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Methods for Ecological Surveys with Detection Observation Error

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  • Full or part time
    Prof D Borchers
    Prof S Buckland
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Please note that this project is co-supervised by Dr. Rachel Fewster, University of Auckland.

There is often uncertainty over some aspect of a detection in a wildlife survey. Here are a few examples: (1) It is easier to observe fish lengths on trawl surveys than to observe fish ages, but age is more informative for fitting population dynamics models. We can view length as a "noisy" observation of age. (2) When surveying multiple species simultaneously, species may be uncertain for some detected animals, but a predictor of species (e.g. size or colour) is often available, and this can be treated as a noisy observation of species. (3) When using mark-recapture methods, there may be uncertainty about whether or not detected animals are recaptures of previously-detected animals. There is usually ancillary data available that can act as predictors of recapture status (for example, two things detected close together in space are more likely to be recaptures than things detected far apart). In all these cases, the status of at least some responses (age/species/recapture status/etc.) is unknown, but predictors of status are available. This project will involve the development of methods for density and abundance estimation when key responses are observed with error. The intention is to focus on distance sampling and capture-recapture survey methods but there is scope to consider other methods as well. Methods will be developed in the context of specific survey datasets, a wide range of which are available to choose from.

Potential applicants are encouraged to contact the Postgraduate Officer responsible for PhDs in Statistics, in advance of making a formal application. He is: Len Thomas, email [email protected]

To make a formal application, complete the appropriate online form at http://www.st-andrews.ac.uk/admissions/pg/apply/research/ (click on “Apply Now” on that page). You also need to provide the following supporting documentation: CV, evidence of qualifications and evidence of English language (if applicable). You are welcome to include a covering letter. You don’t need to provide a research proposal or a sample of academic written work. You will need to ask two referees to provide academic references for you – once you fill in their name on the form, they will be sent emails asking them to upload their references. Please note that we give serious consideration to both the stature of your referees and the remarks that they make about you. More details about the application procedure are given at http://www.st-andrews.ac.uk/admissions/pg/apply/research/

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 scientific discipline with a substantial numerical component. Applicants with degrees in other subjects, such as biology, are invited to discuss their qualifications with the Postgraduate Officer. A masters-level degree is an advantage.

Many details of the general requirements and admissions procedure are given at the university web site http://www.st-andrews.ac.uk/admissions/pg/apply/research/

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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