Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River.

ABSTRACT:

Background

Hidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling approaches and software exist, including both maximum likelihood and Bayesian methods. The diversity of these methods obscures the underlying HMM and can exaggerate minor differences in parameterization.

Methods

In this paper, we describe a general framework for modelling multi-state capture-recapture data via HMMs using both maximum likelihood and Bayesian methods. We then apply an HMM to invasive silver carp telemetry data from the Illinois River and compare the results estimated by both methods.

Results

Our analysis demonstrates disadvantages of relying on a single approach and highlights insights obtained from implementin

SUBMITTER: Labuzzetta CJ 

PROVIDER: S-EPMC10775585 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature