Freshwater carbon and nutrient cycles revealed through reconstructed population genomes

Sampling

Samples were collected from Lake Mendota and Trout Bog Lake as previously described (Bendall et al., 2016). Briefly, integrated samples of the water column were collected during the ice-free periods of 2007–2009 in Trout Bog and 2008–2012 in Mendota. In Mendota, the top 12 m of the water column were sampled, approximating the epilimnion (upper, oxygenated, and warm thermal layer). The epilimnion and hypolimnion (bottom, anoxic, and cold thermal layer) of Trout Bog were sampled separately at depths determined by measuring temperature and dissolved oxygen concentrations. The sampling depths were most often zero to two meters for the epilimnion and two to seven meters for the hypolimnion. DNA was collected by filtering 150 mL of the integrated water samples through 0.2-μm pore size polyethersulfone Supor filters (Pall Corp., Port Washington, NY, USA). Filters were stored at −80 °C until extraction using the FastDNA Spin Kit (MP Biomedicals, Burlingame, CA, USA) with minor modifications (Shade et al., 2007).

Sequencing

As previously described (Bendall et al., 2016; Roux et al., 2017), metagenomic sequencing was performed by the Department of Energy Joint Genome Institute (DOE JGI) (Walnut Creek, CA, USA). A total of 94 samples collected over 5 years were sequenced for Mendota, while 47 metagenomes collected over 3 years were sequenced for each layer in Trout Bog (Table S2). Samples were sequenced on the Illumina HiSeq 2500 platform (Illumina, San Diego, CA, USA), except for four libraries (two from each layer of Trout Bog) that were sequenced using the Illumina TruSeq protocol on the Illumina GAIIx platform; all samples were sequenced using paired ends with read lengths of 150 base pairs (Data S1). Paired-end sequencing reads were merged with FLASH v1.0.3 with a mismatch value of less than 0.25 and a minimum of 10 overlapping bases (Magooc & Salzberg, 2011). 16S rRNA gene amplicon sequencing was also performed on samples collected with the same method over the same time periods. These datasets are available under DOE JGI project IDs 1078703 and 1018581 for Trout Bog and Mendota, respectively. Samples from Trout Bog were sequenced on the 454 GS FLX-Titanium platform (Roche, Branford, CT, USA) targeting the V8 hypervariable region (primer 1392R: ACGGGCGGTGTGTRC) (Engelbrektson et al., 2010), and sequences were trimmed to 324 base pairs using VSEARCH (v2.3.4) (Rognes et al., 2016). Samples from Mendota were sequenced on an Illumina MiSeq, and the V4 region was targeted using paired-end sequencing (primers 525F: GTGCCAGCMGCCGCGGTAA and 806R: GGACTACHVGGGTWTCTAAT) (Caporaso et al., 2012). Both datasets were trimmed based on alignment quality and chimera checking using mothur v.1.39.5 (Schloss et al., 2009). Unclustered, unique sequences were classified using a custom database of freshwater 16S rRNA gene sequences (Newton et al., 2011) and the Greengenes database (DeSantis et al., 2006) with the classification pipeline TaxAss (Rohwer et al., 2018).

Assembly and binning

To recover MAGs, metagenomic reads from the same sampling sites (Mendota’s epilimnion, Trout Bog’s epilimnion, and Trout Bog’s hypolimnion) were pooled (Table S2) and then assembled as previously described (Bendall et al., 2016; Roux et al., 2017). In metagenomes from Trout Bog, this assembly was performed using SOAPdenovo2 at various k-mer sizes (Luo et al., 2012), and the resulting contigs were combined using Minimus (Sommer et al., 2007). In Mendota, merged reads were assembled using Ray v2.2.0 with a single k-mer size (Boisvert et al., 2012). Contigs from the combined assemblies were binned using MetaBAT (“-veryspecific” settings, minimum bin size of 20 kb, and minimum contig size of 2.5 kb) (Kang et al., 2015), and reads from individual metagenomes were mapped to the assembled contigs using the Burrows–Wheeler Aligner (≥95% sequence identity, n = 0.05) (Li & Durbin, 2010), which allowed time-series resolved binning (Table S2). DOE JGI’s Integrated Microbial Genome (IMG) database tool (https://img.jgi.doe.gov/mer/) (Markowitz et al., 2012) was used for gene prediction and annotation. Annotated MAGs can be retrieved directly from the IMG database and JGI’s Genome Portal using the IMG Genome ID provided (also known as IMG Taxon ID). MAG completeness and contamination/redundancy was estimated based on the presence of a core set of genes with CheckM (Rinke et al., 2013; Parks et al., 2015), and MAGs were taxonomically classified using Phylosift (Darling et al., 2014) or the phylogeny-based “guilt by association” method (Hamilton et al., 2017). As recommended by Bowers et al. (2017), only MAGs that were at least approximately 50% complete with less than 10% estimated contamination/redundancy (meeting the MIMARKS definition of a medium or high quality MAG) (Bowers et al., 2017) were included in the study.

A total of 193 medium to high quality bacterial MAGs were recovered from the three combined time series metagenomes in Trout Bog and Mendota: 99 from Mendota, 31 from Trout Bog’s epilimnion, and 63 from Trout Bog’s hypolimnion (Data S2). These population genomes ranged in estimated completeness from 50 to 99% based on CheckM estimates. Several MAGs from Trout Bog’s epilimnion and hypolimnion appeared to belong to the same population based on average nucleotide identities greater than 99% calculated using DOE JGI’s ANI calculator (Data S3) (Varghese et al., 2015). This is likely because assembly and binning were carried out separately for each thermal layer, even though some populations were present throughout the water column.

Functional marker gene analysis

To analyze functional marker genes in the unassembled, unpooled metagenomes, we used a curated database of reference protein sequences (Data S4) (Anantharaman et al., 2016) and identified open reading frames (ORFs) in our unassembled metagenomic time series using Prodigal (Hyatt et al., 2010). This analysis was conducted on merged reads. The protein sequences and ORFs were compared using BLASTx (Camacho et al., 2009) with a cutoff of 30% identity. Read abundance was normalized by metagenome size for plotting. We chose to perform this analysis because gene content in unassembled metagenomes is likely more quantitative and more representative of the entire microbial community than gene content in the MAGs, due to limitations of assembly and binning algorithms.

These comparisons were run between the epilimnia of Trout Bog and Mendota, and between the epilimnion and hypolimnion of Trout Bog. We did not compare Mendota’s epilimnion to Trout Bog’s hypolimnion, as the multitude of factors differing between these two sites make this comparison illogical. We aggregated marker genes by function (as several marker genes from a phylogenetic range were included in the database for each type of function) and tested for significant differences in distribution between lakes and layers using a Wilcoxon rank sum test in R with a Bonferroni correction for multiple pairwise testing.

Pathway prediction

Pathways were analyzed by exporting IMG’s functional annotations for the MAGs, including KEGG, COG, PFAM, and TIGRFAM annotations, and mapping to pathways in the KEGG and MetaCyc databases as previously described (He et al., 2017). To score presence, a pathway needed at least 50% of the required enzymes encoded by genes in a MAG, and if there were steps unique to a pathway, at least one gene encoding each unique step. Putative pathway presence was aggregated by lake and phylum in order to link potential functions identified in the metagenomes to taxonomic groups that may perform those functions in each lake. Glycoside hydrolases were identified using dbCAN2’s implementation of HMMER (Zhang et al., 2018). Nitrogen usage in amino acids was calculated by taking the average number of nitrogen atoms in translated ORF sequences across each MAG.

Data formatting and plotting was performed in R (R Core Team, 2017) using the following packages: ggplot2 (Wickham, 2009.), cowplot (Wilke, 2017), reshape2 (Wickham, 2007), and APE (Paradis, Claude & Strimmer, 2004). The datasets, scripts, and intermediate files used to predict pathway presence and absence are available at https://github.com/McMahonLab/MAGstravaganza. Any future updates or refinements to this dataset will be available at this link.

Community functional marker gene analysis

Due to the contrasting water chemistry of Mendota and Trout Bog (Table 1; Table S1), we expected that microbial metabolisms would differ between lakes, and that these differences would be reflected in metagenomic gene content. To assess the potential for differing microbial metabolisms by lake, we tested whether functional marker genes identified in the unassembled merged metagenomic reads appeared more frequently in one lake or layer compared to the others. Many functional markers were found to be significantly more abundant in specific sites; more will be reported in each of the following sections (Fig. 1; Table S3). The recovered MAGs represent a diverse set of genomes assigned to taxonomic groups typically observed in freshwater (Fig. S2).

Overview of the MAGs dataset

To identify the phylogenetic affiliations of the microbes carrying marker genes and the co-occurrences of key marker genes within the same population genomes, we used MAGs from each metagenomic time series to predict metabolic pathways based on genomic content. To assess the diversity of our MAGs, we constructed an approximate maximum likelihood tree of all the MAGs in FastTree (Price, Dehal & Arkin, 2010) using whole genome alignments (Fig. S1). The tree is not intended to infer detailed evolutionary history, but to provide an overall picture of similarity between genomes. MAGs recovered are a diverse set of genomes assigned to taxa typically observed in freshwater (Fig. S2).

We also compared 16S rRNA gene amplicon sequencing data from the same timeframe as the metagenomes to confirm that the microbial community composition for these lakes and years was not “abnormal” compared to previous published studies (Fig. S3). The observed taxonomic compositions were consistent with other 16S-based studies carried out on these lakes (Linz et al., 2017; Hall et al., 2017) and with freshwater bacterial community compositions in general (Newton et al., 2011).

Nitrogen cycling

Nitrogen availability is an important factor structuring freshwater microbial communities. It is often a determining factor in a lake’s trophic status and a risk factor for the development of toxic cyanobacterial blooms (Smith, 2003; Beversdorf, Miller & McMahon, 2013). Because of the significance of nitrogen in freshwater, we analyzed nitrogen-related marker genes and identified MAGs containing characteristic nitrogen cycling pathways. We discovered significant differences in the abundances of marker genes, along with differences in phylogenetic affiliations of the MAGs containing these pathways.

Genes encoding nitrogenase, the key enzyme in nitrogen fixation, were observed most frequently in metagenomes from Trout Bog’s hypolimnion, followed by Trout Bog’s epilimnion, and lastly by Mendota’s epilimnion (Fig. 1; Table S3). We analyzed MAGs predicted to fix nitrogen and found differences in the identities of putative diazotrophs between the two ecosystems (Fig. 2; Fig. S1 and Data S5). In Mendota, two-thirds of MAGs encoding the nitrogen fixation pathway were classified as Cyanobacteria, while the other third was assigned to Betaproteobacteria and Gammaproteobacteria. Although not all Cyanobacteria fix nitrogen, previous studies of nitrogen fixation in Mendota have reported a strong correlation between this pathway and the cyanobacterium affiliated with Aphanizomenon (Beversdorf, Miller & McMahon, 2013). MAGs containing genes encoding nitrogen fixation were more phylogenetically diverse in Trout Bog and included Deltaproteobacteria, Gammaproteobacteria, Epsilonproteobacteria, Acidobacteria, Verrucomicrobia, Chlorobi, and Bacteroidetes. The higher diversity of diazotrophs in Trout Bog compared to Mendota suggests that nitrogen fixation may be a more advantageous trait in humic lakes than in eutrophic lakes.

We noted a high frequency of genes related to polyamine biosynthesis and degradation in our MAGs. We found that 94% of MAGs encoded pathways for polyamine synthesis, and 87% encoded pathways for polyamine degradation. These pathways were predicted in diverse MAGs from both lakes, including Actinobacteria as previously observed (Ghylin et al., 2014; Hamilton et al., 2017). While there is some evidence for the importance of polyamines in aquatic systems (Mou et al., 2011), the ecological roles of these compounds in freshwater are not fully resolved. Polyamines are known to play a critical but poorly understood role in bacterial metabolism (Igarashi & Kashiwagi, 1999), and the exchange of these nitrogen compounds between populations may be a factor structuring freshwater microbial communities. Polyamines can also result from the decomposition of amino acids, so higher trophic levels such as fish or zooplankton may represent an additional polyamine source (Al Bulushi et al., 2009). The frequent appearance of polyamine-related pathways in our MAGs lends support to the hypothesis that these compounds are important but largely unrecognized parts of the dissolved organic nitrogen and carbon pool in freshwater.

We analyzed genes for denitrification, including reductases for nitrous oxide, nitrite, and nitrate. Denitrification genes were observed most frequently in Trout Bog’s hypolimnion, with the exception of nitrous oxide reductase, which was found more frequently in Mendota. Genes encoding urease were not identified more frequently in any site. Denitrification and urea degradation pathways were predicted in similar proportions of MAGs from both lakes.

Sulfur cycling

Sulfur is another essential element in freshwater that is cycled between oxidized and reduced forms by microbes. Our marker gene analysis demonstrated that genes encoding sulfide:quinone reductase (for sulfide oxidation) and the sox pathway (for thiosulfate oxidation) were significantly more abundant in Trout Bog compared to Mendota, with no significant differences between the layers of Trout Bog (Fig. 1; Table S3). Genes encoding sulfite reductases were the least abundant sulfur cycling marker genes in all sites. Dissimilatory sulfite reductase was observed only in MAGs from Trout Bog, especially those classified as Chlorobiales. Because this enzyme is thought to operate in reverse in green sulfur-oxidizing phototrophs such as Chlorobiales (Holkenbrink et al., 2011), this may indicate an oxidation process rather than a reductive sulfur pathway. Sulfur oxidation pathways were observed in MAGs classified as Betaproteobacteria from both lakes and Epsilonproteobacteria in Trout Bog’s hypolimnion. Assimilatory sulfate reduction was overall the most common sulfur-related pathway identified in the MAGs (Fig. 2; Data S5).

Assimilatory sulfate reduction was observed more frequently than dissimilatory sulfate reduction; this suggests that sulfate is more commonly used for biosynthesis, while reduced forms of sulfur are used as electron donors for energy mobilization in these populations. This is in contrast to marine systems, where sulfate reduction holds a central role as an energy source for organotrophic energy acquisition (Bowles et al., 2014), although sulfate reduction could also be occurring in Mendota’s hypolimnion.

Phototrophy

Primary production (the coupling of photosynthesis and carbon fixation) is a critical component of the freshwater carbon cycle. To identify differences in routes of primary production between freshwater environments, we compared marker genes for carbon fixation across sites. Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), the marker gene for carbon fixation via the Calvin–Benson–Bassham (CBB) pathway, was most frequently observed in Trout Bog’s epilimnion (Fig. 1; Table S3).

We assessed the MAGs for photoautotrophy, expecting to find differences between our two study sites based on the observed contrasts in the functional marker gene analysis (Fig. 2; Data S5). In Mendota, the majority of MAGs encoding phototrophic pathways were classified as Cyanobacteria. These MAGs contained genes encoding enzymes in the CBB pathway. In Trout Bog, most MAGs encoding phototrophy were classified as Chlorobium clathratiforme, a species of Chlorobiales widespread in humic lakes (Karhunen et al., 2013). The Chlorobiales MAGs in Trout Bog contained genes encoding citrate lyase and other key enzymes in the reductive tricarboxylic acid (TCA) cycle, an alternative carbon fixation method commonly found in green sulfur bacteria such as Chlorobi (Kanao et al., 2002; Tang & Blankenship, 2010). Although we found genes annotated as the RuBisCO large subunit (rbcL) in some of the Chlorobiales MAGs, the reductive TCA cycle is the only carbon fixation pathway known to be active in cultured representatives of Chlorobiales. Homologs of rbcL have been previously identified in isolates of Chlorobium, and were associated with sulfur metabolism and oxidative stress (Hanson & Tabita, 2001). Given this information, it seems likely that this rbcL homolog encodes a function other than carbon fixation in our Chlorobiales MAGs. MAGs affiliated with Cyanobacteria in Mendota and Chlorobi in Trout Bog also possessed genes encoding diazotrophy, providing a link between carbon and nitrogen fixation. As both Chlorobi and Cyanobacteria are often abundant members of freshwater communities (Eiler & Bertilsson, 2004; Peura et al., 2012), their fixation capabilities may be relevant even at the ecosystem scale.

The potential for photoheterotrophy via the aerobic anoxygenic phototrophic (AAP) pathway was identified in several MAGs from all lake environments, especially from epilimnia, based on the presence of genes annotated as pufABCLMX, puhA, and pucAB encoding the core reaction center RC-LH1 (Martinez-Garcia et al., 2012b). Betaproteobacteria and Gammaproteobacteria, particularly MAGs classified as Burkholderiales (including PnecC, LD28, and Zwartia alpina), most often contained these genes, although they were not broadly shared across the phylum (Fig. 2). As AAP has previously been associated with freshwater Proteobacteria (Martinez-Garcia et al., 2012b), these results are not surprising. However, an Acidobacteria MAG from the Trout Bog epilimnion also contained genes suggesting AAP, which to our knowledge has not previously been found in this phylum.

Another form of photoheterotrophy previously identified in freshwater is the use of light-activated proteins such as rhodopsins (Martinez-Garcia et al., 2012b). We observed genes encoding rhodopsins in MAGs from each lake environment, but more frequently in Actinobacteria and Bacteroidetes MAGs from Mendota (Fig. 2). Trout Bog, especially the hypolimnion, harbored fewer and less diverse MAGs encoding rhodopsins than those from Mendota.

Glycoside hydrolases

Degradation of high-complexity, recalcitrant carbon compounds requires specialized enzymes, but wide availability of these carbon compounds can make complex carbon degradation an advantageous trait. One way to predict the ability to degrade high-complexity carbon in microbial populations is by identifying genes annotated as glycoside hydrolases (GHs), which encode enzymes that break the glycosidic bonds found in complex carbohydrates. However, it is important to keep in mind that GHs can also play structural roles in microbial cells in addition to the degradation of complex carbon substrates (Henrissat & Davies, 1997). A previous study of Verrucomicrobia MAGs from our dataset found that the profiles of GHs differed between Mendota and Trout Bog, potentially reflecting the differences in available carbon sources (He et al., 2017). We expanded this analysis of GHs to all of the MAGs in our dataset to identify differences in how populations from our two study sites degrade complex carbohydrates.

We calculated the coding density of GHs, defined as the percentage of coding regions in a MAG annotated as a GH, to identify differences in carbon metabolism between MAGs from different lake environments (Fig. 3; Data S6). Our GH coding density metric was significantly correlated with the diversity of GHs identified (r² = 0.92, p < 2.2 × 10⁻¹⁶), which is an indicator of the number of substrates an organism can utilize. The MAGs with the highest GH coding densities were classified as Bacteroidales, Ignavibacteriales, Sphingobacteriales, and Verrucomicrobiales from Trout Bog’s hypolimnion. Two of these orders, Sphingobacteriales and Verrucomicrobiales, also contained MAGs with high GH coding densities in Mendota and Trout Bog’s epilimnion. There were several additional orders with high GH coding density that were unique to Mendota, including Mycoplasmatales (Tenericutes), Cytophagales (Bacteroidetes), Planctomycetales (Planctomycetes), and Puniceicoccales (Verrucomicrobia). Members of Verrucomicrobia have been previously identified as potential polysaccharide degraders in freshwater, although our coding densities for this phylum are higher than previously reported (Martinez-Garcia et al., 2012a). This may be due to differences in trophic status between our lakes and those previously studied, or it may be that MAGs capture more pan-genomic content than isolate or single amplified genomes. In concordance with their ability to hydrolytically degrade biopolymers to sugars, MAGs with high GH coding densities also contained putative degradation pathways for a variety of sugars (Fig. 2). The increased diversity of these genes found in Trout Bog’s hypolimnion compared to the other study sites suggests differing diversity and complexity of the available organic carbon.

Central metabolism and simple carbon degradation

Freshwater microbes are exposed to a great variety of low-complexity carbon sources such as carbohydrates, carboxylic acids, and single-carbon (C1) compounds. The central metabolic pathways shared by most living cells are often an entry point for the least complex carbon compounds. Therefore, the specific routing of central metabolism predicted in our MAGs may reveal how low complexity carbon compounds are used within freshwater populations.

We investigated the types of cytochrome oxidases encoded in our MAGs to compare oxidative phosphorylation between lakes and layers (Fig. 2; Data S5). Cytochrome c oxidases, both aa3- and cbb3-type, were widespread in all three lake environments and frequently co-occurred within MAGs. aa3-type cytochromes are associated with high oxygen concentrations, while cbb3-type cytochromes are associated with low oxygen concentrations (Gong et al., 2018). The presence of genes encoding both types suggests the flexibility to operate under a range of oxygen concentrations.

Similarly, hydrogen metabolism can influence and be influenced by other aspects of nutrient usage. Iron-only hydrogenases were found primarily in MAGs from Trout Bog’s hypolimnion (Fig. 2; Table S3), consistent with their previously identified presence in anaerobic, often fermentative bacteria (Peters et al., 2015). Group 3 [Ni–Fe] hydrogenases were identified in MAGs belonging to Cyanobacteria and Chlorobiales in both lakes. This finding is consistent with the proposed function of Group 3d, which is to remove excess electrons produced by photosynthesis (Peters et al., 2015).

Low molecular weight carbohydrates may be derived either from autochthonous (such as algae) or allochthonous (such as terrestrial plants) sources (Giroldo, Augusto & Vieira, 2005; Ramanan et al., 2016). The pathway for mannose degradation was encoded in many MAGs from all three sites (Fig. 2; Data S5). Predicted pathways for rhamnose, fucose, and galactose degradation were often found within the same MAGs (including members of Planctomycetes and Verrucomicrobia from Mendota, and members of Bacteroidetes, Ignavibacteria, and Verrucomicrobia from Trout Bog). Xylose is a common freshwater sugar which has already been proposed as a potential carbon source for streamlined Actinobacteria (Ghylin et al., 2014). We confirmed this in our MAGs and also identified Bacteroidetes, Planctomycetes, and Verrucomicrobia from Mendota and Bacteroidetes and Verrucomicrobia from Trout Bog as additional potential xylose degraders. Genes for the degradation of glycolate, an acid produced by algae and consumed by heterotrophic bacteria (Paver & Kent, 2010), were identified in Cyanobacteria and Betaproteobacteria MAGs from Mendota and in Acidobacteria, Verrucomicrobia, Alpha-, Beta-, Gamma-, and Epsilonproteobacteria MAGs from Trout Bog. The pathways predicted in our MAGs may inform us about which low molecular weight compounds are important carbon substrates in freshwater.

Methylotrophy, the ability to grow solely on C1 compounds such as methane or methanol, was predicted in MAGs from both Trout Bog and Mendota. Putative pathways for methanol and methylamine degradation were found in MAGs classified as Methylophilales (now merged with Nitrosomonadales; Boden, Hutt & Rae, 2017), while Methylococcales MAGs were potential methane degraders based on the presence of genes encoding methane monooxygenase. Methylococcales MAGs from Trout Bog also encoded the pathway for nitrogen fixation, consistent with reports of nitrogen fixation in cultured isolates of this taxon (Bowman, Sly & Stackebrandt, 1995). Methylotrophy in cultured freshwater isolates from Methylococcales and Nitrosomonadales is well-documented (Kalyuzhnaya et al., 2011; Salcher et al., 2015). We also found predicted pathways for methanol degradation in MAGs classified as Burkholderiales and Rhizobiales in Trout Bog. Methylotrophy has been identified in members of Rhizobiales, such as Methylobacterium and Methylocystaceae, and in Burkholderiales, including Methylibium (Auman et al., 2000; Chistoserdova et al., 2003; Kane et al., 2007). Our MAGs may represent populations related to these known methylotrophs.

Using MAGs to track population abundances over time

Because our metagenomes comprise a time series, we can investigate potential changes in function over time using our MAGs and functional marker genes. We analyzed nitrogen fixation over time in Cyanobacteria, known to be highly variable over time in Mendota. We found that in each year, one Cyanobacteria MAG was substantially more abundant (based on read coverage) than the rest; this single MAG was plotted for each year in Mendota (Figs. 4A–4E). We compared read coverage-based abundance of the dominant Cyanobacteria MAG to the normalized number of BLAST hits in the metagenomes from abundant functional marker genes encoding nitrogenase subunits (TIGR1282 (nifD), TIGR1286 (nifK specific for molybdenum–iron nitrogenase), and TIGR1287 (nifH, common among different types of nitrogenases)) (Figs. 4F–4J). As expected, we detected significant correlations (p < 0.05) between MAG abundance and nitrogen fixation marker genes in 2008, 2011, and 2012. In these years, the dominant Cyanobacteria MAGs were predicted to fix nitrogen based on gene content, while the dominant MAGs in 2009 and 2010 were not predicted to fix nitrogen. In agreement with this, the number of hits for the nitrogenase marker genes were an order of magnitude lower in 2009 and 2010 compared to 2008 and 2012. While genome incompleteness precludes us from concluding that the potential for nitrogen fixation in Mendota was lower in 2009 and 2010 because the dominant Cyanobacteria populations were not diazotrophic, it does suggest a strong link between Cyanobacteria dynamics and nitrogen fixation in this ecosystem (Beversdorf, Miller & McMahon, 2013). This could also have important implications for cyanotoxin production, since nitrogen stress has been linked to toxin production (Beversdorf et al., 2015).