  Mechanistic statistical models are commonly used to study the flow of
biological processes. For example, in landscape genetics, the aim is to infer
spatial mechanisms that govern gene flow in populations. Existing statistical
approaches in landscape genetics do not account for temporal dependence in the
data and may be computationally prohibitive. We infer mechanisms with a
Bayesian hierarchical dyadic model that scales well with large data sets and
that accounts for spatial and temporal dependence. We construct a
fully-connected network comprising spatio-temporal data for the dyadic model
and use normalized composite likelihoods to account for the dependence
structure in space and time. We develop a dyadic model to account for physical
mechanisms commonly found in physical-statistical models and apply our methods
to ancient human DNA data to infer the mechanisms that affected human movement
in Bronze Age Europe.
