Quantifying the spatial pattern of dialect words spreading from a central population

Spatial evolution of human cultures inferred through Bayesian phylogenetic analysis

Spatial distribution of human culture reflects both descent from the common ancestor and horizontal transmission among neighbouring populations. To analyse empirically documented geographical variations in cultural repertoire, we will describe a framework for Bayesian statistics in a spatially explicit model. To consider both horizontal transmission and mutation of the cultural trait in question, our method employs a network model in which populations are represented by nodes. Using algorithms borrowed from Bayesian phylogenetic analysis, we will perform a Markov chain Monte Carlo (MCMC) method to compute the posterior distributions of parameters, such as the rate of horizontal transmission and the mutation rates among trait variants, as well as the identity of trait variants in unobserved populations. Besides the inference of model parameters, our method enables the reconstruction of the genealogical tree of the focal trait, provided that the mutation rate is sufficiently small. We will also describe a heuristic algorithm to reduce the dimension of the parameter space explored in the MCMC method, where we simulate the coalescent process in the network of populations. Numerical examples show that our algorithms compute the posterior distribution of model parameters within a practical computation time, although the posterior distribution tends to be broad if we use uninformative priors.

Application of a Markovian ancestral model to the temporal and spatial dynamics of cultural evolution on a population network

Cultural macroevolution concerns a long-term evolutionary process involving transmission of non-genetic or cultural traits between populations as well as birth and death of populations. To understand the spatial dynamics of cultural macroevolution, we present a one-locus model of cultural diffusion in which a cultural trait is transmitted on a network of populations. Borrowing the method of ancestral backward process from population genetics, our model explores the lineage of a trait variant sampled in the present generation to quantify when and where the variant was invented. Mathematical analysis of the model enables us to predict the distribution of cultural age in each population of the network, estimate the frequencies of trait variants originating from given populations, and discuss the time it takes for a trait variant to diffuse between a given pair of populations. We also perform numerical analysis on random scale-free network of populations to investigate the effect of network topology and innovation rate on the age and origin of variants in each population. The result suggests that trait variants are more likely to derive from a population with higher innovation rate. Our numerical analysis also shows that trait variants invented in populations with higher network-centrality values are likely to be maintained at a higher frequency and transmitted to other populations in a shorter time period.