The work presented in this thesis falls naturally into two parts. The first part (Chapter 2), is concerned with the benefit of perturbing a population into an immediately undesirable state, in order to improve estimates of a static probability which may improve long-term management. We consider finding the optimal harvest policy for a theoretical harvested population when a key parameter is unknown. We employ an adaptive management framework to study when it is worth sacrificing short term rewards in order to increase long term profits. / Active adaptive management has been increasingly advocated in natural resource management and conservation biology as a methodology for resolving key uncertainties about population dynamics and responses to management. However, when comparing management policies it is traditional to weigh future rewards geometrically (at a constant discount rate) which results in far-distant rewards making a negligible contribution to the total benefit. Under such a discounting scheme active adaptive management is rarely of much benefit, especially if learning is slow. In Chapter 2, we consider two proposed alternative forms of discounting for evaluating optimal policies for long term decisions which have a social component. / We demonstrate that discount functions which weigh future rewards more heavily result in more conservative harvesting strategies, but do not necessarily encourage active learning. Furthermore, the optimal management strategy is not equivalent to employing geometric discounting at a lower rate. If alternative discount functions are made mandatory in calculating optimal management policies for environmental management, then this will affect the structure of optimal management regimes and change when and how much we are willing to invest in learning. / The second part of this thesis is concerned with how to account for partial observability when calculating optimal management policies. We consider the problem of controlling an invasive pest species when only partial observations are available at each time step. In the model considered, the monitoring data available are binomial observations of a probability which is an index of the population size. We are again concerned with estimating a probability, however, in this model the probability is changing over time. / Before including partial observability explicitly, we consider a model in which perfect observations of the population are available at each time step (Chapter 3). It is intuitive that monitoring will be beneficial only if the management decision depends on the outcome. Hence, a necessary condition for monitoring to be worthwhile is that control polices which are specified in terms of the system state, out-perform simpler time-based control policies. Consequently, in addition to providing a benchmark against which we can compare the optimal management policy in the case of partial observations, analysing the perfect observation case also provides insight into when monitoring is likely to be most valuable. / In Chapters 4 and 5 we include partial observability by modelling the control problem as a partially observable Markov decision process (POMDP). We outline several tests which stem from a property of conservation of expected utility under monitoring, which aid in validating the model. We discuss the optimal management policy prescribed by the POMDP for a range of model scenarios, and use simulation to compare the POMDP management policy to several alternative policies, including controlling with perfect observations and no observations. / In Chapter 6 we propose an alternative model, developed in the spirit of a POMDP, that does not strictly satisfy the definition of a POMDP. We find that although the second model has some conceptually appealing attributes, it makes an undesirable implicit assumption about the underlying population dynamics.

Since the first reserve selection algorithm was developed in the early 1980s, systematic approaches to reserve design have attracted widespread support due to their ability to identify repeatable and efficient solutions to conservation planning problems. Yet there has been limited application of these methods to the problem of designing reserve systems for biodiversity conservation in the marine environment. In my dissertation research, I apply systematic methods to examine four fundamental issues in marine reserve system design. These issues consider how conservation planning outcomes are influenced when design constraints such as spatial compactness, efficiency, economic costs and incremental reserve establishment are formulated as part of the reserve design problem. First, I consider the trade-offs between spatial design and cost efficiency. In particular, I examine how well marine reserve systems can satisfy the design requirement to minimise the degree of fragmentation whilst minimising reserve system cost. In this case cost refers to the number of sites required to achieve biodiversity conservation objectives. The second issue is the inefficiency of ad hoc marine reserve system design. In terrestrial systems, ad hoc reserve design has been shown to produce inefficient reserve systems, limiting opportunities to achieve conservation targets. I examine how efficiently South Australia’s existing marine reserves contribute to quantitative conservation targets and introduce a new measure of irreplaceability. This metric reflects the potential value of a site’s contribution to reservation goals, by assessing whether a site is selected more than could be expected from chance alone. Sites selected as often as would be expected by chance, fail to contribute to the design of efficient marine reserve systems and represent an opportunity cost. The third issue addresses the demands on reserve systems to achieve both conservation and socio-economic objectives. Options for the design of marine reserve systems, which achieve better economic outcomes for commercial users without compromising conservation targets, are examined using a cost function that serves to make trade–offs early in the design process. The fourth issue is one of shifting targets and incremental reserve design. The problem was most recently highlighted with the rezoning of the Great Barrier Reef Marine Park, where the amount of no-take areas increased from 5% to over 30% but the original zoning arrangements were left in place. The consequence this has on the efficiency of the final marine reserve system is examined when different starting targets are used as the base. Each issue is examined by formulating planning scenarios using data for South Australia’s state waters as a case study. The marine reserve systems are configured using the mathematical optimisation program MARXAN to examine the complex trade-offs of conservation planning problems. The program offers the flexibility to incorporate new approaches and developing theory in marine conservation into the formal statement of the reserve design problem. The results offer some important insights for the future of marine reserve system design. These include 1) efficient representation of biodiversity is only part of the reserve design problem, with small increases in reserve system cost reported as a trade-off for more spatially compact marine reserve systems, 2) despite spanning less than 4% of South Australian state waters, the existing ad hoc marine reserves presented considerable opportunity costs that did not improve even when conservation targets were increased. Hence ad hoc reserve selection is likely to constrain effective conservation of marine biodiversity by compromising the ability to select more suitable sites, 3) integrating conservation and socio-economic objectives presents opportunities to design representative, efficient and practical marine reserve systems that minimise potential loss to commercial users with only small increases to the areal extent of the reserve system and 4) incrementally changing target levels of reservation has a minor affect on the efficiency of the final reserve system, though is likely to influence which planning units are in the final reserve system.

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Project (M.S.)--Brigham Young University. Dept. of Statistics, 2007. / Includes bibliographical references (p. 60-61).

Thesis (Ph. D. ) -- McMaster University, 1998. / Includes bibliographical references. Also available via World Wide Web.

Thesis (Ph.D.)--George Mason University, 1999. / Includes bibliographical references (leaves [180]-189).

Thesis (Ph. D.)--University of New South Wales, 2003. / Also available online.

Thesis (M. Eng. (Electrical, Electronic and Computer Engineering))--University of Pretoria, 2007. / Includes bibliographical references (leaves 107-112).

Thesis (Ph. D.)--University of Sydney, 2009. / Title from title screen (viewed May 1, 2009) Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Economics to the Faculty of Economics and Business, University of Sydney. Degree awarded 2009; thesis submitted 2008. Bibliography: leaves 93-97. Also available in print form.

Thesis (Ph. D.)--University of Washington, 1994. / Vita. Includes bibliographical references (leaves [142]-150).