CR2025_24 Keeping pace with climate change: facilitating species dispersal through productive landscapes.

Lead Supervisor: Andrew Bladon, Department of Ecology and Evolutionary Biology, University of Reading

Email: a.j.bladon@reading.ac.uk

Co-supervisors: David R Williams, School of Earth and Environment, University of Leeds; Amelia Hood, Department of Sustainable Land Management, University of Reading; Ben Woodcock, UK Centre of Ecology and Hydrology

Protecting biodiversity underpins our ability to mitigate and adapt to climate change. The UK is one of the most biodiversity-depleted countries¹, primarily due to land-use change (e.g. converting natural habitat to agriculture) and land management (e.g. pesticides)². Maintaining or restoring large patches of natural or semi-natural habitat is essential for conserving species. However, to meet food demand, this may require intensification on remaining agricultural land (e.g. increasing agrochemical use or mechanisation)³. Intensively managed land is hostile to biodiversity, creating a barrier to dispersal. This risks isolating populations within habitat patches, limiting their ability to persist under climate change as they are unable to migrate to more suitable conditions. At the same time, intensively managed agricultural systems are less resilient to climate change^4,5,6, as reduced biodiversity supports fewer ecosystem services (e.g. pollination, soil health) which maintain crop yields under environmental change⁷.

Combined, these challenges highlight the need to create agricultural landscapes which simultaneously support high and resilient agricultural yields while facilitating species migration due to climate change. Current policy approaches to conserving agricultural biodiversity in the UK are delivered through agri-environment schemes (AES), with farmers paid to create “wildlife-friendly” farmland. Unfortunately, many schemes benefit a narrow suite of generalist species, while specialists⁸ with unusual morphologies or behaviour^9.10 require larger areas of different habitat interventions. More importantly, very little practical consideration has been given to how these management interventions affect how species move through agricultural systems, allowing adaptation to changing environments for species with diverse functional characteristics.

Trait-based modelling (using species’ morphological and behavioural attributes to predict responses to change) represents a means of understanding whether AES management could be improved (in terms of habitat quality or spatial configuration) to work for more species without significant yield losses from land forgone to production. However, there are outstanding challenges both in understanding how traits affect species’ migration ability¹¹, and the interactions between traits and environmental conditions, especially in a changing climate.

This project will use mechanistic metacommunity modelling of multiple invertebrate taxa, parameterised using trait data compiled by UKCEH, and borrowing analytical techniques from disciplines including circuit theory¹². This approach represents a major advance in connectivity modelling, substantially increasing the accuracy of predictions for each species, while working for multiple taxa. The student will build an understanding of how species’ traits and landscape configuration (e.g. width, structure, and composition of features like hedgerows) mediate migration and survival. By modelling realistic species’ communities, they will assess the potential for different AES options to boost connectivity through agricultural landscapes, and produce a set of options that, if implemented together, maximise movement through the landscape.

Using a quasi-experimental mark-release-recapture set-up, this project will validate model results by comparing the success of a species community (e.g. light-attracted moths) at traversing agricultural landscapes containing different combinations of AES options. The student will use automated field recorders using machine learning to identify species (e.g. UKCEH AMI¹³) to record the movement of marked individuals, using these data to assess model accuracy and make iterative improvements to model predictions.

Throughout, the project will use existing connections between the supervisory team and key decision-makers and stakeholder groups to build an understanding of barriers to, and facilitators of, change in agricultural policy, and incorporate these views into the modelling as a cost layer. This could include workshops tailoring project elements to stakeholder interests, for instance establishing priority species for farmers. Ultimately, the project will make recommendations for policy improvements which enable agricultural resilience and minimise biodiversity loss by helping both rare and functionally important species to persist under climate change.

Training opportunities: 

The student will be trained as an interdisciplinary conservation scientist. They will learn to engage with stakeholders (e.g. farmers and policy-makers) when conducting applied research. They will develop field ecology and analytical skills, including using cutting-edge equipment (e.g. CEH AMI), statistical methods (e.g. mechanistic meta-community modelling), coding (e.g. R, Julia), and reviewing literature on landscape design and connectivity. They will undertake a three-month CASE internship with Natural England, learning how to integrate scientific evidence into environmental policy. Personal development training will also be provided following the student’s interests and needs, including leadership and inclusivity training. 

Student profile: 

The student will hold a BSc (or equivalent) in a relevant subject (ecology, zoology, environmental science, mathematics). A MSc (or equivalent) in a relevant subject is desirable. They will have a keen interest in ecology, conservation science and agroecology, and a desire to work at the science-policy interface. The student will have experience in conducting ecological fieldwork, preferably including field entomology, including working with land managers and owners. They will have strong quantitative skills, including experience with scientific modelling, systems analysis, functional programming (e.g. R, Julia) and Geographic Information Systems (GIS). 

Co-Sponsorship details: 

The project will receive co-sponsorship from Natural England. This co-sponsorship will be in the form of a placement.

References

1. Burns, F. et al. State of Nature 2023. The State of Nature Partnership. (2023). https://stateofnature.org.uk/
2. IPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services. https://zenodo.org/doi/10.5281/zenodo.3553579 (2019) doi:10.5281/ZENODO.3553579.
3. Phalan, B. et al. How can higher-yield farming help to spare nature? Science, 351, 450–451 (2016).
4. Lesk, C. et al. Compound heat and moisture extreme impacts on global crop yields under climate change. Nat. Rev. Earth Environ. 3, 872–889 (2022).
5. Tendall, D. M. et al. Food system resilience: Defining the concept. Glob. Food Secur. 6, 17–23 (2015).
6. Altieri, M. A., Nicholls, C. I., Henao, A. & Lana, M. A. Agroecology and the design of climate change-resilient farming systems. Agron. Sustain. Dev. 35, 869–890 (2015).
7. Oliver, T. H. et al. Biodiversity and Resilience of Ecosystem Functions. Trends Ecol. Evol. 30, 673–684 (2015).
8. Sharps, E. et al. Reversing declines in farmland birds: How much agri‐environment provision is needed at farm and landscape scales? J. Appl. Ecol. 60, 568–580 (2023).
9. Image, M. et al. Does agri-environment scheme participation in England increase pollinator populations and crop pollination services? Agric. Ecosyst. Environ. 325, 107755 (2022).
10. Kleijn, D. et al. Mixed biodiversity benefits of agri‐environment schemes in five European countries. Ecol. Lett. 9, 243–254 (2006).
11. Liczner, A. R. et al. Advances and challenges in ecological connectivity science. Ecol. Evol. 14, e70231 (2024).
12. McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89, 2712–2724 (2008).
13. UK Centre for Ecology and Hydrology. UKCEH AMI system. (2024).