Reversal in competitive dominance of a toxic versus non-toxic cyanobacterium in response to rising CO2
Bibliographic record
Abstract
Climate change scenarios predict a doubling of the atmospheric CO(2) concentration by the end of this century. Yet, how rising CO(2) will affect the species composition of aquatic microbial communities is still largely an open question. In this study, we develop a resource competition model to investigate competition for dissolved inorganic carbon in dense algal blooms. The model predicts how dynamic changes in carbon chemistry, pH and light conditions during bloom development feed back on competing phytoplankton species. We test the model predictions in chemostat experiments with monocultures and mixtures of a toxic and non-toxic strain of the freshwater cyanobacterium Microcystis aeruginosa. The toxic strain was able to reduce dissolved CO(2) to lower concentrations than the non-toxic strain, and became dominant in competition at low CO(2) levels. Conversely, the non-toxic strain could grow at lower light levels, and became dominant in competition at high CO(2) levels but low light availability. The model captured the observed reversal in competitive dominance, and was quantitatively in good agreement with the results of the competition experiments. To assess whether microcystins might have a role in this reversal of competitive dominance, we performed further competition experiments with the wild-type strain M. aeruginosa PCC 7806 and its mcyB mutant impaired in microcystin production. The microcystin-producing wild type had a strong selective advantage at low CO(2) levels but not at high CO(2) levels. Our results thus demonstrate both in theory and experiment that rising CO(2) levels can alter the community composition and toxicity of harmful algal blooms.
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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) | 0.001 | 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 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".