This project holds the R code in support of a manuscript:
Title: Modelling complex spatial-temporal drivers of habitat suitability for an imperilled stream fish
Paul A. Bzonek, D. Andrew R. Drake, Jacob W. Brownscombe
Bzonek, P.A., Drake, D.A.R. & Brownscombe, J.W. Modelling complex spatial–temporal drivers of habitat suitability for an imperilled stream fish. Hydrobiologia 851, 2279–2294 (2024). https://doi.org/10.1007/s10750-023-05455-5
Fish populations rely on complex environmental conditions involving physical, chemical, and biological factors. Understanding the factors that control population persistence and productivity is essential for species management. We assessed the distribution and associated habitat features of a species at risk in Canada, Silver Shiner (Notropis photogenis), within Sixteen Mile Creek, a tributary of Lake Ontario. Using random forest models, we quantified a range of ecological factors (n=25) to estimate habitat associations for sampled populations and life stages (juvenile, adult). A complex set of ecological factors were informative predictors of Silver Shiner distribution, including physical (stream morphology, water velocity, substrate type), chemical (conductivity), and biological (aquatic and riparian vegetation) conditions. Juveniles were less responsive to habitat conditions but exhibited high seasonal variability in occurrence. Adults were most common in stream sections with greater than 0.5 m depth and stream velocity less than 0.6 m/s, and areas without silt substrate. Broadly, the models predicted Silver Shiner distribution with 68%-92% accuracy in non-training data. Our findings describe the habitat conditions that Silver Shiner currently occupies in an urban drainage, which may serve as a point of reference for habitat protection and restoration. Further, predictive species distribution models can serve to identify habitat for further monitoring and restoration.
The data and R code used to investiage Silver Shiner habitat suitability are provided in this project. Copy or clone the files and you should be able to reproduce the analysis and figures seen within the manuscript.
All scripts can be run from the intial script: Script0-0_UserInterface.R.
An API key is needed to produce plots with a GoogleMaps basemap. Users would need to provide their own API key to get the basemaps.