Partial paternity does not always select for female‐biased care
Bibliographic record
Abstract
The theoretical literature predicts that parentage differences between the sexes, due to females mating with multiple males, select males to provide less parental care and females to care more for the offspring. We formulate simple evolutionary games to question the generality of this prediction. We find that the relationship between paternal care and fitness gained from extra-pair matings is important. A trade-off between these two quantities is required for partial paternity and complete maternity to bias the evolutionary stable strategy (ESS) toward more female care. We argue that this trade-off has been implicitly or explicitly assumed in most previous theories. However, if there is no trade-off between paternal care and extra-pair matings, parentage differences do not influence the ESS sex roles. Moreover, it is also possible for these two quantities to have a positive relationship, in which case we predict selection for male care is possible. We support these predictions using agent-based simulations. We also consider the possibility that caring males have greater opportunities to guard their paternity, and find that this mechanism can also select for male-biased care. Hence, we derive the conditions under which male care may be selected despite partial paternity and complete maternity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".