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  Exploring the link between genetic relatedness r and social contact structure k in animal social networks

Wolf, J. B. W., Traulsen, A., & James, R. (2011). Exploring the link between genetic relatedness r and social contact structure k in animal social networks. The American Naturalist, 177(1), 135-142. doi:10.1086/657442.

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 Creators:
Wolf, Jochen B. W.1, Author           
Traulsen, Arne2, Author           
James, Richard, Author
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1Department Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445635              
2Research Group Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445641              

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Free keywords: cooperation; inclusive fitness theory; evolutionary graph theory; social network; relatedness; population substructuring
 Abstract: Our understanding of how cooperation can arise in a population of selfish individuals has been greatly advanced by theory. More than one approach has been used to explore the effect of population structure. Inclusive fitness theory uses genetic relatedness r to express the role of population structure. Evolutionary graph theory models the evolution of cooperation on network structures and focuses on the number of interacting partners k as a quantity of interest. Here we use empirical data from a hierarchically structured animal contact network to examine the interplay between independent, measurable proxies for these key parameters. We find strong inverse correlations between estimates of r and k over three levels of social organization, suggesting that genetic relatedness and social contact structure capture similar structural information in a real population.

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Language(s): eng - English
 Dates: 2011-01
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: eDoc: 556572
DOI: 10.1086/657442
Other: 2824/S 39134
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Title: The American Naturalist
Source Genre: Journal
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Pages: - Volume / Issue: 177 (1) Sequence Number: - Start / End Page: 135 - 142 Identifier: ISSN: 0003-0147 (print)
ISSN: 1537-5323 (online)