Rewilding is an approach to conservation which seeks to regenerate degraded ecosystems in a self-sustaining way, with relatively little ongoing management. Rewilding projects are proliferating in the UK, especially following the high-profile success of the Knepp Wildland project in West Sussex, and are potentially transformative in attempts to protect and enhance biodiversity nationally. There is an urgent need to monitor and evaluate the success of rewilding projects, to facilitate evidence-based design and management of sites for maximal conservation value.
Central to evaluating the impact of conservation projects is accurate and cost-effective surveying of biodiversity. However, the challenges of species identification often lead to patchy or ineffective monitoring of all but the most easily identifiable species groups. Recent technological developments, especially using machine learning (ML) in species recognition, have the potential to reduce dramatically the costs of biodiversity monitoring.
This project will evaluate the effectiveness of ML species recognition tools for monitoring biodiversity in the context of a major new rewilding project (Boothby Wildland) being implemented Nattergal Ltd, founded by the creators of the Knepp Wildland. We will survey key pollinators over the first three years of the Boothby project, assessing the impact of the retreat from arable farming on spatial and temporal patterns in biodiversity. Simultaneously, we will validate an ensemble of species recognition apps with conventional expert-led insect identification, enabling us to assess the long-term feasibility of rapid, low-cost monitoring of biodiversity.
The successful applicant will gain practical experience of biodiversity monitoring in the field, cutting-edge species identification technology, and diverse approaches to data management/analysis. They will work in partnership with a flagship rewilding project in the earliest stages of its development. The focus of the work will be shaped by the student’s interests, working with a cross-disciplinary supervisory team, including conservation practitioners and specialists in machine learning technology.
Essential: The student should hold at least an upper-second class (2.1) undergraduate degree or equivalent in a biological science; they should have experience of ecology and conservation, including some field experience; they should have practical research project experience and be familiar with basic statistical analysis.
Highly desirable: A Masters-level qualification in a relevant subject area; full UK driving licence; experience of practical entomology; experience of statistical/mathematical modelling; experience of programming (e.g. Python, R).
By email to Dr Tom Reader, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD; email@example.com.