Spatio‐Temporal Resolution of Microbial Functions and Taxa Associated With Cyanobacterial Harmful Algae Blooms Along a 500‐Km Aquatic Continuum in the Lake Erie Watershed
Bibliographic record
Abstract
Biogeochemical processes rendered by the aquatic microbiome could influence the development of cyanobacterial harmful algal blooms (cHABs), but those biotic factors are poorly understood and rarely considered. We focused on the link between microbial functions, community composition and environmental gradients along the Thames River-Lake St. Clair-Detroit River-Lake Erie corridor across different seasons. We measured the abundance and expression (transcripts) of genes involved in nutrient cycling and microcystin toxin production with qPCR and determined microbial community composition with high-throughput sequencing of the 16S rRNA gene. Throughout the year, genes and transcripts involved in P acquisition, denitrification and N fixation were in higher abundance upstream in the Thames River and Lake St. Clair. Gene abundance, rather than expression, correlated with environmental variables, but functional changes were linked to changes in the aquatic microbiome and did not respond directly to larger environmental gradients. Network analysis revealed tighter connections between gene expression and biotic variables than gene presence, with ubiquitous and streamlined-genomes microbes associated with the dominant bloom-causing cyanobacteria, highlighting the cooperative dynamic of these associations. Overall, the results highlight the link between the changing microbiome, microbial processes and the watershed influence in the presence of cHABs.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".