title: Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River.
abstract: Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species' presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale.
authors.affiliation: Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Radinger
authors.affiliation: Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Wolter
authors.affiliation: Department of Aquatic Ecology, University of Duisburg-Essen, Essen, Germany.
authors.forename: Jochem
authors.initials: J
authors.lastname: Kail
mesh_terms: D000818:Animals; D019540:Area Under Curve; D017753:Ecosystem; D005399:Fishes / Q000502:physiology*; D005843:Geography; D005858:Germany; D016014:Linear Models; D008962:Models, Theoretical; D015203:Reproducibility of Results; D045483:Rivers; D013045:Species Specificity