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Record W3009315083 · doi:10.1002/edn3.76

Validating environmental DNA metabarcoding for marine fishes in diverse ecosystems using a public aquarium

2020· article· en· W3009315083 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnvironmental DNA · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of TorontoUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationOntario Genomics InstituteGenome Canada
KeywordsEnvironmental DNABiodiversityEcologyBiologyEcosystemMarine ecosystemKey (lock)

Abstract

fetched live from OpenAlex

Abstract Environmental DNA metabarcoding has been widely touted as a powerful tool for monitoring biodiversity in marine ecosystems. However, this method still requires thorough validation and standardization before it can be widely applied for ecological monitoring. The potential utility of environmental DNA metabarcoding is greatest in systems with high levels of diversity, yet environmental DNA metabarcoding has typically been validated in closed systems with relatively low levels of diversity. Additionally, the use of a multiple marker approach has been minimally explored under controlled, closed systems in the literature. Using a pilot study, we assess the ability of eDNA metabarcoding to capture biodiversity in a highly diverse closed system at the Ripley's Aquarium of Canada in Toronto, Ontario. Our pilot study highlights several key knowledge gaps that must be addressed before metabarcoding can be employed for widespread use in ecological monitoring. We found that environmental DNA metabarcoding recovered just over 50% of target species and 80% of target genera within a closed marine system containing 107 species and 44 genera using previously published markers for COI, 12S, and 16S . Additionally, COI and 12S were found to identify fewer target species than 16S, with COI generating the most off‐target identifications, but maximum detection success was achievable by combining all three markers. We discuss numerous key limitations which currently present barriers to the application of eDNA metabarcoding for studying highly diverse marine systems. These include marker selection, data validation and confidence, and the complexity of abundant and diverse novel systems. This study highlights important, yet incompletely resolved, challenges of environmental DNA metabarcoding for the detection of marine fishes in a diverse closed system environment using a multiple marker approach.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.050
GPT teacher head0.219
Teacher spread0.169 · 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