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Record W4289981979 · doi:10.1002/edn3.347

Environmental DNA tracks zooplankton population dynamics in experimental mesocosms

2022· article· en· W4289981979 on OpenAlexafffund
Danielle Bourque, Xueqi Wang, John M. Fryxell, Gustavo S. Betini, Kevin S. McCann, Robert Hanner

Bibliographic record

VenueEnvironmental DNA · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsMesocosmAbiotic componentZooplanktonDaphnia magnaEcologyBiologyAbundance (ecology)Environmental DNAPopulationPlanktonBiomass (ecology)Environmental scienceBiodiversityEcosystemChemistry

Abstract

fetched live from OpenAlex

Abstract Environmental DNA (eDNA) holds notable potential for biomonitoring and ecological research. However, its utility for quantifying temporal changes in abundance, biomass, and diversity remains contentious. We investigated biotic and abiotic factors influencing temporal variation in eDNA concentration in large, 28,000 L experimental mesocosms. Time series data demonstrated a positive relationship between the population abundance and biomass of the Cladoceran zooplankton species Daphnia magna and eDNA concentration, with a time lag of ~3.5 days in 25°C conditions, and up to 28 days in 15°C. Water temperature variations within mesocosms did not consistently influence eDNA quantity, although water temperature negatively predicted DNA concentration across mesocosms. Algal density negatively correlated with D. magna eDNA quantity across and within mesocosms. We demonstrate that eDNA signal detection can be used as a proxy for relative abundance and suggest that future investigation into the dynamics of eDNA shedding and degradation processes is warranted.

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 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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.002

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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