Avian introgression in the genomic era
Bibliographic record
Abstract
Introgression, the incorporation of genetic material from one (sub)species into the gene pool of another by means of hybridization and backcrossing, is a common phenomenon in birds and can provide important insights into the speciation process. In the last decade, the toolkit for studying introgression has expanded together with the development of molecular markers. In this review, we explore how genomic data, the most recent step in this methodological progress, impacts different aspects in the study of avian introgression. First, the detection of hybrids and backcrosses has improved dramatically. The most widely used software package is STRUCTURE. Phylogenetic discordance (i.e. different loci resulting in discordant gene trees) is another means for the detection of introgression, although it should be regarded as a starting point for further analyses, not as a definitive proof of introgression. Specifically, disentangling introgression from other biological processes, such as incomplete lineage sorting, remains a challenging endeavour, although new techniques, such as the D-statistic, are being developed. In addition, phylogenetics might require a shift from trees to networks. Second, the study of hybrid zones by means of geographical or genomic cline analysis has led to important insights into the complex interplay between hybridization and speciation. However, because each hybrid zone study is just a single snapshot of a complex and continuously changing interaction, hybrid zones should be studied across different temporal and/or spatial scales. A third powerful tool is the genome scan. The debate on which evolutionary processes underlie the genomic landscape is still ongoing, as is the question whether loci involved in reproductive isolation cluster together in ‘islands of speciation’ or whether they are scattered throughout the genome. Exploring genomic landscapes across the avian tree of life will be an exciting field for further research. Finally, the findings from these different methods should be incorporated into specific speciation scenarios, which can consequently be tested using a modelling approach. All in all, this genomic perspective on avian hybridization and speciation will further our understanding in evolution in general.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".