Metals Alter Membership but Not Diversity of a Headwater Stream Microbiome.

Metal contamination from mining or natural weathering is a common feature of surface waters in the American west. Advances in microbial analyses have created the potential for routine sampling of aquatic microbiomes as a tool to assess the quality of stream habitat. We sought to determine if microbiome diversity and membership were affected by metal contamination and identify candidate microbial taxa to be used to indicate metal stress in stream ecosystems. We evaluated microbiome membership from sediments at multiple sites within the principal drainage of an EPA superfund site near the headwaters of the Upper Arkansas River, Leadville, CO. From each sample, we extracted DNA and sequenced the 16S rRNA gene amplicon on the Illumina MiSeq platform. We used the remaining sediments to simultaneously evaluate environmental metal concentrations. We also conducted an artificial stream mesocosm experiment using sediments collected from two of the observational study sites. The mesocosm experiment had a two-by-two factorial design: (i) location (upstream or downstream of contaminating tributary), and (ii) treatment (metal exposure or control). We found no difference in diversity between upstream and downstream sites in the field. Similarly, diversity changed very little following experimental metal exposure. However, microbiome membership differed between upstream and downstream locations and experimental metal exposure changed microbiome membership in a manner that depended on origin of the sediments used in each mesocosm.IMPORTANCE Our results suggest that microbiomes can be reliable indicators of ecosystem metal stress even when surface water chemistry and other metrics used to assess ecosystem health do not indicate ecosystem stress. Results presented in this study, in combination with previously published work on this same ecosystem, are consistent with the idea that a microbial response to metals at the base of the food web may be affecting primary consumers. If effects of metals are mediated through shifts in the microbiome, then microbial metrics, as presented here, may aid in the assessment of stream ecosystem health, which currently does not include assessments of the microbiome.

Evaluating the stoichiometric trait distributions of cultured bacterial populations and uncultured microbial communities.

We measured the stoichiometric trait distribution of cultured freshwater bacterial populations under different resource conditions and compared them to natural microbial communities sampled from three lakes. Trait distributions showed population differences among growth phases and community differences among lakes that would have been masked by only reporting the mean biomass value. The stoichiometric trait distribution of the environmental isolates changed with P availability, growth phase and genotype, with P availability having the strongest effect. The distribution of biomass ratios within each isolate growth experiment were the most constrained during the stages of rapid growth and commonly had unimodal distributions. In contrast to the population distributions, the distribution of N:P and C:P for a similar number of cells from each of the lake communities had narrower stoichiometric distributions and more commonly exhibited multiple modes. © 2019 Society for Applied Microbiology and John Wiley & Sons Ltd.

A functional microbiome catalog crowdsourced from North American rivers.

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept 1 , we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures 2, 3 so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.

The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years.

Many lakes across the world are entering novel states and experiencing altered biogeochemical cycling due to local anthropogenic stressors. In the tropics, understanding the drivers of these changes can be difficult due to a lack of documented historic conditions or an absence of continuous monitoring that can distinguish between intra- and inter-annual variation. Over the last forty years (1980-2020), Lake Yojoa (Honduras) has experienced increased watershed development as well as the introduction of a large net-pen Tilapia farm, resulting in a dramatic reduction in seasonal water clarity, increased trophic state and altered nutrient dynamics, shifting Lake Yojoa from an oligotrophic (low productivity) to mesotrophic (moderate productivity) ecosystem. To assess the changes that have occurred in Lake Yojoa as well as putative drivers for those changes, we compared Secchi depth (water clarity), dissolved inorganic nitrogen (DIN), and total phosphorus (TP) concentrations at continuous semi-monthly intervals for the three years between 1979 and 1983 and again at continuous 16-day intervals for 2018-2020. Between those two periods we observed the loss of a clear water phase that previously occurred in the months when the water column was fully mixed. Seasonal peaks in DIN coincident with mixing suggest that an enhanced accumulation of ammonium in the hypolimnion (the bottom layer of a stratified lake) during stratification, and release to the epilimnion (the top layer of a stratified lake) with mixing maintains high algal abundance and subsequently low Secchi depth during what was previously the clear water phase. This interaction of nutrient loading and Lake Yojoa's monomictic stratification regime illustrates a key phenomenon in how physical water column structure and nutrients interact in tropical monomictic lakes. This work highlights the need to consider nutrient dynamics of warm anoxic hypolimnions, not just surface water nutrient concentrations, to understand environmental change in these societally important but understudied ecosystems.

Confounding factors in algal phosphorus limitation experiments.

Assessing algal nutrient limitation is critical for understanding the interaction of primary production and nutrient cycling in streams, and nutrient diffusing substrate (NDS) experiments are often used to determine limiting nutrients such as nitrogen (N) and phosphorus (P). Unexpectedly, many experiments have also shown decreased algal biomass on NDS P treatments compared to controls. To address whether inhibition of algal growth results from direct P toxicity, NDS preparation artifacts, or environmental covariates, we first quantified the frequency of nutrient inhibition in published experiments. We also conducted a meta-analysis to determine whether heterotrophic microbial competition or selective grazing could explain decreases in algal biomass with P additions. We then deployed field experiments to determine whether P-inhibition of algal growth could be explained by P toxicity, differences in phosphate cation (K vs. Na), differences in phosphate form (monobasic vs. dibasic), or production of H2O2 during NDS preparation. We found significant inhibition of algal growth in 12.9% of published NDS P experiments as compared to 4.7% and 3.6% of N and NP experiments. The meta-analysis linear models did not show enhanced heterotrophy on NDS P treatments or selective grazing of P-rich algae. Our field experiments did not show inhibition of autotrophic growth with P additions, but we found significantly lower gross primary productivity (GPP) and biomass-specific GPP of benthic algae on monobasic phosphate salts as compared to dibasic phosphate salts, likely because of reduced pH levels. Additionally, we note that past field experiments and meta-analyses support the plausibility of direct P toxicity or phosphate form (monobasic vs. dibasic) leading to inhibition of algal growth, particularly when other resources such as N or light are limiting. Given that multiple mechanisms may be acting simultaneously, we recommend practical, cost-effective steps to minimize the potential for P- inhibition of algal growth as an artifact of NDS experimental design.

Understanding how microbiomes influence the systems they inhabit.

Translating the ever-increasing wealth of information on microbiomes (environment, host or built environment) to advance our understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes that they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here, we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system-level processes that they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted using constituent taxa (community-aggregated traits) from those properties that cannot currently be predicted using constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these three categories of microbial characteristics and connect them with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.