Ironing out the question: what is limiting cyanobacteria in freshwater lakes in the Prairie Pothole Region?
Bibliographic record
Abstract
The Canadian Prairie Pothole Region is a notable hotspot for cyanobacteria-dominated lakes. This study found minor variations in cyanobacterial genera across these lakes yet observed significant differences in standing biomass, as the lakes ranged from oligotrophic to hypereutrophic classifications. A correlational analysis of nutrients, specifically total phosphorus (TP) and total nitrogen (TN) revealed that the limiting nutrients varied considerably across the region. Of the lakes studied, cyanobacterial biomass was P-limited in 21 lakes, N-limited in 3 lakes, and co-limited by both P and N in 23 lakes. Surprisingly, in 32 lakes, the biomass was limited by neither P nor N. In these lakes, iron (Fe) emerged as the most likely limiting nutrient, given a relatively narrow range of free ferric Fe (pFe) between 18 and 26. Cyanobacteria can create biomass under Fe stress by producing Fe-scavenging siderophores that target pFe. However, in neither P- nor N-limited lakes, there was a lack of correlation between siderophore concentrations and cyanobacterial biomass (r = 0.05), indicating that the siderophores were unable to scavenge Fe and thereby utilize the available P and N to produce further cyanobacterial biomass. Our findings suggest that these Fe-starved eutrophic lakes exhibited a paradox of slow-growing yet high cyanobacterial biomass, challenging the notion that only oligotrophic lakes embody slow-growing metabolisms. Overall, our study highlights the importance of considering nutrient limitations on cyanobacterial growth and incorporating macro- (P and N) and micro- (Fe) nutrient limitation considerations into existing nutrient management strategies to mitigate cyanobacterial dominance effectively. Supplementary Information: The online version contains supplementary material available at 10.1007/s10533-025-01234-7.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".