Nitrogen and phosphorus cycling in an ombrotrophic peatland: a benchmark for assessing change

AIMS

Slow decomposition and isolation from groundwater mean that ombrotrophic peatlands store a large amount of soil carbon (C) but have low availability of nitrogen (N) and phosphorus (P). To better understand the role these limiting nutrients play in determining the C balance of peatland ecosystems, we compile comprehensive N and P budgets for a forested bog in northern Minnesota, USA.

METHODS

N and P within plants, soils, and water are quantified based on field measurements. The resulting empirical dataset are then compared to modern-day, site-level simulations from the peatland land surface version of the Energy Exascale Earth System Model (ELM-SPRUCE).

RESULTS

Our results reveal N is accumulating in the ecosystem at 0.2 ± 0.1 g N m^-2 year^-1 but annual P inputs to this ecosystem are balanced by losses. Biomass stoichiometry indicates that plant functional types differ in N versus P limitation, with trees exhibiting a stronger N limitation than ericaceous shrubs or Sphagnum moss. High biomass and productivity of Sphagnum results in the moss layer storing and cycling a large proportion of plant N and P. Comparing our empirically-derived nutrient budgets to ELM-SPRUCE shows the model captures N cycling within dominant plant functional types well.

CONCLUSIONS

The nutrient budgets and stoichiometry presented serve as a baseline for quantifying the nutrient cycling response of peatland ecosystems to both observed and simulated climate change. Our analysis improves our understanding of N and P dynamics within nutrient-limited peatlands and represents a crucial step toward improving C-cycle projections into the twenty-first century.

Microbial ecoenzyme stoichiometry, nutrient limitation, and organic matter decomposition in wetlands of the conterminous United States

Microbial respiration (Rm) and ecoenzyme activities (EEA) related to microbial carbon, nitrogen, and phosphorus acquisition were measured in 792 freshwater and estuarine wetlands (representing a cumulative area of 217,480 km2) across the continental United States as part of the US EPA's 2011 National Wetland Condition Assessment. EEA stoichiometry was used to construct models for and assess nutrient limitation, carbon use efficiency (CUE), and organic matter decomposition (- k). The wetlands were classified into ten groups based on aggregated ecoregion and wetland type. The wetlands were also assigned to least, intermediate, and most disturbed classes, based on the extent of human influences. Ecoenzyme activity related to C, N and P acquisition, Rm, CUE, and (- k differed among ecoregion-wetland types and, with the exception of C acquisition and (- k, among disturbance classes. Rm and EEA were positively correlated with soil C, N and P content (r = 0.15-0.64) and stoichiometry (r = 0.15-0.48), and negatively correlated with an index of carbon quality (r = - 0.22 to - 0.39). EEA stoichiometry revealed that wetlands were more often P- than N-limited, and that P-limitation increases with increasing disturbance. Our enzyme-based approach for modeling C, N, and P acquisition, and organic matter decomposition, all rooted in stoichiometric theory, provides a mechanism for modeling resource limitations of microbial metabolism and biogeochemical cycling in wetlands. Given the ease of collecting and analyzing soil EEA and their response to wetland disturbance gradients, enzyme stoichiometry models are a cost-effective tool for monitoring ecosystem responses to resource availability and the environmental drivers of microbial metabolism, including those related to global climate changes.

A synoptic survey of microbial respiration, organic matter decomposition, and carbon efflux in U.S. streams and rivers

We analyzed ecoenzyme activities related to organic matter processing in 1879 streams and rivers across the continental U.S. as part of the USEPA's National Rivers and Streams Assessment. Ecoenzymatic stoichiometry was used to construct models for carbon use efficiency (CUE) and decomposition (-k). Microbial respiration (R_m) was estimated from sediment organic carbon stocks, CUE and -k. The streams and rivers were classified by size (headwaters: 1^st-order; streams: 2^nd-3^rd order; small rivers: 4^th-5^th order; big rivers 6^th-7^th order; and great rivers ≥ 8^th order) and condition class (least, intermediate and most disturbed), and grouped into nine ecoregions. There were ecoregion, stream size, and condition class effects for CUE, -k, and R_m, with R_m increasing from eastern ecoregions through the plains to the western ecoregions. CUE, -k, and R_m decreased with increasing streams size and increased with increasing disturbance. R_m, CUE, and -k were correlated with water and sediment chemistry; CUE and -k were also correlated with stream bed fine sediments; and CUE was further correlated with catchment land cover. R_m was extrapolated to ecoregional and national scales, and the results suggest that microbial assemblages account for 12% of the total CO₂ outgassing, and nearly 50% of the aquatic metabolism C losses, from U.S. streams and rivers. Cumulative respiratory C losses increased from headwaters to small streams, then decreased with increasing stream size. This U-shaped respiration curve was not evident when streams were viewed by disturbance classes, suggesting that anthropogenic disturbances mask the expected organic matter processing signature of the river continuum.

Comparisons of soil nitrogen mass balances for an ombrotrophic bog and a minerotrophic fen in northern Minnesota

We compared nitrogen (N) storage and flux in soils from an ombrotrophic bog with that of a minerotrophic fen to quantify the differences in N cycling between these two peatlands types in northern Minnesota (USA). Precipitation, atmospheric deposition, and bog and fen outflows were analyzed for nitrogen species. Upland and peatland soil samples were analyzed for N content, and for ambient (DN) and potential (DEA) denitrification rates. Annual atmospheric deposition was: 0.88-3.07kg NH4(+)ha(-1)y(-1); 1.37-1.42kg NO3(-)ha(-1)y(-1); 2.79-4.69kg TNha(-1)y(-1). Annual N outflows were: bog-0.01-0.04kg NH4(+)ha(-1)y(-1), NO3(-) 0.01-0.06kgha(-1)y(-1), and TN 0.11-0.69kgha(-1)y(-1); fen-NH4(+) 0.01-0.16kgha(-1)y(-1), NO3(-) 0.29-0.48kgha(-1)y(-1), and TN 1.14-1.61kgha(-1)y(-1). Soil N content depended on location within the bog or fen, and on soil depth. DN and DEA rates were low throughout the uplands and peatlands, and were correlated with atmospheric N deposition, soil N storage, and N outflow. DEA was significantly greater than DN indicating C or N limitation of the denitrification process. We highlight differences between the bog and fen, between the upland mineral soils and peat, and the importance of biogeochemical hotspots within the peatlands. We point out the importance of organic N storage, as a source of N for denitrification, and propose a plausible link between organic N storage, denitrification and N export from peatlands. Finally, we considered the interactions of microbial metabolism with nutrient availability and stoichiometry, and how N dynamics might be affected by climate change in peatland ecosystems.

Quantifying Urban Watershed Stressor Gradients and Evaluating How Different Land Cover Datasets Affect Stream Management

Watershed management and policies affecting downstream ecosystems benefit from identifying relationships between land cover and water quality. However, different data sources can create dissimilarities in land cover estimates and models that characterize ecosystem responses. We used a spatially balanced stream study (1) to effectively sample development and urban stressor gradients while representing the extent of a large coastal watershed (>4400 km(2)), (2) to document differences between estimates of watershed land cover using 30-m resolution national land cover database (NLCD) and <1-m resolution land cover data, and (3) to determine if predictive models and relationships between water quality and land cover differed when using these two land cover datasets. Increased concentrations of nutrients, anions, and cations had similarly significant correlations with increased watershed percent impervious cover (IC), regardless of data resolution. The NLCD underestimated percent forest for 71/76 sites by a mean of 11 % and overestimated percent wetlands for 71/76 sites by a mean of 8 %. The NLCD almost always underestimated IC at low development intensities and overestimated IC at high development intensities. As a result of underestimated IC, regression models using NLCD data predicted mean background concentrations of NO3 (-) and Cl(-) that were 475 and 177 %, respectively, of those predicted when using finer resolution land cover data. Our sampling design could help states and other agencies seeking to create monitoring programs and indicators responsive to anthropogenic impacts. Differences between land cover datasets could affect resource protection due to misguided management targets, watershed development and conservation practices, or water quality criteria.

Persistent organic pollutants in fish tissue in the mid-continental great rivers of the United States

Great rivers of the central United States (Upper Mississippi, Missouri, and Ohio rivers) are valuable economic and cultural resources, yet until recently their ecological condition has not been well quantified. In 2004-2005, as part of the Environmental Monitoring and Assessment Program for Great River Ecosystems (EMAP-GRE), we measured legacy organochlorines (OCs) (pesticides and polychlorinated biphenyls, PCBs) and emerging compounds (polybrominated diphenyl ethers, PBDEs) in whole fish to estimate human and wildlife exposure risks from fish consumption. PCBs, PBDEs, chlordane, dieldrin and dichlorodiphenyltrichloroethane (DDT) were detected in most samples across all rivers, and hexachlorobenzene was detected in most Ohio River samples. Concentrations were highest in the Ohio River, followed by the Mississippi and Missouri Rivers, respectively. Dieldrin and PCBs posed the greatest risk to humans. Their concentrations exceeded human screening values for cancer risk in 27-54% and 16-98% of river km, respectively. Chlordane exceeded wildlife risk values for kingfisher in 11-96% of river km. PBDE concentrations were highest in large fish in the Missouri and Ohio Rivers (mean>1000 ng g(-1) lipid), with congener 47 most prevalent. OC and PBDE concentrations were positively related to fish size, lipid content, trophic guild, and proximity to urban areas. Contamination of fishes by OCs is widespread among great rivers, although exposure risks appear to be more localized and limited in scope. As an indicator of ecological condition, fish tissue contamination contributes to the overall assessment of great river ecosystems in the U.S.

A bioassessment approach for mid-continent great rivers: the Upper Mississippi, Missouri, and Ohio (USA)

The objectives of the Environmental Monitoring and Assessment Program for Great River Ecosystems (EMAP-GRE) are to (1) develop and demonstrate, in collaboration with states, an assessment program yielding spatially unbiased estimates of the condition of mid-continent great rivers; (2) evaluate environmental indicators for assessing great rivers; and (3) assess the current condition of selected great river resources. The purpose of this paper is to describe EMAP-GRE using examples based on data collected in 2004-2006 with emphasis on an approach to determining reference conditions. EMAP-GRE includes the Upper Mississippi River, the Missouri River, and the Ohio River. Indicators include biotic assemblages (fish, macroinvertebrates, plankton, algae), water chemistry, and aquatic and riparian physical habitat. Reference strata (river reaches for which a single reference expectation is appropriate) were determined by ordination of the fish assemblage and examination of spatial variation in environmental variables. Least disturbed condition of fish assemblages for reference strata was determined by empirical modeling in which we related fish assemblage metrics to a multimetric stressor gradient. We inferred least disturbed condition from the y-intercept, the predicted condition when stress was least. Thresholds for dividing the resource into management-relevant condition classes for biotic indicators were derived using predicted least disturbed condition to set the upper bound on the least disturbed condition class. Also discussed are the outputs of EMAP-GRE, including the assessment document, multimetric indices of condition, and unbiased data supporting state and tribal Clean Water Act reporting, adaptive management, and river restoration.