Evidence map›Paper›PMID 30389965›Full record

ArticleScientific reports2018

Combining morpho-taxonomy and metabarcoding enhances the detection of non-indigenous marine pests in biofouling communities.

Ulla von Ammon, Susanna A Wood, Olivier Laroche, Anastasija Zaiko, Leigh Tait, Shane Lavery, Graeme J Inglis, Xavier Pochon

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  13. Comparison of Larval Fish Detections Using Morphology-Based Taxonomy versus High-Throughput Sequencing for Invasive Species Early Detection.Canadian journal of fisheries and aquatic sciences. Journal canadien des sciences halieutiques et aquatiques · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ulla von AmmonEnvironmental Technologies, Coastal and Freshwater Group, Cawthron Institute, Private Bag 2, Nelson, 7042, New Zealand. ulla.ammon@cawthron.org.nz.
Susanna A WoodEnvironmental Technologies, Coastal and Freshwater Group, Cawthron Institute, Private Bag 2, Nelson, 7042, New Zealand.
Olivier LarocheEnvironmental Technologies, Coastal and Freshwater Group, Cawthron Institute, Private Bag 2, Nelson, 7042, New Zealand.
Anastasija ZaikoEnvironmental Technologies, Coastal and Freshwater Group, Cawthron Institute, Private Bag 2, Nelson, 7042, New Zealand.
Leigh TaitNational Institute of Water & Atmospheric Research Ltd, PO Box 8602, Riccarton, Christchurch, 8440, New Zealand.ORCID http://orcid.org/0000-0001-9153-139X
Shane LaverySchool of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.
Graeme J InglisNational Institute of Water & Atmospheric Research Ltd, PO Box 8602, Riccarton, Christchurch, 8440, New Zealand.
Xavier PochonEnvironmental Technologies, Coastal and Freshwater Group, Cawthron Institute, Private Bag 2, Nelson, 7042, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Marine infrastructure can favor the spread of non-indigenous marine biofouling species by providing a suitable habitat for them to proliferate. Cryptic organisms or those in early life stages can be difficult to distinguish by conventional morphological taxonomy. Molecular tools, such as metabarcoding, may improve their detection. In this study, the ability of morpho-taxonomy and metabarcoding (18S rRNA and COI) using three reference databases (PR2, BOLD and NCBI) to characterize biodiversity and detect non-indigenous species (NIS) in biofouling was compared on 60 passive samplers deployed over summer and winter in a New Zealand marina. Highest resolution of metazoan taxa was identified using 18S rRNA assigned to PR2. There were higher assignment rates to NCBI reference sequences, but poorer taxonomic identification. Using all methods, 48 potential NIS were identified. Metabarcoding detected the largest proportion of those NIS: 77% via 18S rRNA/PR2 and NCBI and 35% via COI/BOLD and NCBI. Morpho-taxonomy detected an additional 14% of all identified NIS comprising mainly of bryozoan taxa. The data highlight several on-going challenges, including: differential marker resolution, primer biases, incomplete sequence reference databases, and variations in bioinformatic pipelines. Combining morpho-taxonomy and molecular analysis methods will likely enhance the detection of NIS from complex biofouling.

Indexed as

BiofoulingIntroduced SpeciesAnimalsAquatic OrganismsBiodiversityDatabases, GeneticDNA Barcoding, TaxonomicMetagenomeMetagenomicsMicrobiotaNew ZealandPhylogenyRNA, Ribosomal, 18SRNA, Ribosomal, 18S

Identifiers

PMID30389965
PMCPMC6215007

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.