Reproductive seasonality in African ungulates in relation to rainfall
Bibliographic record
Abstract
Context Reproductive seasonality in ungulates has important fitness consequences but its relationship to resource seasonality is not yet fully understood, especially for ungulates inhabiting equatorial environments. Aims We test hypotheses concerning synchronisation of conception or parturition peaks among African ungulates with seasonal peaks in forage quality and quantity, indexed by rainfall. Methods We relate monthly apparent fecundity and juvenile recruitment rates to monthly rainfall for six ungulate species inhabiting the Masai Mara National Reserve (Mara) of Kenya, using cross-correlation analysis and distributed lag non-linear models. We compare the phenology and synchrony of breeding among the Mara ungulates with those for other parts of equatorial East Africa, with bimodal rainfall and less seasonal forage variation, and for subtropical southern Africa, with unimodal rainfall distribution and greater seasonal forage variation. Key results Births were more synchronised for topi, warthog and zebra than for hartebeest, impala and giraffe in the Mara, and for impala and hartebeest in southern than in eastern Africa. This pattern is likely to reflect regional differences in climate and plant phenology, hider–follower dichotomy and grazing versus browsing. All six species except the browsing giraffe apparently time the conception to occur in one wet season and births to occur just before the onset or during the next wet season, so as to maximise high-quality forage intake during conception and parturition. Fecundity and recruitment rates among the African ungulates peak at intermediate levels of rainfall and are reduced at low or excessive levels of rainfall. Fecundity rate is most strongly positively correlated with rainfall pre-conception, during conception and during early gestation, followed by rainfall at about the time of parturition for all the grazers. For giraffe, fecundity rate is most strongly correlated with rainfall during the gestation period. Conclusions Rainfall seasonality strongly influences reproductive seasonality and juvenile recruitment among African ungulates. The interaction of the rainfall influence with life-history traits and other factors leads to wide interspecific and regional variation. Implications Global climate change, especially widening annual rainfall variation expected to result from global warming, could reduce the predictability of the timing of peak forage availability and quality based on meteorological cues, the length of time with adequate nutrition or both, and hence reduce reproductive success among tropical ungulates.
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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 | 0.001 | 0.001 |
| 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".