OOS 45-3 - Forecasting range shifts with process-based models and big data

Background/Question/Methods
Population dynamic theory can forecast demography and is widely used in managing natural resources including fisheries. Less is understood about how predictable these demographic processes are over space and time, particularly in the context of global change. At short time scales, transient dynamics of populations not in equilibrium with the environment may strongly influence species distributions, underscoring the need to explicitly model demography. We developed a process-based dynamic range model that estimates demographic rates, and the relationship between those rates and the environment, to forecast species range shifts in response to temperature change. This hierarchical Bayesian model uses large datasets on species’ occurrences and abundances to estimate parameters and simulate future states.

Results/Conclusions
We fitted this model to historical occurrence and abundance data from demersal trawl surveys conducted annually by the National Oceanic and Atmospheric Administration since the 1960s. We focused on four species of importance to fisheries in the mid-Atlantic region — summer flounder, shortfin squid, spiny dogfish, and gray triggerfish — and made forecasts for every year up to a decade (2007-2016). We then compared our annual forecasts to the real data from the testing interval, and to predictions from correlative ecological niche models, revealing that dynamic range models have skill at forecasting near-term range shifts. This work is among the first applications to real data of a class of models that shows great promise in ecological forecasting: dynamic range models that can make mechanistic predictions about the future by estimating process rates from survey data. By explicitly modeling demographic processes, this study advances the ability to predict short-term range dynamics of species on the move.