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A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population

Diana, Alex, Griffin, Jim E., Matechou, Eleni (2019) A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population. In: Springer Proceedings in Mathematics & Statistics. BAYSM 2018: Bayesian Statistics and New Generations. 296. Springer (doi:10.1007/978-3-030-30611-3_1) (KAR id:72492)

Abstract

Many ecological sampling schemes do not allow for unique marking of individuals. Instead, only counts of individuals detected on each sampling occasion are available. In this paper, we propose a novel approach for modelling count data in an open population where individuals can arrive and depart from the site during the sampling period. A Bayesian nonparametric prior, known as Polya Tree, is used for modelling the bivariate density of arrival and departure times. Thanks to this choice, we can easily incorporate prior information on arrival and departure density while still allowing the model to flexibly adjust the posterior inference according to the observed data. Moreover, the model provides great scalability as the complexity does not depend on the population size but just on the number of sampling occasions, making it particularly suitable for data-sets with high numbers of detections. We apply the new model to count data of newts collected by the Durrell Institute of Conservation and Ecology, University of Kent.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1007/978-3-030-30611-3_1
Uncontrolled keywords: Bayesian nonparametrics, Polya Tree, Count Data, Statistical Ecology, SEAK
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: Eleni Matechou
Date Deposited: 14 Feb 2019 12:46 UTC
Last Modified: 21 Nov 2020 00:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72492 (The current URI for this page, for reference purposes)
Griffin, Jim E.: https://orcid.org/0000-0002-4828-7368
Matechou, Eleni: https://orcid.org/0000-0003-3626-844X
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