United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Proceedings

RMRS-P-61

December 2010

Integrated Management of Carbon 

Sequestration and Biomass Utilization 

Opportunities in a Changing Climate: 

Proceedings of the 2009 National Silviculture Workshop; 

2009 June 15-18; Boise, ID

2009 NATIONAL SILVICULTURE WORKSHOP

BOISE, IDAHO

SILVICULTURE

CLIMATE

BIOMASS

CARBON SEQUESTRATION
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Jain, Theresa B.; Graham, Russell T.; and Sandquist, Jonathan, tech. eds. Integrated management of 

carbon sequestration and biomass utilization opportunities in a changing climate: Proceedings 

of the 2009 National Silviculture Workshop; 2009 June 15-18; Boise, ID. Proceedings RMRS-P-61. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 351 p.

Abstract

Forests are important for carbon sequestration and how they are manipulated either through natural or human 

induced disturbances can have an effect on CO2

 emissions and carbon sequestration. The 2009 National 

Silviculture Workshop presented scientific information and management strategies to meet a variety of objectives while simultaneously addressing carbon sequestration and biomass utilization. The focus areas were: 

the role of climate change in science and management; silvicultural methods to address carbon sequestration 

and biomass utilization; alternative silvicultural strategies to address the growth and development of forests; 

and current applications of computer simulation models or modeling techniques designed to provide decision 

support. 

Keywords: silviculture, carbon sequestration, climate change, forest management
Integrated Management of Carbon Sequestration and Biomass 

Utilization Opportunities in a Changing Climate: 

Proceedings of the 2009 National Silviculture Workshop; 

2009 June 15-18; Boise, ID

Technical Editors

Theresa B. Jain

Russell T. Graham

Jonathan Sandquist
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Theresa B. Jain

Part 1: Climate Change in Science and Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Cecil D. Andrus

Ecosystem Services and Climate Change: Understanding the Differences and Identifying

Opportunities for Forest Carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Robert L. Deal, Crystal Raymond, David L. Peterson, and Cindy Glick

Integrating Climate Change Considerations into Forest Management Tools and Training . . . . . . . . 27

Linda M. Nagel, Christopher W. Swanston, and Maria K. Janowiak

Part 2: Carbon Sequestration and Biomass Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged

Forests of the Rocky Mountains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Theresa B. Jain, Russell T. Graham, and David Adams

Carbon Benefits from Fuel Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Jim Cathcart, Alan A. Ager, Andrew McMahan, Mark Finney, and Brian Watt

Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range . . . . . . . . . . 81

Mike A. Battaglia, Kellen Nelson, Dan Kashian, and Michael G. Ryan

To Manage or not to Manage: The Role of Silviculture in Sequestering Carbon in the

Specter of Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Jianwei Zhang, Robert F. Powers, and Carl N. Skinner

Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged

Southern Pine Stands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Don C. Bragg and James M. Guldin

The Role of Forests in Energy and Climate Change - Integrating Objectives . . . . . . . . . . . . . . . . . 125

Dave Atkins

The Forest—Bioenergy—Carbon Connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Jay O’Laughlin

Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals . . . . . . . . . . . . . . . . . . . 135

Barry Wynsma and Christopher R. Keyes

Emissions, Energy Return and Economics from Utilizing Forest Residues for Thermal

Energy Compared to Onsite Pile Burning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Greg Jones, Dan Loeffler, Edward Butler, Woodam Chung, and Susan Hummel

Can Portable Pyrolysis Units Make Biomass Utilization Affordable While Using Bio-Char

to Enhance Soil Productivity and Sequester Carbon? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Mark Coleman, Deborah Page-Dumroese, Jim Archuleta, Phil Badger, Woodum Chung, 

Tyron Venn, Dan Loeffler, Greg Jones, and Kristin McElligott

Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed

Western Conifers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Andrew Youngblood, Junyong Zhu, and C. Tim Scott

Contents
Part 3: Alternative Silvicultural Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

Estimating Site Index from Tree Species Composition in Mixed Stands of Upland

Eastern Hardwoods: Should Shrubs be Included? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

W. Henry McNab

Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture

and Stand Projection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

John D. Shaw and James N. Long

The role of strategic forest inventories in aiding land management decision-making:

Examples from the U.S. Forest Inventory and Analysis Program . . . . . . . . . . . . . . . . . . . . . . . 211

W. Keith Moser, Renate Bush, John D. Shaw, Mark H. Hansen, and Mark D. Nelson

Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at

Humboldt Redwoods State Park, California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

Christopher R. Keyes, Thomas E. Perry, and Jesse F. Plummer

Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned

Ponderosa Pine Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Matt Busse

Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho . . . . . . . . . . . . . 251

Douglas D. Basford, John Sloan, and Joy Roberts

Built for the Future: New Directions in Silviculture Research and Demonstration at

Montana’s Lubrecht Experimental Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

Christopher R. Keyes and Thomas E. Perry

Part 4: Decision Support Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271

Addressing Climate Change in the Forest Vegetation Simulator to Assess Impacts on

Landscape Forest Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

Nicholas L. Crookston, Gerald E. Rehfeldt, Gary E. Dixon, and Aaron R. Weiskittel

Calibration of State and Transition Models with FVS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275

Melinda Moeur and Don Vandendriesche

FVS Out of the Box – Assembly Required . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289

Don Vandendriesche

An Empirical Approach for Estimating Natural Regeneration for the Forest

Vegetation Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307

Don Vandendriesche

Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem

in Northern Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321

Reuben Weisz, Jack Triepke, Don Vandendriesche, Mike Manthei, Jim Youtz, 

Jerry Simon, and Wayne Robbie

Part 5: Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337

Summary of the 2009 National Silviculture Workshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339

James M. Guldin

USDA Forest Service Proceedings RMRS-P-61. 2010. 1

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 USDA Forest Service, Rocky Mountain Research Station

Introduction

Theresa B. Jain1

Forests can play a role in carbon sequestration and mitigating CO2 emissions. 

However, what course of action needed to meet issues concerning carbon management and other ecosystem services for specific situations is not always clear. 

The National Silviculture Workshop, held in Boise, Idaho on June 15-18, 2009, 

focused on scientific information and management opportunities and strategies 

applicable for meeting a variety of objectives, while simultaneously addressing 

carbon sequestration and biomass utilization. The symposium and subsequent 

proceedings covered four general areas of interest: the role of climate change in 

science and management; silvicultural methods to address carbon sequestration 

and biomass utilization; alternative silvicultural strategies to address the growth 

and development of forests; and current applications of computer simulation 

models or modeling techniques designed to provide decision support.

Climate change in science and management was introduced by emeritus 

Governor of Idaho and current director of the Andrus Center for Public Policy, 

Cecil Andrus. He emphasized that society depends on forest scientists and managers in silviculture to provide information, including the trade-offs, benefits, 

and alternatives, involved in making sound public policy. The challenge facing 

managers is finding the ways for forests to meet energy, economic development, 

and environmental quality in a changing climate. Bob Deal and others define and 

provide alternatives that integrate ecosystem services and climate change. Linda 

Nagel and others discuss methods and techniques that provide state-of-the-art 

knowledge about climate change and potential impacts to facilitate development of 

silvicultural objectives and prescriptions that are flexible and enhance ecosystem 

resistance and resilience.

Forest management is linked to the manipulation of carbon through either 

carbon sequestration or the use of biomass for other resource needs. For example, 

there is a trade-off between management activities that decrease the consumption 

of biomass from a wildfire and meeting other objectives (for example, timber 

production) and the amount of biomass a site can hold for carbon sequestration. 

Theresa Jain and others provide estimates of carbon amounts in different forest 

components. Jim Cathart and others, Mike Battaglia and others, and Jianwei 

Zhang and others discuss the effectiveness of fuel treatments in avoiding carbon 

emissions from wildfires. Don Bragg and James Guldin discuss the tradeoffs of 

thinning in southern forests of the United States in relation to carbon sequestration. 

A variety of alternatives and techniques are presented, including the application 

of logging systems (Barry Wynsma and Christopher Keyes) and innovative ideas 

for biomass utilization (Dave Atkins, Jay O’Laughlin). Greg Jones and others 

compare total emissions from delivering and burning forest residue biomass for 

thermal energy to onsite disposal by pile-burning and using fossil fuels to produce 

the equivalent amount of useable energy. Two other alternatives for using biomass 

were presented: Mark Coleman and others discuss the economic feasibility of 

producing biochar and its role in soil productivity and Andrew Youngblood and 

others present the use of biomass for ethanol production.

Ultimately, any type of silvicultural activity has an effect on carbon sequestration and the production of biomass. Therefore, we encouraged discussions 

that provide alternative silvicultural strategies for insuring the growth and 
2 USDA Forest Service Proceedings RMRS-P-61. 2010.

development of forests that meet a variety of objectives. Because quantification of 

forest metrics continues to be a challenge, alternative approaches were presented. 

Henry McNab evaluates the inclusion of shrubs for estimating site index in oak 

dominated forests. John Shaw and James Long discuss the limitations and confusion concerning the application of Reineke’s Stand Density Index and provide 

guidelines to avoid the misapplication of this metric. Moser and others discuss 

how FIA data can be used for Forest Plan revision, monitoring conditions and 

trends at mid- to broad spatial scales over time, and to set the context for proposed 

projects. Two manuscripts focus on implementation of silviculture treatments: 

Christopher Keyes and others evaluate the effectiveness and challenges involved 

with accomplishing variable density prescriptions and Matt Busse discusses the 

tradeoff between maintaining long-term soil productivity and removing biomass, 

an issue that creates much discussion among managers. Although silviculture 

can influence the role of disturbances at multiple spatial scales, it is a discipline 

that focuses on site specific management activities. Douglas Basford and others 

present a method, adapted from a model developed for tree species on the Salmon 

National Forest, for estimating stand growth from stand density and average diameter in stands of pure and mixed species in southwest Idaho. Christopher Keys 

and Thomas Perry highlight the role of experimental forests, using Lubrecht as 

an example, to implement and demonstrate alternative silvicultural strategies.

Associated with climate change is an element of uncertainty. Typically 

uncertainty is addressed through the development and application of models 

which are used to evaluate a variety of scenarios that are applicable in decision 

support. Nicholas Crookston and others introduce a modification of the Forest 

Vegetation Simulator (FVS) to account for climate change. Melinda Moeur and 

Don Vandendriesche describe how FVS is being used with regional inventory 

data to empirically derive state transition models such as residence times in 

states, pathways between states, and transition probabilities between states, and 

to link these outputs to vegetation states. Don Vandendriesche provides a set of 

guidelines for using FVS and also discusses the process for estimating natural 

regeneration. Reuben Weisz and others present a methodology for developing 

a state-and-transition model with the Vegetation Dynamics Development Tool 

(VDDT), using outputs from FVS.

James Guldin closes this proceedings by summarizing the workshop, including the field tour and discussions. He concludes that there was a consensus in 

the meeting that climate change will be the defining issue for this generation of 

resource managers and that silviculture will play a key role.

Jain Introduction

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
USDA Forest Service Proceedings RMRS-P-61. 2010. 3

Part 1: Climate Change in 

Science and Management

2009 NATIONAL SILVICULTURE WORKSHOP

BOISE, IDAHO

SILVICULTURE

CLIMATE

BIOMASS

CARBON SEQUESTRATION

USDA Forest Service Proceedings RMRS-P-61. 2010. 5

Remarks

Cecil D. Andrus1

I want to thank Harv Forsgren, the Intermountain Regional Forester, for his 

invitation to me to offer a few remarks to this very impressive gathering. I was 

raised in western Oregon, which provided the genesis for my love of forests. My 

beginnings of government service started over 50 years ago by serving in the 

Korean War and I subsequently became a political accident. As many of you 

know, I was Idaho’s only 4-term governor and served as Secretary of the Interior in the Carter Administration, which was only a portion of my long career 

in public service. I am impressed with the agenda and really impressed that you 

would invite a former lumberjack to keynote a conference on science. For those 

of you from out of town—welcome. Spend your per-diem. We need the revenue.

When you have been around politics and government as long as I have—and 

in my case it has been a pretty steady preoccupation for close to 50 years—you 

sometimes think that no big problem ever gets fully resolved. If we are lucky, 

we make a little progress at the edges and maybe things get a little better, but it 

is pretty rare that we ever say: “Well, we got that problem licked.”

It reminds me of the old story about the inmates at a prison who had all been 

locked up for many years. They had been locked up for so long that they had 

heard every joke from everyone on the inside a thousand times. So, to streamline 

the joke telling, they just started numbering the jokes. One of the inmates would 

say, for example, I feel like telling old number 12 and everyone would laugh. 

Finally, one day one of the newer inmates who had only been in the joint for a 

few years decided he would try a joke and he said “how about number 7.” All the 

other inmates just stared at him and no one was laughing. Finally, the joke teller 

said, “What’s wrong—no one is laughing.” One of the real old timers spoke up 

and said: “You didn’t tell it right.”

I do sometimes feel we could just assign a number to forest health, another 

number to climate change, a different number to multiple use, and so forth, and 

I could just recite the numbers and sit down. The policy makers make endless 

speeches on these kinds of issues, but it frequently seems we make little real 

progress in solving problems. I am hoping that with a new administration now 

in place we will start solving some problems and I do have some optimism. I am 

encouraged by the stated determination of the new Obama Administration to 

sweep aside what I can only call an anti-science bias that existed in the previous 

administration and begin once again to rely on the kind of science and analysis 

that all of you produce. We simply must use the best science, regardless of how 

it may test the popular will or the politically easy position, if we are to make 

progress on terribly important policies related to climate change and utilization 

of biomass, among many others.

So, part of my message today is to encourage you to do all you can to recapture 

a central role for science—in your case silviculture—in the making of public 

policy. You are the experts and the rest of us depend on you to give us the data 

and the facts about trade-offs and options in order to make sound, smart public 

policy. Unfortunately, for too many years, the voices of the experts were drowned 

out by the political operators who think every problem is a partisan problem 

that can be solved by making the loudest argument. I truly hope the new 

administration values science. It is only common sense to do so.

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Directed the Andrus Center for Public 

Policy of Boise State University, Boise, 

Idaho since 1995.
6 USDA Forest Service Proceedings RMRS-P-61. 2010.

As some of you know, when I left the Idaho Governor’s office in 1995, we 

established the Andrus Center for Public Policy at Boise State University. We 

have convened a number of conferences over the last 10 years or so that often 

deal with western natural resource issues. Back in 2004, we did a conference 

to commemorate the 100th anniversary of the Forest Service. We talked a great 

deal about the management challenges the Forest Service has always faced and 

we reflected on the job going forward. As I was thinking about this gathering, I 

went back and looked at the report from that conference and I was struck by one 

section that really jumped out. It was a remark about climate change, and I quote 

the prominent climatologist, Dr. Tim Brown of the Desert Research Institute when 

he noted “there is scientific consensus that we are in the midst of climate change. 

For the West in the 21st Century, this means a warmer winter, less snow pack but 

more precipitation, and warmer summers. Drought will continue, comparable to 

the period of the 1930s or 1950s.” In Dr. Brown’s understated summary, he said, 

“This will be the challenge for management.”

I think that is about right and that makes your work all the more important. 

Harv Forsgren’s letter inviting me to offer some remarks today asked me to ad-

dress how the Forest Service—and each of you—meets the challenge of managing 

the national forests for energy, economic development and environmental quality 

“in a changing climate.” That will be a tall order and as Dr. Brown suggested in 

2004, “a challenge for Forest Service management.” I don’t need to tell you that 

all three of these purposes (energy, economic development, and environmental 

quality) are very important. They can also be contradictory and competing. Some 

groups will push for one outcome at the expense of the other two. Finding the 

right balance is the real challenge because I doubt whether you can maximize all 

three objectives at the same time.

The facts are that the American public wants—and the politicians want to 

give—everything all the time. We do not like making tradeoffs and we do not like 

having to choose. For years—maybe forever—the Forest Service has been caught 

in this struggle. As we continue the debate over just what exactly the purposes 

of the national forests are, how do we find something approaching agreement 

around that question? One Idahoan would tell you the national forests exist to 

produce wood fiber. Another would tell you they exist to provide hunting and 

fishing opportunities. Another would tell you the forests help drive the economy 

of the state and particularly rural communities. This Idahoan would tell you that 

there is a measure of large truth in each of those answers. So what you do, in my 

opinion, and what policy makers must do, is find the delicate balance that creates 

an equilibrium that gives the American public the opportunity to have it all. That 

is, increased energy from biomass, a stronger economy, and the hunting, fishing 

and outdoor recreation that we so enjoy in Idaho and the remainder of the West.

Let me make a couple of comments about the debate over climate change, and 

in addition, offer some suggestions for you and the leadership of the Forest Ser-

vice to help the policy makers sort through this debate. I suspect we have some 

consensus around this issue that climate is changing. If some of the change is the 

result of a “natural cycle” then the predictable human reaction will want to have 

us focus on how we can keep forests the way they are because we value them 

this way, even if we must go up against “Mother Nature.” On the other hand, if it 

becomes more certain—as I believe it will—that human activity is at the center 

of climate change, it may become easier to attempt to manage forests to “keep 

them the way we want them.” The real point is that climate change is likely to 

make it more difficult for you to manage for the three purposes—energy, eco-

nomic development, and the environment. If climate change really has become 

your new overarching management issue then, I believe, the Forest Service should 

Andrus Remarks
USDA Forest Service Proceedings RMRS-P-61. 2010.

7

be extremely transparent about how managing for climate change will affect the 

three big objectives.

A couple of thoughts on the biomass to energy issue. According to the Depart-

ment of Energy, biomass is right now providing about 3 percent of all energy 

consumed in the United States and nearly 50 percent of all the energy from 

renewable sources. Biomass supplies more BTU’s than hydroelectric energy. 

The latest number in Idaho, I’m told, is that wood bioenergy accounts for about 

4 to 5 percent of all the energy consumed. Electric generation from biomass (not 

counting municipal solidwaste) represents about 11 percent of all generation from 

renewable sources in the United States. Biomass supplies almost six times the 

energy of geothermal, solar, and wind energy sources combined and worldwide 

biomass meets about 14 percent of the world’s energy needs. But, no one as far 

as I know has a good handle on how much potential really exists in the West or 

the rest of the nation. It is not clear to me—maybe you’ll get it figured out at this 

conference—how, once we figure out the potential, we will maximize the op-

portunity to generate more energy while still managing the national forests for 

wood products and recreation.

We need to think about the infrastructure needed to get the potential energy 

delivered. In addition, we probably should not be rushing to construct a lot of 

infrastructure until we know the amount of biomass realistically available from 

national forest lands and better understand what is involved with making it 

available. Because Forest Service silviculturists play a critical role in provid-

ing this kind of information, realistic plans can be pursued for utilizing forest 

biomass to produce energy. There appear to be two primary challenges. One is 

cost, and transportation is the largest component of cost. The second challenge 

is supply. Biofuels facilities must have a consistent supply of material, day in and 

day out, for the life of a project and this could mean 20 to 25 years. That steady 

supply issue presents a real challenge for you and the national forests. And the 

data about the amount of material available should drive decisions for how large 

an energy facility should be built, not the other way around. On this point, the 

developers of these big biomass plans need to hear from the technical experts 

about how much biomass is realistically available and how much it costs to get it 

out of the woods. I would encourage you to continue to play the role of honest, 

science-based reality checkers and resist the pressure that sometimes occurs to 

come up with research that justifies a political position. Don’t get me wrong. I 

think we need to improve biomass utilization—and quickly. I do not think we 

will be able to move as quickly as we might like, because we have to do all this 

sorting out of priorities first and we have barely begun that effort. For example, 

we may need to reconsider a whole range of issues related to how we manage the 

national forests. We know that a lack of harvest can have serious adverse conse-

quences. I’ve had a preview of some research that will soon be public that says, 

among other things, that tree mortality in Idaho forests due to overcrowding and 

drought is at the highest level since we started keeping records nearly 60 years 

ago. Mortality in the forests is now removing more timber on an annual basis than 

harvest and the accumulation of dead wood has now reached an all-time high. 

The overwhelming majority of the dead wood is in the national forests where it 

contributes the fuel to feed big fires that in turn have major cost and environ-

mental impacts. Here is one other challenge for you in the Forest Service: I think 

it is very important that you be as clear as possible about the state of the science 

on carbon sequestration, specifically growing trees to hold carbon. As far as I 

can tell, we have been talking about this for 15 or more years and the state of the 

science—at least in terms of public understanding—has not advanced much. I 

would like to see more attention—quickly—on just what various sequestration 

Remarks Andrus
8 USDA Forest Service Proceedings RMRS-P-61. 2010.

strategies really mean to a more effective policy aimed at controlling carbon. We 

have limped along in the United States for 25 years or more without a coherent 

national energy strategy. We continue to import way too much energy and clearly 

we can’t drill our way to energy independence. We need to attack the issue in a 

comprehensive way and make some tough decisions. We haven’t, until recently, 

been aggressive enough about fuel efficiency standards and we haven’t placed 

nearly enough attention on better mass transit. We cannot summon the political 

will to deal with nuclear waste, so that technology remains largely on the shelf. 

Everyone agrees now, I think, that we must aggressively pursue alternatives, but 

doing so will not be fast or easy. Nevertheless, we must get on with it. The best 

policy, whether it’s related to biomass, climate change, or carbon, will come about 

when we really utilize the science that you offer to give us the best, most honest 

information about which way we should move. I’m optimistic, as I’ve said, that 

we have some new leadership and new commitment to solving some of these old 

and perplexing problems. I hope if Harv were to invite me back in a couple of 

years, I would have the opportunity to thank you all for moving the ball on these 

issues. Good luck. We’re depending on you.

Andrus Remarks

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
USDA Forest Service Proceedings RMRS-P-61. 2010. 9

Ecosystem Services and Climate Change: 

Understanding the Differences and 

Identifying Opportunities for Forest Carbon

Robert L. Deal1, Crystal Raymond2, David L. Peterson3, and 

Cindy Glick4

Abstract—There are a number of misunderstandings about “ecosystem services” and 

“climate change” and these terms are often used incorrectly to describe different concepts. These concepts address different issues and objectives but have some important 

integrating themes relating to carbon and carbon sequestration. In this paper, we 

provide definitions and distinctions between ecosystem services and climate change. 

We describe some of the emerging markets for ecosystem services including carbon, 

water, wetland mitigation and species conservation banking and some of the national 

initiatives to address climate change with carbon markets. We also discuss some of 

the potential effects of climate change on forest ecosystems in the USA. Finally, we 

develop the concept of using an ecosystem services marketplace and the potential for 

mitigating climate change specifically focusing on the emerging markets for carbon. 

This integration of ecosystem services and climate change may provide some new 

opportunities for forest landowners and managers to enhance forest stewardship in 

addition to reducing greenhouse gas emissions through forest carbon sequestration.

Keywords: ecosystem services, climate change, carbon and forestry, carbon markets.

Introduction

Ecosystem Services and Climate Change

Ecosystem services are, in the broadest sense, the contributions or benefits 

that come from the natural environment. The Millenium Ecosystem Assessment 

(MEA 2005) provided a simple definition of ecosystem services as “the benefits 

people obtain from ecosystems.” There is some disagreement among ecologists 

and economists on whether these benefits should include only the direct benefits 

for people or also include some of the supporting functions of ecosystems. The 

ecologist viewpoint (Daily 1997) focuses on the function and process of the ecosystem, and these services may include water purification, climate regulation, 

and biodiversity (figs. 1 and 2). The economist viewpoint generally focuses on 

ecosystem services that are components of nature, directly enjoyed, consumed, or

used to yield human well-being (Boyd 2004; Boyd and Banzhaf 2006; Kroeger and 

Casey 2007). For our paper, we use the broad and previously defined typology for 

ecosystem services (MEA 2005) that highlights the wide-ranging importance and 

value of these services. The MEA divided up these services into four categories 

including provisioning, regulating, supporting, and cultural services (table 1). 

Provisioning services are a familiar part of the economy that provides goods such 

as food, freshwater, timber and fiber for direct human use. Regulating services 

maintain a world in which it is possible for people to live, and provide benefits 

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Pacific Northwest Research Station, 

Portland OR.

2 University of Washington, School of 

Forestry, Seattle, WA.

3 Pacific Northwest Research Station, 

Seattle, WA.

4 Deschutes National Forest, Bend, OR.
10 USDA Forest Service Proceedings RMRS-P-61. 2010.

Deal, Raymond, Peterson, and Glick Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon

a

a

b

b

c d

Figure 1—Examples of water as a global ecosystem service: a) Steelhead Creek, southeast Alaska, providing clear, cold 

water for high quality fish habitat and aquatic resources, b) headwater stream in Shikoku, Japan providing clear drinking 

water, c) Clearwater River in Idaho providing water for irrigation and flood control but with reduced habitat for migrating 

salmon, d) cement-walled river in Kochi, Japan, with highly degraded water quality and aquatic services.

Figure 2—Examples of managed forest as an ecosystem service for biodiversity: a) pure 40-year-old conifer plantation 

in southeast Alaska with no plant understory and reduced wildlife habitat, b) mixed red alder-conifer 40-year-old forest 

in southeast Alaska with abundant plant understory and improved habitat for deer and small mammals.
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Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon Deal, Raymond, Peterson, and Glick

Table 1—Categories of ecosystem services provided by nature. Modified 

from the Millenium Ecosystem Assessment (MEA 2005).

Ecosystem Services

Supporting Services

Nutrient cycling

Soil formation

Primary production

Provisioning Services

Food (crops, livestock, wild foods, etc...)

Fiber (timber, cotton/hemp/silk, wood fuel)

Genetic resources

Biochemicals, natural medicines, pharmaceuticals

Fresh water

Regulating Services

Air quality regulation

Climate regulation (global, regional, and local)

Water regulation

Erosion regulation

Water purification and waste treatment

Disease regulation

Pest regulation

Pollination

Natural hazard regulation

Cultural Services

Aesthetic values

Spiritual and religious values

Recreation and ecotourism

such as flood and disease control, water purification, climate stabilization and 

crop pollination. Supporting services are the underlying processes that maintain 

the conditions for life on Earth and include nutrient cycling, soil formation and 

primary production from our ecosystems. Cultural services make the world a 

place where people want to live and include recreational, spiritual, aesthetic and 

cultural values.

Climate in a narrow sense is usually defined as the average weather over a period of time ranging from months to thousands or millions of years. The relevant 

quantities are most often surface variables such as temperature, precipitation and 

wind. Climate, in a wider sense, is the state of the climate system. Climate change 

refers to a change in the state of the climate that can be identified (e.g., by using 

statistical tests) by changes in the mean and/or the variability of its properties, 

and that persists for an extended period, typically decades or longer. Climate 

change may be due to natural internal processes or external forcings, or to persistent anthropogenic changes in the composition of the atmosphere or changes 

in land use. Note that the United Nations Framework Convention on Climate 

Change (UNFCCC 2007) defines climate change as: “a change of climate which 

is attributed directly or indirectly to human activity that alters the composition 

of the global atmosphere and which is in addition to natural climate variability 

observed over comparable time periods.” The UNFCCC thus makes a distinction 

between climate change attributable to human activities altering the atmospheric 

composition, and climate variability attributable to natural causes.
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Deal, Raymond, Peterson, and Glick Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon

Integration of Ecosystem Services and Climate Change

Ecosystem service providers are increasingly recognized as having an important role to play in ecosystem protection. The emerging regulated market for 

carbon could also provide incentives for reducing greenhouse gas emissions and 

sequestering more carbon; both important considerations for mitigating climate 

change. The concept of providing incentives through market-based programs 

for ecosystem services, and the recent emergence of markets for carbon, water, 

wetlands and biodiversity has stimulated interest from a broad suite of new stakeholders. These markets provide incentives for landowners to provide clean air 

and water, wildlife habitat, and other goods and services from their forests and 

wetlands. These new financial incentives expand opportunities for forest landowners to gain revenue from their lands while providing public goods to society. 

Ecosystem services when considered as “natural capital” leads land managers to 

regard landscapes as natural assets that requires measuring different ecosystem 

services and ensuring the people who use these services know their value and 

the cost of losing them (Collins and Larry 2008; Kline 2006).

The importance of healthy, functioning ecosystems is widely recognized.

Forests play a major role in the global carbon cycle through the ability of trees to 

withdraw or sequester carbon, and forests serve as a terrestrial carbon sink during most stages of forest development. Forests also have high conservation value 

for a number of threatened and endangered species, for mitigating pollution, for 

flood control and for other ecosystem services. Forests can play a major role in 

reducing greenhouse gas emissions through maintaining current carbon stores 

and by increasing the rate of carbon sequestration. Forest carbon is a particularly 

important ecosystem service to monitor and manage because there is interest in 

both maintaining current forest carbon stocks and also increasing carbon sequestration as a mitigation strategy for reducing atmospheric CO2.

Deforestation accounts for approximately 20 percent of total greenhouse gas 

emissions (FAO 2005) and one of the major forestry concerns is reducing the loss 

of forestland from development. Maintaining these carbon stores is an important 

component of global carbon management. Forests can sequester large amounts of 

carbon in several ways including as carbon sinks in the standing forest, in wood 

products, and in avoided emissions when wood is used as a substitute for more 

fossil fuel-consuming products such as steel, concrete and brick. Other considerations include forest management practices that increase carbon sequestration 

such as extended rotations or increased growth rates through intensive forest management. Forest management will be required to help forests adapt and maintain 

high levels of carbon sequestration as climate changes.

The integration of ecosystem services markets and the role of forest carbon 

to reduce greenhouse gas emissions may be an effective framework for mitigating some of the effects of climate change. These markets can be helpful for both 

increasing carbon sequestration as part of a regulated carbon market and as 

financial incentives for landowners to prevent forestland from being developed. 

We explore some of these concepts in our paper and describe some of the markets 

for ecosystem services and the potential effects of climate change on forestry and 

some of the management practices relating to forests and carbon sequestration. 

The specific objectives of this paper include: 1) describe the policy and regulatory 

frameworks of emerging markets for ecosystem services in the USA, 2) describe 

the relationship between climate change, forest ecosystems and carbon, and some 

of the opportunities to mitigate climate change, and 3) develop a framework for 

integrating ecosystem services markets and climate change using forest carbon.
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Markets for Ecosystem Services

Policy and regulations have an essential role for establishing markets for 

ecosystem services and market-based programs have developed in response to 

regulations for water, wetlands and endangered species. Examples of regulation 

driven markets include the Clean Water Act (33 U.S.C. 1344) that helped establish wetland mitigation banking and water quality trading (Brauman and others 

2007; Gaddie and Regens 2000), and the Endangered Species Act (USFWS 1988) 

that led to the emergence of species conservation banking (Carroll and others 

2007). However, these different ecosystem services are regulated and controlled 

by several different federal and state agencies with their own sets of policies and 

regulatory frameworks. For instance, at the national level, air and water quality is 

regulated by the Environmental Protection Agency (EPA), wetlands are regulated 

by the Army Corps of Engineers (USACE), and species conservation is controlled 

by the U.S. Fish and Wildlife Service (USFWS). Several markets for ecosystem 

services are emerging in the U.S. with potentially new revenue streams for forest 

landowners. These new markets offer potential financial incentives to landowners 

to maintain and manage forestlands rather than converting these forests to other 

uses. Overviews of U.S. carbon markets, water quality trading and wetland and 

species mitigation banking are outlined here.

Emerging Carbon Markets in the USA

The United States is not a signatory of the Kyoto Protocol (UNFCCC 2007) and 

the U.S. does not have a comprehensive national policy mandating limits in CO2

emissions. Instead, the U.S. has voluntary, or state and region-based programs to 

reduce greenhouse gas emissions. Project-based transactions can generate offset 

credits by an approved activity that compensates for emissions by a business in 

a regulated sector. Examples of offset credits include forest carbon sequestration, 

methane recapture, and alternative energy use. Since about 20 percent of humaninduced carbon dioxide emissions are due to land-use change and deforestation 

(FAO 2005), sustainable forest management can play an important role in climate 

change mitigation. Forestry offsets also provide a range of environmental benefits, 

such as wildlife habitat and water quality improvement.

Due to the absence of a comprehensive GHG regulatory emissions reduction 

standards (e.g. national cap-and-trade legislation), voluntary carbon markets have 

dominated in the USA and state and region-based programs are being developed 

to reduce greenhouse gas emissions. Regional and state programs include the 

Regional Greenhouse Gas Initiative (RGGI) in the Northeast USA (RGGI 2007), 

the Western Climate Initiative (WCI) in the Western USA (Capoor and Ambrosi 

2008) and the Climate Action Registry in California (CCAR 2005). However, 

due to different regulatory frameworks being developed in each region and state, 

there is a need for developing national standards to help develop the registration 

and trading of carbon offset projects (Sampson 2004). Ruddell and others (2007) 

further contends that in the absence of such national standards, forestry offset 

projects will continue to be limited and inconsistent.

Although the voluntary U.S. carbon market is small compared with the global 

carbon market estimated at about $130 billion ($US) in 2009, the U.S. voluntary 

carbon market increased by 200 percent in 2007 with 13 percent of the carbon 

trading including carbon sequestration or forestry credits (Forestry Source 2007). 

By comparison, no forestry credits are accepted under the European Union Emission trading scheme and less than 1 percent of total transactions of 475 million 

tons made under the Kyoto protocol’s Clean Development Mechanism involved 
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Deal, Raymond, Peterson, and Glick Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon

forestry-based credits (UNFCCC 2007). With a regulated cap-and-trade mechanism that provides higher prices than current carbon values and the allowance of 

forest carbon offsets, the carbon market could provide a huge incentive for forestry. 

However, it is important that these forestry offsets provide high-quality carbon 

sequestration credits in order to assure early investors in the carbon market that 

these carbon offsets are credible and provide true reductions in GHG emissions.

To address GHG policy, the forestry community has a significant opportunity 

to shape what kinds of forest projects are included. Lawmakers in the U.S. have 

a variety of pending legislation with significant implications for carbon and forestry including the 2008 Farm Bill, 2009 American Clean Energy and Security 

(ACES) Waxman-Markey bill, and other federal and state legislation. Two key 

components for any forestry offset project include keeping forestland in forests, 

and increasing carbon sequestration through forest management. There are also 

a number of important policy issues to incorporate in forestry offsets including 

clear definitions for carbon baselines and additionality, permanence and leakage, possible inclusion of wood products for the long-term storage of carbon, and 

projects that promote additional carbon sequestration and discourage conversion 

of forests to other land uses (Cathcart 2000; Ruddell and others 2007).

Water Quality Trading

Ecosystem services for water include water supply, water damage mitigation, 

and water-related cultural services (Brauman and others 2007; fig. 1). Unlike 

global carbon markets, market-based schemes for improving water quality are 

generally limited to local or regional programs within a specific watershed. Forest landowners and farmers can be included as sellers of water quality credits in 

many programs. Other participants include water quality permitting authorities, 

third-party brokers, conservation organizations, watershed councils, and private industry groups. Local examples of water quality trading include the EPA 

watershed-based permit for the Tualatin River in Oregon that allows trading to 

achieve the permit requirement for temperature (Cochran 2007). Here, instead of 

installing refrigeration systems at two Tualatin River treatment plants (at a cost 

of $60 million), the wastewater utility paid upstream farmers to plant shade trees 

in the riparian areas (at a cost of $6 million).

Wetland and Species Mitigation Banking

Another market for ecosystem services is wetland mitigation and species conservation banking. These markets are based on regulations that require developers 

to obtain a permit to offset any loss of wetland or habitat before they are allowed 

to harm a wetland or an endangered species. Wetland mitigation banking has 

developed into a well-established, market-based system where buyers and sellers of credits conduct transactions through wetland banks. Wetland ecosystems 

provide a broad range of ecological services for people, and studies have shown 

the importance of services provided by wetlands including water quality and 

quantity, recreation, wildlife habitat, flood control and pollution interception 

(Azevedo and others 2000; Hoehn and others 2001). On-site wetland mitigation 

has been largely unsuccessful for restoring original wetland functions but larger 

offsite-wetland banks are now recognized for their broader functionality and 

production of multiple ecosystem services (Gaddie and Regens 2000; Willamette 

Partnership 2008).

Conservation banking, the creation and trading of credits that represent wildlife 

conservation values on private lands is more than a decade old, and the State of 

California has developed most of the conservation banking agreements in the 

U.S. (Fox and Nino-Murcia 2005). A conservation bank is a parcel of protected 
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natural land that is authorized to sell a set number of credits, usually in the form 

of land area of habitat, to the customer that is required by law to mitigate their 

impact to the same species and habitat on nearby land. Private landowners reported 

that financial motives were behind most of their interest in conservation banking 

but bureaucracy was the biggest challenge with the average time for establishing banks more than 2 years and varied from 8 months to over 6 years (Fox and 

Nino-Murcia 2005). However, as Fox and Nino-Murcia (2005) contend, the fact 

that banks are profitable in most cases is an indication that conservation banking 

offers viable incentives to protect species on private land.

Climate Change and Forestry

Climate Change and Forest Ecosystems

Some effects of climate change on forest ecosystems and natural resources 

in North America are already detectable (IPCC 2007), and no historical analog 

exists for the combination of future climate conditions, disturbance regimes, 

and land-use patterns expected in the future. Climate provides an overarching 

control on the distribution of tree species (Woodward 1987). Climate-induced 

stress occurs in areas where species may be marginally suited, such as the edge of 

their geographic distribution. As a result, a warmer climate will lead to potential 

changes in species distribution and abundance at various spatial scales. Changes 

in composition may be slow even in a rapidly warming climate, because mature 

individuals are typically resistant to climatic variation. Therefore, disturbance 

will be a major agent of change and will promote change through forest regeneration at shorter time scales than the direct influence of climate (McKenzie and 

others 2004; fig. 3).

Figure 3—Conceptual model of the effects of climatic change and disturbance on 

forest ecosystems. Times are approximate. Adapted from McKenzie and others (2004).
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Deal, Raymond, Peterson, and Glick Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon

Limiting factors act at the interface between organisms and their environment, 

and plant performance is affected when one or more resources (e.g., energy, water, 

nutrients) limits physiological function. Forests of western North America can be 

considered as energy limited, water limited, or some combination thereof (Littell 

and others 2008). Energy limitations are primarily light (e.g., productive forests 

where high leaf area reduces light exposure in the canopy) and temperature (e.g., 

subalpine and boreal forests). Some energy-limited forest systems appear to be 

responding positively to warming temperatures over the past 100 years (Peterson 

1998).

Productivity in water-limited forests will decrease in a warmer climate, because 

negative water balances will reduce photosynthesis (Hicke and others 2002), 

although this may be partially offset if CO2 fertilization increases water-use 

efficiency (Neilson and others 2005). For example, most montane Douglas-fir 

(Pseudotsuga menziesii) forests across the northwestern United States are water 

limited (Littell and others 2008), and the area and magnitude of this limitation 

will increase as the climate continues to warm. Limiting factors typically vary 

within species (Peterson and Peterson 2001), between seasons, and with respect 

to the balance between water and energy needs (Stephenson 1998).

The conceptual model of a “disease spiral” (sensu Manion 1991) in which tree 

death is caused by the accretion of multiple stresses can be scaled up to the concept 

of a “stress complex” for populations of tree species and for multiple populations 

at the ecosystem level (McKenzie and others 2009). Temperature increase is a 

predisposing factor causing stress in forest ecosystems of western North America 

by exacerbating negative water balance (Littell and others 2008; Stephenson 1998) 

and through increased frequency, severity, and extent of disturbances. Climate 

change and the combination of warmer temperatures, drought, and more severe 

disturbance regimes can create stressful conditions for forest ecosystems over 

large geographic areas.

The principal disturbance regimes of western North America, wildfire, and 

insect outbreaks, respond to short-term weather and annual-to-decadal cycles 

in climate. For example, synchronous fire years are associated with the El Niño 

Southern Oscillation cycle in the American Southwest and southern Rocky 

Mountains (Swetnam and Betancourt 1998; Veblen and others 2000) and to some 

extent in the Pacific Northwest (Hessl and others 2004). Short-term weather 

anomalies associated with atmospheric blocking ridges of high pressure promote 

extreme wildfire years in some areas of the West (Gedalof and others 2005). 

Insect defoliators are favored in years during which vegetation productivity is 

high (Weber and Schweingruber 1995), but overall forest vigor is low (Swetnam 

and Betancourt 1998).

Higher temperatures are expected to alter the frequency, severity, and extent 

of natural disturbances, and wildfire (McKenzie and others 2004; Westerling and 

others 2006) and mountain pine beetle outbreaks (Logan and Powell 2001) may 

become a more dominant feature of western landscapes. Where fire and insect 

disturbances interact, changes in forest ecosystem structure and function may be 

accelerated (Veblen and others 1994), resulting in altered combinations of species, 

productivity, and disturbance regimes (table 2).

Forest Management for Carbon Sequestration

Carbon sequestration in forests is one ecosystem service that will be sensitive 

to climate change, and forest management will be necessary to facilitate forest 

adaptation as the climate changes. Sustainable forest management can not only 

maintain carbon sequestration at current levels, but can also increase carbon 

sequestration to mitigate atmospheric CO2 concentrations. Sustainable forest 
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Table 2—Examples of stress complexes in western North American forests that could be 

affected by a warmer climate.

Pinyon-juniper woodland (American Southwest)

Pinyon pine (Pinus edulis Engelm.) and various juniper species (Juniperus spp.) are among 

the most drought-tolerant trees in western North America and clearly occur in water-limited 

systems. Multi-year droughts have caused historical diebacks of pinyon pines over large 

geographic areas in the American Southwest, but the current dieback is unprecedented in 

terms of the scale of response to a period of low precipitation and high temperature (Breshears and others 2005). A warmer climate has been a predisposing factor, and wood-boring 

insects have contributed to weakening and ultimately killing trees.

Mixed conifer forest (Sierra Nevada, southern California)

Forests in central and southern California have a Mediterranean climate with long dry summers, and mild winters during which most of the annual precipitation occurs. Fire exclusion 

has increased fuel loadings (McKelvey and others 1996) and competitive stress as stand 

densities have increased (van Mantgem and others 2004). Elevated levels of ambient ozone 

have reduced net photosynthesis, growth, and interannual accumulation of biomass in ponderosa pine (Pinus ponderosa C. Lawson var. ponderosa) and Jeffrey pine (P. jeffreyi Balf.) in 

the Sierra Nevada and southern California mountains (Byternowicz and Grulke 1992; Miller 

1992; Peterson and Arbaugh 1988; Peterson and others 1991). Bark beetle outbreaks in 

these regions have caused extensive mortality in recent years following protracted droughts.

Lodgepole pine forest (western North America)

Lodgepole pine (Pinus contorta Douglas ex Louden var. latifolia Engelm. ex S. Watson) 

is the principal host of the mountain pine beetle (Dendroctonus ponderosae), and dense 

stands that are stressed from low soil moisture are particularly vulnerable to mortality during 

beetle outbreaks (Hicke and others 2006). Recent beetle outbreaks have caused extensive 

mortality across millions of hectares in western North America (Logan and Powell 2001), 

with large mature cohorts (age 70-80 yr) contributing to widespread vulnerability. Tree mortality caused by beetles produces rapid necromass (fuel) accumulation, and the potential 

for species conversion following stand-replacing fires, including a favorable environment 

in some locations for establishment of drought-tolerant species such as interior Douglas-fir 

and ponderosa pine.

Boreal forest (central and southern Alaska)

Alaska has experienced historically unprecedented areas burned by wildfire in the 

last decade (NIFC 2006). Concurrently, large outbreaks of the spruce bark beetle 

(Dendroctonus rufipennis) occurred in white spruce (Picea glauca [Moench] Voss) forests 

on and near the Kenai Peninsula in southern Alaska (Berg and others 2006). Fire and beetle 

outbreaks are likely associated with warmer temperatures in recent decades (Duffy and 

others 2005, Werner and others 2006). In interior Alaska, white spruce and black spruce 

(Picea mariana [Mill.] Britton, Sterns & Poggenb.) are more flammable than co-occurring 

deciduous species such as paper birch (Betula papyrifera Marsh.). Conifers are the target 

of bark beetles, so in southern Alaska they are disadvantaged compared to deciduous 

species. As a result, this system may transition to deciduous trees via more frequent and 

extensive disturbance associated with a warmer climate.

management practices aimed at mitigating atmospheric CO2 are more likely to be 

successful if they are specific to different forest types and disturbance regimes 

within western North America. Furthermore, these mitigation strategies will be 

more effective if they are implemented with consideration of the expected effects 

of climate change on forest ecosystems given that some degree of climate change 

is inevitable despite current mitigation actions.

Afforestation and reforestation of previously forested lands are two forest management practices with the greatest potential to increase carbon sequestration. 

These management practices, if properly implemented, can remove additional 

carbon from the atmosphere and sequester it for decades to centuries. These 

projects will be more successful if they are implemented in combination with 
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Deal, Raymond, Peterson, and Glick Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon

management practices that also facilitate forest adaptation to climate change. 

Adaptation strategies include selecting for planting species or varieties that are 

adapted to a warmer climate, planting a greater diversity of species, and planting 

at lower initial densities to reduce moisture stress in water-limited forests. These 

adaptation strategies will help maintain carbon storage by increasing forest productivity and resilience to warmer temperatures and more frequent disturbances.

Climate-driven increases in wildfire frequency, extent, and severity are 

expected to affect the potential of forest ecosystems to sequester carbon. In 

water-limited forests, climate change may also reduce regeneration success after 

severe wildfires due to greater climate-induced stress in seedlings. A vegetation 

type conversion (e.g. from forest to shrubland) or a reduction in forest density 

can reduce carbon sequestration more than the wildfire itself (Kashian and others 2006). Therefore, forest management practices that ensure adequate post-fire 

regeneration with appropriate species, genotypes, and densities are important 

for enhancing forest resilience to climate change and maintaining the carbon 

sequestration functionality of the forest ecosystem.

Thinning forests to reduce disturbance severity and extent (fuel treatments) is 

another forest management practice that can enhance resilience to disturbances, 

as well as maintain and enhance carbon sequestration. Individual wildfires are a 

large, one-time source of carbon emissions that can be significant in the short-term 

(Turner and others 2007; fig. 4). However, the carbon sequestration benefits of 

fuel treatments may be less than expected because of four common misconceptions regarding carbon and wildfires. First, wildfires, even those burning with 

high severity, typically consume less than 20 percent of total live and dead forest 

biomass (Campbell and others 2007). Although more than 80 percent of trees 

can be killed in high severity fires, the carbon is generally released slowly over 

decades as the biomass decomposes. Second, the difference between biomass 

consumption in high and low severity fires is small, about 10 percent (Campbell 

and others 2007). Third, as fire-killed material decomposes and releases carbon, 

carbon is returned to the system as post-fire regeneration and the productivity 

of these young regenerating forests is higher than that of the older forests they 

Figure 4—Examples of fire as an ecosystem service: a)severe fire intensity after B&B fire in central Oregon with loss 

of organic layers, exposed mineral soil and reduced forest productivity, b) moderate fire intensity after B& B fire with 

pre-fire thinning treatment to reduce fire severity resulting in relatively healthy forest ecosystem after fire.

a b
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Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon Deal, Raymond, Peterson, and Glick

replace. Fourth, at long temporal scales (the scale being relative to the ecosystemspecific fire return interval) the net release of carbon from any fire-disturbed 

ecosystem may be zero as long as the forest regenerates and reaches the pre-fire 

age and density (Kashian and others 2006).

The carbon benefits of fuel treatments in forest ecosystems depend on the fire 

regime characteristic of the ecosystem (fig. 4). Fuel treatments will not incur 

carbon storage benefits in high severity, low frequency fire regimes (fire return 

intervals on the order of centuries) (Mitchell and others 2009). Treatments may 

need to be repeated to maintain low fire hazard and the total carbon removed in 

successive treatments over centuries can exceed the carbon emitted in a single, 

high severity fire (Hurteau and North 2009). However, fuel treatments are unlikely to reduce fire severity in these forest types because fire severity is more a 

function of weather than fuel availability (Brown and others 2004).

Conversely, fuel treatments can enhance carbon storage in forests with low 

severity, high frequency fire regimes (fire return intervals on the order of years 

to decades) (Hurteau and North 2009; Mitchell and others 2009), especially forests that have experienced biomass accumulation due to fire suppression (Brown 

and others 2004). In these ecosystems, fuel treatments can effectively reduce 

subsequent wildfire severity and carbon emissions. Fuel treatments reduce forest 

productivity in the short term (1-3 years), but ecosystem productivity often returns 

to or exceeds pre-treatment levels within only a few years (Boerner and others 

2008). Furthermore, reduced productivity in the tree component (in proportion to 

tree removal) is compensated by increased productivity in roots and understory 

vegetation, which respond positively in more open stands (Campbell and others 

2009). However, the carbon benefits of fuel treatments are marginal even in low 

severity fire regimes. Fuel treatments remove substantial carbon from the site 

and a subsequent wildfire, even with effective fire severity reduction, will release additional carbon (Mitchell and others 2009). The total carbon removed in 

only a few treatments may exceed the carbon gains from fire severity reduction 

because of the small difference in biomass consumption between high and low 

severity wildfires (Hurteau and North 2009; Mitchell and others 2009). However, 

fuel treatments are a useful management tool for maintaining other ecosystem 

services, including air quality, water quantity, and wildlife habitat, and should 

be considered based on their benefits to multiple ecosystem services, not just 

carbon sequestration (fig. 2).

Certain forest management practices may increase the carbon sequestration 

potential of fuel treatments. Fuel treatments will have greater carbon storage 

benefits if a small area can be treated to reduce fire severity over a larger area 

through the strategic placement of treatments on the landscape (Finney 2001). 

Carbon sequestration can also be enhanced with specific uses of the biomass that 

is removed in treatments. The carbon may be stored for up to 100 years or longer 

if the material is used in long-lived forest products. Carbon benefits also increase 

if the biomass is used as an energy source that is substituted for energy that would 

otherwise be derived from fossil fuels (Mitchell and others 2009). Increasing the 

production of biofuels using biomass removed from thinning forests can increase 

the carbon benefits of fuel treatments (fig. 5).

Discussion

The Role of Ecosystem Services for Ecosystem Protection

Ecosystem services provide provisioning, supporting, and regulating services 

that are critical for the functioning of life on Earth and provide natural assets that 

are intrinsic components of our economy. However, recent evaluation of the state 
20 USDA Forest Service Proceedings RMRS-P-61. 2010.

Deal, Raymond, Peterson, and Glick Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon

a b

c

Figure 5—Examples of biomass utilization and use of forest carbon: a) small-to-medium size diameter logs following forest 

thinning, b) grinding of branches and small diameter trees for wood chips and hogg fuel for biomass energy, c) bundling 

of biomass for hogg fuel for co-generation energy source.

of the world’s ecosystems shows that about 60 percent of all ecosystems are rapidly 

degrading or are being used unsustainably (MEA 2005). Emerging markets for 

ecosystem services are increasingly recognized as having an important role to play 

in ecosystem protection. Market mechanisms can be used to provide incentives to 

private forest landowners to enhance provision of ecosystem services, often with 

the associated objective of providing a counterbalance to financial incentives to 

convert forests to other land uses (Kline 2006). These new financial incentives 

expand opportunities for forest landowners to gain revenue from their lands while 

providing public goods to society.

Collaborative efforts are being developed at local, regional, national and international levels to better conserve our natural resources (Boyd 2004; Daily 1997; 

Heal and others 2005; Oliver and Deal 2007). There are several organizations in 

the United States that are interested in developing an ecosystem marketplace that 

could buy and trade different ecosystem services (Bay Bank 2008; Katoomba 

2007; Willamette Partnership 2008). This marketplace could help a single large 
USDA Forest Service Proceedings RMRS-P-61. 2010. 21

Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon Deal, Raymond, Peterson, and Glick

landowner or a group of landowners sell wetland, endangered species, water 

quality and carbon credits from the same piece of land. For example, in Oregon, 

the Willamette Partnership recently received a NRCS Conservation Innovation 

Grant to develop a multi-credit market system to measure and account for multiple types of ecosystem service credits for use within the Willamette Ecosystem 

marketplace (Willamette Partnership 2008). This multi-credit marketplace would 

be able to take advantage of efforts to combine or bundle different ecosystem 

services. Other examples include the 2008 USDA Farm Bill, section 2709 (USDA 

2008) that shifted an emphasis from commodity-oriented programs to more 

market-based payment programs, achieving movement toward this ecosystem 

services goal. Pending carbon cap and trade legislation such as the 2009 ACES 

Waxman-Markey bill could further reduce greenhouse gas emissions using market 

mechanisms and forestry and agricultural offset programs. Market-based incentives for ecosystem services has provided a new framework for a diverse coalition 

of conservationists, forest landowners and other stakeholders to work together to 

develop market based strategies for conserving ecosystem services. This has led 

to a shift from thinking about conservation as a burden or endangered species as 

a liability, to the concept of restoration and stewardship of ecosystem services 

as a profit making enterprise (Collins and Larry 2008; Heal and others 2005).

The Role of Forests, Forestry, and Wood Products for 

Sequestering Carbon

Forests and forestry have an important role for sequestering carbon and reducing greenhouse gas emissions. Forests can sequester large amounts of carbon in 

several ways including as carbon sinks in the standing forest, in wood products, 

and in avoided emissions when wood is used as a substitute for more fossil fuelconsuming products such as steel, concrete and brick. One of the obvious and most 

important roles for reducing CO2 emissions is avoiding deforestation and keeping 

forestlands in forests. Globally, about 20 percent of human-induced carbon dioxide emissions are due to land-use change and deforestation (FAO 2005). This is 

important at the global scale and here in the United States where land conversion 

and development has led to more than 2,500 acres of forest loss each day, with 

more area being impacted by forest fragmentation (Alig 2007). Afforestation and 

reforestation of previously forested lands is an important and widely accepted 

forest management practice to increase carbon sequestration. Storage of carbon in 

wood products can also have a significant impact in storing carbon and avoiding 

use of more fossil fuel-intensive products. Preliminary calculations suggest a 20 

percent to 50 percent decrease in fossil fuel use if forests and wood products are 

used to sequester carbon in place of more fossil fuel-consuming products such as 

steel, concrete and brick (Lippke and others 2004). An example of local biomass 

utilization is highlighted on the Deschutes National Forest in central Oregon. In 

fiscal year 2008, biomass utilization included 150,000 green tons of small diameter 

wood converted into mulch, pulp chips, animal bedding, lumber and poles with an 

additional 69,000 green tons converted into hogg fuel or firewood that was used 

for energy or heat as an offset to fossil fuel consumption (fig. 5). Wood can play 

an important role as a substitute for fossil fuels; however, it is important to note 

that wood used for energy is much less efficient than wood used for construction. Currently, approximately 50 percent of the world’s wood harvest is used for 

fuel, primarily in areas of low economy such as Africa where approximately 90 

percent of the wood harvest is used for fuel for cooking (Oliver and Deal 2007). 

Another key consideration is how carbon markets, forestry management, wood 

products and carbon credit programs are administered. Actual carbon market 

trading will involve a number of complex variables relating to establishment of 
22 USDA Forest Service Proceedings RMRS-P-61. 2010.

Deal, Raymond, Peterson, and Glick Ecosystem Services and Climate Change: Understanding the Differences and Identifying Opportunities for Forest Carbon

existing carbon baselines and additionality, leakage and permanence, inclusion 

of wood products for the long-term storage of carbon, and programs that promote 

additional carbon storage through forest management practices that discourages 

forest land conversion (Cathcart 2000; Ruddell and others 2007).

Conclusion

Ecosystem services when considered as “natural capital” leads land managers 

to regard landscapes as natural assets. Furthermore, the integration of ecosystem 

services markets and the use of forests to sequester carbon may be an effective 

framework for mitigating some of the effects of climate change. Several markets 

for ecosystem services are emerging in the USA with potentially new revenue 

streams for forest landowners. These new markets offer potential financial incentives to landowners to maintain and manage forestlands rather than converting 

these forests to other uses. There is increasing interest in the use of market-based 

approaches to add value for these services and assist conservation of natural resources. This integration of ecosystem services and climate change may provide 

some new opportunities for forest landowners and managers to enhance forest 

stewardship in addition to reducing greenhouse gas emissions through forest 

carbon sequestration. There is a also a need for a more integrated approach that 

combines different ecosystem services and provides financial incentives for forest 

landowners to achieve broad conservation goals.

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 27

Abstract—Silviculturists are currently facing the challenge of developing management strategies that meet broad ecological and social considerations in spite of a high 

degree of uncertainty in future climatic conditions. Forest managers need state-of-theart knowledge about climate change and potential impacts to facilitate development 

of silvicultural objectives and prescriptions that are flexible and enhance ecosystem 

resistance and resilience. Existing approaches also must be infused with adaptive 

techniques and strategies. We are working on a project that will help address these 

needs for forest management. Specifically, our objectives are to: (1) provide training 

on the ecological impacts of climate change to forestry professionals; (2) incorporate 

current understanding of species and forest responses to climate change into silvicultural strategies that meet management objectives while encouraging adaptation to 

changing climate conditions; (3) provide decision support tools to guide forest management planning under climate change; and (4) integrate climate change adaptation 

strategies into Forest Service silvicultural education and implementation. We describe 

the incorporation of training and tools from the larger project into the USDA Forest 

Service silviculture certification program, and report on lessons we have learned about 

the process attendant to the design and implementation of climate change training 

for land managers.

Keywords: climate change, forest management, NASP, silviculture certification, 

uncertainty

Introduction

Evidence for global climate change is unequivocal, as is the implication that 

human activity has caused substantial rises in greenhouse gases that are contributing to the earth’s changing climate (IPCC 2007). Temperature, precipitation, 

and other climate variables are expected to continue changing worldwide, with 

substantial implications for the composition and function of terrestrial ecosystems 

(Kirilenko and Sedjo 2007). In the United States, forestland covers 33 percent 

of the land surface (Smith and others 2004) and provides extensive ecosystem 

services. Managers of these lands are seeking information and direction to create 

flexible and adaptive management approaches that balance the need to manage 

forests today for multiple objectives with the reality that future climatic conditions are uncertain.

An abundance of information is available about global climate change and 

how to promote forests that are able to adapt, but the mechanisms to combine 

and apply this information to specific situations are less common. This leads to 

a great need for educating land managers about climate change, its uncertainties, and the importance of incorporating both into silvicultural prescriptions. 

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 School of Forest Resources and 

Environmental Science, Michigan Technological University, 1400 Townsend 

Drive, Houghton, MI 49931; lmnagel@

mtu.edu.

2 Northern Research Station, USDA 

Forest Service, 410 MacInnes Dr., 

Houghton, MI 49931; cswanston@

fs.fed.us.

3 Northern Institute of Applied Carbon Science, Michigan Technological 

University, 1400 Townsend Drive, 

Houghton, MI 49931; mjanowiak02@

fs.fed.us.

Integrating Climate Change Considerations 

into Forest Management Tools and Training

Linda M. Nagel1, Christopher W. Swanston2, and 

Maria K. Janowiak3
28 USDA Forest Service Proceedings RMRS-P-61. 2010.

Nagel, Swanston, and Janowiak Integrating Climate Change Considerations into Forest Management Tools and Training

Our approach to this complex issue centers on a combination of education and 

problem-solving exercises. We first educate participants about state-of-the-art 

climate change science and the potential ecophysiological responses of forests. 

We then facilitate problem-solving by engaging critical thinking skills so that 

approaches and strategies are developed from the ground up, building on local 

knowledge and experience of participants.

This overall approach provides participants with fundamental information 

on likely climate change impacts on forests, and arms them with a structure for 

incorporating these considerations into future management strategies. A common 

criticism of continuing education training is that the information gained often lacks 

practical application and does not strengthen attendee’s ability to solve complex, 

real-world problems largely due to a one-way flow of knowledge (Salwasser and 

others 1990). The approach we outline here does not instill a one-size-fits-all 

solution, but rather builds in mechanisms and tools for further creative problemsolving to generate approaches to complex problems relevant to local forest types. 

This acknowledges that every situation confronting land managers is unique 

(i.e., forest plans, vegetation types, stand conditions, conflicting objectives) and 

requires management strategies particular to those conditions.

As part of a larger project funded by the USDA Forest Service Global Change 

Program aimed at providing advanced training in climate change science to land 

managers within the USDA Forest Service, we extended a training module geared 

toward forest management in the context of an uncertain climate future to the 

contingent of agency silviculturists in training for certification. Silviculture is 

an interdisciplinary practice used to manage for multiple objectives at a variety 

of scales with the local-level (stand), the scale at which on-the-ground actions 

(prescriptions) are implemented. Silviculture is recognized as a leading discipline 

within the forestry profession for implementing science-based practices, and leads 

the forestry profession by setting standards in continuing education and training 

(Graham and Jain 2004). The National Advanced Silviculture Program (NASP) 

is the current silviculture certification program within the USDA Forest Service. 

The program contains four core training courses led by academic institutions, 

and a fifth local area module developed specifically for expertise in a given forest type. Each course consists of a variety of topics taught by experts in their 

respective fields with the goal of preparing silviculturists to design prescriptions 

that meet the Minimum National Standards for Silviculturist Certification (Forest Service Handbook 2005). We incorporated a climate change module into the 

most recent NASP class, with these overall goals: 1) provide the latest scientific 

information about climate change and forest ecosystem responses; 2) develop a 

mindset for silviculturists to think about climate change considerations as part 

of decision making in forest management; and 3) develop a list of general forest 

management strategies that are applicable across national forests in the context 

of an uncertain climate future. The process implemented and outcomes achieved 

from this module are described below.

Educational Process

Context

The two-week Ecological Systems Course of NASP is the first course of the 

certification program, and is conducted at Michigan Technological University, 

Houghton, MI. A pilot four-hour climate change module was integrated into the 

third cohort of NASP (May 2009) as a new and essential component of the curriculum, and represents a logical and appropriate place to begin incorporating the 

complex issue of climate change into formal silviculture training. The module was 
USDA Forest Service Proceedings RMRS-P-61. 2010. 29

Integrating Climate Change Considerations into Forest Management Tools and Training Nagel, Swanston, and Janowiak

presented at the beginning of the second week, with the students already having 

several hours of basic forest ecology, geology, landforms, soils, hydrology, tree 

physiology, and disturbance ecology.

Approach

The process implemented during the climate change module consisted of a 

modified version of approaches utilized during an initial Silviculture Workshop 

geared toward Region 9 silviculturists held in fall, 2008 (Janowiak and others, 

unpublished paper). The pedagogical approach utilized components of the learning 

cycle (Allard and Barman 1994) through participatory engagement on the topic 

(fig. 1). To begin, the class was presented with a brief overall introduction of the 

topic at hand, and given a description of the goals for the climate change module 

in the context of NASP.

Inform and educate—To provide a sound and consistent scientific basis for 

building discussion, the first hour of the module consisted of a presentation of 

climate change science and forest response followed by an extensive question 

and answer session (fig. 2). The presentation covered several topics central to 

climate change issues in forest management. It included an overview of climate 

change science (observations, mechanisms, and predictions), the global carbon 

cycle, distribution and density of forest carbon, and how climate change affects 

forest ecosystems including benefits and deleterious effects on forest productivity. 

Uncertainty surrounding climate change and forest response was emphasized as 

material was presented, encouraging students to synthesize and evaluate information at higher levels of learning (Bloom 1956). To help students prepare for this 

topic and to provide context for climate change as it affects silviculture prescription development, three papers were assigned as pre-work: Birdsey and others 

2006, Millar and others 2007, and the USDA Forest Service Strategic Framework 

for Responding to Climate Change (2008). The goals here were twofold: 1) to 

inform and educate participants about climate change providing a common level 

of understanding; and 2) to address misconceptions and skepticism that may be 

present among the group about climate change as a pervasive and real issue facing forest managers.

Engage—The goal of the first learning activity (fig. 1) was to generate interest, 

enable active participation, and begin the flow of problem-solving ideas by posing this question to the group: What new or altered considerations does climate 

Figure 1—Modified learning cycle used in the climate change module of the Ecological Systems 

Course of the National Advanced Silviculture Program (NASP).
30 USDA Forest Service Proceedings RMRS-P-61. 2010.

Nagel, Swanston, and Janowiak Integrating Climate Change Considerations into Forest Management Tools and Training

change bring to the process of making silvicultural decisions and devising strategies? Each person was given large sticky notes to write down individual thoughts, 

and then asked to share these ideas with the group by bringing them forward and 

posting them onto the whiteboard in the front of the classroom. As facilitators, 

we then guided the participants through a re-organization of ideas under broad 

themes that emerged from the group (fig. 3). This exercise actively engaged each 

participant and facilitated sharing of many individual ideas. The goal was not to 

seek full agreement or consensus among the group, but rather to generate a list 

of considerations that would help trigger discussion for the next exercise.

Explore and extend—The second activity involved brainstorming silvicultural 

strategies that could be used to sustain forests and reach management objectives. 

Each person was given a set of maps (fig. 4) representing projected change in 

Figure 2—Presentation of climate change science and forest response. 

Photo credit: Jill C. Witt.

Figure 3—Responses to the question for Activity 1: What 

new or altered considerations does climate change bring to the 

process of making silvicultural decisions and devising strategies?

Photo credit: Jill C. Witt.
USDA Forest Service Proceedings RMRS-P-61. 2010. 31

Integrating Climate Change Considerations into Forest Management Tools and Training Nagel, Swanston, and Janowiak

Figure 4—Climate change scenarios used for the activity to develop silvicultural strategies for 

forest management in the context of an uncertain climate future. The maps represent projected 

change in temperature and precipitation for the US using two climate models and emissions 

scenarios. Top: Projections using CSIRO global climate model and low (B1) emissions scenario 

represent a lesser degree of change in temperature and precipitation at the end of the century. 

Bottom: Projections using MIROC global climate model and high (A2) emissions scenario represent 

a greater degree of change in temperature and precipitation at the end of the century. Map data 

courtesy of R. Neilson and the MAPSS Vegetation Modeling Lab.
32 USDA Forest Service Proceedings RMRS-P-61. 2010.

Nagel, Swanston, and Janowiak Integrating Climate Change Considerations into Forest Management Tools and Training

precipitation and temperature for summer and winter months at the end of 

the 21st century across the United States based on different emissions scenarios 

developed by the IPCC (2007). For simplicity and due to time constraints, two 

extremes (low model sensitivity and low emissions, high model sensitivity and 

high emissions) were used to frame the discussion. The class (32 participants) 

was then divided into five breakout groups by region or location consisting of 

five to eight people focused on common forest types in order to bring together 

similar experiences and to facilitate regionally oriented dialog specific to these 

two extremes.

The groups were instructed to address the following question given the range 

of uncertainty depicted by the different climate projections: What silvicultural 

strategies may be helpful or necessary to sustain our regional forests in the face 

of climate change? Each group was given a flipchart, an easel and markers, and 

the groups were relocated to a large open area conducive to group discussions 

outside the classroom. Each group chose a note-taker who recorded main points 

of the discussion on the flipchart. The groups were instructed to summarize five 

or six key points onto one flipchart page that would then be shared with the class 

by a volunteer spokesperson.

We acted as facilitators during this process, observing groups during their 

discussion to work through the overarching question. We deliberately avoided 

providing feedback or giving direction during these small group sessions. This 

enabled the participants to independently identify and grapple with complex issues, it encouraged open sharing of ideas, and resulted in the group framing their 

own problems and solutions. This exercise helped make climate change a real 

issue by forcing participants to extend and apply what they know conceptually 

about climate change to localized regions, and further required them to invoke 

local expertise and experience at the forest- and stand-level in order to develop 

practical strategies to address the issue.

Explain—The groups reconvened in the main classroom, and volunteer spokespersons shared a summary of the main ideas generated through the small group 

discussions with the full group. Their summary flipchart pages were posted on 

the board in the front of the room, and commonalities and unique ideas were 

identified. Participation from the entire group was encouraged during this process. This exercise allowed for a distillation of many different ideas that were 

discussed, provided an opportunity for individuals to practice organizing and 

presenting information, and exposed participants to general management issues 

from across different regions of the country.

Synthesis and wrap-up—The penultimate stage of the module was a synthesis 

of the day’s activities and a brief description of how this module fit into the larger 

climate change project. Results from the initial Silviculture Workshop (Janowiak 

and others, unpublished paper) were shared, highlighting the many similarities 

to the ideas generated by the NASP participants during the current module. Final 

wrap-up comments were made emphasizing these broad concepts: 1) we know 

climate is changing, therefore we must be proactive in our approach to forest 

management; 2) there is no single shiny new silvicultural tool to use against climate change; instead we must be creative in developing silvicultural prescriptions 

and management approaches that are flexible and adaptive; and 3) continuing to 

practice sustainable forest management is our best approach toward addressing 

the uncertainties of climate change and other complex issues.
USDA Forest Service Proceedings RMRS-P-61. 2010. 33

Integrating Climate Change Considerations into Forest Management Tools and Training Nagel, Swanston, and Janowiak

Evaluate process—We were interested in how effective our approach toward 

educating and training were for this group, so we implemented a survey at the 

end of the module to obtain feedback from the participants. We asked for comments on the information presented and activities conducted during the module. 

Participants were also asked to identify to what extent they had thought about 

this topic before, to what extent it has been discussed on their district or forest, 

if they thought they would incorporate ideas related to climate change into their 

management activities, and if their perceptions have changed during the module. 

We also asked them to identify the most and least useful parts of the module.

Outcomes

One of our main questions was whether this approach would be effective in 

helping participants develop silvicultural problem-solving skills to address the 

issue of climate change. We believe the process used in this module was highly 

effective at achieving our goals. The high response rate among the group, and 

the content of those responses on the evaluations, gave us positive evidence that 

our approach was effective at communicating climate change science and arming 

participants with a model structure for silvicultural strategy development. Some 

common themes are described below.

Feedback

Overall, the level of material presented was deemed appropriate for the audience, though many respondents desired more time, more in-depth information, 

and more activities for this topic within the overall curriculum of the two-week 

course. The detailed up-to-date information presented at the beginning of the 

module was often mentioned as a strength because it was clearly articulated with 

very useful graphics. The hands-on activities were highly rated and thought to 

be very effective at getting people to really think about the topic. The activities 

were consistently identified as a major strength of the module.

This reaction is in line with our overall experience with conducting training 

workshops and seminars: providing a detailed yet concise summary of climate 

change science is an essential component of educating land managers. But ultimately the most beneficial part of the process is the discussion generated and 

ideas shared within small groups. Our experience in this module was that the 

individual groups were reluctant to end their discussions and move to the final 

synthesis stage back in the classroom. It is clear that both components are necessary, but this finding reinforces the value and effectiveness of interactive teaching 

strategies that ultimately result in better learning and application of knowledge 

and skills (McNeal and D’Avanzo 1997).

Answers to the question “To what extent have you thought about this topic 

before?” ranged from “a lot” to “not very much. ” Most people indicated they had 

thought about climate change, and active measures were being taken in some locations. Many faced limitations to incorporating climate change-oriented concepts 

and approaches into forest management plans from colleagues, and some perceived 

resistance to the idea across interdisciplinary boundaries as well. Time constraints 

and pressure to address other issues (i.e., restoration, invasive species) were also 

cited as major limitations to directly dealing with climate change in everyday 

activities. Several also mentioned that current forest plans were not developed in 

ways that allowed management flexibility in the face of climate change.
34 USDA Forest Service Proceedings RMRS-P-61. 2010.

Nagel, Swanston, and Janowiak Integrating Climate Change Considerations into Forest Management Tools and Training

Conversely, carbon footprint reduction was identified as a positive step many 

local offices are taking. It was also pointed out that many of the silvicultural 

strategies currently implemented are geared toward increasing forest health 

and sustainability, which indirectly addresses many concerns regarding climate 

change. An overwhelming majority indicated that they would be incorporating 

ideas related to climate change into future management activities.

There was a mix of “yes” and “no” answers to the question of whether the 

module changed the respondent’s perceptions about climate change. Many people 

responded that their understanding was enhanced and that they now feel more 

prepared for dealing with the issue on their forests. Some identified that climate 

change felt less overwhelming following the module. Though diminishing throughout the day, some skepticism remained within the group, largely surrounding the 

use of climate change models. There was appreciation for the overall approach 

in that silvicultural possibilities were elucidated and solutions were not dictated, 

recognizing that every situation is different. There was also a call for presenting 

this information to broader audiences.

Summary

As evidenced by the theme of this workshop, climate change is a real issue 

that must be confronted by land managers, and the USDA Forest Service is in 

a strategic position of opportunity to lead this charge. Education and training 

are necessary and pertinent first steps to move forward within the agency and 

beyond. The training module we described was effective at communicating 

state-of-the-art climate change science, and the hands-on activities successfully 

engaged participants into developing silvicultural strategies that will contribute 

to sustainable forest management in the context of an uncertain climate future. 

Feedback we received from participants is informative for improving content 

for future NASP sessions, including building in additional time for the topic of 

climate change. This ground-up approach to education and problem-solving, and 

the interactive flow of knowledge between academic, research, and management 

sectors, may serve as a model for future climate change training programs as 

well as for programs dealing with other contemporary issues of great importance 

facing natural resource managers.

References

Allard, D.W.; Barman, C.R. 1994. The learning cycle as an alternative method for college science 

teaching. BioScience 44(2): 99-101.

Birdsey, R.; Pregitzer, K.; Lucier, A. 2006. Forest carbon management in the United States: 1600-

2100. Journal of Environmental Quality 35: 1461-146.

Bloom, B.S. 1956. Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain. 

New York: David McKay Co Inc.

Forest Service Handbook, National Headquarters (WO) Washington, DC. FSH 2409.17: Silvicultural 

Practices Handbook. Chapter 8: Silvicultural Examinations, Prescriptions, and Evaluations. 

Effective Date: 01/18/2005. 19 p.

Graham, R.T.; Jain, T.B. 2004. Past, Present and Future Role of Silviculture in Forest Management. 

In: Shepperd, W.D.; Eskew, L.G. (Compilers). Silviculture in Special Places: Proceedings of the 

National Silviculture Workshop, Grandy, CO. RMRS-P-34. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. pp 1-14.

Intergovernmental Panel on Climate Change. 2007. Climate Change 2007: The Physical Science 

Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, 

K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United 

Kingdom and New York, NY, USA.
USDA Forest Service Proceedings RMRS-P-61. 2010. 35

Integrating Climate Change Considerations into Forest Management Tools and Training Nagel, Swanston, and Janowiak

Janowiak, M.K.; Swanston, C.W.; Nagel, L.M.; Palik, B.J.; Twery, M.J.; Bradford, J.B.; Webster, 

C.R.; Parker, L.R.; Hille, A.T.; Johnson, S.M. Silvicultural Decision-making in an Uncertain 

Climate Future: A Process to Explore Considerations, Strategies, and Approaches. Unpublished 

paper on file at: U.S. Department of Agriculture, Forest Service, Northern Research Station, 

Houghton, MI. 29 p.

Kirilenko, A.P.; Sedjo, R.A. 2007. Climate change impacts on forestry. Proceedings of the National 

Academy of Sciences of the United States of America. 104(50): 19697-19702.

McNeal, A.P.; D’Avanzo, C., eds. 1997. Student-active science: models of innovation in college 

science teaching. Proceedings on the NSF sponsored conference on Inquiry Approaches to 

Science Teaching held at Hampshire College, June 1996. Saunders College Publishing. 490 p.

Millar, C.; Stephenson, N.L.; Stephens, S.L. 2007. Climate change and forests of the future: managing in the face of uncertainty. Ecological Applications 17: 2145-2151.

Salwasser, H.; Cross, G.H.; Sidle, W.B. 1990. Continuing Education for Biologists: the Forest 

Service Program. Transactions of the North American Wildlife and Natural Resources Conference. Volume 55: 144-153.

Smith, W.B.; Miles, P.D.; Vissage, J.S.; Pugh, S.A. 2004. Forest Resources of the United States, 

2002. General Technical Report NC-241. St. Paul, MN: U.S. Department of Agriculture, Forest 

Service, North Central Research Station. 137 pp.

USDA Forest Service Strategic Framework for Responding to Climate Change, Version 1.0. 

October 2, 2008. 21 p.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 37

Part 2: Carbon Sequestration 

and Biomass Utilization

2009 NATIONAL SILVICULTURE WORKSHOP

BOISE, IDAHO

SILVICULTURE

CLIMATE

BIOMASS

CARBON SEQUESTRATION

USDA Forest Service Proceedings RMRS-P-61. 2010. 39

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 USDA Forest Service, Rocky Mountain Research Station.

2 Retired Professor from the University 

of Idaho.

Carbon Concentrations and Carbon Pool 

Distributions in Dry, Moist, and Cold 

Mid-Aged Forests of the Rocky Mountains

Theresa B. Jain1, Russell T. Graham1, and David Adams2

Abstract—Although “carbon” management may not be a primary objective in forest 

management, influencing the distribution, composition, growth, and development of 

biomass to fulfill multiple objectives is; therefore, given a changing climate, managing 

carbon could influence future management decisions. Also, typically, the conversion 

from total biomass to total carbon is 50 percent; however, we believe this value is not 

consistent across all forest components. Therefore, the objectives of this study are to: 

acknowledge the appropriate carbon concentrations and distribution of carbon pools 

and provide improved estimates of carbon content in four habitat types with different 

climatic regimes—(dry (Arizona), cold (Montana), and moist (Idaho)—of the Rocky 

Mountains, USA. We quantified biomass, carbon concentrations, and carbon amounts 

for trees, soils, woody debris, and coarse and fine roots. We found that in most cases 

our carbon concentrations were less than the typical conversion of 50 percent. Thus 

we recommend the following conversions from biomass to carbon: trees should be 

49 percent for overstory crown, 48 percent for boles, 48 percent for understory trees, 

and 47 percent for coarse roots; for understory plants concentrations should be 

47 percent for shrubs and 41 percent for forbs and grasses; woody residue should 

be 48 percent for solid logs, 49 percent for rotten logs, 48 percent for brown cubical rotten wood, and 44 percent for buried wood; cones should be 48 percent in 

ponderosa pine forests and 46 percent in cold and moist forests; sticks in ponderosa 

pine forests should be 49 percent and in the moist and cold climate regimes sticks 

should be 47 percent. Unique carbon pools often overlooked include cones, woody 

debris, and buried wood. Given these results, additional research questions could 

be pursued, such as the effect of successional stage on carbon pool distributions, 

or as forests grow and develop, if carbon concentrations change or if only biomass 

distribution changes over time.

Introduction

Forest plans and prescriptions on public lands emphasize a variety of values, 

such as biological diversity, scenery, wildlife, water quality, sustainable ecosystems, and other values, in addition to commodity production. Past forest practices 

consisted of managing individual stands of trees (Graham 1990) as separate entities; today managers need to consider overall ecosystem processes and functions 

before developing management prescriptions of large landscapes (Jain and Graham 

2005), particularly with the uncertainty associated with climate change (Joyce 

and others 2008). In addition, because management actions have the potential to 

manipulate carbon, acknowledging changes in carbon pools may be a critical element that will need documentation in the future (Waring and Schlesinger 1985).

In forest ecosystems, organic carbon is stored in different locations, including live and dead standing biomass, down woody debris, litter, and soils. Thus 
40 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

the manipulation of these organic substances not only affects carbon storage but 

also other essential nutrients such as nitrogen, calcium, potassium, sulfur, and 

phosphorous (Binkley and Richter 1987; Jorgensen and Wells 1986). Therefore, 

recognizing the role of carbon and organic matter in the structure and function of forest ecosystems is essential for sustaining long- and short-term forest 

productivity.

Although a large portion of carbon is in live biomass, a significant amount of 

carbon is also stored in coarse woody debris (CWD), the forest floor, and soils. 

The forest floor and soils contain five organic components that contribute to carbon storage (fig. 1): 1) litter, which encompasses recognizable plant and animal 

materials such as conifer needles, insect frass, and deciduous leaves; 2) humus, 

which is unrecognizable, decomposed plant and animal material having a high 

content of complex hydrocarbons located above the mineral soil; 3) brown cubical 

rotten wood (BCR), which consists of woody debris in an advanced state of decay 

located on the surface; 4) soil wood, which is decaying wood incorporated within 

the mineral layers; and 5) mineral soil organic matter, which is organic matter 

incorporated in the mineral soil (Aber and Melillo 1991; Harmon and others 1986; 

Harvey and others 1987; Waring and Schlesinger 1985). The dead organic matter 

components of forests represent different substrate qualities, including sizes and 

state of decomposition; thus each has its own unique carbon pools.

Because the type of vegetation influences the kinds of carbon compounds 

present, carbon pools vary depending on forest type. This, combined with the 

Figure 1—The forest floor and mineral soils contain five organic elements: 

litter, humus, brown cubical rotten wood (BCR), soil wood, and organic 

matter in the mineral soil. All these elements contribute to storing carbon. 

The difference between BCR and soil wood is the location of the material; 

soil wood is buried, often below the humus and litter, while BCR is on the 

surface. Soil organic matter typically decreases with depth (Woods 1989). 
USDA Forest Service Proceedings RMRS-P-61. 2010. 41

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

local climate, subsequently affects decomposition rates. For example, Douglas-fir 

(Pseudotsuga menziesii (Mirb.) Franco) decays more slowly than most conifers 

because the heartwood contains fungi-toxic compounds and high amounts 

of lignin (Scheffer and Cowling 1966). Therefore, if all other factors controlling decomposition were similar, a Douglas-fir forest may store more carbon in 

CWD, BCR, and soil wood than a true fir (Abies spp.) forest. In turn, the amount 

of CWD created within a forest type also affects soil wood amounts, which is 

incorporated into soil mineral layers through freeze-thaw action, soil mixing, and 

erosion (Harvey and others 1987). For example, on moist forests the accumulation of CWD and soils wood is much greater than dry forests in the southwestern 

United States (Graham and others 1994).

Carbon Estimates

There is wide variation in carbon storage among and within forest ecosystems. 

In forests of the Lake States, (Minnesota, Wisconsin, and Michigan), Grigal and 

Ohmann (1992) concluded that both stand age or successional stage and forest 

type influence the amount of carbon stored in the forest floor. They found that 

carbon continued to accumulate over time because in these ecosystems biomass 

was produced more rapidly than it decomposed. Other research has also indicated 

that forest type may affect carbon storage but only if ecosystems were significantly 

different (Post and others 1982). However, Grigal and Ohmann (1992) determined 

that wide variations in forest type were not necessary to notice subtle differences 

in carbon storage.

The role of CWD, BCR, and soil wood in storing carbon is often overlooked because most estimates consider only living biomass, forest floor (litter and surface 

humus), and mineral soil (Buringh 1984; Eswaran and others 1993; Franzmeier 

and others 1985; Huntington and others 1988; Post and others 1990; Schlesinger 

1977). Studies have compared carbon storage in CWD between different forests 

(Harmon and Hua 1991; Keenan and others 1993). The results of these studies 

indicate that a large fraction of the terrestrial sink could potentially be located in 

woody debris. For example, Keenan and others (1993) reported that 60 percent of 

the forest floor in northern Vancouver Island was composed of woody material. 

In the Northern Rocky Mountains, up to 58 percent of the organic components 

can consist of CWD and soil wood (Harvey and others 1987).

To estimate carbon storage in vegetation, the amount of carbon is estimated 

to be 50 percent of the biomass (Grigal and Ohmann 1992; Hendrickson 1990; 

Lamlom and Savidge 2003; Linder and Axelsson 1982). Using this ratio assumes 

that all organic biomass has the same carbon concentration across different vegetation types and species. Although this is the best and most popular information 

currently available for estimating carbon, we hypothesize that ratios should differ 

among and between vegetation types.

Because estimating carbon storage is a key element in predicting the effects of 

climate change and determining carbon pools, it is important that valid conversion 

factors be used to minimize the amount of error these estimates may provide. 

Moreover, knowing where carbon is stored is important across vegetation types 

within the Rocky Mountains. Therefore, the objectives of this study are to acknowledge the appropriate carbon concentrations and distribution of carbon pools and 

provide improved estimates of carbon content in three forests types with different 

climatic regimes (dry, cold, and moist) of the Rocky Mountains. Although carbon 

management may not be a primary objective in forest management, knowing the 

changes and distribution of carbon pools may potentially influence management 

decisions in a future with climate change.
42 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

Methods

Site Selection

The sites selected for the study (fig. 2) include three climatic regimes: coolwet, cold-dry, and warm-dry. The habitat types chosen to represent each of these 

regimes were selected after consultation with soil scientists, silviculturists, and 

forest managers. The wettest and most productive site was a western hemlock/

queen cup beadlily (Tsuga heterophylla (Raf.) Sarg.)/ (Clintonia uniflora (Schult.) 

Kunth) (WH/CLUN) habitat type (Cooper and others 1991) on the Priest River 

Experimental Forest in northern Idaho (sites 1-3). A cold-dry subalpine fir/dwarf 

huckleberry (Abies lasiocarpa (Hook.)Nutt.)/(Vaccinium scoparium Leib.) (SAF/

VASC) habitat type (Pfister and others 1977) was located on the Deerlodge National Forest near Butte, Montana (sites 4-6). Two warm-dry sites were selected 

in northern Arizona: a ponderosa pine (Pinus ponderosa C. Lawson)/gambel 

Figure 2—The general locations of study areas. Study sites 1-3 are located in northern 

Idaho within the western hemlock (Tsuga heterophylla (Raf.) Sarg./queencup beadlily 

(Clintonia uniflora (Schult.) Kunth) (WH/CLUN) habitat type (Cooper and others 1991). 

Study sites 4-6 are located in western Montana within the subalpine fir (Abies lasiocarpa

(Hook.) Nutt.)/dwarf huckleberry (Vaccinium scoparium Leib.) (SAF/VASC) habitat type 

(Pfister and others 1977). Study sites 7-12 are located in northern Arizona: 7-9 are located 

within the ponderosa pine (Pinus ponderosa C. Larson)/gambel oak (Quercus gambelii

Nutt.) (PP/QUGA) (Larson and Moir 1986) and 10-12 are located within the ponderosa 

pine (Pinus ponderosa Dougi. ex Lawsi/Arizona fescue (Festuca arizonica Vasey) (PP/

FEAR). Please refer to table 1 for specific characteristics of each site.
USDA Forest Service Proceedings RMRS-P-61. 2010. 43

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

oak (Quercus gambelii Nutt.) (PP/QUGA) habitat type on the Coconino National 

Forest (Larson and Moir 1986) (sites 7-9) and a ponderosa pine/Arizona fescue 

(Festuca arizonica Vasey) (PP/FEAR) habitat type on the Kaibab National Forest 

(sites 10-12).

The WH/CLUN habitat type (Cooper and others 1991) occurs at elevations 

from 760 to 1,580 m (2,500 to 5,200 ft). Parent material is an ash cap over belt 

metasedimentary rocks (Alt and Hyndman 1989). Tree species include Douglasfir, western larch (Larix occidentalis Nutt), western white pine (Pinus monticola

Dougl. ex D. Don.), lodgepole pine (Pinus contorta Dougl. ax Loud.), grand 

fir (Abies grandis Dougl. ex D. Don) Lindl.), subalpine fir, Engelmann spruce 

(Picea engelmannii Parry ex Engelm.), western redcedar (Thuja plicata Donn ex 

D. Don), and western hemlock. The overstory canopy of late seral stands is usually dense with a sparse herbaceous layer. WH/CLUN climate is characterized 

by dry summers with the majority of precipitation occurring during the fall and 

winter. Total precipitation averages between 710 to 1,520 mm (28 to 60 inches); 

snowfall averages 262 cm (103 inches). Average annual air temperature ranges 

from 4 to 10 °C (40 to 50 °F) (Graham 1990).

SAF/VASC is one of the most abundant habitat types east of the Continental 

Divide in Montana. Elevations range from 2,130 to 2,590 m (7,000 to 8,500 ft). 

The parent material of the study site is volcanic (Hunt 1972). The overstory in the 

sites for this study are dominated by lodgepole pine, with a minor component of 

Engelmann spruce, subalpine fir, and Douglas-fir. The understory is carpeted with 

dwarf huckleberry, scattered common juniper (Juniperus communis Pall.) and a 

minor component of pine grass (Calamagrostis rubescens Buckl.). Precipitation 

ranges from 280 to 740 mm (11 to 29 inches), with snowfall averaging 686 cm 

(270 inches). Average annual air temperature ranges from –4 to 2 °C (25 to 35 °F) 

(Alexander and others 1990; Pfister and others 1977).

The PP/QUGA habitat type occurs at elevations from 1,860 to 2,590 m 

(6,100 to 8,500 ft) with basalt parent material. The overstory consists of ponderosa 

pine with a minor component of gambel oak. Understory vegetation includes 

rose (Rosa spp.), skunk bush (Rhus trilobata Nutt.), New Mexico locust (Robinia neomexicana A. Gray), muttongrass (Pea fendleriana (Steud.) Vasey), and 

mountain muhly (Muhlenbergia montana Nutt.). PP/QUGA climate is similar to 

PP/FEAR (described below) but unlike the Kaibab Plateau, the majority of the 

precipitation falls during July through October (Brewer and others 1991; Larson 

and Moir 1986). Sites 7 through 9 were located on the Coconino National Forest 

in Arizona (table 1, fig. 2).

The PP/FEAR habitat type occurs at elevations from 2,300 to 2,500 m 

(7,540 to 8,200 ft) in northern Arizona. The parent material of the study site is 

limestone (Hunt 1972). The overstory consists of ponderosa pine with a small 

amount of quaking aspen (Populus tremuloides Michx.). Understory vegetation includes Arizona fescue, Oregon grape (Berberis repens Lindl.), Fendler’s 

ceanothus (Ceanothus fendleri A. Gray), wax gooseberry (Ribes cereum Lindl.), 

mountain muhly, and muttongrass. Precipitation has a bimodal distribution with 

one wet season occurring July through October and another December through 

March. However, on the Kaibab plateau, greater than 50 percent of the precipitation falls between December and March. Total precipitation ranges from 520 to 

600 mm (20 to 24 inches), with snowfall averaging 1,120 mm (47 inches). Mean 

annual air temperature ranges from 4 to 6 °C (39 to 43 °F) (Brewer and others 

1991; Larson and Moir 1986).

Although we recognize that successional stage and/or stand age may influence 

the amount and distribution of carbon pools, our objective was to determine if 

the ratios and carbon pool location varied across different habitat types. To accomplish this we acquired a list, within each habitat and soil type, from forest 
44 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

silviculturists and soil scientists of undisturbed stands consisting of mid-to late 

seral vegetation. From each list, three sites were randomly selected and then 

verified (table 1).

Data Collection

Twelve points were systematically located on a random transect bisecting the 

site. From these points, forest components and data for biomass estimates were 

sampled using five plot types: 1) variable, 2) fixed, 3) microsite, 4) soil core, and 

5) line intersect (table 2). A variable plot using probability proportional to size was 

used to sample total height and d.b.h. (diameter at 4.5 ft; 1.4 m) for trees ≥12.7 cm 

(5 inches) d.b.h. Sapwood, heartwood, coarse roots, and overstory crown samples 

were collected for carbon analysis from each tree species. Increment cores at d.b.h. 

were used to sample sapwood and heartwood. Coarse roots (>1 cm; 0.5 inches 

diameter) were sampled 20 to 25 cm (7 to 10 inches) below the soil surface on 

the down-hill side of the tree. A sub-sample of overstory crown (branches and 

needles) was collected from three trees per species. For consistency, crown samples 

were removed from the third highest whorl and from the north side of the tree.

The second plot type was a 13.5 m2 (1/300 acre) fixed-area circular plot (table 2). 

Trees <12.7 cm (5 inches) d.b.h. occurring on this plot were tallied and their heights 

were measured; for trees ≥1.4 m (4.5 ft) tall, d.b.h was measured, while basal diameter was measured on trees <1.4 m (4.5 ft) tall. In addition, foliage samples for 

carbon analysis were taken from the understory trees. Average basal diameter and 

number of stems occurring on the plot were also recorded for the following shrub 

Table 1—Description of selected stands within each habitat type. Refer to figure 2 for study site locations.

Cover type-study site	 Age	 Aspect (°)	 Slope (%)	 Elevation (m)	 Parent materialb

The WH/CLUNa on the Idaho Panhandle National Forest—Priest Lake Ranger District

(Priest River Experimental Forest)

WH/DF/WL/WP-1c 100 310 45 1280 Ash/Belt

WH/DF/WL/WP-2 100 340 45 1340 Ash/Belt

WH/DF/WL/WP-3 100 340 45 1340 Ash/Belt

The SAF/VASCa on the Deerlodge National Forest—Butte Ranger District

LP-4c 65 124 21 2073 Volcanic

LP-5 65 124 21 2073 Volcanic

LP-6 65 110 33 2073 Volcanic

The PP/QUGAa on the Coconino National Forest—Mormon Lake Ranger District

PP-7c 141 0 0 2134 Basalt

PP-8 150 0 0 2134 Basalt

PP-9 145 0 0 2134 Basalt

The PP/FEARa on the Kaibab National Forest—North Kaibab Ranger District

PP-10c 127 0 0 2470 Limestone

PP-11 125 0 0 2470 Limestone

PP-12 123 0 0 2487 Limestone 

a Habitat types and species for cover types: In northern Idaho (Cooper and others 1991) WH/CLUN = western hemlock (Tsuga 

heterophylla (Raf.) Sarg./queencup beadlily (Clintonia uniflora (Schult.) Kunth). In western Montana (Pfister and others 1977) 

SAF/VASC = subalpine fir (Abies lasiocarpa (Hook.) Nutt.)/dwarf huckleberry (Vaccinium scoparium Leib.). In northern Arizona 

(Larson and Moir 1986) PP/FEAR = ponderosa pine (Pinus ponderosa C. Larson/Arizona fescue (Festuca arizonica (Vasey) 

and PP/QUGA = ponderosa pine/gambel oak (Quercus gambelii Nutt.).

b Parent materials are (Alt and Hyndman 1989; Hunt 1972) Ash, fine shreds of lava blown from Mount Mazama; Belt, mildly 

metamorphosed sedimentary rocks, including argillites, siltites, quartzites, and dolomites; Volcanic (Rhyolite), lava or shallow 

intrusion, fine grained, with composition equivalent to granite. Basalt: Black volcanic rock rich in iron, calcium, and magnesium, 

composed primarily of plagioclase; and Limestone, sedimentary rock or surface deposit of calcium carbonate.

c Species are WH = western hemlock, DF = Douglas fir (Pseudotsuga menziesii (Mirb.) Franco), WL = western larch (Larix 

occidentalis Nutt), WP = western white pine (Pinus monticola Dougl. ex D. Don.), LP = lodgepole pine (Pinus contorta Dougl. 

ax Loud.), and PP = ponderosa pine. The number following species cover type refers to the site number located on figure 2.
USDA Forest Service Proceedings RMRS-P-61. 2010. 45

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

size-classes from Brown (1976): low (0 to 0.5 cm; 0 to 0.2 inches) and medium 

(0.51 to 2.0 cm; 0.2 to 0.75 inches); tall shrubs (2.01 to 5.0 cm; 0.76 to 2 inches) 

were not present in any of the plots. When present, foliage samples for carbon 

analysis were taken for medium shrubs. The third plot type (microsite) was a 30 

cm (12 inches) diameter hoop (table 2). All cones and organic soil components 

were collected for carbon analysis on this plot. Next to the microsite plot, a fourth 

type of plot consisted of extracting a 10 by 30 cm (4 by 12 inches) soil core. Litter, humus, soilwood (rotten wood buried in established humus or mineral soil 

horizon), and BCR were separated. Mineral soil was separated into two depths: 

shallow mineral (0 to 10 cm depth) and deep mineral (10-30 cm depth). Depth of 

each horizon was recorded and the entire sample was taken from the core from 

each horizon and placed in a cloth bag. Mineral soil bulk density was sampled 

at 0 to 10 cm (0 to 4 inches) and 10 to 20 cm (4 to 8 inches) depths using a 135.7 

cm3 (8.4 in3) core sampler. Samples of BCR, litter, humus, soil wood, and mineral 

soils at two depths were collected for carbon analysis.

The fifth type of plot was a line intersect to determine woody residue biomass (Brown 1974) (table 2). Two 7.5 m (25 ft) transects were located in random 

directions from plot center. Woody residues were separated into stick (<7.5 cm; 

3 inches), and solid and rotten logs ≥7.5 cm (3 inches). For carbon analysis, a 

sample was taken from each residue class.

Table 2—Forest components and plot types where measurements and samples were obtained.

Plot typea

Forest component	 Variable	 Fixed	 Microsite	 Line intersect	 Soil core

Trees

Overstory crown X

Sapwoodb X

Heartwoodb X

Coarse roots (≥1 cm diameter) X

Fine roots (<1 cm diameter) X

Understory crown (<12.7 cm tall) X

Understory vegetationc

Small shrubs (<0.5 cm) X

Medium shrubs (0.5 to 2.0 cm) X

Forbs and grasses X

Woody residued

Sticks (<7.5 cm diameter) X

Solid logs (≥7.5 cm diameter) X

Rotten logs (≥7.5 cm diameter) X

Cones X

Organic soil

Brown cubical rotten wood X X

Litter X X

Humus X X

Soil wood X X

Mineral soile

Shallow mineral (0-10 cm) X

Deep mineral (>10, up to 30 cm) X

a Plot types: 1) variable, based on probability, proportional to tree diameter. On the Idaho Panhandle, Kaibab, and 

Coconino National Forests, we used a 40 basal area factor (BAF); on the Deerlodge we used a 20 BAF; 2) fixed, a 

0.0013 ha (24th acre) circular plot; 3) microsite, a 30 cm (12 inches) circular plot; 4) transect, 15.2 m (50 ft) in length 

(Brown 1974); and 5) soil core, l0 by 30 cm (4 by 12 inches).

b Sampled using increment cores at 1.4 m (4.5 ft).

c Shrub size classes are from Brown (1976).

d Size classes are from Brown (1974).

e Soil depths are from Jurgensen and others (1977).
46 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

Biomass Estimates

The Forest Vegetation Simulator (FVS) (Dixon 2002; Wykoff and others 1982) 

and field data were used to estimate tree biomass (tables 3 and 4). FVS provided 

a list that included total cubic foot volume, species, diameter, height, and number of trees per unit area for each sample tree represented. Published regression 

equations (tables 3 and 4) in combination with this tree list, provided estimates on 

crown, bole, bark, and coarse root weight (Baskerville 1965; Brown 1978; Feller 

1992; Johnstone 1971; Kuiper and Coutts 1992; Whittaker and others 1974; Will 

1966, cited in Santantonio 1977).

To estimate shrub biomass, we used regression equations (table 4) for the 

basal stem diameter-based size-classes (Brown 1976) described above. If grasses 

covered more than 10 percent of the site, their biomass was estimated using overstory basal area (Covington and Fox 1991) (table 4). Weight estimates of sticks 

and logs (solid and rotten) were determined using Brown’s (1974) down woody 

debris transect methods. Cone and soil biomass were estimated directly from field 

sampling. Mineral soil biomass was estimated using core volume, percent coarse 

fragments, and bulk density. Oven-dry (60° C; 140° F for 12 hours) weights of 

cones, organic components, and fine roots were expanded to a per unit area basis.

Laboratory Analysis

Field collections were taken to the Forestry Sciences Laboratory in Moscow, 

Idaho, and prepared for carbon and organic matter analysis. Soils were oven-dried 

and sieved using screens with 2 mm (0.08 inches) openings. Roots were removed 

Table 3—Regression equations used for estimating biomass for components of trees ≥5 cm (2 inches). Columns A, B, 

and C show coefficient values for specific species. 

Bole and barkc Coarse roots

Wt (lb) =VolA +VolBC	 Log10WT (kg) =B(log10DBH(cm) +Log10A

Speciesa Crownb A	 B	 C	 B	 Log10A	 Reference

PP exp[0.2680+2.0740(lnd)] 25.0 0.24 21.8 2.445 –0.94 Will 1966d

ES exp[l.0404+l.7096(lnd)] 21.8 0.19 30.6 2.151 –1.24 Whittaker and others 1974 

GF exp[l.3094+l.6076(lnd)] 23.1 0.20 37.4 2.445 –1.71 Baskerville 1965 

SAF 7.345+1.255(diameter2) 20.0 0.19 27.4 2.445 –1.71 Baskerville 1965 

Logl0Wt (lb) =Blog10DBH2(in) Ht(ft) +log10A

WWP exp(0.7276+l.5497(lnd)] 23.7 0.21 26.2 1.022 1.879 Johnstone 1971

WL exp[0.4373+l.6786) (lnd)] 32.4 0.24 24.3 1.022 1.879 Johnstone 1971

LP exp[0.1224+l.8820(lnd)] 25.6 0.11 26.5 1.022 1.879 Johnstone 1971

Wt(kg)=A(DBH) (cm)

A	 B

DF <43 cm exp[l.368+l.5819(lnd)] 30.0 0.19 27.4 0.01 2.630 Kuiper and Coutts 1992

DF ≥43 cm 1.0237(diameter2) - 20.74

lnWt(kg)=B+Aln(DBH) (cm)

WH exp[0.72l8+l.7502(lnd)] 28.1 0.19 31.2 –4.159 2.519 Feller 1992 

WRC exp(0.88l5+1.6389(lnd)] 20.0 0.15 23.1 –4.159 2.519 Feller 1992

a Tree species: PP-ponderosa pine (Pinus ponderosa Dougl. ex Laws.); ES-Engelmann spruce (Picea engelmannii Parry ex Engelm.); 

GF-grand fir (Abies grandis Dougl. cx D. Don); SAF-subalpine fir (Abies lasiocarpa (Dougl. ex D. Don) Lindl.; WWP-western white pine 

(Pinus monticola Dougl. ex D. Don); WL-western larch (Larix occidentalis Butt.); LP-lodgepole pine (Pinus contorta Dougl. ex Loud.); 

DF-Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco); WH-western hemlock (Tsuga heterophylla (Raf.) Sarg.); WRC-western red cedar 

(Thuja plicata Dorm ex D. Don). 

b Ind = natural log diameter in inches (Brown 1978); d2 = diameter2.

c Wt = Weight; Vol = ft3 ; A = specific gravity of wood; B = percentage of bark; C = specific gravity of bark. 

d Cited in Santantonio and others 1977. 
USDA Forest Service Proceedings RMRS-P-61. 2010. 47

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

from the soils and coarse fragments greater than 2 mm (0.08 inches) diameter 

were weighed. The twelve samples from the litter, humus, BCR, soil wood, and 

mineral soils were each combined into four composites for each stand. Each 

composite was composed of three adjacent samples that were collected along the 

transect line. Similar to the soils, 12 samples were collected from the other forest 

components, were oven-dried and ground, and then placed into three composites. 

Before conducting any laboratory analyses, mineral soils were tested for carbonates using 10 percent hydrochloric acid (Soil Survey Staff 1992). The LECO 

Carbon, Hydrogen, Nitrogen (CHN-600) Autoanalyzer was used to determine 

the organic carbon concentration of soil and forest components.

Statistical Analysis

Analysis of variance for a randomized complete block design was used to 

analyze the data. Within each habitat type, a variety of data were collected from 

three individual randomly selected stands. These stands within each habitat type 

were used as replications in the analysis of variance (Steel and Torrie 1960). Mean 

values for each variable were computed for the stand prior to analysis. Therefore, 

the four habitat types described above served as the main effects in three replications (stands). Scheffé’s (1953) S test was used to separate main effect means when 

more than two means were compared. Carbon proportional data (percent) were 

transformed using the square root of the arcsine prior to performing the analysis 

of variance. The analyses were conducted at P-level of ≤0.05.

Results

Organic Biomass

In all habitat types, outside the mineral soil, the highest amount of biomass 

occurred in tree boles and the least amount occurred in fine roots (table 5), while 

coarse roots contributed the second largest amount. Among habitat types, PP/

FEAR and PP/QUGA tree bole biomass was greater than other habitat types 

Table 4—Regression equations used to estimate forb, grass, shrub, and tree crown 

weight for trees less than 5 cm (2 inches). Columns A and B show coefficient values 

for specific species.

	 Speciesa Crown weightb Reference

Douglas-fir exp[–4.212 + 2.7168 (lnht] Brown 1978

Lodgepole pine exp[0.0311(height2) Brown 1978

Western larch exp(–5.126 + 2.5639(lnht)] Brown 1978

Grand fir 0.0538 (height2) Brown 1978

Western redcedar 0.0307 (height2) Brown 1978

Ponderosa pine exp[–2.7297 + 1.1707 (lnht)] Brown 1978

Shrubs, forbs, and grasses

lnWt(g)= A+ Bln basal stem diameter (cm) Brown 1976

A B

Vaccinium scoparium 2.113 2.148

Juniperus communis 4.081 2.202

Combined low shrub 3.565 3.565

Forbs and grassesc 1nWt(kg/ha)=2.517 – 0.294 (10–1)BAc Covington & Fox 1991

a Refer to table 3 for scientific names of species.

b lnht=natural log of height (ft)

c BA is basal area in m2/ha
48 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

(table 5) and coarse root biomass was significantly greater (>215 Mg/ha; 100 tons/

acre) than the other habitat types (<85 Mg/ha; 40 tons/acre). However, fine root 

biomass was greater in the WH/CLUN and SAF/VASC habitat types.

In addition to tree components, table 5 shows biomass estimates for understory 

vegetation, woody residue, organic soil, and mineral soil. With the exception of 

SAF/VASC, where shrubs were a significant component (>40 Mg/ha; 18 tons/acre), 

understory vegetation did not contribute large amounts of biomass. PP/QUGA 

had only 0.1 Mg/ha (0.05 tons/acre), while WH/CLUN did not have any. Among 

habitat types, there were significant differences in biomass across woody debris 

classes. For example, solid and rotten logs contributed most of the woody residue 

biomass for WH/CLUN, while cones contributed significantly in the ponderosa 

pine habitat types. Mineral soil had the greatest total weight, with deep mineral 

having more mass than shallow mineral.

Table 5—Forest component biomass (Mg/ha) estimates (mean) and standard error (Sx

–) by forest habitat 

type. The different letters (x, y, or z) indicate significant differences; if letters are the same there 

were no significant difference among the habitat types. Refer to table 1 for habitat type designation. 

Habitat type

PP/FEAR	 PP/QUGA	 WH/CLUN	 SAF/VASC

Forest component	 Mg/ha	 Sx

–	 Mg/ha	 Sx–	 Mg/ha	 Sx–	 Mg/ha	 Sx–

Trees

Overstory crown 99 7 113 10 51 3 31 4

Bole 415 16 388 46 268 21 160 31

Coarse roots (≥1 cm diameter) 215 10 252 22 84 11 68 13

Fine roots (<1 cm diameter) 0.l 0.1 0.3 0.1 9 2 5 4

Understory trees (<12.7 cm tall) 1 0.6 1 0.5 0.3 0.2 1 0.2

x x y y

Total 730.1 33.7 754.3 78.6 412.3 37.2 265.3 52.2

Understory vegetation

Medium shrub (0.5-2.0 cm) — — — — — — 9 4

Low shrub (<0.5 cm) — — 0.l 0.1 — — 32 5

Forbs and grasses 0.1 0.01 0.3 0.2 — — — —

y y x

Total 0.1 0.01 0.4 0.2 — — 41 9

Woody residue

Sticks (<7.5 cm diameter) 2 1 5 1 11 0.3 3 0.4

Solid Log (≥7.5 cm diameter) 4 3 0.l 0.1 42 10 1 1

Rotten log (≥7.5 cm diameter) 2 1 4 1 47 6 8 3

Cones 23 8 27 7 9 2 7 3

y y x y

Total 31.4 13 36.1 9.1 109 18.3 19 7.4

Organic soil

Brown cubicle rot 0.4 0.2 3 1 3 3 10 5 

Litter 33 6 63 4 33 16 40 11 

Humus 65 14 87 8 127 29 98 22 

Soil wood 1 0.4 18 8 103 65 36 18 

x x x x

Total 99.4 20.6 171 21 266 113 184 56

Mineral soil

x x z y

Shallow mineral (0-10 cm) 1198 16 1170 52 512 48 756 41 

X x Y y

Deep mineral (>10, up to cm) 2992 18 2757 45 1306 121 1637 20
USDA Forest Service Proceedings RMRS-P-61. 2010. 49

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

Several tree species contributed to total tree biomass (no table shown). Eight 

species occurred in the WH/CLUN habitat type, with the majority consisting of 

western redcedar (134.5 Mg/ha; 62 tons/acre) and western hemlock (129.1 Mg/

ha; 60 tons/acre), along with a minor component of western white pine (17.5 

Mg/ha; 8 tons/acre) and subalpine fir (4.7 Mg/ha; 2 tons/acre). Tree biomass in 

the ponderosa pine habitat types consisted of only ponderosa pine and for SAF/

VASC, only lodgepole pine.

Carbon Concentrations

Carbon concentrations were compared among forest components and habitat 

types (table 6). In all habitat types, carbon concentrations in fine and coarse 

roots were lower (44 percent to 47 percent) than the concentrations in overstory 

crown, bole, and understory trees (47 percent to 50 percent). Some of the lowest 

carbon concentrations (32 percent to 42 percent) occurred in the forbs and grasses 

component of understory vegetation. Among habitat types, the only significant 

differences occurred in the understory trees, where WH/CLUN had a significantly 

lower carbon concentration (47 percent) than the understory trees in both the 

ponderosa pine (50 percent) and SAF/VASC (49 percent) habitat types.

For woody residue, among the habitat types, solid log carbon concentrations 

were significantly lower in the SAF/VASC habitat type than the other habitat 

types; however, this was not statistically significant (table 7). We also had nonstatistically significant results concerning rotten logs. However, those created 

from ponderosa pine had some of the lowest carbon concentrations (46 percent) 

compared to the other habitat types. Sticks in the ponderosa pine habitat types had 

significantly more carbon (49 percent) than sticks in the WH/CLUN (47 percent) 

and SAF/VASC (48 percent) habitat types. A similar trend occurred with cones, 

with 49 percent for ponderosa pine habitat types, 46 percent for WH/CLUN, and 

47 percent for SAF/VASC.

Table 6—Carbon concentrations (%) are for vegetation by habitat type with the mean and standard error (Sx

–).

Significant differences among the means across habitat types are presented as x, y, and z located above the 

value. Significant differences among the means across different forest components within a habitat type are 

presented as “a” and “b” located next to the value. If the letter is the same no significant differences were 

identified. For habitat type designation, refer to table 1.

PP/FEAR	 PP/QUGA	 WH/CLUN	 SAF/VASC

Forest component	 %	 Sx

–	 %	 Sx–	 %	 Sx–	 %	 Sx–

Trees

x x x x

Overstory crown 49.9 a 0.2 49.9 a 0.2 49.2 a 0.2 49.4 a 0.1

Bole xy x yz z

49.0 a 0.4 49.5 a 0.2 47.3 a 0.2 47.2 b 0.4

x x x x

Coarse roots (≥1 cm diameter) 47.2 ab 0.7 48.6 a 0.6 46.8 a 0.3 47.l b 0.3

x x x x

Fine roots (<1 cm diameter) 44.0 b 1.2 46.2 b 0.4 46.1 a 0.4 44.9 c 0.4

X x y x

Understory trees (<12.7 cm dbh) 49.9 a 0.03 49.8 a 0.1 47.3 a 0.2 49.l ab 0.2

Understory vegetationa

Medium shrub (0.5-2.0 cm basal diameter) — — — — — — 48.3 a 0.2

x x x x

Low shrub (<0.5 cm basal diameter) 47.l a 0.6 46.7 a 0.5 45.5 a 0.5 47.8 a 0.2

x x x x

Forbs and grasses 42.7 b 0.4 41.5 b 0.6 41.6 b 1.3 42.0 b 0.4

aShrub size classes are from Brown (1976).
50 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

In the forest soil component, BCR tended to have statistically significant higher 

carbon concentrations (ranging from 47 percent to 49 percent) than the other soil 

components within each habitat type (table 7). More importantly, in all habitat 

types, the mineral soils had the lowest carbon concentrations, ranging from 1 percent to 6 percent. Within habitat types, concentrations for deep mineral soil were 

lower than those for shallow mineral soil, except WH/CLUN, which had 4 percent 

for both. The similarities that did occur were in the ponderosa pine habitat types 

where concentrations for humus were similar to those for litter.

Nine different tree species contributed to carbon concentrations for the tree 

components (table 8). For the overstory crowns, there were significant differences 

among species. For example, subalpine fir crowns had the highest (51 percent), 

while Engelmann spruce crowns had the lowest (48 percent). The results revealed 

no significant differences among species in sapwood carbon concentrations; however, significant differences were noted for heartwood, ranging from 46 percent 

for Engelmann spruce to 53 percent for ponderosa pine. For example, subalpine 

fir contained 46 percent while Douglas-fir contained 50 percent. Understory concentrations did not range as much (48 percent to 50 percent), with ponderosa pine 

having the highest (50 percent). For coarse root concentrations among species, 

Douglas fir had the highest (50 percent), while western redcedar and subalpine 

fir had the lowest concentrations (each had 46 percent).

Table 7—Carbon concentrations (%) for vegetation by habitat type with the mean and standard error (Sx

–). Refer 

to table 1 for habitat type designation. Significant differences among the means across habitat types are 

presented as x, y, and z located above the value. Significant differences among the means across different 

forest components within a habitat type are presented as “a” and “b” located next to the value. If the letter 

is the same no significant differences were identified.

PP/FEAR	 PP/QUGA	 WH/CLUN	 SAF/VASC

Forest component	 %	 Sx

–	 %	 Sx–	 %	 Sx–	 %	 Sx–

Woody Residue

x x y y

Sticks (<7.5 cm diam.) 49.l ab 0.3 49.0 a 0.1 46.9 a 0.3 47.6 ab 0.2

x x x x

Solid log (≥7.5 cm diam.) 50.5 a 1.5 49.5 a 1.2 47.0 a 0.2 46.0 bc 0.1

x x x x

Rotten log (≥7.5 cm diam.) 46.3 ab 0.9 50.8 a 1.6 47.9 a 0.7 48.9 a 0.6

x x y xy

Cones 48.6 ab 0.6 48.7 a 0.4 46.0 a 0.2 46.5 abc 0.3

Soil

x x x x

Brown cubical rot 48.2 a 0.9 47.l a 1.5 48.l a 0.9 49.0 a 0.7

x x x x

Litter 41.3 ab 1.5 32.7 ab 3.4 34.0 b 2.6 43.6 ab 1.2

x xy y xy

Humus 36.2b 3.6 29.l b 4.4 21.9 c 2.0 26.8 c 1.9

x x x x

Soil wood 47.2 a 2.4 44.6 ab 3.0 42.9 ab 0.9 41.6 b 1.0

x x x y

Shallow mineral (0-10 cm) 6.l c 0.5 4.5 c 0.6 4.4 d 0.6 1.4 d 0.1

yz xy x z

Deep mineral (>10, up to 30 cm) 1.9c 0.5 2.5c 0.6 3.5d 0.3 0.7d 0.04
USDA Forest Service Proceedings RMRS-P-61. 2010. 51

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

Table 8—Carbon concentrations (%) for individual species with the mean and standard error (Sx

–). 

Significant differences among the means across habitat types are presented as x, y, and z located 

above the value. Significant differences among the means across different forest components within a 

habitat type are presented as “a” and “b” located next to the value. If the letter is the same no significant 

differences were identified. Refer to table 1 for habitat type designation.

	 Overstory crown	 Bole wood	 Coarse roots	 Understory crown

	 	 Sap	 	 Heart

	 	 wood	 	 wood

Speciesa	 %	 Sx

–	 %	 Sx–	 %	 Sx–	 %	 Sx–	 %	 Sx–

x xy xy xy y

WWP 50.0 ab 0.4 48.5a 0.9 48.9 b 0.8 48.4 ab 0.1 47.3 ab 0.1

y z z x z

DF 48.7 bc 0.3 47.l a 0.2 47.4 bc 0.3 49.8 a 0.6 47.5 ab 0.4

x y y z y

GF 49.3 ab 0.3 47.7 a 0.1 47.7 bc 0.1 45.9 cd 0.2 47.8 ab 0.3

x y y y y

WH 49.9 ab 0.3 46.4 a 0.1 47.l bc 0.2 47.3 bcd 0.2 47.3 ab 0.2

x xy xy y xy

WRC 48.8 bc 0.2 47.l a 0.1 48.0 bc 0.3 45.7 cd 0.1 46.6 b 0.9

x y y y x

LP 49.5 ab 0.1 47.4 a 0.03 47.S bc 0.1 47.l bcd 0.3 49.l ab 0.2

xy y y y x

ES 47.6 c 0.1 46.7 a 0.2 46.4 c 0.2 46.5 bcd 0.3 48.0 ab 0.1

x x x x

SAF 50.5 a 0.2 43.7 a 3.5 47.4 bc 0.2 45.7 d 0.1 — —

xy y x y xy

PP 49.9 ab 0.1 47.5 a 0.2 52.5 a 0.8 47.9 abc 0.4 49.9 a 0.1

a Tree species are WWP-western white pine (Pinus monticola Dougl. ex D. Don); DF-Douglas fir (Pseudotsuga menziesii

(Mirb.) Franco); GF-grand fir (Abies grandis Dougl. cx D. Don); WH-western hemlock (Tsuga heterophylla (Raf.) Sarg.); 

WRC-western red cedar (Thuja plicata Dorm cx D.Don>; LP-lodgepole pine (Pinus contorta oougl. cx Loud.); ES-Engelmann 

spruce (Picea Engelmannii Parry cx Engelm.); SAF-subalpine fir (Abies lasiocarpa (Dougl. cx D. Don) Lindl; PP-ponderosa 

pine (Pinus ponderosa Dougl. ex Laws.) 

Carbon Content

We compared total carbon content in each classification among and within 

habitat types (fig. 3). The ponderosa pine types were considerably higher than 

others, with 562 Mg/ha (251 tons/acre) for PP/QUGA and 533 Mg/ha (238 tons/

acre) for PP/FEAR; WH/CLUN had 394 Mg/ha (176 tons/acre) and SAF/VASC 

had 239 Mg/ha (107 tons/acre).

Within all habitat types, the highest proportion of carbon content was in trees; 

PP/FEAR had 62 percent, PP/QUGA 61 percent, SAF/VASC 42 percent, and WH/

CLUN 40 percent. The component with the lowest proportion of the total carbon 

content for all four habitat types was woody residue, with WH/CLUN having the 

highest proportion (11 percent) compared to the others. Carbon content in the soils 

was not significantly different among habitat types.

In all habitat types (no figure shown), carbon content in trees was dominated 

by the boles, followed by coarse roots and understory crowns; fine roots had the 

least amount of carbon (fig. 3). Interestingly, carbon content in fine roots tended 

to be higher in the WH/CLUN (4.0 Mg/ha; 1.8 tons/acre) and SAF/VASC (2.2 

Mg/ha; 0.9 tons/acre) habitat types compared to the ponderosa pine types (0.1 

to 0.2 Mg/ha; 0.04 to 0.09 tons/acre). In SAF/VASC, shrubs had higher carbon 

content (15.0 Mg/ha; 7 tons/acre) compared to the forbs and grasses. In contrast, 

grasses and forbs had higher carbon content in PP/FEAR.
52 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

For woody residue components, cones comprised a rather large proportion of 

total carbon content in the ponderosa pine habitat types; PP/FEAR had 73 percent of the 15 Mg/ha in cones and PP/QUGA had 74 percent of the 18 Mg/ha in 

cones (fig. 4). In the WH/CLUN habitat type rotten (44 percent) and solid logs 

(38 percent) contributed the most carbon. In SAF/VASC, no significant differences occurred among woody residue components but the proportion of carbon 

content tended to be higher in cones (35 percent) and rotten logs (44 percent). 

Significant differences did occur among habitat types for sticks; SAF/VASC 

(15 percent) and PP/QUGA (14 percent) had more than WH/CLUN (10 percent) 

and PP/FEAR (7 percent).

Carbon content within the forest floor was dominated by mineral soil in the PP/

FEAR habitat type (fig. 5). Within the organic soil components, humus and litter 

in PP/FEAR had significantly more than BCR and soil wood, while in PP/QUGA 

organic components (litter, humus, soil wood, and BCR) were not a significant 

contribution. In the SAF/VASC and WH/CLUN habitat types, no significant 

differences occurred among any of the soil components, indicating that carbon 

was well distributed among the different soil components. Comparisons among 

habitat types showed no significant differences in carbon content across the soil 

components, except in mineral soils. Generally, the content in shallow mineral soils 

for the ponderosa pine habitat types was higher than WH/CLUN or SAF/VASC. 

Figure 3—The distribution of carbon for trees, forest floor, woody residue, mineral 

soils, and understory vegetation (shrubs, forbs, and grasses) in the four habitat types 

(see figure 2 for habitat type definitions). The ponderosa pine sites (PP/FEAR and PP/

QUGA) had significantly more carbon in the trees than the WH/CLUN or SAF/VASC 

sites. Woody residue, although a minor component, was significantly more abundant in 

WH/CLUN than the other sites. Shrubs and grasses tended to contribute more towards 

total carbon content in WH/CLUN and SAF/VASC compared to the ponderosa pine sites.
USDA Forest Service Proceedings RMRS-P-61. 2010. 53

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

Figure 5—The distribution of carbon 

content varied widely among the different 

habitat types. In the ponderosa pine 

habitat types (PP/FEAR and PP/QUGA), 

mineral soil (shallow and deep) dominated. 

In contrast, for WH/CLUN and SAF/VASC, 

carbon was located in many more places 

besides litter (for example, humus, BCR, 

and soil wood). In addition, shallow 

mineral soil contained more carbon in 

WH/CLUN than in SAF/VASC.

Figure 4—The distribution of 

woody residue in each of the 

habitat types. For the PP/FEAR 

and PP/ QUGA , most of the 

woody residue was in cones, with 

73% and 74% respectively. WH/

CLUN had the highest amount 

of carbon in woody residue, 

with 51 Mg /ha; the greatest 

proportions of this occurred 

in solid and rotten logs. SAF/

VASC had the lowest amount of 

carbon in woody residue; a large 

percentage of this was in cones 

(35%) and rotten wood (44%). 
54 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

In the deep mineral layers, carbon content (10.9 Mg/ha; 5 tons/acre) in SAF/VASC 

was significantly less than the carbon content among the other habitat types.

Discussion

Carbon is a critical element that plants accrue and use to support their structure 

and sustain physiological processes in temperate forest ecosystems (Waring and 

Schlesinger 1985). Besides being a key element in forest ecosystems, carbon is 

also essential for sustaining life on a global scale (Bolin and others 1979). Estimating carbon reserves in all ecosystems is critical if we are to understand the 

role carbon plays in climate change (Post and others 1990; Schlesinger 1977). 

It is also essential that we understand the potential human impacts on carbon 

storage in forest ecosystems and the resulting effects on the global carbon cycle. 

Although there are gross estimates available, additional information on carbon 

concentrations in forest components could improve those estimates.

Carbon Concentrations

To help improve the accuracy of estimates of carbon reserves in Rocky Mountain forests, this study quantified carbon concentrations for forest components 

in four habitat types (table 1). These results are comparable to other studies on 

carbon in the Rocky Mountains. For example, Klemmedson’s (1975) carbon concentrations for tree, understory, coarse woody debris, and other components in 

southwest ponderosa pine forests ranged from 39 percent to 50 percent. The forest 

components from the ponderosa pine sites in our study had similar concentrations 

(table 9). In addition, concentrations in all forest types sampled were similar to 

those of Klemmedson’s (1975) and Lamlom and Savidge (2003).

Traditionally, a concentration of 50 percent was used for calculating carbon content from tree biomass, as evaluated by Lamlom and Savidge (2003). This estimate 

is reasonable if the objective is to provide approximate estimates of total carbon. 

In this study, we found there to be significant differences in carbon concentrations 

of coarse roots and overstory crowns among species; however, the maximum differences were only 1.7 percent for the overstory and 2.7 percent for coarse roots. 

These small differences would have an insignificant impact on carbon estimates 

when the variations in biomass estimates are included. For example, the amount 

of error introduced in root biomass estimations outweighs the small differences 

detected in carbon concentrations. On the other hand, to improve carbon content 

estimates for trees, an average carbon concentration for each tree component 

could be used. Based on this study, we recommend 49.5 percent for overstory 

crown, 47.6 percent for boles, 47.2 percent for coarse roots, and 48.4 percent for 

understory trees (table 9). Similarly, better carbon content estimates for other 

forest components can be achieved by using more precise carbon concentrations.

Carbon Storage

In the ponderosa pine habitat types, carbon weights in trees were 20 percent 

higher than those reported by Klemmedson (1975), who conducted a similar study 

near Flagstaff, Arizona. This discrepancy is probably due to the difference in tree 

size between the two studies and our successional stage. Klemmedson sampled 

sapling and pole-sized trees, while trees sampled in this study were mature and 

ranged from 50 to 100 cm (20 to 40 inches) d.b.h. The amount of carbon storage 

in WH/CLUN trees was less than the amount stored on the ponderosa pine sites. 

This appears to suggest that the ponderosa pine sites are more productive than 

the WM/CLUN sites. However, this can be misleading because the sites sampled 
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Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

on the WH/CLUN habitat type were not at their maximum growth potential and 

relatively young, while the ponderosa pine sites were older and maximum growth 

potential may have been reached (Pearson 1950). When western hemlock habitat 

types are at their full growing capacity, the carbon storage potential could be 

much higher (Haig 1932).

Several studies have reported the importance of shrubs, forbs, and grasses for 

nutrient cycling (Chapin 1983; Jorgensen and Wells 1986), yet rarely quantify the 

amount of carbon they can store. Dwarf huckleberry in the SAF/VASC is a small 

shrub rarely considered for its ability to store carbon; in this study, 15 Mg/ha (6.7 

tons/acre) of carbon were stored in this component. Forbs and grasses may also 

have the potential to be important for carbon storage. This study, however, found 

a maximum of only 0.1 Mg/ha (.04 tons/acre) across all habitat types. Before 

fire suppression, forbs and grasses were abundant in the ponderosa pine types. 

However, due to lack of fire as well as over-grazing, in this study, they were an 

insignificant element for carbon storage (Covington and Moore 1994). However, 

with the advent of more wild and prescribed fire, these pools could shift and thus 

grass and forbs could play a greater role in storing carbon both above and below 

(rapid root turnover may increase carbon concentration in mineral soil) the soils 

surface. Therefore, depending on forest type and forest history, small components 

within forest ecosystems should not be overlooked when estimating carbon pools.

Coarse roots have the potential to store large amounts of carbon. In the 

ponderosa pine habitat types, we found that coarse roots stored more carbon 

than in the WH/CLUN and SAF/VASC habitat types. Carbon allocation to roots 

varies widely among sites, depending on growing season, nutrient availability, 

climate, tree species, age, and genetic materials (Kramer and Kozlowski 1979). 

For example, the ponderosa pine forests sampled in this study were 25 to 50 years 

older than the WH/CLUN forest and 60 years older than the SAF/VASC forest. 

Table 9—Carbon concentrations recommended for converting organic biomass 

to carbon content for habitat types evaluated in this study. Because significant 

differences occurred among habitat types, separate carbon concentrations are 

recommended. Refer to table 1 for habitat type designation. 

Forest component	 Carbon concentration (%)

Trees

Overstory crown 49.5

Bole 47.6

Coarse roots (>1 cm diameter.) 47.2

Fine roots (≤1 cm diameter) 45.3

Understory trees (<12.7 cm tall) 48.4

Understory vegetation

Shrubs 47.2

Forbs and grasses 41.4

Woody residue

Sticks (<7.5 cm diameter)—PP/FEAR and PP/QUGA 49.1

Sticks (<7.5 cm diameter)—WR/CLUN and SAF/VASC 47.2

Solid log (≥7.5 cm diameter) 48.2

Rotten log (≥7.5 cm diameter) 48.7

Cones—PP/FEAR and PP/QUGA 47.9

Cones—WR/CLUN and SAF/VASC 46.0

Soils

Brown cubicle rotten wood 48.0

Litter 37.9

Soil wood 44.2
56 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jain, Graham, and Adams Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains

This difference in age most likely influenced the amount of carbon stored in 

roots and other carbon pools. The Northern Rocky Mountain habitat types had 

more fine woody roots than the ponderosa pine forests of the southwest. This may 

be because moisture and nutrients are located in the surface layers of Northern 

Rocky Mountain forests, causing trees to allocate more carbon to fine root growth 

(Aber and Melillo 1991; Kramer and Kozlowski 1979; Page-Dumroese and others 

1990). Schlesinger (1977) discussed the importance detritus plays in ecosystem 

function and carbon cycling, referring to root turnover, undecomposed litter, and 

soil humus, but did not mention the contribution of woody residue. Keenan and 

others (1993) recognized the importance of woody residue, reporting 161 Mg/

ha (71.8 tons/acre) of carbon in woody material in western redcedar and western 

hemlock forests in northern Vancouver Island. Similarly, in the younger inland 

western hemlock forest we sampled, 51 Mg/ha (23.8 tons/acre) was found in woody 

materials. Although woody material is beginning to be recognized as a carbon 

sink, usually only coarse woody debris is considered, while other components that 

may be important for storing carbon are ignored. For this reason, we quantified 

where carbon is located within some of these other woody components (fig. 4).

This study found that cones are a major component of the woody materials of 

three (PP/FEAR, PP/QUGA, and SAF/VASC) of the four habitat types (fig. 4). 

In the ponderosa pine habitat types, greater than 70 percent of the carbon in the 

woody residue was in cones. In the SAF/VASC habitat type, cones also stored a 

significant proportion (35 percent); WH/CLUN had the least amount (8 percent). 

In vegetation types such as ponderosa and lodgepole pine, where cones represent 

a large portion of the woody residue, it is important to consider these components 

when estimating total carbon reserves. Other types of woody residue also store 

large amounts of carbon. For example, in the WH/CLUN habitat type, large and 

small woody residue contributed 11 percent of the total carbon on the site (fig. 3), 

with more than 80 percent in solid and rotten logs (fig. 4). These results show 

that CWD plays a major role in storing carbon in WH/CLUN habitat types while 

sticks are important in ponderosa pine habitat types.

As snags, CWD, sticks, cones, and coarse roots decompose, they form soil 

wood and BCR, important soil components of Rocky Mountain forest ecosystems 

(Graham and others 1994; Harvey and others 1987). Graham and others (1994) 

suggested that forest floors may consist of 30 to 60 percent woody material. In 

this study, we found that 25 to 30 percent of the soil carbon was in soil wood and 

BCR (fig. 5). Although in the ponderosa pine sites less than 7 Mg/ha (3.1 tons/

acre) of the soil carbon consisted of soil wood, there is a large potential for soil 

wood recruitment after trees die and root biomass becomes soil wood. In Rocky 

Mountain forests, soil wood and BCR are important carbon sinks that are often 

overlooked.

Litter and humus also store large amounts of carbon. The large amounts of litter 

in the ponderosa pine and SAF/VASC habitat types are the result of the continuous shedding of needles (Kilgore 1981; Olson 1981). The proportion of carbon 

in the litter and humus located in the soils of the ponderosa pine habitat types in 

this study ranged from 20 percent to 25 percent. These proportions were larger 

than the 10 percent reported by Klemmedson (1975). This is probably due again 

to the differences in stand ages or successional stage between the two studies. 

Klemmedson’s (1975) stands were younger and did not produce as much litter and 

surface humus, while this study’s stands were over 200 years old.

Carbon content in mineral soils varied among habitat types. The results 

from this study show that in the ponderosa pine habitat types, greater than 

70 percent of the forest soil carbon was stored in the mineral soils (fig. 5). In 

comparison, Klemmedson (1975) found that 89 percent of the total soil carbon 

content was in mineral soils. This amount was the result of root turnover from 
USDA Forest Service Proceedings RMRS-P-61. 2010. 57

Carbon Concentrations and Carbon Pool Distributions in Dry, Moist, and Cold Mid-Aged Forests of the Rocky Mountains Jain, Graham, and Adams

the grass component (Buol and others 1989). In the WH/CLUN and SAF/VASC 

habitat types, litter and humus contained 39 to 43 Mg/ha (17.4 to 19.8 tons/acre) 

of carbon (fig. 5). These results did not differ greatly from the 50 to 60 Mg/ha 

(22.3 to 26.8 tons/acre) in litter and humus reported by Keenan and others (1993) 

from sites in northern Vancouver Island. The ponderosa pine habitat types had 

more carbon in mineral soils than WH/CLUN and SAF/VASC (fig. 5), which 

may be due to higher clay content in soils of the ponderosa pine habitat types.

Other reasons for the variation in carbon storage among the forest types may 

be differences in climate and decomposition (Aber and Melillo 1991; Harmon 

and Hua 1991; Harvey and others 1987). For example, warm temperatures in the 

southwest coupled with summer rains provide favorable conditions for microbes 

to decompose woody material (Clark 1957), while WH/CLUN and SAF/VASC 

have colder temperatures, thus slowing decomposition rates (Harmon and others 

1986). These environmental factors controlling decomposition contribute to the 

differences in carbon storage among the habitat types.

On a global scale, researchers have theorized that the major carbon sink is in 

mineral soils (Post and others 1982; Schlesinger 1986). However, most global 

carbon estimates ignore many other forest components. In this study, we found 

that other forest components such as shrubs, cones, CWD, BCR, and soil wood 

can be major carbon sinks in Rocky Mountain forest ecosystems.

Conclusions

Typically, the conversion of 0.50 is used to provide estimates of carbon pools; 

however, this study provided a suite of values that vary depending on the species, substrate, and location. Moreover, these values tended to be less than the 

conventional value, leading one to overestimate total carbon amounts in these 

forest types if the typical conversion is used. In addition to carbon concentrations, we showed the variability in carbon content as a function of forest type 

and that minor elements such as cones, shrubs, and brown cubical rotten wood 

can contribute to the total carbon pool. Given these results, a series of additional 

research questions could be pursued such as the effect of successional stage on 

carbon pool distributions. For example, young forests may not contain the brown 

cubical rotten wood mid- to late-seral moist forests contain. Also, as forests grow 

and develop, do carbon concentrations change or does only biomass distribution 

change over time? Thus, determining if carbon concentrations vary as a function 

of successional change could provide invaluable information concerning variation 

of carbon over time and space.

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Whittaker, R. H.; Bormann, F. H.; Likens, G. E.; Siccama, T. G. 1974. The Hubbard Brook ecosystem study: forest biomass and production. Ecological Monographs. 442: 233-254.

Will, G. H. 1966. Root growth and dry-matter production in a high-producing stand of Pinus 

radiata. New Zealand Forestry. Research Note 44.

Wykoff, William R.; Crookston, Nicholas L.; Stage, Albert R. 1982. User’s guide to the stand 

prognosis model. Gen. Tech. Rep. INT-33. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station. 112 p.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 61

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Oregon Department of Forestry.

2 USDA Forest Service, Pacific Northwest Research Station, Western 

Wildlands Environmental Threat Assessment Center.

3 SofTec Solutions.

4 USDA Forest Service, Rocky Mountain Research Station, Missoula Fire 

Sciences Laboratory.

5Formerly with, USDA Forest Service, 

Fremont-Winema National Forest.

Carbon Benefits from Fuel Treatments

Jim Cathcart1, Alan A. Ager2, Andrew McMahan3, 

Mark Finney4, and Brian Watt5

Abstract—Landscape simulation modeling is used to examine whether fuel treatments 

result in a carbon offset from avoided wildfire emissions. The study landscape was a 

169,200-acre watershed located in south-central Oregon. Burn probability modeling 

was employed under extreme weather and fuel moisture conditions. Expected carbon stocks post-treatment, post-wildfire were calculated for all stands on the treated 

landscape; post-wildfire on the untreated landscape. Results show a negative carbon 

offset initially—the known reduction of carbon stocks from treatment is greater than 

expected carbon benefit from reduced wildfire emissions. Treatment may break even 

as a carbon offset after 9 years.

Introduction

Many of the forests in the western United States (U.S.) are significantly altered as a result of decades of continuous fire suppression activity. The absence 

of wildfire in many forest types that historically have had frequent fire return 

intervals results in increased density of overstory trees and understory vegetation. Competition for limited moisture and nutrients further reduces tree growth 

and vigor. Add drought, insects or disease and the result is a perfect storm for 

uncharacteristically severe wildfire events—overstocked stands containing dead 

trees, dying crowns, ladder fuels, and high surface fuel loadings.

In response to these trends, the U.S. has increased appropriations for wildfire 

fuel treatment projects through the National Fire Plan and complementary programs (Healthy Forest Restoration Act 2003; Sexton 2006; USDA Forest Service 

and USDI Bureau of Land Management 2001). Appropriations for fuel treatments 

have also been awarded through the 2009 American Recovery and Reinvestment 

Act as an economic stimulus to the national recession. The goals of fuel treatment 

projects are to reduce wildfire fuels within stands, to provide defendable space 

around homes and communities, and to create fuel breaks that allow for more aggressive and effective initial attack suppression efforts (Finney and Cohen 2003; 

Reinhardt and others 2008). Mechanical methods include slash busting, chipping, 

bundling, piling and burning, as well as pre-commercial and commercial thinning. 

Prescribed fire is also used as a treatment, often in combination with mechanical 

methods. Effective treatments result in the removal of woody biomass from treated 

areas and improved residual stand vigor, which promote resiliency to fire, insect 

and disease. Fuel treatment projects also reduce fire spread and intensity both 

inside and outside the portions of the landscape treated (Finney 2001; Finney and 

others 2007). In most cases, fuel treatment projects are completed at a financial 

loss; the stumpage value of merchantable trees is usually insufficient to cover the 

cost of the overall treatment. Moreover, fuel treatment projects are not without 

controversy with respect to their impact on forest ecological values, even though 

there is general agreement as to their need (Brown 2008).
62 USDA Forest Service Proceedings RMRS-P-61. 2010.

Cathcart, Ager, McMahan, Finney, and Watt Carbon Benefits from Fuel Treatments

Carbon Dioxide Emissions and Wildfires

For the period 2002-2006, wild and prescribed fire emissions from forests, range 

and agriculture in the U.S, averaged 4-6 percent of human derived carbon dioxide 

(CO2) emissions from fossil fuel sources (Wiedinmyer and Neff 2007). However, 

the level of emissions varies widely among states and time of year. In Oregon, 

fire emissions are about 50 percent of the fossil fuel CO2 emissions (Wiedinmyer 

and Neff 2007). A similar study estimates emissions from catastrophic wildfire 

in California during 2001-2007 as equivalent to three and a half years worth of 

CO2 emissions from 14 million cars (Bonnicksen 2009). Wiedinmyer and Neff 

(2007) conclude that very large wildfires in a severe fire season lasting only one 

or two months can release as much carbon as the annual emissions from the entire 

transportation or energy sector of an individual state.

Increases in wildfire severity and extent will lead to increases in carbon dioxide (CO2) emissions to the atmosphere. Simulations of how Oregon’s forests will 

be affected by climate change in Bachelet and others (2001) show that Oregon’s 

forests will accumulate more woody biomass, not less, especially in the eastern 

region of the state where continued biomass accumulation in the understory increases the risk of uncharacteristically severe wildfire. Nielson (2004) suggests 

that Oregon’s current wildfire risk problem is not only the result of fire suppression efforts, but also the result of an underlying climate signal that has increased 

biomass accumulation. This signal is expected to continue into the future. In the 

Pacific Northwest, area burned can be expected to increase despite management 

efforts to reduce fuel loadings through treatment in fire prone forests (Gedalof 

and others 2005). As a result, the risk of high severity wildfires in western forests 

will increase, not decrease, in the future (McKenzie and others 2004). The area 

in need of fuel treatment far exceeds what is being planned for and accomplished 

despite increases in federal appropriations. This situation only stands to get worse 

as climate change leads to more biomass accumulation in western forests (USDA 

Forest Service 2005). Law and others (2004) conclude that large fire events, 

such as Oregon’s 2002 Biscuit fire can significantly reverse the role forests play 

in the carbon cycle from being net carbon sink that sequesters atmospheric CO2

to being a net source of CO2 to the atmosphere, not only in the year of the fire, 

but in subsequent years due to continued decomposition of fire-killed material.

The wildfire risk problem can in part be attributed to an excess of stored carbon 

in the forest as a result of many decades of fire suppression (Hurteau and North 

2009; North and others 2009; Stephens and others 2009a). Fuels reduction treatments in general reduce the amount of stored carbon on treated areas and are thus 

a source of carbon to the atmosphere. Girod and others (2007) concluded that 

strategies that promote both carbon storage and reduced wildfire risk will need to 

involve increased fuel treatment efforts that are spatially adjusted to avoid unnecessary reductions in carbon stocks. Despite the fact that fuel treatment practices 

result in carbon releases back to the atmosphere, several studies conclude that 

fuel treatment projects can result in a net gain in carbon storage landscape-wide 

because of the treatment’s effect on reducing and avoiding CO2 emissions from 

severe wildfires (Bonnicksen 2009; Hurteau and others 2008; North and others 

2009; Stephens and others 2009a). Other studies disagree; fuel treatment leads 

to a net loss of carbon on the forest landscape even when the avoided emissions 

from uncharacteristically severe wildfire are factored in (Krankina and others 

2008; Mitchell and others 2009).

These differing conclusions can be attributed to the fire regime and disturbance 

return interval of the forest vegetation type being studied and how wildfire risk 

is assessed. For example, Mitchell and others (2009) concluded that for hemlockspruce-Douglas-fir forest types of western Oregon, fuel treatment projects fell 
USDA Forest Service Proceedings RMRS-P-61. 2010. 63

Carbon Benefits from Fuel Treatments Cathcart, Ager, McMahan, Finney, and Watt

at odds with effectively managing forests for long-term carbon storage. This is 

because extreme weather conditions, not fuel loadings, are the leading cause of 

high severity wildfires in these forest types. Krankina and others (2008) found 

a net carbon benefit to treated stands, but only in short fire return interval dry 

ponderosa pine forests where the removal of non-merchantable understory material was sufficient to reduce wildfire risk.

Unless the surface fuels, understory and ladder fuels found in small, unmerchantable trees are all treated to reduce wildfire risk, the treatment projects 

will not be effective in reducing wildfire severity and extent (Cram and others 

2006; Stephens and others 2009b). There are not many facilities that will take this 

type of material and when they do, the delivered value of the material is usually 

not sufficient to cover the costs of its removal and transportation (Mason Bruce 

& Girard Inc. and others 2006). Recognizing the potential of utilizing forest 

slash and other residues as a source of biomass energy, thereby displacing fossil 

fuel use, Oregon’s strategy for reducing greenhouse gases included the creation 

of markets for forest biomass as a significant greenhouse gas mitigation action 

(Governor’s Advisory Group on Global Warming 2004). The question remains 

though on whether the implementation of fuel treatment projects creates an additional carbon benefit accruing to the treated forest landscape (as the Advisory 

Group hypothesized), or results in an overall decrease in carbon stores that should 

be deducted from any displaced CO2 emissions benefit claims being attributed 

to biomass energy.

Purpose

The purpose of this study is to properly account for the net balance of carbon 

storage resulting from a landscape fuel treatment project. On the treated acres 

themselves, the goal is to reduce or remove biomass, which may reduce on-site 

carbon stocks, even when continued carbon storage in long-lived wood products 

and avoided wildfire emissions is accounted for. However, strategically located 

fuel treatments can reduce fire spread and intensity both inside and outside the 

portions of the landscape treated. Therefore, the accounting of carbon gains and 

losses resulting from the fuel treatment project must be calculated in a way that 

includes the reduction in fire risk to and corresponding change in carbon stores 

within untreated areas of the landscape.

Quantifying whether a treatment’s effect on stored carbon is positive or negative is especially important if the fuel treatment project attempts to seek funding 

from entities investing in such a project as a means to generate carbon offsets 

to their CO2 emissions. If the treatment effect is positive (more carbon is stored 

on the landscape as a result of treatment), then a carbon offset might be able to 

be claimed by the project6. Clearly, if the opposite is true, then no carbon offset 

can be claimed. Such a “negative offset” means that there is an emission cost, in 

terms of carbon released to the atmosphere. But this result does not mean the fuel 

treatment project is any less effective in its primary purpose—to make forested 

landscapes more healthy and resilient to wildfire—and as a result, more suited 

for providing clean water, fish and wildlife habitat, and protecting Oregon’s communities from the risk of wildfire.

6 Whether or not the amount of carbon benefit can actually be claimed as on offset in a voluntary 

carbon market or compliance carbon trading program is beyond the study’s scope.
64 USDA Forest Service Proceedings RMRS-P-61. 2010.

Cathcart, Ager, McMahan, Finney, and Watt Carbon Benefits from Fuel Treatments

Methods

Because wildfire is a chance event, this study estimates the expected carbon 

stored in forests based on the probability of wildfire on the landscape. Using a 

simulation modeling approach, expected carbon loss from wildfire on the untreated 

landscape is estimated and compared to the expected carbon loss from wildfire 

and treatment for the treated landscape. There are two ways to look at carbon 

loss. The first is to look at the expected change in carbon stocks; the second is to 

look at the expected carbon emissions from the landscape. If all affected carbon 

pools are accounted for, the two measures of carbon loss mirror one another. The 

expected change in carbon stocks equals expected carbon emissions when both 

changes are measured in the same units such as CO2 equivalent. At the time of a 

wildfire event, carbon loss increases as the intensity of wildfire increases and in 

all cases, regardless of intensity, wildfire reduces carbon stocks. Only in the case 

where wildfire does not occur can carbon stocks increase. This study’s approach is 

to simulate all possible wildfire intensities on all possible stand conditions (both 

treated and untreated) to develop carbon loss functions for each stand condition. Then, by simulating thousands of wildfires on the landscape, a probability 

surface of wildfire is developed both for the treated landscape and the untreated 

landscape. The difference in the probability surfaces (treated minus untreated) 

is a measure of the treatment effect on reducing both the likelihood of wildfire 

on the landscape given an ignition, and the intensity of wildfire given that wildfire occurs. Simulating carbon loss functions using this probabilistic approach 

and comparing the results from treated and untreated landscapes quantifies the 

expected change in carbon stocks resulting from treatment. Table 1 summarizes 

the major steps for the analysis.

Study Area

The study area is the Drews Creek watershed located within the Goose Lake 

basin of southern Oregon (fig. 1). The Drews Creek watershed was selected because it contains dry ponderosa pine and mixed conifer forest types at risk of 

uncharacteristically severe wildfire, is at the beginning stages of fuel treatment 

planning by the Fremont-Winema National Forest, and is within a collaborative 

terrestrial sequestration research pilot through the West Coast Regional Carbon 

Sequestration Partnership7. The watershed is approximately 169,200 acres of 

which approximately 77,500 acres are privately owned and the remaining 91,700 

acres are owned and managed by the U.S. Department of Agriculture (USDA) 

Forest Service.

The Drews Creek watershed encompasses a relatively narrow band of topographical relief. Elevations of the watershed range from 6,400 - 7,900 ft in the 

northeast portion to 4,200 - 5,000 ft in the valley adjacent to Goose Lake in the 

southwest. Although slope ranges from 0 - 200 percent, much of the watershed 

slopes relatively gently with the average slope being 14 percent. The forested area 

of the watershed is 140,526 acres. Stands dominated by ponderosa pine (Pinus 

ponderosa Laws.) account for about 68 percent of the forest land in the watershed. Approximately 17 percent of the area is in juniper woodlands, and western 

juniper (Juniperus occidentalis Hook.) dominates 26 percent of all forested types, 

encroaching on the hot dry ponderosa pine sites. Stands dominated by white fir 

(Abies spp.) represent a minor contingent of the landscape, at about 6 percent of 

7 For more information on WESTCARB, see: http://www.westcarb.org/.
USDA Forest Service Proceedings RMRS-P-61. 2010. 65

Carbon Benefits from Fuel Treatments Cathcart, Ager, McMahan, Finney, and Watt

Table 1—Steps for modeling wildfire risk, fuel treatments and expected carbon.

Step	 Description	 Data source	 Model

1 Calculate the amount of stored carbon GNNa treelist and LANDFIRE FFE-FVSb with fuel

by carbon pool for each untreated stand fuel model data. model override and

condition under all possible wildfire FlameAdj keyword.

intensity conditions, including no fire.

2 Calculate the amount of stored carbon Post treatment FVS treelist FFE-FVS with fuel

by carbon pool for each treated stand output. model override and

condition under all possible wildfire FlameAdj keyword.

intensity conditions, including no fire.

3 Create untreated landscape file. Slope, Aspect, Elevation, ArcFuels

GNN treelist, and LANDFIRE 

fuel model data; FVS outputs 

(initial conditions) for Canopy 

Bulk Density, Canopy Base 

Height, Crown Closure and 

Canopy Height.

4 Create treated landscape file. Same as Step 3 except use ArcFuels

FVS outputs from Step 2 for 

Canopy Bulk Density, Canopy 

Base Height, Crown Closure 

and Canopy Height and switch 

fuel model to 181 for treated 

stands.

5 Conduct repeated landscape Landscape files from Steps 3 Randig

simulations of wildfire from a random and 4.

wildfire ignition for both the untreated 

and treated Drews Creek landscapes. 

6 Calculate conditional burn intensity Output from Step 5. Randig

probabilities for each 90- by 90-meter

pixel for both the untreated and 

treated landscapes.

7 Calculate the amount of expected Outputs from Step 1, Step 2 ArcFuels

stored carbon for each 90- by 90-meter and Step 6.

pixel for both the untreated and treated 

landscapes.

8 Calculate and map the expected carbon Outputs from Step 7; the offset ArcFuels

offset for each 90- by 90-meter pixel; is treated landscape results

summarize results to get the carbon minus untreated landscape

offset accruing to the treated landscape. results.

a GNN—Gradient nearest neighbor.

b FFE-FVS—The Fire and Fuels Extension to the Forest Vegetation Simulator.

the forested acres. Smaller stands of lodgepole pine (Pinus contorta Dougl. ex 

Loud.) as well as small stands of aspen (Populus tremuloides Michx.) are scattered across the landscape. Past harvesting activities have significantly reduced 

the area dominated by large diameter old growth ponderosa pine. Dry grasslands, 

dry shrub lands and dry meadows comprise nearly one-half of non-forested lands 

(tree cover less than 10 percent) with the balance being agricultural lands and wet 

meadows associated with the major streams.

Fuel loadings are variable across the drainage, but follow various gradients. As 

elevation increases, stands types move from pure low elevation ponderosa pine, 

to ponderosa pine dominated mixed conifer, in association with white fir, western 

white pine (Pinus monticola Douglas ex D. Don), sugar pine (Pinus lambertiana 

Dougl), and incense-cedar (Libocedrus decurrens Torr.), to upper elevation fringes 

where white fir dominates or more frequently co-dominates with ponderosa pine. 
66 USDA Forest Service Proceedings RMRS-P-61. 2010.

Cathcart, Ager, McMahan, Finney, and Watt Carbon Benefits from Fuel Treatments

Another gradient is aspect, and a third is whether there has been a prescribed 

burning treatment. Most of the pine dominated stands below 5,500 feet in elevation have received at least one prescribed burn treatment, the exceptions being 

the Quartz Creek sub-watershed and the Chandler Creek drainage, forming the 

northern tier of the watershed.

In the low elevation treated pine stands, typical fuel loadings range from 2-5 

tons per acre. Untreated pine stands tend to be more variable, averaging 3-15 tons 

per acre. As white fir joins the stands at low elevations, loadings increase rapidly, 

particularly where root disease is present. Typical loadings here can range from 

15 tons to as high as 50 tons per acre or more if there has been recent disturbance 

without fuel treatment. For the period of 1949-1999 the watershed has had 688 

wildfire ignitions, with an average of fourteen fire starts per year. The high was 

38 in 1977 and the low was one in 1963. All fires were actively suppressed. Of 

these, 88 percent were suppressed at less than 0.25 acres, 10 percent between 0.26 

to 9.9 acres with the balance larger. Forty-four fires larger than 10 acres occurred 

over this period; the total burned for these fires is approximately 9,000 acres.

Figure 1—Vicinity and relief map for the Drews Creek Watershed, Fremont-Winema National Forest 

showing watershed boundaries and treatment units. 
USDA Forest Service Proceedings RMRS-P-61. 2010. 67

Carbon Benefits from Fuel Treatments Cathcart, Ager, McMahan, Finney, and Watt

Spatial Modeling of Wildfire, Fuel Treatments and 

Expected Carbon Loss

The study used ArcFuels8 to automate much of the analyses (Ager and others 

2006). ArcFuels is a library of ArcGIS9 macros developed to streamline spatial 

modeling of wildfire behavior, stand growth and yield, and fuel treatments for 

planning purposes. ArcFuels brings together various data layers (e.g., gradient 

nearest neighbor (GNN10) treelists, digital elevation grids, stand polygons, Forest 

Vegetation Simulator11 (FVS) growth and yield outputs, LANDFIRE12 fuel model 

data, slope, and aspect) and processes them in ways that facilitate communication 

between fire simulation, stand growth and yield, and spatial modeling programs. 

ArcFuels links the FlamMap13 wildfire behavior models with fuels and vegetation 

data through a Microsoft© Office and ArcGIS platform. Carbon stocks were modeled through the Fire and Fuels Extension (FFE) of FVS14. Specifically, FFE-FVS 

accounts for the following carbon pools: aboveground total and merchantable live, 

below ground live and dead, standing dead, dead and down woody debris, forest 

floor (litter and duff), and understory (shrub and herb). FFE-FVS also accounts 

for the fate of carbon stored in merchantable material removed—specifically 

the amount of continued storage in wood products and landfills—as well as the 

amount emitted for energy and non-energy use (Rebain 2009).

Stand treatments—Stands were selected for treatment based on criteria 

developed by staff on the Fremont-Winema National Forest. Virtually all stands 

eligible based on basal area (70 ft2 per acre), also met additional distance to road 

and slope criteria. Pixels selected for treatment were then aggregated into treatment units having a minimum size of 15 acres by first kernel density smoothing 

the selected pixels, and then retaining only those pixels that exceeded a density 

threshold. The result was 94 treatment polygons, consisting of 17,740 acres, averaging 175 acres each (fig. 1). The treatment units covered approximately 12.6 

percent of the watershed’s forestland. Of the 17,740 acres, only 12,825 acres met 

thresholds for treatment (9.1 percent of the watershed’s forestland).

Stands (represented by the imputed GNN treelist data to 30- by 30-meter pixels) 

that were within treatment polygons were assigned a treatment prescription and 

the treatment was then simulated in FVS. The treatment prescriptions called for 

thinning from below to a residual basal area of 70 ft2 per acre for mixed conifer 

or fir dominated stands and 50 ft2 per acre for pine dominated stands, followed by 

slash removal and under burning. The specific parameters for the fuel reduction 

prescription were chosen based on operational guidelines from the FremontWinema National Forest. The treatments were simulated with FVS and consisted 

of a three-year sequence of thinning from below, site removal of surface fuels, 

and under burning. Under burning and mechanical treatment of surface fuels was 

simulated with the FFE-FVS keywords SIMFIRE and FUELMOVE (Reinhardt 

and Crookston 2003). Fuel treatment prescriptions for thinning from below had 

no upper diameter limit and specified retention of fire tolerant ponderosa pine and 

8 For more information on ArcFuels, see: http://www.fs.fed.us/wwetac/arcfuels/.

9 For more information on ArcGIS, see: http://resources.esri.com/gateway/index.cfm.

10 For more information on gradient nearest neighbor data, see Ohmann and Gregory (2002).

11 For more information on the Forest Vegetation Simulator, see: http://www.fs.fed.us/fmsc/fvs/.

12 Landscape Fire and Resource Management Planning Tools Project. For more information see: 

http://www.landfire.gov/. 13 For more information on FlamMap, see Finney (2006).

14 See pp. 3 through 7 in Rebain (2009) and Hoover and Rebain (2008).
68 USDA Forest Service Proceedings RMRS-P-61. 2010.

Cathcart, Ager, McMahan, Finney, and Watt Carbon Benefits from Fuel Treatments

favored the removal of white fir. Surface fuel treatments simulated the removal 

of 90 percent of the material up to 12 inches. Under burning was then simulated 

using weather conditions and fuel moisture guidelines provided by fuels specialists on the Fremont-Winema National Forest. The prescription was supported by 

empirical studies as effective for reducing potential wildfire behavior (Peterson 

and others 2003; Stephens and Moghaddas 2005; Stephens and others 2009b; van 

Wagtendonk 1996).

Burn probability modeling—We simulated wildfires using the minimum 

travel time (MTT) fire spread algorithm of Finney (2002) as implemented in a 

command line version of FlamMap called “Randig” (Finney 2006). The MTT 

algorithm replicates fire growth by Huygens’ Principle where the growth and 

behavior of the fire edge acts as a vector or wave front (Finney 2002). Extensive 

testing over the years has demonstrated that the Huygens’ Principle can accurately 

predict fire spread and replicate large fire boundaries on heterogeneous wildlands 

(Knight and Coleman 1993; LaCroix and others 2006; Yohay and others 2009). The 

MTT algorithm was parallelized for multi-threaded processing making it computationally feasible to perform Monte Carlo simulations of many fires (>100,000). 

The MTT algorithm is now being applied daily for operational wildfire problems 

throughout the U.S15 and can generate burn probability surfaces for very large 

(> 2 million ha) landscapes (Ager and Finney 2009; Ager and others 2006; Ager 

and others 2007). The MTT algorithm assumes constant weather and is used to 

model individual burn periods within a wildfire rather than continuous spread 

of a wildfire over many days and weather scenarios.

Relatively few burn periods generally account for the majority of the total 

area burned in large (e.g. >5,000 ha) wildfires in the western U.S., and wildfire 

suppression efforts have little influence of fire perimeters during these extreme 

events. Based on input from forest staff, and supported by historical data from 

remote automated weather stations, each fire event was simulated as an 8-hour 

burn period with a 25 mph wind under the fuel moisture conditions listed in table 2. 

Wind was randomly simulated from three directions (225, 235, and 245 degrees) 

for each burn period. Ignition locations were random. The target simulated problem fire under these conditions, on average, was 11,000 acres. Randig outputs a 

vector of conditional burn probabilities (BPi

) for each pixel that represents the 

probability of a fire at the ith 0.5 m flame length category. Different flame lengths 

15 See: http://www.fpa.nifc.gov and http://wfdss.usgs.gov/wfdss/WFDSS_About.shtml.

Table 2—Fuel moisture values used in 

the wildfire simulationsa.

Parameter	 Percent moisture

1 hour 4

10 hour 5

100 hour 7

Live herbaceous 60

Live woody 62

aPersonal communication. Fire management 

and silviculture specialists, Lakeview Ranger 

District, Fremont-Winema National Forest.
USDA Forest Service Proceedings RMRS-P-61. 2010. 69

Carbon Benefits from Fuel Treatments Cathcart, Ager, McMahan, Finney, and Watt

are predicted by the MTT fire spread algorithm depending on the direction the 

fire encounters a pixel relative to the major direction of spread (i.e. heading, 

flanking, or backing fire) (Finney 2002). The conditional burn probability for a 

given pixel is an estimate of the likelihood that a pixel will burn given a random 

ignition somewhere in the watershed under the weather conditions represented 

in the simulation. Random ignitions were also allowed to originate outside the 

watershed to include wildfire events that burned into the watershed. The treated 

and untreated landscapes were simulated with 10,000 wildfires to generate burn 

probability and intensity (flame length) surfaces at 90 m resolution.

Carbon loss—To quantify the potential effects of wildfire on carbon stocks, 

each possible stand condition (as represented by the GNN treelist data—both 

treated and untreated) for each possible wildfire intensity (as represented by 

flame length category) was burned through FFE-FVS. Each stand condition in 

the study area was burned within FFE-FVS under a pre-defined surface fire 

flame length ranging from 0.5 m to 10 m in 0.5 m increments (SIMFIRE and 

FLAMEADJ keywords). The post-wildfire carbon reports in FFE (FVS_Carbon 

and FVS_Hrv_Carbon reports) were then examined to determine the amount of 

carbon in each carbon pool post burning at each flame length. Treatments and 

wildfire were simulated within the first four years of the simulation; the FFEFVS outputs from the fifth simulation year were used as the results. The result 

was a carbon loss function for each stand condition representing all the possible 

post-wildfire carbon stocks by wildfire intensity class including no wildfire. 

Carbon loss is defined here as the reduction in post-wildfire carbon stocks for a 

given wildfire intensity class when compared to the carbon stocks present if no 

wildfire occurred; the amount reduced being equivalent to CO2 emissions to the 

atmosphere from the fire. For treated stand conditions, carbon loss included the 

sum of carbon loss from treatment and from wildfire. Treatment carbon losses 

occur as a result of the disposal of non-merchantable material either removed or 

left on site, carbon losses associated with the end-use and fate of merchantable 

material removed and from CO2 emissions from the under burns. The fate of the 

non-merchantable material removed was not accounted for; it was treated as a 

CO2 emission at the time of treatment.

The current configurations of FVS and Randig do not allow for exact matching 

of fire behaviors. Randig reports total flame length of the surface and, if initiated, crown fire. In contrast, the FVS FLAMEADJ keyword does not allow for 

specifying a total flame length (surface and crown); rather it allows specification of a flame length for surface fires only. Moreover, FVS FLAMEADJ will 

not simulate crown fire initiation if it is parameterized with only a fire flame 

length (except as reported in the potential fire report). To simulate crown fires 

in FVS, we calculated a critical flame length (representing the threshold flame 

length between a surface fire and a crown fire) and imposed 100 percent crown 

consumption (via parameter 3 of the FLAMEADJ keyword) when the surface 

fire flame length exceeded the critical flame length.

Expected Carbon

The carbon stocks representing the amount of stored carbon post-wildfire 

(for untreated stand conditions) and post-treatment and post-wildfire (for treated 

stands) was matched with the burn probability data to calculate expected carbon 

for each 90- by 90-meter pixel as follows:

E C LS BPij SCij WPC i

j

0

20

j = # + =

6 6 @ ;/ @E (1)
70 USDA Forest Service Proceedings RMRS-P-61. 2010.

Cathcart, Ager, McMahan, Finney, and Watt Carbon Benefits from Fuel Treatments

where:

E[C]LSj = Expected carbon (mass per unit area) post-wildfire for the jth pixel and 

LS = TRT for the treated landscape and NO-TRT for the untreated 

landscape.

BPij = Conditional burn probability of wildfire intensity class i reaching the pixel 

j; where:

BPij BP i

j

1

20

= =

/ where BPj is the overall burn probability of wildfire reaching pixel j

BP0 j = Conditional probability of no fire = 1-BPj; 

and BPij 1 i

20

0

= =

/

SCi j = total stand carbon, post-wildfire of wildfire intensity class i burning in 

pixel j; i=1 to 20.

SC0j = total stand carbon in pixel j if no wildfire occurs.

WPCj = carbon stored in wood products from treatment in pixel j.

For the untreated landscape, WPCj = 0 for all j. For treated pixels on the treated 

landscape, SCij represents total stand carbon post treatment and post-wildfire 

for intensity class i burning in pixel j for i = 1 to 20 and for treated pixels on the 

treated landscape, SC0j represents total stand carbon post treatment if no wildfire 

occurred.

Carbon offset calculations—The expected carbon offset is calculated for each 

90- by 90-meter pixel by comparing the expected post-wildfire amount of carbon 

stored in the pixel post-treatment (if the pixel is treated) for the treated landscape 

with the amount of carbon stored in the same pixel post-wildfire on the untreated 

landscape. If the carbon offset is positive—meaning that the amount of carbon 

stored on the treated landscape is greater than the amount of carbon stored on 

the untreated landscape—then a positive CO2 emission reduction benefit occurs 

as a result of undertaking the treatments.

The expected carbon offset, E[(ΔC)], for the treated landscape is calculated 

as follows:

E C E C TRT E C NO TRT j

n

1

3 = j - - j

=

6^ h@ @ /^ 6 6 @ h (2)

where:

n = is the total number of pixels in the landscape

E[C]TRTj

= is the expected carbon post treatment and wildfire in pixel j; treated 

landscape

E[C]NO-TRTj

= is the expected carbon post-wildfire, pixel j; untreated landscape, 

and

E[C]TRTj

 – E[C]NO-TRTj = the carbon offset occurring in pixel j as a result of 

treatment.

E[(ΔC)] > 0 is a necessary condition for the offset to be used as mitigation for 

CO2 emissions from an unrelated source. However, if E[(ΔC)] < 0, then the fuel 

treatment project resulted in more CO2 emissions (less stored carbon) than if the 

landscape remained untreated. This does not mean the fuel treatment project 

should not be implemented; rather, those other benefits of conducting the fuel 

treatment project (e.g., resource protection, lowering community wildfire risk) 

come with a net loss of carbon from the landscape to the atmosphere.
USDA Forest Service Proceedings RMRS-P-61. 2010. 71

Carbon Benefits from Fuel Treatments Cathcart, Ager, McMahan, Finney, and Watt

Results

Fuels treatment had the desired effect of reducing the likelihood of fire reaching 

a given pixel as measured by burn probability (figs. 2 and 3; table 3). For untreated 

stands on the treated landscape, the likelihood of wildfire spreading to untreated 

stands was also reduced as a result of applying the treatments (fig. 4a). For treated 

stands, there was a shift in the conditional burn probability distribution making 

low intensity fires much more likely than if the stands had not been treated, as 

well as reducing the overall likelihood of wildfire in those stands (fig. 4b). This 

lowered the average per acre carbon loss from wildfire in post-treated stands 

when compared to untreated stand conditions (fig. 4b). Comparing the frequency 

distribution of the wildfire simulations for both landscapes also shows that the 

fuel treatments were effective in reducing the intensity and extent of wildfire 

(fig. 5). Average fire size on the treated landscape was 32 percent lower than 

average fire size on the untreated landscape. Secondly, the largest fire simulated 

on the treated landscape was 15,000 acres compared to over 19,000 acres for 

the untreated landscape. In general, the treated landscape experienced a greater 

number of smaller sized wildfires when compared to the untreated landscape.

Figure 2—Conditional burn probability map from 10,000 randomly located ignitions and 8-hour burn 

periods (untreated landscape). 
72 USDA Forest Service Proceedings RMRS-P-61. 2010.

Cathcart, Ager, McMahan, Finney, and Watt Carbon Benefits from Fuel Treatments

Figure 3—Difference in burn probability between untreated and treated landscape. Areas not shaded 

within watershed boundary had differences less than 0.0025.

Table 3—Mean overall conditional burn probabilities for Drews Creek—

forestland.

Treated 	 Untreated

landscape	 landscape	 Difference

Treated stands 0.01235 0.02602 –0.01367

Untreated stands 0.01709 0.02106 –0.00397

All stands 0.01665 0.02152 –0.00487
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Carbon Benefits from Fuel Treatments Cathcart, Ager, McMahan, Finney, and Watt

Figure 4—Frequency histograms for burn probabilities for untreated (a) and treated (b) scenarios for the 

Drews Creek watershed study area.

(a)

(b)

When compared to the carbon stocks post-wildfire for the same areas on the 

untreated landscape, fuels treatment resulted in a net carbon loss to the atmosphere 

of –303,458 tons (table 4) (–23.7 tons per treated acre). The expected carbon stocks 

within the untreated area of the treated landscape did increase to a small degree 

(3,536 tons; or 0.027 tons per untreated acre) as a result of the treatment’s effect 

of reducing the likelihood that wildfire reaches the untreated stands. However, 

the cost in terms of carbon released to the atmosphere as a result of treatment 

was overwhelming with a net, negative carbon offset, E[(ΔC)] = –299,622 tons 

(table 4) (–2.13 tons per forested acre).
74 USDA Forest Service Proceedings RMRS-P-61. 2010.

Cathcart, Ager, McMahan, Finney, and Watt Carbon Benefits from Fuel Treatments

Biomass removed through thinning from below and movement of the excess 

material (slash) resulting from harvest activities removed 215,071 tons of carbon 

(16.8 tons per treated acre) representing 19.1 percent of the total biomass in treated 

stands. Of this amount, 159,440 tons (74 percent) was emitted to the atmosphere 

(12.4 tons per treated acre) with the remaining 55,631 tons (26 percent) remaining 

stored in long-lived wood products (4.3 tons per treated acre). Under burning emitted another 111,893 tons carbon (8.7 tons per treated acre) representing13.3 percent 

of the total biomass in treated stands. In total, carbon lost to the atmosphere from 

the fuel treatment activity totaled –271,333 tons carbon (–21.2 tons per treated 

acre) (table 5). In comparison, only an expected 3,700 tons (0.21 tons per acre) 

of avoided carbon loss accrued to the treatment polygons as a result of the treatment’s effect of reducing both the likelihood and intensity of wildfire in treated 

stands. Similarly, only 3,087 tons of expected avoided carbon loss accrued to the 

untreated polygons (0.025 tons per acre) as a result of the treatment’s effect of 

reducing the likelihood of wildfire, for a total benefit of 6,787 tons of expected 

Figure 5—Frequency histogram of simulated fire sizes for untreated and treated scenarios for the 

Drews Creek watershed study area.

Table 4—Expected stored carbon immediately after wildfire—Drews Creek—forestland

Treated 	 Untreated

landscape	 landscape	 Difference

short tons carbon

Treated standsa 538,940 842,398 –303,458

Untreated stands 2,961,484 2,957,948 3,536

OFFSET –299,622

aThis is the area selected for treatment on the treated landscape but remained untreated on the untreated 

landscape. The treated stand area plus the untreated stand area equals the total forestland area.
USDA Forest Service Proceedings RMRS-P-61. 2010. 75

Carbon Benefits from Fuel Treatments Cathcart, Ager, McMahan, Finney, and Watt

Table 5—Expected carbon emissionsa—Drews Creek—forestland.

Treated	 Untreated

Emission source	 landscape	 landscape	 Difference

short tons carbon

Merchantable 

material removed –41,884 0 –41,884

From treatment, but 

not stored

Non-merchantable 

material removed –62,796 0 –62,796 

from thinning 

treatment

Non-merchantable 

material removed –54,760 0 –54,760 

from FUELMOVE 

treatment

Total, thinning activity –159,440 –159,440

Prescribed fire 

in treatments –111,893 0 –111,893

Total, all treatment 

practices –271,333 –271,333

Wildfire

treatedb stands –157 –3,857 3,700

Wildfire

non-treated stands –8,936 –12,023 3,087

OFFSET –264,546

a Negative sign indicates a loss of stored carbon to the atmosphere. 

b This is the area selected for treatment on the treated landscape but remained untreated on the untreated 

landscape. The treated stand area plus the untreated stand area equals the total forestland area.

carbon loss avoided (0.048 tons per forested acre). The net expected carbon loss 

accruing to the treated landscape when compared to the untreated landscape is 

–264,546 tons (–1.9 tons per forested acre) carbon (table 5)16. 

Discussion

The Law of Averages

The question is—if the implementation of fuels treatments within the Drews 

Creek watershed had the beneficial effect of reducing the likelihood of wildfire 

intensity and extent as simulated in this study, why is the expected carbon offset 

from fuels treatment so negative? The answer lies in the probabilistic nature of 

wildfire. Fuels treatment comes with a carbon loss from biomass removal and 

prescribed fire with a probability of 1. In contrast, the benefit of avoided wildfire 

emissions is probabilistic. The law of averages is heavily influenced that given 

16 FVS does not account for all carbon losses as emissions and as a result the offset as calculated in 

table 4 (from comparing carbon stock changes) does not balance with the offset calculated in table 5 

(from comparing carbon emission changes).
76 USDA Forest Service Proceedings RMRS-P-61. 2010.

Cathcart, Ager, McMahan, Finney, and Watt Carbon Benefits from Fuel Treatments

a wildfire ignition somewhere within the watershed, the probability that a stand 

is not burned by the corresponding wildfire is 0.98 (1 minus the average overall 

conditional burn probability in table 3). The simulations also included all possible 

wildfire scenarios from one random ignition within the Drews Creek watershed 

under the severe weather and fuel moisture conditions that could result in the problem fire. But, in many of the simulations, the problem fire did not occur (fig. 5).

Thus, the expected benefit of avoided wildfire emissions is an average that 

includes the predominant scenario that no wildfire reaches the stand. And if the 

predominate scenario for each stand is that the fire never reaches it, there is no 

avoided CO2 emissions benefit to be had from treatment. So even though severe 

wildfire can be a significant CO2 emissions event, its chance of occurring and 

reaching a given stand relative to where the wildfire started is still very low, with 

or without fuel treatments on the landscape. Further, when wildfire does occur, 

the immediate effect is to transfer the stored carbon on the landscape from the 

live tree pool to the standing dead tree pool. So, most of the carbon stored before 

wildfire is still there after wildfire, and the amount of carbon loss compared to 

the stored carbon before wildfire is still relatively small. Had this study included 

the continued carbon loss over time from the decomposition of the dead tree pool 

post-wildfire, then the total avoided wildfire emissions would have been greater.

Life Expectancy of Fuel Treatments

The carbon offset result of –299,622 tons carbon (–2.13 tons per forested acre) is 

the carbon offset of avoiding the possibility of a severe wildfire based on a single 

wildfire ignition under extreme weather and fuel moisture conditions. Our study 

results show that for any given ignition in the year after the completion of fuel 

treatments, the expected avoided carbon loss from one wildfire ignition is 6,787 

tons carbon. But the Drews Creek watershed experiences on average, 14 wildfire 

ignitions a year. For example, if 1/3 (i.e., say 5) of the 14 ignitions per year for 

Drews Creek occurred during the severe weather and fuel moisture conditions 

used in this study and independence is assumed in the wildfire outcomes from 

one ignition to another, then the expected avoided carbon loss from the same fuel 

treatment investment (and treatment carbon loss) is 33,935 (=5*6,787) tons carbon17. This amount is still not enough benefit to make the carbon offset positive 

for the year following the completion of the fuel treatment project.

But, the fuel treatment project is actually an investment in reducing wildfire 

risk over an extended period of time beyond the year following treatment. If 

the life expectancy of the implemented fuel treatments is at least 9 years, then 

the expected carbon offset becomes positive. That is, if for each year, the fuel 

treatments avoid an expected 33,935 tons of carbon loss to the atmosphere, then 

305,415 (=9*33,935) tons of expected carbon loss is avoided after 9 years for the 

same –299,622 treatment carbon loss investment18. While the carbon offset from 

fuel treatments is clearly negative for any one ignition in the year immediately 

17 This is a simplifying assumption used for illustrative purposes. If each random ignition wildfire 

outcome is an independent event—meaning the spread and intensity of the wildfire from the second 

ignition is not influenced by how the wildfire burned from the first ignition—the overall burn probability 

of wildfire reaching the stands from 5 random ignitions on the landscape is 5 times the probability of 

it reaching the stand from one ignition. Any dependency would lower this result, but the concept still 

holds. 18 Again, this assumes independence between wildfire outcomes from one year to the next. In this 

case, this is a conservative estimate. It may be that the burn probability landscape intensifies—meaning 

the probability of wildfire not reaching a stand (1-BP) decreases if the problem fire event does not occur 

in the preceding year.
USDA Forest Service Proceedings RMRS-P-61. 2010. 77

Carbon Benefits from Fuel Treatments Cathcart, Ager, McMahan, Finney, and Watt

following the completion of treatments, it may become positive within the course 

of the shelf life of the treatment’s effectiveness in reducing wildfire risk. Further, 

accounting for the consequences of continued decomposition from the in-stand 

dead tree pools and continued sequestration from the live tree pool not lost to 

wildfire as a result of treatment improves the carbon offset potential of the fuel 

treatment. While not accounted for in this study, the expected carbon benefit from 

the fuel treatment project would be better reflected as the cumulative expected 

avoided carbon loss and improved carbon sequestration gain over time from 

avoiding the problem fire from multiple ignitions in multiple years after treatment.

Future Work

This study had several limitations. First, the treatment design did not explicitly 

minimize the carbon loss from conducting the fuel treatment while at the same 

time maintaining the same level of effectiveness in reducing the risk of catastrophic 

wildfire. Better utilization of the non-merchantable material removed in products 

that continue to store carbon would reduce the carbon cost of treatment. Second, 

the analysis does not account for the continued loss of carbon post-wildfire from 

decomposition of burned material; nor does the study account for the continued 

growth and corresponding sequestration that occurs both in treated stands (where 

fire severity and extent is reduced) and in untreated stands (where the chance of 

wildfire reaching them is reduced) outside of the treatment areas. Third, we did not 

convert the conditional burn probabilities of wildfire occurring given an ignition 

on the landscape to the absolute probability of wildfire occurring. This step would 

take into account the probability of ignition and could take into account other 

factors such as the improved effectiveness of suppression afforded by treatment. 

Finally, modeling the shelf life of fuel treatments using burn probability maps 

for more than one year following treatment that take into account the wildfire 

outcomes in previous years might better reveal the cumulative carbon offset of 

conducting the fuel treatment project.

Acknowledgment

Funding for this research was provided by the U. S. Department of Energy 

through the West Coast Regional Carbon Sequestration Partnership (WESTCARB) 

administered by the California Energy Commission.

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1USFS Rocky Mountain Research 

Station, Fort Collins, CO.

2Colorado State University, Fort 

Collins, CO.

3Wayne State University, Detroit, MI.

Forest Biomass and Tree Planting for 

Fossil Fuel Offsets in the Colorado 

Front Range

Mike A. Battaglia1, Kellen Nelson2, Dan Kashian3, 

and Michael G. Ryan1

Abstract—This study estimates the amount of carbon available for removal in fuel 

reduction and reforestation treatments in montane forests of the Colorado Front Range 

based on site productivity, pre-treatment basal area, and planting density. Thinning 

dense stands will yield the greatest offsets for biomass fuel. However, this will also 

yield the greatest carbon losses, if the removals remain on site to decompose. Stands 

that regrow the biomass removed will recover the carbon. However, if these treatments 

are maintained at low basal areas, then the treatments may permanently convert a 

large quantity of carbon from the forest to the atmosphere.

Keywords: ponderosa pine, carbon, wildfire risk, FVS, biomass

Introduction

Over the past century, the exclusion of wildfires in ponderosa pine/Douglasfir (Pinus ponderosa / Pseudotsuga menziesii) forests has increased tree density 

in most landscapes. While this increase in tree density serves as a carbon sink, 

these same areas are at higher risk for a high severity fire, which could lead to 

a substantial initial release of carbon if trees are killed and the fire consumes 

the forest floor, foliage, and wood (Hurteau and others 2008; Hurteau and North 

2009). Several recent high severity wildfires in the ponderosa pine/Douglas-fir 

forests of the Colorado Front Range have converted dense forests into large areas with no living trees and minimal ability to regenerate (Romme and others 

2003). Since these areas have little to no regeneration, natural forest regrowth 

will not replace the carbon lost to combustion and decomposition and the area 

will become a carbon source (Dore and others 2008; Kashian and others 2006) 

for the foreseeable future. These fires have prompted the implementation of fuel 

reduction treatments intended to reduce crown fire risk. These treatments often 

involve removing small-diameter, non-merchantable trees that established over 

the past century. Since the biomass removed is non-merchantable, it is typically 

piled and burned or masticated, which moves carbon dioxide (CO2) stored in the 

forest to the atmosphere. Furthermore, the lack of removable merchantable material makes these treatments costly.

The increase in atmospheric CO2 and its role in global climate change have 

encouraged the search for ways to reduce or offset greenhouse-gas emissions. 

Emissions cap-and-trade programs or voluntary carbon trading programs can 

reduce CO2 input to the atmosphere by creating a market aimed at decreasing 

fossil fuel emissions (Ellerman and others 1998; Petty and Ball 2001; Zhang 

and Folmer 1995). Carbon credit trading allows industries to buy credits from 
82 USDA Forest Service Proceedings RMRS-P-61. 2010.

Battaglia, Nelson, Kashian, and Ryan Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range

industries or entities that have reduced their emissions. Companies can choose to 

invest in reforestation projects that remove CO2 from the atmosphere, or to use 

alternative energy sources for fossil fuels in heat and power generation. The large 

area currently storing carbon but in need of thinning for fire hazard reduction 

could benefit from these programs.

Biomass harvested from fuel reduction treatments could substitute for fossil 

fuels currently used for heat and power generation. The credits generated by the 

use of the biomass fuel could help compensate for the costs of the management 

activities, depending on the price of the credits. Also, planting trees in burned 

areas can help restore forests and promote carbon sequestration. While the initial 

harvests to reduce crown fire risk may result in immediate CO2 offsets if used for 

biomass energy, the maintenance of low forest density to maintain low crown fire 

risk will reduce potential carbon stored on the landscape. This study estimates the 

amount of carbon available now for removal in fuel reduction and reforestation 

treatments in the ponderosa pine/Douglas-fir forests of the Colorado Front Range 

based on site productivity, pre-treatment basal area, and planting density. We 

also estimate the carbon storage potential of tree planting on areas not expected 

to regenerate naturally. Finally, we estimate the potential carbon offsets and associated crown fire risk for different management scenarios.

Methods

Study Area

Our study area was located within the Pike and San Isabel National Forest 

(PSINF) in Colorado, southwest of Denver (fig. 1). Tree species are ponderosa 

pine, Douglas-fir, quaking aspen (Populus tremuloides Michx.), lodgepole pine 

(Pinus contorta Douglas ex Louden), Engelmann Spruce (Picea engelmannii 

Parry ex Engelm.), and subalpine fir (Abies lasiocarpa [Hook.] Nutt.). Site index 

on the PSINF varies from 30 to 80 ft (index age of 100 years).

Figure 1—Location of the Pike-San Isabel National Forest (shaded) within the state 

of Colorado.
USDA Forest Service Proceedings RMRS-P-61. 2010. 83

Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range Battaglia, Nelson, Kashian, and Ryan

We studied the dry ponderosa pine/Douglas-fir forest because this is the forest 

type that has experienced severe wildfires and would likely be the area treated 

for fuel reduction. The dry ponderosa pine/Douglas-fir forests of the PSINF are 

found from 6,500 to 9,000 ft in elevation. Ponderosa pine dominates the south 

and west facing slopes and the Douglas-fir dominates the northern slopes. The 

understories are typically grassy, shrubby, or both. Soils are gravelly coarse sandy 

loams derived from weathered Pikes Peak granite (Moore 1992).

Overview

We modeled two scenarios (Fuel Reduction Treatment and Reforestation) to 

develop estimates for the amount of carbon that is currently and potentially available. We calibrated the Forest Vegetation Simulator (FVS) forest growth model 

to match growth conditions found on the PSINF and modeled carbon accumulation for different management and site index scenarios. Specifically, carbon 

accumulation was estimated by 1) using USDA Forest Service inventory data 

to determine the range of site indices and actual forest growth for areas within 

the PSINF either in need of fuels reduction or in areas burned in the Hayman 

wildfire, 2) calibrating FVS to match actual forest growth for the different site 

index values, 3) using the FVS model to model organic material accumulation for 

a range of site index values, and 4) applying literature-based values to estimate 

carbon content of biomass and carbon dioxide equivalent in metric tons per acre 

(MT/ac). We included belowground biomass and organic material accumulation 

(forest floor) in our carbon estimates.

The Forest Vegetation Simulator

The Forest Vegetation Simulator (Dixon 2002) is an individual tree and stand 

level growth and yield model that is based on the Prognosis model (Stage 1973). 

The Extensions to Prognosis began in 1978, and GENGYM (Edminster and others 

1991), today known as the “Central Rockies Variant,” was developed in 1991. Like 

the Prognosis model, the Central Rockies Variant is a set of allometric equations 

that defines growth rates for tree species in New Mexico, Arizona, Colorado, 

and South Dakota. This variant is broken into five sub-variants. The sub-variant 

specific to the PSINF region was used to generate the information in this study.

Scenarios

Current management guidelines in the PSINF for fuels reduction and forest 

restoration involve reducing standing forest biomass to a target basal area of 

40 to 60 ft2/ac. This guideline is based on research done by Merrill Kaufmann 

and others (Kaufmann and others 2000; Kaufmann and others 2001; Romme and 

others 2003) in the area surrounding Cheeseman Reservoir, Deckers, Colorado.

Fuel reduction treatment—We downloaded inventory data from the PSINF 

Resource Information System (RIS) database. The RIS database was queried for 

stand identification numbers of suitable ponderosa pine/Douglas-fir sites. Stands 

were sorted into 4 site index groups (45, 55, 65, and 75). Using FSVeg, specific 

stand exams were downloaded and entered into the FVS program.

We used FVS to grow each site index group until a specific management criterion was met and then had the model simulate thinning. For each of these groups, 

the FVS model was set up to grow to 6 different basal areas (90, 100, 110, 120, 

130, and 140 ft2/ac). Each basal area class was thinned to three post-treatment 

basal area targets (40, 50 and 60 ft2/ac). These values encompass the range of 

possible management targets that a manager may chose, given the flexibility in 
84 USDA Forest Service Proceedings RMRS-P-61. 2010.

Battaglia, Nelson, Kashian, and Ryan Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range

potential management practices. The ThinBBA keyword (thin from below to a 

specific basal area target) was used in the FVS model to allow the model stand 

to grow to and be thinned to a specified basal area target.

We simulated two additional scenarios to assess the potential carbon offsets 

and associated fire risk for stands managed to maintain fuel treatment effectiveness. For both scenarios, we chose to model the stands with a site index of 45. In 

the first scenario, the initial basal area was 100 ft2/ac, and then it was thinned 

from below to 50 ft2/ac and allowed to grow back to 100 ft2/ac and thinned again 

to 50 ft2/ac. In the second scenario, the initial basal area was 160 ft2/ac, and then 

it was thinned from below to 50 ft2/ac and only allowed to grow to 100 ft2/ac 

and thinned again to 50 ft2/ac. In both scenarios, the fire risk associated with the 

management regime was assessed with the fire and fuels extension (FFE) to FVS. 

FFE provides estimates of the torching and crowning index based on canopy base 

height, canopy bulk density, and an estimated fuel model. The torching index is 

the windspeed at 20 ft required to initiate a passive crown fire. The crowning 

index is the windspeed at 20 ft required to sustain an active crown fire (Scott and 

Reinhardt 2001). These scenarios were simulated for a 200 year period.

Reforestation—We selected ponderosa pine stands from the post-Hayman 

fire reforestation needs GIS coverage map. The polygons identified areas within 

the Hayman wildfire that had no living trees and lacked a seed source. These 

polygons had records that include stand inventory codes that allow the user to 

download stand exams from the PSINF database using the Forest Service’s FSVeg 

network application.

The reforestation scenario used a matrix of site indices and planting densities 

to compare the differences in carbon storage for these scenarios. Site index values 

were taken from PSINF stand inventories completed between 1978 and 2000. Site 

index values ranged from 30 to 58, with only a few stands having a site index 

>55. We modeled growth for site index values of 35 to 55 in increments of 5. We 

modeled surviving tree density from a planting density of 100, 200, and 300 trees 

per acre for each site index value. The “surviving planting number” is the number 

of seedlings per acre remaining on a site after any post-planting mortality. These 

surviving planting densities were chosen to represent the tradeoff between high 

density with rapid carbon accumulation and frequent thinning to reduce fire 

risk, to low density with slow carbon accumulation but few necessary thinnings. 

These scenarios were simulated for one harvest cycle (typically 80 to 100 years).

Similar to the ‘biofuels’ simulations, two additional scenarios were simulated to 

assess the potential carbon offsets and associated fire risk for reforested stands that 

would be managed to maintain fuel treatment effectiveness. For both scenarios, 

we chose to model the stands with a site index of 45 and initial planting density 

of 450 trees per acre. In the first scenario, we allowed the stand to grow with no 

thinning activities for 400 years. In the second scenario, we allowed the stand to 

grow to 100 ft2/ac and thinned from below to 50 ft2/ac. We repeated this management regime for 400 years. In both scenarios, the fire risk associated with the 

management regime was assessed with the fire and fuels extension (FFE) to FVS.

Calculating Carbon Stores

To determine the amount of carbon stored for each scenario, we used the 

‘FuelOut’ keyword in FVS to request a report of biomass in all forest carbon 

pools. Forest floor (sum of surface litter, surface duff, and surface wood—mostly 

litter and duff) and total standing biomass (sum of standing live wood, large 

roots, standing snags, and foliage) were calculated and summed as the total 

biomass accumulation in a forest stand. Although FVS now has the ability to 

calculate forest carbon, when the simulations were run, the extension was 
USDA Forest Service Proceedings RMRS-P-61. 2010. 85

Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range Battaglia, Nelson, Kashian, and Ryan

still in development. We estimated the belowground biomass (large roots) as 

25 percent of the aboveground biomass (Jackson and Chittenden 1981). We assumed that biomass was 50 percent carbon (Schlesinger 1997). Carbon trading 

uses CO2 instead of carbon, so we multiplied total carbon by 3.67, the ratio of the 

molecular weights of CO2 to carbon (Chicago Climate Exchange, Inc. 2004; US 

Department of Energy 2007). We report our values in metric tons CO2 equivalent 

per acre (MT CO2/ac)

Results

Fuel Reduction Treatment: Initial CO2 offsets

Biomass available from fire hazard reduction activities in ponderosa pine/

Douglas-fir forests of the PSINF varied by pre-treatment basal area, site index, 

and the post-treatment basal area. Dense stands will yield the most biomass and 

offsets, especially if they are thinned to the lower target basal area of 40 ft2/ac 

(table 1). For example, stands with a site index of 45 and pre-treatment basal 

areas between 90 and 140 ft2/ac will generate fossil fuel offsets between 18.6 to 

43.6 MT CO2/ac if thinned to a target basal area of 40 ft2/ac (table 1). If these 

same stands were thinned to a target basal area of 60 ft2/ac, they will generate 

fossil fuel offsets between 9.0 to 32.5 MT CO2/ac (table 1). If these stands had a 

site index of 75, they would generate fossil fuel offsets between 22 to 55.3 CO2

MT/ac if thinned to a target basal area of 40 ft2/ac and 10.5 to 41.3 CO2 MT/ac if 

thinned to a target basal area of 60 ft2/ac (table 1).

Maintenance of Fuel Reduction Treatment: Future CO2

Offsets and Fire Risk

Maintaining ponderosa pine/Douglas-fir forests between 50 and 100 ft2/ac 

reduces fire risk by increasing the windspeed required to sustain an active crown 

fire. Before the initial harvest, the 100 ft2/ac and the 160 ft2/ac stands had low 

crowning indices, 26 (fig. 2) and 13 (fig. 3) mph, respectively. After the initial 

entry, crowning indices increased substantially (figs. 2 and 3). As the stands accuTable 1—Average (+ standard error, in parentheses) metric tons of CO2 equivalent per acre available for immediate biomass 

removal for different site productivities, pre-treatment basal areaa, and residual post-treatment basal area of ponderosa pine/

Douglas-fir stands in need of fuel reduction treatments on the Pike and San Isabel National Forest, Colorado.

Pre-trt 

BA a

Site index = 45 Site index = 55 Site index = 65 Site index = 75

Target basal area (ft2/ac)

0 40 50 60 0 40 50 60 0 40 50 60 0 40 50 60

ft2/ac ----------------------------------------Metric tons of CO2 equivalent per acre (± standard error)----------------------------------------

90 39.1

(1.2)

18.6 

(0.77)

13.7 

(0.57)

9.0 

(0.54)

42.4

(1.2)

20.1 

(0.75)

14.8 

(0.70)

9.9 

(0.59)

47.5

(2.0)

22.8 

(1.12)

17.2 

(1.04)

10.9 

(0.65)

46.5

(1.7)

22.0 

(1.18)

15.7 

(0.96)

10.5 

(0.87)

100 45.3

(1.4)

23.9 

(0.90)

19.9 

(0.79)

13.9 

(0.63)

48.0

(1.2)

25.1 

(0.81)

19.9 

(0.81)

14.7 

(0.70)

52.1

(1.7)

27.4 

(1.10)

21.4 

(1.03)

16.2 

(0.81)

53.5

(1.9)

28.4 

(1.14)

21.7 

(0.94)

16.2 

(0.75)

110 50.4

(1.6)

28.9 

(1.07)

23.6 

(0.97)

18.3 

(0.86)

52.9

(1.2)

30.0 

(0.87)

24.4 

(0.84)

19.1 

(0.77)

57.4

(1.7)

32.7 

(1.23)

26.3 

(1.10)

20.9 

(0.91)

60.1

(2.1)

34.1 

(1.37)

27.8 

(1.25)

21.4 

(1.00)

120 55.5

(1.8)

33.8 

(1.22)

28.4 

(1.11)

23.1 

(0.99)

59.1

(1.2)

35.6 

(0.91)

29.7 

(0.86)

24.4 

(0.84)

63.9

(1.8)

38.6 

(1.29)

32.1 

(1.20)

26.9 

(0.96)

66.4

(3.0)

40.1 

(2.07)

33.8 

(1.87)

27.2 

(1.64)

130 60.0

(1.8)

38.4 

(1.36)

32.8 

(1.22)

27.5 

(1.22)

63.8

(1.4)

40.4 

(1.05)

34.4 

(0.98)

28.7 

(0.88)

71.0

(2.3)

45.4 

(1.75)

38.2 

(1.28)

32.2 

(1.25)

75.3

(3.3)

48.1 

(2.33)

41.2 

(2.13)

34.5 

(1.96)

140 65.6

(2.1)

43.6 

(1.63)

38.0 

(1.50)

32.5 

(1.43)

70.4

(1.5)

46.8 

(1.12)

40.4 

(1.02)

34.7 

(0.94)

77.9

(2.2)

51.7 

(1.63)

45.2 

(1.46)

38.5 

(1.32)

83.0

(3.8)

55.3 

(2.74)

48.1 

(2.6)

41.3 

(2.34)
86 USDA Forest Service Proceedings RMRS-P-61. 2010.

Battaglia, Nelson, Kashian, and Ryan Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range

Figure 3—CO2 equivalent per acre accumulated, potential CO2 offsets, and the 

associated fire hazard for a stand maintained between 50 and 100 ft2/ac. The 

pre-treatment stand basal area was 160 ft2/ac and site index= 45.

Figure 2—CO2 equivalent per acre accumulated, potential CO2 offsets, and the 

associated fire hazard for a stand maintained between 50 and 100 ft2/ac. The pretreatment stand basal area was 100 ft2/ac and site index= 45.
USDA Forest Service Proceedings RMRS-P-61. 2010. 87

Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range Battaglia, Nelson, Kashian, and Ryan

Table 2—Metric tons of CO2 equivalent per acre 

accumulated 90 years after a reforestation effort 

for different site productivities and ponderosa pine 

planting densities within the Hayman Fire on the 

Pike and San Isabel National Forest, Colorado.

Planting densities (Trees per acre)

Site Index	 100	 200	 300

Metric tons of CO2 equivalent per acre

35 27.5 41.3 51.9

40 29.7 46.1 57.3

45 34.8 54.0 67.4

50 39.6 63.6 79.8

55 50.1 80.2 102.3

mulated more carbon, the crowning index decreased slightly, but never decreased 

to the pre-treatment value (figs. 2 and 3). This pattern continued throughout the 

simulation.

Although active crown fire risk was decreased, the success of thinning from 

below to prevent passive crown fire (crown fire initiation) was variable for both 

maintenance scenarios (figs. 2 and 3). For both scenarios, the torching index would 

increase substantially after each entry for about 30 years, but it would decrease 

due to the ingrowth of new tree regeneration.

The carbon consequences of maintaining stands at tree densities that reduce 

crown fire risk differed depending on the initial pre-treatment basal area. In the 

scenario where initial basal area was 100 ft2/ac, thinning from below to 50 ft2/ac 

would provide approximately 18 CO2 MT/ac in the initial harvest (fig. 2). However, 

the scenario where initial basal area was 160 ft2/ac and was thinned from below 

to 50 ft2/ac would provide approximately 38 CO2 MT/ac in the initial harvest 

(fig. 3). Throughout the next several rotations, thinning from below occurred 

whenever the basal area reached 100 ft2/ac. For the scenario that was initially 

at 100 ft2/ac, each maintenance harvest yielded approximately the same amount 

of CO2 MT/ac as the initial harvest (fig. 2), replacing the carbon harvested each 

rotation. However, the scenario that was initially at 160 ft2/ac only yielded approximately 20 CO2 MT/ac in the maintenance harvests, never fully replacing 

the carbon harvested in the initial harvest (fig. 3).

Reforestation of Hayman Fire

The CO2 equivalent accumulation rates for tree planting within the Hayman 

wildfire burn area varied with site index and planting density, but planting density 

was more important in determining carbon accumulation (table 2). After 90 years, 

forest CO2 equivalent ranged from 22.5 to 50.1 MT CO2/ac for a planting density 

of 100 trees per acre, 34.2 to 80.2 MT CO2/ac for a planting density of 200 trees 

per acre, and 42.8 to 102.3 MT CO2/ac for 300 trees per acre.

Reforestation: Future CO2 Offsets and Fire Risk

The potential carbon offsets and associated fire risk for reforested stands differed depending on the management regime. The amount of CO2 that accumulated 

after 400 years and available for an offset at the end of a 400-year rotation was 

143 CO2 MT/ac (fig. 4). For the stands that were maintained to reduce fire hazard, 

the CO2 offsets from the harvests combined with the end of the 400-year rotation 

would yield 108 CO2 MT/ac (fig. 5). The consequence of increased carbon sequestered in the no thin scenario was a higher risk for active crown fire with crowning 

indices ranging between 20 and 40 mph for the majority of the rotation (fig. 4). 
88 USDA Forest Service Proceedings RMRS-P-61. 2010.

Battaglia, Nelson, Kashian, and Ryan Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range

Figure 4—CO2 equivalent per acre accumulated, potential CO2 offsets, and the 

associated fire hazard for a reforested stand (site index= 45) planted at a density 

of 450 trees per acre.

Figure 5—CO2 equivalent per acre accumulated, potential CO2 offsets, and the 

associated fire hazard for a reforested stand (site index= 45) planted at a density 

of 450 trees per acre and maintained between 50 and 100 ft2/ac.
USDA Forest Service Proceedings RMRS-P-61. 2010. 89

Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range Battaglia, Nelson, Kashian, and Ryan

In contrast, maintaining basal areas between 50 and 100 ft2/ac resulted in lower 

active crown fire risk with crowning indices ranging between 40 and 80 mph 

(fig. 5). However, the risk of passive crown fire was high for both scenarios, with 

torching indices never exceeding 20 mph (figs. 4 and 5).

Discussion

The dense ponderosa pine/Douglas-fir forests of the Colorado Front Range 

currently serve as a carbon sink, but are at high risk for crown fire. Residents that 

live within the wildland urban interface and their urban counterparts recognize 

the increased wildfire risk (Kaval 2009) and are willing to pay to reduce the 

risk (Kaval 2009; Walker and others 2007). These residents favor thinning over 

prescribed fire (Walker and others 2007) to reduce crown fire hazard which will 

produce large quantities of non-merchantable material. If the biomass removed is 

not used as a substitute for fossil fuels then the area will become a carbon source. 

Also, maintenance of forests with low crown fire risk will result in lower carbon 

storage on the landscape.

Fuel treatments can reduce CO2 emissions during a wildfire (Hurteau and 

others 2008) by reducing tree mortality and maintaining an intact post-fire 

forested stand. In our simulations, maintaining stand basal area between 50 and 

100 ft2/ac reduced the risk of a severe wildfire by substantially increasing the 

windspeed estimated to initiate a passive crown fire (torching event) or sustain 

an active crown fire. Other studies found similar results. In simulations of East 

Cascade ponderosa pine forests, the removal of understory vegetation led to a 

reduction in potential fire severity that consequently lowered overall biomass 

combustion (Mitchell and others 2009). A thin from below and whole-tree harvests 

were implemented, which resulted in several stand characteristics that reduce 

undesirable fire behavior (Agee and Skinner 2005). Thinning from below focuses 

on reducing the ladder fuel component of the stand, which raises the canopy base 

height of the stand. Taller canopy base heights place the flammable foliage further 

from the surface fuels and require taller and more intense flames for crown fire 

ignition. However, thinning from below may not accomplish target basal area 

reductions and some overstory may also need to be removed (Agee and Skinner 

2005). In stands without a ladder fuel component, thinning would be focused in 

the overstory to reduce canopy bulk density in order to reduce crown fire risk.

As expected, in our simulations, as the residual trees grew and new regeneration established, stands added more foliage biomass, canopy bulk density 

increased, and canopy base height decreased. These changes in stand characteristics increased the fire risk until the next entry. Two things are notable. First, 

although the crowning index decreased throughout the rotation, it never reached 

the pretreatment values due to the lack of vegetation in the midstory, and met the 

objective of reducing active crown fire risk. Second, the torching index was variable throughout each rotation due to the ingrowth of regeneration that evidently 

reduced the average stand crown base height. It should be noted, however, that 

FVS is not a spatially explicit model and the placement of the regeneration in 

the understory is continuous. In reality, the regeneration could be quite patchy, 

creating situations in the stand where torching is more probable in one area versus 

another. Nevertheless, the lowering in torching index suggests that managers will 

need to have additional, more frequent entries that address the regeneration issue 

to maintain low crown fire hazard (Battaglia and others 2008).

While fuel treatments reduce the risk of severe wildfire and increase stand 

resiliency to fire, the removal of biomass from the stand also reduces the amount 

of carbon sequestered and produces carbon emissions (Finkral and Evans 2008; 
90 USDA Forest Service Proceedings RMRS-P-61. 2010.

Battaglia, Nelson, Kashian, and Ryan Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range

Mitchell and others 2009; North and others in press). From a carbon standpoint, 

the thinning of forests to reduce fire hazard is only effective if a wildfire were 

to burn over the treated area within the rotation period. Therefore, if a wildfire 

doesn’t burn over the fuel treatment, then that area will become a carbon source 

if the biomass removed is not used to replace fossil fuels to generate energy. The 

magnitude of the CO2 emitted will depend on the equipment used for harvesting, 

the amount of biomass removed, the utilization of the material removed, and the 

biomass replaced during the rotation.

The potential amounts of CO2 equivalent per acre emitted by fuel reduction 

treatments in the ponderosa pine/Douglas-fir forests of the PSINF will vary 

depending upon site productivity and thinning intensity if the biomass is not 

utilized to offset fossil fuels (table 1). In our simulations, stands with a high 

basal area (160 ft2/ac) did not replace the carbon removed in the first thinning, 

because they were maintained at a lower basal area. Stands with lower basal area 

(100 ft2/ac) would regrow the biomass removed, because the target for the next entry 

was 100 ft2/ac. In the 160 ft2/ac basal area stand, even if all the removed biomass 

was used as a substitute for fossil fuels, this treatment would still emit about 18 

MT CO2/ac, because the stand did not fully replace the carbon removed in the 

first entry. However, if a wildfire burned either of these treated areas, the stands 

would likely remain intact and CO2 emissions would be lower than if not treated.

Assessing the carbon value of fuel reductions weighed against potential losses 

in a stand-replacing fire is complicated, but requires consideration of the total area 

treated and the probability of fire in that area if not treated. For any individual 

stand that burns, the calculation is fairly simple, and depends on the probability 

of regeneration. If a wildfire does burn over an untreated area the amount of CO2

emitted over time would be substantial (Hurteau and others 2008), with initial 

losses from combustion of the foliage, small twigs, forest floor, and some dead 

wood, and subsequent losses from decomposition of the killed trees. If the burned 

area was small enough to be near live trees with seeds, natural regeneration would 

establish a new forest, and the area would eventually recover most or all of the 

carbon lost. Contemporary wildfires in ponderosa pine forests often produce large 

patches far from seed sources that prevent natural regeneration (Bonnet and others 2005; Lentile and others 2005; Romme and others 2003). In this case, unless 

replanted, the forest will change to a meadow and lose substantial carbon. But, 

fires do not always conveniently burn only the treated areas, and thinned areas 

are a substantial carbon source unless the biomass removed is used instead of 

fossil fuel and the trees allowed to regrow the carbon lost. If the entire PSFNF 

ponderosa pine/Douglas-fir zone were treated for fuels reduction, we estimate 

that about 60 percent of the biomass carbon would be moved from the forest to the 

atmosphere unless it was used for biomass fuel and the forest allowed to regrow. 

Contrast this with a historic probability of wildfire of ~2 percent of forested area 

per year, with a lower amount of area in a large, high severity burn that would 

preclude timely natural regeneration. From a carbon perspective, large-scale fuel 

treatments are likely to be a carbon source, unless biomass is used for fuel.

In our reforestation simulations, more carbon accumulated within 90 years as 

site productivity and planting densities increased (table 2). At sites with low productivity (SI=35), planting densities of 300 TPA would yield similar CO2 equivalent 

sequestration as planting densities of 100 TPA on a higher site productivity of 55. 

These differences in CO2 equivalent accumulated based on planting densities and 

site productivity should help guide managers with strategic reforestation planning. 

However, high planting densities would recreate forests with high active crown 

fire and bark beetle risk and more need for thinning, while low planting densities 

would not. Instead, low planting densities would develop forests with lower crown 
USDA Forest Service Proceedings RMRS-P-61. 2010. 91

Forest Biomass and Tree Planting for Fossil Fuel Offsets in the Colorado Front Range Battaglia, Nelson, Kashian, and Ryan

base heights more susceptible to passive crown fire. In our reforestation simulation, the no thin scenario sequestered more CO2 than the fuel treatment scenario. 

After a 120 years of stand development, the active crown fire hazard was high 

(30 mph) and the stands were in potential danger of high mortality and substantial 

CO2 release. The fuel treatment scenario follows the same story as mentioned in 

the fuel treatment maintenance scenario above; increase in CO2 sequestered and 

lower fire risk over several rotations. The no thin scenario continues to accumulate 

CO2 throughout the simulation, but density-dependent mortality begins around 

180 years. Also around this time, basal areas are exceeding 180 ft2/ac, making 

this stand highly susceptible to mountain pine beetle (Dendroctonus ponderosae) 

attack (Schmid and others 1994). Crown fire risk is moderate, with windspeeds 

less than 40 mph required to sustain an active crown fire due to the dense overstory canopy. The risk of passive crown fire is high in both scenarios, suggesting 

that some additional treatments such as pruning in the no thin and addressing the 

regeneration issue in the fuel treatment scenario is needed.

Conclusions

Many acres in the ponderosa pine/Douglas-fir forests of the Colorado Front 

Range are currently sequestering substantial amounts of carbon, but are also at 

high risk of sustaining a crown fire if ignited. Thinning these stands to reduce 

crown fire risk will result in CO2 emissions if the biomass is not used as a substitute for fossil fuels. Furthermore, if stands are maintained at densities that are 

lower than the pre-treatment basal area, carbon will move from the forest to the 

atmosphere because the carbon from the total biomass removed will not be recovered. From a carbon standpoint, the argument that thinning a forest to reduce 

crown fire risk and CO2 emissions is valid, but only if a fire actually burns the 

dense stand. Regardless of the carbon impact, thinning ponderosa pine/Douglasfir forests to reduce crown fire risk has other ecological benefits.

A recent report by the Front Range Fuel Treatment Partnership concluded 

that heating with woody biomass in institutional buildings along the Colorado 

Front Range appears to be the best opportunity to lower fuel reduction treatment 

costs while utilizing woody biomass (FRFTP 2006). The FRFTP estimates that 

institutional buildings require only ~300 tons of woody biomass a year, which is 

feasible in terms of the level of forest treatment and transportation activity that 

is sustainable and desirable. Furthermore, the demand for institutions heated by 

biomass along the Colorado Front Range is expected to rise due to the building 

of new schools and existing schools that will replace aging boilers in the next few 

decades. Since most of the public institutions along the Colorado Front Range 

are dispersed within the same forests that need treatment, transportation costs 

to individual boilers would likely be cheaper than transporting it to a centralized 

biopower facility. However, until the infrastructure is developed, the utilization 

of biomass generated from fuel reduction treatments will remain a challenge.

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of the Hayman Fire. Pages 181-262 in R.T. Graham, technical editor, Hayman Fire Case Study. 

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Diego, CA.

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and crown fire behavior. Res. Pap. RMRS-29. USDA Forest Service, Rocky Mountain Research 

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 95

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Research Forester, USDA Forest Service, Redding Silviculture Laboratory, 

Pacific Southwest Research Station, 

Redding, CA.

2 Scientist Emeritus, USDA Forest Service, Redding Silviculture Laboratory, 

Pacific Southwest Research Station, 

Redding, CA.

3 Research Geographer, USDA Forest 

Service, Redding Silviculture Laboratory, Pacific Southwest Research Station, 

Redding, CA.

To Manage or Not to Manage: The Role 

of Silviculture in Sequestering Carbon in 

the Specter of Climate Change

Jianwei Zhang1, Robert F. Powers2, and Carl N. Skinner3

Abstract—Forests and the soils beneath them are a major sink for atmospheric CO2

and play a significant role in offsetting CO2 emissions by converting CO2 into wood 

through photosynthesis and storing it for an extended period. However, forest fires 

counter carbon sequestration because pyrolysis converts organic C to CO and CO2, 

releasing decades or centuries of bound C to the atmosphere as a pulse, exacerbating 

the greenhouse gas effect. With global warming, the probability of fire has increased. 

Silviculture is an important tool for reducing wildfire risk and enhancing long-term 

carbon sequestration and—through this—mitigating the effect of climate change. Using the data collected from three studies over the last several decades, we compared 

treatment effects (density manipulation, fertilization, vegetation control, and interactions among some of them) on tree growth and subsequently carbon accumulation, 

fire risks predicted with fire behavior simulations, and responses of stand to future 

climate changes modeled by a process-based model (3-PG). With these case studies, we found that (1) intensive management (vegetation control and fertilization) 

increased C sequestration 400 percent and decreased fire caused tree mortality 50 

percent compared to control at age 21 (Whitmore Garden of Eden study). (2) Density 

manipulation and vegetation control increased C sequestration 30 percent and decreased fire caused tree mortality 50 percent compared to control at age 40 (Challenge 

Initial Spacing study). (3) Density manipulation increased C sequestration 9 percent 

and decreased fire caused tree mortality 40 percent compared to control at age 55 

(Elliot Ranch LOGS study). In addition, bark beetles killed significantly more trees in 

the control (high density plots) than in the lower density plots. (4) The 3-PG model 

predicts that global warming impacts carbon sequestration more in unmanaged than 

managed stands. These findings suggest that if carbon sequestration and storage are 

goals, our forests should be managed more aggressively in the future.

Introduction

Global climate is changing at an unprecedented rate. The latest assessment 

from the Intergovernmental Panel on Climate Change (IPCC 2007) states that 

the global average surface temperature has increased 0.74 °C from 1906 to 2005. 

By 2100, increases of 1.1-6.4 °C are projected over the 1990 level using different models with various scenarios. Warming trends are believed to be due to the 

anthropogenic increase of greenhouse gases (GHG), with an increase of 70 percent between 1970 and 2004. Carbon dioxide (CO2) is one of the most important 

anthropogenic GHGs. Annual emissions grew by about 80 percent between 1970 

and 2004. Not only have CO2 and GHG concentration increased greatly since 

1750, but the rate of increase far exceeds pre-industrial values determined from 

ice cores spanning many thousands of years (IPCC 2007).

Forests play a significant role in offsetting CO2 emissions by converting CO2

into organic C through photosynthesis. Much of the product of photosynthesis 
96 USDA Forest Service Proceedings RMRS-P-61. 2010.

Zhang, Powers, and Skinner To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change

is stored in the forest for decades or centuries. Despite uncertainties, annual 

carbon sequestration is estimated to vary between 149 and 330 million tonnes C 

by forests in the United States (Woodbury and others 2007), which offsets about 

10 percent of US CO2 emissions. Wildland fires have annually affected about 

1.7 million ha of forests across the United States in recent decades with the area 

increasing in the last 10 years, thereby releasing vast pulses of ecosystem carbon 

back to the atmosphere.

Fire was historically an integral ecosystem process in the forests, especially 

forests in the Interior West. Prior to Euro-American settlement, forests of the 

region were more open, containing fewer trees and wider crown spacing than 

today (Agee 1993; Cooper 1960; Covington and Moore 1994; Skinner and Taylor 

2006). Fires historically burned every 4-25 years (Graham and others 2004), thinning forest stands of small trees but also creating bare mineral soil environments 

favored by seedlings of some tree species, such as light-demanding pines. Starting 

in the late 19th and early 20th century, logging, livestock overgrazing, and fire 

suppression created conditions more suitable for tree regeneration and survival. 

Today, fire suppression and a lack of density management in both young and old 

stands have resulted in forests dominated by dense thickets of saplings and polesized trees, often with a higher proportion of shade-tolerant species. Furthermore, 

long intervals between fire events have led to heavy accumulations of litter, duff, 

and woody fuels in many areas. Therefore, today’s forests are more susceptible 

to stand-replacing crown fires (Agee and Skinner 2005).

Managing these forests has become a great challenge to forest managers. In 

recent decades, Federal land management agencies and private land owners have 

treated millions of acres of hazardous fuels using mechanical thinning, prescribed 

fires, and other means. These treatments are absolutely necessary because forest 

structure and function would not be restored without them (Agee 2007). However, 

there is limited information on how these treatments affect carbon sequestration 

and storage and how treated stands are likely to respond to future climate. On 

one hand, fuel treatment may remove carbon by harvesting small-size trees and 

shrubs and by disturbing soils that may stimulate soil respiration. On the other, 

treatment may increase the vigor of remaining trees that will sequester more carbon. At the stand level, we would reallocate more carbon to residual living trees.

Manipulating stand density through thinning is not new. Since the birth of 

silviculture, thinning has been a major means for controlling stand density, 

structure, and composition (Smith and others 1996). Standing fuel reduction is 

merely a modern extension of density manipulation. The results from growth and 

yield studies established across the US in the past century can be interpreted to 

answer some of today’s questions such as effect of density and competing vegetation control on carbon sequestration and storage, as well as fuel accumulations.

In this paper, we present three case studies that were conducted over several 

decades by the PSW Redding Silviculture Laboratory demonstrating how forest 

vegetation management has affected the fate of carbon and how silviculture may 

help mitigate climate change effects on our forests (table 1). These long-term permanent installations are the (1) Whitmore “Garden of Eden” study of the effects 

of understory vegetation control and fertilization on stand dynamics, established 

by the second author; (2) Challenge Initial Spacing study of the effects of stand 

density and understory vegetation control on stand growth; and (3) Elliot Ranch 

Levels-of-Growing Stock study of the effect of density on stand growth. Both 

established by retired Research Silviculturist William W. Oliver. These plantations represent 21, 40, and 55 years of development respectively and include a 

range of silvicultural treatments.
USDA Forest Service Proceedings RMRS-P-61. 2010. 97

To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change Zhang, Powers, and Skinner

General Procedures

Aboveground Carbon Comparison

Using historical inventory data for each tree at each plot, we calculated and 

compared aboveground biomass among silvicultural treatments and controls. A 

diameter-based biomass regression (fig. 1) was established with 36 ponderosa 

pine (Pinus ponderosa C. Lawson) trees harvested at the Whitmore Garden of 

Eden (5-24 cm DBH) and 40 smaller diameter trees harvested from the LongTerm Soil Productivity Study installations (4-8 cm) in northern California (R.F. 

Powers, unpublished data). The biomass estimate for trees with DBH smaller than 

4 cm must be used with caution. The allometric equation for trees with DBH of 

25 cm and above was developed from 110 mature ponderosa pine trees harvested 

in natural stands prior to the Long-Term Soil Productivity Study installations 

(15 – 152 cm) at 11 sites in Northern California (fig. 2; R.F. Powers, unpublished 

data). After plotting predicted values from two equations, we found that both 

were remarkably similar for trees with DBH smaller than 25 cm (inset of fig. 2). 

For trees that are larger than 25 cm DBH, the equation extrapolated from small 

diameter trees (fig. 1) clearly underestimates the aboveground biomass. We concentrated on aboveground biomass because (1) it is more sensitive to disturbance 

such as wildfires and insect infestation and (2) it is relatively easier to measure 

the aboveground biomass than the below-ground biomass, which is not available 

for the larger-diameter trees. The biomass data were converted to carbon stock 

assuming a carbon content of approximately 50 percent by dry weight.

Fire Effects

Fire Family Plus (FFP) (http://www.fs.fed.us/fire/planning/nist/distribu.htm) 

software was employed to derive weather variables to use in fire simulations. We 

used data for Station 040615-Whitmore (http://nwcg.gov/fam-web/) to represent 

Table 1—Study site, silvicultural treatments, and climate change effect (percentage 

reduction) relative to normal climate on aboveground live tree carbon stock modeled 

with 3-PG for three plantations by assuming three scenarios (25% precipitation [ppt] 

reduction, 2 °C increase, and both) since the trees were planted at these sites. 

Spacing/

Study site	 density	 Treatment	 –25% ppt	 +2 °C	 –25% ppt +2 °C

Whitmore 2.4 x 2.4 m Control (C) –1.6 –14.5 –15.2

Fertilizer (F) –2.6 –9.6 –10.9

Herbicide (H) –3.3 –7.2 –9.7

HF –2.4 –4.5 –6.9

Challenge 1.8 x 1.8 m Shrub present –4.7 –3.4 –6.0

Shrub absent –2.3 –1.3 –3.7

3.7 x 3.7 m Shrub present –1.5 –1.9 –3.7

Shrub absent –0.4 –1.7 –2.2

5.5 x 5.5 m Shrub present –0.1 –1.8 –1.9

Shrub absent 0.0 –1.8 –1.8

Elliot Ranch 38 m2 ha–1 Shrub present –21.0 –16.0 –18.9

16 m2 ha–1 Shrub present –8.4 –0.3 –6.2
98 USDA Forest Service Proceedings RMRS-P-61. 2010.

Zhang, Powers, and Skinner To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change

Figure 1—Ponderosa pine diameter-based biomass regression for 21-yr-old trees 

at Whitmore and some small diameter trees harvested at the Long-Term Soil 

Productivity installation in northern California. The range of DBH is between 

3.6 cm and 24.3 cm.

Figure 2—Ponderosa pine diameter-based biomass regression for mature trees (15-152 cm) 

harvested prior to the Long-Term Soil Productivity installations in northern California. The 

predictions from this power equation are remarkably similar with predictions from the 

polynomial equation established for trees with DBH less than 25 cm (inset figure). 
USDA Forest Service Proceedings RMRS-P-61. 2010. 99

To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change Zhang, Powers, and Skinner

conditions for all of our study sites in order to facilitate cross-site comparisons. 

Environmental conditions for fire behavior simulations were derived from climatological reports in FFP. Most variables are for 97.5 percent burning conditions 

(conditions exceeded only 2.5 percent of the time each year). Windspeed was 

set at the 97 percent condition of 8 km/hr (conservative for windspeeds in highintensity wildfires in our study areas), but temperature and moisture conditions 

are mean high/low August values (35 °C, 10 percent relative humidity). Moisture 

contents of ground fuels were set at 2 percent, 4 percent, and 4 percent for 1-, 10-, 

and 100-hour fuels, respectively. Live fuel moistures were set at 30 percent, 56 

percent, and 80 percent for herbaceous, woody, and foliage materials, respectively. 

For each simulation we used a standard fuel model from one of three sources: 

Rothermel (1983), Fire Program Solutions (FPS) (2005), or Scott and Burgan 

(2005). We did not attempt to create a custom fuel model since we did not have 

an opportunity to calibrate fire behavior output with an actual fire.

All fire behavior simulations were performed using the CrownMass routine 

of the Fuels Management Analyst Suite 3.01 (FPS 2005). This program allows 

the entry of a tree list from the site to estimate the canopy fuel conditions. This 

is then combined with a standard surface fuel model in the scenario to simulate 

surface fire spread and intensity as well as the potential for torching and crowning as described by Scott and Reinhardt (2001).

Calibrating the 3-PG Model

To examine climate change effects on aboveground biomass, we needed a 

process-based model to predict stand dynamics by varying temperature and 

precipitation. Because the Whitmore “Garden of Eden” study includes vegetation control and fertilization treatments and was intensively measured with the 

detailed records for 20 years, we used these records to calibrate the 3-PG (Physiological Principles Predicting Growth) model (Landsberg and Waring 1997) for 

ponderosa pine grown in northern California. 3-PG is a process-based model to 

predict forest performance with climatic variables as drivers. It holds several advantages for managers. First, 3-PG is a relatively simple stand-level model. Like 

other process-based models, it includes subroutines to calculate photosynthesis, 

transpiration, respiration, growth allocation, and litter production. Notably, 3-PG 

differs from most process-based models in that it requires only readily available 

site and climatic data as inputs, and predicts the time-course of stand development in a form familiar to the forest manager. Second, it is very user-friendly; 

a Microsoft Excel workbook includes everything—input and code that you can 

change, and output results in a normal spreadsheet. Finally, it has been extensively 

tested in the world’s major forest species, including Eucalyptus spp., Picea sitchensis

(Bong.) Carrière, Pinus ponderosa C. Lawson, P. radiata D. Don, P. taeda L., 

Pseudotsuga menziesii (Mirb.) Franco, etc. (Landsberg and others 2001; Law and 

others 2000; Sands and Landsberg 2002; Waring 2000). After calibrating key 

parameters with one treatment at Whitmore, we applied the model to other treatments at this site, the Challenge Initial Spacing study, and the Elliot Ranch LOGS 

study. We used the aboveground biomass output to compare the aboveground 

biomass estimation from the allometric equations for the silvicultural treatments 

at each site. Similarly, as calculated biomass from observed DBH, biomass was 

converted to carbon stock.

During model calibration, we used all parameters given by Law and others 

(2000) as our prototype blueprint for ponderosa plantations in northern California. Based on our biomass data at Whitmore, we found that the mathematical 

relationship between stem mass and diameter was:

Ws = 0.0456*(DBH)2.5687, r2 = 0.99
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Zhang, Powers, and Skinner To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change

Where: Ws is the stem and branch biomass in kg and DBH is diameter at breast 

height in cm. Therefore, both the constant and power differ from the parameters 

used by Law and others (2000) in Oregon.

In addition, ratios of foliage to stem partitioning differed from those for mature 

natural stands in Oregon. Our ratios were about 0.85 at DBH = 2 cm and 0.14 at 

DBH = 20 cm.

Maximum quantum use efficiency also differed from mature Oregon stands. 

By measuring sun and shade leaves from 36 trees grown from the different treatments at three Garden of Eden sites, quantum use efficiency was found to be 

0.05 (Liang Wei, unpublished data, University of Idaho). Remarkably, there was 

no difference between sun and shade leaves, among treatments, or among sites.

We also added in maximum stand density of 365 as a constraint for tree mortality because stand density is strongly influenced by Dendroctonus bark beetles 

in northern California (Oliver 1995).

Climatic Data and Climate Change Scenarios

Climatic data for the model were obtained from weather stations nearest to 

each site. Temperature and precipitation are commonly available and are musthave variables. Incident solar radiation was calculated following Coops and 

others (2000). The 3-PG simulator can run with monthly data for specific years 

(Whitmore and Challenge) or with mean monthly data for many years (Elliot 

Ranch) if yearly data are not available. Whitmore Garden of Eden data were from 

a Remote Automated Weather Station (RAWS) at Whitmore, about a km from 

the study site (http://www.wrcc.dri.edu/cgi-bin/rawMAIN.pl?caCWHT). The data 

before 1990 when the station was installed were obtained from the average of 

1990-2007. Two weather stations were used to make a complete climatic data for 

the Challenge Initial Spacing study since 1966: Strawberry Valley (http://www.

wrcc.dri.edu/cgi-bin/cliMAIN.pl?ca8606) and Challenge Ranger Station (http://

www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?ca1653). Due to a lack of data for the last 

60 years, we used station mean data for Elliot Ranch site; two stations were used: 

Foresthill (http://www.wrcc.dri.edu/cgi-bin/rawMAIN.pl?caCFOR) and Iowa Hill, 

CA (http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?ca4288).

After the model was calibrated, we made projection runs for each treatment 

with the past climate. The modeled values were compared with calculated values 

from the allometric equations based on periodic DBH measurements. Then, we 

ran the model again by increasing temperature 2 oC, reducing precipitation by 

25 percent, or both. The final changes relative to the first run were presented as 

the climate change effect for each treatment itself. Lastly, impact of silvicultural 

treatments was compared.

Case Study I: Whitmore “Garden of Eden”

Site Characteristics and Experimental Design

The Garden of Eden study was established to determine the biological response 

of ponderosa pine to a broad array of silvicultural treatments over a range of sites 

typifying plantation management in northern California. These plantations were 

established on lands cleared of brushfields or natural forest from 1986 to 1988. 

Each plot measuring 19.7 by 21.9 m was hand-planted at a standard square spacing of 2.4 m with seedlings from superior genetic stock known to perform well 

at each seed zone and elevation. Following planting, 8 treatments were applied 

to 24 plots in a completely randomized design. The standard suite of treatments 
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To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change Zhang, Powers, and Skinner

consisted of a control (C: planting and no further treatment); fertilization 

only (F: eight nutrients applied at planting, and at an exponential rate over the 

next 6 years); vegetation control only (H: glyphosate herbicide applied annually 

to control all understory vegetation for the first 5 years); and fertilization and 

vegetation control combined (HF). An additional silvicultural treatment, systemic 

insecticide (I), was applied as well, producing 3 replications each of 24 plots. 

Each treatment plot of 0.04 ha consisted of 72 trees, but only the innermost 20 

were used for measurement. Treatment details can be found in Powers and Ferrell 

(1996) and Powers and Reynolds (1999).

The Whitmore Garden of Eden plantation, the oldest in the series, was planted 

in spring 1986 on land managed by W.M. Beaty and Associates in eastern Shasta 

County on the southwestern slope of the Cascade Range (Lat. 40.62 N; Long. 

121.90 W). Elevation of the Whitmore plantation is 730 m and precipitation averages 1,140 mm annually. The soil is the Aiken soil series (clayey, mesic Xeric 

Haplohumults), a widespread soil developed from a Pleistocene volcanic mudflow 

and typical of the west slope pine forest. Prior to clearing, the site supported a 

brushfield dominated by whiteleaf and common manzanitas (Arctostaphylos 

viscida Parry and A. manzanita Parry, respectively) that originated from a 1967 

wildfire. Site index (Powers and Oliver 1978), based on measurements of older 

trees in surrounding stands, averages 23 m (78 ft) at 50 years.

After installation, all measurement plots were inventoried at ages 2, 5, 6, 10, 15, 

21 years. Each tree was measured for DBH, height to the base of the live crown, 

and total tree height. In the earlier years when trees were less than 1.4 m tall, 

diameter at 10 cm above ground was measured. All understory vegetation was 

measured for height and percent cover by four line intercept transects per plot, 

each 10 m long. Sample trees spanning the range of tree sizes were felled at ages 

15 and 21 for biomass analyses (3 trees per treatment plot). Each tree stem was 

measured and sectioned at several stem positions and rounds taken for determining 

oven-dry weights after drying to a constant weight at 70 °C. Bole mass was then 

estimated by applying mass/volume ratios of each round to the bole volume in 

each sequential sector. Crowns were divided into 5 equal-length sections and the 

branch of average basal area was taken from each section for dry weight analysis 

after separation into wood and needles. Crown mass per sector was estimated as:

Crown sector mass = ∑(mass/basal area of sample 

branch x basal area of all branches)

Crown sector masses for each sample tree were summed for a single estimate 

of individual crown mass. This, added to the mass estimate for the bole, produced 

a mass estimate for each individual sample tree. Due to lack of an allometric 

equation to calculate biomass for trees that are under 1.4-m tall, we used data 

collected at ages 5 and older to tune the 3-PG model.

Aboveground Tree Carbon

At age 21, aboveground tree carbon stock was 73.3, 55.1, 44.2, and 20.0 Mg ha–1

on HF, H, F, and C, respectively (fig. 3), and treatment effects were highly significant (P<0.001). Relative to control trees (that is, do nothing after trees were 

planted), trees in the fertilizer only treatment (F) accumulated twice the mass of 

carbon, while the herbicide only treatment (H) nearly trebled the amount and HF 

treatment almost quadrupled the amount, compared with control trees. Therefore, 

managed stands stored much more carbon in trees than unmanaged stands.
102 USDA Forest Service Proceedings RMRS-P-61. 2010.

Zhang, Powers, and Skinner To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change

Fire Effect

By age 21, shrub cover was 57 percent and average height was 1.3 m (table 2) in 

the control plots so that fire behavior was modeled as a shrubfield fuelbed. Flames 

exceeded tree height leading to a crown fire. Mortality was projected as close to 

100 percent if fire occurred at this age. Similar results would have occurred in the 

fertilization treatment with 99 percent mortality because fertilization stimulated 

the growth of understory shrubs before pine canopies had closed (Powers and 

Ferrell 1996) and left a continuum of dead and dying fuels that reached to the 

live crown once canopies had closed. In contrast, because herbicides eliminated 

understory in the H and HF plots, a surface litter model was used for fire simulation, which projected that flame length was less than 1 m and fire was confined to 

the surface. No crowns were ignited in the simulations, but scorch was sufficient 

to cause 40-50 percent mortality in lower crown classes at age 21. These results 

clearly suggest that managed stands are more resilient to wildfire even early in 

stand development.

Modeled Effects

Projections made with 3-PG predicts aboveground biomass well; an intercept 

and a slope of regression between modeled and measured values did not differ 

from zero and one, respectively (fig. 4). After applying three climate change 

scenarios in the 3-PG model, we found that a reduction of 25 percent precipitation for each of 21 years would have reduced aboveground tree carbon only 1.6 to 

3.3 percent (table 1). Yet, a temperature increase of 2 °C would have reduced 

carbon accumulation by 7.2-14.5 percent. This result seems surprising because 

water is always considered a limiting factor during late growing season under 

the Mediterranean climate as at these sites in California. A possible, but unlikely, 

explanation is that these trees would not consume all precipitation so that the 

Figure 3—Age-related aboveground tree carbon stock accumulated on HF, H, F, 

and control plots at the Whitmore Garden of Eden study, Whitmore, California.
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To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change Zhang, Powers, and Skinner

Table 2—Results of fire behavior simulations for ponderosa pine plantations at Whitmore Garden of Eden treatment plots 

at age 21, Challenge Initial Spacing plots at age 30 when shrub data were available, and Elliot Ranch LOGS study at 

age 55. All fire behavior fuel models are standard fuel models from either (1) Rothermel (1983), (2) Scott and Burgan 

(2001), or (3) FPS (2005). Fire types are: PC = passive crown fire; SURF = surface fire.

Spacing/	 	 Fuel model	 Stand	 Shrub	 Shrub	 Fire	 Flame	 Mortality

Study site	 Density	 Treatment	 (source)	 HT (m)	 HT (m)	 cover (%)	 type	 length (m)	 (%)

Whitmore 2.4 x 2.4 m Control (C) SH4 (2) 6.7 1.3 57.4 PC 7.3 100

Fertilizer (F) TU3 (2) 9.4 1.4 28.8 PC 6.1 99

Herbicide (H) TL8 (2) 9.8 0.0 0.0 SURF 0.6 48

HF FB9 (1) 11.6 0.0 0.0 SURF 0.5 42

Challenge 1.8 x 1.8 m Shrub present 5A (3) 15.3 3.1 54.9 SURF 1.5 91.3

Shrub absent FB9 (1) 16.5 0.0 0.0 SURF 0.6 42.1

3.7 x 3.7 m Shrub present 5M (3) 16.8 2.3 64.0 SURF 1.4 77.6

Shrub absent FB9 (1) 19.3 0.0 0.0 SURF 0.6 24.8

5.5 x 5.5 m Shrub present 5Z (3) 20.2 2.3 86.2 SURF 1.8 84.8

Shrub absent FB9 (1) 22.9 0.0 0.0 SURF 0.4 12.6

Elliot Ranch 38 m2 ha–1 Shrub present 6M (3) 20.2 4.3 38.8 SURF 1.8 61.5

16 m2 ha–1 Shrub present 6M (3) 18.0 2.4 22.1 SURF 1.4 36.6

Figure 4—The relationship in aboveground tree carbon between modeled with 3-PG 

and calculated from measured DBH on HF, H, F, and control plots at the Whitmore 

Garden of Eden study, Whitmore, California.
104 USDA Forest Service Proceedings RMRS-P-61. 2010.

Zhang, Powers, and Skinner To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change

25 percent reduction represents water lost in the system but with little effect 

on tree growth. Otherwise, the water relation submodel within 3-PG may need 

to be further improved concerning water use and transpiration. Because 3-PG 

is a stand model and does not account for leaf area in understory vegetation, it 

probably underestimates water use by trees in a droughty climate. By assuming 

both 25 percent reduction of precipitation and a temperature increase of 2 °C 

since 1986, we found that carbon storage would have reduced 6.9 to 15.2 percent. 

Carbon accumulation aboveground was reduced more in the control plot trees 

than in any managed plots, suggesting that untended plantations are particularly 

sensitive to climate change.

Case Study II: Challenge Initial Spacing Study

Site Characteristics and Experimental Design

This study was established with planted seedlings near the lower edge of 

the mixed-conifer forest, on the west slope of the northern Sierra Nevada 

(Lat. 39.48 N; Long. 121.22 W). Elevation is 810 m and precipitation averages 

1,730 mm annually. Soil, an Aiken clayey, mesic Xeric Haplohumult, is more than 

1.5 m deep. Dominant shrub species are whiteleaf manzanita, deerbrush (Ceanothus integerrimus Hook. & Arn.), squaw carpet (C. prostratus Benth.), Indian 

manzanita (A. mewukka Merriam), small numbers of Sierra gooseberry (Ribes 

roezlii Regel) and sprouts of California black oak (Quercus kelloggii Newberry) 

and tanoak (Lithocarpus densiflorus [Hook. & Aan.] Rehd.).

The original stand was 70-year-old ponderosa pine with a site index of 30 m at 

50 years, which was clearcut for this experiment. Logging slash was raked, piled, 

and burned off the unit. In March 1966, ponderosa pine seedlings were planted 

in two randomized blocks. Each block contains five plots that were planted at 

square spacings of 1.8, 2.7, 3.7, 4.6, and 5.5 m. Each plot was split into two adjacent 

subplots. On one subplot, brush seedlings were grubbed out by hand for the first 

year and then herbicide 2,4,5-T (2,4,5-trichlorophenoxy acetic acid) was applied 

by hand sprayer in the second and fourth years after planting. Subsequent shrub 

seedlings were removed by hand for about five more years. On the other subplot, 

shrubs were allowed to develop naturally. Each subplot contained 12 measurement 

trees that were buffered from adjacent plots by at least 7 m, minimally two rows 

of trees. Because the same number of trees was used among plots, subplot size 

with buffer varied among treatments, covering 0.05 ha for 1.8 m spacing plots to 

0.15 ha for 5.5 m spacing plots.

Height and DBH (if tree height reached 1.37 m) were measured every year 

from 1968 to 1975, every two years from 1975 to 1985, and every four years from 

1985 to 2006. Other measurements include height to live crown, crown width, 

and tree condition.

Aboveground Tree Carbon

Based on our biomass equation, aboveground tree carbon stocks for shrub present and shrub absent treatments at 40 years were 78.0 and 70.3 Mg ha–1 on 1.8 m 

spacing, 80.8 and 99.2 Mg ha–1 on 3.7 m spacing, and 72.6 and 92.8 Mg ha–1 on 

5.5 m spacing, respectively (fig. 5). The results suggest that control of competing 

vegetation enhances tree growth and carbon storage compared to the plots with 

shrubs present at the wider spacings. At the narrowest spacing of 1.8 x 1.8 m, 

the plots with shrubs absent developed so quickly that mortality occurred much 

earlier than the treatments leaving shrubs present. As a result, carbon stocks are 

higher on the shrub present plots.
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To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change Zhang, Powers, and Skinner

Fire Effect

By age 30, in plots where shrubs were not controlled, shrub cover ranged from 

54.9 percent to 86.2 percent and average height varied from 2.3 to 3.1 m (table 2). 

These plots were modeled as a shrub-dominated fuelbed. Fire intensity in these 

plots was estimated to cause between 77.6 percent and 91.3 percent mortality 

depending on density of plantings (table 2). Where shrubs were controlled, a 

surface litter model was used for fire simulation, which projected fire mortality 

to range only from 12.6 percent to 42.1 percent—again depending on tree density. 

No crowns were ignited in the simulations, and mortality was caused primarily by 

scorch and cambium damage. Again, these results suggest that more intensively 

managed stands are more likely to be resilient to wildfire.

Modeled Effects

Overall, aboveground tree carbon stock based on DBH measurements was 

strongly correlated to 3-PG modeled aboveground tree carbon (fig. 5). Because 

our allometric equation was developed from trees with DBHs 3.6 cm and greater, 

a weaker relationship was expected at young ages. At later years, heavy mortality 

at 1.8 m spacing with shrub absence yielded a C stock overestimated by 3-PG.

After applying the three climate change scenarios in the 3-PG model, we 

found that a reduction of 25 percent precipitation for each of 40 years would 

have reduced aboveground tree carbon only 0 to 4.7 percent (table 1). A climate 

warming of 2 oC would reduce carbon 1.3-3.4 percent and the combination of 

both changes would reduce carbon by 1.9 to 6.0 percent, varying with densities 

and whether shrubs were controlled. In general, high density stands are more 

sensitive to temperature increase and precipitation reduction because of greater 

competition for soil resources. Similarly, shrub present-plots are more sensitive 

to climate change.

Figure 5—The relationship in aboveground tree carbon between modeled with 3-PG 

and calculated from measured DBH on three spacings split with either shrub present 

or shrub absent plots at the Challenge Initial Spacing study, Challenge Experiment 

Forest, California.
106 USDA Forest Service Proceedings RMRS-P-61. 2010.

Zhang, Powers, and Skinner To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change

Case Study III: Elliot Ranch Levels-of-Growing 

Stock Study

Site Characteristics and Experimental Design

These plots were established in a plantation that originated after the Elliot Ranch 

Fire, which burned a deerbrush shrub and snag field that had developed following 

the 1949 Elliot Ranch Burn. The area was planted at 1.8 by 2.4 m spacing in 1950 

with 1-1 ponderosa pine stock from the appropriate seed zone. The plantation is 

located on the Foresthill Ranger District, Tahoe National Forest (Lat. 39.16 N; 

Long. 120.74 W) on the western slope of the Sierra Nevada. Elevation is about 

1,200 m and precipitation averages 1,524 mm annually. Three clay-loam soils, 

Cohasset and Horseshoe Series (loamy, mesic Ultic Haploxeralfs) and an unclassified alluvium, underlie the study area (Oliver 1979). The average site index is 

estimated to be 35 m at 50 years. Cohasset Series is slightly more productive than 

Horseshoe, and trees growing in Cohasset soil in the study area are estimated to 

be 36.5 m tall at 50 years. Trees on the alluvial soil express a site index similar 

to Horseshoe Series.

The study plots were established in 1969 when the plantation was 20 years old. 

This is one of six installations in the west-wide levels-of-growing-stock study for 

even-aged ponderosa pine guided by Myers’ (1967) study plan. Portions of the 

Elliot Ranch plantation were used in developing yield tables for managed stands 

of ponderosa pine (Oliver and Powers 1978).

All plots are buffered with a 9-m isolation strip. The study design is fully 

randomized with three replications. All plots are 0.2 ha in size, exclusive of buffer. Five thinning treatments of 9, 16, 23, 30, and 37 m2 basal area per ha were 

applied in 1969. Rethinnings in 1974 and 1979 restored the original basal area 

stand densities. The third rethinning in 1989 used Stand Density Index as the 

measure of stand density and resulted in an increase of approximately 10 percent 

in growing stock for each density treatment.

All trees within the plots were measured for DBH. Stem deformities and evidence of insect and disease attack were also noted. Total height and height to live 

crown were measured on a 20 percent systematic sample of the trees during all but 

the last measurement in 2004. At that time, all trees were measured for total height. 

A probability-proportional-to-size sample of six trees per plot was measured with 

an optical dendrometer for stem volumes during several remeasurements.

Aboveground Tree Carbon

To demonstrate effect of treatments, we only calculated tree carbon for two 

stand densities: 16 m2 ha–1 (70 ft2 ac–1) and 38 m2 ha–1 (160 ft2 ac–1); the latter 

similar to that of the natural untreated stand. Treatment plots were installed 

when the plantation was 20 years old and stand densities were achieved with 

repeated thinning. After each thinning, only bole wood was removed from the 

site. Thinning slash from the original thinning was piled and burned in the isolation strips between the measurement plots, while that from subsequent thinnings 

was lopped and scattered. We calculated live-tree carbon using a specific gravity 

of 0.38 Mg m–3 in converting wood volume to biomass. Collectively, there was 

201 Mg ha–1 carbon stock (current live trees, 156; initial thin, 16; repeated thins, 

30) on 16 m2 ha–1 plots and 185 Mg ha–1 (current live trees, 166; initial thin, 2.4; 

repeated thins, 17) on 38 m2 ha–1 plots. Not only did moderately thinned plots 

produce large-sized healthy trees within 55 years, but also they stored about 

16 Mg ha–1 more carbon than lightly thinned plots. The National Forests across 

the Sierra Nevada consider 76 cm (30 in.) trees as must-keep “old growth” trees 
USDA Forest Service Proceedings RMRS-P-61. 2010. 107

To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change Zhang, Powers, and Skinner

during fuel reduction projects (fig. 6). With an appropriate silvicultural treatment, some trees can reach that category of apparent “old growth” in only 55 

years. In addition, higher density plots suffered greater mortality, mainly caused 

by bark beetles, than lower density plots (fig. 6). These dead materials have become hazardous fuels for the forests.

Fire Effect

Shrubs were present in both stand conditions at this site and cover varied 

from 22.1 percent to 38.8 percent. Where shrubs were present, the fire simulation estimated flame lengths exceeding 1 m. Fire mortality was estimated to be 

36.6 percent in the lower density plots and 61.5 percent in the higher density plots 

(table 2). This difference is due primarily to greater shrub height and cover, and 

smaller trees in the later plots leading to greater area experiencing the higher 

intensity fire. As at Challenge, the fire simulation indicated mortality was due 

mainly to crown scorch and cambium damage.

Figure 6—Measured and modeled aboveground tree carbon stock (A) and quadratic 

mean diameter (QMD) and tree mortality (B) on two growing stock levels at 16 m2 ha–1

(70 ft2 ac–1) and 38 m2 ha–1 (160 ft2 ac–1) plots across the last 55 years at the Elliot Ranch 

Level-of-Growing-Stock study, near Foresthill, California. 
108 USDA Forest Service Proceedings RMRS-P-61. 2010.

Zhang, Powers, and Skinner To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change

Modeled Effects

The 3-PG projection also tracked the thinning events well (fig. 6). We performed 3-PG runs based on the three scenarios of climate change that included 

the initial and repeated thinning treatments. Interestingly, the 25 percent precipitation reduction yielded the most reduction of carbon stocks with 8.4 percent for 

16 m2 ha–1 plots and 21 percent for 38 m2 ha–1 plots, respectively (table 1). After 

55 years, 2 oC increases would have reduced C by 0.3-16 percent. Together, 

lower precipitation and higher temperature would have reduced C storage by 

6.2 percent and 18.9 percent for the two densities. However, the higher density

plots are more sensitive to climate change.

Concluding Remarks

Results from these case studies indicated that silviculture can play a significant 

role on managing forests for carbon and for mitigating the deleterious effects of 

climate change. Although what we presented here are results from plantations, 

the concept should hold for natural stands, especially when we consider wildfire 

as a part of ecosystem processes.

Managed stands accumulated more aboveground tree carbon than unmanaged 

stands. The result should not surprise forest managers because the goal of silviculture in earlier years was to maximize the highest wood production on a given 

land unit (Fernow 1914) and carbon stock is directly related to stem volume. We 

concentrated only on the aboveground tree carbon in this chapter because this 

represents stabilized carbon that varies considerably with stand ages. Understory 

vegetation and forest floor components are also important, but relatively unstable 

carbon because they are more susceptible to wildfire.

Managed stands are more resilient to wildfires or bark beetle infestation than 

unmanaged stands. This result supports the experience of seasoned forest managers, and echoes a major conclusion reached by Agee (2007) in his keynote at 

an earlier National Silviculture Workshop. In the past few decades, federal land 

management agencies and private land owners have treated millions of hectares 

of hazardous fuels using mechanical thinning, prescribed fires, and other means 

in order to create forests resilient to intensified wildfires or insect infestation. 

The challenge facing us is how we can use silviculture in meeting the multipleuse objectives for our forests.

Unmanaged stands are more sensitive to global climate change than managed 

stands in terms of carbon accumulations. Our three study sites are located near the 

lower edge of the mixed-conifer forest, on the western side of the Sierra Nevada 

and are dominated by a Mediterranean climate with wet and mild winters and 

hot and dry summers. Weather patterns suggest that growing season is controlled 

by water availability. Any management tools that improve availability of water 

and other resources during the growing season will benefit individual trees as 

well as stands.

Western ponderosa pine ecosystems have changed dramatically in structure 

and composition over the past century. More open forests with fewer trees and 

wider crown spacing have often been replaced by forests dominated by dense 

thickets of saplings and pole-sized trees due to various reasons. More than ever, 

these forests need to be managed in order to preserve their ecosystem services 

for this and future generations.
USDA Forest Service Proceedings RMRS-P-61. 2010. 109

To Manage or Not to Manage: The Role of Silviculture in Sequestering Carbon in the Specter of Climate Change Zhang, Powers, and Skinner

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1 Research Forester, USDA Forest 

Service, Southern Research Station, 

Monticello, AR.

2 Supervisory Ecologist and Project 

Leader, USDA Forest Service, Southern 

Research Station, Hot Springs, AR.

Estimating Long-Term Carbon 

Sequestration Patterns in Even- and 

Uneven-Aged Southern Pine Stands

Don C. Bragg1 and James M. Guldin2

Abstract—Carbon (C) sequestration has become an increasingly important consideration for forest management in North America, and has particular potential in 

pine-dominated forests of the southern United States. Using existing literature on 

plantations and long-term studies of naturally regenerated loblolly (Pinus taeda) and 

shortleaf (Pinus echinata) pine-dominated stands on the Crossett Experimental Forest, allometric biomass equations, and reasonable assumptions about forest product 

life cycles, we projected the net C pools of the following silvicultural systems over 

a 100-year period: a short rotation loblolly pine plantation (4 rotations); a seed treebased (natural origin) even-aged loblolly/shortleaf pine stand (2 rotations); and an 

uneven-aged loblolly/shortleaf pine stand (20 cutting-cycle harvests under the selection method). Both the seed tree stand and the intensively managed pine plantation 

produced large (if fluctuating) quantities (up to almost 190 tons/ha) of aboveground 

live biomass. Though not as productive as the even-aged treatments, the uneven-aged 

pine stands produced a steady stream of sequestered C in the form of high quality 

sawtimber while simultaneously maintaining a steady stock of 61.5 to 78.5 tons/ha 

of live aboveground biomass. Belowground C sequestration was also substantial in 

uneven-aged stands, with a fairly constant 13.3 to 16.9 tons/ha of coarse roots in 

the standing live pine crop. While shorter rotation even-aged stands tend to produce 

smaller coarse roots, their higher stocking levels more than offset this, and hence 

these stands have more live belowground biomass during most of the rotation (up to 

almost 41 tons/ha). By the end of the 100-year simulation, the even-aged stands had 

sequestered approximately 120 tons/ha of C in live tree and product pools, or about 

50 percent more than the uneven-aged stand. The uneven-aged stand, however, 

maintained a more stable residual live tree C store, and fluctuated (only ± 2 tons/ha/yr) 

far less than either even-aged treatment. Averaged over the period, annual C storage 

ranged from 0.38 to 1.11 to 1.16 tons/ha for the uneven-aged, seed tree, and plantation, respectively. Though it is difficult to compare these values to other simulations, 

the data show that managing loblolly pine stands is an effective way to sequester C, 

even if their patterns differ appreciably.

Keywords: Crossett Experimental Forest, Loblolly pine, shortleaf pine, silviculture

Introduction

Bioenergy production and carbon (C) sequestration have become a major emphasis for silviculture in recent years (for example, Gan and Smith 2007; Mead 

and Pimentel 2006). To date, afforestation has garnered the bulk of the economic 

interest because current commercial markets for C sequestration require the longterm accumulation of atmospheric C on locations presently lacking tree cover 

(Birdsey 2006). However, foresters and policy makers are trying to modify the 

nature of these markets to get credit for C accumulated in and the forest products 
112 USDA Forest Service Proceedings RMRS-P-61. 2010.

Bragg and Guldin Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands

generated from existing stands. If successful, this broadening of C credits for 

silvicultural practices other than afforestation may significantly boost the income 

potential on millions of hectares of productive timberlands.

The possibility of C credits is fueling research into understanding the differences between management practices and their ability to sequester C, which in 

turn has raised a number of questions. For instance, are there quantifiable differences between long-term C sequestration patterns by silvicultural system? Many 

people assume that productivity is a reasonable measure of C accumulation, 

and therefore, fast growing pine plantations may be considered most desirable. 

However, much of the long-term C storage benefits may be found in end product 

usage. Commodities such as dimensional lumber or plywood last much longer 

than short-lived products (such as paper) that are often the primary output of 

short-rotation plantations (Johnsen and others 2001; Markewitz 2006). Would 

naturally regenerated southern pine stands geared towards quality sawlog production sequester more C over the long-term than a short-rotation, intensively 

managed pine plantation that produces more fiber than boards?

We will address these questions using long-term data accumulated on the USDA 

Forest Service’s Crossett Experimental Forest (CEF) and adjacent industrial lands 

in extreme southern Arkansas. The naturally regenerated forests of the CEF are 

predominantly loblolly pine (Pinus taeda L.), with lesser amounts of shortleaf 

pine (Pinus echinata Mill.) and limited quantities of oaks (Quercus spp.), gums 

(Liquidambar styraciflua L. and Nyssa sylvatica Marsh.), and other hardwoods. 

The CEF is characteristic of mesic, relatively productive Upper West Gulf Coastal 

Plain upland sites, most of which have been in timber production for decades, and 

many of which have been recently converted to intensively cultured loblolly pine 

plantations (Wear and Greis 2002). This makes the results from studies on the 

CEF an appropriate source of information to compare long-term C sequestration 

patterns under different silvicultural systems.

Methods

Silvicultural Systems

To consider broad-scale differences between silvicultural systems rather than 

specific real-world stands, a number of “model” systems will be used to represent 

stand dynamics related to C sequestration. These idealized systems are based on 

examples of long-term growth and yield from southern pine stands in the Upper 

West Gulf Coastal Plain, which has a rich history of diverse and sustainable forestry varying from even-aged plantations to uneven-aged selection. As with all 

silvicultural systems, there are a number of possible conditions and management 

objectives—the treatments described below follow typical standards and practices 

for loblolly pine-dominated forests in southern Arkansas.

Uneven-aged stand—The loblolly pine-dominated uneven-aged stand in this 

exercise was modeled after examples of the selection method using periodic 

cutting-cycle harvests provided in Baker and others (1996), primarily adapted 

from data on the CEF’s Good and Poor Farm Forestry Forties. Our simulated 

stand has a reverse J-shaped distribution, with a residual basal area of 13.8 m2/ha 

and 345 stems/ha (merchantable pines only), a maximum d.b.h. of 53.3 cm, and 

a q-factor of approximately 1.2 for 2.5 cm d.b.h. classes (table 1). In the 5 years 

following any given cutting-cycle harvest, this stand is assumed to reach 

17.2 m2/ha of basal area and just under 400 merchantable pines/ha, producing 

22.0 m3/ha of sawtimber.
USDA Forest Service Proceedings RMRS-P-61. 2010. 113

Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands Bragg and Guldin

Seed tree stand—There are many possible permutations in how to conduct 

seed tree management in southern pine stands, depending on initial conditions, 

desired rotation length, the number of seed trees to retain following the regeneration harvest, the duration of overwood retention, etc. For convenience, we chose 

a strategy that incorporated a 50-year rotation (hence, 2 full rotations during our 

100-year simulation period) and retained just under 20 seed trees/ha. We assume 

that 12,355 new loblolly pines/ha are successfully established after the regeneration cut, and all of the seed trees are harvested in year 5 in conjunction with a 

precommercial thinning to reduce the number of pines to 1,347 stems/ha by year 

6. Commercial thinnings were performed in years 18, 25, 35, and 43, reducing 

stand density down to 494, 309, 222, and 124 pines/ha, respectively. After 50 years 

under this prescription, loblolly pines in the simulation were assumed to reach an 

average of 47.8 cm in d.b.h. An excellent description of the shelterwood system 

in loblolly pine-dominated stands in this region is presented in Zeide and Sharer 

(2000). Specific data used to develop this silvicultural regime were adapted from 

a variety of studies in even-aged, naturally regenerated, loblolly pine-dominated 

stands on the CEF, including Cain (1996), Cain and Shelton (2001, 2003), and 

Bragg (2010).

Table 1—Structure of uneven-aged loblolly pine-dominated stand immediately post-harvest 

(year = 0) and immediately prior to the next harvest (year = 5).

	 	 Live pine stocking	 Live pine basal area	 5-year harvested

	 D.b.h.	 by d.b.h. class	 by d.b.h. class	 sawtimber

	class midpoint	 Year = 0	 Year = 5	 Year = 0	 Year = 5	 volume

cm - - - - - trees/ha - - - - - - - - - - m2/ha - - - - - - - - - - m3/ha a- - - -

Submerchantable stems

1.3 579.4 695.2 0.07 0.09 —

2.5 331.1 397.3 0.17 0.20 —

5.1 189.2 227.0 0.38 0.46 —

7.6 108.1 129.7 0.49 0.59 —

Subtotals: 1207.8 1449.2 1.11 1.34 —

Merchantable stems

10.2 61.8 74.1 0.50 0.60 —

12.7 46.2 49.4 0.59 0.63 —

15.2 40.8 44.5 0.74 0.81 —

17.8 35.8 39.5 0.89 0.98 —

20.3 30.9 34.6 1.00 1.12 —

22.8 25.9 29.7 1.07 1.22 —

25.4 24.5 24.7 1.24 1.25 0.05

27.9 18.5 20.0 1.14 1.23 0.55

30.5 13.6 17.3 0.99 1.26 1.65

33.0 11.4 14.6 0.97 1.25 1.84

35.6 9.4 12.1 0.93 1.20 1.92

38.1 7.7 10.4 0.87 1.18 2.31

40.6 6.7 9.6 0.87 1.25 2.98

43.2 5.4 7.4 0.80 1.09 2.31

45.7 3.5 4.9 0.57 0.81 2.00

48.3 2.0 3.7 0.36 0.68 2.68

50.8 1.2 2.5 0.25 0.50 2.17

53.3 0.0 0.7 0.00 0.17 1.47

Subtotals: 345.3 399.7 13.78 17.23 21.93

Grand totals: 1553.1 1848.9 14.89 18.57 21.93

a Calculated from equations in Farrar and others (1984) for sawtimber-sized pines only.
114 USDA Forest Service Proceedings RMRS-P-61. 2010.

Bragg and Guldin Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands

Plantation—The plantation used to exemplify artificial regeneration typical of 

this region assumed that 1,347 seedlings/ha of 1-0 genetically improved loblolly 

pine stock were planted on 2.4- by 3.0-meter spacing and were managed on a 

25‐year rotation. Although plantations in this region commonly recruit volunteer 

pines from nearby seed sources, we assumed that site preparation and release 

eliminated these unwanted volunteers. At 11 and 17 years, commercial thinning 

treatments were implemented to reduce this stocking to 445 and 222 pines/ha, 

respectively. At 25 years, when these improved loblolly pines were assumed to 

average 37.1 cm at d.b.h., all trees were cut and the site was replanted. For convenience of the calculation, we assumed that loblolly pines were immediately 

planted to 1,347 seedlings/ha after logging was completed so that no year went 

without the presence of a plantation (though in the real world, plantation reestablishment often occurs months to a couple years after harvest).

Model Design and Assumptions

Modularity—This modeling exercise assumes modularity of the treatments, 

with each silvicultural system module based on either cutting cycle (5-year cutting 

cycle for the uneven-aged example) or rotation length (25-year rotation for the 

plantation example or 50-year rotation for the seed tree example). Each module 

within each treatment is modeled exactly the same, with identical stand developmental trajectories and treatment implementations. Furthermore, no mortality 

or other stochastic events are assumed, making all of the modules in this system 

deterministic. However, biomass and sequestered C values are carried over from 

one module to the next, producing a running total. Hence, the results reported 

are for a single projection in ExcelTM (in other words, no replication). While this 

focuses on the silvicultural treatments under idealized circumstances, it also likely 

produces overly optimistic growth and yield predictions.

Tree allometry—To standardize biomass values, a nationally derived set of 

estimators was applied. What the equations of Jenkins and others (2003) sacrifice 

in terms of local accuracy is offset by the needs of this simulation to segment 

individual trees into their primary biomass components (in other words, coarse 

roots, stem wood, stem bark, branches, and foliage) in a compatible system. 

Undoubtedly, in any given stand (even intensely managed pine plantations) there 

will be individual-based differences in attributes such as species, wood density, 

shoot:root ratio, bark thickness, leaf area efficiency, decay presence, among 

many other variables. We do not have the ability to account for all of these differences—hence, we are better off recognizing this inadequacy and emulating 

a “standard” tree following well-described factors we can control (for example, 

silvicultural system, rotation or cutting cycle length, stand density).

Jenkins and others (2003) used a series of published equations on biomass 

for different tree species groups to derive “pseudodata” that were then used to 

generate a set of equations for species groups based on a number of factors, including phylogenetic relationship, adequacy of the original equations and data, 

and similarity of wood specific gravity. Each species group equation was fit to a 

common logarithmic model form:

B e

1000

b b 1 . n d b. . h

= 0 1 +

[1]

where B = total aboveground biomass (in metric tons), d.b.h. = diameter at breast 

height, and b0 and b1 are group-specific coefficients. Note that in this paper, all 

biomass or C sequestration weights are given in terms of oven-dry metric tons 

(1 metric ton = 1,000 kilograms = 1 megagram). Because we considered only 

loblolly and shortleaf pine-dominated natural stands and loblolly pine plantations, 
USDA Forest Service Proceedings RMRS-P-61. 2010. 115

Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands Bragg and Guldin

we used their pine species group coefficients (b0 = -2.5356 and b1 = 2.4349, table 4 

in Jenkins and others (2003)).

Jenkins and others (2003, their table 6) also developed a series of coefficients 

to estimate the proportions of different tree components for conifers based on 

the following ratio equation:

Ri = eb0,i+b1,i/d.b.h. [2]

where Ri = the ratio (0.0 to 1.0) of component i to total aboveground biomass. 

Note that the coarse root component is in addition to the biomass calculated in 

equation [1], and that the proportion of branch biomass (RBRANCH) = 1.0 – RWOOD – 

RBARK – RFOLIAGE. The ratios predicted by equation [2] do not explain a lot of the 

variation in the data they were derived from, but follow logical proportioning 

patterns, and vary reasonably as a function of tree diameter. Future iterations 

of this effort may be improved with the adaptation of more conceptually robust 

allometric relationships (for example, Enquist 2002; Enquist and Niklas 2002).

Biomass and post-harvest C storage pools—Live tree biomass (both aboveand belowground) is tracked every year from the start of the even-aged stands. 

For uneven-aged stands, the residual contains the live trees retained to meet the 

minimum basal area target (in this case, 13.8 m2/ha)—neither the seed tree stand 

nor the plantation has such a consistent base C storage.

Once harvested, there are two post-harvest biomass pools that each tree is 

proportioned to—the fast (short-term) and slow (long-term) pools. These pools 

represent the dynamics of felled trees post-harvest whether utilized as forest 

products or left on the site as slash. Fast pool biomass consists of finer materials 

such as smaller roots, bark, foliage, and certain portions of the bole that either 

decompose quickly or are converted into short-lived consumer goods such as 

paper products. Slow pool biomass is assumed to be larger portions of the bole 

and large coarse roots that are left on-site as coarse woody debris or converted 

into long-lived consumer goods such as boards or structural panels. These pools 

assume half-lives of 1 year for the fast pool and 50 years for the slow pool (Birdsey 

1996) using the following exponential decay function:

Ψt = Ψ0e –λt [3]

where the original harvested biomass (Ψ0, fast or slow) reduces to Ψt after t years. 

For equation [3], the decay coefficient λ = 0.69315 for the fast pool and 0.01386 

for the slow pool.

All stems less than 25.4 cm d.b.h. are automatically assigned to the fast pool 

because of their rapid decay or use as short-lived consumer products. Large bolewood was partitioned into slow and fast pools based on the lumber recovery 

work of Fonseca (2005, his table 6.6). For 25 cm d.b.h. stems, only 30 percent of 

their stem wood is assigned to the slow pool because the rest is either converted to 

sawdust, slabs, chips, or planer shavings, or is in part of the bole that is too small 

to decay slowly. The proportion of slow pool biomass from stem wood gradually 

increases to 45 percent when the tree reaches 40 cm d.b.h., and is held constant 

at this ratio no matter how much larger the stem gets. The 55 to 70 percent of 

bole biomass not assigned to the slow pool is transferred to the fast pool for timerelated decay, as were all of the foliage, bark, and branches.

To convert from biomass (in both the live biomass and post-harvest pools) 

to weight of sequestered C only, we simply multiplied the biomass total by 0.5 

(Johnsen and others 2004; Skog 2008). For this paper, sequestered C weight was 

defined as the sum of the above- and belowground live C weight plus the weight 

of the C in the current fast and slow post-harvest pools for any given year.
116 USDA Forest Service Proceedings RMRS-P-61. 2010.

Bragg and Guldin Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands

Other assumptions—Because this research is initial and exploratory, certain 

significant C pools are not expressly included in this analysis. For instance, the 

only vegetation being tracked are the crop loblolly pines that are either planted 

or explicitly included in the modular natural regeneration scenario. No other 

non-pine vegetation (for example, grasses, forbs, shrubs, vines, hardwoods, etc.) 

is tracked in the C pools. Likewise, no “volunteer” pines are incorporated in 

plantations, nor are pines that would otherwise seed in following the thinnings 

of even-aged natural stands. Though all of these components are noticeable parts 

of the ecosystem C pools, we lacked good, quantifiable information on how they 

would respond to the specific treatments presented over time, and therefore, we 

chose not to include them. Note, however, that the uneven-aged stand did assume 

continuous pine regeneration since this is how this stand condition perpetuates 

itself (rather than relying on discrete establishment events).

Two other dynamic components likely to be very important but poorly described are the soil and forest floor C pools (Birdsey 2006). Rather than trying 

to estimate these values and simulate their behavior, we will assume there are no 

significant differences between the size of the pools in any of the silvicultural 

treatments we are comparing. Studies performed on loblolly pine stands in the 

southeastern US have provided mixed results regarding these components, with 

some treatments decreasing and others increasing soil C (for example, Laiho and 

others 2003; Samuelson and others 2004). Most of these studies are relatively 

short-term (less than 10 years) and often focus on soil C dynamics for plantations 

managed under a gradient of treatment intensity rather than mature loblolly pine 

stands of natural origin. So, given the paucity of reliable information, we believed 

the bulk of the belowground contribution to C sequestration in these pine stands 

would be best dealt with in a later analysis. Hence, the only subsurface sequestration components in this paper are the coarse roots.

There are also C emission impacts related to harvest system and equipment 

type/usage by silvicultural system (Eriksson and others 2007; Markewitz 2006). 

Because this particular part of the C cycle depends strongly on the type of equipment and how it is used, coupled with the quantities of fossil fuel-based fertilizers 

and other chemicals applied and the nature of the site being treated, we have 

chosen to assume that there are no significant differences between our silvicultural systems. Ignoring this component is not likely to have a marked influence 

on overall C dynamics, anyhow—Markewitz (2006) estimated that cumulative 

C emissions for all silvicultural activities on an intensive fiber farming system 

using southern pines over an entire 25 year rotation was only about 3 tons/ha.

Finally, we did not explore the economic ramifications of these treatments (or 

any variations of them), so any conclusions on the efficacy of these silvicultural 

regimes are based on their ability to store C in either slow or fast post-harvest 

pools or residual (live) crop tree biomass on the site. We also did not assume any 

post-harvest consumer products were recycled, nor did we include C offsets due 

to product displacement or substitution.

Results

Aboveground Live Tree Biomass

Marked differences arose between the biomass patterns between these silvicultural systems in loblolly pine dominated stands (fig. 1). The seed tree (natural 

origin) stand, starting from the regeneration cut, contained more biomass (19.2 tons 

versus 0.0 tons/ha) than the plantation, which started from a true clearcut. Aboveground biomass in the seed tree increased rapidly as both the newly established 
USDA Forest Service Proceedings RMRS-P-61. 2010. 117

Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands Bragg and Guldin

pine seedlings and overwood pine grew quickly, reaching 47.2 tons/ha in year 5. 

The harvest of the seed trees and concurrent precommercial thinning resulted in 

a sharp decrease in biomass, noticeably below the level of the similarly stocked 

loblolly pine plantation. This biomass difference after 6 years (5.6 versus 49.5 tons/

ha) is attributable to the significantly larger size of the improved pine seedlings 

in the plantation, which had been growing at a low density since planting compared to the considerably higher stocking of the natural origin seedlings during 

the first 5 years of their life.

The seed tree stand and the plantation both rapidly added biomass during the 

remainder of their rotations, periodically experiencing sharp drops as thinning 

operations and regeneration harvests removed biomass (fig. 1). Not surprisingly, 

the more intensively managed even-aged stands experienced substantially 

higher peak live biomass totals than the uneven-aged stand. Both even-aged stands 

Figure 1—Biomass fractions in live loblolly pines (aboveground biomass + coarse roots) and contributions 

to different post-harvest product biomass based on fast (1-year half-life) and slow (50-year half-life) 

decomposition (loss) pools for three silvicultural systems.
118 USDA Forest Service Proceedings RMRS-P-61. 2010.

Bragg and Guldin Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands

approached 190 tons/ha of aboveground live biomass—the plantation reached just 

under 190 tons/ha first, immediately prior to the first commercial thinning at age 

11, before fluctuating between 69 and 117 tons/ha for the rest of the rotation. The 

seed tree stand peaked much later in its rotation, just exceeding 190 tons/ha in 

year 35 (right before the third thinning). The seed tree stand contained between 

100 and 150 tons/ha of live pine biomass during the last 3 decades.

In contrast to the even-aged stands, biomass in the well-regulated unevenaged stand managed under the selection system (Baker and others 1996) varied 

little over time. Immediately following each cutting-cycle harvest, the simulated 

uneven-aged loblolly pine-dominated stand started out at 61.5 tons/ha of live pine 

aboveground biomass and quickly increased to 78.5 tons/ha 5 years later (fig. 1). 

The closely controlled stand density of the uneven-aged stand kept any of the 

high or low aboveground live pine biomass amounts from fluctuating nearly as 

much as the even-aged stands.

Belowground Live Tree Biomass

Because belowground live tree biomass was determined as a relatively fixed 

proportion of aboveground live pine biomass (a function of diameter), this component closely paralleled the aboveground patterns. Even-aged stands peaked near 

41 tons/ha of belowground biomass when aboveground biomass was peaking at 

190 tons/ha, and would decline to less than 2 tons/ha at the end of each cutting 

cycle while the new stands were establishing themselves (fig. 1). The uneven-aged 

stand varied between 13.3 and 16.9 tons/ha of live pine belowground biomass 

across the entire 100-year simulation period.

Post-harvest Biomass Pools

The harvested biomass showed a steady increase in quantity for the slow pools 

(fig. 1). In all cases, the slow pools decayed at a rate that did not reach equilibrium 

with new inputs during the 100-year simulation period. Thus, all of the silvicultural treatments continually added to their respective slow biomass pools. Fast 

biomass pools, however, decayed quickly enough so that even the fairly substantial 

(greater than 60 tons/ha) periodic contributions by either even-aged treatment 

failed to build upon other pulses of fast pool biomass, and typically remained at 

less than 1 ton/ha during most of the simulation period.

C Sequestration Patterns

Long-term C stocks varied considerably over time, with the highest fluctuations 

once again being found in the even-aged treatments (fig. 2). The combination of 

above- and belowground biomass with the post-harvest product pools helped to 

dampen some of the more pronounced oscillations in the even-aged stands, but 

both still experienced dramatic changes. At their peaks, both even-aged stands 

exceeded 160 tons of C in their respective simulated stand developmental trajectories (before settling down to about 120 tons C at 100 years), and seem destined 

to continue to accumulate C well into the future.

The uneven-aged stand surpassed the other even-aged treatments in C storage 

only briefly during the first 3 simulated decades—from the first 8 to 14 years for 

the plantation and seed tree stands, respectively, and then again between 26 and 

31 years after the logging and reestablishment of the plantation (fig. 2). C steadily 

accumulated in the uneven-aged stand and its related post-harvest pools from a low 

of 37.4 tons/ha to about 76 tons/ha at the end of the 100-year simulation period. 

Variation from year to year in the uneven-aged stand showed a much more stable 

pattern, with fluctuations of less than 2 tons/ha typical during the simulation.
USDA Forest Service Proceedings RMRS-P-61. 2010. 119

Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands Bragg and Guldin

Considerable variation appears in C storage patterns from one year to the 

next (fig. 3), although this pattern was far less pronounced for the uneven-aged 

stand. The uneven-aged scenario varied by ± 2.2 tons/ha/yr (standard deviation 

(SD) = 1.36 tons/ha/yr)), compared to between +14.4 and -24.2 (SD = 7.25) tons/

ha/yr for the seed tree stand and +21.7 and -32.3 (SD = 11.90) tons/ha/yr in the pine 

plantation. Over the 100-year simulation period, average annual C sequestration 

was positive for all treatments, ranging from 0.38 tons/ha/yr in the uneven-aged 

stand to 1.11 tons/ha/yr for the seed tree stand to 1.16 tons/ha/yr for the plantation.

Figure 2—Sequestered carbon (C) 

in above- and belowground live 

trees and fast and slow product 

pools over a 100-year simulation 

of three silvicultural systems in 

loblolly pine stands.

Figure 3—Change in sequestered 

carbon (C) from one year to the 

next during the 100-year simulation 

period as a function of silvicultural 

system in loblolly pine-dominated 

stands.
120 USDA Forest Service Proceedings RMRS-P-61. 2010.

Bragg and Guldin Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands

Discussion

This preliminary study makes a number of key oversimplifications in order 

to examine silvicultural systems in isolation. For instance, some of our assumptions (such as the decay coefficients for the fast and slow pools) apply commonly 

accepted values, but no sensitivity testing is presented here to evaluate the consequences of altering the rates of change on C sequestration patterns. Given the wide 

range of differences between fast and slow pool behavior with the two extremes 

of decay rates, we would expect this to vary considerably based on the values 

used. For instance, it may be more appropriate to use a slower decay rate for the 

belowground slow pool for the large sawtimber trees produced in the natural origin 

stands, as these stumps are often resin-soaked and therefore much more resistant 

to decay than younger, faster-grown plantation stumps of comparable size. The 

multitudes of possible management activities, coupled with the likelihood of stochastic mortality events, changing climate patterns, and even developments in the 

genetic improvement of loblolly pine may also drastically affect the outcomes of 

the silvicultural scenarios presented. It is simply not possible to determine every 

possible interaction and predict their influence on the outcomes.

The aforementioned qualifications notwithstanding, it is obvious that C sequestration patterns in southern pine stands, when the fate of the materials produced 

from them is included, show considerable potential to offset some atmospheric 

CO2 increases. The sustained accumulation of C in this study is comparable to 

other simulation-based research (for example, Baral and Guha 2004; Johnsen and 

others 2001) and suggests that the active management of southern pinelands may 

increase atmospheric CO2 sequestration over no-treatment options.

C storage patterns fluctuate dramatically in managed ecosystems, especially 

those under even-aged management (fig. 3). Because of how this exercise was 

designed, large negative values in the live biomass portion of this metric only 

occur in a single year, as regeneration and residual tree growth immediately begin 

accumulating new C. Any prolonged deficits in C storage shown in figure 3 reflect 

periods after major timber harvests, when new growth fails to offset C losses due 

to post-harvest product decay. Even with these prolonged deficits, stand-level C 

accumulation over the whole simulation period was positive for each treatment, 

ranging from 0.38 to 1.16 metric tons/ha/yr.

However, it is hard to compare these results with other published studies because different components are often used to derive C accumulation. For instance, 

Luyssaert and others (2008) used carbon flux estimates to find that old-growth 

temperate and boreal forests sequestered an average of 2.4 tons of C per hectare per 

year, a majority (83 percent) of which was attributed to C stored in coarse woody 

debris (CWD), roots, and soil organic matter. While the CWD pool in their study 

is analogous to our post-harvest product pools, Luyssaert and others (2008) also 

incorporated fine root biomass in their root category (we only considered coarse 

roots) and our study has no information on soil organic matter. It is also unclear 

how Luyssaert and others (2008) accounted for the decomposition of wood. Other 

estimates of C sequestration by temperate forests (for example, Baral and Guha 

2004; Hall and others 1991) consider only C accumulation via growth, and do 

not account for simultaneous losses to the system. If couched in accretion-only 

terms, average annual C accumulation values of 0.81, 3.36, and 4.82 tons/ha/yr 

were calculated for the uneven-aged stand, the seed tree stand, and the plantation 

in this study, respectively. Undoubtedly, these values would increase even more 

if soil C storage was also factored into these estimates.

The long-term C sequestration benefits of southern pine forests under management also depend on the end-use of the biomass produced. Shifting products 
USDA Forest Service Proceedings RMRS-P-61. 2010. 121

Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands Bragg and Guldin

from slow pool into fast pool consumer goods (for example, dimension lumber 

or plywood into paper products) would have a marked effect on C sequestration, 

especially if this happens at large scales. While this study has focused on C storage 

based on either biomass retained on the landscape or preserved within long-term 

commercial products, net C benefits may also be derived if woody biomass (rather 

than fossil fuels) are used to produce energy or other derived products (for example, 

Baral and Guha 2004; Frederick and others 2008; Gan and Smith 2007; Hall and 

others 1991), replaces corn-based ethanol with cellulosic ethanol (Piñeiro and 

others 2009), or is substituted for higher C-emitting building materials such as 

steel or concrete (Perez-Garcia and others 2005).

Conclusions

Undoubtedly, our results are sensitive to a number of possible variables, 

including the decay coefficient used for both fast and slow pools, mortality of individual pines as a function of treatment and random factors, regeneration success 

or failure, and differences in wood density related to genetics and growth habit. 

Our goal was not to consider all possible conditions and permutations related to 

the C cycle, but to explore the role of silvicultural practices on an aspect of stand 

management (C sequestration) only poorly considered to date.

Hence, we believe our results show that C sequestration patterns in southern 

pine-dominated forests depend considerably on the silvicultural system being applied. According to our projections and the work of others (for example, Johnsen 

and others 2001; Smith and others 2006), all of the conventional management 

practices used in loblolly pine ecosystems of the southern US have potential to 

accumulate C in standing timber, the soil, and long-term end products. Intensively 

managed loblolly pine plantations, due to their high rate of fiber production (and 

assuming that most of their products end up in slow decaying uses), were predicted 

to accumulate C at the highest rate over the course of a 100-year simulation.

However, it is possible to have mature, natural origin southern pine forests produce significant amounts of C storage. Loblolly pine-dominated stands managed 

under a seed tree regeneration approach accumulated C at a rate very comparable 

to a loblolly pine plantation. The timing of harvest and regeneration in the two 

even-aged harvest regimes produced some differences in C sequestration patterns, 

but both were predicted to store around 120 metric tons of C per hectare after 100 

years of growth and harvesting. This total is at least 50 percent higher than that 

accumulated under uneven-aged management of loblolly pine-dominated stands 

at the end of the same period.

The perpetual understocking of uneven-aged southern pine stands to ensure 

adequate regeneration also assures lower C sequestration. Uneven-aged pine 

stands are also much less variable in their C accumulation patterns, since they 

always retain a large quantity of live biomass on the site. However, it may be 

possible to manage uneven-aged southern pine stands on a more irregular basis 

with cutting cycles longer than conventional 5-year intervals. Doing so would 

likely result in increased rates of C accumulation somewhat similar to that seen 

in the seed tree method, and concurrently would increase C sequestration while 

retaining the continuous cover canopy attributes sought by managers who utilize 

this silvicultural system.

The opportunities presented by bioenergy and C sequestration may have particular appeal for public lands management in the southeastern US. Given that 

few governmental agencies can engage in the large-scale industrial forestry of 

intensively cultured loblolly pine plantations, the ability to produce significant C 

storage while harvesting high-value timber products under naturally regenerated, 
122 USDA Forest Service Proceedings RMRS-P-61. 2010.

Bragg and Guldin Estimating Long-Term Carbon Sequestration Patterns in Even- and Uneven-Aged Southern Pine Stands

sustainable forest conditions would seem to fulfill multiple resource objectives 

simultaneously. To ensure this, it is vital that work (for example, Johnsen and others 2004; Mayfield and others 2007) considering the possibility of using forests 

for bioenergy and C sequestration in the southeastern US be expanded to include 

the unique statutory, regulatory, and policy obstacles that may supersede these 

opportunities on federal lands.

Acknowledgments

We would like to thank the following for their contributions to this paper: 

Teddy Reynolds (Reynolds Forestry Consulting) and Mike Shelton (USDA Forest 

Service, Southern Research Station), plus all of the former USFS scientists and 

staff who helped collect the long-term data on the CEF.

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 125

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Northern and Intermountain Region 

Biomass Utilization, Program Manager.

The Role of Forests in Energy 

and Climate Change—Integrating 

Objectives

Dave Atkins1

Abstract—Woody biomass utilization presents a tremendous opportunity to address 

energy independence directly through the use of domestic wood as an energy source 

that offsets fossil fuel. It also works indirectly through conservation by the substitution of wood for higher embodied energy construction materials such as concrete 

and steel. Both the direct and indirect measures mitigate climate change by reducing 

greenhouse gas emissions. By integrating these goals with treatments of forests to make 

them more resilient to disturbances, longer term C sequestration is accomplished in 

the live forest on the landscape. This paper explores future and current technologies 

that are available to accomplish utilization and sustainable treatments in the field.

Introduction

Woody biomass utilization presents a tremendous opportunity to address 

energy independence directly through the use of domestic wood as an energy 

source that offsets fossil fuel and indirectly by the substitution of wood for higher 

embodied energy construction materials such as concrete and steel; moreover, 

it mitigates climate change by reducing greenhouse gas emissions. Integrating 

forest treatments that use woody biomass can also fulfill other objectives, such 

as creating forests that are resilient to disturbances and able to provide long-term 

C sequestration. In this paper, I present current and future technologies that are 

available to accomplish utilization and sustainable treatments in the field.

There is a need to move toward more sustainable systems over the next 30-

40 years to achieve the goal of reducing C emissions. Forests are natural solar 

collectors that store energy and filter carbon from the atmosphere and store it in 

“wood batteries.” Given that forests can store carbon for long periods (decades to 

centuries), conducting sustainable forest management activities and techniques 

to insure continued forest growth and development is one method of reducing 

carbon emissions in addition to sequestering carbon. One measure of resiliency 

is the post-wildfire environment. For example, in Colorado, the 1989 Sheep Rock 

fuel treatment area burned more variable and moderate compared to surrounding 

untreated sites. Because there were more abundant live trees, these sites continue 

to sequester C in the trees.

Wood as a Conservation Tool

The use of wood in place of steel and concrete results in C being sequestered 

in the wood for the life of the building; importantly, it can also save significant 

amounts of fossil C from being emitted during production. Figure 1 shows an 

example of the net energy consumed to extract, transport, manufacture, and 

erect 100 square feet of interior wall using various framing materials (fig. 1a) 
126 USDA Forest Service Proceedings RMRS-P-61. 2010.

Atkins The Role of Forests in Energy and Climate Change—Integrating Objectives

and CO2 emissions from different materials used for floor construction (fig. 1b). 

Products created from small wood, such as Glulam beams, and the use of wood 

for flooring, furniture, and engineering trusses are ways to store C and reduce 

energy consumption.

Energy Technologies With Wood

There are numerous technologies under research and development at various 

stages, such as fast pyrolysis to make bio-oil and biochar, gasification to liquid 

fuels, fermentation processing of ligno-cellulose to liquid fuel, and small-scale 

gasification for producing heat and power for power plants. However these new 

products are not commercially available. Because of a lack of existing markets for 

the product, they are untested in the marketplace; thus there is reluctance on the 

part of buyers to convert to the new product. Most significantly, the volatile nature 

of fossil fuels results in these products being more competitive during times when 

the price of fossil fuels is high compared to when it is low. Therefore, investors 

Figure 1—(a) The net energy consumed to extract, transport, 

manufacture, and erect an interior wall that is 100 square feet using 

wood, aluminum, and steel (CORRIM 1976). (b) The CO2 emissions 

from floors constructed from wood I-joists, wood dimensional joists, 

concrete slab, and steel joists (CORRIM 2009).
USDA Forest Service Proceedings RMRS-P-61. 2010. 127

The Role of Forests in Energy and Climate Change—Integrating Objectives Atkins

are reluctant to make large investments given the risk that the low price times will 

kill the viability of the product. Along with a move towards a more sustainable 

set of energy sources, the efficiency of various uses of woody biomass should be 

considered. Current policies in the United States provide incentives to electrical 

and liquid production that are only 25 and 40 percent, respectively, efficient at 

converting the energy in the wood to useable energy for the consumer. On the 

other hand, thermal and combined heat and power are 65-90 percent efficient, 

yet have no incentives at the federal level. Policies to encourage efficient use of 

energy would certainly be better in the long-run.

Efficient Production and Transportation of Fuel

Slash and small trees have traditionally been viewed as a disposal problem; 

a cost problem rather than a potential product and revenue stream. The negative 

value of the material results in it being treated like trash with dirt and rocks being mixed into the fuel. While industrial systems can tolerate the dirt and rocks, 

albeit with a higher operating and maintenance cost, smaller commercial and 

institutional systems often cannot handle the low quality fuel without significant 

costs. Chipped material is preferable to ground material because it is more efficient 

in smaller combustion systems. However, chipping is typically more expensive 

than grinding, road systems to access many projects that generate potential fuel 

cannot handle highway chip vans, and chipping and grinding in the woods can be 

expensive due to significant lost production while moving to and from concentrations of slash. Many different methods and types of equipment are being developed 

and tested to improve the efficiency of production and transportation of material.

Conclusion

Wood utilization by an integrated industry can provide a wide range of products 

from solid wood and composites that can conserve energy as a result of lower 

energy content compared to concrete, steel, and plastics. It can provide a direct 

substitution for fossil fuel and thus reduce fossil C emissions. The sustainable use 

of forest products can provide for greater resilience of the forest to disturbances 

and thus continue to sequester more C in live forests as well as in forest products. 

The integration of management objectives for watershed protection, wildlife 

habitat enhancements, reduction of the potential for greater severity of wildfires 

in the context of climate change, and production of renewable energy adds to 

the considerations a silviculturist must make in developing landscape and stand 

level prescriptions. To achieve this level of integration requires consideration and 

communication of the various trade-offs.

References

Committee on Renewable Resources for Industrial Materials (CORRIM)—National Research 

Council. 1976. Wood for structural and architectural purposes. Wood and Fire 8:1-72.

Committee on Renewable Resources for Industrial Materials (CORRIM)—National Research 

Council (CORRIM)—National Research Council. 2009. Fact Sheet #5: Maximizing forest 

contributions to carbon mitigation. http://www.corrim.org/factsheets/fs_05/index.asp. 6 p.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 129

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 College of Natural Resources, University of Idaho, Moscow, ID.

The Forest—Bioenergy—Carbon 

Connection

Jay O’Laughlin1

Extended Abstract—Burning wood for energy is a back-to-the-future approach for 

solving modern problems. The burning of fossil fuels for energy and resultant carbon 

emissions are global concerns: “The world needs ever increasing energy supplies to sustain economic growth and development. But energy resources are under pressure and 

carbon dioxide (CO2) emissions from today’s energy use already threaten our climate. 

What options do we have for switching to a cleaner and more efficient energy future?” 

(IEA 2008). The basic choice is between action and delay, and forest management 

is among the set of options that have the capacity to provide “stabilization wedges” 

and solve the climate problem for the next half-century (Pacala & Socolow 2004).

Wood bioenergy is a proven, cost-effective technology for producing homegrown, 

reliable baseload energy (O’Laughlin, in press). In 2007 wood provided 1.8 percent 

of the energy consumed in the United States (EIA 2009). Wood bioenergy use is 

generally higher in states with extensive forest resources and a substantial forest 

products business sector, like Idaho, where 4.7 percent of the energy consumed is 

from wood (Idaho Legislature 2007). Forest products manufacturing mill residues are 

the low hanging fruit for wood bioenergy, as they have already been transported out 

of the woods, but almost all mill residues are already used to make energy (Nicholls 

and others 2008).

Forest health thinnings and logging residues are a potential source of feedstocks 

for wood bioenergy, whether for space heating with thermal energy, electric biopower, or liquid biofuels. The benefits of producing thermal energy and biopower 

from wood can be substantial; e.g., the University of Idaho (2008) saves on average 

$1.5 million per year burning wood residues from local sawmills instead of natural 

gas, and Fuels for Schools projects save $1.8 million per year in fuel costs. These 

thermal heating solutions are the most efficient use of wood bioenergy and widely 

used in some European countries (see Richter and others 2009).

Using forest residues (thinnings and logging slash) for bioenergy is an opportunity 

to restore forest health, wildfire resiliency, and wildlife habitat. Silvicultural operations to improve forest conditions can help revitalize rural economies while providing 

renewable energy feedstocks (Cloughesy & Lord 2006). In addition to this “triple 

win” (IFPC 2009; OFRI 2009), forests play a key role in the global carbon cycle by 

capturing, storing, and cycling carbon (EPA 2009a, see also California Forest Foundation 2009). Forests in the conterminous U.S. sequestered, on average, 162 million 

metric tons of carbon per year during 1990-2005 (Woodbury and others 2007). This 

is sufficient to offset at least 10 percent of all U.S. carbon dioxide emissions (data 

from EPA 2009b). However, from 2002-2006 wildfires in the conterminous U.S. emitted, on an annual average, 59 million metric tons of carbon as CO2 and two million 

metric tons as particulate matter (Wiedinmyer & Neff 2007).

Considerations for safe storage of carbon on, in, or deep under the soil create 

roles for forestry (Read 2009). Five types of carbon reservoirs are preferable to storing carbon in the atmosphere, and only the last item does not have an obvious role 

for forestry: 1) new forestry plantations; 2) new timber structures and other durable 

wood products from harvested wood; 3) underground wood burial, perhaps in 

abandoned mines; 4) biochar storage in soil reservoirs with co-produced bio-oil; 

and 5) carbon capture and storage in deep geological strata or as bicarbonates in 

the ocean or insoluble carbonates on land in played-out coal mines. In addition, the 
130 USDA Forest Service Proceedings RMRS-P-61. 2010.

O’Laughlin The Forest—Bioenergy—Carbon Connection

existing fossil carbon reservoir is maintained in situ through technology chains that 

involve bioenergy and other renewable sources of energy that substitute for fossil 

fuel (Read 2009).

Silviculture designed to reduce stand-replacing wildfires is the most important 

forestry strategy for mitigating climate change, followed by keeping forest lands in 

forest cover, putting trees back on the land through afforestation and reforestation, 

using wood products instead of substitutes like concrete and steel, and modifying 

harvest rotation length (Cloughesy 2006). Beginning in the mid-1980s, the acreage 

burned by wildfires in the 11 western states began to increase considerably from 

levels of the previous 50 years. According to information provided to the U.S. Gov-

ernment Accountability Office by the U.S. Forest Service, “The most extensive and 

serious problems related to the health of national forests in the Interior West is the 

over-accumulation of vegetation, which has caused an increasing number of large, 

intense, uncontrollable, and catastrophically destructive wildfires” (GAO 1999).

Climate change concerns heighten the issue: “The overall importance of climate 

in wildfire activity underscores the urgency of ecological restoration and fuels 

management to reduce wildfire hazards to human communities and to mitigate eco-

logical impacts of climate change” (Westerling and others 2006). According to the 

Intergovernmental Panel on Climate Change, “...a sustainable forest management 

strategy aimed at maintaining or increasing forest carbon stocks, while producing 

an annual sustained yield of timber fibre or energy from the forest, will generate the 

largest sustained mitigation benefit” (Nabuurs and others 2007). Two types of barriers 

impede implementing this strategy on federal lands.

One of the two primary challenges to utilization of wood biomass that could 

provide energy feedstocks are high harvesting and transportation costs (GAO 2005, 

2006). Perhaps the best way to create more favorable economics that can make 

biomass removal projects feasible is harvesting higher value timber along with bio-

mass removals (Evans 2008; Nicholls and others 2008). Furthermore, in dry forest 

types comprehensive restoration treatments are not only more effective at reducing 

hazard than thin-from-below approaches designed to remove smaller trees only, the 

economics are more favorable and in many situations can return more revenue than 

the treatment costs, whereas thin-from-below requires out-of-pocket expenditures 

(Fiedler and others 2004).

Besides harvesting high value timber along with biomass, the only other way to 

deal with unfavorable economics of biomass harvesting is through public subsidies 

(Nicholls and others 2008). A rationale for subsidizing fuel treatments is that the 

benefits to society from hazardous fuel treatments are worth more than the value of 

electricity produced by wood biopower plants. These include the uncompensated 

benefits of reduced costs of fire suppression costs and avoided costs of site reha-

bilitation as well as avoided costs of carbon emissions, calculated at a conservative 

estimate of $10 per metric ton (WGA 2006). Other researchers have also quantified 

the value of these silvicultural benefits (see Mason and others 2006). In part to support 

bioenergy, the Western Governors’ Association “call[s] on the federal government 

to create a substantial, long-term national public investment on the scale of tens of 

billions of dollars annually, and encourage at least the same investment from the 

private sector, to support the kind of basic and applied research and deployment of 

clean energy technology and infrastructure” (WGA 2009).

The other primary challenge to using wood as an energy feedstock is the lack of 

a long-term reliable supply (GAO 2005, 2006). To deal with that, some have sug-

gested public programs that create a demand for biomass material (e.g., Williams 

2005). One such program is “Fuels for Schools and Beyond” (see Nicholls and 

others 2008). However, unless entrepreneurs can demonstrate that they have 

reliable feedstock supplies for 10 or 20 years they will have difficulty attracting 

capital to wood bioenergy projects. Whether demand will pull out supply or sup-

ply will push new demand is a chicken-and-egg argument; both are necessary.

Since the advent of the National Fire Plan in 2000 there has been much positive 

activity to reduce hazardous fuels, notes Doug Crandall (2006), currently the legisla-

tive liaison for the U.S. Forest Service. Referring to the relationship of forest growth, 
USDA Forest Service Proceedings RMRS-P-61. 2010. 131

The Forest—Bioenergy—Carbon Connection O’Laughlin

mortality, and removals he said, “Momentum has shifted from conflict and neglect 

to a recognition that there’s actually a gorilla in the room.” He opined that with 

190 million acres of forests needing treatment, much more than the current level of 

four to five million acres per year nationwide needs to be accomplished (Crandall 

2006). While serving as Chief of the U.S. Forest Service, Dale Bosworth described 

the situation as unsustainable: “We have some 73 million acres of national forest 

land at risk from wildland fires that could compromise human safety and ecosystem 

integrity. . . . The situation is simply not sustainable—not socially, not economically, 

not ecologically” (Bosworth 2003).

Idaho and Montana face forest health and wildfire risks from overstocked forests. 

Together the two states have 7 million acres of forests in the high-risk Fire Regime 

Condition Class (FRCC) 3 category and another 10.5 million acres in FRCC 2 (Schmidt 

and others 2002). Federal land managers are reducing fuels on an average of 250,000 

acres per year (Healthy Forests and Rangelands 2009). At that rate it would take 28 

years to treat just the FRCC 3 lands, at which time vegetation would have grown 

back and retreatment would be necessary to maintain fuel reduction benefits. Forest 

managers, stakeholders, and policy-makers need to consider whether passively waiting 

for the inevitable wildfire to burn is better that actively managing fuels, and whether 

piling and burning biomass onsite is preferable to moving it offsite and burning it in 

boilers that produce bioenergy. The State of Oregon subsidizes biomass removal for 

energy production at $10 per green ton (ODE 2007). A similar program failed to pass 

the Montana legislature in 2009 (O’Laughlin, in press).

In conclusion, wood bioenergy opportunities are substantial and sustainable. Many 

communities are interested in installing wood bioenergy facilities to reduce costs to 

heat public buildings and provide local jobs. In addition, the uncompensated social 

benefits of reduced wildfire suppression costs, plus the avoided costs of site rehabilita-

tion and carbon emissions, exceed the value of bioenergy and create a rationale for 

subsidizing hazardous fuel treatments. The implementation question in the forestry 

sector is whether the subsidy should be in the form of timber from the forest or cash 

payments from the public treasury and taxpayers’ pockets. In the short term hazardous 

fuel reduction provides a triple win: improved forest conditions, renewable energy 

feedstocks, and revitalized rural communities. The reduction of carbon emissions 

from burning wood in a boiler instead of in the woods is a substantial bonus. The 

long-term payoff from wood bioenergy will be enhanced energy security.

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Management Today. 65(4): 4-8.

Woodbury, P.B.; Smith, J.E.; Heath, L.S. 2007. Carbon sequestration in the U.S. forest sector from 

1990 to 2010. Forest Ecology and Management. 241: 14-27.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 135

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 North Zone Idaho Panhandle NF 

Project Leader, USDA Forest Service, 

Bonners Ferry Ranger District, Bonners Ferry, ID.

2 Applied Forest Management Program 

Director, Montana Forest & Conservation Experiment Station, University of 

Montana, Missoula, MT.

Biomass Utilization Opportunities to 

Achieve Diverse Silvicultural Goals

Barry Wynsma1 and Christopher R. Keyes2

Abstract—Silviculturists and ecologists may recommend land management prescriptions that are designed to be resilient to changing climatic conditions. When 

considering biomass utilization opportunities that may result from climate-change 

treatments, it really doesn’t matter what species mix or stocking levels are to be retained: if there are trees that need to be harvested, there will usually be opportunities 

for utilizing woody biomass.

Keywords: biomass utilization, harvesting systems, designation by description, silvicultural prescriptions

Introduction

This paper focuses on biomass utilization opportunities in the context of Goals 

2 and 3 of the recently released Forest Service Strategic Framework for Responding to Climate Change. Goal 2 is Adaptation—“Enhance capacity of forests and 

grasslands to adapt.” Goal 3 is Mitigation—“Promote management of forests 

and grasslands to reduce greenhouse gases while sustaining the multiple benefits 

and services of these ecosystems.” The take-home message is simple: it doesn’t 

matter what management prescription you want to apply or habitat type you are 

working in—as long as you are proposing to cut trees, there will be opportunities 

to utilize biomass.

Biomass Utilization to Achieve Mitigation in 

Young Stands

Goal #3, Mitigation, is to promote the management of forests and grasslands 

to reduce the buildup of greenhouse gases, while sustaining the multiple benefits and services of these ecosystems. There is a wide variety of management 

scenarios that can integrate biomass utilization solutions while meeting climate 

change objectives. One possible way to meet the goal is to find ways to utilize 

logging slash that is excess to other resource needs and is normally disposed of 

by open burning, which releases CO2 into the atmosphere. CO2 emissions from 

open burning could be vastly reduced if the slash were instead burned in very-low 

emission biomass-electric generation facilities, which could convert an averagesized grapple pile of slash into about 2,000 kilowatt-hours of electricity. A single 

grapple pile may also contain enough biomass to convert to between 40 and 80 

gallons of ethanol.
136 USDA Forest Service Proceedings RMRS-P-61. 2010.

Wynsma and Keyes Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals

Operational Alternatives to Piling and Burning

One of the most high-tech, and expensive, ways to remove logging slash from 

harvest units is to employ a “slash bundler” such as the John Deere 1490 (fig. 1). 

The bundler is capable of operating on slopes up to around 40 percent and can 

maneuver through residual stands with a leave-tree spacing of about 20 to 30 feet 

without excessive damage to residual trees. The cost of this machine approaches 

$500,000, so it needs to be used in locations that have a high volume of biomass 

removed on a steady basis in order to be economically feasible.

Figure 1—Slash can be densely packaged into movable 

units (top) with the John Deere 1490 slash bundler 

(middle), and transported with a typical forwarder 

(bottom).
USDA Forest Service Proceedings RMRS-P-61. 2010. 137

Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals Wynsma and Keyes

Some bundling systems include the capability of removing the bundler attachment to convert the machine to a forwarder, while others require the use of a 

second piece of equipment that can transport the bundles to a roadside for trucking or on-site chipping. If logging slash is needed to be left on-site in a scattered 

fashion for a period of time to permit nutrient cycling for instance, this system is 

useful because slash can be bundled at any time after the timber harvest occurs. 

A forwarder is the most likely equipment for yarding bundles (fig. 1, bottom), 

but grapple skidders or other equipment could also be used. Still other bundlers 

or “balers” are designed to either be pulled through the woods behind a machine 

or set up at a landing. Data gathered by the Southern Research Station and Forest 

Products Lab during a slash bundler study in 2003 (Rummer and others 2004) 

indicate that slash bundles contain about 750 to 1,000 kilowatt-hours worth of 

convertible energy.

An alternative to using the slash bundler technology is to simply remove loose 

slash from harvest areas on forwarders (fig. 2). Some entrepreneurs are experimenting with making “grapple piles on wheels” that can be gathered in the woods 

and transported to roadside for chipping or hauled off in loose form in something 

like a roll-off container (i.e. large dumpster). An advantage to this system over the 

slash bundler is that if local loggers don’t have the money to invest in the bundler, 

they can make do with this. Disadvantages may include slower production rates 

and less capability for terrain than the bundler. Probably more cost effective 

than gathering scattered slash from within harvest units, chipping at landings 

in combination with whole-tree-yarding is yet another way to utilize biomass.

Perhaps the ultimate way to utilize biomass while managing for climate change 

objectives is to stop thinking about biomass as a “forest residue” and start thinking about it as an energy resource to be integrated in management activities. 

We can begin doing this by integrating biomass utilization in young age-class 

timber stands and plantations to accomplish what in the past has been referred 

to as “pre-commercial thinning” treatments. The ultimate goal is to decide what 

tree species, stocking levels, coarse woody debris and other biomass we want to 

leave in a harvest unit to meet our climate change management prescriptions, and 

then remove and utilize all the excess wood for the full range of forest products, 

including biomass for energy.

Figure 2—Loose slash transported from harvest area on a small-scale forwarder.
138 USDA Forest Service Proceedings RMRS-P-61. 2010.

Wynsma and Keyes Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals

Case study: Templemental Stewardship contract—The Templemental 

Stewardship contract “biomass thinning” unit is being conducted in a 300 acre 

plantation that was established in 1981 after the Templeman Lake fire in 1978. 

The average tree diameter is 5 1/2” inches dbh and average tree height is about 

40 feet (fig. 3, top). This plantation is being thinned to spacings between 12 

and 17 feet (variable-density thinning; Designation by Prescription) and 

Figure 3—Young plantation (Templemental Stewardship 

Project, top) is subjected to variable-density thinning 

using Cat 314 (middle) and Rottne 2002 (bottom) miniharvesters (shown working recently in private property 

areas).
USDA Forest Service Proceedings RMRS-P-61. 2010. 139

Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals Wynsma and Keyes

requires mandatory removal of trees as small as 1-inch dbh and 4 feet tall, with 

the exception of leaving coarse down woody debris based on guidelines by Graham 

and others (1994) and also some material for slash mats. This biomass thinning 

treatment also includes long-term soil productivity research to be conducted by 

the Rocky Mountain Research Station (RMRS) and production rate and recovery 

research on the equipment to be used (mini-harvesters and small-scale forwarders) 

to be conducted by the Southern Research Station (SRS). Two of the machines 

to be used in the Templemental Stewardship Project are the Cat 314 and Rottne 

2002 “mini-harvesters” (fig. 3). Both the Cat 314 and Rottne 2002 can cut trees 

as small as 1-inch diameter. The Cat 314 is less than 10 feet wide; the Rottne 

2002 is less than 9 feet wide and can reach about 23 feet with the harvester head.

Biomass Utilization to Achieve Resilience in 

Mature Stands

Improved biomass utilization can be achieved while harvesting mature timber 

stands using conventional and cut-to-length harvest systems. Goal #2, Adaptive 

Climate Change strategies, can be achieved through biomass utilization by restoring resiliency in timber stands.

This section provides examples wherein biomass utilization advances Goal #2. 

Requiring smaller diameter trees, smaller top diameters and shorter minimum 

pieces to be removed during initial harvest treatments not only improves timber 

stand resilience, but also increases biomass supplies and could also reduce the 

footprint of management activities by eliminating the need to have follow-up 

slash disposal treatments.

Case Study: Deerskin Roundwood Timber Sale Unit 26

This case study of the Deerskin Roundwood timber sale is to illustrate how a 

prescription for a small diameter timber sale was implemented using Designation 

by Description (DxD), weight-scale contract and utilization of smallwood material having minimum specs of 5 inch dbh to a 3 inch top on a 16 foot piece. This 

treatment would probably be considered a “restoration” treatment, promoting a 

condition that is more resilient to climate change.

The long-term objective for Unit 26 is to develop large diameter western larch 

and white pine, while maintaining a mix of other species and also maintaining 

coarse woody debris on-site. A reconnaissance cruise collected current stand 

composition data within the proposed harvest unit in order to determine the best 

mix of species and diameters to be left in the residual timber stand. Contract provisions for Designation by Description were then designed to meet the objectives 

of the silvicultural prescription, while at the same time providing a cost savings 

for sale preparation of about $50 per acre.

Extremely dense understory (fig. 4) illustrates why DxD is a necessary costsaving tool for managing these kinds of stands. DxD should be considered the 

“leatherman tool” of sale preparation foresters, in that there are many ways to 

use it, but site-specific stands usually require only a combination of 2 or 3 tools 

(i.e. designations). This stand used two designations, which included a diameter 

limit cut (understory removal) along with a requirement to maintain a minimum 

spacing of 15 feet between leave trees if 8-inch trees were available.

Because resource protection measures were built into the contract to protect 

soils from excessive compaction and disturbance, harvesting was accomplished 

in a low-impact manner. Soil compaction and disturbance in these types of stands 

with these types of equipment can be kept to a minimum. An in-woods processor 
140 USDA Forest Service Proceedings RMRS-P-61. 2010.

Wynsma and Keyes Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals

Figure 4—The dense understories of some restoration treatments make the 

Designation by Description contract provision approach invaluable (Deerskin 

Roundwood Timber Sale).

(fig. 5, top) not only sorted small sawlogs and small pulplogs, but also provided 

a slash mat for operating on along with grapple skidders. The system’s grapple 

skidder (fig. 5, middle) operated on slash mat and elevated most logs completely 

off the ground, further reducing soil disturbance. Even with utilization set at 

5 inch dbh to a 3-inch top on a 16-foot piece, substantial slash was left after grapple 

piling and leaving the recommended level of coarse woody debris (fig. 5, bottom).

Two More Scenarios

An example is the 2001 Kat Tail 2 timber sale on the Bonners Ferry RD Idaho 

Panhandle National Forests (IPNF) (fig. 6). The minimum size tree to be cut 

and removed in this project was 4 inches dbh having a minimum top diameter 

of 21⁄2 inches on a 13-foot piece. Small trees were removed from the understory, 

leaving larger overstory trees in a more resilient condition.

Perhaps one of the most challenging opportunities for biomass utilization is 

being accomplished on the Apache-Sitgreaves NF White Mountain Stewardship 

project. This project includes a range restoration treatment area designed to reduce 

the grossly overstocked condition in a pinyon-juniper habitat type, with utilization 

of the “trees” for biomass-electric energy at the nearby Renegy electric facility 

near Snowflake, Arizona. Trees were whole-tree harvested, roots and all, and 

were to be chipped on-site and then transported to the Renegy facility (fig. 7).
USDA Forest Service Proceedings RMRS-P-61. 2010. 141

Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals Wynsma and Keyes

Figure 5—Slash and soil management at the Deerskin Roundwood 

Timber Sale. Slash mat created by in-woods processor (top) 

cushioned the grapple skidder (middle). Most slash was grapplepiled (bottom) yet sufficient coarse woody debris was retained.
142 USDA Forest Service Proceedings RMRS-P-61. 2010.

Wynsma and Keyes Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals

Figure 6—Kit Tail 2 Timber Sale on the Bonners Ferry RD before 

(top), during (middle), and after thinning of smallwood to promote 

stand resilience.
USDA Forest Service Proceedings RMRS-P-61. 2010. 143

Biomass Utilization Opportunities to Achieve Diverse Silvicultural Goals Wynsma and Keyes

Figure 7—Biomass utilization as part of range restoration 

on the Apache-Sitgreaves NF White Mountain Stewardship 

Project. Biomass in this project is utilized as feedstocks for 

the Renegy electric facility.

Conclusion

Silviculturists and ecologists may recommend land management prescriptions 

that are designed to be resilient to changing climatic conditions. When considering 

biomass utilization opportunities that may result from climate-change treatments, 

it really doesn’t matter what species mix or stocking levels are to be retained. If 

there are trees that need to be cut as a result of the prescription, there will usually 

be opportunities for recovering these as woody biofuels.

References

Graham, R. T., A. E. Harvey, M. F. Jurgensen, T. B. Jain, J. R. Tonn and D. S. Page-Dumroese. 

1994. Managing coarse woody debris in forests of the Rocky Mountains. Intermountain Research 

Station. Research paper INT-RP-477.

Rummer, Bob, Len, Dan and O’Brien, Obie. 2004. Forest residues bundling project, new technology for residue removal. Final Report. USDA Forest Service, Forest Operations Research Unit, 

Southern Research Station, Auburn, Alabama. 18 p.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 145

Emissions, Energy Return and Economics 

From Utilizing Forest Residues for Thermal 

Energy Compared to Onsite Pile Burning

Greg Jones1, Dan Loeffler2, Edward Butler2, Woodam 

Chung2, Susan Hummel3

Abstract—The emissions from delivering and burning forest treatment residue biomass 

in a boiler for thermal energy were compared with onsite disposal by pile-burning 

and using fossil fuels for the equivalent energy. Using biomass for thermal energy 

reduced carbon dioxide emissions on average by 39 percent and particulate matter 

emissions by 89 percent for boilers with emission control. Over 21 units of bioenergy 

were produced for each unit of diesel energy used to collect, grind, and haul biomass. 

At prices in place at the time of the study, utilizing biomass was economically viable 

on 49 percent of the study area.

Keywords: biomass energy, bioenergy, carbon emissions, greenhouse gases, logging 

residues

Introduction

In the western U.S. approximately 16.8 million acres of accessible forestland 

could benefit from mechanical fuel treatments that reduce hazardous fuels (Rummer and others 2003). Such treatments have the potential to produce significant 

quantities of forest residue biomass, which includes the tops and limbs from 

merchantable trees and smaller trees removed by prescription (Barbour and others 2004; Loeffler and others 2006; Perlack and others 2005, Rummer and others 

2003). The common practice of disposing of these residues via onsite open burning 

has drawbacks, however, including negative effects on air quality, potential for 

escaped fires, and seasonal limits on burning. Open burning also releases atmospheric carbon dioxide and methane, two internationally recognized greenhouse 

gases and prominent compounds of interest in the global warming literature (IPCC 

2007a; US Environmental Protection Agency 2009a). Furthermore, no energy is 

captured by open burning.

An alternative to onsite, open burning of forest residues is to utilize them instead 

as feedstock for energy production. Most of the wood-based energy in the US 

has historically been generated from industrial mill residues (Malmsheimer and 

others 2008), but there is increasing interest in generating energy directly from 

forest treatment residue biomass. Additionally, new research is investigating different methods for expanding the use of forest residues as a feedstock for various 

approaches to energy production. There are a number of potential advantages to 

utilizing forest residues for energy including: reducing smoke from onsite burning, 

providing a source of energy for offsetting fossil fuel consumption, promoting 

new industries in rural economies, and improving the balance sheet for forest fuel 

reduction and forest restoration treatments by opportunities to add product value.

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Human Dimensions Science Program, 

USDA Forest Service, Rocky Mountain 

Research Station, Missoula, MT.

2 College of Forestry and Conservation, 

The University of Montana, Missoula, 

MT.

3 USDA Forest Service, Pacific Northwest Research Station, Portland, OR.
146 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jones, Loeffler, Butler, Chung, and Hummel Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning

Many questions remain regarding the contributions that expanding the use of 

forest residues for energy can make toward offsetting fossil fuel consumption or 

for meeting objectives for carbon and particulate matter emissions and sequestration (Tilman and others 2009). Forest residues are often dispersed over forested 

landscapes, sometimes requiring long haul distances for energy utilization to 

occur. We contend that the spatial configuration of forest residues will influence 

their energetic and economic contribution to management or policy goals. In this 

analysis we consider the following questions: How much fossil fuel is required to 

harvest, grind, and haul these forest residues from various landscape locations, and 

how does it compare with the amount of bioenergy that can be produced? What 

are the net emissions of key greenhouse gases and particulate matter produced by 

utilizing forest residues from various landscape locations? How do these emissions compare with the common practice of burning these forest residues onsite? 

Under what conditions is it economically viable to utilize these forest residues?

To address these questions, we considered the case of collecting, grinding, 

and hauling forest residue biomass from potential treatment units (74,352 acres) 

spread across a forested 1.3 million-acre landscape in western Montana. We 

computed the consumption of diesel fuel needed to utilize these forest residues 

and compared it with the thermal energy that they would produce in a boiler. In 

addition, the total greenhouse gas and particulate matter emissions from delivering and burning forest residues in a boiler for thermal energy were compared 

with onsite disposal by pile-burning and then using fossil fuels to produce the 

equivalent amount of useable energy (fig. 1). We also compared the fossil fuel 

requirements to use this forest biomass in a boiler for thermal energy with the 

fossil fuels needed to provide the equivalent heat in a boiler. Finally, we analyzed 

where biomass utilization is economically viable within the study area for various 

diesel and delivered biomass prices.

Figure 1—Comparison of burning forest residues in a boiler for thermal energy with 

onsite disposal by pile-burning and then using fossil fuels to produce the equivalent 

amount of useable energy.
USDA Forest Service Proceedings RMRS-P-61. 2010. 147

Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning Jones, Loeffler, Butler, Chung, and Hummel

Methods

Study Area

The study area included the Bitterroot National Forest and adjacent forested 

lands in the Bitterroot Valley of western Montana, comprising a total of 1.3 million 

acres (fig. 2). Past fire suppression, together with other factors, has contributed 

to increased densities of shade-tolerant trees over much of the study area. This 

forest cohort creates “ladder fuels,” which can increase the risk of crown fire and 

can reduce the growth and vigor of larger trees via competitive stress. Thinning 

and other density reduction treatments offer ways to accomplish forest and fuels 

management objectives of reducing fire severity, promoting tree growth, and 

fostering natural regeneration. We examined two options for disposal of forest 

residues produced by mechanical fuel treatments, onsite burning and removal for 

producing energy. Disposal of these forest residues is important to accomplish the 

treatment objectives of reducing forest fuels to in turn reduce the risk of wildfire.

A GIS-based forest vegetation classification system, R1-VMP (Brewer and 

others 2004), was used to identify the locations for mechanical fuel treatments 

within the mapped study area. R1-VMP categorizes polygons based on dominant 

and co-dominant tree species, stand size class, and stand density as measured by 

percent canopy cover. The R1-VMP polygons selected as candidates for treatment 

Figure 2—Study area showing treatment polygons and mill locations for consuming sawlogs, pulpwood, 

and forest treatment residue biomass.
148 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jones, Loeffler, Butler, Chung, and Hummel Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning

contained species that are associated with low-elevation, frequent low-intensity 

fire regimes (ponderosa pine [Pinus ponderosa C. Lawson] and mixtures of Pinus 

ponderosa, western larch [Larix occidentalis Nutt.], and Douglas-fir [Pseudotsuga 

menziessi (Mirb.) Franco] and miscellaneous shade-tolerant species) and fell into 

fire regime condition classes 2 and 3. Land categorized as condition classes 2 and 

3 contain fuel loading that places these forests at the greatest risk of environmental damage from uncharacteristic wildfire (Hardy and others 2001, Schmidt and 

others 2002). Candidate polygons were further restricted to those with average 

slopes less than 35 percent, that lie within 1500 feet from polygon center to existing roads, and are classified as Forest Service non-reserved or non-industrial 

privately owned land. This resulted in 15,800 polygons (average size is five acres) 

comprising 74,352 acres.

The treatment residues were assumed to go to a wood residue boiler 17 road 

miles north of the study area boundary that produces electricity and heat for 

a commercial manufacturing plant. Pulpwood was assumed to go to the same 

facility and sawlogs to a mill 67 road miles north of the study area boundary. 

Transportation to these mills is over forest roads and secondary roads that feed 

into a main highway that exits the north end of the study area.

Modeling Silvicultural Treatments

A variety of silvicultural treatments are available to land managers to achieve 

differing fuel treatment and/or forest health restoration objectives. For this analysis 

we focused on a mechanical treatment called “comprehensive restoration” that 

was designed to reduce ladder and crown fuels, thereby mitigating severe wildfire 

effects and restoring forests to historical conditions (Fiedler and others 1999). 

This mechanical treatment removes all trees below seven inches diameter at breast 

height plus some larger diameter trees with a target residual stand basal area in 

the range of 40-60 ft2 per acre comprised of fire resistant tree species such as 

ponderosa pine and western larch. This treatment is designed to produce an open 

stand of trees that reduces the potential for crown fire and promotes health of the 

residual trees by removing competition for moisture and nutrients.

We assumed that whole tree harvesting is used to cut and skid trees to a landing accessible by road. Further, we assumed the tree boles that are suitable for 

sawlogs and pulpwood are removed and the portion that remains is the residue 

available for bioenergy. This residue consists of the tops and limbs of the commercial trees, and all of the smaller, noncommercial trees that are skidded to the 

landing to meet treatment objectives. This green biomass typically has a moisture 

content around 50 percent and is allowed to air dry to 30 percent moisture content 

prior to grinding and hauling offsite (Han and others 2008).

Volumes of logs and treatment residues produced by this treatment were estimated using the method described in Loeffler and others (2006). The Northern 

Idaho/Inland Empire variant of the Forest Vegetation Simulator (FVS, www.

fs.fed.us/fmsc/fvs) was used to model the outcome of applying the comprehensive 

treatment prescription to Forest Inventory and Analysis (FIA, http://www.fs.fed.

us/rm/ogden) plot data. To ensure adequate data, we supplemented the FIA plots 

from within the study area with similar inventory plots from outside the study 

boundary. Analyzing all plots provided estimates of merchantable timber volumes 

and non-merchantable biomass volumes that would be removed per acre, assuming 

that all cut trees are whole tree skidded to the landing (table 1). Quadratic mean 

diameter (QMD) and trees cut per FIA plot were tallied for both the merchantable 

and non-merchantable categories. The Fire and Fuels Extension of FVS was used 

to estimate the weight of the total biomass removed. Subtracting the removed 

merchantable log weight from the weight of the total biomass removed yielded the 
USDA Forest Service Proceedings RMRS-P-61. 2010. 149

Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning Jones, Loeffler, Butler, Chung, and Hummel

weight of the non-merchantable biomass. Based on the default residue recovery 

fraction in the Fuel Reduction Cost Simulator (FRCS; Fight and others 2006), we 

assumed 80 percent of the non-merchantable biomass was skidded to a landing; 

the remaining 20 percent represented breakage that stays in the treatment unit.

The volumes estimated from analyzing the FIA plots were assigned to the 

R1-VMP vegetation categories based on dominant species, tree size class, and 

stand canopy cover. The results from analyzing the plots were averaged within 

the R1-VMP categories such that each R1-VMP category contained the average 

tree attributes calculated from the FIA plots in the corresponding category.

Treatment costs (excluding administrative and planning) were modeled for 

each application of the comprehensive treatment using the FRCS. Required FRCS 

input variables include trees per acre removed, QMD, average tree volume, green 

wood weight, and residue weight to bole weight fractions. These were calculated 

from the FVS-generated cut tree lists (table 1), regression equations from Jenkins 

and others (2003) and dry wood weights from Reinhardt and Crookston (2003) 

adjusted to 50 percent wood fiber moisture content. We classified the treatment 

polygons into three slope categories and assumed an average skidding distance 

of 1,000 feet. Average skidding distance is approximately 2/3 of the maximum 

skid distance assuming logs are skidded to a centralized landing for a triangular 

treatment unit (Matthews 1942). The model was calibrated to reflect western 

Montana wage rates – $14.72 per hour (ACINET 2008). The model’s default labor 

benefit rate of 35 percent was retained and move-in costs were included.

Mill-delivered prices at the time of the analysis were used to value the products 

produced by the comprehensive treatment: $28 per ton at 30 percent moisture 

content for ground biomass, $40 per ton for pulpwood, and $425 per MBF for 

sawlogs.

Modeling Transportation

A GIS roads coverage obtained from the Bitterroot National Forest (www.

fs.fed.us/r1/bitterroot) provided the road network for modeling haul of treatment 

residue biomass, pulpwood, and sawlogs from the candidate treatment polygons 

to the respective processing facilities. This GIS coverage contains road segments 

separated by nodes, which were placed at every road intersection and in the vicinity of candidate treatment polygons. The location where biomass volume from 

each polygon enters the road system was approximated by choosing the nearest 

down-slope node.

Table 1—Summary statistics from modeling application of the comprehensive restoration treatment on 

1-acre plots (n=458).

Treatment	 QMDa of	 Number of	 Volume of

residue	 merchantableb trees merchantable trees	 merchantable trees

biomass	 removed	 removed	 removed

(dry tons) (inches) (number) (cubic feet)

Mean 11.6 10.5 96.0 1,371.1

Median 10.3 10.3 82.5 1,091.7

Standard Deviation 7.3 2.2 64.0 1,162.7

Minimum .5 7.0 1.9 24.1

Maximum 47.6 24.4 364.0 6,556.3

a Quadratic mean diameter.

b Merchantable trees are greater than four inches diameter at breast height.
150 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jones, Loeffler, Butler, Chung, and Hummel Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning

Many of the treatment polygons are next to roads inaccessible by large chip 

vans, which are generally considered the most cost-effective way of trucking 

biomass on paved surfaces. Therefore, we assumed the biomass was hauled 

from the polygons to the bioenergy facility by hook-lift trucks hauling roll-on/

off containers resembling extremely large trash bins (Han and others 2008). 

These trucks are suitable for low-standard mountain roads and have essentially 

the same access capabilities as a logging truck. These hook-lift trucks haul one 

roll-on/off container and pull a pup trailer with a second container, providing a 

total payload of approximately 25 tons (Thomas, personal communication). This 

compares with 27 to 30 ton payloads for a chip van. We assumed that the biomass 

is ground into these roll-off containers at the landings. The hook-lift trucks then 

pick up the loaded containers and haul them to the biomass utilization facility. 

Empty containers are returned to the landing on the return trip.

Haul costs were estimated on a per mile basis for each of two types of roads, 

paved and non-paved, using the Forest Residue Trucking Model (FoRTS; http://

www.srs.fs.usda.gov/forestops/). Costs were calibrated to reflect local wages 

and conditions and various diesel fuel prices. Standard log trucks were assumed 

for haul of pulpwood and sawlogs. Log trucks were assumed to haul 30 tons of 

pulpwood and five MBF of sawlogs. The average haul distance from all the potential treatment polygons to the biomass utilization facility was 85 miles (fig. 2).

Fossil Fuel Consumption Associated with Utilization of 

Forest Biomass

Diesel fuel is used in cutting, skidding, and processing the whole trees at the 

landing into merchantable logs, for grinding the biomass into the roll-on/off containers, and for hauling the ground biomass to the energy utilization site. Diesel 

consumption for cutting, skidding, and processing was estimated at 0.022 gallon 

per cubic foot of harvested timber (CORRIM 2004). We assumed the diesel attributable to biomass removal was proportional to the biomass percentage of the 

total weight of material delivered to the landing, which based on FVS analysis 

averaged 25 percent of total weight.

Diesel consumption for grinding into the roll-on/off containers was estimated 

using the FoRTS model at 0.42 gallon per ton of biomass, which had been allowed 

to dry in piles to an average 30 percent moisture content. In addition, we used 

FoRTS to estimate the diesel consumed during a 20 minute idle time for each 

hook-lift truck and pup trailer to be loaded at 0.21 gallon. The diesel consumption for trucks hauling biomass was estimated at four miles per gallon (Thomas, 

personal communication). This consumption rate was applied to the loaded haul 

distance as well as the return trips with empty containers.

Spatial Modeling of Components

MAGIS, a spatial decision support system for scheduling vegetation treatments 

and road-related activities (www.fs.fed.us/rm/econ/magis) was used to simulate 

the treatments on the study area. The spatial R1-VMP polygons and road network data, vegetation treatment data, costs, delivered product prices, and fossil 

fuel consumption data served as inputs in the MAGIS model. MAGIS was then 

applied to simulate the application of the comprehensive restoration treatment 

on the relevant polygons on the landscape, load the biomass residue, pulpwood, 

and sawlogs onto the road network, and route the loaded trucks over the shortest 

path to their respective mill facility locations. In this process MAGIS calculated 

the acres receiving treatment, tons of biomass produced by the treatments and 

either hauled for energy production or burned onsite, the truck-miles required to 

haul the biomass for energy production, and the diesel consumption involved 
USDA Forest Service Proceedings RMRS-P-61. 2010. 151

Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning Jones, Loeffler, Butler, Chung, and Hummel

in collecting, grinding, and hauling the biomass. The emission factors discussed 

below were applied to the model results. The scheduling capability in MAGIS was 

used to analyze applying the comprehensive treatment to incremental portions of 

the study area having increasing average haul distances.

Emission Factors

This paper focuses on two greenhouse gases, carbon dioxide and methane, as 

well as particulate matter emissions less than 10 microns in size (PM10). PM10 is 

one of several measurements of air quality used by the US Environment Protection Agency (2009b). For the alternative of utilizing forest residues in a boiler, we 

include emissions from internal combustion diesel engines, and stack emissions 

produced by burning biomass in a boiler for generating electricity and/or thermal 

energy. For the alternative of onsite disposal of forest residues by pile-burning and 

using fossil fuels to produce the equivalent amount of useable energy we include 

the emissions from pile-burning as well as the stack emissions from using either 

#2 distillate oil or natural gas to produce the equivalent usable energy in a boiler. 

The pile-burn emission calculations assume 95 percent of the residues in the piles 

are burned based on the assumption that unburned material at the edge of the 

piles is manually thrown into the fire (Hardy 1998; Fox, personal communication; 

Parks, personal communication)

Carbon dioxide, methane, and PM10 emissions for internal combustion diesel 

engines were estimated using the US Environmental Protection Agency AP-42 

report (US Environmental Protection Agency 1995) and data from the US Energy Information Administration (US Energy Information Administration 2008). 

Stack emissions from burning biomass in a boiler both with and without a wet 

scrubber were estimated using the AP-42 report and data from the USDA Forest 

Service Forest Products Lab (USDA Forest Service Forest Products Laboratory 

2004). AP-42 factors were also used for the stack emissions from burning either 

#2 distillate oil or natural gas in a boiler and emission factors for pile-burning 

the biomass in the forest came from published fuel management data (Hardy and 

others 2001). We assume boiler efficiency ratings of 83 percent, 80 percent, and 

74 percent respectively, for distillate oil, natural gas, and biomass at 30 percent 

moisture content (USDA Forest Service Forest Products Laboratory 2004) to 

calculate the amounts of distillate oil and natural gas required in the pile-burn 

alternatives to produce the equivalent heating value of bioenergy.

A fossil energy ratio factor was incorporated into our estimates of fossil energy used in the alternatives. The fossil energy ratio is the useable fuel energy 

divided by the total fossil energy inputs required to collect, refine, and deliver the 

fossil fuel to market (National Renewable Energy Laboratory 1998). The direct 

consumption of diesel, #2 distillate oil, and natural gas was divided by the fossil 

energy ratio of 0.8337 (National Renewable Energy Laboratory 1998) to include 

the fossil fuel energy required to deliver the fossil fuels to the final market as 

well as the direct usage of fossil fuels in the alternatives analyzed.

Results

Emissions

Figure 3 compares the total carbon dioxide emissions from using forest treatment residues for thermal energy (the bioenergy alternative) with disposal of 

treatment residues by on-site pile burning and using fossil fuels in a boiler to 

produce the equivalent amount of usable thermal energy. Carbon dioxide emissions 

from the bioenergy alternative are only 57 percent of the pile-burn alternative 
152 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jones, Loeffler, Butler, Chung, and Hummel Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning

using distillate oil and 65 percent of the pile-burn alternative using natural gas. 

Notice that the carbon dioxide emissions from the consumption of diesel fuel to 

collect, grind, and haul the biomass to the boiler facility represents only a very 

small percentage of the total carbon dioxide emissions associated with using 

fossil fuels in boilers to provide the equivalent heat in the pile-burn alternatives.

The reductions in methane emissions (fig. 4) are much greater than the reductions calculated for carbon dioxide, with the methane emissions from the 

bioenergy alternative representing only about 3 percent of the pile-burn alternatives. Methane is not produced in appreciable amounts by burning fossil fuels in 

a boiler or in diesel engines. The methane production, while small compared to 

carbon dioxide, is important because the global warming potential of methane is 

about 21 times that of carbon dioxide (IPCC 2007b, US Environmental Protection Agency 2009c).

For the PM10 comparison, stack emissions were computed for biomass boilers 

both with and without wet scrubber particulate matter emission control (fig. 5). 

Although large biomass boilers would be expected to have particulate matter 

emission controls, we were also interested in comparing emissions from small 

boilers without these controls. PM10 emissions from the bioenergy alternative 

with wet scrubber emission control were 11 percent of the pile-burn alternatives, 

and without the emission control were 44 percent of the pile-burn alternatives. 

For the pile-burn alternatives, PM10 emissions are almost entirely produced by 

pile-burning, very little is produced by burning either distillate oil or natural gas 

in a boiler.

Figure 3—Carbon dioxide emissions per dry ton of forest treatment residues utilized in the 

bioenergy alternative compared with disposal by on-site pile burning and using either distillate 

oil or natural gas to provide the equivalent thermal heat in a boiler.
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Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning Jones, Loeffler, Butler, Chung, and Hummel

Figure 4—Methane emissions per dry ton of forest treatment residues utilized in the bioenergy 

alternative compared with disposal by on-site pile burning and using either distillate oil or natural 

gas to provide the equivalent thermal heat in a boiler.

Figure 5—PM10 emissions per dry ton of forest treatment residues utilized in the bioenergy 

alternatives compared with disposal by on-site pile burning and using either distillate oil or natural 

gas to provide the equivalent thermal heat in a boiler.
154 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jones, Loeffler, Butler, Chung, and Hummel Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning

Biomass Energy Returns

The scheduling capability in MAGIS was used to analyze energy returns from 

delivering forest residues in ten percent increments of total potential residues 

available on the study area. The first ten percent cost the least to haul to the bioenergy consumer; the second ten percent costs the next least, and so on. Figure 6 

shows the average haul distances for each of the ten increments and the units of 

biomass energy obtained for each unit of diesel energy expended to collect, grind, 

and haul the biomass over these ten percent increments. As in the boiler emission 

calculations, the fossil energy ratio of 0.8337 was applied to our estimate of the 

amount of diesel consumed by these activities to account for the total amount of 

energy required for a gallon of fuel. At the 47-mile average haul distance, 26 units 

of energy are obtained for each unit of diesel fuel energy required to deliver the 

ground biomass to the energy facility. This ratio decreases to 21 units of energy 

per unit of diesel fuel energy consumed at the 85-mile average haul distance.

These bioenergy returns compare well with other bioenergy alternatives. For 

example, in a survey of literature of energy return Hammerschlag (2006) reported 

ratios for corn ethanol energy produced per unit of nonrenewable energy expended 

ranging from 0.84 to 1.65. For cellulosic ethanol Hammerschlag reported ratios 

of 6.61 for a mixed feedstock, 4.55 for poplar, 4.40 for corn stover, and 0.69 for 

switchgrass. Wu and others (2008) estimated year 2030 production cellulosic 

ethanol energy returns per unit of nonrenewable energy expended at 6.25 for 

wood residue and 11.11 for corn stover.

The fossil fuel energy consumed to collect, grind, and haul one dry ton of 

biomass in the bioenergy alternative is on average four percent of the fossil fuel 

energy required to provide the equivalent usable thermal energy in a boiler. In 

other words, the fossil fuel energy required in a boiler to provide the equivalent 

heat in the pile burn alternatives is many times greater than the fossil energy 

consumed in the bioenergy option.

Figure 6—Biomass energy obtained for each unit of diesel fuel used to collect, grind, and haul forest 

treatment residue biomass across increasing average haul distances.
USDA Forest Service Proceedings RMRS-P-61. 2010. 155

Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning Jones, Loeffler, Butler, Chung, and Hummel

Economics of Biomass Utilization

Figure 7 identifies the candidate treatment units within the study area where 

utilization of biomass for thermal energy production is economically feasible. For 

these units, the delivered value of removed treatment residue biomass is greater 

than or equal to the cost of handing, grinding, and hauling biomass to that mill 

location. We assumed whole tree harvesting, so the biomass costs apply to piled 

treatment residues either at a landing or at road-side.

When diesel price is $4 per gallon and the delivered biomass price is $28 per 

ton at 30 percent moisture content, 36,447 acres (49 percent of the 74,352 total 

acres in polygons analyzed for potential treatment) are economically viable (leftmost map in fig. 7). If the diesel price were to increase 50 percent to six dollars 

per gallon (center map in fig. 7), the number of economically viable acres drops 

to 23,445 (31 percent of the potential acres). The polygons that drop out at this 

higher diesel price are those with the longer hauling distances and/or more unpaved road hauling distance. If both the diesel and delivered biomass price were 

to increase 50 percent from the base case (right-most map in fig. 7), then the 

economically viable acres increases to 37,915 acres (51 percent of the potential 

acres). This suggests that changes in the delivered price of biomass are slightly 

more important in economic feasibility than changes in the price of diesel fuel.

Figure 7—Where biomass utilization is economically viable within the study area across various diesel 

and delivered biomass prices.
156 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jones, Loeffler, Butler, Chung, and Hummel Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning

Conclusions

These results suggest that when a bioenergy alternative to onsite pile-burning 

is available, far fewer carbon dioxide, methane, and particulate matter emissions 

would be generated and useable energy is produced that could offset the use of 

fossil fuels for thermal energy production. In addition, the fossil fuel energy required for the bioenergy alternative is small compared to the energy produced in 

the bioenergy alternative. Based on the economics of biomass utilization results, 

these relationships hold for haul distances that are many times longer than what 

are financially feasible.

The analysis we present in this paper is based on whole tree harvesting, grinding 

the skidded biomass residue into containers, and trucking these containers to the 

location where the biomass is burned for heat energy. We expect that other wood 

utilization standard and ground-based harvesting or biomass handling methods 

would produce different emission trade-offs and energy consumption ratios.

Our results indicate that utilizing woody residues for thermal heat can contribute 

to generating energy while also reducing greenhouse gas and particulate matter 

emissions compared to alternative methods of residue disposal. The reduction in 

particulate matter emissions may also provide an advantage in areas where open 

burning is restricted by air quality standards.

Acknowledgments:

We thank Janet Sullivan and Kurt Krueger of the Rocky Mountain Research 

Station for their help in applying the MAGIS model in this study. We also thank 

Mark Nechodom of the USDA Forest Service Pacific Southwest Research Station 

for advice on methodology.

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158 USDA Forest Service Proceedings RMRS-P-61. 2010.

Jones, Loeffler, Butler, Chung, and Hummel Emissions, Energy Return and Economics From Utilizing Forest Residues for Thermal Energy Compared to Onsite Pile Burning

Author Contact Information:

Greg Jones, Human Dimensions Science Program, Rocky Mountain Research 

Station, USDA Forest Service, PO Box 7669, 200 East Broadway, Missoula, MT 

59807, 406-329-3396, jgjones@fs.fed.us

Dan Loeffler, College of Forestry and Conservation, The University of Montana, 

Forestry Sciences Laboratory, PO Box 7669, 200 East Broadway, Missoula, MT 

59807, 406-329-3397, drloeffler@fs.fed.us drloeffler@fs.fed.us

Edward Butler, College of Forestry and Conservation, The University of Montana, 

Forestry Sciences Laboratory, PO Box 7669, 200 East Broadway, Missoula, MT 

59807, 406-329-3396, ebbutler@fs.fed.us

Woodam Chung, College of Forestry and Conservation, The University of Montana, Missoula, MT 59812, 406- 243-6606, woodam.chung@umontana.edu

Susan Hummel, Pacific Northwest Research Station, USDA Forest Service, 333 

SW First Avenue, Portland, OR 97204, 503-808-2084, shummel@fs.fed.us

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
USDA Forest Service Proceedings RMRS-P-61. 2010. 159

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 University of Idaho, Moscow, ID.

2 USDA Forest Service, Rocky Mountain 

Research Station, Moscow, ID.

3 USDA-Forest Service, Umpqua NF, 

Idleyld Park, OR.

4 Renewable Oil International, LLC,

Florence, AL.

5 University of Montana, Missoula, MT.

6USDA Forest Service, Rocky Mountain 

Research Station, Missoula, MT.

Can Portable Pyrolysis Units Make Biomass 

Utilization Affordable While Using Bio-Char to 

Enhance Soil Productivity and Sequester Carbon?

Mark Coleman1, Deborah Page-Dumroese2, Jim Archuleta3, 

Phil Badger4, Woodum Chung5, Tyron Venn5, Dan Loeffler5

, 

Greg Jones6 and Kristin McElligott1

Abstract—We describe a portable pyrolysis system for bioenergy production from forest 

biomass that minimizes long-distance transport costs and provides for nutrient return 

and long-term soil carbon storage. The cost for transporting biomass to conversion 

facilities is a major impediment to utilizing forest biomass. If forest biomass could be 

converted into bio-oil in the field, it may be more profitable to utilize forest biomass 

for bioenergy. Bio-oil can substitute for fuel oil, or be used as a crude oil and further 

refined into additional products. Transporting energy-dense bio-oil is more cost effective than transporting bulky, low-value biomass. In-woods pyrolysis can also address 

concerns over removing nutrients and carbon from forest sites through reapplication 

of bio-char, a pyrolysis byproduct, which is equivalent to the charcoal found in all fire 

ecosystems. Bio-char is 70-80 percent carbon and retains most nutrients contained in 

biomass. It can be used as a soil amendment to enhance soil productivity through a 

liming effect, which improves cation exchange capacity and base saturation, increasing anion availability, improving water holding capacity and decreasing bulk density. 

Charcoal is known to remain stable in soils for hundreds to thousands of years. Long 

charcoal residence times provide a way to quickly sequester atmospheric carbon by 

assimilating it into a recalcitrant form that can be applied to soils. In total the portable 

pyrolysis approach has the potential to improve the economic efficiency of biomass 

removal from overstocked forests through the in-woods conversion of biomass to 

bio-oil that avoids the costs and emissions of transportation to central facilities. Biochar can be returned to the forest economically if pyrolysis occurs at or near the site 

of biomass removal. Reapplication of bio-char will sequester carbon in soil and may 

enhance site productivity.

Keywords: bioenergy, bio-oil, carbon sequestration, fuels reduction, soil 

productivity

Introduction

Forest biomass accumulation is both a problem and an opportunity. Increasing forest biomass is a consequence of continuous forest growth, effective fire 

suppression tactics, lack of harvest activities, and other management practices. 

Young growing forest stands quickly become overstocked with numerous small 

diameter tree stems, slowing individual tree growth and causing stem exclusion 

processes to initiate (Oliver and Larson 1990). Prior to implementing effective fire 

suppression tactics, some fire-adapted ecosystems (i.e. low-elevation, frequent 

fire regime forests), burned regularly, often as cooler understory fires or moderate severity fires that served to limit biomass accumulation, release nutrients 
160 USDA Forest Service Proceedings RMRS-P-61. 2010.

Coleman, Page-Dumroese, Archuleta, Badger, Chung, Venn, Loeffler, Jones and McElligott Can Portable Pyrolysis Units Make Biomass Utilization Affordable While Using...

and improve stand vigor (Agee 1996; Stanturf and others 2002). Pre-commercial 

thinning is also used to restore ecological function (Thibodeau and others 2000). 

Without frequent burning or thinning such overstocked forests contain abundant 

ground level biomass and experience considerable mortality of subordinate trees 

as dominant stems emerge. This fuel buildup has resulted in high-severity stand 

replacing fires, which captures the attention of those living in developments at 

the wild land interface. As a consequence of increasing wildfire occurrence and 

intensity, public land managers in fire-prone areas have once again begun to thin 

overstocked stands with a focus on fuels reduction, even though the area actually 

being treated is small relative to that in need of treatment.

Removed biomass adds to the equally large volume of biomass that is commonly 

found at landings of logging operations where whole-tree yarding is practiced 

(Perlack and others 2005). This accumulated biomass from thinning and harvesting 

practices is typically flared to avoid continued risk of fire as the slash piles dry. 

Onsite flaring releases greenhouse gases, energy and carbon captured by natural 

forest processes, and concentrates nutrients at burn pile locations.

Opportunity for Bioenergy

Utilization of biomass offers a potential solution to the problem of hazard fuel 

accumulation. Developing markets for biomass may provide managers and land 

owners a way to achieve management objectives if forest operators have a viable opportunity to sell biomass and land managers have the ability to contract 

for product removal. Potential markets for biomass utilization include products 

such as small-wood furniture and structures, garden mulch, bioenergy, chemicals, and other products (Hakkila 1989; LeVan-Green and Livingston 2003). Of 

particular interest at this time is abundant energy contained in biomass that can 

be tapped as an alternative to fossil fuels and avoid greenhouse gas emissions. 

Bioenergy production is most attractive when fossil fuel energy prices increase, 

but as greenhouse gas emissions become an increasing concern it also causes us 

to look for alternative, renewable and low emissions energy sources. Bioenergy 

production from forests may meet that need. It is particularly interesting with the 

coincident occurrence of enhanced energy security needs, requirements to reduce 

emissions of carbon, and the requirement to remove biomass from forest stands.

Biomass utilization for bioenergy has a long history. Much of the nation’s energy 

needs were met by wood fuel prior to widespread use of coal and petroleum. Even 

now it is common to find combined heat and power production operations where 

there are abundant biomass supplies such as in pulp mills and lumber yards. Recent 

interest has also been spurred by government programs promoting alternatives 

such as heat for schools, prisons, hospitals, etc. (Richter and others 2009). Even 

with this level of utilization, there is still over 300 million tons of unused biomass coming available annually nationwide (Perlack and others 2005). However, 

adoption of bioenergy production practices typically occurs only where there is 

a ready biomass supply on site, such as forest product facilities, or where modest 

feedstock requirements are met within close proximity to the energy conversion 

facility, such as a low-demand educational heating facility in a forested region.

The importance of the biomass supply being localized to minimize transport 

costs cannot be overstated. While there are significant costs for biomass removal, 

those costs may be exceeded by revenue gained through the sale of that material 

to a local conversion facility (Evans 2008). However, delivery to distant conversion facilities frequently causes the delivered cost to exceed revenues making 

the biomass utilization process economically unviable (Stokes and others 1993). 
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Can Portable Pyrolysis Units Make Biomass Utilization Affordable While Using... Coleman, Page-Dumroese, Archuleta, Badger, Chung, Venn, Loeffler, Jones and McElligott

Consequently, despite abundant supply, biomass is commonly not removed for utilization due to expenditures exceeding potential revenues and instead is cut, piled 

and burned at significant expense and with important consequences to consider.

Biomass Disposal Concerns

Both off- and on-site consequences occur from pile-burning biomass. Dried 

biomass is about 50 percent carbon and when biomass slash piles are flared that 

carbon is oxidized and released back to the atmosphere as carbon dioxide or other 

organic compounds. Such disposal is questionable in light of efforts to reduce 

greenhouse gas emissions and sequester carbon. In addition to volatilizing carbon, other essential plant nutrients are also lost from the site by burning. These 

losses include several processes such as oxidation, vaporization, convective ash 

losses, leaching and erosion (Fisher and Binkley 2000). The two main inorganic 

nutrients lost to oxidation are nitrogen and sulfur, which are typically released 

as air pollutants in the smoke produced by open-air burning. Phosphorous can 

also be lost, but in lower quantities than nitrogen and sulfur. In hot fires, such 

as in well seasoned slash piles, oxidative losses of nitrogen can be 25-65 percent 

and for sulfur they can be 25-90 percent. These nutrients are frequently growth 

limiting in forest environments (Fox and others 2007; Kishchuk and Brockley 

2002), so it is equally unwise to cause such losses rather than conserving onsite 

stores. Nutrients are also lost from site in smoke emissions. Convective losses of 

particulates occur during burning that contain the full range of mineral nutrients 

found in biomass, many of which are concentrated in ash (Fisher and Binkley 

2000). Finally, other pollutants including particulates, carbon monoxide, and a 

variety of volatile aromatic carbon compounds are also released in smoke (U.S. 

National Research Council 2004). These pollutants are typically regulated in 

urban and agricultural areas requiring permits to release. Smoke management 

procedures are also in place to limit forest biomass pile burning to favorable 

atmospheric conditions (e.g. http://www.smokemu.org/).

Piling and burning slash concentrates nutrients in the fire ring, which may lead 

to lower average site productivity. The site preparation practice of shearing, piling 

and burning was discontinued in southern pine plantations after it was recognized 

that the redistribution of nutrients resulted in productivity declines (Carter and 

Foster 2006). Similar results were observed in other regions (Binkley 1986), some 

of which may be explained by topsoil displacement as well as biomass redistribution. Regardless, the concentration of biomass into piles and release of nutrients 

localizes nutrients and can potentially saturate nutrient exchange capacity in the 

burned area, leading to greater leaching loss.

While utilization of abundant forest biomass for bioenergy is appealing, it 

too may result in removal of nutrients from sites. Environmental critics of forest 

bioenergy production systems frequently cite the concern of nutrient removal 

and over-exploitation of the resource as an expected negative consequence of 

biomass harvesting for energy production (Kimmins 1997). We know from timber 

harvesting that bole-only removal has an undetectable impact on the regrowth of 

subsequent forest stands; however, if we remove whole trees from nutrient poor 

sites, impacts on growth of the next forest rotation have been detected (Kimmins 

2004). More certainly we know that removing litter and displacing soil will have 

significant impact on the next rotation (Fleming and others 2006; Van Miegroet 

and Johnson 2009). But we have little or no information on the impacts of 

removing small diameter biomass material, such as tops, branches and needles 

that contain high concentrations of nutrients (Evans 2008; Palviainen and others 2004). We do not know if those removals will impact subsequent forest 
162 USDA Forest Service Proceedings RMRS-P-61. 2010.

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productivity, but it will likely depend on the inherent site quality, the frequency 

and intensity of harvest and the ability of the site to replenish nutrients removed 

(Kimmins 2004). The forest system is resilient and maintains large stocks of 

nutrients that, given adequate time, can meet the requirements of forest growth, 

but an accelerated frequency of removal may exceed the replenishment capacity. 

Consequently, there is an urgent need to understand the implications of biomass 

removal. A sustained bioenergy production system might include removing the 

energy and not the nutrients, or returning the nutrients after energy is extracted 

from the biomass.

Pyrolytic Biomass Conversion Solution

Both profit and sustainability are essential where financial analysis controls 

the viability of alternative energy projects and the feedstock derives from venerated forested ecosystems. The mobile fast pyrolysis bioenergy production system 

(Badger and Fransham 2006) may be one approach to profitable and sustainable 

biomass utilization. The mobile pyrolysis unit has potential to cover the cost of 

biomass removal through the production of a crude oil product known as “bio-oil” 

that has higher density and energy content than biomass. In addition to the bio-oil, 

there is also a “bio-char” byproduct that has market value of its own, but might 

best be used by returning it to the site of energy extraction as a soil amendment 

and as a means of soil carbon sequestration. Such an approach has recently been 

advocated for agricultural systems (Laird 2008; Lehmann and others 2006), but 

it makes even greater sense for forest ecosystems when the bio-char is produced 

at and immediately returned to the site of energy extraction.

Table 1 shows value comparisons for fast-pyrolysis products. The pyrolysis 

actually has three product phases: gas, liquid and solid (Bridgwater 2004). The 

flammable gas is used to fuel the pyrolysis process in a self sustaining combustion. 

So although in some situations the heating value of the gas can be quantified as a 

product, in this case it provides the energy for producing the other products. The 

gas amounts to ~48 percent of the energy in dry wood (Raveendran and Ganesh 

1996). The bio-oil is the liquid phase product and fast pyrolysis will produce more 

than 120 gallons per dry ton of biomass (Mohan and others 2006). We determined 

the value of bio-oil by comparing it to substitute market products. Bio-oil is 

discounted by 60 percent in this analysis to account for the lower heating value 

relative to the petroleum products. Minor furnace or boiler modifications are also 

Table 1—Value of pyrolysis products from one air dry ton of biomass. 

1. Syngas (fuel for Pyrolysis) 

2. Bio-oil = 120 gal of bio-oil

• $64 ($0.89 / gal1 Bunker Fuel Houston, TX, Bunkerworld.com)

• $94 ($1.30 / gal1 Wholesale fuel oil, tonto.eia.doe.gov)

3. Bio-char = 500 lbs of bio-char

• $65 ($260/ton, author market survey)

• $9-$18 ($35-$70 / ton1 EU carbon trading EU ETS, www.pointcarbon.com)

One ton Forest Biomass = $73-$159 (sum of bio-oil and bio-char products

1 Prices as of 20 April 2009
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required in handling and burner/boiler design to allow for unique chemical and 

physical bio-oil properties (Mohan and others 2006). This comparison gives a 

value of $64 - $78 of bio-oil produced per dry ton of biomass. The third product 

of pyrolysis is the solid bio-char and it is similarly valued by substitute market 

products. Bio-char can be sold for horticulture or barbeque charcoal at a value 

of ~$65 of bio-char per dry ton of biomass. Although bio-char does have this 

wholesale market, the real benefit of the bio-char produced from forest biomass 

using a portable pyrolyis unit might be in leaving it on the site from where the 

biomass was extracted and using it for soil conditioning and carbon sequestration. As with biomass, the bio-char is a low-density, bulky material (0.35 specific 

gravity, (Antal and Gronli 2003)) and transport cost may overcome the value and 

favor leaving it on site. Carbon sequestration might provide a value of $9 and $18 

per air dry ton. If ten air dry tons of biomass can be removed from an acre, the 

potential market value of bio-oil plus bio-char might result in revenue of $730 to 

$1430 per acre. In comparison to the median cost of biomass removal of $625 per 

acre (Evans 2008), there appears to be a reasonable potential for profit considering 

production, relocation, and transport costs must still be accounted.

One of the key features of the mobile pyrolysis approach is the ability to 

take the conversion unit to the biomass source and avoid biomass transport. 

In-woods pyrolysis operations allows us to convert biomass into an energy rich 

high-density bio oil. Transporting a value-added high-density product not only 

decreases transportation costs, but also decreases fossil fuel emissions required 

for transport. Therefore, life-cycle analysis is another aspect of the portable vs. 

centralized pyrolysis plant for which accounting should occur.

The capital and operating costs of small scale conversion units are high relative to larger units (Bridgwater 2004). Greater efficiencies are created by using 

higher capacity pre-processing and handling equipment: relatively fewer personnel requirements, lower maintenance and greater operating hours per year. For 

instance, moving the mobile pyrolysis unit into the woods, conducting startup 

procedures, consuming available biomass, shutting down and relocating may 

have a significant impact on operating efficiency. It is likely that the portable 

pyrolysis unit will be located at a single central location within one or more project 

area(s) and operated at that one location for considerable time, requiring minimal 

transport of biomass, but still incurring some short-distance biomass transport 

costs. Consequently, mobile pyrolysis units have both the advantage of limiting 

transport distance over that of the centralized fixed-location conversion facility 

and the disadvantage of having greater capital, operating, and relocation costs. 

Our research is evaluating these operational and economic tradeoffs.

Figure 1 demonstrates the hypothetical operating range of mobile pyrolysis 

units within the Umpqua and Willamette National Forest woodshed. Biomass 

from the Umpqua would otherwise be transported to a centralized plant located in 

Roseberg, OR. The central plant draws from a broader region beyond the indicated 

National Forests, including surrounding Bureau of Land Management ground as 

well as other public and private lands in and beyond the area illustrated. Travel 

routes affect the efficiency with which biomass can be moved to Roseburg and 

road networks are being used to calculate transportation requirements. Operational 

efficiency of fixed and mobile pyrolysis units is being evaluated. Capital costs 

and operational requirements of fixed location units are known through commercial applications (Bridgwater 2004) and are being compared to information 

from development-stage mobile units.
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Bio-Char Advantage

The bio-char produced through these mobile units is equivalent to charcoal 

that is manufactured for numerous other purposes through traditional and modern 

pyrolysis techniques. Charcoal manufacture has been used throughout human 

history including fuel for iron and bronze metallurgy starting 4,000 years ago 

and lasting until the use of fossil fuel became widespread during the 19th century 

(Rackham 1980). Modern charcoal uses include air and water filtration, cooking 

charcoal, horticultural media, bioremediation, medicinal purposes, among others. 

As an equivalent to charcoal, bio-char is also an artificially produced analog to 

charcoal found in many fire ecosystems. This black carbon has been defined as 

a natural component of fire ecosystems that lends favorable properties to soils 

and enhances soil productivity (DeLuca and others 2008; Pietikainen and others 

2000; Zackrisson and others 1996). Therefore, it can be applied to native ecosystems without concerns of contamination. Bio-char presents an opportunity to 

return nutrients removed in the biomass from project locations, and as mentioned 

above, reapplication of bio-char to project sites also has potential value in carbon 

Figure 1—Map of biomass supply area with circles representing portable pyrolyzer 

supply areas within the Willamette and Umpqua National Forests and a centralized 

processing facility located in Roseburg, OR with a supply area extending beyond the 

map area. 
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sequestration. Both the nutrient return and carbon sequestration values of biochar reapplication to project sites may outweigh other potential uses. Segments 

of the public are increasing demands for limits on forest product utilization from 

public land, which may prompt requirements for nutrient conservation. Geopolitical decisions are expected to expand limits on carbon emissions and reward 

carbon sequestration. On-site retention may be the best option in light of these 

social pressures.

Charcoal also has important horticultural values and soil enhancement characteristics. It can be used in greenhouses as a plant growth media. Figure 2 

compares poplar trees growing in potting soil blends with increasing bio-char 

proportions. Poplar was used as a bioassay because of its responsiveness to variable growing conditions and sensitivity to soil growth media. In this case, each 

is growing equally well regardless of the amount of char included. Bio-char can 

be used as an effective soil media in the greenhouse and at forest sites because 

of the favorable properties provided to the soil.

Bio-char contains the majority of nutrients found in biomass feedstock (Gaskin 

and others 2008). Nutrients such as nitrogen and sulfur can be volatilized during 

the pyrolysis process, but the bio-char produced may also contain significant 

amounts of these nutrients. This means that the bio-char resulting from extracting 

energy in bio-oil production can be returned to the site to replenish soil nutrient 

stocks.

Returning the bio-char to the site can also enhance soil organic matter. Biochar is mainly carbon held in aromatic form, which results in it being inert when 

added as an amendment. As a consequence, it quickly builds the recalcitrant soil 

carbon fraction of soil. We know from research on wildfire occurrence and the 

development of anthrosols that charcoal-derived carbon can remain in the soil 

for hundreds to thousands of years (Agee 1996; Lehmann and Rondon 2006). 

Enhancement of the soil organic matter pool with charcoal provides the numerous benefits of other organic matter including large surface area for exchange of 

water and nutrients; however bio-char also has other characteristics that create 

additional soil improvements.

Figure 2—Poplar trees growing for 12 weeks in potting soil with 

different proportions of bio-char. Each pot received 1.5 g slow release 

fertilizer (18-6-12). Differences between treatment were not significant 

(P = 0.63). Error bars are standard errors.
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Bio-char acts as a liming agent resulting in increased soil pH and nutrient 

availability for a number of different soil types (Glaser and others 2002; Lehmann and Rondon 2006). Soil liming results in pH increases of one-half to one pH 

units. The liming of acidic soils decreases Al saturation, while increasing cation 

exchange capacity and base saturation. These responses following bio-char additions are common soils responses to lime additions (Tisdale and Nelson 1975) 

indicating that the effects of bio-char are similar to those of other liming agents. 

Nutrient availability may actually increase beyond the amount expected by cation 

exchange sites due to soluble salts available in the char. Anion availability may 

also increase suggesting that anion exchange may be enhanced by bio-char additions to soils (Glaser and others 2002). Microbial biomass and diversity is also 

known to increase with greater bio-char including more abundant mycorrhizal 

associations and enhanced biological nitrogen fixation (Lehmann and Rondon 

2006). Therefore, when bio-char is added to soil it “sweetens” the soil by raising 

the pH, improving the fertility level through additions of nutrient ions commonly 

associated with ash additions, and enhances symbiotic soil microbe populations.

Bio-char may also increase the water holding capacity of forest soils. This is 

especially important on western soils where the growing season is determined by 

the length of time into seasonal summer droughts where soil moisture remains 

favorable to growth. It may become more important in other forest ecosystems 

where extended summer drought can significantly decrease growth and the frequency and amount of summer rain events are expected to decrease with predicted 

climate change. Improved water holding capacity through char additions is most 

commonly observed in coarse textured or sandy soils (Gaskin and others 2007; 

Glaser and others 2002). Just as increased surface area improves water holding 

capacity of ash deposits (Dahlgren and others 2004; McDaniel and Wilson 2007), 

the impact of bio-char additions on moisture content may be due to increased 

surface area relative to that found in coarse textured soils (Glaser and others 2002).

The residence time of bio-char in soils may be in excess of 1000 years making 

it a potential tool for carbon sequestration. Bio-char consists of highly aromatic 

organic material having carbon concentrations of 70 to 80 percent (Lehmann and 

others 2006), and it is highly resistant to decay by common soil saprophytes. Evidence for the residence time of bio-char comes from several lines of research. Fire 

ecology typically makes use of the long residence times of bio-char in dating fire 

events through the latest interglacial period (Agee 1996). Archeologists similarly 

have demonstrated the use of coppiced woodlands for prehistoric metallurgy by 

dating the charcoal remains of historic operations back some four millennium 

(Rackham 1980). Furthermore, the rich Terra Preta soils produced through charcoal additions by a poorly understood Amazonian society occur in a matrix of 

highly weathered tropical Oxisols (Mann 2008). These soils were developed over 

2000 years ago as the agricultural basis of this sophisticated society and are still 

regarded today as high quality top-soils with charcoal as the vital component 

(Glaser and others 2001).

The potential to sequester carbon by char additions to soils creates an important 

possibility to mitigate greenhouse gas emissions. This idea is not new (Seifritz 

1993), but has recently gained interest with greater public awareness of the effect 

of greenhouse gas emissions on climate. The portable pyrolysis units at scattered 

locations throughout the forest may create greater opportunity to sequester carbon 

than pyrolysis conversion at a centralized plant. For large fixed-location pyrolysis 

plants, the economic incentive to return bio-char back to the woods is low because 

of high transport costs and alternative uses for filtration, clean energy, cooking, 

horticulture, etc. Furthermore, biomass moved from the woods is just as likely 

to be used by any number of other processes in addition to pyrolysis including 

fueling industrial boilers where char would not be a significant byproduct. From 
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a forest management perspective, the preferred use for bio-char may not be for 

transport to alternative use markets, but as an on-site soil amendment. Bio-char 

reapplication represents the middle ground that might make biomass utilization 

a reality.

Conclusion

The portable pyrolysis system offers a solution to biomass accumulation in 

forest ecosystems. By utilizing the abundant forest biomass that is annually 

produced through forest harvest residues and hazard fuel reduction projects it 

may be possible to produce a liquid fuel that will reduce dependence on foreign 

energy sources. If biomass conversion can occur in the woods it will improve the 

economic and environmental impact of biomass utilization for energy production. In addition, the bio-char byproduct can be redistributed to the site of energy 

extraction and thereby return nutrients to the site to maintain site quality. The 

additional properties of char additions, including liming, microbial enhancement 

and improved water holding capacity, create the opportunity to maintain or improve soil quality. Furthermore, bio-char’s recalcitrance can sequester carbon for 

centuries. Such an approach is advocated for agricultural systems (Laird 2008; 

Lehmann and others 2006), but the arguments are even stronger for portable 

pyrolysis units used in forestry systems where long distances make onsite reapplication a better option than long-distance transport of biomass to and return of 

char from a centralized processing facility.

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
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In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

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1 Pacific Northwest Research Station, 

USDA Forest Service, LaGrande, OR.

2 Forest Products Laboratory USDA 

Forest Service, Madison, WI.

Ethanol Production from Woody 

Biomass: Silvicultural Opportunities 

for Suppressed Western Conifers

Andrew Youngblood1, Junyong Zhu2, and C. Tim Scott2

Abstract—The 2007 Energy Security and Independence Act (ESIA) requires 16 billion 

gallons of ethanol to be produced from lignocellulose biomass by 2022 in the United 

States. Forests can be a key source of renewable lignocellulose for ethanol production 

if cost and conversion efficiency barriers can be overcome. We explored opportunities 

for using woody biomass from thinning western conifers as source materials for conversion to biofuel. We present preliminary results using suppressed lodgepole (Pinus

contorta Douglas ex Louden var. latifolia Engelm. ex S. Watson) and ponderosa pine 

(Pinus ponderosa C. Lawson var. ponderosa) from Pringle Falls Experimental Forest 

in central Oregon. We first examined growth rates of suppressed and presumably unsuppressed lodgepole pine and ponderosa pine planned for removal during thinning 

operations, and determined that all sampled trees were equally suppressed. We found 

component polysaccharides in relatively high concentrations among all sample trees. 

Finally, we used a new sulfite pretreatment technique for biochemical conversion of 

lignocellulose in wood chips from our sampled lodgepole pine and ponderosa pine 

to ethanol, and discuss the efficacy of the sulfite pretreatment in terms of dissolved 

carbohydrate composition, hydrolysis reaction rates, and sugar yield after hydrolysis. 

Using biomass from forest thinning to make biofuel may help mitigate the cost of fuel 

reduction treatments and potentially offset the cost of sustaining healthy forests and 

reducing the risk of catastrophic fires.

Introduction

In the United States, grain-ethanol, a biofuel, is mainly produced from the 

starch in kernels of field corn. Ethanol production from starches is limited by 

supplies of agricultural crops and costs associated with production. Currently, 

grain ethanol supplies 3 percent of US demand for fuel (about 4.5 billion gallons 

of ethanol produced annually and about 140 billion gallons of fuel used for ground 

transportation annually) (Somerville 2006). Using lignocellulose as biomass to 

produce ethanol could contribute to meeting US transportation fuel demands and 

help the US to achieve its goal of energy independence. Forests can be a key source 

of renewable biomass feedstock for ethanol production if cost and conversion 

efficiency barriers can be overcome. Using woody biomass in the form of small 

diameter trees removed during thinning projects as source materials for biofuel 

may mitigate the cost of thinning treatments and potentially offset the cost of 

sustaining healthy forests and reducing the risk of catastrophic fires.

The biochemical conversion of lignocellulose to ethanol typically consists of 

four major unit operations: pretreatment/size reduction, enzymatic hydrolysis, 

fermentation, and product separation/purification. Most pretreatment processes 

are designed to alter the structure of biomass cellulose by increasing the accessibility of cellulose to enzymes that convert the carbohydrate polymers into 
170 USDA Forest Service Proceedings RMRS-P-61. 2010.

Youngblood, Zhu, and Scott Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers

fermentable sugars (Lynd 1996). The pretreatment stage can account for as much 

as 30 percent of the total production cost in the conversion of cellulosic biomass 

to ethanol (Aden and others 2002). Despite extensive research and development, 

limited progress has been achieved in the economic development of an effective 

pretreatment method for woody biomass, especially conifer species.

A novel pretreatment process under development at the Forest Products Laboratory has the capability of removing woody biomass recalcitrance (the natural 

resistance of plant cell walls to decomposition from microbes and enzymes) for 

subsequent hydrolysis to glucose (Zhu and others 2009a). This process utilizes a 

Sulfite Pretreatment to Overcome the Recalcitrance of Lignocellulose (SPORL). 

The SPORL pretreatment can be applied directly to wood chips in a digester 

providing a low liquid to wood ratio of 3 or less in aqueous pretreatment to 

significantly reduce thermal energy requirements (Zhu and others 2009b). After 

pretreatment, size reduction by disk milling results in a coarse wood pulp at 

greatly reduced specific energy levels (~50 W-hr/kg) (Zhu and others 2009b). 

Furthermore, the resulting wood substrate has excellent digestibility in subsequent enzymatic hydrolysis. This SPORL pretreatment, however, has yet to be 

evaluated with western conifers.

Recent work at the Forest Products Laboratory indicated that red pine (Pinus

resinosa Aiton), grown at high stand density, produced wood with higher concentrations of glucan and xylan (both polysaccharides) compared to wood produced 

by red pine grown at more open stand densities. This suggests that trees grown 

under suppressed conditions, especially small diameter trees in dense, even-aged 

stands, may be useful for biofuel production. In this paper, we expand this work to 

further consider two conifers, lodgepole pine (Pinus contorta Douglas ex Louden 

var. latifolia Engelm. ex S. Watson) and ponderosa pine (Pinus ponderosa C. 

Lawson var. ponderosa). Together these species represent a large proportion of 

the timber targeted for thinning in overstocked stands throughout the western US.

The overall goal of this study was to evaluate the potential of western conifers, 

particularly trees targeted for thinning, for biochemical conversion to biofuel. We 

used the SPORL pretreatment process, disk milling, and enzymatic hydrolysis to 

determine the effective yield of glucose from wood substrate samples. Specific 

objectives of the study were:

1. Quantify the growth rate of lodgepole pine and ponderosa pine from areas 

selected for thinning.

2. Relate the growth rate to the chemical composition of lodgepole pine and 

ponderosa pine.

3. Quantify the efficacy of the SPORL pretreatment process when combined 

with enzymatic hydrolysis for lodgepole pine and ponderosa pine chips.

In this paper, we introduce the study area, and present preliminary results in 

quantifying growth rates, relating the growth rates to chemical composition, and 

quantifying cellulose conversion to glucose through enzymatic hydrolysis.

Methods

Study Area

Wood was obtained from Pringle Falls Experimental Forest (lat. 43°42’ N, long. 

121°37’ W). Pringle Falls Experimental Forest lies within the Deschutes National 

Forest in central Oregon about 48 kilometers southwest of Bend, Oregon, and 

was established in May 1931 as a center for silviculture, forest management, and 

insect and disease research in ponderosa pine forests east of the Oregon Cascade 
USDA Forest Service Proceedings RMRS-P-61. 2010. 171

Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers Youngblood, Zhu, and Scott

Range. The entire experimental forest is addressed in the Deschutes National 

Forest Management Plan as a single management area, with Forest-level standards 

and guides applied when the management activities do not conflict with research.

The 1430 ha Lookout Mountain Unit of the experimental forest was added to the 

Pringle Butte Unit in 1936. The Lookout Mountain Unit lies on an ancient shield 

volcano with an upper elevation of 1900 m above a relatively flat ancient lakebed 

surface at 1300 m. Currently, this relatively large block of closed-canopy forest has 

undergone little major disturbance since stand-replacement fires occurred in 1845 

and 1890, resulting in the establishment of dense lodgepole pine and ponderosa 

pine at lower elevations and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), 

grand fir (Abies grandis Douglas ex D. Don) Lindl.), white fir (Abies concolor 

(Gord. & Glend.) Lindl. ex Hildebr.), sugar pine (Pinus lambertiana Douglas), 

western white pine (Pinus monticola Douglas ex D. Don), and mountain hemlock 

(Tsuga mertensiana (Bong.) Carrière) at higher elevations. These stands may 

represent some of the most productive ponderosa pine sites in central Oregon. 

Limited low thinning occurred in 1969 when the road network was established. 

Lookout Mountain is the site of limited past research, but several silvicultural 

studies continue as long-term projects with regional and national significance, 

such as the Lookout Mountain installation of the Levels-of-Growing Stock study 

(Oliver 2005). Compared to structural characteristics of nearby old-growth stands 

(at least 600 years old) (Youngblood and others 2004), ponderosa pine in the 

Lookout Mountain stands are relatively young, have grown exceptionally well but 

have declined in radial growth over the past several decades, and currently have 

structural characteristics that place them at imminent risk of catastrophic loss 

to wildfire or bark beetles. Thinning and fuel reduction treatments are planned 

for 1413 ha of Lookout Mountain to restore tree vigor, reduce susceptibility to 

insect infestation, and reduce the risk of stand-replacement wildfire (Youngblood 

2009). Wood for this study came from trees targeted for removal during thinning 

treatments. The study area is similar in environmental characteristics and stand 

structures to other low elevation ponderosa pine forest-dominated landscapes 

throughout the pumice zone of eastern Washington, central Oregon, and northern 

California.

Field Methods

Lodgepole pine and ponderosa pine wood samples were collected from two 

stands near the lower elevation of Lookout Mountain (1370 meters above sea level). 

In each case, an initial assessment was conducted to determine the diameter distribution of live trees. The plant community at both sample sites is best described 

as belonging to the ponderosa pine/bitterbrush-snowbrush/sedge plant association 

(CPS312) (Volland 1985). Soils are well-drained, relatively undeveloped, and 

derived from aerial deposits of pumice or scoria flow that exceed 2 m in depth 

after the explosion of ancient Mount Mazama. Surface horizons are loamy coarse 

sand to fine sandy loam. The closest weather station is 40 m lower in elevation 

at Wickiup Dam, OR (latitude 43°41’N, longitude 121°41’W), about 11 km to the 

southwest, with continuous records since 1941. The mean annual temperature at 

Wickiup Dam is 6.3 °C, and the mean annual precipitation totals 534 mm, mostly 

as snow during winter or rain during early summer convection storms.

Measurements of stand structure were conducted in circular 0.04-ha plots, 

with two plots at each sampling site. Trees selected for growth analysis and 

wood chemistry sampling were between about 10 and 36 cm diameter at breast 

height (1.37 m), were representative of the lower 75 percent of the stand diameter 

distribution, had low crown ratios and low to mid canopy positions, and were 

presumed to be suppressed. In addition, a single tree of both species with large 
172 USDA Forest Service Proceedings RMRS-P-61. 2010.

Youngblood, Zhu, and Scott Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers

diameter, high crown ratio, and dominant or codominant crown position was selected to represent relatively unsuppressed growth. A total of 13 lodgepole pines 

and 16 ponderosa pines were felled by hand using Deschutes National Forest fire 

crews during the last half of June, 2008. Trees were delimbed, total bole length 

measured, and then the bole was bucked into sections. From each tree, a 10‐cm 

thick cross-sectional disk was cut from immediately below breast height, and a 

71‐cm short bolt was cut from below this cross-sectional disk. Additional 71‐cm 

short bolts with minimum small-end diameter greater than 10 cm were cut at 

6.1, 12.2, 18.3, and 24.4 m on all trees. All short bolts and cross-sectional disks 

were marked with numbered metal tags for identification. All short bolts were 

wrapped in plastic to minimize drying, and were shipped to the Forest Products 

Laboratory in Madison, WI, for analysis.

Stand Structure and Tree Growth

Stand structure was characterized by basal area, stem density, diameter class 

distribution, stand density index, and tree height. Basal area was computed 

separately by live and dead trees for each species as a summation of the crosssectional areas of all trees equal to or greater than 1.37 m in height. Trees were 

grouped into 10 cm diameter size classes to give size-frequency distributions 

for each species. Stand density index (SDI) is a relative density measure based 

on the relationship between mean tree size and number of trees per unit area in 

a stand (Reineke 1933). This statistic has proved useful for quantifying relative 

density across a wide variety of stand conditions because it is independent of site 

quality and stand age (Long and Daniel 1990). We used the individual tree summation approach rather than the more easily applied but biased approach based 

on uniform diameter classes (Woodall and others 2003). Because the diameter 

distribution was unknown or was not normal, SDI was calculated as a summation 

of individual tree values as:

SDI = ∑(

T⁄

A)(dbhi⁄10)b,

where SDI is stand density index, T/A is trees per acre, dbhi is diameter in inches 

at breast height of the ith tree in the plot, and the exponent b is a species-specific 

value (Shaw 2000). Values of the exponent b were 1.77 for ponderosa pine and 1.74 

for lodgepole pine (Cochran and others 1994). For comparison, SDI at full stocking 

is 365 for ponderosa pine and 277 for lodgepole pine (Cochran and others 1994).

The cross-sectional disks cut at breast height were sanded with increasingly 

fine grit sandpaper (#150, 220, and 320) to clarify ring structure, and annual 

ring widths measured by two people under a binocular microscope by using a 

Velmex measuring stage with an AccuRite linear encoder (resolution 0.001 mm) 

with a Metronics digital readout unit coupled to a computer with MEASURE 

J2X software (version 4.1.2). On each cross-sectional disk, ring widths along the 

largest radii and the radii opposite were measured. Crossdating and measurement 

quality control were completed for each radii by using the computer program 

COFECHA version 6.06 (Holmes 1986) (accessed at the Tree-Ring Lab, LamontDoherty Earth Observatory, Columbia University). Undated ring width series 

were correlated with a master dated series (Lookout Mountain Lower, OR046; 

downloaded from the International Tree-ring Data Bank, World Data Center for 

Paleoclimatology) collected in 1995 from an adjacent ponderosa pine old-growth 

stand for identifying pandora moth outbreak frequency (Speer and others 2000) 

with emphasis on the most recent century. For example, the OR046 chronology 

documented exceptionally low radial growth in 1930 and 1995 associated with 

insect herbivory, and also low radial growth in 1977.

Trees growing at a constant rate under uniform environmental conditions 

will increase in diameter by producing increasingly smaller growth rings as 
USDA Forest Service Proceedings RMRS-P-61. 2010. 173

Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers Youngblood, Zhu, and Scott

the circumference of the bole increases, and this pattern of ring widths is easily 

modeled as a negative exponential growth curve. All tree ring width series were 

standardized in the R statistical programming environment (dplR) (Bunn 2008) 

by first fitting each ring width series to the model:

Gt = ae–bt + k,

where the growth trend Gt is estimated as a function of time t with coefficients 

a, b, and k. Annual values from each ring width series were then divided by the 

corresponding value of the growth trend Gt. The resulting ring width indices have 

no residual age effect, a mean of 1.0, and equal variances. Finally, ring-width 

indices were averaged across trees from each site to build a common chronology. Deviations above or below this standardized index represent periods when 

growth was influenced by exogenous factors including climate, insect herbivory, 

and density-influenced suppression. While climate and insect herbivory may 

cause short-term changes in radial growth, suppression is more chronic. Ring 

width indices were examined for periods when growth indices were ≤ 0.9 for at 

least 5 years. Descriptive statistics were used to compare suppressed and reputed 

unsuppressed chronologies. These statistics include the mean ring width, mean 

sensitivity (MS), the first-order serial autocorrelation (AC) to detect eventual 

persistence retained after the standardization, the mean correlation between 

trees (r

–

wt) and within trees (r

–

wt), and the expressed population signal (EPS) to 

estimate the amount of year-to-year growth variations shared among trees of the 

same chronology (Briffa and Jones 1990; Wigley and others 1984). The EPS is 

based on the mean correlation between all tree-ring series included in the chronology and increases from zero to one with sample size and with the strength of 

the mean correlation.

Growth Analysis and Wood Chemistry

Thin disk samples were cut from each short bolt, oven dried, and sanded 

to expose the ring structure. A representative pith-to-bark wedge sample with 

companion strip (fig. 1) was then split from each disk for analysis of chemical 

composition and growth characteristics, respectively. Strips were then progressively sanded with 600-, 800-, 1000-, 1200-, and 1500-grit sandpaper and polished 

on clean copier paper. To determine the growth characteristics of each strip, 

portions were viewed under a stereo-microscope at various magnifications to 

measure rings, and in some cases, individual tracheids. Once the growth characteristics were known, the wedge samples were split into sections of relatively 

uniform growth by first scoring the row of earlywood cells with a razor blade at 

the beginning of the section of interest and then fracturing the section from the 

remainder of the wedge (fig. 2). This technique resulted in a very clean fracture 

between the latewood band of the previous section and the earlywood band of the 

next section. The fractured sections were then chipped into smaller fragments with 

a sharp chisel and chopped into 20-mesh particles in a Wiley Mill in preparation 

for chemical analysis. The samples were first disintegrated in concentrated sulfuric acid and then centrifuged to produce an aliquot that could be injected into 

a Dionex ion chromatograph with pulsed amperometric detection to determine 

polysaccharide composition (Davis 1998).

To date, analysis of growth and chemical composition has been conducted on 

only a subset of short bolts selected to represent extreme differences between trees. 

For lodgepole pine, three short bolts with the smallest breast-height diameters 

were analyzed in addition to the reputed “unsuppressed” tree. For ponderosa pine, 

three samples with the smallest breast-height diameters, one additional sample 

with larger diameter yet a discrete and extreme period of suppressed growth, and 

the short bolt from the reputed “unsuppressed” tree were analyzed.
174 USDA Forest Service Proceedings RMRS-P-61. 2010.

Youngblood, Zhu, and Scott Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers

Figure 1—Breast height wedge section with companion strip removed from the reputed unsuppressed 

tree in a ponderosa-dominated stand sampled for biofuel potential at Lookout Mountain, Pringle Falls 

Experimental Forest, central Oregon.

Figure 2— Breast height wedge section, divided into separate parts based on radial growth patterns, in 

preparation for polysaccharide composition analysis of a reputed unsuppressed tree in a ponderosadominated stand sampled for biofuel potential at Lookout Mountain, Pringle Falls Experimental Forest, 

central Oregon.
USDA Forest Service Proceedings RMRS-P-61. 2010. 175

Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers Youngblood, Zhu, and Scott

Enzymatic Cellulose Conversion

Short bolts were hand debarked and chipped. The wood chips were screened, 

retaining only particles greater than 6 mm and less than 38 mm in length to ensure smooth operation in disk milling. The thickness of the chips ranged from 

3 to 8 mm.

The wood chips were conditioned to 30 wt% solids, then loaded into a laboratory batch digester (capacity 23 liters) and subjected to a SPORL pretreatment at 

180 °C for 30 minutes. An 8 percent sodium sulfite charge on wood (dry basis) 

was added to the pretreatment liquor for an initial sodium sulfite concentration 

of 2.67 percent. A second pretreatment was conducted in more acidic conditions 

(pH 1.9) by adding sulfuric acid to the liquor at a dosage of 2.21 percent on wood 

(dry base) or acid concentration of 0.4 percent (volume-to-volume). After the 

SPORL pretreatments, the chips were directly fed into a 305 mm atmospheric disk 

refiner (plate pattern D2B-505, Andritz Sprout-Bauer, Springfield, OH) for fiberization. The chemical compositions of the untreated wood and pretreated wood 

substrates were analyzed using an improved high-performance anion exchange 

chromatograph with pulsed amperometric detection (HPAEC-PAD) (Davis 1998). 

Reported data averages of duplicate measurements conducted three weeks apart.

Enzymatic hydrolysis was conducted using commercial enzymes at 2 percent 

of substrate solid (weight-to-volume percent) in 50-mL sodium acetate buffer using a shaker/incubator (Thermo Fisher Scientific, Model 4450, Waltham, MA) at 

200 rpm. The pH and temperature were adjusted to 4.8 and 50 °C, respectively. 

A mixture of Novozyme Celluclast 1.5 L cellulose with an activity loading of 

approximately 15 filter-paper units per gram (FPU/g) substrate and Novozyme 

188 (β-glucosidase) cellobiase with an activity loading of approximately 22.5 

cellulose-binding module per gram (CBU/g) substrate were used for enzymatic 

hydrolysis1. Hydrolysates were sampled periodically for glucose analysis using 

a Glucose Analyzer (YSI 2700S, YSI Inc., Yellow Springs, OH).

Results

Stand Structure and Tree Growth

Both the lodgepole pine stand and the ponderosa pine stand were overstocked, 

with high density, high SDI, and high basal area (table 1). These values are 

consistent with the outcome of more extensive field sampling of ponderosa pine 

stand structure throughout the Lookout Mountain Unit2, indicating that basal area 

generally ranged from 34.4 to 41.3 m2 ha–1 and SDI generally ranged from 223 to 

257. Cochran and Barrett (1999) showed that continued mortality in ponderosa 

pine from mountain pine beetle occurred when the SDI was above 240, and set 

an upper management zone (UMZ) for SDI to sustain healthy stand conditions 

for forest stands based on the dominant plant association. Upper management 

zones defined specifically for the Deschutes National Forest represent the level of 

stocking or relative density beyond which there is imminent risk of catastrophic 

loss of overstory trees to bark beetles3. Values for the UMZ for the lower portion 

of Lookout Mountain containing the lodgepole pine and ponderosa pine stands 

1 Enzymes were generously donated by Novozymes (Franklinton, NC).

2 Data on file, LaGrande Forestry Sciences Laboratory, LaGrande, OR.

3 Booser, J, and J. White. Calculating maximum stand density indexes (SDI) for Deschutes National 

Forest plant associations. Unpublished paper, on file, Deschutes National Forest.
176 USDA Forest Service Proceedings RMRS-P-61. 2010.

Youngblood, Zhu, and Scott Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers

Table 1—Structural attributes of density, mean diameter at breast height, stand density 

index, and basal area by species in two stands sampled for biofuel potential at Lookout 

Mountain, Pringle Falls Experimental Forest, central Oregon. 

	 Site	 Density	 Diameter	 Stand density index	 Basal area

stems ha–1 cm m2

 ha–1

Lodgepole stand

Live lodgepole pine 375 20.0 109.2 13.72

Dead lodgepole pine 50 12.6 21.2 2.74

Live ponderosa pine 538 14.6 132.5 18.58

Dead ponderosa pine 87 2.8 2.7 0.26

Total 1050 16.7 265.6 35.28

Ponderosa stand

Live lodgepole pine 112 8.2 15.8 2.02

Dead lodgepole pine 0 0 0 0

Live ponderosa pine 938 18.3 293.5 39.97

Dead ponderosa pine 100 6.6 4.5 0.44

Total 1150 16.3 313.8 42.44

sampled in this study fall between an SDI of 102 and 156, thus these two stands 

are 225 percent above the UMZ and are in imminent risk of complete overstory 

mortality from bark beetles.

Sampled diameter distributions for both stands had steeply decreasing or reverse 

J shapes, indicating that these stands were relatively young and individual trees 

were experiencing strong competition for space and resources (fig. 3). Diameter 

distributions and stem densities for both stands did not resemble diameter distributions and stem densities of nearby old-growth ponderosa pine stands (Youngblood 

and others 2004).

Diameter-total tree height relations were consistent with relatively young stand 

structure; lodgepole pine showed steeper height growth with diameter compared 

to ponderosa pine (fig. 4). Total height of lodgepole pine likely is near the upper 

limit for this region, while ponderosa pine on similar relatively productive sites 

will reach 45 m in total height (Youngblood and others 2004).

As expected, breast-height ages in both the lodgepole pine- and ponderosa 

pine‐dominated stands were consistent with single-cohort structure originating 

from a single stand-replacement event (fig. 5). Lodgepole pine breast-height ages 

ranged from 91 to 107 years. Assuming 10 years to reach breast height, this suggests a stand replacement disturbance such as a fire and a re-establishment date 

of 1890. Ponderosa pine breast-height ages ranged from 73 to 107 years. This 

wider period of re-establishment is not unusual, especially since ponderosa pine 

seedlings are known to be more sensitive to low temperature than lodgepole pine 

in this region (Cochran 1972).

Both the lodgepole pine and ponderosa pine ring-width analysis indicated that 

the reputed “unsuppressed” trees were actually suppressed. Reputed unsuppressed 

trees had similar establishment dates, sensitivity, and first-order autocorrelation 

as suppressed trees at the same location (table 2). In addition, the chronology 

developed for the reputed unsuppressed trees showed little difference from the 

chronology developed for all other suppressed trees, as shown for lodgepole pine 

in figure 6. For lodgepole, all chronologies contained periods with less than expected growth from 1944 through 1957, from 1994 through 1998, and since 2002. 

Ponderosa pine chronologies contained extended periods with less than expected 

growth from 1960 through 1977 and from 1995 through 1999.
USDA Forest Service Proceedings RMRS-P-61. 2010. 177

Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers Youngblood, Zhu, and Scott

Figure 3—Diameter distribution of 

lodgepole pine and ponderosa pine 

at (A) a lodgepole-dominated stand and 

(B) a ponderosa-dominated stand sampled for biofuel potential at Lookout 

Mountain, Pringle Falls Experimental 

Forest, central Oregon.

Figure 4—Breast-height diameter‐total 

height distribution in a lodgepole‐dominated stand (n = 13 ) and a 

ponderosa-dominated stand (n = 16) 

sampled for biofuel potential at Lookout Mountain, Pringle Falls Experimental Forest, central Oregon.
178 USDA Forest Service Proceedings RMRS-P-61. 2010.

Youngblood, Zhu, and Scott Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers

Figure 5—Breast-height age-diameter distribution in a lodgepoledominated stand (n = 13) and a ponderosa-dominated stand (n = 

16) sampled for biofuel potential at Lookout Mountain, Pringle Falls 

Experimental Forest, central Oregon.

Table 2—Statistics of tree ring chronologies by species in two stands sampled for biofuel potential at Lookout 

Mountain, Pringle Falls Experimental Forest, central Oregon.

	 Site	 Sample size	 First year	 Ring width 	 MS	 AC	 r

–

bt r

–

wt	 EPS

mm

Lodgepole pine stand

Suppressed 24 1900 0.94 0.179 0.858 0.144 0.608 0.94

Unsuppressed 2 1905 1.59 0.132 0.918 NA 0.701 NA

All trees 26 1900 0.99 0.176 0.862 0.162 0.615 0.87

Ponderosa pine stand

Suppressed 30 1900 1.04 0.221 0.902 0.034 0.523 0.59

Unsuppressed 2 1900 1.99 0.209 0.964 NA –0.015 NA

All cores 32 1900 1.11 0.220 0.906 0.034 0.489 0.61

Mean ring width; MS, Mean sensitivity; AC, first-order autocorrelation; r

–

bt, mean correlation between trees; r

–

wt, mean correlation 

within trees; EPS, expressed population signal, a measure of the amount of year-to-year growth variations shared among 

trees of the same chronology.
USDA Forest Service Proceedings RMRS-P-61. 2010. 179

Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers Youngblood, Zhu, and Scott

Growth Analysis and Wood Chemistry

Preliminary analysis relating wood chemistry to tree growth focused on the 

reputed unsuppressed ponderosa pine. Ten discrete sections with unique growth 

characteristics were identified in the disk extracted at breast height (table 3). The 

first four sections (A through D) contained the first 17 years of growth and include 

rings formed under relatively rapid radial growth. Section E began beyond the 

apparent extractives region of heartwood and contained rings formed by rapid and 

Figure 6—Standardized ring-width chronology with 95% confidence interval (left 

axis) and sample depth (right axis) from a suppressed lodgepole pine-dominated 

stand sampled for biofuel potential at Lookout Mountain, Pringle Falls Experimental 

Forest, central Oregon. Standardized ring-width index for the “unsuppressed” tree 

is overlaid.

Table 3—Statistics of radial growth patterns by section in a reputed unsuppressed ponderosa pine sampled for biofuel 

potential at Lookout Mountain, Pringle Falls Experimental Forest, central Oregon.

	 	 Number	 Cumulative	 Section	 Cumulative	 Mean ring	 Section	 Disk	 Ring	 Growth

Section	 rings	 age 	 width	 radius	 width	 area	 area	 area	 rate

year mm mm μm cm2 cm2 % cm2yr –1

A a 6 6 23.6 23.6 3937 18 18 1.1 2.9

B 3 9 17.5 41.1 5842 36 53 2.3 11.9

C 4 13 25.4 66.5 6350 86 139 5.6 21.5

D 4 17 21.1 87.6 5271 102 241 6.6 25.5

E 11 28 49.0 136.6 4457 345 587 22.3 31.4

F 20 48 39.1 175.7 1956 383 971 24.8 19.2

G 7 55 12.7 188.4 1814 145 1116 9.4 20.7

H 11 66 12.7 201.1 1155 155 1271 10.0 14.1

I 18 84 10.5 211.6 586 137 1408 8.8 7.6

J 28 112 10.2 221.8 363 138 1546 8.9 4.9

a Section A closest to pith; section J closest to bark
180 USDA Forest Service Proceedings RMRS-P-61. 2010.

Youngblood, Zhu, and Scott Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers

consistent growth over the next 11 years, with mean ring width of 4.5 mm. This 

section likely represents normal or unsuppressed growth. Radial growth slowed 

over the next 27 years (sections F and G) and then rapidly declined. Over the next 

57 years (sections H through J), annual radial growth dropped from about 1814 

μm year–1 to 363 μm year–1 and the corresponding disk area only increased by 

27 percent. Over a period of 7 years from 1994 to 2000, annual rings averaged 

only 3 earlywood fibers and 2 latewood fibers in the radial direction. These 

sectional results are consistent with the full ponderosa pine chronology results 

presented above. Despite its outward appearance, this reputed “unsuppressed” 

tree was as suppressed as other trees selected on the basis of physical appearance 

to represent suppression.

Numerous polysaccharides, including glucan, were identified in the wood of the 

reputed unsuppressed ponderosa pine (table 4). Only modest differences in glucan 

concentration were found among the various sections of the reputed unsuppressed 

ponderosa pine (sections E through I described above). In addition, sampling of 

short bolts from higher in the same tree, representing areas of recent growth, 

failed to establish a clear trend of increasing or decreasing glucan concentration. 

Other samples, corresponding to the most recent 60 years from several trees with 

the smaller diameters and thus representing potentially the more suppressed trees 

of those sampled, also contained glucan in similar amounts.

A similar preliminary evaluation of polysaccharide composition of lodgepole 

indicated that component polysaccharides occur in similar concentrations to 

those found in ponderosa pine, with perhaps slightly higher concentrations of 

galactan and xylan.

Enzymatic Cellulose Conversion

Substrate chemical composition after SPORL pretreatment differed from 

untreated samples at pH 4.2 and pH 1.9 (table 5). For both pretreatment levels of 

pH, lignin loss was about 10 percent while most of the hemicellulose (xylan and 

mannan) was removed. Glucan loss, undesirable for our purposes, was undetected 

at the pH 4.2 level (measurement uncertainty resulted in no loss and repeated 

measurements showed consistent results), and was moderate (10 percent) at the 

pH 1.9 level. Enzymatic cellulose conversion of fiberized lodgepole pine using 

the SPORL pretreatment process was time dependent as expected (fig. 7). Preliminary results suggest that near complete cellulose conversion to glucose can 

Table 4—Composite polysaccharide composition (%) by component and glucan/carbohydrate ratio (G/C ratio) of lodgepole 

pine and ponderosa pine cross-sectional samples analyzed for biofuel potential at Lookout Mountain, Pringle Falls 

Experimental Forest, central Oregon, and reported values from other sources.

Component composition

Ash	 Lignin	 Arabinan	 Galactan	 Glucan	 Xylan	 Mannan	 Carbohydrates	 Yield	 G/C ratio

- - - - - - - - - - - - - - - - - - - - - - - - - - -percent - - - - - - - - - - - - - - - - - - - - - - - - - -

Lodgepole pine 0.0 27.9 1.6 2.1 42.5 5.5 11.6 63.4 91.1 0.67

Ponderosa pine 0.0 26.9 1.8 3.9 41.7 6.3 10.8 64.6 91.4 0.65

Aspen1 23.0 0.0 0.0 45.9 16.7 1.2 63.8 86.8 0.72

Corn stover2 13.3 19.7 2.1 0.8 34.0 19.5 0.5 56.9 89.9 0.60

Hybrid poplar2 1.0 23.5 0.5 0.7 43.7 16.6 2.8 64.3 88.8 0.68

Switch grass2 5.8 19.5 2.9 1.2 33.6 23.1 0.4 61.2 86.5 0.55

Wheat straw2 10.2 16.9 2.4 0.8 32.6 19.2 0.3 55.3 82.4 0.59 

1 Wang et al., 2009. 

2 DOE. 
USDA Forest Service Proceedings RMRS-P-61. 2010. 181

Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers Youngblood, Zhu, and Scott

Table 5—Chemical composition of lodgepole pine wood, untreated and pretreated using a Sulfite Pretreatment to 

Overcome the Recalcitrance of Lignocellulose (SPORL) process at two initial levels of pH, evaluated for biofuel 

potential as suppressed trees sampled at Lookout Mountain, Pringle Falls Experimental Forest, central Oregon.

Component weight

Liquor		 Klason

Pretreatment	 Initial	 Final	 lignin	 Arabinan	 Galactan	 Rhamnan	 Glucan	 Xylan	 Mannan	 Sum	 Total

- - - - pH - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - g - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Untreated 27.09 1.56 2.23 0.07 42.55 6.93 10.99 91.42 100.0

SPORL 4.2 2.5 25.26 0.0 0.02 0.0 46.03 1.80 0.86 73.97 76.9

SPORL 1.9 1.5 25.43 0.01 0.05 0.0 38.19 0.32 0.25 64.25 66.7

Figure 7—Mean time-dependent enzymatic cellulose conversion (%) (left axis) and 

glucose yield (wt % wood) (right axis) from substrates of lodgepole pine pretreated using 

a Sulfite Pretreatment to Overcome the Recalcitrance of Lignocellulose (SPORL) process 

to evaluate biofuel potential of suppressed trees sampled at Lookout Mountain, Pringle 

Falls Experimental Forest, central Oregon.

be achieved in 48 hours at an enzyme dosage of 15 FPU/g substrate (equivalent to 

about 25 FPU/g cellulose). Considering an initial glucan content of about 42 percent in untreated chips, the SPORL pretreatment, with subsequent disk milling 

and enzymatic hydrolysis, produced high glucose yields from the lodgepole pine 

chips, suggesting that highly suppressed lodgepole pine could be a viable source 

of lignocellulosic biomass for conversion to biofuel.

Discussion

In this work, we explored opportunities to use small diameter lodgepole pine 

and ponderosa pine, scheduled for removal as part of a landscape-scale thinning 

and fuel reduction project in Pringle Falls Experimental Forest, as biomass to 
182 USDA Forest Service Proceedings RMRS-P-61. 2010.

Youngblood, Zhu, and Scott Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers

support biofuel production. Using dendrochronological techniques, we confirmed 

that trees selected in the field representing suppressed crown classes were severely suppressed based on the criterion of reduced diameter growth for at least 

five years. In addition, we found that both the reputed “unsuppressed” lodgepole 

pine and the reputed “unsuppressed” ponderosa pine were also suppressed. This 

limited our comparison of glucose conversion.

Analysis of polysaccharide components was limited to a portion of our ponderosa pine and lodgepole pine samples. While subtle differences were detected 

among the samples, the apparent differences that were expected to occur in relation to bole height were not readily distinguishable. Overall, we found similar 

amounts of glucan in the selected wood samples. The amounts we found suggest 

that both lodgepole pine and ponderosa pine, when suppressed, contain sufficient 

glucan and other polysaccharides that conversion to biofuel is possible.

Pretreatment is one of the most expensive operations in the conversion of 

cellulosic biomass to fermentable sugars. Other pretreatment processes have 

yet to effectively overcome recalcitrance of woody biomass, especially conifer 

biomass, and thus fail to enhance the conversion of cellulose to glucose through 

enzymatic saccharification. For example, weak acid pretreatment can achieve only 

about 40 percent cellulose conversion to glucose (Wyman and others 2009; Zhu 

and others 2009a). Ammonia pretreatment is not effective for biomass with high 

lignin content (Sun and Cheng 2002; Wyman and others 2009). Steam catalyzedsteam explosion produced slightly better conversions of cellulose to glucose (up 

to about 70 percent) with greater energy demands (Tengborg and others 2001; 

Wyman 2009). Organosolv pretreatment can result in over 90 percent enzymatic 

cellulose conversion to glucose, but hemicellulose recovery from the solvent is 

low. The economic production of ethanol from woody biomass using organosolv 

process requires that high value lignin products be developed and marketed. A 

proposed $80 million ethanol demonstration plant in Colorado that would use 

the organosolv process was recently terminated due to the instability of energy 

prices and uncertainty in the capital markets, before markets for lignin products 

could be developed. In contrast, pretreatment of lodgepole pine and ponderosa 

pine chips with the SPORL proved both cost and time effective. The SPORL pretreatment resulted in excellent conversion of cellulose to glucose as demonstrated 

in this study, has excellent fermentable hemicellulose sugar recovery (Zhu and 

others 2009a), low energy consumption (Zhu and others 2009b), and commercial 

scalability. We believe the SPORL pretreatment offers an enormous potential for 

cellulosic ethanol production using small diameter woody biomass resulting from 

forest thinning and fuels reduction activities.

While field corn currently is the most common source of biofuel, our preliminary results indicate that the vast area in the western US currently supporting 

high density stands of lodgepole pine and ponderosa pine could contribute to 

energy demands by providing sources of woody biomass for conversion to biofuel. 

Lodgepole pine and ponderosa pine occur across the western US at low elevations as dry forests; few areas in the western US have the potential to support 

production of field corn at the same extent. Woody biomass in these stands has 

far greater mass per unit area than field corn, thus transportation and processing 

costs should be evaluated in more detail. Further, field corn is harvested within a 

relatively short window of time and requires storage prior to processing. Woody 

biomass is relatively easy to store, and could be harvested from western forests 

and transported throughout the year, thus providing greater stability in workforce 

demands and equipment needs. Finally, the SPORL pretreatment process and 

other technological advances have removed any remaining technological barrier 

to bioconversion of woody biomass to biofuel.
USDA Forest Service Proceedings RMRS-P-61. 2010. 183

Ethanol Production from Woody Biomass: Silvicultural Opportunities for Suppressed Western Conifers Youngblood, Zhu, and Scott

Although the two western conifers tested in this study proved capable of contributing to ethanol production through SPORL pretreatment technology, major 

research and development will be needed before silvicultural treatments with 

ethanol objectives might be implemented. First, additional effort is essential to 

ensure that the technology, developed with relatively small digesters and incubators, is scalable in terms of sugar and ethanol yields and chemical and enzyme 

dosages. Pilot or full-scale production facilities are needed to confirm that 

energy savings associated with wood size reduction observed in our laboratory 

study can be expected across a range of commercial facilities. The commercial 

marketability of dissolved lignosulfonate, as a byproduct of the SPORL process, 

is largely unknown. While the technology builds on existing equipment, technology, and infrastructure that have long been used in the pulp and paper industry, 

this infrastructure is not readily available throughout much of the western US 

where lodgepole pine and ponderosa pine occur. In addition, operational-scale 

ethanol production would likely require a similar scale logging infrastructure, 

yet throughout much of the western US the logging infrastructure has disappeared over the past decade. Finally, our work is limited to two tree species, and 

much of their distribution in the western US is on federally administered lands. 

Management of forests dominated by lodgepole pine and ponderosa pine on these 

public lands for ethanol conversion must be balanced with management of other 

resource objectives.

Acknowledgments

We thank Kevin Stock and fire crews on the Deschutes National Forest for 

assistance in collecting wood samples, and Kerry Lackey for logistical support. 

We acknowledge the Analytical Chemistry and Microscopy Laboratory at the 

US Forest Service, Forest Products Laboratory for carrying out the carbohydrate 

analyses. We also acknowledge Mr. W. Zhu, a visiting graduate student from 

South China University of Technology, for carrying out SPORL pretreatment, 

substrate production, and enzymatic hydrolysis. Financial support of this research 

included the U.S. Forest Service Program of Woody Biomass, Bioenergy, and 

Bioproducts (2008).

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
USDA Forest Service Proceedings RMRS-P-61. 2010. 185

Part 3: Alternative 

Silvicultural Strategies

2009 NATIONAL SILVICULTURE WORKSHOP

BOISE, IDAHO

SILVICULTURE

CLIMATE

BIOMASS

CARBON SEQUESTRATION

USDA Forest Service Proceedings RMRS-P-61. 2010. 187

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Research Forester, USDA Forest Service, Southern Research Station.

Estimating Site Index From Tree 

Species Composition in Mixed Stands 

of Upland Eastern Hardwoods: Should 

Shrubs be Included?

W. Henry McNab1

Abstract—Site index is the most widely used method for site quality assessment 

in hardwood forests of the eastern United States. Its application in most oak 

(Quercus sp. L.) dominated stands is often problematic, however, because available 

sample trees usually do not meet important underlying assumptions of the method. A 

prototype method for predicting site index from tree species composition has shown 

promising results in the southern Appalachian Mountains. The objective of this 

study was to determine if upland oak site index was associated with two common 

understory shrubs: mountain laurel (Kalmia latifolia (L) and spice bush (Lindera 

benzoin (L) Blume). Regression analysis indicated that including these shrubs in the 

model increased r2 from 0.63 to 0.67. Predicted upland oak site index decreased by 

5.4 ft if mountain laurel was present on a sample plot and increased by 7.4 ft if spice 

bush was present. Results from this exploratory study conducted in a small watershed 

should be validated elsewhere, but the findings do suggest that estimates of upland 

oak site index using the species composition method can be improved by observing 

the presence of mountain laurel and spice bush.

Keywords: mesophytic, site quality, soil moisture, upland oaks, xerophytic, yellowpoplar.

Introduction

The productive capacity of forest stands strongly influences the response to 

silvicultural treatments (Smith 1962). Except for short rotation plantation biomass 

culture, however, estimation of forest site productivity by direct measurement of 

product yields is generally not feasible. Indirect estimation of forest site quality 

using site index is the method most used in upland hardwoods (Spurr and Barnes 

1973). Beck and Trousdell (1973) provide a thorough description of the method, 

particularly its underlying assumptions and limitations. Estimation of site index 

in mixed hardwoods is often problematic, however, because suitable sample 

trees are generally lacking particularly on intermediate and lower quality sites, 

resulting in biased estimates of site quality. Also, site index is a timber measure 

and cannot be readily adapted for other applications, such as ecological studies 

or assessment of wildlife habitat. Replacement of conventional site index estimation with an alternate method is particularly desirable for ecosystems where oaks 

(Quercus sp. L.) are a dominant component of the overstory because many stands 

lack trees suitable for determination of site quality. One such alternative method 

to site index is a procedure reported by Whittaker (1956) for arraying stands on 

environmental gradients based on composition of the tree stratum.
188 USDA Forest Service Proceedings RMRS-P-61. 2010.

McNab Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included?

Forest productivity in the southern Appalachian Mountains is associated 

primarily with temperature and moisture gradients (Whittaker 1966) and some-

what with fertility. Whittaker (1966) reported that “... an index of site moisture 

conditions based on weighted averages of stand composition...” was highly 

correlated with forest production. He subdivided the topographic-soil moisture 

gradient within an elevation zone into four soil moisture classes (mesic, submesic, 

subxeric, and xeric) and assigned a weight (0, 1, 2, or 3) to each class. Each tree 

species was assigned to a soil moisture class based on its modal frequency of oc-

currence along the gradient. Whittaker used the average weight of the frequency 

of each species present >1 in diameter at breast height (dbh) as an index of the 

soil moisture conditions for a site. The index, which was a means for quantify-

ing the relative position of sites on the moisture gradient, was highly correlated 

with primary forest production for vegetative communities occupying environ-

ments ranging from xeric to mesic in the Great Smoky Mountains National Park 

(GSMNP) (Whittaker 1966). The simplicity of such a site classification system is 

appealing for several reasons: it can be applied with data typically collected from 

sample plots in a systematic inventory of stand conditions, it is easily adapted to 

other ecosystems with their associated species, and the system can be extended 

to other environmental gradients of temperature and nutrients.

Whittaker’s (1966) methods were used in a previous study (McNab and Loftis, 

in press) to array forested sites along a moisture gradient that was quantified with 

a moisture regime index (MRI) calculated from arborescent vegetation present 

on each plot. Results of that study indicated that upland oak site index was cor-

related with the MRI. Shrubs were not included in that study, but a number of 

species occur in the Southern Appalachians and appear to be associated with sites 

of varying environmental conditions (Stupka 1964). Whittaker (1966) reported 

about a dozen shrubs species were common in the GSMNP and reported the “...

distributions of undergrowth species do not appear closely related to those of 

dominant [tree] species.” The independence of tree and shrub species distribu-

tions observed by Whittaker (1966) suggests that each form of vegetation was 

responding to different environmental conditions and could account for different 

sources of variation that affect site quality. My study investigated the question: 

Are common species of forest shrubs associated with forest site quality? This 

study is an extension of a previous study of site quality that was based only on 

tree species (McNab and Loftis, in press). The purpose of this exploratory study 

was primarily to determine if additional study on this topic is warranted and not 

to develop an application for immediate use by managers.

Methods

Study Area

The study was conducted in the Bent Creek Experimental Forest; a 6,000-ac 

watershed located about 10 miles southwest of Asheville, North Carolina. This 

area is characterized by short, mild winters and long, warm summers; elevation 

ranges from 2000 to 4000 ft. Annual precipitation averages about 45 in and is 

evenly distributed throughout the year. Geologic formations consist of gneisses 

and schists of Precambrian Age that have weathered to form a complex, dissected 

land surface consisting of ridges and coves. Soils, which consist mainly of 

Ultisols with lesser areas of Inceptisols, are generally deep (>40 in), highly acidic 

(pH<5.5) and range in moisture regimes from xeric to hydric.

The arborescent canopy of xeric to subxeric sites on upper slopes and ridges 

typically consists of communities dominated by scarlet oak (Quercus coccinea
USDA Forest Service Proceedings RMRS-P-61. 2010. 189

Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included? McNab

Muenchh.), white oak (Q. alba L.) chestnut oak (Q. prinus L.), and black oak (Q.

velutina Lam.). Typical mesophytic species occurring on moist slopes and 

coves include yellow-poplar (Liriodendron tulipifera L.), northern red oak (Q. ru-

bra L.), white ash (Fraxinus americana L.), eastern hemlock (Tsuga canadensis

(L.) Carr.), cucumbertree (Magnolia acuminata L.), and black locust (Robinia pseu-

doacacia L.). Midstory species include, dogwood (Cornus florida L.), blackgum (Nyssa sylvatica Marsh.), sourwood (Oxydendrum arboreum (L.) DC.); shortleaf 

pine (Pinus echinata L.) or pitch pine (P. rigida L.) may occur on disturbed dry 

sites. Various hickory species occur on dry and moist sites. Red maple (Acer ru-

brum L.) is usually present on sites of all moisture regimes. American chestnut (Castanea dentata L.) was a major component of most stands before it was lost 

as a canopy species as a result of the introduced blight disease (Cryphonectria 

parisitica) in the 1920s; it was typically replaced by oaks (Quercus spp. L.) on 

many sites. Common shrubs of dry sites include mountain laurel (Kalmia lati-

folia L.) and deerberry (Vaccinnium staminium L.); moist sites support rosebay 

rhododendron (Rhododendron maximum L.) and spicebush (Lindera benzoin

(L.), Blume). More than 250 trees, shrubs, and vines are native to this area of the 

southern Appalachian Mountains (Stupka 1964).

Principal types of disturbance in the watershed have resulted from natural and 

human causes. Most stands in Bent Creek Experimental Forest have been affected 

by past land-use. Extensive areas of gentle slopes were cleared for agricultural purposes from 1800 until about 1900, when land abandonment resulted in conversion 

to pine-hardwood mixtures on dry sites and yellow-poplar on moist sites. Stands 

on areas of steeper slopes, which were not cultivated or cleared for pasture, were 

typically burned to promote sprouting for woodland grazing by livestock, and 

periodically harvested for timber by high grading. Following acquisition of the 

watershed by the USDA Forest Service around 1915, timber stand improvement 

work was done in selected areas to reduce stocking of non-commercial species 

such as red maple, sourwood, and dogwood. Lightning-caused fires are seldom 

a source of extensive disturbance in this humid region.

Field Plots and Vegetation Inventory

I used field data from several earlier silvicultural studies installed in the 

experimental forest for my exploratory investigation. Three requirements were 

necessary for the previously collected data to be suitable for use: (1) inventory 

of all tree and shrub species present on each plot, (2) accurate estimation of site 

index for stands on each sample plot, and (3) the sample plots represented the 

range of site qualities in the experimental forest. Field data from two previous 

studies satisfied these requirements: one study dealing with prediction of site 

index for xerophytic upland oaks and the other study to investigate growth and 

yield of mesophytic yellow-poplar after thinning. In the site index study, Doolittle 

(1957) established 114, 0.2-acre plots in even-aged stands of upland oaks rang-

ing in age from 30 to 120 years. Arborescent and shrub vegetation in the sapling 

and tree strata had been re-inventoried by species in 1970 (unpublished data on 

file). Vegetation on this group of plots is typically described as xerophytic. The 

yellow-poplar growth and yield study consisted of 34, 0.25-ac plots that had been 

established in even-aged stands, ranging in age from 30 to 76 years, for which site 

index had been determined (Beck and Della-Bianca 1970). Vegetation associated 

with this group of plots is characterized as mesophytic. I pooled the inventories 

from these two studies to obtain a data set consisting of 148 sample plots that 

extended over the range of site qualities present on upland sites in the Bent Creek 

Experimental Forest.
190 USDA Forest Service Proceedings RMRS-P-61. 2010.

McNab Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included?

Site index relationships developed by Schnur (1937) for upland oak stands 

in the central hardwood region were used as the standard measure of timber 

productivity. I converted yellow-poplar site index to upland oak site index using 

the relationship presented by Doolittle (1958). Upland oak site index overlapped 

between the two groups of plots characterized by composition of the arborescent 

vegetative community (Stupka 1964):

Group of plots (Source of data) Range of oak site index (ft)

Mesophytic (Beck and Della-Bianca 1970) 71 - 95

Xerophytic (Doolittle 1957) 36 - 87

Across all 148 plots, oak site index averaged 65 ft. The plots established for 

the growth and yield study are dominated by yellow-poplar and other mesophytic 

species and are considered representative of high to moderately productive stands 

on mesic and submesic sites. The plots established for the site index study are 

dominated by oaks and other xerophytic species and are considered representative 

of moderate to low productivity on xeric and subxeric sites.

Woody perennial vegetation on each sample plot was recorded by species in 

two physiognomic groups of plants (i.e. tree or shrub) and two size classes (i.e. 

overstory or midstory). A tree was a plant with a well-defined stem carrying a 

definite crown; shrubs were plants without a well-defined stem (Helms 1998). 

Each tree on a sample plot was recorded by species in two arbitrary size classes: 

overstory (>4.5-in dbh), or midstory (0.01 to 4.5 in dbh). Each shrub was recorded 

by species without regard to size. The number of tree and shrub species present 

on each plot was determined for each size class from the inventory data.

Moisture Regime Index

Following the rationale of Whittaker (1956) I subdivided the apparent soil 

moisture gradient on upland sites in the Bent Creek watershed into four classes: 

xeric, subxeric, submesic, and mesic. A fifth class, hydric, was not included in 

this study because the tree species of interest do not occur on soils of that mois-

ture regime. The xeric class of sites was typically associated with upper slopes of 

ridges; mesic sites occurred on lower slopes of valleys. The subxeric and submesic 

classes were on sites perceived as somewhat less dry or less moist than xeric or 

mesic, respectively. I assigned each tree species occurring in the watershed to a 

moisture regime class based on its perceived modal position (i.e. distributional 

center) along the gradient (e.g. post oak (Quercus stellata Wangenh.), xeric; 

chestnut oak, subxeric; northern red oak, submesic, etc). Soil moisture deficits 

would likely develop on xeric sites in the mid to late growing season and on sub-

xeric sites during the late season of most years. On the submesic class of sites, 

soil moisture deficits would occur occasionally during the mid to late growing 

season and on mesic sites, lack of moisture for plant growth would likely occur 

only rarely during an interval of 30 years. Each moisture class was assigned a 

weight, ranging from 1 to 4 (e.g. xeric = 1; subxeric = 2; etc), which represented 

the perceived relative availability of soil moisture for plant growth during the 

frost-free season. Moisture class weights were refined by assigning half values to 

some species where their modal position appeared to occur between two classes 

(e.g. scarlet oak = 1.5; white oak = 2.5, etc).

The combined information provided by all tree species present on a sample 

plot is used to determine the location of the site on the moisture gradient. The 

numerical location of the plot (e.g. site) on the moisture gradient, which ranges in 

magnitude from 1 to 4, is defined as the moisture regime index (MRI). The plot 

MRI is calculated as the average moisture weight of all species present on the plot:
USDA Forest Service Proceedings RMRS-P-61. 2010. 191

Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included? McNab

MRI = ∑(Speciesi * MW i + ... + speciesj * MWj) / N species

where: MRI is moisture regime index, species is each tree species present, MW is 

moisture weight value for each species (values 1 to 4), and N is number of species 

used in calculation of the index.

Because MRI can be calculated with various combinations of vegetation size 

classes and frequencies of occurrence, I used the method of calculation reported 

as best in an earlier study (McNab and Loftis, in press). I combined the midstory 

and overstory size classes for each plot and calculated MRI using tree species 

represented by more than one individual (N > 1). I evaluated the effect of shrubs 

on estimation of site index by including them as binary variables (e.g. 0 or 1) in 

regression analyses, where a species was present on ten or more sample plots.

Data Analysis

I used a completely randomized design where sample plots had been established 

previously in stands meeting criteria for study of timber productivity and without 

regard to topographic or soil characteristics.

Regression model without shrubs—Simple linear regression was used to 

determine the relationship of upland oak site index with MRI by the model:

Yi = α + βXi + ε

where Yi are observed values of upland oak site index, α and β are population 

parameters, Xi are observed values of MRI, and ε is unexplained error.

Regression model with shrubs—A Spearman rank correlation test was used 

to determine the relationship between the frequencies of occurrence of the inventoried tree and shrub species in relation to the xeric (oak dominated) plots and 

the mesic (yellow-poplar dominated) plots. T-tests were used to determine if site 

index was significantly associated with the presence of each species of shrub. 

For those shrubs significantly associated with oak site index, multiple regression 

was used to determine the combined effect of moisture regime index and shrub 

species by the relationship:

Yi = α + βXi + βZi + ε

where Yi are observed values of upland oak site index, Xi are observed values of 

MRI, Zi are species of shrubs absent or present (represented as 0 or 1), α and β 

are population parameters, and ε is unexplained error. Relative performance of 

the two types of models (e.g. site index estimated from MRI without shrubs, and 

with shrubs) was evaluated using the proportion of variation of the dependent 

variable explained by the independent variables (adjusted r2). Statistical tests of 

significance were made at the probability level of 0.05.

Results and Discussion

Thirty-one arborescent species were present with frequency of occurrence of 

n >1 stems on the 148 sample plots (table 1) and were used in calculation of MRI 

for each plot. Although dry-site and moist-site species occurred on plots of all 

moisture regimes, their frequencies of occurrence were generally consistent with 

the perceived soil moisture conditions (e.g. xerophytic oaks tended to dominate 

plots characterized by xeric and subxeric soil moisture regimes). Spearman 
192 USDA Forest Service Proceedings RMRS-P-61. 2010.

McNab Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included?

r = 0.49 (n = 31, p < 0.01), however, indicated a moderate similarity of tree species 

occurrence between the two sets of plots.

Six species of shrubs were present on the sample plots, but the principal species were mountain laurel and spice bush (table 2). There was little correlation 

between shrub species occurring on the two sets of plots (Spearman r = 0.11, n = 6, 

p > 0.05). Four shrubs were present on less than ten plots, leaving only mountain 

laurel and spice bush present with frequency judged adequate for analysis. These 

two species rarely occurred on the same plot.

Table 1—Arborescent species present (>0.05 inch dbh) on plots, moisture weight assigned 

to each species, and their frequencies of occurrence (percent of total plots sampled) 

in two studies installed previously for investigations of productivity for xerophytic and 

mesophytic vegetation types in the Bent Creek Experimental Forest.

	Common	 Scientific	 Moisture	 Plot moisture regime

	 name	 name	 weight	 Xerophytica	 Mesophyticb

- - - - - - Percent- - - - - - - -

Post oak Quercus stellata 1.0 1 0

Virginia pine Pinus virginiana 1.0 0 6

Scarlet oak Quercus coccinea 1.5 75 0

Black oak Quercus velutina 2.0 60 32

Black gum Nyssa sylvatica 2.0 32 21

Chestnut oak Quercus prinus 2.0 71 15

White pine Pinus strobus L. 2.0 0 18

Persimmon Diosporis virginiana 2.0 1 0

Sassafras Sassafras albiumn 2.0 2 15

Shortleaf pine Pinus echinata 2.0 32 9

Sourwood Oxydendrum arboreum 2.0 86 56

Southern red oak Quercus falcata 2.0 2 0

Hickory spp Carya spp. 2.3 0 62

Holly Ixex opaca 2.5 0 3

White oak Quercus alba 2.5 80 32

Red maple Acer rubrum 2.5 74 82

American beech Fagus grandifolia 3.0 3 3

Black locust Robinia pseudoacacia 3.0 37 71

Dogwood Cornus florida 3.0 42 47

Frazier magnolia Magnolia fraserii 3.0 0 6

Mulberry Morus alba 3.0 0 3

Northern red oak Quercus rubra 3.0 31 35

Black cherry Prunus serotina 3.5 0 3

Cucumber Magnolia acuminata 3.5 2 0

Eastern hemlock Tsuga canadensis 3.5 3 21

Hornbeam Ostraya virginiana 3.5 1 0

Sweet birch Betula lenta 3.5 3 74

Yellow-poplar Liriodendron tulipifera 3.5 39 100

Black walnut Juglans nigra 4.0 0 3

White ash Fraxinus americana 4.0 4 26

Yellow birch Betula lutea 4.0 0 3

a 114, 0.2-ac plots situated in stands dominated by oaks.

b 34, 0.25-ac plots situated in stands dominated by yellow-poplar.
USDA Forest Service Proceedings RMRS-P-61. 2010. 193

Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included? McNab

Site Index Model With Shrubs Excluded

Regression analysis of site index as a function of the moisture index produced 

the relationship:

Oak SI (ft) = -21.972 + 36.524*(MRI)

where SI is site index (50 years) in feet for mixed oaks and MRI is moisture 

regime index based on midstory and overstory tree species with plot density >1. 

This equation has an r2 of 0.63 and mean square error of 8.10 ft. The MRI variable was highly significant (p<0.001).

The distribution of upland oak site index in relation to moisture regime index 

without shrubs is shown in figure 1. The simple regression model fitted the field 

data reasonably well, and the pattern of residuals from the regression appeared 

to be uniformly distributed, suggesting a prediction equation with little bias. 

Table 2—Shrub species present on sample plots, and their frequencies of occurrence (percent 

of total plots sampled) in two studies installed previously for investigations of productivity of 

xerophytic and mesophytic vegetation types in the Bent Creek Experimental Forest.

	 Common	 Scientific	 Plots	 Plot moisture regime

	 namea name	 sampled	 Xerophyticb	 Mesophyticc

N - - - - - - Percent- - - - - - - -

Mountain laurel Kalmia latifolia (L.) 32 25 2

Spice bush Lindera benzoin (L., Blume) 21 6 47

Rhododendron Rhododendron maximum (L.) 2 2 0

Flame azalea R. calendulaceum (Michx., Torr.) 4 3 3

Deerberry Vaccinium stamineum (L.) 1 0 3

Black huckleberry Gaylussacia baccata (Wang., K. Koch) 6 4 0

a 95 plots had no shrubs and two or more species occurred on 13 plots. 

b 114 plots were located in stands dominated primarily by oaks.

c 34 plots were located in stands dominated primarily by yellow-poplar.

Figure 1—Observed site index measured on 148 sample plots (plotted 

points) dominated by either xerophytic or mesophytic species and 

predicted site index (dashed line) in relation to the moisture regime 

index. The inset shows residuals (actual - predicted) of predicted site 

index in relation to the moisture regime index.
194 USDA Forest Service Proceedings RMRS-P-61. 2010.

McNab Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included?

Observed site index varied by about ±20 feet from predicted values for the xerophytic plots and about ±10 feet from the mesophytic plots, however, suggesting 

that other variables might explain part of the variation in site index not accounted 

for by the tree species and their perceived association with a moisture gradient.

Site fertility could account for additional variation of site index not explained 

by moisture (Kimmins 1987), but relatively little has been reported on the effects 

of nutrients on height growth for most tree species. Gilmore and others (1968) 

found yellow-poplar height was better correlated with soil pH than nutrients. 

Carmean (1975) reported that for various species of oaks, fertility (particularly 

pH) had a stronger influence on the distribution of some species (e.g. chinquapin 

oak (Quercus muehlenbergii Engelm.) than on height growth.

Site Index Model with Shrubs Included

The two predominant shrubs were each associated with oak site index (table 3). 

Compared to plots with no shrubs, average oak site index was lower by 8.2 ft 

where mountain laurel was present (t = -3.70, P<0.003) and site index was higher 

by 19.6 ft on plots were spice bush was present (t = 7.48, P <0.001). Regression 

analysis of moisture regime index and the two significant shrub variables produced 

the following relationship:

Oak SI (ft) = 15.564 + 21.918*(MRI) - 5.432*(KALA) + 7.368 * (LIBE)

where: SI is site index (50 years) in feet for mixed upland oaks, MRI is moisture 

regime index calculated from tree species, KALA is absence (0) or presence (1) of 

mountain laurel, and LIBE is absence (0) or presence (1) of spice bush. This equation has an r2 of 0.67 and mean square error of 7.7 ft. All variables were highly 

significant (p<0.001). The relationships between observed and predicted upland 

oak site index for the two models is shown in figure 2. The practical importance 

of the shrub variables is to reduce upland oak site index predicted from MRI by 

5.4 feet if mountain laurel is present or increase site index by 7.4 feet if spice 

bush is present (fig. 3).

Table 3—Mean and SD of upland oak site index 

observed on sample plots where either mountain 

laurel, spice bush, or neither species, was present in 

the Bent Creek Experimental Forest.

	 Shrub	 Sample	 Upland oak site index

	 present	 plots	 Mean	 SD

N - - - - - - -feet - - - - - - 

None 95 66.8 11.2

Mountain laurel 32 58.6a 9.4

Spice bush 21 86.4b 8.7

All 148 67.8 13.2

aSignificantly lower compared to plots where the species was 

not present.

bSignificantly higher compared to plots where the species was 

not present.
USDA Forest Service Proceedings RMRS-P-61. 2010. 195

Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included? McNab

Figure 2—Relationships between observed and predicted upland oak site 

index for two models: (1) site index as a function of moisture regime index 

without shrubs (dotted line) and (2) site index as a function of moisture 

regime index with the occurrence of two shrubs (dashed line). Both models 

under predict site index on high quality sites (SI > 70) and over predict 

site index on lower quality sites. The diagonal line represents perfect fit 

between observed and predicted upland oak site index.

Figure 3—Relationship between moisture regime index and predicted 

upland oak site index in relation to the presence or absence of two 

species of shrubs in the Bent Creek Experimental Forest. 
196 USDA Forest Service Proceedings RMRS-P-61. 2010.

McNab Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included?

Results of this exploratory study in a small watershed suggest that both trees 

and shrubs are beneficial for estimating site index from species composition. These 

findings are not surprising because shrubs have different rooting zones compared 

to trees and may have different environmental requirements for their establish-

ment, growth, and reproduction (Kimmins 1987). Including shrubs with MRI for 

estimation of site index was an unplanned part of the overall study that resulted 

from serendipitous observations in the study area. Mountain laurel, normally a 

dry-site species, occurred as scattered plants in several yellow-poplar stands that 

had become established on sites with low site index, which were located on moist 

flood plains. This unusual composition of tree and shrub vegetation provided a 

clue to the value of non-arborescent data for assessment of site quality. A number 

of shrub species are common in this region, mostly xerophytic species, although 

several mesophytic species occur also, such as rosebay rhododendron (Rhododen-

dron maximum L.) and sweet shrub (Calycanthus floridus L.). Additional study 

is needed to determine the value of other shrubs for estimation of site quality 

based on species composition. These results should be applicable within similar 

temperature regimes of the southern Appalachian Mountains, similar to where 

my study was conducted.

Application of the method requires several considerations by the resource 

manager. First, an adequate design must be derived for adequate inventory of the 

stand. A systematic grid of sample points is recommended similar in intensity to 

that used for estimation of the timber resources. Next, the tree species list must 

be completed for the area of application. The lists of trees in table 1 and shrubs 

in table 2 are limited to species encountered in the study area, which is about a 

third of the species occurring throughout the southern Appalachian Mountains. 

Finally, moisture weights assigned to each species must be adjusted for the re-

gion in which the method will be applied. The location of some species on the 

moisture gradient could change somewhat if the area of application is near the 

limits of the natural range or if there are compensating factors for the area, such 

as temperature or fertility. For example, Whittaker (1956) assigned northern red 

oak a value weight of 4.0 for submesic in the overstory. In my area of application, 

which is somewhat lower and drier, northern red oak was assigned a value of 3.5. 

As this example illustrates, one strength of the MRI method of site classification 

is the ease by which it can be adapted and extended to other areas.

In summary, results of this exploratory study demonstrate that at least two 

species of shrubs may be useful for classification of forest site productivity in 

the southern Appalachian Mountains. More specifically, I found that upland oak 

site index is associated with the moisture regime index and the occurrence of 

either mountain laurel or spice bush. An equation based only on the moisture 

index accounted for 63 percent of the variation in oak site index on sample plots 

in the study area. Including the presence of either mountain laurel or spice bush 

accounted for an additional four percent of variation in site index. Although this 

system of site classification has been under intermittent development for over ten 

years, this is the first formal test of the method using nonarborescent species. The 

relationships with shrubs should be evaluated for application beyond the limited 

area of this test. Many shrubs occur throughout the Southern Appalachians, and 

the benefits of using other species for estimation of site index should be tested. 

Considerable additional work is needed to refine the system and particularly to 

establish a direct relationship of the moisture regime index with site quality based 

on volume increment.
USDA Forest Service Proceedings RMRS-P-61. 2010. 197

Estimating Site Index From Tree Species Composition in Mixed Stands of Upland Eastern Hardwoods: Should Shrubs be Included? McNab

References

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Forest Science 14:420-426.

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Agric. Tech. Bull. 560. 88 p.

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Knoxville, TN: The University of Tennessee Press. 186 p.

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 199

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and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

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1Forest Inventory and Analysis, USDA 

Forest Service, Rocky Mountain Research Station, Ogden, UT.

2Department of Wildland Resources, 

Utah State University, Logan, UT.

Consistent Definition and Application 

of Reineke’s Stand Density Index in 

Silviculture and Stand Projection

John D. Shaw1 and James N. Long2

Abstract—Reineke’s Stand Density Index (SDI) has been available to silviculturists for 

over 75 years, but application of this stand metric has been inconsistent. Originally 

described as a measurement of relative density in single-species, even-aged stands, it 

has since been generalized for use in uneven-aged stands and mixed-species stands. 

However, methods used to establish the maximum SDI for various forest types have 

varied widely. As a result, there are maximum SDI values for some forest types that 

do not appear to be supported by adequate analysis. This situation has led to confusion and lack of confidence in SDI among some practitioners. We describe several 

issues related to the determination of maximum SDI, and propose guidelines for future 

research and application.

Keywords: density management diagram, Forest Vegetation Simulator (FVS), Forest 

Inventory and Analysis (FIA), SDI, self-thinning, stand development

Introduction

Reineke’s Stand Density index (SDI; Reineke 1933) is a useful measure of relative density that, in silvicultural practice today, forms the basis for most density 

management diagrams (Jack and Long 1996; Newton 1997). SDI is based on the 

relationship between tree size and number, and the changes in both as a stand 

develops and self-thins as it matures. As such, SDI and density management are 

grounded in the so-called -3/2 self thinning law, although the “law” itself was 

not well-described until the 1950s and 1960s by Japanese researchers (Kira and 

others 1953; Shinozaki and Kira 1956; Yoda and others 1963). In its original form, 

the law describes the inverse relationship between the average mass of plants in 

a population and their number (Zeide 1987):

w = kN–3/2 [1]

where: w is average plant mass,

N is the number of individuals per unit area,

and k is a constant, generally varying by species.

In common forestry application, including SDI, the quadratic mean diameter is 

substituted for average mass. Although the universality of the law was affirmed 

by subsequent investigation (White and Harper 1970), it has been a source of 

controversy among some researchers (Osawa and Sugita 1989; Pretzsch and 

Biber 2005; Sackville Hamilton and others 1995; Weller 1987; Weller 1990) and 

the discussion continues today.
200 USDA Forest Service Proceedings RMRS-P-61. 2010.

Shaw and Long Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection

Although many of the issues surrounding the characteristics and use of SDI 

have been synthesized elsewhere (Shaw 2006), several issues are particularly relevant to the use of SDI in silviculture, stand characterization, and stand projection 

within the Forest Service. Some of these issues are as fundamental as a lack of 

agreement on the maximum SDI value that should be used for a particular species 

or forest type across different Forest Service Regions. Indeed, at this workshop 

Basford (this proceedings) described how the lack of a suitable maximum SDI led 

to adoption of a different, although related approach to density management in 

ponderosa pine forests of Idaho. Guidelines specified a maximum SDI of 830, when 

the appropriate maximum should not have exceeded 450 (Long and Shaw 2005).

The maximum SDI of 450, which is used in other parts of the Interior West, 

was supported by a range-wide analysis of ponderosa pine (Long and Shaw 2005). 

However, the 830 maximum used in Idaho should have been questioned much 

earlier because it can be traced back to Reineke’s (1933) original paper. Close 

examination of Reineke’s graph of ponderosa pine data reveals that the ponderosa 

pine maximum of 830 was based on fewer than two dozen data points, and in 

none of the cases did mean diameter exceed 6 inches. Surely, the persistence of 

this unsupportable value in Forest Service documentation suggests that it was 

never examined closely. Through experience, practitioners suspected there was 

a problem, but it was not caught by the research community. As a result, experiences such as Basford’s have led to diminished trust in SDI as a useful index, in 

turn causing some to abandon its use. Because of controversy in the literature and 

perceived shortcomings in practical use, other practitioners have been hesitant to 

adopt the use of SDI or density management diagrams (fig. 1).

Figure 1—Density management 

diagram for ponderosa pine. 

Analysis of data from across the 

range of ponderosa pine in the 

western United States supported 

the use of 450 as the maximum SDI 

(Long and Shaw 2005). The slope of 

the maximum SDI line is based on 

the self-thinning relationship. 
USDA Forest Service Proceedings RMRS-P-61. 2010. 201

Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection Shaw and Long

However, we have found that many of the “problems” with SDI may be traceable to inadequate data, inconsistent methods, or even the perpetuation of weakly 

supported results. From the perspective of the silviculturist, these problems 

represent a failure of the research and development community to address the 

needs of the practitioner—i.e., a lack of clarity in research leads to confusion in 

application. In this paper we highlight the most important issues and propose a 

path to resolution. The need for resolution is driven by the need for consistent 

definition and use of SDI within the Forest Service:

• Forest Service silviculturists need defensible measures of relative stand density 

to use as the basis for silvicultural prescriptions; these may be in the form 

of a maximum SDI for a given forest type or a well-constructed density 

management diagram. Research that relates certain stand characteristics, 

such as risk of insect infestation or suitability as wildlife habitat, to SDI, 

must measure stand density in a way that is consistent with the values used 

by the silviculturist.

• The Forest Vegetation Simulator (FVS) uses SDI as a driving variable during 

projections of stand development (Dixon 2003). FVS requires generalized 

rules for using SDI in models that predict stand response to changing composition, structure, and density. Values used by these models should be 

consistent, in terms of the data and analysis used, with the methods employed 

by model users—e.g., silviculturists.

• The Forest Inventory and Analysis (FIA) program has a need to assess and 

interpret stand characteristics, including relative density, in all forest types 

of the U.S. Regional inconsistencies in the characterization and use of SDI 

create difficulties with FIA analysis and reporting. The FIA program needs 

a sound basis for calculation of relative density.

Although some of these needs apply more broadly than to the Forest Service 

alone, it is important for the agency to strive for internal consistency among 

various tools and programs, such as FVS and FIA. Once this consistency is accomplished it will benefit the silviculture community at large.

The Issues

Some of the issues surrounding the use of SDI can be related back to one of 

the first questions that a silviculturist might ask in the development of a density 

management regime—“What is the appropriate maximum SDI to use for this 

stand?” This question requires a definition of the term “maximum SDI,” and 

implies some population to which it applies—i.e., the stand. The establishment 

of a maximum SDI for a particular forest also establishes the variable k for that 

type. In our experience, it is necessary to address four important issues in order 

to achieve consistent definition and application of SDI:

• Sampling methodology and error—stands at maximum SDI are rarely observed in the field, so it is necessary to understand the role of sampling in 

the estimation of maximum SDI.

• The scale issue—to what entity should a stated maximum SDI apply—the 

plot, the stand, or something else?

• Assumptions of universal slope and linearity—is the slope of the size-density 

relationship truly constant when plotted in log-log space, is it nonlinear, or 

are there other limitations to size-density?

• Lack of consistent definition—is there consistency between the data and 

methods used to determine maximum SDI and the application of the index 

in the field?
202 USDA Forest Service Proceedings RMRS-P-61. 2010.

Shaw and Long Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection

Sampling Error

Maximum SDI is generally understood to be a hypothetical limit of relative 

density that is very rarely observed in the field. However, it is common for data 

to include observations of higher SDI (sometimes much higher) than would be 

expected for well-studied forest types. These seemingly contradictory data are 

typically plot-based measurements that sample a relatively small portion of the 

population in question (i.e., the stand). One reasonable explanation for these 

“excessive” density observations could simply be that they represent a form of 

sampling error—i.e., the sample design captures a high-density portion of the 

stand but does not account for lower-density areas that affect the competitive 

status of the measured area. This possibility is related to the scale issue, which 

is discussed in more detail below.

Another possibility is that the methods used to establish maximum SDI for 

the forest type were different from the methods used to assess the stand at hand. 

In effect, the researcher who documents maximum SDI and the practitioner who 

assesses the stand are using different measurement scales. For example, consider 

a situation where the maximum SDI for a species was determined using plot-level 

measurements as the sample data. Although various methods may be used to 

establish the maximum, most methods allow for a small percentage of “outliers”—

high-density plots that are considered to be the result of sampling error—and 

establish the maximum based on the remainder of observations or some subset.

In practice, however, density is assessed at the stand level. As a result, a potential problem arises because plot-level density and stand-level density are not 

equivalent measures. This can be illustrated with a simple example. Given an 

acre of forest (representative of a single stand) that is sampled by a series of four 

plots (fig. 2), SDI can be calculated five times—once for each plot and again at 

the stand level.

Figure 2—Example of within-stand variability that results 

in widely varying SDI at the plot level. SDI is 37 percent of 

maximum at the stand level, but the SDI of individual plots may 

be nearly double that.
USDA Forest Service Proceedings RMRS-P-61. 2010. 203

Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection Shaw and Long

Note that even in a relatively homogeneous stand, there is variability in SDI 

among the plots. In this example, SDI ranges from 0 to 245 among the plots, with 

a stand average of 137. Stand-level SDI is always lower than the maximum plotlevel SDI, except in the special case that SDI is identical on all plots measured 

within a stand, because it is an average of the plot-level measurements. If we 

assume the example stand to be ponderosa pine, then plot-level SDI ranges from 

0 percent to 54 percent of maximum SDI for the forest type, with stand-level 

SDI at just over 30 percent of maximum. From a silvicultural perspective, some 

parts of the stand have open growing space, some parts are on the verge of selfthinning (which is expected to begin at around 50% of maximum SDI), and the 

stand, on average, is relatively well-stocked. How then should the silviculturist 

assess this stand, when within-stand variation suggests a need for regeneration 

in some areas, thinning in others, and the average stand condition suggests that 

no intervention is needed? We suggest that the answer partly depends on how 

well-matched the assessment methods are to the methods used to determine the 

reference condition (i.e., maximum SDI). This situation changes the issue from 

one of sampling error (accurately measuring the condition) to one of plot scale 

(measuring the same thing in research and application).

The Scale Issue

When the scale issue is examined using a large sample of plots and stands, such 

as would be used to develop maximum SDI values, differences in the characteristics of the sample become apparent. Data from the Forest Inventory and Analysis 

(FIA) program are used to illustrate the effect of using plot-level vs stand-level 

data for analysis. The FIA plot design uses four 1/24-acre subplots, arranged as 

in figure 2. In most cases, all four subplots sample a single “condition,” which 

is equivalent to a stand in most cases. Where multiple conditions (stands) occur 

across the plot footprint, the changes are mapped and the proportions of plot area 

occupied by each condition are recorded. For the purpose of this analysis, data 

from single-condition FIA plots were compiled two ways: 1) treating each subplot as a separate sample, using subplot data as the observation, and 2) averaging 

all subplots on a plot to represent a stand-level observation. The two estimates 

represent the exact same sampled area, with the plot-based data (1) having four 

times the number of observations as the stand-based data (2). The results of this 

comparison for two common forest types are shown in table 1.

Note that the measures of central tendency are relatively close for each forest 

type when the plot-level data are compared with stand-level data. However, the 

discrepancy between the two data sets increases substantially as the upper limits of 

apparent density are approached in each sample. The magnitude of this discrepancy 

is extremely important with respect to the estimation of maximum SDI, because 

Table 1–Summary statistics for two forest types, using FIA data compiled at the plot 

level (FIA subplot) and at the stand level (FIA plot).

FIA Forest Type

	Sample Statistic Douglas-fir	 Aspen

	 for SDI	 Stand-based	 Plot-based	 Stand-based	 Plot-based

Mean 198 207 220 231

Median 191 186 211 207

Maximum 581 987 639 1683
204 USDA Forest Service Proceedings RMRS-P-61. 2010.

Shaw and Long Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection

all of the methods that have been used to estimate the maximum—from Reineke’s 

pencil and ruler to RMA regression (Leduc 1987) to binning methods (Bi 2001; 

Bi and Turvey 1997)—manipulate the upper limits of the data in order to arrive 

at an estimate. As a result, it is possible for two studies to sample the same area 

of one population, apply the same analysis methods, but estimate substantially 

different maximum SDIs solely due to the sample design.

Assumptions of Universal Slope and Linearity

Another approach to SDI analysis that can be a source of inconsistency involves 

the determination of the slope of the self-thinning trajectory. As mentioned earlier, 

this has been a source of debate. However, this debate is more generally focused 

on the merits of the -3/2 self-thinning law as opposed to Reineke’s SDI in particular (although the two are closely linked). The slope representing constant SDI is 

fixed by definition as reflected in the equation used to calculate it (equation 2):

10 SDI TPA Dq 1 6.

= : c m [2]

where SDI is stand density index,

Dq is quadratic mean stand diameter in inches at breast height,

TPA is the number of trees per acre,

10 is the reference diameter in inches, and

1.6 is the slope factor.

In our view, it is necessary to consider the characteristics of slope as two separate issues: 1) the self-thinning trajectory based on a universal slope factor, and 

2) the upper boundary of the size-density relationship as indicated by observed 

stand conditions. We separate these characteristics on the basis that the former 

may be a manifestation of density-dependent mortality, whereas the latter may 

represent a combination of density-dependent and density-independent stand 

dynamics. This is an important distinction, because with few exceptions (e.g., 

Pretsch and Biber 2005), most analyses of the size-density relationship are based 

on one-time observational data, and not on long-term repeat measurements. As a 

result, many SDI analyses employ space-for-time substitution.

The issue of a universal slope factor (i.e., the exponent that represents the selfthinning trajectory is always 1.6) will largely be avoided in this discussion for 

three reasons: 1) it can be argued that a slope factor of 1.6 is inherent in Reineke’s 

(1933) definition of SDI, and therefore immutable; 2) like the maximum SDI 

example above, it can be shown that some differences in slope estimation are 

possible solely due to the characteristics of the data used in the analysis (e.g., 

finding different slopes for two populations of the same forest type, one of which 

consists of relatively young stands and the other of which includes a broad range 

of age classes); and 3) although the self-thinning trajectory is generally assumed 

to be in effect throughout stand development, broad-scale data suggest that this 

assumption may be incorrect (Shaw and Long 2007). For the sake of further 

discussion, we will assume that the “self-thinning law” is indeed a law, but we 

also consider the possibility that it may not be in operation at all stages of stand 

development or under all stand conditions (e.g., in stands at low relative densities 

or experiencing non-density-related mortality).

Reasons #2 and #3, then, are related to the observation that most stands fail to 

maintain the expected maximum density in a mature state. This situation may be 

more common than is currently appreciated. FIA data collected over a wide range 

of stand conditions have shown that relative density begins to fall off in many 

forest types after quadratic mean diameter reaches about 10 inches (fig. 3, left 

panels). Reineke (1933) suggested that most species conformed to the “universal” 
USDA Forest Service Proceedings RMRS-P-61. 2010. 205

Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection Shaw and Long

1

10

100

10 100 1000 10000

Ponderosa pine

Fit

MSB

1

10

100

10 100 1000 10000

Douglas-fir

Fit

MSB

1

10

100

10 100 1000 10000

Aspen

Fit

MSB

Quadratic Mean Diameter

Trees per Acre

Figure 3—Size-density data showing relative density fall-off for several forest types. Left panels are Forest Inventory and 

Analysis data for ponderosa pine, Douglas-fir, and aspen. Solid black lines represent the approximate limit of size-density 

observed in natural stands, described by Shaw and Long (2007) as the mature stand boundary. Red lines represent the 

expected self-thinning line as defined by SDI. The right panel is taken from figure 7 in Reineke (1933), where the image has 

been rotated and mirrored to match the orientation of the left-side graphs. From top to bottom the graphs show data from 

shortleaf pine, slash pine, longleaf pine, loblolly pine from temporary plot, and loblolly pine from remeasured plots. Reineke 

only acknowledged divergence from the expected slope in the shortleaf pine and slash pine data (thin black lines); longleaf 

pine and loblolly pine data were considered to conform to the common self-thinning slope. However, in this orientation it 

appears that relative density falls off when quadratic mean diameter is greater than 10 inches (thick black lines) for loblolly 

and longleaf pines.
206 USDA Forest Service Proceedings RMRS-P-61. 2010.

Shaw and Long Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection

self-thinning trajectory, but acknowledged that data for a few species showed 

some degree of divergence (fig. 3, right panels). He interpreted this divergence 

by presenting an altered linear slope. However, when nonlinear curves are applied to the frontier of Reineke’s data, it is apparent that the data he used revealed 

the same fall-off in relative density seen in FIA data. Reineke’s (1933) data for 

longleaf pine therefore appear to be consistent with the mature stand boundary 

described by Shaw and Long (2007).

The evidence for fall-off of relative density suggests that the assumption of 

linearity over the entire range of size-density is incorrect. A more precise characterization would be to say that density-dependent mortality (self-thinning) may 

only be in effect during a certain period of stand development, and that there is 

a transition point where other factors limit relative density. One hypothesis for 

the cause of the mature stand boundary is that the mortality rate of large trees 

exceeds the capacity of the residual stand to capture the available growing space. 

This characterization of relative density transition is compatible with the Oliver 

and Larson (1990) stand development model, with maximum SDI limiting stand 

density during the stem exclusion stage and the mature stand boundary occurring 

during the understory re-initiation and old-growth stages.

Lack of Consistent Definitions

Another question that might be asked by the silviculturist is: What SDI should 

be applied in the case of irregularly structured or mixed-species stands? Because 

the title of Reineke’s (1933) paper specified “even-aged forests,” the purist approach suggests that the index is inappropriate for use in irregularly structured 

stands. However, Stage (1968) illustrated how SDI could be partitioned, and this 

approach was further developed for irregularly structured stands by Long and 

Daniel (1990). More recent literature on the subject (Ducey 2009; Ducey and 

Larson 2003; Shaw 2000) has dealt with the implications of calculating SDI by 

summation (equation 3), so there appears to be adequate information available 

to guide practitioners on how to use SDI with irregular stand structures. The 

alternative formulation of SDI in equation [3] therefore provides a more flexible application of the index, and avoids some issues related to the comparison 

of relative density in even-aged and irregularly structured stands (Shaw 2006).

10 SDI TPA D 1.6

sum i i = /c : c m m [3]

where Di is the breast height diameter of the ith tally tree on the plot and

TPAi is the number of trees per acre represented by the ith tree.

10 is the reference diameter in inches, and

1.6 is the slope factor.

Less attention has been paid to the question of species mixtures, although 

various approaches to weighting maximum SDI by stand composition have been 

discussed by Puettmann and others (1992), Dean and Baldwin (1996), Torres-Rojo 

and Martinez (2000), and Woodall and others (2005). Although the approaches 

vary, these studies and examination of FIA data indicate that composition is a factor in the determination of potential maximum SDI for a stand. All of these studies 

suggest that the appropriate “adjustment” for SDI in mixed-species stands should 

be based on weighting of the individual species maximum SDI, as determined 

through analysis of pure stands, by the relative composition of each species in 

the mixed stand. In other words, the expected maximum SDI for a 50:50 mixture 

of two species should be approximately the average of the individual species 

maxima. If such a stand is a candidate for thinning and one species will become 
USDA Forest Service Proceedings RMRS-P-61. 2010. 207

Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection Shaw and Long

more dominant than the other after treatment, then a new maximum should be 

calculated for the resulting stand based on the post-thinning proportions.

The questions about effects of structure and mixtures are relevant to the issue of consistency in the development and use of SDI. Most studies on SDI are 

silent with respect to the ranges of composition and structure that are represented 

in the analyzed data. Because both stand characteristics have an effect on SDI 

calculations, they should be considered in research used to estimate maximum 

SDI or develop tools such as density management diagrams, and they should be 

considered by the silviculturist when designing density management regimes. As 

with the plot scale issue, problems may be caused for SDI users simply because of 

a lack of stated or consistent definitions as part of research methods. For example, 

if two studies are designed to determine the maximum SDI for a species, but one 

uses data largely acquired from pure stands (e.g., >90% composition of the target 

species) and the other uses data acquired from stands where the species is merely 

dominant (e.g., >50% composition of the target species), there is a high likelihood 

that they will find two different SDI maxima for the same species. Without explicit 

definition of the analyzed population, users may be confused by these results.

Proposals for Future Research and Application

In the discussion above we describe four factors that can cause inconsistencies 

between values of SDI as determined in research studies and field application: 

1) sampling error, 2) scale at which the data are obtained (plot scale), 3) assumption of linearity of the self-thinning dynamic during advanced stages of stand 

development, and 4) lack of consistent definitions or a mismatch between the 

population used to develop SDI-related values and the population being assessed 

in management practice. We will not discuss sampling error here, because it is 

an inevitable artifact of estimation and a minor issue in comparison to the others. 

We believe that the remainder of the issues can be resolved through the adoption 

of a few basic guidelines for use during research and field application of SDI.

Researchers should ensure the data used in SDI-related analyses are compatible with the scale expected to be used by the practitioner when the results of 

research are applied in the field. Methods or recommendations for application 

should state the appropriate scale of application. This is not to say that data of 

different scale are not useful in the research process. Small-scale (plot-level) data 

may be useful to determine the biological capacity of a species for packing on the 

site, whereas larger scale (stand-level) data are more likely to describe the range 

and variability of density that would be measured at management scales. Both 

scales of information might be useful to the silviculturist, who might simultaneously seek to manage for dense pockets and more moderate stand-level density. 

In addition to matching of source data and application scales, it is important to 

define the population to which the results apply. The population of interest is 

commonly thought of in terms of geographic extent, but the description should 

include both the range of composition and stand structure. Scale, composition, 

and structure have the potential to affect determination of maximum SDI, and 

it is likely that one or more of these characteristics have been a factor in cases 

where there are apparent regional differences among maximum SDI values. If 

the population of interest is adequately identified, then apparent regional differences should be minimal.

The upper boundary of the size-density relationship is complex, so there is 

little to be gained through continued evaluation of the self-thinning slope where a 

linear relationship is assumed. Instead, research should focus on the mature stand 

boundary, including mechanisms underlying the fall-off phenomenon and stand 
208 USDA Forest Service Proceedings RMRS-P-61. 2010.

Shaw and Long Consistent Definition and Application of Reineke’s Stand Density Index in Silviculture and Stand Projection

dynamics when stem exclusion is not the driving factor. Increasing knowledge 

in this area should substantially improve the realism of stand projection models 

such as the Forest Vegetation Simulator.

Finally, a definition of maximum SDI should be consistent with “benchmark” 

percentages of the maximum that correspond to canopy closure, the onset of competition, and the zone of imminent competition mortality (Drew and Flewelling 

1977). This compatibility is required in order for density management regimes 

to produce desired results. If density is scaled incorrectly in tools and growth 

models, then silviculturists risk missing management objectives because density 

may inadvertently be managed higher or lower than the density target would 

indicate. This is the case that led Basford (this proceedings) to abandon SDI in 

favor of an alternative. Anyone attempting to manage ponderosa pine using a 

maximum SDI of 830, when 450 is the appropriate maximum, is likely to fail 

to meet their objectives because the true relative density (and by extension, the 

state of competition) in the managed stands would always be much higher than 

suggested by the SDI percentage.

Conclusion

In this paper we have attempted to highlight inconsistencies in research and 

application of SDI, and suggested opportunities for resolution of the issues causing the most confusion when applying the index in silvicultural practice. Most of 

these issues may be resolved simply by full disclosure of the data and methods 

used for analysis, coupled with use by the practitioner that is consistent with the 

results. In some cases where inconsistencies have been found, they may be traced 

back to weakly supported conclusions. For some reason, an incorrect maximum 

SDI for one of the most important timber species in the West, ponderosa pine, 

went unquestioned for over 75 years. This might not be surprising, considering the 

foundational nature of Reineke’s work, but the persistence of this unsupportable 

value in Forest Service documentation suggests that it was never examined closely. 

We believe that situations like this can be avoided in the future, if SDI research 

and application are conducted with the adoption of a few simple guidelines:

• Density management guidelines should be developed using spatial scales 

comparable to what would be used in application.

• Research documentation should explicitly describe the population from which 

the data were drawn, in terms of composition and structure, so that practitioners understand the range of conditions represented in the analysis.

• Definitions of terms used in research should be consistent with accepted definitions, or explicitly defined in cases where they are open to interpretation.

Adoption of standards and guidelines such as these, especially in cases where 

the same concept may be described in many different geographic areas and forest 

types using multiple methods, should do much to avoid confusion during technology transfer from researcher to the practitioner.

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 211

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1USDA Forest Service, Northern Research Station, Forest Inventory and 

Analysis Program, St. Paul, MN.

2USDA Forest Service, Region 1, 

Missoula, MT.

3USDA Forest Service, Rocky Mountain 

Research Station, Interior West Forest Inventory and Analysis Program, 

Ogden, UT.

The Role of Strategic Forest Inventories 

in Aiding Land Management Decision 

Making: Examples From the U.S. Forest 

Inventory and Analysis Program

W. Keith Moser1, Renate Bush2, John D. Shaw3, 

Mark H. Hansen1, and Mark D. Nelson1

Abstract—A major challenge for today’s resource managers is the linking of standand landscape-scale dynamics. The U.S. Forest Service has made major investments 

in programs at both the stand- (national forest project) and landscape/regional (Forest 

Inventory and Analysis [FIA] program) levels. FIA produces the only comprehensive 

and consistent statistical information on the status, changes, and trends in the condition 

and health of all forest ecosystems in the USA. Intended to be a strategic inventory, FIA 

data have not been used very much for small area inventory, planning, and analysis 

due to the high variation associated with the estimates. Yet, trends observed over 

landscape and regional scales can help managers making decisions at the local level. 

FIA data can be used to assist with project-level decision making, adding scientifically 

defensible data and framing local management in a larger context. FIA data are helpful 

in understanding stocking and density limitations and inter-species interactions. They 

also can provide insight into future growth and yield. FIA data provide opportunities 

to conduct scale-independent analyses to examine relationships between stand characteristics and forest health and invasive species, as well as methods for establishing 

ecological benchmarks and prioritizing restoration opportunities.

This paper explains how FIA data can be used at three different levels of analysis. 

At the project level, FIA data can be used for Forest Plan revision, monitoring conditions and trends at mid- to broad spatial scales over time, and setting the context 

for proposed projects. One step beyond management decision making, FIA data can 

provide input for stocking guides and other quantitative tools that aid silvicultural planning. Finally, FIA data can help detect and analyze patterns across broader geographic 

areas that reveal (or illustrate) relationships and processes that can be applied at the 

local level or provide policy guidelines that can guide prioritization and allocation 

of scarce management resources.

Introduction

The original objective of the Forest Inventory and Analysis Program was to 

estimate resource availability at the strategic or state-level as an aid to understanding trends in resource utilization potential. Early inventories were referred to as 

“Forest Survey” and focused on growing stock volume and productive timberland 

area, both reflecting an emphasis on the potential for wood utilization. With the 

evolution of our nation’s attitudes toward the environment and the role of forest 

resources in providing ecosystem services, the purview of the “Forest Survey” 

has expanded to include not only productive and accessible timberland, but also 

(protected) forest land reserved from timber harvest and forest land that may not 

be considered productive enough to grow crops of timber. Additionally, selected 
212 USDA Forest Service Proceedings RMRS-P-61. 2010.

Moser, Bush, Shaw, Hansen, and Nelson The Role of Strategic Forest Inventories in Aiding Land Management Decision-Making: ...

criteria and indicators of ecological sustainability, such as estimates of coarse 

woody debris, understory vegetation, and lichens, are now incorporated into the 

sample design. A robust forest inventory requires a large network of sample plots 

measured consistently over time. Over the past decade, the USDA Forest Service, 

Forest Inventory and Analysis (FIA) program has implemented an inventory 

system that seeks to achieve national and international consistency4. This system 

utilizes three phases of inventory designed to make estimates of forest extent, 

composition, structure, health, and sustainability: Phase 1 – remote sensing, Phase 

2 – the actual on-the-ground sampling in a systematic grid, and Phase 3 – a subset 

of Phase 2 that examines certain variables that are considered to be indicators 

of forest health (McRoberts 1999). The design of the FIA program gives greater 

certitude to estimates across larger geographic extents. While FIA does publish 

estimates of county-level attributes, the uncertainty at such scales often is high, 

especially in sparsely-forested locales. The paucity of plots for small geographic 

areas and elements of stratification by ownership or forest type can limit usefulness at the local level. Nonetheless, FIA data can still provide great information 

value to forest managers.

The potential benefits of FIA data for forest resource managers exist at three 

levels that are loosely represented by their distance from the project-level, onthe-ground decision-making process. At the most immediate level is the use of 

FIA data for formulating or updating land management plans for large tracts, 

monitoring forest conditions and trends at the broad level over time, and setting 

the context for proposed projects. We present three examples: monitoring trends 

of target species or forest types, managing snag habitat, and estimating the extent 

of old-growth forest.

Further removed from the management decision, FIA data provide input for 

stocking guides and other quantitative tools that aid silvicultural planning. In 

the past, many of these tools were developed using limited data, and they were 

regional in scope. As a result, some tools have been used in areas outside those 

for which they were developed, perhaps without local validation. The fact that 

FIA data are geographically unbiased and represent a range of conditions that 

exist within a given forest type in proportion to the abundance of those conditions on the landscape permits regional comparisons of modeling results and 

geographically appropriate application of silvicultural tools. In addition, the data 

permit analysis and generalizations of stand dynamics and growth-growing stock 

relationships that are not possible or appropriate with more limited data sets. To 

illustrate this point, we provide an example of using FIA data to develop density 

management diagrams.

Finally, FIA data can detect and analyze patterns at large geographic extents 

that reveal relationships and processes that can be applied at the local level. The 

data also can inform policy guidelines for prioritizing and allocating scarce management resources. We present two examples of this capability, one that provides 

guidance in prioritizing restoration activities by comparing FIA data to historic 

estimates of structure and abundance, and the other that examines the extent of 

non-native invasive plants in forest land and the factors that appear to influence 

their presence. At all three scales, decision makers will find FIA data to be a 

valuable information resource.

4 Complete documentation of the plot design and all measurements is at http://socrates.lv-hrc.

nevada.edu/fia/dab/databandindex.html.
USDA Forest Service Proceedings RMRS-P-61. 2010. 213

The Role of Strategic Forest Inventories in Aiding Land Management Decision-Making: ... Moser, Bush, Shaw, Hansen, and Nelson

Use of FIA Data for Project Level Planning

Statistically sound estimates of the current condition and trends of vegetation 

and associated attributes are fundamental to developing forest plan components, 

monitoring in relation to forest plan standards, guidelines, and desired conditions, and managing wildlife habitat, including cumulative effects analysis for 

project-level planning. Furthermore, statistically reliable data are needed to 

address controversial management issues, such as climate change effects and 

carbon accounting.

FIA data allow for regional monitoring based on an unbiased, representative 

sample of forest lands that are remeasured regularly. Many attributes of trees and 

the site are collected on an FIA plot. The FIA sampling frame uniformly covers 

all forested lands, regardless of ownership status or management emphasis; thus, 

wilderness and roadless areas, as well as more intensively managed lands, have 

equivalent sampling probabilities. As a result, spatial data sets can be intersected 

with FIA plot locations to describe vegetation characteristics within various map 

strata. This section contains 3 examples that incorporate these capabilities.

Example 1 — Identifying Target Species in Silvicultural 

Prescriptions

Within a national forest’s planning area, FIA data can be used to assess current 

condition. Since plots are remeasured on a 5- to 10-year cycle (depending upon 

geographic area), progress towards desired condition or compliance with achievement standards can be monitored. Furthermore, comparing current condition to 

future condition allows prioritizing project-level vegetation treatments needed to 

achieve management objectives. For example, FIA data can be used to compare 

current estimates to desired conditions for the historically most-common forest 

types found on the Idaho Panhandle National Forests (IPNF) in the Northern 

Region (Region 1, which covers Montana and parts of Idaho and North Dakota).

The comparison suggests that projects that encourage restoration and regeneration 

of specific forest types, such as white pine, western larch, and ponderosa pine, 

should take priority other forest types.

Example 2 — Managing Snag Habitat

In another example, the Northern Region analyzed snag densities for planning 

development of project-level snag retention and recruitment options. FIA data 

were used to assess the density and distribution of snags within and outside of 

wilderness/roadless areas and categorized by vegetation classifications, such as 

habitat type groups and seral stages (table 1). This analysis took into consideration 

recent findings on the effects that timber harvest and human access have on snag 

density, how snag density relates to stand succession and disturbances, and the 

spatial pattern of snags. After obtaining these results, all national forests in the 

Northern Region began to monitor snag densities over time at the broad level and 

use the data to adaptively manage at the project-level.

Example 3 — Managing Old Growth

Additionally, the Northern Region uses FIA data to estimate and monitor 

forest plan standards such as the percentage of a national forest in old growth 

status (table 2). The Region has a documented definition of old growth based on 

geographic area, old growth forest type, and habitat type groups that are applied 

to FIA inventory data. Using FIA data, one can estimate that 9.4 percent of the 

Clearwater National Forest (CNF) is in an old growth condition; an estimate with a 
214 USDA Forest Service Proceedings RMRS-P-61. 2010.

Moser, Bush, Shaw, Hansen, and Nelson The Role of Strategic Forest Inventories in Aiding Land Management Decision-Making: ...

Table 1—Mean snag densities per acre with 90% confidence interval, by diameter classes, inside and outside of wilderness/roadless areas 

for all northern Idaho Forests and for each Forest.

Area

Wilderness/

Roadless

Snags per acre 10”+ Snags per acre 15”+ Snags per acre 20”+

Total

Number

PSUs

Number

Forester

Mean PSUs

90% CI

- Lower

bound

90% CI

- Upper

bound Mean

90%

CI -

Lower

bound

90%

CI -

Upper

bound Mean

90%

CI -

Lower

bound

90%

CI -

Upper

bound

North Idaho

Forests

IN 10.3 9.3 11.4 3.9 3.4 4.4 1.6 1.4 1.9 514 514

Idaho 

Panhandle 10.4 8.4 12.6 3.7 2.8 4.7 1.6 1.1 2.1 133 133

Clearwater 8.9 7.3 10.5 3.7 2.9 4.5 1.6 1.2 2.1 189 189

Nez Perce 11.7 9.9 13.7 4.2 3.3 5.2 1.7 1.3 2.1 192 192

North Idaho

Forests

OUT 11.7 10.5 12.9 4.3 3.7 4.9 1.6 1.4 1.9 478 478

Idaho 

Panhandle 12.7 11.1 14.5 4.2 3.5 4.9 1.4 1.1 1.7 260 260

Clearwater 9.6 7.5 11.9 4.4 3.3 5.7 2.1 1.4 2.8 106 106

Nez Perce 11.2 8.6 14.0 4.3 3.0 5.9 1.8 1.2 2.4 112 112

Table 2—Northern Region and individual National Forest estimates of percent of old growth and 90%-confidence 

intervals.

	 	 90 percent	 90 percent

confidence	 confidence

	 	 Percent old	 interval—	 interval—	 Total number	 Number of

	 Unit	 growth estimate	 Lower bound	 Upper bound	 of PSUs	 forested PSUs

Northern Region 13.7 12.9 14.4 3883 3423

Beaverhead—Deerlodge 22.9 20.5 25.4 547 442

Bitterroot 12.8 10.1 15.6 252 226

Idaho Panhandle 11.8 9.6 14.0 413 397

Clearwater 9.4 7.3 11.8 305 300

90 percent confidence interval and a lower bound of 7.3 percent. The CNF’s Forest 

Plan Standard is to retain at least 10 percent old growth forest-wide. Currently, 

the CNF is not proposing any treatments that impact old growth stands or stands 

approaching old-growth status. When FIA annual data show an increase in the 

amount of old growth on the Forest, the Forest will reconsider its current position.

FIA data can be associated with various map products to describe and explore 

various map strata. For example, FIA inventory data were analyzed to determine 

whether or not the Region’s old growth criteria were met. Forests collapse their 

existing vegetation layers into several cover types. The percentage of plots within 

each cover type, which meet old growth criteria can then be extracted and compared (fig. 1).

Use of FIA Data for Development of 

Silvicultural Tools

Although the FIA inventory system was designed to estimate population 

characteristics and trends across broad geographic areas, plot-level FIA data are 

increasingly being used to describe and model stand growth and structural characteristics. This use of FIA data is partly driven by the fact that FIA reporting, 

by design, requires wall-to-wall coverage (i.e., all forest types in all ownerships 
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The Role of Strategic Forest Inventories in Aiding Land Management Decision-Making: ... Moser, Bush, Shaw, Hansen, and Nelson

under all management scenarios). Unfortunately, some of the most basic tools and 

models, such as stocking charts, yield equations, and site index curves, have been 

developed locally using relatively small data sets. Often, the geographic extent is 

not well documented. Because of the extensive demand for these tools, the FIA 

program applies these models over geographic areas that may be greater than 

appropriate for the scope of the original research. This extrapolation may occur 

without substantial validation, because the process for adopting models typically 

relies on expert opinion of model developers, or in some cases, model users.

Because of this situation, the FIA program has relied largely on third parties 

(such as non-FIA Forest Service and university-based researchers) to develop the 

tools and models underlying many FIA variables. Problems arise where there are 

large knowledge gaps, such as for species that are less studied because they are 

not commercially important in all or part of their range. These gaps tend to persist 

for a long time because there has generally been no systematic mechanism available to fill them. Until recently, the FIA program had little capacity to develop 

new models and so used the “best available” tools and models, based on limited 

testing and expert opinion. As a result, many “surrogate” models persist in the 

FIA computation system.

However, with the advent of the current, nationally consistent, mapped-plot 

design, the FIA program began compiling data that can be used to validate existing tools and models or to develop new ones from scratch. Plot-level FIA data can 

be compiled to describe characteristics and conditions, such as stand structure 

and composition, individual tree- and stand-level growth, and mortality rates. In 

comparison to the studies that produced many models and tools in use today, data 

developed on FIA plots offer several potential advantages.

First, many older studies were geographically limited, or one species may have 

been represented by multiple studies across its range, with differing experimental 

procedures and analysis being used among studies. FIA data cover the entire 

ranges of species, or at least their range within the boundaries of the United States, 

and sample all parts of the range with one core protocol. As a result, the data are 

consistent across large geographic areas. Sub-regions of a species’ range can be 

compared without the introduction of noise from varying methods.

Figure 1—Percent of old growth, based on FIA data, by Western Montana Planning Zone (WPMZ) 

Cover Strata.
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Second, older studies were frequently based on relatively small sample sizes, 

usually confined to one geographic area, and with unknown geographic bias. For 

common species and forest types, FIA plots available for analysis typically number 

in the thousands, depending on the criteria imposed by the research question. FIA 

data are geographically unbiased because FIA uses a systematic sampling design.

Finally, many older studies relied on temporary plot data. FIA data from the 

legacy periodic inventories and initial measurement of annual inventory plots are 

also treated as temporary plot data for many current analyses. However, annual 

inventory plots are permanent and will be remeasured on a 5- to 10-year cycle, 

depending on the state (Bechtold and Patterson 2005). The annual inventories 

eventually will yield long-term data on growth, mortality, successional change, 

and other tree- and stand-level characteristics. These data can serve as validation 

data for existing models, or time-series data for the development of new models.

Although the points above are broad generalities, they highlight some of the potential advantages of using plot-level FIA data in a variety of analyses. Of course, 

the data collected on FIA plots cannot satisfy every research question, so the need 

for “experimentally based” research will always exist. What the FIA program 

offers is a well documented, statistically sound sample design that produces the 

kind of data used in many observational studies, without the pitfalls associated 

with ad-hoc selection of plots. In the longer term, FIA data will produce valuable 

time-series data with unparalleled geographic scope.

Example 4 — FIA Data and Development of Density 

Management Diagrams

An example of the broad applicability of FIA data is the development of density 

management diagrams for ponderosa and longleaf pines (Long and Shaw 2005; 

Shaw and Long 2007). Density management diagrams relate yield and density 

and allow forest managers to use current stand density to project what the future 

stand would look like (Kershaw and Fischer 1991). Both of these species are 

commercially and ecologically important, and many aspects of their ecology 

and associated silvicultural practices have been studied. Density management 

diagrams, which are graphic models of stand structure and development (Jack 

and Long 1996; Newton 1997), are commonly used in the western U.S. and much 

of Canada. They are less commonly used in eastern states, but availability and 

use there is gradually increasing.

The lack of a density management diagram (DMD) for ponderosa pine represented a major knowledge gap for that species. Even the maximum relative 

density of ponderosa pine, expressed as stand density index (Reineke 1933), was 

not well understood across the species’ range. The need for a ponderosa pine 

DMD and the availability of FIA data presented an opportunity to create a “test 

case.” One aspect of this test would be the use of “off-the-shelf” FIA data — i.e., 

the data that are freely available to the public through the FIA Datamart (http://

fiatools.fs.fed.us/fiadb-downloads/datamart.html). Given this approach, it would 

be possible for users of the DMD to independently reproduce the results of the 

research using publicly available data and the methods described by Long and 

Shaw (2005). This feature would add a level of transparency to the study that is 

relatively rare in research today.

The number of plots needed to construct a DMD is about 300 (J.N. Long, pers. 

comm.). FIA data provided 766 plots (out of 8,183 plots with ponderosa pine) for 

development of the ponderosa pine DMD based on compositional and structural 

criteria. The DMD appeared to be robust across several sub-regions of the range 

of ponderosa pine, suggesting that the tool can be used over a large geographic 

area without concern for local bias (Long and Shaw 2005). Although the data used 
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in construction of the diagram were necessarily treated as temporary plot data, 

many of the plots used in the original analysis will be remeasured in the future, 

providing data that can be used for periodic validation of the stand dynamics 

represented in the DMD.

From a management perspective, the lack of a DMD for longleaf pine did not 

represent as great an unmet need as did the lack of a DMD for ponderosa pine. 

However, two issues related to longleaf pine could be addressed with the development of a DMD. The first was an apparent fall-off in the relative density of 

mature stands that resulted in gross over-prediction of potential basal area and 

volume when a full-stocking scenario was extended beyond a certain limit. The 

second was the need for tools that would assist silviculturists in the development 

of treatments for the benefit of the endangered Red-cockaded Woodpecker (RCW 

Picoides borealis). Although management goals for the RCW were well established 

by recovery guidelines (U.S. Fish and Wildlife Service 2003), the fall-off issue 

in mature stands was not well understood. Were the apparent limits of relative 

density that were derived from local data universal for the longleaf forest type 

or were they only local?

Again, the broad-ranging availability of FIA data was able to provide a defensible answer. The FIA database yielded 5,222 plots with longleaf pine present, 

leaving 343 available for development of the DMD after applying somewhat 

stricter compositional and structural evaluation criteria than were used in the 

case of ponderosa pine. A somewhat larger pool of plots was used to characterize the “fall-off” phenomenon, which appeared to be common to the longleaf 

forest type throughout its range. As a result, the longleaf pine DMD was published with the inclusion of a “mature stand boundary” (fig. 2) that represented 

the practical upper limit of stand-level management in size-density space (Shaw 

and Long 2007). Finally, FIA data were used to analyze the relationships among 

mean stand diameter, stand basal area, and cumulative basal area by diameter 

class. These relationships were used to translate stand structural characteristics 

specified in the RCW recovery guidelines (U.S. Fish and Wildlife Service 2003) 

into a “suitable habitat zone” on the DMD (fig. 2). Addition of the mature stand 

boundary and the RCW habitat suitability zone considerably enhance the utility 

of the basic longleaf pine DMD.

Although the construction of density management diagrams has been used 

to illustrate the usefulness of FIA data for development of new tools, the list of 

potential applications is long. There are many species and forest types for which 

DMDs are in demand; DMDs for the Sierra mixed conifer and aspen forest types 

are under development using FIA data, and several more are planned. In addition, FIA data are being brought to bear on a variety of questions related to forest 

growth and yield and stand dynamics. In contrast to past studies that may have 

been limited in scope and developed from data collected at one point in time, it 

will be possible to establish a cycle of development, validation, and revision as 

the flow of annual FIA data continues.

Use of FIA Data at the Landscape Level

Because of the extent of the FIA inventory design, at least 1 plot per 6,000 acres, 

patterns and trends that are not obvious at a project level might reveal themselves 

at a larger scale. Some of the questions are explicitly a function of this landscape 

view, like the restoration example below. Others merely need a large number of 

data points to examine specific cause-and-effect relationships, like the invasive 

plant example that follows.
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Figure 2—Density management diagram for longleaf pine, with the mature stand 

boundary (in blue) and zone of Red-cockaded Woodpecker habitat suitability (shaded 

polygon) (based on Shaw and Long 2007). 

Example 5 — Restoration Ecology

As a counterpoint to the highly altered landscapes of today, some ecologists and 

resource professionals trying to establish criteria for sustainability have pointed 

to the pre-Euro-American-settlement landscape as a benchmark for restoration 

efforts (Bragg 2003; Foti 2004; Landres and others 1999; Swetnam and others 

1999). To prioritize restoration efforts, a robust method for comparing current 

and historical landscapes would be extremely useful. In the western two-thirds 

of the United States, public land managers are fortunate to have an historical 

inventory in the Public Land Survey (PLS) and the FIA Program’s inventory of 

current forest conditions.
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The U.S. government’s General Land Office (GLO) conducted public land 

surveys (PLS) across most of the country during the 19th century. Most of the 

lands west of the 13 original colonies were subject to the GLO surveys. These 

public records are an excellent source of historical landscape conditions when 

properly interpreted. The PLS is a rectangular, rule-based system that divided the 

landscape into a series of townships and ranges associated with a point of origin 

and a series of meridians and baselines (usually by state). These north-south and 

east-west running demarcations divided the land into nominal 36 mi2 townships, 

which were then further subdivided into 36 640-acre sections (National Atlas 

2006). These townships were replicated across the landscape. Along this hierarchical grid, survey markers (posts) were set at 1/2 mile and 1 mile intervals. At 

these locations, information such as species, estimated diameter, and distance were 

collected on two to four trees near the posts and recorded in survey notebooks. 

Despite significant deficiencies (e.g., a not particularly intensive sampling regime, 

and numerous biases, ambiguities, and inconsistencies [Bourdo 1956; Bragg 2004; 

Mladenoff and others 2002; Nelson 1997; Schulte and Mladenoff 2001]), the PLS 

records still provide landscape-to-regional information on the vegetation of the 

period due to their detail, wide extent, and resolution.

To test the effectiveness of a system comparing PLS to FIA data, Moser and 

others (2006a) compared historic and current data covering the southeast Missouri 

Ozarks. Missouri was surveyed by General Land Office surveyors from 1816 

through 1855. FIA data have been collected annually in Missouri since 1999. 

Moser and others (2006a) used data from the 1999-2003 FIA inventory of the State 

of Missouri to depict current forest conditions. The two datasets – historic (PLS) 

and current (FIA) — had to be reduced to a common data structure to compare 

species and structures and determine potential for restoration.

During a review in 2002 of current forest management technical specifications 

by the Missouri Department of Conservation, a team produced a table of current 

and potential hardwood forest type groups with the suitability (and, by implication, the ease) of conversion based on site index. This table (updated to include 

pine forest types) became the basis for comparing the PLS data to the FIA data to 

determine how easy it might be to “convert” current forest types to historic ones.

In order to create common variables of structure and composition between the 

historic and current data, Moser and others (2006a) used a simple moving window to classify each pixel in the study area to a structure and forest type, using 

data from land survey plot corners (PLS) and inventory plots (FIA). The moving 

window centers on a target pixel and then looks at all points within a specified 

distance (window) of the target pixel. It assigns the value of most of the points in 

the window to the target pixel and moves to the next pixel. The size of the moving 

window was adjusted to reflect the spacing of the data points, using a smaller 

window for the PLS data and a larger window for the FIA data.

After the moving window maps were combined with the forest conversion table, 

a restoration difficulty/suitability map was created. While there were combinations of past/present that had no information, a substantial amount of acreage was 

classified into restoration categories: Low Effort, Medium Effort, High Effort, 

Maximum Effort, and Not Possible.

A map that identifies the effort required to restore a landscape to pre-settlement 

condition (fig. 3) was produced by combining PLS and FIA data.

Of the 3 million acres in the study area, 14 percent was classified as low-effort 

sites, 17 percent as medium-effort sites, 11 percent as high-effort sites, 21 percent 

as non-forest, and 11 percent as not possible (table 3). The remaining 25 percent 

was classified as no information.

Available funds and time are usually not sufficient to restore all deserving 

sites, so choices must be made. Resource managers can use this methodology 
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Figure 3—Map of categories of restoration suitability and effort. Suitability is inversely related to 

effort, e.g., high suitability for restoration is assumed to equal low effort required to do so. The “Not 

possible” category” represents those binary combinations that were deemed highly unsuitable. “No 

information” represents those pairs of current and historic forest types that were not considered.

Table 3—Summary of categories of conversion suitability, 

in 1000s of acres.

	Suitability	 Acres (1000s)	 Percentage of total

Low Effort 406 14

Medium Effort 503 17

High Effort 333 11

Maximum Effort 25 1

Non-Forest 609 21

Not Possible 334 11

No Information 752 25

Grand Total 2,962

to prioritize restoration opportunities as part of a larger management plan. This 

methodology is limited by the spatial distribution of past and present species 

groups and the simplification of a fairly complex vegetation pattern into a more 

manageable number of categories. Nested within these broad geographic categories 

will be individual, stand-level decisions that will be based on site conditions and 

the local manager’s individual knowledge and expertise.
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Example 6 — Invasive Plants

Non-native invasive plants (NNIP)5 are expanding across the U.S. Once established, NNIP threaten the sustainability of native forest composition, structure, 

function, and resource productivity (Moser and others 2009; Webster and others 

2006). Factors influencing exotic plants’ invasion of forests include: disturbance, 

competitive release, resource availability, and competitive pressure (Richardson 

and Pyšek 2006). Moser and others (2008) analyzed FIA plot data with three 

objectives in mind: 1) document the distribution of species, 2) compare invasive 

presence to site characteristics, and 3) determine the role of disturbance in invasive 

species presence and coverage in Missouri. This study was more a snapshot than 

a trend analysis, as the full extent of NNIP had not previously been documented 

statewide on FIA plots in Missouri.

Located at the juncture of several ecoregions, Missouri’s pre-settlement landscape ranged from upland forest in the Ozarks to bottomland ecosystems of the 

Mississippi Embayment in the southeast of the state to savannas and prairies in 

the north and west. The fertile soils of northern Missouri were ideal for farming 

and settlers quickly cleared the land for agriculture and grazing. In the heavily timbered areas of southern Missouri, commercial harvesting exploited the 

magnificent stands of shortleaf pine and other species (Beilmann and Brenner 

1951). The combination of clearing, settlement, and timber harvesting resulted 

in a highly fragmented landscape, creating many opportunities for non-native 

invasive plants to become established in forests.

During 2005-2006, Phase 2 FIA plots were assessed for presence and cover 

of any of 25 non-native invasive woody, vine, grass, and herbaceous species of 

interest6 (table 4). Moser and others (2008) used these NNIP data, along with a 

geographic information system and geospatial data about road location (ESRI, 

Redlands, CA, 2006 version) and density and summaries of forest fragmentation data from the Conservation Biology Institute (Heilmann and others 2001), 

to look for relationships among evidence of human disturbance, forest structure 

and composition, and invasive species presence.

Of the 25 NNIP species sampled for in the 2005 and 2006 annual inventory 

panels, only 13 were observed in Missouri and only three—multiflora rose (Rosa 

multiflora Thunb.), non-native bush honeysuckles (Lonicera spp.), and Japanese 

honeysuckle Lonicera japonica Thunb.)—were recorded in substantial number. 

Of the 1,264 plots sampled in this study, 42 percent had at least one invasive 

species of interest. Multiflora rose was the most frequently recorded species, 

being observed on 36 percent of the plots (fig. 4). Woody invasive species were 

especially prominent.

Like all plants, NNIPs benefit from higher site productivity. FIA measures 

elements of site productivity in three ways: 1) site index, based on representative 

trees near the plot, 2) aspect, based on measurements taken by the field crews, 

and 3) physiographic class code, a determination made by field crews based on 

their assessment of land form, topographic position, and soil type.

5 This paper will also refer to NNIP as “exotics” or “invasives.”

6 This list was not exhaustive but represented those species likely to have a significant impact somewhere in 

the 11-state Upper Midwest.
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Table 4—Non-native invasive plants surveyed on FIA plots in the Upper Midwest 

of the U.S., 2005-2006.

Common name	 Scientific name

Woody species

Multiflora rose Rosa multiflora

Japanese barberry Berberis thunbergii

Common buckthorn Rhamnus cathartica

Glossy buckthorn Frangula alnus

Autumn olive Elaeagnus umbellata

Non-native bush honeysuckles Lonicera spp.

European privet Ligustrum vulgare

Vines

Kudzu Pueraria montana 

Porcelain berry Ampelopsis brevipendunculata

Asian bittersweet Celastrus orbiculatus

Japanese honeysuckle Lonicera japonica

Chinese yam Dioscorea oppositifolia

Black swallowwort Cynanchum louiseae

Wintercreeper Euonymus fortunei

Grasses

Reed canary grass Phalaris arundiacea

Phragmites, Common reed Phragmites australis

Nepalese browntop, Japanese stiltgrass Microstegium vimineum

Herbaceous

Garlic mustard Alliaria petiolata

Leafy spurge Euphorbia esula

Spotted knapweed Centaurea bierbersteinii

Dame’s rocket Hesperis matronalis

Mile-a-minute weed, Asiatic tearthumb Polygonum perfoliatum

Common burdock Arctium minus

Japanese knotweed Polygonum cuspidatum

Marsh thistle Cirsium palustre

Figure 4—Distribution of plots containing non-native invasive plants in Missouri in 

2005-2006 inventory years.
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Moser and others (2008) reported the following significant relationships:

• site index and the presence of multiflora rose and non-native bush honeysuckles,

• level aspects and multiflora rose and Japanese honeysuckle, and

• mesic physiographic class and multiflora rose and Japanese honeysuckle.

Like young tree seedlings, ground flora—both native and exotic—are influenced by the presence of trees in the overstory. Not only is the presence of a 

particular basal area an indicator of the likely microenvironment below the canopy, 

but in addition it is likely the result of past disturbance events that may also have 

facilitated the establishment of NNIP. Of the three most prominent invasive plants 

that Moser and others (2008) reported in Missouri, only multiflora rose appeared 

to benefit from reduced basal area.

Analysis of the presence and cover of invasive species at a single point in 

time does not usually provide enough data to evaluate trends in regeneration, 

expansion, or growth (Rejmánek 1989). The FIA database can elicit evidence 

of disturbances and/or management activities, but only in the interval since the 

previous inventory. Non-native invasive plant sampling has only recently been 

initiated in certain parts of the country; remeasurements of these plots will provide 

information about the extent and trends of these unwanted guests.

Summary

Strategic forest inventories—such as FIA in the United States—have traditionally focused on estimating the total number of trees or the total tree volume in a 

state, and they have performed that task well. Such inventories can also be valuable to land managers working at smaller scales, such as management projects on 

national forests. However, the increased level of statistical variation when using 

FIA data for small area estimation tends to dissuade people from considering 

its usefulness altogether at the local level. Yet, the unbiased statistical design of 

FIA can provide valuable information to support planning and decision making 

at the project, landscape, and regional levels. To provide useful and defensible 

information at the local and regional levels, the analysis must be focused and take 

into account both the opportunities and limitations of the data.

In this paper, we presented examples of how FIA data aids managers at the 

project level (target tree species, snag management, old-growth estimation), the 

planning level (density management diagrams), and the landscape level (forest 

restoration prioritization and factors influencing the presence of exotic plants). 

While the examples we presented were not explicitly linked across scales, it is 

quite possible to use the same subset of FIA data to aid decision-making at each 

of these levels.

The FIA program is designed to estimate resource availability and forest health 

trends at broad scales. National estimates of carbon storage, forest health indicators, and wood product utilization potential depend on these data. But FIA data 

also have great potential to assist in achieving management objectives at the local 

level, while still satisfying broad-level, long-term goals.

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 227

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Applied Forest Management Program 

Director, Montana Forest & Conservation Experiment Station, University of 

Montana, Missoula, MT.

2 Research Forester, Applied Forest 

Management Program, Montana Forest 

& Conservation Experiment Station, 

University of Montana, Missoula, MT.

3 Forester, USDA Forest Service, 

Fremont-Winema National Forests, 

Lakeview, OR.

Variable-Density Thinning for Parks 

and Reserves: An Experimental Case 

Study at Humboldt Redwoods State 

Park, California

Christopher R. Keyes1, Thomas E. Perry2, and 

Jesse F. Plummer3

Abstract—Variable-density thinning is emerging as a valuable tool for the silvicultural 

promotion of old-growth conditions in second-growth forests of the Pacific Coast. 

This paper reports on an experimental variable-density thinning prescription applied 

between 2006 and 2007 at north coastal California’s Humboldt Redwoods State Park. 

The prescription strategy relied on known patterns of second-growth stand development 

during the stem exclusion phase, and was designed to alter current stand development 

trajectories in order to promote reference forest conditions. Prescription outcomes are 

described and tradeoffs are discussed, with management constraints unique to parks 

and reserves providing the context for this analysis.

Keywords: ecological restoration, forest stand dynamics, disturbance, stand structure.

Introduction

Silvicultural practices and systems in timberlands management are increasingly 

modeled after patterns of natural disturbance (Puettmann and others 2009), but the 

converse is also occurring in parks and reserves, where the potential of silviculture 

as a disturbance force to restore natural conditions in re-growth forests is being 

developed. In the coastal areas of the western United States, thinning to promote 

gaps and stand complexity has become a dominant management paradigm for the 

active restoration of old-forest attributes to second-growth forests (Carey 2003; 

Carey and Curtis 1996). In this region, vegetation composition and structure in old 

and young forests differ widely (Bailey and others 1998; Lindh and Muir 2004), 

and biodiversity is favored by the structural complexity that thinning promotes 

in second-growth forests (Bailey and Tappeiner 1998; Carey and Wilson 2001; 

Lindh and Muir 2004; Schowalter and others 2003). New silvicultural techniques 

are being devised to optimally promote that diversity (O’Hara and Waring 2005).

For these forests of the Pacific Coast, restoration practices are conducted to 

accelerate and make more certain a developmental pathway that will eventually 

result in stand structures and compositions comparable to old-growth forests. In 

younger stands, silvicultural restoration treatments are conducted to manipulate 

stand structure and composition in order to promote a subset of trees with structural attributes (low height:diameter ratios, high live crown ratios) that enhance 

their potential for resilience and persistence, and to promote patterns of development that promote the sustained dominance of those individuals (Chittick and 

Keyes 2007; Plummer 2008). Treatments may also be conducted to remediate 
228 USDA Forest Service Proceedings RMRS-P-61. 2010.

Keyes, Perry, and Plummer Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California

composition imbalances. In older stands, restoration treatments may be conducted 

to expedite the development of biodiversity and structural complexity by establishing canopy gaps that promote understory plant recruitment and the initiation of 

a new tree cohort (Harrington and others 2005; Thysell and Carey 2001). Parks 

and reserves represent a unique context for the restoration of old-forest features to 

second-growth forests; in many cases the restoration strategy is constrained to a 

single-entry opportunity that must maximize ecological benefits while minimizing the potential for negative consequences.

This paper presents a case study in forest restoration at Humboldt Redwoods 

State Park, where an experimental variable-density thinning prescription was 

applied during 2006 and 2007. The treatment is discussed from both ecological 

and operational standpoints.

Methods

Project Area

Humboldt Redwoods State Park was established in 1921 to preserve massive 

alluvial stands of old-growth redwood along lower Bull Creek and the Eel River. 

Such forests account for more than 17,000 of the park’s nearly 53,000 acres. The 

remaining 36,000 acres consist primarily of second-growth upland forests. In the 

aftermath of flooding in 1955, which damaged Rockefeller Forest, a need was 

recognized to claim and control the upland forests that surround the old-growth 

stands at higher stream orders. The park was steadily expanded between 1963 

and 1984, until the whole of the Bull Creek watershed was annexed (Rohde and 

Rohde 1992). Most of the upland forests of the Bull Creek watershed (including 

Panther Creek), however, had been logged between 1950 and 1962, and are now 

in varying conditions of ecological impairment.

Stands within the Panther Creek watershed are aptly described by three impaired forest condition classes defined in an earlier assessment of second-growth 

forests in Humboldt Redwoods State Park (Keyes 2005). The project area did not 

have distinct stand boundaries, but instead was comprised of a mosaic of conditions typical of second-growth forests in this region: namely, compositionally 

diverse but structurally homogenous even-aged mixed stands (figs. 1 and 2). 

Due to management history, overstory density was very high, the proportional 

composition of Douglas-fir (Pseudotsuga menziessi (Mirb.) Franco) was very low, 

and spatial heterogeneity and vertical stand structure were minimal. Remediation of those conditions was the objective of the prescription. The pre-treatment 

conditions found in these stands were likely beyond the natural range of variability, and given our understanding of second-growth forest stand development 

patterns (Oliver 1980; Oliver 1981; Oliver and Larson 1996), we believe they are 

unlikely to achieve the pre-disturbance forest structure that defines the area’s 

reference condition.

Restoration Prescription

A form of variable-density thinning (VDT) was the recommended technique 

to achieve ecological restoration goals for the forests of Panther Creek. VDT 

differs from most traditional methods of thinning, which typically emphasize 

uniformity in tree spacing and form (Nyland 2007; Smith and others 1997), in 

that it promotes spatial heterogeneity and height differentiation among canopy 

trees to enhance structural complexity. While several approaches to VDT have 

been proposed and tested in experimental settings (e.g. Carey 2003), techniques 

for the efficient implementation of VDT as an operational practice have not yet 
USDA Forest Service Proceedings RMRS-P-61. 2010. 229

Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California Keyes, Perry, and Plummer

been developed or standardized. Existing experimental efforts that have been 

documented generally introduce spatial heterogeneity in a random manner, without capitalizing on variability that already occurs within the stand. Approaches 

relying on randomness or stand-level targets (such as inter-tree spacing levels) 

fail to permit flexibility in tree selection that allows for adaptation to existing 

heterogeneity, and hence can delay the achievement of treatment goals.

The VDT technique developed for the forests of Panther Creek was designed 

to capitalize upon differentiation that had already occurred. The prescription 

consists of a basic size-constrained, species-specific, diameter-based multiplier 

cutting rule (Dx Rule; see fig. 3 for a schematic):

A) Identify the largest Douglas-fir or redwood tree larger than five inches dbh in 

the vicinity, multiply the diameter by two, and cut trees for that many feet 

around the Douglas-fir in a radius up to, but not greater than, twenty feet 

(the maximum treatment radius is twenty feet).

B) Within the cutting radius, cut no trees smaller than five inches dbh, or conifers greater than ten inches dbh, or hardwoods (broadleaf trees) over fifteen 

inches dbh.

C) Move on to next closest Douglas-fir or redwood release tree (including 

Douglas-firs retained in Step B), and repeat Steps A and B.

Figure 1—Panther Creek watershed, 1959. Panther Creek drains from southwest to 

northeast through the middle of this aerial photo. Landslides resulting from cutting are 

evident at bottom center of photo. The photo illustrates the intensity of harvesting activity 

and high density of skid roads. 
230 USDA Forest Service Proceedings RMRS-P-61. 2010.

Keyes, Perry, and Plummer Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California

Figure 2—Panther Creek watershed, 1997. Photo reveals the 

widespread distribution of hardwood re-growth (light green) amidst 

lesser conifer re-growth (dark green) (red lines reference project 

area boundaries and landmarks). Although spatially uniform in age 

structure, the area is compositionally diverse, posing difficulty to 

identification of stands with distinct boundaries, and difficulty in 

preparing stand-based prescriptions. Such variability lends itself to 

tree-scale prescriptions that operate adequately and efficiently across 

a spatial range of compositions.

Figure 3—Conceptual diagram of variable-density thinning prescription.
USDA Forest Service Proceedings RMRS-P-61. 2010. 231

Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California Keyes, Perry, and Plummer

For stands with little structural variation, the Dx Rule does not significantly 

alter spatial heterogeneity. But in partly-differentiated stands, the Dx Rule exacerbates inequalities in growing space by appropriating proportionally larger gaps 

around larger trees than around smaller trees. Moreover, two goals in prescription 

development were simplicity and universality; this necessitated to some extent a 

compromise that worked across the range of forest conditions. An advantage of 

the approach is its universal applicability across forest conditions, and an ability 

to remain effective as contractors move through unanticipated changes in those 

conditions.

Analysis

We utilized pre-treatment and post-treatment data (1790 tree records) from 

a semi-permanent plot network of 84 plots. This dataset captured tree species, 

diameter at breast height, and harvest history (cut versus uncut). Both compositional and structural changes were analyzed to quantify or evaluate general stand 

characteristics, re-distribution of stand volume, prescription effectiveness, and 

operator adherence to the prescription.

Prescription implementation effectiveness was indicated by a redistribution of 

basal area from smaller hardwoods to conifers greater than 10” and hardwoods 

greater than 15”. Along with a species composition shift to a higher proportion 

of conifers in the post-treatment stands, an overall reduction in trees per acre, 

with the greatest reduction in hardwood stems less than 15” is also indicative 

of effective implementation of the prescription. Maintenance or exacerbation of 

spatial variability in stand density was another desirable outcome. Adherence of 

the operator to the prescription was evaluated by focusing on the percentage of 

plots in which the prescription was correctly versus incorrectly implemented. 

Consistent operator errors would have indicated either an overly complex prescription or a lower level of operator professionalism.

In addition to the quantitative analysis of plot data, we made numerous qualitative observations during and after the implementation of the prescription. Our 

goal here was to understand the operational linkages between the prescription 

and its implementation by forest workers, in order to improve and simplify future 

prescriptions for similar complex forest objectives.

Results and Discussion

Quantitative Analysis

Figures 4 and 5 reveal that the prescription reduced overall stand density 

while shifting proportional composition from hardwood to softwood dominance. 

Hardwoods accounted for 52.6 percent of the original stand basal area and only 

5.4 percent of the residual stands basal area. In terms of trees per acre, hardwoods 

accounted for 71.9 percent of the stems in the pre-cut stand and only 17.8 percent 

of the residual stand. The greatest reduction in both basal area and trees per acre 

came from the tanoak component of the stand. Although a desired compositional 

shift occurred, the removal intensity on this site may be higher than desired given 

the potential for hardwood sprout regeneration. The intention of the cutting was 

to release existing conifers, not create new space for stem initiation that would 

favor those sprouting hardwoods.

Figure 6 displays the pre-cut and post-cut stand structure in terms of both basal 

area and trees per acre. Cutters by-and-large accurately observed the diameter and 

species guidelines defined by the prescription, but they did improperly remove 

some hardwoods above the 10” diameter limit. Such cuttings represented only 
232 USDA Forest Service Proceedings RMRS-P-61. 2010.

Keyes, Perry, and Plummer Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California

Figure 4—Pre -har vest and post-har vest species 

compositions expressed in basal area (ft2 per acre) and 

Tanoak (Lithocarpus densiflorus) Blume, Madrone (Arbutus

L.) as a percentage of the total basal area.

Figure 5—Pre -har vest and post-har vest species 

compositions expressed in trees per acre and as a 

percentage of the total trees per acre.

4 trees per acre, but removed a substantial amount of hardwood basal area in 

these larger diameter classes. This removal was a deviation from the prescribed 

treatment rather than a failing of the prescription itself; correct implementation 

would have retained these large diameter hardwoods.

Cutters accurately distinguished between conifers and hardwoods, but did not 

distinguish between hardwoods. All of the species in the “other hardwoods” group 

(California laurel [Umbellularia californica (Hook & Arn.) Nutt.], coastal live 

oak [Quercus agrifolia Née var. oxyadenia (Torr.) J.T. Howell], and California 

black oak [Quercus kelloggii] Newberry) were eliminated by the cutting. Those 

trees were few in number, representing an average of less than one tree per acre. 

However, maintaining the site’s species richness was an inherent goal of the prescription. Hence, proper species identification proved an important criterion for 

proper prescription implementation.

To concisely explore the prescription’s effect of spatial heterogeneity in stand 

density, we conducted analysis of grouped decile classes (representing 1/10th of 

the range of pre-cut density) for trees per acre and basal area (fig. 7), similar to 
USDA Forest Service Proceedings RMRS-P-61. 2010. 233

Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California Keyes, Perry, and Plummer

Figure 6—Species-designated diameter distributions displaying pre-cut (top) and post-cut (bottom) 

trees per acre.

diameter classes. This approach allowed for a contrast of the pre-cut and post-cut 

structural variation in density. The range of stem densities (in terms of trees per 

acre) did not change as a result of the prescription; in both the pre-cut and postcut stand, eight of the ten density classes were occupied. There was a notable 

redistribution of plots falling into the lowest stem density class. The basal area 

density classes exhibited the same shift towards the lowest class; however, seven 

of the eight density classes in the original stand were still occupied. Some structural diversity in basal area was lost, since no post-treatment plots occupied the 

three highest basal area density classes observed prior to treatment.

According to the rule, no cutting was to be conducted in pure hardwood areas 

where releasable conifers were not present. Analysis of cut and uncut areas combined revealed that the overall species composition shift was tempered by high 

hardwood and low softwood stem densities in the uncut areas. Generally, the uncut 

areas either did not contain releasable softwoods, or if softwoods were present, 

the hardwood density was low enough to preclude thinning. These untreated plots 
234 USDA Forest Service Proceedings RMRS-P-61. 2010.

Keyes, Perry, and Plummer Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California

contribute to the large diameter hardwood component and maintained some areas 

of high stem density, retaining the upper end of the overall range of densities 

within the stand.

Two different contractors implemented the prescriptions in separate parts of 

the project area, yielding different trends in removal intensity and the structural 

retention. Contractor implementation of the prescription proved to be a strong 

determinant of post-treatment structure, and a critical aspect of a successfully 

designed and implemented restoration treatment (discussed below).

Development of the stand structures established by this treatment will reveal 

whether the treatment’s objectives will be met over longer timeframes. Occlusion 

of overstory space by released Douglas-firs and their dominance over hardwood 

Figure 7—Pre-cut and post-cut plot counts grouped by 

decile density classes for trees per acre (top) and basal area 

(bottom). The range of stand densities was reduced since 

highest density areas no longer exist; however, substantial 

variation remains in the treated stand, with greater 

representation in the lowest density classes. Variation in 

stand density classes indicates spatial heterogeneity in 

stand structure.
USDA Forest Service Proceedings RMRS-P-61. 2010. 235

Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California Keyes, Perry, and Plummer

sprouts was the intended result, but whether this will be achieved depends upon 

rates of crown expansion among residual trees and rates of height growth among 

hardwood stump sprouts

Qualitative Observations

Thinning crews failed to thin small pockets within the designated project area. 

It is estimated that the skipped areas comprised less than five percent of the total 

project area. These mostly occurred in small pockets, apparently between steep 

stream channels. They were small enough not to be detected during the project 

implementation, hence even the presence of an on-site compliance worker would 

unlikely detect them in real-time. At lower slope positions throughout the project 

area, class I and II streams were impassable chasms. This made contour-based 

foot-travel impossible, and required substantial upslope and downslope travel 

during project reconnaissance; it was also probably responsible for many of the 

pockets that were missed by thinning crews.

Communications between foresters and forest workers posed a potential challenge to the correct implementation of the thinning rule. Communications with 

the leader of the migrant crew appeared to suffer from both language and cultural 

differences. The fortunate presence of a compliance forester that was fluent in 

both English and Spanish, and with a good understanding of forestry principles 

and the prescription itself, is credited with enhancing treatment implementation 

at Panther Creek. This element is a vital consideration for similar situations where 

communication is difficult, the prescription is by description, and the prescription objective and post-treatment structure is unlike that which forest workers 

are accustomed.

Workers were impatient to start cutting even prior to receiving instructions on 

the thinning rule. Workers are believed to have been accustomed to the simple 

instructions associated with pre-commercial thinning’s regular spacing method. 

In retrospect we would have planned and scheduled with the contractor a formal 

saw-free instruction with all crews present and attentive prior to removal of cutting 

equipment and prior to entering treatment area. In the absence of tree marking, it 

is vital that crews recognize that VDT thinning differs in important ways from 

traditional thinning methods. We provided a demonstration area for the contractor bidding and to familiarize workers with the prescription, but should have 

contractually required its use in a tutorial provided by us at the outset.

With our makeshift reactions to these apparent deficiencies, immediate posttreatment impressions of the VDT proved positive in regard to achieving project 

objectives of shifted species composition and reduced density. Residual spacing 

was variable throughout the area, hence crews did not thin in regular spacing 

patterns as might have been feared with communications limitations. Workers appeared capable of identifying tree diameters correctly. Diameter-based constraints 

appeared to be adhered to quite well. Large trees were not cut inadvertently. 

However, it was difficult to determine whether the diameter multiplier (2x) was 

being implemented by crews or whether a constant 20-foot radius was applied to 

all focus trees. Many residual trees by prescription had large diameters, hence 

had target thinning radii that were capped at 20 feet. In future efforts, we would 

like to test the radius-multiplier in a stand with smaller pre-treatment dbh’s; if the 

multiplier is not being adhered to by crews, yet the outcomes achieve the treatment objectives, then a standard thinning radius would offer greater simplicity.

Special treatment areas of reserve forest were flagged for exclusion. Such areas 

included pockets that were not logged during the mid-century, did not require 

restoration, and could potentially be damaged by cutters. However, this special 

treatment was not necessary. A pocket that went unnoticed during pre-treatment 
236 USDA Forest Service Proceedings RMRS-P-61. 2010.

Keyes, Perry, and Plummer Variable-Density Thinning for Parks and Reserves: An Experimental Case Study at Humboldt Redwoods State Park, California

reconnaissance, and which was not flagged off as an exclusion area, was subjected 

to the crews and the thinning rule. The thinning rule as written prohibited cutting 

the large trees that occurred in that area, and it was correctly implemented. We 

believe that crews with a proven track record in prior contracts can be trusted 

to adhere to and be able to distinguish accurately diameters and diameter-based 

constraints for this type of prescription. Hence, the expense and potential for 

confusion associated with flagging off reserve pockets can be avoided, unless 

they are not specifically protected by the language of the thinning rule.

Conclusions

The outcomes of restoration treatment applied to Panther Creek were successful in most regards. Concerns regarded the prescription implementation aspect, 

rather than the prescription formulation itself. Future efforts can benefit by reducing worker confusion, enhancing worker understanding of the prescription, and 

reducing pre-treatment expense. Structurally and compositionally, the prescription appeared to achieve forest objectives. Since implementation appeared to be a 

limiting factor to project success, further simplifications to the prescription would 

prove beneficial if they can be shown to reduce worker confusion and expense 

while yet resulting in similar post-treatment forest complexity.

Acknowledgments

Staff at the North Coast Sector of California State Parks proposed the opportunity, provided logistical and financial support, and implemented the prescription. 

Humboldt State University students were instrumental in data collection. The 

study was made possible in part by the Applied Forest Management Program 

at the University of Montana, a research and outreach program of the Montana 

Forest & Conservation Experiment Station.

References

Bailey, J.D., and J.C. Tappeiner. 1998. Effects of thinning on structural development in 40- to 

100-year-old Douglas-fir stands in western Oregon. Forest Ecology and Management 108:99-113.

Bailey, J.D., C. Mayrsohn, P.S. Doescher, E. St. Pierre, and J.C. Tappeiner. 1998. Understory 

vegetation in old and young Douglas-fir forests of western Oregon. Forest Ecology and Management 112:289-302.

Carey, A.B. 2003. Biocomplexity and restoration of biodiversity in temperate coniferous forest: 

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Carey, A.B., and S.M. Wilson. 2001. Induced spatial heterogeneity in forest canopies: responses 

of small mammals. Journal of Wildlife Management 65(4):1014-1027.

Chittick, A.J., and C.R. Keyes. 2007. Holter Ridge Thinning Study, Redwood National Park: 

preliminary results of a 25-year retrospective. Pp 271-280 in Proceedings of the Redwood 

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Harrington, C.A., S.D. Roberts, and L.C. Brodie. 2005. Tree and understory responses to variabledensity thinning in western Washington. Pp. 97-106 in C.E. Peterson and D.A. Maguire, eds., 

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State Park. Report on file at California Department of Parks and Recreation, North Coast Redwoods District Headquarters, Eureka, CA. 41 p.

Lindh, B.C., and P.S. Muir. 2004. Understory vegetation in young Douglas-fir forests: does thinning help restore old-growth composition? Forest Ecology and Management 192:285-296.

Nyland, R.D. 2007. Silviculture: Concepts and Applications, 2nd ed. Waveland Press, Long Grove, 

IL. 682 p.
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O’Hara, K.L., and K.M. Waring. 2005. Forest restoration practices in the Pacific Northwest and 

California. Pp. 445-461 in J.A. Stanturf and P. Madsen, eds., Restoration of Boreal and Temperate Forests. CRC Press, Boca Raton, FL.

Oliver, C.D. 1980. Even-aged development of mixed-species stands. Journal of Forestry 78(4):201-

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Oliver, C.D. 1981. Forest development in North America following major disturbances. Forest 

Ecology and Management 3:153-168.

Oliver, C.D., and B.C. Larson. 1996. Forest Stand Dynamics, Update Edition. John Wiley and 

Sons, New York, NY. 520 p.

Plummer, J.F. 2008. Effects of Precommercial Thinning on Structural Development of Young

Puettmann, K.J., K.D. Coates, and C. Messier. 2009. A Critique of Silviculture – Managing for 

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 239

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 USDA Forest Service, Pacific Southwest Research Station, Davis, CA.

Site Quality Changes in Response to 

Slash Retention and Prescribed Fire in 

Thinned Ponderosa Pine Forests

Matt Busse1

Abstract—The ecological effects of post-thinning slash retention on vegetation, 

wildlife browse, and soil were evaluated in sixty-year-old stands of second-growth 

pine in central Oregon. Three slash-retention treatments were compared: whole-tree 

removal, bole-only removal, and thin no removal (boles and slash scattered on site). 

The study intent was to create a wide gradient of surface organic matter mass among 

treatments and assess any ensuing changes in site quality. No differences in site or soil 

productivity indices were found among the slash-retention treatments after 20 years. 

Tree growth, understory plant production and diversity, wildlife browse cover, litter 

decay, soil nutrients, and soil biological activity were similar among the treatments, 

suggesting that the retention of thinning slash is trivial to the health of these forests. In 

general, thinning alone, regardless of slash treatment, and thinning with subsequent 

burning were sound options for reducing wildfire hazard and maintaining site quality 

in these pine ecosystems.

Keywords: site productivity, thinning slash, prescribed fire, organic matter, pumice soil

Introduction

Forest management is a contentious topic. Unresolved issues are numerous 

and they range in scope from local concerns about public safety and wildfire to 

the politically charged world of protecting old-growth ecosystems and to the scientific unknown of global climate change. Should forests be managed as carbon 

stores in order to slow the warming of our planet? Is it unacceptable to harvest 

moderate-to-large trees on public lands, or should such practices be encouraged 

on occasion in order to maintain healthy, long-lived forests? Which forests should 

be actively managed and which should be allowed to develop “naturally” with 

minimal or no human intrusion? These are complex questions whose practical 

solutions have evaded some of the brightest minds of today despite, at times, 

reasonably clear policy and legal direction.

But the beauty of today’s forestry issues is that they require discourse, debate, 

and scientific inquiry. They require the involvement of assorted individuals, 

groups, and agencies; they require understanding of complex ecosystems; and, 

perhaps, they require compromise. However, as current issues, many will likely 

be replaced in time by other contentious issues, just as some of the key concerns 

of today were not on the radar screen 20 years ago. And this is where the current 

story begins, with a forestry issue that was on people’s minds 20 to 25 years ago, 

but has since been dropped from many dance cards: long-term site productivity. 

Within the forest management and research community, this topic was crackling 

with energy well before biofuel production, biochar, or hybrid cars gained their 

notoriety.
240 USDA Forest Service Proceedings RMRS-P-61. 2010.

Busse Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests

The notion of long-term site productivity on public forests owes much to the 

congressional mandate set by the Multiple Use—Sustained Yield Act of 1960 

(National Forests shall provide “sustained yield of the several products and ser-

vices... without impairment of the productivity of the land”) and the National 

Forest Management Act of 1976 (“Soil will not be irreversibly damaged”). The 

responsibility of sustaining our nation’s forests for future generations was set 

into law and placed in the hands of forest managers, with open public partici-

pation and with full reliance on professionals from a multitude of forestry and 

ecosystem-related disciplines. Soil scientists, for example, spoke for the land with 

their concerns about detrimental erosion, compaction, and nutrient losses. By the 

1980s, numerous soils-related studies were installed on public forests in an attempt 

to better understand how forest management practices affected the long-term 

productivity of the land. Studies such as the Long-Term Soil Productivity study 

(Powers 2006) and the Long-term Ecosystem Productivity experiment (Bormann 

and others 2008) steamrolled into view. It was during this time period that the 

Bend Long-term Site Productivity study (LTSP) was conceived.

The Bend LTSP study began as a collaboration between the Deschutes Na-

tional Forest, located in the rainshadow of the central Oregon Cascades, and Pat 

Cochran, a research soil scientist with the Pacific Northwest Research Station. 

The National Forest was alarmed about bark beetle infestations in young, dense 

stands of ponderosa pine, and needed to monitor the effects of proposed thinning 

operations on forest health. Was wide-scale thinning an effective practice to limit 

bark beetle attack? And how would pine vegetation, fuels, soils, and wildlife re-

spond to an aggressive thinning regime? For Pat Cochran, a golden opportunity 

was presented to study the fate of thinning residues in pine forests. Should slash 

material be left on site in order to maintain or enhance site productivity? Soil 

scientists had long recognized the quintessential quality of soil organic matter 

as a cornerstone of productive ecosystems. So the definitive question was asked: 

how important is it to retain site organic matter in the form of thinning slash?

Preliminary evidence from central Oregon suggested that thinning residues 

were, indeed, important to site productivity. Little and Shainsky (1995) assessed 

the organic carbon, nitrogen, and phosphorus pools in trees and soils and recom-

mended bole-only harvesting as the best management practice for maintaining 

site productivity. They suggested that whole-tree harvesting could remove a 

substantial percentage of the above-ground nitrogen pool. This finding is sup-

ported by studies in the boreal forests of Scandinavia, in which growth declines 

resulted when harvest slash was removed from site (Jacobson and others 2000). 

And as a general recommendation, Page-Dumroese and others (2010) suggested 

leaving thinning residues on site, particularly in areas with low fertility soils, to 

help (1) maintain soil organic matter levels, (2) increase N cycling, (3) promote 

mycorrhizae development, and (4) minimize detrimental soil compaction.

Evidence to support such recommendations is far from definitive, however. 

In an effort to resolve this issue, LTSP study plots were installed in 1988, and 

the effects of retaining thinning residues were evaluated during the succeeding 

20 years. The objective of this work was to determine whether surface residues 

are advantageous to tree growth, understory plant composition and production, 

wildlife browse, or soil fertility.

Methods

Site Description

The study is located on the Deschutes National Forest, in the pumice plateau 

region of central Oregon between the Cascade Range to the west and the Great 
USDA Forest Service Proceedings RMRS-P-61. 2010. 241

Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests Busse

Basin to the east. Ponderosa pine (Pinus ponderosa C. Lawson) forests are com-

mon throughout the plateau, with their production limited by cold winters and 

dry summers. Annual precipitation ranges from about 30 to 50 cm and occurs 

primarily as snow during the winter months. The growing season for pine typi-

cally lasts from mid-May to mid-August.

Three sites were selected in 1988 along a precipitation gradient that extends east 

from the Cascade crest to the desert fringe. East Fort Rock, the least productive of 

the three sites (site index of 25 m at 100 y), is located near the desert fringe, 17 km 

southeast of Bend, OR; Sugar Cast, a medium site (site index of 31 m at 100 y), is 

located 5 km east of Sunriver, OR; and Swede Ridge, the most productive of the 

three sites (site index of 35 at 100y), is located 20 km west of Bend, OR. These 

sites had been clearcut logged in the 1930s and allowed to regenerate naturally. 

By 1988, stand densities ranged from 480 to 780 trees per hectare (195-315 trees 

per acre), mean tree diameter at breast height (DBH) was 26 cm (10.2 inches), 

and basal areas ranged from 24-33 m

2 ha–1 (105-144 ft2 ac–1).

The soil at the three sites is coarse textured (sandy loam), developing from 

wind blown deposits of pumice and ash from the eruption of Mt. Mazama. Soil 

fertility is low, with both organic matter and total nitrogen contents particularly 

sparse at soil depths below 10 cm. Understory vegetation is comprised primar-

ily of woody shrubs, including antelope bitterbrush (Purshia tridentata [Pursh] 

DC.), snowbrush (Ceanothus velutinus Douglas ex Hook.), and greenleaf man-

zanita (Arctostaphylos patula Greene). Herbaceous species such as bottlebrush 

squirreltail (Elymus elymoides [Raf.] Swezey), western needlegrass (Achnatherum 

occidentale [Thurb.] Barkworth), Idaho fescue (Festuca idahoensis Elmer), Ross’ 

sedge (Carex rossii Boott), Virginia strawberry (Fragaria virginiana Duchesne), 

cryptantha (Cryptantha affinis [A. Gray] Greene), and silverleaf phacelia (Phacelia 

hastata Douglas ex Lehm.) are present at low coverage.

Study Design

Four slash-retention treatments, components of the larger Bend LTSP study 

(see Busse and others 2009 for overall experimental design), were compared: 

whole-tree removal, bole-only removal, thin no removal, plus a no-thin control. 

The experiment was a randomized complete block with three replications (one 

replication per site). Treatment plots were 0.4 ha (1 ac) with 20 m (66 ft) or greater 

between adjacent plots. Thinning guidelines followed the prescriptions used 

by the Deschutes National Forest in 1988. Target basal area was 13.7 m

2 ha–1

(60 ft

2 ac–1) or less with a tree spacing of approximately 5.5 x 6.1 m (18 x 20 ft), 

favoring the removal of damaged or smaller trees. Trees marked for thinning were 

cut by chainsaw between November 1988 and October 1989, and felled trees were 

removed using either a rubber-tire or track grapple skidder. All harvest material 

was removed from whole-tree removal plots; boles were removed from boleonly removal plots, with tree crowns lopped and scattered across the plots; and 

all harvest material was retained on the thin no removal plots, with tree crowns 

lopped and scattered and boles left intact on the ground (fig. 1). Surface residues 

at the start of the experiment averaged 12 Mg ha–1 (5.3 tons ac–1) for both wholetree removal and no thin plots, 26 Mg ha–1 (11.6 tons ac–1) for bole-only removal 

plots, and 48 Mg ha–1 (21.4 tons ac–1) for thin no removal plots.

Two additional LTSP treatments, repeated prescribed fire (fig. 2) and fertil-

ization, will be introduced briefly in the results of this report. Three replicate 

plots of each slash-retention treatment were (1) burned in spring 1991 and spring 

2002 by low-to moderate-intensity prescribed fire, (2) fertilized in 1991 and 1996 

with nitrogen (224 kg ha–1), phosphorus (112 kg ha–1), and sulfur (37 kg ha–1), or 

(3) left unburned and unfertilized. Details of the burn conditions, fire behavior, 

and fertilizer application rates are presented by Busse and others (2009).
242 USDA Forest Service Proceedings RMRS-P-61. 2010.

Busse Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests

Figure 1—Thinning and slash-removal treatments, clockwise from upper left: whole-tree removal; 

bolewood-only removal; thin, no-removal; and no thin.

Figure 2—Typical low- to moderate-intensity prescribed burning in spring 2002. Average 

flame lengths were 0.4 to 0.8 m, with litter and duff depths reduced by about 50 percent.
USDA Forest Service Proceedings RMRS-P-61. 2010. 243

Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests Busse

Site Productivity Measurements

Trees—Diameter at breast height (DBH) of all trees on all plots was measured 

in 1988 (pre-treatment), 1991, 1996, 2001, and 2006. Any mortality or damage 

to trees was recorded at each date. Total tree height was measured in 1991 and 

1996 using an optical dendrometer, with a subset of 15 trees per plot (representing 

a cross-section of tree sizes) measured for volume. Regression equations were 

then developed that predicted tree volume in 2001 and 2006 as a function of field 

measurements of DBH. Periodic annual increment diameter and volume growth 

were calculated for live trees only.

Understory vegetation—Shrub cover was estimated ocularly prior to treatment (1988), and then measured using belt transects in 1993, 1996, 1999, 2003, 

and 2006 (Busse and Riegel 2009). Three belt transects (5 x 20 m) were located 

systematically in each plot, and each shrub within a belt transect was measured 

for canopy length and width. Coverage of an individual shrub was calculated 

assuming an ellipse-shaped canopy. Total cover on a plot basis was calculated as 

the sum of the individual shrubs within the three transects.

Herbaceous plants were clipped at ground level for biomass determination during peak season (mid-June to mid-July) in 1992, 1994, 1997, 1998, 

and 2003. Circular plot frames (1.5 m diam.) were used in 1992 and 1994, and 

rectangular-shaped frames (0.5 x 0.5 m) were used subsequently. All plants within 

8 systematically located plot frames per plot were clipped at each sample date. 

Plant species were identified for richness and diversity indices, clipped, dried, 

and weighed separately for dry matter production for 1992 and 1994 samples 

only. Species diversity was estimated by Simpson’s Diversity Index. After 1994, 

plants were clipped separately by lifeform (forb, graminoid) prior to drying and 

weighing, and no attempt was made to determine species diversity.

Fuel load—Surface woody fuel mass was measured in 1990 (pre-burn), 1991, 

1996, 2001 (preburn), 2002, and 2007 using a modified planar-intercept method 

(Brown 1974). Downed wood was counted on 12 transect lines per plot. Measurement length on each transect line was 3 m for the 1-h and 10-h timelag fuels; 10 m 

for 100-h timelag fuels; and 15 m for 1000-h timelag fuels and larger. Litter and 

duff mass was estimated by collecting 12, 50 x 50 cm samples per plot, located 

adjacent to each transect line. Dry weights were determined following oven drying at 70 °C for 48 h.

Soil—Soil samples were collected periodically from 1989 to 2006 for measurements of organic matter content, total carbon, nutrient content, pH, fertility index 

(carbon:nitrogen ratio), microbial biomass and activity, and microbial diversity 

(phospholipid fatty acids) using standard analytical procedures. Ten samples from 

the surface 10 cm of mineral soil were composited per plot at each sample date. 

In addition, a two-year litter decay study examined differences in decomposition rates among the slash-retention treatments. Briefly, litterfall was collected 

in fall 1992 from areas outside of the treatment plots. A pre-weighed amount of 

litter (about 5 g) was placed in 3 mm-mesh litterbags, and 8 replicate litterbags 

were placed on the forest floor surface of each plot. Two litterbags were collected 

from each plot semi-annually, and the litter was dried and weighed for mass loss.
244 USDA Forest Service Proceedings RMRS-P-61. 2010.

Busse Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests

Data Analyses

The main effects of thinning and slash-retention treatments on vegetation, fuel 

load, and litter decay were analyzed by repeated measures analysis (PROC MIXED 

in SAS 9.1). Treatment effects on soil properties for 2003 samples only were 

tested using ANOVA. Significance for all statistical analyses was set at α = 0.05.

Findings and Implications

Twenty years is a brief period in the life of most ponderosa pine forests. Thus, 

the findings here may represent little more than an extended snapshot in time, 

far from a complete story. However, 20 years is most likely sufficient to draw a 

few conclusions about the fate of thinning residues and their contribution to site 

productivity. Woody residues decay surprisingly fast in this region (Busse 1994), 

a fact that is evident on the LTSP plots: the organic residues have either been incorporated in the duff layer or they have decayed sufficiently that their presence 

is visually undetected. Any measurable effect from this pulse addition of foliage 

and woody material should have registered by now. Of course, whether the pulse 

effect registers in succeeding years remains to be seen. Nevertheless, the results 

from the study were unambiguous, no changes in site productivity indices resulted 

from the gradient of thinning slash left on site. Whole-tree harvesting, bole-only 

harvesting, and leaving all material on site produced similar vegetation and soil 

responses in these forests. The following sections document this observation.

Tree Growth and Mortality

No differences in tree diameter or volume growth were detected among the 

3 slash-retention treatments during the 15-year span between 1991 and 2006 

(fig. 3). This trend held whether the plots were burned or not. In comparison, 

unthinned plots had a 50 percent lower rate of diameter growth compared to the 

thinned treatments, as expected following a thinning. The thinning release was 

expressed in diameter growth only, however. Volume growth was comparable 

between thinned and unthinned plots, reflecting the greater number of trees of 

smaller diameter on the unthinned plots.

Figure 3—Tree-growth increment between 1991 and 2006. Thinning and slash-removal 

treatments were completed in 1989, and repeated prescribed burning occurred in spring 

1991 and spring 2002. Error bars represent the standard error of the mean (n = 3).
USDA Forest Service Proceedings RMRS-P-61. 2010. 245

Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests Busse

Tree mortality was nominal in thinned plots in the absence of fire, with no 

differences found among the slash treatments (table 1). Repeated fire, in comparison, resulted in a reasonably high rate of mortality in the thin no removal 

treatment. This finding was triggered by high-velocity, swirling winds that hit a 

single plot at the time of ignition, killing numerous trees. Still, unthinned plots 

had the highest rate of mortality during the study due to bark beetles (without 

fire) and crown scorch (with fire).

Shrub Cover

Bitterbrush, an important wildlife browse species in central Oregon (Gay 1998), 

was the dominant shrub at two sites, while snowbrush was dominant at Swede 

Ridge, the highest elevation site. No differences in total cover (fig. 4) or cover 

of individual shrub species (data not shown) resulted between 1993 and 2006 

due to the slash treatments. An initial decline in shrub presence was noted after 

thinning (pre-thin cover averaged 28 percent), followed by a steady increase in 

shrub cover which, again, was independent of the slash treatment. Without thinning, the majority of shrubs were gradually eliminated by the end of the study.

Table 1—Stand density (trees per hectare) in 1991 and 2006 and percent mortality 

during the 15-year growth period for whole-tree removal (WT), bole-only removal 

(BR), thin no removal (NR), and no thin (NT) treatments. Plots were thinned in 1989 

and burned in spring 1991 and 2002. Values are means (n = 3) plus standard errors 

in parentheses. 

No fire	 Repeated fire

WT	 BR	 NR	 NT	 WT	 BR	 NR	 NT

1991 276 278 256 615 298 266 274 603

(53) (59) (56) (75) (92) (37) (51) (77)

2006 274 276 256 561 288 262 220 458

(51) (57) (56) (42) (92) (30) (42) (23)

Percent mortality 1 1 0 9 3 2 20 24

Figure 4—Effect of thinning and slash removal on shrub cover. Average shrub 

cover in 1988 prior to thinning was 28 percent. Error bars represent the standard 

error of the mean (n = 3).
246 USDA Forest Service Proceedings RMRS-P-61. 2010.

Busse Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests

Thinning, regardless of slash treatment, was particularly effective at stimulating bitterbrush cover (fig. 5). In comparison, thinning plus repeated fire led to a 

rollercoaster-like response by bitterbrush. Bitterbrush recovery was fairly rapid 

after the initial burn, with no statistical difference in cover detected between 

burned and unburned plots by 6 years after burning (Busse and Riegel 2009). 

However, the second entry of fire resulted in a near collapse of bitterbrush presence 

(fig. 5). Busse and Riegel (2009) surmised that this resulted both from extensive 

seed germination after the first burn, depleting the seed bank, and from the inability of bitterbrush germinants to produce a sizable seed crop in the 11 years 

between burns. This finding has relevance to the management of bitterbrush as 

both a desirable wildlife browse and as an unwanted ladder fuel, and it points to 

a few interesting possibilities for land managers to consider. If the presence of 

bitterbrush for wildlife habitat is preferred, then thinning alone may offer the best 

option. Alternatively, repeated burning offers the best option to eliminate bitterbrush from the understory and promote low wildfire hazard. For an ecological 

balance, a landscape-scale strategy that incorporates a mosaic pattern of thinning 

alone, thinning plus repeated fire, and occasional unthinned stands based on local 

knowledge of wildlife needs and wildfire hazard may be preferred.

Herbaceous Plants

Grasses and forbs were largely missing-in-action at the LTSP study sites. Low 

annual precipitation, the presence of competitive shrubs, and a pumice soil that 

challenges even the hardiest of seedbanks to survive with its diurnal temperature 

flux all contribute to low herbaceous production throughout much of the pumice 

Figure 5—Stand conditions in 2006, 17 years after thinning, with (a) no thinning, (b) thinning, 

(c) thinning plus 1 burn, and (d) thinning plus 2 burns.

(a)

(c)

(b)

(d)
USDA Forest Service Proceedings RMRS-P-61. 2010. 247

Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests Busse

plateau. As testimony, herbaceous biomass was exceedingly low for every LTSP 

treatment except one during the study. This response held for the 3 slash-retention 

treatments, no thin plots, and thin plus burn plots (fig. 6). In concurrence, plant 

diversity was low and no differences were found among LTSP treatments (Busse 

and others 2009). Only the fertilizer plots (nitrogen + phosphorus + sulfur) produced sizeable herbaceous biomass for 2 to 3 years following application in 1991 

and 1996. This observation suggests a few fact-busting possibilities: (1) fire is not 

essential to stimulating herbaceous plants in these forests, (2) poor herbaceous 

production on the pumice plateau is not necessarily related to an impoverished 

seedbank; instead, a large nutrient pulse is required to stimulate plant germination and growth.

Fuel Loading

Downed woody fuels were measured periodically between 1991 and 2007 to 

determine if sizeable or long-lasting differences in fuel loads existed between 

whole-tree removal and bole-only removal thinning practices. Does bole-only 

harvesting result in undesirable fuel loads from a wildfire hazard perspective? By 

the second growing season after thinning (1991), the fuel load of small-diameter 

wood (< 7.5 cm) on bole-only removal plots was about double the amount found 

on whole-tree removal plots (fig. 7). Five years later, however, the difference 

between treatments had subsided and, importantly, the absolute quantity of fuel 

for all treatments was low, and remained low throughout the study. The important 

results to consider are, (1) whole-tree removal was an effective means to maintain 

low fuel loads without prescribed burning, and (2) there was not a long-lasting 

difference in fuel loads between treatments, suggesting that the wildfire hazard 

due to retention of thinning slash is transient.

Figure 6—Herbaceous biomass production during peak growing season. Plots were 

thinned in 1989 and burned in spring 1991 and spring 2002. Error bars represent the 

standard error of the mean (n = 3). 
248 USDA Forest Service Proceedings RMRS-P-61. 2010.

Busse Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests

Soil Productivity

The slash-retention treatments had little or no effect on the soil properties by 

2003, 14 years after thinning. In addition to the basic measures reported in 

table 2, no differences among treatments were detected in nutrient concentrations 

(Ca, Mg, K), microbial respiration and diversity, or duff depth. Similar results 

showing little effect of slash treatment were noted for soils collected earlier in the 

study (1993 and 1998). Some soil compaction due to the thinning operation was 

found on whole-tree removal plots (Parker and others 2007). However, the effects 

were not extensive, as no detrimental effects to trees or understory vegetation 

were detected on a plot basis (Busse and others 2009).

Figure 7—Changes in fine woody fuel mass (0 to 7.5 cm diameter) between 1991 and 

2007. The fuel load in 1991 for “Bole-only removal + fire” was measured after the initial 

prescribed burn. Error bars represent the standard error of the mean (n = 3).

Table 2—Effect of thinning and slash removal on selected soil quality characteristics. 

Samples were collected from the surface 10 cm of mineral soil in 2003, fourteen 

years after thinning. Means plus standard errors in parentheses within a row followed 

by different letters are significantly different at a = 0.05 (Tukey’s HSD).

	 	 Whole-tree	 Bole-only	 Thin, no

	 Soil property	 removal	 removal	 removal	 No thin

pH 6.5 (0.2) 6.5 (0.1) 6.4 (0.1) 6.5 (0.1)

Carbon (g kg–1) 25.3 (3.6) 29.3 (4.0) 26.0 (3.6) 23.4 (3.2)

Nitrogen (g kg–1) 0.6 (0.1) 0.7 (0.1) 0.6 (0.1) 0.6 (0.1)

C:N 40 (2) ab 40 (1) ab 43 (2)a 37 (1)b

Phosphorus (mg kg–1) 39 (10) 47 (8) 44 (8) 47 (11)

Litter decay (percent 36 (3) 37 (8) 40 (2) 37 (4)

 mass loss in 2 years)

Microbial biomass (mg kg–1) 772 (106) 902 (174) 818 (124) 652 (28)
USDA Forest Service Proceedings RMRS-P-61. 2010. 249

Site Quality Changes in Response to Slash Retention and Prescribed Fire in Thinned Ponderosa Pine Forests Busse

Conclusions

No differences in vegetation, wildlife browse, or soils due to whole-tree harvesting versus either bole-only harvesting or leaving all material on site were 

identified in 20 years of study. Thus, to answer Pat Cochran’s original question 

about the importance of retaining site organic matter: thinning slash apparently does not need to be left on site in order to maintain site productivity. This 

suggests that single-entry thinning operations on the pumice plateau in central 

Oregon should treat slash residues based on practical concerns (type of logging 

equipment available, budget constraints), not based on ecological concerns for 

site productivity. The rapid reduction in fuel load found for the bole-only removal 

treatment additionally suggests that wildfire hazard due to post-thinning slash 

is a transient concern.

How universal are the study results? Certainly any inferences made beyond 

the geographical constraints of the Bend LTSP study in central Oregon should be 

made with caution. Differences in forest type, soil, climate, thinning prescription, 

or harvest equipment may all contribute to unique responses to slash treatment 

among forests and regions. In this regard, Page-Dumroese and others (2010) suggest that the value of retaining thinning slash is greatest for low fertility soils, 

those that would benefit from added sources of organic matter and nutrients. 

However, this recommendation was not supported by the LTSP results despite 

the low fertility of the region’s pumice soils. Examination of the nutrient pools 

in central Oregon pine forests provides clarification for this assertion. Based on 

the nutrient profile reported by Little and Shainsky (1995) for the three LTSP 

sites, only about 4 percent of the total site nutrient pool was removed by wholetree thinning compared to 1 percent by bole-only harvesting. Thus, removal of 

foliage and limbs during thinning accounted for only a 3 percent loss of the site 

nutrient pool, helping explain the lack of vegetation and soil responses to the 

slash treatments.

To summarize the key findings of the Bend LTSP study, (1) the retention of 

thinning slash was trivial to the health of central Oregon pine forests, (2) increased 

fuel loads for bole-only compared to whole-tree harvesting were transient, (3) thinning alone had a positive effect on wildlife browse and tree vigor without causing 

detrimental changes to understory vegetation or soil productivity, and (4) thinning 

plus repeated fire eliminated wildlife browse (bitterbrush), yet was an effective 

treatment for reducing wildfire hazard and maintaining site and soil productivity.

References

Bormann, Bernard, T.; Homann, Peter S.; Darbyshire, Robyn L.; Morrissette, Bret A. 2008. Intense forest wildfire sharply reduces mineral soil C and N: the first direct evidence. Canadian 

Journal of Forest Research 38: 2771-2783.

Brown, James K. 1974. Handbook for inventorying downed woody material. Gen. Tech. Rep. 

INT-16. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and 

Range Experiment Station. 24 p.

Busse, Matt D. 1994. Downed bole-wood decomposition in lodgepole pine forests of central Oregon. Soil Science Society of America Journal 58:221-227.

Busse, Matt D.; Riegel, Gregg M. 2009. Response of antelope bitterbrush to repeated burning in 

central Oregon ponderosa pine forests. Forest Ecology and Management 257:904-910.

Busse, Matt D.; Cochran, P.H.; Hopkins, William E.; Johnson, William H.; Fiddler, Gary O.; 

Riegel, Gregg M.; Ratcliff, Alice W.; Carol J. Shestak. 2009. Developing resilient ponderosa 

pine forests with mechanical thinning and prescribed fire in central Oregon’s pumice region. 

Canadian Journal of Forest Research 39: 1171-1185.

Gay, D., 1998. A test of the southcentral Oregon mule deer habitat suitability index model. MS 

Thesis, Univ. Idaho, Moscow, USA.
250 USDA Forest Service Proceedings RMRS-P-61. 2010.

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Jacobson, S.; Kukkola, M.; Mälkönen, E.; Tveite, B. 2000. Impact of whole-tree harvesting 

and compensatory fertilization on growth of coniferous thinning stands. Forest Ecology and 

Management 129:41-51.

Little, Susan N.; Shainsky, Lauri J. 1995. Biomass and nutrient distributions in central Oregon 

second-growth ponderosa pine ecosystems. Res. Pap. PNW-RP-481. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 17 p.

Page-Dumroese, Debbie S.; Jurgensen, Martin; Terry, Thomas. 2010. Maintaining soil productivity during forest or biomass-to-energy thinning harvests in the western United States. Western 

Journal of Applied Forestry 25:5-11.

Parker, R.T., D.A. Maguire, D.D. Marshall, and P. Cochran. 2007. Ponderosa pine growth response 

to soil strength in the volcanic ash soils of Central Oregon. Western Journal of Applied Forestry 

22:134-141.

Powers, Robert F. 2006. Long-Term Soil Productivity: genesis of the concept and principles behind 

the program. Canadian Journal of Forest Research 36: 519-528.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
USDA Forest Service Proceedings RMRS-P-61. 2010. 251

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Silviculturist (retired), USDA Forest 

Service, Salmon-Challis National Forest, Salmon, ID.

2 Assistant Manager, Lucky Peak Nursery, USDA Forest Service, Boise, ID.

3 Computer Programmer Analyst, 

USDA Forest Service, Boise, ID.

Stand Density Guides for Predicting

Growth of Forest Trees of Southwest 

Idaho

Douglas D. Basford1, John Sloan2, and Joy Roberts3

Abstract—This paper presents a method for estimating stand growth from stand density and average diameter in stands of pure and mixed species in Southwest Idaho. 

The methods are adapted from a model developed for Douglas-fir, ponderosa pine, 

and lodgepole pine on the Salmon National Forest. Growth data were derived from 

ponderosa pine increment cores taken from sample plots on the Boise and Payette 

National Forests. The model was tested against growth plots and permanent inventory 

plots located on the Boise and Payette National Forests. Model results closely fitted 

observed dbh’s on these managed and unmanaged stands. These stands were summarized by group with average errors ranging from 0.8 to 2.9 percent (r2 = 0.90 to 0.96).

Introduction

Stand density is a critical element in managing forests of the Western United 

States. Although managing stand density is commonly associated with timber 

production, it also affects wildlife habitat, water yield, and other forest resources. 

To manage forests, resource managers need accurate tools to predict how stand 

density affects future growth, which in turn affects various forest uses and values. 

The Relative Stand Density Index (Drew and Flewelling 1979) and Reineke’s 

Stand Density Index (Gingrich 1967; Reineke 1933) provide useful formats in 

some situations.

Basford and others (1991) discussed the stand density considerations in forest 

treatments and the procedure of establishing and measuring permanent growth 

plots over time. Subjective decision processes based only on experience or related 

published data, are less time consuming but can lead to less accurate results. 

Consequently, inappropriate growing space applied during thinning treatments, 

or a delay in thinning, has caused reduced canopies and slower stand growth 

than expected.

To address these questions, a 1991 study on the Salmon NF developed stocking guides for Douglas-fir (Pseudotsuga menziesii var. glauca [Beissn.] Franco), 

ponderosa pine (Pinus ponderosa Dougl. Ex Laws.), and lodgepole pine (Pinus 

contorta var. latifolia Engelm.) (Basford and others 1991). The model developed 

for ponderosa pine is called the Salmon-Challis ponderosa pine model (SCPP), 

and using this model helped foresters on the Salmon NF make better predictions 

about growth expected after thinning.

In 1999, personnel from the timber staff on the Boise and Payette National 

Forests (NFs) requested that a study be undertaken to determine stand density 

recommendations for ponderosa pine, because current recommendations were 

not working. The majority of all 30 to 50 year old plantations on the Boise and 

Payette NFs had been thinned at least once in the last 20 to 30 years. Most did 
252 USDA Forest Service Proceedings RMRS-P-61. 2010.

Basford, Sloan, and Roberts Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho

not respond to thinning treatments as desired due to inadequate spacing to allow 

stand release.

As a result of this concern, staffers adopted the approach that had worked to 

build the SCPP model. That result, the Boise-Payette ponderosa pine model (BPPP), 

predicts the future diameter growth and density of both thinned and unthinned 

stands in southwest Idaho. The model allows managers to closely replicate stand 

growth rates using an automated process that otherwise would be acquired only 

through repetitive permanent sample plots.

Development of the BPPP stand growth models for the southwest Idaho forests 

included the use of computer program (BKTHIN) that allows for data input, model 

processing and tabular output. The program is easy to use and does not require 

large amounts of complex data. The growth models are based on measurements 

from individual trees and their observed growth patterns. To validate the accuracy 

of the models, tests were run using stand data from permanent growth and inventory plots. The predicted growth was compared to actual measured growth. This 

computer program, BKTHIN, is an important and efficient tool to help manage 

forest stands to meet identified objectives.

Methods, Results, and Discussion

The following discussion is organized to first summarize data collection of 

sample, released, and “free-to-grow” trees, followed by a description of the development of mathematical models (BPPP and SCPP, within the BKTHIN computer 

program), and ending with a summary of model validation.

Data Collection

The growth equations for the Boise and Payette NFs were derived by analyzing increment cores, rather than by measuring permanent sample plots over time. 

Increment cores were taken from ponderosa pine trees in plots of different density 

levels on the two National Forests. A total of 133 plots were selected, representing 

an average diameter range of 2 inches up to 22 inches. Each plot contained a sample 

tree, surrounded by other trees of the same species, uniform in size and spacing, 

so that each sample tree experienced consistent competition on all four sides 

(fig. 1). All sample plots were free from mortality and from insects and disease. 

It was assumed that any reductions in growth rates would be related to density.

Figure 1—Distribution of sample tree to adjacent trees (A through D)
USDA Forest Service Proceedings RMRS-P-61. 2010. 253

Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho Basford, Sloan, and Roberts

Sample tree selection and data collection followed procedures outlined in Basford and others (1991). Sample plots of uniform condition were located. Sample 

trees were selected from stands with a relatively narrow range of tree diameters 

and spacing, with at least four adjacent competing trees (fig. 1). Sample trees were 

selected so that the diameter differences between the sample tree and adjacent 

competing trees did not exceed one third of the diameter of the sample tree (for 

example, if the sample tree diameter at breast height was 9 inches, the diameter of 

the adjacent trees was between 6 and 12 inches.). Also, the distance between the 

sample tree and the four adjacent trees could not differ by more than 25 percent.

Increment cores were taken from the uphill and downhill side of each sample 

tree. Measurements on each increment core to record radial distance inside bark 

to each identified growth period were taken and then averaged (fig. 2). Diameter 

outside bark was calculated using the equation described by Husch and others 

(1972).

The average number of rings per inch (rpi) for each growth period was calculated 

by dividing the number of rings by the length of the core within that period, and 

then averaging the measurements from the uphill and downhill cores.

On each sample plot, site and tree data were collected. Data from sample plots 

of similar site quality (habitat type) were grouped, and the distances between the 

boles of the sample tree and adjacent trees were averaged. A linear regression line 

for the transition points between each growth period was drawn. For all regressions, graphs of residuals versus predicted values were examined, and standard 

deviations about the regressions were calculated as measures of reliability.

“Free-to-grow” trees—To establish potential diameter growth rates for trees 

without competition, large open-grown ponderosa pine trees were sampled. These 

were called “free-to-grow” trees. A total of 17 of these trees were sampled, 11 on 

the Idaho City Ranger District, Boise NF, and 6 on the Council Ranger District, 

Payette NF. Increment cores and bark thickness were taken from each sample 

tree, and rpi was calculated using the same tree sampling procedures as identified earlier.

Figure 2—These cores are from a sample tree to illustrate growth rates, periods of uniform growth, and 

points at which growth reduction occurs. An additional 6th growth period was identified on other core 

samples. Rings per inch (RPI) are computed for each growth period.
254 USDA Forest Service Proceedings RMRS-P-61. 2010.

Basford, Sloan, and Roberts Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho

Live crown ratio of released trees—Released trees are those that have been 

under competition at some previous point but, that at the time of measurement, 

were not competing with other trees. These trees had a smaller live crown ratio 

than open-grown trees of similar diameter, due to competition before thinning. 

Live crown ratios were measured and growth rates calculated in order to determine 

potential growth rates when a stand had been thinned and released from competition (table 1). This aspect is important to the model because it will determine 

what the maximum growth rate will be after thinning.

Analysis of Data

Sample tree—The cores from trees that met the strict sample criteria all showed 

identifiable transitions, usually over a period of two to three rings where growth 

slowed. After each identifiable growth reduction point (transition), the rings in the 

core will again grow at a fairly constant, although slower, rate. The cores in figure 

2 show these growth reduction points and growth periods. If the strict sampling 

criteria were not met, these key transition points between growth periods, which 

are very gradual, and could be misidentified. The analysis also shows that the 

timing and diameter at these reduction points can be predicted (fig. 3).

Each of these five growth reduction points was grouped. From these groups, a 

linear regression was computed between average tree spacing (Y) and diameter 

(X) of the sample trees. The resulting equations represent lines at which the 

growth reduction is expected to take place as a stand grows, at any given density 

and average diameter (table 2).

Table 1—Crown ratio measurements. The spacing and average diameters were grouped into crown ratio 

classes and a regression calculated. The independent variable, X, is the tree diameter (inches). The 

dependent variable, Y, is tree spacing (ft).

	 	 	 	 	 Predicted	 10 year

	 	 	 	 	 rings/inch	 diameter

	 	 Crown	 Sample	 Crown competition	 	 after	 growth after

	 Species	 ratio	 size	 equations	 r2	 release	 release

(percent) (inches)

Douglas-fir 60 - 70 17 Y = 1.86 + 1.15X 0.85 8.4 2.4

(Salmon-Challis N.F.) 50 - 59 20 Y = 1.42 + 0.98X 0.79 8.8 2.3

40 - 49 26 Y = 0.89 + 0.94X 0.68 10.4 1.9

30 - 39 10 Y = 0.88 + 0.88X 0.81 12.8 1.6

< 30 — — 17.2 1.2

Ponderosa pine 60 - 70 7 Y = 0.46 + 1.14X 0.90 8.3 2.4

(Salmon-Challis N.F.) 50 - 59 12 Y = 0.45 + 1.04X 0.92 9.4 2.1

40 - 49 18 Y = –0.22 + 1.02X 0.78 10.8 1.9

30 - 39 11 Y = –1.29 + 1.04X 0.87 13.0 1.5

Lodgepole pine 50 - 70 38 Y = –0.39 + 1.32X 0.52 No Data No Data

(Salmon-Challis N.F.) 30 - 40 15 Y = –1.26 + 1.26X 0.65 No Data No Data

Ponderosa pine 60 - 80 55 Y = –0.50 + 1.35X 0.84 5.4 3.7

(Boise-Payette N.F.’s) 50 - 59 57 Y = –0.48 + 1.18X 0.71 6.9 2.9

40 - 49 24 Y = –1.44 + 1.17X 0.82 6.9 2.9

30 - 39 11 Y = –1.46 + 1.05X 0.85 7.4 2.7

< 30 — — 10.7 1.9
USDA Forest Service Proceedings RMRS-P-61. 2010. 255

Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho Basford, Sloan, and Roberts

Figure 3—Spacing and diameter related to growth reduction lines and growth periods.

Table 2—Comparison of tree spacing / diameter regression equations. Regression equations 

represent the transitions between the maximun average growth rates. The independent variable, X, is the 

tree diameter. The dependent variable, Y, is tree spacing. The Boise-Payette equations are presented 

graphically in figure 4.

	 	 Average	 10 year	 	 Range

	 	 growth	 diameter	 	 of data	 Equations

	 	 rate	 growth	 Sample	 (dbh)	 (X = Diameter)

	 Species	 (rings/in)	 (inches)	 size	 (inches)	 (Y = Spacing)	 r2

Douglas-fir

(Salmon-Challis N.F.)8.4 2.4 36 1.1-14.3 Y = 1.11 + 1.85X 0.91

16.0 1.3 29 2.4-21.2 Y = 1.10 + 1.25X 0.88

29.6 0.7 7 5.6-14.2 Y = 0.08 + 1.01X 0.92

Ponderosa pine 

(Salmon-Challis N.F.)

8.3 2.4 25 1.0-11.9 Y = 2.68 + 1.58X 0.86

16.4 1.2 21 2.2-16.2 Y = 1.09 + 1.23X 0.91

31.6 0.6 8 4.1-20.8 Y = 0.59 + 1.02X 0.96

Lodgepole pine

(Salmon-Challis N.F.)

8.9 2.2 39 1.6-12.4 Y = 1.63 +1.80X 0.9

19.4 1.0 37 2.2-10.6 Y = 0.62 + 1.39X 0.81

35.1 0.6 22 2.7-12.6 Y = –1.10 + 1.41X 0.89

Ponderosa pine

(Boise and Payette N.F.’s) 5.4 3.7 63 2.3-16.4 Y = 1.72 + 1.72X 0.88

9.6 2.1 30 4.4-22.1 Y = 1.72 + 1.43X 0.81

13.4 1.5 19 2.5-17.5 Y = 1.57 + 1.24X 0.77

23.1 0.9 14 3.2-19.7 Y = 1.48 + 1.01X 0.99

44.3 0.5 7 3.6-19.7 Y = 0.60 + 0.93X 0.96
256 USDA Forest Service Proceedings RMRS-P-61. 2010.

Basford, Sloan, and Roberts Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho

Free-to-grow—Data from the free-to-grow trees were used to determine the 

potential diameter growth rates in the absence of intertree competition as described above. Increment core analysis of the free-to-grow trees showed periods 

of relatively uniform diameter growth separated by points of identifiable growth 

reduction (table 3). Average diameter was computed at each growth reduction 

point and average growth rate was computed for each growth period. However, 

in these trees, growth reductions were due to tree physiology and occurred at 

larger diameters than those under competition.

The potential radial growth rates from free-to-grow trees were used to validate 

the average rpi for the first growth period of the sample trees. Sample trees that 

showed growth rates that were not within three rings of the potential rate for the 

site, as indicated by the free-to-grow trees, were rejected because their growth 

rates exceeded the identified variance. The reduced growth rates were likely due 

to an unidentified cause such as competition from nearby trees that had since 

disappeared.

Crown ratio of released trees—The last, and important, part of the analysis 

is the live crown ratio component. As stand competition increases, live crown 

ratios become smaller. Crown ratios and diameter growth rates were measured on 

trees that had been released from competition through thinning. The live crown 

ratios were grouped and the growth rates (rpi) within each group were averaged. It was found that when the live crown ratios are 60 percent or greater, the 

predicted growth rate closely reflected potential diameter growth rate (table 1). 

When the live crown ratio is less than 60 percent, the reduced photosynthetic 

base prevents the trees from reaching its predicted growth potential. When live 

crown ratio falls below 20 percent, little or no response is expected from a tree 

retained after thinning.

After calculating the predicted radial growth rate following thinning, the 

model compares it to the growth rate based on the crown ratio of release trees, 

and chooses the slower of the two. In this way, the model prediction would not 

allow a stand to grow faster than a reduced crown ratio would allow.

Computer Program Development

The mathematical models were programmed into a framework to facilitate 

user input, data processing and tabular output. The program was compiled using 

Microsoft Visual Basic®6.0. This computer application called BKTHIN can be 

run on most computers using Windows operating systems. The software and a 

user’s guide can be obtained upon request. The minimum inputs are stand density 

and diameter. Remaining inputs can be used to further refine the results.

The BPPP Model

We compared results for the Boise-Payette model (BPPP) with three similar 

models from the Salmon-Challis National Forest (Salmon-Challis ponderosa 

pine = (SCPP), Salmon-Challis Douglas-fir = (SCDF), and Salmon-Challis lodgepole pine = (SCLP) (Basford and others 1991). The Boise-Payette model is based 

on more sample plots than the Salmon- Challis models, but the range of sample 

tree size is very close. The BPPP model generates faster growth rates than the 

other three, and has five growth reduction regression lines compared to three 

regression lines for the other models (table 2). The growth rates are slower in the 

Salmon-Challis models because they were developed from drier sites with 100-

year site indices ranging from 60 to 80, whereas Boise and Payette NFs sites have 

100-year site indices in the range of 80 to 115. These groups of growth reduction 

lines are limited by the size of sample trees available to sample. If larger sample 
USDA Forest Service Proceedings RMRS-P-61. 2010. 257

Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho Basford, Sloan, and Roberts Table 3—Mean diameter growth rates during each growth period and the mean diameter at the growth reduction points for free-to-grow trees. The formula to convert rings/ inch to diameter growth per decade = 20/rings per inch. Growth reduction points Site	 No. of	 	 	 	 Rings/inch	 	 Rings/inch	 	 Rings/inch 	Ranger District/ index	 trees in	 Rings/inch	 dbh	 between	 dbh	 between	 dbh	 between 	 Forest	 Speciesa	 Habitat Type 	 meanb	 sample	 before 1st	 at 1st	 1st and 2nd	 at 2nd	 2nd and 3rd	 at 3rd	 3rd and 4th North Fork/SCNFc PP Psme/Caru-Pipo 75 11 7.5 13.2 12.4 19.8 19.8 26.1 26.8 Psme/Cage-Pipo Psme/Syal-Pipo Idaho City/BNFde PP Psme/Caru-Pipo 109 11 7.4 22.3 11.5 30.9 20.2 36.4 32.1 Psme/Spbe-Pipo Psme/Syal-Pipo Psme/Cage-Pipo Psme/Phma-Pipo Council/PNFf PP Abgr/Spbe 115 6 6.9 25.8 11.6 31.7 17.7 35.2 36.3 Abgr/Caru Salmon/SCNF DF Psme/Feid-Feid 65 4 9.2 12.8 16.3 21.9 21.2 Psme/Caru-Feid Leadore/SCNF DF Psme/Caru-Caru 65 3 8.9 11.5 12.4 18.3 25.9 Salmon/SCNF DF Psme/Caru-Caru 75 4 7.8 16.4 10.8 26.6 17.0 33.2 29.2 North Fork/SCNF DF Psme/Caru-Pipo 82 3 8.1 20.5 13.7 28.5 23.9 30.6 44.6 Salmon/SCNF LPP Psme/Caru-Caru 68 6 7.1 9.5 11.8 a PP = ponderosa pine, DF = Douglas-fir, LPP = Lodgepole pine b of 100-year base age c SCNF = Salmon-Challis National Forest d BNF = Boise National Forest e Tree diameters on the BNF were larger than the SCNF resulting in the additional growth reduction points listed as follows: dbh at 4th = 42.2, rpi between 4th and 5th = 43.3, dbh at 5th = 46.6, rpi between 5th and 6th = 60.5, dbh at 6th = 52.4, rpi after 6th reduction = 77.5 f PNF = Payette National Forest
258 USDA Forest Service Proceedings RMRS-P-61. 2010.

Basford, Sloan, and Roberts Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho

trees could have been found that met the sample criteria, more growth reduction 

equations would have been developed. The r2 values for the BPPP model reduction equations for all models show that the data points (Y=spacing and X=dbh) 

form acceptable linear relationships.

The growth reduction equations in table 2 for ponderosa pine on the BoisePayette NFs are graphed in figure 4. This figure shows the simplified results of 

the BPPP model. The solid lines represent the range of data. Dotted lines indicate 

where growth rates have been extrapolated. Using the average spacing and the 

average diameter, the growth of a ponderosa pine stand can be predicted using 

the graph. For instance, a stand grown for 80 years, starting with a mean diameter of 4 inches and a spacing of 16 feet, will grow at a rate of 5.4 rpi (3.7 inches/

decade), reaching a mean diameter of 8.3 inches in the first growth period. In the 

second growth period, growth will slow to 9.6 rpi (2.08 inches/decade), and the 

stand will reach a mean diameter of 9.9 inches. Growth will then slow to 13.4 rpi 

(1.49 inches/decade) in the third growth period, and the stand will attain a mean 

diameter of 11.5 inches. Growth then slows to 23.1 rpi (0.87 inches/decade) in the 

fourth growth period with a mean diameter of 14.4 inches. Finally, in the fifth 

growth period the rpi slows to 44.3 rpi (0.45 inches/decade), reaching a mean 

diameter of 16.8 inches within the 80 years allowed by the model. The stand will 

have grown from 4 inches to 16.8 inches in an 80 year period without thinning. 

To predict the results of a thinning to increase the growth rate, simply move up 

the spacing axis to the new spacing and continue from there.

The other component of the BPPP model is live crown ratio. The live crown 

ratio observations were averaged and show that as crown decreases, tree diameter 

growth slows. This average is used in BPPP to determine the predicted growth of 

Figure 4—Ponderosa pine diameter growth rates (rings-per-inch, rpi) associated with 

spacing and tree size in the Boise-Payette National Forests. Equations for each regression 

line are presented in table 2. The solid lines represent the range of data and the dotted 

lines where growth rates have been extrapolated. The diameter growth before the first 

regression line (first growth period) is 5.4 rpi, second growth period 9.6 rpi, third growth 

period 13.4 rpi, fourth growth period 23.1 rpi, fifth growth period 44.3 rpi, and the sixth 

growth period 68.6 rpi.
USDA Forest Service Proceedings RMRS-P-61. 2010. 259

Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho Basford, Sloan, and Roberts

a stand after its release from intertree competition. For example, if the live crown 

ratio is above 60 percent, then the predicted growth rate in the first growth period 

will be 5.4 rpi. Below 60 percent the growth rate slows. Below 30 percent live 

crown ratio, the expected growth rate should be only 10.7 rpi (table 1).

BKTHIN Output

Table 4 presents an example of the BKTHIN program output. All of the settings 

that have been entered are displayed in the heading of the output. In this example, 

using the Boise/Payette model, the beginning stand diameter was 4.2 inches at 

Table 4—Example of an output from the Boise-Payette BPPP model. YR = Year; AGE = average stand age; 

DIAMETER = average diameter at breast height (inches); D-Gr RPI = diameter growth in rings per inch; T/A = trees 

per acre; HT = average height (ft); CU-FT = stand volume in cubic feet per acre; MAI = mean annual increment 

in cubic feet per acre; MBF = stand volume in 1000 board feet per acre; MAIBF = mean annual increment in 

board feet per acre; BA/A = basal area per acre; R-SDI = Reinke’s stand density index; MCA= maximum crown 

area, CCF = crown competition factor; CR-% = crown ratio percent/10; CW = crown width in feet; CSEP = crown 

separation distance in feet; WPB = western pine beetle index from low (0), to high (11).

7/9/2005 Rev 4.0.9 8/24/2004 BOISE/PAYETTE NF BASFORD-KEENE PROGRAM

INITIAL STAND CONDITIONS

Stand Description: PP/DF Site Stand Table Mortality Factor for 6 Growth Periods: .05, 

Productivity Class: Medium 0.1, 0.3, 0.3, 0.5, 0.5 

Height Relation: DBH/HT

Average Stand Diameter: 4.2 THINNING INFORMATION

Optimum Diameter Growth: 5.4 Distribution: User Specified

 Initial Spacing: 9.0 X 9.0 ft. Thinning Diameters: 4.2, 12.1, 0.0, 0.0, 0.0 in.

Stockability %: 100

Stand Age: 30 After Thin Spacing: 18.0, 24.0, 0.0 ft

Years Projected: 27 Minimum Cut MBF: 0.0

	 	 	 D-G	 	 	 	 	 	 	 	 	 	 	 	 	

YR	 AGE DBH RPI T/A HT CU-FT MAI MBF MAIBF BA/A R-SDI MCA CR-% CW CSEP WPB

0	 30 4.2 5.4 538 24 0 0.0 0.0 0 52 134 51 7-9 7 1.9 0.0

COMM THIN REMOVAL	403 0 0.0 Thinned to: 18.0 X 18.0 Ft.

1	 31 4.6 5.4 134 26 0 0.0 0.0 0 16 39 14 7-9 8 10.5 1.0

2	 32 5.1 5.4 134 28 0 0.0 0.0 0 19 45 16 7-9 8 10.1 1.2

3	 33 5.5 5.4 134 30 0 0.0 0.0 0 22 51 18 7-9 8 9.7 1.2

4	 34 5.9 5.4 134 32 0 0.0 0.0 0 26 58 20 7-9 9 9.2 1.2

5	 35 6.3 5.4 134 34 0 0.0 0.0 0 29 64 23 7-9 9 8.8 1.2

6	 36 6.8 5.4 134 35 0 0.0 0.0 0 33 71 25 7-9 10 8.4 1.2

7	 37 7.2 5.4 134 37 301 13.7 1.3 244 38 79 27 7-9 10 8.0 1.2

8	 38 7.6 5.4 134 39 392 17.1 1.3 233 42 86 30 7-9 10 7.6 1.2

9	 39 8.0 5.4 134 41 494 20.6 1.3 224 47 94 32 7-9 11 7.1 1.2

10	 40 8.5 5.4 134 43 606 24.2 1.3 215 52 102 35 7-9 11 6.7 1.2

11	 41 8.9 5.4 134 44 728 28.0 1.3 206 58 111 38 7-9 12 6.3 1.2

12	 42 9.3 5.4 134 46 861 31.9 1.3 199 63 119 41 7-9 12 5.9 1.2

13	 43 9.7 9.6 133 48 1006 35.9 1.3 192 69 128 44 7-9 13 5.5 1.2

14	 44 10.0 9.6 133 49 1092 37.6 1.3 185 72 133 46 7-9 13 5.2 1.2

15	 45 10.2 9.6 133 50 1181 39.4 1.6 187 76 138 47 7-9 13 5.0 1.4

16	 46 10.5 9.6 133 51 1274 41.1 2.1 199 79 143 49 7-9 13 4.8 1.4

17	 47 10.7 9.6 133 52 1371 42.8 2.7 211 83 148 51 7-9 13 4.5 1.4

18	 48 10.9 9.6 133 52 1471 44.6 3.3 223 87 153 53 7-9 14 4.3 1.4

19	 49 11.2 9.6 133 53 1576 46.3 3.9 234 90 159 55 7-9 14 4.1 4.8

20	 50 11.4 13.4 132 54 1684 48.1 4.6 246 94 164 57 7-9 14 3.8 4.8

21	 51 11.6 13.4 132 55 1760 48.9 5.1 252 97 167 58 7-9 14 3.7 4.8

22	 52 11.8 13.4 131 56 1838 49.7 5.5 258 99 171 59 7-9 15 3.5 4.8

23	 53 11.9 13.4 131 56 1917 50.5 6.0 264 102 174 61 7-9 15 3.3 4.8

24	 54 12.1 13.4 131 57 1999 51.2 6.5 270 105 178 62 7-9 15 3.2 4.8

COMM THIN REMOVAL	 55 839 2.7 Thinned to: 24.0 X 24.0 Ft.

25	 55 12.5 5.4 76 58 1283 53.0 4.5 181 65 109 38 7-9 15 8.7 1.4

26	 56 13.0 5.4 76 60 1420 55.1 5.3 197 69 114 40 7-9 16 8.3 1.4

27	 57 13.4 9.6 75 62 1565 57.2 6.2 213 74 120 42 7-9 16 7.9 1.4
260 USDA Forest Service Proceedings RMRS-P-61. 2010.

Basford, Sloan, and Roberts Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho

age 30, and rate of growth was 5.4 rpi. Initial spacing was 9- by 9-feet with two 

projected thinnings (using user-specified spacing): one initially at 4.2 inches 

(18- by 18-feet spacing), and the second at 12.1 inches in diameter (24- by 24-feet 

spacing). The output is given in a year by year summary. After “Year 0” the stand 

was thinned to a spacing of 18- by 18-feet removing 403 trees per acre. Yearly 

updates of stand conditions are reported. After “Year 24,” at stand age 54, and 

mean diameter 12.1 inches, a second thinning removed 55 trees per acre and the 

final spacing was 24- by 24-feet. The table ends in “Year 27” with a mean stand 

diameter of 13.4. If no thinning had taken place in this example the resulting 

average stand diameter would have been 7.7 instead of 13.4 inches.

Model Validation

The model results were validated using data from four sets of inventory and 

permanent growth plots in thinned and unthinned stands. The data used for 

validation include average density (trees per acre), number of years to last measurement, and average beginning diameter. This data was entered into the BPPP 

to calculate growth. The computed ending diameter was then compared to the 

measured mean diameter and percent error was calculated. For thinned stands, 

the measurement periods went from the time of thinning to the final inventory. 

For the unthinned plots, the measurement period ran from age 0 (dbh) to the age 

of the stand at final measurement. To validate, the actual average diameters were 

compared to those predicted by the model. Each line in tables 5 to 8 represents 

an individual stand and a model prediction for that stand.

The first set of data came from permanent growth plots on the Payette NF 

(Unpublished data on file at the Supervisors Office, Payette National Forest, 

McCall, Idaho. 2003a). These 61 plots contained mixed species and pure stands 

of ponderosa pine. They were thinned then re-measured 12 to 21 years later. The 

average thinned spacing ranged from 13.5 to 28.7 feet. Comparing the computed 

Table 5—Summary of actual versus predicted diameter growth for a 12 to 21 period on 61 thinned permanent growth 

plots of Payette NF ponderosa pine and mixed stands using the Boise-Payette model. Percent growth error was 

calculated according to (predicted diameter - ending diameter)/ending diameter. The r2 value for actual diameter vs. 

predicted diameter is 0.91.

Number 	 	 	 Number of	 Diameter	 	 	 	 Percent

of	 	 Spacing	 years for	 spread	 Average dbh (inches)	 error

Species	 stands	 Trees/acre	 (ft)	 run	 (in)	 Initial	 Ending	 Predicted	 (%)

PP 34 85 - 213 13.5 - 22.6 15 3.0 - 23.3 7.2 11.4 11.3 –0.9

PP,DF 10 67 - 200 14.8 - 25.5 15 3.5 - 22.9 8.0 11.5 11.8 2.6

PP,DF 3 113 - 167 16.2 - 19.6 12 4.3 - 19.0 11.8 12.8 13.7 7.0

PP,DF 1 93 21.6 21 7.7 - 27.4 14.8 18.2 17.5 –3.8

PP,DF,LP 1 167 16.2 15 3.7 - 17.2 8.9 11.9 11.8 –0.8

PP,WL 1 213 14.3 15 5.3 - 11.2 4.7 8.9 9.4 5.6

PP,LP 1 180 15.6 15 4.0 - 17.1 8.2 12.7 11.3 –11.0

PP,GF 1 140 17.6 15 7.2 - 16.2 5.5 10.3 10.8 4.9

PP,WL,GF 1 160 16.5 15 4.4 - 9.8 5.3 9.6 10.4 8.3

GF,DF 4 53 - 127 18.5 - 28.7 15 6.7 - 27.1 15.1 17.0 17.1 0.6

ES,WL 2 100 - 127 18.5 - 20.9 15 5.5 - 20.0 12.0 14.0 14.6 4.2

ES 1 87 22.4 15 9.5 - 16.8 11.9 13.6 15.3 12.5

ES,DF,PP,GF,WL 1 147 17.2 15 4.1 - 13.1 3.7 8.9 9.7 9.0

Average across 

all stands 61 53 - 213 13.5 - 28.7 12 - 21 3.0 - 27.4 8.3 12.0 12.1 0.8
USDA Forest Service Proceedings RMRS-P-61. 2010. 261

Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho Basford, Sloan, and Roberts

Table 6—Actual versus predicted diameter growth on 24 permanent control growth plots of Payette 

NF ponderosa pine and mixed stands using the Boise-Payette model. These untreated stands are 

control plots for the treated stands in table 5. Percent error was calculated according to (predicted 

diameter - ending diameter)/ending diameter. The r2 value for actual vs. predicted diameter is 0.97.

	 Diameter

	 Spacing	 Number of	 spread	 Average dbh (in)	 Error

Species	 Trees/acre	 (ft)	 years	 (in)	 Initial	 Ending	 Predicted	 (%)

PP 253 13.1 12 3.5-7.8 0 5.1 5.1 0.0

PP 367 10.9 11 3.5-7.4 0 5.0 4.7 –6.0

PP 520 9.2 11 3.4-7.4 0 4.6 4.7 2.2

PP 167 16.2 11 3.4-5.4 0 4.4 4.7 6.8

PP,DF,ES 427 10.1 28 3.3-13.4 0 8.0 7.6 –5.0

PP 380 10.7 14 3.5-8.4 0 5.6 5.8 3.6

PP 293 12.2 21 3.7-10.1 0 7.3 7.7 5.5

PP,DF 493 9.4 60 3.4-16.6 0 9.1 9.2 1.1

PP 327 11.5 11 3.5-7.0 0 4.6 4.7 2.2

PP,GF,DF 327 11.5 58 3.6-19.4 0 11.2 10.7 –4.5

PP,DF 213 14.3 81 5.4-27.1 0 13.9 14.0 0.7

PP,DF 380 10.7 24 3.3-9.6 0 6.6 7.6 15.2

PP 347 11.2 12 3.5-11.4 0 5.1 5.1 0.0

PP 293 12.2 10 3.4-7.4 0 4.5 4.3 –4.4

PP 320 11.7 29 3.4-12.0 0 7.9 8.6 8.9

ES,WL,LP.DF 527 9.1 69 3.6-17.7 0 9.3 9.3 0.0

GF,DF,ES 473 9.6 69 3.2-17.9 0 10.0 9.8 –2.0

PP 573 8.7 26 3.5-11.1 0 6.0 6.6 10.0

PP 620 8.4 30 3.4-14.0 0 7.2 6.9 –4.2

PP,DF 407 10.4 60 3.4-18.2 0 9.0 9.9 10.0

PP,GF 253 13.1 12 3.4-7.5 0 4.8 5.1 6.2

PP,GF 347 11.2 18 3.6-8.3 0 5.9 6.7 13.6

PP,WL 167 16.2 12 3.3-5.4 0 4.0 5.1 27.5

PP,DF 327 11.5 56 3.4-19.4 0 10.9 10.5 –2.8

Average 366.7 11.4 31.0 0 7.1 7.3 2.8

Table 7—Actual versus predicted diameter growth on 19 untreated mixed plots from Timber Inventory data 

collected on the Payette National Forest using the Boise-Payette model. The plot measurement interval 

ranges from 57 to 114 years. Percent error was calculated according to (predicted diameter - ending 

diameter)/ending diameter. The r2 value for actual vs. predicted diameter is 0.90.

	 Spacing	 Number of	 Diameter	 Average dbh (in)	 Error

Species	 Trees/acre	 (ft)	 years	 spread	 Initial	 Ending	 Predicted	 (%)

GF,DF,PP,ES 213 14.3 57 5.5 - 19.3 0 12.3 12.8 4.8

DF,PP 235 13.6 74 4.0 - 38.0 0 11.7 13.2 11.9

DF 263 12.9 97 7.0 - 40.1 0 11.8 13.6 15.1

PP,DF,GF 226 13.9 81 0.0 - 46.0 0 13.9 13.7 –0.7

DF,PP 277 12.5 70 6.7 - 24.4 0 11.9 12.1 1.7

AF,ES,LP,DF 423 10.2 69 5.5 - 28.8 0 10.0 10.2 2.0

GF,PP,DF 143 17.5 77 5.9 - 34.1 0 16.8 16.2 –3.0

GF,DF,PP 550 8.9 76 0.5 - 33.0 0 9.3 9.4 1.0

GF,PP,DF 363 11.0 82 0.0 - 25.1 0 11.4 11.4 0.0

ES,LP,DF,WL,AF 654 8.2 66 0.0 - 16.1 0 8.1 8.4 3.7

GF,PP,ES,DF 120 19.1 62 0.0 - 34.8 0 16.8 16.0 –4.2

GF,PP,DF 255 13.1 68 0.0 - 23.0 0 11.5 12.4 7.8

GF,DF,PP 490 9.4 89 0.0 - 28.5 0 10.0 10.3 3.0

GF,LP,ES,DF 403 10.4 63 0.0 - 47.8 0 10.9 10.1 –7.3

GF,ES,DF 242 13.4 109 1.0 - 38.8 0 14.1 14.5 2.8

GF,DF 502 9.3 70 1.0 - 23.0 0 10.1 9.6 –5.0

GF,ES,LP,WL 647 8.2 59 0.0 - 25.4 0 7.6 8.2 7.9

ES,AF,LP 793 7.4 83 5.6 - 18.7 0 9.8 8.3 –15.3

GF,DF,ES,PP 273 12.6 114 2.0 - 31.7 0 13.2 13.9 5.3

Average 372.2 11.9 77.2 0 11.6 11.8 1.7
262 USDA Forest Service Proceedings RMRS-P-61. 2010.

Basford, Sloan, and Roberts Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho

Table 8—Actual diameter growth on 18 treated ponderosa pine permanent growth plots on the Boise 

National Forest is compared to predicted growth using the Boise-Payette model. Percent error 

was calculated according to (predicted diameter-ending diameter)/ending diameter. The r2 value 

for actual vs. predicted diameter is 0.96.

	 Spacing	 Number of	 Diameter	 Average dbh (in)	 Error

Species	 Trees/acre	 (ft)	 years	 spread	 Initial	 Ending	 Predicted	 (%)

PP 380 10.7 14 3.4-7.4 0.0 5.0 5.8 16.0

PP 480 9.5 15 4.8-18.4 5.1 7.2 7.2 0.0

PP 140 17.6 20 6.9-14.3 5.8 11.7 12.0 2.6

PP 440 10.0 20 5.5-14.4 4.6 8.7 7.8 –10.3

PP 220 14.1 15 4.4-11.6 5.6 9.1 9.6 5.5

PP 340 11.3 15 4.5-10.8 5.8 8.3 8.4 1.2

PP,DF 300 12.1 20 4.2-14.2 5.2 8.6 9.1 5.8

PP 380 10.7 20 5.0-10.8 4.7 8.0 8.3 3.8

PP 280 12.5 12 3.5-5.8 0.0 4.5 5.1 13.3

PP 160 16.5 9 3.1-5.7 0.0 3.9 3.8 –2.6

PP 220 14.1 12 7.0-11.1 5.0 8.3 8.9 7.2

PP 140 17.6 10 3.4-4.9 0.0 4.3 4.3 0.0

PP 80 23.3 9 3.0-5.3 0.0 4.0 3.8 –5.0

PP 120 19.1 12 5.0-8.3 4.5 8.8 9.6 9.1

PP 60 26.9 12 8.2-8.8 4.3 8.6 9.4 9.3

PP 60 26.9 9 3.2-5.2 0.0 4.3 3.8 –11.6

PP 120 19.1 12 5.2-9.8 4.2 8.2 9.3 13.4

PP 80 23.3 11 3.7-5.4 0.0 4.4 4.7 6.8

Average222.2 16.4 13.7 3.0 7.0 7.3 2.9

stand growth output to the Payette permanent growth plot data revealed the model 

did a very good job of predicting stand growth in thinned stands, with an error 

between predicted and observed data of less than one percent (table 5).

The second set of plots used for validation was a group of 24 permanent growth 

plots from the same area (Unpublished data on file at the Supervisors Office, 

Payette National Forest, McCall, Idaho. 2003b). These had similar composition 

but they were unthinned control plots. Average spacing in these plots was between 

9.1 and 16.2 feet. Comparison of the computed diameter to the actual measured 

diameter resulted in a percent error of less than 3 percent (table 6).

The third set of data from the Payette NF inventory plots contained 19 stands of 

mixed composition of which 11 plots contained ponderosa pine (Unpublished data 

on file at the Supervisors Office, Payette National Forest, McCall, Idaho. 2003c). 

None were pure ponderosa pine. The other species were Douglas-fir, grand fir 

(Abies grandis (Douglas ex D. Don) Lindl.), Engelmann spruce (Picea engelmannii

Parry ex Engelm), subalpine fir (Abies lasiocarpa (Hook.) Nutt. var. lasiocarpa), 

western larch (Larix occidentalis Nutt.), and lodgepole pine. These stands have 

never been thinned. Again, the predicted average stand diameter was close to the 

actual measured stand diameter, with an error of less than 2 percent (table 7).

The final data set was from Boise NF permanent growth plots (Unpublished 

data on file at the Supervisors Office, Boise National Forest, Boise, Idaho. 2003). 

These 18 ponderosa pine stands were thinned to a range of 9.5 to 26.9 feet and 

re-measured anywhere from 9 to 20 years later (table 8). The results were similar 

to those from the Payette NF, with an error between observed and predicted dbh 

of less than 3 percent.

BPPP seems to work well in mixed forest types of southwest Idaho, even when 

ponderosa pine is a minor component of the stand. Stand density (range, 7.4 ft 

to 28.7 ft) and number of years of stand growth did not seem to affect the error. 
USDA Forest Service Proceedings RMRS-P-61. 2010. 263

Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho Basford, Sloan, and Roberts

It could be expected that the error would increase over time; however, the comparison showed little difference between short and long term projections. One 

stand grew for 109 years and the error was 2.8 percent. Another BPPP projection 

of 82 years predicted diameter exactly as observed. Also, there did not seem to 

be much difference in error when predicting small versus large average diameter stands (the initial diameter range was from 0 to 17.9 in). Comparing all four 

tables together, only 17 percent of the plots had an error between observed and 

predicted dbh greater than 10 percent, whereas 51 percent were within 5 percent 

of the observed diameter.

Other Growth Models

It was shown by Basford and others (1991) that the SCPP model could effectively be used to predict ponderosa pine growth in other dry locations outside 

the southwest Idaho region. A direct comparison between actual growth data and 

predicted growth of ponderosa pine on 82 plots produced an r2 of 0.98 in northern 

Arizona (Ronco and others 1985), the Blackhills of South Dakota (Edminster, 

C.B. 1988. [Unpublished data]. Fort Collins, CO: USDA Forest Service Rocky 

Mountain Research Station), central Oregon (Barrett 1982), Pringle Falls, Oregon 

(Cochran and Barrett 1999a), Lookout Mountain in Oregon (Cochran and Barrett 

1999b), the Methow Valley in Washington (Cochran and Barrett 1998), and the 

Malheur NF in Oregon (Cochran and Barrett 1995). Only 10 percent of the plots 

showed an error of more than 10 percent. Sixty-eight percent of the plots were 

within 5 percent. The data is summarized by location in table 9.

Another computer modeling system called the Forest Vegetation Simulator 

(FVS) (Dixon 2006) also allows users to predict densities and diameters. It is 

reasonable for users to compare BKTHIN to FVS, but this should be done cautiously as these tools have different purposes with different levels of input. The 

FVS is a stand level, growth and yield system, using a variety of site and tree 

conditions within specific vegetative classes such as habitat type or site index 

within a variant region. Individual tree sample diameters are input, incremented, 

and compared with other tree samples in the plot. Each cycle interval (i.e. 10 

years) is based on all tree samples in the plot. Tree specific information as well 

as stand summary information is reported.

Table 9—Actual versus predicted diameter growth (inches) on 82 ponderosa pine growth plots using the Salmon-Challis 

model. It was tested against the following studies: northern Arizona (Ronco and others 1985), Black Hills, South 

Dakota (Edmininster 1988), Pringle Falls, Oregon (Cochran and Barrett 1999), Lookout Mountain, Oregon (Cochran 

and Barrett 1999), Methow Valley, Washington (Cochran and Barrett 1998), and Malheur N.F., Oregon (Cochran and 

Barrett 1995). Percent error was calculated according to (predicted diameter - ending diameter)/starting diameter. 

The r2 value for actual vs. predicted diameter is 0.98. 

	 	 	 Number	 	 Range of

	 	 	 of	 Range of	 spacing	 Number of	 	Average dbh (in)	 	 Error

	 Study area	 Species	 stands	 trees/acre	 (ft)	 years	 Initial	 Ending	 Predicted	 (%)

Arizona PP 12 61 - 681 8.0 - 26.7 10 6.6 8.5 8.2 –3.5

South Dakota PP 24 41 - 617 8.4 - 32.5 10 6.0 7.8 8.1 3.8

Pringle Falls, Oregon PP 5 62.5 - 1000 6.6 - 26.4 35 2.0 9.0 9.1 1.1

Lookout Mountain, Oregon PP 18 22 - 308 11.9 - 44.5 10 12.6 14.4 14 –2.8

Methow Valley, Washington PP 5 62.5 - 2387 4.3 - 26.3 30 to 35 4.3 9.1 10.0 9.9

Malheur N.F, Oregon PP 18 56 - 455 9.8 - 27.5 4 to 10 8.7 9.8 10.0 2.0

Average PP 82 22 - 2387 4.3 - 44.5 7.8 9.9 9.9 1.0
264 USDA Forest Service Proceedings RMRS-P-61. 2010.

Basford, Sloan, and Roberts Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho

Conversely, BKTHIN is a tree species level method, driven by spacing and 

diameter criteria that can be applied to broader regions. The average stand diameter is used as the initial input for the model and that diameter is incremented 

yearly over the planning horizon based on growth regression equations. Only 

stand summary information is reported at each one-year cycle.

A simple comparison was completed to see how both methods predict diameter 

and growth (diameter increment) for managed permanent growth plots on the 

Payette NF. These plots were initially inventoried in 1983 and 1988 with three to 

four 5-year re-measurements. The model was projected for a 15-20 year period 

using both BKTHIN and FVS. Table 10 displays the results of these projections 

as compared to the actual beginning data and the actual last re-measurement. 

Both methods started with the same initial QMD for a given plot. The diameter 

increment results are recorded for the actual, FVS, and BKTHIN, which are 

computed by calculating the change from the beginning to ending diameter and 

then dividing the result by the total years. Finally, all plot results were averaged 

by habitat type group. It may not be clear which method predicts most closely to 

actual. This is not a large data set, but both approaches appear to be reasonably 

similar. It would be interesting to conduct this comparison between FVS and the 

empirical approach reported here across a larger and more comprehensive data set.

Management Application

 The BPPP model is based on average size/density measurements. It allows 

thinning of stands to a given density, assuming growing stock is fairly uniform 

throughout the total area. It also uses crown ratio when considering whether the 

trees can fully respond to release. The model works best with even-aged stands, 

but data in table 7 suggest that it also works in uneven-aged stands. It predicts 

growth in unmanaged stands and has been found effective in pure or mixed 

stands of ponderosa pine, Douglas-fir, grand fir, subalpine fir, Engelmann spruce, 

western larch, and lodgepole pine.

BPPP can meet the needs of managers on the Boise and Payette NFs. The 

BKTHIN program can be used to maximize tree diameter through thinning at 

the right times before growth rates slow. It can also maximize stand volume by 

accepting slower growth rates but allowing more trees to better utilize the site. 

Table 10—Actual versus predicted diameters (inches) from permanent plot data using FVS and BKTHIN are 

summarized by habitat type and number of years between measurements. “Average diameter increment” 

represents the average annual growth rate. 

	 Number	 Ave. actual 	 Ave. predicted

Years	 of	 diameter	 QMD 2003	 Ave. diameter increment

Habitat type	 projected	 stands	 Start	 End	 FVS 	 BKTHIN 	 Actual	 FVS	 BKTHIN

Abgr/Acgl/Phma 20 3 4.93 10.43 11.27 11.27 0.28 0.32 0.32

Abgr/Libo 15 3 8.90 12.60 11.73 11.40 0.25 0.19 0.17

Abgr/Spbe 15 3 9.60 13.67 12.37 11.70 0.27 0.18 0.14

Abgr/Spbe20 8 5.39 12.64 12.24 11.76 0.37 0.33 0.32

Abgr/Vagl 15 3 7.37 11.03 11.10 10.63 0.24 0.25 0.22

Pipo/Syal 20 3 5.40 10.63 12.13 12.60 0.26 0.34 0.36

Psme/Caru/Pipo 15 3 9.30 13.13 12.23 11.90 0.26 0.20 0.17

Psme/Caru/Pipo20 3 4.57 10.90 11.50 12.27 0.32 0.35 0.39

Psme/Spbe 15 3 7.17 11.00 10.97 11.40 0.25 0.26 0.28

Psme/Syal/Pipo 15 7 9.49 12.73 12.36 12.24 0.21 0.19 0.19

Psme/Syal/Pipo 20 3 8.83 13.20 12.43 12.90 0.22 0.18 0.21
USDA Forest Service Proceedings RMRS-P-61. 2010. 265

Stand Density Guides for Predicting Growth of Forest Tress of Southwest Idaho Basford, Sloan, and Roberts

When spacing is to be kept relatively uniform, the size of the trees at harvest 

dictates the number and spacing of trees left after each thinning. Lack of consideration for final tree size and the timing of each intermediate harvest could 

result in reduced stand growth.

This model has several advantages. It can accurately predict diameter growth 

in forest stands of southwest Idaho. The input required to operate the model is 

very simple and easily obtained from National Forest stand inventory plots. BKTHIN is fast, easy to use and can be used by managers in the field. The methods 

reported above can be easily replicated for use in other regions.

With BKTHIN it is possible to manage diameter growth in even-aged stands 

by scheduling treatments to meet management objectives. However, as others 

have reminded us (Ernst and Knapp 1985, Leak 1981), we must continue to evaluate recommended stocking levels (stand density) to see how close they come to 

satisfying our management objectives. Through this process, refinements can 

be made and the stocking guides can be improved. For a copy of the BKTHIN 

computer model, please contact one of the authors.

References

Basford, D.D.; Sloan, J.P.; Ryker, R.A. 1991. Developing stand density guides for predicting growth 

of timber species on the Salmon National Forest, Idaho. In: Interior Douglas-fir, the species 

and its management. Proc. Spokane, WA, Feb. 27 – Mar. 1, 1990. D.M. Baumgartner and J.E. 

Lotan, eds. Pullman, WA. Wash. St. Univ. 8 pp.

Barrett, J.W. 1982. Twenty-year growth of ponderosa pine saplings thinned to five spacings in 

central Oregon. USDA For. Serv. Res. Pap. PNW-301 18 pp.

Cochran, P.H.; Barrett, J.W. 1995. Growth and mortality of ponderosa pine poles thinned to various densities in the Blue Mountains of Oregon. USDA For. Serv. Res. Pap. PNW-483. 27 p.

Cochran, P.H.; Barrett, J.W. 1998. Thirty-five-year growth of thinned and unthinned ponderosa 

pine in the Methow Valley of northern Washington. USDA For. Serv. Res. Pap. PNW-502. 24p.

Cochran, P.H.; Barrett, J.W. 1999a. Thirty-five-year growth of ponderosa pine sapplings in response to thinning and understory removal. Portland, OR: U.S. Dept. of Ag., For. Serv. Res. 

Pap. PNW-512. 28 pp.

Cochran, P.H.; Barrett, J.W. 1999b. Growth of ponderosa pine thinned to different stocking levels 

in central Oregon: 30-year results. Portland, OR: U.S. Dept. of Ag., For. Serv. Res. Pap. PNW508. 27 p.

Drew, T.J.; Flewelling, J.W. 1979. Stand density management: an alternative approach and its application to Douglas-fir plantations. For. Sci. 25:518-532.

Dixon, Gary E. 2002. Essential FVS: A user’s guide to the Forest Vegetation Simulator. Internal 

Rep. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Forest Management 

Service Center. 204p. (Last Revised: January 2006).

Ernst, R.; Knapp, W.H. 1985. Forest stand density and stocking: concepts, terms, and the use of 

stocking guides. USDA For. Serv. Gen. Tech. Rep. WO-44. 8pp.

Gingrich, S.F. 1967. Measuring and evaluating stocking and stand density in upland hardwood 

forests in the central states. For. Sci. 13, 38–53.

Husch, B.; Miller, C.I.; Beers, T.W. 1972; Forest mensuration, New York. 410 p.

Leak, W.B. 1981. Do stocking guides in the Eastern United States relate to stand growth? J. of 

For. 79:661-664.

Reineke, L.H. 1933. Perfecting a stand density index for even-aged forests. J. Agri. Res. 46:627-638.

Ronco, F. Jr.; Edminster, C.B.; Trujillo,D.B. 1985. Growth of ponderosa pine thinned to different 

stocking levels in northern Arizona. USDA For. Serv. Res. Pap. RM-262. 15 pp.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 267

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Applied Forest Management Program, 

Montana Forest & Conservation Experiment Station, University of Montana, 

Missoula, MT.

Built for the Future: New Directions in 

Silviculture Research and Demonstration 

at Montana’s Lubrecht Experimental Forest

Christopher R. Keyes1 and Thomas E. Perry1

Abstract—Manipulative experiments at the University of Montana’s Lubrecht 

Experimental Forest have long been set aside as permanent research and demonstration areas (RDA’s) to communicate the tradeoffs among different stand 

management strategies. However, most of these have either degraded over time 

or have diminished relevance to contemporary forest management issues. An 

evaluation and rehabilitation of Lubrecht Forest’s research and demonstration 

infrastructure is currently underway. Examples are presented of existing RDA’s 

that are being refurbished, replaced, revised, or retired. New demonstration areas 

that exemplify the central theme of this rehabilitation effort—stand complexity 

and regeneration—are also described.

Keywords: silvicultural systems, stand structure, complexity, regeneration, 

reforestation.

Introduction

From its inception, the University of Montana’s Lubrecht Experimental Forest 

(LEF) has included in its mission the study and demonstration of silvicultural 

practices and systems. At this 28,000-acre forest laboratory, manipulative experiments have been set aside as permanent reserves, or research/demonstration 

areas (RDA’s), to communicate the tradeoffs among varying stand management 

strategies for ubiquitous forest types of the northern Rocky Mountains. Lubrecht 

Forest’s RDA’s have been widely utilized by many agencies, organizations, and 

landowner groups over the years, as well as legions of University of Montana 

students. But few new RDA’s have been created since the 1980s. Many existing 

areas have suffered degradation, declined in their interpretative value, and lost 

much of their relevancy to contemporary issues.

An evaluation and rehabilitation of Lubrecht Forest’s research and demonstration infrastructure is currently underway. Stand complexity and regeneration are 

the central themes that underlie this forest-wide rehabilitation effort. It is an effort 

that is designed to equip Lubrecht with a deliberately prepared suite of relevant 

RDA’s, and a plan for their use and maintenance that will sustain this experimental 

forest’s relevance into the future.

Lubrecht Forest Management

Lubrecht Forest was established in 1937, a gift from the Anaconda Company 

to the State of Montana under the auspices of the Montana Forest & Conservation 

Experiment Station (MFCES). Through subsequent additions, LEF now totals 
268 USDA Forest Service Proceedings RMRS-P-61. 2010.

Keyes and Perry Built for the Future: New Directions in Silviculture Research and Demonstration at Montana’s Lubrecht Experimental Forest

28,000 acres of forest and range. Permanent study areas date back to 1948, but 

most RDA’s were established after 1981 when the state legislature established the 

Mission-Oriented Research Program, an MFCES program that would continue to 

2007 and provide much of the impetus for research and demonstration occurring 

at LEF. Today the MFCES’s Applied Forest Management Program, initiated in 

2007, oversees the long-term studies and demonstration areas at Lubrecht and 

leads the development of new research and demonstration opportunities.

Lacking a full management plan, Lubrecht Forest’s management framework is 

summarized in a 1996 brief, titled “LEF Management Guidelines.” LEF’s stated 

mission (according to that document) is, “Natural resource study, demonstration, 

and learning, and public use in a forest setting.” This goal is to be met via five 

stated management goals (underlining and italics added for emphasis):

1) Serve Montana citizens through research, demonstration, instruction, and 

public use.

2) Be available for a wide variety of research studies, demonstrations, and learning opportunities.

3) Demonstrate management which leads to healthy, sustainable ecosystems

4) Demonstrate a variety of forest management techniques applicable to ecosystems in the northern Rocky Mountains.

5) Generate revenue to support MFCES activities through the sale of goods and 

services in a manner consistent with other goals for the forest.

Operational management at LEF is best described as a conservative custodial 

model, with origins as a young regrowth forest with limited economic value. Much 

of the forest consists of same-aged thinned or unthinned forests. Over time, this 

custodial management model has changed little, but the forest itself has changed 

dramatically through growth and succession. Young stands on the forest are rare, 

the result of infrequent regeneration harvests or occasional stand-replacing disturbance events. The limited emphasis on strategic regeneration harvesting has 

limited structural diversity across LEF and has fostered the development of unmanaged understory cohorts that are uniformly dominated by Douglas-fir. These 

unfortunate consequences demand an innovative silvicultural approach to their 

remediation. Recently, substantial and widespread bark beetle mortality is forcing 

a more deliberate management focus on the next generation forest. A dynamic 

approach to promoting resilience and adaptability in that forest is a top priority.

Lubrecht’s Silviculture RDA’s

Lubrecht has a notable collection of long-term installations for research and 

demonstration of silvicultural practices. Although Lubrecht has been the site of 

numerous studies, relatively few of these have been established as permanent 

installations. Some of Lubrecht’s RDA highlights are frequently used by diverse 

users, and hence they are considered among the most important for protection 

and maintenance. Two examples are the Fire & Fire Surrogates study area and 

the Uneven-aged Silviculture study areas. Part of a national network, LEF’s large 

Fire & Fire Surrogates study, established in 2002, is used to evaluate the longterm effects and tradeoffs of fire and thinning to reduce forest fuels and restore 

ponderosa pine (Pinus ponderosa C. Lawson) forests. A smaller example is the 

Uneven-aged Silviculture study, established in 1984 with the objective of demonstrating the potential of uneven-aged silvicultural methods to achieve forest 

health and restoration goals. With regular data collection on a 5-year basis since 

1984, and located immediately adjacent to Highway 200, it lends itself well to 

frequent use.
USDA Forest Service Proceedings RMRS-P-61. 2010. 269

Built for the Future: New Directions in Silviculture Research and Demonstration at Montana’s Lubrecht Experimental Forest Keyes and Perry

These and a few other gems notwithstanding, examples also exist at Lubrecht 

of RDA’s that are essentially demonstrating decay. Missing, outdated, and damaged signs minimize the interpretive value of otherwise useful sites. In other 

areas, normal growth and decay have put RDA’s beyond their original shelf lives. 

Examples include the group selection cutting unit of the Silvicultural Systems 

area, where growth has made the group selection gaps indistinguishable from 

the forested matrix; in another RDA designed to illustrate thinning and slash 

management techniques, piles and twitches have deteriorated to indistinguishable mounds covered in needles. Most recently, major levels of mortality from a 

landscape scale bark beetle outbreak has substantially altered many RDA’s, forcing 

rapid assessments of their usefulness and creative responses to their treatment.

Evaluation of Existing RDA’s

To address these situations, a complete evaluation of Lubrecht’s RDA’s is underway. The effort began with an inventory and compilation of relevant data, and 

is now progressing in a continuous screening and sorting of RDA’s into condition 

classes that will help dictate their future management. These categories are:

• Refurbish—useful and relevant, but requiring service; continue the original 

management strategy with periodic treatment updates consistent with the 

original intent of the RDA.

• Replace—still conceptually useful, but has outlived its research and/or demonstration shelf-life due to mortality or decay; conduct fresh treatments to 

revitalize lost elements, or else identify new location where original conditions can be re-established.

• Revise—useful stand structure but treatments reflect outdated strategies or 

are lacking in contemporary value; enhance the site with new hypotheses 

and/or treatments that best exploit the treatment histories and longitudinal 

data.

• Retire—beyond salvage; site is so deteriorated that its usefulness is minimal; 

return to operational landbase.

The most challenging of these is the “Revise” category. An example is provided by the Levels-of-Growing-Stock study areas. One-time low thinnings were 

conducted in 1982 (three pure stands, three mixtures) to demonstrate the effect 

of thinning on stand structure and tree growth. Unmanaged since 1982, they adequately exhibit the effects of a one-time thinning, but they are not representative 

of a silvicultural strategy for continued density management. For these RDA’s, 

strategies of density management based on stand density index (SDI) are being 

developed to demonstrate the potential of long-rotation thinning regimes and to 

evaluate tradeoffs in growth, value, and stand structure among them. Alternatively, 

where understory development has been substantial, treatment options to address 

advanced regeneration may also be desirable, for example, overlaying a precommercial thinning of the understory cohort to promote vigor, or else conducting 

understory burn treatments to retard ladder fuels development.

New Directions

Even as existing studies are being evaluated and modified, new studies are 

being developed and installed. These are designed to address topical voids in the 

Lubrecht RDA infrastructure and to meet contemporary and projected needs. 

They mainly focus on structural complexity and regeneration. Examples include 

managing for spatially irregular stands using the experimental ‘irregular selection’ technique that has been applied in Idaho (Graham and Jain 2005, Graham 

and others 2007), and by an adaptation of the experimental ‘Acadian Femelschlag’ 
270 USDA Forest Service Proceedings RMRS-P-61. 2010.

Keyes and Perry Built for the Future: New Directions in Silviculture Research and Demonstration at Montana’s Lubrecht Experimental Forest

method being tested in Maine (Seymour 2005). Others include the establishment 

of Nelder spacing wheels with pure and mixed species plantings; analysis of 

regeneration dynamics in salvaged and unsalvaged beetle-kill stands; spatially 

irregular planting to promote stand differentiation; and the transitional silviculture 

of aging, even-aged regrowth stands to develop pure and mixed two-age stands. 

Consistent with this focus on fostering resilience during the regeneration phase, 

Lubrecht Forest’s operational capacity for reforestation will be enhanced with the 

addition of a micro-nursery over the next several years. This new infrastructure 

will provide the forest with the ability to better link reforestation to harvesting 

activities, and will also provide LEF with new opportunities for studying and 

demonstrating aspects of small-scale seedling production.

Conclusions

Understanding and forecasting Lubrecht Forest’s current and potential future 

users is helping to guide this planning effort. In the past, Lubrecht’s silviculture 

RDA’s were used by many agencies, organizations, and landowner groups, as well 

as legions of University of Montana students. In the future, public participation 

in natural resource decision-making is expected to grow as it has during past 

decades. By providing effective, accessible research and demonstration areas 

with relevance to contemporary and anticipated forest management issues, by 

creating an array of stand structures and compositions for yet unknowable future 

uses, and by developing new and innovative opportunities for their utilization in 

public outreach and learning, a refreshed and revitalized Lubrecht Forest will be 

positioned for continued relevance and leadership in the study and promotion of 

sustainable silvicultural management.

References

Graham, R. T., and T. B. Jain. 2005. Application of free selection in mixed forests of the inland 

northwestern United States. Forest Ecology and Management 209:131–145.

Graham, R. T., T. B. Jain, and J. Sandquist. 2007. Free selection: A silvicultural option. P. 121-156 

in Restoring Fire-Adapted Ecosystems: Proceedings of the 2005 National Silviculture Workshop, 

R. F. Powers, tech. ed. USDA Forest Service General Tech Report PSW-GTR-203.

Seymour, R. S. 2005. Integrating natural disturbance parameters into conventional silvicultural 

systems: Experience from the Acadian forest of northeastern North America. P. 41-48 in Balancing Ecosystem Values: Innovative Experiments for Sustainable Forestry, C.E. Peterson and 

D. A. Maguire, eds. USDA Forest Service General Technical Report PNW-GTR-635.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
USDA Forest Service Proceedings RMRS-P-61. 2010. 271

Part 4: Decision Support Tools

2009 NATIONAL SILVICULTURE WORKSHOP

BOISE, IDAHO

SILVICULTURE

CLIMATE

BIOMASS

CARBON SEQUESTRATION

USDA Forest Service Proceedings RMRS-P-61. 2010. 273

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1Forest and Woodland Ecosystems, 

Rocky Mountain Research Station, US 

Forest Service, Moscow, ID.

2 Forest and Woodland Ecosystems, 

Rocky Mountain Research Station, US 

Forest Service, Moscow, ID.

3 Forest Management Service Center, 

US Forest Service, Fort Collins, CO.

4 School of Forest Resources, University 

of Maine, Orono, ME.

Nicholas L. Crookston1, Gerald E. Rehfeldt, (Retired)2, 

Gary E. Dixon, (Retired)3, and Aaron R. Weiskittel4

Abstract—To simulate stand-level impacts of climate change, predictors in the widely 

used Forest Vegetation Simulator (FVS) were adjusted to account for expected climate 

effects. This was accomplished by: (1) adding functions that link mortality and regeneration of species to climate variables expressing climatic suitability, (2) constructing 

a function linking site index to climate and using it to modify growth rates, and 

(3) adding functions accounting for changing growth rates due to climate-induced 

genetic responses. For three climatically diverse landscapes, simulations were used 

to explore the change in species composition and tree growth that should accompany 

climate change during the 21st century. The simulations illustrated the changes in 

forest composition that could accompany climate change. Projections were the most 

sensitive to mortality, as the loss of trees of a dominant species heavily influenced 

stand dynamics. While additional work is needed on fundamental plant–climate relationships, this work incorporates climatic effects into FVS to produce a new model 

called Climate–FVS. This model provides for managers a tool that allows climate 

change impacts to be incorporated in forest plans.

See this web site for access to the model and supporting literature: http://www.fs.fed.

us/fmsc/fvs/description/climate-fvs.shtml.

Keywords: species climate relationships, stand dynamics, species composition, genetic adaptation, general circulation model, climate change, carbon loads, site index, 

growth and yield

Note: This paper has been published. Citation: Crookston, Nicholas L.; Rehfeldt, 

Gerald E.; Dixon, Gary E.; Weiskittel, Aaron R. 2010. Addressing climate change in 

the forest vegetation simulator to assess impacts on landscape forest dynamics. Forest 

Ecology and Management. 260: 1198-1211.

Addressing Climate Change in the 

Forest Vegetation Simulator to Assess 

Impacts on Landscape Forest Dynamics

USDA Forest Service Proceedings RMRS-P-61. 2010. 275

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 USDA Forest Service, Pacific Northwest Regional Office, Portland, OR.

2 USDA Forest Service, Forest Management Service Center, Fort Collins, CO.

Calibration of State and Transition 

Models With FVS

Melinda Moeur1 and Don Vandendriesche2

Abstract—The Interagency Mapping and Assessment Project (IMAP), a partnership 

between federal and state agencies, is developing mid-scale vegetation data and 

state and transition models (STM) for comparing the likely outcomes of alternative 

management policies on forested landscapes across the Pacific Northwest Region. In 

an STM, acres within a forested ecosystem transition between state classes defined 

by cover type and seral stage. Transitions resulting from natural and human-induced 

change can be modeled over long time horizons (centuries). Natural growth and 

mortality, silvicultural treatments, and stochastic disturbances such as wildfire and 

insect outbreaks can all be modeled in an STM. In Washington and Oregon, STM 

models are being used to explore proposed actions that affect wildfire risk, forest health 

and restoration, habitats for species whose populations are declining, and long-term 

timber supply. We describe how the Forest Vegetation Simulator (FVS) is being used 

with regional inventory data to empirically derive STM parameters (residence times 

in states, pathways between states, and transition probabilities between states), and 

to link outputs to vegetation states. For example, active fuel treatments in overstocked 

stands can be simulated to track how management might influence long term probabilities of wildfire risk. A range of outputs from FVS—for example, harvest volumes 

and carbon balances associated with thinning—can be captured and linked to treatments through the modeling framework. The ability to empirically forecast complex 

growth and disturbance scenarios using real data makes STM tools more applicable 

to real-world management questions.

Introduction

The diverse landscapes of the Pacific Northwest are managed by different 

owners for different objectives. Key considerations facing all forestland planners 

include managing fuel conditions and wildfire risk, old-growth, wildlife habitat, 

supply and demand of forest products, biomass supplies and carbon budgets, among 

others. Ideally, planning efforts on lands with a variety of management objectives 

would all share a common set of landscape data and modeling methods to help 

insure consistent answers to common questions. The Interagency Mapping and 

Assessment Project (IMAP) is responding to this need to foster cooperation on 

important landscape issues affecting different management entities at different 

scales. IMAP supports federal and state partners in Washington and Oregon by 

building integrated datasets, models, and tools for conducting mid-scale assessments (Hemstrom and others 2008).

IMAP uses a state and transition model (STM) approach to project the effects 

of disturbance and management on the forested landscape. We use the Vegetation 

Dynamics Development Tool (VDDT) developed by ESSA Technologies Ltd. 

(2005) as the IMAP STM. An STM treats vegetation as combinations of cover 

type and structural classes linked by pathways resulting from natural succession, 

management actions, and disturbances (example, fig. 1). The cover type/structural states (boxes) represent the most important developmental stages. Pathways 
276 USDA Forest Service Proceedings RMRS-P-61. 2010.

Moeur and Vandendriesche Calibration of State and Transition Models With FVS

(arrows) are the linkages between the states. Residence time is the average length 

of time that vegetation typically remains in the same class before transitioning to 

another state through successional dynamics. For disturbance pathways, transition 

probabilities control the frequency with which movements between states occur.

An STM modeler specifies the model parameters that control states, pathways, 

and transition probabilities (see sidebar). Of course, model projections will be 

most realistic when the model parameters are informed by best science and relevant experience. Typically, model users obtain as much information as possible 

on vegetation dynamics and disturbance ecology from the literature; however, 

expert opinion is often heavily relied upon to specify model parameters.

Stand projections of inventory data with the Forest Vegetation Simulator (FVS) 

(Dixon 2003) provide another important source of information for helping make 

state and transition models behave in a more realistic way. This approach was first 

Figure 1—Successional pathways diagram for the Warm Dry Ponderosa Pine VDDT STM, 

Blue Mountains modeling region. Boxes represent cover type/structural state classes, 

and arrows represent transitions. Green arrows depict deterministic pathways resulting 

from natural growth dynamics. Brown or blue arrows depict stochastic pathways 

following disturbance events such as stand replacing wildfire, non-lethal underburning, 

or commercial thinning. A key to state box codes follows:

Cover type definitions:

PPine Ponderosa pine cover type

State class definitions:

EAD Early seral, Dense (0-10 inches qmd, > 40% canopy cover)

MID Mid seral, Dense (10-20 inches qmd, > 40% canopy cover)

LSD Late seral, Dense (> 20 inches qmd, > 40% canopy cover)

EAO Early seral, Open (0-10 inches qmd, 10-40% canopy cover)

MIO Mid seral, Open (10-20 inches qmd, 10-40% canopy cover)

LSO Late seral, Open (> 20 inches qmd, 10-40% canopy cover)
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Calibration of State and Transition Models With FVS Moeur and Vandendriesche

SIDEBAR

Virtually any successional pathway, natural disturbance, or management 

prescription can be constructed and modeled within VDDT. The examples 

below are verbal descriptions of important vegetation dynamics within the 

cool moist mixed conifer model type. 

Succession and ingrowth, with and without management

• Ingrowth in medium density stands grow to a closed stand after 20 years 

of no disturbance.

• Grass/forbs stage can last for as long as 100 years under the harshest 

conditions. However, under many conditions saplings will establish. 

These circumstances are represented by an alternate succession pathway 

with a 0.01 annual probability. 

• Common harvesting activities include thinning in mature stands, shelterwood cutting, first entry, second entry, and clearcut with reserves. 

All have been entered in with a 0.01 annual probability.

• A sapling stand will naturally go to a closed canopy condition unless 

pre-commercial thinning keeps it in a medium density state.

• Ingrowth within the post-disturbance classes recognizes that snags are 

falling down and stands are recovering from stand replacing events. A 

time since disturbance value of 30 years is required before movement 

out of a post disturbance class.

Fires

• Stand replacing fires (mean fire return interval 300 yrs) transition the 

area to a grass/forbs post-disturbance state if the stand was in a Medium, Large, Very Large, or Giant size class.

• Mixed severity fires (mean fire return interval 500 yrs) convert 50 percent of the area to grass/forbs post disturbance and the other 50 percent 

remains in the same class.

Insect & Disease

• Beetle mortality and wind damage is lumped into an insect and disease 

disturbance with a low probability (0.0001). Only in open stands do 

these events not decimate the entire stand.

proposed by Stage (1997), as a way of providing “an empirical link between the 

Columbia River Basin Successional Model (CRBSUM) and the real world.” The 

idea was that current vegetation inventories representing different stages of stand 

development could be integrated with insect, disease, management activities, and 

fire effects available in the FVS system to empirically inform parameters and 

outputs in STMs. In concept, pursuit of such an analysis in Region 6 is particularly 

appealing because large amounts of plot sample data from regional strategic inventories are available for projection. Thus, it is likely that representative samples 

exist for most or all modeled states in the STM.
278 USDA Forest Service Proceedings RMRS-P-61. 2010.

Moeur and Vandendriesche Calibration of State and Transition Models With FVS

Case Study

In this paper, we describe the process for using FVS in parallel with VDDT to 

help inform IMAP state and transition models. One objective of this dual modeling system is to test the assumptions made by the STM modeler—in some cases, 

this process may lead to modification of some STM model parameters. Another 

objective is to use FVS/empirical data in combination to more fully understand 

important vegetation pathways that may not have been adequately represented in 

the original STM—and perhaps, to expand the STM model. Conversely, a development pathway conceived to be important in the original STM may be shown 

through the FVS process to be not as prevalent as originally thought—perhaps 

leading to eliminating a particular pathway in a revised STM model. Finally, we 

know of no better way than an FVS analysis to estimate outputs for the many 

complex transitions that are likely to be modeled in an STM—FVS, especially 

when used with the Event Monitor, can be used to develop outputs such as standing and harvest volumes, fuel conditions, and stand structural attributes that can 

be linked to states and transitions in a VDDT model.

Warm Dry Ponderosa Pine in the Blue Mountains Modeling 

Region

Within IMAP, the STM work is organized hierarchically around geographic 

and ecological study regions. In Region 6, the entire project area has been divided 

into modeling regions scheduled around the Forest Plan Revision cycle (fig. 2). In 

subsequent examples, we’ll refer to the 13.1-million acre Blue Mountains modeling region where the Wallowa Whitman, Umatilla, and Malheur National Forests 

are revising their forest plans. Within a modeling region, the landscape is divided 

into ecological strata called potential vegetation types (PVTs). A PVT represents 

a particular combination of environment, disturbance regimes, and vegetation 

growth potential. Unique VDDT state and transition models are designed for 

each PVT (Hemstrom and others 2006). For example, the Blue Mountains project 

area is stratified into eight PVTs that are depicted by separate models (table 1). 

Within each model, combinations of cover type and structure—standard classes 

of tree size, canopy density, and canopy layering (table 2)—define the state boxes.

Each acre in the landscape is initialized to its PVT, cover type, and structural 

state based on a map of current vegetation (fig. 3). These maps have been created using the gradient nearest neighbor imputation approach (GNN). GNN uses 

tree-level attributes collected on Current Vegetation Survey (CVS) and Forest 

Inventory and Analysis (FIA) inventory plots (Max and others 1996; FIA 2006), 

coupled with satellite imagery and other spatial data such as topography and climatic gradients, to populate 30-meter pixel maps (Ohmann and Gregory 2002). 

The resulting vegetation maps are used to define the initial conditions that assign 

acres to state classes according to the definitions in table 2. Other spatial layers 

are used to stratify the study region by ownership and land allocation.

In IMAP, the primary spatial unit for modeling is the watershed (5th –order 

hydrologic code, units averaging about 100,000 ac), and within the watershed, 

additional sub-strata are created for owner/land allocation groups defined primarily by distinct management goals. Examples are timber suitable public lands, 

wilderness areas, species habitat reserves, and private industrial lands managed 

for intensive wood production. Summarizing the acres and outputs for these substrata generates useful information about the spatial distribution of landscape 

characteristics without implying pixel or stand-level accuracy. Results at the 

watershed/owner/allocation scale are fine enough for most mid-scale assessments.
USDA Forest Service Proceedings RMRS-P-61. 2010. 279

Calibration of State and Transition Models With FVS Moeur and Vandendriesche

Figure 2—IMAP modeling regions in Washington and Oregon. Modeling 

regions are scheduled to coincide with National Forest Plan revisions. Because 

the Columbia Basin and Interior Great Basin have little forested land, they are 

not currently being modeled. 

Table 1—Eight VDDT models are defined by forested potential vegetation type in the Blue Mountains 

modeling region. Shown are the numbers of inventory plots on National Forests by potential vegetation 

type. The Blue Mountains variant of FVS was used for modeling all plots.

	 VDDT potential	 VDDT	 Number of CVS plots by National Foresta

	 vegetation type	 code	 Cover typesb	 MAL	 UMA	 WAW	 Total

Subalpine whitebark pine sw WB 16 8 46 70

Cold dry mixed conifer cd GF; ES/AF; WL/LP 22 126 282 430

Cool moist mixed conifer cm DF/GF/ES; WL/LP 206 432 426 1064

Warm dry grand fir dg PP; DF/GF 420 122 242 784

Warm dry Douglas-fir dd PP; DF 280 302 440 1022

Warm dry ponderosa pinec	 dp	 PP	 312	 66	 86	 464

Hot dry ponderosa pine xp PP 288 60 100 448

Woodland western juniper jw WJ 94 26 8 128

Non-forest nf none 120 128 444 692

Total: 1758 1270 2074 5102

a National Forest codes: MAL = Malheur; UMA = Umatilla; WAW = Wallowa-Whitman.

b Species codes: AF = subalpine fir; DF = Douglas-fir; ES = Engelmann spruce; GF = grand fir; LP = lodgepole pine; PP = 

ponderosa pine; WB = whitebark pine; WJ = western juniper.

c Used as the modeling example in the text.
280 USDA Forest Service Proceedings RMRS-P-61. 2010.

Moeur and Vandendriesche Calibration of State and Transition Models With FVS

Table 2—Standard structure classes that along with 

cover type (see table 1) and disturbance history, 

define states in the IMAP VDDT models.

Size class (DBH—inches) a	 Class description

0.0 to 4.99 Seedling-Sapling

5.0 to 9.99 Small tree

10.0 to 14.99 Medium tree

15.0 to 19.99 Large tree

20.0 to 24.99 Very large tree

25.0 and larger Giant tree

Canopy cover (percent)	 Class description

0 to 9.9 Non-tree

10 to 39.9 Open

40 to 69.9 Medium

70 to 100 Closed

Canopy layers	 Class description

1 Single

2 or more Multiple

a Quadratic mean diameter of dominants and codominants.

Figure 3—Existing vegetation for the Blue Mountains modeling region from Gradient 

Nearest Neighbor modeling (Ohmann and Gregory 2002).
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Calibration of State and Transition Models With FVS Moeur and Vandendriesche

FVS Analysis

In order to accomplish the integration of FVS within the STM approach, a 

computer program was developed to classify inventory data into vegetation classes 

(i.e. cover type, size class, canopy cover, canopy layers) for initial conditions and 

for subsequent projection cycles. The Preside program (Vandendriesche 2009a) 

summarizes various vegetation classes into states and provides average time in 

a particular state and the probability of movement to associated states. Armed 

with this tool, the general sequence of steps in an analysis process that integrates 

FVS projections with state and transition models is this:

1. Prepare the inventory data for projection by FVS.

2. Adjust FVS parameters to the current inventory.

3. Develop natural growth projections to estimate parameters for successional 

pathways (without disturbance).

4. Process the tree list output through the Preside program and accumulate the 

results into a matrix from which mean residence times within states and 

transition probabilities between states can be summarized.

5. Aggregate stand attributes by vegetation states.

6. Repeat steps for projections with management, insect and disease, and fire 

to estimate parameters for disturbance pathways.

7. Review state and transition empirical parameters in relation to conceptual 

parameters and adjust the STM where necessary.

Inventory data—Data for an FVS-VDDT analysis can be obtained from 

regional strategic inventories such as CVS and FIA. On U.S. Forest Service nonwilderness lands (as well as on BLM lands in western Oregon), one CVS plot is 

installed every 1.7 miles (1 plot per about 7,200 ac). On U.S. Forest Service wilderness lands, plots are installed at one-fourth the intensity of non-wilderness (one 

plot every 3.4 miles). On non-U.S. Forest Service or non-BLM lands, we are using 

FIA data from the periodic inventories collected between 1988 and 1999 in Oregon 

and Washington, and also from FIA’s National Annual Inventory beginning in 

2000 in Oregon and 2002 in Washington. FIA plots are installed at a sampling 

intensity of one plot every 6,000 acres. For the example analysis, we used data 

only from the Region 6 CVS inventory on the Wallowa Whitman, Umatilla, and 

Malheur National Forests. There were 5,102 samples (2,551 plots, each measured 

at two sampling occasions) available for the Blue Mountains modeling region, 

of which 464 samples fall in the warm dry ponderosa pine (Pinus ponderosa C. 

Lawson) PVT (table 1).

CVS data are prepared for projection using a translator program developed at 

Region 6 (Gregg and other 2004). Before further processing, each plot must be 

assigned to the appropriate VDDT model. The ecoclass code (Hall 1998) recorded 

for the plot is used to assign the potential vegetation type. A GIS intersection of 

the plot location with ownership and land allocation coverages are used to assign 

the administrative classification.

FVS calibration—Before projecting the large set of inventory plots for a project area, it is important to adjust FVS default parameters for growth, mortality, 

and regeneration for each combination of PVT model type and owner/allocation 

stratum. Table 1 shows the number of inventory samples used for calibration 

and modeling for each PVT in the Blue Mountains study area. The purpose of 

performing these calibration steps is so that the projections more closely mimic 

the empirical conditions determined from the actual field measurements. One 

example of a situation where calibration is essential is for projecting old-growth 

stands. The sample base upon which the empirical growth and mortality equations 
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Moeur and Vandendriesche Calibration of State and Transition Models With FVS

in FVS are built is not well suited to modeling old-growth forests over long time 

horizons, and yet typically VDDT simulations are performed for 200- to 300‐year 

intervals. Thus, thoughtful calibration can greatly improve the realism of 

simulations when projecting stands over long time periods by attenuating height 

and diameter growth and mortality during stand senescence.

Calibration procedures include using the FVS self-calibrating feature (for example, altering the baseline estimate of the large-tree diameter growth models), 

estimating and inputting natural regeneration response (querying existing stands 

to tabulate their seedling component), accounting for tree defect for volume 

estimates (adjusting net merchantable volume from gross tree dimensions), and 

determining tree species size attainment and limiting stand maximum density. 

Another paper in this volume (Vandendriesche 2009b) deals with this topic in 

more detail, and so we will not elaborate further in this paper.

Natural growth projections—In VDDT, the successional classes, pathways, 

and transition probabilities are defined for each potential vegetation type. A single 

PVT may have more than one set of probabilities defined to represent different 

management regimes or ecological conditions. In general, two types of transitions 

can occur (fig. 1). One type is movement between states due to natural succession. This process integrates background disturbances that affect regeneration, 

growth, and self-thinning, but not extrinsic disturbances such as insect or disease 

outbreaks, wildfire, or silvicultural treatment. Transitions representing natural successional dynamics (or ‘natural growth’) are modeled deterministically in VDDT. 

What this means is that transitions from one class to the next class occur when 

the residence time (a surrogate for successional ‘age’) has exceeded the value set 

for the state. For transitions in VDDT related to disturbances, movement between 

states is determined stochastically according to probabilities set by the user.

Once the FVS calibration procedure has been completed, we use FVS commands (keywords) to adjust growth, mortality, and regeneration responses as 

outlined in the above section. To model natural succession in FVS, we track 

residence time in a state—the average length of time that vegetation typically 

remains in that state before transitioning to the next state along the successional 

pathway. We do this by projecting all the plots in the study region without invoking any extrinsic disturbance or pest effects in FVS. Then 300-yr projections are 

performed for every plot, outputting tree lists and stand summaries each cycle 

for completing the next two steps.

Classify the tree lists, calculate residence times—Preside classifies the 

current tree list for each plot at each cycle boundary into the cover type, size 

class, canopy closure, and canopy layer that define the possible states (tables 1 

and 2). Following Stage’s approach (1997), estimates of the residence times and 

pathways are summarized by use of an array of all possible transitions from one 

state to another, and indexed by PVT, owner, and allocation groups to which a 

plot belongs. For each plot at each cycle, its source (that is what state it began the 

cycle in) and destination (that is what state it ended the cycle in) are recorded. 

The length of time each plot remains within a state class between cycles is accumulated and the mean and variance of residence times is summarized over all 

the cycles and transitions in the projection (fig. 4). The pathways (direction of 

movement between source and destination) between states are also summarized 

using the array.

Accumulate and summarize outputs—At the end of an FVS projection, a set 

of FVS post-processing steps have been bundled together that produce aggregate 

summaries for each of the state classes, using the sample of plots populating each 
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Calibration of State and Transition Models With FVS Moeur and Vandendriesche

state class during the projection. It is then easy to display graphics for communicating the STM results. For example, images from the Stand Visualization System 

(SVS) can be displayed for each vegetation state that is an aggregate of the plots 

in that state (fig. 5). The post-processing programs also index the aggregate state 

classes to summary values derived from the tree lists, attributes from standard 

FVS output reports, and variables computed from the Event Monitor. This feature 

is useful for tracking important values such as stand volume and biomass across 

states (example, fig. 6). Other attributes tracked by this process are shown for the 

warm dry ponderosa pine (example, table 3).

Figure 4—Preside program relay matrix. Preside calculates mean residence times and 

transition probabilities for the sample of plots in a PVT projection.

State class definitions:

A_GFB VDDT-State A: Grass/Forbs/Brush

B_EAA VDDT-State B: Seed/Sap size class (0-5 inches qmd)

C_SAA VDDT-State C: Small Tree size class (5-10 inches qmd)

D_MAA VDDT-State D: Medium Tree size class (10-15 inches qmd)

E_LAA VDDT-State E: Large Tree size class (15-20 inches qmd)

F_VAA VDDT-State F: Very Large Tree size class (20-25 inches qmd)

G_GAA VDDT-State G: Giant Tree size class (25+ inches qmd)

Figure 5—Stand Visualization System images of the dry ponderosa pine model 

arrayed by size class.
284 USDA Forest Service Proceedings RMRS-P-61. 2010.

Moeur and Vandendriesche Calibration of State and Transition Models With FVS

Figure 6—Summary attributes of aggregate state classes for the warm dry 

ponderosa pine model tracked by the FVS-VDDT post-processing programs.

Feedback loop between FVS and VDDT model—The mean residence times 

and transition probabilities that are computed by the Preside program can be used 

to substantiate assumptions built into the VDDT model by the user. In the warm 

dry ponderosa pine example, we compared the residence times computed from the 

464 sample plots projected through the FVS system with VDDT parameter values 

obtained from the literature and from expert opinion (table 4). Recognizing five 

size classes spanning a 200-year time horizon for dry ponderosa pine sites in the 

Interior West, note the disparity of residence times by source method within the 

developmental stages. The most simplistic view renders an even splicing of the 

time distribution as cited by the literature review. A group of experts recognized 

an emphasis on enhanced early growth with a slow down occurring in later size 

classes. The FVS modeled runs conform to empirical knowledge gained from 

inventory data and indicate that the STM residence times should be further adjusted, especially shortening the time-in-state for the smallest size classes and 

lengthening it for the largest state classes.
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Calibration of State and Transition Models With FVS Moeur and Vandendriesche

Table 3—Aggregate values by VDDT vegetation state derived from FVS computed variables for the warm dry ponderosa 

pine model, by size class.

	 Small	 Medium	 Large	 Very large	 Giant

Seed/sap	 tree	 tree	 tree	 tree	 tree

(0 - 5” )	 (5-10”)	 (10-15”)	 (15-20”)	 (20-25”)	 (25”+)

Vegetation Structure Variables:

Dominance Type PIPO PIPO PIPO PIPO PIPO PIPO

Size Class 1 2 3 4 5 6

Canopy Class 1 2 2 2 2 2

Canopy Layers 1 1 1 2 2 2

Stand Age – Dominant Story 15 46 93 140 199 241

Total Plot Sample Count 23 558 3,406 4,242 2,561 394

Stand-Stock Variables:

Basal Area/Acre, trees >= 1.0” diameter 19 91 112 122 131 130

QMD – Top 20 % trees, diameter 4.0 8.6 13.1 17.5 22.3 25.7

Percent Canopy Cover 24 53 51 46 43 40

Live – Cubic Feet/Acre 198 943 1563 2272 2997 3268

Live – Board Feet/Acre 1,139 5,043 7,868 11,661 16,136 18,534

Wildlife Habitat Variables:

Standing Snags/Acre

 Small = 5-10” diameter 4.8 28.9 35.1 18.3 6.6 1.9

 Large = 10”+ diameter 2.6 6.8 17.9 21.2 18.4 14.0

 Extra-large = 18”+ diameter 0.6 2.0 3.2 5.0 8.0 9.7

Snag Recruitment/Acre

 Small = 5-10” diameter 2.2 43.1 30.5 12.4 3.8 1.2

 Large = 10”+ diameter 1.0 4.9 11.3 10.8 7.4 4.3

 Extra-large = 18”+ diameter 0.2 0.9 1.2 2.1 2.9 2.7

Wildfire Risk Variables:

Crown Bulk Density 0.05 0.10 0.07 0.06 0.05 0.05

Fire Hazard Rating 3 4 4 3 3 2

Biomass-Carbon Variables:

Tree Biomass – Dry weight live & dead/boles & crown 9 37 50 62 76 83

Stand Carbon – Total carbon above & below ground 10 31 49 61 73 80

Table 4—Mean residence time by size class for Dry Ponderosa Pine type.

Seed/sap	 Small	 Medium	 Large	 Very large

0-5”	 5-10”	 10-15”	 15-20”	 20”+

Literature Review a 40 40 40 40 40

Expert Opinion b 35 35 45 45 45

Empirical Basis (FVS) c 14 21 43 52 72

a The Nature Conservancy, under contract with the U.S. Forest Service Southwestern Regional Office, for 

the Southwest Forest Assessment Project, values for Ponderosa Pine/Bunchgrass PVT.

b U.S. Forest Service Pacific Northwest Regional Office and Fremont-Winema National Forests, Southwest 

Oregon, collaborative forest plan working group, values for Dry Ponderosa Pine PVT.

c Interagency Mapping and Assessment Project, U.S. Forest Service Pacific Northwest Regional Office, Blue 

Mountain Modeling Region, from the Forest Vegetation Simulator, values for Warm Dry Ponderosa Pine PVT.
286 USDA Forest Service Proceedings RMRS-P-61. 2010.

Moeur and Vandendriesche Calibration of State and Transition Models With FVS

For testing pathways, a simple test is to verify the correspondence between the 

FVS projection and the STM projection at ‘equilibrium’ over a long projection 

period for the natural growth scenario. To record the successional growth pattern, 

the inventory plots are forecasted for 200-300 years through FVS under endemic 

stand development (without disturbance). Ending vegetation states for the inventory plots as reported by Preside are compared with the equilibrium conditions 

projected by the STM. If the VDDT model is configured appropriately, it should 

predict reasonably well the ending inventory conditions projected by FVS. Appropriate adjustments can then be made.

Discussion

Note that both FVS and VDDT models predict state transitions (a change from 

one vegetation state to another). Transitions in VDDT can only occur along natural 

growth and disturbance pathways specified by the modeler. But transitions in FVS 

result from the detailed regeneration, growth, and mortality of individual trees, 

condensed to VDDT vegetation classes. As a result, FVS typically models transitional pathways not explicitly represented in the corresponding VDDT model. For 

example, if size class (calculated as the qmd of the dominant overstory) decreases 

between cycles in FVS as a result of the mortality of just a few large trees in the 

tree list, the resulting transition may indicate “retrogression” from a larger state 

class to a smaller state class. Unlike FVS, VDDT won’t model retrogressions 

unless such pathways are explicitly specified by the user. For example, ESSA 

Technologies Ltd (Beukema & Robinson 2009) investigated a dry mixed conifer 

PVT from central Washington with a VDDT model having 32 transitions in total 

covering about 40 classes. For the same PVT, the FVS analysis using about 500 

plot samples resulted in 174 transitions only of which 15 pathways were explicitly 

specified in the associated VDDT model. The remaining transitions were retrogressions, or ‘jumps’ between non-adjacent state classes.

FVS and STM can be viewed as models with similar objectives, but at different 

ends of the spectrum in terms of resolution. Both are designed to project vegetation changes due to successional dynamics, and changes driven by disturbances. 

An STM like VDDT represents vegetation change as movement between classes 

along pre-determined pathways. An individual tree model like FVS represents 

vegetation change along a continuum as the sum of regeneration, accretion, and 

mortality of individual trees. The objective of our paper has been to demonstrate 

how these models can be used in conjunction to provide an informed decision 

process. Building linkages between a strong empirical base represented by inventory data and FVS, and an STM calibrated largely by expert opinion, will lead 

to an improved modeling system.

FVS’s expanded resolution accounts for complex changes in the stand development due to regeneration and mortality and the changing impacts of having 

multi-species stands. However, the challenge of building a comprehensive VDDT 

model is to represent the important successional, disturbance, and management 

dynamics in sufficient detail, but not too much detail. If the ultimate goal of 

landscape planning is to understand the underlying processes, coupling FVS with 

VDDT will aid the endeavor.

In building STMs, we recognize the importance of striking a balance between 

FVS projection results, expert judgment (i.e., the collective experience of silviculturists, ecologists, and others about how forest vegetation is likely to develop 

and respond to management and disturbance), and additional scientific knowledge (i.e., vegetation responses drawn from the literature). The VDDT modeler 

should review all three types of information and add best professional judgment 

in selecting the final transition times.
USDA Forest Service Proceedings RMRS-P-61. 2010. 287

Calibration of State and Transition Models With FVS Moeur and Vandendriesche

More information about the data, models, and tools mentioned in this paper 

can be obtained on-line. FVS can be obtained from the FVS website (http://www.

fs.fed.us/fmsc/fvs/index.shtml) and Preside, and associated post-processors can be 

obtained by contacting the authors. A comprehensive user guide that documents 

the primary computer processing steps is also available (Moeur & Vandendriesche 

2009). The Vegetation Dynamics Development Tool can be acquired from ESSA 

Technologies Ltd. (http://www.essa.com/tools/VDDT/index.html). IMAP data 

and more information about the IMAP program can be found at http://www.reo.

gov/ecoshare/mapping/index-issues.asp.

Acknowledgments

The authors wish to thank Miles Hemstrom with the Pacific Northwest Research Station and James Merzenich with the Pacific Northwest Regional Office 

for their contributions to modeling analysis and this paper.

References

Beukema, Sarah; Robinson, Don. ESSA Technologies Ltd. 2009. FVS-VDDT-IMAP Progress 

Report. May 31, 2009. Joint Venture Agreement PNW 08-JV-11261976-362.

Dixon, Gary E. comp. 2003. Essential FVS: A User’s Guide to the Forest Vegetation Simulator. 

Internal Rep. Fort Collins, CO: U. S. Department of Agriculture, Forest Service, Forest Management Service Center. 193p.

ESSA Technologies Ltd. 2005. Vegetation Dynamics Development Tool user guide, Version 5.1. 

Prepared by ESSA Technologies Ltd., Vancouver, B.C., Canada. 188 pp.

Forest Inventory and Analysis Program. 2006. Field instructions for the annual inventory of 

Washington, Oregon, and California, 2006. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 188 p + app.

Gregg, Tom; Moeur, Melinda; Apple, Carol. 2004. User guide to VIMTrans software for translating Current Vegetation Survey data to Forest Vegetation Simulator format. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Region. Interoffice publication.

Hall, Frederick C. 1998. Pacific Northwest ecoclass codes for seral and potential natural communities. Gen. Tech. Rep. PNW-GTR-418. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 290 p.

Hemstrom, M.; Barbour, J; Lettman, G; DeMeo, T; Evers, L. 2008. Interagency Mapping and Assessment Project (IMAP). Interoffice publication. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 1 p.

Hemstrom, M.A., Merzenich, J., Reger, A., Wales, B.C., 2006. Integrated analysis of landscape 

management scenarios using state and transition models in the upper Grande Ronde River Subbasin, Oregon, USA. Landscape and Urban Planning. 80:198–211.

Max, T.A.; Schreuder, H.T.; Hazard, J.W.; Oswald, D.D; Teply, J.; Alegria, J. 1996. The Pacific Northwest 

Region vegetation and inventory monitoring system. Res. Pap. PNW-RP-493. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station. 22 p.

Moeur, Melinda; Vandendriesche, Don. 2009. FVS Processing Procedures for Landscape Assessments. Interoffice publication. U.S. Department of Agriculture, Forest Service, Forest 

Management Service Center. Fort Collins, CO. 50 p.

Ohmann, J.L.; Gregory, M.J. 2002. Predictive mapping of forest composition and structure with 

direct gradient analysis and nearest-neighbor imputation in coastal Oregon, USA. Canadian 

Journal of Forest Research. 32: 725-741.

Stage, Albert R. 1997. Using FVS to provide structural class attributes to a forest succession 

model (CRBSUM). In: Teck, Richard, Melinda Moeur, and Judy Adams (compilers). 1997. 

Proceedings: Forest Vegetation Simulator conference. 1997 February 3-7, Fort Collins, CO. 

Gen. Tech. Rep. INT-GTR-373. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station. 222 p.

Vandendriesche, Donald A. 2009a. Advanced FVS Topics for Forest Planning. Preside: Process 

Residence Times. Interoffice publication. U.S. Department of Agriculture, Forest Service, Forest 

Management Service Center, Fort Collins, CO. 129 p.

Vandendriesche, Donald A. 2009b. FVS Out of the Box – Assembly Required. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. Ogden, UT. Proceedings: 

National Silviculture Workshop; June 15-19, 2009. Boise, ID.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.

USDA Forest Service Proceedings RMRS-P-61. 2010. 289

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Forester, U.S. Department of Agriculture, Forest Service, Forest 

Management Service Center, Vegetation Simulation Group, Natural 

Resources Research Center, 2150A 

Centre Avenue, Fort Collins, CO.

FVS Out of the Box—Assembly Required

Don Vandendriesche1

Abstract—The Forest Vegetation Simulator (FVS) is a prominent growth and yield 

model used for forecasting stand dynamics. However, users need to be aware of model 

behavior regarding stocking density, tree senescence, and understory recruitment; 

otherwise over long projections, FVS tends to concentrate substantial growth on few 

survivor trees. If the intent is to forecast endemic conditions, model performance can be 

modified by establishing baseline trends from measured inventory data and configuring 

FVS to stay within the observed ranges. This paper will present the steps needed to 

carefully craft an endemic FVS run. Techniques include using the FVS self-calibrating 

feature; accounting for tree defect; limiting stand maximum density; determining tree 

species size attainment; and, accounting for regeneration response. A case example 

from the Blue Mountains project area in northeast Oregon will be presented.

Introduction

Have you ever purchased an item from a store that comes in a box only to realize 

that once opened, all the components are there but you must carefully assemble 

the parts to properly construct the object? You may have a manual or diagram to 

consult, but getting from the raw materials to the final product can be challenging. This analogy can be applied to using the Forest Vegetation Simulator (Dixon 

2002). Users may naively assume that after initially installing the software, they 

are fully ready to go. This can be problematic in matching measured trends to 

modeled projections.

Three projection scenarios are recognized in this paper: (1) full site occupancy, 

(2) endemic stand development, and (3) epidemic area disturbance2. By default, 

FVS will forecast stand development to full stocking. In contrast, compilation of 

large field inventory data sets void of significant impacts will typically render 

endemic conditions. Whereas, bark beetle outbreaks, dwarf mistletoe epicenters, 

and active crown fires render epidemic, stand replacing events. Users of FVS 

need to be cognizant of the modeling context and take steps necessary to ensure 

simulation results portray logical outcomes. The purpose of this paper will be 

to present the fundamental steps needed to project stand development under 

endemic conditions with FVS. The methodology prescribed will be based on the 

procedures used to construct vegetation pathways for regional assessments and 

forest planning.

2Endemic implies being constantly present in a particular region and generally considered under control. 

In contrast, epidemic refers to out of control situations where the vector is spreading rapidly among many 

individuals (Ref: Webster’s New World Dictionary, Third College Edition).
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Whether planning at the national, broad, mid, or base level, the analysis process 

for projecting vegetation should contain the following elementary steps:

P erform vegetation stratification

I dentify data sources

C alibrate FVS model

T ailor natural growth runs

U tilize treatment prescriptions

R eport vegetation pathways

E valuate output values

Having a good ‘PICTURE’ in mind before beginning a project will aid the 

endeavor. Each of these analysis topics will be elaborated upon relative to development of vegetation pathways for a landscape assessment of the Blue Mountains 

project area.

Vegetation Stratification

Forest planning efforts often require developing estimates of conditions and 

outcomes by stand type. A stand type is a combination of the physical, vegetative, 

and developmental characteristics used to identify homogeneous forest strata 

(Davis and Johnson 1987). Physical attributes describe the site aspects of the forest, 

such as topography, soils, and habitat type. Vegetative attributes characterize the 

flora aspects of the forest, such as overstory dominant species, its relative size and 

density. Developmental attributes portray the human aspects of the forest, such as 

roads, buildings, and administrative boundaries. In this context, stand types are 

non-spatial but are comprised of many geographically identifiable forest stands.

Biophysical settings of plant associations (habitat types) arrayed by prevailing tree species comprised the stand types used to describe the vegetation strata 

for the Blue Mountains project area. Figure 1 provides a relational schematic of 

ascending elevation adjoining to the potential vegetation types that represent the 

various temperature/hydrologic regimes.

The Warm Dry Ponderosa Pine (Pinus ponderosa C. Lawson) stand type will 

be used to demonstrate FVS assembly techniques employed in the construction 

of an endemic model run. Figure 2 displays a Stand Visualization System (SVS) 

(McGaughey 2004) rendering of this biome.

Figure 1—Biophysical settings of potential vegetation types within the 

Blue Mountains project area.
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Figure 2—SVS ten-acre depiction of the Warm Dry Ponderosa Pine stand type.

Data Sources

Two types of data are generally used for planning projects: spatial and temporal. Spatial data is usually compiled from remote sensing imagery, and acreage 

compilation is accomplished by summing the various stand types residing 

within mapped polygons. Temporal data is collected during a field inventory, and 

place-in-time attributes are gathered to provide an estimate of forest conditions. 

Inventory values provide per acre estimates. When spatial data that complies 

with the vegetation stratification scheme is multiplied by temporal data obtained 

from field inventories, total strata estimates are produced. These values are then 

incorporated into the landscape assessment.

The spatial data source for the Blue Mountains was assembled using Gradient 

Nearest Neighbor technology (Ohmann and Gregory 2002). The primary data 

source available for the area was the USFS Pacific Northwest Region Current 

Vegetation Survey (CVS) (Max and others 1996) that predates the standardized 

installation of the Forest Inventory and Analysis (FIA) (Miles 2001) sample design. Table 1 summarizes the CVS inventory sample used to represent the Warm 

Dry Ponderosa Pine stand type on the Malheur, Umatilla, and Wallowa-Whitman 

National Forests.

Table 1—Data set used for the Warm Dry Ponderosa Pine 

stand type on the Blue Mountains Project Area.

National	 Inventory	 Inventory	 Total

Forest	 Occasion 1 a	 Occasion 2 b	 Sample

- - - - - - - - number of plots - - - - - - - - -

Malheur 156 120 276

Umatilla 33 33 66

Wallowa-Whitman 42 31 73

Total: 231 184 415

a Occasion 1: Initial Installation of Current Vegetation Survey

b Occasion 2: First Remeasurement of Current Vegetation Survey
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Table 2—Plot sample distribution by size class.

	 Stand	 Size	 Stand	 Plot

	 type	 class a age b	 sample

Seedling-Sapling (0-5”) 0 -20 7

Warm Small Tree (5-10”) 20 -50 31

Dry Medium Tree (10-15”) 50 -90 178

Ponderosa Large Tree (15-20”) 90 -140 143

Pine Very Large Tree (20-25”) 140 -200 31

Giant Tree (25”+) 200 -270+ 6

Non-Vegetated c Grass/Forbs/Brush ~ 19

Total: 415

a Based on the quadratic mean diameter (qmd) of the largest 20 percent of the trees 

with a minimum of 20 trees.

b Origin date of the oldest cohort (i.e. qmd size class) inferred as time since the last 

stand replacement disturbance.

c Although non-vegetated with tree cover at the time of inventory, these plot samples 

reside in Warm Dry Ponderosa Pine plant associations.

Portioning the Warm Dry Ponderosa Pine stratum by size class resulted in the 

plot distribution listed in table 2. Stand age was incorporated into the definition of 

size class. Stand age provides a general measure of important ecological processes. 

Each successional stage provides structural components critical for particular 

plants and animals. Vegetation pathways developed for contemporary mid-scale 

plans account for distribution of stand structure across the forest landscape.

Model Calibration

An essential step in appropriate use of the Forest Vegetation Simulator is calibration of the model. The FVS geographic variants are comprised of numerous 

mathematical relationships. One prediction equation may provide input to another. 

For long-term projections, users should validate virtual world estimates generated 

by FVS against real world values obtained from inventory data.

Determining the modeling context matters in regards to constructing a verifiable FVS projection. As conceived and implemented, stand development within 

FVS trends toward full site occupancy. If endemic or epidemic conditions are to 

be portrayed, users should be aware of existing software extensions and addfiles 

that account for insect, disease, and fire effects. These utilities address additional 

mortality impacts during long-term projections. In the absence of available disturbance model extensions, users assume the responsibility to ensure simulation 

runs provide reasonable results. Inventory data should be acquired that represents 

either full stocking, common conditions, or impacted landscapes to provide the 

sideboards for FVS model predictions. The focus of the remainder of this paper 

will be targeting endemic conditions that characterize vegetation pathways for 

landscape planning.

Figure 3 depicts measured trends in the Warm Dry Ponderosa Pine stand type 

as compared to modeled FVS projections of the seedling-sapling size class. The 

images were derived by averaging individual plots. The graphics portray structural 

trends from stand ages 10 to 260 years by size class increments. Note that the 

initial seedling-sapling size class images for the measured and modeled frames 

are identical. The modeled projections were produced by the Blue Mountain 

variant of FVS without and with user intervention. Comparing measured trends 

(left column in fig. 3) with FVS modeled projections, without adjustments (middle 

column in fig. 3), demonstrates vast differences in stand development.
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Figure 3—Measured versus modeled trends of Warm Dry Ponderosa Pine stratum. a Left column: Measured inventory data from stand age 10 to 260 by size class increments.

b Middle column: Modeled strata projected from stand age 10 to 260 by size class increments without adjustments.

c Right column: Modeled strata projected from stand age 10 to 260 by size class increments with adjustments.
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Delving into two key attributes highlights the anomalies. Trees per acre plotted over stand age by size class increments are displayed in figure 4 (Inventory 

Data vs. FVS w/o Adjustment). Board foot volume per acre plotted over stand 

age by size class increments is displayed in figure 5 (Inventory Data vs. FVS w/o 

Adjustment). Contrasting the measured inventory data to the unadjusted modeled 

run over time, tree density is too low and stand volume is too high. Mortality 

and regeneration aspects are difficult components to model. One subtracts trees 

from the ecosystem; the other adds trees to it. The default mortality paradigm 

embedded in FVS allows stands to progress to full site occupancy (i.e. maximum 

stand density). However, this ceiling does not represent the generalized endemic 

growth pattern typified by inventory data sets used for landscape assessments. 

Consequently, densely stocked conditions are forecast. Note also that most FVS 

variants include only the partial establishment extension that lacks automatic 

natural regeneration features. Thus, tree frequencies steadily decline over time. 

Growth dynamics are accumulated in the survivor trees resulting in excessive 

stand volume.

FVS Self-Calibration

A unique feature of the Forest Vegetation Simulator is its ability to self-calibrate 

the small-tree height and large-tree diameter increment models based on measured 

growth rates per inventory plot (Stage 1981). This mechanism alters the interspecies tree competition from the basis that was used to build the model. Over 

long projections, species composition may differ from the base model forecast 

as a result of local observations.

Figure 3—Continued.
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Figure 4—Comparison of trees per acre over stand age by size class increments for measured and 

modeled runs. Within grouping, recommended assembly steps are added to the unadjusted model run 

to properly configure FVS to conform to measured values.

Figure 5—Comparison of board foot volume per acre over stand age by size class increments for measured 

and modeled runs. Within grouping, recommended assembly steps are added to the unadjusted model 

run to properly configure FVS to conform to measured values.
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The FVS self-calibration process computes a scale factor that is used as a multiplier to the base growth equations. This scaling procedure is really quite simple. 

The affected models are linear with logarithmically scaled dependent variables. 

Therefore, the model intercepts are in effect growth multipliers. FVS predicts 

a growth increment to match each observed increment for a given species on a 

specific plot. The median difference is then added to the model for the species 

as an intercept term.

Growth multipliers can be developed across a large geographic area for a particular stand type. Using the CalbStat keyword in conjunction with the Calibration 

Summary Statistics post processing program produces average scale factors for 

all qualifying plots. ReadCorR (Readjust Correction for Regeneration) and ReadCorD (Readjust Correction for Diameter) keywords can be constructed from the 

mean multipliers listed in the Calibration Statistics Report. The ReadCorR and 

ReadCorD keywords alter the baseline estimate for small-tree height and largetree diameter growth, respectively. For a particular species, the original baseline 

estimate is multiplied by the scale factor and the result becomes the new baseline 

estimate. These adjustments are done prior to the FVS self-calibration procedures 

per plot. Large-tree diameter increment scale factors attenuate toward the new 

baseline estimate at twenty-five year intervals as depicted in figure 6. Average 

scale factors computed for the Warm Dry Ponderosa Pine stand type within the 

Blue Mountains project area are displayed in table 3.

With respect to large-tree diameter growth, western larch (Larix occidentalis 

Nutt.) and Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) grow slightly faster 

whereas grand fir (Abies grandis), lodgepole pine (Pinus contorta Douglas ex 

Louden), and ponderosa pine (Pinus ponderosa C. Lawson) grow slightly slower 

(compared to a mean ReadCorD multiplier equal to 1.000) relative to the base 

model equations. Regarding this data set for long-term projections, the species 

composition differs slightly with the inclusion of the ReadCorD keyword than 

without.

Figure 6—Scale factor attenuation occurs over time to the 

adjustment of the large-tree diameter growth equation.
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FVS self-calibration does not address all ‘measured’ versus ‘modeled’ stand 

development discrepancies but rather only individual tree species small-tree height 

and large-tree diameter growth performance. In this case, due to the nominal differences in mean multipliers, board foot attainment was not dramatically improved 

by the inclusion of ReadCorR and ReadCorD keywords in the runstream. Refer 

to figure 5 (FVS w/ Self-Calib).

Tree Defect

Determining net merchantable volume from gross tree dimensions requires 

an estimate of tree defect. Values can be obtained from field inventory data or 

recent timber sales. For example, defect estimates obtained from CVS data for 

the Blue Mountains project area was arrayed by tree species, by 5-inch diameter 

classes, to populate the Defect keyword. Figure 7 displays the board foot defect 

trend for ponderosa pine and grand fir.

Consult the Keyword Reference Guide (Van Dyck 2006) or the Suppose 

(Crookston 1997) interface for specific parameter fields associated with the Defect keyword. Applying board foot defect factors normally affects larger diameter 

trees and aids in reining in runaway sawtimber volume. Given that the Warm Dry 

Ponderosa Pine strata is comprised primarily of ponderosa pine tree species, the 

magnitude of board foot reduction is slight as observed in figure 5 (+Tree Defect). 

Certainly, the Warm Dry Grand Fir strata would display a more dramatic effect 

from accounting for board foot volume defect.

Natural Growth Runs

Landscapes that have been heavily impacted by disturbances may require 

forest planners to estimate stand development beyond the pool of existing stand 

structures. Very large and giant tree size classes may be absent among currently 

inventoried stands. In these situations, projections of old growth development are 

needed. A reasonable assumption would be an extrapolation of existing circumstances toward a ‘steady-state’ condition extending into the future.

The goal of developing natural growth runs is to try to capture ecosystem processes that sustain stand types to their reasonable extent. Adjustments are taken 

into account for stand and tree level mortality components. As growing space 

opens, regeneration fills the void. Crafting an endemic natural growth profile 

requires addressing mortality and regeneration interactions.

Table 3—Calibration scale factors for the 

Warm Dry Ponderosa Pine stand type.

Total	 Mean

Tree	 tree	 ReadCorR

species	 records	 multiplier

Small-Tree Height Growth

Ponderosa Pine 5 1.205

Large-Tree Diameter Growth

Western Larch 32 1.096

Douglas-fir 118 1.001

Grand Fir 36 0.772

Lodgepole Pine 17 0.735

Ponderosa Pine 2741 0.813
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Stand Size Caps

The Forest Vegetation Simulator base model mortality predictions are intended 

to reflect mortality rates that allow for stand development to full site occupancy. 

Increases in mortality from insects, pathogens, and fire are accounted for in the 

various FVS model extensions. Mortality from other causes, such as logging 

operations, animal damage, or wind events, needs to be simulated with appropriate FVS keywords. There are three types of mortality base models used in FVS: 

(1) the original Prognosis type mortality model; (2) the Stand Density Index based 

mortality model; and (3) the Stand Density Index/TWIGS based mortality models.

The Prognosis type mortality model (Stage 1973) is used in western FVS variants where there were enough inventory data suitable for developing the associated 

equations. Two independent equations are involved. The first equation predicts 

an annual mortality rate as a function of habitat type, species, diameter, diameter 

Figure 7—Measured board foot defect for ponderosa pine (PP) and 

grand fir (GF) on the Blue Mountains Project Area.
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increment, estimated potential diameter increment, stand basal area, and relative 

diameter. The estimated annual mortality rate is multiplied by a factor based on 

Reineke’s (1933) Stand Density Index (SDI) that accounts for expected differences 

in mortality rates on different habitat types and National Forests. The second 

equation estimates mortality loss and is dependent on the proximity of stand basal 

area (BA) to the assumed maximum for a site, and on the estimated rate of basal 

area increment. The mortality rate applied to a tree record is a weighted average 

between equation one and two. The weights applied to the respective estimates 

are dependent on the proximity of the stand basal area to the maximum basal 

area specified for a site or strata (BAMax).

The Stand Density Index (SDI) based mortality model (Dixon 1986) is used 

in western FVS variants where there were not enough inventory data suitable for 

developing the Prognosis type mortality model, and no other suitable mortality 

model existed. Mortality predictions for the Blue Mountains variant are SDI 

based. The model has two steps. In the first step, the number of mortality trees is 

determined; in the second step, this mortality is dispersed to the individual tree 

records in FVS. Two types of base model mortality are estimated: (1) background 

mortality and (2) density related mortality. Density related mortality accounts for 

mortality in stands that are dense enough for competition to be the causal agent. 

All other mortality is attributable to background mortality. Background mortality 

gives way to density related mortality based on the relationship between current 

and maximum Stand Density Index. By default within FVS, density related mor-

tality begins when the stand SDI is above 55 percent of maximum SDI, and stand 

density peaks at 85 percent of maximum SDI. Background mortality is used when 

current stand SDI is below 55 percent of maximum SDI. In FVS terminology, the 

55 percent value is referred to as the lower limit of density related mortality, and 

the 85 percent value is the upper limit.

The Stand Density Index/TWIGS based mortality models are used in all eastern 

FVS variants. Mortality losses are determined using the SDI base model method. 

Mortality values are then dispersed to individual tree records using relationships 

found in the TWIGS type mortality models (Buchman 1983; Buchman and Lentz 

1984; Buchman and others 1983; Teck and Hilt 1990). These equations are variant 

dependent and actually predict survival rate, rather than mortality rate. Survival 

rate is predicted as a function of diameter, diameter growth, basal area in larger 

trees, and/or site index. The survival rate is converted to a mortality rate for 

FVS processing. In addition, background mortality is estimated as 1/10th of the 

calculated TWIGS mortality rate for each individual tree record.

Thus, basal area or stand density index maximum control mortality predic-

tions and associated stand-stocking attainment in all FVS variants. The bases 

for the default maximum density relationships within FVS are research studies 

that principally include pure, fully stocked, uniformly even-aged stands. Since 

most forest stands on public lands are very heterogeneous in regard to stocking 

and structure, FVS without adjustment tends to overestimate the carrying capac-

ity of common conditions. Model projections tend toward full site occupancy 

estimates. To account for endemic or epidemic mortality loss caused by insects, 

diseases, fires, or other disturbance agents, use of FVS model extensions should 

be investigated. In their absence, if exogenous data is available that can be used 

to parameterize mortality modifying keywords; this method ought to be explored. 

Lacking model extensions or mortality keyword support, a proxy for estimating 

the endemic average maximum density can be derived from the inventory data 

sets that are used for the landscape analysis.

Users can set maximum SDI and BA values that represent endemic conditions. 

The Forest Vegetation Simulator can be used to compute the measured SDI value 

for each inventory plot. Filtering the SDI calculation to include trees 1.0-inch and 
300 USDA Forest Service Proceedings RMRS-P-61. 2010.

Vandendriesche FVS Out of the Box—Assembly Required

greater in diameter enables excluding seedling tallies that can overwhelm the 

resultant value. Generally, the cluster of the top three percent of the inventory 

plots can then be averaged to determine the observed SDI maximum value3. To 

derive the average stand basal area maximum of the measured plots, multiply the 

observed SDI maximum value by 85 percent to represent an endemic maximum 

limit4. The observed basal area maximum is simply the basal area stocking most 

closely aligned with 85 percent of the observed Stand Density Index maximum 

value. The upper limit of density related mortality for the SDImax keyword should 

be set to 75 percent to represent the empirically derived endemic condition.

Table 4 contrasts the FVS default values for maximum SDI and BA (full site 

occupancy) against those derived from the inventory data (endemic conditions) 

for the Blue Mountains project area. Note that these two metrics have different 

basis and intended use. The FVS default values are indicative of theoretical stand 

density maximums. The data derived values are representative of average maximum stand density and have use for landscape planning projects.

The variant overview document should be consulted to determine the specific 

mortality model being employed for a given geographic area. The Blue Mountains variant uses the Stand Density Index based mortality model. Setting the 

stand density index maximum affects mortality predictions and stand-stocking 

attainment. Specifying a complementary basal area maximum is recommended. 

The effects of including an endemic stand density maximum that is more closely 

aligned to measurement data can be observed in figure 5 (+Stand Caps). Notice 

that as bars are being added to the right per stand age/size class grouping, the gap 

is closing between the inventory data and model projections with adjustments.

Tree Size Caps

The TreeSzCp keyword assists in setting the morphological limits for individual 

tree diameter and height development. The adjusted mortality rate is applied 

when a tree’s diameter exceeds the threshold minimum diameter indicated for a 

given tree species. The threshold diameter acts as a surrogate for age to invoke 

senescence mortality. The process to parameterize the TreeSzCp keyword entails 

choosing the minimum diameter class that contains approximately one tree per 

acre (TPAmin) (the exact number is dependent on the relative abundance of a 

particular tree species) and targeting a maximum diameter class that contains 

3 Edminster (1988) stated that: “For each stand in the database (1400 stands from USFS Region 2 and 2939 

stands from USFS Region 3), Stand Density Index (SDI) was calculated, and stands with SDI values in the 

upper two percent from each Region were selected for further analysis to develop the Average Maximum 

Density (AMD) line. There was nothing special about using the top two percent, but for the six forest types 

analyzed, the selection has resulted in AMD lines which represent ‘average maximum’ conditions”. Given 

smaller data sets to work with for landscape assessments, using the top three percent of the inventory plot 

set to determine AMD is reasonable and has worked repeatedly well for determining “average maximum” 

stand density index for FVS.

4 Powell (1999) indicated in “Table 3 – Characterization of selected stand development benchmarks or 

stocking thresholds as percentages of maximum density and full stocking” that: “Maximum Density is the 

maximum stand density observed for a tree species; although rare in nature, it represents an upper limit. 

Full stocking refers to ‘normal yield table’ values; it has also been termed as ‘Average Maximum Density.’” 

As a “percent of maximum density,” full stocking was cited by Powell at 80 percent. (Recall in FVS, that 

density related mortality trends toward full site occupancy and peaks at 85 percent of maximum SDI.) 

The “Lower Limit of Self Thinning Zone, also referred to as the ‘zone of imminent competition mortality’ 

(Drew and Flewelling 1979)” is referenced by Powell at 60 percent of maximum density. (Recall in FVS, 

that density related mortality begins when the stand SDI is above 55 percent of maximum SDI.) Use of 75 

percent of average maximum density as represented by the top three percent of selected plots renders an 

upper mid-range value between full stocking and the lower limit of self thinning zone. This is the suggested 

target for the Endemic Stand Density Index Maximum. Through repeat application of this process, FVS 

projection results support using this methodology to set stand density maximums
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approximately one-tenth tree per acre (TPAmax)5. Subtracting the associated 

diameter minimum from the diameter maximum, then dividing by the average 

diameter growth rate renders the number of FVS projection cycles needed to get 

from the minimum to the maximum size diameter. Determining the mortality rate 

is akin to computing the discount interest rate needed to pay off a capital sum. 

The mortality rate equals one minus the ratio of TPAmax to TPAmin raised to the 

power of one over the number of projection cycles. Note that the mortality rate 

compounds each projection cycle. Thus, this is the factor needed to diminish the 

tree count from one to one-tenth. The mortality rate becomes the proportion of 

trees to succumb to mortality agents during successive projection cycles.

Table 5 displays stand table values for ponderosa pine based on the inventory 

plots from the Blue Mountains project area. The goal of developing the TreeSzCp 

keyword is to ensure morphological senescence for a given tree species based on 

measured observations. On average, ponderosa pine tree frequencies diminish 

from one to one-tenth in the 22-inch to 36-inch diameter range. The weighted 

average annual diameter growth rate within this diameter range was measured to be 0.088 inches. This implies that it would take approximately 

160 years (i.e. sixteen 10-year FVS projection cycles) for ponderosa pine trees 

to grow from 22 inches in diameter to 36 inches. This aligns with the stand age 

estimates associated with the progression from the very large to giant tree size 

class listed in table 2.

Table 6 presents the values used in the determination of the mortality rate for 

ponderosa pine. Since the TreeSzCp keyword is applied from projection cycle to 

projection cycle during simulation runs, its effect is exponential. The relationship 

between the mortality rate and surviving trees is asymptotic and therefore not all 

trees die. A few trees (or portions of tree records in FVS terms) remain and grow 

to larger diameter greater than the maximum diameter value.

5 Shaw and others (2006) observed that: “However, the density-dependent self-thinning dynamic projected 

in the Southern variant of FVS may not be realistic for mature longleaf pine stands. Recent work suggests 

that the expected self-thinning trajectory does not hold for stands with a quadratic mean diameter greater 

than about 10 inches. Specifically, FVS projections of longleaf pine growth exceed the maximum limit of 

the size-density relationship, or “mature stand boundary,” proposed by Shaw and Long (2007).” Application 

of the TreeSzCp keyword to the upper diameter range per tree species addresses this stand dynamic. Given 

that the mortality rate is applied exponentially over several projection cycles and across the “one-tenth” 

tree distribution target range, the TreeSzCp keyword implements the mature stand boundary concept. 

Repeat trials have supported methods used to construct the TreeSzCp keyword.

Table 4—Stand density maximums: Default values from Blue Mountains variant; 

derived values from inventory data.

SDI Maximum	 BA Maximum (ft2/acre)

	 	 FVS	 Data	 FVS	 Data

	 Stand type	 default	 derived	 default	 derived

Subalpine Whitebark Pine 700 535 325 205

Cold Dry Mixed Conifer 593 505 275 175

Cold Moist Mixed Conifer 597 460 277 170

Warm Dry Grand Fir 604 415 280 160

Warm Dry Douglas-fir 446 340 207 150

Warm Dry Ponderosa Pine 416 330 193 135

Hot Dry Ponderosa Pine 361 290 167 115

Woodland Western Juniper 420 170 195 75
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Table 5—Ponderosa Pine stand table, Blue Mountains Project Area, 415 inventory plots.

Diameter

class (inches)

Trees/

acre

Average

diameter (inches)

Diameter

growth/year

(inches)

Average

height (ft)

<1. 261.518 0.10 0.302 1.0

 2. 61.855 1.84 0.154 10.3

 4. 42.051 3.87 0.111 18.4

 6. 21.775 5.89 0.137 27.5

 8. 18.171 7.92 0.132 36.3

10. 14.305 9.88 0.149 44.5

12. 10.320 11.87 0.141 52.5

14. 5.718 13.90 0.141 59.8

16. 3.727 15.86 0.133 66.6

18. 2.181 17.90 0.130 73.3

20. 1.538 19.86 0.100 79.6

22. 1.057 21.91 0.101 86.4

24. 0.715 23.84 0.092 91.6

26. 0.536 25.92 0.084 97.4

28. 0.406 27.89 0.082 100.7

30. 0.338 29.88 0.074 104.9

32. 0.193 31.91 0.072 109.3

34. 0.144 33.87 0.070 114.0

36. 0.091 35.93 0.079 118.2

38. 0.052 38.01 0.064 120.3

40. 0.088 42.12 0.063 128.0

 Average: a 0.088

a Weighted average annual diameter growth within specified range.

Table 6—Ponderosa Pine mortality rate computation.

	 	 	 Avg. diameter

	 Diameter	 Trees/	 growth/year	 10-Year	 Mortality

	class (inches)	 acre	 (inches)	 project. cycles	 ratea

22. 1.057

— — 0.088 16 0.143

36. 0.091

a Mortality Rate = 1 – (TPAmax/TPAmin)1/Proj_Cycles
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Although the impact of including size caps per tree species appears minimal 

in regards to board foot volume achievement, the importance can be observed in 

terms of tree frequency per stand size class. Refer to figure 4 (+Tree Caps +Regeneration). Having larger trees die provides opportunity for new trees to become 

established. These young recruits then grow in diameter and progress through the 

various size classes as would be expected in natural stand development.

Regeneration Inference

In the last stand visualization image for the FVS modeled run “without adjustments” (fig. 3, middle column), the lack of understory trees is readily apparent. 

Two versions of the Regeneration Establishment extension are used in FVS variants. One version is referred to as the “full” establishment model (Ferguson and 

Carlson 1993). This version has been calibrated for western Montana, central and 

northern Idaho, and coastal Alaska. It includes the full array of establishment 

options. The other version is referred to as the “partial” establishment model. 

This version only simulates regeneration from planting or stump sprouting; users must provide estimates of natural regeneration using keywords. The partial 

establishment model is used in variants for which the full establishment model 

has not been calibrated.

Most FVS variants rely on the partial establishment model. For those variants 

that support the full establishment model, the “with adjustment” run would have 

shown improved results in overall stand structure. The Blue Mountains variant 

relies on the partial establishment model. An empirical approach was used for 

estimating the natural regeneration response over time. Note that the tree count 

and board foot volume in figures 4 and 5 (+Regeneration) respectively reveal a 

comparable trend between measured data and model results. When regeneration 

is excluded, tree counts per acre steadily decline. When regeneration is included, 

canopy gaps caused by mortality agents are readily filled. Standing board foot 

volume is maintained in older age classes.

There are several methods available to induce a natural regeneration response for FVS variants that support only the partial establishment model. A 

portal in the FVS source code allows external programs to interact during 

the regeneration processing sequence (Robinson 2007). An understory estimation procedure has also been developed to import seedling/sapling recruitment 

(Vandendriesche 2009). One last method that should not be overlooked is local 

sources of expert opinion. Sophisticated FVS keyword sets can be constructed 

that depict expected natural regeneration response. Regardless of the process 

used, whether automatically invoked or user supplied, appropriate regeneration 

inferences are needed to configure a realistic FVS projection.

Board foot volume displayed in figure 5 (+Regeneration) reveals a significant 

reduction as a result of including regeneration impulses into the FVS projections. 

In conjunction with the tree size cap that injects overstory senescence, small trees 

can then occupy open space and influence the mortality prediction/stand density 

dynamic. Large trees are not allowed to simply grow and accumulate board foot 

volume. Large trees succumb to morphologic processes, which makes room for 

small tree establishment.

Assembly Process Summarized

Natural growth runs are a common starting point in the development of vegetation pathways for landscape assessments. It is quite possible in this day of 

constrained management that many stands will be left to let grow through the 

planning horizon. From a vegetation modeling standpoint, this scenario may appear to be the simplest to construct. However, due to our limited knowledge of 
304 USDA Forest Service Proceedings RMRS-P-61. 2010.

Vandendriesche FVS Out of the Box—Assembly Required

older stand structures, this run stream may require the most time and imagination. Cultured stands are fairly straightforward with regard to stocking density 

at various stand ages. Also, the regeneration response may be highly regulated. 

Natural stands that are left to grow are more intricate to model. Forests are not 

static and in some cases are very dynamic over short periods of time.

Table 7 summarizes the assembly steps and associated FVS keywords that 

were employed to craft the endemic FVS runs for the Blue Mountains landscape 

assessment. Note that while the assembly steps are imperative, the suite of recommended FVS keywords is not exhaustive or conclusive. Use of the CrnMult 

or Prune keywords may be needed to adjust crown estimates during the model 

calibration phase. If priori information is available, perhaps the MortMult and 

FixMort keywords would be more appropriate for configuring the stand and tree 

size caps. If supportive expertise is accessible, use of existing insect and disease 

extensions and keyword component addfiles should also be explored.

Stand visualizations displayed in the right column of figure 3 show the improvements of applying the recommended assembly process. Comparing FVS “out of 

the box” with recommended adjustments, the difference between the measured 

trends and modeled projections is minimal. Individual plots have varying species 

compositions and structures but given sufficient time in the absence of disturbance 

will strive toward normality.

Looking again at two key characteristics highlights the similarities between 

the measured inventory data and the modeled FVS run with adjustments. Trees 

per acre plotted over stand age are displayed in figure 4. Board foot volume per 

acre plotted over stand age is presented in figure 5. Notice the magnitude in the 

inventory data bars on the left versus the fully assembly bars on the right of stand 

age/size class groupings. Reasonable similarity exists in terms of trees per acre 

and board foot volume attainment.

Table 7—Recommended checklist for assembling a FVS run.

Assembly steps:	 FVS keywords:

Model calibration

1. FVS Self-Calibration {ReadCorR, ReadCorD}

2. Tree Volume Defect {Defect}

Natural growth run

3. Stand Stocking Attainment {SDIMax, BAmax}

4. Tree Senescence Caps {TreeSzCp}

5. Regeneration Response {Natural}

Treatment Prescriptions

Once natural growth runs have been constructed using recommended FVS 

assembly techniques, various silvicultural prescription scenarios can be modeled. Management direction suggest action, be it passive or active. For planning 

projects, certain stand level treatments are postulated as potential activities to 

move the forest toward desired outcomes. For example, it may be proposed to 

reduce stocking densities to lessen insect impacts. Also, it may be recommended 

to provide remedial fuel treatments to minimize wildland fire intensity. Additionally, it may be advocated to produce a balance of stand size classes throughout 

the forest to furnish a full spectrum of wildlife habitats. Furthermore, it may be 
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beneficial to explore predicted changes in climate. For each proposed action, a 

stand treatment schedule needs to be formulated to achieve the stated goal. The 

natural growth runs described in the previous section are a de facto prescription 

option to let stands grow with minimal management intervention.

Vegetation Pathways

A fundamental step in landscape planning is the analysis of the management 

situation. Various alternatives are proposed to guide future programmatic direction. Inherent to the analysis process is the gathering of inventory data and the 

projection of potential outcomes. Computer models play an important role in the 

projection process and formulation of management alternatives. Generally, two 

types of computer models are used for mid-scale forest planning: yield forecasting 

models and decision support systems. Yield models summarize current conditions 

and project future developments thus providing point-in-time value estimates. 

Decision support models pull together the state and transition components of forest planning. Coefficients computed by yield models are used by decision support 

systems to address management issues.

Evaluate Output

Preparing vegetation profiles in support of landscape planning projects is dissimilar to processing inventory data through static software. You can not simply 

feed data in one end and produce meaningful output at the other. Professional 

talents, including those of a mensurationist, ecologist, silviculturist, and forest 

analyst, are required to construct valid trends in vegetation development. This is 

not a complete list of specialty skills. Possessing these abilities does not ensure 

proper integration of tasks. Formal experience on several projects aids in solidifying the corporate memory to conduct such analyses. There is as much art as 

science that goes into the process.

Conclusions

In the ideal world of vegetation modeling, each of the fundamental biological 

processes that occur on an individual tree basis (i.e. diameter, height, and crown 

development) should be examined to verify expected performance. Does the diameter increment seem reasonable; do dominant trees obtain site-height at given 

stand ages; does tree crown development appear correct in open and closed canopy 

situations? Each of these aspects should be questioned and verified. Stand dynamics should also be checked. Are average maximum stocking densities obtainable; 

are successional trends captured; are forest gaps reclaimed by regeneration? These 

are basic stand level tenets that should be confirmed.

In the real world of vegetation modeling, time constrains such rigorous substantiation of predicted outcomes. Regardless, if a project requires analysis of 

one stand or many stand types, inventory data should be acquired that depicts the 

condition of interest. Calibration and maximum values can be derived from the 

associated data set. Regeneration response can also be gleaned empirically from 

measurement data. These are the facets that need to be examined and incorporated 

into a fully assembled FVS projection.
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Vandendriesche FVS Out of the Box—Assembly Required

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St. Paul, MN: U. S. Department of Agriculture, Forest Service, North Central Forest Experi-

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Species of the Great Lakes Region. Can. J. For. 13(4): 601-608.

Buchman, R. G.; Lentz, E. L. 1984. More Lake States Tree Survival Predictions. Res. Note 

NC-312. St. Paul, MN: U. S. Department of Agriculture, Forest Service, North Central Forest 

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Crookston, Nicholas L. 1997. Suppose: An Interface to the Forest Vegetation Simulator. U.S. 

Department of Agriculture, Forest Service, Intermountain Research Station. Ogden, UT. In: 

GTR-375, Proceeding: [1st] Forest Vegetation Simulator Conference. Fort Collins, CO: 7-14.

Davis, Lawrence S.; Johnson, K. Norman. 1987. Forest Management. 3d ed. New York, NY: 

McGraw-Hill Book Company. 790 p.

Dixon, Gary E. 1986. Prognosis Mortality Modeling. Internal Rep. Fort Collins, CO: U. S. Depart-

ment of Agriculture, Forest Service, Forest Management Service Center. 10 p.

Dixon, Gary E. 2002. Essential FVS: A User’s Guide to the Forest Vegetation Simulator. U.S. 

Department of Agriculture, Forest Service, Forest Management Service Center. Fort Collins, 

CO. Interoffice publication. 204 p.

Drew, John T., Flewelling, James W. 1979. Stand Density Management: An Alternative Approach 

and its Application to Douglas-fir Plantations. Forest Science. 25(3): 518-532.

Edminster, Carl B. 1988. Stand Density and Stocking in Even-Aged Ponderosa Pine Stands. In: 

Ponderosa Pine: The Species and Its Management. Baumgartner, D.M., and J.E. Lotan (eds.). 

Washington State Univ. Cooperative Extension, Pullman, WA: 253-260.

Ferguson, Dennis E.; Carlson, Clinton E. 1993. Predicting Regeneration Establishment with the 

Prognosis Model. U.S. Department of Agriculture, Forest Service, Intermountain Research 

Station. Ogden UT. Res. Paper INT-467. 54 p.

Max, T A.; Schreuder, H. T.; Hazard, J. W.; Oswald, D.D; Teply, J.; Alegria, J. 1996. The Pacific 

Northwest Region vegetation and inventory monitoring system. Res. Pap. PNW-RP-493. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 22 p.

McGaughey, Robert J. 2004. Stand Visualization System, Version 3.3. U.S. Department of Agri-

culture, Forest Service, Pacific Northwest Research Station. Seattle, WA. 141 p.

Miles, Patrick D. 2001. The Forest Inventory and Analysis Database: Database Description and 

Users Manual Version 1.0. U.S. Department of Agriculture, Forest Service, North Central 

Research Station. St. Paul, MN. NC-GTR-218. 130 p.

Ohmann, J. L.; Gregory, M. J. 2002. Predictive mapping of forest composition and structure with 

direct gradient analysis and nearest-neighbor imputation in coastal Oregon, USA. Canadian 

Journal of Forest Research. 32: 725-741.

Powell, David C. 1999. Suggested Stocking Levels for Forest Stands in Northeastern Oregon and 

Southeastern Washington: An Implementation Guide for the Umatilla National Forest. U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Region, Umatilla National Forest. 

Pendleton, OR. Tech Pub F14-SO-TP-03-99. 300 p.

Reineke, L. H. 1933. Perfecting a Stand Density Index for Even-Aged Forests. Journal of Agric. 

Res., U.S. Department of Agriculture, Forest Service. 46: 627-638.

Robinson, Donald C. 2007. Development of External Regeneration Models for FVS – Another 

Wrench in the Toolkit. U.S. Department of Agriculture, Forest Service, Rocky Mountain Re-

search Station. Ogden, UT. Proceedings: Third Forest Vegetation Simulator (FVS) Conference; 

February 13-15, 2007. Fort Collins, CO.

Shaw, J. D., G. Vacchiano, R. J. DeRose, A. Brough, A. Kusbach, and J. N. Long. 2006. Local 

Calibration of the Forest Vegetation Simulator (FVS) using Custom Inventory Data. Proceedings: 

Society of American Foresters 2006 National Convention. October 25-29, 2006, Pittsburgh, PA.

Shaw, John D.; Long, James N. 2007. A Density Management Diagram for Longleaf Pine Stands 

with Application to Red-Cockaded Woodpecker Habitat. Southern Journal of Applied Forestry 

31(1) 2007. Society of American Foresters. Bethesda, MD: 28-38.

Stage, Albert R. 1973. Prognosis Model for Stand Development. U.S. Department of Agriculture, 

Forest Service, Intermountain Forest Experiment Station. Ogden, UT. Res. Pap. INT-137. 32 p.

Stage, Albert R. 1981. Use of Self Calibration Procedures to Adjust General Regional Yield Models 

to Local Conditions. Paper presented to XVII IUFRO World Congress S4.01, Sept. 6-17, 1981.

Teck, Richard M.; Hilt, David E. 1990. Individual-Tree Probability of Survival Model for the 

Northeastern United States. Res. Pap. NE-642. Radnor, PA: U.S. Department of Agriculture, 

Forest Service, Northeastern Forest Experiment Station. 10 p.

Vandendriesche, Donald A. 2009. An Empirical Approach for Estimating Natural Regeneration 

for the Forest Vegetation Simulator. U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. Ogden, UT. Proceedings: National Silviculture Workshop; June 

15-19, 2009. Boise, ID.

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The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
USDA Forest Service Proceedings RMRS-P-61. 2010. 307

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

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Station. 351 p.

1 Forester, U.S. Department of Agriculture, 

Forest Service, Forest Management Service Center, Vegetation Simulation Group, 

Natural Resources Research Center, 2150A 

Centre Avenue, Fort Collins, CO 80526.

An Empirical Approach for Estimating 

Natural Regeneration for the Forest 

Vegetation Simulator

Don Vandendriesche1

Abstract—The “partial” establishment model that is available for most Forest 

Vegetation Simulator (FVS) geographic variants does not provide an estimate of 

natural regeneration. Users are responsible for supplying this key aspect of stand 

development. The process presented for estimating natural regeneration begins 

by summarizing small tree components based on observations from local Forest 

Inventory and Analysis (FIA) data sets. Average frequency and height for seedlings and small saplings by tree species are calculated from the data. Seedling 

recruitment is determined by relationships to the maximum stand density index. 

Small saplings regeneration is derived from a distribution pattern computed from 

current stand size and density conditions. Apportioning the small sapling frequencies according to the observed distribution pattern provides an expected natural 

regeneration estimate by stand size/density condition, or vegetation state. Associated small sapling shade tolerance factors can be applied to govern progressions 

in species composition across the stand size/density continuum. Repute, an FVS 

post-processing program, has been developed to automate the estimation process. 

An example from the Southwest U.S. is presented to demonstrate the methodology.

Introduction

Predicting the abundance of natural regeneration, that is seedling and sapling 

recruitment beneath an existing overstory, is a difficult task required of forest 

growth modeling. Regeneration response is a stochastic event and the phenomena 

that trigger adequate natural regeneration are not easily determined. Inventory data 

sets that could be used to develop algorithms rarely cite the statistical parameters 

needed to effectively predict regeneration establishment (Ferguson 1997). As a 

result, prediction estimates for regeneration are generally weak in a system that 

must account for all facets of stand development. The objective of this paper is 

to present a process based on seedling and sapling counts as summarized from 

existing inventory conditions that can be used to provide understory recruitment 

for the Forest Vegetation Simulator (FVS) (Dixon 2002).

Forest models can be designed based on either growth or yield processes. The 

distinction is that growth architecture integrates growth processes over time to 

estimate future states whereas yield architecture directly estimates the future state 

as a function of time. Regeneration models that use a growth-based approach tend 

to be complex and predict too many poorly understood processes (Stage 2002). 

Such models are rare. Instead, most use a yield-based approach to assemble a 

picture of stand regeneration (Ferguson and others 1986). The estimation process 

described here follows the yield-based method. Observed regeneration attributes 

are drawn from FIA samples to estimate the natural regeneration component of a 

given plot of the same ecological stratum. This approach implicitly presumes that 
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Vandendriesche An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator

the inferences driving regeneration in the inventoried plots applies to regeneration 

of modeled plots (Ek and others 1997).

Relevant Questions

Simply assuming that regeneration will only occur following a disturbance 

is wholly inadequate for providing a fully functioning regeneration model. Tree 

based mortality resulting from morphological senescence creates canopy gaps 

enabling thrifty understory recruits to fill the void. Stand based mortality resulting from overstory crown closure provides a successional opportunity for shade 

tolerant understory tree species to take hold. Regeneration occupancy must be 

considered for each combination of stand size and density class. For every vegetation state, there are two basic questions to be addressed:

Quantity  How many seedlings/saplings should be established?

Quality2  What is the species composition of those established?

For this estimation procedure, both seedlings and small saplings are taken into 

account for natural regeneration. Flushes of seedlings pulse into stand projections 

on a twenty-year period. Mortality algorithms within FVS take their toll on the 

seedling component. To ensure recruitment into larger diameter size classes, 

regulation of small saplings (trees between one and three inches diameter) is 

pursued. A distribution pattern for small sapling abundance is computed relative 

to the current vegetation state based on stand size/density criteria.

Stand structure, in terms of size and density, is defined using overstory quadratic mean diameter (qmd) and percent canopy cover, respectively. There are 

five recognized stand size classes: 0-5 inch, 5-10 inch, 10-15 inch, 15-20 inch, and 

greater than 20 inches overstory qmd. Overstory qmd is comprised of the largest 

20 percent of the trees by diameter, with a minimum of 20 trees needed for the 

classification. Otherwise, overstory qmd is determined by the largest diameter 

trees that constituted 25 percent of the canopy cover. Three stand density classes 

are defined: 10-40 percent, 40-70 percent, and greater than 70 percent canopy 

cover (Crookston and Stage 1999). If the canopy cover is less than 10 percent, the 

density class is classified as nonstocked.

The size and density classification system was derived from forest plan revision work for the Colville, Okanogan, and Wenatchee National Forests located in 

Washington State (Jahns 2006). Vegetation states were developed from National 

Standards for Geospatial Data (USDA Forest Service 2003) and supplemented 

with Standards for Mapping of Vegetation in the Pacific Northwest Region (USDA 

Forest Service 2004). The national/regional standards proposed a resolution of 

vegetation classification that was far more detailed than necessary to address forest 

plan revision efforts. However, the standards allowed flexibility for aggregation 

into appropriate vegetation classes. Regional wildlife specialists were consulted 

for advice on appropriate size and density aggregations for species viability and 

proposed the aforementioned classification scheme.

2 Quality in terms of species composition may be ambiguous but is used in the context of regeneration 

establishment to imply a tree species’ ability to survive relative to a given site potential or relative to its 

tolerance of overstory shade.
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An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator Vandendriesche

Pattern Revelation

A supporting premise of the proposed natural regeneration procedure is that 

there is a regular periodicity of good seed years and subsequent seedling establishment that is normally followed by equally impressive seedling mortality. This 

“burst” and “bust” dynamic relative to actual survivorship is difficult to capture in 

forest growth modeling. Consequently, the tactic devised for predicting adequate 

natural regeneration targets the more stable population of small saplings. These 

trees generally have an acceptable girth and height to indicate having a root system 

firmly entrenched in mineral soil. Furthermore, they possess the capacity either 

to fill gaps due to overstory tree mortality or to follow successional recruitment 

trends as dictated by species shade tolerance.

Conditionally, seedlings are input into an FVS projection once per twentyyear period. During that time, seedlings are subject to the various FVS mortality 

mechanisms. A complimentary check is added to evaluate whether sufficient 

small saplings survived from the pool of seedlings to ensure a viable stream of 

ingrowth into larger diameter trees. Thus, a fully functioning regeneration system 

is in-place. The following text will detail the process.

Southwest Prototype

Many of the concepts behind the natural regeneration estimation process arose 

from work for landscape analysis. An example is presented based on recent forest 

planning efforts from the U.S. Forest Service Southwest Region. There are six National Forests in Arizona and five National Forests in New Mexico encompassing 

20.6 million acres (fig. 1). Several terrestrial life zones exist within this diverse 

geographic area. Recent periodic and annual Forest Inventory and Analysis data 

sets were assembled to represent this landscape. Forest cover types, as comprised 

of plant associations, were used to stratify the data. FIA plot counts by cover type 

are shown in table 1. The Forest Vegetation Simulator can be used to model stand 

development of timberland and woodland communities. Methods developed for 

estimating natural regeneration will be described for the Mixed Conifer—Dry type.

Figure 1—Geographic location of National Forests within the USFS Southwestern Region.
310 USDA Forest Service Proceedings RMRS-P-61. 2010.

Vandendriesche An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator

Inspection of the observed small sapling density within the Mixed Conifer-Dry 

strata (table 2) reveals the following pattern: Starting with any canopy cover class 

(row), then reading from the largest to smallest stand size class (columns, right 

to left), each smaller size class contains two to three times as many saplings as 

the next larger size class. Applying the average observed small sapling distribution factor (2.6330) among the size and density classes as shown in the bottom 

half of table 2 does a good job of smoothing inordinate jumps between adjacent 

classes. Derivation of the small sapling distribution factor will be described in 

the following paragraphs.

The natural regeneration estimation process begins by determining the vegetation state (i.e. size/density class) of each plot in the inventory. A reference 

template is displayed in table 3. A two-digit number indexes the size/density class 

combination. The first digit represents columns (qmd stand size classes), and the 

second digit represents rows (canopy cover density classes). For example, vegetation state 22 would indicate a size class of 5-10 inch qmd and a density class of 

40-70 percent canopy cover.

Table 1—FIA plot count by forest cover type.

	 Forest	 Type	 Plot

	 cover	 descriptor	 sample

Spruce-fir Upper subalpine 69

Lower subalpine 72

Mixed conifer Wet—infrequent fire 48

Dry—frequent fire 444

Ponderosa pine Grass 495

Gambel oak 295

Evergreen oak 132

Pinyon-juniper Woodland 1,354

Oak Woodland 308

Total: 3,217

Table 2—Mixed conifer—dry: small sapling distribution.

	 Canopy	 Overstory QMD size class

cover class	 0-5	 5-10	 10-15	 15-20	 > 20

(%) - - - - - - - - - - - - - (inches)- - - - - - - - - - - - - - 

Observed small sapling frequency (trees/acre)

10-40 739 233 59 62 15

40-70 1816 417 179 36 25

> 70 545 256 112

Predicted small sapling frequency (trees/acre)a

10-40 417 158 60 23 9

40-70 1098 417 158 60 23

> 70 2891 1098 417 158 60

a Based on small sapling distribution pattern factor of 2.6330 derived from 

observed data.
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An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator Vandendriesche

The six size/density classes used to compute the small sapling distribution 

factor are vegetation states: 21, 22, 31, 32, 41, and 42 (referred to as base cells). 

These cells contain the majority of FIA plots as displayed in the bottom half of 

table 3. Base cells are of primary interest in the natural regeneration estimation 

procedure. Note there is little need for additional natural regeneration in smaller 

size or closed density classes. These vegetation states are by definition either a 

“seedling/sapling” size class (i.e. 0-5 inches) or at “full site occupancy” in terms 

of stand density (> 70%). Accordingly, additional natural regeneration is not 

targeted for the 12, 13, 23, 33, 43, or 53 size/density classes.

The distribution factor is calculated as a weighted average of the base cells. 

Table 4 presents the calculation method. Vegetation states lying adjacent to base 

cells are used to determine a distribution factor for the base cell. The representative FIA plot samples serve as weights in the calculation. A composite distribution 

factor is determined by summing the base cells multiplied by their FIA plot sample 

and dividing by the total number of FIA plots residing in the base cells.

Table 5 displays composite small sapling distribution factors for each of the 

major forest cover types in the Southwestern Region. A trend can be noted related 

to the composition of timberland tree species compared to woodland. As timberland species diminish and woodland species increase in prevalence, the composite 

small sapling distribution factor increases. Given the relative proportional size 

and site occupancy of woodland tree species, this trend is reasonable.

Repute Program

Repute, an FVS post-processing program, has been written to implement the 

concepts of natural regeneration estimation process. The main Repute window 

is shown in figure 2. In brief, the Repute program processes output files from 

the Fvsstand Alone program (Vandendriesche 1997) to develop regeneration 

“addfiles” (i.e. auxiliary FVS keyword files). Within a FVS run, the Repute 

addfile determines the vegetation state (size/density class) as each projection 

cycle is processed; associates the composite distribution matrix that was based 

Table 3—Mixed Conifer—Dry: Size/Density Template and FIA 

plot count.

	 Canopy	 Overstory QMD size class

cover class	 0-5	 5-10	 10-15	 15-20	 > 20

(%) - - - - - - - - - - - - - (inches)- - - - - - - - - - - - - - 

Code reference guidea

10-40 11 21 31 41 51

40-70 12 22 32 42 52

> 70 13 23 33 43 53

FIA plot sampleb

10-40 14 50 32 6 5

40-70 13 121 92 19 6

> 70 11 12 4

a Code reference guide provides a template for the size/density classes. A 

22 code indicates a 5-10 inch qmd size class and a 40-70 percent canopy 

cover density class.

b Of the 444 total FIA plots sampled within the Mixed Conifer-Dry strata, 

59 were classified as nonstocked (canopy cover less than 10 percent) 

rendering 385 plots for small sapling analysis.
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Vandendriesche An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator

Table 4—Mixed conifer—dry: calculation method for composite 

distribution factor.

	 Canopy	 Overstory QMD size class

cover class	 0-5	 5-10	 10-15	 15-20	 > 20

(%) - - - - - - - - - - - - - (inches)- - - - - - - - - - - - - - 

Size/density distribution factorsa

10-40 2.3270 3.2815 1.1006

40-70 2.2573b,c 2.6764 4.5281

> 70

Composite distribution factor: 2.6330d,e,f,g

a Refer to Table 2 (T2) – Observed Small Samplings Frequency for tree 

per acres values; Table 3 – Code Reference Guide for indicated size/

density class; and, Table 3 (T3) – FIA plot sample for associated weights.

b Size/Density 22 Distribution Factor = ((T2:12/T2:22)*T3:12+(T2:22/

T2:21)*T3:21+(T2:22/T2:32)*T3:32+(T2:23/T2:22)*T3:23) / 

(T3:12+T3:21+T3:32+T3:23)

c 2.2573 = ((1816/417*13)+(417/233)*50+(417/179)*92+(545/417)*11) / 

(13+50+92+11)

d Refer to Table 3 – Code Reference Guide for indicated size/density 

class; Table 3 (T3) – FIA plot sample for associated weights; and, Table 

4 (T4) – Size/Density Distribution Factors for class values.

e Composite Distribution Factor = (T4:21*T3:21+T4:22*T3:22+T4:31*T

3:31+T4:32*T3:32+T4:41*T3:41+T4:42*T3:42) / (T3:21+T3:22+T3:31+

T3:32+T3:41+T3:42)

f Base Cell sample size must contain 10 or more counts. Note that the 

plot sample for Base Cell 41 (T3:41) equaled six and therefore was reset 

to zero.

g 2.6330 = (2.3270*50+2.2573*121+3.2815*32+2.6764*92+1.1006*0+4.

5281*19) / (50+121+32+92+0+19)

Table 5—Composite small sapling distribution factors by 

forest cover type.

	 Forest	 Type	 Distribution

	 cover	 descriptor	 factor

Spruce-fir Upper subalpine 2.1199

Lower subalpine 1.9529

Mixed conifer Wet—Infrequent fire 1.9700

Dry—Frequent fire 2.6330

Ponderosa pine Grass 2.9019

Gambel oak 3.0383

Evergreen oak 2.8769

Pinyon-juniper Woodland 2.7820

Oak Woodland 2.8083
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An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator Vandendriesche

on observed small sapling frequencies; applies shade tolerance multipliers if 

applicable; inputs seedling reproduction; and, estimates small sapling ingrowth.

Implementation of the natural regeneration estimation procedure relies heavily 

on the Event Monitor (Crookston 1990) capabilities within the FVS model. There 

are limitations associated with using the Event Monitor that must be recognized 

such as the number of “compute variables” (199) and “conditional statements” 

(160). Conserving available resources of the Event Monitor is important. The 

natural regeneration estimation procedure was developed to be as efficient as possible. “Economies of Scale” principles were invoked. As developed, the vegetation 

classification system identifies fifteen combinations of size and density. Computing a composite distribution factor based on observed small sapling data allows 

rendering the fifteen size/density classes as a function of one multiplicative value.

Computed Size/Density Class

The natural regeneration estimation process begins by determining the vegetation state (i.e. size/density class) at each FVS projection cycle (fig. 3). The most 

important steps are outlined as follows. The Event Monitor DBHDist function is 

used to compute size class based on the largest 20 percent of the trees. Canopy 

cover, corrected for overlap, is used to calculate density class based on all trees. 

A qualifier variable (CCx) is computed to determine whether a plot is stocked 

(greater than or equal to 10 percent canopy cover) or nonstocked (less than 10 

percent canopy cover) at the beginning of the projection cycle. If classified as 

nonstocked, the vegetation state is set to zero. A “regeneration state” variable is 

defined and set equal to the overstory vegetation state. However, there are times 

when a “bumper” regeneration pulse is allowed. Two additional statements are 

used to indicate the need. If a plot’s current stocking level is less than 25 percent 

of the specified Stand Density Index maximum (Long 1985) and if there is a 

minimum of at least 10 trees greater than nine inches in diameter (seed trees), 

then the regeneration state is set to equal one resulting in a significant flush of 

natural regeneration injected into the projection.

Figure 2—Repute user interface.
314 USDA Forest Service Proceedings RMRS-P-61. 2010.

Vandendriesche An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator

Multiplicative Distribution Factor

A predominant vegetation state is determined from the observed FIA sample 

and is simply the size/density class that contains the most plots. This state is 

designated as the “index” cell. For the Mixed Conifer—Dry strata, size/density 

class 22 is the predominant vegetation state as a result of being comprised of 121 

FIA plots from a total of 444 FIA plots (bottom of table 3). Across all FIA plots 

within this vegetation state, the average small sapling frequency is 417 trees per 

acre. The composite distribution factor is applied relative to the index cell. In 

this example, the 22 size/density class receives a multiplicative factor equal to 

1.0000 (fig. 4). Successive smaller size classes and denser canopy cover classes 

Figure 3—FVS keyword addfile segment that computes size/density class (vegetation 

state) for each projection cycle.

Figure 4—FVS keyword addfile segment that associates a multiple of the composite 

distribution factor based on dominant “22” size/density class.
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An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator Vandendriesche

are multiplied by the composite distribution factor. Successive larger size classes 

and less dense canopy cover classes are divided by the composite distribution 

factor. This allows apportioning the index cell’s sapling frequency (417 trees per 

acre) across all size/density classes and renders the predicted values as displayed 

in the bottom portion of table 2. As such, the question of regeneration “quantity” 

is addressed.

Shade Tolerance Multipliers—Small Saplings

In the absence of disturbance events, either human or natural, tree species succession follows capacities for survival in light of overstory shading (Spurr and 

Barnes 1973). Transitions in species composition do not affect all forest cover 

types equally. For example, the species ponderosa pine (Pinus ponderosa Dougl. 

ex Laws) is considered shade “intolerant” (USDA 1990). If found in a Ponderosa 

Pine forest cover type, it is very resilient and will regenerate as opportunities 

arise. However, if ponderosa pine is found in a Mixed Conifer forest cover type, 

it will struggle to survive without disturbance. Shade tolerance considerations 

governing ponderosa pine regeneration are important in the latter situation, but 

not in the former. The Repute program allows the user the option to include shade 

tolerance effects for small saplings. This feature addresses the “quality” aspect 

regarding species composition of natural regeneration.

Figure 5 displays small sapling shade tolerance multipliers used for intolerant species such as ponderosa pine. Shade tolerance multipliers were developed 

using inventory data sources from throughout the Interior West (ColvilleOkanogan-Wenatchee National Forests, Clearwater-Nez Perce National Forests, 

Figure 5—FVS keyword addfile segment that determines a multiplier relative to size/

density class and associated overstory shade tolerance for small saplings.
316 USDA Forest Service Proceedings RMRS-P-61. 2010.

Vandendriesche An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator

and Southwestern Region National Forests). Tree species were sorted by five 

shade tolerance ratings: very intolerant, intolerant, intermediate, tolerant, and 

very tolerant. Within these groupings, small sapling frequencies were averaged 

in accordance with the size/density classification matrix. All cells were then divided by the index cell in order to scale the shade tolerance matrix relative to the 

composite distribution matrix. In this manner, the two matrices could be joined 

(i.e. multiplied) to account for regeneration quantity and quality.

Table 6 provides an example for ponderosa pine small saplings within the Mixed 

Conifer—Dry strata. On average, 26.34 trees per acre were observed on all FIA 

plots. This value is distributed by the shade tolerance multipliers displayed in 

the upper portion of table 6 to render the expected ponderosa pine small sapling 

frequency as presented in the bottom portion of table 6.

Table 6—Mixed conifer—dry: Ponderosa pine small saplings 

expected abundance.

Canopy 

cover 

class

Overstory QMD size class (inches)

0-5 5-10 10-15 15-20 > 20

(%) Small sapling shade intolerant multipliersa

10-40

40-70

> 70

1.75

1.50

1.00

1.25

1.00

0.75

0.10

0.25

0.35

0.05

0.01

0.00

0.01

0.00

0.00

(%) Expected small sapling frequency (trees/acre)b

10-40

40-70

> 70

46.09

39.51

26.34

32.92

26.34

19.75

2.63

6.59

9.22

1.32

0.26

0.00

0.26

0.00

0.00

aComparative analysis using observed small sapling data from 

throughout the Interior West. Index cell was divided into all other size/

density class cells to compute the multiplier.

bObserved ponderosa pine small sapling frequency dispersed from 

Index Cell to all other size/density cells based on shade intolerant 

multiplier. Values indicate expected small sapling frequency abundance.

Shade Tolerance Effects—Seedlings

Five phases of stand development based on relative stand density index have 

been postulated by Long (1985). They are:

	 •	 0-25% of SDImax—A stand or group of trees at this density level is opengrown. There is little competition for moisture, nutrients, and sunlight.

	 •	 25-35% of SDImax—A stand begins to experience tree-to-tree competition for 

moisture and nutrients. Less sunlight reaches the ground but site productivity is still increasing.

	 •	 35-60% of SDImax—This is the range in which full site occupancy has been 

reached. Tree-to-tree competition has resulted in individual trees sacrificing 

growth. Site productivity is still increasing but has begun to plateau.

	 •	 60-100% of SDImax—At this stage, tree-to-tree competition is so great that 

individual trees can no longer receive enough moisture, nutrients, and sunlight to survive. Self-thinning occurs but openings created by trees dying 

are quickly filled by the growth of surviving trees.
USDA Forest Service Proceedings RMRS-P-61. 2010. 317

An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator Vandendriesche

	 •	 100%+ of SDImax—Full site occupancy and growth are lost as overstory trees 

become weakened and die. As mature trees die, openings are created for 

more shade tolerant tree species if the site is capable of supporting those 

species.

By default within FVS, background mortality occurs when the current stand 

SDI is below 55 percent of maximum SDI. Density related mortality begins when 

the relative stand density is above 55 percent of maximum SDI. Stand density 

peaks at 85 percent of maximum SDI (Dixon 2002). These thresholds were incorporated into the survivability logic for seedling recruitment for three realms 

of shade tolerance in the following manner:

– Intolerant: 25-55% of SDImax

– Mid-tolerant: 35-85% of SDImax

– Tolerant: 55-100% of SDImax

Within the specified range for a shade tolerance realm, input of natural 

regeneration for seedlings is tapered from 100 to 0 percent of the target 

value.

Natural Regeneration Estimates

Repute selects tree counts and average height by species for the seedling 

(< 1-inch) and small sapling (2-inch) diameter classes from the Fvsstand Alone 

‘stand tables’ to create separate tree species regeneration keywords (figs. 6 and 

7). On a twenty-year basis, the natural regeneration estimation process checks 

whether conditions warrant a pulse of seedling/sapling reproduction. Target 

Figure 6—FVS keyword addfile segment that establishes seedling reproduction on a 

20-year basis. Values by species (PP: Ponderosa Pine) for tree count (40.690 trees/ac) 

and average height (1.0 ft) are extracted from Fvsstand Alone ‘stand tables.’
318 USDA Forest Service Proceedings RMRS-P-61. 2010.

Vandendriesche An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator

Figure 7—FVS keyword addfile segment that ensure small sapling regeneration on a 

20-year basis.

thresholds are based on the classified vegetation state. If existing frequencies 

are less than specified thresholds, natural regeneration is invoked. Checks are 

included that account for existing seedlings and small saplings so as not to exceed 

expected targets. Also, a test is performed to inquire whether enough mature trees 

of a given species are present to produce an adequate seed source. For a more 

in-depth discussion of the Repute program, a detailed Users Guide is available 

(Vandendriesche 2005).

Recall that seedlings are input as pawns for small sapling recruitment. The 

crux of the natural regeneration estimation process is insuring that a stable small 

sapling ingrowth component is available that will transition into larger diameter 

classes in subsequent projection cycles. An example is provided in figure 7. Input 

values and the resultant small sapling target for size/density class 32 are displayed. 

If existing small ponderosa pine saplings exceed the target value of 7.54, then 
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An Empirical Approach for Estimating Natural Regeneration for the Forest Vegetation Simulator Vandendriesche

the “Natural” keyword operation is not performed. Conversely, if existing small 

ponderosa pine saplings are lacking, the target frequency of 7.54 trees per acre 

will be regenerated so long as there is a sufficient number of mature ponderosa 

pine capable of providing an adequate seed source.

Conclusions

In the absence of having the “full” regeneration establishment model available 

to provide natural reproduction into an FVS projection, users need to account for 

this important aspect of forest growth modeling. External regeneration imputation 

models have been developed and should be explored where available (Robinson 

2007). Lacking these resources, the natural regeneration estimation procedure 

presented in this paper may serve to fill the void. The procedure presented describes the process of inputting a stable supply of small saplings into projection 

runs to procure a vital source of recruitment into larger diameter trees. Flushes 

of seedlings are also input to account for the smallest form of forest reproduction. 

The regeneration estimation method is well suited for natural growth simulations. 

It may also be suited following certain silvicultural treatments but may need 

augmentation based on professional experience.

Special situations may require creative solutions when running the Repute 

program. For example, in areas with intense deer browsing, it may be necessary 

to input larger diameter trees, greater than the small sapling size class, to represent the stable recruitment component. Having a representative supply of natural 

regeneration to input into a simulation run is vital.

References

Crookston, Nicholas L. 1990. User’s Guide to the Event Monitor: Part of Prognosis Model Version 

6. U.S. Department of Agriculture, Forest Service, Intermountain Research Station. Ogden, UT. 

Gen. Tech. Rep. INT-275. 21 p.

Crookston, Nicholas L.; Stage, Albert R. 1999. Percent Canopy Cover and Stand Structure Statistics from the Forest Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR-24. Ogden, UT: U. S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 11 p.

Dixon, Gary E. 2002. Essential FVS: A User’s Guide to the Forest Vegetation Simulator. U.S. 

Department of Agriculture, Forest Service, Forest Management Service Center, Fort Collins, 

CO. Interoffice publication. 204 p.

Ek, Alan R.; Robinson, Andrew P.; Radtke, David K.; Walters, David K. 1997. Development 

and Testing of Regeneration Imputation Models for Forests in Minnesota. Forest Ecology and 

Management 94 129-140. 12 p.

Ferguson, Dennis E.; Stage, Albert R.; Boyd, R.J. 1986. Predicting Regeneration in the Grand FirCedar-Hemlock Ecosystem of the Northern Rocky Mountains. Forest Science Monograph 26. 41p.

Ferguson, Dennis E. 1997. Regeneration Models for FVS Variants. First Forest Vegetation Simulator (FVS) Conference; February 3-7, 1997. Fort Collins, Proceedings INT-GTR-373. Ogden UT: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Jahns, Phil. 2006. Vegetation Projection, Colville and Okanogan—Wenatchee National Forests, 

Forest Plan Revision. On file.

Long, James N. 1985. A Practical Approach to Density Management. Forestry Chronicle: 23-27.

Robinson, Donald, C.E. 2007. Development of External Regeneration Models for FVS—Another 

Wrench in the Toolkit. Third Forest Vegetation Simulator Conference; February 13-15, 2007. 

Fort Collins, Proceedings RMRS-P-000. Ogden UT: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station.

Spurr, Stephen H.; Barnes, Burton V. Forest Ecology. 2nd ed. New York, NY. The Ronald Press 

Company. 571 p.

Stage, Albert R. 2002. Modeling Regeneration by Imputation: Some background. Workshop: 

Imputation Techniques for Modeling Natural Regeneration in Complex Stands. Sponsored by 

The Research Branch of the BC Ministry of Forests, University Of British Columbia. 18 p.

U.S. Department of Agriculture (USDA), Forest Service. 2004. Standards for Mapping of Vegetation 

in the Pacific Northwest Region. Unpublished document. On file: USDA Forest Service, Pacific 

Northwest Region. Superseded by DeMeo, Tom and others. 2005. Providing Existing Vegetation 

Maps. Unpublished document. On file: USDA Forest Service, Pacific Northwest Region. 168 p.
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U.S. Department of Agriculture (USDA), Forest Service. 2003. National Standards for Geospatial 

Data. Unpublished document. 16 p. On file: USDA Forest Service, Washington Office, Ecosystem 

Management Coordination Staff. Superseded by Brohman, R.; Bryant, L. eds. 2005. Existing 

Vegetation Classification and Mapping Technical Guide. Gen. Tech. Rep. WO-67. 305 p.

U.S. Department of Agriculture (USDA), Forest Service. 1990. Silvics of North America: Summary 

of Tree Characteristics. Agriculture Handbook 654. Volume 1. Conifers. 675 p.

Vandendriesche, Donald A. 1997. FVSSTAND Alone: Generate Dynamic Yield Reports from the 

Forest Vegetation Simulator. First Forest Vegetation Simulator (FVS) Conference; February 3-7, 

1997. Fort Collins, Proceedings INT-GTR-373. Ogden UT: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station.

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Agriculture, Forest Service, Forest Management Service Center, Fort Collins, CO. Interoffice 

publication. 119 p.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.
USDA Forest Service Proceedings RMRS-P-61. 2010. 321

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 USDA Forest Service, Southwestern 

Region, Albuquerque, NM, 87102, 

USA.

2 USDA Forest Service, Forest Management Service Center, Fort Collins, CO, 

80526, USA.

3 Coconino National Forest, Flagstaff, 

AZ, 86001, USA.

* Corresponding Author: Tel.: 01 505 

842 3217; email: rweisz@fs.fed.us.

Evaluating the Ecological Sustainability 

of a Pinyon-Juniper Grassland 

Ecosystem in Northern Arizona

Reuben Weisz1*, Jack Triepke1, Don Vandendriesche2, 

Mike Manthei3, Jim Youtz1, Jerry Simon1, Wayne Robbie1

Abstract—In order to develop strategic land management plans, managers must assess current and future ecological conditions. Climate change has expanded the need 

to assess the sustainability of ecosystems and predict their conditions under different 

climate change and management scenarios using landscape dynamics simulation 

models. We present a methodology for developing a state-and-transition model 

(STM) with the Vegetation Dynamics Development Tool (VDDT), using outputs from 

the Forest Vegetation Simulator (FVS). Preside, a recently developed accessory to the 

FVS program, is used to process and report FVS outputs in terms of succession probabilities and residence times for each STM. We’ve applied these tools with a case 

study based on the pinyon-juniper grassland ecosystem in northern Arizona. After 

applying local probability values for natural growth, contemporary fire, insect and 

disease, and management activities, VDDT simulations were conducted to project 

future ecosystem conditions including carbon accounting. Finally, we also describe 

how these models can be retooled with FVS support to reflect the effects of climate 

change so that managers can consider adaptation and mitigation strategies.

Introduction

The objective of this paper is to illustrate through a case study how State-andTransition Models (STMs) can be developed and used to evaluate the ecological 

sustainability of ecosystems.

Projecting transitions in vegetation states (composition and structure) over 

time facilitates evaluating the ecological sustainability of ecosystems. Vegetation 

states can change in “the absence of disturbance” through natural regeneration, 

growth, competition and mortality; change also can result from disturbances and 

other discrete events in time such as fire, management activity, insect and disease 

outbreaks, etc. To facilitate projecting the effects of the interactions of these agents 

of change, landscape STMs such as the Vegetation Dynamics Development Tool 

(VDDT) developed by ESSA Technologies Limited (2006) can be used to quantify 

the dynamics of vegetation change (He 2008).

Ecological sustainability analysis evaluates both ecosystems (ecosystem diversity) and their associated species (species diversity). A guiding principle for 

ecosystem management (FEMAT 1993) is to use ecosystem reference conditions, 

the range of variation, as an inference of ecological sustainability to enable the 

persistence of ecosystem function and species diversity. In this paper, we focus 

on vegetation diversity and related ecological processes such as fire, and apply a 

case study assessing the ecological sustainability of the pinyon-juniper grassland

ecosystem on the Coconino National Forest (NF) in northern Arizona.
322 USDA Forest Service Proceedings RMRS-P-61. 2010.

Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona

Methods

Framing the Analysis

We stratified the Coconino NF by potential natural vegetation types (PNVTs) 

(Schussman and Smith 2006), a coarse ecosystem framework defined by site 

potential and historic fire regimes, that provides a basic framework for analyzing 

ecosystem diversity. Although the same process was used on each PNVT, this 

paper documents the analysis process conducted on the 122,086 hectare (301,675 

acres) pinyon-juniper grassland PNVT.

The pinyon-juniper grassland type occurs across the States of Arizona and 

New Mexico, in what was historically open woodlands with grassy understories 

(Ffolliott and Gottfried 2002). On the Coconino NF tree species include twoneedle 

pinyon (Pinus edulis Engelm.), oneseed juniper (Juniperus monosperma (Engelm.) 

Sarg.), Utah juniper (Juniperus osteosperma (Torr.) Little), and alligator juniper 

(Juniperus deppeana Steud.). On reference sites, native understories are made 

up of predominantly cool season perennial grasses including muttongrass (Poa 

fendleriana (Steud.) Vasey), squirreltail (Elymus elymoides (Raf.) Swezey ssp. 

brevifolius (J.G. Sm.) Barkworth) and western wheatgrass (Pascopyrum smithii 

(Rydb.) A. Löve), with both annual and perennial forbs, while shrubs are absent 

or scarce (<1 percent cover)(Miller and others 1995). Contemporary understories 

often include invasive grasses such as cheatgrass (Bromus tectorum L.) and a 

dominance of warm season species such as blue grama (Bouteloua gracilis (Willd. 

ex Kunth) Lag. ex Griffiths), and have uncharacteristically high shrub cover. This 

pinyon-juniper woodland type is typically found on sites with well-developed 

and moderately deep soils with loam and clay loam surface textures. Soil orders 

include mollisols derived from basaltic parent materials, andisols formed from 

cinder deposits and alfisols developed from sedimentary sources. Climate is 

characterized by a seasonal distribution of precipitation of which over half occurs 

between the months of April through September, with an annual rainfall ranging 

from 15-18 inches.

Information on the historic condition of this type is sparse. The ability to 

reconstruct historic stand structure and fire chronologies in pinyon-juniper is 

problematic, so the role of fire and the resulting vegetation structure is often 

speculative (Jacobs 2008). However, site productivity suggests that the development of a grass and fine fuels layer would have supported frequent fire, open 

forest dynamics, and perhaps uneven-aged conditions (Gottfried 2003).

We described historic (reference), current, and future structural conditions according to standard classification schemes based on average tree size (diameter) 

and canopy cover class (Brohman and Bryant 2005; table 1). Due to disparities 

in historic and current condition references, and how they were developed, it 

was necessary to develop crosswalks to normalize across references and enable 

comparisons between historic and current conditions. For instance, the U.S. 

Department of Agriculture (USDA), Forest Service mid-scale mapping, used to 

depict current conditions (Mellin and others 2008), uses a canopy cover break 

of 30 percent to distinguish open and closed, versus the LANDFIRE model 

(Havlina 2005) that employs a 40 percent break. We portrayed historic, current, 

and future composition conditions according to a southwestern regional classification of existing vegetation based on dominance types (Triepke and others 

2005). Dominance types, defined by the relative abundance and dominance of 

tree species, are similar to Society of American Foresters or Society for Range 

Management cover types (Eyre 1980; Shiflet 1994), but are keyable, exhaustive, 

and mutually exclusive.
USDA Forest Service Proceedings RMRS-P-61. 2010. 323

Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie

Specific combinations of dominance type, size class, and canopy cover that are 

characteristic to each PNVT are expressed in terms of vegetation states identified for each PNVT, and configured in PRESIDE (Process RESIDEnce Times), 

a recently developed ancillary program (Vandendriesche 2009) to the Forest 

Vegetation Simulator (FVS) model (Dixon 2002). Each vegetative state represents 

an important phase in the ecosystem dynamics of a PNVT. The historic pinyonjuniper grassland ecosystem has been described (LANDFIRE 2007) as a five-state 

model that includes a grass-forb state (A), two open forest states (C and D), and 

two closed forest states (B and E) (table 1). Frequent surface fires maintained 

the forest in these reference conditions. Ecological process reflects the ability 

of natural and anthropogenic events such as fire, forest insect and disease, and 

resource management activities to alter vegetation composition and structure 

and, in turn, wildlife habitat and species diversity (Perry and Amaranthus 1997). 

Along with site potential, the characteristic frequency and severity of fire are 

differentia of the PNVT classes themselves.

Describing Reference Conditions

The Vegetation Dynamic Development Tool (VDDT, ESSA 2006) has been 

used by the National LANDFIRE program (Ryan and others 2006) and others 

such as the Nature Conservancy (TNC) (Schussman and Smith 2006) to develop 

state-and-transition models that describe reference conditions. The VDDT software moves cells (representing a unit of area) from one state to another based on 

a set of “transitions.” Traditionally, “deterministic transitions” describe succession (aging) in the absence of disturbance. Probabilistic transitions reflect the 

quantitative assessments of discrete natural and anthropogenic events including 

fire, insects, diseases, grazing, harvesting, and severe weather events. Each 

probabilistic transition typically has three characteristics that define its pathway: 

1) its return frequency or probability, 2) its severity or impact on vegetation, and 

3) the destination state in which the cell will reside after transition.

Table 1—Crosswalk to facilitate comparison of historic, current, and future conditions of the pinyon-juniper grassland ecosystem.

Reference Condition	 Current Condition	 Future Trends

LANDFIRE RA JUPI1 Model	 USDA FS R3 Mid-Scale EV Map	 USDA FS R3 PJ Grassland Model

State	 Meana	 Description	 Dominance Unit	 Structureb State	 Description

A 5% Post Non-tree: Recently Grass-forb-shrub A GFB/SHR

replacement burned and all shrub,

grass, and forb types

Seed/sap-open B SSO

C 25% Mid-open — Seed/sap-closed E SSC

All pinyon, juniper,

and mixed shade Small-open C SMO

intolerant tree Med to very

D 50% Late-open dominance types large-open D MVO

occurring within the

B 10% Mid development pinyon-juniper Small-closed F SMC

closed grassland PNVT

E 10% Late-closed — Medium to

very large- G MVC

closed 

a Average proportion of the landscape during the reference period (circa 880-1880 (Schussman and Smith 2006)).

b Size classes based on diameter at breast height for forest tree species and diameter at root collar for woodland species: seedling/sapling 

(< 13cm), small (13–24.9cm), medium (25–50cm), and very large (>50cm): overstory cover classes are sparse (<10% tree canopy cover), open 

(10 – 29.9% cover), and closed (>29.9% cover). 
324 USDA Forest Service Proceedings RMRS-P-61. 2010.

Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona

We retooled these models to project future conditions by replacing historic probabilities and transitions with contemporary transitions and attendant frequencies 

that reflect current land management. We also added contemporary and possible 

future vegetative states. We detail the development of the pinyon-juniper grassland 

model below. Reference condition descriptions and models typically are based 

on peer-reviewed journal articles as well as published conference proceedings, 

reports, theses, dissertations, and book chapters along with some consideration 

of professional judgment provided by model developers. In contrast, the models 

that we developed for projecting future conditions were more empirical, using 

Forest Inventory and Analysis (FIA) data, FVS simulation runs, and related 

software tools.

Describing Current Conditions

We mapped PNVTs using Terrestrial Ecosystem Survey (TES) data for the 

Coconino NF (Miller and others 1995). The TES is a terrestrial ecological unit 

inventory that formulates map units based on similarities in climate, soils, landform, and potential vegetation at the map scale of 1:24,000 (Winthers and others 

2005). Among the map unit attributes, disclimax classes (zootic, fire) indicate 

historic disturbance regime, making TES map data the best available resource 

for PNVT mapping.

In 2004, the Southwestern Region initiated mid-scale mapping of existing 

vegetation at 1:100,000 across all National Forests and Grasslands (Mellin and 

others 2008). This mapping includes the three principle existing vegetation map 

components previously mentioned—dominance type, size class, canopy cover. 

With the description of vegetation states (table 1), these map data allowed for the 

quantitative analysis of current conditions within each PNVT. We intersected 

PNVT mapping in GIS with the existing dominance type, size class, and canopy 

cover layers from mid-scale vegetation mapping products to produce tabular 

summaries of current conditions within each PNVT class. These summaries 

were in turn synthesized to give hectares and percent of each vegetation state 

within each PNVT. We then compared these percents to historic and projected 

conditions for the ecosystem.

Along with each condition reported (historic, current, or projected), we calculated ecosystem condition class values using the same equation employed 

by LANDFIRE to compute Fire Regime Condition Class (FRCC) (Hann and 

others 2005). But unlike FRCC, which provides percentages for each departure 

class (1, 2, or 3), our own ecosystem condition class (ECC) provides one overall 

departure rating for a given analysis area (Weisz and others 2009). The ECC is 

computed for each comparison, either current vs. reference condition, or projected 

vs. reference condition (table 2) based on the departure of all states in total from 

their reference conditions. In each calculation, the sum of the lesser of percent 

values for each state, either reference or current, is subtracted from 100 to provide 

one overall departure index on a scale of 0 percent to 100 percent, higher values 

representing more departed conditions. From there, three classes make up the 

ECC rating system:

• ECC 1 (within reference condition) represents departure index values < 33;

• ECC 2 (moderately departed) represents departure index values >33 and < 66; 

and

• ECC 3 (severely departed) represents departure index values > 66.

Recently developed FRCC map data for LANDFIRE map zones in Arizona 

(LANDFIRE 2008) corroborate our findings, as do regional studies of these 

systems.
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Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie

Projecting Future Conditions

Retooling models—Typically reference conditions models are based on a 

survey of the literature, supplemented by empirical data as well as expert opinion (LANDFIRE 2008). Often these models are applicable to a large map zone 

or to a large region like Arizona and New Mexico (Gori and Bate 2007; Havlina 

2005). To retool these models to project conditions under existing or proposed 

management schemes, managers can modify reference condition models to: 

1) include new states or modified states that reflect vegetation classes that did 

not exist under reference conditions; 2) incorporate current and projected natural 

and anthropogenic processes; and 3) incorporate current and projected transition 

probabilities. We illustrate by example how the Coconino NF retooled the pinyonjuniper grassland reference condition model for this purpose (see below), with 

the assumption of no climate change. Carbon accounting was also provided using 

the carbon extension of FVS (Havis and Crookston 2008; Hoover and Rebain 

2008). The carbon extension provides values for dead and live standing trees, 

and dead and live belowground tree tissue (Hoover and Rebain 2008). Standard 

values are provided for carbon held in herbs and shrubs, downed wood, and litter 

and duff, based on similarly measured plant communities. The paper concludes 

with a description of how the model could be retooled in the future to consider 

climate change.

New or modified states—Typically, models for current and projected conditions contain as many or more states than reference condition models. In the 

case of the pinyon-juniper grassland PNVT, we developed a seven-state model 

to describe current conditions in contrast to the reference conditions model that 

had five states. Table 1 illustrates how we cross walked the reference conditions 

states with the current states.

Quantifying current transitions—To retool reference condition models to 

reflect contemporary processes, four steps are followed: 1) identify the contemporary transitions; 2) replace reference transitions with contemporary ones 

(tables 3, 4a and 4b); 3) model future conditions; and, 4) interpret the results. Each 

contemporary transition is identified in terms of its type, transition class (groups 

Table 2—Calculation of departure and ecosystem condition class based on 

the disparity between reference and current conditions for the PJ grassland 

ecosystem on the Coconino NF.

	 Reference condition	 Current condition

State	 Description	 Meana	 Proportion	 Calculationb

A Post replacement 5% 23% 5%

C Mid-open 25% 14% 14%

D Late-open 50% 27% 27%

B Mid development closed 10% 17% 10%

E Late-closed 10% 19% 10%

Sum 66%

Departure index value = 100% - Sum 34

Ecosystem condition class 1 (0–33), 2 (34–65), 3 (66+) “2”

a Average proportion of the landscape during the reference period (circa 880-1880 (Schussman 

and Smith 2006)).

b Lesser of reference condition and current condition.
326 USDA Forest Service Proceedings RMRS-P-61. 2010.

Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona

Table 3—Canopy cover and fire mortality proportion table.

Canopy Cover / Fire Mortality Proportion Table

Beginning Canopy Cover 

Class Fire Severity Class Ending Percentage by Canopy Cover Classes

10 – 30% (open)

non-lethal

9% → sparse (0 – 10%)

91% → open (10 – 30%)

mixed severity

55% → sparse (0 – 10%) 

45% → open (10 – 30%)

stand replacement 100% → sparse (0 – 10%)

30 – 60% (closed)

non-lethal

16% → open (10 – 30%)

84% → closed (30 – 60%)

mixed severity

2% → sparse (0 – 10%)

79% → open (10 – 30%)

19% → closed (30 – 60%)

stand replacement

87% → sparse (0 – 10%)

13% → open (10 – 30%)

of transition types), frequency, and effects. We used four transition classes in 

our current model: wildland fire, management activities, insect and disease, and 

natural growth transitions in the absence of disturbance. Transition types within 

each transition class may have unique frequencies and effects unto themselves. 

The management activities transition class contains, for example, mechanical 

thinning, prescribed burning, etc.

Wildland fire transitions—We used LANDFIRE definitions of fire severity 

based on how much overstory canopy mortality would occur during a wildland 

fire: nonlethal (or low severity), <25 percent mortality; mixed severity fire, 25 

percent to 75 percent mortality; and stand replacement fire, >75 percent mortality (Hann and others 2005). We generated fire frequencies for each of the 

transition classes using local fire history data on the planning unit for the period 

1988 through 2006. Spatial data was available for approximately five wildland 

fires greater than 40 hectares (16 acres) in size for the period 1960 to 2005. Fire 

mortality mapping was available for three incidents including the Lizard (2003), 

Mormon (2003), and Jacket (2004) fires. For other fires that occurred after 1975, 

fire officials provided estimates of the percentage of non-lethal, mixed severity, 

and stand replacement fire that occurred. We corroborated fire mortality for the 

fires using orthophotos in GIS, estimating fire extent and mortality based on 

patterns of top-kill and regeneration.

We summarized these results as average annual probabilities per hectare for 

each fire type: nonlethal fire (0.0002), mixed severity fire (0.0021), and stand 

replacing fire (0.0032) and assigned these probabilities to each model state (table 

4a). The mixed severity and stand replacement probabilities can be attributed to 

significant Pinyon Ips bark beetle activity since 1996. The effects of a fire on a 

cell within the model depend on pre-fire canopy cover and the severity of the fire 

(the fire mortality class; table 3).

Management activity transitions—We quantified management activities using the Forest Activity Tracking System (FACTS) database (M. Pitts, unpublished 

data). We queried all activities recorded on the planning unit from 1988 through 

2006, and then eliminated activities that did not affect broad-scale vegetation 
USDA Forest Service Proceedings RMRS-P-61. 2010. 327

Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie

Table 4a—Pinyon-juniper grassland natural and anthropogenic disturbance transitions expressed 

as the average annual probability per hectare per year.

	From State Code: Acronym: Description:

	 Transition Type	 Probability	 Proportiona	 To State Acronym

A: GFB: Grass/Forb/Brush

Nonlethal Fire 0.0002 1.00 GFB

Stand Replacing Fire 0.0032 1.00 GFB

B: SSO: Seed/Sap, Open

All Regeneration 0.0011 1.00 GFB

Insect and Disease 0.0100 1.00 SSO

Mixed Severity Fire 0.0021 0.55 GFB

Mixed Severity Fire 0.0021 0.45 SSO

Nonlethal Fire 0.0002 0.09 GFB

Nonlethal Fire 0.0002 0.91 SSO

Stand Replacing Fire 0.0032 1.00 GFB

C: SMO: Small, Open

All Regeneration 0.0011 1.00 GFB

Insect and Disease 0.0100 1.00 SMO

Mixed Severity Fire 0.0021 0.55 GFB

Mixed Severity Fire 0.0021 0.45 SMO

Nonlethal Fire 0.0002 0.09 GFB

Nonlethal Fire 0.0002 0.91 SMO

Stand Replacing Fire 0.0032 1.00 GFB

D: MVO: Medium to Very Large Open

All Regeneration 0.0011 1.00 GFB

Insect and Disease 0.0100 1.00 MVO

Mixed Severity Fire 0.0021 0.55 GFB

Mixed Severity Fire 0.0021 0.45 MVO

Nonlethal Fire 0.0002 0.09 GFB

Nonlethal Fire 0.0002 0.91 MVO

Stand Replacing Fire 0.0032 1.00 GFB

E: SSC: Seed/Sap Closed

All Regeneration 0.0011 1.00 GFB

Insect and Disease 0.0100 1.00 SSO

Mixed Severity Fire 0.0021 0.02 GFB

Mixed Severity Fire 0.0021 0.19 SSC

Mixed Severity Fire 0.0021 0.79 SSO

Nonlethal Fire 0.0002 0.84 SSC

Nonlethal Fire 0.0002 0.16 SSO

Stand Replacing Fire 0.0032 0.87 GFB

Stand Replacing Fire 0.0032 0.13 SSO

F: SMC: Small, Closed

All Regeneration 0.0011 1.00 GFB

Insect and Disease 0.0100 1.00 SMO

Mixed Severity Fire 0.0021 0.02 GFB

Mixed Severity Fire 0.0021 0.19 SMC

Mixed Severity Fire 0.0021 0.79 SMO

Nonlethal Fire 0.0002 0.84 SMC

Nonlethal Fire 0.0002 0.16 SMO

Stand Replacing Fire 0.0032 0.87 GFB

Stand Replacing Fire 0.0032 0.13 SMO

G: MVC: Medium to Very Large Closed

All Regeneration 0.0011 1.00 GFB

Insect and Disease 0.0100 1.00 MVO

Mixed Severity Fire 0.0021 0.02 GFB

Mixed Severity Fire 0.0021 0.19 MVC

Mixed Severity Fire 0.0021 0.79 MVO

Nonlethal Fire 0.0002 0.84 MVC

Nonlethal Fire 0.0002 0.16 MVO

Stand Replacing Fire 0.0032 0.87 GFB

Stand Replacing Fire 0.0032 0.13 MVO

a Proportion of acres affected by a transition that will move to the destination state.
328 USDA Forest Service Proceedings RMRS-P-61. 2010.

Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona

composition and structure from further analysis; thus, we eliminated wildlife 

inventories, mine reclamation activities, etc. We summarized the remaining 8,747 

management activities into standardized transition classes such as prescribed burning, fuels treatment, and harvest thinning. As with the wildland fire transitions, 

we calculated average annual probability-per-hectare values for each PNVT and 

assigned these probabilities to each model state (table 4a). In a typical year during the sampled time period, 0.1 percent of the pinyon-juniper grassland PNVT 

was affected by these activities.

Insect and disease transitions—Both localized and widespread mortality 

events have occurred in the pinyon-juniper woodlands on the Coconino NF (Lynch 

and others 2007). These events have typically been pinyon ips outbreaks associated 

with periods of drought, such as occurred in the 1950s, and more recently in the 

mid-1990s and 2001-2003. Localized outbreaks resulted from range improvement 

projects that generated large amounts of fresh pinyon slash (Negrón and Wilson 

2003; Yasinski and Pierce 1962). Although pinyon ips outbreaks can be severe, 

with pinyon mortality approaching 100 percent within a given stand, they are 

generally short lived (1-2 years). The pinyon ips outbreak during the late-1990s 

east of Flagstaff near Twin Arrows encompassed almost 5,261 hectares (13,000 

acres) at its peak (Negrón and Wilson 2003).

Table 4b—Pinyon-juniper grassland natural growth in the absence of disturbance 

successional transitions expressed as the average annual probability per hectare per 

year.

From State Code: Acronym: Description	 Probability	 To State Acronym

A: GFB: Grass/Forb/Brush .9691 GFB

.0136 SSO

.0041 SMO

.0132 MVO

B: SSO: Seed/Sap, Open .9249 SSO

.0269 SMO

.0247 SSC

.0236 SMC

C: SMO: Small, Open .0045 SSO

.9175 SMO

.0193 MVO

.0024 SSC

.0494 SMC

.0070 MVC

D: MVO: Medium to Very Large Open .0078 SSO

.0036 SMO

.9714 MVO

.0014 SSC

.0016 SMC

.0142 MVC

E: SSC: Seed/Sap Closed .9093 SSC

.0907 SMC

F: SMC: Small, Closed .0004 SSC

.9759 SMC

.0237 MVC

G: MVC: Medium to Very Large Closed .0003 MVO

.0002 SSC

.0036 SMC

.9960 MVC
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Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie

At least within the historic period, the size and severity of the recent droughtand pinyon ips-related die-off is unprecedented for the Coconino NF and northern 

Arizona (Allen 2007; Mueller and others 2005). The contemporary pinyon dieoff is 100 times as large (two orders of magnitude) as any previously recorded 

acreage for pinyon ips beetle mortality for the Coconino NF, Kaibab NF, and 

Grand Canyon National Park. High levels of pinyon mortality were detected by 

aerial survey during 2001 through 2003, with approximately 809,389 hectares 

(2,000,000 acres) impacted Region-wide and more than 60,704 hectares (150,000 

acres) on the Coconino NF. The mortality was primarily attributed to pinyon ips 

attacking drought-stressed pinyon; however, twig beetles (Pityophthorus spp.) 

were also observed killing smaller pinyon in 2003. Pinyon mortality averaged 

41.4 percent within an 80-km (50-mile) radius of Flagstaff, with mortality being 

significantly greater on southern aspects and shallow soils developed in volcanic 

cinders (Gitlin and others 2006).

Using data from the above insect and disease outbreaks, we calculated average annual probability-per-hectare values for each model state. These transition 

probabilities were assigned to each model state (table 4a).

Natural growth transitions in the absence of disturbance—To quantify 

the natural growth transitions that will occur in the absence of natural and 

anthropogenic disturbances, we used the PRESIDE software (Vandendriesche 

2009) to process the outputs of FVS (Dixon 2002). In our case study, we show 

the results of applying this methodology in the Southwestern Region. The steps 

in this process include:

1. Prepare the FIA inventory data for projection by FVS: Each FIA plot for the 

PNVT in the Southwestern Region is assigned to the appropriate model 

state.

2. Perform FVS calibration steps: Calibration procedures include using the 

FVS self-calibrating feature, estimating and inputting natural regeneration 

response, accounting for tree defect for volume estimates, and determining 

tree species size attainment and limiting stand maximum density.

3. Run natural growth projections for each FIA plot using the calibrated FVS 

to simulate growth over a 250-year time period.

4. Process the tree list output through the PRESIDE post-processor classifier 

and accumulate the results into a matrix from which to estimate the average 

annual probability per hectare that in the absence of disturbance a plot will 

transition from one state to another state (table 4b).

5. Using the sample of plots populating each state at each point in time during 

the projection, summarize the vegetation characteristics of each model state 

(table 5). The post-processing software indexes the aggregate state classes 

to summary values derived from the tree lists and attributes from standard 

FVS outputs. Several dozen vegetation characteristics such as stand volume 

and stand carbon can be quantified for each model state.

Model runs—Model simulations from VDDT are non-spatial and reflect a 

summary of up to 50,000 sample units or cells. For our study, we opted for 1,000 

sample units because our earlier work, and work conducted by TNC and LANDFIRE, indicated that this number produced reasonable and consistent projections 

(TNC and others 2006). If we increased the number of cells beyond 1000, results 

of the analysis would not be significantly changed, but running time would be 

increased significantly. In the next step of the modeling process, we initialized the 

starting hectares in each state based on current conditions indicated by mid-scale 

vegetation mapping data. We ran multiple simulations to estimate the long-term 
330 USDA Forest Service Proceedings RMRS-P-61. 2010.

Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona

Table 5—FVS Outputs.

Vegetation Structure Variables:

VDDT State A _ GFB	B _ SSO	C _ SMO	D _ MVO	E _ SSC	F _ SMC	G _ MVC

Dominance Type PIED	 PIED	 PIED	 PIED	 PIED	 PIED	 PIED

Size Class 0	 1	 2	 3	 1	 2	 3

Canopy Class 0	 1	 1	 1	 2	 2	 2

Canopy Layers 1	 2	 1	 1	 1	 1	 1

Stand Age – Overstory 17	 76	 98	 118	 97	 146	 207

Stand Age – Dominant Story 0	 55	 92	 130	 68	 121	 196

Total Plot/Activity Count 323	 277	 317	 1222	 194	 3084	 9895

Stand-Stock Variables:

Seedlings/Acre < 1.0” diameter 61	 148	 117	 103	 265	 159	 89

Trees/Acre = 1.0” diameter 206	 441	 395	 286	 777	 493	 315

Basal Area/Acre = 1.0” diameter 11	 37	 44	 57	 84	 117	 135

Quadratic Mean Diameter – Trees = 1.0” diameter 3	 4	 5	 6	 5	 7	 9

Quadratic Mean Diameter – Top 20 percent, diameter 0	 7	 9	 14	 8	 11	 15

Stand Density Index – SDI_Summation 20	 90	 94	 99	 195	 233	 235

Stand Density Index – SDI_Dq 30	 106	 111	 128	 221	 260	 263

Canopy Cover 6	 21	 22	 21	 40	 48	 48

Live – Cubic Feet/Acre 88	 233	 328	 568	 515	 1176	 1741

Live – Board Feet/Acre 0	 5	 12	 16	 30	 18	 11

Wildlife Habitat Variables:

R3 – Vegetative Structural Stage 1	 3ASS	 4ASS	 5ASS	 1C	 4CSS	 5CSS

Standing Snags

Small = 5-10” diameter 1.1	 0.6	 2.8	 2.4	 3.4	 26.4	 14.0

Large = 10”+ diameter 1.6	 2.7	 2.9	 5.9	 4.3	 6.2	 12.1

Extra-large = 18”+ diameter 0.7	 1.2	 1.3	 2.8	 1.5	 2.0	 2.6

Snag Recruitment

Small = 5-10” diameter 0.2	 0.4	 2.0	 0.9	 4.9	 26.0	 11.4

Large = 10”+ diameter 0.5	 1.3	 1.0	 2.5	 2.4	 3.5	 7.5

Extra-large = 18”+ diameter 0.2	 0.4	 0.4	 1.0	 0.6	 0.7	 0.9

Pestilent Disturbance Variables:

Dwarf Mistletoe Rating 0.04	 0.09	 0.14	 0.15	 0.22	 0.34	 0.41

Wildfire Risk Variables:

Crown Bulk Density 0.00	 0.02	 0.02	 0.01	 0.04	 0.04	 0.03

Crown Base Height 4.5	 4.7	 6.2	 7.6	 4.9	 5.7	 7.5

Crowning Index 170.5	 72.4	 85.7	 121.8	 52.3	 49.0	 61.3

Torching Index 5.0	 3.2	 6.4	 8.0	 3.1	 5.8	 10.1

Fire Hazard Rating 3.0	 3.0	 3.0	 3.0	 3.0	 3.0	 3.0

Fuel Load – Coarse Woody Debris = 0-3” diameter 0.1	 0.4	 0.5	 0.6	 1.2	 1.7	 1.5

Fuel Load – Coarse Woody Debris = 3-12” diameter 0.2	 0.7	 1.3	 2.1	 1.9	 5.1	 9.4

Fuel Load – Coarse Woody Debris = 12”+ diameter 0.2	 0.8	 1.3	 2.0	 1.2	 3.1	 5.2

Biomass-Carbon Variables:

Tree Biomass – Dry weight live & dead/boles & crown 2.4	 7.6	 8.6	 14.3	 16.7	 27.8	 36.4

Stand Carbon – Total carbon above & below ground 2.8	 7.2	 8.5	 12.4	 14.8	 24.9	 32.6
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Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie

effects of continuing current management under the existing land management 

plan. We ran ten simulations with each simulation projecting conditions annually 

for 200 years based on data and assumptions described earlier. We compared the 

average annual results of these simulations with current conditions and reference 

conditions (table 6)

Table 6—Proportion of area in pinyon-juniper grassland states in current and 

projected conditions.

Vegetation state A B C D E F G Percent

departure

34

ECC

Current condition 23 1 13 27 0 17 19 2

Projected trends

10 years 20 4 9 25 1 18 23 36 2

50 years

200 years

15

13

6

4

6

5

22

22

1

2

18

17

32

37

40

42

2

2

Results

Here we provide results to answer the question, “How do current and projected 

conditions compare to reference conditions?” Again, reference conditions were 

derived from VDDT models, developed by LANDFIRE, to quantify the historical proportion of major vegetation states of the pinyon-juniper grassland system 

(table 2). Current conditions and projected conditions are summarized in table 6.

Current conditions represent existing vegetation mapping synthesized according to the vegetation states contained in the pinyon-juniper grassland model. An 

ECC value of 34 indicates a system that is somewhat departed from reference 

conditions, on the low end of the moderate range. Current conditions indicate an 

uncharacteristic excess of grass-forbs communities (state A), an excess of closed 

woodlands (states B and E), and a reciprocal deficit of open woodlands (states 

C and D).

Likewise future projections indicate a continuing trend towards departure, 

from an index value of 36 at 10 years, to 40 at 50 years. If current management 

continues, departure from reference conditions as measured by Ecosystem Condition Class will increase over time due to more acres moving to the closed states.

These results relate to the ecological sustainability concepts stated in our introduction and restated more simply here: Every species around today persisted 

over time in its environment under reference conditions. If current and proposed 

future management creates or approximates that environment, then the species 

is not likely or is less likely to be at risk. On the other hand, as in this case study, 

if the ecosystem is departed from reference conditions, and if that departure 

increases over time, then both the ecosystem and its associated species are less 

likely to be sustainable.
332 USDA Forest Service Proceedings RMRS-P-61. 2010.

Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona

Discussion

The Analysis Process

As mentioned, TNC, LANDFIRE, and others have made a significant investment in the development of reference condition descriptions and models. We’ve 

complimented these models with the development of calibrated and more detailed 

models that depict current trends and project future conditions. Current and future 

conditions can be compared with reference conditions to answer two questions: 

1) is there a current departure from reference conditions, and 2) will conditions 

remain static, trend towards, or trend away from reference conditions? Trends 

away from reference condition may indicate an ecosystem at risk and, if so, the 

model can be further tooled to evaluate the potential effectiveness of management strategies.

Also, as discussed under Methods, in retooling the models, several assumptions 

were necessary in the face of uncertainties concerning the historic condition. 

While additional information is needed to supplement and refine concepts for the 

pinyon-juniper grassland PNVT, working assumptions on fire frequency and stand 

dynamics were necessary to enable useful modeling of the system. For example, 

we assumed that a plurality of tree diameters existed to indicate one of four treedominated states, acknowledging that multiple tree cohorts within any one plant 

community were likely due to fire frequency and productivity.

Evaluation of Results

Southwestern pinyon-juniper woodlands span a wide range of environmental 

settings over 8.6 million hectares (3.5 million acres), yet historical descriptions 

are extremely limited. The pinyon-juniper grassland type is thought to have been 

maintained historically by frequent, low-severity surface fires that spread from 

and into adjacent systems including semi-desert grassland, juniper grassland, 

and ponderosa pine forest. Some references (e.g., Gottfried 2003) suggest that 

the resulting stand structure would have been uneven-aged, dominated by open 

grown trees. Modern fire suppression and grazing would have since favored 

closed canopy structures susceptible to drought-insect induced mortality and 

uncharacteristic fire (stand replacement). The current surplus of grass-forbs 

communities has likely resulted from stand clearing and pasture development, 

and from increased drought-insect mortality and fire activity. Long term VDDT 

modeling based on current practices, as reflected in management records from 

1988 to 2006, indicates the perpetuation of dense canopies with regular conversion to a grass-forbs state.

The objective of this paper is to illustrate through a case study how State-andTransition Models (STMs) can be developed and used to evaluate the ecological 

sustainability of ecosystems. We accomplished this objective by using an empirical 

approach to create and calibrate our models based on existing inventory data and 

FVS simulations based on existing data; this allowed us to compare and contrast 

reference conditions, existing conditions and projected conditions to quantify the 

departure of existing and projected conditions from reference conditions.

Our analysis indicates that the pinyon-juniper grassland ecosystem on the 

Coconino NF is moderately departed from reference conditions and that this 

trend will continue into the future under the existing land management plan. Fire 

suppression coupled with infrequent forest management activities contributes 

to an existing departure from reference conditions. Thus, the continued current 

implementation of the existing land management plan may pose a risk to the 

ecological sustainability of this ecosystem.
USDA Forest Service Proceedings RMRS-P-61. 2010. 333

Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie

Others such as Arno and Fiedler (2005) have explored deteriorated forest and 

woodland conditions in western North America and reached similar conclusions. 

By developing empirically based landscape dynamics models, we can quantify 

woodland conditions with more reliability to assess the ecological sustainability 

of these ecosystems within a more credible, systematic framework for strategic 

land management plans.

Addressing Carbon Accounting and Climate Change

Future extensions of our methodology include projecting the effects of climate 

change on ecological sustainability and providing spatial simulations (Miller 

2007). We also advocate evaluating adaptive and mitigation strategies as outlined 

by Millar and others (2007).

Carbon accounting for mitigation strategies was provided by the carbon extension of the Forest Vegetation Simulator (Havis and Crookston 2008). Carbon 

accounting attributes are shown in table 7. Our results indicate that as the ecosystem moves further away from reference conditions over time (due to more acres 

moving into the closed states), the ecosystem sequesters more total carbon (above 

and below ground), because the closed states contain more stand carbon per acre 

than do the open states. This represents a trade off that must be evaluated by land 

managers: managing toward reference conditions versus managing to maximize 

short-term carbon sequestration.

The long-term sustainability of these uncharacteristic closed states is dependent 

upon insect, drought, and fire occurrence. For example, as closed canopy states 

are removed by wildfire, the sequestered carbon is released to the atmosphere. 

Sequestration of excess carbon in closed canopy states in frequent-fire adapted 

forest types may result in a net long-term loss of carbon sequestration values 

when uncharacteristic stand-replacing fires occur (Hurteau and others 2008).

The current model does not provide for charcoal or soil organic carbon, though 

future analyses are likely to include these components (DeLuca and Aplet 2008; 

Jenkins and others 2003). The amount of soil carbon in the pinyon-juniper woodlands is significant; it can be up to 8 tons per acre in the A horizon and up to 

12 tons per acre in the total solum (Meurisse and others 1991). The amount of 

organic carbon in soils within the pinyon-juniper woodlands is inherently lower 

than higher elevation montane forest types (Meurisse and others 1991).

Adaptation strategies necessitate predictions about future vegetation patterns 

and at this time, we are considering the advantages and disadvantages of alternative approaches to modeling climate change. The assumptions in our projection 

Table 7—Thousands of tons of stand carbon occurring above and below ground.

Vegetation state A B C D E F G Total

Current condition 194 22 333 1,010 0 1,277 1,869 4,705

Projected trends

10 years 169 87 231 935 45 1,352 2,262 5,081

50 years

200 years

127

110

130

87

154

128

823

823

45

89

1,352

1,277

3,147

3,639

5,778

6,153

a Stand carbon per acre is taken from table 5 and does not reflect charcoal or organic soil 

carbon. Acres are taken from table 6.
334 USDA Forest Service Proceedings RMRS-P-61. 2010.

Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona

models can be modified in the following ways to incorporate the emerging evidence from climate research (Hemstrom and Merzenich, unpublished document):

1. Types of states: Climate change may result in the addition or removal of states 

within a PNVT as new vegetation composition and structural patterns are 

introduced with changing site potential and processes (such as the introduction of exotic species).

2. Types of transitions: Climate change may result in the addition or removal of 

transitions within a PNVT, with novel patterns of vegetation composition, 

structure, and process.

3. Rates of transitions: The rates of transitions between model states for existing 

transitions, for example, stand replacing fire, may change within the PNVT 

and planning area.

4. New (adventive) PNVTs: Adventive PNVTs may need to be modeled, depending on the climate scenario.

5. Transitions between PNVTs: In addition to transitions within PNVT models, 

transitions between PNVTs may be necessary to reflect the movement of 

area between PNVT classes as climate changes.

6. New management activities: New management activities may be necessary 

to respond to adaptive and mitigation strategies (Millar and others 2007), 

along with modification to the rates of existing transitions.

7. Projected climate variability: Changes in the annual variation of phenomena 

such as wet years, dry years, insect and disease incidence, etc. may be explicitly modeled within existing VDDT software.

8. Addressing multiple climate scenarios: Current assumptions can be modified to reflect each climate change scenario that needs to be considered by 

management; for example in scenario 1 the planning area may be getting 

warmer and drier, and in scenario 2 the planning area may be getting warmer 

and wetter.

Acknowledgments

We would like to thank Melinda Moeur, Jim Merzenich, and Miles Hemstrom 

for their inspiration and critical feedback. Also, we would like to thank Leonardo Frid for his thoughtful insights and suggestions. Rory Steinke enhanced our 

understanding of the pinyon-juniper terrestrial ecosystem on the Coconino NF.

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USDA Forest Service Proceedings RMRS-P-61. 2010. 337

Part 5: Summary

2009 NATIONAL SILVICULTURE WORKSHOP

BOISE, IDAHO

SILVICULTURE

CLIMATE

BIOMASS

CARBON SEQUESTRATION

USDA Forest Service Proceedings RMRS-P-61. 2010. 339

In: Jain, Theresa B.; Graham, Russell T.; 

and Sandquist, Jonathan, tech. eds. 

2010. Integrated management of 

carbon sequestration and biomass 

utilization opportunities in a changing climate: Proceedings of the 2009 

National Silviculture Workshop; 2009 

June 15-18; Boise, ID. Proceedings 

RMRS-P-61. Fort Collins, CO: U.S. 

Department of Agriculture, Forest 

Service, Rocky Mountain Research 

Station. 351 p.

1 Supervisory Ecologist and Project 

Leader, Ecology and Management of 

Southern Pines, Southern Research 

Station, US Forest Service, Hot Springs, 

AR 71902. Contact: jguldin@fs.fed.us

Summary of the 2009 National 

Silviculture Workshop

James M. Guldin1

Abstract—The theme of the 2009 National Silviculture Workshop held in Boise Idaho 

in June 2009 was, “Integrated management of carbon sequestration and biomass utilization opportunities in a changing climate.” The session had a series of outstanding 

presentations and field tours focused on the theme of the meeting nationally, and 

with specific reference to the forests of the northern Rocky Mountains. There was 

consensus in the meeting that climate change will be the defining issue for this generation of resource managers. Silviculture will play a key role in the future of climate 

change, and it will be option-creating silviculture, not option-reducing silviculture. 

Silviculturists and decision-makers must use the best science regardless of how it 

tests the popular will and the politically easy decision—and this is no simple task in 

light of the administrative issues that govern forest management on Federal lands. An 

adaptation strategy for climate change will also require integrating the principles of 

landscape ecology modeled via landscape succession models with principles of forest 

ecology and silviculture modeled using tools such as the Forest Vegetation Simulator. 

Data from long-term experiments will be increasingly important to validate simulation 

outcomes. Finally, state-of-the-art science delivery programs will be needed to think 

about and develop silvicultural prescriptions that address climate change adaptation 

strategies in project-level decisions, and that are implemented spatially in a strategic 

way across the forested landscape.

Introduction

Approximately 150 attendees convened in Boise, Idaho, in early June 2009 for 

the 12th biennial meeting of the National Silviculture Workshop. Amid pleasant 

weather and a fine setting along the Boise River, the speakers and attendees met 

to discuss topics on the theme, “Integrated management of carbon sequestration 

and biomass utilization opportunities in a changing climate.” The session had a 

series of outstanding presentations and field tours focused on the theme of the 

meeting nationally, and with specific reference to the forests of the northern 

Rocky Mountains.

The speakers welcoming the group succinctly summarized the challenges in 

science and management facing silviculturists. Mr. Bill LeVere, Director of Natural 

Resources for the USDA Forest Service (FS) Intermountain Region (Region 4) 

in Ogden, UT, noted that 12 of the warmest years on record have occurred since 

the 1990s; that we have experienced earlier snowpack melt, longer growing seasons, earlier greenup rates, and a 30 percent decline in August streamflows in 

the northern Rocky Mountains; that warmer climate provides longer periods for 

forests to be under stress from insects and disease; and that resource managers 

have lots of questions but few answers. Dr. Tom Crow, Program Manager with 

the FS Rocky Mountain Research Station in Fort Collins, CO, suggested that 

climate change will be the single defining issue facing the current generation 

of resource managers, that there are deep scientific questions underlying the 

concepts of managing forests for resilience and adaptation in the face of climate 
340 USDA Forest Service Proceedings RMRS-P-61. 2010.

Guldin Summary of the 2009 National Silviculture Workshop

change—and that all of this is occurring in an era of declining Federal budgets. 

Ms. Cecelia Romero Seesholtz, the Forest Supervisor of the Boise National Forest in Boise, ID, offered a generous welcome, and thanked her staff for working 

so hard and successfully to convene the Workshop. To Supervisor Seesholtz, the 

organizing committee and attendees at the Workshop offer heartfelt thanks for 

her leadership and support.

Plenary Session

The keynote address was provided by the Hon. Cecil Andrus, Director of the 

Andrus Center for Public Policy in Boise, ID. He engaged the Workshop with a 

highly entertaining presence, and included two key concepts in his comments. 

First, he challenged attendees to manage National Forests for energy, economic 

development, and environmental quality for a changing climate—and suggested 

that these three objectives are important and they can be contradictory. And he 

challenged research scientists and resource managers alike to use the best science 

in making resource management decisions, regardless of how it tests the popular 

will and the politically easy decision.

Two other speakers were invited to participate in the Plenary Session. The first 

was Dr. Dave Cleaves, Acting Deputy Chief for FS Research and Development 

(R&D), who suggested a number of key elements from his position of research 

leadership in Washington, DC. He noted that silviculturally, there is “no sequestration without adaptation”; that the Forest Service mission under changing climate 

will not change, but we’ll have to work in a different context to fulfill the mission; 

and that silviculture is a key role in the future of climate change—specifically, 

option-creating silviculture that expands opportunities available for future managers, not option-reducing silviculture that constrains what future managers can 

do in the field. The second was Mr. Mike DeBonis, Southwest Region Director 

for the Forest Guild in Santa Fe, NM, who reminded us of two key points: that 

the forester in the field is the eyes and ears, or the first respondent, when things 

go awry in the woods, and that collaboration and alliances are critical.

Technical Papers, Day 1

The first day of technical papers provided some perspective on the workshop 

theme using computer models, academic training, and plot data from the Forest Inventory and Analysis (FIA) program of the USDA Forest Service. Nick 

Crookston of the Rocky Mountain Research Station in Moscow, ID, described 

the first version of Climate-FVS, an extension of the widely used Forest Vegetation Simulator (FVS). A key component of this model is based on species-level 

climate profile models that predict the climatic range of species occurrence. He 

included some example outputs of this model that were quite sobering, suggesting 

that climate change will drastically alter at least some of our forest ecosystems 

during this century.

Bob Deal of the Pacific Northwest Research Station in Portland, OR, defined 

terms such as sequestration and ecosystem services; he then suggested that 

there will be opportunities in formulation of policy and in field practice to link 

sequestration, ecosystem services, and climate change—but cautioned that we 

have to get it right.

Keith Moser, with the North Central Research Station in St. Paul, MN, pointed 

out the extraordinary value of FIA data across the Nation. The FIA survey is 
USDA Forest Service Proceedings RMRS-P-61. 2010. 341

Summary of the 2009 National Silviculture Workshop Guldin

designed to describe forest resources at a large scale (States and Regions), but 

increasingly has value at small scales (such as National Forests, and large private 

land holdings), especially when supplemented with state-of-the-art aerial 

imagery.

Linda Nagel from Michigan Technological University, Houghton, MI, described 

a science delivery program for the consideration of climate change in projectlevel decisions that she and her colleagues have developed as part of the Forest 

Service National Advanced Silviculture Program. It was generally agreed in side 

conversations at the Workshop that this program is an outstanding model for other 

regions of the nation as well, not only from the perspective of content but also 

highlighting the cooperation between the academic community and the Forest 

Service to address a timely issue from a practical perspective.

Don Vandendriesche from the National Forest System (NFS) Forest Manage-

ment Service Center in Fort Collins, CO, described the use of FVS to calibrate state 

and transition models (a Rube Goldberg-style modeling approach with algorithms 

for buckets, pipes, and valves) that are being used for landscape assessments to 

quantify long-term trends in forest structure. He posed the question: “Are analysts 

truly able to assist decision makers or just add complexity to already overburdened 

planning staffs?” Indeed, the methods presented support the effort by providing 

an empirical basis to an otherwise subjective process.

Reuben Weisz from the Southwest Region (Region 3), in Albuquerque, NM, 

reported on a pinyon-juniper (Pinus edulis-Juniperus spp.) grassland case study, 

and spoke to a practical tradeoff between restoration and sequestration—an inherent contradiction between managing for open canopy conditions in a restoration 

context versus on-site opportunities for carbon sequestration through accumulation 

of biomass. He cautioned that balancing such contradictory concepts requires a 

long-term, life-cycle-based perspective.

Technical Papers, Day 2

The second day of the Workshop was introduced by a panel addressing climate 

change at the local, regional, and national scales. Ed Gee, National Woody Biomass 

Utilization Team Leader, Forest Management Staff, in the Washington Office, 

brought the Workshop up to speed on national issues. Dave Atkins, Biomass 

Utilization Program Manager in the Northern Region (Region 1) office in Mis-

soula, MT, introduced the concept of “negawatts”—efficiencies that feed power 

into the grid or conserve the drawing of power from the grid. Jay O’Laughlin, 

Professor of Forest Resources at the University of Idaho in Moscow, ID, noted 

that wood energy has its own byproducts—restoring forest health, providing 

renewable energy alternatives, restoring local economies, and yielding a bonus 

in carbon management. Barry Wynsma, a field forester on the Idaho Panhandle 

NF in Bonner’s Ferry, ID, described the silvicultural tactic of “designation by 

description” as the “Leatherman tool” for foresters, who save $50/ac in time spent 

in sale preparation when using that tactic in combination with weight scaling, 

compared to individual tree scaling by volume.

Following the panel, three technical papers were included in the remainder of 

the morning session, and seemed to share a theme of carbon sequestration. Doug 

Basford with the Salmon-Challis NF in Salmon, ID, described a growth model 

for mixed conifer stands in southwestern Idaho without using FVS—and reported 

excellent results. This descriptive analysis shows that analog approaches to data 

analysis sometimes work as well as computer models, and point to the value of 

experience in interpreting data.
342 USDA Forest Service Proceedings RMRS-P-61. 2010.

Guldin Summary of the 2009 National Silviculture Workshop

Matt Busse with the Pacific Southwest Research Station in Redding, CA, reminded the workshop of the “Miracle of Photosynthesis” in which carbon dioxide 

is taken in by growing trees, and stored as cellulose under the familiar equation:

 6 CO2 + 6 H20 => C6H12O6 + 6 O2

Busse then described what he called the lesson of mitigation: that managed 

stands store more carbon than unmanaged stands (in the short term); that managed stands are more stable in sequestration of carbon than unmanaged stands; 

and that as a result, forest management can help mitigate climate change.

Don Bragg with the Southern Research Station in Monticello, AR, noted that 

reality is a special case in the context of computer modeling. He suggested that 

southern pines have a role to play in carbon sequestration, and that data from the 

real world—specifically, from long-term experiments—are useful to validate 

simulation outcomes.

The afternoon session featured five papers continuing along the theme of 

carbon sequestration. Alan Ager with the Pacific Northwest Research Station in 

Prineville, OR, discussed risk analysis at a landscape scale from the perspective 

of catastrophic fires and then extended the idea to climate change. He suggested 

that the impact of climate change can be calculated as a risk analysis, or the 

probability of some event occurring coupled with the changing value of the loss 

associated with that event.

Mike Battaglia with the Rocky Mountain Research Station in Fort Collins, 

CO, discussed the practice of thinning, noting that thinning removes carbon 

from the site—but that removal is preferable to losing all the carbon on the site 

in a catastrophic fire. He further noted that there are substantial amounts of CO2

offsets in areas that need fuel reduction, and that denser and more productive 

stands will provide greater benefits.

Terrie Jain with the Rocky Mountain Research Station in Moscow, ID, pondered 

the origins of the commonly used conversion:

[C]= biomass * 0.5

Her data show that [C] ~42-48 percent, which led her to wonder whether is it worth 

the effort to use actual carbon content rather than the commonly used conversion. 

Her reply suggested that it depends on the analysis, but her demeanor implied 

that of course using the real value of the conversion is appropriate, especially at 

the stand level.

Tara Keyser with the Southern Research Station in Asheville, NC, reported 

results from her dissertation research in Black Hills ponderosa pine (P. ponderosa) 

crown physiognomy. She observed that ponderosa pine there features an unusually 

large lollipop-sized crown that contributes to growth and to flammability; she 

concluded that it was important to properly model canopy physiognomy.

Finally, Chris Keyes with the University of Montana in Missoula, MT, gave 

an update on research and management of the school’s renowned Lubrecht Experimental Forest (EF). He reported on the status and planning for continuing 

several classic long-term studies and for initiating new research, and observed 

that challenges facing the Lubrecht EF included concerns about infrastructural 

support and competing uses other than research.

Field Trip, Day 3

The Workshop always includes a field trip as part of the session, and this 

year the trip headed for the Boise Basin Experimental Forest, on the Idaho City 

Ranger District (RD) of the Boise National Forest. The tour guides were 
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Summary of the 2009 National Silviculture Workshop Guldin

Russ Graham, Terrie Jain, Bob Denner, and Jonathan Sandquist of the Rocky 

Mountain Research Station in Moscow, ID; Tom Martin, Regional Silviculturist with the Intermountain Region in Odgen, UT; Barry Stern of the Boise NF 

Supervisor’s Office; John Sloan of the Lucky Peak Nursery in Boise, ID; Ray 

Eklund, Shannon Hitch, and Allyn Spanfellner of the Idaho City RD; and John 

Roberts with the Idaho State Department of Lands, Idaho City, ID. Thanks to all 

of these folks for an outstanding tour.

Tour stop 1 featured a 40-year-old ponderosa pine plantation established on terraces created by bulldozers (fig. 1). Terracing was a highly controversial practice 

in the 1960s and 1970s. It was designed to remove competition, stabilize erosion, 

and provide a more or less level planting site on steep slopes after clearcutting 

in the northern Rocky Mountains. The practice fell into disrepute in the 1970s, 

largely because of the cost and the critical response from the public about the 

aesthetic and ecological effects. However, after 40 years, this plantation appears 

to be growing at an acceptable rate, and the harshness of the terraces is somewhat 

diminished by time.

The second tour stop demonstrated the effects of fire exclusion in mixed conifer 

stands in the northern Rocky Mountains (fig. 2). Fire exclusion in mixed conifer 

stands in the northern Rocky Mountains results in high densities of seedlings and 

saplings, which can serve as hazardous ladder fuels in fire-adapted ecosystems.

One way to ameliorate that condition is to engage in a restoration treatment conducted using the free selection approach championed by Graham and Jain (2005) 

in these stands. The third tour stop illustrated a recently conducted free selection 

restoration treatment (fig. 3). This stand features the heterogeneous clumped and 

scattered distribution of trees that Graham and Jain seek when implementing the 

prescription. However, the Boise Basin EF also supports a number of more traditional long-term uneven-aged selection reproduction cutting studies in ponderosa 

pine stands, in which the classic reverse J-shaped distribution of stand structure 

are readily apparent (fig. 4).

Figure 1—A terraced 40-year-old ponderosa pine plantation on the Idaho City Ranger 

District, Boise NF, in southern Idaho. (USFS photo by James M. Guldin).
344 USDA Forest Service Proceedings RMRS-P-61. 2010.

Guldin Summary of the 2009 National Silviculture Workshop

Figure 2—Fire exclusion in a mixed conifer stand in the northern Rocky Mountains 

results in high densities of seedlings and saplings, which can serve as hazardous ladder 

fuels in fire-adapted ecosystems, as illustrated in this image from the Boise Basin 

Experimental Forest in southern Idaho. (USFS photo by James M. Guldin).

Figure 3—Implementation of the free selection reproduction cutting method for oldgrowth ponderosa pine restoration on the Boise Basin Experimental Forest in southern 

Idaho. The stand featured a heterogeneous clumped and scattered distribution of 

trees—and ironically, of tour participants as well. (USFS photo by James M. Guldin).
USDA Forest Service Proceedings RMRS-P-61. 2010. 345

Summary of the 2009 National Silviculture Workshop Guldin

Wildfire is of course an important issue in the northern Rocky Mountains 

and Intermountain West, and managers seek information about the ecological 

effects of salvaging trees in burned areas. That led Jain and Graham to develop a 

research study that simulates the effects of wildfire and follows up the simulated 

fire with salvage, in a controlled context where soils and water can be monitored 

(fig. 5). The study was implemented by the Idaho City RD timber and fire staff, 

and workshop participants admired the deft creativity and attention to detail that 

the staff used to meet the researchers’ needs.

Participants then viewed an operational ponderosa pine thinning study on the 

Boise Basin EF in southern Idaho (fig. 6). The size of the slash piles was impressive, not only from the perspective of the cost of conducting follow-up fuels 

treatments in forest operations on difficult terrain, but also as an indication of 

the potential of these stands to produce supplemental biomass associated with 

harvest of merchantable trees.

At the final tour stop, the group observed an application of the shelterwood 

reproduction cutting method on forest lands belonging to the State of Idaho 

(fig. 7). The differences between the shelterwood method as imposed by State 

foresters and the free selection method discussed earlier in the day were apparent, 

and largely as expected. The shelterwood method removed more merchantable 

Figure 4—Classic reverse J-shaped stand structure in an unevenaged reproduction cutting study in ponderosa pine on the Boise 

Basin EF in southern Idaho. (USFS photo by James M. Guldin).
346 USDA Forest Service Proceedings RMRS-P-61. 2010.

Guldin Summary of the 2009 National Silviculture Workshop

Figure 5—Simulated wildfire and salvage research study in northern Rocky Mountain 

mixed conifers, on the Boise Basin EF, southern Idaho. (USFS photo by James M. Guldin).

Figure 6—View through an operational ponderosa pine thinning study on the Boise Basin 

EF in southern Idaho; the slash piles contain unmerchantable material harvested but 

not hauled, and show the potential of these stands to produce supplemental biomass 

associated with harvest of merchantable trees. (USFS photo by James M. Guldin).
USDA Forest Service Proceedings RMRS-P-61. 2010. 347

Summary of the 2009 National Silviculture Workshop Guldin

stems and thus retained lower post-harvest residual basal area than was observed 

in stands marked using the free selection method.

The day was capped with the official banquet routinely held at the Workshop, 

with a long and detailed presentation on the history of the region presented by Susie 

Osgood, Forest Historian on the Boise NF. The highlight of the banquet was the 

presentation of the National Silviculturist of the Year awards. Honorees this year 

from the National Forest System were Joseph F. Myers with the Coeur d’Alene 

Nursery, Idaho Panhandle National Forests, in Coeur d’Alene, ID, Thomas Martin 

and Donald Vandendriesche. Honorees from FS Research and Development 

(R&D) were Marilyn Buford, National Program Leader for Silviculture on the 

R&D staff in Washington, DC., Daniel Dey, Research Forester with the Northern 

Research Station in Columbia, MO., and Henry Mcnab, Research Forester with the 

Southern Research Station in Asheville, NC. The names of these recipients have 

been added to the National Silviculturist of the Year recipient data table (table 1).

Figure 7—Application of the shelterwood reproduction cutting 

method on forest lands of the State of Idaho. As expected, the 

shelterwood method removes more merchantable stems and thus 

retains lower post-harvest residual basal area than was observed 

in the free selection reproduction cutting studies on the Boise 

Basin EF. (USFS photo by James M. Guldin).
348 USDA Forest Service Proceedings RMRS-P-61. 2010.

Guldin Summary of the 2009 National Silviculture Workshop

Technical Papers, Day 4

The final day of the meeting featured papers on the topic of biomass and bioenergy, and a broad spectrum of research was reported. Matt Busse summarized 

20-year results of the Long-Term Site Productivity study plots in California, and 

in doing so illustrated the value of long-term studies, especially studies networked 

across the Nation.

Mark Coleman with the University of Idaho in Moscow, ID, gave an extensive 

review of the University’s research on pyrolysis. He concluded that the science 

of pyrolysis requires thoughtful development and testing before it becomes 

operational. However, issues associated with char and char disposal will be a 

significant hurdle—or perhaps a significant opportunity as a bridge between 

carbon sequestration and biomass utilization.

Greg Jones with the Rocky Mountain Research Station in Missoula, MT, 

addressed concepts of greenhouse gas emissions. He noted that there is less 

greenhouse gas emission if biomass is processed for energy rather than if it is 

burned. He also suggested that that energy used to harvest, collect, and transport 

biomass is tiny compared to the energy lost when biomass is burned.

Tim Swedberg with the Joint Fire Science Program Office in Boise, ID, spoke 

about the development of integrated decision support systems for fuels treatments.

Henry Mcnab discussed a project in the southern Appalachians using shrubs 

to support overstory tree site index predictions. This project was inspired by field 

observations by an experienced professional, and serves as a testament to multiple 

applications of long-term research studies.

Andy Youngblood with the Pacific Northwest Research Station, La Grande, 

OR, examined the silvicultural suitability and practical application of ponderosa 

and lodgepole (Pinus contorta Douglas ex Louden) pines for biofuels. In doing 

Table 1—National Silviculture Workshop Award Recipients, 2001-2009.

YEAR-Region (Location)	 National Forest System	 Research and Development

2001—Region 6 (Hood River, Oregon) Fred Zensen, R6 TM Ray Shearer, Rocky Mt. Res. Stn. 

Jim Jenkinson, Pacific Southwest Res. Stn. 

Bob Curtis, Pacific Northwest Res. Stn.

Nicholas Crookston, Rocky Mt. Res. Stn. 

2003—Region 2 (Granby, Colorado) Brian Ferguson, R-4 Jim Guldin, Southern Res. Stn. 

Mary Frances Maholovich, R-1 Kurt W. Gottschalk, Northeastern Res. Stn. 

Monty Maldonado, WO-FM Paul Johnson, North Central Res. Stn. 

Tom Tibbs, R-8 

2005—Region 5 (Tahoe City, CA) William “Bill” Jones, R-9 Jim Barnett, Southern Res. Stn. 

Glenda L. Scott, R-1 

Tom Landis, S&PF

Jim Russell, R-10

2007—Region 10 (Ketchikan, Alaska) Marlin Johnson, R-3 Terrie Jain, Rocky Mt. Res. Stn. 

Kathy Sleavin, WO-Ft. Collins Steve Shifley, North Central Res. Stn. 

Dave Evans, R-5

Bill McArthur, R-6

2009—Region 4 (Boise, Idaho) Thomas Martin, R1 Henry Mcnab, Southern Res. Stn. 

Donald Vandendriesche, WO-Ft. Collins Marilyn Buford, WO

Joseph F. Myers, R1 Daniel Dey, Northern Res. Stn.
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Summary of the 2009 National Silviculture Workshop Guldin

so, he suggested that such studies illustrate the ongoing value of the agency’s 

network of Experimental Forests and Ranges into the 21st century.

John Shaw with the Rocky Mountain Research Station, Ogden, UT, explained 

current research opportunities and future applications of stand density index re-

search using Forest Survey data, which further points to the value of that national 

data set in scientific inquiry.

Finally, Mike Ryan with the Rocky Mountain Research Station in Fort Collins, 

CO, provided a primer on carbon, and reminded the Workshop attendees that 

forest disturbance does not cause carbon loss unless the area remains unforested.

Discussion

The management actions associated with climate change will change the nature 

of silviculture in the 21st century. The scale of treatment needs threaten to overwhelm agency capacity, with escalating need for even the simplest ameliorative 

treatments, such as thinning in the face of declining numbers of personnel and 

declining budget capacity in the field. With the political overtones of climate 

change among the public and the ecological overtones implicit in decisions to 

promote species migration through silvicultural assistance, gridlock in project 

execution will likely further stifle widespread implementation of management 

activities conducted specifically for climate change.

Even if gridlock melted away, the agency’s capacity to apply silvicultural 

treatments to forest stands nationwide is limited, given the extensive land base 

in roadless areas, in unsuitable condition for access or silvicultural operations, or 

in stands that cannot be economically managed. Some thought should be given to 

merging approaches for mitigating the effects of climate change on the Nation’s 

forests and rangelands by combining stand-level silvicultural treatments with a 

much better understanding than we have today as to where on the landscape those 

treated stands should be located.

There is a need for interim silvicultural recommendations that managers 

can use to practice robust or resilient forestry in the face of changing climatic 

conditions. This can most effectively be accomplished through a collaborative 

partnership between our most creative and experienced practicing silviculturists 

in the field, and our best silviculture researchers and landscape ecologists in FS 

R&D and academia. That need exists at two levels—one level for professionals 

making silvicultural prescriptions consistent with stand dynamics and landscape 

ecology, and the other for field forestry and biological science technicians who 

are most often the personnel in the woods with the paint guns implementing the 

prescribed treatments. The work reported at this meeting by Nagel and others is 

an excellent step, and unfortunately at this time is limited to the federal agency 

foresters working for their silvicultural certification in the National Advanced 

Silviculture Program. There’s a much broader need to provide this continuing 

education across the agency, and there’s a concurrent need to carry this training 

beyond the green line to foresters working for forest industry, forest investment 

organizations, and non-industrial private forest landowners as well.

There seems to be an inherent dichotomy between management for carbon 

sequestration and management for biomass and bioenergy, where an intensive 

degree of utilization almost seems like the antithesis of sequestration. More 

thought should be given to connecting these apparently different ideas, especially 

in the context of policies that try to separate them. There are some silvicultural 

opportunities in this context if a stand could be partitioned into elements ap-

propriate for biomass and elements appropriate for sequestration. Unfortunately, 

offset providers under a program such as the Chicago Climate Exchange typically 
350 USDA Forest Service Proceedings RMRS-P-61. 2010.

Guldin Summary of the 2009 National Silviculture Workshop

operate under a contract that involves all of the forested lands in the ownership. 

These contracts currently don’t allow the owner to enroll some stands but not 

others, nor do they allow owners to include reserved portions of stands being 

harvested. This piecemeal approach is inconsistent with the concept of climate 

change adaptations at the landscape scale.

This all-or-nothing approach limits the ability of a landowner to be rewarded 

for forest practices that sequester carbon using simple tools such as partitioning 

some arbitrary portion of the stand into components appropriate for biomass or 

sequestration. For example, one of the stands on the Boise Basin EF had recently 

been thinned using the free selection method, with a residual stand containing 

about 60 ft

2/ac. If the long-term goal in this stand is to retain 60 ft2/ac indefinitely, 

that portion of the stand is essentially serving the purpose of carbon sequestra-

tion, and the harvested component is serving as a biomass source. Much of the 

timber-available acreage in National Forest System would fall in this category. 

However, the retained trees would not qualify as an offset-providing stand for sup-

port under existing authorities. The concept of partitioning stands into sequestered 

residuals versus harvestable surplus is a practical way to combine sequestration 

and biomass production, but there’s currently no provision for this mutualism in 

current cap and trade markets.

A number of the presentations in the meeting were prepared using FVS, and 

some of the results presented from these models portend dramatic changes in 

forest ecosystems of the Nation. It is important to develop the methodology us-

ing stand-level models such as FVS as well as landscape succession models to 

answer hypothetical questions about the long-term effects of silvicultural practices 

and ecological changes on forest ecosystems. A word of caution from this is that 

models are better interpreted in a relative sense than an absolute sense. One might 

ask whether broader efforts should be made to validate models such as FVS and 

landscape succession models with independent long-term data sets such as those 

available from FIA on forest growth, yield, and developmental dynamics—as 

well as data from long-term studies with repeated measurements over time, such 

as can be found on many of the agency’s Experimental Forests and Ranges.

In a similar vein, program organizers should continue to include presentations 

from real data, especially long-term data, during future workshops. These papers 

often provide a field-based context for the discussions that occur. They also remind 

us that the development of tools such as FVS and landscape succession models 

depend on data collected in the field over time, and on high-quality spatial data 

from our Nation’s forests. It should also remind us of the importance of retaining 

field-going FS R&D scientists who maintain existing long-term studies and install 

new ones. If our R&D capacity creates a new generation of research foresters 

who work only in FVS or other modeling applications rather than in field studies, 

our infrastructural capacity to build and maintain the field studies necessary to 

refine existing models and develop new ones will be compromised.

In this workshop, presenters from FS R&D and academia outnumbered those 

from Regional Offices and National Forests. This may have been due to admin-

istrative constraints on travel during the fiscal year in which the meeting was 

held. One of the great opportunities enabled by the National Silviculture Workshop is to hear the success and failures from the field perspective, and to have 

practicing silviculturists at Ranger Districts, Supervisor’s Offices, and Regional 

Offices interact with R&D researchers and academics. Speaking as one of the 

researchers, this interaction clearly flows both ways, and those of us who inhabit 

the ivory tower learn as much if not more than our colleagues in the field from 

these interactions. Future meeting organizers are encouraged to continue, and 

perhaps even to slightly broaden, the opportunities for professionals from the field 

to give case study presentations during the technical sessions.
USDA Forest Service Proceedings RMRS-P-61. 2010. 351

Summary of the 2009 National Silviculture Workshop Guldin

This was the first workshop where a deliberate effort was made to broaden 

participation so that University research scientists could attend. This was an 

unequivocal success at this meeting. The professors who attended, gave presentations, and participated in the field tour were a welcome addition to the structure 

of the meeting. In a forestry research environment where Universities and R&D 

both are losing capacity through erosion of budgets and staff, opportunities to 

expand participation of University research silviculturists at the workshop 

should continue. This will provide an additional venue for mutual interaction, 

and will continue to help University researchers understand and appreciate the 

silvicultural challenges and opportunities available on Federal forest lands. This 

also gives field silviculturists the opportunity to interact with academics as well 

as R&D researchers, with attendant benefits for the range of expertise they can 

tap when necessary. This concept also applies as the agency tackles the study 

and application of both mitigation and adaptation strategies for climate change.

The next workshop will be held in Region 8, with tentative plans to schedule 

the workshop in Tallahassee, Florida, in May 2011. The workshop will feature 

opportunities to discuss the ecology and silviculture of southern pines during 

the most pleasant time of year to visit the South. Here’s hoping the meeting in 

Tallahassee can be as successful as was this current workshop in Boise.

References

Graham, Russell T.; Jain, Theresa B. 2005. Application of free selection in mixed forests of the 

inland northwestern United States. Forest Ecology and Management. 209: 131-145.

The content of this paper reflects the views of the authors, who are responsible 

for the facts and accuracy of the information presented herein.


Federal Recycling Program Printed on Recycled Paper

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