Predicting cyanobacterial dynamics in the face of global change: the importance of scale and environmental context
Bibliographic record
Abstract
Abstract There is growing concern that harmful cyanobacterial blooms are increasing in frequency and occurrence around the world. Although nutrient enrichment is commonly identified as a key predictor of cyanobacterial abundance and dominance in freshwaters, several studies have shown that variables related to climate change can also play an important role. Based on our analysis of the literature, we hypothesized that temperature or water‐column stability will be the primary drivers of cyanobacterial abundance in stratified lakes whereas nutrients will be the stronger predictors in frequently mixing water bodies. To test this hypothesis, as well as quantify the drivers of cyanobacteria over different scales and identify interactions between nutrients and climate‐related variables, we applied linear and nonlinear mixed‐effect modeling techniques to seasonal time‐series data from multiple lakes. We first compared time series of cyanobacterial dominance to a published lake survey and found that the models were similar. Using time‐series data of cyanobacterial biomass, we identified important interactions among nutrients and climate‐related variables; dimictic basin experienced a heightened susceptibility to cyanobacterial blooms under stratified eutrophic conditions, whereas polymictic basins were less sensitive to changes in temperature or stratification. Overall, our results show that due to predictable interactions among nutrients and temperature, polymictic and dimictic lakes are expected to respond differently to future climate warming and eutrophication.
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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".