Porcupines on the prairie: how do vegetation, cover, and predators influence occupancy?
Bibliographic record
Abstract
The North American Great Plains is an imperiled temperate grassland ecosystem that continues to be reduced by habitat conversion. These system-wide losses coincide with a decline in populations of prairie wildlife, including understudied species that require management. North American porcupines ( Erethizon dorsatum Linnaeus, 1758) have a widespread distribution, yet few studies evaluate their ecology in prairie-dominated landscapes. We used a camera trap network ( n = 1340 locations) along streams and at random locations within a prairie-dominated landscape in north-central Montana, USA to assess porcupine habitat associations. We fit single-season occupancy models to evaluate porcupine occupancy as a function of (1) vegetation characteristics, (2) predation risk, and (3) topographic features. We predicted that porcupines here would be associated with riparian woody cover within prairies to provide forage and limit predation risk from mountain lion ( Puma concolor (Linnaeus, 1771)), their likeliest predator. Our results suggest that porcupines are associated with areas of intermediate plant productivity near riparian areas in modest terrain, likely to exploit vegetative food resources and localized woody cover. Managers looking to support porcupines in prairie ecosystems could increase vegetation in riparian habitats through techniques such as beaver ( Castor canadensis Kuhl, 1820) dam analogs or grazing exclosures.
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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 | 0.003 |
| 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".