Do small mammals avoid roads because of the traffic?
Bibliographic record
Abstract
Summary Roads can act as barriers to animal movement, which may reduce population persistence by reducing recolonization of empty habitats and limiting immigration. Appropriate mitigation of this barrier effect (e.g. seasonal road closures, location and design of wildlife over‐ or underpasses) depends upon whether the animals avoid the road itself or the traffic on the road. Empirical studies of road avoidance to date do not generally differentiate between these. We conducted short‐ and long‐distance translocations and trapping studies of white‐footed mice ( Peromyscus leucopus ) and eastern chipmunks ( Tamias striatus ) near two‐lane paved roads, which differed widely in traffic amount, from 47 to 15 433 vehicles per day. In the trapping study (13 sites) only five animals moved across a road, in comparison to 36 animals that moved the same distance without an intervening road ( P < 0·0001). In the short‐distance translocations (15 sites), 51% of the small mammals that were translocated across roads returned, in comparison to a return rate of 77% of animals that were translocated a similar distance with no intervening road ( P = 0·009). In the long‐distance translocation study (24 sites) we found that each intervening road reduced the probability of successful return by about 50%. We found no significant effects of traffic amount on return rates in either the short‐distance or the long‐distance translocations studies. Small mammal densities were not lower near roads and we found no evidence for a decrease in density near roads with increasing traffic amount. Synthesis and applications. Our results suggest that small mammals avoid the road itself, and not emissions such as noise from the traffic on the roads. Our results imply that the barrier effect of roads on these species cannot be mitigated by measures aimed at reducing traffic amount; other measures such as wildlife passages would be needed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".