Sex-specific behavior by a monomorphic seabird represents risk partitioning
Bibliographic record
Abstract
The presence of sex-stereotyped behavior in monomorphic animals, where there are no sexual differences in form to account for sexual differences in function, is often attributed to intraspecific competition or to differential parental investment. The possibility that the use of different behavioral strategies by each parent may increase reproductive success for both partners through risk partitioning is seldom considered. We studied thick-billed murres (Uria lomvia), where the male exclusively feeds the offspring during the late chick rearing. During the period of biparental care, males fed on “risk-averse” prey (consistent across time and space; unitized risk = 0.29), whereas females fed on “risk-prone” prey (risk = 0.59). Males fed at night at 1 colony, during the day at 2 colonies, and there was no pattern at another colony. We suggest that these differences reflect the availability of risk-prone prey. Modeling suggested that mixed-risk pairs had higher success than “risky” or “riskless” pairs. Males accumulated reserves and reduced chick provisioning just prior to fledging. Thus, sex-specific patterns at 1 period (male-only care during postfledging) may have led to sex-specific patterns at earlier periods through the need for specialization in foraging habits and risk. We propose that risk partitioning may contribute to the prevalence of sex-specific behaviors in monomorphic animals and that patterns are likely context specific rather than species specific.
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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 | 0.000 |
| 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) | 0.002 | 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".