Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics

Abstract

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods’ effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.

A longitudinal genetic survey identifies temporal shifts in the population structure of Dutch house sparrows

“…However, contrary to these authors, the spatially explicit clustering analysis applied in our study also provided evidence for a temporal decrease in genetic admixture, possibly because of the a priori assumption of spatial dependence of individuals that is thought to be biologically sound (Guillot et al, 2005a). Indeed, analyses based on this assumption earlier proved to perform well under weak levels of population differentiation (Guillot, 2008;Safner et al, 2011), in particular for detecting recent barriers to gene flow (Coulon et al, 2006;Safner et al, 2011;Blair et al, 2012). In our study, post-decline population clusters most likely resulted from a progressive connectivity loss at the landscape level, rather than from a distinct geographical barrier to dispersal (Jensen et al, 2013).…”

Gene flow from North Africa contributes to differential human genetic diversity in southern Europe

“…Isolation by distance. It has been shown that ADMIXTURE may misidentify ancestral components when the populations tested follow an isolation by distance model (37 Fig. S5).…”

Bayesian analysis of the genetic structure of a Brazilian popcorn germplasm using data from simple sequence repeats (SSR)

“…According to Latch et al (2006), most of the recent advances in clustering techniques have been made in a Bayesian statistical framework to allow simultaneous estimation of many interdependent parameters in complex models. The advantages of utilizing a Bayesian framework in the analysis of genetic experiments have been emphasized and discussed by several authors (Mora et al, 2009;Cané-Retamales et al, 2011;Li et al, 2011;Safner et al, 2011;Arriagada et al, 2012).…”