Interpreting the flock algorithm: a reply to Anderson & Barry (2015)

Anderson & Barry (Molecular Ecology Resources, 2015, 10, 1020-1030) compared a reprogrammed version of flock (Duchesne & Turgeon , Molecular Ecology Resources, 2009, 9, 1333-1344), flockture, to a particular model of structure (Pritchard , Genetics, 2000, 155, 945-959) that they propose is equivalent to flock, a non-MCMC, non-Bayesian algorithm. They conclude that structure performs better than flockture at clustering individuals from simulated populations with very low level of differentiation (FST c. 0.008) based on 15 microsatellites or 96 SNPs. We rather consider that both algorithms failed, with proportions of correct allocations lower than 50%. The authors also noted the slightly better performance of flockture with SNPs at intermediate FST values (c. 0.02-0.04) but did not comment. Finally, we disagree with the way the processing time of each program was compared. When compared on the basis of a run leading to a clustering solution, the main output of any clustering algorithm, flock, is, as users can readily experience, much faster. In all, we feel that flock performs at least as well as structure as a clustering algorithm. Moreover, flock has two major assets: high speed and clear, well validated, rules to estimate K, the number of populations. It thus provides a valuable addition to the set of tools at the disposal of the many researchers dealing with real empirical data sets.