Accurate age estimation in small-scale societies

Abstract

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. We address this issue by proposing a Bayesian approach that accounts for age uncertainty inherent to fieldwork data. We 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. We first validate our method on 65 Agta foragers from the Philippines with known ages, and show that our method generates age estimations that are superior to previously published regression-based approaches. We 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, we 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. Our 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. A ccurate estimation of the age of individuals is essential in evolutionary anthropology. Major questions in the field require an accurate inference of the timing of life history events, such as age at menarche, age at first reproduction, age at cessation of reproduction, interbirth intervals, and death. Age is also essential when assessing infant growth or developmental trajectories and when estimating age structure properties of a population (e.g., the potential for population growth or decline, recovering signatures of epidemics, assessing vulnerability to ecological perturbations). Humans have important derived life history features, such as shorter interbirth intervals, longer life span, extended postreproductive longevity, and childhood dependence (1). These life history traits vary across species in the slow/fast continuum (2), and they likely vary within humans in response to differences in ecology, such as differential mortality rates (3) and energetics (4). However, due to unreliable age estimations, very few studies have highlighted variability in life history traits in traditional societies (3,5,6). The challenge of estimating ages is particularly problematic for populations where individuals do not relate their age to calendar years, as is the case among many hunter-gatherer and other small-scale societies (7,8). Although longitudinal studies (7, 9) are an ideal approach to address questions about variation in life history traits in smallscale populations, these studies are rare. There is consequently a need for methods to estimate ages based on cross-sectional data fro...

Engagement in agricultural work is associated with reduced leisure time among Agta hunter-gatherers

“…To estimate age, we took the mean values from posterior probability distributions of age produced using a Gibbs sampling MCMC algorithm based on age ranking order data provided by the Agta and a plausible a priori age range for each individual provided by the ethnographers 36 .…”

Sharing and caring: Testosterone, fathering, and generosity among BaYaka foragers of the Congo Basin

“…Moreover, to help attenuate sample size concerns, we collected repeated samples across participants and also used data analytical techniques that maximized the information from these repeated observations. www.nature.com/scientificreports/ In addition, the BaYaka do not record their calendar ages, thus we estimated an approximate age based on a procedure from Diekmann and colleagues and with their assistance 84 . In validating their method, Diekmann et al found the calculations were reliable within a year of known ages (median: 4 months; mean: 11 months) for another forager society, giving us confidence in the BaYaka calculated ages 84 .…”

“…www.nature.com/scientificreports/ In addition, the BaYaka do not record their calendar ages, thus we estimated an approximate age based on a procedure from Diekmann and colleagues and with their assistance 84 . In validating their method, Diekmann et al found the calculations were reliable within a year of known ages (median: 4 months; mean: 11 months) for another forager society, giving us confidence in the BaYaka calculated ages 84 . Still, because of inter-correlations between BaYaka age, T, and fathering rankings, the reliability of these age calculations could be potentially concerning in terms of adjusting our models for age.…”

Reproduction in the Baka pygmies and drop in their fertility with the arrival of alcohol

“…Except for Early and Headland's work (7) on the Agta, in which the ages of 271 of the 857 individuals (31.6%) were known (117 individuals to within a year, 80 to within a month, and 74 exactly to the day; see table 4.1 in ref. 7), authors have had to develop various methods to estimate the missing chronological information (15). From data provided by many scholars, Weiss (16) built model life tables to infer, from a limited number of variables, many demographic/life-history parameters, such as life expectancy or the ASFR for each 5-y age cohort.…”