Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models

This study aims to apply seven data-driven methods (i.e. artificial neural networks [ANNs], classification and regression trees [CARTs], fuzzy habitat suitability models [FHSMs], generalized additive models [GAMs], generalized linear models [GLMs], random forests [RF] and support vector machines [SVMs]) to develop data-driven species distribution models (SDMs) for spawning European grayling (Thymallus thymallus), and to compare the predictive performance and the ecological relevance, quantified by the habitat information retrieved from these SDMs (i.e. variable importance and habitat suitability curves [HSCs]). The results suggest RF to yield the most accurate SDM, followed by SVM, CART, ANN, GAM, FHSM and GLM. However, inconsistencies between different performance measures were observed, indicating that different models may obtain a high score on a particular aspect and perform worse on other aspects. Despite their lower predictive ability, GAM, GLM and FHSM proved to be useful, since HSCs could be obtained and thus these techniques allow testing of ecological relevance and habitat suitability. Water depth and flow velocity appeared to be important variables for spawning grayling. The HSCs clearly indicate higher habitat suitability at a lower water depth, a low to medium flow velocity and a higher percentage of medium-sized gravel, whereas the models disagreed on the habitat suitability for the percentage of small-sized gravel. These findings demonstrate the applicability of data-driven SDMs for both habitat prediction and ecological knowledge extraction that are useful for management of a target species.