The good, the bad, and the ugly: space use and intraguild interactions among three opportunistic predators—cat (<i>Felis catus</i>), dog (<i>Canis lupus familiaris</i>), and red fox (<i>Vulpes vulpes</i>)—under human pressure
Bibliographic record
Abstract
Examples of interspecific interactions have been described for mammalian predators, but less is known regarding disturbances of native predator guilds by domestic predators. We investigated intraguild interactions among three opportunistic predators (dog ( Canis lupus familiaris L., 1758), cat ( Felis catus L., 1758), and red fox ( Vulpes vulpes (L., 1758)) co-occurring in the extensive farmlands of central Poland. Their space use was monitored using tracking stations distributed in field and forest plots along a distance gradient from buildings and analyzed using the occupancy-modeling framework. For all three species occupancy decreased with increased distance from buildings, although for the fox the pattern was relatively weak. The occurrence of cats at the stations was higher in the forest than in the field; for fox and dog, there was a strong variation between study plots. For all three predators, the probability of detection was higher during the night than during the day and varied between the seasons; however, the exact patterns were species-specific. The presence of one predator was also linked to the presence of the other two species—generally, a given species was detected more frequently in the absence of the other two species. We recorded spatiotemporal niche segregation among the three species. We conclude that interspecific antagonistic interactions and differences in foraging ecology are the main drivers shaping co-occurrence of the three species in the agriculture landscape.
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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 | 0.001 | 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".