Data from: Assessing how uncertainty and stochasticity affect the dispersal of fish in river networks

Data from: Assessing how uncertainty and stochasticity affect the dispersal of fish in river networksData and results tables:Data01: csv-table of LHS-generated parameter values (300 combinations, see R01)Data02: Results of the model runs of the coupled fish dispersal and population growth model for 300 LHS-combinations x 5 years x 10 random replicates = 15000 model runs (see GRASS03)Data03: Results of the model runs of the coupled fish dispersal and population growth model for the analysis of stochastic uncertainty by varying the random seeds (n=15 seeds for both sources of stochastic uncertainty). 15x15 seeds x 5 years = 1125 model runs (see GRASS04) GRASS GIS 7.0 scripts:GRASS01: Bash script for to extract the example stream network (River Erlauf, Austria) from the hydrosheds digital elevation modelGRASS02: Python script for the generation of different habitat suitabilities with specified distance (1000, 2500, 7500 m) and amount (1, 5, 10%) of suitable habitats within the example river networkGRASS03: Python script for the calculation of the coupled fish dispersal and population growth model based on the LHS sampling scheme (for results see Data02)GRASS04: Python script for the calculation of the coupled fish dispersal and population growth model with fixed mean parameter values and varying random seeds to analyze stochastic model uncertainty (for results see Data03)R statistical analysis scripts:R01: Generation of the LHS (Latin Hypercube Sampling) parameter combinations (n=300) using the R-package 'lhs' (see Data01)R02: Detailed analysis of the model parameter uncertainty including partial rank correlations (based on the LHS combinations), corresponding plots and habitat related analysisR03: Analysis and quantification of the variation related to model stochasticity based on the ratio of the 95% interval width to the medianR04: Detailed analysis and partitioning of the stochastic model uncertainty using an analysis of variance (aov) model and corresponding plots (Venn-Euler diagrams)