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

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.

Climate change and its impacts in the Balearic Islands: a guide for policy design in Mediterranean regions

“… 3 Expert opinion elicitation has been frequently used to predict the effect of CC on ecosystems, technology or management, among others (Barons et al 2018 ; Dessai et al 2018 ; Few et al 2018 ; Verdolini et al 2018 ; Wilcox et al 2018 ; Reside et al 2019 ; Abad et al 2020 ). …”

Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China

“…All algorithms were repeated 10 times with 50% of presence sites randomly selected for testing and three common evaluating statistics (Cohen's Kappa, KAPPA; True Skill Statistic, TSS and Areas Under the Receiver Operating Characteristic Curve, AUC) were selected to evaluate the performance of SDMs (Supplementary File 2). MaxEnt (Phillips et al, 2006) is a commonly used SDM algorithm for presence-only data, and it has also been shown to perform well in comparison to different algorithms (Reside et al, 2019) although explicit relationships between suitability and environmental variables are difficult to obtain from such a machine-learning algorithm (Phillips et al, 2006). We used MaxEnt (version 3.4.1) to constructed SDMs for three suites of presence data (P_West, P_East, and P_Whole) in the current situation and in two future periods under four RCPs, as explained in section Climate Data.…”

What Will the Future Bring for Biological Invasions on Islands? An Expert-Based Assessment

“…Moreover, biological invasions have arisen from a complex interplay of environmental, socio-economic and societal changes that are difficult to project using classical modeling techniques, like static habitat suitability models, population dynamic models or cellular automata (see Buchadas et al, 2017;Capinha et al, 2018;Lenzner et al, 2019). To overcome such multi-disciplinary challenges, combining classical forecasting techniques with expert-based assessments has proven to be a promising approach (e.g., through qualitative surveys; Berg et al, 2016;Symstad et al, 2017;Reside et al, 2018).…”