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
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
61. IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Sci- ence-Policy Platform on Biodiversity and Ecosystem Services. Bonn, Germany: IPBES Secretariat; 2019. 62. Ji Y, Ashton L, Pedley SM, Edwards DP, Tang Y, Nakamura A, et al. Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Holyoak M, editor. Ecol Lett. 2013; 16: 1245–1257. https://doi.org/10.1111/ele.12162 PMID: 23910579 63. Lacoursière-Roussel A, Howland K, Normandeau E, Grey EK, Archambault P, Deiner K, et al. eDNA metabarcoding as a new surveillance approach for coastal Arctic biodiversity. Ecol Evol. 2018; 8: 7763– 7777. https://doi.org/10.1002/ece3.4213 PMID: 30250661 64. Cristescu ME, Hebert PDN. Uses and misuses of environmental DNA in biodiversity science and con- servation. Annu Rev Ecol Evol Syst. 2018; 49: 209–230. https://doi.org/10.1146/annurev-ecolsys- 110617-062306 65. Garcia M. Racist in the machine: The disturbing implications of algorithmic bias. World Policy J. 2016; 33: 111–117. https://doi.org/10.1215/07402775-3813015 66. Comte L, Grenouillet G. Species distribution modelling and imperfect detection: comparing occupancy versus consensus methods. Robertson M, editor. Divers Distrib. 2013; 19: 996–1007. https://doi.org/ 10.1111/ddi.12078 67. DeWan AA, Zipkin EF. An integrated sampling and analysis approach for improved biodiversity monitor- ing. Environ Manage. 2010; 45: 1223–1230. https://doi.org/10.1007/s00267-010-9457-7 PMID: 20237922 PLOS ONE Multi-species occupancy models for metabarcoding
PLOS ONE | https://doi.org/10.1371/journal.pone.0224119 March 19, 2020 17 / 17