Genetic Divergence of an Avian Endemic on the Californian Channel Islands
Bibliographic record
Abstract
The Californian Channel Islands are near-shore islands with high levels of endemism, but extensive habitat loss has contributed to the decline or extinction of several endemic taxa. A key parameter for understanding patterns of endemism and demography in island populations is the magnitude of inter-island dispersal. This paper estimates the extent of migration and genetic differentiation in three extant and two extinct populations of Channel Island song sparrows (Melospiza melodia graminea). Inter-island differentiation was substantial (G''ST: 0.14-0.37), with San Miguel Island having the highest genetic divergence and lowest migration rates. Santa Rosa and Santa Cruz Island populations were less diverged with higher migration rates. Genetic signals of past population declines were detected in all of the extant populations. The Channel Island populations were significantly diverged from mainland populations of M. m. heermanni (G''ST: 0.30-0.64). Ten mtDNA haplotypes were recovered across the extant and extinct Channel Island population samples. Two of the ten haplotypes were shared between the Northern and Southern Channel Islands, with one of these haplotypes being detected on the Californian mainland. Our results suggest that there is little contemporary migration between islands, consistent with early explanations of avian biogeography in the Channel Islands, and that song sparrow populations on the northern Channel Islands are demographically independent.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".