A systematic approach to planning, decision-making and management has become best-practice in conservation over the past two of decades. The field of ‘systematic conservation planning’ is concerned with identifying cost-effective places and actions to protect biological diversity. Past research has focused on static assessments. However, given the fact that biological diversity and processes that threaten its persistence vary in space and time, conservation assessments might need to be made in a dynamic context. In addition, we must explicitly account for the trade-offs associated with implementing conservation actions and investing in improved knowledge and learning to reduce uncertainty on where, how and when to act. The aim of this thesis was to develop novel approaches for accounting for both ecosystem dynamics and uncertainty in conservation planning. Ecosystems are generally treated as static in conservation planning despite many being spatially and temporally dynamic. For example, pelagic marine ecosystems are quite dynamic because ecological processes, such as eddies, that produce resources that many species depend on can be erratic. In chapter two we explored the issue of developing a system of fixed protected areas that consider the physical and biological dynamics typical of the pelagic realm. The approach was to maximize the representation of key fisheries species and species of conservation concern due to significant declines in their abundance, within a network of protected areas. We also ensured that protected area design reflected system dynamics and this was achieved by representing key oceanographic process (such as upwellings and eddies), and biological processes (such as the abundance of small pelagic fish) in protected areas. To account for the variability where these processes occur, we used time series data to find both predictable areas and anomalies, assuming that their past location was somewhat reflective of their future locations. Implementing conservation actions that are fixed in space and time are probably not the most effective strategy in ecosystems that are dynamic. This is because of the movements of particular species. For example, many species have distributions and abundances that change seasonally and might only require temporary management in particular areas. In chapter three, we tested the utility of three approaches to implementing fisheries closures to reduce bycatch in the South African Longline Fishery; 1) time closures, 2) permanent spatial closures and 3) episodic spatial closures. In chapter three, we identified these closures using an existing database containing catch and bycatch data from 1998 to 2005. There was variation where and when different species were caught as bycatch, and it was determined seasonal area closures were the best strategy. This was because it achieved the same conservation objectives for bycatch species as the other types of closures, but impacted less on the long-lining industry. While this result is intuitive, it demonstrated quantitatively, how much more effective moveable management can be. Decisions on where conservation actions are implemented are always based on incomplete knowledge about biological diversity. It is generally assumed that gathering more data is a good investment for conservation planning. However, data can take time and incur costs to collect and given habitat loss, there are both costs and benefits associated with different levels of investments in knowledge versus conservation implementation. In chapter four, the aim was to determine the return on investment from spending different amounts on survey data before undertaking a program of implementing new protected areas. We found that, after an investment of only US$100,000, there was little increase in the effectiveness of conservation actions, despite the full species dataset costing at least 25 times that amount. Surveying can take time because of expertise limitations, logistics and funding shortfalls. Biological diversity may be lost while data collection occurs conversely, not collecting enough data can lead to erroneous decisions. Additionally, resources spent on learning may be better spent on other actions. In chapter five, in a series of retrospective simulations, we compared the impact of spending different amounts of time collecting biological data prior to the implementation of new protected areas. The aim was to find the optimal survey period given the trade-off between gaining knowledge to improve conservation decisions while there is concurrent loss of habitat. We discovered that surveying beyond two years rarely increased the effectiveness of conservation decisions, despite a substantial increase in the knowledge of species distributions. Often there are choices between different actions and uncertainty as to which are the most effective. In chapter six, we discuss how the principles of adaptive management might be applied to conservation planning. Improving future management decisions through learning should be viewed as essential in all conservation plans but such learning is often included as a minor step, or is completely ignored. In this chapter we provide a brief overview of an adaptive framework for conservation planning and ideas for future research.

Much of the discourse on cultural change has been descriptive and explanatory, with few attempts to be predictive. Where indicators of and buffers to change are identified, they tend only to be post-event assessable. The need for a tool with strong predictive power is fundamental to cultural (and environmental) impact assessment and the rationale behind this developmental work. Focusing on traditional cultures and their environmental context, and based on a case study of the Juatinga Ecological Reserve, Brasil, this research advances knowledge on modelling cultural and environmental changes, and how to manage these changes for accepted goals. A heuristic tool is presented for assessing the impacts of pressures on a culture and its related environment as well as the efficacy of management responses. This tool is associated with methods to assist in developing predictive models representing the change processes. The change model building process involves consulting stakeholders as a way of integrating different perceptions, to identify pressures, responses and links associated with cultural and environmental change. This assists in creating a co-learning environment, which facilitates communication between stakeholders. The change modelling approach permits incorporation of the complexity and uncertainty of the system represented, and enables scenario analyses. These allow expected local and flow-on impacts of management interventions to be tested. This approach is more efficient than stand-alone performance indicators that do not allow for the synergic impacts of management interventions to be observed and assessed. Using the models representing the cultural and environmental change processes of the Caiçaras of the Juatinga Ecological Reserve, this research identifies that tourism is a major pressure for change (at that locality). This study also identified that tourist numbers at new and small tourism destinations, as well as on a continental scale, can be forecast using exponential and polynomial functions. Yet, tourism flow may be perturbed at any given time by, for example, acts of violence and when the type of marketing changes. In addition, tourist numbers cannot be greater than the total population, therefore it cannot grow indefinitely as exponential and polynomial functions suggest. Hence, the use of exponential and polynomial functions to forecast tourist numbers is more reliable for short periods, such as four or five years, and when based on six or more sets of data points. The greatest contribution of this research to the cultural change discourse is its innovative approach to study, forecast and manage cultural and environmental changes. The continuation of this research may lead to identifying general theories relating pressures and responses to indicators of cultural and environmental changes.

A systematic approach to planning, decision-making and management has become best-practice in conservation over the past two of decades. The field of ‘systematic conservation planning’ is concerned with identifying cost-effective places and actions to protect biological diversity. Past research has focused on static assessments. However, given the fact that biological diversity and processes that threaten its persistence vary in space and time, conservation assessments might need to be made in a dynamic context. In addition, we must explicitly account for the trade-offs associated with implementing conservation actions and investing in improved knowledge and learning to reduce uncertainty on where, how and when to act. The aim of this thesis was to develop novel approaches for accounting for both ecosystem dynamics and uncertainty in conservation planning. Ecosystems are generally treated as static in conservation planning despite many being spatially and temporally dynamic. For example, pelagic marine ecosystems are quite dynamic because ecological processes, such as eddies, that produce resources that many species depend on can be erratic. In chapter two we explored the issue of developing a system of fixed protected areas that consider the physical and biological dynamics typical of the pelagic realm. The approach was to maximize the representation of key fisheries species and species of conservation concern due to significant declines in their abundance, within a network of protected areas. We also ensured that protected area design reflected system dynamics and this was achieved by representing key oceanographic process (such as upwellings and eddies), and biological processes (such as the abundance of small pelagic fish) in protected areas. To account for the variability where these processes occur, we used time series data to find both predictable areas and anomalies, assuming that their past location was somewhat reflective of their future locations. Implementing conservation actions that are fixed in space and time are probably not the most effective strategy in ecosystems that are dynamic. This is because of the movements of particular species. For example, many species have distributions and abundances that change seasonally and might only require temporary management in particular areas. In chapter three, we tested the utility of three approaches to implementing fisheries closures to reduce bycatch in the South African Longline Fishery; 1) time closures, 2) permanent spatial closures and 3) episodic spatial closures. In chapter three, we identified these closures using an existing database containing catch and bycatch data from 1998 to 2005. There was variation where and when different species were caught as bycatch, and it was determined seasonal area closures were the best strategy. This was because it achieved the same conservation objectives for bycatch species as the other types of closures, but impacted less on the long-lining industry. While this result is intuitive, it demonstrated quantitatively, how much more effective moveable management can be. Decisions on where conservation actions are implemented are always based on incomplete knowledge about biological diversity. It is generally assumed that gathering more data is a good investment for conservation planning. However, data can take time and incur costs to collect and given habitat loss, there are both costs and benefits associated with different levels of investments in knowledge versus conservation implementation. In chapter four, the aim was to determine the return on investment from spending different amounts on survey data before undertaking a program of implementing new protected areas. We found that, after an investment of only US$100,000, there was little increase in the effectiveness of conservation actions, despite the full species dataset costing at least 25 times that amount. Surveying can take time because of expertise limitations, logistics and funding shortfalls. Biological diversity may be lost while data collection occurs conversely, not collecting enough data can lead to erroneous decisions. Additionally, resources spent on learning may be better spent on other actions. In chapter five, in a series of retrospective simulations, we compared the impact of spending different amounts of time collecting biological data prior to the implementation of new protected areas. The aim was to find the optimal survey period given the trade-off between gaining knowledge to improve conservation decisions while there is concurrent loss of habitat. We discovered that surveying beyond two years rarely increased the effectiveness of conservation decisions, despite a substantial increase in the knowledge of species distributions. Often there are choices between different actions and uncertainty as to which are the most effective. In chapter six, we discuss how the principles of adaptive management might be applied to conservation planning. Improving future management decisions through learning should be viewed as essential in all conservation plans but such learning is often included as a minor step, or is completely ignored. In this chapter we provide a brief overview of an adaptive framework for conservation planning and ideas for future research.

Much of the discourse on cultural change has been descriptive and explanatory, with few attempts to be predictive. Where indicators of and buffers to change are identified, they tend only to be post-event assessable. The need for a tool with strong predictive power is fundamental to cultural (and environmental) impact assessment and the rationale behind this developmental work. Focusing on traditional cultures and their environmental context, and based on a case study of the Juatinga Ecological Reserve, Brasil, this research advances knowledge on modelling cultural and environmental changes, and how to manage these changes for accepted goals. A heuristic tool is presented for assessing the impacts of pressures on a culture and its related environment as well as the efficacy of management responses. This tool is associated with methods to assist in developing predictive models representing the change processes. The change model building process involves consulting stakeholders as a way of integrating different perceptions, to identify pressures, responses and links associated with cultural and environmental change. This assists in creating a co-learning environment, which facilitates communication between stakeholders. The change modelling approach permits incorporation of the complexity and uncertainty of the system represented, and enables scenario analyses. These allow expected local and flow-on impacts of management interventions to be tested. This approach is more efficient than stand-alone performance indicators that do not allow for the synergic impacts of management interventions to be observed and assessed. Using the models representing the cultural and environmental change processes of the Caiçaras of the Juatinga Ecological Reserve, this research identifies that tourism is a major pressure for change (at that locality). This study also identified that tourist numbers at new and small tourism destinations, as well as on a continental scale, can be forecast using exponential and polynomial functions. Yet, tourism flow may be perturbed at any given time by, for example, acts of violence and when the type of marketing changes. In addition, tourist numbers cannot be greater than the total population, therefore it cannot grow indefinitely as exponential and polynomial functions suggest. Hence, the use of exponential and polynomial functions to forecast tourist numbers is more reliable for short periods, such as four or five years, and when based on six or more sets of data points. The greatest contribution of this research to the cultural change discourse is its innovative approach to study, forecast and manage cultural and environmental changes. The continuation of this research may lead to identifying general theories relating pressures and responses to indicators of cultural and environmental changes.

Protected areas are one of the main tourist attractions and home to many poor people living in and around them in many developing countries. Tourism development in such areas requires linking nature conservation with poverty elimination. Although attracting more concern, the actual benefits accessible to the local poor is inevitably neglected or misunderstood. This work identifies the correlation between tourism and poverty alleviation by tracing the cash flows to the local poor, review the social and environmental effects of tourism on the local area and establishes a community-based sustainable tourism-eliminating poverty strategy framework. The case study was conducted in a group of protected areas in the Qinling Mountain Region in Western China. The study focuses on local poor people, but does not exclude other stakeholders. Since the case study region is a typical representation of the combination and overlapping of the multiple needs for tourism development, poverty reduction and biodiversity conservation, which are faced by many other developing countries in the world, it is expected that the findings and recommendations can be applied to other regions in China, as well as other developing countries.

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 (Ph.D.)-- University of Western Sydney, 2008. / A thesis submitted to the University of Western Sydney in fulfilment of the requirements for the degree of Doctor of Philosophy, School of Natural Sciences, College of Health and Science. Includes bibliographies.

Thesis (M.A.)--University of Texas at Austin, 1997. / Includes bibliographical references. Also available via the Internet at the Cuatro Ciénagas de Carranza, Coahuila, México, Web site.

Thesis (Ph. D.)--Dalhousie University, 1998. / Includes bibliographical references (v. 1, p. 212-234).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.