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Accurate age estimation in small-scale societies
13 July 2017
Understanding demographic and evolutionary processes shaping human life history diversity depends on precise age estimations.
Inferring age is a challenge in small-scale societies, and especially in those societies that do not follow a calendar year. This method opens possibilities in demographic and life history studies allowing cross-sectional data to be incorporated in cross-cultural comparisons and a better understanding of the adaptive importance of human life history variation. This research was conducted by a team of anthropologists and geneticists at the UCL Department of Anthropology and the UCL Genetics Institute
Precise estimation of age is essential in evolutionary anthropology, especially to infer population age structures and understand the evolution of human life history diversity. However, in small-scale societies, such as hunter-gatherer populations, time is often not referred to in calendar years, and accurate age estimation remains a challenge. A team of anthropologists and geneticists address this issue by proposing a Bayesian approach that accounts for age uncertainty inherent to fieldwork data. They developed a Gibbs sampling Markov chain Monte Carlo algorithm that produces posterior distributions of ages for each individual, based on a ranking order of individuals from youngest to oldest and age ranges for each individual. The team first validate our method on 65 Agta foragers from the Philippines with known ages, and show that their method generates age estimations that are superior to previously published regression-based approaches. They then use data on 587 Agta collected during recent fieldwork to demonstrate how multiple partial age ranks coming from multiple camps of hunter-gatherers can be integrated. Finally, they exemplify how the distributions generated by our method can be used to estimate important demographic parameters in small-scale societies: here, age-specific fertility patterns. This flexible Bayesian approach will be especially useful to improve cross-cultural life history datasets for small-scale societies for which reliable age records are difficult to acquire.
Accurate age estimation in small-scale societies
Yoan Diekmann, Daniel Smith, Pascale Gerbault, Mark Dyble, Abigail E. Page, Nikhil Chaudhary, Andrea Bamberg Migliano, and Mark G. Thomas