sandwrm: an R package for estimating Wright's neighborhood size and species-level genetic diversity

In most natural populations, individuals in close proximity are more related on average than those at greater distances; this pattern gives rise to geographic population genetic structure. Despite extensive theoretical work on spatial population genetics, few empirical methods exist to estimate the components of theoretical models of genetic relatedness in continuous space. One classic model of relatedness in continuous space is the Wright-Malécot model, which predicts that the probability of identity-by-descent decays as a function of geographic distances. The shape of this decay curve is dictated by the dynamics of local dispersal and mating, as well as population density. This model can be reformulated to describe the probability of identity-by-state, in which case it decays to an asymptote, the value of which is determined by the historical demography of the population. Collectively, these features can be modeled in a likelihood-based framework to estimate neighborhood size and long-term diversity from pairwise genetic and geographic distance. In this article, we introduce the R package sandwrm (Spatial Analysis of Neighborhood size and Diversity using WRight-Malécot), which takes a Bayesian approach to estimate key parameters of populations that are both dispersal-limited and distributed continuously across a landscape.