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.

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The development of collaborative spatial decision support systems presents a host of challenges, ranging from technical to societal and institutional. Resource managers and environmental planners often need to understand the effect of the distributed and uncoordinated land management practices of individual decision-makers, which in the long run causes significant environmental impact. In many cases environmental planning requires collaborative decision-making tools where complex interacting agents with conflicting goals need to work without any prior idea of the counterpart. This paper identifies research issues on the design and implementation of a Web-based collaborative spatial decision making in the specific context of distributed environmental planning. We demonstrate a Web-based Spatial Decision Support System GEO-ELCA (Exploratory Land Use Change Assessment) for typical decision-making tasks by urban or municipal planning agencies where resource managers or stakeholders of different interest groups can express their options for future land use changes and assess the resulting hydrological impacts in a collaborative environment.

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