Larval salamanders are as effective at short-term mosquito predation as mosquitofish
Bibliographic record
Abstract
Biological control of mosquitoes can have unintended ecological consequences. One example is the introduction of the genus Gambusia Poey, 1854 into ponds and wetlands. Gambusia spp. are invasive in many parts of the world and have the potential to alter ecosystems by changing trophic interactions and extirpating amphibians. We sought to determine whether larval amphibians are capable of consuming larval mosquitoes as effectively as Gambusia spp. We tested the predation ability of larval Spotted Salamanders (Ambystoma maculatum (Shaw, 1802); n = 13), Southern Leopard Frog (Rana sphenocephala Cope, 1886; n = 12) tadpoles, and western mosquitofish (Gambusia affinis (Baird and Girard, 1853); n = 13) across a range of body sizes in laboratory experiments. Our results showed that over a 24 h period, salamander larvae (mean ± SE; 238 ± 14) and mosquitofish (195 ± 17) consumed a statistically equivalent number of mosquito larvae, whereas tadpoles consumed a large number (113 ± 14) but significantly less. All species had significant (or marginally significant) positive relationships between body size and rate of mosquito consumption. Further studies into the ability of native larval amphibians to consume mosquito larvae are needed to assess whether amphibians can be successful at mosquito control instead of introducing nonnative species to new areas.
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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) | 0.003 | 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".