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Interpreting the flock algorithm: a reply to Anderson & Barry (2015)
Author(s) -
Duchesne P.,
Turgeon J.
Publication year - 2016
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.12480
Subject(s) - flock , cluster analysis , biology , set (abstract data type) , algorithm , bayesian probability , computer science , evolutionary biology , ecology , artificial intelligence , programming language
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 ( F ST c . 0.008) based on 15 microsatellites or 96 SNP s. 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 SNP s at intermediate F ST 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.