Spider Monkeys in Human-Modified Landscapes
Bibliographic record
Abstract
With the extant of tropical forest degradation, primates increasingly inhabit forest patches embedded in anthropogenic matrices. Such matrices are composed of different land cover types (e.g., agricultural lands and cattle pastures), but large uncertainty remains about the ability of primates to use these land covers. Here, we assessed the use of the landscape matrix by spider monkeys ( Ateles geoffroyi) in 13 forest sites from three countries (Mexico, Costa Rica, and El Salvador). Based on ad libitum records from >212 months of field observations, we found that spider monkeys used four types of land covers for feeding or traveling: secondary vegetation, isolated trees, tree crops, and vegetation corridors. Secondary vegetation was more frequently used than the other land covers. The number of land covers present in the matrix was positively related to the number of land covers used for traveling and feeding. Monkeys consumed 53 plant species in the matrix, mostly native and old-growth or late-successional forest species, although they also used three cultivated tree species. Most species were trees, especially from preferred food species, although monkeys also used palms, lianas, and shrubs. Monkeys fed principally from fruits, but they also used leaves, wood, and flowers. Most species were used from secondary vegetation and isolated trees. These findings suggest that the landscape matrix can provide supplementary food sources for this endangered primate and opportunities for traveling (i.e., spatial connectivity) in human-modified landscapes—information that can be used to improve conservation strategies, especially under the context of land-sharing management strategies (e.g., agroforestry).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".