The ecology of bloom forming cyanobacteria: Food web interactions and environmental correlates
Bibliographic record
Abstract
Cyanobacterial blooms occur when one or a few species of cyanobacteria dominate an ecosystem. These blooms can be detrimental to human well being and ecosystems as many bloom-forming cyanobacterial species produce toxins. At a regional scale, cyanobacterial blooms are more likely in eutrophic lakes. In mesotrophic lakes, however, it is difficult to predict cyanobacterial blooms. This may be due in part to a lack of working models to describe cyanobacteria-zooplankton interactions as well as a lack of field studies at appropriate scales. This thesis investigates how, at different temporal scales, toxin-producing, bloom-forming cyanobacteria interact with zooplankton and the importance of biotic and abiotic factors in explaining both microcystin content and cyanobacterial population dynamics. To accomplish this objective, a range of methods was employed: meta-analysis of literature data, experimentation and time-series analysis of a shallow mesotrophic lake (Constance Lake, Ontario) based on high frequency sampling over 2--3 years. The meta-analysis revealed high variability both within and across zooplankton species in their response to cyanobacteria. However, in most cases, zooplankton maintained positive growth rates when fed a diet containing cyanobacteria. A laboratory experiment with Daphnia showed that zooplankton can adapt to avoid toxic strains of cyanobacteria. Both these results suggest that zooplankton may play a role in controlling cyanobacterial biomass and potentially shifting cyanobacterial strains towards toxic genotypes. Field observations found that abiotic conditions, mainly temperature and pH, were more important than zooplankton when explaining the variability of microcystins. This lack of relationship between zooplankton and microcystins may have been due to the low cladoceran abundances in Constance Lake. Negative correlations, however, were detected between Anabaena and Daphnia and with cyclopoid copepods. Generally across cyanobacterial taxa, correlations with biotic variables such as the growth rates of rotifers and other cyanobacterial taxa, were more important than abiotic variables in explaining variability in cyanobacterial population growth rates. Surprisingly, these biotic relationships were predominantly positive suggesting that facilitation may play a larger role than previously thought in regulating cyanobacterial populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".