LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer

Forests

Michael Ewald¹  Claudia Dupke¹  Marco Heurich²  Jörg Müller² 

[1] Biogeographical Modelling, Bay CEER, University of Bayreuth, Bayreuth 95447, Germany; E-Mails:;Bavarian Forest National Park, Grafenau 94481, Germany; E-Mails:

关键词: remote sensing;    forest structure;    animal behavior;    site selection;    step selection functions;   

DOI  :  10.3390/f5061374

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.