Data from: Use of simulation-based statistical models to complement bioclimatic models in predicting continental scale invasion risks
Muthukrishnan, Ranjan, University of Minnesota
Jordan, Nicholas R., University of Minnesota
Davis, Adam S., United States Department of Agriculture
Forester, James D., University of Minnesota
Published Oct 22, 2018 on Dryad.
Cite this dataset
Muthukrishnan, Ranjan; Jordan, Nicholas R.; Davis, Adam S.; Forester, James D. (2018). Data from: Use of simulation-based statistical models to complement bioclimatic models in predicting continental scale invasion risks [Dataset]. Dryad. https://doi.org/10.5061/dryad.ms768r4
Invasive species represent one of the greatest risks to global biodiversity and economic productivity of agroecosystems. The development of certain novel crops—e.g., herbaceous perennial biomass crops—may create a risk of novel invasions by these crops. Therefore, potential benefits and risks need to be weighed in making decisions about their introduction and subsequent management. Ideally, such a weighing will be based on good estimates of invasion risks in realistic scenarios pertaining to actual landscapes of concern regarding invasion. Most previous large-scale analyses of invasion risk have used species distribution models and their established methods. Unfortunately, these approaches are unable to incorporate local scale biotic and spatial factors that influence invasion risk. Here we present a case study for how such factors can be efficiently incorporated in large-scale analyses of invasion risk, by extending simulation models with statistical modeling tools. By these means, we predict invasion risk at the scale of the entire United States for a major biomass crop, Miscanthus × giganteus. We then combine invasion risk predictions for this method with those from bioclimatic methods, producing a map of aggregated invasion risk that can offer more nuanced predictions of invasion risk than either approach alone. Lastly, we evaluate potential risks for invasive crops that differ in invasiveness traits, to examine how geographic patterns of invasion risk vary among invaders as a result of their particular constellation of traits.
This compressed archive includes multiple other files including data files (in .rdata format) GIS shapefiles (in folders with the associated .shp, .shx, .dbf, and .prj files for each map) and an R script that will run all analyses and plot all figures. Specific descriptions of each file are supplied in the README.TXT file.