Fitting Population Dynamics Models to Count and Cull Data Using Sequential Importance Sampling.

VM Trenkel, DA Elston, Stephen Terrence Buckland

Research output: Other contribution

36 Citations (Scopus)

Abstract

For prudent wildlife management based on population dynamics models, it is important to incorporate parameter uncertainty into the management advice. Much parameter uncertainty originates when It Is not possible to parameterize the population management model for a population of interest using data from that population alone. Instead, information about parameter values obtained from other populations of the same species, or even from similar species, must be used. In addition, the age structure of wildlife populations is generally unknown. We show how sequential importance sampling can be used for combining information on demographic processes, obtained from closely studied populations, with aggregated count and cull information from the population to be managed. We resample parameter sets using kernel smoothing, which has the effect of perturbing parameter values. We show how the fitted model can be used to explore alternative culling strategies for red deer in Scotland.

Original languageEnglish
Volume95
Publication statusPublished - Jun 2000

Keywords

  • Bayesian filter
  • deer management models
  • kernel smoothing
  • state-space models
  • IMPORTANCE RESAMPLING ALGORITHM
  • GAUSSIAN STATE-SPACE
  • FEMALE RED DEER
  • MONTE-CARLO
  • POSTERIOR DISTRIBUTIONS
  • STOCK ASSESSMENT

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