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Spatial capture-recapture methods for snow leopards


Project Description

We do not know how many snow leopards are left in the world. Snow leopard range country governments and scientists have launched an ambitious initiative to develop a robust assessment of the global snow leopard population. Spatial capture-recapture (SCR) methods are central to these efforts. The very heterogeneous nature and massive range of suitable snow leopard habitat, the tiny fraction of the range that can be surveyed in any year, and the variety of data types (camera trap data, GPS tag data, genetic sampling data, prey survey data, environmental data) that are available for informing estimates of abundance and density, raises methodological challenges for statistical analysis and for survey design. In collaboration with the Snow Leopard Trust (SLT), and using data provided by the SLT, this PhD will develop statistical methods to address some of these challenges. This might involve developing open- or closed-population methods that integrate a variety of data sources, developing models that use times of detection to draw inferences about activity patterns and habitat use, incorporating uncertainty in identifying individuals from photographs, and integrating automated identification methods into inference.

Funding Notes

Multiple sources of scholarship funding are potentially available, including university, research council (EPSRC) and research group (CREEM). Some are open to international students, some to EU and some UK only.

Applicants should have a good first degree in mathematics, statistics or another discipline with substantial numerical component. Applicants with degrees in other subjects (e.g., biology) should have the equivalent of A-level/Higher mathematics, and experience using statistical methods; such candidates should discuss their qualifications with the Postgraduate Officer. A masters-level degree is an advantage.

Further details of the application procedure, including contact details for the Postgraduate Officer, are available at View Website

How good is research at University of St Andrews in Mathematical Sciences?

FTE Category A staff submitted: 30.60

Research output data provided by the Research Excellence Framework (REF)

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