Importance of data selection and filtering in species distribution models: A case study on the Cantabrian brown bear

Species distribution models (SDMs) are powerful tools in ecology and conservation. Choosing the right environmental drivers filtering species' occurrences taking their biases into account key factors to consider before modeling. In this case study, we address five common problems arising during selection of input data for presence-only SDMs on an example a generalist species: endangered Cantabrian brown bear. First, focus variables that may drive its distribution, testing if climatic should be considered at 1-km analysis grain. Second, investigate how view (1) collection procedures, (2) different time frames, (3) dispersal areas, (4) subpopulations affects performance outputs three spatial grains (500 m, 1 km, 5 km). Our results show with yielded only minor differences behaved properly terms model validation, although coarsening grain deteriorated performance. Still, contribution individual habitat suitability predictions differed among models. We combination limited availability poor can lead inaccurate predictions. Specifically bear, conclude not exploring best modeling study area originate from observations traces most recent period (2006–2019) which population is expanding, considering cells dispersing bear independently (as they distinct preferences). conclusion, serve as useful tool species including all available data; still, expert evaluation perspective purpose possible recommended. This especially important when intended management conservation purposes local level, respond environment coarse grains.