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Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors

Griffin, Jim E., Matechou, Eleni, Buxton, Andrew S., Bormpoudakis, Dimitrios, Griffiths, Richard A. (2019) Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors. Journal of the Royal Statistical Society: Series C (Applied Statistics), . ISSN 0035-9254. (doi:10.1111/rssc.12390) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:78219)

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Language: English

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Environmental DNA (eDNA) is a survey tool with rapidly expanding applications for assessing presence of a species at surveyed sites. eDNA methodology is known to be prone to false negative and positive errors at the data collection and laboratory analysis stage. Existing models for eDNA data require augmentation with additional sources of information to overcome identifiability issues of the likelihood function and do not account for environmental covariates that predict the probability of species presence or the proba-bilities of error. We present a novel Bayesian model for analysing eDNA data by proposing informative prior distributions for logistic regression coefficients that allow us to overcome parameter identifiability, while performing efficient Bayesian model-selection. Our methodology does not require the use of trans-dimensional algorithms and provides a general framework for performing Bayesian variable selection under informative prior distributions in logistic regression models.

Item Type: Article
DOI/Identification number: 10.1111/rssc.12390
Uncontrolled keywords: Informative prior distributions, known presences, likelihood symmetries, logistic regression, occupancy probability, Polya-Gamma scheme
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Social Sciences > School of Anthropology and Conservation
Depositing User: Eleni Matechou
Date Deposited: 04 Nov 2019 12:26 UTC
Last Modified: 14 Feb 2020 15:48 UTC
Resource URI: (The current URI for this page, for reference purposes)
Matechou, Eleni:
Buxton, Andrew S.:
Griffiths, Richard A.:
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