A cross-city molecular biogeographic investigation of arbuscular mycorrhizas in Conyza canadensis rhizosphere across native and non-native regions
Bibliographic record
Abstract
The ecological processes underlying the suppressive impacts of invasive species on native species diversity, both above- and below-ground, in non-native regions are not well understood. We therefore aimed to investigate the cross-city biogeographic patterns of arbuscular mycorrhizal (AM) diversity in Conyza canadensis rhizosphere in native (North American) and non-native (Kashmir Himalayan) regions. We recovered AMF spores from rhizospheric soils of Conyza in native and non-native ranges, besides doing so from the uninvaded sites in the introduced region. DNA extracted from AMF spores was processed for cloning and PCR-RFLP of SSU rRNA gene to yield the restriction groups (RGs) followed by their sequence analysis to determine the sequence groups (SGs). The results indicated greater diversity of RGs and SGs in Conyza rhizosphere in native than in non-native sites. In the introduced region, however, the AMF diversity was more in uninvaded than in invaded sites. The species composition of AMF varied significantly between native and non-native regions and so also between invaded and uninvaded habitats. Though difference in AMF diversity between Conyza invaded and uninvaded sites may be attributed to invasion, the role of other evolutionary factors seems likely for differences between the native and non-native regions. We suggest that the ecological processes underlying these evolutionary differences in two biogeographic regions, besides the intensity of urbanization, might play some role in these differences.
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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 | 0.000 | 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".