Improving predictions of range expansion for invasive species using joint species distribution models and surrogate co‐occurring species

Abstract

AbstractAimsSpecies 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 projecting distributions into environments that are non‐analogous to the environments used for model building.LocationMinnesota, USA.TaxonCardamine impatiens (Narrowleaf Bittercress), Celastrus orbiculatus (Oriental Bittersweet), and Humulus japonicus (Japanese Hops).MethodsWe 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).ResultsSurrogate 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 conclusionsThese 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.