A scenario-guided strategy for the future management of biological invasions
Bibliographic record
Abstract
Abstract Future dynamics of biological invasions are highly uncertain because they depend on multiple environmental, societal and socio-economic drivers. We adopted a qualitative scenario approach to explore the future of invasive alien species (IAS) in Europe and created an overall strategy for their management that considers different plausible future developments. The scenarios and strategy were developed during two online workshops with a multidisciplinary team of experts. First, we downscaled four global scenarios of biological invasions to the European level. Second, we developed a management strategy structured into 19 goals that cover a broad array of IAS-related topics (i.e. policy, research, public awareness and biosecurity), and provided solutions for achieving these goals considering the European scenarios. Third, we identified four interrelated recommendations around which any long-term strategy for managing IAS in Europe can be structured: (i) a European biosecurity regime, (ii) a dedicated communication strategy, (iii) data standardization and management tools, and (iv) a monitoring and assessment system. Finally, we identified the feasibility of the IAS management strategy, finding strong differences among the four scenarios. High levels of technological development, public environmental awareness, and effectiveness of IAS policies facilitated the implementation of the overall management strategy. Together, our results indicate that it is time for a new management of biological invasions in Europe based on a more integrative perspective across sectors and countries to be better prepared for future challenges.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".