The effectiveness of non-native fish removal techniques in freshwater ecosystems: a systematic review
Bibliographic record
Abstract
In aquatic systems, biological invasions can result in adverse ecological effects. Management techniques available for non-native fish removal programs (including eradication and population size control) vary widely, but include chemicals, harvest regimes, physical removal, or biological control. For management agencies, deciding on what non-native fish removal program to use has been challenging because there is little reliable information about the relative effectiveness of these measures in controlling or eradicating non-native fish. We conducted a systematic review, including a critical appraisal of study validity, to assess the effectiveness of different non-native fish removal methods and to identify the factors that influence the overall success rate of each type of method. We found 95 relevant studies, generating 158 data sets. The evidence base was dominated by poorly documented studies with inadequate experimental designs (76% of removal projects). When the management goal was non-native fish eradication, chemical treatments were relatively successful (antimycin 89%; rotenone 75%) compared with other interventions. Electrofishing and passive removal measure studies indicated successful eradication was possible (58% each) but required intensive effort and multiple treatments over a number of years. Of these studies with sufficient information, electrofishing had the highest success for population size control (56% of data sets). Overall, inadequate data quality and completeness severely limited our ability to make strong conclusions about the relationships between non-native fish abundance and different methods of eradication and population control and the factors influencing the overall success rate of each method. Our review highlights that there is considerable scope for improving our evaluations of non-native fish removal methods. It is recommended that programs should have explicitly stated objectives, better data reporting, and study designs that (when possible and appropriate) incorporate replicated and controlled investigations with rigorous, long-term quantitative monitoring. Future research on the effectiveness of non-native fish removal methods should focus on: (i) the efficacy of existing or potentially new removal measures in larger, more complex environments; (ii) a broader range of removal measures in general; and (iii) phenotypic characteristics of individual fish within a population that fail to be eradicated or controlled.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".