The efficacy of highway wildlife collision mitigation in preventing elk mortality in central Ontario
Bibliographic record
Abstract
Abstract In the Burwash area of north‐central Ontario, Canada, expansion of the Trans‐Canada highway from 2 to 4 lanes was accompanied by installation of a range of wildlife collision‐mitigation infrastructure (e.g., exclusion fencing, underpasses). To assess the overall effectiveness of these measures, we monitored the spatial distribution and mortality rates of elk ( Cervus canadensis ) prior to and following highway expansion, distinguished by season (winter, snowfree) and corridor‐type (highway, railway). We measured herd‐level risk by the proportion of positions falling within 200‐m railway and highway buffer zones using Bayesian methods. Spatial analysis confirmed that there was a distinct northward shift in the winter distribution of elk following construction, situating the elk past the north end of the exclusion fence. This increased the herd's exposure to highway traffic by 3.6 times (proportion of points before = 0.0041 ± 0.002 [SE], after = 0.0147 ± 0.003, P = 0.005), and resulted in a more than 2‐fold increase in elk road mortality from 0.6 elk/yr/20 km during 8 years prior to implementation to 1.5 elk/yr/20 km during 8 years after implementation. Exposure to railways remained unchanged and consistently higher than highway exposure regardless of season (e.g., post‐mitigation, winter proportion of points = 0.0453 ± 0.005), matched by consistently high mortality counts (proportion of points before = 6.4 elk/yr/20 km, after = 6.6 elk/yr/20 km). Our results demonstrate that while wildlife‐vehicle collision mitigation is generally beneficial to wildlife and humans, failure to account for the local characteristics of wildlife populations can lead to suboptimal mitigation designs that reduce their effectiveness and lead to unintended wildlife impacts.
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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 | 0.002 | 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) | 0.000 | 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".