Journal
GLOBAL CHANGE BIOLOGY
Volume 22, Issue 7, Pages 2415-2424Publisher
WILEY
DOI: 10.1111/gcb.13271
Keywords
climate change velocity; demographic models; dispersal; integrodifference equations; life-history traits; population spread rate; range shift; rangeShifter; trait space; virtual species
Funding
- COST Action - COST (European Cooperation in Science and Technology) [ES1101]
- CEH projects [NEC05264, NEC05100]
- Natural Environment Research Council UK [NE/J008001/1]
- NERC [NE/J008001/1, ceh020002] Funding Source: UKRI
- Natural Environment Research Council [ceh020002, NE/J008001/1] Funding Source: researchfish
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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 15000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life-historytraits 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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