Facilitating species dispersal through productive landscapes

Year
2024
Primary Supervisor
David Williams <d.r.williams@leeds.ac.uk>
Institution
University of Leeds (School of Earth and Environment)
Collaborative Project
Yes
Academic Supervisors
Steven Sait <s.m.sait@leeds.ac.uk> (University of Leeds, School of Biology)
Research Themes
,
Project Partners
Natural England
Research Keywords
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Relevant Degree Courses
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Background

Protecting biodiversity underpins our ability to mitigate and adapt to climate change. The UK is extremely biodiversity-depleted1, due to land-use change and intensive land management2. Conserving or restoring large patches of natural habitat is essential for preventing extinctions3, but this takes space and so may require intensification (e.g. increasing mechanisation) on remaining farmland to maintain food production. This intensive farmland is often hostile to biodiversity, creating a barrier to dispersal. This risks isolating populations, limiting their ability to adapt to climate change by migrating to more suitable conditions. 

In addition, these intensively managed agricultural systems may be less resilient to climate change,7, as they contain less biodiversity to support critical ecosystem services (e.g. soil health) which maintain crop yields under environmental change8.

Combined, these challenges highlight the need to create agricultural landscapes which simultaneously support high and resilient agricultural yields and allow species dispersal and migration in response to climate change.

Current approaches to conserving UK agricultural biodiversity are focused on agri-environment schemes (AES), with farmers paid to create “wildlife-friendly” farmland. Unfortunately, many schemes focus on generalist species, neglecting rarer species9 with unusual  morphologies or behaviour10,11. In addition, little consideration has been given to how AES affect species movement (and thus dispersal or responses to climate change), while many AES reduce yields and thus may drive agricultural expansion to maintain food production. 

There is therefore a potential trade-off between the area of natural habitat that can be conserved and the ability of a landscape to maintain functional metapopulations, or support adaptation to climate change (Fig. 1).

Schematic of how agricultural landscapes could affect population dynamics and dispersal under climate change
Fig 1. Alternative land-use strategies. (a) A low-yield agricultural system includes small natural habitat patches, with high connectivity between them. This allows movement (black arrows), ecosystem service provision, and colonisation of new patches in response to climate change (grey arrows). Small patch size and low total habitat area means specialist species are unlikely to survive. In contrast, (b) has large habitat patches, allowing specialists to survive, but the high-yielding agricultural land between patches reduces connectivity and migration in response to climate change. Reconciling the two could allow more species to persist and support agricultural resilience by promoting key ecosystem services.

Approach

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 to support more species without significant yield losses. However, there are major knowledge gaps around understanding how traits affect species’ migration ability12, and the interactions between traits and environmental conditions, especially in a changing climate.

This project will use cutting-edge mechanistic metacommunity modelling to address these gaps. The student will build an understanding of how species’ traits and landscape configuration affect metacommunity dynamics by using interdisciplinary analytical approaches (e.g. circuit theory13) and leveraging trait databases of multiple  invertebrate taxa. This represents a major advance in connectivity modelling and will enable the student to identify potential AES options which could maximise movement through landscapes while minimising yield losses.

The project will then validate model results using a quasi-experimental mark-release-recapture set-up in agricultural landscapes with different combinations of AES options. Using automated field recorders and machine learning to identify species (e.g. UKCEH AMI14) and record the movement of marked individuals, they will assess model accuracy and make iterative improvements to predictions.

Throughout, the project will use existing connections between the supervisory team and key stakeholder groups to understand barriers and enablers of change in agricultural policy. 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.

Student specification:

This project would suit someone with an interest in spatial ecology and conservation science. You will need excellent quantitative skills and a willingness to combine fieldwork with computer modelling.

References

  1.   Burns, F. et al. State of Nature 2023, https://stateofnature.org.uk/;
  2.   IPBES (2019) https://zenodo.org/doi/10.5281/zenodo.3553579
  3.   Williams et al (2018) Curr. Biol.
  4.   Hanlon et al (2021) Clim. Change
  5.   Lesk et al. (2022). Nat. Rev. Earth Environ.
  6.   Tendall et al. (2015) Glob. Food Secur.
  7.   Altieri (2015) Agron. Sustain. Dev.
  8.   Oliver et al. (2015) Trends Ecol. Evol.
  9. Sharps et al. (2023) J. Appl. Ecol.
  10. Kleijn et al. (2006) Ecol. Lett.
  11. Image et al. (2022) Agric. Ecosyst. Environ.
  12. Liczner et al. (2024) Ecol. Evol.
  13. McRae (2008) Ecology
  14. UK CEH (2024) UKCEH AMI system