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Cited by 297

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Female space use is the best predictor of monogamy in mammals

Open access
Komers, Petr E., Brotherton, Peter N. M.·1997·Proceedings of the Royal Society of London. Series B: Biological Sciences

Abstract

Monogamy is typically considered to have evolved either because biparental care is important for offspring survival, or because males are unable to monopolize more than one female due to females being too dispersed. Here, in the first phylogenetic analysis of the evolution of monogamy in mammals, we show that neither of these explanations is consistent with the distribution of monogamy across mammal species. Monogamy evolved significantly more often in the absence of paternal care than in its presence. Furthermore, monogamy does not normally occur in species where female ranges are large. Rather, the most common feature of mammalian monogamy is that it evolved where females were solitary and occupied small, exclusive ranges, enabling males to monopolize them.

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Crossref · 10.1098/rspb.1997.0174

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Metadata

Authors
Komers, P. E., & Brotherton, P. N. M.
Venue
Proceedings of the Royal Society of London. Series B: Biological Sciences
Published
1997-09-22
Volume
264
Issue
1386
Pages
1261-1270
ISSN
  • 0080-4649 (ISSN-L)
  • 0962-8452
  • 1471-2954
OpenAlex
W2007102388
Cited by
297
References
51
FWCI
8.47
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Female space use is the best predictor of monogamy in mammals. https://app.cochat.ai/papers/10.1098/rspb.1997.0174
The paper
Komers, P. E., & Brotherton, P. N. M. (1997). Female space use is the best predictor of monogamy in mammals. Proceedings of the Royal Society of London. Series B: Biological Sciences. https://doi.org/10.1098/rspb.1997.0174

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.