Applying structured decision making for large-scale wildlife management programmes : Project Janszoon as a case study : a thesis presented in partial fulfilment for the degree of Doctor of Philosophy in Conservation Biology at Massey University, Palmerston North, New Zealand

Abstract

Managing threatened populations is challenging due to the delicate balance between urgency and uncertainty. While swift action is often needed to prevent further decline or extinction, significant uncertainty frequently surrounds the effectiveness of various management strategies and the future trajectory of populations. This uncertainty complicates the identification of the most effective course of action, especially when resources are limited. Structured decision making (SDM) is an approach that supports informed decision making in the face of uncertainty in conservation projects. The primary aim of this thesis is to develop a decision making framework for Project Janszoon’s bird translocations, guiding management and monitoring decisions to maximise establishment and persistence probabilities for the kākā (Nestor meridionalis) and pāteke (Anas chlorotis). This framework can serve as a blueprint for implementing SDM and adaptive management (AM), promoting their broader use in conservation initiatives within New Zealand and beyond. In Chapter 2, I discuss expert elicitation techniques for generating predictions from expert knowledge while accounting for epistemic uncertainty. Numerical improvements in handling elicited data are proposed, focusing on aggregating and transforming expert-provided values while maintaining their associated uncertainty. Preserving this uncertainty is critical to avoid generating overconfident predictions from expert judgment. In Chapter 3, I explore which uncertainties are worth reducing and to what degree. Value of information (VOI) analysis offers a way to understand how reducing uncertainty affects decision making and conservation outcomes. A key insight from this chapter is that while monitoring is valuable for reducing uncertainty, such reductions do not always improve conservation outcomes. Beyond a certain point, further reductions in uncertainty do not alter decision making. Practitioners must estimate the optimal level of monitoring for each conservation challenge. In Chapter 4, I outline a passive adaptive management framework to reduce uncertainty as management actions are implemented and monitored. The framework’s extendable nature makes it adaptable to other management problems. The tools and concepts presented here are valuable assets for effective decision making for managed populations under uncertainty.