Contrasting effects of acidification and warming on dimethylsulfide concentrations during a temperate estuarine fall bloom mesocosm experiment
Bibliographic record
Abstract
The effects of ocean acidification and warming on the concentrations of dimethylsulfoniopropionate (DMSP) and dimethylsulfide (DMS) were investigated during a mesocosm experiment in the Lower St. Lawrence Estuary (LSLE) in the fall of 2014. Twelve mesocosms covering a range of pH T (pH on the total hydrogen ion concentration scale) from 8.0 to 7.2, corresponding to a range of CO 2 partial pressures ( p CO 2 ) from 440 to 2900 µatm, at two temperatures (in situ and +5 ∘ C; 10 and 15 ∘ C) were monitored during 13 days. All mesocosms were characterized by the rapid development of a diatom bloom dominated by Skeletonema costatum , followed by its decline upon the exhaustion of nitrate and silicic acid. Neither the acidification nor the warming resulted in a significant impact on the abundance of bacteria over the experiment. However, warming the water by 5 ∘ C resulted in a significant increase in the average bacterial production (BP) in all 15 ∘ C mesocosms as compared to 10 ∘ C, with no detectable effect of p CO 2 on BP. Variations in total DMSP (DMSP t = particulate + dissolved DMSP) concentrations tracked the development of the bloom, although the rise in DMSP t persisted for a few days after the peaks in chlorophyll a . Average concentrations of DMSP t were not affected by acidification or warming. Initially low concentrations of DMS (<1 nmol L −1 ) increased to reach peak values ranging from 30 to 130 nmol L −1 towards the end of the experiment. Increasing the p CO 2 reduced the averaged DMS concentrations by 66 % and 69 % at 10 and 15 ∘ C, respectively, over the duration of the experiment. On the other hand, a 5 ∘ C warming increased DMS concentrations by an average of 240 % as compared to in situ temperature, resulting in a positive offset of the adverse p CO 2 impact. Significant positive correlations found between bacterial production and concentrations of DMS throughout our experiment point towards temperature-associated enhancement of bacterial DMSP metabolism as a likely driver of the mitigating effect of warming on the negative impact of acidification on the net production of DMS in the LSLE and potentially the global ocean.
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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 | 0.001 |
| 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".