Description du poste
AI is transforming how we monitor biodiversity, but could errors in what machines detect lead us to the wrong conservation decisions? This project will establish when automated monitoring provides reliable ecological evidence and how to improve it. Combining large biodiversity datasets with simulation and statistical modelling, the student will investigate which species and environmental conditions challenge AI, quantify how detection errors affect estimates of occupancy, abundance and population trends, and test whether ecological context or complementary sensors improve reliability. The student will examine how traits, taxonomy, habitat, season, environmental conditions and training-data availability shape AI performance, then track how false positives, false negatives and uncertainty influence ecological conclusions. They will also explore ways to make automated monitoring more trustworthy, for example by using ecological information such as location, habitat and season to improve uncertainty estimates, or by combining cameras, acoustics, eDNA and remote sensing. The project has a clear core structure, but the student will be strongly encouraged to develop their own ideas, choose focal taxa and datasets, and shape the final direction of the research. Training will cover reproducible data science, AI and computer vision, hierarchical ecological modelling, uncertainty quantification, high-performance computing, scientific writing and communication. Benno Simmons (Exeter, Ecology), Sareh Rowlands (Exeter, Computer Science) and Chris Clements (Bristol, Ecology) will provide hands-on training through coding, joint analysis, research meetings and paper writing. The student will also gain experience communicating technical results to conservation organisations, local environmental partnerships and policy audiences, learning how to turn methodological advances into evidence that can be used in practice. The Exeter–Bristol collaboration brings together complementary expertise in ecology and computer science. Joint supervision, regular interaction and research visits to Bristol will give the student access to both research communities, broaden their networks and support them in developing an independent research profile.