Anomalous rise in algal production linked to lakewater calcium decline through food web interactions
Bibliographic record
Abstract
Increased algal blooms are a threat to aquatic ecosystems worldwide, although the combined effects of multiple stressors make it difficult to determine the underlying causes. We explore whether changes in trophic interactions in response to declining calcium (Ca) concentrations, a water quality issue only recently recognized in Europe and North America, can be linked with unexplained bloom production. Using a palaeolimnological approach analysing the remains of Cladocera (herbivorous grazers) and visual reflectance spectroscopically inferred chlorophyll a from the sediments of a Nova Scotia (Canada) lake, we show that a keystone grazer, Daphnia, declined in the early 1990s and was replaced by a less effective grazer, Bosmina, while inferred chlorophyll a levels tripled at constant total phosphorus (TP) concentrations. The decline in Daphnia cannot be attributed to changes in pH, thermal stratification or predation, but instead is linked to declining lakewater [Ca]. The consistency in the timing of changes in Daphnia and inferred chlorophyll a suggests top-down control on algal production, providing, to our knowledge, the first evidence of a link between lakewater [Ca] decline and elevated algal production mediated through the effects of [Ca] decline on Daphnia. [Ca] decline has severe implications for whole-lake food webs, and presents yet another mechanism for potential increases in algal blooms.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".