PhD Studentship: Calling in the wilderness: the use of Passive Acoustic Monitoring in biodiversity surveys
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Employer: University of Essex |
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Job location: Colchester UK |
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Apply before: 15 Jan 2020 |
Summary
This project will examine the potential of passive acoustic monitoring (PAM) as a tool for providing large-scale baseline data for nocturnal wildlife. Specifically, the student will combine the deployment of acoustic recorders in the Prypiat and Polesia wilderness area with analysis of acoustic data. As call libraries are essential for building supervised automatic classifiers, gaps in species coverage will be identified and prioritised for fieldwork effort in 2020.
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Scientific background
By 2020, the BTO will be in its second year of an exciting 5-year landscape restoration program in Belarus and Ukraine – ‘Wilderness without borders: creating one of the largest natural landscapes in Europe’. The project aims to designate new, and upgrade existing conservation areas, to create a transboundary protected and interconnected core area of 1.2 million ha, within the wider Prypiat / Polesia area covering approximately 5.8 million ha.
Underpinning this process, it is crucial for decisions to be made on robust and representative assessment of the biodiversity and ecological value of the region. However, large-scale monitoring of wildlife, and particularly nocturnal wildlife remains challenging.
Research methodology
This project will examine the potential of passive acoustic monitoring (PAM) as a tool for providing large-scale baseline data for nocturnal wildlife. Specifically, the student will combine the deployment of acoustic recorders in the Prypiat and Polesia wilderness area with analysis of acoustic data. As call libraries are essential for building supervised automatic classifiers, gaps in species coverage will be identified and prioritised for fieldwork effort in 2020. The student will evaluate the BTO’s existing approach for building random forest classifiers, in relation to new deep learning algorithms (Convolutional Neural Networks, CNNs), to develop a robust framework and tools for automated species identification. With four seasons of data (2019-2022), the student will evaluate the potential of the approach for providing robust data on the distribution, relative abundance and habitat requirements of the focal taxonomic groups.
Training
The successful candidate will receive training in passive biodiversity monitoring approaches; the construction, management and analyses of large, long-term monitoring and acoustic databases; machine-learning including CNN’s and is expected to achieve a high level of competency in statistical modelling. Furthermore, the student will obtain field research and design skills including in large-scale sample design, small mammal trapping and handling, and multi-taxa identification.
Person specification
Candidates will have a degree in biology, ecology, environmental sciences, mathematics or computing with a strong interest in biology. Experience of fieldwork, handling large datasets and familiarity with computer packages such as R, MATLAB and Python will be an advantage.
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