Responding to spatial and temporal variations in predation risk: space use of a game species in a changing landscape of fear
Bibliographic record
Abstract
Predators generate a “landscape of fear” within which prey can minimize the risk of predation by selecting low-risk areas. Depending on the spatial structure of this “landscape”, i.e., whether it is coarse- or fine-grained, prey may respond to increased risk by shifting their home ranges or by fine-scale redistributions within these ranges, respectively. We studied how wild boar ( Sus scrofa L., 1758) responded to temporal changes in risk in hunted areas (risky habitat) surrounding a nature reserve (refuge habitat). Animals with home ranges “in contact” with the reserve during the low-risk season were the only ones to shift toward the refuge when the risk increased. These shifts occurred at two temporal scales in response to the increased risk during the daytime and during the hunting season. Whereas animals not influenced by the reserve found food and shelter in forest during the hunting season, shifts to the refuge area were detrimental to the rather scarce forest areas in the reserve. This confirms that spatiotemporal changes in risk are major drivers of animal distribution when predation strongly limits their fitness. Their response is, however, scale-dependent and reflects at the individual level the perceived structure of their “landscape of fear”.
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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 | 0.000 |
| 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".