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 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 | 0.000 |
| 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 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".