The consequences of genomic architecture on ecological speciation in postglacial fishes
Bibliographic record
Abstract
Abstract The quest for the origin of species has entered the genomics era. Despite decades of evidence confirming the role of the environment in ecological speciation, an understanding of the genomics of ecological speciation is still in its infancy. In this review, we explore the role of genomic architecture in ecological speciation in postglacial fishes. Growing evidence for the number, location, effect size, and interactions among the genes underlying population persistence, adaptive trait divergence, and reproductive isolation in these fishes reinforces the importance of considering genomic architecture in studies of ecological speci-ation. Additionally, these populations likely adapt to new freshwater environments by selection on standing genetic variation, as de novo mutations are unlikely under such recent divergence times. We hypothesize that modular genomic architectures in postglacial fish taxa may be associated with the probability of population persistence. Empirical studies have confirmed the genic nature of ecological speciation, implicating surprisingly extensive linkage disequilibrium across the genome. An understanding of these genomic mosaics and how they contribute to reproductive barriers remains unclear, but migration rates and the strength of selection at these loci is predicted to influence the likelihood of population divergence. Altogether, understanding the role of ge-nomic architecture is an important component of speciation research and postglacial fishes continue to provide excellent organisms to test these questions, both from the perspective of variation in architectures among taxa, and with respect to the distinct environments they have colonized. However, more empirical tests of ecological speciation predictions are needed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".