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Cited by 196

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Individual Variation in Mammals

Hayes, J. P., Jenkins, S. H.·1997·Journal of Mammalogy

Abstract

The study of individual variation offers an underexploited wealth of opportunities for mammalogists. This paper addresses recent developments in the study of both intra- and inter-individual variation. After reviewing several methods (e.g., intraclass correlation, product-moment correlation, and confirmatory factor analysis) for quantifying intra-individual consistency or repeatability, we discuss how these measures of repeatability can serve as guides for appropriately defining traits and how they may be helpful in ensuring that appropriate statistical models are used (e.g., in accounting for measurement errors in regression analyses). We discuss three aspects of inter-individual variation; phenotypic selection, alternative individual strategies and phenotypic integration, and quantitative genetic analyses. The value of these approaches for studying inter-individual variation is illustrated with recent examples from the literature. Finally, we discuss how many field studies of mammals may be well poised to exploit the unique insights that can be gained from studying individual variation.

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Metadata

Authors
Hayes, J. P., & Jenkins, S. H.
Venue
Journal of Mammalogy
Published
1997-05-21
Volume
78
Issue
2
Pages
274-293
ISSN
  • 0022-2372 (ISSN-L)
  • 1545-1542
OpenAlex
W2324878224
Cited by
196
References
2
FWCI
9.04
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Individual Variation in Mammals. https://app.cochat.ai/papers/10.2307/1382882
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Hayes, J. P., & Jenkins, S. H. (1997). Individual Variation in Mammals. Journal of Mammalogy. https://doi.org/10.2307/1382882

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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.