New recruit safety expectations: Relationships with trust and perceived job risk

Very little research has examined the safety expectations of new recruits, particularly those of individuals about to enter full-time work for the first time. There is evidence that new recruits have proportionally more accidents in the first period of their employment. One possible explanation for this is that the safety expectations of new recruits do not match the reality of the workplace they are about to enter. In Study 1 data on workplace safety expectations were collected from 142 final year high school students from six schools. Study 2 collected data from 40 organizations on the safety expectations of a new recruit and compared it with safety expectation data from a manager of the job they were entering. Both studies found that new recruit safety expectations were significantly correlated with ratings of safety specific trust in co-workers and management. Study 2 found that new recruits safety expectation scores were significantly higher than those given by managers. The results suggest that organizations need to develop a clear safety-specific psychological contract with new recruits.     

Social network analysis and valid Markov chain Monte Carlo tests of null models

Analyses of animal social networks derived from group-based associations often rely on randomisation methods developed in ecology (Manly, Ecology 76:1109–1115, 1995) and made available to the animal behaviour community through implementation of a pair-wise swapping algorithm by Bejder et al. (Anim Behav 56:719–725, 1998). We report a correctable flaw in this method and point the reader to a wider literature on the subject of null models in the ecology literature. We illustrate the importance of correcting the method using a toy network and use it to make a preliminary analysis of a network of associations among eagle rays.

Valuing floodplain protection and avoidance in a coastal watershed

Flood‐related losses in the United States are increasing despite large‐scale mitigation efforts. To offset the rising cost of floods, the US Congress passed legislation in 2014 that will augment insurance premiums to make the National Flood Insurance Program more actuarially sound. Consequently, there is interest in lowering flood‐related costs to the homeowner, both in terms of premiums and damage. This study addresses the issue by integrating premium savings and damages avoided based on several mitigation scenarios. Specifically, it examines how much policyholders within a watershed near Houston, Texas, could have saved between 1999 and 2009 had their communities introduced specific avoidance‐based mitigation activities. The results indicate that homeowners and communities can offset premium rises and a majority of the damage suffered through marginal expansions of such initiatives. However, the costs associated with their implementation could counter some of these savings, and hence they need to be considered in future work.     

Identifying the possibilities and pitfalls of conducting IUCN Red List assessments from remotely sensed habitat information based on insights from poorly known Cuban mammals

The International Union for Conservation of Nature's Red List of Threatened Species (RLS) is the key global tool for objective, repeatable assessment of species’ extinction risk status, and plays an essential role in tracking biodiversity loss and guiding conservation action. Satellite remote sensing (SRS) data sets on global ecosystem distributions and functioning show exciting potential for informing range-based RLS assessment, but their incorporation has been restricted by low temporal resolution and coverage of data sets, lack of incorporation of degradation-driven habitat loss, and noninclusion of assumptions related to identification of changing habitat distributions for taxa with varying habitat dependency and ecologies. For poorly known mangrove-associated Cuban hutias (Mesocapromys spp.), we tested the impact of possible assumptions regarding these issues on range-based RLS assessment outcomes. Specifically, we used annual (1985–2018) Landsat data and land-cover classification and habitat degradation analyses across different internal time series slices to simulate range-based RLS assessments for our case study taxa to explore potential assessment uncertainty arising from temporal SRS data set coverage, incorporating proxies of (change in) habitat quality, and assumptions on spatial scaling of habitat extent for RLS parameter generation. We found extensive variation in simulated species-specific range-based RLS assessments, and this variation was mostly associated with the time series over which parameters were estimated. However, results of some species-specific assessments differed by up to 3 categories (near threatened to critically endangered) within the same time series, due to the effects of incorporating habitat quality and the spatial scaling used in RLS parameter estimation. Our results showed that a one-size-fits-all approach to incorporating SRS information in RLS assessment is inappropriate, and we urge caution in conducting range-based assessments with SRS for species for which habitat dependence on specific ecosystem types is incompletely understood. We propose novel revisions to parameter spatial scaling guidelines to improve integration of existing time series data on ecosystem change into the RLS assessment process.     

Blockage of saline intrusions in restricted,two-layer exchange flows across a submerged sill obstruction

Results are presented from a series of large-scale experiments investigating the internal and near-bed dynamics of bi-directional stratified flows with a net-barotropic component across a submerged, trapezoidal, sill obstruction. High-resolution velocity and density profiles are obtained in the vicinity of the obstruction to observe internal-flow dynamics under a range of parametric forcing conditions (i.e. variable saline and fresh water volume fluxes; density differences; sill obstruction submergence depths). Detailed synoptic velocity fields are measured across the sill crest using 2D particle image velocimetry, while the density structure of the two-layer exchange flows is measured using micro-conductivity probes at several sill locations. These measurements are designed to aid qualitative and quantitative interpretation of the internal-flow processes associated with the lower saline intrusion layer blockage conditions, and indicate that the primary mechanism for this blockage is mass exchange from the saline intrusion layer due to significant interfacial mixing and entrainment under dominant, net-barotropic, flow conditions in the upper freshwater layer. This interfacial mixing is quantified by considering both the isopycnal separation of vertically-sorted density profiles across the sill, as well as calculation of corresponding Thorpe overturning length scales. Analysis of the synoptic velocity fields and density profiles also indicates that the net exchange flow conditions remain subcritical (G < 1) across the sill for all parametric conditions tested. An analytical two-layer exchange flow model is then developed to include frictional and entrainment effects, both of which are needed to account for turbulent stresses and saline entrainment into the upper freshwater layer. The experimental results are used to validate two key model parameters: (1) the internal-flow head loss associated with boundary friction and interfacial shear; and (2) the mass exchange from the lower saline layer into the upper fresh layer due to entrainment.     

Conservation decision-making in large state spaces

When looking for the best course of management decisions to efficiently conserve metapopulation systems, a classic approach in the ecology literature is to model the optimisation problem as a Markov decision process and find an optimal control policy using exact stochastic dynamic programming techniques. Stochastic dynamic programming is an iterative procedure that seeks to optimise a value function at each timestep by evaluating the benefits of each of the actions in each state of the system defined in the Markov decision process.Although stochastic dynamic programming methods provide an optimal solution to conservation management questions in a stochastic world, their applicability in metapopulation problems has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. The high computational requirements of stochastic dynamic programming methods mean that only simple metapopulation management problems can be analysed. In this paper we overcome the complexity burden of exact stochastic dynamic programming methods and present the benefits of an on-line sparse sampling algorithm proposed by Kearns, Mansour and Ng (2002). The algorithm is particularly attractive for problems with large state spaces as the running time is independent of the size of the state space of the problem. This appealing improvement is achieved at a cost: the solutions found are no longer guaranteed to be optimal.We apply the algorithm of Kearns et al. (2002) to a hypothetical fish metapopulation problem where the management objective is to maximise the number of occupied patches over the management time horizon. Our model has multiple management options to combat the threats of water abstraction and waterhole sedimentation. We compare the performance of the optimal solution to the results of the on-line sparse sampling algorithm for a simple 3-waterhole case. We find that three look-ahead steps minimises the error between the optimal solution and the approximation algorithm. This paper introduces a new algorithm to conservation management that provides a way to avoid the effects of the curse of dimensionality. The work has the potential to allow us to approximate solutions to much more complex metapopulation management problems in the future.     

Environmental risk assessment in five rivers of Parana River basin,Southern Brazil,through biomarkers in Astyanax spp.

Environmental Science and Pollution Research - In the current study, water quality of five river sites in Parana River basin (Brazil), utilized for public water supply, was assessed through a set...     

Revealing the Diversity of Natural Hydrologic Regimes in California with Relevance for Environmental Flows Applications

Alterations to flow regimes for water management objectives have degraded river ecosystems worldwide. These alterations are particularly profound in Mediterranean climate regions such as California with strong climatic variability and riverine species highly adapted to the resulting flooding and drought disturbances. However, defining environmental flow targets for Mediterranean rivers is complicated by extreme hydrologic variability and often intensive water management legacies. Improved understanding of the diversity of natural streamflow patterns and their spatial arrangement across Mediterranean regions is needed to support the future development of effective flow targets at appropriate scales for management applications with minimal resource and data requirements. Our study addresses this need through the development of a spatially explicit reach‐scale hydrologic classification for California. Dominant hydrologic regimes and their physio‐climatic controls are revealed, using available unimpaired and naturalized streamflow time‐series and generally publicly available geospatial datasets. This methodology identifies eight natural flow classes representing distinct flow sources, hydrologic characteristics, and catchment controls over rainfall‐runoff response. The study provides a broad‐scale hydrologic framework upon which flow‐ecology relationships could subsequently be established towards reach‐scale environmental flows applications in a complex, highly altered Mediterranean region.     

Modeling Summer Month Hydrological Drought Probabilities in the United States Using Antecedent Flow Conditions

Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States (U.S.) using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5‐11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows. We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification. Some of the best‐performing models coincide with areas of high concern including the West, the Midwest, Texas, the Southeast, and the Mid‐Atlantic. Using hydrological drought MLLR probability estimates in a water management context can inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making.     

Building a freshman-year foundation for sustainability studies: Terrascope, a case study

Terrascope is a freshman learning community at the Massachusetts Institute of Technology (MIT) in which teams of students work to find solutions to large ‘unsolvable’ problems and to communicate about those problems with a wide variety of audiences in multiple formats. The program strongly promotes students’ autonomy in focusing and structuring their work, and student projects culminate in public presentations, both to general audiences and to panels of technical specialists. Students who have completed the program tend to show strong engagement with environmental and sustainability issues, as well as the skills and experience to work intensively on such issues within multidisciplinary teams. Here, we present the program as a case study, with some discussion of the factors that are key to its operation.