Global invasive alien plant management lists: Assessing current practices and adapting to new demands
Bibliographic record
Abstract
Invasive alien species (IAS) significantly threaten global biodiversity and ecosystem stability. Despite increasing management efforts, a critical knowledge gap existed in understanding commonalities and disparities among national strategies. We analyzed several IAS management lists from 23 countries and the European Union, focusing specifically on vascular plant species within these lists. List composition, characteristics, and associated management measures were analyzed. Key patterns in species prioritization across national lists and intercontinental exchange of invasive alien plants (IAPs) were identified. Pistia stratiotes , Pontederia crassipes , Salvinia molesta , Cabomba caroliniana , Ulex europaeus were identified as globally recognized threats, being listed by at least 33.3% of analyzed countries and invading five or more continents. Aquatic plants were found to be more frequently included in management lists. A significant directional invasion pattern between the Eastern and Western Hemispheres was identified. Species native to Asia were observed to dominate as significant donors of IAPs across continents. The analysis of list management strategies highlighted substantial gaps in achieving Target 6 of the Kunming-Montreal Global Biodiversity Framework, particularly in species prioritization and inclusion of potential IAPs. In response to these challenges, a tiered classification system for invasive alien species list was proposed, encompassing High-Priority, Watchlist, Potential, and Priority Site categories, which aimed at enhancing management effectiveness by tailoring strategies to different invasion stages and ecological contexts. This study could contribute to understanding global IAPs management strategies and serve as a reference for policymakers and conservation managers to identify priority IAPs and refine management approaches.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".