Abstract

Abstract

Species extinctions are primarily driven by loss of habitat, which is relatively easy to monitor by satellite remote sensing; other anthropogenic threats to biodiversity, like hunting, are much more difficult to observe directly. My PhD project draws on local studies which capture the population effect of some anthropogenic threat, scaling these results using machine learning and remote sensing. In this talk, I will discuss my first attempt at this through quantifying species-specific responses to hunting pressure. I find that machine learning methods can offer marked improvements over (linear) statistical models, which are commonly used in ecology, but model validation must be done carefully to properly contextualise predictive performance. I will preview my plans for integrating these hunting pressure models with the LIFE biodiversity metric framework to express pressure in terms of extinction risk. If there is time, I will also discuss future plans for my PhD.

Bio

Emilio is a PhD student in the Department of Zoology at the University of Cambridge in the Conservation Science Group and the Energy and Environment Group. He is supervised by Andrew Balmford, with co-supervision from Anil Madhavapeddy and Tom Swinfield. He is also part of the AI for Environmental Risks Centre for Doctoral Training, a researcher at the Cambridge Centre for Carbon Credits, and a member of Churchill College. His research focuses on the uses of predictive modeling for biodiversity conservation, with an emphasis on quantifying species-specific responses to human disturbance.