Environmental DNA reveals that rivers are conveyer belts of biodiversity information

Nature Communications

Volumn 7, Issue , 2016, Pages

  Deiner, Kristy ^a,b   Fronhofer, Emanuel A ^a,c   Mächler, Elvira ^a,c   Walser, Jean Claude ^d   Altermatt, Florian ^a,c  

^a SWISS FEDERAL INSTITUTE OF AQUATIC SCIENCE AND TECHNOLOGY   (Switzerland)

^b UNIVERSITY OF NOTRE DAME   (United States)

^c UNIVERSITY OF ZURICH   (Switzerland)

^d ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

DNA;

BIODIVERSITY; BIOME; CATCHMENT; COMMUNITY DYNAMICS; DATA ACQUISITION; DNA; ECOLOGICAL THEORY; ECOSYSTEM MODELING; EMPIRICAL ANALYSIS; EUKARYOTE; GENETIC ANALYSIS; RIVER; TERRESTRIAL ENVIRONMENT;

AQUATIC ENVIRONMENT; ARTICLE; BIODIVERSITY; COMMUNITY ASSESSMENT; ECOLOGICAL GENETICS; EMPIRICAL RESEARCH; EUKARYOTE; IN VITRO STUDY; LANDSCAPE; MACROINVERTEBRATE; NONHUMAN; RIVER; TERRESTRIAL SPECIES; DNA BARCODING; ENVIRONMENTAL MONITORING; GENETICS; PROCEDURES; VALIDATION STUDY;

EUKARYOTA;

BIODIVERSITY; DNA; DNA BARCODING, TAXONOMIC; ENVIRONMENTAL MONITORING; EUKARYOTA; RIVERS;

EID: 84984910785     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms12544     Document Type: Article

References (52)

1. Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555-561 (2010).
2. Willett, S. D., McCoy, S. W., Perron, J. T., Goren, L. & Chen, C.-Y. Dynamic reorganization of river basins. Science 343, 1248765 (2014).
3. Rodríguez-Iturbe, I. & Rinaldo, A. Fractal River Basins: chance and Self-Organization (Cambridge University Press, 1997).
4. Clift, P. D. & Blusztajn, J. Reorganization of the western Himalayan river system after five million years ago. Nature 438, 1001-1003 (2005).
5. Ward, N. D. et al. Degradation of terrestrially derived macromolecules in the Amazon River. Nat. Geosci. 6, 530-533 (2013).
6. Altermatt, F. Diversity in riverine metacommunities: a network perspective. Aquat. Ecol. 47, 365-377 (2013).
7. Mari, L., Casagrandi, R., Bertuzzo, E., Rinaldo, A. & Gatto, M. Metapopulation persistence and species spread in river networks. Ecol. Lett. 17, 426-434 (2014).
8. Deiner, K. & Altermatt, F. Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9, e88786 (2014).
9. Turner, C. R. et al. Particle size distribution and optimal capture of aqueous macrobial eDNA. Methods Ecol. Evol. 5, 676-684 (2014).
10. Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789-1793 (2012).
11. Ji, Y. et al. Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecol. Lett. 16, 1245-1257 (2013).
12. Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358-367 (2014).
13. Cristescu, M. E. From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity. Trends Ecol. Evol. 29, 566-571 (2014).
14. Goldberg, C. S., Strickler, K. M. & Pilliod, D. S. Moving environmental DNA methods from concept to practice for monitoring aquatic macroorganisms. Biol. Conserv. 183, 1-3 (2015).
15. Kelly, R. P. et al. Harnessing DNA to improve environmental management. Science 344, 1455-1456 (2014).
16. Lawson Handley, L. How will the 'molecular revolution' contribute to biological recording? Biol. J. Linn. Soc. 115, 750-766 (2015).
17. Rees, H. C., Maddison, B. C., Middleditch, D. J., Patmore, J. R. & Gough, K. C. REVIEW: The detection of aquatic animal species using environmental DNA-a review of eDNA as a survey tool in ecology. J. Appl. Ecol. 51, 1450-1459 (2014).
18. Laramie, M. B., Pilliod, D. S. & Goldberg, C. S. Characterizing the distribution of an endangered salmonid using environmental DNA analysis. Biol. Conserv. 183, 29-37 (2015).
19. Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853-858 (2000).
20. Noss, R. F. et al. How global biodiversity hotspots may go unrecognized: lessons from the North American Coastal Plain. Divers. Distrib. 21, 236-244 (2015).
21. Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379-391 (2001).
22. Barbour, M. T., Gerritsen, J., Snyder, B. & Stribling, J. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates, and fish. USEPA 339, 1-340 (1999).
23. Stucki, P. Methoden zur Untersuchung und Beurteilung der Fliessgewässer: Makrozoobenthos-Stufe F (Bundesamt für Umwelt-Vollzug, 2010).
24. Altermatt, F., Seymour, M. & Martinez, N. River network properties shape αdiversity and community similarity patterns of aquatic insect communities across major drainage basins. J. Biogeogr. 40, 2249-2260 (2013).
25. Jane, S. F. et al. Distance, flow and PCR inhibition: eDNA dynamics in two headwater streams. Mol. Ecol. Resour. 15, 216-227 (2015).
26. Sheldon, F. et al. Identifying the spatial scale of land use that most strongly influences overall river ecosystem health score. Ecol. Appl. 22, 2188-2203 (2012).
27. Stein, E. D., Martinez, M. C., Stiles, S., Miller, P. E. & Zakharov, E. V. Is DNA barcoding actually cheaper and faster than traditional morphological methods: results from a survey of freshwater bioassessment efforts in the United States? PLoS ONE 9, e95525 (2014).
28. Heino, J. et al. A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels. Ecol. Evol. 5, 1235-1248 (2015).
29. Sundermann, A., Stoll, S. & Haase, P. River restoration success depends on the species pool of the immediate surroundings. Ecol. Appl. 21, 1962-1971 (2011).
30. Strickler, K. M., Fremier, A. K. & Goldberg, C. S. Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol. Conserv. 183, 85-92 (2015).
31. Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: not a perfect match. Biol. Lett. 10, 20140562 (2014).
32. Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PeerJ PrePrints 3, e1258 (2015).
33. Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294-299 (1994).
34. Ivanova, N. V., Zemlak, T. S., Hanner, R. H. & Hebert, P. D. Universal primer cocktails for fish DNA barcoding. Mol. Ecol. Notes 7, 544-548 (2007).
35. Moszczynska, A., Locke, S. A., McLaughlin, J. D., Marcogliese, D. J. & Crease, T. J. Development of primers for the mitochondrial cytochrome c oxidase I gene in digenetic trematodes (Platyhelminthes) illustrates the challenge of barcoding parasitic helminths. Mol. Ecol. Resour. 9, 75-82 (2009).
36. Zimmermann, J., Glöckner, G., Jahn, R., Enke, N. & Gemeinholzer, B. Metabarcoding vs. morphological identification to assess diatom diversity in environmental studies. Mol. Ecol. Resour. 15, 526-542 (2015).
37. Gibson, J. et al. Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics. Proc. Natl Acad. Sci. 111, 8007-8012 (2014).
38. Drummond, A. J. et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. GigaScience 4, 1 (2015).
39. Stribling, J. B., Pavlik, K. L., Holdsworth, S. M. & Leppo, E. W. Data quality, performance, and uncertainty in taxonomic identification for biological assessments. J. N. Am. Benthol. Soc. 27, 906-919 (2008).
40. Haase, P., Pauls, S. U., Schindehütte, K. & Sundermann, A. First audit of macroinvertebrate samples from an EU Water Framework Directive monitoring program: human error greatly lowers precision of assessment results. J. N. Am. Benthol. Soc. 29, 1279-1291 (2010).
41. Ratnasingham, S. & Hebert, P. D.N. bold: the barcode of life data system. Mol. Ecol. Notes 7, 355-364 (2007).
42. AWEL. Zürcher Gewässer 2012: Entwicklung - Zustand - Ausblick. Amt für Abfall, Wasser, Energie und Luft, Zürich, Switzerland. Available at http://www.awel.zh.ch/internet/baudirektion/awel/de/wasser/gewaesserschutz/wasserqualitaet.html (2012).
43. Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Conserv. 183, 53-63 (2015).
44. SeqPrep. Available at https://github.com/jstjohn/SeqPrep (2011).
45. Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863-864 (2011).
46. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460-2461 (2010).
47. Benson, D. A. et al. GenBank. Nucleic Acids Res. 40, D48-D53 (2012).
48. Deiner, K., Knapp, R. A., Boiano, D. M. & May, B. Increased accuracy of species lists developed for alpine lakes using morphology and cytochrome oxidase I for identification of specimens. Mol. Ecol. Resour. 13, 820-831 (2013).
49. Mächler, E., Deiner, K., Steinmann, P. & Altermatt, F. Utility of Environmental DNA for Monitoring Rare and Indicator Macroinvertebrate Species. Freshw. Sci. 33, 1174-1183 (2014).
50. QGIS Geographic Information System. Available at http://qgis.osgeo.org (Open Source Geospatial Foundation Project, 2015).
51. Cardoso, P., Borges, P. A. & Veech, J. A. Testing the performance of beta diversity measures based on incidence data: the robustness to undersampling. Divers. Distrib. 15, 1081-1090 (2009).
52. R: A language and environment for statistical computing. Version 3.0.1 (R Foundation for Statistical Computing, 2013).