The landscape of fear: habitat use by a predator (<i>Canis latrans</i>) and its main prey (<i>Lepus californicus</i> and <i>Sylvilagus audubonii</i>)
Bibliographic record
Abstract
We evaluated the degree of mutual exclusivity of distributions of coyotes ( Canis latrans Say, 1823) and their main prey (two lagomorph species: the black-tailed jackrabbit, Lepus californicus Gray, 1837, and the desert cottontail rabbit, Sylvilagus audubonii (Baird, 1858)) within the landscape by testing two models. The first assumes that prey seek high resource patches and, subsequently, predators seek prey within these patches, and predicts a high degree of overlap in patch use by both. The second model assumes that predator and prey balance not only food resources but reciprocal levels of predation risk and predation success in making decisions on whether or not to use a patch. This model predicts discordance in patch use between predator and prey. We used a combination of GPS-telemetry and camera-trapping data to assess habitat use patterns of predator and prey. Results from this study support the second model regarding spatial use of the landscape by a predator and its prey. Where the use of the landscape by predators and prey seem to be mediated by environmental constraints, both will adjust their predatory or antipredatory strategies based on these constraints. This results in a partial spatial separation of predator and prey across the landscape, providing patches of relative safety for prey but sufficient areas of overlap for predators to be successful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".