Ecological risk-benefit analysis of a wetland development based on risk assessment using "expected loss of biodiversity".

Ecological risk from the development of a wetland is assessed quantitatively by means of a new risk measure, expected loss of biodiversity (ELB). ELB is defined as the weighted sum of the increments in the probabilities of extinction of the species living in the wetland due to its loss. The weighting for a particular species is calculated according to the length of the branch on the phylogenetic tree that will be lost if the species becomes extinct. The length of the branch on the phylogenetic tree is regarded as reflecting the extent of contribution of the species to the taxonomic diversity of the world of living things. The increments in the probabilities of extinction are calculated by a simulation used for making the Red List for vascular plants in Japan. The resulting ELB for the loss of Nakaikemi wetland is 9,200 years. This result is combined with the economic costs for conservation of the wetland to produce a value for the indicator of the "cost per unit of biodiversity saved." Depending on the scenario, the value is 13,000 yen per year-ELB or 110,000 to 420,000 yen per year-ELB (1 US dollar = 110 yen in 1999).     

Evaluation of Two Approaches to Defining Extinction Risk under the U.S. Endangered Species Act

The predominant definition of extinction risk in conservation biology involves evaluating the cumulative distribution function (CDF) of extinction time at a particular point (the “time horizon”). Using the principles of decision theory, this article develops an alternative definition of extinction risk as the expected loss (EL) to society resulting from eventual extinction of a species. Distinct roles are identified for time preference and risk aversion. Ranges of tentative values for the parameters of the two approaches are proposed, and the performances of the two approaches are compared and contrasted for a small set of real‐world species with published extinction time distributions and a large set of hypothetical extinction time distributions. Potential issues with each approach are evaluated, and the EL approach is recommended as the better of the two. The CDF approach suffers from the fact that extinctions that occur at any time before the specified time horizon are weighted equally, while extinctions that occur beyond the specified time horizon receive no weight at all. It also suffers from the fact that the time horizon does not correspond to any natural phenomenon, and so is impossible to specify nonarbitrarily; yet the results can depend critically on the specified value. In contrast, the EL approach has the advantage of weighting extinction time continuously, with no artificial time horizon, and the parameters of the approach (the rates of time preference and risk aversion) do correspond to natural phenomena, and so can be specified nonarbitrarily.     

Allee Effects and the Risk of Biological Invasion

The Allee effect is a nonlinear phenomenon exhibited in the population dynamics of sparse populations in which the per capita population growth rate increases with increasing population density. In sufficiently sparse populations, the Allee effect may lead to extinction and is known to generate a threshold in the probability of establishment when presented as a function of introduced population size or density. As introduced populations are generally small, Allee effects are probably common in biological invasions and their consideration is necessary for accurately assessing the risk of invasion by many species, including all sexually reproducing species. Bythotrephes longimanus, an invasive, freshwater, cladoceran zooplankter from Europe, is one such species. Here, I review a previously published model of the Allee effect for continuously sexually reproducing species. Then, I develop a new model for seasonally parthenogenetic species such as Bythotrephes, and thereby demonstrate the potential consequences of Allee effects. This result underscores the importance of considering nonlinear phenomena, including thresholds, when conducting risk analysis for biological introductions.     

Assessing the Risk of Invasive Spread in Fragmented Landscapes

Little theoretical work has investigated how landscape structure affects invasive spread, even though broad-scale disturbances caused by habitat loss and fragmentation are believed to facilitate the spread of exotic species. Neutral landscape models (NLMs), derived from percolation theory in the field of landscape ecology, provide a tool for assessing the risk of invasive spread in fragmented landscapes. A percolation-based analysis of the potential for invasive spread in fragmented landscapes predicts that invasive spread may be enormously enhanced beyond some threshold level of habitat loss, which depends upon the species' dispersal abilities and the degree of habitat fragmentation. Assuming that invasive species spread primarily through disturbed areas of the landscape, poor dispersers may spread better in landscapes in which disturbances are concentrated in space, whereas good dispersers are predicted to spread better in landscapes where disturbances are small and dispersed (i.e., fragmented landscape). Assessing the risk of invasive spread in fragmented landscapes ultimately requires understanding the relative effects of landscape structure on processes that contribute to invasive spread--dispersal (successful colonization) and demography (successful establishment). Colonization success is predicted to be highest when >20% of the landscape has been disturbed, particularly if disturbances are large or aggregated in space, because propagules are more likely to encounter sites suitable for colonization and establishment. However, landscape pattern becomes less important for predicting colonization success if species are capable of occasional long-distance dispersal events. Invasive species are also more likely to persist and achieve positive population growth rates (successful establishment) in landscapes with clumped disturbance patterns, which can then function as population sources that produce immigrants that invade other landscapes. Finally, the invasibility of communities may be greatest in landscapes with a concentrated pattern of disturbance, especially below some critical threshold of biodiversity. Below the critical biodiversity threshold, the introduction of a single species can trigger a cascade of extinctions among indigenous species. The application of NLMs may thus offer new insights and opportunities for the management and restoration of landscapes so as to slow the spread of invasive species.     

A Risk Assessment Method for Biological Introductions

This article describes an application of a method for assessing risks associated with the introduction of an organism into a new environment. The test organism was a binucleate Rhizoctonia fungal isolate that has potential for commercial development as a biological control agent for damping-off diseases in bedding plants. A test sample of host plant species was selected using the centrifugal phylogenetic host range principles, but with an emphasis on economic species. The effect of the fungus on the plant was measured for each species and expressed on a logarithmic scale. The effects on weights of shoots and roots per container were not normally distributed, nor were the effects on the number of plants standing (those which survived). Statements about the effect on the number standing and the shoot weight per container involved using the observed (empirical) distribution. This is illustrated with an example. Problems were encountered in defining the population of species at risk, and in deciding how this population should be formally sampled. The limitations of the method are discussed.     

Cancer Risk Estimation of Genotoxic Chemicals Based on Target Dose and a Multiplicative Model

A mechanistic model and associated procedures are proposed for cancer risk assessment of genotoxic chemicals. As previously shown for ionizing radiation, a linear multiplicative model was found to be compatible with published experimental data for ethylene oxide, acrylamide, and butadiene. The validity of this model was anticipated in view of the multiplicative interaction of mutation with inherited and acquired growth-promoting conditions. Concurrent analysis led to rejection of an additive model (i.e. the model commonly applied for cancer risk assessment). A reanalysis of data for radiogenic cancer in mouse, dog and man shows that the relative risk coefficient is approximately the same (0.4 to 0.5 percent per rad) for tumours induced in the three species.Doses in vivo, defined as the time-integrated concentrations of ultimate mutagens, expressed in millimol × kg^–1 × h (mMh) are, like radiation doses given in Gy or rad, proportional to frequencies of potentially mutagenic events. The radiation dose equivalents of chemical doses are, calculated by multiplying chemical doses (in mMh) with the relative genotoxic potencies (in rad × mMh^–1) determined in vitro. In this way the relative cancer incidence increments in rats and mice exposed to ethylene oxide were shown to be about 0.4 percent per rad-equivalent, in agreement with the data for radiogenic cancer.Our analyses suggest that values of the relative risk coefficients for genotoxic chemicals are independent of species and that relative cancer risks determined in animal tests apply also to humans. If reliable animal test data are not available, cancer risks may be estimated by the relative potency. In both cases exposure dose/target dose relationships, the latter via macromolecule adducts, should be determined.     

A Distributional Approach to Characterizing Low-Dose Cancer Risk

Since cancer risk at very low doses cannot be directly measured in humans or animals, mathematical extrapolation models and scientific judgment are required. This article demonstrates a probabilistic approach to carcinogen risk assessment that employs probability trees, subjective probabilities, and standard bootstrapping procedures. The probabilistic approach is applied to the carcinogenic risk of formaldehyde in environmental and occupational settings. Sensitivity analyses illustrate conditional estimates of risk for each path in the probability tree. Fundamental mechanistic uncertainties are characterized. A strength of the analysis is the explicit treatment of alternative beliefs about pharmacokinetics and pharmacodynamics. The resulting probability distributions on cancer risk are compared with the point estimates reported by federal agencies. Limitations of the approach are discussed as well as future research directions.     

A Comprehensive Model for Managing Credit Risk on Home Mortgage Portfolios*

Managing credit risk in financial institutions requires the ability to forecast aggregate losses on existing loans, predict the length of time that loans will be on the books before prepayment or default, analyze the expected performance of particular segments in the existing portfolio, and project payment patterns of new loans. Described in this paper are tools created for these functions in a large California financial institution. A forecasting model with Markovian structure and nonstationary transition probabilities is used to model the life of a mortgage. Logistic and regression models are used to estimate severity of losses. These models are integrated into a system that allows analysts and managers to depict the expected performance of individual loans and portfolio segments under different economic scenarios. With this information, analysts and managers can establish appropriate loss reserves, suggest pricing differentials to compensate for risk, and make strategic lending decisions.     

Communicating Conservation Status: How Different Statistical Assessment Criteria Affect Perceptions of Extinction Risk

Although alternative forms of statistical and verbal information are routinely used to convey species’ extinction risk to policymakers and the public, little is known about their effects on audience information processing and risk perceptions. To address this gap in literature, we report on an experiment that was designed to explore how perceptions of extinction risk differ as a function of five different assessment benchmarks (Criteria A–E) used by scientists to classify species within IUCN Red List risk levels (e.g., Critically Endangered, Vulnerable), as well as the role of key individual differences in these effects (e.g., rational and experiential thinking styles, environmental concern). Despite their normative equivalence within the IUCN classification system, results revealed divergent effects of specific assessment criteria: on average, describing extinction risk in terms of proportional population decline over time (Criterion A) and number of remaining individuals (Criterion D) evoked the highest level of perceived risk, whereas the single‐event probability of a species becoming extinct (Criterion E) engendered the least perceived risk. Furthermore, participants scoring high in rationality (analytic thinking) were less prone to exhibit these biases compared to those low in rationality. Our findings suggest that despite their equivalence in the eyes of scientific experts, IUCN criteria are indeed capable of engendering different levels of risk perception among lay audiences, effects that carry direct and important implications for those tasked with communicating about conservation status to diverse publics.     

基于Copula的外汇投资组合风险分析

本文简要介绍了Archimedean Copula,并运用Archimedean Copula给出了确定两种外汇最小风险(VaR)投资组合的方法。并用这个方法,对欧元和日元的投资组合做了相应的风险分析,得到了二者的最小风险投资组合,并对不同置信水平下VaR和组合系数做了敏感性分析。     

Why Risk Is Not Variance: An Expository Note

Variance (or standard deviation) of return is widely used as a measure of risk in financial investment risk analysis applications, where mean‐variance analysis is applied to calculate efficient frontiers and undominated portfolios. Why, then, do health, safety, and environmental (HS&E) and reliability engineering risk analysts insist on defining risk more flexibly, as being determined by probabilities and consequences, rather than simply by variances? This note suggests an answer by providing a simple proof that mean‐variance decision making violates the principle that a rational decisionmaker should prefer higher to lower probabilities of receiving a fixed gain, all else being equal. Indeed, simply hypothesizing a continuous increasing indifference curve for mean‐variance combinations at the origin is enough to imply that a decisionmaker must find unacceptable some prospects that offer a positive probability of gain and zero probability of loss. Unlike some previous analyses of limitations of variance as a risk metric, this expository note uses only simple mathematics and does not require the additional framework of von Neumann Morgenstern utility theory.     

Spatial Risk Assessment Across Large Landscapes with Varied Land Use: Lessons from a Conservation Assessment of Military Lands

Spatial decision-support tools are necessary for assessment and management of threats to biodiversity, which in turn is necessary for biodiversity conservation. In conjunction with the U.S. Geological Survey-Biological Resources Division's Species at Risk program, we developed a GIS-based spatial decision-support tool for relative risk assessments of threats to biodiversity on the U.S. Army's White Sands Missile Range and Fort Bliss (New Mexico and Texas) due to land uses associated with military missions of the two bases. The project tested use of spatial habitat models, land-use scenarios, and species-specific impacts to produce an assessment of relative risks for use in conservation planning on the 1.2 million-hectare study region. Our procedure allows spatially explicit analyses of risks to multiple species from multiple sources by identifying a set of hazards faced by all species of interest, identifying a set of feasible management alternatives, assigning scores to each species for each hazard, and mapping the distribution of these hazard scores across the region of interest for each combination of species/management alternatives. We illustrate the procedure with examples. We demonstrate that our risk-based approach to conservation planning can provide resource managers with a useful tool for spatial assessment of threats to species of concern.     

High Risk or Low: How Location on a "Risk Ladder" Affects Perceived Risk

Efforts to explain risk magnitude often rely on a "risk ladder" in which exposure levels and associated risk estimates are arrayed with low levels at the bottom and high ones at the top. Two experiments were conducted to test the hypothesis that perceived threat and intended mitigation vary with the location of the subject's assigned level on the risk ladder. Subjects were New Jersey homeowners, asked to assume a particular level of radon or asbestos contamination in their homes, to read a brochure explaining the risk, and then to complete a questionnaire. Both studies found that the difference between an assigned level one-quarter of the way up the ladder and the same level three-quarters of the way up the ladder significantly affected threat perception; the effect on mitigation intentions was significant in only one of the studies. Variations in assigned risk also affected threat perception and mitigation intentions. Variations in test magnitude (e.g., 15 fibers per liter vs. 450 fibers per cubic foot, roughly equivalent risks) had no effect, nor did the distinction between radon and asbestos affect the dependent variables. These findings suggest that communicators can design risk ladders to emphasize particular risk characteristics.     

The Quantitative Estimation of IT‐Related Risk Probabilities

How well can people estimate IT‐related risk? Although estimating risk is a fundamental activity in software management and risk is the basis for many decisions, little is known about how well IT‐related risk can be estimated at all. Therefore, we executed a risk estimation experiment with 36 participants. They estimated the probabilities of IT‐related risks and we investigated the effect of the following factors on the quality of the risk estimation: the estimator's age, work experience in computing, (self‐reported) safety awareness and previous experience with this risk, the absolute value of the risk's probability, and the effect of knowing the estimates of the other participants (see: Delphi method). Our main findings are: risk probabilities are difficult to estimate. Younger and inexperienced estimators were not significantly worse than older and more experienced estimators, but the older and more experienced subjects better used the knowledge gained by knowing the other estimators' results. Persons with higher safety awareness tend to overestimate risk probabilities, but can better estimate ordinal ranks of risk probabilities. Previous own experience with a risk leads to an overestimation of its probability (unlike in other fields like medicine or disasters, where experience with a disease leads to more realistic probability estimates and nonexperience to an underestimation).     

Big Data and Predictive Analytics in Fire Risk Using Weather Data

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—-fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R² value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R² value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.     

A Heuristic Risk Assessment Technique for Birdstrike Management at Airports

Collisions between birds and aircraft (birdstrikes) have caused the loss of at least 88 aircraft and 243 lives in world civil aviation. Conservative estimates suggest that more routine damage and delays following birdstrikes cost the industry and its insurers US$1.2-1.5 billion per year. The majority of strikes happen close to airports and most countries have regulations that require airport managers to control the birdstrike risk on their property. Birdstrike prevention has, however, lagged behind other aspects of flight safety in the development and implementation of risk assessment protocols, possibly because of the inherent difficulty in quantifying the variability in the populations and behavior of the various bird species involved. This article presents a technique that uses both national and airport-specific data to evaluate risk by creating a simple probability-times-severity matrix. It uses the frequency of strikes reported for different bird species at a given airport over the preceding five years as a measure of strike probability, and the proportion of strikes with each species that result in damage to aircraft, in the national birdstrike database, as a measure of likely severity. Action thresholds for risk levels for particular bird species are then defined, above which the airport should take action to reduce the risk further. The assessment is designed for airports where the reporting and collation of birdstrike events is reasonably consistent over time and where a bird hazard management program of some sort is already in place. This risk assessment is designed to measure risk to the airport as a business rather than risk to the traveling passenger individually. It therefore takes no account of aircraft movement rate in the calculations and is aimed at minimizing the number of damaging incidents rather than concentrating on catastrophic events. Once set up at an airport, the technique is simple to implement for nonexperts, and it allows managers to focus bird control resources on the species causing the greatest risk, hence maximizing the return on investment. This protocol is now being successfully used at major airports in the United Kingdom and elsewhere in the world.     

Anesthesia Patient Risk: A Quantitative Approach to Organizational Factors and Risk Management Options

The risk of death or brain damage to anesthesia patients is relatively low, particularly for healthy patients in modern hospitals. When an accident does occur, its cause is usually an error made by the anesthesiologist, either in triggering the accident sequence, or failing to take timely corrective measures. This paper presents a pilot study which explores the feasibility of extending probabilistic risk analysis (PRA) of anesthesia accidents to assess the effects of human and management components on the patient risk. We develop first a classic PRA model for the patient risk per operation. We then link the probabilities of the different accident types to their root causes using a probabilistic analysis of the performance shaping factors. These factors are described here as the "state of the anesthesiologist" characterized both in terms of alertness and competence. We then analyze the effects of different management factors that affect the state of the anesthesiologist and we compute the risk reduction benefits of several risk management policies. Our data sources include the published version of the Australian Incident Monitoring Study as well as expert opinions. We conclude that patient risk could be reduced substantially by closer supervision of residents, the use of anesthesia simulators both in training and for periodic recertification, and regular medical examinations for all anesthesiologists.     

Risk to Groundlings of Death Due to Airplane Accidents: A Risk Communication Tool

During the period 1975-85 in the United States the 70 year lifetime risk of dying from being hit by an airplane when the individual is on the ground was 4.2 per million people. In contrast to many other risks used for comparison purposes, risk to those on the ground from an airplane crash is not a function of our own skills; is not optional; provides no benefit to anyone involved; and is not an act of nature. As a risk comparison tool it also has the useful characteristics of being something about which we can agree that regulatory action, such as control of airplane use and traffic, is warranted; but that no significant change in personal behavior, such as living in the basement to protect against dying from a plane hitting the home, is commensurate with the extent of risk.     

How Probabilistic Risk Assessment Can Mislead Terrorism Risk Analysts

Traditional probabilistic risk assessment (PRA), of the type originally developed for engineered systems, is still proposed for terrorism risk analysis. We show that such PRA applications are unjustified in general. The capacity of terrorists to seek and use information and to actively research different attack options before deciding what to do raises unique features of terrorism risk assessment that are not adequately addressed by conventional PRA for natural and engineered systems—in part because decisions based on such PRA estimates do not adequately hedge against the different probabilities that attackers may eventually act upon. These probabilities may differ from the defender's (even if the defender's experts are thoroughly trained, well calibrated, unbiased probability assessors) because they may be conditioned on different information. We illustrate the fundamental differences between PRA and terrorism risk analysis, and suggest use of robust decision analysis for risk management when attackers may know more about some attack options than we do.     

不同“融资铜”模式的风险识别与警示

针对现实中防范"融资铜"风险的需要,本文归纳提炼出三种不同的"融资铜"模式,并就其操作过程中所面临的利率风险、人民币价格波动风险和标的物价格波动风险及其联动性进行深入剖析;采用GARCH-Copula模型,利用上述风险的收益数据及风险因子数据(2011年5月至2014年4月)对单一风险及综合风险进行识别与模拟,对不同模式中综合风险分布进行对比分析,根据在险价值做出警示。结果表明:"融资铜"是一种套利行为,其综合风险收益具有右偏特性,并明显呈现出尖峰厚尾的风险分布特征,相同置信水平下在险价值显著高于单一风险在险价值;其存在极值损失,发生极值损失对企业自身及金融系统会造成严重影响,因此相关监管机构必须对"融资铜"加以重视并实施控制,以引导资金回归实体经济之中。