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Currently, there are insufficient resources available across the world to secure all threatened species. In the past decade there has been increasing research in the field of resource allocation for conservation actions. Deciding how to allocate resources optimally poses a challenging problem that is difficult to solve due to a multitude of complexities associated with each action. This requires us to solve the problem using a multidisciplinary framework. The research in this thesis is about addressing resource allocation problems using a decision theory framework. Specifically, we answer the general question about how much resource (time and money) we should allocate among multiple interacting management actions. In chapter 2, we address the question of how social, technological and habitat limitations affect the allocation of money among multiple management actions to mitigate multiple threats. We examine this question using an example of the koala inhabiting the Koala Coast that is limited by constraints: the unwillingness of owners to enclose their dogs a night (social limitations), the effectiveness of road crossing structures (technological limitations) and the amount of suitable koala habitat available for restoration (habitat limitations). Using numerical optimisation, we found the best management option for any budget but we also found that that these limitations significantly reduce the effectiveness of management. Thus, it reduces our ability to achieve a stable population growth rate. The only plausible alternative is to find ways to alleviate these limitations. In chapter 3, we addressed the question of how several key ecological variables influences the amount of resources (time and money) we spend on monitoring a population that we could be managing. Using a simulation model we examined how several demographic parameters influence the optimal monitoring strategy. We found that the amount of time one should spend on monitoring before translocating a population should increase as the unmanaged population growth rate or the initial population size increases. The optimal amount of money to invest in annual monitoring increases as the uncertainty associated with the wild or captive population growth rate, or the initial population size, increases. In chapter 4, we considered the question of whether or not we should abandon our current population management strategy with reliable outcomes, or if we try a new and uncertain strategy, for how long should we pursue that new strategy before reverting to the old strategy. To do this, we uncovered an analytical solution to help us decide when to cease a new action before reverting back to an existing action. We applied this theory to the conservation management of the Christmas Island Pipistrelle, where existing actions appear to have failed and a new strategy, captive breeding, might have secured the species. Our model revealed the time at which we should stop captive breeding before releasing animals back into the wild. We found that the optimal switching time depends on the growth rate of the population under different management actions, the management time frame and the management goal. Chapter 5 and 6 are extensions of Chapter 4. Although Chapter 5 is an extension of chapter 4, it answers a different question. It address the question of finding the optimal time to stop an existing action that has an outcome that is known (certain) and replace it with a new action that is uncertain. We found that it is possible to integrate invasive and threatened species management issues under the same general framework. We illustrated the approach for the conservation of the malleefowl (Leipoa ocellata) where fox baiting has been ongoing and fire management is the new action being considered and for the invasive fire ant (Solenopsis invicta) where nothing was done initially before eradication was considered as the new management action. Even though the optimal time to change an action from the existing to the new management action was affected by similar variables compared to Chapter 4, the analytical solution is different. In Chapter 6, we used stochastic dynamic programming (SDP) to find the exact solution for whether to trial a new action that is uncertain, or continue with an existing action that is certain under an active adaptive management framework. Chapter 6 differs from Chapter 4 and 5 because it uses a stochastic model. We compared the performance of the analytical solutions (Chapter 4 and 5) to the SDP. We found that the best management action depends on the population growth rate of the old, relative to the new management action. Overall, my thesis shows how to allocate resources (money or time) in a wide variety of situations, solved using different mathematical techniques, within a decision theory framework. This thesis provide tools for conservation managers in an uncertain world. |
