Alien plant invasion hotspots and invasion debt in European woodlands
Bibliographic record
Abstract
Abstract Questions European woodlands harbor at least 386 alien plant species but the factors driving local invasions remain unknown. By using a large vegetation‐plot database, we asked how local richness and abundance of alien species vary by regions, elevation, climate, soil properties, human disturbance, and habitat types. Location Western, central and southern Europe. Methods We linked consolidated data from the European Vegetation Archive (16,211 plots) to a habitat classification scheme, climate, soil properties and human disturbance variables. In addition, we used 250 km × 250 km regional grid cells to test whether local patterns differ among regions. We used generalized additive models (GAMs) and quantile GAMs to explore how relative alien species richness and the sum of alien species covers per plot relate to predictors. Random Forest analyses (RFs) were employed to assess the importance of individual predictors that were not multicollinear. Results Relative alien species richness and the sum of alien species covers varied across regions and habitat types, with effects being more pronounced at the maximum rather than average responses. Both response variables declined with increasing elevation and distance to the nearest road or railroad and increased with the amount of sealed soil. Maxima in fitted functions matched plots from regional invasion hotspots in northwestern and central Europe. RFs accounted for 39.6% and 20.9% of the total variation in relative alien species richness and the sum of alien species covers, respectively, with region and habitat being the most important variables. Conclusions The importance of maximum response quantiles and the prevalence of regional hotspots point to invasion debt in European woodlands. As alien plants expand further, their species richness and abundance in woodlands will be likely driven by the shared effects of the introduction and planting history, differences in the invaded habitat types, and dispersal corridors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".