Integrating environmental DNA into national biodiversity datasets to explain drivers of biodiversity loss

About the Project

Biodiversity monitoring is the cornerstone of understanding responses to disturbance, ecological resilience, the effect of interventions in degraded ecosystems and progress towards conservation targets. Considerable biodiversity data are collected from large-scale monitoring efforts as well as from opportunistic observations and are collated into databases and repositories to support our understanding of the state of biodiversity. As a result, it has become clear that much of our planet’s biodiversity is in decline as a direct result of human pressures (WWF 2024, Burns et al 2023, IPBES 2019) However, much of the biodiversity data we collect is primarily focused on charismatic animals, in particular vertebrates (Troudet et al 2017), giving us a limited picture of ecosystem health and function from lower trophic levels. The challenge of using observation-based data also manifests in limitations in spatial and temporal scales and coverage.

This studentship will investigate environmental DNA as a tool to complement and supplement biodiversity datasets to facilitate a wider understanding of ecosystem health and function. Dr Littlefair’s lab and collaborators recently explored the possibility of biodiversity monitoring through airborne environmental DNA by air quality networks operating at weekly temporal intervals (Littlefair et al 2023). By combining eDNA with autonomous monitoring systems, as well as investigating the wealth of existing open source eDNA data, we will increase the coverage of the spatio-temporal-taxonomic continuum of existing types of biodiversity data (Tournayre et al 2025a,b). This studentship will achieve this through joint modelling approaches of observation and DNA-based data at varying temporal and spatial scales. Depending on the student’s interest, it will be possible to investigate 1) coverage and uncertainties inherent in each data type (Diana et al, 2025), 2) the response to ecological perturbation evaluated by each data type (Ji et al, 2025), 3) the role of eDNA in assessing progress towards biodiversity targets.

This project will support the student to gain cutting-edge data handling and statistical modelling skills, as well as an in-depth knowledge of a diversity of data types including citizen science observations / structured surveys / and high throughput environmental genetics. The student will gain knowledge of broad scale biodiversity trends which will serve them in further research-focused positions, or in industry, governmental or NGO work. The student will join a dynamic and growing lab which is supported by several funding sources including a Future Leaders Fellowship. Dr Littlefair’s lab is based at the newly opened People and Nature group at UCL East (Queen Elizabeth Olympic Park campus, Stratford). The student will also benefit from the academic network of co-supervisors Dr Charlie Outhwaite (ZSL) and Dr Eleni Matechou (School of Mathematical Sciences, Queen Mary University of London), as well as the rich intellectual environment of London (CBER, CEE, NHM, Kew).

We are open to hearing from students from interdisciplinary backgrounds in ecology, environmental science, quantitative/computational ecology, data science, AI, or mathematics. If from an ecological background, students must have demonstrable experience or interest in big datasets/statistical modelling. If from a data science or mathematical background, students must have demonstrable experience or interest in answering ecological questions.

The application process is in two stages which both need to be completed. You will need to 1) Complete the UCL GEE application process here, selecting admission in October 2026, and 2) Email the following documents to Dr Joanne Littlefair j.littlefair@ucl.ac.uk with the subject heading "Environmental DNA for biodiversity data PhD studentship":

1. Your CV
2. A motivation statement which includes:
   • a brief description of up to two research projects that you’ve undertaken, written for a non-specialist (but still scientific) audience (1 paragraph per project)
   • a summary of your pathway to this opportunity, including any challenges that you’ve had to overcome in your research or educational career so far if you’d like to share them, and an explanation of why this opportunity appeals to you (max 3 paragraphs)
   • Confirmation that you meet the requirements for Home fee status, set out here: https://www.ucl.ac.uk/students/fees-and-funding/pay-your-fees/fee-schedules/student-fee-status (fee status is formally assessed by UCL after application).

Funding Notes

This is a directly funded PhD covering home (UK) fees and an annual stipend for three years at the UKRI standard rate with London allowance.