Multilevel landscape utilization of the Siberian flying squirrel: Scale effects on species habitat use. Issue 20 (7th September 2017) - Remm, Jaanus; Hanski, Ilpo K.; Tuominen, Sakari; Selonen, Vesa
Abstract: Animals use and select habitat at multiple hierarchical levels and at different spatial scales within each level. Still, there is little knowledge on the scale effects at different spatial levels of species occupancy patterns. The objective of this study was to examine nonlinear effects and optimal‐scale landscape characteristics that affect occupancy of the Siberian flying squirrel, Pteromys volans, in South‐ and Mid‐Finland. We used presence–absence data ( n  = 10, 032 plots of 9 ha) and novel approach to separate the effects on site‐, landscape‐, and regional‐level occupancy patterns. Our main results were: landscape variables predicted the placement of population patches at least twice as well as they predicted the occupancy of particular sites; the clear optimal value of preferred habitat cover for species landscape‐level abundance is a surprisingly low value (10% within a 4 km buffer); landscape metrics exert different effects on species occupancy and abundance in high versus low population density regions of our study area. We conclude that knowledge of regional variation in landscape utilization will be essential for successful conservation of the species. The results also support the view that large‐scale landscape variables have high predictive power in explaining species abundance. Our study demonstrates the complex response of species occurrence at different levels of population configuration on landscape structure. The study also highlights the need for data in large spatial scale to increase the precision of biodiversity mapping and prediction of future trends. Abstract : In this study, we distinguished between scale‐dependent effects of habitat characteristics on site occupancy and landscape‐level abundance of flying squirrels, a conservation flagship species. We separated the effects of site‐, landscape‐, and regional‐level processes. Our results show that large‐scale landscape variables and nonlinear approach have high predictive power on species abundance.