Quantifying adaptive evolution: three‐dimensional phenotypes provide new insight into mechanisms of rapid evolutionary change in threespine stickleback (918.28)
Bibliographic record
Abstract
How do evolutionary transitions occur following environmental change? Threespine stickleback (Gasterosteus aculeatus) are among the most well‐studied natural examples of rapid evolutionary change. They provide an ideal model with which to examine the means by which many parallel examples of adaptive phenotypic modification can occur over very short periods of time. Here we discuss the results of two studies employing a three‐dimensional (3D) approach to phenotypic quantification, combining the techniques of micro‐computed tomography and geometric morphometrics with the tools of molecular ecology to gain new insight into the mechanisms driving adaptive change. First, we tested the hypothesis that two freshwater populations originating from the same putative ancestral population have diverged to more closely resemble each other than they do their recent common ancestor. We demonstrate that sticklebacks establishing new populations in Alaskan freshwater lakes follow similar phenotypic trajectories. Second, we used a quantitative trait locus analysis of a marine‐freshwater cross from British Columbia to test the hypothesis that a small number of loci of large effect are responsible for marine‐freshwater phenotypic divergence. We recover both previously identified QTL as well as a new, previously unreported loci with the potential for wide‐ranging effects on skeletal development. We discuss the framework of an integrative research program aimed at elucidating the means by which key characteristics of the phenotype may allow organisms to adapt rapidly to changing environments. Grant Funding Source : Supported by the Natural Sciences and Engineering Research Council of Canada
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".