Search

When evaluating the risks of future invasions, we often have sparse information on the likelihood that a species will arrive, establish and spread in a new environment, and on the potential impacts should this occur. Conventional risk assessment, therefore, is limited in providing guidance in managing the risk of non-indigenous species (NIS). However, risk management decisions must be made facing these uncertainties to avoid high and irreversible impacts. We develop an integrated ecological economic modeling and deliberative multi-criteria evaluation (DMCE) approach to support group decision-making in risk prioritization, using an example of ten NIS that could potentially impact Australian plant industries. This innovative approach seeks to combine the advantages of dynamic modeling with the benefits of DMCE in assessing and communicating uncertainty. The model unveils the complexity of the socio-ecological system of biological invasion, with a scenario analysis designed to interactively communicate scientific uncertainty to decision-makers. The DMCE provides a structured approach to identifying stakeholders' key concerns in addressing economic, social, and environmental dimensions of NIS risk explicitly. Functioning as a platform for risk communication, the DMCE also offers an opportunity for diverse views to enter the decision-making process and for the negotiation of consensus consensuses.

When evaluating the risks of future invasions, we often have sparse information on the likelihood that a species will arrive, establish and spread in a new environment, and on the potential impacts should this occur. Conventional risk assessment, therefore, is limited in providing guidance in managing the risk of non-indigenous species (NIS). However, risk management decisions must be made facing these uncertainties to avoid high and irreversible impacts. We develop an integrated ecological economic modeling and deliberative multi-criteria evaluation (DMCE) approach to support group decision-making in risk prioritization, using an example of ten NIS that could potentially impact Australian plant industries. This innovative approach seeks to combine the advantages of dynamic modeling with the benefits of DMCE in assessing and communicating uncertainty. The model unveils the complexity of the socio-ecological system of biological invasion, with a scenario analysis designed to interactively communicate scientific uncertainty to decision-makers. The DMCE provides a structured approach to identifying stakeholders' key concerns in addressing economic, social, and environmental dimensions of NIS risk explicitly. Functioning as a platform for risk communication, the DMCE also offers an opportunity for diverse views to enter the decision-making process and for the negotiation of consensus consensuses.

A decision framework called Deliberative Multi-Criteria Evaluation (DMCE) was developed and deployed to prioritize biosecurity risks using a variety of subjective and objective information. To aid stakeholders in the prioritization of Emergency Plant Pest (EPP) species risk, we presented them with outputs from a Stella-based bioeconomic pest risk model, and probable ecological and socioeconomic impacts. The stakeholder participants weighted the consequence criteria they deemed to have the highest expected impact. Themethodology featured an uncertain set of parameters, multiple iterations of criteria weighting along with real-time sensitivity analysis. Of the five criteria, economic cost was weighted the highest at 26% while landscape amenity was weighted the lowest at the 10-12% range. The increased understanding and support gained by stakeholders through the DMCE process provided a greater likelihood of the sanctioning of the policies concerned and progress toward desired outcomes.

The sheer number and complexity of biophysical and socio-economic models and their outputs can quickly overwhelm stakeholders who are trying to make sense of a natural resource-related issue. Add uncertainty to this bulk of multi-faceted information from

Uncertainty is a ubiquitous feature of biosecurity and for this reason managing invasive species has long been a reactive business. In order to move towards proactive management it is critical to communicate uncertainties to the stakeholders. The uncertainty components exist not only in biological but also socio-economic processes during an invader's entry, establishment, spread and impact creation. A STELLA model was developed to capture the dynamics of this socio-ecological system and to estimate the expected economic costs of the apple maggot (Rhagoletis pomonella) over the next 30 years for the entire country of Australia. Like complex climate models, it cannot be meaningfully calibrated because it is simulating a never before experienced state of the system. Therefore it is inappropriate to apply generic techniques which utilize observations to calibrate models to forecast the economic cost of bio-invasion in the real world. Instead of forecasting, a more proper use is as a communication tool for uncertainty, which organizes our existing understanding and present "what if" scenarios in front of stakeholders in a DMCE environment. One of the major modelling outputs, the potential economic cost of the apple maggot, was used in DMCE as a criterion. The DMCE participants were asked to weigh the same criterion twice, before and after the modelling uncertainty was exposed and discussed. The effectiveness of the model as a communication tool was examined by comparing the average group weights and standard deviation of the individual weights before and after the uncertainty injection. Preliminary results showed that after the parameter uncertainty was exposed the mean weight did not change significantly yet the standard deviation of the weights did become smaller. We hypothesized that this could be because of the anchoring effect: economic cost was one of the most important criteria therefore the uncertainty exposure would not make much of a difference in weighting. Th

A fuzzy methodology is presented for evaluating the reliability of satellite imagery-derived, continent-wide (Australia) land-cover deforestation/regrowth maps covering the period of 1972 to 2000 in ten discrete time periods. The methodology uses aerial photographs as its reference data and accommodates the difficulty inherent in determining definitively from an aerial photograph, whether a sample point is Forest or Non-forest by permitting interpreters to identify their level of certainty, i.e., Definitely Forest, Probably Forest, Uncertain, Probably Non-forest, or Definitely Non-forest. This information is then cross-tabulated against the Forest/Non-forest classification for the classified image closest in date to the photo date. Information from several photographs is summarized over a larger geographic area and over all time periods. Subsequently, temporal lineage information for each sample pixel is extracted from the 1972 to 2000 series of classified images to determine if a pixel's lineage is Forest Throughout, Non-forest Throughout, Deforestation, Regrowth, or Cyclic. The fuzzy evaluation for individual pixels is then tabulated against this lineage information to identify if pixels of any particular lineage have an elevated tendency to be misclassified. The methodology provides a means by which problems in the map production methodology can be improved as future time slices are added.