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Implementation options for DNA-based identification into ecological status assessment under the European Water Framework Directive

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Hering, Daniel, Borja, Angel, Jones, J.Iwan, Pont, Didier, Boets, Pieter, Bouchez, Agnes, Bruce, Kat, Drakare, Stina, Hänfling, Bernd, Kahlert, Maria, Leese, Florian, Meissner, Kristian, Mergen, Patricia, Reyjol, Yorick, Segurado, Pedro, Vogler, Alfried, Kelly, Martyn·2018·Water Research
DOI 10.1016/j.watres.2018.03.003
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Abstract

Assessment of ecological status for the European Water Framework Directive (WFD) is based on "Biological Quality Elements" (BQEs), namely phytoplankton, benthic flora, benthic invertebrates and fish. Morphological identification of these organisms is a time-consuming and expensive procedure. Here, we assess the options for complementing and, perhaps, replacing morphological identification with procedures using eDNA, metabarcoding or similar approaches. We rate the applicability of DNA-based identification for the individual BQEs and water categories (rivers, lakes, transitional and coastal waters) against eleven criteria, summarised under the headlines representativeness (for example suitability of current sampling methods for DNA-based identification, errors from DNA-based species detection), sensitivity (for example capability to detect sensitive taxa, unassigned reads), precision of DNA-based identification (knowledge about uncertainty), comparability with conventional approaches (for example sensitivity of metrics to differences in DNA-based identification), cost effectiveness and environmental impact. Overall, suitability of DNA-based identification is particularly high for fish, as eDNA is a well-suited sampling approach which can replace expensive and potentially harmful methods such as gill-netting, trawling or electrofishing. Furthermore, there are attempts to replace absolute by relative abundance in metric calculations. For invertebrates and phytobenthos, the main challenges include the modification of indices and completing barcode libraries. For phytoplankton, the barcode libraries are even more problematic, due to the high taxonomic diversity in plankton samples. If current assessment concepts are kept, DNA-based identification is least appropriate for macrophytes (rivers, lakes) and angiosperms/macroalgae (transitional and coastal waters), which are surveyed rather than sampled. We discuss general implications of implementing DNA-based identification into standard ecological assessment, in particular considering any adaptations to the WFD that may be required to facilitate the transition to molecular data.

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  1. Albaina, 2016, 18S rRNA V9 metabarcoding for diet characterization: a critical evaluation with two sympatric zooplanktivorous fish species, Ecol. Evol., 6, 1809, 10.1002/ece3.1986 · DOI
  2. Andújar, 2018, Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill, Mol. Ecol., 27, 146, 10.1111/mec.14410 · DOI
  3. Ärje, 2016, Understanding the statistical properties of the percent model affinity index can improve biomonitoring related decision making, Stoch. Environ. Res. Risk Assess., 30, 1981, 10.1007/s00477-015-1202-6 · DOI
  4. Ärje, 2017, The effect of automated taxa identification errors on biological indices, Expert Syst. Appl., 72, 108, 10.1016/j.eswa.2016.12.015 · DOI
  5. Avó, 2017, DNA barcoding and morphological identification of benthic nematodes assemblages of estuarine intertidal sediments: advances in molecular tools for biodiversity assessment, Front. Mar. Sci., 4, 10.3389/fmars.2017.00066 · DOI
  6. Aylagas, 2016, Benchmarking DNA metabarcoding for biodiversity-based monitoring and assessment, Front. Mar. Sci., 3, 10.3389/fmars.2016.00096 · DOI
  7. Baird, 2012, Biomonitoring 2.0: a new paradigm in ecosystem assessment made possible by next-generation DNA sequencing, Mol. Ecol., 21, 2039, 10.1111/j.1365-294X.2012.05519.x · DOI · Paper page
  8. Baldigo, 2017, Efficacy of environmental DNA to detect and quantify brook trout populations in headwater streams of the Adirondack Mountains, New York. Trans. Am. Fisheries Soc., 146, 99, 10.1080/00028487.2016.1243578 · DOI
  9. Barnes, 2015, The ecology of environmental DNA and implications for conservation genetics, Conserv. Genet., 17, 1, 10.1007/s10592-015-0775-4 · DOI · Paper page
  10. Bergsten, 2012, The effect of geographical scale of sampling on DNA barcoding, Syst. Biol., 61, 851, 10.1093/sysbio/sys037 · DOI
  11. Birk, 2012, Three hundred ways to assess Europe's surface waters: an almost complete overview of biological methods to implement the Water Framework Directive, Ecol. Indicat., 18, 31, 10.1016/j.ecolind.2011.10.009 · DOI · Paper page
  12. Birk, 2013, Intercalibrating classifications of ecological status: Europe's quest for common management objectives for aquatic ecosystems, Sci. Total Environ., 454–455, 490, 10.1016/j.scitotenv.2013.03.037 · DOI
  13. Bista, 2017, Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity, Nat. Commun., 8, 14087, 10.1038/ncomms14087 · DOI
  14. Blackman, 2017, Detection of a new non-native freshwater species by DNA metabarcoding of environmental samples - first record of Gammarus fossarum in the UK, Aquat. Invasions, 12, 177, 10.3391/ai.2017.12.2.06 · DOI
  15. Bohmann, 2014, Environmental DNA for wildlife biology and biodiversity monitoring, Trends Ecol. Evol., 29, 358, 10.1016/j.tree.2014.04.003 · DOI · Paper page
  16. Borja, 2013, Marine monitoring during an economic crisis: the cure is worse than the disease, Mar. Pollut. Bull., 68, 1, 10.1016/j.marpolbul.2013.01.041 · DOI
  17. Bourlat, 2013, Genomics in marine monitoring: new opportunities for assessing marine health status, Mar. Pollut. Bull., 74, 19, 10.1016/j.marpolbul.2013.05.042 · DOI · Paper page
  18. Carugati, 2015, Metagenetic tools for the census of marine meiofaunal biodiversity: an overview, Mar. Genom., 24, 11, 10.1016/j.margen.2015.04.010 · DOI
  19. Choo, 2017, Shotgun mitogenomics across body size classes in a local assemblage of tropical Diptera: phylogeny, species diversity and mitochondrial abundance spectrum, Mol. Ecol., 26, 5086, 10.1111/mec.14258 · DOI
  20. Civade, 2016, Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system, PLoS One, 10.1371/journal.pone.0157366 · DOI
  21. Clarke, 2006, Errors and uncertainty in bioassessment methods – major results and conclusions from the STAR project and their application using STARBUGS, Hydrobiologia, 566, 433, 10.1007/s10750-006-0079-2 · DOI
  22. Clarke, 2006, Effects of locally rare taxa on the precision and sensitivity of RIVPACS bioassessment of freshwaters, Freshw. Biol., 51, 1924, 10.1111/j.1365-2427.2006.01611.x · DOI
  23. Creer, 2016, The ecologist's field guide to sequence-based identification of biodiversity, Meth. Ecol. Evol., 7, 1008, 10.1111/2041-210X.12574 · DOI
  24. Cristescu, 2014, From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity, Trends Ecol. Evol., 29, 566, 10.1016/j.tree.2014.08.001 · DOI
  25. Dafforn, 2014, Chapter one - faster, higher and Stronger? The pros and cons of molecular faunal data for assessing ecosystem condition, Adv. Ecol. Res., 51, 1, 10.1016/B978-0-08-099970-8.00003-8 · DOI
  26. Danovaro, 2016, Implementing and innovating marine monitoring approaches for assessing marine environmental status, Front. Mar. Sci., 3, 10.3389/fmars.2016.00213 · DOI
  27. Darling, 2011, From molecules to management: adopting DNA-based methods for monitoring biological invasions in aquatic environments, Environ. Res., 111, 978, 10.1016/j.envres.2011.02.001 · DOI · Paper page
  28. De Jonge, 2006, Marine monitoring: its shortcomings and mismatch with the EU Water Framework Directive's objectives, Mar. Pollut. Bull., 53, 5, 10.1016/j.marpolbul.2005.11.026 · DOI
  29. Deiner, 2014, Transport distance of invertebrate environmental DNA in a natural river, PLoS One, 9, 10.1371/journal.pone.0088786 · DOI · Paper page
  30. Deiner, 2017, Environmental DNA metabarcoding: transforming how we survey animal and plant communities, Mol. Ecol., 2017, 1
  31. Deiner, 2016, Environmental DNA reveals that rivers are conveyer belts of biodiversity information, Nature Comms, 7, 12544, 10.1038/ncomms12544 · DOI · Paper page
  32. Deiner, 2015, Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA, Biol. Conserv., 183, 53, 10.1016/j.biocon.2014.11.018 · DOI · Paper page
  33. Dejean, 2012, Improved detection of an alien invasive species through environmental DNA barcoding: the example of the American bullfrog Lithobates catesbeianus, J. Appl. Ecol., 49, 953, 10.1111/j.1365-2664.2012.02171.x · DOI · Paper page
  34. EEA, 2012, European waters - assessment of status and pressures, EEA Report, 8, 100
  35. Elbrecht, 2017, Validation and development of COI metabarcoding primers for freshwater macroinvertebrate bioassessment, Front. Environ. Sci.
  36. Elbrecht, 2017, Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring, Methods Ecol Evol., 10.1111/2041-210X.12789 · DOI · Paper page
  37. Ferraro, 1989, Power-cost efficiency of eight macrobenthic sampling schemes in puget sound, Washington, USA, Can. J. Fish. Aquat. Sci., 46, 2157, 10.1139/f89-267 · DOI
  38. Ficetola, 2008, Species detection using environmental DNA from water samples, Biol. Lett., 4, 423, 10.1098/rsbl.2008.0118 · DOI
  39. Ficetola, 2015, Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data, Mol. Ecol. Resour., 15, 543, 10.1111/1755-0998.12338 · DOI · Paper page
  40. Foden, 2008, A critical review of approaches to aquatic environmental assessment, Mar. Pollut. Bull., 56, 1825, 10.1016/j.marpolbul.2008.08.017 · DOI
  41. Goodwin, 2017, DNA sequencing as a tool to monitor marine ecological status, Front. Mar. Sci., 10.3389/fmars.2017.00107 · DOI
  42. Grossmann, 2016, Trade-off between taxon diversity and functional diversity in European lake ecosystems, Mol. Ecol., 25, 5876, 10.1111/mec.13878 · DOI
  43. Haase, 2004, Development of a practical standardized protocol for macroinvertebrate sampling and sorting in streams, Limnologica, 34, 349, 10.1016/S0075-9511(04)80005-7 · DOI
  44. Haase, 2006, Assessing the impact of errors in sorting and identifying macroinvertebrate samples, Hydrobiologia, 566, 505, 10.1007/s10750-006-0075-6 · DOI
  45. Hallett, 2016, A review of Australian approaches for monitoring, assessing and reporting estuarine condition: I. International context and evaluation criteria, Environ. Sci. Pol., 66, 260, 10.1016/j.envsci.2016.07.014 · DOI
  46. Hanfling, 2016, Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods, Mol. Ecol., 25, 3101, 10.1111/mec.13660 · DOI · Paper page
  47. Hering, 2006, Cook book for the development of a Multimetric Index for biological condition of aquatic ecosystems: experiences from the European AQEM and STAR projects and related initiatives, Hydrobiologia, 566, 311, 10.1007/s10750-006-0087-2 · DOI
  48. Jane, 2015, Distance, flow, and PCR inhibition: eDNA dynamics in two headwater streams, Mol. Ecol. Resour., 15, 216, 10.1111/1755-0998.12285 · DOI · Paper page
  49. Ji, 2013, Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding, Ecol. Lett., 16, 1245, 10.1111/ele.12162 · DOI · Paper page
  50. Keck, 2017, Freshwater biomonitoring in the information age, Front. Ecol. Environ., 15, 266, 10.1002/fee.1490 · DOI
  51. Kelly, 2009, Uncertainty in ecological status assessments of lakes and rivers using diatoms, Hydrobiologia, 633, 5, 10.1007/s10750-009-9872-z · DOI
  52. Kelly, 2014, Using environmental DNA to census marine fishes in a large mesocosm, PLoS One, 9, 10.1371/journal.pone.0086175 · DOI
  53. Kermarrec, 2014, A next-generation sequencing approach to river biomonitoring using benthic diatoms, Freshw. Sci., 33, 349, 10.1086/675079 · DOI
  54. Kiranyaz, 2011, Classification and retrieval on macroinvertebrate image databases, Comput. Biol. Med., 41, 463, 10.1016/j.compbiomed.2011.04.008 · DOI
  55. Klymus, 2017, Environmental DNA (eDNA) metabarcoding assays to detect invasive invertebrate species in the Great Lakes, PLoS One, 12, 10.1371/journal.pone.0177643 · DOI
  56. Klymus, 2015, Quantification of eDNA shedding rates from invasive bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix, Biol. Conserv., 183, 77, 10.1016/j.biocon.2014.11.020 · DOI
  57. Konopka, 2012, Application of meta-transcriptomics and -proteomics to analysis of in situ physiological state, Front. Microbiol., 18, 184
  58. Lawson Handley, 2015, How will the ‘molecular revolution’ contribute to biological recording?, Biol. J. Linn. Soc., 115, 750, 10.1111/bij.12516 · DOI
  59. Leese, 2016, DNAqua-Net: developing new genetic tools for bioassessment and monitoring of aquatic ecosystems in Europe, Res. Ideas and Outcomes, 2, 10.3897/rio.2.e11321 · DOI
  60. Leese, 2018, Chapter two - why we need sustainable networks bridging countries, disciplines, cultures and generations for aquatic biomonitoring 2.0: a perspective derived from the DNAqua-net COST action, Adv. Ecol. Res., 58, 63, 10.1016/bs.aecr.2018.01.001 · DOI
  61. Lyon, 2014, Efficiency of electrofishing in turbid lowland rivers: implications for measuring temporal change in fish populations, Can. J. Fish. Aquat. Sci., 71, 878, 10.1139/cjfas-2013-0287 · DOI
  62. Medinger, 2010, Diversity in a hidden world: potential and limitation of next generation sequencing for surveys of molecular diversity of eukaryotic microorganisms, Mol. Ecol., 19, 32, 10.1111/j.1365-294X.2009.04478.x · DOI
  63. Mohrbeck, 2015, High-throughput sequencing-the key to rapid biodiversity assessment of marine metazoa?, PLoS One, 10, 10.1371/journal.pone.0140342 · DOI
  64. Mondy, 2012, A new macroinvertebrate-based multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: a taxonomical and trait approach, Ecol. Indicat., 18, 452, 10.1016/j.ecolind.2011.12.013 · DOI
  65. Nathan, 2014, Quantifying environmental DNA signals for aquatic invasive species across multiple detection platforms, Environ. Sci. Technol., 48, 12800, 10.1021/es5034052 · DOI
  66. Nygård, 2016, Price versus value of marine monitoring, Front. Mar. Sci., 3, 10.3389/fmars.2016.00205 · DOI
  67. Patrício, 2016, European marine biodiversity monitoring networks: strengths, weaknesses, opportunities and threats, Front. Mar. Sci., 3, 10.3389/fmars.2016.00161 · DOI
  68. Pilliod, 2013, Estimating occupancy and abundance of stream amphibians using environmental DNA from filtered water samples, Can. J. Fish. Aquat. Sci., 70, 1123, 10.1139/cjfas-2013-0047 · DOI
  69. Pinol, 2014, A pragmatic approach to the analysis of diets of generalist predators: the use of next-generation sequencing with no blocking probes, Mol. Ecol. Resour., 14, 18, 10.1111/1755-0998.12156 · DOI
  70. Poikane, 2014, Intercalibration of aquatic ecological assessment methods in the European Union: lessons learned and way forward, Environ. Sci. Pol., 44, 237, 10.1016/j.envsci.2014.08.006 · DOI
  71. Pont, 2006, Assessing river biotic condition at the continental scale: a European approach using functional metrics and fish assemblages, J. Appl. Ecol., 43, 70, 10.1111/j.1365-2664.2005.01126.x · DOI
  72. Pont, D., Rocle, M., Valentini, A., Civade, R., Jean, P., Maire, A., Roset, N., Schabuss, M., Zornig, H. & Dejean, T., submitted. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Under review.
  73. Rees, 2014, The detection of aquatic animal species using environmental DNA – a review of eDNA as a survey tool in ecology, J. Appl. Ecol., 51, 1450, 10.1111/1365-2664.12306 · DOI · Paper page
  74. Schluter, 2014, Temporal and spatial variability of phytoplankton monitored by a combination of monitoring buoys, pigment analysis and fast screening microscopy in the Fehmarn Belt Estuary, Environ. Monit. Assess., 186, 5167, 10.1007/s10661-014-3767-9 · DOI
  75. Schmidt-Kloiber, 2015, An online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences, Ecol. Indicat., 53, 271, 10.1016/j.ecolind.2015.02.007 · DOI
  76. Shaw, 2016, Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system, Biol. Conserv., 197, 131, 10.1016/j.biocon.2016.03.010 · DOI
  77. Shokralla, 2012, Next-generation sequencing technologies for environmental DNA research, Mol. Ecol., 21, 1794, 10.1111/j.1365-294X.2012.05538.x · DOI · Paper page
  78. Sigsgaard, 2015, Monitoring the near-extinct European weather loach in Denmark based on environmental DNA from water samples, Biol. Conserv., 183, 46, 10.1016/j.biocon.2014.11.023 · DOI · Paper page
  79. Smart, 2016, Assessing the cost-efficiency of environmental DNA sampling, Meth. Ecol. Evol., 7, 1291, 10.1111/2041-210X.12598 · DOI
  80. Stoeckle, 2017, Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary, PLoS One, 12, 10.1371/journal.pone.0175186 · DOI
  81. Sweeney, 2011, Can DNA barcodes of stream macroinvertebrates improve descriptions of community structure and water quality?, J. N. Am. Benthol. Soc., 30, 195, 10.1899/10-016.1 · DOI · Paper page
  82. Taberlet, 2012, Environmental DNA, Mol. Ecol., 21, 1789, 10.1111/j.1365-294X.2012.05542.x · DOI · Paper page
  83. Taberlet, 2012, Towards next-generation biodiversity assessment using DNA metabarcoding, Mol. Ecol., 21, 2045, 10.1111/j.1365-294X.2012.05470.x · DOI · Paper page
  84. Takahara, 2012, Estimation of fish biomass using environmental DNA, PLoS One, 7, 10.1371/journal.pone.0035868 · DOI
  85. Thomsen, 2016, Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes, PLoS One, 11, 10.1371/journal.pone.0165252 · DOI
  86. Thomsen, 2012, Detection of a diverse marine fish fauna using environmental DNA from seawater samples, PLoS One, 7, 10.1371/journal.pone.0041732 · DOI · Paper page
  87. Valentini, 2016, Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding, Mol. Ecol., 25, 929, 10.1111/mec.13428 · DOI · Paper page
  88. Vasselon, 2017, Assessing ecological status with diatom DNA metabarcoding: scaling-up on a WFD monitoring network (Mayotte Island, France), Ecol. Indicat., 82, 1, 10.1016/j.ecolind.2017.06.024 · DOI
  89. Wilcox, 2016, Understanding environmental DNA detection probabilities: a case study using a stream-dwelling char Salvelinus fontinalis, Biol. Conserv., 194, 209, 10.1016/j.biocon.2015.12.023 · DOI · Paper page
  90. Yamamoto, 2017, Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea, Scie. Rep., 7, 40368, 10.1038/srep40368 · DOI · Paper page

Cited by

Metadata

Authors
Hering, D., Borja, A., Jones, J. I., Pont, D., Boets, P., Bouchez, A., Bruce, K., Drakare, S., Hänfling, B., Kahlert, M., Leese, F., Meissner, K., Mergen, P., Reyjol, Y., Segurado, P., Vogler, A., & Kelly, M.
Venue
Water Research
Publisher
Elsevier BV
Published
2018-07
Volume
138
Pages
192-205
ISSN
0043-1354 (ISSN-L)
DOI
10.1016/j.watres.2018.03.003
OpenAlex
W2793229774
Cited by
455
References
5
FWCI
32.05
Type
journal article
Language
en
Open access
hybrid
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
European Union (COST Action CA15219)

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