Amino acid δ13C and δ15N fingerprinting of sea ice and pelagic algae in Canadian Arctic and Subarctic Seas
Bibliographic record
Abstract
The on-going decline in Arctic sea ice represents a significant loss of habitat for sea ice algae, which are a major contributor to primary production in the Arctic. Data on sea ice algal production is limited due to difficulties in both accessing sea-ice and sampling under-ice algae. Compound-specific isotope analysis (CSIA) of amino acids (AAs) is emerging as a powerful tool to trace element origins and biogeochemical processes in marine food webs and may address the knowledge gaps in sea ice algal productivity dynamics. Here we measured δ13C-AA and δ15N-AA in natural communities of sea ice and pelagic algae collected from regions offshore Labrador and Nunavut, Canada. Significant difference in δ13C-AA patterns between sea ice and pelagic algae was observed in different size classes. This difference was further supported by multivariate analyses based on normalized δ13C of essential amino acids (EAAs), which demonstrated a clear separation between sea ice and pelagic algae. Beta (β) values and trophic position (TP) calculated from δ15N of Glutamic Acid and Phenylalanine (Phe) and ∑V parameter for microbial resynthesis indicated a slightly higher heterotrophic biomass in pelagic and sea ice samples as compared to cultured samples. This finding is consistent with the Phe-normalized δ15N of Alanine and Threonine, which provided better separations between sea ice/pelagic algae and other end-member groups. Overall, our study provides first insights into the potential differences in δ13C-AA and δ15N-AA patterns between sea ice and pelagic algae and suggests carbon of sea ice origins may be distinguished from pelagic sources using CSIA-AA approach. These observations highlight the potential of CSIA-AA to estimate proportional contributions of sea ice and pelagic algae to export production and efficiency of benthic-pelagic coupling in polar marine environments.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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".