Stable isotope turnover and variability in tail hairs of captive and free-ranging African elephants (<i>Loxodonta africana</i>) reveal dietary niche differences within populations
Bibliographic record
Abstract
Many herbivore species expand their dietary niche breadths by switching from browse-rich diets in dry seasons to grass-rich diets in rainy seasons, in response to phenological changes in plant availability and quality. We analyzed stable isotope series along tail hairs of captive and free-ranging African elephant (Loxodonta africana (Blumenbach, 1797)) to compare patterns of seasonal dietary variability across individuals. Results from elephants translocated from the wild into captivity, where their diets are semicontrolled, revealed tail hair growth rates of ∼0.34 mm/day, on average, and relatively rapid isotope turnover through the transition from wild into captivity. Sampling hairs at 10 mm increments thus archives dietary chronologies at a resolution suitable for tracking diet switches at seasonal, and even subseasonal, scales. Hairs of free-ranging elephants showed extensive carbon isotopic variability within individuals, consistent with seasonal switches between C3-browsing and C4-grazing. Similarly extensive, but asynchronous, shifts in nitrogen isotope ratios were also observed, suggesting an influence of factors other than seasonality. Across individuals, switching patterns differed across habitats, and across age classes, with older, larger animals including increasing amounts of C3browse into their diets. These results demonstrate how stable isotope approaches characterize complex patterns of resource use in wildlife populations.
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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 | 0.001 | 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".