Modeling the impact of landscape types on the distribution of stream fish species

Abstract

Modifications of the landscape adjoining streams perturb their local habitat and their biological diversity, but little quantitative information is available on land cover classes that influence the fish species individually. Data collected from 191 sites in the Adour–Garonne Basin (France) were analyzed to assess the effects of land cover on the distribution of fish species. A multimodel approach was carried out to predict fish species using land cover classes and to define the most important classes applying a hierarchical filtering based on artificial neural network method and sensitivity analysis. Firstly, using three single-class models, a selection of the land cover subclasses contributing the most was carried out for each fish species and each class. Secondly, multiclass models were built with all the previously selected subclasses to predict each species (n-selected subclass model). Finally, the percentages of contribution for artificial, agricultural, and forest areas obtained for the different model architectures (three class, n-selected subclass, and global multiclass models) were compared. The majority of the distribution of fish species was correctly predicted by the single-class models, and different land cover subclasses have been selected depending on the species. Using the n-selected subclass models, the predictive performances were globally better than those obtained with other multiclass models.