Thông tin tài nguyên

Validating metabarcoding-based biodiversity assessments with multi-species occupancy models: A case study using coastal marine eDNA

pmc-PMC7082047.pdf · Phiên bản 1 · 17 trang

18
Bản gốc
Trang 16 · Đoạn 39Vị trí nguồn

38. R Core Team. R: A language and environment for statistical computing. R Found Stat Comput Vienna Austria. 2018. Available: https://www.R-project.org/ 39. Plummer M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proc 3rd Int Workshop Dsitributed Stat Comput. 2003. Available: https://www.ci.tuwien.ac.at/Conferences/ DSC-2003/ 40. Kellner K. jagUI: a wrapper around “rjags” to streamline “JAGS” analyses. 2018;R package version 1.5.0. Available: https://CRAN.R-project.org/package=jagsUI 41. Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat. 1998; 7: 434–455. https://doi.org/10.2307/1390675 42. Kruschke JK. Doing Bayesian Data Analysis. 2nd ed. London: Academic Press; 2015. 43. Meredith M, Kruschke J. HDInterval: Highest (Posterior) Density Intervals. R Package Version 020. 2018. Available: https://CRAN.R-project.org/package=HDInterval 44. Leray M, Knowlton N. Censusing marine eukaryotic diversity in the twenty-first century. Philos Trans R Soc B Biol Sci. 2016; 371: 20150331. https://doi.org/10.1098/rstb.2015.0331 PMID: 27481783 45. Sigsgaard EE, Nielsen IB, Carl H, Krag MA, Knudsen SW, Xing Y, et al. Seawater environmental DNA reflects seasonality of a coastal fish community. Mar Biol. 2017;164. https://doi.org/10.1007/s00227- 017-3147-4 46. Alberdi A, Aizpurua O, Gilbert MTP, Bohmann K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Mahon A, editor. Methods Ecol Evol. 2018; 9: 134–147. https://doi.org/10. 1111/2041-210X.12849

Trang 16 · Đoạn 40Vị trí nguồn

47. MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA. Estimating site occu- pancy rates when detection probabilities are less than one. Ecology. 2002; 83: 2248–2255. https://doi. org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 48. Evans NT, Li Y, Renshaw MA, Olds BP, Deiner K, Turner CR, et al. Fish community assessment with eDNA metabarcoding: effects of sampling design and bioinformatic filtering. Can J Fish Aquat Sci. 2017; 74: 1362–1374. https://doi.org/10.1139/cjfas-2016-0306 49. Kebschull JM, Zador AM. Sources of PCR-induced distortions in high-throughput sequencing data sets. Nucleic Acids Res. 2015; 43: e143. https://doi.org/10.1093/nar/gkv717 PMID: 26187991 50. Royle JA, Link WA. Generalized site occupancy model allowing for false positive and false negative errors. Ecology. 2006; 87: 835–841. https://doi.org/10.1890/0012-9658(2006)87[835:gsomaf]2.0.co;2 PMID: 16676527 51. Lahoz-Monfort JJ, Guillera-Arroita G, Tingley R. Statistical approaches to account for false-positive errors in environmental DNA samples. Mol Ecol Resour. 2016; 16: 673–685. https://doi.org/10.1111/ 1755-0998.12486 PMID: 26558345 52. Guillera-Arroita G, Lahoz-Monfort JJ, van Rooyen AR, Weeks AR, Tingley R. Dealing with false-positive and false-negative errors about species occurrence at multiple levels. McCrea R, editor. Methods Ecol Evol. 2017; 8: 1081–1091. https://doi.org/10.1111/2041-210X.12743 53. Ficetola GF, Taberlet P, Coissac E. How to limit false positives in environmental DNA and metabarcod-

Trang 16 · Đoạn 41Vị trí nguồn

ing? Mol Ecol Resour. 2016; 16: 604–607. https://doi.org/10.1111/1755-0998.12508 PMID: 27062589 54. Porter TM, Hajibabaei M. Automated high throughput animal CO1 metabarcode classification. Sci Rep. 2018; 8: 4226. https://doi.org/10.1038/s41598-018-22505-4 PMID: 29523803 55. Fonseca VG. Pitfalls in relative abundance estimation using eDNA metabarcoding. Mol Ecol Resour. 2018; 18: 923–926. https://doi.org/10.1111/1755-0998.12902 56. Lamb PD, Hunter E, Pinnegar JK, Creer S, Davies RG, Taylor MI. How quantitative is metabarcoding: A meta-analytical approach. Mol Ecol. 2019; 28: 420–430. https://doi.org/10.1111/mec.14920 PMID: 30408260 57. Hall K, MacLeod CD, Mandleberg L, Schweder-Goad CM, Bannon SM, Pierce GJ. Do abundance– occupancy relationships exist in cetaceans? J Mar Biol Assoc U K. 2010; 90: 1571–1581. https://doi. org/10.1017/S0025315410000263 58. Miranda LE, Killgore KJ. Abundance–occupancy patterns in a riverine fish assemblage. Freshw Biol. 2019; 64: 2221–2233. https://doi.org/10.1111/fwb.13408 59. Habel JC, Trusch R, Schmitt T, Ochse M, Ulrich W. Long-term large-scale decline in relative abun- dances of butterfly and burnet moth species across south-western Germany. Sci Rep. 2019;9. https:// doi.org/10.1038/s41598-018-36956-2 60. Steffen W, Broadgate W, Deutsch L, Gaffney O, Ludwig C. The trajectory of the Anthropocene: The great acceleration. Anthr Rev. 2015; 2: 81–98. https://doi.org/10.1177/2053019614564785 PLOS ONE Multi-species occupancy models for metabarcoding PLOS ONE | https://doi.org/10.1371/journal.pone.0224119 March 19, 2020 16 / 17