Mitigation reduces road mortality of a threatened rattlesnake
Bibliographic record
Abstract
Context Reducing road mortality is essential to reptile conservation in regions with dense road networks. The Georgian Bay, Ontario population of the eastern massasauga rattlesnake (Sistrurus catenatus) is designated as Threatened, in part because of high road mortality. In Killbear Provincial Park, four ecopassages and barrier fencing were constructed along three busy park roads to reduce road mortality of massasaugas. Aim Although mitigation of road mortality has been widely recommended and in some instances implemented for reptiles, effectiveness of mitigation efforts is often inadequately evaluated. The goals of our study were to use long-term data to quantify the effectiveness of ecopassages and barrier fencing in reducing massasauga fatalities on roads, and to evaluate the potential of these structures to serve as movement corridors for individual snakes. Methods We used five approaches to assess the overall efficacy of mitigation efforts: (1) comparison of pre- and post-mitigation road mortality; (2) camera traps in ecopassages to document massasauga and predator presence; (3) automated tag readers in ecopassage entrances to detect PIT-tagged individuals; (4) an experiment to assess massasauga willingness to enter and travel through ecopassages; and (5) measurement of temperature fluctuations in ecopassages to assess thermal suitability for massasaugas. Key results We found a significant decrease in road mortality of massasaugas on stretches of park roads associated with ecopassages and barrier fencing post construction. Automated tag readers and cameras detected the presence of massasaugas and other animals within the ecopassages, and experimental data showed that massasaugas willingly entered, and in some cases crossed through, ecopassages. Conclusion Our evaluation of mitigation structures determined that they successfully reduce road mortality and provide potential movement corridors between bisected habitats, provided that intense maintenance of the fencing is conducted yearly. We also demonstrated the need to utilise a combination of multiple post-monitoring methods to effectively evaluate mitigation structures. Implications This study provides a template for construction of similar mitigation in other key locations where reptile road mortality occurs.
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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 | 0.000 | 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) | 0.001 | 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".