Sea-ice habitat minimizes grazing impact and predation risk for larval Antarctic krill
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".