A comparison of contributing effects on 2-vehicle alcohol-related crashes between impaired and nonimpaired operators

Objective: Driving under the influence of alcohol is a crime that places the lives of all motorists in danger. Though it is a largely preventable act, impaired driving has accounted for 31 to 38% of fatal crashes across the country over the last decade. When an impaired operator crashes his or her vehicle, there is often a second unit, of which the operator is not impaired, involved in the crash.

Methods: This research looks at approximately 14,000 2-unit crashes involving an impaired operator in the State of Ohio from 2008 through 2012. The research is focused on determining the effects of crash and operator characteristics in 2-unit alcohol-related crashes through the use of 2 mixed logit models.

Results: It is found that several factors have similar effects on the injury severities of both the impaired and nonimpaired operators, including head-on crashes, the use of seat belts, and the deployment of airbags. There are, however, several factors that affect the 2 operators differently. It is found that the impaired operator's injury severity is based on the type and, more important, the size of the vehicle he or she is driving, the roadway geometry, and the speed of the vehicle driven by the nonimpaired operator. The nonimpaired operator is equally affected by the speed of the impaired vehicle as much as his or her own speed, and the nonimpaired operator's injury severity is virtually independent of the type of vehicle being driven.

Conclusions: Researchers may disseminate the results to community groups such as Mothers Against Drunk Driving and Safe Communities to increase awareness of the dangers of drunk driving in an effort to reduce the number of alcohol-related crashes.     

Adaptive management of marine mega-fauna in a changing climate

Management of marine mega-fauna in a changing climate is constrained by a series of uncertainties, often related to climate change projections, ecological responses, and the effectiveness of strategies in alleviating climate change impacts. Uncertainties can be reduced over time through adaptive management. Adaptive management is a framework for resource conservation that promotes iterative learning-based decision making. To successfully implement the adaptive management cycle, different steps (planning, designing, learning and adjusting) need to be systematically implemented to inform earlier steps in an iterative way. Despite the critical role that adaptive management is likely to play in addressing the impacts of climate change on marine mega-fauna few managers have successfully implemented an adaptive management approach. We discuss the approaches necessary to implement each step of an adaptive management cycle to manage marine mega-fauna in a changing climate, highlighting the steps that require further attention to fully implement the process. Examples of sharks and rays (Selachimorpha and Batoidea) on the Great Barrier Reef and little penguins, Eudyptula minor, in south-eastern Australia are used as case studies. We found that successful implementation of the full adaptive management cycle to marine mega-fauna needs managers and researchers to: (1) obtain a better understanding of the capacity of species to adapt to climate change to inform the planning step; (2) identify strategies to directly address impacts in the marine environment to inform the designing step; and (3) develop systematic evaluation and monitoring programs to inform the learning step. Further, legislation needs to flexible to allow for management to respond.     

Females go where the food is: does the socio-ecological model explain variation in social organisation of solitary foragers?

The socio-ecological model (SEM) links ecological factors with characteristics of social systems and allows predictions about the relationships between resource distribution, type of competition and social organisation. It has been mainly applied to group-living species but ought to explain variation in social organisation of solitary species as well. The aim of this study was to test basic predictions of the SEM in two solitary primates, which differ in two characteristics of female association patterns: (1) spatial ranging and (2) sleeping associations. Beginning in August 2002, we regularly (re-)captured and marked individuals of sympatric populations of Madame Berthe's and grey mouse lemurs (Microcebus berthae, Microcebus murinus) in Kirindy Forest (Madagascar). We recorded data on spatial patterns, feeding and social behaviour by means of direct observation of radio-collared females. The major food sources of M. berthae occurred in small dispersed patches leading to strong within-group scramble competition and over-dispersed females with a low potential for female associations. In contrast, M. murinus additionally used patchily distributed, high-quality (large) resources facilitating within-group contest competition. The combined influence of less strong within-group scramble and contest as well as between-group contest over non-food resources allowed females of this species to cluster in space. Additionally, we experimentally manipulated the spatial distribution of food sources and found that females adjusted their spatial patterns to food resource distribution. Thus, our results support basic predictions of the SEM and demonstrated that it can also explain variation in social organisation of solitary foragers.     

Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach

Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.     

Risk-based economic decision analysis of remediation options at a PCE-contaminated site

Remediation methods for contaminated sites cover a wide range of technical solutions with different remedial efficiencies and costs. Additionally, they may vary in their secondary impacts on the environment i.e. the potential impacts generated due to emissions and resource use caused by the remediation activities. More attention is increasingly being given to these secondary environmental impacts when evaluating remediation options. This paper presents a methodology for an integrated economic decision analysis which combines assessments of remediation costs, health risk costs and potential environmental costs. The health risks costs are associated with the residual contamination left at the site and its migration to groundwater used for drinking water. A probabilistic exposure model using first- and second-order reliability methods (FORM/SORM) is used to estimate the contaminant concentrations at a downstream groundwater well. Potential environmental impacts on the local, regional and global scales due to the site remediation activities are evaluated using life cycle assessments (LCA). The potential impacts on health and environment are converted to monetary units using a simplified cost model.A case study based upon the developed methodology is presented in which the following remediation scenarios are analyzed and compared: (a) no action, (b) excavation and off-site treatment of soil, (c) soil vapor extraction and (d) thermally enhanced soil vapor extraction by electrical heating of the soil. Ultimately, the developed methodology facilitates societal cost estimations of remediation scenarios which can be used for internal ranking of the analyzed options. Despite the inherent uncertainties of placing a value on health and environmental impacts, the presented methodology is believed to be valuable in supporting decisions on remedial interventions.