Nonparametric Projections of Forest and Rangeland Condition Indicators

Nonparametric Projections of Forest and Rangeland 

Condition Indicators

John Hof, Curtis Flather, Tony Baltic, and Rudy King

U.S. Department of Agriculture

A Technical Document Supporting 

the USDA Forest Service 2005 Update of the RPA Assessment

A Technical Document Supporting 

the USDA Forest Service 2005 Update of the RPA Assessment

Forest Service
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Hof, John; Flather, Curtis; Baltic, Tony; King, Rudy. 2006. Nonparametric projections of forest and rangeland condition indicators: A technical document supporting the 2005 USDA Forest Service RPA Assessment Update.

Gen. Tech. Rep. RMRS-GTR-166. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 39 p.

Abstract

The 2005 Forest and Rangeland Condition Indicator Model is a set of classification trees for forest and rangeland condition indicators at the national scale. This report documents the development of the database and the nonparametric 

statistical estimation for this analytical structure, with emphasis on three special characteristics of condition indicator 

production processes: (1) the inability of humans to completely control ecological systems; (2) the lack of a theoretical 

basis for specific relational functional forms, suggesting the need for a highly flexible model structure; and (3) the broadscale spatial nature of the problem (and data). The resolution of data deficiencies is also examined. Finally, the model 

projections themselves are presented and discussed using national-scale maps.

Keywords: Resource interactions, land management planning, classification trees, modeling, environmental outputs

Authors

John Hof is a Research Forester (retired), Rocky Mountain Research Station, USDA Forest Service, 

Fort Collins, CO.

Curtis Flather is a Research Wildlife Biologist, Rocky Mountain Research Station, USDA Forest Service, 

Fort Collins, CO.

Tony Baltic is an Operations Research Analyst, Rocky Mountain Research Station, USDA Forest Service, 

Fort Collins, CO.

Rudy King is Station Statistician, Rocky Mountain Research Station, USDA Forest Service, Fort Collins, CO.
Nonparametric Projections of Forest and Rangeland 

Condition Indicators: A Technical Document 

Supporting the 2005 USDA Forest Service RPA 

Assessment Update

John Hof, Curtis Flather, Tony Baltic, and Rudy King
Contents

Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Structure of the Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Ecosystem Independence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Classification Tree Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Spatial Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Database Development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Model Estimation and Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Projection of Explanatory Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Projection of Forest and Rangeland Condition Indicators. . . . . . . . . . . . . . . 7

Individual Indicator Hotspot Projections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Edge (EDG). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Patch Size (PCH). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Exotic Breeding Birds (EXT). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Timber Mortality (MOR). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Timber Growth (GRO). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Negative Deviations From Mean Streamflows (STR). . . . . . . . . . . . . . . . . 18

Total Nitrogen in Surface Waters (NTG). . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Total Phosphorous in Surface Waters (PHO). . . . . . . . . . . . . . . . . . . . . . . 19

Ph in Precipitation (PHL). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Total Toxic Chemical Releases to the Environment (TRI). . . . . . . . . . . . . . 20

Overlays of the Projected Hotspots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Appendix A: Variable Descriptions and Data Sources. . . . . . . . . . . . . . . . . 24

Appendix B: Mapped Current and Projected Explanatory Variables. . . . . . 33

iii

USDA Forest Service RMRS-GTR-166. 2006. 1

Background_____________

The Forest and Rangeland Renewable Resources 

Planning Act of 1974 (RPA) as amended by the National 

Forest Management Act of 1976 (NFMA) and the six 

national renewable resource assessments that have been 

completed as mandated by that legislation have all emphasized the importance of quantitative information on 

renewable resource interactions. The interactions analysis for the 1989 RPA Assessment (Hof and Baltic 1988) 

focused on the National Forest System lands. That report 

analyzed the capability of the National Forest System 

to maintain a constant share of total national resource 

production, as indicated by the demand (consumption) 

projections developed for individual resources in other 

recent assessment analyses and the impacts on costs 

and environmental conditions. The analysis was not 

completely successful in determining the environmental 

impacts of increasing output levels, but it did indicate 

previously unrecognized limits to the production capabilities of the National Forest System. These results were 

used in the development of the Recommended 1990 RPA 

Program (USDA Forest Service 1990). In the “Implications” chapter, the Program also emphasized the critical 

importance of interactions research:

Without improved information [about resource 

interactions], there could be misjudgments about 

the resource output capability of the Nation’s 

forests and rangelands. This could

• Lead to errors in management decisions that 

could stress the resource base or, conversely, 

underutilize the resource capability.

• Misdirect public and private programs that 

target just one renewable resource, without 

giving adequate attention to effects on other 

resources.

Since that assessment and program development, 

the Forest Service has undergone a significant change 

in the way it considers and manages natural resources. 

The 1989 interactions analysis focused on harvestable resource outputs and the 1990 Program endorsed 

this approach to interactions analyses when it stated 

that the objective of research in resource interactions 

“is to determine which management systems and 

practices are most suitable for the production and use 

of natural resources.” In 1992, however, the Forest 

Service officially committed to using an approach to 

management called “ecosystem management” defined 

as “an ecological approach to achieve the multiple-use 

management of the National Forests and Grasslands by 

blending the needs of people and environmental values 

in such a way that the National Forests and Grasslands 

represent diverse, healthy, productive, and sustainable 

ecosystems” (Robertson 1992). The subsequent 1993 

RPA Assessment Update (USDA Forest Service 1994) 

stated in the section on “Ecosystems Management and 

Resource Interactions,” that “an emphasis on ecosystem 

management may change the nature of production possibilities and feasibilities.” This section concluded that 

future resource interactions analyses needed to involve 

“assessments of [ecosystem] function, process, and 

condition.” Both the 1993 Update and the Draft 1995 

RPA Program (USDA Forest Service 1995) continued 

to emphasize the importance of resource interactions 

research, but within this new context.

Based on this new direction for management and 

research, the 2000 resource interactions model and supporting database (Hof and others 1999a, b) was oriented 

toward analysis of the interactions between resource use 

and condition indicators, as opposed to the emphasis 

on production possibilities and feasibilities in the 1989 

interactions analysis. The focus on condition indicators 

is a direct result of a shift in natural resource management that is now focusing on long-term sustainability 

of ecosystems as the measure of responsible stewardship (Nobel and Dirzo 1997; Heinz Center 2002). This 

shift is attributable to a growing recognition that the 

human economy is very much dependent upon goods 

and services derived directly from ecosystems (Dailey 

1997) and that intensive use of natural resources may 

be stressing ecosystems to a point where their ability to 

provide these benefits is compromised (Vitousek and 

others 1997; Loreau and others 2001). The 2000 analysis 

used econometric methods to project “hotspots” of stress 

as suggested by a set of forest and rangeland condition 

indicators. Now, for the 2005 Update of the 2000 Assessment, a new model has been built that has an improved 

set of indicators, a more tenable set of driving variable 

projections, and a more flexible, nonparametric methodology. Although the indicators of forest and rangeland 

condition chosen were limited by data availability, they 

were chosen to reflect (as much as possible) the scope 

and intent of ongoing efforts to define appropriate sets 

of ecosystem condition indicators (Rapport and others 1985; Coulombe 1995; National Research Council 

2000). This report describes the rationale and structure 

of this new model, the development of its supporting 

database, the implementation of the model in projecting 
2 USDA Forest Service RMRS-GTR-166. 2006.

hotspots for resource condition indicators, and the results 

of our analysis as displayed with national-level maps.

Structure of the Model____

The purpose of this analysis is to identify broad-scale 

(national) relationships between natural characteristics, 

land use/land cover/land ownership variables, human 

population, and indicators of the forest and rangeland 

condition. Many studies have quantified these types of 

relationships at the local scale (see Hof and Baltic [1988] 

for a survey). Far less is known about these relationships 

at the national scale, other than the econometric results 

of our previous study (Hof and others 1998). This study 

does not analyze biological processes or capture detailed 

impacts. Rather, its intent is to analyze coarse effects at 

a very broad spatial scale. This study certainly misses 

many fine-scale relationships, but it may capture broadscale effects that are missed with a tightly focused view. 

We are, so to speak, trying to see the forest, not the trees. 

We begin with a brief discussion of the theory behind this 

analysis and then focus on the suggested empirics.

The production processes for the indicators of forest 

and rangeland condition are obviously different from 

those for traditional economic outputs. The ecosystems 

that “produce” environmental outputs on forests and 

rangelands are far more complex and far less controllable by human management than a traditional economic 

production unit such as a factory or a farm. In addition, 

the production unit for forest and rangeland ecosystems 

covers very large landscapes rather than the spatially 

limited traditional economic production units. Thus, in 

this paper, we will focus on three special characteristics 

of the environmental output production process: (1) the 

inability of humans to completely control ecological 

systems; (2) the lack of a theoretical basis for specific 

relational functional forms, suggesting the need for a 

highly flexible model structure; and (3) the broad-scale 

spatial nature of the problem (and data). Each of these 

will be discussed in more detail before proceeding.

Ecosystem Independence

First, we define three vectors of variables: X% is a 

vector of inputs that include human-generated inputs 

embodied in management actions as well as “natural” 

inputs such as climate and landscape characteristics; Y%

is a vector of harvested outputs such as timber, livestock 

grazing, recreation use, and mining activity; and Z% is a 

vector of forest and rangeland condition indicators. The 

traditional economic analysis would treat the Y% vector 

as the outputs, produced from the X% vector, with the 

Z% vector left largely unaccounted for. Our focus here 

is the Z% vector, so we will treat it as the output vector 

with the X% and Y% vectors as inputs. It might be more 

appealing in this context to regard the Y% vector as 

harvest-related management intensity variables that are 

inputs (positive or negative) to the production of the 

condition indicators.

The textbook treatment of joint production would use 

an implicit-form production function as:

0 = f X%,Y%, Z% ( ) (1)

to relate these three variable vectors. Mittelhammer and 

others (1981) show, however, that this approach is quite 

limiting because it does not allow any of the variables 

(and Z% in our case) to be unrelated. In a traditional economic production unit, we expect to be able to fix inputs 

and then define a locus of output combinations—the 

product transformation curve. Because our outputs are 

the Z% forest and rangeland condition indicators, this 

expectation may not be appropriate. In ecological systems theory, management actions are viewed as altering 

the structure and function of the ecosystem, which then 

results in a particular system response (see Barrett and 

others 1976; Hall and Day 1977; Allen and Hoekstra 

1992). Viewed deterministically, any combination of 

X% and Y% is associated with a particular set of environmental outputs Z% , and the product transformation curve 

would be a single point. For example, once a certain 

fire suppression and harvesting schedule is applied, 

a set of environmental outputs such as sedimentation 

and wildlife habitat are determined by the resulting 

ecosystem structure and function. This would suggest 

that an appropriate production structure should have 

the property that:

∂Zi / ∂Zj = 0 i ≠ j (2)

with all X% and Y% held constant. Mittelhammer and others show that such a property is not obtainable with (1). A 

production structure that has this property would be:

Zi = gi X%,Y% ( )∀i. (3)

Note that (3) is not a simultaneous system, but is 

potentially a set of seemingly unrelated regressions. 

Equation (3) is still a joint production structure, because 

the Z% simultaneously utilize the X% and Y% inputs. If 

the X% and Y% vectors are fixed, however, only a single 

Z% results, reflecting the autonomy of the ecosystem, as 
USDA Forest Service RMRS-GTR-166. 2006. 3

desired. In a complex ecosystem, interactions between 

any of the X% and Y% variables in affecting the Z% variables are potentially important, suggesting the flexible, 

interactive approach referred to as “Classification Trees” 

(discussed next).

Classification Tree Structure

The ultimate purpose of this study is to project “hotspots” of environmental stress. We will use indicators 

of forest and rangeland condition stress (environmental 

outputs) to accomplish this purpose, and the current 

hotspots for each indicator will be defined as the areas 

in the country with the highest 10 percent of the values 

for that indicator. When we project these hotspots, this 

definition will be retained—we will project areas that, in 

the future, would be considered hotspots by our current 

standard just defined. Thus, analytically, we have a classification problem and we need a flexible classification 

tool that can be used for projection purposes. Let us look 

at the need for flexibility a bit closer.

The production functions in (3) are written in closed 

form for the purposes of discussion, but it is actually 

quite difficult to determine what sort of specific functional form would be theoretically tenable for such a 

relationship. In Hof and others (1999a, b), we used a 

translog functional form to be as flexible as possible in 

a (parametric) econometric estimation. However, the relationships between variables and environmental outputs 

may be strongly nonlinear and involve complex interaction terms that remain undetectable by traditional statistical modeling approaches (De’ ath and Fabricius 2000). 

An alternative modeling approach that can represent 

complex variable interactions and is robust to violation 

of assumptions that typically constrain parametric modeling techniques is called “classification and regression 

trees” (CART; see Breiman and others 1984). A CART 

model can be built for either a categorical (generating 

a classification tree) or continuous (generating a regression tree) response variable. Because our interest lies in 

projecting whether a given geographic unit qualifies as a 

hotspot of environmental stress (a categorical response), 

we will generate a set of classification trees.

A classification tree amounts to a set of binary differentiations (“splits”), each based on a threshold value 

of a particular explanatory variable. The explanatory 

variable and its associated threshold value (split point) 

are selected in the CART algorithm so as to minimize 

a “misclassification function.” At each split observations are partitioned into two groups, with the set of 

observations in each partition being more homogeneous 

with respect to membership in the response variable’s 

categorical classes than the set of observations prior to 

the split. The CART classification algorithm continues 

to construct additional splitting rules until all observations are assigned to class categories in a way that minimizes misclassification. This initial classification tree is 

then “pruned” in a search for the simplest tree without 

significantly eroding classification accuracy (Breiman 

and others 1984:59-81). The CART software uses “10-

fold” cross-validation of the tree estimation process to 

compute an unbiased estimate of misclassification rate 

(Steinberg and Colla 2000). In this fashion CART essentially specifies a dichotomous key that is used to assign 

new observations to the response variable categories. 

Flexibility is achieved because no pre-specifications on 

the structure or content of the trees are necessary.

In this paper, the coterminous United States is divided 

into a grid of uniform-sized cells, which we call “analysis 

grid cells.” The objective is to classify all analysis grid 

cells as a “hotspot” or a “non-hotspot” with regard to each 

of a set of indicators. The discussion above suggests that 

each indicator should be analyzed separately, so a classification tree will be estimated for each indicator. Figure 1 

presents a very simple example. Each node in the inverted 

tree represents the separation of analysis grid cells into 

two groups. For example, the first node separates the cells 

into two groups depending on each cell’s value for the 

variable “Agr.” Those cells that have more than 85 percent of their land in agricultural land use are then further 

split according to the variable Urb reflecting the percent 

of the cell in urban land. The cells that have less than 85 

percent of their land in agricultural land use are further 

split according to their density of human population (pop/

acre). The binary splits continue until all of the cells are 

classified into sets that are labeled as either “hotspot” or 

“non-hotspot.” These final sets are referred to as terminal 

nodes and together they represent a mutually exclusive 

and exhaustive partitioning of all analysis grid cells. Once 

a classification tree has been constructed, projections of 

hotspot and non-hotspot sets can be obtained by substituting the projected values of the explanatory variables 

associated with each analysis grid cell and allowing the 

tree to re-classify these “new” cells.

Spatial considerations are critical both in the process 

of estimating classification trees and in the process of 

using them for projection purposes. These spatial factors 

are discussed next.
4 USDA Forest Service RMRS-GTR-166. 2006.

Spatial Considerations

Because the problem defined for this paper has a 

large spatial extent and because the process of producing environmental outputs takes place over very large 

landscapes, the estimation of the desired functions 

requires consideration of spatial issues that are not 

normally a concern in traditional economic models of 

production. Our desired sampling scheme for environmental output production analyses would yield observations that: (1) provide a representative sample of all 

ecological systems in the study area; and (2) provide 

a reasonable representation of the system’s boundary 

when aggregated by ecosystem. Unfortunately, broadscale data are often not available on this basis. At our 

scale, data are available in many different formats, from 

micro data with many sample points to county-level 

numbers that are relatively reliable but are also highly 

aggregated. This was problematic since we needed to 

have our response and explanatory variables on a common spatial frame.

In Hof and others (1999a, b), we tested two approaches to this problem. The “COUNTY” approach 

simply aggregated all micro data into county averages 

and treated the county as the observation unit. This 

approach is typical in broad scale statistical modeling (see Cressie 1991:383), but differential county 

size results in inequitable sampling of landscapes 

across the coterminous United States. In our “GRID” 

approach, we started with the data in its most disaggregated (and nonhomogeneously defined) format, and 

then we used kriging (Robertson 2000) to spatially 

interpolate observations onto a uniform grid 

across the country (the reader is referred to 

Cressie [1991], Ripley [1981], and Haining 

[1990] for a detailed discussion of kriging 

and geostatistical techniques in general). This 

approximated the more equitable sampling 

scheme that would have been desirable in 

the first place. Using this intermediate step 

increases the possibility of information loss 

from smoothing or averaging in the interpolation process but creates a much more 

homogenous spatial density of observation 

units than counties.

The results of the test in Hof and others 

(1999a, b) suggested statistical advantages 

in the GRID approach, and it has two particular advantages in this study. First, in the classification tree 

method, goodness of fit is largely based on counts of 

misclassified observations. If the observations are of 

heterogeneous size then these goodness of fit measures 

are more difficult to interpret and are less reliable. 

Second, the interpretation of the classification tree and 

the definition of the hotspots projected with it are much 

more straightforward if the observations are of uniform 

size. We did experiment with the COUNTY approach 

in CART and found that the indicated performance was 

greatly superior with the GRID approach.

In Hof and others (1999a,b), we maintained approximately the same number of observations in the 

GRID approach as there were in the COUNTY approach, because we did not want to inflate the statistical 

hypothesis tests by artificially increasing sample size. 

In this case, however, the nonparametric model is not 

influenced by sample size, and we wanted to retain as 

much detail as possible (especially in the parts of the 

country with small counties). Thus, the analysis grid 

that we implemented contained 17,000 cells, which 

resulted in a cell size (21,376.5 meters square or 

45,695.5 ha) that approximated the 5 percent quantile 

level of county sizes (only 5 percent of the counties 

in the coterminous United States are smaller than our 

cell size).

Database Development____

The theoretical production structure discussed 

above suggests that indicators of forest and rangeland 

condition should be related to measures (including surrogate measures) of land use, land ownership, climate 

Yes No

No

No No

No Yes

Yes

Yes

Yes

Hotspot Urb>5%

Hotspot Non-hotspot Hotspot Non-hotspot

Non-hotspot

Pop/acre>.06?

Elev Var<50%?

Urb>10%?

Agr>85%?

Figure 1. An Example Classification Tree.
USDA Forest Service RMRS-GTR-166. 2006. 5

variables, topography variables, human population 

levels, economic activity levels, and commodity harvest 

levels. For purposes of projecting hotspots, however, 

projections of these explanatory variables are needed, 

and geographically specific projections are only available for land use/land cover variables (Alig and others 

2003) and for human population (Woods and Poole 

Economics, Inc. 2003). In addition, we assumed that 

climate, topography, and land ownership variables are 

constant during the projection period (to 2025). We 

limited the variables in the projection model to these, so 

as to maximize the explanatory power of the variables 

that we have projections for. Including explanatory variables that cannot be tenably projected has the potential 

to compromise the power of the explanatory variables 

that we do have projections for and also to create a 

scenario among the explanatory variable projections 

that is internally inconsistent. This points up the most 

critical weakness of projection models—the projection 

of the response variable is only as good as the projections of the explanatory variables that are available. 

Of course, the question remains: are these explanatory 

variables sufficient for the classification tree to be able 

to generate tenable hotspot projections? Basically, the 

hypothesis is that population, land ownership, and the 

land use/land cover variables capture the basic pattern 

of human activity and that the climate and topography 

variables capture the environmental context. Our CART 

estimations will provide a test of this hypothesis below. 

We should note that the available projections of the 

explanatory variables are defined on a county basis, so 

they needed to be translated onto our uniform grid by 

spatially interpolating (kriging) from county centroids 

to grid centroids.

The database includes 10 response variables representing forest and rangeland conditions. Based on 

the previous discussion, we included 21 explanatory 

variables including seven measures of human activity 

(including six land use/land cover types and human 

population), two variables that relate to federal/nonfederal ownership, a categorical variable to account for 

ecoregional differences, and 11 measures of climate and 

topographic variation. All of these were included in 

model estimation, and then the ownership, ecoregional 

strata, and climate and topographic variables were assumed constant in the projection of the hotspots.

All of these variables are defined, with sources, in Appendix A. Again, only the coterminous 

United States is included. Data were obtained from 

numerous sources in formats ranging from digital spatial 

databases to highly aggregated county data and micro 

data with many sample points. Most of the source databases included many variables and individual variables 

were often reported in multiple temporal and spatial 

dimensions. Thus, extraction was a significant undertaking. The digital data required geospatial analysis 

using a Geographic Information System (GIS) and 

other data had to be reformatted or otherwise processed 

and synthesized to be consistent with the data structure 

requirements of the analytical approach. This processing was not trivial because of the spatial scope of the 

analysis, the wide range of the variables of interest, 

and the sheer magnitude and complexity of many of 

the source databases. Data gaps were particularly problematic because the threshold for rejecting variables 

on the grounds of insufficient coverage is not clear. 

Interpolations and other estimations were necessary 

to ensure data completeness of the variables included. 

As indicated earlier, kriging was used to extrapolate 

micro level data to the grid structure and to interpolate 

county level data to the grid structure.

Standardization involved four kinds of processing. First, for county data, there were some data with 

observations for different parts of a given county that 

had to be averaged or apportioned to obtain a single 

county observation. Second, spatial standardization to 

a common land area (acres) was often required because 

counties and other spatial reporting areas (for example, 

watersheds) vary greatly in size. Third, temporal standardization was also necessary for data that varied over 

time (climate data for example). Fourth, much of the data 

had to be standardized to analysis grid cells in a GIS 

geospatial analysis involving the calculation of “zonal 

means” (where the analysis grid cells were defined as 

the zones).

Georeferencing, the geographic location of data, was 

another necessary processing step. An identifier was 

assigned as a unique variable for each data observation 

representing either a land area or specific point on the 

ground. For example, FIPS codes identify the county 

data and each observation in the grid data is identified 

by a geographic coordinate (point of latitude and longitude) for the cell centroid. These centroids represent 

the observation units in the grid model structure.

The sources and processing procedures are unique 

to each variable and, again, are summarized in Appendix A for each variable included in the analysis. All 
6 USDA Forest Service RMRS-GTR-166. 2006.

database files and documentation are stored at the Rocky 

Mountain Research Station, Fort Collins, Colorado.

Model Estimation and 

Results_________________

Classification trees were estimated for each of the 

10 forest and rangeland condition indicators using the 

procedures in Breiman and others (1984). These trees 

attempt to correctly classify the current hotspots from the 

database just described. Table 1 presents the basic measures of goodness of fit for this nonparametric estimation 

procedure. Overall, the results are fairly encouraging. 

The total classification errors in the estimation of the 

trees (the LEARNING data errors) are typically less than 

10 percent, and the TEST data errors typically total less 

than 12 percent. The TEST data errors are based on a 

10-fold cross-validation test, as mentioned earlier (see 

Breiman and others 1984; Steinberg and Colla 2000). 

In terms of model error, our main concern is the false 

hotspot errors, because of the possibility that they would 

influence the hotspot projections. In a later section, we 

will plot the false hotspot cells from the CART estimation and compare them to the actual current hotspots. 

Overall, there is typically a spatial association between 

the actual hotspots and the false hotspot errors. This suggests that when the classification trees make systematic 

errors, it is on the margin of the hotspot areas (which is 

what one might hope for).

The results in table 1 suggest that the variables included in the model (for projection purposes) do appear 

to be adequate to provide a fairly accurate classification 

of current hotspots with the CART algorithm. Thus, for 

our purposes here, we will tentatively accept the hypothesis that these explanatory variables adequately provide 

for a classification of analysis grid cells into hotspot and 

non-hotspot groups for the indicator variables included. 

Caveats regarding our projections based on this model 

will be emphasized in the Conclusion.

Projection of Explanatory 

Variables________________

Using the classification trees to project changes in 

forest and rangeland condition indicators over time required the projection of selected explanatory variables. 

FED and PAD were assumed to be constant because 

RPA assessments in the past have concluded that ownership patterns for forest and rangeland are expected to 

change little over the projection period (USDA Forest 

Service 1989). The climate and topography variables 

were assumed to be constant (recognizing that we 

are ignoring the possibility of global climate change 

within the projection time period). Projections to the 

year 2025 were developed for the land use/land cover 

and human population variables: RNG, CRO, FOR, 

DEV, PAS, CRP, and POP (described in Appendix A). 

Projections for the land use/cover variables were based 

on projections from Alig and others (2003). These 

projections were county-based and only applied to nonfederal land. Land uses on federal land were assumed 

to be constant throughout the projection period based 

on the findings of recent RPA assessments of land use 

and land cover changes (USDA Forest Service 1989; 

2001). County-level projections for population (POP) 

were obtained from Woods & Poole Economics, Inc. 

Table 1. Goodness of fit statistics for classification trees.

Test data % error Learning data % error

No. of False False False False

Indicator terminal nodes Non-hotspot Hotspot Total Non-hotspot Hotspot Total

EDG 97 14.29 13.42 13.51 5.24 11.96 11.29

PCH 70 13.47 10.25 10.57 5.88 9.47 9.11

EXT 75 4.47 2.45 2.65 0.12 1.90 1.72

MOR 101 7.71 3.73 4.13 0.70 2.74 2.54

GRO 78 7.20 6.13 6.24 0.91 5.37 4.92

STR 149 15.82 11.29 11.75 2.59 10.56 9.76

NTG 213 17.12 7.35 8.32 1.47 5.88 5.44

PHO 188 19.00 9.27 10.25 2.47 8.74 8.11

PHL 81 3.18 2.82 2.86 0.12 2.62 2.19

TRI 68 6.06 10.00 9.61 1.18 7.55 6.91
USDA Forest Service RMRS-GTR-166. 2006. 7

(2003) an independent corporation that specializes in 

long-term county economic and demographic projections. All of the explanatory variable projections (that 

change) are mapped in Appendix B.

Projection of Forest and 

Rangeland Condition 

Indicators_______________

The classification tree is used to project hotspots for 

each indicator by inserting the projected values of the 

explanatory variables and then reclassifying all cells 

accordingly, as previously discussed. For each indicator, we report three maps: (a) the current condition 

hotspots, (b) the false hotspot errors from the estimation 

of the CART model, and (c) the CART model hotspot 

projections for the year 2025. The false hotspot errors 

are areas where the CART model classified cells as current hotspots, but they are actually non-hotspots (these 

errors were tabulated in table 1). These errors may be 

pure model errors, or they could reflect areas that are 

“very nearly” hotspots.

The false hotspot error maps are important to consider when viewing the CART projections, because 

the projections inevitably include model error (and the 

only indication we have of those are the false hotspot 

errors for the current time period). It is impossible to 

determine if any false hotspot errors that appear in the 

projections occur because of pure model error or because 

they are currently trending toward being hotspots. An 

area that is erroneously predicted by CART to be a current hotspot might be correctly projected to be a future 

hotspot by the same model, and vice-versa. Such is the 

nature of statistical projection modeling. Clearly, the 

hotspots discussed should be regarded as candidates for 

further study—our analysis is suggestive, not definitive 

in projecting trends.

Individual Indicator Hotspot 

Projections______________

Edge (EDG)

As the amount of native habitat is reduced, there 

comes a point when the arrangement of that habitat 

becomes critically important to maintaining ecosystems 

(Saunders and others 1991; Muradian 2001). Edge effects are thought to be a predominant factor affecting 

the structure and function of ecological systems (Harrison and Bruna 1999). Although most of the empirical 

literature has focused on documenting edge effects 

at local scales, there is accumulating evidence that 

the influence of edges can be wide-ranging, affecting 

ecosystem properties over broad geographic regions 

(Laurance 2000).

Current condition hotspots for the amount of edge 

between native habitats and human-dominated land uses 

are generally scattered across the country, with some 

concentrations in the eastern half of the country (figure 

2a). The false hotspot errors from the CART model 

(figure 2b) are also more concentrated in the east and do 

tend to be located on the fringes of the current hotspot 

concentrations. The CART model projects significant 

increases in the hotspot areas by 2025, with new hotspot 

areas being very much concentrated in the eastern half 

of the country (figure 2c).

Patch Size (PCH)

Another attribute of habitat arrangement that affects 

ecosystem structure and function is the size of patches 

on a landscape. One of the most obvious, and nearly 

ever-present, effects of habitat destruction is a reduction in average patch size (Bender and others 1998). 

Reductions in the average size of patches can compound 

the effects associated with a loss of habitat (Flather 

and Bevers 2002) stemming from a reduced capacity 

to support biological diversity, altered disturbance regimes, and subtle changes to microclimate (Saunders 

and others 1991).

Current concentrations of low average patch size for 

native habitats are fairly clustered in the upper Midwest 

and Great Lake states (figure 3a). Peninsular Florida, the 

Gulf coast, and the entire West also exhibit some smaller 

current condition hotspot areas. The false hotspot errors 

from the CART model (figure 3b) are quite scattered but 

still appear to be associated spatially with the current 

hotspots. The CART model projects increases in the 

PCH hotspots by 2025, located similarly to the current 

condition hotspots (figure 3c). The increases seem particularly noticeable in Florida, California, Arizona, New 

Mexico, and the Midwest from Texas north to North 

Dakota and east to Ohio.
8 USDA Forest Service RMRS-GTR-166. 2006.

Upper 10 percent by area

Figure 2. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Hotspots to 2025 

for EDG. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the EDG values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

False hotspot errors

Projected hotspots
USDA Forest Service RMRS-GTR-166. 2006. 9

Figure 3. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Hotspots to 2025 

for PCH. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the PCH values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
10 USDA Forest Service RMRS-GTR-166. 2006.

Figure 4. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Hotspots to 2025 

for EXT. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the EXT values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
USDA Forest Service RMRS-GTR-166. 2006. 11

Figure 5. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Hotspots to 2025 

for MOR. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the MOR values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
12 USDA Forest Service RMRS-GTR-166. 2006.

Figure 6. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Hotspots to 2025 

for GRO. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the GRO values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
USDA Forest Service RMRS-GTR-166. 2006. 13

Figure 7. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Projected Hotspots to 

2025 for STR. The Current Condition Hotspots are the grid cells with 

the worst 10 percent of the STR values in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
14 USDA Forest Service RMRS-GTR-166. 2006.

Figure 8. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Hotspots to 2025 

for NTG. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the NTG values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
USDA Forest Service RMRS-GTR-166. 2006. 15

Figure 9. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Hotspots to 2025 

for PHO. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the PHO values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
16 USDA Forest Service RMRS-GTR-166. 2006.

Figure 10. (a) Current Condition Hotspots, (b) False Hotspot Errors from 

the CART Model, and (c) Projected 

CART Model Hotspots to 2025 

for PHL. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the PHL values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
USDA Forest Service RMRS-GTR-166. 2006. 17

Figure 11. (a) Current Condition 

Hotspots, (b) False Hotspot Errors from the CART Model, and (c) 

Projected CART Model Hotspots to 

2025 for TRI. The Current Condition 

Hotspots are the grid cells with the 

worst 10 percent of the TRI values 

in the current data set, the False 

Hotspot Errors are the grid cells 

that the CART model incorrectly 

classified as hotspots based on 

the current values for the independent variables, and the Projected 

Hotspots are the grid cells that the 

CART model classified as hotspots 

based on the projected values of the 

independent variables.

Upper 10 percent by area

False hotspot errors

Projected hotspots
18 USDA Forest Service RMRS-GTR-166. 2006.

Exotic Breeding Birds (EXT)

A common observation in the ecosystem stress 

literature is that as resource development intensifies 

there is typically an increase in the abundance of exotic (non-native) species that tolerate human activity 

(Rapport and others 1985; Pimentel and others 2000). 

The number of exotic individuals compared to the total 

number of individuals within some taxonomic group 

has been proposed as a useful indicator of ecosystem 

health (National Research Council 2000) and has been 

used recently with bird monitoring data as a broad-scale 

indicator of ecosystem condition (Hof and others 1999a; 

Sieg and others 1999).

The proportion of exotic species currently comprising 

the bird community is especially prominent throughout 

the upper Midwest (figure 4a). There are some smaller, 

isolated current condition hotspots in the mid-Atlantic 

region, Texas, Idaho, southern California, and the Pacific 

Northwest. The false hotspot errors from the CART 

model (figure 4b) closely surround the current EXT hotspots. The CART model projections (figure 4c) indicate 

stability in the large Midwestern hotspot, a reduction 

in the hotspot area in the mid-Atlantic region, and new 

hotspots in Colorado, Kansas, Oklahoma, and Texas.

Timber Mortality (MOR)

A less well-studied aspect of ecosystem condition 

indicators involves the effect of various stressors on 

disease incidence (Rapport and others 1985). One group 

of host organisms where there has been a number of 

studies examining the relationship between stress and 

disease is trees (Manion 1981). The ultimate stress that 

predisposes a tree to death may be quite different from 

the proximate mortality factor. For example, Dahlsten 

and Rowney (1980) found that air pollution rendered 

ponderosa pine (Pinus ponderosa) more vulnerable to 

insect infestation. Our interest here is not to attribute 

cause to increasing tree mortality, but to simply use the 

mortality rate as an indicator of some undefined stress 

or set of stresses. Because the data for this indicator are 

limited to timberland, we excluded those counties that 

had no timberland (light gray areas in figures 5a-c). 

Timberland is defined as “Forest land that is producing 

or capable of producing in excess of 20 cubic feet per 

acre per year of wood at culmination of mean annual 

increment...” and is not withdrawn from timber utilization by statute or administrative regulation (Bechtold 

and Patterson 2005:84).

High mortality rates for timberland currently occur 

in four distinct areas throughout the country (figure 5a). 

Notable areas of clustering occur in the Great Plains, 

the coastal plain and piedmont regions of South Carolina and North Carolina (and a bit in Georgia), New 

Mexico, and the interior West. The ultimate cause of 

the increased mortality is likely very different in each of 

these hotspots. The false hotspot errors from the CART 

model (figure 5b) are closely associated with the current hotspots. The CART model projections (figure 5c) 

indicate a strong reduction in the New Mexico hotspot, 

a diffusion of the Interior West hotspot, a concentration 

of the Great Plains hotspot, and some diffusion of the 

Southeastern hotspot. Small new hotspots are projected 

in the Gulf Coast region of Texas, Arizona, California, 

and Florida

Timber Growth (GRO)

When under stress, trees alter their normal pattern of 

carbon allocation by decreasing stem growth (Waring 

1987). Consequently, reductions in tree growth will often 

precede a detectable change in tree mortality. Although 

timber growth and mortality rates are expected to be 

correlated, reductions in timber growth may actually 

serve as an early warning sign, relative to timber mortality, of ecosystem stress. Again, because the data for 

this indicator are limited to timberland, we excluded 

those counties that had no timberland (light gray areas 

in figures 6a-c).

The results for GRO (in figures 6a-c) are quite 

similar to those discussed for MOR (note that GRO is 

defined as 1 minus the ratio of actual growth to potential 

growth). The current condition hotspots (figure 6a) are, 

if anything, slightly more diffuse than timber mortality 

with additional hotpots in the Northeast, Northwest, 

and California. The false hotspot errors from the CART 

model (figure 6b) are generally associated with the current hotspots but the errors are scattered across the East 

Coast and the West. The CART projections (figure 6c) 

indicate a scattering of the hotspots across the West, 

some expansion of the Great Plains and the Northeast 

hotspots, and essentially a disappearance of the hotspot 

in South Carolina.

Negative Deviations From 

Mean Streamflows (STR)

Human alteration of land cover has obvious direct 

effects on terrestrial systems. Those alterations coupled 
USDA Forest Service RMRS-GTR-166. 2006. 19

with increasing human populations also affect aquatic 

systems through their influence on the hydrologic cycle. 

Clearing of land for agriculture or urban development 

can increase the amount of water reaching water courses 

by reducing transpiration, interception, and infiltration 

(Arnold and Gibbons 1996; Goudie 2000; Weng 2001); 

however, we assume that the effects of concern from agricultural development and human population growth are 

the reductions in stream flows that result from increased 

water withdrawals (Brown 2000).

The current condition hotspots for STR (figure 7a) are 

spread across the western half of the country, with a few 

areas in the Southeast. The false hotspot errors from the 

CART model (figure 7b) are generally associated with 

the current hotspots but the errors are rather extensive 

in some areas. The projections from the CART model 

(figure 7c) indicate some expansion of the hotspots in 

the West, as well as new hotspots in the Great Lakes 

states and the far Northeast. The projected hotspots 

in the Southeast shift somewhat relative to the current 

condition hotspots, and the hotspots in Texas all but 

disappear.

Total Nitrogen in 

Surface Waters (NTG)

In disturbed systems there is often a loss of nutrients 

through leaching and soil erosion (Likens and others 

1978). Chemical cycles become leaky causing elevated 

nutrient delivery and accumulation in aquatic systems 

(Rapport and others 1985; Magdoff and others 1997; 

National Research Council 2000). Furthermore, nutrient 

loads in aquatic systems can become elevated due to 

direct input from sewage, animal waste, and synthetic 

fertilizer application (Goudie 2000). Nitrogen is an 

important element in plant and animal nutrition, but elevated levels can have detrimental effects on ecosystem 

function and human health (National Research Council 

2000). We use a measure of total nitrogen in surface 

waters (also called total Kjeldahl nitrogen) as described 

in Mueller and others (1995:7).

Current areas of high total nitrogen levels (figure 8a) 

appear diffusely across the country with some spatial 

clustering in the Interior West, the Midwest, and the 

arid Southwest. The false hotspot errors from the CART 

model (figure 8b) are generally associated with the 

current hotspots but the errors are also rather scattered 

in some areas. The CART model projections (figure 

8c) indicate intensification of stress in the Midwest, 

the Pacific Northwest, the Southwest, and the far East. 

Texas and Nebraska exhibit particularly large hotspot 

expansions.

Total Phosphorous in 

Surface Waters (PHO)

Phosphorous concentrations are another common 

indicator of water quality (O’Neill and others 1997) that 

can alter ecosystem condition through their accelerating effect on eutrophication (Goudie 2000). Elevated 

phosphorous levels are caused by many of the same 

agents as nitrogen—namely sewage, animal waste, fertilizers, and detergents. Because of the shared origins, 

phosphorous levels are expected to be correlated with 

nitrogen levels. However, there are reasons why these 

two nutrients are not necessarily redundant indictors of 

water quality. Phosphorous is less mobile than nitrogen, 

adhering strongly to soil constituents. Consequently, 

phosphorous levels in surface waters are affected by 

the type, texture, and level of organic matter in the soil 

(National Research Council 2000). We use a measure of 

total phosphorous (mg of P/L) as described in Mueller 

and others (1995:7).

Areas of high total phosphorous currently appear 

diffusely across the country (figure 9a). There is some 

geographic similarity with nitrogen—namely concentration of analysis grid cells supporting high levels of 

phosphorous in the upper Midwest, the Southwest, the 

Interior West, and the Gulf Coast. The false hotspot 

errors from the CART model (figure 9b) are generally 

associated with the current hotspots but the errors show 

some general scattering as well. The projections from 

the CART model (figure 9c) indicate a dramatic increase 

in stress in the Midwest from Texas to the Dakotas. 

Increases are also evident in the Southeast and Utah, 

with decreases in hotspot area in the arid Southwest. 

The hotspot in Maine also disappears.

Ph in Precipitation (PHL)

Chemical compounds can also impact ecosystem 

condition through the process of atmospheric deposition. Increased acidification of precipitation (higher 

loads of H+) is prominent among these concerns (Likens and Bormann 1974). Sulphur oxides and nitrogen 

oxides from fossil-fuel combustion are the primary 

compounds causing increased acidity in precipitation, 

and its effects on the structure and function of ecosystems are potentially widespread—causing, among other 

things, accelerated leaching of soil nutrients, increased 
20 USDA Forest Service RMRS-GTR-166. 2006.

solubility of toxic heavy metals, reductions in fish and 

algae diversity, slower growth in forests, and reduced 

seed germination (Goudie 2000).

The current condition hotspots for PHL (figure 10a) 

are essentially restricted to the Northeast. The false 

hotspot errors from the CART model (figure 10b) are 

on the margin of the current hotspot cluster but are 

quite noticeable in Maine, Illinois, and the Great Lakes 

States. The projections from the CART model (figure 

10c) indicate a continuance of this area of stress in the 

Northeast, with some expansion into the areas identified as false hotspot errors in figure 10b. Whether this is 

model error or a tenable projection is difficult to assess 

(as discussed previously).

Total Toxic Chemical Releases 

to the Environment (TRI)

Chemical pollutants can take many forms. Previous 

indicators have been targeted to a restricted set of compounds. This indicator attempts to capture changes in 

ecosystem condition caused by a much broader suite of 

contaminants. The Toxics Release Inventory contains 

information on the releases of nearly 650 chemical 

categories to the air, water, and land. Because of the 

diversity of contaminants and their widely varying toxicities, it is difficult to outline generally the ecosystem 

effects stemming from these pollutants.

The current condition hotspots for TRI (figure 11a) 

are spread throughout the eastern half of the country 

(with notable exceptions in the far Northeast and Southeast). There is also a large current hotspot in Utah. The 

false hotspot errors from the CART model (figure 11b) 

are generally associated with the current hotspots but the 

errors also appear in a few areas quite separated from the 

current condition hotspots. The CART model projections 

(figure 11c) indicate a significant intensification of the 

hotspots in the East, as well as many new (and smaller) 

hotspot areas in the West.

Overlays of the Projected 

Hotspots________________

Figure 12 presents an “overlay” of the individual indicator projected hotspots. By this, we mean that figure 

12 shows the number of hotspots that were projected to 

occur in each grid cell across the country. Moving from 

east to west, the first noteworthy area of projected hotspot 

Figure 12. Overlay of 

Projected CART Model 

Hotspots to 2025.
USDA Forest Service RMRS-GTR-166. 2006. 21

concentration is on the Atlantic seaboard, running from 

South Carolina, through the North Carolina piedmont, 

to Massachusetts. The most expansive concentration 

of projected hotspots includes a broad area through the 

Midwest extending from Ohio in the east, to the till plains 

of Iowa and western Nebraska in the west, south into Kansas, and north into Minnesota and South Dakota. Three 

much smaller concentrations appear in Texas, around the 

Houston area, the Dallas-Fort Worth area, and in western 

Texas around Lubbock and Amarillo. Additional areas 

with high concentrations of projected hotspots include 

the Front Range of Colorado, the Wasatch Range of Utah, 

the area around Las Vegas, and southern California. Additional areas with two or three projected hotspots are 

located throughout the West, the Northeast, Florida, and 

the large area south of the Great Lakes states.

The overall picture in figure 12 is very different from 

that depicted in Hof and others (1999a, b). We attribute 

this to at least three factors. First, the hotspots in Hof 

and others (1999a, b) were defined as those grid cells 

that had the greatest degree of change in an indicator 

over the projection period. Here, the hotspots are defined according to the actual magnitude of the projected 

condition as it compares to the definition of the current 

condition hotspots. Second, the hotspots in Hof and others (1999a, b) were based on the worst 5 percent whereas 

a 10 percent threshold is used here. Thus, the hotspots 

were defined in a totally different way in the two studies. Third, the indicators in this study are quite different than those in the previous study. In Hof and others 

(1999a, b), the indicators were dominated by threatened 

and endangered (T and E) species indicators, while the 

indicators here include a broader emphasis, including 

such factors as water and air pollution, forest growth and 

mortality, and more general measures of wildlife habitat and community structure. In this sense, the current 

indicators better represent those indicators suggested in 

ongoing efforts to evaluate ecosystem conditions (see for 

example Rapport and others 1985; Coulombe 1995; National Research Council 2000). Clearly, we cannot draw 

any conclusions from comparing the results of the two 

studies. Looking only at the 10 indicators in this GTR, 

however, it is clear that different indicators paint a different picture of environmental stress. In the individual 

indicator results section, we discuss why each indicator 

might be of some use in assessing environmental stress. 

We would interpret all of our results in light of those 

more specific descriptions. The overlay in figure 12 is 

just intended to provide a summary of the indicators 

analyzed in this study, not as a comprehensive picture 

of environmental stress in general.

Conclusion______________

The RPA directs the Forest Service to prepare broadscaled evaluations of the current status and condition 

of natural resources, and to anticipate future resource 

conditions based on trend projections. One concern in 

meeting this mandate is that evaluation of each resource 

area independently (for example, timber, forage, wildlife, 

and recreation) runs the risk of developing conflicting 

resource management policy—benefiting one resource to 

the detriment of others. This report represents an attempt 

to consider many indicators of resource condition simultaneously for the purpose of identifying those areas of the 

country where ecosystem condition appears problematic. 

We delineated those problem areas based on the coincident occurrence of indicator values defined to represent 

the worst condition and defined these as hotspots.

Classification tree analysis was shown to be potentially useful as a hotspot classification tool. Even when 

limiting the model to explanatory variables that are 

projected at a geographically specific level, the ability 

of CART models to identify areas of relative poor ecosystem condition was still fairly good.

With the projection model, we identified potential hotspots in 10 condition indicators for forests 

and rangelands based on projected changes in land 

use/cover variables as well as human population. We 

avoided any interpretations that suggest actual causation because the methods used identify only patterns 

of association between response and explanatory 

variables, not cause-effect relationships. Thus, these 

potential hotspots should be viewed only as candidates 

for areas of significant change. One policy implication 

that does seem to be fairly tenable is that the projected 

hotspot areas for the indicators included in this study 

do not coincide extensively with National Forest System lands. Thus, addressing the areas of ecosystem 

stress identified in figure 12 will most likely require 

cooperation with state and private land owners. This 

analysis is clearly exploratory in nature, however, and 

all results reported are tentative. Hopefully, the results 

will serve to focus additional study in potentially high 

priority areas, both in terms of research and planning analysis effort. Focused, indicator-by-indicator 

analyses are expected to lead to better understanding 

of the potential causes of these broad-scale patterns of 
22 USDA Forest Service RMRS-GTR-166. 2006.

ecosystem condition. Further research is clearly needed 

in developing the methods for studies such as this one, 

which can serve the purpose of the triage in large scale 

planning efforts.

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24 USDA Forest Service RMRS-GTR-166. 2006.

The database includes 10 response variables representing forest and rangeland conditions. Based 

on the previous discussion, we included 21 explanatory variables including seven measures of human 

activity (including six land use/land cover types and human population), two variables that relate to 

federal/non-federal ownership, a categorical variable to account for ecoregional differences, and 11 

measures of climate and topographic variation. Because of data availability, only the coterminous 

United States is included. The database contains observations on each variable by uniform grid 

cell. There are 17,000 cells in the database, and each cell is approximately 21,376.5 meters square 

(45,695.5 ha) in size. The size of the grid cells was based on a univariate analysis of the range in 

county sizes, such that our grid cell size corresponds to the 5 percent quantile level of county sizes 

(only 5 percent of the counties in the coterminous United States are smaller than our cell size). Our 

17,000-cell grid will be referred to here as the “analysis grid.”

The indicators of forest and rangeland condition were chosen because of their likelihood of being 

affected by human activity, their availability, and their consistency (as possible) with the Montréal 

Process indicators (for a description of Montréal Process indicators see Coulombe [1995]). Data 

were obtained from numerous sources in formats ranging from digital spatial databases to highly 

aggregated county data and micro data with many sample points. Most of the data had to be reformatted or otherwise processed and synthesized to be consistent with the data structure requirements 

of the analytical approach. In the case of micro data with many sampling points, analysis grid cell 

observations were obtained by “kriging” to the cell centroid (see Ripley [1981]; Haining [1990]; 

Cressie [1991] for a detailed discussion of kriging and geostatistical techniques in general).

The data for this analysis is described, with sources, below:

Variable	 Description	 Source

Condition

Indicators:

EDG A measure of total linear edge between Edge-based measure of fragmentation, 2002:

natural land cover (forest, shrub, grass) Data made available on CD-ROM in ASCII

and anthropogenic land use (developed, and SAS format / prepared by USDA Forest

disturbed, agriculture) within a 7.5 km x Service, Research Triangle Park, NC: Forestry

7.5 km square analysis unit gridded at Sciences Lab, Forest Health Monitoring (SRS30 m resolution. In a GIS procedure, the 4803), 2002.

analysis grid cells were defined as “zones” 

and zonal means of total linear edge were Fragmentation databases will be available at 

calculated based on the 30 m resolution http://www.srs.fs.usda.gov/4803/landscapes/in

cells that fell in each zone. The broad land dex.html.

cover classes are defined as follows: forest 

includes deciduous, evergreen, mixed, and See also Riitters (2004).

woody wetland; shrub includes shrubland; 

grass includes grasslands/herbaceous and 

herbaceous wetland; developed includes 

low intensity residential, high intensity 

residential, commercial/industrial/

transportation, and urban/recreational 

Appendix A: Variable Descriptions and Data 

Sources
USDA Forest Service RMRS-GTR-166. 2006. 25

grasses; disturbed includes bare rock/sand/

clay, quarries/strip mines/gravel pits, and 

transitional; agriculture includes row crops, 

small grains, pasture/hay, fallow, and 

orchards/vineyards/other.

PCH A measure of average patch size (area Patch-based measure of fragmentation, 2002: 

weighted average) within a 7.5 km x 7.5 km Data made available on CD-ROM in ASCII

square analysis unit gridded at 30 m resolution. and SAS format, prepared by USDA Forest 

These analysis units were overlaid on land- Service, Research Triangle Park, NC: Forestry 

cover maps for the lower 48 states from the Sciences Lab, Forest Health Monitoring (SRSNational Land Cover Data (NLCD) database. 4803), 2002.

A patch was defined as a block of contiguous 

(in the 4 cardinal directions) natural land Fragmentation databases will be available at 

cover (forest, shrub, grass). The average http://www.srs.fs.usda.gov/4803/landscapes/in

patch size for each analysis unit was estimated dex.html.

for the land cover type that was expected to 

be the potential natural vegetation within the See also Riitters (2004) and Küchler (1993).

analysis unit based on an overlay of the analysis 

unit grid with a Küchler PNV coverage 

(Küchler 1964). In a GIS procedure, the 

analysis grid cells were defined as “zones” 

and zonal means of average patch size were 

calculated based on the 30 m resolution cells 

that fell in each zone. The actual measure used 

in the analysis is 1 minus the ratio of average 

patch size to the maximum possible patch size. 

The broad land cover classes (forest, shrub, 

and grass) are as defined in EDG except that 

grass patches do not include herbaceous wetlands.

EXT Exotic breeding birds. Proportion of total exotic North American Breeding Bird Survey (BBS), 

individuals to total individuals (exotic + native) USGS Patuxent Wildlife Research Center,

for each BBS route averaged over the years the Laurel, MD. 2001. 

route was run between 1990 – 1998, then kriged http://www.pwrc.usgs.gov/bbs/retrieval/menu

to analysis grid cell centroids. .cfm.

MOR Mortality of growing stock on timberlands in National RPA Forest Data Base, 1997: Data 

1000 cu. ft./acre/yr standardized to volume of made available in ASCII file format, prepared

growing stock on timberlands in 1000 cu. ft./ by USDA Forest Service, Asheville, NC: Forest

acre/yr by county, then kriged to analysis grid Inventory and Monitoring (SRS-4801), 1999.

cell centroids.

USDA Forest Service. Forest Inventory and 

Analysis (FIA). 1997 National RPA Forest Data 

Base (Eastwide /Westwide format). http://srs

fia2.fs.fed.us/html/tables.htm.

See also Hansen and others (1992) and 

Woudenberg and Farrenkopf (1995).
26 USDA Forest Service RMRS-GTR-166. 2006.

GRO Measure of actual productivity on timberlands National RPA Forest Data Base, 1997: Data 

(growth in cu. ft./acre/yr) divided by potential made available in ASCII file format, prepared 

productivity on timberlands. Potential by USDA Forest Service, Asheville, NC: Forest

productivity is also in cu. ft./acre/yr and is Inventory and Monitoring (SRS-4801), 1999.

calculated based on site productivity class. 

The actual measure used in the analysis is USDA Forest Service. Forest Inventory and 

1 minus this proportion. This data is recorded Analysis (FIA). 1997 National RPA Forest Data

by county and was kriged to analysis grid cell Base (Eastwide /Westwide format). http://srs

centroids. fia2.fs.fed.us/html/tables.htm.

See also Hansen and others (1992) and 

Woudenberg and Farrenkopf (1995).

STR The mean yearly streamflows (in cfs) in the USGS daily and peak values data compiled on

1990’s and the 30-year period 1960 to 1989 CD-ROM by Hydrosphere Data Products, Inc. 

were standardized to drainage areas in sq. Boulder, CO. 1999. http://www.hydrosphere.

mi. with 6826 and 1240 sample points, com/hdp/.

respectively. Both of these values were 

kriged to analysis grid cell centroids. Then, USGS Fact Sheet FS-027-98. National Water 

the 30-year averages were divided into the Information System (NWIS). http://pubs.usgs.

streamflows from the 1990s. The actual gov/fs/FS-027-98/.

measure used in the analysis is 1 minus this 

proportion. See also Yorke and Williams (1991).

NTG Total nitrogen measured as mg/L in surface STORET (mainframe) Water Quality File (now

waters. Kriged to analysis grid cell centroids STORET Legacy Data), 1998: Nutrient

from 34,131 sample point locations. parameter files obtained from USEPA

anonymous ftp site, prepared by staff of the 

USEPA, Washington, DC: USEPA Data Storage 

and Retrieval System (STORET), 1999.

USEPA, Office of Water, STORET Legacy Data

Center (USGS data removed), 1999. http://

www.epa.gov/storpubl/legacy/gateway.htm.

USEPA, STORET, About STORET, 2002. 

http://www.epa.gov/STORET/about.html.

USGS, National Water Information System 

(NWISWeb), Water-Quality Data for the Nation,

1999. http://waterdata.usgs.gov/nwis/qw.

USGS Fact Sheet FS-027-98. National Water 

Information System (NWIS). http://pubs.usgs.

gov/fs/FS-027-98/.

See also Yorke and Williams (1991).

PHO Total phosphorus measured as mg/L in surface STORET (mainframe) Water Quality File (now

waters. Kriged to analysis grid cell centroids STORET Legacy Data), 1998: Nutrient 

from 45,283 sample point locations. parameter files obtained from USEPA anony

mous ftp site, prepared by staff of the USEPA, 

Washington, DC: USEPA Data Storage and 

Retrieval System (STORET), 1999.
USDA Forest Service RMRS-GTR-166. 2006. 27

USEPA, Office of Water, STORET Legacy Data 

Center (USGS data removed), 1999. http://

www.epa.gov/storpubl/legacy/gateway.htm.

USEPA, STORET, About STORET, 2002. 

http://www.epa.gov/STORET/about.html.

USGS, National Water Information System 

(NWISWeb), Water-Quality Data for the Nation,

1999. http://waterdata.usgs.gov/nwis/qw.

USGS Fact Sheet FS-027-98. National Water 

Information System (NWIS). http://pubs.usgs.

gov/fs/FS-027-98/.

See also Yorke and Williams (1991).

PHL Lab pH. Calculated using a 2500 m grid of National Atmospheric Deposition Program 

hydrogen ion concentrations in precipitation (NADP) Maps, 1998: ARC/INFO grids of lab 

collected at field locations and measured in the pH obtained from NADP anonymous ftp site, 

lab. In a GIS procedure, the analysis grid cells prepared by NADP Program Office, 

were defined as “zones” and zonal means of Champaign, IL: Illinois State Water Survey. 

hydrogen ion concentrations were calculated 2000.

based on the 2500 m resolution cells that fell 

in each zone. The hydrogen ion concentrations National Atmospheric Deposition Program 

were then converted to pH. The actual measure (NRSP-3) National Trends Network. 1998. 

used in the analysis was the observed average NADP Program Office, Illinois State Water

pH subtracted from the value 7 (all observations Survey, 2204 Griffith Dr., Champaign, IL 

were less than 7). 61820. http://nadp.sws.uiuc.edu/.

TRI Total toxic chemical releases to the environment USEPA, Toxics Release Inventory (TRI), EZ 

for each county (air, water, and land). Measured Query, 1997. http://www.epa.gov/enviro/html/

in pounds per acre. These data were kriged to tris/ez.html.

analysis grid cell centroids. 

USEPA, Toxics Release Inventory (TRI) 

Program, What is the Toxics Release Inventory 

(TRI) Program, 2002. http://www.epa.gov/tri/

whatis.htm.

Human

Activity

Variables:

FED Federal land areas as a proportion of total area USGS, Federal Lands and Indian Reservations

by analysis grid cell. In a GIS procedure, a of the United States, 2003. http://nationalatlas.

geographic intersection of a polygon coverage gov/atlasftp.html.

of federal land areas and a polygon coverage 

of the analysis grid enabled the calculation of 

this proportion. The data included only federal 

areas greater than 640 acres. It also did not 

include “linear” areas such as federally 

administered parkways.
28 USDA Forest Service RMRS-GTR-166. 2006.

PAD Protected areas of the U.S. as a proportion of CBI/WWF Protected Areas Database (PAD), 

total area by analysis grid cell. In a GIS Second Edition. 2001. http://www.consbio.

procedure, a geographic intersection of a org/cbi/applied_research/pad_2005/pad2005.

polygon coverage of protected areas and a htm.

polygon coverage of the analysis grid enabled 

the calculation of this proportion. Protected See also DellaSala and others (2001).

areas are defined as parcels with a Gap Analysis 

Program (GAP) code of 1 or 2. The actual 

measure used in the analysis is 1 minus this 

proportion.

RNG Proportion of counties in rangeland. A land Summary Report, 1997 National Resources 

cover/use category on which the climax or Inventory (NRI). See Appendix 3, Glossary of

potential plant cover is composed principally Selected Terms, revised December 2000.

of native grasses, grasslike plants, forbs or 

shrubs suitable for grazing and browsing, and

introduced forage species that are managed like 

rangeland. This would include areas where 

introduced hardy and persistent grasses, such 

as crested wheatgrass, are planted and such 

practices as deferred grazing, burning, chaining, 

and rotational grazing are used, with little or 

no chemicals or fertilizer being applied. 

Grasslands, savannas, many wetlands, some 

deserts, and tundra are considered to be 

rangeland. Certain communities of low forbs 

and shrubs, such as mesquite, chaparral, 

mountain shrub, and pinyon-juniper, are also 

considered as rangeland. These data were 

kriged to analysis grid cell centroids.

CRO Proportion of counties in crops. A land cover/ Same as RNG.

use category that includes areas used for the 

production of adapted crops for harvest. 

Cropland includes cultivated and noncultivated 

lands. Cultivated cropland includes row crops, 

close-grown crops, and other cultivated 

cropland such as hayland or pastureland that is 

in a rotation with row or close-grown crops. 

Noncultivated cropland includes permanent 

hayland and horticultural cropland. These data 

were kriged to analysis grid cell centroids.

FOR Proportion of counties in forest land. A land Same as RNG.

cover/use category that is at least 10 percent 

stocked by single-stemmed woody species of 

any size that will be at least 4 meters tall at 

maturity. Land bearing evidence of natural 

regeneration of tree cover (cut over forest or 

abandoned farmland) and not currently 

developed for nonforest use is also included. 

Ten percent stocking, when viewed from a 
USDA Forest Service RMRS-GTR-166. 2006. 29

vertical direction, equates to an areal canopy 

cover of leaves and branches of 25 percent or 

greater. The minimum area for classification 

as forest land is 1 acre, and the area must be 

at least 100 feet wide. These data were kriged 

to analysis grid cell centroids.

DEV Proportion of counties in developed land. A Same as RNG.

land cover/use category that includes “large” 

(>10 acres) and “small” (.25-10 acres) urban 

and built-up areas. These include residential, 

industrial, commercial, and institutional land; 

construction sites; public administrative sites; 

railroad yards; cemeteries; airports; golf 

courses; sanitary landfills; sewage treatment 

plants; water control structures and spillways; 

other land used for such purposes; small parks 

within urban and built-up areas; and highways, 

railroads, and other transportation facilities if 

they are surrounded by urban areas. Also 

included are tracts of less than 10 acres that do 

not meet the above definition but are completely 

surrounded by urban and built-up land. These 

data were kriged to analysis grid centroids.

PAS Proportion of counties in pastureland. A land Same as RNG.

cover/use category of land managed primarily 

for the production of introduced forage plants 

for livestock grazing. Pastureland may consist 

of a single species in a pure stand, a grass 

mixture, or a grass-legume mixture. 

Management usually consists of cultural 

treatments: fertilization, weed control, 

reseeding or renovation, and control of grazing. 

For the NRI, includes land that has a vegetative 

cover of grasses, legumes, and/or forbs, 

regardless of whether or not it is being grazed 

by livestock. These data were kriged to analysis 

grid cell centroids.

CRP Proportion of counties in the Conservation Same as RNG.

Reserve Program (CRP), a federal program 

established under the Food Security Act of 1985 

to assist private landowners to convert highly 

erodible cropland to vegetative cover for 10 

years. These data were kriged to analysis grid 

centroids.
30 USDA Forest Service RMRS-GTR-166. 2006.

POP Human population density within each county. Census 2000 Summary File 1 United States.

These data were kriged to analysis grid cell Prepared by the U.S. Census Bureau, 2001.

centroids.

Stratification:

DIV A categorical variable that stratifies the Bailey, R. G. 1995. Description of the

coterminous U.S. into 3 ecoregional ecoregions of the United States, 2nd ed. 

classifications based on “divisional” boundaries Revised and expanded (1st ed. 1980). Misc.

from Bailey’s Ecoregions of the United States. Publ. No. 1391 (rev.), USDA Forest Service,

Each analysis grid cell was assigned to an Washington, DC. 108p. with separate map.

ecoregion category with a GIS procedure (using 

a geographic intersection of polygon coverages).

Climate and

Topographic

Variables:

TMP Mean annual temperature. In a GIS Procedure, Oregon State University, Spatial Climate 

analysis grid cells were defined as zones and Analysis Service (SCAS) and Oregon Climate

zonal means of gridded mean annual Service (OCS), PRISM digital climate data,

temperature (30-year normals, 1961-1990) 1996. http://www.ocs.orst.edu/prism/.

were calculated. Measured in Fo . 10. Original 

data was on a 4 km grid such that the zonal Oregon State University, Spatial Climate 

mean was based on ~25 observations. Analysis Service (SCAS) and Oregon Climate

 Service (OCS), Climate Mapping with PRISM,

Reports and Papers. 1993-1998. http://www.

ocs.orst.edu/prism/docs/index.phtml.

TMS Spatial variation in temperature. In a GIS Oregon State University, Spatial Climate 

procedure, analysis grid cells were defined as Analysis Service (SCAS) and Oregon Climate

zones and zonal standard deviations of gridded Service (OCS), PRISM digital climate data,

mean annual temperature (30-year normals, 1996. http://www.ocs.orst.edu/prism/.

1961-1990) were calculated. Original data was 

on a 4 km grid such that the zonal mean was Oregon State University, Spatial Climate 

based on ~25 observations. Analysis Service (SCAS) and Oregon Climate 

Service (OCS), Climate Mapping with PRISM, 

Reports and Papers. 1993-1998. http://www.

ocs.orst.edu/prism/docs/index.phtml.

SST Seasonal variation in temperature. In a GIS Oregon State University, Spatial Climate 

procedure, analysis grid cells were defined as Analysis Service (SCAS) and Oregon Climate

zones and zonal means of gridded mean monthly Service (OCS), PRISM digital climate data, 

temperatures (30-year normals, 1961-1990) 1996. http://www.ocs.orst.edu/prism/.

were calculated. The standard deviation across 

mean monthly temperatures then was used as Oregon State University, Spatial Climate 

a measure of seasonal variation in temperature. Analysis Service (SCAS) and Oregon Climate 

Original data was on a 4 km grid such that the Service (OCS), Climate Mapping with PRISM,

zonal mean was based on ~25 observations. Reports and Papers. 1993-1998. http://www.

ocs.orst.edu/prism/docs/index.phtml.
USDA Forest Service RMRS-GTR-166. 2006. 31

AVT Temporal variation in temperature. VEMAP National Center for Atmospheric Research 

data (historical data, 1895 to 1993) on a 0.5oX (NCAR), Ecosystem Dynamics and the 

0.5o degree grid of latitude and longitude were Atmosphere Section, VEMAP 2 Transient 

used to calculate the standard deviation of 99 Climate Datasets (TCLIMATE), Monthly Files, 

years of mean annual temperature observations Historical Climate (1895-1993). 1998. http://

at each of 3261 lat/lon locations, then kriged www.cgd.ucar.edu:80/vemap/ve298.html.

to analysis grid cell centroids. See also Kittel and others (1997).

PRC Mean annual precipitation. Calculated using Oregon State University, Spatial Climate 

same method as for TMP. Measured in mm. Analysis Service (SCAS) and Oregon Climate 

Original data was on a 4 km grid such that the Service (OCS), PRISM digital climate data, 

zonal mean was based on ~25 observations. 1996. http://www.ocs.orst.edu/prism/.

Oregon State University, Spatial Climate 

Analysis Service (SCAS) and Oregon Climate 

Service (OCS), Climate Mapping with PRISM, 

Reports and Papers. 1993-1998. http://www.

ocs.orst.edu/prism/docs/index.phtml.

PRS Spatial variation in precipitation. Calculated Oregon State University, Spatial Climate 

using same method as for TMS. Original data Analysis Service (SCAS) and Oregon Climate 

was on a 4 km grid such that the zonal mean Service (OCS), PRISM digital climate data, 

was based on ~25 observations. 1996. http://www.ocs.orst.edu/prism/.

Oregon State University, Spatial Climate 

Analysis Service (SCAS) and Oregon Climate 

Service (OCS), Climate Mapping with PRISM, 

Reports and Papers. 1993-1998. http://www.

ocs.orst.edu/prism/docs/index.phtml.

SSP Seasonal variation in precipitation. Calculated Oregon State University, Spatial Climate 

using same method as for SST. Original data Analysis Service (SCAS) and Oregon Climate

was on a 4 km grid such that the zonal mean Service (OCS), PRISM digital climate data, 

was based on ~25 observations. 1996. http://www.ocs.orst.edu/prism/.

Oregon State University, Spatial Climate 

Analysis Service (SCAS) and Oregon Climate 

Service (OCS), Climate Mapping with PRISM, 

Reports and Papers. 1993-1998. http://www.ocs.

orst.edu/prism/docs/index.phtml.

AVP Temporal variation in precipitation. Calculated National Center for Atmospheric Research 

using same method as for AVT. (NCAR), Ecosystem Dynamics and the 

Atmosphere Section, VEMAP 2 Transient 

Climate Datasets (TCLIMATE), Monthly Files, 

Historical Climate (1895-1993). 1998. http://

www.cgd.ucar.edu:80/vemap/ve298.html.

See also Kittel and others (1997).
32 USDA Forest Service RMRS-GTR-166. 2006.

VEG Total vegetation carbon (potential – no land National Center for Atmospheric Research 

use effects). Thirty-year annual average (NCAR), Ecosystem Dynamics and the 

(1961-1990) gC/m2 at 3168 lat/lon locations Atmosphere Section, VEMAP2 Transient 

representing the center of a 0.5oX 0.5o grid, Dynamics Data, distributed by the University of 

then kriged to analysis grid cell centroids. New Hampshire, EOS-WEBSTER Earth 

Science Information Partner (ESIP), dataset 

TResults-CGCM1-Increasing CO2. 2000. http://

eos-webster.sr.unh.edu/data_guides/vemap_

trans_dg.jsp.

See also Schimel and others (2000).

DEM Mean elevation. In a GIS procedure, analysis Oregon State University, Oregon Climate 

grid cells were defined as zones and zonal Service FTP Archive, Conterminous U.S. 

means of gridded elevation data were 2.5-minute Digital Elevation Model (DEM), 

calculated. Measured in meters. 1995. ftp://www.ocs.orst.edu/pub/maps/Other/

U.S./.

See also Barnes (1964)

DMS Elevation variance. In a GIS procedure, Oregon State University, Oregon Climate 

analysis grid cells were defined as zones and Service FTP Archive, Conterminous U.S. 

zonal standard deviations of gridded 2.5-minute Digital Elevation Model (DEM), 

elevation data were calculated. 1995. ftp://www.ocs.orst.edu/pub/maps/Other/

U.S./.

See also Barnes (1964).
USDA Forest Service RMRS-GTR-166. 2006. 33

Appendix B: Mapped Current and Projected 

Explanatory Variables

Figure 13. (a) Current Conditions and 

(b) Projections to 2025 for RNG. 

Classes depicted in the projection 

map (b) have maintained the class 

cutpoints used to define the current 

condition (a).
34 USDA Forest Service RMRS-GTR-166. 2006.

Figure 14. (a) Current Conditions and (b) Projections to 2025 for CRO. Classes depicted in the projection map (b) have maintained the class cutpoints used to define the current condition (a).
USDA Forest Service RMRS-GTR-166. 2006. 35

Figure 15. (a) Current Conditions and (b) Projections to 2025 for FOR. Classes depicted in the projection map (b) have maintained 

the class cutpoints used to define the current condition (a).
36 USDA Forest Service RMRS-GTR-166. 2006.

Figure 16. (a) Current Conditions and (b) Projections to 2025 for DEV. Classes depicted in the projection map (b) have maintained 

the class cutpoints used to define the current condition (a).
USDA Forest Service RMRS-GTR-166. 2006. 37

Figure 17. (a) Current Conditions and (b) Projections to 2025 for PAS. Classes depicted in the projection map (b) have maintained 

the class cutpoints used to define the current condition (a).
38 USDA Forest Service RMRS-GTR-166. 2006.

Figure 18. (a) Current Conditions and (b) Projections to 2025 for CRP. Classes depicted in the projection map (b) have maintained 

the class cutpoints used to define the current condition (a).
USDA Forest Service RMRS-GTR-166. 2006. 39

Figure 19. (a) Current Conditions and (b) Projections to 2025 for POP. Classes depicted in the projection map (b) have maintained 

the class cutpoints used to define the current condition (a).
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United States Department of Agriculture

Forest Service

Rocky Mountain Research Station

General Technical Report

RMRS-GTR-166

April 2006