The gill symbiont of the hydrothermal vent mussel Bathymodiolus thermophilus is a psychrophilic,chemoautotrophic, sulfur bacterium

Certain hydrothermal vent invertebrates, e.g. Riftia pachyptila and Calyptogena magnifica, are clearly established as harboring dense populations of chemoautotrophic sulfur bacteria in specialized tissues. By contrast, the physiological characteristics of the abundant intracellular gill symbiont of the vent mussel Bathymodiolus thermophilus have been questioned. The low activities of enzymes diagnostic for CO₂ fixation (Calvin cycle) and for sulfur-driven energy generation, as measured by other investigators, have been attributed to bacterial contamination of the gill surface. Based on research at the Galápagos Rift hydrothermal vents in 1988 and subsequent laboratory experiments, the current study confirms that the B. thermophilus symbiont is a psychrophile for which thiosulfate and sulfide stimulate CO₂ fixation. It strongly indicates that the symbiont is a chemoautotroph by establishing the following: (1) Sulfide and thiosulfate can stimulate CO₂ fixation by partially purified symbionts by up to 43-fold and 120-fold, respectively; (2) the ribulose-1,5-bisphosphate carboxylase/oxygenase activity of the symbiont is sufficient to account for its sulfide- or thiosulfate-stimulated CO₂ incorporation; (3) the symbiont's molar growth yield on thiosulfate, as judged by CO₂ incorporation, is indistinguishable from that of free-living chemoautotrophs. Due to the high protein-degrading activity of B. thermophilus gill lysate, it is also suggested that host lysis of symbionts plays a more important role in the nutrition of the vent mussel than in R. pachyptila or C. magnifica, for which no comparable protein-degrading activity was found.     

Volunteer Macroinvertebrate Monitoring: Tensions Among Group Goals,Data Quality,and Outcomes

Volunteer monitoring of natural resources is promoted for its ability to increase public awareness, to provide valuable knowledge, and to encourage policy change that promotes ecosystem health. We used the case of volunteer macroinvertebrate monitoring (VMM) in streams to investigate whether the quality of data collected is correlated with data use and organizers' perception of whether they have achieved these outcomes. We examined the relation between site and group characteristics, data quality, data use, and perceived outcomes (education, social capital, and policy change). We found that group size and the degree to which citizen groups perform tasks on their own (rather than aided by professionals) positively correlated with the quality of data collected. Group size and number of years monitoring positively influenced whether a group used their data. While one might expect that groups committed to collecting good-quality data would be more likely to use it, there was no relation between data quality and data use, and no relation between data quality and perceived outcomes. More data use was, however, correlated with a group's feeling of connection to a network of engaged citizens and professionals. While VMM may hold promise for bringing citizens and scientists together to work on joint conservation agendas, our data illustrate that data quality does not correlate with a volunteer group's desire to use their data to promote regulatory change. Therefore, we encourage scientists and citizens alike to recognize this potential disconnect and strive to be explicit about the role of data in conservation efforts.     

Numerical Analyses of Soft Bottom Macroinvertebrates to Diagnose the Pollution in Tropical Coastal Waters

Soft bottom benthic organisms especially polychaetes are known to adapt as r or k selected strategies to different gradients of pollution. This will result in changes of benthic community structure from that of normal structure. There are a number of techniques to assess the impact of pollution on benthic community structure. Hence, to test this hypotheses some of the univariate and multivariate techniques were applied to soft bottom macro-invertebrates data of coastal waters of Mangalore, central west coast of India, a hot-spot area. Univariate techniques such as Pearson Rosenberg Model (PRM), abundance biomass comparison curve (ABC), geometrical class distribution, dominance-diversity curve, benthic community structure indices and multivariate techniques such as cluster classification, multidimensional scaling (MDS) and principal component analysis (PCA) were used to discriminate and diagnose the disturbance among the sites. Effectiveness and applicability of some of the above techniques are highlighted and discussed with the present set of data for environmental impact assessment studies.     

Pitfalls in the prenatal diagnosis of peroxisomal β-oxidation defects by chorionic villus sampling

Variability in the level of expression of very long chain fatty acids (VLCFAs) is documented in cultured chorionic villus (CV) cells derived from two fetuses, one at risk for an unusual peroxisomal fatty acid β-oxidation defect, and the other at risk for the X-linked form of adrenoleucodystrophy (ALD). Cells from early subcultures of chorionic cells from both cases gave normal values for VLCFA ratios. The results for the fetus at risk for the β-oxidation defect were interpreted to indicate that the fetus was not affected; however, at birth, the infant was clinically and biochemically affected. In the case of the fetus at risk for X-linked ALD, although VLCFAs were normal in subculture 1, the levels of these fatty acids increased dramatically in subculture 3, suggesting an abnormal fetus. Termination of the pregnancy and subsequent biochemical and morphological follow-up confirmed that the fetus was indeed affected by ALD.     

In-situ sparging: Mass transfer mechanisms

In-situ sparging has been accepted as a method to rapidly remediate groundwater at considerably lower costs compared to remedies based on groundwater recovery alone. The success of in-situ sparging depends on effective mass transfer between air and contaminated media in the subsurface. Factors affecting mass transfer include advective airflow, diffusive transport, interphase chemical partitioning, and chemical and biological reaction rates between sparged gases and subsurface contaminants, minerals, and naturally occurring organic compounds. Understanding these factors can increase the design efficiency of in-situ sparging and assist in developing sparging systems that use gases other than air (i.e., oxygen, ozone, and methane).     

Techniques for accuracy assessment of tree locations extracted from remotely sensed imagery

Remotely sensed imagery is becoming a common source of environmental data. Consequently, there is an increasing need for tools to assess the accuracy and information content of such data. Particularly when the spatial resolution of imagery is fine, the accuracy of image processing is determined by comparisons with field data. However, the nature of error is more difficult to assess. In this paper we describe a set of tools intended for such an assessment when tree objects are extracted and field data are available for comparison. These techniques are demonstrated on individual tree locations extracted from an IKONOS image via local maximum filtering. The locations of the extracted trees are compared with field data to determine the number of found and missed trees. Aspatial and spatial (Voronoi) analysis methods are used to examine the nature of errors by searching for trends in characteristics of found and missed trees. As well, analysis is conducted to assess the information content of found trees.     

Degree of phosphorus saturation of an Oxisol amended with biosolids in a long-term field experiment

When applied to agricultural soils, phosphate fertilizers and the mineral or organic compounds present in solid and/or liquid waste may raise phosphorus (P) content and increase soil P saturation. The degree of phosphorus saturation (DPS) is a good indicator of potential P loss from agricultural soils. The purpose of this study was to calculate the DPS of samples from an Oxisol amended for 5 years with biosolids and mineral fertilizer. DPS was calculated based on P, iron, and aluminum extracted by ammonium oxalate and oxalic acid (DPS_ox) or by Mehlich-1 solution (DPS_M1). Treatments included NPK mineral fertilization (175 kg ha^?1 of P), B1?=?19.02 t ha^?1 of biosolids (350 kg ha^?1 of P), B2?=?38.17 t ha^?1 of biosolids (703 kg ha^?1 of P), B3?=?76.26 t ha^–1 of biosolids (1,405 kg ha^?1 of P), and a control (no P added). Water-extractable P (WEP) was also measured. Critical levels of DPS_ox and DPS_M1 (21 and 24 %, respectively) were only achieved in the topsoil (0–0.1 m) at the highest biosolid dose. Concentration of WEP was positively correlated to DPS_ox and DPS_M1. The DPS_M1 method may be an alternative to DPS_ox for assessing the environmental risk of P loss from soil into surface runoff.     

Soil criteria to protect terrestrial wildlife and open-range livestock from metal toxicity at mining sites

Thousands of hard rock mines exist in the western USA and in other parts of the world as a result of historic and current gold, silver, lead, and mercury mining. Many of these sites in the USA are on public lands. Typical mine waste associated with these sites are tailings and waste rock dumps that may be used by wildlife and open-range livestock. This report provides wildlife screening criteria levels for metals in soil and mine waste to evaluate risk and to determine the need for site-specific risk assessment, remediation, or a change in management practices. The screening levels are calculated from toxicity reference values based on maximum tolerable levels of metals in feed, on soil and plant ingestion rates, and on soil to plant uptake factors for a variety of receptors. The metals chosen for this report are common toxic metals found at mining sites: arsenic, cadmium, copper, lead, mercury, and zinc. The resulting soil screening values are well above those developed by the US Environmental Protection Agency. The difference in values was mainly a result of using toxicity reference values that were more specific to the receptors addressed rather than the most sensitive receptor.     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.