Data from: Spatial scaling of environmental variables improves species-habitat models of fishes in a small, sand-bed lowland river ...

• Main Authors: Radinger, Johannes, Wolter, Christian, Kail, Jochem
• Format: Dataset
• Language: English
• Published: Dryad 2016
• Subjects: Gobio gobio; Gymnocephalus cernua; Anguilla anguilla; Salmo salar; stream fish occurrence; low-land sand-bed river; fish mobility; Cobitis taenia; Pungitius pungitius; river habitat; Leuciscus leuciscus; Rutilus rutilus; Perca fluviatilis; Phoxinus phoxinus; Salmo trutta fario; hydromorphological assessment; Gasterosteus aculeatus; Tinca tinca
• Online Access: https://dx.doi.org/10.5061/dryad.b6k1k; https://datadryad.org/stash/dataset/doi:10.5061/dryad.b6k1k

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 ... : Set_AV: Single boosted regression tree (BRT) habitat models for 13 fish species based on assessed hydromorphological variables without topological variablesResults for single boosted regression tree (BRT) models for 13 fish species, 4 modelled distance classes (0, 200, 2500, 4000 m) and assessed hydromorphological data (AV) without topological variables. Model results include a global BRT model (brt.model.global, including all variables) and a final BRT model (brt.model.final, including only statistically relevant variables) for each species. Furthermore, summarizing statistics (brt.stats.final) for each final model (e.g. cross-validated AUC) and associated plots showing the influence of selected selected variables (species_response.pdf) are provided. Models are stored as R objects in the *.rds format and can be loaded with the R command readRDS().Set_AV.zipSet_AV_TV: Single boosted regression tree (BRT) habitat models for 13 fish species based on assessed hydromorphological variables and topological ...