Demographic decline and lineage-specific adaptations characterize New Zealand kiwi
Bibliographic record
Abstract
Small and fragmented populations may become rapidly differentiated due to genetic drift, making it difficult to distinguish whether neutral genetic structure is a signature of recent demographic events, or of long-term evolutionary processes that could have allowed populations to adaptively diverge. We sequenced 52 whole genomes to examine Holocene demographic history and patterns of adaptation in kiwi ( Apteryx ), and recovered 11 strongly differentiated genetic clusters corresponding to previously recognized lineages. Demographic models suggest that all 11 lineages experienced dramatic population crashes relative to early- or mid-Holocene levels. Small population size is associated with low genetic diversity and elevated genetic differentiation ( F ST ), suggesting that population declines have strengthened genetic structure and led to the loss of genetic diversity. However, population size is not correlated with inbreeding rates. Eight lineages show signatures of lineage-specific selective sweeps (284 sweeps total) that are unlikely to have been caused by demographic stochasticity. Overall, these results suggest that despite strong genetic drift associated with recent bottlenecks, most kiwi lineages possess unique adaptations and should be recognized as separate adaptive units in conservation contexts. Our work highlights how whole-genome datasets can address longstanding uncertainty about the evolutionary and conservation significance of small and fragmented populations of threatened species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".