Assessing the success of a horizon scanning approach in predicting invasive non‐native species arrival
Bibliographic record
Abstract
Abstract Despite increasing awareness of invasive non‐native species (INNS) and enhanced biosecurity controls in many countries, INNS are still arriving and establishing in new destinations, remaining a globally acknowledged threat to native biodiversity. Preventing the introduction of INNS, as opposed to controlling them once they have arrived, is recognised as the most effective approach to their management. Horizon scanning represents one of the key tools to identify high‐risk INNS that have yet to arrive within a region and has been applied in many contexts around the world, but to date there have been no studies that systematically assess the effectiveness of this approach. Here, we revisit the horizon scan for Great Britain conducted in 2013 that assessed the likelihood of high‐risk INNS arriving within the next 10 years, establishing and having an impact on biodiversity and ecosystems. We evaluated the success of this exercise in predicting arrival of these species within the subsequent 10 years. Ninety‐two species were shortlisted in the 2013 horizon scan. In total, 31 of the 92 species identified in the 2013 horizon scan had arrived by 2023. We found that 12 of the top 20 species had arrived within 10 years. In predicting arrival, there was a significant effect of species having arrived previously to Great Britain, and the number of countries in Western Europe and Baltic countries in which an INNS was found prior to 2013. Policy implications : We conclude that horizon scanning provides a rapid, affordable and successful mechanism to predict the arrival of high‐risk INNS. We highlight the importance of citizen science, including biological recording, and of local expertise for detecting and documenting arrival of INNS. We discuss knowledge gaps that could help inform and improve future horizon scanning. In addition, we recommend regularly repeating horizon scanning exercises to support biosecurity and awareness raising for INNS.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".