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Record W3160769752 · doi:10.1007/s00300-021-02868-7

Sea-ice habitat minimizes grazing impact and predation risk for larval Antarctic krill

2021· article· en· W3160769752 on OpenAlexaff
Carmen David, Fokje L. Schaafsma, J.A. van Franeker, Evgeny A. Pakhomov, Brian P. V. Hunt, Benjamin Lange, Giulia Castellani, Angelika Brandt, Hauke Flores

Bibliographic record

VenuePolar Biology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsTula FoundationUniversity of British ColumbiaFisheries and Oceans Canada
FundersSeventh Framework ProgrammeHelmholtz-Gemeinschaft
KeywordsEuphausiaBiologyWater columnKrillPredationPelagic zoneAntarctic krillZooplanktonSea icePhytoplanktonOceanographyFisheryEcologyNutrient

Abstract

fetched live from OpenAlex

Abstract Survival of larval Antarctic krill ( Euphausia superba ) during winter is largely dependent upon the presence of sea ice as it provides an important source of food and shelter. We hypothesized that sea ice provides additional benefits because it hosts fewer competitors and provides reduced predation risk for krill larvae than the water column. To test our hypothesis, zooplankton were sampled in the Weddell-Scotia Confluence Zone at the ice-water interface (0–2 m) and in the water column (0–500 m) during August–October 2013. Grazing by mesozooplankton, expressed as a percentage of the phytoplankton standing stock, was higher in the water column (1.97 ± 1.84%) than at the ice-water interface (0.08 ± 0.09%), due to a high abundance of pelagic copepods. Predation risk by carnivorous macrozooplankton, expressed as a percentage of the mesozooplankton standing stock, was significantly lower at the ice-water interface (0.83 ± 0.57%; main predators amphipods, siphonophores and ctenophores) than in the water column (4.72 ± 5.85%; main predators chaetognaths and medusae). These results emphasize the important role of sea ice as a suitable winter habitat for larval krill with fewer competitors and lower predation risk. These benefits should be taken into account when considering the response of Antarctic krill to projected declines in sea ice. Whether reduced sea-ice algal production may be compensated for by increased water column production remains unclear, but the shelter provided by sea ice would be significantly reduced or disappear, thus increasing the predation risk on krill larvae.

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 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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.015
GPT teacher head0.291
Teacher spread0.276 · 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 teacher head, 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

Citations15
Published2021
Admission routes1
Has abstractyes

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