Antibiotic pollution promotes dominance by harmful cyanobacteria: A case study examining norfloxacin exposure in competition experiments
Bibliographic record
Abstract
Cyanobacterial harmful algal blooms (cyanoHABs) in freshwater lakes across the globe are often combined with other stressors. Pharmaceutical pollution, especially antibiotics in water bodies, poses a potential hazard in aquatic ecosystems. However, how antibiotics influence the risk of cyanoHABs remains unclear. Here, we investigated the effects of norfloxacin (NOR), one of the most widely used antibiotics globally, to a bloom‐forming cyanobacterium ( Microcystis aeruginosa ) and a common green alga ( Scenedesmus quadricauda ), under both mono‐ and coculture conditions. Taxon‐specific responses to NOR were evaluated in monoculture. In addition, the growth rate and change in ratio of cyanobacteria to green algae when cocultured with exposure to NOR were determined. In monocultures of Microcystis , exposure to low concentrations of NOR resulted in decreases in biomass, chlorophyll a and soluble protein content, while superoxide anion content and superoxide dismutase activity increased. However, NOR at high concentration only slightly affected Scenedesmus . During the co‐culture trials of Microcystis and Scenedesmus , the 5 μg · L −1 NOR treatment increased the ratio of Microcystis to co‐cultured Scenedesmus by 47.2%. Meanwhile, although Scenedesmus growth was enhanced by 4.2% under NOR treatment in monoculture, it was conversely inhibited by 63.4% and 38.2% when co‐cultured with Microcystis with and without NOR, respectively. Our results indicate that antibiotic pollution has a potential risk to enhance the perniciousness of cyanoHABs by disturbing interspecific interaction between cyanobacteria and green algae. These results reinforce the need for scientists and managers to consider the influence of xenobiotics in shaping the outcome of interactions among multiple species in aquatic ecosystems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".