A railway increases the abundance and accelerates the phenology of bear‐attracting plants in a forested, mountain park
Bibliographic record
Abstract
Abstract Vegetation enhancement along railways has not been well studied, despite high rates of mortality from train strikes across numerous species, including sensitive populations in protected areas. This situation describes grizzly bears ( Ursus arctos ) in the mountain parks of Canada, where train strikes have become the leading source of known mortality. We hypothesized that attraction by bears to railways occurs partly because of increases in the richness, diversity, cover, and maturation rate of plants consumed by bears relative to adjacent forest and that this effect may increase with elevation. We quantified and compared responses in plants used by bears to the railway (ballast), forest edge, and within adjacent forest for two growing seasons (May–October) at 19 locations spanning an elevational gradient in Banff and Yoho National Parks in the Canadian Rocky Mountains. Overall, richness, diversity, and total cover of plants consumed by bears were greatest at the forest edge. On the forest edge or ballast, flowering rates of some species were over three times higher and fruit occurrence was up to five times higher than 50 m into the adjacent forest. Enhancement of berry productivity along rail edges increased with elevation. Buffaloberry ( Shepherdia canadensis ), an important regional pre‐hibernation resource, had more fruit, faster ripening, and higher sugar content for shrubs located within 15 m of the rail than within surrounding interior forest. Our results demonstrate that railway edges can increase the quantity and quality of palatable vegetation resources of both native and introduced species, potentially increasing strike risk for bears and other wildlife. Potential mitigation of this risk could include removal of attractants along the rail, particularly at locations where other factors increase the risk of collisions, and creation of forest openings, either through natural processes or through forest modification that would provide forage in less risky habitats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".