Predicting occurrence of juvenile shark habitat to improve conservation planning

Fishing and habitat degradation have increased the extinction risk of sharks, and conservation strategies recognize that survival of juveniles is critical for the effective management of shark populations. Despite the rapid expansion of marine protected areas (MPAs) globally, the paucity of shark‐monitoring data on large scales (100s–1000s km) means that the effectiveness of MPAs in halting shark declines remains unclear. Using data collected by baited remote underwater video systems (BRUVS) in northwestern Australia, we developed generalized linear models to elucidate the ecological drivers of habitat suitability for juvenile sharks. We assessed occurrence patterns at the order and species levels. We included all juvenile sharks sampled and the 3 most abundant species sampled separately (grey reef [Carcharhinus amblyrhynchos], sandbar [Carcharhinus plumbeus], and whitetip reef sharks [Triaenodon obesus]). We predicted the occurrence of juvenile sharks across 490,515 km² of coastal waters and quantified the representation of highly suitable habitats within MPAs. Our species‐level models had higher accuracy (? ≥ 0.69) and deviance explained (≥48%) than our order‐level model (? = 0.36 and deviance explained of 10%). Maps of predicted occurrence revealed different species‐specific patterns of highly suitable habitat. These differences likely reflect different physiological or resource requirements between individual species and validate concerns over the utility of conservation targets based on aggregate species groups as opposed to a species‐focused approach. Highly suitable habitats were poorly represented in MPAs with the most restrictions on extractive activities. This spatial mismatch possibly indicates a lack of explicit conservation targets and information on species distribution during the planning process. Non‐extractive BRUVS provided a useful platform for building the suitability models across large scales to assist conservation planning across multiple maritime jurisdictions, and our approach provides a simple for method for testing the effectiveness of MPAs.     

Effects of particle size on chemical speciation and bioavailability of copper to earthworms (Eisenia fetida) exposed to copper nanoparticles

To investigate the role of particle size on the oxidation, bioavailability, and adverse effects of manufactured Cu nanoparticles (NPs) in soils, we exposed the earthworm Eisenia ferida to a series of concentrations of commercially produced NPs labeled as 20- to 40-nm or < 100-nm Cu in artificial soil media. Effects on growth, mortality, reproduction, and expression of a variety of genes associated with metal homeostasis, general stress, and oxidative stress were measured. We also used X-ray absorption spectroscopy and scanning X-ray fluorescence microscopy to characterize changes in chemical speciation and spatial distribution of the NPs in soil media and earthworm tissues. Exposure concentrations of Cu NPs up to 65 mg kg(-1) caused no adverse effects on ecologically relevant endpoints. Increases in metallothionein expression occurred at concentrations exceeding 20 mg kg(-1) of Cu NPs and concentrations exceeding 10 mg kg(-1) of CuSO4. Based on the relationship of Cu tissue concentration to metallothionein expression level and the spatial distribution and chemical speciation of Cu in the tissues, we conclude that Cu ions and oxidized Cu NPs were taken up by the earthworms. This study suggests that oxidized Cu NPs may enter food chains from soil but that adverse effects in earthworms are likely to occur only at relatively high concentrations (> 65 mg Cu kg(-1) soil).     

Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island

Surveillance for invasive non-indigenous species (NIS) is an integral part of a quarantine system. Estimating the efficiency of a surveillance strategy relies on many uncertain parameters estimated by experts, such as the efficiency of its components in face of the specific NIS, the ability of the NIS to inhabit different environments, and so on. Due to the importance of detecting an invasive NIS within a critical period of time, it is crucial that these uncertainties be accounted for in the design of the surveillance system. We formulate a detection model that takes into account, in addition to structured sampling for incursive NIS, incidental detection by untrained workers. We use info-gap theory for satisficing (not minimizing) the probability of detection, while at the same time maximizing the robustness to uncertainty. We demonstrate the trade-off between robustness to uncertainty, and an increase in the required probability of detection. An empirical example based on the detection of Pheidole megacephala on Barrow Island demonstrates the use of info-gap analysis to select a surveillance strategy.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

The macroeconomic determinants of volatility in precious metals markets

This paper models the monthly price volatilities of four precious metals (gold, silver, platinum and palladium prices) and investigates the macroeconomic determinants (business cycle, monetary environment and financial market sentiment) of these volatilities. Gold volatility is shown to be explained by monetary variables, but this is not true for silver. Overall, there is limited evidence that the same macroeconomic factors jointly influence the volatility processes of the four precious metal price series, although there is evidence of volatility feedback between the precious metals. These results are consistent with the view that precious metals are too distinct to be considered a single asset class, or represented by a single index. This finding is of importance for portfolio managers and investors.     

Polybrominated diphenyl ethers (PBDEs) in leachates from selected landfill sites in South Africa

The last few decades have seen dramatic growth in the scale of production and the use of polybrominated diphenyl ethers (PBDEs) as flame retardants. Consequently, PBDEs such as BDE -28, -47, -66, -71, -75, -77, -85, -99, -100, -119, -138, -153, -154, and -183 have been detected in various environmental matrices. Generally, in South Africa, once the products containing these chemicals have outlived their usefulness, they are discarded into landfill sites. Consequently, the levels of PBDEs in leachates from landfill sites may give an indication of the general exposure and use of these compounds. The present study was aimed at determining the occurrence and concentrations of most common PBDEs in leachates from selected landfill sites. The extraction capacities of the solvents were also tested. Spiked landfill leachate samples were used for the recovery tests. Separation and determination of the PBDE congeners were carried out with a gas chromatograph equipped with Ni63 electron capture detector. The mean percentage recoveries ranged from 63% to 108% (n=3) for landfill leachate samples with petroleum ether giving the highest percentage extraction. The mean concentrations of PBDEs obtained ranged from ND to 2670pgl(-1), ND to 6638pgl(-1), ND to 7230pgl(-1), 41 to 4009pgl(-1), 90 to 9793pgl(-1) for the Garankuwa, Hatherly, Kwaggarsrand, Soshanguve and Temba landfill sites, respectively. Also BDE -28, -47, -71 and BDE-77 were detected in the leachate samples from all the landfill sites; and all the congeners were detected in two of the oldest landfill sites. The peak concentrations were recorded for BDE-47 at three sites and BDE-71 and BDE-75 at two sites. The highest concentration, 9793+/-1.5pgl(-1), was obtained for the Temba landfill site with the highest BOD value. This may suggest some influence of organics on the level of PBDEs. Considering the leaching characteristics of brominated flame retardants, there is a high possibility that with time these compounds may infiltrate into the groundwater around the sites since most of the sites are not adequately lined.     

Spatial and seasonal variations in atrazine and metolachlor surface water concentrations in Ontario (Canada) using ELISA

Concerns regarding the impacts of pesticides on aquatic species and drinking water sources have increased demands on water quality monitoring programs; however the costs of sample analysis can be prohibitive. In this study we investigated enzyme-linked immunosorbent assay (ELISA) as a cost-effective, high through-put method for measuring pesticide concentrations in surface waters. Seven hundred and thirty-nine samples from 158 locations throughout Ontario were analysed for atrazine and metolachlor from April to October 2007. Concentrations ranged from <0.1 to 3.91 μg L⁻¹ (median = 0.12 μg L⁻¹) for atrazine and from <0.1 to 1.83 μg L⁻¹ (median = 0.09 μg L⁻¹) for metolachlor. Peak concentrations occurred in late spring/early summer, in rural agricultural locations, and decreased over the remainder of the growing season for both herbicides. About 3% of the samples that had ELISA results occurring above the limit of quantification (0.10 μg L⁻¹) were evaluated against gas chromatography-mass spectrometry (GC-MS). Linear regression analysis revealed a R² value of 0.88 and 0.39, for atrazine and metolachlor, respectively. ELISA tended to overestimate concentrations for atrazine and metolachlor, most likely because the ELISA kits also detect their metabolites. Atrazine data suggest that ELISA may be used complementary with GC-MS analysis to enhance the spatial and temporal resolution of a water quality monitoring study. The commercially available metolachlor ELISA kit requires further investigation. ELISA may be used to detect atrazine and metolachlor in surface water samples, but it is not recommended as a quantitative replacement for traditional analytical methods.     

Social learning in a policy-mandated collaboration: community wildfire protection planning in the eastern United States

Policies such as the US Healthy Forests Restoration Act (HFRA) mandate collaboration in planning to create benefits such as social learning and shared understanding among partners. However, some question the ability of top-down policy to foster successful local collaboration. Through in-depth interviews and document analysis, this paper investigates social learning and transformative learning in three case studies of Community Wildfire Protection Planning (CWPP), a policy-mandated collaboration under HFRA. Not all CWPP groups engaged in social learning. Those that did learned most about organisational priorities and values through communicative learning. Few participants gained new skills or knowledge through instrumental learning. CWPP groups had to commit to learning, but the design of the collaborative-mandate influenced the type of learning that was most likely to occur. This research suggests a potential role for top-down policy in setting the structural context for learning at the local level, but also confirms the importance of collaborative context and process in fostering social learning.     

Decreased Runoff Response to Precipitation,Little Missouri River Basin,Northern Great Plains,USA

High variability in precipitation and streamflow in the semiarid northern Great Plains causes large uncertainty in water availability. This uncertainty is compounded by potential effects of future climate change. We examined historical variability in annual and growing season precipitation, temperature, and streamflow within the Little Missouri River Basin and identified differences in the runoff response to precipitation for the period 1976‐2012 compared to 1939‐1975 (n = 37 years in both cases). Computed mean values for the second half of the record showed little change (<5%) in annual or growing season precipitation, but average annual runoff at the basin outlet decreased by 22%, with 66% of the reduction in flow occurring during the growing season. Our results show a statistically significant (p < 0.10) 27% decrease in the annual runoff response to precipitation (runoff ratio). Surface‐water withdrawals for various uses appear to account for <12% of the reduction in average annual flow volume, and we found no published or reported evidence of substantial flow reduction caused by groundwater pumping in this basin. Results of our analysis suggest that increases in monthly average maximum and minimum temperatures, including >1°C increases in January through March, are the dominant driver of the observed decrease in runoff response to precipitation in the Little Missouri River Basin.