United States

Department of 

Agriculture

Forest Service

Northeastern 

Research Station

Research Paper NE-729

A Habitat Model for the Virginia 

Northern Flying Squirrel 

(Glaucomys sabrinus fuscus) in 

the Central Appalachian Mountains

Jennifer M. Menzel

W. Mark Ford

John W. Edwards

Leah J. Ceperley
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Authors

JENNIFER M. MENZEL is a research wildlife biologist, formerly with the USDA Forest 

Service, Northeastern Research Station, Parsons, WV 26287.

W. MARK FORD is a research wildlife biologist, USDA Forest Service, Northeastern 

Research Station, Parsons, WV 26287.

JOHN W. EDWARDS is an associate professor, Division of Forestry, West Virginia 

University, Morgantown, WV 26506.

LEAH J. CEPERLEY is an ecologist, USDI Fish and Wildlife Service, Canaan Valley 

National Wildlife Refuge, Davis, WV 26260.

Abstract

The Virginia northern flying squirrel (Glaucomys sabrinus fuscus) is an endangered sciurid 

that occurs in the Allegheny Mountains of Virginia and West Virginia. Despite its status, 

few of its ecological requirements have been synthesized for landscape-level predictive 

distributions to facilitate habitat delineation efforts. Using logistic regression, we developed 

a GIS-based habitat model for the Virginia northern flying squirrel using micro- and 

macrohabitat relationship data in West Virginia. Important habitat characteristics obtained 

from radio-collared squirrels included: (1) elevation over 1036 m; (2) northerly aspects; and 

(3) red spruce (Picea rubens) and mixed northern hardwood-red spruce cover types. A final 

model retaining elevation and forest cover type showed reasonably high predictive power 

across a large portion of the Allegheny Mountains in West Virginia.

Cover Photos

Left, view of North Fork Mountain looking east from Spruce Knob, Pendleton County, West 

Virginia; right, Virginia northern flying squirrel (Glaucomys sabrinus fuscus) courtesy of Craig 

Stihler, West Virginia Division of Natural Resources.

Manuscript received for publication 16 September 2005
1

Introduction

Managing endangered species is often a daunting task 

for natural resource professionals, as typically there is a 

paucity of species-specifi c conservation guidelines. For 

common wildlife species, data are gathered through 

both detailed macro- and microhabitat research on 

habitat use, home ranges, food habits, etc. (Shriner et 

al. 2002) that enables natural resource professionals 

to develop scientifi cally based management strategies 

(Vanderpoorten et al. 2005). However, for many 

endangered species, the consequence of uncertainty is 

that management plans frequently are not developed. 

Geographic information systems (GIS) can be used 

to construct spatially explicit habitat models for both 

common and rare wildlife species (McCombs 1997, 

Gibson et al. 2004, Posillico et al. 2004). With adequate 

data collection, such models often can analytically 

identify specifi c habitat characteristics across wider 

landscapes with high predictive power and therefore 

provide considerable utility. Accordingly, habitat 

models are especially useful for rare, threatened, or 

endangered species, such as the Virginia northern fl ying 

squirrel (Glaucomys sabrinus fuscus) that inhabit forested 

landscapes, yet are diffi cult to physically survey because 

of their low population density, patchy distribution, and 

cryptic nature. By identifying occupied or potentially 

occupied areas for the Virginia northern fl ying squirrel, 

recovery efforts through forest-habitat conservation and 

active restoration can focus on specifi c habitats delineated 

by the habitat model (Schuler et al. 2002).

The Virginia northern fl ying squirrel is one of two 

federally endangered subspecies of the northern 

fl ying squirrel restricted to relict, montane boreal and 

northern hardwood forests in the central and southern 

Appalachians. The Virginia subspecies occurs or could 

possibly occur in nine counties located in the high 

elevations of the Allegheny Mountains in eastern West 

Virginia and extreme northwestern Virginia (WellGosling and Heaney 1984, U.S. Fish and Wildlife Service 

1985, Odom et al. 2001). Live trapping and nest box 

surveys over the past two decades have shown that the 

Virginia northern fl ying squirrel is mostly associated 

with forest stands dominated by red spruce (Picea 

rubens) (Urban 1988, Stihler et al. 1995, Ford et al. 

2004). This forest type was estimated to exceed 200,000 

ha in this region prior to the exploitative logging and 

widespread wildfi res in the early 20th century (Korstian 

1937). At present, however, less than 15 percent of the 

original area remains dominated by red spruce with the 

remainder having been replaced by northern hardwood 

forests with a much reduced conifer component (Adams 

and Stephenson 1989, Schuler et al. 2002). Under the 

aegis of the United States Endangered Species Act of 

1973, the presence of Virginia northern fl ying squirrels 

severely restricts active management for most purposes 

of remaining montane boreal stands in the Allegheny 

Mountains. Trepidation to undertake management 

activities, such as red spruce restoration that could benefi t 

the Virginia northern fl ying squirrel, is due in part to 

the poorly defi ned distribution of the species in the 

region. Nonetheless, the restricted distribution of this 

subspecies makes it an excellent candidate for a habitat 

model because the range is large enough to encompass 

a landscape-level analysis, but small enough to be 

geographically defi nable and logistically manageable.

Preliminary predictive habitat models were developed 

for a restricted portion of the Virginia northern fl ying 

squirrel’s distribution in Virginia and West Virginia using 

an “exclusionary approach” generated from capture data 

from trapping and nest box monitoring (McCombs 

1997, Odom et al. 2001). This method provided a 

useful quantitative “fi rst step” for modeling endangered 

species distributions where specifi c habitat requirements 

are diffi cult to obtain. It is important to note, however, 

a generally untested assumption of the exclusionary 

modeling approach is that capture locations are indicative 

of quality habitat. If individuals are artifi cially attracted 

to an area, or if presence is enhanced via baiting, or if 

a capture method such as nest boxes provides artifi cial 

den sites, the results of the habitat model could be 

misleading.

Although robust for predicting nonoccurrence, the 

attempt by Odom et al. (2001) to model Virginia 

northern fl ying squirrel habitat was inconclusive in its 

ability to accurately predict presence in habitats not 

surveyed directly by nest boxes or trapping. Odom et al. 

(2001) suggested that more detailed microhabitat data 
2

would be required to develop more accurate habitat 

models. Therefore, our primary objective was to use 

spring and summer nest-tree and habitat-use data from 

a concurrent radiotelemetry study of Virginia northern 

fl ying squirrels (Menzel 2003) to quantify a descriptive 

habitat model that would prioritize areas for conservation 

and management of the subspecies. Following Ford et 

al. (2004) as well as other anecdotal evidence and expert 

opinion, we hypothesized that increasing elevation and 

forest stand dominance of red spruce and other highelevation montane conifers, such as the widely planted 

exotic Norway spruce (Picea abies) and the native eastern 

hemlock (Tsuga canadensis), would result in progressively 

increasing probability of occurrence of Virginia northern 

fl ying squirrels.

Materials and Methods

Study Area

To obtain habitat-use data to incorporate into our 

habitat modeling, we radio-tracked Virginia northern 

fl ying squirrels in the Allegheny Mountains from May 

to September 2000 and from May to August 2001 

on the MeadWestvaco Ecosystem Research Forest 

(MWERF) near Adolph, WV (38.74oN, 80.05oW; 

Randolph County) and on the Monongahela National 

Forest (MNF) near Parsons, WV (38.55o N, 79.83o 

W; Randolph and Tucker counties). Specifi c sites on 

the MNF included Stuart Knob, Canaan Heights, and 

McGowan Mountain areas.

The MWERF is 3,360 ha intensively managed industrial 

forest (Ford and Rodrigue 2001); whereas the MNF 

is a 367,455 ha national forest that contains a variety 

of forest conditions and stand ages from seminatural 

unmanaged mature second-growth to large plantations 

of Norway spruce. Our habitat model study site was 

centered on the proclamation boundary of the MNF and 

the MWERF; however, we included several other areas 

of state, federal and privately owned land within and 

adjacent to the MNF and the MWERF, such as Canaan 

Valley National Wildlife Refuge, Kumbrabow State 

Forest, and Snowshoe Mountain Resort.

Regionally, topography consisted of northeast- to 

southwest-oriented steep ridges divided by narrow 

valleys. Elevation ranged from 700 to 1450 m. The 

climate is generally cool and moist with annual 

precipitation ranging from 120 to 150 cm, much 

of which occurs as snow during the winter months 

(Stephenson 1993). The forest community at the higher 

elevations (>1000 m) was comprised of red spruce, 

eastern hemlock, sugar maple (Acer saccharum), red 

maple (A. rubrum), yellow birch (Betula alleghaniensis), 

American beech (Fagus grandifolia) and black cherry 

(Prunus serotina).

Scattered throughout the higher elevations were planted 

stands of Norway spruce. The forest community at the 

lower elevations (<1000 m) was a mixed mesophytic 

type composed of yellow-poplar (Liriodendron tulipifera), 

basswood (Tilia americana), northern red oak (Quercus 

rubra), black birch (B. lenta), red maple, sugar maple, 

American beech, hickory (Carya spp.), chestnut oak (Q. 

montana), and white oak (Q. alba) in varying amounts, 

depending on aspect and site quality.

For the habitat model, we used all or portions of 44 

U.S. Geological Survey (USGS) 7.5-minute topographic 

quadrangle maps from eastern West Virginia: Adolph, 

Bergoo, Beverly East, Blackbird Knob, Bowden, Cass, 

Circleville, Davis, Droop Mountain, Durbin, Edray, 

Elkins, Fork Mountain, Glady, Greenbank, Greenland 

Gap, Harman, Hightown, Hillsboro, Hopeville, 

Laneville, Lead Mine, Lobelia, Marlinton, Mingo, 

Mill Creek, Mount Storm Lake, Mozark Mountain, 

Onego, Parsons, Pickens, Samp, Sharp Knob, Sinks of 

Gandy, Snowy Mountain, Spruce Knob, Synder Knob, 

Thornwood, Valley Head, Webster Springs SE, Webster 

Springs SW, Whitmer, Widell and Woodrow. These areas 

are known to encompass the known distribution of the 

Virginia northern fl ying squirrel and/or high elevation 

montane habitats in West Virginia.

Habitat Model Construction

Data from Menzel (2003), Menzel et al. (2004), and 

Menzel et al. (in press) were used to build a predictive 

model of occupancy. These data showed that forest 

types dominated by either red or Norway spruce 

and northern hardwoods with a considerable spruce 

component (≥ 50 percent) were selected preferentially 
3

over pure northern hardwood or mixed mesophytic forest 

types. Additionally, north-facing slopes and elevations 

greater than 1036 m were used more than would be 

expected based on landscape availability. Therefore, we 

incorporated these parameters into the construction of 

our habitat model.

Because previous attempts to model Virginia northern 

fl ying squirrel habitat using the exclusionary approach 

had been equivocal (McCombs 1997, Odom 2001), 

we included quantitative microhabitat data into a 

model to more accurately refl ect habitat quality. We 

constructed our habitat model using the variables of nest 

tree (elevation) and vegetative community (montane 

conifer) characteristics (Menzel 2003, Ford et al. 2004). 

Signifi cant variables in the habitat analysis included 

land cover types and topographic surface characteristics 

obtained from spatial information generated from 

monitoring radio-collared Virginia northern fl ying 

squirrels. A more detailed explanation of the variables 

in the nest tree and habitat use portion is provided by 

Menzel (2003) and Menzel et al. (2004, In Press).

We determined land-cover types from a variety of 

sources, including stand data (Combined Data System, 

CDS) from the MNF, West Virginia Gap Analysis (GAP) 

data from IKONOS satellite imagery (Space Imaging , 

Thornton, CO), and MeadWestvaco’s Forest Resource 

Inventory System (FRIS) data for the MWERF. We used 

images from IKONOS and GAP data for areas of private 

ownership inside the MNF proclamation boundary and 

outside the boundary, respectively, where forest stand 

data largely was absent. The IKONOS imagery was 

obtained in October 2001 with leaf-off conditions and 

<20 percent cloud cover. Leaf-off imagery was necessary 

to identify areas of spruce and spruce-northern hardwood 

areas. Imagery was 1-m resolution in true color with a 

Universal Transverse Mercator (UTM) projection using 

the World Geodetic System 1984 (WGS84) datum. We 

classifi ed the IKONOS imagery into land-cover types 

using both unsupervised and supervised classifi cation 

in ERDAS IMAGINE® (ERDAS 1997). We used the 

ISODATA method for land cover classifi cation in the 

unsupervised classifi cation.

To determine cover types for areas not included in the 

CDS or FRIS data, we used classifi cations from the 

GAP data (Glenn and Ripple 2004). With these three 

sources to determine land cover, we were able to create 

a complete data set of land cover for the entire study 

area, albeit one with variable resolution and classifi cation 

accuracy. Only forested land cover types were used in the 

analysis and were given binary values for analysis (i.e., 

oak = 0, spruce = 1). We created topographic surfaces, 

such as elevation and aspect, for the GIS using ArcView 

Spatial Analyst® from a digital elevation model (DEM) 

for each 7.5-minute topographic quadrangle obtained 

from the USGS. Aspect in degrees was transformed to 

the format of the habitat model using the formula [1-

cosine (aspect)] + [1-sine (aspect)] to linearize the data so 

that xeric, southern aspects ranged from 135-225o and 

mesic northern aspect ranged from 315-45o (McCombs 

1997).

The variables used in the Virginia northern fl ying squirrel 

habitat model were determined using stepwise logistic 

regression (Hosmer and Lemeshow 2000). Habitat 

characteristics at 884 (87 occupied and 797 vacant) nest 

box sites were entered into a logistic regression model 

to determine characteristics indicative of fl ying squirrel 

habitat. Because previous studies in the area found 

nest trees not a limiting factor for fl ying squirrels, it 

is possible that the use of nest boxes do provide some 

insight into preferred habitat, though an untested 

assumption (Menzel et al. 2004). To assess fi t and relative 

strength of the selected model, we used the Hosmer 

and Lemeshow goodness-of-fi t test and Nagelkerke’s 

rescaled R2

, respectively (SAS Institute 1995). We used 

the probabilities from the logistic regression to derive 

a predictive habitat map from the signifi cant habitat 

characteristics across the landscape at threshold values 

of <50, 50-75, and >75 percent predictive probabilities 

of occurrence to show possible distribution of Virginia 

northern fl ying squirrels. For model validation, we used 

a jackknife procedure to compute a percentage correct 

classifi cation for correctly assigned presence (sensitivity) 

and absence (specifi city) for the model (SAS Institute 

1995).
4

Results

Our stepwise logistic regression identifi ed two signifi cant 

variables, elevation and forest cover type, that contributed 

to the most parsimonious model identifying Virginia 

northern fl ying squirrel habitat based on habitat variables 

at nest box locations (Table 1). The best approximating 

model equation was as follows:

Virginia northern fl ying squirrel occupancy = 

(1/1+exp (-4.4279 -(2.3424*Forest Type)-(0.0027*Elevation (m))).

This model had a reasonably high predictive capability 

(Nagelkerke’s adjusted R2

 = 0.4967) in identifying 

Virginia northern fl ying squirrel habitat with good overall 

model fi t (Hosmer-Lemeshow χ2

 = 13.79; P = 0.087). 

Overall correct classifi cation was 81 percent with a model 

sensitivity of 79.3 percent and a model specifi city of 

82.8 percent. Predicted occurrence by probability class 

analysis indicated that we could successfully predict 

the spatial extent of high probability of occupancy (> 

75 percent), potentially occupied (50-75 percent) and 

low probabilities (< 50 percent) of occupancy (Table 

2). The inclusion of aspect in a three-variable model 

did not improve model performance. Spatially, our 

model identifi ed 19,162 ha of area with >75 percent 

predicted probability of occurrence and 224,285 ha 

of 50-75 percent predicted probability of across eight 

counties in eastern West Virginia (Fig. 1). Approximately 

63.2 percent of area designated 50 percent or greater 

of probability occurred on public land such as MNF, 

Canaan Valley National Wildlife Refuge or state parks 

or forests. The model is available at http://www.fs.fed.

us/ne/parsons/webdata/data/gis/.

Discussion

Our Virginia northern fl ying squirrel habitat model is not 

a defi nitive attempt to predict presence or absence, but 

rather an attempt to identify areas where conservation 

and/or forest-habitat enhancement could be prioritized. 

Our deductive modeling approach allows the use of 

statistically signifi cant quantitative data to build a habitat 

model that describes areas similar to those used by 

radio-collared animals (DeMers 2000). By identifying 

congruencies in habitat features across the Allegheny 

Mountains that are considered potentially occupied by 

Virginia northern fl ying squirrels, restoration efforts 

could be directed to increase suitable habitat patch size 

and connect neighboring patches (Rosenblatt et al. 

1999, Miller and Cale 2000). Outside of the MNF and 

MWERF areas, detailed forest habitat information is 

less resolute; however, our model’s utility in predicting 

marginal probability of occurrence also is useful for 

understanding the potential extent of the subspecies’ 

Table 1.—Logistic regression model of habitat variables surrounding occupied Virginia 

northern fl ying squirrel nest boxes (n = 87) and randomly located vacant boxes (n=797) in 

the Monongahela National Forest, West Virginia.

Variable

Parameter 

estimate SE

Wald’s 

Chi-square P Odds ratio

Land cover 2.3424 0.3945 35.255 0.0001 10.407

Elevation (m) 0.0027 0.0010 7.689 0.0056 1.003

Table 2.—Occurrence of Virginia northern fl ying squirrel occupied 

and vacant nest box locations by probability class for areas across the 

Monongahela National Forest, West Virginia. 

Probability class Interval Present Absent Total

1 0-49 % 5 84 89

2 50-75 % 24 3 27

3 >75-100 % 58 0 58

Total 87 87
5

former and future distribution in this high-elevation 

landscape (Fig. 2). From a regulatory management 

standpoint, this model could contribute to protection 

and mitigation efforts on private lands in the region 

where mining, recreational development, and windenergy development currently are under way (Schuler et 

al. 2002, Arnett et al. 2005).

Virginia northern fl ying squirrels essentially are restricted 

to high elevation forest “islands” surrounded by a matrix 

of less- or unsuitable forest habitats (Lomolino and Davis 

1997, Weigl et al. 1999). Both endangered subspecies 

of northern fl ying squirrel in the Appalachians already 

have begun to show indications of restricted gene fl ow 

and loss of genetic variability (Brown 1984, Brown et 

al. 1999, Steele and Powell 1999). Genetic isolation 

combined with unsuitable habitat greatly increases the 

chances of localized extirpation or complete extinction 

of these subspecies. Also, there are many other natural 

and modifi ed landscape features that could function as 

physical barriers to movement by fl ying squirrels. Our 

predictive maps could be used as a fi rst step to identify 

marginal habitat in or near optimal patches where active 

forest management or restoration activities could improve 

vegetative or structural conditions in the future (Schuler 

et al. 2002). Such efforts could improve the Virginia 

northern fl ying squirrel’s potential for long-term viability 

and/or for eventual subspecies recovery and de-listing by 

increasing genetic diversity and decreasing competitive 

pressures by increasing available habitat (Browne et al. 

1999). With increasing habitat patch size in an island 

biogeography theory approach (MacArthur and Wilson 

1967) or a meta-population context (Castleberry et al. 

2002), resources should become more abundant making 

Figure 1.—Location of predicted Virginia northern fl ying 

squirrel habitat in 8 counties of eastern West Virginia. The 

dark gray outline denotes the proclamation boundary of the 

Monongahela National Forest. See text for details.

Figure 2.—Predicted Virginia northern fl ying squirrel habitat 

on a section of the Leadmine 7.5-minute topographic 

quadrangle map in Tucker County, West Virginia.
6

competition less severe. With decreasing distance between 

patches, Virginia northern fl ying squirrel populations 

should become more panmictic and genetic diversity 

conserved or enhanced (Brown et al. 1999).

Optimal areas of potential occupancy that are both 

surrounded by marginal habitat and are in close 

proximity to other optimal habitat patches could have the 

highest success rate for restoration. Additionally, optimal 

patches that are surrounded by poor habitat and are 

isolated from other patches likely would be more diffi cult 

to connect to other optimal habitats. These patches 

may represent areas where Virginia northern fl ying 

squirrels were extirpated and were unable to recolonize 

because of the unsuitable surrounding habitat. Many 

of these isolated patches are areas that in most need of 

management. Nonetheless, it is important to note that 

our habitat model possibly failed to identify all existing 

patches of potentially occupiable habitat, especially 

the smaller suitable forest patches. Due to resolution 

problems, this is a common error that occurs in habitat 

modeling that must be recognized. However, the scale of 

this project and the simplicity of the habitat model may 

signifi cantly reduce this error (Moilanen 2002).

Wildlife habitat modeling is replete with assumptions. 

For example, we assumed that the vegetative 

communities and habitat characteristics used by the 

radio-collared fl ying squirrels reported in Menzel (2003) 

and Menzel et al. (2004) represented typical selection 

by the targeted species (Millspaugh and Marzluff 2001). 

Because of the diffi culty in measuring factors, such as 

competition and the effects of human activity (Posillico 

et al. 2004), our model assumes that the collared Virginia 

northern fl ying squirrels were selecting habitats that 

were most desirable and not selecting habitats that were 

in suboptimal areas (Cody 1981, Ozesmi and Mitsch 

1997, Rosenblatt et al. 1999, Miller and Cale 2000). 

There has been a dramatic alteration in the extent 

and quality (i.e., stand structure and composition) of 

high elevation red spruce forests of West Virginia over 

the past century (Clarkson 1968, Mielke et al. 1986). 

Accordingly, it may be possible that the areas occupied 

by Virginia northern fl ying squirrels today represent 

the best of what is currently available even though it 

may not be “optimal” from the subspecies’ perspective. 

Quite likely, the best forest habitats represented by 

overmature or old-growth montane-boreal communities 

have characteristics that can only develop over time. For 

the purpose our study, we were constrained to describe 

the vegetational communities and habitat characteristics 

used by the radio-collared fl ying squirrels as potentially 

occupied habitat in the present landscape. Although we 

cannot say with certainty what characteristics constitute 

highly optimal habitat, we believe our designations were 

justifi ed due to the evidence of successful breeding and 

natal production observed in the study areas (Menzel 

2003, Menzel et al. in press).

Despite our relatively high percentage of correct 

classifi cation, we do urge caution in overly extending 

the results of our modeling based on the quality and 

quantity of data used. There was a small sample size of 

nest boxes that were occupied - only 87 (10 percent) of 

the total 884 boxes, most of which were not placed across 

the landscape at random but in forested areas that were 

similar to areas where Virginia northern fl ying squirrels 

have been previously captured (e.g., red spruce stands 

or other high elevation forests with signifi cant conifer 

cover). This bias undoubtedly made it more diffi cult to 

distinguish differences between occupied and vacant sites. 

Additionally, the presence of natural cavities in forest 

stands with nest-boxes also could infl uence nest-box use, 

as stands with high cavity abundance might have a lower 

rate of nest box use whereas the opposite might be true in 

stands with low cavity availability.

Although the earlier habitat model produced by Odom 

et al. (2001) produced equivocal results, it demonstrated 

the importance of montane conifer cover in explaining 

Virginia northern fl ying squirrel presence and absence 

on the landscape. Certainly our model, combined with 

the stand-level work provided by Ford et al. (2004), 

provides a more compelling link between the subspecies 

and montane confi er habitats. Odom et al. (2001) used 

an inductive approach to habitat modeling, whereas we 

used a deductive approach based on previously gathered 

quantitative data. Additionally, our habitat model adds 

a more detailed forest composition component to the 

habitat model of Odom et al. (2001).
7

In summary, our habitat model should be useful to 

land managers as it shows where the critical or highly 

optimal habitats from an occupancy perspective exist 

in the Allegheny Mountains. Secondly, based on the 

proposition that Virginia northern fl ying squirrels 

should be dependent on the areas outlined by the 

model, this model spatially shows where the highest 

concentrations of Virginia northern fl ying squirrel 

habitat (and therefore presumably squirrels themselves) 

is probable. Because this species is secretive and diffi cult 

to capture, our model should help reduce the time and 

efforts for future Virginia northern fl ying squirrel surveys 

and/or research activities. Thirdly, this model provides 

a baseline for future management prior to any active 

habitat management for the northern fl ying squirrel 

(Schuler et al. 2002, Carey 2003a). Finally, our model 

provides a spatial map that shows how all of the optimal 

habitat patches are spatially related to each other. This 

could be useful in conservation planning and/or longterm habitat restoration efforts such as the release of 

understory spruce or the connecting of optimal patches 

with habitat corridors that would allow inter-patch 

movement. Single-species focal management efforts 

usually fail over the long term, therefore ecosystembased approaches with spatial elements that introduce 

forest structure heterogeneity and increase biocomplexity 

typically benefi t a greater number of species, including 

the targeted single species (Carey 2001, Nordlind and 

Ostund 2003, Carey 2003b). For example, of other 

wildlife in the region that are associated with the 

montane boreal forest type, greater than 80 percent of 

both the known endangered Cheat Mountain salamander 

(Plethodon nettingi) collection sites and the USDA Forest 

Service Region 9 sensitive-listed northern goshawk 

(Accipiter gentilis) nesting sites that occur within the 

MNF proclamation boundary have been located where 

the predicted probability of Virginia northern fl ying 

is 50 percent squirrels or greater (T. Evans, USDA 

Forest Service, pers. comm.). Accordingly, we believe 

our habitat model for the Virginia northern fl ying 

squirrel can help facilitate multi-species ecosystem-based 

management efforts within high elevation forests in the 

Allegheny Mountains.

Acknowledgments

Funding for this study was provided by the West Virginia 

University Division of Forestry and the USDA Forest 

Service’s Scientist Recruitment Initiative program. We 

thank E. Rowan, K. Nightingale, K. Leonard, and J. 

Rodrigue for study assistance. 

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Printed on Recycled Paper

Menzel, J.M.; Ford, W.M.; Edwards, J.W.; Ceperley, L.J. 2006. A habitat model for 

the Virginia northern flying squirrel (Glaucomys sabrinus fuscus) in the 

central Appalachian Mountains. Res. Pap. NE-729. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, Northeastern Research Station. 10 p.

The Virginia northern flying squirrel (Glaucomys sabrinus fuscus) is an endangered 

sciurid that occurs in the Allegheny Mountains of Virginia and West Virginia. Despite its 

status, few of its ecological requirements have been synthesized for landscape-level 

predictive distributions to facilitate habitat delineation efforts. Using logistic regression, 

we developed a GIS-based habitat model for the Virginia northern flying squirrel 

using micro- and macrohabitat relationship data in West Virginia. Important habitat 

characteristics obtained from radio-collared squirrels included: (1) elevation over 

1036 m; (2) northerly aspects; and (3) red spruce (Picea rubens) and mixed northern 

hardwood-red spruce cover types. A final model retaining elevation and forest cover 

type showed reasonably high predictive power across a large portion of the Allegheny 

Mountains in West Virginia.

Key Words: Allegheny Mountains, Glaucomys sabrinus fuscus, GIS, habitat model, 

Virginia northern flying squirrel 
Headquarters of the Northeastern Research Station is in Newtown Square, 

Pennsylvania. Field laboratories are maintained at:

Amherst, Massachusetts, in cooperation with the University of Massachusetts

Burlington, Vermont, in cooperation with the University of Vermont

Delaware, Ohio

Durham, New Hampshire, in cooperation with the University of New Hampshire

Hamden, Connecticut, in cooperation with Yale University

Morgantown, West Virginia, in cooperation with West Virginia University

Parsons, West Virginia

Princeton, West Virginia

Syracuse, New York, in cooperation with the State University of New York, 

College of Environmental Sciences and Forestry at Syracuse University

Warren, Pennsylvania

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