Effects of experimental greenhouse warming on phytoplankton and zooplankton communities in fishless alpine ponds
Bibliographic record
Abstract
The impacts of global warming on aquatic ecosystems are expected to be most pronounced at higher trophic levels in coldwater environments. Therefore, we hypothesized that warming of fishless alpine ponds would suppress large‐bodied consumers (e.g., cladocerans, copepods) and stimulate fast‐growing microorganisms (e.g., phytoflagellates, rotifers), thereby altering the community composition and total abundance of zooplankton and phytoplankton. This hypothesis was tested using three blocks of four experimental mesocosms (1000‐liter capacity) that were located next to alpine ponds in Banff National Park, Canada. Each block received unfiltered pond water and sediment from a pond following ice out in June 2000. A warming treatment (control vs. 3.6°C warmed) was achieved by controlling the ventilation of greenhouse canopies that were suspended over each of the mesocosms. By the end of our 50‐d experiment, warming significantly suppressed total zooplankton biomass because large cladocerans (Daphnia pulex) declined while rotifer (Keratella cochlearis, Conochilus unicornis) abundance increased during the second half of the experiment. In contrast, warming did not affect total phytoplankton biomass but significantly altered community composition by favoring phytoflagellates (Mallomonas, Synura, Trachelomonas) over larger filamentous green algae (Mougeotia, w). Warming did not significantly increase dissolved nitrogen and phosphorus concentrations. Therefore, warmer growing conditions and reduced grazer biomass best explained the increased abundance of more edible, fast‐growing phytoflagellates in the warmed mesocosms. Our findings support the hypothesis that moderate warming can destabilize plankton dynamics, thereby potentially reducing the reliability of water quality and food resources for higher trophic levels (e.g., planktivorous fish) in shallow cold‐water ecosystems.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".