Warming combined with experimental eutrophication intensifies lake phytoplankton blooms
Bibliographic record
Abstract
Abstract Phytoplankton blooms are a global water quality issue, and successful management depends on understanding their responses to multiple and interacting drivers, including nutrient loading and climate change. Here, we examine a long‐term dataset from Lake 227, a site subject to a fertilization experiment (1969–present) with changing nitrogen:phosphorus (N:P) ratios. We applied a process‐oriented model, MyLake, and updated the model structure with nutrient uptake kinetics that incorporated shifting N:P and competition among phytoplankton functional groups. We also tested different temperature and P‐loading scenarios to examine the interacting effects of climate change and nutrient loading on phytoplankton blooms. The model successfully reproduced lake physics over 48 yr and the timing, overall magnitude, and shifting community structure (diazotrophs vs. non‐diazotrophs) of phytoplankton blooms. Intra‐ and interannual variability was captured more accurately for the P‐only fertilization period than for the high N:P and low N:P fertilization periods, highlighting the difficulty of modeling complex blooms even in well‐studied systems. A model scenario was also run which removed climate‐driven temperature trends, allowing us to disentangle concurrent drivers of blooms. Results showed that increases in water temperature in the spring led to earlier and larger phytoplankton blooms under climate change than under the effects of nutrient fertilization alone. These findings suggest that successful lake management efforts should incorporate the effects of climate change in addition to nutrient reductions, including intensifying and/or expanding monitoring periods and incorporating climate change into uncertainty estimates around future conditions.
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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".