Biological responses to change in Antarctic sea ice habitats
Bibliographic record
Abstract
Sea ice is a key habitat in the high latitude Southern Ocean and is predicted to change in its extent, thickness and duration in coming decades. The sea-ice cover is instrumental in mediating ocean–atmosphere exchanges and provides an important substrate for organisms from microbes and algae to predators. Antarctic krill,Euphausia superba,is reliant on sea ice during key phases of its life cycle, particularly during the larval stages, for food and refuge from their predators, while other small grazers, including copepods and amphipods, either live in the brine channel system or find food and shelter at the ice-water interface and in gaps between rafted ice blocks. Fish, such as the Antarctic silverfishPleuragramma antarcticum, use platelet ice (loosely-formed frazil crystals) as an essential hatching and nursery ground. In this paper, we apply the framework of the Marine Ecosystem Assessment for the Southern Ocean (MEASO) to review current knowledge about relationships between sea ice and associated primary production and secondary consumers, their status and the drivers of sea-ice change in this ocean. We then use qualitative network modelling to explore possible responses of lower trophic level sea-ice biota to different perturbations, including warming air and ocean temperatures, increased storminess and reduced annual sea-ice duration. This modelling shows that pelagic algae, copepods, krill and fish are likely to decrease in response to warming temperatures and reduced sea-ice duration, while salp populations will likely increase under conditions of reduced sea-ice duration and increased number of days of >0°C. Differences in responses to these pressures between the five MEASO sectors were also explored. Greater impacts of environmental pressures on ice-related biota occurring presently were found for the West and East Pacific sectors (notably the Ross Sea and western Antarctic Peninsula), with likely flow-on effects to the wider ecosystem. All sectors are expected to be impacted over coming decades. Finally, we highlight priorities for future sea ice biological research to address knowledge gaps in this field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".