Resource Partitioning and Sympatric Differentiation Among Closely Related Bacterioplankton

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Science  23 May 2008:
Vol. 320, Issue 5879, pp. 1081-1085
DOI: 10.1126/science.1157890


Identifying ecologically differentiated populations within complex microbial communities remains challenging, yet is critical for interpreting the evolution and ecology of microbes in the wild. Here we describe spatial and temporal resource partitioning among Vibrionaceae strains coexisting in coastal bacterioplankton. A quantitative model (AdaptML) establishes the evolutionary history of ecological differentiation, thus revealing populations specific for seasons and life-styles (combinations of free-living, particle, or zooplankton associations). These ecological population boundaries frequently occur at deep phylogenetic levels (consistent with named species); however, recent and perhaps ongoing adaptive radiation is evident in Vibrio splendidus, which comprises numerous ecologically distinct populations at different levels of phylogenetic differentiation. Thus, environmental specialization may be an important correlate or even trigger of speciation among sympatric microbes.

Microbes dominate biomass and control biogeochemical cycling in the ocean, but we know little about the mechanisms and dynamics of their functional differentiation in the environment. Culture-independent analysis typically reveals vast microbial diversity, and although some taxa and gene families are differentially distributed among environments (1, 2), it is not clear to what extent coexisting genotypic diversity can be divided into functionally cohesive populations (1, 3). First, we lack broad surveys of nonpathogenic free-living bacteria that establish robust associations of individual strains with spatiotemporal conditions (4, 5); second, it remains controversial what level of genetic diversification reflects ecological differentiation. Phylogenetic clusters have been proposed to correspond to ecological populations that arise by neutral diversification after niche-specific selective sweeps (6). Clusters are indeed observed among closely related isolates (e.g., when examined by multilocus sequence analysis) (7) and in culture-independent analyses of coastal bacterioplankton (8). Yet recent theoretical studies suggest that clusters can result from neutral evolution alone (9), and evidence for clusters as ecologically distinct populations remains sparse, having been most conclusively demonstrated for cyanobacteria along ocean-scale gradients (10) and in a depth profile of a microbial mat (11). Further, horizontal gene transfer (HGT) may erode the ecological cohesion of clusters if adaptive genes are transferred (12), and recombination can homogenize genes between ecologically distinct populations (13). Thus, exploring the relationship between phylogenetic and ecological differentiation is a critical step toward understanding the evolutionary mechanisms of bacterial speciation (9).

In this study, we investigated ecological differentiation by spatial and temporal resource partitioning in coastal waters among coexisting bacteria of the family Vibrionaceae, which are ubiquitous, metabolically versatile heterotrophs (14). The coastal ocean is well suited to test population-level effects of microhabitat preferences, because tidal mixing and oceanic circulation ensure a high probability of migration, reducing biogeographic effects on population structure. In the plankton, heterotrophs may adopt alternate ecological strategies: exploiting either the generally lower concentration but more evenly distributed dissolved nutrients or attaching to and degrading small suspended organic particles, originating from algal exopolysaccharides and detritus (3). Bacterial microhabitat preferences may develop because resources are distributed on the same scale as the dispersal range of individuals, due to turbulent mixing and active motility (15). Of potential microhabitats, particles represent abundant but relatively short-lived resources, as labile components are rapidly utilized (on time scales of hours to days) (16, 17), implying that particle colonization is a dynamic process. Moreover, particulate matter may change composition with macroecological conditions (such as seasonal algal blooms). Zooplankton provide additional, more stable microhabitats; vibrios attach to and metabolize chitinous zooplankton exoskeletons (18, 19) but may also live in the gut or occupy niches specific to pathogens. The extent to which microenvironmental preferences contribute to resource partitioning in this complex ecological landscape remains an important question in microbial ecology (20).

We aimed to conservatively identify ecologically coherent groups by examining distribution patterns of Vibrionaceae genotypes among free-living and associated (with suspended particles and zooplankton) compartments of the planktonic environment under different macroecological conditions (spring and fall) (fig. S1 and table S1). Because the level of genetic differentiation at which ecological preferences develop is not known, we focused on a range of relationships [0 to 10% small subunit ribosomal RNA (rRNA) divergence] among co-occurring vibrios (21). Particle-associated and free-living cells were separated into four consecutive size fractions by sequential filtration (four replicate water samples, each subsampled with at least four replicate filters per size fraction); each fraction contained organisms and dead organic material of different origins [detailed in the supporting online material (SOM)]. For simplicity, we refer to these fractions as enriched in zooplankton (≥63 μm), in large (5 to 63 μm) and small (1 to 5 μm) particles, and in free-living cells (0.22 to 1 μm) (fig. S1B). The 1- to 5-μm size fraction was somewhat ambiguous, probably containing small particles as well as large or dividing cells; however, it provided a firm buffer between obviously particle-associated (>5 μm) and free-living (<1 μm) cells. Vibrionaceae strains were isolated by plating filters on selective media, previously shown by quantitative polymerase chain reaction to yield good correspondence between genotypes recovered in culture and those present in environmental samples (21). Roughly 1000 isolates were characterized by partial sequencing of a protein-coding gene (hsp60). To obtain added resolution, between one and three additional gene fragments (mdh, adk, and pgi) were sequenced for over half of the isolates (SOM), including V. splendidus strains, the most abundant group (21).

Our rationale for testing environmental associations grows out of the following considerations. First, as in most ecological sampling, the true habitats or niches are unknown and can only be observed as projections onto the sampling dimensions (“projected habitats”). Thus, associations can be detected as distinct distributions of groups of strains if habitats/niches are differentially apportioned among samples. Second, the lack of an accepted microbial species concept implies that it is imprudent to use any measure of genetic relationships to define a priori the populations whose environmental association should be assessed. Therefore, we first tested the null hypothesis that there is no environmental association across the phylogeny of the strains. We then refined such estimates by developing a new model to simultaneously identify populations and their projected habitats. Finally, these model-based results were tested with nonparametric empirical statistics.

The initial null hypothesis of no association between phylogeny and ecology is strongly rejected (seasons: P <10–79; size fractions: P <10–49) by comparing the parsimony score of observed environments on the tree to that expected by chance (22) (SOM), confirming the visual impression of differential patterns of clustering among seasons and size fractions (Fig. 1A). This result is robust toward uncertainty in the phylogeny, which should diminish but not strengthen associations, and is confirmed by introducing additional uncertainty in the phylogeny (fig. S2). The observed overall association with season and size fraction therefore suggests that water-column vibrios partition resources, but neither provides insights into the phylogenetic bounds of populations or the composition of their habitats.

Fig. 1.

Season and size fraction distributions and habitat predictions mapped onto Vibrionaceae isolate phylogeny inferred by maximum likelihood analysis of partial hsp60 gene sequences. Projected habitats are identified by colored circles at the parent nodes. (A) Phylogenetic tree of all strains, with outer and inner rings indicating seasons and size fractions of strain origin, respectively. Ecological populations predicted by the model are indicated by alternating blue and gray shading of clusters if they pass an empirical confidence threshold of 99.99% (see SOM for details). Bootstrap confidence levels are shown in fig. S10. (B) Ultrametric tree summarizing habitat-associated populations identified by the model and the distribution of each population among seasons and size fractions. The habitat legend matches the colored circles in (A) and (B) with the habitat distribution over seasons and size fractions inferred by the model. Distributions are normalized by the total number of counts in each environmental category to reduce the effects of uneven sampling. The insets at the lower right of (A) show two nested clusters (I.A and I.B and II.A and II.B) for which recent ecological differentiation is inferred, including habitat predictions at each node. The closest named species to numbered groups are as follows: G1, V. calviensis; G2, Enterovibrio norvegicus; G3, V. ordalii; G4, V. rumoiensis; G5, V. alginolyticus; G6, V. aestuarianus; G7, V. fischeri/logei; G8, V. fischeri; G9, V. superstes; G10, V. penaeicida; G11 to G25, V. splendidus.

We therefore developed an evolutionary model (AdaptML) to identify populations as groups of related strains sharing a common projected habitat, which reflects their relative abundance in the measured environmental categories (size fractions and seasons) (SOM). In practice, the model inputs are the phylogeny, season, and size fraction of the strains. It then maps changes in environmental preference onto the tree by predicting projected habitats for each extant and ancestral strain in the phylogeny. Although similar in spirit to existing parsimony, likelihood, and Bayesian methods, which map ancestral states onto trees (23), the model accounts for the complexities and uncertainties of environmental sampling. First, projected habitats can span multiple sampling dimensions to account for complex life cycles (such as time spent in multiple true habitats) and problems inherent in environmental sampling: Discrete samples rarely equate to true habitats, and true habitats are frequently misplaced among their typical sample categories (for example, zooplankton fragments may also be found in smaller size fractions). Second, projected habitats can span multiple phylogenetic clusters to allow for the possibility that clusters may arise neutrally or that the relevant parameters differentiating them ecologically have not been measured.

Briefly, AdaptML builds a hidden Markov model for the evolution of habitat associations: Adjacent nodes on the phylogeny transition between habitats according to a probability function that is dependent on branch length and a transition rate, which is learned from the data (SOM) (fig. S3). Subsequently, we optimize the model parameters (the transition rate and the composition of each projected habitat) to maximize the likelihood of the observed data. Finally, we use a simple ad hoc rule for reducing noninformative parameters: We merge habitats that converge to similar distributions (simple correlation of distribution vectors >90%) during the model-fitting procedure (SOM). This reproducibly identified six nonredundant habitats for the observed data set (HA to HF in Fig. 1B and fig. S5). Moreover, the algorithm acts conservatively, as suggested by two tests. First, the model did not overfit the data when there was no ecological signal present: When the environments were shuffled, only a single generalist habitat (evenly distributed over all size fractions and seasons) was recovered. Second, when simulated habitats were used to generate environmental assignments, the model usually identified a number of habitats equal to or less than the true number present (fig. S6).

The analysis suggests that a single bacterial family coexisting in the water column resolves into a striking number of ecologically distinct populations with clearly identifiable preferences (habitats). The algorithm identified 25 populations, associated with one of the six habitats defined by distinct distributions of isolates over seasons and size fractions (Fig. 1 and fig. S7). Most clusters have a strong seasonal signal; interestingly, two pairs of highly similar habitats are observed in both seasons (Fig. 1B). The first of the habitat pairs corresponds to populations occurring both free-living and on particles but lacking zooplankton-associated isolates (HB and HC); the second indicates a preference for zooplankton and large particles (HE and HF) (Fig. 1B). The remaining two habitats were season-specific. Habitat HA combines all primarily free-living populations in the fall, whereas habitat HD identifies a second particle- and zooplankton-associated group in spring, but unlike HE and HF it has a higher proportion of large particles and maps onto a single small group (G25) (Fig. 1). However, we cannot place high confidence in the absence of the free-living habitat in the spring, because relatively few strains were recovered from that fraction. Moreover, the distribution of individual populations among seasons and size fractions varies considerably, with remarkably narrow preferences for some populations whereas others are more broadly distributed. For example, V. ordalii (G3) is almost exclusively free-living in both seasons, whereas V. alginolyticus (G5) has a significant representation in both zooplankton and free-living size fractions but occurs exclusively in the fall (Fig. 1, A and B). The sequences of three additional genes for V. alginolyticus isolates were identical, arguing against misidentification due to recombination or additional population substructuring. Similarly, there was good agreement when two different gene phylogenies (hsp60 and mdh) were used to identify habitats for V. splendidus (fig. S8), although fewer habitats were identified using the mdh tree, most likely because it is less well-resolved. Overall, across all vibrios sampled, association with the zooplankton-enriched and free-living fractions dominated, and although several populations contain particle-associated isolates, only a few appear to be specifically particle-adapted. Because vibrios are generally regarded as particle and zooplankton specialists (14), this observed partitioning offers new insight into their ecology.

Thus, in spite of the highly variable conditions of the water column, populations appear to finely partition resources, especially because our habitat estimates are conservative, as clusters occupying the same habitat may be differentiated along additional (unobserved) resource axes. For example, different zooplankton-associated groups may be host- or body region–specific, and the strong seasonal signal of most clusters may be due to a variety of factors; however, temperature is a likely candidate because it has so far arisen as the strongest correlate of microbial population changes both over a seasonal cycle (24) and along ocean-scale gradients (10). Finally, populations, which appear unassociated in our study, may be true generalists with respect to the resource space sampled or may be adapted to environments not sampled in this study, such as animal intestines or sediments (14). Despite these uncertainties, the observed strong partitioning among associated and free-living clusters may have important implications for population biology in the bacterioplankton. As recently suggested (9), for attached bacteria, the effective population size (Ne) may be considerably smaller than the census size because colonization serves as a population bottleneck, whereas in free-living clusters, Ne may be closer to the census size. Although computing the true magnitude of Ne in microbial populations remains controversial (25), it is an important parameter that determines the relative strength of selection and drift. Thus, attached and free-living populations may evolve under different constraints (9).

The phylogenetic structure of populations also provides insights into the history of habitat switches. Deeply branching populations may have remained associated with habitats over long evolutionary time, and shallow branches may have diversified more recently (Fig. 1, A and B). These stable habitat-associated clusters roughly correlate to named species within the Vibrionaceae. For example, V. ordalii (G3) and Enterovibrio norvegicus (G2) both represent clusters without close relatives containing >50 isolates, which are overwhelmingly predicted to follow primarily free-living (HA) and free-living/particle-associated life-styles (HC), respectively (Fig. 1A). On the other hand, some very closely related clusters are associated with different habitats; V. splendidus, which is composed of strains that are ∼99% identical in rDNA gene sequence (21), differentiates into 15 microdiverse habitat-associated clusters, of which one is distributed roughly evenly among both seasons, and 9 and 5 predominantly occur in spring and fall, respectively. Thus, V. splendidus appears to have ecologically diversified, possibly by invading new niches or partitioning resources at increasingly fine scales.

Recent or perhaps ongoing radiation by sympatric resource partitioning is most strongly suggested for two nested clusters within V. splendidus, where groups of strains differing by as little as a single nucleotide in hsp60 display distinct ecological preferences (Fig. 1A, insets, and table S2). These strains were isolated from multiple independent samples and thus do not represent clonal expansion, suggesting that this may reflect a true habitat switch; nonetheless, homologous recombination could also move alleles between distantly related, ecologically distinct clusters, creating spurious phylogenetic relationships, which can be detected by comparison with other genes. Multilocus sequence analysis shows that for nested cluster I, a close relationship was artificially created because hsp60 gene phylogeny is discordant with three other genes (Fig. 2). However, this still represents a habitat switch, just at a slightly larger sequence distance, as I.A is nested within the much larger G16 cluster in both the hsp60 and the mdh-pgi-adk phylogenies. For the second nested cluster, the three additional genes confirm partial separation of the subclusters II.A and II.B by a single base pair difference in one of the genes, whereas the other genes consist of identical alleles. This reinforces the idea that subcluster II.A is not incorrectly grouped because of recombination, despite its distinct ecological affiliation (Fig. 2). In combination, these data support the idea that there is ecological differentiation among recently diverged genotypes and show that such changes might be recognized in protein-coding genes as soon as they accumulate (neutral) sequence changes.

Fig. 2.

Multilocus sequence analysis of nested clusters (IA and IB and IIA and IIB) with differential habitat association by comparison of partial hsp60 (left) and concatenated partial mdh, adk, and pgi (right) gene phylogenies. Habitat predictions (indicated by colored boxes) and the numbering of clusters correspond to Fig. 1. Scale bar is in units of nucleotide substitutions per site.

How might adaptation to a new habitat relate to speciation, the generation of distinct clusters of closely related bacteria? Mathematical modeling has recently shown that the dynamics of speciation depend on the ratio of homologous recombination to mutation rates (r/m) (9). When this ratio per allele exceeds ∼1, populations transition from essentially clonal to sexual, with the major consequence that selection is probably required for the formation of clusters (9). Our preliminary multilocus sequence analysis on a set of strains with similar taxonomic composition suggests that their r/m is well above that threshold. Thus, our observations of habitat separation for highly similar but clearly distinct genotypes suggest that ecological selection may have triggered phylogenetic differentiation. A plausible mechanism is that differential distribution among habitats (possibly caused by few adaptive loci) is sufficient to depress gene flow between associated genotypes (9, 26). Consequently, mutations will no longer be homogenized but instead accumulate within specialized populations, even for ecologically neutral genes. Over time, genetic isolation may increase because homologous recombination rates decrease log-linearly with sequence distance (27). We detected associations with different habitats among sister clades over a wide range of phylogenetic distances, possibly representing populations at various stages of differentiation (Fig. 1A). Although we cannot determine whether clusters represent transiently adapted populations or nascent species, our observations of differential distributions of genotypes suggest that there exists a small-scale adaptive landscape in the water column allowing the initiation of (sympatric) speciation within this community.

Although it has recently been suggested that microbial lineages remain specific to macroenvironments over long evolutionary times (28), this study demonstrates switches in ecological associations within a bacterial family coexisting in the coastal ocean. In the V. splendidus clade, speciation could be ongoing, but the divergence between most other ecologically defined groups appears large. This is consistent with our previous suggestion that rRNA gene clusters, which are roughly congruent with the deeply divergent protein-coding gene clusters detected here, represent ecological populations (8). However, the example of V. splendidus highlights the fact that using marker genes to assess community-wide diversity may not capture some ecological specialization. Moreover, different groups of organisms could evolve under different constraints, and the mechanisms suggested here apply to the invasion of new habitats and are thus different from (but compatible with) the widely discussed niche-specific selective sweeps (29). Why V. splendidus appears to have radiated recently into new habitats whereas other groups appear to be more constant is not known but may be related to its high heterogeneity in genome architecture (21). This could indicate a large (flexible) gene pool that, if shared by horizontal gene transfer, gives rise to large numbers of ecologically adaptive phenotypes. It will therefore be important to compare whole genomes within recently ecologically diverged clusters to identify specific changes leading to adaptive evolution.

Supporting Online Material

Materials and Methods

Figs. S1 to S10

Tables S1 and S2


References and Notes

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