DOI: 10.5061/dryad.b6k1k
title: Data from: Spatial scaling of environmental variables improves species-habitat models of fishes in a small, sand-bed lowland river

description: 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.
description: 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 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) 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.zipSet_MV: Single boosted regression tree (BRT) habitat models for 13 fish species based on measured 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 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.zipSet_MV_TV: Single boosted regression tree (BRT) habitat models for 13 fish species based on measured hydromorphological variables and topological variablesResults 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.zipGRASS GIS scriptsGRASS 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.zipR scriptsR 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.zipModel performance (AUC) - dataframeR-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.rdsVariable contribution - dataframeR-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.rdsVariable contribution rank - dataframeR-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.rdsREADMEREADME including a description of the results, dataframes and variables.