River edge feeding: Howler monkey feeding ecology in a fragmented riparian forest
Bibliographic record
Abstract
Abstract Rivers are important components of animal habitats worldwide. The area near riparian edge (⩽100 m from the river) has different abiotic characteristics and vegetation than both forest interior and areas bordering human development, which may lead to differences in animal feeding behaviour. To better contextualize the impact of human-caused habitat destruction on animal feeding ecology, it is important to study both natural riparian and anthropogenic forest edges within the same habitat. We compared howler monkey ( Alouatta palliata ) feeding behaviour and tree use across four forest zones (riparian edge, anthropogenic edge, forest interior, and combined riparian and anthropogenic edge) in a fragmented riparian rainforest in Costa Rica, La Suerte Biological Research Station (LSBRS). We predicted that monkey feeding behaviour and tree use would differ across forest zones, and especially between riparian and anthropogenic edges due to higher vegetation quality near the river. We observed individual focal monkeys for 30-minute periods, collecting data on monkey feeding behaviour and tree use every 2 minutes. We recorded plant parts eaten and feeding tree taxonomy, and measured feeding trees. Monkeys ate more leaves in riparian edge than in other forest zones, and fed from fewer tree families in riparian edge and forest interior compared to anthropogenic edge. Monkeys also fed from trees with smaller DBH in riparian edge compared to other forest zones, but trees of similar height to forest interior and taller than anthropogenic edge. Our results indicate that riparian zones are rich habitats for howler monkeys and conservation efforts should prioritize their preservation.
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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 | 0.001 | 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".