Genetics of adaptation: Experimental test of a biotic mechanism driving divergence in traits and genes
Bibliographic record
Abstract
Abstract The genes underlying adaptations are becoming known, yet the causes of selection on genes—a key step in the study of the genetics of adaptation—remains uncertain. We address this issue experimentally in a threespine stickleback species pair showing exaggerated divergence in bony defensive armor in association with competition-driven character displacement. We used semi-natural ponds to test the role of a native predator in causing divergent evolution of armor and two known underlying genes. Predator presence/absence altered selection on dorsal spines and allele frequencies at the Msx2a gene across a generation. Evolutionary trajectories of alleles at a second gene, Pitx1, and the pelvic spine trait it controls, were more variable. Our experiment demonstrates how manipulation of putative selective agents helps to identify causes of evolutionary divergence at key genes, rule out phenotypic plasticity as a sole determinant of phenotypic differences, and eliminate reliance on fitness surrogates. Divergence of predation regimes in sympatric stickleback is associated with coevolution in response to resource competition, implying a cascade of biotic interactions driving species divergence. We suggest that as divergence proceeds, an increasing number of biotic interactions generate divergent selection, causing more evolution in turn. In this way, biotic adaptation perpetuates species divergence through time during adaptive radiation in an expanding number of traits and genes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".