Curbing the major and growing threats from invasive alien species is urgent and achievable
Bibliographic record
Abstract
Although invasive alien species have long been recognized as a major threat to nature and people, until now there has been no comprehensive global review of the status, trends, drivers, impacts, management and governance challenges of biological invasions. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Thematic Assessment Report on Invasive Alien Species and Their Control (hereafter ‘IPBES invasive alien species assessment’) drew on more than 13,000 scientific publications and reports in 15 languages as well as Indigenous and local knowledge on all taxa, ecosystems and regions across the globe. Therefore, it provides unequivocal evidence of the major and growing threat of invasive alien species alongside ambitious but realistic approaches to manage biological invasions. The extent of the threat and impacts has been recognized by the 143 member states of IPBES who approved the summary for policymakers of this assessment. Here, the authors of the IPBES assessment outline the main findings of the IPBES invasive alien species assessment and highlight the urgency to act now. This Perspective highlights the global consensus on the urgency and growing threat of invasive alien species, and management needs, as found by the 2023 report on invasive alien species conducted by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".