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Cited by 534

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

The Ecological Context of Life History Evolution

Partridge, Linda, Harvey, Paul H.·1988·Science

Abstract

There is now a good theoretical understanding of life history evolution, and detailed explicit optimality models have been constructed. These present a challenge for empirical work examining some of the assumptions, such as the extent and mechanisms of the costs of growth and reproduction. In addition, there is an obvious need for comparative tests of the models. These tests, properly applied, may be particularly informative because they can deal with multiple independent variables, including ecological variables, and can reveal broad trends against a background of constraints on optima and the rate of evolutionary approach to them. Life histories are the probabilities of survival and the rates of reproduction at each age in the life-span. Reproduction is costly, so that fertility at all ages cannot simultaneously be maximized by natural selection. Allocation of reproductive effort has evolved in response to the demographic impact of different environments but is constrained by genetic variance and evolutionary history.

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Crossref · 10.1126/science.241.4872.1449

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Metadata

Authors
Partridge, L., & Harvey, P. H.
Venue
Science
Published
1988-09-16
Volume
241
Issue
4872
Pages
1449-1455
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W1974377563
Cited by
534
References
6
FWCI
36.18
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Ecological Context of Life History Evolution. https://app.cochat.ai/papers/10.1126/science.241.4872.1449
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Partridge, L., & Harvey, P. H. (1988). The Ecological Context of Life History Evolution. Science. https://doi.org/10.1126/science.241.4872.1449

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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.