Avian clutch size in relation to rainfall seasonality and stochasticity along an aridity gradient across South Africa
Bibliographic record
Abstract
Abstract The question of how aridity might influence avian clutch size, through the influences of rainfall seasonality and environmental stochasticity (unpredictability), has received little attention. A marked east-west gradient in aridity across South Africa provides a unique opportunity to test for such influences. Using an extensive collection of nest records for 106 terrestrial bird species from the South African Nest Record Card Scheme, we tested three predictions related to rainfall seasonality and stochasticity. Analyses were conducted at two levels, the first examining each species independently, and the second grouping species into five dietary guilds. The first prediction, that clutch size should generally increase with higher rainfall seasonality (i.e. higher seasonal fluctuation of food availability), was supported, particularly in the most arid environments where food abundance is more closely linked to rainfall. Controlling for rainfall seasonality, the second prediction, that clutch size should generally decrease as a bet-hedging strategy in arid, stochastic environments, was also supported. Although the timing of the rainy season differs among regions in South Africa (winter, early summer, later summer, year-round), birds primarily nest during spring. The relative timing of rainfall and breeding is expected to have different consequences for seasonal variation in clutch size among rainfall regions. The third prediction, of different patterns of seasonal variation in clutch size between rainfall regions, was also supported. In the winter and early-summer rainfall regions, early-nesting birds (breeding with or soon after the rains) generally had a larger clutch size than late-nesting birds. In the late-summer rainfall region, early-nesting birds (breeding well before the rains) had a smaller clutch size than late-nesting birds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".