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Modelling and Movement by Interacting Individuals

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  • Full or part time
    Prof P Blackwell
    Dr J Pitchford
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Improvements in tracking technology mean that it is now often possible to monitor the movements of several individual animals in a wild population simultaneously, using GPS tags or collars, or similar devices. This creates exciting opportunities to learn about the way in which animals interact, and so improve our understanding of social structure, reproductive behaviour, potential transmission of disease, etc. At the moment, however, these opportunities are largely unexploited, as typically the analysis of movement data is carried out separately for each animal. Existing models of collective movement are largely theoretical and are generally not directly fitted to real data.

The aim of this project is to develop realistic models of the ways in which animals influence each others’ movement, and statistical methods to fit these models to data. The initial emphasis will be on how members of the same species interact in the wild, making use of datasets on badgers and wild boar collected by the National Wildlife Management Centre (NWMC), part of the UK government’s Animal and Plant Health Agency. As well as the general scientific interest, badgers are of interest because of concern about their role in the spread of bovine TB, and understanding wild boar behaviour is important because of the need to monitor and control their population.

The models and methods developed will build on recent work by the academic supervisors which apply ideas from Markov chains, diffusion processes and Bayesian statistics to movement modelling for a wide range of species. The student will spend one or more periods based at NWMC in Sand Hutton, near York, working closely with the applied supervisor and other scientists there.

This project would suit a student with a strong mathematical background, with skills in statistics, probability or computing, and an interest in - or willingness to learn about - wildlife ecology.

Details of some recent papers relevant to this project are given below.

Blackwell PG, Niu M, Lambert MS, LaPoint SD (2016) Exact Bayesian inference for animal movement in continuous time. Methods Ecol Evol 7:184-195.
Croft S, Budgey R, Pitchford JW, Wood AJ (2015) Obstacle avoidance in social groups: new insights from asynchronous models. J Roy Soc Interface 12:20150178.
Langrock, R, Hopcraft, JGC, Blackwell, PG, Goodall, V, King, R, Niu., M, Patterson, TA, Pedersen, MW, Skarin, A, Schick, RS (2014) Modelling group dynamic animal movement. Methods in Ecology and Evolution 5:190-199.
Niu M, Blackwell PG & Skarin A (2016) Modelling interdependent animal movement in continuous time. Biometrics 72:315-324.
Quy RJ, Massei G, Lambert MS, Coats J, Miller LA & Cowan DP (2014) Effects of a GnRH vaccine on the movement and activity of free-living wild boar (Sus scrofa). Wildlife Research 41:185-193

Funding Notes

Fully funded for a minimum of 3.5 years, studentships cover: (i) a tax-free stipend at the standard Research Council rate (at least £14,296 per annum for 2017-2018), (ii) research costs, and (iii) tuition fees at the UK/EU rate. Studentship(s) are available to UK and EU students who meet the UK residency requirements. Students from EU countries who do not meet residency requirements may still be eligible for a fees-only award.

References

This PhD project is part of the NERC funded Doctoral Training Partnership “ACCE” (Adapting to the Challenges of a Changing Environment). This is a partnership between the Universities of Sheffield, Liverpool, York and the Centre for Ecology and Hydrology.
Selection process: Shortlisting will take place as soon as possible after the closing date and successful applicants will be notified promptly. Shortlisted applicants will be invited for an interview to take place at the University of Sheffield the w/c 13th February 2017.


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