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Cited by 192

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Neutral theory: a historical perspective

LEIGH, E. G.·2007·Journal of Evolutionary Biology

Abstract

To resolve a panselectionist paradox, the population geneticist Kimura invented a neutral theory, where each gene is equally likely to enter the next generation whatever its allelic type. To learn what could be explained without invoking Darwinian adaptive divergence, Hubbell devised a similar neutral theory for forest ecology, assuming each tree is equally likely to reproduce whatever its species. In both theories, some predictions worked; neither theory proved universally true. Simple assumptions allow neutral theorists to treat many subjects still immune to more realistic theory. Ecologists exploit far fewer of these possibilities than population geneticists, focussing instead on species abundance distributions, where their predictions work best, but most closely match non‐neutral predictions. Neutral theory cannot explain adaptive divergence or ecosystem function, which ecologists must understand. By addressing new topics and predicting changes in time, however, ecological neutral theory can provide probing null hypotheses and stimulate more realistic theory.

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Metadata

Authors
LEIGH, E. G.
Venue
Journal of Evolutionary Biology
Published
2007-10
Volume
20
Issue
6
Pages
2075-2091
ISSN
  • 1010-061X (ISSN-L)
  • 1420-9101
OpenAlex
W2138330738
Cited by
192
References
9
FWCI
9.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Neutral theory: a historical perspective. https://app.cochat.ai/papers/10.1111/j.1420-9101.2007.01410.x
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LEIGH, E. G. (2007). Neutral theory: a historical perspective. Journal of Evolutionary Biology. https://doi.org/10.1111/j.1420-9101.2007.01410.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.