A review of techniques for spatial modeling in geographical, conservation and landscape genetics

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.

Population genetic structure of Penaeus monodon, in relation to monsoon current patterns in Southwest, East and Andaman coastal waters of India

“…To test the confidence level of each branch in the dendrogram, data were bootstrapped 1000 times. Multidimensional scaling (MDS) is a procedure for reducing among-sample variance in allele frequencies to a smaller number of dimensions to generate a "synthetic map" (Menozzi et al, 1978;Wang et al, 2010) which can be compared with a geographical map (Diniz-Filho et al, 2009). Multidimensional scaling was performed on a 400x8, RxC matrix of frequencies of all 400 distinct alleles observed in the eight population samples of ten loci, using SYSTAT version 12 (2007) package.…”

Spatial Trends of Genetic Variation of Domestic Ruminants in Europe

“…Various demographic events have created geographic patterns of genetic diversity [1], such as domestication, migration, selection, isolation, and expansion of successful breeds. Several techniques have been developed to analyze spatial patterns of genetic variation among populations [2,3]. One widely used approach is the analysis of spatial auto-correlation [4][5][6], which is detected if proximate individuals or populations are, for a given variable, more similar or dissimilar than expected for a random distribution.…”

Geographical Patterns in Biodiversity: Towards an Integration of Concepts and Methods from Genes to Species Diversity

“…However, recent developments in landscape genetics (Manel et al 2004;Holderegger & Wagner 2006;Storfer et al 2007) are now revealing that other processes (e.g., human-induced modifications of the landscape and disruption of dispersal routes) can confound a simple test of IBD. We believe that the modeling tools widely used in ecological analyses for variance partitioning and to detect spatial discontinuities will soon start to play a major role in analyzing broad-scale patterns of genetic variation among local populations (e.g., Diniz-Filho et al 2009). The opposite is also true, as Diniz-Filho et al (2011) recently showed that the same protocols used to infer IBD based on spatial autocorrelation analyses can be used to test Hubbell's (2001) neutral dynamic.…”