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PhD Studentship: Climate, Land use and Protect Area effects on Condition, Behaviour and Distribution of Migratory Waterbirds

University of Essex - School of Life Sciences

Qualification Type: PhD
Location: Essex
Funding for: UK Students, EU Students, International Students
Funding amount: £20,780
Hours: Full Time
Placed On: 13th February 2026
Closes: 13th April 2026
 

Funding for: UK Students, EU Students, International Students

Funding amount: Full time Home fees and a standard tax-free stipend for three years equivalent to the UKRI National Minimum Doctoral Stipend (£20,780 in 2025-26, the 2026-27 stipend is TBC), as well as a research allowance for costs such as supplies, field work etc. International applicants are welcome, but will need to cover the difference between Home and International fees.

Hours: Full Time

Closes: 13 April 2026

Background

Animals with complex life histories are particularly vulnerable to environmental change. Afro-Palearctic migratory waterbirds experience environmental and anthropogenic pressures in both their boreal and arctic breeding areas, and their passage and winter ranges.

The United Kingdom supports internationally significant waterbird populations in the non-breeding season. The relative role of environmental changes at different points in the life cycle informs the UK’s responsibility to conserve waterbirds of conservation concern.

In this conservation science PhD, the candidate will explore the role of land use and its management, climate, weather on the spatial distribution of protected waterbirds. The project will explore and scrutinise alternative approaches to assessing the ecological status of protected areas in providing effective conservation. A key knowledge gap is how many birds of different species should we expect to observe in different landscapes.

The project

Key questions on the physiological, behavioural and spatial population dynamic response of waterbirds to land use, season and climate can be examined with existing population and demographic monitoring data held by the British Trust for Ornithology. Statistical modelling approaches will examine spatiotemporal changes in bird distributions in response to protection, land use change, management weather and, on longer-term scales, climate.

Opportunities for developing bespoke field monitoring, international collaborations, tagging and movement studies can be explored.

Data from well-designed monitoring schemes (e.g. the BTO/RSPB/JNCC Wetland Bird Survey, Sentinel satellite) and others from less organised citizen science and remote sensing data (e.g. BirdTrack) can be integrated with emerging statistical procedures in such a way that greater inference is possible on what drives wildlife distributions and abundance.

Significant and diverse statistical modelling and data science experience exists across the supervisor team (Essex and BTO); for example, recent related projects have used GLMMs and hierarchical models using INLA to model these datasets. Further training will be provided by the supervisors and lead stakeholder partner in fieldwork methods, research impact and policy engagement.

Funding

The successful applicant will receive a full PhD scholarship for up to a maximum of 3.5 years of study, including:

  • Home tuition fee waiver. International candidates would need to demonstrate they had plans in place to cover International Fees.
  • Living costs stipend at the UK Research and Innovation recommended level per year. The stipend rate for 2025-26 is £20,780. The stipend for 2026-27 is to be confirmed.
  • A research allowance of to be spent on authorised research costs for example supplies and equipment, field work etc.

Person specification

We seek a candidate interested in data science for conservation; additional interest(s) in evidence-led conservation policy, effectiveness of protected areas, spatial animal ecology, land use change or ornithology are desirable.

We welcome candidates with backgrounds such as ecology, statistical modelling or geographic modelling — it’s a broad search, so please apply.

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