Background

Climate change and biodiversity loss create positive feedback loops: the climate changes, more habitats are disrupted and carbon absorption is reduced. Around the world, countries use different methods to decide which habitats to protect. However, these decisions have no shared, structured, data-driven approach. An adaptable framework informed by literature on decisions under uncertainty, data science, and climate science would help identify which habitats are most important to protect. This project will explore three key areas: (a) the range of factors relevant to conservation (e.g., ecological value, climate impact, and social importance), (b) the views and priorities of people involved, and (c) how to assess each habitat on these factors with appropriate margins for uncertainty. The project aims to mitigate biodiversity loss and climate change. Our adaptive methodological framework will guide the development of a tool to support conservation-related policies.

PhD Project

Ecosystems are complex, and deciding which habitats to save can have outsized effects on our planet. Providing an adaptive framework can guide conservation policy. We list the research questions and aims below:

A. The prioritisation problem
1. What is the structure of criteria for a conservation priority decision, and what are the preferences of the various stakeholders across them?
2. How should each habitat be evaluated on these criteria (ecological value, climate impact, cost, interdependency with other habitats), and which habitats are disproportionately affected by harsher IPCC AR6 climate scenarios?
3. Combining this information via MCDA, what ranking of priorities results, and how does it correspond to guidelines from (inter)national conservation organisations?

B. Handling uncertainty
4. What are the dominant sources of uncertainty in climate projections, ecological data, and stakeholder preferences, and how large is each relative to the others?
5. How can simulation-based, stochastic MCDA methods rank habitats by producing rank-robustness measures rather than a single deterministic ranking, and which habitats’ priority is robust versus sensitive to that uncertainty?
6. What is the most effective way, including simulation-based interactive visualisation, to communicate the ranking and its uncertainty so policymakers can judge how much to trust it?

Candidate Outcomes:
You will develop your data collection skills using surveys, interviews, and accessing secondary data. You will learn to apply MCDA to problems under multidimensional uncertainty, likely producing a novel approach. You will expand your network among activists, experts and policymakers. You will also gain topic expertise in decision science, climate science and conservation.

Research Outcomes:
– Open-source database with the relevant criteria for conservation priorities and anonymised preferences of stakeholders.
– An interactive data-driven decision support tool for policymakers.

Applicant Profile

We, as a research team, acknowledge there are systemic inequities in higher education and research, especially at the doctoral level. Furthermore, especially when it comes to biodiversity loss and climate change, the research is usually conducted by people distant from those who are impacted most heavily, primarily in the Global South. We would encourage international students from LMIC countries to apply for this research project.

What do you bring:

Required
– Previous exposure to developing your academic writing.
– You have a strong interest in conservation.
– You have a background in ecology, conservation, or climate science.

Desired
– You are confident in managing data and have programming skills (preferably R or Python).

Other Information

A good starting point for background reading on the topic:
A UN blog on biodiversity and climate change:
https://www.un.org/en/climatechange/science/climate-issues/biodiversity#:~:text=The%20Earth’s%20land%20and%20the,and%20adapting%20to%20climate%20impacts.

Previous major advances in the field:
– https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001991
– https://www.cpsg.org/sites/cbsg.org/files/documents/method%20for%20the%20assessment%20of%20priorities_0.pdf
Uncertainty in MCDA:
–https://link.springer.com/chapter/10.1007/978-3-319-47540-0_5
An inspiration for how we could think of solving this problem:
– https://github.com/sjharw/Biobank-Prioritisation-Tool