ENVIRONMENT SPECIFIC PLEIOTROPY FACILITATES DIVERGENCE AT THE<i>ECTODYSPLASIN</i>LOCUS IN THREESPINE STICKLEBACK
Bibliographic record
Abstract
Adaptive radiation occurs when divergent natural selection in different environments leads to phenotypic differentiation. The pleiotropic effects of underlying genes can either promote or constrain this diversification. Identifying the pleiotropic effects of genes responsible for divergent traits, and testing how the environment influences these effects, can therefore help to provide an understanding of how ecology drives evolutionary change between populations. Positive selection on low-armor alleles at the Ectodysplasin (Eda) locus in threespine stickleback has led to the repeated evolution of reduced armor in populations following freshwater colonization by fully armored marine sticklebacks. Here, we demonstrate that Eda has environmentally determined pleiotropic effects on armor and growth. When raised in freshwater, reduced armor sticklebacks carrying "low" alleles at Eda had increased growth rate relative to fully armored sticklebacks carrying "complete" alleles. In saltwater treatments this growth advantage was present during juvenile growth but lost during adult growth, suggesting that in this environment stickleback are able to develop full armor plates without sacrificing overall growth rate. The environment specific pleiotropic effects of Eda demonstrate that ecological factors can mediate the influence of genetic architecture in driving phenotypic evolution. Furthermore, because size is important for mate choice in stickleback, the growth rate differences influenced by Eda may have effects on reproductive isolation between marine and freshwater populations.
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 | 0.000 |
| 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".