Flying insect abundance declines with increasing road traffic
Bibliographic record
Abstract
Abstract One potentially important but underappreciated threat to insects is road mortality. Road kill studies clearly show that insects are killed on roads, leading to the hypothesis that road mortality causes declines in local insect population sizes. In this study we used custom‐made sticky traps attached to a vehicle to target diurnal flying insects that interact with roads, sampling along 10 high‐traffic and 10 low‐traffic rural roads in southeastern Ontario, Canada. We used a paired sampling design to control for potentially confounding differences in the road characteristics (e.g. road width) and surrounding land covers (e.g. housing density) between high‐traffic and low‐traffic roads. We then used these data to test the prediction that fewer flying insects collide with vehicles, per vehicle (i.e. insect abundance is lower), on high‐traffic than low‐traffic roads. We found significantly fewer insects at the high‐traffic roads than at the low‐traffic roads as predicted. There was a 23.5% decline in the number of insects/km/vehicle on high‐traffic relative to low‐traffic roads. Given the high rates of insect mortality observed in previous studies, it is likely that road mortality contributes to these observed negative effects of traffic intensity. Thus the growing global road network is a concern for conservationists and land managers, not only because insect population declines contribute to the ongoing global losses of biodiversity but also because insects play a vital role in food webs and provide important ecosystem services.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".