Impacts of invasive plant species on soil biodiversity: a case study of dog-strangling vine (<i>Vincetoxicum rossicum</i>) in a Canadian National Park
Bibliographic record
Abstract
Soil microbial diversity is expected to be altered by the establishment of invasive plant species, such as dog-strangling vine (DSV) [Vincetoxicum rossicum (Apocynaceae)]. However, in urban ecosystems where DSV invasion is high, there is little research evaluating the impacts of DSV and other anthropogenic disturbances on microbial diversity. Our study was based in Rouge National Urban Park, Canada, where we used terminal restriction fragment length polymorphism data to evaluate (i) if DSV has a detectable impact on soil bacterial community composition and (ii) if these impacts occur independently of other anthropogenic change or soil characteristics. Variation in soil bacterial communities was greatly reduced in DSV-invaded sites vs. less-invaded sites. The degree of DSV invasion independently explained 23.8% of variation in bacterial community composition: a value similar to the explanatory power of proximity to roadways (which explained 22.6% of the variation in community composition), and considerably greater than soil parameters (pH, moisture, carbon, and nitrogen concentrations) which explained only between 6.0% and 10.0% of variation in bacterial community composition. Our findings indicate that DSV influences soil bacterial community composition independent of other anthropogenic disturbances and soil parameters, with potential impacts on multiple facets of plant–soil interactions and plant invasion 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".