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A review of techniques for spatial modeling in geographical, conservation and landscape genetics.
MedLine Citation:
PMID:  21637669     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
Most evolutionary processes occur in a spatial context and several spatial analysis techniques have been employed 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 explanatory variables. In this case, more complex models incorporating the effects of autocorrelation must be used. Here we review those models and compared their relative performances in a simple simulation, in which spatial patterns in allele 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 autocorrelation affects Type I errors and that standard linear regression does not provide minimum variance estimators. 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 commonly used spatial regression techniques in biology and ecology may aid population geneticists towards providing better explanations for population structures dealing with more complex regression problems throughout geographic space.
Authors:
José Alexandre Felizola Diniz-Filho; João Carlos Nabout; Mariana Pires de Campos Telles; Thannya Nascimento Soares; Thiago Fernando L V B Rangel
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Publication Detail:
Type:  Journal Article     Date:  2009-06-01
Journal Detail:
Title:  Genetics and molecular biology     Volume:  32     ISSN:  1415-4757     ISO Abbreviation:  Genet. Mol. Biol.     Publication Date:  2009 Apr 
Date Detail:
Created Date:  2011-06-03     Completed Date:  2011-07-14     Revised Date:  2013-05-29    
Medline Journal Info:
Nlm Unique ID:  100883590     Medline TA:  Genet Mol Biol     Country:  Brazil    
Other Details:
Languages:  eng     Pagination:  203-11     Citation Subset:  -    
Affiliation:
Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, GO Brazil.
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