Why Are Male Social Relationships Complex in the Doubtful Sound Bottlenose Dolphin Population?
Bibliographic record
Abstract
BACKGROUND: Access to oestrus females tends to be the main driver of male sociality. This factor can lead to complex behavioural interactions between males and groups of males. Male bottlenose dolphins may form alliances to consort females and to compete with other males. In some populations these alliances may form temporary coalitions when competing for females. I examined the role of dyadic and group interactions in the association patterns of male bottlenose dolphins in Doubtful Sound, New Zealand. There is no apparent mating competition in this population and no consortship has been observed, yet agonistic interactions between males occur regularly. METHODOLOGY/PRINCIPAL FINDINGS: By comparing the network of male interactions in several social dimensions (affiliative, agonistic, and associative) I show that while agonistic interactions relate to dyadic association patterns, affiliative interactions seem to relate to group association patterns. Some evidence suggests that groups of males also formed temporary coalitions during agonistic interactions. While different groups of males had similar relationships with non-oestrus females, the time they spent with oestrus females and mothers of newborns differed greatly. CONCLUSIONS/SIGNIFICANCE: After considering several hypotheses, I propose that the evolution of these complex relationships was driven by sexual competition probably to out-compete other males for female choice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".