Forest Service Northern Research Station General Technical Report NRS-184

Gaps in Available Data for Modeling 

Tree Biomass in the United States

Jereme Frank, Aaron Weiskittel, David Walker, 

James A. Westfall, Philip J. Radtke, David L.R. Affleck, 

John Coulston, David W. MacFarlane

United States Department of Agriculture

March 2019
Published by

USDA FOREST SERVICE

11 CAMPUS BLVD SUITE 200

NEWTOWN SQUARE PA 19073

March 2019

https://www.nrs.fs.fed.us/pubs/

Manuscript received for publication 17 April 2018

Abstract

When estimating tree-level biomass and carbon, it is common practice to develop generalized 

models across numerous species and large spatial extents. However, sampling efforts are 

generally incomplete and trees are not randomly selected. In this analysis, of the more than 1,000 

biomass-related articles that were reviewed, trees were destructively sampled in over 300 studies 

to estimate biomass in the United States. Studies were summarized and past sampling efforts 

were explored to illuminate where the largest data gaps occurred in terms of tree components 

sampled, tree size, tree form, tree species, and location. The most prominent gaps were in large 

trees, particularly in Douglas-fir trees in the Pacific Northwest. In addition, tree roots were notably 

undersampled. Lastly, trees of poor or unusual form and low vigor were often not sampled, and 

this may introduce a systematic bias if not dealt with appropriately. More than 200 species did 

not have a biomass model or a single data point. The gaps presented here can be viewed as 

suggestions for future destructive sampling efforts, but the magnitude of a gap for a given model 

will ultimately depend on the selected modeling framework and the user’s objectives.

The Authors

JEREME FRANK is a research assistant with the University of Maine, School of Forest Resources, 

Orono, ME 04469; jeremem.frank@gmail.com.

AARON WEISKITTEL is a professor of forest biometrics and modeling with the University of Maine, 

Center for Research on Sustainable Forests, Orono, ME 04469.

DAVID WALKER is a research associate with Virginia Tech, Department of Forest Resources and 

Environmental Conservation, Blacksburg, VA 24061.

JAMES A. WESTFALL is a research forester with the USDA Forest Service, Northern Research 

Station, Newtown Square, PA 19703.

PHILIP J. RADTKE is an associate professor of forest biometrics with Virginia Tech, Department of 

Forest Resources and Environmental Conservation, Blacksburg, VA 24061.

DAVID L.R. AFFLECK is a professor of biometrics with the University of Montana, W.A. Franke 

College of Forestry and Conservation, Missoula, MT 59812.

JOHN COULSTON is a supervisory research forester with the USDA Forest Service, Southern 

Research Station, Blacksburg, VA 24060.

DAVID W. MACFARLANE is a professor of forest measurements and modeling with Michigan State 

University, Department of Forestry, East Lansing, MI 48824.

Cover Art

Cover artwork by D.L.R. Affleck, D.W. MacFarlane, and J. Frank, used with permission.
Gaps in Available Data for Modeling 

Tree Biomass in the United States

Jereme Frank, Aaron Weiskittel, David Walker, James A. Westfall, 

Philip J. Radtke, David L.R. Affleck, John Coulston, David W. MacFarlane

General Technical Report NRS-184
CONTENTS

Introduction ................................................................................................................................................1

Rationale .................................................................................................................................................1

Generalized Allometric Models.......................................................................................................1

Previous Tree Biomass Allometric Model Compilations.........................................................1

Current Biomass Estimation Approaches for the U.S. Forest Inventory ............................2

Objectives and Expectations...........................................................................................................3

Methods ........................................................................................................................................................3

Recording Information for the Gap Analysis..............................................................................3

Assessment and Quantification of Data Gaps by Location, Species, and Diameter......5

Results............................................................................................................................................................6

Tree and Component Mass...............................................................................................................6

Tree Selection........................................................................................................................................6

Geography, Site Selection, and Location...................................................................................10

Data Gaps by Region, Tree Species, and Diameter Class......................................................11

Discussion...................................................................................................................................................11

Alternatives for Further Sampling................................................................................................11

Model-based Approaches to Fill Data Gaps.............................................................................12

Sampling for Trees of Varying Form, Risk, and Vigor, and Improving 

Estimates of Component Allometry............................................................................................14

Summary, Limitations, and Recommendations for Future Research ................................15

Summary...............................................................................................................................................15

Limitations............................................................................................................................................16

Recommendations for Continued Research.............................................................................16

Acknowledgments..................................................................................................................................26

Literature Cited ........................................................................................................................................26

Appendixes..............................................................................................................................33

Appendix 1: Bibliography of Reviewed Studies.......................................................................33

Appendix 2: Common and scientific names for species mentioned in the 

text and the 30 tree species with the most biomass in the FIADB ...................................57

Supplemental Figures ...............................................(https://doi.org/10.2737/NRS-GTR-184.s1)

Supplemental Tables and Appendix S2 ............(https://doi.org/10.2737/NRS-GTR-184.s2)
1

INTRODUCTION

Rationale

Given increasing interest in accounting for forest biomass and carbon stocks (Aalde et al. 

2006, Pan et al. 2011) across the world, and specifically in the United States, there is a need 

to more closely examine limitations to current tree biomass estimation methods. At a broad 

level, biomass estimation methods are often categorized as (1) regional biomass conversion/

expansion factors (e.g., Fang and Wang 2001), (2) stand-level biomass models, or (3) treelevel biomass models (Temesgen et al. 2015). In particular, there are two primary factors that 

influence biomass estimation accuracy: limitations to modeling techniques, and sampling gaps 

that limit available data and the geographic extent of model applicability. This review explores 

gaps in previous sampling efforts that limit the available data required to develop tree-level 

biomass models across the United States. Other important limitations of biomass estimation 

methods (including modeling techniques) are discussed in Weiskittel et al. (2015).

Generalized Allometric Models

Tree-level biomass models are generally derived by destructively sampling a subset of live 

trees, drying and weighing the separate tree components (e.g., stems, branches, foliage), and 

using allometry to relate some easily measured metric (e.g., diameter and sometimes height) 

to the dry weight of the entire tree or some portion or component of the tree. Destructive 

sampling is extremely costly and time intensive; thus, most biomass studies sample a relatively 

small number of trees over a generally small area. In addition, sampling units are difficult 

to select objectively or at random due to operating restrictions and landowner limitations. 

Consequently, sampling efforts are often coordinated with ongoing management objectives or 

otherwise constrained to areas where destructive sampling is feasible. As such, it is difficult to 

be entirely representative in both site and tree selection.

Variations in wood and bark properties, crown architecture, and stem form can result from 

different climatic, soil, or management factors (Larson 1963), making it challenging to 

extrapolate biomass to a different location or larger area. Those seeking to derive stand- and 

landscape-level biomass and carbon estimates often turn to geographically generalized treelevel allometric models, which use data from multiple studies and locations to refit models to 

a larger area (e.g., Schmitt and Grigal 1981).

In addition to considering how sites are selected, it is important to consider how individual 

trees are selected. Standards for sampling trees for volume and yield tables (e.g., Behre et 

al. 1926), and by extension biomass models, generally exclude trees with abnormalities and 

defects from model fitting because they detract from a study’s ability to identify allometric 

relationships. This selection effect can result in a significant amount of systematic bias, leading 

to overestimates, particularly in hardwood biomass (e.g., MacFarlane and Weiskittel 2016).

Previous Tree Biomass Allometric Model Compilations

The plethora of published allometric models and their frequent use in estimating biomass and 

carbon across various scales has necessitated literature syntheses to understand limitations in 

both the models and the data used to build them. In Europe, Zianis et al. (2005) summarized 

the number of biomass models in terms of species by tree component and country. Similar 

syntheses have summarized models in Australia (Keith et al. 2000), sub-Saharan Africa 

(Henry et al. 2011), southeast Asia (Yuen et al. 2016), South America (Cifuentes-Jara et 

al. 2013), and North America (Ter-Mikaelian and Korzukhin 1997). Jenkins et al. (2004) 
2

summarized biomass models from 177 biomass studies for tree species commonly found in 

the United States. More recently, Chojnacky et al. (2014) updated these models. While most 

of these compilations focus on developing or comparing models, recent work in the United 

States has led to a compilation of actual tree-level volume, biomass, and component biomass 

data (Radtke et al. 2015).

Current Biomass Estimation Approaches for the 

U.S. Forest Inventory

Perhaps the most widely used standing tree inventory available in the United States is the 

USDA Forest Service, Forest Inventory and Analysis Database (FIADB) (O’Connell et al. 

2016). This database is also considered the best source for estimating biomass and carbon 

at the landscape level in the United States. For example, the Forest Inventory and Analysis 

(FIA) program reports its carbon estimates to the United Nations Framework Convention 

on Climate Change (UNFCCC) (USEPA 2017), and the Department of Energy relies on 

FIA’s estimates of biomass (Aalde et al. 2006). Furthermore, biomass is the basic unit of 

productivity used in numerous ecological studies (e.g., Clark et al. 2001, Parker and Schneider 

1975, Whittaker et al. 1974), and it is clearly important that mean biomass predictions and 

characterizations of uncertainty are accurate.

To estimate tree-level biomass, FIA uses the Component Ratio Method (CRM) (Woodall et 

al. 2011b), having switched in 2008 from previously using regionally-specific biomass models 

(e.g., Wharton and Griffith 1993). CRM uses species-specific regional volume models, deducts 

cull to estimate sound cubic-foot wood volume, and uses biomass conversion and expansion 

factors (BCEFs) to estimate total aboveground biomass without foliage. These BCEFs include 

wood and bark density values (as presented in Miles and Smith 2009) as well as nationally and 

taxonomically generalized CRM models (as presented in Jenkins et al. 2003). These models 

are then applied to the tree list in the FIADB to estimate state, regional, and national biomass. 

Despite this recent switch to the CRM, assessment of the new approach has been minimal 

(e.g., Domke et al. 2012). A recent analysis using observed tree-level biomass data from the 

eastern states suggests that the CRM method underestimates biomass by 6-15 percent across 

the eastern United States (Radtke et al. 2017). Because a fully comprehensive destructive 

sampling effort to obtain tree biomass data across the United States has not been conducted, it 

is also important to understand how sampling gaps may influence past and future assessments.

An important underlying assumption of this work is that tree biomass can vary between 

species, diameter at breast height (d.b.h.) or d.b.h./height, and region even when other 

factors are held constant. Basic allometry suggests that for a given species within a region, 

biomass will increase as d.b.h. increases (Jenkins et al. 2003). In addition, for a given tree size, 

biomass will vary between species within a region due to differences in wood density (Miles 

and Smith 2009) and tree architecture. Regional differences, however, are more difficult to 

detect. For instance, Jenkins et al. (2003) found that biomass regressions of the same species 

varied considerably, but no regional patterns were apparent. This brings up the question as 

to whether apparent biomass differences should be attributed to regional variation, noise in 

biomass measurements, or high site-to-site variability.
3

Objectives and Expectations

The primary objectives for this study were threefold: 

1. Report on the number of studies that have been conducted and the number of trees 

sampled by core component groups to estimate biomass in the United States.

2. Assess the sample sizes and diameter ranges for key species evaluated in previous studies.

3. Characterize the sampling locations of certain species and compare that to the 

geographic range of the species.

Data collected across these studies will ultimately be used to test the assumptions mentioned 

in the previous section. Thus, data gaps were considered in terms of (1) how they may affect 

generalized models and CRM estimators used to assess biomass at various scales across 

the United States; (2) how the data can serve to validate current models; and (3) how they 

might restrict future model applicability when using actual data. Ideally, from a validation 

perspective, the relative representation of data from destructively sampled trees would match 

the standing tree inventory. However, from a modeling perspective, ensuring sampling across 

the range of attributes may be preferred (i.e., ensuring some samples of large trees at the tail 

of size distribution rather than a high number of trees at the peak). To illustrate, the publicly 

available FIADB was used to compare and formally assess gaps in terms of tree size classes, 

species, and location.

METHODS

Recording Information for the Gap Analysis

Literature Review

To complete this analysis, an exhaustive search of the literature on tree biomass studies 

in the United States was conducted. First, previous tree biomass model syntheses and 

databases (Chojnacky et al. 2014, Jenkins et al. 2004, Ter-Mikaelian and Korzukhin 1997) 

were examined, and the literature describing the studies cited in these articles were used as a 

source for additional resources. The literature review leveraged these past works by providing 

additional details on each study, synthesizing the additional information, and incorporating 

newer datasets. In addition, this review encompassed a recent (and ongoing) compilation 

of biomass data from destructively sampled trees (Radtke et al. 2015), hereafter referred to 

as the “legacy tree database.” The data search for the legacy tree database extended beyond 

the peer-reviewed literature and incorporated a number of unpublished studies. As a matter 

of accounting and considering the widespread use of the models developed by Jenkins et al. 

(2004) and Chojnacky et al. (2014) in the United States, it was noted if the reviewed studies 

appeared in these works. It was also recorded if actual biomass data or only a model could be 

obtained from each study. For studies that had a model, it was noted if the model contained 

height as a predictor and if the author reported standard error. The objective was to quantify 

the number of trees greater than 1 inch d.b.h. within the United States for which at least 

one tree component was destructively sampled for dry weight. Studies were not included 

in the analysis if they were located outside of the United States, were compilations, were 

more theoretical or statistical in nature, measured only external characteristics (without any 

destructive sampling) on standing trees, sampled only seedlings (i.e., trees less than 1 inch 

d.b.h.) or shrubs, measured only green weight or volume, or focused on biomass estimates at 

the stand or regional level rather than the tree level. Exceptions to these rules included studies 

where both seedlings and larger trees were sampled and the number of each could not be 

determined. In these cases, the total number of sampled trees included seedlings.
4

Tree Components

The primary interest of this study was in estimating aboveground biomass (AGB). However, 

given that other researchers may be interested in the mass of individual tree components, all 

components sampled were reported. For instance, the CRM and biomass expansion factors 

(BEFs) in general rely on the relationship between a component (particularly bole biomass) 

and AGB (without foliage). In addition, recent studies suggest that variations in whole-tree 

mass are related to differences in the way mass is allocated to tree components (e.g., the 

crown) (Goodman et al. 2014), and that bole volume is strongly affected by the allocation 

of wood to branches (MacFarlane and Weiskittel 2016). Filling gaps in tree component data 

is also important because tree utilization is best understood when the tree is modeled as 

the sum of its interrelated parts (MacFarlane 2015). Finally, branch and bark models might 

provide preliminary estimates for bioenergy harvests (Conner and Johnson 2011, USDOE 

2011), while foliage estimates are integral for ecosystem process modeling (e.g., Chapin et al. 

2002).

The USDA Forest Service National Biomass Estimation Library (Wang 2014), an extension 

of the Jenkins et al. (2004) database, identified 47 different tree component classes, many of 

which can be seen as subclasses of other components. For this analysis, gaps in available data 

were assessed for nine tree component groups: (1) total stem (wood and bark) biomass; (2) 

branch wood and bark biomass; (3) total above-stump biomass (including foliage); (4) root 

biomass; (5) stem wood biomass; (6) stem bark biomass; (7) total above-stump wood and 

bark biomass (excluding foliage); (8) crown (foliage and branch) biomass; and (9) foliage 

biomass. Studies vary considerably in how they differentiate between size classes of roots 

or branches, and often models calculate the biomass of the entire component and do not 

separate by size. In this analysis, a study was considered to have examined root biomass if any 

portion of the root biomass was measured. For branches (wood and bark), the study had to 

include at least the live branch component, although for most studies, the branch component 

included both live and dead branches. There were also differences in how components 

were separated. For example, in most cases the foliage component was separated from the 

twig; however, in some cases, foliage was defined as leaf and twig (e.g., Lambert et al. 2005, 

Whittaker et al. 1974). Finally, studies varied in how they defined the stem, specifically 

whether it constituted the entire stem or merchantable stem, and how the merchantable stem 

was delimited (i.e., “topped”). For this analysis, the stem was considered to be sampled if the 

entire merchantable stem was sampled regardless of where it was delimited.

Tree and Site Selection Criteria

For each reviewed study, the author, year, species, and location were recorded. In addition, 

for each location by species combination, the sample size; average, minimum, and maximum 

tree diameter; and tree components sampled were recorded. Each study was then examined to 

determine if tree-sampling restrictions were imposed as evidenced by avoiding trees of poor 

form (e.g., low forks, excessive branching, or broken tops) or poor health (e.g., showing signs 

of low vigor, damage, or disease, and at high risk of mortality). The best available information 

for site locations was used to estimate latitude and longitude for each study. Frequently, 

coordinates could be estimated to within 0.05°, although accuracy was much lower for 

studies that had a location description that was either too general or the extent of the area 

sampled was too large and little site-specific information was provided. Based on the location 

data, trees were assigned to one of four FIA regional units: Northern, which was split into 

Northeast (NE) and North Central (NC); Southeastern (SE); Intermountain West (IMW); and 

Pacific Northwest (PNW).
5

The lack of uniformity in recording site characteristics across studies makes it difficult to 

utilize site factors in the modeling framework. Authors of many articles do not adequately 

explain how sites are selected and do not describe physiography, landscape, or stand 

attributes. Some studies utilize classification systems, but groupings often differ across studies 

or use qualitative descriptors such as poor and good, which makes intra-study comparisons 

difficult. Site index can be used to classify studies, but in a previous review, site index was 

recorded in fewer than 10 studies (Ter-Mikaelian and Korzukhin 1997). Instead, in this 

analysis, data gaps were assessed by location and region, two factors that can be viewed as 

proxies for variations in climatic conditions, edaphic characteristics (such as soil type or 

depth), and treatment.

Assessment and Quantification of Data Gaps by Location, Species, 

and Diameter

This analysis examined available models and actual data (i.e., data available in the legacy tree 

database) and includes summaries of (1) the total number of trees sampled across all studies; 

(2) the total number of studies conducted; and (3) the number of trees available in the legacy 

tree database across all species by components and region. The summaries can be seen as a 

first assessment of gaps in available data.

As a means to assess gaps in data by tree diameter, diameter distributions for a given species 

were compared between the FIADB and the legacy tree database. Boxplots showing the 

maximum, minimum, median, and 25th and 95th quantiles were used to show discrepancies 

between the distributions of the two datasets and to highlight the limitations in the range of 

trees sampled in the legacy tree database. Distributions of the data were further examined for 

species groups and species by proportion of trees and proportion of biomass.

To visualize and assess spatial gaps in data (i.e., do the number of trees sampled represent the 

biomass across a species’ range), maps showing the FIADB-estimated biomass per acre for 

the 30 species with the most biomass across the continental United States were overlaid with 

the locations of past biomass studies. Latitudinal and longitudinal biases were depicted by 

calculating and plotting spherical centroids weighted by FIA plot biomass data and sample 

size in the legacy tree database. The FIA estimate for total tree biomass was calculated as 

oven-dry biomass in the merchantable bole + dry biomass of tops and limbs of timber species 

(DRYBIO_BOLE + DRYBIO_TOP in O’Connell et al. 2016). Percent error was calculated by 

taking the ratio of the distance between the centroids and the diagonal distance between the 

minimum and maximum coordinates and then converting this to a percentage.

A goal of this study was to quantify whether past sampling efforts undersampled trees of 

different sizes and species relative to the amount of biomass reported for trees of the specified 

sizes and species represented in the U.S. forest carbon inventory. The number of trees for a 

given species, diameter size class (d.b.h. <5 inches = 1; 5 to <15 inches = 10, 15 to <25 inches 

= 20, etc.), and FIA region in the legacy tree database and the percentage of the biomass 

present for the same groups in the FIADB were calculated. Potential gaps were described for 

illustrative purposes by using a baseline tree sampling objective equal to the FIADB biomass 

percentage x 10. As a measure of sampling effectiveness, the sampling completeness value 

(SCV) was calculated as the product of the number of trees in the legacy tree database and 

the sampling objective. In addition, representation for a biomass pool was calculated as the 

difference between the percentage of the number of trees in the legacy tree database and the 

percentage of biomass in the FIADB.
6

RESULTS

Over 1,060 studies were identified that were potentially related to tree-level biomass 

estimation. After eliminating studies determined not to contain data relevant to this analysis, 

844 studies were further examined. A preliminary review of these 844 studies determined 

that 351 had actual tree-level aboveground or component biomass (e.g., stem, foliage, roots, 

or branches) measurements assessed in the United States. Trees were destructively sampled 

in most of the studies, but in some cases trees were cored and climbed with limited branch 

sampling (e.g., Sillett et al. 2015). Of the biomass studies that used destructive sampling, 25 

were duplicates and were removed, leaving 326 unique studies with a combined total of 47,684 

trees in the analysis (see Appendix 1 for a list of all studies). A total of 240 of these studies (74 

percent) were not included in Jenkins et al. (2003), which developed generalized models using 

numerous finer-scale models to generate pseudodata.

Tree and Component Mass

A total of 24,791 trees (52 percent of the 47,684 trees sampled) were identified from 199 

studies that employed destructive sampling for above-stump biomass across the United States 

(Table 1; tables begin on p. 17). Stem biomass was measured for 27,080 trees (57 percent); 

stem wood biomass estimates were available for 25,618 trees (54 percent); and wood biomass 

estimates were provided for 20,283 trees (43 percent). Stem measurements generally were 

taken from a variable stump (generally <1 foot) to a variable top, which was almost always 

reported as a value <4 inches diameter outside bark. Branch, crown, and foliage biomass 

was estimated for 22,197 (47 percent); 19,154 (40 percent); and 21,262 (45 percent) trees, 

respectively. Estimates for the stump and root portions of the tree were less common, with 

root biomass measurements identified for only 2,840 (6 percent) trees.

When considering legacy tree data only, the percentage of known trees measured ranged from 

72 percent (14,548 out of 20,283 trees) for stem bark biomass to 13 percent for root biomass 

(363 out of 2,840 trees). Trees that had both stem and above-stump biomass measured 

included 11,402 trees from the legacy tree database and 17,251 trees across all studies. In the 

legacy tree database, above-stump biomass (with or without foliage) was measured for 14,073 

trees. This number is slightly higher than the number of trees with above-stump biomass and 

the number of trees with total wood biomass (Table 1) because it included trees both with 

and without foliage. For the 30 species with the greatest biomass in the FIADB, subalpine fir1, 

Sitka spruce, white fir, and grand fir were lacking in total aboveground estimates. Mountain 

hemlock and pignut hickory were undersampled for all components and have no known 

destructively sampled trees. Data for all 148 species that had at least one tree sampled can be 

found in Table S1 (supplemental files for tables S1-S8 and Appendix S2 are available at https://

doi.org/10.2737/NRS-GTR-184.s2).

Tree Selection

Species

Gaps in species data were depicted by tree component (Table 1), study type (Table 2), and 

region (Table 3). Excluding more general taxonomic groupings and species in the Caribbean 

and Pacific, 354 unique tree species were identified in the FIADB. Models or legacy tree data 

were available for 148 of these species (see Appendix S2 in supplemental tables file for list), 

leaving 206 species for which a published biomass model could not be located. These 206 

species, however, accounted for less than 10 percent of the total biomass in the FIADB. The 

1

Scientific names for all tree species are listed in Appendix 2. 
7

10, 20, and 30 species with the greatest amount of biomass in the FIADB made up 42 percent, 

57 percent, and 67 percent, respectively, of the total biomass in the FIADB and 33 percent, 42 

percent, and 61 percent, respectively, of the destructively sampled trees in the literature. The 

species with the greatest number of trees was loblolly pine, with 9,967 trees sampled (Table 2). 

By comparison, only 1,177 Douglas-fir trees have been sampled for at least one component 

of tree biomass. Of the 30 species with the greatest amount of biomass in the FIADB, pignut 

hickory and mountain hemlock had no samples in the legacy tree database (Table 2). 

Additional species with no samples included Pacific silver fir, shagbark hickory, and common 

pinyon (Table S2). It is worth noting that although more hickory trees were sampled, they were 

rarely identified to species (e.g., Clark et al. 1986, Wiant 1977). Additional ash, poplar, elm, 

birch, hackberry, and oak species were also sampled, but were not identified to species (see 

Appendix 2 in this document and Appendix S2 in supplemental tables file for the complete list 

of species and genera in this study).

Sample Size, Size Classes, and Diameter Distributions

Relatively few studies sample trees over 20 inches (500 mm) in d.b.h. and sample more than 

500 trees. Given this finding, studies that sampled a high number of trees, including large trees, 

were noted as higher in importance. An importance index (sample size * maximum d.b.h. 

in inches/100) was calculated for each study, and the studies with the 30 highest importance 

indexes (Table 4, Fig. 1) contained 21,659 of the 47,684 trees evaluated for this analysis. These 

included the work of Alexander Clark (Clark and Saucier 1990; Clark and Schroeder 1986; 

Clark et al. 1985, 1986), which contributed 4,712 trees in a rather comprehensive sampling of 

the southeast region; Young et al. (1980), who sampled an estimated 965 trees in Maine; and 

4000

6000

8000

D.B.H. (in mm)

Sample size

0

500

1000

1500

2000

Bailey et al. 1982

Bailey et al. 1985

Brenneman et al. 1978

Brown 1978

Burkhart and Clutter 1971

Burkhart et al. 1972

Clark and Saucier 1990

Clark and Taras 1976

Clark et al. 1985 & 1986

Clutter et al. 1984

FIA 2016

Flowers 1978

Hyink et al. 1972

Jordan et al. 2008

Lohrey 1985

Madgwick and Kreh 1980

Monteith 1979

Perala and Alban 1994

Pienaar et al. 1990

Queen and Pienaar 1977

Reed et al. 1995

Reinhardt 2000

Schlaegel (n.d)

Schlaegel 1975a

Sillet et al. 2010

Sillett et al. 2015

Snell and Max 1985

Sollins and Anderson 1971

Storey et al. 1955

Young et al. 1980

Author

Number of trees sampled and maximum d.b.h. (mm)

Figure 1.—Sample size and maximum d.b.h. (in mm = inches * 25.4) sampled for the 30 studies with the 

greatest importance index (simply sample size * maximum d.b.h. (mm) sampled). A threshold of 500 (red line) 

highlights the studies that sampled a maximum diameter tree greater than 19.7 inches (500 mm) or sampled 

more than 500 trees. Please note the discontinued scale. (See Appendix 1 for study citations.)
8

Perala and Alban (1994), who sampled extensively across the Great Lakes region (NC region). 

The works of Sillett et al. (2010, 2015) are highly influential, being the only U.S. studies to 

measure above-stump biomass for trees over 50 inches in d.b.h. The redwoods sampled in 

California are by far the largest trees sampled across North America. Otherwise, the majority 

of the 30 studies with the highest importance index occurred in the eastern United States, with 

more than half from the southeast (Table 4).

For most species, the largest trees were generally undersampled (Fig. 2). Across the United 

States, the largest tree sampled for biomass was generally greater than 95 percent of the trees 

in the FIADB; however, for a given species, the largest destructively sampled tree was often 

less than half the size of the largest tree in the FIADB (Fig. 2). By count, hardwood trees with a 

d.b.h. from 1 to 19 inches and conifers with a d.b.h. from 1 to 23 inches represented 99 percent 

of trees across the landscape. While the vast majority of trees were in the smaller diameter 

classes, the greatest proportion of biomass was in the mid-range diameter classes (Fig. 3). By 

individual species, distribution peaks consistently shifted from saplings (1 to <5 inches d.b.h.) 

when examining the proportion of trees by count to small trees (5 to <15 inches d.b.h.) when 

examining the proportion of biomass by size class (Figs. S3.1 to S3.30). A shift in the peaks 

of the distribution from small diameter classes in the FIADB to larger diameter classes in 

the legacy tree database was also generally observed, suggesting that trees were destructively 

sampled to cover a selected range of diameters rather than being sampled strictly according to 

the distribution. However, these trends likely vary by state based on current composition and 

past management.

0

25

50

75

100

125

Douglas−fir

Loblolly pine

Red maple

White oak

Sugar maple

Western hemlock

Northern red oak

Ponderosa pine

Yellow−poplar

Sweetgum

Diameter (inches)

Source

FIA Inventory

Legacy Data

Figure 2.—The boxplot shows the diameter distributions of forest land trees in the FIA database compared 

to trees in the legacy tree database for the 10 species with the most biomass. The 25th and 95th quantiles 

are represented by the bottom and top of the box, respectively, and maximum and minimum values are 

represented by the top and bottom of the lines, respectively. The line that dissects the box marks the median 

value. Additional figures for the remaining 30 species with the most biomass in the FIADB are available as 

supplemental materials (Figs. S2.2 and S2.3).
9

Tree Sampling Restrictions Based on Form, Risk of Mortality, or Vigor

For many studies, it was difficult to determine whether sampling restrictions were imposed. 

Of the 326 studies (47,684 trees) examined, it was determined that 67 studies (10,080 trees) 

imposed sampling restrictions, while 46 studies (10,903 trees) described their sampling design 

as random and did not indicate any sampling restrictions. For the remaining 211 studies, tree 

selection methods were unclear. Clark et al. (1985, 1986) classified trees as growing stock, 

rough, and rotten/cull, and we assumed that there were no sampling restrictions; Perala and 

Alban (1994) randomly selected trees and recorded rot and stain, while Schlaegel (1975) took 

diligent note of external defect and measured rot and stain. Generally, sampling restrictions 

were evidenced in methodologies that avoided trees that were open grown, heavily defoliated, 

broken at the top, low-forked, diseased, or otherwise distorted (Brown 1978). In other 

cases, only good to average form or “healthy” trees were selected (Hocker and Earley 1983, 

Levia 2008). In one case, if trees were over 12 inches d.b.h., only poor form or less vigorous 

trees were selected (Bridge 1979). In other cases, the sample may have been biased toward 

a particular class of trees according to the objectives of a thinning study (Goldsmith and 

Hocker 1978).

0.00

0.01

0.02

0.03

0

10

20

30

40

50

60

70

Proportion (trees)

Hardwoods

0.00

0.01

0.02

0.03

0.04

0

10

20

30

40

50

60

70

Proportion (trees)

Source

FIA Inventory

Legacy Data

Conifers

0.000

0.004

0.008

0.012

0

10

20

30

40

50

60

70

Diameter (inches)

Proportion (biomass)

0.00

0.01

0.02

0.03

0

10

20

30

40

50

60

70

Diameter (inches)

Proportion (biomass)

Figure 3.—Tree frequency by diameter class in the FIA and legacy tree databases for hardwoods and conifers 

(top panels) and the proportion of biomass by diameter in the FIA and legacy tree databases (bottom panels). 

Given the overwhelming proportion of biomass in redwood trees in the legacy tree database, they were excluded 

from this figure. Individual species figures for the 30 species with the most biomass in the FIADB are available as 

supplemental materials (Fig. S3).
10

Geography, Site Selection, and Location

A comparison of FIADB-estimated biomass per acre across the species range and known 

biomass study locations showed some variation for the four species with the most biomass 

(Fig. 4). For red maple, over 95 percent of reported biomass was in only 22 of the 31 states 

where this species occurs. In particular, the Allegheny Plateau in northwestern Pennsylvania 

and southwestern New York represented areas of high red maple biomass. However, prior 

to FIA sampling in 2014, no more than eight red maple trees had been sampled in these 

areas (Wood 1971), and none of these trees had a d.b.h. greater than 11.8 inches. In contrast, 

sampling for loblolly pine appeared to be relatively complete across its range, while Douglasfir could be further studied in the southeast portion of its range, and white oak could be 

further studied in eastern Missouri. Comparisons between the legacy and FIA biomass 

centroids for Douglas-fir, loblolly pine, and red maple showed relatively good agreement, 

with differences of 6.4, 4.0, and 3.5 percent, respectively (Table 5). By comparison, white oak 

showed a southeasterly sampling bias of approximately 9.7 percent (311 miles), which would 

be alleviated by sampling towards the western edge of its range. Individual maps comparing 

the FIADB-estimated biomass per acre across the species range and known biomass study 

locations are available for the 30 species with the greatest biomass (Fig. S4.1-S4.30).

45 

Q) 

"O 

:::J 

E 40 ro 

_J 

35 

45 

a> 40 "O 

..... 

:::J 

:;:::. 

ro 

....J 35 

30 

25 

•o-

-125 -120

-95 -90

Douglas-fir 

-115

Longitude 

Red maple 

-85 -80

Longitude 

• 

• 

• 

0 

-110 -105

< 10 trees sampled 10-20 trees sampled > 20 trees sampled other components

legacy centroid FIA biomass centroid

-75 -70

40 

Q) 

"O 

:::J 

E 35 ro 

_J 

30 

-95

45 

a> 40 1 , _ 

"O 

.. 

..... 

:::J 

:;:::. 

ro 

_J 

35 

30 

-95 -90

Loblolly pine 

-90 -85

Longitude 

White oak 

-85 -80

Longitude 

-80 -75

-75 -70

Figure 4.—Known biomass study locations and maximum sample size sampled for the four most prevalent species 

by biomass across the United States plotted over biomass per acre as estimated from the FIA database. Low biomass 

sites appear in lighter shades of yellow and green and high biomass sites appear in shades of dark green and blue. 

Species level maps for the 30 species with the most biomass in the FIADB are available as supplemental materials 

(Fig. S4).
11

Over half of the trees sampled in the United States came from the southeastern region (Table 

3), which included states as far west as Texas. While 42,962 trees were sampled in the eastern 

states (NE, NC, SE), only 4,722 trees (9.9 percent) were sampled in the western states (PNW 

and IMW).

Data Gaps by Region, Tree Species, and Diameter Class

The percentage of total biomass and the number of legacy trees sampled for aboveground 

wood and bark biomass (with or without foliage) were calculated for species by region by 

diameter class combinations. The 30 combinations with the greatest amount of biomass 

accounted for approximately a third of the cumulative biomass in the FIADB, and tentative 

sampling objectives were met for most of these groupings (Table 6). The sampling objective 

was not met in all cases, however, and the 30 species with the most biomass where the 

sampling objective was not met (i.e., SCV<1) are highlighted in Table 7. In the SE, species 

including loblolly pine, yellow-poplar, and white oak were adequately sampled in both the 

10 and 20 inch diameter classes, as were slash pine, red maple, and water oak in the 10 inch 

diameter class. However, when examining all groupings, species such as pignut hickory and 

sugar maple in the 10 inch diameter class and loblolly pine in the 30 inch diameter class were 

undersampled (see Table S7 for all tree groupings). In the PNW, large Douglas-fir (40 to 50 

inch diameter classes) and western hemlock (10 to 30 inch diameter classes) were noticeably 

undersampled, along with 20 to 30 inch Douglas-fir and grand fir in the IMW. In the northcentral region, no 20 inch black oak, 10 inch green ash, or 10 inch black ash trees were 

sampled. In the northeast region, species such as red maple and northern red oak (Quercus 

rubra L.) in the 20 inch diameter class were notably undersampled. Large Douglas-fir was also 

underrepresented, while loblolly pine was perhaps overrepresented with more than 1,400 trees 

sampled in the 10 inch diameter class (i.e., 10 inch loblolly pine make up 4.6 percent of the 

biomass in the FIADB and 10.1 percent of the trees in the legacy tree database). In addition to 

20-40 inch Douglas-fir, the most underrepresented trees included 10 inch red maple, 20 inch 

western hemlock, 10 inch lodgepole pine, and 10 inch sugar maple (Table 8).

DISCUSSION

Alternatives for Further Sampling

The main objective of this research was to identify current gaps in tree biomass data so that 

future studies might optimize sampling, and thereby reduce uncertainty in predictive biomass 

models. This work highlighted some of the gaps that exist in currently available data by tree 

components (Table 1), species (Table 2), and regions (Table 3). In addition, the maps (Figs. 4 

and 4S) can be used to assess where spatial gaps exist for a given species. For example, a total 

of 219 Douglas-fir; 2,168 loblolly pine; 445 red maple; and 360 white oak legacy trees were 

destructively sampled for above stump biomass (above stump legacy trees in Table 1). This 

might provide an adequate sample for predicting aboveground biomass for these species if 

the trees were spatially well-distributed. However, because they are not (see Fig. 4), additional 

sampling may be necessary to fill gaps across the species ranges where key landscapes and 

habitats are not represented, thereby minimizing the potential influence of spatially distinct 

patterns. Lastly, the results illustrate the lack of a representative sample in terms of diameter 

distributions, which are shifted towards larger trees but rarely contain the largest trees. (Figs. 2 

and 3).
12

Overall, the most robust assessment of gaps seems to come from comparing biomass pool 

proportions in the FIADB to sample sizes in the legacy tree database. To match the effective 

population, the largest gaps for a given biomass pool are defined as having a low number of 

trees and a high amount of biomass in the FIADB. Regionally, the most prominent data gaps 

were in the PNW and IMW regions and tended to align with tree size rather than species. 

Despite being well represented in the FIADB, groupings in the 20- to 50-inch diameter classes 

appeared to be the most undersampled. For future sampling efforts, one approach might be 

to set a tentative sampling completeness value (SCV) goal of one and sample from each group 

until this goal is met. In particular, this approach would suggest sampling large Douglas-fir 

trees in the PNW region as well as northern red oak and red maple in the NE region (Table 

7). The sampling objectives presented in Tables 6 and 7 could be increased based on user’s 

objectives and available resources. Representation (i.e., the proportion of trees in the legacy 

tree database compared to the proportion of biomass for a given biomass pool) is important to 

consider because an unrepresentative database would result in erroneous projections of model 

error if not dealt with appropriately.

Although these are imperfect assessments, they serve as reasonable prioritizations of 

current needs (i.e., by species, diameter class, and region) until a more robust assessment of 

uncertainty and required sample size is undertaken within a specific modeling framework. For 

instance, recent work by Clough et al. (2016) suggests that the greatest relative uncertainties 

in AGB are in the southern portions of the IMW (where woodland species dominate) and 

eastern portions of the PNW. Ultimately, continued sampling for a given biomass pool would 

be determined by the number of trees sampled and model uncertainty after accounting for 

diameter and height.

Sampling to mirror the effective population is practical for many applications. From a 

modeling perspective, however, it may be useful to sample across the range of attributes 

in the population. Using this approach, the legacy tree database and literature may have 

oversampled small trees, undersampled large trees, and failed to sample species up to the 

edge of their range, and thus, the current literature and available data may not adequately 

capture the full range of attributes or variability in the population. In contrast to sampling to 

match the effective population, an alternative approach would be to sample at the “fringe” to 

best understand the broad range of variability for a population. This would include sampling 

less prevalent species, sampling at the edge of a species’ range, and sampling the largest trees. 

For instance, large trees are presently a relatively small part of the landscape, but shifts in 

management practices may create the potential to greatly increase carbon stores (Stephenson 

et al. 2014).

Model-based Approaches to Fill Data Gaps

The cost of extensive sampling may make it prohibitive to adequately represent species and 

size classes across the entire United States. As such, in this section modeling techniques are 

considered, including (1) validating and developing species-group models; (2) using spatially 

explicit models; and (3) using pseudodata to fill gaps where the greatest paucity of data exists. 

These approaches may offer solutions for modeling gaps in available data, but species-group 

and spatial models may support a rationale to sample across the widest range of possible 

attributes rather than sampling to match the effective population. Errors associated with 

each approach are largely unquantified so these approaches will need testing, and continued 

sampling is recommended to update and validate a selected modeling approach.
13

Nonspecific Models and Species-group Models

When few data points are available for a given species, a species-group approach (i.e., 

predicting biomass for a single species using species that share similar traits or phylogeny) 

may be useful. However, a model fit to genus-level data then applied to individual species may 

incur bias, and this bias has largely been unquantified (Weiskittel et al. 2015). The literature 

review revealed that only a few studies have grouped species. When employed, the grouping 

was mainly based on apical dominance (e.g., Brenneman et al. 1978). Hence, the relative 

advantages and disadvantages of grouping species remain largely unexplored. The species 

groups presented in Jenkins et al. (2003) were based primarily on phylogenetic similarities, 

while Chojnacky et al. (2013) incorporated species’ specific gravity. A nonspecific modeling 

approach was supported when quantifying volume across a large area since estimates and 

uncertainties using nonspecific models did not substantially deviate from specific models 

(i.e., models developed for each species). Mean volume estimates were within 3 percent, and 

standard errors improved with the nonspecific models for both coniferous and deciduous 

species (McRoberts and Westfall 2014).

A species-group approach may further support sampling a broad spectrum of species, 

including the 200+ species that currently do not have a model or data point. This approach 

also suggests sampling less common species with no observations to validate species-group 

models since there is a theoretical threshold at which the sampling of the most common 

species ceases to improve predictions of biomass and uncertainty. In addition, considering 

that species distributions and abundance are ever-changing in the face of climate change 

(Iverson and Prasad 1998), disease and pest outbreaks (e.g., eastern hemlock and white ash), 

and human disturbance (e.g., red maple [Abrams 1998]), models that are sensitive to these 

factors should be sought. These prospects may warrant model exploration that extends beyond 

taxonomy to include physiologically meaningful categories such as species tolerances (e.g., 

Niinemets and Valladares 2006), or not grouping at all and predicting with wood properties 

such as wood density. For instance, modeling approaches that incorporate wood density 

across many species have been shown to work well in the tropics (e.g., Chave et al. 2014) and 

in the northern United States (MacFarlane 2015).

Spatial Considerations and Spatially Explicit Models

Since site selection is generally opportunistic, bias may occur; thus users will need to consider 

whether a model is suitable for a given application. Ideally, site-related factors would be 

included in models, but as noted earlier, problems arise because there is little consistency in 

how sites and treatments are described, and different authors use various classification systems 

for soils and geographic factors. Incorporating tree height may help to account for these 

potential site differences, but only 44 percent of the studies with models incorporated height 

as a predictor variable (Table 4). It is interesting that height is not incorporated into biomass 

models more frequently. This may, in part, be due to the study scope. For example, studies that 

collect data only across homogeneous growing conditions probably warrant a diameter-only 

regression model. In contrast, studies that sample across a wide range of sites require height in 

their models (e.g., Clark et al. 1985, 1986) or height and site for some species (e.g., Perala and 

Alban 1994). Incorporating height (which is generally available in the legacy tree database) 

into models will likely help to explain site and stand characteristics that cannot be explained 

in diameter-only models. Additional variation may be explained by age and soil, but this 

information is not always easily measured in an inventory.

The general lack of a representative sample across the entire United States may require that 

users employ more sophisticated modeling techniques to improve parameter and error 

estimates that result from spatial dependence and clustering for some species. Work in 
14

the northeastern United States suggests spatial variability in height-diameter relationships 

between sites, but no systematic relationship to latitude and longitude were found (Westfall 

2015). Mixed-effects modeling may offer a solution considering that the fixed-effects structure 

predicts across the entire dataset, while the inclusion of random effects parameters may 

improve local predictions (De-Miguel et al. 2014, Westfall 2016). By assessing differences in a 

fixed-only versus mixed-modeling approach, one may determine whether variation between 

ecoregions (e.g., Bailey 1995) or states exists and to what extent the variation exists, but sample 

size may be a limiting factor. Models that include explicit spatial modifiers on parameters and 

parameter standard error estimates might be most appropriate (e.g., Babcock et al. 2013). The 

effect of this would likely be expressed as greater uncertainty as predictions are made beyond 

the general availability of existing data.

Pseudodata Vs. Actual Data

The number of destructively sampled trees in terms of the whole body of “literature” (i.e., all 

studies with and without models) and the number of trees in the legacy tree database were 

quantified. Over half of the trees (25,187) that were identified as having been destructively 

sampled are not currently available in the legacy tree database, but the majority of these trees 

(24,388) had associated models. The trees in the legacy tree database can be viewed as actual 

data, and while using actual data is preferred, a model could be used to predict pseudodata 

where large spatial, species, or size gaps exist.

The generalized models presented in Jenkins et al. (2003) were fit after generating pseudodata 

from regression models reported in the literature (Pastor et al. 1984). Models developed from 

pseudodata, however, have some important limitations (see Lambert et al. 2005) and may 

introduce error when back-predicting from the original regression model (Baskerville 1972, 

Snowdon 1991). Additionally, the models have only a tenuous link to empirical data, and the 

error estimates are propagated using pseudo-ranges (see Jenkins et al. 2004). As such, while 

the approach may lead to reasonable mean estimates across multiple studies and large regions, 

it does not provide realistic error estimates, particularly within a site or study.

Generating more realistic measures of uncertainty is integral to providing more realistic 

simulated data. This requires measures of uncertainty, such as the standard error, that 

accompany models used to generate pseudodata. Of the 326 studies that were reviewed, 

252 studies presented models to predict biomass of at least one tree component. Across all 

studies, standard error (generally on the mean rather than the parameters) was reported for 55 

percent of the models. R2 values were reported more frequently, and recent work shows that 

using R2 values may provide improved pseudodata estimates (Wayson et al. 2015). Bayesian 

approaches to simulating data are also proposed in Henry et al. (2015). However, questions 

remain about the suitability of pseudodata, and standard errors on the parameter estimates 

tend to be underestimated (Magnussen and Negrete 2015). The legacy tree database could be 

a valuable tool for testing methodologies for generating pseudodata. However, when available, 

we advocate using actual data in all model development.

Sampling for Trees of Varying Form, Risk, and Vigor, and Improving 

Estimates of Component Allometry

Of the 113 studies for which tree selection methods were determined, more than half of the 

studies imposed sampling restrictions by selecting trees of average and better vigor and by 

avoiding forked (as noted by MacFarlane and Weiskittel 2016) and otherwise deformed trees. 

Using models that avoid trees that are poorly formed (e.g., low forking or with a broken top) 

or at a high risk of mortality (i.e., of low vigor), as evidenced by broken branches, severe 
15

mechanical damage, and fungal pathogens (Pelletier et al. 2013), may overestimate the 

biomass of low vigor/high risk trees, leading to overestimates at larger strata.

The height to the tree’s lowest branch and the size of the largest branch may help explain 

the proportion of bole-to-branch biomass (MacFarlane 2011) and bole-to-AGB. Height to 

the lowest branch was almost never included as a predictor, and only a few biomass studies 

assessed the influence of form or health on biomass estimates (e.g., Bickelhaupt 1979). In 

addition, classifying trees in terms of form and risk (Pelletier et al. 2013) may improve 

estimates of tree merchantable volume (Castle et al. 2017) and decay (Frank et al. 2018), 

which in turn affects volume and biomass estimates when discounted from gross volume. It is 

recommend that future studies examine how variation in tree form and health may influence 

biomass estimates by sampling diseased and deformed trees. Further, a clear and broadly 

applicable classification or measurement protocol is necessary for assessing standing tree form 

and risk to mortality.

Although root biomass can constitute approximately 30 percent of the total tree biomass 

(aboveground + belowground) (Grier and Milne 1981, Young et al. 1980), roots were the most 

undersampled component. Of the studies observed, only 4 sampled root biomass for Douglasfir and only 13 sampled root biomass for loblolly pine, while only a single study examined 

red maple root mass, and no studies included belowground biomass for western hemlock. 

Hence, it is important to investigate root biomass in western hemlock and red maple. In 

addition, although above-stump biomass (including foliage) is available in over 12,000 trees in 

the legacy tree database, only 9,383 of the trees were sampled for all major components (i.e., 

stem wood, stem bark, branch, and foliage). We recommend weighing all major aboveground 

components separately for any destructive sampling endeavor.

SUMMARY, LIMITATIONS, AND 

RECOMMENDATIONS FOR FUTURE RESEARCH

Summary

This review included 326 studies with 47,684 trees that were destructively sampled for at 

least one tree component. This is a conservative estimate of the data available because not 

all existing biomass studies were located and reviewed, and more trees have been sampled 

since these numbers were tabulated. For example, the following articles are known to contain 

relevant root biomass data in the United States, but were located after this analysis was 

completed: Samuelson et al. 2014 (36 longleaf pine trees); Litton et al. 2003 (45 lodgepole pine 

trees); Omdal et al. 2001 (80 lodgepole pine trees); Pearson et al. 1984 (89 ponderosa pine 

trees); Foster (1985 (9 balsam fir trees). Additional studies and root biomass data are also 

presented in Cairns et al. (1997).

As of March 2016, the actual data from 159 studies have been located, accounting for 22,497 

trees in the continental United States (including Alaska), including 9,383 trees that have data 

for each major component (i.e., stem wood, stem bark, branch, and foliage). Presently, these 

data are available in a permanent online repository available at legacytreedata.org (Radtke 

et al. 2015) where researchers can share data and analyses to improve biomass and carbon 

estimation at various scales.

Based on this comprehensive assessment of existing literature, the most notable gaps in tree 

biomass data included (1) very large trees (>40 inches d.b.h.), particularly large conifers in 

the PNW region; (2) root and stump biomass from most species; and (3) trees that diverge 
16

from an idealized form for timber production or that have low vigor. In addition, although the 

literature was not formally explored for these factors, very few studies included open-grown 

trees, trees in urban landscapes, and dead and dying standing trees. The lack of studies of the 

latter supports recommendations that additional research is required to quantify this carbon 

pool and meet UNFCCC reporting requirements (Woodall et al. 2011a).

Limitations

This gap assessment focused on how existing data and past studies are limited in terms of the 

species, tree components, and size classes of trees sampled, but there are some limitations 

to this approach. The most obvious data gaps were defined and compared to the effective 

tree population, which was estimated using the FIA database. However, exactly matching 

the effective population ultimately may not be necessary because the variation in a biomass 

pool determines whether there are enough trees sampled. In addition, depending on the 

adopted modeling framework (e.g., a nonspecific approach could replace a specific approach), 

sampling a wider range of less common species may be warranted rather than sampling a 

higher number of common species.

In addition to limitations in this gap assessment approach, how these data were collected must 

also be considered. First, since the data were often collected opportunistically and do not 

reflect a representative sample, more sophisticated modeling techniques may be necessary, and 

careful consideration should be given to the appropriateness of a model for a given biomass 

pool. Second, within-tree variation is often not accounted for and protocols can vary widely 

between studies, leading to random sources of variation.

Recommendations for Continued Research

The data that are preserved in the legacy tree database provide a valuable resource for 

addressing questions pertaining to biomass and carbon estimation at multiple scales (ranging 

from within-tree to national). A substantial number of biomass prediction studies hailing 

from the past 60 years have been preserved; however, data are missing from nearly half of the 

trees sampled. This result highlights the value of archiving all data because models and data 

generated from these efforts can be useful for large-scale modeling efforts. The FIA program 

continues to collaborate with university and industry partners to archive and digitize past 

datasets and to target trees for destructive sampling from those species and size classes needed 

to fill in data gaps. Additional collaboration is sought where feasible, and future destructive 

sampling efforts should consider adopting existing protocols (available at legacytreedata.org) 

to improve data compatibility across studies.

Studies that examine within-tree variation and compare different methodologies are needed 

and may shift prioritizations of future sampling requirements. In addition, optimal sample 

sizes will need to be considered as a function of uncertainty for a given biomass pool, as these 

will offer more appropriate objectives for future sampling.
17

Table 1.—Summary of number of trees and studies sampled for component biomass for the 30 species with the greatest biomastree component, estimates are given for (1) the total numbers sampled in the literature (including trees in the legacy tree databstudies in the literature (including trees in the legacy tree database; in brackets); and (3) the number of trees available in the legindicates that there are no known destructively sampled trees or studies. Data for all 148 species sampled are available in Table Common name

Stem Branch Above stump Stem and above stump 

Total # 

of trees

# of 

studies

# trees 

in DB

Total # 

of trees

# of 

studies

# trees 

in DB

Total # 

of trees

# of 

studies

# trees 

in DB

Total # 

of trees

# of 

studies

# trees 

in DB

Douglas-fir 761 [16] 332 829 [21] 431 598 [16] 219 585 [14] 211 Loblolly pine 5307 [46] 4203 2962 [44] 1810 2743 [41] 2168 2265 [29] 2078 Red maple 921 [22] 544 783 [17] 488 825 [21] 445 700 [15] 412 White oak 528 [13] 444 451 [10] 431 428 [11] 360 359 [8] 348 Sugar maple 427 [17] 273 326 [14] 196 421 [16] 238 377 [15] 238 Western hemlock 79 [4] 60 90 [5] 71 37 [3] 18 30 [2] 11 Northern red oak 257 [11] 204 313 [12] 171 137 [9] 82 102 [7] 78 Ponderosa pine 366 [11] 246 459 [15] 394 232 [10] 161 226 [9] 161 Yellow-poplar 411 [13] 335 377 [11] 331 291 [11] 210 224 [9] 208 Sweetgum 1040 [10] 806 1188 [10] 793 879 [8] 734 868 [7] 723 Lodgepole pine 205 [8] 135 202 [10] 202 95 [7] 76 91 [6] 76 Chestnut oak 193 [8] 140 209 [9] 140 159 [6] 87 127 [5] 87 Black oak 90 [5] 64 90 [5] 64 89 [5] 55 43 [2] 43 Engelmann spruce 113 [4] 84 121 [6] 92 51 [3] 19 48 [2] 19 Quaking aspen 717 [13] 672 438 [15] 393 389 [13] 240 249 [10] 214 American beech 196 [10] 134 146 [8] 84 150 [11] 86 134 [9] 72 Black cherry 113 [8] 82 102 [7] 82 130 [9] 61 91 [6] 60 White ash 90 [5] 72 58 [4] 40 65 [4] 7 25 [3] 7 Eastern white pine 299 [9] 221 263 [8] 190 189 [7] 136 155 [6] 102 Water oak 206 [2] 206 396 [3] 206 215 [3] 215 206 [2] 206 Slash pine 2582 [21] 1281 1318 [18] 784 1308 [17] 988 1176 [13] 988 White fir 22 [2] 10 34 [3] 22 12 [1] 0 12 [1] 0 Post oak 30 [3] 30 30 [3] 30 63 [3] 30 28 [1] 28 Mountain hemlock 6 [1] 0 6 [1] 0 6 [1] 0 6 [1] 0 Subalpine fir 72 [4] 63 68 [4] 59 9 [1] 0 9 [1] 0 Sitka spruce 40 [2] 23 24 [2] 6 17 [1] 0 17 [1] 0 Grand fir 27 [1] 27 69 [3] 69 0 [0] 0 0 [0] 0 Pignut hickory 0 [0] 0 0 [0] 0 0 [0] 0 0 [0] 0 Eastern hemlock 167 [7] 120 136 [6] 89 83 [5] 36 83 [5] 36 Scarlet oak 141 [5] 141 127 [4] 127 80 [3] 80 78 [2] 78 All 148 species 27080 [221] 17301 22197 [197] 12783 24791 [199] 12177 17251 [160] 11402 
18

Table 1.—continuedCommon name

Stem wood Stem bark Total wood Crown 

Total # 

of trees

# of 

studies

# trees 

in DB

Total # 

of trees

# of 

studies

# trees 

in DB

Total # 

of trees

# of 

studies

# trees 

in DB

Total # 

of trees

# of 

studies

# trees 

in DB

Douglas-fir 503 [10] 147 503 [10] 147 694 [16] 315 906 [21] 434 Loblolly pine 7280 [41] 5418 4003 [34] 3583 2057 [26] 1695 2726 [29] 2280 Red maple 595 [11] 418 591 [10] 418 930 [23] 537 682 [14] 414 White oak 397 [8] 379 393 [7] 379 605 [15] 468 375 [9] 364 Sugar maple 265 [11] 231 265 [11] 231 444 [14] 196 377 [15] 238 Western hemlock 40 [3] 21 40 [3] 21 65 [4] 46 164 [6] 66 Northern red oak 170 [5] 170 170 [5] 170 302 [14] 185 158 [8] 78 Ponderosa pine 77 [6] 50 77 [6] 50 255 [11] 184 444 [14] 397 Yellow-poplar 327 [8] 283 327 [8] 283 468 [14] 377 224 [9] 208 Sweetgum 1146 [7] 802 1146 [7] 802 1195 [11] 795 783 [5] 723 Lodgepole pine 63 [4] 48 63 [4] 48 139 [7] 120 239 [11] 224 Chestnut oak 130 [4] 130 130 [4] 130 270 [11] 143 129 [5] 90 Black oak 87 [4] 61 87 [4] 61 173 [8] 107 69 [3] 43 Engelmann spruce 79 [3] 50 79 [3] 50 108 [5] 76 98 [5] 69 Quaking aspen 690 [11] 671 664 [10] 645 415 [14] 363 279 [12] 244 American beech 65 [6] 65 65 [6] 65 216 [10] 98 135 [9] 73 Black cherry 58 [4] 58 58 [4] 58 132 [8] 86 91 [6] 60 White ash 39 [3] 39 39 [3] 39 113 [6] 80 26 [3] 8 Eastern white pine 121 [4] 121 121 [4] 121 206 [6] 153 207 [6] 154 Water oak 396 [3] 206 396 [3] 206 396 [3] 206 206 [2] 206 Slash pine 3423 [23] 2137 1526 [19] 1255 844 [11] 784 1048 [12] 988 White fir 12 [1] 0 12 [1] 0 22 [2] 10 34 [3] 22 Post oak 29 [2] 29 29 [2] 29 32 [3] 32 28 [1] 28 Mountain hemlock 6 [1] 0 6 [1] 0 6 [1] 0 6 [1] 0 Subalpine fir 43 [3] 34 46 [3] 37 50 [3] 41 69 [4] 60 Sitka spruce 0 [0] 0 0 [0] 0 23 [2] 6 24 [2] 6 Grand fir 0 [0] 0 0 [0] 0 15 [1] 15 72 [4] 72 Pignut hickory 0 [0] 0 0 [0] 0 0 [0] 0 0 [0] 0 Eastern hemlock 64 [4] 64 64 [4] 64 126 [6] 58 104 [5] 57 Scarlet oak 138 [5] 138 138 [5] 138 147 [6] 147 92 [3] 92 All 148 species 25618 [164] 17328 20283 [150] 14548 22560 [178] 12308 19154 [166] 13057 
19

Table 2.—Tree sample sizes and number of studies [shown in brackets] for the 30 tree species with the greatest biomass in the tree component) and the minimum (min.) and maximum (max.) diameters for trees previously summarized in Jenkins et al. (20tree data presently accumulated. Totals also include legacy tree data from previously summarized articles (Both) and from artipreviously summarized and that have no legacy tree data (Additional literature). Additional literature was further separated inand studies without models. Total n = 47,684 trees for all species. Data for all 148 species sampled are available in Table S2.Common name

Tree sample size [number of studies] by study type D.B.H

Legacy tree 

database

Jenkins 

database Both

Additional 

literature with 

models

Additional 

literature 

without models Total

Jenkins 

min.

Jenk

ma

Douglas-fir 458 [15] 555 [7] 54 [3] 61 [2] 49 [3] 1177 [30] 0.3 63.Loblolly pine 6318 [37] 200 [5] 86 [4] 3347 [26] 16 [1] 9967 [73] 1.3 22.Red maple 202 [7] 479 [9] 400 [10] 65 [4] 23 [1] 1169 [31] 0.1 26.White oak 98 [5] 182 [6] 383 [6] 82 [4] 6 [1] 751 [22] 0.1 25.Sugar maple 136 [5] 247 [6] 137 [6] 85 [2] 26 [2] 631 [21] 0.1 27.Western hemlock 65 [3] 98 [2] 38 [2] 0 [0] 0 [0] 201 [7] 1.1 43.Northern red oak 36 [2] 96 [5] 182 [7] 108 [3] 33 [1] 455 [18] 0.1 28.Ponderosa pine 445 [11] 47 [2] 42 [1] 107 [3] 6 [1] 647 [18] 1.0 34.Yellow-poplar 90 [5] 102 [5] 291 [4] 55 [3] 59 [2] 597 [19] 0.2 25.Sweetgum 79 [3] 0 [0] 738 [2] 485 [9] 0 [0] 1302 [14] 1.0 20.Lodgepole pine 189 [7] 0 [0] 35 [3] 55 [2] 4 [1] 283 [13] 1.1 11.Chestnut oak 7 [1] 110 [4] 136 [4] 49 [3] 16 [1] 318 [13] 1.1 22.Black oak 7 [2] 60 [2] 100 [4] 40 [1] 0 [0] 207 [9] 0.2 34.Engelmann spruce 98 [4] 29 [1] 9 [1] 0 [0] 3 [1] 139 [7] 1.1 30.Quaking aspen 549 [9] 47 [2] 185 [3] 143 [3] 7 [1] 931 [18] 1.1 19.American beech 42 [4] 101 [3] 106 [5] 19 [1] 0 [0] 268 [13] 0.1 26.Black cherry 32 [3] 75 [4] 54 [3] 20 [1] 0 [0] 181 [11] 0.1 20.White ash 49 [3] 55 [2] 63 [2] 18 [1] 0 [0] 185 [8] 0.2 20.Eastern white pine 172 [4] 78 [3] 93 [4] 0 [0] 0 [0] 343 [11] 0.1 26.Water oak 13 [2] 0 [0] 202 [1] 190 [1] 0 [0] 405 [4] 1.0 20.Slash pine 2163 [16] 100 [1] 0 [0] 2508 [14] 0 [0] 4771 [31] - White fir 22 [2] 12 [1] 0 [0] 0 [0] 0 [0] 34 [3] 2.8 38.Post oak 1 [1] 0 [0] 31 [2] 33 [1] 0 [0] 65 [4] 3.0 20.Mountain hemlock 0 [0] 6 [1] 0 [0] 0 [0] 0 [0] 6 [1] 6.7 21.Subalpine fir 66 [3] 0 [0] 16 [1] 9 [1] 0 [0] 91 [5] 1.0 12.Sitka spruce 23 [1] 18 [1] 0 [0] 0 [0] 0 [0] 41 [2] 1.2 30.Grand fir 52 [3] 0 [0] 32 [1] 0 [0] 0 [0] 84 [4] 1.0 15.Pignut hickory 0 [0] 0 [0] 0 [0] 0 [0] 0 [0] 0 [0] - Eastern hemlock 89 [5] 68 [2] 34 [2] 0 [0] 0 [0] 191 [9] 0.1 33.Scarlet oak 4 [1] 0 [0] 157 [6] 0 [0] 0 [0] 161 [7] 1.1 22.All 148 species 16013 [133] 5704 [60] 6484 [26] 18684 [94] 799 [13] 47684 [326] 0.1 63.
20

Table 3.—Estimates of the number of trees sampled by species and region for the 30 tree species with the greatest biomass in thestimates are given for (1) the total numbers sampled in the literature (including trees in the legacy tree database); (2) the numbliterature (including trees in the legacy tree database; in brackets); and (3) the number of trees available in the legacy tree databspecies is not present in a given region in the FIA database, and zero indicates that there are no known destructively sampled trmay bridge more than one region, leading to discrepancies between the apparent number of studies listed here and the totals lall 148 species sampled are available in Table S3.Common name

Intermountain West (IMW) Pacific Northwest (PNW) Northeast (NE) Southeast (SE) 

Total # 

of trees

# of 

studies

# trees in 

DB

Total # 

of trees

# of 

studies

# trees 

in DB

Total # 

of trees

# of 

studies

# trees 

in DB

Total # 

of trees

# of 

studies

# trees in 

DB

Douglas-fir 203 [8] 130 974 [24] 382 0 [0] 0 0 [0] 0 Loblolly pine - - - - - - 0 [0] 0 9967 [72] 6404 Red maple - - - - - - 495 [18] 188 346 [9] 259 White oak - - - - - - 217 [10] 65 478 [12] 400 Sugar maple - - - - - - 380 [12] 115 11 [3] 10 Western hemlock 12 [1] 12 189 [6] 91 - - - - - - Northern red oak - - - - - - 203 [11] 78 142 [6] 108 Ponderosa pine 383 [13] 316 264 [7] 171 - - - 0 [0] 0 Yellow-poplar - - - - - - 189 [9] 43 352 [10] 282 Sweetgum - - - 0 [0] 0 0 [0] 0 1302 [14] 817 Lodgepole pine 138 [8] 134 145 [7] 90 - - - - - - Chestnut oak - - - - - - 95 [5] 21 207 [8] 122 Black oak - - - - - - 89 [4] 55 78 [4] 52 Engelmann spruce 125 [6] 93 14 [1] 14 - - - 0 [0] 0 Quaking aspen 48 [3] 41 160 [3] 27 30 [3] 30 0 [0] 0 American beech - - - - - - 200 [10] 80 52 [4] 52 Black cherry - - - - - - 141 [8] 46 34 [3] 34 White ash - - - - - - 114 [6] 41 31 [1] 31 Eastern white pine - - - - - - 115 [4] 72 183 [6] 163 Water oak - - - - - - 0 [0] 0 405 [4] 215 Slash pine - - - - - - - - - 4771 [31] 2163 White fir 12 [1] 12 22 [2] 10 - - - 0 [0] 0 Post oak - - - - - - 0 [0] 0 65 [4] 32 Mountain hemlock 0 [0] 0 6 [1] 0 - - - - - - Subalpine fir 91 [5] 82 0 [0] 0 - - - 0 [0] 0 Sitka spruce - - - 41 [2] 23 - - - - - - Grand fir 50 [2] 50 34 [3] 34 - - - - - - Pignut hickory - - - - - - 0 [0] 0 0 [0] 0 Eastern hemlock - - - - - - 104 [5] 36 71 [5] 71 Scarlet oak - - - - - - 49 [3] 49 112 [4] 112 All species 1555 [28] 1173 3167 [52] 1526 6121 [50] 1774 29901 [167] 16094 
21

Table 4.—The 30 studies with the highest importance (import.) index (=sample size * maximum d.b.h.[in inches]/100) ranked from Indicator (ind.) values of 1 = yes and 0 = no identify if a biomass model (Eq.) was presented, height (Ht.) was included, standard (Stsampling restrictions (Samp. rest.) imposed based on form/risk/vigor class. It is also noted if the article was present in the legacy tJenkins et al. (2004), or Chojnacky et.al (2014). A proportion between 0 and 1 indicates that only a portion of the data has the givestudies are available in Table S4.Author Region

Import. 

index

Sample size

(no.)

Min. d.b.h.

(inches)

Max. d.b.h.

(inches) Eq. ind. Ht. ind.

Std. error 

ind.

Samp. 

rest. ind.

Leg. t

ind

Clark et al. 1985, 1986 SE 916 3580a 1 25.6 1 1 1 0 1 FIA 2016 All 642 1430 1 44.9 0 - - 1 1 Sillett et al. 2015 PNW 466 139 1.6 334.9 1 0 1 0 1 Young et al. 1980 NE 251 965 0.1 26 1 0 0 - 0 Schlaegel (n.d.)b SE 221 582 1 37.9 - - - - 1 Clark and Saucier 1990 SE 217 1132 5 19.2 0 - - - 1 Perala and Alban 1994 NC 151 774 1.1 19.5 1 1 0 1 1 Sollins and Anderson 1971 SE 120 357 1.1 33.5 1 1 0 - 1 Pienaar et al. 1990 SE 117 838 3 14 1 1 0 - 0 Clutter et al. 1984 SE 107 762 2 14 1 1 0 - 0 Snell and Max 1985 PNW 101 160 3.9 63 1 0 0 1 0 Flowers 1978 SE 98 724 2.2 13.6 1 1 0 1 1 Reed et al. 1995 NC 96 3083 0.1 3.1 1 1 0 0 0 Lohrey 1985 SE 89 467 1.3 19.1 1 1 1 - 1 Hyink et al. 1972 SE 88 632 5 14 1 1 0 0 0 Madgwick and Kreh 1980 SE 87 501 0.6 17.4 1 1 1 - 0 Burkhart and Clutter 1971 SE 86 701 3.1 12.2 1 1 0 - 1 Monteith 1979 NE 84 402 1 20.8 1 1 - 1 0.8 Bailey et al. 1982 SE 82 686 3 12 1 1 0 - 0 Queen and Pienaar 1977 SE 82 685 2.6 12 1 1 0 1 1 Brenneman et al. 1978 NE 81 407 2 20 1 0 0 0 0 Burkhart et al. 1972 SE 78 551 2.5 14.1 0 - - 1 1 Brown 1978 IMW 71 210 1 34 1 1 1 1 1 Schlaegel 1975a NC 71 426 1.1 16.7 1 1 1 0 1 Jordan et al. 2008 SE 59 407 5.5 14.4 - - - - 1 Bailey et al. 1985 SE 57 472 1 12 1 1 0 - 0 Sillet et al. 2010 PNW 54 21 22.8 255.1 0 - - - 1 Clark and Taras 1976 SE 53 221 5.6 24 0 - - - 1 Reinhardt 2000 IMW 53 201 1 26.4 0 0 0 1 1 Storey et al. 1955 IMW+ PNW 53 143 1.5 37 1 1 1 1 1 a The figure of 3,580 trees sampled by Alexander Clark likely includes trees sampled from Clark et al. (1985), Clark et al. (1986), and Clark and Schroeder (198Clark and Saucier (1990) reused data from four previous species-specific studies that he did on yellow-poplar, southern red oak, northern red oak, and scarnot cited in this analysis.b Schlaegel (n.d.) includes multiple bottomland hardwood reports from the early to mid-1980s.
22

Table 5.—Centroid coordinates for the legacy and FIA databases for the 30 species with the greatest estimated biomass acrStates. Distances between the centroids and the diagonal distance across the range of the species’ are given in miles (mi). Eusing the centroid error and the diagonal distance error. A dash indicates that there was insufficient data to determine the FIA species code

Legacy 

centroid 

longitude

Legacy 

centroid 

latitude

FIA centroid 

longitude

FIA 

centroid 

latitude

Distance between 

centroids 

(mi)

Longitudinal 

difference

(mi)

Latitudinal 

difference

(mi)

FIA 

d

Douglas-fir -121.9 45.9 -120.8 44.6 167.4 -83.7 145.0 Loblolly pine -85.3 33.0 -86.3 33.4 102.8 95.3 -38.5 Red maple -81.4 40.2 -80.5 41.1 124.6 -74.6 -99.8 White oak -82.5 36.1 -85.4 37.6 310.9 257.9 -173.7 Sugar maple -83.7 45.0 -81.8 43.2 254.5 -158.0 199.5 Western hemlock -124.3 46.4 -124.9 49.3 320.9 41.5 -318.2 Northern red oak -82.0 38.6 -82.4 41.2 297.7 34.7 -295.7 Ponderosa pine -115.1 39.1 -116.3 42.2 358.0 102.0 -343.2 Yellow-poplar -83.0 36.1 -82.3 36.8 99.1 -65.2 -74.6 Sweetgum -83.5 33.7 -85.9 34.0 221.9 219.9 -29.9 Lodgepole pine -114.6 43.2 -115.2 44.3 133.8 45.8 -125.7 Chestnut oak -82.6 36.5 -81.5 37.6 157.9 -97.7 -124.0 Black oak -79.5 38.3 -85.4 38.9 518.4 514.1 -67.3 Engelmann spruce -111.2 43.7 -111.3 42.9 91.7 6.9 91.4 Quaking aspen -93.2 48.3 -96.5 44.6 480.6 250.8 410.1 American beech -79.3 40.5 -78.8 40.9 56.9 -34.8 -45.0 Black cherry -80.5 38.3 -81.4 40.5 258.5 84.6 -244.2 White ash -84.9 37.4 -80.6 41.1 555.3 -371.9 -412.5 Eastern white pine -81.6 38.5 -79.0 42.9 530.2 -217.7 -483.5 Water oak -82.5 32.5 -87.6 32.5 479.3 479.3 3.4 Slash pine -88.0 31.3 -84.5 30.7 339.0 -331.5 70.8 White fir -123.0 44.1 -119.8 40.4 488.1 -266.9 408.8 Post oak -86.8 36.4 -92.0 35.0 495.4 470.4 155.7 Mountain hemlock - - -125.4 49.7 - - - Subalpine fir -113.3 45.2 -113.7 44.9 37.4 26.2 26.7 Sitka spruce -133.2 55.9 -135.2 55.7 126.6 125.3 18.3 Grand fir -121.1 45.7 -118.1 46.0 234.1 -232.8 -24.8 Pignut hickory - - -83.9 36.9 - - - Eastern hemlock -82.3 38.8 -77.1 43.1 648.4 -441.2 -475.4 Scarlet oak -82.1 36.8 -82.2 37.4 62.2 9.1 -61.5 
23

Table 6.—Percentage of biomass (% bio) and cumulative biomass percentage estimated by using the FIADB, numbtrees in the legacy tree database (nleg) with above-stump woody biomass (with or without foliage), and diameter height or diameter at ground line for the 30 region/species/d.b.h. class combinations with the greatest biomass in Diameter classes are grouped by saplings (1 to <5 inches) then by 10 inch increments (d.b.h. class 10 = 5 to <15 incThe sampling completeness value (SCV) = nleg/%bio *10 and indicates how well sampled a group is. Representatithe difference between the percentage that the group comprises out of the entire legacy tree database by numbepercentage of biomass that the group comprises in the FIA database. In total 14,073a trees with aboveground biomand without foliage) were used here. Data for all region/species/d.b.h. class combinations are available in Table S6Region Common name

D.b.h. 

class

% of biomass

(%)

Cumulative 

biomass 

(%) 

Number of trees 

in legacy tree 

database

Sampling 

Objective SCV 

SE Loblolly pine 10 4.6 4.6 1431 46 30.9 PNW Douglas-fir 20 2.4 7.0 83 24 3.4 PNW Douglas-fir 10 1.9 8.9 116 19 6.2 NE Red maple 10 1.7 10.6 80 17 4.7 SE Loblolly pine 20 1.6 12.2 240 16 14.7 PNW Douglas-fir 30 1.5 13.7 16 15 1.1 SE White oak 10 1.2 14.9 237 12 20.6 IMW Lodgepole pine 10 1.1 16.0 38 11 3.3 NE Sugar maple 10 1.1 17.1 38 11 3.4 SE Sweetgum 10 1.1 18.2 444 11 40.8 PNW Western hemlock 20 1.1 19.3 14 11 1.3 IMW Douglas-fir 10 1.1 20.4 30 11 2.8 SE White oak 20 1.0 21.4 86 10 8.4 IMW Douglas-fir 20 1.0 22.4 1 10 0.1 PNW Douglas-fir 40 1.0 23.4 6 10 0.6 PNW Western hemlock 10 0.9 24.3 21 9 2.2 SE Slash pine 10 0.9 25.2 599 9 67.0 NE Northern red oak 20 0.8 26.0 7 8 0.9 SE Red maple 10 0.8 26.8 134 8 17.7 SE Yellow-poplar 20 0.7 27.5 65 7 8.9 SE Post oak 10 0.7 28.2 26 7 3.6 NE Red maple 20 0.7 28.9 5 7 0.7 NC Sugar maple 10 0.7 29.6 66 7 9.9 SE Yellow-poplar 10 0.7 30.3 159 7 23.8 NE Sugar maple 20 0.6 30.9 15 6 2.3 PNW Western hemlock 30 0.6 31.5 4 6 0.6 IMW Engelmann spruce 20 0.6 32.1 10 6 1.7 IMW Engelmann spruce 10 0.6 32.7 36 6 6.2 SE Water oak 10 0.6 33.3 98 6 17.3 PNW Douglas-fir 50 0.6 33.8 0 6 0.0 
24

Table 7.—The 30 groups that were most undersampled by region/species/d.b.h. class group where SCV was leAll groupings are ranked according to the percentage of biomass in the FIA database. Data for all region/specclass combinations are available in Table S7.Region Common name

D.B.H.

class

% of 

biomass

FIADB 

Rank

Number of trees 

in legacy tree 

database

Samplin

Objectiv

IMW Douglas-fir 20 1.0 14 1 10 PNW Douglas-fir 40 1.0 15 6 10 NE Northern red oak 20 0.8 18 7 8 NE Red maple 20 0.7 22 5 7 PNW Western hemlock 30 0.6 26 4 6 PNW Douglas-fir 50 0.6 30 0 6 NE White ash 10 0.5 43 3 5 NE Eastern hemlock 10 0.4 45 1 4 IMW Utah juniper 20 0.4 49 3 4 PNW Mountain hemlock 20 0.4 50 0 4 NE Yellow-poplar 20 0.4 53 2 4 SE Pignut hickory 10 0.3 58 0 3 SE Mockernut hickory 10 0.3 59 0 3 NE White oak 20 0.3 62 2 3 PNW Ponderosa pine 30 0.3 63 0 3 NE Black cherry 20 0.3 64 1 3 NE Chestnut oak 20 0.3 67 2 3 PNW Mountain hemlock 10 0.3 69 0 3 NE White ash 20 0.3 70 2 3 IMW Common or two-needle pinyon 10 0.3 77 0 3 NE Eastern hemlock 20 0.3 85 0 3 PNW Douglas-fir 60 0.3 87 0 3 PNW Sitka spruce 20 0.3 89 2 3 PNW Western hemlock 40 0.3 91 0 3 IMW Douglas-fir 30 0.3 92 0 3 PNW Sitka spruce 30 0.3 96 0 3 PNW Pacific silver fir 20 0.3 98 0 3 PNW White fir 30 0.2 100 1 2 NE Black oak 20 0.2 101 1 2 PNW Canyon live oak 10 0.2 104 0 2 
25

Table 8.—The 30 most underrepresented region/species/d.b.h class combinations. Representation was calcthe percentage of trees in legacy tree database – percentage of biomass in the FIADB. Data for all region/spd.b.h. class combinations are available in Table S8.Region Common name

D.B.H

 class

% of 

biomass

FIADB 

Rank

Number of trees 

in legacy tree 

database Repre

PNW Douglas-fir 20 2.4 2 83 PNW Douglas-fir 30 1.5 6 16 NE Red maple 10 1.7 4 80 IMW Douglas-fir 20 1.0 14 1 PNW Western hemlock 20 1.1 11 14 PNW Douglas-fir 10 1.9 3 116 PNW Douglas-fir 40 1.0 15 6 IMW Lodgepole pine 10 1.1 8 38 NE Sugar maple 10 1.1 9 38 IMW Douglas-fir 10 1.1 12 30 PNW Western hemlock 10 0.9 16 21 NE Northern red oak 20 0.8 18 7 NE Red maple 20 0.7 22 5 PNW Western hemlock 30 0.6 26 4 PNW Douglas-fir 50 0.6 30 0 NE Sugar maple 20 0.6 25 15 IMW Engelmann spruce 20 0.6 27 10 SE Post oak 10 0.7 21 26 IMW Ponderosa pine 20 0.5 33 9 NE White ash 10 0.5 43 3 NE Eastern hemlock 10 0.4 45 1 PNW Mountain hemlock 20 0.4 50 0 SE Pignut hickory 10 0.3 58 0 SE Mockernut hickory 10 0.3 59 0 NE Eastern white pine 20 0.4 46 4 IMW Utah juniper 20 0.4 49 3 NE Yellow-poplar 20 0.4 53 2 PNW Ponderosa pine 30 0.3 63 0 IMW Quaking aspen 10 0.5 42 12 NE Black cherry 20 0.3 64 1 
26

ACKNOWLEDGMENTS

We thank the U.S.D.A. Forest Service Forest Inventory and Analysis program for their 

support of this project. In addition, we would like to acknowledge those who helped peruse 

the literature, format citations, and edit early drafts. In particular, we would like to thank 

Grayson O’Connor, William Thomas, Brittany Cline, Caleb Winslow, Mark Castle, and Cody 

Dillingham. Two anonymous reviewers helped to improve a prior draft of this report.

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APPENDIXES

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Appendix 2: Common and scientific names for species mentioned in the text and the 

30 tree species with the most biomass in the FIADB

Common namea Genus Species Full scientific name with authorityb

FIA Species 

codec

Pacific silver fir Abies amabilis Abies Amabilis Dougl. ex Forbes 11

White fir Abies concolor Abies concolor (Gord. & Glend.) Lindl. Ex Hildebr. 15

Grand fir Abies grandis Abies grandis (Dougl. ex D. Don) Lindl. 17

Subalpine fir Abies lasiocarpa Abies lasiocarpa (Hook.) Nutt. 19

Engelmann spruce Picea engelmannii Picea engelmannii Parry ex Engelm. 93

Sitka spruce Picea sitchensis Picea sitchensis (Bong.) Carr. 98

Common or two-needle pinyon Pinus edulis Pinus edulis Engelm. 106

Lodgepole pine Pinus contorta Pinus contorta Dougl. ex. Loud. 108

Slash pine Pinus elliottii Pinus elliottii Engelm. 111

Ponderosa pine Pinus ponderosa Pinus ponderosa Dougl. ex Laws. 122

Eastern white pine Pinus strobus Pinus strobus L. 129

Loblolly pine Pinus taeda Pinus taeda L. 131

Douglas-fir Pseudotsuga menziesii Pseudotsuga menziesii (Mirb.) Franco 202

Redwood Sequoia sempervirens Sequioa sempervirens (D. Don) Endl. 211

Eastern hemlock Tsuga canadensis Tsuga canadensis (L.) Carr. 261

Western hemlock Tsuga heterophylla Tsuga heterophylla (Raf.) Sarg. 263

Mountain hemlock Tsuga mertensiana Tsuga mertensia (Bong.) Carr. 264

Red maple Acer rubrum Acer rubrum L. 316

Sugar maple Acer saccharum Acer saccharum Marsh. 318

Birch spp. Betula spp. Betula L. 370

Pignut hickory Carya glabra Carya glabra (Mill.) Sweet 403

Shagbark hickory Carya ovata Carya ovata (Mill.) K. Koch 407

Hackberry spp. Celtis spp. Celtis L. 460

American beech Fagus grandifolia Fagus grandifolia Ehrh. 531

Ash spp. Fraxinus spp. Fraxinus L. 540

White ash Fraxinus americana Fraxinus americana L. 541

Black ash Fraxinus nigra Fraxinus nigra Marsh. 543

Green ash Fraxinus pennsylvanica Fraxinus pensylvanica Marsh. 544

Sweetgum Liquidambar styraciflua Liquidambar styraciflua L. 611

Yellow-poplar Liriodendron tulipifera Liriodendron tulipifera L. 621

Cottonwood and poplar spp. Populus spp. Populus L. 740

Quaking aspen Populus tremuloides Populus tremuloides Michx. 746

Black cherry Prunus serotina Prunus serotina Ehrh. 762

Oak spp. Quercus spp. Quercus L. 800

White oak Quercus alba Quercus alba L. 802

Scarlet oak Quercus coccinea Quercus coccinea Muenchh. 806

Water oak Quercus nigra Quercus nigra L. 827

Chestnut oak Quercus prinus Quercus prinus L. 832

Northern red oak Quercus rubra Quercus rubra L. 833

Post oak Quercus stellata Quercus stellata Wangenh. 835

Black oak Quercus velutina Quercus velutina Lam. 837

Elm spp. Ulmus spp. Ulmus L. 970

a A list of all species examined in this analysis is available in Appendix S2 located in supplemental tables file. b Scientific names from Burns and Honkala 1990a and 1990b.

c Forest Inventory and Analysis species codes (O’Connell et al. 2016).
Frank, Jereme; Weiskittel, Aaron; Walker, David; Westfall, James A.; Radtke, Philip J.; Affleck, 

David L.R.; Coulston, John; MacFarlane, David W. 2019. Gaps in available data for 

modeling tree biomass in the United States. Gen. Tech. Rep. NRS-184. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 57 p. https://

doi.org/10.2737/NRS-GTR-184.

When estimating tree-level biomass and carbon, it is common practice to develop generalized 

models across numerous species and large spatial extents. However, sampling efforts are 

generally incomplete and trees are not randomly selected. In this analysis, of the more 

than 1,000 biomass-related articles that were reviewed, trees were destructively sampled 

in over 300 studies to estimate biomass in the United States. Studies were summarized and 

past sampling efforts were explored to illuminate where the largest data gaps occurred in 

terms of tree components sampled, tree size, tree form, tree species, and location. The most 

prominent gaps were in large trees, particularly in Douglas-fir trees in the Pacific Northwest. 

In addition, tree roots were notably undersampled. Lastly, trees of poor or unusual form and 

low vigor were often not sampled, and this may introduce a systematic bias if not dealt with 

appropriately. More than 200 species did not have a biomass model or a single data point. The 

gaps presented here can be viewed as suggestions for future destructive sampling efforts, but 

the magnitude of a gap for a given model will ultimately depend on the selected modeling 

framework and the user’s objectives.

KEY WORDS: U.S. Forest Carbon Inventory, allometric modeling, tree component biomass, 

gaps, tree form and vigor

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