Paula J. Mouser

Comparison of Bacterial Communities in New England Sphagnum Bogs Using Terminal Restriction Fragment Length Polymorphism (T-RFLP)

Wetlands are major sources of carbon dioxide, methane, and other greenhouse gases released during microbial degradation. Despite the fact that decomposition is mainly driven by bacteria and fungi, little is known about the taxonomic diversity of bacterial communities in wetlands, particularly Sphagnum bogs. To explore bacterial community composition, 24 bogs in Vermont and Massachusetts were censused for bacterial diversity at the surface (oxic) and 1xa0m (anoxic) regions. Bacterial diversity was characterized by a terminal restriction fragment length (T-RFLP) fingerprinting technique and a cloning strategy that targeted the 16S rRNA gene. T-RFLP analysis revealed a high level of diversity, and a canonical correspondence analysis demonstrated marked similarity among bogs, but consistent differences between surface and subsurface assemblages. 16S rDNA sequences derived from one of the sites showed high numbers of clones belonging to the Deltaproteobacteria group. Several other phyla were represented, as well as two Candidate Division-level taxonomic groups. These data suggest that bog microbial communities are complex, possibly stratified, and similar among multiple sites.

The comparison of four dynamic systems-based software packages: Translation and sensitivity analysis

Abstract Dynamic model development for describing complex ecological systems continues to grow in popularity. For both academic research and project management, understanding the benefits and limitations of systems-based software could improve the accuracy of results and enlarge the user audience. A Surface Wetness Energy Balance (SWEB) model for canopy surface wetness has been translated into four software packages and their strengths and weaknesses were compared based on ‘novice’ user interpretations. We found expression-based models such as Simulink and GoldSim with Expressions were able to model the SWEB more accurately; however, stock and flow-based models such as STELLA, Madonna, and GoldSim with Flows provided the user a better conceptual understanding of the ecologic system. Although the original objective of this study was to identify an ‘appropriate’ software package for predicting canopy surface wetness using SWEB, our outcomes suggest that many factors must be considered by the stakeholders when selecting a model because the modeling software becomes part of the model and of the calibration process. These constraints may include user demographics, budget limitations, built-in sensitivity and optimization tools, and the preference of user friendliness vs. computational power. Furthermore, the multitude of closed proprietary software may present a disservice to the modeling community, creating model artifacts that originate somewhere deep inside the undocumented features of the software, and masking the underlying properties of the model.

Enhanced detection of groundwater contamination from a leaking waste disposal site by microbial community profiles

[1]xa0Groundwater biogeochemistry is adversely impacted when municipal solid waste leachate, rich in nutrients and anthropogenic compounds, percolates into the subsurface from leaking landfills. Detecting leachate contamination using statistical techniques is challenging because well strategies or analytical techniques may be insufficient for detecting low levels of groundwater contamination. We sampled profiles of the microbial community from monitoring wells surrounding a leaking landfill using terminal restriction fragment length polymorphism (T-RFLP) targeting the 16S rRNA gene. Results show in situ monitoring of bacteria, archaea, and the family Geobacteraceae improves characterization of groundwater quality. Bacterial T-RFLP profiles showed shifts correlated to known gradients of leachate and effectively detected changes along plume fringes that were not detected using hydrochemical data. Experimental sediment microcosms exposed to leachate-contaminated groundwater revealed a shift from a β-Proteobacteria and Actinobacteria dominated community to one dominated by Firmicutes and δ-Proteobacteria. This shift is consistent with the transition from oxic conditions to an anoxic, iron-reducing environment as a result of landfill leachate-derived contaminants and associated redox conditions. We suggest microbial communities are more sensitive than hydrochemistry data for characterizing low levels of groundwater contamination and thus provide a novel source of information for optimizing detection and long-term monitoring strategies at landfill sites.

GEOGRAPHIC VARIATION IN NUTRIENT AVAILABILITY, STOICHIOMETRY, AND METAL CONCENTRATIONS OF PLANTS AND PORE-WATER IN OMBROTROPHIC BOGS IN NEW ENGLAND, USA

Geographic trends in surface water chemistry and leaf tissue nutrients may reflect gradients of nutrient limitation and broad-scale anthropogenic inputs. In 24 rain-fed (ombrotrophic) peatland bogs in Massachusetts and Vermont, we measured nutrient and metal concentrations in pore-water and in leaf tissues of three common bog plant genera — leather leaf (Chamaedaphne calyculata), northern pitcher plant (Sarracenia purpurea), and peat moss (Sphagnum spp.). The concentrations of N, P, and K were low in leaf tissues of all three plant genera, as were the concentrations of many trace heavy metals, including Cr, Cu, Co, Cd, Mo, and Pb. Stoichiometric ratios of macronutrients (N:P, P:K, and N:K) in plant leaves suggested that plant growth in the sampled bogs was limited by P, or was co-limited by all three macronutrients. N:P and N:K nutrient ratios of Sarracenia purpurea and Sphagnum spp. increased toward the northwest and with elevation, but stoichiometric ratios of Chamaedaphne calyculata did not show any clear geographic trends. A principal components analysis revealed additional distinct differences among the three plant genera in their nutrient and metal concentrations. Furthermore, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), Cu, Mg, NO3, Al, and K in porewater increased from the northwest (northwestern Vermont) to the southeast (Cape Cod and eastern Massachusetts near Boston), a gradient of increasing human population density and urbanization. In contrast, pore-water concentrations of SO4 and Al were highest in the western sites, and SO4 concentrations increased with elevations. These patterns may reflect atmospheric inputs from the Ohio River Valley leading to increased acidic deposition, causing Al to be leached from soils. Because bogs are naturally low in nutrients and do not receive substantial inputs from surrounding groundwater, the chemical signatures and nutrient stoichiometry of specific bog plant species or genera may provide useful indicators for assessing spatiotemporal changes in atmospheric deposition.

Parameter Estimation Using an Artificial Neural Network to Incorporate Multiple Types of Data

We apply a modified counterpropagation artificial neural network (ANN) that uses multivariate data to several parameter estimation problems: (1) estimation of small scale Berea sandstone geophysical properties, (2) estimation of apparent conductivity at a leaking landfill using electromagnetic data and (3) estimation of hydraulic conductivity field at a landfill in New York State using pumping test and well log data. The counterpropagation algorithm has been enhanced in this research to allow for spatial interpolation that is comparable to traditional kriging methods. This enhanced ANN is data-driven, can incorporate large amounts of multiple data types to produce parameter estimates in real-time and does not require the computation of large covariance matrices associated with traditional geostatistical methods (kriging).