Importance of ice algae versus phytoplankton in the diet of megabenthic organisms under contrasting sea ice conditions (Canadian Arctic): a dual biochemical approach (SIA and HBIs)
Bibliographic record
Abstract
The effects of global warming are most pronounced at high latitudes and are a threat to primary productivity patterns and, in particular, to sea ice algae. Here, we investigated the importance of ice algae in the diet of megabenthic organisms belonging to several feeding guilds across several locations in the Canadian Arctic characterised by different sea ice conditions using two biochemical approaches i.e., stable isotope and highly branched isoprenoid (HBI) lipids analysis. In addition, the short-term ingestion (gut contents) versus mid to long-term assimilation (tissues) of carbon were investigated to depict momentary condition in the present and the recent past. Our results show firstly that, as soon as the ice breaks up, ice algae accounts for a high proportion of the organic matter deposited to the seafloor and can provide a substantial carbon input to benthic communities for a long period of time (up to 79 days after sea ice break up in our case). Overall, organisms responded rapidly and efficiently to this pulse of fresh organic matter but trends in resource utilisation (quality and quantity) were observed based on feeding strategy. Deposit feeders (except those from lasting sea ice cover) and predators/scavengers showed a dominance of ice algae feeding, while suspension feeder showed a stronger reliance on phytoplankton. Finally, the spatial variability in resource utilisation by ophiuroids is likely related to area’s specificities (e.g., primary production, ice break-up timing, grazer abundance) and highlighted their ability to adapt to available food by switching their feeding types. Our data show that sympagic (ice-associated) carbon represents a significant proportion of the carbon ingested by the megabenthic organisms in the Canadian Arctic during spring/summer but appears to be highly variable depending on sea ice conditions and availability (e.g., patchiness, depth) on the seafloor. Overall, the ongoing decline in seasonal sea ice could alter the functioning and dynamic of the benthic food web in the Canadian Arctic if certain feeding types (e.g., deposit feeders) are unable to adapt to a change in primary productivity patterns.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".