Family feud: permanent group splitting in a highly philopatric mammal, the killer whale (Orcinus orca)
Bibliographic record
Abstract
Abstract For animals that tend to remain with their natal group rather than individually disperse, group sizes may become too large to benefit individual fitness. In such cases, group splitting (or fission) allows philopatric animals to form more optimal group sizes without sacrificing all familiar social relationships. Although permanent group splitting is observed in many mammals, it occurs relatively infrequently. Here, we use combined generalized modeling and machine learning approaches to provide a comprehensive examination of group splitting in a population of killer whales (Orcinus orca) that occurred over three decades. Fission occurred both along and across maternal lines, where animals dispersed in parallel with their closest maternal kin. Group splitting was more common: (1) in larger natal groups, (2) when the common maternal ancestor was no longer alive, and (3) among groups with greater substructuring. The death of a matriarch did not appear to immediately trigger splitting. Our data suggest intragroup competition for food, leadership experience and kinship are important factors that influence group splitting in this population. Our approach provides a foundation for future studies to examine the dynamics and consequences of matrilineal fission in killer whales and other taxa. Significance statement Group living among mammals often involves long-term social affiliation, strengthened by kinship and cooperative behaviours. As such, changes in group membership may have significant consequences for individuals’ fitness and a population’s genetic structure. Permanent group splitting is a complex and relatively rare phenomenon that has yet to be examined in detail in killer whales. In the context of a growing population, in which offspring of both sexes remain with their mothers for life, we provide the first in-depth examination of group splitting in killer whales, where splitting occurs both along and across maternal lines. We also undertake the first comprehensive assessment of how killer whale intragroup cohesion is influenced by both external and internal factors, including group structure, population and group demography, and resource abundance.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".