Sympatric speciation: when is it possible in bacteria?

Abstract

According to theory, sympatric speciation in sexual eukaryotes is favored when relatively few loci in the genome are sufficient for reproductive isolation and adaptation to different niches. Here we show a similar result for clonally reproducing bacteria, but which comes about for different reasons. In simulated microbial populations, there is an evolutionary tradeoff between early and late stages of niche adaptation, which is resolved when relatively few loci are required for adaptation. At early stages, recombination accelerates adaptation to new niches (ecological speciation) by combining multiple adaptive alleles into a single genome. Later on, without assortative mating or other barriers to gene flow, recombination generates unfit intermediate genotypes and homogenizes incipient species. The solution to this tradeoff may be simply to reduce the number of loci required for speciation, or to reduce recombination between species over time. Both solutions appear to be relevant in natural microbial populations, allowing them to diverge into ecological species under similar constraints as sexual eukaryotes, despite differences in their life histories.

Figure 1. Schematic of the symsim model.

(A) Additive fitness. The steps of the simulation are (i) growth/selection according to relative additive fitness within each niche, (ii) small probability of recombination (r) by gene conversion of homologous loci (diagonal lines) in a sympatric, mixed pool of genotypes from both niches, and (iii) individuals return to the niche to which their genotype is best adapted (e.g. in this 3-locus example, genotypes 000 and 010 go to niche 0, while 111 and 011 go to niche 1). Steps (i), (ii) and (iii) are iterated for a set number of generations or until any of the derived alleles go extinct. (B) Step fitness. The steps of the simulations are the same as for additive fitness, except that individuals can grow and be selected in both niches. Optimally adapted genotypes (111 and 000) compete in just one niche. Intermediates compete in both niches, but pay a fitness cost s for niche switching. In the example shown, an individual of genotype 010 obtains 2/3 of its resources in niche 0 (and adds a count of 2/3 of an individual to the population size of niche 0), and 1/3 from niche 1 (and adds a count of 1/3 of an individual to niche 1).

Figure 2. Results of symsim model under additive fitness.

(A, B, C) Weak selection. (D, E, F) Strong selection. (A, D) Probability of appearance (p) of the derived (niche 1) optimal genotype in 100 replicate simulations for each combination of the number of loci involved in niche adaptation, L and the recombination rate, r. High probabilities (p = 1) are shown in white, low probabilities in black, and intermediate probabilities in grey scale. The space under the red line indicates extinction of the niche-1 optimal genotype in all 100 replicates (p<0.01). (B, E) Time to appearance of the niche 1 optimal genotype (mean over 100 replicate simulations). The red line is the same as in (A); n.d. refers to appearance time not determined, or effectively infinite, because extinction of niche-1 alleles occurred before the optimal genotype could appear. Shorter times are shown in white, effectively infinite times in black, and intermediate times in grey scale. (C, F) Completeness of speciation. The mean fraction of the pooled populations (niche 0 and 1) occupied by optimally-adapted genotypes is based on 10 replicate simulations for every combination of L and r. Complete speciation (optimal genotype fraction near 1) shown in white, incomplete in black, and intermediate completeness in grey scale. Magenta letters in C refer to the same simulations depicted in panels (A, B, C, D) of Figure 4.

Figure 3. Results of symsim model under step fitness.

(A, B, C) Weak selection. (D, E, F) Strong selection. See Figure 2 legend.

Figure 4. Higher recombination rates maintain intermediate genotypes and reduce the completeness of speciation.

Panels A, B, C and D show dynamics of a single simulation under different combinations of L (number of adaptive loci) and r (recombination rate), corresponding to magenta letters in Figure 2C. The y-axis shows the frequency of optimal genotypes in a given niche (ancestral or derived).