Assessing the efficacy of fathead minnows (Pimephales promelas) for mosquito control
Bibliographic record
Abstract
Mosquitoes function as important vectors for many diseases globally and can have substantial negative economic, environmental, and health impacts. Specifically, West Nile virus (WNv) is a significant and increasing threat to wildlife populations and human health throughout North America. Mosquito control is an important means of controlling the spread of WNv, as the virus is primarily spread between avian and mosquito vectors. This is of particular concern for avian host species such as the Greater sage-grouse (Centrocercus urophasianus), in which WNv negatively impacts fitness parameters. Most mosquito control methods focus on the larval stages. In North America, control efforts are largely limited to larvicides, which require repeated application and have potentially negative ecological impacts. There are multiple potential advantages to using indigenous fish species as an alternative for larval control including lowered environmental impact, decreased costs in terms of time and financial inputs, and the potential for the establishment of self-sustaining fish populations. We tested the efficacy of using fathead minnows (Pimephales promelas) as biological control for mosquito populations in livestock reservoirs of semiarid rangelands. We introduced minnows into 10 treatment reservoirs and monitored an additional 6 non-treated reservoirs as controls over 3 years. Adult mosquitoes of species known to transmit WNv (e.g., Culex tarsalis) were captured at each site and mosquito larvae were also present at all sites. Stable isotope analysis confirmed that introduced fathead minnows were feeding at the mosquito larvae trophic level in all but one treatment pond. Treatment ponds demonstrated suppressed levels of mosquito larva over each season compared to controls with a model-predicted 114% decrease in larva density within treatment ponds. Minnows established self-sustaining populations throughout the study in all reservoirs that maintained sufficient water levels. Minnow survival was not influenced by water quality. Though minnows did not completely eradicate mosquito larvae, minnows are a promising alternative to controlling mosquito larvae density within reservoirs. We caution that careful site selection is critical to avoid potential negative impacts, but suggest the introduction of fathead minnows in reservoirs can dramatically reduce mosquito larva abundance and potentially help mitigate vector-borne disease transmission.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".