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June 2021 A Bayesian semiparametric Jolly–Seber model with individual heterogeneity: An application to migratory mallards at stopover
Guohui Wu, Scott H. Holan, Alexis Avril, Jonas Waldenström
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Ann. Appl. Stat. 15(2): 813-830 (June 2021). DOI: 10.1214/20-AOAS1421

Abstract

We propose a Bayesian hierarchical Jolly–Seber model that can accommodate a semiparametric functional relationship between external covariates and capture probabilities, individual heterogeneity in departure due to an internal time-varying covariate and the dependence of arrival time on external covariates. Modelwise, we consider a stochastic process to characterize the evolution of the partially observable internal covariate that is linked to departure probabilities. Computationally, we develop a well-tailored Markov chain Monte Carlo algorithm that is free of tuning through data augmentation. Inferentially, our model allows us to make inference about stopover duration and population sizes, the impacts of various covariates on departure and arrival time and to identify flexible yet data-driven functional relationships between external covariates and capture probabilities. We demonstrate the effectiveness of our model through a motivating dataset collected for studying the migration of mallards (Anas platyrhynchos) in Sweden.

Funding Statement

This research was partially supported by the U.S. National Science Foundation (NSF) under NSF Grant SES-1853096. The duck surveillance data was supported by the Swedish Research Council (2011-3568, 2015-03877). This is contribution number 318 from Ottenby Bird Observatory.

Acknowledgments

The authors would like to thank the Editor, Thomas Brendan Murphy, Associate Editor and anonymous referees for providing valuable comments that have helped strengthen this manuscript.

Citation

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Guohui Wu. Scott H. Holan. Alexis Avril. Jonas Waldenström. "A Bayesian semiparametric Jolly–Seber model with individual heterogeneity: An application to migratory mallards at stopover." Ann. Appl. Stat. 15 (2) 813 - 830, June 2021. https://doi.org/10.1214/20-AOAS1421

Information

Received: 1 December 2019; Revised: 1 November 2020; Published: June 2021
First available in Project Euclid: 12 July 2021

Digital Object Identifier: 10.1214/20-AOAS1421

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.15 • No. 2 • June 2021
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