Effects of spatial scale and sample size in GPS-based species distribution models: are the best models trivial for red deer management?

摘要

Species distribution models (SDMs) are popular in conservation and management of a wide array of taxa. Often parameterized with coarse GIS-based environmental maps, they perform well in macro-ecological settings but it is debated if the models can predict distribution within broadly suitable “known” habitats of interest to local managers. We parameterized SDMs with GIS-derived environmental variables and location data from 82 GPS-collared female red deer (Cervus elaphus) from two study areas in Norway. Candidate GLM models were fitted to address the effect of spatial scale (landscape vs. home range), sample size, and transferability between study areas, with respect to predictability (AUC) and explained variance (Generalized R 2 and deviance). The landscape level SDM captured variation in deer distribution well and performed best on all diagnostic measures of model quality, caused mainly by a trivial effect of avoidance of non-habitat (barren mountains). The home range level SDMs were far less predictable and explained comparatively little variation in space use. Landscape scale models stabilized at the low sample size of 5–10 individuals and were highly transferrable between study areas implying a low degree of individual variation in habitat selection at this scale. It is important to have realistic expectations of SDMs derived from digital elevation models and coarse habitat maps. They do perform well in highlighting potential habitat on a landscape scale, but often miss nuances necessary to predict more fine-scaled distribution of wildlife populations. Currently, there seems to be a trade-off between model quality and usefulness in local management.