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Modelling population dynamics from detection survey data

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  • Full or part time
    Prof Len Thomas
    Dr R Glennie
  • Application Deadline
    Applications accepted all year round

Project Description

Ecologists collect data on wild animal populations by a variety of survey methods and infer from these how the population changes over time through recruitment, survival, and movement processes. Conceptually, the population size is a hidden quantity that varies over space and time, and data are observed that depends on this hidden quantity and how it changes. Capture-recapture surveys (marked surveys), such as photo ID or camera trapping, can provide long-term data on individuals in the population; while count data or distance sampling surveys (unmarked surveys) provide information on the population level.

In this project, statistical methods would be developed toward the following aims:

1. Improve the modelling of population dynamics from spatial capture-recapture (SCR) data. Photo ID and camera trapping, in particular, can provide long-term data on individuals in a population. This aim would concentrate on developing open population SCR models suited to these survey methods allowing for animal movement, age-structured demographics, and spatially-varying demographics.

2. Integrate data across multiple sources. Data can be collected from breeding surveys, capture-recapture surveys, point counts, citizen science, and distance sampling. Integrated population models (IPMs) are a framework to allow for data from multiple sources to inform a common population process; however, the current methods to fit these models are limited by computation time. Here, the aim is to develop efficient computational methods using hidden Markov models to improve computation time and, therefore, improve model flexibility.

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 http://tinyurl.com/StAndStatsPhD

References

Buckland, S.T., Newman, K.B., Fernández, C., Thomas, L. and Harwood, J., 2007.Embedding population dynamics models in inference. Statistical Science, pp.44-58.
Chandler, R.B., Hepinstall-Cymerman, J., Merker, S., Abernathy-Conners, H. and Cooper, R.J., 2018. Characterizing spatio-temporal variation in survival and recruitment with integrated population models. The Auk, 135(3), pp.409-426.
Ergon, T. and Gardner, B., 2014. Separating mortality and emigration: modelling space use, dispersal and survival with robust‐design spatial captureÔÇôrecapture data. Methods in Ecology and Evolution, 5(12), pp.1327-1336.
Glennie, R., Borchers, D.L., Murchie, M., Harmsen, B. and Foster, R., 2018. Open population maximum likelihood spatial capture-recapture. Pre-print, URI: http://hdl.handle.net/10023/11758.
Newman, K.B., Buckland, S.T., Morgan, B., King, R., Borchers, D.L., Cole, D., Besbeas, P., Gimenez, O. and Thomas, L., 2014. Modelling population dynamics. New York, NY, USA: Springer.

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FTE Category A staff submitted: 30.60

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