Knowledge‐Based Risk Assessment Under Uncertainty for Species Invasion

Abstract

Management of invasive species depends on developing prevention and control strategies through comprehensive risk assessment frameworks that need a thorough analysis of exposure to invasive species. However, accurate exposure analysis of invasive species can be a daunting task because of the inherent uncertainty in invasion processes. Risk assessment of invasive species under uncertainty requires potential integration of expert judgment with empirical information, which often can be incomplete, imprecise, and fragmentary. The representation of knowledge in classical risk models depends on the formulation of a precise probabilistic value or well-defined joint distribution of unknown parameters. However, expert knowledge and judgments are often represented in value-laden terms or preference-ordered criteria. We offer a novel approach to risk assessment by using a dominance-based rough set approach to account for preference order in the domains of attributes in the set of risk classes. The model is illustrated with an example showing how a knowledge-centric risk model can be integrated with the dominance-based principle of rough set to derive minimal covering "if ... , then...," decision rules to reason over a set of possible invasion scenarios. The inconsistency and ambiguity in the data set is modeled using the rough set concept of boundary region adjoining lower and upper approximation of risk classes. Finally, we present an extension of rough set to evidence a theoretic interpretation of risk measures of invasive species in a spatial context. In this approach, the multispecies interactions in an invasion risk are approximated with imprecise probability measures through a combination of spatial neighborhood information of risk estimation in terms of belief and plausibility.

Predicting establishment success for alien reptiles and amphibians: a role for climate matching

“…Establishment success involves complex interactions between the invading species and the physical and biological characteristics of the recipient environment, which may be case-specific and include positive feedback mechanisms (Noble 1989), Allee effects (Drake 2004), and genetic variability (Holdgate 1986). To cope with these complexities it may be desirable to use a combination of qualitative and quantitative methods to provide a framework for risk assessments for alien species introductions (Sikder et al 2006;Stohlgren and Schnase 2006). Sikder et al (2006) suggest that this combination approach is required, as quantitative data alone are insufficient to deal with the complexities and uncertainties inherent in invasive species' interactions with their environment.…”

“…To cope with these complexities it may be desirable to use a combination of qualitative and quantitative methods to provide a framework for risk assessments for alien species introductions (Sikder et al 2006;Stohlgren and Schnase 2006). Sikder et al (2006) suggest that this combination approach is required, as quantitative data alone are insufficient to deal with the complexities and uncertainties inherent in invasive species' interactions with their environment. We used ROC curves to determine the range of model predictions likely to indicate species establishment.…”

Predicting establishment success for introduced freshwater fishes: a role for climate matching

“…In addition, it may be desirable to take account of species-level attributes that may influence introduction outcomes, particularly for species that have no history of being introduced elsewhere. To cope with these complexities it may be desirable to use a combination of qualitative and quantitative methods to provide a framework for risk assessments for exotic species introductions (Sikder et al 2006;Stohlgren and Schnase 2006). Sikder et al (2006) suggest that this combination approach is required, as quantitative data alone are insufficient to deal with the complexities and uncertainties inherent in invasive species' interactions with their environment.…”

Advancing effects analysis for integrated, large-scale wildfire risk assessment

“…Risk assessments are decision support tools that integrate information regarding the likelihood and magnitude of resource response to risk factors, in order to synthesize a conclusion about risk that can inform decision making (Sikder et al 2006). Two key steps in risk assessment are exposure analysis and effects analysis (U.S. EPA 1998).…”