CR2025_23 Spatially informed biodiversity restoration strategies to benefit multiple species
Lead Supervisor: Konstans Wells, Department of Biosciences, Swansea University
Email: k.l.wells@swansea.ac.uk
Co-supervisors: James Bullock, UK Centre for Ecology and Hydrology; Penny Neyland, Department of Biosciences, Swansea University;
Biodiversity restoration strategies have become a compulsory part of land management and development policy in parts of the UK and elsewhere (e.g. Biodiversity Net Gain (BNG), scheme in England and Net Benefit for Biodiversity (NBB) in Wales). These policies require the development of actions that should increase biodiversity, such as habitat creation and enrichment, for planning permissions to be issued. These new policies form an exciting opportunity to ameliorate urban development with nature restoration. But the existing approaches of trading alternative habitats are not necessarily based on the underlying biodiversity and conservation landscape and therefore require better informed practices and systemic oversight to ensure biodiversity recovery is informed by key ecological principles that take the spatiotemporal scales of biodiversity dynamics and ecosystem processes into account. Challenges include, for example, balancing restoration efforts across different habitat types and species communities, and the allocation of restoration efforts in an appropriate spatiotemporal context to facilitate habitat development and succession according to the needs of local species and ecological networks.
Other challenges are linked to optimizing biodiversity enrichment in newly emerging anthropogenic land uses such as agrivoltaic and recreational areas. Agrivoltaics is the practice of farming crops or livestock under solar panels as an approach to doubling up on land use, but ecologically-informed biodiversity enrichment strategies in such habitats requires research-based evidence.
In this PhD the student will work in an interdisciplinary team (biodiversity, ecosystem restoration, botany and zoology, macroecology, movement ecology, ecological modelling) to evaluate spatiotemporal heterogeneity in the value of habitats and landscapes according to multispecies distributions and interactions, using vertebrates and plant-pollinator ecological networks in Wales as model systems. The approach will include multi-species distribution models (mostly focused around various types of multispecies distribution models based on statistical models and machine learning tools) and process-based modelling of species persistence in heterogeneous landscapes that will help to quantify for any given habitat patch in a landscape the biodiversity value (‘what species are most likely affected if a habitat patch is subject to development’) and the restoration capacity (‘what species are likely to colonise a habitat patch subject to restoration efforts and how likely does a patch successfully sustain biodiversity over long times’). The key focus of the project is on big-data and spatially-explicit model-based approaches to evaluate biodiversity value and restoration capacity in a landscape context and to work towards spatially informed biodiversity restoration strategies. The outcome of this project may aid in improving existing biodiversity net gain schemes, such as BNG and NBB and hopefully will generate real-world impact by informing future biodiversity restoration and rewilding strategies.
The student will also have the opportunity to carry out greenhouse and field experiments to explore biodiversity enrichment towards resilient plant-pollinator networks under variable climate conditions in Welsh grasslands, including sites used for agrivoltaics (with the aim to parameterise some of the modelling for the selected species).
Ultimately, this project will be imbedded in broader scientific ambitions to generate a digital twin for biological restoration strategies (a digital replica of the real environment contextualising biodiversity value and restoration capacity for ecologically informed and reproducible biodiversity net gain strategies).
Training opportunities:
A comprehensive personal and professional training programme will be provided together with extensive opportunities for student to engage in multidisciplinary activities through interactions with a wide network of academic, research and industrial/policy partners. Training will include data analysis, ecological modelling, programming, geographical information systems, techniques in biodiversity assessment, ecological concepts and wider skills as relevant for professional development, impact and knowledge transfer. There will be opportunities to engage in field work of direct relevance to parameterise the modelling. There will be multiple placement opportunities with regional authorities and industry partners to apply for during the course of the PhD.
Student profile:
This project is suitable for students with a degree in Biological Sciences with a quantitative focus, Biomathematics, or Computer Science, or any related disciplines. It is essential that the PhD student is self-motivated and curious, interested in working as part of an interdisciplinary team, and combining ecological knowledge and concepts with the development of statistical and mechanistic modelling. Suitable candidates will be eager to engage with peers and enthusiastic in engaging with quantitatively informed policy making. Candidates must hold a UK bachelor’s degree with a minimum of Upper Second Class honours or higher or overseas bachelor’s degree deemed equivalent.