Fluvial geomorphic evolution and stream fish community trajectories in the Bayou Pierre, Mississippi

Changing environments place stresses on ecosystems, and are contributing to widespread losses of biodiversity and ecosystem function. Comparisons of historical and contemporary data offer considerable utility in understanding how ecosystems respond to, adapt to, or recover from changing environments. Stream fishes offer a particularly interesting study system for this topic, as streams are naturally dynamic environments and human needs have placed increasing pressure on aquatic systems. The effects of fine sediments on stream fishes and aquatic ecosystems more broadly have been well studied. Yet studies from fluvial geomorphology have resulted in models of watershed morphological evolution which encompass far broader processes and changes to aquatic systems. Our dataset integrates a fluvial geomorphic approach to characterize stream channel and habitat evolution over a four decade period in the Bayou Pierre, Mississippi, an ecological approach to study related change in stream fish comm..., Fluvial geomorphic data were collected from remote sensing sources using QGIS (open-source) geospatial software. Measurements were taken from aerial imagery sources (National High Altitude Imagery Program 1982 and National Agricultural Imagery Program 2020) and two digital elevation models (Mississippi Digital Earth Model, data source 1958-1980, and 2015/2016 Lidar-based 3DEP DEM) and exported to shapefile formats. Spatial sampling locations were determined by creating a 200m chain object using QChainage on the trunk stream and five tributaries via the NHD+ V2 flowline files. At each sample location we digitized channel width line features (1982 and 2020) and extracted channel thalweg elevations (both DEMs). We employed visual identification of knickpoint features using channel profiles generated from DEMs. These measurements were processed in R to create csv files of measurements for further analyses. Historical fish community data were extracted from the University of Southern Mississ..., Data files (csv) can be opened by either proprietary spreadsheet software (e.g., MS Word), any open-source spreadsheet software, or any software which can read csv files. R code files can be opened in any text editor. R code files require the R Environment for Statistical Computing (open-source) for running analyses.,