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Modeling species occurrence dynamics with multiple states and imperfect detection.

Darryl I MacKenzie1, James D Nichols, Mark E Seamans

  • 1Proteus Wildlife Research Consultants, P.O. Box 5193, Dunedin 9058, New Zealand. darryl@proteus.co.nz

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|April 4, 2009
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Summary
This summary is machine-generated.

Multistate occupancy modeling extends ecological studies beyond species distribution to include additional states like reproduction or disease. This approach enhances understanding of population dynamics and ecological processes, even with imperfect detection.

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Area of Science:

  • Ecology
  • Population Dynamics
  • Statistical Modeling

Background:

  • Occupancy modeling traditionally focuses on species distribution across space.
  • Recent extensions incorporate additional state variables (e.g., reproduction, disease presence, abundance categories) for richer ecological insights.
  • Imperfect detection introduces ambiguity in both species presence and state classification.

Purpose of the Study:

  • To unify and extend existing multistate occupancy modeling approaches.
  • To develop methods for estimating state transition probabilities using multi-season or multi-year data.
  • To demonstrate the application of these methods for addressing key ecological questions.

Main Methods:

  • Review and synthesize independently published multistate occupancy modeling approaches.
  • Extend pattern-based modeling to incorporate temporal dynamics (multiple seasons/years).
  • Employ maximum likelihood and Markov chain Monte Carlo (MCMC) estimation techniques.

Main Results:

  • Demonstrated the relationships between various multistate occupancy modeling frameworks.
  • Developed a methodology to estimate state transition probabilities, crucial for understanding system dynamics.
  • Successfully applied the methodology to real-world ecological data.

Conclusions:

  • Multistate occupancy modeling offers a powerful framework for ecological research, analogous to capture-recapture methods for individual animals.
  • This approach significantly enhances the ability to investigate complex ecological processes underlying population dynamics.
  • The methods provide valuable tools for studying species' ecological states and their temporal changes.