Integrating past evolution, adaptive capacity, and dispersal in population viability analysis under climate change

Climate change may threaten biodiversity, particularly in the Arctic, but our empirical understanding of its eco-evolutionary consequences is poor. One challenge has been to obtain and integrate data on demographic, genetic, and spatial processes in joint statistical modelling frameworks accounting for environmental stochasticity. This has restricted our ability to predict spatially explicit population viability under global warming, an urgent conservation and management task. We will apply an interdisciplinary approach on wild Svalbard reindeer to (1) understand how the interplay of short- and long-term ecological and evolutionary processes can buffer against (or increase vulnerability to) environmental change, for then to (2) develop novel methodology for empirical-based predictions of population viability accounting for spatial, demographic, and genetic processes under climate change and habitat fragmentation. This requires new interdisciplinary frameworks. We will apply state-of-the-art eco-evolutionary theory and statistical modelling to empirical data on dispersal, demography, genotype-phenotype-fitness distributions, and ancient and contemporary DNA. This allows quantification of selection processes and environmental impacts on population dynamics and genetics over decades and millennia. By linking stochastic processes in ecology and evolution, this enables us to predict spatially explicit population viability under future climate and habitat connectivity (e.g., sea-ice) scenarios. The results and methodology will be generalisable and applicable to other systems and contribute to improved conservation and management by providing realistic predictions of population viability in fragmented landscapes subject to climate change. New guidelines can be developed that reduce threatened and fragmented species’ extinction probabilities, guiding future management and conservation decisions that minimize loss of biodiversity.

Climate change may threaten biodiversity, particularly in the Arctic, but our empirical understanding of its eco-evolutionary consequences is poor. One challenge has been to obtain and integrate data on demographic, genetic, and spatial processes in joint statistical modelling frameworks accounting for environmental stochasticity. This has restricted our ability to predict spatially explicit population viability under global warming, an urgent conservation and management task. We will apply an interdisciplinary approach on wild Svalbard reindeer to (1) understand how the interplay of short- and long-term ecological and evolutionary processes can buffer against (or increase vulnerability to) environmental change, for then to (2) develop novel methodology for empirical-based predictions of population viability accounting for spatial, demographic, and genetic processes under climate change and habitat fragmentation. This requires new interdisciplinary frameworks. We will apply state-of-the-art eco-evolutionary theory and statistical modelling to empirical data on dispersal, demography, genotype-phenotype-fitness distributions, and ancient and contemporary DNA. This allows quantification of selection processes and environmental impacts on population dynamics and genetics over decades and millennia. By linking stochastic processes in ecology and evolution, this enables us to predict spatially explicit population viability under future climate and habitat connectivity (e.g., sea-ice) scenarios. The results and methodology will be generalisable and applicable to other systems and contribute to improved conservation and management by providing realistic predictions of population viability in fragmented landscapes subject to climate change. New guidelines can be developed that reduce threatened and fragmented species’ extinction probabilities, guiding future management and conservation decisions that minimize loss of biodiversity.