The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change
Abstract
Based on the sensitivity of species to ongoing climate change, and numerous challenges they face tracking suitable conditions, there is growing interest in species' capacity to adapt to climatic stress. Here, we develop and apply a new generic modelling approach (AdaptR) that incorporates adaptive capacity through physiological limits, phenotypic plasticity, evolutionary adaptation and dispersal into a species distribution modelling framework. Using AdaptR to predict change in the distribution of 17 species of Australian fruit flies (Drosophilidae), we show that accounting for adaptive capacity reduces projected range losses by up to 33% by 2105. We identify where local adaptation is likely to occur and apply sensitivity analyses to identify the critical factors of interest when parameters are uncertain. Our study suggests some species could be less vulnerable than previously thought, and indicates that spatiotemporal adaptive models could help improve management interventions that support increased species' resilience to climate change.
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Cites
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Physiological plasticity increases resilience of ectothermic animals to climate change
Seebacher, F., White, C. R., & Franklin, C. E. (2015) · Nature Climate Change · doi:10.1038/nclimate2457 · Cited by 998
The shifting influence of drought and heat stress for crops in northeast Australia
Lobell, D. B., Hammer, G. L., Chenu, K., Zheng, B., McLean, G., & Chapman, S. C. (2015) · Global Change Biology · doi:10.1111/gcb.13022 · Cited by 310
García-Robledo, C., Kuprewicz, E. K., Staines, C. L., Erwin, T. L., & Kress, W. J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507681113 · Cited by 306
Cited by
Evolutionary genomics can improve prediction of species’ responses to climate change
Waldvogel, A.-M., Feldmeyer, B., Rolshausen, G., Exposito-Alonso, M., Rellstab, C., Kofler, R., Mock, T., Schmid, K., Schmitt, I., Bataillon, T., et al. (2020) · Evolution Letters · doi:10.1002/evl3.154 · Cited by 348
Metadata
- Authors
- Bush, A., Mokany, K., Catullo, R., Hoffmann, A., Kellermann, V., Sgrò, C., McEvey, S., & Ferrier, S.
- Venue
- Ecology Letters
- Publisher
- Wiley
- Published
- 2016-11-22
- Volume
- 19
- Issue
- 12
- Pages
- 1468-1478
- ISSN
- 1461-023X (ISSN-L)
- 1461-0248
- OpenAlex
- W2555381849
- Cited by
- 261
- References
- 4
- FWCI
- 13.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bush, A., Mokany, K., Catullo, R., Hoffmann, A., Kellermann, V., Sgrò, C., McEvey, S., & Ferrier, S. (2016). Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecology Letters. https://doi.org/10.1111/ele.12696
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.