Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Fear of certain threat and anxiety about uncertain threat are distinct emotions with unique behavioral, cognitive-attentional, and neuroanatomical components. Both anxiety and fear can be studied in the laboratory by measuring the potentiation of the startle reflex. The startle reflex is a defensive reflex that is potentiated when an organism is threatened and the need for defense is high. The startle reflex is assessed via electromyography (EMG) in the orbicularis oculi muscle elicited by brief, intense, bursts of acoustic white noise (i.e., “startle probes”). Startle potentiation is calculated as the increase in startle response magnitude during presentation of sets of visual threat cues that signal delivery of mild electric shock relative to sets of matched cues that signal the absence of shock (no-threat cues). In the Threat Probability Task, fear is measured via startle potentiation to high probability (100% cue-contingent shock; certain) threat cues whereas anxiety is measured via startle potentiation to low probability (20% cue-contingent shock; uncertain) threat cues. Measurement of startle potentiation during the Threat Probability Task provides an objective and easily implemented alternative to assessment of negative affect via self-report or other methods (e.g., neuroimaging) that may be inappropriate or impractical for some researchers. Startle potentiation has been studied rigorously in both animals (e.g., rodents, non-human primates) and humans which facilitates animal-to-human translational research. Startle potentiation during certain and uncertain threat provides an objective measure of negative affective and distinct emotional states (fear, anxiety) to use in research on psychopathology, substance use/abuse and broadly in affective science. As such, it has been used extensively by clinical scientists interested in psychopathology etiology and by affective scientists interested in individual differences in emotion.     

Monte Carlo Vadose Zone Model for Soil Remedial Criteria

Many vadose zone models are available for environmental remediation, but few offer the procedures for verifying model predictions with field data and for dealing with uncertainties associated with model input parameters. This article presents a modified model combining a one-dimensional vadose-zone transport model and a simple groundwater mixing model with a function of Monte Carlo simulation (MCS). The modified model is applied to determine soil remedial concentrations for methyl tertiary butyl ether (MTBE). The modified model generates a distribution of MTBE ground-water concentrations at the point of compliance. This distribution can be used to estimate the risk of exceeding groundwater quality standard given soil remedial concentrations. In a case study, soil remedial concentration for MTBE is established to be 5?µg/kg, with a 95% and 10?µg/kg with a 50% probability that groundwater concentration will not exceed the water quality objective of 13?µg/L. Furthermore, this study uses MCS to investigate uncertainties of model input parameter hydraulic conductivity (K). One set of data (K1) is based on the results of hydraulic conductivity laboratory tests, and the other (K2) is based on the results of slug tests conducted in the field. As expected, the laboratory data show smaller K values than the field data. The comparison of the MCS results obtained from the two sets of K data indicates that the MTBE groundwater concentrations calculated based on K1 are generally 160 to 625% greater than those calculated based on K2 at the same percentiles of the MCS distribution. A higher soil remedial concentration of9jig/kg is then calculated based on the MCS results from K2 at 95%ile and 19?µg/kg at 50%ile.     

Modeling the effect of fire on the demography of <Emphasis Type="Italic">Dicerandra frutescens</Emphasis> ssp. <Emphasis Type="Italic">frutescens</Emphasis> (Lamiaceae), an endangered plant endemic to Florida scrub

Managing populations, either for conservation, harvesting, or control, requires a mechanistic or semi-mechanistic understanding of population dynamics. Here, we investigate how time-since-fire affects demographic transitions in an endangered plant, Dicerandra frutescens ssp. frutescens (Lamiaceae), which is specialized to gaps created by fire. We used a hierarchical Bayesian model to estimate transition probabilities (i.e., the elements of population projection matrices) as a function of time-since-fire and random effects, from 13 years of data on marked individuals in five populations. Using a standard Bayesian criterion to compare models, we find that death becomes increasingly probable and progression increasingly improbable with time-since-fire. The magnitude of some of the time-since-fire effects is substantial: death is 3–5 times more likely for flowering plants >6 years versus 3–6 years post-fire, 3-step progression is almost 7 times less likely, and large flowering plants are more than 6 times more likely to stop flowering. These insights inspire new hypotheses about the underlying cause of decline with time-since-fire, and how it can be managed. Our approach can be used by others who wish to model the effect of an exogenous factor on demography, while rigorously accounting for uncertainty and variability. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Twenty‐first‐century climate change impacts on marine animal biomass and ecosystem structure across ocean basins

Climate change effects on marine ecosystems include impacts on primary production, ocean temperature, species distributions, and abundance at local to global scales. These changes will significantly alter marine ecosystem structure and function with associated socio‐economic impacts on ecosystem services, marine fisheries, and fishery‐dependent societies. Yet how these changes may play out among ocean basins over the 21st century remains unclear, with most projections coming from single ecosystem models that do not adequately capture the range of model uncertainty. We address this by using six marine ecosystem models within the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish‐MIP) to analyze responses of marine animal biomass in all major ocean basins to contrasting climate change scenarios. Under a high emissions scenario (RCP8.5), total marine animal biomass declined by an ensemble mean of 15%–30% (±12%–17%) in the North and South Atlantic and Pacific, and the Indian Ocean by 2100, whereas polar ocean basins experienced a 20%–80% (±35%–200%) increase. Uncertainty and model disagreement were greatest in the Arctic and smallest in the South Pacific Ocean. Projected changes were reduced under a low (RCP2.6) emissions scenario. Under RCP2.6 and RCP8.5, biomass projections were highly correlated with changes in net primary production and negatively correlated with projected sea surface temperature increases across all ocean basins except the polar oceans. Ecosystem structure was projected to shift as animal biomass concentrated in different size‐classes across ocean basins and emissions scenarios. We highlight that climate change mitigation measures could moderate the impacts on marine animal biomass by reducing biomass declines in the Pacific, Atlantic, and Indian Ocean basins. The range of individual model projections emphasizes the importance of using an ensemble approach in assessing uncertainty of future change.     

Evaluation of Sampling-Based Methods for Sensitivity Analysis: Case Study for the E. coli Food Safety Process Risk Model

This article evaluates selected sensitivity analysis methods applicable to risk assessment models with two-dimensional probabilistic frameworks, using a microbial food safety process risk model as a test-bed. Six sampling-based sensitivity analysis methods were evaluated including Pearson and Spearman correlation, sample and rank linear regression, and sample and rank stepwise regression. In a two-dimensional risk model, the identification of key controllable inputs that can be priorities for risk management can be confounded by uncertainty. However, despite uncertainty, results show that key inputs can be distinguished from those that are unimportant, and inputs can be grouped into categories of similar levels of importance. All selected methods are capable of identifying unimportant inputs, which is helpful in that efforts to collect data to improve the assessment or to focus risk management strategies can be prioritized elsewhere. Rank-based methods provided more robust insights with respect to the key sources of variability in that they produced narrower ranges of uncertainty for sensitivity results and more clear distinctions when comparing the importance of inputs or groups of inputs. Regression-based methods have advantages over correlation approaches because they can be configured to provide insight regarding interactions and nonlinearities in the model.     

Application of Uncertainty Factors in the Priority Substances Program and International Harmonization

The Canadian Environmental Protection Act (CEPA) authorizes the Ministers of the Environment and of Health in Canada to investigate a wide variety of substances that may contaminate the environment and cause adverse effects on the environment and/or on human health. Under the Act, assessments have been completed for 44 environmental contaminants on the first Priority Substances List (PSL) and are relatively advanced for 25 compounds on the second PSL. The principles developed for the application of uncertainty factors in assessment of risks to human health for Priority Substances under CEPA are outlined, with emphasis on those aspects which are somewhat unique and/or evolving. The interface of developments in the Priority Substances program with an initiative of the International Programme on Chemical Safety in this area to effect greater harmonization of approaches is also described.     

Delaying conservation actions for improved knowledge: how long should we wait?

Decisions about where conservation actions are implemented are based on incomplete knowledge about biodiversity. The Protea Atlas is a comprehensive database, containing information collated over a decade. Using this data set in a series of retrospective simulations, we compared the outcome from different scenarios of information gain, and habitat protection and loss, over a 20-year period. We assumed that there was no information on proteas at the beginning of the simulation but knowledge improved each year. Our aim was to find out how much time we should spend collecting data before protecting habitat when there is ongoing loss of habitat. We found that, in this case, surveying for more than 2 years rarely increased the effectiveness of conservation decisions in terms of representation of proteas in protected areas and retention within the landscape. If the delay is too long, it can sometimes be more effective just using a readily available habitat map. These results reveal the opportunity costs of delaying conservation action to improve knowledge.     

Climate variability and vulnerability to climate change: a review

The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here, we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food systems, with a focus on the developing world. We present new analysis that tentatively links increases in climate variability with increasing food insecurity in the future. We consider the ways in which people deal with climate variability and extremes and how they may adapt in the future. Key knowledge and data gaps are highlighted. These include the timing and interactions of different climatic stresses on plant growth and development, particularly at higher temperatures, and the impacts on crops, livestock and farming systems of changes in climate variability and extreme events on pest‐weed‐disease complexes. We highlight the need to reframe research questions in such a way that they can provide decision makers throughout the food system with actionable answers, and the need for investment in climate and environmental monitoring. Improved understanding of the full range of impacts of climate change on biological and food systems is a critical step in being able to address effectively the effects of climate variability and extreme events on human vulnerability and food security, particularly in agriculturally based developing countries facing the challenge of having to feed rapidly growing populations in the coming decades.     

Sampling designs, field techniques and analytical methods for systematic plant population surveys

Summary Intense pressures on the use and management of land underscore the need for reliable and up-to-date information on the status of native species. The outcomes of the most recent plant population surveys commissioned by agencies are generally limited by faults or omissions in survey design. There is little guidance on how to design and implement field surveys of plant populations in ways that address the most pertinent gaps in our current knowledge and provide answers of known reliability. In this paper, I used the International Union for the Conservation of Nature (IUCN) Red List criteria as a framework to define the data required from surveys to assess the conservation status of potentially threatened species. The criteria address the location and geographical range of extant populations, aspects of species' life history, the size and structure of extant populations and rates of change in abundance and range. I have described survey designs and sampling techniques for estimating these parameters. Choices of appropriate methods that consider trade-offs between desired levels of precision and rigour and sampling effort are illustrated using surveys of 13 Tasmanian Epacris species as examples. Key elements of the approach are: (i) systematic approaches to field searches and recording both positive and negative search outcomes; (ii) construction and testing of intuitive or quantitative distribution models in an explicit experimental framework; (iii) rigorous cost-effective sampling designs, systematic field methodologies and simple analytical techniques to estimate both the magnitude and uncertainty of distribution and abundance; (iv) assessment of the merits and limitations of alternative sampling options; and (v) inference of changes in distribution and abundance by judicious use of historical data and field evidence of recent population processes.