Temporal variation in habitat use, co-occurrence, and risk among generalist predators and a shared prey
Bibliographic record
Abstract
Generalist predators typically have broad diets, but their diets may become constrained when one species of prey becomes disproportionately available. Yet there is poor understanding regarding whether generalist predators exhibit stereotypic relationships with pulsed prey resources. We used telemetry data from 959 woodland caribou (Rangifer tarandus caribou (Gmelin, 1788); 146 adult females, 813 calves), 61 coyotes (Canis latrans Say, 1823), and 55 black bears (Ursus americanus Pallas, 1780) to investigate how two generalist predators interacted with caribou neonates on the island of Newfoundland. We examined the similarity of patterns of habitat use between caribou and their predators across time and related this similarity to interspecific spatiotemporal co-occurrence and mortality risk for caribou neonates. The similarity in habitat use between coyotes and caribou mirrored variation in juvenile hazard risk, but had weak association with actual co-occurrence with caribou. Bears and caribou exhibited less similarity in habitat use during the calving season than coyotes and caribou. The relationship between habitat use of bear and caribou did not correspond with either co-occurrence patterns or overall risk for caribou neonates. Our work illustrates how risk for a prey species can be shaped differently based upon differences between the behavioural strategies of generalist predator species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".