Diatom-Specific Highly Branched Isoprenoids as Biomarkers in Antarctic Consumers
Bibliographic record
Abstract
The structure, functioning and dynamics of polar marine ecosystems are strongly influenced by the extent of sea ice. Ice algae and pelagic phytoplankton represent the primary sources of nutrition for higher trophic-level organisms in seasonally ice-covered areas, but their relative contributions to polar marine consumers remain largely unexplored. Here, we investigated the potential of diatom-specific lipid markers and highly branched isoprenoids (HBIs) for estimating the importance of these two carbon pools in an Antarctic pelagic ecosystem. Using GC-MS analysis, we studied HBI biomarkers in key marine species over three years in Adélie Land, Antarctica: euphausiids (ice krill Euphausia crystallorophias and Antarctic krill E. superba), fish (bald notothens Pagothenia borchgrevinki and Antarctic silverfish Pleuragramma antarcticum) and seabirds (Adélie penguins Pygoscelis adeliae, snow petrels Pagodroma nivea and cape petrels Daption capense). This study provides the first evidence of the incorporation of HBI lipids in Antarctic pelagic consumers. Specifically, a di-unsaturated HBI (diene) of sea ice origin was more abundant in ice-associated species than in pelagic species, whereas a tri-unsaturated HBI (triene) of phytoplanktonic origin was more abundant in pelagic species than in ice-associated species. Moreover, the relative abundances of diene and triene in seabird tissues and eggs were higher during a year of good sea ice conditions than in a year of poor ice conditions. In turn, the higher contribution of ice algal derived organic matter to the diet of seabirds was related to earlier breeding and higher breeding success. HBI biomarkers are a promising tool for estimating the contribution of organic matter derived from ice algae in pelagic consumers from Antarctica.
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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 | 0.000 | 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) | 0.000 | 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".