United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

Research Paper RMRS-RP-84

December 2010

Toward Gleasonian Landscape Ecology: 

From Communities to Species, 

From Patches to Pixels

Samuel A. Cushman, Jeffrey S. Evans, 

Kevin McGarigal, Joseph M. Kiesecker
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Cushman, Samuel A.; Evans, Jeffrey S.; McGarigal, Kevin; Kiesecker, Joseph M. 

2010. Toward Gleasonian Landscape Ecology: From Communities to Species, 

From Patches to Pixels. Res. Pap. RMRS-RP-84. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 12 p.

Abstract

The fusion of individualistic community ecology with the Hutchinsonian niche 

concept enabled a broad integration of ecological theory, spanning all the 

way from the niche characteristics of individual species, to the composition, 

structure, and dynamics of ecological communities. Landscape ecology has been 

variously described as the study of the structure, function, and management of 

large heterogeneous land areas. Any reading of the published landscape ecology 

literature shows near uniformity in the adoption of a categorical patch-mosaic 

paradigm. However, if biological communities are multivariate gradients of 

species composition, with each species responding individualistically to particular 

combinations of limiting factors, is a categorical patch-based representation 

appropriate? We evaluate the sufficiency of several patch-based representations 

of vegetation at the landscape level to explain the composition of the plant 

community. Classified vegetation maps all performed poorly in explaining the 

composition and structure of forest trees among plots. Different categorical 

vegetation maps provided largely independent explanations of species variability. 

Individual species models based on spectral, topographic, and climatic variables 

vastly out-performed those produced using the classified maps. By moving from 

a landscape ecological paradigm based on categorical patches to one based 

on quantitative species and environmental responses across continuous space, 

it will be possible to both produce much more effective predictions of species 

distributions and ecological processes and remove much of the disjunction 

between landscape ecology and mainstream community ecology theory.

Keywords: Vegetation, variance partitioning, categorical maps, gradient theory, 

landscape ecology

The Authors

Samuel A. Cushman is a Research Ecologist with the USDA Forest Service, Rocky 

Mountain Research Station, in Missoula, Montana.

Jeffrey S. Evans is a Senior Landscape Ecologist with The Nature Conservancy—

Central Science, in Fort Collins, Colorado.

Kevin McGarigal is a Professor with the Department of Natural Resources 

Conservation, University of Massachusetts, in Amherst, Massachusetts.

Joseph M. Kiesecker is a Director of Science with The Nature Conservancy—

Central Science, in Fort Collins, Colorado.
CONTENTS

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

Clementsian Landscape Ecology ........................................................ 1

Objectives and Hypotheses .............................................................. 1

Methods............................................................................................ 2

Vegetation Data.............................................................................. 2

Classified Vegetation Maps ............................................................... 3

Independent Variables..................................................................... 4

Hypothesis 1: Variance Partitioning With Redundancy Analysis ................. 4

Hypothesis 2: Predicting Occurrence of Four Major Species 

With Random Forest..................................................................... 4

Results .............................................................................................. 5

Hypothesis 1: Variance Partitioning With Canonical 

Correspondence Analysis .............................................................. 5

Hypothesis 2: Predicting Species Occurrence With 

Random Forests........................................................................... 5

Discussion......................................................................................... 7

Hypothesis 1 ................................................................................. 7

Hypothesis 2 ................................................................................. 9

Categorical Landscape Ecology as Historical Legacy and 

Management Convenience ............................................................ 9

Landscape Structure Is Not Vegetation Type ......................................... 9

Toward Gleasonian Landscape Ecology.............................................. 10

References....................................................................................... 11

Toward Gleasonian Landscape Ecology:

From Communities to Species, From Patches to Pixels

USDA Forest Service RMRS-RP-84. 2010. 1

Introduction_______________

The modern scientific study of ecological communities is often traced to Clements (1907, 1916), 

who posited that the composition of species within a 

community is a deterministic product of regional climate and time since disturbance. Species composition 

within a community was thought to be highly predictable as deterministic functions of regional climate and 

seral condition. However, the Clementsian view of 

communities as analogs to super-organisms was fundamentally challenged by Gleason (1917, 1926), who 

argued that identification of categorical vegetation 

types was inconsistent with the heterogeneous structure of plant communities. He questioned delineating 

patch-mosaic maps of community types and opposed 

grouping of species in nameable associations. As an 

alternative, Gleason offered the individualistic concept 

of the plant association in which “the phenomena of 

vegetation depend completely upon the phenomena of 

the individual” species (Gleason 1917).

This individualistic concept of vegetation ecology 

is the foundation of modern community ecology. The 

fusion of individualistic community ecology (Gleason 

1926; Curtis and McIntosh 1951; Whittaker 1967) 

with the Hutchinsonian niche concept (Hutchinson 

1957) enabled a broad integration of ecological theory, spanning all the way from Darwinian evolution, 

to the niche characteristics of individual species, to 

the composition, structure, and dynamics of ecological communities. Each species is seen responding to 

local environmental and biotic conditions. The biotic 

community in this context is conceived as a collection 

of species that occur together at a particular place and 

a particular time due to overlapping tolerances of environmental conditions, biotic interactions (Lortie and 

others 2004), and vagaries of history, rather than an 

integrated and deterministic mixture. Research in this 

paradigm focuses on extending the individual concept 

to quantitative analysis of species distribution along 

environmental gradients and the effort to quantify the 

fundamental niche of each species in terms of the range 

of resources and conditions needed for that species to 

survive. The natural level of focus of such analyses 

is the species, not community type, assemblage, or 

patch type; the natural focal scale for such analyses 

is the location or pixel, rather than the stand or patch 

(McGarigal and Cushman 2005; Cushman and others 

2007; Cushman and others 2010a).

Clementsian Landscape Ecology

Landscape ecology has been variously described as 

the study of the structure, function, and management 

of large heterogeneous land areas (Forman 1995) or, 

more generally, the study of spatial pattern and process (Turner 1989, 2005). Likewise, landscapes are 

typically described in terms of patches, corridors, and 

matrix (Forman 1995). These definitions explicitly 

frame the scope of landscape ecology within a patchmosaic paradigm. This is a fundamentally Clementsian 

approach, and any reading of the published landscape 

ecology literature shows near uniformity in the adoption of this approach (McGarigal and others 2009). In 

the patch-mosaic paradigm (Forman 1995), each patch 

is implicitly treated like an individual of the super organism of each “patch type,” and it is assumed that by 

measuring the area and configuration of patch types 

we can represent the most important attributes of the 

landscape, including the distribution and abundance of 

plant and animal species.

However, if biological communities are multivariate gradients of species composition, with each species 

responding individualistically to particular combinations of limiting factors, is a categorical, patch-based 

representation appropriate? Put another way, isn’t representing biological communities as categorical patches 

in a mosaic a de facto ratification of a Clementsian 

model of community composition at the landscape 

level? This disjunction between gradient-based, 

individualistic community ecology theory and patchbased, categorical landscape ecology has provoked 

some recent discussion (McIntyre and Barrett 1992; 

Manning and others 2004; McGarigal and Cushman 

2005; Cushman and others 2008; McGarigal and others 2009; Cushman and others 2010c). However, these 

papers have largely been discussions of conceptual arguments and have had limited ability to provide real 

empirical evaluations of the strengths and limitations 

of alternative approaches.

Objectives and Hypotheses

Our goal in this paper is to evaluate the sufficiency of 

several patch-based representations of vegetation at the 

landscape level to explain the composition of the plant 

community. Our basic premise is that if a patch-based 

model of vegetation at the landscape-level actually 

represents the dominant scales and patterns of variation in the plant community, then classified vegetation 
2 USDA Forest Service RMRS-RP-84. 2010.

maps should explain a large portion of the variability 

in the density, basal area, and importance value of the 

tree species that comprise the community. The failure 

of classified vegetation maps to explain the structure 

and composition of the forest community would indicate that other approaches, such as species-based 

gradient approaches at the pixel scale, are needed to 

explain the patterns of biological communities at the 

landscape-level.

We break this central question into two hypotheses.

Hypothesis 1: If the structure and composition of 

the plant community is well represented by classified 

vegetation maps, which purportedly represent the major tree assemblages in this landscape, then these maps 

should explain (a) a large and (b) a similar amount of 

the variance in the variation of tree species abundance 

among plots. If these maps do not explain a large proportion of the variance in species abundance among plots 

then they do not represent the actual scales and patterns 

at which vegetation is primarily varying. Second, if 

different versions of these maps do not explain a similar portion of variance in the species assemblage then 

they do not provide a consistent measure of the vegetation community. As these maps were created with 

the same purpose, to describe the major patterns of the 

plant community, if they describe different portions of 

the structure of the species assemblage then this is an 

indication that these maps provide equivocal measures 

of the structure of the plant community.

Hypothesis 2: If the structure and composition of 

the plant community is well represented by classified vegetation maps, then those maps should be able 

to predict the occurrence of individual species among 

plots nearly as well as species-level, pixel-scale gradient models developed using spectral, topographical, 

and climatic variables. The combination of community-level (Hypothesis 1) and species-level (Hypothesis 

2) tests provides the means to evaluate both classified 

maps’ abilities to describe major patterns of co-occurrence and to predict the distribution of individual 

species.

Methods___________________

Vegetation Data

The tree data used in this study were collected from 

a uniform grid of 411 vegetation plots distributed on a 

1.6-km spacing across approximately 2500 square kilometers of northern Idaho, USA (Figure 1). Each plot 

consisted of a central 0.10 ha (17.95 m radius) plot and 

three 0.01 ha (7.32 m radius) subplots. For each plot 

the species and diameter were recorded for each tree, 

along with canopy closure, tree height, coarse woody 

debris, shrubs, forbs, and grasses. This analysis uses 

the tree data only.

Figure 1. Study area orientation map.
USDA Forest Service RMRS-RP-84. 2010. 3

We produced three sites-by-species matrices for the 

tree data, summarizing the density, basal area, and importance value for each species in each plot. Density 

is recorded in total number of trees on the plot, basal 

area is in units of m2

 per hectare, and importance value 

is based equally on the relative number of stems and 

the relative basal area in each plot (Iverson and Prasad 

1998). We produced these three versions of the species 

response data to evaluate the sensitivity of the results 

to how the dependent variables are measured.

Classified Vegetation Maps

We chose three widely used vegetation maps for the 

study area to provide a broad evaluation of the range 

of classified maps currently in use by scientists and 

managers to represent vegetation community composition and structure at the landscape level. These 

maps included: (1) Idaho Panhandle National Forest 

(IPNF) stands map (R1 TSMRS handbook, Art Zack 

pers. comm., (2) GAP vegetation type map (http://gapanalysis.nbi.gov), and (3) U.S. Forest Service Northern 

Region Dominance Type Group (R1 VMAP; http://

www.fs.fed.us/r1/gis/vmap_v06.html). These maps 

collectively represent the full range of available spatial 

predictions of vegetation assemblages in the study area 

and are those that are currently being utilized by ecologists and land managers.

The IPNF data consists of photo-interpreted stand 

delineations (Table 1) and is comparable to stand data 

currently used by most National Forests for their planning and management decisionmaking (R1 TSMRS 

handbook, Art Zack pers. comm.). The GAP map was 

a modification of the Idaho and Montana GAP analysis vegetation type maps. The study area spanned parts 

of both states, so it was necessary to combine the two 

maps with reclassification to the patch types shown in 

Table 1. This map was chosen because GAP maps are 

often used in landscape-level research and conservation 

planning. VMAP is a product of the Northern Region 

U.S. Forest Service and was intended to provide consistent and empirically error-checked vegetation type 

classification across the 14 national forests in USFS 

Region 1. It represents “dominance type group,” which 

is a comparable level of classification resolution as the 

other maps (Table 1). The three maps collectively represent those typically used in research, conservation, 

and planning in the northern Rocky Mountains and are 

representative of the kind and accuracy of such maps 

used elsewhere.

Table 1. List of classes in the three land cover maps.

Gap code Gap class Name IPNF code IPNF class Name VMAP Code VMAP Class name

1 Urban 1 Aspen 3100 Grass

2 Agricultural 2 BGA 3300 Shrub

3 Mining 3 Cedar 5000 Water

6 Water 4 Douglas fir 7000 Sparsely vegetated

7 Rock/soil 5 Grand fir 8010 Ponderosa pine

8 Mixed barren 6 Western larch 8020 Douglas fir

9 Snow 7 Lodgepole pine 8030 Grand fir

10 Clear cut conifer 8 No data 8040 Western larch

11 Alpine meadow 9 Non-forest 8050 Lodgepole pine

12 Burned forest 10 Ponderosa pine 8060 Subalpine fir

13 Forested riparian 11 Subalpine fir 8070 Engelmann spruce

14 Shrub riparian 12 Western hemlock 8090 Western red cedar

15 Grass riparian 13 WLP 8110 Mountain hemlock

16 Wetlands 14 Western white pine 8200 IMXS

19 Grassland 15 WSL 8300 TASH

21 Shrub-grass 8400 TGCH

24 Mesic shrub

25 Xeric shrub

26 Aspen

27 Ponderosa pine

28 Lodgepole pine

29 Western Red cedar

30 Western hemlock

31 Mixed conifer

32 Mixed subalpine forest

33 Mixed whitebark pine forest

34 Subalpine meadow
4 USDA Forest Service RMRS-RP-84. 2010.

Independent Variables

The independent variable set used in this analysis 

consisted of the percentage of a 90 m radius window 

centered on each vegetation plot comprised of each 

patch type for each of the three classified vegetation 

maps. There were three motivations for this approach. 

First, our goal was to evaluate the ability of the classified map to predict species composition of plots across 

the landscape, so the key variable is the patch type the 

plot resides in. Second, we wanted to minimize any errors related to patch edge delineation. Classified map 

error rates tend to be higher near edges of patches 

where heterogeneity of continuously varying vegetation is highest. The 90 m window size reduces this 

effect. Third, we chose to compare the ability of the 

three classified vegetation maps to predict tree community composition and structure and to explicitly 

compare their independent and over-lapping explanatory ability.

Hypothesis 1: Variance Partitioning 

With Redundancy Analysis

We used hierarchical variance partitioning 

(Cushman and McGarigal 2002) with redundancy 

analysis (RDA) (Legendre and Legendre 1998) to test 

Hypothesis 1. Hierarchical variance partitioning uses 

a series of partial canonical ordinations to partition 

explained variance into its components. The method is described in detail in Cushman and McGarigal 

(2002). In the analyses in this paper, we completed 

three separate decompositions, corresponding to the 

three forms of the dependent variable data set (density, basal area, and importance value by tree species). 

The decomposition separates the explanatory ability 

of each classified map. This decomposition extracts 

seven discrete components of explained community 

variation and one component of unexplained variation: (1) variance uniquely explained by map IPNF; (2) 

variance uniquely explained by map GAP; (3) variance 

uniquely explained by map VMAP; (4) joint explanation of community composition by IPNF and GAP; (5) 

joint explanation of community composition by GAP 

and VMAP; (6) joint explanation of community composition by IPNF and VMAP; and (7) joint effects of 

all three maps.

We conducted each variance decomposition with 

RDA after row normalization of the species data (dependent variables), as recommended by Legendre 

and Gallagher (2001). Row normalization converts 

the species data to relative profiles within each site. 

This approach allows for the use of RDA, which is 

Euclidean-based, for the analysis of community data, 

while circumventing the problems associated with the 

Euclidean distance and avoiding certain problems associated with Canonical Correspondence Analysis 

(CCA) such as the differential weighting of species 

and sites preserved in the chi-square distance measure 

of CCA (Legendre and Gallagher 2001). We conducted 

the variance decomposition both with and without the 

data standardization using both RDA and CCA and the 

results did not differ substantially. After standardizing 

each of the dependent variable sets, we used forward 

stepwise variable selection based on minimizing 

Akaike’s Information Criterion to select a parsimonious set of variables from each explanatory variable set 

(i.e., vegetation maps). This resulted in three to nine 

explanatory variables in the final sets of explanatory 

variables. All statistical analyses were conducted using 

the vegan library of R (rproject.org) and scripts written 

by the authors.

Hypothesis 2: Predicting Occurrence 

of Four Major Species With 

Random Forest

Random Forest is a powerful refinement of classification and regression tree (CART) approaches for 

predicting species occurrence. CART approaches have 

gained broad usage in ecological studies (Déath and 

Fabricius 2000). However, CART suffers from several 

problems, such as over-fitting and difficulty in parameter selection. Several solutions have been proposed 

that incorporate iterative approaches (Schapire 1990; 

Breiman 1996). One approach in particular, Random 

Forests (Brieman 2001), has risen to prominence 

due to its ability to handle large numbers of predictor variables, accurately reflect complex non-linear 

species-environment relationships (Cutler and others 

2007), and provide local and global measures of variable importance (Evans and Cushman, 2009).

Evans and Cushman (2009) predicted occurrence 

probabilities for four selected species using the Random 

Forest algorithmic method (Brieman 2001; Cutler and 

others 2007) as implemented in R (R development core 

2007; Liaw and Wiener 2002). The selected species are 

Thuja plicata (THPL), Pseudotsuga menzesii (PSME), 

Abies lasiocarpa (ABLA), and Pinus ponderosa 

(PIPO). These species were selected due to their dominance in each of four major biophysical conditions: 

THPL dominant in mesic, middle elevation montane 
USDA Forest Service RMRS-RP-84. 2010. 5

valleys; PSME as a widespread co-dominant on dry to 

mesic sites at low to middle elevation; ABLA dominant in cold and moist upper elevation sites; and PIPO 

dominant in warm and dry lower elevations. The analysis predicted occurrence at the species-level and the 

pixel scale, using the same species occurrence dataset 

as used in the community-level, stand-scale analysis 

above and a set of 40 spectral, topographical, and climatic independent variables.

Here we use identical methods to predict the same 

species, but as functions of the three classified vegetation maps rather than spectral, topographical, and 

climatic variables. The difference in the analyses is that 

the independent variable set is the percentage of a 90 m 

radius window centered on each vegetation plot comprised of each patch type for each of the three classified 

vegetation maps. The response variable was defined 

as presence (1) if proportion > 0.10 and absence if 

less than 0.10. Previous studies (Chawala and others

2002; Chen and others 2004) demonstrated that imbalance between presences and absences bias prediction 

and model-fit error. To correct this, we used a random 

down-sample method (Evans and Cushman 2009) to 

correct for imbalance in the number of presences and 

absences. We ran 5000 bootstrap replicates (k) with replacement using a 36% out-of-bag (OOB) sample, and 

we set the number of variables permutated at each node 

(the m parameter) at [SQRT(number of x variables)], 

with a minimum of m=2. The final model was built by 

combining trees from all the resulting random forest 

models (Evans and Cushman, 2009).

Comparison of these model results with those of 

Evans and Cushman (2009) provides a test the hypothesis that classified community-level, patch scale 

vegetation maps will provide similar predictive power 

as models predicting species-level, pixel scale responses based on continuous environmental variables. We 

evaluate this (Hypothesis 2) by comparing the kappa statistics and area under the Receiver Operating 

Characteristic (AUC) curve between the three classified map models and the model based on spectral, 

topographic, and climatic variables (Evans and 

Cushman 2009) for all four species. The kappa statistic 

is a corrected index of classification accuracy that indicates the percentage improvement of the classification 

over random assignment. The area under the Receiver 

Operating Characteristic (AUC) curve is a cut-point 

independent measure of the model’s discriminatory 

ability. AUC values of greater than 0.9 indicate excellent discriminating ability, those between 0.7-0.9 are 

fair, and values less than 0.7 indicate poor model ability to discriminate between presences and absences.

Results____________________

Hypothesis 1: Variance Partitioning 

With Canonical Correspondence 

Analysis

In none of the three decompositions did the three 

maps jointly explain more than 35% of the variance in 

the tree community (Table 2, Figure 2). The three maps 

had the highest explanatory power when the species 

data were in importance value form, and the lowest 

when in density form, in which only 16% of species 

variance was explainable by the three maps in combination (Table 2, Figure 2).

No single vegetation map explained more than 25% 

of the variance in the tree community in any of the three 

partitionings (Table 2, Figure 2). In all three analyses, 

GAP was the poorest predictor. For the density partitioning, GAP explained a total of 6.2% of the variance 

in the tree community and was able to uniquely explain 

only 2.1% (after accounting for explanatory ability of 

the other two maps, Figure 2). Likewise, for the basal 

area partitioning GAP had total and unique explanatory 

abilities of 12.2% and 2.9% respectively. In the importance value partitioning, GAP explained 15.5% and 

3.6%. The IPNF stands map was the strongest predictor in two of the three partitionings, explaining 22.9% 

in total and 9.6% uniquely in the basal area analysis 

and 24.9% total and 8.7% uniquely in the importance 

value analysis. In contrast, the R1 VMAP map was the 

strongest predictor in the density partitioning, explaining 9.1% in total and 4.6% uniquely.

Hypothesis 2: Predicting Species 

Occurrence With Random Forests

Comparing kappa and AUC for four conifer species 

predicted on the basis of the three classified vegetation 

maps with those for models based on spectral, topographic, and climatic variables (Evans and Cushman 

2009) indicated substantial variability among species 

in how well they were represented by the classified 

maps, as well as variability among maps in how well 

they described the occurrence patterns across species 

(Table 3). ABLA and PSME were predicted with “fair” 

accuracy using the classified maps as predictor variables, with AUC values over 0.7 and kappa indicating 

roughly 50% improvement in classification over random assignment (Table 3). In contrast, THPL had low 

kappa values and AUC of less than 0.620 and kappa 
6 USDA Forest Service RMRS-RP-84. 2010.

Table 2. Variance partitioning results for each of the three analyses. There are eight 

components of variation in each partitioning: VMAP—variance explainable uniquely by 

the VMAP map; GAP—variance explainable uniquely by the GAP map; IPNF—variance 

explainable uniquely by the IPNF map; VMAP-GAP—variance jointly explained by 

VMAP and GAP, but not explained by IPNF; VMAP-IPNF—variance explained jointly 

by VMAP and IPNF but not explained by GAP; GAP-IPNF—variance explained jointly 

be GAP and IPNF but not by VMAP; VMAP-GAP-IPNF—variance explained by all three 

maps. Each component corresponds to a single exclusive (non-overlapping) partition of 

the total species variance, as can be represented in a Venn diagram (see Figure 2). The 

remaining “unexplained” variance is usually referred to as the “residual” variance and is 

equal to the sum of the unconstrained eigenvalues.

Importance Value Partitioning

Inertia Prop. total Prop. constrained P value

VMAP 0.215 0.044 0.127 0.020

GAP 0.465 0.036 0.104 0.005

IPNF 0.44 0.087 0.251 0.000

VMAP-GAP 0.039 0.017 0.049 NA

VMAP-IPNF 0.205 0.06.0 0.173 NA

GAP-IPNF 0.061 0.014 0.041 NA

VMAP-GAP-IPNF 0.257 0.088 0.254 NA

Unexplained 0.654

Basal Area Partitioning

Inertia Prop. total Prop. constrained P value

VMAP 0.185 0.036 0.117 0.210

GAP 0.437 0.029 0.094 0.046

IPNF 0.476 0.096 0.313 0.000

VMAP-GAP 0.031 0.012 0.039 NA

VMAP-IPNR 0.182 0.052 0.169 NA

GAP-IPNF 0.044 0.012 0.039 NA

VMAP-GAP-IPNF 0.271 0.069 0.225 NA

Unexplained 0.693

Density Partitioning

Inertia Prop. total Prop. constrained P value

VMAP 0.349 0.046 0.287 0.044

GAP 0.829 0.021 0.131 0.017

IPNF 0.473 0.039 0.244 0.010

VMAP-GAP 0.128 0.008 0.050 NA

VMAP-IPNR 0.123 0.013 0.081 NA

GAP-IPNF 0.022 0.009 0.056 NA

VMAP-GAP-IPNF 0.253 0.024 0.15 NA

Figure 2 Unexplained 0.840

A B C

Figure 2. Venn diagrams showing partitioning for the three analyses. A. Density Partitioning, B. Basal Area 

Partitioning, C. Importance Value Partitioning. Values in the Venn diagram represent proportion of total 

variance explained.
USDA Forest Service RMRS-RP-84. 2010. 7

less than 0.27 for all three maps. Finally, PIPO was 

very poorly predicted by all three maps, with kappa 

values less than 0.12 and AUC values very near 0.5 

indicating very poor prediction of this species.

In all cases the models developed based on spectral, topographic, and climatic variables (Evans and 

Cushman 2009) vastly outperformed those based on 

the classified maps. In all cases, these models showed 

much higher kappa and AUC values than those produced by any of the classified map based models for 

any of the four species (Table 3). All four species had 

kappa and AUC values very near 1 in the Random 

Forests predictions produced using spectral, topographic, and climatic variables, indicating that the 

occurrence of these species across the landscape is very 

highly predictable based on environmental variables. 

In comparison, the models developed using the classified maps performed much more poorly. The PIPO 

model completely fails to discriminate presences from 

absences. The THPL model performs poorly (AUC < 

0.620). The models for ABLA and PSME were only 

marginally effective at discriminating presences from 

absences, with AUC values over 0.7.

Discussion_________________

Do Classified Vegetation Maps Explain the 

Composition and Structure of Forest Communities?

For classified vegetation maps to be reliable in representing the structure and composition of the plant 

community, they must meet at least three critical tests. 

First, at the community-level they should explain a 

considerable proportion of the variability in the species distribution data. Second, we would like different 

classified map products that have the same goal of 

representing forest “vegetation types” as patches 

to provide comparable explanations of community 

composition (Figure 3). Third, at the species-level, 

classified maps should be able to explain the variability in the occurrence of individual species nearly as 

well as models produced directly from selected environmental variables.

Hypothesis 1

In Hypothesis 1a, the question is: Do classified 

vegetation maps provide a powerful explanation of 

the structure of the plant community? In Hypothesis 

1b, the question is: Do alternative classified vegetation 

maps provide similar explanations of plant community structure? If the first condition is not met then 

we have little confidence interpreting the meaning of 

patch-mosaic maps of vegetation types. If vegetation 

is not discretely grouped into meaningful associations then there will be no way to produce meaningful 

spatial classifications into patch mosaic maps. This 

condition is more fundamental than those typically 

considered in evaluating classified map accuracy, such 

as the sufficiency of spectral or textural separability 

among classes, the adequacy of available imagery or 

other GIS products to predict class assignment, the 

availability of sufficient training and truthing data, 

and identification of the proper grain and extent for 

analysis. However, if this first condition is not met intrinsically within the ecological system then none of 

the technical considerations that follow is of importance. If communities do not exist as discrete entities 

then categorical patch mosaic maps will not meaningfully represent biotic landscape patterns.

Hypothesis 1a: In this case our analysis indicates 

that the three classified vegetation maps all performed 

poorly in explaining the composition and structure of 

Table 3. Kappa and AUC for the four study species predicted on the basis of the three classified vegetation maps (IPNF, GAP, 

VMAP), and the spectral, topographic, and climatic variables utilized in Evans and Cushman (submitted; GRADIENT). 

The classes included in the three maps are described in Table 1. The kappa statistic is a corrected index of classification 

accuracy that indicates the percentage the classification is superior to random assignment. The area under the Receiver 

Operating Characteristic (AUC) curve is a cut-point independent measure of the models discriminatory ability. AUC values 

of greater than 0.9 indicate excellent discriminating ability, those between 0.7—0.9 are fair, and values less than 0.7 

indicate poor model ability to discriminate between presences and absences.

Kappa Kappa Kappa Kappa AUC AUC AUC AUC

Species IPNF GAP VMAP GRADIENT IPNF GAP VMAP GRADIENT

ABLA 0.559 0.519 0.498 0.97 0.777 0.766 0.749 0.98

PIPO 0.117 -0.005 0.000 0.86 0.538 0.501 0.500 0.98

PSME 0.496 0.333 0.540 0.99 0.731 0.657 0.773 0.99

THPL 0.250 0.083 0.268 0.98 0.612 0.536 0.620 0.99
8 USDA Forest Service RMRS-RP-84. 2010.

Figure 3. Illustration of incongruity of the three classified vegetation cover type maps with each other and with the underlying 

pattern of variation in forest vegetation in a portion of the study area. The figure shows the vegetation in a high-resolution 

color air photo and shows the boundaries of the patches in each of the three classified maps. Blue line—VMAP; Red line—

GAP map; Yellow line—Idaho Panhandle National Forest stands map.

forest trees among plots. No map singly was able to 

explain more than 25% of the variability in the plant 

community. If over 75% of the variability in species 

composition in a landscape is unexplainable by a classified map, does that map adequately represent species 

composition? Recently, Cushman and others (2008) 

conducted a similar analysis for breeding birds. In that 

study they found that classified maps of vegetation 

types were largely unable to predict the occurrence 

and relative abundance of species within the bird community. This analysis shows that classified vegetation 

maps also may often fail to predict vegetation community composition itself.

Hypothesis 1b: Even in cases where the first criterion 

of explanatory power is met, it is important to evaluate whether different map products which purportedly 

represent the same information provide comparable 

explanations. If two maps explain largely separate 

and independent portions of species variability among 

plots, then they do not provide the same information 

and cannot be used interchangeably. If this were the 

case, then the kind of partitioning analysis done in this 

paper would be warranted any time a classified map 

is used to describe vegetation and any time a different map is used to substitute for another. This latter 

condition is very common, such as when assessments 

of landscape structure are conducted in different areas 

with different available map products. It is therefore 

important to consider how comparable are different 

maps produced for the same landscape with the same 

purpose of describing forest vegetation types.

In this analysis the three maps provided largely independent explanations of species variability (Figure 

3). For example, in the partitionings based on importance value, basal area, and density, less than 15% of 

the variability in species data was shared among any 

combination of two of the maps, and less than 9% was 

explainable jointly by all three in any of the three cases 

(Figure 2). These maps were made for the same purpose, to describe the important attributes of vegetation 

composition across the landscape. That they provide 

such different and independent information is telling. 

Two maps made in the same landscape to describe the 

same ecological attributes at the same spatial scales 
USDA Forest Service RMRS-RP-84. 2010. 9

should provide comparable information if they are 

reliable. Indeed, one way to determine the validity 

of spatial predictions is to look for concordance between independent predictions made using different 

data. The failure of these maps to provide concordant 

predictions of vegetation is further evidence that these 

classified vegetation maps fail to represent the major 

patterns of vegetation in this landscape (Figure 3).

Hypothesis 2

At the species-level, perhaps the best evaluation 

of the effectiveness of the classified maps in reflecting the major scales and patterns of variability in 

the tree community is to compare individual-species 

models produced based on the classified maps with 

those produced for the same dependent variable set 

using topographic, climatic, and spectral independent 

variables. This allows a formal comparison of model 

performance based on the two sets of independent 

variables.

Individual species models based on spectral, topographic, and climatic variables vastly out-performed 

those produced using the classified maps. The former 

set of models had kappa and AUC values very near 

1 for all four species, indicating very high success in 

predicting occurrence vs. absence. In contrast, the latter models, in which species occurrence is predicted 

solely on the basis of the cover types that occur at or 

in proximity to each vegetation plot, produced much 

lower kappa and AUC values. Models for one species 

(PIPO) failed to effectively discriminate presences 

from absences in any of the three maps, and no models 

for any species based on any map were able to strongly 

discriminate between occurrences and absences based 

on kappa and AUC.

The large differences among species in their relationship to the classified maps further suggests a 

decoupling of the scales and patterns of individual 

species response to environmental drivers from the 

scales and patterns of structure represented in the classified vegetation maps. Further, the high variability 

in the ability of the three classified maps to predict 

the four species echoes the point made in evaluating 

Hypothesis 1b above; different maps made in the same 

landscape to describe the same ecological attributes at 

the same spatial scales should provide comparable information. The fact that these maps provide divergent 

predictions is further evidence that they fail to represent the major patterns of vegetation in this landscape.

Categorical Landscape Ecology as 

Historical Legacy and Management 

Convenience

The predominance of classified, categorical maps as 

the basis for landscape ecology is a historical legacy 

and a management convenience. German geographer 

Carl Troll, who introduced the term “landscape ecology” in 1939 (Troll 1971), defined the new field as 

“the study of the main complex causal relationships 

between the life communities and their environment 

in a given section of the landscape.” Troll’s original 

focus was on continuous patterns of environmental 

variability and continuous population processes, in 

a way embracing the gradient concepts of American 

community ecology proposed by Gleason (1926) and 

Whittaker (1967). However, landscape ecology shortly 

thereafter departed from this gradient framework, and 

evolved into an effort to divide landscapes into small 

components and ascertain the logic through which they 

were grouped and interacted as a landscape mosaic 

(Cushman and others 2010a). This transition was the 

result of the simultaneous emergence of hierarchy theory and the advent of categorical map analysis in GIS. 

As is frequently the case, this feedback between theory 

and methodology led to a “boring in” of the patch mosaic paradigm (Cushman and Huettmann, 2010), such 

that even as technological advances in remote sensing, 

GIS and spatial statistics enabled vast improvements in 

measuring, mapping, and analyzing continuous landscape gradients, the field remained nearly completely 

dominated by the categorical paradigm (McGarigal 

and others 2009; Cushman and others 2010b). The 

dominance of the patch mosaic paradigm was further 

strengthened by its adoption by land management 

agencies due to its congruence with ownership maps 

and the ease of making management decisions based 

on patch mosaic representations of landscape structure.

Landscape Structure Is Not 

Vegetation Type

Defining vegetation types is often problematic. 

Classified vegetation type maps fail to explain a 

large amount of variance in vegetation structure and 

composition due to error in map classification, error 

in delineating categories for the classification, and 

mismatch between individualistic species responses 

and categorical definitions. In addition, several other 
10 USDA Forest Service RMRS-RP-84. 2010.

factors contribute variance to species density, basal 

area, and importance value, including differences in 

biophysical setting and seral stage.

It is conceivable that one could stretch the patchmosaic paradigm to include more of these factors, with 

unique classes for each vegetation class, in each biophysical setting and each seral stage. However, even if 

vegetation type classes could be meaningfully and accurately defined, this combination would be unfeasible 

for several reasons. First, given a modest number of 

vegetation types, seral stages, and biophysical settings, 

this combination would result in dozens of individual 

classes, at a minimum. For example, nine biophysical 

classes, five seral stages and 10 vegetation types would 

result in 450 patch types. It is difficult to conceive how 

to effectively map this many classes with accuracy and 

even more challenging to analyze and interpret them. 

Further, it is readily argued that biophysical settings 

and seral stages themselves are more appropriately 

represented as gradient phenomena than categorical 

entities (McGarigal and others 2009; Cushman and 

others 2010b), which compounds the issues discussed 

above for categorizing inherently continuous entities.

Toward Gleasonian Landscape Ecology

If classified vegetation maps are poor surrogates 

for important aspects of vegetation pattern across 

landscapes, what is the alternative? We believe that 

explicitly embracing the Gleason-Hutchinson model 

of individualistic response to spatially structured limiting factors provides a powerful general framework 

(McGarigal and Cushman 2005; Cushman and others 2007; Evans and Cushman 2009). Specifically, by 

moving focus from community types to individual species we can avoid the Clementsian pitfall of trying to 

map and analyze ephemeral and unstable multi-species 

groups, and instead focus on the realized niches of 

individual species, which puts analysis within the context of the full scope of ecological theory (Evans and 

Cushman 2009; Cushman 2010).

This is particularly relevant given the current climate change crisis. Past changes in climate often 

triggered disassembly of communities. Subsequently, 

communities reassembled unpredictably, producing 

mixtures of species that are rare or absent at present 

(Barnosky and others 1987; Bartlein and others 1997). 

Typically, displacement of entire vegetation zones or 

communities did not occur. Instead, species responded individualistically to climate change according to 

environmental tolerance, dispersal ability, and biotic 

interactions (Davis 1986; McLachlan and Brubaker 

1995). Because species do not respond en masse, predicting changes in vegetation composition requires 

species-level resolution; all hierarchical levels above 

the species are transient.

Accordingly, we advocate addressing vegetation 

response to climate and disturbance regime at the 

level of individual species and their interactions with 

key resources and limiting factors (Cushman and others 2007). Multivariate models characterizing the 

responses of individual species to gradients of ecological conditions are a primary tool in this effort. These 

multivariate models predict the response of individual 

plant species and the composition of plant communities. Importantly, they avoid questionable assumptions 

about the reality and stability of categorical vegetation 

types (Gleason 1926; Whittaker 1967).

The fundamental challenge to integrating landscape 

and community ecology is linking non-spatial niche 

relationships with the complex patterns of how environmental gradients overlay heterogeneous landscapes 

(Austin 1985; Cushman 2010). By establishing species 

optima and tolerances along environmental gradients, 

researchers can quantify the characteristics of each 

species’ environmental niche. The resulting statistical 

models can be used to predict the biophysical suitability of each location on a landscape for each species 

(e.g., Evans and Cushman 2009). This species-level, 

pixel-scale framework for modeling individualistic 

species responses can also flexibly include biotic interactions as predictors. This is particularly important, 

as individualistic species response includes response 

to the influences of other members of the community 

as well as abiotic environmental gradients (Lortie and 

others 2004; Cushman and others 2007). This mapping of niche suitability onto complex landscapes is 

the fundamental task required to predict individualistic 

species responses, and it is fundamentally important as 

a foundation for predicting effects of altered climate 

and disturbance regimes.

There is no single correct scale of analysis for forest 

ecology. The fundamental unit of ecological analysis is 

the organism (Schneider 1994) and fundamental scales 

are those at which the organism strongly interacts 

with critical or limiting resources in its environment 

(Spomer 1973; Cushman and others 2007). Patches, 

therefore, like “communities,” are ephemeral and unstable. Moving from analysis at the “assemblage” to the 

species level thus simultaneously requires a move from 

patch to pixel scale. Instead of predicting a categorical entity as a discrete patch, we advocate predicting a 

continuous entity at each unique location (e.g., Evans 

and Cushman 2009). These locations become pixels 
USDA Forest Service RMRS-RP-84. 2010. 11

in the context of GIS and remote sensing databases. 

Accounting for multiple interactions across ranges of 

spatial and temporal scales is substantially facilitated by 

adopting an individualistic, pixel-scale, gradient-based 

framework (McGarigal and Cushman 2005; Cushman 

and McKelvey 2010). Studies of relationships between 

forest communities, climate, and disturbance regimes 

should use multi-scale approaches that are directly tied 

to the dominant scales of organism interaction with the 

environment. Where data allow, it is advantageous to 

quantitatively measure the relationships among driving factors across a range of scales simultaneously to 

identify these dominant scales and quantify interaction 

of factors across scale (e.g., Cushman and McGarigal 

2002). Ideally, ecological analysis will therefore not 

be between hierarchical “levels,” such as populations, 

communities, or ecosystems, but instead will focus 

on relationships among organisms and driving processes across continuous ranges of scale (Levin 1992; 

Cushman 2010; Cushman and others 2010b).

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