United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-567

March 2003

Assessing the Viability and

Adaptability of Forest-Dependent

Communities in the United States

Richard W. Haynes
Richard W. Haynes is a research forester, Forestry Sciences Laboratory, P.O. Box

3890, Portland, OR 97208-3890.

Author
Haynes, Richard W. 2003. Assessing the viability and adaptability of forest-dependent

communities in the United States. Gen. Tech. Rep. PNW-GTR-567. Portland, OR:

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

33 p.

The work responds to the need to assess progress toward sustainable forest management as established by the Montréal Process of Criteria and Indicators. The focus is on

a single indicator (commonly referred to as Indicator 46), which addresses the “viability

and adaptability to changing economic conditions, of forest-dependent communities,

including indigenous communities.” From county-level data, a composite measure was

developed that combined population density, lifestyle diversity, and economic resiliency.

There are 837 counties assigned a low rating representing 36 percent of the area of the

United States but that contain less than 3 percent of the U.S. population. The rest of the

population is roughly divided among the 2,064 counties assigned medium ratings and the

209 counties assigned high ratings. Of the forest-dependent communities, there are 742

counties with a high proportion of forest land, but only 14 percent are classified as having

low viability and adaptability.

Keywords: Community resiliency, criteria and indicators, forest dependency, Montréal

Process, socioeconomic well-being, sustainable forest management.

Abstract
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1

A fundamental societal concern increasingly debated in the past two decades has been

the need for human actions that lead to sustainable development that meets the needs

of the present without comprising the ability of future generations to meet their needs

(WCED 1987). This debate has broadened to include notions of sustainability as a normative concept reflecting the persistence over an indefinite future of certain necessary

and desirable characteristics of both ecological and human parts of an ecosystem (modified from Hodge 1997). Within the forestry community, these concerns manifested themselves in the development of frameworks for assessing the progress toward sustainable

forest management.

The United States is a signatory participant of the Montréal Process for assessing progress toward sustainable forest management. The Montréal Process is largely used by

counties with temperate forest resources and involves seven criteria and 67 indicators.1

Two of the criteria (numbers six and seven and containing 39 of the 67 indicators) address various human concerns including components of social well-being. These two

criteria address the need for forest management to meet societal needs and maintain

and enhance long-term multiple socioeconomic benefits. This is a broader context than

the frequently cited goal of managing forests to sustain a flow of timber and other benefits to promote the stability of forest industries and communities.

The focus on communities is the subject of this paper. Specifically, the purpose of this

paper is to describe an approach to assess community viability and adaptability in the

context of the Montréal Process of Criteria and Indicators for sustainable forest management (Montréal Process Working Group 1998). This is Indicator 46 of Criterion 6, which

deals with the viability and adaptability to changing economic conditions of forest-dependent communities, including indigenous communities. The term community refers to

both the physical place and to the group of people who live in and identify with that

place.

Criterion 6 and its 19 associated indicators (including Indicator 46) reflect one of the

enduring goals of land management: the concern that forest management sustains a

flow of timber and other benefits to promote the well-being of forest industries and communities. This criterion takes a broad view of how sustainable forest management influences social well-being that includes the expected concerns about determinants of

economic well-being (often measured by jobs) as well as concerns about community

well-being. The criterion reflects a notable evolution in thinking on the part of decisionmakers, researchers, resource managers, and the public about the relation between

communities and forests, as well as what constitutes sustainable forest management.

Introduction

1 See www.mpci.org for a description of the Criteria

and Indicators for the Conservation and Sustainable

Management of Temperate and Boreal Forests. The

seven criteria are Conservation of Biological Diversity;

Maintenance of Productive Capacity of Forest Ecosystems; Maintenance of Forest Ecosystem Health and

Vitality; Conservation and Maintenance of Soil and

Water Resources; Maintenance of Forest Contribution to

Global Carbon Cycles; Maintenance and Enhancement

of Long-Term Multiple Socio-Economic Benefits to Meet

the Needs of Societies; and Legal, Institutional, and

Economic Framework for Forest Conservation and

Sustainable Forest Management.

Indicator

Interpretation
2

The discussions2 of this indicator suggested that there are two types of variables for this

indicator: (a) measures of economic dependency on forests and (b) social well-being of

communities. Social well-being would be a measure of both the capacity of communities

to respond to changes and the socioeconomic status of people. In the reviews of the

U.S. efforts to develop a national report on sustainable forests,3 there were suggestions

that measures of dependency should be considered as part of Indicator 44 (employment

in the forest sector), but for clarity, both economic and social aspects of well-being are

considered in the development of this indicator.

The Montréal Process provides little guidance for defining communities although the information that is available largely addresses communities of place. The various technical

notes on the subject refer to communities generically as forest-dependent communities

and indigenous communities. They stress the need to assess using quantitative and

qualitative information of the trends on the status of a community’s flexibility and capacity to adapt to changing economic conditions. Little guidance is available for which

scales need to be considered other than general direction to deal with national, regional,

and local concerns. No guidance is provided for how to scale community information

upward to broader spatial scales.

I acknowledge that there are severe data limitations for assessing community viability

and adaptability at the national scale as required in the Montréal Process. Foremost,

there is a lack of systematic community-level databases except in some unique cases

(such as the Pacific Northwest) where they have been assembled as part of ecoregion

assessments. Even where the data have been assembled, there are severe limitations

for measuring certain elements of community viability and adaptability.

The past two decades have seen an evolution of many terms used to depict communities that have distinct connections to forest resources: community stability, forest dependence, forest based, community capacity, community resiliency, and now with the

Montréal Process, community viability and adaptability. Viability and adaptability are not

explicitly defined in the Montréal Process but have the connotation of some of the more

recent terms such as resiliency.

This evolution of terms reveals the evolving emphasis on the complex, dynamic, and

interrelated aspects of rural communities and the natural resources that surround them.

The earliest terms dealt with the limits between improved forest management and stable

communities achieved through stable employment. By the late 1980s, the notion of community stability as reflective of sustained-yield timber management was being called into

question (Lee 1990, Schallau 1989). Although the use of the term stability continued to

endure in policy debates, concern was raised about the lack of a clear definition of stability and how it might be measured (see Richardson 1996). Seeking alternative terms,

some researchers began looking beyond employment indicators to other aspects of

The Evolution of Viability

and Adaptability4

2 See www.mpci.org/meetings/tac/mexico/tn1-6_e.html

for a discussion of the rationale and approaches to

measurement for Indicator 46.

3 Roundtable workshops were held in Portland, Oregon,

and Washington, D.C., in spring 2002. Reports are on

file with: David Darr (ddarr@fs.fed.us).

4 This section is condensed from Donoghue and Haynes

(2002).
3

community life in order to assess community well-being (Doak and Kusel 1996, Kusel

and Fortmann 1991). In addition to economic measures, indicators for poverty, education, crime, and other sociodemographic measures have been used to assess conditions

within communities.

There are two definitions key to developing a measure for Indicator 46. First, communities are defined in terms of a sense of place, organization, or structure. Second, economies are defined by transactions among people that allocate scarce resources among

alternative uses. The spatial configurations of both differ, with economies generally being

spatially the larger of the two.

Concurrent with discussions about stability and well-being were discussions about the

term forest dependence, including several appeals to redefine that term (Richardson and

Christensen 1997). Forest and timber dependence were initially defined in terms of commodity production. Various research studies suggest that communities are more complex than traditional measurements of timber dependency would imply (see Haynes and

others 1996). Most communities have mixed economies, and their vitality is often linked

to other factors besides commodity production. Many of the communities thought of as

timber dependent have been confronted with economically significant challenges, such

as mill closures, and have displayed resilient behavior as they have dealt with change.

Arguments for redefining the term forest dependence emphasized that the economic ties

that some communities have to forests are not wood product based, but reside in recreation and other amenities (Kusel 1996). Another concern was that the term forest dependence did not reflect the local living traditions and sense of place held by many communities (Kusel 1996). This broader connotation of the term forest dependence is often

what is implied by the term forest based.

The terms community capacity and community resiliency connote the ability of a community to take advantage of opportunities and deal with change (Doak and Kusel 1996,

Harris and others 2000). The terms resiliency and, arguably to a lesser extent, capacity

differ in meaning from terms such as forest dependence because they represent a projected condition or ability of a community over some period. Levels of resiliency are dynamic, just like external factors that might induce change within a community. The

Forest Service is shifting its focus from dependency to concepts like resiliency (see

Horne and Haynes 1999, McCool and others 1997). Based on Harris and others (2000),

factors useful in assessing community resiliency or adaptability include:

• Population size—Resiliency ratings vary directly with population size:

• Small (and often lower resiliency), less than 1,500 people.

• Large (often associated with higher resiliency), greater than 5,000 people.

• Economic diversity—Resiliency ratings vary directly with population size.

• Civic infrastructure—Higher resiliency is associated with strong civic leadership,

positive attitudes toward changes, strong social cohesion.

• Amenities—Combines both civic amenities as well as natural amenities.

• Location—Locations on major trade routes; near service centers; shopping, service,

or resort destinations are associated with higher resiliency. Spatial isolation is often

a characteristic of lower resiliency.
4

The history behind the evolution of terms combined with the results of recent and current

work suggest that connectivity to broad regional economies, community cohesiveness

and place attachment, and civic leadership are greater factors in determining community

viability and adaptability than factors related to employment.

There are three considerations necessary for the development of a measure of community viability and adaptability. First, there are various factors listed in the previous section

used to depict communities and their ability to deal with changes such as changes in

the management of the surrounding forests. Second, there is a need to be specific about

the geographic scale at which to construct the measure. Third, there is also the issue

about how to judge the “goodness” of any measure that is developed. Recent work on

ecological indicators (see National Research Council 2000) suggests the following criteria for such judgments: the extent the measure is based on a well-understood conceptual model, its reliability, its ability to detect differences at appropriate spatial scales,

and its robustness in the sense of being able to yield reliable measures given the variability in the data.

The first step is to select what factors to use and how to develop measurements of those

factors. From the previous review, the following factors were selected for assessing the

degree of community viability and adaptability: population size, economic diversity, and

amenities lifestyle. The proportion of forest land in the surrounding area was used to

measure forest dependency.

The second step involves at what spatial scale this work needs to be conducted. The

notion of a social hierarchy combines both political and individual person organization

units. For example, one representation is from broad common markets to nations, to

states, to counties, to communities and neighborhoods, to families and persons (see for

example, Quigley and others 1996). In the context of the Montréal Process, Indicator 46

needs to assess conditions at the national level based on an understanding developed

from the next lower spatial scale. In the United States, that scale would most likely be

states; however, it is difficult to generalize community conditions at that level.5 A compromise to gain both greater spatial resolution and to overcome the lack of community data

sets is to use county data. These data sets have been used to monitor well-being and to

reveal geographic variation, but most social scientists do not like to use these data sets

as proxies for communities, primarily because of the lack of data richness for various

components of social well-being such as community leadership and infrastructure, quality of life, welfare, and social and economic health of a community.

Using county data, we can use the general approach developed by Horne and Haynes

(1999), which used county data to develop measures of socioeconomic resiliency (defined as the ability of human institutions [both communities and economies] to adapt to

change) for the interior Columbia basin. The advantage of this approach was that it was

measurable in the context of the definition of social well-being by using existing information. Horne and Haynes (1999) developed a composite measure of three factors: economic resiliency (as a measure of economic diversity in employment), population density

(a proxy for civic infrastructure), and lifestyle diversity (a proxy for social and cultural

Developing a

Measure of

Community Viability

and Adaptability

5 For example, the state of Oregon (a relatively small

state) has some 400 communities. Donoghue and

Haynes (2002) found that about 10 percent of Oregon’s

communities with about 2 percent of Oregonians had

low viability and adaptability.
5

diversity). Using this approach, we find that counties that have high socioeconomic resiliency demonstrate quick adaptation as indicated by rebounding measures of socioeconomic well-being, whereas counties with low resiliency have more lingering negative

impacts. The terms high or low should not be thought of as good or bad but rather as a

reflection of the ability of a county to respond to changes in social or economic factors.

Here, a similar approach was developed for the 3,110 counties (and some city-county

and borough combinations) in the United States. A composite measure was developed

for each county that considered civic infrastructure, economic diversity (in terms of employment), and lifestyle diversity. Forest dependency was interpreted as being related to

the extent that each county was forested (measured by proportion of forest land in each

county).

Proxy measures were developed for each component of the composite measure. Each

proxy was assigned a rating of 1 for low, 2 for medium, and 3 for high. The definitions

differ for each measure. Population density represents civic infrastructure. Here, counties

with fewer than six people per square mile were assigned a rating of 0. These have been

called frontier counties following the definition from the 1890 census. Counties with 6 to

27 people per square mile represent rural areas. Counties with 28 to 250 people per

square mile are called intermix counties, and those with over 250 people per square mile

are called interface counties6 and essentially represent urbanlike characteristics. Economic diversity (based on a Shannon-Weaver diversity index) is computed from employment in major sectors including agriculture. Ratings are assigned that reflect how the

diversity of a county compares relative to the average of U.S. counties. A high rating

reflects counties that are in the top 16 percent (one standard deviation) of counties, and

a low rating reflects those counties in the bottom 16 percent of counties. A proxy for

lifestyle diversity is composed (in equal parts) of proportion of the county population that

is minority (as a proxy for ethnic, income, and lifestyle diversity) and the availability of

national forest lands (as a proxy for access to opportunities for outdoor recreation activities). Minority status is based on variation around the country average (15.6 percent).

Counties with minority populations greater than 23.94 percent were rated high, whereas

those with minority populations less than 7.3 percent were rated low. The ratings for

national forest are based on actual acres in each county. Counties with fewer than

35,000 acres are assigned a rating of 1, whereas those counties with over 157,000 acres

are assigned a rating of 3.

These indicators were used to develop a composite measure that combined population

density, lifestyle diversity, and economic resiliency. Those counties with a composite

score of 4 or less were assigned as having low adaptability and viability. Those counties

with a composite score of 4.5 to 6.5 were assigned as having medium adaptability and

viability. Those counties with a composite score of 7 or greater were assigned as having

high adaptability and viability. A summary of the results is shown in table 1. There are

837 counties assigned a low rating representing 36 percent of the area of the United

States but just less than 3 percent of the U.S. population. The rest of the population is

roughly evenly divided among the 2,064 counties assigned medium ratings and the 209

counties assigned high ratings.

6 The definitions for intermix and interface are consistent

with research being undertaken as part of the fire plan.
6

Table 1—Summary of county data and ratings

Population Minority Native Economic Forested National Composite

Ranking density status American diversity acres forest acres score

Number of counties

Lowa 1,133 1,409 2,497 299 1,511 2,594 837

Medium 1,603 948 434 2,403 857 259 2,064

High 374 753 179 408 742 257 209

Percent (by area)

Low 62.6 32.9 49.7 9.0 47.1 67.4 36.3

Medium 31.3 32.6 26.5 70.3 35.4 9.0 56.3

High 6.1 34.5 23.9 20.7 17.5 23.6 7.4

Percent (by population)

Low 5.0 17.0 85.8 2.2 53.9 81.1 3.0

Medium 31.2 37.2 12.6 49.7 35.3 5.6 49.5

High 63.8 45.8 1.6 48.1 10.8 13.2 47.5

a Includes 384 counties assigned a rating of 0 because they are frontier counties.

Indicator 46 emphasizes the viability and adaptability of forest-dependent communities.

Traditionally, this has been measured (in the United States) by the extent of employment

in forest industries, but this tends to overstate the importance of manufacturing (relative

to trade and services associated with activities such as recreation) and understates personal connections to the forest for subsistence and nontraditional economic activities.

Here, I use the extent of forest land in each county as a proxy for the dependence of

local residents on forest resources. Three maps show the results (figs. 1 through 3).

The colors denote the extent of forest land in each county. Those with less than the U.S.

average (32.5 percent) are shown in yellow, those with more than the U.S. average are

shown in green (32.5 to 66.2 percent forest land), and those with greater than 66.3 percent forest land are shown in blue. For the purpose of this indicator, it is the counties

shown in blue that represent forest-dependent communities. Figure 1 shows the counties

assigned a rating of low viability and adaptability. These counties contain 8.3 million

Americans but comprise 36 percent of the area of the United States. Figures 2 and 3

show the counties assigned a rating of medium or high viability and adaptability, respectively. Of the 742 counties with a high level of forest dependence (shown in blue), 102 are

assigned a rating of low viability and adaptability.

Because the discussions of the indicator indicate a concern about those communities

with less viability and adaptability, the detailed information for the 837 counties (and boroughs) assigned a low composite rating (shown in fig. 1) are listed in the appendix. The

results for those counties with a low rating are summarized for both population and area

in table 2 by four broad regions. The regional data illustrate how most of the people affected are in the two Eastern regions, whereas most of the area is in the West.

Discussion
7

Figure 1—Counties with low viability and adaptability to changing economic conditions.
8

Figure 2—Counties with medium viability and adaptability to changing economic conditions.
9

Figure 3—Counties with high viability and adaptability to changing economic conditions.
10

Although much of the emphasis has been on the counties with low composite rankings,

most (640 counties) of the heavily forested counties have actually medium or high

rankings. Many of these are counties with extensive urban populations such as those in

Atlanta, Seattle, or Portland, whereas others contain significant recreation areas such

as Shenandoah and Olympic National Parks or the Upper Peninsula in Michigan. These

results suggest that forests often contribute to positive notions of the amenities associated with residential and recreation areas.

There is a question of how well this indicator rates viability and adaptability. In the last

section, several criteria were presented for such judgments. The first pertains to the extent to which the measure is based on a well-understood conceptual model. Throughout

this paper, I have argued that the evolution of the concern about the viability and adaptability of a community reflects the broader changes in our understanding of community

well-being and its relation to the surrounding forest or natural resources. Although there

is often ambiguity about the definition of social well-being, there is acceptance of its

utility (see McCool and others 1997). The second criterion addresses the reliability of the

indicator. One demonstration of this has been the previous use of similar indicators in

both community (Harris and others 2000) and bioregional assessments (see Horne and

Haynes 1999, McCool and others 1997), and in state-level attempts to address sustainable forest management (see Donoghue and Haynes 2002). In all cases, useful measures were developed and helped expand our understanding of how communities differ in

their propensity to deal with external changes. The third criterion pertains to the ability of

an indicator to detect differences at appropriate spatial scales. The above examples also

demonstrate these abilities in the various measures that were developed. The fourth criterion is robustness of the indicator to provide reliable measures given the variability in

the data. The various maps do illustrate the ability to deal with spatial differences, but

additional work will be needed to demonstrate the extent of temporal variation inherent in

social and economic data. In summary, as a first approximation, the measure developed

here for Indicator 46 meets most of the criteria for “goodness.”

These results (like those of Horne and Haynes 1999) suggest that most Americans live

in areas with moderate to high viability and adaptability, but there are extensive areas

characterized by low viability and adaptability for economic and social changes. Figure 4

illustrates this dilemma. It also suggests that extent of forest land is more or less evenly

distributed across areas of both low and high adaptability. Finally, it should not be concluded that these areas of low adaptability are in some type of distress. They simply

have low adaptability or resiliency to social and economic changes.

Table 2—Population and area of the United States by region, with low variability and

adaptability by region

Population Area

Extent of forest land Extent of forest land

Region Low Medium High Low Medium High

 – – – Thousand/people – – – – – – – Thousand/acre – – – –

North 2,702 492 513 164,223 14,328 22,844

South 1,843 367 682 120,405 9,272 21,347

Rocky Mountains 1,088 154 62 196,329 46,506 10,023

Pacific Coast 189 205 49 25,651 178,429 12,752

Total United States 5,823 1,219 1,306 506,608 248,535 66,966

Note: Individual columns may not add to the total owing to rounding.

Interpreting the

Results
11

It is interesting to note those counties that are both heavily forested and that have medium or high viability and adaptability. Of the 209 counties with high viability and adaptability, only 22 are heavily forested. Most of these are in urban areas such as Seattle. Of

the 2,064 counties with medium viability and adaptability, 30 percent are heavily forested.

Several are near urban areas, but others contain significant residential developments

including counties thought of as recreation counties (see Johnson and Beale 1995) affirming the rich connections between forests and human populations beyond just timber

for wood products. Of the 285 counties identified by Johnson and Beale (1995), 44 percent are heavily forested.

The intent of the original indicator was to identify forest-dependent communities. As was

argued in an earlier section, this is both difficult to do and is probably inappropriate at

the broad scale of national indicators. However, we do have community data for eastern

Oregon where we can examine the relation between the counties rated as having low

adaptability and the communities in those counties. In eastern Oregon, there are 10

counties rated as having low adaptability. These 10 counties contain 46 communities

ranging in size from a population of 3 to nearly 10,000. Of the 46 communities, 26 were

rated by Reyna (1998) as being small, isolated from major population centers, and

lacking in economic diversity. The results suggest that the approach taken to develop

a measure of Indicator 46 does provide a useful first approximation of areas where community assistance may be needed to offset the effects of changes in broad-scale land

management.

Another inference concerns the intent of Indicator 46 to address those areas with indigenous communities. Each county was assigned a scale for its extent of American Indian

or Alaska Native population. Counties with an American Indian and Alaska Native population less than 1 percent were assigned a rating of 0. Those counties with a population

proportion between 1 and 5.77 percent were assigned a rating of 1 (the U.S. average is

Figure 4—Population and area by degree of adaptability and extent of forest land.
12

1.9 percent). Those with a population greater than 5.78 percent were assigned a rating

of 2. Generally, indigenous communities are difficult to identify other than as communities associated with American Indian reservations. For example, in eastern Oregon,

Reyna (1998) identified seven communities as being associated with American Indian

reservations. Only 1 of these communities is in 1 of the 10 counties identified as having

low adaptability. Seven of the ten counties are identified as having average (1.9 percent

of population) or higher populations of American Indians, but none has more than 5.77

percent of its population as being American Indian. There are 65 counties and boroughs

that do have significant American Indian or Alaska Native populations. The largest numbers are concentrated in six states: Alaska, Kansas, Montana, North Dakota, South

Dakota, and Oklahoma.

Finally, I caution against drawing inferences about economic conditions from the assignment of low viability and adaptability. In other studies (Horne and Haynes 1999), little

relation is found between reduced economic opportunities and extent of forest land.

Indicator 46 attempts to address the links among land management, the flow of goods

and services, and social well-being of human communities. In that sense it reflects the

current scientific thinking about the relation between the biophysical and socioeconomic

components of ecosystems. It is difficult, however, to reconcile its intent with the almost

mystical reference to communities without specific definition or clarity in terms of spatial

hierarchy among communities and higher levels of administrative governments.

There are three possible research directions that can be taken to improve our treatment

of this indicator. First, there is the need for refinement of the specific proxies used in the

composite measure. There is the opportunity to develop other midscale measures that

might better represent social and economic conditions. Included in this should be an

examination of the relations between the variables used in developing the composite

measure. Second, there is the need to use existing (or to develop new) community databases to better understand the relation between midscale measures of social and economic conditions and actual community-level measures. Third, there is the opportunity to

improve our ability to estimate nontimber outputs (both goods and services) and to value

them. These are key to better describing the benefits of forest management.

Thinking more broadly, there are significant science challenges in developing an indicator for community condition. First, there is the intellectual challenge of developing a

measure for viability and adaptability. Multiple terms like adaptability, viability, and resiliency add confusion, but the essential point is the need to capture the essence of the

dynamics of communities and changes in their functioning. Second, the development of

indicators is a pressing managerial problem. Indicator 46 addresses the need to identify

which communities may need assistance in adjusting to changes in land management

approaches, outputs, or other changes. This poses two challenges: the need to better

understand the relation between forest management and communities and the need to

develop adequate proxies for broad-scale measurement of certain attributes of community adaptability. Finally, we need to develop a coincidental understanding of how to engage community development interests in a discussion of viability and adaptability as

they are often the agents of change.

Closing
13

When you know: Multiply by: To find:

Acres 0.40 Hectares

Square miles 2.59 Square kilometers

The basic county data sets were provided by Mike Vasievich. Xiaoping Zhou prepared the

maps. Judy Mikowski helped in the construction of the composite measure and data

indexes.

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assessment. In: Sierra Nevada Ecosystem Project: final report to Congress—

assessments and scientific basis for management options. Davis, CA: University

of California, Centers for Water and Wildland Resources: 375-402. Vol 2.

Donoghue, E.M.; Haynes, R.W. 2002. Assessing the viability and adaptability of

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Harris, C.C.; McLaughlin, W.; Brown, G.; Decker, D. 2000. Rural communities in

the inland Northwest: an assessment of small communities in the interior and upper

Columbia River basins. Gen. Tech. Rep. PNW-GTR-477. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 120 p.

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scientific assessment).

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Metric Equivalents

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Reyna, N.E. 1998. Economic and social characteristics of communities in the interior

Columbia basin. In: Economic and social conditions: economic and social characteristics of interior Columbia basin communities and an estimation of effects on communities from the alternatives of the eastside and Upper Columbia River basin draft

environmental impact statements. Part 1. A report prepared by the Interior Columbia

Basin Ecosystem Management Project. [Place of publication unknown]: U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior, Bureau of Land

Management: 4-81.

Richardson, C.W. 1996. Stability and change in forest-based communities: a selected

bibliography. Gen. Tech. Rep. PNW-GTR-366. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 36 p.

Richardson, C.W.; Christensen, H. 1997. From rhetoric to reality: research on the

well-being of forest-based communities. In: Integrating social science and ecosystem management: a national challenge, proceedings. Gen. Tech. Rep. SRS-17.

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

Station: 195-200.

Schallau, C.H. 1989. Sustained yield versus community stability: An unfortunate

wedding? Journal of Forestry. 87(9): 16-23.

World Commission on Environment and Development [WCED]. 1987. Our common

future—report of the World Commission on Environment and Development. London:

Oxford University Press. [Pages unknown].
15

Table 3—List of 837 forest-dependent counties with low viability and adaptability

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

01029 AL, Cleburne County 1 1 0 2 1 3.5 3 201057 AL, Fayette County 2 2 0 2 1 4.0 301061 AL, Geneva County 2 2 0 2 1 4.0 201063 AL, Greene County 1 3 0 2 1 3.5 301065 AL, Hale County 1 3 0 2 1 4.0 2 101075 AL, Lamar County 1 2 0 2 2 4.0 301085 AL, Lowndes County 1 3 0 2 1 3.5 301105 AL, Perry County 1 3 0 2 1 4.0 3 101107 AL, Pickens County 1 3 0 2 1 3.5 301131 AL, Wilcox County 1 3 0 2 1 3.5 302013 AK, Aleutians East Borough 0 3 2 4 2 3.5 202016 AK, Aleutians West Census Area 0 3 2 4 2 3.5 202050 AK, Bethel Census Area 0 3 2 4 2 3.5 202060 AK, Bristol Bay Borough 0 3 2 4 2 3.5 202068 AK, Denali 0 2 1 4 3 4.0 202090 AK, Fairbanks North Star Borough 1 2 2 4 2 4.0 202150 AK, Kodiak Island Borough 0 3 2 4 1 3.0 2 102164 AK, Lake and Peninsula Borough 0 3 2 4 2 3.5 202185 AK, North Slope Borough 0 3 2 4 2 3.5 202201 AK, Prince of Wales-Outer

 Ketchikan Census Area 0 3 2 4 2 3.5 3

02240 AK, Southeast Fairbanks Census

Area 0 2 2 4 3 4.0 2

02261 AK, Valdez-Cordova Census Area 0 3 2 4 2 3.5 202270 AK, Wade Hampton Census Area 0 3 2 4 2 3.5 202280 AK, Wrangell-Petersburg Census

Area 0 3 2 4 1 2.5 3

04012 AZ, La Paz County 0 3 2 3 2 3.5 005011 AR, Bradley County 1 3 0 2 1 3.5 305017 AR, Chicot County 1 3 0 2 1 3.5 105025 AR, Cleveland County 1 2 0 2 1 3.0 3

Appendix
16Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

05049 AR, Fulton County 1 1 0 2 2 3.5 205053 AR, Grant County 1 1 0 2 2 3.5 305065 AR, Izard County 1 1 0 2 2 3.5 305073 AR, Lafayette County 1 3 0 2 1 3.5 205079 AR, Lincoln County 1 3 0 2 1 3.5 205087 AR, Madison County 1 1 1 2 1 3.5 2 205089 AR, Marion County 1 1 0 2 2 4.0 3 105109 AR, Pike County 1 2 0 2 1 3.5 3 105111 AR, Poinsett County 2 2 0 2 1 4.0 105117 AR, Prairie County 1 2 0 2 1 3.0 105127 AR, Scott County 1 1 1 2 1 4.0 3 305129 AR, Searcy County 1 1 0 2 2 4.0 3 105133 AR, Sevier County 2 2 1 2 1 4.0 305141 AR, Van Buren County 1 1 0 2 2 4.0 3 105147 AR, Woodruff County 1 3 0 2 1 3.5 106091 CA, Sierra County 0 1 1 4 2 4.0 3 308009 CO, Baca County 0 1 1 3 2 4.0 1 308011 CO, Bent County 0 2 1 3 2 3.0 008017 CO, Cheyenne County 0 1 0 3 2 2.5 008023 CO, Costilla County 0 3 1 3 2 4.0 2 108025 CO, Crowley County 1 2 1 3 2 4.0 008027 CO, Custer County 0 1 1 3 2 4.0 3 308033 CO, Dolores County 0 1 1 3 2 4.0 2 308039 CO, Elbert County 1 1 0 3 2 3.5 008055 CO, Huerfano County 0 2 1 3 2 4.0 2 208057 CO, Jackson County 0 1 0 3 2 4.0 2 308061 CO, Kiowa County 0 1 1 3 2 2.5 008063 CO, Kit Carson County 0 2 0 3 2 3.0 008071 CO, Las Animas County 0 2 1 3 2 4.0 1 208073 CO, Lincoln County 0 2 0 3 2 3.0 008075 CO, Logan County 1 2 0 3 2 4.0 008081 CO, Moffat County 0 1 0 3 2 3.5 1 208087 CO, Morgan County 1 2 0 3 1 3.0 008095 CO, Phillips County 1 1 0 3 2 3.5 008099 CO, Prowers County 1 2 1 3 1 3.0 008103 CO, Rio Blanco County 0 1 0 3 1 3.0 1 308115 CO, Sedgwick County 0 2 0 3 2 3.0 008121 CO, Washington County 0 1 0 3 2 2.5 008125 CO, Yuma County 0 1 0 3 2 2.5 012013 FL, Calhoun County 1 2 1 2 2 4.0 312029 FL, Dixie County 1 2 0 2 2 4.0 312041 FL, Gilchrist County 2 2 0 2 1 4.0 212043 FL, Glades County 1 2 1 2 2 4.0 112045 FL, Gulf County 1 2 0 2 2 4.0 312067 FL, Lafayette County 1 2 0 2 1 3.0 312123 FL, Taylor County 1 2 0 2 2 4.0 313003 GA, Atkinson County 1 3 0 2 1 3.5 3
17

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

13005 GA, Bacon County 2 2 0 2 1 4.0 313007 GA, Baker County 1 3 0 2 1 3.5 213065 GA, Clinch County 1 3 0 2 1 3.5 313099 GA, Early County 1 3 0 2 1 3.5 213101 GA, Echols County 1 2 1 2 1 3.0 313125 GA, Glascock County 1 2 0 2 2 4.0 313165 GA, Jenkins County 1 3 0 2 1 3.5 313239 GA, Quitman County 1 3 0 2 1 3.5 313243 GA, Randolph County 1 3 0 2 1 3.5 213259 GA, Stewart County 1 3 0 2 1 3.5 313301 GA, Warren County 1 3 0 2 1 3.5 313307 GA, Webster County 1 3 0 2 1 3.5 313319 GA, Wilkinson County 1 3 0 2 1 3.5 316003 ID, Adams County 0 1 1 3 2 4.0 3 316011 ID, Bingham County 1 2 2 3 2 4.0 116015 ID, Boise County 0 1 0 3 2 4.0 3 316025 ID, Camas County 0 1 0 3 2 4.0 1 316029 ID, Caribou County 0 1 0 3 1 3.0 2 316035 ID, Clearwater County 0 1 1 3 1 3.0 3 316037 ID, Custer County 0 1 0 3 2 4.0 2 316047 ID, Gooding County 1 2 0 3 1 3.0 016049 ID, Idaho County 0 1 1 3 1 3.0 2 316051 ID, Jefferson County 1 2 0 3 1 3.5 0 116059 ID, Lemhi County 0 1 0 3 2 4.0 2 316063 ID, Lincoln County 0 2 1 3 2 3.0 016067 ID, Minidoka County 1 2 0 3 1 3.0 016071 ID, Oneida County 0 1 0 3 2 3.5 1 216073 ID, Owyhee County 0 2 1 3 1 2.0 116077 ID, Power County 0 2 1 3 1 3.0 1 216079 ID, Shoshone County 0 1 1 3 2 4.0 3 316087 ID, Washington County 1 2 0 3 1 4.0 1 217009 IL, Brown County 1 2 0 1 2 4.0 117013 IL, Calhoun County 1 1 0 1 2 3.5 217023 IL, Clark County 2 1 0 1 1 3.5 117025 IL, Clay County 2 1 0 1 1 3.5 117059 IL, Gallatin County 1 1 0 1 2 4.0 1 117061 IL, Greene County 1 1 0 1 2 3.5 117065 IL, Hamilton County 1 1 0 1 2 3.5 117069 IL, Hardin County 1 1 0 1 2 4.0 2 117071 IL, Henderson County 1 1 0 1 2 3.5 117079 IL, Jasper County 1 1 0 1 1 2.5 117149 IL, Pike County 1 1 0 1 2 3.5 117169 IL, Schuyler County 1 1 0 1 2 3.5 117171 IL, Scott County 1 1 0 1 2 3.5 117175 IL, Stark County 1 1 0 1 2 3.5 017189 IL, Washington County 1 1 0 1 2 3.5 1
18Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

17191 IL, Wayne County 1 1 0 1 1 2.5 118007 IN, Benton County 1 1 0 1 2 3.5 018171 IN, Warren County 1 1 0 1 2 3.5 119001 IA, Adair County 1 1 0 1 2 3.5 019003 IA, Adams County 1 1 0 1 2 3.5 019005 IA, Allamakee County 1 1 0 1 2 3.5 219009 IA, Audubon County 1 1 0 1 2 3.5 019023 IA, Butler County 1 1 0 1 2 3.5 019025 IA, Calhoun County 1 1 0 1 2 3.5 019029 IA, Cass County 1 1 0 1 2 3.5 019035 IA, Cherokee County 1 1 0 1 2 3.5 019037 IA, Chickasaw County 1 1 0 1 2 3.5 019039 IA, Clarke County 1 1 0 1 2 3.5 119043 IA, Clayton County 1 1 1 1 2 3.5 119047 IA, Crawford County 1 1 0 1 2 3.5 019051 IA, Davis County 1 1 0 1 2 3.5 119053 IA, Decatur County 1 1 0 1 2 3.5 119069 IA, Franklin County 1 1 0 1 2 3.5 119071 IA, Fremont County 1 1 0 1 2 3.5 019073 IA, Greene County 1 1 0 1 2 3.5 019075 IA, Grundy County 1 1 0 1 2 3.5 019077 IA, Guthrie County 1 1 0 1 2 3.5 119081 IA, Hancock County 1 1 0 1 2 3.5 019085 IA, Harrison County 1 1 0 1 2 3.5 119089 IA, Howard County 1 1 0 1 1 2.5 019091 IA, Humboldt County 1 1 0 1 2 3.5 019093 IA, Ida County 1 1 0 1 2 3.5 019095 IA, Iowa County 1 1 0 1 2 3.5 119107 IA, Keokuk County 1 1 0 1 2 3.5 119109 IA, Kossuth County 1 1 0 1 2 3.5 019117 IA, Lucas County 1 1 0 1 2 3.5 119119 IA, Lyon County 1 1 0 1 2 3.5 019121 IA, Madison County 1 1 0 1 2 3.5 119131 IA, Mitchell County 1 1 0 1 2 3.5 019133 IA, Monona County 1 1 0 1 2 3.5 119135 IA, Monroe County 1 1 0 1 2 3.5 119141 IA, O’Brien County 1 1 0 1 2 3.5 019143 IA, Osceola County 1 1 0 1 2 3.5 019147 IA, Palo Alto County 1 1 0 1 1 2.5 019151 IA, Pocahontas County 1 1 0 1 2 3.5 019159 IA, Ringgold County 1 1 0 1 2 3.5 119161 IA, Sac County 1 1 0 1 2 3.5 019165 IA, Shelby County 1 1 0 1 2 3.5 019171 IA, Tama County 1 2 2 1 2 4.0 119173 IA, Taylor County 1 1 0 1 1 2.5 119177 IA, Van Buren County 1 1 0 1 1 2.5 1
19

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

19185 IA, Wayne County 1 1 0 1 1 2.5 119195 IA, Worth County 1 1 0 1 2 3.5 019197 IA, Wright County 1 1 0 1 2 3.5 020003 KS, Anderson County 1 1 0 1 2 3.5 120007 KS, Barber County 0 1 0 1 2 2.5 020011 KS, Bourbon County 1 1 0 1 2 3.5 120013 KS, Brown County 1 2 2 1 2 4.0 020017 KS, Chase County 0 1 0 1 2 2.5 020019 KS, Chautauqua County 1 1 1 1 2 3.5 120023 KS, Cheyenne County 0 1 0 1 2 2.5 020025 KS, Clark County 0 1 1 1 2 2.5 020027 KS, Clay County 1 1 0 1 2 3.5 020029 KS, Cloud County 1 1 0 1 2 3.5 020031 KS, Coffey County 1 1 0 1 2 3.5 120033 KS, Comanche County 0 1 0 1 2 2.5 020039 KS, Decatur County 0 1 0 1 2 2.5 020041 KS, Dickinson County 1 1 0 1 2 3.5 020043 KS, Doniphan County 1 1 1 1 1 2.5 120047 KS, Edwards County 1 2 0 1 2 4.0 020049 KS, Elk County 0 1 0 1 2 2.5 120053 KS, Ellsworth County 1 1 0 1 2 3.5 020063 KS, Gove County 0 1 0 1 1 1.5 020065 KS, Graham County 0 1 0 1 2 2.5 020067 KS, Grant County 1 2 0 1 1 3.0 020069 KS, Gray County 1 2 0 1 2 4.0 020071 KS, Greeley County 0 1 0 1 2 2.5 020073 KS, Greenwood County 1 1 0 1 1 2.5 020075 KS, Hamilton County 0 2 0 1 2 3.0 020077 KS, Harper County 1 1 0 1 1 2.5 020081 KS, Haskell County 1 2 0 1 1 3.0 020083 KS, Hodgeman County 0 1 0 1 2 2.5 020085 KS, Jackson County 1 2 2 1 2 4.0 120089 KS, Jewell County 0 1 0 1 2 2.5 020093 KS, Kearny County 0 2 0 1 1 2.0 020095 KS, Kingman County 1 1 0 1 2 3.5 020097 KS, Kiowa County 0 1 0 1 2 2.5 020099 KS, Labette County 2 2 1 1 1 4.0 120101 KS, Lane County 0 1 0 1 2 2.5 020105 KS, Lincoln County 0 1 0 1 2 2.5 020107 KS, Linn County 1 1 0 1 2 3.5 120109 KS, Logan County 0 1 0 1 2 2.5 020115 KS, Marion County 1 1 0 1 2 3.5 020117 KS, Marshall County 1 1 0 1 2 3.5 020119 KS, Meade County 0 2 0 1 2 3.0 020123 KS, Mitchell County 1 1 0 1 2 3.5 020127 KS, Morris County 1 1 0 1 2 3.5 0
20Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

20129 KS, Morton County 0 2 1 1 2 4.0 0 220131 KS, Nemaha County 1 1 0 1 2 3.5 020135 KS, Ness County 0 1 0 1 1 1.5 020137 KS, Norton County 1 1 0 1 2 3.5 020139 KS, Osage County 1 1 0 1 2 3.5 120141 KS, Osborne County 0 1 0 1 2 2.5 020143 KS, Ottawa County 1 1 0 1 2 3.5 020145 KS, Pawnee County 1 2 0 1 2 4.0 020147 KS, Phillips County 1 1 0 1 2 3.5 020149 KS, Pottawatomie County 1 1 0 1 2 3.5 120151 KS, Pratt County 1 1 0 1 2 3.5 020153 KS, Rawlins County 0 1 0 1 2 2.5 020157 KS, Republic County 1 1 0 1 1 2.5 020159 KS, Rice County 1 1 0 1 1 2.5 020163 KS, Rooks County 1 1 0 1 2 3.5 020165 KS, Rush County 0 1 0 1 1 1.5 020167 KS, Russell County 1 1 0 1 1 2.5 020171 KS, Scott County 1 1 0 1 2 3.5 020179 KS, Sheridan County 0 1 1 1 1 1.5 020181 KS, Sherman County 1 1 0 1 2 3.5 020183 KS, Smith County 0 1 0 1 2 2.5 020185 KS, Stafford County 1 1 1 1 1 2.5 020187 KS, Stanton County 0 2 2 1 2 3.0 020191 KS, Sumner County 1 1 0 1 2 3.5 020195 KS, Trego County 0 1 1 1 2 2.5 020197 KS, Wabaunsee County 1 1 0 1 2 3.5 020199 KS, Wallace County 0 1 1 1 2 2.5 020201 KS, Washington County 1 1 0 1 2 3.5 020203 KS, Wichita County 0 2 2 1 2 3.0 020205 KS, Wilson County 1 1 0 1 2 3.5 120207 KS, Woodson County 1 1 2 1 2 3.5 121017 KY, Bourbon County 2 2 0 2 1 4.0 121055 KY, Crittenden County 1 1 0 2 2 3.5 221057 KY, Cumberland County 1 1 0 2 2 3.5 321077 KY, Gallatin County 2 1 0 2 1 3.5 221079 KY, Garrard County 2 1 0 2 1 3.5 121097 KY, Harrison County 2 1 0 2 1 3.5 121105 KY, Hickman County 1 2 0 2 2 4.0 121123 KY, Larue County 2 1 0 2 1 3.5 121149 KY, McLean County 2 1 0 2 1 3.5 221159 KY, Martin County 2 1 0 2 1 3.5 321169 KY, Metcalfe County 2 1 0 2 1 3.5 221171 KY, Monroe County 2 1 0 2 1 3.5 221189 KY, Owsley County 1 1 0 2 2 4.0 3 121201 KY, Robertson County 1 1 0 2 2 3.5 221219 KY, Todd County 2 2 0 2 1 4.0 1
21

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

21225 KY, Union County 2 2 0 2 1 4.0 121233 KY, Webster County 2 1 0 2 1 3.5 121237 KY, Wolfe County 2 1 0 2 1 4.0 3 121239 KY, Woodford County 2 2 0 2 1 4.0 122021 LA, Caldwell Parish 1 2 0 2 2 4.0 322023 LA, Cameron Parish 1 1 0 2 1 2.5 022027 LA, Claiborne Parish 1 3 0 2 1 4.0 3 122035 LA, East Carroll Parish 1 3 0 2 1 3.5 122059 LA, La Salle Parish 1 2 0 2 1 3.0 322091 LA, St. Helena Parish 1 3 0 2 1 3.5 322113 LA, Vermilion Parish 2 2 0 2 1 4.0 023003 ME, Aroostook County 1 1 1 1 2 3.5 323007 ME, Franklin County 1 1 0 1 2 3.5 323021 ME, Piscataquis County 0 1 0 1 2 2.5 323023 ME, Sagadahoc County 2 1 0 1 1 3.5 323025 ME, Somerset County 1 1 0 1 2 3.5 323029 ME, Washington County 1 1 1 1 1 2.5 326083 MI, Keweenaw County 0 1 0 1 3 3.5 326095 MI, Luce County 1 2 1 1 2 4.0 326109 MI, Menominee County 1 1 1 1 2 3.5 326113 MI, Missaukee County 1 1 0 1 1 2.5 226119 MI, Montmorency County 1 1 0 1 2 3.5 326141 MI, Presque Isle County 1 1 0 1 2 3.5 326151 MI, Sanilac County 2 1 0 1 1 3.5 127001 MN, Aitkin County 1 1 1 1 2 3.5 227005 MN, Becker County 1 2 2 1 2 4.0 227011 MN, Big Stone County 1 1 0 1 2 3.5 027023 MN, Chippewa County 1 1 1 1 2 3.5 027029 MN, Clearwater County 1 2 2 1 2 4.0 227033 MN, Cottonwood County 1 1 0 1 2 3.5 027043 MN, Faribault County 1 1 0 1 2 3.5 027045 MN, Fillmore County 1 1 0 1 2 3.5 127051 MN, Grant County 1 1 0 1 2 3.5 027057 MN, Hubbard County 1 1 1 1 2 3.5 327063 MN, Jackson County 1 1 0 1 2 3.5 027069 MN, Kittson County 0 1 0 1 2 2.5 127071 MN, Koochiching County 0 1 1 1 2 3.0 3 127073 MN, Lac qui Parle County 1 1 0 1 2 3.5 027077 MN, Lake of the Woods County 0 1 1 1 3 3.5 227081 MN, Lincoln County 1 1 0 1 2 3.5 027089 MN, Marshall County 1 1 0 1 1 2.5 127101 MN, Murray County 1 1 0 1 1 2.5 027107 MN, Norman County 1 1 1 1 1 2.5 027113 MN, Pennington County 1 1 0 1 2 3.5 127115 MN, Pine County 1 1 1 1 2 3.5 227117 MN, Pipestone County 1 1 1 1 2 3.5 0
22Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

27119 MN, Polk County 1 1 1 1 2 3.5 027121 MN, Pope County 1 1 0 1 2 3.5 027125 MN, Red Lake County 1 1 1 1 2 3.5 127127 MN, Redwood County 1 1 1 1 2 3.5 027129 MN, Renville County 1 1 0 1 1 2.5 027133 MN, Rock County 1 1 0 1 2 3.5 027135 MN, Roseau County 1 1 1 1 2 3.5 127143 MN, Sibley County 1 1 0 1 2 3.5 127149 MN, Stevens County 1 1 0 1 2 3.5 027151 MN, Swift County 1 2 0 1 2 4.0 027153 MN, Todd County 1 1 0 1 2 3.5 127155 MN, Traverse County 1 1 1 1 2 3.5 027159 MN, Wadena County 1 1 0 1 2 3.5 227165 MN, Watonwan County 1 2 0 1 2 4.0 027167 MN, Wilkin County 1 1 0 1 2 3.5 027173 MN, Yellow Medicine County 1 1 1 1 2 3.5 028013 MS, Calhoun County 1 3 0 2 1 3.5 228015 MS, Carroll County 1 3 0 2 1 3.5 328023 MS, Clarke County 1 3 0 2 1 3.5 328053 MS, Humphreys County 1 3 0 2 1 3.5 128055 MS, Issaquena County 1 3 0 2 1 4.0 2 128061 MS, Jasper County 1 3 0 2 1 4.0 3 128069 MS, Kemper County 1 3 1 2 1 3.5 328103 MS, Noxubee County 1 3 0 2 1 3.5 228119 MS, Quitman County 1 3 0 2 1 3.5 128129 MS, Smith County 1 2 0 2 1 4.0 3 228155 MS, Webster County 1 2 0 2 2 4.0 329005 MO, Atchison County 1 1 0 1 2 3.5 029011 MO, Barton County 1 1 0 1 1 2.5 129013 MO, Bates County 1 1 0 1 2 3.5 129015 MO, Benton County 1 1 0 1 2 3.5 229017 MO, Bollinger County 1 1 0 1 2 4.0 2 129025 MO, Caldwell County 1 1 0 1 2 3.5 129033 MO, Carroll County 1 1 0 1 2 3.5 129041 MO, Chariton County 1 1 0 1 2 3.5 129045 MO, Clark County 1 1 0 1 2 3.5 129057 MO, Dade County 1 1 0 1 2 3.5 129061 MO, Daviess County 1 1 0 1 2 3.5 129063 MO, DeKalb County 1 2 0 1 2 4.0 029075 MO, Gentry County 1 1 0 1 2 3.5 129079 MO, Grundy County 1 1 0 1 2 3.5 129081 MO, Harrison County 1 1 0 1 2 3.5 129085 MO, Hickory County 1 1 0 1 2 3.5 229087 MO, Holt County 1 1 0 1 2 3.5 129089 MO, Howard County 1 2 0 1 2 4.0 129093 MO, Iron County 1 1 0 1 1 3.5 3 2
23

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

29103 MO, Knox County 1 1 0 1 2 3.5 129111 MO, Lewis County 1 1 0 1 2 3.5 129115 MO, Linn County 1 1 0 1 2 3.5 129117 MO, Livingston County 1 1 0 1 2 3.5 129119 MO, McDonald County 2 2 1 1 1 4.0 229121 MO, Macon County 1 1 0 1 2 3.5 129125 MO, Maries County 1 1 0 1 2 3.5 229129 MO, Mercer County 1 1 0 1 2 3.5 129137 MO, Monroe County 1 1 0 1 1 2.5 129139 MO, Montgomery County 1 1 0 1 2 3.5 129147 MO, Nodaway County 1 1 0 1 2 3.5 129151 MO, Osage County 1 1 0 1 1 2.5 229163 MO, Pike County 1 2 0 1 1 3.0 129171 MO, Putnam County 1 1 0 1 2 3.5 129173 MO, Ralls County 1 1 0 1 2 3.5 129185 MO, St. Clair County 1 1 0 1 2 3.5 229197 MO, Schuyler County 1 1 0 1 2 3.5 129199 MO, Scotland County 1 1 0 1 2 3.5 129205 MO, Shelby County 1 1 0 1 1 2.5 129211 MO, Sullivan County 1 1 0 1 1 2.5 129217 MO, Vernon County 1 1 0 1 2 3.5 129227 MO, Worth County 1 1 0 1 2 3.5 129229 MO, Wright County 1 1 0 1 2 4.0 2 130001 MT, Beaverhead County 0 1 1 3 2 4.0 2 330003 MT, Big Horn County 0 3 2 3 2 3.5 130005 MT, Blaine County 0 3 2 3 2 3.5 030007 MT, Broadwater County 0 1 1 3 2 4.0 1 330009 MT, Carbon County 0 1 0 3 2 4.0 1 330011 MT, Carter County 0 1 0 3 2 3.5 0 230015 MT, Chouteau County 0 2 2 3 2 3.5 0 130017 MT, Custer County 0 1 1 3 2 2.5 030019 MT, Daniels County 0 1 1 3 2 2.5 030021 MT, Dawson County 0 1 1 3 2 2.5 030025 MT, Fallon County 0 1 0 3 2 2.5 030027 MT, Fergus County 0 1 1 3 2 3.5 1 230033 MT, Garfield County 0 1 0 3 2 2.5 030035 MT, Glacier County 0 3 2 3 2 4.0 1 130037 MT, Golden Valley County 0 1 0 3 2 3.0 1 130039 MT, Granite County 0 1 1 3 2 4.0 3 330041 MT, Hill County 1 2 2 3 2 4.0 030043 MT, Jefferson County 1 1 1 3 1 4.0 2 330045 MT, Judith Basin County 0 1 0 3 2 4.0 1 330051 MT, Liberty County 0 1 0 3 2 2.5 030053 MT, Lincoln County 0 1 1 3 2 4.0 3 330055 MT, McCone County 0 1 1 3 2 2.5 030057 MT, Madison County 0 1 0 3 2 4.0 1 3
24Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

30059 MT, Meagher County 0 1 1 3 2 4.0 2 330061 MT, Mineral County 0 1 1 3 2 4.0 3 330065 MT, Musselshell County 0 1 1 3 2 2.5 130069 MT, Petroleum County 0 1 0 3 2 2.5 030071 MT, Phillips County 0 2 2 3 2 3.0 030073 MT, Pondera County 0 2 2 3 2 4.0 1 230075 MT, Powder River County 0 1 1 3 2 4.0 1 330077 MT, Powell County 0 2 1 3 1 3.5 2 330079 MT, Prairie County 0 1 0 3 2 2.5 030083 MT, Richland County 0 1 1 3 2 2.5 030085 MT, Roosevelt County 0 3 2 3 2 3.5 030091 MT, Sheridan County 0 1 1 3 2 2.5 030095 MT, Stillwater County 0 1 0 3 2 4.0 1 330097 MT, Sweet Grass County 0 1 0 3 2 4.0 1 330099 MT, Teton County 0 1 1 3 2 4.0 1 330101 MT, Toole County 0 1 1 3 2 2.5 030103 MT, Treasure County 0 1 1 3 2 2.5 130105 MT, Valley County 0 2 2 3 2 3.0 030107 MT, Wheatland County 0 1 0 3 2 3.5 1 230109 MT, Wibaux County 0 1 0 3 2 2.5 031003 NE, Antelope County 1 1 0 1 2 3.5 031005 NE, Arthur County 0 1 0 1 2 2.5 031007 NE, Banner County 0 1 0 1 2 2.5 031009 NE, Blaine County 0 1 0 1 2 3.0 0 131011 NE, Boone County 1 1 0 1 2 3.5 031015 NE, Boyd County 0 1 0 1 2 2.5 031017 NE, Brown County 0 1 0 1 2 2.5 031021 NE, Burt County 1 1 1 1 2 3.5 031023 NE, Butler County 1 1 0 1 1 2.5 031027 NE, Cedar County 1 1 0 1 2 3.5 031029 NE, Chase County 0 1 0 1 2 2.5 031031 NE, Cherry County 0 1 1 1 2 3.5 0 231033 NE, Cheyenne County 1 1 0 1 2 3.5 031035 NE, Clay County 1 1 0 1 2 3.5 031039 NE, Cuming County 1 1 0 1 2 3.5 031041 NE, Custer County 0 1 0 1 2 2.5 031047 NE, Dawson County 1 2 0 1 2 4.0 031049 NE, Deuel County 0 1 0 1 2 2.5 031051 NE, Dixon County 1 1 0 1 2 3.5 031057 NE, Dundy County 0 1 0 1 2 2.5 031059 NE, Fillmore County 1 1 0 1 2 3.5 031061 NE, Franklin County 1 1 0 1 2 3.5 031063 NE, Frontier County 0 1 0 1 2 2.5 031065 NE, Furnas County 1 1 0 1 2 3.5 031067 NE, Gage County 1 1 0 1 2 3.5 031069 NE, Garden County 0 1 0 1 2 2.5 0
25

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

31071 NE, Garfield County 0 1 0 1 2 2.5 031073 NE, Gosper County 0 1 0 1 2 2.5 031075 NE, Grant County 0 1 0 1 2 2.5 031077 NE, Greeley County 0 1 0 1 2 2.5 031081 NE, Hamilton County 1 1 0 1 2 3.5 031083 NE, Harlan County 1 1 0 1 2 3.5 031085 NE, Hayes County 0 1 0 1 2 2.5 031087 NE, Hitchcock County 0 1 0 1 2 2.5 031089 NE, Holt County 0 1 0 1 2 2.5 031091 NE, Hooker County 0 1 0 1 2 2.5 031093 NE, Howard County 1 1 0 1 2 3.5 131095 NE, Jefferson County 1 1 0 1 2 3.5 131097 NE, Johnson County 1 1 0 1 2 3.5 031099 NE, Kearney County 1 1 0 1 2 3.5 031101 NE, Keith County 1 1 0 1 2 3.5 031103 NE, Keya Paha County 0 1 0 1 2 2.5 131105 NE, Kimball County 0 1 0 1 2 2.5 031107 NE, Knox County 1 2 2 1 2 4.0 031113 NE, Logan County 0 1 1 1 2 2.5 031115 NE, Loup County 0 1 0 1 2 2.5 031117 NE, McPherson County 0 1 0 1 2 2.5 031121 NE, Merrick County 1 1 0 1 2 3.5 031123 NE, Morrill County 0 1 0 1 2 2.5 031125 NE, Nance County 1 1 0 1 2 3.5 031127 NE, Nemaha County 1 1 0 1 2 3.5 031129 NE, Nuckolls County 1 1 0 1 2 3.5 031131 NE, Otoe County 1 1 0 1 2 3.5 031133 NE, Pawnee County 1 1 0 1 2 3.5 031135 NE, Perkins County 0 1 0 1 2 2.5 031137 NE, Phelps County 1 1 0 1 2 3.5 031139 NE, Pierce County 1 1 0 1 2 3.5 031143 NE, Polk County 1 1 0 1 2 3.5 131145 NE, Red Willow County 1 1 0 1 2 3.5 031147 NE, Richardson County 1 1 1 1 2 3.5 131149 NE, Rock County 0 1 0 1 2 2.5 031151 NE, Saline County 1 1 0 1 2 3.5 031155 NE, Saunders County 1 1 0 1 2 3.5 031161 NE, Sheridan County 0 2 2 1 2 3.0 031163 NE, Sherman County 1 1 0 1 2 3.5 031165 NE, Sioux County 0 1 0 1 2 3.5 1 231167 NE, Stanton County 1 1 0 1 2 3.5 031169 NE, Thayer County 1 1 0 1 1 2.5 031171 NE, Thomas County 0 1 0 1 2 3.5 0 231175 NE, Valley County 1 1 0 1 2 3.5 031179 NE, Wayne County 1 1 0 1 2 3.5 031181 NE, Webster County 1 1 0 1 2 3.5 0
26Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

31183 NE, Wheeler County 0 1 0 1 2 2.5 031185 NE, York County 1 1 0 1 2 3.5 032001 NV, Churchill County 0 2 1 3 2 3.0 132009 NV, Esmeralda County 0 2 1 3 1 3.0 1 232017 NV, Lincoln County 0 2 1 3 2 3.5 1 132027 NV, Pershing County 0 2 1 3 1 2.0 035003 NM, Catron County 0 2 1 3 1 3.5 2 335007 NM, Colfax County 0 2 1 3 2 4.0 1 235011 NM, De Baca County 0 2 0 3 2 3.0 035019 NM, Guadalupe County 0 3 1 3 2 3.5 035021 NM, Harding County 0 2 1 3 2 4.0 1 235029 NM, Luna County 1 3 1 3 1 3.5 035037 NM, Quay County 0 2 1 3 2 3.0 035059 NM, Union County 0 2 0 3 2 4.0 1 236031 NY, Essex County 1 1 0 1 2 3.5 236041 NY, Hamilton County 0 1 0 1 2 2.5 136049 NY, Lewis County 1 1 0 1 2 3.5 337095 NC, Hyde County 1 3 0 2 1 3.5 237177 NC, Tyrrell County 1 3 0 2 1 3.5 238001 ND, Adams County 0 1 0 1 2 2.5 038003 ND, Barnes County 1 1 0 1 2 3.5 038005 ND, Benson County 0 3 2 1 2 3.5 038007 ND, Billings County 0 1 0 1 2 4.0 1 338009 ND, Bottineau County 0 1 1 1 2 2.5 038011 ND, Bowman County 0 1 0 1 2 2.5 038013 ND, Burke County 0 1 0 1 2 2.5 038019 ND, Cavalier County 0 1 0 1 2 2.5 038021 ND, Dickey County 0 1 0 1 2 2.5 038023 ND, Divide County 0 1 0 1 2 2.5 038025 ND, Dunn County 0 2 2 1 2 3.0 038027 ND, Eddy County 0 1 1 1 2 2.5 038029 ND, Emmons County 0 1 0 1 2 2.5 038033 ND, Golden Valley County 0 1 0 1 2 3.5 0 238037 ND, Grant County 0 1 1 1 2 3.0 0 138039 ND, Griggs County 0 1 0 1 2 2.5 038041 ND, Hettinger County 0 1 0 1 2 2.5 038043 ND, Kidder County 0 1 0 1 2 2.5 038045 ND, LaMoure County 0 1 0 1 2 2.5 038047 ND, Logan County 0 1 0 1 2 2.5 038049 ND, McHenry County 0 1 0 1 2 3.0 0 138051 ND, McIntosh County 0 1 0 1 2 2.5 038055 ND, McLean County 0 2 2 1 2 3.0 038057 ND, Mercer County 1 1 1 1 2 3.5 038059 ND, Morton County 1 1 1 1 2 3.5 038061 ND, Mountrail County 0 3 2 1 2 3.5 038063 ND, Nelson County 0 1 0 1 2 2.5 0
27

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

38065 ND, Oliver County 0 1 1 1 2 2.5 038067 ND, Pembina County 1 1 1 1 1 2.5 038069 ND, Pierce County 0 1 0 1 2 2.5 038071 ND, Ramsey County 1 2 1 1 2 4.0 038075 ND, Renville County 0 1 0 1 2 2.5 038077 ND, Richland County 1 1 1 1 2 4.0 0 138081 ND, Sargent County 0 1 0 1 3 3.5 038083 ND, Sheridan County 0 1 0 1 2 2.5 038085 ND, Sioux County 0 3 2 1 2 4.0 0 138089 ND, Stark County 1 1 0 1 2 3.5 038091 ND, Steele County 0 2 0 1 2 3.0 038093 ND, Stutsman County 1 1 0 1 2 3.5 038095 ND, Towner County 0 1 1 1 2 2.5 038097 ND, Traill County 1 1 0 1 2 3.5 038099 ND, Walsh County 1 2 0 1 2 4.0 038103 ND, Wells County 0 2 0 1 2 3.0 038105 ND, Williams County 1 2 1 1 2 4.0 039127 OH, Perry County 2 1 0 1 1 4.0 2 139175 OH, Wyandot County 2 1 0 1 1 3.5 040003 OK, Alfalfa County 1 2 1 2 2 4.0 040007 OK, Beaver County 0 1 1 2 2 2.5 040009 OK, Beckham County 1 2 1 2 2 4.0 040011 OK, Blaine County 1 2 2 2 1 3.0 040025 OK, Cimarron County 0 2 1 2 2 3.5 0 140033 OK, Cotton County 1 2 2 2 2 4.0 040039 OK, Custer County 1 2 2 2 2 4.0 040043 OK, Dewey County 0 2 1 2 2 3.0 040045 OK, Ellis County 0 1 1 2 2 2.5 040049 OK, Garvin County 2 2 2 2 1 4.0 040051 OK, Grady County 2 2 1 2 1 4.0 040053 OK, Grant County 0 1 1 2 2 2.5 040055 OK, Greer County 1 2 1 2 2 4.0 040059 OK, Harper County 0 1 0 2 2 2.5 040061 OK, Haskell County 1 2 2 2 2 4.0 240067 OK, Jefferson County 1 2 1 2 1 3.0 040069 OK, Johnston County 1 2 2 2 2 4.0 040073 OK, Kingfisher County 1 2 1 2 2 4.0 040075 OK, Kiowa County 1 2 2 2 2 4.0 040077 OK, Latimer County 1 3 2 2 1 3.5 340085 OK, Love County 1 2 2 2 2 4.0 040093 OK, Major County 1 1 0 2 1 2.5 040099 OK, Murray County 2 2 2 2 1 4.0 040103 OK, Noble County 1 2 2 2 2 4.0 040107 OK, Okfuskee County 1 3 2 2 1 3.5 040113 OK, Osage County 1 3 2 2 1 3.5 040127 OK, Pushmataha County 1 2 2 2 2 4.0 340129 OK, Roger Mills County 0 2 1 2 2 3.5 0 1
28Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

40149 OK, Washita County 1 2 1 2 2 4.0 040151 OK, Woods County 1 1 1 2 1 2.5 040153 OK, Woodward County 1 2 1 2 2 4.0 041001 OR, Baker County 0 1 1 4 2 4.0 2 341021 OR, Gilliam County 0 1 0 4 2 2.5 041023 OR, Grant County 0 1 1 4 2 4.0 3 341025 OR, Harney County 0 2 1 4 1 3.5 1 341037 OR, Lake County 0 2 1 4 1 3.5 1 341045 OR, Malheur County 0 3 1 4 1 3.0 0 141049 OR, Morrow County 0 2 1 4 1 3.0 1 241055 OR, Sherman County 0 1 1 4 1 1.5 041063 OR, Wallowa County 0 1 0 4 2 4.0 2 341069 OR, Wheeler County 0 1 0 4 2 4.0 2 342023 PA, Cameron County 1 1 0 1 2 3.5 342059 PA, Greene County 2 1 0 1 1 3.5 242105 PA, Potter County 1 1 0 1 2 3.5 346003 SD, Aurora County 0 1 1 3 2 2.5 046005 SD, Beadle County 1 1 0 3 2 3.5 046007 SD, Bennett County 0 3 2 3 2 3.5 046009 SD, Bon Homme County 1 1 1 3 2 3.5 046013 SD, Brown County 1 1 1 3 2 3.5 046017 SD, Buffalo County 0 3 2 3 2 3.5 046019 SD, Butte County 0 1 1 3 2 2.5 046021 SD, Campbell County 0 1 0 3 2 2.5 046025 SD, Clark County 0 1 0 3 2 2.5 046031 SD, Corson County 0 3 2 3 2 4.0 0 146033 SD, Custer County 0 1 1 3 2 4.0 1 346037 SD, Day County 1 2 2 3 2 4.0 046039 SD, Deuel County 1 1 0 3 2 3.5 046041 SD, Dewey County 0 3 2 3 2 3.5 046043 SD, Douglas County 1 1 0 3 2 3.5 046045 SD, Edmunds County 0 1 0 3 2 2.5 046049 SD, Faulk County 0 1 0 3 1 1.5 046051 SD, Grant County 1 1 0 3 2 3.5 046053 SD, Gregory County 0 1 1 3 2 2.5 046055 SD, Haakon County 0 1 1 3 2 2.5 046057 SD, Hamlin County 1 1 0 3 2 3.5 046059 SD, Hand County 0 1 0 3 2 2.5 046061 SD, Hanson County 1 1 0 3 2 3.5 046063 SD, Harding County 0 1 0 3 2 3.5 0 246067 SD, Hutchinson County 1 1 0 3 2 3.5 046069 SD, Hyde County 0 2 2 3 2 3.0 046073 SD, Jerauld County 0 1 0 3 2 2.5 046075 SD, Jones County 0 1 1 3 2 3.0 0 146077 SD, Kingsbury County 1 1 0 3 2 3.5 046079 SD, Lake County 1 1 0 3 2 3.5 046087 SD, McCook County 1 1 0 3 2 3.5 0
29

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

46089 SD, McPherson County 0 1 0 3 2 2.5 046091 SD, Marshall County 0 2 2 3 2 3.0 046095 SD, Mellette County 0 3 2 3 2 3.5 046097 SD, Miner County 0 1 0 3 2 2.5 046101 SD, Moody County 1 2 2 3 2 4.0 046105 SD, Perkins County 0 1 1 3 2 3.5 0 246107 SD, Potter County 0 1 0 3 2 2.5 046111 SD, Sanborn County 0 1 0 3 2 2.5 046115 SD, Spink County 0 1 1 3 2 2.5 046119 SD, Sully County 0 1 0 3 2 2.5 046123 SD, Tripp County 0 2 2 3 2 3.0 046125 SD, Turner County 1 1 0 3 2 3.5 046127 SD, Union County 1 1 0 3 2 3.5 046137 SD, Ziebach County 0 3 2 3 2 4.0 0 147007 TN, Bledsoe County 2 1 0 2 1 3.5 347033 TN, Crockett County 2 2 0 2 1 4.0 147057 TN, Grainger County 2 1 0 2 1 3.5 247067 TN, Hancock County 2 1 0 2 1 3.5 347135 TN, Perry County 1 1 0 2 2 3.5 347175 TN, Van Buren County 1 1 0 2 2 3.5 347181 TN, Wayne County 1 2 0 2 2 4.0 347183 TN, Weakley County 2 2 0 2 1 4.0 148003 TX, Andrews County 1 2 0 2 1 3.0 048009 TX, Archer County 1 1 0 2 1 2.5 048011 TX, Armstrong County 0 1 0 2 2 2.5 048017 TX, Bailey County 1 3 0 2 1 3.5 048019 TX, Bandera County 1 1 0 2 2 3.5 048023 TX, Baylor County 0 2 0 2 2 3.0 048031 TX, Blanco County 1 2 0 2 2 4.0 048033 TX, Borden County 0 2 0 2 2 3.0 048035 TX, Bosque County 1 2 0 2 2 4.0 048043 TX, Brewster County 0 2 0 2 2 3.0 048045 TX, Briscoe County 0 2 0 2 2 3.0 048051 TX, Burleson County 1 3 0 2 1 3.5 048059 TX, Callahan County 1 1 0 2 1 2.5 048065 TX, Carson County 1 1 1 2 2 3.5 048069 TX, Castro County 1 3 1 2 1 3.5 048071 TX, Chambers County 2 2 0 2 1 4.0 048077 TX, Clay County 1 1 1 2 2 3.5 048079 TX, Cochran County 0 3 0 2 1 2.5 048081 TX, Coke County 0 2 0 2 2 3.0 048083 TX, Coleman County 1 2 0 2 2 4.0 048087 TX, Collingsworth County 0 2 1 2 1 2.0 048093 TX, Comanche County 1 2 0 2 2 4.0 048095 TX, Concho County 0 2 0 2 2 3.0 048101 TX, Cottle County 0 2 0 2 2 3.0 0
30Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

48103 TX, Crane County 0 3 0 2 2 3.5 048105 TX, Crockett County 0 2 0 2 2 3.0 048109 TX, Culberson County 0 3 0 2 2 3.5 048111 TX, Dallam County 0 2 0 2 2 4.0 0 248115 TX, Dawson County 1 3 0 2 1 3.5 048117 TX, Deaf Smith County 1 3 0 2 1 3.5 048119 TX, Delta County 1 2 0 2 2 4.0 048123 TX, De Witt County 1 2 0 2 1 3.0 048125 TX, Dickens County 0 2 0 2 2 3.0 048127 TX, Dimmit County 1 2 0 2 2 4.0 048129 TX, Donley County 0 2 0 2 2 3.0 048131 TX, Duval County 1 2 0 2 1 3.0 048133 TX, Eastland County 1 2 0 2 1 3.0 048137 TX, Edwards County 0 2 0 2 2 3.0 048143 TX, Erath County 2 2 0 2 1 4.0 048149 TX, Fayette County 1 2 0 2 2 4.0 048151 TX, Fisher County 0 2 0 2 2 3.0 048153 TX, Floyd County 1 3 0 2 1 3.5 048155 TX, Foard County 0 2 0 2 2 3.0 048161 TX, Freestone County 1 3 0 2 1 3.5 048165 TX, Gaines County 1 2 0 2 1 3.0 048169 TX, Garza County 0 3 0 2 1 2.5 048171 TX, Gillespie County 1 1 0 2 2 3.5 048173 TX, Glasscock County 0 2 0 2 2 3.0 048175 TX, Goliad County 1 2 0 2 2 4.0 048191 TX, Hall County 0 3 0 2 2 3.5 048193 TX, Hamilton County 1 1 0 2 2 3.5 048195 TX, Hansford County 1 2 0 2 1 3.0 048197 TX, Hardeman County 1 2 0 2 1 3.0 048205 TX, Hartley County 0 2 0 2 1 2.0 048207 TX, Haskell County 1 2 0 2 1 3.0 048211 TX, Hemphill County 0 2 0 2 1 2.5 0 148219 TX, Hockley County 1 3 0 2 1 3.5 048229 TX, Hudspeth County 0 2 1 2 2 3.0 048233 TX, Hutchinson County 1 2 1 2 2 4.0 048235 TX, Irion County 0 2 0 2 2 3.0 048237 TX, Jack County 1 2 0 2 1 3.0 048239 TX, Jackson County 1 2 0 2 2 4.0 048243 TX, Jeff Davis County 0 2 0 2 2 3.0 048247 TX, Jim Hogg County 0 2 0 2 2 3.0 048253 TX, Jones County 1 2 0 2 1 3.0 048255 TX, Karnes County 1 3 0 2 1 3.5 048261 TX, Kenedy County 0 3 0 2 2 3.5 048263 TX, Kent County 0 1 0 2 2 2.5 048267 TX, Kimble County 0 2 0 2 2 3.0 048269 TX, King County 0 1 1 2 2 2.5 0
31

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

48271 TX, Kinney County 0 3 0 2 2 3.5 048275 TX, Knox County 0 3 1 2 1 2.5 048279 TX, Lamb County 1 2 0 2 2 4.0 048281 TX, Lampasas County 1 2 0 2 2 4.0 048283 TX, La Salle County 0 2 0 2 2 3.0 048285 TX, Lavaca County 1 2 0 2 1 3.0 048287 TX, Lee County 1 2 0 2 1 3.0 048289 TX, Leon County 1 2 0 2 2 4.0 248293 TX, Limestone County 1 3 0 2 1 3.5 048295 TX, Lipscomb County 0 2 1 2 2 3.0 048297 TX, Live Oak County 1 2 0 2 2 4.0 048301 TX, Loving County 0 2 0 2 2 3.0 048305 TX, Lynn County 1 3 1 2 1 3.5 048307 TX, McCulloch County 1 2 0 2 2 4.0 048311 TX, McMullen County 0 2 0 2 2 3.0 048317 TX, Martin County 0 2 0 2 1 2.0 048319 TX, Mason County 0 2 0 2 2 3.0 048327 TX, Menard County 0 2 0 2 2 3.0 048331 TX, Milam County 1 2 0 2 1 3.0 048333 TX, Mills County 1 2 0 2 2 4.0 048335 TX, Mitchell County 1 3 0 2 1 3.5 048337 TX, Montague County 1 1 0 2 2 4.0 0 148341 TX, Moore County 1 3 0 2 1 3.5 048345 TX, Motley County 0 2 0 2 2 3.0 048353 TX, Nolan County 1 2 0 2 1 3.0 048357 TX, Ochiltree County 1 2 0 2 2 4.0 048359 TX, Oldham County 0 2 1 2 2 3.0 048365 TX, Panola County 2 2 0 2 1 4.0 248371 TX, Pecos County 0 3 0 2 2 3.5 048377 TX, Presidio County 0 2 0 2 2 3.0 048383 TX, Reagan County 0 3 0 2 1 2.5 048385 TX, Real County 0 2 0 2 2 3.0 048387 TX, Red River County 1 2 0 2 2 4.0 248389 TX, Reeves County 0 2 0 2 1 2.0 048391 TX, Refugio County 1 2 0 2 1 3.0 048393 TX, Roberts County 0 1 0 2 2 2.5 048399 TX, Runnels County 1 2 0 2 1 3.0 048411 TX, San Saba County 0 2 1 2 2 3.0 048413 TX, Schleicher County 0 2 0 2 1 2.0 048415 TX, Scurry County 1 2 0 2 1 3.0 048417 TX, Shackelford County 0 1 0 2 1 1.5 048421 TX, Sherman County 0 2 0 2 1 2.0 048429 TX, Stephens County 1 2 0 2 1 3.0 048431 TX, Sterling County 0 2 0 2 2 3.0 048433 TX, Stonewall County 0 2 0 2 2 3.0 048435 TX, Sutton County 0 3 0 2 2 3.5 0
32Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

48437 TX, Swisher County 1 3 0 2 1 3.5 048443 TX, Terrell County 0 2 1 2 2 3.0 048445 TX, Terry County 1 2 0 2 2 4.0 048447 TX, Throckmorton County 0 2 0 2 2 3.0 048457 TX, Tyler County 1 2 0 2 2 4.0 348461 TX, Upton County 0 2 1 2 2 3.0 048465 TX, Val Verde County 1 2 0 2 2 4.0 048475 TX, Ward County 1 2 0 2 2 4.0 048483 TX, Wheeler County 1 2 0 2 1 3.0 048487 TX, Wilbarger County 1 2 0 2 1 3.0 048501 TX, Yoakum County 1 3 0 2 1 3.5 048503 TX, Young County 1 2 0 2 1 3.0 048505 TX, Zapata County 1 2 0 2 1 3.0 049001 UT, Beaver County 0 1 0 3 2 3.5 2 249009 UT, Daggett County 0 1 0 3 2 4.0 2 349015 UT, Emery County 0 1 0 3 2 4.0 1 349017 UT, Garfield County 0 1 1 3 2 4.0 2 349023 UT, Juab County 0 1 1 3 2 3.5 1 249027 UT, Millard County 0 1 1 3 2 4.0 1 349029 UT, Morgan County 1 1 0 3 2 4.0 2 149031 UT, Piute County 0 1 1 3 2 4.0 2 349033 UT, Rich County 0 1 0 3 2 3.5 1 249055 UT, Wayne County 0 1 0 3 2 4.0 1 351021 VA, Bland County 1 1 0 2 1 3.5 3 251029 VA, Buckingham County 1 3 0 2 1 3.5 351037 VA, Charlotte County 1 3 0 2 1 3.5 351097 VA, King and Queen County 1 3 1 2 1 3.5 351105 VA, Lee County 2 1 0 2 1 4.0 2 151157 VA, Rappahannock County 1 2 0 2 2 4.0 251167 VA, Russell County 2 1 0 2 1 4.0 2 153013 WA, Columbia County 0 1 0 4 2 4.0 2 353023 WA, Garfield County 0 1 0 4 2 3.5 1 253025 WA, Grant County 2 2 1 4 1 4.0 053039 WA, Klickitat County 1 2 1 4 1 3.5 2 153043 WA, Lincoln County 0 1 1 4 2 2.5 153049 WA, Pacific County 1 2 1 4 2 4.0 353069 WA, Wahkiakum County 1 1 1 4 1 2.5 353075 WA, Whitman County 1 2 0 4 2 4.0 054013 WV, Calhoun County 1 1 0 1 1 2.5 354017 WV, Doddridge County 1 1 0 1 2 3.5 354021 WV, Gilmer County 1 1 0 1 1 2.5 354023 WV, Grant County 1 1 0 1 1 3.0 3 154085 WV, Ritchie County 1 1 0 1 1 2.5 354087 WV, Roane County 2 1 0 1 1 3.5 354101 WV, Webster County 1 1 0 1 1 3.5 3 254105 WV, Wirt County 1 1 0 1 1 2.5 3
33

Table 3—List of 837 forest-dependent counties with low viability and adaptability (continued)

Population Native National

Area key Area name densitya Minorityb Americanc Regiond Employmente Compositef Forest landg foresth

55011 WI, Buffalo County 1 1 0 1 2 3.5 255013 WI, Burnett County 1 1 1 1 2 3.5 255019 WI, Clark County 2 1 0 1 1 3.5 255051 WI, Iron County 1 1 0 1 2 3.5 355053 WI, Jackson County 1 2 2 1 2 4.0 255065 WI, Lafayette County 1 1 0 1 1 2.5 155067 WI, Langlade County 1 1 0 1 2 4.0 3 155103 WI, Richland County 2 1 0 1 1 3.5 255107 WI, Rusk County 1 1 0 1 2 3.5 255129 WI, Washburn County 1 1 1 1 2 3.5 356003 WY, Big Horn County 0 1 0 3 1 3.0 1 356009 WY, Converse County 0 1 0 3 2 4.0 1 356011 WY, Crook County 0 1 1 3 1 3.0 2 356015 WY, Goshen County 1 1 0 3 2 3.5 056019 WY, Johnson County 0 1 0 3 2 4.0 1 356023 WY, Lincoln County 0 1 0 3 2 4.0 2 356025 WY, Natrona County 1 1 1 3 2 4.0 1 156027 WY, Niobrara County 0 1 0 3 2 3.0 0 156029 WY, Park County 0 1 0 3 2 4.0 2 356031 WY, Platte County 0 1 0 3 2 3.0 1 156035 WY, Sublette County 0 1 0 3 2 4.0 2 356037 WY, Sweetwater County 0 2 1 3 2 4.0 0 256043 WY, Washakie County 0 2 0 3 2 4.0 1 256045 WY, Weston County 0 1 1 3 2 4.0 1 3a Population density (person per square mile): 0 = 0–5, 1 = 6–28, 2 = 29–250, 3 = >250.b Minority (percent): 1 = 0–7.33, 2 = 7.34–23.93, 3 = >23.94.c Native American: 0 = 0–1, 1 = 1.1–5.77, 2 = >5.78.d Region: 1 = North, 2 = South, 3 = Rocky Mountains, 4 = Pacific coast.e Employment: (Shannon Weaver Index): 1 = 0–0.76684, 2 = 0.76685–0.87421, 3 = >0.87422–1.f Composite: (population+[(minority+NF area)/2]+employment).g Forest land (percent): 0 = 0–5.0, 1 = 5.1–32.5, 2 = 32.6–66.2, 3 = ≥66.3.h National forest: 1 = 0–35,000, 2 = 36,000–157,000, 3 = >157,000 acres.
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