Uncertainty and risk assessment for nonindigenous species

Ecological predictions and management strategies are sensitive to uncertainty in model structure and variability in model parameters. Systematic analysis of the effect of alternative model structures however is often beyond the resources typically available to ecologists, ecological risk practioners and natural resource managers. Many of these practioners are also using Bayesian Belief Networks based on expert opinion to fill empirical information gaps. The practical application of this approach, however, is limited by the need to populate large conditional probability tables and the complexity associated with ecological feedback cycles. In this presentation we describe a modelling approach that helps solve these problems by embedding a qualitative analysis of sign directed graphs into the probabilistic framework of a Bayesian Belief Network. Our approach incorporates the effects of feedback on the model’s response to a sustained changes in one or more of its parameters, provides an efficient means to explore the effect of alternative model structures, mitigates the cognitive bias in expert opinion and is amenable to stakeholder input. We demonstrate our approach by hypothetical and real case studies, including a host-parasitoid community centered on a non-native, agricultural pest of citrus cultivars. Observations are drawn from these case to studies to diagnose alternative model structures and to predict the system’s response following management intervention