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Evaluating kin and group selection as tools for quantitative analysis of microbial data.

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.

SUBMITTER: Smith J

PROVIDER: S-EPMC8131122 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Evaluating kin and group selection as tools for quantitative analysis of microbial data.

Smith Jeff J Inglis R Fredrik RF

Proceedings. Biological sciences 20210519 1951

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 ...[more]

PMID: 34004128

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Project description:BackgroundConventional wisdom in evolutionary theory considers aging as a non-selected byproduct of natural selection. Based on this, conviction aging was regarded as an inevitable phenomenon. It was also thought that in the wild organisms tend to die from diseases, predation and other accidents before they could reach the time when senescence takes its course. Evidence has accumulated, however, that aging is not inevitable and there are organisms that show negative aging even. Furthermore, old age does play a role in the deaths of many different organisms in the wild also. The hypothesis of programmed aging posits that a limited lifespan can evolve as an adaptation (i.e., positively selected for) in its own right, partly because it can enhance evolvability by eliminating "outdated" genotypes. A major shortcoming of this idea is that non-aging sexual individuals that fail to pay the demographic cost of aging would be able to steal good genes by recombination from aging ones.ResultsHere, we show by a spatially explicit, individual-based simulation model that aging can positively be selected for if a sufficient degree of kin selection complements directional selection. Under such conditions, senescence enhances evolvability because the rate of aging and the rate of recombination play complementary roles. The selected aging rate is highest at zero recombination (clonal reproduction). In our model, increasing extrinsic mortality favors evolved aging by making up free space, thereby decreasing competition and increasing drift, even when selection is stabilizing and the level of aging is set by mutation-selection balance. Importantly, higher extrinsic mortality is not a substitute for evolved aging under directional selection either. Reduction of relatedness decreases the evolved level of aging; chance relatedness favors non-aging genotypes. The applicability of our results depends on empirical values of directional and kin selection in the wild.ConclusionsWe found that aging can positively be selected for in a spatially explicit population model when sufficiently strong directional and kin selection prevail, even if reproduction is sexual. The view that there is a conceptual link between giving up clonal reproduction and evolving an aging genotype is supported by computational results.

Dataset Information

Evaluating kin and group selection as tools for quantitative analysis of microbial data.

Publications

Evaluating kin and group selection as tools for quantitative analysis of microbial data.

Similar Datasets

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