Effects of temperature on isotopic enrichment in <i>Daphnia magna</i> : implications for aquatic food‐web studies
Bibliographic record
Abstract
Laboratory experiments were conducted with Daphnia magna and Hyalella sp. grown on a single food source of known isotopic composition at a range of temperatures spanning the physiological optima for each species. Daphnia raised at 26.5 degrees C were enriched in delta(13)C and delta(15)N by 3.1 and 2.8 per thousand, respectively, relative to diet. Daphnia raised at 12.8 degrees C were enriched 1.7 and 5.0 per thousand in delta(13)C and delta(15)N, respectively. Results imply a significant negative relationship between the delta(13)C and delta(15)N of primary consumers when a temperature gradient exists. Similar responses were observed for Hyalella. Results indicate a general increase in delta(13)C enrichment and decrease in delta(15)N enrichment as temperature rises. Deviations from the commonly applied isotopic enrichment values used in aquatic ecology were attributed to changes in temperature-mediated physiological rates. Field data from a variety of sources also showed a general trend toward delta(13)C enrichment with increasing temperature in marine and lacustrine zooplankton. Multivariate regression models demonstrated that, in oligotrophic and mesotrophic lakes, zooplankton delta(13)C was related to lake-specific POM delta(13)C, lake surface temperature and latitude. Temperature-dependent isotopic separation (enrichment) between predator and prey should be taken into consideration when interpreting the significance of isotopic differences within and among aquatic organisms and ecosystems, and when assigning organisms to food-web positions on the basis of observed isotope values.
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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".