Wildlife mortality on roads and railways following highway mitigation
Bibliographic record
Abstract
Abstract Wildlife mortality caused by collisions with vehicles on roads is increasingly and effectively mitigated with exclusion fencing and crossing structures, but this solution potentially changes wildlife habitat use and distribution to increase the risk of mortality on adjacent, unmitigated railways. We investigated this potential side‐effect of mitigating the TransCanada Highway, which was completed in sections between 1983 and 2013, on the rate of wildlife mortality on the nearby transcontinental mainline of the Canadian Pacific Railway in Banff National Park. For each transportation class (highway and railway), we calculated collision rate as the number of collisions per year and km for two guilds (carnivores and ungulates) before and after mitigation occurred between 1981 and 2014. We constructed additional models for each transportation class and each of four species groups with adequate sample sizes: elk (Cervus canadensis), other ungulates (family Cervidae), bears (Ursus spp.), and coyotes (Canis latrans). Across guilds, mortality rates declined after mitigation, particularly on the highway (as expected) and most strongly for ungulates. For individual species groups, mortality on the railway for elk was best predicted by year and population size, without the inclusion of mitigation status on the adjacent highway. However, collision rates on the railway increased after mitigation for other ungulates (mostly deer, Odocoileus spp.) while also increasing over time. Collision rates on the railway increased over time for bears, but not in relation to highway mitigation. We found no evidence that the spatial distribution of collisions on the railway changed after highway mitigation, as might be expected from a funneling effect of crossing structures. Our results support and extend previous work demonstrating that exclusion fencing and wildlife crossing structures reduce wildlife mortalities on the highway at this location, and provide limited evidence, for other ungulates alone, that such mitigation may increase mortality on the adjacent railway. Similar analyses are warranted in other locations, particularly mountainous regions, where major transportation features often occur in close proximity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".