An experimental test of cyanotoxins as a potential driver of microbial community structure
Bibliographic record
Abstract
Cyanobacterial harmful algal blooms (CyanoHABs) are common biological disturbances in freshwater ecosystems, impacting microbial community diversity and composition. While extensive research has focused on these blooms, the direct effects of cyanotoxins on microbial communities remain less understood. In this study, we investigated the impact of various cyanotoxins on the microbial community of an oligotrophic lake in Quebec, Canada (45.99°N, 74.00°W). Water samples were exposed to different concentrations of MC-LR, MC-RR, MC-LF, and CYN, both individually and in combination. These toxins were selected based on their prevalence, toxicity, and distinct chemical properties. Toxin concentrations were chosen in relation to the World Health Organization (WHO) regulatory thresholds, 1 μg/L as indicative of low toxin exposure (drinking water limit) and 1000 μg/L as indicative of high exposure (lake threshold). We performed a longitudinal analysis of 16S rRNA to assess changes in microbial community diversity and composition at 24-h, 48-h, and 72-h intervals. Our findings showed a significant change in alpha and beta diversity, highlighting shifts in community structure in response to high cyanotoxin doses. Conversely, no significant changes were detected across diverse cyanotoxin compositions. We then performed a differential analysis and identified several amplicon sequence variants (ASVs) with significant changes in relative abundance across cyanotoxin doses. This analysis highlighted potential cyanotoxins degrading bacteria, such as Paucibacter and Ideonella. Overall, our results showed that the changes were more associated with cyanotoxin doses than with composition. Understanding how cyanotoxins could impact oligotrophic lakes is essential for better predicting their ecological impacts, especially as these lakes are increasingly affected by cyanobacterial blooms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".