Behavioral adaptations to heat stress and water scarcity in white‐faced capuchins (<i>Cebus capucinus</i>) in Santa Rosa National Park, Costa Rica
Bibliographic record
Abstract
We examined thermoregulatory behaviors in a wild population of white-faced capuchins (Cebus capucinus) inhabiting a highly seasonal dry forest in Santa Rosa National Park (SRNP), Costa Rica. The dry season in SRNP lasts approximately 5 months and is characterized by high ambient temperatures regularly exceeding 37 degrees C, low relative humidity, and the near absence of precipitation. This study demonstrates that capuchins rest more and travel shorter distances during the hottest and driest hours of the day, and suggests that they extend their tongues to lower body temperature via evaporative cooling. Seasonal weather patterns and group movement data reported here are based on 940 h of observations on three social groups of capuchins (wet season: 370 h, dry season: 570 h). In the dry season, the proportion of time spent resting increased at higher temperatures whereas the proportion of time spent traveling decreased. Distance traveled between location points taken at half-hour intervals decreased significantly as temperature increased, although the correlation was not strong. Capuchins exposed their tongues during hot, dry, windy conditions, and this behavior was much more frequent in the dry season. Temperature was significantly higher and humidity significantly lower for "tongue-out" events than expected for a random event in the dry season. Finally, as surface water became scarce, home-range areas of heavy use became increasingly centered on the remaining permanent water sources. These results suggest that heat stress and water scarcity are significant influences on the behavior of capuchins in hot, dry conditions.
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 | 0.000 |
| 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".