Dr Iain Barr (UEA)
Dr Simon Gillings (British Trust for Ornithology)
Dr Stuart Newson (British Trust for Ornithology)
Passive acoustic monitoring (PAM) is increasingly used both to monitor rare and/or nocturnal species that are often undetectable with traditional active monitoring approaches and to draw inference on biodiversity health and community structure from the characteristics of the composite soundscape. PAM has the potential to provide cheap, efficient and large-scale biodiversity monitoring but this requires improved understanding of the drivers of spatial and temporal variation in acoustic characteristics and the development of robust sampling protocols.
This project will critically examine the potential of PAM as a tool for biodiversity monitoring in the UK. Specifically, the student will combine the deployment of acoustic recorders with analysis of existing monitoring data, biodiversity surveys and the development of acoustic classifiers to assess the use of PAM to i) establish species occurrence; ii) detect large-scale species movements and iii) monitor wider biodiversity health. This will involve both targeted data collection at sites across East Anglia and establishing and managing networks of citizen scientist recorders to collect data from across the UK. Working closely with the British Trust for Ornithology, the project aims to determine the key drivers of spatial and temporal patterns in soundscape structure and composition and, through comprehensive sub-sampling and scheduling analyses, establish effective and efficient PAM sampling protocols.
The successful candidate will receive training in active and passive biodiversity monitoring approaches; invertebrate identification; the construction, management and analyses of large, long-term monitoring and acoustic databases; and is expected to achieve a high level of competency in statistical modelling.
Candidates will have a first degree in biology, ecology, environmental sciences or related subject. Experience of undertaking biodiversity surveys, handling large datasets and familiarity with computer packages such as R will be an advantage.