Next‐generation monitoring of aquatic biodiversity using environmental DNA metabarcoding
Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for...
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| Vydáno v: | Molecular ecology Ročník 25; číslo 4; s. 929 - 942 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Blackwell Scientific Publications
01.02.2016
Blackwell Publishing Ltd Wiley |
| Témata: | |
| ISSN: | 0962-1083, 1365-294X, 1365-294X |
| On-line přístup: | Získat plný text |
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| Abstract | Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90–0.99) vs. 0.58 (CI = 0.50–0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA‐based approach has the potential to become the next‐generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems. |
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| AbstractList | Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, invitro and insitu. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI=0.90-0.99) vs. 0.58 (CI=0.50-0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA-based approach has the potential to become the next-generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems. see also the Perspective by Hoffmann, Schubert and Calvignac-Spencer. Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90–0.99) vs. 0.58 (CI = 0.50–0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA‐based approach has the potential to become the next‐generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems. see also the Perspective by Hoffmann, Schubert and Calvignac‐Spencer Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90–0.99) vs. 0.58 (CI = 0.50–0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA‐based approach has the potential to become the next‐generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems. Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90-0.99) vs. 0.58 (CI = 0.50-0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA-based approach has the potential to become the next-generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems.Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90-0.99) vs. 0.58 (CI = 0.50-0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA-based approach has the potential to become the next-generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems. Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90-0.99) vs. 0.58 (CI = 0.50-0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA-based approach has the potential to become the next-generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems. see also the Perspective by Hoffmann, Schubert and Calvignac-Spencer Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90–0.99) vs. 0.58 (CI = 0.50–0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA‐based approach has the potential to become the next‐generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems. Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA ( eDNA ) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 ( CI = 0.90–0.99) vs. 0.58 ( CI = 0.50–0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA ‐based approach has the potential to become the next‐generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems. see also the Perspective by Hoffmann, Schubert and Calvignac‐Spencer |
| Author | Civade, Raphaël Le Brun, Matthieu Taberlet, Pierre Herder, Jelger Pont, Didier Baudoin, Jean‐Marc Jean, Pauline Poulet, Nicolas Acqueberge, Manon Miaud, Claude Coissac, Eric Copp, Gordon H Besnard, Aurélien Olivier, Anthony Bellemain, Eva Argillier, Christine Valentini, Alice Crivelli, Alain J Willerslev, Eske Dejean, Tony Roset, Nicolas Boyer, Frédéric Gaboriaud, Coline Peroux, Tiphaine Møller, Peter R Thomsen, Philip Francis Geniez, Philippe |
| Author_xml | – sequence: 1 fullname: Valentini, Alice – sequence: 2 fullname: Taberlet, Pierre – sequence: 3 fullname: Miaud, Claude – sequence: 4 fullname: Civade, Raphaël – sequence: 5 fullname: Herder, Jelger – sequence: 6 fullname: Thomsen, Philip Francis – sequence: 7 fullname: Bellemain, Eva – sequence: 8 fullname: Besnard, Aurélien – sequence: 9 fullname: Coissac, Eric – sequence: 10 fullname: Boyer, Frédéric – sequence: 11 fullname: Gaboriaud, Coline – sequence: 12 fullname: Jean, Pauline – sequence: 13 fullname: Poulet, Nicolas – sequence: 14 fullname: Roset, Nicolas – sequence: 15 fullname: Copp, Gordon H – sequence: 16 fullname: Geniez, Philippe – sequence: 17 fullname: Pont, Didier – sequence: 18 fullname: Argillier, Christine – sequence: 19 fullname: Baudoin, Jean‐Marc – sequence: 20 fullname: Peroux, Tiphaine – sequence: 21 fullname: Crivelli, Alain J – sequence: 22 fullname: Olivier, Anthony – sequence: 23 fullname: Acqueberge, Manon – sequence: 24 fullname: Le Brun, Matthieu – sequence: 25 fullname: Møller, Peter R – sequence: 26 fullname: Willerslev, Eske – sequence: 27 fullname: Dejean, Tony |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26479867$$D View this record in MEDLINE/PubMed https://hal.science/hal-01419572$$DView record in HAL |
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| Keywords | wildlife management detection probability environmental DNA amphibian monitoring fish Environmental DNA FAUNE AQUATIQUE aquatic fauna ADN LAC INVENTAIRE FAUNISTIQUE dna ichthyology animal population Fish ICHTYOLOGIE lakes Wildlife management Amphibian Detection probability Monitoring |
| Language | English |
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| Notes | http://dx.doi.org/10.1111/mec.13428 STOWA ark:/67375/WNG-2M5B4DXD-1 Waterboard de Dommel Waterboard Brabantse Delta EDF SPYGEN ONEMA Table S1 Amphibian tissue samples used for the reference database construction. Table S2 Fish tissue samples used for the reference database construction. Table S3 Geographical coordinates, sampling date and results of traditional and eDNA metabarcoding surveys for amphibians. Table S4 Location, site characteristics and sampling methods used for eDNA metabarcoding and traditional surveys for the fish comparative study. Table S5 In silico assessment of different primer pairs targeting Batrachia and Teleostei. Table S6 Number of reads obtained from the NGS runs per sample before and after bioinformatic filtering. Table S7 Number of detection events of amphibian species using eDNA metabarcoding and traditional surveys; species detection probability with confidence intervals between brackets for eDNA metabarcoding, traditional surveys and historical data and number of visits required to achieve a 95% chance of species detection. Table S8 Results from the eDNA metabarcoding survey and historical data for fish in control sites (sites 1-4). Table S9 Results from eDNA metabarcoding and traditional surveys for fish in pond ecosystems (sites 5-8). Table S10 Results from eDNA metabarcoding and traditional surveys for fish in ditch ecosystems (sites 9-12). Table S11 Results from eDNA metabarcoding and traditional surveys for fish in lake ecosystems (site 13). Table S12 Results from eDNA metabarcoding and traditional surveys for fish in stream ecosystems in the Netherlands (sites 14-17). Table S13 Results from eDNA metabarcoding and traditional surveys for fish in stream ecosystems in France (sites 18-19). Table S14 Results from eDNA metabarcoding and traditional surveys for fish in river ecosystems (sites 20-23). Table S15 Results from eDNA metabarcoding for fish in the marine ecosystem (site 23). istex:4D7493F68E343E2BAB7D9DE51124DF4CBF78D950 Parc National du Mercantour Waterboard Rijn en Ijssel Network Ecological Monitoring (NEM) ArticleID:MEC13428 Waterboard Vallei en Veluwe ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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Fiske I, Chandler R (2011) Unmarked: an R package for fitting hierarchical models of wildlife occurrence and Abundance. Journal of Statistical Software, 43, 1-23. 2015; 2 2010; 11 2010; 10 2015; 15 2015; 183 2004; 303 2014; 516 2013; 22 2012 1985; 5 2013; 704 2012a; 21 2008; 9 2003; 18 2011; 471 2011; 39 2004; 306 2011; 6 2007; 35 2011; 111 2007; 14 2014; 21 1991; 6 2014b; 9 2004; 33 2000; 289 2000 2002; 83 2004; 14 1980; 77 2014a; 344 2014; 14 2011; 43 2001; 3 2008; 21 2015 2012; 49 2014 2012; 27 2012; 7 2014; 51 2014; 6 2012; 21 2014; 10 2012b; 7 2014; 344 e_1_2_8_28_1 Geniez P (e_1_2_8_24_1) 2012 e_1_2_8_47_1 Hubert WA (e_1_2_8_27_1) 2012 e_1_2_8_26_1 R Core Team (e_1_2_8_41_1) 2014 e_1_2_8_49_1 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 e_1_2_8_43_1 e_1_2_8_22_1 e_1_2_8_45_1 e_1_2_8_17_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_57_1 European Council (e_1_2_8_20_1) 2000 Daan N (e_1_2_8_14_1) 2001; 3 e_1_2_8_32_1 e_1_2_8_55_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_51_1 e_1_2_8_30_1 e_1_2_8_29_1 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_48_1 e_1_2_8_2_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_23_1 e_1_2_8_44_1 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_56_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_52_1 e_1_2_8_50_1 26876232 - Mol Ecol. 2016 Feb;25(4):846-8 |
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| SubjectTerms | amphibian Amphibians Amphibians - classification Amphibians - genetics Animals Aquatic ecosystems Biodiversity Biodiversity and Ecology Conservation biology Deoxyribonucleic acid detection probability DNA DNA Barcoding, Taxonomic - methods DNA Primers DNA, Mitochondrial - genetics Ecological studies Ecosystem Environmental DNA Environmental Monitoring Environmental Sciences fish Fishes - classification Fishes - genetics Fresh Water freshwater monitoring Oceans Oceans and Seas probability Rare species sequence analysis surveys Water analysis Water sampling wildlife management |
| Title | Next‐generation monitoring of aquatic biodiversity using environmental DNA metabarcoding |
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