Costs of injury for scent signalling in a strepsirrhine primate
Bibliographic record
Abstract
Honesty is crucial in animal communication when signallers are conveying information about their condition. Condition dependence implies a cost to signal production; yet, evidence of such cost is scarce. We examined the effects of naturally occurring injury on the quality and salience of olfactory signals in ring-tailed lemurs (Lemur catta). Over a decade, we collected genital secretions from 23 (13 male, 10 female) adults across 34 unique injuries, owing primarily to intra-group fights. Using gas chromatography-mass spectrometry, we tested for differences in the chemical composition of secretions across pre-injury, injury and recovery, in animals that did and did not receive antibiotics. Lemur genital secretions were significantly dampened and altered during injury, with patterns of change varying by sex, season and antibiotics. Using behavioural bioassays (excluding odorants from antibiotic-treated animals), we showed that male 'recipients' discriminated injury status based on scent alone, directing more competitive counter marking towards odorants from injured vs. uninjured male 'signallers.' That injured animals could not maintain their normal signatures provides rare evidence of the energetic cost to signal production. That conspecifics detected olfactory-encoded 'weakness' suggests added behavioural costs: By influencing the likelihood of intra- or inter-sexual conflict, condition-dependent signals could have important implications for socio-reproductive behaviour.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".