Statistical Science

Embedding Population Dynamics Models in Inference

Stephen T. Buckland, Ken B. Newman, Carmen Fernández, Len Thomas, and John Harwood

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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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Statist. Sci. Volume 22, Number 1 (2007), 44-58.

First available in Project Euclid: 1 August 2007

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Hidden process models filtering Kalman filter matrix population models Markov chain Monte Carlo particle filter sequential importance sampling state-space models


Buckland, Stephen T.; Newman, Ken B.; Fernández, Carmen; Thomas, Len; Harwood, John. Embedding Population Dynamics Models in Inference. Statist. Sci. 22 (2007), no. 1, 44--58. doi:10.1214/088342306000000673.

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