Effects of fungal pathogens on seeds of native and exotic plants: a test using congeneric pairs
Bibliographic record
Abstract
Summary It has previously been hypothesized that low rates of attack by natural enemies may contribute to the invasiveness of exotic plants. We tested this hypothesis by investigating the influence of pathogens on survival during a critical life‐history stage: the seed bank. We used fungicide treatments to estimate the impacts of soil fungi on buried seeds of a taxonomically broad suite of congeneric natives and exotics, in both upland and wetland meadows. Seeds of both natives and exotics were recovered at lower rates in wetlands than in uplands. Fungicide addition reduced this difference by improving recovery in wetlands, indicating that the lower recovery was largely attributable to a higher level of fungal mortality. This suggests that fungal pathogens may contribute to the exclusion of upland species from wetlands. The effects of fungicide on the recovery of buried seeds did not differ between natives and exotics. Seeds of exotics were recovered at a higher rate than seeds of natives in uplands, but this effect was not attributable to fungal pathogens. Fungal seed pathogens may offer poor prospects for the management of most exotic species. The lack of consistent differences in the responses of natives vs. exotics to fungicide suggests few aliens owe their success to low seed pathogen loads, while impacts of seed‐pathogenic biocontrol agents on non‐target species would be frequent.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".