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Mechanistic statistical models are commonly used to study the flow ofbiological processes. For example, in landscape genetics, the aim is to infermechanisms that govern gene flow in populations. Existing statisticalapproaches in landscape genetics do not account for temporal dependence in thedata and may be computationally prohibitive. We infer mechanisms with aBayesian hierarchical dyadic model that scales well with large data sets andthat accounts for spatial and temporal dependence. We construct afully-connected network comprising spatio-temporal data for the dyadic modeland use normalized composite likelihoods to account for the dependencestructure in space and time. Our motivation for developing a dyadic model wasto account for physical mechanisms commonly found in physical-statisticalmodels. However, a numerical solver is not required in our approach because wemodel first-order changes directly. We apply our methods to ancient human DNAdata to infer the mechanisms that affected human movement in Bronze Age Europe.

Composite Dyadic Models for Spatio-Temporal Data