United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Research Paper 

RMRS-RP-94

May 2012

Multi Scale Habitat Relationships 

of Martes americana in 

Northern Idaho, U.S.A.

Tzeidle N. Wasserman, Samuel A. Cushman, 

David O. Wallin, Jim Hayden
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Wasserman, Tzeidle N.; Cushman, Samuel A.; Wallin, David O.; Hayden, Jim. 

2012. Multi scale habitat relationships of Martes americana in northern Idaho, 

U.S.A. Res. Pap. RMRS-RP-94. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 21 p.

Abstract

We used bivariate scaling and logistic regression to investigate multiple-scale 

habitat selection by American marten (Martes americana). Bivariate scaling 

reveals dramatic differences in the apparent nature and strength of relationships 

between marten occupancy and a number of habitat variables across a range of 

spatial scales. These differences include reversals in the direction of an observed 

association from positive to negative and frequent dramatic changes in the 

apparent importance of a habitat variable as a predictor of marten occurrence. 

Logistic regression on the optimally scaled input variables suggests that at the scale 

of home ranges, marten select landscapes with high average canopy closure and 

low fragmentation. Within these low fragmented landscapes, marten select foraging 

habitat at a fine scale within late-seral, middle-elevation mesic forests. In northern 

Idaho, optimum American marten habitat, therefore, consists of landscapes with 

low road density, low density of non-forest patches with high canopy closure, 

and large areas of middle-elevation, late successional mesic forest. Comparison 

of current landscape conditions to those expected under the historic range of 

variability indicates that road building and timber harvest in the past century may 

have substantially reduced the amount of suitable marten habitat in northern 

Idaho. Our results are generally consistent with previous research in the Rocky 

Mountains, with additional insights related to the relative importance, functional 

form, and scale at which each habitat variable has the largest influence on marten 

occurrence.

Keywords: Martes americana, multi scale, habitat modeling, logistic regression

Authors

Tzeidle N. Wasserman, School of Forestry, Northern Arizona University, Flagstaff, 

Arizona 86001; 406-531-1515; tnw23@nau.edu.

Samuel A. Cushman, USDA Forest Service, Rocky Mountain Research Station, 

Forestry Sciences Laboratory, 2500 S. Pine Knoll Dr., Flagstaff, Arizona 86001.

David O. Wallin, Huxley College of the Environment, Western Washington 

University, 516 High St., Bellingham, Washington 98225.

Jim Hayden, Idaho Department of Fish and Game, 2885 W. Kathleen Ave., Coeur 

D’Alene, Idaho 83815.

Cover photo credit: Michael Lucid, Idaho Department of Fish and Game.
Contents

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

Materials and Methods............................................................................... 1

Study Area .......................................................................................... 1

Data Collection .................................................................................. 1

Variable Selection............................................................................... 2

Bivariate Scaling ................................................................................. 4

Logistic Regression Analysis................................................................ 5

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

Bivariate Scaling ................................................................................. 5

Canopy Closure.................................................................................. 5

Elevation............................................................................................. 5

Logistic Regression Analysis................................................................ 9

Variable Importance ........................................................................... 9

Model Assessment ............................................................................ 10

Model Prediction.............................................................................. 10

Discussion................................................................................................ 12

Scale Dependency of Marten Habitat Relationships.......................... 12

Predicting Habitat Quality and Management Effects ......................... 12

Management and Conservation Implications .................................... 16

Using Models to Assess and Monitor Habitat Conditions for 

 American Marten....................................................................... 17

Importance of Scale Optimization .................................................... 18

Differences Between Habitat Quality and Habitat Connectivity ........ 18

Conclusions.............................................................................................. 19

References ............................................................................................... 20
USDA Forest Service RMRS-RP-94. 2012. 1

Introduction

The influence of changes in spatial scale on observed 

pattern-process relationships is a central question in ecology (Levin 1992). Each process may be driven by multiple 

factors, each acting at different characteristic scales, and 

apparent pattern-process relationships may change fundamentally across scale (Wiens 1989). It is critical not only 

to identify correct mechanisms, but also the correct scales 

at which they act. Failure to do so often results in failure 

to observe an effect when in fact one exists, observation of 

spurious effects that are incorrect, or misconstrual of the 

nature or strength of the pattern-process relationship (e.g., 

Thompson and McGarigal 2002).

Scaling analysis is particularly important in speciesenvironment relationship analysis. Animals often hierarchically select habitats; therefore, it is important to evaluate 

habitat selection at a range of spatial scales in order to reveal 

the true grain at which the animal responds within the landscape (Kotliar and Wiens 1990). Thompson and McGarigal 

(2002) demonstrated the sensitivity of apparent species-environment relationships to grain, extent, and thematic resolution 

of habitat variables. The authors observed that bald eagles 

(Haliaeetus leucocephalus) select different habitat variables 

at widely divergent scales; and incorrect specification of 

the grain, extent or habitat definition could lead to incorrect 

conclusions about the habitat relationships of this species. 

Importantly, the environmental variables of importance and 

grain of response for some processes, such as establishing 

home ranges, may differ greatly from the variables and scales 

of importance for other processes, such as habitat selection 

for foraging within home ranges.

The information theoretic approach has become the dominant paradigm for use in wildlife habitat selection studies 

(Burnham and Anderson 2002; Johnson and Omland 2004). 

Although the advantages of this approach have been extensively reviewed, very little attention has been given to scaling 

issues when selecting variables for inclusion in candidate 

models. For most species, information is available to guide 

decisions regarding appropriate variables for inclusion in 

candidate models. However, the effect of a given variable on 

habitat selection may manifest itself at spatial scales ranging from a few meters to kilometers (e.g., Thompson and 

McGarigal 2002; Grand and others 2004). Most habitat selection studies fail to address this issue and simply evaluate 

alternative models that are based on predictor variables from 

a single, arbitrarily selected scale.

For most environmental variables, there is no advance 

way to know the spatial extent surrounding the sample point 

at which the variable is most strongly related to species presence. Therefore, it is best to measure each environmental 

variable across a range of radii surrounding each sampled 

plot to determine the scale at which each predictor variable 

is most related to the response. Bivariate scaling (Thompson 

and McGarigal 2002; Grand and others 2004) has been 

shown to be a highly effective method for identifying the 

appropriate scale in species-environment relationship modeling. Characterizing the landscape surrounding each point 

at multiple scales facilitates the selection of the appropriate scale at which each aspect of landscape composition was 

most significant for our focal species.

American marten (Martes americana) have been 

shown to be particularly sensitive to fragmentation of late 

seral forest habitats by road building and timber harvest. 

Perturbations such as timber harvest remove canopy cover, 

reduce coarse woody debris, change mesic sites into xeric sites, remove riparian dispersal zones, and change prey 

communities (Buskirk and Ruggiero 1994). Previous studies 

have revealed that marten respond to small amounts of forest 

fragmentation and rarely use sites where more than 25% of 

forest cover was removed (Hargis and others 1999). Highly 

contrasted edges and areas of open canopy are strongly 

avoided by American marten (Buskirk and Ruggiero 1994; 

Hargis and others 1999). However, previous habitat relationship studies on the species have not thoroughly considered 

scaling issues in habitat selection. The objectives of this 

study are: to identify the variables that most strongly influence habitat selection by American marten in northern Idaho 

and the scale at which each of these variables is most important, and to predict the probability of marten occupancy 

across a large, mountainous landscape.

Materials and Methods

Study Area

The study area is a 3000-km2 section of the Selkirk, 

Purcell, and Cabinet Mountains, encompassing the Bonners 

Ferry and Priest River Ranger Districts of the Idaho 

Panhandle National Forest (IPNF; Figure 1). The topography is mountainous, with steep ridges, narrow valleys, and 

many cliffs and cirques at the highest elevations. Elevation 

ranges from approximately 700 m to 2400 m above sea level. 

The climate is characterized by cold, wet winters and mild 

summers. The area is heavily forested, with subalpine fir 

(Abies lasiocarpa) and Engelmann Spruce (Picea engelmannii) codominant above 1300 m, and a diverse mixed forest of 

Douglas-fir (Pseudotsuga menziesii), lodgepole pine (Pinus 

contorta), ponderosa pine (Pinus ponderosa), western white 

pine (Pinus monticola), grand fir (Abies grandis), western 

hemlock (Tsuga heterophylla), western red cedar (Thuja 

plicata), western larch (Larix occidentalis), paper birch 

(Betula papyrifera), quaking aspen (Populus tremuloides), 

and black cottonwood (Populus trichocarpa) dominating 

below 1300 m.

Data Collection

Hair snares were set along transects across the portions of 

the study area during the winter months of January through 

March in 2005, 2006, and 2007. Transects were selected to 
2 USDA Forest Service RMRS-RP-94. 2012.

provide near complete sample coverage across all areas of 

Federal or State ownership that are accessible with a snowmobile. Each snare consisted of a 1 m x 1 m corrugated plastic 

sheet folded into an equilateral triangular tunnel with hardware cloth mesh wired to the back end of the snare, creating 

a one-way opening (Cushman and others 2008; Figure 2). 

Snares were baited with a ~30 cm3 cube of deer meat and 

~1 tsp beaver castor. Approximately 1⁄4 tsp Gusto, a commercial call lure (Minnesota Trapline Products), was also dabbed 

onto a sponge and hung above the trap. Each trap was lined 

with five 30-caliber rifle bore brushes to non-invasively 

obtain hair samples from animals visiting the snare. Snares 

were set for two weeks, after which each station was revisited to collect hair samples and re-bait for another two-week 

cycle. During the check, brushes with hair were removed 

from the trap and put into sterile tubes filled with desiccant. 

Each brush is considered a single sample.

All genetic analysis was conducted by the U.S. Forest 

Service Wildlife Conservation Genetics Laboratory in 

Missoula, Montana. Hair samples taken from each brush were 

genetically analyzed at both the species and individual level. 

Mitochondrial DNA was extracted from hair samples using 

Qiagen tissue protocols with slight modifications (Mills and 

others 2000) and amplified with polymerase chain reaction 

(PCR). Following DNA amplification, PCR products were 

digested in three restriction reactions with HinfI, HaeIII, 

and MboI (Riddle and others 2003). This method allows 

the discrimination of mustelid species from all other species. Species identification was determined using diagnostic 

restriction enzyme patterns followed by amplification of a 

region of cytochrome b on mitochondrial DNA (Riddle and 

others 2003). Over the three winter seasons of 2005, 2006, 

and 2007, 361 locations were surveyed. American marten

were detected at 159 individual hair snare stations.

Variable Selection

In advance, we selected several variables we believed 

would be strongly related to American marten occurrence 

based on previous research (Buskirk and Ruggiero 1994; 

Hargis and others 1999; Tomson 1999): elevation, percent 

canopy closure, road density, patch density, percentage of 

the landscape occupied by late seral forests, percentage of 

the landscape occupied by non-stocked clear-cuts, and probability of occurrence of western red cedar (Thuja plicata). 

All input variables were co-rectified to a UTM projection 

with 30-m cell size.

Elevation

Elevation source data were from the 30-m2 Shuttle 

Topographic Radar Mission (Rabus and others 2003) downloaded from the United States Geological Survey (USGS) 

national map (http://nationalmap.gov). The effects of elevation on the probability of marten occurrence were modeled 

Figure 1. Study area orientation map. Idaho Panhandle National Forest located in northern Idaho, U.S.A.
USDA Forest Service RMRS-RP-94. 2012. 3

as Gaussian functions on the expectation that martens should 

show a unimodal optimum in habitat quality with respect to 

elevation. We evaluated 72 Gaussian elevation variables, consisting of a factorial of 9 levels of elevation (1200 m-2000 m) 

and 8 levels of standard deviation (300 m-1000 m in 

100-m increments (e.g., Cushman and others 2006).

Landcover

We created a consistent seral stage layer by merging the 

IPNF stands map (R1 TSMRS handbook, Art Zack, pers. 

comm.) with the Idaho Department of Lands Timber Type 

map (IDL 2006) after confirming a comparable cross-walk 

of diameter at breast height (DBH) size classes. Final classes 

in the seral stage map and their descriptions are found in 

Table 1.

We calculated landscape composition and configuration 

variables for 11 focal landscape extents surrounding each 

marten sampling station using FRAGSTATS (McGarigal 

and others 2002). The variables consisted of one landscapelevel metric characterizing the mosaic of cover types and 

two class-level metrics characterizing a focal cover type 

(Table 2). We chose patch density (PD) as a landscapelevel metric due to previous work showing that marten 

avoid fragmented landscapes (Hargis and others 1999). 

The class-level metrics were only calculated for particular 

cover classes thought to be important in influencing species 

distribution (Buskirk and Powell 1994). Specifically, we 

calculated the percentage of the focal landscape (PLAND) 

for the large size class, as past work has shown strong effects of the area of late seral forests on the probability of 

Figure 2. Non-invasive hair snare station with five gun brushes, bait, and mesh wire at back to create a oneway opening.

Table 1. Vegetation and timber classification on both State and Federal lands in the study 

area. Seral stage is presented with DBH in cm.

Class Description

1. Large sawtimber Dominant and codominant trees with DBH>40.6 cm

2. Small sawtimber Dominant and codominant tress with DBH 20.3-40.6 cm

3. Pole timber Dominant and codominant trees with DBH 7.62-20.3 cm

4. Seedling/sapling Crop trees <1.37 m and <7.62 DBH (open canopy)

5. Non-stocked Forest land less than 10% stocked with growing trees

6. Non-forest Non-forested areas
4 USDA Forest Service RMRS-RP-94. 2012.

marten occurrence (Hargis and others 1999). Likewise, 

we calculated percentage of the landscape for non-stocked 

seral stage consisting of unregenerated past timber harvest 

areas, as past work has shown marten avoid landscapes 

with even moderate amounts of recent clear-cuts (Hargis 

and others 1999). All metrics were derived at 11 scales using circular windows with radii window sizes from 90 m to 

990 m in 90-m increments around each sample point with 

FRAGSTATS (McGarigal and others 2002).

Canopy Closure

Canopy closure was taken from the National Land Cover 

Data canopy closure layer (NLCD 2001). We calculated 

focal mean percent canopy across 11 scales ranging from 

90 m radius to 990 m radius in 90-m increments around 

each point. We produced 88 functionally scaled surfaces 

for canopy closure by transforming the focal mean layers 

with 8 power functions expressing alternative hypothetical 

response curves of marten habitat suitability as a function of 

canopy closure: the linear and power functions of 0.2, 0.4, 

0.6, 0.8, 2nd, 3rd, and 4th power (Figure 3). These curves 

collectively describe a broad range of potential changes in 

marten habitat suitability across canopy closure, including 

non-linear threshold relationships.

Road Density

We used two forms of the roads layer (Cushman and 

others 2006), one depicting only roads that are currently 

maintained, and one depicting so-called “ghost” roads that 

do not appear on current travel maps and include abandoned 

roads in various stages of recolonization by vegetation. We 

calculated the density of roads within focal landscapes centered on each marten detection station at 12 scales ranging 

from 90 m to 1980 m in 180-m intervals around each sample 

point.

Cedar Forest Type

The site suitability for western red cedar was derived for 

each marten detection station from species probability maps. 

We selected this species as a predictor of marten occurrence 

given the strong associations of marten in northern Idaho 

with mesic, riparian forest types (Tomson 1999). These 

maps were created using a random forest ensemble modeling approach based on climatic, topographic, and spectral 

predictor variables (Evans and Cushman 2009).

Bivariate Scaling

The first step in the analysis is bivariate scaling 

(Thompson and McGarigal 2002; Grand and others 2004) 

to identify the scale at which each independent variable is 

most strongly related to American marten occurrence. We 

used a non-parametric difference in means test (PROC 

NPAR1WAY Wilcoxon; SAS Institute 1999-2000; Grand 

and others 2004) to evaluate the nature and strength of relationship between each environmental variable and marten 

Table 2. List of final predictor variables used in logistic regression models after univariate analyses.

Predictor variable Most significant scale (m) Variable acronym

Elevation 1400, s.d. 400 el4s.x14

All Roads 1980 ar1980

Percent Canopy Closure to 4th power 990 canopy4xs_990

Western Red Cedar 90 thpl

Patch Density (landscape level) 990 pd990

PLAND Large Sawtimber (Class 1) 90 pland190

PLAND Non-stocked Areas (Class 5) 990 pland5990

0 

0.2 

0.4 

0.6 

0.8 

1 

1.2 

1 10 19 28 37 46 55 64 73 82 91 100 

Relative Suitability 

% Canopy Closure 

0.2 

0.4 

0.6 

0.8 

Linear 

2nd 

3rd 

4th 

Figure 3. Power functions 

transforming predicted habitat 

suitability as a function of 

canopy closure. We evaluated 

eight power transformations 

ranging from 0.2 to 4th power.
USDA Forest Service RMRS-RP-94. 2012. 5

occurrence across the spatial scales previously described. 

We evaluated the sensitivity of the apparent relationship between each variable and marten occurrence to changes in 

focal scale and identified the scale at which each variable 

was most highly related to marten occurrence. We selected the scale producing the smallest p-value less than 0.05 

and excluded all other scales of each variable from further 

analysis. After this scale selection process was completed, 

we eliminated one of each pair of variables with a Pearson’s 

correlation greater than 0.5.

Logistic Regression Analysis

We used an all-subsets approach to test all combinations 

of our seven variables (Table 3). We used model averaging 

based on AIC weights to produce parameter estimates for a 

final model. To assess model predictive ability, we used the 

max-Kappa cut-point to calculate percent of observations 

correctly classified, sensitivity, and specificity. We computed 

Cohen’s Kappa statistic, percent observations correctly classified, and area under the Receiver Operating Characteristic 

(ROC) curve—known as the “area under curve” (AUC). We 

computed variance inflation factors for all variables in the 

model. We computed the significance of each variable in the 

model with Wald statistics. We checked for over-dispersion 

by computing the ratio of residual scaled deviance to the residual degrees of freedom.

We measured the effects sizes of the variables in the final 

model by calculating probability of marten presence as each 

variable ranged from the 10th to the 100th percentile of values in the sampled dataset, while holding all other variables 

constant at their medians. We calculated the AIC variable 

importance of each variable by summing the AIC model 

weights of all models that included each variable.

Results

Bivariate Scaling

The bivariate scaling analysis revealed that the strength 

and nature of observed relationships between marten occurrence and all measured environmental variables is highly 

dependent on the focal scale within which the variable is 

derived.

Landscape Fragmentation Metrics

We produced bivariate scaling plots for the landscape 

fragmentation metric Patch Density. The highest degree 

of support was at the largest focal landscape scale of 990 

m, with p-value less than 0.005, indicating strong association between Patch Density and marten occurrence at broad 

spatial scales (Figure 4). Also, the strength of the observed 

relationship with these variables was highly scale dependent, with non-significant relationships at the 0.05 level 

when calculated at the finest scale of 90-m focal landscape. 

American marten in the study area occur more frequently 

in landscapes with low density of patches, indicative of unfragmented landscapes dominated by large and contiguous 

patches.

Landscape Composition Metrics

We produced bivariate scaling plots for two landscape 

composition metrics: Percentage of Landscape (PLAND 1; 

Figure 5) for the late seral forest class, and Percentage of the 

Landscape in non-stocked (clear-cut and recent fire) class 

(PLAND 5; Figure 6). Scaling suggests significant positive 

relationships between marten occurrence and area of late 

seral forest at the finest (90 m) scale tested. There were highly significant negative associations with landscape area in 

non-stocked cover types across all 11 tested focal landscape 

scales. This metric also showed two peaks of support, with 

very highly significant negative association between marten 

occurrence and area of non-stocked cover type at 360 m and 

990 m radius focal landscape.

Road Density

We evaluated two forms of the road data: roads that are 

maintained in drivable condition (Open Roads), and All 

Roads, including so-called abandoned and unmaintained 

“ghost” roads that don’t appear in current travel maps but 

still remain in varying levels of regeneration in the landscape (Figure 7). There was a clear difference between the 

two forms of road data, with the All Roads form having lower p-value (stronger relationship) at all spatial scales. Marten 

detection was most strongly affected by road density at the 

broadest scale tested, corresponding to focal landscapes with 

1980-m radius.

Canopy Closure

We evaluated the relationship between marten occurrence and canopy closure across 88 combinations of focal 

landscape extent and power transformation of percent canopy closure. This bivariate scaling indicated a strong peak 

in significance at the broadest landscape extent tested, with 

martens positively associated with high amounts of average canopy closure at a 990-m radius focal landscape scale 

(Figure 8). There was relatively little difference among power transformations (Figure 3) of canopy closure and marten 

occurrence, with marginally higher support for canopy closure to the 4th power compared to the other transformations.

Elevation

We evaluated 72 Gaussian elevation variables consisting 

of a factorial combination of nine levels of mean elevation 

and eight levels of standard deviation. Bivariate scaling revealed very strong positive association with the Gaussian 

elevation index with mean of 1400 m and a 400-m standard deviation. There was relatively little differentiation 

among different standard deviations of the elevation relationship compared to the strong differentiation among mean 

elevations.
6 USDA Forest Service RMRS-RP-94. 2012.

Table 3. Models, AICc, and AIC model weights for all candidate models with non-zero AIC weights. The variable acronyms 

correspond to those give in Table 2. Delta—difference in AICc between the model on that line and the model with the 

lowest AICc; wi—AIC weight of the model on that line.

Model AICc Delta wi

canopy4xs_990+ar1980+el4s.x14+thpl+pland5990 469.539 0 0.087834

canopy4xs_990+ar1980+el4s.x14+thpl+pd990+pland5990 469.644 0.105 0.083341

canopy4xs_990+el4s.x14+thpl+pd990+pland5990 470.317 0.778 0.059528

canopy4xs_990+ar1980+el4s.x14+thpl+pland5990+pland190 470.841 1.302 0.045807

canopy4xs_990+el4s.x14+pd990+pland5990 470.997 1.458 0.04237

canopy4xs_990+ar1980+el4s.x14+pd990+pland5990 471.083 1.544 0.040587

canopy4xs_990+ar1980+el4s.x14+thpl+pd990 471.09 1.551 0.040445

canopy4xs_990+ar1980+el4s.x14+thpl+pd990+pland5990+pland190 471.225 1.686 0.037805

canopy4xs_990+ar1980+el4s.x14+pland5990 471.29 1.751 0.036596

canopy4xs_990+el4s.x14+thpl+pd990 471.291 1.752 0.036578 

canopy4xs_990+el4s.x14+thpl+pd990+pland5990+pland190 471.388 1.849 0.034846

canopy4xs_990+ar1980+el4s.x14+thpl 471.768 2.229 0.028816

canopy4xs_990+el4s.x14+thpl+pland5990 471.957 2.418 0.026218

canopy4xs_990+el4s.x14+thpl+pd990+pland190 472.064 2.525 0.024852

canopy4xs_990+el4s.x14+pd990+pland5990+pland190 472.159 2.62 0.023699

canopy4xs_990+el4s.x14+pd990 472.197 2.658 0.023253

canopy4xs_990+el4s.x14+thpl+pland5990+pland190 472.28 2.741 0.022308

canopy4xs_990+ar1980+el4s.x14+thpl+pd990+pland190 472.381 2.842 0.021209

canopy4xs_990+ar1980+el4s.x14+pland5990+pland190 472.581 3.042 0.019191

canopy4xs_990+ar1980+el4s.x14+thpl+pland190 472.622 3.083 0.018802

canopy4xs_990+ar1980+el4s.x14+pd990+pland5990+pland190 472.669 3.13 0.018365

canopy4xs_990+ar1980+el4s.x14+pd990 472.687 3.148 0.0182

canopy4xs_990+el4s.x14+pland5990 472.758 3.219 0.017566

canopy4xs_990+el4s.x14+pd990+pland190 473.073 3.534 0.015006

canopy4xs_990+el4s.x14+pland5990+pland190 473.191 3.652 0.014146

canopy4xs_990+el4s.x14+thpl+pland190 473.495 3.956 0.012151

canopy4xs_990+el4s.x14+thpl 473.664 4.125 0.011167

canopy4xs_990+ar1980+el4s.x14 473.747 4.208 0.010713

canopy4xs_990+ar1980+el4s.x14+pd990+pland190 473.987 4.448 0.009501

canopy4xs_990+thpl+pd990+pland5990+pland190 474.577 5.038 0.007074

canopy4xs_990+ar1980+el4s.x14+pland190 474.586 5.047 0.007042

canopy4xs_990+pd990+pland5990+pland190 474.605 5.066 0.006976

canopy4xs_990+ar1980+thpl+pd990+pland5990 474.663 5.124 0.006776

canopy4xs_990+el4s.x14+pland190 474.689 5.15 0.006689

canopy4xs_990+pd990+pland5990 474.719 5.18 0.006589

canopy4xs_990+el4s.x14 474.745 5.206 0.006504

canopy4xs_990+thpl+pd990+pland5990 474.937 5.398 0.005909

canopy4xs_990+ar1980+thpl+pd990+pland5990+pland190 474.98 5.441 0.005783

canopy4xs_990+ar1980+pd990+pland5990 475.016 5.477 0.00568

canopy4xs_990+ar1980+pd990+pland5990+pland190 475.476 5.937 0.004513

canopy4xs_990+ar1980+thpl+pland5990+pland190 475.661 6.122 0.004114

ar1980+el4s.x14+thpl+pd990+pland5990 476.07 6.531 0.003353

canopy4xs_990+ar1980+thpl+pland5990 476.194 6.655 0.003152

canopy4xs_990+ar1980+pland5990+pland190 476.391 6.852 0.002856

el4s.x14+thpl+pd990+pland5990 476.479 6.94 0.002733

canopy4xs_990+thpl+pland5990+pland190 476.634 7.095 0.002529
USDA Forest Service RMRS-RP-94. 2012. 7

Table 3. Continued.

Model AICc Delta wi

canopy4xs_990+pland5990+pland190 476.725 7.186 0.002417

canopy4xs_990+ar1980+pland5990 476.768 7.229 0.002365

ar1980+el4s.x14+thpl+pland5990 476.808 7.269 0.002319

el4s.x14+thpl+pd990+pland5990+pland190 477.415 7.876 0.001712

ar1980+el4s.x14+thpl+pd990+pland5990+pland190 477.534 7.995 0.001613

canopy4xs_990+thpl+pd990+pland190 477.576 8.037 0.001579

el4s.x14+pd990+pland5990 477.587 8.048 0.001571

canopy4xs_990+pd990+pland190 477.738 8.199 0.001456

ar1980+el4s.x14+thpl+pland5990+pland190 477.864 8.325 0.001367

ar1980+el4s.x14+pd990+pland5990 477.95 8.411 0.00131

canopy4xs_990+pland5990 478.409 8.87 0.001041

el4s.x14+thpl+pd990 478.547 9.008 0.000972

canopy4xs_990+thpl+pland5990 478.628 9.089 0.000933

ar1980+el4s.x14+thpl+pd990 478.637 9.098 0.000929

el4s.x14+pd990+pland5990+pland190 478.638 9.099 0.000929

canopy4xs_990+ar1980+thpl+pd990+pland190 478.687 9.148 0.000906

canopy4xs_990+pd990 478.969 9.43 0.000787

el4s.x14+thpl+pd990+pland190 479.013 9.474 0.00077

canopy4xs_990+thpl+pd990 479.11 9.571 0.000733

el4s.x14+thpl+pland5990+pland190 479.165 9.626 0.000714

canopy4xs_990+ar1980+pd990+pland190 479.182 9.643 0.000707

ar1980+el4s.x14+pland5990 479.194 9.655 0.000703

el4s.x14+thpl+pland5990 479.233 9.694 0.00069

ar1980+el4s.x14+pd990+pland5990+pland190 479.425 9.886 0.000627

canopy4xs_990+ar1980+thpl+pd990 479.638 10.099 0.000563

ar1980+el4s.x14+thpl+pd990+pland190 479.646 10.107 0.000561

el4s.x14+pd990 479.687 10.148 0.00055

0 

0.05 

0.1 

0.15 

0.2 

0.25 

0.3 

0.35 

0.4 

90 180 270 360 450 540 630 720 810 900 990 

1-tail KW p-value 

Window Radius 

Figure 4. Bivariate scaling plot of 1-tail 

Kruskal-Wallis p-value for relationship 

between marten occurrence and 

landscape-level patch density. The graph 

indicates highly significant association of 

marten with low patch density at spatial 

scales above 450, with highest support at 

the largest focal extent tested, 990 m.
8 USDA Forest Service RMRS-RP-94. 2012.

0	
  

0.05	
  

0.1	
  

0.15	
  

0.2	
  

0.25	
  

90	
   180	
  270	
  360	
  450	
  540	
  630	
  720	
  810	
  900	
  990	
  

1-­‐tail	
  KW	
  p-­‐value	
  

Window	
  Radius	
  

Figure 5. Bivariate scaling plot of 1-tail KruskalWallis p-value for relationship between marten 

occurrence and percentage area in late seral 

forest cover across 11 spatial scales. The graph 

indicates significant association between marten 

occurrence and area of late seral forest in a focal 

landscape at scales from 90 m up to 450 m, with 

the strongest relationship at the smallest window 

size tested, 90 m.

Figure 6. Bivariate scaling plot of 1-tail KruskalWallis p-value for relationship between marten 

occurrence and percentage area in the nonstocked cover type across 11 spatial scales 

The graph shows significant negative effect 

of area of non-stocked clear-cuts in the focal 

landscape surrounding detection stations at 

all scales tested, with the highest effect at the 

largest window size tested, 990 m. 0	
  

0.002	
  

0.004	
  

0.006	
  

0.008	
  

0.01	
  

0.012	
  

0.014	
  

0.016	
  

0.018	
  

0.02	
  

90	
   180	
  270	
  360	
  450	
  540	
  630	
  720	
  810	
  900	
  990	
  

1-­‐tail	
  KW	
  p-­‐value	
  

Window	
  Radius	
  

Figure 7. Bivariate scaling plot 

of 1-tail Kruskal-Wallis 

p-value for relationship 

between marten 

occurrence and road 

density across 12 spatial 

scales. All Road—

maintained, unmaintained, 

and abandoned roads; 

Open Road—maintained 

roads. The figure shows a 

stronger negative effect of 

road density of all roads 

than maintained roads 

across all scales evaluated. 

The graph also shows the 

largest negative effect of 

road density on marten 

occurrence at the largest 

window size tested, 

1980 m.
USDA Forest Service RMRS-RP-94. 2012. 9

Figure 8. Bivariate scaling factorial for 88 combinations of focal landscape window radius and mean canopy closure 

transformed by 8 power functions (Figure 2). The surface indicates a strong peak of support for a positive relationship 

between mean landscape canopy closure at a 990-m landscape radius, with marginally higher support for canopy closure 

to the 4th power than other power transformations.

Logistic Regression Analysis

Following bivariate scaling, there were seven final predictor variables for logistic regression modeling (Table 2). 

Seventy-three models produced from an all-subsets analysis of predictor variables have non-zero AIC weights, with 

11 models within 2 AICc (AIC corrected for small sample 

size) units of the top model. Thus, the support of the nominally top model as the exclusive basis for interpretation is very 

low. Thus, we used model averaging based on AIC weights to 

produce a final averaged model that equitably represents the 

support for all candidate models (Table 4). The averaged model indicated that occurrence probability for American marten 

increases with the average percent canopy closure to the 4th

power within a 990-m focal radius, percentage of a 90-m focal 

landscape occupied by late seral forest, and site suitability for 

western red cedar. Occurrence probability peaked with respect to elevation at 1400 m and 400 m standard deviation. 

The averaged model indicated that occurrence probability of 

American marten decreased with density of all roads within 

a 1980-m focal radius, percentage of 990-m focal radii in 

non-stocked timber areas, and landscape-level patch density 

within a 990-m focal radius.

Variable Importance

We assessed variable importance in two ways. First, we 

calculated the AIC variable importance, which measured 

the cumulative AIC weight of all models that include a given variable (Table 4). AIC importance value of 1.0 indicates 

Table 4. Parameters of final averaged model, AIC variable importance values, and variable effects size. AIC 

importance values are the sum of AIC model weights for all models that include the variable. The variable 

effects size (change in P [10%-100%]) is proportional change in probability of detection as the focal 

variable is changed from the 10th to the 100th percentile of values in the dataset.

intercept canopy roads elev cedar pland5 pd pland1

Averaged coefficient -0.30 0.42 -0.11 0.35 0.13 -0.07 -0.20 0.13

Averaged standard error 0.11 0.15 0.12 0.13 0.11 0.22 0.12 0.15

Importance values 1.00 0.59 1.00 0.64 0.57 0.60 0.35

Change in P (10%-100%) 61.05 -53.05 19.78 77.21 -35.99 -46.26 13.21
10 USDA Forest Service RMRS-RP-94. 2012.

that the variable is included in all models that have non-zero 

AIC weight. Values less than one indicate the cumulative 

AIC weight of all models including that variable. Based 

on AIC variable importance, canopy closure within a 990-

m focal radius and a Gaussian function of elevation with 

optimum at 1400 m are equally important, with AIC importance value of 1.0. Four other variables had AIC importance 

values over 0.5, indicating substantial importance in the 

averaged model. In rank order importance, these variables 

are: site-level suitability for western red cedar, patch density, road density, and percentage of the landscape within 

a 990-m focal radius occupied by non-stocked clear-cuts.

Our second measure of variable importance presents the 

change in probability of marten occupancy as each variable 

changes from its 10th to 100th percentile in the sample data 

set, holding all other variables constant at their medians 

(Table 4). Based on this measure of variable importance, 

site-level suitability for cedar was identified as the most 

important variable. Probability of marten occurrence increased 77% as site suitability for cedar rose from its 10th

percentile (approximately 23% probability of cedar occurrence) to 100th percentile (approximately 78% probability 

of cedar occurrence). The second most important variable 

based on effect size was canopy cover within a 990-m focal landscape. Probability of marten occurrence increased 

61% as canopy cover rose from its 10th to 100th percentile 

(66% to 95% canopy closure). Road density was the third 

most important variable based on effects size, with marten 

probability of occurrence decreasing 53% as road density 

rose from its 10th to 100th percentile. Density of patches 

of different seral stages in the landscape is the next most 

important variable based on effects size, with probability 

of marten occurrence dropping by 46% as patch density 

changes from the 10th to the 100th percentile of values in 

our dataset. Similarly, probability of marten occurrence 

decreases by 36% as the proportion of 990-m radius focal 

landscape occupied by non-stocked clear cuts rises from 

the 10th to the 100th percentile. Elevation and percentage of 

local area occupied by late seral forest were the least important variables based on effects size.

Model Assessment

The final average model was very highly significant 

based on Wald statistics (Table 5). Three of seven variables in the final model are statistically significant based on 

Wald scores and p-values at a 0.05 level—(canopy4sx_990, 

THPL, elev1400). All eight variables had variance inflation 

factors near 1, indicating no effect of unaddressed collinearity inflating explained variance (Table 5). The ratio of 

scaled residual deviance to scaled degrees of freedom was 

1.2745, indicating little model over-dispersion.

Model Prediction

The Generalized R2 N index of Nagelkerke indicates that 

approximately 15% of the variance in marten occurrence 

Table 5. Wald statistics and variance inflation factors (VIF) for 

each variable in the final averaged model.

Factor Chi-square P VIF

canopy4xs_990 7.15 0.0075 1.101

All Roads (ar1980) 2.51 0.1134 1.140

Western Red Cedar (thpl) 3.9 0.0483 1.037

pd990 1.19 0.2758 1.144

pland5990 1.92 0.1655 1.076

pland190 0.41 0.5221 1.125

elev5_m1000 9.83 0.0017 1.129

TOTAL 33.74 <.0001

Figure 9. Receiver Operator Characteristic (ROC) plot 

showing the MaxKappa cutpoint of 0.52 and the area 

under the ROC curve (0.70) for the final averaged model.

among sites is accounted for by the model. The AUC is 

0.70, indicating that the model performs moderately well 

in discriminating marten occurrence from non-detection 

(Figure 9). The model has moderate Percent Correctly 

Classified (PCC), high specificity, and moderate sensitivity 

(Table 6; Figure 10). The Kappa statistic indicates that the 

model discriminates between detections and non-detections 

31% better than chance. Figure 11 is a map of predicted 

habitat quality across the study area produced by applying 

the final averaged model.
USDA Forest Service RMRS-RP-94. 2012. 11

Table 6. Cutpoint threshold, percent of observations correctly classified, 

sensitivity, specificity, Kappa, and AUC. We present results for two cutpoints: 

(1) the MaxKappa cutpoint, with a threshold of 0.52, which maximizes the 

Kappa statistic and PCC; and (2) the default cutpoint of 0.50.

Threshold PCC Sensitivity Specificity Kappa AUC

MaxKappa 0.52 0.678 0.541 0.787 0.335 0.70

Default 0.50 0.659 0.57 0.723 0.303 0.70

Figure 10. Histogram of 

presence and absence 

data points along the 

gradient of predicted 

probability from 0 to 

1. There is a peak of 

presence points at high 

predicted probability of 

occurrence.

Figure 11. Predicted 

probability of marten 

occurrence across the 

study area as function 

of the final averaged 

model.
12 USDA Forest Service RMRS-RP-94. 2012.

Discussion

A fundamental concept in animal ecology is that each 

species occurs within a limited range of environmental conditions, defining its habitat niche (Hutchinson 1957). Most past 

wildlife habitat relationships modeling has focused on identifying the most important habitat variables. However, in the 

past several decades, it has become evident that identifying 

the operative scale for these variables is equally important to 

the extent that scaling has been proposed as a central question 

in ecology (Levin 1992). Identifying the proper variables, but 

at an incorrect scale, may lead to weak or incorrect apparent 

relationships (Wiens 1989; Thompson and McGarigal 2002; 

Grand and others 2004). In this analysis, we focused explicitly on evaluating the relationships between American marten 

occupancy and several potentially important environmental 

variables across a range of spatial scales.

Scale Dependency of Marten Habitat 

Relationships

The bivariate scaling analysis revealed high sensitivity 

of marten habitat relationships to the scale at which habitat 

variables are measured. We observed strong and consistent 

relationships with the landscape fragmentation variables, with 

uniformly positive association of marten occurrence with 

landscapes that contain high canopy closure, low density, and 

low area of non-stocked clear-cuts.

The roads scaling analysis revealed two interesting patterns. First, it indicated that unmaintained and closed roads 

add explanatory power to the road density relationship, indicating the importance of legacy and “ghost” roads to habitat 

quality for this species. Second, the bivariate scaling analysis 

for marten revealed that the effects of road density on marten 

occurrence have a very large spatial range, with habitat quality diminished by road density at distances up to 2 km from a 

road. This long-distance, diffuse effect suggests that roads can 

have large cumulative effects that extend across broad landscapes and can reduce habitat quality over large extents.

Bivariate scaling of the relationship between canopy 

closure and marten occurrence suggested a strong positive association between high levels of canopy closure at a broad 

landscape scale and marten occurrence. The factorial also 

indicated higher support for canopy closure to the 4th power (Figure 3), suggesting a non-linear relationship in which 

habitat suitability decreases rapidly as average canopy closure 

drops below about 75% at the landscape level. Interestingly, 

this is very similar to the fragmentation threshold for marten 

identified by Hargis and others (1999).

Scaling analysis identified a strong peak of association between marten occurrence and elevation, with marten occurrence 

most likely at 1400 m elevation, with suitability decreasing as a 

Gaussian function with 400-m standard deviation. Interestingly, 

marten are commonly assumed to be high-elevation and subalpine forest associated species in the Rocky Mountains. 

However, our elevation results suggest that this is not the case 

in northern Idaho, with highest marten suitability in middleelevation forest in our study area. We hypothesize this may be 

due to competition with other mustelids (e.g., fisher), forest 

type and presence of western red cedar, and/or winter snow 

depth, where higher elevations and deeper snow would prohibit efficient movement of marten across the landscape.

Predicting Habitat Quality and 

Management Effects

The final averaged model suggested that at scales approximately the size of home ranges within the study area 

(Wasserman 2008), marten select landscapes with high 

average canopy closure, low road density, and low forest fragmentation. Importantly, our analysis indicated that 

American marten respond to canopy closure in a strongly 

non-linear, threshold-like way (Figure 3), such that habitat 

suitability within a focal landscape remains quite low until 

average canopy closure exceeds 40%, with suitability rising 

rapidly as average canopy closure approaches 100%. Within 

these low-fragmentation landscapes, marten select foraging 

habitat at a fine scale within middle-elevation, late-seral, 

mesic forests, often with a large component of western red 

cedar. In northern Idaho, optimum American marten habitat, 

therefore, consists of landscapes with low road density, low 

density of patches, and low percentage of landscape comprised of non-stocked areas, with high canopy closure and 

large areas of middle-elevation, late successional forest.

 While these generalities are informative, managers need 

specific guidance regarding targets in desired amounts and 

configurations of habitat elements at the landscape level. 

Calculation of change in probability of occurrence across 

each variable provides a detailed understanding of both the 

relative effects of each variable in the final model (Table 4; 

Figures 12-16). This information is useful to managers in 

order to quantitatively evaluate the probable effects of particular management alternatives on marten habitat suitability 

at the project and landscape scales.

Biophysical site potential for western red cedar is the 

variable with the largest effect on marten probability of occurrence (Figure 12). A change from 23% (10th percentile in 

the dataset) probability of cedar occurrence to 75% (100th

percentile in the dataset) probability of cedar occurrence 

resulted in a 77% increase in the probability of marten occurrence (from ~35% to ~62%). This indicates that American 

marten favor wet and productive forest types that have high 

biophysical suitability for the occurrence of western red cedar. Tomson and others (1999) also found that marten habitat 

use was highest in mesic forest in riparian zones in northern 

Idaho. However, the very strong association with this mesic, 

middle-elevation forest type is at odds with the commonly 

held belief that marten habitat is optimum in late seral, highelevation forest. Our results suggest that high-elevation forests, such as those dominated by subalpine fir and Engelmann 

spruce in our study area, are substantially less valuable as 

habitat to marten.
USDA Forest Service RMRS-RP-94. 2012. 13

The average canopy cover within a 990-m focal landscape 

was the variable with the second largest marginal effect on 

probability of marten detection (Figure 13). An increase in 

average canopy closure within a 990-m radius focal landscape from its 100th percentile in the database (67% canopy 

closure) to the 100th percentile in the database (94% canopy 

closure) results in an increase in the probability of marten 

detection from 37% to 61%, holding all other variables constant at their medians. This indicates that canopy clover at a 

broad landscape scale has very strong associations with marten occurrence. This broad scale effect suggests that marten 

select home ranges within landscapes with high canopy cover at scales at of 1 km or larger.

The density of all roads at the broadest landscape scale 

tested (1980 m) was the third strongest predictor variable 

based on effects size (Figure 14). An increase in road density 

from 1.1 km per km2 (10th percentile) to 7.2 km per km2

(100th percentile) resulted in a decrease in probability of 

marten occurrence from 53% to 35%. This shows substantial 

effects of road density at very broad landscape extents on the 

probability of marten occurrence. Importantly, our analysis 

found that the density of all roads, including those closed 

and abandoned up to several decades in the past, was a higher predictor of marten occurrence than currently maintained 

roads. This emphasizes the import effects of these so-called 

“ghost” roads on landscape fragmentation and wildlife habitat suitability.

Landscape fragmentation, as measured by Patch 

Density, was the variable with the fourth largest effects size 

(Figure 15). Specifically, probability of marten occurrence 

decreased from 56% to 32% as patch density increased from 

its 10th percentile (2.6 patches per 100 ha) to its 100th percentile (12.2 patches per 100 ha). The large effect of patch 

density at broad landscape extents (990-m focal landscapes) 

is consistent with past research that revealed that marten 

respond strongly to small amounts of forest fragmentation 

(Hargis and others 1999).

The area of a 990-m focal landscape occupied by nonstocked clear-cuts was the variable with the fifth largest 

effect size (Figure 16). Probability of marten occurrence 

0.3	
  

0.35	
  

0.4	
  

0.45	
  

0.5	
  

0.55	
  

0.6	
  

0.65	
  

0.2	
   0.3	
   0.4	
   0.5	
   0.6	
   0.7	
   0.8	
  

Probability	
  of	
  Presence	
  

Probability	
  of	
  Western	
  Red	
  Cedar	
  

Figure 12. Change in probability 

of marten detection as the 

site suitabilty for western red 

cedar changes from the 10th

to the 100th percentile in the 

database, holding all other 

variables constant at their 

medians.

0.35	
  

0.4	
  

0.45	
  

0.5	
  

0.55	
  

0.6	
  

0.65	
  

65	
   70	
   75	
   80	
   85	
   90	
   95	
  

Probabiliyt	
  of	
  Presence	
  

Average	
  Canopy	
  Closure	
  990m	
  

Figure 13. Change in 

probability of marten 

detection as the average 

canopy closure within a 

990-m focal landscape 

increases from the 10th

to the 100th percentile in 

the database, holding all 

other variables constant 

at their medians.
14 USDA Forest Service RMRS-RP-94. 2012.

0.3	
  

0.35	
  

0.4	
  

0.45	
  

0.5	
  

0.55	
  

0	
   1	
   2	
   3	
   4	
   5	
   6	
   7	
   8	
  

Probability	
  of	
  Presence	
  

Density	
  of	
  Roads	
  km/km2	
  

Figure 14. Change in probability 

of marten detection as the road 

density within a 1980-m focal 

landscape increases from the 

10th to the 100th percentile 

in the database, holding all 

other variables constant at their 

medians.

0.3	
  

0.35	
  

0.4	
  

0.45	
  

0.5	
  

0.55	
  

0.6	
  

1	
   3	
   5	
   7	
   9	
   11	
   13	
  

Probability	
  of	
  Presence	
  

Patch	
  Density	
  /	
  100	
  ha	
  

Figure 15. Change in probability of 

marten detection as density of 

patches of different seral stage 

within a 990-m focal landscape 

extent increases from the 10th

to the 100th percentile in the 

database, holding all other 

variables constant at their medians.

0.3	
  

0.35	
  

0.4	
  

0.45	
  

0.5	
  

0.55	
  

0	
   5	
   10	
   15	
   20	
   25	
   30	
  

Probability	
  of	
  Presence	
  

Propor1on	
  Non-­‐stocked	
  990m	
  

Figure 16. Change in probability of 

marten detection as the proportion 

of 990-m focal landscape occupied 

by non-stocked clear-cuts increases 

from the 10th to the 100th percentile 

in the database, holding all other 

variables constant at their medians.
USDA Forest Service RMRS-RP-94. 2012. 15

decreased from 52% to 33% as the proportion of a 990-m focal landscape occupied by non-stocked clear-cuts increased 

from 2% (10th percentile) to 28% (100th percentile). This is 

consistent with previous research that showed that marten 

rarely used sites where more than 25% of forest cover was 

removed (Hargis and others 1999).

Elevation was tied with canopy cover as the variable with 

the highest AIC importance value (Table 4). However, it 

was the sixth of seven variables in terms of its effects size 

(Figure 17). Bivariate scaling identified a Gaussian function 

of elevation, with maximum suitability at 1400 m and a standard deviation of 400 m, as the most supported functional 

relationship between marten occurrence and elevation. The 

effects size calculation indicated that a change from the 10th

(751 tm or 2051 m) to the 100th (1400 m) percentile of the 

transformed index increased probability of marten occurrence from 45% to 54%. This result indicates that while there 

is a very strong statistical relationship between elevation and 

marten occurrence (based on AIC variable importance), it 

has a relatively small effect size on actual probability of 

marten being present in comparison to the major landscape 

composition and configuration variables (canopy cover, 

western red cedar, road density, patch density, area nonstocked clear-cuts).

The percentage of a 90-m radius focal landscape consisting of large sawtimber was the least influential predictor 

variable, as judged by both AIC importance and effects size 

(Figure 18). However, local landscape area occupied by 

late seral forest was included in the final averaged model, 

indicating that marten detection was related to the area of 

a local 90-m focal landscape covered in late seral forest. A 

change from 0% to 100% of the focal landscape comprised 

of large sawtimber resulted in an increase in probability of 

occurrence from 48% to 55%. This indicates that American 

marten select for fine-scale foraging habitat within large size 

class, older forests. We can make this inference about foraging habitat due to the scale at which this variable entered the 

model; it was most highly significant at the finest scale tested 

(90 m), which corresponds to fine-scale behavioral choices 

within home ranges, and the hair-snare sampling method 

was built around marten foraging.

0.4	
  

0.42	
  

0.44	
  

0.46	
  

0.48	
  

0.5	
  

0.52	
  

0.54	
  

0.56	
  

700	
   900	
   1100	
   1300	
   1500	
   1700	
   1900	
   2100	
  

Probability	
  of	
  Presence	
  

Meters	
  Eleva3on	
  

Figure 17. Change in probability of 

marten detection from the lowest 

to highest elevation in the study 

area, holding all other variables 

constant at their medians.

0.47	
  

0.48	
  

0.49	
  

0.5	
  

0.51	
  

0.52	
  

0.53	
  

0.54	
  

0.55	
  

-­‐20	
   0	
   20	
   40	
   60	
   80	
   100	
   120	
   Probability	
  of	
  Presence	
  

Propor1on	
  Old	
  Forest	
  90m	
  

Figure 18. Change in probability of 

marten detection as proportion 

of late seral forest within a 90-m 

focal landscape extent increases 

from the 10th to the 100th

percentile in the database, holding 

all other variables constant at their 

medians.
16 USDA Forest Service RMRS-RP-94. 2012.

Management and Conservation 

Implications

The results of this study suggest that unfragmented, 

middle-elevation, mesic forest landscapes with high canopy 

closure are optimal habitat for American marten in northern 

Idaho. This is consistent with the results of previous studies, 

which have shown high sensitivity to landscape fragmentation and perforation by non-stocked clear-cuts (Hargis 

and others 1999), and strong preference of American marten in northern Idaho for mesic riparian forest conditions 

in unfragmented watersheds (Tomson 1999). However, 

this research has revealed several new insights that have 

substantial implications for conservation and management. 

The first is that marten select habitat at multiple spatial 

scales, selecting home ranges within unfragmented landscapes with high canopy closure and low road density; and 

those marten select foraging habitat within late seral, mesic, 

middle-elevation forests. This suggests that marten select 

home ranges largely based on the degree of fragmentation 

at broad landscape scales, which emphasizes the importance 

of maintaining large, unfragmented landscapes. Conversely, 

it suggests that local stand-level late seral forest conditions, 

while necessary, are insufficient to fully characterize habitat. 

Our results also suggest that perforations in the late seral 

matrix can have cumulative effects that extend much farther 

than the boundaries of disturbed patches. The results further 

suggest that late-seral forest types are important for finescale foraging behavior. This multiple-scale habitat selection 

emphasizes that critical habitat elements from both fine and 

broad scales are necessary to create suitable habitat for this 

species. For example, a patch of late-seral forest in a highly 

fragmented landscape with low canopy closure is not likely 

to be utilized. Conversely, a large, un-roaded landscape with 

high canopy closure is not likely to be highly utilized if it 

does not contain substantial areas of mesic, middle-elevation, late seral forest.

The second management implication relates to the importance of low fragmentation, middle-elevation forests. Our 

results show that middle-elevation forests with high biophysical suitability for western red cedar are particularly valuable 

as American marten habitat in northern Idaho. In the study 

area, most past timber harvest has occurred in lower and 

middle elevations, often concentrating on the most productive western red cedar cover types. Past timber management 

in northern Idaho may have been implemented in a way that 

disproportionately affected marten habitat quality, due to 

the: (1) pervasive road network, (2) dispersed clear-cutting 

in small patches, and (3) importance of middle-elevation 

cedar forest. In the post-World War II era, the U.S. Forest 

Service adopted a land management model based on regulated forests. The goal was to exert full control over harvest 

and wildfire across the land base. The first step in implementing a regulated forest was installation of an extensive 

road network. Roads were built throughout National Forest 

System lands, except for administratively withdrawn areas 

such as Wilderness or areas that were too topographically 

extreme for road construction. This resulted in a pervasive 

network of roads throughout the Forest. Our results show 

that road density has long-distance effects and can reduce 

habitat quality up to 2 km from a road. Given the highly extensive road network on National Forest lands in our study 

area, few areas are more than 2 km from a road, and road 

density in most parts of the study area is high enough to substantially affect marten habitat quality (Figure 14).

Second, timber harvest on National Forest System lands 

from the late 1950s until the 1990s was based on dispersing 

relatively small clear-cuts (10-20 ha) widely across the landscape. However, this style of management maximizes forest 

fragmentation and edge density (Wallin and others 1994). 

Forest fragmentation and perforation are among the largest 

negative impacts on marten habitat quality and occurrence 

(e.g., Hargis and others 1999). Our results confirmed this in 

northern Idaho, with large effects of the area of non-stocked 

clear cuts and density of forest patches with different seral 

stages at broad landscape extents. Cushman and McGarigal 

(2006) simulated a range of timber harvest patterns on the 

extent and pattern of American marten habitat and found 

that dispersed clear-cutting, similar to that previously implemented in northern Idaho on Forest Service lands, led 

to much faster and more severe loss of habitat quality for 

American marten than alternative cutting scenarios such as 

aggregated harvest blocks.

Third, past timber harvest in northern Idaho National 

Forest System lands was disproportionately concentrated in 

the high-productivity and highly valuable middle-elevation 

mesic forest types. These forest types provide the highest 

quality marten habitat; thus, harvest focused within these areas may have had more impact on marten populations than 

would be anticipated based on area harvested alone.

The fourth major management implication of 

our work is that the existence of late seral, middleelevation, mesic forests is not a sufficient predictor of highquality marten habitat. Specifically, our results indicate that 

marten are highly sensitive to road density and patch density at a broad landscape scale, suggesting that even if large 

portions of a landscape are composed of late seral, mesic 

forest, the quality of the landscape as marten habitat could 

be markedly reduced if the landscape is traversed by a relatively high density of roads or is perforated by a number 

of small patches with high-contrast edges. Importantly, our 

results also show that abandoned and decommissioned roads 

that do not appear on current travel maps still have substantial impact on marten habitat.

In combination, our findings suggest that marten habitat quality in northern Idaho may have been substantially 

reduced by timber harvest and road building in the past 

100 years. This suggests that remaining roadless areas may 

be important source habitats for this species. Second, if 

marten conservation is a management objective, remaining 

late-seral, mesic forest types should be protected from harvest, some existing roads should be decommissioned and 

revegetated to reduce road densities, and additional roads 

should not be built in high-quality marten habitat.
USDA Forest Service RMRS-RP-94. 2012. 17

Using Models to Assess and Monitor 

Habitat Conditions for American 

Marten

Generally speaking, most habitat models account for 

less than half the variation in species density or abundance (Morrison and others 2008). For example, Cushman 

and others (2008) empirically evaluated a suite of habitat 

models for multiple species and found that even dozens of 

habitat attributes from multiple spatial scales were unable 

to explain a majority of the variance in species abundances. 

Even when a model indicates strong associations between 

the probability of species occurrence and habitat gradients, 

it will usually fail to explain the majority of variability. 

In our case, even though our averaged model was highly 

statistically significant and performed moderately well 

discriminating presences from absences, it only explained 

approximately 15% of the variance in marten occurrence 

among sites. Therefore, the model predictions are not a 

surrogate for population estimation (Cushman and others 

2008). However, models can be effective in evaluating the 

suitability of habitat for the species and the likely effects 

of past and potential future landscape change on habitat 

suitability. It is important for managers to understand the 

distinction between habitat suitability and actual population size.

For habitat monitoring to be useful as a guide for assessing population status, information on how habitat amounts 

or configurations can be linked with population viability or 

detection/non-detection is absolutely essential (Cushman 

and others 2011b). Adaptive management works by specifying resource goals, managing with the purpose to create 

or maintain these desired conditions, and monitoring results to confirm that the system is behaving as expected and 

that resources are moving toward the desired conditions 

(Cushman and McKelvey 2010). This approach presupposes that the state of the system is well known across 

time. The adaptive management paradigm sets high priority on developing ongoing analyses, based on monitoring, 

to continually adjust or change land management planning 

decisions and thereby efficiently move toward desired 

conditions (Cushman and McKelvey 2010). The adaptive 

management cycle involves: (1) a comprehensive evaluation of current resource conditions, (2) frequent monitoring 

and evaluation of conditions and trends relative to desired 

conditions, and (3) adaptation of management to improve 

performance in approaching or maintaining desired conditions. For this cycle to be effective, all three steps must be 

robust.

One way National Forest System managers evaluate 

current habitat conditions for emphasis species in light 

of past management actions is through cumulative effects 

analysis (CEA), a legal requirement under the National 

Environmental Policy Act (Schultz 2010). However, current CEA practice often fails to account for long-term or 

broad-scale impacts. Schultz (2010) identified insufficient 

monitoring and lack of formal thresholds and trigger points 

as a primary impediment to improving CEA. One way to 

improve assessment of habitat conditions of species of concern relative to desired conditions and to assess cumulative 

effects of past management is to integrate formal thresholds 

and trigger points with detailed and specific desired conditions statements (Cushman and others 2011c). It is critical 

to provide a formal declaration of desired conditions, how 

current conditions departure from desired conditions are 

quantified, and how management triggers are established 

to most efficiently move toward desired conditions.

In the context of our habitat model for American marten in northern Idaho, the model-averaged final logistic 

regression equation provides a means to predict habitat 

quality across the study area. Current habitat conditions on 

the analysis area can be mapped using this equation (e.g., 

Figure 11). Cumulative effects of past management activities, such as road building and logging, can be calculated 

by comparing the predicted habitat quality in the current 

landscape to that predicted in the analysis area prior to the 

specified management actions (e.g., Cushman and others 

2006, 2011a). For use in adaptive management, a quantitative amount and configuration of quality habitat in the 

analysis area would be defined as an objective and specified as a desired condition. The model prediction of habitat 

quality would be the indicator used to quantify condition 

and trend relative to the objective. Quantitative triggers, 

expressed in terms of measured attributes, such as amount 

and configuration of quality habitat, would be established 

to identify when management actions would change. For 

adaptive management to be effective, practitioners must 

define their objectives and choose appropriate indicators 

and triggers up front and formally.

Future changes in habitat quality and pattern can be 

monitored over time by reapplying the habitat model to 

the analysis area after each management action or natural 

disturbance changes landscape composition and configuration (e.g., Cushman and others 2011b, 2011c). The area 

and configuration of high-quality marten habitat can then 

be compared with the quantitative and specific desired conditions statements that specify the desired range of quality 

marten habitat in terms of area, patch size, and patch isolation. Observing change away from desired conditions 

would trigger a change in management, while observing 

change toward desired conditions would not.

If a management change is indicated by surpassing a 

specified threshold in habitat area or fragmentation, the 

habitat model can provide useful guidance as to what 

changes are most likely to improve habitat condition effectively in relation to objectives. Specifically, the effects size 

graphs (Figures 12-16) provide a means to prioritize management actions that would have the biggest effects. For 

example, the model predicts that landscape-level canopy 

closure, road density, and forest perforation by clear cuts 

are all strong predictors of broad-scale patterns of marten 

occurrence. Mangers can use the graphs in Figures 12-16 

to determine the expected effects of changing management 
18 USDA Forest Service RMRS-RP-94. 2012.

to increase canopy closure, decrease road density, and/or 

reduce forest perforation by specific amounts. This information can guide a cost-benefit analysis of the management 

tradeoffs necessary to simultaneously meet multiple desired conditions objectives for multiple resources.

Simulation modeling can be particularly useful to guide 

managers in their evaluating alternative management strategies to achieve habitat desired conditions. For example, 

Cushman and others (2011a) used the habitat relationships model for American marten produced by Wasserman 

(2008) in conjunction with landscape dynamic simulation 

to evaluate the interaction among climate change, fire suppression, and vegetation harvest on the extent and quality 

of marten habitat in a watershed in western Montana. The 

analysis simulated landscape conditions under all combinations of a 2x2x2 factorial modeling experiment, which 

enabled formal quantification of the relative effects and 

interactions of climate change, fire suppression, and timber harvest on marten habitat. Importantly, by basing the 

analysis on an empirically developed statistical model, the 

analysis was able to quantitatively predict changes in the 

extent and pattern of quality habitat under each scenario, 

which provided the detailed and specific information managers need to evaluate alternative management scenarios.

Importance of Scale Optimization

For most species, biological information is available 

to guide decisions regarding appropriate variables for 

inclusion in candidate models, which greatly facilitates development of a suite of candidate models. However, the 

effect of a given variable on habitat selection may manifest itself at spatial scales ranging from a few meters to 

kilometers (e.g., Thompson and McGarigal 2002; Grand 

and others 2004). Most habitat selection studies fail to address this issue and simply evaluate alternative models that 

are based on predictor variables from a single, arbitrarily 

selected scale. Scientists have rarely evaluated the optimality of the scales at which they represent variables or the 

effect of incorrectly specifying scale (but see Thompson 

and McGarigal 2002; Grand and others 2004). The analysis 

presented here used a univariate optimization of variable 

scale in relation to marten occupancy, using the approach 

first published by Grand and others (2004). Shirk and others (in press) evaluated the sensitivity of habitat predictions 

in our northern Idaho study area to the scale of variables 

included. To test this, they compared a logistic regression 

model that was scale optimized, as in this paper, to a model 

that was constructed from a single scale of input variables. 

They chose a 90-m focal scale to represent the local patch 

character surrounding each detection station.

Shirk and Cushman (2011) found large differences in 

the scale at which habitat variables optimally predicted 

marten occurrence and the naïve scale of the original data 

set. There were also large differences between the scaled 

and unscaled models in terms of variable coefficients. Two 

variables (Percent Canopy Closure and Roads) changed 

signs between the scaled and unscaled forms of the model. 

This indicates an apparent reversal of relationship with 

marten occurrence as a function of scale, which has large 

implications for interpreting marten habitat selection. In 

addition, the magnitude of the coefficients changed dramatically for most of the other variables included in the 

model.

 There was also substantial difference in predictive performance between the scaled and unscaled models from the 

Idaho study area. The scaled model had a substantially lower AICc value, higher percentage of observations correctly 

classified, and higher Kappa and AUC values. The scaling 

analysis also had a large impact on the apparent effects size 

of variables. Together, these results show that failure to optimize the scale at which each predictor variable relates to 

marten occurrence can have large effects on model performance and interpretation. Figure 19 shows the difference 

in predicted probability of occurrence between the scaled 

and unscaled models. The scaled model predicts substantially higher probability of occurrence than the unscaled 

model in middle-elevation, mesic forested landscapes with 

low road density, while the unscaled map over-predicts 

probability of marten occurrence in less optimal situations, 

such as in low-elevation, non-forested sites. The unscaled 

model is much weaker and provides substantially different predictions, which would lead to dramatically different 

interpretations in relation to what factors are important as 

components of marten habitat and at what spatial scale 

marten most strongly select them.

Differences Between Habitat Quality 

and Habitat Connectivity

We found that marten occurrence in this study area is 

highly dependent on high canopy cover, low forest fragmentation, and low road density at broad spatial scales. 

This sensitivity to forest fragmentation and mesic, middleelevation forest is highly consistent with the preponderance 

of past studies of American marten habitat associations 

(Buskirk and Powell 1994; Ruggiero and others 1994; 

Hargis 1996; Bissonette and others 1997; Chapin and 

others 1998; Hargis and others 1999; Tomson and others 

1999). However, Wasserman and others (2010) found that 

genetic distances were not independently related to any 

of these factors, indicating that habitat selection and gene 

flow of American marten may be driven by different factors 

at different scales. This may not be surprising, as habitat 

selection reflects the behavior of individual organisms to 

maximize fitness within home ranges, while gene flow is 

driven by mating and dispersal events. These are functionally and biologically different processes. Habitat suitability 

may not be a reliable proxy for predicting landscape effects 

on gene flow. This highlights the importance of not assuming that a known habitat relationship optimally reflects the 

landscape features governing gene flow.
USDA Forest Service RMRS-RP-94. 2012. 19

Conclusions

We explored the scale dependency of habitat relationships of American marten in northern Idaho and found 

dramatic differences in the apparent nature and strength of 

relationships between marten occurrence and a number of 

habitat variables across a range of spatial scales. These differences include reversals in the direction of an observed 

association from positive to negative and frequent dramatic 

changes in the apparent importance of a habitat variable as 

a predictor of marten occurrence. Logistic regression on the 

optimally scaled input variables suggests that at the scale 

of home ranges, marten select landscapes with high average canopy closure and low fragmentation. Within these 

low-fragmentation landscapes, marten select foraging habitat at a fine scale within late-seral, middle-elevation mesic 

forests. In northern Idaho, optimum American marten habitat, therefore, consists of landscapes with low road density 

and low density of non-forest patches with high canopy closure and large areas of middle-elevation, late successional 

mesic forest. Comparison of current landscape conditions to 

those expected under the historic range of variability indicated that road building and timber harvest in the past century 

may have substantially reduced the amount of suitable marten habitat in northern Idaho. Our results are generally 

consistent with previous research in the Rocky Mountains, 

with additional insights related to the relative importance, 

functional form, and scale at which each habitat variable has 

the largest influence on marten occurrence.

Figure 19. Difference in predicted probability of presence between the optimized scaled model in this paper and 

a single-scale model from Shirk and others (2011). The areas in blue are predicted to have higher probability of 

marten occurrence in the scaled model than the unscaled model, while areas in red are predicted to have higher 

probability of marten occurrence in the unscaled model than the scaled model. The scaled model predicted 

higher probability of occurrence in high-quality habitat and lower probability of occurrence in low-quality 

habitat than did the unscaled model.
20 USDA Forest Service RMRS-RP-94. 2012.

References

Bissonette, J.X., D.J. Harrison, C.D. Hargis, and T.G. 

Chapin. 1997. The influence of spatial scale and scalesensitive properties on habitat selection by American 

marten. In: J.A. Bissonette, ed. Wildlife and landscape 

ecology. Springer-Verlag, New York: 368-385.

Burnham, K.P., and D.R. Anderson. 2002. Model selection 

and multimodel inference: a practical informationtheoretic approach. 2nd Ed. Springer-Verlag, New York. 

488 p.

Buskirk, S.W., and L.F. Ruggiero. 1994. The American 

marten. In: L.F. Ruggiero, K.B. Aubry, S.W. Buskirk, L.J. 

Lyon, and W.J. Zielinski, eds. American marten, fisher, 

lynx, and wolverine in the western United States. Gen. 

Tech. Rep. RM-254. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Forest 

and Range Experiment Station.

Buskirk, S.W., and R.A. Powell. 1994. Habitat ecology of 

fishers and American martens. In: S.W. Buskirk, A.S. 

Harestad, M.G. Raphael, and R.A. Powell, eds. Martens, 

sables, and fishers: Biology and conservation. Cornell 

University Press, Ithaca. NY: 283-296.

Campbell, T.M. 1979. Short-term effects of timber harvests 

on pine marten ecology. Fort Collins: Colorado State 

University. Thesis. 71 p.

Chapin, T.G., D.J. Hamson, and D.D. Katnik. 1998. In 

audience of landscape pattern on habitat use by American 

marten in an industrial forest. Conservation Biology 12: 

1327-1337.

Cushman, S.A., and K. McGarigal. 2006. Multi-variate 

landscape trajectory analysis: an example using simulation 

modeling of American marten habitat change under three 

disturbance regimes. In: J.A. Bissonette, and I. Storch. 

Temporal explicitness in landscape ecology: wildlife 

responses to changes in time. Springer, New York.

Cushman, S.A., and K.S. McKelvey. 2010. Data on 

distribution and abundance: Monitoring for research and 

management. In: S.A. Cushman and F. Huettman, eds. 

Spatial complexity, informatics and wildlife conservation. 

Springer, Tokyo: 111-130.

Cushman, S.A., K. McGarigal, K. McKelvey, C. Reagan, T. 

Demeo, and C. Vojta. In press a. Landscape analysis of 

wildlife habitat. In: C. Vojta, ed. USFS Wildlife Habitat 

Technical Guide: Chapter 6.

Cushman, S.A., K.S. McKelvey, and M.K. Schwartz. 2008. 

Case study 6.1: DNA survey for fisher in northern Idaho. 

In: R.A. Long, P. MacKay, J.C. Ray, and W.J. Zielinski, 

eds. Noninvasive survey methods for North American 

carnivores. Island Press: 179-190.

Cushman, S.A., K.S. McKelvey, J. Hayden, and M.K. 

Schwartz. 2006. Gene-flow in complex landscapes: 

testing multiple models with causal modeling. American 

Naturalist 168: 486-499.

Cushman, S.A., T. Merrsman, P. Hyatt, T. DeMeo, K. 

McKelvey, K. Brewer, and G. Moisen. In press b. Using 

habitat models for habitat monitoring. In: C. Vojta, ed. 

USFS Wildlife Habitat Technical Guide: Chapter 5.

Cushman, S.A., T.N. Wasserman, and K. McGarigal. 2011a. 

Landscape fire and wildlife habitat. In: D. McKenzie, 

and L.-K. Kellog, eds. The landscape ecology of fire. 

Ecological Studies 213. Springer, New York: Chapter 5.

Evans, J.S., and S.A. Cushman. 2009. Gradient modeling of 

conifer species using random forests. Landscape Ecology 

24(5): 673-683.

Grand, J., J. Buonaccorsi, S.A. Cushman, C.R. Griffin, and 

M.C. Neel. 2004. A multiscale landscape approach to 

predicting bird and moth rarity hotspots in a threatened 

pitch pine-scrub oak community. Conservation Biology 

18(4): 1063-1077.

Hargis, C.D. 1996. The influence of forest fragmentation 

and landscape pattern on American marten and their prey. 

Logan: Utah State University. Dissertation.

Hargis, C.D., and J.A. Bissonette. 1997. Effects of forest 

fragmentation on populations of American marten in the 

intermountain West. In: G. Proulx, H.N. Bryant, and P.M. 

Woodard, eds. Martes: taxonomy, ecology, techniques, 

and management. Provincial Museum of Alberta, 

Edmonton, Canada: 437-451.

Hargis, C.D., J.A. Bissonette, and D.L. Turner. 1999. The 

influence of forest fragmentation and landscape pattern 

on American martens. Journal of Applied Ecology 36: 

157-172.

Hosmer, D.H., and S. Lemshow. 2000. Applied logistic 

regression, 2nd edition. John Wiley and Sons Ltd., New 

York. 392 p.

Idaho Department of Lands. 2006. GIS department for IDL. 

Online GIS layers for IDL. Available at: http://gis1.idl.

idaho.gov/index.htm.

Johnson, J.B., and K.S. Omland. 2004. Model selection in 

ecology and evolution. Trends in Ecology and Evolution 

19: 101-108.

Kotliar, N.B., and J.A. Wiens. 1990. Multiple scales of 

patchiness and patch structure: A hierarchical framework 

for the study of heterogeneity. Oikos 59: 253-260.

Levin, S.A. 1992. The problem of pattern and scale in 

ecology. Ecology 73(6): 1943-1967.

McGarigal, K., S.A. Cushman, M.C. Neel, and E. Ene. 

2002. FRAGSTATS: Spatial pattern analysis program for 

categorical maps. Computer software program produced 

by the authors at the University of Massachusetts, 

Amherst. Available at: www.umass.edu/landeco/research/

fragstats/fragstats.html.

Mills, L.S., K.L. Pilgrim, M.K. Schwartz, and K. McKelvey. 

2000. Identifying lynx and other North American felids 

based on mtDNA analysis. Conservation Genetics 1: 

285-288.
USDA Forest Service RMRS-RP-94. 2012. 21

Morrison, M.L., W.M. Block, M.D. Strickland, B.A. Collier, 

and M.J. Peterson. 2008. Wildlife study design. 2nd ed. 

New York, Springer.

Nagelkerke, N. 1991. A note on a general definition of the 

coefficient of determination. Biometrika 78(3): 691-692.

National Land Cover Data. 2001. Available at: http://www.

epa.gov/mrlc/nlcd-2001.html.

Rabus, B., M. Eineder, A. Roth, R. Balmer. 2003. The 

shuttle radar topography mission—a new class of 

digital elevation models acquired by spaceborne radar. 

Photogrammetric Engineering and Remote Sensing 57: 

241-262.

Riddle, A., K.L. Pilgrim, L.S. Mills, K.S. McKelvey, 

and L.F. Ruggiero. 2003. Identification of mustelids 

using mitochondrial DNA and non-invasive sampling. 

Conservation Genetics 4: 241-243.

R-project. 2008. Version 2.7.0. Available at: http://www.rproject.org/.

Ruggiero, L.F., K.B. Aubry, S.W. Buskirk, L.J. Lyon, and 

W.J. Zielinski. 1994. American marten, fisher, lynx 

and wolverine in the western United States. Gen. Tech. 

Rep. RM-254. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station.

SAS Institute Inc., Cary, NC. 2007. SAS Software Package. 

Available at: http://www.sas.com. Copyright © 2007 

SAS Institute Inc.

Schultz, C. 2010. Challenges in connecting cumulative 

effects analysis to effective wildlife conservation 

planning. Bioscience 60(7): 545-551.

Shirk, A.J, and S.A. Cushman. 2011. sGD software for 

estimating spatially explicit indices of genetic diversity. 

Molecular Ecology Resources 11: 923-934.

Shirk, A.J., T.N. Wasserman, S.A. Cushman, and M.G. 

Raphael. In press. Scale dependency of American marten 

(Martes americana) habitat relationships. Biology and 

Conservation of Martens, Sables, and Fishers: a New 

Synthesis. Cornell University Press, Ithaca, NY.

Thompson, C.M., and K. McGarigal. 2002. The influence 

of research scale on bald eagle habitat selection along 

the lower Hudson River, New York (USA). Landscape 

Ecology 17: 569-586.

Thompson, I.D., and A.S. Harestad. 1994. Effect of 

logging on American martens, and models for habitat 

management. In: S.W. Buskirk, A.S. Harestad, M.G. 

Raphaeland, and R.A. Powell, eds. Martens, sables, and 

fishers: biology and conservation. Cornell University 

Press, Ithaca, NY: 355-367.

Tomson, S.D. 1999. Ecology and summer/fall habitat 

selection of American marten in northern Idaho. Missoula: 

University of Montana. Thesis. 80 p.

Wallin, D.O., F.J. Swanson and B. Marks. 1994. Landscape 

pattern response to changes in the pattern-generation rules: 

land-use legacies in forestry. Ecological Applications 

4(3): 569-580.

Wasserman, T.N. 2008. Habitat relationships and gene flow 

of Martes americana in northern Idaho. Bellingham: 

Western Washington University. Thesis. 145 p.

Wasserman, T.N., S.A. Cushman, A.S. Shirk, E.L. Landugth, 

and J.S. Littell. 2011 Simulating the effects of climate 

change on population connectivity of American marten 

(Martes americana) in the northern Rocky Mountains, 

USA. Landscape Ecology. 10.1007/s10980-011-9653-8.

Wasserman, T.N., S.A. Cushman, M.K. Schwartz, and D.O. 

Wallin. 2010. Spatial scaling and multi-model inference in 

landscape genetics: Martes americana in northern Idaho. 

Landscape Ecology. doi: 10.1007/s10980-010-9525-7.

Wiens, J.A. 1989. Spatial scaling in ecology. Functional 

Ecology 3(4): 385-397.
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