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Use of simulation-based statistical models to complement bioclimatic models in predicting continental scale invasion risks

  • Ranjan Muthukrishnan
  • Nicholas R. Jordan
  • Adam S. Davis
  • James D. Forester
Original Paper
  • 72 Downloads

Abstract

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. 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.

Keywords

Invasion risk Species distribution model Hybrid model Biofuel Landscape Spatial structure 

Notes

Acknowledgements

This work was funded by USDA-NIFA Grant #2012-67013 to NRJ, ASD and JDF. We are grateful for computational resources from the University of Minnesota Supercomputing Institute. We thank Rob Venette and Umakant Mishra for assistance with the bioclimatic modeling.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Fisheries, Wildlife, and Conservation BiologyUniversity of MinnesotaSt. PaulUSA
  2. 2.Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulUSA
  3. 3.USDA-ARS Global Change and Photosynthesis Research UnitUrbanaUSA
  4. 4.Environmental Resilience InstituteIndiana UniversityBloomingtonUSA

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