Using Hidden Markov Models to characterise intermittent social behaviour in fish shoals

The movement of animals in groups is widespread in nature. Understanding this phenomenon presents an important problem in ecology with many applications that range from conservation to robotics. Underlying all group movements are interactions between individual animals and it is therefore crucial to understand the mechanisms of this social behaviour. To date, despite promising methodological developments, there are few applications to data of practical statistical techniques that inferentially investigate the extent and nature of social interactions in group movement. We address this gap by demonstrating the usefulness of a Hidden Markov Model approach to characterise individual-level social movement in published trajectory data on three-spined stickleback shoals (Gasterosteus aculeatus) and novel data on guppy shoals (Poecilia reticulata). With these models, we formally test for speed-mediated social interactions and verify that they are present. We further characterise this inferred social behaviour and find that despite the substantial shoal-level differences in movement dynamics between species, it is qualitatively similar in guppies and sticklebacks. It is intermittent, occurring in varying numbers of individuals at different time points. The speeds of interacting fish follow a bimodal distribution, indicating that they are either stationary or move at a preferred mean speed, and social fish with more social neighbours move at higher speeds, on average. Our findings and methodology present steps towards characterising social behaviour in animal groups.     

Contaminant Exposure and Effects—Terrestrial Vertebrates Database: Trends and Data Gaps for Atlantic Coast Estuaries

In order to examine the condition of biota in Atlantic coast estuaries, "Contaminant Exposure and Effects—Terrestrial Vertebrates" database (CEE-TV) has been compiled through computerized search of published literature, review of existing databases, and solicitation of unpublished reports from conservation agencies, private groups, and universities. Summary information has been entered into the database, including species, collection date (1965–present), site coordinates, estuary name, hydrologic unit code, sample matrix, contaminant concentrations, biomarker and bioindicator responses, and reference source, utilizing a 98-field character and numeric format. Currently, the CEE-TV database contains 3699 geo-referenced records representing 190 vertebrate species and >140,000 individuals residing in estuaries from Maine through Florida. This relational database can be directly queried or imported into a Geographic Information System to examine spatial patterns, identify data gaps and areas of concern, generate hypotheses, and focus ecotoxicological field assessments. Information on birds made up the vast majority (83%) of the database, with only a modicum of data on amphibians (<0.1%). Of the >75,000 chemical compounds in commerce, only 118 commonly measured environmental contaminants were quantified in tissues of terrestrial vertebrates. There were no CEE-TV data records in 15 of the 67 estuaries located along the Atlantic coast and Florida Gulf coast. The CEE-TV database has a number of potential applications including focusing biomonitoring efforts to generate critically needed ecotoxicological data in the numerous "gaps" along the coast, reducing uncertainty about contaminant risk, identifying areas for mitigation, restoration or special management, and ranking ecological conditions of estuaries.     

Value congruence,control, sense of community and demands as determinants of burnout syndrome among hospitality workers

Employees working in the hospitality industry are constantly exposed to occupational stressors that may lead employees into experiencing burnout syndrome. Research addressing the interactive effects of control, community and value congruence to alleviate the impact of workplace demands on experiencing burnout is relatively limited. The present study examined relationships among control, community and value congruence, workplace demands and the three components of burnout. A sample of 418 employees working in a variety of hospitality associations including restaurants and hotels in Spain were recruited. Moderation analyses and linear regressions analyzed the predictive power of control, community and value congruence as moderating variables. Results indicate that control, community and value congruence were successful buffers in the relationships between workplace demands and the burnout dimensions. The present findings offer suggestions for future research on potential moderating variables, as well as implications for reducing burnout among hospitality employees.     

Practical Perspectives of 1,4‐Dioxane Investigation and Remediation

1,4‐Dioxane (dioxane) is a contaminant of emerging concern that is classified by the U.S. Environmental Protection Agency as a likely human carcinogen. Dioxane has been used as a minor or major ingredient in many applications, and is also generated as an unwanted by‐product of industrial processes associated with the manufacturing of polyethylene, nonionic surfactants, and many consumer products (cosmetics, laundry detergents, shampoos, etc.). Dioxane is also a known stabilizer of chlorinated solvents, particularly 1,1,1‐trichloroethane, and has been commonly found comingled with chlorinated solvent plumes. Dioxane plumes at chlorinated solvent sites can complicate site closure strategies, which to date have not typically focused on dioxane. Aggressive treatment technologies have greatly advanced and are clearly capable of achieving lower parts per billion cleanup criteria using ex situ advanced oxidation processes and sorption media. In situ chemical oxidation has also been demonstrated to effectively remediate dioxane and chlorinated solvents. Other in situ remedies, such as enhanced bioremediation, phytoremediation, and monitored natural attenuation, have been studied; however, their ability to achieve cleanup levels is still somewhat questionable and is limited by co‐occurring contaminants. This article summarizes and provides practical perspectives on dioxane analysis, plume stability relative to other contaminants, and the development of investigation tools and treatment technologies.     

Climate change impacts,vulnerability and adaptive capacity of the electrical energy sector in Cyprus

The purpose of this study was to investigate the climate change impacts, vulnerability and adaptive capacity of the electrical energy sector in Cyprus. Spatial vulnerability of the island was assessed using the degree-day indicator to investigate heating and cooling demands in the near future using daily temperature projections from regional climate models (RCMs). Using daily electrical energy consumption data for the present climate, an impact model linking consumption and temperature was constructed and this relationship was projected to the future climate using the data from the RCMs and assuming the same technology use. Our impact model results showed that for the period between November and April (‘cold period’), a decreasing trend in electrical energy consumption is evident due to warmer conditions in the near future, while for the period between May and October (‘warm period’), an increasing trend in electricity consumption is evident as warmer conditions dominate by 2050. Regarding the spatial vulnerability assessment, the cooling degree-day indicator testified that major increases in cooling demand, between 100 and 200 degree-days, are expected in inland and southern regions during the summer in the near future. In addition, increases of about 20–50 degree-days are anticipated during autumn. Conversely, energy demand for heating is projected to decrease during spring and winter, especially in the higher elevation parts of the island. More precisely, reductions of about 30–75 degree-days are projected during spring, while greater reductions of about 60–90 degree-days are expected during winter in heating demand, especially for in the near future. The ability of the energy sector to adapt and follow these changes was deemed to be satisfactory reducing the overall vulnerability of the sector to future climate change.     

A Method for Setting the Size of Plant Conservation Target Areas

Abstract: Realistic time frames in which management decisions are made often preclude the completion of the detailed analyses necessary for conservation planning. Under these circumstances, efficient alternatives may assist in approximating the results of more thorough studies that require extensive resources and time. We outline a set of concepts and formulas that may be used in lieu of detailed population viability analyses and habitat modeling exercises to estimate the protected areas required to provide desirable conservation outcomes for a suite of threatened plant species. We used expert judgment of parameters and assessment of a population size that results in a specified quasiextinction risk based on simple dynamic models. The area required to support a population of this size is adjusted to take into account deterministic and stochastic human influences, including small-scale disturbance, deterministic trends such as habitat loss, and changes in population density through processes such as predation and competition. We set targets for different disturbance regimes and geographic regions. We applied our methods to Banksia cuneata, Boronia keysii, and Parsonsia dorrigoensis, resulting in target areas for conservation of 1102, 733, and 1084 ha, respectively. These results provide guidance on target areas and priorities for conservation strategies.     

Total versus urban: Well-to-wheels assessment of criteria pollutant emissions from various vehicle/fuel systems

The potential impact on the environment of alternative vehicle/fuel systems needs to be evaluated, especially with respect to human health effects resulting from air pollution. We used the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model to examine the well-to-wheels (WTW) emissions of five criteria pollutants (VOCs, NO_x, PM₁₀, PM_2.5, and CO) for nine vehicle/fuel systems: (1) conventional gasoline vehicles; (2) conventional diesel vehicles; (3) ethanol (E85) flexible-fuel vehicles (FFVs) fueled with corn-based ethanol; (4) E85 FFVs fueled with switchgrass-based ethanol; (5) gasoline hybrid vehicles (HEVs); (6) diesel HEVs; (7) electric vehicles (EVs) charged using the average U.S. generation mix; (8) EVs charged using the California generation mix; and (9) hydrogen fuel cell vehicles (FCVs). Pollutant emissions were separated into total and urban emissions to differentiate the locations of emissions, and emissions were presented by sources. The results show that WTW emissions of the vehicle/fuel systems differ significantly, in terms of not only the amounts but also with respect to locations and sources, both of which are important in evaluating alternative vehicle/fuel systems. E85 FFVs increase total emissions but reduce urban emissions by up to 30% because the majority of emissions are released from farming equipment, fertilizer manufacture, and ethanol plants, all of which are located in rural areas. HEVs reduce both total and urban emissions because of the improved fuel economy and lower emissions. While EVs significantly reduce total emissions of VOCs and CO by more than 90%, they increase total emissions of PM₁₀ and PM_2.5 by 35–325%. However, EVs can reduce urban PM emissions by more than 40%. FCVs reduce VOCs, CO, and NO_x emissions, but they increase both total and urban PM emissions because of the high process emissions that occur during hydrogen production. This study emphasizes the importance of specifying a thorough life-cycle emissions inventory that can account for both the locations and sources of the emissions to assist in achieving a fair comparison of alternative vehicle/fuel options in terms of their environmental impacts.     

Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1

Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross‐validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3‐fold cross‐validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.