Monitoring perchlorate exposure and thyroid hormone status among raccoons inhabiting a perchlorate-contaminated site

Perchlorate is a water soluble anion that is readily accumulated in vegetation. It inhibits uptake of iodide into thyroid gland tissue, thereby reducing production of thyroid hormones. Potential raccoon food items including berries, fish, and vegetation collected at a contaminated site contained quantifiable concentrations of perchlorate as determined by ion chromatography. Therefore, we monitored resident raccoons for exposure to perchlorate by examining plasma perchlorate and thyroid hormone concentrations. Resulting analytical data failed to demonstrate perchlorate exposure among raccoons that likely consumed food items collected along perchlorate-contaminated water bodies. There were no correlations between triiodothyronine or thyroxine and thyroid stimulating hormone concentrations, but triiodothyronine concentrations in raccoon plasma were significantly higher in 2000 than in 2001 (p = 0.0081). These data suggest that natural attenuation and remedial efforts initiated in January of 2001 may have reduced perchlorate exposure among raccoons inhabiting this site from 2000 to 2001. Temporal, spatial, and analytical factors limited our ability to quantify exposure among raccoons, however, our data do not indicate that raccoons currently inhabiting this site are at risk for significant exposure to perchlorate and subsequent effects.     

Effect of gervital in attenuating hepatotoxicity caused by methotrexate or azathioprine in adult albino rats

Environmental Science and Pollution Research - Methotrexate (MTX) and azathioprine (AZA) are chemotherapeutic, antimetabolic, and immunosuppressive agents with substantial risks such as oxidative...     

Behavior of potentially toxic elements from stoker-boiler fly ash in Interior Alaska: paired batch leaching and solid-phase characterization

Environmental Science and Pollution Research - Despite significant investigation of fly ash spills and mineralogical controls on the release of potentially toxic elements (PTEs) from fly ash,...     

Effects of experimental protocol on global vegetation model accuracy: A comparison of simulated and observed vegetation patterns for Asia

Prognostic vegetation models have been widely used to study the interactions between environmental change and biological systems. This study examines the sensitivity of vegetation model simulations to: (i) the selection of input climatologies representing different time periods and their associated atmospheric CO₂ concentrations, (ii) the choice of observed vegetation data for evaluating the model results, and (iii) the methods used to compare simulated and observed vegetation. We use vegetation simulated for Asia by the equilibrium vegetation model BIOME4 as a typical example of vegetation model output. BIOME4 was run using 19 different climatologies and their associated atmospheric CO₂ concentrations. The Kappa statistic, Fuzzy Kappa statistic and a newly developed map-comparison method, the Nomad index, were used to quantify the agreement between the biomes simulated under each scenario and the observed vegetation from three different global land- and tree-cover data sets: the global Potential Natural Vegetation data set (PNV), the Global Land Cover Characteristics data set (GLCC), and the Global Land Cover Facility data set (GLCF). The results indicate that the 30-year mean climatology (and its associated atmospheric CO₂ concentration) for the time period immediately preceding the collection date of the observed vegetation data produce the most accurate vegetation simulations when compared with all three observed vegetation data sets. The study also indicates that the BIOME4-simulated vegetation for Asia more closely matches the PNV data than the other two observed vegetation data sets. Given the same observed data, the accuracy assessments of the BIOME4 simulations made using the Kappa, Fuzzy Kappa and Nomad index map-comparison methods agree well when the compared vegetation types consist of a large number of spatially continuous grid cells. The results of this analysis can assist model users in designing experimental protocols for simulating vegetation.     

Managing plant population spread: prediction and analysis using a simple model

Models can be used to direct the management of population spread for the control of invasives or to encourage species of conservation value. Analytical models are attractive because of their theoretical basis and limited data requirements, but there is concern that their simplicity may limit their practical utility. We address the applied use of simple models in a study of a declining annual herb, Rhinanthus minor. We parameterized a population-spread model using field data on demography and dispersal for four management systems: grazed only (GR), hay-cut once (H1), hay-cut twice (H2), and hay-cut with autumn grazing (HG). Within a replicated experiment we measured spread rates of introduced R. minor populations over eight years. The modeled and measured spread rates were very similar in terms of both patterns of management effects and absolute values, so that in both cases HG > H2, H1 > GR. The treatments affected both dispersal and demography (establishment and survival) and so we used decomposition approaches to analyze the major causes of differences in population spread. Increased dispersal under hay-cutting was more important than demographic changes and accounted for approximately 70% of the differences in spread rate between the hay-cut and grazed-only treatments. Furthermore, management effects on the tail of the dispersal curve were by far the most critical in governing spread. This study suggests that simple models can be used to inform practical conservation management, and we demonstrate straightforward uses of our model to predict the impacts of different management strategies. While simple models can give accurate projections, we emphasize that they must be parameterized with high-quality data gathered at the appropriate spatial scale.     

Group navigation and the "many-wrongs principle" in models of animal movement

Traditional studies of animal navigation over both long and short distances have usually considered the orientation ability of the individual only, without reference to the implications of group membership. However, recent work has suggested that being in a group can significantly improve the ability of an individual to align toward and reach a target direction or point, even when all group members have limited navigational ability and there are no leaders. This effect is known as the "many-wrongs principle" since the large number of individual navigational errors across the group are suppressed by interactions and group cohesion. In this paper, we simulate the many-wrongs principle using a simple individual-based model of movement based on a biased random walk that includes group interactions. We study the ability of the group as a whole to reach a target given different levels of individual navigation error, group size, interaction radius, and environmental turbulence. In scenarios with low levels of environmental turbulence, simulation results demonstrate a navigational benefit from group membership, particularly for small group sizes. In contrast, when movement takes place in a highly turbulent environment, simulation results suggest that the best strategy is to navigate as individuals rather than as a group.     

Plant diversity, CO2, and N influence inorganic and organic N leaching in grasslands

In nitrogen (N)-limited systems, the potential to sequester carbon depends on the balance between N inputs and losses as well as on how efficiently N is used, yet little is known about responses of these processes to changes in plant species richness, atmospheric CO2 concentration ([CO2]), and N deposition. We examined how plant species richness (1 or 16 species), elevated [CO2] (ambient or 560 ppm), and inorganic N addition (0 or 4 g x m(-2) x yr(-1)) affected ecosystem N losses, specifically leaching of dissolved inorganic N (DIN) and organic N (DON) in a grassland field experiment in Minnesota, USA. We observed greater DIN leaching below 60 cm soil depth in the monoculture plots (on average 1.8 and 3.1 g N x m(-2) x yr(-1) for ambient N and N-fertilized plots respectively) than in the 16-species plots (0.2 g N x m(-2) x yr(-1) for both ambient N and N-fertilized plots), particularly when inorganic N was added. Most likely, loss of complementary resource use and reduced biological N demand in the monoculture plots caused the increase in DIN leaching relative to the high-diversity plots. Elevated [CO2] reduced DIN concentrations under conditions when DIN concentrations were high (i.e., in N-fertilized and monoculture plots). Contrary to the results for DIN, DON leaching was greater in the 16-species plots than in the monoculture plots (on average 0.4 g N x m(-2) x yr(-1) in 16-species plots and 0.2 g N x m(-2) x yr(-1) in monoculture plots). In fact, DON dominated N leaching in the 16-species plots (64% of total N leaching as DON), suggesting that, even with high biological demand for N, substantial amounts of N can be lost as DON. We found no significant main effects of elevated [CO2] on DIN or DON leaching; however, elevated [CO2] reduced the positive effect of inorganic N addition on DON leaching, especially during the second year of observation. Our results suggest that plant species richness, elevated [CO2], and N deposition alter DIN loss primarily through changes in biological N demand. DON losses can be as large as DIN loss but are more sensitive to organic matter production and turnover.     

Plant competition varies with community composition in an edaphically complex landscape

There is currently no consensus on how physical and biological factors affect competitive intensity. Tests of whether competitive intensity varies along axes of environmental change have commonly been conducted in systems with a single strong environmental gradient, such as productivity, a soil resource, or an environmental stress. Frequently, these same axes are associated with changes in species composition, yet few studies have asked whether shifts in the identity of competitors affect competitive intensity. We ask whether resources (nutrients, water), stressors (heavy metals, Ca:Mg ratio), productivity (aboveground biomass), or species identity (an ordination axis of plant community composition) were the best predictors of the intensity of competition in a heterogeneous grassland landscape that included multiple independent environmental gradients. The reproductive fitness of six annual plant species was measured in the presence and absence of competitors and used to calculate relative interaction intensity (RII). We found that RII was best predicted by community composition. Nutrient availability was also important, and a post hoc test showed that competitive intensity was best explained by the combined effects of community composition and nutrient availability. We argue that community composition may be the most effective metric for predicting competitive intensity in many ecosystems because it includes both the competitive effects of the local community and information about covarying environmental characteristics.     

Policy Windows, Policy Change, and Organizational Learning: Watersheds in the Evolution of Watershed Management

Employing in-depth, elite interviews, this empirical research contributes to understanding the dynamics among policy windows, policy change, and organizational learning. First, although much of the research on agenda setting—how issues attract enough attention that action is taken to address them—has been conducted at the national scale, this work explores the subnational, regional scale. With decentralization, regional-scale environmental decision-making has become increasingly important. Second, this research highlights the role of policy windows and instances of related organizational learning identified by natural resources managers. Having practitioners identify focusing events contrasts with the more typical approach of the researcher identifying a particular focusing event or events to investigate. A focusing event is a sudden, exceptional experience that, because of how it leads to harm or exposes the prospect for great devastation, is perceived as the impetus for policy change.     

Biochar as low-cost sorbent of volatile fuel organic compounds: potential application to water remediation

Pyrolysis of waste materials to produce biochar is an excellent and suitable alternative supporting a circular bio-based economy. One of the properties attributed to biochar is the capacity for sorbing organic contaminants, which is determined by its composition and physicochemical characteristics. In this study, the capacity of waste-derived biochar to retain volatile fuel organic compounds (benzene, toluene, ethylbenzene and xylene (BTEX) and fuel oxygenates (FO)) from artificially contaminated water was assessed using batch-based sorption experiments. Additionally, the sorption isotherms were established. The results showed significant differences between BTEX and FO sorption on biochar, being the most hydrophobic and non-polar contaminants those showing the highest retention. Furthermore, the sorption process reflected a multilayer behaviour and a relatively high sorption capacity of the biochar materials. Langmuir and Freundlich models were adequate to describe the experimental results and to detect general differences in the sorption behaviour of volatile fuel organic compounds. It was also observed that the feedstock material and biochar pyrolysis conditions had a significant influence in the sorption process. The highest sorption capacity was found in biochars produced at high temperature (>?400 °C) and thus rich in aromatic C, such as eucalyptus and corn cob biochars. Overall, waste-derived biochar offers a viable alternative to be used in the remediation of volatile fuel organic compounds from water due to its high sorption capacity.