Down by the riverside: Riparian edge effects on three monkey species in a fragmented Costa Rican forest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Rivers represent natural edges in forests, serving as transition zones between landscapes. Natural edge effects are important to study to understand how intrinsic habitat variations affect wildlife as well as the impact of human‐induced forest fragmentation. We examined the influence of riparian and anthropogenic edge on mantled howler, white‐faced capuchin, Central American spider monkeys, and vegetation structure at La Suerte Biological Research Station (abbreviated as LSBRS), Costa Rica. We predicted lower monkey encounter rate, tree species richness, and median dbh at both edge types compared to interior and that monkeys would show species‐specific responses to edge based on size and diet. We expected large, folivorous–frugivorous howler monkeys and small, generalist capuchins would be found at increased density in forest edge, while large, frugivorous spider monkeys would be found at decreased density in forest edge. We conducted population and vegetation surveys along interior, riparian, and anthropogenic edge transects at LSBRS and used GLMM to compare vegetation and monkey encounter rate. Tree species richness and median dbh were higher in forest interior than anthropogenic edge zones. Although spider monkey encounter rate did not vary between forest edges and interior, howler monkeys were encountered at highest density in riparian edge, while capuchins were encountered at highest density in anthropogenic edge. Our results indicate that diverse forest edges have varying effects on biota. Vegetation was negatively affected by forest edges, while monkey species showed species‐specific edge preferences. Our findings suggest that riparian zones should be prioritized for conservation in Neotropical forests. Abstract in Spanish is available with online material.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 it