Sick of attention: The effect of a stress-related disease on juvenile green sea turtle behaviour in the face of intense and prolonged tourism
Bibliographic record
Abstract
Anthropogenic activities are increasingly linked to emerging diseases that cause mortality across many taxa. Human interference through ecotourism, in particular, can increase the stress levels of wild populations and promote the spread of disease. In Akumal Bay, Mexico, green sea turtles (Chelonia mydas) are increasingly infected with fibropapillomatosis (FP), an infectious disease associated with stress-induced immunosuppression linked to high human density, which is particularly high in this area because of intense and prolonged ecotourism. To examine if FP might be associated with behavioural indicators of stress and varying levels of tourist pressure, we observed the behaviour of turtles and the number of tourists through 20-minute focal sampling periods from May to August 2017. We related disease presence and tourist pressure to several aspects of turtle behaviour, specifically feeding, resting, vertical movements (i.e., surfacing and diving), and evasive responses. Turtles that had FP engaged in fewer feeding periods, vertical movements, and evasive responses. Additionally, with increasing tourist pressure, all turtles spent less time engaging in vertical movements and had more evasive responses. Our results suggest that the presence of FP affects green sea turtle behavior, potentially increasing their exposure to tourists. Sick and healthy turtles appear to react differently to tourists, suggesting that FP weakens behavioural responses to tourist pressure. Future management strategies should consider regulating tourist pressure on turtles to reduce the incidence and progression of FP.
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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 | 0.001 |
| 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 | 0.001 |
| 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".