Institutional Constraints on Cost‐Effective Water Management: Selenium Contamination in Colorado's Lower Arkansas River Valley

Ground and surface water selenium (Se) contamination is problematic throughout the world, leading to harmful impacts on aquatic life, wildlife, livestock, and humans. A groundwater reactive transport model was applied to a regional‐scale irrigated groundwater system in the Lower Arkansas River Basin in southeastern Colorado to identify management practices that remediate Se contamination. The system has levels of surface water and groundwater Se concentrations exceeding the respective chronic standard and guidelines. We evaluate potential solutions by combining the transport model with an assessment of the cost to employ those practices. We use a framework common in economics and engineering fields alike, the Pareto frontier, to show the impact of four different best management practices on the tradeoffs between Se and cost objectives. We then extend that analysis to include institutional constraints that affect the economic feasibility associated with each practice. Results indicate that although water‐reducing strategies have the greatest impact on Se, they are the hardest for farmers to implement given constraints common to western water rights institutions. Therefore, our analysis shows that estimating economic and environmental tradeoffs, as is typically done with a Pareto frontier, will not provide an accurate picture of choices available to farmers where institutional constraints should also be considered.     

A comparison of storm-based and annual-based indices of hydrologic variability: a case study in Fort Benning, Georgia

The magnitude, frequency, duration, timing, and rate of change of hydrologic conditions regulate ecological processes in aquatic ecosystems. Conditions are typically characterized using annual-based hydrologic indices derived from daily and/or monthly stream flow data. In this study, we present an alternative approach to identify hydrologic indices based on storm hydrographs. Hydrologic indices derived from long-term daily flow data were compared to those from storm events for two headwater watersheds in Fort Benning, Georgia. Five hydrologic indices derived from daily flow data and storm events shared common features. Storm-based magnitude of mean peak discharge and mean response factor, frequency of bankfull discharge, rate of change in mean slopes of rising, and falling limb of the hydrograph were consistent with the results from long-term daily flow data. The annual flow increases and decreases were well matched by stormflow rising and falling. Both indicators showed one watershed having three times the response rates as compared to the other. Results suggested that select storm-based indices may be used as surrogates to the indices derived from long-term data.     

Essential and non-essential element concentrations in two sleeper shark species collected in arctic waters

A number of elements/metals have increased in arctic biota and are of concern due to their potential toxicity. Most studies on elements in the Arctic have focused on marine mammals and seabirds, but concentrations in the Greenland shark (Somniosus microcephalus) and Pacific sleeper shark (Somniosus pacificus), the only two shark species known to regularly inhabit arctic waters, have never been reported. To address this data gap, concentrations and patterns of 25 elements were analyzed in liver of Greenland sharks collected about Cumberland Sound (n=24) and Pacific sleeper sharks collected about Prince William Sound (n=14). Several non-essential elements differed between species/locations, which could suggest geographical exposure differences or ecological (e.g., diet) differences between the species. Certain essential elements also differed between the two sleeper sharks, which may indicate different physiological requirements between these closely related shark species, although information on such relationships are lacking for sharks and fish.     

Estimating naphthenic acids concentrations in laboratory-exposed fish and in fish from the wild

Naphthenic acids (NAs) are the most water-soluble organic components found in the Athabasca oil sands in Alberta, Canada, and these acids are released into aqueous tailing waters as a result of bitumen extraction. Although the toxicity of NAs to fish is well known, there has been no method available to estimate NAs concentrations in fish. This paper describes a newly developed analytical method using single ion monitoring gas chromatography-mass spectrometry (GC-MS) to measure NAs in fish, down to concentrations of approximately 0.1mgkg(-1) of fish flesh. This method was used to measure the uptake and depuration of commercial NAs in laboratory experiments. Exposure of rainbow trout (Oncorhynchus mykiss) to 3mg NAsl(-1) for 9d gave a bioconcentration factor of approximately 2 at pH 8.2. Within 1d after the fish were transferred to NAs-free water, about 95% of the NAs were depurated. In addition, the analytical method was used to determine if NAs were present in four species of wild fish - northern pike (Esox lucius), lake whitefish (Coregonus clupeaformis), white sucker (Catostomus commersoni), walleye (Sander vitreus) - collected from near the oil sands. Flesh samples from 23 wild fish were analyzed, and 18 of these had no detectable NAs. Four fish (one of each species) contained NAs at concentrations from 0.2 to 2.8mgkg(-1). The GC-MS results from one wild fish presented a unique problem. However, with additional work it was concluded that the NAs concentration in this fish was <0.1mgkg(-1).     

Calibration and validation of a regionally and seasonally stratified macroinvertebrate index for West Virginia wadeable streams

Multimetric indices (MMIs) are routinely used by federal, state, and tribal entities to assess the quality of aquatic resources. Because of their diversity, abundance, ubiquity, and sensitivity to environmental stress, benthic macroinvertebrates are well suited for MMIs. West Virginia has used a statewide family-level stream condition index (WVSCI) since 2002. We describe the development, validation, and application of a geographically- and seasonally partitioned genus-level index of most probable stream status (GLIMPSS) for West Virginia wadeable streams. Natural classification strata were evaluated with reference site communities using mean similarity analysis and non-metric multidimensional scaling ordination. Forty-one metrics spanning six ecological categories (richness, composition, tolerance, dominance, trophic groups, and habits) were evaluated for sensitivity, responsiveness, redundancy, range and variability across seasonal (spring and summer) and regional (mountains and plateau) strata. Through a step-wise metric selection process, 8–10 metrics were aggregated to comprise four stratum-specific GLIMPSS models (mountain/plateau and spring/summer). A comparison of GLIMPSS with WVSCI exhibited marked improvements where GLIMPSS detecting greater stream impacts. A variation of the GLIMPSS, which differs only in the family-level taxonomic identification of Chironomidae (GLIMPSS (CF)), was comparable to the full GLIMPSS. These MMIs are robust yet practical tools for evaluating impacts to water quality, instream and riparian habitat, and aquatic wildlife in wadeable riffle-run streams based on sensitivity, responsiveness, precision, and independent validation. These models may be used effectively to detect degradation of the naturally occurring benthic community, assess causes of biological degradation, and plan and evaluate remediation of damaged stream ecosystems.     

Patterns,correlates, and paternity consequences of extraterritorial foray behavior in the field sparrow (Spizella pusilla): an automated telemetry approach

Behavioral Ecology and Sociobiology - ᅟMales of fiddler crabs (genus: Uca) construct courtship structures using mud or sand to attract mate-searching females for underground mating. A sensory...     

Resident Motivations and Willingness-to-Pay for Urban Biodiversity Conservation in Guangzhou (China)

The monetary assessment of biodiversity measures the welfare damages brought by biodiversity losses and the cost-benefit analysis of conservation projects in a socio-economic context. The contingent valuation method could include motivational factors to strengthen economic analysis of nature conservation. This study analyzed Guangzhou residents’ motivations and willingness-to-pay (WTP) for an urban biodiversity conservation program in the National Baiyun Mountain Scenic Area (BMSA). The peri-urban natural site, offering refuge to some endemic species, is under increasing development pressures for recreational and residential use. A questionnaire survey was conducted in the Guangzhou metropolitan area during June to October 2007. We interviewed face-to-face 720 stratified sampled households to probe residents’ attitudes towards the city’s environmental issues, motivations for urban nature conservation, and WTP for biodiversity conservation. Principal component analysis identified five motivational factors, including environmental benefit, ecological diversity, nature-culture interaction, landscape-recreation function, and intergenerational sustainability, which illustrated the general economic values of urban nature. Logistic regression was applied to predict the probability of people being willing to pay for the urban biodiversity conservation in BMSA. The significant predictors of WTP included household income and the factor nature-culture interaction. The median WTP estimated RMB149/household (about US$19.5/household) per year and an aggregate of RMB291 million (approximately US$19.5/household) per year and an aggregate of RMB291 million (approximately US38.2 million) annually to support the urban conservation project. Including public motivations into contingent valuation presents a promising approach to conduct cost-benefit analysis of public projects in China.     

Preventing corruption in humanitarian assistance: perceptions, gaps and challenges

Corruption is a threat to the purpose of humanitarian assistance. Until fairly recently, humanitarian assistance has not been considered an important arena in broader efforts aimed at curbing corruption, and corruption has not always been considered a particularly important concern for humanitarian assistance despite the obviously challenging nature of the context of humanitarian emergencies. Corruption, though, is a threat to humanitarian action because it can prevent assistance from getting to the people who most need it, and because it can potentially undermine public support for such assistance. This paper examines perceptions of corruption and its affects, documents best practices, and outlines gaps in understanding. It suggests recommendations for improving the capacity of humanitarian agencies to prevent and manage the risk of corruption. Agencies have taken steps to combat corruption and improve accountability--downwards and upwards--but scope remains for improvement and for greater sharing of learning and good practice.     

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.