Centre for Research into Ecological and Environmental Modelling and School of Mathematics and Statistics, The Observatory, University of St Andrews, St Andrews, Fife KY16 9LZ, UK. e-mail: email@example.com
A UNIFIED FRAMEWORK FOR MODELLING WILDLIFE POPULATION DYNAMICS†
Article first published online: 24 FEB 2005
Australian & New Zealand Journal of Statistics
Volume 47, Issue 1, pages 19–34, March 2005
How to Cite
Thomas, L., Buckland, S. T., Newman, K. B. and Harwood, J. (2005), A UNIFIED FRAMEWORK FOR MODELLING WILDLIFE POPULATION DYNAMICS. Australian & New Zealand Journal of Statistics, 47: 19–34. doi: 10.1111/j.1467-842X.2005.00369.x
Acknowledgments. The authors thank Byron Morgan, Steve Brooks, Carmen Fernandez, Dave Elston and Simon Wood for helpful discussions on this work, and two anonymous referees for their comments on the paper.
This paper was presented at the 4th Conference on Statistics in Ecology and Environmental Monitoring, ‘Population dynamics: the interface between models and data’, 9–12 December 2002, University of Otago, Dunedin, New Zealand.
- Issue published online: 24 FEB 2005
- Article first published online: 24 FEB 2005
- Received February 2003; revised August 2003; accepted August 2003.
- Cited By
- auxiliary particle filter;
- grey seals;
- Halichoerus grypus;
- nonlinear stochastic matrix models;
- sequential importance sampling;
- state-space models;
- wildlife conservation and management
This paper proposes a unified framework for defining and fitting stochastic, discrete-time, discrete-stage population dynamics models. The biological system is described by a state-space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources of uncertainty in the inputs, including uncertainty about model specification, are readily incorporated. The paper shows how the state process can be represented as a generalization of the standard Leslie or Lefkovitch matrix. By dividing the state process into subprocesses, complex models can be constructed from manageable building blocks. The paper illustrates the approach with a model of the British grey seal metapopulation, using sequential importance sampling with kernel smoothing to fit the model.