Social relationships and death-related behaviour in aquatic mammals: a systematic review
Bibliographic record
Abstract
Some aquatic mammals appear to care for their dead, whereas others abandon their live offspring when conditions are unfavourable. This incredible variety in behaviours suggests the importance of comparing and contrasting mechanisms driving death-related behaviours among these species. We reviewed 106 cases of aquatic mammals (81 cetaceans and 25 non-cetaceans) reacting to a death event, and extrapolated ‘participant’ ( age class , sex , relationship and decomposition ) and ‘social’ characteristics ( escorting , calf dependence , alloparental care , herding and dispersal patterns ) from published and unpublished literature. A multiple correspondence analysis (MCA) was performed to explore the relationships between these characteristics and death-related behaviours, with species clustered based on MCA scores. Results showed that both cetaceans and non-cetaceans react to death but in different ways. Non-cetaceans, characterized by a short maternal investment, were observed to protect the dead (defending it from external attacks), while cetaceans spent much longer with their offspring and display carrying (hauling, spinning, mouthing with the carcass and diving with it) and breathing-related (lifting and sinking the carcass) activities with the dead generally in association with other conspecifics. Our work emphasizes the need of increased documentation of death-related cases around the world to improve our understanding of aquatic mammals and their responses to death. This article is part of the theme issue ‘Evolutionary thanatology: impacts of the dead on the living in humans and other animals’.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.004 | 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".