Different food preferences in four sympatric deep-sea Macrourid fishes

Feeding habits of the four most abundant deep-sea demersal trawl-caught macrourids on Chatham Rise, New Zealand, were examined from stomach contents. Two species were predominantly benthic foragers: Coelorinchus bollonsi on infaunal and epifaunal polychaetes, and C. aspercephalus on epifaunal crustaceans; and two species were predominantly mesopelagic foragers; C. oliverianus on calanoid copepods, and Lepidorhynchus denticulatus on mesopelagic crustaceans. The most important predictors of diet variability were identified using distance-based linear models and included areal predictors in all four species, fish size in C. aspercephalus, C. bollonsi and L. denticulatus, and sample year in C. bollonsi. Cluster analyses showed that the diets of C. aspercephalus and C. bollonsi were most distinct. There was a greater interspecific similarity in diet in the spatial and fish size subgroups of C. oliverianus and L. denticulatus, than at the species level. Failing to account for areal, temporal, and ontogenetic variability in diets may bias evaluations of resource competition.     

Spatially stratified sampling using auxiliary information for geostatistical mapping

This paper presents a method of spatial sampling based on stratification by Local Moran’s I _i calculated using auxiliary information. The sampling technique is compared to other design-based approaches including simple random sampling, systematic sampling on a regular grid, conditional Latin Hypercube sampling and stratified sampling based on auxiliary information, and is illustrated using two different spatial data sets. Each of the samples for the two data sets is interpolated using regression kriging to form a geostatistical map for their respective areas. The proposed technique is shown to be competitive in reproducing specific areas of interest with high accuracy.     

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.     

A contingency model of union commitment and participation: Meta‐analysis of the antecedents of militant and nonmilitant activities

Correlation estimates from a meta‐analysis of 108 studies including 126 independent samples and nearly 70 000 participants were used to test multiple path models of the antecedents of militant and nonmilitant union participation. Variables moderating the relationship between union commitment and participation were examined. The magnitude of the relationship between commitment and militant participation was moderated by status‐based group membership (i.e., white collar versus blue collar), supporting a contingency model of union participation. Results updated and further developed an integrative model of union participation. Copyright © 2010 John Wiley & Sons, Ltd.     

Challenges for physical characterization of silver nanoparticles under pristine and environmentally relevant conditions

The reported size distribution of silver nanoparticles (AgNPs) is strongly affected by the underlying measurement method, agglomeration state, and dispersion conditions. A selection of AgNP materials with vendor-reported diameters ranging from 1 nm to 100 nm, various size distributions, and biocompatible capping agents including citrate, starch and polyvinylpyrrolidone were studied. AgNPs were diluted with either deionized water, moderately hard reconstituted water, or moderately hard reconstituted water containing natural organic matter. Rigorous physico-chemical characterization by consensus methods and protocols where available enables an understanding of how the underlying measurement method impacts the reported size measurements, which in turn provides a more complete understanding of the state (size, size distribution, agglomeration, etc.) of the AgNPs with respect to the dispersion conditions. An approach to developing routine screening is also presented.     

Climate change risk management: a Mental Modeling application

The potential impacts of climate change are varied and highly uncertain, and pose a significant challenge to agencies charged with managing environmental risks. This paper presents a comprehensive and structured Mental Modeling approach to elicit, organize and present relevant information from experts and stakeholders about the factors influencing environmental risk management in the face of climate change. We present and review an initiative undertaken by the United States Army Corps of Engineers (USACE) to characterize climate change challenges to USACE environmental risk management activities, and to identify gaps with respect to science, engineering, and organizational processes for addressing these challenges. By employing Mental Modeling, the research has characterized the influences of climate change on USACE environmental risk management, and aggregating recommendations from 28 experts. In addition, the study identifies the most important opportunities to improve organizational response to climate change, ranging from focused research and development of technical capabilities to broad paradigm shifts and systemic organizational improvements within the USACE environmental risk management programs. This study demonstrates that Mental Modeling is a useful tool for understanding complex problems, identifying gaps, and formulating strategies, and can be used by a multitude of organizations and agencies.     

Amsterdam: planning and policy for the ideal city?

Is Amsterdam the “ideal city?” Many of the social, economic, and environmental problems facing Amsterdam are considerably less than those in cities in the USA, and in most cases, Western Europe. Amsterdam, at this moment in history, might be the world's greatest city because of its ability to ensure basic necessities, freedom, and creativity. Tolerance of drugs, sexual freedom, along with the integration of different races helps reduce many of the “social problems” faced by most cities. We have compared, on a per capita basis, differences between Amsterdam/Holland and the USA. The Netherlands is a tiny country, and comparisons are made on a city and per capita numbers at the national level. Our data show that Amsterdam has lower crime, murder, rape, drug usage (cocaine, marijuana), teenage pregnancy, diabetes, obesity, suicide, abortion rates, infant mortality, dependence on fossil fuels, and homelessness, and racial segregation is considerably less. People live longer because of Amsterdam's walkability and bike usage and access to parks. Indeed, the Netherlands leads both Western European and the USA in the proportion of trips made walking and bicycling, with significantly reduced car dependency. Ghettos are nearly non-existent compared with the segregation in the 1940s/1950s. Quality housing is supplied to everyone that gives pride of place compared with the stark, cold, and institutional “projects” provided by the US federal government. Amsterdam leads Western Europe by 35% in social housing, compared with runner-up UK, which has significantly less. The modernism of the 1960s where the poor were warehoused is nearly all gone. People living in Amsterdam seem more tolerant, secure, happier, and healthier compared with citizens in the USA. Great cities provide opportunities for all citizens to enhance their lives and ensure an unrivalled level of freedom. We demonstrate that social and environmental justice are tied together. Amsterdam is by no means perfect, but in comparison with many other democratic industrial cities, it is a far better place for citizens of all races, religions, and incomes.     

Polychlorinated biphenyls and omega-3 fatty acid exposure from fish consumption, and thyroid cancer among New York anglers

Fish from the Great Lakes contain polychlorinated biphenyls(PCBs)which have been shown to disrupt endocrine function and mimic thyroid hormones,but they also contain beneficial omega-3 fatty acids that may offer protection against endocrine cancers.The purpose of this study was to examine the effects of Lake Ontario fish consumption and the estimated consumption of PCBs and omega-3 fatty acids on the risk of thyroid cancer in a group of sport fishermen.Anglers from the New York State Angler Cohort Study were followed for cancer incidence from 1991–2008.Twenty-seven cases of incident thyroid cancer and 108 controls were included in the analyses.Total estimated fish consumption,estimated omega-3 fatty acid consumption,and estimated PCB consumption from Lake Ontario fish were examined for an association with the incidence of thyroid cancer,while matching on sex,and controlling for age and smoking status.Results from logistic regression indicate no significant associations between fish consumption,short-term estimated omega-3 fatty acids,or estimated PCB consumption from Great Lakes fish and the development of thyroid cancer,but it was suggested that long-term omega-3 fatty acid from Great Lakes fish may be protective of the development of thyroid cancer.In conclusion,fish consumption,with the possible concomitant PCBs,from the Great Lakes does not appear to increase the risk of thyroid cancer in New York anglers.Further research is needed in order to separate the individual health effects of PCBs from omega-3 fatty acids contained within the fish.