Beyond the model: expert knowledge improves predictions of species' fates under climate change

Reside, April E., Critchell, Kay, Crayn, Darren M., Goosem, Miriam, Goosem, Stephen, Hoskin, Conrad J., Sydes, Travis, Vanderduys, Eric P., and Pressey, Robert L. (2019) Beyond the model: expert knowledge improves predictions of species' fates under climate change. Ecological Applications, 29 (1). e01824.

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Abstract

The need to proactively manage landscapes and species to aid their adaptation to climate change is widely acknowledged. Current approaches to prioritizing investment in species conservation generally rely on correlative models, which predict the likely fate of species under different climate change scenarios. Yet, while model statistics can be improved by refining modeling techniques, gaps remain in understanding the relationship between model performance and ecological reality. To investigate this, we compared standard correlative species distribution models to highly accurate, fine-scale, distribution models. We critically assessed the ecological realism of each species' model, using expert knowledge of the geography and habitat in the study area and the biology of the study species. Using interactive software and an iterative vetting with experts, we identified seven general principles that explain why the distribution modeling under- or overestimated habitat suitability, under both current and predicted future climates. Importantly, we found that, while temperature estimates can be dramatically improved through better climate downscaling, many models still inaccurately reflected moisture availability. Furthermore, the correlative models did not account for biotic factors, such as disease or competitor species, and were unable to account for the likely presence of micro refugia. Under-performing current models resulted in widely divergent future projections of species' distributions. Expert vetting identified regions that were likely to contain micro refugia, even where the fine-scale future projections of species distributions predicted population losses. Based on the results, we identify four priority conservation actions required for more effective climate change adaptation responses. This approach to improving the ecological realism of correlative models to understand climate change impacts on species can be applied broadly to improve the evidence base underpinning management responses.

Item ID: 56870
Item Type: Article (Research - C1)
ISSN: 1939-5582
Keywords: climate change impact, endemic species, expert knowledge, fine-scale data, Maxent, rainforest, refugia, species distribution modeling
Copyright Information: © 2018 by the Ecological Society of America
Funders: Australian Government (AG), Australian Research Council (ARC)
Projects and Grants: AG Regional NRM planning for climate change program
Date Deposited: 16 Jan 2019 07:32
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment @ 100%
SEO Codes: 96 ENVIRONMENT > 9603 Climate and Climate Change > 960301 Climate Change Adaptation Measures @ 100%
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