Susceptibility of non-native invasive plants to novel pathogen attack -- do plant traits matter?
Bibliographic record
Abstract
1. Invasive plants are considered major threats to biodiversity globally; however, our understanding of the long-term dynamics of invasion remains limited. 2. Over time, invasive plants can accumulate pathogens capable of causing population declines because invaders have a greater chance of encountering such pathogens as they spread and native pathogens adapt to use invasive plants as a resource over time. However, reports typically focus on individual species and ecologists lack a synthesis approach capable of predicting pathogen susceptibility in plant invaders. 3. Pathogen resistance and tolerance are tightly coupled to plant traits, which we suggest can provide a framework for understanding and predicting novel pathogen accumulation. 4. We reviewed the literature to synthesize plant traits associated with pathogen susceptibility and to determine the prevalence of novel pathogen accumulation on invasive plants. We then used these data and applied a multivariate model to associate plant traits with pathogen effects to predict pathogen susceptibility of invasive plants. Finally, we provide directions for future research. 5. Considering the emergence of trait-based approaches, comprehensive databases, and new data on individual invasions, advances in our understanding of invasive plant-pathogen interactions can lead to breakthroughs both at fundamental and management decision-making levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".