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Record W2980835560 · doi:10.1002/edn3.35

Comparing eDNA metabarcoding and species collection for documenting Arctic metazoan biodiversity

2019· article· en· W2980835560 on OpenAlexafffundabout
Noémie Leduc, Anaïs Lacoursière‐Roussel, Katie E. Howland, Philippe Archambault, Maelle Sevellec, Éric Normandeau, Antoine Dispas, Gesche Winkler, Christopher W. McKindsey, Nathalie Simard, Louis Bernatchez

Bibliographic record

VenueEnvironmental DNA · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversité du Québec à RimouskiFisheries and Oceans CanadaUniversité Laval
FundersFisheries and Oceans CanadaChurchill Northern Studies CentreArcticNet
KeywordsEnvironmental DNABiodiversityArcticEcologyPhylumSpecies richnessBiologyInvertebrateBeta diversityMarine biodiversityDNA barcodingPelagic zoneFishery

Abstract

fetched live from OpenAlex

Abstract Background Arctic biodiversity has long been poorly documented and is now facing rapid transformations due to ongoing climate change and other impacts, including shipping activities. These changes are placing marine coastal invertebrate communities at greater risk, especially in sensitive areas such as commercial ports. Preserving biodiversity is a significant challenge, going far beyond the protection of charismatic species and involving suitable knowledge of the spatiotemporal organization of species. Therefore, knowledge of alpha, beta, and gamma biodiversity is of great importance to achieve this objective, particularly when partnered with new cost‐effective approaches to monitor biodiversity. Method and results This study compares metabarcoding of COI mitochondrial and 18S rRNA genes from environmental DNA (eDNA) water samples with standard invertebrate species collection methods to document community patterns at multiple spatial scales. Water samples (250 ml) were collected at three different depths within three Canadian Arctic ports: Churchill, MB; Iqaluit, NU; and Deception Bay, QC. From these samples, 202 genera distributed across more than 15 phyla were detected using eDNA metabarcoding, of which only 9%–15% were also identified through species collection at the same sites. Significant differences in taxonomic richness and community composition were observed between eDNA and species collections at both local and regional scales. This study shows that eDNA dispersion in the Arctic Ocean reduces beta diversity in comparison with species collections while emphasizing the importance of pelagic life stages for eDNA detection. Conclusion The study also highlights the potential of eDNA metabarcoding to assess large‐scale Arctic marine invertebrate diversity while emphasizing that eDNA and species collection should be considered as complementary tools to provide a more holistic picture of coastal marine invertebrate communities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.190
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations103
Published2019
Admission routes3
Has abstractyes

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