An options based bioeconomic model for biological and chemical control of invasive species

The management of non-native invasive species is a complex but crucial task given the potential for economic and environmental damages. For many invasions the development of socially optimal control strategies requires more than is offered by the single-species, single-control models that have dominated this area of research. We develop a general stochastic optimal control framework that accommodates multiple interacting species while accounting for uncertainty in the temporal population dynamics. This extension to the current line of bioeconomic control models allows for the design of optimal integrated pest management strategies that utilize both chemical and biological controls in an environment of uncertainty and irreversibility. We demonstrate the benefits of combining chemical and biological controls in long term management strategies through a case study of the hemlock wooly adelgid (Adelges tsugae) infestation in the eastern United States. In this application we find that the introduction of natural predators is usually sufficient to manage the infestation, though chemical insecticides can play an important role when detection of the infestation is delayed or when the biological control agent does not sufficiently increase mortality of the invasive species.     

Species Invasion as Catastrophe: The Case of the Brown Tree Snake

This paper develops a two-stage model for the optimal management of a potential invasive species. The arrival of an invasive species is modeled as an irreversible event with an uncertain arrival time. The model is solved in two stages, beginning with the post-invasion stage. Once the arrival occurs, the optimal path of species removal is that which minimizes the present value of damage and removal costs plus the expected present value of prevention costs. An expenditure-dependent, conditional hazard rate describing species arrival is developed based on discussions with natural resource managers. We solve for the optimal sequence of prevention expenditures, given the minimum invasion penalty as just described. For the case of the Brown Tree Snake potentially invading Hawaii, we find that pre-invasion expenditures on prevention are inverse U-shaped in the hazard rate. Efficient prevention should be approximately $2.9?million today and held constant until invasion. Once invasion occurs, optimal prevention requires $3.1?million annually and $1.6?million per year on species removal to keep the population at its steady state level, due to high search costs at very small population levels.     

Monitoring as a partially observable decision problem

Monitoring is an important and costly activity in resource management problems such as containing invasive species, protecting endangered species, preventing soil erosion, and regulating contracts for environmental services. Recent studies have viewed optimal monitoring as a Partially Observable Markov Decision Process (POMDP), which provides a framework for sequential decision making under stochastic resource dynamics and uncertainty about the resource state. We present an overview of the POMDP framework and its applications to resource monitoring. We discuss the concept of the information content provided by monitoring systems and illustrate how information content affects optimal monitoring strategies. Finally, we demonstrate how the timing of monitoring in relation to resource treatment and transition can have substantial effects on optimal monitoring strategies.     

A bioeconomic model for determining the optimal response strategies for a new weed incursion

Invasions by non-indigenous plant species pose serious economic threats to Australian agricultural industries. When a new invader is identified a rapid response is critical, particularly if the invasive species has the ability to spread rapidly. An early decision is required whether to attempt to eradicate or contain the infestation, or do nothing and leave it to landholders to manage. These decisions should be based on economic considerations that account for long term benefits and costs. This paper describes a bioeconomic simulation framework with a mathematical model representing weed spread linked to a dynamic programming model to provide a means of determining the economically optimal weed management strategies over time, from the government’s perspective. The modelling framework is used to evaluate hypothetical case study invasive weed control scenarios in the Australian cropping systems. The benefit–cost ratios of invasion control are shown to be generally very high and clearly, there are significant benefits to be achieved by controlling highly invasive weeds when initial infestations are at a low level. Even if the invasion cannot be eradicated due to its high invasiveness or budget constraints, it still pays to maintain invasions at low levels.     

Technology adoption and mitigation of invasive species damage and risk: application to zebra mussels

Using a bio-economic model of zebra mussels (Dreissena polymorpha), we examine the expected economic value of prevention, control and eradication alternatives for the freshwater mussel in Lake Okeechobee (Florida, USA). We include two emerging technologies for zebra mussel (ZM) control: (1) a natural pesticide called Zequanox, and (2) hot wash stations at boat ramps. We employ water management district data, user data collected via a phone survey, and mitigation expenditures from infested locations elsewhere to estimate the potential damage from the introduction of zebra mussels in Florida. Methods used include static cost transfer estimation, econometric cost estimation, and stochastic-dynamic simulation. We use our bio-economic model to compare costs and risks with and without the emerging technologies. We also consider the impact of technology adoption rates by anglers, management policy efficacy, and opportunity costs associated with ZM control. Results indicate that, without investment in prevention, there is a very high probability that Florida waterways will be infested with zebra mussels by year 2025, and expected environmental damages and management costs are high. Slow response due to poor detection methods or insufficient control efforts will lead to a moderate probability of a significant infestation. Rapid reaction and enhanced prevention efforts are expected to greatly reduce the probability of ZM infesting Lake Okeechobee by 2025, and to generate much higher expected net benefits.     

Spatial economic analysis of early detection and rapid response strategies for an invasive species

Economic impacts from invasive species, conveyed as expected damages to assets from invasion and expected costs of successful prevention and/or removal, may vary significantly across spatially differentiated landscapes. We develop a spatial–dynamic model for optimal early detection and rapid response (EDRR) policies, commonly exploited in the management of potential invaders around the world, and apply it to the case of the Brown treesnake (Boiga irregularis) in Oahu, Hawaii. EDRR consists of search activities beyond the ports of entry, where search (and potentially removal) efforts are targeted toward areas where credible evidence suggests the presence of an invader. EDRR costs are a spatially dependent variable related to the ease or difficulty of searching an area, while damages are assumed to be a population-dependent variable. A myopic strategy in which search only occurs when and where current expected net returns are positive is attractive to managers, and, we find, significantly lowers present value losses (by $270 m over 30 years). We find further that in the tradeoff between search costs and damages avoided, early and aggressive measures that search some high priority areas beyond points of entry even when current costs of search exceed current damages can save the island more ($295 m over 30 years). Extensive or non-targeted search is not advised however.     

Asymmetric information without common priors: an indirect evolutionary analysis of quantity competition

The common prior assumption justifies private beliefs as posterior probabilities when updating a common prior based on individual information. We dispose of the common prior assumption for a homogeneous oligopoly market with uncertain costs and firms entertaining arbitrary priors about other firms’ cost-type. We show that true prior beliefs can not be evolutionarily stable when truly expected profit measures (reproductive) success.     

Optimal Dynamic Control of Visitors and Endangered Species in a National Park

To enable visitors to enjoy nature while protecting endangered species is the key challenge for national parks around the world. In our optimal dynamic control model, a park management maximizes tourism revenues and conservation benefits net of control costs by choosing optimal dynamic levels of conservation and visitor management. The optimization is constrained by an extended food chain model representing species-habitat-visitor interactions. We illustrate for a game bird in an Alpine national park that ecotourism can indeed lead to ecosystem degradation when the park management is more concerned about increasing tourism revenues than about achieving the conservation target and if the endangered species is unknown to most visitors. If, however, the park management is well aware of the potentially negative consequences of ecotourism and hence cares for species conservation, limited ecotourism can provide funding for species conservation which ensures population levels above the uncontrolled steady state.     

Heterogeneous guilt sensitivities and incentive effects

Psychological games of guilt aversion assume that preferences depend on (beliefs about) beliefs and on the guilt sensitivity of the decision-maker. We present an experiment designed to measure guilt sensitivities at the individual level for various stake sizes. We use the data to estimate a structural choice model that allows for heterogeneity, and permits that guilt sensitivities depend on stake size. We find substantial heterogeneity of guilt sensitivities in our population, with 60% of decision makers displaying stake-dependent guilt sensitivity. For these decision makers, we find that average guilt sensitivities are significantly different from zero for all stakes considered, while significantly decreasing with the level of stakes.     

First- and second-order subjective expectations in strategic decision-making: Experimental evidence

We study first- and second-order subjective expectations (beliefs) in strategic decision-making. We elicit probabilistically both first- and second-order beliefs and apply the method to a Hide-and-Seek experiment. We study the relationship between choice and beliefs in terms of whether observed choice coincides with the optimal action given elicited beliefs. We study the relationship between first- and second-order beliefs under a coherence criterion. Weak coherence requires that if an event is assigned, according to first-order beliefs, a probability higher/lower/equal to the one assigned to another event, then the same holds according to second-order beliefs. Strong coherence requires the probability assigned according to first- and second-order beliefs to coincide. Evidence of heterogeneity across participants is reported. Verbal comments collected at the end of the experiment shed light on how subjects think and decide in a complex environment that is strategic, dynamic and populated by potentially heterogeneous individuals.     

Standards and the regulation of environmental risk

We study regulatory design for a pollution-generating firm who is better informed than the regulator regarding pollution mitigation possibilities, and who chooses an unobservable action when employing a particular mitigation plan. We distinguish among performance, process, and design standards, and study the relative merit of each type of regulatory instrument. Relative to previous work on standards design, we emphasize technology and process verification. An optimal performance standard is relatively strict when regulator and firm preferences are congruent, but the regulator may prefer no performance standard at all if verification costs are sufficiently high. A process standard unambiguously increases expected surplus (relative to no regulation) in some environments, and otherwise improves welfare only when it is unlikely to generate a “bad” technology choice by the firm. A design standard can improve welfare if the regulator is sufficiently well informed about the technological possibilities for pollution control, but only when the firm’s private benefits from technology choice are sufficiently small.     

Repeated Experimentation to Learn About a Flow-Pollutant Threshold

We examine in discrete time the management of a flow pollutant that causes damage when it crosses a fixed but unknown threshold. The manager sequentially chooses a pollution level that allows learning about the threshold, thereby improving future decisions. If crossed, damage can be reversed at some cost. We analyze the conditions under which experimentation is optimal, and explore how experimentation depends on restoration costs, information about the threshold, and the discount rate. Our results suggest that the level of experimentation, defined as the difference between the optimal activity with and without learning, is non-monotonic in costs and decreasing in the discount rate. We identify two stopping boundaries for the experiment, depending on cost levels compared to the lower bound of the threshold’s interval. We show that when costs are high the stopping boundary under an infinite number of decisions is the same as when there are only two decision moments. A computational extension to more than two decisions suggests that an optimal sequence of experiments can cross the same threshold several times before experimentation ceases. These results shed light on a large class of environmental decision problems that has not been examined in the literature.     

An optimization framework for addressing aquatic invasive species

This study develops a bio-economic model framework to optimize the management of aquatic invasive species. Stochastic dynamic programming is applied to investigate when and to what extent a society should engage in efforts to reduce the likelihood of an invasion, to control and eradicate a newly established population, and to adapt to damages. The framework is parameterized for a potential Asian clam (Corbicula fluminea) invasion in the warm water discharge area of a nuclear power plant planned on the northern shores of the Baltic Sea. The sensitivity analysis reveals three distinct strategies: an adaptive strategy, which reduces the damage that an existing invasive species population causes to the private sector; a preventive strategy, which delays the invasion and the resulting damage; and a mitigative strategy, which puts effort into timely detection, control and eradication of the newly established population. Choice of the optimal strategy is sensitive to the unit costs and effectiveness of the measures required, to the level of externalities and to the size of the clam population after the invasion has been detected. The results emphasize the need for the energy sector to identify and internalize the external costs of potential invasions when making any large-scale investment plans.     

Additional costs of nature management caused by deposition

Effects of atmospheric deposition on natural areas can be mitigated by management. For example, the effects of excessive nitrogen deposition can partly be overcome by intensifying measures like mowing or sod cutting. The costs of this extra management may, in the future, no longer be required when deposition rates decrease. We developed a method to calculate the costs of the intensified nature management that is required at increased deposition rates. We used a set of models that simulate the biomass development under different management regimes necessary to maintain a certain level of biodiversity. The costs were calculated for several vegetation types based on, among others, the biomass and litter that was removed. Preliminary results show that the models can be applied with some success to assess these costs. Model outcomes show a clear effect of management intensity on biomass growth in heathland and grassland although no differences were found in forest. The biodiversity in heathlands clearly increased when management was intensified. As an example, the extra costs in heathlands that are made to counteract the effects of atmospheric deposition were calculated for The Netherlands. The costs of maintaining heathlands with sods being removed from heathlands every 20 years (presently the usual rate) exceed those in a situation of sod cutting every 60 years (the expected rate without atmospheric deposition) by 1.4 m Euro per year.     

Optimal strategies for the surveillance and control of forest pathogens: A case study with oak wilt

Cost-effective strategies are needed to find and remove diseased trees in forests damaged by pathogens. We develop a model of cost-minimizing surveillance and control of forest pathogens across multiple sites where there is uncertainty about the extent of the infestation in each site and when the goal is to minimize the expected number of new infections. We allow for a heterogeneous landscape, where grid cells may be differentiated by the number of trees, the expected number of infected trees, rates of infection growth, and costs of surveillance and control. In our application to oak wilt in Anoka County, Minnesota, USA, we develop a cost curve associated with saving healthy trees from infection. Assuming an annual infection growth rate of 8%, a $1 million budget would save an expected 185 trees from infection for an average of $5400 per tree.We investigate how more precise prior estimates of disease and reduced detection sensitivity affect model performance. We evaluate rules of thumb, finding that prioritizing sites with high proportions of infected trees is best. Our model provides practical guidance about the spatial allocation of surveillance and control resources for well-studied forest pathogens when only modest information about their geographic distribution is available.     

Non-Laplacian beliefs in a global game with noisy signaling

In standard global games, individual behavior is optimal if it constitutes a best response to agnostic—Laplacian—beliefs about the aggregate behavior of other agents. This paper considers a standard binary action global game augmented with noisy signaling by an informed policy-maker and shows that in this game, equilibrium beliefs depart in quite stark ways from the Laplacian benchmark. In the limit as signals become arbitrarily precise, so that all fundamental uncertainty is removed (leaving only strategic uncertainty), the equilibrium beliefs of the marginal individual concerning the aggregate action collapse to a discrete Bernoulli distribution, giving probability mass only to the polar extreme outcomes. By contrast in the underlying standard global game the marginal individual believes the aggregate action has a continuous uniform distribution, giving equal likelihood to all possible outcomes.     

A Behavioral Learning Process in Games

This paper studies the cumulative proportional reinforcement (CPR) rule, according to which an agent plays, at each period, an action with a probability proportional to the cumulative utility that the agent has obtained with that action. The asymptotic properties of this learning process are examined for a decision-maker under risk, where it converges almost surely toward the expected utility maximizing action(s). The process is further considered in a two-player game; it converges with positive probability toward any strict pure Nash equilibrium and converges with zero probability toward some mixed equilibria (which are characterized). The CPR rule is compared in its principles with other reinforcement rules and with replicator dynamics. Journal of Economic Literature Classification Number: C72.     

Factors determining awareness and knowledge of aquatic invasive species

Public perceptions of invasive species may influence policies and programs initiated by public and private stakeholders. We investigate the determinants of the public's awareness and knowledge of invasive species as few studies have examined this relationship. We focus on aquatic invasive species (AIS) and employ survey data from property owners in a lake district. A major contribution is that we estimate a mixed trivariate binary-ordered probit regression model that accommodates correlations among unobserved characteristics, produces statistically more efficient estimates, and allows a more proper investigation of the probability of knowledge conditional on awareness. Our results provide insights for invasive species education and management programs. We find that individuals are more likely to be aware of AIS if they participate in water-based recreation, visit lakes outside their area, have a boat, belong to a lake association, or are college educated. This has a policy implication: Given high levels of AIS awareness by those most involved in activities around lakes and those with a higher education, it may be beneficial to target informational campaigns at those who do not display these characteristics, so that they can better make informed decisions about whether to support and expend money on invasive species management programs.     

Challenging the enforcement of environmental regulation

If a firm can contest the enforcement of an environmental regulation, neither increasing the probability nor severity of the fine will guarantee a reduction in a firm's illegally dumped waste. A policy that can unambiguously decrease illegal dumping is lowering the cost of legal disposal. This result occurs because the use of monitoring and fines to increase the probability or severity of enforcement triggers investment to evade enforcement, while a decrease in the costs of legal disposal does not. Investment in the resources to evade enforcement decreases the attractiveness of monitoring by significantly increasing the costs of environmental audits, administrative hearings, and judicial procedures. This occurs even with a high degree of regulator information about the firm's cost structure and no monitoring errors. In addition, if the regulator can only imperfectly monitor a firm's behavior so the firm can be accused of another firm's behavior, observable commitment to challenge enforcement will lead to overinvestment in resources to evade enforcement, an increased level of illegal dumping, and an overall increase in total costs relative to the unobservable case.