Urban Matrices Threaten Patch Occurrence of Howler Monkeys in Anthropogenic Landscapes
Bibliographic record
Abstract
Habitat modification due to human activities threatens species survival. While some species can inhabit habitat patches in anthropogenic landscapes, their occurrence often depends on landscape structure. We assessed the effects of landscape structure on brown howler monkey (Alouatta guariba clamitans) occurrence in an urban scenario. We conducted censuses in 59 forest patches from 2014 to 2016 in Rio Grande do Sul State, Brazil. We evaluated patch occurrence (presence/absence) in response to landscape composition (forest cover, arboreal crops, urban areas, open areas, and water) and configuration (patch density), considering the scale of effect. Water, urban, and open areas were the most important predictors of howler presence. Their presence was notably higher in landscapes with more water, likely because these landscapes consist of rural areas with low urbanization, less farming, and relatively high forest cover. Presence of howlers was positively associated with forest cover and negatively related to urban areas, open areas, and arboreal crops. Resource scarcity and increased mortality risks from human pressures, such as domestic dog attacks, electrocution, and roadkill on these land covers may explain these relationships. We highlight the importance of conserving and increasing forest cover in anthropogenic landscapes to protect species reliant on forested habitats, like howler monkeys.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".