Balancing foraging opportunity and predation risk: influence of dependent neonates on spatial co-occurrence of northern cervids and omnivorous predators
Bibliographic record
Abstract
For female ungulates, the vulnerability of neonates can lead to trade-offs between predation and access to forage. We explored the spatial overlap between moose ( Alces americanus (Clinton, 1822)), deer ( Odocoileus hemionus (Rafinesque, 1817), O. virginianus (Zimmermann, 1780)), elk ( Cervus canadensis Erxleben, 1777), grizzly bear ( Ursus arctos Linnaeus, 1758), and black bear ( U. americanus Pallas, 1780) during parturition (15 May to 28 June) and after the parturition period (29 June to 30 October). We used camera images collected at 66 sites between 2020 and 2021 to parameterize integrated multi-species occupancy models. Covariates measured at the scale of the camera and grid cell were useful for assessing detection and occupancy of the six focal species, respectively. For both reproductive periods, occupancy models representing human disturbance (e.g., road density, cutblock size) explained detection while occurrence was explained by habitat composition (e.g., elevation, basal area, canopy closure). Grizzly bears, elk, and deer occupied similar low-elevation areas during the parturition period, supporting the foraging hypothesis. Consistent with the predator avoidance hypothesis, elk occupied habitats with forest structure that would reduce detection by predators during the post-parturition season. Although results represented the coarse-scale distribution of sympatric prey and omnivorous predators, ungulates with young likely experienced a trade-off between accessing high-quality forage and minimizing predation from bears.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".