Abstract
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.
Original language | English |
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Pages (from-to) | 2415-2424 |
Number of pages | 10 |
Journal | Global Change Biology |
Volume | 22 |
Issue number | 7 |
Early online date | 13 Apr 2016 |
DOIs | |
Publication status | Published - Jul 2016 |
Keywords
- climate change velocity
- demographic models
- dispersal
- integrodifference equations
- life-history traits
- population spread rate
- range shift
- rangeShifter
- trait space
- virtual species
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Profiles
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Thomas Cornulier
- Earth Systems and Environmental Sciences
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Research Fellow
Person: Academic Related - Research
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Justin Travis
- Earth Systems and Environmental Sciences
- Biological Sciences, Aberdeen Centre For Environmental Sustainability - Personal Chair
- Marine Alliance for Science and Technology for Scotland (MASTS) - Professor
Person: Academic