Dennis, Emily B., Diana, Alex, Matechou, Eleni, Morgan, Byron J. T. (2024) Efficient statistical inference methods for assessing changes in species’ populations using citizen science data. Journal of the Royal Statistical Society: Series A (Statistics in Society), . ISSN 0964-1998. E-ISSN 1467-985X. (In press) (doi:10.1093/jrsssa/qnae105) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:107274)
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Official URL: https://doi.org/10.1093/jrsssa/qnae105 |
Abstract
The global decline of biodiversity, driven by habitat degradation and climate breakdown, is a significant concern.
Accurate measures of change are crucial to provide reliable evidence of species’ population changes. Meanwhile
citizen science data have witnessed a remarkable expansion in both quantity and sources and serve as the
foundation for assessing species’ status. The growing data reservoir presents opportunities for novel and improved
inference but often comes with computational costs: computational efficiency is paramount, especially as regular
analysis updates are necessary. Building upon recent research, we present illustrations of computationally efficient
methods for fitting new models, applied to three major citizen science data sets for butterflies. We extend a
method for modelling abundance changes of seasonal organisms, firstly to accommodate multiple years of
count data efficiently, and secondly for application to counts from a snapshot mass-participation survey. We
also present a variational inference approach for fitting occupancy models efficiently to opportunistic citizen
science data. The continuous growth of citizen science data offers unprecedented opportunities to enhance our
understanding of how species respond to anthropogenic pressures. Efficient techniques in fitting new models are
vital for accurately assessing species’ status, supporting policy-making, setting measurable targets, and enabling
effective conservation efforts.
Item Type: | Article |
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DOI/Identification number: | 10.1093/jrsssa/qnae105 |
Additional information: | For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. |
Uncontrolled keywords: | biodiversity change; citizen science; concentrated likelihood; generalised abundance index; occupancy models; variational bayes |
Subjects: | Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Funders: |
University of Kent (https://ror.org/00xkeyj56)
Natural Environment Research Council (https://ror.org/02b5d8509) |
Depositing User: | Eleni Matechou |
Date Deposited: | 19 Sep 2024 12:40 UTC |
Last Modified: | 23 Oct 2024 15:38 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/107274 (The current URI for this page, for reference purposes) |
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