How Rainfall Variation Influences Reproductive Patterns of African Savanna Ungulates in an Equatorial Region Where Photoperiod Variation Is Absent
Bibliographic record
Abstract
In high temperate latitudes, ungulates generally give birth within a narrow time window when conditions are optimal for offspring survival in spring or early summer, and use changing photoperiod to time conceptions so as to anticipate these conditions. However, in low tropical latitudes day length variation is minimal, and rainfall variation makes the seasonal cycle less predictable. Nevertheless, several ungulate species retain narrow birth peaks under such conditions, while others show births spread quite widely through the year. We investigated how within-year and between-year variation in rainfall influenced the reproductive timing of four ungulate species showing these contrasting patterns in the Masai Mara region of Kenya. All four species exhibited birth peaks during the putative optimal period in the early wet season. For hartebeest and impala, the birth peak was diffuse and offspring were born throughout the year. In contrast, topi and warthog showed a narrow seasonal concentration of births, with conceptions suppressed once monthly rainfall fell below a threshold level. High rainfall in the previous season and high early rains in the current year enhanced survival into the juvenile stage for all the species except impala. Our findings reveal how rainfall variation affecting grass growth and hence herbivore nutrition can govern the reproductive phenology of ungulates in tropical latitudes where day length variation is minimal. The underlying mechanism seems to be the suppression of conceptions once nutritional gains become insufficient. Through responding proximally to within-year variation in rainfall, tropical savanna ungulates are less likely to be affected adversely by the consequences of global warming for vegetation phenology than northern ungulates showing more rigid photoperiodic control over reproductive timing.
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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 | 0.000 | 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".