Effective design and use of indicators for marine conservation

• Main Author: Burgass, Michael John
• Format: Text
• Language: unknown
• Published: Imperial College London 2019
• Subjects: Arctic; Arctic Ocean; Norway; Climate change
• Online Access: https://dx.doi.org/10.25560/76025; http://spiral.imperial.ac.uk/handle/10044/1/76025

The design, selection and use of indicators for large-scale conservation policy has been of great interest since the Convention on Biological Diversity (CBD) committed to a significant reduction in the rate of biodiversity loss by 2010. Following the introduction of the 2020 Aichi Targets, there was an increase, not only in demand for numbers of indicators, but the requirements that they are expected to meet. The complexities of social-ecological systems and the inevitable trade-offs that exist within them mean understanding and validating indicator responses are critical if they are to play a role in active management. In this thesis, I look critically at uncertainties around how indicators are constructed and used, through the lens of marine science and conservation. I start the thesis by exploring the different types of uncertainty found when using composite indicators and from reviewing the literature, suggest possible methods of dealing with them. I find that structural uncertainties of indicators are rarely acknowledged. As a case study of application of composite indicators, I developed an Ocean Health Index assessment for the Arctic Ocean, demonstrating how a structured framework can be of great use for taking a data-driven approach to assessing social-ecological systems in large, data-poor regions. I show the Arctic is sustainably delivering a range of benefits to people, but with room for improvement in all areas, particularly tourism, fisheries, and protected places. Successful management of biological resources and short-term positive impacts on biodiversity in response to climate change underlie these high goal scores. I then explore how two biodiversity indicators (Living Planet Index and Norway Nature Index) can be better interpreted and validated using an end-to-end ecosystem model, Atlantis, in the Nordic and Barents Seas. By simulating different fishing scenarios, I evaluated the extent to which the model-based testing approach gave insights into indicator behaviour; while the LPI is able to distinguish clearly between three different fishing scenarios, the NNI is only able to distinguish the most heavily fished scenario from the other two. I discuss how this approach is useful for indicator testing and to advance integration of large-scale biodiversity indicators with goal-setting and decision making at the system scale. I then use the model to explore how different indicators of biodiversity from across fisheries and conservation respond to management interventions in Norway in the face of climate change. I find that despite having the same intentions, fisheries and conservation biodiversity indicators respond differently to each other under the same scenarios, due to how they are constructed. This means that without proper validation, indicators can potentially give different pictures of the same system to different interest groups, meaning greater integration and understanding of conservation and fisheries management objectives is necessary. Finally, I reflect on the findings of my thesis in light of the CBD Post-2020 Framework. I discuss several core areas where the process could be revised to improve biodiversity outcomes. This includes formulating a robust theory of change to give the framework a clear conceptual basis and explicitly articulate the causal assumptions about the relationship between actions and outcomes. I do not focus on what targets should look like, but instead seek proactive, solutions-oriented approaches that can help ‘bend the curve’ for biodiversity. This thesis highlights the uncertainties and challenges associated with large-scale indicator design and use and demonstrates how countries can take steps to reduce these. Greater consideration of the systems within which indicators are based can lead to better validation and ultimately better decision making.