Nature-Based Tourism Elicits a Phenotypic Shift in the Coping Abilities of Fish
Bibliographic record
Abstract
Nature-based tourism is gaining extensive popularity, increasing the intensity and frequency of human-wildlife contacts. As a consequence, behavioral and physiological alterations were observed in most exposed animals. However, while the majority of these studies investigated the effects of punctual exposure to tourists, the consequences of constant exposition to humans in the wild remains overlooked. This is an important gap considering the exponential interest for recreational outdoor activities. To infer long-term effects of intensive tourism, we capitalized on Odontostilbe pequira, a short-lived sedentary Tetra fish who spends its life close to humans, on which it feeds on dead skin. Hence, those fish are constantly exposed to tourists throughout their lifecycle. Here we provide an integrated picture of the whole phenomenon by investigating, for the first time, the expression of genes involved in stress response and neurogenesis, as well as behavioral and hormonal responses of animals consistently exposed to tourists. Gene expression of the mineralocorticoid (and cortisol) receptor (mr) and the neurogenic differentiation factor (NeuroD) were significantly higher in fish sampled in the touristic zone compared to those sampled in the control zone. Additionally, after a simulated stress in artificial and controlled conditions, those fish previously exposed to visitors produced more cortisol and presented increased behavioral signs of stress compared to their non-exposed conspecifics. Overall, nature-based tourism appeared to shift selection pressures, favoring a sensitive phenotype that does not thrive under natural conditions. The ecological implications of this change in coping style remain, nevertheless, an open question.
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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 | 0.000 |
| 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 | 0.000 |
| 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".