Santini, Luca, Cornulier, Thomas, Bullock, James M, Palmer, Stephen CF, White, Steven M, Hodgson, Jenny A ORCID: 0000-0003-2297-3631, Bocedi, Greta and Travis, Justin MJ
(2016)
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, 22 (7).
pp. 2415-2424.
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Santini et al. 2016.pdf - Published version Download (579kB) |
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.
Item Type: | Article |
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Uncontrolled Keywords: | climate change velocity, demographic models, dispersal, integrodifference equations, life-history traits, population spread rate, range shift, rangeShifter, trait space, virtual species |
Depositing User: | Symplectic Admin |
Date Deposited: | 13 Jul 2016 13:45 |
Last Modified: | 19 Jan 2023 07:33 |
DOI: | 10.1111/gcb.13271 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3002303 |