Behavioural transition probabilities in dugongs change with habitat and predator presence: implications for sirenian conservation
Bibliographic record
Abstract
Although not widely appreciated, prey can manage predation risk by modifying the sequence of their behavioural states. We explored this phenomenon in dugongs (Dugong dugon) subject to spatially and temporally variable risk of tiger shark (Galeocerdo cuvier) predation in Shark Bay, Australia. Dugong behaviour was assayed using focal follows and organised into sequences of foraging, resting, and travelling bouts. We used log-linear analysis to test for sequence differences in relation to habitat (deep, shallow) and predation danger (sharks present, largely absent). Dugongs modified their behavioural sequences between periods of high and low shark abundance: those at risk alternated more frequently between foraging, which constrains anti-predator vigilance, and travelling, which facilitates predator detection. Dugongs also avoided continuous series of resting bouts, during which awareness is reduced, when sharks were present. These changes were only observed in relatively dangerous shallow habitat, which is hunted disproportionately by tiger sharks; behavioural responses to sharks in deep habitat were modest. We conclude that dugongs in risky habitat resort to safer behavioural sequences in response to sharks. Given that human disturbance and predators are perceived similarly by many species, some forms of vessel interaction could compromise the fitness of sirenians by eliciting similar behavioural adjustment.
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 | 0.000 | 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) | 0.001 | 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".