Nitrogen cycling in the coastal marine environment
N cycling and Nitrous Oxide fluxes in Great Sippewissett Marsh (with Dr. Jen Bowen, U. Mass. Boston)
Salt marshes are an essential feature of coastal landscapes and microbial processes in marsh sediments are critical to the exchange of nitrogen between land, sea and the atmosphere. Perturbations such as the dramatic increase in the amount of fixed nitrogen (N) in the biosphere through the combined effects of fossil fuel combustion and fertilizer application, however, have led to a fundamental restructuring of the ecology of these habitats, since much of the excess N applied on land and released to the atmosphere eventually finds its way to estuarine and coastal waters. The primary effect of this N enrichment is eutrophication – enhanced production and high organic loading to water and sediments. Microbial activity is essential in the decomposition and remineralization of this organic matter. One of the important byproducts of microbial activities in salt marshes is nitrous oxide (N2O), which ranks as the number one ozone-depleting chemical (usurping chloroflorocarbons, which have been successfully controlled by the Montreal Protocol) and is an increasingly important greenhouse gas. Understanding and controlling the flux of N2O is complicated because it is produced by several different microbial pathways that are favored on different environmental conditions, but which may cooccur in the sediments of salt marshes.
We will examine the effects of two key environmental variables, N supply and redox conditions, on the fluxes of N2O and N2 in salt marshes. The work will be performed at Great Sippewissett Marsh, Cape Cod, MA. Great Sippewissett Marsh (See Research, Figure 3) is the site of a long-term fertilization experiment, in which varying amounts of nitrogen have been added to different test sites since the early 1970s. Previous work, led by former post doc Dr. Jen Bowen (now Asst. Professor at U Mass Boston), investigated the response of microbial assemblages in the marsh to varying levels of N addition. To our surprise, we found that neither the total assembly, based on pyrosequencing of the bacterial 16S rRNA genes, nor the denitrifying assemblage, based on microarray analysis of the nirS gene (Figure 1), had changed significantly between control and high fertilizer addition plots (Bowen et al 2011).
This resistance in the composition of the microbial community is in contrast the increases in rates of denitrification that have been measured in the marsh by other workers. Our current focus therefore, is to investigate the sources and controls on N2O production by the microbial assemblage of the marsh. Molecular biological methods will be used to examine the microbial community composition, activity and abundance using functional genes that are diagnostic for N2O production, nitrification and denitrification. Rate measurements and gene distribution/expression analyses will examine how N supply and redox conditions independently affect the proportion of N2O and N2 produced at each site and the pathways that are responsible for that production. This research will improve our understanding of the interaction between the activity of key microorganisms responsible for nitrogen cycling, the gaseous N fluxes associated with those microorganisms, and the environmental factors that regulate both.
Figure 1. Stacked bar plot of the relative abundance of nirS targets from the Sippewissett Marsh fertilization. Each symbol represents an archetype probe from a functional gene microarray that spans the diversity of most known denitrifiers. The relative fluorescence ratio (RFR) indicates the relative abundance of that taxonomic group in the control, and in the low, medium, and high dose fertilizer experiment. Fertilization had little effect on the community composition and relative abundance of denitrifiers in the marsh sediments. Only two probes had significant responses to fertilization (inset; CB3-S-128: F = 4.295, p=0.028, CB3-S-15: F=3.1705, p=0.043).
Modeling N metabolism in estuarine sediments, a mesocosm approach.
Sediment systems are ideal experimental environments in which to study N cycling because of their ability to simultaneously support aerobic and anaerobic processes. One complication of these systems, however, is their intrinsic spatial heterogeneity, arising from physical limitations of transport in porous media. To overcome the experimental drawbacks of heterogeneity we employ a mesocosm approach. Semi-closed systems of sediment and overlying water are setup in the laboratory such that gas exchange is the only mass transfer permitted in or out of the system (Figure 2). Sampling the water column allows spatially-integrated changes in nitrogen speciation to be observed. We have found the mesocosm incubations to be highly reproducible within treatments, exhibiting reproducible patterns of dissolved inorganic nitrogen fluxes (Figure 3). Based on the changes in DIN concentrations, we can then calculate the N transformation rates of the complex system through numerical analysis of the changes in the nitrogen speciation observed in the overlying water. We are using this setup to test hypotheses about the controls on anammox and denitrification and the response of the biological community to episodic or sudden changes such as anthropogenic sewage discharge or ocean acidification (Babbin and Ward, submitted).
Figure 2. Sediment mesocosms used to investigate N transformations in sediments. About 2.5 cm of homogenized Chesapeake Bay surface sediment was evenly distributed in the container and covered with about 18 cm of Chesapeake Bay water. The overlying water was stirred gently and aerated by bubbling with an aquarium pump. This maintained atmospheric saturation oxygen levels in the water, but allowed the sediments to set up their oxic/anoxic stratification.
Figure 3. Example of the sequence of dissolved organic nitrogen concentrations observed after addition of organic material to the sediment surface. The DIN data provide the input for least squares model, which was used to extract estimates of the magnitude and time sequence of N transformations rates that are responsible for the DIN patterns.
Population structure of ammonia oxidizing bacteria and archaea in marine environments.
Water and sediment samples from upper and lower Chesapeake Bay were analyzed using functional gene microarrays for ammonia oxidizing bacteria (AOB) to determine temporal and spatial patterns over four years and relationships between AOB communities and environmental variables. Covarying AOB assemblages reoccurred seasonally in concert with specific environmental conditions, potentially revealing patterns of niche differentiation. Among the most notable patterns were correlations of AOB archetypes with temperature, DON and ammonium concentrations. Different AOB archetypes were more prevalent at certain times of the year, e.g., some were more abundant every autumn and others every spring (Figure 4). This data-set also documents the successional return to an indigenous community following massive perturbation (hurricane induced flooding) as well as the seasonal reoccurrence of specific lineages, identified by key functional genes, associated with the biogeochemically important process nitrification (Bouskill et al 2011).
Using a similar functional gene microarray for ammonia oxidizing archaea (AOA), we investigated the relative abundance of both groups of ammonia-oxidizing organisms (AOO) across large-scale gradients in temperature, salinity and substrate concentration and dissolved oxygen. The relative abundance of AOB and AOA varies across environments, with AOB dominating in the freshwater region of the Chesapeake Bay and AOA more abundant in the water column of the coastal and open ocean. Perhaps surprising, we found very high abundance of the AOA amoA gene in the oxygen minimum zones (OMZ) of the Eastern Tropical South Pacific (ETSP) and the Arabian Sea (AS). Phylogenetic diversity within physicochemically congruent stations was more similar than would be expected by chance. This suggests that the prevailing geochemistry, rather than localized dispersal, is the major driving factor determining OTU distribution.
Figure 4. Temporal classification of covarying archetypes based on K-means discrimination analysis at the near bottom depth at Chesapeake Bay Station 1 (head of the estuary). The left panel shows the allocation of archetypes into different temporal patterns (TP) based on their temporal RFR signal (i.e., increasing or decreasing signal between sample dates). The r-value on the plot represents a Cophenetic correlation (and bootstrapped 200 times) to judge how well the dendrogram reflects the original data. Within the dendrogram, the branches are color-coded to represent individual TPs (TP1 – red, TP2 – blue, TP3 – green). The middle panel represents the RFR of individual archetypes in each TP over time. The right panel shows a centroid compiled from the cumulative behavior of all the archetypes in each cluster over time. This centroid is without magnitude and incorporates the trend of the RFR signal through time.