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

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 modelling techniques, gaps remain in understanding the relationship between model performance and ecological reality. To investigate this we compared standard correlative species distributionmodels 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 vettingwith experts, we identified seven general principles that explain why the distribution modelling under- or over-estimated 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 vettingidentified regions that were likely to contain micro refugia, even where the fine-scale future projections of species distributionspredicted population losses. Based on the results we identify four priority conservation actions required for more effective climate change adaptationresponses. 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. This article is protected by copyright. All rights reserved.