2010
DOI: 10.1126/science.1189675
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A Generalization of Hamilton’s Rule for the Evolution of Microbial Cooperation

Abstract: Hamilton’s rule states that cooperation will evolve if the fitness cost to actors is less than the benefit to recipients multiplied by their genetic relatedness. This rule makes many simplifying assumptions, however, and does not accurately describe social evolution in organisms like microbes where selection is both strong and nonadditive. We derived a generalization of Hamilton’s rule and measured its parameters in Myxococcus xanthus bacteria. Nonadditivity made cooperative sporulation surprisingly resistant … Show more

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Cited by 73 publications
(95 citation statements)
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“…Analytical methods for studying collective behaviour often focus either on heterogeneities in structure [4,[20][21][22] or on heterogeneities in cell group composition [23][24][25], but rarely on both. Similarly, powerful theory has been developed for understanding the evolution of social interaction [26][27][28][29][30], but this theory is often difficult to apply directly to cell groups in realistic contexts (but see [31][32][33]). Computational individual-based modelling offers an alternative approach, implementing cells in two-or threedimensional space that behave independently in response to their local microenvironments [34,35].…”
Section: Introductionmentioning
confidence: 99%
“…Analytical methods for studying collective behaviour often focus either on heterogeneities in structure [4,[20][21][22] or on heterogeneities in cell group composition [23][24][25], but rarely on both. Similarly, powerful theory has been developed for understanding the evolution of social interaction [26][27][28][29][30], but this theory is often difficult to apply directly to cell groups in realistic contexts (but see [31][32][33]). Computational individual-based modelling offers an alternative approach, implementing cells in two-or threedimensional space that behave independently in response to their local microenvironments [34,35].…”
Section: Introductionmentioning
confidence: 99%
“…In either case, the way to overcome the problem is to add more predictors to the regression model in order to take deviations from additivity explicitly into account (Queller 1992b;Smith et al 2010;Cornforth et al 2012). …”
Section: B the Multi-level Selection Casementioning
confidence: 99%
“…These extended formulations have already proved invaluable in lab studies of microbial cooperation, a context in which accounting for deviations from additivity turns out to be particularly important (Smith et al 2010;Cornforth et al 2012).…”
Section: Introductionmentioning
confidence: 99%
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