Does Wildlife Transportation Mitigation Make Cents: A Case Study of Highway 3 in the Crowsnest Pass in the Southern Canadian Rockies
Bibliographic record
Abstract
The Highway 3 transportation corridor, including land use and development adjacent to the highway, has been identified as a major challenge to maintaining wildlife connectivity in the southern Canadian Rockies. Highway 3 is a two-lane, east–west highway supporting 6,000 to 9,000 vehicles per day traveling over the Continental Divide at Crowsnest Pass. Wildlife – Vehicle Collisions (WVC’s) involving large mammals along Highway 3 predominantly involve deer; collisions also occur with less common species such as elk, moose, bighorn sheep, grizzly bear, wolf, lynx and cougar. A cost–benefit analysis was conducted using annual rates of WVCs for 31 mitigation emphasis sites identified along Highway 3 in British Columbia and Alberta. Mitigation emphasis sites (MES) were identified based on a synthesis of carnivore and ungulate use of the landscape surrounding Highway 3, the site’s local and regional conservation value, development status of land adjacent to the MES, and the highway mitigation potential. At each MES an analysis of wildlife vehicle collision mortality data was conducted. The number of collisions per kilometer per year involving deer, elk, moose and bighorn sheep were summarized at each mitigation emphasis site and the total cost of the ungulate– vehicle collisions (UVCs) was compiled. Using the UVC rates at each MES, the annual costs of the UVCs were then derived based on each ungulate species’ average cost per collision (i.e., human fatalities and injuries, vehicle damage): deer ($6,617), elk ($17,483), moose ($30,760) and bighorn sheep ($6,617). In British Columbia, total annual costs of UVCs at MES ranged from a low of $1,323 to $28,329 (includes 23 MES). In Alberta, total annual costs of UVCs varied from a low of $6,617 to a high of $31,405 (includes 14 MES)(all figures in 2007 Canadian dollars). A recent cost–benefit analysis for a variety of highway mitigation measures across North America found the average cost of building and maintaining a wildlife underpass with fencing and jumpouts (escape ramps for wildlife) is $18,123 km/yr. Although underpasses are often considered an “expensive” infrastructure investment for wildlife, nearly onethird of the monetary costs for the sites in British Columbia were estimated in excess of $18,123 km/yr and half of the MES in Alberta had estimated annual costs in excess of this threshold number. These costs indicate many of the MES along Highway 3 in the study area are excellent candidates for underpasses or other infrastructure investments. Further, if the underreporting of WVCs were accounted for, then investment in mitigation at even more sites would have been considered cost effective for using infrastructure investments. Focusing highway mitigation efforts in these areas could improve motorist safety, reduce wildlife mortalities, improve habitat linkage and animal movements across Highway 3 and be cost effective.
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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 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".