Allocation of offspring size and sex by female black ratsnakes
Bibliographic record
Abstract
How females allocate resources to each offspring and how they allocate the sex of their offspring are two powerful potential avenues by which mothers can affect offspring fitness. Previous research has focussed extensively on mean offspring size, with much less attention given to variance in offspring size. Here we focussed on variation in offspring size in black ratsnakes, Elaphe obsoleta . We collected and hatched 105 clutches (1283 eggs) over 9 years. We predicted that females should lay larger eggs, or more variable eggs, when the environment is less predictable. We also predicted that females laying early or laying larger eggs should produce mostly sons because adult males are larger than adult female ratsnakes. The largest hatchling was more than twice the length and almost four times the mass of the smallest hatchling. Variation in offspring size was itself highly variable, with CVs in offspring mass among clutches ranging from 1% to 25%. With one exception, the variables we expected should influence variation in offspring size had little effect. We found that clutch size increased with maternal size and that egg size decreased with clutch size, but we found no evidence that variance in egg size among clutches increased as the season progressed or that females increased the mean size of their offspring the later in the season they laid their eggs. Females in better condition after they finish laying their eggs did produce larger eggs. There was no relationship between within‐clutch variation in egg size and laying date or mean egg size. Finally, sex ratio did not vary with mean egg size or hatching date. Given evidence that offspring size in snakes affects survival, selection should reduce variation in offspring size unless that variance enhances maternal fitness and yet we found little support for hypothesized advantages of varying offspring size.
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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".