Experimental assessment of the sensitivity of an estuarine phytoplankton fall bloom to acidification and warming
Bibliographic record
Abstract
Abstract. We investigated the combined effect of ocean acidification and warming on the dynamics of the phytoplankton fall bloom in the Lower St. Lawrence Estuary (LSLE), Canada. Twelve 2600 L mesocosms were set to initially cover a wide range of pHT (pH on the total proton scale) from 8.0 to 7.2 corresponding to a range of pCO2 from 440 to 2900 µatm, and two temperatures (in situ and +5 ∘C). The 13-day experiment captured the development and decline of a nanophytoplankton bloom dominated by the chain-forming diatom Skeletonema costatum. During the development phase of the bloom, increasing pCO2 influenced neither the magnitude nor the net growth rate of the nanophytoplankton bloom, whereas increasing the temperature by 5 ∘C stimulated the chlorophyll a (Chl a) growth rate and maximal particulate primary production (PP) by 76 % and 63 %, respectively. During the declining phase of the bloom, warming accelerated the loss of diatom cells, paralleled by a gradual decrease in the abundance of photosynthetic picoeukaryotes and a bloom of picocyanobacteria. Increasing pCO2 and warming did not influence the abundance of picoeukaryotes, while picocyanobacteria abundance was reduced by the increase in pCO2 when combined with warming in the latter phase of the experiment. Over the full duration of the experiment, the time-integrated net primary production was not significantly affected by the pCO2 treatments or warming. Overall, our results suggest that warming, rather than acidification, is more likely to alter phytoplankton autumnal bloom development in the LSLE in the decades to come. Future studies examining a broader gradient of temperatures should be conducted over a larger seasonal window in order to better constrain the potential effect of warming on the development of blooms in the LSLE and its impact on the fate of primary production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".