Oysters and eelgrass: potential partners in a high <scp>pCO</scp><sub>2</sub> ocean
Bibliographic record
Abstract
Abstract Climate change is affecting the health and physiology of marine organisms and altering species interactions. Ocean acidification (OA) threatens calcifying organisms such as the Pacific oyster, Crassostrea gigas. In contrast, seagrasses, such as the eelgrass Zostera marina, can benefit from the increase in available carbon for photosynthesis found at a lower seawater pH. Seagrasses can remove dissolved inorganic carbon from OA environments, creating local daytime pH refugia. Pacific oysters may improve the health of eelgrass by filtering out pathogens such as Labyrinthula zosterae (LZ), which causes eelgrass wasting disease (EWD). We examined how co‐culture of eelgrass ramets and juvenile oysters affected the health and growth of eelgrass and the mass of oysters under different pCO2 exposures. In Phase I, each species was cultured alone or in co‐culture at 12°C across ambient, medium, and high pCO2 conditions, (656, 1,158 and 1,606 μatm pCO2, respectively). Under high pCO2, eelgrass grew faster and had less severe EWD (contracted in the field prior to the experiment). Co‐culture with oysters also reduced the severity of EWD. While the presence of eelgrass decreased daytime pCO2, this reduction was not substantial enough to ameliorate the negative impact of high pCO2 on oyster mass. In Phase II, eelgrass alone or oysters and eelgrass in co‐culture were held at 15°C under ambient and high pCO2 conditions, (488 and 2,013 μatm pCO2, respectively). Half of the replicates were challenged with cultured LZ. Concentrations of defensive compounds in eelgrass (total phenolics and tannins), were altered by LZ exposure and pCO2 treatments. Greater pathogen loads and increased EWD severity were detected in LZ exposed eelgrass ramets; EWD severity was reduced at high relative to low pCO2. Oyster presence did not influence pathogen load or EWD severity; high LZ concentrations in experimental treatments may have masked the effect of this treatment. Collectively, these results indicate that, when exposed to natural concentrations of LZ under high pCO2 conditions, eelgrass can benefit from co‐culture with oysters. Further experimentation is necessary to quantify how oysters may benefit from co‐culture with eelgrass, examine these interactions in the field and quantify context‐dependency.
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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 | 0.001 |
| 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".