UNITED STATES

DEPARTMENT OF

AGRICULTURE

FOREST SERVICE

PACIFIC NORTHWEST

REGION

SEPTEMBER 2012

CLIMATE CHANGE AND FOREST 

TREES IN THE PACIFIC 

NORTHWEST:

GUIDE TO VULNERABILITY ASSESSMENT 

METHODOLOGY
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SUGGESTED CITATION

Devine, W.; Aubry, C.; Miller, J.; Potter, K.; Bower, A. 2012. Climate change and forest trees in the Pacific Northwest: 

guide to vulnerability assessment methodology. Olympia, WA: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Region. 49 p.

Available at: http://ecoshare.info/projects/ccft/
CLIMATE CHANGE AND FOREST TREES 

IN THE PACIFIC NORTHWEST:

GUIDE TO VULNERABILITY ASSESSMENT 

METHODOLOGY

SEPTEMBER 2012

Prepared by:

Warren Devine, Carol Aubry, Jeanne Miller, Kevin Potter, and Andrew 

Bower

Authors

Warren Devine is a biological scientist, and Jeanne Miller is a GIS specialist, 1835 Black Lake 

Blvd. SW, Suite A, Olympia, WA 98512, Carol Aubry and Andrew Bower are geneticists, U.S. 

Department of Agriculture, Forest Service, Olympic National Forest, 1835 Black Lake Blvd. 

SW, Suite A, Olympia, WA 98512. Kevin Potter is a research assistant professor, North 

Carolina State University, Department of Forestry and Environmental Resources, U.S. 

Department of Agriculture, Forest Service, Eastern Forest Environmental Threat Assessment 

Center, 3041 Cornwallis Road, Research Triangle Park, NC 27709.
TABLE OF CONTENTS

Chapter 1. Introduction 5

Chapter 2. Applying the ForGRAS vulnerability assessment model 8

Chapter 3. Risk Factor: Distribution 16

Chapter 4. Risk Factor: Reproductive Capacity 22

Chapter 5. Risk Factor: Habitat Affinity 24

Chapter 6. Risk Factor: Adaptive Genetic Variation 27

Chapter 7. Risk Factor: Insect and Disease Threats 30

Acknowledgments 31

References 32

Appendix 1: Data Retrieval and Preparation 34

Appendix 2. Data Processing and Calculations for the Distribution Risk Factor 44

Appendix 3. Data Processing and Calculations for the Habitat Affinity Risk Factor 48
Guide to Vulnerability Assessment Methodology 5

CHAPTER 1. INTRODUCTION

This guide provides a step-by-step description of 

the methodology used to apply the Forest Tree 

Genetic Risk Assessment System (ForGRAS; 

Potter and Crane 2010) to the tree species of the 

Pacific Northwest in a recent climate change 

vulnerability assessment (Devine et al. 2012). We 

describe our modified version of the ForGRAS

model, and we review the model’s basic 

principles and operation. We also discuss further 

adjustments to the model 

that managers may find 

useful if applying it to 

other regions or to fit 

other objectives. Chapter 

2 of this guide describes 

the overall methodology 

of the vulnerability 

assessment; chapters 3 

through 7 describe each 

of the five risk factors 

that we selected for the 

Pacific Northwest vulnerability assessment.

Appendices provide step-by-step procedures used 

to prepare the model input data.

Background: The Pacific 

Northwest Forest Tree 

Vulnerability Assessment

Climate change projections for the Pacific 

Northwest include year-round warming and 

potentially increased winter precipitation and 

decreased summer precipitation (Mote and 

Salathé 2009). The extent and duration of the 

regional snowpack is projected to decrease, 

particularly at lower elevations (Elsner et al. 

2010, Mote 2003). In addition to potential direct 

effects of climate on individual tree vigor and 

survival, climate-related stress could increase 

vulnerability of forests to large-scale disturbance 

including fire and damage from insects and 

diseases (Dale et al. 2001). The effects of longterm climate changes on the composition and 

structure of Pacific Northwest plant communities 

are difficult to predict. There is limited 

information on the climatic tolerances of many 

species and even less information on what 

complex interactions could result from 

ecosystem-wide exposure to a changing 

environment.

In 2010, we launched an 

effort to address the 

projected effects of climate 

change on forest tree 

species in Washington and 

Oregon (Forest Service 

Pacific Northwest Region).

We sought to determine the 

potential implications of 

climate change on the 

management, restoration, 

and conservation of forest 

tree species of the Pacific 

Northwest. The project emphasized National 

Forest System lands and management issues but 

considered the full distribution of each tree 

species, across all land ownerships.

This project led to the publication of a climate 

change vulnerability assessment focused on the 

forests of western Washington, with emphasis on 

the Olympic, Mt. Baker-Snoqualmie, and 

Gifford-Pinchot National Forests (Aubry et al. 

2011). In 2012, we completed a regional report 

that included six subregional vulnerability 

assessments that covered all of the national 

forests of the Pacific Northwest Region (Devine 

et al. 2012) (fig. 1). Based on the results of these 

assessments, the reports include

recommendations and specific actions for 

managers to address the projected effects of 

climate change on forest tree species in 

Washington and Oregon.

The goal of this guide is to 

describe the methodology 

used to apply the Forest Tree 

Genetic Risk Assessment 

System (Potter and Crane 

2010) to the tree species of 

the Pacific Northwest.
 6 Guide to Vulnerability Assessment Methodology

Figure 1. Boundaries of the six study areas of the Pacific Northwest forest tree climate 

change vulnerability assessment (Devine et al. 2012)
Guide to Vulnerability Assessment Methodology 7

At the heart of the project was a 

vulnerability assessment—a 

systematic process of identifying 

and quantifying areas of 

vulnerability within a system 

(Glick et al. 2011). The assessment 

was undertaken to identify: (1) 

characteristics of individual forest 

tree species that could influence 

their response to long-term 

changes in climate, and (2) relative 

levels of vulnerability to climate 

change among tree species. This 

vulnerability assessment is a tool 

for prioritizing tree species to help 

managers more efficiently allocate 

limited resources to the most 

vulnerable tree species.

In contrast to some of the 

previously published analyses of 

climate change effects on forests, 

the Pacific Northwest forest tree

vulnerability assessment was not

designed to create maps of 

projected future suitable habitat for 

each species. Instead, we used

documented life history traits, 

current distribution, and pest and pathogen data 

for individual tree species—combined with 

consensus regional climate projections—to rate 

each species’ relative vulnerability to a changing 

climate. We chose this approach because we felt 

that it would be most useful to managers and 

would provide a biological basis to assist 

decision-making in the near future.

After evaluating several different vulnerability 

assessment models, we chose ForGRAS because 

it is straightforward to apply, transparent, and can 

be easily modified to fit specific objectives and 

assumptions. Originally developed for the 

Southern Appalachian region, the ForGRAS 

model quantities each tree species’ climate 

change vulnerability, as well as the specific 

factors that contribute to that vulnerability (Potter 

and Crane 2010). The primary goal of this guide 

is to provide a detailed description of the 

methodology that we recently used (Devine et al. 

2012) to apply the ForGRAS model to the tree 

species of the Pacific Northwest.

The Forest Tree Genetic Risk 

Assessment System

ForGRAS was developed to evaluate the vulnerability of 

individual tree species within a region of interest, under 

anticipated long-term climate change. Given projected 

changes in climate, conservation of species’ existing 

adaptedness (specifically variation in adaptive traits) is key to 

long-term viability. However, because this detailed level of 

genetic information does not currently exist for many tree 

species, ForGRAS uses documented ecological and lifehistory traits to assess the vulnerability of individual tree 

species to climate change and related threats (Myking 2002, 

Potter and Crane 2010). 

For a defined study area, ForGRAS produces an overall 

vulnerability score for each tree species; this score is then 

used to rank the species according to their climate change 

vulnerability. A species’ overall vulnerability score is 

calculated by averaging the scores of multiple risk factors 

(e.g., distribution, reproductive capacity), each of which 

range from 0 to 100. Each risk factor contains a set of 

variables that are used to quantify a species’ climate change 

vulnerability according to intrinsic attributes or external 

threats.
 8 Guide to Vulnerability Assessment Methodology

CHAPTER 2. APPLICATION 

OF THE FORGRAS

VULNERABILITY 

ASSESSMENT

In this section, we describe the four steps used to 

perform the Pacific Northwest vulnerability 

assessment using the ForGRAS model: (1) 

determine the area and species of interest, (2) 

select risk factors, (3) collect data and calculate 

risk factor values, and (4) calculate overall scores 

and rank species (Potter and Crane 2010). We 

also describe, in detail, the procedures used to 

acquire and prepare data for the model.

This section describes the process of applying the 

ForGRAS model to an individual study area, as 

we did in each of the six subregional analyses 

(Devine et al. 2012) (fig. 1). In the regional

report, we also calculated region-level scores by 

averaging each tree species’ vulnerability score 

across the six study areas (Devine et al. 2012).

Step 1: Determine Area and 

Species of Interest

Area of Interest

ForGRAS can be applied at a range 

of different geographic scales. We 

delineated six study areas within 

the Pacific Northwest Region so 

that we could focus on the species 

and management issues unique to 

each area. Although we assessed all 

of the national forests of the region, 

we chose not to conduct analyses at 

the individual-forest level because 

it would have been more difficult 

to identify broad trends. For 

example, if the analysis included 

only the Olympic National Forest, 

it would not account for the fact 

that populations of high-elevation 

tree species in the Olympic Range 

are geographically separated from 

Cascade Range populations. 

Conversely, if a study area were

too large and ecologically diverse, 

it could be more difficult to 

identify trends. In the case of 

Washington, combining eastern 

and western portions of the state in 

a single analysis would involve 

simultaneously evaluating the 

Minimum Technical Skills Needed to Run the 

ForGRAS Model

The original ForGRAS spreadsheet model (Potter and Crane 

2010), and the version of ForGRAS modified for the Pacific 

Northwest assessment, is designed to be run by users 

possessing only a basic knowledge of Microsoft Excel. 

However, some of the data preparation that is necessary to 

calculate the model input data requires additional software 

skills. Detailed procedures for calculating the model input 

data appear in appendices 1 through 3. The software and 

skills needed to perform these procedures are:

Microsoft Excel: Basic knowledge only; detailed steps are 

provided in the appendix procedures.

Microsoft Access: No prior experience needed for 

analyzing the FIA annual inventory tree data; detailed steps 

are provided. Optional analyses of other types of map data 

may, in some cases, require additional skills, such as 

managing variable relationships and creating queries.

ESRI ArcGIS: An intermediate skill level is necessary, 

including ability to create and edit shapefiles and to run 

tabular statistical analysis, including use of tools such as the 

Frequency Tool.

Procedures described in this publication are based on the 

2010 versions of Microsoft Excel and Access and on ArcGIC 

10.
Guide to Vulnerability Assessment Methodology 9

influences of substantially different disturbance 

regimes and climates.

The study area can be based on ownership 

boundaries, ecological considerations, or a 

combination of both. In the study areas in eastern 

Washington and Oregon, where study area

boundaries were based on the extent of the

national forests, we added a 5-km-wide buffer so 

that the study area would fully encompass 

populations of tree species occurring at the edge 

of the national forest that would have otherwise 

been partially excluded from the analysis. Thus, 

these study areas were determined by a 

combination of ownership and ecological factors.

Species List

After defining the study area, the next task is to 

assemble a complete list of forest tree species 

within that area. In the Pacific Northwest

assessment, we defined a tree as a woody 

perennial capable of producing a single stem with 

apical dominance and reaching at least 20 ft (6 m) 

in height.

There are at least four potential approaches for 

compiling a list of all tree species within a study

area (all of these were used in the Pacific 

Northwest assessment):

1. Use tree species distribution datasets to 

create a species list; examples of such

datasets appear in table 1. Procedures for 

Table 1. Primary sources of tree species distribution data used in a climate change vulnerability 

assessment of the Pacific Northwest Region

Dataset Source Coverage Inventory design

Forest Inventory and 

Analysis (FIA)

U.S. Forest Service, 

Pacific Northwest Region

All public and private 

lands1

Regularly spaced plots

Current Vegetation 

Survey (CVS)

U.S. Forest Service, 

Pacific Northwest Region

National forests1 Regularly spaced plots

USFS Region 6 

Ecology Program

U.S. Forest Service, 

Pacific Northwest Region

National forests Plots located according to 

plant community type

Oregon Flora Project Department of Botany 

and Plant Pathology, 

Oregon State University

Public and private land 

in Oregon

Specimen collections and 

recorded observations by 

many individuals with 

various objectives

University of 

Washington 

Herbarium

Burke Museum, 

University of Washington

Public and private 

lands in Washington

and elsewhere in the 

Pacific Northwest

Specimen collections and 

recorded observations by 

many individuals with 

various objectives

National parks National Park Service Lands managed by the 

National Park Service

Irregularly spaced plots; 

data availability varies by 

park and inventory 

objectives

Other national forest 

surveys

USDA Forest Service, 

Region 6

National forests Inventory based on 

individual species of interest

Other Washington Department 

of Natural Resources; 

USDA Forest Service 

Pacific Northwest 

Research Station; 

Department of Defense

Various Inventory based on 

individual species of interest

1 A small amount of random error was intentionally added to the plot locations to protect the identity of landowners.
 10 Guide to Vulnerability Assessment Methodology

acquiring and processing data appear in 

appendix 1. The only tree distribution 

data source that is mandatory for running 

this version of the ForGRAS model is the 

Forest Inventory and Analysis (FIA) 

annual inventory dataset; however, 

because FIA sampling is at a low 

intensity (one plot per 6,000 ac), 

acquiring data from additional sources 

may significantly improve understanding

of a species’ distribution, particularly 

when working at fine scale or in a 

relatively small study area. Availability

of data varies widely by land ownership 

(“coverage” column in table 1). Outside 

of Washington and Oregon, many of 

these data sources will not apply; 

however, they should provide a starting 

point for locating similar data sources 

available in other regions.

2. Consult local publications and field 

guides. These are likely to include rare, 

non-commercial, and small tree species 

that occasionally are excluded from, or 

missed by, timber-based forest 

inventories. In the Pacific Northwest

study area, Northwest Trees (Arno and 

Hammerly 2007) was a valuable resource

for identifying such tree species. We also 

consulted local plant association guides

that were published by the Forest Service 

(e.g., Henderson et al. 1989, 1992).

3. Consult local experts, such as botanists or 

ecologists.

4. Examine national tree species distribution 

maps (e.g., Little 1971, 1976).

Selecting Species for Analysis

After creating a comprehensive species list for 

the study area, the next step is to determine how 

these species will be analyzed in the vulnerability 

assessment. Four potential approaches include:

1. Analyze all tree species in a single group.

It is important to remember that the

ForGRAS assessment is only as good as 

the data on which it is based. Thus, if tree 

species are included for which data are 

scarce or unavailable, the analysis will be 

weakened. For example, if tree 

distribution is included as a risk factor

(chapter 3), comparable distribution data 

are required for all tree species in order to 

rate the variables in that risk factor. The

primary dataset used for these ratings (in 

the original ForGRAS model and in the 

Pacific Northwest assessment) is the

national FIA annual inventory dataset. 

However, in this inventory, some smaller 

tree species (e.g., willows (Salix)) were 

recorded by genus rather than at the 

species level. Thus, data availability and 

selection of variables may influence the 

tree species list.

2. Select a subset of species that fit the 

objectives of the study; analyze only 

these species. It may not be necessary or 

relevant to include every single tree 

species in the analysis.

3. Group the tree species, and analyze the 

groups separately. For example, it may be 

desirable to separately analyze the 

vulnerability of overstory trees and 

understory trees, or to separately analyze 

riparian species.

4. Group the tree species, but do not analyze 

all groups. This is the approach that we 

took in the Pacific Northwest 

vulnerability assessment (Devine et al. 

2012), based on the objective of 

assessing the major overstory tree species 

in the study area (group 1) to prioritize 

these species for conservation and 

monitoring. We defined other species 

groups for less-common and non-canopy 

species (group 2) and for rare species

(group 3). We did not apply ForGRAS to 
Guide to Vulnerability Assessment Methodology 11

groups 2 or 3 because

the full range of data 

required to rate the 

risk factor variables

was not available 

(e.g., genetic or 

distribution data were 

often lacking).

Instead, we took an 

alternative approach 

for group 2 and 3 

species (Aubry et al. 

2011, Devine et al. 

2012).

Step 2: Select Risk 

Factors and 

Variables

The ForGRAS model is 

designed to be transparent 

and flexible so that the user 

can customize it to meet their 

specific objectives (Potter and 

Crane 2010). Using the 

original model as a starting 

point, we selected five areas 

of climate change 

vulnerability—risk factors—

based on our objectives, data 

availability, and the results of 

an early peer review. Each of 

these five risk factors 

included multiple variables 

that were used to determine a 

species’ vulnerability score 

(fig. 2). 

The five selected risk factors 

were based on biological and 

ecological attributes that are 

well-established in the

Fire and Climate Change Pressure as Risk 

Factors

During development and refinement of the Pacific Northwest 

vulnerability assessment model, we considered adding two risk 

factors that we ultimately did not include in the final model: (1) 

effects of future fire regimes, and (2) predicted geographic shifts in 

species distributions resulting from climate pressure.

The five risk factors that we chose for the model were based on 

consensus regional climate change projections that are widely 

accepted and relatively conservative (i.e., increases in temperature 

and summer drought conditions; Littell et al. 2010). The fire and 

climate pressure risk factors would have required additional 

assumptions, and therefore uncertainty, beyond those necessary for 

the other five risk factors.

Although future wildfire regimes—for example, area burned per 

year—can be modeled using temperature and precipitation 

projections, incorporating wildfire as a risk factor would require:

• Assumptions regarding the frequency and intensity of 

wildfire (and the extent of future wildfire suppression) across 

the range of habitats that each tree species occupies at a given 

point in the future; and

• For each tree species, quantification of whether its existing 

ecological adaptations to fire would be sufficient under this 

predicted future fire regime. For example, will a species’ bark 

thickness and foliar flammability affect its vulnerability under 

a future fire regime differently than they do at present?

It should be noted that wildfire effects are incorporated in the Pacific 

Northwest vulnerability assessment model inasmuch as the 

reproductive capacity risk factor is designed to quantify a species’ 

capacity to regenerate following large-scale disturbance. 

Climate change pressure is a risk factor in the original ForGRAS 

model; its variables are based on the differences between the current 

distribution of a species and its predicted future distribution according 

to climate envelope models (Hargrove et al. 2010, Rehfeldt et al. 

2006). After initially including this risk factor in the Pacific 

Northwest assessment model, concerns were raised during the peer 

review process over the uncertainty associated with the future species 

distributions predicted by the climate envelope models. After further 

consideration, we decided to exclude this risk factor from the 

analysis. Climate change pressure, as a potential risk factor, is 

described in detail in appendix E of Aubry et al. (2011).
 12 Guide to Vulnerability Assessment Methodology

scientific literature, or, in the case of the insect 

and disease threats risk factor, on information 

provided by local experts. We chose not to 

include projected future shifts in the distribution 

of species’ habitats as a risk factor (appendix E, 

Aubry et al. 2011). We included this risk factor 

initially, but removed it after an early peer review 

underscored the uncertainties of such models and 

the lack of agreement among scientists on the 

assumptions on which those models are based. 

We also considered but did not use the effects of 

future fire regimes as a risk factor (see box on 

Fire and Climate Change Pressure as Risk 

Factors).

Step 3: Collect Data and 

Calculate Risk Factor Scores

The process of rating the vulnerability of each 

tree species requires information from a variety 

of sources. In the Pacific Northwest assessment, 

the types of data collected fell into three 

categories: (1) life history traits expected to 

influence a species’ resistance and resilience to 

the effects of anticipated climate change, (2) 

distribution data, and (3) data on forest insect and 

disease threats that were provided by U.S. Forest 

Service, Pacific Northwest Region Forest Health 

Protection Program. The sources of data used to 

Figure 2. The five risk factors of the Pacific Northwest ForGRAS vulnerability assessment model 

and the variables used to quantify each risk factor. For each variable, the characteristic that 

increases a species’ vulnerability is shown in parentheses

Reproductive 

Capacity

Habitat 

Affinity

Distribution Adaptive 

Genetic 

Variation

Insect and 

Disease 

Threats

Frequency 

of 

occurrence

(rare ↑)

Proportion 

of canopy 

trees 

(smaller ↑)

Distribution 

within study 

area 

(narrow ↑)

Seed 

dispersal 

vector 

(animal ↑)

Fecundity 

(low ↑)

Seed 

dispersal 

distance 

(short ↑)

Mean 

elevation 

(high ↑)

Successional

stage 

(late ↑)

Habitat 

specificity 

(high ↑)

Seed zone 

elevation 

band width 

(narrow ↑)

Pollen 

dispersal 

vector 

(animal ↑)

Disjunct 

populations 

(one or more ↑)

Threats 

from major 

insects and 

diseases 

(immediacy, 

severity ↑)

Minimum 

seed-bearing 

age 

(high ↑)

Dioecy 

(dioecious ↑)

Drought 

tolerance 

(low ↑)
Guide to Vulnerability Assessment Methodology 13

rate variables within each of the five risk factors 

are shown in table 2.

The tree species’ vulnerability scores for each of 

the five risk factors are calculated using a 

Microsoft Excel spreadsheet model. This 

spreadsheet model is available online at 

www.ecoshare.info/projects/ccft in two versions: 

one containing no data and one populated with 

the data from the Pacific Northwest vulnerability 

assessment. For each risk factor, the model 

calculates a score ranging from 0 to 100, with a 

higher score indicating greater vulnerability. The 

details of how each risk factor is scored are 

described later in this guide, in the sections

devoted to each risk factor.

Step 4: Calculate Overall Scores

and Rank Species

After calculating a score from 0 to 100 for each 

species for each risk factor, the model calculates 

an overall score for each tree species by 

averaging its score across the five risk factors.

Species can then be sorted and ranked according 

to this overall vulnerability score.

In the Pacific Northwest vulnerability assessment, 

we weighted the five risk factors equally, based 

on the assumption that we did not know which 

factors would become most important under 

future climate scenarios. However, users of this 

vulnerability assessment system may find it 

desirable to weight risk factors differently or to 

calculate the overall species’ scores by using a 

method other than a simple average. Some 

examples of alternative methods of calculating 

the overall vulnerability score for each species 

are:

• Use risk factor weightings—Weight the 

risk factors according to how important 

each is considered to be. This is based on 

the assumption that, in a given study 

area, one predicts certain risk factors to 

be more important than others. For 

example, if the distribution and insect 

and disease threats risk factors are 

deemed twice as important as the other 

three factors, one would apply the 

following steps to determine the overall 

vulnerability score for each species:

1. Assign a weighting factor to each of 

the five risk factor scores. In this 

example, the distribution and insect 

and disease threats risk factors would 

each be assigned a multiplier of 2, 

because they were deemed twice as 

important as the other factors; the 

other three risk factors would each be 

assigned a multiplier of 1.

ForGRAS Terminology

Risk factor—An aspect of vulnerability to 

climate change that is quantified by a set of 

variables. The Pacific Northwest application of 

ForGRAS used five risk factors.

Risk factor score—For each tree species, the 

ForGRAS model calculates a vulnerability score 

for each risk factor. The model first calculates a 

raw risk factor score; it then scales this raw score 

so that the species scores range from 0 (least 

vulnerable) to 100 (most vulnerable).

Variable—Each risk factor has multiple 

variables to quantify that aspect of vulnerability 

to climate change.

Rating—Each variable has a rating, in most 

cases a value between 0 and 100.

Overall vulnerability score—For each tree 

species, an overall climate change vulnerability 

score is calculated by averaging the five risk 

factor scores.
 14 Guide to Vulnerability Assessment Methodology

2. Multiply each risk factor score by its 

weighting factor.

3. Average the risk factor scores to 

create an overall vulnerability score.

4. To ensure that the overall

vulnerability scores for all species

range from 0 to 100, the overall 

vulnerability score calculated in step 

3 must be multiplied by the number 

of risk factors and then divided by 

the sum of the multipliers. In this 

example, one would multiply the 

Table 2. Major sources of data used to rate climate change vulnerability within five risk factors in the Pacific 

Northwest climate change vulnerability assessment

Risk factor Data source Application of data

Distribution Forest Inventory and Analysis (FIA) annual 

inventory Phase 2 data

Other sources listed in table 1

FIA data were used to calculate each species’ 

frequency of occurrence and the proportion of the 

canopy trees that it composed

Distribution maps were created using all data 

sources available; these maps were used to 

assess the geographic distribution of a species’ 

occurrences

Reproductive capacity The Woody Plant Seed Manual (Bonner 

and Karrfalt 2008)

Regional compilations of tree data (Klinka 

et al. 2000)

Comprehensive tree species guides and 

literature reviews (e.g., Burns and Honkala 

1990, Fischer et al. 1996)

Information used to rate fecundity and seedbearing age

Information used to rate fecundity and seed 

dispersal distance

Information used to rate seed-dispersal vectors, 

seed dispersal distance, and dioecy

Habitat affinity Forest Inventory and Analysis (FIA) annual 

inventory Phase 2 data

Regional compilations of tree data (Arno 

and Hammerly 2007, Klinka et al. 2000)

Comprehensive tree species guides and 

literature reviews (e.g., Burns and Honkala 

1990, Fischer et al. 1996)

Used to calculate mean elevation of occurrences

Information used to rate successional stage, 

habitat specificity, and drought tolerance

Information used to rate successional stage, 

habitat specificity, and drought tolerance

Adaptive genetic 

variation

Forest tree seed zone manuals (e.g., 

Randall 1996, Randall and Berrang 2002) 

and other sources in the scientific literature

Comprehensive tree species guides and 

literature reviews (e.g., Burns and Honkala 

1990, Fischer et al. 1996)

All sources listed in table 1; tree 

distribution maps of Little (1971, 1976)

Information used to determine width of seed zone 

elevation bands 

Information used to rate pollen dispersal vector

Data used to identify disjunct populations

Insect and disease 

threats

Major insect and disease threats rated by 

experts at U.S. Forest Service Pacific 

Northwest Region Forest Health Protection 

Service Centers

Data used to quantify the vulnerability of each tree 

species to insect and disease threats

See Aubry et al. (2011) and Devine et al. (2012)
Guide to Vulnerability Assessment Methodology 15

score by 5 and then divide it by 7 

(i.e., 2+1+1+1+2).

• Base overall score on each species’ 

most vulnerable factors—This method 

is based on the assumption that a species’ 

overall climate change vulnerability is 

best described by averaging only those 

risk factors that make it most vulnerable 

(i.e., the risk factors with the highest 

scores). For example, using a “top three” 

approach, the three risk factors with the 

highest vulnerability scores would be 

averaged to create the overall 

vulnerability score for each species. 

These top three factors would likely 

differ among species. This approach 

assumes that, if a species has certain risk 

factors making it highly vulnerable, those 

vulnerabilities should be emphasized in 

creating its overall vulnerability score.

Likewise, it also assumes that it is 

irrelevant to the final score if the other 

two risk factors have very low 

vulnerability scores. Examples of the

“top three” analysis are given in 

appendices 2 through 6 of Devine et al. 

(2012). Overall vulnerability scores also

could be calculated using the top one, 

two, or four risk factor scores.

• Use the limiting factor approach—This 

approach is based on the assumption that 

a selected risk factor is important enough 

that a species’ overall vulnerability score 

should be no lower than its score for that 

risk factor. For example, if the insect and 

disease threats risk factor were

determined to be of particular importance 

(i.e., a limiting factor), one would:

1. Calculate each species’ overall 

vulnerability score by averaging the 

five risk factor scores (or using 

another method, if desired).

2. Compare each species’ overall score 

to its score for the insect and disease 

threats risk factor.

3. Select the higher of these two scores.

A broader approach is to apply several

calculation methods (such as the three described 

above), as well as a simple average of the risk 

factor scores, to evaluate how the overall species’ 

scores and rankings are influenced by the 

different methods. This approach can be used to 

gauge the sensitivity of the overall model to the 

assumptions of the various calculation methods.
 16 Guide to Vulnerability Assessment Methodology

CHAPTER 3. RISK FACTOR:

DISTRIBUTION

The distribution risk factor quantifies a species’ 

prevalence and distribution throughout the study 

area. Species with narrow distributions are 

considered more susceptible to genetic 

degradation or even extinction, given pressures 

associated with long-term change in climate

(Willi et al. 2006; Potter and Crane 2010). This

risk factor is composed of three variables selected 

to describe a species’ distribution: frequency of 

occurrence, proportion of canopy trees, and 

distribution within study area.

Required Data

To rate tree species for this risk factor, the Forest 

Inventory and Analysis annual inventory Phase 2 

data are required for the study area. These data 

are used to calculate ratings for the frequency of 

occurrence and proportion of canopy trees

variables for each species. The distribution within 

study area variable can also be rated using only

the FIA dataset, although we found that our 

precision, and our confidence in the ratings,

increased when we supplemented the FIA dataset

with data from some of the other sources listed in 

table 1. If these other data sources are not 

available, Little’s range maps are a useful 

resource (Little 1971, 1976). Figures 3 through 6

illustrate the differences in coverage among four 

of the major data sources used in the Pacific 

Northwest vulnerability assessment western 

Washington study area (Aubry et al. 2011). 

Figures 3 through 5 show all sample plot 

locations, regardless of which species were found 

on the plots; figure 6 shows the locations of 

specimen collections only for species in the 

Pacific Northwest vulnerability assessment.

Procedures used for acquiring and processing 

data from major data sources are in appendix 1.

Rating the Variables

Frequency of occurrence—This variable is 

calculated as the percentage of FIA plots on 

which a given species occurred as a live tree. The 

vulnerability rating ranges from 0 (for the species 

occurring on the most plots) to 100 (for the 

species occurring on the fewest plots), with 

values for all other species assigned 

proportionally, according to the percentage of 

plots on which each occurred. The procedure for 

calculating this variable is described in appendix 

2 (assuming the FIA data acquisition and 

processing described in appendix 1 have already 

been completed).

Proportion of canopy trees—The dominant and 

co-dominant canopy trees are generally the trees 

with the greatest vigor and reproductive capacity; 

thus, a species with a high degree of canopy 

dominance is assumed to be more likely to 

maintain its presence. For each of the plots on 

which a species occurred, we calculated the 

percentage of all canopy trees (i.e., a tree coded 

by FIA as dominant, co-dominant, or opengrown) that the given species represented; then 

we averaged this percentage across these plots. 

We calculated the vulnerability rating in the same 

manner as the frequency of occurrence variable, 

with the species composing the lowest percentage 

of canopy trees receiving the highest vulnerability 

rating (100) and the species with the highest 

percentage of canopy trees receiving the lowest 

vulnerability rating (0). The full procedure for 

calculating this value is described in appendix 2.

Distribution within study area—Although the 

frequency of occurrence variable describes the 

number of plots on which a species occurs, it 

does not describe the distribution of the plots 

within the study area. This variable, distribution 

within study area, is rated according to how the 

occurrences are distributed. Because this rating is 

qualitative, the user should examine the
Guide to Vulnerability Assessment Methodology 17

Figure 3. Forest Inventory and Analysis 

(FIA) plot locations in western Washington

(locations are approximate)
 18 Guide to Vulnerability Assessment Methodology

Figure 4. Current Vegetation Survey (CVS) 

plot locations in western Washington

(locations are approximate)
Guide to Vulnerability Assessment Methodology 19

Figure 5. Ecology Core Dataset plot 

locations in western Washington
 20 Guide to Vulnerability Assessment Methodology

Figure 6. Burke Museum herbarium 

specimen collection locations used in the 

western Washington vulnerability assessment
Guide to Vulnerability Assessment Methodology 21

distributions of all tree species before assigning

the ratings. We used the following guidelines for 

rating this variable:

• Wide distribution (species occurs across 

all of the study area) = 0

• Moderate distribution (species absent 

from up to half of the study area—e.g., 

because elevation limits the presence of 

habitat—but common in the rest of the 

study area) = 25

• Narrow distribution (species present in 

less than half of study area, occurring 

there at a moderate to low density) = 50

• Very narrow distribution (species present 

only in a small part of the study area) = 

75

• Rare (species occurs on only a few plots) 

= 100

Risk Factor Score

The ForGRAS model calculates a raw 

distribution factor score by averaging the scores 

of the three variables. It then scales this score so 

that the tree species with the lowest score is

assigned a risk factor score of 0 and the species 

with the highest score is assigned 100. The 

purpose of this scaling is to make the species 

scores within each risk factor range from 0 to 

100, thereby giving each risk factor the same 

influence on the overall vulnerability score.
 22 Guide to Vulnerability Assessment Methodology

CHAPTER 4. RISK FACTOR:

REPRODUCTIVE CAPACITY

The reproductive capacity risk factor quantifies a 

species’ capacity to migrate and regenerate in 

openings created by disturbance. Species that are 

limited in their production of seed or in seed 

dispersal are considered more vulnerable, given 

an expectation of an increase in large-scale 

disturbances under a changing climate (Littell et 

al. 2010). The variables in this risk factor are: 

seed dispersal vector, fecundity, seed dispersal 

capacity, minimum seed-bearing age, and dioecy 

(breeding system).

Required Data

To rate the variables in the reproductive capacity 

risk factor, several sources of data are required:

• The Woody Plant Seed Manual (Bonner 

and Karrfalt 2008). This publication, 

available freely online, is the most 

comprehensive single source of data for 

information on tree seeds and seed 

production. It summarizes all available 

information on tree species of the U.S.; 

therefore, it is applicable to the tree 

species of any study area in the country.

• Literature reviews and guides to tree 

species. Silvics of North America (Burns 

and Honkala 1990) and the Fire Effects 

Information System database (Fischer et 

al. 1996) are excellent compilations of 

biological and ecological data on U.S. 

tree species. Both are free online.

• Regional guides to tree species. For the

Pacific Northwest assessment, The 

Distribution and Synopsis of Ecological 

and Silvical Characteristics of Tree 

Species of British Columbia’s Forests

(Klinka et al. 2000) was a useful source 

of information for this risk factor. It 

includes tables that summarize

reproductive information for each tree 

species.

Rating the Variables

Seed dispersal vector—Tree species that depend 

primarily on animal seed dispersal vectors are 

potentially vulnerable to climate change because 

behavior of animal species could be altered by 

changes in annual climate patterns. Tree species 

not adapted to long-distance dispersal of 

propagules, such as those with seeds dispersed 

primarily by gravity or those reproducing 

vegetatively, are also vulnerable.

• Species with wind or water dispersal 

vectors are assigned a vulnerability rating 

of 0.

• Species with animal dispersal vectors,

seed dispersed by gravity, and species 

with minimal seed production (e.g., those 

reproducing primarily vegetatively) are

assigned a rating of 100.

Fecundity—Fecundity is defined as the capacity 

to reproduce. For each species, gather 

information on the typical interval between seed 

crops, the interval between large seed crops, 

sound seed percentage, seed germination 

capacity, and how long seed can retain viability 

in the forest floor. Assign vulnerability ratings to 

species based on their fecundity relative to the 

other species in the assessment:

• Assign a rating of 0 to species with high 

fecundity.

• Assign a rating of 50 to species with 

medium fecundity.

• Assign a rating of 100 to species with 

low fecundity.

Seed dispersal capacity—For this variable, rate

species according to the distance to which seed is 

typically dispersed. Often, values in the scientific 
Guide to Vulnerability Assessment Methodology 23

literature are based on the maximum distance to 

which the majority of seed (e.g., 90 percent) is 

dispersed; these are the values that should be used

to rate this variable. For some tree species, 

information on seed dispersal distance is lacking; 

thus, values must be interpolated using dispersal 

distance values of other species within the same 

genus that have the most similar seed size, 

weight, and structure.

• Assign a rating of 0 for a dispersal 

distance of greater than 0.5 mi.

• Assign a rating of 50 for a dispersal 

distance of 400 ft to 0.5 mi.

• Assign a rating of 100 for a dispersal 

distance of less than 400 ft.

Minimum seed-bearing age—This variable is 

based on the premise that species that produce 

seed at a younger age have a shorter generation 

time and thus can more rapidly adapt to 

environmental change, migrate, and reproduce 

after large-scale disturbances. Migration rate is 

important for facilitating natural regeneration, 

given potential geographic shifts in suitable 

habitat. The age at which seed production begins 

for a given species can be variable; thus, for 

consistency, we recommend using the values 

given in The Woody Plant Seed Manual (Bonner 

and Karrfalt 2008), which describe minimum 

seed-bearing age under good growing conditions.

• Assign a rating of 0 for a minimum seedbearing age of less than 10 years.

• Assign a rating of 50 for a minimum 

seed-bearing age of 10 to 20 years.

• Assign a rating of 100 for a minimum 

seed-bearing age of greater than 20 years.

Dioecy—Species with more complex breeding 

systems, for example, dioecious species which 

have separate male and female individuals, are at 

higher risk of extinction (Vamosi and Vamosi 

2005). For this reason, we assigned these species 

a higher vulnerability rating.

• Species that are always or usually 

monoecious were assigned a rating of 0.

• Species that are always or usually 

dioecious were assigned a rating of 100.

Risk Factor Score

The ForGRAS model calculates a raw 

reproductive capacity factor score by averaging 

the scores of the five variables. It then scales this 

score so that the tree species with the lowest 

score is assigned a risk factor score of 0 and the 

species with the highest score is assigned 100.
 24 Guide to Vulnerability Assessment Methodology

CHAPTER 5. RISK FACTOR:

HABITAT AFFINITY

The habitat affinity risk factor rates the degree to 

which a tree species’ habitat affinities are 

expected to increase its vulnerability to projected 

changes in climate. For example, a species with a 

high level of drought tolerance is better-suited to 

warmer summer conditions with high evaporative 

demand, compared to a species with low drought 

tolerance. Thus, in the Pacific Northwest 

assessment, drought-tolerant species were

assigned lower vulnerability ratings, given 

projected increases in summer temperatures 

during the 21st century (Littell et al. 2010). Four 

variables were selected for the habitat affinity 

risk factor: mean elevation, successional stage, 

habitat specificity, and drought tolerance.

Required Data

Several sources of data are used to rate the four 

variables in the habitat affinity risk factor:

• To rate the mean elevation variable, use

FIA annual inventory data for the study 

area. A detailed description of the data 

preparation and analysis process appears 

in appendices 1 and 3.

• To rate the other three variables 

(successional stage, habitat specificity, 

drought tolerance), use information from 

literature reviews (e.g., Burns and 

Honkala 1990 and Fischer et al. 1996) 

and regional tree species guides (e.g.,

Arno and Hammerly 2007 and Klinka et 

al. 2000).

Rating the Variables

Mean elevation—Species with habitat limited to

higher elevations are considered more vulnerable 

to climate change because the extent of their 

habitat and their pathways of migration become 

increasingly limited under long-term warming 

(Parmesan 2006). For each species, calculate the

mean elevation of all FIA plots on which that 

species occurs within the study area (appendix 3). 

The ForGRAS model will then calculate a rating 

for each species ranging from 0 (the species with 

the lowest mean elevation) to 100 (the species 

with the highest mean elevation), with values for 

all other species assigned proportionally,

according to where their mean elevations fall 

between these two extremes.

Although not necessary for rating this variable, 

graphs illustrating the distribution of species 

occurrences by elevation can be useful in 

understanding spatial patterns in habitat. An 

example of this type of graph is shown near the 

bottom of figure 7; instructions for creating an 

elevation graph appear in appendix 3.

Successional stage—Species adapted to late 

successional stages generally have greater withinpopulation genetic diversity than species of early 

successional stages (Hamrick et al. 1992) and 

thus are assumed to be more vulnerable to loss of 

genetic diversity (Myking 2002, Potter and Crane 

2010). We based the successional stage variable 

ratings on the successional stage at which a tree 

species typically becomes a significant

component of the forest canopy:

• Significant canopy component in early 

successional stages only = 0.

• Significant canopy component in early 

and late successional stages = 50.

• Significant canopy component in late 

successional stages only = 100.

Habitat specificity—Habitat specificity 

represents the specificity of a given species’ 

habitat requirements relative to the other tree 

species in the study area. A species with high 

habitat specificity is assigned a high vulnerability
Guide to Vulnerability Assessment Methodology 25

Figure 7. Occurrences 

of Douglas-fir in the 

northwestern Oregon 

study area
 26 Guide to Vulnerability Assessment Methodology

rating because it is considered more vulnerable to 

habitat loss associated with climate change than a 

species adapted to a broad range of habitats. 

Because ratings are based on habitat specificity 

relative to the other tree species, one must 

consider the range of habitats of each of the 

species in the study area. Habitat specificity is 

rated qualitatively, as low, medium, or high:

• Species with low habitat specificity are

assigned a rating of 0. These species 

occur in a habitat type that is very 

common within the study area or in a 

range of habitats that, when combined,

represent a substantial portion of the 

study area.

• Species with medium habitat specificity 

are assigned a rating of 50. These species 

may be limited to a certain elevational 

range that represents only a portion of the 

study area or they may have other habitat 

requirements that prevent them from 

becoming widespread.

• Species with high habitat specificity are

assigned a rating of 100. These species 

are limited to very specific habitat within 

the study area. It is possible that, within a 

study area, no species have this level of 

habitat specificity. For example, in the

western Washington study area, none of 

the group 1 tree species (i.e., major 

canopy trees) were rated as having high 

habitat specificity (Devine et al. 2012).

Drought tolerance—Projected increases in 

summer temperatures are likely to be associated 

with greater evaporative demand, even if no 

substantial change occurs in summer 

precipitation. Thus, tree species with low drought 

tolerance have a higher level of vulnerability to 

projected increases in summer temperature in 

regions such as the Pacific Northwest where 

summer moisture is generally growth-limiting 

(Littell et al. 2010). Drought tolerance is rated as 

low, medium, or high. Ratings are assigned 

relative to the other tree species within the study 

area; therefore, drought tolerance information for 

all species must be examined before ratings can 

be assigned.

• Species of high drought tolerance are

assigned a vulnerability rating of 0.

• Species of medium drought tolerance are

assigned a vulnerability rating of 50.

• Species of low drought tolerance are

assigned a vulnerability rating of 100.

Risk Factor Score

The ForGRAS model calculates a raw habitat 

affinity risk factor score by averaging the scores 

of the four variables. It then scales this score so 

that the tree species with the lowest score is 

assigned a risk factor score of 0 and the species 

with the highest score is assigned 100.
Guide to Vulnerability Assessment Methodology 27

CHAPTER 6. RISK FACTOR:

ADAPTIVE GENETIC 

VARIATION

The adaptive genetic variation risk factor 

describes a tree species’ ability to adapt to a 

changing climate using three variables: elevation 

band width of seed zones, pollen dispersal vector, 

and the presence of disjunct populations. This 

risk factor is based on elements that describe 

genetic diversity, gene flow, and population 

structure. Genetic variation in traits related to 

local adaptation is critically important in 

assessing vulnerability to climate change. We 

chose to use seed zone elevation band width 

(within the study area) as a surrogate for adaptive 

genetic variation. Species with one or no 

elevation bands are considered generalists with 

wide climatic tolerances, whereas species with 

several narrow elevation bands are considered 

specialists, highly adapted to their local 

environment, with specific climatic requirements.

Gene flow occurs through the movement of 

pollen and seed; species that are insect-pollinated 

are more vulnerable to climate change because of 

the required interaction with another organism

whose behavior may be influenced by a changing 

climate. Some tree species with disjunct 

populations are considered to have elevated 

vulnerability to climate change because loss of 

the genetic variation represented by the disjunct 

population could negatively affect the climate 

change vulnerability of the species as a whole.

Required Data

• Local forest tree seed zone manuals (e.g., 

Randall 1996, Randall and Berrang 2002) 

provide specific information on seed 

zone elevation band width for many tree 

species. For some tree species, 

particularly non-commercial species, data 

on elevation band width are absent from 

the manuals and therefore must be 

extracted from other sources in the 

scientific literature. In a few cases, data 

are not explicitly available in the 

literature, and values must be interpolated

by a geneticist, by using data from 

closely related species.

• Tree species guides and literature reviews 

(Burns and Honkala 1990, Fischer et al. 

1996) provide data on pollen dispersal 

vectors.

• To identify disjunct populations, use the

species distribution maps created in the 

distribution risk factor (chapter 3) in 

conjunction with range-wide maps 

(available online at: 

http://esp.cr.usgs.gov/data/atlas/little/ and 

in publications such as Little (1971, 

1976)). The tree distribution maps 

created with documented occurrences 

provide greater fine-scale accuracy for 

the study area (fig. 8), whereas the rangewide maps are necessary to evaluate the 

location of disjunct populations relative 

to a species’ full distribution.

Rating the Variables

Elevation band width of seed zones—Seed zone 

band width is defined as the range in elevation 

within which maladaption due to seed movement 

is minimized. Species with narrow elevation band 

widths are highly adapted to their local 

environment, with specific climatic requirements, 

and are thus considered more vulnerable to 

climate change.

• Species with no seed zone elevation 

bands (i.e., seed may be moved across the 

species’ full elevational range without

maladaption) are assigned a rating of 0.
 28 Guide to Vulnerability Assessment Methodology

Figure 8. Recent documented 

occurrences, and Little’s 

(1971) distribution data, for 

Rocky Mountain juniper in 

western Washington. Note that 

the mapped recent 

occurrences show additional 

populations
Guide to Vulnerability Assessment Methodology 29

• Species with seed zone elevation bands 

greater than 1,500 ft wide are assigned a 

rating of 33.

• Species with seed zone elevation bands 

1,000 to 1,500 ft wide are assigned a 

rating of 67.

• Species with seed zone elevation bands 

less than 1,000 ft wide are assigned a 

rating of 100.

Pollen dispersal vector—Tree species that 

depend on insects, rather than wind, for 

pollination (and therefore gene flow) have a 

potentially increased vulnerability to climate 

change. Changes in climate could affect behavior 

of pollinators and influence tree phenology; thus, 

gene flow could be negatively affected if the 

interaction between trees and pollinators are 

altered. 

• Primarily wind-pollinated tree species are 

assigned a vulnerability rating of 0.

• Primarily insect-pollinated tree species 

are assigned a vulnerability rating of 100.

Disjunct populations—A tree species is 

assigned a high vulnerability rating if the 

potential loss of a disjunct population is deemed 

likely to impact the climate change vulnerability 

of the species as a whole. For example, if a study 

area includes a disjunct population that is known 

to be genetically distinct, or if the disjunct 

population represents the edge of the species’ 

distribution, the loss of this population could 

represent loss of adaptive genetic variation for the 

species as a whole.

• If the study area does not include a 

disjunct population that either (a) 

represents the edge of the species’ 

distribution, or (b) is known to be 

genetically distinct, then assign a rating 

of 0.

• If the study area includes a disjunct 

population that represents the edge of the 

species’ distribution (e.g., the species’ 

southernmost or easternmost occurrence),

assign a vulnerability rating of 100. Or, if 

a disjunct population in the study area is 

known to be genetically distinct, even if 

it does not represent the edge of the 

species’ distribution, it should also be

assigned a rating of 100.

Risk Factor Score

The ForGRAS model calculates a raw adaptive 

genetic variation risk factor score by averaging 

the scores of the three variables. It then scales this 

score so that the tree species with the lowest 

score is assigned a risk factor score of 0 and the 

species with the highest score is assigned 100.
 30 Guide to Vulnerability Assessment Methodology

CHAPTER 7. RISK FACTOR:

INSECT AND DISEASE 

THREATS

This risk factor quantifies insect and disease

threats that presently affect a tree species or are 

expected to exacerbate the negative impacts of 

climate changes on tree survival, growth, or 

vigor. For each tree species, as many as five of 

the most important threats are rated according the 

immediacy and severity of the threat.

Required Data

Data for this risk factor are ideally provided by 

experts in forest entomology and pathology. In 

the Pacific Northwest assessment, data were 

provided by personnel from the Forest Service 

Forest Health Protection Service Center (i.e., 

field office) that served the area in which each

study area was located. We provided the 

pathologists and entomologists at the service 

center with a list of tree species and a detailed 

description of the overall objectives and the 

rating system. The service center personnel then 

listed and rated each of the major insect and 

disease threats for each of the tree species.

Rating the Variables

In the Pacific Northwest vulnerability assessment,

Service Center personnel identified the most 

important insect and disease threats for each tree 

species (to a maximum of five threats per tree 

species). They rated two variables for each threat: 

severity and immediacy.

Severity—Severity is defined as the present 

impact of a threat. Potential ratings range from 1 

to 10:

• Minor mortality, usually of alreadystressed trees = 1

• Moderate mortality in association with 

other threats = 3

• Moderate mortality of mature trees = 5

• Significant/complete mortality in related 

species = 6

• Significant mortality of mature trees = 8

• Complete mortality of all mature trees = 

10.

Immediacy—The immediacy of each threat is

rated on a scale of 1 to 3:

• Potential to reach region of interest = 1

• Present in region = 2

• Present in region and climate change 

appears to be a contributing factor in 

increases in distribution and impact = 3.

Risk Factor Score

For each tree species, the ForGRAS model 

calculates a score for each threat by multiplying 

the threat’s severity and immediacy values. The 

model next calculates the tree species’ raw risk 

factor score by summing all of the threat scores

for that species. It then scales all of the tree 

species’ raw scores so that the tree species with 

the lowest score is assigned a risk factor score of 

0 and the species with the highest score is 

assigned 100.
Guide to Vulnerability Assessment Methodology 31

ACKNOWLEDGMENTS

The authors would like to thank Vicky Erickson 

and Marc Kramer for providing financial and 

program support for this project.

In addition to the numerous individuals who 

provided technical support, advice, and peer 

reviews during the preparation of the Pacific 

Northwest vulnerability assessment (names listed 

in Devine et al. 2012), we thank Jeff Muehleck 

for providing a technical review of this guide.

We thank Mary Carr of Forest Service Publishing 

Arts Staff for editorial support and Carol LoSapio 

for document design.

We thank Steffen Rasile for website support.
 32 Guide to Vulnerability Assessment Methodology

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the Washington climate change impacts 

assessment. Seattle, WA: Center for Science in 

the Earth System, Joint Institute for the Study of 

the Atmosphere and Oceans, University of 

Washington: 21-43.

Myking, T. 2002. Evaluating genetic resources of 

forest trees by means of life history traits—a 

Norwegian example. Biodiversity and 

Conservation. 11(9): 1681–1696.

Parmesan, C. 2006. Ecological and evolutionary 

responses to recent climate change. Annual 

Review of Ecology, Evolution, and Systematics. 

37: 637–669.

Potter, K.M.; Crane, B.S. 2010. Forest tree 

genetic risk assessment system: a tool for 

conservation decision-making in changing times. 

Version 1.2. http://www.forestthreats.org/currentprojects/project-summaries/genetic-riskassessment-system. (25 February 2011).

Randall, W.K. 1996. Forest tree seed zones 

for western Oregon. Salem, OR: Oregon 

Department of Forestry. 82 p. 

Randall, W.K.; Berrang, P. 2002. Washington 

tree seed transfer zones. Olympia: Washington 

Department of Natural Resources. 63 p.

Rehfeldt, G.E.; Crookston, N.L.; Warwell, M.V.; 

Evans, J.S. 2006. Empirical analyses of plantclimate relationships for the western United 

States. International Journal of Plant Sciences. 

167(6): 1123–1150.

Vamosi, J.C.; Vamosi, S.M. 2005. Present day 

risk of extinction may exacerbate the lower 

species richness of dioecious clades. Diversity 

and Distributions. 11(1):25-32.

Willi, Y.; Van Buskirk, J.; Hoffmann, A.A. 2006. 

Limits to the adaptive potential of small 

populations. Annual Review of Ecology, 

Evolution, and Systematics. 37:433-458.
 34 Guide to Vulnerability Assessment Methodology

APPENDIX 1: DATA 

RETRIEVAL AND 

PREPARATION

This appendix contains a description of the steps 

followed to retrieve and prepare tree distribution 

data for the Pacific Northwest vulnerability 

assessment. It includes the major data sources 

used: FIA, CVS, Regional Ecology Program Core 

Dataset, the Burke Museum herbarium, and the 

Oregon Flora Project. When applying the 

ForGRAS model to other regions (i.e., areas

outside Washington and Oregon), available

sources of data will differ; however, the 

descriptions of these processes should provide a

useful starting point.

Determine Tree Species List and 

Associated Codes

Before extracting data from various sources, it is 

helpful to compile a comprehensive list of species 

names and synonyms, and their associated 

species codes. Different sources may use 

different names and/or codes to represent the 

same species; sometimes multiple codes are even 

used within the same dataset (table 3). The online 

USDA Plants Database (www.plants.usda.gov) 

lists many pseudonyms and associated codes but 

should not be solely relied upon, especially if 

searches are based on species codes. For 

example, the Plants Database code for Acer 

macrophyllum (bigleaf maple) is ACMA3, but 

searching the Ecology Core Dataset (which uses 

codes but not species names) for “ACMA3” will 

result in zero selections because the Ecology 

Core Dataset code is “ACMA” (without the “3”). 

Searching for FRPU (not FRPU7, as listed in the 

Plants Database) will result in a partial selection 

of Frangula purshiana (cascara) because many of 

the species’ records in that dataset are coded 

RHPU, an older synonym. Other agencies, such

as the USDI National Park Service, may use a 

completely different coding system, in which 

case it might be more appropriate to base 

searches on species names, where included, or on

multiple fields. It is useful to run a frequency on 

the search field (code or name) in each shapefile 

to obtain a list of all choices within each dataset.

Data Sources

The ForGRAS model, as applied in the Pacific 

Northwest Vulnerability Assessment, can be run 

using only data from the FIA annual inventory.

However, the Pacific Northwest Assessment 

included data from several secondary sources as 

well. The most frequently used of these 

secondary sources were CVS, Ecology Program 

Core Dataset, the Oregon Flora Project, and the 

Burke Museum herbarium. These secondary data 

sources were not used to calculate the 

quantitative variable ratings for the model; rather, 

they were used to improve the precision of the 

distribution maps and better our understanding of 

species’ distributions. In the case of less-common 

tree species, the FIA annual inventory often did 

not full capture the full distribution, and the 

secondary data sources proved highly useful. A 

drawback of using the secondary sources is that it 

can be time-consuming to process and compile 

the data, particularly for large study areas. If a 

model user chooses not to acquire secondary data 

(i.e., only uses FIA data), it is recommended that 

the user augment their data with Little’s range 

maps (Little 1971, 1976).

FIA Annual Inventory Data

(Available Nationwide)

The FIA dataset is used for vulnerability ratings 

and for creating maps of species occurrences; the 

other datasets, described later, are used only for 

creating maps of occurrences. For the purpose of 

the ForGRAS vulnerability assessment, the FIA 

annual inventory dataset is the most important
Guide to Vulnerability Assessment Methodology 35

source of tree distribution data. The FIA annual

inventory consists of sample plots on all forested 

lands, regardless of ownership, at a frequency of 

one plot per 6,000 acres. This inventory is 

conducted on a 5- to 10-year cycle (depending on 

the state) in which 10 to 20 percent of all plots 

are measured each year. This annual inventory 

began around 2000 in most states, and for some 

states operating on a 10-year cycle, the full 

dataset is not publicly available yet. However, a 

partial state dataset consisting of at least 70 

percent of plots is sufficient for this type of 

assessment because there is no spatial bias in 

sequence of plots measured (i.e., the 70 percent 

of plots that have been measured are uniformly 

distributed across the state).

The FIA database is publicly available at the FIA 

DataMart: http://apps.fs.fed.us/fiadbdownloads/datamart.html. The User Manual can 

be downloaded from 

http://fia.fs.fed.us/library/databasedocumentation/, and the Field Guide can be found 

at http://www.fia.fs.fed.us/library/field-guidesmethods-proc/. To retrieve and prepare an FIA 

dataset:

Table 3. Examples of variation in tree species codes among data sources used in the Pacific Northwest 

vulnerability assessment

Dataset

Scientific name

Working 

code

Synonyms listed in 

USDA Plants 

Database

Forest 

Inventory and 

Analysis

Ecology 

Core Data

Current 

Vegetation 

Survey NPS

Abies amabilis ABAM None. ABAM ABAMT,

ABAM1

ABAM ABIAMA

Acer macrophyllum ACMA3 None. ACMA3 ACMAT,

ACMA1

ACMA and 

ACMA3

ACEMAC

Cupressus 

nootkatensis

CUNO CHNO, CUNO, 

XANO4, CANO92

CHNO CHNOT, 

CHNO1

CUNO and 

CHNO

CHANOO

Frangula purshiana FRPU7 RHPU FRPU7 (in 

shrub 

dataset)

RHPUT None. None.

Populus balsamifera

ssp. trichocarpa

POBAT POBAC3, POHA15, 

POTR15, POTRH, 

POTRC, POTRH2, 

POTRI

POBAT POTR2T,

POTR21

POBAT or 

POTR2

POPBALTRI

Populus tremuloides POTR5 POAU8, POCE6, 

POPO12, POTRT, 

POTRA, POTRC2, 

POTRI2, POTRM, 

POTRR, POTRV, 

POVA11

POTR5 None. 

(although 

POTR3 

was listed)

POTR5 POPTRE

1 Code used on the Gifford Pinchot National Forest only.

2 Code currently used in USDA Plants Database, which is different from the working code.
 36 Guide to Vulnerability Assessment Methodology

1) Download the FIADB Microsoft Access 

database for the state of interest. At 

http://apps.fs.fed.us/fiadbdownloads/datamart.html click on the 

state abbreviation on the right-hand map 

of the U.S. Note that the species codes, 

common names, and scientific names 

used in the FIA database can be found in 

Appendix F of the FIA Database User 

Manual. Unzip the downloaded file if 

necessary.

2) Query this database for plot number, 

coordinates, and elevation, species codes 

and names, and tree status of all trees 

(defined as one inch in diameter and 

larger). To perform this query, first open 

Microsoft Access, and then open the 

database that was just downloaded in step 

1. Click on the Create menu, click Query 

Design on the ribbon, close the window 

that pops up, and then click the SQL 

button on the ribbon. In the window that 

appears, delete “SELECT;” and replace it 

with the query in the box below (i.e., the

capitalized paragraph in the box). Next, 

click the Run button on the ribbon. The 

result of the query will appear in a new 

window. Export the results of this query 

to an Excel spreadsheet by clicking on 

the External Data menu; then, within the 

Export ribbon, click on the Excel button. 

In the resulting window, name the file, 

and check the box titled “Export data 

with formatting and layout.” Click OK.

3) In ArcGIS, use the Excel spreadsheet 

data to create an ArcGIS shapefile (or 

feature class). FIA coordinates are 

recorded in decimal degrees, using NAD

83 datum. (Note that these coordinates 

are “fuzzed” and are accurate at least to 

within one mile of the actual location. In 

addition, up to 20 percent of coordinates 

representing plots on private land have 

been “swapped” with similar locations 

within the same county in an effort to 

protect landowner privacy.)

4) Optional step, only for analyses that 

include species recorded as shrubs in 

FIA data:

Query the FIA database for tree species 

that may have been recorded in the 

SQL Query for selecting FIA trees

SELECT DISTINCT PLOT.PLOT, PLOT.LAT, PLOT.LON, PLOT.ELEV, TREE.SPCD, 

REF_SPECIES.GENUS, REF_SPECIES.SPECIES, REF_SPECIES.VARIETY, 

REF_SPECIES.SUBSPECIES, REF_SPECIES.SPECIES_SYMBOL, 

REF_SPECIES.COMMON_NAME, TREE.STATUSCD

FROM (PLOT INNER JOIN TREE ON PLOT.CN = TREE.PLT_CN) INNER JOIN REF_SPECIES 

ON TREE.SPCD = REF_SPECIES.SPCD

WHERE ((TREE.STATUSCD)=1);

For each plot, this query will list each tree species (live trees only, which is specified by 

TREE.STATUSCD=1) that exists within the plot (genus, species, variety, subspecies, species symbol, 

and common name) and the lat/long location and elevation of each plot.) This query DOES NOT select 

species classified as shrubs.
Guide to Vulnerability Assessment Methodology 37

database as understory vegetation, or 

“shrubs” (due to having multiple stems 

instead of single stems, or due to a

change in recording protocol). For the

Pacific Northwest vulnerability 

assessment, records containing trees 

classified as shrubs were selected with 

the help of FIA personnel from the Forest 

Service Pacific Northwest Research 

Station FIA Program. We supplied them

with a list of all FIA plot numbers within 

the study area and a list of all the species 

in the analysis, and then they created a 

table that included all plot numbers 

where each shrub species was recorded. 

We then imported this table into the FIA 

database and used it to retrieve the 

location coordinates and related 

information for each of those species. For 

the Washington and Oregon study areas, 

the tree species classified as shrubs by 

FIA were Douglas and Rocky Mountain 

maple, black and Suksdorf’s hawthorn, 

cascara, golden chinquapin, western 

chokecherry, and all willow (Salix) 

species. An additional shapefile was 

created for each of these shrub species, 

which was then selectively combined 

with the tree shapefile: only points

representing new locations for a species 

(i.e., those plots where the species was 

not already represented as a tree) were 

transferred to the tree shapefile.

5) Create maps showing the distribution of 

each species, incorporating any desired 

base maps for the study area. Visually 

inspect the maps for each species and 

remove from the shapefile any point that 

is obviously in error. Detecting these 

errors requires some judgment when 

locations show a species occurring in 

areas where it is highly unlikely to occur

(example in fig. 9). Such unusual 

locations are typically the result of error. 

These errors usually result from data 

entry mishaps, particularly in older 

records that were input prior to the 

inception of software checks. Examples 

are mistyping species codes (“101” 

instead of “110”), or mistyping a 

coordinate.

Current Vegetation Survey Data 

(Forest Service Region 6 Only)

The CVS dataset is used only for creating species 

occurrence maps. Compressed dBase files of 

CVS data for national forests within Oregon and

Washington can be downloaded at 

http://www.fs.fed.us/r6/survey/data_tables.htm. 

The User Guide can be found at 

http://www.fs.fed.us/r6/survey/document.htm.

1) Download and then unzip the data files 

for each national forest in the area of 

interest. It is best to unzip each forest’s 

data into a separate folder, as each forest 

has multiple data files. For each national 

forest within the study area, complete the 

following steps:

a. Import the data into Access by 

first opening Access and creating 

a new blank database (under the 

File menu), named for the 

national forest. Next, go to the 

External Data menu and select 

More>dBASE File from the 

Import & Link ribbon. Import the 

TREEDATA.DBF for the forest. 

b. Go again to the External Data 

menu and select More>dBASE 

File from the Import & Link 

ribbon. This time import the 

PSUDATA.DBF for the same 
 38 Guide to Vulnerability Assessment Methodology

Figure 9. Questionable locations of 

noble fir (Abies procera) in western 

Washington
Guide to Vulnerability Assessment Methodology 39

forest. The database will now 

have two tables, one named 

PSUDATA and one named 

TREEDATA.

c. After steps 1a and 1b have been 

repeated for each national forest 

in the study area, there should be 

a database for each forest, and 

each database should contain two 

tables.

2) Repeat this step for each national forest’s 

database: Query the database for plot 

number, coordinates, and elevation, and 

species codes and names. To do this, 

click on the Create menu, click Query 

Design on the ribbon, close the window 

that pops up, and then click the SQL 

button on the ribbon. In the window that 

appears, delete “SELECT;” and replace it 

with the query in the box to the right. 

Next, in the Query Type group, click on 

Make Table. Name the table and leave 

“current database” selected. Click OK. 

Next, click the Run button on the ribbon. 

A warning box will appear; click Yes to 

create the new table. Open the new table 

in Datasheet View. Click on the left-most 

column header and then click on Fields 

tab under Table Tools. The Data Type

shown in the ribbon should appear as 

“Number”; click the next column header. 

With the exception of the species code 

column, which should be “Text”, all 

other columns must be set to “Number.”

a. Export this table by clicking on 

the External Data menu; then, 

within the Export ribbon group, 

click on the Excel button. In the 

resulting window, name the file 

after the national forest, and 

check the box titled “Export data 

with formatting and layout.” 

Click OK.

b. Open the Excel spreadsheet, and 

examine the UTM zone data. If 

multiple zones are represented 

within a single national forest

(i.e., a single spreadsheet), then 

use cut and paste to separate the

records into additional 

spreadsheets based on UTM 

zone. Each sheet must include 

only one UTM zone and should 

be named according to the forest 

and the zone.

3) Use the Excel spreadsheets to create 

ArcGIS shapefiles (or feature classes). 

Coordinates are UTM, NAD 1983, zones 

10 or 11. Note that these coordinates are 

“fuzzed”, but most are accurate to within 

0.85 mile of the actual location.

4) In ArcMap, add a “Source” field to each 

shapefile and populate with the forest 

name, then merge all shapefiles.

5) Add an “ELEV” field to the final 

shapefile and calculate equal to 

“ELEVATIO * 100” (the ones and tens 

SQL Query for selecting trees in 

CVS data

SELECT DISTINCT TreeData.PSUNr, 

TreeData.SPECSCIA, 

PSUData.UTMZone, 

PSUData.Northing, PSUData.Easting, 

TreeData.VegCode, 

PSUData.ELEVATIO FROM TreeData 

LEFT JOIN PSUData ON 

TreeData.PSUNr = PSUData.PSUNr;
 40 Guide to Vulnerability Assessment Methodology

digits for elevation values were not 

entered in the database).

6) Create maps showing the distribution of 

each species. Visually inspect the maps 

for each species and remove from the 

shapefile any point that is obviously in 

error (as described above in step 5 of the 

FIA data preparation process).

Regional Ecology Program Core 

Dataset (Forest Service Region 6

Only)

This dataset is used only for creating species 

occurrence maps. Zipfiles containing Access 

databases of USFS Region 6 Ecology Program 

core dataset are available at 

http://ecoshare.info/category/data-sets/. Data 

dictionaries are included.

1) Download and unzip the Core Data file

or files for the study area from 

http://ecoshare.info/category/data-sets/.

2) Repeat this step for each Core Data

dataset needed for the study area (e.g., 

SW Oregon, NW Oregon, etc.):

a. Open the Access database file

(i.e., the “.mdb” file) in Access.

b. The procedure will involve 

joining plot number fields from 

different tables. However, these 

field must be of the same data 

type. Verify this first by opening 

(double-clicking) the table name 

ending in 

“VEGETATION_LOADER”. 

Click on the PLOT_NUMBER 

column header at the top of the 

table. Click on the Fields tab 

under Table Tools at the top of 

the window. Data Type should be 

set to “Number”. Now open 

(double-click) the table name on 

the left side of the screen ending 

in “EXPORT_VU”. Click on the 

PLOTNBR column header at the 

top of the table. Click on the 

Fields tab under Table Tools at 

the top of the window. Data Type 

should be set to “Number”. If it 

is not, change it to “Number”.

c. Query the database for plot 

number, coordinates, elevation, 

and species codes. To perform 

this query, click on the Create 

menu, click Query Design on the 

ribbon, close the window that 

pops up, and then click the SQL 

button on the ribbon. In the 

window that appears, delete 

“SELECT;” and replace it with 

the query in the box below (i.e., 

all of the non-italicized text from 

the box). Next, click the Run 

button on the ribbon. The result 

of the query will appear in a new 

window. Export the results of 

this query to an Excel 

spreadsheet by clicking on the 

External Data menu; then, within 

the Export ribbon, click on the 

Excel button. In the resulting 

window, name the file, and check 

the box titled “Export data with 

formatting and layout.” Click 

OK.

3) Open the exported Excel spreadsheet, and 

examine the UTM zone data. If multiple 

zones are represented within a single 

spreadsheet, then use cut and paste to 

separate the records into additional 

spreadsheets based on UTM zone. Each 

sheet must include only one UTM zone 
Guide to Vulnerability Assessment Methodology 41

and should be named according to the 

area and the zone.

4) Use the Excel spreadsheets to create 

ArcGIS shapefiles (or feature classes). 

Coordinates for these data are UTM, 

NAD 1983, zones 10 or 11.

5) In ArcMap, delete points that do not

represent species of interest. Also delete 

any points that were created for records 

that do not have UTM coordinates. Add a 

“SOURCE” field to each shapefile and

populate with the region name.

6) Standardize the item names and 

definitions so that multiple shapefiles can 

be combined, then merge all shapefiles.

7) Create maps showing the distribution of 

each species. Visually inspect the maps 

for each species and remove from the 

shapefile any point that is obviously in 

error. 

SQL query for selecting Ecology Program Core Dataset tree data

This example is from the Program’s southwestern Oregon area. Note that the final line of this query 

excludes understory species.

SELECT DISTINCT

FS_R6ECD_ECD_SW_OR_VEGETATION_LOADER.PLOT_NUMBER, 

FS_R6ECD_ECD_SW_OR_VEGETATION_LOADER.LAYER, 

FS_R6ECD_ECD_SW_OR_VEGETATION_LOADER.SPECIES_CODE, 

FS_R6ECD_ECD_SWO_PLOT_EXPORT_VU.AGENCY, 

FS_R6ECD_ECD_SWO_PLOT_EXPORT_VU.REGION, 

FS_R6ECD_ECD_SWO_PLOT_EXPORT_VU.ELEV, 

FS_R6ECD_ECD_SWO_PLOT_EXPORT_VU.UTMZONE, 

FS_R6ECD_ECD_SWO_PLOT_EXPORT_VU.UTMEAST, 

FS_R6ECD_ECD_SWO_PLOT_EXPORT_VU.UTMNORTH

FROM FS_R6ECD_ECD_SW_OR_VEGETATION_LOADER LEFT JOIN 

FS_R6ECD_ECD_SWO_PLOT_EXPORT_VU ON 

FS_R6ECD_ECD_SW_OR_VEGETATION_LOADER.PLOT_NUMBER = 

FS_R6ECD_ECD_SWO_PLOT_EXPORT_VU.PLOTNBR

WHERE ((Not (FS_R6ECD_ECD_SW_OR_VEGETATION_LOADER.LAYER)="U"));
 42 Guide to Vulnerability Assessment Methodology

Burke Museum Herbarium Data 

(Primarily Washington State)

This dataset is used only for creating species 

occurrence maps. The Burke Museum Herbarium 

maintains an online database of its specimens, 

and many of these specimen records include 

coordinates indicating where the specimen was 

collected and a date of collection. In the Pacific 

Northwest assessment, we assumed that locations 

of herbarium specimens collected within the last 

20 years still represent current locations of that 

species (this timeframe could be longer or shorter 

based on user objectives). A description of the 

Burke Museum’s online herbarium database is at: 

http://biology.burkemuseum.org/herbarium/collec

tions/list.php. To download and map specimen 

collection locations: 

1) Search database by species, genus, 

family, or other variables:

http://biology.burke.washington.edu/herb

arium/collections/vascular/search.php.

Because data must be downloaded 

separately for each species or genus, it 

may be more efficient to acquire data 

only for those species for which the other 

data sources provide inadequate 

coverage. For example, we did not 

download herbarium data for Douglas-fir 

or western hemlock, which were wellcovered by other data sources, but we did 

download data for the less-common 

Rocky Mountain juniper and Oregon 

white oak. Note that you can narrow the 

query by state, if desired.

2) Download the results of a species search

by clicking on the DOWNLOAD tab. 

Next, click on the “Tab-Delimited Text” 

option, and save the file.

3) To open this saved file in Microsoft 

Excel, first load Excel, then choose 

“Open” and specify the saved “.txt” data 

file. This will automatically launch the 

Text Import Wizard. The default settings 

in the Wizard (i.e., “delimited” data type; 

“tab” delimiters; “general” column data 

format) will import the file with proper 

formatting. 

4) Save this imported file as an Excel 

spreadsheet. 

5) Edit the data as desired, to retain only the 

records that you want to map. We deleted 

records that did not include coordinates. 

(If necessary, for records of rare species 

that do not have coordinates, you may 

want to screen digitize points based on 

written descriptions.) We also deleted 

records from states outside of the study 

area and records of specimen collections 

older than 20 years. Alternatively, some 

of these criteria can be included in the 

initial query in step 1.

6) Repeat steps 1 through 5 for all species.

7) Using Copy and Paste functions, transfer 

data for all species into a single Excel 

sheet.

8) Prepare spreadsheet by removing spaces 

from field names and shortening field 

names to be ArcGIS compatible. Delete 

any columns that do not contain data 

needed for mapping or species 

identification.

9) Use the Excel spreadsheets to create 

shapefiles (or feature classes) after 

separating records based on coordinate 

type. Most coordinates are given in 

decimal degrees, with various datums 

(some datums are unknown). Some 

records also have UTM coordinates, 

while some only have UTM coordinates. 

Others have only Public Land Survey 

System locations and/or written 

descriptions.

10) Create maps showing the distribution of 

each species. Visually inspect the maps 

and remove from the shapefile(s) any 

point that is obviously in error, or that 
Guide to Vulnerability Assessment Methodology 43

represents an occurrence of a species that 

is likely planted.

Oregon Flora Project (Oregon 

Only)

This dataset is used only for creating species 

occurrence maps. The Oregon Flora Project 

(http://oregonflora.org/about.php) makes 

available the geographic coordinates of 

herbarium specimen collections and observations 

of approximately 4,500 vascular plant taxa within 

the state of Oregon. Data are available through 

the “atlas” feature at 

http://oregonflora.org/atlas.php. To extract and 

process data:

1) At the web page 

http://oregonflora.org/atlas.php, search 

for a species by scientific or common 

name and then click the CREATE MAP

button. The resulting map will include a 

DOWNLOAD RESULTS button. Click 

this button and choose a location to save 

the “.csv” data file. Alternatively, the 

website also allows one to map multiple 

species at the same time, so it is possible 

to map and then download data for all 

species of interest to a single “.csv” file. 

If this option is chosen, then skip steps 3 

and 4.

2) Edit the data file as desired using 

Microsoft Excel or another program, to 

retain only the records that you want to 

include on the GIS map.

3) Repeat steps 1 and 2 for all species of 

interest.

4) Using Copy and Paste functions, transfer 

data for all species into a single Excel 

sheet.

5) Prepare spreadsheet by removing spaces 

from field names and shortening field 

names to be ArcGIS compatible.

6) Use the spreadsheet to create an ArcGIS 

shapefile (or feature class). Geographic 

coordinates are given in decimal degrees, 

with no datum specified (we assumed the 

datum was NAD 1983).

7) Create maps showing the distribution of 

each species. Visually inspect the maps 

and remove from the shapefile(s) any 

point that is obviously in error, or that 

represents an occurrence of a species that 

is likely planted.
 44 Guide to Vulnerability Assessment Methodology

APPENDIX 2. DATA 

PROCESSING AND 

CALCULATIONS FOR THE 

DISTRIBUTION RISK FACTOR

Frequency of Occurrence 

Variable (Percent of FIA Plots)

1) Prepare the FIA shapefile (the file 

already processed as described in 

appendix 1) by completing these steps:

a. Determine whether any species is 

represented by more than one 

code. Do this by comparing the 

list of species under assessment

to the list of species in the FIA 

dataset. The requires some 

familiarity with the species under 

assessment (and, if applicable, 

subspecies or varieties).

b. If the vulnerability assessment is 

conducted at the subspecies or 

variety level, and if these 

subspecies or varieties are not 

each represented by a unique 

code in the FIA dataset, then it is 

necessary to add an additional 

field using ArcMap and calculate 

a unique code for each

subspecies or variety. For 

example, in the Pacific 

Northwest assessment, PICO 

represented two varieties in the 

FIA database: shore pine 

(PICOC) and lodgepole pine

(PICOL). For these varieties, we 

coded a new field and identified 

observations as PICOC or 

PICOL, based on their 

geographic locations.

2) Determine the total number of plots in 

the study area.

a. Clip the FIA shapefile with the 

study area boundary shapefile.

This clipped shapefile will also 

be used later in appendix 3.

b. In ArcMap, select all points in 

the clipped shapefile. Run the 

Frequency Tool on PLOT. Note 

the number of records returned 

by the frequency operation to 

obtain the total number of plots 

within the study area (this is not 

the same as the highest OID 

number). Export the results to a 

textfile to obtain a list of all plot 

numbers in the study area (this 

will be be used in the next 

section).

3) Calculate the percentage of plots in the 

study area on which each species occurs.

a. In ArcMap, run the frequency 

tool on the field representing 

unique species codes (either field 

SPECIES_SYMBOL from the 

FIA database, or the field added 

in step 1b, above).

b. Export the frequency results to a 

text file, and then import the text 

file to a spreadsheet (the file will 

be comma delimited; do not 

import the OID field). To do this 

using Excel, first open Excel, 

then choose Open under the File 

menu. Make sure “All Files” is 

selected as the file type; this will 

allow a text file to be opened. 

Column B of the imported 

spreadsheet should contain the 

frequency (number of plots) for 

each species.
Guide to Vulnerability Assessment Methodology 45

c. In a new column in the 

spreadsheet, calculate the 

percentage of plots on which a 

given species occurs (e.g., 

=B2/totnum × 100, where totnum

is the total number of plots 

within the study area from step 

2b).

Proportion of Canopy Trees 

Variable

1) Download the FIA data for the state in 

which the study area is located by going 

to http://apps.fs.fed.us/fiadbdownloads/datamart.html, scrolling down 

to the appropriate state, and clicking on 

the “TREE.CSV” file. For example, for 

the state of Oregon, the file is called 

OR_TREE.CSV. The following steps 

described how to extract from this 

TREE.CSV file only the records for plots 

that are in the study area:

a. To extract study area plots only, 

you will first need the list of FIA 

plots within the study area. This 

list was created, as a text file, in 

step 2c for the frequency of 

occurrence variable above.

b. Next, open a blank database in 

Microsoft Access. Under the 

External Data ribbon, choose 

“Text file”. In the new window 

that appears, click the Browse 

button to locate the plot list text 

file described in the previous step 

(1a). Click OK. In the next 

window, accept the Delimited 

option, and click Next. Comma 

should be selected; check the box 

“First Row Contains Field 

Names” and click Next. In the 

next window, change the name of 

the plot field to PLOT_LIST and 

then click Finish and then Close.

c. Under the External Data ribbon, 

again choose “Text file”. In the 

new window that appears, click 

the Browse button to locate the 

“TREE.CSV” file described at 

the beginning of step 1 above. 

Click OK. In the next window, 

accept the Delimited option, and 

click Next. Comma should be 

selected; check the box “First 

Row Contains Field Names” and 

click Finish and then Close. 

Import errors may occur 

regarding some of the date fields, 

but these may be ignored because 

they are not needed for this 

process.

d. Under the Database Tools 

ribbon, select Relationships. In 

the window that appears, add the 

two tables from steps 1b and 1c. 

Click Close. Click the Edit 

Relationships button in the 

Design ribbon. Click the Create 

New button in the Edit 

Relationships window. For Left 

Table Name, select the plot list 

table created in 1b; for Left 

Column Name select the 

PLOT_LIST variable. For Right 

Table Name, select the tree data 

table created in 1c; for Right

Column Name select the PLOT 

variable. Click OK. In the Edit 

Relationships window click 

Create.

e. Under the Create ribbon, click 

Query Design. In the Show Table 

window, select each of the two 

tables from steps 1b and 1c and 

click Add to add each one. Click 
 46 Guide to Vulnerability Assessment Methodology

Close to close the Show Table 

window. In the small window 

showing the plot list table 

variables, double-click the 

PLOT_LIST variable. In the 

other small window, showing the 

tree data variables, double click 

on each of the following 

variables: STATUSCD, SPCD, 

CCLCD, and TPA_UNADJ (the 

last variable is near the bottom of 

the list). In the lower window, in 

the STATUSCD column, enter a 

value of 1 in the Criteria row. In 

the lower window, in the 

CCLCD column, enter <4 in the 

Criteria row. Uncheck the Show 

boxes in the STATUSCD and 

CCLCD columns. In the Design 

ribbon, click Run.

f. Export this query (named Query1 

by default): with the query 

results showing, go to the 

External Data ribbon and click 

Excel in the Export group. 

Choose the location to save the 

file, and then check the box 

“Export data with formatting and 

layout.”

2) Open the exported file from step 1f in 

Microsoft Excel. Click on any cell with 

the numerical data, then go to the Insert 

ribbon and click on PivotTable to create a 

PivotTable. In the Create PivotTable 

window, click OK.

3) In the PivotTable Field List: right-click 

on PLOT_LIST and select Add To Row 

Labels, right-click on TPA_UNADJ and 

select Add To Values, and right-click on 

SPCD and select Add To Report Filter.

4) Near the bottom of the PivotTable Field 

List window, find the small Row Labels 

window and left-click on the small arrow 

to the right of PLOT_LIST. Choose Field 

Settings. Click on the Layout & Print tab. 

Check the box that says “Show items 

with no data.” Then click OK. This step 

assures that the lists of plots in columns 

A and B are properly aligned; the lists of 

values in these two columns should 

always be of the same length after 

completing this step.

5) Two columns appear at the left of the 

sheet. Column A is a list of Row Labels 

which is a list of every plot in the study 

area with a live canopy tree on it. 

Column B is the sum of the trees per acre 

(TPA) represented by all live canopy 

trees on the plot. (Note that each “plot” 

actually consists of cluster of plots of 

different sizes. For this reason, the same 

tree could represent different TPA values 

depending on which plot or subplot it 

occurred on. For the purposes of this 

analysis, we combine data from all of 

clustered plots.) Click on the B at the top 

of column B and right-click to choose 

Copy. Right-click on the C of Column C 

and choose Paste Special, Paste as 

Values. Column C is now the total TPA 

of all species on each plot.

6) Column D will be used to calculate the 

percentage TPA represented by a given 

species on each plot. In the column D cell 

to the right of the topmost numerical 

value in column C (this should be cell 

SQL Query for step 1e (an alternative 

way of completing step 1e, for users 

familiar with Access)

SELECT PLOT_LIST.PLOT, TREE.SPCD, 

TREE.TPA_UNADJ

FROM PLOT_LIST INNER JOIN TREE ON 

PLOT_LIST.PLOT = TREE.PLOT

WHERE (((TREE.STATUSCD)=1) AND 

((TREE.CCLCD)<"4"));
Guide to Vulnerability Assessment Methodology 47

D4) type: “=B4/C4*100” (without 

quotes). Copy this formula down to the 

bottom of the column of numbers, except 

for the final value which is a grand total. 

Do not paste the formula in the Grand 

Total line.

7) In cell E1, type: 

=SUM(D:D)/COUNTIF(D:D,">0") and 

then press enter.

8) This is the value that will, in the next 

step, represent the average proportion of 

canopy cover for each tree species.

9) In the upper left corner of the spreadsheet 

(cells A1 and B1), there is a small arrow. 

Click on this arrow and a list of numbers 

will appear. These numbers are the FIA 

species codes. You can cross-reference 

these numerical species codes with 

species names using the list in appendix 

F at: 

http://www.fia.fs.fed.us/library/databasedocumentation/historic/ver3/FIADB_user

%20manual_v3-0_p2_7_10_08.pdf

Now, the click on the numerical code 

representing a species of interest. In cell 

E1, mean proportion of canopy trees 

represented by this species will appear 

(as a percentage). Select the code for the 

next species of interest and that species’ 

percentage will then appear.
 48 Guide to Vulnerability Assessment Methodology

APPENDIX 3. DATA 

PROCESSING AND 

CALCULATIONS FOR THE

HABITAT AFFINITY RISK 

FACTOR

Mean Elevation Variable

1) Locate the FIA shapefile that was clipped 

to the study area boundary in appendix 2, 

in the frequency of occurrence variable 

calculation procedure, step 2a.

2) Run the Summary Statistics Tool using 

the FIA shapefile as the input table, 

“ELEV” (elevation) as the statistics field, 

“MEAN” for Statistic Type, and 

“SPECIES_SYMBOL” (from the FIA 

database, or the field representing unique 

species codes) as the case field.

3) Open the resulting table, turn off the OID 

field, and then export the results to a text 

file.

Creating Elevation Graphs 

(Optional)

Graphs showing the distribution of species 

occurrences by elevation are not required for 

rating any of the variables; however, such graphs 

can be helpful in understanding spatial patterns in 

habitat. To create elevation distribution graphs:

1) Elevation records for FIA plots are 

supposed to be recorded to the nearest 

100 feet; however, some records have 

been recorded to the nearest foot. These 

can be corrected in ArcMap using the 

following equation in the field calculator: 

ROUND([ELEV]/100)*100. In order to 

preserve the original elevation records (if 

desired), create a duplicate of the ELEV 

field before performing this calculation.

2) Run the summary statistics tool using the 

clipped shapefile as the input table, 

“ELEV” (elevation) as the statistics field, 

“COUNT” for statistic type, 

“SPECIES_SYMBOL” (from the FIA 

database, or the field representing unique 

species codes) as the first case field, and 

“ELEV” as a second case field.

3) Open the resulting table, turn off the OID 

and FREQUENCY fields, and then 

export the results to a text file.

4) Import the text file into a spreadsheet, 

sum the number of plots in which each 

species occurs, add a new column for 

“Plot (%)” and calculate the percentage 

of plots at each elevation.

5) For each species, create a graph of 

elevation versus percentage of FIA plots 

and save as a “.jpg”. If desired, import 

into ArcMAP to include graph on a map.

 50 Guide to Vulnerability Assessment Methodology

CLIMATE 

CHANGE AND 

FOREST TREES 

IN THE PACIFIC 

NORTHWEST:

GUIDE TO 

VULNERABILITY 

ASSESSMENT 

METHODOLOGY

United States

Department of

Agriculture

Forest Service

Pacific Northwest

Region

September 2012