Experimental blooms of the cyanobacterium Gloeotrichia echinulata increase phytoplankton biomass, richness and diversity in an oligotrophic lake
Bibliographic record
Abstract
Cyanobacterial blooms are increasing in lakes, both eutrophic and oligotrophic, in many parts of the world. Freshwater cyanobacteria generally have negative effects on eukaryotic phytoplankton in eutrophic systems because of their ability to form dense surface aggregations (scums) that reduce light availability. However, less is known about the effects of cyanobacteria on other phytoplankton in oligotrophic lakes. Because Gloeotrichia echinulata, a large colonial cyanobacterium, has been increasingly observed in low-nutrient lakes in the northeastern USA and Canada, we investigated its effects on phytoplankton biomass and community structure. In field and laboratory experiments, high densities of Gloeotrichia had significant positive effects on the biomass of small phytoplankton (<30 μm, typically considered edible to zooplankton) relative to no-Gloeotrichia controls. Interestingly, Gloeotrichia also increased phytoplankton taxa richness and Shannon diversity, primarily by stimulating the richness and biovolume of Bacillariophyta (diatoms) and Chlorophyta (green algae). Our laboratory experiment further suggests that at high densities, Gloeotrichia may have stimulated the other phytoplankton by leaking nitrogen and phosphorus. Thus, this study suggests that continued increases in Gloeotrichia in low-nutrient lakes are likely to increase phytoplankton biomass and alter community structure in these systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".