Dynamic range models improve the near-term forecast for a marine species on the move

Population dynamic models are widely used to predict demography. However, they have rarely been extended to biogeographical applications despite widespread calls to do so. We developed a process-based dynamic range model (DRM) that estimated demographic rates and the effects of the environment on demographic rates to forecast species range shifts in response to temperature change. As a proof of concept, we fitted DRMs to historical observations of summer flounder (Paralichthys dentatus), a fish species in the Northwest Atlantic, and evaluated model skill at retrospective forecasting. The best DRMs outperformed a statistical species distribution model and a persistence forecast at predicting biogeographical dynamics across a decade. The DRM approach is general and can be applied to a wide range of species with historical observations across space and time. By explicitly modeling demographic processes and their relationship to climate, DRMs promise to substantially advance prediction of species on the move.