Embedding population dynamics models in inference.

Stephen Terrence Buckland, Kenneth Brian Newman, C Fernandez, Len Thomas, John Harwood

Research output: Contribution to journalArticlepeer-review

104 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)44-58
Number of pages15
JournalStatistical Science
Volume22
Issue number1
DOIs
Publication statusPublished - Feb 2007

Keywords

  • hidden process models
  • filtering
  • Kalman filter
  • matrix population models
  • Markov chain Monte Carlo
  • particle filter
  • sequential importance sampling
  • state-space models
  • CAPTURE-RECAPTURE DATA
  • IMPORTANCE RESAMPLING ALGORITHM
  • STOCK ASSESSMENT
  • BAYESIAN-ESTIMATION
  • BOWHEAD WHALES
  • TIME-SERIES
  • AGE DATA
  • FRAMEWORK
  • ABUNDANCE
  • SALMON

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