Data from: A macroecological approach to evolutionary rescue and adaptation to climate change
Cite this dataset
Diniz-Filho, Jose Alexandre Felizola et al. (2019). Data from: A macroecological approach to evolutionary rescue and adaptation to climate change [Dataset]. Dryad. https://doi.org/10.5061/dryad.11d0f29
Despite the widespread use of Ecological Niche Models (ENMs) for predicting the responses of species to climate change, these models do not explicitly incorporate any population-level mechanism. On the other hand, mechanistic models adding population processes (e.g., biotic interactions, dispersal and adaptive potential to abiotic constraints) are much more complex and difficult to parameterize, especially if the goal is to predict range shifts for many species simultaneously. In particular, the adaptive potential (based on genetic adaptations, phenotypic plasticity and behavioral adjustments for physiological responses) of local populations has been the less studied mechanism affecting species’ responses to climatic change so far. Here, we discuss and apply an alternative macroecological framework to evaluate the potential role of evolutionary rescue under climate change based on ENMs. We begin by reviewing eco-evolutionary models that evaluate the maximum sustainable evolutionary rate under a scenario of environmental change, showing how they can be used to understand the impact of temperature change on a Neotropical anuran species, the Schneider’s toad Rhinella diptycha. Then we show how to evaluate spatial patterns of species’ geographic range shift using such models, by estimating evolutionary rates at the species’ trailing edge distribution estimated by ENMs and by recalculating the relative amount of total range loss under climate change. We show how different models can reduce the expected range loss predicted for the studied species by potential ecophysiological adaptations in some regions of the trailing edge predicted by ENMs. For general applications, we believe that parameters for large numbers of species and populations can be obtained from macroecological generalizations (e.g. allometric equations and ecogeographical rules), so our framework coupling ENMs with eco-evolutionary models can be applied to achieve a more accurate picture of potential impacts from climate changes and other threats to biodiversity.