Author(s): Allyn, A. J., M. A. Alexander, B. S. Franklin, F. Massiot-Granier, A. J. Pershing, J. D. Scott, and K. E. Mills.
Project PI: Mills
Species distribution shifts are a widely reported biological consequence of climate-driven warming across marine ecosystems, creating ecological and social challenges. To meet these challenges and inform management decisions, we need accurate projections of species distributions. Quantitative species distribution models (SDMs) are routinely used to make these projections, while qualitative climate change vulnerability assessments are becoming more common. We constructed SDMs, compared SDM projections to expectations from a qualitative expert climate change vulnerability assessment, and developed a novel approach for combining the two methods to project the distribution and relative biomass of 49 marine species in the Northeast Shelf Large Marine Ecosystem under a “business as usual” climate change scenario. A forecasting experiment using SDMs highlighted their ability to capture relative biomass patterns fairly well (mean Pearson’s correlation coefficient between predicted and observed biomass = 0.24, range = 0–0.6) and pointed to areas needing improvement, including reducing prediction error and better capturing fine-scale spatial variability. SDM projections suggest the region will undergo considerable biological changes, especially in the Gulf of Maine, where commercially-important groundfish and traditional forage species are expected to decline as coastal fish species and warmer-water forage species historically found in the southern New England/Mid-Atlantic Bight area increase. The SDM projections only occasionally aligned with vulnerability assessment expectations, with agreement more common for species with adult mobility and population growth rates that showed low sensitivity to climate change. Although our blended approach tried to build from the strengths of each method, it had no noticeable improvement in predictive ability over SDMs. This work rigorously evaluates the predictive ability of SDMs, quantifies expected species distribution shifts under future climate conditions, and tests a new approach for integrating SDMs and vulnerability assessments to help address the complex challenges arising from climate-driven species distribution shifts.