GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Supervised machine learning outperforms taxonomy‐based environmental DNA metabarcoding applied to biomonitoring
    Wiley ; 2018
    In:  Molecular Ecology Resources Vol. 18, No. 6 ( 2018-11), p. 1381-1391
    Details
    In: Molecular Ecology Resources, Wiley, Vol. 18, No. 6 ( 2018-11), p. 1381-1391
    Abstract: Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high‐throughput amplicon sequencing of environmental DNA ( eDNA metabarcoding) could overcome many limitations of the traditional morphotaxonomy‐based bioassessment. Recently, we demonstrated that supervised machine learning ( SML ) can be used to predict accurate biotic indices values from eDNA metabarcoding data, regardless of the taxonomic affiliation of the sequences. However, it is unknown to which extent the accuracy of such models depends on taxonomic resolution of molecular markers or how SML compares with metabarcoding approaches targeting well‐established bioindicator species. In this study, we address these issues by training predictive models upon five different ribosomal bacterial and eukaryotic markers and measuring their performance to assess the environmental impact of marine aquaculture on independent data sets. Our results show that all tested markers are yielding accurate predictive models and that they all outperform the assessment relying solely on taxonomically assigned sequences. Remarkably, we did not find any significant difference in the performance of the models built using universal eukaryotic or prokaryotic markers. Using any molecular marker with a taxonomic range broad enough to comprise different potential bioindicator taxa, SML approach can overcome the limits of taxonomy‐based eDNA bioassessment.
    Type of Medium: Online Resource
    ISSN: 1755-098X , 1755-0998
    URL: Issue
    URL: Article
    DOI: 10.1111/men.2018.18.issue-6
    DOI: 10.1111/1755-0998.12926
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2406833-0
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes
    Wiley ; 2021
    In:  Molecular Ecology Vol. 30, No. 13 ( 2021-07), p. 2988-3006
    Details
    In: Molecular Ecology, Wiley, Vol. 30, No. 13 ( 2021-07), p. 2988-3006
    Abstract: Increasing anthropogenic impact and global change effects on natural ecosystems has prompted the development of less expensive and more efficient bioassessments methodologies. One promising approach is the integration of DNA metabarcoding in environmental monitoring. A critical step in this process is the inference of ecological quality (EQ) status from identified molecular bioindicator signatures that mirror environmental classification based on standard macroinvertebrate surveys. The most promising approaches to infer EQ from biotic indices (BI) are supervised machine learning (SML) and the calculation of indicator values (IndVal). In this study we compared the performance of both approaches using DNA metabarcodes of bacteria and ciliates as bioindicators obtained from 152 samples collected from seven Norwegian salmon farms. Results from standard macroinvertebrate‐monitoring of the same samples were used as reference to compare the accuracy of both approaches. First, SML outperformed the IndVal approach to infer EQ from eDNA metabarcodes. The Random Forest (RF) algorithm appeared to be less sensitive to noisy data (a typical feature of massive environmental sequence data sets) and uneven data coverage across EQ classes (a typical feature of environmental compliance monitoring scheme) compared to a widely used method to infer IndVals for the calculation of a BI. Second, bacteria allowed for a more accurate EQ assessment than ciliate eDNA metabarcodes. For the implementation of DNA metabarcoding into routine monitoring programmes to assess EQ around salmon aquaculture cages, we therefore recommend bacterial DNA metabarcodes in combination with SML to classify EQ categories based on molecular signatures.
    Type of Medium: Online Resource
    ISSN: 0962-1083 , 1365-294X
    URL: Issue
    URL: Article
    DOI: 10.1111/mec.v30.13
    DOI: 10.1111/mec.15434
    RVK:
    WA 15000
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2020749-9
    detail.hit.zdb_id: 1126687-9
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap
    Wiley ; 2021
    In:  Molecular Ecology Vol. 30, No. 13 ( 2021-07), p. 2937-2958
    Details
    In: Molecular Ecology, Wiley, Vol. 30, No. 13 ( 2021-07), p. 2937-2958
    Abstract: A decade after environmental scientists integrated high‐throughput sequencing technologies in their toolbox, the genomics‐based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end‐users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or “in development”, hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics‐based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy‐based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.
    Type of Medium: Online Resource
    ISSN: 0962-1083 , 1365-294X
    URL: Issue
    URL: Article
    DOI: 10.1111/mec.v30.13
    DOI: 10.1111/mec.15472
    RVK:
    WA 15000
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2020749-9
    detail.hit.zdb_id: 1126687-9
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    A Comparison of Different Ciliate Metabarcode Genes as Bioindicators for Environmental Impact Assessments of Salmon Aquaculture
    Wiley ; 2019
    In:  Journal of Eukaryotic Microbiology Vol. 66, No. 2 ( 2019-03), p. 294-308
    Details
    In: Journal of Eukaryotic Microbiology, Wiley, Vol. 66, No. 2 ( 2019-03), p. 294-308
    Abstract: Ciliates are powerful indicators for monitoring the impact of aquaculture and other industrial activities in the marine environment. Here, we tested the efficiency of four different genetic markers (V4 and V9 regions of the SSU rRNA gene, D1 and D2 regions of the LSU rRNA gene, obtained from environmental (e) DNA and environmental (e) RNA ) of benthic ciliate communities for environmental monitoring. We obtained these genetic metabarcodes from sediment samples collected along a transect extending from below salmon cages toward the open sea. These data were compared to benchmark data from traditional macrofauna surveys of the same samples. In beta diversity analyses of ciliate community structures, the V4 and V9 markers had a higher resolution power for sampling sites with different degrees of organic enrichment compared to the D1 and D2 markers. The eDNA and eRNA V4 markers had a higher discriminatory power than the V9 markers. However, results obtained with the eDNA V9 marker corroborated better with the traditional macrofauna monitoring. This allows for a more direct comparison of ciliate metabarcoding with the traditional monitoring. We conclude that the ciliate eDNA V9 marker is the best choice for implementation in routine monitoring programs in marine aquaculture.
    Type of Medium: Online Resource
    ISSN: 1066-5234 , 1550-7408
    URL: Issue
    URL: Article
    DOI: 10.1111/jeu.2019.66.issue-2
    DOI: 10.1111/jeu.12670
    RVK:
    WA 15000
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2126326-7
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Marine protist diversity in E uropean coastal waters and sediments as revealed by high‐throughput sequencing
    Wiley ; 2015
    In:  Environmental Microbiology Vol. 17, No. 10 ( 2015-10), p. 4035-4049
    Details
    In: Environmental Microbiology, Wiley, Vol. 17, No. 10 ( 2015-10), p. 4035-4049
    Abstract: Although protists are critical components of marine ecosystems, they are still poorly characterized. Here we analysed the taxonomic diversity of planktonic and benthic protist communities collected in six distant E uropean coastal sites. Environmental deoxyribonucleic acid ( DNA ) and ribonucleic acid ( RNA ) from three size fractions (pico‐, nano‐ and micro/mesoplankton), as well as from dissolved DNA and surface sediments were used as templates for tag pyrosequencing of the V4 region of the 18S ribosomal DNA. Beta‐diversity analyses split the protist community structure into three main clusters: picoplankton‐nanoplankton‐dissolved DNA , micro/mesoplankton and sediments. Within each cluster, protist communities from the same site and time clustered together, while communities from the same site but different seasons were unrelated. Both DNA and RNA ‐based surveys provided similar relative abundances for most class‐level taxonomic groups. Yet, particular groups were overrepresented in one of the two templates, such as marine alveolates ( MALV )‐ I and MALV‐II that were much more abundant in DNA surveys. Overall, the groups displaying the highest relative contribution were Dinophyceae, Diatomea, Ciliophora and Acantharia. Also, well represented were Mamiellophyceae, Cryptomonadales, marine alveolates and marine stramenopiles in the picoplankton, and Monadofilosa and basal F ungi in sediments. Our extensive and systematic sequencing of geographically separated sites provides the most comprehensive molecular description of coastal marine protist diversity to date.
    Type of Medium: Online Resource
    ISSN: 1462-2912 , 1462-2920
    URL: Issue
    URL: Article
    DOI: 10.1111/emi.2015.17.issue-10
    DOI: 10.1111/1462-2920.12955
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 2020213-1
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...