Genomic differentiation and local adaptation on a microgeographic scale in a resident songbird
Bibliographic record
Abstract
AbstractElucidating forces capable of driving species diversification in the face of gene flow remains a key goal in evolutionary biology. Song sparrows, Melospiza melodia, occur as 25 subspecies in diverse habitats across North America, are among the continent’s most widespread vertebrate species, and are exemplary of many highly variable species for which the conservation of locally adapted populations may be critical to their range-wide persistence. We focus here on six morphologically distinct subspecies resident in the San Francisco Bay region, including three salt-marsh endemics and three residents in upland and riparian habitats adjacent to the Bay. We used reduced-representation sequencing to generate 2,773 SNPs to explore genetic differentiation, spatial population structure, and demographic history. Clustering separated individuals from each of the six subspecies, indicating subtle differentiation at microgeographic scales. Evidence of limited gene flow and low nucleotide diversity across all six subspecies further supports a hypothesis of isolation among locally adapted populations. We suggest that natural selection for genotypes adapted to salt marsh environments and changes in demography over the past century have acted in concert to drive the patterns of diversification reported here. Our results offer evidence of microgeographic specialization in a highly polytypic bird species long discussed as a model of sympatric speciation and rapid adaptation, and they support the hypothesis that conserving locally adapted populations may be critical to the range-wide persistence of similarly highly variable species.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".