Synergistic Oxidative Stress of Co‐Occurring Cyanobacterial Bloom and Invasive Fish on an Endangered Macrophyte
Bibliographic record
Abstract
ABSTRACT In freshwater ecosystems, aquatic macrophytes often have to cope with multiple stressors, in particular those caused by human activities. Cyanobacterial blooms spurred by eutrophication and the introduction of non‐native species can cause significant changes in macrophyte health and stability. Here we examined how the bloom‐forming cyanobacteria Microcystis aeruginosa and the invasive fish Pseudorasbora parva impact the growth and survival of Ottelia acuminata and how these stressors may interact to impair ongoing restoration efforts of this endemic macrophyte. Macrophytes were exposed to each species alone or in combination, and their effects on growth, antioxidant mechanisms, anti‐grazing deterrents, and osmotic regulation were assessed. Exposure to either M. aeruginosa or P. parva increased oxidative damage, stimulated the production of antioxidant enzymes catalase (CAT), ascorbate peroxidase (APX), and peroxidase (POD), and increased concentrations of protective compounds tannins and total phenols in O. acuminata leaves. The significantly elevated oxidative damage observed with combined exposure indicated a potential synergistic effect on lipid peroxidation. Glutathione reductase and total flavonoids were also significantly increased in this treatment, with the combined cyanobacteria‐fish exposure producing an exacerbated effect compared to the single treatments. Coinciding stressors of cyanobacterial blooms and invasions by P. parva have the potential to interact synergistically by exacerbating oxidative stress and stimulating both enzymatic (e.g., GR) and nonenzymatic (e.g., flavonoids) stress response components while also stimulating other protective systems, including grazing deterrence and osmotic solute production. Interactions between invasive species and harmful algal blooms, two common stressors across freshwater ecosystems, represent a critical area of study for future restoration efforts for both O. acuminata and other imperilled aquatic macrophytes, allowing stakeholders and researchers to optimise the effectiveness of management plans.
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 | 0.000 |
| 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 | 0.001 |
| 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".