Seasonal effects of food quality and temperature on body stoichiometry, biochemistry, and biomass production in <i>Daphnia</i> populations
Bibliographic record
Abstract
Abstract Food quality and temperature can affect zooplankton production in lakes by altering organismal metabolism. However, the influence of these factors on consumer nutritional physiology and population biomass remains relatively understudied in natural populations. Here, we examined seasonal changes in body stoichiometry, biochemistry, and population biomass in two Daphnia species collected from two separate lakes differing in dietary phosphorus (P) supply. Food quality, measured as seston carbon:P (C:P) ratios, varied throughout the study in each lake, and water temperatures generally increased across the growing season. Daphnid elemental composition was correlated with food quality in both populations, but relationships between daphnid body stoichiometry and temperature were consistently stronger as Daphnia body C:P ratios and content of major biochemical pools declined simultaneously throughout the summer, which largely coincided with increased water temperatures. Warmer temperatures were associated with relaxed %P‐RNA coupling as daphnid body RNA content declined and P content remained relatively high. These responses combined with temperature related decreases in Daphnia body %lipids and %C appeared to explain declines in daphnid body C:P ratios in both lakes over the growing season. Seasonal changes in population biomass were related to both food quality and water temperature in the lower nutrient lake. Biomass production under more eutrophic conditions however was unrelated to food quality and was instead associated with seasonal temperature changes in the higher nutrient lake. Overall, our study shows that seasonal changes in temperature and resource quality may differentially affect consumer stoichiometry and biomass production in lake ecosystems by altering consumer elemental metabolism.
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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) | 0.000 | 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 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".