OOS 24-2 - Spatial ecological forecasting: applications to marine fish range dynamics

Population dynamic theory can forecast demography and is widely used in managing natural resources including fisheries. Because population dynamic models are already process-based, they are also good candidates for probabilistic ecological forecasts that quantify and propagate uncertainty. Indeed, several successful ecological forecasts used these models. Less is known, however, about making these mechanistic population forecasts spatial. A spatial population dynamic model could predict dispersal across landscapes or seascapes and patch-level demographic effects of climate and other drivers. As such, it would advance our ability to predict species distributions at relatively short time scales with appropriate uncertainty, which has proved challenging with existing species distribution modeling techniques. 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. We fitted the 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 species distribution models, revealing that dynamic range models are more accurate and less biased 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 spatial 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.