The influence of iron, siderophores and refractory <scp>DOM</scp> on cyanobacterial biomass in oligotrophic lakes
Bibliographic record
Abstract
Summary Our conceptual understanding of factors that promote cyanobacterial growth is inadequate in the face of rising public concern about cyanobacterial blooms in oligotrophic freshwater lakes. We hypothesised that cyanobacterial density would be highest in lakes with low levels of phosphorus (P), nitrogen (N), total dissolved iron (TDFe) and dissolved organic matter (DOM) with labile properties, where cyanobacteria use siderophores to scavenge Fe and overcome Fe limitation. We tested this hypothesis by measuring cyanobacterial density during peak biomass in 25 oligotrophic lakes representing gradients in total P (TP), nitrate, TDFe and DOM concentrations. Total phytoplankton biomass, using chlorophyll‐a (chl‐a) as a proxy, was a function of TP (r2 = 0.83, P < 0.001). Cyanobacterial density was highest in lakes with low chl‐a, low TP, variable (low and high) nitrate and low TDFe. Regression tree analysis confirmed that TDFe, specifically low concentrations (<3.2 μg L−1), gave rise to the highest cyanobacterial densities in lakes. All lakes had detectable concentrations of hydroxamate and/or catecholate siderophores. In lakes with relatively low TDFe (<3.2 μg L−1), cyanobacterial density was positively correlated with hydroxamate siderophore concentration (r2 = 0.77, P = 0.01). In lakes with higher TDFe (≥3.2 μg L−1), cyanobacterial density was positively correlated with nitrate (r2 = 0.84, P < 0.001) and ammonium (r2 = 0.75, P < 0.001) concentrations. Dissolved organic matter may have an overriding control on cyanobacterial density, with cyanobacterial densities typically highest where DOM concentrations were low (<5 mg L−1) and with a humification index <5. These findings suggest that DOM with labile properties may allow cyanobacteria to gain access to Fe complexed with DOM and thus to overcome Fe limitation, while DOM with refractory properties may bind Fe tightly so that Fe is not readily bioavailable to cyanobacteria. A new conceptual model is presented that emphasises the potential influence of DOM quantity and quality on the functioning of siderophores and the provision of a supply of Fe to cyanobacteria in lakes with low macronutrient supply.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".