FLOCK provides reliable solutions to the "number of populations" problem

Identifying groups of individuals forming coherent genetic clusters is relevant to many fields of biology. This paper addresses the K-partition problem: given a collection of genotypes, partition those genotypes into K groups, each group being a sample of the K source populations that are represented in the collection of genotypes. This problem involves allocating genotypes to genetic groups while building those groups at the same time without the use of any other a priori information. FLOCK is a non-Markov chain Monte Carlo (MCMC) algorithm that uses an iterative method to partition a collection of genotypes into k groups. Rules to estimate K are formulated and their validity firmly established by running simulations under several migration rates, migration regimes, number of loci, and values of K. FLOCK tended to build clusters largely consistent with the source samples. The performance of FLOCK was also compared with that of STRUCTURE and BAPS. FLOCK provided more accurate allocations to clusters and more reliable estimates of K; it also ran much faster than STRUCTURE. FLOCK is based on an entirely novel approach and provides a true alternative to the existing, MCMC based, algorithms. FLOCK v.2.0 for microsatellites or for AFLP markers can be downloaded from http://www.bio.ulaval.ca/no_cache/departement/professeurs/fiche_des_professeurs/professeur/11/13/.

A simulation-based evaluation of methods for inferring linear barriers to gene flow

Different analytical techniques used on the same data set may lead to different conclusions about the existence and strength of genetic structure. Therefore, reliable interpretation of the results from different methods depends on the efficacy and reliability of different statistical methods. In this paper, we evaluated the performance of multiple analytical methods to detect the presence of a linear barrier dividing populations. We were specifically interested in determining if simulation conditions, such as dispersal ability and genetic equilibrium, affect the power of different analytical methods for detecting barriers. We evaluated two boundary detection methods (Monmonier's algorithm and WOMBLING), two spatial Bayesian clustering methods (TESS and GENELAND), an aspatial clustering approach (STRUCTURE), and two recently developed, non-Bayesian clustering methods [PSMIX and discriminant analysis of principal components (DAPC)]. We found that clustering methods had higher success rates than boundary detection methods and also detected the barrier more quickly. All methods detected the barrier more quickly when dispersal was long distance in comparison to short-distance dispersal scenarios. Bayesian clustering methods performed best overall, both in terms of highest success rates and lowest time to barrier detection, with GENELAND showing the highest power. None of the methods suggested a continuous linear barrier when the data were generated under an isolation-by-distance (IBD) model. However, the clustering methods had higher potential for leading to incorrect barrier inferences under IBD unless strict criteria for successful barrier detection were implemented. Based on our findings and those of previous simulation studies, we discuss the utility of different methods for detecting linear barriers to gene flow.

Statistical methods in spatial genetics

The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only the potential of various approaches but also methodological pitfalls.