Protecting endangered species under future climate change: From single-species preservation to an anticipatory policy approach

Anthropogenic climate climate change presents a unique challenge for endangered species policy and an opportunity for policy makers to develop a more predictive and robust approach to preserving the nation's biological resources. Biological and ecological reactions to shifting climate conditions and the potential feedbacks and synergistic effects of such changes may threaten the well-being of many species, particularly of those already in jeopardy of extinction. The United States Endangered Species Act of 1973 will fail to keep pace with increasing numbers of species needing protection as long as it remains focused on protecting species individually. The actmust not be abandoned, however; it holds tremendous promise for preserving biological diversity through a more proactive, anticipatory perspective. The current Endangered Species Act should be reinforced and improved by better integration of scientific expertise into habitat and community preservation listing decisions and recovery plan devlopment. Given the uncertainties surrounding long-term environmental consequences of human activities and resource use, a longer-term perspective must be integrated into all efforts to protect our biotic resources. Under appointment from the Graduate Fellowships for Global Change administered by the Oak Ridge Institute for Science and Ecducation for the US Department of Energy.     

Buying Time: Real and Hypothetical Offers

This article provides the results of a field test of contingent valuation estimates within a willingness to accept framework. Using dichotomous choice questions in telephone–mail–telephone interviews, we compare survey respondents' responses to real and hypothetical offers for the opportunity to spend time in a second set of interviews on an undisclosed topic. Five hundred and forty people were randomly split between the real and hypothetical treatments. Our findings indicate no significant differences between people's choices with real and hypothetical offers. Choice models werenotsignificantly different between real and hypothetical offers.     

Understanding Human–Landscape Interactions in the “Anthropocene”

This article summarizes the primary outcomes of an interdisciplinary workshop in 2010, sponsored by the U.S. National Science Foundation, focused on developing key questions and integrative themes for advancing the science of human–landscape systems. The workshop was a response to a grand challenge identified recently by the U.S. National Research Council (2010a)—“How will Earth’s surface evolve in the “Anthropocene?”—suggesting that new theories and methodological approaches are needed to tackle increasingly complex human–landscape interactions in the new era. A new science of human–landscape systems recognizes the interdependence of hydro-geomorphological, ecological, and human processes and functions. Advances within a range of disciplines spanning the physical, biological, and social sciences are therefore needed to contribute toward interdisciplinary research that lies at the heart of the science. Four integrative research themes were identified—thresholds/tipping points, time scales and time lags, spatial scales and boundaries, and feedback loops—serving as potential focal points around which theory can be built for human–landscape systems. Implementing the integrative themes requires that the research communities: (1) establish common metrics to describe and quantify human, biological, and geomorphological systems; (2) develop new ways to integrate diverse data and methods; and (3) focus on synthesis, generalization, and meta-analyses, as individual case studies continue to accumulate. Challenges to meeting these needs center on effective communication and collaboration across diverse disciplines spanning the natural and social scientific divide. Creating venues and mechanisms for sustained focused interdisciplinary collaborations, such as synthesis centers, becomes extraordinarily important for advancing the science.     

Differential Assessment of Designations of Wetland Status Using Two Delineation Methods

Two different methods are commonly used to delineate and characterize wetlands. The U.S. Army Corps of Engineers (ACOE) delineation method uses field observation of hydrology, soils, and vegetation. The U.S. Fish and Wildlife Service’s National Wetland Inventory Program (NWI) relies on remote sensing and photointerpretation. This study compared designations of wetland status at selected study sites using both methods. Twenty wetlands from the Wetland Boundaries Map of the Ausable–Boquet River Basin (created using the revised NWI method) in the Ausable River watershed in Essex and Clinton Counties, NY, were selected for this study. Sampling sites within and beyond the NWI wetland boundaries were selected. During the summers of 2008 and 2009, wetland hydrology, soils, and vegetation were examined for wetland indicators following the methods described in the ACOE delineation manual. The study shows that the two methods agree at 78 % of the sampling sites and disagree at 22 % of the sites. Ninety percent of the sampling locations within the wetland boundaries on the NWI maps were categorized as ACOE wetlands with all three ACOE wetland indicators present. A binary linear logistic regression model analyzed the relationship between the designations of the two methods. The outcome of the model indicates that 83 % of the time, the two wetland designation methods agree. When discrepancies are found, it is the presence or absence of wetland hydrology and vegetation that causes the differences in delineation.     

Environmentally stratified sampling design for the development of Great Lakes environmental indicators

Understanding the relationship between human disturbance and ecological response is essential to the process of indicator development. For large-scale observational studies, sites should be selected across gradients of anthropogenic stress, but such gradients are often unknown for a population of sites prior to site selection. Stress data available from public sources can be used in a geographic information system (GIS) to partially characterize environmental conditions for large geographic areas without visiting the sites. We divided the U.S. Great Lakes coastal region into 762 units consisting of a shoreline reach and drainage-shed and then summarized over 200 environmental variables in seven categories for the units using a GIS. Redundancy within the categories of environmental variables was reduced using principal components analysis. Environmental strata were generated from cluster analysis using principal component scores as input. To protect against site selection bias, sites were selected in random order from clusters. The site selection process allowed us to exclude sites that were inaccessible and was shown to successfully distribute sites across the range of environmental variation in our GIS data. This design has broad applicability when the goal is to develop ecological indicators using observational data from large-scale surveys.     

Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.     

Maternal exposure to polybrominated and polychlorinated biphenyls: infant birth weight and gestational age

Understanding the influence of maternal exposures on gestational age and birth weight is essential given that pre-term and/or low birth weight infants are at risk for increased mortality and morbidity. We performed a retrospective analysis of a cohort exposed to polybrominated biphenyls (PBB) through accidental contamination of cattle feed and polychlorinated biphenyls (PCB) through residual contamination in the geographic region. Our study population consisted of 444 mothers and their 899 infants born between 1975 and 1997. Using restricted maximum likelihood estimation, no significant association was found between estimated maternal serum PBB at conception or enrollment PCB levels and gestational age or infant birth weight in unadjusted models or in models that adjusted for maternal age, smoking, parity, infant gender, and decade of birth. For enrollment maternal serum PBB, no association was observed for gestational age. However, a negative association with high levels of enrollment maternal serum PBB and birth weight was suggested. We also examined the birth weight and gestational age among offspring of women with the highest (10%) PBB or PCB exposure, and observed no significant association. Because brominated compounds are currently used in consumer products and therefore, are increasingly prevalent in the environment, additional research is needed to better understand the potential relationship between in utero exposure to brominated compounds and adverse health outcomes.     

Predictors of restraint use among child occupants

Objectives: The objective of this study was to identify factors that predict restraint use and optimal restraint use among children aged 0 to 13 years.

Methods: The data set is a national sample of police-reported crashes for years 2010–2014 in which type of child restraint is recorded. The data set was supplemented with demographic census data linked by driver ZIP code, as well as a score for the state child restraint law during the year of the crash relative to best practice recommendations for protecting child occupants. Analysis used linear regression techniques.

Results: The main predictor of unrestrained child occupants was the presence of an unrestrained driver. Among restrained children, children had 1.66 (95% confidence interval, 1.27, 2.17) times higher odds of using the recommended type of restraint system if the state law at the time of the crash included requirements based on best practice recommendations.

Conclusions: Children are more likely to ride in the recommended type of child restraint when their state's child restraint law includes wording that follows best practice recommendations for child occupant protection. However, state child restraint law requirements do not influence when caregivers fail to use an occupant restraint for their child passengers.