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Fast Bayesian inference for large occupancy datasets

Diana, Alex, Dennis, Emily B., Matechou, Eleni, Morgan, Byron J. T. (2023) Fast Bayesian inference for large occupancy datasets. Biometrics, 79 (3). pp. 2503-2515. ISSN 0006-341X. E-ISSN 1541-0420. (doi:10.1111/biom.13816) (KAR id:98286)

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In recent years, the study of species’ occurrence has benefited from the increased availability of large-scale citizen-science data. Whilst abundance data from standardized monitoring schemes are biased towards well-studied taxa and locations, opportunistic data are available for many taxonomic groups, from a large number of locations and across long timescales. Hence, these data provide opportunities to measure species’ changes in occurrence, particularly

through the use of occupancy models, which account for imperfect detection. These opportunistic datasets can be substantially large, numbering hundreds of thousands of sites, and hence present a challenge from a computational perspective, especially within a Bayesian framework. In this paper, we develop a unifying framework for Bayesian inference in occupancy models that account for both spatial and temporal autocorrelation. We make use of the P´olyaGamma scheme, which allows for fast inference, and incorporate spatio-temporal random effects using Gaussian processes (GPs), for which we consider two efficient approximations: Subset of Regressors and Nearest neighbour GPs. We apply our model to data on two UK butterfly species, one common and widespread and one rare, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years. Our framework can be applied to a wide range of taxa, providing measures of variation in species’ occurrence, which are used to assess biodiversity change.

Item Type: Article
DOI/Identification number: 10.1111/biom.13816
Additional information: For the purpose of open access, the author has applied a CC BY public copyright licence (where permitted by UKRI, an Open Government Licence or CC BY ND public copyright licence may be used instead) to any Author Accepted Manuscript version arising
Uncontrolled keywords: Bayesian analysis; Biodiversity change; Citizen-science data; Occupancy models; P´olya-Gamma; Species distribution models.
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Funders: Natural Environment Research Council (
Depositing User: Eleni Matechou
Date Deposited: 23 Nov 2022 17:20 UTC
Last Modified: 01 Nov 2023 14:54 UTC
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