In the quest of isotope equilibrium for trophic discrimination estimation: diet–tissue dynamics in Nile crocodiles ( <i>Crocodylus niloticus</i> )
Bibliographic record
Abstract
Stable isotopes of carbon (δ13C) and nitrogen (δ15N) are increasingly employed to study the foraging ecology of ectothermic predators like crocodilians. However, accurate and precise estimations of trophic discrimination factors between diet and crocodile tissues (Δ13C and Δ15N) from captive experiments under controlled conditions are necessary to reliably quantify the contribution of different prey items make to their diet. The issue of an isotopically constant diet which leads to isotope equilibrium is an important factor influencing accurate estimations of diet–tissue discrimination factors. We raised Nile crocodiles (Crocodylus niloticus) under controlled experimental conditions feeding them with two isotopically distinct (but constant) diets until tissues reached isotopic equilibrium. We sampled blood (plasma and red blood cells, RBC), scute keratin and collagen, and nail tissues throughout the experiment to estimate diet–tissue discrimination factors. Overall, our estimations of average diet–tissue discrimination factors for δ13C were +0.2 ‰ for plasma, +0.1 ‰ for RBC, +0.2 ‰ for keratin, +1.9 ‰ for collagen, and +1.2 ‰ for nail tissue, while for δ15N values were −0.6 ‰ for plasma, +1.5 ‰ for RBC, +1.5 ‰ for keratin, +2.3 ‰ for collagen, and +1.8 ‰ for nail tissue. Body size did not have a significant effect on these tissue estimates, but plasma Δ15N was influenced slightly. Understanding these differences in ectotherm isotope ecology is crucial for interpreting trophic relationships within food webs that include animals such as reptiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".