Positive effects of roads on small mammals: a test of the predation release hypothesis
Bibliographic record
Abstract
Abstract Some authors have hypothesized that observed increases in small mammal populations with increasing road density (after controlling for habitat effects) are due to predation release. Predation could be reduced in areas with high road density because of negative effects of roads on predator numbers and/or hunting activity. However, there are no studies testing the relationship between road density and predation rate on small mammals. Based on the predation release hypothesis, we predicted that white‐footed mouse ( Peromyscus leucopus ) individuals placed in sites with higher surrounding paved road density and/or closer to a paved road would experience fewer predation attempts than P. leucopus individuals placed in sites with lower surrounding paved road density and/or farther from a paved road. We recorded predation attempts on P. leucopus placed in wire mesh enclosures, using motion‐triggered cameras, at 28 sites ranging widely in surrounding road density. There was no overall decline in predation attempts with increasing paved road density, or increase in predation attempts with increasing distance to the nearest paved roads. However, we cannot rule out the predation release hypothesis for larger mammalian predators, as they were not well sampled in our study. For predatory birds, we found weak evidence in support of the predation release hypothesis, but this conclusion is very tentative, as we only recorded three predation attempts by birds. We suggest that the predation release hypothesis for positive road effects on small mammals merits further investigation, using methods tailored to the particular predators most likely to impact small mammal populations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".