Population Genomics of Adaptive Radiation
Bibliographic record
Abstract
Adaptive radiations are rich laboratories for exploring, testing, and understanding key theories in evolution and ecology because they offer spectacular displays of speciation and ecological adaptation. Particular challenges to the study of adaptive radiation include high levels of species richness, rapid speciation, and gene flow between species. Over the last decade, high-throughput sequencing technologies and access to population genomic data have lessened these challenges by enabling the analysis of samples from many individual organisms at whole-genome scales. Here we review how population genomic data have facilitated our knowledge of adaptive radiation in five key areas: 1) phylogenetics, 2) hybridization, 3) timing and rates of diversification, 4) genomic basis of trait evolution, and 5) the role of genome structure in divergence. We review current knowledge in each area, highlight outstanding questions, and focus on methods that facilitate detection of complex patterns in population evolutionary demography. Among patterns that emerge, we particularly note that hybridization—and the hypothesized processes by which it shapes diversification—and questions seeking to bridge the divide between microevolutionary and macroevolutionary processes as rich areas for future study. Overall, access to population genomic data have facilitated an exciting era in adaptive radiation research, with implications for deeper understanding of fundamental evolutionary processes across the tree of life.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".