A century of Azolla filiculoides biocontrol: the economic value of Stenopelmus rufinasus to Great Britain
Bibliographic record
Abstract
Abstract Background The invasive aquatic fern Azolla filiculoides has been present in Great Britain (GB) since the end of the nineteenth century, while its specialist natural enemy, the weevil Stenopelmus rufinasus was first recorded nearly four decades later, in 1921. The purpose of this study was to estimate the economic value of management cost savings resulting from the presence of S. rufinasus as a biocontrol agent of A. filiculoides in GB, including the value of additional augmentative releases of the weevil made since the mid-2000s, compared with the expected costs of control in the absence of S. rufinasus. Methods Estimated economic costs (based on the length/area of affected waterbodies, their infestation rates, and the proportion targeted for management) were calculated for three scenarios in which A. filiculoides occurs in GB: (1) without weevils; (2) with naturalised weevil populations; and (3) with naturalised weevil populations plus augmentative weevil releases. Results In the absence of biocontrol, the expected average annual costs of A. filiculoides management were estimated to range from £8.4 to 16.9 million (US$9.4 to 18.9 million) (£1 = US$1.12). The impacts of naturalised S. rufinasus populations on A. filiculoides were expected to reduce management costs to £0.8 to 1.6 million (US$0.9 to 1.8 million) per year. With additional augmentative releases of the weevil, A. filiculoides management costs were estimated to be lower still, ranging from £31.5 to 45.8 thousand (US$35.3 to 51.3 thousand) per year, giving an estimated benefit to cost ratio of augmentative S. rufinasus releases of 43.7:1 to 88.4:1. Conclusions The unintentional introduction of the weevil S. rufinasus to GB is estimated to have resulted in millions of pounds of savings annually in management costs for A. filiculoides. Additional augmentative releases of the weevil provide further net cost savings, tackling A. filiculoides outbreaks and bolstering naturalised populations. The use of herbicides in the aquatic environment is likely greatly reduced due to A. filiculoides biocontrol. Although somewhat climate-limited at present in GB, climate change may result in even more effective biocontrol of A. filiculoides by S. rufinasus as has been observed in warmer regions such as South Africa, where the plant is no longer considered a threat since the introduction of the weevil.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".