An Adaptive Design to Reduce Animal Road Mortality: Analyzing the Effectiveness of a Fence-Culvert Ecopassage Design on Highway 401, Ontario
Bibliographic record
Abstract
Road mortality has become a serious threat to turtle populations. Mitigation strategies using exclusion fencing and some form of crossing structure are becoming increasingly common, yet studies that evaluate the efficacy of these designs still remain rare. Field monitoring that was conducted from 2014-2017 had identified a 1 km stretch of Highway 401 in eastern Ontario with especially high levels of wildlife mortality, including the endangered Blanding’s turtle (Emydoidea blandingii). In 2018 the Ontario Ministry of Transportation installed two types of fencing in this location to prevent animals from entering the roadway and to funnel them into existing drainage culverts to allow for habitat connectivity. The goal of this research was to evaluate the effectiveness of the fence-culvert design by collecting two years of post-mitigation road mortality data, and then comparing this to the pre-installation data. I implemented a before-after-control-impact (BACI) study design to interpret and assess the results. In addition to road mortality surveys, a combination of camera trapping, sand trapping, and field observations of wildlife behavior were used in the post-mitigation survey years. Kernel density analysis (KDE+) was used to analyze the mortality data and showed that the mitigation structure was effective in reducing turtle and mammal mortality, but also suggested that the fencing may have contributed to an increased mortality of snakes. Camera trapping at culvert entrances indicated that the majority of complete crossings through the culverts were mammals (97%), with few herpetofaunal crossings (3%). Nevertheless, there was a distinct presence of herpetofauna at the openings of culverts (n=789), suggesting that adequately sized and configured ecopassages may aid in connectivity. Based on these results, as well as ongoing maintenance considerations, I conclude that a ‘best practice’ design to reduce road mortality of turtles and other wildlife on Highway 401 may be the installation of 3/8” chain link fence with a ground- level screen of fine mesh or smooth plastic (~30cm), adequately sized and designed ecopassages, and full coverage fencing of pre-defined hot spot locations of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".