Project Description

The challenges of our time are becoming increasingly complex, for example, how we address the biodiversity crisis and climate change are multifaceted problems. The post 2020 global biodiversity framework drafts have outlined ambitious conservation targets and strategies to prevent further species extinctions. Management strategies require an understanding of how biodiversity might respond to future scenarios and threats. However, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) expressed low confidence in abilities to forecast ecological change. While there have been large-scale efforts devoted to projecting climate change, modelling frameworks for projecting future biodiversity and ecological changes have been neglected.

Species responses to climate and land use change are typically modelled in a correlative manner, yet it is increasingly recognised that process-based models are essential for understanding the complex feedbacks between threats to species and their responses1. Data for parameterising such models are however frequently sparse or lacking for individual species2. To facilitate the development of process-based models for forecasting, the integration of machine learning (ML) methods to address data limitation problems is needed.

This project will develop ML methodologies for predicting species’ traits with uncertainty quantification. The development of computational approaches that synthesise and address scarcities in species data through generative ML models could transform research and how the current biodiversity crisis is managed. Potential methods could include generative deep learning models, e.g. transformers with variational autoencoders for multitasking, with data sourced from ever-growing trait repositories (e.g. TRY plant database, MammalBase). These approaches will be used to generate data sets for different taxa that can be used in process-based species range models. This pipeline will allow for projections of how future land use change and/or climate change might affect the distribution of species and the identification of species that may be more vulnerable to changing landscapes. As such, this project will address key knowledge and methodological gaps in biodiversity forecasting and ML and help to prioritise conservation efforts.

This project will be supervised by Dr Roslyn Henry, Dr Georgios Leontidis, Prof. David Burslem and Prof. Justin Travis. Roslyn Henry is an ecologist and land use modeller with research expertise in how future land use change may affect biodiversity and species responses. Georgios Leontidis is The University of Aberdeen’s Interdisciplinary research director for Data and AI and has expertise in machine/deep learning and the application of these techniques across various domains. David Burslem is The University of Aberdeen’s Interdisciplinary research director for Environment and Biodiversity and has extensive experience researching the mechanisms and traits that maintain the genetic and species diversity of tropical forest trees. Justin Travis is an evolutionary ecologist with research interests focused on modelling eco-evolutionary species responses to environmental change in a process-based manner. This project will benefit from being embedded within The University of Aberdeen’s new Interdisciplinary Research Centres.

Please submit a CV and a motivation letter (max 1 page, addressed to Dr Roslyn Henry) with your application.

For any queries, contact Dr Roslyn Henry (, Interdisciplinary Fellow) and Dr Georgios Leontidis (, Interdisciplinary Director of Data and AI).

Essential Candidate Background:

  • Honours degree (minimum 2:1) in Computer Science, Engineering, Mathematics, or Biological Sciences with a quantitative focus that included, for example, programming, modelling, data science, machine learning and AI. Training in AI/ML will be available within the studentship for individuals without previous experience.
  • It is essential that the PhD student is self-driven, curious, interested in working across disciplines and exploring new areas, as well as eager to work as part of a large interdisciplinary team.
  • The student will be expected to engage with their peers and other academic staff, get involved in departmental events and seminars, show enthusiasm for public/policy engagement activities.

Desirable Candidate Background:

  • Knowledge of biological sciences, climate science, environmental sciences.
  • A master’s degree in AI, Machine Learning, Environmental Sciences, Climate Science, Biological Science or similar, is highly desirable
  • Quantitative or technically focused Dissertation/Thesis.


Photo by CRYSTAL MIRALLEGRO on Unsplash


Primary Supervisor:

Dr Roslyn Henry – University of Aberdeen, School of Biological Sciences

Academic profile:



Secondary Supervisor:

Dr Georgios Leontidis – University of Aberdeen, Interdisciplinary Centre for Data and AI

Academic profile:




David Burslem

Additional Supervisor:

Profile: David Burslem
Institution: University of Aberdeen
Department/School: School of Biological Sciences

Justin Travis

Additional Supervisor:

Profile: Justin Travis
Institution: University of Aberdeen
Department/School: School of Biological Sciences


  • Urban et al. (2022) Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity, BioScience, 72:1.
  • Urban et al. (2016) Improving the forecast for biodiversity under climate change. Science, 353.6304: aad8466.


  • biodiversity
  • environmental-management

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