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Camera trapping and the ecology of the imperilled Scottish wildcat


Project Description

Scottish wildcats have recently been recognised as one of Britain’s most endangered mammal species, with a population unlikely to number in excess of a few hundred individuals. Wildcats are currently the focus of a conservation plan intended to determine their distribution and abundance with greater precision, to ameliorate current threats in key areas of abundance, to augment the existing population through conservation breeding, and to restore a viable population in the north of Scotland. The conservation plan is being implemented by Scottish Wildcat Action, a consortium of 20 organisations, including Scottish Natural Heritage. To monitor the existing population of wildcats, a key approach has been to conduct camera trapping each winter across large areas of northern Scotland. Initially, the focus was on 5 priority areas where wildcats were known to persist. Recently, however, this has been broadened to involve the wider public in camera trapping across much larger areas. To do this, Scottish Wildcat Action has teamed up with MammalWeb, a citizen science platform that facilitates the remote upload of camera trap images and metadata, and – crucially – allows much wider participation in classifying the contents of captured images. Those images typically number in the hundreds of thousands each year and, hitherto, it has only been possible to assess them rapidly, ignoring images that don’t contain putative wildcats. With the involvement of MammalWeb, it is now possible to assess those images in greater depth, learning more about the ecology of the wildcat. Here, we propose a project to use the accumulated images, in conjunction with the assistance of citizen scientists, to learn more about the basic community ecology of the wildcat.

The main aims of the project are to: (i) enlist citizen scientists in classifying the accumulated wildcat monitoring data set; (ii) determine confidence in citizen-submitted consensus classifications for the wildcat image data base; (iii) work with a wider team to implement side-by-side comparisons of putative wildcats, so that contributors can assign individual identities; (iv) conduct standardised camera trapping within priority wildcat areas to determine the occupancy and abundance of wildcats, and to assess potential biases arising from current sampling approaches; and (v) assess patterns of co-occurrence and avoidance between wildcats and potential competitor species.

The student will help to enlist and deliver training for members of the public interested in participating in the wildcat monitoring programme. They will upload the backdated image data sets and will refine existing statistical approaches to determine confidence in consensus classifications. They will devise and contribute to the implementation of a new module on MammalWeb, enabling identity matching for pictured wildcats and spatially-explicit capture-recapture approaches to abundance estimation. Focusing on priority areas of known occurrence, the student will also deploy camera traps to a standardised protocol, in order to employ standard methods for estimating both occupancy and abundance. Finally, the student will use recently-proposed methods to assess the full wildcat image data set for evidence of spatial co-occurrence and avoidance with other mammalian species, and to assess evidence for temporal partitioning among competitors.

Funding Notes

This project is in competition with others for funding. Success will depend on the quality of applications received, relative to those for competing projects. If you are interested in applying, contact Dr Stephens in the first instance, with a CV and covering letter, detailing your interest in and fit to the project. Note that competition for this funding scheme is intense; competitive applicants usually have exemplary academic records in addition to relevant experience

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