CR2025_02 Network approaches to rewilding Britain’s ecosystems

Lead Supervisor: Miguel Lurgi Rivera, Department of Biosciences, Swansea University 

Email: miguel.lurgi@swansea.ac.uk  

Co-supervisors: Nathalie Pettorelli, Institute of Zoology; Sahran Higgins, Rewilding Britain 

The (re)introduction of species is seen by many as a key part of addressing the current environmental crisis and restoring natural processes, with the return of native and keystone species expected to help ecosystems function and enhance the delivery of vital ecosystem services (Bullock et al. 2022). However, species (re)introductions could have unintended consequences for the recipient communities and ecosystems, which can manifest through the network of ecological interactions between the resident and the newly introduced species (Pearson et al. 2021). To fully understand and predict the potential effects of such actions on ecosystem functioning, we need quantitative frameworks that allow for a holistic assessment of the effects of species (re)introductions on communities, with a specific focus on the persistence and stability of populations and ecosystems. 

Recent advances in ecological theory have enabled the development of a good understanding of the mechanisms responsible for the stability and persistence of complex ecological communities (Lurgi et al. 2016). This has supported the emergence of a series of tools that can be used to approach these systems from a quantitative perspective and tackle ecological questions using formal frameworks. In parallel, advances in satellite remote sensing technologies have allowed for a better understanding of changes in vegetation and community patterns across large spatial scales (Pettorelli 2019). They have additionally provided quantitative methods to understand these changes from a community ecology perspective by predicting changes prompted by the recovery of formerly degraded habitats. 

In this project, the student will extend these methods and make use of classical ecological theories of meta-communities and ecological networks, to develop a decision-making framework to inform nature recovery efforts across Britain, focusing on rewilding initiatives (Fig. 1). Britain is an interesting choice for this, as its wildlife is intensively monitored, and the number of rewilding projects has recently dramatically increased across the island. The framework will bring together ecological theory and modelling with modern remote sensing techniques to develop a comprehensive picture of recovered ecosystems. Specifically, remote sensing techniques will be used to predict ecological changes in communities (as per, e.g., Schulte to Bühne et al. 2022) and integrated with dynamical ecological frameworks (as found, e.g., in Lurgi et al. 2018) to produce a quantitative and dynamic picture of ecosystems assembly across spatial scales under rewilding scenarios. 

The specific objectives of the project are: 

1.- Build a database bringing together information on the climatic, environmental and community context for the case studies to be considered (i.e., the Knepp estate, Hepple Wilds; Gilfach, and Wild Woodbury) 

2.- Analyse the data collected in (1) to develop a comprehensive picture of assembly dynamics via rewilding 

3.- Construct a theoretical framework incorporating regional (e.g. temporal processes such as climate change and spatial processes such as dispersal) and local (e.g. biotic interactions such as competition and food webs) processes to develop a multi-scale understanding of the effects of rewilding on community persistence and stability 

4.- Integrate remote sensing data into the modelling framework to allow for realistic predictions of community assembly based on underlying environmental conditions and habitat characteristics 

5.- Parameterise and validate the theoretical model using data from (1) and (2) 

 6.- Design and run simulated scenarios of species rewilding to predict future outcomes of community assembly in these proposed conservation sites 

 The student will be based in the Computational Ecology Lab at Swansea University, with additional co-supervision from the world leading conservation research Institute of Zoology at the Zoological Society of London. The project is supported by a CASE partnership with Rewilding Britain. 

The outcomes of this project will contribute to the strategic goal of bending the curve on biodiversity loss in Britain by identifying areas where species (re)introductions could be particularly effective at enhancing biodiversity, ecological complexity and ecosystem functioning. The proposed framework and associated models will be integrated into a decision-making tool to support Rewilding Britain’s network members as they make decisions around reintroductions  

Training opportunities: 

The student will have the opportunity to work with internationally recognised experts in the field of computational ecology (Dr Miguel Lurgi, Swansea University [SU]) and conservation science (Prof Nathalie Pettorelli, IoZ). Although based in SU, the student will be given the opportunity to visit IoZ for specific training on remote sensing data manipulation and analyses. The project has been developed with Rewilding Britain (Dr Sahran Higgins), which act as a case partner on this studentship. Rewilding Britain is at the forefront of catalysing rewilding in Britain and support a thriving, autonomous practitioner network. 

Student profile: 

This project would be suitable for students with a degree in Ecology, Computer Science, Complex Systems, Earth and Remote Sensing, or a closely related environmental, computational, or physical science. We encourage applications from students with diverse quantitative backgrounds, who are interested in applications of mathematical and computational approaches to solving ecological problems. and who possess a creative, problem-solving attitude. Students with strong quantitative skills, including proficiency in developing complex computer code in python, C, or similar programming languages, would be particularly well suited to this PhD. Proficiency in R for statistical analysis, or willingness to independently learn is highly desirable. 

Co-Sponsorship details:

The project will receive a CASE award from Rewilding Britain.  

References 

• Bullock et al. (2022) Future restoration should enhance ecological complexity and emergent properties at multiple scales. Ecography 4: https://doi.org/10.1111/ecog.05780  

• Lurgi et al. (2016) The effects of space and diversity of interaction types on the stability of complex ecological networks. Theoretical Ecology 9: 3-13. 

• Lurgi et al. (2018). Eradicating abundant invasive prey could cause unexpected and varied biodiversity outcomes: The importance of multispecies interactions. Journal of Applied Ecology. 55, 2396-2407. 

• Pearson et al. (2021) Evaluating unintended consequences of intentional species introductions and eradications for improved conservation management. Conservation Biology 36(1):e13734 

• Pettorelli (2019) Satellite remote sensing and the management of natural resources, Oxford University Press 
• Schulte to Bühne et al. (2022) Monitoring rewilding from space: The Knepp estate as a case study. Journal of Environmental Management 312, 114867