An updated assessment of the direct costs of invasive non-native species to the United Kingdom
Bibliographic record
Abstract
Abstract Estimates of the economic impact of invasive non-native species (INNS) are important to support informed decision-making and prioritise resources. A detailed estimate of the direct costs of INNS to Great Britain, covering many sectors of the economy and the impacts of many INNS in diverse habitats, was made in 2010 and extended to Northern Ireland in 2013. These estimates are increasingly out of date as a result of changes in distribution and impacts of species, new knowledge, changes in management and newly established INNS. We, therefore, updated the estimated costs for the United Kingdom (UK) for sectors where new information was available and applied an inflation factor to the remaining sectors and species. The results show changes in all sectors and species and the new estimated annual costs to the UK economy is £4014 m, with £3022 m, £499 m, £343 m and £150 m to England, Scotland, Wales and Northern Ireland, respectively. Overall, we found a 45% increase in comparable costs since 2010, with most estimated costs increased, often more than inflation, although in some cases the costs have decreased as a result of changes in the population size of INNS, such as was the case for rabbits. A comparison with the previously estimated costs revealed that the costliest species and sectors of 2010 remain the same, but the newly established, widely distributed and highly impactful ash dieback is now one of the most costly diseases caused by an INNS. We discuss reasons for these changes and the evolution of costs in comparison to other studies. Overall, these results confirm the enormous cost of INNS to the UK economy and highlight the need for continued efforts to mitigate the impacts of established INNS, whilst also supporting measures to limit the entry and establishment of new, potentially harmful non-native species.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".