Using Multilevel Models to Estimate Variation in Foraging Returns
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Distributions of human foraging success across age have implications for many aspects of human evolution. Estimating the distribution of foraging returns is complicated by (1) the zero-inflated nature of hunting returns, as many if not most trips fail, and (2) the substantial variation among hunters, independent of age. We develop a multilevel mixture analysis of human foraging data to address these difficulties. Using a previously published 20-year record of hunts by 147 individual Aché hunters in eastern Paraguay, we estimate returns-by-age functions for both hunting failures and the size of harvests, while also estimating the heterogeneity among hunters. Consistent with previous analyses, we find that most hunters peak around 40 years of age. We can also show, however, that much more of the variation among Aché hunters arises from heterogeneity in failure rates (zero returns), not harvest sizes. We also introduce a new R package, glmer2stan, to assist in defining and fitting similar multilevel mixture models.
KeywordsHuman behavioral ecology Foraging Multilevel modeling Life history
Thanks to Mark Grote, Jamie Holland Jones, Bruce Winterhalder, members of the UC Davis Cultural Evolution and Human Behavioral Ecology labs, and two anonymous reviewers for advice and comments. Bob Carpenter and the members of the Stan Development Team helped us improve the efficiency of our Stan code. Keith O’Rourke suggested the analogy between Whorf’s gasoline drums and uninformative priors. Nicholas Blurton-Jones, back in 1997, encouraged RM to find a solution to modeling imbalanced field data, leading him eventually to Bayesian data analysis.
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