Quantitative Uncertainty Analysis for a Weed Risk Assessment System.

Area of Science:

• Ecology
• Invasive Species Management
• Risk Assessment

Background:

• Weed risk assessments (WRA) are crucial for preventing invasive plant introductions.
• Existing WRAs often lack robust methods for incorporating and evaluating uncertainty.
• Uncertainty is a fundamental component of risk assessment that needs quantitative evaluation.

Purpose of the Study:

• To develop and test a probabilistic model for quantitatively evaluating the effects of uncertainty on question-based WRAs in the United States.
• To assess how different levels of uncertainty (Negligible, Low, Moderate, High) influence WRA outcomes.
• To improve the interpretation and reliability of WRA results for regulatory and management decisions.

Main Methods:

• Developed a probabilistic model to simulate the impact of response uncertainty on WRA scores.
• Assigned likelihoods of response changes based on four uncertainty ratings.
• Simulated WRA outcomes by determining response changes, selecting new responses, and summing scores.
• Tested the model on 204 species assessments to analyze variations in risk ratings.

Main Results:

• Simulated risk score ranges increased with higher uncertainty levels.
• Most species assessments (87%) produced simulated risk ratings that matched the baseline WRA rating.
• High uncertainty generally led to a wider range of potential risk ratings, with most frequent ratings aligning with baseline for High Risk species.
• Accuracy of WRA did not improve by changing final ratings based on uncertainty analysis alone.

Conclusions:

• The probabilistic model effectively quantifies the influence of uncertainty on WRA outcomes.
• While most ratings remained consistent, uncertainty analysis highlights potential variability, especially for Low Risk assessments.
• Reducing assessment uncertainty is most effectively achieved by gathering evidence for unanswered questions.
• This approach enhances the regulation and management of potential invasive weed species by providing a more nuanced understanding of risk.