Whole genomes show contrasting trends of population size changes and genomic diversity for an Amazonian endemic passerine over the late quaternary
Bibliographic record
Abstract
Abstract The “Amazon tipping point” is a global change scenario resulting in replacement of uplandterra‐firmeforests by large‐scale “savannization” of mostly southern and eastern Amazon. Reduced rainfall accompanying the Last Glacial Maximum (LGM) has been proposed to have acted as such a tipping point in the past, with the prediction thatterra‐firmeinhabiting species should have experienced reductions in population size as drier habitats expanded. Here, we use whole‐genomes of an Amazonian endemic organism (Scale‐backed antbirds –Willisornisspp.) sampled from nine populations across the region to test this historical demography scenario. Populations from southeastern Amazonia and close to the Amazon–Cerradoecotone exhibited a wide range of demographic patterns, while most of those from northern and western Amazonia experienced uniform expansions between 400 kya and 80–60 kya, with gradual declines toward 20 kya. Southeastern populations ofWillisorniswere the last to diversify and showed smaller heterozygosity and higher runs of homozygosity values than western and northern populations. These patterns support historical population declines throughout the Amazon that affected more strongly lineages in the southern and eastern areas, where historical “tipping point” conditions existed due to the widespread replacement of humid forest by drier and open vegetation during the LGM.
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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".