PROJECT HIGHLIGHTS
- Multidisciplinary science – This project combines life and computer science. Within computer science, it applies cutting-edge AI technologies such as digital twins, deep learning, graph machine learning, and temporal data modelling. Within the life sciences, it applies high throughput sequencing technologies, bioinformatics and biostatistics.
- Big data science – This project uses holistic computational methodologies to analyse spatiotemporal high-dimensional ecological data to predict biodiversity loss under various climate and pollution scenarios. It can be applied to assess the potential impact of industrial processes on biodiversity.
- Action research —This project aims to translate fundamental science into a digital twin system with an intuitive analytical dashboard. It will allow industries, NGOs, and regulators to simulate the impact of environmental changes on biodiversity loss, facilitating more precise management and conservation actions.
Overview
Biodiversity is declining at an alarming rate, affecting the delivery of ecosystem services on which we all rely for our well-being and the economy. These services include climate regulation, food provisioning, clean water and recreation, to name a few. With the exit from the EU, the UK established the 25-year environment plan to improve environmental quality within a generation, and the Environment Act 2021 to maintain biodiversity net gain while enabling growth. These necessary regulations demand a rapid response from industry to assess the impact of production processes on biodiversity.
However, we lack the tools to predict the impact of industrial processes on natural biological diversity, thereby limiting our ability to conserve critical resources and the services they provide. One of the main challenges that conservationists face is predicting the severity of biodiversity loss and identifying the main drivers of this loss. Traditional methods focus on individual species, missing by design the species interactions and with the environment. Holistic, community-level approaches are still largely missing.
In this project, the DR will develop state-of-the-art AI algorithms to monitor and predict biodiversity loss under different climate and pollution scenarios. The DR will apply graph neural networks (GNNs), especially temporal graph networks (TGNs) and spatiotemporal graph neural networks (STGNNs), to model historical biodiversity data obtained from freshwater lake ecosystems across England. This will be done using sediment core environmental DNA. GNN is a cutting-edge AI model. It will integrate spatiotemporal eDNA and environmental data to create a digital twin to simulate the dynamics of freshwater lake biodiversity. This Biodiversity Digital Twin will offer multi-scale, holistic modelling that tracks changes across taxonomic groups over space and time and links them to environmental change, identifying the main drivers of loss.
To make the tool accessible to end-users, the DR will develop an intuitive analytical dashboard. This dashboard will allow the direct assessment of production processes, land use, and other human activities on biodiversity. By synergistically combining advanced computational and bioinformatics technologies with end-user insights, the team aims to accelerate the transition from traditional to science-driven, technologically enhanced solutions for biodiversity conservation.
Case Projects
This project does not offer a CASE studentship
Host
University of Birmingham
Theme
Organisms and Ecosystems
Supervisors
Project investigator
Dr Jiarui Zhou (J.Zhou.3@bham.ac.uk)
Co-investigators
Prof Luisa Orsini, University of Birmingham and The Alan Turing Institute, (l.orsini@bham.ac.uk)
How to apply
Each host has a slightly different application process.
Find out how to apply for this studentship.
All applications must include the CENTA application form.
Methodology
Biological Data Science. Metabarcoding or marker gene sequencing data will be collected from freshwater ecosystems to capture community-level biodiversity. The DR will learn to analyse these data using bioinformatics and multivariate statistics.
AI. Graph neural networks (GNNs) will be employed to model dynamic changes in biodiversity over space and time in response to environmental factors. Designed to model network-structured data that evolve over time, GNNs can effectively capture temporal dependencies and node interactions, facilitating monitoring and prediction of biodiversity loss.
Digital Twins. Digital twins will be constructed as a data-driven representation of real-world ecosystems. The biodiversity digital twin will integrate environmental factors and biodiversity quantified with eDNA, utilising GNNs to dynamically model their interactions, offering multi-scale and multi-taxa predictions.
Translation. To create long-lasting impacts beyond the project, the DR will design a user-friendly interface that will be tested and applied to real-world cases by potential partners in industry and government agencies to ensure the transfer of knowledge and drive the translation of research findings into end-user applications.
Training and skills
Partners and collaboration
Possible timeline
Further reading
Further details and How to Apply
For inquiries, please contact Dr Jiarui Zhou (j.zhou.3@bham.ac.uk).
To apply to this project:
- You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2027
- You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
- Please submit your application and complete the host institution application process via: https://admissions.bham.ac.uk/course-finder-landing-page/?code=LES068 Please select the PhD in School of Biosciences (CENTA) 2027 entry year Apply Now button. The CENTA Studentship Application Form 2027 and CV can both be uploaded to the Personal Statement section of the online form. In the funding section of the online form please select Research Council Funding and then choose Natural Environment Research Council (NERC). Please quote CENTA 2027-B07 when completing the application form.
