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.
Set_AV: Single boosted regression tree (BRT) habitat models for 13 fish species based on assessed hydromorphological variables without topological variables
Results 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.zip
Set_AV_TV: Single boosted regression tree (BRT) habitat models for 13 fish species based on assessed hydromorphological variables and topological variables
Results 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) with topological variables (stream orders, distance from mouth). 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_TV.zip
Set_MV: Single boosted regression tree (BRT) habitat models for 13 fish species based on measured hydromorphological variables without topological variables
Results for single boosted regression tree (BRT) models for 13 fish species, 4 modelled distance classes (0, 200, 2500, 4000 m) and measured hydromorphological data (MV) without topological variables (stream orders, distance from mouth). 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_MV.zip
Set_MV_TV: Single boosted regression tree (BRT) habitat models for 13 fish species based on measured hydromorphological variables and topological variables
Results for single boosted regression tree (BRT) models for 13 fish species, 4 modelled distance classes (0, 200, 2500, 4000 m) and measured hydromorphological data (MV) with 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_MV_TV.zip
GRASS GIS scripts
GRASS GIS scripts for (i) transforming vector based hydromorphological data into GRASS raster format and (ii) calculating distance based average focal predictors for 4 predefined distance classes (0, 200, 2500, 4000 m) using the GRASS rdfilter add-on.
GRASS_GIS_scripts.zip
R scripts
R scripts for (i) calculating the boosted regression tree models (BRT), (ii) analyzing and plotting the model performance based on AUC and (iii) analyzing and plotting the contribution of single variables.
R_scripts.zip
Model performance (AUC) - dataframe
R-dataframe storing the model performance (AUC) of the single models for 13 species, four modelled distance classes, assessed vs. measured variables and with/without topological variables. The dataframe is stored as R object in the *.rds format and can be loaded with the R command readRDS().
AUC_df.rds
Variable contribution - dataframe
R-dataframe storing the contribution of single variables for each of the final BRT models. The dataframe is stored as R object in the *.rds format and can be loaded with the R command readRDS().
VC.df.rds
Variable contribution rank - dataframe
R-dataframe storing the importance rank of single variables for each of the final BRT models. The dataframe is stored as R object in the *.rds format and can be loaded with the R command readRDS().
VC.df.rank.rds
README
README including a description of the results, dataframes and variables.