RiverOtterSDM: Data and code for species distribution and connectivity modeling of river otters in Texas

This archive provides the data, code, intermediate products, and derived outputs needed to reproduce the species distribution and connectivity analyses reported in Werdel (2026), “Human-created surface waters facilitate habitat suitability and connectivity for river otters (Lontra canadensis) in Texas.” The repository includes all data, code, intermediate products, and derived outputs used to model the distribution and landscape connectivity of the North American river otter (Lontra canadensis) across Texas, USA. Species distribution models (SDMs) were developed using presence-only occurrence records from the Global Biodiversity Information Facility (GBIF), hydrological predictors derived from the U.S. National Hydrography Dataset (NHD), and the Maxent algorithm implemented via the maxnet R package. Contents of this archive include:• Raw and processed hydrological raster inputs (natural and anthropogenic water features)• Derived predictor rasters (water density and distance-to-river surfaces)• Cleaned and spatially thinned occurrence data• Model objects, coefficients, tuning results, and evaluation metrics• Scenario prediction rasters (current vs. natural-only hydrology)• Connectivity and accessibility summaries at the HUC-8 watershed scale• All scripts required to reproduce analyses and figures reported in the manuscript Large raster datasets are included here to ensure complete reproducibility independent of GitHub file-size constraints. The analysis pipeline is executed via the script SDM_Pipeline_EndToEnd.R, which reproduces all intermediate and final outputs when run from the project root. This archive supports the manuscript:“Human-Created Surface Waters Increase Suitability and Connectivity for River Otters (Lontra canadensis) in Texas”. All spatial analyses were conducted in an equal-area projection (NAD83 / CONUS Albers, EPSG:5070). Connectivity and cartographic post-processing were performed in ArcGIS Pro as described in the manuscript.