Howling on the edge: Mantled howler monkey (<i>Alouatta palliata</i>) howling behaviour and anthropogenic edge effects in a fragmented tropical rainforest in Costa Rica
Bibliographic record
Abstract
Abstract The function of long calling is a subject of interest across animal behaviour study, particularly within primatology. Many primate species have male‐specific long‐distance calls, including platyrrhines like the folivorous howler monkey (Alouattaspp.). Howler monkeys may howl to defend resources such as feeding trees or areas of rich vegetation from other monkey groups. This study tests the ecological resource defence hypothesis for howling behaviour in the mantled howler monkey (Alouatta palliata) and investigates how anthropogenic forest fragmentation may influence howling behaviour. More specifically, this study examines how howling bout rate, duration, precursors and tree species richness, DBH, and canopy cover vary in 100 m anthropogenic edge and interior forest zones at La Suerte Biological Research Station (LSBRS), a fragmented tropical rainforest in Costa Rica. Results show that tree species richness and canopy cover are higher in forest interior at this site, suggesting that monkeys should howl at greater rates in the interior to defend access to these higher‐quality vegetation resources. Overall, our results supported the ecological resource defence hypothesis. The main howl precursor was howling from neighbouring groups. Although howling rate did not differ between forest zones, howling bouts from forest interior were longer, had a greater number of howls per bout and were preceded by different precursors than howls from anthropogenic edge zones, including more howls from neighbouring groups. Our findings provide some of the first evidence for behavioural edge effects in primate vocal communication behaviour.
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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".