Analysis of Cyanobacteria and its Abundance in the Turkey Lakes Watershed using Molecular Techniques
Bibliographic record
Abstract
Toxic cyanobacterial blooms continue to pose a threat to the quality and safety of drinking water globally by producing toxins and forming dense surface blooms. Forested watersheds naturally provide high quality drinking water to various communities but are threatened by bloom events that are increasing due to warming climates and anthropogenic land use. Monitoring programs utilized in drinking water sources are required to adapt to the changing intensity and frequency of these blooms where observation of cyanobacterial composition and abundance may vary based on sampling efforts. However, due to the variation and adaptability of these organisms spatially and temporally, cyanobacteria are often overlooked if surface blooms are not visualized, where these organisms may be present and abundant throughout the water column at different depths and vary throughout the day. The undetected organisms may release potent toxins that are threats to drinking water security if left untreated. The harmful toxic blooms comprise of cyclic hepatotoxins, involved in causing severe liver damage and affecting human and aquatic health. The aim of this study was to identify and quantify the cyanobacterial community composition and abundance of potential toxin producing genes in an oligotrophic northern forested watershed (Turkey Lakes Watershed, Ontario, Canada). \n\tTo evaluate the composition and abundance, water samples were collected from Little Turkey Lake in May, June, July, and August 2022 at integrated and varying depths to determine variability over a summer season and at different timepoints in a single day. Microbial DNA was extracted from the water samples for 16S rRNA gene sequencing where data was obtained for bioinformatic and phylogenetic analyses. Extracted samples underwent quantitative PCR analysis for identification of gene copy numbers of cyanobacteria and potential microcystin producing organisms. With the extension of the ice-free season through warmer temperatures, and changes in environmental parameters, cyanobacteria and potential cyanotoxin producers appeared as early as May in this oligotrophic lake system. Peak abundances of cyanobacterial and potential cyanotoxin producing gene copy numbers were observed in the months of July and August, without visible blooms during sampling. Cyanobacterial composition had variability between the months, days, and timepoints when sampling, demonstrating the importance of consisting monitoring and sampling efforts due to the changing composition and abundance. Variation was observed among the depths within the water column, where integrated sampling provided a snapshot of the water system and can be useful for efficient analysis of the system, but multiple depth sampling is more representative of the community composition and abundance of cyanobacteria. This illustrates that monitoring protocols for drinking water sources require evaluation for the appropriate sampling protocol, timepoints, and location of the water column as each water system is unique. \n\tThis research provides insight into cyanobacterial emergence in earlier summer months in an oligotrophic water system. It is applicable for the development of monitoring and drinking water treatment protocols for toxin-producing cyanobacteria, where analysis of the full water column is required with consistent sampling and integrated sampling is efficient, especially when there is an absence of a visible surface bloom. The inclusion of molecular characterization (amplicon sequencing and qPCR) is a valuable tool that can be cost efficient and effective ways to analyze samples. This research can then be expanded to other toxins and secondary metabolites produced by cyanobacteria.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".