Oh the places they’ll go: improving species distribution modelling for invasive forest pests in an uncertain world

• Published in: Biological invasions 2021, Vol.23 (1), p.297-349
• Main Authors: Srivastava, Vivek, Roe, Amanda D., Keena, Melody A., Hamelin, Richard C., Griess, Verena C.
• Format: Article
• Language: English
• Subjects: Biomedical and Life Sciences; Complexity; Developmental Biology; Dispersal; Dispersion; Ecology; Entropy; Freshwater & Marine Ecology; Geographical distribution; Introduced species; Invasive species; Life Sciences; Maximum entropy; Modelling; Nonnative species; Original Paper; Pests; Plant Sciences
• ISSN: 1387-3547; 1573-1464
• DOI: 10.1007/s10530-020-02372-9

Species distribution modelling (SDM) is a valuable tool for predicting the potential distribution of invasive species across space and time. Maximum entropy modelling (MaxEnt) is a popular choice for SDM, but questions have been raised about how these models are developed. Without biologically informed baseline assumptions, complex default SDM models could be selected, even though alternative settings may be more appropriate. Here we explored the effects of various SDM design strategies on distribution mapping of four forest invasive species (FIS) in Canada. We found that if we ignored the underlying FIS biology such as use of biologically relevant predictors, appropriate feature selection and inclusion of dispersal and biotic interactions when we developed our SDMs, we obtained complex SDMs (default) that provided an incomplete picture of the potential FIS invasion. We recommend simplifying SDM complexity and including biologically informed assumptions to achieve more accurate dispersal predictions, particularly when projecting FIS spread across time. We strongly encourage SDM users to perform species-specific tuning when modeling FIS distributions with MaxEnt to determine the best SDM design.