Developing a Topographic Model to Predict the Northern Hardwood Forest Type within Carolina Northern Flying Squirrel (<i>Glaucomys sabrinus coloratus</i>) Recovery Areas of the Southern Appalachians

Evans, Andrew M.; Odom, Richard H.; Resler, Lynn M.; Ford, W. Mark; Prisley, Stephen P.

doi:10.1155/2014/179415

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…We derived elevation (meters above sea level), slope (degrees), aspect (sine‐transformed) using 30‐m digital elevation models (DEM; U.S. Geological Survey 2000). We calculated a topographic exposure index (TEI), where higher TEI values indicate greater topographic exposure, using the zonal statistics tools in ArcMap, by subtracting the average elevation of a 1.75‐km 2 circular area around each camera (Evans et al , Ford et al ). We buffered each camera location based on average male winter home range estimate for the species (1.75 km 2 ; Lesmeister et al ).…”

Section: Methodsmentioning

confidence: 99%

Winter habitat associations of eastern spotted skunks in Virginia

et al. 2017

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Eastern spotted skunk (Spilogale putorius) populations have declined throughout much of their range in the eastern United States over recent decades. Declines have been attributed to habitat loss or change, increased competition with sympatric mesocarnivore species, or disease. To better understand the extant distribution of spotted skunks in the Appalachian Mountains of western Virginia, USA, we used a detection‐non‐detection sampling approach using baited camera traps to evaluate the influence of landscape‐level environmental covariates on spotted skunk detection probability and site occupancy. We conducted camera trap surveys at 91 sites from January to May in 2014 and 2015. Spotted skunk occupancy was associated with young‐aged forest stands at lower elevations and more mature forest stands at higher elevations. Both land cover types in this region can be characterized as having complex forest structure, providing cover that varies with stand age, species composition, elevation, and management regime. Our results provide insight into factors that influence spotted skunk spatial distribution and habitat selection, information that can be used to generate conservation assessments and inform management decisions. © 2017 The Wildlife Society.

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Section: Methodsmentioning

confidence: 99%

Winter habitat associations of eastern spotted skunks in Virginia

et al. 2017

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“…Topographic exposure was derived by subtracting the average elevation of the area within a 1-km radius surrounding a pixel from the elevation of the pixel. Relatively high TEI values indicate that sites were exposed peaks or ridges, whereas low or negative values indicate that sites were sheltered landforms such as coves, lower slopes, and stream corridors (Evans et al 2014;Ford et al 2015). Ford et al (2005) and Owen et al (2003) indicated that divisions among riparian and upland forests locally on the FEF were important predictors of bat occurrence and bat foraging habitat selection.…”

Section: Methodsmentioning

confidence: 99%

Deriving Habitat Models for Northern Long-Eared Bats from Historical Detection Data: A Case Study Using the Fernow Experimental Forest

Ford

Silvis

Rodrigue

et al. 2016

Journal of Fish and Wildlife Management

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The listing of the northern long-eared bat (Myotis septentrionalis) as federally threatened under the Endangered Species Act following severe population declines from white-nose syndrome presents considerable challenges to natural resource managers. Because the northern long-eared bat is a forest habitat generalist, development of effective conservation measures will depend on appropriate understanding of its habitat relationships at individual locations. However, severely reduced population sizes make gathering data for such models difficult. As a result, historical data may be essential in development of habitat models. To date, there has been little evaluation of how effective historical bat presence data, such as data derived from mist-net captures, acoustic detection, and day-roost locations, may be in developing habitat models, nor is it clear how models created using different data sources may differ. We explored this issue by creating presence probability models for the northern long-eared bat on the Fernow Experimental Forest in the central Appalachian Mountains of West Virginia using a historical, presence-only data set. Each presence data type produced outputs that were dissimilar but that still corresponded with known traits of the northern long-eared bat or are easily explained in the context of the particular data collection protocol. However, our results also highlight potential limitations of individual data types. For example, models from mist-net capture data only showed high probability of presence along the dendritic network of riparian areas, an obvious artifact of sampling methodology. Development of ecological niche and presence models for northern long-eared bat populations could be highly valuable for resource managers going forward with this species. We caution, however, that efforts to create such models should consider the substantial limitations of models derived from historical data, and address model assumptions.

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Home range, den selection and habitat use of Carolina northern flying squirrels (Glaucomys sabrinus coloratus)

et al. 2017

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Context Understanding habitat selection is important for determining conservation and management strategies for endangered species. The Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus) is an endangered subspecies found in the high-elevation montane forests of the southern Appalachians, USA. The primary use of nest boxes to monitor CNFS has provided biased information on habitat use for this subspecies, as nest boxes are typically placed in suitable denning habitat. Aims We conducted a radio-telemetry study on CNFS to determine home range, den site selection and habitat use at multiple spatial scales. Methods We radio-collared 21 CNFS in 2012 and 2014–15. We tracked squirrels to diurnal den sites and during night-time activity. Key results The MCP (minimum convex polygon) home range at 95% for males was 5.2±1.2ha and for females was 4.0±0.7. The BRB (biased random bridge) home range at 95% for males was 10.8±3.8ha and for females was 8.3±2.1. Den site (n=81) selection occurred more frequently in montane conifer dominate forests (81.4%) vs northern hardwood forests or conifer–northern hardwood forests (9.9% and 8.7%, respectively). We assessed habitat selection using Euclidean distance-based analysis at the 2nd order and 3rd order scale. We found that squirrels were non-randomly selecting for habitat at both 2nd and 3rd order scales. Conclusions At both spatial scales, CNFS preferentially selected for montane conifer forests more than expected based on availability on the landscape. Squirrels selected neither for nor against northern hardwood forests, regardless of availability on the landscape. Additionally, CNFS denned in montane conifer forests more than other habitat types. Implications Our results highlight the importance of montane conifer to CNFS in the southern Appalachians. Management and restoration activities that increase the quality, connectivity and extent of this naturally rare forest type may be important for long-term conservation of this subspecies, especially with the impending threat of anthropogenic climate change.

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Developing a Topographic Model to Predict the Northern Hardwood Forest Type within Carolina Northern Flying Squirrel (Glaucomys sabrinus coloratus) Recovery Areas of the Southern Appalachians

Cited by 6 publications

References 48 publications

Winter habitat associations of eastern spotted skunks in Virginia

Winter habitat associations of eastern spotted skunks in Virginia

Deriving Habitat Models for Northern Long-Eared Bats from Historical Detection Data: A Case Study Using the Fernow Experimental Forest

Home range, den selection and habitat use of Carolina northern flying squirrels (Glaucomys sabrinus coloratus)

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