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Selecting biological control agents

Predicting direct non-target impacts

Modelling non-target impacts

How can models improve the biosafety of future introductions?

Minimum Standards (section 36 of the HSNO Act 1996) must be met for the EPA to approve an application. Grounds for declining an application include displacement of any native species within its natural habitat, significant deterioration of natural habitats, or any adverse effect on New Zealand's inherent genetic diversity. The applicant's risk analysis must address these issues, but its quality is severely constrained by limitations both of testing organisms in containment, and in the information that can be derived from the receiving environment before release. The models discussed in this section can help applicants, the EPA, and other stakeholders to predict when, where and how much impact will arise from a proposed biological control agent introduction. In addition, the models highlight the biological parameters that have the greatest influence on impact, thereby identifying the most important data to obtain. Application of the analytical frameworks provided by predictive models can add considerable value to the applicant's risk/benefit analysis.

Models can also help to optimise post-release monitoring by indicating which nontarget species should be monitored, what time of year they should be examined, and what geographic regions should be sampled. Retrospective post-release analysis of impacts is the only way for regulators to validate their risk analysis methods and decision making processes. As demonstrated above, models can also assist in understanding post-release validation case studies. Well structured and analysed information on previous case studies adds significant value to the decision-making process by providing insights to likely outcomes from future introductions.

Validation of the models is a priority for future research, and there is significant potential to capitalise on other research projects for this. In particular, there is considerable scope to learn from deliberate introductions to improve models for predicting the impacts of accidentally introduced species.