Could increased cyanobacterial biomass following forest harvesting cause a reduction in zooplankton body size structure?
Bibliographic record
Abstract
Changes in phytoplankton and zooplankton communities were studied in 16 lake-years during a large-scale experimental before–after study as part of the Terrestrial Riparian Organisms Lakes and Streams (TROLS) project. After forest harvesting, phytoplankton biomass and especially cyanobacterial biomass increased in some of the experimental lakes, up to an order of magnitude. Although the response of phytoplankton was not directly related to the intensity of forest harvesting, this large-scale experiment provided a unique opportunity for studying the effects of an increasing gradient in cyanobacterial biomass on zooplankton communities. Cladoceran size structure and biomass were negatively correlated with cyanobacterial biomass, suggesting that large cladocerans and especially daphnids were inhibited by the increase in cyanobacterial biomass. The increase in cyanobacterial biomass seems to have caused a significant reduction in the size structure of zooplankton communities. As no correlation was found between cladocerans and cyanotoxins, our results point more towards feeding inhibition by filamentous and colonial cyanobacteria as a possible cause for the decline in the size structure and biomass of zooplankton communities. These results suggest that the increase in cyanobacterial biomass is likely to cause a shift in the size of zooplankton communities, which will lead to a profound change in functioning of lake 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".