Uncertainty, Economics, and the Spread of an Invasive Plant Species

Abstract

The invasion of ecosystems by nonindigenous (nonnative) species is a global economic problem. In the United States alone, rough estimates suggest that environmental damage and other losses from nonindigenous species range from tens to hundreds of billions of dollars per year (Pimentel et al.), though the true value is uncertain. Uncertainty also pervades attempts to identify economically efficient management strategies for any given nonindigenous invasive species. Sources of uncertainty regarding relevant state variables (e.g., the biomass of an invasive plant species in a particular geographic area) include a paucity of data, measurement errors, and substantial variability in intrinsic rates of spread. Significant uncertainty, regarding the impacts of various management measures applied to any invasive species, also exists. In addition, the problem of invasive species management has characteristics that suggest the use of fuzzy logic. Descriptive terms for categorizing the state of an infestation are inherently vague and often depend on sitespecific contexts. For example, one scientist may categorize a particular weed infestation as minimal, while another classifies the same state as highly infested. Such observational categorizations affect management decisions since numerical (objective) measures of the degree of invasion (e.g., a density or percent cover measure) are often completely lacking or based on incomplete field data. Further, any given scientist may classify an infestation of a given density as minimal at one location but high at another, depending on the nature and human uses of the sites. This means that invasions possess the properties associated with fuzzy sets, and are thereby subject to analysis through fuzzy membership functions (see, e.g., Zadeh). In addition, decisions for managing invasive species need to account for fuzzy variables, such as rate of spread, whose values are uncertain. Fuzzy membership functions developed to deal with such variables are different from traditional probability distribution functions (Kosko). The above characteristics of the problem recommend the collection of data, and ultimately the use of a bioeconomic modeling approach that addresses both the stochastic and fuzzy properties of the invasive species' management problem. In this study, we employ insights from fuzzy set theory to develop an expert survey of the spread and damage caused by an invasive plant species that adversely affects rangeland in California and Nevada. The survey instrument is described as are preliminary results based on survey responses to date. In particular, we construct and discuss membership functions describing infestation levels for nonindigenous weed management. Then, we employ a stochastic dynamic programming (SDP) model to identify economically optimal management choices from a portfolio of potential options, with the results compared to those of a program that seeks to eradicate the invasive species. Our approach differs from previous ones in the following ways. First, while a number of dynamic models for invasive weed management decisions have been constructed, most are deterministic (Taylor and Burt; Gorddard, Pannell, and Hertzler; Jones and Medd; Wu; Eiswerth and Johnson). Second, we utilize formally elicited probabilistic expert judgments within an SDP framework for nonindigenous invasive species. Previous studies of weed control decisions utilize SDP (e.g., Pandey and Medd), but apply it to agricultural crop systems for which good data are available, rather than eliciting probabilistic judgments. Third, our application focuses on a non-cropland setting for which data on invasive species state Mark E. Eiswerth is research assistant professor in the Department of Applied Economics and Statistics, University of Nevada, Reno and G. Cornelis van Kooten is professor in the Department of Economics, University of Victoria in Canada. The authors wish to thank Steve Schoenig, Joseph DiTomaso, and Wayne Johnson for suggestions in survey design, and John Zimmerman for research assistance.