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A trait-based approach for predicting species responses to environmental change from sparse data : how well might terrestrial mammals track climate change ? [Global Change Biology]

Keywords : climate change velocity, demographic models, dispersal, integrodifference equations, life-history traits, population spread rate, range shift, rangeShifter, trait space, virtual species

par Frédéric Magné - publié le

Estimating population spread rates across multiple species is vital for projecting biodiversity responses to climate change. A major challenge is to parameterise spread models for many species. We introduce an approach that addresses this challenge, coupling a trait-based analysis with spatial population modelling to project spread rates for 15 000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life-history traits are estimated from an extensive terrestrial mammal data set using Bayesian inference. We elucidate the relative roles of different life-history traits in driving modelled spread rates, demonstrating that any one alone will be a poor predictor. We also estimate that around 30% of mammal species have potential spread rates slower than the global mean velocity of climate change. This novel trait-space-demographic modelling approach has broad applicability for tackling many key ecological questions for which we have the models but are hindered by data availability.

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