Details of Award
NERC Reference : NE/R008485/1
Camera traps, image analysis and wild mammal monitoring
Training Grant Award
- Lead Supervisor:
- Professor PA Stephens, Durham University, Biosciences
- Grant held at:
- Durham University, Biosciences
- Science Area:
- Terrestrial
- Overall Classification:
- Terrestrial
- ENRIs:
- Biodiversity
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Science Topics:
- Behavioural Ecology
- Community Ecology
- Conservation Ecology
- Population Ecology
- Statistics & Appl. Probability
- Abstract:
- 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 that we have 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. Our CASE partners, Durham Wildlife Trust (DWT) and Zoological Society of London (ZSL), both have a strong interest in improving the efficiency of camera trap data processing. Over the past 2 years, DWT has been running a citizen science project in which members of the public deploy cameras and classify images, delivering information about mammals in an under-recorded part of the UK, while increasing engagement of the public with the biodiversity around them. Both outcomes are core to DWT's mission. DWT has identified that engagement of participants with image processing would be higher if it were possible to screen out images that clearly do not contain wildlife. ZSL uses camera trapping to monitor key mammal populations and evaluate the impact of conservation interventions globally, generating large volumes of imagery that require substantial valuable staff time to process. For the last 6 years, the ZSL Conservation Technology Unit has also been working to develop both citizen-based and automated image processing solutions to the problem. 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 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).
- NERC Reference:
- NE/R008485/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Industrial CASE
This training grant award has a total value of £90,226
FDAB - Financial Details (Award breakdown by headings)
| Total - DSA | Total - Fees | Total - RTSG | Total - Student Stipend |
|---|---|---|---|
| £707 | £17,480 | £11,000 | £61,042 |
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