Journal
FORESTS
Volume 5, Issue 6, Pages 1374-1390Publisher
MDPI AG
DOI: 10.3390/f5061374
Keywords
remote sensing; forest structure; animal behavior; site selection; step selection functions
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Funding
- German Research Foundation (DFG)
- University of Bayreuth
- project, Modelluntersuchungen zum Wildtiermanagement in Schutzgebieten am Beispiel des Nationalparks Bayerischer Wald, of the German Federal Environmental Foundation (Deutsche Bundesstiftung Umwelt)
- EU-program INTERREG IV (EFRE Ziel 3)
- Bavarian Forest National Park Administration
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The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the cold period in winter forces individuals to optimize their trade off in searching for food and shelter. We analyzed the winter habitat selection of roe deer (Capreolus capreolus) in a montane forest landscape combining estimates of vegetation cover in three different height strata, derived from high resolution airborne Laser-scanning (LiDAR, Light detection and ranging), and activity data from GPS telemetry. Specifically, we tested the influence of temperature, snow height, and wind speed on site selection, differentiating between active and resting animals using mixed-effects conditional logistic regression models in a case-control design. Site selection was best explained by temperature deviations from hourly means, snow height, and activity status of the animals. Roe deer tended to use forests of high canopy cover more frequently with decreasing temperature, and when snow height exceeded 0.6 m. Active animals preferred lower canopy cover, but higher understory cover. Our approach demonstrates the potential of LiDAR measures for studying fine scale habitat selection in complex three-dimensional habitats, such as forests.
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