Aquatic Macrophytes Are Associated With Variation in Biogeochemistry and Bacterial Assemblages of Mountain Lakes

Abstract

In aquatic systems, microbes likely play critical roles in biogeochemical cycling and ecosystem processes, but much remains to be learned regarding microbial biogeography and ecology. The microbial ecology of mountain lakes is particularly understudied. We hypothesized that microbial distribution among lakes is shaped, in part, by aquatic plant communities and the biogeochemistry of the lake. Specifically, we investigated the associations of yellow water lilies (Nuphar polysepala) with the biogeochemistry and microbial assemblages within mountain lakes at two scales: within a single lake and among lakes within a mountain range. We first compared the biogeochemistry of lakes without water lilies to those colonized to varying degrees by water lilies. Lakes with >10% of the surface occupied by water lilies had lower pH and higher dissolved organic carbon than those without water lilies and had a different microbial composition. Notably, cyanobacteria were negatively associated with water lily presence, a result consistent with the past observation that macrophytes outcompete phytoplankton and can suppress cyanobacterial and algal blooms. To examine the influence of macrophytes on microbial distribution within a lake, we characterized microbial assemblages present on abaxial and adaxial water lily leaf surfaces and in the water column. Microbial diversity and composition varied among all three habitats, with the highest diversity of microbes observed on the adaxial side of leaves. Overall, this study suggests that water lilies influence the biogeochemistry and microbiology of mountains lakes.

Keywords: Nuphar polysepalum; biogeochemistry; macrophytes; microbes; mountain lakes; water lilies.

FIGURE 1

Average pH (A) and dissolved organic carbon (B) in each lake. Error bars (where visible) represent standard deviation.

FIGURE 2

Point estimates for microbial richness estimated with Breakaway (A) and Shannon Diversity (B) for water samples from all 12 lakes.

FIGURE 3

NMDS ordination of Bray-Curtis dissimilarities for microbial communities from all 12 lakes (A). Biogeochemical parameters overlayed on an NMDS ordination of microbial communities for all lakes except Hanging Lake, a non-water lily lake with elevated pH and ion concentrations (B). Biogeochemical measures significantly associated with the ordination (p < 0.01) are shown where the length of the arrow is proportional to the correlation coefficient (2r).

FIGURE 4

Phylum level taxonomic composition in each lake. Lakes are divided into those without water lilies (No) and with water lilies (Yes). Lakes with water lilies are ordered from left to right from lowest to highest water lily surface coverage. Only phyla with mean relative abundance greater than 0.01% across all samples are included and the phylum Proteobacteria has been broken down by subphylum (A). Beta coefficient estimates from the corncob regression model for differentially abundant families that met our three criteria: (1) significantly differentially abundant between lakes with none and many water lilies, (2) the effect of few and many water lilies was the same direction, and (3) the magnitude of the effect of many water lilies was greater than the effect of few water lilies. Negative estimates indicate taxa more abundant in lakes with no water lilies while positive estimates indicate taxa more abundant in lakes with many water lilies. Individual families are represented by points and are grouped by phylum (B).

FIGURE 5

Point estimates for microbial richness estimated with Breakaway (A), Shannon Diversity (B), NMDS ordination of Bray-Curtis dissimilarities (colors correspond to the legend shown in the top row) (C), and Phylum level taxonomic composition (D) for microbial samples collected from leaf surface and the water column within Long Lake. Panel (D) only includes phyla with mean relative abundance greater than 0.1% across all samples and the phylum Proteobacteria has been broken down by subphylum.

FIGURE 6

Beta coefficient estimates from the corncob regression model for differentially abundant families for each pairwise comparison between sample types in Long Lake: Abaxial (bottom) vs. adaxial (top) leaf surfaces, where negative beta estimates indicate taxa more abundant on the bottom while positive estimates indicate taxa more abundant top (A), abaxial leaf surface vs. water, where negative beta estimates indicate taxa more abundant on the bottoms of leaves while positive estimates indicate taxa more abundant in the water (B), adaxial leaf surface vs. water where negative beta estimates indicate taxa more abundant on the tops of leaves while positive estimates indicate taxa more abundant on adaxial surfaces (C), and high vs. low in the water column across all samples where negative beta estimates indicate taxa more abundant on the surface and positive estimates indicate taxa more abundant lower in the water column (D). Individual families are represented by points and are grouped by phylum.