Spatiotemporal dynamics of cyanobacterium Dolichospermum lemmermannii populations in a bloom-prone region of Lake Superior
Bibliographic record
Abstract
Cyanobacterial Harmful Algal Blooms (cHABs) are increasingly common in marine and freshwater environments, including the Laurentian Great Lakes (LGL). Lake Superior has seen two large-scale cyanobacterial blooms (2012 and 2018) along the Wisconsin shoreline west of the Apostle Islands, caused by the cyanobacterium Dolichospermum lemmermannii. The drivers of bloom formation in Lake Superior are not yet certain, with many factors known to trigger blooms elsewhere in the LGL being absent in Lake Superior. Furthermore, little is known about D. lemmermannii ’s spatial structure or phenology. Thus, we sought to track the seasonal population dynamics of D. lemmermannii to shed light on its growth, physiology, and abundance. In 2021, we used 16S rRNA amplicon and shotgun metagenomic sequencing to characterize spatiotemporal patterns of D. lemmermannii abundance and diversity along the bloom-prone Wisconsin shore of Lake Superior. In 2022, we performed net tows and direct colony counts in another localized area. No large-scale bloom event was observed during either year, though several smaller localized blooms were observed. D. lemmermannii abundances were low at nearly all sites and sampling times. Spikes in abundance occurred in July and September, particularly near Siskiwit Bay, a hotspot of bloom formation. We also observed a seasonal shift in heterocyte and akinete abundance indicative of late-season nutrient limitation. Most striking was the seasonal turnover of D. lemmermannii strains, suggesting strain adaptation to specific environmental conditions. These results offer valuable and actionable insights for managers and provide a foundation for additional work to clarify drivers of bloom formation in Lake Superior.
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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 | 0.000 | 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".