Resistance of Arctic phytoplankton to ocean acidification and enhanced irradiance
Bibliographic record
Abstract
The Arctic Ocean is a region particularly prone to ongoing ocean acidification (OA) and climate-driven changes. The influence of these changes on Arctic phytoplankton assemblages, however, remains poorly understood. In order to understand how OA and enhanced irradiances (e.g., resulting from sea–ice retreat) will alter the species composition, primary production, and eco-physiology of Arctic phytoplankton, we conducted an incubation experiment with an assemblage from Baffin Bay (71°N, 68°W) under different carbonate chemistry and irradiance regimes. Seawater was collected from just below the deep Chl a maximum, and the resident phytoplankton were exposed to 380 and 1000 µatm pCO 2 at both 15 and 35% incident irradiance. On-deck incubations, in which temperatures were 6 °C above in situ conditions, were monitored for phytoplankton growth, biomass stoichiometry, net primary production, photo-physiology, and taxonomic composition. During the 8-day experiment, taxonomic diversity decreased and the diatom Chaetoceros socialis became increasingly dominant irrespective of light or CO 2 levels. We found no statistically significant effects from either higher CO 2 or light on physiological properties of phytoplankton during the experiment. We did, however, observe an initial 2-day stress response in all treatments, and slight photo-physiological responses to higher CO 2 and light during the first five days of the incubation. Our results thus indicate high resistance of Arctic phytoplankton to OA and enhanced irradiance levels, challenging the commonly predicted stimulatory effects of enhanced CO 2 and light availability for primary production.
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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 | 0.001 | 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".