Forest

Service

United States Department of Agriculture

Northern

Research Station April 2015

General Technical

Report NRS-148

A Regeneration Indicator 

for Forest Inventory and Analysis: 

History, Sampling, Estimation, 

Analytics, and Potential Use in the 

Midwest and Northeast United States
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Manuscript received for publication September 2014

Abstract

The density and composition of regeneration drives future forest character for forests in need of 

replacement. Forested ecosystems face numerous regeneration stressors including invasive plants, 

insects and diseases, herbivory, lack of management, and climate change. As stands that make 

up these systems age, it is imperative to track the viability of forest reproduction. The information 

required for understanding the complexity of forest dynamics during the stand establishment stage 

has been lacking in our Nation’s forest inventory. This poses a particular problem for analysts 

working with the major deciduous forest systems of the Midwest and Northeast United States that 

require detailed information on advance reproduction. To address this need, the Forest Inventory 

and Analysis (FIA) program of the U.S. Forest Service, Northern Research Station (NRS) has 

added protocols for measuring all established tree seedlings and for assessing browse impact. 

This information is compiled using new NRS-FIA forest sampling and analytical methodologies—

the regeneration indicator. The regeneration indicator is described along with examples and 

suggestions to guide research on the difficult question of whether the region’s forests are 

able to regenerate in the face of numerous stressors.

Cover Photos

Top left: Eastern white pine seedlings. Photo by Steven Katovich, U.S. Forest Service, 

Bugwood.org.

Top right: Red spruce and balsam fir seedlings. Photo by William H. McWilliams, U.S. Forest 

Service.

Bottom left: Oak reproduction within a white-tailed deer exclosure. Photo by William H. McWilliams, 

U.S. Forest Service.

Bottom right: Sugar maple seedlings. Photo by William H. McWilliams, U.S. Forest Service.

The use of trade, firm, or corporation names in this publication is for the information and 

convenience of the reader. Such use does not constitute an official endorsement or approval by the 

U.S. Department of Agriculture or the Forest Service of any product or service to the exclusion of 

others that may be suitable. 
A Regeneration Indicator for Forest Inventory 

and Analysis: History, Sampling, Estimation, 

Analytics, and Potential Use in the Midwest 

and Northeast United States

William H. McWilliams, James A. Westfall, Patrick H. Brose, 

Daniel C. Dey, Mark Hatfield, Katherine Johnson, 

Kenneth M. Laustsen, Shawn L. Lehman, Randall S. Morin, 

Mark D. Nelson, Todd E. Ristau, Alejandro A. Royo, Susan L. Stout, 

Thomas Willard, and Christopher W. Woodall

About the Authors

William H. McWilliams is a research forester with 

the Forest Inventory and Analysis program, Northern 

Research Station, U.S. Forest Service, Newtown Square, 

PA 19073.

James A. Westfall is a research forester with 

the Forest Inventory and Analysis program, Northern 

Research Station, U.S. Forest Service, Newtown Square, 

PA 19073.

Patrick H. Brose is a research forester with the 

Sustaining Forests in a Changing Environment project, 

Northern Research Station, U.S. Forest Service, Irvine, 

PA 16329.

Daniel C. Dey is research forester and project leader 

of the Sustainable Management of Central Hardwood 

Ecosystems and Landscapes project, Northern Research 

Station, U.S. Forest Service, Columbia, MO 65211.

Mark Hatfield is a research forester with the Forest 

Inventory and Analysis program, Northern Research 

Station, U.S. Forest Service, St. Paul, MN 55108.

Katherine Johnson is a forester with the Forest 

Inventory and Analysis program, Northern Research 

Station, U.S. Forest Service, Newtown Square, PA 19073.

Kenneth M. Laustsen is a forest biometrician with the 

Maine Forest Service, Augusta, ME 04330.

Shawn L. Lehman is section chief of the Inventory 

& Monitoring Section, Pennsylvania Department of 

Conservation and Natural Resources, Bureau of Forestry, 

Harrisburg, PA 17105.

Randall S. Morin is a research forester with the Forest 

Inventory and Analysis program, Northern Research 

Station, U.S. Forest Service, Newtown Square, PA 19073.

Mark D. Nelson is a research forester with the Forest 

Inventory and Analysis program, Northern Research 

Station, U.S. Forest Service, St. Paul, MN 55108.

Todd E. Ristau is a research forester with the 

Sustaining Forests in a Changing Environment project, 

Northern Research Station, U.S. Forest Service, Irvine, 

PA 16329.

Alejandro A. Royo is a research forester with the 

Sustaining Forests in a Changing Environment project, 

Northern Research Station, U.S. Forest Service, Irvine, 

PA 16329.

Susan L. Stout is a research forester and project 

leader with the Sustaining Forests in a Changing 

Environment project, Northern Research Station, 

U.S. Forest Service, Irvine, PA 16329.

Thomas Willard is a forester with the Forest Inventory 

and Analysis program, Northern Research Station, 

U.S. Forest Service, Newtown Square, PA 19073.

Christopher W. Woodall is a research forester 

with the Forest Inventory and Analysis program, Northern 

Research Station, U.S. Forest Service, St. Paul, MN 

55108.
METRIC EQUIVALENTS

When you know: Multiply by: To find:

Inches (in) 2.54 Centimeters (cm)

Feet (ft) 0.305 Meters (m)

Acres (ac) 0.405 Hectares (ha)
Contents

Research Problem and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

National Core Sample Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Phase 2-plus Regeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Estimation and Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Appendix I: Common and Scientific Names of Tree Species Measured on FIA Plots . . . . 63

Appendix II: Forest Inventory and Analysis Database Documentation . . . . . . . . . . . . . . . 65

Appendix III: Table 5 and Table 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Page intentionally left blank
Yellow-poplar reproduction following a fire. Photo by Rich Widmann, U.S. Forest Service.

Research Problem

and Objectives
	 Research Problem and Objectives

Introduction

The U.S. Forest Service, Northern Research 

Station (NRS) conducts research for a 24-state 

region across the Midwest and Northeast United 

States (Fig. 1). Forest systems of the region face 

numerous regeneration stressors including invasive 

plants, insects and diseases, herbivory, lack of 

management, and climate change. As stands that 

make up these systems mature, it is imperative to 

know the viability of forest reproduction. Although 

artificial methods such as planting or seeding are 

an option in some stands, the region is dominated 

by forest systems that regenerate naturally. In most 

situations, establishing desirable reproduction is the 

key to successful systems of natural regeneration 

that replace existing stands with high canopy species 

that meet manager’s objectives (Nyland 2002, Smith 

1997, Wenger 1984). Information on how well 

Figure 1.—Location of the Midwest and Northeast United States.

forests are regenerating is critically important for 

understanding and projecting future forest character 

that ultimately determines sustainability of forest 

values. 

To address the need for more detailed information on 

regeneration, the NRS Forest Inventory and Analysis 

(FIA) program has added new measurement 

protocols for data collected on a subset of NRSFIA sample plots measured during the growing 

season. The new procedures measure all established 

trees less than 1 inch in diameter at breast height 

(d.b.h.) and include a browse assessment. This 

information improves NRS-FIA’s ability to 

evaluate this important aspect of forest health and 

sustainability. The new procedures are referred to as 

the regeneration indicator.
Research Problem and Objectives 

The main topic of this research is inventory 

and monitoring of regeneration for large forest 

landscapes. As used here, landscape refers to large 

sample domains designed for informing managers 

and policymakers on the quality of regeneration. 

Typical landscapes of interest are states, foresttype groups, wildlife management areas, and other 

geographic areas. The areas cover heterogeneous 

patches of habitat across a collection of land 

features. The landscape approach includes the 

capacity to evaluate broad ecological units, such as 

those developed by Cleland et al. (2007). Ecoregions 

are defined as groupings of landscape mosaics, 

typically defined in nested hierarchies that consider 

climate (moisture and solar radiation), landform, 

and vegetation (Bailey 1996). Other homogenous 

land characteristics, such as southwest facing 

mountainous slopes or low-lying flatlands, can also 

be analyzed. In this sense, the regeneration indicator 

provides a metric for evaluating groups of stands 

that make up landscape-level forest ecosystems. 

Evaluation of landscape-level patterns requires 

some method to accommodate a mix of regeneration 

sources. The Midwest and Northeast span a broad 

array of forest types that regenerate either naturally, 

or in some cases artificially from planting or 

seeding. Natural regeneration originates from seed 

trees, seed stored in the soil, seed blown from 

adjacent stands, and stump or root sprouts (where 

stumps are defined as at least 2 inches in diameter) 

(Dey et al. 2012). This study includes all sources that 

produce seedlings or sprouts that occupy the FIA 

sample plot. The regeneration indicator considers 

only tree seedlings that are at least 2.0 inches tall, 

less than 1.0-inch d.b.h., and have been established 

for at least 1 year.

This report defines the research problem and 

includes a literature review, a brief history of the 

Pennsylvania Regeneration Study (PRS) that is 

a precursor of the approach used here, and an 

overview of Midwest and Northeast forest systems. 

This is followed by sampling and estimation 

techniques for large landscapes. A series of 

computational analytics for quantifying regeneration 

character are also provided along with examples to 

guide analyses using the new data.

Regeneration Research 

Problem Statement 

The research need addressed in this report is the 

lack of measurements, metrics, and analytics 

necessary to quantify the tree seedling component 

for forests at policy-relevant scales for the Midwest 

and Northeast in order to address the reproductive 

capacity following natural or anthropogenic 

processes, such as wind storms or harvesting, that 

make forests eligible for regeneration. The overall 

goal is to inform forest management planning, 

policy, and modeling efforts by quantifying and 

assessing the abundance, composition, structure, and 

quality of forest regeneration. The intent is to apply 

a consistent, transparent, and seamless research 

method across large scales, such as ecological 

provinces. 

Objectives

Specific objectives are:

1. Prepare baseline estimates of numbers of tree 

seedlings by species, length class, and other 

explanatory variables, such as forest-type group.

2. Implement metrics of regeneration adequacy 

using accepted regeneration stocking guidelines.

3. Estimate trends for numbers of seedlings, 

advance regeneration, and related analytics.

4. Explore geospatial relationships of estimates and 

metrics for use in geographic visualizations. 

5. Investigate modeling methods for quantifying 

advance tree regeneration, and evaluate the utility 

of the models for providing input to forest-stand 

dynamics models.

6. Incorporate regeneration estimates into resource 

projections and assessments.
	 Research Problem and Objectives

The data and methods described herein are intended 

to provide a platform for accomplishing these 

objectives. Objectives that require additional 

research are discussed in enough detail to guide 

future work. Examples in this report draw heavily 

on experience in Pennsylvania because sampling 

there has been ongoing since 1989, and the sample 

intensity is twice that of other states. Across all 24 

states in the study region, the new protocols are now 

being used for the third year.
Golden-winged warbler. Photo by Laura Erickson, Cornell University, used with permission.

Background
	 Background

Literature Review

Importance of Young Forests 

The composition of early successional, young forest 

is critical for maintaining quality habitat for plant 

and animal species that depend on them (DeGraaf 

and Yamasaki 2003, Gilbert 2012, Greenberg et 

al. 2011, Lorimer and White 2003, Swanson et al. 

2011). Declines in the area of young forest have 

been documented for North America (Hunter et al. 

2001, Trani et al. 2001) and the northeastern United 

States (Brooks 2003). The FIA sapling-seedling 

stand-size class quantifies the area of young forest, 

with sapling-seedling stands defined as forest land 

with a plurality of stand stocking in trees with a 

d.b.h. less than 5.0 inches. Currently, these young 

forests occupy 29 million acres, or 16 percent of 

total forest land of the Midwest and Northeast 

(Miles 2014). The percentage of young forest 

is lower for mixed oak (9 percent) and northern 

hardwoods (10 percent), and is much lower in some 

states (4 percent in Massachusetts). 

Shifley et al. (2014) list a lack of age class diversity, 

most notably the declining area of young early 

successional forest habitat, as one of five factors 

that will radically alter future forest conditions and 

management needs. The issue is important because 

a lack of advance tree reproduction threatens the 

sustainability of northern forest values (Loftis and 

McGee 1993, McWilliams et al. 1995). Shifley and 

Thompson (2011) demonstrate that practices that 

increase the rate of regeneration and establishment 

of young forests will increase landscape-scale 

structural diversity. It is important to achieve a more 

balanced distribution of forest land by conserving 

the area of late successional forest with the potential 

of becoming old growth forest because this stage 

of development is also rare in extent compared to 

middle to late successional forest.

Forest Ecology, Silvics, Management, 

and Regeneration Requirements

The breadth and depth of literature on the 

basic forest ecology, silvics, management, and 

regeneration requirements for forest types of the 

Midwest and Northeast reflects the challenge of 

adapting regeneration guidelines for use with FIA 

measurements. The history of research for these 

forest types is wide and precludes a full discussion 

so is only briefly mentioned here. A selected 

summary of pertinent literature is shown in 

Table 1. The references listed contain general 

information and details useful for understanding 

fundamental processes and dynamics of the region’s 

forest systems. The literature also provides guidance 

on regeneration stocking requirements. In general, 

there are few publications on specific regeneration 

stocking requirements for established stands, 

but many silviculture and management guides 

are available and regeneration requirements are 

embedded in some of these guides. The references 

in the table include some older, foundational 

sources (e.g., Braun 1950, Frothingham 1915, 

Hough and Forbes 1943, Westveld 1949) and 

more contemporary work for general contextual 

background.

Management of mixed oak, northern hardwoods, 

and aspen/birch forest-type groups is described in 

numerous publications on regeneration requirements. 

(Scientific names for trees are listed in Appendix I.) 

The management of spruce/fir has also been well 

studied, but specific regeneration requirements for 

using with NRS-FIA data are needed. Information 

for management and regeneration for other 

forest types is largely lacking. In some cases, the 

regeneration guidelines are published for a specific 

forest type, such as black ash (Erdman and others 

1987), but guidelines cannot always be generalized 

to other similar types. More information for the 

elm/ash/cottonwood and white/red/jack pine groups 

is needed to develop seedling stocking requirements 

for natural and planted stands. 
Background 

Forest-type group General

Ecology 

and silvics

Management 

guidelines

Regeneration 

requirements

Mixed oak Abrams 2003, 

Oliver 1978

Dey et al. 2010, 

Johnson et al. 2009, 

Hicks 1998

Brose et al. 2008, 

Sander 1977, 

Gingrich 1971

Sander 1972, 

Sander et al. 1976, 

Sander et al. 1984, 

Steiner et al. 2007

Northern hardwoods Nyland 1998, 

Frothingham 

1915

Hornbeck and Leak 

1992, Leak et al. 

1969, Hough and 

Forbes 1943

Leak et al. 2014, 

Marquis 1994, 

Tubbs 1977, 

Eyre 1953

Marquis and 

Bjorkbom 1982, 

Grisez and Peace 

1973

Aspen/birch Adams 1990, 

Alban et al. 1991, 

U.S. Forest 

Service 1969

Safford 1983, 

Steneker 1976

Perala 1977, 

Marquis et al. 1969

Doucet 1989, 

Perala and Alm 1990

Spruce/fir Seymour and 

Hunter 1992, 

Eagar and 

Adams 1992

Frank and Bjorkbom 

1973, Seymour 1992

Wilson et al. 1999

Elm/ash/cottonwood Shifley and 

Brown 1978

Burns and Honkala 

1990

Larsen et al. 2010, 

Myers and Buchman 1984

White/red/jack pine Leak and 

Yamasaki 2013

Stine and Baughman 

1992, Horton and 

Bedell 1960

Gilmore and Palik 2006, 

Lancaster and Leak 1978, 

Seymour and Smith 1987, 

Benzie 1977

All groups Nowacki and 

Abrams 2008, 

Braun 1950

Nyland 2002, Smith 

et al. 1997, Burns 

and Honkala 1990, 

Westveld 1949

Burns and Honkala 

1990

Dey et al. 2012, Burns 

and Honkala 1990

Table 1.—Selected references for the major NRS-FIA forest-type groups

Regeneration Stressors

The success or failure of regeneration in the stand 

initiation and understory re-initiation phases of stand 

development depends on intricate interactions of 

abiotic, biotic, and anthropogenic factors (Oliver and 

Larson 1996). 

Important abiotic factors that affect forest 

regeneration needs for seedling establishment and 

development are:

• Soil chemistry (Cronan and Grigal 1995, Driscoll 

et al. 2001, Drohan and Sharpe 1997, Sharpe and 

Drohan 1999, Zaccherio and Finzi 2007) 

• Available light (Latham et al. 2005)

• Local weather history including drought, wind, 

and ice events (Irland 2000)

• Climate change (Dukes et al. 2009, Glick et al. 

2011, Iverson et al. 2008)
	 Background

Notable biotic factors that affect forest regeneration 

are:

• Native and nonnative competing vegetation, 

including invasive species (Engleman and Nyland 

2006, Kelty and Cretaz 2002, Knight et al. 2009, 

Royo et al. 2010b) 

• Herbivory (Horsley et al. 2003, Royo et al. 

2010a, Tremblay et al. 2007, White 2012) 

• Native and nonnative insects and diseases 

(Latham et al. 2005)

• Available seed sources, seed production, 

and seed predation (Brose et al. 2008)

Anthropogenic stresses resulting from people 

interacting with forest ecosystems and obstructing 

the development of quality forest habitat include:

• Exclusion of fire (Brose et al. 2012, 2013)

• Lack of regeneration management actions 

(Marquis 1994)

• Poor cutting practices (Nyland 1998, 2002)

• Multiple owner objectives (Butler 2008)

• Lack of management planning (Butler 2008)

• High ungulate populations relative to agriculture 

food crops (Marquis and Brenneman 1981)

• Forest fragmentation, e.g., patch size and 

connectivity (Allen et al. 2013)

It is important to note that not all anthropogenic 

factors have a negative influence. Positive 

anthropogenic factors include tree planting, 

competing vegetation control, and other management 

treatments to encourage desirable reproduction and 

limit negative abiotic and biotic factors.

The mix of stressors and the scales at which they 

operate make it particularly difficult to understand 

management challenges and assess consequences. 

Research on cross-relationships and multiple 

interactions is in its infancy. For example, Dukes 

et al. (2009) found that several pests, diseases, and 

invasive species are likely to have more of an impact 

under projected climate change scenarios and that 

relationships depend on incompletely understood 

and unstable interactions.

Understory Vegetation and Herbivory

The pervasive nature of the herbivory factor bears 

further discussion. The reproductive capacity of 

a forest ecosystem depends on the stocking of 

tree reproduction and the composition, structure, 

and abundance of shrubs, vines, and herbaceous 

vegetation that occupy vital growing space as stands 

are being replaced (Jackson and Finley 2011). 

Herbivory is a process that removes palatable 

understory plants and leaves unpalatable plants; 

however, research has shown that high levels of 

herbivory can also cause a decline in unpalatable 

herbaceous plants (Heckel et al. 2010). The overall 

question of ecological impacts of white-tailed deer 

(Odocoileus virginianus) overabundance has been 

thoroughly described by Cote et al. (2004). Deer 

continue to be a persistent force controlling the 

amount and composition of tree regeneration in the 

Midwest and Northeast (Jenkins et al. 2014, Nuttle 

et al. 2014, Shelton et al. 2014).

The impact of herbivory on regeneration quality and 

abundance was recognized in the literature as early 

as 1930 (Frontz 1930) and later by Leopold et al. 

(1947). Much of the concern and research within the 

Midwest and Northeast has centered on white-tailed 

deer where studies have revealed the relationship 

between deer density and regeneration (Marquis 

1974, Marquis and Brenneman 1981, Tilghman 

1989). Kittredge and Ashton (1995), Abrams and 

Johnson (2012), Rooney and Waller (2003), and 

Sage et al. (2003) provide important edification of 

deer impacts. Latham et al. (2005) offer a detailed 

review based on experience in Pennsylvania and 

stress that quality forest regeneration and wildlife 

habitat are codependent. DiTommaso et al. (2014) 

found that impacts of deer herbivory in their study 

in New York were severe and immediate, resulting 

in significantly more bare soil, reduced plant 

biomass, reduced recruitment of woody species, and 

relatively fewer native species. Russell et al. (2001) 

provide a comprehensive review of deer effects for 

north-central and northeastern states. Jenkins et al. 

(2014) found that reducing deer density increased 
Background 

the cover of tree seedlings and recovery of degraded 

vegetation communities.

Research on vegetation specific to the understory 

has addressed the development of new and novel 

formations of species that result from overbrowsing 

combined with invasion by native and nonnative 

plants (Horsley et al. 2003; Kain et al. 2011; 

Royo and Carson 2006; Royo et al. 2010a, 2010b; 

Wiegmann and Waller 2006). Native and nonnative 

invasive plants and communities are discussed by 

Baumer and Runkle (2010), Engleman and Nyland 

(2006), Fei et al. (2010), Huebner et al. (2010), and 

Knight et al. (2009).

Regeneration Sampling

A technical session on regeneration surveys was 

convened as part of the 1983 Society of American 

Foresters national convention. The proceedings 

provided a wealth of information to consider when 

designing regeneration surveys. Stage and Ferguson 

(1984) and Stein (1984b) state that the purposes of 

regeneration surveys are to determine regeneration 

status, demonstrate conservation potential, evaluate 

effectiveness of the regeneration method, identify 

cultural needs, and project future forest yield. Stein 

(1984a) and Dennis (1984) cover fixed-plot and 

distance sampling methods, respectively. Kaltenberg 

(1984) and MaClean (1984) cover other sampling 

issues. Recent work by Ristau and Stout (2014) 

indicates that percentage of cover of seedlings is not 

a reliable surrogate for seedling counts.

Regeneration Assessments

Studies that utilize FIA seedling measurements to 

evaluate regenerative capacity are rare compared 

to work on other forest components, such as 

established trees in the main canopy. In the eastern 

United States, most of the work has been for pine 

forests in the South (McWilliams 1990, McWilliams 

and Birdsey 1984). Landscape-level regeneration 

results are available for Pennsylvania (McCaskill 

et al. 2013; McWilliams et al. 1995, 2007). General 

findings using traditional FIA variables are available 

in state resource reports including those for Virginia 

(Rose 2009) and West Virginia (Widmann et al. 

2012). Shirer and Zimmerman (2010) used FIA 

seedling counts to evaluate regeneration for the state 

of New York. The National Park Service adopted 

a similar approach as part of their inventory and 

monitoring program due to concerns over high deer 

impacts affecting tree regeneration (National Park 

Service 2012, Schmit and Nortrup 2013). Increased 

emphasis on future forests, impacts of changing 

climate, and species migration have strengthened the 

need for novel analytics. A prime example of this 

burgeoning field of inquiry is the work of Woodall 

et al. (2008) that includes an indicator of oak 

regeneration as a driver of species migration.

Pennsylvania Regeneration Study—

A Precursor to the Regeneration 

Indicator

In 1989, FIA began the PRS with the Pennsylvania 

Department of Conservation and Natural Resources, 

Bureau of Forestry to gauge tree regeneration 

adequacy in the State. The study was prompted by 

concerns over the impact of deer and other factors 

on a perceived lack of regeneration across large 

areas (Frye 2006). Measuring advance reproduction 

and competing vegetation was included on all FIA 

sample plots visited during the summer leaf-on 

season. Sampling protocols included an evaluation 

of deer impact on trees, measurement of all 

established seedlings, measurement of root collar 

diameter (RCD) for large-seeded species such as 

oaks, and a complete count of tree seedlings for 

six length classes. Note that while seedling length 

is the term used in the NRS-FIA field manual, for 

simplicity it will be referred to as seedling height in 

the rest of this report.

Results from the PRS provided the first largescale empirical data describing the impoverished 

condition of advance regeneration in the State 

(McWilliams et al. 1995). The PRS continued with 
10 Background

the implementation of the FIA annual inventory 

system in 2000. Since then, the PRS has documented 

regeneration adequacy on an annual basis.

Over the past two decades, the Pennsylvania 

Game Commission has been developing strategies 

for managing deer populations to improve 

habitat quality across Pennsylvania. Wildlife 

Management Units (WMUs) were designed using 

land characteristics such as land use, ownership, 

physiography, habitat type, and road networks that 

are related to game management. The WMUs are 

used to relate deer harvest goals to habitat quality 

and available food sources. The current deer 

management plan uses PRS results as the primary 

indicator of deer habitat quality in appraisals of 

prospective harvest for the WMUs (Rosenberry et al. 

2009).

Forest Systems of the Midwest 

and Northeast United States

The Midwest and Northeast span a wide range of 

physiographic and climatic conditions that support 

a diverse mix of forest systems with differing 

requirements for regeneration stocking. The 80 

forest types found in the region are assembled into 

18 forest-type groups. Detailed types, groups, and 

forest land estimates are shown in Table 2. The most 

widespread forest-type groups are oak/hickory and 

maple/beech/birch, referred to herein as mixed oak 

and northern hardwoods, respectively. 

The distribution of forest land by forest-type group 

for the Midwest and Northeast United States in 2013 

was:

Forest-type group Percentage of forest land (%) 

Mixed oak 36

Northern hardwoods 25

Aspen/birch 9

Spruce/fir 9

Elm/ash/cottonwood 8

White/red/jack pine 5

Other groups 8

100

The mixed oak and northern hardwood groups 

dominated 6 out of every 10 acres of the region’s 

forest land. Aspen/birch, spruce/fir, elm/ash/

cottonwood, and white/red/jack pine combined for 

31 percent of the forest land, with the 12 remaining 

groups contributing 8 percent of the total. FIA foresttype groups each include multiple forest types. The 

aggregation of forest types is needed because of the 

large number of forest types that exist; however, 

grouping often results in the combining of specific 

types that have vastly different silvics. For example, 

the spruce/fir group includes tamarack and northern 

white-cedar, two forest types that have disparate 

phenology. Therefore, guidelines for specific types 

that have established silvicultural guidelines should 

be considered whenever possible in regeneration 

assessments. 

The work described in this report is aimed at 

quantifying observed patterns of tree regeneration 

for forest-type groups. Information about tree 

species can also be used to compare understory 

composition to overstory composition; for example, 

red maple seedlings and saplings under an oak 

canopy.
Background 11

White/red/jack pine

Jack pineb 1,392,696

Red pine 2,476,580

Eastern white pine 3,312,052

Eastern white pine-eastern hemlock 556,489

Eastern hemlock 1,353,054

Spruce/fir

Balsam fir 3,821,455

White spruce 721,585

Red spruce 1,262,378

Red spruce-balsam fir 1,449,779

Black spruce 2,949,959

Tamarack 1,992,786

Northern white-cedar 3,424,659

Loblolly/shortleaf pine

Loblolly pine 400,637

Shortleaf pine 293,576

Virginia pine 296,086

Table Mountain pine 9,485

Pitch pine 650,810

Other eastern softwoods

Eastern redcedar 1,099,929

Pinyon/juniper

Rocky Mountain juniper 182,262

Douglas-fir

Douglas-fir 5,313

Ponderosa pine

Ponderosa pine 1,344,783

Fir/spruce/mountain hemlock

Blue spruce 25,620

Exotic softwoods

Scotch pine 387,390

Other exotic softwoods 34,224

Norway spruce 203,516

Introduced larch 51,455

Other softwoods

Other softwoods 6,047

Oak/pine

Eastern white pine-

 northern red oak-white ash 2,665,277

Eastern redcedar-hardwood 1,153,390

Shortleaf pine-oak 341,537

Virginia pine-southern oak 384,676

Loblolly pine-hardwood 203,908

Other pine-hardwood 1,169,757

Oak/hickory (mixed oak)

Post oak-blackjack oak 1,629,485

Chestnut oak 1,919,177

White oak-red oak-hickory 26,735,839

White oak 4,040,223

Northern red oak 4,185,014

Yellow-poplar-white oak-northern red oak 2,380,573

Sassafras-persimmon 639,906

Sweetgum-yellow-poplar 418,094

Bur oak 1,313,496

Scarlet oak 448,713

Yellow-poplar 974,669

Black walnut 897,117

Black locust 759,347

Southern shrub oak 52,710

Chestnut oak-black oak-scarlet oak 3,690,651

Cherry-white ash-yellow-poplar 3,210,612

Elm-ash-black locust 4,081,849

Red maple-oak 2,536,465

Mixed upland hardwoods 5,688,604

Oak/gum/cypress

Swamp chestnut-cherrybark oak 67,602

Sweetgum-Nuttal oak-willow oak 85,869

Overcup oak-water hickory 91,229

Atlantic white-cedar 35,600

Baldcypress-water tupelo 28,203

Sweetbay-swamp tupelo-red maple 430,415

Elm/ash/cottonwood

Black ash-American elm-red maple 3,219,966

River birch-sycamore 952,934

Cottonwood 983,068

Willow 482,017

Sycamore-pecan-American elm 911,982

Sugarberry-hackberry-elm-green ash 4,360,058

Silver maple-American elm 1,370,845

Red maple-lowland 1,934,415

Cottonwood-willow 324,291

FIA forest-type groups (in bold) 

and forest types Acresa

Table 2.—Area of forest land by NRS-FIA forest-type group and forest type, Midwest and Northeast, 

United States, 2013

FIA forest-type groups (in bold) 

and forest types Acres

(continued on next page)
12 Background

Maple/beech/birch (northern hardwoods)

Sugar maple-beech-yellow birch 30,847,000

Black cherry 1,460,773

Hard maple-basswood 8,658,191

Red maple-upland 4,738,700

Aspen/birch

Aspen 12,003,534

Paper birch 3,027,855

Gray birch 343,268

Balsam poplar 850,477

Pin cherry 297,054

FIA forest-type groups (in bold) 

and forest types Acres

Table 2 (continued).

FIA forest-type groups (in bold) 

and forest types Acres

Other hardwoods

Other hardwoods 2,119,359

Exotic hardwoods

Paulownia 5,979

Other exotic hardwoods 257,860

Nonstocked forest 1,769,685

Total forest land 182,895,106

a Estimates of roughly 78,000 acres are associated with a 

25 percent sampling error at the 68 percent confidence level and 

have low reliability. Estimates for smaller areas are not reliable for 

most uses. 

b See Appendix I for scientific names.
Competitive oak reproduction following a shelterwood cut. Photo by William H. McWilliams, U.S. Forest Service.

National Core 

Sample Design 
14 NATIONAL CORE SAMPLE DESIGN 

The national FIA annual inventory sampling frame 

consists of three phases (Bechtold and Patterson 

2005). The core measurements are collected using 

the same protocols and methods across the Nation 

(U.S. Forest Service 2013b). The development of 

the national design from differing regional designs 

has improved FIA’s ability to track geospatial 

relationships across traditional boundaries and to 

monitor a wider array of forest values and services, 

such as ecological indicators (Gillespie 1999).

Phase 1

In Phase 1, classified remotely sensed imagery 

is used to post-stratify sample plots, allowing for 

stratified estimation, a procedure that can reduce 

uncertainty (variance) for estimates of population 

totals (McRoberts et al. 2006, Scott et al. 2005). 

The stratification layer for NRS-FIA is based on 

thresholds applied to the 2001 National Land Cover 

Database (NLCD) canopy density dataset (Homer 

et al. 2004, 2007; Huang et al. 2001) for all NRSFIA states through 2012 and for roughly half the 

states processed for 2013. The other half of 2013 

NRS-FIA states and future processing is based on 

the 2011 Tree Canopy Cover dataset (Coulston 

et al. 2013) using the same stratum thresholds as 

previously used for the 2001-based stratifications. 

The NLCD canopy density classification of 30-meter 

pixels is used along with sample plot coordinates to 

stratify NRS-FIA sample locations. This procedure 

is done in addition to and independent from pre-field 

aerial photo interpretation, which provides a basis 

for selecting which plots receive field-crew visits. 

Field-visited plots include those that were previously 

forested along with additional plots that now appear 

to be in or very near forest land.

Phase 2

The Phase 2 sample uses a national grid of 

hexagons, each occupying roughly 6,000 acres. 

Figure 2 depicts the hexagonal grid design and the 

sample footprint. A Phase 2 sample plot is located 

randomly within each hexagon. Forested sample 

plots are visited each year using an interpenetrating 

design that yields statistically independent “panels” 

of inventory measurement data. The inventory cycle 

is defined by the number of years (or panels) it 

takes to measure all of the sample plots in a State; 

thus, a 5-year cycle yields five independent panels. 

Although each annual panel can be used individually 

for estimates, the more panels that are used, the 

higher the statistical power of the estimates. Multiple 

inventory cycles provide data for change estimation 

because the same plots are revisited across cycles if 

they were in a forested condition previously and are 

forested at the time of the next visit.

The national FIA program adopted a mapped sample 

design where each sample plot is examined for 

boundaries separating forest-nonforest and withinforest condition changes. Separate within-forest 

conditions are defined by changes in vegetation and 

ownership that occur along distinct boundaries based 

on reserved status, owner group, forest type, stand 

size, stand origin, and stand density (U.S. Forest 

Service 2013b). Condition boundaries are mapped, 

condition proportions are calculated, and separate 

conditions are used to reduce bias and improve 

classification for Phase 2 sample plots. For example, 

the assignment of a single forest type to a sample 

plot that is split between coniferous and broadleaf 

forest could yield a misleading classification. To 

delineate condition boundaries, the FIA field staff 

examines an area of at least 1 acre or at least 120 

feet in width for contrasting conditions that affect 

the plurality of live tree stocking over the total area 

covered by the sample plot. 

The standard Phase 2 inventory provides data and 

information at the tree, condition, and plot level. 

Some condition-level variables, such as stand age, 

are based on field measurements. Other variables, 

including forest type and stocking class, are 

developed by classification algorithms that use the 

individual tree measurements.
NATIONAL CORE SAMPLE DESIGN 15

Figure 2.—(A) Pennsylvania Phase 2 hexagonal sample grid (hexagon locations are approximate), (B) 5-year interpenetrating panel design, (C) example of a forested sample plot randomly located within a hexagon, and (D) Phase 2 

sample plot footprint spanning two condition classes (U.S. Forest Service 2013b).

(A)

(B) (C)

(D)
16 NATIONAL CORE SAMPLE DESIGN 

The sample of forest vegetation consists of a cluster 

of four fixed-radius subplots, with one subplot 

centered on the plot location and three peripheral 

subplots each centered 120 feet from plot center 

(Fig. 2d). Each subplot has a radius of 24.0 feet and 

contains a 6.8-foot radius microplot offset from 

subplot center. In Phase 2, field crews visit each plot, 

subplot, and microplot to measure a suite of 

forest attributes specified in the national core sample 

design. Trees at least 5.0 inches in diameter at 

4.5 feet above ground line (d.b.h.) are measured on 

the subplot. Trees with a d.b.h from 1 inch to 

4.9 inches (saplings) are measured on the microplot. 

Azimuth from microplot center, distance, species, 

d.b.h., status (live, dead, or removed), crown class, 

crown ratio, and total height are measured for each 

sapling. Tree seedlings (less than 1-inch d.b.h.) are 

also counted on the microplot. Having both saplings 

and seedlings measured on the microplot improves 

the spatial integrity of regeneration estimates that 

include saplings.

Phase 3

The Phase 3 sample was collected prior to 2012 and 

encompassed the following ecological indicators: 

vegetation composition/structure (Schulz et al. 

2009), down woody material (Woodall and Monleon 

2007), tree-crown condition (Schomaker et al. 

2007), tree damage (U.S. Forest Service 2013b), 

soils (O’Neill et al. 2005), lichens (Jovan 2008), 

and ozone damage (Smith et al. 2008). In the 

Midwest and Northeast, these measurements were 

collected on 6.25 percent of the Phase 2 samples 

during the summer leaf-on season. Each sample plot 

represented about 96,000 acres. 
Painted trillium. Photo by Thomas G. Barnes, U.S. Fish and Wildlife Service, via http://commons.wikimedia.org/wiki.

Phase 2-plus 

Regeneration 
18 PHASE 2-PLUS REGENERATION 

Beginning with the 2012 field season, Phase 3 

ecological indicators were streamlined to gain cost 

efficiencies based on lessons learned from over a 

decade of collection (U.S. Forest Service 2013a). 

The revised set of ecological indicators is vegetation 

structure, down woody material, tree-crown 

condition, and soils. The tree damage indicator data 

are now collected on Phase 2 plots. The vegetation 

structure indicator consists of a vegetation profile, 

an invasive plants survey, and the regeneration 

survey. The vegetation profile and invasive plant 

survey data are collected on the subplot (Fig. 3). 

The regeneration indicator survey is conducted on 

the microplot that is nested within the subplot 

(Fig. 4). The new approach, referred to as “Phase 

2-plus,” doubles the number of samples that were 

previously collected to 12.5 percent, so each plot 

represents approximately 48,000 acres. The time 

it takes to complete the entire set of Phase 2-plus 

samples depends on the inventory cycle length. 

Seedling Sample History and Use

The seedling tally is one of the oldest FIA 

measurements, primarily because the FIA definition 

of forest land requires at least 10 percent stocking 

of live trees, and in the absence of saplings or 

larger trees, seedlings become the basis for the 

classification. This is important because the number 

of forested samples is used to estimate the area of 

forest land in a State, and FIA only conducts field 

measurements on forested samples.

Historically, the detail of the tree seedling inventory 

has been limited to the use of the data for estimating 

the area of forest land. The national core seedling 

tally has always used minimum height thresholds 

for defining seedlings. For most of FIA’s history, 

seedlings were defined as at least 6.0 inches in 

height for softwoods and at least 12.0 inches in 

height for hardwoods; however, a single 12-inch 

threshold has also been used. This approach ignored 

smaller established seedlings that are an important 

component of reproduction.

The process of implementing the annual inventory 

system resulted in the consolidation of regional 

manuals into a single national manual (U.S. Forest 

Service 2013b), and seedling count was expanded 

to include all seedlings by species. Although this 

was a major improvement, the approach lacked any 

assessment of seedling establishment other than 

the minimum height thresholds, making it difficult 

to evaluate regeneration because the best science 

has shown that seedling height and RCD are better 

indicators of seedling establishment and competitive 

status (Brose et al. 2008). The implementation of 

the regeneration indicator has extended counts to 

include all established seedlings that are at least 2 

inches tall; however, the 6-inch and 12-inch counts 

have been maintained to ensure temporal consistency 

for forest land estimates. The NRS-FIA Phase 2-plus 

regeneration measurements are collected on the 

sample plot, subplot, and the microplot.

Sample Plot Regeneration 

Measurements

Browse Impact

The browse impact code indicates the amount 

of pressure that herbivores are exerting on tree 

seedlings and other understory flora for the area 

surrounding the sample plot. Browse is defined as 

the consumption of tender shoots, twigs, and leaves 

of trees and shrubs used by animals for food (U.S. 

Forest Service 2013a). Browse impact is a function 

of browser population and the amount of available 

food sources within 1 square mile surrounding 

the plot. The approach is distinguished from other 

browse studies that assess impact of browsing on 

understory plants (Latham et al. 2005). The reason 

for assessing impact is that it is difficult to identify 

browse if there are no seedlings or other vegetation 

present to be browsed (Latham et al. 2009). This 
PHASE 2-PLUS REGENERATION 19

Figure 3.—Subplot vegetation profile (U.S. Forest Service 2013a).

(>1.64 feet)

(Nonwoody)

(<1.64 feet)
20 PHASE 2-PLUS REGENERATION 

Figure 4.—Microplot tree seedling sample (U.S. Forest Service 2013a).

White oak (Quercus alba)

5.0 to 9.9 feet in height

3.0 to 4.9 feet in height

1.0 to 2.9 feet in height

6.0 to 11.9 inches in height

2.0 to 5.9 inches in height

Greater than or equal to 10.0 feet in height 

Black birch (Betula lenta) 

Red maple (Acer rubrum) 

6.8 feet

Microplot
PHASE 2-PLUS REGENERATION 21

study examines the existing forest understory 

vegetation and tree seedling abundance to evaluate 

browse impact. The following five codes are used:

1 Very low impact—Plot is inside a wellmaintained exclosure.

2 Low impact—No browsing observed, vigorous 

seedlings present (no exclosure present).

3 Medium impact—Browsing evidence observed 

but not common, seedlings common.

4 High impact—Browsing evidence common.

5 Very high impact—Browsing evidence 

omnipresent or severe browse line.

Subplot Regeneration Measurements

Subplot Site Limitations

Site limitations that are observable over a minimum 

of 30 percent of the forested condition are recorded 

for each subplot. The variable is used to explain 

why some subplots may have no tree seedlings. The 

limitations include rocky surfaces with little or no 

soil and soil that is saturated during the growing 

season.

Microplot Regeneration Measurements

Microplot Site Limitations

Site limitations that are observable over a minimum 

of 30 percent of the forested condition of the 

microplot are also recorded. The limitations are the 

same as the subplot site limitations with the addition 

of a duff layer in excess of 2 inches thick. Thick 

duff layers are not limiting for all species, such as 

hemlock and spruce.

Tree Seedling Counts

Tree seedlings are counted by species and condition 

class on each microplot that is in a forested condition 

within each subplot (U.S. Forest Service 2013a). 

The tally is designed to capture seedlings that are 

in their second growing season and are established. 

Established seedlings have survived for at least a 

year and are at least 2 inches in height. 

The purpose of the NRS-FIA seedling count is to 

provide estimates of the number of seedlings per unit 

area for populations of interest. These evaluations 

require thresholds for the number of seedlings 

needed to satisfy regeneration stocking requirements 

for FIA forest types. Seedling height is an important 

factor for estimating likelihood of survival for 

naturally and artificially established seedlings, 

especially in areas with competing vegetation and 

herbivory (Brose et al. 2008, Oswalt et al. 2006, 

Sander 1972). 

The methodology for tree seedling counts, seedling 

height measurements, and RCD evaluation were 

adapted from long-term research for mixed oak 

(Brose et al. 2008) and northern hardwood (Marquis 

1994) forests of the mid-Atlantic region. Most 

factors that influence tree seedling establishment 

and development are in play in the region, including 

competing vegetation, invasive plants, and 

herbivory. The approach was necessarily simplified 

to cover the broad conditions of the Midwest and 

Northeast; however, the methodology is flexible 

enough to adapt for other forest types and forest-type 

groups.

Tree seedlings are counted by source and height 

class (Fig. 5). Seedling source is coded as seedling, 

stump sprout, or a separate code expressing 

competitiveness of large-seeded species such as 

oak, hickory, black walnut, pecan, and butternut. 

The code for stump sprouts includes stumps at least 

2 inches in diameter. 

In the case of large-seeded species, the level of 

establishment is determined primarily by evaluating 

RCD. A RCD of at least 0.25 inch defines a stem 

that is established. Stems at least 3.0 feet in height 

are also included as established. Seedlings with an 

RCD of at least 0.75 inch and a d.b.h. less than 

1.0 inch are coded as competitive. The competitive 

code is used in the analysis of regeneration stocking 
22 PHASE 2-PLUS REGENERATION Figure 5.—Tree seedling source count guide (U.S. Forest Service 2013a).
PHASE 2-PLUS REGENERATION 23

for large-seeded species (see Adequacy of Advance 

Tree Seedling Regeneration on page 36). 

Public Database

All regeneration indicator data are stored both 

internally and in the FIA public database that is 

available for download (http://apps.fs.fed.us/fiadbdownloads/datamart.html). The structure follows the 

three levels of data collection: sample plot, subplot, 

and microplot. The Oracle® tables, structure, and 

variable definitions are described in Appendix II.

Tree-of-heaven seedlings. Photo by Chris Evans, Illinois Wildlife Action Plan, via http://www.forestryimages.org.

Estimation and Analytics 
26 ESTIMATION AND ANALYTICS 

Population Estimates

Estimates of the number of seedlings provide critical 

input for the study of regeneration, wildlife habitat, 

carbon accounting, species migration, future forest 

dynamics, and other topics. Scott et al. (2005) 

describe the details of FIA’s post-stratified sampling 

design and the associated statistical procedures for 

calculating population estimates. Briefly, estimates 

are made by summing the numbers of seedlings in 

the domain of interest for each plot and computing 

the mean and variance in each stratum. These 

statistics are then combined across all strata using 

stratum weights derived from maps of the population 

area to obtain estimates and sampling errors. 

Estimates of seedling frequency, volume, weight 

(biomass), or carbon can be made for unit area and 

population totals.

The differentiation of seedlings by species and 

height class provides numerous opportunities to 

develop core products that address composition 

and structure of the seedling component (Objective 

1). Sampling errors for seedling estimates will be 

relatively high until the measurement of all the 

baseline Phase 2-plus sample plots is completed. 

To illustrate, consider estimates of the number of 

seedlings by species for Wisconsin using the 2012 

and 2013 inventory data (App. III, Table 5). The 

estimate of the total number of seedlings is 

92 billion stems with a sampling error of 11 percent 

at the 68 percent confidence level. The estimate 

for the most abundant species, red maple, is 

27 billion subject to a 25 percent sampling error. The 

estimates for some species and height classes are 

in excess of 50 percent, meaning the results should 

be used with caution. When all inventory panels are 

completed, the estimates for the total and for red 

maple are projected to be 7 percent and 16 percent, 

respectively. Once the baseline sample is complete, 

more detailed products, such as the analytics 

mentioned in this report, can be completed.

Core Reporting Product Examples

Core products are needed to address common 

issues, and having information available for the 

regeneration indicator provides the opportunity to 

analyze results across states, ecoregions, and other 

areas. This section presents a basic set of reporting 

products that can be used prior to the completion of 

the baseline set of samples. Examples are provided 

for the first 2 years of data collected for Wisconsin 

(2012 and 2013). Wisconsin was chosen as a case 

study because it has a relatively large amount 

of forest land, and it provides the opportunity to 

develop core reporting products in the early stages of 

data collection.

Estimates of the number of seedlings and number 

of seedlings per acre on forest land by species are 

fundamental tabular products (App. III, Tables 5 

and 6). Expanding the reporting template to include 

graphical summaries of important variables and 

geospatial visualizations of the results provide 

interim analytical products. Graphics showing the 

location of samples and frequency distributions for 

browse impact, species, and height class offer basic 

information describing the State’s tree seedling 

inventory (Figs. 6 through 12). Profiles of the 

number of seedlings per acre by species and height 

class highlight composition and structure of the 

seedling component. The six seedling height classes 

were combined into three groups to take advantage 

of the larger number of samples and reduce the 

size of the confidence intervals for species-level 

estimates. Profiles of per acre estimates for saplings 

(Fig. 13) and dominant/codominant adult trees 

(Fig. 14) provide a comparison of stand structural 

components and indicate differences between 

understory and overstory composition. The estimates 

for saplings and adult trees are based on five 

inventory panels collected from 2009-2013. 
ESTIMATION AND ANALYTICS 27

Figure 6.—Distribution of forested Phase 2-plus samples on forest land by land-use class, Wisconsin, 2012-2013.

Figure 7.—Percentage of forested Phase 2-plus samples on forest land by browse impact class, Wisconsin, 2012-2013. 

Sampling errors are shown for the 68 percent confidence level.

0 10 20 30 40 50 60

Low

Medium

High

Very High

Percent

Browse Impact
28 ESTIMATION AND ANALYTICS 

Figure 8.—Distribution of forested Phase 2-plus samples on forest land by browse impact class, Wisconsin, 2012-2013

Low

Medium

High

Very High

Nonforest

(All plot locations are approximate)

Browse Impact
ESTIMATION AND ANALYTICS 29

Figure 9.—Total number of seedlings (A) and seedlings per acre (B) on forest land by species for species with at least 

1 percent of the total, Wisconsin, 2012-2013. Sampling errors are shown for the 68 percent confidence level.

0 20,000,000,000 40,000,000,000

northern white-cedar

black spruce

American basswood

silver maple

paper birch

mountain maple

bitternut hickory

American elm

green ash

northern pin oak

eastern white pine

eastern hophornbeam

northern red oak

white oak

chokecherry

serviceberry spp.

white ash

balsam fir

quaking aspen

American hornbeam

black ash

black cherry

sugar maple

red maple

0 500 1000 1500 2000

northern white-cedar

black spruce

American basswood

silver maple

paper birch

mountain maple

bitternut hickory

American elm

green ash

northern pin oak

eastern white pine

eastern hophornbeam

northern red oak

white oak

chokecherry

serviceberryspp.

white ash

balsam fir

quaking aspen

American hornbeam

black ash

black cherry

sugar maple

red maple

(A) (B)

Number of Seedlings per Acre

0 20,000,000,000 40,000,000,000

10.0 inches and greater

5.0 feet to 9.9 feet

3.0 feet to 4.9 feet

1.0 feet to 2.9 feet

6.0 inchesto 11.9 inches

2.0 inchesto 5.9 inches

Height Class

Figure 10.—Number of seedlings on forest land by height class, Wisconsin, 2012-2013. Sampling errors are shown for the 

68 percent confidence level.

Number of Seedlings (millions)

Number of Seedlings (millions)

0 20,000 40,000

2.0 to 5.9 inches

6.0 to 11.9 inches

1.0 to 2.9 feet

3.0 to 4.9 feet

5.0 to 9.9 feet

≥10 feet

0 20,000 40,000 0 500 1,000 1,500 2,000
30 ESTIMATION AND ANALYTICS 

Figure 11.—Distribution of forested Phase 2-plus samples on forest land by number of seedlings per acre, Wisconsin, 

2012-2013.

Nonforest
ESTIMATION AND ANALYTICS 31

Figure 12.—Number of seedlings per acre on forest land by species and height class for species with at least 1 percent of 

the total, Wisconsin, 2012-2013. Sampling errors are shown for the 68 percent confidence level.

0 500 1,0001,5002,000

mountain maple

American elm

green ash

eastern hophornbeam

bitternut hickory

quaking aspen

northern pin oak

silver maple

chokecherry

white ash

eastern white pine

white oak

northern red oak

serviceberry spp.

black ash

balsam fir

American hornbeam

black cherry

sugar maple

red maple

0 200 400 600

American basswood

mountain maple

paper birch

black spruce

American elm

eastern white pine

northern pin oak

northern red oak

white oak

green ash

eastern hophornbeam

chokecherry

balsam fir

serviceberry spp.

quaking aspen

white ash

American hornbeam

black cherry

black ash

red maple

sugar maple

0 50 100 150

green ash

mountain maple

white oak

northern pin oak

chokecherry

black willow

serviceberry spp.

eastern white pine

paper birch

white ash

eastern hophornbeam

balsam fir

American hornbeam

black cherry

black ash

red maple

quaking aspen

sugar maple

Number of Seedlings per Acre Number of Seedlings per Acre

Number of Seedlings per Acre

2.0 inches to 11.9 inches 1.0 foot to 4.9 feet

5.0 feet and larger
32 ESTIMATION AND ANALYTICS 

Number of Saplings per Acre

0 20 40 60 80

serviceberry spp.

jack pine

boxelder

northern red oak

green ash

American basswood

tamarack (native)

northern pin oak

white ash

paper birch

American elm

eastern white pine

American hornbeam

bigtooth aspen

black cherry

black spruce

black ash

eastern hophornbeam

sugar maple

red maple

balsam fir

quaking aspen

0 5 10 15 20

white ash

boxelder

northern pin oak

northern red oak

yellowbirch

green ash

red pine

American basswood

jack pine

tamarack (native)

northern white-cedar

paper birch

black cherry

eastern hophornbeam

eastern white pine

American elm

bigtooth aspen

black spruce

black ash

sugar maple

red maple

balsam fir

quaking aspen

Number of Saplings per Acre

1.0 inches to 2.9 inches d.b.h. 3.0 inches to 4.9 inches d.b.h.

Figure 13.—Number of live saplings per acre on forest land by species and diameter class for species with at least 

1 percent of the total, Wisconsin, 2009-2013. Sampling errors are shown for the 68 percent confidence level.

Seedling data used with condition-level variables 

and classifications provide considerable 

opportunities to apply analytics for specific 

domains of interest if associated sample errors are 

acceptable. These include examining predefined 

geographic areas such as states, ecoregions, or 

wildlife management areas, or using sample-based 

classifications for a particular species or structural 

type.

Potential Use

According to Leak (2007), one purpose of 

regeneration surveys is to estimate future tree 

species composition based on seedlings and 

saplings. The stand re-initiation and initiation stages 

of development ultimately impact adult forest 

composition, structure, and function (Oliver 1980), 

making the choice of an approach for evaluating 
ESTIMATION AND ANALYTICS 33

Number of Stems per Acre

0 5 10 15

bitternut hickory

shagbark hickory

eastern hemlock

bur oak

yellowbirch

white spruce

black cherry

American elm

black oak

white ash

white oak

green ash

northern pin oak

black spruce

jack pine

balsam fir

northern white-cedar

tamarack (native)

paper birch

American basswood

bigtooth aspen

eastern white pine

northern red oak

black ash

sugar maple

red pine

red maple

quaking aspen

Figure 14.—Number of dominant and codominant 

growing-stock stems per acre at least 5 inches in diameter 

on forest land by species and diameter class for species 

with at least 1 percent of the total, Wisconsin, 2009-2013. 

Sampling errors are shown for the 68 percent confidence 

level.

regeneration critical for holistic assessments and 

models to predict future forest conditions. The 

regeneration indicator data offer considerable 

flexibility for designing analytical constructs for 

the many forest systems of the 24-state region. The 

NRS-FIA species and other explanatory variables, 

including forest type and stocking level, provide 

added detail. This section provides some examples 

of approaches that have been used and offers 

suggestions to guide further work.

Regeneration Composition

The FIA definition of a tree includes all perennial 

woody species that typically produce a single stem 

and grow to at least 15 feet in height. Because this 

definition includes such a wide array of phenotypes, 

simply assuming that a tree seedling of any species 

is acceptable for adequate regeneration can produce 

uninformed results. 

The FIA species provide the opportunity to 

characterize regeneration for a wide range of 

research objectives. Some common analytical 

questions of interest include evaluating FIA species 

that are: (1) native, (2) endemic, (3) nonnative, 

(4) invasive, (5) preferred for timber production, 

(6) able to achieve high canopy position, (7) match 

overstory species, and (8) combinations of these 

and other characteristics. Matching regeneration 

to the overstory is not always the preferred 

condition, particularly if the overstory is made up 

of undesirable species.

To illustrate the use of species to quantify 

regeneration stocking, consider a stand with a 

regeneration component containing sugar maple, 

black cherry, tree-of-heaven, and striped maple 

where the objective is to evaluate the adequacy of 

regeneration for future timber production. Both 

sugar maple and black cherry have the potential to 

achieve high canopy positions and are preferred 

for timber. Tree-of-heaven is a nonnative invasive 

plant that is not a preferred timber species. Striped 

maple is a low canopy species and is not desirable 

for timber, but it may dominate regeneration 

stocking and serve as a competitor to more desirable 

species (Nyland et al. 2006). In this case, the sugar 

maple and black cherry seedlings could be used for 

quantifying regeneration, and the striped maple and 

tree-of-heaven could be included as a measure of 

competing vegetation.
34 ESTIMATION AND ANALYTICS 

The approach used to quantify regeneration in the 

PRS further illustrates a practical application of 

using FIA species. The objective of the PRS has 

been to include all species with the ability to achieve 

high canopy and that are native to Pennsylvania. 

Potential canopy position (PCP) was assigned to 

each species using existing literature, primarily 

Burns and Honkala (1990). The PRS only considers 

species with at least 2 percent of the State’s total 

biomass and that are capable of reaching high 

canopy. The purpose of this definition is to capture 

common native species that could replace existing 

high canopy forests with a future forest of similar 

character. The approach and results of this appraisal 

are described in the section entitled Adequacy of 

Advance Tree Seedling Regeneration on page 36.

Another approach is to use PCP as an indicator of 

quality. Consider a partition of species using low, 

middle, and high canopy potential, with a maximum 

height at maturity of at least 80 feet for high canopy 

species, 60-80 feet for the mid canopy species, 

and less than 60 feet for low canopy species. It 

should be noted that FIA species groups interject 

the unavoidable possibility that some low canopy 

species are included as high canopy species. For 

example, mountain ash may be coded as Fraxinus

spp. The generic genus code is only for sample trees 

where species is not distinguishable. Also, assessing 

PCP requires some judgment for species with a 

wide range of height potential that depend on site 

productivity, stand history, and other factors. For 

example, the maximum height for Atlantic whitecedar in the northern United States is 50 to 60 feet; 

however, the species typically occurs in pure stands 

where it is the canopy dominant.

Other species-specific issues can be addressed. 

Exceptions can be made to include species that have 

special value beyond their contribution to existing 

biomass or PCP. A good example is black ash, a 

species used for traditional Native American baskets 

that is a relatively rare species that usually does not 

attain a high canopy position throughout much of its 

range. Because of its special value for nontraditional 

forest products and high threat of mortality resulting 

from the emerald ash borer (Agrilus planipennis), 

black ash can be included even though it may not 

satisfy other objectives. Also, some high canopy 

species may be encountered outside of their natural 

range where they do not achieve typical form and 

height. An example of this is an abandoned Douglasfir Christmas tree plantation that has reverted to 

forest. 

Phase 2 Explanatory Variables

Other variables from the Phase 2 data can be used to 

refine regeneration analytics. For example, relative 

stocking, forest-type group, and site productivity 

class are important (McWilliams et al. 1995). 

Condition-level relative stocking percent serves as a 

proxy for available light. Stocking percent is similar 

to basal area per acre but takes into consideration 

site occupancy differences between species (Stout 

and Nyland 1986). For example, black cherry has 

the potential to occupy a site with more stems 

per unit area than sugar maple. This means that 

individual sugar maple trees are assigned a slightly 

higher stocking percent than black cherry trees of 

the same d.b.h. because fewer are needed to achieve 

full stocking. Stocking percent values are summed 

across tree species sampled on subplots to yield 

per acre stocking estimates. Summing basal area 

per unit area would yield identical values for the 

two species in this example. Thus, the advantage of 

relative stocking is that it accounts for variability 

in crowding tolerance by species in stands with the 

same average diameter.

Stocking percent is assigned to each sample tree 

using species-level equations (e.g., Leak et al. 

1969, Roach and Gingrich 1962, Stout and Nyland 

1986). Stocking values are summed for each sample 

condition and then grouped into classes. For a full 

explanation of the FIA stocking algorithm, see Arner 

et al. (2001).
ESTIMATION AND ANALYTICS 35

The FIA stocking classes roughly correspond to 

silvicultural stocking guides such as those developed 

by Gingrich (1967), Leak (1981), and Wilson et al. 

(1999). Stocking classes are: 0 to 34 percent (poor), 

35 to 59 percent (moderate), 60 to 99 percent (full), 

and 100 and greater (overstocked). The moderate, 

full, and overstocked classes correspond to C, B, 

and A levels described in most stocking guides; that 

is, overstocked represents stands above the A level 

and full stocking represents the area between A and 

B levels where stocking is considered acceptable 

(Ernst and Knapp 1985). Moderate stocking 

corresponds to the area between the B and C levels, 

a zone where B level stocking is expected to be 

reached in 10 years. Below the C level, a stand 

is considered understocked for traditional timber 

management, but may be adequately stocked for a 

savanna or woodland management objective.

Stands in the moderate range of stocking are 

generally assumed to have enough light for 

establishment and development of juvenile trees 

across a range of shade tolerances. Very often in 

areas with regeneration challenges, poorly stocked 

stands are the result of a failure due to lack of 

management planning to ensure a future stand 

to replace harvested trees and control competing 

vegetation. In the absence of competing vegetation, 

stands below the C level should eventually 

regenerate with trees, although often with unplanned 

undesirable species. These conditions can persist 

for long periods of time. Stands in the full and 

overstocked classes have little available light for 

regeneration establishment and development due to 

dense shade conditions, such as occurs in eastern 

hemlock stands. 

It is recommended that analyses of advance 

reproduction adequacy focus on stocking conditions 

where tree seedlings should be able to establish and 

develop. For example, the PRS uses a range of 40 to 

75 percent stocked that is specific to requirements 

for the mid-Atlantic region. Seedling counts are 

conducted across all stocking levels to allow 

evaluation of any range that meets individual study 

objectives.

Forest-type group is a broad classification of the 

composition of forested conditions. Segmenting 

regeneration analyses by forest-type group facilitates 

the application of relevant management guides. So 

far, guides for northern hardwood and mixed-oak 

forests of the mid-Atlantic States have been used to 

develop minimum thresholds for seedling stocking 

found on NRS-FIA microplots (Brose et al. 2008, 

Marquis 1994). The thresholds gauge the ability of 

existing advance regeneration to replace overstory 

trees when they are removed. These thresholds 

have necessarily been simplified to reflect the wide 

number of types found in the mid-Atlantic region. 

For example, regeneration stocking thresholds are 

not specific to aspen stands, but rather aspen types 

are treated the same as the northern hardwood group. 

Future work is needed to widen the geographic 

scope to include thresholds for other regions, 

including the Central States, Lake States, and New 

England (Objective 2).

FIA site productivity classification provides a means 

for including species-level adaptation to edaphic 

conditions. For example, oak species generally 

have a competitive advantage on steep, dry, rocky 

sites in eastern mountain biomes. Site productivity 

class is estimated by field measurement of site 

index. Productivity codes are assigned based on 

existing site-curve equations and a derivation of the 

culmination of mean annual increment (MAI) for 

merchantable trees in fully stocked natural stands 

(Scott and Voorhis 1986). The codes are grouped 

into classes based on net growth per acre expressed 

in cubic feet.

The PRS included an approach using site 

productivity class to address the competitiveness 

of advance reproduction based on the premise that 

fewer large-seeded stems are required for adequate 
36 ESTIMATION AND ANALYTICS 

stocking on poorer sites. Stems with a RCD of at 

least 0.75 inch and a d.b.h. less than 1.0 inch were 

classified as competitive seedlings. Minimum 

thresholds were developed using expert opinion to 

classify microplots as adequately stocked based on 

the number of competitive seedlings encountered. 

The thresholds range from one to three:

Minimum number

Site productivity class of competitive stems

and description (ft3/acre) for adequate stocking

1 225+ 3

2 165-224 3

3 120-164 3

4 85-119 2

5 50-84 2

6 20-49 1

7 0-19 1

This approach is illustrated in the Examples section 

on page 37.

Presence of Dominant Trees

For some applications, a dominant adult tree on 

or in close proximity to the microplot can be used 

for evaluating stands in need of replacement. A 

dominant adult is defined as a live tree with at 

least a 5.0-inch d.b.h. To illustrate, a dominant 

American beech with a dense crown may preclude 

the need for the microplot to contain adequate 

stocking of seedlings. The presence or absence of a 

dominant tree can be determined from the distance 

and azimuth recorded for each tree sampled on the 

subplot, and tree attributes such as crown class can 

be used to adjust the metrics. Variables attached to 

adult trees can also be used to make adjustments to 

the analysis. For example, adult trees with an overtopped crown class code should not be counted.

Adequacy of Advance Tree Seedling Regeneration

It is well documented that advance regeneration is 

the best indicator of future composition in stands 

undergoing replacement. The challenge of assessing 

regeneration adequacy for large landscapes is to 

develop a credible methodology for evaluating 

the likelihood that a seedling will survive and 

attain a high canopy position. Here, a method for 

evaluating the adequacy of advance tree regeneration 

is presented based on experience with forest 

systems of Pennsylvania where the measurements 

and assumptions have been documented by the 

PRS. The large number of research questions the 

regeneration indicator can address precludes full 

coverage in this report, but the methods described 

provide a foundation on which to build a more 

widely applicable approach. The breadth of 

prospective topics is addressed through examples 

and suggestions to guide future work. Development 

of variants for regeneration requirements is a clear 

research need and will improve the usefulness of 

the regeneration indicator. The purpose here is 

to document a template that is flexible enough to 

accommodate extension to the wider range of forest 

systems of the Midwest and Northeast. 

Seedling height and RCD were chosen as the 

primary tree regeneration indicators based on 

the assumption that competitiveness of advance 

seedlings along with species can be used effectively 

to quantify whether regeneration is adequate or not 

for specific management objectives. It is understood 

that assuming RCD thresholds are similar across 

diverse local factors, such as ecoregion, type of 

competing vegetation, amount of invasive species, 

herbivory pressure, or management level, is a 

limitation. The competitive code for large-seeded 

species can be ignored if this limitation obstructs a 

useful analysis. 

Regeneration stocking may be classified as 

adequate for a particular objective, but the species 

composition of the understory and overstory 

components may not necessarily match. For 

example, stands classified as mixed oak often 

have regeneration composed of other species (see 

Examples section on page 37). The new NRS-FIA 

measurements facilitate comparison of understory 
ESTIMATION AND ANALYTICS 37

and overstory composition to evaluate success or 

failure of management objectives. For example, 

if the NRS-FIA forest type is classified as eastern 

white pine-eastern hemlock, one approach might 

be to limit the assessment to white pine and eastern 

hemlock seedlings. Another example is to consider 

the actual existing canopy dominants and use them 

to compare with regeneration composition as an 

indicator of overstory replacement. The number of 

permutations leaves the analyst with many options; 

hence, the use of broad groupings in the strategy 

described in this report.

Weighting factors were developed for the six 

seedling-height classes to account for the positive 

relationship between seedling height and likelihood 

of long-term survival. The weighting factors shown 

below are based on an adaptation of existing science 

for all forest types of Pennsylvania where herbivory 

stress is common (McWilliams et al. 1995). Each 

seedling is assigned a stem weight according to 

height (see Equation 1):

Height class Stem weight 

2.0 to 5.9 inches 1

6.0 to 11.9 inches 1

1.0 foot to 2.9 feet 2

3.0 to 4.9 feet 20

5.0 to 9.9 feet 50

>10.0 feet 50 

The formula for summing the weighted number of 

stems per microplot (WNS) is:

(1)

where W is the stem weight and NS is the number of 

seedlings tallied by height class (i).

WNS is compared to thresholds developed for 

the PRS expressed as the minimum number of 

seedlings needed for adequate stocking by browse 

level (see Incorporating Browse Impact on page 

38). The thresholds were developed by consensus of 

silvicultural experts as part of the PRS study design. 

The minimum thresholds are:

Browse impact Minimum threshold

Very low 15

Low 30

Medium 50

High 100

Very High 200

The broad assumption that a single weighting 

scheme is applicable across all species groups may 

not be appropriate for all forest types found in the 

Midwest and Northeast. These thresholds should 

be adjusted if other weighing schemes are more 

applicable or herbivory is not an issue.

Examples for Mixed-oak 

and Northern Hardwood Forests

Two examples using microplot seedling tallies 

demonstrate the assessment of regeneration 

adequacy using the WNS framework. The first 

example is an NRS-FIA sample located in a mixedoak forest dominated by white oak and other species. 

The sample plot is split by a boundary between two 

distinct forest conditions. All but one microplot is 

located in the condition dominated by white oak. 

Microplot 3 is in a reverting field with abundant 

white pine and other species. The overstory of the 

oak forest condition is 54 percent stocked following 

selection cutting of white oak. The white pine 

condition is 48 percent stocked. Site productivity 

class is from 20-49 cubic feet per acre per year at the 

culmination of MAI. Browse impact is medium. The 

seedling component is made up of white oak, black 

birch, red maple, black cherry, sassafras, white pine, 

and tree-of-heaven. Although sparse, oak stump 
38 ESTIMATION AND ANALYTICS 

sprouts and seedlings are present. Table 3 contains a 

tally of seedlings for this example. The computation 

of the weighted number of stems (Equation 1) for 

each microplot is: 

Microplot 1: WNS = [(12+3)*1] + [(2+4)*1] + 

 [(1+1+1)*2] = 27

Microplot 2: WNS = [(10+22)*1] + [(5+1)*1] + 

 [(1)*2] + [(1)*20] = 60

Microplot 3: WNS = [(19+9+11+5)*1] + 

 [(24+6+5+1)*1] + [(1+4)*2] + [(1)*20] = 110

Microplot 4: WNS = [(8+23)*1] + [(11+9+5)*1] + 

 [(2+2+1)*2] + [(1)*50] = 116

Incorporating Browse Impact

The impact of ungulate browsing on forest 

regeneration has been a growing concern in 

many states in the NRS region. While browsing 

is a natural forest process, too much browsing 

can eliminate palatable tree seedlings like oaks 

that are often a preferred species for the next 

generation forest. Most of the concern has been 

over the impact of white-tailed deer, but browsing 

by moose and other mammals can also be a 

concern. 

Incorporating the impact of browsing into studies 

of regeneration quality and abundance is difficult 

because deer density estimates are nonexistent 

or lack the spatial detail or statistical confidence 

needed for developing useful analytics. Also, 

extrapolating data measured from within deer 

exclosures can lead to erroneous results because 

deer move freely through landscapes with 

varying degrees of disturbance and alternate 

food sources (Horsley et al. 2003). For areas 

under browse pressure, this means it inhibits the 

ability to evaluate potential success using FIA

measurements. To overcome these limitations, 

a browse impact code was included in the 

Phase 2-plus suite of regeneration indicator 

measurements.

The effectiveness of traditional approaches for 

evaluating browse of understory plants is limited 

if there are no palatable plants to evaluate. 

The browse code was designed to be effective 

across the wide diversity of conditions found on 

NRS-FIA sample plots. The code introduces the 

ability to account for browsing in analyses using 

site-specific information not available from other 

sources. This report provides a means of using 

the code along with regeneration stocking guides 

expressing the number of seedlings required for 

success. Typically, the higher the browse impact, 

the greater the number of seedlings required. 

If the impact of browsing is not considered 

important in the analysis of tree seedlings and 

other understory vegetation, the browse code can 

be ignored.

White-tailed deer. Photo by Scott Bauer, USDA

Agricultural Research Service, via http://commons.

wikimedia.org/wiki.

The results indicate that the weighted number of 

stems for microplots 2, 3, and 4 meet the WNS 

minimum threshold of 50 stems for adequate 

regeneration; however, microplot 1 contains a 

competitive white oak seedling with a RCD larger 

than 0.75 inch. For site productivity class 6, only 

one competitive seedling is required, so microplot 

1 is also considered adequately stocked with tree 

seedlings. Tree-of-heaven was excluded from the 

tally because it is an undesirable species.
ESTIMATION AND ANALYTICS 39

Species

Subplot number White Black Red Black White Tree-ofand height class oak birch maple cherry Sassafras pine heaven Total

Microplot 1:

2.0 inches to 5.9 inches 0 12 3 0 0 0 0 15

6.0 inches to 11.9 inches 0 2 4 0 0 0 0 6

1.0 foot to 2.9 feet 1 0 1 1 0 0 0 3

3.0 feet to 4.9 feet 0 0 0 0 0 0 0 0

5.0 feet to 9.9 feet 0 0 0 0 0 0 0 0

Greater than or equal to 10.0 feet 0 0 0 0 0 0 0 0

Total 1 14 8 1 0 0 0 24

Competitive 1 0 0 0 0 0 0 1

Saplings 0 0 0 0 0 0 0 0

Microplot 2:

2.0 inches to 5.9 inches 0 10 0 22 0 0 0 32

6.0 inches to 11.9 inches 0 0 5 0 1 0 0 6

1.0 foot to 2.9 feet 0 0 0 0 1 0 0 1

3.0 feet to 4.9 feet 0 0 1 0 0 0 0 1

5.0 feet to 9.9 feet 0 0 0 0 0 0 0 0

Greater than or equal to 10.0 feet 0 0 0 0 0 0 0 0

Total 0 10 6 22 2 0 0 40

Competitive 0 0 0 0 0 0 0 0

Saplings 0 0 0 0 0 0 0 0

Microplot 3:

2.0 inches to 5.9 inches 0 19 9 11 0 5 0 44

6.0 inches to 11.9 inches 0 0 24 6 5 1 2 38

1.0 foot to 2.9 feet 0 0 0 0 1 4 1 6

3.0 feet to 4.9 feet 0 0 0 0 0 1 0 1

5.0 feet to 9.9 feet 0 0 0 0 0 0 1 1

Greater than or equal to 10.0 feet 0 0 0 0 0 0 0 0

Total 0 19 33 17 6 11 4 90

Competitive 0 0 0 0 0 0 0 0

Saplings 0 0 0 0 0 0 1 0

Microplot 4:

2.0 inches to 5.9 inches 0 8 23 0 0 0 0 31

6.0 inches to 11.9 inches 0 11 9 0 5 0 0 25

1.0 foot to 2.9 feet 2 0 2 1 0 0 0 5

3.0 feet to 4.9 feet 0 0 0 0 0 0 0 0

5.0 feet to 9.9 feet 0 0 1 0 0 0 0 1

Greater than or equal to 10.0 feet 0 0 0 0 0 0 0 0

Total 2 19 35 1 5 0 0 62

Competitive 1 0 0 0 0 0 0 1

Saplings 0 0 0 0 0 0 0 0

Table 3.—Tree seedling and sapling tally by microplot, height class, and species for an NRS-FIA 

sample plot classified as mixed-oak forest
40 ESTIMATION AND ANALYTICS 

The second example is a northern hardwoods stand 

with an overstory dominated by sugar maple with 

some interspersed beech. FIA algorithms generate 

a relative stocking percent of 68 percent following 

mortality of mature black cherry. Site productivity 

class is 50-84 cubic feet per acre per year at the 

culmination of MAI. Browse impact is high with 

numerous beech root suckers and a few sugar 

maple seedlings. The understory flora includes 

dense patches of rhizomatous ferns (Dennstaedtia

punctilobula and Thelypteris oveboracensis). 

 Species

Subplot number Sugar American Black Red Striped

and height class maple beech cherry maple maple Total

Microplot 1:

2.0 inches to 5.9 inches 1 0 147 42 9 199

6.0 inches to 11.9 inches 0 0 57 26 5 88

1.0 foot to 2.9 feet 0 0 0 0 1 1

3.0 feet to 4.9 feet 0 0 0 0 0 0

5.0 feet to 9.9 feet 0 0 1 0 0 1

Greater than or equal to 10.0 feet 0 0 0 0 0 0

Total 1 0 205 68 15 289

Competitive 0 0 0 0 0 0

Saplings 0 0 0 0 0 0

Microplot 2: Nonforest—No Tally

Microplot 3:

2.0 inches to 5.9 inches 0 0 12 0 2 14

6.0 inches to 11.9 inches 0 0 21 6 5 32

1.0 foot to 2.9 feet 0 1 0 0 4 5

3.0 feet to 4.9 feet 0 2 0 0 0 2

5.0 feet to 9.9 feet 0 0 0 0 0 0

Greater than or equal to 10.0 feet 0 0 0 0 0 0

Total 0 3 33 6 11 53

Competitive 0 0 0 0 0 0

Saplings 0 0 0 0 0 0

Microplot 4:

2.0 inches to 5.9 inches 0 0 0 9 0 9

6.0 inches to 11.9 inches 0 0 0 6 14 20

1.0 foot to 2.9 feet 0 3 0 3 1 7

3.0 feet to 4.9 feet 1 0 0 0 1 2

5.0 feet to 9.9 feet 0 0 0 0 0 0

Greater than or equal to 10.0 feet 0 0 0 0 0 0

Total 1 3 0 18 16 38

Competitive 0 0 0 0 0 0

Saplings 0 0 0 0 0 0

Table 4.—Tree seedling and sapling tally by microplot, height class, and species for an NRS-FIA 

sample plot classified as northern hardwood forest

Table 4 contains the tally of seedlings for this 

example. The computation of WNS for each 

microplot is: 

Microplot 1: WNS = [(1+147+42)*1] + 

 [(57+26)*1] + [(1)*50] = 323

Microplot 2: Nonforest plot; no tally

Microplot 3: WNS = [(12)*1] + [(21+6)*1] + 

 [(1)*2] + [(2)*20] = 81

Microplot 4: WNS = [(9)*1] + [6*1] + 

 [(3+3)*2] + [(1)*20] = 47
ESTIMATION AND ANALYTICS 41

The northern hardwood example has three 

microplots in a forested condition. Only microplot 

1 exceeded the 100-stem threshold. Although sugar 

maple was sampled on two microplots, black cherry 

and red maple were most prevalent. Striped maple 

was abundant on all the forested microplots, but it 

was not included in the weighted sum because it is 

a low canopy species.

Role of Saplings as Advance Regeneration

Saplings can be added to the evaluation of 

regeneration stocking for the microplot. For 

example, the PRS used two saplings from 1.0 to 

1.9 inches d.b.h. or one sapling at least 2.0 inches 

d.b.h. as the minimum thresholds for adequate 

regeneration for timber management species. In 

some cases this approach can overestimate the 

regeneration adequacy because some saplings, such 

as sugar maple or American beech, are suppressed 

shade tolerant species that may not respond fast 

enough to increased light to achieve high canopy. As 

with seedlings, the choice of species and thresholds 

to use for saplings depends on the forest types being 

studied and the assumptions regarding regeneration 

stocking. The mixed oak example is a case where 

including the tree-of-heaven (a nonnative invasive 

plant) sapling would change the composition of the 

regeneration component of microplot 3 from mostly 

black cherry, black birch, and red maple regeneration 

to tree-of-heaven.

Population Proportions

For each sample plot, the proportion of microplots 

with adequate regeneration is defined as the number 

of microplots meeting seedling thresholds divided 

by the number of microplots sampled in forested 

conditions. In the previous example for mixed-oak 

forests, the proportion is 1.00 because all of the 

forested microplots have adequate regeneration. 

For the northern hardwood forest example, the 

proportion is 0.33. 

At the population level, the overall rate of 

advance regeneration adequacy can be estimated 

as the proportion of the microplots sampled in 

the state or subregion that qualify as adequately 

stocked (Westfall and McWilliams 2012). It is 

important to consider sample sizes when making 

population estimates because specifying subsets 

of the population, either geographically or via 

classification variables, reduces the precision 

of the estimate.

Sampling Error and Trend Estimation

Detecting change in the population is a primary 

need for regeneration monitoring (Objective 3). 

When conducting statistical analyses, hypothesis 

tests should consider both Type I and II sources 

of error (Westfall and McWilliams 2012). In this 

context, the null hypothesis is that no change in 

the population parameter has occurred, while the 

research hypothesis is that some specified amount 

of change has transpired. A Type I error occurs 

when the actual change in the population does not 

cross the threshold value specified in the research 

hypothesis but the research hypothesis is accepted 

as true due to the sample statistics. Similarly, a Type 

II error is committed when the actual change in the 

population crosses the threshold value specified in 

the research hypothesis but the null hypothesis of no 

change is accepted as true (Di Stefano 2001). Low 

error rates are sought to minimize the probability of 

drawing erroneous conclusions. Type I and II errors 

are inversely related, which challenges the analyst 

to balance the two error types based on the negative 

consequences of each. Westfall and McWilliams 

(2012) demonstrate that when the change in the 

proportion of adequately stocked forested samples 

is less than 0.05, it is likely not statistically different 

from zero (i.e., the null hypothesis of no change is 

accepted). The problem is exacerbated for substate 

regions as sample sizes become smaller. In these 

cases, it may be useful to consider other metrics and 

indicators, such as a direct analysis of the sum of 

weighted seedlings. 
42 ESTIMATION AND ANALYTICS 

To illustrate the evaluation of change estimates 

for substate regions, Wildlife Management Unit 

(WMU) 2G in Pennsylvania was used (Fig. 15). 

WMU 2G is a major deer hunting area that has 

been heavily impacted by numerous, interacting 

regeneration stressors. Figure 15 shows estimates 

and sampling errors of the proportion of adequately 

stocked forest land located on WMU 2G using 

nine full sets of five inventory panels. Estimates 

for sets of remeasured inventory panels are not 

Figure 15.—(A) Pennsylvania Wildlife Management Units (WMU), and (B) the proportion of forest land adequately stocked 

with advance tree seedling and sapling regeneration for canopy replacement species and samples 40 to 75 percent 

stocked with trees, Wildlife Management Unit 2G, Pennsylvania, 2001-2005 to 2009-2013. Sampling errors are shown for 

the 68 percent confidence level. Number of samples (n) is shown for each estimate. Proportion

(A)

(B)
ESTIMATION AND ANALYTICS 43

independent because most of the sample plots 

were measured (Westfall et al. 2013). Examining 

the change between remeasurements of the same 

inventory panels requires accounting for this in the 

calculation of standard errors. The longest trend data 

available for Pennsylvania are the 2001-2005 and 

2009-2013 inventory panels. The results for WMU 

2G indicate an increase from 0.46 to 0.55 in the 

proportion of forest land area with adequate advance 

regeneration. The standard error of the difference 

was calculated using the variances of the estimates 

at each time period, as well as the covariance 

between the two, which was derived from Cochran 

(1977). The p-value associated with this test for 

significant differences was 0.0093. Assuming a null 

hypothesis that no change has occurred, the small 

p-value indicates strong statistical evidence that the 

proportion of forest land with adequate regeneration 

has increased, and there is a low likelihood that the 

null hypothesis of no change is true.

Geospatial Products

Exploring regeneration analytics in a geospatial 

(mapped) context offers the ability to search for 

spatial patterns, or the lack of patterns, for indicators 

of interest (Objective 4). Options for mapping 

attributes include thematic, grid, and modeled 

products. Examples of grid maps for Pennsylvania 

show the result of applying the PRS approach to 

mixed-oak forests (Fig. 16) and northern hardwood 

forests (Fig. 17) in Pennsylvania for 2009 through 

2013. The grid points depict NRS-FIA samples 

(sample locations approximate) coded as having 

50 percent or more of the subplots adequately 

stocked with advance regeneration. All of the 

samples shown are for plots in the 40 to 74 percent 

range of relative stocking. Neither map shows a 

visually strong spatial pattern. This information 

is useful to managers and policymakers as they 

consider actions. 

Figure 16.—Distribution of samples on forest land classified as the mixed oak forest-type group and 40 to 75 percent 

stocked with live trees by percentage of microplots with adequate advance regeneration of canopy replacement species, 

Pennsylvania, 2009 and 2013.

70 percent and greater

50 to 69 percent

Less than 50 percent

(All plot locations are approximate)
44 ESTIMATION AND ANALYTICS 

Figure 17.—Distribution of samples on forest land classified as the northern hardwood forest-type group and 40 to 

75 percent stocked with live trees by percentage of microplots with adequate advance regeneration of canopy 

replacement species, Pennsylvania, 2009 and 2013.

Opportunities for spatial analyses include a wide 

array of potential products and will continue to 

multiply as the first full baseline set of inventory 

panels and remeasurements are completed and 

distributed publicly. Examples of the types of maps 

NRS-FIA produces can be found in standard statelevel reports (Perry et al. 2012) online at the NRSFIA map Web portal (http://www.nrs.fs.fed.us/fia/

maps/default.asp). The Forest Inventory Data Online 

(FIDO) tool (http://apps.fs.fed.us/fia/fido/index.

html) also generates user-defined maps (Wilson et al. 

2012). The new seedling measurements have yet to 

be included in the online map products.

Modeling Applications

Broad-scale forest projection models, such as the 

Forest Vegetation Simulator (FVS) (Crookston 

and Dixon 2005), typically require a method to 

account for initial seedling recruitment. Existing 

models often include input parameters that are too 

costly or ephemeral to collect over large areas. 

The seed production used in FOREGEN (Solomon 

and Leak 2002) is an example. In the past, forest 

system modelers have had limited tree seedling 

data available to construct recruitment models that 

are consistent across large regions. The new NRSFIA seedling measurements were developed to fill 

this gap (Objective 5). Brief discussions of two 

prominent models of forest dynamics are presented 

to illustrate potential applications. This is followed 

by a discussion of models to predict abundance of 

advance regeneration.

SORTIE-Neighborhood Dynamics (SORTIEND) is an individual tree simulation model with 

integrated components for seedling recruitment, light 

availability, tree growth, and tree mortality (Pacala 

et al. 1996). The recruitment functions in SORTIEND were developed to compare seedling and adult 

tree population distributions, thus, eliminating the 

70 percent and greater

50 to 69 percent

Less than 50 percent (All plot locations are approximate)
ESTIMATION AND ANALYTICS 45

challenge of identifying the specific parent for each 

recruit (Ribbens et al. 1994). The approach has 

further advantages of adding a spatial component 

to the model and the ability to use more readily 

available field data. Recent work refined the model 

by parameterizing tree growth functions using FIA 

plot data (Canham et al. 2006). The recruitment 

function uses basal area of adult trees within the 

immediate neighborhood of the microplots to 

incorporate shading. These developments have 

allowed a variety of scenarios to be incorporated 

within the SORTIE-ND model to study large 

regions.

The Forest Vegetation Simulator is a stand-level 

growth and yield model that simulates forest 

dynamics to project future characteristics. The 

FVS model has a variety of options for simulating 

different management regimes, including 

intermediate cuttings and regeneration harvests, 

and allows adjustments to the timing of treatments 

over the projection period. Although FVS has 

been calibrated for most of the United States, the 

flexibility of the model provides many opportunities 

for improving projections by customizing models 

and inputs (Dixon 2002). For example, Crookston 

et al. (2010) summarized methods for using FVS 

to address impacts of climate change on forest 

dynamics.

An advantage of FVS is the ability to download and 

input FIA data directly from the national data portal 

(http://apps.fs.fed.us/fiadb-downloads/datamart.

html). These features allow the user to make 

improvements in forest projections that go beyond 

the default regeneration parameters, thus improving 

regeneration estimates that are such important 

factors affecting the composition and stocking of 

future forest stands. Adding seedling height, level 

of establishment for large-seeded species, and 

browse impact to the FIA measurements provides 

the opportunity for improving simulation models 

for the stand initiation and re-initiation stages of 

development. 

The regeneration establishment component used in 

the northeast FVS variant has three options for stand 

initiation: planting, stump sprouts, and user input 

(Dixon and Keyser 2008). Currently, users making 

landscape-level projections need more detailed 

empirical data, particularly for tree seedlings. 

Statistical models that predict regeneration 

composition and structure have been developed for 

the Rocky Mountains (Ferguson and Carlson 1993), 

but elsewhere more models for advance regeneration 

are needed due to complexities and challenges 

such as those mentioned herein for northern forest 

systems.

Research is also being directed towards models 

that predict the quality of advance regeneration 

using PRS regeneration measurements as the 

dependent variable and other stand characteristics 

as independent variables. The focus is to provide a 

stochastic model of regeneration that can be used in 

FVS projections. Although challenging due to the 

many factors that determine regeneration quality, 

developing reliable models would represent a major 

advancement for predicting future stand character.

The new FIA measurements fit well with efforts 

to predict future composition of dominant and 

codominant trees. Vickers et al. (2011, 2013) 

developed the REGEN expert system that addresses 

the complexity of dynamic interactions of species, 

height, and density. REGEN uses existing stand 

conditions along with literature and expert opinion 

to assign competitive ranking factors to advance 

reproduction for hardwood species. The ranking 

factors range from 1 to 20 and are assigned to each 

seedling by species for four height classes and for 

potential stump sprouts. These parameters make up 

Regeneration Knowledge Bases (RKB) developed 

for the southern and central Appalachian hardwoods. 

The model uses the information in the RKBs to 

predict which stems will grow to dominate future 

species composition, and hence, the future stand.
46 ESTIMATION AND ANALYTICS 

Research Extensions (Objective 6)

Wildlife Habitat

Ripley and McClure (1963) describe the use of 

forest inventory data for assessing deer browse 

resources of North Georgia. Three attributes of 

browse were recorded by field crews: (1) the 

dominant tree or shrub species making up the 

greatest winter browse dry biomass within each plot, 

(2) the total winter browse dry biomass within each 

plot, and (3) presence or absence of deer browsing 

activity. These data were used to produce estimates 

of percentage of plots dominated by desirable and 

undesirable deer browse species, and winter dry 

biomass for desirable and undesirable deer browse 

species. Estimates were stratified by ownership 

category, forest type, and stand size.

A timber resource inventory was used to assess 

deer browse resources of the Uwharrie National 

Forest in North Carolina (Moore and Strode 1966). 

Data collection included frequency sampling for 

the occurrence and utilization of woody understory 

plants and estimates of the weight of annual growth. 

Browse species were assigned to one of four 

preference categories: preferred, staple, emergency, 

or stuffing. Sample data were stratified by major 

forest type, age class, stand size, and management 

class. Estimates were produced for occurrence, 

utilization, and weight (biomass). Estimates of daily 

browse were applied to deer population estimates.

Pearson and Sternitzke (1976) augmented forest 

survey protocols with additional attributes of deer 

browse availability and quality in the Louisiana 

Coastal Plain. Browse resources were classified into 

one of three categories of desirability: good, fair, and 

poor. The study compared browse resources among 

forest types. They concluded that cattle grazing 

and deer browsing did not unduly restrict timber 

production.

McWilliams et al. (1995) used FIA data to 

determine that advance tree seedling regeneration 

in Pennsylvania was inadequate for new stand 

establishment across most of the state. Although 

specific causal factors were not quantified, it was 

surmised that high deer densities likely were the 

most pervasive obstacle to regeneration.

Didier and Porter (2003) used FIA data to calculate 

an index of sugar maple reproductive success in 

northern New York State, which they related to 

relative deer densities via regression analysis. 

They concluded that high deer densities were not 

necessarily associated with poor maple reproductive 

success within the range of densities observed within 

their study area.

Phase 2-plus regeneration data and estimators 

will provide deer browse information similar to 

the historical approaches described above. This 

capability previously has not been available at 

regional or national scales. Additional work is 

needed to provide estimates of seedling and sapling 

biomass, with linkages to deer carrying capacity. 

Regeneration information products likely will 

support assessments for other species of wildlife as 

well.

Carbon Accounting

Forest carbon monitoring is another potential 

application of the regeneration indicator. First, 

more robust estimates of seedling attributes may 

inform efforts to estimate understory vegetation 

carbon pools (Russell et al. 2014). Second, domestic 

energy policies are currently exploring the carbon 

consequences of using forest biomass as feed stocks 

for bioenergy efforts versus managing forests for 

maximizing carbon sequestration in ecosystem 

components such as soils (Mitchell et al. 2013). 

In the face of future prospective disturbances, 
ESTIMATION AND ANALYTICS 47

reproductive capacity will be critical to maintaining 

ecosystem processes and carbon stocks (Woodall 

et al. 2013a). Although tree regeneration is a minor 

carbon pool compared to total carbon stocks, the 

new measurements will inform future carbon stock 

estimates and questions regarding the sustainability 

of the ecosystem process of carbon sequestration.

Tree Species Migration

Methods for estimating migration of forest 

vegetation were outlined by Leak and Graber (1974) 

and more recently by Iverson et al. (2008) using 

FIA Phase 2 data to consider the impact of climate 

change scenarios. Woodall et al. (2009) proposed an 

indicator of tree-range dynamics where the attributes 

and location of trees greater than 1.0 or 5.0 inches 

(i.e., microplot and subplot trees respectively) were 

compared to seedling metrics across large scales 

using FIA data for the conterminous United States. 

The basic indicator that explores divergences 

between tree and seedling metrics has been 

extended to numerous studies that have expanded 

fundamental information for monitoring tree ranges 

across the United States (Bell et al. 2014; Woodall 

et al. 2013b; Zhu et al. 2012, 2014). The additional 

seedling measurements improve the ability to assess 

tree regeneration beyond that of basic seedling 

abundance because all seedlings are assumed to 

be equal when using existing Phase 2 data. Two 

critical pieces of information missing from previous 

studies are height and status of individual seedlings. 

Collecting more detailed measurements will refine 

and improve the monitoring of tree ranges across 

the large scales used for estimating climate change 

impact on vegetation. 
48 ESTIMATION AND ANALYTICS 

SUMMARY

The Phase 2-plus regeneration indicator fills a void 

for information on tree seedlings and completes 

the NRS-FIA goal of tracking trees from birth, to 

adulthood, to death, to down woody material, and 

eventually, to components of soil. The indicator 

provides basic information on composition and 

structure of the regeneration component that can be 

used along with information on saplings and adult 

trees to construct a full profile of forest systems of 

the region. In the early years of data collection, the 

ability to fully address information needs will be 

limited to geographic areas with enough forested 

samples to develop estimates with acceptable 

sampling errors. Once the full set of baseline 

measurements is complete, the process for building 

credible trend estimates can begin, and results can be 

used to improve state- and region-level assessments 

of forest sustainability.

One of the more important uses of the regeneration 

indicator is for quantifying and assessing the 

character of advance regeneration and shifts in tree 

species composition. Species, seedling height, and 

browse impact data offer the flexibility to evaluate 

a wide array of metrics that can be used to predict 

future forest conditions; however, new approaches 

need to be developed for forest types and forest-type 

groups currently not covered. Developing and testing 

models to predict regeneration are noteworthy 

opportunities for research. The data can also be 

included in research on carbon accounting, species 

migration, and wildlife habitat. 

ACKNOWLEDGMENTS

The authors would like to thank all of the field staff 

who collected the regeneration indicator data. The 

work is completed during the leaf-on season along 

with the collection of other ecological indicator 

variables, making for an arduous and challenging 

field season. Without the hard work of these folks, 

the difficult job of measuring regeneration could 

not have been completed within quality assurance 

tolerances.

We are also grateful to those who provided valuable 

feedback on an earlier version of this document: 

Bill Leak, U.S. Forest Service; Steve Shifley, U.S. 

Forest Service; Anthony D’Amato, University of 

Minnesota; and Dave McGill, University of West 

Virginia.
Oak seedlings emerging from fern dominated understory. Photo by Will McWilliams, U.S. Forest Service.

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251-264.
Appendix I 63

Appendix I 

Common and Scientific Names of Tree Species Measured on FIA Plots

ailanthus Ailanthus altissima

American basswood Tilia americana

American beech Fagus grandifolia

American chestnut Castanea dentata

American elm Ulmus americana

American holly Ilex opaca

American hornbeam Carprinus caroliniana

American mountain-ash Sorbus americans

American plum Prunus americana

American sycamore Platanus occidentalis

apple spp. Malus spp.

ash spp. Fraxinus spp.

Atlantic white-cedar Chamaecyparis thyoides

Austrian pine Pinus nigra

baldcypress Taxodium distichum

balsam fir Abies balsamea

balsam poplar Populus balsamifera

balsam willow Salix pyrifolia

basswood spp. Tilia spp.

Bebb willow Salix bebbiana

bigleaf magnolia Magnolia macrophylla

bigtooth aspen Populus grandidenta

bitternut hickory Carya cordiformis

black ash Fraxinus nigra

black cherry Prunus serotina

black hickory Carya texana

black locust Robinia pseudoacacia

black maple Acer nigrum

black oak Quercus velutina

black spruce Picea mariana

black walnut Juglans nigra

black willow Salix nigra

blackgum Nyssa silvatica

blackjack oak Quercus marilandica

blue ash Fraxinus quadrangulata

blue spruce Picea pungens

boxelder Acer negundo

buckeye, horsechestnut spp. Aesculus spp.

bur oak Quercus macrocarpa

butternut Juglans cinerea

Canada plum Prunus nigra

catalpa spp. Catalpa spp.

cherry and plum spp. Prunus spp.

cherrybark oak Quercus pagoda

chestnut oak Quercus prinus

chestnut spp. Castanea spp.

Chinese chestnut Castanea mollissima

chinkapin oak Quercus muehlenbergii

chittamwood, gum bumelia Sideroxylon lanuginosum

chokecherry Prunus virginiana

coastal plain willow Salix caroliniana

cockspur hawthorn Crataegus crus-galli

common persimmon Diospyros virginiana

common serviceberry Amelanchier arborea

cottonwood and poplar spp. Populus spp.

cucumbertree Magnolia acuminata

Douglas-fir Pseudotsuga menzeisii

downy hawthorn Crataegus mollis

dwarf chinkapin oak Quercus prinoides

eastern cottonwood Populus deltoides

eastern hemlock Tsuga canadensis

eastern hophornbeam Ostrya virginiana

eastern redbud Cercis canadensis

eastern redcedar Juniperus virginiana

eastern white pine Pinus strobus

elm spp. Ulmus spp.

European alder Alnus glutenosa

European mountain ash Sorbus aucuparia

fir spp. Abies spp.

flowering dogwood Cornus florida

Fraser fir Abies fraseri

ginkgo, maidenhair tree Ginkgo biloba

gray birch Betula populifolia

green ash Fraxinus pennsylvanica

hackberry Celtis occidentalis

hackberry spp. Celtis spp.

hawthorn spp. Craetagus spp.

hickory spp. Carya spp.

honeylocust Gleditsia triancanthos

jack pine Pinus banksiana

Kentucky coffeetree Gymnogladus dioicus

larch spp. Larix spp.

laurel oak Quercus laurifolia

loblolly pine Pinus taeda

Lombardy poplar Populus nigra

magnolia spp. Magnolia spp.

maple spp. Acer spp.

mimosa, silktree Albizia julibrissin

mockernut hickory Carya alba

mountain maple Acer spicatum

mountain or Fraser magnolia Magnolia fraseri

mountain-ash spp. Sorbus spp.

mulberry spp. Morus spp.

northern catalpa Catalpa speciosa

northern mountain-ash Sorbus decora 

northern pin oak Quercus ellipsoidalis

northern red oak Quercus rubra

northern white-cedar Thuja occidentalis

Norway maple Acer platanoides

Norway spruce Picea abies

Nuttal oak Quercus nuttallii

oak spp. Quercus spp.

Ohio buckeye Aesculus glabra

Osage-orange Maclura pomifera

Common name Scientific namea Common name Scientific name

(continued on next page)
64 Appendix I

overcup oak Quercus lyrata

paper birch Betula papyrifera

paulownia, empress-tree Paulownia tomentosa

pawpaw Asimina triloba

peachleaf willow Salix amygdaloides

pecan Carya illinoinensis

persimmon spp. Diospyros spp.

pignut hickory Carya glabra

pin cherry Prunus pensylvanica

pin oak Quercus palustris

pitch pine Pinus rigida

pond pine Pinus serotina

ponderosa pine Pinus ponderosa

post oak Quercus stellata

prairie crab apple Malus ioensis

pumpkin ash Fraxinus profuna

quaking aspen Populus tremuloides

red hickory Carya ovalis

red maple Acer rubrum

red mulberry Morus rubra

red pine Pinus resinosa

red spruce Picea rubens

redcedar/juniper spp. Juniperus spp.

river birch Betula nigra

rock elm Ulmus thomasii

Rocky Mountain juniper Junipoerus scopulorum

roundleaf serviceberry Amelanchier sanguinea

Russian olive Elaeangnus augustifolia

sand hickory Carya pallida

sassafras Sassafras albidum

scarlet oak Quercus coccinea

Scotch pine Pinus sylvestris

scrub oak Quercus ilicifolia

serviceberry spp. Amelanchier spp.

shagbark hickory Carya oovata

shellbark hickory Carya laciniosa

shingle oak Quercus imbricaria

shortleaf pine Pinus echinata

Shumard oak Quercus shumardii

Siberian elm Ulmus pumila

silver maple Acer saccharinum

slippery elm Ulmus rubra

smoketree Cotinus obovatus

sourwood Oxydendron arboreum

southern catalpa Catalpa bignoniodes

southern crab apple Malus angustifolia

southern red oak Quercus falcata

spruce spp. Picea spp.

striped maple Acer pensylvanicum

sugar maple Acer saccharum

sugarberry Celtis laevagata

swamp chestnut oak Quercus michauxii

swamp cottonwood Populus heterophylla

swamp tupelo Nyssa biflora

swamp white oak Quercus bicolor

sweet birch Betula lenta

sweet cherry, domesticated Prunus avium

sweet crab apple Malus coronaria

sweetbay Magnolia virginiana

sweetgum Liquidambar styraciflua

Table Mountain pine Pinus pungens

tamarack (native) Larix larcinia

Texas buckeye Aesculus glabra 

 var. arguta 

Texas red oak Quercus texana

umbrella magnolia Magnolia tripetala

Virginia pine Pinus virginiana

water hickory Carya aquatica

water oak Quercus nigra

water tupelo Nyssa aquatica

waterlocust Gledistia aquatica

weeping willow Salix sepulcralis

western soapberry Sapindus saponaria 

 var. drummondii

white ash Fraxinus americana

white basswood Tilia americana 

 var. heterophylla

white fir Abies concolor

white mulberry Morus alba

white oak Quercus alba

white spruce Picea glauca

white willow Salix alba

willow oak Quercus phellos

willow spp. Salix spp.

winged elm Ulmus alata

yellow birch Betula alleghaniensis

yellow buckeye Aesculus flava

yellow-poplar Liriodendron tulipifera

yellowwood Cladrastis kentukea

Appendix I (continued)

Common name Scientific name Common name Scientific name

a Common names are from the PLANTS database (Natural 

Resources Conservation Service 2014).
Appendix II 65

Appendix II

Forest Inventory and Analysis Database Documentation

Background

The regeneration indicator protocols include a 

small number of variables collected at the PLOT, 

SUBPLOT, and SEEDLING levels of information. 

These data are organized by topic into a new group 

of Oracle® REGEN tables in the Forest Inventory 

and Analysis database (U.S. Forest Service 2014). 

If additional variables related to forest regeneration 

are collected in the future, they can be organized 

in this table group. The following is draft 

documentation for these data tables (December 

9, 2014). References to any appendices in this 

documentation refer to appendices in the database 

document (U.S. Forest Service 2014).

Regeneration Data Table (PLOT_REGEN)

Column Order Column name Descriptive name Data type

1 CN Sequence number VARCHAR2(34)

2 PLT_CN Plot sequence number VARCHAR2(34)

3 INVYR Inventory year NUMBER(4)

4 STATECD State code NUMBER(4)

5 UNITCD Survey unit code NUMBER(2)

6 COUNTYCD County code NUMBER(3)

7 PLOT Phase 2 plot number NUMBER(5)

8 BROWSE_IMPACT Browse impact code NUMBER(1)

11 CREATED_BY Created by VARCHAR2(30)

12 CREATED_DATE Created date DATE

13 CREATED_IN_INSTANCE Created in instance VARCHAR2(6)

14 MODIFIED_BY Modified by VARCHAR2(30)

15 MODIFIED_DATE Modified date DATE

16 MODIFIED_IN_INSTANCE Modified in instance VARCHAR2(6)

17 CYCLE Inventory cycle number NUMBER(2)

18 SUBCYCLE Inventory subcycle number NUMBER(2)

Type of key Column(s) order Tables to link Abbreviated notation

Primary CN N/A PLTREGEN_PK

Unique STATECD, COUNTYCD, PLOT, 

INVYR

N/A PLTREGEN_UK1

Unique STATECD, COUNTYCD, PLOT, 

CYCLE, SUBCYCLE

N/A PLTREGEN_UK2

Foreign PLT_CN PLOT_REGEN to PLOT PLTREGEN_PLT_FK
66 Appendix II

1. CN Sequence number. A unique sequence number used to identify a 

Regeneration Plot record.

2. PLT_CN Plot sequence number. Foreign key linking the Regeneration Plot 

record to the Plot record for this location.

3. INVYR Inventory year. See SURVEY.INVYR description for definition.

4. STATECD State code. Bureau of the Census Federal Information Processing 

Standards (FIPS) two-digit code for each State. Refer to appendix B.

5. UNITCD Survey unit code. Forest Inventory and Analysis survey unit 

identification number. Survey units are usually groups of counties 

within each State. For periodic inventories, Survey units may be 

made up of lands of particular owners. Refer to appendix B for codes.

6. COUNTYCD County code. The identification number for a county, parish, 

watershed, borough, or similar governmental unit in a State. FIPS

codes from the Bureau of the Census are used. Refer to appendix B 

for codes.

7. PLOT Phase 2 plot number. An identifier for a plot. Along with STATECD, 

INVYR, UNITCD, COUNTYCD and/or some other combinations of 

variables, PLOT may be used to uniquely identify a plot.

8. BROWSE_IMPACT A code designating the amount of ungulate browse pressure exerted 

on regeneration of the forest. Pressure need not be exerted only by 

deer, but by other wildlife as well including but not limited to deer, elk, 

feral hogs, livestock, moose and others.

9. CREATED_BY Created by. See SURVEY.CREATED_BY description for definition.

10. CREATED_DATE Created date. See SURVEY.CREATED_DATE description for 

definition.

11. CREATED_IN_INSTANCE Created in instance. See SURVEY.CREATED_IN_INSTANCE

description for definition.

12. MODIFIED_BY Modified by. See SURVEY.MODIFIED_BY description for definition.

13. MODIFIED_DATE Modified by. See SURVEY.MODIFIED_BY description for definition.

14. MODIFIED_IN_INSTANCE Modified in instance. See SURVEY.MODIFIED_IN_INSTANCE

description for definition.

15. CYCLE Inventory cycle number. See SURVEY.CYCLE description for 

definition.

16. SUBCYCLE Inventory subcycle number. See SURVEY.SUBCYCLE description for 

definition.

Browse Impact Codes Table (Browse_Impact)

Code Definition

1 Very Low - Plot is inside a well-maintained exclosure.

2 Low - No browsing observed, vigorous seedlings present (no exclosure present).

3 Medium - Browsing evidence observed but not common, seedlings common.

4 High - Browsing evidence common OR seedlings are rare.

5 Very High - Browsing evidence omnipresent OR forest floor bare, severe browse line.
Appendix II 67

Column Order Column name Descriptive name Data type

1 CN Sequence number VARCHAR2(34)

2 PLT_CN Plot sequence number VARCHAR2(34)

3 SBP_CN Subplot sequence number VARCHAR2(34)

4 INVYR Inventory year NUMBER(4)

5 STATECD State code NUMBER(4)

6 UNITCD Survey unit code NUMBER(2)

7 COUNTYCD County code NUMBER(3)

8 PLOT Phase 2 plot number NUMBER(5)

9 SUBP Subplot number NUMBER(1)

10 REGEN_SUBP_STATUS_CD Regeneration subplot 

status code

NUMBER(1)

11 REGEN_SUBP_NONSAMPLE_

REASN_CD

Regeneration subplot 

nonsampled reason code

NUMBER(2)

12 SUBPLOT_SITE_LIMITATIONS Subplot site limitations NUMBER(1)

13 MICROPLOT_SITE_LIMITATIONS Microplot site limitations NUMBER(1)

14 CREATED_BY Created by VARCHAR2(30)

15 CREATED_DATE Created Date DATE

16 CREATED_IN_INSTANCE Created in Instance VARCHAR2(6)

17 MODIFIED_BY Modified by VARCHAR2(30)

18 MODIFIED_DATE Modified Date DATE

19 MODIFIED_IN_INSTANCE Modified in Instance VARCHAR2(6)

20 CYCLE Inventory cycle number NUMBER(2)

21 SUBCYCLE Inventory subcycle number NUMBER(2)

Subplot Regeneration Table (SUBPLOT_REGEN)

Type of key Column(s) order Tables to link Abbreviated notation

Primary CN N/A SBPREGEN_PK

Unique STATECD, COUNTYCD, 

PLOT, SUBP, INVYR

N/A SBPREGEN_UK

Foreign PLT_CN SUBPLOT_REGEN to PLOT SBPREGEN_PLT_FK

Foreign SBP_CN SUBPLOT_REGEN to SUBPLOT SBPREGEN_SBP_FK
68 Appendix II

1. CN Sequence number. A unique sequence number used to identify a 

Regeneration Subplot record.

2. PLT_CN Plot sequence number. Foreign key linking the Regeneration Subplot record 

to the Plot record for this location.

3. SBP_CN Subplot sequence number. Foreign key linking the Regeneration Subplot 

record to the Subplot record for this location.

4. INVYR Inventory year. See SURVEY.INVYR description for definition.

5. STATECD State code. Bureau of the Census Federal Information Processing Standards 

(FIPS) two-digit code for each State. Refer to appendix B.

6. UNITCD Survey unit code. Forest Inventory and Analysis survey unit identification 

number. Survey units are usually groups of counties within each State. For 

periodic inventories, Survey units may be made up of lands of particular 

owners. Refer to appendix B for codes.

7. COUNTYCD County code. The identification number for a county, parish, watershed, 

borough, or similar governmental unit in a State. FIPS codes from the 

Bureau of the Census are used. Refer to appendix B for codes.

8. PLOT Phase 2 plot number. An identifier for a plot. Along with STATECD, INVYR, 

UNITCD, COUNTYCD and/or some other combinations of variables, PLOT

may be used to uniquely identify a plot.

9. SUBP Subplot number. The number assigned to the subplot. The national plot 

design (PLOT.DESIGNCD = 1) has subplot number values of 1 through 

4. Other plot designs have various subplot number values. See PLOT.

DESIGNCD and appendix I for information about plot designs. For more 

explanation about SUBP, contact the appropriate FIA work unit.

10. REGEN_SUBP_

STATUS_CD

A code indicating whether or not the subplot was sampled for advanced 

regeneration.

11. REGEN_SUBP_

NONSAMPLE_

REASN_CN

A code designating the reason why a subplot was not sampled for advanced 

regeneration. 

12. SUBPLOT_SITE_

LIMITATIONS

A code indicating if the site (as defined by the subplot) has a limitation that 

would inhibit or preclude the presence of regenerating seedlings.

13. MICROPLOT_

SITE_

LIMITATION

A code indicating if the site (as defined by the microplot) has a limitation that 

would inhibit or preclude the presence of regenerating seedlings.

14. CREATED_BY Created by. See SURVEY.CREATED_BY description for definition.

15. CREATED_DATE Created date. See SURVEY.CREATED_DATE description for definition.

16. CREATED_IN_

INSTANCE

Created in instance. See SURVEY.CREATED_IN_INSTANCE description for 

definition.

17. MODIFIED_BY Modified by. See SURVEY.MODIFIED_BY description for definition.

18. MODIFIED_DATE Modified by. See SURVEY.MODIFIED_BY description for definition.

19. MODIFIED_IN_

INSTANCE

Modified in instance. See SURVEY.MODIFIED_IN_INSTANCE description 

for definition.

20. CYCLE Inventory cycle number. See SURVEY.CYCLE description for definition.

21. SUBCYCLE Inventory subcycle number. See SURVEY.SUBCYCLE description for 

definition.
Appendix II 69

Regeneration Subplot Status Code Definitions Table 

(REGEN_SUBP_STATUS_CD)

Code Definition

1 Subplot sampled for advanced regeneration

2 Subplot not sampled for advanced regeneration

Subplot Site Limitations Definitions Table (SUBPLOT_SITE_LIMITATIONS)

Code Definition

1 No site limitation

2 Rocky surface with little or no soil

3 Water-saturated soils (during the growing season)

Microplot Site Limitations Definitions Table (MICROPLOT_SITE_LIMITATIONS)

Code Definition

1 No site limitation

2 Rocky surface with little or no soil

3 Water-saturated soil (during the growing season)

4 Thick duff layer (in excess of two-inches thick)
70 Appendix II

Seedling Regeneration Table (SEEDLING_REGEN)

Column Order Column name Descriptive name Oracle data type

1 CN Sequence number VARCHAR2(34)

2 PLT_CN Plot sequence number VARCHAR2(34)

3 CND_CN Condition sequence number VARCHAR2(34)

4 SCD_CN Subplot-condition sequence number VARCHAR2(34)

5 INVYR Inventory year NUMBER(4)

6 STATECD State code NUMBER(4)

7 UNITCD Survey unit code NUMBER(2)

8 COUNTYCD County code NUMBER(3)

9 PLOT Phase 2 plot number NUMBER(5)

10 SUBP Subplot number NUMBER(1)

11 CONDID Condition number NUMBER(1)

12 SPCD Species code NUMBER

13 SPGRPCD Species group code NUMBER(2)

14 SEEDLING_SOURCE_CD Seedling source code VARCHAR2(2)

15 LENGTH_CLASS_CD Length class code NUMBER(1)

16 TREECOUNT Count of qualifying seedlings NUMBER(3)

17 CREATED_BY Created by VARCHAR2(30)

18 CREATED_DATE Created date DATE

19 CREATED_IN_INSTANCE Created in Instance VARCHAR2(6)

20 MODIFIED_BY Modified by VARCHAR2(30)

21 MODIFIED_DATE Modified date DATE

22 MODIFIED_IN_INSTANCE Modified in Instance VARCHAR2(6)

23 CYCLE Inventory cycle number NUMBER(2)

24 SUBCYCLE Inventory subcycle number NUMBER(2)

Type of key Column(s) order Tables to link Abbreviated notation

Primary CN SDLREGEN_PK

Unique STATECD, COUNTYCD, 

PLOT, SUBP, INVYR, SPCD, 

CONDID, SEEDLING_

SOURCE_CD, LENGTH_

CLASS_CD

SDLREGEN_UK

Foreign CND_CN SEEDLING_REGEN to COND SDLREGEN_CND_FK

Foreign PLT_CN SEEDLING_REGEN to PLOT SDLREGEN_PLT_FK

Foreign SCD_CN SEEDLING_REGEN to SDLREGEN_SCD_FK
Appendix II 71

1. CN Sequence number. A unique sequence number used to identify a Regeneration 

Subplot record.

2. PLT_CN Plot sequence number. Foreign key linking the Regeneration Seedling record 

to the Plot record for this location.

3. CND_CN Condition sequence number. Foreign key linking the Regeneration Seedling 

record to the Condition record for this location.

4. SCD_CN Subplot-condition sequence number. Foreign key linking the Regeneration 

Seedling record to the Subplot-condition record for this location.

5. INVYR Inventory year. See SURVEY.INVYR description for definition.

6. STATECD State code. Bureau of the Census Federal Information Processing Standards 

(FIPS) two-digit code for each State. Refer to appendix B.

7. UNITCD Survey unit code. Forest Inventory and Analysis survey unit identification 

number. Survey units are usually groups of counties within each State. For 

periodic inventories, Survey units may be made up of lands of particular 

owners. Refer to appendix B for codes.

8. COUNTYCD County code. The identification number for a county, parish, watershed, 

borough, or similar governmental unit in a State. FIPS codes from the Bureau 

of the Census are used. Refer to appendix B for codes.

9. PLOT Phase 2 plot number. An identifier for a plot. Along with STATECD, INVYR, 

UNITCD, COUNTYCD and/or some other combinations of variables, PLOT

may be used to uniquely identify a plot.

10. SUBP Subplot number. The number assigned to the subplot. The national plot design 

(PLOT.DESIGNCD = 1) has subplot number values of 1 through 4. Other plot 

designs have various subplot number values. See PLOT.DESIGNCD and 

appendix I for information about plot designs. For more explanation about 

SUBP, contact the appropriate FIA work unit.

11. CONDID Condition class number. The unique identifying number assigned to a condition 

that exists on the subplot, and is defined in the COND table. See COND.

CONDID for details on the attributes which delineate a condition.

12. SPCD Species code. An FIA tree species code. Refer to appendix F for codes.

13. SPGRPCD Species group code. A code assigned to each tree species in order to group 

them for reporting purposes on presentation tables. Codes and their associated 

names (see REF_SPECIES_GROUP.NAME) are shown in appendix E. 

Individual tree species and corresponding species group codes are shown in 

appendix F.

14. SEEDLING_

SOURCE_CD

A code designating the source of the seedlings. 

15. LENGTH_

CLASS_CD

A code designating the length class of the seedlings. 

16. TREECOUNT A count of the number of established live tally tree seedlings at least 2-

inches in length (with at least two normal-size leaves that do not still bear the 

cotelyledons) and less than 1.0-inch at d.b.h.

17. CREATED_BY Created by. See SURVEY.CREATED_BY description for definition.

18. CREATED_DATE Created date. See SURVEY.CREATED_DATE description for definition.
72 Appendix II

19. CREATED_IN_

INSTANCE

Created in instance. See SURVEY.CREATED_IN_INSTANCE description for 

definition.

20. MODIFIED_BY Modified by. See SURVEY.MODIFIED_BY description for definition.

21. MODIFIED_DATE Modified by. See SURVEY.MODIFIED_BY description for definition.

22. MODIFIED_IN_

INSTANCE

Modified in instance. See SURVEY.MODIFIED_IN_INSTANCE description for 

definition.

23. CYCLE Inventory cycle number. See SURVEY.CYCLE description for definition.

24. SUBCYCLE Inventory subcycle number. See SURVEY.SUBCYCLE description for 

definition.

Seedling Source Definitions Table (SEEDLING_SOURCE_CD)

Code Definition

1 Other seedling

2 Stump sprout

3 Competitive oak, hickory, walnut, or butternut seedling1

1

Research indicates that competitive seedlings are highly likely to become dominant or codominant stems in the next 

stand during forest succession. To be classified as competitive, stems must have a root collar diameter (RCD) > 0.75 inch 

or have a length of at least 3 feet. In situations with a relatively high tally, check at least 10% of RCDs.

Seedling Length Class Table (LENGTH_CLASS_CD)

Code Definition

1 2.0 to 5.9 inches

2 6.0 to 11.9 inches

3 1.0 to 2.9 feet

4 3.0 to 4.9 feet

5 5.0 to 9.9 feet

6 Greater than or equal to 10.0 feet
Appendix III 73

Appendix IIIred maple 15,856,996,489 33.0 4,835,520,211 24.3 4,433,388,226 24.7 747,077,671 29.4 484,101,337 31.0 331,520,202 31.0 26,688,604,137 25.3

sugar maple 3,105,183,933 33.8 2,323,931,871 29.4 4,409,237,725 34.0 1,678,349,917 48.6 697,087,102 53.3 412,449,094 38.4 12,626,239,642 30.1

black cherry 1,524,576,982 33.5 1,386,564,656 16.4 1,829,739,252 16.8 479,855,550 22.2 397,162,604 24.8 129,660,268 34.8 5,747,559,312 16.7black ash 930,682,257 44.1 746,280,773 59.4 1,995,076,666 47.5 722,769,918 55.8 353,601,607 68.1 211,513,751 51.7 4,959,924,972 47.4American hornbeam 1,011,148,854 49.8 777,048,839 38.1 1,744,369,486 38.1 484,898,235 47.0 313,009,468 32.8 192,206,118 42.9 4,522,681,000 33.1

quaking aspen 240,894,930 45.5 522,296,870 29.5 1,383,371,153 24.5 359,557,060 34.2 457,944,402 37.3 556,289,180 45.5 3,520,353,594 20.6balsam fir 1,009,761,968 29.8 677,317,175 32.0 888,830,925 28.0 411,524,503 31.9 443,217,160 38.2 10,116,111 100.4 3,440,767,842 24.0white ash 181,165,416 37.8 824,898,188 40.0 1,800,151,163 35.2 312,212,570 46.0 157,697,125 50.7 77,692,824 78.3 3,353,817,286 32.9serviceberry spp. 576,113,495 32.2 988,562,590 29.2 1,322,084,495 26.9 149,764,798 34.2 137,842,069 61.0 20,232,223 70.6 3,194,599,670 19.7chokecherry 474,532,649 54.6 521,877,139 34.5 902,074,329 26.8 224,349,247 49.6 99,774,229 58.7 9,653,816 102.2 2,232,261,409 25.6white oak 473,900,097 57.1 787,499,553 69.7 705,311,468 56.6 69,888,190 75.3 70,812,780 87.1 40,464,446 100.4 2,147,876,534 52.0northern red oak 442,748,953 32.8 999,135,085 27.4 564,525,369 32.0 80,928,892 46.0 0 0 0 0 2,087,338,298 23.9eastern hophornbeam 354,063,902 34.6 283,251,122 34.9 708,127,805 46.4 343,485,495 41.8 222,092,157 40.9 40,464,446 49.4 1,951,484,927 35.1eastern white pine 787,669,810 77.1 252,440,492 35.4 362,793,127 38.6 201,859,934 55.4 121,393,338 85.1 91,045,003 74.5 1,817,201,705 42.4northern pin oak 217,006,908 59.1 557,256,335 40.9 494,710,485 48.3 99,774,229 44.7 90,582,708 62.0 20,232,223 70.6 1,479,562,887 36.0green ash 131,509,449 43.3 382,897,658 38.7 614,686,195 37.3 160,933,193 48.2 80,004,301 46.4 10,116,111 100.4 1,380,146,908 35.8American elm 230,359,087 49.5 280,939,645 41.6 402,247,854 26.6 100,698,820 63.4 49,655,967 44.5 10,116,111 100.4 1,074,017,484 25.8bitternut hickory 340,711,723 48.6 390,325,125 41.1 251,011,041 41.4 40,002,151 80.0 10,116,111 100.4 0 0 1,032,166,151 34.4mountain maple 303,483,345 51.7 192,206,118 53.4 333,831,679 45.3 10,116,111 100.4 60,696,669 57.3 40,464,446 60.9 940,798,369 44.0paper birch 181,457,454 52.4 70,307,921 37.5 167,350,941 52.3 264,308,833 62.0 188,507,755 56.6 50,118,262 52.8 922,051,166 43.6

silver maple 875,777,878 83.7 28,961,448 100.2 0 0 0 0 0 0 0 0 904,739,326 84.2

American basswood 141,625,561 57.6 141,163,265 46.9 316,781,137 52.4 19,307,632 70.4 19,307,632 102.2 9,653,816 102.2 647,839,043 35.8black spruce 141,625,561 93.4 40,464,446 49.4 310,363,388 50.5 151,279,377 48.1 0 0 0 0 643,732,772 43.3northern white-cedar 131,509,449 56.6 171,973,895 55.6 182,090,007 79.8 30,348,334 74.5 50,580,557 72.0 0 0 566,502,244 57.3bur oak 20,232,223 100.4 100,236,524 44.4 140,238,675 60.1 137,002,607 61.5 40,002,151 61.5 0 0 437,712,179 46.7

boxelder 241,807,697 92.1 28,961,448 100.2 40,002,151 61.6 60,234,374 71.2 10,116,111 100.4 9,653,816 102.2 390,775,597 67.8hackberry 177,169,103 57.5 48,731,376 66.4 129,362,318 61.4 0 0 10,073,547 103.9 0 0 365,336,343 52.9

hawthorn spp. 40,464,446 100.4 91,045,003 80.9 89,658,117 66.4 19,769,928 71.6 40,002,151 61.6 0 0 280,939,645 68.8

black oak 0 0 90,120,413 53.4 99,774,229 58.8 9,653,816 102.2 10,116,111 100.4 19,769,928 70.9 229,434,496 47.6

bigtooth aspen 101,161,115 57.9 30,348,334 74.5 30,348,334 74.5 10,116,111 100.4 0 0 48,731,376 83.6 220,705,271 38.0

black willow 0 0 19,307,632 100.2 19,307,632 100.2 28,961,448 100.2 144,807,241 100.2 0 0 212,383,954 100.2

eastern hemlock 151,103,208 103.9 40,379,317 62.8 20,232,223 100.4 0 0 0 0 0 0 211,714,749 84.9

red pine 19,769,928 71.6 29,886,039 75.5 98,387,342 53.2 28,961,448 74.6 10,116,111 100.4 0 0 187,120,869 55.5

tamarack (native) 0 0 30,348,334 74.5 29,886,039 57.7 59,772,078 75.2 50,580,557 82.5 10,116,111 100.4 180,703,121 65.1Siberian elm 0 0 0 0 125,499,609 100.2 38,615,264 100.2 9,653,816 100.2 0 0 173,768,689 100.2

yellow birch 10,116,111 100.4 30,348,334 74.5 80,928,892 49.4 10,116,111 100.4 10,116,111 100.4 0 0 141,625,561 50.9

shagbark hickory 0 0 59,309,783 46.9 40,002,151 50.0 0 0 9,653,816 102.2 0 0 108,965,749 43.2

rock elm 10,116,111 100.4 0 0 50,580,557 100.4 0 0 20,232,223 100.4 20,232,223 100.4 101,161,115 100.4

jack pine 0 0 0 0 28,961,448 102.2 19,307,632 102.2 28,961,448 102.2 0 0 77,230,529 102.2

black walnut 0 0 9,653,816 102.2 29,423,744 57.7 0 0 9,653,816 102.2 0 0 48,731,376 66.4

white mulberry 28,961,448 75.0 0 0 9,653,816 102.2 9,653,816 102.2 0 0 0 0 48,269,080 73.2

Bebb willow 10,116,111 100.4 20,232,223 100.4 10,116,111 100.4 0 0 0 0 0 0 40,464,446 100.4

eastern redcedar 19,769,928 71.6 0 0 0 0 9,653,816 102.2 9,653,816 100.2 0 0 39,077,560 50.0

American beech 0 0 0 0 10,116,111 100.4 0 0 10,116,111 100.4 10,116,111 100.4 30,348,334 100.4

pin cherry 0 0 10,116,111 100.4 0 0 0 0 10,116,111 100.4 0 0 20,232,223 70.6

red mulberry 0 0 9,653,816 102.2 9,653,816 102.2 0 0 0 0 0 0 19,307,632 102.2

white spruce 0 0 0 0 0 0 10,116,111 100.4 0 0 0 0 10,116,111 100.4

slippery elm 0 0 0 0 10,116,111 100.4 0 0 0 0 0 0 10,116,111 100.4

Scotch pine 0 0 9,653,816 100.2 0 0 0 0 0 0 0 0 9,653,816 100.2

black locust 0 0 9,653,816 102.2 0 0 0 0 0 0 0 0 9,653,816 102.2

Total 30,495,293,581 19.8 18,851,106,558 12.2 27,224,403,344 10.7 7,595,225,221 14.5 4,939,158,331 12.8 2,382,628,018 17.7 91,487,815,053 11.2Common namea 2.0 to 5.9 in SEb 6.0 to 11.9 in SE 1.0 to 2.9 ft SE 3.0 to 4.9 ft SE 5.0 to 9.9 ft SE ≥10.0 ft SE Total SE

Table 5.—Number of seedlings by species and height class on forest land, Wisconsin, 2012-2013a Scientific names for all species are listed in Appendix I.b Sampling error (SE) expressed as a percent are shown for the 68 percent confidence level and are not applicable for estimates of zero. 
74 Appendix III Appendix III (continued). Table 6.—Number of seedlings per acre by species and height class on forest land, Wisconsin, 2012-2013 Common namea 2 in to 6 in SEb 6 in to 1 ft SE 1 ft to 3 ft SE 3 ft to 5 ft SE 5 ft to 10 ft’ SE ≥10 ft SE Total SE red maple 1,046 33.0 319 24.2 293 24.6 49 29.4 32 30.9 22 30.9 1,761 25.3 sugar maple 205 33.8 153 29.4 291 34.0 111 48.5 46 53.3 27 38.3 833 30.1 black cherry 101 33.1 91 16.1 121 16.7 32 22.1 26 24.8 9 34.7 379 16.4 black ash 61 44.2 49 59.3 132 47.4 48 55.7 23 68.1 14 51.7 327 47.4 American hornbeam 67 49.8 51 38.0 115 38.1 32 46.9 21 32.7 13 42.8 298 33.0 quaking aspen 16 45.5 34 29.5 91 24.6 24 34.5 30 37.3 37 45.4 232 20.6 balsam fir 67 29.7 45 32.0 59 28.0 27 31.9 29 38.3 1 100.3 227 24.0 white ash 12 37.7 54 39.9 119 35.1 21 46.0 10 50.5 5 78.1 221 32.8 serviceberry spp. 38 32.1 65 29.0 87 26.3 10 33.8 9 61.0 1 70.6 211 19.3 chokecherry 31 54.6 34 34.3 60 26.2 15 48.9 7 58.7 1 102.2 147 25.1 white oak 31 57.0 52 69.6 47 56.4 5 75.1 5 87.0 3 100.3 142 51.9 northern red oak 29 32.7 66 27.3 37 31.9 5 46.0 0 0 0 0 138 23.8 eastern hophornbeam 23 34.6 19 34.8 47 46.4 23 41.8 15 40.9 3 49.4 129 35.1 eastern white pine 52 77.0 17 35.3 24 38.4 13 55.3 8 85.1 6 74.5 120 42.3 northern pin oak 14 58.5 37 40.3 33 48.0 7 44.4 6 61.9 1 70.6 98 35.5 green ash 9 43.4 25 38.6 41 37.2 11 48.1 5 46.4 1 100.5 91 35.7 American elm 15 49.3 19 41.4 27 26.4 7 63.4 3 44.5 1 100.3 71 25.5 bitternut hickory 22 48.6 26 41.0 17 41.4 3 80.0 1 100.3 0 0 68 34.3 mountain maple 20 51.8 13 53.5 22 45.3 1 100.6 4 57.4 3 61.0 62 44.1 paper birch 12 52.3 5 37.2 11 51.6 17 61.2 12 56.0 3 52.5 61 43.0 silver maple 58 83.6 2 100.0 0 0 0 0 0 0 0 0 60 84.1 American basswood 9 57.6 9 46.9 21 52.2 1 70.1 1 102.0 1 102.0 43 35.6 black spruce 9 93.4 3 49.3 20 50.4 10 48.0 0 0 0 0 42 43.2 northern white-cedar 9 56.6 11 55.6 12 79.8 2 74.5 3 72.0 0 0 37 57.2 bur oak 1 100.3 7 44.3 9 60.0 9 60.8 3 61.1 0 0 29 46.3 boxelder 16 91.4 2 99.4 3 61.7 4 71.2 1 100.3 1 102.2 26 67.1 hackberry 12 57.1 3 66.3 9 61.3 0 0 1 103.8 0 0 24 52.6 hawthorn spp. 3 100.3 6 80.9 6 66.3 1 71.4 3 61.5 0 0 19 68.7 black oak 0 . 6 53.4 7 58.5 1 102.2 1 100.3 1 70.6 15 47.4 bigtooth aspen 7 57.9 2 74.5 2 74.5 1 100.3 0 0 3 83.4 15 37.9 black willow 0 . 1 99.4 1 99.4 2 99.4 10 99.4 0 0 14 99.4 eastern hemlock 10 103.7 3 62.6 1 100.3 0 0 0 0 0 0 14 84.6 red pine 1 71.4 2 75.4 6 52.7 2 74.4 1 100.3 0 0 12 55.1 tamarack (native) 0 0 2 74.5 2 57.3 4 74.9 3 82.5 1 100.3 12 64.9 Siberian elm 0 0 0 0 8 100.0 3 100.0 1 100.0 0 0 11 100.0 yellow birch 1 100.5 2 74.6 5 49.4 1 100.3 1 100.3 0 0 9 51.0 shagbark hickory 0 0 4 46.9 3 50.2 0 0 1 102.2 0 0 7 43.3 rock elm 1 100.3 0 0 3 100.3 0 0 1 100.3 1 100.3 7 100.3 jack pine 0 0 0 0 2 102.0 1 102.0 2 102.0 0 0 5 102.0 black walnut 0 0 1 102.0 2 57.6 0 0 1 102.0 0 0 3 66.2 white mulberry 2 74.3 0 0 1 102.2 1 102.2 0 0 0 0 3 72.8 Bebb willow 1 100.6 1 100.6 1 100.6 0 0 0 0 0 0 3 100.6 eastern redcedar 1 71.6 0 0 0 0 1 102.0 1 100.0 0 0 3 49.8 American beech 0 0 0 0 1 100.3 0 0 1 100.3 1 100.3 2 100.3 pin cherry 0 0 1 100.3 0 0 0 0 1 100.3 0 0 1 70.6 red mulberry 0 0 1 102.2 1 102.2 0 0 0 0 0 0 1 102.2 white spruce 0 0 0 0 0 0 1 100.3 0 0 0 0 1 100.3 slippery elm 0 0 0 0 1 100.3 0 0 0 0 0 0 1 100.3

Scotch pine 0 0 1 99.4 0 0 0 0 0 0 0 0 1 99.4

black locust 0 0 1 102.2 0 0 0 0 0 0 0 0 1 102.2Total 2,012 19.7 1,244 11.9 1,796 10.3 501 14.1 326 12.4 157 17.5 6,036 10.9

a Scientific names for all species are listed in Appendix I.b Sampling error (SE) expressed as a percent are shown for the 68 percent confidence level and are not applicable for estimates of zero. 
Printed on recycled paper

McWilliams, William H.; Westfall, James A.; Brose, Patrick H.; Dey, Daniel C.; 

Hatfield, Mark; Johnson, Katherine; Laustsen, Kenneth M.; Lehman, Shawn L.; 

Morin, Randall S.; Nelson, Mark D.; Ristau, Todd E.; Royo, Alejandro A.; Stout, 

Susan L.; Willard, Thomas; Woodall, Christopher W. 2015. A regeneration 

indicator for Forest Inventory and Analysis: history, sampling, estimation, 

analytics, and potential use in the midwest and northeast United States.

Gen. Tech. Rep. NRS-148. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 74 p.

The density and composition of regeneration drives future forest character for 

forests in need of replacement. Forested ecosystems face numerous regeneration 

stressors including invasive plants, insects and diseases, herbivory, lack of 

management, and climate change. As stands that make up these systems age, it 

is imperative to track the viability of forest reproduction. The information required 

for understanding the complexity of forest dynamics during the stand establishment 

stage has been lacking in our Nation’s forest inventory. This poses a particular 

problem for analysts working with the major deciduous forest systems of the 

Midwest and Northeast United States that require detailed information on advance 

reproduction. To address this need, the Forest Inventory and Analysis (FIA) program 

of the U.S. Forest Service, Northern Research Station (NRS) has added protocols 

for measuring all established tree seedlings and for assessing browse impact. 

This information is compiled using new NRS-FIA forest sampling and analytical 

methodologies—the regeneration indicator. The regeneration indicator is described 

along with examples and suggestions to guide research on the difficult question 

of whether the region’s forests are able to regenerate in the face of numerous 

stressors.

Key words: forest sustainability, forest management, forest regeneration, 

 tree reproduction, mixed oaks, northern hardwoods

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all prohibited bases apply to all programs.) Persons with disabilities who require alternate means for 

communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s 

TARGET Center at 202-720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, 

Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, DC 20250-9410, or call 

800-795-3272 (voice) or 202-720-6382 (TDD). USDA is an equal opportunity provider and employer.
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