Effects of cyanobacterial toxicity and morphology on the population growth of freshwater zooplankton: Meta‐analyses of laboratory experiments
Bibliographic record
Abstract
We synthesized data from 66 published laboratory studies, representing 597 experimental comparisons, examining the effects of cyanobacterial toxicity and morphology on the population growth rate and survivorship of 17 genera (34 species) of freshwater, herbivorous zooplankton. Two meta‐analyses were conducted with these data. The primary analysis compared herbivore population growth rates for grazers fed treatment diets containing cyanobacteria versus control diets comprising phytoplankton that are generally considered to be nutritious for zooplankton (chlorophytes and/or flagellates). This analysis confirmed that cyanobacteria were poor foods relative to small chlorophytes and flagellates. More importantly, filamentous cyanobacteria were found to be significantly better foods for grazers than single‐celled cyanobacteria over all studies. Surprisingly, the presence or absence of commonly‐measured toxic compounds (microcystins in 70% of the cases) in the diet had no overall influence on grazer population growth relative to control diets. A secondary analysis compared survival rates for grazers fed cyanobacteria versus no food. In contrast to the primary analysis, grazer survival was more negatively affected by toxic cyanobacteria than non‐toxic cyanobacteria, relative to starvation. However, this difference was attributable to the effects of a single Microcystis strain, PCC7820. Thus, though some cyanobacterial strains appear to be toxic to some strains of zooplankton, the overall role of commonly‐assayed cyanobacterial toxins as a determinant of food quality may be less than widely assumed. We suggest that more attention be focused on nutritional deficiencies, morphology, and the toxicity of undescribed cyanobacterial compounds as mediators of the poor food quality of cyanobacteria for zooplankton.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".