Efficient MCMC implementation of multi-state mark-recapture models

TL;DR

This paper introduces an efficient MCMC approach for multi-state mark-recapture models that incorporate individual heterogeneity, improving estimation accuracy and computational efficiency in ecological studies.

Contribution

It presents a novel combination of Beta-Binomial Gibbs sampling and an Independent Metropolis-Hastings sampler for hyper-parameters in mark-recapture models.

Findings

Simulation studies confirm convergence to true parameters.

Application to humpback whales reveals heterogeneity in sighting and site preference.

The methods improve estimation efficiency in ecological data analysis.

Abstract

Inherent differences in behaviour of individual animal movement can introduce bias into estimates of population parameters derived from mark-recapture data. Additionally, quantifying individual heterogeneity is of considerable interest in it's own right as numerous studies have shown how heterogeneity can drive population dynamics. In this paper we incorporate multiple measures of individual heterogeneity into a multi-state mark-recapture model, using a Beta-Binomial Gibbs sampler using MCMC estimation. We also present a novel Independent Metropolis-Hastings sampler which allows for efficient updating of the hyper-parameters which cannot be updated using Gibbs sampling. We tested the model using simulation studies and applied the model to mark-resight data of North Atlantic humpback whales observed in the Stellwagen Bank National Marine Sanctuary where heterogeneity is present in both sighting probability and site preference. Simulation studies show asymptotic convergence of the posterior distribution for each of the hyper-parameters to true parameter values. In application to humpback whales individual heterogeneity is evident in sighting probability and propensity to use the marine sanctuary.