Influence of environmental conditions on sex allocation in the black rhinoceros population of Mkhuze Game Reserve, South Africa
Bibliographic record
Abstract
According to the Trivers–Willard and local resource competition (LRC) hypotheses, for species where reproductive success is more variable in one sex, natural selection may lead to a bias in sex allocation of a female’s offspring according to her body condition. The extrinsic modification hypothesis (EMH) suggests that offspring sex can also be influenced by environmental conditions experienced by mothers. We investigated the influence of rainfall, El Niño-Southern Oscillation (ENSO), population size and burning, in the year before conception and during pregnancy, on sex allocation in the black rhinoceros population of Mkhuze Game Reserve, South Africa, during 1970–2007. Females were more likely to have a male calf as rainfall during pregnancy increased, supporting the Trivers–Willard hypothesis. Also, the probability of having a male calf increased with population size, supporting the LRC hypothesis. Calf sex allocation was not influenced by ENSO. In conclusion, local environmental conditions may influence sex allocation in black rhinoceros, thereby supporting the EMH. Burning and population size may influence sex allocation in black rhinoceros, and yet can be manipulated by managers. Thus, this knowledge can be applied to improve population structure assessments and management regimes, especially in enclosed reserves, which is essential to maintain endangered species’ productivity.
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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".