Spatial Stochastic Simulation Offers Potential as a Quantitative Method for Pest Risk Analysis

Pest risk analysis represents an emerging field of risk analysis that evaluates the potential risks of the introduction and establishment of plant pests into a new geographic location and then assesses the management options to reduce those potential risks. Development of new and adapted methodology is required to answer questions concerning pest risk analysis of exotic plant pests. This research describes a new method for predicting the potential establishment and spread of a plant pest into new areas using a case study, Ralstonia solanacearum, a bacterial disease of potato. This method combines current quantitative methodologies, stochastic simulation, and geographic information systems with knowledge of pest biology and environmental data to derive new information about pest establishment potential in a geographical region where a pest had not been introduced. This proposed method extends an existing methodology for matching pest characteristics with environmental conditions by modeling and simulating dissemination behavior of a pest organism. Issues related to integrating spatial variables into risk analysis models are further discussed in this article.     

Assessing potential hybridization between a hypothetical gene drive-modified Drosophila suzukii and nontarget Drosophila species

Genetically engineered gene drives (geGD) are potentially powerful tools for suppressing or even eradicating populations of pest insects. Before living geGD insects can be released into the environment, they must pass an environmental risk assessment to ensure that their release will not cause unacceptable harm to non-targeted entities of the environment. A key research question concerns the likelihood that nontarget species will acquire the functional GD elements; such acquisition could lead to reduced abundance or loss of those species and to a disruption of the ecosystem services they provide. The main route for gene flow is through hybridization between the geGD insect strain and closely related species that co-occur in the area of release and its expected dispersal. Using the invasive spotted-wing drosophila, Drosophila suzukii, as a case study, we provide a generally applicable strategy on how a combination of interspecific hybridization experiments, behavioral observations, and molecular genetic analyses can be used to assess the potential for hybridization.     

Beauty Is a Beast,Frog Is a Prince: Assortative Matching with Nontransferabilities

We present sufficient conditions for monotone matching in environments where utility is not fully transferable between partners. These conditions involve not only complementarity in types of the total payoff to a match, as in the transferable utility case, but also monotonicity in type of the degree of transferability between partners. We apply our conditions to study some models of risk sharing and incentive problems, deriving new results for predicted matching patterns in those contexts.     

Adaptive Approaches to Biosecurity Governance

This article discusses institutional changes that may facilitate an adaptive approach to biosecurity risk management where governance is viewed as a multidisciplinary, interactive experiment acknowledging uncertainty. Using the principles of adaptive governance, evolved from institutional theory, we explore how the concepts of lateral information flows, incentive alignment, and policy experimentation might shape Australia's invasive species defense mechanisms. We suggest design principles for biosecurity policies emphasizing overlapping complementary response capabilities and the sharing of invasive species risks via a polycentric system of governance.     

A New Multicriteria Risk Mapping Approach Based on a Multiattribute Frontier Concept

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance, or introduction potential) as independent entities. These independent risk components are integrated using various multicriteria analysis techniques that typically require prior knowledge of the risk components’ importance. Such information is often nonexistent for many invasive pests. This study proposes a new approach for building integrated risk maps using the principle of a multiattribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multiattribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high‐risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multiattribute ranking against a multicriteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info‐gap decision theory. The results show major geographic hotspots where the consideration of tradeoffs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multiattribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base.     

Application of FISK,an Invasiveness Screening Tool for Non‐Native Freshwater Fishes,in the Murray‐Darling Basin (Southeastern Australia)

The Fish Invasiveness Scoring Kit (FISK) is currently one of the most popular pre‐screening tools for freshwater fishes. A recent upgrade has ensured its wider climatic relevance to countries with subtropical regions. This enhancement is of particular importance to Australia, which encompasses tropical, arid, and temperate zones, and where the introduction of non‐native fish species poses a significant risk to biodiversity. In this study, 55 fish species previously evaluated in a U.K.‐based calibration of FISK are reassessed for their potential invasiveness in the Murray‐Darling Basin (MDB; southeastern Australia), the continent's largest catchment encompassing arid and temperate climates. Approximately half of the species were classed as “medium risk” and the other half as “high risk,” and the ≥19 threshold previously identified from the calibration study was confirmed. The three highest scoring species (common carp Cyprinus carpio carpio, goldfish Carassius auratus, and eastern mosquitofish Gambusia holbrooki) were those already present and invasive in the area, whereas nearly half of the tropical and subtropical species had lower scores compared to U.K. assessments, possibly because of climate change predictions of drier conditions across the MDB. There were some discordances between FISK and two Australian‐based assessment protocols, one of which is qualitative and the other represents a simplified version of FISK. Notably, the Australian origins of FISK should provide for an additional reason for further applications of the tool in other RA areas (i.e., drainage basins) of the continent, ultimately encouraging adoption as the country's reference screening tool for management and conservation purposes.     

Quantifying the Establishment Likelihood of Invasive Alien Species Introductions Through Ports with Application to Honeybees in Australia

The cost of an uncontrolled incursion of invasive alien species (IAS) arising from undetected entry through ports can be substantial, and knowledge of port‐specific risks is needed to help allocate limited surveillance resources. Quantifying the establishment likelihood of such an incursion requires quantifying the ability of a species to enter, establish, and spread. Estimation of the approach rate of IAS into ports provides a measure of likelihood of entry. Data on the approach rate of IAS are typically sparse, and the combinations of risk factors relating to country of origin and port of arrival diverse. This presents challenges to making formal statistical inference on establishment likelihood. Here we demonstrate how these challenges can be overcome with judicious use of mixed‐effects models when estimating the incursion likelihood into Australia of the European (Apis mellifera) and Asian (A. cerana) honeybees, along with the invasive parasites of biosecurity concern they host (e.g., Varroa destructor). Our results demonstrate how skewed the establishment likelihood is, with one‐tenth of the ports accounting for 80% or more of the likelihood for both species. These results have been utilized by biosecurity agencies in the allocation of resources to the surveillance of maritime ports.     

Contaminated consignment simulation to support risk-based inspection design

Invasive nonnative plant pests can cause extensive environmental and economic damage and are very difficult to eradicate once established. Phytosanitary inspections that aim to prevent biological invasions by limiting movement of nonnative plant pests across borders are a critical component of the biosecurity continuum. Inspections can also provide valuable information about when and where plant pests are crossing national boundaries. However, only a limited portion of the massive volume of goods imported daily can be inspected, necessitating a highly targeted, risk-based strategy. Furthermore, since inspections must prioritize detection and efficiency, their outcomes generally cannot be used to make inferences about risk for cargo pathways as a whole. Phytosanitary agencies need better tools for quantifying pests going undetected and designing risk-based inspection strategies appropriate for changing operational conditions. In this research, we present PoPS (Pest or Pathogen Spread) Border, an open-source consignment inspection simulator for measuring inspection outcomes under various cargo contamination scenarios to support recommendations for inspection protocols and estimate pest slippage rates. We used the tool to estimate contamination rates of historical interception data, quantify tradeoffs in effectiveness and workload for inspection strategies, and identify vulnerabilities in sampling protocols as changes in cargo configurations and contamination occur. These use cases demonstrate how this simulation approach permits testing inspection strategies and measuring quantities that would otherwise be impossible in a field-based setting. This work represents the first steps toward a decision support tool for creating dynamic inspection protocols that respond to changes in available resources, workload, and commerce trends.     

Effectiveness of FISK,an Invasiveness Screening Tool for Non‐Native Freshwater Fishes,to Perform Risk Identification Assessments in the Iberian Peninsula

Risk assessments are crucial for identifying and mitigating impacts from biological invasions. The Fish Invasiveness Scoring Kit (FISK) is a risk identification (screening) tool for freshwater fishes consisting of two subject areas: biogeography/history and biology/ecology. According to the outcomes, species can be classified under particular risk categories. The aim of this study was to apply FISK to the Iberian Peninsula, a Mediterranean climate region highly important for freshwater fish conservation due to a high level of endemism. In total, 89 fish species were assessed by three independent assessors. Results from receiver operating characteristic analysis showed that FISK can discriminate reliably between noninvasive and invasive fishes for Iberia, with a threshold of 20.25, similar to those obtained in several regions around the world. Based on mean scores, no species was categorized as “low risk,” 50 species as “medium risk,” 17 as “moderately high risk,” 11 as “high risk,” and 11 as “very high risk.” The highest scoring species was goldfish Carassius auratus. Mean certainty in response was above the category “mostly certain,” ranging from tinfoil barb Barbonymus schwanenfeldii with the lowest certainty to eastern mosquitofish Gambusia holbrooki with the highest level. Pair‐wise comparison showed significant differences between one assessor and the other two on mean certainty, with these two assessors showing a high coincidence rate for the species categorization. Overall, the results suggest that FISK is a useful and viable tool for assessing risks posed by non‐native fish in the Iberian Peninsula and contributes to a “watch list” in this region.     

Ensemble Habitat Mapping of Invasive Plant Species

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species‐environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species‐environment matching models for risk analysis.     

Risk and Climate Change: Perceptions of Key Policy Actors in Canada

This article examines factors that predict perceptions of risk associated with global climate change. The research focuses on the perceptions of those associated with climate change policy making in the prairie region of Canada. The data are from an online survey ( n = 851) of those policy actors. The analysis integrates several dominant approaches to the study of risk perception: psychometric approaches that examine the effects of cognitive structure; demographic assessments that examine, for example, differences in perception based on gender or family status; and political approaches that suggest that one's position in the policy process may affect his or her perceived risk. Attitudes toward climate change are to a degree predicted by all of these factors, but only when indirect effects are observed. Sociodemographic characteristics have little direct effect on perceived risk, but do affect general beliefs that affect risk perceptions. Perceived risk is related more strongly to these general beliefs or world views than to more specific beliefs about the effects of climate change on weather patterns. Position within the policy process also contributes to our understanding of perceptions, with industry and governmental actors demonstrating similar attitudes, which contrast with environmental groups and university researchers.     

Projecting Rates of Spread for Invasive Species

All else being equal, the faster an invading species spreads, the more dangerous its invasion. The projection of spread rate therefore ought to be a central part of the determination of invasion risk. Originally formulated in the 1970s to describe the spatial spread of advantageous alleles, integrodifference equation (IDE) models have since been co-opted by population biologists to describe the spread of populations. More recently, they have been modified to include population structure and environmental variability. We review how IDE models are formulated, how they are parameterized, and how they can be analyzed to project spread rates and the sensitivity of those rates to changes in model parameters. For illustrative purposes, we apply these models to Cytisus scoparius, a large shrub in the legume family that is considered a noxious invasive species in eastern and western North America, Chile, Australia, and New Zealand.     

Establishment Risks for Invasive Species

This article presents a quantitative methodology for evaluating the probability of invasive pest species establishing persistent populations. The estimation of pest establishment relies on data and information describing the biology and ecology of the pest and its interactions with potential host species and the regional environment. This information is developed using a model construct borrowed from theoretical population ecology. The methodology for estimating the probability of pest establishment is part of an overall framework that explores the implications of reductions in pest invasions on subsequent establishment. The risk reduction framework integrates the engineering aspects of different technologies for reducing pest entry, the biology and ecology of pest species, the suitability of potentially susceptible hosts, and the quality of available habitats. The methodology for estimating the risk of establishment is presented using an example pest, the Asian longhorned beetle ( Anoplophora glabripennis ), which has been introduced into the United States via solid wood packing materials (SWPM) used in international commerce. Uncertainties inherent to the estimation of model parameters that determine the risk of establishment are defined, quantified, and propagated through the population model. Advantages and limitations of the proposed methodology are discussed along with recommendations to make the approach more useful in the management of risks posed by the establishment of pest populations.     

Portfolio value-at-risk estimation for spot chartering decisions under changing trade patterns: A copula approach

Evolving geopolitical relationships between countries (especially between China and the United States) in recent years have highlighted dynamically changing trade patterns across the globe, all of which elevate risk and uncertainty for transport service providers. In order to mitigate risks, shipowners and operators must be able to estimate risks appropriately; one potentially promising method of doing so is through the value-at-risk (VaR) method. VaR describes the worst loss a portfolio is likely to sustain, which will not be exceeded over a target time horizon at a given level of confidence. This article proposes a copula-based GARCH model to estimate the joint multivariate distribution, which is a key component in VaR estimation. We show that the copula model can capture the VaR more successfully, as compared with the traditional method of calculation. As an empirical study, the expected portfolio VaR is examined when a shipowner chooses among Panamax soybean trading routes under a condition of reduced trade volumes between the United States and China due to the ongoing trade turmoil. This study serves as one of the very few papers in the literature on shipping portfolio VaR analysis. The results have significant implications for shipowners regarding fleet repositioning, decision making, and risk management.     

Time‐Series Models for Border Inspection Data

We propose a new modeling approach for inspection data that provides a more useful interpretation of the patterns of detections of invasive pests, using cargo inspection as a motivating example. Methods that are currently in use generally classify shipments according to their likelihood of carrying biosecurity risk material, given available historical and contextual data. Ideally, decisions regarding which cargo containers to inspect should be made in real time, and the models used should be able to focus efforts when the risk is higher. In this study, we propose a dynamic approach that treats the data as a time series in order to detect periods of high risk. A regulatory organization will respond differently to evidence of systematic problems than evidence of random problems, so testing for serial correlation is of major interest. We compare three models that account for various degrees of serial dependence within the data. First is the independence model where the prediction of the arrival of a risky shipment is made solely on the basis of contextual information. We also consider a Markov chain that allows dependence between successive observations, and a hidden Markov model that allows further dependence on past data. The predictive performance of the models is then evaluated using ROC and leakage curves. We illustrate this methodology on two sets of real inspection data.     

Robustness of Risk Maps and Survey Networks to Knowledge Gaps About a New Invasive Pest

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk‐ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.     

Principles for Conduct of Pest Risk Analyses: Report of an Expert Workshop

The North American Free Trade Agreement (NAFTA) and the General Agreement on Tariffs and Trade (GATT) have focused attention on risk assessment of potential insect, weed, and animal pests and diseases of livestock. These risks have traditionally been addressed through quarantine protocols ranging from limits on the geographical areas from which a product may originate, postharvest disinfestation procedures like fumigation, and inspections at points of export and import, to outright bans. To ensure that plant and animal protection measures are not used as nontariff trade barriers, GATT and NAFTA require pest risk analysis (PRA) to support quarantine decisions. The increased emphasis on PRA has spurred multiple efforts at the national and international level to design frameworks for the conduct of these analyses. As approaches to pest risk analysis proliferate, and the importance of the analyses grows, concerns have arisen about the scientific and technical conduct of pest risk analysis. In January of 1997, the Harvard Center for Risk Analysis (HCRA) held an invitation-only workshop in Washington, D.C. to bring experts in risk analysis and pest characterization together to develop general principles for pest risk analysis. Workshop participants examined current frameworks for PRA, discussed strengths and weaknesses of the approaches, and formulated principles, based on years of experience with risk analysis in other setting and knowledge of the issues specific to analysis of pests. The principles developed highlight the both the similarities of pest risk analysis to other forms of risk analysis, and its unique attributes.     

基于多种污染物损害视角的地方政府间环境规制合作策略研究

考虑多种(非累积性和累积性)污染物对环境造成不同损害的前提下,首先基于Stackelberg博弈分析占主导的地方政府和跟随的工业企业各自的动态决策过程,确定工业企业的最优污染物排放量;随后运用最优控制理论构建两个相邻地区在非合作和合作博弈下关于跨界污染最优控制的博弈模型,分析地方政府的环境治理策略,包括最优的环境保护税、污染治理投资,探讨污染物存量的动态变化情况,并对此两种博弈结构进行了比较分析。理论及仿真分析表明：无论地区间是非合作还是合作博弈,工业企业的最优污染物排放量与其污染物减排比例的相关性不确定;每个地区的最优环境保护税与污染物减排比例呈正相关。但合作博弈下,每个地区均会考虑其非累积性污染物排放对其相邻地区造成的损害;每个地区均会增加污染治理投资;所有地区的总收益高于非合作博弈下,且高出部分(合作剩余)受累积性污染物的损害以及非累积性污染物对相邻地区损害的影响,但是与非累积性污染物对本地区的损害无关。     

Priority Topics in the Study of Environmental Risk in Developing Countries: Report on a Workshop Held at the East–West Center, August, 1988

The rapid industrialization occurring in developing regions of the world brings not only economic benefits, but changes in the types and severity of health and environmental problems that each region experiences. As the industrialized world moves toward the use of risk assessment methodologies to aid in problem evaluation and regulatory and policy decision analysis, it seems inevitable that these methodologies will be applied globally. The changes brought about by rapid industrialization, however, must be viewed within the context of societies that are still struggling with the more traditional and basic environmental problems associated with urban and rural poverty. The urgency of development and the lack of adequate resources for characterizing health and environmental changes, often present under these circumstances, offer special challenges to the application of risk assessment methodologies.