Mountain roads across the globe significantly alter local soil microclimates
Bibliographic record
Abstract
Abstract Mountain roads have repeatedly shown to host significantly different plant species communities compared to the adjacent natural vegetation. Besides the effect of propagule pressure, altered disturbance regime and soil processes, one of the reasons given for the strong influence of mountain roads on species distributions is a significantly altered microclimate in the roadside compared to the adjacent vegetation, a direct consequence of the altered disturbance regime. However, the microclimatic differences between roadside and natural vegetation have rarely been quantified, particularly lacking global analyses, hampering a better understanding of their importance for mountain biodiversity. In this study, we analysed in-situ measured soil temperatures along mountain roads in seven mountain regions across the globe, in order to assess the impact of mountain roads on a range of bioclimatic variables across the elevational gradient. Our results undeniably show the importance of roadsides as unique microhabitats, even in heterogeneous mountain environments. In most regions, roadside soils had warmer maxima (3.95 ± 2.35°C warmer) and colder minima (0.85 ± 1.11 °C colder) than the soil in the adjacent vegetation, with higher frost risks in winter. Therefore, we recommend future research to incorporate the notion that the local microclimates created by mountain roads could play a critical role in species redistributions in space and time.
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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 | 0.000 |
| 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) | 0.001 | 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".