Nutrient-Controlled Niche Differentiation of Western Lake Erie Cyanobacterial Populations Revealed via Metatranscriptomic Surveys
Bibliographic record
Abstract
Although toxic cyanobacterial blooms in western Lake Erie threaten drinking water supplies and are promoted by nutrient loading, the precise nutrient regime that selects specific cyanobacteria populations is poorly understood. Here, we assess shifts in cyanobacterial abundances and global gene-expression patterns in response to natural and manipulated gradients in nitrogen and phosphorus to identify gene pathways that facilitate dominance by different cyanobacteria. Gradients in soluble reactive phosphorus shaped cyanobacterial communities and elicited the largest transcriptomic responses. Under high-P conditions (closest to the mouth of the Maumee River), Anabaena and Planktothrix were the dominant cyanobacterial populations, and experimental P and ammonium enrichment promoted nitrogen fixation gene (nifH) expression in Anabaena. For Microcystis, experimental additions of P up-regulated genes involved in phage defense, genomic rearrangement, and nitrogen acquisition but led to lower abundances. Within offshore, low-P regions of the western basin of Lake Erie, Microcystis up-regulated genes associated with P scavenging (pstSCAB, phoX) and dominated cyanobacterial communities. Experimental additions of ammonium and urea did not alter Microcystis abundances but did up-regulate protease inhibitors (aer and mcn gene sets) and microcystin synthetase genes (mcy), with urea enrichment yielding significant increases in microcystin concentrations. Our findings suggest that management plans that reduce P loads alone may not significantly reduce the risk of cyanobacterial blooms in western Lake Erie but rather may promote a shift among cyanobacterial populations (Microcystis, Anabaena, and Planktothrix) toward a greater dominance by toxic strains of Microcystis.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".