PhD Studentship in Thermal Futures: Digital Twin Modelling of Marine Turtles under Climate Change

About the Project

Project details:

Predicting how biological systems will respond to a warming climate remains one of the most pressing challenges in environmental science. Developing robust, quantitative tools to forecast these responses is essential for effective conservation and management. A compelling and urgent example lies with marine turtles, in which six of seven species are currently listed as threatened by the IUCN. As ectothermic vertebrates, turtles are highly sensitive to increases in both ocean and beach temperatures. Even small temperature shifts can alter offspring sex ratios, with potentially severe long-term demographic consequences. In addition, temperature-driven changes in ocean conditions influence maternal migration and nesting behaviour, further impacting population viability. 

Despite decades of research, there remains limited capacity to identify which turtle populations are most vulnerable to warming, and to evaluate the effectiveness of mitigation strategies such as nest shading or relocation. Addressing this gap requires the integration of physiological models, environmental sensing, and data-driven forecasting, tools that can provide the predictive power urgently needed to guide conservation under accelerating climate change. This PhD project will therefore integrate climate data with physiological and behavioural systems, by creating a digital twin framework that integrates real-time environmental data and physiological information to simulate and forecast how marine turtle populations respond to changing thermal conditions. The proposed Digital Twin for Marine Turtle Reproduction will couple satellite-derived ocean temperature data, microclimate forecasts of nesting beaches with behavioural and physiological models derived from biological first principles. This dynamic model will allow conservation biologist to predict impacts of warming on individual nesting cycles to population-level reproductive trends, under varying climate scenarios.  

The project aligns with EPSRC’s strategic priorities in Digital Twins, Environmental Intelligence, and Data-Driven Engineering, using advanced computational modelling to support ecosystem resilience and sustainable management. The project’s aims are (i) to develop a digital twin architecture capable of integrating heterogeneous environmental data streams (satellite-measured sea-surface temperatures), in-situ beach temperatures collected using microsensors at the surface or in nests, long-term datasets of turtle populations involving nestling success, seasonality and migration and behavioural and physiological models from first principles. This architecture can then be used to (ii) simulate nesting and hatching responses to projected temperature trajectories and identify tipping points affecting reproductive success and demography; (iii) deliver interactive, year-by-year visualisations of model outputs to inform policymakers and conservation planners on how current and future temperatures affect different turtle populations.  

Impact: This project will provide a decision-support tool for conservation agencies, enabling proactive responses to climate-driven reproductive challenges in marine turtles. Beyond ecology, it will demonstrate how digital twins that combine climate and physiological principles can lead to more accurate predictions on vulnerable populations. The long-term aim of this project is to develop transferable technology that can be used to predict population trajectories in other thermally sensitive species.  

Training Environment: The student will receive interdisciplinary training on a combination of physiological and ecological modelling, data science, field work on turtle populations and how to develop impactful collaborations with conservation NGOs and other policy partners.  

Please direct project specific enquiries to: Bram Kuijper, a.l.w.kuijper@exeter.ac.uk. Please ensure you read the entry requirements for the potential programme you are applying for. To Apply for this project please click the 'Apply' button 

Funding Comment

Payment of tuition fees (Home), Research Training Support Grant £5,000 over 3.5 years

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