Biological Control of Salvinia molesta (D.S. Mitchell) Drives Aquatic Ecosystem Recovery
Bibliographic record
Abstract
Salvinia molesta D.S. Mitchell (Salviniaceae) is a damaging free-floating invasive alien macrophyte native to South America. The biological control programme against S. molesta by the weevil Cyrtobagous salviniae Calder and Sands (Erirhinidae) has been successful in controlling S. molesta infestations in the introduced range, however, there is some debate as to how biological control success is measured. This study measured the response of epilithic algae and aquatic macroinvertebrate communities in a S. molesta-dominated state and subsequently where the weed had been cleared by biological control, as a proxy for ecosystem recovery in a before–after control–impact mesocosm experiment. The restored treatment (S. molesta and C. salviniae) demonstrated epilithic algae and aquatic macroinvertebrate recovery during the “after” biological control phase, defined as similar to the control treatment. Comparatively, the impacted treatment (100% S. molesta) showed a drastic decline in biodiversity and shifts in community assemblages. We conclude that the biological control effort by C. salviniae facilitated biodiversity recovery of the impacted treatment. Furthermore, epilithic algae and aquatic macroinvertebrate communities were reliable biological indicators for measuring ecological impacts of invasion and ecosystem recovery following biological control, and thus represent potential tools for evaluating biological control success and ecological restoration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".