Details of Grant 

EPSRC Reference:	GR/M76546/01
Title:	STOCHASTIC MODELLING OF WILDLIFE POPULATIONS
Principal Investigator:	Morgan, Professor B
Other Investigators:	Brooks, Professor SP
Researcher Co-Investigators:
Project Partners:	Inst of Terrestrial Ecology Institute of Arable Crops Res Zoological Soc London Inst of Zoology
Department:	Sch of Maths Statistics & Actuarial Sci
Organisation:	University of Kent
Scheme:	Standard Research (Pre-FEC)
Starts:	01 October 1999	Ends:	31 March 2003	Value (£):	41,376
EPSRC Research Topic Classifications:	Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:	Environment
Related Grants:	GR/M76157/01
Panel History:
Summary on Grant Application Form
Detailed studies of wildlife populations are, at the present time, of vital interest to ecologists interested in the impact of environmental and other factors on animal population dynamics. Such studies requires rare, long-term, individual life-history data and close collaboration between statisticians and ecologists. The proposed project builds upon established and successful collaboration of this kind as well as developing new collaborative relationships with scientists interested in modelling dynamics of this sort. Recent work by the investigators and co-workers has developed new methodology for the analysis of capture recapture and band return data on wildlife populations. This work has focussed upon the importance of including co-variate information in the corresponding analyses, and introduced both classical and Bayesian methodology. We are performing statistical analyses on some of the most important ecological data sets in the world and there still remains a great deal of work to be done. Novel methodology needs to be developed in order to undertake these analyses and the work involves the investigation and application of innovative classical techniques for model selection together with the simultaneous development of powerful computational Bayesian techniques capable of analysing realistically complex models, and of differentiating competing models for analysis.
Key Findings
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Potential use in non-academic contexts
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Further Information:
Organisation Website:	http://www.kent.ac.uk