Ecology of Speciation in the Genus Bacillus

Abstract

Microbial ecologists and systematists are challenged to discover the early ecological changes that drive the splitting of one bacterial population into two ecologically distinct populations. We have aimed to identify newly divergent lineages ("ecotypes") bearing the dynamic properties attributed to species, with the rationale that discovering their ecological differences would reveal the ecological dimensions of speciation. To this end, we have sampled bacteria from the Bacillus subtilis-Bacillus licheniformis clade from sites differing in solar exposure and soil texture within a Death Valley canyon. Within this clade, we hypothesized ecotype demarcations based on DNA sequence diversity, through analysis of the clade's evolutionary history by Ecotype Simulation (ES) and AdaptML. Ecotypes so demarcated were found to be significantly different in their associations with solar exposure and soil texture, suggesting that these and covarying environmental parameters are among the dimensions of ecological divergence for newly divergent Bacillus ecotypes. Fatty acid composition appeared to contribute to ecotype differences in temperature adaptation, since those ecotypes with more warm-adapting fatty acids were isolated more frequently from sites with greater solar exposure. The recognized species and subspecies of the B. subtilis-B. licheniformis clade were found to be nearly identical to the ecotypes demarcated by ES, with a few exceptions where a recognized taxon is split at most into three putative ecotypes. Nevertheless, the taxa recognized do not appear to encompass the full ecological diversity of the B. subtilis-B. licheniformis clade: ES and AdaptML identified several newly discovered clades as ecotypes that are distinct from any recognized taxon.

Demarcation of bacterial ecotypes from DNA sequence data: A comparative analysis of four algorithms

“…This data set is generated under the assumptions of the Stable Ecotype model, with periodic selection acting as the force of cohesion within ecotypes. We also present the algorithms' demarcations of Bacillus strains in the Radio Facility Wash canyon in Death Valley [2]. Finally, we compare the running times of the algorithms.…”

“…The disadvantage of AdaptML is that it is limited to identifying ecotypes that are significantly different in preferences to habitat types that are anticipated by the investigators. Interestingly, three recent studies have shown that Ecotype Simulation and AdaptML reach highly similar ecotype demarcations [2,28,29], indicating that investigators' a priori guesses about the significant habitat types have been fairly accurate. Finally, a disadvantage of each class of algorithms is that their ability to find the adaptively homogeneous ecotypes is limited by the phylogenetic resolution of the sequences analyzed, and it is difficult to test whether a putative ecotype is homogeneous in its ecological adaptations [4].…”

“…comm.). The hypothesized ecotypes were found to differ in their associations with solar exposure [2,17], soil texture [2], salinity, and rhizospheres (S. Kopac, pers. comm.).…”

“…David Ward and colleagues have used it to identify ecologically distinct populations of Synechococcus in hot spring microbial mats [28][29][30], and the hypothesized ecotypes were shown to differ in their associations with temperature and light intensity and quality. The algorithm has also been used for an analysis of Bacillus isolates on different slopes of canyons in Death Valley, California [2] and in Israel [17] and on a salinity gradient in Death Valley (S. Kopac, pers. comm.).…”

“…AdaptML places strains into ecotypes based on the assumption that the origin of each ecotype is driven by a change in habitat preferences. This algorithm has been used to demarcate ecotypes within the Vibrionaceae in coastal estuaries [18], within Bacillus in Death Valley [2], and within Synechococcus in Yellowstone hot springs [28,29].…”