ArticleGenetics and molecular biology2009
A review of techniques for spatial modeling in geographical, conservation and landscape genetics.
Article in Genetics and molecular biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Disparate and parallel craniofacial climatic adaptations in native populations of Asia, North America, and South America.Journal of anatomy · 2024Article
- When Bigger Is Better: 3D RNA Profiling of the Developing Head in the CatsharkFrontiers in cell and developmental biology · 2021Article
- Where to rewild? A conceptual framework to spatially optimize ecological function.Proceedings. Biological sciences · 2020Article
- Ecological drivers of plant genetic diversity at the southern edge of geographical distributions: Forestal vines in a temperate region.Genetics and molecular biology · 2018Article
- Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference.International journal of health geographics · 2017Article
- Geographically weighted regression as a generalized Wombling to detect barriers to gene flow.Genetica · 2016Article
- The Impact of Global Climate Change on the Geographic Distribution and Sustainable Harvest of Hancornia speciosa Gomes (Apocynaceae) in Brazil.Environmental management · 2016Article
- Mantel test in population genetics.Genetics and molecular biology · 2013Review
- Article
- Geographical patterns of turnover and nestedness-resultant components of allelic diversity among populations.Genetica · 2012Article
- The modifiable areal unit problem (MAUP) in the relationship between exposure to NO2 and respiratory health.International journal of health geographics · 2011Article
- A geographical genetics framework for inferring homing reproductive behavior in fishes.Genetica · 2011Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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