Optimal Size and Number of Propagules: Allowance for Discrete Stages and Effects of Maternal Size on Reproductive Output and Offspring Fitness
Bibliographic record
Abstract
Existing optimality models of propagule size and number are not appropriate for many organisms. First, existing models assume a monotonically increasing offspring fitness/propagule size relationship. However, offspring survival during certain stages may decrease with increasing propagule size, generating a peaked offspring fitness/propagule size function (e.g., egg size in oxygen-limited aquatic environments). Second, existing models typically do not consider maternal effects on total reproductive output and the expression of offspring survival/propagule size relationships. However, larger females often have greater total egg production and may provide better habitats for their offspring. We develop a specific optimality model that incorporates these effects and test its predictions using data from salmonid fishes. We then outline a general model without assuming specific functional forms and test its predictions using data from freshwater fishes. Our theoretical and empirical results illustrate that, when offspring survival is negatively correlated with propagule size, optimal propagule size is larger in better habitats. When larger females provide better habitats, their optimal propagule size is larger. Nevertheless, propagule number should increase more rapidly than propagule size for a given increase in maternal size. In the absence of density dependence, females with greater relative reproductive output (i.e., for a given body size) should produce more but not larger propagules.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".