Spatial evolution of human cultures inferred through Bayesian phylogenetic analysis.

Area of Science:

• Anthropology
• Computational Biology
• Bayesian Statistics

Background:

• Human cultural diversity arises from both ancestral inheritance and transmission between contemporary groups.
• Understanding the interplay of vertical (descent) and horizontal (neighboring populations) cultural transmission is crucial for evolutionary studies.
• Existing models often struggle to disentangle these transmission pathways and their geographical influences.

Purpose of the Study:

• To develop a Bayesian statistical framework for analyzing spatial variations in cultural repertoires.
• To model both horizontal transmission and mutation of cultural traits within a spatially explicit network model.
• To infer transmission rates, mutation rates, and trait identities in unobserved populations.

Main Methods:

• A network model representing populations as nodes, incorporating horizontal transmission and trait mutation.
• Application of Markov chain Monte Carlo (MCMC) algorithms, adapted from Bayesian phylogenetic analysis.
• Development of a heuristic algorithm to reduce computational complexity by simulating coalescent processes.

Main Results:

• The framework successfully computes posterior distributions for key parameters like horizontal transmission and mutation rates.
• The method allows for the reconstruction of the genealogical tree of cultural traits under low mutation rates.
• Numerical simulations demonstrate computational feasibility, though broad posteriors may arise with uninformative priors.

Conclusions:

• The proposed Bayesian framework provides a robust method for analyzing complex cultural transmission patterns.
• This approach enhances our ability to understand the spatial dynamics of cultural evolution and trait diversification.
• The model offers a powerful tool for inferring historical and contemporary cultural exchange across populations.