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.
Highlights
In spatial ecology, a boundary is a region of abrupt change in a map of biological variables.Boundaries are of interest because their locations reflect underlying biological, physiological or social processes [1], including barriers to dispersal
We examined five simulation parameter combinations that allowed us to evaluate the effects of average dispersal distances ( ), mutation rates ( ) and amount of gene flow across barriers (b) on the ability of the methods to correctly detect boundaries
The most striking result of our simulations was that the spatial Bayesian clustering methods outperformed the direct edge detection methods
Summary
A boundary is a region of abrupt change in a map of biological variables.Boundaries are of interest because their locations reflect underlying biological, physiological or social processes [1], including barriers to dispersal. The detection of genetic boundaries may help to determine the underlying generating factors such as important historical events [2] or ongoing barriers to gene flow (e.g., [3,4,5]). Bayesian clustering algorithms intrinsically strive to identify discrete sets of individuals based on the analysis of the multilocus genotypes [9,10,11]. They simultaneously delineate clusters of individuals based on the analysis of individual genotypes and assign individuals to the identified cluster where their posterior probability is highest. The probability that two individuals belong to the same cluster is influenced by the geographic distance between them whereas geographic proximity is ignored in aspatial models
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