Urbanisation, agricultural intensification and climate change strongly affect the distribution and abundances of species, globally. This raises ethical concerns, diminishes the aesthetic value of nature and affects functioning of the ecosystems on which we rely. Management and mitigation of these processes requires a good understanding of how they affect wildlife. That understanding must come from sound information on the distribution and abundances of species, and how those change over time. This requires that we monitor wildlife over large areas and long time periods.
Mammals are often scarce, shy, elusive or nocturnal, and so are difficult to monitor over large areas. This is changing, thanks to the advent and increasing affordability of "camera traps", devices that can be deployed over long periods to take photographs of passing wildlife, 24 hours a day. Camera trapping has revolutionised prospects for monitoring mammals but is associated with significant challenges. Monitoring over large areas and long periods generates huge numbers of images to be analysed. Some projects have successfully turned to "citizen scientists", concerned members of the public, to help with image processing. However, the research and conservation communities are generating rapidly increasing quantities of data, both in the UK and globally, and, under current processing models, demand for citizen scientists is likely to outstrip supply in the near future.
In the past, camera traps were predominantly used to monitor mammals with individually recognisable coat patterns, allowing the numbers of individuals in an area to be estimated. Over the past decade, however, techniques have been developed (notably by our project partners at ZSL Institute of Zoology) to enable the analysis of unmarked populations from camera trapping, yielding data on both abundance and movement parameters. However, these techniques further increase the burden of image processing, begging the question, can citizen science and automated image analysis make the process more accessible and widely used?
In this project, the student will work with an interdisciplinary team of ecologists, mathematicians, specialists in public engagement, and conservation technologists, including the CASE partners: Durham Wildlife Trust (DWT) and Zoological Society of London (ZSL), both of which have a strong interest in improving the efficiency of camera trap data processing. The aim will be to develop techniques to radically improve our ability to monitor mammal communities by: 1) increasing engagement of citizen scientists in data processing by automatically screening images for the presence of species of interest; 2) automating aspects of image processing, including camera-subject distance estimation, in order to accelerate the uptake of innovative approaches to community-wide mammal monitoring; and 3) using these advances in automated analysis to determine the environmental drivers of travel speed (an important illustration of the contribution of the technique to ecology).
Funding for this project, at standard RCUK rates, has been secured from the NERC Industrial CASE Studentship scheme. Eligibility for funding follows RCUK guidelines
(http://www.rcuk.ac.uk/documents/publications/traininggrantguidance-pdf/); non-UK applicants may be eligible for fees-only awards and should check the guidelines. Interested applicants should contact the listed supervisor with a CV and covering letter in the first instance. Shortlisted applicants will ultimately be selected by interview
How good is research at Durham University in Biological Sciences?
FTE Category A staff submitted: 39.00
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