Mantled Howler Monkeys (Alouatta palliata) in a Costa Rican Forest Fragment Do Not Modify Activity Budgets or Spatial Cohesion in Response to Anthropogenic Edges
Bibliographic record
Abstract
Forest fragmentation increases forest edge relative to forest interior, with lower vegetation quality common for primates in edge zones. Because most primates live in human-modified tropical forests within 1 km of their edges, it is critical to understand how primates cope with edge effects. Few studies have investigated how primates inhabiting a fragment alter their behaviour across forest edge and interior zones. Here we investigate how anthropogenic edges affect the activity and spatial cohesion of mantled howler monkeys (Alouatta palliata) at the La Suerte Biological Research Station (LSBRS), a Costa Rican forest fragment. We predicted the monkeys would spend greater proportions of their activity budget feeding and resting and a lower proportion travelling in edge compared to forest interior to compensate for lower resource availability in the edge. We also predicted that spatial cohesion would be lower in the edge to mitigate feeding competition. We collected data on activity and spatial cohesion (nearest neighbour distance; number of individuals within 5 m) in forest edge and interior zones via instantaneous sampling of focal animals. Contrary to predictions, the monkeys spent equal proportions of time feeding, resting and travelling in forest edge and interior. Similarly, there were no biologically meaningful differences in the number of individuals or the distance between nearest neighbours in the edge (1.0 individuals; 1.56 m) versus the interior (0.8 individuals; 1.73 m). Our results indicate that A. palliata at LSBRS do not adjust their activity or spatial cohesion patterns in response to anthropogenic edge effects, suggesting that the monkeys here exhibit less behavioural flexibility than A. palliata at some other sites. To develop effective primate conservation plans, it is therefore crucial to study primate species' responses to fragmentation across their geographic range.
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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".