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 |
|---|---|
| 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 |
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Keywords
- climate change velocity
- demographic models
- dispersal
- integrodifference equations
- life-history traits
- population spread rate
- range shift
- rangeShifter
- trait space
- virtual species
Cite this
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 C. F.; White, Steven M.; Hodgson, Jenny A.; Bocedi, Greta; Travis, Justin M. J.
In: Global Change Biology, Vol. 22, No. 7, 07.2016, p. 2415-2424.Research output: Contribution to journal › Article
}
TY - JOUR
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.
PY - 2016/7
Y1 - 2016/7
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
C2 - 27073017
VL - 22
SP - 2415
EP - 2424
JO - Global Change Biology
JF - Global Change Biology
SN - 1354-1013
IS - 7
ER -