Unveiling the hidden economic toll of biological invasions in the European Union
Bibliographic record
Abstract
Background: Biological invasions threaten the functioning of ecosystems, biodiversity, and human well-being by degrading ecosystem services and eliciting massive economic costs. The European Union has historically been a hub for cultural development and global trade, and thus, has extensive opportunities for the introduction and spread of alien species. While reported costs of biological invasions to some member states have been recently assessed, ongoing knowledge gaps in taxonomic and spatio-temporal data suggest that these costs were considerably underestimated. Results: (v4.1)-the most comprehensive database on the costs of biological invasions-to assess the magnitude of this underestimation within the European Union via projections of current and future invasion costs. We used macroeconomic scaling and temporal modelling approaches to project available cost information over gaps in taxa, space, and time, thereby producing a more complete estimate for the European Union economy. We identified that only 259 out of 13,331 (~ 1%) known invasive alien species have reported costs in the European Union. Using a conservative subset of highly reliable, observed, country-level cost entries from 49 species (totalling US$4.7 billion; 2017 value), combined with the establishment data of alien species within European Union member states, we projected unreported cost data for all member states. Conclusions: Our corrected estimate of observed costs was potentially 501% higher (US$28.0 billion) than currently recorded. Using future projections of current estimates, we also identified a substantial increase in costs and costly species (US$148.2 billion) by 2040. We urge that cost reporting be improved to clarify the economic impacts of greatest concern, concomitant with coordinated international action to prevent and mitigate the impacts of invasive alien species in the European Union and globally. Supplementary Information: The online version contains supplementary material available at 10.1186/s12302-023-00750-3.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".