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A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming.


ABSTRACT: Withstanding extinction while facing rapid climate change depends on a species' ability to track its ecological niche or to evolve a new one. Current methods that predict climate-driven species' range shifts use ecological modelling without eco-evolutionary dynamics. Here we present an eco-evolutionary forecasting framework that combines niche modelling with individual-based demographic and genetic simulations. Applying our approach to four endemic perennial plant species of the Austrian Alps, we show that accounting for eco-evolutionary dynamics when predicting species' responses to climate change is crucial. Perennial species persist in unsuitable habitats longer than predicted by niche modelling, causing delayed range losses; however, their evolutionary responses are constrained because long-lived adults produce increasingly maladapted offspring. Decreasing population size due to maladaptation occurs faster than the contraction of the species range, especially for the most abundant species. Monitoring of species' local abundance rather than their range may likely better inform on species' extinction risks under climate change.

SUBMITTER: Cotto O 

PROVIDER: S-EPMC5424169 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming.

Cotto Olivier O   Wessely Johannes J   Georges Damien D   Klonner Günther G   Schmid Max M   Dullinger Stefan S   Thuiller Wilfried W   Guillaume Frédéric F  

Nature communications 20170505


Withstanding extinction while facing rapid climate change depends on a species' ability to track its ecological niche or to evolve a new one. Current methods that predict climate-driven species' range shifts use ecological modelling without eco-evolutionary dynamics. Here we present an eco-evolutionary forecasting framework that combines niche modelling with individual-based demographic and genetic simulations. Applying our approach to four endemic perennial plant species of the Austrian Alps, w  ...[more]

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