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

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.