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TangkokoAI: empowering biodiversity monitoring and phenology mapping through AI and citizen science
Project Description
Globally significant biodiversity hotspots face mounting threats from climate change and human activity. These pressures are expected to impact ecological cycles with potential consequences for critically endangered species like the endemic crested macaque (Macaca nigra). Yet, little is currently known about how phenology and species interactions are responding to environmental change.
This PhD will pioneer AI-assisted camera trap monitoring to generate high-resolution data on wildlife behaviour and plant phenology in the Tangkoko Nature Reserve, North Sulwesi, Indonesia. By combining machine learning, long-term ecological datasets, and citizen science, the student will investigate ecological dynamics under climate variability and explore how new technologies can strengthen biodiversity monitoring.
The project builds on the internationally recognised Macaca Nigra Project, a long-term research programme in primatology that has operated in Tangkoko for over 15 years. With its permanent field station and strong partnerships with local communities and authorities, the Macaca Nigra Project provides a robust foundation for this PhD. Collaboration with ConservationAI further ensures feasibility and global impact.
Key research questions:
How effective are camera traps, combined with AI-assisted image recognition, at documenting the diversity and abundance of Sulawesi’s wildlife across habitats in Tangkoko?
How do flowering and fruiting patterns of fig trees and other key plant species vary over time in Tangkoko?
Which animal species (e.g., macaques, hornbills, civets, fruit bats) are most dependent on fig trees, and how do their foraging behaviours vary with fruiting availability?
Research themes
Project Specific Training
The student will gain one-to-one interdisciplinary training by the supervisory team and their network of collaborators in a number of technical skills including camera-trap deployment, AI/machine learning for image recognition, biodiversity informatics, phenology datasets. Fieldwork will further their knowledge in behavioural ecology and ecological monitoring. They will engage in designing and evaluating citizen science initiatives, working with conservation stakeholders, open science and data dissemination. This project provides a range of networking opportunities, including collaborations with the Macaca Nigra Project, ConservationAI, several local conservation NGOs and the relevant authorities in Indonesia (e.g., Natural Resources Conservation Agency).
Potential Career Trajectory
This PhD project will equip the student with a strong portfolio of transferable skills in remote sensing, ecological modelling, and data science, alongside training in open science practices, including reproducible workflows, transparent data management, and database sharing. These skills are highly valued across academic and non-academic sectors. Within academia, the project provides a clear pathway into careers in primate behavioural ecology, conservation biology, environmental science, and ecological informatics. The student will be perfectly positioned for postdoctoral research and lectureships, particularly in fields integrating animal behaviour, ecology, and environmental change. Beyond academia, expertise in remote sensing, and open science methods is increasingly sought after by government agencies and conservation NGOs. The graduate will also be well placed to develop and apply AI and big data approaches within organisations such as ConservationAI or Zooniverse as well as leading participatory monitoring initiatives in biodiversity hotspots worldwide. Finally, skills in open, reproducible science also align with growing requirements for transparency and accountability across research, policy, and industry sectors.
Project supervisor/s
Jérôme Micheletta
School of Psychology, Sport and Health Sciences
University of Portsmouth
jerome.micheletta@port.ac.uk
Marine Joly
School of Psychology, Sport and Health Sciences
University of Portsmouth
marine.joly@port.ac.uk