Open population maximum likelihood spatial capture-recapture

Area of Science:

• Ecology and Wildlife Biology
• Population Dynamics
• Statistical Modeling

Background:

• Open population capture-recapture models are essential for estimating animal population demographics and abundance over time.
• Bayesian methods have been developed for spatial capture-recapture (SCR) in open populations, enabling estimation of effective sampling area and density.
• Existing methods often face computational challenges or limitations in accuracy.

Purpose of the Study:

• To formulate open population SCR as a hidden Markov model (HMM) for maximum likelihood inference.
• To apply the HMM approach to estimate survival and abundance of jaguars (Panthera onca) in Belize.
• To compare the HMM method with existing Bayesian and closed population models.

Main Methods:

• Formulation of open population SCR as a hidden Markov model (HMM).
• Inference using maximum likelihood for Cormack-Jolly-Seber and Jolly-Seber models, with and without activity center movement.
• Application to a 12-year jaguar survey data from Belize and simulation studies.

Main Results:

• The HMM approach provides accurate population density and survival estimates for jaguars.
• Assuming fixed activity centers leads to biased inference, while modeling activity center movement minimizes bias.
• The HMM method is computationally efficient and yields lower root-mean-square error than closed population models.

Conclusions:

• Hidden Markov models offer a computationally efficient and accurate alternative for open population spatial capture-recapture analysis.
• Accounting for animal movement (activity center changes) is crucial for unbiased inference in SCR studies.
• This HMM framework advances ecological modeling for wildlife population estimation.