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. It will predict biodiversity loss under business as usual and restoration plans.  

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. 

An illustrative framework for digital twins.

Figure 1: The framework that uses digital twins to monitor and predict biodiversity loss under different climate and pollution scenarios. The lake Biodiversity Digital Twin uses AI-supported data fusion, data analytics and predictions to improve biodiversity management 

Host

University of Birmingham

Theme

  • Organisms and Ecosystems

Supervisors

Project investigator

Co-investigators

  • Prof Luisa Orsini, School of Biosciences, University of Birmingham and The Alan Turing Institute ([email protected])

How to apply

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

DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.  

The DR will receive multidisciplinary training spanning from AI and computational modelling (Zhou) and biodiversity science (Orsini). The student will join the research activities in the cross-college Centre for Environmental Research and Justice (CERJ) at the UoB, benefiting from the training, collaborations, and resources it provides in support of environmental science research. The DR will benefit from access to some of the most up-to-date high-performance computing facilities in the country, including UoB and Alan Turing facilities. They will learn the skills of translational science.  

Partners and collaboration

This PhD synergies with our case study in the Horizon Europe PARC project that focuses on biodiversity monitoring and prediction. The student will collaborate with our PARC partners in the UK and EU on eDNA data processing and modelling. The PhD student may also work closely with our collaborators in industry and government agencies, including Severn Trent Water (eDNA), Environment Agency (environmental data), and the UK Centre for Ecology & Hydrology (molecular ecology). 

Further details

For inquiries, please contact Dr Jiarui Zhou ([email protected]).

To apply to this project: 

  • 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://sits.bham.ac.uk/lpages/LES068.htm.   Please select the PhD Bioscience (CENTA) 2025/26 Apply Now button. The  CENTA Studentship Application Form 2025 and CV can be uploaded to the Application Information section of the online form.  Please quote CENTA 2025-B33 when completing the application form.  

 Applications must be submitted by 23:59 GMT on Wednesday 8th January 2025.

Possible timeline

Year 1

Bioinformatics and statistical analysis of high throughput sequencing data generated using eDNA metabarcoding. These data are extant at the Co-I Orsini’s laboratory. Student conference in Birmingham. Write a literature review.  

Year 2

Development of graph neural networks and other AI models to identify drivers of biodiversity loss under various scenarios of pollution and climate change. Write methodology and case study papers. 

Year 3

Working with potential industry partners and government agencies. Develop and test the user-friendly dashboard for the end-user application. Write thesis. Attend international conferences. 

Further reading

  • Eastwood N., Zhou J., Derelle R., Jia Y., Crawford S., Abdallah M., Davidson T. A., Brown J. B., Hollert H., Colbourne J.K., Creer S., Bik H., Orsini L. (2023) 100 years of anthropogenic impact causes changes in freshwater functional biodiversity. eLife, 12. (https://doi.org/10.7554/eLife.86576.3) 
  • Eastwood N, Stubbings WA, Abdallah MA, Durance I, Paavola J, Dallimer M, Pantel JH., Johnson S, Zhou J, Hosking JS, Brown JB, Ullah S, Krause S, Hannah DM, Crawford SE, Widmann M, Orsini L (2022) The Time Machine framework: monitoring and prediction of biodiversity loss’. Trends in Ecology and Evolution (Invited opinion paper), 37: 138-146. (https://doi.org/10.1016/j.tree.2021.09.008 
  • Rossi E., Chamberlain B., Frasca F., Eynard D., Monti F., & Bronstein M. (2020). Temporal graph networks for deep learning on dynamic graphs. arXiv preprint, arXiv:2006.10637. (https://doi.org/10.48550/arXiv.2006.10637) 
  • Borowiec M. L., Dikow R. B., Frandsen, P. B. McKeeken, A. Valentini, G. & White A. E. (2022). Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13(8): 1640-1660. (https://doi.org/10.1111/2041-210X.13901)