Road kill hotspots do not effectively indicate mitigation locations when past road kill has depressed populations
Bibliographic record
Abstract
ABSTRACT Negative effects of roads on wildlife include mortality caused by attempted road crossings. The most common method to choose locations for road kill mitigation is to identify hotspots of current road mortality. We evaluated the effect of traffic volume on current road kill hotspots. We used a road kill survey to test for differential traffic effects on road kill by taxonomic group, controlling for effects of habitat. Anuran road kill was negatively related, whereas bird road kill was positively related, to traffic volume. The negative effects of traffic on the birds are at broader spatial extents than we measured, and effective mitigation could be directed by hotspot analysis at this scale. Decreased anuran road kill with increasing traffic volume could be caused by road avoidance or depressed populations, but focusing mitigation efforts on anuran road kill hotspots may ignore populations that have been reduced by past traffic‐related mortality. Road kill hotspot analyses should therefore be used with caution when evaluating mitigation options, since when past mortality reduces populations (e.g., Bouchard et al. 2009, in this region), current road kill numbers can be smallest in precisely the sites with the greatest historical road impact on the population size. Sites with high traffic volume in locations where wildlife habitat is near the road, and particularly where it straddles the road, will often correspond with road kill hotspots, but instances where there is good habitat but low current road kill can indicate particularly important locations for mitigation to restore populations. © 2013 The Wildlife Society.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".