OFFSPRING SIZE VARIATION WITHIN BROODS AS A BET‐HEDGING STRATEGY IN UNPREDICTABLE ENVIRONMENTS
Bibliographic record
Abstract
Offspring size is strikingly variable within species. Although theory can account for variation in offspring size among mothers, an adaptive explanation for variation within individual broods has proved elusive. Theoretical considerations of this problem assume that producing offspring that are too small results in reduced offspring viability, but producing offspring that are too large (for that environment) results only in a lost opportunity for increased fecundity. However, logic and recent evidence suggest that offspring above a certain size will also have lower fitness, such that mothers face fitness penalties on either side of an optimum. Although theory assuming intermediate optima has been developed for other diversification traits, the implications of this idea for selection on intra-brood variance in offspring size have not been explored theoretically. Here we model the fitness of mothers producing offspring of uniform vs. variable size in unpredictably variable environments and compare these two strategies under a variety of conditions. Our model predicts that producing variably sized offspring results in higher mean maternal fitness and less variation in fitness among generations when there is a maximum and minimum viable offspring size, and when many mothers under- or overestimate this optimum. This effect is especially strong when the viable offspring size range is narrow relative to the range of environmental variation. To determine whether this prediction is consistent with empirical evidence, we compared within- and among-mother variation in offspring size for five phyla of marine invertebrates with different developmental modes corresponding to contrasting levels of environmental predictability. Our comparative analysis reveals that, in the developmental mode in which mothers are unlikely to anticipate the relationship between offspring size and performance, size variation within mothers exceeds variation among mothers, but the converse is true when optimal offspring size is likely to be more predictable. Together, our results support the hypothesis that variation in offspring size within broods can reflect an adaptive strategy for dealing with unpredictably variable environments. We suggest that, when there is a minimum and a maximum viable offspring size and the environment is unpredictable, selection will act on both the mean and variance of offspring size.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".