Integrated animal movement and spatial capture-recapture models: Simulation, implementation, and inference.

Area of Science:

• Ecology
• Wildlife Biology
• Computational Biology

Background:

• Spatial capture-recapture (SCR) models are widely used for estimating wildlife population parameters.
• Traditional SCR models often rely on unrealistic assumptions about animal movement and space use.
• This limits their ability to address key ecological questions about behavior and habitat selection.

Purpose of the Study:

• To develop and evaluate advanced spatial capture-recapture (SCR) models that incorporate more realistic animal movement processes.
• To demonstrate the application of these models using standard SCR data and Bayesian analysis.
• To assess the performance of these integrated models under various simulation scenarios.

Main Methods:

• Developed novel SCR models integrating simple random walk, correlated random walk, and Langevin diffusion movement processes.
• Utilized data-augmented Bayesian analysis for model formulation, simulation, and fitting.
• Conducted simulation studies varying detection, movement, and resource selection parameters, sampling occasions, and auxiliary data.

Main Results:

• Integrated SCR movement models demonstrated good performance in estimating abundance, detection, and movement parameters.
• The Langevin model successfully recovered resource selection parameters, linking movement to habitat selection and density.
• Incorporating auxiliary telemetry data improved parameter estimates and model fitting for correlated random walk scenarios.

Conclusions:

• Advanced SCR models offer a flexible framework for linking animal movement behavior to population dynamics and distribution.
• These models can be extended to open populations and incorporate additional movement behaviors like territoriality.
• The developed methods provide a powerful tool for addressing complex ecological questions with standard SCR data.