Decision-support for marine spatial prioritisation

Abstract

Human demands on natural resources result in habitat degradation, resource exploitation, prey competition, pollution, and threats to species viability. Halting the decline of biodiversity and alleviating human threats requires investing in conservation actions such as restoration, protection, and management of species and ecosystems. As funding for conservation is often limited, strategies are needed to ensure investments are allocated to places that will best deliver conservation outcomes. However, the development and execution of such strategies are confounded by our limited knowledge of natural systems and uncertainty about both the state of the systems we aim to conserve, and the costs and feasibility of our conservation efforts. Tools to inform decision-making exist, having emerged from fields such as economics, operations research, and mathematics, but are not often tailored and applied to solve conservation challenges. In this thesis, I examine a broad spectrum of applications around problem-based conservation prioritisations to illustrate their utility for decision-making. I chose a diversity of problems linked by this common theme deliberately. In chapters 2 and 3, I investigated common approaches to collecting and collating data for conservation decision-making. In chapter 2, I challenged the notion that all data are useful for conservation and argued that unless new data changes a decision about an action in space or time, it is of limited utility for conservation decision-making. I present a conceptual framework of the types of impacts new data delivers to conservation. My primary message was to urge scientists collecting data with the motivation of informing conservation to examine which uncertainties (e.g. uncertainty about habitat condition or aspects of species demography) are the most important to reduce. I drew from the emerging field of animal telemetry to argue my case. Finally, I provide a decision tree to illustrate when new information should be pursued as opposed to managing with uncertainty and suggest value of information analysis as a tool to address this challenge. In chapter 3, I evaluated one of the most comprehensive species-focused spatial datasets available for the global oceans, BirdLife International’s Important Bird and Biodiversity Areas (IBAs). IBAs are intended to delineate the most important marine habitats for conservation. Using Australia’s Exclusive Economic Zone as a case-study, I first tested the ability of the Australian IBA inventory to act as effective surrogates for other known biodiversity (e.g. known seabird ranges, ecoregions and benthic habitats) and then examined how various treatments of IBAs influenced the cost-efficiency of marine reserve networks. Based on my findings, I present the first “best practice” guidelines for including IBAs into systematic conservation planning processes. Chapters 4 and 5 focused on the development of decision-support tools for two different aspects of marine conservation, spatial planning policy and global prioritisation strategies, respectively. In chapter 4, I developed a system model that optimizes marine zoning allocations for three actions; establishing: no-take marine reserves, managed fishing areas and open-access fishing zones. My aim was to develop a simple, purpose driven model to inform decisions about how to optimally partition marine systems into different zones that maximise a conservation benefit (e.g. standing stock biomass) given a fixed budget, while maintaining a minimum level of fisheries catch. I found that when management budgets are small, investing the entire budget into no-take protected areas is the optimal strategy. As the management budget increases, growing the size of the management zone enters into the optimal zoning allocation. This rule of thumb was robust to changes in parameters and provides a starting point for managers overseeing coastal resources in countries where over-fishing and exploitation are concerns, to better tailor policy around proportional zoning allocations. In chapter 5, I developed a novel, flexible decision-support tool to inform a specific conservation financing mechanism called “Debt for Nature” swaps – the conversion of country’s debt in exchange for a commitment to protect nature. Small Island Developing States (SIDS) often have significant financial constraints (high debt ratios) that make it difficult to finance conservation. Thus, there is a need to prioritise future Debt for Nature swaps in those countries that can achieve the greatest return on investment. The tool leads users through a prioritisation protocol of enabling factors, the consideration of abatable (e.g. fishing) and unabatable (e.g. sea level rise) threats, benefits, weightings, costs and the likelihood of success to rank countries on their cost-effectiveness to inform global investment strategies. I provide a proof-of-concept to demonstrate the tool using Caribbean SIDS and coastal nations.I conclude that solving complex conservation challenges requires clearly defined problems and user-inspired decision support tools that link actions (e.g. what can be done) to objectives, threats, and costs. Translating the hopes, dreams and fears of end-users is a timeconsuming and challenging task. But in the end, it is only through a deep and detailed understanding of end-user needs that we can deliver the best decision-support tools for conservation.