A review of techniques for spatial modeling in geographical, conservation and landscape genetics
| Autor: | Thannya Nascimento Soares, João Carlos Nabout, José Alexandre Felizola Diniz-Filho, Mariana Pires de Campos Telles, Thiago F. Rangel |
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| Rok vydání: | 2009 |
| Předmět: |
isolation-by-distance
Spatial contextual awareness education.field_of_study lcsh:QH426-470 spatial regression autocorrelation geographical genetics Autocorrelation Population landscape genetics Context (language use) Review Article Biology Regression lcsh:Genetics Linear regression Genetics Econometrics Spatial ecology education Molecular Biology Spatial analysis |
| Zdroj: | Genetics and Molecular Biology ResearcherID Genetics and Molecular Biology, Vol 32, Iss 2, Pp 203-211 (2009) Genetics and Molecular Biology, Volume: 32, Issue: 2, Pages: 203-211, Published: 2009 Genetics and Molecular Biology v.32 n.2 2009 Sociedade Brasileira de Genética (SBG) instacron:SBG |
| ISSN: | 1415-4757 |
| DOI: | 10.1590/s1415-47572009000200001 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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