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Open AccessJournal ArticleDOI

Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding

TLDR
Compared with standard biodiversity data sets, metabarcoded samples are taxonomically more comprehensive, many times quicker to produce, less reliant on taxonomic expertise and auditable by third parties, which is essential for dispute resolution.
Abstract
To manage and conserve biodiversity, one must know what is being lost, where, and why, as well as which remedies are likely to be most effective. Metabarcoding technology can characterise the species compositions of mass samples of eukaryotes or of environmental DNA. Here, we validate metabarcoding by testing it against three high-quality standard data sets that were collected in Malaysia (tropical), China (subtropical) and the United Kingdom (temperate) and that comprised 55,813 arthropod and bird specimens identified to species level with the expenditure of 2,505 person-hours of taxonomic expertise. The metabarcode and standard data sets exhibit statistically correlated alpha- and beta-diversities, and the two data sets produce similar policy conclusions for two conservation applications: restoration ecology and systematic conservation planning. Compared with standard biodiversity data sets, metabarcoded samples are taxonomically more comprehensive, many times quicker to produce, less reliant on taxonomic expertise and auditable by third parties, which is essential for dispute resolution.

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Environmental DNA - An emerging tool in conservation for monitoring past and present biodiversity

TL;DR: The achievements gained through analyses of eDNA from macro-organisms in a conservation context are reviewed, its potential advantages and limitations are discussed, and it is expected the eDNA-based approaches to move from single-marker analyses of species or communities to meta-genomic surveys of entire ecosystems to predict spatial and temporal biodiversity patterns.
Journal ArticleDOI

How Many Species of Insects and Other Terrestrial Arthropods Are There on Earth

TL;DR: With 1 million insect species named, this suggests that 80% remain to be discovered and that a greater focus should be placed on less-studied taxa such as many families of Coleoptera, Diptera, and Hymenoptera and on poorly sampled parts of the world.
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Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass—Sequence Relationships with an Innovative Metabarcoding Protocol

TL;DR: A DNA metabarcoding protocol that utilises the standard cytochrome c oxidase subunit I (COI) barcoding fragment to detect freshwater macroinvertebrate taxa and indicated that primer efficiency is highly species-specific would prevent straightforward assessments of species abundance and biomass in a sample.
Journal ArticleDOI

Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data.

TL;DR: The level of replication required for accurate detection of targeted taxa in different contexts was evaluated and whether statistical approaches developed to estimate occupancy in the presence of observational errors can successfully estimate true prevalence, detection probability and false‐positive rates was evaluated.
Journal ArticleDOI

DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match

TL;DR: It is argued that COI does not contain suitably conserved regions for most amplicon-based metabarcoding applications and available marker choices should be broadened in order to maximize potential in this exciting field of research.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI

Search and clustering orders of magnitude faster than BLAST

Robert C. Edgar
- 01 Oct 2010 - 
TL;DR: UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters and offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.
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