Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Isabel Cantera, Kévin Cilleros, Alice Valentini, Axel Cerdan, Tony Dejean, Amaia Iribar, Pierre Taberlet, Régis Vigouroux, Sébastien Brosse

ABSTRACT

Environmental DNA (eDNA) metabarcoding is a promising tool to estimate aquatic biodiversity. It is based on the capture of DNA from a water sample. The sampled water volume, a crucial aspect for efficient species detection, has been empirically variable (ranging from few centiliters to tens of liters). This results in a high variability of sampling effort across studies, making comparisons difficult and raising uncertainties about the completeness of eDNA inventories. Our aim was to determine the sampling effort (filtered water volume) needed to get optimal inventories of fish assemblages in species-rich tropical streams and rivers using eDNA. Ten DNA replicates were collected in six Guianese sites (3 streams and 3 rivers), resulting in sampling efforts ranging from 17 to 340 liters of water. We show that sampling 34 liters of water detected more than 64% of the expected fish fauna and permitted to distinguish the fauna between sites and between ecosystem types (stream versus rivers). Above 68 liters, the number of detected species per site increased slightly, with a detection rate higher than 71%. Increasing sampling effort up to 340 liters provided little additional information, testifying that filtering 34 to 68 liters is sufficient to inventory most of the fauna in highly diverse tropical aquatic ecosystems. More... »

PAGES

3085

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-39399-5

DOI

http://dx.doi.org/10.1038/s41598-019-39399-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112440467

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30816174


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0602", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Ecology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Laboratoire \u00c9volution & Diversit\u00e9 Biologique (EDB UMR5174), Universit\u00e9 Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cantera", 
        "givenName": "Isabel", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Research Institute of Science and Technology for Environment and Agriculture", 
          "id": "https://www.grid.ac/institutes/grid.48142.3b", 
          "name": [
            "Laboratoire \u00c9volution & Diversit\u00e9 Biologique (EDB UMR5174), Universit\u00e9 Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France", 
            "Irstea, UR RECOVER, Equipe FRESHCO, 3275 Route de C\u00e9zanne, CS 40061, 13182, Aix en Provence, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cilleros", 
        "givenName": "K\u00e9vin", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "SPYGEN, 17 rue du Lac Saint-Andr\u00e9 Savoie Technolac - BP 274, 73375, Le Bourget-du-Lac, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Valentini", 
        "givenName": "Alice", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Laboratoire \u00c9volution & Diversit\u00e9 Biologique (EDB UMR5174), Universit\u00e9 Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France", 
            "\u00c9cologie des For\u00eats de Guyane (UMR EcoFoG), Campus Agronomique, Kourou, French Guiana"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cerdan", 
        "givenName": "Axel", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "SPYGEN, 17 rue du Lac Saint-Andr\u00e9 Savoie Technolac - BP 274, 73375, Le Bourget-du-Lac, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dejean", 
        "givenName": "Tony", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Laboratoire \u00c9volution & Diversit\u00e9 Biologique (EDB UMR5174), Universit\u00e9 Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Iribar", 
        "givenName": "Amaia", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratoire d'\u00c9cologie Alpine", 
          "id": "https://www.grid.ac/institutes/grid.462909.0", 
          "name": [
            "Laboratoire d\u2019Ecologie Alpine (LECA UMR5553), CNRS, Universit\u00e9 Grenoble Alpes, 38041, Grenoble, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Taberlet", 
        "givenName": "Pierre", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "HYDRECO, Laboratoire Environnement de Petit Saut, B.P 823, F-97388, Kourou Cedex, French Guiana"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Vigouroux", 
        "givenName": "R\u00e9gis", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Laboratoire \u00c9volution & Diversit\u00e9 Biologique (EDB UMR5174), Universit\u00e9 Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brosse", 
        "givenName": "S\u00e9bastien", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1111/j.1442-9993.1993.tb00438.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001935240"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/2041-210x.12595", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002861823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biocon.2014.11.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005284001"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/mec.13481", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007365302"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12421", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007617029"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12402", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008593461"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12188", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009579296"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-294x.2012.05542.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009756127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-294x.2011.05418.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013769784"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1461-0248.2001.00230.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015653758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1469-185x.1984.tb00711.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017866363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0157366", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020460876"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/mec.13660", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020741418"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/mec.13660", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020741418"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biocon.2014.11.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022469880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-011-0701-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022591970", 
          "https://doi.org/10.1007/978-94-011-0701-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-011-0701-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022591970", 
          "https://doi.org/10.1007/978-94-011-0701-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1139/cjfas-2016-0306", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024625181"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12428", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025851006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep40368", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027130834", 
          "https://doi.org/10.1038/srep40368"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pbio.1001569", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033766451"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsbl.2008.0118", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037392305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biocon.2014.11.029", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038952395"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1365-2664.12306", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039452762"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00027-015-0433-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039576159", 
          "https://doi.org/10.1007/s00027-015-0433-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12285", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039834589"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12643", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044286584"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/mec.13428", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046457524"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms12544", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047053651", 
          "https://doi.org/10.1038/ncomms12544"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047074108"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ece3.2186", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047439047"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00006595", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049617110", 
          "https://doi.org/10.1007/bf00006595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biocon.2014.11.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050094277"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/es404734p", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051041083"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1317625111", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053574680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.est.5b04188", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055088316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1051/kmae/2001046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057019109"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1051/limn/2010013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057031905"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2174/1874940201003010038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069237316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2531487", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069976928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/fee.1490", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085409284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/fwb.12981", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090896889"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/2041-210x.12994", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101855726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12900", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104021500"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1755-0998.12900", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104021500"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/cla.12341", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105268357"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-018-30179-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105928952", 
          "https://doi.org/10.1038/s41598-018-30179-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1525/california/9780520268685.001.0001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1108331214"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Environmental DNA (eDNA) metabarcoding is a promising tool to estimate aquatic biodiversity. It is based on the capture of DNA from a water sample. The sampled water volume, a crucial aspect for efficient species detection, has been empirically variable (ranging from few centiliters to tens of liters). This results in a high variability of sampling effort across studies, making comparisons difficult and raising uncertainties about the completeness of eDNA inventories. Our aim was to determine the sampling effort (filtered water volume) needed to get optimal inventories of fish assemblages in species-rich tropical streams and rivers using eDNA. Ten DNA replicates were collected in six Guianese sites (3 streams and 3 rivers), resulting in sampling efforts ranging from 17 to 340 liters of water. We show that sampling 34 liters of water detected more than 64% of the expected fish fauna and permitted to distinguish the fauna between sites and between ecosystem types (stream versus rivers). Above 68 liters, the number of detected species per site increased slightly, with a detection rate higher than 71%. Increasing sampling effort up to 340 liters provided little additional information, testifying that filtering 34 to 68 liters is sufficient to inventory most of the fauna in highly diverse tropical aquatic ecosystems.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41598-019-39399-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers", 
    "pagination": "3085", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "cd41fb5352f726a1c58b328187845bb9789fb43d35297d6dc4bb9b65e974103b"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30816174"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101563288"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-019-39399-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112440467"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-019-39399-5", 
      "https://app.dimensions.ai/details/publication/pub.1112440467"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:00", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000352_0000000352/records_60338_00000004.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41598-019-39399-5"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-39399-5'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-39399-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-39399-5'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-39399-5'


 

This table displays all metadata directly associated to this object as RDF triples.

275 TRIPLES      21 PREDICATES      74 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-019-39399-5 schema:about anzsrc-for:06
2 anzsrc-for:0602
3 schema:author Na4950b4ed784462b8b41fa59cd6ed3d0
4 schema:citation sg:pub.10.1007/978-94-011-0701-3
5 sg:pub.10.1007/bf00006595
6 sg:pub.10.1007/s00027-015-0433-4
7 sg:pub.10.1038/ncomms12544
8 sg:pub.10.1038/s41598-018-30179-1
9 sg:pub.10.1038/srep40368
10 https://doi.org/10.1002/ece3.2186
11 https://doi.org/10.1002/fee.1490
12 https://doi.org/10.1016/j.biocon.2014.11.018
13 https://doi.org/10.1016/j.biocon.2014.11.019
14 https://doi.org/10.1016/j.biocon.2014.11.020
15 https://doi.org/10.1016/j.biocon.2014.11.029
16 https://doi.org/10.1021/acs.est.5b04188
17 https://doi.org/10.1021/es404734p
18 https://doi.org/10.1046/j.1461-0248.2001.00230.x
19 https://doi.org/10.1051/kmae/2001046
20 https://doi.org/10.1051/limn/2010013
21 https://doi.org/10.1073/pnas.1317625111
22 https://doi.org/10.1098/rsbl.2008.0118
23 https://doi.org/10.1111/1365-2664.12306
24 https://doi.org/10.1111/1755-0998.12159
25 https://doi.org/10.1111/1755-0998.12188
26 https://doi.org/10.1111/1755-0998.12285
27 https://doi.org/10.1111/1755-0998.12402
28 https://doi.org/10.1111/1755-0998.12421
29 https://doi.org/10.1111/1755-0998.12428
30 https://doi.org/10.1111/1755-0998.12643
31 https://doi.org/10.1111/1755-0998.12900
32 https://doi.org/10.1111/2041-210x.12595
33 https://doi.org/10.1111/2041-210x.12994
34 https://doi.org/10.1111/cla.12341
35 https://doi.org/10.1111/fwb.12981
36 https://doi.org/10.1111/j.1365-294x.2011.05418.x
37 https://doi.org/10.1111/j.1365-294x.2012.05542.x
38 https://doi.org/10.1111/j.1442-9993.1993.tb00438.x
39 https://doi.org/10.1111/j.1469-185x.1984.tb00711.x
40 https://doi.org/10.1111/mec.13428
41 https://doi.org/10.1111/mec.13481
42 https://doi.org/10.1111/mec.13660
43 https://doi.org/10.1139/cjfas-2016-0306
44 https://doi.org/10.1371/journal.pbio.1001569
45 https://doi.org/10.1371/journal.pone.0157366
46 https://doi.org/10.1525/california/9780520268685.001.0001
47 https://doi.org/10.2174/1874940201003010038
48 https://doi.org/10.2307/2531487
49 schema:datePublished 2019-12
50 schema:datePublishedReg 2019-12-01
51 schema:description Environmental DNA (eDNA) metabarcoding is a promising tool to estimate aquatic biodiversity. It is based on the capture of DNA from a water sample. The sampled water volume, a crucial aspect for efficient species detection, has been empirically variable (ranging from few centiliters to tens of liters). This results in a high variability of sampling effort across studies, making comparisons difficult and raising uncertainties about the completeness of eDNA inventories. Our aim was to determine the sampling effort (filtered water volume) needed to get optimal inventories of fish assemblages in species-rich tropical streams and rivers using eDNA. Ten DNA replicates were collected in six Guianese sites (3 streams and 3 rivers), resulting in sampling efforts ranging from 17 to 340 liters of water. We show that sampling 34 liters of water detected more than 64% of the expected fish fauna and permitted to distinguish the fauna between sites and between ecosystem types (stream versus rivers). Above 68 liters, the number of detected species per site increased slightly, with a detection rate higher than 71%. Increasing sampling effort up to 340 liters provided little additional information, testifying that filtering 34 to 68 liters is sufficient to inventory most of the fauna in highly diverse tropical aquatic ecosystems.
52 schema:genre research_article
53 schema:inLanguage en
54 schema:isAccessibleForFree true
55 schema:isPartOf N6c77ed36943348c1afd7d8cc88d6e357
56 N89a9f895f0954e0bb06196e18b8a661a
57 sg:journal.1045337
58 schema:name Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers
59 schema:pagination 3085
60 schema:productId N7945317c17554b709a68e726b3feb89b
61 Nbe4095365b2d4340b4b1780cd4abb89b
62 Nc4db48a66e9b460aa9071b0ebc08d21d
63 Ndf6beb9dda6a48feaf67c1eea503c17b
64 Neadaca905909436699d8c90aeb8e04db
65 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112440467
66 https://doi.org/10.1038/s41598-019-39399-5
67 schema:sdDatePublished 2019-04-11T11:00
68 schema:sdLicense https://scigraph.springernature.com/explorer/license/
69 schema:sdPublisher Nec19e3a44e8949c2a52268945f08c87c
70 schema:url https://www.nature.com/articles/s41598-019-39399-5
71 sgo:license sg:explorer/license/
72 sgo:sdDataset articles
73 rdf:type schema:ScholarlyArticle
74 N0008cf8125f548fc977ab250e0cb304e schema:name Laboratoire Évolution & Diversité Biologique (EDB UMR5174), Université Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France
75 rdf:type schema:Organization
76 N012ac24811e24b8ba6bdfc1b0b69994b schema:affiliation N25aeed150852491f86a3b9ec684f70de
77 schema:familyName Iribar
78 schema:givenName Amaia
79 rdf:type schema:Person
80 N09e4d645e94647eea749f4c591ae8b26 rdf:first N012ac24811e24b8ba6bdfc1b0b69994b
81 rdf:rest N6a2b5d39d5f9428dbbacdcca78c1b484
82 N0d036a3a0bf9408798c0393cc2acec07 schema:name HYDRECO, Laboratoire Environnement de Petit Saut, B.P 823, F-97388, Kourou Cedex, French Guiana
83 rdf:type schema:Organization
84 N175dfc02415745e6accda36ec034185d rdf:first N4bcae42aedf041219b0c474bee5fc01a
85 rdf:rest N9ff5a761d8514379b9228227099ccf5b
86 N1aa9c3384ce4461ead892f7d56e3d1b6 schema:affiliation N71665b5d40d04b729140071851ab6e56
87 schema:familyName Cantera
88 schema:givenName Isabel
89 rdf:type schema:Person
90 N25aeed150852491f86a3b9ec684f70de schema:name Laboratoire Évolution & Diversité Biologique (EDB UMR5174), Université Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France
91 rdf:type schema:Organization
92 N2733f29ba5b8436f951960b37f5ab176 schema:affiliation N0008cf8125f548fc977ab250e0cb304e
93 schema:familyName Brosse
94 schema:givenName Sébastien
95 rdf:type schema:Person
96 N288c5372d646427fa2eb3880ef2e8b62 schema:affiliation N9a023336695940eb8cce6c7a03713b98
97 schema:familyName Cerdan
98 schema:givenName Axel
99 rdf:type schema:Person
100 N314a1f7e11e743c4858bdd5523e40120 rdf:first Nd536f5bf02574dda98a683d1604e8666
101 rdf:rest N09e4d645e94647eea749f4c591ae8b26
102 N3a7d6a0329d946d29881a07d02487c65 schema:affiliation https://www.grid.ac/institutes/grid.48142.3b
103 schema:familyName Cilleros
104 schema:givenName Kévin
105 rdf:type schema:Person
106 N431f45c2c58f44899bd2ca58634745db schema:name SPYGEN, 17 rue du Lac Saint-André Savoie Technolac - BP 274, 73375, Le Bourget-du-Lac, France
107 rdf:type schema:Organization
108 N49cb4f5014e2499695377188791bb68f rdf:first Nac8eba2c1c61479eb106617dd020ee3e
109 rdf:rest N5a61430224594f89a946a6a05c05e22c
110 N4bcae42aedf041219b0c474bee5fc01a schema:affiliation N431f45c2c58f44899bd2ca58634745db
111 schema:familyName Valentini
112 schema:givenName Alice
113 rdf:type schema:Person
114 N532a4c34f0864080953ae09755114898 schema:affiliation https://www.grid.ac/institutes/grid.462909.0
115 schema:familyName Taberlet
116 schema:givenName Pierre
117 rdf:type schema:Person
118 N5a61430224594f89a946a6a05c05e22c rdf:first N2733f29ba5b8436f951960b37f5ab176
119 rdf:rest rdf:nil
120 N6a2b5d39d5f9428dbbacdcca78c1b484 rdf:first N532a4c34f0864080953ae09755114898
121 rdf:rest N49cb4f5014e2499695377188791bb68f
122 N6c77ed36943348c1afd7d8cc88d6e357 schema:volumeNumber 9
123 rdf:type schema:PublicationVolume
124 N71665b5d40d04b729140071851ab6e56 schema:name Laboratoire Évolution & Diversité Biologique (EDB UMR5174), Université Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France
125 rdf:type schema:Organization
126 N7945317c17554b709a68e726b3feb89b schema:name doi
127 schema:value 10.1038/s41598-019-39399-5
128 rdf:type schema:PropertyValue
129 N89a9f895f0954e0bb06196e18b8a661a schema:issueNumber 1
130 rdf:type schema:PublicationIssue
131 N9a023336695940eb8cce6c7a03713b98 schema:name Laboratoire Évolution & Diversité Biologique (EDB UMR5174), Université Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France
132 Écologie des Forêts de Guyane (UMR EcoFoG), Campus Agronomique, Kourou, French Guiana
133 rdf:type schema:Organization
134 N9ff5a761d8514379b9228227099ccf5b rdf:first N288c5372d646427fa2eb3880ef2e8b62
135 rdf:rest N314a1f7e11e743c4858bdd5523e40120
136 Na4950b4ed784462b8b41fa59cd6ed3d0 rdf:first N1aa9c3384ce4461ead892f7d56e3d1b6
137 rdf:rest Nfd523538cf584ae29d1d37557ebdfa5c
138 Nac8eba2c1c61479eb106617dd020ee3e schema:affiliation N0d036a3a0bf9408798c0393cc2acec07
139 schema:familyName Vigouroux
140 schema:givenName Régis
141 rdf:type schema:Person
142 Nbe4095365b2d4340b4b1780cd4abb89b schema:name readcube_id
143 schema:value cd41fb5352f726a1c58b328187845bb9789fb43d35297d6dc4bb9b65e974103b
144 rdf:type schema:PropertyValue
145 Nc4db48a66e9b460aa9071b0ebc08d21d schema:name dimensions_id
146 schema:value pub.1112440467
147 rdf:type schema:PropertyValue
148 Nd536f5bf02574dda98a683d1604e8666 schema:affiliation Nfdab5e1beaa1473f87a58451048c8a9c
149 schema:familyName Dejean
150 schema:givenName Tony
151 rdf:type schema:Person
152 Ndf6beb9dda6a48feaf67c1eea503c17b schema:name pubmed_id
153 schema:value 30816174
154 rdf:type schema:PropertyValue
155 Neadaca905909436699d8c90aeb8e04db schema:name nlm_unique_id
156 schema:value 101563288
157 rdf:type schema:PropertyValue
158 Nec19e3a44e8949c2a52268945f08c87c schema:name Springer Nature - SN SciGraph project
159 rdf:type schema:Organization
160 Nfd523538cf584ae29d1d37557ebdfa5c rdf:first N3a7d6a0329d946d29881a07d02487c65
161 rdf:rest N175dfc02415745e6accda36ec034185d
162 Nfdab5e1beaa1473f87a58451048c8a9c schema:name SPYGEN, 17 rue du Lac Saint-André Savoie Technolac - BP 274, 73375, Le Bourget-du-Lac, France
163 rdf:type schema:Organization
164 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
165 schema:name Biological Sciences
166 rdf:type schema:DefinedTerm
167 anzsrc-for:0602 schema:inDefinedTermSet anzsrc-for:
168 schema:name Ecology
169 rdf:type schema:DefinedTerm
170 sg:journal.1045337 schema:issn 2045-2322
171 schema:name Scientific Reports
172 rdf:type schema:Periodical
173 sg:pub.10.1007/978-94-011-0701-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022591970
174 https://doi.org/10.1007/978-94-011-0701-3
175 rdf:type schema:CreativeWork
176 sg:pub.10.1007/bf00006595 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049617110
177 https://doi.org/10.1007/bf00006595
178 rdf:type schema:CreativeWork
179 sg:pub.10.1007/s00027-015-0433-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039576159
180 https://doi.org/10.1007/s00027-015-0433-4
181 rdf:type schema:CreativeWork
182 sg:pub.10.1038/ncomms12544 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047053651
183 https://doi.org/10.1038/ncomms12544
184 rdf:type schema:CreativeWork
185 sg:pub.10.1038/s41598-018-30179-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105928952
186 https://doi.org/10.1038/s41598-018-30179-1
187 rdf:type schema:CreativeWork
188 sg:pub.10.1038/srep40368 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027130834
189 https://doi.org/10.1038/srep40368
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1002/ece3.2186 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047439047
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1002/fee.1490 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085409284
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1016/j.biocon.2014.11.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050094277
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1016/j.biocon.2014.11.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005284001
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1016/j.biocon.2014.11.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022469880
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1016/j.biocon.2014.11.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038952395
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1021/acs.est.5b04188 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055088316
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1021/es404734p schema:sameAs https://app.dimensions.ai/details/publication/pub.1051041083
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1046/j.1461-0248.2001.00230.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1015653758
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1051/kmae/2001046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057019109
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1051/limn/2010013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057031905
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1073/pnas.1317625111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053574680
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1098/rsbl.2008.0118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037392305
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1111/1365-2664.12306 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039452762
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1111/1755-0998.12159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047074108
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1111/1755-0998.12188 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009579296
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1111/1755-0998.12285 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039834589
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1111/1755-0998.12402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008593461
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1111/1755-0998.12421 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007617029
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1111/1755-0998.12428 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025851006
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1111/1755-0998.12643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044286584
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1111/1755-0998.12900 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104021500
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1111/2041-210x.12595 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002861823
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1111/2041-210x.12994 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101855726
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1111/cla.12341 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105268357
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1111/fwb.12981 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090896889
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1111/j.1365-294x.2011.05418.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1013769784
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1111/j.1365-294x.2012.05542.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1009756127
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1111/j.1442-9993.1993.tb00438.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1001935240
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1111/j.1469-185x.1984.tb00711.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1017866363
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1111/mec.13428 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046457524
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1111/mec.13481 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007365302
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1111/mec.13660 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020741418
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1139/cjfas-2016-0306 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024625181
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1371/journal.pbio.1001569 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033766451
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1371/journal.pone.0157366 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020460876
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1525/california/9780520268685.001.0001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1108331214
264 rdf:type schema:CreativeWork
265 https://doi.org/10.2174/1874940201003010038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069237316
266 rdf:type schema:CreativeWork
267 https://doi.org/10.2307/2531487 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069976928
268 rdf:type schema:CreativeWork
269 https://www.grid.ac/institutes/grid.462909.0 schema:alternateName Laboratoire d'Écologie Alpine
270 schema:name Laboratoire d’Ecologie Alpine (LECA UMR5553), CNRS, Université Grenoble Alpes, 38041, Grenoble, France
271 rdf:type schema:Organization
272 https://www.grid.ac/institutes/grid.48142.3b schema:alternateName National Research Institute of Science and Technology for Environment and Agriculture
273 schema:name Irstea, UR RECOVER, Equipe FRESHCO, 3275 Route de Cézanne, CS 40061, 13182, Aix en Provence, France
274 Laboratoire Évolution & Diversité Biologique (EDB UMR5174), Université Paul Sabatier - Toulouse 3, CNRS, IRD, UPS, 118 route de Narbonne, 31062, Toulouse Cedex, France
275 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...