Management measures and trends of biological invasions in Europe
Bibliographic record
Abstract
Biological invasions are a major threat to biodiversity, ecosystem functioning and nature's contributions to people worldwide. However, the effectiveness of invasive alien species (IAS) management measures and the progress toward achieving biodiversity targets remain uncertain due to limited and nonuniform data availability. Management success is usually assessed at a local level and documented in technical reports, often written in languages other than English, which makes such data notoriously difficult to collect at large geographic scales. Here we present the first European assessment of how managers perceive trends in IAS and the effectiveness of management measures to mitigate biological invasions. We developed a structured questionnaire translated into 18 languages and disseminated it to local and regional managers of IAS in Europe. We received responses from 1928 participants from 41 European countries, including 24 European Union (EU) Member States. Our results reveal substantial efforts in IAS monitoring and control, with invasive plants being the primary focus. Yet, there is a general perception of an increase in the numbers, occupied areas, and impacts of IAS across environment and taxonomic groups, particularly plants, over time. This perceived increase is consistent across both EU and non-EU countries, with respondents from EU countries demonstrating more certainty in their responses. Our results also indicate a lack of data on alien vertebrates and invertebrates, reflecting a need for more targeted monitoring and knowledge sharing between managers and policymakers and between countries. Overall, our study suggests that Europe's current strategies are insufficient to substantially reduce IAS by 2030 and hence to meet the Kunming-Montreal Global Biodiversity Framework target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".