Composite effects of fire and seismic lines reduce non-native plant infiltration along roads in a western North American boreal forest
Bibliographic record
Abstract
Invasion by non-native species threatens biodiversity, disrupts population dynamics and alters community composition. Roads are major contributors to the infiltration of non-native plants into adjacent native habitats. Less is known about whether these effects are magnified by the composite effects of other adjacent or overlapping disturbances. Here, we assessed how the spatial co-occurrence of wildfires and seismic lines associated with oil exploration influences the abundance of non-native plants along roads in Alberta, Canada's boreal forest. Specifically, we tested differences in the ratio of non-native to native plant cover between burned and unburned mesic upland boreal forests and on/off seismic lines, at increasing distances from roads. For unburned forest sites, non-native plant cover was highest adjacent to the road, decreasing threefold at the farthest distances. Wildfire and seismic line disturbances facilitated the infiltration of non-native species from roadsides into forests, but when combined, they produced an antagonistic effect that mitigated these effects, depending on the distance from roadsides. We found an equal ratio of non-native to native plant cover 7 m from road verges and declining thereafter. As natural and anthropogenic disturbances increase, understanding their combined influence on non-native plant invasion is essential for understanding threats and guiding effective conservation and management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".