Diurnal Human Activity and Introduced Species Affect Occurrence of Carnivores in a Human-Dominated Landscape
Bibliographic record
Abstract
Diurnal human activity and domestic dogs in agro-forestry mosaics should theoretically modify the diurnal habitat use patterns of native carnivores, with these effects being scale-dependent. We combined intensive camera trapping data with Bayesian occurrence probability models to evaluate both diurnal and nocturnal patterns of space use by carnivores in a mosaic of land-use types in southern Chile. A total of eight carnivores species were recorded, including human-introduced dogs. During the day the most frequently detected species were the culpeo fox and the cougar. Conversely, during the night, the kodkod and chilla fox were the most detected species. The best supported models showed that native carnivores responded differently to landscape attributes and dogs depending on both the time of day as well as the spatial scale of landscape attributes. The positive effect of native forest cover at 250 m and 500 m radius buffers was stronger during the night for the Darwin's fox and cougar. Road density at 250 m scale negatively affected the diurnal occurrence of Darwin´s fox, whereas at 500 m scale roads had a stronger negative effect on the diurnal occurrence of Darwin´s foxes and cougars. A positive effect of road density on dog occurrence was evidenced during both night and day. Patch size had a positive effect on cougar occurrence during night whereas it affected negatively the occurrence of culpeo foxes and skunks during day. Dog occurrence had a negative effect on Darwin's fox occurrence during day-time and night-time, whereas its negative effect on the occurrence of cougar was evidenced only during day-time. Carnivore occurrences were not influenced by the proximity to a conservation area. Our results provided support for the hypothesis that diurnal changes to carnivore occurrence were associated with human and dog activity. Landscape planning in our study area should be focused in reducing both the levels of diurnal human activity in native forest remnants and the dispersion rates of dogs into these habitats.
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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 | 0.001 |
| 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 | 0.001 | 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".