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Modelling encounters in surveys of unmarked animal populations

Project Description

Many wild animal populations are monitored by placing detectors (e.g. cameras or microphones) in a study area and recording encounters with individual animals. From these encounters, the goal is to estimate population density. A common problem is that individuals are not identifiable, often termed “unmarked”, so we cannot know which individuals were seen in each encounter.

Random encounter models are a method to estimate population density from unmarked populations by using auxiliary information on how individuals move: if you know how individuals move, you can estimate how many times a single individual would be encountered, and so deduce how many individuals produced the total number of encounters observed.

This PhD project will focus on developing random encounter models (REM) in one or more of the following ways: (1) construct a maximum likelihood based framework for estimation; (2) incorporate alternative models for animal movement; (3) extend density and encounter models to vary, with correlation, spatially and temporally; (4) build joint models for partially marked populations.

This project is supervised jointly by David Borchers and RIchard Glennie in St Andrews, together with Marcus Rowcliffe (institute of Zoology, Zoological Society of London).

Funding Notes

Multiple sources of scholarship funding are potentially available, including university, research council (EPSRC) and research group. 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 (e.g., biology, computer science), with substantial statistical component. 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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