Long-term influence of climate and experimental eutrophication regimes on phytoplankton blooms
Bibliographic record
Abstract
Abstract Phytoplankton blooms respond to multiple drivers, including climate change and nutrient loading. Here we examine a long-term dataset from Lake 227, a site exposed to a fertilization experiment (1969–present). Changes in nitrogen:phosphorus loading ratios (high N:P, low N:P, P-only) did not impact mean annual biomass, but blooms exhibited substantial inter- and intra-annual variability. We used a process-oriented lake model, MyLake, to successfully reproduce lake physics over 48 years and test if a P-limited model structure predicted blooms. The timing and magnitude of blooms was reproduced during the P-only period but not for the high and low N:P periods, perhaps due to N acquisition pathways not currently included in the model. A model scenario with no experimental fertilization confirmed P loading is the major driver of blooms, while a scenario that removed climate-driven temperature trends showed that increased spring temperatures have exacerbated blooms beyond the effects of fertilization alone. Significance Statement Harmful algal blooms and eutrophication are key water quality issues worldwide. Managing algal blooms is often difficult because multiple drivers, such as climate change and nutrient loading, act concurrently and potentially synergistically. Long-term datasets and simulation models allow us to parse the effects of interacting drivers of blooms. The performance of our model depended on the ratio of nitrogen to phosphorus inputs, suggesting that complex biological dynamics control blooms under variable nutrient loads. We found that blooms were dampened under a “no climate change” scenario, suggesting that the interaction of nutrient loading and increased temperature intensifies blooms. Our results highlight successes and gaps in our ability to model blooms, helping to establish future management recommendations. Data Availability Statement Data and metadata will be made available in a GitHub repository ( https://github.com/biogeochemistry/Lake-227 ). Upon manuscript acceptance, the repository will be made publicly available and a DOI will be provided. We request that data users contact the Experimental Lakes Area directly, per their data use policy ( http://www.iisd.org/ela/wp-content/uploads/2016/04/Data-Terms-And-Conditions.pdf ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".