Occupational boundary play: Crafting a sense of identity legitimacy in an emerging occupation

In emerging occupations, individuals are given very little prepackaged identity “content”—for example, occupational values, legitimating ideologies, clear goals, tasks, and/or routines—to help them build their individual-level occupational identities. By contrast, individuals in well-established occupations (e.g., professions) are given ample identity content, and prior identity research has examined identity work processes almost exclusively in the context of such occupations. Consequently, prior theory assumes that identity work is mostly a matter of tailoring prepackaged identity content to fit one's individual-level preferences and objectives. Prior theory is therefore of limited use in emerging occupations, where the key identity problem is not one of tailoring identity content effectively but creating an identity in the first place—more specifically, an identity whose existence feels justified and valid. Thus, in this paper, we ask: how do individuals in emerging occupations construct an internal sense that “who they are” is necessary, desirable, and appropriate (i.e., legitimate) within the broader occupational landscape? On the basis of a grounded theory study of health coaches, we suggest that individuals in such circumstances can craft this sense of “identity legitimacy” via a sensemaking process we call occupational boundary play. This process consists of both “occupational boundary setting” and “occupational boundary blurring,” the former providing for individuals a sense of identity novelty and the latter providing a sense of identity familiarity. Taken together, this subjective experience of both novelty and familiarity provides for individuals the sense that “who they are” is legitimate within the broader occupational landscape.     

Evaluation of land use and water quality in an agricultural watershed in the USA indicates multiple sources of bacterial impairment

Pathogens are the number one cause of impairments of assessed rivers and streams in the USA and pose a significant human health hazard. The Dry Run Creek Watershed in Northeast Iowa has been designated as impaired by the State of Iowa because of high levels of Escherichia coli bacteria. To investigate the nature of this impairment, land use and stream bank assessments were coupled with comprehensive water quality monitoring. Physical, chemical, and biological parameters were measured at 13 different sites in the watershed, including pH, temperature, conductivity, dissolved oxygen, turbidity, total Kjeldahl nitrogen, ammonia-N, nitrate?+?nitrite-N, total phosphorus, and E. coli. In addition, benthic macroinvertebrate communities were analyzed at seven sites, and optical brightener tests were performed late in the season. Results identified segments of the watershed that were more prominent contributors of E. coli, and correlations were observed between levels of E. coli and several chemical parameters, including ammonia-N, total Kjeldahl nitrogen, and total phosphorus. Interestingly, distinct sites emerged as more prominent contributors of these elements during rain vs. non-rain events, suggesting different types of sources. Both the amount of rainfall and the time elapsed between the rain event and the sampling influenced E. coli levels during wet weather conditions. Nitrate?+?nitrite-N displayed a unique response to rain events compared with the other parameters, suggesting a different delivery route. Analyses of benthic macroinvertebrate communities were consistent with pollution trends. Collectively, these data suggest distinct agriculturally related E. coli contributions, as well as specific areas and practices for water quality improvement strategies. This study can serve as a resource for evaluating agricultural watersheds that are impaired for bacteria.     

Using a relative health indicator (RHI) metric to estimate health risk reductions in drinking water

When a new drinking water regulation is being developed, the USEPA conducts a health risk reduction and cost analysis to, in part, estimate quantifiable and non-quantifiable cost and benefits of the various regulatory alternatives. Numerous methodologies are available for cumulative risk assessment ranging from primarily qualitative to primarily quantitative. This research developed a summary metric of relative cumulative health impacts resulting from drinking water, the relative health indicator (RHI). An intermediate level of quantification and modeling was chosen, one which retains the concept of an aggregated metric of public health impact and hence allows for comparisons to be made across “cups of water,” but avoids the need for development and use of complex models that are beyond the existing state of the science. Using the USEPA Six-Year Review data and available national occurrence surveys of drinking water contaminants, the metric is used to test risk reduction as it pertains to the implementation of the arsenic and uranium maximum contaminant levels and quantify “meaningful” risk reduction. Uranium represented the threshold risk reduction against which national non-compliance risk reduction was compared for arsenic, nitrate, and radium. Arsenic non-compliance is most significant and efforts focused on bringing those non-compliant utilities into compliance with the 10 μg/L maximum contaminant level would meet the threshold for meaningful risk reduction.     

Greenhouse gas balance for composting operations

The greenhouse gas (GHG) impact of composting a range of potential feedstocks was evaluated through a review of the existing literature with a focus on methane (CH(4)) avoidance by composting and GHG emissions during composting. The primary carbon credits associated with composting are through CH(4) avoidance when feedstocks are composted instead of landfilled (municipal solid waste and biosolids) or lagooned (animal manures). Methane generation potential is given based on total volatile solids, expected volatile solids destruction, and CH(4) generation from lab and field incubations. For example, a facility that composts an equal mixture of manure, newsprint, and food waste could conserve the equivalent of 3.1 Mg CO(2) per 1 dry Mg of feedstocks composted if feedstocks were diverted from anaerobic storage lagoons and landfills with no gas collection mechanisms. The composting process is a source of GHG emissions from the use of electricity and fossil fuels and through GHG emissions during composting. Greenhouse gas emissions during composting are highest for high-nitrogen materials with high moisture contents. These debits are minimal in comparison to avoidance credits and can be further minimized through the use of higher carbon:nitrogen feedstock mixtures and lower-moisture-content mixtures. Compost end use has the potential to generate carbon credits through avoidance and sequestration of carbon; however, these are highly project specific and need to be quantified on an individual project basis.     

Hydrogen peroxide-assisted UV photodegradation of Lindane

Aqueous solutions of gamma-hexachlorocyclohexane (Lindane) were photolyzed (lambda=254 nm) under a variety of solution conditions. The initial concentrations of hydrogen peroxide (H(2)O(2)) and Lindane varied from 0 to 20 mM and 0.21 to 0.22 microM, respectively, the pH ranged from 3 to 11, and several concentration ratios of Suwannee River humic acid and fulvic acid were dissolved in the irradiated solutions. Lindane rapidly reacted, and the maximum reaction rate constant (9.7 x 10(-3) s(-1)) was observed at pH 7 and initial [H(2)O(2)]=1 mM. Thus, 90% of the Lindane is destroyed in approximately 4 min under these conditions. In addition, within 15 min, all chlorine atoms were converted to chloride ion, indicating that chlorinated organic by-products do not accumulate. The reactor was characterized by measuring the photon flux (7.04 x 10(-6) E s(-1)) and the cumulative production of *OH during irradiation. The cumulative *OH production during irradiation was fastest at an initial [H(2)O(2)]=5 mM (k=0.77 micro M s(-1)).     

Spatial Variability in Cost and Success of Revegetation in a Wyoming Big Sagebrush Community

The ecological integrity of the Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle and A. Young) alliance is being severely interrupted by post-fire invasion of non-native annual grasses. To curtail this invasion, successful post-fire revegetation of perennial grasses is required. Environmental factors impacting post-fire restoration success vary across space within the Wyoming big sagebrush alliance; however, most restorative management practices are applied uniformly. Our objectives were to define probability of revegetation success over space using relevant soil-related environmental factors, use this information to model cost of successful revegetation and compare the importance of vegetation competition and soil factors to revegetation success. We studied a burned Wyoming big sagebrush landscape in southeast Oregon that was reseeded with perennial grasses. We collected soil and vegetation data at plots spaced at 30?m intervals along a 1.5?km transect in the first two years post-burn. Plots were classified as successful (>5?seedlings/m(2)) or unsuccessful based on density of seeded species. Using logistic regression we found that abundance of competing vegetation correctly predicted revegetation success on 51?% of plots, and soil-related variables correctly predicted revegetation performance on 82.4?% of plots. Revegetation estimates varied from $167.06 to $43,033.94/ha across the 1.5?km transect based on probability of success, but were more homogenous at larger scales. Our experimental protocol provides managers with a technique to identify important environmental drivers of restoration success and this process will be of value for spatially allocating logistical and capital expenditures in a variable restoration environment.     

Hexavalent chromium reduction kinetics in rodent stomach contents

Reduction of hexavalent chromium (Cr(VI)) to trivalent chromium (Cr(III)) in the stomach prior to absorption is a well-recognized detoxification process thought to limit the toxicity of ingested Cr(VI). However, administration of high concentrations of Cr(VI) in drinking water cause mouse small intestinal tumors, and quantitative measures of Cr(VI) reduction rate and capacity for rodent stomach contents are needed for interspecies extrapolation using physiologically-based toxicokinetic (PBTK) models. Ex vivo studies using stomach contents of rats and mice were conducted to quantify Cr(VI) reduction rate and capacity for loading rates (1-400 mg Cr(VI) L⁻¹ stomach contents) in the range of recent bioassays. Cr(VI) reduction was measured with speciated isotope dilution mass spectrometry to quantify dynamic Cr(VI) and Cr(III) concentrations in stomach contents at select time points over 1 h. Cr(VI) reduction followed mixed second-order kinetics, dependent upon concentrations of both Cr(VI) and the native reducing agents. Approximately 16 mg Cr(VI)-equivalents of reducing capacity per L of fed stomach contents (containing gastric secretions, saliva, water and food) was found for both species. The second-order rate constants were 0.2 and 0.3 L mg⁻¹ h⁻¹ for mice and rats, respectively. These findings support that, at the doses that caused cancer in the mouse small intestine (?20 mg Cr(VI) L⁻¹ in drinking water), the reducing capacity of stomach contents was likely exceeded. Thus, for extrapolation of target tissue dose in risk assessment, PBTK models are necessary to account for competing kinetic rates including second order capacity-limited reduction of Cr(VI) to Cr(III).     

Potential sources of methylmercury in tree foliage

Litterfall is a major source of mercury (Hg) and toxic methylmercury (MeHg) to forest soils and influences exposures of wildlife in terrestrial and aquatic ecosystems. However, the origin of MeHg associated with tree foliage is largely unknown. We tested the hypothesis that leaf MeHg is influenced by root uptake and thereby related to MeHg levels in soils. Concentrations of MeHg and total Hg in deciduous and coniferous foliage were unrelated to those in soil at 30 urban and rural forested locations in southwest Ohio. In contrast, tree genera and trunk diameter were significant variables influencing Hg in leaves. The fraction of total Hg as MeHg averaged 0.4% and did not differ among tree genera. Given that uptake of atmospheric Hg(0) appears to be the dominant source of total Hg in foliage, we infer that MeHg is formed by in vivo transformation of Hg in proportion to the amount accumulated.     

An introduction to decision science for conservation

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.     

Fundamental insights on when social network data are most critical for conservation planning

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state-dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small-scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species-poor sites are subsets of species-rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small-scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un-nested and when social networks are likely to be centralized.