Liver lesions in demersal fishes near a large ocean outfall on the San Pedro Shelf, California

The prevalence of toxicopathic liver lesions in demersal fish on the San Pedro Shelf, California was determined for a 15-year period (1988–2003). Fish livers were sampled at fixed locations as part of the Orange County Sanitation Districts (OCSD) ocean monitoring program. Histopathological examination of selected fish liver tissues was studied to determine whether the wastewater discharge had affected fish health. The prevalence of toxicopathic lesion classes neoplasms (NEO), preneoplastic foci of cellular alteration (FCA), and hydropic vacuolation (HYDVAC) varied among species and locations. For all species sampled, severe lesions occurred in 6.2% of the fish examined (n = 7,694). HYDVAC (4.1%) was the most common toxicopathic lesion type followed by FCA (1.4%) and NEO (0.7%). HYDVAC occurred only in white croaker (Genyonemus lineatus), accounting for 84.8% of the toxicopathic lesions for this species. Prevalence of HYDVAC, NEO, and FCA in white croaker was 15.2, 2.0, and 0.7%, respectively. The prevalence of HYDVAC and NEO in white croaker increased with age and size but there was no sexual difference. A linear regression model was used for hypothesis testing to account for significant differences in fish size (and age for croakers) at the different sampling locations. This analysis showed that for HYDVAC there was no spatial or location effect for lesion rate or size/age of onset. For NEO, the model predicted that white croaker near the wastewater outfall may acquire these lesions at a smaller size/younger age, and at a higher rate, than at other sites. However, this result may be biased due to the unequal size frequency distributions and the low prevalence of NEO in white croaker at the different sampling sites. Bigmouth sole (Hippoglossina stomata) had a prevalence of FCA and NEO of 1.3 and 0.35%, respectively, but the prevalence and distribution of lesions was too few for statistical testing. There was no sexual difference for lesion prevalence in hornyhead turbot (Pleuronichthys verticalis) and the prevalence of FCA and NEO was 3.4 and 0.37%, respectively. FCA prevalence increased with size in hornyhead turbot and there were no significant spatial differences for lesion rates and fish size at lesion onset. Overall, consistent spatial differences for lesion prevalence were not demonstrated and highlight the analytical difficulties of detecting a possible point source impact when the effect is rare, correlated with the size/age structure of the population, and may be caused by exposure to unknown multiple sources. Thus, the usefulness of liver histopathology as a point-source monitoring tool is best applied to where the spatial scale of impact generally exceeds the home range of the target species.     

Using the Sediment Quality Triad to characterize baseline conditions in the Anacostia River, Washington, DC, USA

The Sediment Quality Triad (SQT) consists of complementary measures of sediment chemistry, benthic community structure, and sediment toxicity. We applied the SQT at 20 stations in the tidal portion of the Anacostia River from Bladensburg, MD to Washington, DC to establish a baseline of conditions to evaluate the effects of management actions. Sediment toxicity was assessed using 10-day survival and growth tests with the freshwater amphipod, Hyalella azteca and the midge, Chironomus dilutus. Triplicate grabs were taken at each station for benthic community analysis and the Benthic Index of Biotic Integrity (B-IBI) was used to interpret the data. Only one station, #92, exhibited toxicity related to sediment contamination. Sediments from this station significantly inhibited growth of both test species, had the highest concentrations of contaminants, and had a degraded benthic community, indicated by a B-IBI of less than 3. Additional sediment from this station was tested and sediment toxicity identification evaluation (TIE) procedures tentatively characterized organic compounds as the cause of toxicity. Overall, forty percent of the stations were classified as degraded by the B-IBI. However, qualitative and quantitative comparisons with sediment quality benchmarks indicated no clear relationship between benthic community health and contaminant concentrations. This study provides a baseline for assessing the effectiveness of management actions in the Anacostia River.     

Effects of Hurricane Katrina on benthic macroinvertebrate communities along the northern Gulf of Mexico coast

The effects of Hurricane Katrina on benthic fauna and habitat quality in coastal waters of Louisiana, Mississippi, and Alabama, USA, were assessed in October, 2005, 2 months after the hurricane made landfall between New Orleans, LA and Biloxi, MS. Benthic macrofaunal samples, sediment chemical concentrations, and water quality measurements from 60 sites in Lake Pontchartrain and Mississippi Sound were compared with pre-hurricane conditions from 2000–2004. Post-hurricane benthic communities had significant reductions in numbers of taxa, H′ diversity, and abundance as well as shifts in composition and ranking of dominant taxa. These effects were not associated with changes in chemical contamination, organic enrichment of sediments, or hypoxia and were likely due to hurricane-related scouring and changes in salinity.     

流动注射分光光度法在线分析水中总铁

采用流动注射邻菲哕啉分光光度法在线分析水中总铁,考察了显色剂、消解液、酸度、反应盘管长度、载流流量、注样体积、采样时间等因素对试验的影响。方法在0.100mg／L-10.0mg／L范围内线性良好,检出限为0.04mg／L,实际水样平行测定的RSD≤3.7％,加标回收率为95.0％-96.0％。     

Predicting estuarine sediment metal concentrations and inferred ecological conditions: an information theoretic approach

Empirically derived relationships associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. Resources limit the number, magnitude, and frequency of monitoring activities to acquire these data. Models that use available information and simple statistical relationships to predict sediment metal concentrations could provide an important tool for environmental assessment. We developed 45 predictive models for the total concentrations of copper, lead, mercury, and cadmium in estuarine sediments along the Southern New England and Mid-Atlantic regions of the United States. Using information theoretic model-averaging approaches, we found total developed land and percent silt/clay of estuarine sediment were the most important variables for predicting the presence of all four metals. Estuary area, river flow, tidal range, and total agricultural land varied in their importance. The model-averaged predictions explained 78.4, 70.5, 56.4, and 50.3% of the variation for copper, lead, mercury, and cadmium, respectively. Overall prediction accuracies of selected sediment benchmark values (i.e., effects ranges) were 83.9, 84.8, 78.6, and 92.0% for copper, lead, mercury, and cadmium, respectively. Our results further support the generally accepted conclusion that sediment metal concentrations are best described by the physical characteristics of the estuarine sediment and the total amount of urban land in the contributing watershed. We demonstrated that broad-scale predictive models built from existing monitoring data with information theoretic model-averaging approaches provide valuable predictions of estuarine sediment metal concentrations and show promise for future environmental modeling efforts in other regions.     

Risk and opportunity in upgrading the US drinking water infrastructure system

This paper presents a practical risk assessment methodology to provide drinking water infrastructure (DWI) decision-makers with an objective risk assessment tool. The purpose of this risk assessment tool is to maintain the desired level-of-service or systems reliability [r(f)], while managing the financial uncertainty of the expected budgetary impact within the capital improvement program (CIP). The goal of this paper is to demonstrate the value of an objective risk assessment tool for estimating the DWI decision-maker's sensitivity to the risk of systems failure (R). The objectives are to: (1) incorporate probability of systems failure [p(f)] into the CIP budgetary analysis process and (2) evaluate the affects of p(f) on the expected CIP budgetary outcome. The magnitude of the expected budgetary impact is managed through the DWI decision-maker's sensitivity to R, which is represented by the level of the rate of reinvestment (RR). The expected result of the proposed risk assessment tool demonstrates that by proactively managing R to maintain a desired r(f) will effectively manage the impact of uncertainty on the expected budgetary outcome within the CIP. The expected contribution of the practical risk assessment methodology is to provide DWI decision-makers with the ability to reduce budgetary uncertainty when allocating limited financial resources among competing operational, repair, maintenance, and expansion activities within the CIP. The conclusions of the paper reveal that if DWI decision-makers assume risk-avoidance positions through proactive asset management (AM) strategies, they will achieve positive affects on expected budgetary outcomes.     

Testing the Hydrological Landscape Unit Classification System and Other Terrain Analysis Measures for Predicting Low-Flow Nitrate and Chloride in Watersheds

Elevated nitrate concentrations in streamwater are a major environmental management problem. While land use exerts a large control on stream nitrate, hydrology often plays an equally important role. To date, predictions of low-flow nitrate in ungauged watersheds have been poor because of the difficulty in describing the uniqueness of watershed hydrology over large areas. Clearly, hydrologic response varies depending on the states and stocks of water, flow pathways, and residence times. How to capture the dominant hydrological controls that combine with land use to define streamwater nitrate concentration is a major research challenge. This paper tests the new Hydrologic Landscape Regions (HLRs) watershed classification scheme of Wolock and others (Environmental Management 34:S71-S88, 2004) to address the question: Can HLRs be used as a way to predict low-flow nitrate? We also test a number of other indexes including inverse-distance weighting of land use and the well-known topographic index (TI) to address the question: How do other terrain and land use measures compare to HLR in terms of their ability to predict low-flow nitrate concentration? We test this for 76 watersheds in western Oregon using the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program and Regional Environmental Monitoring and Assessment Program data. We found that HLRs did not significantly improve nitrate predictions beyond the standard TI and land-use metrics. Using TI and inverse-distance weighting did not improve nitrate predictions; the best models were the percentage land use—elevation models. We did, however, see an improvement of chloride predictions using HLRs, TI, and inverse-distance weighting; adding HLRs and TI significantly improved model predictions and the best models used inverse-distance weighting and elevation. One interesting result of this study is elevation consistently predicted nitrate better than TI or the hydrologic classification scheme.     

A critical review of the theory and application of social learning in participatory natural resource management processes

Social learning is increasingly cited as an essential component of sustainable natural resource management and the promotion of desirable behavioural change. This paper attempts to contribute to the current debate about social learning and public participation by reviewing the many perspectives on social learning and associated claims and benefits. Based on this analysis the paper identifies conceptual and practical weaknesses of the concept of social learning and their implications for the design of participatory processes in natural resource management.     

Effective monitoring of agriculture: a response

The development of effective agricultural monitoring networks is essential to track, anticipate and manage changes in the social, economic and environmental aspects of agriculture. We welcome the perspective of Lindenmayer and Likens (J. Environ. Monit., 2011, 13, 1559) as published in the Journal of Environmental Monitoring on our earlier paper, "Monitoring the World's Agriculture" (Sachs et al., Nature, 2010, 466, 558-560). In this response, we address their three main critiques labeled as 'the passive approach', 'the problem with uniform metrics' and 'the problem with composite metrics'. We expand on specific research questions at the core of the network design, on the distinction between key universal and site-specific metrics to detect change over time and across scales, and on the need for composite metrics in decision-making. We believe that simultaneously measuring indicators of the three pillars of sustainability (environmentally sound, social responsible and economically viable) in an effectively integrated monitoring system will ultimately allow scientists and land managers alike to find solutions to the most pressing problems facing global food security.     

Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability

Collection of standardized assessment and monitoring data is critically important for supporting policy and management at local to continental scales. Remote sensing techniques, including image interpretation, have shown promise for collecting plant community composition and ground cover data efficiently. More work needs to be done, however, evaluating whether these techniques are sufficiently feasible, cost-effective, and repeatable to be applied in large programs. The goal of this study was to design and test an image-interpretation approach for collecting plant community composition and ground cover data appropriate for local and continental-scale assessment and monitoring of grassland, shrubland, savanna, and pasture ecosystems. We developed a geographic information system image-interpretation tool that uses points classified by experts to calibrate observers, including point-by-point training and quantitative quality control limits. To test this approach, field data and high-resolution imagery (∼3 cm ground sampling distance) were collected concurrently at 54 plots located around the USA. Seven observers with little prior experience used the system to classify 300 points in each plot into ten cover types (grass, shrub, soil, etc.). Good agreement among observers was achieved, with little detectable bias and low variability among observers (coefficient of variation in most plots <0.5). There was a predictable relationship between field and image-interpreter data (R ² > 0.9), suggesting regression-based adjustments can be used to relate image and field data. This approach could extend the utility of expensive-to-collect field data by allowing it to serve as a validation data source for data collected via image interpretation.