Invasion success and impacts depend on different characteristics in non‐native plants
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Aim Biological invasions threaten biodiversity globally. Large‐scale studies of non‐native plant species invasiveness typically focus on identifying ecological differences between naturalized and invasive species that account for their spread from sites of initial establishment (i.e., invasion success). However, invasive species differ widely in the magnitude of their impacts, suggesting the characteristics that favour invasion success might not necessarily predict the consequences of that invasion. Here we test whether those factors that increase the probability of plant species invasion also explain the severity of impacts. Location China. Methods We compiled a database of the invasiveness, biogeographic origins, life history traits, and introduction history for 538 non‐native plants in China and modelled differences in (a) naturalized and invasive species; (b) the spatial extent of invasion; and, (c) the severity of invasion impacts among successful invaders. Results Invasion success and the spatial extent of invasion shared similar influencing factors. However, these clearly differed from the predictors of severe invasion impacts. Unintentionally introduced non‐native plants with shorter life cycles and longer residence times were more likely to become invasive and to invade a larger area, while taller plants introduced from the Americas tended to have more severe impacts on the native ecosystems of China. Main Conclusions These results illustrate the different roles of introduction history, biogeographical origin and biological traits in determining the invasion success and spatial extent of invasion versus the severity of invasive species impacts. We suggest that factors associated with evolutionary adaptation and population expansion might determine invasion success and extent, while traits related to the relative competitive ability of invasive species determine the severity of impacts. Identifying specific characteristics of species that distinguish among successful invaders most likely to result in more severe impacts could help with planning more effective interventions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it