Abstract-Identification of closely related, ecologically distinct populations of bacteria would benefit microbiologists working in many fields including systematics, epidemiology, and biotechnology. Several laboratories have recently developed algorithms aimed at demarcating such "ecotypes." In this paper we examine the ability of four of these algorithms to correctly identify ecotypes from sequence data (along with, in the case of one algorithm, information on the habitats where organisms were isolated). We test the algorithms on synthetic sequences, with known history and habitat associations, generated under the Stable Ecotype model [1], and on data from Bacillus strains isolated from Death Valley where previous work [2] has confirmed the existence of multiple ecotypes. We find that one of the algorithms (Ecotype Simulation) performs significantly better than the others (AdaptML, GMYC, BAPS) in both instances. Unfortunately, it is also shown to be the least efficient of the four.

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