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February 2007 Embedding Population Dynamics Models in Inference
Stephen T. Buckland, Ken B. Newman, Carmen Fernández, Len Thomas, John Harwood
Statist. Sci. 22(1): 44-58 (February 2007). DOI: 10.1214/088342306000000673


Increasing pressures on the environment are generating an ever-increasing need to manage animal and plant populations sustainably, and to protect and rebuild endangered populations. Effective management requires reliable mathematical models, so that the effects of management action can be predicted, and the uncertainty in these predictions quantified. These models must be able to predict the response of populations to anthropogenic change, while handling the major sources of uncertainty. We describe a simple “building block” approach to formulating discrete-time models. We show how to estimate the parameters of such models from time series of data, and how to quantify uncertainty in those estimates and in numbers of individuals of different types in populations, using computer-intensive Bayesian methods. We also discuss advantages and pitfalls of the approach, and give an example using the British grey seal population.


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Stephen T. Buckland. Ken B. Newman. Carmen Fernández. Len Thomas. John Harwood. "Embedding Population Dynamics Models in Inference." Statist. Sci. 22 (1) 44 - 58, February 2007.


Published: February 2007
First available in Project Euclid: 1 August 2007

zbMATH: 1246.62225
MathSciNet: MR2408660
Digital Object Identifier: 10.1214/088342306000000673

Keywords: Filtering , Hidden process models , Kalman filter , Markov chain Monte Carlo , matrix population models , particle filter , sequential importance sampling , state-space models

Rights: Copyright © 2007 Institute of Mathematical Statistics


Vol.22 • No. 1 • February 2007
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