Energetic context determines species and community responses to ocean acidification
Bibliographic record
Abstract
Physiological responses to ocean acidification are thought to be related to energetic trade-offs. Although a number of studies have proposed that negative responses to low pH could be minimized in situations where food resources are more readily available, evidence for such effects on individuals remain mixed, and the consequences of such effects at the community level remain untested. We explored the potential for food availability and diet quality to modify the effects of acidification on developing marine fouling communities in field-deployed mesocosms by supplementing natural food supply with one of two species of phytoplankton, differing in concentration of fatty acids. After 12 weeks, no species demonstrated the interactive effects generally predicted in the literature, where a positive overall effect of diet mitigated the negative overall effects of acidification. Rather, for some species, additional food supply appeared to bring out or exacerbate the negative effects of low pH. Community richness and structure were only altered by acidification, while space occupation and evenness reflected patterns of the most dominant species. Importantly, we find that acidification stress can increase the relative abundance of invasive species, even under resource conditions that otherwise prevented invasive species establishment. Overall, the proposed hypothesis regarding the ability for food addition to mitigate the negative effects of acidification is thus far not widely supported at species or community levels. It is clear that acidification is a strong driving force in these communities but understanding underlying energetic and competitive context is essential to developing mechanistic predictions for climate change responses.
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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 | 0.001 |
| 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".