Data from: Isotopic turnover rates and diet-tissue discrimination depend on feeding habits of freshwater snails
Bibliographic record
Abstract
Estimates of animal diets and trophic structure using stable isotope analysis are strongly affected by diet-tissue discrimination and tissue turnover rates, yet these factors are often unknown for consumers because they must be measured using controlled-feeding studies. Furthermore, these parameters may be influenced by diet quality, growth, and other factors. We measured the effect of dietary protein content on diet-tissue discrimination and tissue turnover in three freshwater snail species. We fed lettuce to individually housed snails (n = 450 per species) for ten weeks, then half were switched to a high-protein diet. Isotopic values of muscle and gonad tissue were assessed at 48 and 80 days post-diet change. Snail discrimination factors varied by diet (low-protein > high-protein) and usually differed among species for both N and C, although species had similar carbon discrimination when fed the low-protein diet. Carbon turnover rates were similar among species for a given tissue type, but nitrogen turnover varied more among species. In addition, diet affected growth of species differently; some species grew larger on high-protein (H. trivolvis) while others grew larger on low-protein diet (Lymnaea spp.). These differences among species in growth influenced turnover rates, which were faster in the species with the highest growth rate following the diet switch from low to high-protein. Thus, growth is one of the main processes that affects tissue turnover, but growth and feeding preference did not affect diet-tissue discrimination, which was greater on low-protein than high-protein diets for all species regardless of growth performance. These results suggest that diet might influence two key parameters of stable isotope analysis differently.
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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 | 0.000 |
| 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) | 0.004 | 0.001 |
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".