Modeling species occurrence dynamics with multiple states and imperfect detection

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.