Abstract

Biodiversity conservation suffers from limited resources, irreversible consequences of mismanagement (species extinction), and significant uncertainty surrounding the environmental systems to be managed. Given such difficulties I will summarise research from my PhD that illustrates why the structured decision making methods of operations research are useful in aiding decision making for biodiversity conservation.

Firstly, I will present a taxonomy of management and learning techniques that highlight the ability to incorporate learning into the decision making framework by specifying the importance of learning about a system relative to actual benefit in the managed system. The said taxonomy makes it clear that a decision policy will choose actions that favour learning in two circumstances: either via an objective that quantifies learning or via an algorithm with a management objective that acknowledges that future management benefit can be obtained through improved knowledge.

Secondly, I will outline a species translocation problem: given a range of translocation sites, each of a different unknown habitat quality, where should captive-bred populations be released to maximise the survival of such populations? This problem draws on methods from the medical clinical trials and ‘bandit problem’ literature for an optimal solution, comparing this with rules of thumb.