Mammals with large home ranges, low reproductive rates and small body sizes are most vulnerable to roads: A meta‐analysis
Bibliographic record
Abstract
Abstract In a previous meta‐analysis, mammals with large home ranges, low reproductive rates and large body sizes were found to respond most negatively to roads. However, due to correlations among these traits, it is not known whether these responses were due to a subset or all three traits. We conducted a multiple meta‐regression of the effects of species traits on mammal responses to roads, using data from 92 studies, to determine whether an analysis with a larger sample size and controlling for correlations among traits will support the previous findings. The results reinforce the findings that mammals with larger home ranges and lower reproductive rates respond more negatively to roads. Surprisingly, we found that when controlling for the effects of home range size and reproductive rate, larger mammals respond less negatively to roads than smaller mammals. We speculate that the positive effect of body size is due to driver avoidance of collisions with larger mammals and/or differences in road attraction and car avoidance behaviours of larger versus smaller species that allow larger mammals to extract benefits of roads while avoiding oncoming vehicles We also found high variability of individual responses to roads, above what could be explained by the species traits model, most likely due to site‐ and/or species‐specific characteristics. Synthesis and applications. Road mitigation for mammals should ideally be informed by site‐level knowledge and generally involve prioritizing species with the combination of larger home ranges, lower reproductive rates and smaller body sizes. To protect these vulnerable mammals from roads, we should maintain low road densities and instal small‐mesh mitigation fencing along roads. This differs from current road mitigation efforts which are typically targeted towards large mammals (e.g. large‐mesh fencing along roads) and are often ineffective for smaller mammals.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.029 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".