Selection for increased allocation to offspring number under environmental unpredictability
Bibliographic record
Abstract
According to life-history theory, the evolution of offspring size is constrained by the trade-off between allocation of resources to individual offspring and the number of offspring produced. Existing models explore the ecological consequences of offspring size, whereas number is invariably treated simply as an outcome of the trade-off with size. Here I ask whether there is a direct evolutionary advantage of increased allocation to offspring number under environmental unpredictability. Variable environments are expected to select for diversification in the timing of egg hatch and seed germination, yet the dependence of the expression of diversification strategies, and thus parental fitness, on offspring number has not previously been recognized. I begin by showing that well-established sampling theory predicts that a target bethedging diversification strategy is more reliably achieved as offspring number increases. I then use a simulation model to demonstrate that higher offspring number leads to greater geometric mean fitness under environmental uncertainty. Natural selection is thus expected to act directly to increase offspring number under assumptions of environmental unpredictability in season quality.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".