Simulating Selective Mortality on Tadpole Populations in the Lab Yields Improved Estimates of Effect Size in Nature
Bibliographic record
Abstract
Many populations normally experience high levels of mortality throughout larval development, but this is generally overlooked with laboratory experimental protocols. Evidence suggests that mortality is nonrandom in natural tadpole populations, so high survivorship, typical of laboratory populations, may poorly represent populations in nature. We compared survival, growth and development, and population variance of tadpoles in natural ponds with those in the laboratory at low and high densities. In the laboratory, high-density groups were reared with no selection and with selection imposed against different size classes to identify if, and how, mortality influences natural tadpole populations and to investigate whether imposing selection against certain size classes produces responses more consistent with those observed in natural systems. Our results suggest that selective mortality removes smaller individuals in natural populations. We demonstrate that introducing selection against small individuals artificially, in the laboratory, results in individual growth and development, population variance, and statistical power that more closely resembles that observed in natural populations. This is important from an ecological perspective because it demonstrates how selection acts on natural tadpole populations. More importantly, this demonstrates that laboratory experiments can be designed to provide better qualitative estimates for responses of natural populations by considering and simulating natural rates of mortality.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".