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Men’s investments in parenting and long-term reproductive relationships are a hallmark feature of human reproduction and life history. The uniqueness of such male involvement among catarrhines has driven an extensive debate surrounding the selective pressures that led to and maintain such capacities in men. Three major pathways have been proposed through which men’s involvement might confer fitness benefits: enhancing child well-being, increasing couple fertility, and decreasing likelihood of partner desertion. Previous research has explored the impact of father involvement on these factors individually, but here we present novel research that explores all three pathways within the same population, the Mayangna/Miskito horticulturalists of Nicaragua. Furthermore, we expand the traditional dichotomous measure of father presence/absence by using a continuous measure of overall male investment, as well as two continuous measures of its subcomponents: direct care and wealth. We find that men’s investments are associated with children’s growth and possibly with wife’s marital satisfaction; however, they are not associated with couple fertility.

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 Ache 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 Ache 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.