Effects of Habitat Loss and Fragmentation on the Occurrence of Alouatta guariba in Brazil
Bibliographic record
Abstract
Habitat loss is considered a major global threat to biodiversity. Yet, the effects of fragmentation are strongly debated, with studies showing positive, negative, or null effects on species. Understanding the effects of fragmentation has key conservation implications as negative effects prioritize large, contiguous habitats; null or weak effects highlight the protection of all habitat patches, regardless of their size; and positive effects support the preservation of small patches. This information is particularly important for highly threatened species with declining populations, such as primates. In this study, we assessed the independent effects of habitat amount (forest cover) and fragmentation (patch density) on the patch occurrence of the brown howler monkey (Alouatta guariba) across 956 forest patches in Brazil, using data from 53 studies. We found that both forest cover and patch density positively, but non-significantly, affect the occurrence of the brown howler monkey in forest patches. Weak effects of forest cover on patch occurrence are likely due to the ability of howlers to cope with habitat loss, although the long-term consequences of habitat destruction are considered negative for the species. Weak effects of fragmentation underscore the importance of maintaining both small and large forest patches for the conservation of the brown howler monkey.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".