Mostevolutionaryprocessesoccurinaspatialcontextandseveralspatialanalysistechniqueshavebeenemployed in an exploratory context. However, the existence of autocorrelation can also perturb significance tests when data is analyzed using standard correlation and regression techniques on modeling genetic data as a function of explanatoryvariables.Inthiscase,morecomplexmodelsincorporatingtheeffectsofautocorrelationmustbeused.Herewe reviewthosemodelsandcomparedtheirrelativeperformancesinasimplesimulation,inwhichspatialpatternsinallele frequencies were generated by a balance between random variation within populations and spatially-structured gene flow. Notwithstanding the somewhat idiosyncratic behavior of the techniques evaluated, it is clear that spatial autocorrelationaffectsTypeIerrorsandthatstandardlinearregressiondoesnotprovideminimumvarianceestimators. Due to its flexibility, we stress that principal coordinate of neighbor matrices (PCNM) and related eigenvector mapping techniques seem to be the best approaches to spatial regression. In general, we hope that our review of commonlyusedspatialregressiontechniquesinbiologyandecologymayaidpopulationgeneticiststowardsproviding better explanations for population structures dealing with more complex regression problems throughout geographic space.
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