Species from different taxonomic groups show similar invasion traits
Bibliographic record
Abstract
Invasion ecology tends to treat taxonomic groups separately. However, given that all invasive species go through the same stages of the invasion process (transport, escape, establishment, spread), it is likely that – across taxa – comparable traits help to successfully complete this process (invasion traits). Perhaps not all invasive species have the same invasion traits, but different combinations of invasion traits can be found among invaders, corresponding to different possibilities to become a successful invader. These combinations of invasion traits might be linked to taxonomic affiliation, but this is not necessarily the case. We created a global dataset with 201 invasive species from seven major taxonomic groups (animals, green plants, fungi, heterokonts, bacteria, red algae, alveolates) and 13 invasion traits that are applicable across all taxa. The dataset was analysed with cluster analysis to search for similarities in combinations of invasion traits. Three of the five clusters, comprising 60% of all species, contain several major taxonomic groups. While some invasion trait frequencies were significantly related to taxonomic affiliation, the results show that invasive species from different taxonomic groups often share similar combinations of invasion traits. A post-hoc analysis suggests that combinations of traits characterizing successful invaders can be associated with invasion stages across taxa. Our findings suggest that there are no universal invasion traits which could explain the invasion success of all invaders, but that invaders are successful for different reasons which are represented by different combinations of invasion traits across taxonomic groups.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".