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

Luca Santini, Thomas Cornulier, James M. Bullock, Stephen C. F. Palmer, Steven M. White, Jenny A. Hodgson, Greta Bocedi, Justin M. J. Travis

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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 languageEnglish
Pages (from-to)2415-2424
Number of pages10
JournalGlobal Change Biology
Volume22
Issue number7
Early online date13 Apr 2016
DOIs
Publication statusPublished - Jul 2016

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Mammals
Climate change
environmental change
mammal
climate change
Biodiversity
population modeling
life history trait
Availability
life history
biodiversity
rate
modeling

Keywords

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

Cite this

@article{37039473a4a140e0942023b6abe36a29,
title = "A trait-based approach for predicting species responses to environmental change from sparse data: how well might terrestrial mammals track climate change?",
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.",
keywords = "climate change velocity, demographic models, dispersal, integrodifference equations, life-history traits, population spread rate, range shift, rangeShifter, trait space, virtual species",
author = "Luca Santini and Thomas Cornulier and Bullock, {James M.} and Palmer, {Stephen C. F.} and White, {Steven M.} and Hodgson, {Jenny A.} and Greta Bocedi and Travis, {Justin M. J.}",
note = "Acknowledgements LS was supported by two STSMs by the COST Action ES1101 ”Harmonising Global Biodiversity Modelling“ (Harmbio), supported by COST (European Cooperation in Science and Technology). JMB and SMW were funded by CEH projects NEC05264 and NEC05100. JMJT and SCFP are grateful for the support of the Natural Environment Research Council UK (NE/J008001/1). LS, JAH and JMJT conceived the original idea. LS, JAH, JMB, TC & JMJT designed the study; LS collected the data; LS and TC performed the statistical analyses; LS conducted the integrodifference modelling assisted by JMB and SMW. LS conducted the individual-based modelling assisted by SCFP. LS led the writing supported by JMJT, JMB, SCFP, SMW, TC, JAH and GB.",
year = "2016",
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language = "English",
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journal = "Global Change Biology",
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T1 - A trait-based approach for predicting species responses to environmental change from sparse data

T2 - how well might terrestrial mammals track climate change?

AU - Santini, Luca

AU - Cornulier, Thomas

AU - Bullock, James M.

AU - Palmer, Stephen C. F.

AU - White, Steven M.

AU - Hodgson, Jenny A.

AU - Bocedi, Greta

AU - Travis, Justin M. J.

N1 - Acknowledgements LS was supported by two STSMs by the COST Action ES1101 ”Harmonising Global Biodiversity Modelling“ (Harmbio), supported by COST (European Cooperation in Science and Technology). JMB and SMW were funded by CEH projects NEC05264 and NEC05100. JMJT and SCFP are grateful for the support of the Natural Environment Research Council UK (NE/J008001/1). LS, JAH and JMJT conceived the original idea. LS, JAH, JMB, TC & JMJT designed the study; LS collected the data; LS and TC performed the statistical analyses; LS conducted the integrodifference modelling assisted by JMB and SMW. LS conducted the individual-based modelling assisted by SCFP. LS led the writing supported by JMJT, JMB, SCFP, SMW, TC, JAH and GB.

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N2 - 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.

AB - 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.

KW - climate change velocity

KW - demographic models

KW - dispersal

KW - integrodifference equations

KW - life-history traits

KW - population spread rate

KW - range shift

KW - rangeShifter

KW - trait space

KW - virtual species

U2 - 10.1111/gcb.13271

DO - 10.1111/gcb.13271

M3 - Article

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JO - Global Change Biology

JF - Global Change Biology

SN - 1354-1013

IS - 7

ER -