Interaction effects of zooplankton and<scp>CO</scp><sub>2</sub>on phytoplankton communities and the deep chlorophyll maximum
Bibliographic record
Abstract
Abstract Studies examining the consequences of increased partial pressure of carbon dioxide (pCO2) in freshwater ecosystems associated with climate change have focused on direct effects for phytoplankton, showing increases in primary productivity, biomass or altered composition. However, in lakes, phytoplankton dynamics are also regulated by zooplankton predation and thermal stratification which can lead to a concentration of phytoplankton biomass in a deep chlorophyll maximum (DCM) layer, making the response toCO2increase important to understand here. Mesocosm experiments were conducted in a meso‐oligotrophic north temperate lake with a strong summer phytoplanktonDCM, to estimate the independent and interaction effects of zooplankton grazing and elevatedpCO2on water column phytoplankton communities and onDCMcharacteristics. Interaction ofCO2effects with zooplankton grazing occurred for three of four phytoplankton spectral groups, influencing water column phytoplankton biomass. Zooplankton selective grazing appeared as important asCO2concentration in controlling phytoplankton population and biomass. Unexpectedly,CO2had an overall negative effect on phytoplankton total biomass despite positive productivity responses. ElevatedCO2led to more peaked (stronger vertical gradient) mid‐water column chlorophyll distribution, but to smallerDCMpeaks overall. Zooplankton had unexpected effects, inducing clustering of more edible phytoplankton and greater temporal variation in theDCM. Our experiment points to interactions in the microbial food web and stoichiometric considerations with nutrient addition that should be explored further in future work with realistic lake food webs to better understand these complex responses toCO2.
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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 | 0.000 |
| 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 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".