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Changes in zooplankton communities from epipelagic to lower mesopelagic waters

2019· article· en· W2922513286 on OpenAlexaff
Paris V. Stefanoudis, Molly Rivers, Helen Ford, Igor Yashayaev, Alex D. Rogers, Lucy C. Woodall

Bibliographic record

VenueMarine Environmental Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersGarfield Weston Foundation
KeywordsMesopelagic zoneZooplanktonTrophic levelCopepodPelagic zoneFood webWater columnSpecies richnessEcologyOceanographyAbundance (ecology)BiologyEnvironmental scienceGelatinous zooplanktonCrustaceanGeology

Abstract

fetched live from OpenAlex

Zooplankton form a trophic link between primary producers and higher trophic levels, and exert significant influence on the vertical transport of carbon through the water column ('biological carbon pump'). Using a MultiNet we sampled and studied mesozooplankton communities (i.e. >0.2 mm) from six locations around Bermuda targeting four depth zones: ∼0-200 m, ∼200-400 m, ∼400-600 m (deep-scattering layer), and ∼600-800 m. Copepoda, our focal taxonomic group, consistently dominated samples (∼80% relative abundance). We report declines in zooplankton and copepod abundance with depth, concurrent with decreases in food availability. Taxonomic richness was lowest at depth and below the deep-scattering layer. In contrast, copepod diversity peaked at these depths, suggesting lower competitive displacement in these more food-limited waters. Finally, omnivory and carnivory, were the dominant trophic traits, each one affecting the biological carbon pump in a different way. This highlights the importance of incorporating data on zooplankton food web structure in future modelling of global ocean carbon cycling.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.087
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1050.018

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 head0.029
GPT teacher head0.263
Teacher spread0.234 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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