Abstract
Kin selection and multilevel selection theory are often used to interpret experiments about the evolution of cooperation and social behaviour among microbes. But while these experiments provide rich, detailed fitness data, theory is mostly used as a conceptual heuristic. Here, we evaluate how kin and multilevel selection theory perform as quantitative analysis tools. We reanalyse published microbial datasets and show that the canonical fitness models of both theories are almost always poor fits because they use statistical regressions misspecified for the strong selection and non-additive effects we show are widespread in microbial systems. We identify analytical practices in empirical research that suggest how theory might be improved, and show that analysing both individual and group fitness outcomes helps clarify the biology of selection. A data-driven approach to theory thus shows how kin and multilevel selection both have untapped potential as tools for quantitative understanding of social evolution in all branches of life.
Highlights
For biologists seeking to understand the evolution of cooperation and social behaviour, mathematical theory has been a valuable guide for empirical research
For example, has been enormously influential in part because it shows that social evolution can be understood from just a few important quantities: how a behaviour affects the fitness of an actor, how that behaviour affects the fitness of other individuals, and the genetic relatedness between social partners [1,2]
For guidance on how theory might better handle the challenges of microbial data, we identify analytical practices in empirical research that are robust across different microbial systems, provide insight into the causes of selection, and allow quantitative comparison of social selection across systems
Summary
For biologists seeking to understand the evolution of cooperation and social behaviour, mathematical theory has been a valuable guide for empirical research. For example, has been enormously influential in part because it shows that social evolution can be understood from just a few important quantities: how a behaviour affects the fitness of an actor, how that behaviour affects the fitness of other individuals, and the genetic relatedness between social partners [1,2] This theory was developed with a major focus on animal behaviour, so its mathematical form was developed in terms of the quantitative-genetic phenotypes most measured in animal systems [3]. Mix experiments examine how selection acts in a local subpopulation during one part of a microbe’s life cycle—within a single infected host, for example Their results can be used to estimate how selection in a larger metapopulation would depend on genetic social structure at this scale (the distribution of initial genotype frequencies among subpopulations). A quantitative, data-driven approach can be a productive way forward to identify how theory can best aid our understanding of social evolution in all branches of life
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