LiDAR metrics predict suitable forest foraging areas of endangered Mouse-eared bats (Myotis myotis)

Abstract

Habitat shift caused by human impact on vegetation structure poses a great threat to species which are specialized on unique habitats. Single layered beech forests, the main foraging habitat of Greater Mouse-eared Bats (Myotis myotis), are threatened by recent changes in forest structure. After this species suffered considerable population losses until the 1970s, their roosts in buildings are strictly protected. However, some populations are still declining. Thus, the spatial identification of suitable foraging habitat would be essential to ensure conservation policy. The aim of this study was (a) to verify the relevance of forest structural variables for the activity of M. myotis and (b) to evaluate the potential of LiDAR (Light Detection and Ranging) in predicting suitable foraging habitat of the species. We systematically sampled bat activity in forests close to 18 maternity roosts in Switzerland and applied a generalized linear mixed model (GLMM) to fit the activity data to forest structure variables recorded in the field and derived from LiDAR. We found that suitable forest foraging habitat is defined by single layered forest, dense canopy, no shrub layer and a free flight space. Most importantly, this key foraging habitat can be well predicted by airborne LiDAR data. This allows for the first time to create nationwide prediction maps of potential foraging habitats of this species to inform conservation management. This method has a special significance for endangered species with large spatial use, whose key resources are hard to identify and widely distributed across the landscape.