Fall Composition of Storage Lipids is Associated with the Overwintering Strategy of <i>Daphnia</i>
Bibliographic record
Abstract
Diapause, which occurs through the production of dormant eggs, is a strategy used by some zooplankton to avoid winter months of persistent low temperatures and low food availability. However, reports of active zooplankton under the ice indicate that other strategies also exist. This study was aimed at evaluating whether the composition of storage lipids in the fall differs between diapausing and active overwintering Daphnia. We assessed the quantity of storage lipids and fatty acid (FA) composition of Daphnia species, along with FA content of seston, in six boreal, alpine and subarctic lakes at the onset of winter, and evaluated the association between storage lipids and Daphnia overwintering strategy. We found that active overwintering Daphnia had >55% body fat and the highest FA concentrations. Polyunsaturated FA, especially stearidonic acid (18:4n-3; SDA) and high ratios of n-3:n-6, were preferentially retained to a greater extent in active overwintering Daphnia than in those that entered diapause. Daphnia FA composition was independent of that of the seston diet, indicating that Daphnia adjusted their storage lipids according to the physiological requirements of a given overwintering strategy. The occurrence of an active overwintering strategy has consequences for zooplankton community structure, and can have important implications for the transfer of high-quality energy at higher trophic levels.
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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 | 0.000 |
| 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) | 0.001 | 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".