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Cited by 732

The DOI matches its Crossref record; 187 of the 187 references it cites were found in a registry or an index.

Polyploidy: an evolutionary and ecological force in stressful times

Open access
Van de Peer, Yves, Ashman, Tia-Lynn, Soltis, Pamela S, Soltis, Douglas E·2021·The Plant Cell

Abstract

Polyploidy has been hypothesized to be both an evolutionary dead-end and a source for evolutionary innovation and species diversification. Although polyploid organisms, especially plants, abound, the apparent nonrandom long-term establishment of genome duplications suggests a link with environmental conditions. Whole-genome duplications seem to correlate with periods of extinction or global change, while polyploids often thrive in harsh or disturbed environments. Evidence is also accumulating that biotic interactions, for instance, with pathogens or mutualists, affect polyploids differently than nonpolyploids. Here, we review recent findings and insights on the effect of both abiotic and biotic stress on polyploids versus nonpolyploids and propose that stress response in general is an important and even determining factor in the establishment and success of polyploidy.

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Crossref · 10.1093/plcell/koaa015

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Metadata

Authors
Van de Peer, Y., Ashman, T.-L., Soltis, P. S., & Soltis, D. E.
Venue
The Plant Cell
Published
2020-12-01
Volume
33
Issue
1
Pages
11-26
ISSN
  • 1040-4651 (ISSN-L)
  • 1532-298X
OpenAlex
W3122540149
Cited by
732
References
187
FWCI
43.20
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Polyploidy: an evolutionary and ecological force in stressful times. https://app.cochat.ai/papers/10.1093/plcell/koaa015
The paper
Van de Peer, Y., Ashman, T.-L., Soltis, P. S., & Soltis, D. E. (2021). Polyploidy: an evolutionary and ecological force in stressful times. The Plant Cell. https://doi.org/10.1093/plcell/koaa015

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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.