Recommendations for quantifying and reducing uncertainty in climate projections of species distributions

Contributors: Stephanie Brodie, James A. Smith,  Barbara A. Muhling, Lewis A. K. Barnett, Gemma Carroll, Paul Fiedler, Steven J. Bograd, Elliott L. Hazen, Michael G. Jacox, Kelly S. Andrews, Cheryl L. Barnes, Lisa G. Crozier, Jerome Fiechter, Alexa Fredston, Melissa A. Haltuch, Chris J. Harvey, Elizabeth Holmes, Melissa A. Karp, Owen R. Liu, Michael J. Malick, Mercedes Pozo Buil, Kate Richerson, Christopher N. Rooper, Jameal Samhouri, Rachel Seary, Rebecca L. Selden, Andrew R. Thompson, Desiree Tommasi, Eric J. Ward, Isaac C. Kaplan

Organizations: Institute of Marine Sciences, University of California Santa Cruz, Monterey, California • Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, California • Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, San Diego, California • Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington • Environmental Defense Fund, Seattle, Washington • Physical Sciences Laboratory, Earth System Research Laboratories, National Oceanic and Atmospheric Administration, Boulder, Colorado • Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington • Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, Washington • Ocean Sciences Department, University of California Santa Cruz, Santa Cruz, California • Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, New Jersey • ECS Tech, in support of, NOAA Fisheries Office of Science and Technology, Silver Spring, Maryland • Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia, Canada • Department of Biological Sciences, Wellesley College, Wellesley, Massachusetts

Summary: Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change—rather than accurately predict specific outcomes—it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling.

The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change.