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DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities

Morphology-based profiling of benthic communities has been extensively applied to aquatic ecosystems’ health assessment. However, it remains a low-throughput, and sometimes ambiguous, procedure. Despite DNA metabarcoding has been applied to marine benthos, a comprehensive approach providing species-...

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Autores principales: Lobo, Jorge, Shokralla, Shadi, Costa, Maria Helena, Hajibabaei, Mehrdad, Costa, Filipe Oliveira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688171/
https://www.ncbi.nlm.nih.gov/pubmed/29142319
http://dx.doi.org/10.1038/s41598-017-15823-6
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author Lobo, Jorge
Shokralla, Shadi
Costa, Maria Helena
Hajibabaei, Mehrdad
Costa, Filipe Oliveira
author_facet Lobo, Jorge
Shokralla, Shadi
Costa, Maria Helena
Hajibabaei, Mehrdad
Costa, Filipe Oliveira
author_sort Lobo, Jorge
collection PubMed
description Morphology-based profiling of benthic communities has been extensively applied to aquatic ecosystems’ health assessment. However, it remains a low-throughput, and sometimes ambiguous, procedure. Despite DNA metabarcoding has been applied to marine benthos, a comprehensive approach providing species-level identifications for estuarine macrobenthos is still lacking. Here we report a combination of experimental and field studies to assess the aptitude of COI metabarcoding to provide robust species-level identifications for high-throughput monitoring of estuarine macrobenthos. To investigate the ability of metabarcoding to detect all species present in bulk DNA extracts, we contrived three phylogenetically diverse communities, and applied four different primer pairs to generate PCR products within the COI barcode region. Between 78–83% of the species in the contrived communities were recovered through HTS. Subsequently, we compared morphology and metabarcoding-based approaches to determine the species composition from four distinct estuarine sites. Our results indicate that species richness would be considerably underestimated if only morphological methods were used: globally 27 species identified through morphology versus 61 detected by metabarcoding. Although further refinement is required to improve efficiency and output of this approach, here we show the great aptitude of COI metabarcoding to provide high quality and auditable species identifications in estuarine macrobenthos monitoring.
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spelling pubmed-56881712017-11-24 DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities Lobo, Jorge Shokralla, Shadi Costa, Maria Helena Hajibabaei, Mehrdad Costa, Filipe Oliveira Sci Rep Article Morphology-based profiling of benthic communities has been extensively applied to aquatic ecosystems’ health assessment. However, it remains a low-throughput, and sometimes ambiguous, procedure. Despite DNA metabarcoding has been applied to marine benthos, a comprehensive approach providing species-level identifications for estuarine macrobenthos is still lacking. Here we report a combination of experimental and field studies to assess the aptitude of COI metabarcoding to provide robust species-level identifications for high-throughput monitoring of estuarine macrobenthos. To investigate the ability of metabarcoding to detect all species present in bulk DNA extracts, we contrived three phylogenetically diverse communities, and applied four different primer pairs to generate PCR products within the COI barcode region. Between 78–83% of the species in the contrived communities were recovered through HTS. Subsequently, we compared morphology and metabarcoding-based approaches to determine the species composition from four distinct estuarine sites. Our results indicate that species richness would be considerably underestimated if only morphological methods were used: globally 27 species identified through morphology versus 61 detected by metabarcoding. Although further refinement is required to improve efficiency and output of this approach, here we show the great aptitude of COI metabarcoding to provide high quality and auditable species identifications in estuarine macrobenthos monitoring. Nature Publishing Group UK 2017-11-15 /pmc/articles/PMC5688171/ /pubmed/29142319 http://dx.doi.org/10.1038/s41598-017-15823-6 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lobo, Jorge
Shokralla, Shadi
Costa, Maria Helena
Hajibabaei, Mehrdad
Costa, Filipe Oliveira
DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities
title DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities
title_full DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities
title_fullStr DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities
title_full_unstemmed DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities
title_short DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities
title_sort dna metabarcoding for high-throughput monitoring of estuarine macrobenthic communities
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688171/
https://www.ncbi.nlm.nih.gov/pubmed/29142319
http://dx.doi.org/10.1038/s41598-017-15823-6
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