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We consider optimal design for changepoint problems with particular attention paid to situations where the only possible change is in the mean. Optimal design for changepoint problems has only been addressed in an unpublished doctoral thesis, and in only one journal article, which was in a frequentist setting. The simplest situation we consider is that of a stochastic process that may undergo a, change at an unknown instant in some interval. The experimenter can take n measurements and is faced with one or more of the following optimal design problems: Where should these n observations be taken in order to best test for a change somewhere in the interval? Where should the observations be taken in order to best test for a change in a specified subinterval? Assuming that a change will take place, where should the observations be taken so that that one may best estimate the before-change mean as well as the after-change mean? We take a Bayesian approach, with a risk based on squared error loss, as a design criterion function for estimation, and a risk based on generalized 0-1 loss, for testing. We also use the Spezzaferri design criterion function for model discrimination, as an alternative criterion function for testing. By insisting that all observations are at least a minimum distance apart in order to ensure rough independence, we find the optimal design for all three problems. We ascertain the optimal designs by writing the design criterion functions as functions of the design measure, rather than of the designs themselves. We then use the geometric form of the design measure space and the concavity of the criterion function to find the optimal design measure. There is a straightforward correspondence between the set of design measures and the set of designs. Our approach is similar in spirit, although rather different in detail, from that introduced by Kiefer. In addition, we consider design for estimation of the changepoint itself, and optimal designs for the multipath changepoint problem. We demonstrate why the former problem most likely has a prior-dependent solution while the latter problems, in their most general settings, are complicated by the lack of concavity of the design criterion function. / Nous considérons, dans cette dissertation, les plans d'expérience bayésiens optimauxpour les problèmes de point de rupture avec changement d'espérance. Un cas de pointde rupture avec changement d'espérance à une seule trajectoire se présente lorsqu'uneséquence de données est prélevée le long d'un axe temporelle (ou son équivalent) etque leur espérance change de valeur. Ce changement, s'il survient, se produit à unendroit sur l'axe inconnu de l'expérimentateur. Cet endroit est appelé "point derupture". Le fait que la position du point de rupture soit inconnue rend les tests etl'inférence difficiles dans les situations de point de rupture à une seule trajectoire.

Biodiversity is declining globally due to mounting anthropogenic threats. Actions to protect biodiversity against threats can be costly, involving land purchase, invasive species management, and inflicting opportunity costs of lost revenue and livelihoods in conservation areas. Governments and conservation organisations are under increasing pressure to deliver the greatest benefits from conservation funds, and to minimise conflicts between conservation and other human priorities. Most conservation planning approaches are limited in their ability to assist with cost-effective funding allocation decisions. First, approaches often lack quantifiable objectives and appropriate tools. Second, approaches rarely consider economic information, such as spatially explicit data on the costs of conservation actions. In this thesis I address these two limitations, which often co-occur, in spatial conservation planning. Problem definition includes specifying a quantifiable objective, a set of constraints and control variables, and knowledge of the system. A simple conservation objective is to protect target amounts of biodiversity features, such as 15% of the range of each species and vegetation type, over a minimal total reserve area. Here, targets are the constraints and the control variables are the decisions of whether or not to conserve each site. Target-based conservation planning is the dominant spatial prioritisation approach, but has been criticised for failing to protect untargeted portions of biodiversity and for employing targets too low to ensure species persistence. In Chapter 2, I review target-based systematic conservation planning, discovering that many perceived limitations can be overcome with current developments in research and software and better communication, whilst acknowledging the value of alternative approaches. Conservation planning objectives are becoming increasingly complex due to the need to conserve many kinds of features, such as species, habitat types, and ecosystem services. Measures of the spatial congruence between features is often used to determine if one feature is a good surrogate for representing another and whether multiple features can be easily captured in a single plan. In Chapter 3 I review the use of congruence metrics in conservation planning research, explaining the differences between the three most common metrics – spatial correlation, hotspot overlap, incidental representation – and demonstrating why high values in one metric can coincide with low values in another. Most importantly, I show that integrated systematic conservation planning, rather than congruence metrics, is the only way to determine how efficient it will be to protect multiple features in a reserve system. While conservation planning has an implicit goal of cost-efficiency, spatially explicit data on the costs of conservation action are rarely considered. Prioritisation analyses that do not consider conservation costs can lead to the misallocation of funds and high opportunity costs. In Chapter 4 I carry out a global analysis at 1º resolution to identify areas that could protect targets of 10% of every mammal species’ range whilst minimising the opportunity costs of forgone agricultural production. The a priori inclusion of opportunity costs reduced the cost of meeting conservation targets by at least 30%. I then compare cost-effective allocation of funds to actual funding allocation by international conservation agencies in 2006, highlighting globally important, threatened and under-funded regions. While estimates of conservation opportunity costs can increase conservation planning efficiency, there are often various actions under consideration, each with different associated costs. The definition of specific actions, and their respective costs, is rarely considered in conservation planning. In Chapter 5 I develop cost surfaces for two conservation actions in Australia (i) land purchase for reservation estimated by unimproved land values and (ii) stewardship payments to private landholders to conserve biodiversity estimated by forgone agricultural production. I then identify priority areas at a 10 km2 resolution for conserving 15% of the pre-clearing extent of a range of biodiversity features by these actions. I demonstrate that using cost data to reflect specific conservation actions minimises improves financial efficiency by up to two-fold. Cost-effective conservation planning is also hindered by uncertainties in estimates of conservation costs. In Chapter 6 I carry out the first comprehensive sensitivity analysis of conservation priorities to cost value, using the same goal as in Chapter 5, but restricting planning to reservation in Queensland, which is the Australian state with the best quality unimproved land value data. First, I show that sites which are essential or unhelpful for meeting conservation targets maintain a high and low priority status respectively, over a large range of cost data (1-400% of their estimated cost). Medium priority sites are sensitive to estimates of cost, and represent the greatest opportunities to make cost-effective decisions. Next I develop a simple approach for planning with uncertain cost data, where priorities can be updated as real information on the cost of a parcel of land becomes available. This chapter shows that uncertain cost data is useful for conservation planning. Potentially cost-effective areas for conservation actions in Australia are identified in Chapters 5 and 6. My final chapter serves to synthesise and interpret this research. Through comprehensive analyses, I have shown that cost-effective conservation planning requires the definition of appropriate objectives and tools, and the integration of conservation costs. Further, I have demonstrated accessible approaches that integrate these crucial factors, showing at least a doubling of efficiency in conservation investments. There are cost-effective opportunities for conservation actions in Australia and around the world: this research will assist Governments, Non-Government Organisations, and other conservation-minded people in finding them. Further investment is required in obtaining and wisely applying socio-economic data for conservation planning and in evaluating conservation projects to improve our knowledge base.

Biodiversity is declining globally due to mounting anthropogenic threats. Actions to protect biodiversity against threats can be costly, involving land purchase, invasive species management, and inflicting opportunity costs of lost revenue and livelihoods in conservation areas. Governments and conservation organisations are under increasing pressure to deliver the greatest benefits from conservation funds, and to minimise conflicts between conservation and other human priorities. Most conservation planning approaches are limited in their ability to assist with cost-effective funding allocation decisions. First, approaches often lack quantifiable objectives and appropriate tools. Second, approaches rarely consider economic information, such as spatially explicit data on the costs of conservation actions. In this thesis I address these two limitations, which often co-occur, in spatial conservation planning. Problem definition includes specifying a quantifiable objective, a set of constraints and control variables, and knowledge of the system. A simple conservation objective is to protect target amounts of biodiversity features, such as 15% of the range of each species and vegetation type, over a minimal total reserve area. Here, targets are the constraints and the control variables are the decisions of whether or not to conserve each site. Target-based conservation planning is the dominant spatial prioritisation approach, but has been criticised for failing to protect untargeted portions of biodiversity and for employing targets too low to ensure species persistence. In Chapter 2, I review target-based systematic conservation planning, discovering that many perceived limitations can be overcome with current developments in research and software and better communication, whilst acknowledging the value of alternative approaches. Conservation planning objectives are becoming increasingly complex due to the need to conserve many kinds of features, such as species, habitat types, and ecosystem services. Measures of the spatial congruence between features is often used to determine if one feature is a good surrogate for representing another and whether multiple features can be easily captured in a single plan. In Chapter 3 I review the use of congruence metrics in conservation planning research, explaining the differences between the three most common metrics – spatial correlation, hotspot overlap, incidental representation – and demonstrating why high values in one metric can coincide with low values in another. Most importantly, I show that integrated systematic conservation planning, rather than congruence metrics, is the only way to determine how efficient it will be to protect multiple features in a reserve system. While conservation planning has an implicit goal of cost-efficiency, spatially explicit data on the costs of conservation action are rarely considered. Prioritisation analyses that do not consider conservation costs can lead to the misallocation of funds and high opportunity costs. In Chapter 4 I carry out a global analysis at 1º resolution to identify areas that could protect targets of 10% of every mammal species’ range whilst minimising the opportunity costs of forgone agricultural production. The a priori inclusion of opportunity costs reduced the cost of meeting conservation targets by at least 30%. I then compare cost-effective allocation of funds to actual funding allocation by international conservation agencies in 2006, highlighting globally important, threatened and under-funded regions. While estimates of conservation opportunity costs can increase conservation planning efficiency, there are often various actions under consideration, each with different associated costs. The definition of specific actions, and their respective costs, is rarely considered in conservation planning. In Chapter 5 I develop cost surfaces for two conservation actions in Australia (i) land purchase for reservation estimated by unimproved land values and (ii) stewardship payments to private landholders to conserve biodiversity estimated by forgone agricultural production. I then identify priority areas at a 10 km2 resolution for conserving 15% of the pre-clearing extent of a range of biodiversity features by these actions. I demonstrate that using cost data to reflect specific conservation actions minimises improves financial efficiency by up to two-fold. Cost-effective conservation planning is also hindered by uncertainties in estimates of conservation costs. In Chapter 6 I carry out the first comprehensive sensitivity analysis of conservation priorities to cost value, using the same goal as in Chapter 5, but restricting planning to reservation in Queensland, which is the Australian state with the best quality unimproved land value data. First, I show that sites which are essential or unhelpful for meeting conservation targets maintain a high and low priority status respectively, over a large range of cost data (1-400% of their estimated cost). Medium priority sites are sensitive to estimates of cost, and represent the greatest opportunities to make cost-effective decisions. Next I develop a simple approach for planning with uncertain cost data, where priorities can be updated as real information on the cost of a parcel of land becomes available. This chapter shows that uncertain cost data is useful for conservation planning. Potentially cost-effective areas for conservation actions in Australia are identified in Chapters 5 and 6. My final chapter serves to synthesise and interpret this research. Through comprehensive analyses, I have shown that cost-effective conservation planning requires the definition of appropriate objectives and tools, and the integration of conservation costs. Further, I have demonstrated accessible approaches that integrate these crucial factors, showing at least a doubling of efficiency in conservation investments. There are cost-effective opportunities for conservation actions in Australia and around the world: this research will assist Governments, Non-Government Organisations, and other conservation-minded people in finding them. Further investment is required in obtaining and wisely applying socio-economic data for conservation planning and in evaluating conservation projects to improve our knowledge base.

Biodiversity is declining globally due to mounting anthropogenic threats. Actions to protect biodiversity against threats can be costly, involving land purchase, invasive species management, and inflicting opportunity costs of lost revenue and livelihoods in conservation areas. Governments and conservation organisations are under increasing pressure to deliver the greatest benefits from conservation funds, and to minimise conflicts between conservation and other human priorities. Most conservation planning approaches are limited in their ability to assist with cost-effective funding allocation decisions. First, approaches often lack quantifiable objectives and appropriate tools. Second, approaches rarely consider economic information, such as spatially explicit data on the costs of conservation actions. In this thesis I address these two limitations, which often co-occur, in spatial conservation planning. Problem definition includes specifying a quantifiable objective, a set of constraints and control variables, and knowledge of the system. A simple conservation objective is to protect target amounts of biodiversity features, such as 15% of the range of each species and vegetation type, over a minimal total reserve area. Here, targets are the constraints and the control variables are the decisions of whether or not to conserve each site. Target-based conservation planning is the dominant spatial prioritisation approach, but has been criticised for failing to protect untargeted portions of biodiversity and for employing targets too low to ensure species persistence. In Chapter 2, I review target-based systematic conservation planning, discovering that many perceived limitations can be overcome with current developments in research and software and better communication, whilst acknowledging the value of alternative approaches. Conservation planning objectives are becoming increasingly complex due to the need to conserve many kinds of features, such as species, habitat types, and ecosystem services. Measures of the spatial congruence between features is often used to determine if one feature is a good surrogate for representing another and whether multiple features can be easily captured in a single plan. In Chapter 3 I review the use of congruence metrics in conservation planning research, explaining the differences between the three most common metrics – spatial correlation, hotspot overlap, incidental representation – and demonstrating why high values in one metric can coincide with low values in another. Most importantly, I show that integrated systematic conservation planning, rather than congruence metrics, is the only way to determine how efficient it will be to protect multiple features in a reserve system. While conservation planning has an implicit goal of cost-efficiency, spatially explicit data on the costs of conservation action are rarely considered. Prioritisation analyses that do not consider conservation costs can lead to the misallocation of funds and high opportunity costs. In Chapter 4 I carry out a global analysis at 1º resolution to identify areas that could protect targets of 10% of every mammal species’ range whilst minimising the opportunity costs of forgone agricultural production. The a priori inclusion of opportunity costs reduced the cost of meeting conservation targets by at least 30%. I then compare cost-effective allocation of funds to actual funding allocation by international conservation agencies in 2006, highlighting globally important, threatened and under-funded regions. While estimates of conservation opportunity costs can increase conservation planning efficiency, there are often various actions under consideration, each with different associated costs. The definition of specific actions, and their respective costs, is rarely considered in conservation planning. In Chapter 5 I develop cost surfaces for two conservation actions in Australia (i) land purchase for reservation estimated by unimproved land values and (ii) stewardship payments to private landholders to conserve biodiversity estimated by forgone agricultural production. I then identify priority areas at a 10 km2 resolution for conserving 15% of the pre-clearing extent of a range of biodiversity features by these actions. I demonstrate that using cost data to reflect specific conservation actions minimises improves financial efficiency by up to two-fold. Cost-effective conservation planning is also hindered by uncertainties in estimates of conservation costs. In Chapter 6 I carry out the first comprehensive sensitivity analysis of conservation priorities to cost value, using the same goal as in Chapter 5, but restricting planning to reservation in Queensland, which is the Australian state with the best quality unimproved land value data. First, I show that sites which are essential or unhelpful for meeting conservation targets maintain a high and low priority status respectively, over a large range of cost data (1-400% of their estimated cost). Medium priority sites are sensitive to estimates of cost, and represent the greatest opportunities to make cost-effective decisions. Next I develop a simple approach for planning with uncertain cost data, where priorities can be updated as real information on the cost of a parcel of land becomes available. This chapter shows that uncertain cost data is useful for conservation planning. Potentially cost-effective areas for conservation actions in Australia are identified in Chapters 5 and 6. My final chapter serves to synthesise and interpret this research. Through comprehensive analyses, I have shown that cost-effective conservation planning requires the definition of appropriate objectives and tools, and the integration of conservation costs. Further, I have demonstrated accessible approaches that integrate these crucial factors, showing at least a doubling of efficiency in conservation investments. There are cost-effective opportunities for conservation actions in Australia and around the world: this research will assist Governments, Non-Government Organisations, and other conservation-minded people in finding them. Further investment is required in obtaining and wisely applying socio-economic data for conservation planning and in evaluating conservation projects to improve our knowledge base.

Thesis (Ph. D.)--Virginia Polytechnic Institute, 1963. / Vita. Abstract. Includes bibliographical references (leaves 113-114). Also available via the Internet.

Thesis (Ph. D.)--George Mason University, 2007. / Title from PDF t.p. (viewed Jan. 22, 2008). Thesis director: KC Chang. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Vita: p. 117. Includes bibliographical references (p. 108-116). Also available in print.

Thesis (Ph.D.)--Baylor University, 2007. / Includes bibliographical references (p. 84-87).

Thesis (Ph.D.) -- McMaster University, 1999. / Includes bibliographical references (leaves 76-79). Also available via World Wide Web.