Abstract
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.
Highlights
Understanding a species’ habitat selection is a major prerequisite for wildlife management, concerning both conservation and forest management
We aimed at integrating high-resolution remote sensing data of forest structure and behavioral data on a forest-dwelling ungulate to study site selection, combining GPS-telemetry with a resource selection function approach
We could show that habitat selection of roe deer under rough winter conditions cannot be generalized, but is strongly dependent on external influences, like weather, and on the activity status of the animal
Summary
Understanding a species’ habitat selection is a major prerequisite for wildlife management, concerning both conservation and forest management. Our study aimed at utilizing LiDAR-derived measures of forest structure for testing three predictions on the winter habitat selection of roe deer in a mountain area with harsh weather conditions: We predicted that in winter: (1) Roe deer should prefer semi open habitats with short distances between vegetation structure providing suitable shelter and food; (2) With increasing harsh weather conditions (temperature, snow height) roe deer should prefer sites with dense canopy cover due to lower heat emission and lower snow levels; and (3) Sites used for resting should be characterized by higher canopy cover than sites visited by active roe deer due to higher thermal cover
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