About Me

Welcome! I am a DPhil (PhD) 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? Two Decades of Empirical Evaluation of Conservation Investments
  Twenty years ago, the landmark paper “Money for Nothing?” argued that biodiversity conservation relied too little on empirical evidence. It called for more evaluations of conservation effectiveness based on explicit counterfactuals, comparing observed outcomes with those that would likely have occurred in the absence of intervention. To assess progress towards this goal, we systematically reviewed the study designs used to evaluate one of the most widely implemented conservation interventions: protected areas. Across 614 studies published over the past two decades, half still relied on simple Before-After or Control-Impact designs that do not reliably support causal inferences, although their use has declined in recent years. The other half used more formal causal identification strategies, most commonly conditioning strategies that control for observed confounders. However, most of these studies lacked pre-protection outcome data, limiting their ability to address unobserved confounders. Because causal claims depend on causal assumptions, it is notable that few studies stated these assumptions explicitly, let alone interrogated their plausibility. Although the design of conservation impact evaluations has advanced substantially, much remains to be improved. Combining causal inference methods with expanding data streams from remote sensing and biodiversity monitoring offers a major opportunity to strengthen the evidence base for conservation. Pre-print available on EcoEvoRxiv

• 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!