PhD Studentship: Leverhulme ‘Space for Nature’ Doctoral Scholars (PhD or MSc by Research+PhD) Leveraging Machine Learning to Assess the Impact of Other Effective Area-Based Conservation Measures (OECMs) on Bumblebee Populations in the UK

Project Details: Monitoring bumblebee populations is imperative due to their critical role as pollinators. ‘Other Effective Area-Based Conservation Measures’ (OECMs) are places that are outside the protected site network, but which are managed in a nature-friendly way alongside management for other reasons. The OECM approach - conservation beyond nature reserves – has the potential to support bumblebees at a population scale for the first time. The extent of this support (foraging, nesting and overwintering resources) is highly dependent on the habitats present within the OECM and OECM location, size, shape, landscape connectivity, and surrounding habitats. However, no metrics or methods currently exist for quantifying the impact of OECM characteristics on bumblebee populations, understanding the potential value of OECMs for supporting these important pollinators and informing policy and conservation actions accordingly.

BBCT manage the only GB bumblebee monitoring scheme (BeeWalk), numbering ~800 volunteers, collecting monthly bumblebee count data (48,000 records in 2023). Additionally, opportunistic bumblebee sightings are recorded nationwide using mobile-phone apps (e.g. iRecord, iNaturalist). Together, these schemes provide unprecedented spatio-temporal data, alongside regularly-updated remote-sensed

data associated with the locations and times of observations, including novel metrics (e.g. non-visible-spectrum reflectance).

This project brings together statistical modelling of the data-generating process with machine learning, including deep learning, techniques, to model and predict bumblebee populations comparing potential OECMs and existing protected areas in terms of their effect on bumblebee populations, using all available, diverse, data. The ongoing collection of bumblebee and remote-sensed data has the potential to provide near-real-time reliable feedback on the value of OECMs for bumblebee populations, and by extension the wider pollinator guild, informing conservation management as well as the concomitant benefits of pollination for surrounding land users.

This interdisciplinary project merges applied ecology, statistics, and computing to construct a robust modelling framework to infer bumblebee population dynamics and trends, link these to OECMs and their characteristics, and ultimately derive insights into the potential effectiveness of OECMs in bumblebee conservation and inform corresponding conservation strategies.

The selected candidate will receive comprehensive training in the latest machine learning and deep learning techniques. Suitable applicants have a strong background in statistics, mathematics or computing.

Scholarship value

The MSc by Research (if relevant) and PhD scholarships include a stipend (equivalent to the Research Councils UK National Minimum Doctoral Stipend; the 2024/25 rate is £19,237, which is not taxed income). Tuition fees are covered at the home student rate. The PhD scholarship comes with a £10,000 research and training fund.

For eligibility criteria and details on how to apply, please see the full advertisement.

For more background information about the Leverhulme ‘Space for Nature’ Doctoral Scholars, please go to the DICE website

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