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The life history of human foraging: Cross-cultural and individual variation

MPG-Autoren
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Koster,  Jeremy       
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Mcelreath,  Richard       
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Ready,  Elspeth       
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Ross,  Cody       
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Zitation

Koster, J., Mcelreath, R., Hill, K., Yu, D., Shepard, G., Vliet, N. V., et al. (2020). The life history of human foraging: Cross-cultural and individual variation. Science Advances, 6(26): eaax9070. doi:10.1126/sciadv.aax9070.


Zitierlink: https://hdl.handle.net/21.11116/0000-0003-BF23-F
Zusammenfassung
Human adaptation depends upon the integration of slow life history, complex production skills, and extensive sociality. Refining and testing models of the evolution of human life history and cultural learning will benefit from increasingly accurate measurement of knowledge, skills, and rates of production with age. We pursue this goal by inferring individual hunters’ of hunting skill gain and loss from approximately 23,000 hunting records generated by more than 1,800 individuals at 40 locations. The model provides an improved picture of ages of peak productivity as well as variation within and among ages. The data reveal an average age of peak productivity between 30 and 35 years of age, though high skill is maintained throughout much of adulthood. In addition, there is substantial variation both among individuals and sites. Within study sites, variation among individuals depends more upon heterogeneity in rates of decline than in rates of increase. This analysis sharpens questions about the co-evolution of human life history and cultural adaptation. It also demonstrates new statistical algorithms and models that expand the potential inferences drawn from detailed quantitative data collected in the field.