The fundamental role of conservation science is to provide land managers and policy-makers with evidence-based practical guidance. Conservation decisions are usually made with limited information and tight budgets. This dictates the need for efficiency and cost-effective actions. Basically, efficiency is the ratio between benefit and cost. The larger the ratio (compared to other systems) the higher the efficiency of that system. When calculating efficiency, benefits and costs are usually in the same currency. In this thesis, cost-effectiveness is the ratio of a non-economic benefit relative to an economic or semi-economic (e.g. area of land) cost. Using decision science and defining objectives (such as achieving certain conservation targets) of the conservation problem we are addressing, can help us find better actions. Once the objectives of the problem are established, decision makers need to decide what features of biodiversity – genes, species, habitat– we intend to benefit. Different conservation problems involve different kinds of biodiversity features, which can represent different levels of coarseness (e.g. single species versus multiple; species versus ecosystems, etc.); each will have different financial costs and biodiversity benefits. However, the cost-effectiveness of choosing conservation features at different levels of coarseness is not well studied. As such, the overarching questions of my thesis are: How does the cost-effectiveness of the conservation outcomes change with the use of different fine- and coarse-scale biodiversity features as target? What are the trade-offs between biodiversity benefit and conservation cost involved when applying fine- and coarse-scale conservation efforts?
To examine these questions, I investigated the cost-effectiveness of conservation planning for two major conservation problems: 1) mitigating the effects of roads on wildlife (Chapters 2-3); and 2) the planning of protected area networks (Chapters 4-5). I explored the cost-effectiveness of several aspects of planning at different scales: single species (Chapter 2); from single to multiple species (Chapter 3) and from multispecies to a set of focal species (Chapter 3); and planning at both the multispecies and multi-ecosystem levels (Chapters 4-5).
The negative impact of roads on wildlife is a major problem worldwide. The two main direct effects are mortality due to animal-vehicle collision and reduced connectivity due to fragmentation. Mitigation measures such as fences and wildlife passages can be used to reduce these effects, however they are expensive. The limitation of available conservation funds indicates the need for cost-effective solutions using decision science to decide which mitigation measures to use and where to place them. As such, I first mathematically formulated the problem of which road mitigation measures to place where, for the conservation of the threatened koala (Phascolarctos cinereus) population in the Koala Coast of south-east Queensland (Chapter 2). Each budget step had an optimal mitigation configuration. However, the linear shape of the trade-off curve between expected population size (the biodiversity benefit) and mitigation cost indicates that there is no clear “win-win” (low cost-high benefit) solution for protecting koala populations through road mitigation management. In Chapter 3, I used the problem formulation from Chapter 2 in combination with a metapopulation mean time-to-extinction model to find optimal mitigation solutions for multiple species. I also compared the cost-effectiveness of using focal species (with selected life history traits) to that of the multispecies analysis. I found that the multispecies analysis was more cost-effective than planning separately for each species. However, using the focal species with the largest home range can provide adequate results and can be used when time or funding are limited and decisions need to be made in a hurry.
The Convention on Biological Diversity (CBD) aims to protect the world’s biodiversity by expanding the current protected area network to comprise 17% of the Earth’s terrestrial area using ecosystem-based targets (Target 11) and preventing the extinction of known threatened species (Target 12). While both targets use protected areas, Target 11 is the main driver for the CBD’s expansion plan. However, the cost-effectiveness of the CBD’s guidelines of using ecosystem-based targets to effectively represent threatened species has not been adequately investigated. In Chapter 4, I used Australia as a case study to test how well ecosystem-based targets protect threatened species, and compared the cost-effectiveness of planning for species and ecosystems separately and simultaneously. I used species-specific targets for 1,320 threatened species and a 10% target for each one of Australia’s 85 bioregions. I discovered that, following the CBD’s ecosystem-based approach for protected area expansion, the outcome would be inadequate and inefficient for representation of threatened species. Even filling in the gaps for threatened species protection later (coarse- then fine-scale) proved to be an inefficient strategy, while the reverse (fine- then coarse-scale) was almost as cost-effective as planning for both simultaneously. In Chapter 5, I extended this problem to explore the trade-off curves between the target sizes of these two conservation features within several protected area networks of different sizes. These curves can be used as a planning tool for countries that have either geographical or monetary limitations. Depending on their needs, countries can use the trade-off curves to place more or less emphasis on either ecosystems or species when planning protected areas.
This research is one of the first to address feature-objective coarseness in conservation planning. The methods develop here allows decision makers to understand the cost-effectiveness and trade-offs involved with engaging with different levels of biodiversity features’ coarseness. The two problems I address are current and pressing issues in conservation. The conclusions of my research show that: (i) Using all available data on the targeted biodiversity features will generate the most cost-effective solutions. (ii) Large-scale environmental surrogates or focal species might be used when monetary or time limitations prevail but are less cost-effective. (iii) Understanding the necessary trade-offs within the planning process can help decision scientists and planners to make informed choices about how to invest limited conservation resources, taking advantage of near win-win solutions where they exist.