Philippa Oppenheimer

Exploring patterns and processes of global biodiversity change through statistical modelling and machine learning.

Philippa's work focuses on exploring the patterns and processes of global biodiversity change, with a particular focus on understanding population abundance change as measured by the Living Planet Index (LPI). 

Being able to measure biodiversity change is a crucial part of assessing progress towards international targets for nature, such as those set by the UN's Convention on Biological Diversity. However, capturing a complete picture of biodiversity change is difficult and would require continuous data on the population size of all species all over the world, an impossible task.

To address these challenges, Philippa uses statistical modelling and machine learning techniques to evaluate how well the LPI represents biodiversity and to enhance the quality of the data it contains. In particular, Philippa examines how well the LPI represents biodiversity in terms of functional traits, and investigates differences in patterns of population change across functional space. She also explores reasons why we might under or over-represent particular species or regions in the LPI, and ways in which this imbalance might be addressed. Additionally, Philippa also works on developing strategies to fill existing gaps in the Living Planet Database, ultimately enriching the quality and scope of our biodiversity assessments.