Impact Factor 4.019

The world's most-cited Microbiology journal

This article is part of the Research Topic

Cyanobacterial Blooms in a Changing World

Original Research ARTICLE

Front. Microbiol., 27 March 2018 | https://doi.org/10.3389/fmicb.2018.00438

Niche Separation Increases With Genetic Distance Among Bloom-Forming Cyanobacteria

Nicolas Tromas1*†, Zofia E. Taranu2†, Bryan D. Martin3, Amy Willis3,4, Nathalie Fortin5, Charles W. Greer5 and B. Jesse Shapiro1*
  • 1Département de Sciences Biologiques, Université de Montréal, Montreal, QC, Canada
  • 2Department of Biology, University of Ottawa, Ottawa, ON, Canada
  • 3Department of Statistics, University of Washington, Seattle, WA, United States
  • 4Department of Biostatistics, University of Washington, Seattle, WA, United States
  • 5Energy, Mining and Environment, National Research Council Canada, Montreal, QC, Canada

Bacterial communities are composed of distinct groups of potentially interacting lineages, each thought to occupy a distinct ecological niche. It remains unclear, however, how quickly niche preference evolves and whether more closely related lineages are more likely to share ecological niches. We addressed these questions by following the dynamics of two bloom-forming cyanobacterial genera over an 8-year time-course in Lake Champlain, Canada, using 16S amplicon sequencing and measurements of several environmental parameters. The two genera, Microcystis (M) and Dolichospermum (D), are frequently observed simultaneously during bloom events and thus have partially overlapping niches. However, the extent of their niche overlap is debated, and it is also unclear to what extent niche partitioning occurs among strains within each genus. To identify strains within each genus, we applied minimum entropy decomposition (MED) to 16S rRNA gene sequences. We confirmed that at a genus level, M and D have different preferences for nitrogen and phosphorus concentrations. Within each genus, we also identified strains differentially associated with temperature, precipitation, and concentrations of nutrients and toxins. In general, niche similarity between strains (as measured by co-occurrence over time) declined with genetic distance. This pattern is consistent with habitat filtering – in which closely related taxa are ecologically similar, and therefore tend to co-occur under similar environmental conditions. In contrast with this general pattern, similarity in certain niche dimensions (notably particulate nitrogen and phosphorus) did not decline linearly with genetic distance, and instead showed a complex polynomial relationship. This observation suggests the importance of processes other than habitat filtering – such as competition between closely related taxa, or convergent trait evolution in distantly related taxa – in shaping particular traits in microbial communities.

Introduction

Microbial communities are composed of potentially diverse groups of lineages, which must be sufficiently similar to survive in the same habitat, yet sufficiently dissimilar to occupy distinct ecological niches and avoid competition. This tension between selection for common traits to survive in a common environment (habitat filtering) and selection for divergent traits to reduce competition (niche partitioning among closely related species) was recognized by Darwin, and the relative impacts of the two processes on communities are still debated (Cavender-Bares et al., 2009). A pioneering study of microbial communities using phylogenetic marker genes found evidence for phylogenetic clustering, suggesting the importance of habitat filtering in selecting for closely related taxa sharing specific traits allowing them to survive in a given habitat (Horner-Devine and Bohannan, 2006). However, phylogenetic overdispersion (the opposite pattern as phylogenetic clustering) has also been observed, suggesting that competition between closely related taxa can lead to niche partitioning (Koeppel and Wu, 2014). Importantly, the power to detect phylogenetic overdispersion depends on the phylogenetic resolution (e.g., whether operational taxonomic units are defined at 97, 98, or 99% identity in a marker gene) (Koeppel and Wu, 2014).

Beyond searching for phylogenetic patterns of clustering or overdispersion, explicitly considering the associations between microbial traits and niches can help understand the selective pressures shaping microbial communities on different evolutionary time scales. It is known that certain traits (e.g., salinity preference, methanogenesis) are relatively slow-evolving and thus restricted to only certain lineages, whereas other traits (e.g., phage resistance, organic phosphate utilization) can be acquired by a single point mutation or gene acquisition, thus evolving rapidly in response to ecological selection and competition (Martiny et al., 2015). Therefore, habitat filtering might be stronger for slow-evolving traits, while niche partitioning will be more likely for fast-evolving traits.

In this study, we use Hutchinson’s definition of a fundamental niche as the set of abiotic conditions under which an organism can survive and reproduce, and a realized niche as the conditions under which it is actually observed in nature, accounting for both abiotic and biotic (e.g., competition, predation, cooperation) interactions (Hutchinson, 1957). If two species have identical ecological niches, one should competitively exclude the other (Gauze, 1934; Tilman, 1982) unless competition is weak due to abundant resources. In practice, closely related taxa often compete for space and resources (Cavender-Bares et al., 2009), favoring specialization to reduce overlap in niche space. For example, coexisting (sympatric) taxa within the same genus tend to have different realized niches, experiencing different seasonal growth patterns or responding differently to environmental parameters (Gray et al., 2004; Jaspers and Overmann, 2004; Hunt et al., 2008; Šimek et al., 2010; Jezbera et al., 2011; Neuenschwander et al., 2015). Pairs of taxa with similar realized niches can be identified when they co-occur in repeated sampling over space and time, and such co-occurrence networks are readily inferred from deep amplicon sequencing datasets (Friedman and Alm, 2012). Typically, niches are considered as features of species. However, when niches are considered as collections of traits or environmental associations, the Hutchinsonian niche concept can be extended to taxonomic groupings more inclusive than species, even if the biological “reality” of such groups is doubtful. Here, we apply the niche concept to both fine-grained (i.e., sub-genus level) and coarse-grained (i.e., genus level) taxonomic units. We focus mainly on niche specialization within genera, showing that specialization is extensive and that lumping bacterial diversity at the genus level obscures finer-scale niche preferences.

Cyanobacteria are widely and naturally present in freshwater ecosystems, and some lineages form blooms under appropriate conditions of temperature and nutrients (Konopka and Brock, 1978; Harke et al., 2016). Several studies have shown that different cyanobacterial genera can co-occur during blooms, thus sharing at least some dimensions of their realized niches (Paerl et al., 2001; Yamamoto and Tsukada, 2009). Nitrogen (N) and phosphorus (P) can both be limiting for bloom formation, and different cyanobacterial taxa apparently have different preferences for N and P concentrations (Dolman et al., 2012). For example, cyanobacteria capable of N-fixation (such as Dolichospermum) are associated with more efficient P-utilization at the community level, suggesting P-limitation when N-fixers are abundant (Andersson et al., 2015; Olli et al., 2015). Clearly, N and P utilization are ecologically important traits for cyanobacteria, and may be important for niche partitioning among closely related strains. In the marine cyanobacterium Prochlorococcus, P uptake and metabolism appears to be relatively fast-evolving perhaps due to horizontal gene transfer of P-related genes (Coleman and Chisholm, 2010) while light preferences are slow-evolving, and temperature preferences are intermediate (Martiny et al., 2015).

We investigated niche partitioning within and between Microcystis and Dolichospermum, two genera of potentially toxigenic cyanobacteria that bloom nearly every year in a large eutrophic North American lake, Lake Champlain. In a previously described 8-year time-course analysis (2006–2013) spanning multiple bloom events, we used 16S amplicon sequencing to broadly survey changes in the lake microbial community over time, generally at the genus level (Tromas et al., 2017). Here, we use Minimum Entropy Decomposition (MED) of amplicon sequences (Eren et al., 2014) to identify sub-genus strains (MED nodes; here used interchangeably with “strains”) within each of the two dominant cyanobacterial genera, Microcystis and Dolichospermum, at single nucleotide resolution (i.e., each MED node is an exact sequence variant, distinguishable from other variants that differ by at least one nucleotide substitution). MED discards low-entropy nucleotide positions, which effectively filters out sequencing errors at the expense of possibly also removing true polymorphism at very low frequency. Therefore, although MED outputs exact sequences that are actually present in the sample (after denoising), it is possible the MED nodes contain finer-scale genetic variation which could be captured using additional marker genes or whole genome sequencing. A previous study demonstrated that oligotypes (similar to MED nodes) lacked the resolution to distinguish toxic and non-toxic Microcystis lineages, but could potentially be informative about other, more phylogenetically conserved niches such as eutrophic (nutrient-rich) vs. oligotrophic (nutrient-poor) lake preferences (Berry et al., 2017). Toxin production is thought to be fast-evolving because toxin biosynthesis genes are widely distributed across cyanobacterial genera, suggesting rapid gain and loss. While horizontal gene transfer is a likely explanation, transfer is probably more frequent among closely related lineages because toxin gene trees are congruent with ribosomal phylogenies of distantly related cyanobacteria genera (Rantala et al., 2004).

We used a combination of genetic data (diversity of MED nodes) and matched environmental data (e.g., temperature, nutrient concentrations, precipitation) to address three specific questions. First, how similar are the niches of the two dominant cyanobacterial genera Microcystis and Dolichospermum? Second, how similar are the niches of strains within each genus? Third, how does niche similarity change with genetic relatedness? We confirm that Microcystis and Dolichospermum are broadly co-occurring during blooms, but have distinct nutrient preferences. We also identified niche partitioning at the sub-genus level, and observe a general decline in realized niche similarity with genetic distance, consistent with habitat filtering. However, certain niche dimensions (particulate nutrient concentrations) show a complex polynomial relationship with genetic distance, suggesting that a combination of habitat filtering and competitive interactions shapes the evolution of these traits.

Materials and Methods

Sampling, DNA Extraction, Purification, and Sequencing

Open-water season samples (April to November) collected over 8 years (2006–2013) from the photic zone of Missisquoi Bay at two different sites (littoral and pelagic) of Lake Champlain, QC, Canada (45°02’45”N, 73°07’58”W) were filtered and extracted for DNA sequencing as described in Tromas et al. (2017).

Sequence Analysis

A total of 7,949,602 sequences of the 16S rRNA gene V4 region were obtained from 150 lake samples, with a median of 41,982 per sample as previously described (Tromas et al., 2017). These sequences were processed with the default parameters of the SmileTrain pipeline1 that includes reads quality filtering, chimera filtering, and merging using USEARCH (version 7.0.10902, default parameter) (Edgar, 2010), Mothur (version 1.33.3) (Schloss et al., 2009), Biopython (version 2.7) and custom scripts. MED was then applied to the filtered and merged reads to partition sequence reads into MED nodes (Eren et al., 2014). MED was performed using the following parameters –M noise filter set to 500 resulting in ∼7% of reads filtered and 941 MED nodes (Supplementary Data Sheet 2). Samples with less than 1,000 reads were removed, yielding a final dataset of 135 samples. Finally, taxonomy was assigned using the assign_taxonomy.py QIIME script (default parameters), and a combination of GreenGenes and a freshwater-specific database (Freshwater database 2016 August 18 release; Newton et al., 2011), using TaxAss3, installation date: September 13th 2016; Rohwer et al., unpublished). After assignment, nodes that belong to Eukaryotes but still present in the database (Cryptophyta, Streptophyta, Chlorophyta, and Stramenopiles orders) were removed, leading to a total of 891 nodes.

Diversity Analysis

Comparing changes in the diversity of Microcystis strains to the diversity of Dolichospermum strains was performed using betta (Willis et al., 2017). Betta accounts for strains that are present in the environment but not observed in the samples due to incomplete sampling. The number of unobserved strains is estimated based on the number of strains that are observed and their abundances. The total strain diversity was estimated using breakaway (R package v4) which accounts for ecological interactions between strains (Willis and Bunge, 2015).

Conditionally Rare Taxa Analysis

We investigated the temporal dynamics of Microcystis and Dolichospermum nodes by measuring the composition for each genus in conditionally rare taxa. The matrix of node absolute abundances was used as input for the R script CRT_Functions_v1.1.R4 (Shade et al., 2014) using the default parameters. Conditionally rare taxa are defined as usually rare taxa that occasionally become very abundant, without showing rhythmic or seasonal patterns.

Node–Environment Relationships Analysis

To investigate node–environment relationships, we used an environmental data matrix that included: particulate phosphorus in μg/L (PP, the difference between TP and DP), particulate nitrogen in mg/L (PN, the difference between TN and DN), total dissolved phosphorus in μg/L (DP), total dissolved nitrogen in mg/L (DN), 1-week-cumulative precipitation in mm and 1-week-average air temperature in Celsius. Total nutrients were measured directly from collected lake water and the dissolved nutrients were measured in filtered water (Glass microfiber Filters grade GF/F, 0.7 microns). The detailed measurements of each environment variable are described in Tromas et al. (2017). In this previous study, we showed that these environmental variables, over the 8 years, were not correlated with one another.

Response to Abiotic Factors

To analyze the response of each node to abiotic environmental data, we used a Latent Variable Model (LVM) framework, which combines Generalized Linear Models with Bayesian Markov Chain Monte Carlo (MCMC) methods (boral package in R; Hui et al., 2015; Warton et al., 2015). LVM is a model-based approach for analyzing multivariate data (e.g., numerous taxa within the response matrix) that partitions the different drivers of taxa co-occurrence patterns into two components: the first is a regression component, which models the taxon-specific environmental responses, and the second is a latent variable component, which is used to identify residual patterns of co-occurrence resulting from unmeasured factors and/or biotic interactions (Letten et al., 2015). In this study, we used LVMs to examine how taxa co-respond to abiotic gradients, and used these co-responses as a proxy for niche similarity. To do so, we extracted the environmental correlation matrix from the regression model, which, for any two taxa, corresponds to the correlation between their fitted values (xiβj). In particular, for each node we calculated the predicted probability of mean abundance for each site on linear and non-linear (i.e., Y ∼ X + X2) response scales, providing a vector of fitted values. Correlations among the fitted responses of any two taxa were then calculated based upon these vectors (correlating the vector of taxon A with the vector of taxon B, for instance).

We first tested LVMs using a response matrix of two columns, corresponding to the relative abundances of the Dolichospermum and Microcystis genera, relative to the rest of the bacterial community. For these models, Dolichospermum and Microcystis abundances were normalized by the total counts of all bacteria using the centered log ratio to correct for data compositionality (clr-inter genus) (Aitchison, 1986; Paliy and Shankar, 2016) using a zero-replacement procedure as suggested in Gloor and Reid (2016). To then examine dynamics within these genera, we ran separate LVMs for Dolichospermum and Microcystis nodes (i.e., response matrix consisted of the Dolichospermum and Microcystis nodes, respectively). For this second set of LVMs, node abundances were normalized by the total counts of Dolichospermum and Microcystis, respectively, to obtain an intra-genus relative abundance (clr-intra genus) for each node. Environmental variables were standardized to mean zero and unit variance to reduce the correlation between the linear (X) and non-linear (X2) fit, which also simplified model interpretation and stabilized MCMC sampling. As most of the Dolichospermum nodes were conditionally rare, we included only the Dolichospermum nodes that were present in at least 70% of samples, to avoid overfitting based on too little data. To complement this multivariate LVM analysis and help visualize the univariate response of each genus and node to the suite of environmental variables, we also tested linear (degree-1 polynomial) and non-linear (degree-2 polynomial) relationships between each response and explanatory variable. We used AIC to find the single best-fit model (either linear or non-linear) for each relationship and plotted the best-fit model.

Lastly, to test whether co-responses or niche separations were stronger for more closely or distantly related nodes, we extracted the correlations between fitted responses of any two nodes from the environmental correlation matrix of each best-fit LVM and examined the relationship between each significant correlation and the pairwise genetic distances of the two respective taxa (percent identity in the V4 region of the 16S gene). Here, significant positive correlations represent co-response between any two taxa whereas significant negative correlations represent the degree of niche separation. The genetic distance between nodes was measured within each genus Microcystis and Dolichospermum using the software MEGA (version 7.0.18) using the p-distance (the proportion of nucleotide sites at which two sequences differ), calculated by dividing the number of sites with nucleotide differences by the total number of sites compared (excluding sites with gaps). R code to reproduce these analyses and relevant figures is provided in Supplementary Data Sheet 3.

Co-occurrence Analysis

Co-occurrences between Microcystis or Dolichospermum and other taxa were calculated with SparCC (Friedman and Alm, 2012), with 20 iterations to estimate the median correlation of each pair of MED nodes, and 500 bootstraps to assess the statistical significance. Correlations were then filtered for statistical significance (P < 0.01) and correlations with R > ±0.6, were selected to build networks using Cytoscape (version 3.1.0).

Results

Do Microcystis and Dolichospermum Co-occur Temporally?

We have previously shown that Microcystis and Dolichospermum were the two most dominant bloom forming cyanobacteria in Lake Champlain’s Missisquoi Bay (Tromas et al., 2017). Both genera were present every year at both littoral and pelagic sampling sites, between 2006 and 2013, with a relative abundance of at least 15% on average during summer (max = 24.8%), with the exception of summer 2007 (0.7%) when no substantial blooms occurred (Figure 1). Dolichospermum was generally the most dominant genus except for 2009 and 2010 when Microcystis dominated (Supplementary Figure S1). Dolichospermum and Microcystis were both present every year (Supplementary Figure S1) suggesting that they share similar physico-temporal niche during a bloom, although other aspects of their niches likely differ. For example, Dolichospermum can fix nitrogen but Microcystis cannot (Hajdu et al., 2007).

FIGURE 1
www.frontiersin.org

FIGURE 1. Temporal dynamics of the two dominant cyanobacterial genera over an 8-year time-course in littoral (A) and pelagic (B) sampling sites. Relative abundance of Microcystis is shown in solid red, Dolichospermum in dashed green and the other members of the bacterial community in dashed light violet. The time scale (x-axis) is in units of years.

Are Microcystis and Dolichospermum Equally Diverse and Dynamic?

To explore the temporal dynamics of the finer-scale taxa within each genus, we partitioned each genus into MED nodes, which we call “strains” (Methods). We obtained 25 Dolichospermum strains and 6 Microcystis strains, and after accounting for low-abundance strains that may be missing from the samples using Breakaway (Methods), we conclude that diversity is significantly lower within Microcystis than within Dolichospermum (P < 0.001). The difference in the number of nodes is unlikely to be an artifact of sequencing depth, because there is no correlation between the number of sequence reads and the number of nodes per genus (Supplementary Figure S2). Dolichospermum thus appears to be more diverse than Microcystis.

We then examined whether nodes within the Microcystis and Dolichospermum genera followed the same temporal dynamics. We observed that 15/25 Dolichospermum nodes were conditionally rare, meaning that they are usually rare but occasionally become relatively abundant, without following any apparent rhythm (Table 1).

TABLE 1
www.frontiersin.org

TABLE 1. Conditionally rarity analysis.

The proportion of rare Dolichospermum nodes was significantly higher than what is observed in other nodes in the lake community (Fisher’s exact test, P < 0.001). Several conditionally rare Dolichospermum nodes seemed to dominate several times without showing seasonal patterns (Supplementary Figure S3). Furthermore, we noticed a shift in node composition after 2011, where node D5505 decreased while D2282 increased in relative abundance. In contrast, we only found one conditionally rare Microcystis node. However, only two Microcystis nodes (M5732 and M5733) were consistently dominant over time (Supplementary Figure S4). Similarly, only two Dolichospermum nodes, but not always the same two, dominated at any given time (Supplementary Figure S3). Overall, these results suggest that Dolichospermum is more genetically diverse, and that this genetic diversity varies over time. In contrast, Microcystis is less diverse and more stable over time.

Do Microcystis and Dolichospermum Share the Same Realized Niche Within the Community?

To investigate the niche separation between Dolichospermum and Microcystis, we analyzed their relationships with several environmental conditions measured at the time of sampling (temperature, nutrient concentrations, precipitation and toxin concentrations). We observed that these two cyanobacterial genera have different responses to nutrients (Supplementary Figure S5) as previously observed (Andersson et al., 2015; Harke et al., 2016). Microcystis relative abundance was positively correlated with DP (Supplementary Figure S5; R2 = 7%), in agreement with previous observations (Homma et al., 2008). In contrast, Dolichospermum was not significantly correlated with DP, and instead was positively correlated with PP and PN (Supplementary Figure S5). Dolichospermum also responded negatively to dissolved nitrogen, in agreement with previous studies demonstrating that Nostocales (the Order containing Dolichospermum) are favored under conditions of low dissolved inorganic nitrogen, due to their ability to utilize atmospheric nitrogen for growth (Suikkanen et al., 2013; Andersson et al., 2015). Overall, these results confirmed that Microcystis and Dolichospermum share a spatio-temporal niche during a bloom, but have distinct nutrient preferences.

How Similar Are the Niches of Strains Within a Genus?

Numerous studies have focused on environmental conditions that favor cyanobacterial blooms, but few studies have examined how different cyanobacterial strains might respond differently to environmental conditions due to niche partitioning. We observed that Dolichospermum and Microcystis strains, within each genus, appear to have qualitatively different dynamics (Supplementary Figures S3, S4). To test whether these different dynamics were due to niche partitioning, we first analyzed how the different strains within each genus were related to environmental variables, and then used LVMs to determine the co-responses (niche similarity) as well as niche separation between strains.

We found that nearly all (19/20) of the significant relationships between strain relative abundances (within each genus) and environmental conditions were linear (Figure 2 and Supplementary Table S2). Dolichospermum nodes D2282 (red) and D5630 (green) showed opposite responses to nutrients (PP and PN) and precipitation (Figure 2A). The LVMs confirmed a significant niche separation for these niche dimensions (Supplementary Figure S6 and Supplementary Table S3). In contrast, nodes D2424 and D5630 both displayed a similar negative relationship (Figure 2A), resulting in a significant co-response (niche similarity) to DN (Supplementary Figure S6 and Supplementary Table S3). Within the Microcystis genus, we observed several nodes with similar responses to PP, PN, and DP (Figure 2B), and significant co-responses between nodes were detected for these niches (Supplementary Figure S7). We also found significant niche separations for DN (involving M5732, M5733, and M5738) and temperature (involving M5734 and M5738) (Supplementary Figure S7). Overall, the LVM analysis showed that niche separation occurs among nodes within a genus (Supplementary Figure S8). In Dolichospermum, niche separation occurred mainly in the niche dimensions of particulate nutrients (PP and PN) and precipitation (Supplementary Figure S6), whereas in Microcystis niche separation occurred mainly according to temperature and DN preferences (Supplementary Figure S7).

FIGURE 2
www.frontiersin.org

FIGURE 2. Niche partitioning at the sub-genus level. Best-fit polynomial models of the response of Dolichospermum (A) and Microcystis (B) nodes to abiotic factors. The relative abundance of each MED node (strain) was computed relative to the total number of reads within each genus using the centered-log ratio (clr) transform. Significant relationships are shown by solid lines and colored confidence intervals. In most cases, the degree-1 polynomial (linear model) provided the best-fit (see Supplementary Table S1 for details). For Dolichospermum, only the dominant nodes (observed in at least 70% of samples) are shown.

How Do Niche Preferences Vary With Genetic Distance?

When two taxa share the same realized niche (i.e., where they are actually able to survive in the wild, including biotic and abiotic niches), they are more likely to be observed together, and thus to be correlated in survey data. We therefore used co-occurrence patterns between pairs of Dolichospermum or Microcystis nodes as a proxy for similarity in their realized niches, and asked if more genetically similar nodes are more likely to have similar realized niches. Indeed, we found that pairwise SparCC correlation coefficients between nodes tends to decline with genetic distance (Figure 3).

FIGURE 3
www.frontiersin.org

FIGURE 3. Co-occurrence of strains declines with their pairwise genetic distance. Relationship between co-occurrence (significant SparCC correlation, P < 0.05) and genetic distance (p-distance) between Dolichospermum (A) and Microcystis (B) nodes. Blue diamonds represents the mean SparCC correlation for each distance. Boxplots show the median (horizontal line), the 25th and 75th percentile (enclosed in box) and 95% confidence intervals (whiskers). The discreteness observed in the x-axis is due to the discrete number of substitutions in the 16S rRNA gene sequence (e.g., exactly 1, 2, 3, …nucleotide differences between pairs).

This pattern was significant within both Dolichospermum [linear regression, F(1,279) = 28.3, P < 0.001, adjusted R2 = 8.9%] and Microcystis [linear regression, F(1,13) = 7.9, P < 0.05, adjusted R2 = 33.0%]. The higher R2 observed for Microcystis might be explained by its more limited genetic diversity (maximum pairwise genetic distance ∼0.01) compared to Dolichospermum (maximum distance ∼0.05). There appears to be a rapid decline in niche similarity as genetic distance goes from 0 to ∼0.01 (adjusted R2 = 23.0% in Dolichospermum when considering only this distance range) followed by a flatter relationship for genetic distance > 0.01.

Ecologically distinct lineages are expected to be associated with distinct surrounding communities, due to a combination of direct microbe-microbe (biotic) interactions and shared preferences for abiotic conditions (Cohan and Koeppel, 2008). We therefore analyzed the co-occurrence patterns between each Dolichospermum or Microcystis node and other bacterial taxa in the lake community. We identified non-cyanobacterial MED nodes that co-occurred with each Dolichospermum and Microcystis node and found that Microcystis nodes were generally more connected with other members of the bacterial community (Supplementary Figure S9). We also found that relatively few taxa (4 out of 26; Supplementary Table S5) are significantly correlated with both Microcystis and Dolichospermum, suggesting that Microcystis and Dolichospermum strains co-occur with distinct sets of other bacteria, and thus have distinct realized niches.

We further investigated whether more closely related Microcystis or Dolichospermum nodes have more similar correlations with potentially interacting community members. We focused on members of the Cytophagaceae family (MED nodes 3667, 5983, and 5984), which are potential predators of cyanobacteria (Rashidan and Bird, 2001), members of the Rhizobiales order (nodes 4737, 3705, and 3726), which are potential N-fixers that could provide nitrogen for non-N-fixing Microcystis (Louati et al., 2015), and all the taxa that co-occurred with both Dolichospermum and Microcystis nodes (nodes 1061, 4674, 7272, and 4756). For each pair of Dolichospermum or Microcystis nodes, we correlated their genetic distance (as in Figure 3) with the absolute difference in the correlations (r) with each potentially interacting community member (Supplementary Figure S11). We found that closely related Dolichospermum nodes tend to have more similar correlations with Cytophagaceae node 3667, whereas more distantly related Dolichospermum nodes have more different correlations with node 3667 (correlation between |Δr| and genetic distance, adjusted R2 = 0.059, P < 0.001). Similar but not statistically significant patterns were observed for several Microcystis nodes. It is difficult to generalize from these results, but at least in some cases, more phylogenetically similar strains may share conserved interactions with other community members.

Finally, we asked whether the decline in niche similarity with genetic distance (Figure 3) was a common feature of all niche dimensions, or if different abiotic parameters showed different patterns. We considered all significant models, both linear and non-linear as shown in Figure 4 (for Dolichospermum) and Supplementary Figure S10 (for Microcystis). We found a negative linear relationship between DN niche similarity and genetic distance (adjusted R2 = 25%, P = 0.0791). Qualitatively, most other niche dimensions showed a similar pattern for Dolichospermum (Figure 4), and among Microcystis nodes we found a significant negative linear relationship between the correlated fitted response to microcystin concentrations and genetic distance (Supplementary Figure S10). However, we also identified non-linear relationships between Dolichospermum niche similarity and genetic distance for PP (adjusted R2 = 69%, P = 0.0184) and PN (adjusted R2 = 63%, P = 0.0280). For these niche dimensions, the correlation of fitted responses declines from genetic distances of 0 to ∼0.01, then rises to another peak around ∼0.03 before declining again (Figure 4). While these non-linear models provided significantly better fits to the data compared to linear models, we cannot exclude the possibility that the fits were driven by a few outlying points particular to our data set. Replicating these findings in additional data sets (e.g., from different lakes, over different time scales) is therefore essential.

FIGURE 4
www.frontiersin.org

FIGURE 4. The relationship between genetic distance and the co-response (niche similarity) for Dolichospermum. LVMs were used to identify correlations between the responses of MED nodes to each measured environmental parameter (separate panels). Positive correlations of fitted responses indicate similar niches; negative correlations indicate different niche preferences. Genetic distances were computed using the p-distance. Separate model fits were tested with the Akaike Information Criterion (AIC) for the relationship between each niche dimension and genetic distance. See Supplementary Table S4 for details of model fits. Significant model fits are shown with thick solid lines; non-significant fits are shown with dashed lines.

Discussion

Overall, our results show how Microcystis and Dolichospermum can achieve similar levels of dominance during cyanobacterial blooms by partitioning niche space within and between genera. Over 8 years of sampling, we observed that Dolichospermum and Microcystis generally co-occurred during blooms, suggesting a broadly similar physico-temporal niche (Figure 1 and Supplementary Figure S1). However, we confirmed that Dolichospermum was associated with lower concentrations of dissolved nitrogen, consistent with its known ability to fix nitrogen (Andersson et al., 2015; Harke et al., 2016) while Microcystis was associated with higher concentrations of dissolved phosphorus (Supplementary Figure S5). These genus-level traits could mask niche differentiation among strains within each genus.

To dissect niche preferences at finer taxonomic resolution, below the genus level, we used MED, allowing single-nucleotide resolution of 16S amplicon sequences, i.e., each MED node is an exact sequence variant. Finer taxonomic resolution has been shown to increase the power to correctly identify phylogenetic overdispersion, a signature of competitive interactions being more important than habitat filtering (Koeppel and Wu, 2014). However, even at high resolution, the 16S marker gene may be too slow-evolving to be a good marker for fast-evolving traits, such as toxin production (Berry et al., 2017) – a trait which likely evolves rapidly by horizontal gene transfer (Moffit and Neilan, 2004).

Despite these limitations, MED analysis provided additional insights into the distinct niches of Microcystis and Dolichospermum. Dolichospermum diversity was higher and most of the MED nodes (strains) were conditionally rare, some of them being bloom-associated in some years but not in others (e.g., D2282, Supplementary Figure S3). Microcystis nodes, on the other hand, were less diverse but more consistently bloom-associated (nodes M5732 and M5733; Supplementary Figure S4) and more correlated with other taxa (Supplementary Figure S9), suggesting that the two dominant bloom-forming genera might use different ecological strategies, beyond what is already known about nitrogen and phosphorus utilization.

Examining niche partitioning below the genus level also allowed us to detect patterns not evident at the genus level. For example, Dolichospermum is associated with higher concentrations of PP and PN at the genus level (Supplementary Figure S5), but contains a strain (D5630) that shows the opposite pattern (Figure 2 and Supplementary Figure S6). Within Microcystis, niche partitioning occurred mainly for temperature and DN, and the two most dominant Microcystis nodes had a significant niche separation for DN (Figure 2 and Supplementary Figure S7). It is known that Microcystis does not fix atmospheric nitrogen, but is able to use refractory N-containing compounds such as urea or amino acids (Dai et al., 2009; Moisander et al., 2009), and DN is likely needed for toxin production (Monchamp et al., 2014). Therefore, different Microcystis strains could specialize in their preference for different forms of nitrogen.

The relative importance of habitat filtering and competition in shaping microbial communities is widely debated, and distinguishing between the two processes can be technically challenging (Koeppel and Wu, 2014; Cadotte and Tucker, 2017). Consistent with a general effect of habitat filtering in selecting for genetically similar cyanobacteria under similar conditions, we found a negative relationship between MED node co-occurrence and pairwise genetic distance (Figure 3). This result is in agreement with an early study showing the importance of habitat filtering in microbial communities (Horner-Devine and Bohannan, 2006) and is also consistent with what was observed by Silverman et al. (2017) using human microbiome data. Here, we considered co-occurrence as a proxy for shared realized niches, including both biotic and abiotic factors.

Consistent with the general importance of habitat filtering in shaping cyanobacterial communities, we observed that closely related strains tended to have similar co-responses to several measured abiotic environmental parameters. For example, we found negative linear relationships between niche similarity and Dolichospermum genetic distance for most niche dimensions, particularly DN (Figure 4). This observation is in agreement with a previous study that showed that bacterial and fungal responses to N fertilization tend to be more similar among close relatives, with a decline in similarity between genetic distances of 0 and 0.05 (Martiny et al., 2015; Amend et al., 2016). In Microcystis, we found that more genetically similar strains tended to be observed at more similar concentrations of the toxin microcystin (Supplementary Figure S10). Although 16S is a poor marker for microcystin production (Berry et al., 2017), our results suggest that phylogenetic relatedness is nevertheless somewhat predictive of microcystin concentrations. This means that microcystin production or tolerance is more likely to be shared by close relatives.

In contrast to the general pattern of declining niche similarity with genetic distance, PN and PP both had non-linear relationships with genetic distance in Dolichospermum (Figure 4). After an initial decline in co-responses to PN and PP to a genetic distance of ∼0.01, co-response rose to another peak at ∼0.03 before declining again. This response is consistent with near-identical MED nodes sharing the same preferences for PN and PP concentrations, and that competition between close-relatives (up to genetic distance of ∼0.01) imposes divergent selection for distinct niche preferences. The similarity in PN and PP niches for more distant relatives (distance ∼0.03) can be explained if distant relatives have diverged to reduce competition in other niche dimensions, allowing them to converge in PN and PP preferences. It is unclear why this non-linear relationship is observed for PN and PP, but not for other niche dimensions. One possibility is that P uptake and metabolism genes are easily acquired by horizontal gene transfer, as observed in marine cyanobacteria (Coleman and Chisholm, 2010) and may thus be more rapidly evolving, resulting in non-linear relationship with phylogenetic distance. Yet it is unclear why the non-linear pattern is observed for particulate but not dissolved N and P. Particulate nutrients could be a marker for biomass (e.g., bloom density), but it is equally unclear how rapidly “bloom preference” would be expected to evolve. The same non-linear pattern might be expected for microcystin, due to frequent gain/loss of the underlying biosynthetic genes. However, we observe a linear relationship (Supplementary Figure S10) between microcystin concentrations and genetic distance among Microcystis strains – but only in a very narrow range of genetic distance (99–100% identity). Therefore, non-linear relationships could become apparent among more distantly related strains (e.g., 95–99% identity).

To investigate how biotic niches change over evolutionary time, we studied how cyanobacterial interactions (SparCC correlations) with non-cyanobacterial community members varied with genetic distance among cyanobacteria. We found that genetically similar Dolichospermum strains tended to have similar interactions with a strain of Cytophagaceae, a potential cyanobacterial predator, whereas more distantly related Dolichospermum strains diverged in terms of their interactions (Supplementary Figure S11). We limited our analyses to only the most common interacting partners (common to Microcystis and Dolichospermum) or those previously suspected to interact with cyanobacteria via predation or cross-feeding. Future studies could expand on these analyses in a more comprehensive fashion to quantify how biotic interactions evolve over different time scales and in different lineages.

Overall, our results show how different traits may have different relationships with genetic distance, highlighting the importance of considering each niche dimension separately, because adaptation to different niche dimensions can occur at dramatically different rates (Martiny et al., 2015). Our results also suggest that the same trait could evolve at different rates in different cyanobacterial lineages. For example, temperature adaptation evolves at an intermediate rate in Prochlorococcus (Martiny et al., 2015) and perhaps at a slower rate in hot spring Synechococcus (Becraft et al., 2011, 2015). Using our comparative framework of two broadly sympatric genera, we identify a general decline in ecological similarity with genetic distance – although certain traits (e.g., PN and PP in Dolichospermum but not in Microcystis) go against this trend. A future challenge in microbial ecology and evolution will be to determine which traits are generally fast- or slow-evolving, and most interestingly, which lineages provide exceptions. For example, particularly rapid evolution of a generally slow-evolving trait in a particular lineage could provide evidence for an unusual genetic architecture or strong selective pressure on that particular trait in that particular lineage.

Data Availability

Raw sequence data have been deposited NCBI GenBank under BioProject number PRJNA353865: SRP099259.

Author Contributions

BS and NT conceived the study. NT, ZT, BM, and AW analyzed the data and made figures. NT, NF, and CG collected the samples and data. NT, ZT, and BS wrote the manuscript. All authors have edited and approved the manuscript.

Funding

This research was funded by a Natural Sciences and Engineering Research Council (NSERC) Discovery grant and a Fonds de Recherche du Québec - Nature et Technologies (FRQNT) New Researcher grant to BS, a FRQNT Programme de Recherche en Partenariat sur les Cyanobactéries grant to CG, and the federal government interdepartmental Genomics Research and Development Initiative (GRDI). NT is funded by a project from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 656647.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank Francis Hui, Andrew Letten, and David Warton for advice on the LVM analyses, and the two peer reviewers for their constructive comments that improved the manuscript. We also thank everyone who participated in sampling, data collection, and analysis.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2018.00438/full#supplementary-material

Footnotes

  1. ^ https://github.com/almlab/SmileTrain/wiki
  2. ^ http://www.drive5.com/usearch/
  3. ^ https://github.com/McMahonLab/TaxAss
  4. ^ https://github.com/ShadeLab/ConditionallyRareTaxa

References

Aitchison, J. (1986). The Statistical Analysis of Compositional Data. London: Chapman and Hall, 416.

Google Scholar

Amend, A. S., Martiny, A. C., Allison, S. D., Berlemont, R., Goulden, M. L., Lu, Y., et al. (2016). Microbial response to simulated global change is phylogenetically conserved and linked with functional potential. ISME J. 10, 109–118. doi: 10.1038/ismej.2015.96

PubMed Abstract | CrossRef Full Text | Google Scholar

Andersson, A., Höglander, H., Karlsson, C., and Huseby, S. (2015). Key role of phosphorus and nitrogen in regulating cyanobacterial community composition in the northern Baltic Sea. Estuar. Coast. Shelf Sci. 164, 161–171. doi: 10.1016/j.ecss.2015.07.013

CrossRef Full Text | Google Scholar

Becraft, E. D., Cohan, F. M., Kuhl, M., Jensen, S. I., and Ward, D. M. (2011). Fine-scale distribution patterns of Synechococcus ecological diversity in microbial mats of Mushroom Spring, Yellowstone National Park. Appl. Environ. Microbiol. 77, 7689–7697. doi: 10.1128/AEM.05927-5911

PubMed Abstract | CrossRef Full Text | Google Scholar

Becraft, E. D., Wood, J. M., Rusch, D. B., Kühl, M., Jensen, S. I., Bryant, D. A., et al. (2015). The molecular dimension of microbial species: 1. Ecological distinctions among, and homogeneity within, putative ecotypes of Synechococcus inhabiting the cyanobacterial mat of Mushroom Spring, Yellowstone National Park. Front. Microbiol. 6:590. doi: 10.3389/fmicb.2015.00590

PubMed Abstract | CrossRef Full Text | Google Scholar

Berry, M. A., White, J. D., Davis, T. W., Jain, S., Johengen, T. H., Dick, G. J., et al. (2017). Are oligotypes meaningful ecological and phylogenetic units? A case study of Microcystis in freshwater lakes. Front. Microbiol. 8:365. doi: 10.3389/fmicb.2017.00365

PubMed Abstract | CrossRef Full Text | Google Scholar

Cadotte, M. W., and Tucker, C. M. (2017). Should environmental filtering be abandoned? Trends Ecol. Evol. 32, 429–437. doi: 10.1016/j.tree.2017.03.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Cavender-Bares, J., Kozak, K. H., Fine, P. V. A., and Kembel, S. W. (2009). The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715. doi: 10.1111/j.1461-0248.2009.01314.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Cohan, F. M., and Koeppel, A. F. (2008). The origins of ecological diversity in Prokaryotes. Curr. Biol. 18, R1024–R1034. doi: 10.1016/j.cub.2008.09.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Coleman, M. L., and Chisholm, S. W. (2010). Ecosystem-specific selection pressures revealed through comparative population genomics. Proc. Natl. Acad. Sci. U.S.A. 107, 18634–18639. doi: 10.1073/pnas.1009480107

PubMed Abstract | CrossRef Full Text | Google Scholar

Dai, R., Liu, H., Qu, J., Zhao, X., and Hou, Y. (2009). Effects of amino acids on microcystin production of the Microcystis aeruginosa. J. Hazard. Mater. 161, 730–736. doi: 10.1016/j.jhazmat.2008.04.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Dolman, A. M., Rücker, J., Pick, F. R., Fastner, J., Rohrlack, T., Mischke, U., et al. (2012). Cyanobacteria and cyanotoxins: the influence of nitrogen versus phosphorus. PLoS One 7:e38757. doi: 10.1371/journal.pone.0038757

PubMed Abstract | CrossRef Full Text | Google Scholar

Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. doi: 10.1093/bioinformatics/btq461

PubMed Abstract | CrossRef Full Text | Google Scholar

Eren, A. M., Morrison, H. G., Vineis, J. H., Reveillaud, J., Sogin, M. L., and Lescault, P. J. (2014). Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J. 9, 968–979. doi: 10.1038/ismej.2014.195

PubMed Abstract | CrossRef Full Text | Google Scholar

Friedman, J., and Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8:e1002687. doi: 10.1371/journal.pcbi.1002687

PubMed Abstract | CrossRef Full Text | Google Scholar

Gauze, G. F. (1934). The Struggle for Existence. Baltimore, MD: The Williams & Wilkins Company.

Google Scholar

Gloor, G. B., and Reid, G. (2016). Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62, 692–703. doi: 10.1139/cjm-2015-0821

PubMed Abstract | CrossRef Full Text | Google Scholar

Gray, N. D., Comaskey, D., Miskin, I. P., Pickup, R. W., Suzuki, K., and Head, I. M. (2004). Adaptation of sympatric Achromatium spp. to different redox conditions as a mechanism for coexistence of functionally similar sulphur bacteria. Environ. Microbiol. 6, 669–677. doi: 10.1111/j.1462-2920.2004.00607.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Hajdu, S., Höglander, H., and Larsson, U. (2007). Phytoplankton vertical distributions and composition in Baltic Sea cyanobacterial blooms. Harmful Algae 6, 189–205. doi: 10.1016/j.hal.2006.07.006

CrossRef Full Text | Google Scholar

Harke, M. J., Davis, T. W., Watson, S. B., and Gobler, C. J. (2016). Nutrient-controlled niche differentiation of western Lake Erie cyanobacterial populations revealed via metatranscriptomic surveys. Environ. Sci. Technol. 50, 604–615. doi: 10.1021/acs.est.5b03931

PubMed Abstract | CrossRef Full Text | Google Scholar

Homma, T., Komatsu, N., Negishi, M., Katagami, Y., Nakamura, K., and Park, H. D. (2008). “Influence of dissolved inorganic nitrogen and phosphorus concentrations on the horizontal and temporal changes of Microcystis population in Lake Kitaura,” in Proceedings of the XII World Lake Conference, Taal, 1423–1429.

Horner-Devine, M. C., and Bohannan, B. J. M. (2006). Phylogenetic clustering and overdispersion in bacterial communities. Ecology 87, S100–S108.

Google Scholar

Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D., and Warton, D. I. (2015). Model-based approaches to unconstrained ordination. Methods Ecol. Evol. 6, 399–411. doi: 10.1111/2041-210X.12236

CrossRef Full Text | Google Scholar

Hunt, D. E., David, L. A., Gevers, D., Preheim, S. P., Alm, E. J., and Polz, M. F. (2008). Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 320, 1081–1085. doi: 10.1126/science.1157890

PubMed Abstract | CrossRef Full Text | Google Scholar

Hutchinson, G. E. (1957). Concluding remarks. Cold. Spring Harb. Symp. Quant. Biol. 22, 415–427. doi: 10.1101/SQB.1957.022.01.039

CrossRef Full Text | Google Scholar

Jaspers, E., and Overmann, J. (2004). Ecological significance of microdiversity: identical 16s rRNA gene sequences can be found in bacteria with highly divergent genomes and ecophysiologies. Appl. Environ. Microbiol. 70, 4831–4839. doi: 10.1128/AEM.70.8.4831-4839.2004

PubMed Abstract | CrossRef Full Text | Google Scholar

Jezbera, J., Jezberová, J., Brandt, U., and Hahn, M. W. (2011). Ubiquity of Polynucleobacter necessarius subspecies asymbioticus results from ecological diversification. Environ. Microbiol. 13, 922–931. doi: 10.1111/j.1462-2920.2010.02396.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Koeppel, A. F., and Wu, M. (2014). Species matter: the role of competition in the assembly of congeneric bacteria. ISME J. 8, 531–540. doi: 10.1038/ismej.2013.180

PubMed Abstract | CrossRef Full Text | Google Scholar

Konopka, A., and Brock, T. D. (1978). Effect of temperature on blue-green algae (Cyanobacteria) in Lake Mendota. Appl. Environ. Microbiol. 36, 572–576.

PubMed Abstract | Google Scholar

Letten, A. D., Keith, D. A., Tozer, M. G., and Hui, F. K. C. (2015). Fine-scale hydrological niche differentiation through the lens of multi-species co-occurrence models. J. Ecol. 103, 1264–1275. doi: 10.1111/1365-2745.12428

CrossRef Full Text | Google Scholar

Louati, I., Pascault, N., Debroas, D., Bernard, C., Humbert, J.-F., and Leloup, J. (2015). Structural diversity of bacterial communities associated with bloom-forming freshwater cyanobacteria differs according to the cyanobacterial Genus. PLoS One 10:e0140614. doi: 10.1371/journal.pone.0140614

PubMed Abstract | CrossRef Full Text | Google Scholar

Martiny, J. B. H., Jones, S. E., Lennon, J. T., and Martiny, A. C. (2015). Microbiomes in light of traits: a phylogenetic perspective. Science 350:aac9323. doi: 10.1126/science.aac9323

PubMed Abstract | CrossRef Full Text | Google Scholar

Moffit, M. C., and Neilan, B. A. (2004). Characterization of the nodularin synthetase gene cluster and proposed theory of the evolution of cyanobacterial hepatotoxins. Appl. Environ. Microbiol. 70, 6353–6362.

PubMed Abstract | Google Scholar

Moisander, P. H., Ochiai, M., and Lincoff, A. (2009). Nutrient limitation of Microcystis aeruginosa in northern California Klamath River reservoirs. Harmful Algae 8, 889–897. doi: 10.1016/j.hal.2009.04.005

CrossRef Full Text | Google Scholar

Monchamp, M.-E., Pick, F. R., Beisner, B. E., and Maranger, R. (2014). Nitrogen forms influence microcystin concentration and composition via changes in cyanobacterial community structure. PLoS One 9:e85573. doi: 10.1371/journal.pone.0085573

PubMed Abstract | CrossRef Full Text | Google Scholar

Neuenschwander, S. M., Pernthaler, J., Posch, T., and Salcher, M. M. (2015). Seasonal growth potential of rare lake water bacteria suggest their disproportional contribution to carbon fluxes. Environ. Microbiol. 17, 781–795. doi: 10.1111/1462-2920.12520

PubMed Abstract | CrossRef Full Text | Google Scholar

Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D., and Bertilsson, S. (2011). A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49. doi: 10.1128/MMBR.00028-10

PubMed Abstract | CrossRef Full Text | Google Scholar

Olli, K., Klais, R., and Tamminen, T. (2015). Rehabilitating the cyanobacteria – niche partitioning, resource use efficiency and phytoplankton community structure during diazotrophic cyanobacterial blooms. J. Ecol. 103, 1153–1164. doi: 10.1111/1365-2745.12437

PubMed Abstract | CrossRef Full Text | Google Scholar

Paerl, H. W., Fulton, R. S., Moisander, P. H., and Dyble, J. (2001). Harmful freshwater algal blooms, with an emphasis on cyanobacteria. ScientificWorldJournal 1, 76–113. doi: 10.1100/tsw.2001.16

PubMed Abstract | CrossRef Full Text | Google Scholar

Paliy, O., and Shankar, V. (2016). Application of multivariate statistical techniques in microbial ecology. Mol. Ecol. 25, 1032–1057. doi: 10.1111/mec.13536

PubMed Abstract | CrossRef Full Text | Google Scholar

Rantala, A., Fewer, D. P., Hisbergues, M., Rouhiainen, L., Vaitomaa, J., Börner, T., et al. (2004). Phylogenetic evidence for the early evolution of microcystin synthesis. Proc. Natl. Acad. Sci. U.S.A. 101, 568–573. doi: 10.1073/pnas.0304489101

PubMed Abstract | CrossRef Full Text | Google Scholar

Rashidan, K. K., and Bird, D. F. (2001). Role of predatory bacteria in the termination of a cyanobacterial bloom. Microb. Ecol. 41, 97–105. doi: 10.1007/s002480000074

PubMed Abstract | CrossRef Full Text | Google Scholar

Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., et al. (2009). Introducing Mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541. doi: 10.1128/AEM.01541-09

PubMed Abstract | CrossRef Full Text | Google Scholar

Shade, A., Jones, S. E., Caporaso, J. G., Handelsman, J., Knight, R., Fierer, N., et al. (2014). Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. mBio 5:e01371-14. doi: 10.1128/mBio.01371-14

PubMed Abstract | CrossRef Full Text | Google Scholar

Silverman, J. D., Washburne, A. D., Mukherjee, S., and David, L. A. (2017). A phylogenetic transform enhances analysis of compositional microbiota data. eLife 6:e21887. doi: 10.7554/eLife.21887

PubMed Abstract | CrossRef Full Text | Google Scholar

Šimek, K., Kasalický, V., Horňák, K., Hahn, M. W., and Weinbauer, M. G. (2010). Assessing niche separation among coexisting Limnohabitans strains through interactions with a competitor, viruses, and a bacterivore. Appl. Environ. Microbiol. 76, 3762–3762. doi: 10.1128/AEM.00774-10

PubMed Abstract | CrossRef Full Text | Google Scholar

Suikkanen, S., Pulina, S., Engström-Öst, J., Lehtiniemi, M., Lehtinen, S., and Brutemark, A. (2013). Climate change and eutrophication induced shifts in northern summer plankton communities. PLoS One 8:e66475. doi: 10.1371/journal.pone.0066475

PubMed Abstract | CrossRef Full Text | Google Scholar

Tilman, D. (1982). Resource Competition and Community Structure. Princeton, NJ: Princeton University Press.

Google Scholar

Tromas, N., Fortin, N., Bedrani, L., Terrat, Y., Cardoso, P., Bird, D., et al. (2017). Characterizing and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course. ISME J. 11, 1746–1763. doi: 10.1038/ismej.2017.58

PubMed Abstract | CrossRef Full Text | Google Scholar

Warton, D. I., Blanchet, F. G., O’Hara, R. B., Ovaskainen, O., Taskinen, S., Walker, S. C., et al. (2015). So many variables: joint modeling in community ecology. Trends Ecol. Evol. 30, 766–779. doi: 10.1016/j.tree.2015.09.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Willis, A., and Bunge, J. (2015). Estimating diversity via frequency ratios. Biometrics 71, 1042–1049. doi: 10.1111/biom.12332

PubMed Abstract | CrossRef Full Text | Google Scholar

Willis, A., Bunge, J., and Whitman, T. (2017). Improved detection of changes in species richness in high diversity microbial communities. J. R. Stat. Soc. C 66, 963–977. doi: 10.1111/rssc.12206

CrossRef Full Text | Google Scholar

Yamamoto, Y., and Tsukada, H. (2009). Measurement of in situ specific growth rates of Microcystis (cyanobacteria) from the frequency of dividing cells. J. Phycol. 45, 1003–1009. doi: 10.1111/j.1529-8817.2009.00723.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: cyanobacteria, ecological niche, niche partitioning, habitat filtering, competition, Microcystis, Dolichospermum

Citation: Tromas N, Taranu ZE, Martin BD, Willis A, Fortin N, Greer CW and Shapiro BJ (2018) Niche Separation Increases With Genetic Distance Among Bloom-Forming Cyanobacteria. Front. Microbiol. 9:438. doi: 10.3389/fmicb.2018.00438

Received: 29 November 2017; Accepted: 26 February 2018;
Published: 27 March 2018.

Edited by:

Ana Beatriz Furlanetto Pacheco, Universidade Federal do Rio de Janeiro, Brazil

Reviewed by:

David Baltrus, University of Arizona, United States
Frederick M. Cohan, Wesleyan University, United States

Copyright © 2018 Tromas, Taranu, Martin, Willis, Fortin, Greer and Shapiro. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nicolas Tromas, nicolas.tromas@umontreal.ca B. Jesse Shapiro, jesse.shapiro@umontreal.ca

These authors have contributed equally to this work.