An extension of Lee and Mitchell's unfolding model of voluntary turnover

Lee and Mitchell ( 1994 ) proposed a decision process‐based voluntary turnover model, which identifies the psychological processes involved in the decision to quit a job. The current study tests and extends the Lee and Mitchell's ( 1994 ) unfolding model of voluntary turnover using a sample of voluntary ‘quitters’ and ‘stayers’. Survey and interview results from 84 practicing accountants suggest that the Lee and Mitchell's ( 1994 ) model does an excellent job of capturing employee decision process‐data in an accounting environment. Additional extension hypotheses pertaining to economic consequences and gender differences are also proposed and subsequently supported. Copyright © 2006 John Wiley & Sons, Ltd.     

Mapping species distributions in one dimension by non-homogeneous hidden Markov models: the case of freshwater pearl mussels in the River Dee

The investigation of species distributions in rivers involves data which are inherently sequential and unlikely to be fully independent. To take these characteristics into account, we develop a Bayesian hierarchical model for mapping the distribution of freshwater pearl mussels in the River Dee (Scotland). At the top of the hierarchy the likelihood is used to describe the sequence of sites in which mussels were observed or not. Given that false observations can occur, and that “not observed” means both that the species was not present and that it was not observed, a Markov prior is introduced at the second level of the hierarchy to represent the sequence of sites in which mussels are estimated to occur. The Markov prior allows modelling the spatial dependency between neighbouring sites. A third level in the hierarchy is given by the representation of the transition probabilities of the Markov chain in terms of site-specific explanatory variables, through a logistic regression. The selection of the explanatory variables which influence the Markov process is performed by means of a simulation-based procedure, in the complex case of association between covariates. Four features were found to be associated with reduced chance of finding a local mussel population: tributaries, bridges, dredging, and waste water treatment works. These results complement the results of a previous study, providing new evidence for the causes of the deterioration of a highly threatened species.     

Modeling toxic endpoints for improving human health risk assessment of polycyclic aromatic hydrocarbons – parent compounds and simple mixtures

Risk assessments for mixtures of polycyclic aromatic hydrocarbons (PAH) are problematic due to the lack of available potency and toxicity data on individual compounds and mixtures. This article examines the toxicity of parent compounds and designed mixtures of PAH in order to bridge the gap between component assessment and mixture assessment for this class of ubiquitous compounds. The objective for this study was to test seven parent PAH compounds and four PAH mixtures in a set of three bioassays to evaluate the toxicity of parent compound PAH and binary mixtures of PAH. PAH and mixtures were examined in the Salmonella/microsome mutagenicity assay, a Gap Junction Intercellular Communication assay, and the 7-ethoxyresorufin-O-deethylase assay. These assays were chosen for their ability to measure specific toxic endpoints related to the carcinogenic process (i.e. initiation, promotion, and progression). Two compounds similar in structure, benzo(a) pyrene (BAP) and benzanthracene, consistently produced positive results in all three bioassays. Conversely, a linear PAH, anthracene, produced negative results in all three bioassays. An antagonistic response was observed for the mixtures in all three bioassays. Chemical structure was important in explaining the observed responses. Using chemical structure–activity relationships with the steps of the carcinogenic process may be used to improve estimates of toxicity for compounds and mixtures for human health risk assessments.     

A discussion of the potential impacts of climate change on the shorelines of the Northeastern USA

An increase in the rate of sea-level rise and potential changes in storminess represent important components of global climate change that will likely affect the extensive coasts of the Northeastern USA. Raising sea level not only increases the likelihood of coastal flooding, but changes the template for waves and tides to sculpt the coast, which can lead to land loss orders of magnitude greater than that from direct inundation alone. There is little question that sea-level rise, and in particular an increased rate of rise, will result in permanent losses of coastal land. However, quantitative predictions of these future coastal change remains difficult due in part to the complexity of coastal systems and the influence of infrequent storm events, and is further confounded by coastal science’s insufficient understanding of the behavior of coastal systems over decadal timescales. Recently, dramatic improvements in technology have greatly improved our capabilities to investigate and characterize processes and sedimentary deposits in the coastal zone, allowing us, for the first time, to address some of the over-arching problems involved in shoreline change. Despite advances in many areas of coastal geology, our fundamental understanding of shoreline change has been limited by a lack of a broad and integrated scientific focus, a lack of resources, and a lack of willingness on the part of policymakers who make crucial decisions about human activity along the coast to support basic research in this area. Although quantitative predictions remain constrained, there remains little doubt that the predicted climates changes will have profound effects upon the Northeastern coast.