How Might Changing Climate Limit Cyanobacteria Growth in Shallow Prairie Lakes? An Empirical Space-For-Time Evaluation of the Potential Role of Increasing Sulfate
Bibliographic record
Abstract
Cyanobacteria blooms alter aquatic ecosystems and occur frequently in shallow prairie lakes, which are predicted to increase in salinity as the regional climate becomes hotter and drier. However, flat landscapes that experience depression bottom salinity with high concentrations of sulfate in addition to sodium and chloride, may mitigate nutrient increases or even inhibit cyanobacteria growth. Cyanobacteria can dominate shallow lakes with low N:P ratios because many cyanobacteria species fix dissolved N2, whether due to in-lake denitrification or exchange with the atmosphere, a process that requires molybdenum as an enzyme cofactor. Sulfate can compete with molybdate at cellular uptake sites, potentially limiting the competitive advantage of cyanobacteria. We studied 25 lakes located in a relatively limited geographic region of southern Alberta (Canada) and used a space-for-time analysis to model scenarios of increased sulfate concentrations under changing climate. Monthly, we measured nitrogen, phosphorus, sulfate, molybdenum, and cyanobacterial pigments and used mixed effects models to identify empirical relationships. Sulfate drives conductivity in the region and we found that most saline lakes we sampled are turbid lakes with high nutrients and high cyanobacteria biomass. In addition to phosphorus, molybdenum predicted cyanobacterial pigments in the top two models, showing a positive relationship with cyanobacterial biomass. We also found a negative relationship between sulfate concentration and cyanobacteria pigments, which suggests that as lakes get saltier, even with increased nutrients, there may not be an incremental increase in cyanobacteria biomass. Our results therefore suggest that competition between sulfate and molybdate may limit future cyanobacteria growth in shallow lakes and that with a warmer and drier climate it may not be inevitable that shallow lakes will continue to be dominated by cyanobacterial blooms, a hypothesis that could be tested directly via experimentation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".