The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Ecological genomics meets community‐level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation
Abstract
Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism–environment relationships that combines genomic data and community‐level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar ( Populus balsamifera ) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA ‐5 ( GI 5 ), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community‐level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species‐level assessments of climate change vulnerability.
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Cites
Very high resolution interpolated climate surfaces for global land areas
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A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
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Conceptual issues in local adaptation
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Cited by
Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions
Hoban, S., Kelley, J. L., Lotterhos, K. E., Antolin, M. F., Bradburd, G., Lowry, D. B., Poss, M. L., Reed, L. K., Storfer, A., & Whitlock, M. C. (2016) · The American Naturalist · doi:10.1086/688018 · Cited by 876
“…to develop and test multivariate approaches that can detect covarying signals across multiple loci (e.g., Turchin et al. 2012; Berg and Coop 2014; Fitzpatrick and Keller 2014; Forester et al. 2015; Laporte et al. 2016), as it is still not known how frequently signals of local adaptation can be…”
Conservation and the Genomics of Populations
Allendorf, F. W., Funk, W. C., Aitken, S. N., Byrne, M., Luikart, G., & Antunes, A. (2022) · doi:10.1093/oso/9780198856566.001.0001 · Cited by 207
Hradilová, I., Duchoslav, M., Brus, J., Pechanec, V., Hýbl, M., Kopecký, P., Smržová, L., Štefelová, N., Vaclávek, T., Bariotakis, M., et al. (2019) · PeerJ · doi:10.7717/peerj.6263 · Cited by 52
Capblancq, T., Butnor, J. R., Deyoung, S., Thibault, E., Munson, H., Nelson, D. M., Fitzpatrick, M. C., & Keller, S. R. (2020) · Evolutionary Applications · doi:10.1111/eva.12985 · Cited by 30
Self-citationSamake, J. N., Lavretsky, P., Gunarathna, I., Follis, M., Brown, J. I., Ali, S., Yared, S., & Carter, T. E. (2023) · Molecular Ecology · doi:10.1111/mec.17136 · Cited by 22
Metadata
- Authors
- Fitzpatrick, M. C., & Keller, S. R.
- Venue
- Ecology Letters
- Publisher
- Wiley
- Published
- 2014-09-30
- Volume
- 18
- Issue
- 1
- Pages
- 1-16
- ISSN
- 1461-023X (ISSN-L)
- 1461-0248
- OpenAlex
- W2122403114
- Cited by
- 699
- References
- 75
- FWCI
- 12.28
- Type
- journal article
- Language
- en
- Open access
- closed
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