Aims: Species distribution models (SDMs) are often used to forecast potential distributions of important invasive or rare species. However, situations where models could be the most valuable ecologically or economically, such as for predicting invasion risk, often pose the greatest challenges to SDM building. These challenges include non-equilibrium range expansion, low or uneven prevalence, and interest in projecting distributions into environments that are non-analogous to the environments used for model building. Location: Minnesota, USA. Taxon: Cardamine impatiens (Narrowleaf Bittercress), Celastrus orbiculatus (Oriental Bittersweet), and Humulus japonicus (Japanese Hops). Methods: We took a novel approach to build robust species distribution models of invasive species using occurrence-environment correlations between invasive species and co-occurring native community members. The correlations were obtained from a joint species distribution model (JSDM) of a densely sampled database of 10,336 MN plant communities from across the state of Minnesota, USA. Positively and negatively associated native species were incorporated into the model as surrogate presences and pseudoabsences (weighted by their environmental correlations) along with invasive species occurrences records (surrogate SDMs). Results: Surrogate models performed better than traditional SDMs in predicting occurrences along the northern invasion margin (outside the training area). Both types of models had similarly high cross-validation metrics in the area of training. Surrogate models also predicted greater range expansion beyond the current geographic range. Main conclusions: These results demonstrate that modelers can take advantage of detailed community data to develop SDMs that leverage surrogate native species as phytometers of environments beyond the current area of occupancy. The additional information in surrogate models generates highly effective predictions of invasive species along expanding range margins.
Bibliographical noteFunding Information:
We thank Rob Venette for early discussions about ideas in this manuscript as well as Kady Wilson and Zack Radford for early help in gathering invasive species occurrence records and field surveys. We thank Andy Holdsworth, Laura Van Riper, and the MN Department of Natural Resources for providing the community relevé data. Funding for this project was provided by the Minnesota Invasive Terrestrial Plants and Pests Center through the Environment and Natural Resources Trust Fund as recommended by the Legislative‐Citizen Commission on Minnesota Resources (LCCMR).
© 2021 John Wiley & Sons Ltd
- boosted regression trees
- ecological niche models
- incipient invasive species
- range margin