The Effect of Road Proximity on Arthropod Communities in Yukon, Canada + Supplementary Appendix 1 (See Article Tools)
Bibliographic record
Abstract
Roads affect the ecosystems they traverse: road construction and maintenance constitute substantial disturbance, while roads and vehicles have hydrological, thermal, and other abiotic effects. Arthropod communities respond to these abiotic effects of roads, as well as to the indirect effects of vegetation changes. The Dempster Highway is a year-round gravel highway that travels 737 km through the Subarctic and Arctic ecosystems of Yukon and the Northwest Territories, Canada. In July 2016, in a Subarctic region of Yukon, we established nine transects spaced at 4 – 12 km intervals along a 56 km segment of the Dempster Highway and sampled arthropods at 1, 10, and 100 m from the highway on each transect. Our objective was to determine the effect of road proximity on the diversity, abundance, and composition of arthropod communities, with a special focus on the functionally and phylogenetically diverse group, Hymenoptera. We found that total arthropod abundance was lowest at the intermediate distance from the road, a pattern driven by high Diptera abundance close to the road and high Collembola abundance far from the road. We suggest that Diptera may be responding to increased moisture and plant biomass near the road, while Collembola may be responding to changes in soil characteristics. Hymenoptera abundance was greater close to the road than farther away, a pattern that may be driven by high Diptera parasitoid abundance near the road. Our results suggest that arthropods are good indicators of ecosystem function and that roads may have important effects on Subarctic ecosystem services (e.g., pollination, pest control). As infrastructural development continues in the Canadian North, it is of vital importance to anticipate and predict the effects of this development on the unique and diverse fauna of the Subarctic ecosystem.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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".