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Related Experiment Videos

A Bayesian state-space formulation of dynamic occupancy models.

J Andrew Royle1, Marc Kéry

  • 1U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA. aroyle@usgs.gov

Ecology
|July 25, 2007
PubMed
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Dynamic occupancy models separate species occurrence from detection probability for accurate conservation assessments. This new hierarchical approach offers flexible, precise estimates of extinction and colonization, crucial for understanding metapopulation dynamics and species distributions.

Area of Science:

  • Ecology
  • Biogeography
  • Conservation Biology

Background:

  • Species occurrence and its dynamics (extinction, colonization) are key in biogeography and conservation.
  • Estimating these dynamics separately from detection probability is crucial to avoid bias from non-detection.
  • Dynamic occupancy models are of significant theoretical and practical interest.

Purpose of the Study:

  • To describe a novel hierarchical parameterization for dynamic occupancy models.
  • To demonstrate the flexibility and extensibility of this state-space approach.
  • To highlight the benefits of finite sample inference for precise parameter estimation.

Main Methods:

  • A hierarchical parameterization analogous to time series state-space models was developed.

Related Experiment Videos

  • The model consists of two components: partially observable occupancy and conditional observations.
  • The approach was applied to two case studies using R and WinBUGS software.

    • Main Results:

    • Dynamic parameters for European Crossbill showed significant annual variation, indicating irruptive population dynamics.
    • Cerulean Warbler analysis revealed low turnover and a stable distribution, contrasting with abundance count data.
    • The model identified declining patch survival and increasing turnover at the edge of the Cerulean Warbler's range.

    Conclusions:

    • The described dynamic occupancy models provide a flexible framework for studying species distributions and range dynamics.
    • Finite sample inference using the state-space representation yields more precise estimates.
    • The models offer valuable insights into metapopulation dynamics and the inertia of occupancy relative to abundance.