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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".