Elements that promote highway crossing structure use by small mammals in Banff National Park
Bibliographic record
Abstract
Summary Corridors provide important structural connectivity in habitats that have been fragmented by human activities, but more empirical work is needed to identify the attributes of effective corridor design under a broad range of ecological conditions. We tested the efficacy with which murid rodents in Banff National Park, Alberta, Canada, used crossing structures constructed across the Trans‐Canada Highway. We studied the effects of size, vegetative cover at entrances, and distance from home ranges as determinants of crossing structure use with potential relevance to other corridor types. We translocated animals across underpasses and overpasses, coated them with fluorescent dye, released them at standardized distances, and followed them to obtain detailed information on two scale‐dependent movement parameters, return success and tortuosity (or complexity) of movement paths, as well as a scale‐independent metric of movement, the fractal dimension. Translocated animals returned with higher success across smaller crossing structures than across larger ones, perhaps because these structures afforded more overhead cover from predators. Adding overhead cover to crossing structure entrances further improved return success and correlated with more tortuous movement of translocated animals. Animals translocated further (60 m) from crossing structures returned with lower success than those translocated closer to them (20 and 40 m). Among the three species studied (deer mice Peromyscus maniculatus, meadow voles Microtus pennsylvanicus and red‐backed voles Clethrionomys gapperi), deer mice had significantly higher return success at 60 m, perhaps owing to their larger home range sizes and willingness to cross the road directly. Synthesis and applications. Ideal crossing structure characteristics will often be species‐specific, even within guilds of animals. Our results imply that wildlife corridors, more generally, need to offer sufficient cover and be placed with a frequency that corresponds to the spatial scale over which targeted species move.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".