Abstract
In the present study, we used both simulations and real data set analyses to show that, under stochastic processes of population differentiation, the concepts of spatial heterogeneity and spatial pattern overlap. In these processes, the proportion of variation among and within a population (measured by G ST and 1 - G ST, respectively) is correlated with the slope and intercept of a Mantel's test relating genetic and geographic distances. Beyond the conceptual interest, the inspection of the relationship between population heterogeneity and spatial pattern can be used to test departures from stochasticity in the study of population differentiation.
Highlights
Spatial analysis of genetic divergence among local populations has always played a central role in population genetics and evolutionary biology
We show that the concepts of spatial heterogeneity and spatial pattern overlap in stochastic models of population differentiation, such as Wright’s (1943) isolation-by-distance or Kimura’s stepping stone (Kimura and Weiss, 1964)
Because they generate functional relationships between genetic divergence and geographic distances, there is a close correspondence between measurements of spatial heterogeneity and parameters of explicit spatial models
Summary
Spatial analysis of genetic divergence among local populations has always played a central role in population genetics and evolutionary biology. These statistics have been criticized because they do not furnish a detailed description of the spatial patterns of genetic divergence (Barbujani, 1987), furnishing only a general description of spatial heterogeneity among local populations.
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