Genetic analyses of historic and modern marbled murrelets suggest decoupling of migration and gene flow after habitat fragmentation
Bibliographic record
Abstract
The dispersal of individuals among fragmented populations is generally thought to prevent genetic and demographic isolation, and ultimately reduce extinction risk. In this study, we show that a century of reduction in coastal old-growth forests, as well as a number of other environmental factors, has probably resulted in the genetic divergence of marbled murrelets (Brachyramphus marmoratus) in central California, despite the fact that 7 per cent of modern-sampled murrelets in this population were classified as migrants using genetic assignment tests. Genetic differentiation appears to persist because individuals dispersing from northern populations contributed relatively few young to the central California population, as indicated by the fact that migrants were much less likely to be members of parent-offspring pairs than residents (10.5% versus 45.4%). Moreover, a recent 1.4 per cent annual increase in the proportion of migrants in central California, without appreciable reproduction, may have masked an underlying decline in the resident population without resulting in demographic rescue. Our results emphasize the need to understand the behaviour of migrants and the extent to which they contribute offspring in order to determine whether dispersal results in gene flow and prevents declines in resident populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".