Mantled howler monkeys (Alouatta palliata) modify activity and spatial cohesion in response to seasonality
Bibliographic record
Abstract
Longer, more severe dry seasons are impacting wildlife populations that depend on stable seasonality. Howler monkeys in tropical rainforests rely on consistent seasonal durations for food. We investigated mantled howler monkeys' (Alouatta palliata) response to seasonality at La Selva Research Station, Costa Rica, by comparing their activity and spatial cohesion patterns across seasons. We predicted monkeys would rest less, feed and travel more, and be less spatially cohesive during the dry season than the wet season due to decreased resources. We collected 553 hours of data on monkey activity and spatial cohesion using instantaneous focal sampling across wet and dry seasons from 2018-2023. Monkeys spent significantly less time resting and more time feeding, with a higher median distance between nearest neighbors in the dry season compared to the wet season (resting: 69.0% vs. 75.3%; feeding: 14.7% vs. 7.9%; neighbor distance: 2.1m vs. 1.5m). These results suggest lower nutritional yields in the dry season require increased feeding while decreased feeding competition during the wet seasons enables higher spatial cohesion. Since many primates rely on stable seasonality, primates across the tropics will likely need to modify their behavior as climate change continues to increase the length and severity of dry seasons. It is therefore crucial to examine how howler monkeys and other primates respond to seasonal changes over many years to understand the impacts of climate change more fully, and to determine the limits of behavioral flexibility in the face of climate change.
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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 | 0.000 |
| 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".