Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models
Various data-driven methods are applied to develop species distribution models (SDMs) for spawning European grayling. The SDMs are compared with regard to predictive accuracy and habitat information. We evaluate predictive accuracy using multiple performance measures. Variable importance and habitat suitability curves are extracted as habitat information. Findings demonstrate the applicability of data-driven SDMs for habitat prediction and knowledge extraction.
knowledge extraction accuracy habitat modelling data-driven model machine learning presence/absence data