Evidence for iron‐regulated cyanobacterial predominance in oligotrophic lakes
Bibliographic record
Abstract
Summary Cyanobacterial harmful algal blooms (cyano HAB s) are most often associated with large eutrophic lakes. In the past decade, an increase in the incidence of cyano HAB s in small oligotrophic lakes within the Laurentian Great Lakes‐St. Lawrence River Basin has been documented. We explored macro‐ and micronutrient conditions during peak phytoplankton biomass that result in high cyanobacterial densities. We hypothesised that the availability of biologically available iron (Fe) both limits the assimilation of phosphorus by competing taxa and selects for the predominance of cyanobacteria. We tested this hypothesis with a comparative study of 25 oligotrophic lakes in central Ontario, Canada. These lakes were sampled during peak phytoplankton biomass for 3 years (from 2009 to 2011) for concentrations of total phosphorus (TP), total nitrogen (TN), ammonium ( ), nitrate ( ), total dissolved Fe (TDFe) and pH for comparison with phytoplankton community biomass and composition. Correlation analyses indicated that predominance of cyanobacteria was assured at all TP levels when the availability of Fe was low. Proportionally high cyanobacterial density occurred at relatively low TP in lakes with a molar ratio of dissolved inorganic N (DIN):TP of 8:1 and a modelled ferric Fe concentration <1.0 × 10 −19 m at which cyanobacteria have been shown to be competitive for Fe in laboratory experiments. Regression tree analysis confirmed the minimal influence of P and N, implying that cyanobacteria thrive in low Fe environments. Our findings suggest that an increased susceptibility of oligotrophic lakes to cyanobacterial bloom formation is based on the presence of cyanobacteria that have adapted sufficiently to low Fe environments and are able to multiply rapidly when pulses of P are made available to the ecosystem. Physiologically prepared transitionary cyanobacteria do not have to compete with the eukaryotic species but rather exploit the new P and replicate faster.
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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) | 0.002 | 0.001 |
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".