Increasing presence of non-native plants and arbuscular mycorrhizal fungi during a 10-year survey along subarctic mountains roads
Bibliographic record
Abstract
not-yet-known not-yet-known not-yet-known unknown Roads in cold climate mountains are known to be important vectors in the introduction and spread of non-native plant species. In the same context, mycorrhizal fungi communities are also altered by roads with a known positive effect on arbuscular mycorrhizal (AM) fungi diversity and abundance in disturbed roadsides. However, to what degree these two effects of roads are intertwined and how they are evolving over time is not well understood. In this study we conducted repeated surveys of non-native plants and AM fungi between 2012 and 2022, in the northern Scandes mountains to investigate temporal changes and interactions between roads, mycorrhizal fungi, and non-native plants. We found that the upward spread of non-native plants and lateral spread away from the roadside into the natural vegetation were so far extremely limited, with only two out of 23 non-native species showing an increase in their upper elevational limit. However, non-native plant species cover did increase over the ten year period, especially at lower elevations, and non-native richness increased from 17 to 23 species. Likewise, we saw an increase in AM fungal abundance over the last four years along the roadsides at lower elevations. Furthermore, our results suggest that increases in non-native species are unlikely to be the driving cause of the observed increase in AM fungal abundance, as AM fungi colonization varied independently of non-native species cover dynamics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".