Estimating dispersal from short distance spatial autocorrelation

A series of theoretical studies has formed a strong connection between spatial statistics observed in populations and summary measures of the amount of dispersal. Synthesized, these developments allow dispersal to be indirectly estimated from standing spatial patterns of genetic variation under a range of conditions broad enough to be likely met in most populations of either plants or animals. The spatial correlations at the shortest distances are particularly robust to range of conditions and have disproportionately high statistical power. This review integrates theoretical results in a way that maximizes robustness and flexibility in the use of short distance autocorrelation to estimate Wright's neighborhood size, or the total variance in dispersal distances. Empirical guidelines are developed that are meant to be as practical and broad as possible. The guidelines focus on Moran's I-statistics for diploid genotypes converted to allele frequencies, but are also extended to or compared with several other approaches.