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

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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Official URL: http://dx.doi.org/10.1111/gcb.13271

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
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:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3002303