Plenary Speaker I

Speaker I: Andrew Allyn

Title: Can we predict species distributions? Understanding the relationship between species distribution model prediction skill and novel environmental conditions

Abstract: Correlative species distribution models (SDMs) are commonly used to predict species occurrence patterns under future environmental conditions, offering essential information to face the challenges arising from climate-driven species distribution shifts. Although widely applied, we still have much to learn about the predictive skill of these models, especially how well they can predict under novel conditions expected with continued global climate change. We investigated this question using a simulation study and assessed whether the relationship between SDM prediction skill and environmental novelty was consistent across two different large marine ecosystems, the California Current and the Northeast U.S. Shelf, and two contrasting species archetypes, a resident-mobile and a seasonally-migrating species. Both marine ecosystems experienced novel conditions during the prediction testing period, however, the degree of novelty was considerably greater for the Northeast U.S. Shelf. While we anticipated consistent declines in prediction skill as environmental novelty increased, this relationship was complicated by the species underlying movement characteristics as prediction skill remained stable and even increased under novel conditions in certain situations. This work adds to our theoretical understanding of SDM prediction skill and provides guidance for distribution forecast and projection efforts, highlighting how underlying system dynamics and species characteristics may interact to create unexpected patterns in model prediction skill.