Large animal-vehicle collisions in the Central Canadian Rocky Mountains: patterns and characteristics
Bibliographic record
Abstract
The trends of increasing traffic volumes and road densities will only magnify the already adverse effects roads have on large mammals and other vertebrates. Development of practical highway mitigation will rely on an understanding of patterns and processes that result from highway accidents, which involve elk Cervus elaphus and other large animals. We specifi cally address three areas relating to the patterns and characteristics of large-animal vehicle collisions on different road-types in the Central Canadian Rocky Mountains. First, we investigate the spatial error associated with reported wildlife-vehicle collisions (WVCs). Second, we look at the demographic and temporal patterns of elk and wildlife-vehicle collisions on different road-types. Finally, we investigate the type of vehicles involved in WVCs and what conditions contribute to injury-related accidents. We found that the average reporting error from park wardens, highway maintenance contractors and from Royal Canadian Mounted Police (RCMP) data ranged from 300m-2000m. The sex ratio of elk-vehicle collisions (EVCs) was signifi cantly different from that found in the population, and highly skewed towards greater male mortality during the 15-year period. The age ratio of EVCs was highly skewed towards greater subadult mortality. We found no difference in marrow fat content between highway and railway killed elk, but both had higher fat content than predator-killed elk. EVCs were signifi cantly higher on the Trans-Canada Highway (TCH) in the province which had the highest traffi c volumes. The TCH in Banff National Park (BNP) had a signifi cantly higher rate of EVCs than the secondary highway (93S) in Kootenay National Park. EVCs declined over time on the unmitigated section of TCH in BNP and on highway 93S, even though traffi c volumes were increasing. We found that local elk abundance was decreasing and was the driving force in EVC rates; however, traffi c volume determined the rate of EVCs on different road types. WVCs occur more often than expected at dusk and night periods and on weekends. Injury-related WVCs are more likely to occur in dry conditions than in slush, snow or icy conditions. Injury-related WVCs are more likely to occur with smaller vehicles than in larger vehicles. Further, larger vehicles were involved in more WVCs than expected on two of our road-types. In conclusion, spatial road-kill data can aid in determining location of mitigation measures, e.g., wildlife signage and crossing structures. Patterns of WVCs can be valuable in devising mitigation based on specifi c hour of day or season when collision frequencies are highest, and what individuals within a population are most susceptible to road-kills. Factors contributing to WVCs, such as traffi c volumes and elk abundance, can help managers predict long-term viability of wildlife populations with incurring road mortality.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".