The effect of elevated CO2 on growth and competition in experimental phytoplankton communities
Bibliographic record
Abstract
We report an experiment designed to identify the effect of elevated CO2 on species of phytoplankton in a simple laboratory system. Major taxa of phytoplankton differ in their ability to take up CO2, which might lead to predictable changes in the growth rate of species and thereby shifts in the composition of phytoplankton communities in response to rising CO2. Six species of phytoplankton belonging to three major taxa (cyanobacteria, diatoms and chlorophytes) were cultured in atmospheres whose CO2 concentration was gradually increased from ambient levels to 1000 parts per million over about 100 generations and then maintained for a further 200 generations at elevated CO2. The experimental design allowed us to trace a predictive sequence, from physiological features to the growth response of species to elevated CO2 in pure culture, from the growth response in pure culture to competitive ability in pairwise mixtures and from pairwise competitive ability to shifts in the relative abundance of species in the full community of all six species. CO2 altered the dynamics of growth in a fashion consistent with known differences among major taxa in their ability to take up and use CO2. This pure-culture response was partly successful in predicting the outcome of competition in pairwise mixtures, especially the enhanced competitive ability of chlorophytes relative to cyanobacteria, although generally statistical support was weak. The competitive response in pairwise mixtures was a good predictor of changes in competitive ability in the full community. Hence, there is a potential for forging a logical chain of inferences for predicting how phytoplankton communities will respond to elevated CO2. Clearly further extensive experiments will be required to validate this approach in the greater complexity found in diverse communities and environments of natural systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".