Heatwave‐mediated decreases in phytoplankton quality negatively affect zooplankton productivity
Bibliographic record
Abstract
Abstract Climate change is expected to increase the frequency of extreme temperature events. The effect of heatwaves on phytoplankton is of particular concern because they are a key source of C, N, P and essential fatty acids to aquatic ecosystems. Laboratory studies have demonstrated that phytoplankton grown at warmer temperatures are a lower quality food source, but how heatwaves affect phytoplankton quality at the community scale is currently unclear. Here we address this knowledge gap by growing natural assemblages of freshwater phytoplankton at “ambient”, “constant warming” or “heatwave” conditions. We next fed these phytoplankton communities to natural assemblages of zooplankton to test the prediction that zooplankton that consume heatwave‐exposed phytoplankton will exhibit reductions in biomass. Our experiment demonstrated that zooplankton that consumed “heatwave” phytoplankton attained lower community biomass than those fed “constant warming” or “ambient” phytoplankton. Additionally, despite receiving similar total heat input, phytoplankton exposed to “heatwave” conditions contained lower C, N, P and fatty acid concentrations compared to phytoplankton grown in “constant warming” conditions. Correlations between zooplankton biomass and all measured phytoplankton traits revealed that decreases in zooplankton biomass were best explained by low quantities of C, N and monounsaturated fatty acids in “heatwave” phytoplankton. Our study demonstrates that the effects of heatwaves on phytoplankton quality are clearly distinct from those caused by constant warming temperatures and that heatwave‐mediated decreases in resource quality have immediate effects on consumer productivity. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".