Abstract

Whether species are capable of adapting to rapid shifts in climate raises considerable interest. Analyses based on niche models often assume niche conservatism and equilibrium with climate, implying that species will persist only in regions where future climatic conditions match their current conditions and that they will colonize these regions promptly. However, species may adapt to changing climate and persist where future climates differ from their current optimum. Here, we provide a first macroecological generalization to the approach of evolutionary rescue, by comparing the expected shift in mean temperature within the geographic range of 7193 species of amphibians worldwide, under alternative warming scenarios. Expected evolutionary change is expressed in units of standard deviations of mean temperature, per generation (Haldanes) and compared with theoretical models defining the maximum sustainable evolutionary rates (MSER) for each species. For the pessimistic emission scenario RCP8.5, shifts in mean temperature vary between near-zero and 6°C within the geographic ranges for most species, with a median equal to 3.75°C. The probability of evolutionary rescue in temperature peaks is higher than 0.05 for about 55% of the species and higher than 0.95 for only 12% of the species. Therefore, the predicted shift in mean temperature would be too extreme to deal with for almost half of the species. When evolutionary plasticity is incorporated, this scenario becomes more optimistic, with about 44% of the species being likely to shift their thermal peaks tracking future warming. These figures are not random in geographical space: evolutionary rescue would be unlikely in the tropics, especially in South America (Amazonia), parts of Africa, Indonesia, and in the Mediterranean region. Given the uncertainty in demographic and genetic parameters for species’ responses to climate change, we caution that it remains difficult to assess the realism of the macroecological generalization. In any case, it may be precautionary to assume that our results are not liberal, showing low probability of adaptation for most of the species and thus that the persistence of populations by evolutionary rescue may, in general, be unlikely in the long term.

Highlights

  • There is a considerable interest in evaluating how species will cope with global changes, in terms of their adaptive potential in relation to rapid shifts in climate and habitat conversion

  • Our analyses revealed that probability of evolutionary rescue is lower than 5% for about 45% of the species, and that only 12% of species would potentially adapt to climate change

  • The predicted evolutionary rate is compared with theoretical expectations built under distinct theoretical evolutionary models for Maximum Sustainable Evolutionary Rates (MSER)

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Summary

Introduction

There is a considerable interest in evaluating how species will cope with global changes, in terms of their adaptive potential in relation to rapid shifts in climate ( temperature) and habitat conversion. In theory, species could adapt to changing climates, even in relatively short times, and persist in regions where future conditions will differ from their current tolerance (Lavergne et al 2010, Hoffmann and Sgrò 2011, Franks and Hoffmann 2012, Norberg et al 2012, Thuiller et al 2015). The idea is to select a target climatic variable (e.g., temperature) and calculate the predicted evolutionary rate to the future, expressed in units of standard deviations of mean temperature, per generation, measured in Haldanes (H), within the species’ range (either for species’ centroid or throughout the trailing edge of the geographic range obtained by niche models). Evolutionary rescue would allow persistence of populations by adaptation to the novel climate conditions occurring within the current geographic range of the species. If H > MSER, evolutionary change in climate is too fast for the species to cope with, and extinction is more likely in the long run, depending on how well populations are able to persist in suboptimum environments

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