Asymmetry matters: A genomic assessment of directional biases in gene flow between hybridizing spruces
Bibliographic record
Abstract
Assessing directional bias in interspecific gene flow might be important in determining the evolutionary trajectory of closely related species pairs. Using a set of 300 single nucleotide polymorphisms (SNPs) having variable propensity to cross species boundary, we evaluated the genomic extent and direction of interspecific gene flow in a progenitor-derivative spruce species pair (black spruce and red spruce). A higher rate of gene flow was found from black spruce toward red spruce purebreds than vice versa. This asymmetry could reflect the historical gene flow between the two taxa at the time of species inception and during postglacial colonization. A clear asymmetry in introgression was depicted by a greater gene flow between red spruce and hybrids than between black spruce and hybrids. While backcrossing toward red spruce was invariably high across the genome, the actual species boundary is between hybrids and black spruce where gene flow is impeded at those genomic regions impermeable to introgression. Associations between hybrid index and climatic variables (total annual precipitation and mean annual temperature) were tested, as these might indicate a role for exogenous selection in maintaining the species boundary. While an apparent association was found between the hybrid index and precipitation, it collapsed when considered in light of the directional bias in interspecific gene flow. Hence, considering asymmetrical patterns of introgression allowed us to falsify an apparent role for exogenous selection. Although this was not formerly tested here, we suggest that this pattern could result from asymmetrical endogenous selection, a contention that deserves further investigations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".