Soil biota composition and the performance of a noxious weed across its invaded range
Bibliographic record
Abstract
The success of invasive plant species is driven, in part, by feedback with soil ecosystems. Yet, how variation in belowground communities across latitudinal gradients affects invader distributions remains poorly understood. To determine the effect of soil communities on the performance of the noxious weed Cirsium arvense across its invaded range, we grew seedlings for 40 days in soils collected across a 699 km linear distance from both inside and outside established populations. We also described the mesofaunal and bacterial communities across all soil samples. We found that C. arvense typically performed better when grown in soils sourced from northern populations than from southern locations where it has a longer invasion history. We also found evidence that C. arvense performed best in soils sourced from outside invaded patches, although this was not consistent across all sites. The bacterial community showed a significant increase in the magnitude of compositional change in invaded sites at higher latitudes, while the mesofaunal community showed the opposite pattern. Bacterial community composition was significantly correlated with C. arvense performance, although mesofaunal community composition was not. Our results demonstrate that the interactions between an invasive plant and associated soil communities change across the invaded range, and the bacterial community in particular may affect variation in plant performance. Observed patterns may be caused by C.arvense presence and time since invasion allowing for an accumulation of species‐specific pathogens in southern soils, while the naïveté of northern soils to invasion results in a more responsive bacterial community. Although these interactions are difficult to predict, such effects could possibly facilitate the establishment of this exotic species to novel locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".