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

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 u...

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Bibliographic Details
Published in:Global change biology 2022-11, Vol.28 (22), p.6586-6601
Main Authors: Brodie, Stephanie, Smith, James A., Muhling, Barbara A., Barnett, Lewis A. K., Carroll, Gemma, Fiedler, Paul, Bograd, Steven J., Hazen, Elliott L., Jacox, Michael G., Andrews, Kelly S., Barnes, Cheryl L., Crozier, Lisa G., Fiechter, Jerome, Fredston, Alexa, Haltuch, Melissa A., Harvey, Chris J., Holmes, Elizabeth, Karp, Melissa A., Liu, Owen R., Malick, Michael J., Pozo Buil, Mercedes, Richerson, Kate, Rooper, Christopher N., Samhouri, Jameal, Seary, Rachel, Selden, Rebecca L., Thompson, Andrew R., Tommasi, Desiree, Ward, Eric J., Kaplan, Isaac C.
Format: Article
Language:English
Subjects:
artificial intelligence
Climate adaptation
Climate Change
Climate change adaptation
Climate models
Distribution
earth system models
Ecological effects
Ecological models
Ecosystem
Ecosystem management
Electric power distribution
Environment models
Environmental conditions
extrapolation
Fisheries
Fisheries management
Fishery resources
Forecasting
Geographical distribution
machine learning
Ocean models
Ocean-atmosphere system
Simulation
Species
species distribution models
Uncertainty
virtual species
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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. Projecting the future distributions of species has become critical for climate adaption planning, but we don't have a precise understanding of how accurate projections can be without waiting decades for validation. Here, we model simulated species responses under climate change to quantify and understand sources
ISSN:1354-1013
1365-2486
DOI:10.1111/gcb.16371