i

Ouachita and Ozark Mountains Symposium:

Ecosystem Management Research

Hot Springs, Arkansas

October 26–28, 1999

James M. Guldin, Technical Compiler

Bibliographic citation:

Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management

research. Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern

Research Station. 321 p.

Symposium objectives:

• to present an overview of ecosystem management research in the Ouachita and Ozark Mountains

• to present results from 5 years of postharvest measurements from the phase II stand-level studies including

silviculture, understory vegetation, wildlife, soils and water quality; logging and economics; visual quality; and

arthropods and microbial diversity

• to present results from 5 years of baseline data collection from the phase III landscape-scale studies including

vegetation, wildlife, aquatic ecology, hydrology, and social sciences.

• to present an overview of other ecosystem-based projects in the Interior Highlands.

Symposium sponsors:

U.S. Department of Agriculture (USDA), Forest Service, Southern Research Station

University of Arkansas School of Forest Resources, Arkansas Forest Resources Center

USDA Forest Service, Ouachita National Forest

USDA Forest Service, Ozark-St. Francis National Forest
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CONTENTS

Page

Opening Session

James B. Baker, Moderator

The History of New Perspectives and Ecosystem

Management

F. Dale Robertson . . . . . . . . . . . . . . . . . . . . . . . . 3

Overview of Ecosystem Management Research in

the Ouachita and Ozark Mountains: Phases I–III

James M. Guldin . . . . . . . . . . . . . . . . . . . . . . . . . 8

Session 1—Phase II Silviculture Research

James B. Baker, Moderator

Regeneration Development Across a Range of

Reproduction Cutting Methods in Shortleaf Pine

and Pine-Hardwood Stands in the Interior Highlands

James M. Guldin, James B. Baker, and

Michael G. Shelton . . . . . . . . . . . . . . . . . . . . . . . 15

A Comparison of Vegetation Within and Outside

Riparian Areas Bordering Ephemeral Streams in

the Ouachita Mountains

David K. Radabaugh, Hal O. Liechty, and

James M. Guldin . . . . . . . . . . . . . . . . . . . . . . . . . 21

Session 2—Phase II Wildlife Research

Larry D. Hedrick, Moderator

Initial Small Mammal Responses to Alternative

Pine Regeneration Methods in Arkansas and

Oklahoma: Preliminary Findings

Ronald E. Thill, Roger W. Perry,

Nancy E. Koerth, Philip A. Tappe, and

David G. Peitz . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Initial Bird Responses to Alternative Pine

Regeneration Methods in Arkansas and

Oklahoma: Preliminary Findings

Ronald E. Thill, Roger W. Perry,

Nancy E. Koerth, Philip A. Tappe, and

David G. Peitz . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Early Succession Bird Communities of

Group-Selection Openings and Clearcuts in the

Ouachita Mountains, Arkansas and Oklahoma

Philip A. Tappe, Ronald E. Thill,

David G. Peitz, and Roger W. Perry . . . . . . . . . . 42

The Relationship Between Basal Area and

Hard Mast Production in the Ouachita Mountains

Roger W. Perry, Ronald E. Thill,

Philip A. Tappe, and David G. Peitz . . . . . . . . . . 55

Initial Response of Individual Soft Mast-Producing

Plants to Different Forest Regeneration Methods

in the Ouachita Mountains

Roger W. Perry, Ronald E. Thill,

Philip A. Tappe, and David G. Peitz . . . . . . . . . . 60

Page

Home Range, Habitat Selection, and Population

Dynamics of Southern Flying Squirrels in

Managed Forests in Arkansas

James F. Taulman and Kimberly G. Smith . . . . . 71

Session 3—Phase II Arthropod and

Microbial Diversity Research

David Hammond, Moderator

The Insect Guild of White Oak Acorns: Its Effect

on Mast Quality in the Ozark and Ouachita

National Forests

Alex C. Mangini and Roger W. Perry . . . . . . . . . 79

Impact of Stand Management Practices on

Beetle Diversity

Stephen P. Cook . . . . . . . . . . . . . . . . . . . . . . . . . 83

Radiographic Analysis of Shortleaf Pine Seeds

From the Ouachita and Ozark National Forests

Alex C. Mangini, William W. Bruce, and

James L. Hanula . . . . . . . . . . . . . . . . . . . . . . . . . 89

Crown Health of Reserve Hardwood Trees

Following Reproduction Cutting in the Ouachita

Mountains

Dale A. Starkey and James M. Guldin . . . . . . . . 92

Session 4—Phase II Soil and Water Resources

Research

Alan Clingenpeel, Moderator

Changes in Organic Matter and Nutrients in

Forest Floor After Applying Several Reproductive

Cutting Methods in Shortleaf Pine-Hardwood Stands

Hal O. Liechty and Michael G. Shelton . . . . . . . 101

Session 5—Phase II Understory Vegetation

and Visual Quality Research

Dan Nolan, Moderator

Scenic Beauty in Summer the Year Before,

the Year After, and 4 Years After Harvest

Victor A. Rudis, Hochan Jang, and

James H. Gramann . . . . . . . . . . . . . . . . . . . . . . . 113

Impact of Recent Timber Harvests on Autumn

Scenic Beauty of Near-Stand Views

Rebecca J. Ray Barlow and Victor A. Rudis . . . 121

A Psychological Model of Scenic Beauty by

Silvicultural Treatment Two Growing Seasons

After Harvest

Ying-Hung Li, Victor A. Rudis, and

Theresa A. Herrick . . . . . . . . . . . . . . . . . . . . . . . 130
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Page

Session 6—Phase III Overview and

Wildlife Research

T. Bently Wigley, Moderator

Landscape-Scale Research in the Ouachita

Mountains of West-Central Arkansas:

General Study Design

James M. Guldin . . . . . . . . . . . . . . . . . . . . . . . . . 143

Landscape Characterization of Four Watersheds

Under Different Forest Management Scenarios in

the Ouachita Mountains of Arkansas

Philip A. Tappe, Robert C. Weih, Jr.,

Ronald E. Thill, M. Anthony Melchiors, and

T. Bently Wigley . . . . . . . . . . . . . . . . . . . . . . . . . 146

Breeding Bird Communities on Four Watersheds

Under Different Forest Management Scenarios in

the Ouachita Mountains of Arkansas

Philip A. Tappe, Ronald E. Thill,

M. Anthony Melchiors, and T. Bently Wigley . . . 154

Amphibian Communities Under Diverse Forest

Management in the Ouachita Mountains, Arkansas

Stanley F. Fox, Paul A. Shipman,

Ronald E. Thill, Joseph P. Phelps, and

David M. Leslie, Jr. . . . . . . . . . . . . . . . . . . . . . . . 164

Reptile Communities Under Diverse Forest

Management in the Ouachita Mountains, Arkansas

Paul A. Shipman, Stanley F. Fox,

Ronald E. Thill, Joseph P. Phelps, and

David M. Leslie, Jr. . . . . . . . . . . . . . . . . . . . . . . . 174

Aquatic Turtles of Diversely Managed Watersheds

in the Ouachita Mountains, Arkansas

Joseph P. Phelps . . . . . . . . . . . . . . . . . . . . . . . . 183

Session 7—Phase III Vegetation and

Social Science Research

Alan Newman, Moderator

Baseline Overstory Conditions in Four Watersheds

of Varying Management Intensity in the Eastern

Ouachita Mountains

James M. Guldin and Thomas Foti . . . . . . . . . . 189

Ordination of Woody Vegetation in a Ouachita

National Forest Watershed

Denise Marion and George Malanson . . . . . . . . 198

Session 8—Phase III Hydrology and

Aquatic Ecology Research

Duane Dipert, Moderator

An Improved Flood-Frequency Model for Small

Watersheds in the Upper Ouachita Mountains

Daniel A. Marion . . . . . . . . . . . . . . . . . . . . . . . . . 207

Page

Nutrient Uptake and Community Metabolism in

Streams Draining Harvested and Old-Growth

Watersheds: A Preliminary Assessment

Brian H. Hill and Frank H. McCormick . . . . . . . . 214

Context-Specific Trophic and Functional Ecology

of Fishes of Small Stream Ecosystems

in the Ouachita National Forest

William J. Matthews, A. Maria Miller-Lemke,

Melvin L. Warren, Jr., Donna Cobb,

Jeffery G. Stewart, Betty Crump, and

Frances P. Gelwick . . . . . . . . . . . . . . . . . . . . . . . 221

The Role of Regional Factors in Structuring

Ouachita Mountain Stream Assemblages

Lance R. Williams, Christopher M. Taylor,

Melvin L. Warren, Jr., and

J. Alan Clingenpeel . . . . . . . . . . . . . . . . . . . . . . . 231

Fish Assemblage Structure Under Variable

Environmental Conditions in the Ouachita

Mountains

Christopher M. Taylor, Lance R. Williams,

Riccardo A. Fiorillo, R. Brent Thomas, and

Melvin L. Warren, Jr. . . . . . . . . . . . . . . . . . . . . . . 239

Consequences of Pool Habitat Isolation on

Stream Fishes

David G. Lonzarich, Melvin L. Warren, Jr., and

Mary E. Lonzarich . . . . . . . . . . . . . . . . . . . . . . . . . . 246

Using Snorkeling to Quantify Fish Assemblage

Structure in Arkansas Streams

David G. Lonzarich, Mary E. Lonzarich, and

Melvin L. Warren, Jr. . . . . . . . . . . . . . . . . . . . . . . 253

Computerization of the Arkansas Fishes Database

Henry W. Robison, L. Gayle Henderson,

Melvin L. Warren, Jr., and Janet S. Rader . . . . . 257

Session 9—Other Ecosystem Management

Research in the Interior Highlands

Charles Richmond, Moderator

Gypsy Moth Defoliation Potential in the

Ouachita/Ozark Highlands Region

Andrew M. Liebhold, Kurt W. Gottschalk,

James M. Guldin, and Rose-Marie Muzika . . . . 267

Herpetological Habitat Relations in the

Ouachita Mountains, Arkansas

Doyle L. Crosswhite, Stanley F. Fox, and

Ronald E. Thill . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

Poster Session

A 5-Year Assessment of Shortleaf Pine and

Hardwood Sprouts Relative to Three Methods of

Hardwood Control in the Arkansas Ozarks

Michael D. Cain . . . . . . . . . . . . . . . . . . . . . . . . . . 285
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Page

Effects of All-Terrain Vehicles on Stream Dynamics

Anne Chin, Deven M. Rohrer, Daniel A. Marion,

and J. Alan Clingenpeel . . . . . . . . . . . . . . . . . . . 292

Effects of Retaining a Hardwood Component

During the Application of Uneven-Aged Silviculture

in a Shortleaf Pine-Oak Stand: 6-Year Results

Michael G. Shelton . . . . . . . . . . . . . . . . . . . . . . . 297

Understory Vegetation and Overstory Growth in

Pine and Pine-Hardwood Shelterwood Stands in

the Ouachita Mountains: 5-Year Results

Michael G. Shelton . . . . . . . . . . . . . . . . . . . . . . . 305

Small Mammal Communities of Streamside Zones

in the Ouachita Mountains

Philip A. Tappe, Ronald E. Thill,

M. Anthony Melchiors, T. Bently Wigley, and

David G. Peitz . . . . . . . . . . . . . . . . . . . . . . . . . . . 312

Page

Avian Communities of Streamside Zones in the

Ouachita Mountains of Arkansas

Ronald E. Thill, Philip A. Tappe,

M. Anthony Melchiors, and T. Bently Wigley . . . 313

Initial Wildlife Habitat Responses to Alternative

Forest Regeneration Methods in the Ouachita

Mountains

Ronald E. Thill, Roger W. Perry,

Nancy E. Koerth, Philip A. Tappe, and

David G. Peitz . . . . . . . . . . . . . . . . . . . . . . . . . . . 314

Shortleaf Pine Seed Production Following

Partial Cutting in the Ouachita Mountains

Robert F. Wittwer and Michael G. Shelton . . . . . 315

Index of Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
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Opening Session

James B. Baker, Moderator
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3

INTRODUCTION

Thanks for this opportunity to come back to my home State,

and to the Ouachita National Forest (NF), where I began

my Forest Service career. Dr. Jim Baker, a key leader of the

research underlying this symposium, and I were classmates

at Arkansas Agricultural and Mechanical (A&M) College, now

the University of Arkansas at Monticello (UAM). He went on

to become a world-class research scientist. I pursued a less

honorable career path and became Chief of the Forest

Service, and by doing so got an opportunity for early retirement—which is an occupational hazard for Chiefs!

This morning, I was asked to kick off your symposium by

talking about the history of the New Perspectives and

Ecosystem Management concepts, the importance of the

Ouachita NF in that history, and my view of changes that

have occurred in the last 9 years. I am eager to hear and

see first-hand how things are going 9 years after the socalled walk in the woods back in August 1990 with Senator

David Pryor. Tomorrow, I’m taking another walk in the woods,

but the good news is that I have absolutely no authority to

make any decisions. So you Rangers can relax, you do not

have anything to worry about!

I will give you my perspective as Chief from the Washington,

D.C., vantage point. National policy making is never a very

pretty thing, even for a highly professional outfit like the

Forest Service, when it involves a scientific topic like Ecosystem Management. It was also a sensitive political issue that

had to play out in the Washington, D.C., political environment. So, here is my story.

THE LIMITS OF MULTIPLE-USE MANAGEMENT

Fortunately or unfortunately during my tenure as Chief,

multiple-use management, as it was being practiced on the

national forests, hit a wall. Management of the national

forests got mired down in intense public controversy, with

many of our land management decisions being appealed

and challenged in the courts. It was taking an extraordinary

amount of time and effort to get even the simplest jobs

done, like timber sales here on the Ouachita NF.

For the first time in its history, the Forest Service began to

fall short in a major way of meeting its financed goals and

targets as outlined in the congressionally approved budget

and appropriation laws. The Forest Service had always

been known as a can-do outfit. I was rapidly becoming the

most experienced Chief in the history of the Forest Service

in trying to explain to Congress and user groups why the

Forest Service was not meeting its financed goals and

targets. I think that probably everyone here pretty well

knows the reasons behind the Forest Service’s situation at

that time, so I won’t spend much time on that.

Even though clearcutting was really a big issue here in

Arkansas, it was not a new issue. In fact, the first professionally trained forester in America, Bernard E. Fernow,

was fired in 1903 for clearcutting on the Cornell University

Experimental Forest in the Adirondack Mountains of New

York. Due to the clearcutting issue, he completely lost the

first forestry school in America when New York State

decided to zero out his budget.

Thus, clearcutting has always been a threatening cloud

hanging over the forestry profession from the very beginning of American forestry. The American people simply do

not like the way clearcuts look, and they highly value the

scenic qualities of their national forests. That is something

that Forest Service people in Arkansas know as well or

better than anyone else in the country.

The primary new drivers of change in management of the

national forests were the Endangered Species Act (ESA),

the viability requirement of the National Forest Management

Act (NFMA), the National Environmental Policy Act (NEPA),

and the courts. The ESA was the most difficult to deal with

because it really established the protection of critical habitat

for endangered species as a dominant use. The Forest

Service has had great difficulty fitting endangered species,

such as the northern spotted owl, within our overall multipleuse management concept.

Over the years, under the multiple-use concept the Forest

Service had gotten very good and comfortable at considering the relative values of many resources, making tradeTHE HISTORY OF NEW PERSPECTIVES AND ECOSYSTEM MANAGEMENT

F. Dale Robertson1

1 Retired, USDA Forest Service, presently residing in Sedona, AZ.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Arkansas occupies a unique and important place in the history of New Perspectives and Ecosystem Management. An historic visit to the Ouachita National Forest by Senator David Pryor (D-Arkansas) in August 1990, thereafter

called the walk in the woods, served as an opportunity to shift the Ouachita’s style of management in a manner that has

served as a model for other national forests in the Nation. This paper summarizes limits of the multiple-use concept,

outlines some important elements that constitute the basis of the New Perspectives program, and discusses from the

author’s perspective the evolution of the Ecosystem Management concept while he served as Chief of the USDA Forest

Service.
4

offs, and arriving at what we thought were good, balanced

decisions. This was the trademark that the Forest Service

had been well known for since the days of Gifford Pinchot.

But, when we got hauled into court, most of the judges

either did not understand our decision-making process,

thought it was procedurally inadequate under NEPA, or

disagreed with the decision. The trade-offs that we had

been making over the years under the concept of multipleuse became no longer acceptable. And, the Forest Service

was fast building up a bookcase full of case law that made

it increasingly difficult to manage the national forests under

the thinking of that time.

So, the time was ripe for some rather drastic conceptual

changes in how the Forest Service managed the national

forests. And I happened to be the Chief at the time serving

in a Republican Administration that was not too keen on

making major policy changes, especially if it would adversely

affect economic benefits from the national forests.

CHANGES IN THINKING AND POLICY

It was relatively easy to conclude that the concept of multiple use, especially with a heavy reliance on clearcutting,

had its shortcomings. As it was being practiced, the concept

was not comprehensive enough to encompass all the considerations and values that the Forest Service had to take

into account in the future management of the national

forests. But few people had better alternatives. At the time,

the choice was either to continue to shore up and strengthen the multiple-use management concept, or to make a

major conceptual change in our way of thinking about how

the national forests should be managed.

Well, we decided to start down the path of change, not

knowing for sure where it was going to lead us. At the time,

the term ecosystem management was not a common term

in our vocabulary—maybe only in the minds of a few of our

ecologists and other scientists.

New Forestry

Jerry Franklin, a research forest ecologist at the Pacific

Northwest Forest and Range Experiment Station, was one

of the first to take an ecological approach in dealing with

the old growth/spotted owl issue. As I remember, Jerry’s

new idea was then that we should view the forest as an

ecosystem and expand our thinking and planning to encompass something called a landscape. An ecosystem at the

landscape level had certain structural characteristics like

old-growth trees, large snags, down logs, and shaded riparian areas. Jerry recommended that all of these ecological

values should be perpetuated in the forest through time in

order to maintain all pieces of the ecosystem (including

endangered species). At about the same time, a few other

Forest Service scientists, like Tom Crow in the Lake States,

were also developing similar ecological concepts.

Jerry began to work with district rangers in the Pacific

Northwest to pilot test his ecological concepts in the design

of timber sales. Soon we were able to see what Jerry’s

ideas looked like on the ground. As Chief, I went out to see

some of the results. It was certainly different and in many

ways looked like a sloppy, unfinished logging job!

Perhaps unfortunately, Jerry called it new forestry. It generated a lot of controversy in the Pacific Northwest. Some old

timers called it old forestry, referring to the kind of highgrade logging that they did back in the 1950’s. As you might

suspect, Jerry was not very popular with many in the forestry community of the Pacific Northwest. But, as Chief I knew

Jerry personally from my days as Forest Supervisor of the

Siuslaw NF and had a lot of confidence in his work. Besides,

no one else was coming forth with any better ideas about

how to solve our old-growth/spotted owl issue in the Pacific

Northwest.

I ended up giving Jerry an award for his pioneering work in

Ecosystem Management. I remember his acceptance speech

saying that the Chief should get an award for having the

courage to give him an award for his controversial work.

New Perspectives

The next stage was something called New Perspectives. By

now, problems managing the national forests were multiplying and the controversy growing more intense. Congress

also was feeling the heat and wanted assurance that the

Forest Service was getting on top of its land management

problems. One of the congressional committees decided to

hold an oversight hearing and the essence of the subject

was, “Chief, what are you doing about all of these problems

in the Forest Service?”

The usual procedure in getting prepared for a congressional hearing is for your staff to prepare draft testimony

along with a 3- to 4-inch-thick briefing book. The staff meets

with the Chief a day or two before the hearing to go over

the proposed testimony and make sure the Chief is comfortable with the Forest Service statement.

Well, that was the first time I ever laid eyes on the words

New Perspectives. The words jumped out at me and I

remember asking a lot of questions like, “Why call it New

Perspectives?” The staff explained that we could not use

the term new forestry because of its controversial history

and baggage. They convinced me that New Perspectives

was a good, neutral term and it would be hard for anyone to

disagree with. Besides, it pretty well represented our

broadening concept of national forest management.

Once I got over the idea of this new terminology, I did some

editing to make it a stronger statement about actions the

Forest Service would take to get on top of our land management problems. New Perspectives was to be a creative,

experimental program to pilot test different concepts of land

management. Researchers and land managers were to

work together as a team in addressing the issues on each

national forest. It meant that we had to shift the priorities of

our researchers, to which Jerry Sesco, the Deputy Chief for

Research, agreed in advance. I was to direct each Forest

Supervisor, through the Regional Foresters, to get together

with the appropriate research scientists and design some

pilot tests or experimental projects to demonstrate a new

and more environmentally sensitive way of managing each

national forest.

The briefing book included write-ups on all of the current and

planned projects going on throughout the Forest Service
5

that could be included in the New Perspectives program.

Congressional hearings are a lot like final exams in college,

except they are oral exams with Members of Congress

asking the questions. Also, Members often have their own

agendas, and they try to get the Chief to answer in a way

that supports their individual viewpoints. So, after reading

and studying all of the write-ups in my briefing book, I probably knew as much or more than anyone else in the Forest

Service, on that particular day, about the intent and meaning of New Perspectives. It is almost as dangerous for the

Chief to go to Congress to testify as it is to go on a walk in

the woods—you never know what sort of policy might get

made in the process.

So off to the Hill I went announcing the New Perspectives

program as the new Forest Service way of dealing with

many of our land management problems. As I recall, it was

a friendly hearing with a lot of good questions by members.

I am sure that over the course of the hearing, I must have

added some details about the intent and meaning of New

Perspectives. I think the Committee was generally convinced

that the Forest Service was making a sincere and strong

effort to get on top of our land management problems.

I was really proud of how quickly Forest Service field people

responded in implementing New Perspectives projects.

Before long, people could see on-the-ground results, including here in Arkansas on the Ouachita NF and the Ozark-St.

Francis NF. It also set in motion an unprecedented degree

of cooperation and teamwork among national forest land

managers and researchers.

Ouachita NF Situation—The Walk In The Woods

In many cases, the Chief gets involved in national forest

issues in which he does not have first-hand knowledge and

experience, and has to depend on what other people tell

him about the issues. However, in the case of the Ouachita

NF, I did have some previous experience, even though it

had been a long time ago. I graduated from the Forestry

School at Arkansas A&M, now the University of Arkansas at

Monticello, and learned a lot about southern pine management, including the uneven-aged management practices on

the Crossett Experimental Forest. During my college years,

I worked for two summers in the late 1950’s on the Womble

Ranger District (RD) at Mount Ida, Arkansas, mainly on a

timber marking crew and a timber stand improvement crew.

So, I had walked up and down a lot of mountains on the

Ouachita NF and knew what the country and forest looked

like before the Forest Service began large-scale clearcutting.

In the mid-1960s, I was the District Ranger on the Choctaw

RD of the Ouachita NF. Ironically, I am the only Chief out of

the 14 in the history of the Forest Service who has ever

been a District Ranger. So, the Ouachita NF has the honor

of being the only national forest ever to have produced a

Chief out of its Ranger ranks.

I also remember participating in a Ouachita NF Ranger/

Staff meeting here in Hot Springs in about 1966. We had

been in the clearcutting business for about 2 years and we

Rangers were getting some flak from the natives. At the

Ranger/Staff meeting we expressed our concerns about

clearcutting. As I remember, the Forest Supervisor and

Timber staff dealt with our concerns rather directly and

bluntly and told us our job was to go home and educate the

public about the merits of clearcutting. So, I spent the next

2 years trying to do just that here on the Ouachita NF.

Well, I left the Ouachita NF in 1968 and did not get involved

again until the famous walk in the woods in 1990, some 22

years later. So, I will tell you about that.

U.S. Senator, David Pryor of Arkansas was on our back

about clearcutting on the Ouachita NF. He had written

me a couple of letters asking me to look into the situation

and see if we could stop it. My very competent staff in

Washington, D.C., with the help of Region 8 staff in Atlanta,

had prepared responses to Senator Pryor that should have

convinced any reasonable person that clearcutting was an

essential silvicultural tool for managing the Ouachita NF.

However, Senator Pryor wasn’t buying it. He wrote me

another letter asking me to stop clearcutting on the Ouachita

NF. Again, my staff prepared a response that told him the

same thing, as though he did not quite understand the first

letter. This time, however, my staff decided that the issue

with Senator Pryor had escalated to the point that the Chief

should sign the letter.

Well, when the letter arrived on my desk for signature and I

reviewed its long history, I decided to rewrite it myself. As I

recall, I wrote a short response saying that I, too, had some

concerns about clearcutting in Arkansas and that we were

experimenting with something called New Perspectives

aimed at gradually getting out of the clearcutting business

as a standard timber harvest practice. I then invited Senator

Pryor to join me in a visit to the Ouachita NF to see firsthand how we were managing the Forest. He accepted my

offer and we were on our way to Arkansas as soon as schedules could be worked out.

The walk in the woods, as it was later called, took place in

August 1990 on the Winona Ranger District of the Ouachita

NF. Along with Senator Pryor and myself were Regional

Forester Jack Alcock, Forest Supervisor Mike Curran, Dr.

Jim Baker from the Southern Forest Experiment Station, and

District Ranger Norman Alley. Thus, we had line officers

from the four levels of the Forest Service, plus a researcher

—a potentially dangerous group, as the Rangers later found

out! We spent the morning looking at some clearcuts, as

well as some New Perspectives timber sales.

As a group, I think we were all impressed with the New

Perspectives projects. I remember Jim Baker explaining

to Senator Pryor how important it was to manage the sunlight so as to be able to regenerate the forest to desirable

species. This really made an impression on the Senator.

Also, I noticed that Ranger Alley had a twinkle in his eye

and sense of excitement about his New Perspectives

projects, which impressed me. And we never succeeded in

convincing the Senator that clearcutting was an essential

silvicultural tool on the Ouachita NF, especially since we

had good examples of successful partial cutting methods

under our New Perspectives program. We ended up at

Lake Sylvia Recreation Area for a picnic lunch, which

concluded our trip with Senator Pryor. The Ranger’s wife

got involved in providing us a fancy tablecloth and some
6

really great food for lunch, which also really impressed the

Senator.

Over lunch, the inevitable question came up, “Why can’t the

Forest Service manage the entire Ouachita NF using the

partial cutting practices that we had just seen on the New

Perspectives projects and do away with clearcutting?” The

main arguments against doing away with clearcutting at

that time were:

(1) New Perspectives was a new, experimental program in

its early stages of implementation and had not been

proven successful yet, except for the fact that we could

harvest timber in a way that was not an eyesore to the

public.

(2) The jump from a few experimental New Perspectives

timber sales to managing the entire Ouachita NF based

on the principles of New Perspectives without the use of

clearcutting had huge operational problems. How would

you like to have been a Ranger at that time with the Chief

and Regional Forester coming to town and turning your

whole timber sale program upside down? It would be

like trying to assemble a bicycle and ride it at the same

time.

In spite of these good reasons to continue clearcutting on

the Ouachita, Jack, Mike, and I knew down deep that we

were on an unpopular, losing path and that some rather

drastic changes were needed. We also knew that those

changes would be difficult regardless of the timing. Dr. Jim

Baker participated in the discussion and said that he was

willing to shift his priorities to help the Rangers. Knowing

that my old college classmate was willing to step up to the

plate and put his professional reputation on the line gave me

added confidence. Forest Supervisor Curran and Ranger

Alley said they were willing to go for the change. We four

line officers agreed that such a change would severely disrupt the timber sale program. However, we were all in this

thing together and would take collective responsibility for

any downfall in target accomplishments. The Rangers were

not to be left hanging out on a limb alone.

So, we made the decision to designate the entire Ouachita

NF as a New Perspective Forest, and the rest is history.

That is why you are having this symposium this week. I

hope you conclude that it turned out well!

This was not only a big decision for the Ouachita NF, but

also a big one for the entire Forest Service. It caught everyone’s attention both inside and outside the agency. They

suddenly understood the importance and implications of the

New Perspectives program—that it was for real and that the

Forest Service was getting serious about making a major

conceptual change in how we managed the national forests.

Ecosystem Management

As time marched on, the New Perspectives program gained

momentum, and more and more projects came on-line with

good success. People began to describe what they were

doing under the New Perspectives and Ecosystem Management programs. These terms began to find their way into

the Forest Service vocabulary with some regularity. There

was a fast-developing consensus in the agency that Ecosystem Management was the new concept that we had been

searching for to guide future management of the national

forests.

However, making a major policy change in the Washington,

D.C., political environment is not easy and takes time. This

is especially the case if it becomes controversial and could

result in changing the way national forests are managed.

So, I will tell you the story of how the policy change was

made in Washington, D.C. Again, our old friend, clearcutting, played a crucial role, just as it did here in Arkansas.

Bill Riley was Administrator of the Environmental Protection

Agency (EPA) during the Bush Administration. I knew Bill

before he was appointed to the EPA job, and we would

occasionally get together for lunch just to talk things over.

Bill was concerned about clearcutting and the old-growth

forests of the Pacific Northwest. We always ended up talking about these issues and Bill became quite knowledgeable about them.

Then in 1992, the Earth Summit (United Nations Conference

on Environment and Development) was held in Rio de

Janeiro, Brazil. Bill Riley was appointed head of the U.S.

delegation. When he got to Rio, several representatives of

American environmental groups and some Members of

Congress were there questioning forestry practices in the

United States and holding press conferences telling the

world about how the U.S. was managing its forests. The

main issues were clearcutting and old-growth forests—the

very issues Bill was concerned about and had been talking

to me about.

Bill and the other U.S. delegates were getting beat up pretty

badly in Rio de Janeiro. I am sure that the other leaders

from around the world were pretty confused by having an

official delegation from the U.S. saying that we were doing

a good job of managing our forests and an unofficial group

of Americans, including some Members of Congress, vehemently disagreeing. President Bush was scheduled to go to

Brazil and make a speech on the closing day of the Earth

Summit.

The end of the Bush Administration was near, and there had

been some major personnel changes in the White House.

As it turned out, my old boss and friend, former Secretary of

Agriculture Clayton Yeutter, was serving as the President’s

Chief of Staff. During all of this controversy, Bill Riley was

talking to Clayton in the White House. Because the U.S. was

getting roughed up pretty badly, there was even discussion

about whether the President should go to Brazil and subject

himself to all the controversy. Bill asked Clayton to talk to

me to see if the U.S. could make a policy statement about

clearcutting on Federal lands and let President Bush

announce the policy change in his speech at the Earth

Summit.

Clayton called me from the White House. Falling back on

my experience here on the Ouachita NF, I told him that the

Forest Service was willing to eliminate clearcutting as a

standard method of timber harvest on the national forests.

However, we still needed to keep the option open for

exceptional cases.
7

Then I made the big move! I called Clayton and told him I

would also like to announce that we were officially adopting

an Ecosystem Management policy for the national forests.

There was a little pause and then he asked, “What in the

world is Ecosystem Management?” I had about 5 minutes

to explain it to him and describe why it was a good policy

for the country. After he had heard my explanation, Clayton

said that the policy sounded pretty good to him and asked

that I write it up and fax it to him so he could discuss it with

the President.

I went home that night and worked until about 11 p.m. on

the proposed Clearcutting and Ecosystem Management

policy statement. I got up at 3 a.m. the next morning and

worked another 3 hours on it. I had to come up with wording that made sense to Forest Service employees, as well

as to Clayton and President Bush in his speech in Brazil. At

about 8 a.m., I faxed the statement to the White House. In

about an hour, Clayton called me and said the President

liked it and wanted to announce the policy in his speech at

the Earth Summit.

In the meantime, back home at the Department of Agriculture, all was not well. My relatively new boss, Secretary of

Agriculture Madigan, had not been in the loop and was

unaware of what was going on. He was not an easy person

to work with, and our relationship was a rather rocky one.

Secretary Madigan, having no experience in forestry, thought

that a good Chief would run a peaceful Forest Service without much controversy. By his standards, I was not performing very well. I knew that it would not be easy to get his

approval. The Acting Assistant Secretary of Agriculture was

John Beuter, a professional forester, and he was a big help

in getting the policy through the Department. After we had

met with Secretary Madigan’s key staff, they reluctantly

agreed to go along with the new policy, especially since

President Bush had already approved it. However, this

situation certainly did not improve my rather poor standing

with Secretary Madigan.

As it played out, I issued the famous policy letter of June 4,

1992, on clearcutting and ecosystem management as the

President was leaving for Rio de Janeiro. The President

gave his speech at the Earth Summit, and an important part

of it was the new policy statement. So, that is the real story

as to how it all happened in Washington, D.C.

CONCLUSIONS

I would just like to conclude by saying that the Chief has to

stand on the shoulders of Forest Service field people. By

being creative and working hard to make the new perspectives program successful, they were the real key players.

They forged the thinking that became the Ecosystem

Management concept. As Chief, I was able to marshal a

unique fleeting opportunity to translate this thinking into a

major policy change for future management of the national

forests. As you can see, the Ouachita NF played a crucial

role in how it all unfolded. And I thank you for that!

Well, all of you know the rest of the story. We had a presidential election in November 1992 and your guy from

Arkansas won. The new Administration strongly supports

ecosystem management, and has made it the centerpiece

of their policy in managing the national forests. However,

they wanted a Chief of their own choosing.

So, I rode off into the sunset to Arizona where I am pursuing a new life unencumbered by the forestry issues of the

day. It has been my great pleasure to return to Arkansas

and, after 9 years, to see strong evidence that the decisions

I made as Chief are working out better than I had hoped.
8

INTRODUCTION

The development of the Land and Resource Management

Plan on the Ouachita National Forest (NF) in the 1980s triggered considerable debate. In 1986, the release of the first

draft of the plan to the public called for the continued and

widespread use of clearcutting and planting as a primary

means to regenerate shortleaf pine (Pinus echinata Mill.)

and pine-hardwood stands in Ouachita Mountains. That

draft attracted 11 appeals from individuals and organizations, and was in part responsible for a change in leadership

on the Ouachita NF (Curran 1994). Between 1986 and 1990,

the Forest Supervisor on the Ouachita NF led the effort to

revise the draft. With the release of the Amended Land and

Resource Management Plan (U.S. Department of Agriculture,

Forest Service 1990), all appeals except one were dropped

(Curran 1994). The locally renowned walk in the woods by

two native Arkansans, Forest Service Chief Dale Robertson

and Senator David Pryor, is described elsewhere (Robertson,

in press).

That walk in the woods led to the establishment of the

Ouachita NF as a “Lead Forest” under the New Perspectives

Program. The Southern Forest Experiment Station (now

part of the Southern Research Station) was directed by the

Chief to provide scientific support for a shift in management

philosophy away from clearcutting and planting, and toward

even-aged and uneven-aged high-forest reproduction cutting

methods that rely on natural regeneration. The MonticelloCrossett Forestry Sciences Laboratory was headed at that

time by Dr. James Baker, who had been a college classmate

of Chief Robertson. That laboratory had considerable expertise in silvicultural treatments using natural regeneration of

mixed loblolly (P. taeda L.)-shortleaf pine stands of the upper

west Gulf Coastal Plain. However, there was not much experience there or elsewhere in applying even-aged or unevenaged silvicultural systems using natural regeneration of the

pure shortleaf pine stands found across the Interior Highlands

(Baker 1994).

As a result, a research team was assembled to develop

silvicultural options for shortleaf pine and pine-hardwood

forests, and to study the effects of those options on a host

of resource attributes and values of interest to managers

and scientists. With the advent of the ecosystem management approach for national forests and grasslands in 1992

(Robertson, in press), the work of this team came to be

known as the Ouachita Mountains Ecosystem Management

Research Project. With the Southern Research Station in

the coordinating role, 10 research units have played a part

in the research program. Other cooperators have included

the Ouachita NF, the Ozark-St.Francis NF, Region 8 headquarters of the USDA Forest Service, more than a dozen

universities in the region, Weyerhaeuser Company, the

National Council of the Pulp and Paper Industry for Air and

Stream Improvement (NCASI), and several State agencies

and nongovernmental organizations.

Highlights of the establishment of the program included

close working support with academia, establishment of a

research liaison position on the staff of the Ouachita NF to

work with the research team, and the development of independent funding through supplemental appropriations from

Congress and through NCASI. Since then, the working

relationship between the Ouachita NF and the Southern

Research Station has been exceptionally close.

ORGANIZATION OF THE RESEARCH PROGRAM

The shortleaf pine forest type covers a majority of the area

managed for timber production on the Ouachita NF. This

forest type is dominated by shortleaf pine and also contains

a minor and varying hardwood component that includes

white and red oaks (Quercus spp.), hickories (Carya spp.),

and elms (Ulmus spp.). Shortleaf pine and pine-hardwood

stands in this forest type are typically found on south and

southwest-facing slopes in the Ouachita Mountains, the

Arkansas River Valley, and the Boston Mountains in Arkansas

and Oklahoma. As a result, research study sites were located

on the Ouachita NF in Arkansas and Oklahoma as well as

the southern portion of the main unit of the Ozark NF in the

Boston Mountains of Arkansas.

The Chief’s interest was in the demonstration of alternatives

to clearcutting and planting that could be quickly implemented

and shown to professional resource managers and the

OVERVIEW OF ECOSYSTEM MANAGEMENT RESEARCH IN

THE OUACHITA AND OZARK MOUNTAINS: PHASES I–III

James M. Guldin1

1 Research Forest Ecologist, USDA Forest Service, Southern Research Station, Hot Springs, AR.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—When the shift away from clearcutting and planting on the Ouachita National Forest was implemented in the

early 1990s, it became apparent that research support for reproduction cutting methods that employ natural regeneration

in shortleaf pine stands in the Interior Highlands was lacking. To fill that need, research scientists and land managers

established a three-phase research program that included demonstration case studies, replicated stand-level studies, and

landscape studies at the watershed scale. This review of each of these three phases includes a brief history and justification, an overview of objectives and a summary of the study design, and a report on the status and future work planned in

each phase.
9

public. That mandate quickly developed into a three-phase

program of research and demonstration. Mersmann and

others (1994) reviewed the origins of the project in detail,

and they described operational planning procedures and

public involvement in phases I and II.

Phase I—Demonstration Stands

The phase I demonstration stands were established in 1990–

91 immediately after inception of the project. These demonstrations were intended as examples of the alternatives to

clearcutting that were being considered. They allowed people

inside and outside the agency to see and discuss those alternatives. They were established by modifying existing open

timber sale projects; with contractor approval, sales were

modified to illustrate the seed tree, shelterwood, single-tree

selection, and group-selection reproduction cutting methods.

Twenty-two demonstrations were established in three general

areas: (1) the Winona Ranger District (RD) for tours originating in Little Rock, (2) the Womble RD for tours emanating

from the Supervisor’s Office in Hot Springs, and (3) the

Kiamichi RD and Choctaw RD in eastern Oklahoma for tours

originating there. The Womble RD tour stops were the most

frequently visited. One of the phase I stands on the Womble

RD that features classic uneven-aged foliar canopy structure in shortleaf pine is still used for demonstration.

By the time the phase I tours were generally retired in 1994,

they had supported more than 50 tours, with an estimated

attendance of more than 1,000 participants. As unreplicated

case studies, these demonstration stands provided little of

scientific value. But they had tremendous value as a tool for

allowing people to envision the changes embodied in the

new perspectives and ecosystem management concepts

being put forth at the time.

Phase II—Stand-Level Research

The phase II study was designed to test different reproduction cutting methods using a replicated experimental

approach, which provides a higher standard of statistical

rigor than was found in the unreplicated phase I demonstrations. The two main objectives of the phase II study are (1)

to evaluate biological and economic feasibility of even-aged

and uneven-aged high-forest reproduction cutting methods

that rely on natural regeneration to establish and maintain

shortleaf pine and pine-hardwood stands in the Interior

Highlands, and (2) to quantify the effects of these alternatives on a spectrum of forest resources and values.

The phase II study design called for a test of 13 reproduction cutting methods (table 1) in 4 ecoregional blocks for a

total of 52 stands (Baker 1994, Guldin and others 1994).

The 52 stands were randomly selected, and treatments

were randomly assigned such that each stand would have

1 reproduction cutting treatment imposed upon it with 4

stands of each given treatment in the study. The random

assignment of treatments led to an interesting effect.

Because each stand in the study was eligible for any of the

reproduction cutting methods that were being tested, and

because the management plans for the national forests

restricted the use of clearcutting in stands near roads, the

phase II stands are all some distance from major roads in

the region.

The phase II study went through the standard operating

procedure for compliance with provisions for public involvement. Mersmann and others (1994) describe in detail the

preparation of the environmental assessment for the phase

II study. As a result, the entire phase II study was considered a single decision, and it was not appealed.

Research crews inventoried stands and prepared marking

guidelines for the treatments being imposed in each stand.

Those marking guidelines were given to the RD marking

crews that marked the stands in an operational manner as

part of the district workload. The research harvest generated

Table 1—Reproduction cutting methods and residual basal area for the

pine and hardwood component within each in the phase II stand-level

studies

Residual basal area

Reproduction cutting method S/W/X Pine Hardwood

square feet per acre

Clearcutting W 0 2 – 5

Seed tree method, pine S 20 2 – 5

Seed tree method, pine-hardwood S 10 10

Shelterwood method, pine S 40 2 – 5

Shelterwood method, pine-hardwood S 30 10

Shelterwood method, pine-hardwood W 30 10

Group selection method, pine X 60 2 – 52

Group selection method, pine-hardwood W 50 102

Single-tree selection method, pine S 60 2 – 5

Single-tree selection method, pine-hardwood S 50 10

Single-tree selection method, pine-hardwood W 50 10

Single-tree selection method, low-impact X 60 15

Unmanaged control X ~100 ~30

S = included in split-plot site preparation and release study; W = included in wildlife

group habitat study; X = not included in either the split-plot or wildlife studies.
10

10 million board feet of sawtimber—roughly 8 percent of

the annual timber harvest on the Ouachita NF in 1993.

As an element of operational work, RD personnel prepared

sale area improvement plans for harvested areas on their

respective districts, and collected Knutsen-Vandenberg (KV)

funds for planting, site preparation, release, and monitoring.

Through this process, the Ouachita and Ozark-St. Francis

NFs allocated roughly $1 million in KV funds to the Southern

Research Station from FY94 through FY98 to monitor the

effects of KV-funded activities in the 48 harvested stands.

Monitoring was structured within a statistically rigorous

study design and it involved repeated visits to stands.

Seven groups contributed to the research and monitoring

activities in phase II stands:

1. The woody vegetation group studied seed production,

seedbed condition, woody plant regeneration development, shortleaf pine genetic diversity, overstory development, and the effects of site preparation and release

2. The understory vegetation group measured density,

frequency, and cover of herbaceous and shrub species

on plots nested within the woody vegetation plots

3. The wildlife research group studied small mammals,

flying squirrels, neotropical migratory and resident birds,

and general wildlife habitat conditions

4. The management economics group quantified harvesting costs and management costs associated with

various reproduction cutting methods

5. The visual quality group measured visual impacts of

recently harvested stands, conducted customer surveys

of scenic preferences, and evaluated the effects of hardwood retention, season, and physiography on perceived

scenic beauty

6. The arthropod and microbial communities group studied

insect diversity (with emphasis on arthropods), cone and

seed insects, southern pine beetle hazard ratings, and

crown health of hardwoods

7. The water, soil, and cultural resources group studied the

water chemistry of ephemeral streams, herbicide movement in streamwater, stream channel morphology and

woody debris, soil disturbance associated with logging,

and harvesting effects on cultural resources.

Some of the results of these many studies are contained in

this proceedings. Other papers can be found in the

refereed literature.

Phase III—Landscape-Scale Research

Early in phase II design, it became apparent that some

questions could not be answered at the stand level. These

questions include effects of management on hydrology and

aquatic ecology, and the ecological ramifications of maintaining or imposing different vegetation patterns across a

watershed or landscape.

The phase III study was developed to support operational

implementation of ecosystem management at the landscape scale, defined for these purposes as watersheds.

Emphasis was placed on research on watershed hydrology

and aquatic ecology, linkages between terrestrial and

aquatic systems, landscape analysis of forest patterns and

processes, landscape-level terrestrial wildlife concerns, and

the social context of ecosystem management.

In the planning for phase III, scientists took advantage of

smaller stand-level studies when that was possible. Two

examples are studies of the use of prescribed fire and studies of the effects of retaining trees within group openings

in stands being managed using the group selection method.

Four watersheds make up the overall study design of phase

III research. Each watershed has a unique desired forest

condition; taken together, the watersheds reflect a range of

models for ecosystem management. Since replication at

the scale associated with this phase is difficult, a different

approach to experimental design was required. That

approach builds on a combination of approaches, including

the use of repeated measurements across years, subdivision of existing watersheds or establishment of new watersheds for validation of modes, and traditional small-scale

replicated research within the larger watersheds (Guldin, in

press).

Of the four watersheds that are included in the landscapescale study, three are in the Upper Lake Winona Basin of

the Winona RD on the Ouachita NF; the fourth is on nearby

forest industry land (table 2). Management intensity varies

widely. The Alum Creek watershed represents essentially

an unmanaged condition. The Little Glazypeau watershed

supports intensive management of pine plantations for

industrial timber production. The other two watersheds—

Bread Creek and North Alum Creek—differ by percentage

of industry ownership and, thus, by intensity of management

overall. Overall, the intensity of management established

for the watersheds during the baseline pretreatment measurement period reflects that distribution of ownership.

Table 2—Watersheds included in the phase III study with

subjective classifications of ownership and management

intensity

Ownership Management

Watershed Area N.F. Industry intensity

acres - - - percent - - -

Alum Creek 3,700 100 0 Unmanaged

Bread Creek 3,800 100 0 Low

North Alum Creek 9,800 50 50 Moderate

Little Glazypeau Creek 5,600 5 95 High

N.F. = national forests.
11

Specific objectives in the phase III study are

1. to quantify core watershed hydrology through a series of

flumes and uncontrolled cross-section gauging stations,

which will be used to model hydrological factors and

cumulative hydrology effects up to basin scale

2. to characterize and quantify sensitive and critical elements

of aquatic and riparian ecology in concert with the studies

of watershed hydrology

3. to quantify terrestrial ecological relationships of vegetation pattern, ecological classification, wildlife, and

biodiversity across the core watersheds

4. to characterize the social dimensions of the landscape in

which the core watersheds lie, including study of the

prehistoric, historic, and current relationships of people

with the land.

Baseline measurements in the phase III study were initiated

in 1994, and carried forth through 1998. Papers included in

this proceedings present baseline measurements of various

types for that pretreatment period.

Treatments to carry the watersheds from their existing condition to their desired future condition are underway. The

desired future condition in the phase III watersheds represents a synthesis of results to date from the stand-level

research study with the interests of land managers with both

the national forest and forest industry. The watershed under

intensive management will remain so. It will quantify the

effects of continued plantation management over time at

the large scale. Half of the watershed in an unmanaged

condition will continue in the unmanaged condition, and the

remainder will be subject to single-tree selection silviculture,

generally thought to be the least intensive of the reproduction cutting methods in the arsenal of the silviculturist. The

Bread Creek watershed, wholly under Forest Service ownership and managed using typical national forest treatments

in the past, will continue to support standard agency actions.

The North Alum Creek watershed will be split three ways to

support two variations of group selection treatments and a

shortleaf pine-bluestem habitat restoration treatment (Guldin,

in press).

As in phase II, these phase III treatments are being imposed

in an operational manner by the regular management staff

of the cooperating forest management organization. On

industry land, typical schedules of harvest and associated

cultural activities associated with intensive management of

pine plantations for commercial timber production will continue as they would normally occur. On the national forest

watersheds, actions have been planned and are being

executed by the staff and field crews of the Jessieville and

Winona RDs. An administrative study plan was written and

approved that contains provisions for monitoring supported

by KV funds. Those monitoring data and other data collected

on public and private land will be indispensable in helping

scientists and forest land managers understand the effects

of concentrating management activities in a forested

watershed.

SUMMARY

This symposium is linked to larger regional science issues

through the Southern Research Station’s strategic science

framework. A crosscutting theme in the Station’s strategic

plan highlights the sustainability and productivity of the

Interior Highlands ecosystem. The Ouachita Mountains

Ecosystem Management Research Program is a key element of that theme, and work reported in this proceedings

supports it directly.

The three-phase approach of demonstration, stand-level

research, and landscape-scale research demonstrates how

team-oriented science efforts bring “critical mass” to complex

experiments at heterogeneous scales. However, the research

project would not be possible without many Station partners

in public and private sectors who have contributed time,

talent, in-kind services, and both direct and indirect financial support.

LITERATURE CITED

Baker, James B. 1994. An overview of stand-level ecosystem

management research in the Ouachita/Ozark National Forests.

In: Baker, James, comp. Proceedings of the symposium on

ecosystem management research in the Ouachita Mountains:

pretreatment conditions and preliminary findings. Gen. Tech.

Rep. SO-112. New Orleans: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment Station: 18-28.

Curran, John M. 1994. Evolution of ecosystem management and

research on the Ouachita and Ozark-St. Francis National

Forests. In: Baker, James, comp. Proceedings of the symposium on ecosystem management research in the Ouachita

Mountains: pretreatment conditions and preliminary findings.

Gen. Tech. Rep. SO-112. New Orleans: U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station: 1-4.

Guldin, James M. [In press]. Landscape scale research in the

Ouachita Mountains of west-central Arkansas: general study

design. In: Guldin, James M., tech. comp. Ouachita and Ozark

Mountains symposium: ecosystem management research.

Gen. Tech. Rep. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station.

Guldin, James M.; Baker, James B.; Shelton, Michael G. 1994.

Midstory and overstory plants in mature pine-hardwood stands

of the Ouachita-Ozark National Forests. In: Baker, James, comp.

Proceedings of the symposium on ecosystem management

research in the Ouachita Mountains: pretreatment conditions

and preliminary findings. Gen. Tech. Rep. SO-112. New

Orleans: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 29-49.

Mersmann, Timothy J.; Baker, James B.; Guldin, James M.; Pell,

William F. 1994. Implementing ecosystem management research:

bringing researchers, managers, and citizens together. In: Baker,

James, comp. Proceedings of the symposium on ecosystem

management research in the Ouachita Mountains: pretreatment

conditions and preliminary findings. Gen. Tech. Rep. SO-112.

New Orleans: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 10-17.

Robertson, F. Dale. [In press]. The history of new perspectives

and ecosystem management. In: Guldin, James M., tech.

comp. Ouachita and Ozark Mountains symposium: ecosystem

management research. Gen. Tech. Rep. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station.

U.S. Department of Agriculture, Forest Service. 1990. Amended

land and resource management plan—Ouachita National

Forest. Hot Springs, AR: U.S. Department of Agriculture, Forest

Service, Southern Region. 409 p. Chapters 1-5 + appendices

A-L. Vol. 1.
12
13

Session 1

Phase II Silviculture Research

James B. Baker, Moderator
14
15

INTRODUCTION

Shortleaf pine (Pinus echinata Mill.), the most widely distributed of the four major southern pines, reaches its ecological

optimum in the Ouachita Mountains. In this region, the topography is characterized by long ridges running generally

from east to west. South-facing slopes are dominated by

pine and pine-hardwood stands, and the pine component

consists exclusively of shortleaf pine. No other part of the

country contains naturally regenerated stands in which

shortleaf pine is the major, dominant, and only pine species

over so large an area of pine-dominated landscapes.

There has been relatively little interest in the silviculture of

natural stands dominated by shortleaf pine. Little is known

about regenerating shortleaf pine with even-aged and

uneven-aged reproduction cutting methods. However,

interest in natural regeneration of shortleaf pine has

increased with the shift in management philosophy away

from clearcutting and planting on the Ouachita National

Forest in the 1990s.

In this paper, we describe results of a variety of even-aged

and uneven-aged high-forest reproduction cutting methods

to establish and maintain shortleaf pine and pine-hardwood

stands in the Ouachita Mountains and southern Boston

Mountains of Arkansas and Oklahoma. Key silvicultural

questions are (1) the early growth of seed-origin shortleaf

pine versus sprout-origin hardwoods, (2) the effects of overstory hardwoods on regeneration development, and (3) the

effects of residual basal area on pine and hardwood regeneration development. We present initial data from a subset

of treatments, and we give suggestions for further analysis.

METHODS

Study Layout

The study layout was established as previously described

(Guldin and others 1994). Fifty-two stands were included in

the study. In each stand, 12 plots were assigned to the

timber management zone (TMZ) where tree harvesting was

permitted and 2 plots were installed in streamside management zones, where harvesting is excluded. All plot centers

were located using geographic positioning systems, and were

digitized on low-level orthogonally rectified aerial photos.

Reproduction Cutting Treatments

Even-aged and uneven-aged reproduction cutting methods

was tested. Even-aged methods were the seed tree and

shelterwood methods. Uneven-aged methods included both

the single-tree selection and group selection methods. At

least two variations of each method were tested—one in

which primarily pines were retained in the residual overstory,

and one in which both pines and hardwoods were retained.

In addition, two control treatments were used—the clearcutting method and an unmanaged treatment. Table 1 lists

the residual basal area target for pines and hardwoods in

each treatment, and the site preparation and release treatments used in each treatment.

Site Preparation and Release Treatments

A subset of stands was subdivided into quarters to measure

effects of site preparation and release (Table 1). One quarter

remained untreated by either site preparation or release. A

second quarter received two manual felling treatments—

manual site preparation early in the first growing season

in the late spring and early summer of 1994, and manual

release early in the fifth growing season in the late spring

and early summer of 1998. Manual felling was done using

either machetes or chain saws. In a third quarter, chemical

site preparation was imposed by applying triclopyr (Garlon®)

to cut surfaces of vegetation prior to the first growing season

in the late spring of 1994. In the fourth quarter, chemical

release was conducted by applying triclopyr (Garlon®) as a

cut surface application or a directed foliar spray immediately prior to the fifth growing season in the late spring and

summer of 1998.

REGENERATION DEVELOPMENT ACROSS A RANGE OF REPRODUCTION

CUTTING METHODS IN SHORTLEAF PINE AND PINE-HARDWOOD STANDS

IN THE INTERIOR HIGHLANDS

James M. Guldin, James B. Baker, and Michael G. Shelton1

1 Research Forest Ecologist and Research Foresters, USDA Forest Service, Southern Research Station, Monticello AR, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Density, milacre stocking, and height of shortleaf pine (Pinus echinata Mill.) regeneration under 13 reproduction cutting methods were measured after 5 growing seasons across a range of reproduction cutting treatments in

shortleaf pine and pine-hardwood stands in the Interior Highlands of Arkansas and Oklahoma. A subset of the full

database was used to suggest trends in the data to date. After five growing seasons, six of the treatments exceeded the

minimum standards for regeneration success—the clearcutting treatment, two of the three shelterwood treatments, and

three of the four single-tree selection treatments. The two seed-tree methods fell short in both pine regeneration density

and pine milacre stocking, whereas the two group selection treatments and the unmanaged control fell short in both pine

milacre stocking and height. Key questions over the next 5-year period are whether regeneration development can be

maintained as residual overstory basal area continues to increase, and whether the 10-year cutting cycle harvest in the

uneven-aged stands or the partial removal cut in the even-aged stands will cause unacceptable mortality in the current

regeneration cohort.
16

In a second subset of treatments, competing vegetation in

the entire stand was manually felled as described earlier

(table 2). The purpose of this treatment was to promote

uniform understory habitat conditions for studies of bird

populations.

The group selection treatments and the controls were

omitted from the split-plot site preparation and release

comparison (table 1).

Measurements

Overstory vegetation (woody plants 3.6 inches in d.b.h. and

larger) and regeneration (woody plants 3.5 inches in d.b.h.

and smaller) were measured separately. Each measurement

cycle required approximately 6 months of field work and

was restricted to the dormant season, so overstory and

regeneration measurements occurred on a 2-year cycle.

Measurement cycles completed to date are shown in table

2. Measurement protocols for overstory and regeneration

measurements have been published elsewhere (Guldin and

others 1994) and are summarized later.

A nested sample was taken to measure the overstory. All

woody plants 9.6 inches in d.b.h. and larger were sampled

by species using variable-radius plots established using a

prism with a basal area factor of 5.0 square feet per acre,

and d.b.h. was recorded to the nearest 0.1 inch. After the

harvest, all sample trees were tagged for remeasurement

and the slope distance and slope percent from plot center

to each tree were recorded. A measurement error of +5 feet

was deliberately added to the formula used to calculate the

horizontal limiting distance such that all trees up to and 5

feet past the exact limiting distance for the prism were

sampled. The use of field data recorders and uploaded

spreadsheet template data files allowed this calculation to

be easily made in the field. In this way, borderline trees in

the prism sample were included in or excluded from the

sample by office computation rather than field technique.

All woody plants with d.b.h. between 3.6 and 9.5 inches

inclusive were sampled by species on a 0.1-acre fixedradius plot. The preharvest measurement was made by

tallying these trees to the nearest 1-inch diameter class.

After harvest, the d.b.h. of these was measured to the

nearest 0.1-inch, and all sample trees were tagged.

The regeneration measurements were slightly modified

from the original study design. Two separate samples—an

inventory tally and a tagged tree tally—were taken. The

Table 1—Reproduction cutting method treatments included in the phase II study in the

Interior Highlands

Site preparation/

release treatmentsa RBA

Reproduction cutting methods Q1 Q2 Q3 Q4 Conifer Hardwood

square feet per acre

Clearcutting C/N C/N C/N C/N 0 2 – 5

Seed tree, pine N/N C/N M/M N/C 20 2 – 5

Seed tree, pine-hardwood N/N C/N M/M N/C 10 10

Shelterwood, pine N/N C/N M/M N/C 40 2 – 5

Shelterwood, pine-hardwood N/N C/N M/M N/C 30 10

Shelterwood, pine-hardwood M/M M/M M/M M/M 30 10

Group selection, pine M/M M/M M/M M/M 60 2 – 5

Group selection, pine-hardwood M/M M/M M/M M/M 50 10

Single-tree selection, pine N/N C/N M/M N/C 60 2 – 5

Single-tree selection, pine-hardwood N/N C/N M/M N/C 50 10

Single-tree selection, pine-hardwood M/M M/M M/M M/M 50 10

Single-tree selection, low impact M/M M/M M/M M/M 60 15

Unmanaged control N/N N/N N/N N/N ~100 ~30

RBA = residual basal area; C = chemical; N = no treatment; M = manual felling.

a Site preparation/release treatment combinations are shown by quarter (Q1-Q4) to illustrate whether a

given treatment was split or uniformly treated; split treatments were randomly assigned by quarter by stand.

Table 2—Measurements to date taken by the

silviculture research group in the stand-level phase II

study in the Interior Highlands

Schedule Season Data collected

Preharvest Winter 1992–93 Overstory

Harvesting occurred Summer 1993 Nonea

Postharvest year 0 Winter 1993–94 Overstory

Postharvest year 1 Winter 1994–95 Regeneration

Postharvest year 2 Winter 1995–96 Overstory

Postharvest year 3 Winter 1996–97 Regeneration

Postharvest year 4 Winter 1997–98 Overstory

Postharvest year 5 Winter 1998–99 Regeneration

a None taken during harvest.
17

inventory tally was designed to sample regeneration 1 year

after the harvest, but was subsequently modified to sample

the entire cohort of woody plants with d.b.h. ≤ 3.5 inches.

Thus, in postharvest year 1, all woody plants between 3

inches in total height to > 4.5 feet in height but with a d.b.h.

≤ 0.5 inch were tallied on 6 milacres located within the 0.1-

acre fixed radius plot. In postharvest year 3 and subsequently, all woody plants between 3 inches in height but with

a d.b.h. ≤ 3.5 inches were tallied on the same 6 milacres.

The tagged tree measurement was designed to follow height

and diameter growth of specific stems. Milacres were divided

into four quadrats along cardinal directions. In postharvest

year 1, the tagged tree sample was imposed on 2 milacres

per plot; in postharvest year 3 and subsequently, the tagged

tree tally was extended to all 6 milacres. During the first

measurement, the tallest pine and the tallest hardwood, if

present, per quadrat were tagged and measured for species

identity, root-collar diameter, and total height. During remeasurement all previously tagged trees were remeasured; in

addition, if a different pine or hardwood was taller than a

previously tagged pine or hardwood, the new dominant was

sampled and tagged.

ANALYSIS

A subset of stands based on rank within treatments was

selected that would be representative of general trends in

regeneration success from reproduction cutting methods.

Results are summarized from only one of the four stands in

each treatment. A broader analysis of the complete data set

will be reported elsewhere.

The decision variable upon which the subset is based was

milacre stocking after the fifth growing season. Thus, in all

treatments except the clearcutting treatment, the four stands

were ranked by percent milacre stocking in the pine component within the TMZ. The stand with the second-lowest milacre stocking was included in this subset to represent that

treatment. The clearcut treatment was represented by the

one stand in that treatment with the highest pine milacre

stocking.

Milacre stocking was calculated as the proportion of the 72

milacres per stand stocked with at least one pine seedling.

Regeneration density for both pine and desired hardwoods,

defined as white oak (Quercus alba L.) and southern red

oak (Q. falcata Michx.), was calculated as the sum of all

pines or desired hardwoods on the 72 milacres per stand

converted to a per-acre basis.

Pine height was calculated by averaging the height of all

tagged pines after the fifth growing season. Thus, this variable represents the average height of the tallest pines in

the regeneration cohort, not the average height of all pines

in the regeneration cohort.

Acceptable standards for pine and hardwood regeneration

density were set at 300 trees per acre in even-aged stands,

and 200 trees per acre in uneven-aged stands (Baker and

others 1996). These are the standards used by the Ouachita

National Forest. If the trees are well spaced, the standards

translate to 30 and 20 percent pine milacre stocking for

even-aged stands and uneven-aged stands, respectively.

Minimum acceptable height growth for pines is 0.5 feet per

year; therefore, the third standard of acceptable height

growth is whether seedlings exceed 2.5 feet in height after

the fifth growing season.

RESULTS

Regeneration Density

In the even-aged treatments, acceptable regeneration density for shortleaf pine and for desired hardwoods is 300 trees

Figure 1—Density of regeneration, shortleaf pine, and combined white and southern red oaks

after five growing seasons, by treatment (cf. table 1). Dashed vertical lines represent minimum

acceptable standard for corresponding treatments.
18

per acre (fig. 1). The clearcut treatment supported more than

three times this number of pines, and more than double the

minimum number of desired hardwoods. The pine regeneration density was not surprising, because the clearcut was

planted in the spring of 1994 with genetically improved 1-0

shortleaf pine seedlings on an 8-foot x 10-foot spacing (544

trees per acre). Scattered volunteer pine seedlings that

seeded in naturally from adjacent stands boosted stocking.

Virtually all of the white and southern red oaks are of stump

sprout or seedling sprout origin.

Desired hardwood regeneration exceeded the minimum

acceptable stocking in all even-aged seed tree and shelterwood treatments (fig. 2). Four treatments out of five had

more than 1,000 stems per acre of desired hardwood

regeneration.

In two of the three shelterwood treatments, pine regeneration exceeded the minimum acceptable density by between

threefold and fourfold. Pine regeneration density was also

greater here than in the clearcut stands. The third shelterwood treatment yielded just slightly less than the minimum

acceptable density. Conversely, both seed-tree treatments

fell well below the acceptable minimum density of pine

regeneration.

The minimum acceptable regeneration density for shortleaf

pine and for desired hardwoods in the uneven-aged treatments is 200 trees per acre. All uneven-aged treatments

exceeded this minimum number in the desired hardwood

component, varying between 1,000 and 2,000 stems per

acre.

In the pine component, all of the single-tree selection treatments produced adequate pine regeneration density. Only

one of the two group selection treatments yielded an acceptable number of pines. Stem density in the single-tree selection treatments varied from 800 to 10,000 trees per acre.

The unmanaged control treatment supports acceptable densities of pine and hardwood regeneration as well (fig. 1).

More than 700 pines and 1,500 desired hardwood seedlings and saplings per acre are present.

Milacre Stocking

In the even-aged treatments, minimum acceptable milacre

stocking of shortleaf pine is 30 percent. After the fifth growing season, milacre stocking of pine exceeded 60 percent

in the clearcutting treatment (fig. 1). The unmanaged control, as expected, had the lowest pine milacre stocking of all

treatments—< 10 percent.

In the even-aged seed tree and shelterwood stands, milacre

stocking of shortleaf pine was much lower than in the clearcut stand. However, pine milacre stocking in two of the three

shelterwood treatments exceeded the minimum of 30 percent, and the third was < 5 percent below the minimum.

Both seed-tree stands had pine milacre stocking less than

half the minimum acceptable stocking.

For the uneven-aged single tree and group selection treatments, minimum acceptable milacre stocking of pine is 20

percent (fig. 2). Three of the four single-tree selection treatments exceeded this minimum. The fourth, the low-impact

single-tree selection, contained < 2 percent below the minimum. Neither of the group selection treatments contained

more pine seedlings than the minimum.

Height of Shortleaf Pine Regeneration

In all treatments, acceptable development of dominant pines

was judged by whether seedlings exceeded 2.5 feet in height

after the fifth growing season. As with the other standards

Figure 2—Milacre stocking, percent, for shortleaf pine regeneration by treatment (cf. table 1).

Dashed vertical lines represent minimum acceptable standard for corresponding treatment.
19

of regeneration success, the clearcut treatment was best,

with dominant pines exceeding 6 feet in height (fig. 3). Of

the other five even-aged treatments, four exceeded the

minimum standard, and two supported dominant pines

whose average height exceeded 5 feet.

In the uneven-aged stands, the average height of dominant

pines exceeded the minimum in three of the four single-tree

selection stands, and was fractionally less than the minimum

in one of the group selection stands (fig. 3). In all of the

uneven-aged stands, the average height of dominant pines

was < 5 feet after 5 years.

The average height of dominant pines in the unmanaged

control stand was < 0.5 foot after five growing seasons.

Seedlings in this treatment apparently can become established and can persist, but they grow very little.

DISCUSSION

Judging success of pine regeneration after five growing

seasons requires consideration of three standards—acceptable density, acceptable stocking, and acceptable growth.

As expected, the clearcut treatment has been most successful. Pine regeneration density exceeded 1,000 trees per acre,

milacre stocking of 60 percent was double the minimum,

and the height of dominant pines exceeded 6 feet after 5

years. These data all exceeded minimum standards by a

comfortable margin.

However, several of the reproduction cutting treatments that

rely on natural regeneration also exceeded all three standards. Specifically, two of the three shelterwood stands met

or exceeded minimum standards; the third, which fell slightly

below minimum in both regeneration density and milacre

Figure 3—Height of dominant shortleaf pine saplings after five growing seasons by treatment

(cf. table 1). Dotted vertical line represents minimum acceptable standard across all

treatments.

stocking, exceeded the other two in average dominant pine

height.

Three of the four single-tree selection treatments also

exceeded all three minimum standards, and the fourth, the

low-impact single-tree selection stand, was only slightly

below the minimum in both regeneration density and milacre

stocking. The relatively high levels of overstory basal area

in these single-tree selection stands was expected to hinder

regeneration development, but this was apparently not the

case over the first 5 years of the study. However, these

stands are expected to grow approximately 2 square feet

per acre of basal area annually. Thus, over the typical 10-

year cutting cycle expected in these stands, the most critical

years for overstory suppression of regeneration development are probably the second, rather than the first, 5-year

period.

Between 1992 and 1996, about 60 percent of the evenaged reproduction cuts on the Ouachita National Forest

were done using the seed-tree method; between 1992 and

1995, about one-fifth of the uneven-aged reproduction cuts

on the same forest were done using group selection (Guldin

and Loewenstein 1999). The data presented here suggest

that these two treatments do not consistently exceeded

minimum standards for regeneration establishment and

development. Both seed-tree stands yielded less that the

minimum standard in pine density and pine milacre stocking; both group selection treatments yielded less than the

minimum standard in pine density and pine height growth.

A decision about whether a stand is successfully regenerated

is commonly required on national forests during the first 5

years after harvest. These data raise a question about that
20

standard, especially in uneven-aged stands. Overstory

basal area is likely to reach a maximum during the last half

of the cutting cycle rather than the first half. Seedling performance should be followed through the next cutting cycle

harvest to determine whether regeneration development

continues to be acceptable in the single-tree selection

stands.

Before they can comfortably rely on natural regeneration,

foresters need to know whether acceptable density and

stocking of regeneration can be retained through the

scheduled removal cut in the even-aged seed tree and

shelterwood stands, and through the next cutting cycle in

the uneven-aged stands. In this study, both are scheduled

for the summer of 2003, the tenth growing season of this

regeneration cohort. Ideally, this study should be continued

beyond that point, so that these and other unresolved

questions can be addressed.

LITERATURE CITED

Baker, James B.; Cain, Michael D.; Guldin, James M. [and others].

1996. Uneven-aged silviculture for the loblolly and shortleaf

pine forest cover types. Gen. Tech. Rep. SO-118. Asheville,

NC: U.S. Department of Agriculture, Forest Service, Southern

Research Station. 65 p.

Guldin, James M.; Baker, James B.; Shelton, Michael G. 1994.

Midstory and overstory plants in mature pine-hardwood stands

on south-facing slopes of the Ouachita/Ozark National Forests.

In: Baker, James, comp. Proceedings of the symposium on

ecosystem management research in the Ouachita Mountains:

pretreatment conditions and preliminary findings. Gen. Tech.

Rep. SO-112. New Orleans: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment Station: 29-49.

Guldin, James M.; Loewenstein, Edward F. 1999. Silvicultural

practices. In: Ozark-Ouachita Highlands assessment: terrestrial

vegetation and wildlife. Rep. 5 of 5. Gen. Tech. Rep. SRS–35.

Asheville, NC: U.S. Department of Agriculture, Forest Service,

Southern Research Station: 73–102. Chapter 4.
21

INTRODUCTION

Riparian areas are located directly adjacent to water and

are a transition between upland and aquatic ecosystems

(Svejcar 1997; Hansen and Law 1994). These riparian

areas have many essential functions. They display high

levels of diversity (Naiman and others 1998), are important

in maintaining water quality (Anderson 1992), reduce flood

occurrence (Stuart and others 1994), provide shade for

streams thereby reducing water temperature, and provide

shelter for birds and other animals (Anderson 1992). The

extent of a riparian area surrounding rivers varies depending on the type of river system, vegetation, topography and

soils in the landscape.

Ephemeral streams primarily flow during snowmelts and

rainstorms, thereby producing temporally and seasonally

diverse environmental conditions for plant growth and adaptation (Banner and MacKenzie 1998). Given the spatially

and temporally diverse nature of ephemeral streams, vegetation bordering these streams could be unique because of

the need for plants to survive in both seasonally hydric and

xeric environments. Species diversity in these areas could

also be high because diversity and richness is frequently

found to be greatest in areas where the disturbances are

ephemeral in frequency and variable in their size and intensity (Malanson 1993). Thus, ephemeral riparian systems

have the potential to be important for enhancing landscape

and species diversity.

Within the Ouachita Mountains there are many ephemeral

streams. These streams likely comprise greater than 50 percent of stream channels within this region (Personal communication. 1999. Alan Clingenpeel, Ouachita National Forest).

Within the Ouachita National Forest, stream management

zones (SMZ) or buffers in upland pine-hardwood stands are

used to protect ephemeral streams and sustain the functions of the riparian vegetation and soils bordering these

streams (U.S. Department of Agriculture, Forest Service

1990). Guidelines require that SMZ of at least 10 m width

be installed on each side of ephemeral stream channels

that are scoured (U.S. Department of Agriculture, Forest

Service 1990). If a steep slope borders the stream, the

SMZ is to be extended to the top or break of the slope.

Forest management activities such as tree removal are

minimized in SMZ to reduce disturbance to these areas.

SMZ along ephemeral streams account for approximately

8-14 percent of area within shortleaf pine-hardwood stands

growing on southwestern facing slopes in the Ouachita

Mountains (David K. Radabaugh. 1999. Unpublished data.

On file with: School of Forest Resources, University of

Arkansas at Monticello, Monticello, AR 71656).

Although the primary objective of installing SMZ along

ephemeral streams in the Ouachita Mountains is to protect

water quality, land managers expect that these SMZ will

also increase landscape diversity and wildlife habitat. However, to our knowledge there have been few if any studies

in this region to quantify whether these ephemeral SMZ

accomplish any of these secondary objectives. It is not

known whether or to what degree the vegetation within

these riparian communities differs from the surrounding

upland forest communities. Without a fundamental knowledge of the vegetation bordering ephemeral streams it is

difficult, if not impossible, to determine their value for fulfilling stand and landscape management objectives or

developing management plans to sustain the ecological

functions of these areas.

Characterization of the woody vegetation is the initial step

to fully understand and better manage riparian communities

that border ephemeral streams. Therefore, the objectives of

this study were (1) to quantify the woody vegetation composition and examine the vegetative community characteristics, such as diversity and density, within SMZ adjacent to

ephemeral streams in pine-hardwood stands and (2) to

compare the composition and characteristics of plant

A COMPARISON OF VEGETATION WITHIN AND OUTSIDE RIPARIAN AREAS

BORDERING EPHEMERAL STREAMS IN THE OUACHITA MOUNTAINS

David K. Radabaugh, Hal O. Liechty, and James M. Guldin1

1 Graduate Student and Assistant Professor, School of Forest Resources, University of Arkansas at Monticello, Monticello, AR 71656; and

Project Leader, USDA Forest Service, Southern Research Station, Monticello, AR 71656, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Ephemeral streams frequently occur in shortleaf pine (Pinus echinata Mill.) hardwood stands that grow on the

upper and mid-slopes of the Ouachita Mountains in Arkansas. Stream management zones are established around these

ephemeral streams in the Ouachita National Forest to minimize impacts of adjacent forest management activities. To

better understand the vegetation communities in these riparian areas, we quantified composition, density and diversity of

the woody vegetation within these riparian areas and upland areas outside these zones. Overstory density outside

riparian areas was significantly greater than inside (p < 0.001) but seedling density was significantly higher inside than

outside the riparian area (p = 0.07). The overstory was more diverse within the riparian areas than outside. However,

midstory, sapling, nor seedling diversity significantly differed between the two areas. Pines dominated the overstory in

riparian and nonriparian areas alike. The midstory, sapling, and seedling size classes contained a greater species

richness and were not dominated by any one single species as was the overstory.
22

communities within these SMZ to plant communities outside riparian areas in pine-hardwood stands located in the

Ouachita Mountains.

METHODS

Study Design and Location

Woody vegetation measurements utilized for this study were

collected at two different times. The first measurements were

collected in 1993, from 16 relatively undisturbed, mature

shortleaf pine-hardwood stands occurring on generally southfacing slopes in the Ouachita Mountains. These 16 stands

were a subset of the stands used for the Phase II Ouachita/

Ozark Ecosystem Management Study described by Baker

(1994). Stands had a minimal size of 14 ha, were dominated

by shortleaf pine that were at least 70 years old, occupied

south or south-west slopes, and contained between 13.8 to

25.3 m2 per ha of pine as well as between 4.6 to 11.5 m2

per ha of hardwood basal area (Baker 1994). Overstory and

midstory tree measurements utilized for our study were from

two woody overstory and midstory plots established in the

SMZ as well as from two of twelve overstory and midstory

plots established outside the SMZ (Guldin and others 1994)

in each of the stands. Plots outside the SMZ were selected

such that they occurred on similar slope positions and as

near as possible as those within the SMZ.

Saplings and seedlings were measured during 1999 in four

of the original 16 stands. These four stands had been

selected as controls, as part of the Phase II USDA Forest

Service Ecosystem Management Study, and were the only

stands of the original 16 that were not harvested. Three

plots corresponding to lower-, mid-, and upper-slope positions were established in at least one SMZ in each of these

four stands. The plots were 15 m wide while the length of

the plot spanned the entire width of the SMZ. Thus plot

length varied from location to location but averaged 32.6 m.

The plots were placed at least 120 m apart and 75 m or

more from the stand boundary or the upslope end of a SMZ.

An additional 15 x 30 m plot was established adjacent to

each of these plots but at a distance of at least 40 m outside the edge of the SMZ. The plots outside the SMZ were

located at the same aspect and slope position to the corresponding plot located within the SMZ.

Overstory and Midstory Measurements

Trees with a diameter at breast height (d.b.h.) ≥ 24.4 cm

were classified as overstory while trees with a d.b.h. ≥ 9.1

and < 24.4 cm were classified as midstory trees. Overstory

trees were sampled using variable radius plots. Species

and d.b.h. to the nearest 0.25 cm was recorded for each

tree sampled. All midstory trees were tallied by 1 cm

classes and species on a 0.04 ha fixed radius plot. A complete description of the midstory and overstory sampling

and measurement methodologies can be found in Guldin

and others (1994).

Sapling and Seedling Measurements

Woody stems > 3 cm in height with a d.b.h. ≤ 1.3 cm were

categorized as seedlings. Seedling presence and coverage

was quantified using 1 m2 quadrats. A transect was established along the length of the plot such that it passed through

the plot center. The quadrats were centered on the transect

at each sampling location. Sampling locations were established at equal distances from each edge of the plot so that

they mirrored each other along the length of the plot. The

first quadrat in the SMZ plot was placed 2 m from the SMZ

edge. The other quadrats were placed 5 m from the first.

When the middle of the plot was less than 5 m away from a

potential quadrat location, the quadrats were located half

the distance from the last established quadrats and the

center of the plot. Quadrats were evenly dispersed along

the transect in plots located outside the SMZ. Density was

determined by tallying the number of individuals of each

specific species located within the quadrats.

Saplings were defined as woody species with a d.b.h. > 1.3

cm and dbh < 9.1 cm. Species and d.b.h. of each sapling in

the 2 x 5 m plots that enclosed each of the 1 x 1 m seedling

plot were recorded. D.b.h. was measured to the nearest 0.1

cm.

Data Analysis and Summaries

Density was determined for overstory, midstory, saplings and

seedlings. Species diversity for each woody vegetation size

class and plot was calculated using the Shannon-Weaver

index (Shannon and Weaver 1949). Species richness values

were also determined for each plot in each area. To determine the degree of similarity between riparian and nonriparian woody vegetation Sorensen index (Sorensen 1948) and

Ellenberg index (Ellenberg 1956) were computed for each

vegetation size class. Paired t-tests were used to test

whether diversity, richness, and density significantly differed

between nonriparian and riparian plots.

Importance values were used to evaluate the differences in

species composition of the vegetation communities within

and outside the riparian areas. Importance values of seedlings were calculated using relative density (stems per ha),

relative dominance (percent cover) and relative frequency

(individual species) (Krebs 1985). Importance values of the

sapling, midstory and overstory were calculated using relative density (stems per ha), relative dominance (basal area

per ha), and relative frequency (individual species) (Krebs

1985).

RESULTS AND DISCUSSION

Density

Overstory stems per ha and basal area per ha were significantly greater (p < 0.01) in the plots inside than outside the

SMZ (table 1). On average the overstory in the nonriparian

plots contained 34 percent more trees and 36 percent more

basal area than overstory in the riparian plots (table 1).

Differences in density of the overstory did not appear to be

related to differences in growth or stand dynamics because

average quadratic mean diameters did not differ between

areas. It is more likely that the lower density in the riparian

areas reflected a reduction in growing space due to the

shallow, rocky soils on the steep slopes that often border

these streams and area occuppied by the stream channels

themselves. The shallow soil depths and rocky nature of the

slopes bordering many of these streams would likely reduce

tree survival and growth of trees in these areas. Crow and

others (2000) indicated that reduced moisture holding capacity of sandy soils in riparian areas may cause trees to be

scattered with low basal areas. Soils surrounding the ephemeral streams did not appear to be coarser than the areas
23

outside the SMZ and thus the cause of the reduced density

in the SMZ. Riparian plots generally had higher seedling

densities than nonriparian plots. Differences were signficant

at p = 0.072 (table 1). Midstory and sapling densities were

similar in the two areas. The higher densities of the seedlings within the riparian plots may reflect the lower densities

of the riparian overstory. The lower density of the overstory

in the riparian plots most likely provides light conditions

underneath the canopy that are more condusive to survival

and regeneration of trees in the lower strata than does the

higher overstory density in the nonriparian plots. On average, the riparian plots contained 78 percent more seedlings

than the nonriparian plots.

Diversity and Similarity

The riparian overstory was significantly more diverse (p <

0.01) than the nonriparian overstory (table 2). Differences

in diversity, in part, occurred because of the greater dominance of shortleaf pine in the nonriparian overstory. Pines

composed 92 percent of all overstory stems in the nonriparian plots compared to 71 percent in the riparian plots. The

higher diversity in the riparian plots also reflected greater

species richness in these plots (table 2). Species richness

of the overstory was significantly greater in riparian plots

than in the nonriparian plots (table 2). Midstory, saplings

and seedlings diversity nor species richness significantly

differed between the two areas (table 2). However, richness

and diversity values were consistently greater in the riparian areas regardless of size class.

It was apparent that diversity differed among size classes

within these areas. Overstory and seedlings were the least

diverse while the midstory size class was the most diverse.

The large diversity values in the midstory in part reflects a

greater diversity in the light environment of this strata. The

midstory size class includes shade intolerant species as

well as a number of mid-tolerant or shade tolerant species.

The dominant overstory species such as shortleaf pine and

post oak (Quercus stellata Wangenh.) are shade intolerant

or in the case of white oak (Q. alba L.) mid-tolerant. Trees

that dominate the midstory also included species such as

blackgum (Nyssa sylvatica L.) and winged elm (Ulmus alata

Michx.) that are shade tolerant. Sapling and seedling size

classes are primarily dominated by shade tolerant species

while importance values of shade intolerant species were

much lower than in overstory or midstory size classes

(tables 3-6). Differences in diversity among woody vegetation size classes may also reflect the number and distribution of plots utilized for the overstory and midstory plots (24

pairs of plots in 12 stands) compared to the sapling and

seedling plots (12 paired plots in 4 stands).

Overstory and midstory vegetation in the riparian areas

were relatively similar to that in the nonriparian areas.

Ellenberg’s and Sorensen’s indices indicated that the

Table 1—Mean stem density (stems ha-1), basal area

(m2

 ha-1) and probability level associated with paired

T-test comparison of riparian and nonriparian plots

Size class Riparian Nonriparian P-value

Stem density

Overstory 185 248 0.002

Midstory 491 480 0.790

Sapling 1516 1564 0.926

Seedling 6640 3733 0.072

Basal area

Overstory 16.7 22.3 < 0.001

Midstory 8.6 8.0 0.455

Saplings 2.0 2.6 0.498

Table 3—Riparian and nonriparian overstory

importance values

Species Riparian Nonriparian

Carya texana Buckl. 1.6 0.7

C. tomentosa (Poir.) Nutt. 3.0 0.7

Fraxinus pennsylvanica (Borkh.)

Sarg. 1.1

Liquidambar styraciflua L. 5.6 0.7

Nyssa sylvatica Marsh. 1.7 1.3

Pinus echinata Mill. 59.3 78.7

P. taeda L. 1.1

Quercus alba L. 9.3 7.1

Q. falcata Michx. var falcata 2.3 0.8

Q. marilandica Muenchh. 0.6

Q. rubra L. 1.5 0.6

Q. stellata Wangenh. 8.9 6.2

Q. velutina Lam. 1.8 2.6

Ulmus alata Michx. 1.8

U. americana L. 0.6

U. rubra Muhl. 0.6

Table 2—Mean Shannon-Weaver’s index computed

using stem density and basal area, mean richness, and

probability level associated with paired T-test

comparison for nonriparian and riparian plots

Size class Riparian Nonriparian P-value

SWI (Stem density)

Overstory 0.65 0.27 < 0.001

Midstory 1.52 1.39 0.201

Sapling 1.21 1.10 0.645

Seedling 0.92 0.89 0.880

SWI (basal area)

Overstory 0.62 0.25 < 0.001

Midstory 1.46 1.31 0.157

Sapling 0.96 0.99 0.830

Richness

Overstory 4.25 2.69 0.006

Midstory 8.81 8.13 0.086

Sapling 4.25 4.00 0.377

Seedling 3.83 3.17 0.221

SWI = Shannon-Weaver’s index.
24

could be attributed to the incised nature of the ephemermal

stream corridors. The borders of the SMZ in these upland

pine-hardwood stands contain short but steep slopes. These

slopes are relative dry and most likely similar to those in the

upland areas. Distribution of plant communities on either

side of streams usually reflects the gradient of environmental factors such as water availability which dominates

these areas (Hancock and others 1996). However, these

slopes increase drainage as well as elevate trees above

standing water during storm events. These factors create a

drier environment more suitable to upland species such as

shortleaf pine, black hickory (Carya texana Buckl.), mockernut hickory [C. tomentosa (Poir.) Nutt.], post oak, white oak,

black oak (Q. velutina Lam.), and blackjack oak (Q. marilandica Muenchh) than species typically found in riparian landscapes. This suggests that more hydric conditions exist

only in close proximity to the stream channel and for only

short periods of time. Thus stream effects on at least these

two size classes of vegetation are spatially and temporally

limited in these stream corridors.

Similarity indices for saplings or seedlings size classes were

much lower than those calculated for the overstory or midstory size classes. Similarities of 27.1 percent and 18.5 percent were calculated using Sorensen’s index for the saplings

and seedlings respectively. Ellenberg’s index indicated a

32.5 percent similarity between riparian and nonriparian

saplings. Reduction in vegetation similarity of the seedling

and sapling size classes compared to that of the midstory

and overstory may again reflect the differences in the number of plots utilized for index calculation of each group.

Table 5—Riparian and nonriparian sapling importance

values

Species Riparian Nonriparian

Acer rubrum L. 9.2 10.2

Carya texana Buckl. 2.6 7.0

C. tomentosa (Poir.) Nutt. 1.3 4.2

Chionanthus virginicus L. 1.4

Cornus florida L. 11.4 12.9

Crataegus marshallii Eggl. 1.5

Diospyros virginiana L. 1.0 1.2

Fraxinus pennsylvanica (Borkh.)

Sarg. 1.6

Juniperus virginiana L. 1.8 1.6

Liquidambar styraciflua L. 10.8 3.4

Ostrya virginiana (Mill.) K.Koch 19.5 11.5

Nyssa sylvatica Marsh. 12.8 11.2

Pinus echinata Mill. 2.2 4.3

P. taeda L. 1.6

Prunus serotina Ehrh. 1.6 1.2

Quercus alba L. 4.1 9.1

Q. marilandica Muenchh. 1.0 2.5

Q. stellata Wangenh. 1.2 7.5

Rhamnus caroliniana Walt. 1.8

Ulmus alata Michx. 12.1 5.9

Vaccinium arboreum Marsh. 1.0 4.3

Vitis rotundifolia Michx. 0.8

Table 4—Riparian and nonriparian midstory importance

values

Species Riparian Nonriparian

Acer rubrum L. 4.7 0.3

Amelanchier arborea

(Michx. f.) Fern. 0.5 0.1

Carya texana Buckl. 8.4 3.6

C. tomentosa (Poir.) Nutt. 7.4 3.6

Cornus florida L. 1.5 4.3

Hardwood misc. 1.3

Juniperus virginiana L. 0.9 1.3

Liquidambar styraciflua L. 5.2 2.4

Nyssa sylvatica Marsh. 5.2 3.0

Ostrya virginiana (Mill.) K.Koch 1.1

Pinus echinata Mill. 18.0 28.6

Prunus serotina Ehrh. 0.5 0.1

Quercus alba L. 11.7 16.0

Q. falcata Michx. var falcata 2.1 3.2

Q. marilandica Muenchh. 0.3 6.2

Q. stellata Wangenh. 16.7 9.2

Q. rubra L. 1.8 0.4

Q. velutina Lam. 2.7 10.0

Ulmus alata Michx. 9.1 7.5

U. americana L. 0.3

U. rubra Muhl. 0.7 0.2

Table 6—Riparian and nonriparian sapling importance

values

Species Riparian Nonriparian

Acer rubrum L. 3.5 15.0

Carpinus caroliniana 0.9

Carya texana Buckl. 0.9 5.6

C. tomentosa (Poir.) Nutt. 1.1

Cercis canadensis L. 0.9

Cornus florida L. 14.9 26.1

Crataegus marshallii Eggl. 0.6

Fraxinus pennsylvanica (Borkh.)

Sarg. 3.7

Hardwood misc. 2.3

Juniperus virginiana L. 0.9 0.9

Liquidambar styraciflua L. 1.0

Nyssa sylvatica Marsh. 14.9 7.6

Ostrya virginiana (Mill.) K. Koch 24.9 4.1

Prunus serotina Ehrh. 1.7

Quercus alba L. 13.1 8.1

Q. marilandica Muenchh. 0.6 4.3

Q. phellos L. 1.0 0.9

Q. rubra L. 0.9 1.5

Q. stellata Wangenh. 5.7 8.1

Q. velutina Lam. 6.3 3.4

Rhamnus caroliniana Walt. 7.5 5.5

Rhus aromatica Ait 1.7

Ulmus alata Michx. 0.3 3.2

overstory was respectively 84.8 percent and 66.6 percent

similar while the midstory was 74.4 percent and 71.8 percent similar. The high degree of similarity in these two areas
25

However, several species in the seedling and sapling size

classes occur on the riparian plots that do not occur in the

nonriparian plots and one or two species occur in the nonriparian plots that do not occur in the riparian plots. Many of

the saplings and seedlings occuring in these areas are considered late successional species within these ecosystems.

Late successional species are typically resource specialists

(Odum 1997). Compared to early successional species in

the larger size class, these late successional species may

better reflect subtle differences in environmental conditions

within or outside the influence of these streams, thus creating the observed differences in similarity among size classes.

Composition

Shortleaf pines dominated the overstory with importance

values of 59.3 percent and 78.7 percent for the riparian and

nonriparian areas respectively (table 3). These two areas

are primarily composed of species that are shade intolerant, favor relative dry habitats, and are intolerant of inudation (Hook 1984). No one species dominated either the

riparian or nonriparian midstory. The importance value of

any one species never exceeded 29 percent (table 4).

Shortleaf pines and oaks (post, white and black) were major

components of both riparian and nonriparian areas. Black

oak and blackjack oak are two species that were prevalent

in the nonriparian midstory. The midstories of both areas

were comprised of intolerant to intermediate shade tolerant

species but some shade tolerant species such as flowering

dogwood (Cornus florida L.) and ironwood (Ostrya virginiana

Mill.) also occurred. The sapling and seedling size classes

were equally dominated by six to seven species (table 5).

White oak was the only species that had importance values

greater than 10 percent within the seedling or sapling size

classes as well as in the midstory and overstory size classes.

Species such as flowering dogwood, ironwood, blackgum,

and red maple (Acer rubrum L.) which were only minor components in the overstory and midstory were major components of the woody vegetation communities in these smaller

size classes (table 5). Species which are shade intolerant

generally had low importance values while more shade

tolerant species had high importance values in the sapling

and seedling size classes (tables 5 and 6). These changes

in composition reflected the lower light levels within these

forest strata.

CONCLUSION

Woody vegetation within SMZ bordering ephemeral streams

within shortleaf pine-hardwood stands generally was found

to be significantly more diverse and have a greater species

richness than woody vegetation growing outside the influence of these streams. Differences in diversity and richness

between riparian and nonriparian areas were generally

greater in the larger than smaller size classes. The density

of the overstory woody vegetation was greater outside than

inside these SMZ but did not significantly differ in the smaller

size classes. The riparian vegetation communities in these

SMZ appear to contribute to the overall diversity of the landscape. However, due to the relatively small size of these

streams and their relatively minor impact on moisture

regimes, vegetation communities are much more similar to

upland communities than would be expected for riparian

communities growing along perrenial streams with larger

floodplains.

LITERATURE CITED

Anderson, S. 1992. Water quality series: riparian forest buffers.

Cooperative Extension Service. Division of Agriculture Sciences

and Natural Resources. Oklahoma State University. 6 p.

Baker, J.B. 1994. An overview of stand-level ecosystem management research on the Ouachita/Ozark National Forests. In:

Baker, J.B., comp. Proceedings of the symposium on ecosystem

management research in the Ouachita Mountains: pretreatment

conditions and preliminary findings. Gen. Tech. Rep. SO-112.

New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station: 29-49.

Banner, A.; MacKenzie, W. 1998. Riparian areas: providing landscape habitat diversity part 5 of 7. British Columbia Ministry of

Forests Research Program

Crow, T.R.; Baker, M.E.; Barnes, B.V. 2000. Diversity in riparian

landscapes. In: Ed. Verry, E.S., Hornbeck, J.W. and C.A. Dolloff.

Riparian management in forests. C.A. Lewis Publishers. 402 p.

Ellenberg, H. 1956. Aufgaten und methoden der vegetationskunde.

Stuttgart:Eugen Ulmer.

Guldin, J.G.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine-hardwood stands on south-facing

slopes of the Ouachita/Ozark National Forests. In: Baker, J.B.,

comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Gen. Tech. Rep. SO-112. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 18-28.

Hancock, C.N.; Ladd, P.G.; Froend, R.H. 1996. Biodiversity and

management of riparian vegetation in Western Australia. Forest

Ecology and Management 85:239-250.

Hansen, W.F.; Law, D.L. 1994. Riparian management on the

National Forests in South Carolina. In: Proceedings of conference on riparian ecosystems in the humid U.S.; functions,

values and management. USDA Forest Service, Cooperative

Forestry Atlanta, Ga. 56-64.

Hook, D.D. 1984. Waterlogging tolerance of lowland tree species

of the South. Southern Journal of Applied Forestry 8:136-149.

Krebs, C.J. 1985. Ecology: the experimental analysis of distribution and abundance. (Third Edition). Harper & Row Publishers

Inc. New York, NY. 800 p.

Malanson, G.P. 1993. Riparian landscapes. Cambridge University

Press. New York, NY. 296 p.

Naiman, R.J.; Fetherston, K.L.; McKay, S.J.; Chen, J. 1998.

Riparian forests. In: Ed. Naiman, R.J. and Bilby, River ecology

and management. R.E.Springer-Verlag. New York, NY. 705 p.

Odum, E.P. 1997. Ecology: a bridge between science and society.

Sinauer Associates, Inc. Massachusetts. 331 p.

Shannon, C.E.; Weaver, W. 1949. The mathematical theory of

communication. Urbana: University of Illinois Press.

Sorensen, T. 1948. A method of establishing groups of equal

amplitude in plant sociology based on similarity of species

content. Det Kong. Danske Vidensk. Selsk. Siol. Skr.

(Copenhagen) 5:1-34.

Stuart, G.; Dolloff, A.C.; Corbett, E.S. 1994. Riparian area functions and values – a forest perspective. In: Proceedings of

conference on riparian ecosystems in the humid U.S.; functions, values and management. U.S. Department of Agriculture,

Forest Service, Cooperative Forestry. Atlanta, GA. 81-86.

Svejcar, T. 1997. Riparian zones: (1) what are they and how do

they work? Rangelands 19:4-7.

USDA Forest Service. 1990. Amended land and resource management plan. Ouachita National Forest. Vol. 1. For. Serv.,

Ouachita Nat. For., Hot Springs, AR.
26
27

Session 2

Phase II Wildlife Research

Larry D. Hedrick, Moderator
28
29

INTRODUCTION

Small mammals play key roles in ecosystem processes.

They occupy important trophic levels of the food web, often

serving both as predators and as prey for many vertebrates.

Fungus-consuming species are largely responsible for dispersal of hypogeous fungal spores that form mycorrhizae,

which are required by most higher plants for optimum growth

and health (Maser and others 1978). Many species of small

mammals consume detrimental insects (for example, see

Elkinton and others 1996) and aid in seed dispersal. Their

digging and burrowing improve soil aeration and friability,

and can significantly influence forest hydrology (Ursic and

Esher 1988).

Consumption of pine seeds by small mammals can adversely

affect pine regeneration (Pank 1974, Smith and Aldous 1947,

Stephenson and others 1963), but impacts likely vary from

trivial during years of bumper seed crops to devastating in

years of below average seed production (Wittwer and

Shelton 1992). For shortleaf pine (Pinus echinata Mill.) in

the Ouachita and southern Ozark Mountains, seed production typically follows a feast or famine pattern with roughly

one-third of the seed crops “good” or “better” (≥80,000

sound seeds per acre) (Shelton and Wittwer 1996). Seed

production varies widely from year to year, and long periods

of low seed production may occur, especially in the drier

western portions of the Ouachita Mountains. Given this

variability in shortleaf pine seed production, small mammal

population responses to silvicultural practices that rely on

natural regeneration are of paramount importance.

In response to growing public concern over clearcutting and

hardwood control practices on national forests in Arkansas

and Oklahoma, a long-term, multidisciplinary, stand-level,

research and demonstration project was begun in the

Ouachita Mountains in 1991 (Baker 1994). A primary objective of this research is to compare effectiveness of different

partial cutting methods for natural regeneration of shortleaf

pine relative to clearcutting and planting. As part of this multidisciplinary project, we are evaluating temporal changes in

small mammal and bird communities and habitat conditions

under these cutting treatments, which were implemented

during the summer of 1993. We studied small mammal

populations during two winters prior to harvesting (Tappe

and others 1994). Here we summarize preliminary findings

on small mammal responses to alternative pine regeneration methods at the ends of the first, third, and fifth growing

seasons after harvest.

METHODS

Study Areas

Four replications of five treatments, blocked by physiographic

zones (Baker 1994), were randomly assigned to 20 stands

located on 9 districts of the Ouachita National Forest and

the southernmost district of the Ozark-St. Francis National

Forest (Thill and others 1994). All stands were late-rotation

(≥60 years old), ≥35 acres in size (and blocky in shape to

minimize confounding edge influences), and occupied predominantly south, southeastern, or southwestern aspects.

All stands contained ephemeral, or occasionally intermittent,

stream drainages that typically flow only for short periods

after heavy storms. Unharvested buffer strips (“greenbelts”

henceforth) were retained along these drainages for watershed protection. Ephemeral and intermittent greenbelts were

typically 65 and 130 feet wide, respectively. Greenbelts

comprised 4 to 20 percent of stand acreage and averaged

10.9 percent across all 16 harvested stands.

INITIAL SMALL MAMMAL RESPONSES TO ALTERNATIVE PINE

REGENERATION METHODS IN ARKANSAS AND OKLAHOMA:

PRELIMINARY FINDINGS

Ronald E. Thill, Roger W. Perry, Nancy E. Koerth,

Philip A. Tappe, and David G. Peitz1

1 Supervisory Research Wildlife Biologist, USDA Forest Service, Southern Research Station, Nacogdoches, TX; Wildlife Biologist, USDA

Forest Service, Southern Research Station, Hot Springs, AR; Statistician, USDA Forest Service, Southern Research Station, Nacogdoches,

TX; Associate Professor and Research Associate, School of Forest Resources and Arkansas Forest Resources Center, University of

Arkansas at Monticello, Monticello, AR, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—We studied winter small mammal communities in the Ouachita Mountains of west-central Arkansas and eastern

Oklahoma using Sherman live traps in four replications of four regeneration treatments (clearcut/plant, shelterwood, singletree selection, and group selection), plus four mature, untreated “controls.” Data on relative small mammal abundance,

species richness, and diversity after one, three, and five growing seasons after harvest are presented. Capture success

(all species included) generally peaked in all harvested treatments after the first growing season, declined markedly thereafter, but remained 2.6 (single tree) to 4.1 (clearcut) times greater than in controls five growing seasons after harvest.

Total small mammal abundance in clearcut and shelterwood stands was significantly higher (P≤0.05) than in controls all

years. With data for all species included, species richness and diversity did not differ among harvested stands in any year,

but shelterwood stands had higher richness and diversity than controls in the third growing season.
30

Pretreatment conifer basal area for all 20 stands averaged

76.7 square feet per acre and consisted almost entirely of

shortleaf pine and a few eastern redcedars (Juniperus

virginiana L.); hardwood basal area averaged 36.6 square

feet per acre (Thill and others 1994). Stand conditions

immediately after treatment are described by Thill and

others (2000).

For additional information on study areas, climate, geology,

stand selection, and experimental design, see Baker (1994)

and Thill and others (2000). For information on pretreatment

stand conditions and wildlife habitat characteristics, see

Guldin and others (1994) and Thill and others (1994),

respectively.

Treatments

Although the larger study involves 13 treatments, we chose

a subset of treatments where an overstory hardwood component was retained to improve wildlife habitat and aesthetics. We also included clearcutting, which had been the

principal Forest Service pine regeneration method in the

Ouachitas for decades. Four treatments (clearcut, shelterwood, single-tree selection, and group selection) plus

untreated controls were compared. Harvesting was completed between June 1 and September 30, 1993; site

preparation, where needed, was conducted during the

winter of 1993-94.

Clearcut—All merchantable pines and hardwoods were

harvested except for 2 to 5 square feet per acre of hardwood basal area retained for mast production and/or cavity

and den sites. All trees not harvested or retained were

injected with herbicide (Baker 1994). After harvesting, all

clearcuts were to be mechanically ripped to facilitate pine

planting, but contractors could not be located to rip two

stands. With this exception, all clearcuts were treated

identically (Thill and others 2000). Genetically improved

shortleaf pines were planted on an 8- by 10-foot spacing

between December 1994 and March 1995. One stand was

replanted in February 1996 due to inadequate stocking.

Shelterwood—From 20 to 40 of the largest pines and hardwoods per acre were retained. The basal area of trees that

were left was 30 to 40 square feet per acre, of which 5 to

15 square feet per acre were overstory hardwoods. All other

pines and hardwoods were harvested or felled and left on

the ground.

Single-tree selection—Partial harvest of pines and hardwoods resulted in residual basal areas of 45 to 65 square

feet per acre. From 5 to 20 square feet per acre of the

residual basal area was in hardwoods. Site preparation

consisted of removing all hardwoods <5.9 inches diameter

at breast height (d.b.h.).

Group selection—Approximately 10 percent of each stand

was clearcut in openings generally ranging from 0.5 to 2.0

acres in size. Most pines were cut within these openings,

but 5 to 10 square feet per acre of overstory hardwood basal

area was retained. Within the matrix surrounding these

openings, pines were thinned to 70 to 80 square feet per

acre basal area, but no hardwoods were removed. Site

preparation in group openings consisted of chain-saw

felling all hardwoods <5.9 inches d.b.h.; no site preparation

occurred in the surrounding matrix.

Untreated controls—These areas supported secondgrowth, late rotation (62 to 76 years old), largely evenaged, pine-hardwood stands. Management consisted of

protection from wildfire and insects.

Transects/Trap Stations

Eighty permanent trap stations were established along

transects oriented parallel to topographic contours of each

stand. No two transects were closer than 98 feet, trap

stations were at least 49 feet apart, and no station was

closer than 164 feet from stand boundaries (Thill and

others 1994).

Trapping

Small mammals were trapped for seven consecutive nights

between January 5-14 in 1995, 1997, and 1999. Consequently, our data characterize small mammal communities

present one, three, and five full growing seasons after harvesting. Two Sherman live traps (3.0 by 3.5 by 9.0 inches)

were placed at each of the 80 trap stations in each stand to

ensure opportunities for multiple captures per trap station.

These traps are sufficient to capture mammals as small as

southern short-tailed shrews (Blarina carolinensis) and as

large as eastern woodrats (Neotoma floridana); however,

we were unable to adjust trigger sensitivity enough to consistently capture least shrews (Cryptotis parva). Traps were

placed on bare ground within 16.4 feet of each station

center, and (where possible) adjacent to down logs, burrows,

stumps, and rocks to increase trap success. At least eight

(10 percent) of the trap stations were placed within greenbelts if transects crossed sufficient greenbelt area.

Traps were baited with rolled oats, and cotton was placed

in each trap to minimize trap mortality. Captured mammals

were marked and released at the site of capture after

recording species, sex (when possible), and trap location.

Small mammal nomenclature follows Sealander and Heidt

(1990). Because accurate separation of our sympatric

Peromyscus species is not possible in the field (Laerm and

Boone 1994, Rich and others 1996), all white-footed mice

(Peromyscus leucopus), cotton mice (P. gossypinus), Texas

mice (P. attwateri), and deer mice (P. maniculatus) were

grouped as Peromyscus spp.

Analyses

We computed captures per 100 trap nights as an index of

relative abundance. Total available trap nights were computed by adjusting for empty sprung traps; traps that contained recaptured animals were also considered unavailable.

An index of species richness was calculated as the number

of species encountered on each area over the 7-day trapping period. Species diversity (Shannon-Weiner diversity

index) was based on these composited data (Magurran

1988). Differences among treatments for all variables were

evaluated using one-way ANOVAs (n = 20 stands). We used

REGWQ multiple range tests (alpha = 0.05) to separate

means (SAS Institute Inc. 1988). Analyses were run with

and without Peromyscus data to ensure that the relatively
31

high abundance of this group was not masking treatment

effects. Variances of new captures per 100 trap nights were

heterogeneous in 1995 with Peromyscus data included and

in 1997 and 1999 with Peromyscus excluded; in each of

these cases, values were rank transformed prior to analyses

of variance. Except where noted, presented results include

Peromyscus data.

Posttreatment surveys sampled a mixture of habitat conditions within the 16 treated stands included treated openings

and thinned areas, temporary logging roads, and greenbelts.

These differences within treated areas are ignored and small

mammal responses to operational, stand-level conditions

are reported here.

RESULTS AND DISCUSSION

Trap Success

Small mammal captures (excluding recaptures) across all

treatments totaled 1,501; 1,151; and 1,091 during 1995,

1997, and 1999. Total trap nights, adjusted for recaptures

and empty sprung traps, were 20,560; 20,774; and 19,131;

respectively, for 1995, 1997, and 1999. Trap success across

all 20 stands declined from an average of 7.3 captures per

100 trap nights in 1995 to 5.5 in 1997 and 5.7 in 1999.

Relative Abundance

Approximately 1.5 years after harvest (January 1995), small

mammal trap success was about 3.0 to 5.5 times higher in

harvested stands than in controls (fig. 1). Clearcut and

shelterwood stands had higher capture rates than controls

0

2

4

6

8

10

12

Clearcut Shelterwood Single tree Group

selection

Control

New captures (no./100 TN)

1st year

3rd year

5th year

0

2

4

6

8

10

12

Clearcut Shelterwood Single tree Group

selection

Control

New captures (no./100 TN)

1st year

3rd year

5th year

(A)

(B)

A

A

A

A

A

A

AB A

AB

AB

AB

AB

B

B

B

A

A AB

AB AB

ABC AB

BC

B C

Figure 1—Relative abundance of small mammals (mean number of new captures per 100 trap

nights, all species combined) by treatment following the first, third, and fifth growing seasons after

harvesting pine-hardwood forests in the Ouachita Mountains of Arkansas and Oklahoma. Within

years, bars with different letters are significantly different (P≤0.05). Top graph (A) includes

Peromyscus spp.; bottom graph excludes Peromyscus spp.
32

all three sampling years; however, except for single-tree

selection in 1997, differences between single-tree and group

selection stands and controls were not significant (P>0.05)

(fig. 1). Among the four harvested treatments, small mammal

abundance was highest the first year after harvest, except

in single-tree selection stands. When data for the four

harvested treatments were averaged within years, small

mammal abundance was 4.6, 6.2, and 3.5 times greater

than in controls during 1995, 1997, and 1999, respectively.

With Peromyscus data excluded, clearcuts had significantly

greater relative abundance of small mammals than the controls during 1995. In 1997, clearcut and shelterwood stands

had significantly greater small mammal abundance than

controls; clearcuts also had greater (P<0.05) abundance

than group selection stands (fig. 1). By 1999, none of the

treatment differences were significant.

Species Richness

Ten taxa were captured, but five (Peromyscus spp., Reithrodontomys fulvescens, Blarina carolinensis, Ochrotomys

nuttalli, and Neotoma floridana) consistently comprised

over 95 percent of captures during all three sampling years

after treatment (table 1). Except for two least shrews captured in one clearcut during 1997, all species trapped after

harvest were also encountered prior to treatment (Tappe

and others 1994).

Small mammal species richness did not differ (P>0.05)

among treatments in 1995 or 1999 (fig. 2). The only significant difference occurred in 1997, when richness was 2.4

times greater in shelterwood stands than in controls.

Peromyscus spp. were present on all areas; excluding

Peromyscus did not change the relationships among the

treatments.

Diversity

As with species richness, there were generally no differences in small mammal diversity among treatments (fig. 3).

The only significant difference occurred in 1997 when diversity was 2.2 times greater in shelterwood than in control

stands. With Peromyscus data excluded, single-tree stands

were more diverse than control stands in 1997.

CONCLUSIONS

Prior to treatment, study areas were characterized by high

canopy coverage, an abundance of relatively small diameter

midstory and overstory hardwoods, and limited understory

browse and herbage (Thill and others 1994). These untreated,

late-rotation stands also were characterized by low small

mammal abundance, species richness, and diversity (Tappe

and other 1994). We suspect this condition is related, at

least partially, to limited soft and hard mast availability

[Perry and others, in press (a); in press (b)]. Following

harvest, these forage items increased rapidly.

Table 1—Nomenclature, trapping effort, total winter captures, and percent composition of small mammals by species/

species group following the first (1995), third (1997), and fifth (1999) growing seasons after harvesting of pinehardwood stands in the Ouachita Mountains of Arkansas and Oklahomaa

1995 1997 1999

Nomenclature Common name Captures Percent Captures Percent Captures Percent

Order Insectivora

Family Soricidae

Blarina carolinensis Southern short-tailed shrew 114 7.6 95 8.2 115 10.5

Cryptotis parva Least shrew 0 0.0 2 0.2 0 0.0

Order Rodentia

Family Muridae

Microtus pinetorum Woodland vole 5 0.3 31 2.7 33 3.0

Mus musculus House mouse 0 0.0 0 0.0 0 0.0

Neotoma floridana Eastern woodrat 82 5.5 71 6.2 101 9.3

Ochrotomys nuttalli Golden mouse 132 8.8 53 4.6 70 6.4

Oryzomys palustris Marsh rice rat 5 0.3 0 0.0 0 0.0

Peromyscus spp.b Mice 866 57.7 676 58.7 609 55.8

Reithrodontomys fulvescens Fulvous harvest mouse 277 18.4 213 18.5 143 13.1

Sigmodon hispidus Hispid cotton rat 19 1.3 9 0.8 5 0.5

Family Sciuridae

Glaucomys volans Southern flying squirrel 1 0.1 0 0.0 15 1.4

Tamias striatus Eastern chipmunk 0 0.0 1 0.1 0 0.0

Total captures: 1,501 1,151 1,091

Total trap nights (TN)c : 20,560 20,774 19,131

Total captures/100TN: 7.3 5.5 5.7

TN = trap nights.

a Logging within the 16 harvested stands occurred during summer 1993. Data from all 20 stands are combined.

b Includes P. leucopus, P. gossypinus, P. maniculatus, and P. attwateri. c Trap nights were adjusted for recaptures and sprung/empty traps; total unadjusted trap nights was 22,400 per year or 1,120 stand per year.
33

0

1

2

3

4

5

6

7

Clearcut Shelterwood Single tree Group selection Control

Species (no.)

1st year

3rd year

5th year

AB

A

AB

AB

B

Figure 3—Small mammal species diversity by treatment following the first, third, and fifth growing

seasons after harvesting pine-hardwood forests in the Ouachita Mountains of Arkansas and Oklahoma.

Within years, bars with different letters are significantly different (P≤0.05). Top graph (A) includes

Peromyscus spp.; bottom graph excludes Peromyscus spp.

Figure 2—Small mammal species richness by treatment following the first, third, and fifth growing

seasons after harvesting pine-hardwood forests in the Ouachita Mountains of Arkansas and

Oklahoma. Within years, bars with different letters are significantly different (P≤0.05). Statistical

results are similar with and without Peromyscus spp.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Clearcut Shelterwood Single tree Group selection Control

Diversity

1st year

3rd year

5th year

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Clearcut Shelterwood Single tree Group selection Control

Diversity

1st year

3rd year

5th year

(A)

(B)

AB

A

AB

AB

B

AB

A

A

AB

B
34

Single-tree and group-selection stands, largely even-aged

at study initiation, are in transition to an uneven-aged stand

structure. Before these stands attain this structure, they

must be harvested additional times and obtain satisfactory

pine regeneration and survival. Consequently, it is too soon

to draw definitive conclusions about small mammal

responses to even- and uneven-aged regeneration methods.

However, during the early stages of this transition, our data

suggest that both uneven-aged treatments should yield

similar small mammal species richness and diversity to

those yielded by the two even-aged treatments on sites

similar to those studied here. Likewise, although small

mammal abundance was somewhat lower in single-tree

and group- selection stands than in clearcut or shelterwood

stands in all 3 years, these differences were not statistically

significant. Additionally, single-tree and group-selection

stands both had substantially (though not always statistically) higher small mammal abundance than unharvested

stands.

From a pine regeneration standpoint, small mammal numbers increased rapidly and were at their highest level soon

after harvesting. Because of logging and site preparation

disturbances, seedbed conditions are also typically optimal

for natural pine regeneration soon after harvesting. Given

these conditions, the seed production characteristics of

shortleaf pine, and the rapid response of small mammals to

harvesting, foresters have legitimate concerns about seed

consumption by small mammals (and other wildlife). Thus,

additional research investigating actual impacts under

these silvicultural systems may be advisable. Increases in

bird abundance following these treatments were not as

rapid as for small mammals (Thill and others 2000).

Small mammal abundance, richness, and diversity did not

differ (P>0.05) between clearcut and shelterwood stands

during any sampling year with or without inclusion of Peromyscus data. Thus, shelterwood regeneration methods can

achieve similar small mammal responses as clearcutting

within aesthetically more-pleasing settings (Personal communication. Victor A. Rudis. 2000. Research Forester, USDA

Forest Service, Southern Research Station, Forestry

Sciences Laboratory, 201 Lincoln Green, Starkville, MS

39759) during at least the first 5 years after harvest. Healthy

small mammal populations should benefit a host of vertebrate predators.

ACKNOWLEDGMENTS

We thank R.E. Brown and D.B. Burt for providing useful

comments on an earlier draft of this manuscript. We thank

J.B. Baker, J.M. Guldin, L.D. Hedrick, T.J. Mersmann, W.F.

Pell, and P. Trenchi for logistical assistance. For trapping

assistance we thank R.E. Brown, R.A. Buford, J.F. Taulman,

J.H. Williamson, and students from Stephen F. Austin State

University and the University of Arkansas at Monticello.

LITERATURE CITED

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, J.B., comp. Proceedings of the symposium on ecosystem

management research in the Ouachita Mountains: pretreatment

conditions and preliminary findings. Gen. Tech. Rep. SO-112.

New Orleans: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 18-28.

Elkinton, J.S.; Healy, W.M.; Buonaccorsi, J.P. [and others]. 1996.

Interactions among gypsy moths, white-footed mice, and

acorns. Ecology 77(8):2332-2342.

Guldin J.M.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine/hardwood stands of the Ouachita/

Ozark National Forests. In: Baker, J.B., comp. Proceedings of

the symposium on ecosystem management research in the

Ouachita Mountains: pretreatment conditions and preliminary

findings. Gen. Tech. Rep. SO-112. New Orleans: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 29-49.

Laerm, J.; Boone, J.L. 1994. Mensural discrimination of four species

of Peromyscus (Rodentia: Muridae) in the Southeastern United

States. Brimleyana 21:107-123.

Magurran, A.E. 1988. Ecological diversity and its measurement.

Princeton University Press, Princton, New Jersey. 179 p.

Maser, C.; Trappe, J.M.; Nussbaum, R.A. 1978. Fungal-small

mammal interrelationships with emphasis on Oregon coniferous

forests. Ecology 59:799-809.

Pank, L.F. 1974. A bibliography on seed-eating mammals and birds

that affect forest regeneration. Washington, DC: Department of

the Interior, U.S. Fish and Wildlife Service Special Science

Report—Wildlife Number 174. 28p.

Perry, R.W; Thill, R.E.; Tappe, P.A. [and others]. [In press (a)].

Initial response of individual soft-mast-producing plants to different forest regeneration methods in the Ouachita Mountains. In:

Guldin, James M., tech. comp. Ouachita and Ozark Mountains

symposium: ecosystem management research. Gen. Tech.

Rep. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station.

Perry, R.W; Thill, R.E.; Tappe, P.A. [and others]. [In press (b)].

Relationship between basal area and hard mast production in

the Ouachita Mountains. In: Guldin, James M., tech. comp.

Ouachita and Ozark Mountains symposium: ecosystem

management research. Gen. Tech. Rep. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station.

Rich, S.M.; Kilpatrick, C.W.; Shippee, J.L. [and others]. 1996.

Morphological differentiation and identification of Peromyscus

leucopus and P. maniculatus in Northeastern North America.

Journal of Mammalogy. 77(4):985-991.

SAS Institute Inc. 1988. SAS/STAT user’s guide. Cary, NC: SAS

Institute Inc. 1028 p.

Sealander, J.A.; Heidt, G.A. 1990. Arkansas mammals: their

natural history, classification, and distribution. The University of

Arkansas Press, Fayetteville, AR. 308 p.

Shelton, M.G.; Wittwer, R.F. 1996. Shortleaf pine seed production

in natural stands in the Ouachita and Ozark Mountains.

Southern Journal of Applied Forestry 20:74-80.

Smith, C.F.; Aldous, S.E. 1947. The influence of mammals and

birds in retarding artificial and natural regeneration of coniferous forests of the United States. Journal of Forestry 45:361-

369.

Stephenson, G.K.; Goodrum, P.D.; Packard, R.L. 1963. Small

rodents as consumers of pine seed in east Texas uplands.

Journal of Forestry 61:523-526.

Tappe, P.A.; Thill, R.E.; Krystofik, J.K. [and others]. 1994. Small

mammal communities of mature pine-hardwood stands in the

Ouachita Mountains. In: Baker, J.B., comp. Proceedings of the

symposium on ecosystem management research in the Ouachita

Mountains: pretreatment conditions and preliminary findings.

Gen. Tech. Rep. SO-112. New Orleans: U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station: 74-81.
35

Thill, R.E.; Perry, R.W.; Tappe, P.A. [and others]. [In press]. Initial

bird responses to alternative pine regeneration methods in

Arkansas and Oklahoma: preliminary findings. In: Guldin, James

M., tech. comp. Ouachita and Ozark Mountains symposium:

ecosystem management research. Gen. Tech. Rep. Asheville,

NC: U.S. Department of Agriculture, Forest Service, Southern

Research Station.

Thill, R.E.; Tappe, P.A.; Koerth, N.E. 1994. Wildlife habitat conditions in mature pine-hardwood stands in the Ouachita/Ozark

National Forests. In: Baker, J.B., comp. Proceedings of the

symposium on ecosystem management research in the

Ouachita Mountains: pretreatment conditions and preliminary

findings. Gen. Tech. Rep. SO-112. New Orleans: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 126-143.

Ursic, S.J.; Esher, R.J. 1988. Influence of small mammals on

stormflow responses of pine-covered catchments. Water

Resources Bulletin 24(1):33-139.

Wittwer, R.F.; Shelton, M.G. 1992. Seed production in natural

shortleaf pine stands. In: Brissette, J.C. and J.P. Barnett, comp.

Proceedings of the Shortleaf Pine Regeneration Workshop.

Gen. Tech. Rep. SO-90. New Orleans, LA: U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station: 113-123.
36

INTRODUCTION

Even-aged silviculture employing clearcutting, site preparation, and planting of pines has dominated forest management practices on forest industry and national forest lands

throughout the South for the last 30 years (Baker 1989).

Although young pine plantations provide excellent habitat

for wildlife species adapted to early seral stages, even-aged

pine silviculture, especially under short rotations, can diminish critical habitat for wildlife that require an abundance of

snags and cavity trees, hardwoods, hard mast, and large

down woody material.

Under recently adopted principles of ecosystem management

(Guldin 1996, Overbay 1992), the USDA Forest Service is

evaluating the effects of even- and uneven-aged regeneration methods on both an experimental and operational basis

(for example, see Baker 1994, Kitchens 1989). One such

project was initiated in Arkansas in 1991 on the Ouachita

National Forest, the largest national forest (nearly 1.8 million

acres) in the southern region. This multiphase project is

evaluating partial cutting methods, some of which include

long-term retention of a mixed-species overstory, employing

natural regeneration as alternatives to clearcutting and

planting (Baker 1994).

Phase II of this initiative is a stand-level, replicated study

comparing an array of forest outputs and outcomes under

12 silvicultural treatments plus untreated controls (Baker

1994). Two years of pretreatment bird and habitat data were

collected in 20 of these stands in 1992 and 1993 (Petit and

others 1994). Stands were harvested during summer 1993,

site preparation was conducted during the 1993-94 dormant

season, and posttreatment bird surveys were initiated during

May 1994. This paper reports breeding bird populations 1,

3, and 5 years after treatment and compares them with

populations in untreated control stands.

METHODS

Study Areas

The 52 stands that comprise the larger study were randomly

selected from a list that included all mixed shortleaf pine

(Pinus echinata Mill.)-hardwood stands from randomly

selected township and range strips in the Ouachita and

Ozark National Forests that were candidates for regeneration. The stands also met the following pretreatment criteria:

≥ 60 years old; ≥ 35 acres in size (and blocky in shape to

minimize confounding edge influences); predominantly

southerly (south, southwest, or southeast) aspect; 60 to

110 square feet per acre pine basal area; and 20 to 50

square feet per acre hardwood basal area (Baker 1994).

These 52 stands were blocked into 4 physiographic zones

with 13 stands per block. Within each block, treatments were

randomly assigned to the stands, yielding a completely

randomized block design (Baker 1994). Because of limited

resources, we chose to study only 5 of the 13 treatments.

Thus, data reported here are from 20 (4 replications of 5

treatments) of the 52 stands (Thill and others 1994).

Eighteen of our study areas were on the Ouachita National

Forest and two were on the southernmost district of the

Ozark-St. Francis National Forest. Study areas were 35 to

40 acres in size and had slopes ranging from 0 to 15 percent.

They generally occupied relatively xeric sites (southerly

aspects, usually of mid- or lower slope position) characterized by high canopy coverage, an abundance of mostly

small midstory and overstory hardwoods, and limited understory browse and herbage prior to treatment (Thill and others

1994). Pretreatment conifer basal area averaged 76.7 square

feet per acre and consisted almost entirely of shortleaf pine

and a few eastern redcedars (Juniperus virginiana L.);

hardwood basal area averaged 36.6 square feet per acre

(Thill and others 1994). Common hardwoods included oaks

(Quercus spp.), hickories (Carya spp.), winged elm (Ulmus

INITIAL BIRD RESPONSES TO ALTERNATIVE PINE REGENERATION

METHODS IN ARKANSAS AND OKLAHOMA: PRELIMINARY FINDINGS

Ronald E. Thill, Roger W. Perry, Nancy E. Koerth,

Philip A. Tappe, and David G. Peitz1

1 Supervisory Research Wildlife Biologist, USDA Forest Service, Southern Research Station, Nacogdoches, TX; Wildlife Biologist, USDA

Forest Service, Southern Research Station, Hot Springs, AR; Statistician, USDA Forest Service, Southern Research Station, Nacogdoches,

TX; Associate Professor and Research Associate, School of Forest Resources and Arkansas Forest Resources Center, University of

Arkansas at Monticello, Monticello, AR.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—We studied spring songbird communities in a predominantly forested landscape in the Ouachita Mountains of

west-central Arkansas and eastern Oklahoma. Relative bird abundance, species richness, diversity, and evenness values

are presented for the first, third, and fifth years following harvesting in four replications of four regeneration treatments

(clearcut/plant, shelterwood, single-tree selection, and group selection), plus mature, untreated “controls.” Within clearcut

and shelterwood stands, relative abundance, species richness, and diversity of birds increased dramatically from the first

to the third year, with smaller increases from the third to the fifth year. Single-tree and group selection stands followed this

same pattern for species richness and diversity, but not for relative bird abundance. Within years, differences in response

to harvesting generally were not significant, but group selection stands typically had the lowest values for the four

harvesting treatments. Of the five treatments, controls typically had the lowest response variable values.
37

alata Michx.), and black gum (Nyssa sylvatica Marsh.)

(Guldin and others 1994). Pine site index (base age 50)

averaged 62.2 feet over all 52 stands. For information on

topography, soils, and climate, see Baker (1994).

All 20 stands contained ephemeral, and occasionally intermittent, drainages that typically flow only during and after

heavy rains. Unharvested linear strips (“greenbelts”) were

established along these drainages in the 16 treated stands

for watershed protection. Ephemeral and intermittent greenbelts were typically 65- and 130-feet wide, respectively.

Greenbelts comprised 4 to 20 percent of the 16 harvested

stands and averaged 10.9 percent. Prior to treatment, there

was generally little difference in woody vegetation composition or structure between greenbelts and the surrounding

habitat. On the upper slopes of most stands, these drainages tend to be steep and rocky and typically contain water

only for brief periods.

Postharvest overstory basal area data were obtained as

described by Guldin and others (1994). For additional information on sites, treatments, general stand characteristics,

and pretreatment wildlife habitat conditions, see Baker

(1994), Guldin and others (1994), and Thill and others

(1994), respectively.

Treatments

Although the larger study involves 13 treatments, we chose

to evaluate those treatments where an overstory hardwood

component was retained to improve wildlife habitat and

aesthetics; we also included clearcutting, which had been

the principle timber harvesting and regeneration method for

decades. Thus, four treatments (clearcut, shelterwood,

single-tree, and group selection) plus an untreated control

were evaluated. Harvesting was completed between June 1

and September 30, 1993.

Clearcut—All merchantable pines and hardwoods were

harvested except for 2 to 5 square feet per acre of hardwood basal area retained for mast production and/or cavity

and den sites. All trees that were not harvested or retained

were injected with herbicide (Baker 1994). After harvesting,

all four sites were scheduled to be ripped on the contour at

10-foot intervals to a depth of 6 to 8 inches and planted with

genetically improved shortleaf pine seedlings on an 8-foot

spacing within the rips. However, contractors could not be

obtained to rip two sites; these sites were treated identically

to the other two clearcuts but were not ripped. Genetically

improved pines were planted between December 1994 and

March 1995. One stand was replanted in February 1996

due to inadequate stocking.

Shelterwood—From 20 to 40 of the largest pines and hardwoods per acre were retained; the combined basal area of

trees that were left was 30 to 40 square feet per acre, of

which 5 to 15 square feet per acre were hardwoods. All

other pines and hardwoods were harvested or felled and

left on the ground.

Single-tree selection—Under this treatment, some pines

and hardwoods will be harvested approximately every 10

years. As with future thinning treatments, residual basal area

after this first cutting cycle ranged from 45 to 65 square feet

per acre, with 5 to 20 square feet per acre in hardwoods.

Site preparation consisted of removing all hardwoods < 5.9

inches diameter at breast height (d.b.h.).

Group selection—As in the single-tree selection stands,

portions of these stands will be harvested approximately

every 10 years. During each harvest entry, approximately

10 percent of each stand will be cut in openings that will

generally range from 0.5 to 2.0 acres in size. Because 1993

was the first entry, about 10 percent of our stands were in

group openings. All pines in these openings were cut, but 5

to 10 square feet per acre of overstory hardwood basal area

was retained. Within the matrix surrounding these openings,

pines were thinned to 70 to 80 square feet per acre basal

area, but no hardwoods were removed. Site preparation in

group openings consisted of chain-saw felling all hardwoods

< 5.9 inches d.b.h.; no site preparation occurred outside

these openings.

Untreated controls—With the exception of protection from

severe loss to wildfire or insects, no management was or

will be conducted in these mature second-growth, pinehardwood stands.

Bird Surveys

Before treatment, bird populations were surveyed in five or

six (depending on stand size and shape) permanent 131-

foot-radius (1.24-acres) point-count plots (Verner 1988)

spaced evenly over each of the 20 study areas (Petit and

others 1994). Plots were usually ≥ 425 feet apart and all

plot edges were ≥ 295 feet from stand boundaries. Beginning in 1994, we randomly deleted one plot from stands

where six plots had been established so that all posttreatment comparisons would be based on identical sampling

effort.

Because group selection opening locations were not known

when permanent sampling plots were established, posttreatment surveys sampled a mix of openings and surrounding

(matrix) habitat. Likewise, posttreatment surveys also sampled some greenbelts in most harvested stands. Thus, data

presented here characterize operational, stand-level conditions rather than individual, within-stand habitat components

such as greenbelts, openings, or matrix habitat.

Plots were surveyed for birds once each by three observers

in 1994 and once each by six observers in 1996 and 1998.

All surveys were conducted between May 3 and June 12.

Surveys at each plot lasted 10 minutes, and all surveys

were conducted within 3.5 hours of sunrise. Surveys were

not conducted during heavy rain or windy conditions. At

each plot, all birds seen or heard within and outside the plot

(but within the same stand) were recorded separately.

Analyses

Observers differed to some extent each year and sampling

effort was increased the last 2 years; consequently, differences among years were not evaluated statistically but are

reported for descriptive purposes. Because our primary

interest is in differences among treatments within years,

statistical tests are presented only for within-year comparisons. Findings reported here are based only on data

collected within 131-foot-radius plots.
38

Relative bird abundance was calculated as the mean number

of birds encountered on each plot across all observers.

Relative species richness was calculated as the number of

species encountered on each plot, composited across observers. Species diversity (Shannon-Weiner diversity index) and

evenness are based on these composited values (Magurran

1988). Differences among treatments for all variables were

evaluated using randomized-block analyses of variance

(with physiographic zones as blocks and plots as subsamples: n = 100, four blocks X five treatments X five plots/

stand). Where sampling error and experimental error were

not different (alpha = 0.15), treatment effect was tested with

sampling error; otherwise, experimental error was used to

test for treatment differences. Mean separation was accomplished using REGWQ multiple range tests (alpha = 0.05)

(SAS Institute Inc. 1988).

RESULTS AND DISCUSSION

Posttreatment Stand Characteristics

Prior to harvesting, pine, hardwood, and total basal area

averaged 76.7, 36.6, and 113.4 square feet per acre, respectively. Pine and hardwood basal area were similar (P>0.05)

among the 20 stands when grouped by future treatment

(Thill and others 1994). The most intensive treatments, as

indicated by residual basal area, occurred in group selection

openings and clearcuts (fig. 1). Average residual hardwood

basal area in shelterwood and single-tree selection stands

was identical (13.5 square feet per acre), and slightly less

than in group openings (17.0 square feet per acre). Residual

hardwood basal area in the group selection matrix habitat

was comparable (27.0 square feet per acre) to that of controls (28.8 square feet).

Relative Abundance

Within treatments, postharvest bird abundance increased

progressively each year in all treatments except controls,

which had slightly lower abundance in 1996 than in 1994 or

1998 (fig. 2). The amount of increase in bird abundance

between years decreased in clearcuts and shelterwoods

from 1996 to 1998 relative to the increase from 1994 to

1996 (fig. 2). This pattern was not apparent in the singletree and group selection treatments. In fact, the differences

in relative abundance for both single-tree and group selection stands between 1996 and 1998 were greater than

between 1994 and 1996.

Differences in relative abundance among treatments were

statistically significant during 1994 (F = 7.54; 4,80 df; P =

0.0001), 1996 (F = 5.17; 4,12 df; P = 0.0118), and 1998 (F

= 27.32; 4,12 df; P = 0.0001). During the first year after treatment, relative abundance was significantly lower in controls

than in other treatments; no differences existed among harvested stands (fig. 2). During the third year after treatment,

abundance was lower in control stands than in clearcut and

shelterwood stands. By the fifth after treatment, relative

abundance was comparable in clearcut, shelterwood, and

single-tree selection stands, but relative abundance in these

three treatments was significantly higher than in group

selection and control stands, both of which also differed

significantly (fig. 2). When data for the four harvested treatments were averaged within years, relative abundance was

2.1, 3.1, and 2.5 times greater than in controls during 1994,

1996, and 1998, respectively.

Species Richness

In harvested stands, avian species richness increased

substantially from the first to the third year for all treatments

except controls; only moderate increases occurred in

harvested stands between the third and fifth years after

treatment (fig. 3). Species richness in control stands also

increased each year, but this increase between years was

more uniform than in treated stands. While the cause for

apparent increases in species richness in control stands is

unknown, the amount of between-year variation is only

0

20

40

60

80

100

120

140

Clearcut Shelterwood Single tree Opening Matrix Control

Bas area (ft2/ac)

Hardwood

Pine

Group selection

Figure 1—Pine and hardwood basal area in untreated controls and four regeneration treatments

immediately after harvest of pine-hardwood stands in the Ouachita Mountains of Arkansas and Oklahoma.
39

0

1

2

3

4

5

6

7

8

Clearcut Shelterwood Single tree Group selection Control

Birds (no./plot)

1st year

3rd year

5th year

A

A

A

A

A

A

A

AB

A

A

AB

B

B B

C

Figure 2—Relative bird abundance by silvicultural treatment during the first (1994), third (1996), and fifth

years (1998) after harvesting of pine-hardwood stands in the Ouachita Mountains of Arkansas and

Oklahoma. Within years, bars with different letters are significantly different (P<0.05).

0

2

4

6

8

10

12

14

16

Clearcut Shelterwood Single tree Group selection Control

Species (no.)

1st year

3rd year

5th year

AB

A

A

A

A

A

A

AB

A

AB

AB

A

B

B

B

Figure 3—Bird species richness by silvicultural treatment during the first (1994), third (1996), and fifth years

(1998) after harvesting of pine-hardwood stands in the Ouachita Mountains of Arkansas and Oklahoma.

Within years, bars with different letters are significantly different (P<0.05).

slightly higher than observed prior to treatment (Petit and

others 1994; fig. 6) and probably reflects inherent temporal

variability and/or observer differences.

Within years, differences in species richness among treatments were significant during 1994 (F = 3.66; 4,12 df; P =

0.0360), 1996 (F = 3.41; 4,12 df; P = 0.0440), and 1998 (F

= 12.66; 4,12 df; P = 0.0003). Within years, species richness was consistently lowest in untreated controls. Among

the four harvested treatments, differences in richness were

not significant during any year (fig. 3). However, in all 3 years

species richness was significantly higher in shelterwood

stands than in controls. Clearcuts and single-tree stands

had higher species richness than controls in 2 years (1996

and 1998, and 1994 and 1998, respectively). Species richness in group selection stands was higher than in controls

only during the fifth year. When data for the four harvested

treatments were averaged within years, species richness

was 1.7, 2.5, and 2.0 times greater than in controls during

1994, 1996, and 1998, respectively.

Diversity

Within harvested treatments, diversity increased each year,

but the amounts of increase declined over time (fig. 4).

Diversity followed a similar pattern to species richness in

control stands.

Within years, controls consistently had the lowest avian

diversity. Differences in diversity among the five treatments

were significant during the first (F = 4.06; 4,12 df; P = 0.0262)

and fifth years (F = 8.60; 4,12 df; P = 0.0016) and nearly so

(F = 3.15; 4,12 df; P = 0.0551) during the third year after
40

0.80

0.82

0.84

0.86

0.88

0.90

0.92

0.94

0.96

Clearcut Shelterwood Single tree Group

selection

Control

Evenness

1st year

3rd year

5th year

Figure 5—Bird evenness by silvicultural treatment during the first (1994), third (1996), and fifth years (1998)

after harvesting of pine-hardwood stands in the Ouachita Mountains of Arkansas and Oklahoma. None of the

within-year differences were significant (P>0.05).

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Clearcut Shelterwood Single tree Group selection Control

Diversity

1st year

3rd year

5th year

AB

A

A

A

A

A

AB

A

B

B

Figure 4—Bird diversity (Shannon-Weiner’s index) by silvicultural treatment during the first (1994), third

(1996), and fifth years (1998) after harvesting of pine-hardwood stands in the Ouachita Mountains of

Arkansas and Oklahoma. Within years, bars with different letters are significantly different (P<0.05). No

differences existed among treatments in 1996.

treatment. Bird diversity was significantly higher in shelterwood and single-tree stands than in controls the first year.

By the fifth year, diversity was significantly higher in all four

harvested treatments than in controls. When data for the

four harvested treatments were averaged within years,

avian diversity was 1.5, 1.6, and 1.4 times greater than in

controls during 1994, 1996, and 1998, respectively.

Evenness

In shelterwood, single-tree, and group selection stands, evenness was somewhat higher in 1994 than in the other 2 years,

while values for 1996 and 1998 were almost identical (fig. 5).

Avian evenness in clearcuts and controls followed different

patterns. Consequently, differences among treatments were

not significant in 1994 (F = 1.53; 4,80 df; P = 0.2011), 1996

(F = 1.06; 4,12 df; P = 0.4197), or 1998 (F = 1.75; 4,12 df;

P = 0.2029).

CONCLUSIONS

Untreated, late-rotation stands occupying southerly aspects

of the Ouachita Mountains seem to be characterized by low

bird abundance, species richness, and diversity. However,

our findings suggest that a variety of regeneration methods

can be used to increase bird abundance, richness, and diversity through at least the fifth year after logging. The general

pattern of these response variables suggests that they may

be close to peak 5 years after treatment, but additional

surveys are needed to substantiate this conclusion.

All stands were largely even-aged at study initiation. Consequently, stands that were selected for uneven-aged treatments are in a transition from an even- to an uneven-aged

stand structure and will be for at least one or two additional

harvest entries. Therefore, it is too early to draw definitive

conclusions comparing uneven-aged treatments (single-tree
41

and group selection) and even-aged (clearcut and shelterwood). Nevertheless, our data suggest that early-transition

single-tree and group selection stands should have similar

avian abundance, richness, and diversity as clearcut and

shelterwood stands. Except for lower abundance in group

selection stands during the fifth year, none of the other

differences in response variables among these four regeneration methods were significant.

Due to space limitations, data on individual bird species

responses to treatments are not presented here. However,

it is important to note that the control stands, despite their

lower avian abundance and diversity, supported some sensitive species that were uncommon or not encountered in

harvested stands. Certain forest interior or disturbancesensitive species, such as ovenbirds (Seiurus aurocapillus)

and scarlet tanagers (Piranga olivacea), were relatively common in control stands but were absent or rare in harvested

stands. Consequently, retention of mature stands is important for the conservation of these species. Furthermore, our

preliminary findings suggest that a mix of silvicultural treatments over the landscape should enhance avian species

diversity.

Young shelterwood stands may be aesthetically more pleasing than stands that are clearcut and planted (Personal

communication. Victor A. Rudis. 2000. Research Forester.

USDA Forest Service, Southern Research Station, Forestry

Sciences Laboratory, 201 Lincoln Green, Starkville, MS

39759). Retention of seed trees in perpetuity should further

increase public acceptance of shelterwood regeneration

methods. Our findings indicate that stands regenerated

using shelterwood methods on similar sites in the Ouachita

Mountains should have comparable levels of avian abundance, richness, and diversity as clearcuts through at least

the fifth year after harvest.

ACKNOWLEDGMENTS

We gratefully acknowledge the field assistance provided

by L. Anderson, R.A. Buford, R.E. Brown, R.S. Daniel,

H. Garner, E.A. Kroese, D.J. Reed, J.F. Taulman, and

J.H. Williamson. J.P. Duguay and R.E. Brown provided

useful suggestions on an earlier draft of this manuscript.

We are also grateful for the technical and logistical assistance provided by J.B. Baker, J.M. Guldin, L.D. Hedrick,

T.J. Mersmann, and W.F. Pell.

LITERATURE CITED

Baker, J.B. 1989. Alternative silvicultural systems—South. In:

Proceedings of the National Silviculture Workshop: Silvicultural

challenges and opportunities in the 1990’s; 1989 July 10-13;

Petersburg, AK. Washington, DC: U.S. Department of

Agriculture, Forest Service, Timber Management: 51-60.

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In: Baker,

J.B., comp. Proceedings of the Symposium on Ecosystem

Management Research in the Ouachita Mountains: Pretreatment conditions and preliminary findings; 1993 October 26-27;

Hot Springs, AR. Gen. Tech. Rep. SO-112. New Orleans, LA:

U.S. Department of Agriculture, Forest Service, Southern

Forest Experiment Station: 18-28.

Guldin, J.M. 1996. The role of uneven-aged silviculture in the

context of ecosystem management. Western Journal of Applied

Forestry. 11(1): 4-12.

Guldin, J.M.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine/hardwood stands of the Ouachita/

Ozark National Forests. In: Baker, J.B., comp. Proceedings of

the Symposium on Ecosystem Management Research in the

Ouachita Mountains: Pretreatment conditions and preliminary

findings; 1993 October 26-27; Hot Springs, AR. Gen. Tech. Rep.

SO-112. New Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment Station: 29-49.

Kitchens, R.N. 1989. Alternative silvicultural systems on southern

national forests: a status report. In: Proceedings of the National

Silviculture Workshop: Silvicultural challenges and opportunities

in the 1990’s; 1989 July 10-13; Petersburg, AK. Washington,

DC: U.S. Department of Agriculture, Forest Service, Timber

Management: 46-50.

Magurran, A.E. 1988. Ecological diversity and its measurement.

Princeton, NJ: Princeton University Press. 179 p.

Overbay, J.C. 1992. Ecosystem management. In: Taking an ecological approach to management; 1992 April 27-30; Salt Lake

City, UT. Washington, DC: U.S. Department of Agriculture,

Forest Service Watershed and Air Management: 3-15.

Petit, D.R.; Petit, L.J.; Martin, T.E. [and others]. 1994. Breeding

birds of late-rotation pine-hardwood stands: community characteristics and similarity to other regional pine forests. In: Baker,

J.B., comp. Proceedings of the Symposium on Ecosystem

Management Research in the Ouachita Mountains: Pretreatment conditions and preliminary findings; 1993 October 26-27;

Hot Springs, AR. Gen. Tech. Rep. SO-112. New Orleans, LA:

U.S. Department of Agriculture, Forest Service, Southern

Forest Experiment Station: 103-116.

SAS Institute Inc. 1988. SAS/STAT user’s guide. release 6.03

edition. Cary, NC: SAS Institute Inc. 1028 p.

Thill, R.E.; Tappe, P.A.; Koerth, N.E. 1994. Wildlife habitat conditions in mature pine hardwood stands in the Ouachita/Ozark

National Forests. In: Baker, J.B., comp. Proceedings of the

Symposium on Ecosystem Management Research in the

Ouachita Mountains: Pretreatment conditions and preliminary

findings; 1993 October 26-27; Hot Springs, AR. Gen. Tech. Rep.

SO-112. New Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment Station: 126-143.

Verner, J. 1988. Optimizing the duration of point counts for monitoring trends in bird populations. Res. Note PSW-395. Berkeley,

CA: U.S. Department of Agriculture, Forest Service, Pacific

Southwest Forest and Range Experiment Station. 4 p.
42

INTRODUCTION

Recent changes in management of national forests has

increased interest in silvicultural alternatives to clearcutting

and planting for the regeneration of pines in the southeastern United States. One alternative silvicultural method

receiving increased use in southern national forests is

group-selection management. Group-selection management

consists of periodic harvesting of small groups of trees to

create and maintain an uneven-aged stand structure with at

least three distinct age/size classes. Cutting intervals on

southern national forests generally range from 5 to 10 years

for pine, with opening sizes from 0.5 to 2.0 ac (Kerpez 1994).

If the stand surrounding these openings (termed the matrix)

contains sufficient timber volume, the matrix is typically

thinned at the same time these openings are created. Groupselection management is normally used to regenerate shade

intolerant tree species. Little information exists on the effects

of group selection harvesting on wildlife in southern pine

and pine-hardwood forests.

Structural and floristic habitat alterations that result from

group-selection harvesting may affect ecologically important faunal groups such as nongame birds. Nongame birds

help facilitate seed and fungi dispersal, help control insect

numbers, play essential roles in food web dynamics, and

can create habitat for other wildlife species through excavation of cavities. In addition to their ecological values, nongame birds are important as a recreational resource to

millions of people that watch and feed birds (U.S. Department

of the Interior 1992). Neotropical migratory birds are of

particular research interest due to recent evidence of longterm population declines in many species (Finch 1991).

Forest edges, such as those created by openings in forest

canopies, are generally considered to be detrimental to

many avian species because of possible increases in nest

predation and parasitism by the brown-headed cowbird

(Molothrus ater) (Brittingham and Temple 1983, Gates and

Gysel 1978, Temple and Cary 1988, Wilcove 1985, Yahner

and Scott 1988). Indices of habitat fragmentation, such as

interior/edge ratios, fragment/matrix ratios, habitat patch

heterogeneity, and the absolute amount of edge, are proportional to opening sizes (Rolstad 1991). Forest interior

and area sensitive bird species may be adversely affected

by management practices that increase forest fragmentation.

Few studies have assessed the effects of different opening

sizes on nongame birds within forested landscapes. Lanham

and Guynn (1998) found that species richness and relative

abundance of neotropical migrants were positively related

to increasing clearcut size, ranging from 1.24 to 32.12 ac,

at piedmont sites in South Carolina. Using mist nets in

bottomland hardwood forest gaps of 0.08, 0.31, and 1.24 ac,

Kilgo and others (1999) captured more fall migrants in

larger than smaller openings. However, the suitability of

group-selection openings for breeding birds that utilize

early succession habitats is unknown. Here, we compared

presence/absence and densities of nongame birds among

a range of group-selection opening sizes with those found

in 35-40 ac clearcuts.

METHODS

Study Areas

We selected a subset of the 52 forest stands included in the

Phase II (stand-level) Ecosystem Management Study described by Baker (1994). This subset consisted of eight

stands located in Arkansas and Oklahoma on the Ouachita

National Forest and the southern-most district of the Ozark/

St. Francis National Forest. All stands were late-rotation

(60-70 years old), 35-40 ac shortleaf pine- (Pinus echinata

EARLY SUCCESSION BIRD COMMUNITIES OF GROUP-SELECTION

OPENINGS AND CLEARCUTS IN THE OUACHITA MOUNTAINS,

ARKANSAS AND OKLAHOMA

Philip A. Tappe, Ronald E. Thill, David G. Peitz, and Roger W. Perry1

1 Associate Professor, School of Forest Resources and Arkansas Forest Resources Center, University of Arkansas—Monticello, Monticello,

AR 71655; Supervisory Research Wildlife Biologist, Southern Research Station, USDA Forest Service, Nacogdoches, TX 75962; Research

Associate, School of Forest Resources and Arkansas Forest Resources Center, University of Arkansas—Monticello, Monticello, AR 71655;

and Wildlife Biologist, Southern Research Station, USDA Forest Service, Hot Springs, AR 71902, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—We compared species presence/absence and density of nongame birds across a range of group-selection

opening sizes and clearcuts. Group openings and clearcuts were harvested the summer of 1993. Birds were surveyed

during 1995, 1996, and 1998 in 12 group openings ranging in size from 0.54 to 2.62 ac and within 4 clearcuts of approximately 35-40 ac each. Several species occurred throughout a variety of available opening sizes and many had their

highest densities recorded in group openings. Bird densities were only related with opening size during 1995. No

correlations with opening size were apparent for densities of individual species. Results indicate that group-selection cuts

in late-rotation, shortleaf pine-dominated stands may help enhance relative breeding bird abundance. However, our data

reflect bird responses to the initial transition of an uneven-aged forest structure, and may not adequately represent

responses in the latter part of this transition. Additionally, data are needed on reproductive success of birds utilizing these

openings under a variety of successional stages.
43

Mill.) dominated stands with mostly south, southeastern, or

southwestern aspects. Prior to harvest, coniferous basal

area of the 52 Phase II stands averaged 95.3 ft2

/ac; hardwood basal area averaged 34.3 ft2/ac (Guldin and others

1994). Pretreatment stand and habitat conditions are

described by Guldin and others (1994) and Thill and others

(1994).

Treatments

Although the overall Phase II study included 13 treatments,

this paper contrasts bird communities within group openings of different size in the pine/hardwood group-selection

treatment and the clearcut treatment. All harvesting was

completed between June 1 and September 30, 1993, and

site preparation was completed between October 1, 1993

and April 30, 1994.

Group-selection stands—Approximately 10 percent of

each of the four stands receiving the pine/hardwood groupselection treatment was harvested, creating a total of

twelve group openings with the following sizes: 0.54, 0.59,

0.60, 0.72, 0.75, 0.92, 0.95, 0.98, 1.12, 1.23, 1.48, and

2.62 ac. All pines, except a few smaller trees totaling an

average of only 1.3 ft2

/ac basal area, were removed from

the openings. Hardwoods < 5.9 in. d.b.h. were felled and

left on site. A residual overstory hardwood component averaging 17.0 ft2

/ac basal area was retained in the openings to

improve aesthetics, forest diversity, and hard mast availability for wildlife. Pines in the matrix surrounding group

openings were thinned to 70 to 80 ft2/ac; no other management occurred within the matrices.

Clearcuts—All four stands receiving the clearcut treatment

contained ephemeral drainages that typically flow only for

brief periods during high rainfall events. Unharvested linear

strips (“greenbelts”) were retained along these drainages

for watershed protection. Averaging about 11 percent per

stand, greenbelts were comprised of woody vegetation that

was similar in composition and structure to pretreatment

stand conditions. Except for these greenbelts and a few

overstory hardwoods (averaging 2-5 ft2/ac basal area), all

merchantable pines and hardwoods were harvested from

the four clearcut stands. All remaining trees were injected

with herbicide (Baker 1994). Although clearcuts were scheduled to be ripped prior to planting, contractors could not

be found to rip two of the stands. All stands were planted

with genetically improved pines on an 8- by 10-ft spacing

between December 1994 and March 1995. One stand was

replanted in February 1996 due to inadequate stocking.

Bird Surveys

Relative bird abundance was quantified within a 131-ftradius (40 m) circular plot centered in each of the 12 group

openings. Additionally, one plot per clearcut stand was

randomly selected from the five plots used for the Phase II

Ecosystem Management bird surveys (Thill and others, in

press). Plots were surveyed six times, once by six different

observers, for 10 minutes. All plots were surveyed within

3.5 hours of sunrise. Surveys were conducted during May

and June of 1995, 1996, and 1998. Birds were recorded as

either in or outside of the 131-ft-radius plots. Birds within

the plots were recorded by species, sex (if known), and

singing status; birds outside the plots were recorded as

present only. Since a 131-ft-radius plot encompasses an

area of 1.24 ac, larger than most group openings studied,

many group-opening plots included both opening and

matrix habitat. Thus, birds were also recorded as either in

the opening or in the surrounding thinned matrix.

Analyses

Because sampling plots within group-selection stands often

contained both opening and matrix habitat, bird data for the

openings only were converted to numbers of birds per ac;

i.e., density values. The mean number of birds encountered

by all observers on each group-opening plot was computed

using birds recorded within the actual opening only, and for

each clearcut plot. Clearcut plots did not include any greenbelt inclusions, thus the area sampled was 1.24 ac per

clearcut.

Densities were computed for each opening size for three

sets of birds: (1) all birds combined (males and females),

(2) migrants, and (3) residents. Pearson’s correlation coefficients were computed to test (α ≤ 0.05) for linear relationships between group-opening size (including the four

clearcuts) and mean total density for all birds combined,

migrants, and residents. Correlation coefficients were also

used to test (α ≤ 0.05) for relationships between groupopening size and densities of individual species.

RESULTS

A total of 1,383 breeding birds representing 53 species were

recorded over all years (tables 1 and 6). Presence/absence

and density information by opening size are summarized in

tabular form (tables 1, 2, 3, and 4). Four species comprised

almost 50 percent of the individuals recorded: indigo bunting, Carolina wren, prairie warbler, and yellow-breasted

chat. Seventeen percent of species were recorded only in

clearcuts, 20.7 percent were recorded only in group-selection openings, and 62.3 percent were recorded in both

clearcuts and group openings (table 1). The indigo bunting

was the only species to occur in all group-opening sizes and

the clearcuts. Over all years, the Carolina wren occurred in

all but the 0.60 ac opening, the summer tanager occurred

in all but the 0.60 and 0.72 ac openings, and the black-andwhite warbler occurred in all but the 0.60 and 0.75 ac openings. All other species were absent in ≥ 3 openings. However, species presence or absence by opening size varied

by year (tables 2, 3, and 4). Over all years, 37 species

(69.8 percent) had highest average densities recorded in a

group opening; 13 species (24.5 percent) had their highest

densities in the clearcut treatment (table 1). Of the 33 species

found in both group openings and clearcuts, only 4 had

their highest average densities in clearcuts: the prairie

warbler, brown-headed cowbird, common yellowthroat, and

eastern bluebird. However, density by opening size also

varied by year (tables 2, 3, and 4).

A significant, positive linear relationship between opening

size and mean total bird density was found during 1995

(r = 0.68, P = 0.004). However, no linear relationships were

evident during 1996 or 1998 (table 5 and fig. 1). Furthermore, no densities of individual species were correlated

with opening size (P > 0.05).
44 Table 1—Densities of birds (birds per ac), averaged across birders and years (1995, 1996, and 1998), during point counts in 12 group openings (0.54-2.62

ac) and 4 clearcuts (35-40 ac) during May-June in the Ouachita Mountains of Arkansas and Oklahoma

Opening size Clearcuts

Species Density Total 0.54 0.59 0.60 0.72 0.75 0.92 0.95 0.98 1.12 1.23 1.48 2.62 35–40

no. percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Indigo bunting* 408 29.5 1.5 2.1 2.5 1.7 1.9 1.6 1.7 1.1 1.0 1.3 0.8 1.0 1.5Carolina wren 101 7.3 1.2 0.5 — 0.3 0.4 0.4 0.2 0.3 0.3 0.4 0.2 0.5 0.3Prairie warbler* 91 6.6 — 0.2 — — 0.2 0.5 — 0.2 0.1 0.1 0.1 — 0.8Yellow-breasted chat* 90 6.5 — — 0.3 — 0.8 0.2 — — 0.3 0.5 — 0.2 0.6Brown-headed cowbird 66 4.8 0.1 0.2 — — 0.2 0.1 0.1 0.1 0.1 0.2 — 0.2 0.5Summer tanager* 64 4.6 0.2 0.3 — — 0.5 0.5 0.1 0.1 0.3 0.5 0.1 0.1 0.2Mourning dove 38 2.7 — — — — — — — — — — — — 0.4American goldfinch 32 2.3 — — 0.3 — — — 0.4 — — — — 0.1 0.2Carolina chickadee 30 2.2 0.2 0.1 0.4 — 0.3 — 0.1 — 0.2 0.1 0.2 0.2 < 0.1Pine warbler 30 2.2 — 0.4 0.2 0.2 0.2 0.2 — 0.2 0.2 — 0.5 — < 0.1Blue-gray gnatcatcher* 29 2.1 0.2 — — — 0.7 0.1 0.1 — 0.2 0.2 — 0.1 0.1Great-crested flycatcher* 28 2.0 — — — — 0.2 — 0.1 0.1 — 0.1 — 0.1 0.2Eastern wood pewee* 27 2.0 0.4 — — — 0.2 — 0.4 — 0.1 0.1 — 0.2 0.1Common yellowthroat* 24 1.7 — — 0.1 — 0.1 — — — — 0.1 — 0.1 0.2Black & white warbler* 24 1.7 0.2 0.1 — 0.1 — 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1Red-headed woodpecker 24 1.7 — — — — — — — — — — — — 0.3Ruby-throated hummingbird* 21 1.5 0.3 — — 0.2 0.3 — 0.2 — 0.1 0.2 — 0.1 < 0.1White-eyed vireo* 19 1.4 0.1 — — — 0.4 — — — 0.3 0.1 — < 0.1 0.1Red-eyed vireo* 19 1.4 — 0.1 — — 0.3 — — 0.1 0.1 0.1 0.1 0.1 < 0.1Tufted-titmouse 19 1.4 0.1 — — — 0.2 0.1 — — — 0.1 — 0.5 < 0.1Eastern bluebird 18 1.3 — — 0.1 — — — — — — — — — 0.2White-breasted nuthatch 17 1.2 0.4 — — — — — 0.1 — 0.1 0.1 — 0.1 0.1Field sparrow 16 1.2 — — — 0.2 — 0.1 — — — — — — 0.1Northern cardinal 15 1.1 — — — — — — — — 0.3 0.4 — — < 0.1Bachman’s sparrow* 14 1.0 — — — — — 0.1 — 0.1 — — — — 0.1Eastern kingbird* 12 0.9 — — — — — — — — — — — — 0.1Bluejay 10 0.7 — — — — 0.2 — — — — 0.1 — — 0.1Common flicker 7 0.5 — — — — — — — — — — — — 0.1Lark sparrow* 7 0.5 — — — — — — — — — — — — 0.1Hooded warbler* 6 0.4 — — — — — — — — — 0.1 — 0.1 < 0.1Downy woodpecker 5 0.4 — — — — 0.1 — — — — — — 0.1 < 0.1Hairy woodpecker 5 0.4 0.1 — 0.1 — — — 0.1 — — 0.1 < 0.1 — —Scarlet tanager* 5 0.4 — — — — — — — — — 0.1 — — < 0.1Pileated woodpecker 4 0.3 — — 0.1 — — 0.1 — — — 0.1 — < 0.1 —Red-bellied woodpecker 4 0.3 0.1 — — — — — — 0.1 0.1 0.1 — — —Yellow-throated vireo* 4 0.3 — — — — — 0.1 — — — — — < 0.1 < 0.1Baltimore oriole* 4 0.3 — — — — 0.1 — — — — — — < 0.1 < 0.1

(continued)
45

Table 1—Densities of birds (birds per ac), averaged across birders and years (1995, 1996, and 1998), during point counts in 12 group openings (0.54-2.62ac) and 4 clearcuts (35-40 ac) during May-June in the Ouachita Mountains of Arkansas and Oklahoma (continued)

Opening size Clearcuts

Species Density Total 0.54 0.59 0.60 0.72 0.75 0.92 0.95 0.98 1.12 1.23 1.48 2.62 35–40

no. percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

American kestrel 4 0.3 — — — — — — — — — — — — < 0.1Chipping sparrow 3 0.2 — — — — 0.1 — — — — — — — < 0.1Broad-winged hawk* 3 0.2 0.1 — — — — — — — — — < 0.1 < 0.1 —Orchard oriole* 3 0.2 — — — — — — — — — — — — < 0.1Eastern phoebe 3 0.2 — — — — — 0.1 — — — — — — < 0.1Yellow-billed cuckoo* 2 0.1 0.2 — — — — — — — — — —— —Brown-headed nuthatch 2 0.1 — — — — — — — — — — — — < 0.1Yellow-throated warbler* 2 0.1 — — — — — — 0.1 — — — — — < 0.1N. rough-winged swallow* 2 0.1 — — — — 0.2 — — — — — — — —Worm-eating warbler* 1 0.1 — — — — — — — — — — — < 0.1 —American crow 1 0.1 — — — — — — 0.1 — — — — — —Brown thrasher 1 0.1 — — — — — — — — — — — — < 0.1Gray catbird* 1 0.1 — — — — — 0.1 — — — — — — —Ovenbird* 1 0.1 — — — — — — — — — 0.1 — — —

Total 1,383 100.0 5.4 4.1 4.2 2.7 7.8 4.4 3.9 2.5 4.0 5.8 2.2 4.0 7.2

Asterisks after bird names denote migrant species.
46Table 2—Densities of birds (birds per ac), averaged across birders, during point counts in 12 group openings (0.54-2.62 ac) and 4 clearcuts (35-40 ac)

during May-June 1995 in the Ouachita Mountains of Arkansas and Oklahoma

Opening size Clearcuts

Species Density Total 0.54 0.59 0.60 0.72 0.75 0.92 0.95 0.98 1.12 1.23 1.48 2.62 35–40

no. percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Indigo bunting* 186 29.6 1.2 1.4 2.5 1.6 2.9 1.8 2.5 1.7 1.3 1.8 1.1 1.6 2.4Carolina wren 71 11.3 3.1 0.9 — 0.9 0.7 — 0.4 0.9 0.7 0.7 0.5 1.2 0.7Prairie warbler* 21 3.3 — 0.3 — — — — — — — — — — 0.7Yellow-breasted chat* 26 4.1 — — — — 0.2 — — — — — — — 0.8Brown-headed cowbird 35 5.5 — 0.6 — — — 0.2 — — 0.3 0.4 — — 0.9Summer tanager* 32 5.1 — — — — 1.1 0.7 0.2 0.3 0.7 0.7 0.3 0.3 0.2Mourning dove 29 4.6 — — — — — — — — — — — — 1.0American goldfinch 22 3.5 — — 0.8 — — — 0.2 — — — — 0.3 0.5Carolina chickadee 1 0.2 — 0.3 —————————— —Pine warbler 9 1.4 — 0.3 0.6 0.2 — — — — 0.3 — 0.3 — < 0.1Blue-gray gnatcatcher* 16 2.5 0.6 — — — 1.1 0.2 0.2 — — 0.5 — 0.3 < 0.1Great-crested flycatcher* 14 2.2 — — — — — — 0.2 — — 0.3 — — 0.4Eastern wood pewee* 12 1.9 0.6 — — — 0.4 — 0.5 — 0.2 0.1 — — 0.1Common yellowthroat* 10 1.6 — — — — — — — — — — — 0.3 0.3Black & white warbler* 9 1.4 — 0.3 — 0.2 — 0.2 — — — 0.3 0.1 0.1 0.1Red-headed woodpecker 19 3.0 — — — — — — — — — — — — 0.6Ruby-throated hummingbird* 4 0.6 — — — 0.2 0.2 — — — — 0.3 — — —White-eyed vireo* 0 0 ———————————— —Red-eyed vireo* 7 1.1 — — — — 0.4 — — — 0.3 0.1 0.1 0.1 —Tufted-titmouse 8 1.3 — — — — — — — — — — — 0.8 < 0.1Eastern bluebird 13 2.1 — — 0.3 — — — — — — — — — 0.4White-breasted nuthatch 6 1.0 — — — — — — 0.2 — 0.2 — — — 0.1Field sparrow 9 1.4 — — — — — — — — — — — — 0.3Northern cardinal 10 1.6 — — — — — — — — 0.5 0.7 — — 0.1Bachman’s sparrow* 14 2.2 — — — — — 0.2 — 0.2 — — — — 0.4Eastern kingbird* 2 0.3 — — — — — — — — — — — — 0.1Bluejay 8 1.3 — — — — — — — — — 0.4 — — 0.2Kentucky warbler* 1 0.2 — — — — — — — — — — — 0.1 —Blue grosbeak* 1 0.2 — — 0.3 ————————— —Common flicker 1 0.2 — — — — — — — — — — — — < 0.1Lark sparrow* 6 1.0 — — — — — — — — — — — — 0.2Hooded warbler* 2 0.3 — — — — — — — — — — — — 0.1Downy woodpecker 3 0.5 — — — — 0.2 — — — — — — 0.1 < 0.1Hairy woodpecker 0 0 ———————————— —Scarlet tanager* 2 0.3 — — — — — — — — — 0.1 — — < 0.1Pileated woodpecker 3 0.5 — — — — — 0.2 — — — 0.1 — 0.1 —Red-bellied woodpecker 1 0.2 — — — — — — 0.2 — — — — — —

(continued)
47

Table 2—Densities of birds (birds per ac), averaged across birders, during point counts in 12 group openings (0.54-2.62 ac) and 4 clearcuts (35-40 ac)during May-June 1995 in the Ouachita Mountains of Arkansas and Oklahoma

Opening size Clearcuts

Species Density Total 0.54 0.59 0.60 0.72 0.75 0.92 0.95 0.98 1.12 1.23 1.48 2.62 35–40

no. percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Yellow-throated vireo* 2 0.3 — — — — — — — — — — — 0.1 < 0.1Baltimore oriole* 1 0.2 — — — — — — — — — — — — < 0.1American kestrel 2 0.3 — — — — — — — — — — — — 0.1Chipping sparrow 1 0.2 — — — — — — — — — — — — < 0.1Broad-winged hawk* 1 0.2 — — — — — — — — — — 0.1 — —Orchard oriole* 0 0 ———————————— —Eastern phoebe 3 0.3 — — — — — 0.2 — — — — — — 0.1Yellow-billed cuckoo* 0 0 ———————————— —Brown-headed nuthatch 0 0 ———————————— —Yellow-throated warbler* 2 0.3 — — — — — — 0.2 — — — — — < 0.1N. rough-winged swallow* 2 0.3 — — — — 0.4 — — — — — — — —Worm-eating warbler* 1 0.2 — — — — — — — — — — — 0.1 —American crow 0 0 ———————————— —Brown thrasher 0 0 ———————————— —Gray catbird* 1 0.2 — — — — — 0.2 — — — — — — —Ovenbird* 0 0 ———————————— —

Total 629 100.0 5.5 4.1 4.5 3.1 7.6 3.9 4.8 3.1 4.5 6.5 2.5 5.5 11.2

Asterisks after bird names denote migrant species.
48 Table 3—Densities of birds (birds per ac), averaged across birders, identified during point counts in 12 group openings (0.54-2.62 ac) and 4 clearcuts (35-

40 ac) during May-June, 1996 in the Ouachita Mountains of Arkansas and Oklahoma

Opening size Clearcuts

Species Density Total 0.54 0.59 0.60 0.72 0.75 0.92 0.95 0.98 1.12 1.23 1.48 2.62 35–40

no. percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Indigo bunting* 106 29.3 1.5 1.7 3.1 2.1 1.8 1.5 1.2 0.5 0.9 1.5 0.3 1.1 0.7Carolina wren 16 4.4 0.6 — — — — 0.7 0.2 — 0.2 0.4 — 0.3 0.1Prairie warbler* 30 8.3 — — — — 0.7 0.9 — — 0.2 0.1 — — 0.7Yellow-breasted chat* 32 8.9 — — — — 0.9 0.5 — — 0.3 0.8 — 0.5 0.4Brown-headed cowbird 17 4.7 — — — — — 0.2 0.4 0.2 — 0.3 — 0.3 0.3Summer tanager* 20 5.5 0.6 0.3 — — 0.4 0.5 0.2 — — 0.7 — — 0.2Mourning dove 5 1.4 — — — — — — — — — — — — 0.2American goldfinch 7 1.9 — — — — — — 0.5 — — — — 0.1 0.1Carolina chickadee 10 2.7 — — 0.6 — — — — — 0.5 — — 0.5 < 0.1Pine warbler 9 2.5 — — — — 0.4 0.5 — 0.3 0.2 — 0.1 — —Blue-gray gnatcatcher* 5 1.4 — — — — 0.2 — — — 0.3 — — — 0.1Great-crested flycatcher* 9 2.5 — — — — 0.4 — — — — — — — 0.2Eastern wood pewee* 11 3.0 0.6 — — — 0.2 — 0.4 — — 0.1 — 0.4 0.1Common yellowthroat* 9 2.5 — — — — 0.2 — — — — 0.4 — — 0.2Black & white warbler* 5 1.4 — — — — — 0.2 0.2 0.2 — — — 0.1 < 0.1Red-headed woodpecker 4 1.1 — — — — — — — — — — — — 0.1Ruby-throated hummingbird* 10 2.7 0.9 — — 0.2 — — 0.4 — 0.3 0.1 — — < 0.1White-eyed vireo* 1 0.3 — — — — — — — — — — — — < 0.1Red-eyed vireo* 5 1.4 — — — — 0.2 — — 0.2 — 0.1 0.1 0.1 —Tufted-titmouse 4 1.1 — — — — — 0.2 — — — 0.1 — 0.3 —Eastern bluebird 3 0.8 — — — — — — — — — — — — 0.1White-breasted nuthatch 8 2.2 1.2 — — — — — — — — 0.3 — — 0.1Field sparrow 3 0.8 — — — — — — — — — — — — 0.1Northern cardinal 2 0.6 — — — — — — — — — 0.3 — — —Bachman’s sparrow* 0 0 ———————————— —Eastern kingbird* 0 0 ———————————— —Bluejay 2 0.6 — — — — 0.4 — — — — — — — —Kentucky warbler* 1 0.3 — — — — — — — — — 0.1 — — —Blue grosbeak* 4 1.1 — — — — — — — — — — — — 0.1Common flicker 5 1.4 — — — — — — — — — — — — 0.2Lark sparrow* 1 0.3 — — — — — — — — — — — — < 0.1Hooded warbler* 0 0 ———————————— —Downy woodpecker 1 0.3 — — — — — — — — — — — 0.1 —Hairy woodpecker 3 0.8 0.3 — — — — — 0.2 — — 0.1 — — —Scarlet tanager* 2 0.6 — — — — — — — — — — — — 0.1Pileated woodpecker 0 0 ———————————— —Red-bellied woodpecker 3 0.8 0.3 — — — — — — — 0.2 0.1 — — —

(continued)
49

Table 3—Densities of birds (birds per ac), averaged across birders, identified during point counts in 12 group openings (0.54-2.62 ac) and 4 clearcuts (35-40 ac) during May-June, 1996 in the Ouachita Mountains of Arkansas and Oklahoma

Opening size Clearcuts

Species Density Total 0.54 0.59 0.60 0.72 0.75 0.92 0.95 0.98 1.12 1.23 1.48 2.62 35–40

no. percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Yellow-throated vireo* 0 0 ———————————— —Baltimore oriole* 1 0.3 — — — — — — — — — — — 0.1 —American kestrel 2 0.6 — — — — — — — — — — — — 0.1Chipping sparrow 1 0.3 — — — — — — — — — — — — < 0.1Broad-winged hawk* 0 0 ———————————— —Orchard oriole* 0 0 ———————————— —Eastern phoebe 0 0 — — — — — — — — — — — — —Yellow-billed cuckoo* 2 0.6 0.6 ——————————— —Brown-headed nuthatch 2 0.6 — — — — — — — — — — — — 0.1Yellow-throated warbler* 0 0 ———————————— —N. rough-winged swallow* 0 0 ———————————— —Worm-eating warbler* 0 0 ———————————— —American crow 0 0 ———————————— —Brown thrasher 0 0 ———————————— —Gray catbird* 0 0 ———————————— —Ovenbird* 0 0 ———————————— —

Total 361 100.0 6.6 2.0 3.7 2.3 5.8 5.2 3.7 1.4 3.1 5.5 0.5 3.9 4.5

Asterisks after bird names denote migrant species.
50 Table 4—Densities of birds (birds per ac), averaged across birders, during point counts in 12 group openings (0.54-2.62 ac) and 4 clearcuts (35-40 ac)

during May-June, 1998 in the Ouachita Mountains of Arkansas and Oklahoma

Opening size Clearcuts

Species Density Total 0.54 0.59 0.60 0.72 0.75 0.92 0.95 0.98 1.12 1.23 1.48 2.62 35–40

no. percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Indigo bunting* 116 29.5 1.8 3.1 1.9 1.4 1.1 1.5 1.4 1.0 0.9 0.5 1.1 0.3 1.3Carolina wren 14 3.6 — 0.6 — — 0.4 0.4 — — — — — 0.1 0.2Prairie warbler* 40 10.1 — 0.3 — — — 0.5 — 0.7 — — 0.4 — 1.0Yellow-breasted chat* 32 8.1 — — 0.8 — 1.3 — — — 0.5 0.7 — — 0.5Brown-headed cowbird 14 3.6 0.3 — — — 0.7 — — — — — — 0.3 0.3Summer tanager* 12 3.1 — 0.6 — — — 0.4 — — 0.2 0.1 0.1 0.1 0.1Mourning dove 4 1.0 — — — — — — — — — — — — 0.1American goldfinch 3 0.8 — — — — — — 0.4 — — — — — < 0.1Carolina chickadee 19 4.8 0.6 0.6 — — 0.9 — 0.4 — — 0.3 0.7 0.1 < 0.1Pine warbler 12 3.1 — 0.9 — 0.2 — — — 0.2 — — 0.9 — —Blue-gray gnatcatcher* 8 2.0 — — — — 0.7 — — — 0.2 0.1 — — 0.1Great-crested flycatcher* 5 1.3 — — — — — — — 0.2 — — — 0.3 0.1Eastern wood pewee* 4 1.0 — — — — — — 0.2 — — — — 0.3 < 0.1Common yellowthroat* 5 1.3 — — 0.3 — — — — — — — — — 0.1Black & white warbler* 10 2.5 0.6 — — — — — — — 0.2 0.3 0.3 0.1 0.1Red-headed woodpecker 1 0.3 — — — — — — — — — — — — < 0.1Ruby-throated hummingbird* 7 1.8 — — — — 0.7 — 0.2 — — 0.1 — 0.3 —White-eyed vireo* 18 4.6 0.3 — — — 1.3 — — — 0.7 0.1 — 0.1 0.1Red-eyed vireo* 7 1.8 — 0.3 — — 0.2 — — 0.2 — 0.1 0.1 0.1 < 0.1Tufted-titmouse 7 1.8 0.3 — — — 0.4 — — — — — — 0.5 —Eastern bluebird 2 0.4 — — — — — — — — — — — — 0.1White-breasted nuthatch 3 0.8 — — — — — — — — — — — 0.3 < 0.1Field sparrow 4 1.0 — — — 0.5 — 0.2 — — — — — — < 0.1Northern cardinal 3 0.8 — — — — — — — — 0.3 0.1 — — —Bachman’s sparrow* 0 0 ———————————— —Eastern kingbird* 10 2.5 — — — — — — — — — — — — 0.3Bluejay 0 0 ———————————— —Kentucky warbler* 7 1.8 — — — — 0.4 — — — 0.3 0.4 — — —Blue grosbeak* 3 0.8 — 0.3 — — — — — — — 0.1 — — < 0.1Common flicker 1 0.3 — — — — — — — — — — — — < 0.1Lark sparrow* 0 0 ———————————— —Hooded warbler* 4 1.0 — — — — — — — — — 0.1 — 0.4 —Downy woodpecker 1 0.3 — — — — — — — — — — — — < 0.1Hairy woodpecker 2 0.4 — — 0.3 — — — — — — — 0.1 — —Scarlet tanager* 1 0.3 — — — — — — — — — — — — < 0.1Pileated woodpecker 1 0.3 — — 0.3 ————————— —Red-bellied woodpecker 0 0 ———————————— —

(continued)
51

Table 4—Densities of birds (birds per ac), averaged across birders, during point counts in 12 group openings (0.54-2.62 ac) and 4 clearcuts (35-40 ac)during May-June, 1998 in the Ouachita Mountains of Arkansas and Oklahoma

Opening size Clearcuts

Species Density Total 0.54 0.59 0.60 0.72 0.75 0.92 0.95 0.98 1.12 1.23 1.48 2.62 35–40

no. percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Yellow-throated vireo* 2 0.4 — — — — — 0.2 — — — — — — < 0.1Baltimore oriole* 2 0.4 — — — — 0.2 — — — — — — — < 0.1American kestrel 0 0 ———————————— —Chipping sparrow 1 0.3 — — — — 0.2 — — — — — — — —Broad-winged hawk* 2 0.4 0.3 — — — — — — — — — — 0.1 —Orchard oriole* 3 0.8 — — — — — — — — — — — — 0.1Eastern phoebe 0 0 — — — — — — — — — — — — —Yellow-billed cuckoo* 0 0 ———————————— —Brown-headed nuthatch 0 0 ———————————— —Yellow-throated warbler* 0 0 ———————————— —N. rough-winged swallow* 0 0 ———————————— —Worm-eating warbler* 0 0 ———————————— —American crow 1 0.3 — — — — — — 0.2 — — — — — —Brown thrasher 1 0.3 — — — — — — — — — — — — < 0.1Gray catbird* 0 0 ———————————— —Ovenbird* 1 0.3 — — — — — — — — — 0.1 — — —

Total 393 100.0 4.2 6.7 3.6 2.1 8.5 3.2 2.8 2.3 3.3 3.1 3.7 3.4 5.0

Asterisks after bird names denote migrant species.
52

Table 5—Correlation coefficients of mean densities (birds per ac)

with opening size (0.54-40 ac) during May-June, 1995, 1996 and 1998

in the Ouachita Mountains of Arkansas and Oklahoma

 1995 1996 1998

Variable r P r P r P

Migrants 0.54 0.031 0.06 0.814 0.38 0.144

Residents 0.70 0.003 0.32 0.224 -0.05 0.868

Combined 0.68 0.004 0.22 0.419 0.31 0.242

Table 6—Common and scientific names of birds identified during point counts in12 group openings (0.54-2.62 ac) and

4 clearcuts (35-40 ac) during May-June, 1995-1996 and1998 in the Ouachita Mountains of Arkansas and Oklahoma

Species Scientific name

Indigo bunting* Passerina cyanea

Carolina wren Thryothorus ludovicianus

Prairie warbler* Dendroica discolor

Yellow-breasted chat* Icteria virens

Brown-headed cowbird Molothrus ater

Summer tanager* Piranga rubra

Mourning dove Zenaida macroura

American goldfinch Carduelis tristis

Carolina chickadee Parus carolinensis

Pine warbler Dendroica pinus

Blue-gray gnatcatcher* Polioptila caerulea

Great-crested flycatcher* Myiarchus crinitus

Eastern wood pewee* Contopus virens

Common yellowthroat* Geothlypis trichas

Black & white warbler* Mniotilta varia

Red-headed woodpecker Melanerpes

erythrocephalus

Ruby-throated hummingbird* Archilochus colubris

White-eyed vireo* Vireo griseus

Red-eyed vireo* Vireo olivaceus

Tufted-titmouse Parus bicolor

Eastern bluebird Sialia sialis

White-breasted nuthatch Sitta carolinensis

Field sparrow Spizella pusilla

Northern cardinal Cardinalis cardinalis

Bachman’s sparrow* Aimophila aestivalis

Eastern kingbird* Tyranus tyranus

Species Scientific name

Bluejay Cyanocitta cristata

Kentucky warbler* Oporomis formosus

Blue grosbeak* Guiraca caerulea

Common flicker Colaptes auratus

Lark sparrow* Chondestes grammacus

Hooded warbler* Wilsonia critrina

Downy woodpecker Picoides pubescens

Hairy woodpecker Picoides villosus

Scarlet tanager* Piranga olivacea

Pileated woodpecker Dryocopus pileatus

Red-bellied woodpecker Melanerpes carolinus

Yellow-throated vireo* Vireo flavifrons

Baltimore oriole* Icterus galbula

American kestrel Falco sparverius

Chipping sparrow Spizella paserina

Broad-winged hawk* Buteo platypterus

Orchard oriole* Icterus spurius

Eastern phoebe Sayomis phoebe

Yellow-billed cuckoo* Coccyzus americanus

Brown-headed nuthatch Sitta pusilla

Yellow-throated warbler* Dendroica dominica

N. rough-winged swallow* Stelgidopteryx serripennis

Worm-eating warbler* Helmitheros vermivorus

American crow Corvus brachyrhynchos

Brown thrasher Toxostoma rufum

Gray catbird* Dumetella carolinensis

Ovenbird* Seiurus aurocapillus

Asterisks after bird names denote migrant species.

Source: Hamel (1992).

Migrants

A total of 928 migrants of 29 species were recorded over all

years (table 1). Migrants represented 54.7 percent of species

recorded. Four species comprised over 70 percent of the

individual migrants recorded: indigo bunting, prairie warbler,

yellow-breasted chat, and summer tanager.

A weak but significant, positive linear relationship between

opening size and mean total migrant density was found

during 1995 (r = 0.54, P = 0.031). However, no linear relationships were evident during 1996 or 1998 (table 5 and fig.

1).

Residents

A total of 455 residents of 24 species were recorded over

all years (table 1). Residents represented 45.3 percent of

species recorded. Seven species comprised over 70 percent of the individual residents recorded: Carolina wren,

brown-headed cowbird, mourning dove, American goldfinch, Carolina chickadee, pine warbler, and red-headed

woodpecker.

A significant, positive linear relationship between opening

size and mean total resident density was found during 1995
53

Figure 1—Densities of birds, averaged across birders, during point counts in 12 group

openings (0.54-2.62 ac) and 4 clearcuts (35-40 ac) during May-June, 1995, 1996, and 1998

in the Ouachita Mountains of Arkansas and Oklahoma.
54

(r = 0.70, P = 0.003). However, no linear relationships were

evident during 1996 or 1998 (table 5 and fig. 1).

CONCLUSIONS

Several species, including species of conservation concern

such as the black and white warbler, occurred throughout a

variety of opening sizes. Additionally, most species had

their highest densities recorded in group openings. Apparently these openings embedded within a more mature

habitat matrix are attractive to a variety of species that

utilize these early successional habitats. These openings

also increase edge and habitat patchiness within a forested

stand. However, the length of time a group opening maintains sufficient dissimilarity and edge contrast with the surrounding matrix habitat to sustain these relationships is

unknown.

Total bird, migrant, and resident densities were only related

with opening size during one of the three years of this

study. This was probably due to the high numbers of birds

recorded in the four clearcuts in 1995. No relationships

between densities of individual species and opening size

were apparent. The group-opening sizes in this study

ranged from 0.54-2 to 62 ac, with the extremes differing by

only 2.08 ac. This distribution of sizes may not be sufficient

to reveal potential relationships between opening size and

densities of individual species. Additionally, other factors

such as opening shape and surrounding habitat matrix

characteristics may also influence these relationships (Sisk

and others 1997). We suggest additional research on a

broader range of opening sizes.

Group-selection stands in our study are in a transition from

an even-aged to an uneven-aged structure. Thus, bird

community characteristics are expected to change over

time as the vegetation composition and structure of these

group openings and clearcuts change. Relative bird abundance was significantly higher within these same groupselection stands than for untreated controls in 1994 and

1998 (Thill and others, in press). Thus, our results suggest

that during the early part of this transition, relative bird

abundance may be enhanced by group-selection cuts in

late-rotation, shortleaf pine dominated stands. However,

before we can endorse this treatment, data are needed on

reproductive success of birds utilizing these openings

under a variety of successional stages.

ACKNOWLEDGMENTS

We are grateful for the field assistance provided by the

following individuals: L. Anderson, R.A. Buford, R.E. Brown,

R.S. Daniel, H. Garner, E.A. Krose, D.J. Reed, J.F. Taulman,

and J.H. Williamson. We also appreciate the logistical and

technical assistance provided by J.B. Baker, J.M. Guldin,

L.D. Hedrick, T.J. Mersmann, and W.F. Pell.

LITERATURE CITED

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, J.B., comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Gen. Tech. Rep. SO112. New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station: 18-28.

Brittingham, M.C.; Temple, S.A. 1983. Have cowbirds caused

forest songbirds to decline? Bioscience. 33:31-35.

Finch, D.M. 1991. Population ecology, habitat requirements, and

conservation of neotropical migratory birds. Gen. Tech. Rep.

RM-205. U.S. Department of Agriculture, Forest Service. 26 p.

Gates, J.E.; Gysel, L.W. 1978. Avian nest dispersion and fledging

success in field-forest ecotones. Ecology. 59:871-883.

Guldin, J.M.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine/hardwood stands of the Ouachita/

Ozark National Forests. In: Baker, J.B., comp. Proceedings of

the symposium on ecosystem management research in the

Ouachita Mountains: pretreatment conditions and preliminary

findings. Gen. Tech. Rep. SO-112. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 29-49.

Hamel, P.B. 1992. Land manager’s guide to the birds of the South.

The Nature Consevancy, Southeastern Region, Chapel Hill,

NC. 437 p.

Kerpez, T.A. 1994. Effects of group selection and clearcut openings on wildlife in Appalachian hardwood forests. Ph.D. dissertation. Blacksburg, Virginia: Virginia Polytechnic Institute and

State University. 110 p.

Kilgo, J.C.; Miller, K.V.; Smith, W.P. 1999. Effects of group-selection timber harvest in bottomland hardwoods on fall migrant

birds. Journal of Field Ornithology. 70:404-413.

Lanham, J.D.; Guynn, D.C. 1998. Habitat-area relationships of

shrub-scrub birds in South Carolina. Proceedings of the Annual

Conference of Southeastern Associations of Fish and Wildlife

Agencies. 52:222-231.

Rolstad, J. 1991. Consequences of forest fragmentation for the

dynamics of bird populations: conceptual issues and the evidence. Biological Journal of the Linnean Society. 42:149-163.

Sisk, T.D.; Haddad, N.M.; Ehrlich, P.R. 1997. Bird assemblages in

patch woodlands: modeling the effects of edge and matrix

habitats. Ecological Applications. 7:1170-1180.

Temple, S.A.; Cary, J.R. 1988. Modeling dynamics of habitatinterior bird populations in fragmented landscapes.

Conservation Biology. 2:340-347.

Thill, R.E.; Perry, R.W.; Tappe, P.A. [and others]. [In press]. Initial

bird responses to alternative pine regeneration methods in

Arkansas and Oklahoma: preliminary findings. In: Guldin, James

M., tech. comp. Ouachita and Ozark Mountains symposium:

ecosystem management research. Gen. Tech. Rep. Asheville,

NC: U.S. Department of Agriculture, Forest Service, Southern

Research Station.

Thill, R.E.; Tappe, P.A.; Koerth, N.E. 1994. Wildlife habitat conditions in mature pine-hardwood stands in the Ouachita/Ozark

National Forests. In: Baker, J.B., comp. Proceedings of the

symposium on ecosystem management research in the

Ouachita Mountains: pretreatment conditions and preliminary

findings. Gen. Tech. Rep. SO-112. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 126-143.

U.S. Department of the Interior. 1992. 1991 national survey of

fishing, hunting, and wildlife-associated recreation: national

overview preliminary findings. U.S.D.I. Fish and Wildlife Serv.

Rep. 16 p.

Wilcove, D.S. 1985. Nest predation in forest tracts and the decline

of migratory songbirds. Ecology. 66:1211-1214.

Yahner, R.H.; Scott, D.P. 1988. Effects of forest fragmentation on

depredation of artificial nests. Journal of Wildlife Management.

52:158-161.
55

INTRODUCTION

Hard mast production is an important element of forest

ecology. Mast abundance affects both forest regeneration

and wildlife that rely on mast as a food source. Many wildlife species are so dependent on hard mast that its supply

may influence their condition, reproduction, movements,

survival, and population parameters (McShea and Schwede

1993, Nixon and others 1975, Wentworth and others 1990).

For decades, researchers have investigated the factors

influencing hard-mast (primarily acorns) production. Their

efforts have provided considerable, and frequently conflicting, information on the variation in production among years,

species, and individual trees. Moreover, these studies have

provided many theories, from genetics to weather, to account

for this variation (e.g., Christisen 1955, Christisen and

Korschgen 1955, Downs and McQuilken 1944, Farmer 1981,

Koenig and others 1996, Sork and others 1993). However,

little is known on how forest management practices affect

mast production.

Land managers who thin forest stands cite increased mast

production as one of the benefits of thinning. Although the

relationship between stand density and seed production has

been thoroughly investigated in pines (Pinus spp.; e.g., Bilan

1960, Croker 1952, Godman 1962, Wenger 1954), information on oaks (Quercus spp.) and hickories (Carya spp.) is

limited mostly to anecdotal and observational references

(e.g., Gysel 1956, Minckler and McDermott 1960, Reid and

Goodrum 1957, Sharp and Sprague 1967). Studies suggest

a relationship may exist in oaks, but this relationship is

poorly understood. For this study, we investigated the relationship between total (pine and hardwood) basal area (BA)

and mast production by oaks and hickories to determine if

reducing stand density increases the mast production of

residual trees.

METHODS

Study Areas

Research was conducted in the 20 phase II wildlife stands

of the USDA Forest Service Ouachita Mountain Ecosystem

Management Research Project located in the Ouachita and

Ozark National Forests of Arkansas and Oklahoma (Baker

1994). Sampling began the first year after four different silvicultural systems were applied to initially even-aged, pinehardwood stands (approximately 40 acres each). Trees were

surveyed in four replications of clearcut (with scattered overstory hardwoods retained for wildlife), single-tree selection,

group selection, and shelterwood. Harvesting occurred in

1993. In addition, trees were surveyed in four closed-canopy,

late-rotation unharvested stands (60-80 years old). For a

complete description of stand treatments, see Baker (1994).

Basal area estimates for trees ≥3.6 inches d.b.h. were

derived using the method of Guldin and others (1994).

Immediately after harvest, average total overstory BA within

harvested portions of stands, excluding greenbelts, was 6.5

(1.3 pine and 5.2 hardwood) square feet per acre in clearcuts, 49.6 (36.1 pine and 13.5 hardwood) square feet per

acre in shelterwoods, 67.5 (54.0 pine and 13.5 hardwood)

square feet per acre in single-tree selection stands, 18.3

(1.3 pine and 17.0 hardwood) square feet per acre in group

openings, and 88.8 (61.8 pine and 27.0 hardwood) square

feet per acre in group selection matrixes. Average basal

area among unharvested stands was 127.5 (98.8 pine and

28.7 hardwood) square feet per acre (Unpublished data.

James M. Guldin, Forest Ecologist, USDA Forest Service,

Southern Research Station, P.O. Box 1270, Hot Springs,

AR 71902).

Production Estimates

In each stand, we sampled all oaks ≥7.9 in. d.b.h. and all

hickories ≥5.9 in. d.b.h. (regardless of crown placement)

located within 4 to 9 belt transects (depending on stand size

and shape) that were parallel and 49.2-feet wide (Thill and

THE RELATIONSHIP BETWEEN BASAL AREA AND HARD MAST

PRODUCTION IN THE OUACHITA MOUNTAINS

Roger W. Perry, Ronald E. Thill, Philip A. Tappe, and David G. Peitz1

1 Wildlife Biologist, USDA Forest Service, Southern Research Station, P.O. Box 1270, Hot Springs, AR 71902; Supervisory Research Wildlife

Biologist, USDA Forest Service, Southern Research Station, P.O. Box 7600 SFA Station, Nacogdoches, TX 75962; and Associate Professor

and Research Associate, School of Forest Resources, University of Arkansas at Monticello, Monticello, AR 71656, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Because the relationship between stand density and hard mast production is not clear, we investigated the

effects of varying total overstory basal area (BA) on acorn and hickory nut production in the Ouachita Mountains. We used

Whitehead visual surveys to estimate mast production in oaks (Quercus spp.) and hickories (Carya spp.) located in 20

stands under five silvicultural treatments, each varying in residual BA. In 5 years of data (1994-1998), we found no linear

relationship between BA and mast production for the red oak subgenus. A significant linear relationship existed between

BA and hickory nut production two of the 5 years. A significant linear relationship existed between acorn production and

BA for white (Q. alba) and post (Q. stellata) oaks all 5 years; trees in stands with lower BA had higher production indices.

Because white and post oaks tend to be the dominant mast producers in pine-hardwood stands on south-facing slopes in

the Ouachita Mountains, thinning these areas should generally increase mast production by residual trees. However, we

did not measure the effects of thinning on stand-wide mast production.
56

others 1994). Transects were 98 to 312 feet apart, perpendicular to slope contours, and >164 feet from the stand edge.

Because residual mast-producing tree densities differed

among treatment types, we sampled less area in stands with

higher tree densities (unharvested, group selection matrix

areas, and single-tree selection stands). Thus, total area

sampled in each stand was 2.5-3.2 acres in unharvested

stands, 2.3-3.5 acres in single-tree selection stands, 2.8-

2.9 acres in group selection stands, 4.5-5.3 acres in shelterwood stands, and 5.1-5.3 acres in clearcut stands.

We sampled each stand in mid- to late-August of 1994-1998.

Because of costs and biases associated with seed traps

(Perry and Thill 1999), we derived indices of oak and hickory production using the Whitehead visual survey method

(Whitehead 1969). These indices were derived by visually

estimating percent of a tree’s crown producing nuts, percent

of twigs with nuts, and the average number of nuts per twig

using binoculars. This method results in an index ranging

from 0 (no production) to 10 (bumper crop) and can be useful for distinguishing differences in mast production among

areas (Perry and Thill 1999).

Data Analysis

Within group selection stands, we considered group openings

and the thinned matrix surrounding openings as separate

treatments because of substantial differences in residual

BA. Therefore, total number of stands and subtreatments

was 24. We did not include trees located in greenbelts

(unharvested buffers surrounding streams and drains) in

the analysis; likewise, we excluded greenbelt BA from total

stand BA calculations. We calculated a mean production

index for each stand or subtreatment where at least eight

trees of a species or species group existed within the sample area. We regressed each mean production index with

the mean BA for that stand or subtreatment using linear

regression (SAS Institute Inc. 1988) at the ≤0.10 level of

probability. We regressed each year separately.

Oak and hickory species composition differed among stands

because of site and/or geographic locality differences. Thus,

post oaks (Q. stellata Wang.) were the dominant oak species

in some stands but were absent in others, whereas white

oaks (Q. alba L.) were the dominant oak in most stands.

Furthermore, the densities of some mast species were too

low to collect adequate sample sizes (≥8 trees per stand or

Table 1—Mean (± SE) number of trees sampled in each stand or stand subtreatment,

by species (or species group) and year, and the number of sample areas (N) included

in the regression analysis between Whitehead mast indices and BA in the Ouachita

Mountains, 1994–1998

White oak Post oak Hickories Red oak subgenus

Year Mean N Mean N Mean N Mean N

1994 16.5 ± 1.86 13 17.4 ± 3.54 7 13.3 ± 1.37 8 20.5 ± 6.03 4

1995 18.8 ± 1.89 14 16.0 ± 3.26 10 15.5 ± 2.02 10 25.4 ± 8.73 5

1996 19.2 ± 2.32 14 17.4 ± 3.26 8 14.2 ± 1.42 10 20.0 ± 6.27 7

1997 22.1 ± 2.31 14 16.5 ± 3.08 10 17.8 ± 2.23 11 21.7 ± 7.98 6

1998 22.8 ± 2.40 14 16.8 ± 3.21 10 17.9 ± 2.26 11 19.9 ± 6.87 7

BA = basal area.

Table 2—Statistics for the yearly

relationship between BA and

production indices for red oak

species sampled in the Ouachita

Mountains, 1994–1998

Year F P df

1994 0.01 0.93 1,2

1995 0.02 0.89 1,3

1996 1.01 0.36 1,5

1997 0.00 0.99 1,4

1998 0.94 0.38 1,5

BA = basal area.

subtreatment). Therefore, total number of areas used in

regression equations did not reflect the total number of

stands and subtreatments. Oaks of the red oak group (subgenus Erythrobalanus) were rare in most stands; therefore,

we combined black (Q. velutina Lam.), northern red (Q.

rubra L.), southern red (Q. falcata Michx.), and blackjack

(Q. marilandica Muenchh.) into a single red oak group. We

also combined mockernut hickory (C. tomentosa Nutt.) and

black hickory (C. texana Buckl.) into a single hickory group.

For each species or species group, the mean number of

trees sampled ranged from 13.3-25.4 per stand or subtreatment and total number of stands or subtreatments in the

analysis was 4-14 (table 1).

RESULTS AND DISCUSSION

Red Oak Subgenus

Among the combined species of the red-oak subgenus, we

found no relationship between BA and production indices

all 5 years (table 2). Healy (1997) found individual northern

red oaks in New England stands thinned to 50 percent stocking produced more acorns than trees in unthinned stands.

Paugh (1970) found individual red oaks in heavily thinned

stands produced more mast than trees in unthinned stands,

but trees in lightly thinned stands produced less mast than

trees in unthinned areas. Harlow and Eikum (1963) found

turkey oaks (Q. laevis Walt.) in stands thinned to 50 or 90

percent of their original BA produced more mast than trees

in unthinned or 75 percent thinned stands. Although these

studies suggest that heavy thinning promotes increased
57

mast production, they also suggest a linear relationship

between density and mast production may not exist for red

oaks. However, grouping data from four species and small

sample sizes may have adversely affected our analysis.

Furthermore, much larger sample sizes are probably

required to detect a relationship given the highly variable

nature of mast production among individual trees, years,

and areas.

White and Post Oaks

A significant relationship existed between BA and production indices in white oaks all 5 years of the study (fig. 1).

Production indices decreased with increases in total overstory BA. This relationship was relatively weak (r2 = 0.31)

in 1994 and relatively strong (r2 = 0.71) in 1995. A similar

significant relationship existed between BA and production

indices in post oaks all 5 years of the study (fig. 2), with the

weakest relationship (r2 = 0.29) occurring in 1997 and the

strongest relationship (r2 = 0.63) in 1996.

Yearly variations in acorn production did not appear to affect

the strength of the relationship between production and BA.

Among white oaks, the strongest relationship (r2 = 0.71) was

during a bumper-crop year (1995), whereas the weakest

relationship was during an average production year (1994).

The r2

 values in 1998 (a near mast-failure year), 1996 (an

average year), and 1997 (an above average year) were

similar. Yearly variation in production did not appear to

affect the strength of the relationship in post oaks.

Equations describing the relationship between stand BA

and mast production indices in white and post oaks differed

each year, depending on the yearly level of production.

Because of these yearly differences, we can only roughly

calculate a predictive equation for the effect of BA on

Whitehead index. An averaged equation for white oaks (all

5 years of data pooled) was: y = 3.5-0.022x, where y = the

predicted mast index and x = BA (square feet per acre). For

post oaks, the averaged equation was: y = 4.3-0.030x. For

example, a total overstory BA of 25 square feet per acre

will, on average, result in a white-oak index of 2.95, whereas a BA of 120 will average 0.86.

Figure 1—Yearly relationship (A = 1994, B = 1995, C = 1996, D = 1997 and E = 1998)

between basal area and mean Whitehead mast production indices for white oaks

(Quercus alba) in the Ouachita Mountains, 1994-1998.
58

Hickories

A significant relationship existed between BA and hickory

production indices only in 1995 and 1997 (fig. 3). No significant relationship occurred in 1994 (F = 1.47, P = 0.27, df =

1,6), 1996 (F = 1.27, P = 0.29, df = 1, 8), or 1998 (F = 0.09,

P = 0.78, df = 1, 9). The relationship was strong (r2 = 0.79)

in 1995 (an above-average hickory-mast year) and moderate (r2 = 0.42) in 1997 (also an above-average hickory-mast

year). Because this relationship existed only two of the 5

sample years, the effect of stand density on hickory production is inconclusive.

Figure 3—Yearly relationship (A = 1995, B = 1997) between basal area and mean

Whitehead mast production indices for hickories (Carya texana and C. tomentosa) in 1995

and 1997 in the Ouachita Mountains. No significant linear relationship existed in 1994,

1996, or 1998 (P > 0.10).

Figure 2—Yearly relationship (A = 1994, B = 1995, C = 1996, D = 1997 and E = 1998)

between basal area and mean Whitehead mast production indices for post oaks (Quercus

stellata) in the Ouachita Mountains, 1994-1998.
59

CONCLUSIONS

Land managers strive to create optimal wildlife habitat by

providing cover, breeding areas, and adequate resources

such as food. Because hard mast is such an important food

source for numerous species of wildlife, managers should

consider optimizing mast production in conjunction with

other management goals. Our study suggests thinning,

even light thinning, can increase mast production in white

and post oaks. Because these are two of the most abundant mast-producing species in pine-hardwood stands on

south-facing slopes in the Ouachita Mountains, thinning

should generally increase mast production of individual

trees in these areas. However, we evaluated the effects

only of stand density on individual trees within those stands,

not the effects of thinning on overall stand production.

Because thinning removes some mast-producing trees, the

overall effects on stand-level production are unknown. Our

results suggest production by hickories or red oaks may not

benefit from thinning, but further investigation is needed to

confirm this.

ACKNOWLEDGMENTS

We thank R. Brown, M.G. Sams, C. Watt, R. Lay, K. Corbin,

and C. Miller for help with field measures; M.E. Shelton and

M. Spetich for reviews; and B. Parresol for biometrics

review.

LITERATURE CITED

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, J.B., comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Gen. Tech. Rep. SO112. New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station: 18-28.

Bilan, V.M. 1960. Stimulation of cone and seed production in polesized loblolly pine. Forest Science. 6: 207-220.

Christisen, D.M. 1955. Yield of seed by oaks in the Missouri

Ozarks. Journal of Forestry. 53: 439-441

Christisen D.M.; Korschgen, L.J. 1955. Acorn yields and wildlife

usage in Missouri. Transactions of the North American Wildlife

Conference. 20: 337-357.

Croker, T.C. 1952. Early release stimulates cone production. Res.

Note 79. New Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment Station. 3 p.

Downs, A.A.; McQuilken, W.E. 1944 Seed production of southern

Appalachian oaks. Journal of Forestry. 42: 913-920.

Farmer, R.E., Jr. 1981. Variation in seed of white oak. Forest

Science. 27: 377-380.

Godman, R.M. 1962. Red pine cone production stimulated by heavy

thinning. Tech. Note 628. Milwaukee, WI: U.S. Department of

Agriculture, Forest Service, Lake States Forest Experiment

Station. 2 p.

Guldin, J.M.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine/hardwood stands of the Ouachita/

Ozark National Forests. In: Baker, J.B., comp. Proceedings of

the symposium on ecosystem management research in the

Ouachita Mountains: pretreatment conditions and preliminary

findings. Gen. Tech. Rep. SO-112. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 29-44.

Gysel, L.W. 1956. Measurement of acorn crops. Forest Science. 2:

305-313.

Harlow, R.F.; Eikum, R.L. 1963. The effects of stand density on the

acorn production of turkey oaks. Proceedings of the Annual

conference of Game and Fish Commissioners. 17: 126-133.

Healy, W.M. 1997. Thinning New England oak stands to enhance

acorn production. Northern Journal of Applied Forestry. 14:

152-156.

Koenig, W.D.; Knops, J.M.H.; Carmen, W.J.; and others. 1996.

Acorn production by oaks in central coastal California: influence

of weather at three levels. Canadian Journal of Forest Research.

26: 1677-1683.

McShea, W.J.; Schwede, G. 1993. Variable acorn crops: responses

of white-tailed deer and other mast consumers. Journal of

Mammalogy. 74: 999-1006.

Minckler, L.S.; McDermott, R.E. 1960. Pin oak acorn production

and regeneration as affected by stand density, structure, and

flooding. University of Missouri. Columbia, MO: Agriculture

Experiment Station Research Bulletin 750. 38 p.

Nixon, C.M.; McClain, M.W.; Donohoe, R.W. 1975. Effects of hunting and mast crops on a squirrel population. Journal of Wildlife

Management. 39: 1-25.

Paugh, J.H. 1970. Effects of thinning on acorn production on the

West Virginia University Forest. Morgantown, WV: West Virginia

University. 100 p. M.S. Thesis.

Perry, R.W.; Thill, R.E. 1999. Estimating mast production: an evaluation of visual surveys and comparison with seed traps using

white oaks. Southern Journal of Applied Forestry. 23: 164-169.

Reid V.H.; Goodrum, P.D. 1957. The effects of hardwood removal

on wildlife. Proceedings of the Society of American Foresters

57: 141-147.

SAS Institute Inc. 1988. SAS/STAT user’s guide, Version 6.03.

Cary, NC: SAS Institute. 1028 p.

Sharp, W.M.; Sprague, V.G. 1967. Flowering and fruiting in the

white oaks. Pistillate flowering, and acorn development,

weather, and yields. Ecology. 48: 243-251.

Sork, V.L.; Bramble, J. 1993. Ecology of mast-fruiting in three

species of North American deciduous oaks. Ecology. 74: 528-

541.

Thill, R.E.; Tappe, P.A.; Koerth, N.E. 1994. Wildlife habitat conditions in mature pine-hardwood stands in the Ouachita/Ozark

National Forests. In: Baker, J.B., comp. Proceedings of the

symposium on ecosystem management research in the

Ouachita Mountains: pretreatment conditions and preliminary

findings. Gen. Tech. Rep. SO-112. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 18-28.

Wenger, K.F. 1954. The stimulation of loblolly pine seed trees by

preharvest release. Journal of Forestry. 53: 115-118.

Wentworth, J.M.; Johnson, A.S.; Hale, P.E. 1990. Influence of

acorn use on nutritional status and reproduction of deer in the

Southern Appalachians. Proceedings of the Annual Conference

of the Southeastern Association of Fish and Wildlife Agencies.

44: 142-154.

Whitehead, C.J. 1969. Oak mast yields on wildlife management

areas in Tennessee. Nashville, TN: Tennessee Wildlife

Resources Agency. 11 p.
60

INTRODUCTION

Quality forest habitats provide not only the vegetation structure needed by wildlife, but also adequate resources such

as food and water. Soft mast is an important component of

forest wildlife habitat, comprising a great percentage of the

seasonal diets of many wildlife species (Martin and others

1951). Soft mast from different plant species varies in nutritional quality (Halls 1977), and individual wildlife species

may prefer certain types of mast. Thus, wildlife managers

need to know what factors affect the abundance and production of individual soft mast species.

Recent policy changes by Federal land management agencies, such as the USDA Forest Service, have increased the

use of forest regeneration methods other than clearcut and

plant. Prior research provides substantial information on

soft mast responses to clearcutting (e.g., Campo and Hurst

1980, Johnson and Landers 1978, Stransky and Halls 1980).

However, the effects of alternative natural regeneration methods on individual soft mast-producing species are unknown.

Therefore, we compared the initial production and coverage

of 37 soft mast-producing species among four regeneration

methods (clearcut, shelterwood, single-tree selection, and

group selection) and in mature, unharvested forest stands

(controls). We sampled stands the first (1994), third (1996),

and fifth (1998) years after initial harvest in 1993. We

present data for all 37 species but focus on the seven

species that produced the most mast.

METHODS

Study Areas

We conducted the study in the 20 wildlife research stands

of the USDA Forest Service Ecosystem Management

(phase II) research study, located in the Ouachita and Ozark

National Forests of Oklahoma and Arkansas (Baker 1994,

Thill and others 1994). Five late-rotation, mixed pine-hardwood stands were selected in four physiographic zones

(north, south, east, and west blocks) of the Ouachita

Mountains (Baker 1994). Within each of the four blocks,

stands randomly received one of five treatments. Treatments

were single-tree selection, group selection, shelterwood,

clearcut, and late-rotation unharvested. Timber harvesting

was conducted in spring and summer of 1993; site preparation in natural regeneration stands occurred the following

winter. Mechanical ripping of clearcuts (prior to pine planting) occurred the following summer (1994). For a detailed

description of treatments see Baker (1994) and Perry and

others (1999).

Unharvested buffer strips or greenbelts (typically 15 m on

both sides of ephemeral and intermittent streams) were

established for water quality protection within harvested

stands. We considered greenbelts a subtreatment of

harvested stands; we averaged greenbelt data from all 16

harvested stands for comparison with other treatments.

Soft Mast Sampling

Prior to timber harvest, in each stand we established 100

permanent sampling stations at 15-m intervals along 4-9

(depending on stand size and shape) parallel transects

(Thill and others 1994). Transects were 30-95 m apart,

oriented perpendicular to stand slope, and >50 m from the

stand edge. We randomly selected a subsample of these

100 stations to estimate plant cover and soft mast

production.

INITIAL RESPONSE OF INDIVIDUAL SOFT MAST-PRODUCING PLANTS

TO DIFFERENT FOREST REGENERATION METHODS

IN THE OUACHITA MOUNTAINS

Roger W. Perry, Ronald E. Thill, Philip A. Tappe, and David G. Peitz1

1 Wildlife Biologist, USDA Forest Service, Southern Research Station, P.O. Box 1270, Hot Springs, AR 71902; Supervisory Research Wildlife

Biologist, USDA Forest Service, Southern Research Station, P.O. Box 7600 SFA Station, Nacogdoches, TX 75962; and Associate Professor

and Research Associate, School of Forest Resources, University of Arkansas at Monticello, Monticello, AR 71656, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Recent policy changes have eliminated clearcutting as the primary pine regeneration method on Federal lands

in the Southern United States. However, the effects of alternative natural regeneration methods on soft mast production

are unknown. We compared plant coverage and mast production of 37 soft mast-producing plants among four regeneration methods (clearcut, shelterwood, single-tree selection, and group selection), and in mature, unharvested stands the

first, third, and fifth years after timber harvest in the Ouachita Mountains of Oklahoma and Arkansas. Species richness

and diversity of plants that produced mast were greatest in unharvested stands the first postharvest year, although total

mast production did not differ among treatments. By the fifth postharvest year, total mast production and species richness

were greater in harvested stands than in unharvested stands. Overall, shelterwoods most closely approximated the

abundant soft mast production usually associated with clearcuts. Most of the 37 taxa investigated produced little or no

mast, regardless of treatment. Mast from only seven taxa made up 48-100 percent of total production in each treatment.

Pokeberry (Phytolacca americana L.) was the most abundant soft mast produced in intensively harvested areas

(clearcuts, shelterwoods, and group openings) the first year, but production declined dramatically in subsequent years.

Blackberry (Rubus spp.) was the most abundant mast in these areas by the fifth year. Muscadine grape (Vitis rotundifolia

Michx.) was generally the most abundant mast in other treatments.
61

We estimated soft mast production and percent coverage,

by species, during the summers of 1994, 1996, and 1998

(table 1). In 1996 and 1998, we included additional percent

cover estimates for some species not measured in 1994,

although we detected no production by these species

throughout the study. In 1994, we sampled three 1-m2

 plots,

located at 30 of the 100 sampling stations (90 m2 sample

area), in each unharvested and group selection stand. In

each clearcut, shelterwood, and single-tree selection stand,

we sampled one 1-m2 plot at 40 of the stations (40 m2

sample area). However, because we observed heterogenous distribution of soft mast within all stands in 1994, we

increased sampling effort at all stations to one 3x3-m plot,

located at each of 60 stations (540 m2 total sample area in

each stand) in 1996 and 1998. For further information on

sampling effort by treatment and year, see Perry and others

(1999).

We conducted soft mast surveys in mid-June, mid-July, and

mid-August. During each sampling period, we measured a

different set of species to coincide with ripening phenology

of the major fruit-producing species. During sampling, we

counted all soft mast, including green fruits, located within

plots to a height of 2 m. To reduce potential bias from herbivory, we tallied evidence of removed fruits when possible.

We developed wet to dry mass conversion factors by collecting, drying to constant mass, and weighing samples of each

fruit type. We visually estimated percent coverage of each

fruit-producing species at each plot in mid-July. A single

observer estimated coverage in west- and south-block

stands, and another observer estimated coverage in eastand north-block stands. Although we measured coverage of

downy serviceberry (Amelanchier arborea Michx.f.), sampling occurred too late in the season to include soft mast

from this early-spring producer.

For species with large seed heads containing numerous

individual fruits [e.g., winged sumac (Rhus copallina L.)],

we developed regression equations to convert volume

estimates to mass. First, we collected 20-50 fruit heads per

species. We then measured these fruit heads in three

dimensions to determine volume, then measured mass to

derive volume-to-mass estimates. During fruit surveys, we

measured volume of each seed head on each plot to

estimate mass produced.

Data Analysis

We derived means of soft mast production (kg/ha dry mass)

and percent coverage for each soft mast species in each

stand. We calculated treatment means from the four stands/

treatment, except for clearcuts in 1994. In 1994, one clearcut was being ripped during the July surveys; therefore, we

only included three clearcuts in the 1994 analysis. We used

the same stands for sampling each year except for one

unharvested stand. In 1997, the east-block unharvested

stand was inadvertently harvested and subsequently

replaced in 1998 with a similar stand. Because data for

individual soft mast species among treatments were not

parametric, we compared means among treatments using

analysis of variance (ANOVA) on ranks and Duncan’s multiple range test at the 0.10 level (SAS Institute Inc. 1988).

Measured values, rather than ranks, are presented in all

tables. However, because of ranking, some values are statistically lower than others even though the presented

means are greater.

Group selection stands were comprised of group openings,

where basal areas (BA) averaged 4.1 ± 0.7 m2

/ha, and the

surrounding forest matrix, which was thinned to 20.3 ± 0.8

m2/ha. Because of notable differences in BA and mast production between the openings and surrounding matrices,

Table 1—Soft-mast-producing taxa surveyed for production and coverage in 20 forest stands under

various treatments in the Ouachita Mountains of Arkansas and Oklahoma during summer 1994, 1996,

and 1998

Downy serviceberry (Amelanchier arborea Michx.f)a Pokeberry (Phytolacca americana L.)b

Rattan (Berchemia scandens Hill) Black cherry (Prunus serotina Ehrh.)

Bumelia (Bumelia lanuginosa Michx.)a Wild plums (Prunus spp.)

American beautyberry (Callicarpa americana L.) Carolina buckthorn (Rhamnus caroliniana Walt.)

Hackberries (Celtis spp.) Fragrant sumac (Rhus aromatica Ait.)

Fringe tree (Chionanthus virginicus L.) Winged sumac (Rhus copallina L.)

Redberry moonseed (Cocculus carolinus L.) Smooth sumac (Rhus glabra L.)

Flowering dogwood (Cornus florida L.) Wild roses (Rosa spp.)

Narrow-leafed dogwood (Cornus obliqua Raf.) Blackberries (Rubus spp.)

Hawthorns (Crataegus spp.) Sassafras (Sassafras albidum Nutt.)

Persimmon (Diospyros virginiana L.) Greenbriers (Smilax spp.)

Deciduous holly (Ilex decidua Ait.) Coralberry (Symphoricarpos orbiculatus Moench)

American holly (Ilex opaca Ait.) Poison ivy (Toxicodendron radicans L.)

Yaupon holly (Ilex vomitoria Ait)a Sparkleberry (Vaccinium arboreum Marsh.)

Eastern redcedar (Juniperus virginiana L.)a Blueberries (Vaccinium spp.)

Partridgeberry (Mitchella repens L.) Rusty blackhaw (Viburnum rufidulum Raf.)

Mulberry (Morus rubra L.) Muscadine grape (Vitis rotundifolia Michx.)

Blackgum (Nyssa sylvatica Marsh.)a Other grapes (Vitis spp.)

Virginia creeper (Parthenocissus quinquefolia L.)a

 a No cover estimates were derived for these species in 1994.

 b Herbaceous species
62Table 2—Mean (± SE) percent cover of soft-mast-producing plants, by treatment and habitat types within treatments, measured during

July 1994 (the first post-harvest year) in the Ouachita Mountains

Single

tree Group selectionc

Species Unharvested Greenbeltb selection Matrix Openings Combined Shelterwood Clearcut

American beautyberry 0.03 ± 0.03 0.06 ± 0.06 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.13 ± 0.13 0.00 ± 0.00American holly 0.15 ± 0.15 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Blackberries 0.04ba ± 0.04 0.17b ± 0.17 0.13ab ± 0.08 0.22 ± 0.11 0.31 ± 0.24 0.25a ± 0.12 1.35a ± 0.59 0.09ab ± 0.06

Black cherry 0.10 ± 0.08 1.59 ± 1.32 0.19 ± 0.15 0.07 ± 0.07 0.02 ± 0.01 0.04 ± 0.04 0.00 ± 0.00 0.34 ± 0.27Blueberries 10.06 ± 3.01 2.19 ± 1.36 5.66 ± 3.22 1.49 ± 1.13 1.22 ± 0.43 1.38 ± 0.87 1.54 ± 1.07 2.10 ± 1.79Flowering dogwood 10.96 ± 5.44 4.01 ± 1.46 6.47 ± 3.51 2.03 ± 0.44 1.01 ± 0.59 1.90 ± 0.35 3.12 ± 1.47 0.68 ± 0.60Fragrant sumac 0.00 ± 0.00 0.00 ± 0.00 0.06 ± 0.06 0.00 ± 0.00 0.01 ± 0.01 <0.01 0.00 ± 0.00 0.00 ± 0.00Greenbriers 2.86 ± 1.51 2.05 ± 1.06 0.49 ± 0.14 0.81 ± 0.23 0.75 ± 0.75 0.95 ± 0.35 0.95 ± 0.47 0.22 ± 0.20Hackberries 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.06 ± 0.06 0.00 ± 0.00 0.06 ± 0.06 0.00 ± 0.00 0.00 ± 0.00Hawthorns 0.07b ± 0.07 0.02b ± 0.02 0.22ab ± 0.21 0.21 ± 0.13 0.00 ± 0.00 0.31a ± 0.15 0.00b ± 0.00 0.00b ± 0.00Muscadine grape 2.64 ± 0.71 2.28 ± 0.80 1.76 ± 0.84 5.11 ± 3.60 6.93 ± 6.32 5.05 ± 3.74 4.25 ± 1.83 1.37 ± 1.35Other grapes 0.43 ± 0.28 0.02 ± 0.02 0.01 ± 0.01 0.72 ± 0.62 0.24 ± 0.24 0.54 ± 0.47 0.35 ± 0.35 0.10 ± 0.10Partridgeberry <0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Persimmon 0.08 ± 0.08 0.38 ± 0.38 0.01 ± 0.01 0.00 ± 0.00 0.02 ± 0.02 <0.01 0.00 ± 0.00 0.00 ± 0.00Poison ivy 2.86 ± 0.60 5.19 ± 1.43 4.67 ± 2.41 3.69 ± 1.70 3.94 ± 1.76 3.73 ± 1.42 1.87 ± 0.19 1.97 ± 1.10Pokeberry 0.00b ± 0.00 0.08b ± 0.08 0.63b ± 0.63 0.02 ± 0.02 1.43 ± 1.43 0.37b ± 0.37 1.46a ± 1.25 1.63a ± 1.37Rattan 0.02 ± 0.02 0.41 ± 0.22 0.08 ± 0.08 0.18 ± 0.17 0.08 ± 0.08 0.17 ± 0.12 0.00 ± 0.00 0.00 ± 0.00Rusty blackhaw 0.00 ± 0.00 1.03 ± 0.76 0.07 ± 0.06 0.02 ± 0.02 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.11 ± 0.10Sassafras 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.02 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00Smooth sumac 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.05 ± 0.05 0.00 ± 0.00Sparkleberry 1.85 ± 1.16 1.81 ± 1.35 0.29 ± 0.25 2.08 ± 1.56 0.42 ± 0.42 1.86 ± 1.46 0.00 ± 0.00 0.01 ± 0.01Wild plums <0.01b 0.07b ± 0.07 0.11ab ± 0.07 0.12 ± 0.11 0.00 ± 0.00 0.06ab ± 0.05 0.00b ± 0.00 0.92a ± 0.77

Winged sumac 0.00b ± 0.00 0.10b ± 0.08 0.01b ± 0.01 0.11 ± 0.09 0.71 ± 0.68 0.32a ± 0.21 0.47a ± 0.16 0.59a ± 0.30

a Within rows, means followed by the same letter were not different (P < 0.10) using ANOVA on ranks and Duncan’s MRT.b Averaged across all harvested stands which contained greenbelt plots (N = 15).c Only combined values were included in the ANOVA.
63

we present data for these two habitats separately. However,

only combined data (derived from stand-wide averages)

were included in the ANOVA analysis. We also determined

total soft mast production and coverage in the greenbelts of

each stand (n = 15 stands in 1994 and 

n = 16 stands in 1996

and 1998). We then determined mean soft mast production

and coverage in greenbelts by averaging across all stands.

We calculated richness, evenness, and diversity of species

which produced mast and compared these measures, by

year, among the five primary treatments (clearcut, shelterwood, single-tree selection, group selection, and unharvested) using analysis of variance and Duncan’s multiple

range test at the 0.10 level (SAS Institute Inc. 1988). Richness was defined as the total number of species producing

soft mast per stand. Species diversity was the Shannon

index (Shannon and Weiner 1949); Pielou’s J was our

evenness measure (Pielou 1969).

RESULTS AND DISCUSSION

Soft mast plant coverage was relatively low in harvested

stands the first postharvest year, apparently due to logging

disturbance (table 2). Only nine species produced mast

among all stands in 1994 (table 3). In group openings and

clearcut stands, pokeberry (Phytolacca americana L.) was

the only mast produced. In the third postharvest year (1996),

coverage of most plant species increased in harvested

stands (table 4). Overall soft mast production in the third

year was greater in harvested stands than in unharvested

stands (Perry and others 1999). In the third year, the number

of species producing mast increased to 16 (table 5). By the

fifth postharvest year, coverage of many plant species was

greater in harvested stands than in unharvested stands or

greenbelts (table 6). Twenty-five species produced mast the

fifth year, although many of these species produced only

trace amounts in a single stand (table 7). Total production

in the fifth year was greatest in clearcut and shelterwood

stands, intermediate in single-tree selection and group

selection stands, and lowest in unharvested stands; shelterwood and clearcut stands produced more than twice as

much mast as single-tree selection and group selection

stands (Perry and others 1999).

Species richness was greatest in unharvested stands the

first postharvest year (table 8). In the third year, no difference existed in richness among treatments. By the fifth

year, richness was greater in harvested stands than in

unharvested stands. Diversity of mast-producing species

was greatest in unharvested stands, and evenness was

greatest in unharvested and group selection stands the first

year. However, differences were not significant (

P > 0.10) in

subsequent years.

Pokeberry, blackberries (Rubus spp.), blueberries (Vaccinium

spp.), muscadine grape (Vitis rotundifolia Michx.), other

grapes (Vitis spp.), winged sumac, and American beautyberry (Callicarpa americana L.) were the seven most

abundant soft mast producers during the study (table 9).

Therefore, the following discussion focuses on these seven

taxa.

Table 3—Mean (± SE) dry-weight of soft mast (kg/ha) produced, by treatment and habitat types within treatments, measured during summer1994 (the first post-harvest year) in the Ouachita Mountains

Single

tree Group selectionc

Species Unharvested Greenbeltb selection Matrix Openings Combined Shelterwood Clearcut

Blueberries 0.58aa ± 0.52 0.00b ± 0.00 0.00b ± 0.00 0.09 ± 0.09 0.00 ± 0.00 0.07b ± 0.07 0.07b ± 0.07 0.00b ± 0.00

Flowering dogwood 1.42 ± 0.98 1.13 ± 1.00 0.23 ± 0.23 0.71 ± 0.42 0.00 ± 0.00 0.47 ± 0.30 0.00 ± 0.00 0.00 ± 0.00

Greenbriers 0.25a ± 0.18 0.02b ± 0.02 0.00b ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00b ± 0.00 0.00b ± 0.00 0.00b ± 0.00

Muscadine grape 0.52a ± 0.29 2.06b ± 2.06 0.22b ± 0.22 0.00 ± 0.00 0.00 ± 0.00 0.00b ± 0.00 3.33b ± 3.33 0.00b ± 0.00Other grapes 0.46 ± 0.46 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

Poison ivy 0.02 ± 0.01 0.04 ± 0.04 0.00 ± 0.00 0.02 ± 0.02 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00Pokeberry 0.00b ± 0.00 2.57b ± 2.57 0.00b ± 0.00 0.00 ± 0.00 13.13 ± 13.13 3.28b ± 3.28 26.51a ± 21.60 23.05a ± 18.56Rattan 0.00 ± 0.00 0.04 ± 0.04 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Sparkleberry 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.02 ± 0.02 0.00 ± 0.00 0.02 ± 0.02 0.00 ± 0.00 0.00 ± 0.00

a Within rows, means followed by the same letter were not different (P < 0.10) using ANOVA on ranks and Duncan’s MRT.b Averaged across all harvested stands which contained greenbelt plots (N = 15).c Only combined values were included in the ANOVA.
64Table 4—Mean (± SE) percent cover of soft-mast-producing plants, by treatment and habitat types within treatments, measured during July

1996 (the third post-harvest year) in the Ouachita Mountains

Single

tree Group selectionc

Species Unharvested Greenbeltb selection Matrix Openings Combined Shelterwood Clearcut

American

beautyberry 0.03aba ± 0.02 0.00b ± 0.00 0.06ab ± 0.05 0.01 ± 0.01 0.42 ± 0.27 0.08ab ± 0.05 0.80a ± 0.78 0.13a ± 0.08

American holly 0.07 ± 0.07 0.06 ± 0.06 0.08 ± 0.08 0.00 ± 0.00 1.38 ± 1.38 0.31 ± 0.31 0.01 ± 0.01 0.00 ± 0.00

Blackberries 0.08b ± 0.02 0.48b ± 0.31 2.08a ± 0.64 0.48 ± 0.10 8.92 ± 2.01 2.59a ± 0.84 7.86a ± 4.02 4.07a ± 1.43Black cherry 0.17b ± 0.12 0.69b ± 0.31 1.76a ± 0.87 0.19 ± 0.10 0.00 ± 0.00 0.12b ± 0.07 1.48ab ± 0.48 1.39ab ± 1.12

Blackgum 4.54a ± 1.88 1.04bc ± 0.44 2.61ab ± 1.27 0.32 ± 0.07 0.57 ± 0.48 0.39bc ± 0.15 2.41abc ± 1.49 0.69c ± 0.68Blueberries 10.40 ± 2.70 3.66 ± 1.35 7.66 ± 5.16 2.88 ± 2.24 2.41 ± 0.88 2.63 ± 1.77 2.78 ± 1.76 2.46 ± 1.36Bumelia 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.15 ± 0.15 0.05 ± 0.05 0.00 ± 0.00 0.00 ± 0.00Carolina buckthorn 0.04 ± 0.04 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.05 ± 0.05 0.00 ± 0.00Coralberry 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.03 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Downy Serviceberry 0.19a ± 0.07 0.06b ± 0.06 0.07b ± 0.07 0.00 ± 0.00 0.00 ± 0.00 0.19b ± 0.19 0.00b ± 0.00 0.00b ± 0.00Eastern redcedar 0.06 ± 0.06 0.10 ± 0.07 0.05 ± 0.05 0.27 ± 0.27 0.00 ± 0.00 0.14 ± 0.14 0.12 ± 0.12 0.00 ± 0.00Flowering dogwood 4.52 ± 1.06 4.11 ± 0.80 8.52 ± 4.28 2.43 ± 0.83 2.04 ± 0.86 2.33 ± 0.73 3.33 ± 1.02 1.03 ± 0.91Fragrant sumac 0.15ab ± 0.15 0.01ab ± 0.01 0.18a ± 0.11 0.16 ± 0.12 0.42 ± 0.27 0.20a ± 0.12 0.19a ± 0.13 0.00b ± 0.00Fringe tree 0.00 ± 0.00 0.25 ± 0.25 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Greenbriers 4.61 ± 2.93 2.18 ± 0.47 1.75 ± 0.61 1.35 ± 0.48 1.87 ± 1.56 1.60 ± 0.82 2.57 ± 0.93 0.98 ± 0.53Hackberries 0.00 ± 0.00 0.05 ± 0.05 0.04 ± 0.04 0.01 ± 0.01 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00Hawthorns 0.08 ± 0.07 0.19 ± 0.14 0.28 ± 0.10 0.21 ± 0.13 0.31 ± 0.24 0.30 ± 0.15 0.11 ± 0.09 0.05 ± 0.05Mulberry 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

Muscadine grape 3.76b ± 1.25 3.24b ± 0.89 6.84ab ± 2.36 6.56 ± 3.46 13.22 ± 7.77 8.11b ± 4.37 17.88a ± 2.79 4.77b ± 2.98Narrow-leaf dogwood 0.17 ± 0.17 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Other grapes 0.43ab ± 0.23 0.05c ± 0.02 0.74a ± 0.28 0.68 ± 0.50 1.17 ± 0.48 0.71ab ± 0.39 0.86ab ± 0.33 0.26bc ± 0.22Partridgeberry 0.49a ± 0.30 0.00b ± 0.00 0.00b ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00b ± 0.00 0.00b ± 0.00 0.00b ± 0.00Persimmon 0.06 ± 0.02 0.24 ± 0.11 0.21 ± 0.06 0.41 ± 0.19 2.20 ± 1.71 0.72 ± 0.36 0.34 ± 0.26 0.17 ± 0.15Poison ivy 4.50 ± 1.47 9.15 ± 2.48 12.62 ± 7.10 10.11 ± 4.74 14.26 ± 5.53 10.69 ± 4.43 10.07 ± 1.76 7.68 ± 5.56Pokeberry 0.00b ± 0.00 0.00b ± 0.00 0.15a ± 0.13 0.06 ± 0.06 1.22 ± 0.78 0.26a ± 0.14 0.37a ± 0.19 1.60a ± 1.28Rattan 0.07 ± 0.05 0.14 ± 0.06 0.23 ± 0.18 0.01 ± 0.01 0.00 ± 0.00 <0.01 0.22 ± 0.19 0.00 ± 0.00Redberry moonseed 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.11 ± 0.11 0.00 ± 0.00Rusty blackhaw 0.02 ± 0.02 0.59 ± 0.37 0.14 ± 0.14 0.06 ± 0.04 0.03 ± 0.03 0.14 ± 0.08 0.01 ± 0.01 0.01 ± 0.01Sassafras 0.02c ± 0.02 0.01c ± 0.01 0.11ab ± 0.07 0.06 ± 0.03 1.58 ± 1.37 0.45a ± 0.23 0.00c ± 0.00 0.15bc ± 0.15Smooth sumac 0.00b ± 0.00 0.00b ± 0.00 0.00b ± 0.00 0.00 ± 0.00 0.14 ± 0.14 0.02ab ± 0.02 0.09a ± 0.06 0.10ab ± 0.10Sparkleberry 0.73 ± 0.52 1.47 ± 0.85 1.11 ± 0.34 2.82 ± 2.17 3.38 ± 3.38 3.00 ± 2.51 0.81 ± 0.61 0.78 ± 0.22Virginia creeper 2.82 ± 2.39 1.52 ± 0.48 2.07 ± 0.87 1.62 ± 0.50 2.94 ± 1.75 1.85 ± 0.57 4.17 ± 2.31 0.73 ± 0.61Wild plums 0.03b ± 0.03 0.09b ± 0.07 0.33ab ± 0.22 0.64 ± 0.37 0.84 ± 0.56 0.79a ± 0.45 0.60 ± 0.43 0.64a ± 0.35Wild rose 0.00c ± 0.00 0.05bc ± 0.03 0.03bc ± 0.03 0.38 ± 0.27 0.58 ± 0.55 0.37a ± 0.30 0.15ab ± 0.09 0.00c ± 0.00Winged sumac 0.01c ± 0.01 0.38c ± 0.37 0.62b ± 0.27 0.26 ± 0.09 3.93 ± 2.39 1.38ab ± 0.81 3.10a ± 0.04 4.62a ± 2.46

a Within rows, means followed by the same letter were not different (P < 0.10) using ANOVA on ranks and Duncan’s MRT.b Averaged across all harvested stands which contained greenbelt plots (N = 16).c Only combined values were included in the ANOVA.
65

Table 5—Mean (± SE) dry-weight of soft mast (kg/ha) produced, by treatment and habitat types within treatments, measured during summer1996 (the third post-harvest year) in the Ouachita Mountains

Single

tree Group selectionc

Species Unharvested Greenbeltb selection Matrix Openings Combined Shelterwood Clearcut

American beautyberry 0.00 ± 0.00 0.00 ± 0.00 0.12 ± 0.12 0.00 ± 0.00 0.01 ± 0.01 <0.01 0.71 ± 0.71 0.03 ± 0.03Blackberries 0.00da ± 0.00 0.01d ± 0.01 0.20bc ± 0.05 0.05 ± 0.02 1.51 ± 1.34 0.51c ± 0.46 3.15a ± 2.08 1.17ab ± 0.80

Blueberries 0.04 ± 0.02 0.10 ± 0.06 0.22 ± 0.12 0.08 ± 0.08 <0.01 0.06 ± 0.06 0.76 ± 0.74 0.76 ± 0.74Carolina buckthorn <0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 <0.01 0.00 ± 0.00

Flowering dogwood 0.02 ± 0.02 0.11 ± 0.09 0.17 ± 0.13 0.03 ± 0.03 0.10 ± 0.10 0.06 ± 0.03 0.10 ± 0.10 0.00 ± 0.00Greenbriers 0.13 ± 0.10 0.02 ± 0.01 0.94 ± 0.94 0.02 ± 0.01 0.48 ± 0.48 0.18 ± 0.17 0.75 ± 0.44 0.01 ± 0.01Muscadine grape 0.09b ± 0.04 1.00b ± 0.65 6.65a ± 2.03 0.53 ± 0.30 4.46 ± 3.94 1.76b ± 1.42 10.25a ± 5.60 0.95b ± 0.52Other grapes 0.43ab ± 0.43 0.00b ± 0.00 0.20ab ± 0.20 0.00 ± 0.00 0.51 ± 0.51 0.09ab ± 0.09 1.59a ± 0.96 0.00b ± 0.00Poison ivy 0.03bc ± 0.02 0.01c ± 0.01 0.13a ± 0.06 0.02 ± 0.01 0.00 ± 0.00 0.01bc ± 0.01 0.21ab ± 0.15 0.34bc ± 0.34Pokeberry 0.00d ± 0.00 0.00d ± 0.00 0.64cd ± 0.64 0.02 ± 0.02 9.72 ± 6.55 2.11bc ± 1.41 1.92a ± 1.00 28.74ab ± 24.98Rattan 0.00 ± 0.00 0.00 ± 0.00 <0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Redberry moonseed 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.02 ± 0.02 0.00 ± 0.00Sassafras 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 1.40 ± 1.40 0.24 ± 0.24 0.00 ± 0.00 0.00 ± 0.00Sparkleberry 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 <0.01Wild rose 0.00b ± 0.00 <0.01b 0.00b ± 0.00 0.15 ± 0.13 0.23 ± 0.23 0.16a ± 0.15 0.29a ± 0.20 0.00b ± 0.00

Winged sumac 0.00c ± 0.00 0.32c ± 0.32 0.00c ± 0.00 0.52 ± 0.52 0.00 ± 0.00 0.40bc ± 0.40 4.25ab ± 4.06 8.86a ± 8.15

a Within rows, means followed by the same letter were not different (P < 0.10) using ANOVA on ranks and Duncan’s MRT.b Averaged across all harvested stands which contained greenbelt plots (N = 16).c Only combined values were included in the ANOVA.
66Table 6—Mean ( ± SE) percent cover of soft-mast-producing plants, by treatment and habitat types within treatments, measured during July 1998 (the fifth post-harvest year) in the Ouachita Mountains

Single

tree Group selectionc

Species Unharvested Greenbeltb selection Matrix Openings Combined Shelterwood Clearcut

American

beautyberry 0.05bca ± 0.02 0.63c ± 0.63 0.04bc ± 0.02 0.07 ± 0.06 0.98 ± 0.47 0.24ab ± 0.09 1.93a ± 1.64 0.26bc ± 0.15

American holly 0.06 ± 0.05 0.65 ± 0.62 0.10 ± 0.09 0.19 ± 0.19 1.32 ± 1.23 0.40 ± 0.26 0.03 ± 0.03 0.00 ± 0.00Blackberries 0.27c ± 0.13 1.39c ± 0.66 3.34b ± 0.84 2.04 ± 1.12 20.84 ± 4.18 6.76ab ± 2.45 14.08a ± 4.32 14.60a ± 1.51Black cherry 0.33 ± 0.08 1.48 ± 0.65 2.05 ± 1.57 0.83 ± 0.46 3.43 ± 2.06 1.52 ± 0.88 3.29 ± 1.92 2.69 ± 1.34Blackgum 4.42 ± 1.89 2.26 ± 0.71 4.17 ± 1.69 1.38 ± 0.72 6.88 ± 6.45 2.28 ± 1.65 3.26 ± 2.04 1.11 ± 0.73Blueberries 10.25 ± 2.60 3.94 ± 1.39 7.51 ± 4.14 3.10 ± 2.07 3.08 ± 1.63 2.91 ± 1.80 3.52 ± 2.02 3.97 ± 1.71Bumelia 0.00 ± 0.00 0.63 ± 0.62 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Carolina buckthorn 0.24 ± 0.24 0.84 ± 0.65 0.10 ± 0.10 0.04 ± 0.04 0.01 ± 0.01 0.03 ± 0.03 0.46 ± 0.46 0.00 ± 0.00Coralberry 0.00 ± 0.00 0.63 ± 0.63 0.04 ± 0.04 0.00 ± 0.00 0.01 ± 0.01 <0.01 0.00 ± 0.00 0.00 ± 0.00

Deciduous holly 0.00 ± 0.00 0.89 ± 0.65 0.00 ± 0.00 0.00 ± 0.00 0.22 ± 0.22 0.07 ± 0.07 0.00 ± 0.00 0.00 ± 0.00Downy Serviceberry 0.07 ± 0.04 0.64 ± 0.62 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.03 0.01 ± 0.01 0.06 ± 0.06 0.00 ± 0.00Eastern redcedar 0.13 ± 0.10 0.66 ± 0.32 0.15 ± 0.11 1.26 ± 0.76 0.50 ± 0.40 0.92 ± 0.43 0.56 ± 0.24 0.08 ± 0.07Flowering dogwood 4.35 ± 1.38 5.56 ± 0.97 9.52 ± 4.85 3.81 ± 0.87 2.37 ± 1.00 3.49 ± 0.70 4.97 ± 1.15 1.75 ± 1.38Fragrant sumac 0.13 ± 0.10 0.94 ± 0.68 0.33 ± 0.19 0.10 ± 0.07 1.56 ± 0.97 0.34 ± 0.21 0.56 ± 0.33 0.01 ± 0.01Fringe tree 0.00 ± 0.00 1.00 ± 0.71 0.03 ± 0.03 0.01 ± 0.01 0.00 ± 0.00 0.01 ± 0.01 0.00 ± 0.00 0.01 ± 0.01Greenbriers 4.42 ± 2.02 5.91 ± 1.47 2.21 ± 0.36 4.03 ± 2.06 4.77 ± 4.39 4.41 ± 2.74 5.75 ± 2.24 2.51 ± 1.73Hackberries 0.00 ± 0.00 0.66 ± 0.62 0.07 ± 0.07 0.01 ± 0.01 0.03 ± 0.03 0.01 ± 0.01 0.00 ± 0.00 0.00 ± 0.00Hawthorns 0.19 ± 0.06 0.75 ± 0.62 0.46 ± 0.13 0.27 ± 0.14 0.42 ± 0.26 0.34 ± 0.17 0.21 ± 0.06 0.29 ± 0.20Mulberry 0.00 ± 0.00 0.66 ± 0.62 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.17 ± 0.10 0.03 ± 0.03Muscadine grape 6.55 ± 2.34 6.87 ± 2.03 8.43 ± 3.15 11.24 ± 5.97 10.30 ± 7.13 10.77 ± 5.92 22.42 ± 4.20 7.52 ± 4.17Narrow-leaf dogwood 0.09 ± 0.09 0.63 ± 0.63 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Other grapes 0.61ab ± 0.34 0.75b ± 0.62 0.95a ± 0.37 0.89 ± 0.47 2.32 ± 1.33 1.12ab ± 0.59 1.79a ± 0.62 0.70ab ± 0.53Partridgeberry 0.49a ± 0.30 0.63b ± 0.63 0.00b ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00b ± 0.00 0.00b ± 0.00 0.00b ± 0.00Persimmon 0.07 ± 0.04 0.73 ± 0.62 0.10 ± 0.06 0.36 ± 0.30 1.26 ± 0.72 0.58 ± 0.40 0.17 ± 0.17 0.04 ± 0.02Poison ivy 6.82 ± 1.97 10.84 ± 2.39 16.61 ± 7.80 13.19 ± 5.94 20.62 ± 6.79 14.52 ± 5.85 15.89 ± 2.80 8.60 ± 5.17Pokeberry 0.00b ± 0.00 0.63b ± 0.63 0.07b ± 0.07 0.00 ± 0.00 0.13 ± 0.13 0.03b ± 0.03 0.00b ± 0.00 0.22a ± 0.14Rattan 0.20 ± 0.16 1.56 ± 0.77 0.41 ± 0.19 0.32 ± 0.18 1.04 ± 1.04 0.77 ± 0.51 0.61 ± 0.53 0.18 ± 0.18Redberry moonseed 0.01 ± 0.01 0.65 ± 0.62 0.01 ± 0.01 0.01 ± 0.01 0.00 ± 0.00 0.01 ± 0.01 0.17 ± 0.16 0.00 ± 0.00Rusty blackhaw 0.08 ± 0.05 1.26 ± 0.63 0.48 ± 0.36 0.10 ± 0.04 0.26 ± 0.15 0.15 ± 0.03 0.26 ± 0.15 0.34 ± 0.32Sassafras 0.04 ± 0.02 0.68 ± 0.62 0.14 ± 0.07 0.19 ± 0.11 1.06 ± 0.67 0.46 ± 0.19 0.13 ± 0.09 0.35 ± 0.35Smooth sumac 0.00c ± 0.00 0.63c ± 0.63 0.07bc ± 0.07 0.00 ± 0.00 0.42 ± 0.27 0.09abc ± 0.05 0.12ab ± 0.05 0.95a ± 0.84Sparkleberry 1.03 ± 0.52 2.03 ± 0.89 1.38 ± 0.48 2.81 ± 2.07 4.24 ± 2.58 2.70 ± 1.52 1.26 ± 0.73 1.31 ± 0.55Virginia creeper 4.50 ± 2.84 2.19 ± 0.74 2.54 ± 0.72 2.31 ± 0.92 4.13 ± 2.33 2.70 ± 1.13 4.98 ± 2.76 0.55 ± 0.41Wild plums 0.27 ± 0.08 1.58 ± 0.73 1.55 ± 0.28 0.86 ± 0.49 1.08 ± 0.64 1.88 ± 1.23 1.26 ± 0.85 0.98 ± 0.81Wild rose 0.05 ± 0.04 0.78 ± 0.62 0.08 ± 0.05 0.26 ± 0.19 1.11 ± 1.11 0.38 ± 0.33 0.40 ± 0.26 0.06 ± 0.06Winged sumac 0.05b ± 0.05 1.21b ± 0.77 1.35a ± 0.39 0.47 ± 0.19 6.88 ± 3.49 2.47a ± 1.34 5.21a ± 0.68 9.04a ± 4.52Yaupon holly 0.00 ± 0.00 0.63 ± 0.63 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00

a Within rows, means followed by the same letter were not different (P < 0.10) using ANOVA on ranks and Duncan’s MRT.b Averaged across all harvested stands which contained greenbelt plots (N = 16).c Only combined values were included in the ANOVA.
67

Table 7—Mean ( ± SE) dry-weight of soft mast (kg/ha) produced, by treatment and habitat types within treatments, measured during summer1998 (the fifth post-harvest year) in the Ouachita Mountains

Single

tree Group selectionc

Species Unharvested Greenbeltb selection Matrix Openings Combined Shelterwood Clearcut

American beautyberry <0.01bca 0.00 ± 0.00c 0.14bc ± 0.14 0.00 ± 0.00 3.42 ± 3.17 0.57ab ± 0.53 3.61a ± 3.36 0.45ab ± 0.31

American holly 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 <0.01 0.00 ± 0.00Blackberries 0.00c ± 0.00 0.04c ± 0.02 1.46b ± 0.66 0.90 ± 0.37 82.34 ± 23.45 18.31a ± 4.02 46.10a ± 32.37 48.37a ± 20.85Black cherry 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 1.67 ± 1.67Blueberries 0.50 ± 0.49 0.39 ± 0.20 0.11 ± 0.04 0.40 ± 0.40 0.78 ± 0.76 0.48 ± 0.47 2.63 ± 2.58 0.85 ± 0.79Carolina buckthorn 0.02 ± 0.02 0.00 ± 0.00 0.09 ± 0.09 0.00 ± 0.00 <0.01 <0.01 0.21 ± 0.21 0.00 ± 0.00Coralberry 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.01 <0.01 0.00 ± 0.00 0.00 ± 0.00Deciduous holly 0.00 ± 0.00 <0.01 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Flowering dogwood 0.00 ± 0.00 0.07 ± 0.07 0.30 ± .030 0.11 ± 0.11 0.00 ± 0.00 0.08 ± 0.08 0.12 ± 0.12 0.00 ± 0.00Fringe tree 0.00 ± 0.00 0.47 ± 0.47 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Fragrant sumac 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 <0.01 0.00 ± 0.00 <0.01 <0.01 0.00 ± 0.00Greenbriers 0.06b ± 0.06 0.18b ± 0.11 0.02b ± 0.02 0.03 ± 0.03 0.11 ± 0.11 0.07b ± 0.07 0.61a ± 0.14 0.06b ± 0.06Hawthorns 0.00 ± 0.00 0.00 ± 0.00 0.05 ± 0.05 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Muscadine grape 0.34c ± 0.31 2.33c ± 2.19 9.89a ± 4.34 2.37 ± 1.55 9.99 ± 9.72 4.76bc ± 3.98 33.87a ± 20.05 6.09ab ± 3.65Other grapes 0.00c ± 0.00 0.00c ± 0.00 0.48ab ± 0.28 0.24 ± 0.24 0.74 ± 0.74 1.25ab ± 1.09 1.16a ± 0.69 0.02bc ± 0.02Partridgeberry 0.01a ± 0.01 0.00b ± 0.00 0.00b ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00b ± 0.00 0.00b ± 0.00 0.00b ± 0.00Poison ivy 0.01ab ± 0.01 0.05b ± 0.03 0.13a ± 0.08 0.04 ± 0.02 0.00 ± 0.00 0.03ab ± 0.02 0.08ab ± 0.07 0.92ab ± 0.82Pokeberry 0.00b ± 0.00 0.00b ± 0.00 0.54b ± 0.54 0.00 ± 0.00 0.22 ± 0.22 0.05b ± 0.05 0.00b ± 0.00 0.86a ± 0.71Rattan 0.00 ± 0.00 0.00 ± 0.00 0.06 ± 0.06 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00Sassafras 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 <0.01Smooth sumac 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.65 ± 0.65Sparkleberry 0.01bc ± 0.01 0.23bc ± 0.23 0.02bc ± 0.02 0.02 ± 0.02 0.12 ± 0.10 0.05b ± 0.03 0.00c ± 0.00 0.28a ± 0.23Wild plums 0.00b ± 0.00 0.00b ± 0.00 0.06ab ± 0.06 0.01 ± 0.01 0.08 ± 0.08 0.04a ± 0.03 0.00b ± 0.00 0.23ab ± 0.23Wild rose 0.00b ± 0.00 0.00b ± 0.00 0.00b ± 0.00 0.01 ± 0.01 0.40 ± 0.40 0.07a ± 0.06 0.45a ± 0.30 <0.01abWinged sumac 0.00c ± 0.00 0.00c ± 0.00 0.00c ± 0.00 0.00 ± 0.00 3.43 ± 3.43 0.58c ± 0.58 11.06a ± 7.41 22.07b ± 12.82

a Within rows, means followed by the same letter were not different (P < 0.10) using ANOVA on ranks and Duncan’s MRT.b Averaged across all harvested stands which contained greenbelt plots (N = 16).c Only combined values were included in the ANOVA.
68

Table 8—Comparison of mean soft-mast species richness (number of species

actually producing soft mast), diversity of production, and evenness among five

silvicultural treatments during the first (1994), third (1996), and fifth (1998) year

after initial harvest in the Ouachita Mountains

Singletree Group

Measure Year Unharvested selection selection Shelterwood Clearcut

Richness 1994 3.75aa ± 0.25 0.50b ± 0.29 1.50b ± 0.65 1.25b ± 0.63 0.50b ± 0.29

1996 3.75 ± 0.25 6.50 ± 0.87 6.00 ± 1.08 7.25 ± 1.03 5.00 ± 1.08

1998 4.00a ± 0.41 7.00b ± 0.41 7.00b ± 1.22 8.75b ± 1.38 7.75b ± 1.03

Diversity 1994 0.87a ± 0.15 0.00b ± 0.00 0.16b ± 0.15 0.16b ± 0.16 0.00b ± 0.00

1996 0.78 ± 0.23 0.83 ± 0.21 0.76 ± 0.12 1.10 ± 0.05 0.67 ± 0.25

1998 0.70 ± 0.21 0.94 ± 0.18 0.74 ± 0.22 0.96 ± 0.24 0.84 ± 0.17

Evenness 1994 0.54a ± 0.19 0.15b ± 0.14 0.64a ± 0.10 0.15b ± 0.15 0.00b ± 0.00

1996 0.34 ± 0.18 0.46 ± 0.06 0.53 ± 0.18 0.39 ± 0.15 0.45 ± 0.12

1998 0.40 ± 0.13 0.50 ± 0.08 0.46 ± 0.12 0.44 ± 0.16 0.38 ± 0.03

a Within rows, means followed by the same letter were not different (P < 0.10) using ANOVA and

Duncan’s MRT.

Table 9–-Percent of total soft-mast production represented by pokeberry,

blackberry, blueberry, winged sumac, American beautyberry, muscadine grape,

and other grapes during first (1994), third (1996), and fifth (1998) year after initial

harvest in five silvicultural treatments and unharvested greenbelts in the

Ouachita Mountains

Single

tree Group

Year Unharvested Greenbelts selection selection Shelterwood Clearcut

1994 48 79 49 87 100 100

1996 76 91 86 89 94 99

1998 90 73 95 99 99 95

Pokeberry

In the first postharvest year (1994), pokeberry coverage was

greatest in clearcuts and shelterwoods (table 2). Pokeberry

plants were not abundant in other treatments and were not

detected in unharvested stands. Pokeberry production was

greatest in shelterwoods and clearcuts in the first year, and

this species produced more soft mast than any other species

(table 3). In the third postharvest year, pokeberry coverage

was greater in harvested stands than in unharvested stands

and greenbelts (table 4), but production had declined. Pokeberry production was greatest in clearcuts, shelterwoods,

and group openings (table 5). By the fifth postharvest year,

pokeberry coverage was low in all stands, although clearcuts had the highest abundance (table 6). Production in the

fifth year declined dramatically, being highest in clearcuts

(table 7).

Pokeberry is probably the most important soft mast producer

immediately after intensive logging because of its ability to

establish quickly and produce abundant levels of soft mast.

Pokeberry, a colonizing herbaceous species, invaded the

more intensely harvested areas (clearcuts, shelterwoods,

and group openings) immediately after harvest, but quickly

disappeared because of its inability to compete with the

intense woody vegetation growth. This trend suggests

pokeberry production reaches a peak about 1-3 years after

harvest and then declines quickly.

Blackberry, Winged Sumac, and

American Beautyberry

Blackberry plants were present in all treatments the first

year (table 2), but produced no soft mast. By the third year,

coverage was greatest in harvested stands (table 4). Production was greatest in shelterwoods; no blackberries were

produced in unharvested stands and production was almost

nonexistent in greenbelts (table 5). By the fifth year, blackberry coverage and production was greatest in clearcuts,

shelterwoods, and group openings (tables 6 and 7). Blackberry was the most abundant soft mast in intensively harvested areas the fifth year.
69

In the first postharvest year, winged sumac coverage was

greatest in group selections, shelterwoods, and clearcuts

(table 2); however, no production occurred in any stands. In

the third year, coverage was greatest in clearcuts, shelterwoods, and group openings (table 4); production was greatest in clearcuts and shelterwoods (table 5). In the fifth year,

coverage was greatest in harvested stands (table 6), and

production occurred only in clearcuts, shelterwoods, and

group openings (table 7).

American beautyberry coverage was low in all stands the

first year (table 2), and production was nonexistent. Coverage and production was still low in the third year, and no

significant differences in production existed among treatments (tables 4 and 5). By the fifth year, production was

greatest in shelterwoods, clearcuts, and group openings

(table 7).

Blackberry, winged sumac, and American beautyberry were

similar in their response to treatments. Each occurred predominantly in intensively logged areas (clearcuts, shelterwoods, and group openings), and each produced the most

soft mast during the fifth postharvest sampling year. These

species tended to produce high levels of soft mast when

stands were thinned to a residual BA ≤11.5 m2/ha. Blackberry

was probably the most important soft mast producer in

intensively logged areas by the fifth postharvest year because

of its extreme abundance and the high carbohydrate and

water content of its mast, which are likely important traits

for wildlife during the relatively dry July and early August

period.

Muscadine and Other Grapes

Although muscadine grape coverage did not differ among

treatments the first postharvest year (table 2), production

was greatest in unharvested stands (table 3). In the third

year, production was greatest in shelterwoods and singletree selection stands (table 5). Although no significant

difference existed in coverage the fifth year (table 6),

production was greatest in shelterwoods and single-tree

selection stands (table 7).

For other species of grapes, no difference in coverage or

production existed among treatments the first year. In the

third year, coverage was greater in single-tree selection

stands than in clearcuts or greenbelts (table 4), and production was greater in shelterwoods than in clearcuts or

greenbelts (table 5). In the fifth year, production in shelterwoods was greater than in clearcuts, greenbelts, or unharvested stands (table 7).

Initial logging disturbance greatly affected muscadine and

other grape species the first year after harvest. However, by

the fifth year, grapes (primarily muscadine) were abundant

in thinned stands with some intact overstory (shelterwoods,

single-tree selections, group openings), but were not abundant in areas with no overstory (clearcuts) or areas with

closed canopies and low ground-level light penetration

(unharvested areas and greenbelts). However, differences in

site, seedbed, previous land uses, or other factors unrelated

to treatment may have affected muscadine abundance. For

example, among the group selection stands in 1998, muscadine grape covered 23.8 percent of the ground in the northzone stand (C1124 S11), but was absent in the south-zone

stand (C35 S42). Among clearcuts, the north-zone stand

(C458 S16) had 19.8 percent muscadine ground cover,

whereas the west-zone stand (C1292 S2) had less than 1.3

percent cover.

Blueberries

Blueberry coverage did not differ among treatments any year.

However, production was greatest in unharvested stands the

first year (table 3); no differences existed in subsequent

years. Logging disturbance appeared to reduce production

in harvested stands the first year, but production in harvested stands increased in years three and five to levels

equal to unharvested stands.

CONCLUSIONS

The importance of individual mast species varied by treatment and years since harvest. Pokeberry was the most

abundant mast produced in intensely harvested areas (clearcuts, shelterwoods, and group openings) the first year after

harvest, but its production quickly declined in subsequent

years. Pokeberry was the only abundant mast-producing

herbaceous species on study areas. Herbaceous species

tend to recover and establish quickly, whereas woody species

tend to recover and establish more slowly. By the fifth year,

blackberry became the dominant soft mast available in

intensively harvested areas. Winged sumac and muscadine

grape were the second and third most abundant mast in

these areas by the fifth year. Muscadine grape was generally the most abundant mast in single-tree selections, unharvested stands, the thinned matrix areas of group-selection

stands, and greenbelts. Differences in site, seedbed, previous land uses or other factors unrelated to treatment, which

were not controlled in this study, may have contributed to

the large variance in production and cover we observed

among stands of similar treatment.

Providing high-quality, early-successional wildlife habitat is

often a priority for land managers. Clearcutting provides

abundant soft mast for the first 5 years after harvest, but

many people are opposed to this management on public

lands. The public generally accepts partial cutting methods

more than clearcuts. Our results suggest young shelterwood

cuts provide soft mast levels comparable to young clearcuts

during the first 5 years of growth. By the fifth postharvest

year, total soft mast production in clearcuts and shelterwoods

were more than twice the levels of other treatments (Perry

and others 1999).

ACKNOWLEDGMENTS

We thank R. Lay, K. Corbin, C. Watt, and M. Lorenz for help

with field measures, M.A. Melchiors and M. Spetich for

reviews, and B. Parresol for biometrics review.

LITERATURE CITED

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, J.B., comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Gen. Tech. Rep. SO112. New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station: 18-28.
70

Campo, J.J.; Hurst, G.A. 1980. Soft mast production in young loblolly plantations. Proceedings of the Annual Conference of the

Southeastern Association of Fish and Wildlife Agencies 34:

470-475.

Halls, L.K., ed. 1977. Southern fruit-producing woody plants used

by wildlife. Gen. Tech. Rep. SO–16. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station. 235 p.

Johnson, A.S.; Landers, J.L. 1978. Fruit production in slash pine

plantations in Georgia. Journal of Wildlife Management 42:

606-613.

Martin, A.C.; Zim, H.S; Nelson, A.L. 1951. American wildlife and

plants. New York: McGraw-Hill. 500 p.

Perry, R.W; Thill, R.E; Peitz, D.G.; Tappe, P.E. 1999. Effects of

different silviculture systems on initial soft mast production.

Wildlife Society Bulletin 27: 915-923.

Pielou, E.C. 1969. An introduction to mathematical ecology. New

York: John Wiley and Sons. 286 p.

SAS Institute Inc. 1988. SAS/STAT user’s guide. Version 6.03.

Cary, NC: SAS Institute. 1028 p.

Shannon, C.E.; Weiner, W. 1949. The mathematical theory of

communication. Urbana, IL: University of Illinois Press. 177 p.

Stransky, J.J.; Halls, L.K. 1980. Fruiting of woody plants affected

by site preparation and prior land use. Journal of Wildlife

Management 44: 258-263.

Thill, R.E.; Tappe, P.A; Koerth N. 1994. Wildlife habitat conditions

in mature pine-hardwood stands in the Ouachita/Ozark National

Forests. In: Baker, J.B., comp. Proceedings of the symposium

on ecosystem management research in the Ouachita

Mountains: pretreatment conditions and preliminary findings.

Gen. Tech. Rep. SO-112. New Orleans, LA: U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station: 18-28.
71

INTRODUCTION

To effectively consider the needs of wildlife in forest management planning, information on the responses of native

species to various disturbance regimes is required (Daily

and others 1996, Hurley 1986). Effects of even-aged silviculture on vertebrates, primarily birds, have been documented (see review by Harlow and Van Lear 1987, Smith

and Petit 1988). However, few studies have dealt with the

autecological responses of mammals to the intermediate

disturbances caused by alternative even-aged and selection harvests (Healy and Brooks 1988, Muul and Lim 1978,

Nixon and others 1980, Pattanavibool and Edge 1996,

Wilson and Johns 1982), and more information is needed

(McCoy 1982, 1983; Simberloff and Abele 1976, 1982;

Verner 1986; Zimmerman and Bierregaard 1986).

Flying squirrels (Glaucomys volans) are common inhabitants

of pine-hardwood forests in the southern United States

(Goertz and others 1975). Because G. volans glides as a

primary means of escape and travel, habitat needs include

trees of considerable height and a relatively open midstory

(Bendel and Gates 1987). Since flying squirrels do not

excavate their own tree cavities, den trees and snags are

also critical habitat components (Bendel and Gates 1987,

Doby 1984, Gilmore and Gates 1985, Muul 1974, Sawyer

and Rose 1985, Weigl 1978). Flying squirrels have rather

omnivorous food habits but rely heavily on hard mast

throughout the year (Harlow and Doyle 1990), e.g. seeds,

hickory nuts, and acorns (Braun 1988, Sawyer and Rose

1985, Sealander and Heidt 1990, Sollberger 1943). The life

history requirements of flying squirrels suggest the species’

utility as a healthy forest indicator for forest-dependent

small mammals.

This study was carried out within a larger interdisciplinary

research effort in which a range of experimental harvest

techniques were evaluated. First, in order to examine the

effect of forest alterations on flying squirrel population

dynamics, winter nest box surveys were completed on 21

stands (three replicates of each forest condition), providing

data on density, fecundity, reproduction, persistence, and

overall robustness. Working hypotheses were that none of

the demographic parameters investigated would be different either among pretreatment study area groups prior to

harvest or among control and harvested study areas during

postharvest years.

Second, in order to examine flying squirrel home range and

habitat selection behaviors within a fragmented forest landscape, squirrels were tracked by radiotelemetry during late

spring and summer on five study areas. Null hypotheses

were that male and female home ranges would be similar in

size and shape among the five telemetry study areas and

that all habitats would be used for nightly foraging in proportion to availability, without selection. It was also assumed

that nesting habitats and tree types would be used as

available, without selection.

MATERIALS AND METHODS

Population Dynamics

Study areas consisted of 21 15-ha mature pine-hardwood

forest stands in the Ouachita National Forest of Arkansas.

In summer 1993, 18 of these stands in six replicated groups

of three each were harvested to the following specifications:

pine-hardwood single-tree selection (STS), pine STS, pinehardwood shelterwood (SW), pine SW, pine-hardwood

seedtree (ST), and pine ST. Detailed descriptions of these

habitat characteristics are provided by Baker (1994). During

fall 1992, 30 nest boxes were installed on each of the 21

experimental stands in a grid with 60-meter spacing. Boxes

were opened in November and nest box surveys were

completed once each month during January - March. Boxes

were closed for the summer after the last survey. The total

number of squirrels found nesting on a study area during

the winter survey was used as the population estimate for

that stand. Weight, gender, and reproductive condition were

recorded for all squirrels.

HOME RANGE, HABITAT SELECTION, AND POPULATION DYNAMICS OF

SOUTHERN FLYING SQUIRRELS IN MANAGED FORESTS IN ARKANSAS

James F. Taulman and Kimberly G. Smith1

1 Postdoctoral Research Associate, University of Arkansas, Fayetteville, AR, 72701; and Professor, University of Arkansas, Department of

Biology, Fayetteville, AR, 72701, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—After experimental harvests on 18 mature pine-hardwood stands in 6 replicated groups, flying squirrels

changed from uniform pre-harvest patterns of nest box use to concentrating on protected greenbelt (GB) areas on

harvested stands. Squirrel densities declined on all harvested stands as densities increased on three control stands. Fifty

squirrels tracked by radiotelemetry on three control and two harvested stands over three years selected mature pinehardwood, GB, and hardwood forest habitats for foraging around control study areas; they foraged in GB and adjacent

hardwood forests around harvested stands. With boxes closed, squirrels nested in mature pine-hardwood forest on

control study areas and GB habitat on harvested stands. Males showed vagility around control areas where mast

production was low; female and male ranges were similar, and smaller, around harvested areas where hard mast

resources were more abundant.
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Habitat macroplots were installed on the 21 stands in a

stratified random array and data were collected by the Silviculture research group in the larger Ecosystem Management

research project (see Taulman 1997). Those data are used

in this report.

Home Range and Habitat Selection

Telemetry—From 8 - 15 squirrels were radiocollared on

each of a subset of five study areas from the total of 21:

three control, a pine-hardwood SW, and a pine-hardwood

ST. Squirrels were tracked by researchers on foot at night

during May - July, 1994 - 1996. One to three independent

locations were obtained on each animal on a study area

during a 6 - 8 hour tour. Bi-weekly diurnal telemetry led to

the discovery of natural nesting locations. After arriving at a

squirrel’s location, that site was documented by measuring

distance and direction to a tree that had been previously

monumented using differentially corrected global positioning system (GPS) fixes. Universal Transverse Mercator

(UTM) coordinates for each squirrel location were later

computed trigonometrically.

Home range computation—Telemetry data sets were

examined through location/area curves; those not showing

a leveling of area with increasing number of locations were

rejected as insufficient to describe an animal’s home range.

Those data sets showing a range shift during the data collection period were divided and only one segment was used to

create a home range estimate. Home ranges were computed

using the kernel method, as described by Silverman (1986),

Worton (1995), and Seaman and Powell (1996); employing

the computer program KERNELHR (Seaman and Powell

1995). The home range used in this report is “the smallest

area containing 95 percent of the utilization distribution”

(Seaman and Powell 1996). Core activity areas are areas

within a contour that contain locations that are closer than

would be expected given uniform use of the home range.

Habitat description—For habitat selection analyses of freeranging squirrels at the five telemetry study areas, additional macroplots were installed and surveyed in habitats

frequented by squirrels adjacent to experimental stands.

Habitat variables provided a description of woody forest

vegetation that was useful in comparisons within and

among study areas (Taulman 1997).

Habitat selection analysis—We followed the methods of

Manly and others (1993) in describing habitat selection

using the resource selection function, or selection ratio,

which represents the proportion of a used resource compared to the proportion of that resource available to the

animal. Ninety-five percent Bonferroni confidence intervals

were constructed for the mean selection ratio for each

habitat type on a study area. Where an interval fell completely below 1.00, significant selection against a habitat

was indicated; an interval completely above 1.00 indicated

significant selection for that habitat type. Available areas

were defined individually for each animal and consisted of

99 percent kernel home ranges computed with the socalled “reference” smoothing parameter. This method used

with nonnormal data produces contours with inflated areas.

This “buffer zone” around the home range provides a useful

estimate of habitats available to an individual based on

known patterns of habitat use (fig. 1).

Selection was evaluated for placement of the home range

within available habitats and for use of habitats within the

home range, Johnson’s (1980) second and third order

selection, respectively. Habitat selection for location of

diurnal nesting sites was also investigated (Taulman 1999).

RESULTS

Population Dynamics

While numbers of adult and immature squirrels captured by

treatment group increased on control stands from 1993 -

1995, a sharp decline was observed on harvested stands in

1994 (Taulman and others 1998). A partial rebound was

seen on some harvested stands during 1995 and 1996;

Figure 1—Habitat types around study area 27, pine-hardwood

shelterwood harvest. This map shows the three contours

computed from a telemetry location data set for one individual on

which habitat selection analyses were based. Similar contours

were created for each of the 50 squirrels in this study for which

telemetry location data sets met criteria described in the text. For

habitat selection analyses, the outer contour represents available

areas for placement of the home range, and is the 99 percent

kernel contour computed with the “reference” smoothing

parameter. The 95 percent kernel home range estimate computed

with least squares cross validation selection of smoothing

parameter and the core activity area contours are also shown.

Areas of habitats within the 95 percent contour were considered

available for the analysis of habitat use within the home range.

The core boundary defines an area within which locations were

closer together than would be expected based on uniform use of

the home range.
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most of the recovery seen was due to population surges on

a few stands that had adjacent hardwood forest habitats.

Squirrels on control stands virtually disappeared in 1996

(Taulman and others 1998); most of the squirrels encountered were recaptures from previous years. The ratio of

litters to adult females was variable among years and

mirrored the change in numbers of litters produced (Taulman

1997), indicating yearly changes in female fecundity.

Nest box use was uniform on all pre-treatment stands, but

per capita box use declined on control stands in 1994, as

squirrel numbers rose, without a corresponding increase in

boxes used (Taulman and others 1998). However, on harvested stands per capita box use increased to a significant

level over that seen on control stands, even as squirrel

numbers decreased. Comparing nest boxes used to those

available, boxes were used significantly more in nonGB

habitat on control stands than nonGB on harvested stands

in all post-harvest years (Taulman and others 1998). Also,

after harvest boxes were used significantly more in GB

habitat on harvested stands than in cutover nonGB habitat.

Squirrel density on the set of 21 mature pine-hardwood

stands in 1993 was significantly correlated with snag

density (rs

 = 0.83, p = 0.006) (Taulman and others 1998).

Five mortalities showed direct or circumstantial evidence

of predation. Four of those involved collars and remains

retrieved either from seedtree harvest habitat (two screech

owls [Otis asio] and two of unknown causes), and one

occurred in a forest/clearing ecotone (rat snake, Elaphe

obsoletus).

Home Range and Habitat Selection

Habitats categorized differently on each study area were

significantly different for many of the measured descriptive

variables (Taulman 1997). Overall, habitat types categorized

similarly were not significantly different for the same set of

variables. Home range areas of males were significantly

different among study areas (Kruskal-Wallis ANOVA p =

0.03); those of females were similar (table 1). Ranges were

smallest and most equal among males and females on

harvested study areas; the large male ranges were associated with control study areas. Comparisons with other

telemetry studies on flying squirrels show similarities to

range areas on the harvested study areas (Taulman 1997).

Mature pine-hardwood forest, GB, and adjacent hardwoodrich forests were selected more than expected for foraging;

the selection of GB for home range placement was signifiTable 1—Kernel 95 percent topographic home range areas, with 1 standard error (SE), of males and

females by study areaa

Study areas

MAT P-H

Squirrel

gender N I N II N III N P-H SW N P-H ST

Male 2 14.3 6 19.09 (5.65) 3 44.0 (15.16) 5 4.79 (0.82) 2 2.76

Female 6 6.43 (1.57) 8 6.09 (1.87) 2 6.04 9 5.57 (0.81) 1 3.38

The Kruskal-Wallis ANOVA results indicated that female range areas did not differ among study areas. The K-W ANOVA

indicated a significant difference among male range areas (p < 0.05), but the multiple comparisons test show no significant

difference for any pair due to small sample size.

a Only squirrels for which number of locations was ≥ 30 are included.

Table 2—Mean selection ratios (with standard error and 95 percent confidence intervals) for young (< 15 years

old) and immature (15 - 40 years old) pine plantation habitats, mature pine-hardwood forest, mature hardwood

forest, and greenbelt habitats, including data from all squirrels (N) for which those habitats were availablea

95 percent home range 95 percent C.I. Locations in range 95 percent C.I.

Habitat Mean SE N Lower Upper Mean SE N Lower Upper

Young plantation 0.401 0.054 31 0.290 0.511b 0.337 0.065 28 0.203 0.471b

Immature plantation 0.395 0.115 14 0.147 0.643b 0.282 0.102 12 0.058 0.506b

Mature pine-hardwood 1.119 0.078 33 0.959 1.279 1.060 0.038 29 0.982 1.137

Mature hardwood 1.555 0.301 15 0.910 2.200 1.414 0.286 11 0.776 2.052

Greenbelt 1.549 0.181 19 1.168 1.930c 1.283 0.147 27 0.981 1.586

a Selection ratio means for 95 percent home range represent the percentage of habitats in 95 percent kernel home ranges (used)

compared with the percentage of those habitats available in a larger area. Available areas and home ranges were computed

individually for each squirrel. Means for locations in range represent the percentage of locations in a habitat type in the home range

(used) compared with percent area of that habitat in the home range (available).

b Indicates significant selection against a habitat type (95 percent confidence interval below 1.00).

c Indicates significant selection for a habitat type (95 percent confidence interval above 1.00).
74

cant (table 2). Mature pine-hardwood forest was significantly

selected for diurnal nesting on control study areas; nests

were concentrated in GB habitat on the two harvested stands

(Taulman 1999). Maps of core activity areas graphically

illustrate habitat use and avoidance patterns at all five

study areas (Taulman 1997).

The densities of overstory oaks and hickories can provide

an indication of the mast production potential in a habitat

(Goodrum and others 1971, Wolff 1996). Overstory oak and

hickory densities in habitats on these study areas, and fall

mast surveys on experimental stands, showed that control

study areas were depauperate in overstory hardwoods and

mast production (Taulman 1997). The two harvested study

areas had habitats containing high densities of overstory

hardwoods within or adjacent to experimental stands.

DISCUSSION

Population declines following harvest have been previously

documented for flying squirrels (Hokkanen and others 1982)

and other vertebrates (Burgess 1971, Wilson and Johns

1982). The marked declines observed on control stands in

1996, and the significantly lower weights of squirrels in

1996, may indicate a metapopulation response to low mast

production in 1994 and 1995 (Taulman 1997). Wolff (1996)

found that over 14 years population densities of three rodent

species varied in synchrony with hard mast production.

Male vagility in areas with limited food resources may be an

evolutionary strategy, increasing survival chances of females

and offspring kin in familiar nesting areas through reduced

competition. Males on distant forays also increase their own

likelihood of encountering new females and producing additional offspring. Female philopatry conserves energy and

permits young to learn the locations of nearby feeding sites

and refugia.

Contrary to the suggestion of some foresters and landscape ecologists (Urban and others 1987) that clearcut

harvests mimic natural disturbance events, clearcut silviculture has produced a pattern in which undisturbed forests

are now found within the matrix of a fragmented landscape

(DellaSala and others 1995, Moloney and Levin 1996), the

reverse of the pattern historically observed under natural

disturbance regimes (Agee 1994). To ensure the persistence of native terrestrial forest-dependent wildlife, public

forests should be managed from a landscape perspective,

preserving areas of mature undisturbed forest, with interconnecting corridors of forest habitat (Bright 1993, Dunstan

and Fox 1996).

Pickett and Thompson (1978) have suggested the “minimum

dynamic area” method as an approach to managing a fragmented ecosystem for the persistence of given species. This

method emphasizes landscape management at a scale that

can ensure the protection of “recolonization sources” in

order to prevent the isolation of small patches and to reduce

the possibility of long-term local extinctions. Henderson and

others (1985) found that eastern chipmunks (Tamias striatus)

inhabiting isolated woodlots amid an “ecologically hostile”

agricultural landscape were able to disperse rapidly along

brushy fencerows to repopulate wooded areas which had

undergone local extinctions.

Results of the present study suggest that the U.S. Forest

Service compartment level (approximately 10 - 20 sq km)

should be an appropriate scale at which to manage habitat

for flying squirrels. Within each management compartment

many 15 - 40 ha patches of mature pine-hardwood forest,

as well as north-slope hardwood forests, should be maintained along with interconnecting corridors through harvested

habitats to other undisturbed areas. Some overstory hardwoods and snags should be retained within harvested areas.

Provided adjacent mature pine-hardwood, or hardwood,

forests are available, and protected GB areas are retained

along ephemeral drainages within a treated stand, harvests

as severe as the shelterwood prescription should not

adversely affect flying squirrel populations. The seedtree

harvest regime, even with retained hardwoods, produces a

reduction of forest cover which greatly enhances opportunities for such predators as screech owls. The higher predation risk, together with the diminution of mast and cavity

resources created by the seedtree harvest regime, appear

to render forests subjected to this prescription unsuitable

for the long-term persistence of flying squirrels.

ACKNOWLEDGMENTS

Equipment loans by D.M. Harris, T.A. Nelson, and D.B.

Griffith were vital to the logistical success of this project. R.

Huston was an invaluable field assistant during much of the

study. Support for this research came from the U.S. Forest

Service, Southern Research Station, Theodore Roosevelt

Fund of the American Museum of Natural History, the Max

McGraw Wildlife Foundation, the University of Arkansas,

the Ouachita National Forest, and the estate of D.C. and

V.S. Taulman. Reviews by J.N. Layne, C. Maser, D.W.

Macdonald, D.A. James, W.J. Etges, and an anonymous

reviewer greatly improved earlier versions of this manuscript.

LITERATURE CITED

Agee, J.K. 1994. Fire and weather disturbances in terrestrial

ecosystems of the Eastern Cascades. Vol. 3: Assessment. U.S.

Department of Agriculture, Forest Service, Gen. Tech. Rep.

PNW-GTR-320.

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, J.B. comp. Proceedings of the symposium on ecosystem

management research in the Ouachita Mountains: pretreatment

conditions and preliminary findings. Gen. Tech. Rep. SO-112,

U.S. Department of Agriculture, Forest Service, Southern

Research Station: 18-28.

Bendel, P.R.; Gates, J.E. 1987. Home range and microhabitat

partitioning of the southern flying squirrel (Glaucomys volans).

Journal of Mammalogy. 68: 243-255.

Braun, J.K. 1988. Systematics and biogeography of the southern

flying squirrel, Glaucomys volans. Journal of Mammalogy. 69:

422-426.

Bright, P.W. 1993. Habitat fragmentation - problems and predictions for British mammals. Mammal Review. 23: 101-111.

Burgess, P.F. 1971. The effect of logging on hill dipterocarp

forests. Malaysian Nature Journal. 24: 231-237.

Daily, G.C.; Ehrlich, P.R.; Alberti, M. 1996. Managing Earth’s life

support systems: the game, the players, and getting everyone

to play. Ecological Applications. 6: 19-21.

DellaSala, D.A.; Olson, D.M.; Barth, S.E. [and others]. 1995.

Forest health: moving beyond rhetoric to restore healthy landscapes in the inland Northwest. Wildlife Society Bulletin. 23:

346-356.
75

Doby, W.J. 1984. Resource base as a determinant of abundance in

the southern flying squirrel (Glaucomys volans). M.S. thesis,

Wake Forest University, Winston-Salem, N.C.

Dunstan, C.E.; Fox, B.J. 1996. The effects of fragmentation and

disturbance of rainforest on ground-dwelling small mammals on

the Robertson Plateau, New South Wales, Australia. Journal of

Biogeography. 23: 187-201.

Gilmore, R.M.; Gates, J.E. 1985. Habitat use by the southern flying squirrel at a hemlock-northern hardwood ecotone. Journal

of Wildlife Management. 49: 703-710.

Goertz, J.W.; Dawson, R.M.; Mowbray, E.E. 1975. Response to

nest boxes and reproduction by Glaucomys volans in northern

Louisiana. Journal of Mammalogy. 56: 933-939.

Goodrum, P.D.; Reid, V.H.; Boyd, C.E. 1971. Acorn yields, characteristics, and management criteria of oaks for wildlife. Journal

of Wildlife Management. 35: 520-532.

Harlow, R.F.; Doyle, A.T. 1990. Food habits of southern flying

squirrels (Glaucomys volans) collected from red-cockaded

woodpecker (Picoides borealis) colonies in South Carolina.

American Midland Naturalist. 124: 187-191.

Harlow, R.F.; Van Lear, D.H. 1987. Silvicultural effects on wildlife

habitat in the South (an annotated bibliography) - 1980-1985.

Clemson University Department of Forestry Technical Paper 17.

Healy, W.M.; Brooks, R.T. 1988. Small mammal abundance in

northern hardwood stands in West Virginia. Journal of Wildlife

Management. 52: 491-496.

Henderson, M.T.; Merriam, G.; Wegner, J. 1985. Patchy environments and species survival: chipmunks in an agricultural

mosaic. Biological Conservation. 31: 95-105.

Hokkanen, H.; Törmälä, T.; Vuorinen, H. 1982. Decline of the flying

squirrel Pteromys volans L. populations in Finland. Biological

Conservation. 23: 273-284.

Hurley, J.F. 1986. Summary: development, testing, and application

of wildlife-habitat models - the manager’s viewpoint. In: Verner,

J; Morrison, M.L.; and Ralph, C.J., eds. Wildlife 2000: modeling

habitat relationships of terrestrial vertebrates. University of

Wisconsin Press, Madison: 151-153.

Johnson, D.H. 1980. The comparison of usage and availability

measurements for evaluating resource preference. Ecology. 61:

65-71.

Manly, B.F.J.; McDonald, L.L.; Thomas, D.L. 1993. Resource

selection by animals: statistical design and analysis for field

studies. Chapman and Hall, New York.

McCoy, E.D. 1982. The application of island-biogeographic theory

to forest tracts: problems in the determination of turnover rates.

Biological Conservation. 22: 217-227.

McCoy, E.D. 1983. The application of island-biogeographic theory

to patches of habitat: how much land is enough? Biological

Conservation. 25: 53-61.

Moloney, K.A.; Levin, S.A. 1996. The effects of disturbance architecture on landscape-level population dynamics. Ecology. 77:

375-394.

Muul, I. 1974. Geographic variation in the nesting habits of

Glaucomys volans. Journal of Mammalogy. 55: 840-844.

Muul, I.; Lim, B.L. 1978. Comparative morphology, food habits,

and ecology of some Malaysian arboreal rodents. In:

Montgomery, G.G., ed. The ecology of arboreal folivores.

Smithsonian Institution Press, Washington, D. C.: 361-368.

Nixon, C.M.; McClain, M.W.; Donohoe, R.W. 1980. Effects of clearcutting on gray squirrels. Journal of Wildlife Management. 44:

403-412.

Pattanavibool, A.; Edge, W.D. 1996. Single-tree selection silviculture affects cavity resources in mixed deciduous forests in

Thailand. Journal of Wildlife Management. 60: 67-73.

Pickett, S.T.A.; Thompson, J.N. 1978. Patch dynamics and the

design of nature reserves. Biological Conservation. 13: 27-37.

Sawyer, S.L.; Rose, R.K. 1985. Homing in and ecology of the

southern flying squirrel Glaucomys volans in southeastern

Virginia. American Midland Naturalist. 113: 238-244.

Sealander, J.A.; Heidt, G.A. 1990. Arkansas mammals: their

natural history, classification, and distribution. University of

Arkansas Press, Fayetteville.

Seaman, D.E.; Powell, R.A. 1995. KERNELHR home range estimation program. Version 3.03. User’s guide.

Seaman, D.E.; Powell, R.A. 1996. An evaluation of the accuracy of

kernel density estimators for home range analysis. Ecology. 77:

2075-2085.

Silverman, B.W. 1986. Density estimation for statistics and data

analysis. Chapman and Hall, New York.

Simberloff, D.S.; Abele, L.G. 1976. Island biogeography theory and

conservation practice. Science. 191: 285-286.

Simberloff, D.S.; Abele, L.G. 1982. Refuge design and island

biogeographic theory: effects of fragmentation. American

Naturalist. 120: 41-50.

Smith, K.G.; Petit, D.R. 1988. Breeding birds and forestry practices

in the Ozarks: past, present, and future relationships. Bird

Conservation. 3: 23-49.

Sollberger, D.E. 1943. Notes on the breeding habits of the eastern

flying squirrel (Glaucomys volans volans). Journal of

Mammalogy. 24: 163-173.

Taulman, J.F. 1997. Effect of forest alterations on population

dynamics, home range, and habitat selection of the southern

flying squirrel (Glaucomys volans). Dissertation, University of

Arkansas, Fayetteville, 308 pp.

Taulman, J.F. 1999. Selection of nest trees by southern flying

squirrels (Sciuridae: Glaucomys volans) in Arkansas. Journal of

Zoology. 248: 369-377.

Taulman, J.F.; Smith, K.G.; Thill, R.E. 1998. Demographic and

behavioral responses of southern flying squirrels to experimental logging in Arkansas. Ecological Applications. 8: 1144-

1155.

Urban, D.L.; O’Neill, R.V.; Shugart, H.H., Jr. 1987. Landscape

ecology: a hierarchical perspective can help scientists understand spatial patterns. BioScience. 37: 119-127.

Verner, J. 1986. Predicting effects of habitat patchiness and fragmentation - the researcher’s viewpoint. In: Verner, J.; Morrison,

M.L.; and Ralph, C.J., eds. Wildlife 2000: modeling habitat

relationships of terrestrial vertebrates. University of Wisconsin

Press, Madison: 327-329.

Weigl, P.D. 1978. Resource overlap, interspecific interactions and

the distribution of the flying squirrels, Glaucomys volans and G.

sabrinus. American Midland Naturalist. 100: 83-96.

Wilson, W.L.; Johns, A.D. 1982. Diversity and abundance of

selected animal species in undisturbed forest, selectively

logged forest and plantations in east Kalimantan, Indonesia.

Biological Conservation. 24: 205-218.

Wolff, J.O. 1996. Population fluctuations of mast-eating rodents

are correlated with production of acorns. Journal of Mammalogy.

77: 850-856.

Worton, B.J. 1995. Using Monte Carlo simulation to evaluate

kernel-based home range estimators. Journal of Wildlife

Management. 59: 794-800.

Zimmerman, B.L.; Bierregaard, R.O. 1986. Relevance of the

equilibrium theory of biogeography and species-area relations

to conservation with a case from Amazonia. Journal of

Biogeography. 13: 133-143.
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77

Session 3

Phase II Arthropod and Microbial Diversity Research

David Hammond, Moderator
78
79

INTRODUCTION

Oak regeneration is essential to the health and sustainability of hardwood and mixed pine-hardwood forests in the

Ouachita and Ozark Mountains of Arkansas. Also, hard

mast produced by oaks is an important food for wildlife in

these ecosystems (e.g. Goodrum and others 1971, McShea

and Schwede 1993, Nixon and others 1975). Estimates of

the effect insects have on acorn quality can help managers

assess regeneration potential and hard mast availability.

The insect guild that utilizes acorns includes species in the

orders Coleoptera, Lepidoptera, Diptera, and Hymenoptera.

Damage to the acorns in most cases results from larvae

feeding on the nutritious endosperm of the acorn. Primary

acorn-infesting insects are weevils in the genus Curculio

(Coleoptera: Curculionidae) and the filbertworm, Melissopus

latiferreanus (Walsingham) (Lepidoptera: Tortricidae). These

primary insects infest developing acorns on the tree and are

able to directly enter the acorn (Myers 1978). They can completely consume the endosperm, thus destroying the acorn

(Gibson 1972). Additional, though less abundant, primary

acorn insects include several species of cynipid gall wasps

whose larvae inhabit galls on or in the acorn (Myers 1978).

Secondary insects include the acorn moth, Valentinia

glandulella Riley (Lepidoptera: Blastobasidae), weevils of

the genus Conotrachelus (Coleoptera: Curculionidae) and

Diptera (midge larvae). These insects are scavengers that

typically enter acorns which are damaged or have been

infested by primary insects (Myers 1978), although Galford

(1986) demonstrated that V. glandulella can be a primary

invader of germinating acorns.

MATERIALS AND METHODS

Our study was part of the USDA Forest Service Ouachita

Mountain Ecosystem Management Research Project (Phase

II), located in the Ouachita and Ozark National Forests of

Arkansas and Oklahoma. This long-term study examines

the effects of different timber harvest methods on numerous

aspects of forest biology (Baker 1994).

We sampled white oak (Quercus alba L.) acorns from four

forest stands, yearly, from 1993 to 1998. However, an

almost complete mast failure combined with extreme predation by wildlife resulted in few acorns collected in 1998.

Therefore, we did not include 1998 data in our analyses.

We selected a single-tree selection (STS) stand and an

undisturbed mature forest stand (control) in both the northern and southern portions of the Ouachita region. Pine-hardwood STS stands (C70 S10 and C1649 S13) were initially

harvested the summer of 1993 with residual hardwood basal

areas (BA) ranging from 2.8 to 5.1 m2

 per ha and total BA

ranging from 15.4 to 16.1 m2 per ha (Baker 1994). Control

stands were approximately 80 years old, previously unmanaged, with approximate hardwood BA of 7.1 m2 per ha and

total BA of 29.9 m2 per ha. In the north zone we used Phase

II control stand C284 S11, however, a complete mast failure

occurred in this stand in 1994 and we used an alternative

stand (C406 S1) as a control that year. Because of low numbers of co-dominant white oaks in the south zone Phase II

stand, we selected an alternative control stand (C1652 S58)

for sampling in 1993. However, harvesting adjacent to this

stand made it unsuitable in 1994. Therefore, from 1994-

1998 we sampled an alternative south-zone control stand

(C1637 S4).

In each stand, we selected 12-25 mast-producing white oaks

and placed 1-6 traps under each tree. Traps were placed

half-way between the trunk and canopy edge and in random

orientation to the trunk. We used two types of traps: metal

trash cans (37 cm tall with a 0.33 m diameter opening) and

wooden peach baskets (30 cm tall with a 0.43 m opening).

All traps had poultry wire covers to prevent wildlife from

removing acorns. In 1993 and 1994 we also collected acorns

from a single 1-m radius ground plot encircling each trap.

All collections were made in 14-day intervals from late

August until all acorns had fallen (late November).

Collected acorns were sorted by stage of development

(Myers 1978). Stage 1 and 2 acorns were dead and hardTHE INSECT GUILD OF WHITE OAK ACORNS: ITS EFFECT ON MAST

QUALITY IN THE OZARK AND OUACHITA NATIONAL FORESTS

Alex C. Mangini and Roger W. Perry1

1 Entomologist, USDA Forest Service, Forest Health Protection, Pineville, LA 71360; and Wildlife Biologist, USDA Forest Service, Southern

Research Station, Hot Springs, AR 71092, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Hardwood regeneration, especially of oaks, is an essential component of ecosystem management in the

Ouachita and Ozark Mountains of Arkansas. In addition, oak mast is an important wildlife food. Several species of insects

inhabit and consume acorns. Data on the insect guild inhabiting white oak (Quercus alba L.) acorns were collected from

two undisturbed mature (control) and two single-tree selection stands in the Ouachita and Ozark National Forests from

1993-1998. Insects collected were: weevils of the genus Curculio and Conotrachelus spp. (Coleoptera: Curculionidae);

filbertworm, Melissopus latifereanus (Walsingham) (Lepidoptera: Tortricidae); acorn moth, Valentinia glandulella Riley

(Lepidoptera: Blastobasidae), cynipid gall wasps (stone galls) (Hymenoptera: Cynipidae) and midge larvae (Diptera). Over

the 5 years of study, sound acorns averaged 31.9 percent. Curculio weevils, with an average infestation rate of 26.6

percent, were the most abundant acorn-infesting insect. Other insect species occurred in much smaller numbers.
80

ened fruits aborted soon after pollination. Stage 3 acorns

were either green acorns not yet fully developed or brown

acorns, apparently mature, but dwarfed. Stage 4 acorns

were fully developed but green. Stage 5 acorns were fully

mature.

Each acorn was dissected, examined, and tallied as either

sound or damaged. Acorn quality categories were: sound

acorn, Curculio, Conotrachelus, filbertworm, acorn moth,

Cynipidae, Diptera (midge), rancid, aborted, and wildlife

damaged (Myers 1978). The “rancid” category included

acorns soured by moisture and/or fungi. If more than one

category or damage was present, the acorn was categorized by the primary invader or cause of damage.

For our developmental stage analysis, we pooled data from

all four stands and averaged yearly percentages for the five

years of data. We included both ground-and trap-collected

acorns for 1993 and 1994. For our acorn quality measures,

we included only stage 4 and 5 acorns; we excluded data

from ground-collections in the analysis of acorn quality

because corresponding data were not available for 1995 to

1997. We pooled data from single-tree selection and control

stands and calculated yearly percent damage rates to

compare damage among groups by year. For overall acorn

quality measures, we averaged yearly damage rates for the

five years data was collected.

RESULTS AND DISCUSSION

Stage of Development

Fully mature stage 5 acorns constituted the greatest portion

of acorns collected (fig. 1). A great portion (34 percent) of

the acorn crop was aborts (Stages 1-3). However, because

traps were placed in mid-late August of each year, just prior

to mature acorn fall, we probably underestimated percentages of aborted and immature.

Acorn Quality by Year

The white oak acorn crop varied yearly (fig. 2); such variation is characteristic of oaks, with very large crops produced

during “mast” years (e.g. Auchmoody and others 1993,

Koenig and others 1994). Although more acorns were

collected in 1995 than in 1996, the total number of sound

acorns was greater in 1996. Total percent soundness was

also greater in 1996 than in 1995 (fig. 3). This suggests

1996 may have been a “true” mast year because a greater

amount of sound acorns were available for wildlife and

regeneration.

Curculio weevils were the most abundant insect infesting

white oak acorns, with a substantial presence each year.

However, in 1995 and 1996, the Curculio infestation rate

was lower than in 1994 despite the larger acorn crop of

these latter years. This suggests that, in years of abundant

mast, the reproductive capacity of this insect is overwhelmed.

The other primary insect species, the filbertworm, maintained a smaller but more consistent percentage across

years.

Secondary insect species were less abundant than the

primary infestors. Percent of acorns damaged by Cynipidae

was noticeably greater in 1993, 1995 and 1997 compared

Figure 1—Average proportion of acorns by development stages

collected from white oaks located in two single-tree selection and

two unharvested mature forest stands in the Ouachita and Ozark

National Forests, Arkansas. Percentages were averaged from

yearly data from 1993 to 1997. Data for 1993 and 1994 include

acorns collected from ground plots and traps; 1995-1997 data

include only trap-collected acorns.

Figure 2—Numbers of white oak acorns, by damage category and

year, collected from traps from 1993 to 1997 in the Ouachita and

Ozark National Forests, Arkansas. Acorns from two single-tree

selection and two unharvested mature forest stands were

combined.
81

with 1994 and 1996. This suggests a periodicity in Cynipid

populations. Acorn destruction by wildlife was fairly consistent across years. However, the damage rate in 1996 was

relatively lower than other years suggesting the mast production of 1996 was greater than the consumption rate of

wildlife, furthermore suggesting 1996 was a mast year.

Overall Acorn Quality

When averaged over the five years of collection (fig. 4),

sound acorns were the largest quality category. Curculio

weevils were by far the most abundant insects infesting the

acorns and surpassed wildlife in numbers of acorns destroyed. However, many Curculio-infested acorns had only a

few small larvae and these acorns could still be consumed

by wildlife and would probably still germinate.

Other insect species were present in much smaller numbers.

The acorn moth and Conotrachelus weevils were relatively

Figure 3—Percentage of white oak acorns in each quality category, by year, collected

from traps from 1993 to 1997 in the Ouachita and Ozark National Forests, Arkansas.

Acorns from two single-tree selection and two unharvested mature forest stands were

combined.

rare inhabitants in acorns. Galford (1986) indicated “secondary” insects such as the acorn moth may destroy significant numbers of acorns that have fallen to the ground and

commenced germination. However, because we used

acorns collected from traps rather than the ground, the

damage rates for these secondary insects may be underrepresented. Perry and Mangini (1997) indicated differences

in damage rate estimates for some secondary infestors

between ground- and trap-collected acorns.

CONCLUSIONS

Insects feeding on white oak acorns had a significant

impact on the quality of the acorn crop. Curculio weevils

were responsible for the greatest amount of the damage.

Curculio infestation, along with wildlife damage and fungi

damaged 56 percent of the total mature acorn crop available for regeneration. In addition, a great portion of the

overall acorn crop was aborted or fell prior to maturity.
82

Our study involved acorns collected during the fall acorn

drop. Consequently, our estimates of insect damage are

likely to be conservative in regards to regeneration potential

because additional infestation and rot could be expected as

acorns lay for an extended period on the forest floor prior to

sprouting. Furthermore, our overall damage rate estimates

were probably reduced because we estimated acorn quality

using only trap-collected acorns.

ACKNOWLEDGMENTS

We thank W. Bruce, R. McMullen, R. Menard, V. Peacher,

and H. Wallace for assistance in dissecting the acorns.

LITERATURE CITED

Auchmoody, L.R.; Smith, H.C.; Walters, R.S. 1993. Acorn production in northern red oak stands in northwestern Pennsylvania.

Research Paper NE-660. Radnor, PA: U.S. Department of

Agriculture, Forest Service, Northeastern Forest Experiment

Station. 5 p.

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, J.B., comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains:

pretreatment conditions and preliminary findings. Gen. Tech.

Rep. SO-112. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station: 18-28.

Figure 4—Average percent of white oak acorns for each quality category, collected from traps in the

Ouachita and Ozark National Forests, Arkansas. Percentages were averaged from yearly data

collected from 1993 to 1997 and represent acorns collected from two single-tree selection and two

unharvested mature forest stands.

Galford, J.R. 1986. Primary infestation of sprouting chestnut, red

and white oak acorns by Valentinia glandulella (Lepidoptera:

Blastobasidae). Entomological News 97:109-112.

Gibson, L.P. 1972. Insects that damage white oak acorns. Research

Paper NE-2, U.S. Department of Agriculture, Forest Service,

Northeastern Forest Experiment Station. 7 p.

Goodrum, P.D.; Reid, V.H.; Boyd, C.E. 1971. Acorn yields, characteristics, and management criteria of oaks for wildlife. Journal

of Wildlife Management 35:520-532.

Koenig, W.D.; Mumme, R.L.; Carmen, W.J.; Stanback, M.T. 1994.

Acorn production by oaks in central coastal California: variation

within and among years. Ecology 75: 99-109.

McShea, W.J.; Schwede, G. 1993. Variable acorn crops: responses

of white-tailed deer and other mast consumers. Journal of

Mammalogy. 74: 999-1006.

Myers, S.A. 1978. Insect impact on acorn production in Missouri.

Columbia, MO: University of Missouri. 246 p. Ph.D. dissertation.

Nixon, C.M.; McClain, W.M.; Donohoe, R.W. 1975. Effects of

hunting and mast crops on a squirrel population. Journal of

Wildlife Management 39: 1-25.

Perry, R.W.; Mangini, A. 1997. A comparison of trap versus ground

collections of acorns to assess insect infestation. Journal of

Entomological Science 32: 412-415.
83

INTRODUCTION

Insects and other arthropods are abundant throughout

terrestrial ecosystems (May 1989, Wheeler 1990, Gaston

1991) where they play a role in most ecosystem processes

and have a major impact on energy flow through a system

(Samways 1994). Because of their abundance, species

richness and functional importance, insects are useful indicators of ecosystem change (Rosenberg and others 1986).

The ecosystem management project in the Ouachita Mountains is examining a variety of timber harvesting treatments

and there were 13 stand management treatments implemented (Baker 1994). The objective of my work has been

to examine the diversity and abundance of populations of

beetles (Coleoptera) following the implementation of 5 of

these treatments (unmanaged controls, single-tree selection

cuts, group selection cuts, shelterwood cuts and clearcuts).

In the past, stand management research involving insects

has primarily focused on species of economic importance,

but stand management practices also impact other insect

species found within a forest. Studies that examined environmental impacts and used insects as bioindicators range

from examining all insect taxonomic groups (i.e. Stork 1991)

to using a single insect family such as the beetle family

Cicindelidae (Pearson and Cassola 1992). The choice of

taxonomic group(s) to utilize for monitoring should be related

to the system under examination and the potential impact of

the applied treatments on the group(s). Therefore, species

do not need to be rare to be useful as bioindicators of

system change. In this study, I examined both the overall

community of beetles present in the stands as well as 2

specific families (Silphidae and Scarabaeidae). In these

stands, most of the individuals of these 2 families that were

captured are carrion-feeding insects. In general, the carrionfeeding guild of insects is dependent upon the presence of

small vertebrates in the system, which in turn is regulated

in large part by the vegetation.

One reason for choosing to examine the carrion-feeding

guild of insects is that there are known populations of the

endangered American burying beetle, Nicrophorus

americanus, in western Arkansas and eastern Oklahoma.

These populations appear to be remaining fairly stable and

are located near where the ecosystem management project

is occurring. In addition to American burying beetle, the

community of carrion-feeding insects is a large and diverse

group. This feeding guild can be sensitive to environmental

changes that impact the carrion resource or that result in

the introduction of new members to the guild (Summerlin

and others 1984, Porter and Savignano 1990, Stoker and

others 1995). The diversity and abundance of the guild,

along with its sensitivity to environmental changes make it a

good candidate group for examining treatment impact on

biodiversity.

METHODS

Treatments

The beetle communities occurring within 20 stands (18 in

Ouachita National Forest and 2 in Ozark National Forest)

were examined. Each stand was approximately 40 acres in

size and treatments were applied in 1993 (Mersmann and

others 1994 for a more detailed description). Stands were

randomly assigned to treatment categories. Four stands per

treatment were examined. The treatments were: 1) unmanaged controls in which no harvesting and no other stand

management practices occurred; 2) single-tree selection

cuts in which some harvesting occurred but a residual basal

area of from 45 – 65 ft2 per acre was retained; 3) group

selection cuts in which harvest openings ranged from 0.1 –

1.0 acres and the pine component outside of the openings

was thinned to 70 – 80 ft2 per acre but no hardwoods were

harvested or removed from outside of the group openings;

4) shelterwood cuts were harvested while retaining from 20

– 40 of the largest trees per acre (approximately 30 – 40 ft2

per acre of basal area); and 5) clearcuts in which all trees

were harvested except 2 – 5 ft2 per acre of hardwoods that

were retained as den trees and mast producers. A more

detailed description of the treatments can be found in Baker

(1994).

IMPACT OF STAND MANAGEMENT PRACTICES ON BEETLE DIVERSITY

Stephen P. Cook1

1 Research Scientist, University of Idaho, Department of Forest Resources, Moscow, ID 83844-1133.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Insects are useful indicators of change within ecosystems because of their abundance, richness and functional

importance. Stand management practices impact the insect community within a forest. Therefore, the objective of the

project is to determine the impact of various stand management practices on the diversity of beetles within selected

treatments. Four harvesting treatments and uncut controls were examined. The lowest number of beetle families captured

was in the control plots. Diversity within individual families can also vary based upon management practices. For

example, the same 6 species of Silphidae were captured in 4 of the 5 treatments but fewer individuals were captured in

the control plots compared with the treatments. Similarly, the lowest number of Scarabaeidae captured (species and

individuals) was in the uncut control plots. Therefore, disturbing stands through tree harvesting increased the overall

diversity of beetle families as well as diversity within select families.
84

Insect Trapping

The beetle communities present within the stands were

examined using 4 types of traps. One malaise trap (7 foot

tall) was placed in each of 15 stands (3 stands per treatment) for 3 consecutive days during May and June of 1998.

These are unbaited traps that indiscriminately capture flying

insects. Insects were removed at the end of the 3-day period.

Carrion-baited pitfall traps (Lomolino and others 1995) were

placed in 10 stands (2 stands per treatment) during May and

June of 1998. An 8-trap transect was used and monitored

daily for 3 consecutive days. Transects were placed near the

center of each stand and traps were approximately 25 yards

apart within the transects. Beetles were removed from the

traps daily and the traps were rebaited as needed. A second

transect of 8 pitfall traps baited with a combination of EtOH

and turpentine were placed in the plots approximately 50

yards from the first transect. These traps were also examined daily for 3 consecutive days. These traps are designed

to capture pine regeneration insects (Hunt and Raffa 1989).

Lindgren funnel traps (45 inches long with a trap surface

area of 538 in2) were placed in 10 stands (2 per treatment)

during May of 1999. There were 2 traps placed in each stand,

1 trap was baited with EtOH and alpha-pinene and the other

trap was baited with EtOH, alpha-pinene and frontalin, the

aggregation pheromone of the southern pine beetle. These

traps were left in place for 3 weeks. These traps capture a

wide variety of beetles but the specific baits were chosen to

sample for bark beetles and their associates.

Analyses

All of the individual beetles captured in all of the traps were

identified to family. The data from all trap types were combined to compare family richness among treatments. Prior

sampling had occurred on several of these sites following

application of the treatments in 1993 (Carlton and others

1994). The two sampling periods had 3 treatment types in

common (uncut controls, group selection cuts and shelterwood cuts). For these 3 treatments, data on beetle families

was compared between the sample dates for family richness and shared families.

The individual beetles of 2 families, Silphidae and

Scarabaeidae, were identified to genus (and species where

possible). Within each of the 2 families, comparisons of

total number of beetles captured, species richness and

species diversity were made among treatments. For these

comparisons, species richness refers to the number of

species captured and species diversity was calculated using

the Shannon-Weaver diversity index (see Price 1975). The

3 most commonly captured species in each of these 2 families was determined and the number of individuals captured

in the carrion-baited pitfall traps compared among treatments. The comparisons were based on the number of

beetles captured per trap per night and only undisturbed

traps were used in the comparison.

RESULTS AND DISCUSSION

Overall Family Diversity

There were a total of 47 families of beetles captured during

the 1998 and 1999 sample periods (table 1). The lowest

number of beetle families (n=30) was captured in the unmanaged control stands. The stands that received some harvesting treatment all had a larger number of beetle families

present and the number of families captured was similar

among stands (n=36 to 39). Therefore, some stand disturbance through harvesting appeared to increase the diversity

of beetle families present in these stands.

A similar pattern can be distinguished in the 1993 data presented by Carlton and others (1994). They reported capturing a total of 54 families. Of the 3 treatments in common

between the 1993 and 1998/99 samples their control plot

also had the lowest number of families captured in 1993

(n=23) followed by the shelterwood cut (n=33) and the

group selection cut (n=40).

It is interesting to note that while the sampling for these 2

studies occurred 5 to 6 years apart and different trapping

schemes were used, there were 36 families of beetles captured in common between the 2 studies. Further, the lowest

number of families was captured in the control stands during

both studies. However, 29 families of beetles were unique

to one or the other study. This may be largely due to the

time difference between when trees were harvested and

when beetle sampling occurred. This demonstrates the need

to sample over a long period of time to establish patterns in

diversity or the impact of disturbance events on insect

populations.

Diversity Within the Family Silphidae

Although there are known populations of American burying

beetle in western Arkansas and eastern Oklahoma, none

were captured on these sites during the 2 summers that

sampling was conducted. One explanation is that the beetle

seems to do best in areas that are undisturbed and have

had little human activity (see Ratcliffe 1996). Also, the study

plots are relatively small and the beetle may be present in

the National Forest(s), just not in these study plots.

There were 6 species of Silphidae captured in 4 of the 5

treatment types (table 2). In the other treatment type, shelterwood cuts, 5 of the 6 species were captured. The lowest

number of individual silphids captured was in the control

stands, followed by the shelterwood cut stands, the clearcuts, the single-tree selection cut stands and the group

selection cut stands. Although the measures of diversity

were similar across treatment types, the lowest diversity

(H’=1.21) occurred in the shelterwood cut stands. The measure of diversity was similar in the control stands (H’=1.22)

and increased in the stands that had been clearcut (H’=1.31)

or had been harvested as single-tree selection cuts (H’=1.43)

or group selection cuts (H’=1.45). The 2 most abundant

species of silphids captured were always the 2 species of

Oceoptoma. These 2 species are the smallest of the silphids

captured during this study and they are both very active

during the spring (see Ratcliffe 1996).

As would be expected, different species demonstrated different responses to the harvesting disturbance. The capture

distributions of the 3 most commonly caught silphids are

presented in figure 1. For Oceoptoma inaequale, there was

a slight increase in abundance with increasing harvest as

long as some residual canopy remained. There was a pronounced decrease in capture of this beetle in the clearcut

stands. In contrast, captures of O. novaboracense typically
85

Table 1—Families of beetles captured within stands that had received

1 of the 5 harvesting treatments

Treatment

Single Group

Family Control tree selection Shelterwood Clearcut

Rhysodidae x x x x x

Carabidae x x x x x

Hydrophilidae x x x x x

Histeridae x x x x x

Pselaphidae x x x x x

Staphylinidae x x x x x

Leiodidae x x x x x

Silphidae x x x x x

Eucinetidae x x

Scarabaeidae x x x x x

Byrrhidae x

Ptilodactylidae x

Buprestidae x x x x x

Elateridae x x x x x

Throscidae x

Eucnemidae x x x x

Phengodidae x x x x

Lampyridae x x

Cantharidae x x x x x

Lycidae x x x x

Dermestidae x x x x x

Anobiidae x x x x x

Bostrichidae x x x

Lyctidae x x x x x

Trogositidae x x x x x

Cleridae x x x x x

Melyridae x x

Nitidulidae x x x

Cucujidae x x

Eroltylidae x x x

Phalacridae x

Coccinellidae x x x x x

Endomychidae x

Lathridiidae x x x

Colydiidae x x x

Tenebrionidae x x x x x

Oedemeridae x x x x x

Melandryidae x x x x x

Mordellidae x x x x x

Pedilidae x x x x x

Cerambycidae x x x x x

Bruchidae x x x x

Chrysomelidae x x x x x

Anthribidae x

Curculionidae x x x x x

Platypodidae x

Scolytidae x x x x x
86

Table 2—Members of the family Silphidae captured within stands that

had received 1 of the 5 harvesting treatments

Treatment

Single Group Shelter- ClearSpecies Control tree selection wood cut

Necrophila americana 1 4 10 5 13

Oiceoptoma inaequale 19 57 53 46 31

O. novaboracense 23 28 63 23 53

Nicrophorus orbicollis 7 14 19 9 1

N. pustulatus 17 401

N. tomentosus 1 10 13 3 15

Total capture 52 120 162 86 114

decreased with increasing tree harvest. Trap catch for the

third species, Nicrophorus orbicollis, initially increased but

then declined as harvest intensity increased beyond the

group selection cuts.

Diversity Within the Family Scarabaeidae

There were 20 species of scarabs captured in the stands

during the 2 years of this project (table 3). The lowest

number of species (n=11) and the fewest individuals (n=67)

were captured in the control stands. The largest number of

species (n=15) and the most individual scarabs (n=191)

were captured in the group selection stands. The measure

of species diversity was lowest in the control stands

(H’=1.72) followed by the single-tree selection stands

(H’=1.93), the clearcut stands (H’=1.94), the shelterwood

cut stands (H’=2.04) and the group selection cut stands

(H’=2.16).

The 3 most abundant species captured were Deltochilum

gibbosus, Ateuchus histeroides and Canthon chalcites. The

capture distributions for these 3 species are presented in

figure 2. Captures of D. gibbosus initially increased with

harvest disturbance in the single-tree selection and group

selection stands but then decreased in the shelterwood cut

and clearcut stands. No A. histeroides were captured in the

control stands, but the species was present in all of the

stands that had received some harvesting treatment. There

was no discernable pattern in the distribution of C. chalcites

captures. As with the silphids, there was a species-specific

response by the beetles to harvest disturbance intensity.

CONCLUSIONS

Overall, stand disturbance through tree harvesting increased

the diversity of beetle families present in stands. For the

individual families examined, species richness was similar

among treatments for the Silphidae but the fewest number

Figure 1—The number of Oiceoptoma inaequale, O.

novaboracense, and Nicrophorus orbicollis captured per trap night

in the pitfall traps that were baited with carrion. There were eight

traps per stand and trapping was conducted over three

consecutive nights.

Figure 2—The number of Deltochilum gibbosus, Ateuchus

histeroides, and Canthon chalcites captured per trap night in the

pitfall traps that were baited with carrion. There were eight traps

per stand and trapping was conducted over three consecutive

nights.
87

Table 3—Members of the family Scarabaeidae captured within stands

that had received 1 of the 5 tree harvesting treatments

Treatment

Single Group Shelter- ClearSpecies Control tree selection wood cut

Aphodius sp. 1 1 0 0 0

Ateuchus histeroides 0 40 44 5 38

Canthon sp. 1 0 0 0 0

Canthon chalcites 10 2 9 3 12

Cloeotus sp. 0 0 1 1 4

Deltochilum gibbosus 32 39 45 31 18

Geotrupes blackburnii 66 942

Glaphyrocanthon viridis 00 120

Onthophagus hectate 0 0 16 2 10

O. orpheus 0 1 2 10 4

O. pennsylvanicus 0 15 29 0 2

O. striatulus 41 301

Phyllophaga sp. 1 2 12 9 4 1

P. sp. 2 1 0 0 0 0

P. sp. 3 0 3 4 1 0

Trichiotinus bibens 00 010

Trox sp. 0 4 6 6 5

T. monachus 7 5 12 2 2

T. punctatus 20 101

T. suberosus 16 030

Unidentified 0 0 0 1 0

Total capture 67 135 191 76 100

of silphids were captured in the control plots. The control

stands also had the lowest abundance, species richness

and species diversity in captured Scarabaeidae and the

highest measure for all 3 of these parameters was in the

group selection cut plots. As expected, there were speciesspecific responses to the intensity of tree harvesting disturbance. In general, stand disturbance caused by tree

harvesting increased the diversity and abundance of

beetles within stands and this could still be measured 5 to

6 years following the tree harvests.

ACKNOWLEDGMENTS

I thank Alex Mangini (USDA-Forest Service, Pineville, LA)

for his assistance in getting the project started and showing

me the treatment stands and Marco Miccozi helped with

field collections during 1998.

Research support was provided through the project “Impact

of Stand Management Practices on the Diversity of Beetles

in the Ouachita and Ozark National Forests” dated August

1, 1999 to September 30, 2001 of Cooperative Agreement

33-CA-99-679. This research was supported in part by

funds provided by the U.S. Department of Agriculture,

Forest Service, Southern Research Station as part of their

Ecosystem Management Research Program.

LITERATURE CITED

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, J.B., Ecosystem management research in the Ouachita

mountains: pretreatment conditions and preliminary findings.

U.S. Department of Agriculture, Forest Service Gen. Tech. Rep.

SO-112: 18-28.

Carlton, C.E.; Bollinger, J.; Thompson, L.C. 1994. Arthropod

biodiversity-sampling protocol development. In: Baker, J.B.,

Ecosystem management research in the Ouachita mountains:

pretreatment conditions and preliminary findings. U.S.

Department of Agriculture, Forest Service Gen. Tech. Rep.

SO-112: 144-153.

Gaston, K.J. 1991. The magnitude of global insect richness.

Conserv. Biol. 5: 283-296.

Hunt, D.W.; Raffa, K.F. 1989. Attraction of Hylobius radicis and

Pachylobius picivorus (Coleoptera: Curculionidae) to ethanol

and turpentine in pitfall traps. Environ. Entomol. 18: 351-355.

Lomolino, M.V.; Creighton, J.C.; Schnell, G.D.; Certain, D.L. 1995.

Ecology and conservation of the endangered American burying

beetle (Nicrophorus americanus). Conserv. Biol. 9: 605-614.

May, R.M. 1989. How many species? In: Friday, L; Laskey, R., The

fragile environment. Cambridge Univ. Press. 61-81.

Mersmann, T.J.; Baker, J.B.; Guldin, J.M.; Pell, W.F. 1994. Implementing ecosystem management research: bringing researchers,

managers, and citizens together. In: Baker, J.B., Ecosystem

management research in the Ouachita mountains: pretreatment

conditions and preliminary findings. U.S. Department of

Agriculture, Forest Service Gen. Tech. Rep. SO-112: 10-17.
88

Pearson, D.L.; Cassola, F. 1992. World-wide species richness

patterns of tiger beetles (Coleoptera: Cicindelidae): indicator

taxon for biodiversity and conservation studies. Conserv. Biol.

6: 376-391.

Porter, S.D.; Savignano, D.A. 1990. Invasion of polygyne fire ants

decimates native ants and disrupts arthropod community.

Ecology 71: 2095-2106.

Price, P.W. 1975. Insect ecology. J. Wiley, New York.

Ratcliffe, B.C. 1996. The carrion beetles (Coleoptera: Silphidae) of

Nebraska. Bull. Univ. Nebraska State Museum 13: 1-100.

Rosenberg, D.M.; Danks, H.V.; Lehmukuhl, D.M. 1986. Importance

of insects in environmental impact assessment. Environ. Manag.

10: 773-783.

Samways, M.J. 1994. Insect conservation biology. Chapman &

Hall, London.

Stoker, R.L.; Grant, W.E.; Vinson, S.B. 1995. Solenopsis invicta

(Hymenoptera: Formicidae) effect on invertebrate decomposers

of carrion in central Texas. Environ. Entomol. 24: 817-822.

Stork, N.E. 1991. The comparison of the arthropod fauna of

Bornean lowland rain forest trees. J. Trop. Ecol. 7: 161-180.

Summerlin, J.W.; Petersen, H.D.; Harris, R.L. 1984. Red imported

fire ant (Hymenoptera: Formicidae): effects on the horn fly

(Diptera: Muscidae) and coprophagous scarabs. Environ.

Entomol. 13: 1405-1410.

Wheeler, Q.D. 1990. Insect diversity and cladistic constraints. Ann.

Entomol. Soc. Am. 83: 1031-1047.
89

INTRODUCTION

Insects feeding on cones and seeds of pines reduce seed

and cone production of southern pines. Seed bugs were

first recognized as pests of seeds when tree improvement

pine seed orchards were established in the South in the late

1960’s (DeBarr 1967, DeBarr and Ebel 1974). Two species

of seed bugs damage to seeds, the leaffooted pine seed

bug, Leptoglossus corculus (Say) (Hemiptera: Coreidae)

and the shieldbacked pine seed bug,Tetyra bipunctata

(Herrich-Shaffer) (Hemiptera: Pentatomidae) (DeBarr 1967,

Ebel and others 1980). Seed bug damage has been extensive in Arkansas shortleaf pines; for example, at a seed

orchard in Mt. Ida, seed bug damage was 43 percent in

1981 (J.C. Weatherby, H.N. Wallace, 1985. Post-treatment

evaluations of the 1981, 1982, 1983, and 1984 pest management programs at the Ouachita seed orchard, Mt. Ida,

Arkansas. Unpublished report 85-2-10. USDA Forest

Service, State and Private Forestry, Southern Region,

Alexandria Field Office, Atlanta, GA).

Seed bugs are important considering that natural seed production is highly variable from year to year (Wittwer and

Shelton, in press). This becomes all the more important

given the desire for natural regeneration on the Ouachita

and Ozark National Forests.

In this study, we too estimated the amount of damage

caused by seed bugs in natural stands of shortleaf pine on

the Ouachita and Ozark National Forests. This is consistent

with the Ecosystem Management Phase II goals because

seed bugs are endemmic to these areas and we need to

estimate their impact in the ecosystem (Mangini and others

1974).

MATERIALS AND METHODS

We collected shortleaf pine cones on or near the twenty

Ecosystem Management Phase II Wildlife sites (Logan,

Montgomery, Polk, Scott and Yell Counties, AR; LeFlore

County, OK) (Baker 1993) during late October-early

November from 1995 through 1998. In stands which had

been clear-cut, we sampled shortleaf pines adjacent to the

site.

Collections were made by shooting cone-bearing branches

from the trees using a rifle, either a “243” bolt-action rifle or

a “22” magnum rifle. Twenty cones were collected at each

site from one or more trees. When cones were sparse at a

given site, we collected as many as possible within a 30

minute period.

Cones were placed in paper bags and left in an air-conditioned room for 4-6 weeks to dry. The bags of cones were

then placed in a drying kiln at 45°C for 24-36 hours in order

to open the cones. Seeds were extracted from the opened

cones, dewinged by hand and placed in 7.6 x 12.6 kraftpaper envelopes. These were radiographed to determine

the number of healthy and damaged seeds and the causes

of damage. Each seed was tallied according to appearance

(Bramlett and others 1977, DeBarr 1970) and placed into

one of the following seed quality categories: full (healthy),

empty, seed bug, abort, seedworm and other.

Data were summarized using standard procedures in SAS

(SAS Institute 1989). Means and percentages for each

seed quality category were calculated by year and site.

RESULTS AND DISCUSSION

Although we sampled from 1995 through 1998, the 1998

cone crop was extremely sparse so those data are not

included in the results. When data from the three years are

taken as a whole, we found that 67 percent of the seeds

were healthy (fig. 1). Only 4 percent of the damage could

be attributed to insects. When the insect damage is further

broken down, only 11 percent of the damage was due

directly to seed bugs (fig. 2). On the other hand, seedworm

caused 38 percent of the insect damage. Three species of

RADIOGRAPHIC ANALYSIS OF SHORTLEAF PINE SEEDS

FROM THE OUACHITA AND OZARK NATIONAL FORESTS

Alex C. Mangini, William W. Bruce, and James L. Hanula1

1 Entomologist and Biological Technician, USDA Forest Service, Forest Health Protection, Pineville, LA 71360; and Research Entomologist,

USDA Forest Service, Southern Research Station, Athens, GA 30602, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Shortleaf pine, Pinus echinata Mill., is indigenous to the Ouachita Mountains and the Magazine Mountain area

of Arkansas. Natural regeneration of shortleaf pine is a priority on National Forest lands in this area. Insects infesting

cones and seeds of shortleaf pine reduce the healthy seeds available for natural regeneration. Two species of seed bug,

the leaffooted pine seed bug, Leptoglossus corculus (Say) (Hemiptera: Coreidae) and the shieldbacked pine seed bug,

Tetyra bipunctata (Herrich-Schaffer) (Hemiptera: Pentatomidae) damage pine seeds. We collected cones on or near twenty

Ecosystem Management Phase II wildlife sites during late October-early November from 1995 through 1998. In 1998,

cone production was too low to include in the analyses. Cones were kiln-dried to open them. Seeds were extracted,

dewinged and radiographed. Radiography allows accurate evaluation of healthy and damaged seed. Seed bug damage

was consistent with that recorded at the Ouachita Seed Orchard in Mt. Ida, AR. Seed bugs were responsible for about 30

percent damage for combined collections from 1995-97.
90

29%

4%

67%

Full

Empty

Insect

Figure 1—Seed quality as percent of total seeds for shortleaf pine

from the 20 Ecosystem Management Phase II wildlife stands on

the Ouachita and Ozark National Forests in Arkansas. Data

represent combined counts of collections made in 1995 through

1997. Quality determined by radiographic examination.

9% 11%

42% 38% Seedworm

Abort

Seedbug

Other

Figure 2—Categorization of “insect” damaged seeds of shortleaf

pine collected from the 20 Ecosystem Management Phase II

wildlife stands on the Ouachita and Ozark National Forests in

Arkansas. Data represent combined counts of collections made in

1995 through 1997. Quality determined by radiographic

examination.

seedworm (Cydia spp. = Laspeyresia spp.) commonly infest

seeds of southern pines (Ebel and others 1980).

These results may be misleading if the empty (fig. 1) and

aborted (fig. 2) categories are not considered. Radiographs

of seeds can clearly show effects of late-season feeding by

seed bugs (Bramlett and others 1977); however, earlier

feeding on second-year cones or seed bug feeding on firstyear cones can cause aborted or empty seeds.

There are two causes of ovule abortion in the first year of

cone development: poor pollination and seed bug damage.

If pollen is not present when flowers are receptive or if the

pollen does not germinate, the ovules abort early in the first

year (Bramlett and Johnson 1975). Nymphs of L. corculus

are known to feed on the flowers of shortleaf pine and

destroy ovules (DeBarr and Kormanik 1975, Ebel and Yates

1974); but the damage is difficult to distinguish from those

aborted by lack of pollen (Bramlett and others 1977).

Consequently, the empty and abort categories in figures 1

and 2, respectively, when added to the radiographicallyconfirmed seedbug damage are likely a more accurate

estimate of the effects of the two seed bug species. This is

consistent with estimated damage in the Ouachita seed

orchard from 1981-84 where the proportion of empty seeds

ranged from 23-70 percent and the confirmed seed bug

damage ranged from 2-43 percent ((J.C. Weatherby, H.N.

Wallace, 1985. Post-treatment evaluations of the 1981,

1982, 1983, and 1984 pest management programs at the

Ouachita seed orchard, Mt. Ida, Arkansas. Unpublished

report 85-2-10. USDA Forest Service, State and Private

Forestry, Southern Region, Alexandria Field Office, Atlanta,

GA).

Our results are consistent with Shelton and Wittwer (1996)

in that the quantity of sound seeds produced is positively

related to total seeds produced. In 1996, shortleaf cone

production was much greater than that in 1995 and 1997

(fig. 3). Healthy seed was substantially higher in 1996

(fig. 4) at 62.5 percent, near the maximum indicated by

Wittwer and Shelton (in press). They demonstrate a positive response of healthy seeds as a function of total seeds

produced up to an asymptotic limit at about 60 percent

healthy seeds for the largest seed crops.

88

485

115

0

100

200

300

400

500

600

1995 1996 1997

Year

Number of cones

Figure 4—Healthy seeds as a percentage of total seeds collected

for shortleaf pine from the 20 Ecosystem Management Phase II

wildlife stands on the Ouachita and Ozark National Forests in

Arkansas.

Figure 3—Shortleaf pine cones collected from the 20 Ecosystem

Management Phase II wildlife stands on the Ouachita and Ozark

National Forests in Arkansas.

21.7

62.7

12.2

0

20

40

60

80

100

1995 1996 1997

Year

Percent healthy seed
91

It is apparent that managers should monitor the shortleaf

seed crop in its first year of development to determine crop

estimates. For natural regeneration, site preparation efforts

can be timed to coincide with large seed crops. This will

also minimize damage due to seed bugs and seedworms.

ACKNOWLEDGMENTS

We wish to acknowledge the assistance of Mike Cody and

Chris Crowe of the USDA Forest Service, Southern Research

Station, Athens, GA; Jack Garner, USDA Forest Service,

Kisatchie National Forest, Pollock, LA; Bob Raines, Ben

Rowland and Charles Studyvin, USDA Forest Service,

Ouachita National Forest, Mt. Ida, AR. Photograph of leaffooted pine seed bug by Scott Cameron. Phototgraph of

shieldbacked pine seed bug by Larry Barber. Both photographs courtesy of University of Georgia Cooperative

Extension Service, Cooperative Series Bulletin No. 383,

G.K. Douce, G.J. Lenhard, B.T. Watson and D.J. Moorhead.

LITERATURE CITED

Baker, J.B., comp. 1993. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Hot Springs, AR.

Gen. Tech. Rep. SO-112. New Orleans, LA; U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station.

Bramlett, D.L.; DeBarr, G.L.; Yates, H.O., III [and others]. 1977.

Cone analysis of southern pines – a guidebook. USDA Forest

Service, Southeastern Forest Expt. Sta. Gen. Tech. Rep. SE13. 28 p.

Bramlett, D.L.; Johnson, C.J. 1975. Pollination in a slash pine seed

orchard. Thirteenth South. For. Tree Improv. Conf. Proc. 161-

165.

DeBarr, G.L. 1967. Two new sucking insect pests of seed in

southern pine seed orchards. USDA Forest Service,

Southeastern Forest Exp. Stn. Res. Note SE-78. 3 p.

DeBarr, G.L. 1970. Characteristics and radiographic detection of

seed bug damage to slash pine seed. Florida Entomol. 53(2):

109-117.

DeBarr, G.L.; Ebel, B.H. 1974. Conelet abortion and seed damage

of shortleaf and loblolly pines by a seedbug, Leptoglossus

corculus. Forest Sci. 20(2): 165-170.

DeBarr, G.L.; Kormanik, P.P. 1975. Anatomical basis for conelet

abortion on Pinus echinata following feeding by Leptoglossus

corculus (Hemiptera: Coreidae). Can. Entomol. 107: 81-86.

Ebel, B.H.; Flavell, T.H.; Drake, L.E. [and others]. 1980. Seed and

cone insects of southern pines. USDA Forest Service,

Southeastern Forest Exp. Stn. Gen. Tech. Rep. SE-8. 43 p.

Revised 1980.

Ebel, B.H.; Yates, H.O., III 1974. Insect-caused damage and

mortality to conelets, cones and seed of shortleaf pine. Journ.

Econ. Entomol. 67(2): 222-226.

Mangini, A.; Carlton, C.; Perry, R.W. [and others]. 1994. Seed,

cone, regeneration and defoliating insects in forest ecosystem

management. In: Baker, J.B., comp. Proceedings of the symposium on ecosystem management research in the Ouachita

Mountains: Pretreatment conditions and preliminary findings.

Gen. Tech. Rep. SO-112. New Orleans, LA; U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station: 154-159.

SAS Institute. 1989. SAS/STAT user’s guide. Version 6, 4th ed.

Cary, NC: SAS Institute, Inc. 846 p.

Shelton, M.G.; Wittwer, R.F. 1996. Shortleaf pine seed production

in natural stands in the Ouachita and Ozark Mountains. South.

J. Appl. For. 20: 74-80.

Wittwer, R.F.; Shelton, M.G. [In press]. Shortleaf pine seed production following partial cutting in the Ouachita Mountains. In:

Guldin, James M., tech. comp. Ouachita and Ozark Mountains

symposium: ecosystem management research. Gen. Tech.

Rep. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station.
92

INTRODUCTION

An important element of the Ecosystem Management

Research Project in the Ouachita Mountains is to investigate whether the shortleaf pine (Pinus echinata)-hardwood

forest type in the Ouachita and Ozark-St. Francis National

Forests can be managed using even-aged and uneven-aged

reproduction cutting methods that rely on natural regeneration. A complete description of the project can be found in

Baker (1994). In about half of the harvest treatments, overstory and midstory hardwoods are being retained in order to

meet ecosystem management objectives such as improved

wildlife habitat, greater biodiversity, reduced visual impact

of harvesting, and perpetuation of the pine-hardwood forest

type.

Retained hardwood trees generally meet the definition of

“reserve trees” (Helms 1998) for the even-aged methods,

since they are retained after the regeneration period. However, in the uneven-aged methods, retained trees are not

truly reserve trees; they are more appropriately considered

as a component of the pine-hardwood forest type being

studied in this project. Nonetheless, we will use the term

“reserve trees” throughout to describe hardwoods retained

after reproduction cutting in these treatments.

The health and longevity of reserve trees are important if

ecosystem management objectives are to be realized. But

little information exists about the health and fate of these

trees that might guide the selection of reserve trees in future

operational harvests under an ecosystem management

regime.

The major concern for the health of reserve hardwoods is

oak or hardwood decline (Starkey and others 1989, Wargo

and others 1983). Decline can generally be described as a

complex disease syndrome resulting from the interaction of

a variety of host, site, and stand factors with biotic and abiotic

agents and stress factors. It is expressed by a progressive

dieback of the crown from the upper and outer portions downward, usually resulting in mortality. Manion (1991) describes

decline as “an interaction of interchangeable, specifically

ordered abiotic and biotic factors to produce a gradual general deterioration, often ending in death of trees”; decline is

depicted as a spiral of (1) one or more predisposing factors,

followed by (2) one or more inciting factors, which are then

followed by (3) one or more contributing factors. Factors

that can be responsible for decline include abiotic, biotic,

site/stand or anthropogenic agents (fig. 1).

In oak decline, specific predisposing, inciting and contributing

factors interact to affect the incidence and severity of symptoms (fig. 2). In the Eastern United States, decline is attributed to climatic events and site/stand factors, defoliation,

drought, frost, root rots and borers (Millers and others 1989,

Starkey and others 1989). In the Ouachita Mountains, the

factors most likely to be operative are site/stand factors (predisposing), stand disturbance from harvesting, or drought

(inciting), and root rot/borers (contributing). Oaks (Quercus

spp.), hickories (Carya spp.), and other hardwoods are susceptible to decline. Oaks in the red oak group are usually

more frequently and severely affected than white oaks or

hickories. Other hardwoods are less affected.

CROWN HEALTH OF RESERVE HARDWOOD TREES FOLLOWING

REPRODUCTION CUTTING IN THE OUACHITA MOUNTAINS

Dale A. Starkey and James M. Guldin1

1 Plant Pathologist, USDA Forest Service, Southern Region, Forest Health Protection, Pineville, LA; and Research Forest Ecologist and

Project Leader, USDA Forest Service, Southern Research Station, Hot Springs, AR, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Monitoring the health of reserve hardwood trees is being performed as part of the Ecosystem Management

Research Project on the Ouachita and Ozark National Forests in Arkansas. A suite of crown variables (diameter, live

crown ratio, density, dieback, and foliage transparency) was used to detect significant changes in reserve tree health over

time. While treatments had some effect on crown variables over time, seasonal climatic conditions (e.g. acute drought)

may have had a greater effect. It was generally apparent that for the most intensive treatments, crown variables worsened

more over time compared to less intensive treatments. Results will provide information about the success of retaining

such trees and provide guidelines for selecting reserve trees in future operational harvests.

Abiotic Factors

 Climatic trends or past events 

 Drought 

 Frost

Biotic Factors

 Borers 

 Canker fungi 

 Defoliators 

OAK

DECLINE

Site/stand Factors

 Density/competition 

 Physiological age 

 Soil depth/texture/fertility 

 Species/genotypes 

 Topography 

Anthropogenic Factors

 Air pollution 

 Stand disturbance

Figure 1—Causal factors of oak decline organized by type.
93

METHODS

We identified seven treatments in which hardwoods were

retained, as follows

(1) UC: unmanaged control treatment

(2) GSPH: group selection treatment, with hardwood

retained within groups

(3) STSW: pine-hardwood single-tree selection treatment

(4) STSL: low-impact single-tree selection treatment

(5) SWW: pine-hardwood shelterwood treatment

(6) STPH: pine-hardwood seed tree treatment

(7) CC: clearcutting treatment.

Each treatment was installed in 4 stands and measurements

were taken on 12 plots per stand for a total of 336 plots in

this study. Details about the treatments and sample plot

configuration are presented in Guldin and others (1994).

Prior to the imposition of harvest treatments, three to five of

the largest hardwoods nearest plot centers were identified

for monitoring at each plot. These constituted the trees most

likely to be designated as reserves during marking and harvesting operations. Preference was given to trees already

marked as reserves, to oaks, hickories, and finally other hardwoods. Azimuth and distance from plot center were recorded

for each tree as well as species and diameter at breast

height (d.b.h.). Plots were visited and trees evaluated prior

to harvest in 1992, 3 years after harvest in 1996, and 5 years

after harvest in 1998.

A suite of crown measurements was utilized to evaluate the

condition (i.e. health) of tree crowns at each sampling—a

procedure currently being used in the National Forest Health

Monitoring Program (U.S. Department of Agriculture, Forest

Service 1992). The suite consists of six variables (table 1),

each requiring two crew members to estimate. Crown

diameter was measured by projecting the crown perimeter

onto the ground and measuring with a tape; measurements

were taken on N-S and E-W axes. The other five variables

were each visually estimated by two observers standing on

opposite sides of the tree about one-half to one tree length

away such that a clear view of the crown was obtained.

Estimates for each variable were made by each crew member individually, and a consensus or average of both is used

as the final estimate.

Crown health data were obtained during the leaf-on, summer

field season. Field crews were trained at the beginning of

the field season to collect crown health data during an allday session. Classroom training and field practice were followed by field testing and evaluation in order to meet quality

assurance goals (U.S. Department of Agriculture, Forest

Service 1992). For all visual crown variables, a goal of ±10

percent (i.e., two 5-percent classes, see table 1), 90 percent

of the time (when compared to estimates of the trainers) was

used. For crown diameter, average diameter was required

to be ±10 percent of the trainers result, 90 percent of the

time. Catastrophic events (windthrow, logging damage, etc.)

occurring to sample trees between visits were identified and

these trees removed from the data set used to evaluate the

effects of decline.

RESULTS

One thousand and three hardwoods were identified as

reserve trees before stand treatments were imposed. Most

of these were white oaks (Q. alba) (WHO) and post oaks

(Q. stellata) (PSO); they comprised about 65 percent of the

population (fig. 3). Other oaks [black oak (Q. velutina) (BLO),

southern red oak (Q. falcate) (SRO), northern red oak (Q.

Predisposing Factors

 Air pollution 

 Climatic trends or past events 

 Density/competition 

 Physiological age 

 Soil depth/texture/fertility 

 Species/genotypes 

 Topography 

 

Inciting Factors

 Drought 

 Defoliation 

 Frost 

 Stand disturbance 

OAK

DECLINE

Contributing Factors

 Borers 

 Canker fungi 

 Root rot fungi 

Figure 2—Causal factors of oak decline organized by their function

in the decline syndrome.

Table 1—Crown health indicators for monitoring

reserve hardwood trees

Indicator Definition and units

Crown diameter Measured on ground in two directions

at 90o; in feet to the nearest foot;

average of measurements.

Crown position Standard forestry definitions; dominant,

codominant, intermediate, or

suppressed.

Live crown ratio Ratio (in percent) of live crown length to

total tree height; visually estimated in

5-percent increments.

Crown density Estimated percentage of foliage, twigs,

(DEN) branches, and reproductive structures

blocking light through the crown;

visually estimated in 5-percent

increments.

Crown dieback Estimated percentage of recent dieback

(DBK) (fine twigs remaining) in upper and

outer portions of the crown compared

to entire crown; visually estimated in

5-percent increments.

Foliage Estimated percentage of light being

transparency transmitted through the foliated

 (TRN) portions of the crown; visually

estimated in 5-percent increments.
94

rubra) (NRO), blackjack oak (Q. marilandica) (BJO)]; hickories (C. texana and C. tomentosa) (HIK); and other hardwoods

[sweetgum (Liquidambar stryracifula), blackgum (Nyssa

sylvatica), winged elm (Ulmus alata), black cherry (Prunus

serotina), white ash (Fraxinus Americana), and sassafras

(Sassafras albidum)] (OTH) made up the remainder. Since

most species were present in relatively small numbers, we

used species groupings for all data analysis involving species

(white oaks, WHTs include PSO and WHO; red oaks, REDs

include the other oaks; and other hardwoods, OTHs including hickories and all other species) (fig. 4).

The d.b.h. of reserve trees varied little among species, ranging from a quadratic mean d.b.h. of 10.1 to 13.1 inches (fig.

5). Most trees were either codominant or intermediate in

crown position with the proportion of each varying by species

(fig. 6). Generally, 80 to 90+ percent of sample trees were

in these two classes. A few dominant or suppressed crown

classes were represented in each species tally.

By the 3- and 5-year measurements a number of trees had

been cut, damaged or died (fig. 7). By 1998, 876 trees

survived in our sample set. Forty-two trees had been cut by

OTH

6%

WHO

32%

BLO

10%

NRO

4%

SRO

5%

HIK

8%

BJO

2%

PSO

33%

Figure 3—Percent of sample population by

species.

RED's

21%

OTH's

14%

WHT's

65%

Figure 4—Percent of sample population by species

group.

0

2

4

6

8

10

12

14

PSO

WHO

BLO

SRO

NRO

BJO

HIK

OTH

Inches

Figure 5—Quadratic mean diameter at breast height for

sample population by species.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

PSO

WHO

BLO

SRONRO

BJO

HIK

OTH

DOMIN

CODOM

INTER

SUPPR

Figure 6—Percent of sample population by crown position and species.
95

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1992 1996 1998

Dead

Cut

Live

Figure 7—Sample population change due to cutting and mortality.

0

10

20

30

40

50

60

70

DBK DEN TRN

Percent

1992

1996

1998

Figure 8—Mean crown variables by year for the uncut control treatment.

1996 and another 4 trees by 1998, either in the initial harvest or subsequent site—preparation treatments. Removing

these from consideration, a total of 81 trees died over the 5

years out of 957 trees—a very acceptable survival rate of

91.5 percent. Of the trees that died, a few were attributed to

logging, lightning or windthrow, but most were from unknown

causes (which could be decline-related) (table 2). If all of

the 64 trees which died from unknown causes are attributed

to decline, then the mortality rate due to decline after 5

years is 6.7 percent.

Previous experience and research suggests that decline is

closely related to inciting events like severe drought (Starkey

and others 1989, Wargo and others 1983). Thus, drought

can have an effect on reserve tree health in spite of cutting

practices imposed. To evaluate this phenomenon we examined the crown variables of trees in the uncut control treatment. Climatic conditions over the 1992-98 period caused a

rather large increase in dieback, a decrease in density, and

an increase in transparency—all indicative of worsening

crown health (fig. 8). The trend is most apparent from 1996-

Table 2—Causes of mortality

Year Herbicide Logging Lightning Windthrow I/D/Unknown Total

1996 1 12 0 2 44 59

1998 0 0 1 1 20 22

Total 1 12 1 3 64 81
96

98, reflecting the acute drought conditions which prevailed

in central and northern Arkansas in the past few years,

particularly 1995, 1996, and 1998.

Past experience and research (Starkey and others 1989,

Wargo and others 1983) shows that trees in the red oak

group are more often and severely affected by decline than

trees in the white oak group or other species of hardwoods.

This too is evident in the uncut control treatments by examining the crown variables for the species groups. Red oak

dieback started out little different from white oaks in 1992,

but increased much more in 1996 and 1998 (fig. 9).

Despite the effects of climate, treatment effects are somewhat apparent on reserve tree dieback for all tree species

(fig. 10). Dieback increased the greatest amount in 1996

and 1998 in the even-aged treatments—the shelterwood,

seed tree, and clearcut—with the greatest increase in the

clearcut.

Red oaks began with a little higher dieback levels in 1992

and also seemed to experience higher dieback by 1998,

even in the uneven-aged treatments and the uncut control

0

1

2

3

4

5

6

7

8

9

10

WHT's RED's OTH's

Percent

1992

1996

1998

Figure 9—Mean dieback in the uncut control treatment for species groups

by year.

Figure 10—Dieback by treatment and year for all species.

(fig. 11). This again is probably a response to the acute

drought conditions that occurred after the treatments were

imposed. Nonetheless, 1996 and 1998 dieback was highest

in the more intensive treatments.

DISCUSSION

Reserve trees were affected by both climatic variation and

by the cutting treatments applied. The largest effect appears

to be due to short-term climatic variation, specifically, drought

over the measurement period. Survival of reserve trees to

date is 91.5 percent, greater than we expected. Although

dieback increased over the measurement period, surviving

trees had mean dieback levels that are not excessively high

(generally < 15 percent) and may not lead to further mortality. We have found in other studies that for oaks, dieback

does not generally indicate a high risk for mortality unless it

is one-third of the crown or more (Steven W. Oak, Dale A.

Starkey [and others]. Unpublished data on file. Asheville, NC:

U.S. Department of Agriculture, Forest Service, Southern

Region, Forest Health Protection).

To date, these results suggest that reserve hardwood trees

selected from the larger diameter and higher crown classes

0

2

4

6

8

10

12

14

UC

STSL

STSW

GSPHSWW

STPH

CCNS

Percent dieback

1992

1996

1998

Percent dieback
97

can be any species desirable in meeting management objectives. Further, survival and health of reserve trees in this

study suggests that reserve tree selection need not be a

complicated task, and acceptable results may obtain from

simply selecting the largest trees of desired species in

numbers that meet the objectives of management.

LITERATURE CITED

Baker, James B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Proceedings of the Symposium on Ecosystem Management

Research in the Ouachita Mountains: Pretreatment Conditions

and Preliminary Findings, James B. Baker, compiler; 1993

October 26-27; Hot Springs, AR. Gen. Tech. Rpt. SO-112. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experimentation Station: 18-28.

Guldin, James M.; Baker, James B.; Shelton, Michael G. 1994.

Midstory and overstory plants in mature pine/hardwood stands

of the Ouachita/Ozark National Forests. In: Proceedings of the

Symposium on Ecosystem Management Research in the

Ouachita Mountains: Pretreatment Conditions and Preliminary

Findings, James B. Baker, compiler; 1993 October 26-27; Hot

Springs, AR. Gen. Tech. Rpt. SO-112. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experimentation Station: 29-49.

Figure 11—Dieback for red oaks by treatment and year.

0

2

4

6

8

10

12

UC

STSL

STSW

GSPHSWW

STPH

CCNS

Percent dieback

1992

1996

1998

Helms, John A., ed. 1998. The dictionary of forestry. Bethesda,

MD: The Society of American Foresters. p. 153.

Manion, P.D. 1991. Tree disease concepts. Englewood Cliffs, NJ:

Prentice-Hall, Inc. 399 p.

Millers, I.; Shriner, D.S.; Rizzo, D. 1989. History of hardwood

decline in the Eastern United States. Gen. Tech. Rep. NE-126.

Broomall, PA: U.S. Department of Agriculture, Forest Service,

Northeastern Forest Experiment Station; 75 p.

Starkey, Dale A.; Oak, Steven W.; Ryan, George W. [and others].

1989. Evaluation of oak decline areas in the South. Protection

Rep. R8-PR17. Atlanta, GA: U.S. Department of Agriculture,

Forest Service, Southern Region, Forest Health. 36 p.

U.S. Department of Agriculture, Forest Service. 1992. Forest health

monitoring field methods guide (eastern guide). Research

Triangle Park, NC: U.S. Department of Agriculture, Forest

Service, Forest Health Monitoring Program.

Wargo, P.M.; Houston, D.R.; LeMadeleine, L.A. 1983. Oak decline.

Forest Insect and Disease Leaflet 165. Washington, DC: U.S.

Department of Agriculture, Forest Service. 8 p.
98
99

Session 4

Phase II Soil and Water Resources Research

Alan Clingenpeel, Moderator
100
101

INTRODUCTION

Timber harvesting and associated silvicultural practices has

the potential to significantly alter nutrient cycling and organic

matter decomposition in forest ecosystems (Yin and others

1989; Montagnini and others 1989; Kimmins 1996; Barnes

and others 1998). Tree and vegetation removal reduces

annual inputs of litter to the forest floor until such time that

aboveground biomass recovers to pre-harvested levels

(Kimmins 1996). This reduction in annual inputs is partially

offset at the time of harvesting by a substantial one-time

flux of woody materials and decomposable tissues added

to the soil surface in the form of logging debris and unmerchantable woody material. The reduction of annual inputs

and addition of harvesting debris not only impacts the amount

but type of organic matter inputs to detritus food chains

resulting in the modification of organic matter decomposition and the availability of nutrients in the forest floor and

soil. Nutrient mineralization, like decomposition, changes

with the modification of organic matter and carbon inputs

after harvesting. Removal of forest canopies can also alter

nutrient mineralization and decomposition in temperate

forests by increasing available water and soil temperature.

Increasing soil temperature occurs after harvesting due to

the increased insolation after canopy removal (Waide and

others 1987; Liechty and others 1992). A reduction in the

amount of canopy also reduces transpiration resulting in an

increase in soil moisture availability (Liechty and others

1992). Depending on original ambient conditions and the

magnitude of change in temperature and moisture, alteration of ambient conditions can either increase microbial

efficiency and population levels or decrease microbial activity (Waide and others 1987). Reductions in decomposition

or mineralization can also occur if nutrient concentrations

are decreased below pre-harvest levels by addition of

carbon rich, nutrient poor organic material.

The need to design and develop alternative regeneration

strategies for shortleaf pine (Pinus echinata Mill.)-hardwood

forests in the Ouachita Mountains has stimulated land managers to question how silvicultural practices affect long-term

sustainability and productivity. One of the keys to answering

this question is a better understanding of the degree to which

these strategies alter nutrient cycling and decomposition.

We have attempted to evaluate the effect of harvesting and

composition manipulation on these processes by quantifying changes in forest floor mass, nutrient contents, nutrient

concentrations, and organic matter 3.5 years after application of several regeneration cutting methods as part of the

USDA Forest Service’s Phase II Ecosystems Management

Study. Our objectives were to quantify changes in the:

(1) amount and composition of forest floor, and (2) nutrient

concentration and content of forest floor. This information

will then be used to address if potential long-term changes

in nutrient and organic matter in these ecosystems could

occur.

METHODS

Study Sites and Design

Relatively undisturbed, mature, shortleaf pine (Pinus

echinata Mill.)-hardwood stands occurring on generally

south facing slopes in the Ouachita Mountains of Arkansas

and Oklahoma were selected for study. A detailed description of the study area and its vegetation is provided in

Baker (1994) and Guldin and others (1994). Baker (1994)

describes the selection criteria for stands and provides an

overview of the full array of thirteen overstory treatments

that were performed in the summer of 1993. Six of these

thirteen treatments were selected for forest floor sampling

in this study. The six treatments selected were: clearcut

(CC), pine shelterwood (PSW), pine-hardwood shelterwood

(PHSW), pine single-tree selection (PSTS), pine-hardwood

single-tree selection (PHSTS), and unharvested control

(UC). These treatments provided a broad range of disturbance levels but also allowed us to evaluate the effects of

different levels of hardwood retention on nutrient cycling.

A total of 24 stands, 4 from each of the 6 treatments, were

CHANGES IN ORGANIC MATTER AND NUTRIENTS IN FOREST FLOOR

AFTER APPLYING SEVERAL REPRODUCTIVE CUTTING METHODS IN

SHORTLEAF PINE-HARDWOOD STANDS

Hal O. Liechty and Michael G. Shelton1

1 Assistant Professor, University of Arkansas, School of Forest Resources, Monticello, AR 71656; Research Forester, USDA Forest Service,

Southern Research Station, Monticello, AR 71556, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—This study was initiated to determine the effects of various regeneration cutting methods on forest floor mass

and nutrient content in shortleaf pine-hardwood communities in the Ouachita and Ozark National Forests. Clearcutting

generally altered forest floor concentrations of N, P, and S as well as loss on ignition by increasing the amount of

herbaceous vegetation and by mixing mineral soil with the forest floor. Reductions in forest floor concentrations of Mg and

Mn were evident in regeneration methods that favored pines and excluded hardwoods (pine single-tree selection and pine

shelterwoods). Changes were due to the overall lower concentrations of these nutrients in pine foliage than in hardwood

foliage and a reduction of Mn in both pine and hardwood foliage after harvesting. Thus reductions in forest floor Mn

appeared not only to be related to changes in stand composition but also reduced soil availability and/or tree uptake.

Reductions in Mn content after harvesting were between 22 and 52 percent. Reduction of the other nutrients were

generally less than 20 percent.
102

used for the study. Each stand was initially subdivided into

quarters to facilitate establishment of 12 randomly located,

permanent subplots that were used for sampling vegetation.

These quarters were oriented perpendicular to the dominant

slope within the stand. In stands receiving a uniform manual

site-preparation treatment, subplots in one of the four quarters were randomly selected. In stands receiving different

site-preparation treatments, forest floor sampling was

restricted to the quarter assigned to the manual site-preparation treatment. This assured that the site preparation would

be the same in all areas used for forest floor sampling. Subplots in the chosen quarter represented the lower, middle,

or upper topographic positions. In total, 72 subplots were

sampled from these 24 stands. Pre-harvest and post-harvest conifer and hardwood basal areas in the selected

subplots (Guldin and others 1994) are presented in table 1.

Field Sampling

Sampling was conducted during February and March of

1993 prior to harvesting and again in 1997 approximately

3.5 years after harvesting. Five sampling locations were

systematically located 11.4 m from each subplot center.

Sampling locations were relocated if abnormal conditions

occurred, such as large surface rocks, woody debris more

than 7.6 cm in diameter, or previous manmade disturbances (e.g. old roads, etc.). Thus, samples and results

reflect forest floor conditions from undisturbed areas that

were not dominated by rocks or woody materials. Less than

5 percent of the sample locations had to be relocated due

to these criteria.

The forest floor, excluding woody debris greater than 7.5 cm

in diameter, was collected within a 0.1-m2 square frame at

each of five sampling locations. Two layers or stages of

decomposition were recognized: (1) a litter L layer, which

included the uppermost, relatively undecomposed material

that was mostly deposited in the autumn pulse of litterfall

and (2) a fermentation F layer consisting of partially decomposed, older material located between the soil surface and

the L layer. The L and F layers are also frequently referred

to as the Oi

 and Oe horizons, respectively. The color, texture,

and level of fragmentation of foliage (especially the hardwoods) were used to define the boundary between the L

and F layers. The boundary between the bottom of the F

layer and the soil surface was also based on decomposition

stage. The F layer contains fragments of vegetation that

could be identified. By contrast, the soil surface was either

mineral soil or dark, amorphous organic matter, representing the humus H layer or the Oa horizon. A well defined H

layer rarely exists in the forest floors of southern forests

because of rapid decomposition rates and incorporation of

organic matter within the soil by fauna (Switzer and others

1979). Thus, any H layer material present was not included

within the forest floor sample.

Laboratory Procedures and Analysis

Forest floor samples were dried to a constant weight at 75

o

C and weighed. Each L layer sample was separated into

woody and foliar components. The woody component (WD)

included branches, bark, small stems, and reproductive

material (e.g., pine cones). The foliar component of each

sample was separated into pine (PF) and hardwood foliage

(HF) and weighed. Thus, the L layer was represented by

PF, HF, and WD in 1993. As a result of the prolific increase

in herbaceous plants in the harvested areas, a miscellaneous class of L layer was added in 1997. All forest floor

woody material from the clearcuts was classified as F layer

due to its highly degraded form 3 years after harvesting and

the lack of any current year inputs of woody material.

The forest floor material for a given component was composited for each subplot and ground to pass a 20-mesh sieve.

Loss on ignition was determined by heating samples at 500

oC for 4 hours. This is a frequently used approximation of

organic matter and is commonly expressed as a percent of

total weight. Forest floor concentrations of P, K, Ca, Mg, Mn,

S, and Fe were determined by inductance coupled plasma

analysis after nitric/perchloric digestion (University of

Arkansas, Soil Test Laboratory, 1990a). Total N concentrations were determined using a Tecator Kjeltec Model 1030

Auto Analyzer after sulfuric acid/hydrogen peroxide digestion (University of Arkansas, Soil Test Laboratory, 1990b).

Statistical Design and Methods

To evaluate whether different regeneration cutting methods

altered forest floor characteristics, a randomized complete

block analysis of variance was used to evaluate differences

among study treatments. Ecoregions, as described by Baker

(1994), were used as the blocking factor. Dependent variables used for the analysis were either individual post-harvest measurements or differences between pre-harvest and

post-harvest measurements (1993-1997). The differences

in pre- and post-harvest measurements were used to evaluate if changes in various forest floor components after

harvesting were similar or differed among treatments. If

differences among treatments were significant (α=0.05),

Dunnet’s multiple range test (Dunnet 1964) was used to

compare the magnitude of changes of the harvested treatments to any changes in the uncut controls. Significant

differences between a given harvesting treatment and the

uncut treatment indicated changes in a forest floor component or nutrients were of greater magnitude than could be

attributed to normal annual variation or inconsistencies in

forest floor sampling techniques.

Table 1— Mean preharvest (1993) and postharvest

(1997) basal area (m2

 ha-1) of conifer and hardwood

trees greater than 10 cm d.b.h. for each treatment

Preharvest Postharvest

Treatment Conifer Hardwood Conifer Hardwood

 - - - - - - - - - - - - - - -m2 ha-1 - - - - - - - - - - - - - -

CC 19.2 6.7 < 0.1 0.9

PSW 19.8 6.7 9.3 1.2

PHSW 25.6 6.4 8.2 3.2

PSTS 20.0 9.7 14.2 1.8

PHSTS 21.6 7.7 11.8 3.1

Uncut 21.5 6.7 20.3 7.0

CC = clearcut; PSW = pine shelterwood; PHSW = pine-hardwood

shelterwood; PSTS = pine single-tree selection; PHSTS = pinehardwood single-tree selection; UC = unharvested control.
103

RESULTS AND DISCUSSION

Mass and Organic Matter

Mass of all forest floor components and organic matter content of the L layer were generally lower, while the loss on

ignition of the total forest floor and F layer was greater in

1997 than in 1993 (table 2). These changes were relatively

consistent among all treatments including the uncut stands.

This variation in mass and loss on ignition appears to be

related to a subtle difference in sampling methodologies

used in 1993 compared to 1997. A greater portion of the

forest floor was designated as the F layer and potentially

less of the H layer/mineral soil interface was included with

the F layer in 1997 than in 1993. Thus loss on ignition and

organic matter contents were greater in the F layer, while

organic matter content and mass were lower in the L layer

in 1997 than in 1993.

These changes in sampling methods were caused by the

manner in which the crews collected and delineated samples in 1993 and 1997. Although one original member of the

1993 crew helped to train the 1997 crew, differences in

collection methods were still evident. Assuming annual

fluctuations in the amount or composition of forest floor are

relatively minor in uncut stands, any changes in these

parameters observed in the uncut stands between 1993

and 1997 most likely represent differences in sampling

methodologies. Thus, any changes in forest floor characteristics within the harvested stands greater or less than those

in the uncut control should represent actual differences

related to silvicultural practices and harvesting, rather than

sampling methodology.

Harvesting did not appear to significantly change total

forest floor, L layer, or F layer mass or organic matter

content. Differences between 1993 and 1997 values in the

harvested stands were similar to those observed in the

uncut stands (table 2). However, clearcutting significantly

reduced loss on ignition of the L layer and F layer. Due to

the alteration in sampling procedures, loss on ignition in

1997 was greater or approximately equal to 1993 values in

the uncut control. Differences in pre- and post-harvest loss

on ignition were reduced in the shelterwood and clearcut

regeneration treatments. In the case of the clearcut, 1993

values were significantly greater than the 1997 values.

Harvesting often churns and mixes mineral soil with the

forest floor (Mroz and others 1985; Alban and Perala 1990;

Liechty and others 1992). Thus, part of this decrease in

loss on ignition could be attributed to mixing of mineral soil

with forest floor as a result of harvesting and associated

disturbances.

The changes in loss on ignition could also be related to the

changes in composition of the forest floor. Figure 1 and

table 3 indicate that composition of the L layer was altered

by the harvesting treatments. The amount of litter classified

as miscellaneous was greatest in the treatments that had

the highest amount of tree removal (CC, PSW, and PHSW).

This increase in the miscellaneous L layer component

occurred with the rapid occupation of harvested sites by

herbaceous plants. The clearcut had the largest amount of

herbaceous vegetation and the lowest amounts of PF in the

L layer. This modification to litter inputs appeared to have

altered the character and physical attributes of the forest

floor in the stands that received the greatest amount of tree

removal. Loss on ignition of miscellaneous material was

lower than HF, which was lower than PF (table 3). Evidently,

the reduction in the loss of ignition in the clearcut was

caused by: (1) the reduction in PF, (2) the increase in

Table 2—Mean mass, organic matter content, and loss on ignition of the total forest floor, L layer, and F layer

for each treatment in 1993 and 1997

CC PSW PHSW PSTS PHSTS UC

Layer 1993 1997 1993 1997 1993 1997 1993 1997 1993 1997 1993 1997

Mass (Mg ha-1 )

Total 22.4 21.1 25.1 22.6 24.9 21.8 26.2 21.4 28.2 19.5 24.0 20.5

L 4.4 1.3 5.7 2.5 5.5 2.8 6.0 1.8 5.1 2.4 5.5 2.9

F 18.0 19.8 19.5 20.1 19.3 19.0 20.1 19.7 23.1 17.1 18.5 17.6

Organic matter content (Mg ha-1 )

Total 17.8 15.0 19.3 18.3 19.8 17.9 19.9 17.3 19.1 16.6 19.0 17.8

L 4.3 1.1 5.4 2.4 5.3 2.6 5.8 1.7 5.0 2.3 5.3 2.8

F 13.6 13.9 13.8 16.0 14.4 15.2 14.1 15.6 14.2 14.4 13.7 15.0

Percent loss on ignition

Total 80.1 71.1*a 77.7 81.5 79.2 81.6 76.6 81.8 69.0 85.2 79.1 86.4

L 96.4 90.9* 95.5 96.0 96.0 95.0 96.2 95.6 96.2 96.1 96.0 96.8

F 76.0 69.7* 72.8 79.7 74.6 79.3 70.5 80.6 62.9 83.8 73.2 84.7

L = litter layer; F = fermentation layer; CC = clearcut; PSW = pine shelterwood; PHSW = pine-hardwood shelterwood; PSTS = pine

single-tree selection; PHSTS = pine-hardwood single-tree selection; UC = unharvested control.

a Changes between preharvest (1993) and postharvest (1997) for a given forest floor attribute and regeneration cutting method

denoted by * significantly differ (α = 0.05) from those observed in the uncut control.
104

Figure 1—Proportion of mass (percent) in L layer hardwood foliage (HF), pine foliage (PF), woody (WD), and

miscellaneous (MS) components by reproduction cutting method in 1997; CC = clearcut, PHSW = pine-hardwood

shelterwood, PHSTS = pine-hardwood single-tree selection, PSW = pine shelterwood, PSTS = pine single-tree

selection, UC = unharvested control.

Table 3—Mean mass and loss on ignition for the L layer hardwood foliage,

pine foliage, and miscellaneous components in 1997

Component CC PSW PHSW PSTS PHSTS UC

 - - - - - - - - - - - - - - - - - -Mass (Mg ha-1 )- - - - - - - - - - - - - - - - -

HF 0.41*a 0.60* 0.81 0.51* 0.81 1.06

PF 0.01* 0.70 0.50* 0.82 0.63 0.83

MS 0.85* 0.15 0.42 0.07 0.10 0.04

 - - - - - - - - - - - - - -Percent loss on ignition- - - - - - - - - - - - - -

HF 95.0 94.5 93.4 94.2 94.5 94.2

PF 96.4 96.6 96.5 96.1 96.5 96.1

MS 89.8* 94.2 92.2 93.4 94.6 94.3

CC = clearcut; PSW = pine shelterwood; PHSW = pine-hardwood shelterwood; PSTS =

pine single-tree selection; PHSTS = pine-hardwood single-tree selection; UC =

unharvested control; HF = hardwood foliage; PF = pine foliage; MS = miscellaneous

components.

a Treatments for a given with an * are significantly (α = 0.05) different than

the uncut control.

miscellaneous material which has the lowest loss on

ignition, and (3) the mixing of mineral soil and forest floor.

The increased amount of herbaceous vegetation also

appeared to reduce potential changes in forest floor mass

attributed to the removal and harvesting of trees within the

clearcut treatments (table 3). In the clearcut during 1997

approximately 62 percent of the L layer mass or 0.85 Mg

ha-1 was classified as miscellaneous. Without the input of

this material to the forest floor and ultimately to the soil,

organic pools would likely have been significantly lower

with this regeneration method.
105

A change in forest floor composition was also noted for the

pine only treatments (PSW and PSTS). In each of these

two treatments, hardwood removal significantly lowered the

amount of HF in the L layer (table 3). However, these

changes in composition did not appear to significantly alter

loss on ignition of the forest floor or the L layer.

Nutrient Concentration

Harvesting treatments also altered the chemical composition of the forest floor. Most conspicuous was a decrease in

N, P, and S concentrations of the overall forest floor and the

F layer within the clearcut stands (table 4). N, P, and S concentrations in the F layer and the forest floor of the uncut

controls in 1997 were higher or similar to concentrations

Table 4—Mean preharvest (1993) and postharvest (1997) total forest floor, L layer, and F layer nutrient

concentrations for each regeneration cutting method

CC PSW PHSW PSTS PHSTS UC

Layer 1993 1997 1993 1997 1993 1997 1993 1997 1993 1997 1993 1997

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

N

Total 0.97 0.89*a 0.90 0.92 0.91 0.97 0.89 0.92 0.91 1.00 0.89 1.01

L 0.68 1.01* 0.68 0.76 0.68 0.88 0.69 0.80 0.70 0.82 0.68 0.78

F 1.04 0.87* 0.98 0.95 0.98 0.98 0.95 0.93 0.96 1.01 0.95 1.05

P

Total 0.059 0.049* 0.055 0.049 0.057 0.050 0.052 0.048 0.055 0.052 0.052 0.052

L 0.047 0.052 0.044 0.037 0.047 0.041 0.047 0.038 0.046 0.041 0.043 0.037

F 0.061 0.049* 0.059 0.052 0.060 0.051 0.054 0.049 0.057 0.054 0.055 0.055

K

Total 0.09 0.06 0.10 0.06 0.09 0.06 0.08 0.06 0.09 0.06 0.07 0.06

L 0.07 0.06 0.07 0.08 0.08 0.10 0.07 0.09 0.07 0.09 0.06 0.10

F 0.10 0.06 0.11 0.06 0.10 0.06 0.09 0.06 0.10 0.06 0.08 0.06

Ca

Total 0.73 0.70 0.88 0.81 0.87 0.89 0.82 0.84 0.79 0.83 0.79 0.81

L 0.85 0.74 1.13 0.75 1.08 0.96 1.08 0.74 1.00 0.82 1.00 0.89

F 0.70 0.69 0.79 0.83 0.80 0.87 0.75 0.85 0.75 0.84 0.72 0.80

Mg

Total 0.098 0.080 0.105 0.076* 0.103 0.083 0.093 0.070* 0.123 0.093* 0.101 0.080

L 0.112 0.097 0.118 0.080* 0.122 0.101 0.118 0.087* 0.114 0.095 0.097 0.095

F 0.095 0.079 0.101 0.075* 0.098 0.081 0.086 0.068 0.125 0.093* 0.082 0.076

S

Total 0.10 0.08* 0.08 0.08 0.09 0.08 0.09 0.08 0.09 0.08 0.09 0.09

L 0.05 0.09 0.08 0.07 0.08 0.07 0.08 0.07 0.08 0.07 0.07 0.07

F 0.10 0.08* 0.09 0.08 0.10 0.08 0.09 0.08 0.09 0.08 0.09 0.09

Mn

Total 0.10 0.05* 0.14 0.09* 0.12 0.10* 0.14 0.09* 0.12 0.08* 0.12 0.14

L 0.08 0.05 0.12 0.07* 0.11 0.08 0.12 0.08* 0.09 0.07 0.11 0.11

F 0.10 0.04* 0.15 0.10* 0.13 0.11 0.14 0.09* 0.13 0.08* 0.13 0.14

Fe

Total 0.39 0.45 0.50 0.33 0.37 0.32 0.33 0.24 0.53 0.21 0.38 0.15

L 0.02 0.12 0.04 0.02 0.02 0.08 0.02 0.02 0.02 0.01 0.02 0.01

F 0.48 0.48 0.63 0.37 0.47 0.38 0.42 0.26 0.65 0.24 0.05 0.17

L = litter layer; F = fermentation layer; CC = clearcut; PSW = pine shelterwood; PHSW = pine-hardwood shelterwood; PSTS = pine

single-tree selection; PHSTS = pine-hardwood single-tree selection; UC = unharvested control.

a Changes between preharvest (1993) and postharvest (1997) forest floor concentrations for a given regeneration cutting method

denoted by * significantly differ (α = 0.05) from those observed in the uncut control.
106

measured in 1993. In contrast concentrations of these three

nutrients in the forest floor of the clearcuts generally

decreased after harvesting (table 4). Decreases in concentrations of these nutrients appeared to be strongly related

to the observed decreases in loss of ignition. N, P, and S

are important elements in organic constituents, such as

proteins, amino acids, etc. Mixing of mineral soil, which has

N, P, and S concentrations, with the forest floor would

significantly reduce the concentrations of these elements in

addition to lowering loss on ignition. However, mixing of

mineral soil with forest floor did not significantly lower base

concentrations due to the greater similarity of concentrations of bases in these two materials.

Although loss on ignition decreased after harvesting in both

the L layer and the F layer, concentrations of N, P, and S in

the L layer generally increased after clearcutting. Average

concentration of N, P, and S in the clearcut stands were

respectively 0.68, 0.047, and 0.08 percent prior to harvesting in 1993 and 1.01, 0.052, and 0.09 percent after harvesting in 1997 (table 4). Increases of N were significantly

greater than those found in the uncut stands (table 4).

These increases in concentration were related to the

decrease of PF and the corresponding increase of HF and

miscellaneous material in the L layer within the clearcuts

(figure 1 and table 5). Concentrations of N, P, and S were

36 to 50 percent greater in HF and miscellaneous material

than in PF (table 5). Thus while mixing of mineral soil and

forest floor tended to decrease N, P, and S in the F layer,

the reduction of PF increased the concentrations of these

nutrients in the L layer. The reduction in N, P, and S levels

in the F layer was apparently great enough to decrease the

overall concentrations of these elements in the forest floor

as a whole.

Regeneration treatments that retained pine and excluded

hardwoods also altered the forest floor nutrient levels.

Decreases in forest floor concentrations of Mg and Mn in

the PSW and PSTS treatments after harvesting were

significantly greater than decreases observed in the uncut

stands (table 4). Tree removal at any level was found to

decrease the concentrations of these nutrients in the forest

floor. However, changes were always greatest in the PSW

and PSTS treatments. Concentrations of bases, such as

Mg and Mn, were consistently higher in hardwood compared

to PF (table 5). Exclusion of HF with an accompanying

dominance of PF ultimately decreased levels of Mg and Mn

in the forest floor. This alteration of chemistry of the forest

floor was most evident in the L layer but changes in concentrations in the F layer and the total forest floor were also

evident. Changes in concentrations of Mg and Mn of the F

layer in the PHSTS were also significant. Calcium levels

were reduced in PSW and PSTS treatments, but reductions

in concentrations after harvesting were not significantly

greater than those found in the uncut stands (table 4). The

prolific growth of herbaceous vegetation did not appear to

have a significant impact on base concentrations of the

forest floor. Average concentrations of Ca, Mg, and Mn in

the miscellaneous L layer component were similar to concentration of these elements in PF (table 5).

Reductions in forest floor bases, such as Mg and Mn, could

not solely be attributed to the increased dominance of PF in

the forest floor. Concentrations of these elements in HF and

PF were also reduced after harvesting (table 6). A reduction

of Mg in HF was evident within all harvesting treatments.

Declining levels of Mg in PF were significantly greater than

reductions observed in the uncut controls within the PSW

and PSTS treatments only. Declining levels of Mn were also

evident in HF and PF collected from the harvested treatments. Reductions were only significantly greater than differences in uncut control for the clearcut and PSTS treatments.

The diminished levels of Mg and Mn in the hardwood and

pine component of the L layer may potentially be attributed

to two specific changes related to harvesting. First, harvesting can result in accelerated soil leaching of NO3

- (Vitousek

1981; Van Lear and others 1990) and cations (Kimmins

1996). Accelerated leaching of Mg and Mn may have

reduced the availability of these two cations in soil and thus

uptake of these nutrients by the residual trees. Reduction in

uptake can lower levels of nutrients in foliage when original

available nutrients in soil are at or below levels required by

the residual stand. Harvesting can also change the composition of HF in the forest floor. For example, removal of

undesirable species in favor of more desirable species in

the PHSW and PHSTS treatments could have altered HF

composition. Treatments that retain minimal residual densities dramatically increased the light environment within the

stands. These conditions favored growth and establishment

of early successional, shade intolerant hardwoods as well

as shrubs and other herbaceous vegetation. Early successional hardwood species that respond to the disturbances

often have foliage physiology and nutrient levels much

different than later successional, shade tolerant species

(Hinesley and others 1991; Kozlowski and others 1991).

Table 5—Mean nutrient concentration in L layer hardwood foliage, pine

foliage, and miscellaneous components from all treatments in 1997

Component N P K Ca Mg S Mn Fe

- - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - -

HF 1.06 0.049 0.07 1.16 0.13 0.09 0.12 0.03

PF 0.75 0.042 0.09 0.46 0.09 0.06 0.08 0.01

MS 1.12 0.057 0.09 0.67 0.08 0.09 0.06 0.09

HF = hardwood foliage; PF = pine foliage; MS = miscellaneous components.
107

The study design did not let us directly determine which of

these factors were responsible for the changes in foliar

chemistry. Since reductions in PF Mg also occurred after

harvesting and shortleaf pine was the dominate species

prior to and after harvesting, it seems likely that changes in

concentrations of Mg were at least in part due to changes

in availability of Mg in the soil. However, greater reductions

in concentrations occurred in HF and a greater number of

treatments showed significant reductions in concentrations

of Mg and Mn in HF than PF. These responses would also

support the hypothesis that a change in hardwood species

composition has altered HF chemistry within the L layer. It

is likely that both nutrient availability and species composition has changed in the harvested stands during the study.

Reductions in nutrient concentrations other than Mg were

not significant. N concentrations of PF in the PSW treatment and HF concentrations of Fe in the clearcut stands

significantly increased after harvesting. Increases in concentrations of these elements within the other treatments

were not significantly greater than in the uncut stands. Thus

increases in N and Fe were considered anomalies.

Nutrient Content

Similar to mass, nutrient contents of the forest floor were

generally lower in all treatments in 1997 compared to 1993

(table 7). Decreased nutrient contents were related to the

differences in sampling methodology and potentially natural

changes in annual fluxes of litter. The lack of any significant

reductions in nutrient content within the forest floor appears

to be related to a high variation in forest floor mass among

plots, treatments, and sampling periods rather than the lack

of a significant change in nutrient concentrations (table 4).

Significant reductions of Mg and Mn in the F layer of the

PHSTS treatment and Mn in the F layer of the PSW were

evident. However, substantial decreases in mass of the F

layer along with changes in concentrations were responsible for changes within the PHSTS treatment. Reductions

in the Mn contents of the PSW treatment reflect changes in

concentrations rather than mass.

Reductions in contents of N, P, Ca, Mg, S, and Mn between

pre- and post-harvested periods were consistently greater

in the harvested than those in the uncut stands. Even if

results of the PHSTS treatment are disregarded, reductions

in amounts of these elements were as much as 3-7 times

greater in the harvested stands than in the uncut control.

Elements which showed the greatest reductions in concentrations, such as Mn, usually had the greatest reductions in

contents (tables 4 and 7). Changes in contents of the L and

F layers were variable and for the most part decreased.

However, Ca content of the F layer and the Fe content of

the L layer in one or more of the harvested treatments

increased. It seems likely from our results that nutrient

contents in the forest floor decreased after harvesting but

these decreases were not substantial enough to be statistically significant.

Given the high variability within forest floor mass and contents, we cannot accurately estimate the amount of loss

associated with a given harvesting treatment. To better

determine the significance of these losses, differences in

contents between pre- and post-harvest periods were

adjusted for observed differences in the uncut control by

subtracting average uncut control differences. These

adjusted values were then expressed as a percent of the

original 1993 content for a given treatment. The PHSTS

treatment had the greatest reduction in forest floor mass

(18.5 percent) and generally the greatest reductions in

nutrient content. Reductions in Mn were substantial for all

harvesting treatments. If the PSTS treatment is disregarded,

reductions in other nutrients were generally less than 20

percent of original pre-harvest contents. Mg and K also

consistently showed relatively high reductions in all or the

majority of treatments. There appeared to be no specific

trends in nutrient content reductions related to intensity of

tree removal as indicated by target residual densities. The

clearcut stands that had the greatest amount of tree

removal often showed the lowest nutrient content reduction.

Table 6—Concentrations of Mg and Mn in L layer hardwood and pine foliage components

CC PSW PHSW PSTS PHSTS UC

Component 1993 1997 1993 1997 1993 1997 1993 1997 1993 1997 1993 1997

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Mg

HF 0.16 0.11*a 0.18 0.14*a 0.14 0.09*a 0.17 0.12*a 0.18 0.14*a 0.15 0.14

PF —b —b 0.12 0.08*a 0.12 0.09 0.12 0.08*a 0.12 0.10 0.10 0.09

Mn

HF 0.14 0.06*a 0.19 0.12 0.19 0.12 0.20 0.11*a 0.17 0.11 0.21 0.18

PF —b —b 0.10 0.08 0.09 0.07 0.09 0.07 0.08 0.07 0.10 0.10

L = litter layer; HF = hardwood foliage; PF = pine foliage.

a Absolute differences between concentrations for pre- and post-harvest sampling (1993 and 1997) denoted with * are significantly

greater than differences for uncut control (α = 0.05). b There was not sufficient pine foliage for chemical analysis in the clearcut stands.
108

Table 7—Preharvest (1993) and postharvest (1997) total forest floor, L layer, and F layer nutrient contents for

each regeneration cutting method

CC PSW PHSW PSTS PHSTS UC

Layer 1993 1997 1993 1997 1993 1997 1993 1997 1993 1997 1993 1997

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - kg ha-1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

N

Total 216 187 223 206 227 211 230 195 254 197 213 206

L 30 13 38 18 37 25 41 14 36 19 38 22

F 186 174 185 188 190 186 190 181 219 178 175 183

P

Total 13.1 10.3 13.7 10.8 14.1 10.8 13.3 10.2 15.2 10.2 12.5 10.7

L 2.1 0.7 2.5 0.8 2.6 1.1 2.6 0.7 2.3 1.0 2.5 1.0

F 10.9 9.7 11.2 10.0 11.6 9.6 10.7 9.5 12.9 9.3 10.0 9.6

K

Total 20.5 12.0 26.3 13.3 22.8 14.2 21.0 13.3 26.4 11.9 17.4 12.7

L 3.2 0.8 4.1 1.4 4.3 2.3 4.1 1.4 3.7 1.8 3.3 1.7

F 17.3 11.2 22.2 11.9 18.4 12.0 16.9 11.9 22.7 10.1 14.1 11.0

Ca

Total 167 148 215 182 212 196 206 174 221 165 187 162

L 39 9 63 19 62 28 60 13 54 20 58 26

F 129 139 152 163 150 168 146 161 168 145 130 136

Mg

Total 22.3 17.2 26.5 16.6 25.5 17.7 23.7 14.7 34.0 17.6*a 20.4 15.7

L 5.0 1.1 6.5 1.9 6.7 2.9 6.7 1.5 6.0 2.3 6.0 2.8

F 17.2 16.1 20.0 14.8 18.8 14.7 17.0 13.2 28.1 15.3* 14.8 13.0

S

Total 21.8 16.7 21.1 17.3 23.4 16.9 22.7 16.6 25.4 16.3 21.7 18.2

L 3.5 1.2 4.5 1.5 4.4 2.0 4.5 1.2 4.0 1.7 4.2 2.1

F 18.3 15.5 16.6 15.8 19.0 15.0 18.2 15.4 21.4 14.7 17.5 16.1

Mn

Total 22.6 9.7 36.5 20.4 30.0 21.7 33.1 19.1 33.1 16.1 28.6 27.4

L 3.9 0.7 6.4 1.6 5.8 2.1 6.6 1.3 4.8 1.7 6.0 3.1

F 18.7 9.0 30.2 18.8* 23.8 19.6 26.4 17.7 28.3 14.5* 22.5 24.3

Fe

Total 88 96 134 76 91 68 84 61 152 38* 90 29

L 1 2* 2 1 1 1 2 0 1 0 1 0

F 887 94 132 75 90 68 82 60 151 38 88 29

L = litter layer; F = fermentation layer; CC = clearcut; PSW = pine shelterwood; PHSW = pine-hardwood shelterwood; PSTS = pine

single-tree selection; PHSTS = pine-hardwood single-tree selection; UC = unharvested control.

a Changes between preharvest (1993) and postharvest (1997) forest floor concentrations for a given regeneration cutting method

denoted by * significantly differ (α = 0.05) from those observed in the uncut control.

CONCLUSION

Several of the reproduction/cutting methods evaluated in

this study were found to alter the characteristics and nutrient compositions of the forest floor. Clearcutting generally

was found to increase mixing of forest floor and mineral soil

as well as the proliferation of herbaceous vegetation. This

resulted in decreasing concentrations of N, P, and S in the

forest floor. Regeneration methods that removed the majority

of hardwoods tended to decrease levels of Mg and Mn in

the forest floor. These decreases were attributed to the

increased proportion of PF with lower concentrations of Mg

and Mn compared to HF. However, reductions in concentrations of these nutrients were also evident in HF of all of

the harvested stands. Reductions in Mn content of the
109

forest floor in the harvested stands were between 22-52

percent but reductions in content of other nutrients were

generally less than 20 percent.

LITERATURE CITED

Alban, D.H.; Perala, D.A. 1990. Impact of aspen timber harvesting

on soils. In: Gessel S.P.; Lacate, D.S.; Weetman, G.F.; Powers,

R.F., eds. Proceedings of the seventh North American forest

soils conference: sustained productivity of forest soils. Faculty

of Forestry Publication. Vancouver, BC: University of British

Columbia: 373-391.

Baker, J.B. 1994. An overview of stand-level ecosystem management research on the Ouachita/Ozark National Forests. In:

Baker, J.B., comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Gen. Tech. Rep. SO112. New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station: 29-49.

Barnes, B.V.; Zak, D.R.; Denton, S.R.; Spurr, S.H. 1998. Forest

ecology. 4th ed. New York, NY: John Wiley and Sons Inc. 774

p.

Dunnett, C.W. 1964. New tables for multiple comparisons with a

control. Biometrics. 20: 482.

Guldin, J.G.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine-hardwood stands on south-facing

slopes of the Ouachita/Ozark National Forests. In: Baker, J.B.,

comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Gen. Tech. Rep. SO-112. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 18-28.

Hinesley, L.E.; Nelson, L.E.; Switzer, G.L. 1991. Weight and

nutrient content of litter during secondary succession on well

drained uplands of the east Gulf Coastal Plain in Mississippi.

Canadian Journal Forest Research. 21: 848-857.

Kimmins, J.P. 1996. Forest ecology: a foundation for sustainable

management. 2nd ed. Upper Saddle River, NJ: Prentice Hall.

596 p.

Kozlowski, T.T.; Kramer, P.J.; Pallardy, S.G. 1991. The physiological ecology of woody plants. San Diego, CA: Academic Press

Inc. 657 p.

Liechty, H.O.; Holmes, M.J.; Reed, D.D.; Mroz, G.D. 1992.

Changes in microclimate after stand conversion in two northern

hardwood stands. Forest Ecology and Management. 50: 253-

254.

Montagnini, F.; Haines, B.L.; Swank, W.T.; Waide, J.B. 1989.

Nitrification in undisturbed mixed hardwoods and manipulated

forests in the Southern Appalachian Mountains of North

Carolina. Canadian Journal Forest Research. 19: 1226-1234.

Mroz, G.D.; Jurgensen, M.F.; Frederick, D.J. 1985. Soil nutrient

changes following whole tree harvesting on three northern

hardwood sites. Soil Science Society of America Journal. 49:

1552-1557.

Soil Test Laboratory. 1990a. Procedure PL 0003: total plant tissue

digest-ICAP analysis. Fayettville, AR: University of Arkansas,

Soil Test Laboratory. 3 p.

Soil Test Laboratory. 1990b. Procedure PL 0004: plant tissue total

nitrogen analysis. Fayettville, AR: University of Arkansas, Soil

Test Laboratory. 3 p.

Switzer, G.L.; Shelton, M.G.; Nelson, L.E. 1979. Successional

development of the forest floor and soil surface on upland sites

of the east Gulf Coastal Plain. Ecology. 60: 1162-1171.

Van Lear, D.H.; Kapeluck, P.R.; Waide, J.B. 1990. Nitrogen pools

and processes during natural regeneration of loblolly pine. In:

Gessel S.P.; Lacate, D.S.; Weetman, G.F.; Powers, R.F., eds.

Proceedings of the seventh North American forest soils conference: sustained productivity of forest soils. Faculty of Forestry

Publication, Vancouver, BC: University of British Columbia:

234-252.

Vitousek, P.M. 1981. Clear-cutting and the nitrogen cycle.

Ecological Bulletin. 33: 631-642.

Waide, J.B.; Caskey, W.H.; Tood, R.L.; Boring, L.R. 1987.

Changes in soil nitrogen pools and transformations following

forest clearcutting. In: Swank, W.T.; Crossely, D.A., Jr., eds.

Forest hydrology and ecology at Coweeta. Ecological Studies

66. New York, NY: Springer-Verlag: 221-232.

Yin, X.; Perry, J.A.; Dixon, R.K. 1989. Influence of canopy removal

on oak forest floor decomposition. Canadian Journal Forest

Research. 19: 204-214.
110
111

Session 5

Phase II Understory Vegetation and Visual Quality Research

Dan Nolan, Moderator
112
113

INTRODUCTION

An attractive, healthy, and visually pleasing natural environment is closely related to the success of tourism and

recreation-related industries (Mieczkowski 1995). Such environments improve the quality of life for nearby communities

and raise their property values (Correll and others 1978,

Crompton 1993, Ulrich and others 1991a, 1991b).

Ecosystem management, a paradigm adopted by the U.S.

Department of Agriculture, Forest Service, National Forest

System, emphasizes the integration of social, economic,

and ecological needs at different scales of time and space

(Salwasser and Pfister 1994). Society places importance on

the public enjoyment of forests, especially their scenic values

(Brunson 1991). Because people’s first response to the

forest is often visual, and their evaluation of place is also

visual (Gobster 1993), they judge forest management and

policy on a forest’s appearance (Hull 1989).

In managing forests, knowledge of the visual impact and

speed of recovery from alternative harvest disturbances are

critical elements of an appearance-sensitive approach to

ecosystem management. Empirical studies have confirmed

the validity and reliability of photographs to represent scenes

of field conditions (Brown and others 1989, Buhyoff and

Wellman 1979, Hoffman and Palmer 1995).

To develop hypotheses about a variety of disturbances,

Benson and Ullrich (1981) studied preferences of foreground,

or near-stand views of Douglas fir-larch and lodgepole pine

forest types in Montana and Wyoming having a range of

stand histories and treatments. Viewers rated scenic values

lower for stands with severe cutting and burning activities,

and rated scenic values higher in stands where time-sincedisturbance was 25 to 50 years old. Had Benson and Ullrich

(1981) conducted long-term monitoring and controlled

experiments at the same location—or in forest types of the

Eastern United States—results might have been different.

Rudis and others (1994, 1999) monitored 0.5 acres of experiment-scale (1.6 acres) treatments in shortleaf pine-oak

vegetation types of the Ouachita Mountains of Arkansas

and found measurable differences in vegetation structure

with the type and amount of cutting two growing seasons

after harvest. Gramann and Rudis (1994) found the amount

of cutting in these same areas negatively associated with

scenic values of within-stand views. In operational-scale

(about 40 acres) treatments elsewhere in the Ouachita

Mountains, the amount of cutting was negatively associated

with scenic values within a year of cutting (Barlow and Rudis,

in press) and a full year (two growing seasons) after cutting

(Li and others, in press), but longer term trends had not

been quantified.

OBJECTIVES AND THE STUDY AREA

We monitored change in scenic beauty the summer before,

the year (two growing seasons) after, and 4 years after cutting disturbance and compared the effects of different harvest

practices. Study locations were part of a 52-stand study

region (Baker 1994, Guldin and others 1994, Mersmann and

others 1994) in national forests in the Ouachita Mountains

of Arkansas. Treated stands were harvested in the winter of

1992-93 and subsequent reproduction treatments occurred

in 1994. Silvicultural treatments were randomly assigned to

1 of 13 stands in each quadrant of the study region (Baker

1994). Cost and time constraints limited our study to four

treatment conditions and the north, east, and south quadrants of the study region. The conditions and stand references for this study were

(a) Untreated: untreated stands (CON) retained in their

natural state, averaging 129 square feet per acre. The

north, east, and south stands were 0284-11, 0605-05,

0023-10, respectively

(b) Group selection: pine-hardwood group selection

(PHGS): 60 square feet per acre retained (70 square

feet per acre removed); openings 0.1 to 2.0 acres.

SCENIC BEAUTY IN SUMMER THE YEAR BEFORE,

THE YEAR AFTER, AND 4 YEARS AFTER HARVEST

Victor A. Rudis, Hochan Jang, and James H. Gramann1

1 Research Forester, USDA Forest Service, Southern Research Station, Forest Inventory and Analysis Unit, Starkville, MS; former Graduate

Research Assistant, Texas A&M University, Department of Park, Recreation, and Tourism Sciences, College Station, TX; and Professor and

Associate Head, Texas A&M University, Department of Park, Recreation, and Tourism Sciences, College Station, TX, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—This study examined the effects of four silvicultural options (clearcut, group selection, shelterwood, and

untreated) on the perceived scenic beauty of shortleaf pine-oak (Pinus echinata-Quercus spp.) stands of national forest

land in the Ouachita Mountains of Arkansas. Twelve randomly selected stands were selected and nine were cut in the

winter of 1993. Color images of the stands were captured in June 1992, July 1994, and July 1997. Texas A&M University

undergraduate students rated the scenic beauty of five images per plot and summer season results are reported here.

The visual impact of cutting these stands was severe a year (two growing seasons) after treatment, with intensive

treatments yielding the lowest scenic beauty preferences. Four years after treatment, however, no statistical differences

were detected among treated and untreated stands. Results are preliminary, but clearly show that the negative visual

aspects of harvesting declines with time.
114

Retention of 20 to 45 trees per acre shortleaf pine and

10 to 20 trees per acre hardwoods. The north, east,

and south stands were 0046-18, 1124-11, 0035-42,

respectively

(c) Shelterwood: pine-hardwood shelterwood (PHSW): 40

square feet per acre retained (90 square feet per acre

removed). Retention of 10 to 30 trees per acre shortleaf pines and 10 to 30 trees per acre hardwoods. The

north, east, and south stands were 0457-12, 1119-21,

0027-01, respectively

(d) Clearcut: all merchantable trees removed (CC): 0 to 5

square feet per acre of trees retained (129 square feet

per acre removed). The north, east, and south stand

compartments were 0458-16, 1067-15, 1658-05,

respectively.

METHODS

We took about 15 images per stand within a 2-week period.

Viewing points were at five to six predetermined locations

(depending on the size of the stand) and spaced evenly

within each stand (Baker 1994). Equipment used included

ASA 400, 35-mm transparency film, push processed to ASA

800, and an f2.8-lens (Olympus XA) camera. Duplicate,

overexposed or underexposed images were discarded. This

study used about five randomly selected scenes per stand

from the remaining images.

We tested scenic beauty at three time periods for four conditions: one untreated (control) and three treatment conditions; and three replicates per condition: one in each of the

north, east, and south quadrants of the study region. The

season and year for images included a pretreatment summer

sample, a year (two growing seasons) after treatment in

spring, summer, fall, and winter, and 4 years after treatment

in summer, fall, and spring. For brevity, analysis reported

here used data only from the summer season; quadrant

and season differences are not included.

Rating sessions followed procedures developed by Daniel

and Boster (1976) and used rating-analysis software (Brown

and others 1990). In each session, raters viewed 80 images,

20 of which were baseline images shown to every group.

These baselines were placed in every fourth position of the

slide carousel. Raters were shown each scene for 8 seconds,

then asked to rate each image on a scale of 1 to 10, where

10 was the highest scenic beauty. The score for an image

represented the average scenic beauty rating, called SBE,

relative to baseline images shown to all raters (Brown and

Daniel 1990). People’s perception, represented by SBE, is

widely used in visual impact estimation (Brown and Daniel

1990, Ribe 1989). However, one is often cautioned that no

true interval exists. Commonly, the resulting ordinal scores

are assumed to provide adequate precision and not violate

assumptions of standard statistical tests.

We conducted two sets of three rating sessions. Each

session consisted of about 5 warm-up images, 20 baseline

images, and 80 images stratified by stand and by 4 pointsin-time. The first set was from summer 1994, fall 1994,

winter 1995, and spring 1995. The second set was from

summer 1992, summer 1997, fall 1997, and spring 1998.

The total set was 480 images. The baseline images were

from four seasons in 1994 and 1995 from another national

forest region in the Ouachita Mountains (Winona Ranger

District); Gritter (1997) also used these images. Thirty-one

students at Texas A&M University viewed a portion of the

images and asked to rate scenes as part of a classroom

assignment. Respondents were students taking undergraduate courses in parks and diverse populations; methods of

park, tourism, and recreation research; and management of

tourism and recreation enterprises. Students were predominantly from the Southeastern United States, and majored in

agricultural development, architecture, or recreation, park,

and tourism sciences.

We used standardized SBE scores, called SBEz, to assure

a uniform scale among different raters (Brown and Daniel

1990) and used nonbaseline image SBEz values for subsequent calculations. For ease of interpretation, we converted

ratings to rankings for conducting an analysis of variance

by year and treatment, calculation of averages, and F- and

t-tests of significance at the 0.05 probability level. Statistical

software employed SAS’s General Linear Model (SAS

Institute, Inc. 1990). By year, significant tests among treatments by year used the Tukey-Kramer option to account for

multiple comparisons (SAS Institute, Inc. 1990).

RESULTS AND CONCLUSIONS

A visual inspection of color images suggests that overt evidence of disturbance was negatively associated with scenic

beauty estimates. (A subset of images in various seasons

and related SBEz values are included in the appendix.) The

amount of sky in the scene changed dramatically in treated

stands, with distant views apparent in areas that were extensively cut. Evidence of cutting disturbance was apparent in

the year following treatment, but evidence was not readily

visible after 4 years.

Quantitatively, our results showed significant differences in

scenic beauty preferences among forest management activities that varied by year (table 1). The chief difference was

Table 1—Analysis of variance in summer scenic beauty ranking by

year and treatment, Ouachita-Ozark National Forests

Degrees of Mean square

Source freedom variance F value P (larger F)

Treatment 3 4,428.49 2.08 0.105

Year 2 5,067.44 2.38 0.096

Year by treatment 6 16,430.42 7.71 < 0.001

Residual 167 2,131.29
115

in the year after treatment, with more intensive treatments

yielding lower scenic beauty (fig. 1). Before treatment and 4

years after treatment, we did not detect significant differences. Failure to detect significant differences in summer

between untreated stands and group selection suggests

this treatment may be the least offensive of the other treatments. A year after treatment, differences in scenic beauty

between group selection and shelterwood were not significant. Analyses that account for quadrant differences and

preference ratings from additional images taken in other

seasons may reveal other fine-scaled differences. Nevertheless, results to date corroborate a common understanding

that, with time, the negative visual aspects of harvests are

indistinguishable from untreated stands.

Benson and Ullrich’s (1981) hypothesis for Douglas fir-larch

and lodgepole pine forests was that the vegetation change in

the years following treatment reduces the negative aspects

of harvest disturbance. Our results for the summer season

in shortleaf pine-oak forest types in Arkansas show definitively that scenic beauty is indistinguishable from untreated

stands 4 years after harvest. This contrasts with data in

Benson and Ullrich’s (1981) report on scenic preferences,

which suggested recoveries of lodgepole pine at 3 to 10

years, and Douglas fir-larch at 23 to 30 years at one site

and 6 to 18 years at three other sites. Apart from methodological differences between the two studies, our study’s

shorter recovery time may be due to the more humid and

longer growing period for vegetative growth in Arkansas

compared with Montana and Wyoming.

ACKNOWLEDGMENTS

We wish to thank USDA Forest Service, Southern Research

Station’s Jim Baker and Jim Guldin for logistical support,

and Mike Shelton and Bernie Parresol for review of an

earlier manuscript draft. For our viewing point locations,

special thanks go to those involved with marking stands

and the bird census points, including Dan Nolen and staff of

the USDA Forest Service, Ouachita National Forest. Also

thanks to Ying-Hung Li, formerly at Clemson University, and

to Theresa Herrick and student staffers at Arkansas Tech

University for 1992 through 1995 image data collection.

We especially thank Rhonda Huston of the USDA Forest

Service,Ouachita National Forest who helped provide

consistent field support throughout the study period and for

guiding the many people involved in data collection. Scenic

beauty estimates were collected in partial fulfillment of

Hochan Jang’s Ph.D. dissertation at Texas A&M University.

LITERATURE CITED

Baker, James B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, James B., compiler. Proceedings of the symposium on

ecosystem management research in the Ouachita Mountains:

pretreatment conditions and preliminary findings; 1993 October

26-27; Hot Springs, AR. Gen. Tech. Rep. SO-112. New Orleans,

LA: USDA Forest Service, Southern Forest Experiment Station:

18-28.

Barlow, Rebecca J. Ray; Rudis, Victor A. [In press]. Impact of

recent timber harvests on autumn scenic beauty of near-stand

views. In: Guldin, James M., tech. comp. Ouachita and Ozark

Mountains symposium: ecosystem management research. Gen.

Tech. Rep. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station.

Benson, Robert E.; Ullrich, James R. 1981. Visual impacts of

forest management activities: findings on public preferences.

Research Paper INT-262. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Forest and Range

Experiment Station. 14 p.

Brown, Thomas C.; Daniel, Terry C. 1990. Scaling of ratings:

concepts and methods. Research Paper RM-293. Ft. Collins,

CO: U.S. Department of Agriculture, Forest Service, Rocky

Mountain Forest and Range Experiment Station. 24 p.

Brown, Thomas C.; Daniel, Terry C.; Schroeder, Herbert W.; Brink,

Glenn E. 1990. Analysis of ratings: a guide to RMRATE. Gen.

Tech. Rep. RM-195. Ft. Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Forest and Range

Experiment Station. 40 p.

Brown, Thomas C.; Richards, Merton T.; Daniel, Terry C.; King,

David A. 1989. Recreation participation and the validity of photobased preference judgments. Journal of Leisure Research. 21:

49-60.



















 

	









  
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Figure 1—Average rank (standardized scenic beauty estimates) in summer scenic beauty the year before (1992),

the year after (1994), and 4 years after harvest (1997) by type of harvest, Ouachita-Ozark National Forest. By year,

averages among harvest treatment with the same letter are not significantly different.
116

Brunson, Martin William. 1991. Effects of traditional and “new

forestry” practices on recreational and scenic quality of

managed forests. Ph.D. dissertation. Corvallis, OR: Oregon

State University. 200 p.

Buhyoff, Gregory J.; Wellman, J. Douglas. 1979. Environmental

preferences: a critical analysis of a critical analysis. Journal of

Leisure Research. 11: 215-218.

Correll, Mark R.; Lillydahl, Jane H.; Singell, Larry D. 1978. The

effects of greenbelts on residential property values: some findings on the political economy of open spaces. Land Economics.

54: 207-217.

Crompton, John L. 1993. Repositioning recreation and park

services: an overview. Trends: Justifying Recreation and Parks

to Decision Makers. 30(4): 2-5.

Daniel, Terry C.; Boster, Ronald S. 1976. Measuring landscape

aesthetics: the scenic beauty estimation method. Research

Paper RM-167. Ft. Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Forest and Range Experiment

Station. 66 p.

Gobster, Paul H. 1993. The aesthetic experience of sustainable

forest ecosystems. In: Sustainable ecological systems: implementing an ecological approach to land management. Gen.

Tech. Rep. RM-247. Ft. Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Forest and Range

Experiment Station: 246-255.

Gramann, James H.; Rudis, Victor A. 1994. Effects of hardwood

retention, season of year, and landform on the perceived scenic

beauty of forest plots in the Ouachita Mountains. In: Baker,

James B., compiler. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains:

pretreatment conditions and preliminary findings; 1993 October

26-27; Hot Springs, AR. Gen. Tech. Rep. SO-112. New

Orleans, LA: USDA Forest Service, Southern Forest

Experiment Station: 223-228.

Gritter, Molly Kay. 1997. The effect of time-since-treatment and

other factors on the perceived scenic beauty of southern pineoak forest plots. M.S. thesis. College Station, TX: Texas A&M

University, Dept. Recreation, Park, and Tourism Sciences. 80 p.

Guldin, James M.; Baker, James B.; Shelton, Michael G. 1994.

Midstory and overstory plants in mature pine/hardwood stands

of the Ouachita/Ozark National Forests. In: Baker, James B.,

compiler, Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment

conditions and preliminary findings; 1993 October 26-27; Hot

Springs, AR. Gen. Tech. Rep. SO-112. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 29-49.

Hoffman, Robin E.; Palmer, James F. 1995. Validity of using photographs to simulate visible qualities of forest recreation environments. In: Vanderstoep, Gail A., ed. Proceedings of the 1994

Northeastern recreation research symposium. Gen. Tech. Rep.

NE-198. Radnor, PA: U.S. Department of Agriculture, Forest

Service, Northeastern Forest Experiment Station: 83-89.

Hull, R. Bruce, IV. 1989. Forest visual quality management and

research. In: Watson, Alan H., comp. Outdoor recreation benchmark 1988: proceedings of the national outdoor recreation

forum; 1988 January 13-14; Tampa, FL. Gen. Tech. Rep. SE52. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southeastern Forest Experiment Station: 485-498.

Li, Ying-Hung; Rudis, Victor A.; Herrick, Theresa A. [In press]. A

psychological model of scenic beauty by silvicultural treatment

two growing seasons after harvest. In: Guldin, James M., tech.

comp. Ouachita and Ozark Mountains symposium: ecosystem

management research. Gen. Tech. Rep. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station.

Mersmann, Timothy J.; Baker, James B.; Guldin, James M.; Pell,

William F. 1994. Implementing ecosystem management research:

bringing researchers, managers, and citizens together. In:

Baker, James B., compiler. Proceedings of the symposium on

ecosystem management research in the Ouachita Mountains:

pretreatment conditions and preliminary findings; 1993 October

26-27; Hot Springs, AR. Gen. Tech. Rep. SO-112. New Orleans,

LA: U.S. Department of Agriculture, Forest Service, Southern

Forest Experiment Station: 10-17.

Mieczkowski, Zbigniew. 1995. Environmental issues of tourism and

recreation. Lanham, MD: University Press of America. 552 p.

Ribe, Richard G. 1989. The aesthetics of forestry: what has empirical preference research taught us? Journal of Environmental

Management 13: 55-74.

Rudis, Victor A.; Gramann, James H.; Herrick, Theresa A. 1994.

Esthetics evaluation. In: Baker, James B., compiler. Proceedlings of the symposium on ecosystem management research in

the Ouachita Mountains: pretreatment conditions and preliminary findings; 1993 October 26-27; Hot Springs, AR. Gen. Tech.

Rep. SO-112. New Orleans, LA: USDA Forest Service, Southern

Forest Experiment Station: 202-211.

Rudis, Victor A.; Thill, Ronald E. [and others]. 1999. Understory

structure by season following uneven-aged reproduction

cutting: a comparison of selected measures two and six years

after treatment. Forest Ecology and Management. 114/2-3:

309-320.

Salwasser, H.; Pfister, R.D. 1994. Ecosystem management: from

theory to practice. In: Covington, W. Wallace.; DeBano,

Leonard F., tech. coordinators. Sustainable ecological systems:

implementing an ecological approach to land management.

Gen. Tech. Rep. RM-247. Fort Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Forest and Range

Experiment Station: 150-161.

SAS Institute, Inc. 1990. SAS/STAT user’s guide. Version 6, 4th ed.

Chapter 24: the GLM procedure. Cary, NC: SAS Institute. Vol.

2: 892-996.

Ulrich, Roger S.; Dimberg, U.; Driver, B.L. 1991a. Psychophysical

indicators of leisure benefits. In: Driver, B.L.; Brown, P.;

Peterson, G.L., eds. Benefits of leisure. State College, PA:

Venture Publishing: 73-89.

Ulrich, Roger S.; Simons, R.F. [and others]. 1991b. Stress

recovery during exposure to natural and urban environments.

Journal of Environmental Psychology. 11:201-230.
117

APPENDIX

The following is a subset of the study’s digitally-archived images with identifying codes by treatment, stand, and vantage point.

References under each image are the season and time period: 1992 = year before treatment; 1994, 1995 = year after

treatment; 1997, 1998 = 4 years after treatment; and standardized scenic beauty estimate (SBEz). SBEz scores ranged from

-170 to +270 for views from all seasons. For summer views, we also list the ranking of SBEz, where 1 = lowest and 179 =

highest. (A seven-digit compact disk/image code is included for archival purposes.)

Untreated (Control), Stand 0284-11, Point 3

Summer (1992) SBEz = 0.06

Rank = 88 CD 2608-069

Winter (1995) SBEz = -62.88

CD 1634-084

Summer (1994) SBEz = 87.87

Rank = 143 CD 0015-022

Fall (1997) SBEz = 68.56

CD 4233-076

Summer (1997) SBEz = -24.89

Rank = 67 CD 4232-014

Spring (1998) SBEz = -16.07

CD 3171-084
118

Summer (1992) SBEz = 77.68

Rank = 135 CD 2609-069

Fall (1994) SBEz = -51.68

CD 1633-048

Summer (1994) SBEz = 38.12

Rank = 116 CD 2607-018

Winter (1995) SBEz = -116.77

CD 1636-072

Summer (1997) SBEz = -292.97

Rank = 2 CD 4232-031

Fall (1997) SBEz = 11.16

CD 4234-044

Group Selection, Stand 1124-11, Point 4
119

Summer (1992) SBEz = -104.99

Rank = 28 CD 2608-010

Winter (1995) SBEz = -189.09

CD 1634-065

Summer (1994) SBEz = 4.11

Rank = 91 CD 2607-058

Spring (1995) SBEz = 31.63

CD 1613-084

Summer (1997) SBEz = -68.15

Rank = 45 CD 4231-045

Spring (1998) SBEz = 75.22

CD 4235-91

Shelterwood, Stand 0027-01

Point 5 Point 2
120

Clearcut, Stand 0458-16, Point 6

Summer (1992) SBEz = 26.28

Rank = 104 CD 2608-054

Fall (1994) SBEz = -188.28

CD 1561-002

Summer (1994) SBEz = -155.81

Rank = 13 CD 0015-012

Spring (1995) SBEz = 235.67

CD 1613-046

Summer (1997) SBEz = 92.82

Rank = 147 CD 4231-066

Spring (1998) SBEz = 121.39

CD 3171-096
121

INTRODUCTION

Public interest in aesthetics for forest management appears

in Federal legislation such as the National Environmental

Policy Act of 1969, the Forest and Rangeland Renewable

Resources Act of 1974, and the National Environmental

Policy Act of 1976. The visual impact of cutting activities on

forests is well known, but predicting the mitigating effects of

different silvicultural treatments is uncertain.

Scenic beauty, as used in this study, is a measure of the

aesthetic significance given to a scene by an observer.

Scenic beauty is influenced by both the observer’s culture

and by the properties of the scene being observed (Smardon

and others 1986). Reaction of observers to the aesthetics

of forest scenery depends partly on the observer’s ability—

or lack of ability—to perceive and distinguish among different management activities (Magill 1990).

Daniel and Boster (1976) contend that scenic beauty is not

totally “in the eye of the beholder,” but inferred from the

observer’s perception of the landscape. The Scenic Beauty

Estimation (SBE) method developed by Daniel and Boster

(1976) is a procedure for rating the visual quality of scenes.

Individuals typically rate representations of these scenes by

numerically scoring their preference for the “scenic beauty”

depicted in photographic images shown to them.

OBJECTIVES AND STUDY AREA

The main objectives of the study were to estimate the impact

of silvicultural treatments on the scenic beauty of shortleaf

pine-hardwood forest stands in the Ouachita and Ozark

National Forests and to compare scenic beauty trade-offs

when selecting silvicultural treatments (Mersmann and

others 1994).

The study locations were part of a 9,600 square mile study

area established by the USDA Forest Service on the

Ouachita and Ozark National Forest land located in northwest Arkansas and eastern Oklahoma (Baker 1994, Guldin

and others 1994). Quadrants of the study area corresponded

to regions with similar land surface forms, potential natural

vegetation, and geology. These quadrants (and nearby

Arkansas cities) were: north (Danville), east (Cedar Creek),

south (Mount Ida), and west (Black Fork). The USDA Forest

Service randomly assigned silvicultural treatments to one of

13 stands in each quadrant of the study region, with trees

mostly cut to promote pine reproduction (Baker 1994). We

used the north, east and south quadrants of the region;

costs and time constraints excluded consideration of the

west quadrant.

Harvesting of treated stands occurred in the winter of 1992

to 1993; reproduction treatments were planned for 1994.

The conditions, treatments, and their abbreviation are listed

first by increasing intensity of harvesting (untreated, singletree selection, group selection, shelterwood, seed tree,

clearcut), second by an estimate of the square feet of basal

area removed, and third by square feet of hardwood basal

area removed:

1. CON: untreated stands retained in their natural state,

averaging 130 ft2 per ac

2. LIST: low impact single-tree selection: about 70 ft2

 per

ac retained (about 60 ft2

 per ac removed). Retention of

30 to 55 trees per ac shortleaf pines and 10 ft2

 per ac

hardwoods

3. PSTS: pine single-tree selection: 63 ft2

 per ac retained

(67 ft2

 per ac removed). Retention of 30 to 55 trees per

ac shortleaf pines and 0 to 5 ft2 per ac hardwoods

4. PHSTS: pine-hardwood single-tree selection: 60 ft2 per

ac retained (70 ft2 per ac removed). Retention of 20 to

45 trees per ac shortleaf pines and 10 ft2 per ac hardwoods. Planned for 1994, vegetative management

treatments, e.g., mechanical versus chemical treatment

IMPACT OF RECENT TIMBER HARVESTS ON

AUTUMN SCENIC BEAUTY OF NEAR-STAND VIEWS

Rebecca J. Ray Barlow and Victor A. Rudis1

1 Forester, Weyerhauser Corporation, Columbus, MS; Research Forester, USDA Forest Service, Southern Research Station, Starkville, MS

39759, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—This study estimated the impact of 10 recent timber cutting regimes on the autumn scenic beauty of shortleaf

pine-hardwood forests in the Ouachita Mountains of Arkansas. Scenes were photographed near forest stand edges—

views typically observed by sightseeing visitors—from 36 treated areas cut the previous winter and 3 comparable

untreated areas. Cutting regimes varied in hardwood retention, spatial arrangement, and harvest intensity. We averaged

scenic beauty ratings from several groups of judges. Results showed that scenic beauty of autumn, near-stand views

were significantly (P<0.05) lower and in inverse proportion to the amount of wood recently removed. Pine vs. pinehardwood retention and differences among groups of judges had no significant effect on scenic beauty ratings among

various treatments. The treatments—grouped into similar impact categories and in order of increasing negative impact—

were: (1) low-impact and pine single-tree selection, (2) pine-hardwood single-tree selection, pine and pine-hardwood

group selection, and pine and pine-hardwood shelterwood, (3) pine and pine-hardwood seed tree, and (4) clearcut

harvest.
122

of residual trees, were to be split (s) in PHSTSs, but

not in nonsplit (ns) PHSTSns stands

5. PHGS: pine-hardwood group selection: 60 ft2

 per ac

retained (70 ft2 per ac removed); clearcut 0.1 to 2.0 ac.

Retention of 20 to 45 trees per ac shortleaf pine and 10

ft2 per ac hardwoods

6. PGS: pine group selection: 60 ft2 per ac retained (70 ft2

per ac removed); clearcut 0.1 to 2.0 ac. Retention of 30

to 55 trees per ac shortleaf pine and 0 to 5 ft2

 per ac

hardwoods

7. PSW: pine shelterwood: 43 ft2

 per ac retained (87 ft2

per ac removed). Retention of 20 to 40 trees per ac

shortleaf pines and 0 to 5ft2 per ac hardwoods

8. PHSW: pine-hardwood shelterwood: 40 ft2 per ac

retained (90 ft2 per ac removed). Retention of 10 to 30

trees per ac shortleaf pines and 10 ft2 per ac hardwoods. Planned for 1994, vegetative management

treatments were to be split (s) in PHSWs, but not in

nonsplit (ns) PHSWns stands

9. PST: pine seed tree: 23 ft2 per ac retained (107 ft2

 per

ac removed). Retention of 15 to 20 trees per ac shortleaf pines and 0 to 5 ft2

 per ac hardwoods

10. PHST: pine-hardwood seed tree: 20 ft2

 per ac retained

(110 ft2

 per ac removed). Retention of 5 to 10 trees per

ac shortleaf pines and 10 ft2 per ac hardwoods

11. CC: clearcut: 0 to 5 ft2

 per ac of trees retained (127 ft2

per ac removed). All trees were removed. Site preparation for planting of genetically improved shortleaf pine

was scheduled to begin in 1994.

METHODS

We examined one untreated stand and stands demonstrating

the 10 different timber-harvest regimes listed above. Table 1

lists stand and compartment numbers, and the square feet

of basal area retained (and removed) for the 39 stands

examined in this study. As outlined elsewhere (Guldin and

others 1994), retained basal area estimates were expected

values following treatment across the study region. We estimated removed basal area by subtraction from the average

basal area prior to harvest treatment (130 ft2 per ac) across

the entire study region. Owing to planned 1994 reproduction

treatments, PHSTS and PHSW harvest treatments were

replicated twice.

The senior author selected three to four representative vantage points per stand from topographic sheets and located

them during July 1993 field visits. These points were just

outside, or up to 100 feet outside, the stand boundary.

Picture taking was conducted from the selected vantage

points during three weekends in October 1993. At that time,

treated areas were recently disturbed, i.e., stand disturbance had occurred within a year after the cut, but without

site preparation.

Stand views were photographed with ASA 100 35 mm transparency film using a single lens reflex camera mounted on

a tripod with a 30 to 70 mm zoom lens fixed at 55 mm. To

randomize view angles and avoid duplicating scenes, images

at each location were taken at three compass directions

(azimuths) toward the stand. The initial image was taken in

a random azimuth toward the stand. The second and third

images were taken 40 degrees to the left and right of the

Table 1—National forest stands sampled in October 1993, approximate square feet of basal area per

acre retained (removed), treatment, and treatment abbreviation, Ouachita-Ozark National Forestsa

Stands sampled by quadrant and

 stand—compartment number

Retained Treatment

North East South (removed) Silvicultural treatment abbreviation

ft2/ac

0284-11 0605-05 0023-10 130 (0) Untreated (control) CON

0367-04 1077-19 0062-08 70 (60) (s) low-impact single-tree selection LIST

1125-05 1044-03 1658-16 63 (67) (s) pine single-tree selection PSTS

0428-02 1073-10 1654-16 60 (70) (s) pine-hardwood single-tree selection

0070-10 0609-09 1649-13 (ns) pine-hardwood single-tree selection PHSTS

0046-18 1124-11 0035-42 60 (70) (ns) pine-hardwood group selection PHGS

0014-18 1106-09 1648-01 60 (70) (ns) pine group selection PGS

0443-03 1097-06 0035-41 43 (87) (s) pine shelterwood PSW

0456-09 1094-04 1660-06 40 (90) (s) pine-hardwood shelterwood

0457-12 1119-21 0027-01 (ns) pine-hardwood shelterwood PHSW

0458-10 1084-07 1646-08 23 (107) (s) pine seed tree PST

1036-17 1119-5S 1651-06 20 (110) (s) pine-hardwood seed tree PHST

0458-16 1067-15 1658-05 3 (127) (ns) clearcut CC

(s) = Split stands; (ns) = nonsplit stands; CON = untreated stands; LIST = low-impact single-tree selection; PSTS = pine

single-tree selection; PHSTS = pine-hardwood single-tree selection; PHGS = pine-hardwood group selection; PGS = pine

group selection; PSW = pine shelterwood; PHSW = pine-hardwood shelterwood; PST = pine seed tree; PHST = pinehardwood seed tree; CC = clearcut.

a Reproduction treatments, planned for 1994, were to differ for half of the stand area of split (s) stands, but not nonsplit

(ns) stands (Baker 1994).
123

initial azimuth. Acquisition of images incorporated a long

depth of view by using an f 22 aperture setting, and

bracketed ± 1 f-stop.

After the film was processed, duplicate, over- and underexposed images were discarded. Only “acceptable” images

from which random samples were selected. To simplify

analysis for this report, we ignored the initial blocking of the

overall study’s sample design, i.e., assignment of stands by

quadrant. Twelve or more images were chosen to represent

each treatment. Random sample selection resulted in one

to six images representing a single stand, with most stands

represented by three images.

We used groups of students at Mississippi State University

to view and rate these images in the spring of 1994. Of the

196 students asked to rate a portion of the images during

their class period, 96 percent turned in completed responses.

Respondents were predominantly from the southeastern

United States, 90 percent were male, with an average age

of 21. Previous research has shown that students’ visual

preferences for natural scenes are representative of the

general public (Schroeder and Daniel 1981).

There were three groups of student judges based on

attendance in particular classes:

1. informed forestry class: 82 students (78 completed

responses) enrolled in an “Introduction to Forest

Survey” course. This group received instructions about

the different types of harvest practices they were to see

2. senior forestry class: 64 students (63 completed

responses) enrolled in a senior-level “Forest

Management” course. This group received no message

about treatments

3. non-forestry class: 50 students (47 completed

responses) enrolled in a “Landscape Architecture

Appreciation” course, primarily for non-landscape

architecture majors. This group also received no

message about treatments. Out of this group, more

than 40 percent majored in professional golf management, 22 percent in business, 14 percent in landscape

architecture, and the remaining 24 percent in other

fields of study.

For the rating sessions, we followed procedures developed

by Daniel and Boster (1976) and used RMRATE software

(Brown and others 1990). Judges were shown each image

for 8 seconds, then asked to rate the image on a scale of

0 to 9, where nine was the highest scenic beauty. In each

session, judges viewed 80 different images, 20 of which

were “baseline” images shown to every group. These “baselines” were placed in every fourth position of the slide

carousel. The score for an image represented the average

scenic beauty rating, called SBE, relative to “baseline”

images shown to all judges (Brown and Daniel 1990).

Subsequent analysis then used ratings only of nonbaseline

images.

We used standardized SBE scores, called SBEz, to assure

a uniform scale among different groups of judges (Brown

and Daniel 1990). A widely accepted and commonly used

practice in social science preference studies is to assume

resulting ordinal scale preference scores are interval data,

with an implied uniform distance between two adjacent

scores (Daniel and Vining 1983). Furthermore, ratings with

from 20 or more individuals, when normally distributed,

have been shown to provide adequate precision and do not

seriously violate assumptions of standard statistical tests

and procedures (Daniel and Vining 1983). However, one is

often cautioned that no true interval exists. That is to say, a

difference in rating of 1.0 between 10.0 and 11.0 does not

necessarily represent the same difference between 90.0

and 91.0.

Traditional parametric tests—where random samples are

taken from one or more populations—assume an underlying normal distribution, but ratings may not follow such a

distribution. The distribution of ratings was tested for kurtosis (0=not skewed and normally distributed; plus or minus

1=skewed and not normally distributed) to determine

whether the data approximated a normal distribution (SAS

Institute Inc. 1990). The kurtosis value of scenic beauty

ratings, -0.5, suggested that rating distributions were

normally distributed.

Nevertheless, we converted ratings to rankings for ease of

interpretation and to minimize our assumptions. Ranked

non-baseline image SBEz values served as the basis for

conducting an analysis of variance by judge and treatment,

calculation of averages, and F- and t-tests of significance at

the 0.05 probability level. For brevity, quadrant differences

are not included in this report. Statistical software used

SAS’s General Linear Model procedure (SAS Institute Inc.

1990). Examination of significant differences used the

Bonferroni approach to ensure an experimentwise error

rate by using t-tests at the 0.05/10=0.005 level (SAS

Institute Inc.1990), and the Duncan multiple range (DMR)

test (Cochran and Cox 1957) at the P=0.05 level.

RESULTS AND DISCUSSION

A visual inspection of color images suggested that the

amount of sky in the scene and overt evidence of disturbance were negatively associated—and retained vegetation

positively associated—with scenic beauty estimates. (A

subset of the images with related SBE values is included in

the appendix.) Scores ranged from –277.5 to +194.8, with a

mean of –48.8 and a median of –58.8. Subsequent analyses used ranked scores ranging from 1 to 179, with a mean

of 90.

Though there was considerable variation among the

images, the analysis of variance revealed that significant

(P(F)<0.05) variation in scenic beauty was due to differences among treatments (table 2). Each group of judges

considered untreated stands highest and clearcut stands

lowest in scenic beauty; other treatments were intermediate

between these extremes (fig. 1). Overall average ranking

for the informed forestry, senior forestry, and nonforestry

classes who rated the images for scenic beauty were 92.0,

93.3, and 84.7, respectively. However, differences were not

statistically significant by judge group (P(F=0.94, df = 2,

166)>0.39), or the treatment by judge group interaction

(P(F=0.56, df=20, 146)>0.93).
124

Treatments were the chief source of variation, with the

pooled variance yielding P(F=18.4, df=10, 168)<0.001.

Averages showed that untreated stands were rated and

ranked highest, clearcut stands lowest, and less extensively

cut stands intermediate between these two extremes (table

3). Treatments, grouped into similar impact categories and

ordered by ascending negative impact, were: (1) low-impact

and pine single-tree selection, (2) pine-hardwood singletree selection, pine and pine-hardwood group selection,

and pine and pine-hardwood shelterwood, (3) pine and

pine-hardwood seed tree, and (4) clearcut harvest. Plotting

the average ranking by the approximate square feet of

basal area removed illustrates an inverse association (fig. 2).

Multiple comparison tests using the Bonferroni approach

yielded the most conservative differences, as experimentwise error rate for t-tests was set to P=0.005. With these

test statistics, scenic beauty rankings of (1) untreated

stands were indistinguishable from low-impact and pine

single-tree selection; (2) group selection and shelterwood

treatments were indistinguishable from each other; (3) pine

and pine-hardwood treatments for a given cutting intensity

were not significant; (4) seed tree cuts ranked lower than

pine and low-impact single-tree selection and pine group

selection; pine-hardwood seed tree cuts ranked still lower

than pine group selection and pine-hardwood single-tree

selection; and (5) pine and pine-hardwood seed tree cuts

were indistinguishable from clearcuts. With Duncan’s

multiple range test, at P=0.05, smaller differences among

closely ranked treatments were significant, but overall

results remained the same.

CONCLUSIONS

Study results showed that timber cutting in the previous

year negatively affected the scenic beauty of near-stand

views in autumn. Differences among judge groups used for

this study were not significant. We concluded that the use

of different judge groups (from informed and uninformed

forestry classes and a nonforestry class) had no significant

influence on our findings. Our results corroborate Benson

and Ullrich’s (1981) suggestion that judge group differences

(college students, public school teachers, and Forest

Service researchers) had little effect on ratings of an array

of treatments in Montana and Wyoming.

Scenic beauty of autumn, near-stand views were significantly lower and in inverse proportion to the amount of wood

recently removed. Harvest intensity (approximated by square

feet of basal area removed), was inversely associated with

scenic beauty. Pine vs. pine-hardwood retention had no

significant effect on scenic beauty for intensively cut stands.

Overt signs of cutting dominated many of the scenes depicted

Table 2—Analysis of variance of fall scenic beauty rating within a year

following treatment, Ouachita-Ozark National Forests

Degrees of Mean square

Source freedom variance F value P (larger F)

Treatment 10 25,008.51 17.45 < 0.001

Judge group 2 1,275.55 0.89 0.413

Judge by treatment 20 805.44 0.56 0.933

Residual 146 1,432.77





















	





























 !"# !$%&

'! !$%&

	! !$%&

Figure 1—Average rank (standardized scenic beauty values) by judge group and year-earlier

treatment for October 1993 shortleaf-hardwood near-stand views, Ouachita-Ozark National

Forests. Treatments are listed in declining scenic beauty estimate rank order; CON = untreated

stands; LIST = low-impact single-tree selection; PSTS = pine single-tree selection; PHSTS = pinehardwood single-tree selection; PHGS = pine-hardwood group selection; PGS = pine group

selection; PSW = pine shelterwood; PHSW = pine-hardwood shelterwood; PST = pine seed tree;

PHST = pine-hardwood seed tree; CC = clearcut.
125

Table 3—Average scenic beauty estimate and average rank by year-earlier treatment, shortleafhardwood near-stand views, Ouachita-Ozark National Forests, October 1993

T-test results, P(|t|<0.005)a

Treatment Treatment number

number Rated Average Average

and code images SBEz rank DMRb 1 2 3 4 5 6 7 8 9 10 11

no.

1. CON 14 91.1 159.9 A + | | ********

2. LIST 13 44.4 139.1 AB | + |||||  

3. PSTS 13 20.1 126.5 BC | | + |||||** *

4. PHSTS 26 - 34.0 99.4 CD * | | + |||||**

5. PHGS 18 - 43.1 92.2 D * | | | + |||||*

6. PGS 13 - 29.6 102.1 CD * | | | | + | | | * *

7. PSW 15 - 61.1 83.3 D * | | | | |+| | |*

8. PHSW 28 - 65.7 79.9 D * * | | | | |+| |*

9. PST 12 - 118.1 48.4 E * * * | | | | |+| |

10. PHST 12 - 133.9 42.2 EF ***** | | | |+|

11. CC 15 - 202.0 18.3 F ********| |+

Overall 179 - 48.8 90.0

SBEz = Scenic beauty estimate; CON = untreated stands; LIST = low-impact single-tree selection; PSTS = pine

single-tree selection; PHSTS = pine-hardwood single-tree selection; PHGS = pine-hardwood group selection;

PGS = pine group selection; PSW = pine shelterwood; PHSW = pine-hardwood shelterwood; PST = pine seed tree;

PHST = pine-hardwood seed tree; CC = clearcut.

a T-tests (Bonferroni approach): compared with treatment (+) averages, other averages were significantly (*) different

at alpha = 0.005, (|) otherwise.

b Duncan’s Multiple Range test: averages with the same letter are not significantly different, (P < 0.05).

Figure 2—Average scenic beauty rank by approximate square feet of basal area removed in

year-earlier treatments for October 1993 shortleaf-hardwood near-stand views, Ouachita-Ozark

National Forests; CON = untreated stands; LIST = low-impact single-tree selection; PSTS =

pine single-tree selection; PHSTS = pine-hardwood single-tree selection; PHGS = pinehardwood group selection; PGS = pine group selection; PSW = pine shelterwood; PHSW =

pine-hardwood shelterwood; PST = pine seed tree; PHST = pine-hardwood seed tree; CC =

clearcut.

180

160

140

120

100

80

60

40

20

0

01 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130

CON

LIST

PSTS

PGS

PHSTS

PHGS

PSW

PHSW

PST

PHST

CC

Average rank (SBEz)

Square feet/acre removed
126

in near-stand images of treated stands and may have overwhelmed the finer-scaled differences in pine vs. pine-hardwood retention, and within-stand grouped vs. single-tree

cutting arrangements. We further concluded that cutting

alternatives fell into four groups according to impact. These

were, in order of increasing impact on scenic beauty: (1) lowimpact and pine single-tree selection, (2) pine-hardwood

single-tree selection, group selection, and shelterwood,

(3) seed tree, and (4) clearcut treatments.

Whether the relationships among treatments suggested by

our results remain the same for other stand types, withinstand views, or different seasons is uncertain. With foreground views of Montana Douglas fir stands, studies by

Benson and Ullrich (1981) yielded scenic beauty ratings for

shelterwood significantly greater than clearcut stands.

Further refinement could include forecasting the decline in

near-stand scenic beauty of shortleaf-hardwood stands as

a function of the actual amount of wood removed, rather

than the approximate values provided in this report. With

the existing images, future image analysis might reveal

color (evergreen pine vs. hardwood) differences between

pine and pine-hardwood-single-tree retention, and discrimination of image attributes among the four groups of treatment impacts noted above.

Forest managers concerned with public perception of harvests need to consider near-stand scenic beauty impacts

when choosing harvest regimes, particularly along roadside

views and in public forests. Our study suggests that—within

a year of treatment—single-tree selective cutting has the

least impact, followed by shelterwood, seed tree, and clearcut treatments. Pine single-tree selective cutting has less of

an impact than pine-hardwood group selection and shelterwood cutting. Shelterwood stands have greater scenic

beauty than seed tree and clearcut stands; pine seed tree

stands have greater scenic beauty than clearcut stands.

Our study suggests that the spatial arrangement or proportion of hardwood retained may not influence scenic beauty

of near-stand views as much as the basal area of trees

removed.

Nevertheless, vegetation structure recovers from harvests,

trees regenerate, and scenic beauty improves in the years

following treatment, as shown for within-stand views of

uneven-aged (Gritter 1997, Rudis and others 1999) and

even-aged treatments (Rudis and others, in press) and

foreground views of a variety of treatments (Benson and

Ullrich 1981). For near-stand views, we suspect that harvest disturbance impacts become less obvious and scenic

beauty improves with the passage of time.

ACKNOWLEDGMENTS

We thank Dennis Jacobs and Bernie Parresol, USDA Forest

Service, Southern Research Station for review of earlier

manuscript drafts. We also thank Billy Watson and Dennis

Cengel, formerly with the Department of Forestry, Mississippi

State University; James Baker, Jim Guldin, and Bryce J.

Stokes, USDA Forest Service, Southern Research Station;

and Tim Mersmann, USDA Forest Service, National Forest

System, for assistance during the period that this study was

conducted. And a special thanks to Stephen Barlow for field

assistance.

LITERATURE CITED

Baker, J.B. 1994. An overview of stand-level ecosystem management research in the Ouachita/Ozark National Forests. In:

Baker, James B., comp. Proceedings: Ecosystem management

research in the Ouachita Mountains: pretreatment conditions

and preliminary findings. Gen. Tech. Rep. SO-112. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 18-28.

Benson, R.E.; Ullrich, J.R. 1981. Visual impacts of forest management activities: findings of public preferences. Res. Pap. INT-262.

Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Forest and Range Experiment Station. 14 p.

Brown, T.C.; Daniel, T.C. 1990. Scaling of ratings: concepts and

methods. Res. Pap. RM-293. Ft. Collins, CO: U.S. Department

of Agriculture, Forest Service, Rocky Mountain Forest and

Range Experiment Station. 24 p.

Brown, T.C.; Daniel, T.C.; Schroeder, H.W.; Brink, G.E. 1990.

Analysis of ratings: a guide to RMRATE. Gen. Tech. Rep. RM195. Ft. Collins, CO: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Forest and Range Experiment

Station. 40 p.

Cochran, W.G.; Cox, G.M. 1957. Experimental designs. 2nd ed.

New York: John Wiley and Sons, Inc. 611 p.

Daniel, T.C.; Boster, R.S. 1976. Measuring landscape aesthetics:

the scenic beauty estimation method. Res. Pap. RM-167. Ft.

Collins, CO: U.S. Department of Agriculture, Forest Service,

Rocky Mountain Forest and Range Experiment Station. 66 p.

Daniel, T.C.; Vining, J. 1983. Methodological issues in the assessment of landscape quality. In: Altman, Irwin.; Wohlwill, Joachim

F., eds. Behavior and the Natural Environment. New York:

Plenum Press: 39-84.

Gramann, J.H.; Rudis, V.A. 1994. Effects of hardwood retention,

season of year, and landform on the perceived scenic beauty of

forest plots in the Ouachita Mountains. In: Baker, James B.,

comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings; 1993 October 26-27; Hot Springs,

AR. Gen. Tech. Rep. SO-112. New Orleans, LA: U.S. Department

of Agriculture, Forest Service, Southern Forest Experiment

Station: 223-228.

Gritter, M.K. 1997. The effect of time-since-treatment and other

factors on the perceived scenic beauty of southern pine-oak

forest plots. College Station, TX: Texas A&M University, Dept.

Recreation, Park, and Tourism Sciences. 80 p. M.S. thesis.

Guldin, J.M.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine/hardwood stands of the Ouachita/

Ozark National Forests. In: Baker, James B., comp., Proceedings

of the symposium on ecosystem management research in the

Ouachita Mountains: pretreatment conditions and preliminary

findings. Gen. Tech. Rep. SO-112. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 29-49.

Magill, A.W. 1990. Assessing public concern for landscape quality:

a potential model to identify visual thresholds. Res. Pap. PNWRP-203. Berkeley, CA: U.S. Department of Agriculture, Forest

Service, Pacific Southwest Forest and Range Experiment

Station. 48 p.

Mersmann, T.J.; Baker, J.B.; Guldin, J.M.; Pell, W.F. 1994. Implementing ecosystem management research: bringing researchers,

managers, and citizens together. In: Baker, James B., comp.

Proceedings of the symposium on ecosystem management

research in the Ouachita Mountains: pretreatment conditions

and preliminary findings. Gen. Tech. Rep. SO-112. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 10-17.
127

Rudis, V.A.; Jang, H.; Gramann, J.H. [In press]. Scenic beauty in

summer the year before, the year after, and 4 years after harvest treatments. In: Guldin, James M., tech. comp. Ouachita

and Ozark Mountains symposium: ecosystem management

research. Gen. Tech. Rep. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southern Research Station.

Rudis, V.A., Thill, R.E.; Gramann, J.H. [and others]. 1999. Understory structure by season following uneven-aged reproduction

cutting: a comparison of selected measures two and six years

after treatment. Forest Ecology and Management. 114/2-3:

309-320.

SAS Institute Inc. 1990. SAS/STAT user’s guide. Version 6. 4th ed.

Chapter 24: the GLM procedure. Cary, NC: SAS Institute Inc.:

892-996. Vol. 2.

Schroeder, H.W.; Daniel, T.C. 1981. Progress in predicting the

perceived scenic beauty of forest landscapes. Forest Science.

27(1): 71-80.

Smardon, R.C.; Palmer, J.F.; Felleman, J.P., eds. 1986. Foundations for visual project analysis. New York: John Wiley. 374 pp.
128

Untreated

Stand 0284-11 CD 0677-066

SBEz 194.80

Stand 0023-10 CD 0329-017

SBEz 155.45

Stand 0023-10 CD 0329-015

SBEz 134.01

Stand 0284-11 CD 0677-060

SBEz 99.35

Stand 0605-05 CD 0677-018

SBEz 62.40

Stand 0605-05 CD 0677-020

SBEz 19.91

APPENDIX

The following 12 photographs (six untreated and six clearcut) is a subset of the study’s digitally archived images with

identifying codes by treatment and stand. Examples are ordered by type of treatment and scenic beauty scores within each

treatment. References under each image are the 6-digit stand and compartment code, the 7-digit original compact disk and

image code, and the standardized scenic beauty estimate (SBEz).
129

Clearcut

Stand 1067-15 CD 0677-084

SBEz -92.56

Stand 1685-05 CD 0329-061

SBEz -159.75

Stand 1658-05 CD 0329-060

SBEz -212.12

Stand 1067-15 CD 0677-083

SBEz -237.76

Stand 0458-16 CD 0677-028

SBEz -260.24

Stand 0458-16 CD 0677-029

SBEz -277.51
130

INTRODUCTION

The National Environmental Policy Act of 1969 requires

environmental impact analyses for major projects on Federal

land, including assessments of aesthetics. In general, older

timber stands and those with open, park-like settings and

limited understory vegetation are preferred; young stands

and the presence of abundant logging slash from recent

cutting are not (Ribe 1989). While the negative impact of

timber harvests on scenic beauty of forested land is well

known (Benson and Ullrich 1981, Jones 1993, Ribe 1989,

Vodak and others 1985), it is poorly understood.

Any scientific assessment of aesthetics uses approaches

rooted in psychology or psychophysics (Daniel and Vining

1983). Psychophysics studies the relationship between

physical objects and the aesthetic preference of respondents. Through measurement of physical objects, such as

the number of tree stems, and a series of preference ratings,

the scientist can develop models that relate objects to

preferences. Models developed with physical objects tied to

psychological theory, such as visual penetration, constitute

psychophysical approaches (e.g., Ruddell and others 1989,

Rudis and others 1988). Psychological studies emphasize

theoretical constructs for the aesthetic response, which

describes both how people perceive (make sense of) and

organize visual information and how previous experience

influences their aesthetic response.

On public land, visual landscape management by U.S. agencies employs the expertise of landscape designers, as well

as empirical public preference research (Anderson 1995).

For managed forests, empirical aesthetics research focuses

on public judgments, largely based on scenic beauty preferences. Yet landscape designers make use of a wider array

of psychological attributes than scenic beauty, such as

mystery and coherence. Most attributes have been empirically studied only within urban environments (Kaplan and

Kaplan 1989). Examination of psychological attributes in

managed forested environments should help us understand

the causes behind public preference for particular scenes

and forest management regimes.

Though landscape designers might not always use the same

terminology, most agree that how one perceives a scene

involves an array of organizing principles (Motloch 1991). In

psychological terms, Kaplan and Kaplan (1989) theorized

that one’s preference for particular natural scenes was

evoked by information processing components, namely

coherence, complexity, mystery, and legibility (finding one’s

way in a scene). Based on Ruddell and others’ (1989) psychophysical modeling, Li and Hammitt (1999) added visual

penetration as another information-processing component.

A theoretical causal model for scenic beauty preferences

includes these five components as principal causal factors

affecting scenic beauty preferences. If these factors control

perception, then comparison of causal attributes by cutting

practice should reveal the psychological factors that determine perceived changes in scenic beauty.

OBJECTIVES AND STUDY AREA

Our overall objective was to better understand the psychological response to timber cutting practices on national

forests of the Ouachita Mountains. Specific objectives were

threefold:

A PSYCHOLOGICAL MODEL OF SCENIC BEAUTY BY SILVICULTURAL

TREATMENT TWO GROWING SEASONS AFTER HARVEST

Ying-Hung Li, Victor A. Rudis, and Theresa A. Herrick1

1

Associate Professor, Providence University, Department of Tourism, Taichung, Taiwan; Research Forester, USDA Forest Service, Southern

Research Station, Forest Inventory and Analysis Unit, Starkville, MS 39759; and Associate Professor and Head, Arkansas Tech University,

Department of Recreation and Park Administration, Russellville, AR, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—This study estimated summer scenic beauty and associated psychological attributes of scenes depicting uncut

and several cutting regimes within shortleaf pine-hardwood forests on national forests. Images were captured in the

summer of 1994 in nine treated and three comparable untreated stands in the Ouachita Mountains of Arkansas. Treatments imposed in the winter of 1992-93 included group selection, pine-hardwood shelterwood, and clearcut in north, east,

and south quadrants of the region. Landscape Architecture professionals, students with professional training, and other

students with no training, rated scenic beauty preferences and associated psychological attributes. Analysis of rankings

showed significant differences (P(F)<0.05) in psychological attributes by treatment type and the background of judges.

For all judges, more intensive cutting yielded significantly less scenic beauty, mystery, coherence, and complexity, and

greater visual penetration. Legibility, a term used to describe finding one’s way, was not significantly associated with

cutting treatment. Scenic beauty preferences were indistinguishable among intermediate (shelterwood and group

selection) treatments, although group selection was likely the least offensive because it provided mystery, complexity, and

visual penetration comparable to untreated areas. There were significant quadrant-by-treatment interactions, suggesting

that local conditions also affect the impact of treatments on scenic beauty. Our results lend quantitative credence to the

qualitative notion that adapting cutting practices to limit visual penetration and increase coherence, complexity, mystery,

and scenic beauty can yield measurable aesthetic benefits.
131

1. To test the effect of landscape architecture knowledge

differences among groups of judges on scenic beauty

ratings

2. To test operational-scale cutting practices on scenic

beauty of shortleaf pine (Pinus echinata Mill.)-hardwood

stands, and as a basis for understanding scenic beauty

ratings

3. To test operational-scale cutting practices on ratings of

causal psychological attributes (coherence, complexity,

legibility, mystery, and visual penetration).

Study locations were part of a 52-stand study region on

national forest land in the Ouachita Mountains, an area 120

miles east-to-west, 80 miles north-to-south, and located in

northwest Arkansas and eastern Oklahoma (Baker 1994,

Guldin and others 1994). For operational purposes, the

9,600 square mile study area was divided into quadrants.

Quadrants and nearby cities were: north (Danville, AR), east

(Cedar Creek, AR) south (Mount Ida, AR), and west (Black

Fork, AR). Trees were cut largely to promote pine reproduction. Treatments were harvests in the winter of 1992-93 and

site preparation for regeneration early in 1994. The study

was designed as a randomized complete block, with silvicultural treatments randomly assigned to 1 of 13 stands in

each of the quadrants (Baker 1994). Because of funding

limitations, we had limited personnel. In taking photographs,

we also sought to minimize variability due to within-season

weather conditions. For these reasons, we restricted our

examination to the north, east, and south quadrants of the

study region.

The north quadrant is in the Arkansas Valley ecoregion, with

the other quadrants in the midst of the Ouachita Mountain

ecoregion, where streams tend to be ephemeral, and in the

south quadrant where they tend to be permanent (Baker

1994, Guldin and others 1994). Stands in the north, east,

and south quadrant have average slopes of 15.1, 12.3, and

9.7 percent; 50-year shortleaf site index values of 61.5, 62.2,

and 65.8 feet; average micro relief severity ratings (1 mild,

2 moderate, 3 severe) of 1.6, 1.5, and 1.4; and tree ages of

65.4, 65.3, and 62.7 years, respectively (Guldin and others

1994).

Cost and time constraints limited this study to four treatment conditions (National Forest stand and compartment

number):

a. Untreated: untreated stands retained in their natural

state, referenced commonly in the design of experiments

as a control (CON), averaging 130 cu ft/ac. (The north,

east, and south stand compartments were 0284-11,

0605-05, and 0023-10, respectively.)

b. Group selection: pine-hardwood group selection (PHGS):

clearcut 0.1 to 2.0 ac. Retention of 20-to-45 trees/ac

shortleaf pine and 10-to-20 trees/ac hardwoods. (The

north, east, and south stand compartments were 0046-

18, 1124-11, and 0035-42, respectively.)

c. Shelterwood: pine-hardwood shelterwood (PHSW):

Retention of 10-to-30 trees/ac shortleaf pines and 10-to30 trees/ac hardwoods. (The north, east, and south

stand-compartments were 0457-12, 1119-21, 0027-01,

respectively.)

d. Clearcut: all merchantable volume removed (CC). (The

north, east, and south stand compartments were 0458-

16, 1067-15, and 1658-05, respectively.)

METHODS

Approximately 15 photographic images per stand were

acquired in July 1994 using 35-mm ASA 400 transparency

film, push-processed to ASA 800, and an f2.8 lens (Olympus

XA) from 5 or 6 point locations stratified within stands that

were designated for periodic bird censuses. Images were

acquired two growing seasons after harvest disturbance,

generally on sunny-to-partly-cloudy days not before 7:30 AM

and not after 6:00 PM. Duplicate, over- or underexposed

images, and those with human subjects, were discarded.

What remained were images from which random samples

were selected by treatment. Random sample selection

yielded one to four images representing a single stand, with

all but two stands represented by three images.

Judges first viewed all 36 images for 2 seconds each to

give them a feel for the range of variation. Then, after being

shown each scene for 8 seconds, they rated each image on

a scale from 1 (lowest) to 10 (highest). This numerical indication of people’s perception is used widely in scenic beauty

estimation (Brown and Daniel 1990; Daniel and Vining

1983, Ribe 1989), and all follow procedures outlined by

Daniel and Boster (1976).

There were 88 judges—all from Clemson University. Of

these, 70 were nonprofessionals, i.e., students from two

undergraduate introductory courses (one in psychology, the

other in horticulture), 14 were professionals, i.e., students

from two upper-level landscape design studio classes, and

4 were experts, i.e., professional educators or researchers

familiar with landscape preference research. The judges

were 56 percent female and 44 percent male. Rating sessions were held in the spring of 1996 during class periods.

To avoid participant fatigue in rating the images, we used

two rating sessions. In one session, judges coded scenic

beauty, coherence, and legibility. In the second session a

week later, judges coded complexity, mystery, and visual

penetration. To avoid conditioning of responses to identical

stimuli, participants rated images one attribute at a time.

Image order was randomly assigned for each psychological

attribute. That is, the sequence of images for scenic beauty

differed from the sequence for coherence and for legibility.

We asked participants to rate images but gave them no

instruction about what constituted scenic beauty. We then

instructed them in the use of causal psychological attributes

and showed them images suggesting high, medium, and

low values for each psychological attribute. Definitions

presented were:

Coherence—how easy it is to visually organize the trees

and surrounding vegetation into a well-ordered pattern,

and how well the patterns “hang together”

Complexity—how much there is to look at in this forest

scene in terms of the number of different kinds of trees,

clusters of trees, and other vegetation compositions or

natural elements
132

Legibility—how easy it would be for them to find their

way around in the forest area using noticeable landmarks

such as big trees, clusters of trees, unusually shaped

trees or bushes, clearings, pathways, or any other

memorable features

Mystery—how much this forest scene attracts and

encourages them to go beyond their standing-viewing

point, enter deeper into the forest, and see things that

are only hinted at from their current position.

Visual Penetration—the ability to see through a forest

scene without interruption by vegetation screening, tree

trunks, or other noticeable objects.

To focus on stated objectives and simplify analysis, we

averaged ratings for each of the 36 images by judge group

(expert, professional, nonprofessional), quadrant (north,

east, south), and treatment (untreated, group selection,

shelterwood, clearcut). In performing statistical tests, we

weighted average values to reflect the number of judges

represented.

For statistical analytical purposes, we assumed the resulting ordinal scale preference ratings were interval data, with

an implied uniform distance between two adjacent ratings.

This is a widely accepted assumption in social science

preference studies and commonly is used in scenic beauty

preference research (Daniel and Vining 1983). The reader

is cautioned, however, that in fact no true interval exists.

That is to say, a difference of 1.0 between 5.0 and 6.0,

does not necessarily represent the same difference between

9.0 and 10.0.

Traditional parametric tests assume an underlying normal

distribution, but ratings may not follow such a distribution.

We tested the distribution of average scenic beauty ratings

for kurtosis (0=not skewed and normally distributed; plus or

minus 1=skewed and not normally distributed) to determine

whether the data approximated a normal distribution (SAS

1990). The kurtosis value for scenic beauty, -0.9, suggested

rejection of the null hypothesis that the distribution was

normally distributed.

Ratings for all psychological attributes were converted to

rankings to conduct an analysis of variance (ANOVA) and

calculation of F (equivalent to nonparametric) tests of

significance at the 0.05 probability level. Analyses were

conducted with average ranked values, but averages are

reported on a 10-point rating scale for ease of interpretation. Statistical software employed SAS’s General Linear

Model (GLM) procedures (SAS 1990). Multiple comparison

tests used the Bonferroni approach to ensure an experimentwise error rate of 0.05 by using t-tests each at the

0.05/3=0.017 level with SAS’s least significant means

(LSMEANS), GLM, and mixed ANOVA (MIXED) procedures

(SAS 1990, 1996).

RESULTS

A visual inspection of color images suggested that the

amount of sky in a scene and overt evidence of disturbance

were negatively associated with scenic beauty estimates,

and retained vegetation was positively associated. Each

group of judges rated untreated stands highest in scenic

beauty and clearcut stands lowest. Other treatments were

intermediate between these extremes. Black-and-white

versions of the images are included in the appendix, along

with an average rating for each psychological attribute.

For all quadrants, untreated stands were rated highest and

clearcut stands lowest. Most of the variation was among

treatments, although the ANOVA revealed that perceived

scenic beauty was significantly affected by the interaction

between quadrant and treatment (table 1). Nevertheless, by

quadrant, clearcut stands were rated lowest and untreated

stands highest (fig. 1).

There was no significant interaction between judges and

treatments (P(larger F)=0.259). F-tests revealed significant

differences in perceived scenic beauty among judge groups

(P(larger F)=0.002). So regardless of treatment, scenic

beauty ratings from the expert, professional, and nonprofessional judges were 4.7, 5.8, and 5.5, respectively. Experts

gave significantly different (P|t|<0.01) and consistently lower

rankings, but differences between judges with professional

and nonprofessional architecture backgrounds did not

(P|t|=0.03) (fig. 2).

Other psychological attributes most closely aligned with

scenic beauty were, in order of association: mystery, coherence, visual penetration, complexity, and legibility (table 2).

Mystery and coherence were the two psychological attributes most closely associated with scenic beauty. Judges

gave untreated stands the highest average value for scenic

beauty, mystery, coherence, and complexity, and lowest

average values for legibility and visual penetration. For

brevity, differences in other psychological attributes by

Table 1—Analysis of variance of scenic beauty ranking two growing seasons

after treatment, Ouachita Mountains

Degrees of Mean square

Source freedom variance F value P (larger F)

Quadrant 2 1,373.01 0.16 0.858

Treatment 3 26,462.82 9.39 0.009

Quadrant by treatment 6 8,802.82 50.53 < 0.001

Judge 2 1,577.11 9.05 0.002

Judge by treatment 6 251.74 1.44 0.259

Residual 16 174.22
133

quadrant and by the landscape architecture background of

judges are not included in this report.

Apart from scenic beauty, statistically significant (P(larger

F)<0.05) differences by treatment and causal psychological

attribute were for mystery, coherence, complexity, and

visual penetration, but not legibility (fig. 3). By treatment,

group selection was not significantly different from untreated

stands for any of the causal attributes. Shelterwood treatments resembled untreated stands only in coherence and

legibility. Clearcut treatments resembled untreated areas

only in legibility.

DISCUSSION

Our study showed that of the three treatments, clearcut

stands were rated lowest in scenic beauty. Alhough there

was no significant difference in scenic beauty ratings of

shelterwood and group selection treatments, examination

of causal attributes revealed differences between these two

in complexity and visual penetration.

Our study was designed to represent the Ouachita Mountains

study region, and we had not anticipated a large and significant quadrant by treatment effect, but our results showed

otherwise. Guldin and others (1994) documented significant

differences in topography by quadrant, with gentler to

steeper slopes and lesser to greater micro relief severity

from south to east to north. Distant vistas afforded by shelterwood cutting could well have influenced scenic beauty

ratings more in the north than in the south. The south quadrant was more mesic (i.e., higher site index) and younger,

on average, than the north; so more abundant logging slash

remained visible there than in the other quadrants. Our

results yielded lower averages for clearcut stands, regardless of quadrant. This is consistent with Vodak and others











	

 
	

 

	 
	

	





	





 	

Figure 1—Average scenic beauty ratings two growing seasons after treatment,

Ouachita Mountains, by treatment and quadrant of the study region.











	

 
	

 

	 
	

	





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Figure 2—Average scenic beauty ratings two growing seasons after treatment, Ouachita

Mountains, by judges with different landscape architecture backgrounds.

Table 2—Correlation between scenic beauty and other

psychological attribute rankings (n=36) two growing

seasons after treatment, Ouachita Mountainsa

Code and

attribute name SBE MY CH CM LE

Visual penetration -0.76 -0.93 -0.91 -0.93 0.86

Legibility -0.52 -0.86 -0.71 -0.75 1.00

Complexity 0.59 0.83 0.58 1.00 —

Coherence 0.91 0.87 1.00 — —

Mystery 0.93 1.00 — — —

SBE = Scenic beauty estimate; MY = mystery; CH = coherence;

CM = complexity; LE = legibility.

a All have significant Pearson correlation coefficients (P|larger

r|<0.001).
134

(1985) study that found recent and abundant logging slash

detracting from scenic beauty.

Obviously, the expert judges had more experience with landscape evaluation than professional and nonprofessional

judges. We anticipated that experts would be more sensitive to differences in scenic beauty, and professionals more

sensitive than nonprofessionals but less sensitive than

experts. We found only that experts provided lower ratings

but otherwise were no different from other groups in their

comparative rating of scenic beauty by treatment. For the

particular mix of individuals in our sample, none of the three

groups of judges rated group selection and shelterwood

treatments differently. Repeated experimentation is needed

to verify such results, because our sample of both experts

(n=4) and professionals (n=14) was limited.

The inclusion of various causal attributes provided insight

into psychological reasons for scenic beauty preferences.

Overall, group selection or shelterwood were favored over

clearcut treatments. Our findings also corroborate an earlier

plot-level study of within-stand scenes on north-facing slopes

2 years after treatment. On the Winona Ranger District,

Gramann and Rudis (1994) showed that group selection was

the favored treatment among other uneven-aged reproduction cutting regimes. They also showed views of northfacing, ridgetop views were more scenic, on average, than

views on more mesic, gentler slopes.

Mystery and coherence were most closely correlated with

scenic beauty. In a related study using structural equation

modeling (SEM), Li and Hammitt (1999) tested a modified

version of Kaplan and Kaplan’s (1989) information processing components. SEM, widely used in the social sciences

(Byrne 1994), is analogous to factor analysis, but with

selection of abstract factors constrained by a theoretical,

causal construct. Factor analysis uses an unconstrained

selection of factors based on an analysis of covariance, i.e.,

the relationship of measured attributes to each other and

upon the dependent attribute of interest. SEM, in brief,

incorporates theoretical attributes that may not be directly

measured. Further details are provided elsewhere (Byrne

1994, Li and Hammitt 1999).

Li and Hammitt (1999) found that most (81 percent) of the

variance in perceived scenic beauty among expert judges

varied with two theoretical factors: one factor directly associated with coherence and mystery; and a second factor

Figure 3—F-value, P(larger F) for the treatment effect by psychological attribute, and

average rating for summer scenes two growing seasons after treatment, Ouachita

Mountains. Averages by attribute with the same letter are not significantly different, using

least squares means tests for multiple comparisons (P|t|<0.01).


	





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135

directly associated with visual penetration and legibility and

inversely associated with complexity. Standardized coefficients were 0.91 for the first factor and –0.08 for the second

factor, which suggested the first factor was the chief theoretical cause for scenic beauty preferences.

Our direct study of individual causal attributes shows group

selection having the most mystery and complexity, and the

least amount of visual penetration of all treatments, and not

substantially different from untreated areas. Legibility was

not a useful metric to distinguish among treatments. We

suggest that digital methods to analyze these scenes

(Kalidindi and others 1996, Rudis and others 1999) should

be helpful in assessing image metrics or features that

reflect these and other theoretically relevant psychological

attributes.

Few physical attributes have been directly associated with

psychological measures, other than scenic beauty and visual

penetration. Our findings differ from Rudis and others (1988)

and Ruddell and others (1989) regarding visual penetration.

Their studies involved an examination of 99 east Texas loblolly pine (Pinus taeda L.)-shortleaf pine (P.echinata Mill.)

and oak (Quercus spp.) sample locations across five counties, largely on private land. Our study involved only 12

stands located in shortleaf pine-hardwood stands located in

National Forests of the Ouachita Mountains of Arkansas,

and by design, three-fourths of the scenes were of 2-yearearlier cutting activities. Our study had many more scenes

of more recently disturbed vegetation than the east Texas

scenes. Their “visual penetration” also referenced an

ocular, scaled physical measurement of visual penetration,

rather than the visual penetration perceived by judges.

MANAGEMENT IMPLICATIONS

Aside from approaches to minimize the aesthetic impacts of

timber harvesting (Jones 1993), the goals of traditional

cutting and management are to maximize timber yield while

minimizing damage to the residual stand. The primary goal

focuses on the selection of merchantable trees and the

status of individual remnant trees, rather than the spatial

arrangement of gaps created in the forest or their association with the surrounding ecoregion. Our study lends quantitative credence to the notion that there is a measurable

benefit to adapting cutting and management regimes that

foster scenic beauty.

Landscape architects commonly design landscapes by

organizing elements to promote mystery and coherence,

e.g., by making winding paths, small openings, and focal

points and creating smooth transitions among objects with

similar form (e.g., Motloch 1991). Our study demonstrates

that standard, operational-scale cutting regimes do affect

mystery and coherence within the scenes changed by traditional logging operations. To retain scenic beauty while

harvesting timber, intermediate cutting practices (group

selection and shelterwood) are least offensive. The choice

between group selection and shelterwood will depend on

characteristics within the ecoregion.

Although we can only speculate on the utility of our findings

for other types of logging operations and other ecoregions,

we offer quantitative credence to the psychological impact

of timber harvesting. Adapting cutting practices to novel

cutting regimes that increase mystery, coherence, complexity, and scenic beauty, while reducing visual penetration

may even foster public acceptance of harvest operations.

ACKNOWLEDGMENTS

We thank William E. Hammitt, Clemson University, Department of Parks, Recreation, and Tourism Management; and

field staff of the Ouachita National Forest, USDA Forest

Service for logistics, and Dennis Jacobs, USDA Forest

Service, Southern Research Station for his review of an

early draft of the manuscript. Stan Zarnoch, a biometrician

with the USDA Forest Service, Southern Research Station,

also offered many helpful suggestions. Data were collected

in partial fulfillment of Li’s Ph.D. dissertation at Clemson

University.

LITERATURE CITED

Anderson, Lee Roger. 1995. Landscape aesthetics: a handbook for

scenery management. Agricultural Handbook No. 701.

Washington, DC: U.S. Department of Agriculture, Forest

Service.

Baker, James B. 1994. An overview of stand-level ecosystem

management research in the Ouachita/Ozark National Forests.

In: Baker, James B., comp. Proc. Ecosystem management

research in the Ouachita Mountains: pretreatment conditions

and preliminary findings. Gen. Tech. Rep. SO-112. New Orleans,

LA: USDA Forest Service, Southern Forest Experiment Station:

18-28.

Benson, Robert E.; Ullrich, James R. 1981. Visual impacts of

forest management activities: findings of public preferences.

Research Paper INT-262. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Forest and Range

Experiment Station. 14 p.

Brown, Thomas C.; Daniel, Terry C. 1990. Scaling of ratings:

concepts and methods. Research Paper RM-293. Ft. Collins,

CO: U.S. Department of Agriculture, Forest Service, Rocky

Mountain Forest and Range Experiment Station. 24 p.

Byrne, B.M. 1994. Structural equations modeling with EOS and

EOS/Windows: basic concepts, application, and programming.

Thousand Oaks, CA: SAGE publications.

Daniel, Terry C.; Boster, Ronald S. 1976. Measuring landscape

aesthetics: the scenic beauty estimation method. Research

Paper RM-167. Ft. Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Forest and Range Experiment

Station. 66 p.

Daniel, Terry C.; Vining, Joanne. 1983. Methodological issues in

the assessment of landscape quality. In: Altman, Irwin.; Wohlwill,

Joachim F., eds. Behavior and the Natural Environment. New

York: Plenum Press: 39-84.

Gramann, James H.; Rudis, Victor A. 1994. Effects of hardwood

retention, season of year, and landform on the perceived scenic

beauty of forest plots in the Ouachita Mountains. In: Baker,

J.B., comp. Proc. ecosystem management research in the

Ouachita Mountains: pretreatment conditions and preliminary

findings. Gen. Tech. Rep. SO-112. New Orleans, LA: USDA

Forest Service, Southern Forest Experiment Station: 223-228.

Guldin, James M.; Baker, James B.; Shelton, Michael G. 1994.

Midstory and overstory plants in mature pine/hardwood stands

of the Ouachita/Ozark National Forests. In: Baker, James B.,

comp., Proceedings of the symposium: ecosystem

management research in the Ouachita Mountains: pretreatment

conditions and preliminary findings. Gen. Tech. Rep. SO-112.

New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station: 29-49.
136

Jones, Geoffrey T. 1993. A guide to logging aesthetics: practical

tips for loggers, foresters, and landowners. Ithaca, NY:

Northeast Regional Agricultural Engineering Service. 28 p.

Kalidindi, N.; Le, A.; Picone, J. [and others]. 1996. Scenic beauty

estimation of forestry images. In: Proceedings: IEEE Southeastcon ’97, 1997 April 12-14; Blacksburg, VA. Piscataway, NJ:

Institute for Electronics and Electrical Engineers: 337-339.

Kaplan, Rachel; Kaplan, Stephen. 1989. The experience of nature:

a psychological perspective. New York: Cambridge University

Press. 336 p.

Li, Ying-Hung; Hammitt, William. 1999. The psychological

perspective of visual penetration in measuring scenic beauty

quality. Taiwan: Journal of Outdoor Recreation Study 12(1): 1-

17.

Motloch, John L. 1991. Introduction to landscape design. New

York: Van Nostrand Reinhold. 307 p.

Ribe, Robert G. 1989. The aesthetics of forestry: what has

empirical preference research taught us? Environmental

Management 13(1): 55-74.

Ruddell, Edward J.; Gramann, James H.; Rudis, Victor A. [and

others]. 1989. The psychological utility of visual penetration in

near-view forest scenic beauty models. Environment and

Behavior 21(4): 393-412.

Rudis, Victor A.; Gramann, James H.; Ruddell, Edward J. [and

others]. 1988. Forest inventory and management-based visual

preference models of southern pine stands. Forest Science 34:

846-863.

Rudis, Victor A.; Thill, Ronald E. [and others]. 1999. Understory

structure by season following uneven-aged reproduction

cutting: a comparison of selected measures 2 and 6 years after

treatment. Forest Ecology and Management. 114/2-3:309-320.

SAS Institute, Inc. 1990. SAS/STAT user’s guide. Version 6, 4th ed.

Cary, NC: SAS Institute, Inc.

SAS Institute, Inc. 1996. The SAS system for windows. Release

6.12. Cary, NC: SAS Institute, Inc.

Vodak, M.C.; Roberts, P.L.; Wellman, J.D. 1985. Scenic impacts of

eastern hardwood management. Forest Science 31:289-301.

APPENDIX

Within each condition, image order is from highest to lowest scenic beauty value.

The legend for each image is:

Treatment condition

Stand: National Forests in Arkansas, stand and compartment code;

Pt: Point number;

Azimuth: compass direction of view when standing at the point:

CD: compact disk and image number.

Psychological attributes, each scaled 1 (lowest) to 10 (highest):

Code Meaning Code Meaning

SBE Scenic beauty estimate CH Coherence

LE Legibility CM Complexity

MY Mystery PV Visual Penetration
137

Untreated

Stand 0284-11 Pt 2 Azimuth 220

CD 0015-024

SBE 8.4 CH 9.0

LE 4.9 CM 3.7

MY 6.6 PV 5.2

Stand 0284-11 Pt 6 Azimuth 150

CD 0015-030

SBE 8.1 CH 5.8

LE 2.4 CM 6.9

MY 8.0 PV 3.0

Stand 0284-11 Pt 3 Azimuth 360

CD 0015-022

SBE 8.1 CH 6.9

LE 2.9 CM 6.6

MY 8.4 PV 2.6

Stand 0605-05 Pt 5 Azimuth 270

CD 2607-068

SBE 7.7 CH 6.5

LE 6.1 CM 5.3

MY 6.5 PV 4.1

Stand 0605-05 Pt 2 Azimuth 120

CD 2607-075

SBE 7.7 CH 6.5

LE 3.2 CM 6.7

MY 7.5 PV 3.0

Stand 0605-05 Pt 4 Azimuth 120

CD 2607-064

SBE 7.7 CH 7.2

LE 4.4 CM 5.6

MY 7.2 PV 3.8

Stand 0023-10 Pt 3 Azimuth 180

CD 2607-007

SBE 7.3 CH 5.2

LE 3.7 CM 6.5

MY 7.2 PV 3.1

Stand 0023-10 Pt 6 Azimuth 150

CD 2607-013

SBE 7.2 CH 3.6

LE 2.9 CM 7.6

MY 6.3 PV 2.1

Stand 0023-10 Pt 6 Azimuth 360

CD 2607-015

SBE 6.9 CH 5.2

LE 3.8 CM 5.8

MY 6.3 PV 2.9
138

Group Selection

Stand 0035-42 Pt 4 Azimuth 300

CD 2607-084

SBE 7.1 CH 5.7

LE 2.0 CM 7.5

MY 7.6 PV 2.6

Stand 0035-42 Pt 3 Azimuth 180

CD 2607-082

SBE 7.0 CH 4.9

LE 2.1 CM 7.6

MY 7.7 PV 1.9

Stand 0035-42 Pt 2 Azimuth 120

CD 2607-081

SBE 6.9 CH 5.2

LE 6.0 CM 7.8

MY 6.9 PV 3.5

Stand 1124-11 Pt 1 Azimuth 240

CD 2607-025

SBE 6.6 CH 6.4

LE 4.4 CM 5.7

MY 7.1 PV 3.5

Stand 1124-11 Pt 4 Azimuth 120

CD 2607-028

SBE 6.5 CH 7.9

LE 5.3 CM 4.2

MY 5.8 PV 4.9

Stand 0046-18 Pt 1 Azimuth 240

CD 0012-050

SBE 5.8 CH 2.8

LE 7.0 CM 7.5

MY 5.8 PV 2.7

Stand 0046-18 Pt 5 Azimuth 270

CD 0012-57

SBE 5.5 CH 4.2

LE 5.1 CM 7.2

MY 5.0 PV 4.1

Stand 1124-11 Pt 3 Azimuth 360

CD 2607-030

SBE 4.8 CH 2.6

LE 3.2 CM 7.6

MY 5.9 PV 2.3

Stand 0046-18 Pt 2 Azimuth 210

CD 0012-052

SBE 2.3 CH 3.9

LE 5.6 CM 4.6

MY 4.0 PV 5.3
139

Shelterwood

Stand 0457-12 Pt 2 Azimuth 030

CD 2607-048

SBE 8.4 CH 6.4

LE 6.9 CM 4.3

MY 4.2 PV 6.7

Stand 1119-21 Pt 3 Azimuth 180

CD 0012-095

SBE 7.0 CH 5.5

LE 5.3 CM 5.4

MY 4.7 PV 5.0

Stand 1119-21 Pt 2 Azimuth 120

CD 0012-090

SBE 7.0 CH 5.0

LE 3.2 CM 5.1

MY 5.0 PV 4.8

Stand 0027-01 Pt 5 Azimuth 090

CD 2607-058

SBE 5.2 CH 4.9

LE 2.1 CM 5.6

MY 3.1 PV 5.9

Stand 0457-12 Pt 4 Azimuth 300

CD 2607-040

SBE 5.2 CH 5.1

LE 5.6 CM 5.3

MY 5.3 PV 4.5

Stand 0457-12 Pt 4 Azimuth 120

CD 2607-039

SBE 5.2 CH 4.1

LE 5.1 CM 5.9

MY 4.9 PV 5.2

Stand 0027-01 Pt 2 Azimuth 030

CD 2607-050

SBE 4.2 CH 3.5

LE 3.6 CM 5.7

MY 4.3 PV 5.4

Stand 1119-21 Pt 1 Azimuth 240

CD 0012-094

SBE 4.2 CH 5.2

LE 4.4 CM 5.1

MY 4.6 PV 5.5

Stand 0027-01 Pt 6 Azimuth 330

CD 2607-060

SBE 3.9 CH 3.3

LE 2.0 CM 5.2

MY 3.4 PV 6.3
140

Clearcut

Stand 0458-16 Pt 6 Azimuth 330

CD 0015-009

SBE 4.2 CH 3.0

LE 5.4 CM 4.2

MY 2.6 PV 6.5

Stand 0458-16 Pt 2 Azimuth 210

CD 0015-002

SBE 4.0 CH 2.6

LE 6.9 CM 4.4

MY 3.5 PV 6.3

Stand 1067-15 Pt 3 Azimuth 360

CD 0012-078

SBE 3.5 CH 3.4

LE 6.5 CM 4.2

MY 3.8 PV 6.8

Stand 1067-15 Pt 5 Azimuth 090

CD 0012-066

SBE 3.2 CH 2.4

LE 5.9 CM 3.2

MY 2.5 PV 7.3

Stand 1067-15 Pt 5 Azimuth 270

CD 0012-067

SBE 2.9 CH 2.8

LE 6.2 CM 4.0

MY 2.6 PV 7.0

Stand 1658-05 Pt 3 Azimuth 180

CD 0012-034

SBE 2.7 CH 2.2

LE 6.9 CM 3.3

MY 2.5 PV 7.4

Stand 1658-05 Pt 5 Azimuth 270

CD 0012-043

SBE 1.9 CH 2.3

LE 8.0 CM 2.3

MY 1.6 PV 8.8

Stand 1067-15 Pt 5 Azimuth 030

CD 0012-068

SBE 1.8 CH 1.7

LE 7.0 CM 2.8

MY 2.1 PV 8.6

Stand 1658-05 Pt 2 Azimuth 210

CD 0012-037

SBE 1.6 CH 1.6

LE 6.4 CM 2.7

MY 1.9 PV 8.5
141

Session 6

Phase III Overview and Wildlife Research

T. Bently Wigley, Moderator
142
143

INTRODUCTION

The phase III landscape study in the Ouachita Mountains

Ecosystem Management Research Project was conceived

as a way to deal with questions about forest management

that cannot be answered at a stand-level scale, but that can

be answered in the context of a watershed that contains

many stands (Hornbeck and Swank 1992, Kessler and others

1992, Lubchenko and others 1991, National Research

Council 1990, O’Hara and others 1994, Swanson and

Franklin 1992). These questions include (1) cumulative

management effects on perennial streams, including both

hydrology and aquatic ecology; (2) the behavior of organisms whose home range encompasses multiple stands or

watersheds; and (3) the degree of change in vegetation in a

watershed as a result of a combination of management

activities in a subset of the stands in that watershed.

The objectives of research in the phase III study are to

(1) quantify core watershed hydrology through modeling

hydrological factors and cumulative hydrological

effects, using a series of flumes and uncontrolled

cross-section gauging stations

(2) characterize and quantify sensitive and critical

elements of aquatic and riparian ecology

(3) quantify terrestrial ecological relationships of

vegetation pattern, ecological classification, wildlife,

and biodiversity at the landscape scale

(4) characterize the social dimensions of the landscape,

including the prehistoric, historic, and current

relationships of people with the land.

To achieve these objectives, four watersheds were selected

to represent a sequence of initial forest management conditions, from relatively unmanaged to intensively managed.

METHODS

Study Area

Three of the four watersheds are east of State Highway 7

on the Winona Ranger District in the combined Jessieville/

Winona Ranger Districts of the Ouachita National Forest in

Saline County, AR. These three contiguous watersheds are

part of the Upper Lake Winona drainage basin, from which

water flows eastward via Alum Creek and its two tributaries,

the North Alum Creek and Bread Creek, into Lake Winona.

Ultimately, those waters flow southerly to the Saline River

of central Arkansas.

The fourth watershed, roughly 10 miles southwest of the

others, is west of Highway 7 on land owned and managed

by Weyerhaeuser Company. Water flows via the Little

Glazypeau Creek into the Ouachita River, below Lake

Ouachita but above Lake Hamilton west of Hot Springs.

The Ouachita and Saline Rivers meet in south Arkansas in

Felsenthal National Wildlife Refuge. Thus, although the four

watersheds are in close proximity and share a similar climatic regime, there are ecological differences between the

Ouachita and Saline river systems that might affect comparisons among the watersheds.

Alum Creek Watershed

The Alum Creek watershed (3,700 acres), the unmanaged

control in the study, was imposed largely on the Alum Creek

Experimental Forest in compartments 1457 and 1460 of the

Winona Ranger District. The Government owns virtually all

of this watershed. The Alum Creek Experimental Forest has

been largely used for upland small-catchment hydrology

research. As a result, most of the experimental forest has

had no management for two decades. The boundary of this

watershed was established using a digital elevation model,

which drew the hydrological unit of interest slightly outside

the current boundary of the Alum Creek Experimental Forest.

Bread Creek Watershed

The Bread Creek watershed (3,800 acres), east of the Alum

Creek watershed, includes compartment 1462 and the south

half of compartment 1456 of the Winona Ranger District.

The Government owns 95 percent of this watershed, and

the balance is owned by Weyerhaeuser Company. The

area lies largely within the timber-available area of the

LANDSCAPE-SCALE RESEARCH IN THE OUACHITA MOUNTAINS OF

WEST-CENTRAL ARKANSAS: GENERAL STUDY DESIGN

James M. Guldin1

1 Research Forest Ecologist and Project Leader, USDA Forest Service, Southern Research Station, Hot Springs, AR.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—A landscape-scale study on forest ecology and management began in 1995 in the eastern Ouachita Mountains. Of four large watersheds, three were within the Winona Ranger District of the Ouachita National Forest, and a

major forest industry landowner largely owned and managed the fourth. These watersheds vary from 3,700 to 9,800

acres. At this scale, replicating treatments for statistical uniformity is impossible; this situation requires other approaches

to statistical design. One approach is temporal replication, which uses multiple years of baseline measurement both prior

to and after treatment as a source of experimental error and hypothesis testing. The second develops models that can be

tested using data subsets, or the development of additional reference watersheds for testing models.
144

Ouachita National Forest. Within the past 20 years, only one

small part of this watershed has been clearcut and replanted

with genetically improved shortleaf pines, mostly in the eastern part of the watershed that was heavily damaged by a

tornado in 1980. Other parts of the watershed have been

thinning and burned by prescription. In this study, these

treatments collectively represent a relatively low intensity of

management.

North Alum Creek Watershed

The third watershed is North Alum Creek (9,800 acres), which

lies north of the Alum Creek and Bread Creek watersheds

in compartments 1445, 1446, 1447, and the north half of

compartment 1456 of the Winona Ranger District. About

half of this watershed is in national forest ownership, and

Weyerhaeuser Company owns and manages the other half.

Most of the industry land has been clearcut in the past 30

years and reforested with genetically improved loblolly pine.

The majority of the national forest land lies within the timberavailable land base and has been managed like the Bread

Creek basin. For this study, the watershed represents a

moderate intensity of forest management.

Glazypeau Watershed

The fourth watershed is the Glazypeau watershed (5,600

acres), approximately 4 miles southwest of Jessieville in

Garland County, AR. Weyerhaeuser Company owns about

95 percent of this watershed, and the balance at the lower

end of the drainage is in national forest. The area supported

mixed shortleaf pine and pine-hardwood forests prior to the

company’s acquisition of the land in the late 1960s. Currently, a large portion of the Glazypeau watershed consists

of genetically improved loblolly pine plantations established

within the past 30 years. As such, it represents the most

intensively managed watershed in the study.

Experimental Design

A key question in watershed research is how to quantify

experimental error among watersheds. Experimental replication of watersheds of this size is impractical, because the

variety of topographic, edaphic, and physiographic conditions in these watersheds invalidates the underlying assumption of homogeneity required to apply parametric statistical

tests. Even if watersheds might have been identified that

would have met this assumption, resources available to the

project prohibit covering a larger area. As a result, alternatives to replication are needed to quantify treatment effects

among watersheds.

Several different approaches to experimental design will be

used in this study, depending on the particular resource

variables and elements of interest. The first is to study

baseline conditions in watersheds for several years prior to

execution of the treatments, then to study conditions after

treatment for a similar number of years. In this way, changes

from baseline conditions can be compared across a number

of years. This approach will generate valid statistical tests

for some parameters, but will result only in descriptive information about other parameters. Yet given the size of the

watersheds and the length of time over which measurements

will occur, descriptive information will be valuable both for

the scientific community and for land managers in the region.

A second approach is to subdivide watersheds into smaller

units, and replicating those. Some elements of the research

objectives will be met using small-scale studies within watersheds. For these, traditional parametric statistical analyses

can be used. The elements for which this approach is

feasible will vary among different research objectives.

A third approach is to use portions of the four watersheds to

develop models of ecosystem attributes, and then to validate

the models in the remaining parts of the watersheds. Work

on model development will proceed by either subdivision of

the existing watersheds or the establishment of separate

watersheds for use in model validation. The use of locations outside the watershed boundaries that meet specific

study objectives will be appropriate in some cases.

Finally, some of the results from the study will be published

with the acknowledgment that data are descriptive rather

than replicated; for some unique and less-intensively studied

attributes or variables, descriptive data will be highly

informative.

RESEARCH GROUPS AND EXPERIMENTAL

APPROACHES

Wildlife Research

The wildlife research group has installed nearly 2,000 sample plots in 4 sets of 500 plots over 4 years. This sampling

design serves as the pretreatment plot network for both

the wildlife and the vegetation groups. The wildlife group

measured habitat variables, and sampled populations of

neotropical migrant and resident birds and herpetofauna.

Vegetation Research

The vegetation research group conducted annual pretreatment baseline surveys on the plot network established by

the wildlife group. The resulting quantitative vegetation data

base quantifies stand structure within and between watersheds. Vegetation measurements include overstory and

midstory woody vegetation, as well as shrub and herbaceous vegetation. Because the vegetation and wildlife

research measured the same plots, they may analyze data

jointly if research dictates an interdisciplinary approach.

Hydrology Research

The hydrological network includes a series of nested, uncontrolled cross sections, three in each core watershed. These

sampling stations gauge streamflow, which will allow scientists to quantify stage-discharge relationships. The study

collects data on water quality as well as quantity in each

watershed.

Aquatic Ecology Research

Aquatic ecological relationships are important in the overall

landscape study design. Studies that quantify those relationships were established in streams within and between

watersheds, as well as off the core watersheds. This

research group studies the nature and composition of fish

communities, the trophic relationships throughout the

aquatic systems, and the changes in aquatic ecological

relationships over time within and between watersheds.
145

Social Science Group

The social science group works primarily outside the core

watersheds. Key responsibilities include studies on decision

making and community involvement, advisory organizations, and forest users.

WATERSHED TREATMENTS

The study is designed to evaluate practical approaches to

forest management on national forest lands. National forest

and ranger district staff worked with research scientists to

identify the desired future conditions proposed as treatments

in this study. This process yielded more than four sets of

desired future condition, so the three contiguous watersheds

were subdivided for imposition of treatment conditions.

Baseline data collected prior to treatment versus data

collected after treatment quantifies differences within and

between watersheds as a result of treatment.

The unmanaged South Alum Creek watershed will support

two desired future conditions. The first is an unmanaged

control condition in the west half of the watershed, which

will be maintained as an unharvested control block. In the

east half of the watershed, a large contiguous block of single-tree selection will be imposed. Monitoring and research

will be conducted to quantify the effects of this desired

future condition across a large area in one operation.

The Bread Creek watershed will not be subdivided and will

support continued management according to standard Forest

Service practice. Reproduction cutting in this block will

emphasize the seed-tree and group selection methods, and

intermediate treatments will emphasize thinning. Prescribed

fire in both immature and mature stands is a hallmark of

standard operational practice on the Ouachita National

Forest, and these stands will be subject to prescribed fire

as called for in operational practice.

The North Alum Creek watershed will be subdivided into

three subwatersheds that reflect different desired future

conditions. In the first, reproduction cutting using the group

selection method will be imposed using large groups

between 2 and 10 acres, in which some groups will retain

overstory trees and others will not. In the second, reproduction cutting under the group selection method will use small

groups < 2 acres; again, some groups will have no residual

trees, while others will. These group selection treatments

are intended to create conditions appropriate for sampling

wildlife species, especially birds, in openings of varied size

and with different within-opening structures. The final treatment in a subunit of this large watershed is shortleaf pinebluestem habitat restoration; this prescription reduces

overstory density and midstory vegetation, and reintroduces

prescribed fire to the landscape.

The Glazypeau watershed will not be subdivided. Standard

industrial forest management practices will continue in

this basin according to schedules and procedures of

Weyerhaeuser Company. In this way, the effects of intensive forest management on a landscape will be quantified.

Scientists and managers will ask several questions as

these treatments are imposed. For example, national forest

management tactics shifted in the past decade from entries

in individual compartments across a district to one larger

scale entry in several contiguous compartments in a given

year. The effect of concentrating management activities on

terrestrial and aquatic ecosystems is not well documented.

The phase III study can address elements of this question,

such as quantifying the differences from baseline levels in

each watershed. In many ways these treatments in the

phase III landscape will represent the most intensive intervention within an agency project because they involve a

concentrated action conducted across a large area in a

short time frame.

On both national forest lands and forest industry lands in

the phase III study, treatments will occur in an operational

context. Within the national forest, treatments are planned

according to standard agency practice. All reproduction

cuttings and thinning will be prepared as commercial timber

sales by the ranger district staff, subject to guidance by the

research group only for meeting specific research objectives

in residual stand condition. All timber sales were scheduled

for the summer of 2000. Site preparation, prescribed burning, and treatment monitoring will commence when treatments have been completed.

SUMMARY

The phase III landscape-scale study was established to

answer specific questions about forest management at

scales larger than an individual stand. Because of difficulties

with assumptions required to apply parametric statistical

tests, the study uses several different sample designs.

These include sampling over a number of years, discrete

smaller studies of a more traditional statistical design, and

subdivision of existing watersheds or identification of new

watersheds for validation testing of models developed in

the core watersheds. Treatments in the watersheds will be

implemented as operational practice by national forest and

industry land managers. Results from the phase III study

will provide valuable guidance for land managers concerned

about effects of forest practices at the landscape scale.

LITERATURE CITED

Hornbeck, J.W.; Swank, W.T. 1992. Watershed ecosystem analysis

as a basis for multiple-use management of eastern forests.

Ecological Applications. 2(3): 238-247.

Kessler, W.B.; Salwasser, H.; Cartwright, C.W., Jr.; Caplan, J.

1992. New perspectives for sustainable natural resources

management. Ecological Applications. 2(3): 221-225.

Lubchenco, Jane; Olson, Annette M.; Brubaker, Linda B. [and

others]. 1991. The sustainable biosphere initiative: an

ecological research agenda. Ecology. 72(2): 371-412.

National Research Council. 1990. Forest research—a mandate for

change. Committee on Forestry Research, Commission on Life

Sciences, Board of Biology [and] Board on Agriculture, National

Research Council. Washington, DC: National Academy Press.

84 p.

O’Hara, Kevin L.; Seymour, Robert S.; Tesch, Steven D.; Guldin,

James M. 1994. Silviculture and our changing profession.

Journal of Forestry. 92(1): 8-13.

Swanson, F.J.; Franklin, J.F. 1992. New forestry principles from

ecosystem analysis of pacific northwest forests. Ecological

Applications. 2(3): 262-274.
146

INTRODUCTION

Recent philosophical changes in natural resource management have focused attention on managing ecosystems at

scales beyond the stand level. This emphasis has led to a

heightened conceptual and theoretical understanding of

landscape patterns and functions. Landscape patterns are

influenced by the composition and development of vegetation following disturbances, as well as the juxtaposition of

these disturbances (Oliver 1981). However, there is a lack

of information regarding these patterns across large-scale

landscapes (Soulé and Kohm 1989, Jeffers 1988).

Properties of forested landscapes, such as patch size, edge

density, and degree of fragmentation have a direct influence

on flora and fauna (Hagan and others 1997, Rolstad 1991,

MacArthur and Wilson 1967). Thus, increased knowledge

of these interactions is needed to make informed management decisions regarding ecologically and sociologically

complex landscapes. However, before studies can address

questions involving landscape functions, landscape structure

must be quantified. Landscape-scale research currently

being conducted in the Ouachita Mountains of Arkansas is

addressing a myriad of questions concerning landscape

interactions of flora and fauna (Fox and others, in press;

Guldin, in press; Tappe and others, in press). This paper

compares structural landscape characteristics for four

watersheds in which these interactions are being studied.

METHODS

Study Areas

We quantified landscape characteristics of four watersheds

included in the Ouachita Mountain Ecosystem Management

Research Project described by Guldin (in press). The watersheds were located in the Ouachita Mountains in Garland

and Saline counties north of Hot Springs, Arkansas. Each

represented different ownerships and management objectives as follows:

1. Little Glazypeau (LG)—2,275 ha predominantly under

Weyerhaeuser Company ownership and intensively

managed for wood products

2. North Alum Creek (NAC)—3,961 ha with approximately

equal mixtures of Weyerhaeuser Company and USDA

Forest Service ownership. Weyerhaeuser Company

lands were intensively managed for wood products and

USDA Forest Service lands were managed under

multiple-use standards and guidelines

3. Bread Creek (BC)—1,535 ha predominantly under USDA

Forest Service ownership and historically managed

under multiple-use standards and guidelines

4. South Alum Creek (SAC)—1,499 ha predominantly

under USDA Forest Service ownership and historically

received minimal management.

Inherent in the differing proportions of ownership and

management objectives of each watershed was an implicit

LANDSCAPE CHARACTERIZATION OF FOUR WATERSHEDS

UNDER DIFFERENT FOREST MANAGEMENT SCENARIOS

IN THE OUACHITA MOUNTAINS OF ARKANSAS

Philip A. Tappe, Robert C. Weih, Jr., Ronald E. Thill,

M. Anthony Melchiors, and T. Bently Wigley1

1 Associate Professors, School of Forest Resources and Arkansas Forest Resources Center, University of Arkansas – Monticello, Monticello,

AR 71656; Supervisory Research Wildlife Biologist, USDA Forest Service, Southern Research Station, Nacogdoches, TX 75962; Wildlife

Research Biologist, Weyerhaeuser Company, Federal Way, WA 98063-9777; and Manager, Wildlife and Watershed Program, National

Council of the Paper Industry for Air and Stream Improvement, Clemson University, Clemson, SC 29634, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Recent changes in philosophy concerning forest management have focused attention on managing ecosystems at scales beyond the stand level. Properties of forested landscapes, such as patch size and shape, edge density,

and interspersion have direct influences on flora and fauna. However, there is little information regarding spatial patterns

and processes across large-scale landscapes. We quantified landscape characteristics for four watersheds in the

Ouachita Mountains that represent different ownerships and management objectives. The watersheds included (1) an

intensively managed landscape predominantly under forest industry ownership, (2) a landscape that has historically been

managed under USDA Forest Service standards and guidelines, (3) a landscape composed of a mixture of forest industry

and USDA Forest Service lands, and (4) a landscape that has received minimal management and now is largely mature

forest. Using multi-temporal Landsat Thematic Mapper satellite imagery in conjunction with field-collected data, landcover

classifications were developed. This information was incorporated into a geographic information system and landscape

metrics were computed. Metrics such as mean patch size and density, edge density, and interspersion varied by watershed. Differences in watersheds at both the watershed and landcover class levels suggest that the dynamics of some

ecological interactions are likely to also be different in each watershed. However, the interpretation of specific watershed

characteristics is dependant on the particular phenomena being investigated. The quantified differences in landscape

characteristics of each watershed provide important information that can aid in making management decisions in our

ecologically and sociologically complex forests.
147

continuum of intensity of forest management activity. The

highest intensity of forest management activity occurred on

LG, followed in descending order by NAC, BC, and SAC.

Remote Sensing and Landcover Classification

A Geographic Information System (GIS) was utilized to help

characterize the study areas. Delineated forest stand boundaries on each watershed represented discrete administrative management units and not necessarily unique landcover

types. Thus, spectral landcover classes initially were identified based on Landsat Thematic Mapper (TM) data (30-m

resolution, bands 1–7) using scenes from February, April,

and July, 1995. The 21 bands (7 from each time period)

were clustered using an isodata clustering algorithm

(unsupervised classification) with a maximum-likelihood

classification to create 255 spectral classes (Richards and

Jia 1999).

Transects with 50-m radius plots spaced approximately

200 m apart were established in each watershed. Vegetation

data were collected during May–August 1995 on a total of

524 plots: 124 plots in LG, 235 plots in NAC, 75 plots in

BC, and 90 plots in SAC. Similar to a classification scheme

used by Hagan and others (1997), each plot was placed

into 1 of 5 forest composition classes, 1 of 3 canopy cover

classes, and 1 of 3 tree height classes (table 1). This information was then used to categorize the unsupervised 255

spectral classes into 12 landcover classes (table 2). This

process used both the spectral information obtained from

the unsupervised classification and ancillary GIS layers

obtained from the Natural State Digital Database (http://

sal.uamont.edu/sal/nsdd/) at the Spatial Analysis Laboratory, University of Arkansas - Monticello. Landcover information was incorporated into a GIS, and watershed-level

and landcover class-level indices were computed using

Fragstats v.2.0 (McGarigal and Marks 1994). These indices

included percent of a landcover class comprising a watershed, patch density (PD), (number per 100 ha), largest

patch index (LPI), (percent of a watershed comprised by

the largest patch), mean patch size (MPS) and coefficient

Table 2—Landcover classes used to characterize four watersheds

under different forest management scenarios in the Ouachita

Mountains of Arkansas

Forest characteristics

Landcover type Composition Canopy cover Height

Young, open pine Pine Low Low

Thin, open pine Pine Medium Medium

Dense canopy pine Pine High Medium

Sparse pine Pine Low Medium

Pine/hardwood Pine/hardwood Medium High

Mixed forest Mixed High High

Hardwood/pine Hardwood/pine High Medium

Hardwood Hardwood High Medium

Sparse hardwood Hardwood Low Medium

Grass Grass NA NA

Rock/road Rock/road NA NA

Water Water NA NA

NA = not applicable.

Table 1—Plot characteristics used to categorize an unsupervised spectral

classification into landcover classes

Characteristic Category Value range

Forest composition (%) Pine ≥ 75 pine

Pine/hardwood ≥ 60 pine < 75

Mixed ≥ 40 pine or hardwood < 60

Hardwood/pine ≥ 60 hardwood < 75

Hardwood ≥ 75 hardwood

Canopy cover (%) Low 0–33

Medium 34–75

High 76–100

Tree height (m) Low 0–7

Medium > 7–13

High > 13
148

of variation (CV), edge density (ED), (m per ha), mean

shape index (MSI), (mean perimeter-to-area ratio), mean

nearest neighbor distance (MNN), (mean distance from a

patch to the nearest neighboring patch of the same type)

and CV, and McGarigal and Mark’s (1994) interspersion/

juxtaposition index (IJI), (extent to which patch types are

interspersed).

RESULTS

Area Metrics

The four watersheds differed in their landcover composition

(table 3). Landcover classes composed of ≥ 75 percent

pine comprised 50.3 percent of LG, 28.7 percent of NAC,

15.8 percent of BC, and 1.2 percent of SAC. Proportions of

pine/hardwood were approximately 1.5 times greater in

SAC than in BC, 2 times greater than in NAC, and 4 times

greater than in LG. Proportions of mixed forest were approximately 1.1 times greater in SAC than in BC, 1.5 times

greater than in NAC, and 3 times greater than in LG. Within

other landcover classes, proportions varied between watersheds by < 8.5 percent (table 3).

Overall, LPI was highest for SAC, followed in decreasing

order by BC, NAC, and LG (table 4). The LPI varied < 4.7

percent within all landcover classes with the exceptions of

dense canopy pine and pine/hardwood classes (table 4).

The largest patch of dense canopy pine comprised a larger

proportion of LG than it did in any other watershed (approximately 6 times greater than NAC, 9 times greater than BC,

and 36 times greater than SAC). The largest patch of pine/

hardwood comprised a larger proportion of SAC than it did

in any other watershed (approximately 1.5 times greater

than BC, 8 times greater than NAC, and 12 times greater

than LG). Young/open pine in BC had notably higher LPI

than in other watersheds.

Patch Metrics

At the watershed-level, LG had approximately 19 patches

per 100 ha more than NAC, 24 patches per 100 ha more

Table 3—Percent and area of 12 landcover classes within 4 watersheds under different

forest management scenarios in the Ouachita Mountains of Arkansas

Little North South

Landcover class Glazypeau Alum Creek Bread Creek Alum Creek

- - - - - - - - - - - - - - - - - percent and area (ha) - - - - - - - - - - - - - - -

Young/open pine 11.2 7.4 11.4 0.5

(255.4) (292.3) (174.6) (7.2)

Thin/open pine 11.0 13.2 2.7 0.4

(249.3) (524.3) (41.5) (6.4)

Dense canopy pine 24.4 7.9 1.7 0.3

(554.2) (314.0) (26.9) (4.8)

Sparse pine 3.7 0.2 0.0 0.0

(84.6) (7.4) (0.0) (0.0)

Pine/hardwood 11.3 24.5 35.6 46.6

(257.5) (970.8) (546.8) (698.2)

Mixed forest 9.0 19.0 25.8 27.8

(204.6) (751.1) (395.2) (416.5)

Hardwood/pine 12.5 16.2 10.7 7.89

(284.1) (639.5) (164.3) (118.3)

Hardwood 9.5 8.8 10.1 14.1

(216.7) (347.7) (154.8) (212.3)

Sparse hardwood 6.4 2.8 2.0 0.5

(145.0) (111.7) (30.5) (7.6)

Grass 0.6 < 0.1 0.0 1.79

(14.4) (1.6) (0.0) (26.7)

Rock/pavement 0.4 0.0 < 0.1 0.1

(8.4) (0.0) (0.1) (1.2)

Water < 0.1 < 0.01 0.0 0.0

(0.4) (0.2) (0.0) (0.0)

 Total 100.0 100.0 100.0 100.0

(2,274.8) (3,960.6) (15,34.7) (1,499.2)
149

than BC, and 33 patches per 100 ha more than BC (table

4). PD for young per open pine was highest in LG, followed

in descending order by NAC, BC, and SAC. PD was also

highest in LG and lowest in SAC for thin/open pine, with

NAC and BC having similar PD. Within other landcover

classes, PD varied among watersheds by < 8 patches per

100 ha.

Overall, SAC had a MPS 0.23 ha larger than BC, 0.32 ha

larger than NAC, and 0.59 ha larger than LG (table 5). MPS

followed a similar pattern for the pine/hardwood class. MPS

for dense canopy pine followed a reverse pattern, with LG

having the largest MPS, followed in decreasing order by

NAC, BC, and SAC. Young/open pine in BC had notably

higher MPS than in other watersheds.

Overall, relative variability of MPS was greatest for SAC,

with MPS CV approximately 1.5 times greater than that for

BC, and 2 times greater than that for NAC and LG (table 5).

MPS CV followed a similar pattern for the mixed forest class.

MPS CV for young/open and dense canopy pine in LG,

followed a reverse pattern, with LG having the largest MPS

CV, followed in decreasing order by NAC, BC, and SAC.

Edge and Shape Metrics

At the watershed-level, LG had approximately 11 m per ha

more edge than NAC, 19 m per ha more edge than BC, and

34 m per ha more edge than SAC (table 6). ED followed a

similar pattern for all landcover classes composed of ≥ 75

percent pine, with LG having, on average, approximately

19 m per ha more edge than NAC, 41 m per ha more edge

than BC, and 56 m per ha more edge than SAC. Pine/hardwood and mixed forest classes followed a reverse pattern.

ED of the pine/hardwood class in SAC was approximately

21 m per ha more than in BC, 50 m per ha more than in

SAC, and 77 m per ha more than in LG. ED of the mixed

pine forest class in SAC was approximately 4 m per ha

more than in BC, 35 m per ha more than in SAC, and 80 m

per ha more than in LG.

Table 4—Patch density and largest patch index for four watersheds under different

forest management scenarios in the Ouachita Mountains of Arkansas

Patch density and largest patch index

Little North South

Landcover class Glazypeau Alum Creek Bread Creek Alum Creek

 - - - - - - - - - - - - - - no./100 ha and (percent) - - - - - - - - - - - - - - -

Young/open pine 11.08 6.54 1.43 0.47

(2.52) (1.01) (7.18) (0.43)

Thin/open pine 14.29 6.34 6.45 0.27

(1.33) (4.39) (0.48) (0.25)

Dense canopy pine 4.26 4.67 1.82 0.67

(7.16) (1.17) (0.76) (0.20)

Sparse pine 1.63 0.03 — —

(1.83) (0.19) — —

Pine/hardwood 9.32 9.85 8.21 9.07

(3.31) (4.91) (22.02) (39.34)

Mixed forest 14.29 15.07 14.6 17.01

(0.63) (1.03) (3.96) (2.53)

Hardwood/pine 16.66 17.24 19.68 16.61

(1.07) (1.31) (0.49) (0.56)

Hardwood 9.63 7.47 11.01 13.07

(1.47) (3.54) (4.94) (5.59)

Sparse hardwood 10.38 7.75 7.04 2.8

(1.50) (0.17) (0.17) (0.04)

Grass 1.71 0.3 — 0.47

(0.13) (0.01) — (1.70)

Rock/pavement 0.84 — 0.07 0.07

(0.12) — (0.01) (0.08)

Water 0.09 0.03 — —

(0.01) (< 0.01) — —

 All classes 94.18 75.28 70.31 60.49

(7.16) (4.91) (22.02) (39.34)
150

Overall, MSI was similar for all watersheds (table 6). No

consistent trends in MSI were observed at the landcover

class level.

Nearest Neighbor and Interspersion Metrics

Overall, MNN was similar for all watersheds (table 7). Likewise, no consistent trends in MNN were observed at the

landcover class level. However, SAC had a notably higher

MNN than other watersheds for both the thin/open (averaging approximately 675 m more) and dense canopy pine

(averaging approximately 227 m more) classes.

Overall, relative variability of MNN was greatest for LG,

followed by SAC (table 7). MNN CV for LG and SAC were

approximately 1.5 to 2 times greater than those for NAC

and BC. No consistent trends in MNN CV were observed at

the landcover class level. However, SAC had a notably

higher MNN CV for both the hardwood pine and hardwood

classes, each averaging approximately 1.5 times greater

than in other watersheds. The MNN CV for the young/open

pine class in BC also had a notable CV, averaging 1.5

times greater than in other watersheds.

At the watershed level, the interspersion of available patch

types is about 75 percent of the maximum possible equitable distribution in LG, whereas NAC and BC are at about

65 percent and SAC is below 50 percent (table 8). The IJI is

< 50 percent for the pine/hardwood and mixed forest classes

in SAC, and > 80 percent for the young/open pine class in

BC, the thin/open pine class in SAC, and the hardwood/

pine class in LG.

CONCLUSIONS

Several landscape characteristics suggest a gradient of

spatial heterogeneity represented by differing proportions of

ownership, management objectives, and intensity of forest

management activity. At the watershed level, PD, ED, and

IJI all were highest for LG, followed in descending order by

Table 5—Mean patch size and coefficient of variation for four watersheds under

different forest management scenarios in the Ouachita Mountains of Arkansas

 Mean patch size and coefficient of variation

Little North South

Landcover class Glazypeau Alum Creek Bread Creek Alum Creek

- - - - - - - - - - - - - - - - - - - ha and percent - - - - - - - - - - - - - - - - - - -

Young/open pine 1.01 1.13 7.94 1.03

(525.43) (381.24) (292.19) (216.41)

Thin/open pine 0.77 2.09 0.42 1.6

(371.89) (585.17) (207.58) (89.82)

Dense canopy pine 5.71 1.70 0.96 0.49

(389.99) (285.52) (240.71) (180.53)

Sparse pine 2.29 7.38 — —

(314.38) (0.00) — —

Pine/hardwood 1.21 2.49 4.34 5.13

(462.00) (523.14) (701.76) (980.97)

Mixed forest 0.63 1.26 1.76 1.63

(215.20) (263.06) (298.34) (270.75)

Hardwood/pine 0.75 0.94 0.54 0.47

(304.52) (339.43) (194.65) (213.04)

Hardwood 0.99 1.17 0.92 1.08

(341.98) (721.50) (641.86) (562.32)

Sparse hardwood 0.61 0.36 0.28 0.18

(491.84) (189.24) (151.95) (77.37)

Grass 0.37 0.14 — 3.83

(147.58) (79.35) — (231.59)

Rock/pavement 0.44 — 0.09 1.17

(145.21) — (0.00) (0.00)

Water 0.18 0.18 — —

(< 0.01) (0.00) — —

 All classes 1.06 1.33 1.42 1.65

(561.47) (531.84) (811.96) (1,205.51)
151

NAC, BC, and SAC. Conversely, LPI and MPS were largest

for SAC, followed in descending order by BC, NAC, and

LG. These observed landscape characteristics suggest that

the watersheds can be ranked in order of decreasing

spatial heterogeneity as LG > NAC > BC > SAC.

For most landcover classes composed of ≥ 75 percent pine,

larger, more dense patches were well interspersed in LG

than in the other watersheds. Additionally, they comprised

50 percent of the area of LG. In contrast, larger pine/hardwood patches occurred in SAC, with the largest accounting

for 39 percent of the watershed area. Mixed forest classes

comprised > 25 percent of BC and SAC, where they also

had their largest MPS and ED. However, interspersion was

poor (< 50 percent) in SAC. Similar proportions and patch

sizes of the hardwood/pine class occurred in all watersheds.

The hardwood class comprised < 15 percent of all watersheds, but proportionally occurred most in SAC.

LG was characterized by a higher proportion of pine landcover classes, more and smaller patches, more edge, and

higher interspersion than the other watersheds. SAC was

characterized by a higher proportion of pine-hardwood and

mixed forest landcover classes, fewer and larger patches,

less edge, and lower interspersion than the other watersheds. The NAC and BC watersheds were intermediate in

these measures and varied relative to the intensity of forest

management. The differences in watersheds at both the

watershed and class levels indicate that the dynamics of

some ecological interactions are likely to also be different in

each watershed. However, the interpretation of specific

watershed characteristics is dependant on the particular

phenomena being investigated.

Table 6—Edge density and mean shape index for four watersheds under different forest

management scenarios in the Ouachita Mountains of Arkansas

 Edge density and mean shape index

Little North South

Vegetation class Glazypeau Alum Creek Bread Creek Alum Creek

 - - - - - - - - - - - - - - - - m/ha and (percent) - - - - - - - - - - - - - - - -

Young/open pine 47.83 30.25 24.65 1.88

(1.28) (1.26) (1.51) (1.19)

Thin/open pine 68.52 48.22 20.00 2.12

(1.36) (1.45) (1.23) (1.55)

Dense canopy pine 59.94 31.66 8.02 1.80

(1.48) (1.38) (1.26) (1.22)

Sparse pine 10.99 0.53 — —

(1.32) (1.93) — —

Pine/hardwood 54.44 81.21 110.37 131.28

(1.35) (1.38) (1.47) (1.38)

Mixed forest 62.06 106.50 137.10 141.57

(1.34) (1.49) (1.58) (1.50)

Hardwood/pine 80.07 94.89 76.59 55.24

(1.38) (1.38) (1.32) (1.24)

Hardwood 42.87 31.09 37.59 59.34

(1.30) (1.22) (1.22) (1.27)

Sparse hardwood 36.51 22.49 17.20 5.32

(1.23) (1.22) (1.16) (1.11)

Grass 5.03 0.48 — 3.44

(1.24) (1.07) — (1.26)

Rock/pavement 2.31 — 0.08 0.50

(1.16) — (1.00) (1.66)

Water 0.16 0.05 — —

(1.06) (1.06) — —

 All classes 235.36 223.68 215.80 201.25

(1.33) (1.36) (1.35) (1.34)
152

Table 7—Mean nearest neighbor and coefficient of variation for four watersheds under

different forest management scenarios in the Ouachita Mountains of Arkansas

 Mean nearest neighbor and coefficient of variation

Little North South

Landcover class Glazypeau Alum Creek Bread Creek Alum Creek

 - - - - - - - - - - - - - - - - - m and (percent) - - - - - - - - - - - - - - - - -

Young/open pine 54.19 56.43 145.66 124.28

(77.49) (76.22) (113.87) (72.33)

Thin/open pine 51.58 43.72 53.03 724.75

(73.43) (63.62) (61.16) (89.97)

Dense canopy pine 54.34 53.99 98.94 305.45

(130.96) (89.29) (206.01) (158.71)

Sparse pine 56.99 0.00 — —

(106.15) (0.00) — —

Pine/hardwood 62.14 46.75 41.16 41.20

(91.00) (73.38) (48.07) (60.35)

Mixed forest 51.39 42.16 37.80 37.89

(87.56) (76.77) (71.43) (47.87)

Hardwood/pine 45.66 46.04 50.52 65.84

(75.57) (73.80) (64.14) (102.00)

Hardwood 67.59 83.72 72.62 60.57

(103.92) (92.58) (88.68) (154.24)

Sparse hardwood 71.85 77.57 90.37 79.70

(84.18) (96.95) (72.56) (109.69)

Grass 163.86 332.91 — 206.77

(122.83) (111.72) — (81.24)

Rock/pavement 190.53 — 0.00 0.00

(109.78) — (0.00) (0.00)

Water 4,185.62 0.00 — —

(< 0.01) (0.00) — —

 All classes 63.10 54.70 57.70 60.90

(223.63) (104.24) (106.67) (175.79)

Table 8—Interspersion/juxtaposition index for four watersheds under different forest

management scenarios in the Ouachita Mountains of Arkansas

 Interspersion/juxtaposition index

Little North South

Landcover class Glazypeau Alum Creek Bread Creek Alum Creek

 - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - -

Young/open pine 71.80 67.50 80.85 77.06

Thin/open pine 62.04 65.42 68.14 84.10

Dense canopy pine 56.45 52.76 64.39 68.12

Sparse pine 53.41 26.94 — —

Pine/hardwood 67.77 57.36 49.46 44.32

Mixed forest 69.55 58.40 58.15 46.51

Hardwood/pine 81.85 66.61 62.39 60.09

Hardwood 74.01 61.88 73.42 50.95

Sparse hardwood 78.19 66.88 73.08 60.65

Grass 78.71 53.57 — 66.22

Rock/pavement 72.72 — 33.33 53.40

Water 44.92 19.57 — —

 All classes 75.26 66.53 64.37 48.78
153

ACKNOWLEDGMENTS

We thank several individuals who provided field assistance

with this study, including H. Garner, D.G. Peitz, R.W. Perry,

and M. Sams. C. Fitzgerald assisted with landcover classification and analyses.

LITERATURE CITED

Fox, S.F.; Shipman, P.A.; Thill, R.E.; Phelps, J.P. [In press].

Amphibian communities under diverse forest management in

the Ouachita Mountains, Arkansas. In: Guldin, James M., tech.

comp. Ouachita and Ozark Mountains symposium: ecosystem

management research. Gen. Tech. Rep. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station.

Guldin, J.M. [In press]. Landscape scale research in the Ouachita

Mountains of west-central Arkansas: general study design. In:

Guldin, James M., tech. comp. Ouachita and Ozark Mountains

symposium: ecosystem management research. Gen. Tech.

Rep. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station.

Hagan, J.M.; McKinley, P.S.; Meehan, A.L.; Grove, S.L. 1997.

Diversity and abundance of landbirds in a northeastern industrial forest. Journal of Wildlife Management 61(3):718-735.

Jeffers, J.N.R. 1988. Statistical and mathematical approaches to

issues of scale in ecology. Pages 47-56 in T. Rosswall, R.G.

Woodmansee, and P.G. Risser, eds. Scales and Global

Change. Wiley, New York, NY.

MacArthur, R.H.; Wilson, E.O. 1967. The theory of island biogeography. Princeton University Press, Princeton, NJ. 203 pp.

McGarigal, K.; Marks, B.J. 1994. FRAGSTATS. version 2.0 user’s

manual. Oregon State University, Corvallis, OR. 67 pp.

Oliver, C.D. 1981. Forest development in North America following

major disturbances. Forest Ecology Management 3:153-168.

Richards, J.A.; Jia, X. 1999. Remote sensing digital image analysis. Springer-Verlag, NY. 384 p.

Rolstad, J. 1991. Consequences of forest fragmentation for the

dynamics of bird populations: conceptual issues and the

evidence. Biological Journal Linnean Society 42: 149-163.

Soule, M.E.; Kohm, K.A. 1989. Research priorities for conservation

biology. Island Press, Washington, D.C. 97 pp.

Tappe, P.A.; Thill, R.E.; Melchiors, M.A.; Wigley, T.B. [In press].

Breeding bird communities on four watersheds under different

forest management scenarios in the Ouachita Mountains

of Arkansas. In: Guldin, James M., tech. comp. Ouachita

and Ozark Mountains symposium: ecosystem management

research. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station.
154

INTRODUCTION

Concern for many migratory landbird species has led to an

increased emphasis on managing ecosystems at scales

beyond the stand level. Birds are often viewed as indicators

of ecosystem health due to their sensitivity to environmental

variability (Maurer 1993). Growing evidence suggests that

populations of many species of neotropical migratory birds

(NTMB), (species that migrate each year between temperate

breeding areas and tropical wintering areas) are declining

and that these declines have accelerated in recent years

(Robbins and others, 1989; Askins and others, 1990; Finch

1991). Available information suggests that these populations

may be limited by circumstances on both their wintering and

breeding habitat, as well as along their migration routes

(Sherry and Holmes 1993). Factors that have been implicated

on their breeding range include habitat destruction/alteration, forest fragmentation, increased nest parasitism by

brown-headed cowbirds (Molothrus ater), and increased

predation, especially in edge-dominated habitat.

Substantial knowledge exists on stand-level avian community

relationships, but information is needed to assess the

impacts of different types, intensities, and spatial arrangements of forest management practices on NTMB at the

landscape/watershed level. In the Ozark-Ouachita Highland

physiographic area, Hunter and others (1993) identified

several neotropical migratory species to be declining in

numbers, including 11 of 22 species that inhabit mature

forest and 4 of 6 species that inhabit forest edges. Where

landscapes are fragmented by agriculture/urbanization,

fragmentation has been implicated in NTMB declines. However, the impacts on bird populations of forest fragmentation that result in a mosaic of different stand compositions

and ages with little or no agriculture/urban inclusions

(characteristic of much of the Ouachita Mountains) are

unknown.

This paper reports on results of a 4-year study (1995 – 1998)

of breeding bird communities on four watersheds under

different levels of forest management intensity. Our objectives were to (1) compare avian community and nesting

guild characteristics (numbers of individuals, species richness, diversity, and evenness) across watersheds, and (2)

relate these community characteristics to landscape attributes. We documented community characteristics of migrant

and resident species, as well as three nesting guilds: canopy

nesters, cavity nesters, and ground and shrub nesters.

METHODS

Study areas

Four watersheds included in the Ouachita Mountain Ecosystem Management Research Project described by Guldin

(this volume) and Tappe and others (this volume) served as

the study areas. The watersheds were located in Garland

and Saline Counties in the Ouachita Mountains near Hot

Springs, Arkansas. Past and current forest management

practices on each watershed reflected different combinations of forest ownership and distinct management strategies, thus providing a range of intensity of management

activities. Consequently, these watersheds differ markedly

in many respects, including landcover composition and

distribution characteristics, mean patch size, and edge

density (Tappe and others, in press).

The Little Glazypeau watershed (LG), consisting of 2,275 ha

and managed largely for sawlog production by Weyerhaeuser

BREEDING BIRD COMMUNITIES ON FOUR WATERSHEDS

UNDER DIFFERENT FOREST MANAGEMENT SCENARIOS IN

THE OUACHITA MOUNTAINS OF ARKANSAS

Philip A. Tappe, Ronald E. Thill, M. Anthony Melchiors, and T. Bently Wigley1

1 Associate Professor, School of Forest Resources and Arkansas Forest Resources Center, University of Arkansas – Monticello, Monticello,

AR 71656; Supervisory Research Wildlife Biologist, USDA Forest Service, Southern Research Station, Nacogdoches, TX 75962; Wildlife

Research Biologist, Weyerhaeuser Company, Federal Way, WA 98063-9777; and Manager, Wildlife and Watershed Program, National

Council of the Paper Industry for Air and Stream Improvement, Clemson University, Clemson, SC 29634, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Concern for many migratory landbird species has led to an increased emphasis on managing ecosystems at

scales beyond the stand level. We characterized breeding bird numbers, species richness, diversity, and evenness on four

watersheds in the Ouachita Mountains of west-central Arkansas. These four watersheds represented different ownerships

and management objectives, and thus differed markedly in many respects, including landcover composition and distribution, mean patch size, and edge density. Point count surveys were conducted by three observers on approximately 525

different 50-m radius plots each spring during 1995-1998, for a total of 2,108 plots. A total of 19,030 birds were encountered representing 97 species. Patterns in numbers of individuals, species richness, and diversity appeared associated

with the level of spatial heterogeneity and landscape composition in each watershed. Numbers of individuals per plot,

species per plot, and diversity per plot for migrant and resident species were generally all highest in watersheds with high

levels of management activity and lowest in watersheds with low levels of management activity. Watersheds were similar

in regard to cavity and canopy nesting bird community characteristics. Ground and shrub nesting bird community

characteristics were related to amounts of available early successional habitats. Species of special concern were

represented in all watersheds.
155

Company, represented the most intensively managed watershed. Much of the second-growth shortleaf pine (Pinus

echinata)-hardwood forest that originally covered this watershed had been harvested and planted to loblolly pine (P.

taeda) plantations that ranged in size from 9 to 142 ha.

These plantations are normally thinned twice, pruned up to

approximately 5 to 8 m high, fertilized, and harvested at 30

to 35 years of age. Other unharvested and selectively

harvested acreage in the watershed occurs on rocky ridge

tops, on steep slopes, and in streamside management

zones that were retained for watershed protection and to

provide wildlife habitat diversity.

The North Alum Creek watershed (NAC) was of mixed ownership, with about half of the area under Weyerhaeuser

Company management and half under USDA Forest Service

management. This watershed encompasses 3,961 ha of

land under a diverse range of management activities, ranging from no management to intensively managed loblolly

pine plantations similar to that in the LG watershed. USDA

Forest Service land within this watershed is primarily mixed

shortleaf pine–hardwood forest and has been managed

using a mixture of silvicultural approaches to obtain multiple

objectives as set by the Ouachita National Forest.

The 1,535-ha Bread Creek watershed (BC), primarily USDA

Forest Service land, has been managed according to

prevailing Forest Service standards and guides for several

decades. It is composed primarily of mixed shortleaf pine–

hardwood forest that has been managed similar to USDA

Forest Service land located in the NAC watershed.

The South Alum Creek watershed (SAC) represented the

lowest management intensity. This 1,499-ha watershed,

which is designated as the Alum Creek Experimental

Forest, is almost entirely USDA Forest Service land and

has received minimal management over the past several

decades; it now consists of a mature, mixed forest over the

majority of the area.

More detailed information on landcover characteristics and

patch and landscape attributes can be found in Tappe and

others (in press). Tappe and others (in press) ranked the

watersheds in order of decreasing spatial and compositional heterogeneity as LG>NAC>BC>SAC.

Bird Surveys

Because of the large sizes of the watersheds, adequate

coverage of all portions of each watershed was not logistically possible within a single year using permanent points.

Thus, we installed 505 – 550 plots each year to allow for

sampling all portions of each watershed. In 1995, nearly

113 km of transects with plots located approximately 200 m

apart were established over the four watersheds. In 1996,

the same transects were used, but new plots were located

between the 1995 plots by shifting plot centers 100 m. In

1997, approximately 113 km of new transects were established between the original transects and a new set of plots

were used. In 1998, new plots were located between the

1997 plots, along the same transects. This resulted in 2,108

sample plots over the duration of the study (table 1).

Birds were sampled between May 6 and June 9 each year

using 50-m fixed-radius point counts. Each plot was censused once by three different observers. All counts lasted 5

minutes and were conducted within 3.5 hours of sunrise on

days with little or no rain and with winds < 6-11 kph.

Analyses

Bird counts from each observer were combined to provide

one data set for each point sampled within a given year.

Species that are not known to breed in the Ouachita Mountains of Arkansas were not used in any analyses. Numbers

of individuals, species richness, diversity (Shannon and

Weiner 1963), and evenness were computed for each point

for all breeding birds combined and for each of the following subsets: migrants (short- and long-distance combined),

residents, canopy nesters, cavity nesters, and ground and

shrub nesters. A 2-way analysis-of-variance was used to

test (α ≤ 0.05) for year, watershed, and year-watershed

interaction effects. If a year-watershed interaction existed,

differences in the watershed effect were interpreted within

years using a one-way analysis-of-variance. Mean separation was accomplished using Fisher’s least significant

differences (LSD) procedure. Because watersheds could

not be replicated, means across the three observers within

years constituted pseudoreplication. This limited our inference space, and thus constrained our results and conclusions to the four specific watersheds on which the study

was conducted.

Table 1—Number of points used each year for sampling breeding bird

communities on four watersheds under different levels of forest management

activity in the Ouachita Mountains of Arkansas

Year

Watershed 1995 1996 1997 1998 Total

 - - - - - - - - - - - - - - - - - - number - - - - - - - - - - - - - - - - - -

Little Glazypeau 124 117 154 141 536

North Alum Creek 235 226 232 224 917

Bread Creek 75 75 76 76 302

South Alum Creek 90 87 88 88 353

 Total 524 505 550 529 2,108
156

Table 2—Species lista and frequencies of occurrence of birds identified during 50-m fixed-radius point counts

during May–June 1995 through 1998 on four watersheds under different levels of forest management activity

in the Ouachita Mountains of Arkansasb

Birds per 100 plots

Species Scientific name n Total LG NAC BC SAC

percent

Red-eyed vireo Vireo olivaceus 3,219 16.92 126.62 173.77 144.92 141.41

Pine warbler Dendroica pinus 1,965 10.33 79.48 83.72 102.95 128.17

Indigo bunting Passerina cyanea 1,556 8.18 107.02 81.64 45.90 25.35

Black and white warbler Mniotilta varia 1,150 6.04 63.96 63.06 42.62 27.32

Carolina chickadee Parus carolinensis 858 4.51 56.38 36.28 35.41 31.83

White-eyed vireo Vireo griseus 798 4.19 75.60 37.38 14.43 0.85

Blue-gray gnatcatcher Polioptila caerulea 708 3.72 75.42 23.83 21.64 4.51

Ovenbird Seiurus aurocapillus 659 3.46 4.81 32.13 31.48 68.45

Yellow-breasted chat Icteria virens 635 3.34 61.92 27.21 16.07 0.56

Prairie warbler Dendroica discolor 613 3.22 55.64 26.34 22.62 0.56

Scarlet tanager Piranga olivacea 592 3.11 19.96 31.26 25.57 33.80

Common yellowthroat Geothlypis trichas 484 2.54 51.39 20.87 4.59 0.28

Tufted-titmouse Parus bicolor 460 2.42 27.17 22.30 11.48 20.85

Hooded warbler Wilsonia critrina 392 2.06 37.15 19.45 3.28 0.85

Carolina wren Thryothorus ludovicianus 371 1.95 34.94 14.21 7.87 7.89

Cedar waxwingb Bombycilla cedrorum 337 1.77 18.11 17.38 16.39 8.45

Northern cardinal Cardinalis cardinalis 296 1.56 32.72 9.84 9.18 0.28

Kentucky warbler Oporomis formosus 289 1.52 29.39 11.80 6.89 0.28

Summer tanager Piranga rubra 272 1.43 16.45 13.11 7.21 11.55

Pileated woodpecker Dryocopus pileatus 247 1.30 8.32 11.37 11.48 17.75

Great-crested flycatcher Myiarchus crinitus 233 1.22 11.65 11.80 7.54 10.99

Yellow-billed cuckoo Coccyzus americanus 210 1.10 7.21 13.44 10.16 4.79

American crow Corvus brachyrhynchos 207 1.09 12.38 9.51 6.23 9.58

Bluejay Cyanocitta cristata 200 1.05 5.36 7.43 12.79 18.03

American goldfinch Carduelis tristis 194 1.02 23.84 4.81 2.30 3.94

Worm-eating warbler Helmitheros vermivorus 189 0.99 11.46 10.93 5.57 2.82

Field sparrow Spizella pusilla 188 0.99 19.22 7.32 5.25 0.28

Acadian flycatcher Empidonax virescens 165 0.87 11.46 9.62 1.64 2.82

Rufous-sided towhee Pipilo erythrophthalmus 132 0.69 14.42 5.68 0.66 0.00

Brown-headed cowbird Molothrus ater 129 0.68 21.26 1.31 0.33 0.28

Hairy woodpecker Picoides villosus 123 0.65 2.40 6.56 7.87 7.32

White-breasted nuthatch Sitta carolinensis 87 0.46 2.03 6.23 0.98 4.51

Downy woodpecker Picoides pubescens 76 0.40 7.58 2.62 2.95 0.56

Northern bobwhite Colinus virginianus 73 0.38 4.81 4.48 1.64 0.28

Swainson’s thrushb Catharus ustulatus 68 0.36 1.85 4.59 4.59 0.56

Wild turkey Meleagris gallopavo 66 0.35 1.66 4.15 1.97 3.66

Yellow-throated vireo Vireo flavifrons 64 0.34 6.28 2.73 0.98 0.56

Eastern wood pewee Contopus virens 63 0.33 7.21 1.97 1.31 0.56

Louisiana waterthrush Seiurus motacilla 57 0.30 3.33 2.51 1.97 2.82

Broad-winged hawk Buteo platypterus 52 0.27 2.03 2.51 1.97 3.38

Ruby-throated hummingbird Archilochus colubris 46 0.24 4.62 2.08 0.00 0.56

Tennessee warblerb Vermivora peregrina 37 0.19 1.85 2.19 1.31 0.85

Chimney swift Chaetura pelagica 34 0.18 0.37 1.20 2.62 3.66

Northern parula warbler Parula americana 29 0.15 4.81 0.22 0.33 0.00

Rose-breasted grosbeakb Pheucticus ludovicianus 26 0.14 0.37 1.64 0.66 1.97

Black-throat green warblerb Dendroica townsendi 24 0.13 2.40 0.44 0.66 1.41

Common flicker Colaptes auratus 21 0.11 1.11 0.87 0.98 1.13

Blue grosbeak Guiraca caerulea 21 0.11 0.92 1.09 1.97 0.00

Chuck-wills widow Caprimulgus carolinensis 18 0.09 1.48 0.87 0.00 0.56

Mourning dove Zenaida macroura 18 0.09 2.96 0.00 0.33 0.28

Wood thrush Hylocichla mustelina 17 0.09 0.55 1.09 0.66 0.56

Red-shouldered hawk Buteo lineatus 17 0.09 1.11 0.11 0.66 2.25

Brown thrasher Toxostoma rufum 17 0.09 1.11 0.66 0.98 0.56

 continued
157

Table 2—Species lista and frequencies of occurrence of birds identified during 50-m fixed-radius point counts

during May–June 1995 through 1998 on four watersheds under different levels of forest management activity

in the Ouachita Mountains of Arkansasb (continued)

Birds per 100 plots

Species Scientific name n Total LG NAC BC SAC

percent

American redstart Setophaga ruticilla 17 0.09 1.29 1.09 0.00 0.00

Bay-breasted warblerb Dendroica castarea 15 0.08 2.40 0.22 0.00 0.00

Magnolia warblerb D. magnolia 15 0.08 0.55 0.77 1.31 0.28

Gray catbird Dumetella carolinensis 15 0.08 0.92 0.87 0.66 0.00

Baltimore oriole Icterus galbula 14 0.07 0.37 1.20 0.00 0.28

Red-bellied woodpecker Melanerpes carolinus 14 0.07 1.85 0.44 0.00 0.00

Turkey vulture Cathartes aura 9 0.05 0.18 0.87 0.00 0.00

Chipping sparrow Spizella paserina 9 0.05 0.55 0.66 0.00 0.00

Veeryb Catharus fuscescens 8 0.04 0.37 0.33 0.00 0.85

Red-tailed hawk Buteo jamaicensis 7 0.04 0.92 0.11 0.33 0.00

Chestnut-sided warblerb Dendroica pensylvanica 7 0.04 1.11 0.11 0.00 0.00

Barred owl Strix varia 7 0.04 0.18 0.33 0.00 0.85

Sharp-shinned hawk Accipiter striatus 6 0.03 0.18 0.11 0.33 0.85

Orchard oriole Icterus spurius 6 0.03 1.11 0.00 0.00 0.00

Belted kingfisher Ceryle alcyon 6 0.03 0.18 0.33 0.33 0.28

Whip-poor-will Caprimulgus vociferus 6 0.03 0.74 0.11 0.00 0.28

Nashville warblerb Vermivora ruficapilla 6 0.03 0.18 0.33 0.66 0.00

Eastern kingbird Tyranus tyranus 6 0.03 1.11 0.00 0.00 0.00

Eastern Phoebe Sayomis phoebe 5 0.03 0.37 0.22 0.00 0.28

Swainson’s warbler Limnothlypis swainsonii 5 0.03 0.37 0.33 0.00 0.00

Eastern bluebird Sialia sialis 5 0.03 0.74 0.11 0.00 0.00

Yellow warblerb Dendroica petechia 4 0.02 0.18 0.33 0.00 0.00

Cooper’s hawk Accipiter cooperii 3 0.02 0.55 0.00 0.00 0.00

Brown-headed nuthatch Sitta pusilla 3 0.02 0.00 0.33 0.00 0.00

Purple martin Progne subis 3 0.02 0.00 0.33 0.00 0.00

Warbling vireo Vireo gilvus 3 0.02 0.00 0.33 0.00 0.00

Great blue heron Ardea herodias 2 0.01 0.00 0.22 0.00 0.00

Hermit thrushb Catharas guttatus 2 0.01 0.00 0.22 0.00 0.00

White-throated sparrowb Zonotrichia albicollis 2 0.01 0.37 0.00 0.00 0.00

Golden-crowned kingletb Regulus satrapa 2 0.01 0.18 0.00 0.00 0.28

Wilson warblerb Wilsonia pusilla 2 0.01 0.18 0.00 0.33 0.00

Red-breasted nuthatchb Sitta canadensis 2 0.01 0.37 0.00 0.00 0.00

Cerulean warbler Dendroica cerulea 1 0.01 0.00 0.11 0.00 0.00

Yellow-bellied flycatcherb Empidonax flaviventris 1 0.01 0.00 0.00 0.00 0.28

Blue-winged warblerb Vermivora pinus 1 0.00 1.00 0.00 0.00 0.28

Red crossbillb Loxia curvirostra 1 0.01 0.00 0.00 0.00 0.28

Red-winged blackbird Agelaius phoeniceus 1 0.01 0.00 0.11 0.00 0.00

Great horned owl Bubo virginianus 1 0.01 0.00 0.00 0.33 0.00

Song sparrowb Melospiza melodia 1 0.01 0.00 0.00 0.33 0.00

Wood duck Aix sponsa 1 0.01 0.00 0.11 0.00 0.00

Screech owl Otus asio 1 0.01 0.00 0.11 0.00 0.00

Canada warblerb Wilsonia canadensis 1 0.01 0.18 0.00 0.00 0.00

American woodcock Scolopax minor 1 0.01 0.18 0.00 0.00 0.00

Blackburnian warblerb Dendroica fusca 1 0.01 0.00 0.00 0.33 0.00

 Total 19,030 100.00

LG = Little Glazypeau; NAC = North Alum Creek; BC = Bread Creek; SAC = South Alum Creek.

a Common and scientific names follow Hamel (1992).

b Nonbreeding species.
158

RESULTS

A total of 19,030 birds representing 97 species were recorded

over all watersheds and years (table 2). Six species comprised approximately 50 percent of the individuals recorded:

red-eyed vireo (Vireo olivaceus), pine warbler (Dendroica

pinus), indigo bunting (Passerina cyanea), black and white

warbler (Mniotilta varia), Carolina chickadee (Parus carolinensis), and white-eyed vireo (Vireo griseus). With the

exception of the rufous-sided towhee (Pipilo erythrophthalmus), species for which >50 individuals were recorded

occurred in all watersheds. A large portion of species

occurred most frequently in LG, followed in decreasing order

by NAC, BC, and SAC. The pine warbler, ovenbird (Seiurus

aurocapillus), and pileated woodpecker (Dryocopus pileatus) followed an opposite pattern, with their highest numbers

occurring in SAC. Species such as the red-eyed vireo and

the tufted-titmouse (Parus bicolor) occurred in similar

numbers across all watersheds. Recorded numbers of nonbreeding migrants (table 2) were a reflection of the timing of

our bird surveys and not a result of habitat differences

between watersheds.

Of the 97 recorded species, 76 are known to breed in the

Ouachita Mountains of Arkansas. Of those breeding birds,

year-watershed interactions existed for numbers of individuals per plot (F = 3.63; df = 9, 2092; P < 0.001), species

per plot (F = 3.76; df = 9, 2092; P < 0.001), and diversity

per plot (F = 3.10; df = 9, 2092; P = 0.001). Thus, these

Table 3—Mean (SE) breeding bird community metrics recorded on four watersheds under

different levels of forest management activity in the Ouachita Mountains of Arkansas

Year

Watershed 1995 1996 1997 1998

number of individuals per plot

Little Glazypeau 8.81 Aa 13.50 A 13.95 A 11.91 A

(0.452) (0.654) (0.484) (0.485)

North Alum Creek 6.02 B 8.33 B 11.36 B 10.30 B

(0.266) (0.289) (0.354) (0.422)

Bread Creek 4.61 C 5.88 C 8.73 C 8.07 C

(0.359) (0.357) (0.652) (0.533)

South Alum Creek 4.02 C 5.67 C 9.26 C 6.34 C

(0.280) (0.316) (0.413) (0.429)

F = 30.44 F = 59.88 F = 22.20 F = 20.77

df = 3, 520 df = 3, 501 df = 3, 546 df = 3, 525

P < 0.001 P < 0.001 P < 0.001 P < 0.001

number of species per plot

Little Glazypeau 5.56 A 7.77 A 9.03 A 6.87 A

(0.240) (0.290) (0.250) (0.240)

North Alum Creek 3.90 B 5.13 B 6.96 B 5.86 B

(0.140) (0.170) (0.200) (0.200)

Bread Creek 3.25 C 3.81 C 5.05 C 4.88 C

(0.230) (0.230) (0.330) (0.270)

South Alum Creek 2.63 C 3.54 C 5.47 C 3.74 D

(0.150) (0.170) (0.210) (0.200)

F = 35.60 F = 63.00 F = 45.64 F = 20.56

df = 3, 520 df = 3, 501 df = 3, 546 df = 3, 525

P < 0.001 P < 0.001 P < 0.001 P < 0.001

diversity per plot

Little Glazypeau 1.50 A 1.83 A 2.02 A 1.72 A

(0.049) (0.041) (0.032) (0.037)

North Alum Creek 1.13 B 1.41 B 1.73 B 1.51 B

(0.039) (0.035) (0.029) (0.036)

Bread Creek 0.96 C 1.13 C 1.35 C 1.38 B

(0.069) (0.061) (0.068) (0.053)

South Alum Creek 0.80 C 1.08 C 1.52 D 1.10 C

(0.054) (0.052) (0.044) (0.057)

F = 29.59 F = 48.53 F = 45.48 F = 28.19

df = 3, 520 df = 3, 501 df = 3, 546 df = 3, 525

P < 0.001 P < 0.001 P < 0.001 P < 0.001

a Means in a column followed by the same letter did not differ (P > 0.05).
159

variables were interpreted within years. Numbers of individuals per plot, species per plot, and diversity per plot were

consistently highest in LG each of the 4 years, followed by

NAC (table 3). BC and SAC recorded similar numbers each

year, with SAC having the lowest species per plot in 1998

and the lowest diversity per plot in 1997 and 1998. No yearwatershed interaction was found for evenness per plot (F =

0.95; df = 9, 1985; P = 0.483), and evenness per plot did

not differ (F = 2.89; df = 3, 9; P = 0.094) among watersheds

(LG = 0.94, SE = 0.002; NAC = 0.94, SE = 0.002; BC = 0.94,

SE = 0.004; SAC = 0.93, SE = 0.003).

Migrant Birds

Year-watershed interactions existed for numbers of individuals per plot (F = 4.32; df = 9, 2035; P < 0.001), species

per plot (F = 3.54; df = 9, 2035; P < 0.001), and diversity

per plot (F = 3.02; df = 9, 2035; P = 0.001). Thus, these

variables were interpreted within years. Numbers of individuals per plot, species per plot, and diversity per plot were

consistently highest for migrants in LG, followed by NAC,

each of the 4 years (table 4). Similar numbers were recorded

on BC and SAC each year. SAC had the lowest species per

plot and diversity per plot in 1995 and 1998. No year-watershed interaction was found for evenness per plot (F = 1.29;

df = 9, 1759; P = 0.236). Evenness per plot was highest in

Table 4—Mean (SE) breeding bird community metrics for migrant species recorded on

four watersheds under different levels of forest management activity in the Ouachita

Mountains of Arkansas

Year

Watershed 1995 1996 1997 1998

number of individuals per plot

Little Glazypeau 6.27 Aa 9.91 A 8.61 A 7.87 A

(0.336) (0.466) (0.332) (0.354)

North Alum Creek 4.53 B 6.44 B 7.81 A 7.00 B

(0.203) (0.252) (0.279) (0.289)

Bread Creek 3.27 C 4.16 C 5.65 B 5.14 C

(0.256) (0.287) (0.471) (0.426)

South Alum Creek 2.40 C 3.61 C 5.30 B 3.69 D

(0.156) (0.203) (0.277) (0.314)

F = 33.19 F = 59.76 F = 18.50 F = 22.30

df = 3, 498 df = 3, 492 df = 3, 539 df = 3, 506

P < 0.001 P < 0.001 P < 0.001 P < 0.001

number of species per plot

Little Glazypeau 3.80 A 5.44 A 5.52 A 4.53 A

(0.080) (0.199) (0.180) (0.163)

North Alum Creek 2.94 B 3.64 B 4.61 B 4.01 B

(0.117) (0.134) (0.151) (0.153)

Bread Creek 2.31 C 2.62 C 3.21 C 3.24 C

(0.173) (0.176) (0.237) (0.228)

South Alum Creek 1.69 D 2.15 C 3.18 C 2.22 D

(0.092) (0.102) (0.140) (0.134)

F = 30.29 F = 64.04 F = 32.43 F = 25.00

df = 3, 498 df = 3, 492 df = 3, 539 df = 3, 506

P < 0.001 P < 0.001 P < 0.001 P < 0.001

diversity per plot

Little Glazypeau 1.13 A 1.49 A 1.53 A 1.32 A

(0.048) (0.043) (0.038) (0.039)

North Alum Creek 0.87 B 1.06 B 1.30 B 1.13 B

(0.037) (0.037) (0.035) (0.041)

Bread Creek 0.64 C 0.76 C 0.91 C 0.94 C

(0.068) (0.062) (0.071) (0.069)

South Alum Creek 0.43 D 0.63 C 1.00 C 0.60 D

(0.045) (0.045) (0.041) (0.056)

F = 31.88 F = 56.26 F = 34.67 F = 29.77

df = 3, 498 df = 3, 492 df = 3, 539 df = 3, 506

P < 0.001 P < 0.001 P < 0.001 P < 0.001

a Means in a column followed by the same letter did not differ (P > 0.05).
160

LG (x_ = 0.94, SE = 0.002) and BC (x_ = 0.94, SE = 0.004)

and lowest in NAC (x_ = 0.93, SE = 0.002) and SAC (x_ = 0.92,

SE = 0.005) (F c= 5.72; df = 3, 9; P = 0.018).

Residents

No year-watershed interactions existed for numbers of individuals per plot (F = 1.17; df = 9, 1760; P = 0.310), species

per plot (F = 1.83; df = 9, 1760; P = 0.059), diversity per

plot (F = 1.74; df = 9, 1760; P = 0.074), or eveness per plot

(F = 1.18; df = 9, 1056; P = 0.303). LG had the highest individuals per plot (F = 25.53; df = 3, 9; P < 0.001), species

per plot (F = 32.05; df = 3, 9; P < 0.001), and diversity per

plot (F = 28.75; df = 3, 9; P < 0.001) of all watersheds

(table 5). NAC, BC, and SAC were comparable for most

community metrics. Evenness did not differ among watersheds (F = 2.19; df = 3, 9; P = 0.152).

Cavity Nesters

No year-watershed interactions existed for numbers of individuals per plot (F = 1.63; df = 9, 1201; P = 0.102), species

per plot (F = 1.81; df = 9, 1201; P = 0.062), or diversity per

plot (F = 1.69; df = 9, 1201; P = 0.087). There were no

differences among watersheds for numbers of individuals

per plot (F = 2.25; df = 3, 9; P = 0.150), species per plot (F

= 2.81; df = 3, 9; P = 0.099), or diversity per plot (F = 2.95;

df = 3, 9; P = 0.089) (table 6). A year-watershed interaction

was found for evenness per plot (F = 2.72; df = 9, 457; P =

0.004), and evenness per plot did not differ between watersheds during 1995 (F = 2.40; df = 3, 90; P = 0.073), 1997

(F = 1.48; df = 3, 180; P = 0.223), or 1998 (F = 0.81; df = 3,

103; P = 0.494). Evenness per plot did differ in 1996 (F =

4.79; df = 3, 84; P = 0.004), with SAC having a lower value

than all other watersheds.

Canopy Nesters

Year-watershed interactions existed for numbers of individuals per plot (F = 2.55; df = 9, 1963; P = 0.007), species

per plot (F = 1.99; df = 9, 1963; P = 0.037), and diversity

per plot (F = 1.87; df = 9, 1963; P = 0.050). Thus, these

variables were interpreted within years. In 1995 and 1997,

there were no differences in numbers of individuals per plot

between watersheds (table 7). Numbers of individuals per

plot were highest in LG in 1996 and lowest in BC in 1998.

In 1995 and 1996, species per plot was highest in LG, with

no differences in the other watersheds. Species per plot

was lowest in SAC in 1998. Diversity per plot was highest in

LG in 1996, with inconsistent differences in other years. No

year-watershed interaction was found for evenness per plot

Table 6—Mean (SE) cavity nesting bird community metrics recorded on four watersheds under

different levels of forest management activity in the Ouachita Mountains of Arkansas

Diversity Evenness

Watershed Individuals per plot Species per plot per plot per plot

- - - - - - - - - - - - number - - - - - - - - - - - -

Little Glazypeau 2.29 1.57 0.34 0.94

(0.080) (0.041) (0.022) (0.010)

North Alum Creek 2.02 1.48 0.29 0.92

(0.064) (0.032) (0.017) (0.013)

Bread Creek 1.89 1.33 0.20 0.93

(0.115) (0.051) (0.029) (0.027)

South Alum Creek 2.07 1.38 0.25 0.85

(0.110) (0.051) (0.027) (0.034)

Table 5—Mean (SE) breeding resident bird community metrics recorded on four watersheds

under different levels of forest management activity in the Ouachita Mountains of Arkansas

Diversity Evenness

Watershed Individuals per plot Species per plot per plot per plot

- - - - - - - - - - - - - number - - - - - - - - - - - - -

Little Glazypeau 4.21 Aa 2.77 A 0.81 A 0.94

(0.238) (0.253) (0.263) (0.267)

North Alum Creek 2.95 B 1.96 B 0.51 B 0.94

(0.172) (0.100) (0.159) (0.190)

Bread Creek 2.76 B 1.79 C 0.43 C 0.95

(0.211) (0.165) (0.242) (0.279)

South Alum Creek 3.08 B 1.83 BC 0.44 C 0.93

(0.263) (0.194) (0.255) (0.237)

a Means in a column followed by the same letter did not differ (P > 0.05).
161

(F = 0.85; df = 9, 1480; P = 0.570), and evenness per plot

did not differ (F = 0.973; df = 3, 9; P = 0.445) among watersheds (LG = 0.92, SE = 0.004; NAC = 0.92, SE = 0.003; BC

= 0.92, SE = 0.006; SAC = 0.92, SE = 0.005).

Ground and Shrub Nesters

A year-watershed interaction existed for numbers of individuals per plot (F = 1.99; df = 9, 1628; P = 0.037); thus, this

variable was interpreted within years. Numbers of individuals per plot were consistently highest in LG and lowest in

SAC over all years (table 8). No year-watershed interactions

existed for species per plot (F = 0.93; df = 9, 1628; P =

0.499), diversity per plot (F = 0.84; df = 9, 1628; P = 0.582),

or evenness per plot (F = 0.55; df = 9, 1035; P = 0.841).

Species per point and diversity per point were consistently

highest in LG and lowest in SAC over all years (table 9).

Evenness did not differ among watersheds.

DISCUSSION AND CONCLUSIONS

Because this study focused on differences and similarities

of breeding bird community characteristics among watersheds, replication was not feasible. Thus, our inferences and

conclusions are limited to the bird communities and landscapes contained within the specific watersheds we studied.

Table 7—Mean (SE) canopy nesting bird community metrics recorded on four watersheds

under different levels of forest management activity in the Ouachita Mountains of

Arkansas

Year

Watershed 1995 1996 1997 1998

number of individuals per plot

Little Glazypeau 3.13 5.29 Aa 5.42 4.84 A

(0.190) (0.654) (0.268) (0.248)

North Alum Creek 2.82 4.05 B 4.92 4.89 A

(0.134) (0.289) (0.186 (0.194)

Bread Creek 2.58 3.59 B 4.86 4.30 AB

(0.189) (0.357) (0.430) (0.310)

South Alum Creek 2.60 3.65 B 5.57 3.94 B

(0.203) (0.316) (0.262) (0.225)

F = 1.68 F = 8.53 F = 1.62 F = 3.13

df = 3, 449 df = 3, 477 df = 3, 531 df = 3, 506

P = 0.170 P < 0.001 P = 0.184 P = 0.025

number of species per plot

Little Glazypeau 2.08 A 2.89 A 3.32 A 2.72 A

(0.103) (0.290) (0.129) (0.112)

North Alum Creek 1.80 B 2.27 B 2.78 BC 2.53 A

(0.062) (0.170) (0.088) (0.075)

Bread Creek 1.77 B 2.13 B 2.53 B 2.45 AB

(0.096) (0.230) (0.140) (0.127)

South Alum Creek 1.65 B 2.01 B 2.97 AC 2.22 B

(0.093) (0.170) (0.109) (0.114)

F = 3.84 F = 12.09 F = 7.38 F = 3.39

df = 3, 449 df = 3, 477 df = 3, 531 df = 3, 506

P = 0.010 P < 0.001 P < 0.001 P = 0.018

diversity per plot

Little Glazypeau 0.58 A 0.83 A 1.00 A 0.81 A

(0.047) (0.041) (0.042) (0.042)

North Alum Creek 0.47 B 0.66 B 0.83 BC 0.74 A

(0.030) (0.035) (0.031) (0.029)

Bread Creek 0.46 AB 0.59 B 0.74 C 0.72 AB

(0.052) (0.061) (0.055) (0.050)

South Alum Creek 0.41 B 0.54 B 0.94 AB 0.63 B

(0.045) (0.052) (0.038) (0.049)

F = 2.65 F = 7.39 F = 6.47 F = 2.98

df = 3, 449 df = 3, 477 df = 3, 531 df = 3, 506

P = 0.049 P < 0.001 P < 0.001 P = 0.031

a Means in a column followed by the same letter did not differ (P > 0.05).
162

The differing ownerships, management objectives, and

intensities of forest management activity among watersheds influenced breeding bird communities. Spatial and

compositional habitat heterogeneity was highest in LG

(Tappe and others, in press) and was reflected in higher

numbers of individuals per point, species per point, and

diversity per point for both migrants and residents. Lower

values were seen in BC and SAC, with NAC being intermediate. Thus, these patterns appear to follow Tappe and

others (in press) ranking of spatial heterogeneity in the

watersheds (LG > NAC > BC > SAC).

Patterns of community characteristics within nesting guilds

were not as consistent as when birds were separated into

migrant and resident species. Though somewhat variable,

watersheds were comparable during most years in respect

to canopy nesters. Community characteristics of cavity

nesters were the same for all watersheds. Each watershed

provided some suitable habitat for species in these nesting

guilds. Ground and shrub nesters had their lowest numbers

of individuals per point, species per point, and diversity per

point in SAC and their highest in LG, with NAC and BC

being intermediate to these values. Values for ground and

shrub nesters reflected the amount of suitable, early successional habitat found in each watershed. LG was composed

of approximately 22 percent young/open and thin/open pine

stands whereas SAC was composed of approximately 1

percent young/open and thin/open pine stands (Tappe and

others, in press).

Each watershed was characterized by differing breeding

bird community structure. LG provided a variety of earlysuccessional and medium-aged habitats. Several species

prefer these habitats, many of which are species of concern, including the black and white warbler, prairie warbler

(Dendroica discolor), and worm-eating warbler (Helmitheros

vermivorus). SAC provided a relatively homogenous landscape of more mature forest, thus benefitting species such

Table 8—Mean (SE) numbers of individual ground and shrub nesting birds per plot

recorded on four watersheds under different levels of forest management activity in the

Ouachita Mountains of Arkansas

Year

Watershed 1995 1996 1997 1998

Little Glazypeau 5.13 Aa 7.26 A 6.04 A 5.99 A

(0.409) (0.654) (0.484) (0.485)

North Alum Creek 3.41 B 4.08 B 5.05 B 4.76 B

(0.234) (0.289) (0.354) (0.422)

Bread Creek 2.66 B 2.98 BC 4.32 B 3.50 C

(0.350) (0.357) (0.652) (0.533)

South Alum Creek 1.19 C 1.85 C 2.59 C 2.47 C

(0.083) (0.316) (0.413) (0.429)

F = 21.53 F = 29.99 F = 14.34 F = 11.96

df = 3, 364 df = 3, 392 df = 3, 467 df = 3, 405

P = < 0.001 P < 0.001 P < 0.001 P = < 0.001

a Means in a column followed by the same letter did not differ (P > 0.05).

Table 9—Mean (SE) ground and shrub nesting bird community metrics

recorded on four watersheds under different levels of forest

management activity in the Ouachita Mountains of Arkansas

Diversity Evenness

Watershed Species per plot per plot per plot

no.

Little Glazypeau 3.70 A 1.07 A 0.94

(0.094) (0.027) (0.003)

North Alum Creek 2.75 B 0.73 B 0.94

(0.075) (0.024) (0.003)

Bread Creek 2.27 C 0.57 C 0.96

(0.123) (0.044) (0.004)

South Alum Creek 1.41 D 0.25 D 0.94

(0.053) (0.024) (0.010)

a Means in a column followed by the same letter did not differ (P > 0.05).
163

as the ovenbird and scarlet tanager (Piranga olivacea). NAC

and BC both provided landscapes that included differing

proportions of each of these habitats, supporting differing

bird communities. Thus, the spatial structure and composition of each watershed resulted in differing breeding bird

community associations.

ACKNOWLEDGMENTS

We are thankful for the assistance of numerous individuals

who were involved with fieldwork and bird surveys during

this study. We particularly appreciate the assistance of D.G.

Peitz, R.W. Perry, and M. Sams.

LITERATURE CITED

Askins, R.A.; Lynch, J.F.; Greenberg, R. 1990. Population declines

in migratory birds in Eastern North America. Current

Ornithology 7:1-57.

Finch, D.M. 1991. Population ecology, habitat requirements, and

conservation of neotropical migratory birds. U.S. For. Serv.

Tech. Rep. RM-205. 26 p.

Guldin, J.M. [In press]. Landscape scale research in the Ouachita

Mountains of west-central Arkansas: general study design. In:

Guldin, James M., tech. comp. Ouachita and Ozark Mountains

symposium: ecosystem management research. Gen. Tech.

Rep. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station.

Hamel, P.B. 1992. Land manager’s guide to the birds of the South.

The Nature Consevancy, Southeastern Region, Chapel Hill,

NC. 437 p.

Hunter, W.C.; Pashley, D.N.; Escano, R.E.F. 1993. Neotropical

migratory landbird species and their habitats of special concern

within the southeast region. Pages 159-169 In: D.M. Finch and

P.W. Stangel, eds. Status and Management of Neotropical

Migratory Birds. Gen. Tech. Rep. RM-229. Fort Collins, CO:

U.S. Department of Agriculture, Forest Service, Rocky

Mountain Forest and Range Experiment Station. 422 p.

Maurer, B.A. 1993. Biological diversity, ecological integrity, and

neotropical migrants: new perspectives for wildlife management. Pages 24-31 In: D.M. Finch and P.W. Stangel, eds.

Status and Management of Neotropical Migratory Birds. Gen.

Tech. Rep. RM-229. Fort Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Forest and Range

Experiment Station. 422 p.

Robbins, C.S.; Dawson, D.K.; Dowell, B.A. 1989. Habitat area

requirements of breeding forest birds of the Middle Atlantic

States. Wildlife Monograph 103:1-34.

Sherry, T.W.; Holmes, R.T. 1993. Are populations of neotropical

migrant birds limited in summer or winter? Implications for

management. Pages 47-57 In: D.M. Finch and P.W. Stangel,

eds. Status and Management of Neotropical Migratory Birds.

Gen. Tech. Rep. RM-229. Fort Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Forest and Range

Experiment Station. 422 p.

Tappe, P.A.; Weih, R.C.; Thill, R.E. [and others]. [In press]. Landscape characterization of four watersheds under different management scenarios in the Ouachita Mountains of Arkansas. In:

Guldin, James M., tech. comp. Ouachita and Ozark Mountains

symposium: ecosystem management research. Gen. Tech.

Rep. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station.
164

INTRODUCTION

Little is known about habitat parameters that influence

amphibian communities, and even less is known about

landscape-level environmental influences (Dodd and Cade

1998). Moreover, many species of amphibians are declining

worldwide in abundance, and more baseline data on

natural populations over time are needed to appraise the

suspected causes.

Amphibian populations, particularly anurans, are catastrophically declining worldwide (Blaustein and Wake 1995,

Houlahan and others 2000, Phillips 1990, Wake 1991).

Habitat loss, or modification to the extent that it is only

marginally suitable for amphibians, is considered by most

biologists to be the principal cause for the decline of

amphibian populations and species (Blaustein and Wake

1995). It has been suggested that local environmental

degradation is insidiously reducing amphibian diversity

(Delis and others 1996). Examples of human activities that

might result in such alterations include impoundment of

natural waterways, mining, agriculture, urbanization, and

forest management.

We report results of pretreatment data collection for a largescale, long-term, field study of amphibian communities and

the influence of habitat and landscape environmental variables in four watersheds of the Ouachita Mountains of westcentral Arkansas. Following the pretreatment stage, sections

of the watersheds will be subjected to different forest management to achieve a variety of specific “desired future

conditions.” After treatment, data on amphibian communities will again be collected and used to quantify community

changes and to compare with the predictions of multivariate

community models that we are developing. This long-term

study is one component of Phase III of the Ouachita

Mountains Ecosystem Management Research Project; the

wildlife component of this cooperative effort involves

Weyerhaeuser Company, the National Council of the Paper

Industry for Air and Stream Improvement, Oklahoma State

University, the Oklahoma Cooperative Fish and Wildlife

Research Unit, the University of Arkansas Monticello, the

Ouachita National Forest, and the Southern Research

Station of the USDA Forest Service.

The objectives of our overall study are to: (1) characterize

reptile and amphibian communities in four watersheds

representing markedly different forest-management strategies in the Ouachita Mountains, Arkansas; (2) develop and

validate models for predicting community composition based

on site, stand, and landscape parameters; and (3) develop

recommendations to promote maintenance of reptile and

amphibian communities in managed forest landscapes.

This report contains results for amphibians of the pretreatment data analysis performed at the end of four survey

years.

METHODS

Study Areas

The study was conducted on four 1500- to 4000-ha watersheds under different intensities of management in Garland

and Saline counties near Hot Springs, AR. The watersheds

differed markedly with respect to factors such as mean rotation lengths, forest type diversity, stand sizes and ages, and

the amount of natural second-growth coverage (Guldin and

others, Tappe and others, in press).

Little Glazypeau, a watershed located some 22 km southwest of the other three watersheds (that were contiguous)

and managed largely for sawlog production by Weyerhaeuser

Company, represented our most intensively managed

AMPHIBIAN COMMUNITIES UNDER DIVERSE FOREST MANAGEMENT

IN THE OUACHITA MOUNTAINS, ARKANSAS

Stanley F. Fox, Paul A. Shipman, Ronald E. Thill,

Joseph P. Phelps, and David M. Leslie, Jr.1

1 Research Professor and Research Technician, Oklahoma State University, Oklahoma Cooperative Fish and Wildlife Research Unit,

Department of Zoology, Stillwater, OK 74078; Supervisory Research Wildlife Biologist, USDA Forest Service, Southern Research Station,

SFA Station, Nacogdoches, TX 75962; Forest Wildlife Biologist, Southern Forest Research Center, Weyerhaeuser Company, Hot Springs,

AR; and Unit Leader, U.S. Geological Survey, Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State University, Stillwater,

OK 74078, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—From May 1995 to March 1999, we censused amphibians in the Ouachita Mountains, Arkansas, on 60 plots on

each of four forested watersheds five times per year, with new plots each year. We found negligible differences in species

richness among watersheds, and community similarities were high, even though most pairwise comparisons were

significantly different. The two most intensively managed watersheds were more similar to each other than to those less

intensively managed, and the former had nominally higher overall species evenness and (beta) diversity. At the plot level,

we found no significant differences in abundance, species richness, or (alpha) diversity, although the two most intensively

managed watersheds had plots with fewer species and less diversity. Detrended correspondence analysis revealed that

communities diverged on the basis of presumed gradients of terrestrial-aquatic, elevation, and canopy cover. Important

communities that differed among watersheds were those of small, often ephemeral ponds and large, permanent ponds.
165

watershed. Much of the second-growth shortleaf pine (Pinus

echinata)-hardwood forest that originally covered this watershed had been harvested and planted to loblolly pine (P.

taeda) plantations of 9-142 ha. Typically, these plantations

were thinned twice, pruned to 5-8 m high, fertilized, and

harvested at 30-35 years old. The remaining, selectivelyharvested acreage in the watershed occured on rocky ridgetops, steep slopes, and streamside management zones that

were retained for watershed protection and to provide habitat diversity for wildlife. South Alum, an experimental section

of the Ouachita National Forest that has received minimal

logging for > 80 years, represented the least intense level

of silviculture. South Alum was almost entirely USDA Forest

Service and consisted of mature forest over most of the

area. Bread Creek and North Alum fell in between these

extremes of forest management. From independent records

of forest management and present composition of number,

age, and distribution of pine (mostly Pinus echinata) plantations, Bread Creek was considered less intensely managed

than North Alum (Tappe and others, in press). Bread Creek

was primarily USDA Forest Service land, and had been

managed according to prevailing Forest Service standards

and guidelines for several decades, whereas North Alum

was of mixed ownership, with about half of the area under

Weyerhaeuser management and half under USDA Forest

Service management. North Alum displayed characteristics

of a diverse range of management activities, ranging from

no management along steeper slopes and higher elevations

to intensively managed pine plantations, mainly at lower

elevations. Thus, the watersheds, in order of intensity of

forest management, ran South Alum, Bread Creek, North

Alum, and Little Glazypeau. These same watersheds, in

order of size, were South Alum (1500 ha), Bread Creek

(1535 ha), Little Glazypeau (2273 ha), and North Alum

(3961 ha).

Sampling Plots

We surveyed amphibians and reptiles using area-constrained

searches on a subset (56) of the 75-235 plots/watershed

that were established each year for breeding bird surveys

(Tappe and others, in press). Bird plots were established at

200-m intervals along > 100 km of parallel transects (oriented

approximately north-south across prevailing topography)

that were established in 1995 over the 4 watersheds. These

same transects were used in 1996, but new plots were

established by shifting plot centers 100 m. In 1997, approximately 110 km of new transects were established between

those of 1995; new plots were established in 1998 along

these new transects as in 1996. The subset of plots that

were used for amphibian surveys were selected to represent

a cross-section of slopes, aspects, forest types, stand conditions, and aquatic habitats. The center of our 20-m-radius

(0.13-ha) plots also was the center of a bird sampling plot.

Plastic flagging was used to delineate plot boundaries on

all amphibian plots. In each watershed, we selected amphibian plots to make sure there were at least 12-15 of them in

aquatic habitat, which consisted of springs, streams, and

man-made ponds that had been established to benefit wildlife (Forest Service wildlife ponds) and/or as sources of

water for fire fighting. An additional four plots per watershed

per year were established off the transects at these ponds

(or at wide pools in the high-order streams at the bottom of

a watershed) to ensure that we had equivalent sampling

effort at these aquatic habitats. Plots at these aquatic sites

were established so that the center of the plot was on land

right next to the shore (roughly half of the plot was over

water and half over land). Thus, we surveyed 60 plots per

year per watershed, in total.

Amphibian Surveys

Trained crews of 3-5 individuals surveyed each set of 60

plots per watershed during daylight hours 5 times a year

from May 1995 to March 1999: early May, late May, midJune, early October, and early the following March. Plots

were surveyed entirely by visually searching vegetation and

the ground surface, and by lifting cover objects (rocks, logs,

and debris); the latter were replaced to minimize impacts

on subsequent surveys. We sampled plots in deep water by

having one person move slowly through the water in chest

waders and carefully scan the water and shoreline for

animals. Some animals were captured in this way by use of

a dipnet. Amphibians that were seen and identified were

tallied; those that required capture for identification were

released at the point of capture before leaving the plot. Additionally, any anurans calling on the plot, but not seen, were

identified by their call and counted. Anuran species calling

off-plot in similar habitat were recorded as present, but no

attempt was made to count the number of individuals.

Data Analysis

Amphibian count data were pooled across the five sampling

periods per year, but data from each plot served as separate

samples. Data collected in the first year (1995) from South

Alum and North Alum were excluded from analyses due to

differences in sampling effort by former collaborators, and

data from a few other plots were discarded when five surveys per year were not attained. Thus, analyses presented

here are based on data from 833 plots, each censused five

times, for a total of 4,165 censuses.

Watershed level—Community indices were first calculated

at the watershed level: number of individuals by species for

all years combined, amphibian abundance, species richness, (beta) species diversity [H’; Shannon-Wiener diversity

index (Shannon and Weaver 1949)], and species evenness

[diversity divided by maximal diversity, or H’/(ln number

of species); Pielou 1966]. With only four watersheds to

compare, no statistical tests were employed. Because the

Shannon-Wiener diversity index is quite sensitive to sample

size and because we did not have an equal effort among all

watersheds (two watersheds with data from only three years;

see previous statement), we randomly reordered the plots

within each watershed, pooling years, and plotted the

Shannon-Wiener diversity index of each watershed for cumulative sets of plots up to the total number of plots for each

watershed. Such a plot would show if diversity approached

an asymptote as cumulative plots increased and if our total

number of plots per watershed was sufficient to adequately

estimate species diversity.

We computed Morisita’s index of community similarity

(Morisita 1959) between all pairs of watersheds, pooling

data for the entire study for each watershed. This index is

desirable because sample sizes and species diversities of

the communities being compared have little influence on its
166

calculation (Morisita 1959; Wolda 1981). Using additive tree

cluster analysis (Sattath and Tversky 1977), we converted

that matrix of similarities into a dendrogram of communities

such that the total length of branches connecting any two

communities was proportional to the compositional difference between those communities. To statistically compare

the various Morisita’s indices of community similarity, we

conducted randomization tests (Biondini and others 1988).

For each pair of watersheds, we randomly reassigned plots

between them (retaining the sample size of each watershed)

and computed Morisita’s index for these two “synthetic”

communities. We repeated this procedure 1,000 times and

tabulated the number of times the recomputed index was

smaller than or equal to the “actual” index. If less than 100

recomputed indices fell below or equal to the “actual” index,

then those two watersheds were considered different (p <

0.10) in species composition.

Plot level—For analyses at the plot level, we used mixed

model, two-way ANOVAs (ANOVAs with both random and

fixed effects; PROC MIXED, SAS 1999) to test for differences among watersheds, years, and year * watershed

interaction. We recognize that our sample of plots drawn

from each of four watersheds is pseudoreplication (Hurlbert,

1984), but the large scale of this study preempted the

sampling of a sufficient number of replicate watersheds per

treatment class. While the results of our ANOVA must be

interpreted with caution due to this pseudoreplication, we

feel that the analysis nevertheless suggests likely ecological patterns that deserve attention. The response variables of the ANOVAs were (1) amphibian abundance per

plot, (2) species richness per plot, and (alpha) plot diversity

(Shannon-Wiener index). We first transformed the count

variables (1 and 2) by the square root, SQR (count + 0.5),

to make distributions within cells of the ANOVA more normally distributed. Even with those transformations, our data

did not fully meet assumptions of normality and homogeneity

of variances, but the Satterthwaite algorithm of the mixed

model ANOVA is relatively robust to abuses of these assumptions, especially of homogeneity of variances (SAS 1999),

and so we proceeded with these parametric analyses. We

recognized differences in weather between years, not of

interest to us here, and included year effects and year *

watershed interaction as random effects, not to be statistically interpreted. The fixed factor (watersheds) was tested

for statistical significance at p < 0.10. If a significant watershed effect was found, we used LSD to evaluate pairwise

differences between any two watersheds.

Detrended canonical correspondence analysis (DCA)—To

appraise amphibian community composition, develop preliminary hypotheses of presumptive environmental gradients

influencing these communities, and compare graphically

the environments and amphibian communities of the four

watersheds, we used DCA (ter Braak and Prentice 1988,

ter Braak and Šmilauer 1998), pooling plots from all four

watersheds and years. Application of DCA to our data

allowed for a more detailed inspection of amphibian communities at the plot level and how they were distributed along

inferred environmental gradients. DCA is probably the most

widely employed eigenanalysis-based ordination technique

used by community ecologists. It is an indirect ordination

method that orders plots with similar compositions of species

along multiple axes simultaneously. The statistical algorithm

is to calculate sample scores of each plot as a weighted

average of the species scores, and species scores as a

weighted average of samples scores; iterations are repeated

until there is no further change in scores, at which time

samples (plots) with similar animal communities appear

clustered when plotted on multiple axes. Environmental

gradients are inferred from the pattern of species and/or

plots and the biologist’s knowledge of the species. As a

step beyond single-number summary statistics like diversity

indices or Morisita’s indices, DCA results in a cloud of

points for separate species in n-dimensional space, conventionally viewed as centroids (averaged central tendency) in

two dimensions at a time. In other words, DCA results in a

pattern, not a number. For our analysis, rare species (less

than three individuals encountered over all four watersheds

for all four years) and plots where no amphibian species

were found were excluded due to computational constraints.

RESULTS AND DISCUSSION

We found 4,214 individuals of 20 amphibian species during

our four-year study (table 1). Total species diversity was

1.76 for all watersheds pooled. We found a mean of 5.06

amphibians per plot (median = 3.00, range = 0-64).

Watershed Level

Despite large differences in size of watersheds and substantial differences in management intensities, the amphibian

communities of these four watersheds were fairly similar.

Species richness was just 17-18 species per watershed,

and 15 of the total set of 20 species (75 percent) were

common to all four watersheds (table 1). The watersheds

differed some in both overall species diversity and evenness; the order from lowest to highest by both measures

was Bread Creek, South Alum, Little Glazypeau, and North

Alum (table 1). Bread Creek and South Alum were virtually

identical in these measures. Recalculated diversity indices

against cumulative plots showed that diversity leveled off

after about 40-100 plots (fig. 1), well below the lowest total

of plots for any watershed. North Alum showed distinctly

higher overall diversity than the other watersheds, and in

rank order, the two watersheds most intensively managed

had higher species diversity than the two least intensively

managed, although Little Glazypeau was not that different

from Bread Creek or South Alum.

By far, the dominant species found in all watersheds was

the western slimy salamander (Plethodon albagula), representing on the average 60.0 percent of each watershed

community. Additionally, species composition of the six

most ubiquitous species of each watershed was strikingly

similar (table 2).

We examined the overall set of species to see if there were

any species absent from all but one watershed, or present

in only one watershed. One species, the central newt

(Notophthalmus viridescens), was absent from Little

Glazypeau; and one species, the Strecker’s chorus frog

(Pseudacris streckeri), was present only in Little

Glazypeau, although represented by only one observed

individual (table 1).
167

Using Morisita’s index of community similarity, watershed

amphibian communities were quite similar, ranging from

0.84 to 0.98 (table 3). South Alum differed the most from

the other watersheds, displaying an index of 0.84 with both

Little Glazypeau and North Alum. The additive tree cluster

analysis grouped the two most intensively managed watersheds (Little Glazypeau and North Alum) and the two least

intensively managed watersheds (Bread Creek and South

Alum) (fig. 2). Despite generally high indices of community

similarity, all of the indices [except that between North Alum

and Little Glazypeau (0.98)] were statistically significant by

the randomization tests; i.e., all watershed pairs except this

one were significantly different from each other beyond that

expected by chance.

Table 1—Amphibian abundance on four watersheds in the Ouachita

Mountains, Arkansas, 1995-1999

Little North Bread South

Species Glazypeau Alum Creek Alum

- - - - - - - - - - - - - number- - - - - - - - - - - -

Acris crepitans

Cricket frog 238 143 48 3

Ambystoma annulatum

Ringed salamander 0 0 3 1

Ambystoma maculatum

Spotted salamander 0 1 4 0

Bufo americanus

American toad 44 56 189 49

Bufo woodhousei

Woodhouse’s toad 2 2 7 2

Desmognathus brimleyorum

Ouachita dusky salamander 74 17 15 9

Eurycea multiplicata

Many-ribbed salamander 17 33 68 48

Gastrophryne carolinensis

Eastern narrowmouth toad 24 22 10 1

Hemidactylium scutatum

Four-toed salamander 14 6 2 2

Hyla chrysoscelis

Gray treefrog 39 17 43 15

Hyla cinerea

Green treefrog 1 1 0 0

Notophthalmus viridescens

Central newt 0 7 3 2

Plethodon albagula

Western slimy salamander 642 355 890 368

Plethodon serratus

Southern redback salamander 13 22 57 195

Pseudacris crucifer

Spring peeper 11 39 11 2

Pseudacris streckeri

Strecker’s chorus frog 1 0 0 0

Pseudacris triseriata

Upland chorus frog 33 26 20 10

Rana catesbeiana

Bullfrog 6 6 2 3

Rana clamitans

Green frog 53 30 70 38

Rana utricularia

Southern leopard frog 7 9 12 1

Total 1,219 792 1,454 749

Species richness 17 18 18 17

Species diversity 1.65 1.94 1.50 1.53

Species evenness 0.58 0.67 0.51 0.53

Data are arrayed (left to right) from the most to the least intensively managed watersheds.
168

Figure 1—Diversity of each watershed against

cumulative number of plots (in random order) included

in recalculation (SA = South Alum, BC = Bread Creek,

NA = North Alum, LG = Little Glazypeau).

Figure 2—Additive tree dendrogram of

amphibian community similarity of four

watersheds constructed from pairwise

Morisita Indices (SA = South Alum, BC =

Bread Creek, NA = North Alum, LG =

Little Glazypeau). Difference between

any two communities is proportional to

the total branch length connecting them.

Plot Level

We found no differences in total number of individuals per

plot, species richness per plot, or species diversity per plot

between the watersheds (table 4), as indicated by ANOVA.

Consequently, we did not perform pairwise contrasts.

Detrended Correspondence Analysis

Detrended correspondence analysis indicated relatively

long environmental gradients along the first three axes for

amphibian communities in the four watersheds (table 5).

The fourth and additional axes contributed little to the

pattern of community organization since each additional

Table 2—Six most common amphibian species found in each watershed in order of decreasing abundance

Little Glazypeau n North Alum n Bread Creek n South Alum n

Plethodon albagula 642 Plethodon albagula 355 Plethodon albagula 890 Plethodon albagula 368

(Slimy salamander) (Slimy salamander) (Slimy salamander) (Slimy salamander)

Acris crepitans 238 Acris crepitans 143 Bufo americanus 189 Plethodon serratus 195

(Cricket frog) (Cricket frog) (American toad) (Redback

salamander)

Desmognathus 74 Bufo americanus 56 Rana clamitans 70 Bufo americanus 49

brimleyorum (American toad) (Green frog) (American toad)

(Ouachita dusky

salamander)

Rana clamitans 53 Pseudacris crucifer 39 Eurycea multiplicata 68 Eurycea multiplicata 48

(Green frog) (Spring peeper) (Many ribbed (Many ribbed

salamander) salamander)

Bufo americanus 44 Eurycea multiplicata 33 Plethodon serratus 57 Rana clamitans 38

(American toad) (Many ribbed (Redback salamander) (Green frog)

salamander)

Hyla chrysoscelis 39 Rana clamitans 30 Acris crepitans 48 Hyla chrysoscelis 15

(Gray treefrog) (Green frog) (Cricket frog) (Gray treefrog)

Table 3—Morisita’s index of community similarity

for all pairs of watersheds

Little North Bread South

Watershed Glazypeau Alum Creek Alum

Little Glazypeau 1.00

North Alum 0.98 1.00

Bread Creek 0.94a 0.92a 1.00

South Alum 0.84a 0.84a 0.91a 1.00

Index ranges from 0 to 1, where 1 means communities are

identical.

a Significantly dissimilar by Randomization test, p < 0.10.
169

axis explains only residual variation not already incorporated into the DCA. In other words, plots and species scores

were relatively tightly correlated with each other along an

appreciable stretch of at least the first three axes (eigenvalues range from 0 to 1: high eigenvalues mean that clouds

of points are spread linearly along each axis, and low eigenvalues mean that points are clustered at the center of each

axis). Species’ centroids plotted against axes two vs. one

(fig. 3) and against axes three vs. one (fig. 4), showed

strong separation of species.

The pattern of species’ centroids along axis one suggested

that it was a moisture gradient from terrestrial (left) to aquatic

conditions (right) (fig. 3). Those species scoring the lowest

on this axis were the very terrestrial woodland salamanders,

Plethodon serratus (southern redback salamander) and P.

albagula (western slimy salamander). Further to the right

were the more aquatic salamanders, Desmognathus

brimleyorum (Ouachita dusky salamander) and Eurycea

multiplicata (many-ribbed salamander), which frequently

were found in small streams or shallow backwaters of larger

water courses. Next to the right along axis one were the

community of amphibians of small, generally fishless, often

ephemeral, ponds (Bufo americanus, Hyla chrysoscelis,

Ambystoma maculatum, Notophthalmus viridescens, and

Figure 3—Centroids of species’ scores from detrended

correspondence analysis (DCA) of censused plots on all

four watersheds pooled: DCA axis two vs. one (ACCR =

Acris crepitans; AMAN = Ambystoma annulata; AMMA = A.

maculatum; BUAM = Bufo americanus; BUWO = B.

woodhousei; DEBR = Desmognathus brimleyorum; EUMU

= Eurycea multiplicata; GACA = Gastrophryne carolinensis;

HESC = Hemidactylium scutatum; HYCH = Hyla

chrysoscelis; NOVI = Notophthalmus viridescens; PLAL =

Plethodon albagula; PLSE = P. serratus; PSCR =

Pseudoacris crucifer; PSTR = P. triseriata; RACA = Rana

catesbeiana; RACL = R. clamitans; RAUT = R. utricularia).

Figure 4—Centroids of species’ scores from detrended

correspondence analysis (DCA) of censused plots on all

four watersheds pooled: DCA axis three vs. one (ACCR

= Acris crepitans; AMAN = Ambystoma annulata; AMMA

= A. maculatum; BUAM = Bufo americanus; BUWO = B.

woodhousei; DEBR = Desmognathus brimleyorum;

EUMU = Eurycea multiplicata; GACA = Gastrophryne

carolinensis; HESC = Hemidactylium scutatum; HYCH =

Hyla chrysoscelis; NOVI = Notophthalmus viridescens;

PLAL = Plethodon albagula; PLSE = P. serratus; PSCR

= Pseudoacris crucifer; PSTR = P. triseriata; RACA =

Rana catesbeiana; RACL = R. clamitans; RAUT = R.

utricularia).

Table 4—Number of plots surveyed (1995–1999), mean

amphibian abundance per plot, mean species richness

per plot, and mean species diversity per plot by

watershed

Mean Mean

Mean species species

Watershed n abundancea richnessb diversityc

Little Glazypeau 236 5.17 1.35 0.25

North Alum 180 4.40 1.51 0.32

Bread Creek 239 6.08 1.90 0.43

South Alum 178 4.21 1.70 0.41

a ANOVA: F3 = 1.73, p = 0.25. b ANOVA: F3 = 2.61, p = 0.14. c ANOVA: F3 = 2.56, p = 0.14.

Table 5—Eigenvalues of first 4 axes of detrended

correspondence analysis of 18 amphibian species

distributed among a pooled total of 681 plots on

the 4 watershedsa

Axis Eigenvalue

One 0.795

Two 0.513

Three 0.430

Four 0.282

a An eigenvalue is the correlation coefficient between the

plot scores and species scores along a given axis where

each axis is orthogonal (independent) to all previous axes

in the analysis.
170

Gastrophryne carolinensis; American toad, gray treefrog,

spotted salamander, central newt, and eastern narrowmouth

toad). Aligned vertically with this community but extending

toward more extreme values of axis one were mapped the

community of salamanders and frogs that were most aquatic

and associated with small to large ponds and quiet pools of

larger streams (Ambystoma annulata, B. woodhousei, Rana

clamitans, R. utricularia, R. catesbeiana, and Acris crepitans;

ringed salamander, Woodhouse’s toad, green frog, southern

leopard frog, bullfrog, and cricket frog), and small frogs

commonly associated with wet to flooded brushy areas

(Pseudacris triseriata and P. crucifer; upland chorus frog

and spring peeper).

Axis two was interpreted as an elevational gradient, with

higher-elevation sites with low scores on this axis, and

lower-elevation sites with high scores (fig. 3). The most

upland community was that of the small, generally fishless,

often ephemeral ponds, which were often artificially created

wildlife ponds, shallow roadside ditches, or scooped-out

depressions next to a road berm and were often found near

roads at the boundary ridges of the watersheds (American

toad, gray treefrog, spotted salamander, central newt, and

eastern narrowmouth toad). Bufo americanus, a member of

this group, also was frequently found at dry, ridge-top sites

away from ponds. Next in the ordination of axis two fell the

terrestrial salamanders (P. albagula and P. serratus), who

were found on terrestrial sites at all elevations and so whose

centroids fell out in the center of the gradient. Species

found lower down in the watersheds included those of

medium-to-large ponds and flooded wetlands, habitats

characteristic of lowlands (ringed salamander, Woodhouse’s

toad, green frog, southern leopard frog, bullfrog, cricket

frog, upland chorus frog, and spring peeper). Finally, those

salamanders that frequented small streams or shallow

backwaters of larger water courses (D. brimleyorum and E.

multiplicata) were plotted; frequently their habitat adjoined

the largest streams at the bottom of the watershed.

Axis three was more difficult to interpret (and had less statistical explanatory power) (table 5), but knowledge of the

species sorting out at the extremes of this presumed gradient

led us to conclude that it reflected differences in canopy

cover, low to high on the vertical axis (fig. 4). Bufo woodhousei breeds along the shores of rather large ponds and is

rarely encountered in other habitats. Such sites have zero

to low canopy cover. Pseudacris triseriata generally was

found in or near water in flooded, brushy areas with sparse

overstory. The central newt usually was found swimming in

small, open ponds with limited canopy cover, and gray treefrog frequented ridge-top hardwoods, also with limited

canopy cover. On the opposite extreme, we found the rather

rare four-toed salamander (Hemidactylium scutatum) on

dark plots with maximal canopy cover, either in closed-in

pine plantations or very dense hardwoods. The centroids of

the rest of the species fell out in the middle of this axis,

implying that these species were limited to intermediate

levels of canopy cover, or they were found on plots with

both low and high canopy cover (thus, their centroids would

fall out along the middle of this axis).

Scattergrams of plot scores onto the same three axes,

aggregating the plots of the separate watersheds, offered

additional support for our interpretation of the environmental

gradients that the axes represented. Ellipses enclosing 95

percent of the plots of the more intensively managed watersheds, Little Glazypeau and North Alum, were a bit larger

than those enclosing 95 percent of the plots of their less

intensively managed counterparts, Bread Creek and South

Alum (figs. 5 and 6). In fact, ellipses for Little Glazypeau

and North Alum completely enclose those for Bread Creek

and South Alum for axes three vs. one (fig. 6). In DCA,

broader extent of plots along axes means that those plots

offer more varied habitat (plot to plot) for more varied

communities of the organisms; i.e., greater beta diversity.

Thus, even though plots of the four watersheds were not

strongly separated (which meant communities were not all

that different), there was the suggestion that North Alum

and Little Glazypeau offered a more heterogeneous, patchy

habitat mosaic than Bread Creek and South Alum. The

former watersheds, in addition to plots with intermediate

DCA scores, had more extreme aquatic plots (axis one) and

both open and closed-canopy plots (axis three) than the

latter. This enhanced patchiness appeared to have furnished suitable habitat conditions for a greater diversity of

amphibians; i.e., greater between-site, or beta, diversity.

CONCLUSIONS

Although watershed sizes varied more than two-fold, there

was no relationship between species richness and watershed area. Likewise, there was no relationship between

species richness and intensity of management. There is the

suggestion, however, that species diversity and species

evenness may relate to intensity of management. The two

most intensively managed watersheds (Little Glazypeau

and North Alum) showed overall species diversity higher

Figure 5—Ninety-five percent sample probability ellipses

of the plot scores from detrended correspondence

analysis (DCA) of the four watersheds: DCA axis two vs.

one (SA = South Alum, BC = Bread Creek, NA = North

Alum, LG = Little Glazypeau).
171

than that of the least intensively managed watersheds

(Bread Creek and South Alum), with the diversity of North

Alum distinctly higher than the rest. Recall that North Alum

had large areas of relatively undisturbed middle and upper

slope forests along with intensively managed Weyerhaeuser

plantations (representing a diversity of successional stages)

on the lower slope and valley topographic positions. Since

species richness of all four watersheds was almost identical, the pattern of species diversity came about because

the most intensively managed watersheds had a more even

distribution of species. Cluster analysis of community similarity using Morisita’s index, which incorporates Simpson’s

index of dominance (Simpson 1949), resulted in the same

pattern. The most divergent watershed was the one least

intensively managed, South Alum, and the two most intensively managed watersheds, Little Glazypeau and North

Alum, grouped together. In fact, of all the pairs, only little

Glazypeau and North Alum fell out statistically indistinguishable based on Morisita’s index. This high degree of similarity was due primarily to the higher species evenness in

both these watersheds.

At the plot level, no relationship to silviculture was strongly

evident. None of our community parameters (total number,

species richness, and species diversity) differed significantly among watersheds, although the least disturbed

watersheds (Bread Creek and South Alum) had plots with

nominally higher species richness and species diversity,

unlike the tendency observed at the watershed level.

All told, the amphibian communities on the four watersheds

did not appear to be that different, even though the level of

similarity of most pairwise comparisons was gauged to be

less than that expected by chance. However, when classifying the four watersheds into just two levels of intensity of

management, there is the qualitative suggestion that

whereas alpha diversity (within-plot diversity) was related

negatively to intensity of forest management, beta diversity

(among-plot diversity) was related positively to intensity of

forest management. Both tendencies, however, were only

weakly demonstrated. At face value, it would appear that

whereas the individual plots on the more intensively

managed watersheds harbored perhaps somewhat less

diverse assemblages of amphibians, the overall diversity of

the watershed was putatively higher for these than the less

intensively managed watersheds. These tendencies

suggest that forest management applied at a large scale in

the Ouachita Mountains of Arkansas may increase overall

amphibian diversity (almost completely through increased

evenness), probably because such habitat manipulation

increases the diversity of habitat patches (timber stands of

different ages and plant species distributions). Along with

an increase of diversity of habitat patches comes increased

diversity of canopy, litter, understory, down wood, soil moisture, stream hydrology, and various other habitat features

that impact amphibians. This suggested increased beta

diversity, which was not demonstrated statistically, comes at

a cost of decreased alpha diversity, however (also not

demonstrated statistically).

One obvious environmental difference among the watersheds that affected beta diversity along axis one was the

presence or absence of large ponds, which represented

important habitat for the more aquatic frogs. South Alum

lacked these large, permanent ponds; Bread Creek also

lacked them but had some moderate-sized ponds; North

Alum had some large ponds; and Little Glazypeau had the

most, primarily because they were constructed as “heliponds” to supply water for fire control. Consequently, Bread

Creek and especially South Alum lacked plots with high

scores along DCA axis one (an environmental gradient from

terrestrial to aquatic) and were relatively impoverished in

species like bullfrog, southern leopard frog, and cricket

frog. The DCA suggests roughly the same pattern of beta

diversity among the watersheds as that implied from the

watershed-wide Shannon-Wiener diversity indices (table 1),

but neither analysis implies a strong difference among

watersheds.

Our data weakly suggest that the more intensely managed

watersheds held a greater diversity (greater evenness of

about the same number of species) of amphibians than the

less intensely managed watersheds. But such a difference

may not relate to management at all. There may be other

characteristics of Little Glazypeau and North Alum that favor

a greater diversity of amphibians. For example, both these

watersheds are larger than either Bread Creek or South

Alum. North Alum is over three times larger than Bread

Creek. It is well known that number of species increases

with area of study plots, the familiar species-area curves of

islands and mainland sites (Pianka 2000). Although we did

not observe more species on the largest watershed, perhaps

area alone furnished conditions that promoted higher species

evenness, hence higher species diversity. The large size of

North Alum means that it may have offered a greater variety

of habitats that might have favored a greater variety of

amphibians, and it was North Alum that held the highest

overall species diversity and evenness of all four watersheds

Figure 6—Ninety-five percent sample probability ellipses

of the plot scores from detrended correspondence

analysis (DCA) of the four watersheds: DCA axis three

vs. one (SA = South Alum, BC = Bread Creek, NA = North

Alum, LG = Little Glazypeau).
172

(table 1), even though it was not the most intensively managed watershed. There also may be other characteristics

that relate to the amphibian communities found on the

watersheds. These are just four watersheds picked to vary

along a management continuum, but they also may vary in

other ways. Without a suitable set of replicate watersheds

representing various levels of forest management, it is

impossible to randomize all these other variables and to

assess the relative effect of forest management on amphibian communities.

One example from our study illustrates that concern. We

found no central newts on Little Glazypeau, but we found

them on the other watersheds, especially North Alum (also

rather intensively managed). Little Glazypeau lacked small,

permanent, fishless ponds whereas the other three watersheds had them, mostly man-made ones established for

wildlife. Predatory fish and newt species often do not coexist

(Beebee 1997, Hecnar and M’Closkey 1997). Both are predatory and have high prey overlap. Apparently, fish are more

efficient predators and can outcompete the newts; introduced centrarchids can quickly eradicate N. viridescens

from small ponds (Hecnar and M’Closkey 1997). Without

the refuge of small, permanent, fishless ponds in which N.

viridescens breeds, Little Glazypeau may not support this

species, and the lack of these ponds may have nothing to

do with commercial forest management. In fact, most of the

small ponds were established artificially in the other watersheds for the benefit of wildlife, but this was not done at

Little Glazypeau. In contrast, permanent heliponds established by Weyerhaeuser in Little Glazypeau were occasionally stocked with fish by local residents or fishermen, who

released catches from nearby lakes or ponds for recreational fishing.

MANAGEMENT IMPLICATIONS

Our study qualitatively suggests that intensive forest management may decrease local, plot-wise (alpha) amphibian

species diversity, but in turn may increase overall, watershed (beta) amphibian species diversity, perhaps by increasing the diversity of available habitats. However, neither

tendency was statistically demonstrated because plots were

not distinctly different among watersheds, and overall amphibian communities of the four watersheds were extremely

similar. Taken together, our data suggest that intensive

silviculture as practiced in the Ouachita Mountains of westcentral Arkansas is not detrimental to landscape-level

amphibian communities. This is probably true because even

under the most intensive forest management, stand sizes

are large, riparian zones are largely left intact, and ponds

are created either for the benefit of wildlife or for a water

supply for fire control. It is important to maintain those practices to conserve and maintain existing amphibian diversity.

We recommend that land managers construct and/or maintain both large and small ponds, critical breeding habitat for

many species of amphibians. Especially small, often ephemeral, vernal ponds are important for amphibians (Dodd

and Cade 1998, Semlitsch and others 1996, Semlitsch and

Bodie 1998). Because fish are important predators of amphibian eggs and larvae (Bradford 1989, Denton and Beebee

1991, Grubb 1972, Webb and Joss 1997) and are known to

completely eliminate some species from permanent ponds

(Fisher and Shaffer 1996, Hecnar and M’Closkey 1997),

some attempt should be made to maintain some small, permanent, fishless ponds on managed watersheds. In addition

to mostly terrestrial species and those characteristic of

small lowland steams and backwaters, we found two large

amphibian communities associated either with small, often

ephemeral, often upland ponds, or large, permanent, more

lowland ponds. Presence and maintenance of these two

classes of ponds should be part of forest-management

practices in the Ouachita Mountains.

ACKNOWLEDGMENTS

We thank the USDA Forest Service, Weyerhaeuser

Company, and the National Council of the Paper Industry

for Air and Stream Improvement for providing funding and

logistical support. Additional logistical support was provided

through the Oklahoma Cooperative Fish and Wildlife

Research Unit (Oklahoma State University, Oklahoma

Department of Wildlife Conservation, U.S. Geological

Survey Biological Resources Division, and Wildlife Management Institute cooperating). We also thank a great number

of paid and volunteer graduate and undergraduate field

assistants for their help in data collection, entry, and errorchecking.

Research support was provided through a 4-year study

“Herptetological Communities under Diverse Forest Management Intensities: a Landscape-Level Assessment of the

Ouachita Mountains, Arkansas” dated May 1995 to March

1999 of Cooperative Agreements 19-95-037, 30-96-022,

and 33-CA-98-354. This research was supported by funds

provided by the U.S. Department of Agriculture, Forest

Service, Southern Research Station as part of the pretreatment sampling in the Winona Basin.

LITERATURE CITED

Beebee, T.J.C. 1997. Changes in dewpond numbers and amphibian

diversity over 20 years on Chalk downland in Sussex, England.

Biological Conservation. 81: 215-219.

Biondini, M.E.; Mielke, P.W., Jr.; Berry, K.J. 1988. Data-dependent

permutation techniques for the analysis of ecological data.

Vegetatio. 75: 161-168.

Blaustein, A.R.; Wake, D.B. 1995. The puzzle of declining amphibian populations. Scientific American. 272: 52-57.

Bradford, D.F. 1989. Allotopic distribution of native frogs and introduced fishes in high Sierra Nevada lakes of California: implication of the negative effect of fish introductions. Copeia. 1989:

775-778.

Delis, P.R.; Mushinsky, H.R.; McCoy, E.D. 1996. Decline of some

west-central Florida anuran populations in response to habitat

degradation. Biodiversity and Conservation. 5: 1579-1595.

Denton, J.; Beebee, T.J.C. 1991. Palatability of anuran eggs and

embryos. Amphibia-Reptilia 12: 111-114.

Dodd, C.K., Jr.; Cade, B.S. 1998. Movement patterns and the conservation of amphibians breeding in small, temporary wetlands.

Conservation Biology. 12: 331-339.

Fisher, R.N.; Shaffer, B. 1996. The decline of amphibians in

California’s great central valley. Conservation Biology. 10:

1387-1397.

Grubb, J.C. 1972. Differential predation by Gambusia affinis on the

eggs of seven species of anuran amphibians. American Midland

Naturalist. 88: 102-108.
173

Guldin, J.M. [In press]. Landscape scale research in the Ouachita

Mountains of west-central Arkansas: general study design. In:

Guldin, James M., tech. comp. Ouachita and Ozark Mountains

symposium: ecosystem management research. Gen. Tech.

Rep. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station.

Hecnar, S.J.; M’Closkey, R.T. 1997. The effects of predatory fish

on amphibian species richness and distribution. Biological

Conservation. 79: 123-131.

Houlahan, J.E.; Findlay, C.S.; Schmidt, B.R. [and others]. 2000.

Quantitative evidence for global amphibian population declines.

Nature 404: 752-755.

Hurlbert, S.H. 1984. Pseudoreplication and the design of ecological field studies. Ecological Monographs. 54: 187-211.

Morisita, M. 1959. Measuring of interspecific association and

similarity between communities. Memoirs of Faculty of Science

at Kyoto University. Series E (Biology) 3: 65-80.

Phillips, K. 1990. Where have all the frogs and toads gone?

Bioscience. 40: 422-424.

Pianka, E.R. 2000. Evolutionary ecology. 6th ed. San Francisco,

CA: Addison Wesley Longman, Inc. 512 p.

Pielou, E.C. 1966. The measurement of diversity in different types

of biological collections. Journal of Theoretical Biology. 13: 131-

144.

SAS Institute Inc. 1999. SAS/STAT user’s guide. Version 8.01

edition. Cary, NC: SAS Institute Inc.

Sattath, S.; Tversky, A. 1977. Additive similarity trees. Psychometrika. 42: 319-345.

Semlitsch, R.D.; Bodie, J.R. 1998. Are small, isolated wetlands

expendable? Conservation Biology. 12: 1129-1133.

Semlitsch, R.D.; Scott, D.E.; Pechmann, J.H.K.; Gibbons, J.W.

1996. Structure and dynamics of an amphibian community:

evidence from a 16-year study of a natural pond. In: Cody,

M.L.; Smallwood, J.A., editors. Long-term studies of vertebrate

communities. San Diego, CA: Academic Press: 217-248.

Shannon, C.E.; Weaver, W. 1949. The mathematical theory of

communication. University of Illinois Press, Urbana.

Simpson, E.H. 1949. Measurement of diversity. Nature. 163: 688.

Tappe, P.A.; Weih, R.C.; Thill, R.E. [and others]. [In press]. Landscape characterization of four watersheds under different management scenarios in the Ouachita Mountains of Arkansas. In:

Guldin, James M., tech. comp. Ouachita and Ozark Mountains

symposium: ecosystem management research. Gen. Tech.

Rep. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station.

ter Braak, C.J.F.; Prentice, I.C. 1988. A theory of gradient analysis.

Advances in Ecological Research. 18: 271-313.

ter Braak, C.J.F.; Šmilauer, P. 1998. CANOCO reference manual

and user’s guide to CANOCO for windows: software for

canonical community ordination. Version 4. Ithaca, NY:

Microcomputer Power. 351 p.

Wake, D.B. 1991. Declining amphibian populations. Science. 253:

860.

Webb, C.; Joss, J. 1997. Does predation by the fish Gambusia

holbrooki (Atheriniformes: Poeciliidae) contribute to declining

frog populations? Australian Zoologist. 30: 316-324.

Wolda, H. 1981. Similarity indices, sample size and diversity.

Oecologia. 50: 296-302.
174

INTRODUCTION

Recently, it has been reported that many reptile populations

are experiencing global declines (Gibbons and others, 2000)

akin to, if not more serious than, the highly publicized

global declines in amphibian populations (Blaustein and

Wake 1995, Houlahan and others 2000, Phillips 1990,

Wake 1991). This realization has led to a call for increased

long-term research on these often ignored taxa.

Reptiles, like amphibians, commonly experience natural

population fluctuations and extinctions (Blaustein and

others, 1994; Pechmann and others, 1991). However, not

all observed declines can be categorized as natural (Gibbons

and others, 2000). Possible causes for these declines

include habitat loss and degradation, climatological change,

introduction of exotic species, environmental pollution,

disease and parasites, and unsustainable harvesting.

Loss of suitable habitat is considered by some scientists to

be the largest single factor responsible for declines in

amphibian populations (Alford and Richards, 1999). Likewise, it has been suggested that habitat loss due to urbanization, agriculture, and silviculture may play an important

role in declines of reptile populations (Gibbons and others,

2000). Despite the implication of silvicultural practices in

these declines, little is known about the habitat parameters

that influence reptile communities, and even less is known

about landscape-level environmental influences.

We report results of pretreatment data collection for a largescale, long-term, field study of reptile communities and the

influence of habitat and landscape environmental variables

in four watersheds of the Ouachita Mountains of west-central Arkansas. Following the pretreatment stage, sections of

the watersheds will be subjected to different forest management to achieve a variety of specific “desired future conditions.” After treatment, data on reptile communities will

again be collected and used to quantify community changes

and to compare with the predictions of multivariate community models that we are developing. This long-term study

is one component of Phase III of the Ouachita Mountains

Ecosystem Management Research Project; the wildlife

component of this cooperative effort involves Weyerhaeuser

Company, National Council of the Paper Industry for Air and

Stream Improvement, Oklahoma State University, Oklahoma

Cooperative Fish and Wildlife Research Unit, University of

Arkansas Monticello, Ouachita National Forest, and Southern

Research Station of the USDA Forest Service.

The objectives of our overall study are to: (1) characterize

reptile and amphibian communities in four watersheds representing markedly different forest-management strategies in

the Ouachita Mountains, Arkansas; (2) develop and validate

models for predicting community composition based on

site, stand, and landscape parameters; and (3) develop

recommendations to promote maintenance of reptile and

amphibian communities in managed forest landscapes.

This report contains results for reptiles of the pretreatment

data analysis performed at the end of four survey years.

METHODS

Study Areas

The study was conducted on four 1500- to 4000-ha watersheds under different intensities of management in Garland

and Saline counties near Hot Springs, Arkansas. The watersheds differed markedly with respect to factors such as

mean rotation lengths, forest type diversity, stand sizes and

ages, and the amount of natural second-growth coverage

(Guldin and others, in press; Tappe and others, in press).

REPTILE COMMUNITIES UNDER DIVERSE FOREST MANAGEMENT

IN THE OUACHITA MOUNTAINS, ARKANSAS

Paul A. Shipman, Stanley F. Fox, Ronald E. Thill,

Joseph P. Phelps, and David M. Leslie, Jr.1

1 Research Technician, Oklahoma Cooperative Fish and Wildlife Research Unit, Department of Zoology, Oklahoma State University,

Stillwater, OK 74078; Research Professor, Department of Zoology, Oklahoma State University, Stillwater, OK 74078; Supervisory Research

Wildlife Biologist, USDA Forest Service, Southern Research Station, SFA Station, Nacogdoches, TX 75962; Forest Wildlife Biologist,

Southern Forest Research Center, Weyerhaeuser Company, Hot Springs, AR 71902; and Unit Leader, U.S. Geological Survey, Oklahoma

Cooperative Fish and Wildlife Research Unit, Oklahoma State University, Stillwater, OK 74078, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—From May 1995 to March 1999, we censused reptiles in the Ouachita Mountains, Arkansas, on approximately

60 plots on each of four forested watersheds five times per year, with new plots each year. We found that the least

intensively managed watershed had significantly lower per-plot reptile abundances, species richness, and diversity.

Despite these differences, community similarities were high (0.89-0.98, Morisita’s index) between all watersheds. The

least intensively managed watershed had nominally higher overall species evenness and diversity. Further inspection

revealed that this was due to high dominance in the more intensively managed watersheds by two species that were

notas as dominant in the least intensively managed watershed. Detrended correspondence analysis revealed communities separating out on the basis of presumed gradients of canopy cover, terrestrial-aquatic, and a complex gradient of

humidity/soil moisture.
175

Little Glazypeau, a watershed located some 22 km southwest of the other three watersheds (that were contiguous)

and managed largely for sawlog production by Weyerhaeuser

Company, represented our most intensively managed

watershed. Much of the second-growth shortleaf pine (Pinus

echinata)-hardwood forest that originally covered this watershed had been harvested and planted to loblolly pine (P.

taeda) plantations of 9-142 ha. Typically, these plantations

were thinned twice, pruned to 5-8 m high, fertilized, and

harvested at 30-35 years old. The remaining, selectivelyharvested acreage in the watershed occured on rocky ridgetops, steep slopes, and streamside management zones that

were retained for watershed protection and to provide habitat diversity for wildlife. South Alum, an experimental section

of the Ouachita National Forest that has received minimal

logging for > 80 years, represented the least intense level

of silviculture. South Alum was almost entirely USDA Forest

Service land and consisted of mature forest over most of

the area. Bread Creek and North Alum fell in between these

extremes of forest management. From independent records

of forest management and present composition of number,

age, and distribution of pine (mostly Pinus echinata) plantations, Bread Creek was considered less intensely managed

than North Alum (Tappe and others, in press). Bread Creek

was primarily USDA Forest Service land and had been

managed according to prevailing Forest Service standards

and guidelines for several decades, whereas North Alum

was of mixed ownership, with about half of the area under

more intensive Weyerhaeuser Company management

(comparable to Little Glazypeau) and half under USDA

Forest Service management. North Alum displayed characteristics of a diverse range of management activities, ranging from no management along steeper slopes and higher

elevations to intensively-managed pine plantations, mainly

at lower elevations. Thus, the watersheds, in order of intensity of forest management (from least to most), were South

Alum, Bread Creek, North Alum, and Little Glazypeau.

These same watersheds, in order of size, were South Alum

(1500 ha), Bread Creek (1535 ha), Little Glazypeau (2273

ha), and North Alum (3961 ha).

Sampling Plots

We surveyed reptiles and amphibians using area-constrained

searches on a subset (56) of the 75-235 plots per watershed that were established each year for breeding bird

surveys (Tappe and others, in press). Bird plots were established at 200-m intervals along >100 km of parallel transects (oriented approximately north-south across prevailing

topography) that were established in 1995 over the 4 watersheds. These same transects were used in 1996, but new

plots were established by shifting plot centers 100 m. In

1997, approximately 110 km of new transects were established between those of 1995; new plots were established

in 1998 along these new transects as in 1996. The subset

of plots that were used for reptile surveys were selected to

represent a cross-section of slopes, aspects, forest types,

stand conditions, and aquatic habitats. The center of our

20-m-radius (0.13-ha) plots also was the center of a bird

sampling plot. Plastic flagging was used to delineate plot

boundaries on all reptile plots. In each watershed, we

selected reptile plots to ensure there were at least 12-15

of them in aquatic habitats, which consisted of springs,

streams, and man-made ponds that had been established

to benefit wildlife (Forest Service wildlife ponds) and/or as

sources of water for fire fighting. An additional four plots per

watershed per year were established off the transects at

these ponds (or at wide pools in streams at the bottom of a

watershed) to ensure that we had equivalent sampling

effort at these aquatic habitats. Plots at these aquatic sites

were established so that roughly half of the plot was water

and half was land. Thus, we surveyed 60 plots per year per

watershed.

Reptile Surveys

Trained crews of 3-5 individuals surveyed each set of 60

plots per watershed during daylight hours 5 times a year

from May 1995 to March 1999: early May, late May, midJune, early October, and early the following March. Plots

were surveyed entirely by visually searching vegetation and

the ground surface, and by lifting cover objects (rocks, logs,

and debris); the latter were replaced to minimize impacts

on subsequent surveys. Reptiles that were seen and identified were tallied; those that required capture for identification were released at the point of capture before leaving the

plot.

Data Analysis

Reptile count data were pooled across the five sampling

periods per year, but data from each plot served as separate samples. Data collected in the first year (1995) from

South Alum and North Alum were excluded from analyses

due to differences in sampling effort by former collaborators, and data from a few other plots were discarded when

five surveys per year were not attained. Thus, analyses

presented here are based on data from 833 plots, each

censused five times, for a total of 4,165 censuses. Watershed- and plot-level data were analyzed differently as

described in the following two paragraphs.

Watershed level—The following community indices were

first calculated at the watershed level: number of individuals

by species for all years combined, reptile abundance,

species richness, (beta) species diversity [H’; ShannonWiener diversity index (Shannon and Weaver 1949)], and

species evenness [diversity divided by maximal diversity, or

H’/(ln number of species); Pielou 1966]. With only four

watersheds to compare, no statistical tests were employed.

Because the Shannon-Wiener diversity index is quite sensitive to sample size and because we did not have an equal

effort among all watersheds (two watersheds with data from

only three years; see above), we randomly reordered the

plots within each watershed, pooling years, and plotted the

Shannon-Wiener diversity index of each watershed for cumulative sets of plots up to the total number of plots for each

watershed. Such a plot would show if diversity approached

an asymptote as cumulative plots increased and if our total

number of plots per watershed was sufficient to adequately

estimate species diversity.

We computed Morisita’s index of community similarity

(Morisita 1959) between all pairs of watersheds, pooling

data for the entire study for each watershed. This index is

desirable because sample sizes and species diversities of

the communities being compared have little influence on its

calculation (Morisita 1959; Wolda 1981). To statistically
176

compare the various Morisita’s indices of community similarity, we conducted randomization tests (Biondini and

others 1988). For each pair of watersheds, we randomly

reassigned plots between them (retaining the sample size

of each watershed) and computed Morisita’s index for these

two “synthetic” communities. We repeated this procedure

1000 times and tabulated the number of times the recomputed index was smaller than or equal to the “actual” index.

If less than 100 recomputed indices fell below or equal to

the “actual” index, then those two watersheds were considered different (p < 0.10) in species composition.

Plot level—For analyses at the plot level, we used mixed

model, two-way ANOVAs (ANOVAs with both random and

fixed effects; PROC MIXED, SAS 1999) to test for differences among watersheds, years, and year * watershed

interaction. We recognize that our sample of plots drawn

from each of four watersheds is pseudoreplicated (Hurlbert,

1984), but the large scale of this study prevented sampling

of replicate watersheds for each treatment. While results of

our ANOVA must be interpreted with caution due to this

pseudoreplication, we feel that the analysis nevertheless

suggests likely ecological patterns that deserve attention.

The response variables of the ANOVAs were (1) reptile

abundance per plot, (2) species richness per plot, and

(alpha) plot diversity (Shannon-Wiener index). For count

variables (1 and 2) we used a square root transformation,

SQR (count + 0.5). Even with those transformations, our

data did not fully meet assumptions of normality and homogeneity of variances, but the Satterthwaite algorithm of the

mixed model ANOVA is relatively robust to abuses of these

assumptions, especially of homogeneity of variances (SAS

1999), and so we proceeded with these parametric analyses. We recognized differences in weather between years,

not of interest to us here, and included year effects and

year * watershed interaction as random effects, not to be

statistically interpreted. The fixed factor (watersheds) was

tested for statistical significance at p < 0.10. If a significant

watershed effect was found, we used LSD to evaluate pairwise differences between any two watersheds.

Detrended canonical correspondence analysis (DCA)—

To appraise reptile community composition, develop preliminary hypotheses of presumptive environmental gradients

influencing these communities, and compare graphically

the environments and reptile communities of the four watersheds, we used DCA (ter Braak and Prentice 1988, ter Braak

and Šmilauer 1998), pooling plots from all four watersheds

and years. Application of DCA to our data allowed for a

more detailed inspection of reptile communities at the plot

level and how they were distributed along inferred environmental gradients. DCA is probably the most widely employed

eigenanalysis-based ordination technique used by community ecologists. It is an indirect ordination method that

orders plots with similar compositions of species along

multiple axes simultaneously. The statistical algorithm is to

calculate sample scores of each plot as a weighted average

of the species scores, and species scores as a weighted

average of samples scores; iterations are repeated until

there is no further change in scores, at which time samples

(plots) with similar animal communities appear clustered

when plotted on multiple axes. Environmental gradients are

inferred from the pattern of species and/or plots and the

biologist’s knowledge of the species. As a step beyond

single-number summary statistics like diversity indices or

Morisita’s indices, DCA results in a cloud of points for separate species in n-dimensional space, conventionally viewed

as centroids (averaged central tendency) in two dimensions

at a time. In other words, DCA results in a pattern, not a

number. For our analysis, rare species (less than three

individuals encountered over all four watersheds for all four

years) and plots where no reptile species were found were

excluded due to computational constraints.

RESULTS AND DISCUSSION

We found 1,877 individuals of 35 reptile species during our

four-year study (table 1). Total species diversity was 2.38

for all watersheds pooled. We found a mean of 2.25 reptiles

per plot (median = 1.00, range = 0-22).

Watershed Level

Despite large differences in size of watersheds and substantial differences in management intensities, the reptile communities of these four watersheds were fairly similar. Species

richness ranged from 26-28 species per watershed, and 19

of the total set of 35 species (54 percent) were found on all

four watersheds (table 1). The watersheds differed some in

both overall species diversity and evenness; the order from

lowest to highest by diversity was Bread Creek, Little Glazypeau, North Alum, and South Alum (table 1). For evenness,

the order from lowest to highest was similarly polarized as

Bread Creek, North Alum, Little Glazypeau, and South Alum.

The middle watersheds in both of these rankings, North

Alum and Little Glazypeau, were virtually identical in these

measures. Recalculated diversity indices against cumulative plots showed that diversity leveled off after about 40-

100 plots (fig. 1), well below the lowest total of plots for any

watershed. South Alum showed distinctly higher overall

diversity than the other watersheds. Bread Creek had the

lowest overall diversity and North Alum and Little Glazypeau

were intermediate and nearly indistinguishible from each

other.

The dominant species found in all watersheds were the

ground skink (Scincella lateralis) and the western fence

lizard (Sceloporus undulatus), representing on average

51.1 percent of total reptiles encountered in each watershed community. Additionally, species composition of the

six most ubiquitous species of each watershed was strikingly similar (table 2).

We examined the overall set of species to see if there were

any species absent from all but one watershed, or present

in only one watershed. The flathead snake (Tantilla gracilis)

was absent from Little Glazypeau; the six-line racerunner

(Cnemidophorus sexlineatus) was not found on any Bread

Creek plots; and the green anole (Anolis carolinensis) and

the speckled kingsnake (Lampropeltis getula) were not

found in South Alum watershed. The western diamondback

rattlesnake (Crotalus atrox) was found only in Little Glazypeau; the Great Plains ratsnake (Elaphe guttata), eastern

coachwhip (Masticophis flagellum), diamondback water

snake (Nerodia rhombifer), and the rough earth snake

(Virginia striatula) were recorded only in North Alum; the

scarlet snake (Cemophora coccinea) was found only in
177

Table 1—Reptile abundance on four watersheds in the Ouachita Mountains,

Arkansas, 1995-1999

Little North Bread South

Species Glazypeau Alum Creek Alum

Agkistrodon contortrix 16 7 8 16

(copperhead)

Agkistrodon piscivorus 4 20 7 10

(cottonmouth)

Anolis carolinensis 43 4 18 0

(green anole)

Carphophis vermis 11 9 29 13

(western worm snake)

Cemophora coccinea 0 0 10

(scarlet snake)

Cnemidophorus sexlineatus 2 7 01

(six-line racerunner)

Coluber constrictor 8 11 7 1

(black racer)

Crotalus atrox 1 0 00

(western diamondback rattlesnake)

Crotalus horridus 1 0 10

(timber rattlesnake)

Diadophis punctatus 35 18 44 29

(western ringneck snake)

Elaphe guttata 0 1 00

(Great Plains ratsnake)

Elaphe obsoleta 3 1 11

(black ratsnake)

Eumeces anthracinus 21 12 26 11

(coal skink)

Eumeces fasciatus 48 22 46 16

(five-line skink)

Eumeces laticeps 10 4 11 3

(broadhead skink)

Heterodon platirhinos 0 0 11

(eastern hognose)

Lampropeltis getula 5 3 10

(speckled kingsnake)

Lampropeltis triangulum 1 1 42

(milksnake)

Masticophis flagellum 0 1 00

(eastern coachwhip)

Nerodia erythrogaster 2 8 12

(yellowbelly watersnake)

Nerodia rhombifer 0 1 00

(diamondback watersnake)

Nerodia sipedon 1 0 04

(midland watersnake)

Opheodrys aestivus 3 9 25

(rough green snake)

Scincella lateralis 187 111 160 69

(ground skink)

Sceloporus undulatus 93 110 209 47

(northern fence lizard)

Sistrurus miliarius 2 4 13

(western pigmy rattlesnake)

Storeria dekayi 6 1 22

(brown snake)

Storeria occipitomaculata 12 5 7 10

(northern redbelly snake)

continued
178

Figure 1—Diversity of each watershed against cumulative number

of plots (in random order) included in recalculation. SA = South

Alum; BC = Bread Creek; NA = North Alum; LG = Little Glazypeau.

Bread Creek; and the western ribbon snake (Thamnophis

proximus) was found only in South Alum watershed.

Based on Morisita’s index of community similarity, watershed

reptile communities were highly similar, ranging from 0.89

to 0.98 (a value of 1.00 means identical communities; table

3). Bread Creek differed the most from the other watersheds,

displaying an index of 0.89 with both Little Glazypeau and

South Alum. Despite generally high indices of community

similarity, all indices [except that between North Alum and

Bread Creek (O.98)] were statistically significant by the randomization tests; i.e., all watershed pairs except this one

were significantly different from each other beyond that

expected by chance.

Plot Level

We found a significant watershed effect for all three plotlevel measures (table 4): reptile abundance (F3 = 2.55, p =

0.05), richness (F3 = 2.53, p = 0.06), and diversity (F3 = 2.64,

p = 0.05). South Alum had plots with significantly fewer reptiles, lower species richness, and lower species diversity

than each of the other watersheds based on ANOVA (table

4). None of the pairwise comparisons for the other watersheds were significant (p > 0.10).

Detrended Correspondence Analysis

Detrended correspondence analysis indicated relatively

long environmental gradients along the first three axes for

reptile communities in the four watersheds (table 5). The

fourth and additional axes contributed little to the pattern of

Table 1—Reptile abundance on four watersheds in the Ouachita Mountains,

Arkansas, 1995-1999 (continued)

Little North Bread South

Species Glazypeau Alum Creek Alum

Tantilla gracilis 0 3 41

(flathead snake)

Terrapene carolina 46 20 16 26

(eastern three-toe box turtle)

Terrapene ornata 7 0 0 1

(ornate box turtle)

Thamnophis proximus 0 0 01

(western ribbon snake)

Thamnophis sirtalis 3 2 72

(red-sided garter snake)

Virginia striatula 0 1 00

(rough earth snake)

Virginia valeriae 4 4 65

(smooth earth snake)

 Total 575 400 620 282

Species richness 27 28 26 26

Species diversity 2.34 2.35 2.12 2.51

Species evenness 0.87 0.86 0.84 0.92

Data are arrayed (left to right) from the most to the least intensively managed watersheds.
179

community organization because each additional axis

explains only residual variation not already incorporated

into the DCA. In other words, plots and species scores

were relatively tightly correlated with each other along an

appreciable stretch of at least the first three axes (eigenvalues range from 0 to 1: high eigenvalues meant that clouds

of points were spread linearly along each axis, and low

eigenvalues meant that points were clustered at the center

of each axis). Species’ centroids plotted against axes two

vs. one (fig. 2) and against axes three vs. one (fig. 3)

showed strong separation of species.

The pattern of species’ centroids along axis one suggested

that it was a measure of low-to-high canopy cover, left to

right (fig. 2). Most of the species scoring lowest on this axis

were typically found in more open or edge habitats. These

species were Tantilla gracilis (flathead snake), Sceloporus

undulatus (northern fence lizard), Nerodia erythrogaster

(yellowbelly watersnake), Elaphe obsoleta (black ratsnake),

and Cnemidophorous sexlineatus (six-line racerunner). To

Table 2—Six most common reptile species found in each watershed in order of decreasing abundance

Little Glazypeau n North Alum n Bread Creek n South Alum n

Scincella lateralis 187 Schincella lateralis 111 Sceloporus undulatus 209 Scincella lateralis 69

(ground skink) (ground skink) (northern fence (ground skink)

lizard)

Sceloporus undulatus 93 Sceloporus undulatus 110 Scincella lateralis 160 Sceloporus undulatus 47

(northern fence lizard) (northern fence lizard) (ground skink) (northern fence lizard)

Eumeces fasciatus 48 Eumeces fasciatus 22 Eumeces fasciatus 46 Diadophis punctatus 29

(five-line skink) (five-line skink) (western ringneck snake) five-line skink)

Terrapene carolina 46 Terrapene carolina 20 Diadophis punctatus 44 Terrapene carolina 26

eastern three-toe (eastern three-toe (western ringneck (eastern three-toe

box turtle) box turtle) snake box turtle)

Anolis carolinensis 43 Agkistrodon piscivorus 20 Carphophis vermis 29 Eumeces fasciatus 16

(green anole) (cottonmouth) (western worm snake) (five-line skink)

Diadophis punctatus 35 Diadophis punctatus 18 Eumeces anthracinus 26 Carphophis vermis 13

(western ringneck (western ringneck (coal skink) (western worm snake)

snake) snake)

Table 3—Morisita’s index of community similarity

for all pairs of watersheds

Little North Bread South

Watershed Glazypeau Alum Creek Alum

Little Glazypeau 1.00

North Alum 0.93a 1.00

Bread Creek 0.89a 0.98 1.00

South Alum 0.96a 0.94a 0.89a 1.00

Index ranges from 0 to 1, where 1 means communities are

identical.

a Significantly dissimilar by Randomization Test, p < 0.10.

Table 5—Eigenvalues of first 4 axes of detrended

correspondence analysis of 25 reptile species

distributed among a pooled total of 403 plots on

the 4 watershedsa

Axis Eigenvalue

One 0.513

Two 0.441

Three 0.334

Four 0.287

a An eigenvalue is the correlation coefficient between the

plot scores and species scores along a given axis where

each axis is orthogonal [independent] to all previous axes

in the analysis.

Table 4—Number of plots surveyed (1995–1999), mean

reptile abundance per plot, mean species richness per

plot, and mean species diversity per plot by watershed

Mean Mean

Mean species species

Watershed n abundancea richnessb diversityc

Little Glazypeau 236 2.44 1.58 0.408

North Alum 180 2.22 1.54 0.404

Bread Creek 239 2.59 1.69 0.453

South Alum 178 1.58 1.23 0.292

Vertical bars connect those watersheds not statistically different as

indicated by post-hoc pairwise contrasts.

a ANOVA: F3

 = 2.55, p = 0.05. b ANOVA: F3

 = 2.53, p = 0.06. c ANOVA: F3

 = 2.64, p = 0.05.
180

the far right, scoring highest on axis one, were mostly species

typically associated with closed-canopy forested habitats,

like Terrapene carolina (eastern three-toe box turtle),

Sistrurus miliarius (western pygmy rattlesnake), Storeria

occipitomaculata (northern redbelly snake), and Agkistrodon

contortrix (copperhead). Species found at the middle of this

axis were found in sites with intermediate or mixed canopy

cover.

Axis two was interpreted as an aquatic gradient, with terrestrial sites having low scores on this axis and aquatic sites

having high scores (fig. 2). The most terrestrial species

(the lowest scores on axis two) included Eumeces laticeps

(broadhead skink), Lampropeltis triangulum (milksnake),

Terrapene ornata (ornate box turtle), and Scincella lateralis

(ground skink). Scoring highest along axis two were the

aquatic species, Agkistrodon piscivorus (cottonmouth) and

Nerodia erythrogaster (yellowbelly watersnake).

Axis three was more difficult to interpret (and had less statistical explanatory power, table 5), but knowledge of the

species displayed at the extremes of this presumed gradient led us to conclude that it reflected a complex gradient

Figure 2—Centroids of species’ scores from detrended correspondence analysis (DCA) of censused plots on all four watersheds

pooled: DCA axis two vs. one. AGCO = Agkistrodon contortrix;

AGPI = A. piscivorous; ANCA = Anolis carolinensis; CAVE =

Carphophis vermis; CNSE = Cnemidophorus sexlineatus; COCO =

Coluber constrictor; DIPU = Diadophis punctatus; ELOB = Elaphe

obsolete; EUAN = Eumeces anthracinus; EUFA = E. fasciatus;

EULA = E. laticeps; LAGE = Lampropeltis getula; LATR = L.

triangulum; NEER = Nerodia erythrogaster; OPAE = Opheodrys

aestivus; SCLA = Scincella lateralis; SCUN = Sceloporus

undulates; SIMI = Sistrurus miliarius; STDE = Storeria dekayi;

STOC = S. occipitomaculata; TAGR = Tantilla gracilis; TECA =

Terrapene Carolina; TEOR = T. ornate; THSI = Thamnophis

sirtalis; VIVA = Virginia valeriae.

Figure 3—Centroids of species’ scores from detrended correspondence analysis (DCA) of censused plots on all four watersheds

pooled: DCA axis three vs. one. AGCO = Agkistrodon contortrix;

AGPI = A. piscivorous; ANCA = Anolis carolinensis; CAVE =

Carphophis vermis; CNSE = Cnemidophorus sexlineatus; DIPU =

Diadophis punctatus; ELOB = Elaphe obsolete; EUAN = Eumeces

anthracinus; EUFA = E. fasciatus; EULA = E. laticeps; LAGE =

Lampropeltis getula; LATR = L. triangulum; NEER = Nerodia

erythrogaster; OPAE = Opheodrys aestivus; SCLA = Scincella

lateralis; SCUN = Sceloporus undulates; SIMI = Sistrurus miliarius;

STDE = Storeria dekayi; STOC = S. occipitomaculata; TAGR =

Tantilla gracilis; TECA = Terrapene Carolina; TEOR = T. ornate;

THSI = Thamnophis sirtalis; VIVA = Virginia valeriae.

of humidity/soil moisture (fig. 3). Species located highest

along axis three are typical of more western, arid habitats

of the United States. These species are Terrapene ornata

(ornate box turtle), Lampropeltis getula (speckled kingsnake), and Diadophis punctatus (ringneck snake). Species

found lowest on the axis were associated with moist substrates or humid environments: Carphophis vermis (western

worm snake), Ophyeodrys aestivus (rough green snake),

and Eumeces anthracinus (coal skink).

Scattergrams of plot scores onto the same three axes, aggregating the plots of the separate watersheds, illustrated the

overall similarity of the watersheds. Ellipses enclosing 95

percent of the plots of the watersheds overlapped considerably (figs. 4 and 5). In DCA, broader extent of plots along

axes means that those plots offer more varied habitat (plot

to plot) in which live more varied communities of the organisms studied, i.e., greater beta diversity. Except perhaps for

Bread Creek, the watersheds had comparable reaches

along each axis. The reptile communities of these four

watersheds were so similar that drawing conclusions about

minor differences among watersheds is not reasonable.
181

CONCLUSIONS

At the watershed level, total species richness per watershed varied little, 26-28 species, and most of these species

were common to all watersheds, as indicated by Morisita’s

comparisons. Also, species diversity per watershed (beta

diversity) differed little. The least intensively managed

watershed, South Alum, had the nominally highest beta

diversity. However, this is not necessarily due to a lack of

species in the other watersheds, but instead is likely due to

the dominance in the other watersheds of two species,

Scincella lateralis (ground skink) and Sceloporus undulatus

(northern fence lizard). The relative lack of dominance by

these two species in South Alum results in greater evenness there. Because the Shannon-Wiener index incorporates evenness for its calculation, South Alum appears to be

most diverse. Thus, if we reduce the recorded abundances

of S. lateralis and S. undulatus in Little Glazypeau, North

Alum, and Bread Creek to those found in South Alum, (69

and 47 animals, respectively) and then recalculate ShannonWiener diversity indices for these three watersheds, we find

that our recalculated diversity index for each watershed

exceeds that of South Alum (2.51) in all cases (Little Glazypeau = 2.65, North Alum = 2.67, Bread Creek = 2.59). In

other words, these other watersheds possessed an inherent diversity that was higher than South Alum, plus more

individuals of two reptile species.

It is well known that number of species increases with area

of study plots; i.e., the familiar species-area curves of

islands and mainland sites (Pianka 2000). This should

relate to differences in beta diversity of entire watersheds.

Little Glazypeau and North Alum were much larger than

Bread Creek and South Alum. We expected, therefore, that

the smaller watersheds should have had lower beta diversity. Our recalculated diversity indices adjusted for species

dominance showed just this—elevated overall species

diversity on the larger watersheds.

At the plot level, our data suggested that the less intensively

managed watershed, South Alum, had plots with lower

reptile abundance, fewer species, and decreased alpha

diversity than the more intensively managed watersheds.

But one must exercise some caution here; such differences

may not relate to management at all. There may be other

characteristics of Little Glazypeau, North Alum, and Bread

Creek that affect species diversity of reptiles at the plot

level. These are just four watersheds picked to vary along a

management continuum, but they also may vary in other

ways. Without a suitable set of replicate watersheds representing various levels of forest management, it is impossible to randomize all these other variables and to assess

the relative effect of forest management on reptile communities. Nevertheless, the plot-level community differences

may well be due to land management. South Alum, in contrast with the other watersheds, had virtually no logging for

over 80 years and differed by having more mature, larger

trees with intermediate canopy, and drastically less herbaceous vegetation, shrubs, and vines. Visually, South Alum

appeared park-like. These differences made for structurally

less complex within-plot habitats, possibly leading to the

lower observed reptile numbers, species richness, and

species diversity (alpha diversity).

MANAGEMENT IMPLICATIONS

Our study suggests that minimal forest management may

result in lower local reptile abundances, species richness,

and plot-wise (alpha) species diversity compared with more

intensive management. However, overall watershed (beta)

reptile species diversity differs little between watersheds of

different management intensities. Overall reptile communiFigure 5—Ninety-five percent sample probability ellipses of

the plot scores from detrended correspondence analysis

(DCA) of the four watersheds: DCA axis three vs. one. LG =

Little Glazypeau; NA = North Alum; BC = Bread Creek; SA =

South Alum.

Figure 4—Ninety-five percent sample probability ellipses of

the plot scores from detrended correspondence analysis

(DCA) of the four watersheds: DCA axis two vs. one. LG =

Little Glazypeau; NA = North Alum; BC = Bread Creek; SA =

South Alum.
182

ties of the four watersheds were extremely similar. Taken

together, our data suggest that intensive silviculture as practiced in the Ouachita Mountains of west-central Arkansas is

not detrimental to landscape-level reptile communities and

in fact, may be beneficial. This is probably true because

even under the most intensive forest management, stand

sizes are large, riparian zones are largely left intact, and

ponds are created either for the benefit of wildlife or for a

water supply for fire control. It is important to maintain those

practices to conserve and maintain existing reptile diversity.

A possible concern with the suggestion that intensive forest

management might benefit reptile communities is that rare

or extremely habitat-sensitive species might suffer under

this strategy. Nonetheless, we found no evidence of rare or

sensitive reptile species that were being harmed by the more

intensive land management. In fact, the one really rare

species, Crotalus atrox (western diamondback rattlesnake),

was found only on the most intensively managed watershed, Little Glazypeau. These findings must be tempered,

though, by the fact that the Ouachita Mountains have been

logged since the arrival of Europeans to the area, and

because no baseline data exist for reptile communities prior

to this, we have only the current diversity of all watersheds

combined (gamma diversity) from our study to evaluate how

rare or sensitive species might be affected by current forest

management practices. Long-term monitoring is the only

way to identify population trends of rare or sensitive species.

Our data suggest that diverse reptile communities in the

Ouachita Mountains of west-central Arkansas would best

be maintained by management decisions that create watershed-level landscape conditions with a diversity of canopy

cover conditions and aquatic habitats. These two factors

can be influenced and controlled by forest managers.

Variation in humidity and soil moisture, also important for

diverse reptile communities, may also vary with canopy

cover and aquatic habitats, but these parameters might be

less influenced by management decisions and more determined by the inherent physiography of a watershed (factors

such as soil type, slope, aspect, elevation, and naturallyoccurring aquatic habitats). We will use our data on reptile

communities, plus available habitat data, to develop and

validate quantitative models for predicting reptile community

composition from habitat and landscape parameters.

ACKNOWLEDGMENTS

We thank the USDA Forest Service, Weyerhaeuser

Company, and the National Council of the Paper Industry

for Air and Stream Improvement for providing funding and

logistical support. Additional logistical support was provided

through the Oklahoma Cooperative Fish and Wildlife

Research Unit (Oklahoma State University, Oklahoma

Department of Wildlife Conservation, U.S. Geological Survey

Biological Resources Division, and Wildlife Management

Institute cooperating). We also thank a great number of paid

and volunteer graduate and undergraduate field assistants

for their help in data collection, entry, and error-checking.

Research support was provided through the study “Herpetological Communities under Diverse Forest Management

Intensities: a Landscape-level Assessment of the Ouachita

Mountains, Arkansas” dated May 1995 to March 1999 of

Cooperative Agreement numbers 19-95-037, 30-96-022,

and 33-CA-98-354. This research was supported by funds

provided by the U.S. Department of Agriculture, Forest

Service, Southern Research Station as part of the Phase III

of the Quachita Mountains Ecosystem Mangement

Research Project.

LITERATURE CITED

Alford, R.A.; Richards, S.J. 1999. Global amphibian declines: a

problem in applied ecology. Annual Review of Ecology and

Systematics. 30: 133-165.

Biondini, M.E.; Mielke, P.W., Jr.; Berry, K.J. 1988. Data-dependent

permutation techniques for the analysis of ecological data.

Vegetatio. 75: 161-168.

Blaustein, A.R.; Wake, D.B. 1995. The puzzle of declining amphibian populations. Scientific American. 272: 52-57.

Blaustein, A.R.; Wake, D.B.; Sousa, W.P. 1994. Amphibian declines:

judging stability, persistence, and susceptibility of populations

to local and global extinctions. Conservation Biology. 8: 60-71.

Gibbons, J.W.; Scott, D.E.; Ryan, T.J. [and others]. 2000. The

global decline of reptiles, déjà vu amphibians. Bioscience.8:

653-666.

Guldin, James M. [In press]. Landscape-scale research in the

Ouachita Mountains of west-central Arkansas: general study

design. In: Guldin, James M., tech. comp. Ouachita and Ozark

Mountains symposium: ecosystem management research. Gen.

Tech. Rep. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station.

Hurlbert, S.H. 1984. Pseudoreplication and the design of ecological field studies. Ecological Monographs. 54: 187-211.

Morisita, M. 1959. Measuring of interspecific association and similarity between communities. Memoirs of Faculty of Science at

Kyoto University. Series E (Biology) 3: 65-80.

Pechman, J.H.K.; Scott, D.E.; Semlitsch, R.D. [and others]. 1991.

Declining amphibian populations: the problem of separating

human impact from natural fluctuations. Science. 253: 892-895.

Pianka, E.R. 2000. Evolutionary ecology. 6th ed. San Francisco,

CA: Addison Wesley Longman, Inc. 512 p.

Pielou, E.C. 1966. The measurement of diversity in different types of

biological collections. Journal of Theoretical Biology. 13: 131-144.

SAS Institute Inc. 1999. SAS/STAT user’s guide. Version 8.01 ed.

Cary, NC: SAS Institute Inc.

Sattath, S.; Tversky, A. 1977. Additive similarity trees.

Psychometrika. 42: 319-345.

Tappe, Philip A.; Weih, Robert C., Jr.; Thill, Ronald E. [and others].

[In press]. Lanscape characterization of four watersheds under

different forest management scenarios in the Ouachita Mountains

of Arkansas. In: Guldin, James M., tech. comp. Ouachita and

Ozark Mountains symposium: ecosystem management

research. Gen. Tech. Rep. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southern Research Station.

ter Braak, C.J.F.; Prentice, I.C. 1988. A theory of gradient analysis.

Advances in Ecological Research. 18: 271-313.

ter Braak, C.J.F.; Šmilauer, P. 1998. CANOCO reference manual

and user’s guide to CANOCO for windows: software for

canonical community ordination. Version 4. Ithaca, NY:

Microcomputer Power. 351 p.

Wolda, H. 1981. Similarity indices, sample size and diversity.

Oecologia. 50: 296-302.
183

INTRODUCTION

Wildlife habitat quality may be influenced by processes

beyond the scale of the traditional stand-level study. The

Phase III watershed research described in many of the

papers in this volume is an attempt to document the impact

of forest management on plant and animal communities at

a landscape scale. The four Phase III watersheds were

chosen to represent a range of management intensities

from industrial forestry at one extreme to a virtually unmanaged forested watershed at the other extreme.

The Little Glazypeau Creek watershed (LG) is 1740 ha in

size and is the most intensively managed of the four watersheds. LG consists largely of loblolly pine (Pinus taeda L.)

plantations less than 35 years old, interspersed with natural

second growth pine-hardwood stands on ridges and near

large streams. LG is owned and managed primarily by

Weyerhaeuser Company. Plantations are harvested by

clearcutting and regenerated by planting of seedlings. The

North Alum Creek watershed (NA) is 3800 ha, and is owned

and managed by Weyerhaeuser Company and the Ouachita

National Forest (ONF) in about equal proportions. NA is

managed operationally by Weyerhaeuser and ONF, so its

management is like that described for LG and Bread Creek,

respectively. The Bread Creek watershed (BC) is 1255 ha

and is owned and managed primarily by ONF. BC is managed operationally for multiple uses. The ONF management

regime is less intensive than Weyerhaeuser management,

with longer rotations, natural regeneration of shortleaf pine

(Pinus echinata Mill.), and in general, no clearcutting. The

South Alum Creek watershed (SA) is 1460 ha and is the

least intensively managed watershed of the four. SA includes

an experimental forest and with the exception of a few

small experimental cutting treatments, it consists of mature

second growth pine-hardwood forest. NA, BC, and SA are

all located in Saline County, AR in the Saline River drainage

basin. LG is located in Garland County, AR in the Ouachita

River drainage basin. Detailed descriptions of these watersheds are found elsewhere in this publication (Tappe and

others, in press).

This study was designed to supplement intensive studies of

reptile and amphibian communities on the four watersheds

(Fox and others, in press; Shipman and others, in press).

These studies were designed to compare diversity, richness

and abundance of all herpetofaunal species. Because their

methods did not adequately sample aquatic turtles, this

trapping effort was initiated.

METHODS

Turtles were trapped with baited hoop nets (Lagler 1943,

Legler 1960). In 1995, chicken liver and strawberries were

used as bait. In 1996, fish and creamed corn were used.

Nets consisted of 2.5-cm nylon mesh stretched over 3 hoops

of galvanized steel 75 cm in diameter. Nets were approximately 2 m in length. They had a funnel entrance at the

downstream end and were closed at the other end. Nets

were placed in pools that were deep enough to submerge

the bait but shallow enough to allow for breathing space at

the top, and were associated with cover (undercut banks,

woody debris, rocks) when possible. In 1995 only, ponds

were sampled in the LG and NA watersheds. The SA and

BC watersheds lacked ponds large enough to allow trapping.

Each trap was kept in the same location for five nights in

1995 and six nights in 1996. Traps were checked daily and

bait replaced as needed. All trapping occurred between 11

July and 29 July 1995, and between 15 July and 19 August

1996. Trapped turtles were identified to species, given a

unique mark by notching the carapace with a triangular file,

measured with tree calipers to the nearest half-centimeter,

and released where caught. Exceptions were one razorback

AQUATIC TURTLES OF DIVERSELY MANAGED WATERSHEDS

IN THE OUACHITA MOUNTAINS, ARKANSAS

Joseph P. Phelps1

1 Graduate Research Assistant, Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47906-2033.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Aquatic turtles were trapped using hoop nets in creeks and ponds located in four Ouachita Mountain watersheds (Little Glazypeau, North Alum, Bread, and South Alum Creeks). These watersheds range in management from one

dominated by industrial loblolly pine (Pinus taeda L.) plantations to one having virtually no management for many

decades. Trapping effort consisted of 212 trapnights (192 in streams, 20 in ponds) during July and August 1995 and 1996.

There were 63 captures for a success rate of 0.297 captures per trap-night. Captured turtles were individually marked and

released. Stream characteristics potentially related to turtle habitat were evaluated at each trapping site. Common

snapping turtles (Chelydra serpentina L.) were the most common species captured, occurring in all watersheds and in

both streams and ponds. Five other species were captured, notably the razorback musk turtle (Sternotherus carinatus

Gray), not previously known to occur in Saline County, and the alligator snapping turtle (Macroclemys temminckii Harlan),

an Arkansas protected species. Both species richness and number of captures were highest in the more heavily managed

watersheds (Little Glazypeau and North Alum). These are also the largest creeks, so effects of management are obscured. The presence of fire-fighting ponds in these watersheds did increase richness. Excluding recaptures, number of

turtles captured in streams was positively correlated (p = 0.0059) with an index of pool size at the trap site.
184

musk turtle (Sternotherus carinatus Gray) that was donated

to the Arkansas State University vertebrate museum on

request, and one common map turtle (Graptemys geographica LeSueur) that appeared to have drowned in the trap.

Habitat data were collected at all 1996 trapping locations.

Pool width and length, and maximum depth of each pool

were combined into an index of pool size. Substrate compositions of each pool were estimated as percentages in each

of four substrate classes: bedrock/boulder, cobble/gravel/

sand, silt/mud, or detritus. Boulders were defined as rocks

greater than 30 cm diameter. Rocks of smaller size were

classified as cobble or gravel. The presence of one or two

undercut banks was noted, as was the presence of accessible basking sites (large logs, large rocks).

Relationships between 1996 turtle captures and habitat

features were investigated using the SAS PROC CORR

procedure (SAS Institute 1993). Pearson correlation coefficients between all habitat variables and number of 1996

turtle captures (all species, excluding recaptures) were

calculated.

RESULTS AND DISCUSSION

In 1995, 64 trap-nights were completed in streams and 20

in ponds. In 1996, 128 trap-nights were completed, all in

streams. Fifty-five individual turtles representing 6 species

were captured a total of 63 times over the 2 years (table 1).

NA and LG watersheds had the richest turtle fauna with four

species each, and these watersheds also had the largest

number of captures, with 19 and 29, respectively. The two

watersheds representing the highest level of management

intensity therefore produced the most abundance and richness. This relationship is obscured by the fact that these are

also the largest watersheds. It is clear that the presence of

large ponds in NA and LG added captures, and these ponds

are a direct result of management.

The most frequently captured species was the common

snapping turtle (Chelydra serpentina L.), with 31 individuals

captured 39 times in all four watersheds. The presence of

ponds in NA and LG contributed to these watersheds having

large numbers of common snapping turtle captures. Eleven

snapping turtles were captured in 20 trap-nights in ponds

(0.55 captures per trap-night). In streams, 28 common snapping turtles were captured in 192 trap-nights (0.15 captures

per trap-night). Although it is a habitat generalist (Ernst and

others 1994), ponds may be an important habitat feature for

this species.

All recaptures were common snapping turtles. This may indicate a strong bond to home range (Ernst and others 1994).

In fact, two individuals were captured in the same location

in both years. A third individual captured in 1995 moved

upstream a short distance and was recaptured in 1996. All

other recaptures involved turtles that were released from a

trap and then caught again in the same trap within a few

days.

Nine razorback musk turtles were captured, all in the Saline

County watersheds. This species is near its northern limit in

the Ouachitas, and these appear to be the first documented

records for this county. No razorback musk turtles were captured in Garland County, although four previous records

exist (Personal communication. 1998. Stan Trauth. Arkansas

State University, State University, AR 72467). Seven common musk turtles (Sternotherus odoratus Latreille) were

captured among three watersheds.

All five captures of common map turtles occurred in LG,

the only watershed in the Ouachita River drainage basin.

Besides geographic isolation, there may be subtle physical

differences that make LG a better habitat for this species.

Habitat features that may be important to common map

turtles include rocky or gravelly substrates (Fuselier and

Table 1—Aquatic turtles captured with hoop-nets in four Ouachita

Mountain watersheds by species (Arkansas), 1995 and 1996

combined data

Watershed

Species NA LG SA BC Total

Common snapping turtle 4 + 3a 13 + 8 7 4 28 + 11

Razorback musk turtle 8 0 0 1 9

Common musk turtle 3 1 3 0 7

Common map turtle 0 5 0 0 5

Red-eared slider 0 0 + 2 0 0 0 + 2

Alligator snapping turtle 1 0 0 0 1

Total 16 + 3 19 + 10 10 5 50 + 13

NA = North Alum Creek; LG = Little Glazypeau Creek; SA = South Alum Creek;

BC = Bread Creek. Watersheds listed in order of decreasing size.

a Data are combined for ponds (20 trap-nights in NA and LG) and streams

(192 trap-nights in all watersheds). Where pond captures are present, data

presented as (stream captures) + (pond captures).
185

Edds 1994), in-stream basking sites (Pluto and Bellis 1986),

and plentiful mollusk food items (Vogt 1981). Red-eared

sliders (Trachemys scripta elegans Wied-Neuwied) were

also captured only in LG, and these were only in ponds.

This species is known to occur in both streams and ponds,

but prefers slow-moving or still waters with muddy bottoms

(Ernst and others 1994).

One alligator snapping turtle (Macroclemys temminckii

Harlan) was captured, in the largest pool in NA. This species

is protected by the states of Arkansas and Oklahoma, and

is thought to be in decline throughout its range. Possible

factors include over-harvest and impoundment of rivers

(George 1987, Pritchard 1989).

Pearson correlation coefficients relating habitat variables to

number of 1996 captures are shown in table 2. Number of

1996 turtle captures was significantly correlated only with

the index of pool volume, indicating that larger pools produced more captures. Percent cobble/gravel/sand was

significantly negatively correlated with percent rock/bedrock

and percent detritus. There are several possible reasons

for the relationship between pool size and turtle captures.

Larger pools are more persistent throughout the dry summer season. They provide greater cover in the form of deep

water than smaller pools. They may provide a greater diversity of habitats such as different water depths. Finally, they

may not provide better habitat, but just more of it. If each

trap tended to attract all the turtles from the pool in which it

was placed, it is obvious that traps in larger pools would

attract more turtles.

The lack of correlation between basking sites and turtle

captures may be due to the heavy bias towards common

snapping turtles, which do not usually bask, and to the use

of presence/absence data, rather than number or density of

basking sites. There were very few large logs in the stream

channels near trapping sites. This is a potential area where

turtle habitat could be improved actively in all four watersheds. Besides serving as basking and hiding habitat for

turtles (Pluto and Bellis 1986), large logs contribute to pool

formation and encourage growth of macro-invertebrates

that form the basis of aquatic food webs (Hilderbrand and

others 1997). As the stream-side forests mature into oldgrowth stands, the input of large logs will increase. Removal

of timber near streams should be restricted, and managers

should consider adding woody debris to the streams

actively.

ACKNOWLEDGMENTS

This research took place while the author was employed by

Weyerhaeuser Company. M.A. Melchiors and P.A. Shipman

provided advice on logistics. Purdue University and

Weyerhaeuser Company shared the cost of the author’s

travel to Hot Springs, AR for the symposium.

Table 2—Pearson correlation coefficients (and p values) relating habitat features to each other and to number of aquatic turtles

captured at 32 trapping sites, Ouachita Mountains, Arkansas, 1996

Percent Percent Percent Percent

Captures Volume cobble rock detritus silt Bank Bask

Turtles captured (no.) 1.0 0.47612 0.10530 - 0.06058 - 0.09431 0.07240 - 0.20777 0.00242

(p = 0.0) (p = 0.0059) (p = 0.5663) (p = 0.7419) (p = 0.6077) (p = 0.6938) (p = 0.2539) (p = 0.9895)

Pool volume index 1.0 0.07619 - 0.12352 0.07044 - 0.06625 0.10908 0.13873

(p = 0.0) (p = 0.6785) (p = 0.5006) (p = 0.7017) (p = 0.7186) (p = 0.5523) (p = 0.4489)

Cobble in substrate (%) 1.0 - 0.77357 - 0.46312 0.02425 0.09338 - 0.01367

(p = 0.0) (p = 0.0001) (p = 0.0076) (p = 0.8952) (p = 0.6112) (p = 0.9408)

Rock in substrate (%) 1.0 - 0.17032 - 0.13039 - 0.20775 0.17730

(p = 0.0) (p = 0.3513) (p = 0.4769) (p = 0.2539) (p = 0.3316)

Detritus in substrate (%) 1.0 - 0.13730 0.17483 - 0.24942

(p = 0.0) (p = 0.4537) (p = 0.3386) (p = 0.1686)

Silt in substrarte (%) 1.0 - 0.22504 0.07059

(p = 0.0) (p = 0.2156) (p = 0.7010)

Banks overhanging (no.) 1.0 0.14832

(p = 0.0) (p = 0.4179)

Presence/absence of

bask site 1.0

(p = 0.0)
186

LITERATURE CITED

Ernst, C.H.; Barbour, R.W.; Lovich, J.E. 1994. Turtles of the United

States and Canada. Smithsonian Institution Press, Washington,

DC.

Fox, S.F.; Shipman, P.A.; Thill, R.E. [and others]. [In press].

Amphibian communities under diverse forest management in

the Ouachita Mountains, Arkansas. In: Guldin, James M., tech.

comp. Ouachita and Ozark Mountains symposium: ecosystem

management research. Gen. Tech. Rep. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station.

Fuselier, L.; Edds, D. 1994. Habitat partitioning among three

sympatric species of map turtles, Genus Graptemys. Journal of

Herpetology 20: 22-31.

George, Gregory. 1987. The current status of the alligator

snapping turtle, Macroclemys temmincki, with a review of its

natural history. Pages 75-81 in M. Rosenberg, editor.

Proceedings of the 11th International Herpetological

Symposium.

Hilderbrand, R.H.; Lemly, A.D.; Dolloff, C.A.; Harpster, K.L. 1997.

Effects of large woody debris placement on stream channels

and benthic macroinvertebrates. Canadian Journal of Fisheries

and Aquatic Science 54: 931-939.

Lagler, K.F. 1943. Methods of collecting freshwater turtles. Copeia

1943: 21-25.

Legler, J.M. 1960. A simple and inexpensive device for trapping

aquatic turtles. Proceedings of the Utah Academy of Science,

Arts, and Letters 37: 63-66.

Pluto, T.G.; Bellis, E.D. 1986. Habitat utilization by the turtle,

Graptemys geographica, along a river. Journal of Herpetology

20: 22-31.

Pritchard, P.C.H. 1989. The alligator snapping turtle: biology and

conservation. Milwaukee Public Museum, Milwaukee, WI.

SAS Institute Inc. 1993. SAS/STAT user’s guide. Version 6. Fourth

edition. SAS Institute, Cary, NC, USA.

Shipman, P.A.; Fox, S.F.; Thill, R.E. [and others]. [In press].

Reptile communities under diverse forest management in the

Ouachita Mountains, Arkansas. In: Guldin, James M., tech.

comp. Ouachita and Ozark Mountains symposium: ecosystem

management research. Gen. Tech. Rep. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station.

Tappe, P.; Weih, R.C., Jr.; Thill, R.E. [and others]. [In press].

Landscape characterization of four watersheds under different

management scenarios in the Ouachita Mountains of Arkansas.

In: Guldin, James M., tech. comp. Ouachita and Ozark

Mountains symposium: ecosystem management research. Gen.

Tech. Rep. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station.

Vogt, R.C. 1981. Food partitioning in three sympatric species of

map turtle, Genus Graptemys (Testudina, Emydidae). American

Midland Naturalist 105: 102-111.
187

Session 7

Phase III Vegetation and Social Science Research

Alan Newman, Moderator
188
189

INTRODUCTION

The phase III landscape study in the Ouachita Mountains

Ecosystem Management Research Project is designed to

describe changes in baseline conditions among four watersheds under different intensities of management. The four

watersheds under study and their attributes are listed in

table 1. An overall description of the study design is presented in Guldin (in press).

Experimental replication of watersheds of substantial size

is impractical; it is impossible to achieve uniformity of topographic, edaphic, and physiographic conditions in such

watersheds. Some other approach to the development of

experimental error is needed if it is necessary to apply parametric statistical tests in this situation. We tackled this question with a study design that used time, rather than space,

as an experimental replicate. Thus, 4 years of measurements

were taken in these areas, and comparisons among watersheds included variation by year to determine whether

treatment or year was the more significant variable.

The objectives of the research reported here are to quantify

existing vegetation conditions of pine, pine-hardwood, and

hardwood stands in the four watersheds; to compare

differences in stand structure by watershed and year; and

to describe the range of variation in forest conditions among

watersheds by year and treatment.

METHODS

Sample Grid

The baseline overstory vegetation data was collected by

systematic sampling over a 4-year period (1995-98). Sampling was conducted using plots on a rectangular grid, with

offsets in the grid by year (fig. 1). The grid used as a basis

for sampling in 1995 and 1996 consisted of north-south

transects 400 m apart. To facilitate access, each transect

was flagged and numbered wherever it crossed as road.

Additional north-south transects, offset 200 m from those

employed in 1995 and 1996, were used in 1997 and 1998.

Sample points were located at 200-m intervals along the

first set of transects in the first year. In the second year, the

sample points were offset 100 m from the first set of sample

points along the same transects. In the third year, sample

points were located at 200-m intervals along the second set

of transects. In the fourth year, sample points were offset

100 m from the third set of sample points along each of the

second set of transects.

BASELINE OVERSTORY CONDITIONS IN FOUR WATERSHEDS OF VARYING

MANAGEMENT INTENSITY IN THE EASTERN OUACHITA MOUNTAINS

James M. Guldin and Thomas Foti1

1 Research Forest Ecologist and Project Leader, USDA Forest Service, Southern Research Station, Hot Springs, AR; and Director of

Research, Arkansas Natural Heritage Commission, Department of Arkansas Heritage, State of Arkansas, Little Rock, AR, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Baseline tree data were collected in four watersheds in the eastern Ouachita Mountains during 1996-98. By

watershed, average basal area ranged from 71 to 102 square feet per acre, average tree density ranged from 234 to 295

trees per acre, and quadratic mean diameter of trees ranged from 7.41 to 8.22 inches. Variables for which the largest

values were associated with the unmanaged South Alum Creek watershed and the smallest values were associated with

the most intensively managed Little Glazypeau Creek watershed include basal area for all trees, stem density for all trees,

basal area and stem density of the white oak-southern red oak species group and for all hardwoods, and basal area and

stem density of shortleaf pine and for all conifers. Conversely, variables for which the largest values were associated with

the Little Glazypeau watershed, and the lowest values were associated with the South Alum Creek watershed, were

loblolly pine basal area and stem density. The data suggest that it may be possible to relate management intensity to

descriptive mensurational variables at the landscape scale in the eastern Ouachita Mountains.

Table 1—Attributes of the four watersheds under study

Ownership Management

Watershed FS Industry intensity Area

 - - percent - - ha

Alum Creek 100 0 None 1460

Bread Creek 95 5 Low 1255

North Alum Creek 50 50 Moderate 3080

Little Glazypeau Creek 5 95 High 2450

FS = Forest Service.
190

These grid points became the plot centers for the plot-based

measurements of vegetation. Approximately 500 plots were

sampled annually.

Plot Layout

Two different sampling methods were used. In 1995, stands

were sampled using variable radius plots, with a basal area

factor (BAF) 10 square-feet-per-acre prism (fig. 2a). All trees

3.0 inches in diameter at breast height (d.b.h.) and larger

that were sampled were recorded by species and 2-inch diameter classes. Examination of the data after the first year of

sampling suggested that this sampling might not yield data

that adequately characterize the vegetation in the watersheds. Because of this concern, sampling in 1996-98 was

done using a nested plot design (fig. 2b). Circular 0.1-acre

fixed-radius plots were used to sample all trees having

d.b.h. from 3.6 to 9.5 inches inclusive, and a BAF 5 squarefeet-per-acre prism was used to sample all trees with d.b.h.

9.6 inches and larger. Species and d.b.h. to the nearest 0.1

inch were recorded for all sampled trees.

Statistical Analysis

Two statistical questions arise. First, because two sampling

schemes were employed, we have to ask whether the 1995

data can be combined with 1996-98 data. Second, we have

to ask whether the replication approach employed was

sensitive enough to yield useful results.

Statistical tests suggest that the total basal area variable is

not normally distributed under either sample design (the

Shapiro-Wilks W statistic was significant at the 0.0001 level

for both). For this reason, comparisons of data obtained by

different sample methods were conducted using nonparametric analysis of variance, using the Wilcoxon statistic to

test significance of mean comparisons. Descriptive baseline

information (basal area per acre, trees per acre, and quadratic mean diameter) will be useful to the research team

and others.

RESULTS

Comparison of Sampling Methods

The 1995 sample gives a basal area distribution sensitive

only to the nearest 10 square feet of basal area (fig. 3a).

This is an artifact of the expansion of a prism sample to a

per-acre basis. Each tree sampled counts as 10 square feet

of basal area per acre in trees of that size class. Therefore,

the prism sample taken in 1995 produces a distribution of

basal area that is obviously discontinuous. Conversely, the

1996-98 sampling scheme, which combined prism sampling

and the use of fixed-radius plots, produces a more continuous distribution (fig. 3b). The shape of the distribution is

similar to that of the 1995 sample, but the nested design

results in a more uniform distribution of observations across

the range of basal areas.

Discontinuities in data, such as that found in the 1995

sample, can violate underlying assumptions in parametrical

statistical tests. One such test suggests that total basal area

variable is not normally distributed under either sample

design (the Shapiro-Wilks W statistic was significant at the

0.0001 level for both). For this reason, comparisons between

variable means for data obtained by different sample

methods were conducted using nonparametric analysis of

variance. The Wilcoxon statistic was used to test significance of mean comparisons. Using this test, significant

differences in variable means by sample design were

detected for virtually all summary variables of basal area

and stem density (table 2). Four of the six species groups

show significantly higher basal area in the 1996-98 sample

design than the 1995 sample design.

Figure 1—Layout of transects from 1995-98. Plots labeled 1 were sampled in 1995, 2 in

1996, 3 in 1997, and 4 in 1998.
191

The largest difference is in the sample for loblolly pine, which

is found exclusively in plantations in these watersheds.

Loblolly pine accounts for only 10 percent of the basal area

in the 1995 sample, but the difference in the loblolly pine

basal area between the two samples is 68 percent of the

total difference between the samples. These loblolly pine

plantations are often very dense and difficult to see through,

which may contribute to inaccuracy in the prism sample.

Of the three broad species groups (all conifers, all hardwoods, and all species), the all conifers and all species

groups show significantly higher basal area in the 1996-98

sample than in the 1995 sample (table 2). Four of the six

species groups show significantly higher stem density in the

1996-98 sample than in the 1995 sample, as do three

broad summary variables.

(A)

(B)

Figure 2—Overstory vegetation sampling methods used in (A) 1995, and (B) 1996-98.

(A) 1995 sampling. Sampling is conducted using a variable radius prism where Θ

corresponds to a basal area factor (BAF) of 10 square feet per acre, which marginally

samples a tree with diameter at breast height (d.b.h.) 12.0 inches at 33 feet; and (B)

1996-98 sampling. Sampling is based on use of a nested fixed radius plot (FRP) of 0.1-

acre to sample midstory trees having d.b.h. 3.6 and 9.5 inches inclusive, combined with

use of a prsim where Θ corresponds to a BAF of 5 square feet per acre to sample trees

with d.b.h. greater than or equal to 9.6 inches. A BAF 5 prism marginally samples a 12.0-

inch tree at 46.67 feet.
192

To determine the effect of sample design on variable means,

we randomly selected and remeasured eighty-one 1995

plots in 1997 according to the 1996-98 sample design. In all

instances, broad summary variables have higher means

and lower coefficients of variation in the 1996-98 sample

design than in the 1995 sample design (table 3). Results for

two important species or species groups are consistent with

these findings—shortleaf pine (fig. 4a) and the white oaksouthern red oak group (fig. 4b).

Based on these results, it is difficult to justify pooling the

1995 data with the 1996-98 data for future analyses. For

the balance of this paper, only the 1996-98 data will be used

as a basis for discussing the conditions in the watersheds.

Overall Stand Conditions

The mean basal area (all species combined) across all watersheds over 1996-98 was 86.4 square feet per acre. Conifer

basal area across all watersheds averaged 49.5 square

feet per acre, of which 35.8 square feet per acre was shortleaf pine and 13.7 square feet per acre was loblolly, with

Figure 3—Number of observations by 1-foot basal area increments, 1995 sample

design versus 1996-98 sample design. (A) 1995 sample design, and (B) 1996-98

sample design.

100

80

60

40

20

0

0 50 100 150 200

Basal area (square feet per acre)

Number of observations

(A)

100

80

60

40

20

0

0 50 100 150 200

Basal area (square feet per acre)

Number of observations

(B)
193

Table 3—Mean and coefficient of variation for summary basal

area and stem density variables for subset of 1995 sample

design plots that were remeasured in 1997 using the 1996–98

sample design

Sample design

1995 1996–98

Mean C.V. Mean C.V.

ft2 /ac percent ft2 /ac percent

A. Basal area

All conifers 48.89 69.1 58.29 61.7

All hardwoods 31.72 80.8 40.32 60.9

All species 80.62 43.3 98.61 33.4

trees/ac percent trees/ac percent

B. Stem density

All conifers 139.2 109.4 138.2 82.6

All hardwoods 122.4 83.2 161.4 50.8

All species 261.5 63.5 299.7 37.0

Table 2—Differences in mean species group summary

variables, by sample design, across all watershedsa

Sample design

1995 1996–98 Prob > |Z|

 - - - ft2 per acre- - -

A. Basal area

Shortleaf pine 34.32 35.75 NS

Loblolly pine 8.28 13.74 0.001

Other conifers 0.00 0.05 0.005

White and southern red oak 16.32 17.53 0.0001

Other oaks 10.12 9.31 0.01

Other hardwoods 9.40 10.02 0.0001

All conifers 42.60 49.54 0.0001

All hardwood 35.83 36.86 NS

All species 78.43 86.39 0.0001

 Trees per acre

B. Stem density

Shortleaf pine 94.97 95.26 0.05

Loblolly pine 40.52 46.16 0.01

Other conifers 0.00 0.21 0.01

White and southern red oak 60.22 58.97 0.0001

Other oaks 31.03 30.01 0.0001

Other hardwoods 41.04 47.84 0.0001

All conifers 135.48 141.64 0.0001

All hardwoods 132.28 136.82 0.001

All species 267.77 278.46 0.0001

a Differences between means are shown by Prob > |Z|, the nonparametric

Wilxocon 2-sample test statistic with normal approximation; the value

shown is the probability level at which the null hypothesis of no difference

between means is rejected.
194

Figure 4—Basal area by plot, for the 81-plot subset of 1995 sample design plots that were remeasured in 1997

using the 1996-98 sample design. (A) shortleaf pine, and (B) white oak and southern red oak.

160

140

120

100

80

60

40

20

0

0 20 40 60 80 100 120 140 160

Basal area, 1995 sample (square feet per acre)

Basal area, 1996-98 sample (square feet per acre)

(A)

Shortleaf pine

80

60

40

20

0

0 20 40 60 80

Basal area, 1995 sample (square feet per acre)

Basal area, 1996-98 sample (square feet per acre)

(B)

White oak and southern red oak
195

the small balance in eastern redcedar. The mean hardwood

basal area across all watersheds was 36.9 square feet per

acre, of which nearly half (17.5 square feet per acre) was in

white and southern red oak, and the balance more or less

equally split between other oaks (9.3 square feet per acre)

and other hardwoods (10.0 square feet per acre).

Mean stem density (all species combined) across all watersheds over 1996-98 was 278.5 trees per acre, nearly equally

split between conifers (141.6 trees per acre) and hardwoods

(136.8 trees per acre). Shortleaf pine was most numerous,

with 95.3 trees per acre, followed by white and southern

red oaks (59.0 trees per acre), other hardwoods (47.8 trees

per acre), loblolly pine (46.2 trees per acre), other oaks

(30.0 trees per acre), and eastern redcedar (0.2 trees per

acre).

South Alum Creek

The unmanaged South Alum Creek watershed had, on

average, 295 trees per acre (table 4). Of these, 174 trees

per acre were hardwoods and 121 trees per acre were

conifers. Almost all of the conifers that were present were

shortleaf pine.

Basal area (all species combined) was higher (102.5 square

feet per acre) than it was in the other watersheds studied

(table 4). Of this, 55.4 square feet per acre was in conifers

and 47.1 square feet per acre was in hardwoods. Shortleaf

pine made up more than 99.9 percent of the conifer basal

area in the South Alum Creek watershed. The remaining

0.1 percent was loblolly pine, recorded in a handful of

research plots that were planted to loblolly in the 1970s and

1980s. No eastern redcedar was sampled during the 3 years

of data collection.

Over half (56.6 percent) of the hardwood basal area in the

South Alum Creek watershed was in white and southern

red oaks. The remaining basal area consisted almost

equally of other oaks (primarily black and post oaks) and

hardwoods other than oak.

Quadratic mean diameter (qdbh) is the diameter calculated

from the mean basal area per tree. In the South Alum Creek

watershed, the qdbh for all species combined during 1996-

98 was 8.23 inches. Conifers were, on average, larger than

hardwoods in this watershed; the qdbh for conifers was

10.59 inches, whereas that for hardwoods was 7.06 inches.

Bread Creek

The Bread Creek watershed, characterized by general Forest

Service management, supported an average of 292.0 trees

per acre (table 4), exactly divided between hardwoods and

conifers. Of the total, over 70 percent of stems were shortleaf pine, white oak, or southern red oak. More than 97 percent of the conifers present were shortleaf pine, and more

than 70 percent of the hardwoods present were oaks.

Basal area averaged 86.2 square feet per acre (table 4), of

which 49.7 square feet per acre was in conifers and 36.5

square feet per acre was in hardwood. Shortleaf pine

accounted for 97.1 percent of the conifer basal area.

Loblolly pine, in plantations, made up 2.8 percent of conifer

basal area. White oak and southern red oak constituted

52.4 percent of hardwood basal area, with an additional

26.6 percent in other oak species and the balance in other

hardwoods.

In the Bread Creek watershed, qdbh for all trees sampled

was 7.4 inches, which was smaller than qdbh in the other

watersheds. Conifer qdbh was 8.5 inches. The qdbh of

Table 4—Mean basal area and stem density by species or species

group and watershed, 1996–98

South North Little

Alum Bread Alum Glazypeau

Creek Creek Creek Creek

A. Basal area

Shortleaf pine 55.43 48.26 35.54 15.92

Loblolly pine 0.02 1.39 14.26 29.01

Other conifers 0.00 0.03 0.09 0.01

White oak-southern red oak 26.64 19.11 18.54 8.88

Other oaks 11.28 9.72 9.82 6.90

Other hardwoods 9.14 7.65 10.57 11.02

Total 102.51 86.15 88.82 71.75

B. Stem density

Shortleaf pine 120.61 142.18 107.32 30.90

Loblolly pine 0.20 3.52 42.46 107.36

Other conifers 0.00 0.28 0.37 0.05

White oak-southern red oak 94.33 67.31 61.82 25.98

Other oaks 39.05 36.83 30.50 19.28

Other hardwoods 41.05 41.85 50.92 50.47

Total 295.24 291.96 293.39 234.04
196

shortleaf pine in this watershed, 8.5 inches, was a full inch

less than qdbh for shortleaf in any of the other watersheds.

The mean qdbh of hardwoods in the Bread Creek watershed, 6.6 inches, was less than qdbh of hardwoods in any

other watershed; and qdbh of white and southern red oaks,

7.4 inches, was 0.1 inch less than qdbh for these species in

any other watershed.

North Alum Creek

Industry owns half of the North Alum Creek watershed, and

the Forest Service owns the other half. Tree density averaged 293.4 trees per acre during 1996-98 (table 4). Density

of conifers was 150.1 trees per acre, and density of hardwoods was 143.2 trees per acre. The industry presence

accounts for the importance of loblolly pine, which constituted 28.3 percent of conifer stem density in the watershed.

Basal area in the North Alum Creek watershed averaged

88.8 square feet per acre (table 4). Of this, 49.9 square feet

per acre was in conifers and 38.9 square feet per acre in

hardwoods. Shortleaf pine accounted for 71.2 percent, and

loblolly for 28.6 percent, of conifer basal area. Oaks constituted 72.8 percent hardwood of basal area, with white and

southern red oak accounting for nearly two-thirds of the oak

basal area.

The qdbh of trees in the North Alum Creek watershed was

7.8 inches. Conifer qdbh was 9.5 inches and hardwood

qdbh was 7.1 inches. The quadratic mean diameters of

shortleaf and loblolly pine were 9.6 and 8.4 inches, respectively. The quadratic mean diameters of the white oaksouthern red oak group and the other oak group were 8.0

and 8.3 inches, respectively.

Little Glazypeau Creek

The Little Glazypeau watershed, managed largely by forest

industry, had 234.0 trees per acre (table 4), about 20 percent fewer stems per acre than were present in the other

three watersheds. The reduction was in hardwood density,

where declined by more than one-third as density of white

and southern red oaks declined by nearly two-thirds.

Basal area in the Little Glazypeau Creek watershed averaged 71.7 square feet per acre (table 4); of this, 44.9 square

feet per acre was in the conifer component and 26.8 square

feet per acre was in the hardwood component. Conifer basal

area in this watershed was about 12 percent lower than

that in the other three watersheds. Loblolly pine constituted

two-thirds of conifer basal area in this watershed (compared

to one-sixth of average conifer basal area across the other

three watersheds). The proportion of hardwood basal area

to total basal area was 33 percent lower than in the other

watersheds, and basal area in white and southern red oaks

was also 33 percent lower in Little Glazypeau than in the

other watersheds.

The qdbh of trees in the Little Glazypeau watershed was

8.0 inches. As in the other watersheds, conifer qdbh (9.9

inches) was greater than hardwood qdbh (6.9 inches).

DISCUSSION

We believe that differences between the sample designs

employed in 1995 and 1996-98 are significant, and we conclude that the two designs should not be combined. All of

the 1995 data were collected by sampling with a BAF 10

square-feet-per-acre prism, and are discontinuous. This

discontinuity has adverse implications for conducting statistical analysis that would not apply to the 1996-98 data.

More importantly, mean values of stem density and basal

area for important species and species groups from the

1995 sample are typically lower than corresponding values

for the 1996-98 data, a difference greater than that likely to

be explained by normal forest growth. For both of these

reasons, summary statistics reported here were prepared

only on the basis of the nested sample design used in

1996-98.

By watershed, average basal area ranged from 71 to 102

square feet per acre, with the lowest basal area in the most

intensively managed Glazypeau watershed and the highest

in the unmanaged South Alum Creek watershed. Average

tree density varied from 234 to 295 trees per acre, again

with the lowest tree density in the Little Glazypeau Creek

watershed and the highest in the South Alum Creek watershed. Quadratic mean diameter varied from 7.41 to 8.22

inches. Quadratic mean diameter was least in the Bread

Creek watershed, and largest in the South Alum Creek

watershed.

Values of other variables discussed here follow a similar

pattern of association. The variables for which the largest

values are associated with the unmanaged South Alum

Creek watershed and the smallest values are associated

with the most intensively managed Little Glazypeau Creek

watershed include the following

• basal area of shortleaf pine

• basal area of all conifers

• basal area of the white oak-southern red oak group

• basal area of the other oak group

• basal area of all hardwoods

• basal area of all trees

• stem density of the white oak-southern red oak group

• stem density of the other oak group

• stem density of all hardwoods

• stem density of all trees.

Conversely, the variables for which the smallest values are

associated with the unmanaged South Alum Creek watershed the largest values are associated with the Little

Glazypeau Creek watershed are

• basal area of loblolly pine

• stem density of loblolly pine

• quadratic mean diameter of loblolly pine.
197

These associations are not unexpected, and reflect the

common silvicultural practices one would conduct in managed landscapes. For example, thinning in the conifer component improves the condition of retained conifers by cutting

the poorer and less vigorous trees, and reduces densitydependent mortality. Timber stand improvement cutting in

the shortleaf pine-hardwood forest type removes hardwoods

to release pines, and is often done in conjunction with thinning of pines. The result of these management actions is to

reduce both stem density and basal area in the conifer and

hardwood component relative to unmanaged stands. Reproduction cutting in the managed watersheds would result in

removal of the larger conifers, which would reduce quadratic

mean diameter in the managed watersheds. Finally, the

most intensive silvicultural practice is to completely remove

the shortleaf pine component and replant with loblolly pine,

which grows more rapidly than shortleaf pine but which is

not naturally found in these watersheds.

In conclusion, these data give some interesting clues about

the effects of varying intensities of management on descriptive mensurational statistics used to characterize average

stand conditions. More comprehensive analysis of the data

is needed to better indicate the robustness of these trends,

and to show whether the trends are statistically significant.

However, this subjective analysis suggests that it may be

possible to quantify landscape attributes associated with

increasing management intensity for the woody plant component in the Ouachita Mountains.

LITERATURE CITED

Guldin, James M. [In press]. Landscape-scale research in the

Ouachita Mountains of west-central Arkansas: general study

design. In: Guldin, James M., tech. comp. Ouachita and Ozark

Mountains symposium: ecosystem management research.

Gen. Tech. Rep. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station.
198

INTRODUCTION

The objective of ecosystem management is to sustain the

structure, function, and diversity of the ecosystem while still

providing goods and services to the public. Meeting these

goals requires a fuller understanding how environmental

factors such as disturbance, soil moisture, and interspecific

competition affect the distribution of plant species across

the landscape. This study uses multivariate ordination to

examine the influence of competition on the pattern of distribution of woody species. The resulting pattern is compared

with the centrifugal model of plant community organization

proposed by Keddy and MacLellan (1990) (fig. 1).

Keddy and MacLellan’s model describes the pattern of

species distribution along biomass gradients2 which radiate

out from a central core which is characterized by fertile,

mesic, and low disturbance environments. The underlying

mechanism in this model is competition for light; consequently the largest species dominate the most productive

habitat. Although all species could thrive in the core habitat,

most species are prevented from doing so by virtue of being

shaded by the large dominant. Species distribution within

the core is primarily a result of gap dynamics. At their core

ends the biomass gradients have similar species composition:

At the less fertile and/or high disturbance end of the gradients

species compositions vary as the environmental stresses

change. Less successful competitors for light in the optimal

habitat achieve greater importance in more stressful environments where adaptations to specific conditions give

them a competitive advantage. A greater beta diversity, or

turnover of species, is thus predicted in the extreme

environments represented at the periphery of the model.

DOMINANCE OF SHORTLEAF PINE

Shortleaf pine (Pinus echinata Mill.) density and volume is

highest in the Ouachita Mountains, where it has regenerated naturally from seedlings on cut-over, burned, or abandoned land to dominate natural stands. It dominates the

Ouachita pine-oak forest where the colder and drier conditions are not suitable for loblolly, and where the moisture,

nutrient, and disturbance regimes discourage the growth of

hardwood species (Guldin 1986).

Shortleaf pine is an example of a species which has its

greatest abundance outside of its optimal habitat. It is most

productive in well drained, sandy loam soils with a low pH

and calcium content, on low gradient north aspects, at lower

elevation and latitude. In these sites, however, it is outcompeted by faster growing pines or overtaken by longer-lived

ORDINATION OF WOODY VEGETATION IN A

OUACHITA NATIONAL FOREST WATERSHED

Denise Marion and George Malanson1

1 Instructor, University of Mississippi, Department of Geology, St. Oxford, MS; and Professor, University of Iowa, Department of Geography,

Iowa City, IA, respectively.

2 Organization is based on biomass, with the greatest biomass in the optimal habitat. Because the model was originally developed for

herbaceous vegetation, biomass was used as a surrogate measure of productivity. This interpretation of biomass does not transfer to woody

species, however, where biomass is the accumulation of productivity of years past. Therefore, no conclusions regarding productivity can be

inferred from this study.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Species response to competition and other environmental gradients has important implications for forest

ecosystem managers who desire to both maintain diversity and provide a sustained flow of forest goods and services.

Woody species on a 140-acre watershed in the Ouachita National Forest are ordinated with detrended correspondence

analysis (DCA) and canonical correspondence analysis (CCA) to identify the important influences on species distribution

at this scale. Species composition is found to respond primarily to a moisture gradient, and secondarily to competition

with shortleaf pine (Pinus echinata Mill.). Centrifugal organization of species along gradients is suggested by the relative

locations of species in the ordination diagrams.

Figure 1—Centrifugal model of plant community

organization. Environmental gradients represented by

lines radiating out from core; e.g. flooding and soil

texture, (A) mesic fertile sites, gap dynamics, one or

two dominant species; (B) competitive hierarchy, shade

tolerance increases toward core; (C) extreme site

conditions, large-scale disturbance, greatest beta

diversity (adapted from Keddy and MacLellan 1990).
199

hardwoods. Its ecological importance increases in drier, less

fertile upland sites where its large root system, lower demand

for nutrients, and tolerance of fire and cold give it a competitive advantage. The success of the shortleaf pine in the

Ouachitas can be attributed to the location of the uplands

relative to the neighboring loblolly-oak forest to the south,

and the oak-hickory forest to the north (Burns and Honkala

1990, Guldin 1986).

STUDY SITE

The study site is the north-facing, 56.7 ha (140 acres)

watershed located in the shortleaf pine and oak forest of

the Ouachita National Forest, northwest of Hot Springs, AR.

The watershed was surveyed and sampled by foresters in

1994 as part of a fluvial geomorphology research project

(Personal communication. Daniel A. Marion. 1996. Research

Hydrologist, USDA Forest Service, Southern Research

Station, 1000 Front St., Oxford, MS 38655). Average

annual precipitation is 51 inches. Elevation ranges from

160 to 396 m (560 to 1,300 feet), with slope gradients up

to 30 percent.

Three soil units occur in the watershed, classified according

to the USDA Forest Service, Southern Region Soil Resource

Guide and the Natural Resources Conservation Service

(NRCS) (Luckow and Lee 1995). The majority of the watershed falls in one soil map unit which is identified as a PirumClebit-Carnasaw complex. These are characteristic of

moderately steep mountain side slopes. Soils at the upper

elevations on the western divide are moderately deep clayey

and loamy soils characteristic of mountain tops. The major

component of this unit is a Carnasaw soil. The third soil unit

occurs on the highest elevations in the watershed, which

form the southeastern divide. It is a Clebit-Carnasaw-Pirum

complex characteristic of upper side slopes and mountain

tops. About 3 inches of very stony, fine sandy loam overlay

a gravelly loam subsoil layer.

Three vegetation patches represent the pattern of disturbance in the watershed: approximately 75 acres are shortleaf pine-hardwood forest, 56 acres are plantation, and 8

acres are riparian. The forest to the west of the creek and in

the southern end of the watershed is roughly 70 years old.

It has been relatively undisturbed since logging in the early

part of this century. Flooding in the riparian zone establishes

a 1- to 5-year disturbance regime adjacent to the channel,

and a 30- to 50-year regime near the break in the slope.

The eastern side of the watershed is a plantation < 10 years

free of disturbance. A seed-tree harvest was completed

sometime between 1979 and 1982. When the seed trees

were removed in 1986 natural reseeding was judged

unsuccessful. In 1988 the plantation site was prepared for

planting with herbicide suppression of hardwoods and

mechanized exposure of bare soil. In 1989 the area was

planted uniformly with 1 year-old shortleaf pine seedlings,

grown from seeds obtained in the Forest Service’s seed

orchard in the Ouachita National Forest.

METHODS

All stems >1 m in height are identified, tallied, and measured.

Stems < 4 inches in diameter are grouped into one sizeclass. Sample sites are located by stratified random sampling: 50 percent of the plots are located in the forested

area; 30 percent of the plots are located in the clear-cut

area; 20 percent of the plots are in the riparian zone. The

riparian zone is defined as the land adjacent to the stream

channel between the slope breaks; a distance of 5 to 30

feet on either side.

Environmental variables used in the analysis are elevation,

percent slope, aspect, height above stream, Natural

Resources Conservation Service (NRCS) soil factor T, litter

cover, and shortleaf pine importance. Elevation, slope,

height above stream, and aspect are taken from the U.S.

Geological Survey 7.5 minute Nimrod SE topographic map,

revised in 1985. An insolation value for aspect was read

from a table calculated by Beers (1966). NRCS factor T is

an estimate of the maximum average annual rate of soil

erosion by wind or water that can occur without affecting

crop productivity over a sustained period. Litter cover is the

percentage of ground with no bare mineral exposed, estimated visually. Importance values are computed as each

species’ percent basal area plus its percent of individuals.

In order to evaluate the effect of competition with shortleaf

pine its importance value is removed from the dependent

(species) variables and used as an environmental variable.

ORDINATION

Ordination is the ordering of species characteristics, such

as abundance or productivity, along an environmental

gradient, or a transect thought to represent a change in

environmental conditions. The goal of ordination is to identify important environmental influences on plant community

composition. An ordination diagram displays compositionally similar sites near each other; dissimilar sites are located

farther apart. Multivariate ordination represents species or

site data in two or more dimensions where axes represent

complex gradients. The first axis explains the greatest proportion of variance in the data, the second axis explains the

next greatest proportion, and so forth for additional axes.

Indirect ordination techniques, such as detrended correspondence analysis (DCA) order only species and sample

plots without direct information regarding habitat conditions.

Environmental variables are then correlated with the ordination in a second step. In direct ordination, such as canonical

correspondence analysis (CCA), axes represent a linear

combination of environmental variables that optimize the

separation of the species response curves. In a CCA graph,

environmental gradients are indicated by arrows pointing in

the direction of the greatest rate of change. Arrows pointing

in a similar direction are highly correlated; arrows pointing

in opposite directions indicate a high negative correlation

(ter Braak and Prentice 1988). The length of the arrow is

proportional to the strength of the correlation with the ordination axes. The length is, therefore, an indicator of the

strength of the relationship of the environmental variable to

the pattern of community variation shown in the graph (ter

Braak 1987).

In both DCA and CCA ordination graphs, points that represent sample sites are located at the center of gravity of the

species that occur there. Species points at the edges of a

DCA ordination are often rare species occupying extreme

environmental conditions (ter Braak and Prentice 1988).
200

Data are ordinated with PC-ORD (McCune and Mefford

1995). DCA ordination is run with all species. CCA ordinations are run with all species, without shortleaf pine, and

with shortleaf pine as an environmental (independent) variable. Shortleaf pine is removed from the species variables

to allow evaluation of the effect of shortleaf pine competition as an environmental variable. Rare species are not

downweighted. DCA and CCA ordination scores are correlated with environmental variables in PC-ORD with both

Pearson’s and Kendall’s correlation coefficients.

RESULTS

Importance Values

Table 1 lists the woody species identified in the watershed.

Shortleaf pine dominates the watershed as a whole at with

an importance value of 48 percent. Only five sample plots

in the watershed are dominated by species other than shortleaf pine: two are plantation sites where snowbell and red

maple (Acer rubrum L.) reach high importance; the others

are riparian sites where ironwood (Ostrya virginiana Scop.),

and sometimes sweetgum (Liquidambar styraciflua L.) and/

or blackgum (Nyssa sylvatica Marsh.) are more important

than pine. Shortleaf pine dominance is greatest in the forest

patch where its importance reaches 48 percent, and its

closest competitor, white oak, achieves only 12 percent (fig.

2). In the plantation patch, the importance of shortleaf pine

is 28 percent, whereas both snowbell and hickory (Carya

spp.) achieve 15 percent importance. In the riparian patch

Table 1—Species and code names in Toots Creek watershed survey

Code Scientific name Common name

BC Prunus serotina Ehrh. Black cherry

BG Nyssa sylvatica Marsh Blackgum

BH Viburnum rufidulum Raf. Rusty blackhaw

BJO Quercus marilandica Muenchh. Blackjack oak

BO Quercus velutina Lam. Black oak

ERC Juniperus virginiana Alt Eastern red cedar

DW Cornus florida L. Flowering dogwood

FM Acer barbatum Michx. Florida maple

HAW Crataegus spp. Hawthorn

HBM Carpinus caroliniana Walt. Hornbeam, blue beech

HIC Carya spp. Hickory

IW Ostrya virginiana Scop. Ironwood, eastern hophornbeam

NRO Quercus rubra Michx. f. Northern red oak

PL Prunus spp. Plum

PO Quercus stellata Wangenh. Post oak

RM Acer rubrum L. Red maple

SB Styrax grandifolius Ait. Bigleaf snowbell

SG Liquidambar styraciflua L. Sweetgum

SLP Pinus echinata Mill. Shortleaf pine

SUM Rhus spp. Sumac smooth, shining

SVB Amelanchier spp. Service berry

VAC Vaccinium spp. Farkleberry, huckleberry

WA Fraxinus americana L. White ash

WE Ulmus alata Michx. Winged elm

WH Hamamelis virginiana L. Witch-hazel

WIO Quercus phellos L. Willow oak

WO Quercus alba L. White oak

WTO Quercus nigra L. Water oak

Figure 2—Species importance values by patch type.

Species with importance values <1 percent are omitted.
201

shortleaf pine’s importance value, 28 percent, is only slightly

greater than that of ironwood at 22 percent. White oak also

reaches 10 percent importance in the riparian patch.

CCA

Figure 3 shows the results of CCA ordination in three different sets: shortleaf pine as a species variable but not an

environmental variable; shortleaf pine omitted from both the

species variables and environmental variables; and shortleaf pine employed as an environmental variable rather

than a species variable. Table 2 lists correlation coefficients

between the axes and the environmental variables.

In the all-species ordination (fig. 3A) forest sites are in the

center, and riparian and plantation sites are distributed on

either end of the first axis. The first CCA axis is most strongly

correlated with height above the stream (r = .786), which represents a moisture gradient. The second axis is correlated

most strongly with soils (r = -.782). The pattern of distribution

of the sites in ordination space is altered radically when

pine is removed from the environmental variables (fig. 3B).

All sites except three, four, and six are tightly clustered in

the middle of axis two, stretched in a slightly negative trend

along axis one. Species composition appears to be influenced primarily by moisture and soil gradients. Patch types

remain in the same relative locations as in the all-species

analysis.

When shortleaf pine is used in the analysis as an environmental variable (fig. 3C) the central cluster is scattered

vertically by the second axis, which is now most strongly

correlated with importance value of shortleaf pine (r = -.652).

The spread of the plantation group is nearly parallel to the

shortleaf pine vector, indicating a strong association with

this environmental variable. The limited change in the riparian group indicates that shortleaf pine has less effect on

species composition in the riparian sites than in the forest

and plantation sites.

Figure 3—Canonical correspondence analysis ordination

(A) all species; (B) shortleaf pine omitted; (C) shortleaf pine

importance used as environmental variable.

Forest

Riparian

Plantation

Sites not enclosed are forest sites

— . —

– – – –
202

The percentage of variance in the data explained by the first

three axes increases from 29.1 percent for the all-species

ordination to 35.2 percent when pine is an environmental

variable (table 3). This increase is largely due to the contribution of the second axis, which is most closely correlated

with shortleaf pine importance. The proportion of variance

explained by soils is displaced to the third axis.

DCA

DCA ordination of sample sites using all species is shown

in figure 4. Patch types are again separated along the first

axis. (Note that the orientation of the first axis is reversed

from the CCA graph.) Riparian plots are grouped at the right

end of first axis and in the middle range of the second axis;

plantation plots form a group in the lower left quadrant.

Forest plots are clustered in the center of the ordination

space, except for outlier sites 3, 17, and 6, indicating

unusual species compositions for these 3 sites.

Table 4 lists the correlation coefficients for the environmental

variables and the distribution of sites in the ordination space.

Height above the stream correlates most closely with the

first axis (r = -.649). Consequently, the riparian sites (sites

10-15) and the upper sites on southwest facing slopes

(sites 3, 4) occupy opposite ends of axis one. The second

axis for the all-species ordination is most strongly correlated with slope (r = -.486) and organic litter (r-.411).

Table 2—Correlation of environmental variables with CCA axes (Pearson’s r)

All species No pine Pine as environmental variable

Axis1 Axis2 Axis3 Axis1 Axis2 Axis3 Axis1 Axis2 Axis3

Height above stream .786 -.344 .437 .777 -.166 .558 .809 -.402 .096

Soils -.214 -.782 -.486 -.534 -.904 -.173 -.527 -.214 -.841

Aspect -.461 -.551 -.055 -.627 -.411 .118 -.588 -.364 -.275

Slope .556 .344 -.563 .476 -.047 -.656 .417 .538 -.334

Elevation .410 .208 .367 .655 .321 .343 .646 .027 .388

Litter -.205 -.235 .627 -.233 .034 .572 -.201 -.372 .268

SLP IV .455 -.652 .299

CCA = canonical correspondence analysis.

SLP = Shortleaf pine.

Table 3—CCA percent of variance in species data

explained by axes

CCA percent of variance explained

Axis 1 Axis 2 Axis 3 Cumulative

All species 15.9 7.5 5.7 29.1

Without SLP 15.7 9.2 8.1 33.1

SLP as environ-

 mental variance 15.4 11.1 8.7 35.2

CCA = canonical correspondence analysis.

SLP = shortleaf pine.

Figure 4—Detrended correspondence analysis ordination.

Table 4—Correlation of environmental variables

with DCA axes (Pearson’s r)

Axis 1 Axis 2 Axis 3

Height above stream -.649 .185 .058

Soils .217 .084 -.251

Aspect .356 .247 -.062

Slope -.382 -.486 -.137

Elevation -.353 .036 -.075

Litter .094 .411 .009

DCA = detrended correspondence analysis.
203

Following the example of Ware and others (1992), sites

with importance values ≥15 percent for given species are

grouped in the ordination graph in figure 5. Since shortleaf

pine exceeds 15 percent on all sites except riparian plot 12,

sites with ≥ 50 percent pine are grouped. Five species other

than shortleaf pine reach importance values ≥15 percent in

two or more sites: Post oak (Quercus Stellata Wangenh.),

snowbell (Styrax grandifolius Ait.), red maple, white oak

(Quercus alba L.), and ironwood. There is little overlap of

these groups. Only four sites have importance values ≥15

percent for other species when importance values for shortleaf pine are ≥ 50 percent: forest sites 17 and 1; and plantation sites 9 and 5. Only three of the sites with shortleaf

pine importance value ≥ 50 percent are not forest; these

are all plantation sites. The groups of secondary competitors

are located at the periphery of the cluster of sites, with the

shortleaf pine cluster in the center of the ordination space.

DISCUSSION AND CONCLUSION

The anomalous positions of site three in the DCA ordinations, and sites three, four, and six in the CCA ordinations

deserve fuller discussion. Plots located at extreme positions

in the ordination space frequently represent rare species

occupying extreme sites (ter Braak 1987). Plot six is the only

site located within the soil unit with the highest T factor. It

has other unusual characteristics as well: it has the highest

importance values for post oak (17 percent) and hickory (9

percent), and it is on a ridge top at 900 feet elevation. Only

three sample plots are higher in elevation, and the two highest of these plots, three and four, are also outliers. Sites

three and four are the two sites on the soil factor with the

lowest T factor, and the only two on exposed southwest

slopes. Plot three has an unusually high importance value

(31 percent) for blackjack oak (Q. marilandica Muench.).

Site four has an extremely high importance value for shortleaf pine (84 percent). CCA puts these unusual species

compositions and unusual environmental conditions in

extreme locations in ordination space.

Gradients

The most important gradient influencing species composition in this watershed is a moisture gradient, represented

by height above the stream. As competition for light represented by shortleaf pine importance value is associated

with the second CCA axis when it is used as an environmental variable, it is concluded that competition is the

second greatest influence on the pattern of species distribution. Improved proportion of variance explained and

improved correlation coefficients when pine is removed

from the list of species variables or added to the environmental variables also support that conclusion.

The effect of competition on the pattern of species distribution is reflected in a comparison of the three CCA ordination

diagrams (fig. 4). There is a marked separation of species

response curves when a shortleaf pine importance gradient

is introduced. The shift in site scores in the plantation patch

(fig. 4C), where the planting of pines was fairly uniform is

interesting. The orientation of the spread nearly parallel to

the shortleaf pine importance vector may be explained by

management practices, including uniform planting, that have

been designed to foster the growth of shortleaf pine and

suppress competition by other woody species. It indicates

the relative success of early successional species. It must

be noted, however, that the patch’s elongation is largely

due to the location of site 4, one of the sites with extreme

environmental conditions. High shortleaf pine dominance at

this site may be more directly the result of environmental

stress that limits the success of other species rather than

the effect of shading.

Centrifugal Organization

The pattern displayed in ordination space when species are

grouped by importance values is consistent with centrifugal

organization as proposed by Keddy and MacLellan (1990)

(figs. 1 and 5). The dominant species, shortleaf pine, is in

the center of the ordination space, while competitor species

are grouped in specific locations around the periphery. In

interpreting this distribution in can be inferred that the more

peripheral sites represent sites with more extreme environmental conditions. The limited overlap among sites with ≥ 50

percent importance value of shortleaf pine and sites with

≥ 15 percent importance value of other species is evidence

that shortleaf pine dominance inhibits the productivity of

other species where competition with the dominant species

is great. The clustering of the most successful species other

than pine in specific locations around the periphery of the

ordination space indicates that these species can achieve

greater productivity in more extreme environmental conditions where adaptations to the specific conditions may give

them a competitive edge.

Figure 5—Detrended correspondence analysis ordination.

Grouping of sites which exceed importance value of 15 percent for

given species, or importance value of 50 percent for shortleaf pine.
204

Time

The Toots Creek ordination diagrams represent species distribution at one point in time—1996. The pattern is expected

to change with time. The effects of disturbance or lack of it,

human intervention, and time must be taken into account

when interpreting the meaning of the pattern shown in

these ordination diagrams.

Ordinations of this watershed in 30 years may show white

oak displacing shortleaf pine from the center of ordination

space. In forest ecology the dominant species in the optimal

habitats are generally shade-tolerant. That is, they are the

best competitors for other plant resources, soil moisture and

nutrients, in low light conditions. In disturbance-free sites

these species are expected to eventually overtop pioneer

or intermediate sequential species. The central location of

white oak in the ordination space suggests that white oak

has this competitive strategy. When the shortleaf pine

reaches the end of its relatively short life span, the white

oak will be in a position in the shared optimal habitat to

respond to increases in plant resources.

Keddy and MacLellan’s centrifugal organization model

assumes a natural species distribution in response to natural

gradients. If management to maximize the productivity of

the commercial species is successful, in 30 years one

would expect an ordination of the plantation patch to show

uniformity of composition, few species other than shortleaf

pine, and little or no response to environmental gradients.

This pattern represents the artificially induced species composition which is the objective of human intervention. As

this pattern is not a result of naturally occurring variation,

application of the centrifugal model is inappropriate.

Management Implications

One might feel justified in concluding that managed stands

on all optimal sites do not reduce diversity, since beta diversity is greater in poorer site conditions. However, species

richness is greatest (p = .05) in this watershed in the 80-yearold natural forest patch, currently occupying the optimal

habitat. Furthermore, in these low stress environments,

where all species could thrive were it not for competition, it

is reasonable to conclude that periodic natural disturbance

such as fire may result in the greatest species diversity.

Forest ecosystem management that uses all optimal environments for plantations may then forfeit the greatest potential

for increasing and sustaining biodiversity.

ACKNOWLEDGMENTS

Data for this study were collected by Gordon McWhirter,

Hydrologic Technician; Clifford Harwell, Hydrologic Technician; and Dennis Carlson, Forester; put in usable form by

Gail Henderson, Computer Specialist; and made available

to me by Dan Marion, Research Hydrologist, USDA Forest

Service, Southern Research Station, Oxford, MS. Hunter

Speed, Silviculturalist, USDA Forest Service, Ouachita

National Forest, Jessieville, AR, researched the management history of the Toots Creek watershed and Steve Brewer,

Assistant Professor, Department of Biology, University of

Mississippi, gave valuable criticism and insight.

LITERATURE CITED

Beers, T.W.; Dress, P.E.; Wensel, L.C. 1966. Aspect transformation

in site productivity research. Journal of Forestry 64: 691-692.

Burns, R.M.; Honkala, B. 1990. Silvics of North America. Agriculture

Handbook 654. Washington, DC: U.S. Department of Agriculture.

vol. 1.

Guldin, J.M. 1994. Ecology of shortleaf pine. In: Murphy, Paul A.

comp. Proceedings of the symposium on the shortleaf pine ecosystem: 1993 October 26-27. Gen. Tech. Rep. SO-112. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Research Station: 25-40.

Keddy, Paul A.; MacLellan, P. 1990. Centrifugal organization in

forests. OIKOS 59: 75-84.

Luckow, Ken; Lee, K. 1995. Soil report: Russelville Ranger District

Winona Compartment 1443. Jessieville, AR: U.S. Department

of Agriculture, Forest Service, Ouachita National Forest. 24 p.

McCune, B.; Mefford, M.J. 1995. PC-ORD: multivariate analysis

of ecological data. version 2.0. Gleneden Beach, OR: MjM

Software Design.

Palmer, M.W. 1993. Putting things in even better order: the advantages of canonical correspondence analysis. Ecology. 74: 2215-

2230.

Smith, K.L. 1986. Sawmill: the story of cutting the last great virgin

forest east of the Rockies. Fayetteville, AR: Univ. Arkansas.

246 p.

ter Braak, C.J.F. 1987. The analysis of vegetation-environment

relationships by canonical correspondence analysis. Vegetatio.

69: 69-77.

ter Braak, C.J.F.; Prentice, I.C. 1988. A theory of gradient analysis.

Advances in Ecological Research. London: Academic Press.

Vol. 18

Ware, S.; Redfearn, P.; Pyrah, G.; Weber, W. 1992. Soil pH,

topography and forest vegetation in the central Ozarks.

American Midland Naturalist. 128: 40-52.
205

Session 8

Phase III Hydrology and Aquatic Ecology Research

Duane Dipert, Moderator
206
207

INTRODUCTION

Accurate predictions of peak streamflow magnitude are

essential for accomplishing forest ecosystem management.

Such predictions are required to design adequate drainage

and stream crossing structures. They are needed to assess

the risk of failure for in-channel or flood-plain structures,

and for planning research and restoration projects. Peak

flow predictions are made using flood-frequency (FF) models

that relate discharge magnitude to the recurrence interval

of such events (tr

, the average number of years between

events), or its inverse, the probability of such a flow

occurring in a given year (P = 1/tr

).

A series of equations have been defined by Neely (1987)

for predicting peak discharge at given recurrence intervals

(Qr) throughout Arkansas.2 These equations model Qr as a

function of various basin and climatic characteristics. They

apply to all unregulated streams with drainage areas

< 3,000 square miles within the entire State. Hereafter, the

term “Neely’s model” is used to refer in general to these

equations.

Two reasons suggest that Neely’s model may not be

accurate in the smaller, headwater streams of the Ouachita

Mountains. First, it is based primarily on catchments with

relatively large basin areas. Within the Ouachita Mountains,

only 3 of the 23 stations used by Neely have basin areas

< 640 acres and only 2 are < 160 acres. Second, Neely’s

model is defined so as to minimize the influence of channel

slope and elevation, two factors that are widely recognized

as greatly influencing streamflow in small, mountain basins

(Lee 1980). Neely states the latter is done to eliminate

“bias” from these variables, but it’s not clear from his

discussion what exactly is meant by “bias.”

In this paper, I present an improved FF model that is applicable to the upper Ouachita Mountain region (see figure 1).

This new model is derived using the regional flood-frequency

(RFF) method of Dalrymple (1960) and data from 10 small,

steep watersheds. The presentation is organized as follows:

first, Neely’s model and procedures for deriving a preliminary RFF model using a data subset are described; second,

Qr predictions from the preliminary RFF model are compared

to those using Neely’s model; and last, the final RFF model

AN IMPROVED FLOOD-FREQUENCY MODEL FOR SMALL WATERSHEDS

IN THE UPPER OUACHITA MOUNTAINS

Daniel A. Marion1

1 Research Hydrologist, USDA Forest Service, Southern Research Station, Hot Springs, AR.

2 Hodge and Tasker (1995) recently published an updated series of models for Arkansas. I discovered this work too late to include it within

this paper, however, I did have time to test their models using the same procedures and found them to actually be less accurate than those

of Neely (1987) for small, steep basins in the upper Ouachita Mountains.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—A new regional flood-frequency (RFF) model is proposed for small, steep watersheds in the upper Ouachita

Mountains of Arkansas. It is derived using Dalrymple’s (1960) method and data from 10 monitoring stations with record

periods of 15 to 33 years. First, I developed a preliminary RFF model and tested it against Neely’s (1987) model using

data from just five stations. Next, I compared the preliminary model’s prediction accuracy to the accuracy of Neely’s

model using data from five stations not used to derive the new model. The preliminary model produced more accurate

predictions than Neely’s for all basins tested. Moreover, it’s prediction errors are unbiased whereas those using Neely’s

model vary with recurrence interval and basin size. I derived a final RFF model using data from 9 of the 10 stations. It is

applicable to sites within steep, forested basins of < 100 acres.

Figure 1—Physiographic provinces in Arkansas (Hodge

and Tasker 1995); location of theu Ouachita Mountains;

and gauging stations used to derive or test a regional

flood-frequency model. Topographic data from Nimrod

SE and Paron SW USGS 7.5-minute quadrangles.
208

is derived using all appropriate data and its usage considerations are discussed. The region of interest is limited to the

upper Ouachita Mountains where data are sufficient for this

analysis.

METHODS AND DATA

Overview of Existing and New Models

Neely (1987) derived models for predicting 2-, 5-, 10-, 25-,

50-, and 100-year peak flows. He divided the State into two

regions wherein different equations are applicable. The

upper Ouachita Mountains region is entirely within Neely’s

Region B. Using multiple regression, he found that basin

area, channel slope, mean annual precipitation, and mean

basin elevation were the important factors in predicting Qr.

For small mountain streams like those in the upper Ouachita

Mountains, slope and elevation are treated as constants

and set equal to 30 feet per mile and 500 feet, respectively.

The RFF method is relatively simple to apply and is used to

derive an alternative model for predicting Qr

. Its only assumption is that all individual basins within the region have streamflows with the similar distribution characteristics (Dunne and

Leopold 1978). The method actually produces two related

models: one for predicting Qr

 given the mean annual flood

(Qmaf) for a basin and the tr of interest, and a second for

predicting the Qmaf from basin characteristics. Hereafter, the

term “RFF model” is used to refer to both models.

The procedures used in deriving a RFF model are explained

in detail in Dalrymple (1960). Briefly, they are

1. Determine the candidate stations and base period. The

base period is the time period that provides the greatest

overlap in record periods between stations

2. Derive a FF model for each station

3. Predict the Qmaf for each station. The Qmaf is used as a

scaling factor to remove the effect of basin size from the

discharge data, so that a single model can predict flows

for basins of varying size. It is determined for each station

using the respective FF model and the discharge at the

2.33-year recurrence interval

4. Test the homogeneity of data from all stations. This test

ensures that all stations have reasonably similar variability in peak discharges for their respective record periods

5. Derive the regional Qr

/Q maf model. This model expresses

the change in Qr

/Q maf with tr. It is derived by predicting

the discharges at selected tr values using the individual

FF models, dividing them by their respective Qmaf values,

determining the median Qr/Qmaf value for all stations at

each tr, and modeling the medians against the tr

6. Derive the Qmaf model. This model predicts Qmaf values

so that the Qr can be determined from the Qr/Qmaf model

at ungauged sites

Two exceptions are made to Dalrymple’s procedures. First,

both the Qr

/Q maf ratio and Qmaf models are derived using

regression procedures rather than fit-by-eye curves. Nonlinear regression is used to model Qr

/Q maf versus return period,

while linear regression is used to model Qmaf. Second,

several basin characteristics are used to model Qmaf rather

than just basin area.

Model Derivation and Testing Procedures

Data from 10 gauging stations located within the region (see

figure 1) were used to develop and test the RFF model.3

These stations are all located in small, steep basins. None

of the other stations in the region were used because either

they have drainage areas that are too large or have catchments that are too low in relief (see Neely 1987 for these

data). The 10 stations used have basin areas from about 1

to 32 acres (see table 1). Their base record period is a little

over 30 years, which is quite long for stations within such

small basins. The data constitute annual maximum instantaneous discharges during the record periods for each station.

The 10 watersheds all have continuous, pine-hardwood

forest cover, but vary in stem density, biomass, and overstory age as a result of past logging (see table 1). In this

way, they are representative of small basins throughout the

upper Ouachita Mountains where forest management has

produced a mosaic of forest conditions.

The data were divided into two groups: one for developing

a preliminary RFF model and a second for testing its accuracy against predictions using Neely’s model. The model

group includes five stations, four of which were chosen at

random. One station, AC04, purposely was included in the

model group because its basin area is substantially larger

than all other stations (see table 1). This ensures that the

preliminary model represents the range of conditions found

among all 10 stations. The testing group includes the remaining five stations. Both groups include data from basins that

cover the range of forest conditions found among the 10

stations.

Data for developing the FF models for each station were

taken from the same base period of 1961–93. Where data

are missing, they were estimated by finding the station

among the other nine that was most highly correlated to the

station with the missing data, regressing the discharges from

those years when both stations have data, then estimating

the missing data using the resulting model. Missing data

estimates were used only in determining the ranks of valid

data; they were not used in computing the FF models.

FF models were derived for each station in both the model

and test groups. Nonlinear regression is used to fit one of

two exponential decay models, either

Qr

2

1

b P b e− = (1)

or

b P

r Q b b e 2

0 1

− = + (2)

where

b0, b1, and b2 = regression coefficients.

3 Unpublished data on file at the USDA Forest Service, Southern

Research Station, P.O. Box 1270, Hot Springs, AR 71902.
209

This allows the FF models to be expressed as equations

rather than graphs, which greatly facilitates their use. The

models were computed using SigmaPlot and derived using

the Marquardt-Levenberg algorithm (SPSS 1998).

Different modeling approaches and weighting schemes were

used to derive a variety of candidate models for each station.

Models were derived both by forcing b0

 to equal 0 and by

computing b0

. Different weighting values were explored to

de-emphasize the leverage of the largest peak flows in

deriving the individual FF models. The largest discharges

have the most uncertain probabilities and can have a disproportionate influence on model parameters. In contrast,

the lower discharges have more certain probabilities, but

have less influence because their residuals are typically

small. Weighted regression can be used to minimize this

effect (Myers 1990). Because this uncertainty varies with

discharge (Q), then 1/Q and 1/Q2 were tested as weights, in

addition to an unweighted model.

I chose the best model to be the one that maximizes overall model fit while conforming most closely to the higher

discharge probabilities. I used the model R2 to measure

overall fit and visual inspection to determine quality of fit at

the lower discharge values. When different models were

equivalent using these criteria, I chose the simplest model

(fewest parameters, least weighting).

The preliminary Qr

/Q maf model was derived using the median

values at 24 probabilities from 0.01 to 0.99. The probabilities were selected so as to adequately define the change in

Qr/Qmaf across the probability range for the five stations in

the model group.

The preliminary Qmaf model was derived using linear regression to relate Qmaf

 to basin morphometry variables for all

stations in the model group. Basin morphometrics considered were basin area, relief, length, and slope (see table

1). The best model was chosen considering R2

, model

mean square error, and Mallow’s Cp factor (Myers 1990).

Model accuracy for both the preliminary RFF and Neely’s

model was tested by comparing discharge predictions using

these models against those predicted by the individual FF

models for stations in the test group. My rationale was that

because the individual models are based solely on the

measured data at each station, they should provide the

best estimate of the “true” Qr at each station. This rationale

is only as good as my assumption that the exponential

decay functions used to fit individual models provide an

accurate representation of annual peak discharge distribution. Because all selected models had R2

 terms of 0.92 to

0.97, I concluded this assumption was valid. Accuracy for

both the preliminary RFF and Neely’s model was evaluated

qualitatively by visually determining which model predicts

Table 1—Environmental characteristics of Forest Service gauging stations

used in the regional flood-frequency model development and testing

Mean

Basin Basin Forest annual Data

Station area relief conditions flood group

acres feet cfs

AC01 1.63 75 Undisturbed 1.34 Test

AC02 1.28 75 Shelterwood harvest in 1970

and 1977, thinned in 1981 1.16 Test

AC03 1.44 60 Clearcut 1970, replanted in

1976 2.04 Model

AC04 32.50 220 Undisturbed 19.94 Model

AC10 14.17 220 Selection harvest in 1980

and 1987 13.14 Test

AC11 12.18 180 Undisturbed 9.00 Model

AC12 14.60 180 Clearcut, burned, and replanted

in 1980 13.91 Model

AC13 11.71 180 Undisturbed 9.78 Model

AC14 10.76 140 Selection harvest in 1980

and 1987 11.14 Test

AC15 12.64 150 Clearcut, burned, and

replanted in 1980 11.86 Test

Note: Record periods are 1961–93 for stations AC01-AC04 and 1979–93 for stations AC10-

AC15.
210

discharges closest to the individual FF predictions. It was

assessed quantitatively by computing the root mean square

(RMS) error of model predictions from corresponding station

Qr values. Hereafter, Qr values predicted from individual

station models are referred to as “station” values while

those predicted using either the RFF or Neely’s model are

referred to as “predicted” values.

RESULTS AND DISCUSSION

Model Comparisons

The FF models for individual stations are fairly similar and

all were fit with a high degree of accuracy, as noted above.

An example is shown in figure 2. Best model fits were most

often obtained using a two-parameter model (b0

 = 0) with

1/Q weighting. Stations AC03 and AC04 were best fit using

the three-parameter model. Stations AC01 and AC10 were

best fit using no weighting, while station AC03 was best fit

using 1/Q2 weighting. With the exception of AC04, the twoparameter model derived using 1/Q weighting is almost as

good as the selected model for these stations. The threeparameter model is clearly better for AC04. A possible—

albeit unproven—physical interpretation of the need for the

b0 value is that due to its larger basin area (table 1), AC04

exhibits a minimum, positive discharge even at the lowest

recurrence intervals, whereas the other, smaller basins

approach 0 discharge.

Station AC10 is the only station that fails to pass the homogeneity test. Its variance exceeds the recommended limits

(Dalrymple 1960); therefore this station was excluded in

deriving both the preliminary and final RFF models. Why it

differs from the other watersheds is not readily apparent. It

is located adjacent to AC11-AC15 and has similar physical

characteristics (see table 1).

The component models of the preliminary RFF are

P

maf

r e

Q

Q ( 1.885) 2.237 − = (3)

and

Qmaf = −0.317 + 0.423A + 0.031E (4)

where

A = basin area (acres)

E = basin relief (feet).

The Qr/Qmaf model is extremely well fit using a two-parameter

function. This exceptional degree of accuracy is due to the

smoothing effect produced by modeling only the median

ratios at each probability. The best Qmaf model uses basin

area and basin relief to predict Qmaf. Its R2 is 0.95.

All comparison tests clearly show that the preliminary RFF

model is more accurate than Neely’s model for the basins

considered. Plots of station versus predicted Qr for three of

the test group stations clearly show the difference (fig. 3).

The stations used in figure 3 show the entire range in overpredictions and underpredictions using the preliminary RFF

model. Surprisingly, the preliminary RFF model is most

accurate for station AC10, the one station whose variance

caused it to be excluded from the model group. The RMS

errors confirm the visual evidence. The RMS error for the

preliminary RFF varies between 0.6 and 4.7 cubic feet per

second; while it varies from 4.7 to 27.9 cubic feet per

second for predictions using Neely’s model.

Figure 2—Flood-frequency models for watershed AC04. Different models are produced

using nonlinear regression with the weighting methods listed. Scales are arithmetic and are

the ones used in all regression analyses.
211

Figure 3—Comparison of discharge predictions using the

preliminary regional and Neely’s (1987) models.

“Observed” discharges are predictions based on observed

data for each station.

Discharge is consistently overpredicted using Neely’s

model and the error increases with both tr

 and basin size.

The distributions of prediction errors are shown in figure 4.

Both the median error and the variance clearly increase in

positive value (overprediction) as tr increases; i.e., probability decreases, and basin size gets larger. Errors are especially pronounced at P < 0.1 (10-year flow) and area > 10

acres. In contrast, the preliminary RFF predictions show no

bias with tr

 and only minor fluctuations around 0 error with

varying basin size.

Given the successful performance of the preliminary RFF

model, a full model is derived using data from all stations

except AC10. Its component Qr/Qmaf and Qmaf models are

P

maf

r e

Q

Q ( 1.922) 2.273 − = (5)

and

Qmaf = −1.262 + 0.416A + 0.040E (6)

The final Qr

/Q maf model is shown in figure 5. Like the preliminary RFF, the selected best model for Qmaf uses basin area

and relief. It has an R2 of 0.94. Similarly, the Qr/Qmaf model

is fit with a two-parameter exponential decay function.

Model Application Considerations

Neely’s (1987) model is not a bad model. The results presented above do not invalidate using Neely’s model for

sites where basin characteristics correspond more closely

to the data used to develop that model. Given that Neely’s

objective was to provide a method for estimating Qr

throughout the entire state of Arkansas, it is not surprising

that his model can be improved on in smaller areas like the

upper Ouachita Mountains where additional data are

available.

The final RFF model resulting from this work has limited

applicability. Its accuracy outside of the upper Ouachita

Mountain region is unknown. The model is based upon data

from sites of approximately 1 to 35 acres and can be

applied to similar basins within this size range. In addition,

it has been my experience that basins with very similar

relief and environmental characteristics occur up to about

100 acres in the upper Ouachita Mountains. Therefore, I

think that where conditions are determined to be similar to

those listed in table 1, the model should be applicable to

catchments up to approximately 100 acres.

Past design and risk assessments based on Neely’s model

for sites in basins like those discussed here may still be

valid. They may overpredict peak discharge, but factors

other than discharge often need to be considered when

designing in-channel structures or assessing their risk of

failure. Such factors include the possibility that a structure

like a culvert will become plugged with woody debris and

leaves during high-flow events. Such factors typically act to

increase the design flow that must be accommodated, thus

overpredictions may be entirely appropriate.
212

Figure 5—Final regional flood-frequency model for small, steep

catchments in the upper Ouachita Mountains. For a given location, discharge for a given recurrence interval is predicted by

calculating the Qr

/Q maf ratio at the probability then multiplying

this value by the Q maf for the location. Qmaf is predicted from the

second model. Both models are based on data from nine

stations.

If a common frequency distribution for small-basin peak

discharges can be determined, then a still better model

might be developed. Such a model would allow more accurate prediction of extreme values, which are always the

most uncertain estimates when using regression methods.

It would also allow shorter record periods to be used in

developing local models. However, identifying a common

frequency distribution is difficult due to the short records

generally available for small, mountain stations and the

relatively large variability inherent in hydrologic processes

at such fine spatial scales.

SUMMARY AND CONCLUSIONS

For small, steep watersheds in the upper Ouachita Mountains, the RFF model described here more accurately

predicts Qr than Neely’s (1987) model. Neely’s model overpredicts discharge for such basins. Using the RFF model

requires measurement of the basin area and relief for the

site in question. The RFF can be applied with confidence to

watersheds of 1 to 35 acres, and probably can be applied

up to 100 acres if environmental characteristics remain

similar to those listed in table 1. Model accuracy is unknown

outside of these limits where, if appropriate, previously

developed models should be used. Factors in addition to

discharge should be considered in designing and assessing

failure potential for in-channel structures in headwater

basins.

ACKNOWLEDGMENTS

My thanks to Tim Diehl, Don Turton, and Howard Halverson

whose reviews helped me to improve this paper. I also thank

the following institutions for their financial support of this

research: USDA Forest Service, Southern Research Station

and Rocky Mountain Station; Stream Systems Technology

Center; the National Council of the Paper Industry for Air

and Stream Improvement; the University of Iowa; and the

USDA Forest Service, Ouachita National Forest. Data used

in this study were obtained through cooperative research

arrangements with Oklahoma State University, the University

of Arkansas, and the Weyerhaeuser Company.

Figure 4—Box plots comparing the effects of (A) recurrence probability and (B) basin area on prediction errors

using both the preliminary regional and Neely’s (1987) models. Errors are plotted at six probabilities corresponding

to 2-, 5-, 10-, 25-, 50-, and 100-year events, and at the basin areas for each of the five stations in the test group.
213

LITERATURE CITED

Dalrymple, Tate. 1960. Flood-frequency analyses. [U.S.] Geological Survey Water-supply Paper 1543-A. Washington: U. S.

Government Printing Office. 80 p.

Dunne, Thomas; Leopold, Luna B. 1978. Water in environmental

planning. New York: W. H. Freeman. 818 p.

Hodge, Scott A.; Tasker, Gary D. 1995. Magnitude and frequency

of floods in Arkansas. U.S. Geological Survey Water Resources

Investigations Report 95-4224. Little Rock, AR: U. S. Geological Survey. 52 p.

Lee, Richard. 1980. Forest hydrology. New York: Columbia

University Press. 349 p.

Myers, Raymond H. 1990. Classical and modern regression with

applications. Boston: PWS-Kent. 488 p.

Neely, Braxtel L., Jr. 1987. Estimating flood hydrographs for

Arkansas streams. [no place of publication]: Arkansas State

Highway and Transportation Department. 19 p.

SPSS. 1998. SigmaPlot 5.0 programming guide. Chicago: SPSS

Inc. 287 p.
214

INTRODUCTION

The Frontier Forest Initiative of the World Resources Institute reported that north temperate regions contain more

than 50 percent of the world’s healthy, intact “frontier” forests

(World Resources Institute 1997). The United States has

about 12 percent of the world’s temperate forest and is harvesting these forests at a rate of more than 500,000 ha/y

(World Resources Institute 1997).

The regenerated forests of temperate regions are often quite

different from the original forests. Studies of the reforested

areas of the northeastern United States have shown changes

in tree species composition and smaller tree sizes compared

with historical forests. Forest plantations may compensate

for the loss of forest resources, but there is little evidence

suggesting that they maintain pre-harvest levels of ecosystem function (Franklin 1988, Swank and Crossley 1988).

Timber harvests affected streams in most forested areas

of the United States (Cummins 1980, Webster and Swank

1985, Webster and others 1992). van der Leeden and others

(1990) reported that 7.5 percent of the total U.S. stream

length has been impacted by logging and silvicultural

practices.

Ecosystems are complex, self-regulating, functional units.

Unlike communities and populations which are structurally

defined, ecosystems are defined by rates and processes,

such as energy flow or material cycling. These processes

are mediated by the trophic structure of the ecosystem. Functional indicators are those metrics that directly or indirectly

measure energy flow and material cycling within ecosystems. Functional indicators (such as ecosystem metabolism

or nutrient cycling) are less likely to be constrained by

regionally restricted biota. Thus, functional approaches lead

to a more global view of stream ecosystems, a view that is

much less variable than one based only on taxa inhabiting

stream communities (Cummins 1988). Hunsaker and others

(1990) stated that for regional ecological risk assessments

to be effective, the system must be functionally defined,

with the spatio-temporal boundaries of the system set by

functional attributes of the communities inhabiting the

system. Assessments that are functionally based are likely

to have greater applicability across regions (Hunsaker and

others 1990).

The most commonly measured functional attributes of ecosystems are primary productivity, or photosynthesis, (P) and

community respiration (R). These two metrics are termed

community metabolism when considered together. Two

additional metrics are calculated from P and R: the ratio of

P to and daily P-R which yields an estimate of net daily

metabolism (NDM). These four metrics have been shown to

be sensitive indicators of ecosystem stress (Bott and others

1985, Hill and others 1997). Metabolism is expected to be

stimulated by nutrients and some organic substances and

suppressed by some pesticides and organic substances,

decreased pH, metals, and habitat degradation stress (Bott

and others 1985, Hill and others 1997).

Many of the transformations which nutrients undergo while

being transported into, through, and out of a stream segment are determined by difference between input and output. The actual pathway an atom of nutrient takes through

this system, usually inferred from movements of radioactive

tracers from one ecosystem component to another, is described as spiraling (Webster and Patten 1979).

NUTRIENT UPTAKE AND COMMUNITY METABOLISM IN STREAMS

DRAINING HARVESTED AND OLD-GROWTH WATERSHEDS:

A PRELIMINARY ASSESSMENT

Brian H. Hill and Frank H. McCormick1

1 Research Ecologists, U.S. Environmental Protection Agency, National Exposure Research Laboratory, Ecological Exposure Research

Division, Cincinnati, OH 45268.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—The effect of timber harvesting on streams is assessed using two measures of ecosystem function: nutrient

spiraling and community metabolism. This research is being conducted in streams of the southern Appalachian Mountains

of North Carolina, the Ouachita Mountains of Arkansas, the Cascade Mountains of Oregon, and the redwood forests of

northern California, in order to understand similarities and differences among stream ecosystem responses to timber

harvesting across diverse geographic regions. Data from Cedar and Peacock Creeks in the redwood forest are used to

illustrate the principles and usefulness of measuring stream ecosystem function for assessing watershed disturbances.

Streams draining logged watersheds had smaller dominant substrate size and more sand and fine sediments in the

channel. Nutrient uptake (NH4

+1, PO4-3) and community metabolism (primary productivity, respiration, P:R, net daily

metabolism) were measured in streams draining old-growth (Cedar Creek) and harvested (Peacock Creek) watersheds.

Phosphate uptake length was significantly shorter in Cedar Creek (212 m) than in Peacock Creek (1020 m). Ammonium

uptake length was not significantly different in these streams (111 m vs. 109 m, respectively). Preliminary analyses of

stream metabolism suggest that primary productivity is greater in streams draining logged watersheds, but community

respiration is greater in stream draining old-growth watersheds, resulting in sustantial differences in P:R and NDM.
215

Elwood and others (1983) listed three aspects of the spiraling concept that make it useful for the analysis of stream

ecosystems. First, it is a simple means of measuring nutrient (including carbon) flow through streams. Second, this

concept emphasizes the interaction of hydrologic transport

and ecological processes governing nutrient cycling. Third,

the interaction of both spatial and temporal dynamics of

streams is emphasized. Additionally, spiral length may be

indicative of increases in nutrient turnover, transport, and

loss from streams as predicted for stressed ecosystems.

Newbold and others (1981) developed an index of nutrient

spiraling known as spiraling length, defined as the average

downstream distance associated with one complete cycle

of a nutrient atom. In developing this index, they assumed

that a nutrient atom passes through water, particulate, and

consumer compartments while being transported. These

components of the spiral can be quantified and the sum of

their average distances is the spiraling length.

Investigations of factors controlling spiral length have shown

a greater role by the biotic component (e.g., Elwood and

others 1981; Newbold and others 1982, 1983; Mulholland

and others 1985b, 1985a) than by abiotic factors. Spiral

length is decreased by those organisms that retain organic

matter and nutrients within a stream (e.g., sessile microorganisms, filter-feeding insects, and grazers), and increased

by organisms which promote particle transport (e.g., shredders). Elwood and others (1981) found that 91 percent of

exchangeable phosphorus uptake by fine particulate organic

matter (FPOM) was due to microorganisms. Similarly, about

80 percent of the exchangeable phosphorus uptake by

stream sediments was due to microorganisms. These findings are in agreement with those of Gregory (1978) who

reported less than 20 percent of the phosphorus uptake by

stream sediments and organic matter was abiotic.

Newbold and others (1983) reported that uptake length

accounted for nearly 90 percent of the total spiral length.

Mulholland and others (1985b) found uptake length to be

as much as 98 percent of spiral length. If spiral length can

be estimated accurately (± 10 percent) from uptake length,

then pulse additions of non-radioactive phosphorus and

ensuing downstream depletion of this phosphorus should

serve as a measure of phosphorus spiraling in those streams

where the use of radioactive tracers would be undesirable

(Stream Solute Workshop 1990, Mulholland and others

1990, Webster and others 1991). Uptake length is expected

to increase with most stressors, though some organic substances may result in uptake length shortening.

The goal of this research is to evaluate stream ecosystem

function in response to different timer harvesting intensities

and time since harvesting. The research is being conducted

in streams of the southern Appalachian Mountains of North

Carolina, the Ouachita Mountains of Arkansas, the Cascade

Mountains of Oregon, and the redwood forests of northern

California. A secondary objective will be the assessment of

intra- and inter-annual variability in measures of stream

ecosystem function. We predict that any amount of watershed disturbance by logging will affect ecosystem function

by depressing community metabolism and increasing nutrient uptake lengths.

METHODS

Study Sites

The study is being conducted at 24 sites grouped by different levels of watershed degradation due to the intensity of

forestry management practice. Sites have been selected at

National Science Foundation Long-Term Ecological Research

(LTER) sites and US Forest Service (USFS) research units

at the Southern Research Station and Redwood Sciences

Laboratory. Sites are of comparable watershed size. The

LTER sites have extensive historical databases on hydrology, chemistry and aquatic community structure and managed impact programs. The USFS sites have historical data

on hydrology, physical habitat, aquatic community structure

and timber harvest.

Coweeta Hydrologic Laboratory—The Coweeta Hydrologic

Laboratory is a 2,185 ha experimental facility in North

Carolina administered by the USFS. It has been dedicated

to forest hydrology research since its establishment in 1933.

Stream sampling has been conducted on eight mixed hardwood control areas and 13 catchments where forest management prescriptions have been applied.

Ouachita National Forest—The Ouachita National Forest

is an 800,000 ha mixed hardwood-conifer forest in central

Arkansas. Research sponsored by the USFS Southern

Research Station has been conducted on streams in the

unit since 1988. In 1990, clear-cutting was eliminated and a

research program initiated to examine the effects of new

forest management strategies on stream ecosystems.

H. J. Andrews Experimental Forest (LTER)—The H.J.

Andrews Experimental Forest, located in the Cascade

Range of Oregon, is a 6,400 ha drainage basin administered by the USFS Pacific Northwest Research Laboratory.

When it was established in 1948, the basin was covered

with virgin forest. Old-growth forest stands with dominant

trees over 400 years old still cover about 40 percent of the

total area. Mature stands (100 to 140 years old) originating

from wildfire cover about 20 percent. About 30 percent of

the Andrews Forest has been harvested, creating young

plantation forests varying in composition and age.

Redwood Sciences Laboratory—Unlike the other study

units, which have their experimental watersheds located in

close proximity, the redwood forest study unit has widely

dispersed experimental watersheds. These study watersheds are an aggregation of management units of the Six

Rivers National Forest and the Redwoods State and

National Park and are scattered over a large portion of

California’s northwestern coastal mountains. Research has

been conducted for several years in these units by the

USFS Redwood Sciences Laboratory and the U.S.

Geological Survey.

At present, only data from the redwood forest study unit has

been analyzed. For the purpose of demonstrating the potential use of community metabolism and nutrient spiraling,

Cedar and Peacock Creeks in the Smith River drainage

basin east of Crescent City were selected.
216

Nutrient Uptake/Spiraling

Phosphate and ammonium uptake lengths were determined

using the method described by the Stream Solute Workshop

(1990), Mulholland and others (1990) and Webster and

others (1991). A solution containing known concentrations

of K2HPO4, NH4Cl, and NaCl tracer was added to the stream

and downstream changes in concentration measured. The

solution, containing 5-10 times background concentrations

of NH4

+1 and PO4-3, was added at a uniform rate until a plateau in tracer concentration was detected at the most downstream station (fig. 1). Once plateau concentration was

achieved, triplicate samples were collected from stations

located every 10 m along the 100 m stream reach. Samples

were refrigerated until analyzed for NH4

+1 and PO4-3 (APHA

1998).

The rise, plateau and decline of solute added to the stream

was monitored by recording Cl-

 concentration and specific

conductivity every 30 seconds for the duration of the nutrient addition. Data logging continued until the specific conductivity returned to pre-release levels. Transient storage

(As), the temporary storage of solutes in water that is moving

more slowly than the main stream flow, was estimated as

the difference between plots of the actual and predicted rise

and fall of the tracer solution and adjusted for total streambed area (Stream Solute Workshop 1990).

Measured concentrations of nutrients and tracer were corrected for background levels and adjusted for hydrologic

dilution (based on reduction in chloride concentration).

Downstream concentrations were divided by the most upstream concentration, and the log of these concentrations

regressed against distance (Webster and others 1991). The

slope of the regression yields an uptake rate per unit stream

length and the inverse of this is uptake length (Newbold

and others 1981).

Stream metabolism—Total stream metabolism was measured in situ using continuously recording dissolved oxygen

meters deployed at a single, downstream point in each

study reach (Owens 1974; Young and Huryn 1996, 1999).

The meters recorded dissolved oxygen (DO), percent DO

saturation, and temperature every 15 minutes for 24 hours.

Reaeration was determined by regressing the rate of change

in DO concentration against the oxygen deficit (saturation

DO concentration less actual DO concentration) (Owens

1974). Daily primary productivity and respiration were estimated by plotting the reaeration adjusted DO concentrations

against time. Mean night-time respiration was extrapolated

to estimate day-time respiration. Primary productivity is the

area above the respiration line, and respiration is the area

below the line (Bott 1996).

RESULTS

Physico-Chemical Characteristics

The two redwood forest study streams were similar in many

of their physical characteristics, with the exception of

wetted width, riffle to pool ratio, dominant substrate, and

the amount of sand in the channel. Cedar Creek had lower

background PO4

-3 and NH4+1(14.6 ± 0.030 and 29.5 ± 2.4μg/

L) than Peacock Creek (18.9 ± 0.106 and 63.0 ± 8.5μg/L)

(table 1).

Figure 1—Actual (solid line) and predicted (dotted line) tracer

concentration for solute releases in Cedar Creek (top) and

Peacock Creek (bottom). The area differences between the two

curves is an estimate of transient storage.

Nutrient Uptake/Spiraling

Three measures related to nutrient uptake and spiraling

were considered in this preliminary study: transient storage,

nutrient uptake rate, and nutrient uptake length. Transient

storage in Cedar Creek (old growth) was 25 m2 compared

to 42 m2 Peacock Creek (clear-cut) (table 2, fig. 1).

Phosphate uptake rate in Cedar Creek was 0.0066 μg/m

compared to 0.0010 mg/m in Peacock Creek. These uptake

rates translate into uptake lengths of 212 m and 1020 m,

respectively (table 2, fig. 2).

Ammonium uptake rates in Cedar and Peacock Creeks

were 0.0167 mg/m and 0.0138 mg/m, resulting in uptake

lengths of 111 m and 109 m, respectively (table 2, fig. 3).

Community Metabolism

Four measures of community metabolism were measured

in each of the study streams: gross primary productivity,

respiration, P/R, and NDM. Gross primary productivity was

450 mg O2/d in Cedar Creek (old growth) and 600 mg O2/d

in Peacock Creek. Respiration in these two streams was

690 mg O2/d and 528 mg O2/d. Net daily metabolism in

Cedar Creek was -240 mg O2/d compared to 72 mg O2/d

for Peacock Creek. The ratio of primary productivity to

respiration (P:R) was 0.65 and 1.13 for Cedar and Peacock

Creeks (table 2, fig. 4).
217

Table 1—Physico-chemical descriptions of Cedar Creek (old growth)

and Peacock Creek (clearcut)

Cedar Creek Peacock Creek

Mean (± SE) channel width (m) 2.9 (0.3) 2.9 (0.2)

Mean (± SE) wetted width (m) 2.1 (0.2) 2.9 (0.3)

Streambed area (m2) 286 291

Wetted width:channel width 0.73 1.00

Mean (± SE) depth (cm) 16 (3) 14 (2)

Maximum depth (cm) 67 46

Mean (± SE) canopy cover (%) 95 (1) 96 (1)

Mean (± SE) channel slope (o

) 1.9 (0.4) 1.4 (0.4)

Riffle:pool 2.3 1.5

Dominant substrate Cobble Coarse

gravel

Sand and fine sediments (%) 0 8

Mean temperature (oC) 13.2 12.9

Temperature range (oC) 12.6–13.5 12.8–13.1

Weather conditions Foggy Foggy

24-h antecedent precipitation (mm) 0 0

Background NH4

+1 (± SE) (μg/L) 29.5 ± 2.4 63.0 ± 8.5

Background PO4

-3 (± SE) (μg/L) 14.6 ± 0.03 18.9 ± 0.11

Harvest history Never logged 2–3 clearcuts

Fire history Unknown Common

Watershed geology Franciscan Franciscan

ultramafic ultramafic

Table 2—Measurements related to nutrient spiraling (transient storage,

uptake rates, uptake lengths) and community metabolism (net primary

productivity, community respiration) in Cedar Creek (old growth) and

Peacock Creek (clearcut)

Cedar Creek Peacock Creek

Transient storage (m2

) 25 42

NH4

+1 uptake rate (mg m-1) 0.0167 0.0138

PO4

-3 uptake rate (mg m-1) 0.0066 0.0010

NH4

+1 uptake length (m) 111 109

PO4

-3 uptake length (m) 212 1020

Gross primary productivity (P)(mg O2

 d-1) 450 600

Community respiration (R) (mg O2

 d-1) 690 528

P:R 0.65 1.13

Net daily metabolism (NDM) (mg O2 d-1) -240 72
218

DISCUSSION

Logging activities, such as trail and road building, skidding,

and slash removal, result in increased sedimentation in

streams draining those watersheds (Lieberman and Hoover

1948, Golladay and others 1987, Webster and others 1992).

Sediment influx to streams following logging is initially high,

decreases slightly as the watershed begins revegetation,

rises again during the transition from pioneer to later successional vegetation, then declines as the forest matures

(Vitousek and Reiners 1975, Webster and others 1992).

Subtle differences in physical characteristics of Cedar and

Peacock Creeks are probably a result of timber harvesting.

Three measures, the ratio of wetted width to channel width,

dominant in-stream substrate, and percent of the streambed

as sand and fine material, were used as indicators of stream

sedimentation. Wetted width:channel width in the clear-cut

stream (Peacock Creek) is 1.00 indicating a lack of channel

braiding and complexity. Substrate in Peacock Creek is

dominated by smaller material (coarse gravel, 16-64 mm

diameter) than that dominating in Cedar Creek (cobble, 64-

250 mm diameter). Likewise, Peacock Creek had more sand

and fine sediments in the channel than did Cedar Creek.

Increased nutrient export from logged watersheds has been

demonstrated in several studies (Bormann and others 1974,

Figure 2—Regression of PO4 concentration against downstream

distance for Cedar Creek (top) and Peacock Creek (bottom). The

slope of the regression line is the PO4

 uptake rate.

Figure 3—Regression of NH4 concentration against downstream

distance for Cedar Creek (top) and Peacock Creek (bottom). The

slope of the regression line is the NH4 uptake rate.

Vitousek and Reiners 1975, Campbell and Doeg 1989,

Webster and others 1992). Peacock Creek was exporting

more nutrients than Cedar Creek, especially PO4

-3.

The physico-chemical similarities between these two streams

suggests that differences in nutrient uptake and community

metabolism are likely due to differences in harvest histories.

Timber harvesting has been shown to affect nutrient processing in streams draining logged watersheds. Webster

and others (1991, 1992) reported decreased PO4

-3 retention

in logged streams, resulting in longer uptake lengths, and

attributed this to biotic and abiotic changes in the stream.

We found no differences between our two study streams

in transient storage or NH4

+1 uptake, but PO4-3 uptake in

Peacock Creek was much lower, resulting in much longer

uptake lengths.

Community metabolism in small streams draining forested

watersheds tends to be dominated by processing of organic

carbon derived from the watershed rather than generated in

situ (Webster and others 1995), resulting in a P:R < 1 and a

NDM < 0. As the riparian canopy opens as a result of logging, primary productivity increases (Webster and others

1983) and P:R approaches 1. Even with no difference in

riparian canopy between Cedar and Peacock Creeks, net
219

primary productivity was higher in Peacock Creek. This may

be related to the higher nutrient levels in this stream. Community respiration, however, was lower in Peacock Creek,

suggesting that the microbial community may have been

carbon limited. Carbon limitation in streams draining logged

watersheds is related to decreases in particulate organic

carbon inputs, the depletion of organic carbon storage, and

decreased dissolved organic carbon influx (Meyer and Tate

1983, Golladay and others 1989). Our results are consistent with the suggestion that disturbed watersheds are less

efficient at processing organic carbon, as indicated by the

lower respiration in Peacock Creek, which results in a positive NDM and a P:R > 1.

The preliminary results of our study of nutrient spiraling and

community metabolism in paired old-growth and clear-cut

watersheds demonstrates the potential use of these measures to assess the impacts of watershed disturbances on

stream ecosystem function. While the patterns are clear, we

must temper our interpretations of the data with the knowledge that our data at present are limited in number, unreplicated, and in every sense preliminary. Over the three-year

term of this study, we will achieve the experimental replication and refinement of measurements of hyporheic storage

and release of nutrients, stream physical characteristics,

stream reaeration, and light regime, to achieve statistically

robust inter-regional comparisons of the impacts of logging

on stream ecosystem function.

ACKNOWLEDGMENTS

The authors wish to thank B. Harvey, R. Nakamoto, and H.

Hendrixson for collecting field data used in this preliminary

study; J. O’Dell, C. Moench and P. Grimmett for chemical

analyses; and comments from B. Daniel, S. Johnson, M.

Warren, and J. Webster. B. Harvey, F. Swanson, J. Vose and

M. Warren are our USDA Forest Service project coordinators.

LITERATURE CITED

APHA. 1989. Standard methods for the analysis of water and

wastewater. 17th edition. American Public Health Association,

Washington, D.C.

Bormann, F.H.; Likens, G.E.; Siccama, T.G. [and others]. 1974.

The export of nutrients and recovery to stable conditions following deforestation at Hubbard Brook. Ecological Monographs 44:

255-277.

Bott, T.L. 1996. Primary productivity and community respiration. In:

Hauer, F.R.; Lamberti, G.A., eds. Methods in Stream Ecology.

Academic Press, New York.

Bott, T.L.; Bock, J.T.; Dunn, C.S. [and others]. 1985. Benthic

metabolism in four temperate stream systems: an interbiome

comparison and evaluation of the river continuum concept.

Hydrobiologia 123: 3-45.

Campbell, I.C.; Doeg, T.J. 1989. Impact of timber harvesting and

production on streams: a review. Australian Journal of Marine

and Freshwater Research 40: 519-539.

Cummins, K.W. 1980. The multiple linkages of forests to streams.

In: Waring, R.H., ed. Forests: fresh perspectives from ecosystem

analysis, Proceedings of the 40th Annual Biology Colloqium, 27-

28 April 1979. Corvallis. Oregon State University Press.

Cummins, K.W. 1988. The study of stream ecosystems: a functional view. pp. 247-262 In: Pomeroy, L.R.; Alberts, J.J., eds.

Concepts of ecosystem ecology. Springer-Verlag, New York.

Elwood, J.W.; Newbold, J.D.; O’Neill, R.V. [and others]. 1981.

The role of microbes associated with organic and inorganic

substrates in phosphorus spiralling in a woodland stream.

Verhandlungen der Internatalen Vereinengung Limnologie 21:

850-856.

Franklin, J. 1988. Structural and functional diversity in temperate

forests, In: Wilson, E.O., ed. Biodiversity. National Academy

Press, Washington, D.C.

Golladay, S.W.; Webster, J.R.; Benfield, E.F. 1987. Changes in

stream morphology and storm transport of organic matter

following watershed disturbance. Journal of the North American

Benthological Society 6: 1-11.

Golladay, S.W.; Webster, J.R.; Benfield, E.F. 1989. Changes in

stream benthic organic matter following watershed disturbance.

Holarctic Ecology 12: 96-105.

Gregory, S.V. 1978. Phosphorus dynamics on organic and inorganic substrates in streams. Verhandlungen der Internationalen

Vereinengung Limnologie 20: 1340-1346.

Hill, B.H.; Lazorchak, J.M.; McCormick, F.H.; Willingham, W.T.

1997. The effects of elevated metals on benthic community

metabolism in a Rocky Mountain stream. Environmental

Pollution 95: 183-190.

Hunsaker, C.T.; Graham, R.L.; Suter, G.W. [and others]. 1990.

Assessing ecological risk on a regional scale. Environmental

Management 14: 325-332.

Figure 4—Diel dissolved oxygen curves for Cedar Creek (top) and

Peacock Creek (bottom). Net primary productivity is the area under

the curve and above the reference line. Community respiration is

the area under the curve and below the reference line; DO =

dissolved oxygen.
220

Lieberman, J.A.; Hoover, M.D. 1948. The effects of uncontrolled

logging on stream turbidity. Water and Sewage Works 95: 255-

258.

Meyer, J.L.; Tate, C.M. 1983. The effects of watershed disturbance

on dissolved organic carbon dynamics of a stream. Ecology 64:

33-44.

Mulholland, P.J.; Elwood, J.W.; Newbold, J.D.; Ferrer, L.A. 1985a.

Effect of a leaf-shredding invertebrate on organic matter dynamics and phosphorus spiralling in heterotrophic laboratory

streams. Oecologia (Berl.) 66: 199-206.

Mulholland, P.J.; Newbold, J.D.; Elwood, J.W. [and others]. 1985b.

Phosphorus spiralling in a woodland stream: Seasonal variations. Ecology 66: 1012 -1023.

Mulholland, P.J.; Steinman, A.D.; Elwood, J.W. 1990. Measurement of phosphorus uptake length in streams: comparison of

radiotracer and stable PO4 releases. Canadian Journal of

Fisheries and Aquatic Sciences 47: 2351-2357.

Newbold, J.D.; Elwood, J.W.; O’Neill, R.V.; Sheldon, A.L. 1983.

Phosphorus dynamics in a woodland stream ecosystem: a

study of nutrient spiralling. Ecology 64: 1249-1265.

Newbold, J.D.; Elwood, J.W.; O’Neill, R.V.; Van Winkle, W. 1981.

Measuring nutrient spiralling in streams. Canadian Journal of

Fisheries and Aquatic Sciences 38: 860-863.

Newbold, J.D.; O’Neill, R.V.; Elwood, J.W.; Van Winkle, W. 1982.

Nutrient spiralling in streams: implications for nutrient limitation

and invertebrate activity. American Naturalist 120: 628-652.

Owens, M. 1974. Measurement on non-isolated natural communities in running waters. In: Vollenweider, R.A., ed. A manual on

methods for measuring primary production in aquatic environments. Blackwell Scientific, Oxford, United Kingdom.

Stream Solute Workshop. 1990. Concepts and methods for

assessing solute dynamics in stream ecosystems. Journal of

the North American Benthological Society 9: 95-119.

Swank, W.T.; Crossley, D.A, eds. 1988. Forest hydrology and

ecology at Coweeta. Springer-Verlag, New York.

van der Leeden, F.; Troise, F.L.; Todd, D.K. 1990. The water

encyclopedia. Lewis Publishers, Chelsea, Michigan.

Vitousek, P.M.; Reiners, W.A. 1975. Ecosystem succession and

nutrient retention: a hypothesis. BioScience 25: 376-381.

Webster, J.R.; D’Angelo, D.J.; Peters. G.T. 1991. Nitrate and phosphate uptake in streams at Coweeta Hydrologic Laboratory.

Verhandlungen der Internationalen Vereinengen Limnologie 24:

1681-1686.

Webster, J.R.; Golladay, S.W.; Benfield, E.F. [and others]. 1992.

Catchment disturbance and stream response: an overview of

stream research at Coweeta Hydrologic Laboratory. In: Boon,

P.J.; Calow, P; Petts, G.E., eds. River conservation and

management, John Wiley and Sons Ltd., New York.

Webster, J.R.; Gurtz, M.E.; Hains, J.J. [and others]. 1983. Stability

of stream ecosystems. In: Barnes, J.R.; Minshall, G.W., eds.

Stream Ecology. Plenum Press, New York.

Webster, J.R.; Patten, B.C. 1979. Effects of watershed perturbation on stream potassium and calcium dynamics. Ecological

Monographs 49: 51-72.

Webster, J.R.; Swank, W.T. 1985. Within-stream factors affecting

nutrient transport from forested and logged watersheds. In:

Blackmon, B.G., ed. Forestry and water quality: a mid-south

symposium, 8-9 May 1985, Little Rock. University of Arkansas

Press.

Webster, J.R.; Wallace, J.B.; Benfield, E.F. 1995. Organic processes

in streams of the Eastern United States. In: Cushing, C.E.;

Cummins, K.W.; Minshall, G.W., eds. River and Stream Ecosystems. Elsevier Science B.V., Amsterdam, The Netherlands.

World Resources Institute. 1997. Last frontier forests. World

Resources Institute, Washington, DC.

Young, R.G.; Huryn, A.D. 1996. Interannual variation in discharge

controls ecosystem metabolism along a grassland river continuum. Canadian Journal of Fisheries and Aquatic Sciences

53: 2199-2211.

Young, R.G.; Huryn, A.D. 1999. Effects of land use on stream

metabolism and organic matter turnover. Ecological Applications 9: 1359-1376.
221

INTRODUCTION

Small streams draining the Ouachita National Forest in

Arkansas and Oklahoma are inhabited by a large number of

native fishes with 117 species known from the Ouachita

Highland (Matthews and Robison 1998). These fishes, dominated numerically by minnows (Cyprinidae), suckers (Catostomidae), catfishes (Ictaluridae), topminnows (Fundulidae),

sunfishes (Centrarchidae), and darters (Percidae), can be

very abundant locally. Thousands of individuals frequently

occupy a single pool-riffle complex in these small upland

streams. Matthews and Robison (1998) found a modal

number of 13 fish species per collection across nearly 200

sites in the Ouachita Mountains, and collection of up to

20 species per site was not unusual. Until the 1980s, little

emphasis was placed on the ecological roles of stream

fishes, but it is now well known that feeding, nutrient release,

and mechanical activities of fishes can have strong and

diverse effects in stream ecosystems (Matthews 1998).

Beginning with work by Power and Matthews (1983), Power

and others (1985), and Grimm (1988), there is clear demonstration that some fish species have strong effects on structural and functional properties of North American stream

ecosystems. Expanding the initial work of Power and others

(1985), several papers (e.g., Gelwick and Matthews 1992,

Gelwick and others 1997, Vaughn and others 1993) demonstrated that a single highly abundant, algivorous species

[central stoneroller, Campostoma anomalum (Rafinesque)]

had direct or indirect effects on at least 20 important structural or functional properties of small stream ecosystems

(Matthews 1998). The central stoneroller can cause differences in algal community composition or productivity, uptake and dynamics of organic matter, invertebrate community composition or life history, movement of materials in

streams, carbon-nitrogen ratios, and standing crops of

bacteria. The species also plays a major role in predatordriven trophic cascades and can alter the manner in which

stream ecosystems function overall. The central stoneroller

is a highly abundant species in many streams of the Ouachita

National Forest where it occurs in schools of up to several

thousand and grazes algae vigorously and actively from

rock substrates of flowing pools and riffles.

The demonstration that a single common species can change

the dynamics of a stream ecosystem, while interesting, falls

short of providing managers with information on the overall

impacts of fish communities on stream ecosystems. The

best assumption from a management view is probably that

all fish species in a small stream have the potential to

change or influence important dynamics in that stream, such

as primary productivity, nutrient relationships, breakdown of

particulate organic matter, linking individual pools with each

other, or changing algal or invertebrate communities. To

demonstrate this concept, (Gido and Matthews, in press),

recently tested the hypothesis that a water-column minnow

species (red shiner, Cyprinella lutrensis Baird and Girard)

could have direct or indirect effects on benthic primary

productivity. Over one fall and winter, they showed a direct

positive relationship between the density of red shiners and

primary productivity on artificial substrates in large outdoor

experimental streams. However, the red shiners did not

cause a decrease in density of benthic stream invertebrates

(many of which graze algae). Presumably, the enhancement

CONTEXT-SPECIFIC TROPHIC AND FUNCTIONAL ECOLOGY OF FISHES OF

SMALL STREAM ECOSYSTEMS IN THE OUACHITA NATIONAL FOREST

William J. Matthews, A. Maria Miller-Lemke, Melvin L. Warren, Jr., Donna Cobb,

Jeffery G. Stewart, Betty Crump, and Frances P. Gelwick1

1 Professor, University of Oklahoma Biological Station, Kingston, OK 73439; Ph.D. Candidate, University of Alabama, Petersburg, IL 62675;

Research Biologist, Center for Bottomland Hardwoods Research, USDA Forest Service, Southern Research Station, Oxford, MS 38655;

Assistant to the Director and Database Manager, University of Oklahoma Biological Station, Kingston, OK 73439; Fish Collection Manager,

Department of Zoology, Southern Illinois University, Carbondale, IL 62901; District Biologist, Caddo Ranger District, USDA Forest Service,

Ouachita National Forest, Glenwood, AR 71943; and Assistant Professor, Wildlife and Fisheries Sciences Department, Texas A & M

University, College Station, TX 77843, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Fish play diverse and important roles in stream ecosystems, but details about ecosystem effects are poorly

known for many freshwater fish species. A requisite first step to understanding functional roles of individual species is

information on their trophic ecology in the context of particular environmental settings. Stomach contents were analyzed

for approximately 1,600 individuals of 21 common fish species (mostly insectivores or omnivores) from four streams in the

Ouachita National Forest, Arkansas. Two streams (South Fork Alum Creek and Crooked Creek) were in watersheds with

little recent timber harvest and two (Bread Creek and Blaylock Creek) were in watersheds harvested under Forest Service

standards and guides. Individual fish species were distinctive in kinds of foods eaten, and were not substitutable for each

other in ecosystems. Benthic and water column species differed in diet, but interspecific differences in food consumption

were not closely linked to taxonomic similarity. Composition of fish diets were overall 70 to 85 percent similar between

harvested and non-harvested watersheds, with dipterans and terrestrial insects the major foods used by all fish assemblages; most individual species had similar diets in different creeks or longitudinally within creeks. Foods eaten per species,

multiplied by estimates of fish density from snorkeling and seining surveys, resulted in an order of magnitude prediction of

consumption of about 13,000 invertebrate food items per day per 100 m of stream by the local fish assemblage.
222

of benthic primary productivity resulted from an increased

rate of nutrient transfer from surface or water column invertebrates, a primary food of red shiners, to nutrient limited

periphytic algae. Regardless of the mechanism, (Gido and

Matthews, in press) provided a clear demonstration that

fishes other than strictly benthic species can have major

impacts on stream ecosystem processes, including processes in the benthic (stream bed) compartment.

Regionally, good information about ecosystem effects is

available for only one species (Campostoma anomalum)

that is common in Ouachita National Forest streams, out of

the more than 200 fish species recorded from Oklahoma

and Arkansas. The long-range goal of our research program is to determine effects of a wide range of fish species

on ecosystems of small streams, adopting the functional

groups approach outlined by Matthews (1998).

The requisite first step to any assessment of the functional

effects of a fish species on stream ecosystems is acquiring

detailed, context specific information on its food use. That

is, what (and how) does a fish eat and what is the specificity or plasticity of its feeding? Does a given species use

largely the same food items regardless of its location in a

stream, the stream system in which it occurs, its potential

competitors for food, or availability of foods? Additionally,

there is great need to understand if a particular fish species

uses foods most like those of closely related species, and

the extent to which species are redundant, or “substitutable”

for one another, in an ecosystem. If a fish species feeds

consistently regardless of environmental context, broad a

priori predictions of its effect in an ecosystem should be

possible. If feeding (and functional role) of a fish is more

context-specific, focus would shift to more local, site-specific studies of fish communities in order to understand ecosystem dynamics. Managers should bear in mind that the

fish community and the ecosystem are intricately linked in

two “directions.” It is becoming clear that fish have effects in

ecosystems, but it is equally clear that the condition of the

ecosystem plays a major role in the kinds and abundances

of fish the system can support. Thus, the fish-ecosystem

relationship can be considered a feedback loop in which the

ecosystem affects the fish and the fish affect the ecosystem.

Only by understanding effects of anthropogenic activities on

the overall ecosystem, and this link to fishes, can an appreciation be gained of the interlinked roles of man, fish, invertebrates, and stream quality that actually comprises the

stream ecosystem at the watershed level.

This study describes a first approximation of the use of

foods by a substantial number of stream fish species of the

Ouachita National Forest. Food use is examined in the

context of stream, season, timber harvest regime, and, for

selected species, longitudinal position in the stream. Specifically, general food use is summarized for 21 species in each

of two pairs of streams in the upper Little Missouri River

drainage and the upper Saline River drainage. Further, similarities and differences in food use are tested among species,

two major feeding groups, and taxonomic relatives. Assemblages are examined from the perspective of food use

differences among streams and timber harvest regimes. For

selected species, intraspecific differences in food use are

examined among or within streams and between harvest

regimes. Finally, an order of magnitude estimate is calculated to ascertain the potential effects of fish assemblages

on the stream invertebrate community.

METHODS AND MATERIALS

Study Area

Stream fish communities were sampled by seining at 24 sites

on four dates (November 1989, May 1990, October 1990,

May 1991). Six sites each were located in Blaylock and

Crooked Creeks (Little Missouri River drainage, northwest

of Glenwood, AR), and six each in South Fork Alum and

Bread Creeks (Saline River drainage, north of Hot Springs,

AR). All sites were on public lands of the Ouachita National

Forest (Matthews 1993). Within each of the two river drainages, one creek was considered “harvested” and one, “nonharvested” on the basis of recency of timber harvest within

the watershed (Clingenpeel 1994) and unpublished basin

area stream survey data (Alan Clingenpeel, Forest Hydrologist, Ouachita National Forest, Box 1270, Hot Springs, AR).

At the time of field sampling, about 12 percent of the stands

in Blaylock Creek had been harvested in the previous 20 yr,

whereas essentially no stands had been harvested in

Crooked Creek in that time period. Similarly, about 21 percent of the stands in the Bread Creek watershed had been

harvested in the last 20 yr, and no stands had been harvested in South Fork Alum Creek watershed during that

period.

At each site, fish were sampled by seining with small seines

in all identifiable habitats (e.g., pools, riffles, runs) for approximately 1 to 1.5 hr over about a 200-m stream reach. Each

site included multiple pools and riffles, although some of

the extreme headwaters sites (particularly in Bread Creek)

were sometimes reduced to a series of disconnected pools.

All fish were preserved in the field in 10 percent formalin,

transported to the University of Oklahoma Biological Station,

and later were identified, enumerated, and transferred to 50

percent isopropanol for permanent storage. A summary of

the fish collected is provided in Matthews (1993).

Subsequent to fish sampling, the Ouachita Mountain Ecosystem Management Research Project (Southern Research

Station) provided funding for assessment of stomach contents of the fishes collected at the 12 sites. Matthews, Miller,

and Stewart revisited all study streams and conducted sampling to make a reference collection of all possible benthic

invertebrates from the study streams. Invertebrates were

sampled qualitatively (i.e., dipnetting, kicknetting, and picking invertebrates from stones). These samples were identified and enumerated primarily by Miller, who holds a Masters

of Science degree on distribution and ecology of benthic

stream invertebrates. She was assisted in identifications by

Dr. David Bass, Central Oklahoma University, an authority

on benthic stream invertebrates in the region. Thus, fish

stomach content was assessed against the background of a

detailed reference collection of invertebrates from the study

streams. This allowed creation of a templet for recording

contents of individual stomachs that included 65 invertebrate taxa arranged hierarchically so that results could be

viewed at the lowest taxonomic categories or aggregated

by family, order, etc.
223

Miller, Stewart, and other technicians (directed by Miller or

Matthews) conducted examination of stomach contents of

over 2,000 individual fishes. Individual fishes were chosen

from across the original field collections and in approximate

proportion to the abundance of the fish species in the field

(table 1). Each fish was weighed and measured, and eviscerated. The stomach was weighed, opened and thoroughly

cleaned of contents, and then re-weighed to obtain weight

of total foods. Stomach contents were identified using binocular dissecting microscopes. Eviscerated fish were returned

to archival storage at the Biological Station. Data on counts

of food items were coded by hand by Miller and others, and

the data sheets sent to Warren who directed entry of the

data into a relational database. This database was ultimately

returned to Cobb who carried out all data manipulations in

a spreadsheet format. In the present study, all data were

aggregated at the level of order. All food items not in the

established taxonomic categories, plus any terrestrial invertebrates (i.e., winged adults presumably taken from the

surface) were classified in the category “other.” Approximately two thirds of all items categorized as “other” were

actually adult terrestrial invertebrates.

Our analyses are based on 1,589 fish stomachs from field

samples in 1989-91. Fish stomachs were distributed among

study streams as follows: Saline Drainage, South Fork Alum

(non-harvested), 425 stomachs and Bread Creek (harvested), 304 stomachs; Little Missouri Drainage, Crooked

Creek (non-harvested), 331 stomachs and Blaylock Creek

(harvested), 529 stomachs. Estimates of abundances of the

15 most common species, excluding the central stoneroller,

were derived from seining and from supplementary snorkeling observations made at all sites by Gelwick in spring 1991.

These abundance estimates were used to extrapolate estimates of effects. Snorkeling observations also were used to

derive classifications of species as “benthic” or “surface and

water column” species. The central stoneroller, a common

species in study streams, was excluded from our analyses

because it depends almost exclusively on an algal diet.

Data analyses included calculations of a standard diversity

measure (Shannon-Weiner) and a percent similarity index

to compare and contrast foods eaten by assemblages and

species among streams and between harvest regimes.

Determination of overall similarities and differences among

species was examined by multivariate clustering and principal components analyses using the NT-SYS computer

programs. Our study was aimed at first approximations of

food use by fishes in the study streams and required an

exploratory approach to data analysis. As such, data analysis is summarized with results so that the method and rationale can be addressed in the context of principal findings.

RESULTS

Stomach contents were represented by 10 to 270 individuals

each of 21 common fish species (table 1). The majority of

invertebrate taxa recovered from fish stomachs were dipteran (midge) larvae and pupae, which occur on stream

substrates, or adult insects that primarily are available to

fishes at the water’s surface (> 6,000 items represented by

dipterans) (fig. 1). Other commonly eaten food items included

(in order of abundance in stomachs): Ephemeroptera (mayflies); Trichoptera (caddisflies); Homoptera (true bugs);

Arachnoidea (spiders, ticks, and mites); Crustacea (mostly

crayfish and excluding microcrustaceans); Coleoptera

(beetles); Hymenoptera (ants, wasps, and bees); Lepidoptera (moths and butterflies); microcrustaceans (mostly cladocerans and copeopods); Annelida (segmented worms);

Plecoptera (stoneflies); and Nematoda (nematodes). Eight

other invertebrate categories occurred frequently enough to

be included in the analyses (fig. 1).

Table 1—Numbers of stomachs examined per fish

per species and number of individuals of that

species surveyed at 12 sites on four creeks in the

Ouachita National Forest (1989-1991)a

Species Stomachs Number

observed

Etheostoma radiosum 270 1982

Orangebelly darter

Lythrurus umbratilis 226 1,764

Redfin shiner

Semotilus atromaculatus 185 2,798

Creek chub

Luxilus chrysocephalus 137 1,183

Striped shiner

Notropis boops 95 704

Bigeye shiner

Fundulus olivaceus 88 229

Blackspotted topminnow

Etheostoma blennioides 79 170

Greenside darter

Etheostoma collettei 72 212

Creole darter

Etheostoma whipplei 67 209

Redfin darter

Notropis ortenburgeri 64 321

Kiamichi shiner

Lepomis megalotis 55 425

Longear sunfish

Labidesthes sicculus 54 57

Brook silverside

Fundulus catenatus 31 41

Northern studfish

Noturus lachneri 31 37

Ouachita madtom

Lepomis cyanellus 25 52

Green sunfish

Esox americanus 24 30

Grass pickerel

Aphredoderus sayanus 19 19

Pirate perch

Hypentelium nigricans 14 40

Northern hog sucker

Erimyzon oblongus 12 176

Creek chubsucker

Micropterus dolomieui 11 44

Smallmouth bass

Ameiurus natalis 10 13

Yellow bullhead

a All seining and snorkeling observations pooled.
224

A principal components analysis (PCA) was carried out for

the 21 most common fish species to determine similarities

and differences among species in foods eaten across 18

taxonomic categories of food items (with finer invertebrate

categories pooled within orders). In PCA, each food item

may be visualized as an individual axis across species that

is aggregated with related food items into a lesser number

of new or principal component axes (PC Axes), each of

which represents original items that were most highly correlated with the new axis (Gauch 1982). The PCA provides a

view of similarities and differences in diet by fishes, and in

two dimensions, arrays fish species in multivariate space

along gradients of foods used by those fishes. The first

three PC axes accounted for 49.2 percent of the total variance in food items among the 21 fish species (19.1, 15.6,

and 14.5 percent, for PC Axes I, II, and III, respectively).

Accounting for nearly half the total variance in food use is a

relatively high result for an ecological analysis. To compare

fish species with respect to their overall similarities and

differences on these “food” axes, two-dimensional scatterplots of fish species scores on PC Axis I versus PC Axis II

and PC Axis I versus PC Axis III were used to interpret food

use by fishes (figs. 2A and 2B).

PC Axis I separated (fig. 2A, from right to left) fish species

with a tendency to eat Homoptera, Annelida, Hymenoptera,

or “other” items (two thirds of which were terrestrial) from

species that fed less on these categories. PC Axis II (fig. 2A)

separated species that fed more on Ephemeroptera, Trichoptera, Lepidoptera, and Nematoda from species that ate less

from these groups and more crustaceans. Interpretation of

the distribution of fish species along the food axes is facilitated if benthic (bottom living) species are delineated from

surface and water column species (fig. 2C). Surface and

water column species are almost completely separable in

overall diet from benthic species (fig. 2C). Surface and watercolumn species included: Luxilus chrysocephalus Rafinesque;

Lythrurus umbratilis (Girard); Fundulus olivaceus (Storer);

Notropis boops Gilbert; Semotilus atromaculatus (Mitchill);

Labidesthes sicculus (Cope); Fundulus catenatus (Storer);

and various sunfish and bass species. Benthic species

included: darters such as Etheostoma blennioides Rafinesque, Etheostoma radiosum (Hubbs and Black), Etheostoma whipplei (Girard), and Etheostoma collettei Birdsong

and Knapp; catfishes like Noturus lachneri Taylor and

Amieurus natalis (Lesuer); and the sucker, Hypentelium

nigricans (Lesueur). The only exception to this separation

in diet is the sucker Erimyzon oblongus (Mitchill), which is a

mostly benthic species but was grouped on both food use

axes with the surface and water column species. Overall,

the PCA axes suggest that the benthic species eat more

aquatic insect larvae (e.g., mayflies, caddisflies, or moth

larvae), whereas the surface and water column species

feed more on items of terrestrial origin. Although not surprising, this finding underscores the high diversity in food

use by a local fish assemblage. Importantly, the patterns

emphasize that food use, which is indicative of potential

role in the ecosystem, relates in a predictable fashion to the

microhabitat use by each species.

PC Axis III accounted for almost as much variance as did

PC Axis II. Thus, it was of interest to examine distribution of

fish species on those two axes (figs. 2B and 2D). The two

axes did, in fact, provide greater overall separation of species

in multivariate diet space with less evidence of outliers.

Importantly, use of some of the most common food items,

Figure 1—Total number of food items by taxonomic category found in stomachs of 1,589 fishes

examined in four streams of the Ouachita National Forest.
225

Figure 2—Principal components analysis ordinating fish species on the basis of food use in four streams of the Ouachita

National Forest (A). PC Axis I verus PC Axis II; (B). PC Axis I versus PC Axis III; (C). PC Axis I verus PC Axis II, but with

benthic and water column species denoted by polygons; (D). PC Axis I versus PC Axis III, but with benthic and water

column species denoted by polygons.
226

including dipterans, was highly associated with PC Axis III.

PC Axis III separated species along a gradient of high to

low consumption of dipterans, odonates, plecopterans, or

coleopterans (upper to lower, respectively, fig. 2B). Again,

benthic species (e.g., Hypentelium nigricans, Amieurus

natalis, Etheostoma whipplei, Etheostoma collettei) fed

more on these items than did the surface and water column

species. Combination of PC axes II and III provided complete segregation of benthic from surface and water column

species on the basis of diet (fig. 2D). Again, Erimyzon

oblongus was an exception (fig. 2C). It grouped with the

surface and water column species in diet in spite of its

benthic microhabitat use. The PCA results indicate distinctive separation in diet of most species that coincides with

microhabitat use. From this, it is suggested that managers

may be able to make rough approximations of trophic roles

of fish in stream ecosystems by using the location of fish

species in the water column.

Principal component analysis also indicated some correspondence between general food consumption and taxonomic

association of species. From the delineation of the three

most abundant families (darters, Percidae; minnows,

Cyprinidae; sunfishes, Centrarchidae) on the PCA plots

(figs. 3A and 3B), it is clear that darters are strongly segregated in diets from the sunfish and minnows. Obviously,

this can relate to the differences in microhabitat use.

Darters live and feed almost exclusively on the stream

bottom predominantly in riffle habitats, and minnows and

sunfishes live and feed more in the water column of pools.

However, it is equally apparent that there is substantial

overlap between minnows and sunfishes in overall diet.

Their overlap in diet is logical in that these two families

include species that overlap strongly in occurrence in the

water column (on the basis of our snorkeling observations),

and it is obvious in the field that members of both families

will take food items (e.g., adult insects) from the surface of

a pool. It would be tempting to conclude that managers

could merely sample a stream, determine the family to

which a species belonged, or the degree of relatedness

between species, and assume some information about the

similarities and differences in roles of the species in the

ecosystem. This appears not to be the case.

Two triangular similarity matrices were created to more rigorously test the hypothesis that food use among fish species

is predictable from taxonomic relatedness. One matrix measured similarity on the basis of overall diet, and the other,

taxonomic distances among the species. Similarity in diets

for the 21 fish species was assessed by a pair-wise product

moment correlation matrix of foods eaten by food category.

The diet similarity matrix (data not shown) was summarized

by clustering of fish species (fig. 4). Taxonomic distances

among species were scored for all possible species pairs

using criteria from Douglas and Matthews (1992). Species

within the same genus were scored with a taxonomic distance of 1; species in the same family, as 2; species in same

order, as 3; and species from different orders, as 4. The

resulting taxonomic similarity matrix gave each species pair

a score of 1 to 4, depending on the relatedness of the species. If similarities (or differences) in diet among species

pairs are associated with taxonomic relatedness, then the

two matrices (diet and taxonomic similarity) should match

closely. To test the degree of association between the matrices, a Mantel test was performed (Douglas and Matthews

1992) in which one matrix was randomly rearranged 9,999

times, and the degree to which the real matrices match

each other was compared to the way the matrices would

Figure 3—Principal components analysis as in figure 2 but with most abundant families denoted by polygons.
227

match after being “reshuffled” at random. The Mantel test of

the food use and taxonomic similarity matrices was not

significant (P = 0.395) which signifies the two matrices are

not closely or significantly matched in structure. Although

Figure 4—Phenogram based on correlation of food use similarities

between 21 common species in four streams of the Ouachita

National Forest.

there are some general differences in food use by darters

versus minnows and sunfishes (explained earlier), there

was no statistically detectable correspondence between

diet and taxonomic relatedness across all species. Hence,

managers could not safely predict trophic similarities among

species on the basis of taxonomy alone. Each common

species in an ecosystem needs detailed study of diets or

their function in that respective system.

The degree of redundancy in a stream fish assemblage or

among species within a region is another question of interest. Redundancy implies that closely related species might

be so similar in their trophic or functional ecology that one

could be substituted for the other. For example, if two

closely related species are fully redundant, it would be

possible for one to be lost from a system, the other to be

introduced, and the system retain all of its original functional properties. In this study, the redundancy hypothesis

can be assessed by examining positions of closely related

fish species in multivariate food use space (figs. 2A and

2B). The PCA plots indicate some pairs of closely related

species are rather similar in diets relative to other species

in general (fig. 2B) including the minnows, Notropis boops

and Notropis ortenburgeri Hubbs; the sunfishes, Lepomis

megalotis (Rafinesque) and Lepomis cyanellus Rafinesque;

and the minnows, Luxilus chrysocephalus and Lythrurus

umbratilis. Conversely, other taxonomically similar species

pairs are broadly separated in multivariate diet space (fig. 2B)

including: the darters, Etheostoma radiosum and Etheostoma whipplei; the suckers, Hypentelium nigricans and

Erimyzon oblongus; the catfishes, Noturus lachneri and

Amieurus natalis; and the topminnows, Fundulus catenatus

and Fundulus olivaceus. Overall, there is no apparent

support for the postulate that closely related species pairs

Figure 5—Use of 13 major food categories by fish assemblages across four streams of the Ouachita

National Forest. Vertical (Y) axis represents total numbers of each item found an all examined fish

stomachs.
228

are highly similar in diets. They do not in general appear

redundant or “substitutable” for each other. For management, this implies intentional or accidental introductions of

species are not likely to allow one species to be lost and

another gained without potential for trenchant changes in

ecosystem function.

Because context-specific diet of fishes influences their potential ecosystem effects, the degree of similarity of trophic

effects for fish assemblages in different creeks is another

important management consideration. To address this, our

entire sample of fish stomach contents was summarized

separately in each creek across all four sampling periods

(fig. 5). In all four creeks, dipterans dominated in diets of

the 21 species (fig. 5), and the “other” category (mostly adult

insects) also was important in all creeks. Differences in importance of mayflies (Ephemeroptera) in fish diets appeared to

be related to river drainage. Far fewer mayflies were eaten

by fish in Alum and Bread Creeks (Saline River drainage)

than in Crooked and Blaylock Creeks (Little Missouri drainage). However, mayfly consumption may be related more to

typical environmental conditions in the streams than to

specific drainage. Alum and Bread Creeks have seasonally

intermittent flows (Taylor and others, in press), (Williams

and others, in press), whereas Crooked and Blaylock Creeks

always exhibited substantial flows during our visits. Persistent flow of the latter streams should favor more consistent

availability of mayflies, particularly in riffles (Brown and

Brussack 1990) which may dry up periodically in Alum and

Bread Creeks. This hypothesis is further supported by the

lower occurrence of Trichoptera (caddisflies; also linked to

flowing habitats) in diets of fishes in Alum and Bread

Creeks than Crooked and Blaylock Creeks (fig. 5). Besides

simple taxonomic composition, diversity of diets can reflect

breadth of potential impact of a fish assemblage in a stream.

However, Shannon-Weiner diversity indices of fish stomach

contents (pooled by creek) were not substantially different

among creeks, ranging from 1.42 in Alum Creek to 1.81 in

Blaylock Creek. Thus, some foods of fishes differed among

streams, likely in the context of the environmental setting

which influences food availability, but the dominant food

item (dipterans) was the same in all four streams.

To assess the degree to which timber harvest or drainage

influences overall similarity in fish diets, a Percent Similarity

Index (PSI) was calculated for all food items across stomachs

in each creek. Within a river drainage, total fish assemblages in harvested and non-harvested creeks showed high

similarity in diets. In the Saline drainage, similarity between

foods eaten in Alum (non-harvested) and Bread (harvested)

Creeks was 85.1 percent. Likewise, the PSI for Crooked

(non-harvested) and Blaylock (harvested) Creeks was 71.5

percent. Creeks with similar harvest history, but in different

river drainages, also showed high similarity values (harvested, Bread and Blaylock, PSI = 74.5 percent; non-harvested, Alum and Crooked, PSI = 77.0 percent). Thus, there

was no evidence that timber harvest histories as defined

here influenced similarities or differences in foods eaten by

fish assemblages among creeks.

Intraspecific difference in diet is the finest scale of resolution to examine context-specific effects of fishes on stream

ecosystems. For selected species, diet was examined to

determine if intraspecific differences could be related to

timber harvest regime or creeks. For each of the seven

most abundant species in our samples, similarity (PSI) was

calculated between stomach contents for all individuals in

harvested and non-harvested watersheds (within the river

drainage where the species was present and/or most abundant, since most species did not occur in all creeks or both

drainages). Intraspecific PSI in diet between harvested and

non-harvested watersheds was high; five of seven species

had PSI values of > 5 percent. Values ranged from a high

of 87.8 percent for Etheostoma radiosum to as low as 48.0

percent for Lepomis megalotis. PSI values for other species

were: Etheostoma whipplei, 84.7 percent; Etheostoma collettei, 81.9 percent; Semotilus atromaculatus, 78.5 percent;

Fundulus olivaceus, 76.2 percent; and Lythrurus umbratilis,

63.1 percent. Intraspecific diversity (Shannon-Weiner Index)

of diets for these species also was similar in harvested and

non-harvested watersheds. There is no compelling evidence

from our data set that timber harvest regimes, at the spatiotemporal scale evaluated here, had strong effects on foods

consumed by individual fish species. Similarity between

streams was highest for the three darter species (Etheostoma spp.), which feed primarily on benthic organisms in

riffles, and lowest for three species (Fundulus olivaceus,

Lythrurus umbratilis, and Lepomis megalotis) that may feed

substantially on surface items (e.g., adult terrestrial insects).

It could be speculated that disturbance from timber harvest

changes availability of terrestrial insects more than that of

riffle dwelling benthic invertebrates, but direct supporting

evidence or plausible mechanisms are lacking.

For a single species, patterns were sought in food use within a stream, longitudinally, seasonally, and in regard to harvest regime. The largest sample of stomachs was available

for Etheostoma radiosum, which is abundant throughout

Crooked and Blaylock Creeks. A PCA with raw data standardized within food item was conducted for this species where

each sample at a site (Fall 1989; May 1991, when most

specimens were available) was ordinated along gradients

of the eleven most used food items. PC Axes I and II

accounted for 25.7 and 18.0 percent of the variance,

respectively, but the results suggested few local patterns in

food use. Visual inspection of plots of PC Axis I versus PC

Axis II (not shown), revealed no difference in amount of

multivariate diet space occupied by E. radiosum in either

creek but did indicate a slightly greater diet breadth in May

than in October. No strong longitudinal patterns in diet were

detected. Likewise, diets in extreme headwaters (where few

or no other species are present) were not notably different

from those at larger, downstream sites.

Fish density and known foods were used to calculate a

crude estimate of the number of invertebrates eaten by a

local fish assemblage. The estimate is instructive in evaluating direct and indirect impacts fish food consumption may

have in these stream ecosystems. In May 1991, Gelwick

counted fish by snorkeling while seine sampling was conducted at the 24 field sites. The total counts by seining and

snorkeling for the 15 most common species (excluding

Campostoma) are provided by Matthews (1993). Potential

use of invertebrate foods by fish in a 100-m reach of the study

streams were estimated for each species as: (individuals/m)

x (detection factor) x (food items/stomach) x (feedings/d).
229

Individuals per meter is the total numbers of a species

detected by snorkel or seine (using greatest value) divided

by 4800 m of stream sampled (approximately 200 m per

site at all 24 sites). A detection factor of four was used to

account for undetected individuals. Detection of 25 percent

of individuals actually present at a sampling site is reasonable in our judgement, but it is emphasized that an increase

or decrease in this factor will drastically change the final

estimate. Food items per stomach were based on the average number of items found in stomachs for a species, and

feedings per day were estimated as six. Based on food

passage times of minnows (Unpublished data. On file with:

William Matthews, Professor, University of Oklahoma

Biological Station, HC 71 Box 205, Kingston, OK 73439),

the observed food items in each stomach were assumed to

represent foods ingested in the last two hours before the

fish were sampled and for purposes here, represents one

feeding. Our use of six feedings per day is derived from

studies that show most minnows and darters feed throughout daylight hours (Matthews and others 1982; Surat and

others 1982). The resulting product was summed across

species, and the sum expressed as invertebrate foods used

per 100 m of stream.

Our calculations yielded a crude estimate of 13,144 invertebrate food items consumed by a “typical” fish assemblage

(i.e., 15 common species) per 100 m of stream per day.

Clearly, this does not reflect reality at any one site but is a

very general extrapolation from stomach contents and sites

that only broadly represent streams in the Ouachita National

Forest. Importantly, even a minor adjustment in assumptions

of detection or number of feedings can drastically affect the

estimate. However, if 13,000 items per l00 m of stream per

day is viewed as an “order of magnitude” estimate, a starting point is established for exploring the degree to which

fishes remove invertebrates from these stream systems or

the rates of transfer of nutrients from living invertebrates to

release in the water column. Exploration of these and similar

dynamic rates could be useful in modeling overall trophic

effects of fishes in stream ecosystems of the region.

DISCUSSION

Many studies have shown the trophic plasticity of stream

fishes, but most also revealed strong relationships between

diet and microhabitat use of stream fishes. Mendelson (1975)

found, for example, that minnows differed in habitat selection, but fed opportunistically on invertebrates available in

those microhabitats. Our findings support the hypothesis of

correspondence between microhabitat use by stream fishes

and their potential trophic effects within the ecosystem.

Importantly, our study also suggests that food use by a

species is not fully predictable from taxonomic relationships. We summarize other major findings as follows:

1. Dipterans and terrestrial insects were major food items

eaten by fish assemblages in all creeks

2. For 18 major invertebrate food items, individual species

were distinctive in feeding habits

3. Major families of fishes were generally distinctive in diets.

4. Similarity in foods eaten was not rigidly related to taxonomic relatedness of species, and closely related species

within families are not redundant or substitutable in

stream ecosystems.

5. For seven common fish species, diets were 75 percent

similar on average between watersheds with little or no

timber harvest and those with timber harvest conducted

under Forest Service standards and guides in the last 20

yr.

6. Likewise, overall diets were 70 to 85 percent similar

between fish assemblages in harvested and non-harvested watersheds.

7. At least one common, widespread species (orangebelly

darter) showed little longitudinal or spatial pattern in

diets use within a stream.

8. Average food intake by each species, multiplied by estimated densities of the species over all study streams

yielded a crude estimate of 13,000 food items eaten per

day per 100 m of stream.

Our study produced evidence related to trophic effects of

fish in ecosystems. Expanded field and manipulative studies

are beginning that ask more detailed questions about functional effects observed from snorkeling observation of feeding, microhabitat use, and behaviors of fishes in these

ecosystems. Our goal in the next several years is to expand

what is now known about food habits and context to focused

experiments linking fish species to stream processes from a

functional groups perspective. This experimental approach

will eventually incorporate in stream experiments and observations, focused mechanistic experiments, and tests of

ecosystem roles of fishes in artificial stream systems.

ACKNOWLEDGMENTS

Research support was provided through the project “Forest

Managment Effects on the Aquatic Food Web: Development of a Data Base and Assessment Protocols in the

Ouachita National Forest” dated April 22, 1993 through May

1, 1995 of Cooperative Agreement 19-90-039. This study

was funded by the Ouachita Mountains Ecosystem Management Research Project, Southern Research Station and

the Ouachita National Forest, U.S. Department of

Agriculture, Forest Service.

We thank the many individuals who assisted in sampling fish

in the field, Alan Clingenpeel for information and assistance

in identification of field sites, and the Caddo Ranger District

for many courtesies during our field studies. We thank Gayle

Henderson for managing the initial food items database and

Carol Watts for data entry. We thank the Arkansas Game

and Fish Commission for requisite Scientific Collecting

Permits for the fishes.

LITERATURE CITED

Brown, A.V.; Brussack, P.B. 1990. Comparisons of benthic invertebrates between riffles and pools. Hydrobiologia. 220: 99-108.

Clingenpeel, J.A. 1994. A cumulative effects analysis of silvicultural best management practices using basin area stream

survey methods. Hot Springs, AR: U.S. Department of

Agriculture, Forest Service, Southern Region, Ouachita

National Forest. 53 p.

Douglas, M.E.; Matthews, W.J. 1992. Does morphology predict

ecology? Hypothesis testing within a freshwater fish assemblage. Oikos. 65: 213-224.

Gauch, H.G., Jr. 1982. Multivariate analysis in community ecology.

Cambridge, UK: Cambridge University Press. 298 p.
230

Gelwick, F.P.; Matthews, W.J. 1992. Effects of an algivorous

minnow on temperate stream ecosystem properties. Ecology.

73: 1630-1645.

Gelwick, F.P.; Stock, M.S.; Matthews, W.J. 1997. Effects of fish,

water depth, and predation risk on patch dynamics in a northtemperate river system. Oikos. 80: 382-389.

Gido, K.B.; Matthews, W.J. [In press]. Effects of a water column

minnow (Cyprinella lutrensis) on benthic stream ecosystem

processes. Oecologia.

Grimm, N.B. 1988. Feeding dynamics, nitrogen budgets, and ecosystem role of a desert stream omnivore, Agosia chrysogaster

(Pisces: Cyprinidae). Environmental Biology of Fishes. 21: 143-

152.

Matthews, W.J. 1993. Fish community composition and biomass

as related to physical stream environments, seasons, and

forest management in the Ouachita Mountains of southwestern

Arkansas. Kingston, OK: University of Oklahoma Biological

Station, Final Report to U.S. Department of Agriculture Forest

Service, Southeastern Forest Experiment Station, Cooperative

Agreement No. 19-89-043. 47 p.

Matthews, W.J. 1998. Patterns in freshwater fish ecology. New

York: Chapman and Hall. 756 p.

Matthews, W.J.; Bek, J.R.; Surat, E. 1982. Comparative ecology of

the darters, Etheostoma podostemone, E. flabellare, and

Percina roanoka in the upper Roanoke River drainage, Virginia.

Copeia. 1982: 805-814.

Matthews, W.J.; Robison, H.W. 1998. Influence of drainage connectivity, drainage area and regional species richness on fishes

of the Interior Highlands in Arkansas. American Midland

Naturalist. 139: 1-19.

Mendelson, J. 1975. Feeding relationships among species of

Notropis (Pisces: Cyprinidae) in a Wisconsin stream. Ecological

Monographs. 45: 199-230.

Power, M.E.; Matthews, W.J. 1983. Algae-grazing minnows

(Campostoma anomalum), piscivorous bass (Micropterus spp.),

and the distribution of attached algae in a small prairie-margin

stream. Oecologia. 60: 328-332.

Power, M.E.; Matthews, W.J.; Stewart, A.J. 1985. Grazing minnows,

piscivorous bass and stream algae: dynamics of a strong interaction. Ecology. 66: 1448-1456.

Surat, E.; Matthews, W.J.; Bek, J.R. 1982. Comparative ecology of

Notropis albeolus, N. ardens, and N. cerasinus (Cyprinidae) in

the upper Roanoke River drainage, Virginia. American Midland

Naturalist. 107: 13-24.

Taylor, C.M.; Williams, L.R.; Fiorillo, R.A. [and others]. [In press].

Fish assemblage structure under variable environmental conditions in the Ouachita Mountains. In: Guldin, James M., tech.

comp. Ouachita and Ozark Mountains symposium: ecosystem

management research. Gen. Tech. Rep. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station.

Vaughn, C.C.; Gelwick, F.P.; Matthews, W.J. 1993. Effects of

algivorous minnows on production of grazing stram invertebrates. Oikos. 66: 119-128.

Williams, L.R.; Taylor, C.M.; Warren, M.L., Jr.; Clingenpeel, J.A.

[In press]. The role of regional factors in structuring Ouachita

Mountain stream assemblages. In: Guldin, James M., tech.

comp. Ouachita and Ozark Mountains symposium: ecosystem

management research. Gen. Tech. Rep. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station.
231

INTRODUCTION

Describing the interactions that determine distribution and

abundance of organisms is a primary goal of ecologists

(Andrewartha and Birch 1954, Townsend and others 2000)

and has defined much of the field of stream ecology (Allan

1995, Gorman and Karr 1978, Tonn and others 1983). These

relationships, however, are complex and vary depending on

spatial, temporal, or taxonomic resolution (Schlosser 1987,

Vinson and Hawkins 1998, Wiens and others 1986). The

assembly of regional stream faunas differs from that of local

assemblages (Tonn 1990). At larger space and time scales,

biota are influenced more by historical events (e.g., biogeographical history and speciation events) and climatic factors

(Cooper and others 1998, Hugueny 1997, Lohr and Fausch

1997, Ricklefs and others 1999, Vinson and Hawkins 1998).

These large-scale, historical influences act as “filters” for

regional faunas and limit the total species pool available at

smaller scales (Cueto and de Casenave 1999, Ricklefs and

others 1999, Tonn 1990).

At smaller scales, local ecological processes (i.e., abiotic

and biotic) act as a final “filter” in structuring assemblages

(Tonn 1990). The role of both environmental factors (Gorman

and Karr 1978, Grimm and Fisher 1989, Grossman and

others 1982, Jackson and Harvey 1993) and biotic interactions (Dahl and Greenberg 1998, Gilliam and others 1989,

Power and Matthews 1983) in structuring aquatic assemblages is well documented for stream systems. Because of

the dependence on factors operating at larger space and

time scales, it is overly simplistic to only consider the role of

local processes in organizing assemblages. Knowledge of

regional and historical influences are critical for refining

hypotheses of community assembly and organization for

streams at smaller scales (Angermeier and Winston 1998,

Brooks and McLennan 1993, Caley and Schluter 1997).

We used a large data set from the USDA Forest Service to

examine patterns of regional fish and macroinvertebrate

assemblage structure. The data were collected from six

hydrologically variable streams paired within three drainage

basins in the Ouachita National Forest, AR. Because much

of the flow in these streams is dependent on rainfall events,

they are prone to flood in the spring and dry to isolated

pools in the summer. Variable flow regimes can strongly

influence the structure of stream assemblages (Boulton and

others 1992, Capone and Kushlan 1991, Delucchi 1988,

Stanley and others 1994, Taylor 1997).

In addition to the natural disturbance regime, streams in the

Ouachita National Forest are impacted by anthropogenic

disturbances, primarily activities associated with timber

harvesting. The effects of timber harvesting practices on

stream fishes in the central United States are poorly understood (Brown and Matthews 1995, Hicks and others 1991,

Rutherford and others 1992). Macroinvertebrates inhabiting

forested streams are particularly responsive to timber harvesting (Wallace and others 1997, Webster and others

1992), and responses may include short-term shifts in

dominant functional groups and/or changes in community

structure (Allan 1995, Campbell and Doeg 1989). Timber

harvesting practices can have both short and long-term

effects on stream biota, with most long-term effects resulting from persistent sedimentation (Campbell and Doeg

1989, Silsbee and Larson 1983).

In this study, we used multivariate techniques to examine

the relationship between regional fish and macroinvertebrate assemblages and environmental variability (both

natural and anthropogenic) in these small, Ouachita

Mountain stream systems. We were particularly interested

in how the assemblages would differ in their response to

THE ROLE OF REGIONAL FACTORS IN STRUCTURING

OUACHITA MOUNTAIN STREAM ASSEMBLAGES

Lance R. Williams, Christopher M. Taylor,

Melvin L. Warren, Jr., and J. Alan Clingenpeel1

1

 Ph.D. Candidate and Assistant Professor, Department of Biological Sciences, Mississippi State University, Mississippi State, MS 39762;

Research Biologist, USDA Forest Service, Southern Research Station, Center for Bottomland Hardwoods Research, Oxford, MS 38655; and

Forest Hydrologist, USDA Forest Service, Ouachita National Forest, Federal Building, Hot Springs, AR 71902, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—We used Basin Area Stream Survey data from the USDA Forest Service, Ouachita National Forest to evaluate

the relationship between regional fish and macroinvertebrate assemblages and environmental variability (both natural and

anthropogenic). Data were collected for three years (1990-1992) from six hydrologically variable stream systems in the

Ouachita Mountains that were paired by management regime within three drainage basins. Most of the variability in

regional fish assemblages was explained by the historically constrained drainage basins themselves rather than measured habitat variables. Macroinvertebrate assemblages also showed some historical constraint but were associated more

closely with stream habitat conditions. Timber harvest regimes showed little effect on regional assemblages. At the basinlevel, taxonomic assemblages of both fishes and macroinvertebrates were a better predictor of environmental variability

than trophic assemblages, bringing into question the use of trophic groups as an ecological measure in large-scale

studies. Regional analyses are critical for understanding how stream systems are organized at different spatial scales and

are important for effective management of streams within an altered landscape.
232

this variability. Fishes are constrained to their historically

defined drainage basins, and we predicted that this historical constraint would strongly influence their response to

environmental variability. In contrast, many macroinvertebrates emerge from the aquatic environment as adults and

choose future breeding sites based on environmental

quality at a stream locality (Anderson and Wallace 1984).

Macroinvertebrates have the ability to rapidly recolonize a

site following a disturbance event (e.g., flood or drought;

Stanley and others 1994). Because of their recolonization

potential and the ability to cross basin barriers, macroinvertebrate assemblages are less constrained by large-scale

processes, and their response to environmental variability

should differ from that of fishes. Previous authors (Lammert

and Allan 1999, Plafkin and others 1989) have suggested

that macroinvertebrates should be more strongly associated with local habitat conditions and fishes influenced

more by factors operating at larger spatial scales.

Finally, we were interested in how these patterns change

with assemblage resolution (taxonomic versus trophic

groupings). Because assemblages based on trophic groups

would be less constrained by phylogeny and biogeographic

history, we predicted that they may be more strongly associated with variability in habitat conditions than taxonomic

assemblages.

MATERIALS AND METHODS

Study Area

The six streams in this study are located within the Saline,

Cossatot, and Arkansas River basins in the Ouachita

Mountains, Ouachita National Forest, AR (table 1). The

Ouachita Mountains, located in southeastern Oklahoma

and southwestern Arkansas, are a series of east-west

oriented ridges and valleys. The mountains are composed

of Paleozoic sedimentary rock, and streams are dominated

by bedrock, boulder, and cobble substrata with some finer

sediments interspersed (Robison 1986). Many small streams

in the Ouachita Mountains have a highly variable flow regime

throughout the year (Brown and Matthews 1995, Taylor

1997). As most of these streams are maintained primarily

by rainfall events, they are prone to flooding in the spring

and drying to isolated pools in the summer months.

Within each of the three drainage basins, streams were

paired with one in a basin managed for timber and the

other in a basin with no harvesting activities (Ponce and

others 1982), (table 1). The extent of timber harvest activities over the last 100 years is reflected in age-class distributions of trees within the basins (table 1). Relative to

reference basins, managed basins show higher areal percentages of forest in early succession (i.e., trees less than

50 years old) and a lower percentage of forest in mid to

late-successional stages (Clingenpeel 1994). Because a

variety of harvesting practices are used, managed basins in

the Ouachita National Forest are a mosaic of stand-types

ranging from small clearcuts to late-successional stands.

Reference basins are also a mosaic of stand ages, but

natural processes dictate the structure of these stands.

Sampling Methods

All six study streams were sampled each summer (late May

to early August) from 1990-1992 by Forest Service inventory teams (led by JAC). A mesohabitat classification system

(McCain and others 1990) was used to divide each stream

into habitat units (e.g., mid-channel pool, backwater pool,

run, glide, high gradient riffle, cascade) from their headwaters to downstream reaches.

Physical stream features were measured within each classified habitat unit. Stream width was measured along a transect at the midpoint of each habitat unit, and depth was

measured at four equidistant points and the thalweg along

this same transect. Several instream variables were visually

estimated as the percentage of area occupied within a

habitat unit, including: substrate composition, embeddedness, undercut banks, large and small woody debris, whitewater, boulder ledges, terrestrial vegetation overhanging

the stream, vegetation clinging to substrate, rooted vegetation, and bank stability (i.e., percent of the bank not eroded).

Bank angle and canopy closure were determined at the

center of each habitat unit. Clingenpeel (1994) provided a

detailed description of these methods.

Water samples were collected in 10 percent of each type of

habitat unit (e.g., if 300 mid-channel pools were present, 30

were sampled). These collections were stratified along the

length of each stream (Clingenpeel and Cochran 1992).

Table 1—Streams sampled by the USDA Forest Service in 1990-1992

Late

Stream Basin Area Mgmt. Early successional

 ha - - - - - - percent - - - - - -

South Alum Saline 1533 R 9 55

Bread Saline 1517 M 40 20

Caney Cossatot 2518 R 2 70

Brushy Cossatot 3428 M 32 18

Dry Arkansas 2170 R 8 30

Jacks Arkansas 2938 M 25 20

Reference streams indicated by ‘R’ and managed by ‘M’, with this assignment

based on the percentages of each basin in early (less than 50 years old) versus

late successional (greater than 80 years old) forest.
233

Dissolved oxygen and temperature were measured in the

field. Water samples were analyzed for conductivity, pH,

bromide, nitrate, phosphorus, manganese, magnesium,

sodium, cobalt, calcium, and sulfate at Berea, KY, with 1983

EPA Methods for Chemical Analysis of Water and Wastes.

Fishes and macroinvertebrates also were collected in 10 percent of each habitat unit type, arrayed longitudinally in each

stream. Fishes were collected with multiple-pass electrofishing and block nets (Clingenpeel and Cochran 1992) and

preserved for identification. Macroinvertebrates were collected at each locality with a kick-net and substrate washing

into a net (5-min each). These samples were pooled and

preserved for later identification.

Statistical Methods

We summarized stream habitat and water chemistry variables for each stream by year and computed means for each.

An overall mean and coefficient of variation were calculated

for depth measurements (transect and thalweg). We used

39 physical variables in a principal components analysis

(PCA) to reduce the dimensionality of these data to a few

significant axes. We were not attempting to interpret these

axes; rather, we used PCA to objectively select a small

subset of the original data that would represent the major

gradients in the study streams. We retained six variables

(conductivity, percent canopy cover, percent boulder substrate, CV depth, percent cover of rooted vegetation, and

bank stability) for further analyses. These variables had the

highest component loadings for the first five axes, which

accounted for about 72 percent of the total variance. We

retained conductivity and canopy cover because they both

had equally high loadings on the first axis.

We also summarized fish and macroinvertebrate data for

each stream by year. For taxonomic analyses, we used fish

species and macroinvertebrate genera (or order or family

when genus was not determined). We classified fishes and

macroinvertebrates into trophic groups according to Horwitz

(1978) and Merritt and Cummins (1984), as summarized by

Allan (1995).

We used Canonical Correspondence Analysis (CCA), (ter

Braak 1986, 1990) to examine the relationship between

regional fish and macroinvertebrate assemblages (taxonomic and trophic) and the reduced set of environmental

variables. We also included dummy environmental variables for the individual river basins (i.e., Arkansas, Cossatot,

Saline) and presence or absence of timber harvesting within

a watershed. These analyses allowed us to simultaneously

examine effects of the historically defined drainage basins,

presence or absence of logging activities, and measured

environmental variables on assemblage structure. Based

on assemblage structure and the environmental variables

modeled, we plotted the site scores for each stream by year

in multivariate space and grouped them by drainage basin.

We constructed these graphs separately for fish and macroinvertebrate trophic and taxonomic assemblages. We used

Monte Carlo tests (1000 permutations) to estimate the

significance of each CCA performed.

We used the Mantel test (Fortin and Gurevitch 1993) to

examine the association between assemblage structure

(taxonomic and trophic) and measured environmental variables. We were interested specifically in how strongly the

trophic matrices were associated with both taxonomic and

habitat matrices. We tested the prediction that trophic

matrices would be associated more strongly with environmental variability than taxonomic matrices because of their

relative freedom from phylogenetic and historical constraint.

For the Mantel test, we used rectangular n x n matrices

(e.g., fish taxa by site) to construct triangular correlation

matrices. The Mantel test determines the association

between pairs of the triangular correlation matrices at a

time, testing the null hypothesis that there is no association

within elements of the matrices. Thus, for these data we

tested the association between trophic assemblages and

both taxonomic assemblages and environmental variability

(i.e., trophic vs. taxonomic and trophic vs. habitat). We also

performed partial Mantel tests (Manly 1997), where a given

test between two matrices is constrained by removing association with a third matrix (e.g., trophic vs. habitat with the

effects of taxonomy removed). Partial Mantel tests are comparable in function and interpretation to partial correlation

analyses (Manly 1997, Zar 1996). We repeated these Mantel

tests for both fish and macroinvertebrate assemblages.

RESULTS AND DISCUSSION

Summary of Collections

A total of 30 species of fishes (9 families) and 152 genera of

macroinvertebrates (65 families, 20 orders) were collected

over the 3-year study period from the six streams. Examples of all major trophic groups (Horwitz 1978, Merritt and

Cummins 1984, Allan 1995) were represented in these data.

For both fishes and macroinvertebrates, overall number of

individuals collected was highest in streams of the Cossatot

River basin (table 2). For fishes, species richness was higher

in the managed streams than in their paired reference for

all three basins (table 2). In two of three cases (Saline and

Arkansas basins), richness of macroinvertebrate taxa was

greater in the managed streams than the paired reference

(table 2). However, because of high variability among basins,

there were no significant differences (T-test, p > 0.05)

between managed and reference watersheds with respect

to number of individuals collected or richness of fishes or

macroinvertebrates.

Fish Assemblage Structure

Based on taxonomic and trophic assemblages of fishes,

streams within individual drainage basins were closely

associated and separated from other basins in multivariate

space (fig. 1). Thus, most of the variation in regional fish

assemblages was explained by differences among the

historically defined drainage basins rather than measured

environmental variables or timber harvesting activities. For

both groups of assemblages, variation in the first CCA axis

was dominated by the individual drainage basins (table 3),

indicating that assemblages differed strongly among the

historically defined basins. The second CCA axis was associated with the percentage of boulder substrate and the

drainage basins (table 3). Logging regime was correlated

weakly with both axes, indicating little effect in structuring

fish assemblages at the regional scale.
234

Figure 1—The distribution of site scores in multivariate space for fish and macroinvertebrate

assemblages (taxonomic and trophic). Streams within a given year are grouped with polygons by

drainage basin. A = Arkansas basin; C = Cossatot basin; S = Saline basin. Shown are the first (xaxis) and second (y-axis) canonical correspondence analysis axes.

Table 2—Total number of individuals collected and taxa richness for each of the six

study streams over the 3-year study period

Measure Stream/Basin

S. Alum Bread* Caney Brushy* Dry Jacks*

Saline Saline Cossatot Cossatot Arkansas Arkansas

Fishes

No. Indiv. 1,694 849* 6,320 7,020* 921 1,550*

Richness 18 19* 16 19* 4 13*

Invertebrates

No. Indiv. 2,402 4,821* 11,648 10,962* 2,185 3,611*

Richness 38 47* 43 40* 28 44*

* = managed streams.

Overall, streams within individual basins differed in environmental conditions. For example, streams in the Saline basin

had greater aerial canopy cover and more rooted vegetation. Streams in the Arkansas basin were characterized by

a higher percentage of boulder substrate and greater bank

stability, and streams in the Cossatot system had higher

conductivity and greater variability in stream depth (L.R.

Williams and others. Environmental variablility, historical
235

Table 3—Correlations between canonical correspondence axes (1 and 2) and environmental

variables for fish and macroinvertebrate assemblages (taxonomic and trophic)

Variable Macroinvertebrate Macroinvertebrate

Fish taxa Fish trophic taxa trophic

Axis 1 2 1 2 1 2 1 2

P-value (0.01) (0.01) (0.01) (0.02) (0.01) (0.02) (0.01) (0.01)

Conductivity 0.42 0.03 0.10 -0.16 0.43 -0.17 0.35 0.08

Boulder -0.33 -0.68 0.42 0.63 -0.78 0.42 -0.81 -0.28

Canopy -0.56 -0.30 -0.51 0.40 -0.29 -0.25 -0.19 -0.04

CV depth 0.55 0.09 0.40 -0.01 0.60 0.09 0.60 0.42

Vegetation -0.43 -0.37 -0.62 0.13 0.07 -0.87 -0.06 0.48

(rooted)

Stability -0.28 0.34 0.22 0.27 -0.53 0.35 -0.43 -0.10

(bank)

Basin

Alum -0.89 -0.42 -0.80 0.33 -0.41 -0.74 -0.38 0.41

Cossatot 0.91 -0.38 0.15 -0.71 0.94 0.28 0.85 -0.26

Logging 0.03 0.17 -0.03 0.11 -0.05 -0.41 -0.15 0.27

Table 4—Correlations between trophic assemblages for

fishes and macroinvertebrates and taxonomic and

habitat matrices

Matrix comparison r

Fish trophic groups x taxa 0.701

Fish trophic groups x habitat 0.343

Partial Mantel (taxonomy effects removed) 0.012

Macroinvertebrate trophic groups x taxa 0.659

Macroinvertebrate trophic groups x habitat 0.212

Partial Mantel (taxonomy effects removed) 0.133

Correlations derived from Mantel and partial Mantel tests.

contingency, and the structure of regional fish and macroinvertebrate assemblages in Ouachita Mountain stream systems. Manuscript in preparation). Based on the relationships

we found, regional fish assemblages are likely influenced

by combinations of spatially constrained environmental variability (i.e., habitat conditions unique to individual basins)

and the historical biogeography of individual drainage

basins.

Macroinvertebrate Assemblage Structure

Taxonomic assemblages of macroinvertebrates showed a

similar pattern to fish assemblages, but the separation of

streams into their respective basins was not as evident for

macroinvertebrate trophic groups (fig. 1). For macroinvertebrate taxa, the first CCA axis was again dominated by

differences among the historically defined drainage basins

and the percentage of boulder substrate (table 3). The

second axis was correlated with drainage basins and percent cover of rooted vegetation (table 3). Macroinvertebrate

trophic groups were also associated with drainage basins;

the Cossatot basin clearly separated from the other two in

multivariate space (fig. 1). Percentage of boulder substrate

and variability in stream depth also had a strong influence

on trophic assemblages (table 3). Logging regime was not

associated strongly with regional macroinvertebrate

assemblages.

The weak relationship between macroinvertebrate trophic

assemblages and drainage basins, and stronger associations with individual environmental variables, indicates that

historical constraint may be less important in structuring

regional macroinvertebrate assemblages. In contrast with

fishes, macroinvertebrate assemblages were associated

more strongly with environmental variability not associated

with the drainage basins (i.e., habitat conditions that are

unrelated to basins). Despite these stronger correlations

with individual environmental variables, historical contingency

still had some influence on macroinvertebrate assemblages

when grouped by taxa (table 3).

Trophic Patterns

For fish assemblages particularly, patterns for taxonomic

and trophic assemblages were quite similar (fig. 1). This

was in contrast to our prediction that trophic assemblages

would be less constrained by historical contingency than

taxa. Mantel tests indicated that fish and macroinvertebrate

trophic groups were associated with both environmental

variables and the taxonomic composition of assemblages

(table 4). Because the trophic matrices are constructed

from species matrices, we focused on the strengths of the

relationships (r) and not associated p-values. As expected,

the strongest correlations were between trophic and taxonomic assemblages (table 4). Correlations between trophic

groups and habitat were not as strong, and when the effect

of taxonomy was removed, there was little correlation left

between trophic assemblages and environmental variables

(table 4). Thus, correlations between trophic and habitat

matrices were confounded by their strong relationships with

the taxonomic matrices. This indicates that taxonomic

assemblages were associated more strongly with environmental variability than the trophic assemblages, and this

pattern was evident for both fishes and macroinvertebrates.
236

CONCLUSIONS

Overall, we found that timber harvesting regime had little

influence on number of individuals collected, taxonomic

richness, or assemblage structure of fishes and macroinvertebrates at the regional scale. The lack of an effect

seems to indicate that these systems are somewhat resistant to such perturbations, and we believe this is related to

the dynamic natural disturbance regime of these streams.

At the large scale of this study, the seasonal flooding and

drying of these streams appears to override effects of

timber harvesting activities as practiced in the Ouachita

National Forest. However, land-use activities often have a

stronger effect on stream biota at local rather than regional

scales (Campbell and Doeg 1989, Lammert and Allan 1999,

Rutherford and others 1992). Because of limitations in

sampling design, we were unable to test this prediction at

smaller spatial scales.

As we predicted for fishes, historical constraint had the

strongest influence on the structure of regional assemblages. This historical explanation, however, has two components that are difficult to separate. First, biogeographical

history influences the potential biota of a stream (Brooks

and McLennan 1993, Matthews and Robison 1988, Tonn

1990). Second, habitat conditions within individual streams

are constrained by the underlying geology of individual

basins (Brown and Matthews 1995, Brussock and others

1985); basin geomorphology determines substrate composition and channel characteristics. Both of these historical

components (i.e., biogeography and habitat variability

unique to individual basins) are important in structuring

regional fish assemblages in the six streams that we

examined.

In contrast, regional macroinvertebrate assemblages were

structured both by historical factors and also environmental

variability that was not associated with the individual basins.

We did not expect such a high degree of historical constraint on macroinvertebrate taxa. As above, this may relate

to the inherent differences in habitat characteristics of the

individual basins.

Regional studies of stream systems are critical for understanding the structure of their biotic assemblages. Huston

(1999) argued that unraveling the interrelationships between

local and regional processes is critical to understanding

global patterns of species diversity. How these ecological

processes are linked at different spatial scales is a fundamental question in the field of ecology (Levin 1992, Lohr

and Fausch 1997). Because of the inherent difficulty in

managing small stream systems (Poff and Ward 1989),

detailed studies at multiple spatial and temporal scales are

necessary to conserve and manage their biological diversity. An understanding of the effects of disturbance is also

necessary for effectively managing aquatic ecosystems

(Resh and others 1988). One of the basic tenets of ecosystem management is that disturbance is a vital attribute of

ecological systems and native species have evolved within

the context of this natural disturbance regime (Hessburg

and others 1999, Landres and others 1999). The effects of

anthropogenic disturbances cannot be fully understood

unless the natural disturbance regime has been taken into

consideration (Landres and others 1999). Because many

government agencies are using basin-level survey methods

(Clingenpeel 1994, Hankin and Reeves 1988) to assess

effects of anthropogenic disturbances, it is important to

evaluate their efficacy. Given the intensive sampling methodologies and expense of these basin-level studies, it is

important to consider the strengths and limitations of these

methods in elucidating ecological relationships.

ACKNOWLEDGMENTS

Research support was provided through two projects “Fish

Community and Habitat Stability under Different Forest

Management Regimes” dated September 1, 1997 through

September 30, 1999 of Cooperative Agreement 33-CA-97-

190 and “Community Dynamics in Small Ouachita Mountain

Streams” dated August 1, 1999 through February 28, 2002

of Cooperative Agreement 33-CA-99-622. Financial support

for this work was provided by the USDA Forest Service,

Southern Research Station, Ouachita Mountains Ecosystem Management Research Project, Ouachita National

Forest and the Department of Biological Sciences at

Mississippi State University.

We thank personnel provided by the Forest Service, Northeast Louisiana University, and Arkansas State University for

their assistance with field and laboratory work. This work

was completed in partial fulfillment of the doctoral degree

requirements for L.R. Williams.

LITERATURE CITED

Allan, J.D. 1995. Stream ecology: structure and function of running

waters. London: Chapman and Hall. 388 p.

Anderson, N.H.; Wallace, J.B. 1984. Habitat, life history, and

behavioral adaptations of aquatic insects. In: Merritt, R.W.;

Cummins, K.W., eds. An introduction to the aquatic insects of

North America, second edition. Dubuque, IA: Kendall/Hunt

Publishing Company: 38-58.

Andrewartha, H.G.; Birch, L.C. 1954. The distribution and abundance of animals. Chicago, IL: University of Chicago Press.

Angermeier, P.L.; Winston, M.R. 1998. Local vs. regional influences on local diversity in stream fish communities of Virginia.

Ecology. 79: 911-927.

Boulton, A.J.; Peterson, C.G.; Grimm, N.B.; Fisher, S.G. 1992.

Stability of an aquatic macroinvertebrate assemblage community in a multiyear hydrologic disturbance regime. Ecology.

73: 2192-2207.

Brooks, D.R.; McLennan, D.A. 1993. Historical ecology: examining

phylogenetic components of community evolution. In: Ricklefs,

R.E.; Schluter, D., eds. Species diversity in ecological communities: historical and geographic perspectives. Chicago, IL:

University of Chicago Press: 267-280.

Brown, A.V.; Matthews, W.J. 1995. Stream ecosystems of the

Central United States. In: Cushing, C.E.; Cummins, K.W.;

Minshall, G.W., eds. River and stream ecosystems. Amsterdam,

Netherlands: Elsevier: 89-116.

Brussock, P.P.; Brown, A.V.; Dixon, J.C. 1985. Channel form and

stream ecosystem models. Water Resources Bulletin. 21: 859-

866.

Caley, M.J.; Schluter, D. 1997. The relationship between local and

regional diversity. Ecology. 78: 70-80.
237

Campbell, I.C.; Doeg, T.J. 1989. Impact of timber harvesting and

production on streams: a review. Australian Journal of Marine

and Freshwater Research. 40: 519-539.

Capone, T.A.; Kushlan, J.A. 1991. Fish community structure in dryseason stream pools. Ecology. 72: 983-992.

Clingenpeel, J.A. 1994. A cumulative effects analysis of silvicultural best management practices using basin area stream

survey methods (BASS). Hot Springs, AR: U.S. Department of

Agriculture, Forest Service, Southern Region, Ouachita

National Forest.

Clingenpeel, J.A.; Cochran, B.G. 1992. Using physical, chemical

and biological indicators to assess water quality on the

Ouachita National Forest using basin area stream survey

methods. Proceedings of the Arkansas Academy of Science.

46: 33-35.

Cooper, S.D.; Diehl, S.; Kratz, K.; Sarnelle, O. 1998. Implications

of scale for patterns and processes in stream ecology.

Australian Journal of Ecology. 23: 27-40.

Cueto, V.R.; de Casenave, J.L. 1999. Determinants of bird species

richness: role of climate and vegetation structure at a regional

scale. Journal of Biogeography. 26: 487-492.

Dahl, J.; Greenberg, L.A. 1998. Effects of fish predation and

habitat type on stream benthic communities. Hydrobiologia.

361: 67-76.

Delucchi, C.M. 1988. Comparison of community structure among

streams with different temporal flow regimes. Canadian Journal

of Zoology. 66: 579-586.

Fortin, M.; Gurevitch, J. 1993. Mantel tests: spatial structure in

field experiments. In: Scheiner, S.M.; Gurevitch, J., eds. Design

and analysis of ecological experiments. New York, NY:

Chapman and Hall: 342-359.

Gilliam, J.F.; Fraser, D.F.; Sabat, A.M. 1989. Strong effects of

foraging minnows on a stream benthic invertebrate community.

Ecology. 70: 445-452.

Gorman, O.T.; Karr, J.R. 1978. Habitat structure and stream fish

communities. Ecology. 59: 507-515.

Grossman, G.D.; Moyle, P.B.; Whitaker, J.O., Jr. 1982. Stochasticity in structural and functional characteristics of an Indiana

stream fish assemblage: a test of community theory. American

Naturalist. 120: 423-454.

Grimm, N.B.; Fisher, S.G. 1989. Stability of periphyton and macroinvertebrates to disturbance by flash floods in a desert stream.

Journal of the North American Benthological Society. 8: 293-

307.

Hankin, D.G.; Reeves, G.H. 1988. Estimating total fish abundance

and total habitat area in small streams based on visual estimation methods. Canadian Journal of Fisheries and Aquatic

Sciences. 45: 834-844.

Hessburg, P.F.; Smith, B.G.; Salter, R.B. 1999. Detecting change

in forest spatial patterns from reference conditions. Ecological

Applications. 9: 1232-1252.

Hicks, B.J.; Hall, J.D.; Bisson, P.A.; Sedell, J.R. 1991. Responses

of salmonids to habitat changes. American Fisheries Society

Special Publication. 19: 458-518.

Horwitz, R.J. 1978. Temporal variability patterns and the distributional patterns of stream fishes. Ecological Monographs. 48:

307-321.

Hugueny, B. 1997. The relationship between local and regional

species richness: comparing biotas with different evolutionary

histories. Oikos. 80: 583-587.

Huston, M.A. 1999. Local processes and regional patterns: appropriate scales for understanding variation in the diversity of

plants and animals. Oikos. 86: 393-401.

Jackson, D.A.; Harvey, H.H. 1993. Fish and benthic invertebrates:

community concordance and community-environment relationships. Canadian Journal of Fisheries and Aquatic Sciences. 50:

2641-2651.

Lammert, M.; Allan, J.D. 1999. Assessing biotic integrity of

streams: effects of scale in measuring the influence of land

use/cover and habitat structure on fish and macroinvertebrates.

Environmental Management. 23: 257-270.

Landres, P.B.; Morgan, P.; Swanson, F.J. 1999. Overview of the

use of natural variability concepts in managing ecological

systems. Ecological Applications. 9: 1179-1188.

Levin, S.A. 1992. The problem of pattern and scale in ecology.

Ecology. 73: 1943-1967.

Lohr, S.C.; Fausch, K.D. 1997. Multiscale analysis of natural

variability in stream fish assemblages of a western Great Plains

watershed. Copeia. 1997: 706-724.

Manly, B.F.J. 1997. Randomization, bootstrap and monte carlo

methods in biology. second edition. London: Chapman and

Hall. 399 p.

Matthews, W.J.; Robison, H.W. 1988. The distribution of fishes in

Arkansas: a multivariate analysis. Copeia. 1988: 358-374.

McCain, M.; Fuller, D.; Decker, L.; Overton, K. 1990. Stream

habitat classification and inventory procedures for northern

California. FHR Currents. San Francisco, CA: U.S. Department

of Agriculture, Forest Service, Pacific Southwest Region.

Merritt, R.W.; Cummins, K.W. 1984. An introduction to the aquatic

insects of North America. second edition. Dubuque, IA: Kendall/

Hunt Publishing Company. 722 p.

Plafkin, J.L.; Barbour, M.T.; Porter, K.D. [and others]. 1989. Rapid

bioassessment protocols for use in stream and rivers: benthic

macroinvertebrates and fish. EPA/444/4-89-011. Washington,

DC: U.S. Environmental Protection Agency.

Poff, N.L.; Ward, J.V. 1989. Implications of streamflow variability

and predictability for community structure: a regional analysis

of stream-flow patterns. Canadian Journal of Fisheries and

Aquatic Sciences. 46: 1805-1818.

Ponce, S.L.; Schindler, D.W.; Avertt, R.C. 1982. The use of the

paired-basin technique in flow-related wildland water-quality

studies. WSDG-TP-00004. Fort Collins, CO: U.S. Department

of Agriculture, Forest Service, Watershed Development Group.

Power, M.E.; Matthews, W.J. 1983. Algae-grazing minnows

(Campostoma anomalum), piscivorous bass (Micropterus spp.),

and the distribution of attached alge in a small prairie-margin

stream. Oecologia. 60: 238-232.

Resh, V.H.; Brown, A.V.; Covich, A.P. [and others]. 1988. The role

of disturbance in stream ecology. Journal of the North American

Benthological Society. 7: 433-455.

Ricklefs, R.E.; Latham, R.E.; Qian, H. 1999. Global patterns of

tree species richness in moist forests: distinguishing ecological

influences and historical contingency. Oikos. 86: 369-373.

Robison, H.W. 1986. Zoogeographic implications of the Mississippi

River basin. In: Hocutt, C.H.; Wiley, E.O., eds. The zoogeography of North American freshwater fishes. New York, NY:

John Wiley and Sons: 267-286.

Rutherford, D.A.; Echelle, A.A.; Maughan, O.E. 1992. Drainagewide effects of timber harvesting on the structure of stream fish

assemblages in southeastern Oklahoma. Transactions of the

American Fisheries Society. 121: 716-728.
238

Schlosser, I.J. 1987. A conceptual framework for fish communities

in small warmwater streams. In: Matthews, W.J.; Heins, D.C.,

eds. Community and evolutionary ecology of North American

stream fishes. Norman, OK: University of Oklahoma Press: 17-

24.

Silsbee, D.G.; Larson, G.L. 1983. A comparison of streams in

logged and unlogged areas of Great Smoky Mountains National

Park. Hydrobiologia. 102: 99-111.

Stanley, E.H.; Buschman, D.L.; Boulton, A.J. [and others]. 1994.

Invertebrate resistance and resilience to intermittency in a

desert stream. American Midland Naturalist. 131: 288-300.

Taylor, C.M. 1997. Fish species richness and incidence patterns in

isolated and connected stream pools: effects of pool volume

and spatial position. Oecologia. 110: 560-566.

ter Braak, C.J.F. 1986. Canonical correspondence analysis: a new

eigenvector technique for multivariate direct gradient analysis

in ecology. Ecology. 67: 1167-1179.

ter Braak, C.J.F. 1990. Update notes: CANOCO. Version 3.10.

Waneningen, The Netherlands: Agricultural Mathematics

Group.

Tonn, W.M. 1990. Climate change and fish communities: a

conceptual framework. Transactions of the American Fisheries

Society. 119: 337-352.

Tonn, W.M.; Magnuson, J.J.; Forbes, A.M. 1983. Community

analysis in fishery management: an application with northern

Wisconsin lakes. Transactions of the American Fisheries

Society. 112: 368-377.

Townsend, C.R.; Harper, J.L.; Begon, M. 2000. Essentials of

ecology. Malden, MA: Blackwell Science. 552 p.

Vinson, M.R.; Hawkins, C.P. 1998. Biodiversity of stream insects:

variation at local, basin, and regional scales. Annual Review of

Entomology. 43: 271-293.

Wallace, J.B.; Eggert, S.L.; Meyer, J.L.; Webster, J.R. 1997.

Multiple trophic linkages of a forest stream linked to terrestrial

litter inputs. Science. 277: 102-104.

Webster, J.R.; Golladay, S.W.; Benfield, E.F. [and others]. 1992.

Catchment disturbance and stream response: an overview of

stream research at Coweeta Hydrological Laboratory. In: Boon,

P.J.; Calow, P.; Petts, G.E., eds. River conservation and

management. Chichester, England: John Wiley and Sons, Ltd.:

231-253.

Wiens, J.A.; Addicott, J.F.; Case, T.J.; Diamond, J. 1986. Overview: the importance of spatial and temporal scale in ecological

investigations. In: Diamond, J.; Case, T.J., eds. Community

ecology. New York, NY: Harper and Row: 145-153.

Zar, J.H. 1996. Biostatistical analysis. 3rd ed. Upper Saddle River,

NJ: Prentice Hall. 662 p.
239

INTRODUCTION

The Ouachita Mountains of Arkansas and Oklahoma contain

a suite of river systems that ultimately drain into the Red and

Arkansas River basins. The region supports a diverse fish

fauna with several endemic species (Robison and Buchanan

1988). The implementation of the Ouachita Mountains Ecosystem Management Research Project (Phase III) by the

USDA Forest Service provided us with the opportunity to

study how environmental variability influences fish assemblage structure across a large spatial scale incorporating

the four Phase III core watersheds.

Many factors affect the distribution and abundance of species, including interaction between the environment and

population processes (Brown 1984). Determining environmental influences on distribution and abundance patterns is

a difficult task because different spatial and temporal scales

yield different types of information (Wiens and others 1986).

Regardless, differences among species in distribution and

abundance patterns within a region are, in part, due to

individualistic responses to the environmental template. In

streams, two complex environmental gradients appear to

influence fish assemblages and habitat structure: stream

size and hydrologic variability (Gorman and Karr 1978;

Horwitz 1978; Poff and Ward 1989; Schlosser 1987; Sheldon

1968). Small, hydrologically dynamic streams in these

Ouachita Mountain watersheds provide an ideal system to

address questions concerning the effects of environmental

variability on the distribution and abundance of fishes.

Our primary objective for this work was to examine how

local fish assemblages change across the landscape and to

see if these patterns were related to spatial and temporal

variability in the environment. Specifically, we used multivariate direct and indirect gradient analyses to ask if patterns

in fish assemblage structure were associated with stream

size and variability in flow.

MATERIALS AND METHODS

Study Area and Species

The Ouachita Mountains of Arkansas and Oklahoma are

characterized by strongly folded, uplifted sedimentary rock

and pine-oak upland forest (Robison 1986). Our sample

sites were located in two river systems (of the Red River

drainage) that drain the eastern side of the uplift. Nine sites

were located in the Alum Fork of the Saline River (hereafter

Alum Fork) and three sites were in the Little Glazypeau

system of the Ouachita River drainage (fig. 1). These are

FISH ASSEMBLAGE STRUCTURE UNDER VARIABLE

ENVIRONMENTAL CONDITIONS IN THE OUACHITA MOUNTAINS

Christopher M. Taylor, Lance R. Williams, Riccardo A. Fiorillo,

R. Brent Thomas, and Melvin L. Warren, Jr.1

1 Assistant Professor, Ph.D. Candidate, and Ph.D. Candidate, Department of Biological Sciences, Mississippi State University, Mississippi

State, MS 39762; Assistant Professor, Division of Natural Sciences and Engineering, University of South Carolina Spartanburg, Spartanburg,

SC 29303; and Research Biologist, USDA Forest Service, Southern Research Station, Center for Bottomland Hardwoods Research, Oxford,

MS 38655, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Spatial and temporal variability of fish assemblages in Ouachita Mountain streams, Arkansas, were examined

for association with stream size and flow variability. Fishes and habitat were sampled quarterly for four years at 12 sites

(144 samples) in the Ouachita Mountains Ecosystem Management Research Project, Phase III watersheds. Detrended

and Canonical Correspondence analyses were used to describe spatial and temporal patterns. Considerable spatial and

temporal variability in fish assemblage structure was observed among sites. Across all sites and samples, assemblage

variability was significantly associated with stream size and flow regime. The flow regime of the Little Glazypeau system

differed from that of the Alum Fork system. Differences in flow regime were significantly associated with differences in fish

assemblages across sites with trenchant differences noted between the Little Glazypeau and Alum Fork systems. The two

systems are historically distinct and reflect large-scale differences in geomorphology, speciation, extinction, and dispersal.

Figure 1—Map showing 12 sites in the Alum Fork and

Little Glazypeau systems. Dashed line indicates that the

geographic proximity of the two systems is not as

shown.
240

clear water streams except during high precipitation events

when dramatic increases in turbidity are evident. Because

of the high gradient of these streams, riffle-pool development is strong. Pools ranged from a few to 50 m or more in

length, and were usually separated by swift riffle habitats

consisting of coarse substrate materials. These streams are

characterized by high variability in the flow regime. Water

levels can rise and fall very rapidly with heavy precipitation

events and headwater reaches are intermittent, frequently

drying to isolated pools during summer and autumn months.

Thus, habitats and their corridors can grow, shrink, and

change configuration rapidly (Taylor 1997).

Data Collection

From November 1995 through August 1998, we sampled

fishes and measured environmental variables at the 12 sites

(fig. 1) for a total of 144 samples. Samples for all years were

taken in November, February, May, and August and were

always completed within a three to four day time period

under ambient conditions. At each site, we electrofished all

habitat types within a 100 to 200-m stream reach (depending on stream size) that spanned at least two pool-riffle

sequences. For stream fishes, the stream reach is a logical

sample unit in a river system (Frissell and others 1986),

and the effects of disturbance on assemblage level properties are measured most commonly at this scale (Matthews

1998). All sampling methods are biased and electrofishing

is no exception. However, our system consisted of small,

clear streams (first to third order) that were wadeable, and

that could be sampled in a consistent manner each time.

The same individual (CMT) always operated the backpack

electroshocker and there were always two to three netters

present to pick up stunned fishes. Thus, we are confident

that we adequately assessed the composition of fish assemblages at a given site for each sample date. After sampling

a site, all fishes were identified, counted, and returned to

the stream alive.

After fish sampling and processing, we measured habitat

variables along transects (perpendicular to stream flow)

spaced at 10 to 15-m intervals along the sampled stream

reach. The number of transects varied (eight to 12) depending on the length of the sampled stream reach. Transects

were permanently marked and re-surveyed during each

visit. At every other meter along transects, we measured

current velocity, depth, and dominant substrate type. We

categorized substrate following Taylor and Lienesch (1996a,

1996b) as bedrock, large boulder (> 300 mm), small boulder

(150 to 300 mm), cobble (50 to 149 mm), gravel (3 to 49

mm), or sand (< 3 mm). We measured stream width at each

transect as well as the presence/absence of aquatic macrophytes, algae, undercuts, and woody debris.

Data Treatment and Analyses

In order to summarize the fish assemblage data and quantify the effects of stream size and flow variability on fish

assemblages, we performed three multivariate analyses.

Detrended correspondence analysis (DCA) is an indirect

ordination technique designed to summarize complex community data (Gauch 1982). We used DCA to qualitatively

compare fish assemblages across space and time. Canonical correspondence analysis (CCA) is a multivariate direct

gradient technique that is commonly used to explore the

relationship between assemblages of organisms and the

environment. The ordination axes (gradients in assemblage

structure) are constrained by the suite of environmental

variables being analyzed, thus maximizing the speciesenvironment correlation (ter Braak 1986). All analyses were

performed with PC-ORD software (McCune and Meford

1997).

The first CCA was designed to quantify the association

between stream size and fish assemblage structure. To

quantify stream size we first calculated the mean and maximum width, mean and maximum depth, and mean and

maximum current velocity for each site/date. All environmental values were log-transformed before analysis. Our

goal was to see if patterns in fish assemblage structure

were associated with stream size variables. We used a

Monte Carlo randomization procedure to determine if the

relationship was significant (1000 permutations).

Next, we used CCA to identify patterns in assemblage

structure that reflect variation through time in stream width,

depth, and flow characteristics. We included coefficients of

variation (CV) calculated for each site across sample dates

for the following variables: mean and maximum width, mean

and maximum depth, and mean and maximum current velocity. The species data used in this analysis included summed

totals for each species at each site across the entire study

period. We used a Monte Carlo randomization procedure to

determine if the relationship between assemblage structure

and flow variability was significant (1000 permutations). Multiresponse permutation procedures (McCune and Meford

1997) were used to determine if there were assemblagelevel differences between the Little Glazypeau system and

the Alum Fork system with regard to the flow regime.

RESULTS AND DISCUSSION

We collected a total of 30 fish species (and numerous individuals of a hybrid sunfish, Lepomis sp.) from the 12 sites.

Species were taxonomically distributed across six families

and functionally distributed across six trophic groups (Allan

1995). We present species relative abundances and distributional information in table 1 and summary environmental

data in table 2.

Species varied greatly in their overall relative abundances

and in their distribution across sites (table 1). At the coarsest

level, five species were collected only from the Little Glazypeau system, and six species were collected only in the

Alum Fork system. Large-scale historic differences in geomorphology, speciation, extinction, and dispersal account

for the non-shared components of the assemblages. Both

systems drain into the Red River, but are isolated by long

distances of lowland, big river habitat. The effectiveness of

isolation is demonstrated by the evolution of one endemic

form (Noturus lachneri, Alum Fork system) and several

species that occur in one system but not the other. Within a

given drainage abundances and occurrences were also

highly variable across space and time, reflecting the different ecological conditions that occur spatially and temporally. These patterns are exemplified in the following indirect

and direct gradient analyses.
241

Table 1—Species, species acronyms, species occurrences in the two drainage systems, total

number of localities occupied (occurrence) by species, and overall relative abundances of species

Little Relative

Species Acronym Glazypeau Alum Fork Occurrence abundance

Ameiurus natalis AMENAT X X 7 < 0.01

(Lesueur)

Aphredoderus sayanus APHSAY X X 9 0.01

(Gilliams)

Campostoma anomalum CAMANO X X 11 0.18

(Rafinesque)

Chaenobryttus gulosus LEPGUL X 1 < 0.01

(Cuvier)

Erimyzon oblongus ERIOBL X X 12 0.04

(Mitchill)

Esox americanus ESOAME X X 10 0.01

Gmelin

Etheostoma blennioides ETHBLE X X 3 < 0.01

Rafinesque

Etheostoma collettei ETHCOL X 6 0.07

Birdsong & Knapp

Etheostoma radiosum ETHRAD X 3 0.05

(Hubbs & Black)

Etheostoma whipplei ETHWHI X 9 0.09

(Girard)

Fundulus catenatus FUNCAT X 2 0.01

(Storer)

Fundulus olivaceous FUNOLI X X 10 0.05

(Storer)

Hypentelium nigricans HYPNIG X X 3 < 0.01

(Lesuer)

Ichthyomyzon gagei ICHGAG X 3 < 0.01

Hubbs and Trautman

Labidesthes sicculus LABSIC X X 6 0.01

(Cope)

Lepomis cyanellus LEPCYA X X 12 0.07

Rafinesque

Lepomis hybrid LEPHYB X X 5 < 0.01

Lepomis macrochirus LEPMAC X X 8 0.01

Rafinesque

Lepomis megalotis LEPMEG X X 11 0.19

(Rafinesque)

Lythrurus umbratilis LYTUMB X X 10 0.07

(Girard)

Luxilus chrysocephalus LUXCHR X 3 0.01

Rafinesque

Micropterus dolomieu MICDOL X 1 < 0.01

Lacepede

Micropterus punctulatus MICPUN X X 6 < 0.01

(Rafinesque)

Micropterus salmoides MICSAL X X 5 < 0.01

(Lacepede)

Notropis boops NOTBOO X X 7 0.02

Gibert

Notropis ortenburgeri NOTORT X 3 0.01

Hubbs

Noturus nocturnus NOTNOC X 1 < 0.01

Jordan & Gilbert

Noturus lachneri NOTLAC X 7 0.05

Taylor

Percina caprodes PERCAP X X 5 0.01

(Rafinesque)

Pimephales notatus PIMNOT X X 5 0.02

(Rafinesque)

Semotilus atromaculatus SEMATR X X 9 0.04

Mitchill
242

Table 2—Substrate characteristics (percent composition of point estimates), mean

stream width, mean stream depth, and mean stream current speed for the 12 sites

Substrate

Mean

Small Large Bed- Mean Mean current

Sites Sand Gravel Cobble boulder boulder rock width depth speed

1 0 30 25 15 10 20 2.6 16.7 0.07

2 0 0 43 36 14 7 3.7 21.9 0.15

3 8 11 70 11 0 0 4.9 34.0 0.14

4 0 18 33 24 6 18 10.8 39.4 0.15

5 0 3 53 29 15 0 4.0 29.1 0.13

6 0 10 23 45 23 0 3.7 30.0 0.02

7 0 16 16 42 26 0 6.7 40.0 0.09

8 0 3 21 21 9 47 6.0 30.0 0.07

9 0 0 21 38 41 0 7.9 36.1 0.19

10 0 62 31 4 0 4 2.8 22.1 0.05

11 3 26 43 23 3 3 4.9 28.6 0.13

12 0 34 6 23 37 0 11.3 34.7 0.13

Substrate compostion is reported for November 1996 samples under ambient flow conditions.

Means for other variables are calculated across the entire study period.

Figure 2—Detrended correspondence analysis (DCA) of all site and samples across

the three year period. Sample scores are plotted in multivariate space defined by

DCA axes 1 and 2.
243

Detrended correspondence analysis of all samples at each

site indicated considerable variability in the structure of

site-specific assemblages across time (fig. 2). The overall

large spread of points in the two-dimensional space was

indicative of high species turnover (beta diversity) among

the sites. This was especially evident when comparing sites

from the Little Glazypeau system (sites 10, 11, and 12) with

sites from the Alum Fork system (sites one through nine).

For samples from individual sites, the variability was considerably less at larger sites as compared to smaller sites.

Sites 4, 9, and 12 were the largest sites (see mean width,

table 2) and samples from these sites clustered together

very closely in ordination space (fig. 2). In contrast, samples

from the smallest sites (e.g. sites 1, 2, 5, 6, and 10) were

much more spread out in ordination space. This pattern

indicated that assemblages at smaller sites were much

more variable across time in species composition and

abundance than assemblages from larger sites. Despite

variability in assemblage structure for each site across time,

each locality was confined to a relatively small proportion of

the overall two-dimensional ordination plot (fig. 2).

Interpretation of the variability in assemblage structure is

facilitated by simultaneously examining the ordination plot

(fig. 3) and correlations of species abundance with the two

DCA axes (table 3). For example, Semotilus atromaculatus

(SEMATR) has the highest species score on DCA axis 2

(top of figure 3). Samples from Site 1 (fig. 2) all clustered in

this region of the ordination space indicating the importance

of this species at this site. As expected, S. atromaculatus

showed a strong positive correlation with DCA axis 2 (r =

0.576; table 3). Lepomis cyanellus (LEPCYA) also was an

important species at site 1 and showed a strong positive

correlation with DCA axis 2 (r = 0.399). Etheostoma radiosum

(ETHRAD) had the highest score on DCA axis 1 (far right in

figure 3) and was found only in the two largest sites in the

Little Glazypeau system (sites 11 and 12, figure 2). This

species showed a very strong correlation with DCA axis 1

(r = 0.620). By superimposing sites in figure 2 onto figure 3

and examining the correlations in table 3, a visual picture

emerges of the species that are important assemblage

components at the 12 localities.

CCA allowed us to examine the importance of stream size

and hydrologic variability in structuring fish assemblages.

Correlations between the environmental variables and CCA

axes one and two (table 4) indicate the relative contribution

of each variable to the CCA axes. For the stream size analysis, the Monte Carlo randomization test indicated that the

relationship between the stream size variables and assemblage structure was highly significant for the first two axes

(p = 0.001, both axes). Thus, assemblage structure was

strongly associated with stream size.

CCA also indicated that variation in stream flow was associated with assemblage structure. A Monte Carlo randomization procedure indicated that the relationship between flow

variability and assemblage structure was significant for the

first CCA axis (p = 0.054) and marginally significant for the

Figure 3—Detrended correspondence analysis (DCA) of all site

and samples across the three year period. Species scores are

plotted in multivariate space defined by DCA axes 1 and 2.

Table 3—Pearson correlations of species

abundance and first two DCA ordination axes

Axis 1 Axis 2

Species correlations correlations

Ameiurus natalis 0.378 -0.152

Aphredoderus sayanus -0.253 -0.270

Campostoma anomalum 0.582 -0.349

Chaenobryttus gulosus -0.058 -0.042

Erimyzon oblongus -0.290 -0.307

Esox americanus -0.363 -0.126

Etheostoma blennioides 0.166 -0.117

Etheostoma collettei -0.027 -0.326

Etheostoma radiosum 0.620 0.103

Etheostoma whipplei -0.531 0.128

Fundulus catenatus 0.508 -0.058

Fundulus olivaceous 0.137 -0.423

Hypentelium nigricans 0.454 -0.042

Ichthyomyzon gagei -0.008 -0.137

Labidesthes sicculus 0.039 -0.138

Lepomis cyanellus 0.120 0.399

Lepomis hybrid -0.186 -0.030

Lepomis macrochirus 0.040 -0.015

Lepomis megalotis 0.359 -0.364

Lythrurus umbratilis -0.085 -0.473

Luxilus chrysocephalus 0.564 0.016

Micropterus dolomieu 0.347 -0.115

Micropterus punctulatus 0.240 -0.165

Micropterus salmoides 0.081 -0.086

Notropis boops 0.012 -0.220

Notropis ortenburgeri -0.155 -0.365

Noturus nocturnus 0.154 -0.029

Noturus lachneri -0.076 -0.367

Percina caprodes 0.204 -0.127

Pimephales notatus 0.138 -0.212

Semotilus atromaculatus -0.143 0.576

DCA = Detrended correspondence analysis.
244

second CCA axis (p = 0.104). The strongest correlate was

variability in maximum current velocity (table 4), and this

vector separated the largest sites (4, 8, 9, 11, and 12) from

the other sites (larger sites showed less variability in maximum current velocity). Other strong correlates on the first

CCA axes were related to CVs of depth, width, and current

(table 4). Thus, variability in the flow regime (CVs of stream

size and current speed) was also an important predictor of

local fish assemblage structure.

An interesting pattern emerged between drainage systems

when sites were plotted in two-dimensional space defined

by CCA axes 1 and 2 from the stream flow analysis. Assemblages from the three sites in the Little Glazypeau system

(sites 10, 11, and 12) separated from the rest of the sites

based on variables associated with the flow regime (fig. 4).

Table 4—Environmental correlates of first two CCA

axes from two separate analysesa b

Size Variability

CCA CCA CCA CCA

Variable Axis 1 Axis 2 Axis 1 Axis 2

Mean width 0.440 0.756 -0.339 0.157

Maximum width 0.238 0.925 0.382 0.041

Mean depth 0.054 0.614 0.466 -0.432

Maximum depth 0.502 0.441 0.594 -0.564

Mean current 0.193 0.326 0.041 0.558

Maximum current 0.076 0.462 0.833 0.408

CCA = Canonical correspondence analysis.

a For the size analysis all environmental variables represent

means, calculated for each site across all sample dates.

b For the variability analysis all environmental variables represent

coefficients of variation, calculated for each site across all sample

dates.

Figure 4—Canonical correspondence analysis (CCA) of 12 sites.

Environmental variables are coefficients of variation, calculated

across the 3-year period. Species abundances are summed across

time for each site.

Multi-response permutation procedures indicated that this

separation was significant (p = 0.032). Thus, the Little

Glazypeau system differed from the Alum Fork system in its

variability with respect to the flow regime, and these differences were also associated with differences in fish assemblage structure.

CONCLUSIONS

Our results suggested that assemblages in these Ouachita

Mountain streams were strongly associated with factors

related to stream size and flow variability. Furthermore,

Taylor and Warren (Taylor, C.M.; Warren, M.L. Dynamics in

species composition for stream fish assemblages: environmental variability and nested species subsets. Manuscript

in preparation) found that stream size and flow variability

were correlated negatively for these streams. Thus, hydrologic variability was spatially structured in this system.

Smaller stream localities were more variable with respect to

flow than larger stream localities, and this variation was associated with the structure of the resident fish assemblages.

Further, the Alum Fork and Little Glazypeau systems

showed significant differences in assemblage structure that

were associated with variability in the flow regime. The

Little Glazypeau system generally has less variable flows

than the Alum Fork system due to geological differences in

groundwater discharge. The two systems also differed for

historical reasons. Little Glazypeau and Alum Fork systems

are geographically separated, and this separation has

produced differences in the regional species pools.

ACKNOWLEDGMENTS

Research support was provided through two projects “Fish

Community and Habitat Stability under Different Forest

Management Regimes” dated September 1, 1997 through

September 30, 1999 of Cooperative Agreement 33-CA-97-

190 and “Community Dynamics in Small Ouachita Mountain

Streams” dated August 1, 1999 through February 28, 2002

of Cooperative Agreement 33-CA-99-622.

We thank L. Irwin, L. Kiner, C. Grove, and T. Holder for help

with fieldwork. The Department of Biological Sciences, at

Mississippi State University, provided vehicle and computer

support. This work was funded by the USDA Forest Service,

Southern Research Station, Ouachita Mountains Ecosystem Management Research Project, Hot Springs, AR.

LITERATURE CITED

Allan, J.D. 1995. Stream ecology: structure and function of running

waters. London: Chapman and Hall. 388 p.

Brown, J.H. 1984. On the relationship between abundance and

distribution of species. American Naturalist. 124: 225-279.

Frissell, C.A.; Liss, W.A.; Warren, C.E.; Hurley, M.D. 1986. A

hierarchical framework for stream habitat classification: viewing

streams in a watershed context. Environmental Management.

10: 199-214.

Gauch, H.G. 1982. Multivariate analysis in community ecology.

London: Cambridge University Press. ??? p.

Gorman, O.T.; Karr, J.R. 1978. Habitat structure and stream fish

communities. Ecology. 59: 507-515.

Horwitz, R.J. 1978. Temporal variability patterns and the distributional patterns of stream fishes. Ecological Monographs. 48:

307-321.
245

Matthews, W.J. 1998. Patterns in freshwater fish ecology. New

York: Chapman and Hall. 756 p.

McCune, B.; Mefford, M.J. 1997. PC-ORD. Multivariate analysis of

ecological data. version 3.0. Gleneden Beach, OR: MjM

Software Design. 47 p.

Poff, N.L.; Ward, J.V. 1989. Implications of streamflow variability

and predictability for community structure: a regional analysis

of stream-flow patterns. Canadian Journal of Fisheries and

Aquatic Sciences. 46: 1805-1818.

Robison, H.W. 1986. Zoogeographic implications of the Mississippi

River basin. In: Hocutt, C.H.; Wiley, E.O. eds. The zoogeography of North American freshwater fishes. New York, NY:

John Wiley & Sons: 267-285.

Robison, H.W.; Buchanan, T.M. 1988. Fishes of Arkansas.

Fayetteville, AR: University of Arkansas Press. 536 p.

Schlosser, I.J. 1987. A conceptual framework for fish communities

in small warmwater streams. In: Matthews, W.J.; Heins, D.C.

eds. Community and evolutionary ecology of North American

stream fishes. Norman, OK: University of Oklahoma Press: 17-

28.

Sheldon, A.L. 1968. Species diversity and longitudinal succession

in stream fishes. Ecology 49: 193-198.

Taylor, C.M. 1997. Fish species richness and incidence patterns in

isolated and connected stream pools: effects of pool volume

and spatial position. Oecologia 110: 560-566.

Taylor, C.M.; Lienesch, P.W. 1996a. Environmental correlates of

distribution and abundance for Lythrurus snelsoni: a range-wide

analysis of an endemic fish species. Southwestern Naturalist.

40: 373-378.

Taylor, C.M.; Lienesch, P.W. 1996b. Regional parapatry for the fish

congeners Lythrurus snelsoni and L. umbratilis: species

replacement along a complex environmental gradient. Copeia.

1996: 493-497.

ter Braak, C.J.F. 1986. Canonical correspondence analysis: a new

eigenvector technique for multivariate direct gradient analysis.

Ecology. 67: 1167-1179.

Wiens, J.A.; Addicott, J.F.; Case, T.J.; Diamond, J. 1986.

Overview: the importance of spatial and temporal scale in

ecological investigations. In: J. Diamond, J.; Case, T.J. eds.

Community ecology. New York: Harper and Row: 145-153.
246

INTRODUCTION

Characteristics of fish assemblages within stream habitats

are shaped by physical and biological factors that operate

at different temporal and spatial scales (e.g., Poff 1997).

Stream ecologists are obligated to determine habitat variables that most influence fish and the spatial scale(s) over

which they operate (Rabeni and Sowa 1996). To make predictions, stream researchers have long used information

about the physical characteristics of streams and knowledge

of the hierarchical nature of the stream environment (e.g.,

Frissell and others 1986, Naiman and others 1992). Physical

features of habitat units (e.g., pools, riffles) such as area or

volume (Schlosser and Angermeier 1989), depth, water current, cover, and substrate (Gorman and Karr 1978), often

correlate strongly with patterns of fish diversity, richness,

and biomass. At large spatial scales (e.g., reaches, watershed), important factors may include flow regime (e.g.,

Horwitz 1978), temperature, valley form, distance to mouth,

and zoogeographic history.

Species distribution and assemblage patterns are shaped

by these natural controls and changes in the physical

environment that can accompany land use activities. Land

use practices, especially those associated with the removal

of riparian vegetation, can lead to changes in stream temperature, flow, nutrient input, and habitat quality (Hicks and

others 1991, Schlosser 1991). In degraded streams, reduced

availability of habitat forming woody debris can reduce the

abundance of pools in a stream (e.g., Keller and Swanson

1979, Schlosser 1991). A potentially important, though

poorly appreciated, consequence of such a change is that

the loss of pool habitat also reduces the frequency (Ralph

and others 1994) and therefore increases the spacing (e.g.,

Beechie and Sibley 1997) of these habitats within the

stream.

Although the ecological impacts of land use disturbances

have been widely studied, the historical focus was on small

scale impacts occurring within stream habitats or reaches

(e.g., Schlosser 1982, Hicks and others 1991). Recent

papers have addressed some of the effects of land use

activities on habitat connectivity and fragmentation of

streams (e.g., Schlosser 1991, Rieman and McIntyre 1995,

Schlosser 1995, Ward 1998), but these efforts focused on

large spatial scales (e.g., riverine landscapes) and moderately long time scales (e.g., generations). Effects of fragmentation on fish behavior (e.g., foraging, habitat selection,

and response to disturbance) remain poorly understood

over short time periods and at small spatial scales.

Riffles are typically the corridors connecting neighboring

pools in stream systems with well-developed habitats.

Characteristics of riffles (e.g., shallow depths, fast currents)

may limit the ability of pool dwelling fishes to access neighboring pools. Access to neighboring pools may be particularly limited if pools are infrequent or spacing of pools is

increased in association with habitat degradation. Although

riffles may not preclude all fish movement, long riffles may

significantly reduce excursions between habitat patches,

slow immigration to new habitats, and limit the ability of fish

to track variability in food resources and predator densities.

Effects of Isolation on Response to Disturbances

Episodic disturbances, such as floods and droughts, are

major factors shaping the organization of stream communities (e.g., Pearsons and others 1992, Strange and others

1992). The rate at which assemblages recover from disturbance will depend strongly on how rapidly different fish species recolonize disturbed stream segments. A large body of

evidence indicates that recolonization of disturbed stream

segments by fishes can be fairly rapid (< 1 yr, e.g., Niemi

and others 1990). Recovery of fish assemblages in short

reaches and habitat units can occur on the scale of days

and weeks (Peterson and Bayley 1993, Sheldon and Meffe

1995). Nevertheless, research thus far has revealed little

about physical factors that contribute to recovery, especially

at small spatial scales (Detenbeck and others 1992). For

example, is recolonization influenced by the size of the

habitats affected or by the degree to which affected habitats

are isolated from source habitats as predicted by Island

Biogeography Theory (MacArthur and Wilson 1967).

CONSEQUENCES OF POOL HABITAT ISOLATION ON STREAM FISHES

David G. Lonzarich, Melvin L. Warren, Jr., and Mary E. Lonzarich1

1 Assistant Professor, University of Wisconsin, Eau Claire, Department of Biology, Eau Claire, WI 54702; Research Biologist, USDA Forest

Service, Southern Research Station, Center for Bottomland Hardwoods Research, Oxford, MS 38655; and Consultant, University of

Wisconsin, Eau Claire, Department of Biology, Eau Claire, WI 54702, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—For fishes, stream habitat units (i.e., pools and riffles) often exist as relatively discrete patches of varying

quality that are distributed in a mosaic along the stream continuum. Under these conditions, the possibility exists that the

spacing of suitable patches within a stream reach may affect interhabitat movements of fishes and their pattern of

distribution within habitat patches. We summarized the findings of two different, but related, studies that demonstrated

how isolation of pools by long and shallow riffles affected recolonization and daily movement patterns of pool dwelling

fishes in two Arkansas streams. Our results show for the first time that the spatial distribution of habitats within streams

can significantly affect the abundance of species and characteristics of fish assemblages within pool habitats. Because

land use activities can alter habitat spacing, our findings also have important implications for fish conservation in

degraded streams.
247

Effects of Isolation on Short Term Movement

Biologists have long been interested in the movement of

fish in streams though few have studied how habitats, when

organized into discrete patches, may affect movement

behavior. Gerking (1953) was probably the first to discuss

the possible effects of patchiness when he proposed that

long riffles separating adjacent pool habitats could act as

behavioral barriers to movement and that fishes within

individual pools could be viewed as discrete populations.

Matthews and others (1994) also speculated that riffles act

as size selective barriers to movement and affect pool

assemblage dynamics. Recent experiments added weight

to earlier observations by showing that long riffles slow fish

recolonization to defaunated pools (Lonzarich and others

1998) and reduce fish movement between pools in experimental streams (Schaeffer 1999).

One message that emerges from field and experimental

studies is that the spatial mosaic of stream habitats may

significantly influence the short term movements of fish. At

least with respect to short time scales (i.e., days, weeks),

the spatial arrangement of pools and riffles in streams may

have significant ecological implications. For example, pool

dwelling fish may need to traverse long, shallow riffles to

access suitable habitats. Depending on the distance between

adjacent patches (i.e., length of intervening riffles), these

movements may entail high energetic costs or high predation risks (e.g., Power 1987).

We strongly believe that efforts to better understand the

ecology of stream fishes and more specifically, the consequences of land use activities, will benefit from research

aimed at addressing the effects of habitat patchiness on

fish movement. In this paper, we summarize the major findings and discuss the general implications of two different,

but closely related, studies that examined the effects of

habitat isolation on recolonization and daily movement

patterns of pool dwelling fishes in two Arkansas streams.

Complete descriptions of the results of both experiments

can be found elsewhere (Lonzarich and others 1998,

Lonzarich and others, in press).

METHODS

Study Area

We carried out the experiments in the summers of 1995

and 1997 in two tributaries of the Little Missouri River in the

Ouachita National Forest, west central Arkansas

(34°22'30"lat and 93°52'30"long). Long and Blaylock creeks

are short (< 10 km), moderate gradient systems that flow

through predominantly forested and mountainous terrain.

General characteristics of the streams included bedrock

and gravel substrates and dense riparian vegetation. The

proportion and average size of pool and riffle habitats in the

two streams were similar (Clingenpeel 1994) as were the

composition, rank abundance, and densities of fish species

(Lonzarich and others 1998).

Experimental Design

General features—We selected pools from the two streams

and assigned them equally into two treatment categories

based primarily on differences in the lengths of adjacent

riffles. Short riffle pools were separated from adjacent upstream and downstream pools by riffles < 10-m long; long

riffle pools were separated from adjacent pools by riffles

which averaged ≥ 50 m in length. With the exception of

these differences, the physical dimensions of pools in the

two treatment groups were similar (Lonzarich and others

1998, Lonzarich and others, in press). The recolonization

experiment included 12 treatment pools (6 pools per treatment), and the movement experiment included 16 pools

(8 pools per treatment).

In both experiments, we collected fishes by isolating survey

pools with 6-mm mesh block nets and sampling with a

Smith Root battery powered, backpack electrofisher. For

the recolonization experiment, we sacrificed collected fish

or relocated them downstream of barriers to upstream

movement. For the movement study, we marked collected

fish with treatment specific caudal fin clips (i.e., long riffle

pools, upper caudal fin; short riffle pools, lower caudal fin).

Species composition, rank dominance, and fish densities in

Long and Blaylock creeks were very similar (Lonzarich and

others 1998). In both experiments, we focused on a subset

of fish species. Because of concerns over sampling efficiency, we did not include small, juvenile fish (< 25 mm) or

bottom dwelling species that often hide within the substrate

and can be difficult to locate without considerably increasing survey times. We excluded four bottom dwelling species

from the survey: northern hog sucker, Hypentelium nigricans

(Lesueur); orangebelly darter, Etheostoma radiosum (Hubbs

and Black); greenside darter, E. blennioides Rafinesque;

and yellow bullhead, Ameiurus natalis (Lesueur). Numerically, these species and small juveniles were a minor component of the pool assemblages, accounting for less than

10 percent of all fish collected by electrofishing (Lonzarich

and others 1998). We included seven target species in the

surveys: central stoneroller, Campostoma anomalum

(Rafinesque); striped shiner, Luxilus chrysocephalus

(Rafinesque); redfin shiner, Lythrurus umbratilis (Girard);

bigeye shiner, Notropis boops Gilbert; northern studfish,

Fundulus catenatus (Storer); creek chub, Semotilus atromaculatus (Mitchill); longear sunfish, Lepomis megalotis

(Rafinesque); and smallmouth bass, Micropterus dolomieui

Lacepede.

We quantified both recolonization and short term movement

using underwater observation rather than electrofishing

largely because our sampling design required repeated fish

counts over short periods. We previously showed that

population estimates generated from snorkeling surveys

are nearly identical to those generated from more invasive

and labor intensive electrofishing surveys (Lonzarich and

others 1998). High water clarity in each stream provided

excellent conditions for identifying marked fish from distances up to 3 m.

Recolonization protocol—We censused fish in experimental pools by snorkeling immediately prior to electrofishing (Day 0, predisturbance census), and then 1, 3, 10,

20, 30, and 40 days following the removal of target species.

To minimize observer error, the same person surveyed all

pools, and two consecutive censuses were performed for

each survey. The mean of these two counts was used in

statistical analyses.
248

For this summary, we limited characterization of recovery

patterns for species and entire pool assemblage to changes

in the relative abundance of fish within each pool. We

divided counts (individuals per pool) obtained for any post

disturbance census by predisturbance counts from Day 0

so that a relative abundance of 100 percent indicated

complete recovery. Using relative abundance data for each

survey date, we generated regression models to characterize recovery patterns and rates for each treatment group.

The model that best explained the relationship between

time and recovery had the following linear form:

ln(Recovery) = ln(a) + b*(ln(Day + 1)) (1)

where recovery is either assemblage or species recovery

(relative numbers), and b, the regression coefficient, represented the recovery rate. We used a two sample t test to

compare differences in mean assemblage and species

recovery rates (b) in the two treatment groups. We determined the effects of other independent variables (i.e., riffle

depth, distance to large pool, pool area and assemblage

size) on pool specific recovery rates by least squares

regression. If percentage data deviated from normality, we

performed square root arcsine transformations prior to

analysis.

Movement protocol—We marked nearly 1,900 individuals

from the 16 experimental pools. After marking, we held all

fish overnight and released them into their experimental

pools 30 min before we initiated snorkeling surveys. In upstream and downstream pools immediately adjacent to the

experimental pool, we completed simultaneous surveys

hourly between 1100 to 1700 h (6 censuses) on Day 0 and

once per day on Day 1 and Day 3 between 1100 to 1400 h

for a total of 8 censuses of marked fish. On Day 1 and Day

3, our snorkeling censuses included the experimental pool

and the second upstream pool and second downstream pool

in the study reach. On Day 0, we limited censuses of the

experimental pool to the final survey (1700 h) to minimize

the risk that snorkeling might artificially inflate emigration.

An important assumption of our study was that marked fish

observed in upstream and downstream pools accurately

reflected emigration from experimental pools and were not

biased downwards by fish emigrating from an experimental

pool to become established in adjacent riffles. We accepted

this assumption on the basis of previous research where

we found strong differences in pool and riffle assemblages

of these streams (Lonzarich and others 1998).

We estimated the rate, direction, and percentage of emigrants from each pool. We determined movement rates

(fish per h) by least squares linear regression models using

data from Day 0 hourly surveys. Because the general

shape of the relationship between time and movement was

logarithmic, the model showing the best fit to the data had

the following general form:

Movement = a + b*ln(h + 1) (2)

where movement represented the percentage of marked

fish observed outside of the experimental pool and b, the

regression coefficient, represented the movement rate. We

generated species specific and assemblage level linear

regression models for each experimental pool. To test for

treatment differences in the rate of movement, we compared

mean movement rates (b) for each treatment using a two

sample t test. We determined the directionality of movement by dividing the number of marked fish that moved

downstream by the combined number of emigrants. Hence,

values above 50 percent indicated an upstream bias in

movement and values below 50 percent, a downstream

bias. We computed the percentage of emigrants from each

pool by dividing the maximum number of known emigrants

by the total number of fish marked in the experimental pool.

We typically observed the maximum number of emigrants

on Day 3 in long riffle pools and on Day 1 in short riffle

pools. We tested for species and assemblage level differences in movement patterns (i.e., rate, proportion, and

direction) between the two treatment groups using two

sample t tests (rate and proportion) and 2 x 2 Chi square

tests of independence (for direction). If percentage data

deviated from normality, we performed square root arcsine

transformations prior to analysis.

RESULTS

Effects of Isolation on Recolonization

Numerical recovery was influenced strongly by factors that

isolated experimental pools from potential colonists. In short

riffle pools, assemblages reached full numerical recovery

by day 30, whereas assemblages in long riffle pools reached

only 75 percent of their predisturbance densities by day 40

(fig. 1). Estimates of full recovery based on data extrapolation ranged from 100 to 130 d for long riffle pools. Over the

entire recovery period, the mean (± 1 S.E.) recovery rate in

long riffle pools was 33 percent lower (20 ± 0.6 percent per

ln(d)) than the rate for short riffle pools (30 ± 1.5 percent

per ln(d), t test, P < 0.05). In addition, recovery was correlated positively with riffle depth (r2 = 0.37, P < 0.05, fig. 2a)

and negatively with distance to the nearest large source

pool (r2 = 0.44, P < 0.05, fig. 2b). These patterns held even

when pools within each riffle length category were examined

separately. Neither predisturbance assemblage size nor

pool area had any detectable influence over numerical

recovery rates of assemblages (P > 0.50, fig. 2c).

Figure 1—Comparisons of percent numerical recovery for each

riffle length category. Error bars represent ± 1 S. E. Data points

and error bars are offset to increase clarity.
249

observed emigrants during the first count, while in long riffle

sites, we did not see emigrants until at least the third hour

and generally observed very few on Day 0 (< 2 percent). By

Day 3, we observed an average of 30 percent (1 SE = ± 3.8

percent) of marked fish outside of experimental short riffle

pools. In contrast, the average for long riffle pools was only

9 percent (1 SE = ± 2.3 percent).

When we account for all marked fish observed within each

site, differences in emigration between treatments become

even more trenchant. Expressed as a percentage of the

total number marked, the proportion of marked fish observed

within long riffle reaches did not change over time (fig. 3).

In contrast, the percentage of marked fish declined sharply

in short riffle reaches from 67 percent for Day 0 to 49 percent for Day 3. Assuming that all of these individuals (18

percent) moved beyond the study area boundaries by Day

3, the actual percentage of fish emigrating from short riffle

pools was much higher than the observed average emigration of 30 percent.

Figure 2—Relationships between estimated recovery rates (A)

riffle depth, (B) Ln distance to large pool, and (C) predisturbance

assemblage size and experimental pool area.

Figure 3—Marked fish (percent) emigrating from and remaining

within experimental pools on each day of the three-day survey

periods.

Effects of Isolation on Short Term Movement

Marked fish emigrated from short riffle pools much more

frequently (up to 10 fold) than from long riffle pools. The

emigration rate (± 1 S.E.) from short riffle pools on Day 0

was seven times higher (b = 6.9 ± 0.7 percent per ln(h) than

the estimated rate for long riffle pools (b = 1.0 ± 0.3 percent

per ln(h), t test, P < 0.01). In short riffle pools, we commonly
250

Marked fish moving from short riffle pools selected upstream

and downstream directions with equal frequency (52 vs. 48

percent, respectively). In contrast, emigrants from long riffle

pools moved downstream nearly twice as often as they

moved upstream (63 percent vs. 37 percent, respectively,

Chi square goodness of fit test, P < 0.01).

DISCUSSION

We summarized here the first studies to quantify how the

habitat mosaic in streams can shape the response of fish to

acute disturbances and limit their short term movements

among habitats. Attributes of the physical environment

responsible for this variability in movement behavior

included riffle length, depth, and distance to large source

pools.

Though poorly studied in streams, the effects of habitat

isolation on the movement of organisms (e.g., Forman and

Godron 1986, Forman 1995) and on the recolonization of

habitat islands (e.g., Simberloff and Wilson 1969) have long

been recognized by ecologists. In fact, Brown and Brussack

(1990) described riffle habitats for benthic invertebrates as

isolated islands that were separated by long pools of low

habitat quality. Like organisms in other spatially heterogeneous environments, fish in streams are distributed within

a mosaic of habitats of varying quality (e.g., resource availability, predator densities, and physical conditions). Thus,

the rate at which individuals move between patches and

recolonize disturbed reaches should depend on whether

intervening habitats (i.e., riffles) possess properties that

could impair movement. For pool dwelling species, we suspect that limited movement through long riffles may be due,

at least in part, to the fact that these habitats are shallow

and fast flowing. Fast flows may be difficult to negotiate,

and shallow water may expose individuals to terrestrial

predators (e.g., Power 1987). Although direct field evidence

supporting either of these potential factors is lacking, Warren

and Pardew (1998) measured a strong effect of water velocity on fish movement through road crossings, and we found

that fish in long riffle pools moved significantly less frequently against the current (upstream) than with the current

(downstream). We also measured a negative relationship

between water depth and fish recolonization of defaunated

stream pools (Lonzarich and others 1998).

We suspect that these factors contribute to variability in fish

movement through long riffles but urge care in extrapolating

beyond the temporal scope (i.e., days and months) and low

flow conditions that characterized our experiments. We

actually question whether potential survival or energy costs

associated with traversing long riffles affect patterns of fish

redistribution during floods or seasonal migrations to spawning or wintering habitats. Nevertheless, the timing of our

study, the summer, coincided with a critical period in the life

cycles of stream fish species. Not only is this the season

when fish experience their most favorable growing conditions, it also is when competition and threats of predation,

especially for juveniles, can be very intense. Further, in

Arkansas, as in many other geographic regions, stream

organisms are often exposed to unpredictable and potentially severe flow disturbances during the summer.

Given this context, we ask how variability in fish movement

between pools affects the dynamics of populations and

assemblages both at the scale of habitats and stream

reaches? As reflected in our study design, we believe that

there are at least two ways to approach this question. When

measured over relatively long time periods (e.g., days,

months), it appears that pool isolation can severely limit

the rate at which fish assemblages recover from acute

disturbances as predicted by Island Biogeography Theory

(MacArthur and Wilson 1967). Not only is recovery affected

by the distance to potential colonists, it also is affected by

the proximity of large pools, which in some respects may be

analogous to the mainland habitats of MacArthur and Wilson

(1967). Sedell and others (1990) argued that large pools

are very important to the ecology of streams because they

provide stream organisms with refugia during stressful

conditions (e.g., droughts, floods). Our results imply that

large pools, which were two times larger than average

sized pools in our study streams, also may serve as important sources of immigrants to neighboring up and downstream habitats. In view of the importance of episodic

disturbances in streams, and because land use activities

can alter both the stream flow regime (e.g., Rosén and

others 1996) and habitat spacing, these results may help

explain variability in the organization of fish assemblages in

stream pools.

When viewed over much shorter time scales (e.g., hours,

days), pool isolation may affect the ability of fish to track

spatially variable resources (e.g., food, mates) or predator

densities. Matthews (1998) described such a possibility. He

speculated that differences in the ability of small bodied

prey and large bodied predators to traverse shallow riffles

might affect predator prey relationships in stream reaches.

The assumption of open fish communities in pools appears

to conflict with the restricted movement paradigm (Gerking

1953) and with the widely held view that fish within pools

can be viewed as discrete populations (e.g., Gerking 1953,

Matthews and others 1994). However, it is consistent with

the findings of Fraser and Sise (1980) who proposed that

stream fish should be sensitive to local resource availability

and move between pools in search of food resources. We

add that the possible benefits of moving from a pool must

be weighed against the potential costs of crossing riffles.

Therefore, in long riffle pools where movement can be

limited, there may be a tendency towards closed community

structure with local (i.e., pool scale) conditions, such as

prey abundance, predator densities, and the habitat template having strong affects on assemblage organization. In

short riffle pools, where the daily turnover of individuals can

be very high (> 20 percent per day), the characteristics of

pool assemblages and populations may show little if any

relationship to local conditions. This view is similar to that of

Cooper and others (1990) who found that the ability of fish

predators to control the local abundance of stream insects

diminished as insect exchange rates (emigration/immigration) into pools increased.

SUMMARY

The extent and nature of animal movements are key factors

affecting the vulnerability of species to landscape changes

(Law and Dickman 1998). Historically, stream ecologists

have focused on local habitat conditions (e.g., pools, riffles)
251

when trying to describe and predict the factors that explain

the abundance and dynamics of fish populations. The importance of habitat isolation to the ecology of stream fishes

may vary widely across streams and geographic regions

due to variability in the physical factors that determine channel features. Riffle lengths in our streams were probably

representative of conditions in many small upland stream

systems in the eastern United States but are much shorter

than those found in small montane streams of the Pacific

Northwest (e.g., > 80 m, Beechie and Sibley 1997). In

contrast, low gradient, sand bottom streams of the Gulf and

Atlantic Coastal Plains may lack physical conditions promoting riffle development. Logging, agriculture, and other

land use activities along streams can lead to changes in the

channel course or reduce the availability of instream elements (e.g., large woody debris) that create habitat. Such

changes can reduce the abundance, and therefore the

spacing, of pools in streams. For this reason, we strongly

recommend that efforts to understand the ecology of stream

fishes and the consequences of land use activities focus

more attention on the spatial distribution and potential

isolation of these habitats in streams.

ACKNOWLEDGMENTS

Research support was provided through two projects “The

Spatial Distribution and Size of Stream Habitat Patches:

Consequences to Stream Fishes” dated August 1, 1996

through August 1, 1997 of Cooperative Agreement 30-CA96-053 and “Trophic Dynamics in Streams: Effects of

Habitat Isolation” dated June 1, 1999 through May 1, 2001

of Cooperative Agreement 33-CA-99-623.

We are grateful to the Ouachita National Forest for granting

access to Long and Blaylock creeks, to W. Matthews for

providing advice in the development of the study, and to J.

Guldin for administrative support. We are deeply indebted

to both A. Clingenpeel and B. Crump for logistical assistance while we were in the field. The work was funded by

the USDA Forest Service, Ouachita Mountain Ecosystem

Management Research Project, Southern Research

Station.

LITERATURE CITED

Beechie, T.J.; Sibley, T.H. 1997. Relationships between channel

characteristics, woody debris and fish habitat in northwestern

Washington streams. Transactions of the American Fisheries

Society. 126: 217-229.

Brown, A.V.; Brussack, P.B. 1990. Comparisons of benthic invertebrates between riffles and pools. Hydrobiologia. 220: 99-108.

Clingenpeel, J.A. 1994. A cumulative effects analysis of silvicultural best management practices using Basin Area Stream

Survey (BASS) methods. Hot Springs, AR: U.S. Department of

Agriculture, Forest Service, Southern Region, Ouachita

National Forest. 53 p.

Cooper, S.D.; Walde, S.J.; Peckarsky, B.L. 1990. Prey exchanges

rates and the impact of predators on prey populations in

streams. Ecology. 71: 1503-14.

Detenbeck, N.E.; DeVore, P.W.; Niemi, G.J.; Lima, A. 1992.

Recovery of temperate stream fish communities from

disturbance: a review of case studies and synthesis of theory.

Environmental Management. 16: 33-53.

Forman, R.T. 1995. Land mosaics: the ecology of landscapes and

regions. Cambridge, England: Cambridge University Press. 632

p.

Forman, R.T.; Godron, M. 1986. Landscape ecology. New York:

John Wiley. 640 p.

Fraser, D.F.; Sise, T.E. 1980. Observations on the stream minnows

in a patchy environment: a test of a theory of habitat distribution. Ecology. 61: 790-797.

Frissell, C.A.; Liss, W.J.; Warren, C.E.; Hurley, M.D. 1986. A

hierarchical framework for stream habitat classification: viewing

streams in a watershed context. Environmental Management.

10:199-214.

Gerking, S.D. 1953. Evidence for the concepts of home range and

territory in stream fishes. Ecology. 34: 347-365.

Gorman, O.T.; Karr, J.R. 1978. Habitat structure and stream fish

communities. Ecology. 59: 507-515.

Hicks, B.J.; Hall, J.D.; Bisson, P.A.; Sedell, J.R. 1991. Responses

of salmonids to habitat changes. American Fisheries Society

Special Publication. 19: 483-418.

Horwitz, R.J. 1978. Temporal variability patterns and the distribution of stream fishes. Ecological Monographs. 48: 307-321.

Keller, E.A.; Swanson, F.J. 1979. Effects of large organic material

on channel form and fluvial processes. Earth Surface

Processes 4: 361-380.

Law, B.S.; Dickman, C.R. 1998. The use of habitat mosaics by

terrestrial vertebrate fauna: implications for conservation and

management. Biodiversity and Conservation. 7: 323-333.

Lonzarich, D.G.; Lonzarich, M.R.; Warren, M.L., Jr. [In press].

Effects of riffle length on the short-term movement of fishes

among stream pools. Canadian Journal of Fisheries and

Aquatic Sciences.

Lonzarich, D.G.; Warren, M.L., Jr.; Elger-Lonzarich, M.R. 1998.

Effects of habitat isolation on the recovery of fish assemblages

in experimentally defaunated stream pools in Arkansas.

Canadian Journal of Fisheries and Aquatic Sciences. 55: 2141-

2149.

MacArthur, R.O.; Wilson, E.O. 1967. The theory of island biogeography. Princeton, NJ: Princeton University Press. 203 p.

Matthews, W.J. 1998. Patterns in freshwater fish ecology. New

York: Chapman and Hall. 756 p.

Matthews, W.J.; Harvey, B.C.; Power, M.E. 1994. Spatial and

temporal patterns in the fish assemblages of individual pools in

a midwestern stream (USA). Environmental Biology of Fishes.

39: 381-397

Naiman, R.; Lonzarich, D.G.; Beechie, T.; Ralph, S. 1992. General

principles of stream classification and the assessment of

conservation potential in rivers. In: River Conservation and

Management. P.J. Boon, P. Calow and G.E. Petts (eds.).

Chichester, England: John Wiley and Sons: 93-124.

Niemi, G.J.; DeVore, P.; Detenbeck, N. [and others]. 1990. Overview of case studies on recovery of aquatic systems from

disturbance. Environmental Management. 14: 571-588.

Pearsons, T.N.; Li, H.W.; Lamberti, G.A. 1992. Influences of

habitat complexity on resistance to flooding and resilience of

stream fish assemblages. Transactions of the American

Fisheries Society. 121: 427-436.

Peterson, J.T.; Bayley, P.B. 1993. Colonization rates of fishes in

experimentally defaunated warmwater streams. Transactions of

the American Fisheries Society. 122: 199-207.
252

Poff, L.N. 1997. Landscape filters and species traits: towards a

mechanistic understanding and prediction in stream ecology.

Journal of the North American Benthological Society. 16: 391-

409.

Power, M.E. 1987. Predator avoidance by grazing fishes in temperate and tropical streams: importance of stream depth and prey

size. In: Predation: Direct and Indirect Impacts on Aquatic

Communities. W.C. Kerfoot and A. Sih (eds.). Hanover, NH:

University Press of New England: 333-352.

Rabeni, C.F.; Sowa, S.P. 1996. Integrating biological realism in

habitat restoration and conservation strategies for small

streams. Canadian Journal of Fisheries and Aquatic Sciences

(supplement). 53: 252-259.

Ralph, S.C.; Poole. G.C.; Conquest, L.L.; Naiman, R.J. 1994.

Stream channel morphology and woody debris in logged and

unlogged basins in western Washington. Canadian Journal of

Fisheries and Aquatic Sciences. 51: 37-51.

Rieman, B.E.; McIntyre, J.D. 1995. Occurrence of bull trout in

naturally fragmented habitat patches of varied size. Transactions of the American Fisheries Society. 124: 285-296.

Rosén, K.; Aronson, J.; Eriksson, H.M. 1996. Effects of clearcutting on streamwater quality in forest catchments of Sweden.

Forest Ecology and Management. 83: 237-244.

Schaefer, J.F. 1999. Movement by three stream-dwelling cyprinids

(Notropis boops, Campostoma anomalum, Cyprinella venusta).

University of Oklahoma, Norman, OK. 96 pp. [unpublished

dissertation].

Schlosser, I.J. 1982. Fish community structure and function along

two habitat gradients in a headwater stream. Ecological

Monographs. 52: 395-414.

Schlosser, I.J. 1991. Stream fish ecology: a landscape perspective. Bioscience. 41: 704-712.

Schlosser, I.J. 1995. Critical landscape attributes that influence

fish population dynamics in headwater streams. Hydrobiologia.

303: 71-81.

Schlosser, I.J.; Angermeier, P.L. 1989. Spatial variation in demographic processes of lotic fishes: conceptual models, empirical

evidence, and implications for conservation. American Fisheries

Society Symposium 17: 392-401.

Sedell, J.R.; Reeves, G.H.; Hauer, F.R. [and others]. 1990. Role of

refugia in recovery from disturbances: modern fragmented and

disconnected river systems. Environmental Management. 14:

711-724.

Sheldon, A.L.; Meffe, G.K. 1995. Short-term recolonization by

fishes of experimentally defaunated pools of a coastal plain

stream. Copeia. 1995: 828-837.

Simberloff, D.S.; Wilson, E.O. 1969. Experimental zoogeography

of islands: the colonization of empty islands. Ecology. 50: 278-

296.

Strange, E.M.; Moyle, P.B.; Foin, T.C. 1992. Interaction between

stochastic and deterministic processes in stream fish community assembly. Environmental Biology of Fishes 36: 1-15.

Ward, J.V. 1998. Riverine landscapes: biodiversity patterns,

disturbance regimes and aquatic conservation. Biological

Conservation. 83: 269-278.

Warren, M.L., Jr.; Pardew, M.G. 1998. Road crossings as barriers

to small-stream fish movement. Transactions of the American

Fisheries Society. 127: 637-644.
253

INTRODUCTION

When planning fish surveys, stream ecologists can choose

from a variety sampling techniques, although seining, electroshocking, and underwater observation are the most widely

used. Seining and electroshocking are both invasive sampling methods that traditionally have been favored in most

stream surveys, especially in studies that do not require

repeated measurements (e.g., collections of baseline population data, microhabitat use or feeding). Unfortunately, as

with all sampling methods, seining and electrofishing

possess potentially severe limitations. For example, habitat

conditions can dramatically affect seining success. In streams

with uneven bottoms and physical obstacles, such as large

rocks, vegetation or woody debris, seining efficiency can be

very low. Electrofishing is more effective under these conditions, but it too can suffer limited efficiency (e.g., Riley and

Fausch 1992). Importantly, electrofishing can injure or kill

collected individuals, an especially serious problem in

waters with endangered or recreationally important fishes

(Nielsen 1998). Finally, all invasive methods require the

handling of individuals to retrieve necessary data. At worst,

handling can cause injuries or death; at best, it may affect

the behaviors of sampled individuals.

In many situations, snorkeling may be a solution to the limitations of invasive sampling. Snorkeling, which has advantages of versatility and cost effectiveness, can provide

information on the composition, distribution, abundance,

and behavior of fishes in streams (Dolloff and others 1996).

Snorkeling is widely used in small, clear streams of the

Pacific Northwest, especially in studies of trout and salmon

(e.g., Hankin and Reeves 1988, Hillman and others 1992).

Snorkeling provides several major benefits. A variety of

habitat conditions can be surveyed in relatively short periods

of time, and labor and equipment costs are low compared

to other techniques. Importantly, fishes are not collected or

handled in snorkeling surveys. Hence, studies requiring

repeated observations (e.g., movement studies) are not

compromised by handling stress, which can adversely affect

behaviors of interest to the researcher and the experimental

results (Lonzarich and others 1998, Lonzarich and others,

in press). Moreover, survey efficiency is not impeded by

physical factors such as wood, undercut banks, or uneven

substrates. A major drawback to snorkeling is the requirement

for clear waters with very high visibility > 2 m, (Whitworth

and Schmidt 1980). Depending on the study objectives,

snorkeling surveys also may require specialized training of

observers to assure accurate, underwater identification of

fishes.

Several studies have assessed the efficacy of snorkeling in

low diversity, clear streams of the Pacific Northwest. The

method can yield very precise results (e.g., Hillman and

others 1992, Dolloff and others 1996), but efforts to assess

snorkeling accuracy are difficult because true population

densities are usually unknown (Dolloff and others 1996). In

the more species rich streams of eastern North America, the

precision and accuracy of this technique as a tool to survey

fish assemblages are poorly documented. In two other studies, we monitored changes in the population and assemblage structure of stream pools. In conjunction with that

research, we report here a study conducted to assess the

effectiveness of snorkeling as a census method in streams

of the Ouachita Mountains, Arkansas. Surveying water

column fish species in two moderately diverse streams, our

objective was to compare estimates of population density

and assemblage structure from snorkeling with results

obtained by backpack electrofishing.

METHODS

We conducted electrofishing and snorkeling surveys between

June and July 1995 in two tributaries of the Little Missouri

River in the Ouachita National Forest, Arkansas (34°22′30′′

lat and 93°52′30′′long). Long and Blaylock creeks are relatively short (< 10 km), low gradient systems that flow through

forested and mountainous terrain. General characteristics

of the streams included bedrock, cobble, and gravel

substrates and dense riparian vegetation. Using habitat

inventory data (Clingenpeel 1994) and data from our own

surveys, we selected 12 pools in the two streams. For each

USING SNORKELING TO QUANTIFY FISH ASSEMBLAGE STRUCTURE

IN ARKANSAS STREAMS

David G. Lonzarich, Mary E. Lonzarich, and Melvin L. Warren, Jr.1

1 Assistant Professor and Consultant, University of Wisconsin, Eau Claire, Department of Biology, Eau Claire, WI 54702; and Research

Biologist, USDA Forest Service, Southern Research Station, Center for Bottomland Hardwoods Research, Oxford, MS 38655, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—As a technique for surveying fish populations in low diversity, clear streams of the Pacific Northwest, snorkeling is a commonly used alternative to electrofishing. While the method is becoming more widely used in the high diversity

streams of eastern North America, its efficacy as a tool for surveying fish assemblages in these streams is not known. In

this study, we compared results from snorkeling and electrofishing surveys obtained from two clear, warmwater streams of

the Ouachita Mountains, Arkansas. Surveying only water column species, we found that fish assemblage structure and

species density estimates obtained by the two methods were statistically indistinguishable. These results have important

implications for ecologists studying warmwater streams as snorkeling holds many advantages over invasive sampling

methods, such as seining and electrofishing.
254

pool, we measured total length, width, area, maximum

depth, and substrate composition (i.e., boulder, cobble, and

gravel). We also measured biological characteristics

including species richness, total assemblage densities, and

individual species densities.

Fish Surveys

Fish species composition, rank dominance, and densities in

Long and Blaylock creeks are very similar (Lonzarich and

others 1998). In this study, we focused on a subset of fish

species. Because of concerns over sampling efficiency, we

did not include small, juvenile fish (< 25 mm) or bottom

dwelling species that often hide within the substrate and

can be difficult to locate without considerably increasing

survey times. We excluded four bottom dwelling species

from the survey: northern hog sucker, Hypentelium nigricans

(Lesueur); orangebelly darter, Etheostoma radiosum (Hubbs

and Black); greenside darter, E. blennioides Rafinesque;

and yellow bullhead, Ameiurus natalis (Lesueur). Numerically, these species and small juveniles were a minor component of the pool assemblages, accounting for less than

10 percent of all fish collected by electrofishing (Lonzarich

and others 1998). We included seven target species in the

surveys: central stoneroller, Campostoma anomalum

(Rafinesque); striped shiner, Luxilus chrysocephalus

(Rafinesque); redfin shiner, Lythrurus umbratilis (Girard);

bigeye shiner, Notropis boops Gilbert; northern studfish,

Fundulus catenatus (Storer); creek chub, Semotilus atromaculatus (Mitchill); longear sunfish, Lepomis megalotis

(Rafinesque); and smallmouth bass, Micropterus dolomieui

Lacepede.

For each survey, we first snorkeled and then immediately

electrofished the study pool. We began snorkeling surveys

at the downstream end of the pool, continued in a zig-zag

fashion to the upstream end of the pool, and then snorkeled

downstream. We considered this a single pass, which generally took less than 30 min to complete. We counted fish

individually except when fish were aggregated; for aggregations, we estimated numbers by counting individuals in

groups of 5 to 10 individuals. To minimize observer error,

the same observer conducted surveys of all pools censused.

Each survey consisted of two passes with the average of

the two counts used in all statistical analyses.

Immediately after the snorkeling survey, we electrofished

experimental pools, isolating them with 6-mm mesh block

seines and sampling with a Smith Root battery powered,

backpack electrofisher. We sampled pools four to seven

times until no fish were collected on two consecutive passes.

Because of this intensive sampling effort and because we

identified fish in the field, surveys of individual pools took

3 to 4 h to complete. We evaluated the effectiveness of the

electrofishing technique in removing all target species by

conducting snorkeling surveys in the isolated pools immediately upon the completion of sampling. In post-electrofishing snorkeling surveys of 6 of the 12 pools, we observed

only 3 fishes from the target group of species.

Data Analysis

We estimated the efficiency of snorkeling relative to electrofishing. To do so, we compared assemblage structure (i.e.,

species richness, percent similarity, and total numbers) and

species abundance (fish per m2) results from snorkeling

surveys with results from electrofishing surveys. We compared assemblage similarity using the Percent Similarity

Index (Wolda 1981). We used correlation analysis to evaluate the strength of relationships between the two methods.

When data met the assumption of normality, we used the

Pearson Product Moment analysis. Otherwise, we used

Spearman Rank analysis. We compared species and

assemblage density estimates for the two methods by a

two-sample t-test (P < 0.05).

RESULTS

Snorkeling surveys yielded results that were very comparable to those obtained in electrofishing surveys. With

respect to the total number of fish per pool, estimates for

the two methods were highly correlated (Pearson product

moment correlation coefficient, r2 = 0.98, n = 12, fig. 1). On

average, total counts from snorkeling observations were

within 10 percent of the total counts from electrofishing

surveys (1.03 fish per m2 versus 1.13 fish per m2, respectively). Neither size of pool nor size of pool assemblages

(range, 63 to 656 individuals per pool) affected this level of

precision.

Estimates of assemblage structure (species composition,

rank abundance, and relative abundance) for the two

methods also were very similar. Across the 12 pools, we

missed species in snorkeling surveys that were captured by

electrofishing on only two occasions (out of 82 possibilities).

Similarly, on a single occasion, we missed only one species

in electrofishing surveys that we observed snorkeling.

Correspondence in the rank abundance of species for the

two methods was very high (Spearman Rank correlation

coefficient, average = 0.97; range, 0.87 to 1.00). The relative abundances of species for each method also were very

Figure 1—The relationship between electrofishing and snorkeling

estimates (total counts per pool) for 12 experimental pools in Long

and Blaylock Creeks, Ouachita Mountains, Arkansas.

Electrofishing (total captured)

Snorkeling (total observed)
255

comparable, with an average assemblage similarity of 91

percent across the 12 pools (range, 84 to 94 percent).

Snorkeling proved to be nearly as effective as electrofishing

in estimating species densities. On average, snorkeling

estimates for the eight target species were slightly lower

than electrofishing densities (table 1). Snorkeling efficiency

was not related to abundance. Estimates for the two most

common species, central stoneroller, and striped shiner,

were nearly identical to electrofishing estimates (table 1).

The weakest relationships were for northern studfish (54

percent deviation), longear sunfish (29 percent deviation)

and bigeye shiner (42 percent deviation). However, neither

these nor any other snorkeling and electrofishing estimates

of species densities were significantly different (t-test, P >

0.25, table 1). We attribute the northern studfish results to

our failure to recognize their preference for shallow, marginal habitats in early surveys. Our efficiency likely improved

in later surveys as we became more aware of their patterns

of habitat use. We believe the longear sunfish results likely

reflect their use of cover in the presence of a snorkeler,

whereas the bigeye shiner finding is related to their rarity in

the study pools.

DISCUSSION

Because we removed all target fish species from experimental pools during electrofishing surveys, we were able to

simultaneously compare the effectiveness of snorkeling

against electrofishing and to determine its accuracy. At

least for the water column species targeted, our results

show that snorkeling is a very accurate method for characterizing fish assemblage and species abundance patterns

in small, clear warmwater streams. Our findings have several

important implications. First, snorkeling required much less

survey time (0.5 h versus 3 to 4 h) and labor than electrofishing. Hence, more area can be sampled by snorkeling

than by electrofishing or seining. Second, snorkeling, unlike

electrofishing, does not adversely affect surveyed fishes

and can be used to describe short-term changes (e.g., days,

weeks) in populations and assemblages. As we demonstrated elsewhere (Lonzarich and others 1998) (Lonzarich,

D.G.; Lonzarich, M.R.; Warren, M.L., Jr. Effects of riffle length

on the short-term movement of fishes among stream pools.

Manuscript in preparation.) repeated snorkeling surveys can

be used to quantify the daily movements of fish and their

patterns of recolonization following disturbance. Recently,

we repeatedly snorkeled pools in one Arkansas stream over

the course of a summer to determine pool specific patterns

of extinction and colonization of water column species

(Lonzarich, D.G.; Lonzarich, M.R.; Warren, M.L., Jr. Effects

of riffle length on the short-term movement of fishes among

stream pools. Unpublished data. On file with: D. Lonzarich,

University of Wisconsin, Eau Claire, Department of Biology,

Eau Claire, WI 54702). Previously in this same region,

Matthews and others (1994) used snorkeling to repeatedly

census fishes from 14 pools over a 19-month period.

While advocating snorkeling surveys for selective fisheries

applications, we are mindful of its many limitations. For example, although benthic species (e.g., darters, catfish, suckers)

can be censused in snorkeling surveys, they likely cannot

be surveyed by snorkeling as efficiently as water column

species. Further, inclusion of benthic fishes would probably

add significantly to the time needed to complete an assemblage survey. Nonetheless, in studies of clear streams, where

it is not necessary (and possibly detrimental) to handle fish,

the technique has important advantages over invasive

sampling methods.

ACKNOWLEDGMENTS

Research support was provided through two projects “The

Spatial Distribution and Size of Stream Habitat Patches:

Consequences to Stream Fishes” dated August 1, 1996

through August 1, 1997 of Cooperative Agreement 30-CA96-053 and “Trophic Dynamics in Streams: Effects of

Habitat Isolation” dated June 1, 1999 through May 1, 2001

of Cooperative Agreement 33-CA-99-623.

We are grateful to the Ouachita National Forest for granting

access to Long and Blaylock Creeks and to J. Guldin for

administrative support. We are deeply indebted to both A.

Table 1—Comparison of mean fish densitiesa estimated from

electrofishing and snorkeling surveys of 12 treatment pools in

Long and Blaylock Creeks, Ouachita Mountains, Arkansas

Species Snorkeling Electrofishing Difference

Percent

Central stoneroller 50.5 ± 13.4 51.6 ± 10.8 2

Striped shiner 30.0 ± 6.1 30.0 ± 6.5 0

Longear sunfish 7.4 ± 1.6 10.4 ± 2.3 29

Creek chub 7.5 ± 3.2 9.3 ± 4.1 19

Northern studfish 2.5 ± 0.7 5.4 ± 1.7 54

Redfin shiner 2.3 ± 0.8 2.0 ± 0.6 15

Bigeye shiner 1.1 ± 0.6 1.9 ± 1.1 42

Smallmouth bass 1.6 ± 0.4 1.9 ± 0.5 16

All species 103.2 ± 14.5 113.2 ± 12.6 8

a Individuals/100 m2

 ± 1 standard error of the mean.
256

Clingenpeel and B. Crump for logistical assistance while we

were in the field. The work was funded by the USDA Forest

Service, Southern Research Station, Ouachita Mountain

Ecosystem Management Research Project.

LITERATURE CITED

Clingenpeel, J.A. 1994. A cumulative effects analysis of silvicultural best management practices using Basin Area Stream

Survey (BASS) methods. Hot Springs, AR: U.S. Department of

Agriculture, Forest Service, Southern Region, Ouachita

National Forest. 53 p.

Dolloff, A.; Kershner, J.; Thurow, R. 1997. Underwater observation.

Chapter 18. In: B.R. Murphy and D.W. Williss (eds.) Fisheries

Techniques. 2nd edition. Bethesda, MD: American Fisheries

Society: 533-554.

Hankin, J.D.; Reeves, G.H. 1988. Estimating total fish abundance

and total habitat area in small streams based on visual estimation methods. Canadian Journal of Fisheries and Aquatic

Sciences. 45: 834-844.

Hillman, T.C.; Mullan, J.W.; Griffth, J.S. 1992. Accuracy of underwater counts of juvenile chinook salmon, coho salmon, and

steelhead. North American Journal of Fisheries Management.

12: 598-603.

Lonzarich, D.G.; Warren, M.L., Jr.; Elger-Lonzarich, M.R. 1998.

Effects of habitat isolation on the recovery of fish assemblages

in experimentally defaunated stream pools in Arkansas.

Canadian Journal of Fisheries and Aquatic Sciences. 55: 2141-

2149.

Matthews, W.J.; Harvey, B.C.; Power, M.E. 1994. Spatial and

temporal patterns in the fish assemblages of individual pools in

a midwestern stream. Environmental Biology of Fishes. 39:

381-397.

Nielsen, J. 1998. Scientific sampling effects: electrofishing

California’s endangered fish populations. Fisheries. 23: 6-12.

Riley, S.C.; Fausch, K.D. 1992. Underestimation of trout population size by maximum-likelihood removal estimates in small

streams. North American Journal of Fisheries Management. 12:

768-776.

Whitworth, W.R.; Schmidt, R.E. 1980. Snorkeling as a means of

evaluating fish populations in small streams. New York Fish and

Game Journal. 27: 91-94.

Wolda, H. 1981. Similarity indices, sample size and diversity.

Oecologia. 50: 296-302.
257

BACKGROUND

In 1994 the first author, Henry W. Robison, and his staff

completed the first tests of a data entry module to capture

recent and historical fish distributional information on

Arkansas fishes that existed only in manual files. The

second author, L. Gayle Henderson, developed the module

with assistance from the third author, Melvin L. Warren, Jr.

The pilot study was completed in September 1995, and the

module proved to be time-efficient and flexible. Data entry

of Robison’s Arkansas fish collections was begun in the fall

of 1995 under a Challenge Cost-Share (CCS) agreement

with the Ouachita National Forest (ONF), Southern Research

Station, and The Nature Conservancy. This CCS agreement

was extended later in partnership with The Nature Conservancy, the ONF Ecosystem Management Program, the ONF

Fisheries Program, and Robison.

Data entry proceeded with records of fishes from within the

proclamation boundaries of the ONF. Initially, the records

entered were those of Robison; however, permission was

secured subsequently from Neil H. Douglas, Northeast

Louisiana University (NLU), to add the data from fish collections housed in their museum. This data was primarily from

the fieldwork of Douglas and NLU graduate students. Later,

data on Arkansas fishes from the large holdings of the Tulane

University fish museum were added to the fish database.

The USDA Forest Service effort focused primarily on fishes

living within or immediately downstream of the forest proclamation boundaries. Initial data entry concentrated on

that subset of fish collections but also included collections

from river systems in Arkansas that drain the ONF. In a subsequent CCS in 1997, fish collection data from the proclamation boundaries of the Ozark National Forest and the St.

Francis National Forest were added to the expanding fish

database. More recently, information from field collections

made by Tom M. Buchanan, John L. Harris, Betty Crump,

George L. Harp, and others in the national forest areas and

elsewhere in Arkansas were added. The addition of other

national forests and field collections of these individuals

substantially increased coverage of federal and surrounding lands in Arkansas.

In 1998-99, supplemental funding was granted from the

Arkansas Game and Fish Commission AGFC to include fish

collections from all drainages in Arkansas. The result is a

database covering the entire state of Arkansas which can

be used by the USDA Forest Service, The Nature Conservancy, the AGFC, the Department of Arkansas Natural

Heritage, and others in future planning, monitoring, and

management efforts.

PERTINENT LITERATURE

The site-specific information now contained in the fishes of

Arkansas database formed the basis of several scientific

contributions that increased our knowledge of fish distribution, fish conservation status, and fish assemblage association with watershed characteristics. Robison and Buchanan

(1988) published “Fishes of Arkansas” with dot distribution

maps depicting over 3,000 fish collections within the state.

These localities were located by hand on paper maps that

are currently in the possession of Robison. None of the

more than 3,000 collections was in a digital format, decreasing their utility for rapid manipulation, analysis, planning,

and monitoring. This manually compiled information was

later used by Matthews and Robison (1988), Matthews and

others (1992), and Matthews and Robison (1998) for studies

analyzing the distributions of Arkansas fishes and the

geological, climatological, and water quality correlates that

described faunal patterns across the state. Recently, the

COMPUTERIZATION OF THE ARKANSAS FISHES DATABASE

Henry W. Robison, L. Gayle Henderson, Melvin L. Warren, Jr., and Janet S. Rader1

1 Professor of Biology, Department of Biology, Southern Arkansas University, Magnolia, AR 71754; Computer Specialist and Research

Biologist, USDA Forest Service, Southern Research Station, Center for Bottomland Hardwoods Research, Oxford, MS 38655; and Data

Manager, Department of Biology, Southern Arkansas University, Magnolia, AR 71754; respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Until recently, distributional data for the fishes of Arkansas existed in the form of museum records, field

notebooks of various ichthyologists, and published fish survey data; none of which was in a digital format. In 1995, a

relational database system was used to design a PC platform data entry module for the capture of information on the

fishes of Arkansas. The graphical user interface (GUI) consists of four screens for data entry of information on locality,

habitat, collection details, and fish species. Values for most of the fields in the data entry screens were standardized to

reduce input error and ensure consistency. Look-up tables allow the user to quickly select standardized values (e.g., fish

species and family, river and stream systems, county, habitat) for many fields. Comment fields were incorporated to allow

the user to record important information on individual specimens that was not amenable to standardization. The database

is fully compatible with geographic information systems and each collection in the database is geo-referenced. The

computerization of this large database of fishes was initiated as part of a cooperative effort with the USDA Forest Service

to digitally capture historic information on fishes of the Ouachita National Forest. The agreement was later expanded to

include fish records from the Ozark and St. Francis National Forests. Recently, records of fishes from the entire state

were added to the database with funding from the Arkansas Game and Fish Commission. Currently, over 3,500 fish

collections have been entered into the Arkansas Fishes Database.
258

maps of Robison and Buchanan (1988), in part, formed the

basis for an assessment of fish distribution, diversity, and

conservation status for hydrologic units in the Ozark-Ouachita

Highlands Assessment led by the USDA Forest Service

(Standage 1999; Warren and Clingenpeel 1999; Warren

and Hlass 1999; Warren and Tinkle 1999).

OBJECTIVES

The computerization of the distributional data for the fishes

of Arkansas was premised on information needs in four

specific areas. First, there was a need to establish a

Geographic Information System (GIS)-compatible fish

research database for the state of Arkansas to document

historical and present fish distributions. Second, a digital

database would allow identification of unique ecological or taxonomic fish community assemblages and centers of fish diversity within and across drainages of Arkansas.

Third, historical changes in stability and persistence of

community assemblage patterns and historical trends in

species distributions needed to be easily associated with

land use. Finally, there was a need for geo-spatial tools to

assess conservation status of individual Arkansas fish species. The primary objective of this paper is to describe the

development and design of the database used to capture

collection records for fishes across Arkansas and that can

be used to meet these information needs.

DATABASE DESIGN

The initial design goal was to create a database structure

that could incorporate diverse sources of data on fishes into

a standardized central, digital repository and that would be

flexible and extensible enough to meet anticipated future

needs. Additional design considerations included standardization, portability, integration with GIS, ease of use, available PC platforms, and support. These factors together with

the one-to-many nature of the primary data dictated the

need for a relational database system (e.g., for each fish

collection, many species were sampled). A relational system

provided the needed master-detail database structures.

Importantly, a relational database system also provided

programming tools for developing modular code units for

data entry, queries, and reports, user-friendly graphical

user-interfaces (GUI), data entry validation procedures, and

on-screen help displays.

Standardization of the fish collection data was a first step in

the design process. The data originated from multiple

sources with varying sampling techniques, measurement

units, and recording methods. Agreement was reached on

standardized values or value ranges for most fields. Attributes of standardized fields were stored in separate lookup

tables and “related” to the main database tables as needed

via a shared code. This approach provided standard variables, and standard categories for development of queries

and analyses and reduced data redundancy and table sizes.

Input errors were also reduced since users select values

from a pick list rather than typing in the entry. In addition,

comment fields were built into the design to capture field

observations or specimen conditions that were important

but not amenable to standardization.

Due to the magnitude of information related to each collection, data fields were grouped into four major categories,

each with a separate data entry screen:

1. Location information (fig. 1), including county; physiographic region and section; river drainage and system;

stream name; and exact locality of the sampling site

Figure 1—Data entry screen for location information.
259

2. Collection information (fig. 2), including collection or field

identification number; collection date; collection interval,

depth, and methods; and collectors

3. Habitat information (fig. 3), including 11 environmental

variables: turbidity; vegetation; substrate; geology; shoreline condition; percent riffle; water depth and width;

current; stream width; and water and air temperatures

4. Species information (fig. 4), including scientific name and

family; number of individual species sampled; museum

number; total length, standard length, and notes concerning sex, weight, spawning colors, and spawning

condition.

Figure 2—Data entry screen for collection information.

Figure 3—Data entry screen for habitat information.
260

To enhance accuracy and ensure standardization, the user

extracts most field entries from pull-down tables of master

lists. Scientific names and respective families for fish species are selected as single entries from a standardized list

of all fishes known to occur in Arkansas as documented by

Robison and Buchanan (1988) and updated to reflect subsequent taxonomic changes (table 1). A hierarchical list of

river drainages and stream systems was adopted from

drainage units defined by (Matthews and Robison 1988)

(table 2). Similar master lists provide standard political

subdivisions (Arkansas county names) and physical and

geologic divisions of the state (physiographic region and

section, surface geology), (table 3). Others indicate the

capture method or provide descriptors of conditions at the

collection site and include: air and water temperature, current category, percent riffle habitat, water depth, substrate

type, turbidity category, stream size, shore classes, and

types of aquatic vegetation (table 4). As needed the master

lists may be updated or expanded to accommodate other

categories of information or create additional entry fields.

Table 1—Sample from species master list

Species Family Species code

Acipenser fulvescens Acipenseridae 1

Alosa alabamae Clupeidae 2

Alosa chrysochloris Clupeidae 3

Ambloplites ariommus Centrarchidae 4

Ambloplites constellatus Centrarchidae 5

Ambloplites rupestris Centrarchidae 6

Amblyopsis rosae Amblyopsidae 7

Ameiurus catus Ictaluridae 8

Ameiurus melas Ictaluridae 9

Ameiurus natalis Ictaluridae 10

Ameiurus nebulosus Ictaluridae 11

Amia calva Amiidae 12

Ammocrypta clara Percidae 13

Ammocrypta vivax Percidae 14

Anguilla rostrata Anguillidae 15

Table 2—Sample from system-drainage master list

Code System Drainage

0 0-Unknown 0-Unknown

1 Mississippi River-Blytheville Mississippi River

2 Mississippi River-West Memphis Mississippi River

3 Mississippi River-Helena Mississippi River

4 Mississippi River-Eudora Mississippi River

5 Upper St. Francis River St. Francis River

6 St. Francis River St. Francis River

7 Tyronza River St. Francis River

8 L’Anguille River St. Francis River

9 Fifteen Mile Bayou St. Francis River

10 Upper White River White River

11 War Eagle Creek White River

12 Kings River White River

13 Long Terrapin-Dry Creek White River

14 Bull Shoals-White River White River

15 Crooked Creek White River

Figure 4—Data entry screen for species information.
261

Help screens were used to clarify instructions and provide

examples of valid entries. Techniques for field-entry validation (e.g., numeric range checking) were employed as

appropriate, and printable summary reports (fig. 5) aided

verifying entry accuracy. An export routine for creating

delimited-text files (ASCII) was provided to ensure the

database could later be moved to other systems. The export

routine also served as a secondary backup mechanism.

SOFTWARE EVOLUTION AND SYSTEM

REQUIREMENTS

The custom application, ONF FISH, has undergone a

series of revisions since its inception. The database was

originally developed for the DOS platform using Paradox, a

relational database development product of Borland (now

Inprise). Factors in the selection of Paradox over other

relational database systems included PC system requirements (Paradox requires less resources than other products,

such as PC Oracle), Borland’s reputation in database development and programming arenas, and previous developer

experience with Paradox. The application includes four basic

modules accessible from a single, menu-driven, graphical

interface: Data, Reports, Queries, and Utilities. Emphasis

to-date has been on data entry and editing, but each module

can easily be further expanded as user needs evolve.

The last major revision converted the application to objectbased Paradox for Windows 5.0 and the Windows 3.1 platforms. The application now functions under Windows ’95 and

Paradox for Windows 7.0 (a Corel product). It is currently

being ported to Delphi 5.0, a Windows ’9x object-oriented

development package originally developed by Borland and

now owned by Corel as a result of their recent acquisition

of Inprise. The new application will have the look-and-feel

of a Windows ’95 or Windows ’98 application. Although still

based on Paradox tables, the new application will not require

Paradox as an underlying package at runtime. It will allow

the use of tables created in any relational database system

Table 3—Physiographic section (region) and geology

master lists

Section (Region) Geology

Arkansas Valley (Arkansas Valley) Alluvium or

terrace deposits

Athens Piedmont Plateau (Ouachita Mtns.) Chert

Boston Mtns. (Ozark Mtns.) Dolomite

Bottomlands (MS Alluvial Plain) Limestone

Central Ouachita Mtns. (Ouachita Mtns.) Sandstone

Crowley’s Ridge (Crowley’s Ridge) Shale

Fourche Mtns. (Ouachita Mtns.) Unknown

Loessial Plains (MS Alluvial Plain)

Salem Plateau (Ozark Mtns.)

Southcentral Arkansas (West Gulf Coastal

Plain)

Southwestern Arkansas (West Gulf Coastal

Plain)

Springfield Plateau (Ozark Mtns.)

Unknown (Unknown)

Table 4—Master list of valid environmental categories

Environmental variables and values

Stream_width:

10-25 m

26-50 m

51-100 m

<10 m

>100 m

Unknown

Water_depth:

Large rivers

Medium-sized rivers

Streams w/pools too deep to wade

Tiniest headwater streams

Unknown

Wadeable streams

Current:

Moderate

None

Slow

Swift

Unknown

Turbidity:

Clear; distinct greenish cast

Clear; tannin stained

Moderately turbid

Slightly turbid

Spring-fed; very clear

Strongly turbid

Unknown

Substrate:

All gravel (mostly small)

All sand

Boulder and bedrock

Gravel-rubble mixture

Mud-sand mixture

Sand-gravel mixture

Unknown

Shore:

0-24 percent wooded

100 percent wooded

25-49 percent wooded

50-74 percent wooded

75-99 percent wooded

Unknown

Vegetation:

Aquatic vegetation beds of rooted aquatic plants

Justicia at stream margins

No vegetation present

Substrate covered with algae

Unknown
262

Figure 5—Sample inventory report.
263

for which an Open Database Connectivity (ODBC) driver is

available (including Paradox, Access, Oracle, or others).

The new version will take advantage of the newest enhancements of Delphi, one of the most powerful Rapid Application

Development (RAD) packages available today.

The current version requires at least an 80386-based PC

with Windows ’95, Paradox for Windows 7.0, 32MB RAM,

and approximately 40MB free disk space for the ONF FISH

application. However, a Pentium or better system with 64MB

RAM is strongly recommended and will be required for

future Delphi-based versions.

INTEGRATION WITH GEOGRAPHIC INFORMATION

SYSTEM

In conjunction with development of the fish database application, efforts were made to ensure the information could

be easily integrated into a GIS. As site-specific collections

were entered, they were also located on paper copies of

USGS 7.5 minute topographic maps (1:24,000) and coded

with a unique identification number. Those maps are being

maintained and continually updated by the first author as a

physical record of fish collection localities in Arkansas. In

late 1999, in an effort led by Alan Clingenpeel (ONF) and

Brian Wagner (AGFC), all sampling locations were georeferenced to state-wide coverages using PC ArcView, a

product of Environmental Software Research Institute. Each

sampling point on the paper maps was matched to the same

point on a 1:24,000 digital topographic coverage of Arkansas.

The resulting point coverage uses an ArcView table linking

collection identification numbers with point labels. This table

will be merged into the original fish database application

tables so that all the fish data attributes may be used in

map creation and geo-spatial analysis.

CONCLUSION

A digital, database repository linked to GIS is now developed

for over 3,500 collections of fishes covering all of Arkansas.

Importantly, the foundation of the database, the individual

collection records, were critically examined so that the

information is up-to-date and as error free as possible

(Robison and Buchanan 1998 and subsequent updates by

Robison). The abundance and distribution of the fishes of

Arkansas are linked intimately to the habitat and water

quality condition of streams and rivers these animals

inhabit (Matthews and Robison 1988, 1998; Matthews

and others 1992). As such, the database of fishes will be

extremely useful to natural resource agencies in management, planning, and monitoring. The database gives natural

resource managers an enhanced ability to examine fish

distribution in association with rehabilitation, enhancement,

or remediation of the state’s running waters.

Future applications of the database are limited primarily by

one’s vision. The fish data application is currently extensible

and will be even more so after full conversion to Delphi,

which supports technologies such as Open Database Connectivity (ODBC), Object Linking and Embedding (OLE),

and Active Data Objects (ADO) which would allow widespread sharing of data with other applications. Uses may

ultimately extend well beyond management. Both Delphi

and certain GIS products could be used to extend database

products to the web. For example, web-based interactive

maps could be created that display recent versus historical

fish distributions. Guides to identification of fish species of

Arkansas could be developed complete with photographs

and detailed distributional maps for each species. Integration of the database with GIS provides managers with decision-making tools and visual communication modes that

assist in prioritizing allocation of scarce resource management dollars, open the door to thoroughly examining

management alternatives, and help convey and justify

management decisions. In sum, the database is a powerful

natural resource tool for the USDA Forest Service, The

Nature Conservancy, the AGFC, the Department of Arkansas

Natural Heritage, and state, federal, and local entities in

future planning, monitoring, and management efforts.

ACKNOWLEDGMENTS

The assistance of a number of government agencies and

individuals who aided in the realization of a long-held dream

of Robison to computerize the fish collection records of

Arkansas is most graciously appreciated. First, thanks go to

the USDA Forest Service, primarily the Ouachita National

Forest and Ecosystem Management Large-Scale Research

Program, Southern Research Station (both in Hot Springs,

AR) and the Center for Bottomland Hardwoods Research,

Southern Research Station (Oxford, MS) and also The Nature

Conservancy, for supplying initial and continued funding in

the early phases of this project. Especially important in

recognizing the value and supporting the project were Alan

Clingenpeel, Jim Guldin, William Pell, and Richard Standage

(USDA Forest Service), and Douglas Zollner of The Nature

Conservancy. We also wish to thank the Arkansas Game

and Fish Commission, which in 1998-1999 funded the

addition of Arkansas fish records outside of the National

Forest. Brian Wagner of the Non-Game Section, Arkansas

Game and Fish Commission, was instrumental in supporting the effort.

Research support was provided through the “Ecosystem

Management Large-Scale Research Program” dated 1994

through September 1995 under Challenge Cost-Share (CCS)

agreement. Special thanks and appreciation is expressed

to Thomas M. Buchanan (Westark College, AR), for his willingness to share important data on Arkansas fish collections

that he has made throughout the State. Other ichthyologists

who graciously shared field notes on fish collections include:

John L. Harris (Arkansas Highway and Transportation

Department), George L. Harp (Arkansas State University),

William J. Matthews and Edie Marsh-Matthews (University

of Oklahoma), and Betty Crump (Caddo Ranger District,

ONF).

LITERATURE CITED

Matthews, W.J.; Hough, D.J.; Robison, H.W. 1992. Similarities in

fish distribution and water quality patterns in streams of

Arkansas: congruence of multivariate analysis. Copeia.

1992(2): 296-305.

Matthews, W.J.; Robison, H.W. 1988. The distribution of the fishes

of Arkansas: a multivariate analysis. Copeia 1988(2): 358-374.

Matthews, W.J.; Robison, H.W. 1998. Influence of drainage

connectivity, drainage area, and regional species richness on

fishes of the Interior Highlands of Arkansas. American Midland

Naturalist. 139: 1-19.
264

Robison, H.W.; Buchanan, T.M. 1988. Fishes of Arkansas.

Fayetteville, AR: University of Arkansas Press. 536 p.

Standage, R.W. 1999. Management indicator species. In: Chapter 2,

U.S. Department of Agriculture, Forest Service. Ozark-Ouachita

Highlands Assessment: aquatic conditions. Report 3 of 5.

Gen. Tech. Rep. SRS-33. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southern Research Station. 317 p.

Warren, M.L., Jr.; Clingenpeel, J. Alan. 1999. Aquatic habitats. In:

Chapter 2, U.S. Department of Agriculture, Forest Service.

Ozark-Ouachita Highlands Assessment: aquatic conditions.

Report 3 of 5. Gen. Tech. Rep. SRS-33. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station. 317 p

Warren, M.L., Jr.; Hlass, L. 1999. Diversity of fishes. In: Chapter 2,

U.S. Department of Agriculture, Forest Service. Ozark-Ouachita

Highlands Assessment: aquatic conditions. Report 3 of 5. Gen.

Tech. Rep. SRS-33. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southern Research Station. 317 p.

Warren, M.L., Jr.; Tinkle, K. 1999. Endangered, threatened, and

other aquatic species of special concern. In: Chapter 2, U.S.

Department of Agriculture, Forest Service. Ozark-Ouachita

Highlands Assessment: aquatic conditions. Report 3 of 5. Gen.

Tech. Rep. SRS-33. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southern Research Station. 317 p
265

Session 9

Other Ecosystem Management Research in the Interior Highlands

Charles Richmond, Moderator
266
267

INTRODUCTION

Since the gypsy moth was originally introduced near Boston

in 1868 or 1869, it has been slowly expanding its range to

include the entire Northeastern United States and portions

of Virginia, West Virginia, North Carolina, Ohio, and Michigan

(Liebhold and others 1992, 1997a). In many of the forested

regions where this insect has become established, outbreaks occur intermittently every 5 to 15 years (Williams and

Liebhold 1995). During these outbreaks, defoliation of host

trees can be extensive and result in severe ecological and

economic effects. It is inevitable that the gypsy moth will

continue to spread to the south and west during this century.

The Ouachita/Ozark Highlands region is currently ca. 750 km

from the expanding front of gypsy moth defoliation. Based

on an historical rate of spread of ca. 21 km/yr (Liebhold and

others 1992), we might expect the first defoliation in the

region around the year 2035, though it is possible that gypsy

moth will be introduced accidentally to the area before then.

Isolated infestations have been discovered in the region but

eradication efforts to date have been successful. Should

future eradication efforts fail or if this strategy is abandoned,

defoliating populations likely will appear before 2035. The

USDA Forest Service has initiated a program aimed at slowing the spread of the gypsy moth in the Midwest and elsewhere (Leonard and Sharov 1995, Sharov and others 1998).

If this program is continued and successful, defoliating

populations may not invade the region until well after 2035.

To plan for the management of the gypsy moth, the distribution of susceptible stands must be limited in currently uninfested areas. Liebhold and others (1997a, 1997b) analyzed

forest inventory data from across the conterminous United

States to evaluate the susceptibility of all forests to gypsy

moth defoliation. The analysis indicated that the Ouachita/

Ozark Highland’s had one of the highest concentrations of

forests that are highly susceptible to the gypsy moth. In this

paper we provide a more detailed description of projected

forest susceptibility to gypsy within this region.

METHODS

The gypsy moth is a polyphagous insect; North American

populations feed on more than 300 different shrub and tree

species (Leonard 1981, Liebhold and others 1995). Despite

this breadth of host preference, there is considerable variation among Northeastern North American forests in their

susceptibility to defoliation. We define susceptibility as the

probability or frequency of defoliation given an established

gypsy moth population in the area (Gottschalk 1993).

Several studies that have focused on relating various characteristics of forests to susceptibility have yielded susceptibility

models of varying levels of complexity. Perhaps the most

important factor affecting stand susceptibility is the proportion of basal area represented by species that are highly

preferred by the gypsy moth (Herrick and Gansner 1986).

Other variables, such as the predominance of chestnut oak,

the abundance of tree structural features; e.g., bark flaps,

and various site characteristics; e.g., soils, are correlated

with susceptibility (Bess and others 1947, Herrick and

Gunner 1986, Valentine and Houston 1979). However

because of the dissimilarity between forests of the Northeast

where the earlier studies were conducted and Ouachita/

Ozark forests; e.g., chestnut oak does not grow there, it is

questionable whether previous correlations could be extrapolated for this region. Also, relatively few site and tree

characteristics used in earlier studies had been measured

in the forest inventory data available to us. As a result, we

excluded all plot characteristics except species composition.

GYPSY MOTH DEFOLIATION POTENTIAL IN

THE OUACHITA/OZARK HIGHLANDS REGION

Andrew M. Liebhold, Kurt W. Gottschalk,

James M. Guldin, and Rose-Marie Muzika1

1 Research Entomologist and Research Forester, USDA Forest Service, Northeastern Research Station, Morgantown, WV; Research Forest

Ecologist, USDA Forest Service, Southern Research Station, University of Arkansas at Monticello, Monticello, AR; and Associate Professor,

Department of Forestry, University of Missouri-Columbia, Columbia, MO, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—The gypsy moth is expanding its range in North America and is likely to invade the Ouachita/ Ozark Highlands

region sometime during this century. A previous analysis indicated that forests in this area are among the most susceptible in North America to defoliation by this insect. We used USDA Forest Service, Forest Inventory and Analysis data to

evaluate forest susceptibility in the region. Susceptibility was estimated as the proportion of basal area composed of tree

species preferred by gypsy moth caterpillars. Analyses were stratified by ecological land type and land ownership. Forest

susceptibility is highest to the north, in the Ozark Highland’s area; ca. 80 percent of the forests in this area have high to

very high susceptibility to defoliation. Forest susceptibility was lower to the south in the Ouachita region. This trend in

susceptibility reflects the increased pine component in southern portions of the region (pine species are not highly

preferred by the gypsy moth). South of the Ozark Highlands, in the Boston and Ouachita Mountains, the lower proportion

of susceptible forests is lower in land owned by the forest industry, presumably because of more intensive management of

softwoods. Most forests in the Ouachita/Ozark region are susceptible to gypsy moth defoliation. Should populations

become established in this area, intense defoliation could result in extensive ecological and economic consequences.
268

Thus we adopted proportion of basal area represented by

preferred species as the measure of forest susceptibility.

Montgomery’s (1991) three-way classification (susceptible,

resistant, immune) was used to categorize each tree species.

This classification was based on a summary of field and

laboratory studies, as well as extrapolations based on taxonomic affinity. It is described in detail in Liebhold and others

(1995). In a previous study in which similar data were used

(Liebhold and others 1997b), proportion of basal area was

highly correlated with defoliation frequency at the county

level for States located within the infested area.

Assessment of forest susceptibility was based on USDA

Forest Service, Forest Inventory and Analysis (FIA) data

compiled in the Eastside Forest Data Base (Hansen and

others 1993). These data are collected in statewide inventories conducted every 10 years. Inventory data are collected

at permanent plots located throughout forested areas of

each State. We selected data from the 9,777 plots in

Arkansas, Missouri, and Oklahoma that fell within the

Ouachita/Ozark Highlands (table 1) (fig. 1).

Summaries of forest susceptibility were stratified using an

ecological land type classification developed by Keys and

others (1995). Expanded, from an earlier classification by

Bailey (1995) the hierarchy of Keys and others begins with

domain at the global ecoregion level and can be refined to

the landscape and land unit, which can be hundreds to < 10

ha in size. Provinces are regional ecological delineations.

The areas of interest for this study in the Ozark-Ouachita

area fall within four provinces. The Ozark Highlands section

is categorized within the Eastern Broadleaf Forest (continental) Province. The Boston Mountains section is the only

one within the Ozark Broadleaf Forest—Meadow Province.

The Arkansas Valley section is one of seven sections within

the Southeastern Mixed Forest Province. The Ouachita

Mountains section is the only one in the Ouachita Mixed

Forest—Meadow Province. Twenty subsections fall within

these four sections (table 2). The location of each FIA plot

within a subsection was determined by comparing approximate survey plot locations with the digital map version of

the Keys and others (1995) classification using a GIS. When

plots fell near the margin of a subsection, final classification

into the appropriate subsection was determined by subjectively comparing individual plot locations with a digital

elevation model of the region (Foti and Bukenhofer 1999).

All inventory data contained information about individual

trees and plots. Individual tree records were used to sum

total basal area by species for each plot. These plot records

were then expanded (using appropriate factors) to subsection-level estimates of basal area per acre. This information

was then used to estimate the proportion of forested land

in each subsection that fell into one of four susceptibility

classes based on the percentage of basal area composed

of preferred tree species: 0 to 20 percent = low, 20 to 50

percent = moderate, 50 to 80 percent = high, 80 to 100 percent = very high. We used this susceptibility classification

scheme in our earlier analysis (Liebhold and others 1997b)

because historical patterns of defoliation seem to be closely

related to these classes (Gottschalk 1993). The type of land

owner is recorded at all FIA plot locations, so we estimated

the proportion of land falling in each of the four susceptibility classes for public forest, forest industry, and other

private land for each subsection.

Table 1—Number of FIA plots used in the analysis

Year of Number of

State inventory forested plots

Arkansas 1995 2,796

Missouri 1989 6,645

Oklahoma 1993 663

FIA = Forest Inventory and Analysis.

Figure 1—Location of USDA Forest Service, Forest Inventory and Analysis plots in

Missouri, Arkansas, and Oklahoma.
269

RESULTS AND DISCUSSION

The Ouachita/Ozark Highlands region is a hilly to mountainous area that differs from the surrounding area both in topography and geological origin. The Highlands consist of two

ecological sections—the Ozark Mountains in southern

Missouri, northern Arkansas, and northeastern Oklahoma;

and the Ouachita Mountains in western Arkansas and eastern Oklahoma. Table 2 shows the estimated percentage of

basal area composed of tree species preferred by the gypsy

moth in each of these sections. As stated earlier, we used

this percentage as our metric of forest susceptibility to the

gypsy moth. The Ozark Highlands section (222A) generally

had the most susceptible mixtures of species of the four

sections. These same data were mapped graphically (fig. 2)

and clearly show a trend with increasing proportions of susceptible species, particularly oaks, in the northern portion of

the region. Examination at a coarser scale of forest type

reveals a north-south trend in forest type that reinforces our

analysis (fig. 3).

The dominant forest type group in the northern portion of the

Ouachita/Ozark Highlands is oak-hickory while the loblollyshortleaf pine type group dominates in the southern portion

of the region. Each of the forest type groups in figure 3

represents many different specific forest types (Eyre 1980).

Nevertheless, these maps depict a real trend in the region;

that is increased dominance of oak to the north and

increased dominance of pine to the south. Analyzing FIA

data from the Ouachita/Ozark Highland regions Guldin and

others (in press) found that the proportion of stands in the

oak-hickory forest type group was greater in the Ozark

Mountain section (north) and that the proportion of stands

in the loblolly-shortleaf type group was greatest in the

Ouachita Mountains section.

The observed trend from oak-dominated to pine-dominated

forests from north to south in the Ouachita/Ozark regions

reflect in part an historical anomaly. At one time much of the

forested area of the Missouri Ozark Mountains currently

dominated by oak-hickory forests were dominated by pure

shortleaf pine or mixed pine-oak forests. Before 1880, pine

and oak-pine cover types were estimated at 6.6 million

acres in Missouri (Liming 1946). Extensive logging and

changes in fire frequency have resulted in shifts in species

composition (Batek and others 1999).

Our results were derived from summaries pooled across all

inventory plots in land type subsections. The question arises:

how is susceptibility to gypsy moth distributed among individual stands? Figure 4 shows estimates of the percentages

Table 2—Characteristics of ecological landtype subsections in the Ouachita

and Ozark Highland’s regiona

Number Preferred

Section Subsection of plots species

% BA

Ozark Highlands (222A)

St. Francis Knobs and Basins 222Aa 256 64

Central Plateau 222Ab 921 70

Osage River Hills 222Ac 279 75

Gasconade River Hills 222Ad 211 66

Meramac River Hills 222Ae 301 71

Current River Hills 222Af 417 66

White River Hills 222Ag 605 59

Elk River Hills 222Ah 87 74

Black River Ozark Border 222Al 210 67

Springfield Plain 222Am 215 65

Springfield Plateau 222An 225 65

Boston Mountains (M222A)

Boston Mountains M222AA 157 55

Boston Hills M222AB 374 51

Arkansas Valley (231G)

Eastern Arkansas Valley and Ridges 231GA 127 39

Mount Magazine 231GB 115 40

Western Arkansas Valley and Ridges 231GC 127 51

Ouachita Mountains (M231A)

Fourche Mountains M231AA 372 38

West Central Ouachita Mountains M231AB 239 34

East Central Ouachita Mountains M231AC 232 38

Athens Piedmont Plateau M231AD 132 23

BA = basal area.

a Subsection codes in parentheses.
270

of land area covered by forest of varying levels of susceptibility. These data indicate that the Ozark Highlands had the

greatest area in highly susceptible stands and the Ouachita

Mountains section had the highest area in stands with low

susceptibility. This reflects the same latitudinal trend seen

in figure 2 and presumably is due to the higher pine component in the more southerly subsections.

Separate analysis of susceptibility by ownership in the Boston

and Ouachita Mountains revealed a lower proportion of

susceptible forests on forest industry lands (fig. 5). Much of

the industry land in these sections is managed for softwood

production. This higher pine component would explain the

lower susceptibility in these sections compared to public and

other private lands. Guldin and others (in press), by contrast, forest industry land in the Ozark Highlands is largely

Figure 3—Distribution of four major forest types in the Ouachita and Ozark Highlands region. Maps were

modified from maps described in Zhu and Evans (1992).

Figure 2—Map of the Ouachita and Ozark Highlands region showing the proportion of basal

area composed of tree species susceptible to the gypsy moth by ecological subsection.
271

managed for oak and seems to be at least as susceptible

as public and other private land (fig. 5).

The classification of forest susceptibility solely on the basis

of species composition may not capture all of the differences

with respect to the true susceptibility to gypsy moth defoliation. One trend that can be extracted from studies of gypsy

moth susceptibility in the Northeastern United States apart

from the association with dominance by preferred species

is that defoliation tends to be more intense on poor sites

(Herrick and Gansner 1986, Valentine and Houston 1979).

Guldin and others (2000) found that site quality generally

was greater to the south in the Ouachita region and lower

to the north in the Ozark Highlands. If forest susceptibility to

the gyspy moth continues to be associated with poor sites,

as has occurred in the northeast, this would tend to reinforce the trend observed in figures 3 and 4, that is forest

susceptibility is greater in the northern portion of the region.

CONCLUSION

The range of the gypsy moth is likely to continue to expand

and defoliating populations are likely to occur in the

Ouachita/Ozark Highlands region during this century. An

analysis by Liebhold and others (1997a, 1997b) indicated

that forests in this area are among the most susceptible in

the Nation to defoliation by this insect. The establishment of

gypsy moth populations in the region could result in extensive ecological and economic consequences. Our analysis

indicates that forest susceptibility is particularly high to the

north, in the Ozark Highlands. The data in figure 4 indicate

that ca. 80 percent of the forests in this area have high to

very high susceptibility to defoliation. It is likely that gypsy

moth populations will not become established for another

30 years. Still forest management practices in this area that

promote pines or other species not favored by the gypsy

moth should reduce the future impact of this insect pest

(Gottschalk 1993).

Figure 4—Percentages of forest land area falling into each of four gypsy moth susceptibility

classes for each subsection. Susceptibility classes were based on percent basal area

composed of species preferred by the gypsy moth: low = 0 to 20 percent, medium = 20 to 50

percent, high = 50 to 80 percent, and very high 80 to 100 percent.

Figure 5—Percentages of forest land area falling into

four gypsy moth susceptibility classes for different land

ownership classes in each land type section. Susceptibility classes were based on percent basal area composed of species preferred by the gypsy moth: low = 0

to 20 percent, medium = 20 to 50 percent, high = 50 to

80 percent, and very high 80 to 100 percent.
272

ACKNOWLEDGMENTS

We thank the USDA Forest Service, Forest Inventory and

Analysis group for supplying the data used in this analysis.

E. Luzader provided assistance with preparation of maps.

We also thank Fred Stephen and Rob Lawrence for reviewing an earlier draft of this paper.

LITERATURE CITED

Bailey, R.G. 1995. Description of the ecoregions of the United

States. 2nd ed. Misc. Publ. No. 1391 (rev.). Washington, DC:

U.S. Department of Agriculture, Forest Service. 108 p.

Batek, M.J.; Rebertus, A.J.; Shroeder, W.A. [and others]. 1999.

Reconstruction of early nineteenth century vegetation and fire

regimes in the Missouri Ozarks. Journal of Biogeography. 26:

397-412.

Bess, H.A.; Spurr, S.H.; Littlefield. E.W. 1947. Forest site conditions and the gypsy moth. Harvard Forest Bulletin No. 22. 56 p.

Eyre, F.H. 1980. Forest cover types of the United States and

Canada. Washington, DC: Society of American Foresters. 148 p.

Foti, T.; Bukenhofer, G. 1999. Ecological classification of the

Highlands. In: Ozark-Ouachita Highlands assessment: terrestrial vegetation and wildlife. Gen. Tech. Rep. SRS-35. Asheville,

NC: U.S. Department of Agriculture, Forest Service, Southern

Research Station: 1-60. Chapter 1.

Gottschalk, K.W. 1993. Silvicultural guidelines for forest stands

threatened by the gypsy moth. General Technical Report NE171. Radnor, PA: U.S. Department of Agriculture, Forest

Service, Northeastern Forest Experiment Station 49 p.

Guldin, James M.; Thompson, Frank R.; Richards, Lynda L.;

Harper, Kyra C. 2000. Status and trends in vegetation. In:

Ozark-Ouachita Highlands assessment: terrestrial vegetation

and wildlife. Gen. Tech. Rep. SRS-35. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station. 201 p. Chapter 3.

Hansen, M.H.; Frieswyk, T.; Glover, J.F.; Kelly, J.F. 1993. The

Eastwide forest inventory data base: users manual. Gen. Tech.

Rep. NC-151. St. Paul, MN: U.S. Department of Agriculture,

Forest Service, North Central Forest Experiment Station. 48 p.

Herrick, O.W.; Gansner, D.A. 1986. Rating forest stands for gypsy

moth defoliation. Res. Pap. NE-583. Broomall, PA: U.S.

Department of Agriculture, Forest Service, Northeastern Forest

Experiment Station. 4 p.

Keys, J., Jr.; Carpenter, C.; Hooks, S. [and others]. 1995. Ecological units of the Eastern United States—first approximation.

Atlanta, GA: U.S. Department of Agriculture, Forest Service.

[Paper map, CD-ROM, and booklet of map unit tables].

Leonard, D.E. 1981. Bioecology of the gypsy moth. In: Doane,

C.C. and M.L. McManus (eds.). The gypsy moth: research

toward integrated pest management. Technical Bulletin 1584.

Washington, DC: U.S. Department of Agriculture, Forest

Service: 9-29.

Leonard, D.S.; Sharov, A.A. 1995. Slow the spread project update:

developing a process for evaluation. In: Gottschalk, K. W.;

Fosbroke, S.L.C. eds. Proceedings U.S. Department of

Agriculture interagency gypsy moth research forum 1995.

Gen. Tech. Rep. NE-213. Radnor, PA: U.S. Department of

Agriculture, Forest Service, Northeastern Forest Experiment

Station: 82-85.

Liebhold, A.M.; Gottschalk, K.W.; Luzader, E.R. [and others].

1997a. Gypsy moth in the United States: an atlas. Gen. Tech.

Rep. NE-233. Radnor, PA: U.S. Department of Agriculture,

Forest Service, Northeastern Forest Experiment Station. 33 p.

Liebhold, A.M.; Gottschalk, K.W.; Mason, D.A.; Bush, R.R. 1997b.

Evaluation of forest susceptibility to the gypsy moth across the

conterminous United States. Journal of Forestry. 95: 20-24.

Liebhold, A.M.; Gottschalk, K.W.; Muzika, R.M. [and others]. 1995.

Suitability of North American tree species to the gypsy moth: a

summary of field and laboratory tests. Gen. Tech. Rep. NE-211.

Radnor, PA: U.S. Department of Agriculture, Forest Service,

Northeastern Forest Experiment Station. 34 p.

Liebhold, A.M.; Halverson J.; Elmes, G. 1992. Quantitative analysis of the invasion of gypsy moth in North America. Journal of

Biogeography. 19: 513-520.

Liming, F.G. 1946. The range and distribution of shortleaf pine in

Missouri. Tech. Pap. No. 106. Columbus, OH: U.S. Department

of Agriculture, Forest Service, Central States Forest Experiment

Station. 4 p.

Montgomery, M.E. 1991. Variation in the suitability of tree species

for gypsy moth. In: Gottschalk, K.W.; Twery, M.J; Smith, S.I.;

eds. Proceedings, U.S. Department of Agriculture interagency

gypsy moth research review. Gen. Tech. Rep. NE-146. Radnor,

PA: 1990 January 22-25. U.S. Department of Agriculture,

Forest Service, Northeastern Forest Experiment Station: 1-13.

Sharov, A.A.; Liebhold, A.M.; Roberts, E.A. 1998. Optimizing the

use of barrier zones to slow the spread of gypsy moth (Lepidoptera: Lymantriidae) in North America. Journal of Economical

Entomology. 91: 165-174.

Valentine, H.T.; Houston, D.R. 1979. A discriminate function for

identifying mixed-oak stand susceptibility to gypsy moth defoliation. Forest Science. 25: 468-474.

Williams, D.W.; Liebhold, A.M. 1995. Influence of weather on the

synchrony of gypsy moth (Lepidoptera: Lymantriidae) outbreaks

in New England. Environmental Entomology. 24: 987-995.

Zhu, Zhiliang; Evans, L.D. 1992. Mapping Midsouth forest distributions. Journal of Forestry. 90(12): 27-30.
273

INTRODUCTION

Several geographical and geological factors have contributed

to the unique fauna and flora of the Interior Highlands of

Arkansas, Oklahoma, and Missouri. Unlike the Southwestern

United States, the Interior Highlands were not covered by

shallow inland seas during the Cretaceous period (Dowling

1956); consequently, these highlands served as an island

refuge. The region also may have served as a refuge for

plants and animals during the Pleistocene epoch when

glaciers covered adjacent northern regions and the formation

of the Arkansas River divided the region into the Ozark

Mountains to the north and the Ouachitas to the south

(Dowling 1956). During the late Cenozoic era, sediments

that had been deposited by inland seas were eroded,

further defining boundaries and isolating the uplift.

The Ouachita uplift has unique habitats that supports a rich

flora and fauna, including more than a dozen endemic plant

species (Mohlenbrock 1993). The herpetofauna is also rich,

with high species densities of both reptiles and amphibians

(Kiester 1971). Reptile faunal assemblages are more or less

representative of adjacent regions and no endemic species

are found within the uplift. Frogs and toads, which are relatively mobile, also are not represented by endemic forms.

Salamanders, however, are represented by five endemic

species, and several endemic subspecies (Connant and

Collins 1998).

Many of the species of reptiles and amphibians in the

Ouachitas are relatively uncommon and some are considered threatened due to limited distributions or low population densities. Ashton (1976), Black (1977), and Reagan

(1974) list the following as rare or threatened: Amphiuma

tridactylum, three-toed amphiuma, Ambystoma annulatum,

ringed salamander, Ambystoma talpoideum, mole salamander, Plethodon ouachitae, Rich Mountain salamander,

Plethodon caddoensis, Caddo Mountain salamander, Hyla

avivoca, bird-voiced tree frog, Cemophora coccinea, scarlet

snake, and Terrapene ornata, ornate box turtle.

Two silvicultural systems are employed in the region: evenaged management (e.g., clearcutting) and selective harvesting. With clearcutting, all or most of the trees are harvested

from an area such that the “forest influence” is removed

from most of the area (Kimmins 1992). A new population of

seedlings is then established through natural regeneration

or planting such that one dominant age-class of trees is

represented. With selective harvesting, individual trees or

groups of trees are removed periodically so that the resulting forest eventually contains trees of several distinct age/

size classes (Kimmins 1992). Even-aged silviculture employing clearcutting, site preparation, and planting of pines has

been the primary method of pine regeneration on southern

forests for >30 years. Although young pine plantations provide excellent habitat for many wildlife species adapted to

early successional stages (such as deer, rabbits, and quail),

clearcutting is generally detrimental to species that require

an abundance of snags and cavity trees, hardwoods, hard

mast, woody debris, and other forest habitat features

(Kimmins 1992, Thill 1990). It has been shown that some

reptiles and amphibians require similar mature habitat features; e.g., oak-hickory habitats supported greater numbers

of amphibians than nearby managed-pine habitats in South

Carolina (Bennett and others 1980). Similarly, Enge and

Marion (1986) found that clearcutting and site preparation

in Florida had a negative impact on reptile and amphibian

numbers and reptile species richness. The decrease in

amphibian numbers in heavily disturbed areas was primarily due to reduced reproductive success in certain species,

such as Scaphiopus spp., Rana utricularia, and Gastrophryne

carolinensis. Low numbers of young-of-the-year were noted

in clearcut areas, possibly due to disappearance of standing water before young frogs and toads could metamorphose.

In another study, presence and numbers of amphibians in

managed stands were strongly affected by the occurrence

and longevity of intermittent ponds and streams during

winter (Whiting and others 1987).

HERPETOLOGICAL HABITAT RELATIONS IN

THE OUACHITA MOUNTAINS, ARKANSAS

Doyle L. Crosswhite, Stanley F. Fox, and Ronald E. Thill1

1 Assistant Professor, St. Gregory’s University, Division of Science, Shawnee, OK; Professor, Oklahoma State University, Department of Zoology,

Stillwater, OK; and Supervisory Research Wildlife Biologist, USDA Forest Service, Southern Research Station, Nacogdoches, TX, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—We studied habitat relationships of the herpetofauna inhabiting managed pine-oak woodlands of the Ouachita

Mountains, Arkansas. We used drift fence arrays with pitfall and double-ended funnel traps to sample two replications

each of three treatments: young clearcuts, selectively harvested stands, and late-rotation untreated controls. Our

objectives were to compare herpetofaunal communities among these treatments and to quantify habitat relationships.

Ninety-one days of trapping over two field seasons yielded 633 captures representing 35 species. Canonical correspondence analysis indicated that species composition differed significantly among treatments. The most distinct separation of

species groups was between reptiles and amphibians; reptiles were far more abundant in the young, xeric clearcuts, while

amphibians were most abundant in the other two treatments. Four habitat parameters (canopy coverage, litter depth,

woody plant cover, and large, woody debris) explained much of the variation in species composition among sample sites.

Several species showed clear preferences for particular habitats.
274

By altering soil structure, hydrology, and horizontal and vertical vegetation structure, clearcutting can affect microclimatic

conditions (Geiger 1971), which influences amphibian and

reptile distribution and abundance (Ash 1988, Matlack 1994,

Pechman and others 1991, Pough and others 1987). These

changes result from canopy removal, reduction in moistureretaining litter, and soil compaction (Bratton 1994, Bury

1983, Raymond and Hardy 1991).

Many terrestrial salamanders require moisture-containing

deciduous leaf litter for site colonization (Jaeger 1971). Thus,

pure stands of conifers are generally unsuitable for salamanders in the Eastern and Central United States (Bennett and

others 1980, Pough and others 1987, Williams and Mullin

1987). In loblolly-shortleaf pine (Pinus taeda and P. echinata)

stands of east Texas, Whiting and others (1987) found that

understory development and degree of deciduous litter

accumulation strongly influenced herpetofaunal communities.

Petranka and others (1993) compared 5-year-old clearcuts

with mature stands over 80 years old and found that terrestrial salamanders were reduced by 75 to 100 percent following clearcutting. Furthermore, Petranka and others (1994)

estimated that it would require a century or more for salamander populations to return to predisturbance levels. There

is concern that this reduction could produce population

bottlenecks and decreased genetic diversity. In some cases,

local populations of sedentary species may be prone to

extinction (Petranka and others 1993).

On a regional scale, survival of a reduced population

depends upon recolonization through immigration from

undisturbed areas (Fahrig and Merriam 1994). However,

several factors limit salamander immigration: (1) salamanders generally only migrate under a narrow set of environmental conditions, (2) migrating individuals may encounter

interspecific competition with other herps, and (3) adult

salamanders are often highly philopatric (Petranka 1994,

Petranka and others 1993). Consequently, recolonization of

heavily disturbed areas by salamanders is slow.

Like amphibians, reptile species richness and community

composition are affected by silvicultural treatments (Enge

and Marion 1986, Whiting and others 1987). Populations of

some reptiles increase in response to clearcutting due to

increased prey abundance, creation of favorable microhabitats or refugia, and other factors (Enge and Marrion 1986).

Cnemidophorus sexlineatus, a cursorial lizard that prefers

open sandy areas, increased in abundance following clearcutting (Enge and Marion 1986). Several grassland species

were also common in young plantations, including Thamnophis proximus, Masticophis flagellum, Lampropeltis calligaster, and L. getula (Whiting and others 1987). Clearcutting

typically results in increased small mammal densities and

species diversity (Atkinson and Johnson 1979; Kirkland

1977, 1990), providing more prey for snakes that feed

primarily on small rodents.

Reptile community composition is related to understory and

overstory development as well as presence of woody debris,

rocky outcroppings, and prey abundance. Many habitat

characteristics affecting herpetofaunal community composition are ultimately dependent upon age of the forest and

degree of disturbance.

Because amphibians are often habitat specialists with

restricted distributions, they may be valuable indicators of

ecosystem health and stability. Despite new evidence that

reptiles and amphibians are important components in many

ecosystems, they continue to be neglected by land managers (Pough and others 1987). Some management plans

may even promote midsuccessional stages to maximize

alpha diversity of other taxa at the expense of sensitive

reptile and amphibian species (Faaborg 1980, Samson and

Knopf 1982).

Our objectives were to (1) determine if herpetofaunal

community structure differs among silvicultural treatments

in the Ouachitas, (2) quantify microhabitat differences

among treatments, and (3) relate herpetological community

composition to microhabitat conditions.

MATERIALS AND METHODS

We sampled six stands (two replicates of three treatments)

located within Perry County, AR, about 70 km north of Hot

Springs. The treatments were young (3 and 5 years old at

study initiation) clearcut plantations, late-rotation (80+ years

old) naturally regenerated stands (hereafter controls), and

selectively harvested stands (table 1). Stands of the first two

treatments were managed by the USDA Forest Service;

selectively harvested stands were managed by forest

industry.

All stands had a predominately south, southeast, or southwest aspect and slopes of 5 to 20 percent. We chose stands

with southerly aspect because these best represent sites

that the USDA Forest Service manages for P. echinata in

the Ouachitas.

Table 1—Treatment histories for six study areas in the Ouachita Mountains

Treatment Year of harvest Year of burna Year of herbicide treatment

Selectively harvested 1972,92 1985,88 1973 (2,4,5-T)

Selectively harvested 1976,91 1988 1973 (2,4,5-T)

Control 1912 — —

Control 1912 — —

Clearcut 1990 — 1990 (Garlon 3A)

Clearcut 1988 — 1988 (Garlon 3A)

a Prescribed burning of understory was conducted to reduce woody debris and young hardwoods.
275

We established three drift fence arrays with associated pitfall and funnel traps on each site (fig. 1a). The array design

was modified from Campbell and Christman (1982), Vogt

and Hine (1982), and Jones (1986). Within each stand,

arrays were positioned at 100-m intervals along a central

transect situated approximately 100 m from roads, streams,

or stand borders to minimize treatment confoundments.

Each array consisted of three 15.2-m by 30.5-cm wings

(galvanized flashing) originating from the center and radiating outward at approximately 120° angles. Drift fences

were buried 5 cm in the ground to help prevent burrowing

under the fence. An 18.9-l pitfall trap (plastic bucket) was

buried at the center of the array and at the end of each of

the three wings (fig. 1a). Pitfalls were buried flush with the

ground, allowing the drift fence to overhang the lip of the

pitfall (fig. 1b). Drain holes were punched in the bottom of

each pitfall. Two double-ended funnel traps, one of hardware cloth (0.64 cm mesh) (Fitch 1951) and one of aluminum window screen (Jones 1986), were placed on both

sides of each wing for a total of 12 funnel traps per array.

Funnel traps were molded and positioned as close to the

fence as possible to prevent animals from moving between

the funnel traps and the drift fence.

Arrays were installed during March 1993, about 2 months

prior to initial trapping. Arrays were checked on alternate

days for a total of 91 days during six periods: three in 1993

(May 22 to June 6, June 15 to 30, and July 15 to 25) and

three in 1994 (March 6 to 21, May 14 to 29, and June 15 to

29). When pitfalls were not in use, they were closed with

18.9-l pitfall

Double-ended

funnel traps

15.2 m

Bucket lid

Double-ended

funnel trap

(15.2-m x 30.5-cm) drift fence

Soil

level

A

B

Figure 1—(A) Array design showing configuration of drift fences, pitfall traps, and

double-ended funnel traps; (B) side view of an array segment showing the intersection

of a pitfall trap with the drift fence.
276

tight-fitting snap lids; funnel traps were closed by lodging a

plug of aluminum foil in their entrances. To minimize mortality from overheating and desiccation, a piece of asphalt

shingle was draped over each funnel trap for shade. Pitfalls

were shaded using small sticks to prop lids 10 to 15 cm

above the container (fig. 1b).

Twelve habitat measurements were collected at each of the

18 arrays during July 15-30, 1993. Habitat changes from

1993 to 1994 were negligible, so the measurements made

in 1993 characterize the total study period. Leaf litter,

exposed rock, woody plant cover, herbaceous cover, and

woody debris were quantified by visually estimating, with an

ocular tube, the percent of the ground surface covered by

each. Percent coverage by woody debris was recorded in

two size classes (Maser and others 1979): total woody debris

and large debris with a mean diameter >20 cm. Forest

canopy cover was estimated using a spherical densiometer

(Lemmon 1957). Pine and hardwood basal area was estimated with a 10-factor English prism. Litter depth was measured with a metric ruler and horizontal cover was estimated

using a 1-m by 1-m density board at two heights: resting on

the ground and centered at 1 m aboveground (MacArthur

and MacArthur 1961). For each array, these data were

collected at right angles to the drift fence, 2 m to either side

of each of the peripheral pitfall traps, for a total of six points

per array. Horizontal cover, litter depth, and all percent

coverage estimates were recorded at these points. Basal

area was estimated from the center of each pitfall trap. The

data for each parameter were then averaged to characterize each array.

DATA ANALYSIS

We employed canonical correspondence analysis (CCA) to

test for differences in herpetofaunal communities among

silvicultural treatments and to identify associations of habitat

variables with the treatments and with particular reptiles and

amphibians. CCA is a gradient analysis that utilizes aspects

of multivariate regression and correspondence analysis to

relate species composition of the samples with measured

habitat variables. Ordination axes are constrained such that

they are linear combinations of the habitat variables and

each subsequent axis explains variation in the data set not

already explained by previous axes (i.e., axes are orthogonal). Ordination diagrams show the relationships among

species abundances, sites, and habitat variables (Taylor

and others 1993, Ter Braak and Smilauer 1998).

In CCA ordination diagrams, sites and species are represented by symbols (points) while habitat variables are

represented by vectors. The length of a vector symbolizes

the importance of the environmental variable while the

direction of vectors indicates the degree of correlation

among habitat variables and sites, and/or habitat variables

and species. Only the positive end of environmental vectors

are shown in the CCA diagrams; therefore, one must remain

aware of the equally important negative portion of each

vector. For each environmental variable shown in the ordination, one can imagine a vector of equal length extending

from the center of the figure and in the opposite direction.

The closer environmental vectors are to one another the

more they are correlated, and the closer these vectors align

with an axis the more the nature of that axis is identified.

The location of sites relative to environmental vectors

indicates the habitat characteristics of the sites, while the

position of species points relative to vectors shows the

environmental associations of individual species.

Analyses were performed using the program CANOCO with

downweighting of rare species (Ter Braak and Smilauer

1998). Each drift fence array was considered a sample site.

Species abundances were log10 transformed and environmental data expressed as proportions were transformed to

the arcsine of the square root of the value. For purposes of

ordination, it was valid to incorporate the total set of variables, but for purposes of hypothesis testing, the number of

habitat variables (12) was large relative to the number of

samples (18) (Ter Braak and Smilauer 1998). Therefore,

before applying the CCA for hypothesis testing, we reduced

the number of habitat variables using Principal Components

Analysis (PCA) to identify those variables that were redundant or superfluous. From this analysis, we identified four

habitat variables (canopy cover, litter depth, woody plant

cover, and large, woody debris) that most influenced the

herp community structure. Although canopy cover and litter

depth are strongly correlated, both were included in the

model because of the known importance of a well-developed litter layer to amphibians (Bury 1983, Diller and

Wallace 1994).

Monte Carlo permutation tests (Manly 1992) were used to

test the overall effects of (1) treatment and (2) the selected

habitat variables on species composition. Monte Carlo

permutation tests were also used to test the effect of the

first CCA axis (CCA1) for each of the analyses.

RESULTS

We captured 633 individuals representing 35 species of reptiles and amphibians (table 2). Of these, 62 percent (395)

were lizards (Phrynosomatidae, Teidae, Scincidae), 26 percent (162) were frogs and toads (Microhylidae, Bufonidae,

Ranidae), and 10 percent (66) were snakes (Colubridae,

Viperidae). Salamanders and turtles (Ambystomatidae,

Plethodontidae and Testudinidae) combined represented

< 2 percent of all captures and therefore will only be briefly

discussed.

Based on CCA using treatment as the only environmental

variable, the pattern of species abundances (overall ordination) was nonrandom along CCA1 (Monte Carlo test, p <

0.01). In other words, the herpetofaunal communities

differed significantly among the three treatments. Using this

same analysis with four preselected variables (canopy

cover, litter depth, woody plant cover, and large, woody

debris), patterns of species abundances among treatments

also differed (P < 0.01).

In the ordination using all habitat variables (fig. 2), CCA1

was positively correlated with leaf litter, pine basal area,

canopy cover, litter depth, and hardwood basal area; CCA 1

was negatively correlated with horizontal cover at 1 m. Over

the first three canonical axes, the three silvicultural treatments were well separated (fig. 3), with CCA1 providing the

greatest separation. Overlaying the habitat variables (fig. 2),

clearcut stands were characterized by dense ground cover

including woody and herbaceous vegetation as well as an
277

Table 2—Amphibians and reptiles captured using drift fence arrays in the Ouchita Mountains of

Arkansas, 1993–94a

Species Acronym Clearcut Selectively harvested Control Total (%)

Frogs and Toads

 Bufo americanus BUFAME 5 65 39 109 (17.2)

 Gastrophryne carolinensis GASCAR 2 37 5 44 (7.0)

 Rana clamitans RANCLA 4 0 2 6 (0.9)

 Rana catesbeiana RANCAT 0 0 2 2 (0.3)

 Rana utricularia RANUTR 1 0 0 1 (0.1)

Salamanders

 Eurycea multiplicata EURMUL 0 2 3 5 (0.7)

 Ambystoma opacum AMBOPA 0 1 0 1 (0.1)

 Ambystoma talpoideum AMBTAL 0 1 0 1 (0.1)

Turtles

 Terrapene carolina TERCAR 1 0 1 2 (0.3)

 Terrapene ornata TERORN 0 1 0 1 (0.1)

Lizards

 Sceloporus undulatus SCEUND 80 54 45 179 (28.3)

 Scincella lateralis SCILAT 16 28 34 78 (12.3)

 Cnemidophorus sexlineatus CNESEX 48 5 2 55 (8.7)

 Eumeces fasciatus EUMFAS 8 20 6 34 (5.4)

 Eumeces anthracinus EUMANT 9 8 6 23 (3.6)

 Eumeces laticeps EUMLAT 10 3 4 17 (2.7)

 Anolis carolinensis ANOCAR 5 4 0 9 (1.4)

Snakes

 Agkistrodon contortrix AGKCON 4 4 4 12 (1.9)

 Coluber constrictor COLCON 6 3 2 11 (1.7)

 Thamnophis proximus THAPRO 2 1 2 5 (0.8)

 Storeria dekayi STODEK 4 0 1 5 (0.7)

 Elaphe guttata ELAGUT 3 1 0 4 (0.6)

 Thamnophis sirtalis THASIS 0 1 2 3 (0.4)

 Storeria occipitomaculata STOOCC 1 0 2 3 (0.4)

 Carphophis amoenus CARVER 2 1 0 3 (0.4)

 Masticophis flagellum MASFLA 1 0 2 3 (0.4)

 Lampropeltis triangulum LAMTRI 2 0 1 3 (0.4)

 Virginia valeriae VIRVAL 1 0 1 2 (0.3)

 Cemphora coccinea CEMCOC 2 0 0 2 (0.2)

 Heterodon platyrhinos HETPLA 1 0 1 2 (0.3)

 Diadophis punctatus DIAPUN 1 0 1 2 (0.3)

 Opheodrys aestivus OPHAES 1 1 0 2 (0.3)

 Tantilla gracilis TANGRA 1 1 0 2 (0.3)

 Elaphe obsoleta ELAOBS 1 0 0 1 (0.1)

 Sistrurus miliarius SISMIL 1 0 0 1 (0.1)

Total 633 (100.0)

a Herpetological nomenclature follows Connant and Collins 1998.

abundance of woody debris. Clearcuts had scanty leaf litter,

reduced canopy cover, and low pine and hardwood basal

areas. Control and selectively harvested stands were closely

grouped along CCA1 to the right and shared several habitat

characteristics including greater litter depth, greater canopy

cover, and greater pine and hardwood basal areas. In turn,

selectively harvested and control stands differed along CCA3

(fig. 3b) due to greater herbaceous cover and more large,

woody debris in selectively harvested stands; conversely,

control stands had a higher proportion of woody plant cover.

These habitat differences are apparent from the variable

means (table 3).

The most distinct separation of species groups (fig. 4) was

between reptiles and amphibians, with reptiles predominating in clearcuts and amphibians being most abundant in
278

Figure 2—Canonical correspondence analysis (CCA) ordination of

habitat variables: CANOPY = canopy cover, DENSG = horizontal

cover at ground level, DENS1M = horizontal cover at 1 m above the

ground, HARDWD = hardwood basal area, HERBS = herbaceous

cover, LITDEPTH = litter depth, LITTER = leaf litter cover, PINE =

pine basal area, ROCK = exposed rock, SLASH20 = woody debris

with a diameter >20 cm, TSLASH = total woody debris, and

WOODY = woody plant cover.

forested stands. Frequencies of captures by species groups

showed the same pattern (table 2).

Although not abundant in control or selectively harvested

stands, salamanders were never observed in either clearcut. Eurycea multiplicata, the most abundant of the three

salamander species (table 2), was not strongly associated

with any habitat variable (fig. 5). All salamanders and most

frogs and toads were collected in forested areas. Gastrophryne carolinensis and Bufo americanus were the most

abundant frogs and toads (table 2) and were strongly associated with the higher canopy cover and litter depth of the

two forested treatments (fig. 5). Both species were most

common in selectively harvested stands (table 2).

The most commonly encountered snakes were Agkistrodon

contortrix and Coluber constrictor (table 2). Agkistrodon

contortrix was found equally in all treatments, whereas we

caught more C. constrictor in clearcuts. These species

occurred in a broad range of habitats within both forested

Figure 3—Canonical correspondence analysis (CCA) ordination of

sample sites against (A) axes 1 and 2, and (B) axes 1 and 3.
279

and open, grassy areas. Both species were associated with

large, woody debris, woody vegetation, and exposed rock

(fig. 5).

Thamnophis spp. were encountered mostly within controls

and were generally observed near water, while both Elaphe

guttata and Storeria dekayi were commonly observed within

the clearcut stands (table 2). E. guttata was strongly associated with dense, herbaceous ground cover (fig. 5).

Lizards were the most abundant taxon (table 2), occupying

most habitats (fig. 5). The most abundant species, Sceloporus undulatus (n = 179), was found in a wide variety of

habitats and is therefore found near the center of the ordination (fig. 5). It was most common in clearcuts, but was

also found in good numbers in the other treatments (table 2).

Scincella lateralis and Eumeces fasciatus were most commonly encountered in forested areas (table 2) in association with abundant litter (fig. 5). Cnemidophorus sexlineatus

Table 3—Means and standard deviations (in parentheses) of habitat variables by treatmenta

Habitat variable Clearcut Selective harvest Control

Slope (percent) 15.0 (1.8) 12.0 (4.5) 12.0 (2.5)

Pine basal area (m2/ha) 0.0 (0.0) 16.1 (3.8) 19.0 (3.8)

Hardwood basal area (m2/ha) 0.2 (0.4) 2.3 (1.6) 7.3 (3.6)

Canopy cover (percent) 7.0 (12.1) 83.0 (7.2) 97.0 (2.0)

Litter depth (cm) 1.0 (0.9) 3.0 (0.5) 3.0 (1.5)

Litter coverage (percent) 25.0 (8.9) 97.0 (2.3) 99.0 (3.2)

Herbaceous cover (percent) 42.0 (14.2) 32.0 (12.6) 7.0 (6.6)

Woody plant cover (percent) 27.0 (24.8) 16.0 (6.2) 17.0 (8.0)

Total woody debris (percent) 20.0 (9.5) 10.0 (3.9) 4.0 (2.1)

Large woody debris (percent) 6.0 (5.8) 4.0 (4.6) 3.0 (2.5)

Exposed rock cover (percent) 3.0 (3.3) 3.0 (2.6) 2.0 (3.4)

Horizontal cover at ground level (percent) 87.0 (8.0) 69.0 (26.1) 31.0 (12.8)

Horizontal cover at 1 m (percent) 63.0 (27.4) 42.0 (22.6) 19.0 (14.6)

a Each value is based on a sample size of 36 measurements.

Figure 4— Canonical correspondence analysis (CCA) ordination of species groups,

which can be superimposed on figures 2A and 3A in order to interpret patterns of

community composition along silvicultural treatments and environmental gradients.
280

and Eumeces laticeps were more prevalent in clearcuts

(figs. 3 and 5), while Eumeces anthracinus was not clearly

associated with any of the treatments (table 2). Anolis carolinensis was common in clearcut and selectively harvested

stands, but was never found in control stands (table 2).

Anolis carolinensis was associated positively and Eumeces

anthracinus negatively to an abundance of large, woody

debris.

DISCUSSION

Herpetofaunal communities differed significantly among

treatments. Generally, microhabitat preferences of species

explain these differences, with reptiles and amphibians

responding predictably to gross changes in habitat structure among treatments. Because of increased insolation,

higher ground temperatures, and higher evaporative water

loss, fewer amphibians would be expected in young clearcuts (Bennett and others 1980; Geiger 1971; Petranka and

others 1993, 1994). Although there was considerable overlap of taxa among habitats, our results suggested that

amphibians generally favored forested areas over clearcuts.

In particular, the two most common amphibians, Gastrophryne carolinensis and Bufo americanus, were strongly

correlated with litter depths.

Unlike amphibians, reptiles preferred the open sunny

habitats of the young clearcuts. Of the variables examined,

horizontal cover and presence of woody debris (positively),

and canopy cover and litter (negatively) seemed to be the

most important factors determining reptile species composition within clearcuts. In loblolly-shortleaf pine (Pinus taeda

and P. echinata) stands of east Texas, Whiting and others

Figure 5— Canonical correspondence analysis (CCA) ordination of species and habitat variables. See table 2

for a key to species’ acronyms.

(1987) also found that vegetative cover and the degree of

deciduous litter accumulation strongly influenced herpetofaunal communities. We found that E. guttata and Cnemidophorus sexlineatus, two grassland species, were both

positively associated with dense, herbaceous ground cover

and negatively with canopy cover. Surprisingly, Eumeces

laticeps, an arboreal lizard, was strongly associated with

woody plant cover and was more abundant in clearcuts

than in either of the forested treatments. Enge and Marion

(1986) found populations of E. laticeps to be reduced within

clearcuts.

Some reptiles (especially lizards) may be attracted to recent

clearcuts because the dense, low-growing vegetation provides an abundance of perching sites. For example, Anolis

carolinensis was positively associated with woody plant

cover and large, woody debris, habitat features largely

absent from control stands. Cnemidophorus sexlineatus, a

cursorial lizard, often inhabits early-succession habitats,

shrubby hillsides, and open, grassy areas (Collins 1993,

Webb 1970). Like us, Enge and Marion (1986) found this

lizard favored the most intensively disturbed clearcuts.

A greater abundance and diversity of prey (invertebrates,

birds, and small mammals) may contribute to higher abundances of reptiles (especially snakes) within clearcuts. A

sharp increase in small mammal densities could attract

large snakes such as E. obsoleta and E. guttata, which

were found primarily in clearcuts. The Fulvous Harvest

Mouse, Reithrodontomys fulvescens, Southern Short-tailed

Shrew, Blarina carolinensis, Golden Mouse, Ochrotomys

nuttalli, and mice, Peromyscus spp. were commonly
281

captured in our pitfall traps in the clearcuts, while only Peromyscus spp. were captured in the other two treatments.

Perhaps the most significant limitation of this study is pseudoreplication (Hurlbert 1984). The three arrays within each

treatment replicate were not spatially independent sites.

Given adequate resources, it would be best to have multiple,

spatially independent replications of each treatment. We

attempted to limit this problem by separating sample sites

by 100 m, but readers should use caution in interpreting the

results.

Some species (e.g., Thamnophis proximus, Storeria dekayi,

and Eurycea multiplicata) were not associated with any of

the habitat variables we measured. Potentially important

variables for future studies include invertebrate and smallmammal prey densities, microclimate, and proximity of sample sites to water. The latter two should aid in predicting

occurrence of most amphibians (especially semiaquatic

salamanders such as E. multiplicata and Desmognathus

brimleyorum) (Crosswhite and others 1998), as well as

reptiles (e.g., T. proximus and T. sirtalis) known to inhabit

riparian areas or sloughs (Collins 1993, Webb 1970).

This and other studies (Crosswhite and others, in press;

Dodd 1991; Gibbons and Bennett 1974; Gibbons and

Semlitsch 1982; Jones 1986) suggest that some species

(e.g. turtles and tree frogs) are not effectively sampled

using pitfall and funnel traps. These species might be more

effectively sampled using alternate techniques such as

quadrat sampling, aural surveys for frogs and toads, or

artificial habitat, (i.e., cover boards, frog houses, and

artificial pools) (Heyer and others 1994).

Reptiles are generally favored by more open canopies and

denser understory, as well as the presence of woody debris

or rocky outcroppings. Most habitat characteristics determining herpetofaunal community composition are ultimately

dependent upon stand age and the degree of site

disturbance.

Reptiles and amphibians play significant roles in many ecosystems. They can be important components of the food web

and may contribute substantially to community biomass

(Burton and Likens 1975, Pough and others 1987). Furthermore, because amphibians are often habitat specialists

with restricted distributions, they may be valuable indicators

of ecosystem health and stability. We hope our findings will

aid land managers in protecting reptile and amphibian

habitat.

ACKNOWLEDGMENTS

We thank the USDA Forest Service for providing funding. We

also thank D.M. Leslie, Jr. and the Oklahoma Cooperative

Fish and Wildlife Research Unit (Oklahoma State University, Oklahoma Department of Wildlife Conservation, U.S.

National Biological Service, and Wildlife Management

Institute Cooperating) for logistical support. We also thank

M. Tiernan, P. Shipman, J. Evans, M. Crosswhite, K. Koehn,

A. Ballou, C. Fincher, J. Whatson, M. Marlow, K. Shannon,

H. Grim, and M. Bruner for their assistance in the field and

M. Palmer and J. Boren for their statistical assistance.

LITERATURE CITED

Ash, A.N. 1988. Disappearance of salamanders from clearcut

plots. Journal Elisha Mitchell Science Society 104:116-122.

Ashton, R.E., Jr. 1976. Threatened and endangered amphibians

and reptiles of the United States. Society for the Study of

Amphibians and Reptiles Herpetological Circular No. 5:1-65.

Atkinson, T.D.; Johnson, A.S. 1979. Succession of small mammals

on pine plantations in the Georgia Piedmont. American Midland

Naturalist. 101:385-392.

Bennett, S.H.; Gibbons, J.W.; Glanville, J. 1980. Terrestrial activity,

abundance and diversity of amphibians in differently managed

forest types. American Midland Naturalist. 103:412-416.

Black, J.H. 1977. Endangered and threatened amphibians and

reptiles of Oklahoma. OHS committee report. Bulletin of the

Oklahoma Herpetological Society 2:42-50.

Bratton, S.P. 1994. Logging and fragmentation of broadleaved

decidious forests: are we asking the right ecological questions?

Conservation Biology 8:295-297.

Burton, T.M.; Likens, G.E. 1975. Energy flow and nutrient cycling

in salamander populations in the Hubbard Brook Experimental

Forest, New Hampshire. Ecology 56:1068-1080.

Bury, R.B. 1983. Differences in amphibian populations in logged

and old-growth redwood forests. Northwest Science 57:167-

178.

Campbell, H.W.; Christman, S.P. 1982. Field techniques for

herpetofaunal community analysis. In: N. J. Scott, Jr. (editor),

Wildlife Research Report 13, pp. 193-200. U.S. Department of

the Interior, Fish and Wildlife Service, Washington DC.

Collins, J.T. 1993. Amphibians and reptiles in Kansas. University of

Kansas Printing. Lawrence, KS. 397 p.

Conant, R.; Collins, J.T. 1998. A field guide to reptiles and amphibians of Eastern/Central North America. Houghton Mifflin Co.,

Boston. 450 p.

Crosswhite, D.L.; Fox, S.F.; Leslie, D.M., Jr.; Gregory, M. 1998.

Survey of the status and distribution of Desmognathus brimleyorum, the Ouachita dusky salamander, in southeast Oklahoma.

Proceedings of the Oklahoma Academy of Science. 78:49-52.

Crosswhite, D.L.; Fox, S.F.; Thill, R.E. [In press]. Techniques and a

comparison of sampling methods for collecting reptiles and

amphibians in the Ouachita Mountains, Arkansas. Proceedings

of the Oklahoma Academy of Science. 79

Diller, L.V.; Wallace, R.L. 1994. Distribution and habitat of Plethodon elongatus on managed, young growth forests in costal

[coastal] California. Journal of Herpetology 28:310-318.

Dodd, K.C., Jr. 1991. Drift fence-associated sampling bias of

amphibians at a Florida sandhills temporary pond. Journal of

Herpetology 25:296-301.

Dowling, H.G. 1956. Geographic relations of Ozarkian amphibians

and reptiles. Southwestern Naturalist 1:174-189.

Enge, K.M.; Marion, W.R. 1986. Effects of clearcutting and site

preperation on herpetofauna of a north Florida flatwoods.

Journal of Forest Ecology and Management. 14:177-192.

Faaborg, J. 1980. Potential uses and abuses of diversity concepts

in wildlife management. Transactions of the Missouri Academy

of Science. 14:41-49.

Fahrig, L.; Merriam, G. 1994. Conservation of fragmented populations. Conservation Biology 8:50-59.

Fitch, H.S. 1951. A simplified type of funnel trap for reptiles.

Herpetologica 7:77-80.
282

Geiger, R. 1971. The climate near the ground. Harvard University

Press. Cambridge, MA.

Gibbons, J.W.; Bennett, D.H. 1974. Determination of frogs and

toads n [in] terrestrial activity patterns by a drift fence method.

Copeia 1974:236-243.

Gibbons, J.W.; Semlitsch, R.D., 1982. Terrestrial drift fences with

pitfall traps: an effective technique for quantitative sampling of

animal populations. Brimleyana 7:1-16.

Heyer, W.R.; Donnelly, M.A.; McDiarmid, R.W. [and others]. 1994.

Measuring and monitoring biological diversity: standard methods

for amphibians. Washington DC: Smithsonian Institution Press.

Chapter VI, Standard techniques for inventory and monitoring;

p 75-141.

Hurlbert, S.H. 1984. Pseudoreplication and the design of ecological field experiments. Ecological Monographs 54, 187-211.

Jaeger, R.G. 1971. Moisture as a factor influencing the distribution

of two species of terrestrial salamanders. Oecologia. 6:191-207.

Jones, K.B. 1986. Amphibians and reptiles. In: Cooperrider, A. Y.,

J. B. Raymond, and R. S. Hanson. Inventory and Monitoring of

Wildlife Habitat, pp. 267-290. U.S. Department of the Interior,

Bureau of Land Management Service Center, Denver.

Kiester, A.R. 1971. Species density of North American amphibians

and reptiles. Systematic Zoology 19:127-137.

Kimmins, H. 1992. Balancing act: environmental issues in forestry.

UBC Press, Vancouver.

Kirkland, G.L. 1977. Responses of small mammals to clearcutting

of northern Appalachian forests. Journal of Mammalogy 58:600-

609.

Kirkland, G.L. 1990. Patterns of initial small mammal community

change after clearcutting of temperate North American forests.

Oikos 59:313-320.

Lemmon, P.E. 1957. A new instrument for measuring forest overstory density. Journal of Forestry 55, 667-668.

MacArthur, R.H.; MacArthur, J.W. 1961. On bird species diversity.

Ecology 42:594-598.

Manly, B.F.J. 1991. Randomization and Monte Carlo methods in

biology. Chapman and Hall, New York. 281 p.

Maser, C.; Anderson, R.G.; Cromack, K., Jr. [and others]. 1979.

Dead and down woody material. In: Thomas, J.W. Wildlife

habitats in managed forests: the Blue Mountains of Oregon and

Washington. Agriculture Handbook 553. Washington D.C.:

United States Department of Agriculture. pp. 77-95.

Matlack, G. 1994. Plant demography, land-use history, and the

commercial use of forests. Conservation Biology 8:298-299.

Mohlenbrock, J.H. 1993. Ouachita Mountains Arkansas. Natural

History Magazine. 102:22-24.

Pechman, J.K.; Scott, D.E.; Semlitsch, R.D. [and others]. 1991.

Declining amphibian populations: the problem of separating

human impacts from natural fluctuations. Science. 253:892-895.

Petranka, J.W. 1994. Response to impact of timber harvesting on

salamanders. Conservation Biology 8:302-304.

Petranka, J.W.; Brannon, M.P.; Hopey, M.E.; Smith, C.H. 1994.

Effects of timber harvesting on low elevation populations of

Southern Appalachian salamanders. Journal of Forest Ecology

and Management. 67:135-147.

Petranka, J.W.; Eldridge, M.E.; Haley, K.E. 1993. Effects of timber

harvest on Southern Appalachian salamanders. Conservation

Biology 7:363-370.

Pough, F.H.; Smith, E.M.; Rhodes, D.H.; Collazo, A. 1987. The

abundance of salamanders in forest stands with different histories of disturbance. Journal of Forest Ecology and Management.

20:1-9.

Raymond, L.R.; Hardy, L.M. 1991. Effects of a clearcut on a population of the mole salamander Ambystoma talpoideum, in an

adjacent unaltered forest. Journal of Herpetology 25:509-512.

Reagan, D.P. 1974. Threatened native amphibians of Arkansas. In:

Arkansas Natural Area Plan, pp. 93-99. Arkansas Dept. of

Planning, Little Rock.

Samson, F.B.; Knopf, F.L. 1982. In search of a diversity ethic for

wildlife management. Transactions of the North American

Wildlife Conference 47:421-431.

Taylor, C.M.; Winston, M.R.; Matthews, W.J. 1993. Fish speciesenvironment and abundance relationships in a Great Plains

river system. Ecography 16:16-23.

ter Braak, C.J.F.; Smilauer, P. 1998. CANOCO reference manual

and users guide to canoco for windows: software for canonical

community ordination (version 4). Microcomputer Power, Ithica,

NY. 352 p.

Thill, R.E. 1990. Managing southern pine plantations for wildlife.

In: Proceedings of the 19th International Union of Forestry

Research Organizations, World Congress; 1990 August 5-11;

Montreal, Canada. Canadian International Union of Forestry

Research Organizations.1:58-68.

Vogt, R.C.; Hine, R.L. 1982. Evaluation of techniques for assessment of amphibian and reptile populations in Wisconsin. In:

Scott N. J., Jr., Wildlife Research Report 13, pp. 201-217. U.S.

Dept. of the Interior, Fish and Wildlife Service, Washington DC.

Webb, R.G. 1970. Reptiles of Oklahoma. University of Oklahoma

Press, Norman. 370 p.

Whiting, M.R., Jr.; Fleet, R.R.; Rakowitz, V.A. 1987. Herpetofauna

in loblolly-shortleaf pine stands of east Texas. In: Pearson, H.A.;

Smeins, F.E.; Thill, R.E. (editors.), Ecological, Physical, and

Socioeconomic Relationships within Southern National Forests.

Proceedings of the Southern Evaluation Project Workshop;

1987 May 26-27; Long Beach, MS. New Orleans, LA: Southern

Forest Experiment Station USDA Forest Service. pp. 39-48.

Williams, K.L.; Mullin, K. 1987. Amphibians and reptiles of loblollyshortleaf pine stands in central Louisiana. In: Pearson, H.A.;

Smeins, F.E.; Thill, R.E. (editors), Ecological, Physical, and

Socioeconomic Relationships within Southern National Forests.

Proceedings of the Southern Evaluation Project Workshop;

1987 May 26-27; Long Beach, MS. New Orleans, LA: Southern

Forest Experiment Station USDA Forest Service. pp. 77-80.
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Poster Session
284
285

INTRODUCTION

For regulated uneven-aged silviculture of shortleaf pine

(Pinus echinata Mill.), the majority of stems in a stand

should be in the seedling, sapling, and pulpwood size

classes to allow for a continuous progression of trees into

more valuable saw logs. To accomplish that objective, the

hardwood component must be periodically controlled;

otherwise, shade-intolerant pine seedlings that develop in

the understory will most likely die (Cain 1987, 1988b).

Alternative methods for vegetation management include

prescribed fire, mechanical methods, manual methods,

herbicide treatments, and biological methods. In the Final

Environmental Impact Statement for the Ozark/Ouachita

Mountains (U.S. Department of Agriculture 1990), the

preferred alternative for vegetation management specified

an increase in the use of manual methods and a decrease

in the use of herbicides and soil-disturbing mechanical

methods. When herbicides are used, priority is to be given

to chemicals and application methods that pose minimum

risks to humans, wildlife, and nontarget plants.

This study was initiated to demonstrate uneven-aged silviculture of shortleaf pine by converting a mature even-aged

stand using single-tree selection. Objectives were (1) to

compare the cost and effectiveness of hardwood control by

manual methods with that of stem-injected herbicides and

(2) to monitor the establishment and development of shortleaf pine regeneration and hardwood regrowth following

manual control and herbicide control of the hardwood

component.

MATERIALS AND METHODS

Study Area

The study was located in Johnson County, AR, on the Bayou

Ranger District of the Ozark National Forest. Plots were

situated on a south-facing slope, and the elevation ranged

from 760 to 920 feet. Soils are Nella (Typic Paleudult) and

Mountainburg (Lithic Hapludult), gravelly or stony, fine

sandy loam (U.S. Department of Agriculture 1977). Annual

precipitation averages 46 inches with seasonal extremes

being wet springs and dry autumns and winters.

The south-facing slope contained a mature stand of shortleaf pines with basal area averaging 85 square feet per acre

in trees > 4.5 inches in diameter at breast height (d.b.h.).

Most pines were from 14 to 18 inches d.b.h., and the understory was devoid of pine regeneration. Overstory and midstory hardwood basal area (> 4.5 inches d.b.h.) averaged

32 square feet per acre. There had been no apparent forest

management activity in the stand during the last 20 years.

Site indices at 50 years for shortleaf pine were determined

by slope position in accordance with U.S. Department of

Agriculture (1976): upper slope = 59 feet, midslope = 64

feet, and lower slope = 69 feet.

Study Installation

Twelve plots of 0.5 acre (147.6 by 147.6 feet) each, with

interior subplots of 0.25 acre (104.4 by 104.4 feet), were

established in the summer of 1990. The experimental design

was a randomized complete block with three replications of

four treatments. Blocking was based on topographic position—upper (22-percent slope), middle (26-percent slope),

and lower (10-percent slope). Within each interior subplot,

25 systematically spaced sample points were permanently

established to serve as centers of circular 1-milacre and 2-

milacre quadrats for monitoring seedbed disturbance, pine

seedling and sapling development, and nonpine competition.

All merchantable-sized pines (> 4.5 inches d.b.h.) were

inventoried by 1-inch d.b.h. classes on a plot-by-plot basis.

These data were used to determine the allowable cut based

on the basal area–maximum diameter–quotient (BDq) technique (Farrar 1984). The BDq technique was applied according to three guidelines, in order of importance: (1) a basal

area of 60 square feet per acre was to be left in merchantable-sized pines after harvest; (2) all pines larger than a

maximum d.b.h. of 18 inches were to be cut, but some larger

pines were retained to achieve the desired basal area; and

(3) the residual diameter distribution should approach a

balanced uneven-aged structure, characterized by a constant ratio (q) between the number of trees in succeeding

diameter classes (q = 1.22 for 1-inch d.b.h. classes).

A 5-YEAR ASSESSMENT OF SHORTLEAF PINE AND HARDWOOD SPROUTS

RELATIVE TO THREE METHODS OF HARDWOOD CONTROL

IN THE ARKANSAS OZARKS

Michael D. Cain1

1 Research Forester, USDA Forest Service, Southern Research Station, Monticello, AR.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Compared with untreated checks, manual hardwood control and herbicide injection of hardwoods facilitated

the development of direct seeded shortleaf pine (Pinus echinata Mill.) regeneration following a single-tree selection

harvest in a mature natural stand of shortleaf pines in northwest Arkansas. Five years after hardwood control, shortleaf

pine seedlings on treated plots were 215 percent taller (P = 0.02) and 242 percent larger (P = 0.01) in groundline diameter

than pine seedlings on check plots. Resprouting hardwoods on herbicide injection plots were 42 percent shorter (P < 0.01)

and had 72 percent less (P = 0.01) crown area compared with those on manual control plots.
286

Pine harvesting on study plots was completed in late June

1990. Cut pines were removed tree-length using an articulated rubber-tired skidder. Merchantable pines on an additional 40 acres surrounding the research plots were also

marked for harvest using single-tree selection guidelines,

but harvesting was delayed until the summer of 1991.

Treatments

Hardwood control was accomplished in early September

1990 by an independent contractor. An untreated check

was compared with three methods of postharvest hardwood

control as follows:

Check—No hardwood control, but there was disturbance

from logging.

Herbicide injection—All hardwood trees and shrubs that

were ≥ 1.0 inch groundline diameter (g.l.d.) were stem

injected near the root collar with Garlon® 3A (triclopyr) at

the rate of 0.02 ounce of undiluted herbicide per incision

and one incision per inch of g.l.d. This treatment was

accomplished with Jim-Gem® tree injectors.

Manual hardwood control—All hardwood trees and shrubs

that were ≥ 1.0 inch g.l.d. were manually cut using chain

saws. Stump heights averaged < 1.0 foot.

Manual hardwood control plus release—Hardwoods were

controlled using the same method as the previous treatment, but pine regeneration was manually released during

the fourth growing season (June 1994) after the initial treatment. For release, brush axes and lopping shears were used

to cut competing vegetation near the groundline but only

within 4 feet of established pine seedlings. If no pine seedlings were visually observed as workers traversed each

plot, then no vegetation was cut in the release treatment.

In order to assess the cost of herbicide and manual treatments for controlling hardwoods, the time required to inject

or manually fell the hardwoods was recorded, and the volume

of injected herbicide was measured. Six 0.5-acre plots were

assessed for herbicide injection cost, and six plots were

assessed for chain saw felling cost. To assess labor cost,

the minimum wage in 1990 was doubled to $8.50 per hour

to account for vendor contract fees. The cost of Garlon

herbicide was based on the retail price in 1991 ($169 for

2.5 gallons). The cost of chain saw operation was based on

dollar amounts taken from Miller (1984) with an addition of

5 percent annually for 6 years to account for inflation and

was determined to be $2.76 per hour of chain saw use.

On the 40-acre nonresearch area that surrounded study

plots, residual hardwoods > 1 inch d.b.h. were controlled

after pine harvesting was completed by using an edge-toedge cut-surface treatment at waist height and applying

Accord® (glyphosate) herbicide at recommended rates

(25 to 50 percent concentration).

Pine Seed Crops

Natural pine seed fall was monitored during 3 consecutive

seed years (1990-91, 1991-92, and 1992-93), from October

through January, using 4 seed traps on each of the 12 plots.

The four interior subplot corners were used as monumentation points for the seed traps. Individual seed traps had an

opening of 0.9 square foot (Cain and Shelton 1993). All 3

seed years were judged to be complete failures, because

the number of potentially viable seeds ranged from 1,000 to

3,000 per acre per year. According to Liming (1945), 40,000

viable shortleaf pine seeds per acre are purported to be

necessary during any one seed year for adequate regeneration under natural conditions.

To ensure regeneration of the area, Bayou Ranger District

personnel obtained from an Ozark Mountain source shortleaf pine seeds that had been in cold storage. Using cyclone

hand-seeders, district personnel broadcast the seeds onto

the 12 study plots at the rate of 3 pounds per plot (6 pounds

per acre) in mid-February 1991. These seeds were reported

to average 28,100 seeds per pound and had a germination

rate of 86 percent. A heavy sowing rate was used because

the seeds were not treated with repellents to control predators or stratified before direct seeding. The recommended

rate for broadcast sowing of repellent-treated, stratified,

shortleaf pine seeds is 0.48 pound per acre (Ezell 1988).

One year after direct seeding, the 40-acre area surrounding

the research plots was hand-planted with 1-0, genetically

improved, bare-root shortleaf pine seedlings from an eastern

Oklahoma seed source. These seedlings were outplanted

with hoedads on a 12- by 12-foot spacing (302 trees per

acre). To monitor the development of planted seedlings, an

additional 0.2-acre plot was established contiguous to each

of the herbicide injection plots in the main study. During

autumn of 1992, 25 planted shortleaf pine seedlings were

randomly selected for measurement within each planted

plot and tagged for identification.

Measurements and Data Analysis

Following harvest and hardwood control, seedbed conditions were ocularly estimated to the nearest 10 percent on

each of the twenty-five 1-milacre sample quadrats per plot.

Eight disturbance codes were used in that assessment

(Cain 1993), but only the percentage of forest floor covered

by manually cut hardwood slash is presented here.

In the winter of 1995-96, 5 years after hardwood control,

merchantable-sized pines (> 4.5 inches d.b.h.) on each

0.5-acre plot were remeasured to 0.1 inch at d.b.h. for calculation of basal area. Pine seedlings were counted within

each of twenty-five 1-milacre sample quadrats per interior

subplot. The two tallest (dominant) pine seedlings (stems

≤ 0.5 inch d.b.h.) within each milacre quadrat were measured for total height to 0.1 foot and g.l.d. to 0.04 inch.

Within a 2-milacre circular plot on even-numbered quadrats,

pine saplings (stems > 0.5 inch but < 4.6 inches d.b.h.)

were measured, but only two saplings were present across

all 12 plots 5 years after harvest. Within the twenty-five

1-milacre quadrats per plot, ground coverage was ocularly

estimated to the nearest 10 percent for herbaceous vegetation (forbs, grasses, semiwoody plants, and vines) and for

merchantable and submerchantable pines and hardwoods

(including shrubs) by vertical projection of foliar cover to the

ground.

During the winter measurements, hardwood rootstocks of

seedling size were counted within each of the twenty-five

1-milacre quadrats per plot. A rootstock was comprised of
287

either single or multiple stems (clump) of seedling size,

which obviously arose from the same root system. The

tallest seedling-sized hardwood was identified by species

and measured for total height to 0.1 foot and for crown

width at the widest axis and perpendicular to that axis to

0.1 foot. Within the 2-milacre quadrats at even-numbered

sample points, hardwood saplings were counted by 1-inch

d.b.h. classes, and the dominant hardwood sapling was

identified by species.

For residual hardwoods that were > 4.5 inches d.b.h., a

100-percent inventory was conducted within each 0.5-acre

plot. The numbers of hardwood stems were recorded by 1-

inch d.b.h. classes and were categorized into four species

groups: red oaks (principally Quercus falcata Michx., Q.

rubra L., and Q. velutina Lam.), white oaks (Q. alba L. and

Q. stellata Wangenh.), hickories (Carya spp.), and other

hardwoods.

On the three plots established for monitoring planted pine

development, surviving crop seedlings were also measured

in the winter of 1995-96 to obtain total height to 0.1 foot

and g.l.d. to 0.04 inch. On these plots, merchantable-sized

pines were measured to 0.1 inch d.b.h. for calculation of

basal area.

Analysis of variance was used to evaluate treatment effects

on measured variables. Percentage values for quadrat

stocking and ground coverage were compared following

arcsine transformation. Treatment means were compared

using orthogonal contrasts. Analysis of variance was also

used to compare the mean size of planted pines with that of

dominant seeded pines on herbicide injection plots in the

main study.

RESULTS AND DISCUSSION

Cost of Postharvest Hardwood Control

Averaged over an area of 3 acres for each method of hardwood control, the initial cost of manually felling the hardwoods was half the cost of tree injection (Cain 1993). Chain

saw felling was accomplished at a cost of $0.07 per treated

stem as compared with $0.14 per stem for herbicide injection. For the injection treatment, Garlon accounted for 78

percent of the cost, with labor at 22 percent. For manual

hardwood control, labor was the highest cost factor at 79

percent, and chain saw use accounted for 21 percent of the

cost. Expenses incurred during the fourth growing season

for release of established pine seedlings averaged $68 per

acre, thereby increasing total cost for manual hardwood

control in that treatment to $100 per acre and exceeding

the cost of herbicide injection by $37 per acre.

During initial hardwood control, all hardwoods > 1.0 inch

g.l.d. were treated, even if they were of merchantable size.

On an operational scale, merchantable hardwoods should

be sold and removed from the site, which would reduce the

cost of postharvest hardwood control.

Postharvest Seedbed Condition

Cut-and-leave hardwood control techniques result in too

much detritus being left on the site, and slash cover has

been shown to impede natural pine regeneration (Trousdell

1950). In the present study, a preliminary analysis of posttreatment seedbed conditions indicated that, on the six plots

subjected to manual hardwood control, 88 percent of sampled 1-milacre quadrats contained hardwood slash, and

ground coverage from felled hardwoods averaged 62 percent of the area within those quadrats. Because such heavy

slash restricted accessibility and was considered to be

unacceptable for pine seedling establishment, the slash

was removed from within plot boundaries if > 4.0 inches in

diameter.

Seeded Pine Regeneration

In uneven-aged silviculture, 200 submerchantable-sized

pines per acre are considered to be the minimum for

adequate density, with 50-percent milacre stocking being

optimum (Cain 1991). The preharvest inventory revealed no

pine seedlings, but five growing seasons after direct seeding, pine seedling density averaged 1,164 stems per acre

across all treatments. Herbicide injection plots had the most

(P = 0.03) pine seedlings compared with other treatments;

even so, all plots averaged > 500 seedlings per acre (table

1). Hardwood control plots also had better milacre stocking

(P = 0.01) of pine seedlings than check plots, and milacre

stocking on herbicide injection plots was better (P = 0.01)

than on manual control plots. Herbicide injection of hardwoods was also the only treatment to result in > 50-percent

milacre stocking of pine seedlings.

With direct seeding, the study area was successfully regenerated. However, stand structure was a long way from a

well-regulated condition. Results from uneven-aged management of shortleaf pines in the Ouachita Mountains of

Arkansas suggest that old, even-aged sawtimber stands

are the most difficult to convert to uneven-aged structure

and will require the longest conversion period of any existing stand type (Murphy and others 1991).

Five growing seasons after establishment, dominant pine

seedlings averaged 1.90 feet tall and 0.34 inch g.l.d. (table

1). Dominant seedlings on hardwood control plots were 215

percent taller (P = 0.02) and 242 percent larger in g.l.d. (P

= 0.01) than those on check plots (table 1). There was no

difference (P > 0.05) in total height or g.l.d. for dominant

pine seedlings on herbicide injection plots versus manual

control plots.

Planted Pine Regeneration

In January 1992, 1 year after direct seeding of the plots in

the main study, the nonresearch areas surrounding the main

study plots were hand-planted with 1-0 bare-root shortleaf

pine seedlings. At the time of remeasurement in the winter

of 1995-96, both the dominant, direct-seeded shortleaf and

the planted shortleaf were assumed to be 5 years old from

seed. At that time, merchantable pine basal area on planted

plots averaged 69 square feet per acre and did not differ (P

= 0.91) from the 70 square feet per acre on herbicide injection plots in the main study. Therefore, the negative effect

of overstory pines on the development of pine regeneration

should have been the same regardless of establishment

technique.
288

Planted shortleaf seedlings averaged 4.44 feet in height

and were 78 percent taller (P < 0.01) than the dominant

seeded shortleaf seedlings on herbicide injection plots in

the main study. At 0.92 inch g.l.d., planted shortleaf seedlings were also 96 percent larger (P = 0.01) than dominant

seedlings on herbicide injection plots. Survival of these

planted shortleaf pines averaged 88 percent.

Because of a natural seed crop failure during the winter after

harvest, direct seeding was chosen in the main study to simulate natural seed fall. Although no cost comparison was made

between planting and direct seeding, 5-year results suggest

that planting of shortleaf seedlings was superior to direct

seeding in terms of growth response after controlling hardwoods by herbicide injection.

Competing Vegetation

Five years after hardwood control, density of seedling-sized

hardwoods averaged nearly 3,700 rootstocks per acre (table

2). Although manual control plots averaged 32 percent more

hardwood rootstocks of seedling size than plots that were

treated by herbicide injection, treatment differences were not

statistically significant (P = 0.20). However, dominant hardwood sprouts of seedling size were 73 percent taller (P <

0.01) on manual control plots than on herbicide injection

plots. In addition to being taller, these dominant sprouts had

crowns that averaged 253 percent larger (P = 0.01) than

those on injected plots (table 2). Obviously, herbicide injection was more effective than chain saw felling by reducing

the regrowth of hardwoods during the first 5 years after

treatment.

Six woody species, or groups of species, predominated the

seedling size class, and they accounted for 78-percent milacre stocking of all nonpine woody vegetation. These were

Cornus florida L. (24 percent), Quercus spp. (20 percent),

Carya spp. (13 percent), Nyssa sylvatica Marsh. (11 percent),

Vaccinium spp. (6 percent), and Acer rubrum L. (4 percent).

Milacre stocking was < 4 percent for other seedling-sized

woody species.

Density of hardwood saplings averaged 556 stems per acre

(table 2), and manual control plots had 633 percent more

(P = 0.02) saplings than herbicide injection plots. Quadrat

stocking of these sapling hardwoods averaged 42 percent

across all plots (table 2). Manual control plots had 159 percent more (P = 0.03) quadrats stocked with hardwood saplings than did herbicide injection plots. This result again

reflects the efficacy of herbicide injection versus chain saw

felling when used to control hardwoods in pine management. As would be expected, the mean d.b.h. of hardwood

saplings on check plots averaged 36 percent larger (P =

0.03) compared with those on treated plots (table 2). On

check plots and manual control plots, Cornus florida L.

were the predominant hardwoods of sapling size, whereas

Ulmus alata Michx. saplings predominated on herbicide

injection plots.

Five years after hardwood control, ground coverage by pine

regeneration averaged only 5 percent (table 3). Herbicide

injection resulted in greater (P = 0.04) ground coverage by

pine seedlings compared with manual control treatments,

but all hardwood control treatments had more (P = 0.03)

pine seedling ground coverage than check plots. Ocular

estimates of percentage ground cover from hardwoods of

submerchantable size substantiates the differences in efficacy of the hardwood control treatments that were tested in

this investigation. Ground coverage from these hardwoods

averaged about 27 percentage points higher (P < 0.01) on

manual control plots than on herbicide injection plots (table

Table 1—Status of shortleaf pine regeneration 5 years after hardwood

control

Hardwood control and Milacre Total

orthogonal contrasts Density stockinga heightb G.l.d.b

stems/ac percent feet inches

1. Check 586 20 0.73 0.12

2. Herbicide injection 1,987 59 2.50 0.47

3. Manual hardwood control 961 41 2.04 0.35

4. Manual hardwood control,

 plus release 1,121 32 2.35 0.42

Mean square error 236,578 0.011 0.59 0.02

PR > F c 0.06 0.01 0.10 0.06

Probabilities of a greater F-ratio

1 vs 2 + 3 + 4 0.06 0.01 0.02 0.01

2 vs 3 + 4 0.03 0.01 0.59 0.37

3 vs 4 0.70 0.28 0.64 0.51

G.l.d. = groundline diameter.

a Based on the presence of at least one pine seedling per 1-milacre quadrat.

b Mean total height and groundline diameter of the two dominant pine seedlings per

1-milacre quadrat.

c The probability of obtaining a larger F-ratio under the null hypothesis.
289

3). Ground coverage from merchantable-sized hardwoods

averaged 48 percentage points higher (P < 0.01) on check

plots compared with plots where these trees were controlled

5 years earlier (table 3). On check plots, the multistoried

hardwood cover, in combination with overstory pines, contributed to almost complete shading of the forest floor.

Residual basal area in merchantable-sized hardwoods on

check plots averaged 36 square feet per acre. These hardwoods ranged from 5 to 18 inches d.b.h. and averaged 116

trees per acre. Of that density, 43 percent was in the white

oak group, 28 percent in hickories, 18 percent in the red

oak group, and 11 percent in other hardwoods.

Table 2—Status of nonpine competition 5 years after hardwood control

Seedling-sized hardwoods Sapling-sized hardwoods

Hardwood control and Rootstock Total Crown Quadrat Weighted

orthogonal contrasts density heighta areaa Density stockingb d.b.h.c

no./ac feet ft2 /rtstk stems/ac percent inches

1. Check 3,080 4.89 11.14 264 30 1.45

2. Herbicide injection 3,213 4.16 4.10 125 22 1.11

3. Manual hardwood

 control 4,693 7.01 14.68 986 67 1.03

4. Manual hardwood

 control, plus release 3,800 7.41 14.28 847 47 1.08

Mean square error 10.46 x 105 0.90 18.42 13.53 x 104 0.04 0.04

PR > Fd 0.30 0.01 0.07 0.07 0.07 0.15

Probabilities of a greater F-ratio

1 vs 2 + 3 + 4 0.27 0.08 0.97 0.16 0.21 0.03

2 vs 3 + 4 0.20 < 0.01 0.01 0.02 0.03 0.73

3 vs 4 0.33 0.62 0.91 0.66 0.21 0.78

D.b.h. = diameter at breast height.

a Data from the dominant seedling-sized hardwood rootstock (rtstk) per 1-milacre quadrat.

b Based on the presence of at least one hardwood sapling per 2-milacre quadrat.

c Means weighted by number of hardwood saplings in the 1-, 2-, 3-, and 4-inch d.b.h. classes.

d The probability of obtaining a larger F-ratio under the null hypothesis.

Table 3—Ground cover by vegetative component 5 years after hardwood control

Ground cover

Hardwood control and Pine Hardwoods Hardwoods Herbaceous

orthogonal contrasts seedlings < 4.6 in. d.b.h. >4.5 in. d.b.h. vegetation

 - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - -

1. Check 1.8 36.5 48.8 29.9

2. Herbicide injection 8.6 24.3 3.9 60.3

3. Manual hardwood control 4.6 52.7 0.0 58.8

4. Manual hardwood control,

plus release 4.6 50.5 0.0 58.2

Mean square error 0.00048 0.006 0.008 0.032

PR > Fa 0.05 0.01 < 0.01 0.14

Probabilities of a greater F-ratio

1 vs 2 + 3 + 4 0.03 0.22 < 0.01 0.03

2 vs 3 + 4 0.04 < 0.01 0.57 0.76

3 vs 4 0.99 0.73 1.00 0.97

in. = inches; d.b.h. = diameter at breast height.

a The probability of obtaining a larger F-ratio under the null hypothesis.
290

Five years after treatment, ground coverage from herbaceous vegetation averaged 52 percent (table 3). Because

of their intolerance to shade, herbaceous plants had less

(P = 0.03) ground cover on check plots than on hardwood

control plots. In the first few years after establishment of

pine regeneration, competition from herbaceous vegetation

can be more detrimental to survival and growth of pine seedlings than woody competition, especially on good sites (site

index > 85 feet at 50 years) where herbaceous ground

cover can approach 100 percent, with multistoried layers

(Cain 1988a). Given the combined ground cover from sprouting hardwoods and herbaceous vegetation in the present

study, and in the absence of site disturbance, it is unlikely

that additional pine regeneration will become established

from natural seed fall on this site, even in better-than-average seed years.

MANAGEMENT IMPLICATIONS AND

CONCLUSIONS

In order for shade-intolerant shortleaf pines to become

established at recommended levels of density and quadrat

stocking and grow at an acceptable rate for sustaining

uneven-aged silviculture, removal of overtopping hardwoods

is required. If a landowner wishes to retain some midstory

and overstory hardwoods while managing for pine, then

clumping of hardwoods would be more desirable than

random distribution (Shelton and Murphy 1993). In fact,

Leopold and others (1985) found that most hardwood

species in a midwestern hardwood forest exhibited an

aggregated distribution under natural conditions.

In the present investigation, herbicide injection of hardwoods

after the pine single-tree selection harvest was more beneficial in terms of enhancing the establishment and growth

of direct-seeded shortleaf pine regeneration when compared with untreated checks or manual hardwood control

treatments. Followup manual release of established pine

regeneration in the fourth growing season resulted in no

measurable benefit in terms of competition control or pine

seedling growth when compared with initial hardwood

control by chain saw felling. The release treatment also

negated the initial cost advantage of chain saw felling. If

herbicide use is not an option on National Forest System

lands because of public concerns, then manual girdling of

the hardwood component is likely to result in fewer sprouts

of smaller size than chain saw felling (Liming 1945, 1946).

During the first 5 years of this study, residual overstory pines

grew at the rate of only 0.14 inch d.b.h. per year. Without

ingrowth from submerchantable to merchantable size classes,

data suggest that cycle cuts may be needed only every 10 to

12 years for conversion of mature even-aged shortleaf pine

stands to uneven-aged structure on south-facing slopes

where site conditions are similar to those tested here. This

assumes that residual basal area averages 60 square feet

per acre in merchantable-size pines after the initial harvest.

Although the evaluation of planted shortleaf pine seedlings

was conducted only as a side test to the main study, the

genetically improved, nursery-grown pines provided better

stocking and outperformed dominant shortleaf seedlings

that were established by seeding. Consequently, when converting mature even-aged shortleaf pine stands to unevenaged structure, planting appears to be a viable regeneration

alternative to direct seeding or natural seeding, especially

at locations where natural seed crops are consistently

below average.

ACKNOWLEDGMENTS

Field support for plot installation and treatment was provided by personnel from the Ozark National Forest, Bayou

Ranger District, Hector, AR.

LITERATURE CITED

Cain, M.D. 1987. Survival patterns of understory woody species in

a pine-hardwood forest during 28 years without timber management. In: Hay, R.L.; Woods, F.W.; DeSelm, H., eds. Proceedings

of the sixth central hardwood forest conference. Knoxville, TN:

University of Tennessee: 141-147.

Cain, M.D. 1988a. Competition impacts on growth of naturally

regenerated loblolly pine seedlings. Res. Note SO-345. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station. 5 p.

Cain, M.D. 1988b. Hardwood control before harvest improves

natural pine regeneration. Res. Pap. SO-249. New Orleans, LA:

U.S. Department of Agriculture, Forest Service, Southern

Forest Experiment Station. 6 p.

Cain, M.D. 1991. Importance of seedyear, seedbed, and overstory

for establishment of natural loblolly and shortleaf pine regeneration in southern Arkansas. Res. Pap. SO-268. New Orleans,

LA: U.S. Department of Agriculture, Forest Service, Southern

Forest Experiment Station. 10 p.

Cain, M.D. 1993. First-year response of direct-seeded shortleaf pine

and hardwood sprouts to three methods of hardwood control in

the Arkansas Ozarks. In: Brissette, J.C., ed. Proceedings of the

seventh biennial southern silvicultural research conference.

Gen. Tech. Rep. SO-93. New Orleans, LA: U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station: 317-326.

Cain, M.D.; Shelton, M.G. 1993. Modification of GSA metal wastebaskets for use as seed and litter traps. Engineering Field

Notes. 26: 65-69.

Ezell, A.W. 1988. Direct seeding—a forest regeneration alternative. Publ. 1588. Mississippi State, MS: Mississippi State

University, Mississippi Cooperative Extension Service. 5 p.

Farrar, R.M., Jr. 1984. Density control—natural stands. In:

Proceedings of the symposium on the loblolly pine ecosystem

(west region). Mississippi State, MS: Mississippi Cooperative

Extension Service, Extension Forestry Department: 129-154.

Leopold, D.J.; Parker, G.R.; Ward, J.S. 1985. Tree spatial patterns

in an old-growth forest in east-central Indiana. In: Dawson, J.O.;

Majerus, K.A., eds. Proceedings of the fifth central hardwood

forest conference. Society of American Foresters Publ. 85-05.

Urbana-Champaign, IL: University of Illinois, Department of

Forestry: 151-164.

Liming, F.G. 1945. Natural regeneration of shortleaf pine in the

Missouri Ozarks. Journal of Forestry. 43: 339-345.

Liming, F.G. 1946. Response of planted shortleaf pine to overhead

release. Tech. Pap. 105. Columbus, OH: U.S. Department of

Agriculture, Forest Service, Central States Forest Experiment

Station. 20 p.

Miller, G.W. 1984. Releasing young hardwood crop trees—use of

a chain saw costs less than herbicides. Res. Pap. NE-550.

Broomall, PA: U.S. Department of Agriculture, Forest Service,

Northeastern Forest Experiment Station. 5 p.
291

Murphy, P.A.; Baker, J.B.; Lawson, E.R. 1991. Selection management of shortleaf pine in the Ouachita Mountains. Southern

Journal of Applied Forestry. 15: 61-67.

Shelton, M.G.; Murphy, P.A. 1993. Pine regeneration and understory vegetation 1 year after implementing uneven-aged silviculture in pine-hardwood stands of the silty uplands of Mississippi.

In: Brissette, J.C., ed. Proceedings of the seventh biennial

southern silvicultural research conference. Gen. Tech. Rep.

SO-93. New Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment Station: 333-341.

Trousdell, K.B. 1950. Seed and seedbed requirements to regenerate loblolly pine. Sta. Pap. No. 8. Asheville, NC: U.S. Department

of Agriculture, Forest Service, Southeastern Forest Experiment

Station. 13 p.

U.S. Department of Agriculture. 1976. Volume, yield, and stand

tables for second-growth southern pines. Rev. Misc. Publ. 50.

Washington, DC: U.S. Department of Agriculture, Forest

Service. 202 p.

U.S. Department of Agriculture. 1977. Soil survey of Johnson

County, Arkansas. Washington, DC: U.S. Department of

Agriculture, Soil Conservation Service and Forest Service. 80

p. + maps. In cooperation with: The Arkansas Agricultural

Experiment Station.

U.S. Department of Agriculture. 1990. Final environmental impact

statement for vegetation management in the Ozark/Ouachita

Mountains. Manage. Bull. R8-MB 45. Atlanta, GA: U.S.

Department of Agriculture, Forest Service. 352 p. Vol. 1.
292

INTRODUCTION

Previous research shows that road and trail crossings

generally have negative impacts on the hydrology and geomorphology of forest streams. Roads and trails are active

sources of sediment, and they increase peak flows in some

cases (Reid and Dunne 1984, Wemple and others 1996).

Heavy usage can accelerate erosion, compact soils, and

decrease infiltration, leading to changes in discharge magnitude and timing, channel structure, sediment routing through

forest streams, and habitat degradation (e.g., Brown 1994,

Eckert and others 1979, Webb 1983). These impacts may

be more pronounced in the case of all-terrain vehicle (ATV)

trails, where users often develop improperly located trails in

addition to designated ones.

In the Ouachita National Forest (ONF), the Wolf Pen Gap

All-Terrain Vehicle Trail was created upon pre-existing roads

and opened in 1991 for public use. The system consists of

67 km of designated trails that loop through an array of

areas, including scenic Gap Creek and Board Camp Creek

(fig. 1). Because of the prevalence for off-road exploration,

and the ease with which ATVs can traverse rugged terrain,

users have also developed a network of unauthorized trails.

These trails totaled 28.2 km by 1997. Because these trails

are undesigned, they can be especially erosive and potentially exacerbate any negative impacts of the planned trail

system on channel integrity.

The ONF began monitoring the effects of the ATV trail

system on water quality and stream characteristics in 1990.

Data collection continued through 1999, wherein we also

completed the mapping of the ATV trails and other roads in

the study area. In this paper, we report preliminary results

comparing pool characteristics between watersheds with

and without ATV usage during the initial period following

ATV trail construction and usage (1990-1992).

STUDY AREA

The study uses four basins within the ONF in Arkansas. The

ONF encompasses much of the Ouachita Mountains, part

of the Southern Interior Highlands of the United States (Smith

1989). The area features east-west trending ridges and

valleys, which were formed by the erosion of tightly folded

sedimentary rocks. The soils tend to be thin Inceptisols and

Ultisols with predominantly forest cover. The vegetation is

largely composed of loblolly and shortleaf pine mixed with

scattered hardwoods. The region has a humid subtropical

EFFECTS OF ALL-TERRAIN VEHICLES ON STREAM DYNAMICS

Anne Chin, Deven M. Rohrer, Daniel A. Marion, and J. Alan Clingenpeel1

1 Assistant Professor and Graduate Assistant, Department of Geography, Texas A&M University, College Station, TX 77843; Research

Hydrologist, USDA Forest Service, Southern Research Station, Oxford, MS 38655-4915; and Forest Hydrologist, USDA Forest Service,

Ouachita National Forest, Hot Springs, AR 71902, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—This paper reports preliminary results from research conducted in the Ouachita National Forest to assess the

effects of all-terrain vehicle (ATV) trails on stream characteristics. The study focuses on the Wolf Pen Gap Trail that has

been in use since 1991. We examine whether that the trail system has caused increased sediment input to and deposition

within stream pools. We compared selected pool characteristics in two watersheds with ATV trails (Gap and Board Camp

Creeks) to those in two control watersheds (Brushy and Caney Creeks). Analysis indicates that watersheds with ATV trails

have pools with higher percentages of sands and fines, lower depths, and lower volumes. More research is needed to

confirm that ATV trails in fact cause these impacts, to determine what factors control impact occurrence, and to better

quantify impact magnitude.

Figure 1—Authorized all-terrain vehicle trail in the Gap

Creek watershed.
293

climate characterized by warm summers and relatively cold

and dry winters. Annual precipitation in Arkansas averages

124 cm, although orographic effects contribute to high rainfall variability in the study area.

The study basins of Board Camp, Gap, Brushy, and Caney

Creeks are all located in the western portion of the ONF

(fig. 2). The basins have similar geology, climate, soils, and

vegetation and are representative of watersheds within the

ONF. Board Camp and Gap Creeks flow generally westward and northward, draining eventually into the Ouachita

River. Their watersheds contain the Wolf Pen Gap Trail,

whereas ATV trails are absent in the Brushy Creek and

Caney Creek basins. A road network does exist in the Brushy

Creek watershed. However, unlike the situation with ATV

trails, these roads only cross the streambed at culverts and

designed fords. Caney Creek is the least disturbed of the

study basins. Situated in the Caney Creek Wilderness

Management Area, only hiking and horse trails are allowed.

Both Caney and Brushy Creeks flow generally westward/

southward and drain into the Cossatot River.

METHODS

The study approach focuses on comparing the stream characteristics of Board Camp Creek and Gap Creek (streams

with ATV trails) with those of Brushy Creek and Caney

Creek (reference streams without ATV trails). We measured

physical characteristics of the study streams using Basin

Area Stream Survey methods (BASS). BASS is a systematic

procedure for inventorying the physical, chemical, and biological characteristics of forest streams (Clingenpeel and

Cochran 1992). As part of the BASS, habitat types are identified according to McCain and others (1990); these include

low- and high-gradient riffle, lateral scour pool, mid-channel

pool, step pool, bedrock sheet, and run. Physical characteristics measured include bankfull width, water depth, substrate

material size, and bank stability. These characteristics were

used to compute parameters for evaluating ATV trail impacts:

percent sands and fines (grains < 2.0 mm), pool depth, pool

volume, and embeddedness. Complete habitat data are

contained in Clingenpeel (1994). For this analysis, only

those habitat types containing pools are examined and data

from all pool habitat types are combined for each basin.

RESULTS AND DISCUSSION

Turbidity

Visual turbidity observations indicate that sediment inputs

differ between the ATV-affected and control streams as a

result of ATV trail usage. High turbidity levels were observed

in surface runoff from ATV trails entering Gap and Board

Camp Creeks during and after light rainstorms in the summer of 1999 (fig. 3). Pools below ATV trail crossings were

no longer clear but muddy and sediment laden (fig. 4). Such

Figure 2—Study basins in the Ouachita National Forest,

Arkansas.

Figure 3—Sediment transport along all-terrain vehicle trails after rainfall, Gap

Creek.
294

observations indicate the erosive nature of the ATV trails,

and they suggest that ATV trails may be significant sediment sources for streams within Gap and Board Camp

Creek basins.

Percent Sands and Fines

Visual observations also indicate that the creeks affected

by ATV trails are experiencing increased sedimentation.

Figure 5 shows typical examples of pools within ATVaffected and unaffected basins. The larger amount of sands

and fines in the affected basin is readily apparent. The

larger fine-sediment fractions indicate that sediment inputs

are large enough that the streams cannot readily transport

these additional loads.

Measured percents confirm that sands and fines are substantially higher in stream pools within the impacted basins.

Box plots in figure 6 show the higher values of percent sands

and fines in Board Camp and Gap Creeks. The median

value approaches 20 percent in both of these streams,

Figure 4—Example of turbid pool in Gap Creek at all-terrain vehicle crossing

after rainfall.

about twice that of Brushy and Caney Creeks. The 25- and

75-percentile values range between 10 and 30 percent

sands and fines for Board Camp and Gap Creeks, whereas

in the reference creeks, they range from 0 to 10 percent.

The 90-percentile values for Board Camp and Gap Creeks

reach 50 percent and greater sands and fines. In contrast,

for the streams without ATV trails, 90 percent of the pools

have only 20 percent sands and fines or less.

Embeddedness

Embeddedness is a measure of the extent to which cobblesized material is surrounded by finer materials. It is

expressed as the percent of a cobble surrounded by (or

embedded in) smaller grains; it is an additional descriptor of

sediment load in streams.

The quantitative data for embeddedness do not show clear

differences between ATV-affected and unaffected streams

(fig. 7). Median values of embeddedness are about 30 to 35

percent for all four streams. The box plots show larger ranges

Figure 5—(A) fine sediment coats cobbles within pool in all-terrain vehicle affected basin, and (B) cobbles in Caney Creek pool are clearly

visible through water.

A B
295

Figure 6—Box-plots illustrating percent sands and fines in pools of

the four study streams. Median is represented by the thick centerline. The box encloses values between the 25- and 75-percentiles.

Whiskers extend from the box to the 10- and 90-percentiles of the

data. Points outside the whiskers are extreme values.

of embeddedness values for the creeks without ATV trails.

Although Board Camp and Gap Creeks exhibit narrower

ranges of embeddedness values, a somewhat larger proportion of the samples in these impacted streams have higher

embeddedness values. For example, there are no samples

in these creeks with < 10 percent embeddedness and 75

percent of their samples have values > 20 percent.

Embeddedness is a measure that has important implications

for fish habitat. When embeddedness increases, the interstitial spaces around cobbles decrease, thereby decreasing

the quantity and quality of habitat for aquatic macroinvertebrates and small fish such as darters. Therefore, any change

in channel embeddedness, particularly if the change is an

increase, can cause a series of complex chain reactions

that may be difficult to clarify and manage effectively.

Pool Depth

Streams impacted by ATV trails are somewhat shallower

than those in the reference watersheds (fig. 8). Median

depths are 20 to 25 cm in Board Camp and Gap Creeks,

about half the values of Brushy and Caney Creeks. The 75-

percentile values for Board Camp and Gap Creeks are also

lower than those of the reference streams.

Decreased depth in ATV-impacted streams is consistent

with increased sedimentation. Depth is an indication of how

viable pool habitat is during low flow or drought conditions.

Shallow pools do not provide the cover of deep pools and

are not as viable during drought. Therefore, these data

suggest a possible decline in the overall health of stream

ecosystems affected by ATV trails.

Pool Volume

Most water volume in Ouachita headwater streams is contained in pools. Because of the intermittent nature of these

streams, pools serve as primary habitats for larger fish and

as a refuge for all fish during low flow or drought conditions.

Figure 9 shows pool volume is notably less for Board Camp

and Gap Creeks. Decreased pool volume is an expected

result if sediment inputs and deposition had increased in

these streams. As with lowered pool depths, smaller pool

volumes suggest possible impairment in the ecological

functioning of the two creeks affected by ATV trails.

CONCLUSIONS

In summary, preliminary analysis of paired watersheds indicates that the Wolf Pen Gap Trail system appears to have

had detectable negative impacts on the structure and habitat quality of stream pools. These impacts are reflected in

an increase in fine sediments in pools along with a decrease

in pool depths and volumes compared to pools in basins

Figure 7—Box-plots of embeddedness for stream pools within the

four study basins.

Figure 9—Box-plots of pool volumes within the four study

basins.

Figure 8—Box-plots of pool depths within the four study basins.
296

unaffected by ATV usage. These changes are logical consequences of accelerated erosion and sediment deposition,

both of which have been associated with roads and trails in

past research. Since the ATV trail system is the only important difference between these basins, we conclude that its

presence and usage have created the observed differences.

More work is needed to determine whether there are differences between designed and undesigned trails, and which

specific features of ATV trails (e.g., slope and distance from

a stream) are most responsible for these impacts. Results

from these new studies will have important implications for

ATV management and the maintenance of healthy stream

ecosystems in the ONF.

ACKNOWLEDGMENTS

We would like to acknowledge the numerous individuals who

contributed to this paper. Jay Vestal was especially helpful

with field work. Kristin Hyche assisted with producing the

figures. Drs. David Butler and Howard Halverson provided

reviews that improved the quality of the paper.

LITERATURE CITED

Brown, K.J. 1994. River-bed sedimentation caused by off-road

vehicles at river fords in the Victorian Highlands, Australia.

Water Resources Bulletin. 30: 239-250.

Clingenpeel, J.A. 1994. A cumulative effects analysis of silvicultural best management practices using Basin Area Stream

Survey methods (BASS). Hot Springs: U.S. Department of

Agriculture, Forest Service, Southern Region. 53 p.

Clingenpeel, J.A.; Cochran, B.G. 1992. Using physical, chemical

and biological indicators to assess water quality on the Ouachita

National Forest utilizing basin area stream survey (BASS)

methods. Proceedings Arkansas Academy of Science. 46: 33-

35.

Eckert, R.E., Jr.; Wood, M.K.; Blackburn, W.H.; Peterson, F.F.

1979. Impacts of off-road vehicles on infiltration and sediment

production of two desert soils. Journal of Range Management.

32: 394-397.

McCain, M.; Fuller, D.; Decker, L.; Overton, K. 1990. Stream

habitat classification and inventory procedures for northern

California. U.S. Department of Agriculture, Forest Service,

Pacific Southwest Region. FHR Currents. 1: 1-16.

Reid, L.M.; Dunne, T. 1984. Sediment production from forest road

surfaces. Water Resources Research. 20: 1753-1761.

Smith, R.M., ed. 1989. The atlas of Arkansas. Fayetteville, AR:

The University of Arkansas Press.

Webb, R.H. 1983. Compaction of desert soils by off-road vehicles.

In: Webb, Robert H. and Wilshire, Howard G., editors. Environmental effects of off-road vehicles: impacts and management in

arid regions. New York: Springer-Verlag: 52-80.

Wemple, B.C.; Jones, J.A.; Grant, G.E. 1996. Channel network

extension by logging roads in two basins, Western Cascades,

Oregon. Water Resources Bulletin. 32: 1195-1207.
297

INTRODUCTION

Knowledge about implementing uneven-aged silviculture in

shortleaf pine (Pinus echinata Mill.) stands is very limited

(Murphy and others 1991). Techniques and guidelines developed for loblolly (P. taeda L.)–shortleaf pine stands at the

Crossett Experimental Forest in the Coastal Plain of Arkansas

(Baker and others 1996) may be adaptable, but most of the

Crossett experience applies to managing existing unevenaged pine stands or rehabilitating understocked pine stands.

Stands in the Ouachita Mountains are mostly mature pines

and oaks (Quercus spp.) that have developed an even-aged

character, and a hardwood component is desired to enhance

nontimber resources. Such stands pose a unique set of

problems: (1) some hardwoods must be removed to create

a favorable environment for the establishment and development of pine regeneration; (2) a reverse-J diameter distribution must be developed, which will likely take several

decades; and (3) the response of suppressed pines of the

original stand to release is questionable (Shelton and

Murphy 1991, 1993).

This ongoing research focuses on some of the problems of

applying uneven-aged silviculture in mature pine-oak stands

on poor sites. Such stands are common on public lands, and

knowledge is needed concerning alternative silvicultural

systems. Study objectives are to test the traditional application of uneven-aged pine silviculture using single-tree

selection and to determine the limits for hardwood retention

within this system. Response of understory vegetation and

merchantable growth is reported at 6 years after implementation of the study. This paper updates an earlier one

reporting 3-year results (Shelton and Murphy 1997).

METHODS

Study Area

The study was installed on the Winona Ranger District of

the Ouachita National Forest in Perry County, AR. Plots

were oriented along an east-west ridge, which is typical of

the physiography of the Ouachita Mountains. Elevations

ranged from 640 to 790 feet above sea level. Blocks were

located on the following slope positions: the lower, middle,

and upper north slopes and the upper south slope. Slopes

of individual plots ranged from 8 to 21 percent; aspects

ranged from north to northwest on the north-slope positions

and from southeast to southwest on the south-slope position.

Soils of the study area are mapped as the Carnasaw and

Pirum series, both Typic Hapludults. These are well-drained,

moderately deep soils that developed in colluvium and

residuum weathered from sandstone and shale. Natural

fertility and organic matter are low, and the soils are strongly

acidic. Site index for shortleaf pine averaged 57 feet at 50

years and ranged from 53 to 64 feet, which is typical of

upland sites in the Ouachita Mountains (Graney 1992). The

lower north slope had a slightly higher site index than the

other three slope positions (61 versus 56 feet). Site index

averaged 53 feet at 50 years for white oak (Quercus alba

L.) and 54 feet for black oak (Q. velutina Lam.).

Vegetation in the study area was typical of forested landscapes in the Ouachita Mountains, where upland forests

are dominated by shortleaf pine and mixed oaks (Guldin

and others 1994). Before treatment, overstory basal area

[trees ≥ 3.6 inches in diameter at breast height (d.b.h.)] in

this mature, second-growth, shortleaf pine-oak stand averaged 90 square feet per acre for shortleaf pine and 32

square feet per acre for hardwoods. Oaks accounted for 84

percent of the total hardwood basal area. White oak was

the most prevalent hardwood, with lesser amounts of post

oak (Q. stellata Wangenh.), black oak, blackjack oak (Q.

marilandica Muenchh.), and southern red oak (Q. falcata

Michx.). The remaining 16 percent of hardwood basal area

was ash (Fraxinus spp.), hickory (Carya spp.), red maple

(Acer rubrum L.), serviceberry [Amelanchier arborea (Michx.

f.) Fern.], blackgum (Nyssa sylvatica Marsh.), and flowering

dogwood (Cornus florida L.). The understory was mainly

EFFECTS OF RETAINING A HARDWOOD COMPONENT

DURING THE APPLICATION OF UNEVEN-AGED SILVICULTURE

IN A SHORTLEAF PINE-OAK STAND: 6-YEAR RESULTS

Michael G. Shelton1

1 Research Forester, USDA Forest Service, Southern Research Station, Monticello, AR 71656-3516

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Treatments were the following hardwood basal areas (square feet per acre) and spatial arrangements: 0, 15-

grouped, 15-scattered, 30-scattered, and an untreated control. Pine basal area was reduced by harvesting to 60 square

feet per acre in all treatments except the control. After six growing seasons, pine regeneration ranged from 8,890 stems

per acre in the 0-hardwood treatment to 0 stems per acre in the control, and stems were 6.2 times taller in the 0-

hardwood treatment than in the 30-scattered treatment. Height growth of pine regeneration was acceptable in the 0-

hardwood and 15-grouped treatments, but was inadequate elsewhere. Oak regeneration averaged 1,510 stems per acre

after 6 years with no significant differences among treatments. Coverage of understory vegetation was greatest in the 0-

hardwood treatment and declined as hardwood retention increased. Volume growth of merchantable pines surviving the 6-

year period was 34 percent higher in the 0-hardwood treatment than in the 15- and 30-scattered treatments. However,

high rates of postharvest mortality resulted in no significant differences in rates of net growth among treatments.
298

regeneration of the more shade-tolerant species and a

variety of common shrubs, such as huckleberries (Vaccinium spp.) and hawthorns (Crataegus spp.).

Overstory pines and oaks ranged in age from 30 to over

110 years (Shelton and Murphy 1991). However, most of

the pines were 50 to 80 years old, and the oaks were 40 to

70 years old. The scarcity of younger overstory trees indicated that regeneration and subsequent recruitment of both

the pines and oaks had been limited for 30 to 40 years of

stand development.

Study Design and Treatments

Sixteen square 0.5-acre plots were installed and surrounded

by a 58.2-foot isolation strip that received the same treatment. Basal area of overstory pines (trees ≥ 3.6 inches

d.b.h.) was reduced to 60 square feet per acre in all plots.

Treatments retained the following hardwood basal areas

(square feet per acre) and spatial arrangements for overstory trees: 0, 15-grouped, 15-scattered, and 30-scattered.

Openings in the pine canopy ranged from 0.10 to 0.25 acre

and often extended from the 0.5-acre plots into the isolation

strip. In the scattered arrangements, hardwoods were

uniformly distributed across each plot, while in the grouped

arrangement they were located outside openings in the

pine canopy. For the grouped arrangement, no attempt was

made to create openings in the pine canopy other than those

resulting from application of single-tree selection. The

grouped spatial arrangement was similar to the 0-hardwood

treatment within openings and to the 30-scattered treatment

outside openings. Treatments were assigned in a randomized, complete block design with four replications of each

treatment. In 1991, permanent 0.5-acre plots were established in untreated areas adjacent to each block to serve as

untreated controls.

The pine component was regulated using the basal area–

maximum diameter–quotient (BDq) method of single-tree

selection (Baker and others 1996). Targets were 60 square

feet per acre for basal area, 18 inches for maximum d.b.h.,

and a quotient of 1.2 for 1-inch d.b.h. classes. Targets for

maximum diameter and quotient were followed as closely

as feasible, because the stand lacked a balanced reverse-J

diameter distribution characteristic of uneven-aged structure. Hardwood retention favored the higher quality red and

white oaks, which typically were the largest hardwoods in

the study area.

Plots were harvested from December 1988 through early

March 1989 using mules to skid logs to landings. Because

there were no local markets for hardwoods, all hardwoods

≥ 1 inch d.b.h. that were not designated for retention were

injected with triclopyr amine in April 1989. Herbicide treatments were applied by contract crews following label directions, although research crews did some followup work.

Measurements

During March 1989, all retained pines and hardwoods (≥ 3.6

inches d.b.h.) in the 0.5-acre plots were measured for d.b.h.,

and the location of each stem was mapped by determining

azimuth and distance from plot center. About one-third of

the trees were measured for height and age. Ten permanent points were systematically located within each 0.5-acre

plot for monitoring the development of understory vegetation. The monitoring points were located so that none were

closer than 30 feet from the 0.5-acre plot boundary and 88

feet from the isolation boundary.

In June 1994, coverage of understory vegetation was visually estimated in milacre plots (3.72-foot radius) centered

around the 10 permanent monitoring points in each 0.5-

acre plot. The following groups were used: grasses, forbs,

vines, shrubs, hardwoods, pines, and total vegetation. Evaluations were conducted in June because coverage was

observed to maximize before the summer droughts that

typically occur on such sites. During mid-September 1994,

all woody seedlings (≤ 0.5 inch d.b.h.) were counted in the

milacre plots by species or species group. Multiple-stemmed

rootstocks were tallied as one individual. Woody saplings

(0.6 to 3.5 inches d.b.h.) were counted in 0.01-acre circular

subplots centered around each of the 10 permanent points

per 0.5-acre plot. The two tallest pines (if any) and two tallest hardwoods in each 0.01-acre subplot were selected as

the dominant regeneration and were measured for groundline diameter, height, and crown width.

Data Analysis

Mean values for understory vegetation were calculated for

the 10 regeneration subplots in each 0.5-acre plot. Regeneration subplots were considered to be stocked by pine and

nonpine woody species if at least one individual was present

to represent the species or species group. To facilitate data

presentation, nonpine species were grouped as follows:

oaks, other overstory trees, midcanopy trees, and shrubs.

Other overstory trees included blackgum, hickory, ash, and

sweetgum (Liquidambar styraciflua L.). Midcanopy trees

included red maple, serviceberry, dogwood, elms (Ulmus

spp.), persimmon (Diospyros virginiana L.), and black cherry

(Prunus serotina Ehrh.). Shrubs were mostly huckleberries,

hawthorns, and plums (Prunus spp.).

Pine volume in each 0.5-acre plot was calculated using

taper curves for natural shortleaf pine (Farrar and Murphy

1987). Inside-bark cubic-foot volume for merchantable trees

(d.b.h. ≥ 3.6 inches) was computed from a 1-foot stump to a

4.0-inch diameter outside-bark top. Volume for sawtimber

trees (d.b.h. ≥ 9.6 inches) was computed from a 1-foot

stump to an 8-inch diameter outside-bark top. Cubic-foot

volume was inside bark. Hardwood volume was calculated

using equations of Clark and others (1986). Hardwood

merchantability limits were the same as for pines except

that stump heights varied as follows: 0.2 foot for trees 3.6

to 4.9 inches d.b.h., 0.6 foot for trees 5.0 to 10.9 inches

d.b.h., and 1.0 foot for larger trees. Sawtimber volume was

not calculated for hardwoods because of their small size

and generally poor quality.

Analysis of variance for a randomized, complete block design

was used to compare expressions of understory vegetation

and volume growth among treatments. Significance was

accepted at a probability level (P) ≤ 0.05. Density and stocking values were transformed to homogenize the error term.

Differences among treatment means were isolated using the

Ryan-Einot-Gabriel-Welsch multiple range test (P = 0.05).

This procedure, which is one of the most powerful step-
299

down, multiple-range tests available, controls the experiment-wise error rate (SAS Institute 1989).

RESULTS AND DISCUSSION

Regeneration Density

Most shortleaf pine seedlings became established from

seeds dispersed following harvest and site preparation.

At 6 years, there was an average of 6,175 seedlings per

acre in treatments with timber harvesting, but no significant

differences occurred among these treatments (table 1).

However, no pine seedlings were found in the untreated

controls. Apparently, environmental conditions in the 30-

scattered treatment were sufficiently different from the

untreated control to allow the establishment of 6,075 seedlings per acre. The 30-scattered treatment had about the

same merchantable hardwood basal area as the control,

but the control had an additional 30 square feet per acre of

pine basal area and 4 square feet per acre of submerchantable hardwoods. Shelton and Murphy (1997) reported a

mean across all harvested treatments of only 756 seedlings

per acre at 3 years, which means that most of the pine seedlings present at 6 years became established after 3 years.

The generally accepted minimum stocking for pine regeneration in uneven-aged pine stands is 200 submerchantable

stems per acre (Cain and others 1987). Although all of the

harvested treatments exceeded this minimum, shortleaf

pine seedlings in the 15-scattered and 30-scattered treatments are not expected to survive. Other researchers have

observed that pine seedlings can become established under

a dense canopy and persist for several years before dying

(Becton 1936, Wahlenberg 1960). This observation suggests

that newly established pine seedlings are moderately shade

tolerant but become shade intolerant with age. Bormann

(1956) reported, for example, that the photosynthetic efficiency of loblolly pine seedlings at low light intensities

declined substantially as secondary growth characteristics

developed.

Shortleaf pine saplings occurred only in the 0-hardwood (65

stems per acre) and 15-grouped (15 stems per acre) treatments at 6 years, and differences among treatments were

significant. Saplings in the 15-grouped treatment were only

in openings where no hardwoods were retained.

For nonpine species, an average of 8,365 rootstocks per

acre occurred in the seedling size class at 6 years with no

significant treatment differences (table 1). This represents a

40-percent decline from levels reported at 3 years (Shelton

and Murphy 1997) and probably reflects mortality from selfthinning and outgrowth to the sapling size class. Shrubs

were the most common nonpine species group, and oaks

represented 17 percent of the total.

Treatments significantly affected the density of sapling oaks

and other canopy trees; the fewest saplings occurred in the

30-scattered treatment where they had been controlled

during site preparation. The greatest density of oak saplings

occurred in the 0-hardwood treatment (128 stems per acre)

and the untreated control (132 stems per acre). Saplings of

other canopy species were most prevalent in the 0-hardwood

treatment (108 stems per acre) followed by the 15-grouped

Table 1—Density of seedlings and saplings 6 years after the initial harvest implementing

uneven-aged silviculture in a shortleaf pine-hardwood stand

Density (number/acre) by hardwood treatment

Mean

Species or 0 15 ft2/ac 15 ft2/ac 30 ft2/ac square

group ft2/ac grouped scattered scattered Control errora Pb

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - seedlings - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Shortleaf pine 8,825ac 3,275a 6,525a 6,075a 0b 571 <0.01

Oaks 825 1,225 1,900 1,100 2,175 135 0.22

Other canopy

trees 1,375a 925ab 925ab 1,300a 100b 111 0.02

Midcanopy trees 350 1,825 1,100 1,300 700 272 0.50

Shrubs 2,275 3,050 4,550 6,425 8,400 1,031 0.37

Nonpine total 4,825 7,025 8,475 10,125 11,375 349 0.12

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - saplings - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Shortleaf pine 65a 15ab 0b 0b 0b 6.1 <0.01

Oaks 128a 35b 28bc 2c 132a 3.1 <0.01

Other canopy

trees 108a 72a 28ab 5b 65a 5.6 <0.01

Midcanopy trees 55 55 10 5 60 13.2 0.32

Shrubs 10 2 0 2 12 3.8 0.42

Nonpine total 301a 164ab 66bc 14c 269a 7.2 <0.01

a Based on transformed values: (number per acre)0.5. b Probability level.

c Row means followed by the same letter or no letter are not significantly different at P = 0.05.
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Table 2—Stocking of seedlings and saplings 6 years after the initial harvest implementing

uneven-aged silviculture in a shortleaf pine-hardwood stand

Stocking (percent) by hardwood treatmenta

Mean

Species or 0 15 ft2

/ac 15 ft2/ac 30 ft2/ac square

group ft2/ac grouped scattered scattered Control errorb Pc

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - seedlings - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Shortleaf pine 78ad 60a 65a 62a 0b 0.062 <0.01

Oaks 50 50 70 52 70 0.041 0.31

Other canopy

trees 52a 50a 42a 45a 8b 0.025 <0.01

Midcanopy trees 25 48 45 45 32 0.080 0.58

Shrubs 38 52 52 58 60 0.061 0.67

All nonpines 88 92 98 92 92 0.062 0.76

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - saplings - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Shortleaf pine 25a 10ab 0b 0b 0b 0.024 <0.01

Oaks 58a 22b 15bc 2c 58a 0.021 <0.01

Other canopy

trees 50a 40a 18ab 5b 45a 0.053 <0.01

Midcanopy trees 28 25 8 5 25 0.076 0.45

Shrubs 8 2 0 2 10 0.034 0.43

All nonpines 88a 58ab 35bc 15c 88a 0.047 <0.01

a Stocking is based on milacre plots for seedlings and 0.01-acre plots for saplings.

b Based on transformed values: arcsine (percent/100)0.5. c Probability level.

d Row means followed by the same letter or no letter are not significantly different at P = 0.05.

treatment (72 stems per acre). Most saplings in the 15-

grouped treatment were located in the openings created by

hardwood control.

Regeneration Stocking

Percentage stocking is often a better measure of regeneration success than density because stocking expresses the

spatial distribution of seedlings, whereas density does not.

Stocking is less sensitive to the clumped-spatial pattern

that often occurs with natural regeneration (Daniels 1978).

The stocking of shortleaf pine seedlings and saplings was

similar in pattern to density. For the harvested treatments,

pine seedling stocking averaged 66 percent with no significant differences (table 2). The generally accepted optimum

stocking limit for pine regeneration in uneven-aged pine

stands is 50 percent (Cain and others 1987). All harvested

treatments either met or exceeded this limit at 6 years,

although seedlings are not expected to survive in the 15-

scattered and 30-scattered treatments. The treatments had

a significant effect on the stocking of shortleaf pine saplings,

which occurred only in the 0-hardwood and 15-grouped

treatments.

A nonpine species in the seedling size class occurred on

nearly all milacre subplots; stocking levels averaged 92 percent with no significant differences among treatments.

Stocking for oaks averaged 58 percent with no significant

differences among treatments. Seedlings of other canopy

trees had a significantly lower stocking in the unharvested

control (8 percent) than in the harvested treatments (47

percent).

For stocking of nonpine saplings, significant differences

occurred among treatments for oaks, other canopy trees, and

all nonpines. The pattern displayed across treatments was

the same for each species group; the 0-hardwood treatment

nearly equaled the control, and stocking decreased in the

following order: 0-hardwood > 15-grouped > 15-scattered

> 30-scattered.

Size of Dominant Regeneration

After the sixth growing season, shortleaf pine regeneration

decreased in size as hardwood retention increased (table 3).

Pines in the 0-hardwood treatment were larger than those

in the 30-scattered treatment by 7.5 times for groundline

diameter, 4.1 times for crown width, and 6.2 times for height.

Differences were highly significant in all cases. Baker and

others (1996) state that annual height growth of pines should

be ≥ 0.5 feet for acceptable development. This guideline

was met in the 0-hardwood and 15-grouped treatment but

not in the other treatments.

The nonpine groups showed a pattern similar to the pines

except that the magnitude of suppression was not as great.

For example, regeneration in the 0-hardwood treatment was

taller than in the 30-scattered treatment by 2.7 times for the

oaks, 1.9 for other canopy trees, and 1.9 for midcanopy trees.

Hardwood regeneration was still larger than shortleaf pine

regeneration at 6 years. This reflected a difference in the

principal reproductive strategy of the two groups—seeds for

pines versus advanced regeneration and sprouts for hardwoods. Experience elsewhere has shown that the height
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Table 3—Mean size of the dominant regeneration 6 years after the initial harvest

implementing uneven-aged silviculture in a shortleaf pine-hardwood stand

Regeneration size by hardwood treatmenta

Mean

Species or 0 15 ft2/ac 15 ft2/ac 30 ft2/ac square

group ft2/ac grouped scattered scattered error Pb

- - - - Groundline diameter (inches) - - - -

Shortleaf pine 0.82ac 0.53ab 0.24bc 0.11c 0.033 <0.01

Oaks 1.71a 0.87b 0.91b 0.66b 0.093 <0.01

Other canopy trees 1.50a 1.21ab 1.08bc 0.80c 0.036 <0.01

Midcanopy trees 1.50a 1.21ab 0.83b 0.76b 0.045 0.03

- - - - - - Crown diameter (feet) - - - - - - -

Shortleaf pine 1.96a 1.48a 0.80b 0.48b 0.104 <0.01

Oaks 5.11a 3.17b 3.33b 2.69b 0.503 <0.01

Other canopy trees 5.45a 4.28ab 4.27ab 3.98b 0.357 0.03

Midcanopy trees 5.72 5.61 3.72 3.62 1.184 0.10

- - - - - - - - Total height (feet) - - - - - - - -

Shortleaf pine 4.66a 3.19ab 1.61bc 0.75c 0.89 <0.01

Oaks 9.07a 5.05b 5.17b 3.41b 2.28 <0.01

Other canopy trees 9.40a 7.34b 7.46b 4.93c 0.90 <0.01

Midcanopy trees 11.47a 9.44ab 6.74b 5.95b 2.07 0.04

a The unharvested control was not included in this comparison because no pines occurred and

the hardwoods present were a different age class than those in the harvested treatments.

b Probability level.

c Row means followed by the same letter or no letter are not significantly different at P = 0.05.

growth of free-to-grow pine regeneration will eventually

exceed that of hardwoods on most upland sites (Wahlenberg

1960), but the 6-year results of this study may be too early

to confirm this. At 3 years, hardwoods were 2.1 times taller

than pines in the 0-hardwood treatment (Shelton and Murphy

1997), and this was the same value observed at 6 years.

Understory Coverage

The effect of treatments on understory coverage varied

among species groups, with significant differences occurring

for grasses, vines, shortleaf pine, and total vegetation

(table 4). Generally, coverage declined as more overstory

hardwoods were retained, although some variation occurred

in this pattern. Coverage was greater in the 0-hardwood

treatment than in the control by 15.1 times for grasses, 2.4

for vines, and 2.1 for hardwoods. In contrast, shrub coverage

was higher in the unharvested control than in the harvested

treatments. Comparison of the 6-year results with those

reported at 3 years (Shelton and Murphy 1997) showed that

pines and hardwoods doubled in coverage, vines increased

slightly, and grasses decreased. This suggests that pines

and hardwoods will dominate the subsequent pattern of

successional change in understory vegetation.

Growth of Merchantable Trees

Postharvest mortality of shortleaf pines was high and required

expressing growth by its functional components—survivor

growth, mortality, and net growth (Husch and others 1982).

Survivor growth for total merchantable volume was significantly higher in the 0-hardwood treatment (42 cubic feet per

acre per year) than in the 15- and 30-scattered treatments

by 34 percent (table 5). Mortality losses of shortleaf pine

averaged 9 cubic feet per acre per year for merchantable

volume with no significant differences among treatments;

mortality averaged 1.0 tree per acre per year and was mostly

from the smaller trees in the stand. Dying trees had a mean

d.b.h. of 7.4 inches compared with 10.1 inches for surviving

trees. Tree mortality was from multiple causes, such as

insects, lightning, suppression, and windthrow, but in the

majority of cases the cause was not discernable (58 percent).

Cain (1998) also observed high rates of inexplicable mortality following the implementation of uneven-aged silviculture

in a shortleaf pine stand in southern Arkansas. Net growth

of merchantable pines averaged 25 cubic feet per acre per

year with no significant differences among treatments.

Net volume growth for sawtimber averaged 122 board feet

(Doyle) per acre per year for shortleaf pine with no significant differences among treatments (table 5). Murphy and

others (1991) reported sawtimber growth in three well-structured, uneven-aged shortleaf pine stands in the Ouachita

Mountains. In a stand with the same site index as this study

(57 feet at 50 years), they reported growth of 209 board

feet (Doyle) per acre per year; on poorer sites they reported

lower growth rates (137 and 126 board feet per acre per

year for stands with site indices of 50 and 55 feet, respectively). Thus, the sawtimber growth for this study, which

lacks an uneven-aged structure, appears somewhat lower

than well-structured stands on similar sites.
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Table 4—Coverage of understory vegetation 6 years after the initial harvest implementing

uneven-aged silviculture in a shortleaf pine-hardwood stand

Understory coverage by hardwood treatment

Mean

Species or 0 15 ft2

/ac 15 ft2/ac 30 ft2/ac square

group ft2/ac grouped scattered scattered Control errora Pb

- - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - -

Grasses 24.1ac 8.4bc 10.8b 4.5cd 1.6d 0.003 <0.01

Forbs 1.8 1.5 0.9 1.4 0.2 0.006 0.59

Vines 22.0ab 27.0a 16.0ab 7.7b 9.0b 0.012 0.03

Shrubs 7.6 6.5 5.5 7.0 10.9 0.016 0.72

Hardwoods 31.9 29.5 16.1 13.2 15.1 0.016 0.07

Shortleaf pine 3.9a 1.1ab 0.3b 0.2b 0.0b 0.003 <0.01

Total vegetationd 78.4a 63.8ab 45.5bc 33.9c 32.0c 0.010 <0.01

a Based on transformed values: arcsine (percent/100)0.5. b Probability level.

c Row means followed by the same letter or no letter are not significantly different at P = 0.05. d Coverage of total vegetation is less than the sum of individual species groups because of multiple occupancy.

Table 5—Growth of merchantable-sized trees for the 6-year period after the

initial harvest implementing uneven-aged silviculture in a shortleaf pinehardwood stand

Growth Annual growth by hardwood treatmenta

component Mean

by species 0 15 ft2/ac 15 ft2/ac 30 ft2

/ac square

or group ft2/ac grouped scattered scattered error Pb

 - - - - Merchantable volume (ft3 /ac) - - - -

Shortleaf pine

Survivor 42.3ac 33.4ab 32.3b 30.6b 24 0.03

Mortality 17.6 7.3 3.1 9.8 269 0.66

Net 24.7 26.1 29.2 20.8 388 0.94

Hardwoods

Survivor — 12.4b 16.0ab 20.0a 19 0.12

Mortality — 2.7 0.4 1.4 9 0.58

Net — 9.7b 15.6ab 18.6a 17 0.05

- - - - - Sawtimber volumed (ft3/ac) - - - - -

Shortleaf pine

Survivor 39.2 38.9 33.7 30.8 41 0.24

Mortality 14.0 4.9 0.0 8.5 248 0.65

Net 25.2 34.0 33.7 22.3 336 0.74

- - Sawtimber volumed (fbm Doyle/ac) - -

Shortleaf pine

Survivor 166 157 133 132 688 0.24

Mortality 56 15 0 31 3,976 0.65

Net 110 142 133 101 5,148 0.83

a Control plots were not monitored for growth.

b Probability level.

c Row means followed by the same letter or no letter are not significantly different at P=0.05. d Too few hardwoods occurred in sawtimber size classes for analysis.
303

Hardwoods contributed to the stand’s growth in total merchantable volume; net growth averaged 15 cubic feet per

acre per year. Mortality of hardwoods was considerably less

than shortleaf pine and averaged only 1.5 cubic feet per acre

per year. Because of their small size and generally low quality,

hardwoods did not contribute to the stand’s sawtimber growth.

CONCLUSIONS AND

MANAGEMENT IMPLICATIONS

The amount and spatial distribution of natural pine regeneration depend on seed supply, seedbed conditions, and

environmental conditions. The treatments evaluated in this

study principally affected the environmental conditions that

govern seedling survival and growth. Because of shortleaf

pine’s shade intolerance, seedling development will be suppressed to some extent in any reproductive cutting method

that retains an overstory. The key to successful application

of uneven-aged silviculture is to reach a balance between

retaining an adequate overstory stocking for acceptable merchantable growth and reducing overstory stocking enough

to provide the environmental conditions needed for regeneration. Pine growth rates in both the overstory and understory

will be below their short-term potential, but this compromise

provides a system that should be sustainable through time.

Recurring pine regeneration is crucial for the long-term

sustainability of uneven-aged stands in which shortleaf pine

is the dominant species. Retaining hardwoods within the

single-tree selection system will most severely impact

environmental conditions in the understory. Hardwoods

apparently suppress the development of pine seedlings

more than an equivalent basal area of pines, which reflects

differences in crown and foliar features. Thus, acceptable

limits for hardwoods within an uneven-aged pine stand are

apparently low, even if the pine basal area is reduced

accordingly. Early results of this study show that retaining

as little as 15 square feet per acre of hardwood basal area

in a scattered distribution with a pine basal area of 60 square

feet per acre (the 15-scattered treatment) will prevent the

acceptable development of sufficient pine regeneration to

sustain long-term pine timber production. However, reducing pine basal area to 45 square feet per acre might allow

retention of 5 to 10 square feet per acre of scattered hardwoods (Baker and others 1996).

Results also suggest that 15 square feet per acre of hardwood basal area can be retained outside openings in the

pine canopy (the 15-grouped treatment) while still obtaining

adequate development of pine regeneration within the openings. However, the success of this treatment can only be

measured by evaluating long-term developmental rates of

regeneration. In addition, the feasibility of implementing the

15-grouped treatment needs to be operationally tested. This

treatment was difficult to implement even on these small

research plots, because it was hard to determine where

opening boundaries occurred. Creating openings to promote

stand regeneration would have been easier using group

selection, where both pines and hardwoods are removed

within well-defined openings (Murphy and others 1993).

This study shows that adequate pine regeneration will occur

in shortleaf pine stands in the Ouachita Mountains when:

(1) traditional guidelines for uneven-aged pine silviculture

are followed (that is, the 0-hardwood treatment), (2) seed

supply and environmental conditions are favorable, and

(3) competing understory vegetation is low. More time,

however, is needed to fully assess developmental rates of

regeneration and to evaluate the growth of overstory trees.

Although traditional guidelines for uneven-aged pine stands

exclude a hardwood component at a local scale, they can

be retained at the stand level along drainages or in clumps

or clusters in an area-wise distribution. Permissible levels

of hardwood retention within this spatial pattern are probably not limited by biology, as long as pine-dominated areas

are large enough to provide relative freedom from the edge

effects of retained hardwoods. Rather, landowner objectives

and operational concerns will probably set realistic limits.

According to Baker and others (1996), the area-wise distribution of pines and hardwoods in uneven-aged stands under

single-tree selection has a number of advantages for landowners who want to retain hardwoods: (1) a favorable

environment is provided for the shade-intolerant pine

regeneration, (2) a significant hardwood component can

be retained, (3) species-site relationships are optimized,

(4) sensitive areas are protected, (5) hardwoods are protected during silvicultural operations, (6) stand regulation

and marking are simplified, and (7) a varied wildlife habitat

is provided.

LITERATURE CITED

Baker, J.B.; Cain, M.D.; Guldin, J.M.; Murphy, P.A.; Shelton, M.G.

1996. Uneven-aged silviculture for the loblolly and shortleaf

pine forest cover types. Gen. Tech. Rep. SO-118. Asheville,

NC: U.S. Department of Agriculture, Forest Service, Southern

Research Station. 65 p.

Becton, W.R. 1936. Effects of varying densities of hardwood cover

on growth and survival of shortleaf pine reproduction. Journal

of Forestry. 34(2): 160-164.

Bormann, F.H. 1956. Ecological implications of changes in the

photosynthetic response of Pinus taeda seedlings during

ontogeny. Ecology. 37(1): 70-75.

Cain, M.D. 1998. Potential problems when partial cutting in overstocked stands of mature shortleaf pines to enhance natural

regeneration. In: Waldrop, T.A., ed. Proceedings of the ninth

biennial southern silvicultural research conference. Gen. Tech.

Rep. SRS-20. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station: 338-342.

Cain, M.D.; Baker, J.B.; Murphy, P.A. 1987. Precommercial thinning of pine regeneration in uneven-aged stands. In: Phillips,

D.R., comp. Proceedings of the fourth biennial southern

silvicultural research conference. Gen. Tech. Rep. SE-42.

Asheville, NC: U.S. Department of Agriculture, Forest Service,

Southeastern Forest Experiment Station: 419-424.

Clark, A., III; Phillips, D.R.; Frederick, D.J. 1986. Weight, volume,

and physical properties of major hardwood species in the uplandSouth. Res. Pap. SE-257. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southern Research Station. 55 p.

Daniels, R.F. 1978. Spatial patterns and distance distributions in

young seeded loblolly pine stands. Forest Science 24(2): 260-

266.

Farrar, R.M., Jr.; Murphy, P.A. 1987. Taper functions for predicting

product volumes in natural shortleaf pines. Res. Pap. SO-234.

New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station. 9 p.
304

Graney, D.L. 1992. Site index relationships for shortleaf pine. In:

Brissette, J.C.; Barnett, J.P., comps. Proceedings of the shortleaf pine regeneration workshop. Gen. Tech. Rep. SO-90. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 142-157.

Guldin, J.M.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine/hardwood stands on south-facing

slopes of the Ouachita/Ozark National Forests. In: Baker, J.B.,

comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Gen. Tech. Rep. SO-112. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 29-49.

Husch, B.; Miller, C.I.; Beers, T.W. 1982. Forest mensuration. New

York: John Wiley & Sons. 402 p.

Murphy, P.A.; Baker, J.B.; Lawson, E.R. 1991. Selection management of shortleaf pine in the Ouachita Mountains. Southern

Journal of Applied Forestry. 15(1): 61-67.

Murphy, P.A.; Shelton, M.G.; Graney, D.L. 1993. Group selection—

problems and possibilities for the more shade-intolerant species.

In: Gillespie, A.R.; Parker, G.R.; Pope, P.E.; Rink, G., eds.

Proceedings of the ninth central hardwood conference. Gen.

Tech. Rep. NC-161. St. Paul, MN: U.S. Department of

Agriculture, Forest Service, North Central Forest Experiment

Station: 229-247.

SAS Institute. 1989. SAS/STAT user’s guide. Version 6, 4th ed.,

vol. 2. Cary, NC: SAS Institute, Inc. 846 p.

Shelton, M.G.; Murphy, P.A. 1991. Age and size structure of a shortleaf pine-oak stand in the Ouachita Mountains—implications for

uneven-aged management. In: Coleman, S.S.; Neary, D.G.,

comps. Proceedings of the sixth biennial southern silvicultural

research conference. Gen. Tech. Rep. SE-70. Asheville, NC:

U.S. Department of Agriculture, Forest Service, Southeastern

Forest Experiment Station: 616-629.

Shelton, M.G.; Murphy, P.A. 1993. Pine regeneration and understory vegetation 1 year after implementing uneven-aged silviculture in pine-hardwood stands of the silty uplands of Mississippi.

In: Brissette, J.C., ed. Proceedings of the seventh biennial

southern silvicultural research conference. Gen. Tech. Rep.

SO-93. New Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment Station: 333-341.

Shelton, M.G.; Murphy, P.A. 1997. Understory vegetation 3 years

after implementing uneven-aged silviculture in a shortleaf pinehardwood stand in the Ouachita Mountains. Res. Pap. SO-296.

Asheville, NC: U.S. Department of Agriculture, Forest Service,

Southern Research Station. 13 p.

Wahlenberg, W.G. 1960. Loblolly pine. Durham, NC: School of

Forestry, Duke University. 603p.
305

INTRODUCTION

The shelterwood reproduction cutting method is a versatile

way of naturally regenerating even-aged or two-aged stands

that is increasingly being used on national forest lands. This

method gradually removes mature trees in a series of partial

cuts and retains more trees than other even-aged reproduction cutting methods. The higher density of retained trees

may satisfy some silvicultural and landowner objectives by

making the stand more visually pleasing during regeneration and increasing timber yields through enhanced growth

of high-quality trees. Shelterwood stands also tend to have

high rates of seed production, which may improve chances

for successful natural regeneration in areas with low or

erratic seed production. For example, Brender and McNab

(1972) reported that enough seeds for successful regeneration were produced 50 percent of the time in shelterwood

stands compared with 21 percent in seed-tree stands. The

shelterwood method may be favorable in regenerating

shortleaf pine (Pinus echinata Mill.) stands in the Ouachita

Mountains, where seed production is variable (Shelton and

Wittwer 1996) and may reduce the intensity of site

preparation needed.

Hardwood retention is desired to enhance nontimber

resources on many public and nonindustrial private lands,

and a pine-hardwood mixture is often the target composition in the regenerated stand. Objectives of this study are to

test the traditional application of the shelterwood reproduction cutting method in shortleaf pine-oak stands, to evaluate

the effects of hardwood retention within this system, and to

determine the type of submerchantable hardwood control

needed. The development of understory vegetation is

reported 5 years after harvesting and hardwood control, and

this paper updates an earlier one reporting 3-year results

(Shelton 1997).

METHODS

Study Area

The study area was located in the Winona Ranger District

of the Ouachita National Forest in Perry County, AR. Plots

were oriented along an east-west ridge typical of the physiography of the Ouachita Mountains. Elevations ranged from

640 to 810 feet above sea level. Blocks were located along

the lower, middle, and upper north slope and the upper

south slope. Slopes of individual subplots ranged from 5 to

26 percent. Aspect was south on the south-slope position

and ranged from north to east on the north-slope positions.

Soils of the study area are mapped as the Carnasaw and

Pirum series, both Typic Hapludults. These are well drained,

moderately deep soils that developed in colluvium and residuum weathered from sandstone and shale. Natural fertility

and organic matter are low, and the soils are strongly acidic.

Site index for shortleaf pine averaged 60 feet at 50 years,

ranging from 56 to 65 feet; this is typical of upland sites in

the Ouachita Mountains (Graney 1992). The lower north

slope had a slightly higher site index than the other slope

positions (62 versus 59 feet). White oak (Quercus alba L.)

site index averaged 54 feet at 50 years. The dominant pines

were slightly older than the dominant white oaks (66 versus

61 years).

Vegetation in the study area was typical of forested landscapes in the Ouachita Mountains, where mature, secondgrowth shortleaf pine and mixed oaks dominate upland

forests (Guldin and others 1994). Preharvest overstory basal

area in trees ≥ 3.6 inches in diameter at breast height

(d.b.h.) averaged 74 square feet per acre for shortleaf pine

and 41 square feet per acre for hardwoods. Oaks accounted

for 92 percent of the hardwood basal area. White oak was

the most prevalent hardwood, followed by post oak (Q.

stellata Wangenh.), black oak (Q. velutina Lam.), blackjack

oak (Q. marilandica Muench.), and southern red oak (Q.

UNDERSTORY VEGETATION AND OVERSTORY GROWTH IN

PINE AND PINE-HARDWOOD SHELTERWOOD STANDS IN

THE OUACHITA MOUNTAINS: 5-YEAR RESULTS

Michael G. Shelton1

1 Research Forester, USDA Forest Service, Southern Research Station, Monticello, AR 71656-3516.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Treatments were two overstory compositions (a pine basal area of 30 square feet per acre with and without 15

square feet per acre of hardwoods) and two methods of submerchantable hardwood control (chainsaw felling with and

without stump-applied herbicide). After the fifth growing season, pine regeneration averaged 1,870 seedlings per acre and

500 saplings per acre in the pine-overstory treatment; there were more pine seedlings when overstory hardwoods were

retained (3,090 seedlings per acre) but fewer saplings (27 saplings per acre). Pine regeneration was twice as tall in the

pine-overstory treatment than in the pine-hardwood treatment. Oak regeneration averaged 1,100 stems per acre.

Overstory pines responded to hardwood removal, averaging 0.84 square feet per acre per year for basal area growth in

the pine-overstory treatment and 0.58 square feet per acre per year in the pine-hardwood treatment. Very few overstory

pines died after harvesting (0.04 trees per acre per year). Results indicate that 15 square feet per acre of scattered

hardwoods can be retained through at least 5 years after harvest.
306

falcata Michx.). The remaining 8 percent of hardwood basal

area was ash (Fraxinus spp.), hickory (Carya spp.), red

maple (Acer rubrum L.), serviceberry [Amelanchier arborea

(Michx. f.) Fern.], blackgum (Nyssa sylvatica Marsh.), and

dogwood (Cornus florida L.). The understory consisted of

tree regeneration, mainly the more shade-tolerant species,

and a variety of common shrubs, such as huckleberries

(Vaccinium spp.), snowbell (Styrax grandifolia Ait.), and

hawthorns (Crataegus spp.).

Study Design and Treatments

The study was a split-plot design with four randomized complete blocks. Two overstory compositions (pine only and

pine-hardwood) were established in eight 3.50-acre whole

plots, each split into two 1.75-acre subplots for testing two

hardwood control methods (manual versus chemical control

of submerchantable hardwoods). Each subplot consisted of

a 0.70-acre measurement area (103 by 295 feet) and an

isolation strip of 1.05-acre. The isolation strip for a subplot

was 66 feet wide when adjacent to the untreated stand, 50

feet wide when adjacent to the neighboring whole plot that

made up the block, and 20 feet wide along the internal boundary separating the two subplots within a whole plot. Within

each 0.70-acre measurement area, 18 permanent points

were systematically located to monitor understory vegetation. These points were at least 80 feet from the external

boundary of the whole plot and 50 feet from the internal

boundary separating the two subplots.

Target retention for all overstories was 30 square feet per

acre of pine basal area. The pine-overstory treatment had

no hardwoods; retention in the pine-hardwood treatment

was 15 square feet per acre of hardwoods and 30 square

feet per acre of pines. Pine seed trees were selected for a

past history of high cone production, a d.b.h. of 12 to 16

inches, good vigor and stem quality, and a uniform spatial

distribution. Selection criteria were sometimes relaxed to

achieve the target basal area. Large, well-formed, vigorous

red and white oaks were preferred for the hardwood component, but less desirable species or low-quality stems were

sometimes kept to meet target basal area.

Whole plots were temporarily subdivided into seven 0.5-

acre areas to facilitate marking the target basal areas for

overstory trees and to ensure a uniform distribution. All

merchantable pines, except seed trees, were harvested

using rubber-tired skidders and tree-length skidding from

November 1989 to early January 1990. A commercial firewood vendor harvested merchantable hardwoods ≥ 6 inches

d.b.h. beginning in early April 1990 but was stopped shortly

afterward by wet weather. Harvesting resumed in July and

was completed in October.

Treatments to control submerchantable hardwoods (0.6 to

5.5 inches d.b.h.) began in mid-August 1990 and were completed in early September 1990. Treatments were either

manual (chainsaw felling) or chemical (chainsaw felling

followed immediately by application of undiluted Garlon® 3A

to the stump). In the manual treatment, submerchantable

pines were chainsaw felled but not treated with herbicide.

Measurements

Understory vegetation was inventoried during mid-September

1995 using 18 permanent plots for each 0.70-acre subplot.

Horizontal coverage of understory vegetation (grasses,

herbs, vines, shrubs, hardwoods, pines, and total vegetation) was estimated on milacre plots centered on each point.

All woody plants in the seedling size class (≤ 0.5 inch d.b.h.)

were counted on the milacre plot, tallying multiple-stemmed

rootstocks as one individual. Woody saplings (0.6 to 3.5

inches d.b.h.) were counted by species or species group

and 1-inch d.b.h. classes on a 0.01-acre plot centered

around each permanent point. On each 0.01-acre plot, the

tallest two pines (if present) and tallest two hardwoods

were measured for groundline diameter, crown width, and

total height.

In March 1990, all retained pines and hardwoods (≥ 3.6

inches d.b.h.) on the 0.7-acre subplots were measured for

d.b.h., and stem location was mapped by determining

azimuth and distance from plot center. All pines and about

a third of the hardwoods were measured for height. Tree

d.b.h. was reinventoried biennially during the dormant

season.

Data Analysis

Milacre plots were considered stocked by pine or deciduous

woody species if at least one seedling was present for the

species or species group; similarly, 0.01-acre plots were

considered stocked if at least one sapling was present.

Means were calculated for understory variables for each

0.7-acre subplot. To facilitate data presentation, deciduous

species were grouped as oaks, other canopy trees, midcanopy trees, or shrubs. Other canopy trees included blackgum, hickory, ash, and sweetgum (Liquidambar styraciflua

L.); midcanopy trees included maple, serviceberry, dogwood,

elms (Ulmus spp.), persimmon (Diospyros virginiana L.),

and black cherry (Prunus serotina Ehrh.). Shrubs included

huckleberries, hawthorns, plums (Prunus spp.), snowbell,

and several other common species.

Pine volumes were calculated from taper curves for natural

shortleaf pine (Farrar and Murphy 1987). Inside-bark, cubicfoot volume for merchantable trees (d.b.h. ≥ 3.6 inches)

was computed from a 1-foot stump to a 4.0-inch outsidebark top. Volumes for sawtimber trees (d.b.h. ≥ 9.6 inches)

were computed from a 1-foot stump to an 8-inch, outsidebark top; cubic-foot volume was inside bark. Hardwood

volumes were calculated from the equations of Clark and

others (1986). Merchantability limits were the same as for

pines except that stump heights varied as follows: 0.2 foot

for trees 3.6 to 4.9 inches d.b.h., 0.6 foot for trees 5.0 to

10.9 inches d.b.h., and 1.0 foot for larger trees. Sawtimber

volumes were not calculated for hardwoods because of

their small size and generally poor quality.

Data were analyzed by analysis of variance for a split-plot

randomized complete block design using the SAS procedure GLM (SAS Institute 1989). Since there were only two

levels for each treatment, means were not separated but

were presented with the associated mean square error

(MSE) and probability level (P). Significance was accepted

at P ≤ 0.05.
307

RESULTS AND DISCUSSION

Regeneration Density and Stocking

Virtually all the shortleaf pine seedlings became established

from seeds dispersed after harvesting and site preparation.

At 5 years, there was an average of 2,479 seedlings per

acre with no significant differences between overstory or

hardwood control treatments (table 1). However, hardwoods

have been shown to substantially reduce the establishment

of shortleaf pine regeneration when levels were greater

than those tested in this study (Becton 1936, Shelton and

Murphy 1997). Shelton (1997) reported an average of only

1,550 seedlings per acre at 3 years, so some shortleaf pine

seedling establishment occurred between 3 and 5 years.

The overstory treatment had a substantial and significant

effect on the density of shortleaf pine saplings, averaging

494 stems per acre in the pine-overstory treatment and 27

stems per acre in the pine-hardwood treatment. By contrast,

the submerchantable control treatment did not have a

significant effect on sapling density. The generally accepted

minimum density for pine regeneration in natural, evenaged pine stands is 700 stems per acre (Campbell and

Mann 1973, Grano 1967). All overstory and hardwood

control treatments either met or exceeded this at 5 years.

In the seedling size class, an average of 6,514 rootstocks

per acre occurred for nonpine species at 5 years after harvesting with no significant treatment differences (table 1).

This represented a 24-percent decline from levels reported

at 3 years (Shelton 1997), which probably reflected mortality

through self-thinning and outgrowth to the sapling size class.

Shrubs were the most common nonpine species group, and

the oaks represented 12 percent of the total. The overstory

treatment significantly affected the sapling density of oaks

and midcanopy trees, but the differences between treatments

were not as great as with the pines. There were 2.5 times

more oak saplings in the pine-overstory treatment than in

the pine-hardwood treatment, but the treatment difference

was only 1.4 times for midcanopy trees.

The stocking of shortleaf pine seedlings and saplings

reflected a similar pattern as density. Stocking averaged 60

percent for seedlings, and no significant treatment differences occurred (table 2). The generally accepted minimum

stocking for pine regeneration in natural, even-aged pine

stands is 40 percent (Campbell and Mann 1973, Grano

1967). All overstory and hardwood control treatments either

met or exceeded this at 5 years. The overstory treatment

had a large and significant effect on sapling stocking; the

pine-overstory treatment averaged 6.8 times more stocking

than the pine-hardwood treatment. The submerchantable

hardwood control treatment had no significant effect. A

nonpine species occurred on nearly all regeneration plots,

with stocking levels averaging 95 percent for seedlings and

90 percent for saplings. For saplings, oaks were the only

group significantly affected by overstory treatment; stocking

averaged 86 percent for the pine-overstory treatment compared with 55 percent for the pine-hardwood treatment.

Regeneration Size

After the fifth growing season, pine regeneration in the pineoverstory treatment was more than twice as large as that in

the pine-hardwood treatment for groundline diameter, crown

width, and height (table 3). Differences were highly significant in all cases. However, the hardwood control treatments

did not significantly affect the size of pine regeneration. Oak

regeneration responded to the overstory treatments in a

manner similar to shortleaf pine, although the magnitude of

the response was not as great. Differences were significant

Table 1—Density of seedlings and saplings in shelterwood stands with pine and pine-hardwood

overstory compositions and manual and chemical methods of submerchantable-hardwood control

Pine-hardwood Overstory Hardwood control

Pine overstory overstory treatment treatment

Species or group Manual Chemical Manual Chemical MSEa Pb MSE P

- - - - - - - Seedlings (number/acre) - - - - - - -

Shortleaf pine 1,500 2,236 2,764 3,416 6.47E5 0.06 4.51E6 0.54

Oaks 736 667 806 1,014 6.93E4 0.21 8.27E4 0.65

Other canopy trees 639 806 694 819 9.28E4 0.84 6.81E4 0.31

Midcanopy trees 444 944 500 514 2.14E5 0.48 2.10E5 0.31

Shrubs 3,680 3,806 4,792 5,194 4.32E6 0.32 5.70E6 0.83

Nonpine total 5,499 6,223 6,792 7,541 4.16E6 0.29 8.58E6 0.63

 - - - - - - - Saplings (number/acre) - - - - - - -

Shortleaf pine 475 514 24 30 5.18E4 0.03 9.23E3 0.65

Oaks 476 374 179 163 1.31E4 0.02 6.13E3 0.18

Other canopy trees 174 182 89 71 4.95E3 0.07 1.28E4 0.94

Midcanopy trees 178 160 178 71 7.19E2 0.05 8.55E3 0.23

Shrubs 24 17 25 21 3.37E2 0.78 2.46E2 0.49

Nonpine total 852 733 471 326 6.20E5 <0.01 2.18E4 0.12

a Mean square error (MSE) are in exponential format; for example, 1.23E4 = 1.23X104 = 12,300. b Probability level (P).
308

Table 2—Stocking of seedlings and saplings in shelterwood stands with pine and pine-hardwood

overstory compositions and manual and chemical methods of submerchantable-hardwood control

Pine-hardwood Overstory Hardwood control

Pine overstory overstory treatment treatment

Species or group Manual Chemical Manual Chemical MSEa Pb MSE P

- - - - - - - - Seedlingsc (percent) - - - - - - - -

Shortleaf pine 48 60 68 65 197 0.16 212 0.54

Oaks 43 40 46 57 90 0.13 225 0.60

Other canopy trees 29 44 30 42 227 0.91 218 0.13

Midcanopy trees 29 43 32 24 416 0.48 175 0.69

Shrubs 54 56 67 61 28 0.04 248 0.81

Nonpine total 87 94 93 92 61 0.75 92 0.57

- - - - - - - - - Saplings (percent) - - - - - - - -

Shortleaf pine 86 76 7 17 292 <0.01 87 0.98

Oaks 82 89 58 52 47 <0.01 120 0.90

Other canopy trees 48 58 43 38 348 0.25 206 0.75

Midcanopy trees 39 44 42 32 313 0.64 225 0.79

Shrubs 20 14 18 8 138 0.57 98 0.17

Nonpine total 96 97 88 80 88 0.07 73 0.53

a Mean square error (MSE).

b Probability level (P). c Stocking is based on milacre plots seedlings and 0.01-acre plots for saplings.

Table 3—Size of the dominant regeneration in shelterwood stands with pine and pine-hardwood

overstory compositions and manual and chemical methods of submerchantable-hardwood control

Pine-hardwood Overstory Hardwood control

Pine overstory overstory treatment treatment

Species or group Manual Chemical Manual Chemical MSEa Pb MSE P

- - - - - Groundline diameter (inches) - - - - -

Shortleaf pine 1.8 1.8 0.7 0.8 0.08 <0.01 0.01 0.31

Oaks 2.6 2.5 1.9 1.8 0.03 <0.01 0.05 0.68

Other canopy trees 2.2 1.8 1.6 1.7 0.10 0.08 0.20 0.42

Midcanopy trees 1.7 1.8 1.7 1.3 0.02 0.02 0.30 0.95

- - - - - - - - - Crown width (feet) - - - - - - - - -

Shortleaf pine 3.2 3.3 1.5 1.7 0.17 <0.01 0.03 0.19

Oaks 6.6 6.4 5.0 5.1 0.22 <0.01 0.49 0.83

Other canopy trees 6.0 5.7 5.7 5.7 0.27 0.22 3.06 0.72

Midcanopy trees 6.1 6.4 5.5 4.9 0.82 0.06 3.43 0.86

- - - - - - - - - - - height (feet) - - - - - - - - - - -

Shortleaf pine 8.4 8.5 3.6 4.0 1.54 <0.01 0.11 0.27

Oaks 11.6 11.0 10.0 9.5 1.08 0.06 1.05 0.36

Other canopy trees 10.0 10.0 9.0 8.5 1.58 0.09 2.14 0.39

Midcanopy trees 11.8 14.2 12.6 9.9 0.74 0.02 12.45 0.90

a Mean square error (MSE).

b Probability level (P).
309

Table 4—Coverage of understory vegetation in shelterwood stands with pine and pine-hardwood

overstory compositions and manual and chemical methods of submerchantable-hardwood control

Pine-hardwood Overstory Hardwood control

Pine overstory overstory treatment treatment

Species or group Manual Chemical Manual Chemical MSEa P b MSE P

 - - - - - - - - Coverage (percent) - - - - - - - -

Grasses 20.6 24.7 11.5 9.4 66.4 0.06 38.7 0.76

Forbs 0.7 2.2 0.5 1.1 0.1 0.01 0.4 0.02

Vines 15.9 13.3 12.1 12.0 29.8 0.42 4.3 0.25

Shrubs 13.4 10.3 18.6 13.8 20.6 0.15 28.6 0.19

Hardwoods 43.8 37.0 25.3 28.5 52.9 0.03 50.2 0.62

Pines 7.9 9.7 2.2 2.5 20.0 0.06 4.6 0.35

Total vegetationc 80.5 75.5 58.6 54.9 1.6 <0.01 13.3 0.05

a Mean square error (MSE).

b Probability level (P). c Coverage of total vegetation is less than the sum of individual species groups because of multiple occupancy.

for groundline diameter and crown width but not for height.

For the other canopy trees and midcanopy trees, regeneration also tended to be slightly larger under the pine-overstory

treatment, but differences were not consistently significant.

Hardwood regeneration was larger than shortleaf pine

regeneration after the fifth growing season. This reflected a

difference in the principal reproductive strategy of the two

groups—seeds for pines versus advanced regeneration and

sprouts for hardwoods. Experience elsewhere has shown

that the height growth of free-to-grow pine regeneration will

eventually exceed that of hardwoods on most upland sites

(Wahlenberg 1960), and the 5-year results of this study

seem to confirm this. At 3 years, hardwoods were 2.0 times

taller than pines for the pine-overstory treatment and 3.8

times for the pine-hardwood treatment, but at 5 years the

differences had diminished to 1.4 times for the pine-overstory treatment and 2.6 for the pine-hardwood treatment.

Understory Coverage

Effects of the overstory treatment on understory coverage

was variable among species groups, with significant differences occurring for forbs, hardwoods, and total vegetation

(table 4). Differences for grasses and pines were nearly

significant. Coverage was greater in the pine-overstory

treatment than in the pine-hardwood treatment: 2.3 times

for grasses, 1.8 for forbs, 1.5 for hardwoods, and 3.7 for

pines. In contrast, coverage of vines and shrubs was only

slightly affected by overstory treatment. Differences in

coverage between hardwood control methods were small

and only significant for herbs and total vegetation. Herbs

had slightly greater coverage in the chemical control treatment. This may be the result of reduced coverage of hardwoods and shrubs, which were the only groups treated with

herbicide. However, it may also be an anomaly because the

coverage of shrubs and hardwoods was only slightly lower

for chemical hardwood control, and these treatment differences were not significant. Total vegetative coverage for

the chemical treatment averaged 70 percent compared with

65 percent for the manual treatment.

Coverage of pines and hardwoods showed large increases

in the pine-overstory treatment between 3 and 5 years, while

the other groups remained fairly constant. Shelton (1997)

reported that hardwoods averaged 27 percent coverage at

3 years and pines 2 percent. Thus, increases from 3 to 5

years were 1.5 times for hardwoods and 3.8 times for pines.

This observation suggests that pines and hardwoods will

dominate subsequent patterns of successional change in

the understory.

Merchantable Tree Growth

Hardwood retention significantly reduced the growth of merchantable-sized pines: by 31 percent for basal area, 30 percent for total merchantable volume, and 28 percent for

sawtimber volume (table 5). Annual growth in pine sawtimber volume averaged 135 board feet (Doyle) per acre in

pine-overstory treatment and 95 board feet per acre in

pine-hardwood treatment. Unthinned shortleaf pine stands

are expected to be growing 260 board feet per acre per

year on similar sites and ages as this shelterwood stand

(U.S. Department of Agriculture 1929). Thus, the shelterwood cutting reduced pine growth by about one-half for the

pine-overstory treatment and about two-thirds for the pinehardwood treatment. Hardwood growth was surprisingly high

after harvesting and actually exceeded that of shortleaf pine

by 45 percent for basal area and 24 percent for total merchantable volume. The different growth rate between pines

and hardwoods may reflect the younger age of the hardwoods (a mean of 61 years for hardwoods versus 66 years

for pines) or inherent differences in growth patterns and

ability to respond to release. Although hardwoods contributed to total merchantable growth, they did not contribute to

the stand’s sawtimber growth because of their small size and

generally low quality. Annual mortality of shortleaf pines was

very low after harvesting, averaging only 0.04 trees per acre.

CONCLUSIONS AND MANAGEMENT

IMPLICATIONS

Results 5 years after harvesting showed that the shelterwood method can effectively regenerate mixed pine-hardwood stands in the Ouachita Mountains when combined
310

Table 5—Mean annual net growth of merchantable-sized trees in shelterwood stands with pine and

pine-hardwood overstory compositions and manual and chemical methods of submerchantablehardwood control

Pine-hardwood Overstory Hardwood control

Pine overstory overstory treatment treatment

Species or group Manual Chemical Manual Chemical MSEa Pb MSE P

- - - - - - - - -Basal area (ft2 /acre)- - - - - - - - -

Shortleaf pine 0.85 0.84 0.55 0.62 0.007 <0.01 0.018 0.64

Hardwoods 0 0 1.01 1.10 — c — 0.019 0.43

 - - - - -Merchantable volume (ft3 /acre)- - - - -

Shortleaf pine 27 27 18 20 6.3 <0.01 15.1 0.73

Hardwoods 0 0 24 26 — — 7.4 0.36

 - - - - - -Sawtimber volume (ft3 /acre)- - - - - -

Shortleaf pine 29 29 20 22 4.0 <0.01 12.6 0.86

Hardwoods 0 0 0 0 — — — —

 - - - - -Sawtimber volume (fbm/acre)- - - - -

Shortleaf pine 137 131 94 100 158 0.01 228 0.98

Hardwoods 0 0 0 0 — — — —

a Mean square error (MSE).

b Probability level (P). c Too few hardwoods existed in sawtimber size class for analysis.

with low-cost, low-impact site preparation that controls the

submerchantable trees left after harvesting. Despite substantial undisturbed litter and slash after harvesting and

hardwood control, enough favorable microsites were available to establish acceptable shortleaf pine and hardwood

regeneration. Pine regeneration principally came from

seeds dispersed after treatment, but hardwoods developed

from advanced reproduction and sprouting. This difference

in reproduction strategy gives hardwoods an initial growth

advantage. However, pines generally grow rapidly after

establishment, providing acceptable regeneration when

density and stocking are at levels similar to those reported

here.

Retention of overstory hardwoods within a shelterwood pine

stand will have the most significant impact on environmental

conditions in the understory. Such hardwoods appear to

suppress development of pine regeneration to a greater

degree than an equivalent pine basal area, reflecting differences in the crown features of the two species groups. The

limit for retaining hardwoods within a pine shelterwood stand

appears to be fairly low. Results suggest that 15 square

feet per acre of hardwood basal area can be retained in a

scattered distribution for at least the first 5 years after the

reproduction cut. This seems logical based on the generality that hardwoods produce about twice the overstory competition as the same pine basal area. An overstory basal

area of 30 square feet per acre of pines and 15 square feet

per acre of hardwoods is equivalent to a pine overstory 60

square feet per acre, which is generally considered to provide an acceptable environment for the development of

pine regeneration in uneven-aged stands (Baker and others

1996). However, the long-term success of either overstory

treatment is doubtful unless overstory trees are removed

entirely or periodically reduced to acceptable stocking levels.

Although subsequent harvesting will damage existing regeneration, Grano (1961) found that the loss in pine milacre

stocking was only 10 percent for a basal area removal of 31

square feet per acre and 16 percent for 42 square feet per

acre.

The upper limit for acceptable overstory stocking has not

been well established for even-aged reproduction cutting

methods, probably because traditional guidelines call for

overstory removal as soon as regeneration reaches acceptable levels. Undoubtedly, the pine-hardwood overstory

treatment of this study will reach the upper limit for acceptable stocking much sooner than the pine-overstory treatment. Based on observed growth rates, basal areas in the

pine-overstory treatment should take well over 20 years to

reach 75 square feet per acre, which is considered the upper

stocking level for adequate development of pine regeneration in uneven-aged pine stands (Baker and others 1996).

Because of its higher initial stocking, the pine-hardwood

overstory treatment should reach this limit in 7 years, when

projected basal areas are 35 square feet per acre for pines

and 20 square feet per acre for hardwoods (equivalent to a

pine basal area of 35 + 2 × 20 = 75 square feet per acre).

Of course, this prediction needs confirmation by the continued monitoring of overstory and understory dynamics, but

the general contrast between overstory treatments is clear:

the pine-hardwood overstory treatment stocking must be

reduced within 5 to 10 years after harvest to sustain the

development of regeneration.
311

Because merchantable hardwoods were removed in this

study during harvesting, subsequent control treatments

were low in intensity and cost. The chemical control treatment was restricted to stumps of individual stems in the 1-

to 5-inch d.b.h. classes and, therefore, was applied to only

a fraction of the stand’s hardwoods. The herbicide effectively controlled sprouting in some but not all species. Early

results suggest that chemical control was not justified in the

conditions tested here because it failed to substantially

improve the amount or size of shortleaf pine regeneration.

Other conditions had more influence on the acceptable

establishment of shortleaf pine regeneration, including

abundant pine seed production and low initial levels of

competing vegetation.

LITERATURE CITED

Baker, J.B.; Cain, M.D.; Guldin, J.M.; Murphy, P.A.; Shelton, M.G.

1996. Uneven-aged silviculture for the loblolly shortleaf pine

forest types. Gen. Tech. Rep. SO-118. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station: 65 p.

Becton, W.R. 1936. Effects of varying densities of hardwood cover

on growth and survival of shortleaf pine reproduction. Journal

of Forestry. 34(2): 160-164.

Brender, E.V.; McNab, W.H. 1972. Loblolly pine seed production in

the lower Piedmont under various harvesting methods. Journal

of Forestry. 70(6): 345-349.

Campbell, T.E.; Mann, W.F., Jr. 1973. Regenerating loblolly pine by

direct seeding, natural seeding, and planting. Res. Pap. SO-84.

New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station. 9 p.

Clark, A., III; Phillips, D.R.; Frederick, D.J. 1986. Weight, volume,

and physical properties of major hardwood species in the

upland-South. Res. Pap. SE-257. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station. 55 p.

Farrar, R.M., Jr.; Murphy, P.A. 1987. Taper functions for predicting

product volumes in natural shortleaf pines. Res. Pap. SO-234.

New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station. 9 p.

Graney, D.L. 1992. Site index relationships for shortleaf pine. In:

Brissette, J.C.; Barnett, J.P. comps. Proceedings of the shortleaf pine regeneration workshop. Gen. Tech. Rep. SO-90. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 142-157.

Grano, C.X. 1961. Shortleaf-loblolly reproduction losses moderate

following seed tree felling. Journal of Forestry. 59(1): 24-25.

Grano, C.X. 1967. Growing loblolly and shortleaf pine in the

Midsouth. Farmer’s Bulletin 2102. Washington, DC: U.S.

Department of Agriculture: 27 p.

Guldin, J.M.; Baker, J.B.; Shelton, M.G. 1994. Midstory and overstory plants in mature pine/hardwood stands on south-facing

slopes of the Ouachita/Ozark National Forests. In: Baker, J.B.,

comp. Proceedings of the symposium on ecosystem management research in the Ouachita Mountains: pretreatment conditions and preliminary findings. Gen. Tech. Rep. SO-112, New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 29-49.

SAS Institute. 1989. SAS/STAT user’s guide. Version 6, 4th ed.,

vol. 2. Cary, NC: SAS Institute, Inc. 846 p.

Shelton, M.G. 1997. Development of understory vegetation in

pine and pine-hardwood shelterwood stands in the Ouachita

Mountains—the first 3 years. Res. Pap. SRS-8. Asheville, NC:

U.S. Department of Agriculture, Forest Service, Southern

Research Station. 18 p.

Shelton, M.G.; Murphy, P.A. 1997. Understory vegetation 3 years

after implementing uneven-aged silviculture in a shortleaf

pine-oak stand. Res. Pap. SO-296. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Research

Station. 13 p.

Shelton, M.G.; Wittwer, R.F. 1996. Seed production in natural

shortleaf pine stands in the Ouachita and Ozark Mountains.

Southern Journal of Applied Forestry. 20(2): 74-80.

U.S. Department of Agriculture. 1929. Volume, yield, and stand

tables for second-growth southern pines. Misc. Pub. 50.

Washington, DC: U.S. Department of Agriculture, Forest

Service. 202 p.

Wahlenberg, W.G. 1960. Loblolly pine. Durham, NC: School of

Forestry, Duke University. 603 p.
312

Natural forest stands along perennial and intermittent

streams, commonly called streamside zones (SZs), frequently are retained for wildlife habitat enhancement and

watershed protection when adjacent stands are harvested.

However, little is known regarding wildlife habitat relationships within SZs, especially as they relate to SZ widths and

influences of habitat surrounding SZs. Thus, our objective

was to quantify effects of SZ width and age/structure of

adjacent pine plantations on small mammal communities in

the Ouachita Mountains of west central Arkansas.

We trapped small mammals in SZs of 6 width classes (0, 1-

20, 21-41, 41-60, 61-100, and > 100 m) flanked by loblolly

pine (Pinus taeda) plantations of 3 age/structural classes

(young/open, closed canopy, and older/thinned) yielding a

matrix of 18 stands. Three sets of these matrices were studied for 2 years each during February 1990–1995. During

each 2-year sampling period we also sampled one untreated

streamside forest with no adjacent plantations. For the

1990-91 sampling period we were unable to locate a > 100

m SZ within an older/thinned plantation; thus including the

3 untreated streamside forests, a total of 56 study areas

were sampled on Weyerhaeuser and Ouachita National

Forest lands.

Small mammals were sampled on each study area for 10

consecutive days using snap traps. Trap lines were parallel

to the general stream course. Each line had 9 trap stations

at 20-m intervals. Within SZs < 20 m wide, one trap line

was placed on each side of and within 5 m of the stream; a

second set of trap lines was placed within the plantations

40 m from each side of the stream. Small mammals were

trapped only in plantation settings where the SZs were < 20

m wide. For SZs 20-60 m wide, two trap lines were again

placed within 5 m of the stream; a second set of trap lines

was placed within 5 m of the SZ/plantation edge (inside the

SZ). For SZs > 60 m wide, we placed two trap lines within

5 m of the stream, 5 m inside the SZ/plantation edge, and

about midway between the stream and SZ/plantation edge

trap lines, for a total of six trap lines.

Structure of adjacent pine plantations had a noticeable

effect on small mammal numbers, richness, and diversity.

SZs flanked by older/thinned and young/open plantations

had higher numbers, richness, and diversity than those

surrounded by closed canopy plantations. Untreated streamside forests were comparable in numbers, richness and

diversity to SZs flanked by young/open and older/thinned

plantations. SZs flanked by closed canopy pine plantations

had the lowest small mammal numbers, richness, and

diversity of all treatments.

Small mammal numbers tended to increase with SZ width.

However, richness and diversity varied little with SZ width.

Numbers, richness, and diversity in untreated streamside

forests were comparable to other SZ widths. Small mammal

species composition varied with SZ width. Species like the

fulvous harvest mouse (Reithrodontomys fulvescens) and

the woodland vole (Microtus pinetorum) tended to be associated more with narrower SZs. Peromyscous spp. tended

to be more associated with wider SZs.

Small mammal richness and diversity did not increase with

increasing SZ width, but was influenced by the structure of

surrounding pine plantations. Young/open and older/thinned

plantations likely support more species of small mammals

than closed canopy plantations. Species from adjacent

plantations with these structures could also utilize SZ habitat, particularly along edges. Thus, within pine-plantation

dominated landscapes, SZs of a variety of widths embedded

in plantations of different age/structural characteristics

should enhance overall small mammal diversity.

SMALL MAMMAL COMMUNITIES OF STREAMSIDE ZONES

IN THE OUACHITA MOUNTAINS

Philip A. Tappe, Ronald E. Thill, M. Anthony Melchiors,

T. Bently Wigley, and David G. Peitz1

1 Associate Professor, School of Forest Resources and Arkansas Forest Resources Center, University of Arkansas – Monticello, Monticello,

AR 71655; Supervisory Research Wildlife Biologist, USDA Forest Service, Southern Research Station, Nacogdoches, TX 75962; Wildlife

Research Biologist, Weyerhaeuser Company WTC 1A5 Federal Way, WA 98063; Wildlife Biologist, National Council for Air and Stream

Improvement, Inc., Clemson University, Clemson, SC 29634; and Research Associate, School of Forest Resources and Arkansas Forest

Resources Center, University of Arkansas – Monticello, Monticello, AR 71655, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.
313

Linear strips of forest along intermittent and perennial

streams, commonly called streamside zones (SZs), are

frequently retained for watershed protection and wildlife

habitat enhancement in southern pine forests when adjacent

stands are harvested. However, little is known regarding

wildlife communities associated with SZs, particularly in

relation to varying SZ widths and interactions with surrounding habitat matrices. The objective of this study was to

assess relationships between forest bird communities and

SZs of various widths within pine plantations of several

age/structural classes within the Ouachita Mountains of

Arkansas.

We studied breeding bird communities in SZs of 6 width

classes (0-246 m) flanked by loblolly pine (Pinus taeda)

plantations of 3 age/structural classes (young/open, closed

canopy, and older/thinned) yielding a matrix of 18 stands.

Three sets of these matrices were studied for 2 years each

between May 1989 and February 1995. During each 2-year

sampling period, we also sampled one untreated riparian

forest that had no adjacent plantations. We were unable to

locate the widest class SZ within a thinned plantation during

the 1989-90 sampling period; consequently, we sampled a

total of 53 SZ/plantation settings plus 3 untreated controls.

Data were generally obtained from one 80-m-wide by 200-

m-long strip transect centered on each stream. Thus, for

SZs averaging < 80 m in total width, varying proportions of

SZ and plantation habitat were sampled. Six observers

sampled each area for 30 minutes twice each spring

between early May and mid-June.

Nearctic-Neotropical migrants comprised > 50 percent of

birds and species encountered, regardless of SZ width.

Although bird abundance, species richness, and diversity

varied little with SZ width, bird composition changed with

increasing SZ width. Birds frequenting narrower SZs were

mainly those associated with young brushy stands and edge

habitat, while those frequenting wider SZs were mostly

species associated with mature pine-hardwood and bottomland hardwood forests. Avian abundance, richness, and

diversity within SZs flanked by plantations were generally

comparable to or higher than those found in the three

untreated controls. These measures tended to be higher

when SZs were flanked by young/open plantations and

lower when flanked by closed-canopy plantations. Brownheaded cowbirds (Molothrus ater) were more abundant in

narrower SZs. Streamside zone policies should accommodate species strongly associated with riparian habitat and

with high conservation priority. Management policies that

promote a variety of SZ widths, related to stream and floodplain characteristics, should enhance avian diversity within

pine-plantation-dominated landscapes.

AVIAN COMMUNITIES OF STREAMSIDE ZONES IN

THE OUACHITA MOUNTAINS OF ARKANSAS

Ronald E. Thill, Philip A. Tappe, M. Anthony Melchiors, and T. Bently Wigley1

1 Supervisory Research Wildlife Biologist, Southern Research Station, Nacogdoches, TX 75962; Associate Professor, School of Forest

Resources, University of Arkansas at Monticello, Monticello, AR 71656; Forest Wildlife Biologist, Weyerhaeuser Company, Hot Springs, AR

71902; Eastern Wildlife Program Leader, National Council of the Paper Industry for Air and Stream Improvement, Clemson University,

Clemson, SC 29634-0362.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.
314

In conjunction with phase II bird and small mammal studies,

we measured an array of habitat features in 20 stands representing 4 replications of 4 pine regeneration treatments

(clearcut/plant, pine-hardwood shelterwood, pine-hardwood

single-tree selection, and pine-hardwood group selection)

plus 4 untreated controls. At study initiation, all stands were

> 60 years old, > 34.6 acres in size, located on southerly

aspects, and had slopes generally < 20 percent. Stands

were harvested during the summer of 1993. Habitat measurements were collected during late winter of 1995, 1997,

and 1999. Here we summarize initial responses of several

key wildlife habitat parameters to these five treatments.

Most habitat measurements were obtained at 30 permanent

sampling points in each stand. Using plots of various sizes,

we ocularly estimated percent cover of forbs, graminoids

(grasses and grasslike plants combined), and woody plants,

and volume of down wood with an average diameter ≥ 4

inches. Densities of snags (standing dead trees ≥ 3.28 feet

tall) were tallied within 49.2-foot-wide belt transects that

traversed each area and averaged 4,833 feet in total length.

None of the treatments resulted in major increases in evergreen forbs or woody plants during late winter; however,

clearcut and shelterwood stands averaged four to five times

more evergreen woody plant cover than controls by the sixth

year after treatment. Evergreen graminoid cover increased

substantially under all regeneration treatments compared to

controls. Total volume of down wood within harvested stands

was consistently two or more times greater than that of

control stands for all 3 sampling years. Pines and hardwoods

contributed equally to down wood volumes. Snag densities

decreased over time under all treatments, indicating that

snag recruitment from insects, diseases, and lightning strikes

is not keeping pace with natural losses. Unfortunately, 44 to

71 percent of the snags were too small [< 6 inches diameter

at breast height (d.b.h.)] for cavity excavation by woodpeckers. Snags 6.0 to 13.7 inches d.b.h. may be utilized as

cavity trees by smaller cavity-nesting birds, but pileated

(Dryocopus pileatus), red-bellied (Melanerpes carolinus),

and red-headed woodpeckers (M. erythrocephalus) generally require snags >13.7 inches d.b.h. When averaged

across years, total densities of 6.0- to 13.7-inch snags (pine

and hardwood) ranged from 1.6 per acre (shelterwood)

to 4.6 per acre (group selection), while densities of snags

> 13.7 ranged from just 0.3 per acre in shelterwood stands

to 0.5 per acre in group selection stands. Retention of

unharvested greenbelt habitat should eventually result in

higher densities of larger snags.

INITIAL WILDLIFE HABITAT RESPONSES TO ALTERNATIVE FOREST

REGENERATION METHODS IN THE OUACHITA MOUNTAINS

Ronald E. Thill, Roger W. Perry, Nancy E. Koerth, Philip A. Tappe, and David G. Peitz1

1 Supervisory Research Wildlife Biologist, USDA Forest Service, Southern Research Station, Nacogdoches, TX; Wildlife Biologist, USDA

Forest Service, Southern Research Station, Hot Springs, AR; Statistician, USDA Forest Service, Southern Research Station, Nacogdoches,

TX; Associate Professor and Research Associate, University of Arkansas at Monticello, School of Forest Resources, Monticello, AR,

respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.
315

INTRODUCTION

Although the most widespread of the southern pines, the

greatest concentration of shortleaf pine (Pinus echinata

Mill.) is in the Ouachita Mountains of central Arkansas and

eastern Oklahoma (Lawson 1990). Within this region, shortleaf pine is the most important commercial species, and it is

the dominant naturally occurring pine species. Shortleaf

pine can be successfully regenerated by both artificial and

natural methods. One of the most critical determinants of

successful natural regeneration of shortleaf pine is an

adequate seed supply (Baker 1992, Lawson 1986). Some

silvicultural strategies, such as retaining fruitful trees and

promoting their general vigor, can be used to enhance seed

production within a stand, but these techniques are overshadowed by the uncontrollable influences of seed and

cone consumers and weather. Considering these restrictions, resource managers relying on natural regeneration of

this important species should be familiar with the periodicity

of seed crops.

The timing of an adequate seed supply with a receptive seedbed and low levels of competing vegetation is the greatest

challenge to managers relying on natural reproduction cutting methods (Shelton and Cain, in press). In this paper, we

compile data on shortleaf pine seed-production from two

research studies and eight operational stands; all sites are

located in the Ouachita Mountains. This information will provide land managers with knowledge about the periodicity of

seed crops within the Ouachitas and the extent to which

they can enhance seed production through silvicultural

manipulations.

The Pine Reproductive Cycle

For shortleaf and the other southern pines, the total time

between strobili initiation and seed dispersal is over 2 yr.

The wide variation observed in annual shortleaf pine seed

crops can be attributed to several factors. Flower production in an old-field stand was observed to vary by a factor

of eight times over a 6-yr period in the Virginia Piedmont

(Bramlett 1972). Flower initiation is influenced by the

interaction of physical environmental factors (nutrients,

moisture, light, temperature) and physiological processes

(Barnett and Haugen 1995).

Subsequent mortality is caused by spring frosts, insects, and

physiological abortion. Spring frosts, late enough to damage

new juvenile leaves on hardwoods, have been observed to

damage female flowers (Campbell 1955, Hutchinson and

Bramlett 1964). Male flowers seem less susceptible to frost

damage. Overall survival of flowers to mature cones varied

from 3 to 65 percent and averaged 29 percent over a 6-yr

observation period (Bramlett 1972). Greatest losses occurred

between May and September during the first year of cone

development. Mortality during the second year was attributed to squirrels and insects. Yearian and Warren (1964)

found 39 insect species to be associated with shortleaf and

loblolly pine (Pinus taeda L.) conelets and cones in Arkansas;

Dioryctria clarioralis (Walker) and D. amatella (Hulst) were

the most destructive. Seed sampling in 22 stands included

in the Phase I, Ecosystem Management Research pilot

study found about 12 percent of the seed to be damaged

by seed bugs (Mangini and others 1994); Leptoglossus

corculus (Say) and Tetyra bipunctata (Herrich-Schaffer)

caused the most damage.

Shortleaf pine seed yields have been found to vary from

failures to over two million per acre in previous studies

(Wittwer and Shelton 1992). Three long-term studies have

been previously conducted: (1) a 10-yr study in the Piedmont

region of the Carolinas and Georgia in the eastern portion

of the natural range (Bramlett 1965), (2) a 10-yr study in

east Texas in the southwest portion of the range (Stephenson

1963) and (3) a 9-yr study in the Ozark and Ouachita

Mountains of Arkansas, Missouri, and Oklahoma in the

northwestern portion of the natural range (Shelton and

Wittwer 1996). In general, all studies recorded three or four

SHORTLEAF PINE SEED PRODUCTION FOLLOWING

PARTIAL CUTTING IN THE OUACHITA MOUNTAINS

Robert F. Wittwer and Michael G. Shelton1

1 Professor, Department of Forestry, Oklahoma State University, Stillwater, OK 74078-6013; and Research Forester, USDA Forest Service,

Southern Research Station, Monticello, AR 71656-3516, respectively.

Citation for proceedings: Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium: ecosystem management research.

Gen. Tech. Rep. SRS–74. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 321 p.

Abstract—Seed production is one of the principal determinants of successful natural regeneration of shortleaf pine (Pinus

echinata Mill.) in both uneven-aged and even-aged silvicultural systems. In this paper, we describe the amount and

periodicity of shortleaf pine seed production observed in a number of stands with monitoring periods of up to 8 yr. Results

were compiled from: (1) two replicated research studies testing hardwood retention in uneven-aged and even-aged reproductive cutting methods in the eastern part of the Ouachitas, and (2) eight operational-level stands, which were mostly

located in the western part of the Ouachitas. The following generalities can be drawn from the combined results: (1) seed

production is highly variable, ranging from zero in some years to over 3 million sound seeds per ac in others, (2) reduced

seed production occurs during some years in stands where midcanopy hardwoods are present but the effects are not

consistent from year to year, (3) seed production tends to decrease in older stands and in overstocked stands, and (4) the

western part of the Ouachitas tends to have more failures and fewer good seed crops. Results indicate that seed production will usually be adequate for natural regeneration of shortleaf pine within most of the Ouachita Mountains.
316

good seed crops during the study periods. In the southeastern Piedmont, seed yields were observed to decrease

with increasing latitude (Bramlett 1965). In the Ozark/

Ouachita Mountain study, yields were lower in the western

portion of the region, an area representing the northwestern

limit of shortleaf’s natural range (Shelton and Wittwer 1996).

Good seed crops may not be distributed at regular intervals; two successive years with good yields may be followed

by three or four poor years.

Seed quality, expressed as the percentage of the total crop

that is sound, appears to increase with the higher yields.

Bramlett’s (1965) 10-yr study in the Piedmont found 57 percent sound seed in the three good seed crops and 41 percent for the other seven crops. In east Texas, Stephenson

(1963) found sound seed to average 61 percent for the four

years with good crops during the 10-yr study. Shelton and

Wittwer (1996) found the percentage to range from about

30 percent in poor seed years to 70 percent for bumper

seed crops. Intensive cone sampling from two Ouachita

Mountain stands in a year with a mediocre seed crop found

between 30 and 50 percent of the seeds to be sound

(Wittwer and others 1997).

METHODS

Study Areas and Treatments

Study 1 (uneven-aged pine-hardwoods)—This study was

installed in the Winona Ranger District of the Ouachita

National Forest in Perry County, AR (Shelton and Murphy

1997). Plots were oriented along an east-west ridge, which

is typical of the physiography of the Ouachita Mountains.

Elevations ranged from 640 to 790 ft above sea level. Slopes

of individual plots ranged from 8 to 21 percent. Soils of the

study area are mapped as the Carnasaw and Pirum series,

both Typic Hapludults. These are well-drained, moderately

deep soils that developed in colluvium and residuum weathered from sandstone and shale. Natural fertility and organic

matter are low, and the soils are strongly acidic. Site index

for shortleaf pine averaged 57 ft at 50 yr and ranged from

53 to 64 ft, which is typical of upland sites in the Ouachita

Mountains (Graney 1992). Annual precipitation averages 52

in., and temperature averages 400 F in the winter and 790 F

in the summer (U.S. Department of Commerce 1968). Winter

is the wettest season and autumn is the driest. Water

deficits typically develop during the summer.

Vegetation in the study area was typical of much of the

forested landscape in the Ouachita Mountains, where

upland forests are dominated by shortleaf pine and mixed

oaks (Guldin and others 1994). Overstory basal area (trees

> 3.6 in. d.b.h.) averaged 90 ft2 per ac for shortleaf pine and

32 ft2 per ac for hardwoods before study implementation.

Oaks accounted for 84 percent of the total hardwood basal

area. Overstory pines and oaks in the initial stand ranged in

age from 30 to over 110 yr (Shelton and Murphy 1991);

dominant pines averaged 76 yr old, while dominant oaks

averaged 70 yr old. No silvicultural treatments had been

applied within a decade of study installation.

Sixteen square 0.50-ac plots were established and surrounded by a 58-ft isolation strip that was treated in an

identical manner. Plots were arranged in a randomized

complete block design with four replicates. One plot per

block was designated as a control that was not treated in

any manner. The pine component for the treated plots was

reduced to a target basal area of 60 ft2 per ac using unevenaged marking guidelines for single-tree selection (Baker

and others 1996). An average of 81 percent of residual

pines were > 10 in. d.b.h., which is usually accepted as the

minimum size for high-seed producing potential (Shelton

and Wittwer 1995). Plots were harvested during the winter

of 1988-89. Imposed treatments were three targeted levels

of retained overstory hardwoods (0, 15, and 30 ft2 per ac in

trees > 4 in. d.b.h.). Hardwoods (> 0.6 in. d.b.h.) that were

not designated for retention were controlled with steminjected herbicide.

Within each plot, four 0.9-ft2 seed traps (Cain and Shelton

1993) were located about 30 ft from the center of the plot in

a square pattern and about 100 ft from the outer boundary

of the plot. Seed collections were generally made during

the middle and end of each October-to-February period,

which is the normally accepted period for dispersal of shortleaf pine seed (Wittwer and Shelton 1992). Monitoring began

in October 1988 for the treated plots and in October 1990

for the untreated control, and the last collection was made

in March 1997.

Study 2 (shelterwood pine-hardwoods)—This study area

was located about 0.5 mile from study 1, and both were

very similar in stand and site conditions (Shelton 1997).

Elevations ranged from 640 to 810 ft above sea level. Slopes

of individual subplots ranged from 5 to 26 percent. Soils

were mapped as the Carnasaw and Pirum series. Site

index for shortleaf pine averaged 60 ft at 50 yr, ranging

from 56 to 65 ft, and the dominant pines averaged 66 yr

old. No silvicultural treatments had been applied within a

decade of study installation. Pre-treatment overstory basal

area averaged 74 ft2 per acre for shortleaf pine and 41 ft2

per acre for hardwoods. Oaks accounted for 92 percent of

the hardwood basal area.

Eight rectangular 1.7-ac plots were established and surrounded by a 60-ft isolation strip that was treated in an

identical manner. Plots were arranged in a randomized

complete block design with four replicates. The pine component on all plots was reduced to a basal area of 30 ft2

 per

ac in trees selected principally for their potential as seed

trees; all trees were > 10 in. d.b.h. Retention of overstory

hardwoods was 0 and 15 ft2 per ac in trees > 4 in. d.b.h. All

merchantable pines were harvested during the winter of

1989-90, and merchantable hardwoods not designated for

retention were harvested during the spring and summer of

1990. After harvesting was completed, plots were split into

halves, and two control methods for submerchantable hardwoods (0.6 to 3.5 in. d.b.h.) were imposed (chain-saw felling with and without a stump-applied herbicide). Three 0.9-

ft2 seed traps were located along the center line of each

split plot; traps were at least 120 ft from the untreated stand

and 100 ft from adjacent whole plots. Monitoring began in

October 1990, and the last collection was made in March

1997.

Operational stands—A 3-ac area within an operational

seed-tree stand was selected for monitoring in the eastern

Ouachitas. The area was located about 2 miles from studies
317

1 and 2 and was similar to those studies in soil and site

conditions. Elevations ranged from 520 to 600 ft. The seedtree cut was made in 1985, and hardwoods were controlled

by chain-saw felling. Nine 0.09-ft2 seed traps were systematically located within the area. Monitoring began in October

1993 and the last collection was made in March 1997.

Five natural stands on the Ouachita National Forest (ONF)

and two stands on Choctaw National Forestry (CNF) lands

in eastern Oklahoma were selected for monitoring (October

1989 through February 1997). Stands were 22 to 40 ac in

area. The ONF stands were 60-80 yr old when sampling

began and had been subjected to even- or uneven-aged

reproduction cutting methods. The CNF stands were 45-50 yr

old when sampling began; one was thinned while the other

was not. Stands on the ONF were in LeFlore County, OK,

while those on CNF lands were in Latimer County, OK. Elevations ranged from 800 to 1200 ft above sea level. The

Carnasaw and Bengal soil series were common in the

stands, although other series were present. All soils were

moderately deep or deep and moderately well-drained or

well-drained. Texture of the surface horizon ranged from

stony- to fine-sandy loams. Annual precipitation averages

approximately 45 in., and temperature averages 430 F in

the winter and 800 F in the summer (U.S. Department of

Commerce 1968). Seed production was sampled with six to

ten, 2 by 2-ft wood-frame and wire mesh traps in each

stand (Scholtens 1979).

Seed Processing

Coarse litter was generally removed from the seed traps in

the field, and fine litter and seeds were brought into the

laboratory for separation. Collected material was refrigerated until processed. Seeds were counted, and viability

was generally determined by cutting seeds and inspecting

the contents (Bonner 1974). Seeds with full, firm, undamaged, and healthy tissue were judged to be potentially

viable and were tallied as sound seeds. Although ineffective

for stored seeds, the cut test can be accurate when applied

to fresh seeds (Bonner and others 1994). To confirm validity

of the cut test in our studies, a subsample from the 1993

and 1994 collections for studies 1 and 2 totaling 1,800 seeds

was germinated under controlled conditions following 30 days

of cold/moist stratification. At the end of the 30-day germination periods, a cut test revealed that only 3.5 percent of

the ungerminated seeds were full, indicating that virtually

all of the full seeds collected in our studies were viable.

Because of the large number of seeds to be processed

from the Oklahoma stands in 1993, seeds were considered

sound if they sank in ethanol (Krugman and Jenkinson

1974). A subsample of these seeds was cut and yielded the

same results as the float test.

Data Analysis

Means were calculated for each plot or subplot for study

1 and 2 and for the entire stand for the operational stands.

For study 1, data were analyzed using analysis of variance

for a randomized, complete block design. Differences

among treatment means were isolated by using the RyanEinot-Gabriel-Welsch Multiple Range Test at a probability

level (P) of 0.05. This procedure, which is one of the most

powerful step-down, multiple-range tests available, controls

the experiment-wise error rate (SAS Institute 1989). For

study 2, data were analyzed by analysis of variance for a 2

by 2 factorial, split-plot randomized complete block design

using the SAS procedure GLM (SAS Institute 1989). Since

there were only two levels for each factor, means were not

separated but were presented with the associated mean

square error (MSE) and P. Significance was accepted at P

< 0.05.

RESULTS AND DISCUSSION

Seed Yield

Seed yields exhibited wide annual variation, ranging from

complete failures in some stands for some years to over 3

million sound seeds per ac in the pine-only shelterwood

stands in 1993 (tables 1, 2, and 3). In the eastern Ouachitas,

five of the eight seed crops sampled in both shelterwood

and uneven-aged stands produced generally good yields

(tables 1 and 2). An adequate seed crop to successfully

regenerate shortleaf pine has usually been specified as

being in excess of 80 thousand seeds per ac (Baker 1992,

Haney 1962, Shelton and Wittwer 1996). Suitable conditions for germination and seedling establishment may be

present for three years after site preparation, perhaps

longer on poor sites and less on more productive sites

(Shelton and Wittwer 1992). In the eastern Ouachita

Mountain stands, an adequate crop was produced at least

once within any three year interval.

Hardwood retention generally had a negative influence on

shortleaf pine seed production, but the effects varied from

year to year. In the uneven-aged stand where hardwoods

were retained, seed production was significantly decreased

during 1993 and 1994 and for the 6-year mean (table 1). A

similar significant decrease occurred in the shelterwood

stand during three seed crops (1993, 1994, and 1996) and

for the 7-yr mean (table 2). The negative effects of hardwoods on seed production were usually greatest for the

better seed crops. The method of controlling submerchantable hardwoods did not significantly affect seed production

in the shelterwood stand.

The importance of reducing shortleaf pine basal area was

apparent in the uneven-aged stand in 1992 and 1996 (table

1). The harvested plots with high hardwood retention produced three times more seeds than the unharvested controls in 1992 and five times more in 1994. These two areas

had about the same hardwood basal area, but the pine

basal area was about 30 ft2

 per ac lower in the harvested

plots. Overall, the harvested plots with no hardwoods produced about twice the sound seeds as the unharvested controls over the 6-yr period when both areas were monitored.

There was wide stand-to-stand variation for the operational

stands sampled in the western Ouachita region for the 8-yr

period from 1989 through 1996 (table 3). Only 1993 can be

characterized as producing a good seed crop, but even

then, three of the seven stands in the western Ouachita

region exhibited failures. This trend agrees with a 9-yr

study of seed production throughout the Interior Highlands

of Arkansas, Missouri, and Oklahoma from 1965 through

1973 (Shelton and Wittwer 1996). Seed crops were only

adequate in the western Ouachitas during 1966 and 1967,

when bumper seed crops occurred throughout the rest of

the region.
318

Seed Quality

The percentage of sound seed was positively related to

total seed production in the sampled stands (fig. 1). Values

ranged from < 20 percent in years with the lower seed production (< 100,000 seeds per ac) to over 60 percent in good

seed years. A 9-yr study conducted between 1965 and 1973

found sound seed to be about 30 percent of total production in poor seed years and 70 percent of total production in

years with good crops (Shelton and Wittwer 1996). Void and

defective seeds result from several factors, which are not

greatly affected by management in natural stands. These

factors include lack of pollen, lethal gene combinations,

self-pollination, insect damage, and climatic factors

(Fatzinger and others 1980).

CONCLUSIONS

Shortleaf pine seed production is highly variable for natural

stands in the Ouachita Mountains. Annual production of

sound seed during the 8-yr observation period from 1989

through 1996 ranged from zero to over 3 million seeds per

ac. The influence of retained hardwoods varied from year

to year. For the better seed crops, stands with retained

Table 1—Annual shortleaf pine seed production after the initial harvest

implementing uneven aged silviculture in a shortleaf pine hardwood stand in

the Ouachita Mountains (study 1)

Mean

 Hardwood retentiona

 Untreated square

Seed None Medium High control error P > F

year - - - - thousands of sound seeds per acreb - - - -

1989 197 144 200 — 6.78E3 0.58

1990 3 3 3 — 3.48E1 1.00

1991 75 75 68 31 8.05E2 0.16

1992 166 260 122 44 2.44E4 0.32

1993 2,654a 1,912ab 1,590ab 964b 3.73E5 0.02

1994 310a 75b 128b 20b 4.36E3 < 0.01

1995 0 6 0 0 1.31E1 0.09

1996 2,313 1,915 1,796 1,596 3.59E5 0.43

Meanc 920a 707ab 617ab 442b 3.37E4 0.03

a After harvest, merchantable basal areas (trees >3.6 inches d.b.h.) in 1988 averaged 62

square feet per acre for shortleaf pine and 0, 16 and 31 square feet per acre for none,

medium, and high hardwood retention treatments, respectively. Unharvested control plots

averaged 94 and 32 square feet per acre for pine and hardwood basal areas, respectively, in

1990, and there were also 8 square feet per acre of understory hardwoods (0.6 to 3.5 inches

d.b.h.).

b Row means followed by different letters are significantly different.

c Means are from 1991 to 1996 so that control plots could be compared to treated plots.

Table 2—Annual shortleaf pine seed production in shelterwood stands with two

overstory compositions and two control treatments for submerchantable hardwoods

(study 2)

Pine onlya Pine hardwooda Overstory Submerchantable

overstory overstory composition hardwood treatment

Seed Manual Chemical Manual Chemical MSE P > F MSE P > F

year - - - thousands of sound seeds per acre - - -

1990 67 50 21 17 2.71E3 0.23 2.50E2 0.23

1991 33 25 17 37 5.28E2 0.87 3.66E2 0.54

1992 142 171 121 175 3.48E3 0.80 3.70E3 0.22

1993 3,442 3,484 2,295 2,291 1.27E5 0.01 1.27E5 0.94

1994 1,431 1,596 517 572 3.14E5 <0.01 5.35E5 0.38

1995 8 17 0 4 6.38E1 0.08 2.26E2 0.44

1996 1,888 2,378 1,652 1,694 3.34E4 0.02 9.32E4 0.13

Mean 1,002 1,103 660 684 4.59E3 <0.01 1.57E4 0.36

a After harvesting in 1990, shortleaf pine merchantable basal area (trees >3.6 inches d.b.h.) averaged 28

square feet per acre, and hardwoods averaged 16 square feet per acre when retained.
319

hardwoods tended to produce less seeds, but the difference

was less evident in years with poor and mediocre seed

crops. Seed production for a shelterwood stand equaled or

exceeded that of a nearby uneven-aged stand, even though

there was a higher basal area in trees of seed-producing

sizes in the uneven-aged stand. Results of this study and

previous work suggest that shortleaf pine stands in the

eastern part of the Ouachita Mountains should produce

adequate seed crops for successful natural regeneration

using both even-aged and uneven-aged methods.

Results from this study support earlier reports of the sparse

and sporadic seed crops in the western Ouachita Mountains

and suggest that regional climatic factors near the western

limit of shortleaf’s natural range are the overriding influence.

Where seed production limits the success of natural regeneration, managers should pay careful attention to: (1) retention of an adequate density of trees with a high potential for

seed production, (2) individual tree characteristics related

to seed production, such as crown class, vigor, size (d.b.h.

> 10 in.), and presence of old cones, (3) preparatory cutting

to stimulate tree vigor, and (4) limiting the amount of retained

hardwoods. Perhaps the best option is to monitor shortleaf

pine’s reproductive structures over the 1.5 yr period from

pollination to the beginning of seed dispersal. Particular

attention should be paid to the amount of maturing cones

during the summer before seed dispersal (Shelton and

Wittwer 1995). Monitoring can forecast when adequate

seed crops are most likely to occur, and site preparation

treatments can be timed to coincide.

ACKNOWLEDGMENTS

The cooperation and assistance of personnel with the USDA

Forest Service, Ouachita National Forest from Perryville,

AR and Talihina, OK; Choctaw National Forestry at Talihina,

OK; and Oklahoma State University, Kiamichi Forestry

Research Station at Idabel, OK is gratefully acknowledged.

LITERATURE CITED

Baker, James B. 1992. Natural regeneration of shortleaf pine. In:

Brissette, John C.; Barnett, James P., comps. Proceedings of

the shortleaf pine regeneration workshop; 1991 October 29-31;

Little Rock, AR. Gen. Tech. Rep. SO-90. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 102-112.

Baker, James B.; Cain, Michael D.; Guldin, James M. [and others].

1996. Uneven-aged silviculture of the loblolly and shortleaf pine

forest types. Gen. Tech. Rep. SO-118. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southern Research

Station. 65 p.

Barnett, James P.; Haugen, Ronald O. 1995. Producing seed crops

to naturally regenerate the southern pines. Res. Pap. SO-286.

New Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station. 10 p.

Figure 1—Relationship between sound seed percentage (P) and

total seed production (T) for shortleaf pine in the Ouachita

Mountains.

Table 3—Annual production of shortleaf pine seed from 1989 through 1996 for

operational stands representing a range of residual shortleaf pine basal areas in the

Ouachita Mountains

 Sound seed production by year

Shortleaf pine

Stand basal area 1989 1990 1991 1992 1993 1994 1995 1996

ft2 per acre - - - - - - - - - - - - thousands of sound seeds per acre - - - - - - - - - - - -

STa 8 NS NS NS NS 188 22 0 1,058

ST 17 0 0 0 20 4 0 0 8

SW 48 0 6 0 12 1,183 0 1 20

UEA 43 NS 39 33 NS 361 0 0 33

UEA 45 NS 2 0 4 96 0 2 15

UEA 51 NS 0 0 2 87 0 2 5

THN 62 265 0 1 NS 0 1 0 0

UTHN 98 272 0 2 NS 0 0 2 2

ST = Seed tree regeneration harvest; NS = not sampled; SW = shelterwood regeneration harvest;

UEA = even-aged stand harvested to implement uneven-aged silviculture; ST, SW, and UEA stands

were 70 to 80 years old; THN = a 50-year old stand thinned to the specified basal area; UTHN = an

unthinned 50-year old stand.

a This stand is in the eastern part of the Ouachita Mountains; all others are in the western part.
320

Bonner, F.T. 1974. Seed testing. In: Schopmeyer, C.S., tech. coord.

Seeds of woody plants in the United States. Agric. Handb. 450.

Washington, DC: U.S. Department of Agriculture: 136-152.

Bonner, F.T.; Vozzo, J.A.; Elam, W.W.; Land, S.B., Jr. 1994. Tree

seed technology training course. Gen. Tech. Rep. SO-107. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station. 171 p.

Bramlett, David L. 1965. Shortleaf pine seed production in the

Piedmont. Res. Note SE-38. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southeastern Forest Experiment

Station. 4 p.

Bramlett, David L. 1972. Cone crop developmental records for six

years in shortleaf pine. Forest Science. 18:31-33.

Cain, Michael D.; Shelton, Michael G. 1993. Modification of GSA

metal wastebaskets for use as seed and litter traps. Engineering

Field Notes. 26(3): 65-69.

Campbell, T.E. 1955. Freeze damages shortleaf pine flowers.

Journal of Forestry. 53: 452.

Fatzinger, Carl W.; Hertel, Gerard D.; Merkel, Edward P. [and

others]. 1980. Identification and sequential occurrence of

mortality factors affecting seed yields of southern pine seed

orchards. Res. Pap. SE-216. Asheville, NC; U.S. Department of

Agriculture, Forest Service, Southeastern Forest Experiment

Station. 41 p.

Graney, David L. 1992. Site index relationships for shortleaf pine.

In: Brissette, J.C.; Barnett, J.P., comps. Proceedings of the

shortleaf pine regeneration workshop; 1991 October 29-31:

Little Rock, AR. Gen. Tech. Rep. SO-90. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Forest

Experiment Station: 142-157.

Guldin, James M.; Baker, James B.; Shelton, Michael G. 1994.

Midstory and overstory plants in mature pine-hardwood stands

on south-facing slopes of the Ouachita/Ozark National Forests.

In: Baker, J.B., comp. Proceeding of the symposium on ecosystem management research in the Ouachita Mountains:

Pretreatment conditions and preliminary findings; 1993 October

26-27; Hot Springs, AR. Gen. Tech. Rep. SO-112. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 29-60.

Haney, Glenn P. 1962. Seedbed scarification aids regeneration of

shortleaf pine. Journal of Forestry. 60: 400-402.

Hutchinson, Jay G.; Bramlett, David L. 1964. Frost damage to

shortleaf pine. Journal of Forestry. 62: 343.

Krugman, S.L.; Jenkinson, J.L. 1974. Pinus L. pine. In: Schopmeyer,

C.S., tech. coord. Seeds of woody plants in the United States.

Agric. Handb. 450. Washington, DC: U.S. Department of

Agriculture: 598-638.

Lawson, Edwin R. 1986. Natural regeneration of shortleaf pine. In:

Murphy, Paul A. (ed.). Proceedings of a symposium on the

shortleaf pine ecosystem; 1986 March 31-April 2; Little Rock,

AR. Monticello, AR; University Arkansas Cooperative Extension

Service: 53-63.

Lawson, Edwin R. 1990. Pinus echinata Mill. shortleaf pine. In:

Burns, R.M. and Honkala, B.H., tech. cords. Silvics of North

America: Volume 1. Conifers. Agric. Handb. 654. Washington,

DC: U.S. Department of Agriculture: 316-326.

Mangini, Alex; Carlton, Chris; Perry, Roger W.; Hanula, James

1994. Seed, cone, regeneration, and defoliating insects in

forest ecosystem management. In: Baker, J.B., comp. Proceeding of the symposium on ecosystem management research in

the Ouachita Mountains: Pretreatment conditions and preliminary findings; 1993 October 26-27; Hot Springs, AR. Gen.

Tech. Rep. SO-112. New Orleans, LA: U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station: 154-159.

SAS Institute. 1989. SAS/STAT user’s guide. Version 6. 4th ed.

Cary, NC: SAS Institute, Inc. 846 p.

Scholtens, John R. 1979. A practical seed trap for pine stands of

coastal South Carolina. Southern Journal of Applied Forestry. 3:

112-113.

Shelton, M.G.; Cain, M.D. [In press]. Regenerating uneven-aged

stands of loblolly and shortleaf pines: current state of knowledge. Forest Ecology and Management.

Shelton, Michael G. 1997. Development of understory vegetation

in pine and pine hardwood shelterwood stands in the Ouachita

Mountains—the first 3 years. Research Paper SRS-8. Asheville,

NC: U.S. Department of Agriculture, Forest Service, Southern

Research Station. 18 pp.

Shelton, Michael G.; Murphy, Paul A. 1991. Age and size structure

of a shortleaf pine-oak stand in the Ouachita Mountains—

implications for uneven-aged management. In: Coleman, S.S.;

Neary, D.G., comps. Proceedings of the 6th biennial southern

silvicultural research conference; 1990 October 30-November

1; Memphis, TN. Gen. Tech. Rep. SE-70. Asheville, NC: U.S.

Department of Agriculture, Forest Service, Southeastern Forest

Experiment Station: 616-629.

Shelton, Michael G.; Murphy, Paul A. 1997. Understory vegetation

3 years after implementing uneven-aged silviculture in a shortleaf pine-hardwood stand in the Ouachita Mountains. Research

Paper SO-296. Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station. 13 p.

Shelton, Michael G.; Wittwer, Robert F. 1992. Effects of seedbed

condition on natural shortleaf pine regeneration. In: Brissett,

J.C.; Barnett, J.P., comps. Proceedings of the shortleaf pine

regeneration workshop; 1991 October 29-31; Little Rock, AR.

Gen. Tech. Rep. SO-90. New Orleans, LA: U.S. Department of

Agriculture, Forest Service, Southern Forest Experiment

Station: 124-139.

Shelton, Michael G.; Wittwer, Robert F. 1995. Forecasting loblolly

and shortleaf pine seed crops. In: Edwards, M. Boyd, comp.

Proceedings of the eighth biennial southern silvicultural research

conference; 1994 November 1-3; Auburn, AL. Gen. Tech. Rep.

SRS-1. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station: 605-612.

Shelton, Michael G.; Wittwer, Robert F. 1996. Shortleaf pine seed

production in natural stands in the Ouachita and Ozark

Mountains. Southern Journal of Applied Forestry. 20: 74-80.

Stephenson, George L. 1963. Ten years of shortleaf pine seed

crops in Texas. Journal of Forestry. 61: 270-272.

U.S. Department of Commerce. 1968. Climatic atlas of the United

States. Washington, DC: U.S. Department of Commerce;

Environmental Science Service Administration. 80 p.

Wittwer, R.F.; Shelton, M.G. 1992. Seed production in natural

shortleaf pine stands. In: Brissett, J.C.; Barnett, J.P., comps.

Proceedings of the shortleaf pine regeneration workshop; 1991

October 29-31; Little Rock, AR. Gen. Tech. Rep. SO-90. New

Orleans, LA: U.S. Department of Agriculture, Forest Service,

Southern Forest Experiment Station: 113-123.

Wittwer, R.F.; Tauer, C.G.; Huebschmann, M.M.; Huang, Y. 1997.

Estimating seed quantity and quality in shortleaf pine (Pinus

echinata Mill.) cones from natural stands. New Forests. 14: 45-

53.

Yearian, W.C.; Warren, L.O. 1964. Insects of pine cones in

Arkansas. Journal of the Kansas Entomological Society. 37:

259-264.
321

INDEX OF AUTHORS

Page

B

Baker, James B. 15

Barlow, Rebecca J. Ray 121

Bruce, William W. 89

C

Cain, Michael D. 285

Chin, Anne 292

Clingenpeel, J. Alan 231, 292

Cobb, Donna 221

Cook, Stephen P. 83

Crosswhite, Doyle L. 273

Crump, Betty 221

F

Fiorillo, Riccardo A. 239

Foti, Thomas 189

Fox, Stanley F. 164, 174, 273

G

Gelwick, Frances P. 221

Gottschalk, Kurt W. 267

Gramann, James H. 113

Guldin, James M. 8, 15, 21, 92,

143, 189, 267

H

Hanula, James L. 89

Henderson, L. Gayle 257

Herrick, Theresa A. 130

Hill, Brian H. 214

J

Jang, Hochan 113

Page

K

Koerth, Nancy E. 29, 36, 314

L

Leslie, David M., Jr. 164, 174

Li, Ying-Hung 130

Liebhold, Andrew M. 267

Liechty, Hal O. 21, 101

Lonzarich, David G. 246, 253

Lonzarich, Mary E. 246, 253

M

Malanson, George 198

Mangini, Alex C. 79, 89

Marion, Daniel A. 207, 292

Marion, Denise 198

Matthews, William J. 221

McCormick, Frank H. 214

Melchiors, M. Anthony 146, 154,

312, 313

Miller-Lemke, A. Maria 221

Muzika, Rose-Marie 267

P

Peitz, David G. 29, 36, 42, 55,

60, 312, 314

Perry, Roger W. 29, 36, 42, 55,

60, 79, 314

Phelps, Joseph P. 164, 174, 183

R

Radabaugh, David K. 21

Rader, Janet S. 257

Robertson, F. Dale 3

Robison, Henry W. 257

Rohrer, Deven M. 292

Rudis, Victor A. 113, 121, 130

Page

S

Shelton, Michael G. 15, 101,

297, 305, 315

Shipman, Paul A. 164, 174

Smith, Kimberly G. 71

Starkey, Dale A. 92

Stewart, Jeffery G. 221

T

Tappe, Philip A. 29, 36, 42, 55,

60, 146, 154,

312, 313, 314

Taulman, James F. 71

Taylor, Christopher M. 231, 239

Thill, Ronald E. 29, 36, 42, 55,

60, 146, 154,

164, 174, 273,

312, 313, 314

Thomas, R. Brent 239

W

Warren, Melvin L., Jr. 221, 231, 239,

246, 253, 257

Weih, Robert C., Jr. 146

Wigley, T. Bently 146, 154,

312, 313

Williams, Lance R. 231, 239

Wittwer, Robert F. 315
DISCLAIMER

The use of trade or firm names in this publication is for reader information and does not imply endorsement by the

U.S. Department of Agriculture of any product or service.

PESTICIDE PRECAUTIONARY STATEMENT

This publication reports research involving pesticides. It does not contain recommendations for their use, nor does it

imply that the uses discussed here have been registered. All uses of pesticides must be registered by appropriate

State and/or Federal agencies before they can be recommended.

CAUTION: Pesticides can be injurious to humans, domestic animals, desirable plants, and fish or other wildlife if

they are not handled or applied properly. Use all herbicides selectively and carefully. Follow recommended practices

for the disposal of surplus pesticides and their containers.

Papers published in these proceedings were submitted by authors in electronic media. Some editing was done to

ensure a consistent format. Authors are responsible for content and accuracy of their individual papers and the

quality of illustrative materials.

April 2004

Southern Research Station

P.O. Box 2680

Asheville, NC 28802

Cover photo: Pedestal Rock Trail View, Pedestal Rock Scenic Area,

Ozark National Forest, Arkansas. Photo by Bill Lea.
Guldin, James M., tech. comp. 2004. Ouachita and Ozark Mountains symposium:

ecosystem management research. Gen. Tech. Rep. SRS–74. Asheville, NC:

U.S. Department of Agriculture, Forest Service, Southern Research Station.

321 p.

This volume presents 5-year results of silvicultural treatments associated with

ecosystem management research in the Ouachita Mountains of Arkansas.

Results from stand-level treatments include regeneration dynamics of pine and

hardwood species, effects of treatment on birds and small mammals, mast

production, visual quality, oak decline, and organic matter. Pretreatment

landscape findings include measurements of woody vegetation; birds, mammals,

and herpetofauna; fish communities and trophic structure; hydrology; and

evaluation of susceptibility to gypsy moth outbreaks.

Keywords: Diversity, ecosystem management, landscape analysis, natural

regeneration, pine-hardwood, reproduction cutting methods, shortleaf pine,

stand-level effects, visual quality.