Integrating count and detection-nondetection data to model population dynamics.

Area of Science:

• Ecology
• Population Dynamics
• Statistical Modeling

Background:

• Expanding spatial and temporal scales in ecological research necessitate integrated analytical frameworks.
• Current methods often focus on capture-recapture data, limiting analyses of unmarked populations.
• Studies on species distributions and trends frequently use data from unmarked individuals across broad scales.

Purpose of the Study:

• To present a novel modeling framework for integrating detection-nondetection and count data.
• To estimate population dynamics, abundance, and individual detection probabilities simultaneously.
• To provide a foundation for incorporating unmarked data into ecological analyses.

Main Methods:

• Developed a dynamic population model incorporating survival, reproduction, and immigration.
• Modeled the observation process assuming equal detection probability for individuals present at a site.
• Utilized simulations to assess the value of count versus detection-nondetection data.
• Applied the model to long-term Barred Owl data (detection-nondetection and count).

Main Results:

• Simulations demonstrated the relative contributions of different data types under various scenarios.
• The empirical example successfully combined historical and recent data for Barred Owl population analysis.
• The model effectively estimated population abundance and dynamics over time.

Conclusions:

• The framework provides a robust method for integrating diverse data types, including unmarked data.
• This approach enhances understanding of factors influencing population abundance.
• Applicable to survey design and incorporating historical or citizen science data.