A review of techniques for spatial modeling in geographical, conservation and landscape genetics
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2009-06
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Sociedade Brasileira de Genética
Resumo
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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Autocorrelation, Geographical genetics, Isolation-by-distance, Landscape genetics, Spatial regression
Citação
DINIZ FILHO, José Alexandre Felizola; NABOUT, João Carlos; TELLES, Marina Pires de Campos; SOARES, Tânia Nascimento; RANGEL, Thiago Fernando L. V. B. A review of techniques for spatial modeling in geographical, conservation and landscape genetics. Genetics and Molecular Biology, Ribeirão Preto, v. 32, n. 2, p. 203-211, 2009.