Unprecedented Harmful algal bloom in the UK and Ireland’s largest lake associated with gastrointestinal bacteria, microcystins and anabaenopeptins presenting an environmental and public health risk
Bibliographic record
Abstract
Harmful Algal Blooms (HABs) are outbreaks of aquatic toxic microalgae emerging as a global problem driven by nutrient enrichment, global climate change and invasive species. We uniquely describe a HAB of unprecedented duration, extent and magnitude during 2023 in Lough Neagh; the UK and Ireland's largest freshwater lake, using an unparalleled combination of satellite imagery, nutrient analysis, 16S rRNA gene sequencing and cyanotoxin profiling. The causative agent Microcystis aeruginosa accounted for over a third of DNA in water samples though common bacterioplankton species also bloomed. Water phosphate levels were hypertrophic and drove local algal biomass. The HAB pervaded the entire ecosystem with algal mats accumulating around jetties, marinas and lock gates. Over 80 % of bacterial DNA isolated from algal mat samples consisted of species associated with wildfowl or livestock faeces and human-effluent wastewater including 13 potential pathogens that can cause serious human illness including: E. coli, Salmonella, Enterobacter and Clostridium among others. Ten microcystins, nodularin and two anabaenopeptin toxins were confirmed as present (with a further microcystin and four anabaenopeptins suspected), with MC-RR and -LR in high concentrations at some locations (1,137-18,493 μg/L) with MC-LR exceeding World Health Organisation (WHO) recreational exposure guidelines in all algal mats sampled. This is the first detection of anabaenopeptins in any waterbody on the island of Ireland. Notwithstanding the ecological impacts, this HAB represented an environmental and public health risk, curtailing recreational activities in-and-around the lake and damaging local businesses. Reducing agricultural runoff and discharge from human-effluent wastewater treatment to manage nutrient loading, and the public health risk, should be the top priority of stakeholders, especially government. Key recommendations include Nature-based Solutions that avoid conflict with the productivity and profitability of the farming sector enhancing sustainability. We hope this stimulates real-world action to resolve the problems besetting this internationally important ecosystem.
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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 | 0.001 | 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".