Contrasting nonphotochemical quenching patterns under high light and darkness aligns with light niche occupancy in Arctic diatoms
Bibliographic record
Abstract
Abstract Over the seasons, Arctic diatom species occupy shifting habitats defined by contrasting light climates, constrained by snow and ice cover dynamics interacting with extreme photoperiod and solar angle variations. How Arctic diatom photoadaptation strategies differ across their heterogeneous light niches remains a poorly documented but crucial missing link to anticipate Arctic Ocean responses to shrinking sea‐ice and increasing light. To address this question, we selected five Arctic diatom species with diverse life traits, representative of distinct light niches across the seasonal light environment continuum: from snow‐covered dimly lit bottom ice to summer stratified waters. We studied their photoacclimation plasticity to two growth light levels and the subsequent responses of their nonphotochemical quenching (NPQ) and xanthophyll cycle to both dark incubations and light shifts. We deciphered NPQ and xanthophyll cycle tuning in darkness and their light‐dependent induction kinetics, which aligned with species' light niche occupancy. In ice‐related species, NPQ was sustained in darkness and its induction was more reactive to moderate light shifts. Open‐water species triggered strong NPQ induction in darkness and reached higher maximal NPQ under high light. Marginal ice zone species showed strong adaptation to light fluctuations with a dark response fine‐tuned depending upon light history. We argue these traits are anchored in diverging photoadaption strategies fostering Arctic diatom success in their respective light niches.
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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 | 0.000 | 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 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".