Effect of ocean acidification on microbial diversity and on microbe-driven biogeochemistry and ecosystem functioning
Bibliographic record
Abstract
The ocean absorbs about 25% of anthropogenic CO 2 emissions, which alters its chemistry.Among the changes of the carbonate system are an increase in the partial pressure of CO 2 (pCO 2 ) and a decline of pH; hence, the whole process is often referred to as 'ocean acidification'.Many microbial processes can be affected either directly or indirectly via a cascade of effects through the response of non-microbial groups and/or through changes in seawater chemistry.We briefly review the current understanding of the impact of ocean acidification on microbial diversity and processes, and highlight the gaps that need to be addressed in future research.The focus is on Bacteria, Archaea, viruses and protistan grazers but also includes total primary production of phytoplankton as well as species composition of eukaryotic phytoplankton.Some species and communities exhibit increased primary production at elevated pCO 2 .In contrast to their heterocystous counterparts, nitrogen fixation by non-heterocystous cyanobacteria is stimulated by elevated pCO 2 .The experimental data on the response of prokaryotic production to ocean acidification are not consistent.Very few other microbial processes have been investigated at environmentally relevant pH levels.The potential for microbes to adapt to ocean acidification, at either the species level by genetic change or at the community level through the replacement of sensitive species or groups by non-or less sensitive ones, is completely unknown.Consequently, the impact of ocean acidification on keystone species and microbial diversity needs to be elucidated.Most experiments used a short-term perturbation approach by using cultured organisms; few were conducted in mesocosms and none in situ.There is likely a lot to be learned from observations in areas naturally enriched with CO 2 , such as vents, upwelling and near-shore areas.
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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 | 0.001 | 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 | 0.001 | 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".