Short term fluctuating temperature alleviates Daphnia stoichiometric constraints
Bibliographic record
Abstract
Abstract In this study, we analysed how short term temperature fluctuation interacts with nutrient limitation in the vertical migrating Daphnia commutata. We hypothesize that short term (daily) temperature fluctuation will alleviate nutrient limitation. We carried out experiments analysing growth rates, phosphorus and RNA content of D. commutate grown under four different temperature regimes and two P-limited conditions. Our experiments showed that individuals grown under fluctuating temperature grew more than at the mean temperature. We estimated the expected sizes for the 15 °C treatment based on the Q10 and for the fluctuating temperature treatment. These expected sizes for both treatments resulted well below the observed ones. The P and RNA content of individuals grown at 10 °C were significantly higher than those at 20 °C, and when individuals grown at 10 °C were translocated to 20 °C they exerted an increased growth rate. Our results suggest that, under a regime of diel vertical migration, the temperature alternation would allow migrating organisms to alleviate the effect of severe nutrient limitation maintaining population growth. Under a scenario of global warming, where epilimnetic temperatures will increase, lake temperature will interact with nutrient limitation for consumers, but, organisms may be able to face these changes if they can still regularly move from a cold hypolimnion to a warmer epilimnion.
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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".