Factors influencing the discovery and use of wildlife passages for small fauna
Bibliographic record
Abstract
Summary While many studies have looked at how large mammals respond to road mitigation measures, few have examined the effects on smaller mammals. We investigated the effectiveness of three different types of wildlife passages along Highway 175 in Quebec, Canada, for small‐ and medium‐sized mammals (<30 kg) using infrared cameras. Wildlife passages (n = 17) were monitored 24 h a day 7 days a week from 2012 to 2015. Two research questions were addressed: (i) What influences passage discovery and use? and (ii) does it differ between species? Global and species‐specific models were produced for both discovery and use. A linear mixed‐effects model was used for the discovery data (log‐transformed counts), and a generalized linear mixed model was used for the crossing data (binary response). Species' responded to the passages differently, with discoveries increasing overall and in particular for marmots Marmota monax as latitude increased. Pipe culverts were significantly more likely to be discovered by micromammals and wooden ledge culverts by red squirrels Tamiasciurus hudsonicus. Older passages were discovered less in general, with the exception of marmots. Marmots were also the only species to show a difference in crossings by passage type, favouring pipe culverts. Passage use was less likely with a median present for all models, except squirrels. More open passages had higher use overall and particularly for marmots and weasels Mustela spp. Synthesis and applications. By separating animal responses to wildlife passages into two types (discovery and use), we have shown it is possible to incorporate multiple dimensions into post‐mitigation evaluation. This study highlights how transportation agencies can engineer more effective wildlife passages by minimizing the barrier effect of the structures themselves and constructing more passages better suited to the needs of the species they are targeting. To benefit the most species, it is recommended that future projects contain a diversity of open, single segment passages requiring long‐term monitoring.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".