Innovative mark-recapture experiment shows patterns of selection on transcript abundance in the wild
Bibliographic record
Abstract
A fundamental aspect of evolutionary biology is natural selection on trait variation. Classically, selection has been estimated primarily on external morphological traits such as beak size and coloration, or on easily-assayable physiological traits such as heat-tolerance. As technologies and methods improved, evolutionary biologists began examining selection on molecular traits such as protein sequences and cellular processes. In a From the Cover manuscript in this issue of Molecular Ecology, Ahmad et al. (2021) continue this trend by estimating parasite driven selection on the molecular trait of transcript abundance in a wild population of brown trout (Salmo trutta) by uniquely combining a mark-recapture experimental design with non-invasive RNA sampling. Using transcript abundance to estimate selection allows for many different traits (each unique gene’s transcript counts) to be tested in a single experiment, providing the opportunity to examine trends in selection. Ahmad et al.(2021) find directional selection strength on transcript counts is generally low and normally distributed. Surprisingly, transcripts under non-linear selection showed a disruptive selection bias contradicting previous comparative studies and theoretical work. This highlights the importance of within-generation selection studies, where mechanisms may differ from longer time frames. Their manuscript also highlights the benefits of an improved 3’ RNA sequencing technique to measure gene expression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".