Effect of high population density of eastern black rhinoceros, a mega‐browser, on the quality of its diet
Bibliographic record
Abstract
Abstract High density of herbivore populations can lead to intense foraging competition and depletion of food consequently lowering diet quality and population performance. We tested for the effects of the density of eastern black rhinoceros (Diceros bicornis michaeli) in ninein situpopulations of 0.01–0.7 individuals per km2density range on the quality of their diet while controlling for plant available moisture and plant available nutrients. We used faecal calcium, phosphorus, copper and zinc concentrations as proxy indices for dietary quality from 473 fresh faecal samples obtained from 77 adult animalsin situ, after determining a positive faeces‐diet mineral correlation in feeding trials with black rhinoceros in zoos. Some populations surpassed 70%–80% of their estimated maximum stocking densities expected to cause impact on forage. However, we did not find significant correlation between rhino population density and dietary quality, as measured via faecal mineral nutrient content. This suggests that black rhinoceros may have sufficient behavioural plasticity to adjust their diet to cover their nutritional requirements when density increases. Instead, 1‐month lagged plant available moisture, reflecting precipitation over the 4 weeks preceding each sampling effort, significantly explained the mineral concentrations in the faeces. By contrast, plant available nutrients had no effect.
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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) | 0.001 | 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".