Accurate age estimation in small-scale societies.

Area of Science:

• Evolutionary Anthropology
• Demography
• Bayesian Statistics

Background:

• Precise age estimation is crucial for understanding population age structures and human life history evolution.
• Accurate age data is difficult to obtain in small-scale societies like hunter-gatherer groups due to the absence of calendar year references.

Purpose of the Study:

• To develop and validate a novel Bayesian approach for estimating individual ages in populations with uncertain age data.
• To demonstrate the method's utility in integrating age information from multiple fieldwork sources and estimating demographic parameters.

Main Methods:

• A Gibbs sampling Markov chain Monte Carlo algorithm was developed to generate posterior age distributions for individuals.
• The method utilizes relative age rankings (youngest to oldest) and age ranges, accounting for fieldwork data uncertainty.
• Validation was performed on Agta foragers with known ages, comparing results to existing regression-based methods.

Main Results:

• The proposed Bayesian method significantly outperformed previous regression-based approaches in age estimation accuracy.
• The approach successfully integrated multiple partial age ranks from different hunter-gatherer camps.
• Age distributions generated by the method allowed for the estimation of age-specific fertility patterns.

Conclusions:

• This flexible Bayesian approach effectively addresses age uncertainty in fieldwork data for small-scale societies.
• The method provides a superior tool for improving cross-cultural life history datasets where reliable age records are scarce.
• Enhanced age estimation facilitates a deeper understanding of demographic dynamics and evolutionary processes in diverse human populations.