Ecoacoustics is the use of sound to understand more about the nature of ecosystems and how they function. The scientific field and practical applications are rapidly developing as new hardware and analytical techniques, in particular AI methods, make the methodologies more accessible and the results more actionable. Many animals produce sounds which can be used to infer presence, activity, behaviour, and in some cases abundance and the group has experience using ecoacoustics in a range of habitats, and to monitor birds, bats, crickets, small mammals and amphibians.
Biodiversity Net Gain (BNG) is a new approach to development in the UK that will be implemented in the forthcoming Environment Act and National Planning Policy Framework. The policy aims to leave the natural environment in a measurably better state than before development – and this gain is assessed through a 30 year monitoring programme. As a result, there is significant interest in for cost-effective methods for monitoring biodiversity that can be deployed at scale. Ecoacoustics has such potential, but key questions still remain. Are differences observed using ecoacoustics consistent with habitat classification? How reliable is ecoacoustics for measuring biodiversity net gain? How do errors and biases affect our conclusions and if so, how do we control for them?
This exciting project will be supported by supervisors at Lancaster and CEH in partnership with a professional consultancy to provide you with support in ecoacoustic monitoring, machine learning and remote sensing. Specific training in these fields, as well as data management, science communication and spatial modelling will be available. The successful candidate will become a highly skilled, interdisciplinary graduate working at the interface of multiple advanced ecological techniques and ideally prepared to assist with the drive to renew biodiversity in the UK.
Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental or Natural Sciences and applicants with first class degrees and/or high quality Masters qualifications are particularly encouraged to apply. Familiarity with the R or Python programming environment would be an advantage.
To find out more about the project in the first instance please contact Dr. Alex Bush (email@example.com).