In recent years, fuzzy models have been acknowledged as a suitable approach for species distribution modelling due to their transparency and their ability to incorporate the ecological gradient theory. Specifically, the overlapping class boundaries of a fuzzy model are similar to the transitions between different environmental conditions. However, the need for ecological expert knowledge is an important constraint when applying fuzzy species distribution models. Moreover, the consistency of the ecological preferences of some fish species across different rivers has been widely contested. Recent research has shown that data-driven fuzzy models may solve this ‘knowledge acquisition bottleneck’ and this paper is a further contribution. The aim was to analyse the brown trout (Salmo trutta fario L.) habitat preferences based on a data-driven fuzzy modelling technique and to compare the resulting fuzzy models with a commonly applied modelling technique, Random Forests. A heuristic nearest ascent hill-climbing algorithm for fuzzy rule optimisation and Random Forests were applied to analyse the ecological preferences of brown trout in 93 mesohabitats. No significant differences in model performance were observed between the optimal fuzzy model and the Random Forests model and both approaches selected river width, the cover index and flow velocity as the most important variables describing brown trout habitat suitability. Further, the fuzzy model combined ecological relevance with reasonable interpretability, whereas the transparency of the Random Forests model was limited. This paper shows that fuzzy models may be a valid approach for species distribution modelling and that their performance is comparable to that of state-of-the-art modelling techniques like Random Forests. Fuzzy models could therefore be a valuable decision support tool for river managers and enhance communication between stakeholders.
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