Genomes of Galápagos Mockingbirds Reveal the Impact of Island Size and Past Demography on Inbreeding and Genetic Load in Contemporary Populations
Bibliographic record
Abstract
ABSTRACT Restricted range size brings about noteworthy genetic consequences that may affect the viability of a population and eventually its extinction. Particularly, the question if an increase in inbreeding can avert the accumulation of genetic load via purging is hotly debated in the conservation genetic field. Insular populations with limited range sizes represent an ideal setup for relating range size to these genetic factors. Leveraging a set of eight differently sized populations of Galápagos mockingbirds (Mimus), we investigated how island size shaped effective population size (Ne), inbreeding and genetic load. We assembled a genome of M. melanotis and genotyped three individuals per population by whole‐genome resequencing. Demographic inference showed that the Ne of most populations remained high after the colonisation of the archipelago 1–2 Mya. Ne decline in M. parvulus happened only 10–20 Kya, whereas the critically endangered M. trifasciatus showed a longer history of reduced Ne. Despite these historical fluctuations, the current island size determines Ne in a linear fashion. In contrast, significant inbreeding coefficients, derived from runs of homozygosity, were identified only in the four smallest populations. The index of additive genetic load suggested purging in M. parvulus, where the smallest populations showed the lowest load. By contrast, M. trifasciatus carried the highest genetic load, possibly due to a recent rapid bottleneck. Overall, our study demonstrates a complex effect of demography on inbreeding and genetic load, providing implications in conservation genetics in general and in a conservation project of M. trifasciatus in particular.
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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 | 0.001 |
| 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) | 0.001 | 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".