Implications of guppy (<i>Poecilia reticulata</i>) life‐history phenotype for mosquito control
Bibliographic record
Abstract
) are frequently introduced to both natural and artificial water bodies as a mosquito control. Laboratory studies have demonstrated that guppies can consume large numbers of larval mosquitoes. Our study investigates how intraspecific variability in guppy phenotype affects their importance as a mosquito biocontrol and how habitat conditions (natural ponds vs. water storage containers) may influence insect biomass and guppy feeding. Using a blocked experimental design, we established stream-side mesocosm ponds with half receiving gravel substrate to simulate pond-bottom habitat. To provide realistic diet choices and insect abundances, we allowed the mesocosms to colonize naturally with aquatic insect larvae for 1 month before introducing guppies. We tested two distinct guppy phenotypes (from high- and low-predation streams) alongside fish-free controls. After 1 month, we measured insect biomass in the mesocosms and examined guppy gut contents to document direct predation. While overall insect biomass was not significantly different across the three fish treatments, we observed a significant reduction in mosquito biomass in fish treatments compared to fish-free controls, as well as intraspecific differences in feeding. Overall insect biomass was significantly higher in mesocosms without gravel, while habitat condition had no effect on mosquito biomass. As guppy phenotype responds to changes in their environments, it is an important consideration for biocontrol policy to anticipate potential ecosystem effects. We close by relating our findings to other studies and by discussing the implications and potential risks of using guppies to control mosquitoes.
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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".