Spatio-temporal interactions between wild and free-ranging domestic ungulates in the Central Pyrenees
Bibliographic record
Abstract
Human encroachment into wilderness areas through deforestation and land-use changes for agriculture and livestock has greatly threatened wild ungulate survival by causing habitat loss and resource depletion. When wild and domestic ungulates share habitats, the former may adopt spatio-temporal strategies to avoid negative interactions such as disease transmission and resource competition. However, such avoidance often forces wild ungulates into lower-quality habitats, in turn affecting their fitness. This study, using camera trap data from summers between 2017 and 2020 in the Central Pyrenees, examined spatio-temporal interactions between wild and domestic ungulates. We employed single-season multispecies occupancy and conditional occupancy models, alongside analyses of activity patterns and temporal overlap. The results indicate that chamois may exhibit weak spatial separation from cattle, whereas wild boar show clear temporal avoidance. These findings suggest that cattle presence may disturb wild ungulates, particularly chamois, and should be managed at sustainable densities to align with conservation goals. Still, livestock grazing supports local livelihoods. A balanced approach is therefore essential, requiring careful monitoring of both wild and domestic ungulate presence to ensure ecosystem integrity while meeting human needs.
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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 | 0.001 |
| 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".