An experimental study of carbon-isotope fractionation between diet, hair, and feces of mammalian herbivores
Bibliographic record
Abstract
The carbon-isotope composition of hair and feces offers a glimpse into the diets of mammalian herbivores. It is particularly useful for determining the relative consumption of browse and graze in tropical environments, as these foods have strongly divergent carbon-isotope compositions. Fecal δ13C values reflect the last few days consumption, whereas hair provides longer term dietary information. Previous studies have shown, however, that some fractionation occurs between dietary δ13C values and those of hair and feces. Accurate dietary reconstruction requires an understanding of these fractionations, but few controlled-feeding studies have been undertaken to investigate these fractionations in any mammalian taxa, fewer still in large mammalian herbivores. Here, we present data from the first study of carbon-isotope fractionation between diet, hair, and feces in multiple herbivore taxa. All taxa were fed pure alfalfa (Medicago sativa) diets for a minimum period of 6 months, at which point recently grown hair was shaved and analyzed for carbon isotopes. The mean observed diethair fractionation was +3.2, with a range of +2.7 to +3.5. We also examined dietfeces fractionation for herbivores on alfalfa and bermudagrass (Cynodon dactylon) feeds. The mean dietfeces fractionation for both diets was 0.8, with less fractionation for alfalfa (0.6) than bermudagrass (1.0). Fecal carbon turnover also varies greatly between taxa. When diets were switched, horse (Equus caballus) feces reflected the new diet within 60 h, but alpaca (Lama pacos) feces did not equilibrate with the new diet for nearly 200 h. Thus, fecal carbon isotopes provide far greater dietary resolution for hindgut-fermenting horses than foregut-fermenting alpacas.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".