Data for: Are immigrants outbred and unrelated? Testing standard assumptions in a wild metapopulation
Bibliographic record
Abstract
Immigration into small recipient populations is expected to alleviate inbreeding and increase genetic variation, and hence facilitate population persistence through genetic and/or evolutionary rescue. Such expectations depend on three standard assumptions: that immigrants are outbred, unrelated to existing natives at arrival, and unrelated to each other. These assumptions are rarely explicitly verified, including in key field systems in evolutionary ecology. Yet, they could be violated due to non-random or repeated immigration from adjacent small populations. We combined molecular genetic marker data for 150-160 microsatellite loci with comprehensive pedigree data to test the three assumptions for a song sparrow (Melospiza melodia) population that is a model system for quantifying effects of inbreeding and immigration in the wild. Immigrants were less homozygous than existing natives on average, with mean homozygosity that closely resembled outbred natives. Immigrants can therefore be considered outbred on the focal population scale. Comparisons of homozygosity of real or hypothetical offspring of immigrant-native, native-native and immigrant-immigrant pairings implied that immigrants were typically unrelated to existing natives and to each other. Indeed, immigrants’ offspring would be even less homozygous than outbred individuals on the focal population scale. The three standard assumptions of population genetic and evolutionary theory were consequently largely validated. Yet, our analyses revealed some deviations that should be accounted for in future analyses of heterosis and inbreeding depression, implying that the three assumptions should be verified in other systems to probe patterns of non-random or repeated dispersal and facilitate precise and unbiased estimation of key evolutionary parameters.
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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 | 0.025 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".