Nations' income inequality predicts ambivalence in stereotype content: How societies mind the gap

Income inequality undermines societies: The more inequality, the more health problems, social tensions, and the lower social mobility, trust, life expectancy. Given people's tendency to legitimate existing social arrangements, the stereotype content model (SCM) argues that ambivalence-perceiving many groups as either warm or competent, but not both-may help maintain socio-economic disparities. The association between stereotype ambivalence and income inequality in 37 cross-national samples from Europe, the Americas, Oceania, Asia, and Africa investigates how groups' overall warmth-competence, status-competence, and competition-warmth correlations vary across societies, and whether these variations associate with income inequality (Gini index). More unequal societies report more ambivalent stereotypes, whereas more equal ones dislike competitive groups and do not necessarily respect them as competent. Unequal societies may need ambivalence for system stability: Income inequality compensates groups with partially positive social images. © 2012 The British Psychological Society.

Impacts of wildlife on household food security and income in northeastern Tanzania

We evaluated the impacts of wildlife on household food security and income in three semi-arid villages adjacent to Lake Manyara National Park (LMNP) and Mkomazi Game Reserve (MGR) in Northeastern Tanzania. Survey data were collected using both household interviews and human-wildlife conflict related archive information from the village government offices. Crop destruction by wildlife influenced both household food security and cash income. Crop damage to households was, on average, 0.08 ton/annum, equivalent to two months household loss of food and reduced household cash income by 1.3%. A combination of measures is proposed as incentives for conservation. These include provision of economic incentives, soft loans to initiate non-farm (e.g., ecotourism, business enterprises) projects to ease dependency on natural resources, increasing of reserves buffer zones and fencing of reserves.

Towards an integrated forecasting system for pelagic fisheries

First results of a coupled modeling and forecasting system for the pelagic fisheries are being presented. The system consists currently of three mathematically fundamentally different model subsystems: POLCOMS-ERSEM providing the physical-biogeochemical environment implemented in the domain of the North-West European shelf and the SPAM model which describes sandeel stocks in the North Sea. The third component, the SLAM model, connects POLCOMS-ERSEM and SPAM by computing the physical-biological interaction. Our major experience by the coupling model subsystems is that well-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher trophic level, in this case the sandeel population, demonstrated here on the base of hindcast data. The coupled forecasting system is tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local and basin scale maximum sustainable yield, stock connectivity and source/sink structure. Our presented simulations indicate that sandeels stocks are currently exploited close to the maximum sustainable yield, but large uncertainty is associated with determining stock maximum sustainable yield due to stock eigen dynamics and climatic variability. Our statistical ensemble simulations indicates that the predictive horizon set by climate interannual variability is 2–6 yr, after which only an asymptotic probability distribution of stock properties, like biomass, are predictable.

Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: The AlgaRisk demonstration service

Coastal zones and shelf-seas are important for tourism, commercial fishing and aquaculture. As a result the importance of good water quality within these regions to support life is recognised worldwide and a number of international directives for monitoring them now exist. This paper describes the AlgaRisk water quality monitoring demonstration service that was developed and operated for the UK Environment Agency in response to the microbiological monitoring needs within the revised European Union Bathing Waters Directive. The AlgaRisk approach used satellite Earth observation to provide a near-real time monitoring of microbiological water quality and a series of nested operational models (atmospheric and hydrodynamic-ecosystem) provided a forecast capability. For the period of the demonstration service (2008–2013) all monitoring and forecast datasets were processed in near-real time on a daily basis and disseminated through a dedicated web portal, with extracted data automatically emailed to agency staff. Near-real time data processing was achieved using a series of supercomputers and an Open Grid approach. The novel web portal and java-based viewer enabled users to visualise and interrogate current and historical data. The system description, the algorithms employed and example results focussing on a case study of an incidence of the harmful algal bloom Karenia mikimotoi are presented. Recommendations and the potential exploitation of web services for future water quality monitoring services are discussed.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

Benchmark Status in Fixed-Income Asset Markets

What is a benchmark bond? We provide a formal theoretical treatment of this concept that relates endogenously determined benchmark status to the location of price discovery and we derive its implications. We describe a rich but little used econometric technique for identifying the benchmark that is congruent with our theoretical framework. We apply this in the context of the US corporate bond market and to the natural experiment that occurred when benchmark status was contested in the European sovereign bond markets after the introduction of the Euro. We show that France provides the benchmark at most maturities in the Euro-denominated sovereign bond market and that IBM provides the benchmark in the 10 year maturity in the US corporate bond market.