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Cited by 326

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Evolutionary responses to global change: lessons from invasive species

Moran, Emily V., Alexander, Jake M.·2014·Ecology Letters

Abstract

Biologists have recently devoted increasing attention to the role of rapid evolution in species' responses to environmental change. However, it is still unclear what evolutionary responses should be expected, at what rates, and whether evolution will save populations at risk of extinction. The potential of biological invasions to provide useful insights has barely been realised, despite the close analogies to species responding to global change, particularly climate change; in both cases, populations encounter novel climatic and biotic selection pressures, with expected evolutionary responses occurring over similar timescales. However, the analogy is not perfect, and invasive species are perhaps best used as an upper bound on expected change. In this article, we review what invasive species can and cannot teach us about likely evolutionary responses to global change and the constraints on those responses. We also discuss the limitations of invasive species as a model and outline directions for future research.

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Metadata

Authors
Moran, E. V., & Alexander, J. M.
Venue
Ecology Letters
Publisher
Wiley
Published
2014-02-24
Volume
17
Issue
5
Pages
637-649
ISSN
  • 1461-023X (ISSN-L)
  • 1461-0248
OpenAlex
W2142860390
Cited by
326
References
3
FWCI
16.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Evolutionary responses to global change: lessons from invasive species. https://app.cochat.ai/papers/10.1111/ele.12262
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Moran, E. V., & Alexander, J. M. (2014). Evolutionary responses to global change: lessons from invasive species. Ecology Letters. https://doi.org/10.1111/ele.12262

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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.