About Me

Welcome! I am a DPhil student at University of Oxford’s Interdisciplinary Centre for Conservation Science, supervised by Professor Joseph Bull and funded through Oxford’s Clarendon Scholarship. Prior to joining Prof. Bull’s lab, I completed my dual master’s degree in Sustainable Forest and Nature Management, specialized in Conservation Biology at the University of Copenhagen and Bangor University, supervised by Professor Julia P. G. Jones. I obtained my bachelor’s degree in Environmental Engineering from Malta’s College of Arts, Science and Technology.

I am also a board member of the Society for Conservation Biology’s Impact Evaluation Working Group, where I co-manage the virtual seminar series, take care of web development and management duties as well as help co-manage grants.

My research focuses on advancing the understanding of whether global conservation initiatives and policies achieve their intended impacts, using rigorous causal inference and counterfactual methods. By identifying gaps in how conservation initiatives are evaluated, I aim to strengthen the evidence base for effective conservation decision-making. I am currently working on these main projects:

• Still Money for Nothing? A systematic review of Empirical Evaluations of Biodiversity Conservation Investments
  Twenty years ago, an essay highlighted that conservation scientists and practitioners largely based their biodiversity conservation investments on intuition and anecdote rather than empirical evidence of impact. The authors called for wider use of experimental and quasi-experimental study designs that make use of counterfactual thinking, from the causal inference to evaluate the impact of behaviourally mediated conservation programmes operated in coupled human natural systems. Since then, conservation science and practice have seen an explosion of interest in applying these designs to better understand the effectiveness of conservation actions. To characterize how these causal inference approaches have been used over the last two decades, and the extent to which users acknowledge and evaluate the method-specific causal assumptions on which their inferences rely, we conduct a systematic review. We focus on a common approach to conservation by identifying studies which evaluate the effectiveness of protected areas at delivering a range of socioeconomic (e.g., poverty alleviation, human-wellbeing, wealth & tourism) and ecological (habitat extent and quality, species populations, rare species presence & species diversity indices) outcomes. In addition to systematically characterizing the status and trends in causal inference applications to biodiversity conservation, we also assess where quality gaps persist and how the conservation community may address those gaps.

• Assessing the effectiveness of revisited Pay-to-Release schemes in Indonesia through randomized controlled trials
  A 2022 randomized controlled trial (RCT) of a Pay-to-Release scheme in Indonesia revealed a critical paradox: while conventional monitoring suggested the scheme reduced mortality for hammerhead sharks (Sphyrna spp.) and wedgefish (Rhynchobatus spp.), the experimental design revealed an estimated 44% increase in hammerhead mortality. The pay-to-release scheme has since undergone two revised iterations, each tested through subsequent RCTs. This chapter re-analyses the newly collected RCT data to determine the efficacy of these new approaches in reducing mortality rates for both species. Our findings will provide the partnering non-profit organisation with a robust evidence base to decide on the scheme future.

• A Causal Inference framework for Conservation Science
  The rapid adoption of Causal Inference in Conservation Science has exposed a significant gap between its use and its rigorous application. Current impact evaluations exhibit substantial heterogeneity in their methodological transparency, frequently overlooking essential components like estimand specification, the treatment-assignment mechanism, and explicit discussion of the causal assumptions and their tenability. This chapter addresses this gap by proposing a standardized framework to guide the design and reporting of such studies. We ultimately aim for this framework to be useful for researchers, journal editors, and practitioners, as it would demystify methodological choices and facilitate a critical assessment of an study’s inferential validity, without being prescriptive to specific designs.

If you’re also interested in these area, do feel free to get in touch!