Evaluating Critical Uncertainty Thresholds in a Spatial Model of Forest Pest Invasion Risk

Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model's numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for entry, establishment, and spread, to estimate the risks of invasion and their variation across a two-dimensional landscape for Sirex noctilio , a nonnative woodwasp recently detected in the United States and Canada. Here, we present a sensitivity analysis of the mapped risk estimates to variation in key model parameters. The tested parameter values were sampled from symmetric uniform distributions defined by a series of nested bounds (±5%, … , ±40%) around the parameters' initial values. The results suggest that the maximum annual spread distance, which governs long-distance dispersal, was by far the most sensitive parameter. At ±15% or larger variability bound increments for this parameter, there were noteworthy shifts in map risk values, but no other parameter had a major effect, even at wider bounds of variation. The methodology presented here is generic and can be used to assess the impact of uncertainties on the stability of pest risk maps as well as to identify geographic areas for which management decisions can be made confidently, regardless of uncertainty.     

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.     

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.     

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.     

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.     

Modeling Extreme Risks in Ecology

Extreme risks in ecology are typified by circumstances in which data are sporadic or unavailable, understanding is poor, and decisions are urgently needed. Expert judgments are pervasive and disagreements among experts are commonplace. We outline approaches to evaluating extreme risks in ecology that rely on stochastic simulation, with a particular focus on methods to evaluate the likelihood of extinction and quasi‐extinction of threatened species, and the likelihood of establishment and spread of invasive pests. We evaluate the importance of assumptions in these assessments and the potential of some new approaches to account for these uncertainties, including hierarchical estimation procedures and generalized extreme value distributions. We conclude by examining the treatment of consequences in extreme risk analysis in ecology and how expert judgment may better be harnessed to evaluate extreme risks.     

Modeling Phytosanitary Risk of Unintended Commodity Use: The Example of U.S. Potato Exports to Mexico

Diversion of commodities from their intended use to an unintended use, e.g., when commodities intended for consumption are used as seed for planting, is an important issue in agricultural trade that has implications for the establishment of pests and pathogens in an importing country and for the appropriate strength of plant health measures. Consequently, understanding and accurately characterizing the risk of diversion from intended use is highly relevant to policymakers, trading partners, and in trade dispute arbitration. To our knowledge, no risk assessments have ever accounted for the likelihood of diversion from intended use. Here we present an approach to analyzing this risk using diversion of U.S. table stock potatoes to seed for planting by Mexican potato producers as a case study. We use probabilistic pathway models to characterize the movement of white, yellow, and russet potatoes from the United States into Mexico at current and double export volumes. We then model the likelihood of these potatoes being diverted for seed and the subsequent establishment of bacteria, nematode, and virus pests in Mexico. Our approach demonstrates how diversion from intended use can be quantified in one scenario and, in particular, how it can be analyzed to estimate the magnitude of diversion required to produce a high risk of pest establishment.     

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.     

Bioeconomics of Managing the Spread of Exotic Pest Species with Barrier Zones

Exotic pests are serious threats to North American ecosystems; thus, economic analysis of decisions about eradication, stopping, or slowing their spread may be critical to ecosystem management. The proposed bioeconomic model assumes that the rate of population expansion can be reduced (even to negative values in a case of eradication) if certain management actions are taken along the population front. The area of management can be viewed as a dynamic barrier zone that moves together with the population front. The lower is the target rate of spread, the higher would be both benefits and costs of the project. The problem is to find the optimal target rate of spread at which the present value of net benefits from managing population spread reaches its maximum value. If a population spreads along an infinite habitat strip, the target rate of spread is optimal if the slope of the cost function versus the rate of spread is equal to the ratio of the average pest-related damage per unit time and unit area to the discount rate. In a more complex model where the potential area of expansion is limited, two local maxima of net benefits may exist: one for eradication and another for slowing the spread. If both maxima are present, their heights are compared and the strategy that corresponds to a higher value of net benefits is selected. The optimal strategy changes from eradication to slowing the spread and finally to doing nothing as the area occupied by the species increases. The model shows that slowing the spread of pest species generates economic benefits even if a relatively small area remains uninfested. The cost of slowing the spread can be estimated from a model of population expansion via establishment of isolated colonies beyond the moving front. The model is applied to managing the spread of the gypsy moth (Lymantria dispar) populations in the United States.     

The evaluation of country risk and economic potential

The need for a framework to evaluate the risks inherent in dealing with different countries is widely recognized. International financial institutions, government agencies and major corporations have in recent years established formal systems of risk assessment as an important input to their operational and planning decisions. It is important to understand the basic thinking underlying the mechanical approach to risk analysis and the results must be treated with care. A structured approach to the evaluation of international risks provides the necessary analytical framework.     

Managing behavioral intentions in diverse cultural environments: an investigation of service quality,service value,and satisfaction for American and Ecuadorian fast-food customers

This study reports a cross-cultural assessment of the effects of service quality, service value, and satisfaction on consumers' behavioral intentions. The study endeavors to extend recent advances in services marketing theory to the international level of analysis. A composite model of a service encounter is developed based on the literature cited and compared across 425 North American and Latin American fast-food customers. A number of significant findings are reported, including the empirical verification of the position that service value and satisfaction drive consumers' behavioral intentions in North America, whereas Latin Americans are found to emphasize satisfaction in their service assessments. These results indicate that American consumers tend to place more emphasis on the tradeoff between what they receive in the service encounter and what they have to give up to receive the benefit. Conversely, Latin American consumers place heightened importance on the more emotive satisfaction judgment. The immediate implication for practitioners in both regions is that North Americans should respond favorably to offerings that emphasize the quality of the meal relative to the expenditure required. Latin Americans, on the other hand, should respond to more emotional appeals that accentuate the pleasure derived from the encounter.     

Genetic Diversity of Invasive Species in the Great Lakes Versus Their Eurasian Source Populations: Insights for Risk Analysis

Combining DNA variation data and risk assessment procedures offers important diagnostic and monitoring tools for evaluating the relative success of exotic species invasions. Risk assessment may allow us to understand how the numbers of founding individuals, genetic variants, population sources, and introduction events affect successful establishment and spread. This is particularly important in habitats that are "hotbeds" for invasive species--such as the North American Great Lakes. This study compares genetic variability and its application to risk assessment within and among three Eurasian groups and five species that successfully invaded the Great Lakes during the mid 1980s through early 1990s; including zebra and quagga mussels, round and tubenose gobies, and the ruffe. DNA sequences are compared from exotic and native populations in order to evaluate the role of genetic diversity in invasions. Close relatives are also examined, since they often invade in concert and several are saline tolerant and are likely to spread to North American estuaries. Results show that very high genetic diversity characterizes the invasions of all five species, indicating that they were founded by very large numbers of propagules and underwent no founder effects. Genetic evidence points to multiple invasion sources for both dreissenid and goby species, which appears related to especially rapid spread and widespread colonization success in a variety of habitats. In contrast, results show that the ruffe population in the Great Lakes originated from a single founding population source from the Elbe River drainage. Both the Great Lakes and the Elbe River populations of ruffe have similar genetic diversity levels--showing no founder effect, as in the other invasive species. In conclusion, high genetic variability, large numbers of founders, and multiple founding sources likely significantly contribute to the risk of an exotic species introduction's success and persistence.     

Communicating Geographical Risks in Crisis Management: The Need for Research

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.     

Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.     

An Agent‐Based Model for Pathogen Persistence and Cross‐Contamination Dynamics in a Food Facility

We used an agent‐based modeling (ABM) framework and developed a mathematical model to explain the complex dynamics of microbial persistence and spread within a food facility and to aid risk managers in identifying effective mitigation options. The model explicitly considered personal hygiene practices by food handlers as well as their activities and simulated a spatially explicit dynamic system representing complex interaction patterns among food handlers, facility environment, and foods. To demonstrate the utility of the model in a decision‐making context, we created a hypothetical case study and used it to compare different risk mitigation strategies for reducing contamination and spread of Listeria monocytogenes in a food facility. Model results indicated that areas with no direct contact with foods (e.g., loading dock and restroom) can serve as contamination niches and recontaminate areas that have direct contact with food products. Furthermore, food handlers’ behaviors, including, for example, hygiene and sanitation practices, can impact the persistence of microbial contamination in the facility environment and the spread of contamination to prepared foods. Using this case study, we also demonstrated benefits of an ABM framework for addressing food safety in a complex system in which emergent system‐level responses are predicted using a bottom‐up approach that observes individual agents (e.g., food handlers) and their behaviors. Our model can be applied to a wide variety of pathogens, food commodities, and activity patterns to evaluate efficacy of food‐safety management practices and quantify contamination reductions associated with proposed mitigation strategies in food facilities.     

Modeling the Risks of Nonindigenous Species Introductions Using a Patch-Dynamics Approach Incorporating Contaminant Effects as a Disturbance

The establishment and spread of invasive or nonindigenous species has caused concern from stakeholders in affected areas, and has prompted many field and modeling studies. We used stochastic two species, circular three patch dynamic models to investigate the patterns of invasion and impacts upon the affected species. Both persistent and degradable toxicants were incorporated as parts of the model system to act as disturbance regimens. There is a clear series of patterns that result from these simulations. Competition increases population variability, but decreases the number of distinct outcomes possible from the same initial conditions. Isolation of the patch of the introduction was the main determinant of successful establishment through a process we call the beachhead effect. Coexistence of species was often possible in local patches, contrary to the analytical solutions of Lotka-Volterra equations and numerous modeling studies. Contaminants and their resultant disturbances are important as contributors to the stochastic nature of models. The stochasticity leads to a variety of outcomes from some sets of initial conditions. Different outcomes have different probabilities of occurrence and are dependent upon the specific initial conditions of the simulation. A clear pattern that is apparent is the "beachhead effect," where the invasive establishes a population within a relatively remote patch before migrating to the remainder of the landscape. We make predictions and provide specific research hypotheses as to the causes and effects of invasive species establishment, spread, and impacts.     

An Ecological Risk Assessment of Nonnative Boas and Pythons as Potentially Invasive Species in the United States

The growing international trade in live wildlife has the potential to result in continuing establishment of nonnative animal populations in the United States. Snakes may pose particularly high risks as potentially invasive species, as exemplified by the decimation of Guam's vertebrate fauna by the accidentally introduced brown tree snake. Herein, ecological and commercial predictors of the likelihood of establishment of invasive populations were used to model risk associated with legal commercial imports of 23 species of boas, pythons, and relatives into the United States during the period 1989-2000. Data on ecological variables were collected from multiple sources, while data on commercial variables were collated from import records maintained by the U.S. Fish and Wildlife Service. Results of the risk-assessment models indicate that species including boa constrictors (Boa constrictor), ball pythons (Python regius), and reticulated pythons (P. reticulatus) may pose particularly high risks as potentially invasive species. Recommendations for reducing risk of establishment of invasive populations of snakes and/or pathogens include temporary quarantine of imports to increase detection rates of nonnative pathogens, increasing research attention to reptile pathogens, reducing the risk that nonnative snakes will reach certain areas with high numbers of federally listed species (such as the Florida Keys), and attempting to better educate individuals purchasing reptiles.     

Life Cycle Risks for Human Health: A Comparison of Petroleum Versus Bio-Based Production of Five Bulk Organic Chemicals

This article describes the development and application of a generic approach to the comparative assessment of risks related to the production of organic chemicals by petrochemical processes versus white biotechnology. White biotechnology, also referred to as industrial biotechnology, typically uses bio-based feedstocks instead of the fossil raw materials used in the petrochemical sector. The purpose of this study was to investigate whether the production of chemicals by means of white biotechnology has lower conventional risks than their production by petrochemical processes. Conventional risks are the risks of well-established processes, and not those related to genetically modified microorganisms and plants. Our approach combines classical risk assessment methods (largely based on toxicology), as developed by the life cycle assessment (LCA) community, with statistics on technological disasters, accidents, and work-related illnesses. Moreover, it covers the total process chain for both petrochemical and bio-based products from cradle to grave. The approach was applied to five products: the plastics polytrimethylene terephthalate (PTT), polyhydroxyalkanoates (PHA), polyethylene terephthalate (PET), polyethylene (PE), and ethanol. Our results show that the conventional risks related to the white biotechnology products studied are lower than those of the petrochemical products. However, considering the uncertainties with respect to the ranges of input data, the (incomplete) coverage of emissions by the environmental priority strategies (EPS) 2000 method, and the uncertainties of the assumptions made in this study (i.e., large to very large), the differences in results between bio-based and petrochemical products fall into the uncertainty range. Because of this, future research is necessary to decrease the uncertainties before we can conclude that the conventional risks of biotechnologically produced chemicals are lower than those of fossil-fuel-derived chemicals.     

A Resilience-Based Approach to Risk Assessments—Building Resilient Organizations under Arctic Conditions

Reliability and higher levels of safety are thought to be achieved by using systematic approaches to managing risks. The assessment of risks has produced a range of different approaches to assessing these uncertainties, presenting models for how risks affect individuals or organizations. Contemporary risk assessment tools based on this approach have proven difficult for practitioners to use as tools for tactical and operational decision making. This article presents an alternative to these assessments by utilizing a resilience perspective, arguing that complex systems are inclined to variety and uncertainty regarding the results they produce and are therefore prone to systemic failures. A continuous improvement approach is a source of reliability when managing complex systems and is necessary to manage varieties and uncertainties. For an organization to understand how risk events occur, it is necessary to define what is believed to be the equilibrium of the system in time and space. By applying a resilience engineering (RE) perspective to risk assessment, it is possible to manage this complexity by assessing the ability to respond, monitor, learn, and anticipate risks, and in so doing to move away from the flawed frequency and consequences approach. Using a research station network in the Arctic as an example illustrates how an RE approach qualifies assessments by bridging risk assessments with value-creation processes. The article concludes by arguing that a resilience-based risk assessment can improve on current practice, including for organizations located outside the Arctic region.     

Life cycle emission distributions within the economy: implications for life cycle impact assessment.

Refinements of methods for life cycle impact assessment (LCIA) are directed at removing unjustified simplifications and quantifying and reducing uncertainties in results. The amount of uncertainty reduction that is actually achieved through LCIA method refinement depends on the structure of the life cycle inventory model. We investigate the general structure of inventory models using an economic input/output (I/O) life cycle assessment model of the U.S. economy. In particular, we study the results of applying a streamlining algorithm to the I/O LCA model. The streamlining algorithm retains only those "branches" of the process tree that are jointly required to account for a specified fraction of the total impacts upstream of each point in the tree. We examine the implications of these "tree pruning" results for site-informed LCIA. Percentiles are presented for U.S. commodities and several important pollutants, for the share of total upstream emissions contributed by the set of processes in each supply tier, that is, each set of processes that directly supply inputs to another set of processes Capturing at least 90% of the total direct plus upstream emissions for criteria air pollutants and toxic releases for at least 75% of the commodities in the U.S. economy requires full modeling of direct emissions plus the first five supply tiers. The requirements for capturing a high percentage (e.g., >80%) of total emissions vary widely across products or commodities. To capture more than 60% of total emissions for more than half of all commodities requires models with more than 4,000 process instances. To well characterize the total impacts of products, life cycle impact assessment methods must characterize foreground process impacts in a site-informed way and mean impacts of far-removed processes in an unbiased way.