Effect of forest loss and fragmentation per se on arboreal and ground mammals of the Lacandon rainforest, Mexico
Bibliographic record
Abstract
Abstract Understanding the effects of forest loss and fragmentation per se (independent of forest loss) on wildlife is urgently needed to design biodiversity-friendly landscape scenarios, particularly for forest-specialist species, such as many ground and arboreal tropical mammals. As this topic remains contentious, we assessed the species-specific response of 14 arboreal and ground mammals to landscape-scale forest loss and fragmentation measured across different scales in the Lacandon rainforest, Mexico. Surprisingly, most species (6 of 14 species, 43%) were weakly related to forest loss, or positively associated with it (7 of 14, 50%), likely because in this young agricultural frontier some individuals can crowd in the remaining forest patches. Only the Geoffroy’s spider monkey was negatively impacted by forest loss. We did not find evidence of extinction thresholds (nonlinear responses to forest loss) in any species. Only in four species fragmentation per se provided a slightly better fit to the data, but as expected, its effect was non-significant. Our multiscale analysis revealed that the scale of effect of forest loss and fragmentation was independent of body mass and habitat use (arboreal vs. ground). Taken together, our findings suggest that landscape composition is more important than configuration, and highlight the conservation value of the studied landscapes for arboreal and ground mammals. In fact, they add to growing evidence indicating that, on a per-area basis, a piece of forest land in a highly deforested landscape has a similar conservation value to that of a more forested one.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".