Periphyton and Flocculent Materials Are Important Ecological Compartments Supporting Abundant and Diverse Mercury Methylator Assemblages in the Florida Everglades

Abstract

Mercury (Hg) methylation in the Florida Everglades is of great environmental concern because of its adverse effects on human and wildlife health through biomagnification in aquatic food webs. Periphyton and flocculant materials (floc) overlaying peat soil are important ecological compartments producing methylmercury (MeHg) in this ecosystem. These compartments retain higher concentrations of MeHg than did soil at study sites across nutrient and/or sulfate gradient(s). To better understand what controls Hg methylation in these compartments, the present study explored the structures and abundances of Hg methylators using genes hgcAB as biomarkers. The hgcA sequences indicated that these compartments hosted a high diversity of Hg methylators, including Deltaproteobacteria, Chloroflexi, Firmicutes, and Methanomicrobia, with community compositions that differed between these habitats. The copy numbers of hgcAB quantified by quantitative PCR revealed that floc and soil supported higher numbers of Hg methylators than periphyton in the Everglades ecosystem. The abundance of Hg methylators was strongly positively correlated with concentrations of carbon and nutrients (e.g., phosphorus and nitrogen) according to redundancy analysis. Strong correlations were also observed among numbers of sulfate reducers, methanogens, and the dominant hgcAB-carrying groups, suggesting that hgcAB would spread primarily through the growth of those assemblages. The abundances of Hg methylators were weakly negatively correlated to MeHg concentrations, suggesting that the size of this population would not solely determine the final concentrations of MeHg in the ecological compartments studied. This study extends the knowledge regarding the distribution of diverse potential mercury methylators in different environmental compartments in a wetland of national concern.IMPORTANCE Methylmercury is a potent neurotoxin that impacts the health of humans and wildlife. Most mercury in wetlands such as the Florida Everglades enters as inorganic mercury via atmospheric deposition, some of which is transformed to the more toxic methylmercury through the activities of anaerobic microorganisms. We investigated the numbers and phylogenetic diversity of hgcAB, genes that are linked to mercury methylation, in the soil, floc, and periphyton in areas of the Everglades with different sulfate and nutrient concentrations. Soil harbored relatively high numbers of cells capable of methylating mercury; however, little detectable methylmercury was present in soil. The greatest concentrations of methylmercury were found in floc and periphyton. The dominant methylators in those compartments included methanogens and Syntrophobacteriales This work provides significant insight into the microbial processes that control methylation and form the basis for accumulation through the food chain in this important environment.

Keywords: flocculent; hgcAB; methylmercury; peat; periphyton; wetlands.

FIG 1

MeHg concentrations in periphyton, floc, and soil across sites F2 and U3 within WCA-2A and DB14 and DB15 within WCA-3A. Error bars in each data point (circle symbol) represent ±1 standard deviation (n ≥ 3). Box-and-whisker plots were generated from the pooled data obtained from each compartment at individual sites. Boxes show the medians (horizontal lines in the boxes) and the lower and upper quartiles (bottoms and tops of the boxes, respectively). The vertical bars (whiskers) on the box plots represent the maximum and minimum values, excluding outliers. Statistical differences between compartments are indicated by capital letters in the panel representing compartments. The difference between sites for each compartment is represented by a small letter(s). Different letters between compartments or between sites mean a significant difference (P ≤ 0.05 by the Tukey-Kramer HSD test in ANOVA).

FIG 2

Phylogeny of Hg methylators detected using hgcAB as a gene marker from the periphyton, floc, and peat. (Left) The phylogeny was determined by maximum likelihood constructed using HgcAB protein sequences deduced from 774 hgcAB sequences collected in the Everglades samples through our previous (220 sequences) (11) and current (554 sequences) studies. (Right) Pie graphs represent the composition of major taxa in the ecological compartments of the sulfate-enriched site U3 within WCA-2A and the sulfate-depleted site DB15 within WCA-3A.

FIG 3

PCoA plot showing the beta diversity of Hg methylator assemblages between three ecological compartments (i.e., soil, floc, and periphyton). The plot was constructed based on UniFrac weighted distance matrix.

FIG 4

Numbers of hgcAB copies from Syntrophobacteriales and methanogenic OTUs hgcAB_MGXa-c in periphyton, floc, and soil sampled in January/February (denoted J), May (M), and November/December (D) 2015. Error bars on each data point (circle symbol) represent ±1 standard error (n = 3). Box-and-whisker plots were constructed from the pooled data within each compartment. Boxes depict the medians (horizontal lines in the boxes) and the lower and upper quartiles (bottoms and tops of the boxes, respectively). The vertical bars (whiskers) show the highest and the lowest values, excluding outliers. Significant differences between compartments in individual sites are denoted on the boxes with letters; different letters mean significant differences (P < 0.05 by the Tukey-Kramer HSD test). The significant differences between compartments across all sites are denoted on the right side of the graph.

FIG 5

Redundancy analysis (RDA) plot representing the relationship between gene copies of hgcAB, dsrB, and mcrA, geochemical parameters, and MeHg concentrations determined in this study. Arrows pointing in the same direction indicate positive correlations, and arrows pointing in opposite directions indicate negative correlations. The arrow length corresponds to the variance explained by the environmental variable. The first two axes explain 82.3% of the total canonical eigenvalues with a significant Monte-Carlo test value (P < 0.001). SO4_PW, sulfate concentration in porewater; SO4_SW, sulfate concentration in surface water; TCa, total calcium concentration.