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ToolsDiana, Alex, Matechou, Eleni, Griffin, Jim, Yu, Douglas W., Luo, Mingjie, Tosa, Marie, Bush, Alex and Griffiths, Richard (2024) eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring. Journal of the American Statistical Association. ISSN 0162-1459
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Abstract
DNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingly being adopted for biodiversity monitoring and decision-making. The most commonly employed method, metabarcoding, combines PCR with high-throughput DNA sequencing to amplify and read “DNA barcode” sequences, generating count data indicating the number of times each DNA barcode was read. However, DNA-based data are noisy and error-prone, with several sources of variation, and cannot alone estimate the species-specific amount of DNA present at a surveyed site (DNA biomass). In this article, we present a unifying modeling framework for DNA-based survey data that allows estimation of changes in DNA biomass within species, across sites and their links to environmental covariates, while for the first time simultaneously accounting for key sources of variation, error and noise in the data-generating process, and for between-species and between-sites correlation. Bayesian inference is performed using MCMC with Laplace approximations. We describe a re-parameterization scheme for crossed-effects models designed to improve mixing, and an adaptive approach for updating latent variables, which reduces computation time. Theoretical and simulation results are used to guide study design, including the level of replication at different survey stages and the use of quality control methods. Finally, we demonstrate our new framework on a dataset of Malaise-trap samples, quantifying the effects of elevation and distance-to-road on each species, and produce maps identifying areas of high biodiversity and species DNA biomass. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
| Item Type: | Article |
|---|---|
| Additional Information: | Data Availability Statement: The sequence data, bioinformatic scripts, and the three sample by species tables and environmental covariates are archived on DataDryad at doi.org/10.5061/dryad.4f4qrfjjb. Acknowledgments: The work was funded by NERC project NE/T010045/1 “Integrating new statistical frameworks into eDNA survey and analysis at the landscape scale” and benefited from the sCom Working Group at iDiv.de. DWY and MJL were supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20050202, the Key Research Program of Frontier Sciences, CAS (QYZDY-SSW-SMC024), the State Key Laboratory of Genetic Resources and Evolution (GREKF19-01, GREKF20-01, GREKF21-01) at the Kunming Institute of Zoology, and the University of Chinese Academy of Sciences. |
| Uncontrolled Keywords: | crossed-effects model,environmental dna,joint species distribution modeling,observation error,occupancy modeling,statistics and probability,statistics, probability and uncertainty ,/dk/atira/pure/subjectarea/asjc/2600/2613 |
| Faculty \ School: | Faculty of Science > School of Biological Sciences |
| UEA Research Groups: | Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation Faculty of Science > Research Groups > Organisms and the Environment |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 30 Sep 2024 15:30 |
| Last Modified: | 03 Jan 2025 01:03 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/96834 |
| DOI: | 10.1080/01621459.2024.2412362 |
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