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Fast Bayesian inference for large occupancy datasets.

Authors :
Diana, Alex
Dennis, Emily Beth
Matechou, Eleni
Morgan, Byron John Treharne
Source :
Biometrics; Sep2023, Vol. 79 Issue 3, p2503-2515, 13p
Publication Year :
2023

Abstract

In recent years, the study of species' occurrence has benefited from the increased availability of large‐scale citizen‐science data. While abundance data from standardized monitoring schemes are biased toward 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 Pólya‐Gamma 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 neighbor 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0006341X
Volume :
79
Issue :
3
Database :
Complementary Index
Journal :
Biometrics
Publication Type :
Academic Journal
Accession number :
171903153
Full Text :
https://doi.org/10.1111/biom.13816