The Composition and Diversity of Soil Microbial Communities Associated With the Invasive Plant <i>Solidago canadensis</i> Vary Across Locations and Time Since Invasion
Bibliographic record
Abstract
ABSTRACT Aim Previous local‐scale research (within plots or landscapes) has shown that invasive plants can alter the composition and diversity of soil microbial communities, with potential feedback effects on their own invasion success. However, the broader ecological patterns of these interactions across different invasion timescales and geographic regions remain poorly understood. This study investigated whether the composition and diversity of soil microbial communities associated with Solidago canadensis (Canada goldenrod) invasion vary across both population residence times and geographic locations. Location South‐eastern China. Taxon Soil fungal and bacterial communities and S. canadensis. Methods We collected rhizospheric soil of S. canadensis, nonrhizospheric soil and adjacent uninvaded soil from each of 36 populations of S. canadensis with varying residence times (8–89 years), totalling 108 samples. We extracted DNA from these samples and sequenced the V3–V4 region of the bacterial 16S rRNA gene and the ITS2 region of the fungal rDNA. We analysed the DNA sequences to assess whether variation in α‐diversity, β‐diversity, arbuscular mycorrhizal fungi (AMF) richness and the ratio of AMF to plant pathogens in the soil microbial communities varied with S. canadensis population residence time, latitude and longitude. Results The α‐diversity and β‐diversity of soil fungal and bacterial communities significantly varied with S. canadensis residence times, latitude and longitude. In locations where S. canadensis had been established for longer periods, there was an increase in AMF richness and a decrease in plant pathogens in the rhizospheric soil. Fungal diversity in rhizospheric soils was positively correlated with longitude, while bacterial diversity in rhizospheric and nonrhizospheric soils was positively correlated with both latitude and longitude. Main Conclusions These findings indicate that the time since invasion and geographical location can both significantly influence the composition and diversity of soil microbial communities associated with invasive plant species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".