ADAPTIVE MATERNAL ADJUSTMENTS OF OFFSPRING SIZE IN RESPONSE TO CONSPECIFIC DENSITY IN TWO POPULATIONS OF THE LEAST KILLIFISH,<i>HETERANDRIA FORMOSA</i>
Bibliographic record
Abstract
Given a trade-off between offspring size and number and an advantage to large size in competition, theory predicts that the offspring size that maximizes maternal fitness will vary with the level of competition that offspring experience. Where the strength of competition varies, selection should favor females that can adjust their offspring size to match the offspring's expected competitive environment. We looked for such phenotypically plastic maternal effects in the least killifish, Heterandria formosa, a livebearing, matrotrophic species. Long-term field observations on this species have revealed that some populations experience relatively constant, low densities, whereas other populations experience more variable, higher densities. We compared sizes of offspring born to females exposed during brood development to either low or high experimental densities, keeping the per capita food ration constant. We examined plastic responses to density for females from one population that experiences high and variable densities and another that experiences low and less-variable densities. We found that, as predicted, female H. formosa produced larger offspring at the higher density. Unexpectedly, we found similar patterns of plasticity in response to density for females from both populations, suggesting that this response is evolutionarily conserved in this species.
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 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 | 0.001 |
| 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 | 0.001 |
| 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".