An integrated framework to identify wildlife populations under threat from climate change
2017
Razgour, Orly | Taggart, John | Manel, Stephanie | Juste, Javier | Ibanez, Carlos | Rebelo, Hugo | Alberdi, Antton | Jones, Gareth | Park, Kirsty | Biological and Environmental Sciences | Institute of Aquaculture | Paris Sciences and Letters Research University | Estación Biológica de Doñana | Estación Biológica de Doñana | University of Bristol | University of the Basque Country | University of Bristol | Biological and Environmental Sciences | 0000-0002-3843-9663 | 0000-0001-6080-7197
Climate change is a major threat to global biodiversity that will produce a range of new selection pressures. Understanding species responses to climate change requires an interdisciplinary perspective, combining ecological, molecular and environmental approaches. We propose an applied integrated framework to identify populations under threat from climate change based on their extent of exposure, inherent sensitivity due to adaptive and neutral genetic variation and range shift potential. We consider intraspecific vulnerability and population-level responses, an important but often neglected conservation research priority. We demonstrate how this framework can be applied to vertebrates with limited dispersal abilities using empirical data for the bat Plecotus austriacus. We use ecological niche modelling and environmental dissimilarity analysis to locate areas at high risk of exposure to future changes. Combining outlier tests with genotype–environment association analysis, we identify potential climate-adaptive SNPs in our genomic data set and differences in the frequency of adaptive and neutral variation between populations. We assess landscape connectivity and show that changing environmental suitability may limit the future movement of individuals, thus affecting both the ability of populations to shift their distribution to climatically suitable areas and the probability of evolutionary rescue through the spread of adaptive genetic variation among populations. Therefore, a better understanding of movement ecology and landscape connectivity is needed for predicting population persistence under climate change. Our study highlights the importance of incorporating genomic data to determine sensitivity, adaptive potential and range shift potential, instead of relying solely on exposure to guide species vulnerability assessments and conservation planning.
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