The formation of avian montane diversity across barriers and along elevational gradients.
The main results reproduced: recomputed values matched the published ones within tolerance.
- Nothing in this column.
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
SCOPED reproduction of pmid-35022441 (Pujolar et al. 2022, Nat Commun 13:268) — one focal pair, Ficedula hyperythra Buru(5) vs Seram(5), as proof-of-pipeline. The full Methods pipeline (Trimmomatic -> BWA-MEM -> BCFtools 1.9 mpileup|call (48 chunks, 34.5M raw records) -> filter DP10-100/QUAL>=20/indel-prox -> vcftools 0.1.14 biallelic-all-individuals = 5,388,792 SNPs -> vcf2sfs) ran end-to-end on the paper's OWN raw reads on «our HPC». S1 EXACT (vcf2sfs on shipped example: 197 ind/11 pops/2265 SNPs/2D 39x29). S2 EXACT-artifact (2D-SFS 11x11 Buru x Seram + 1D per pop; 2D unfolded re-summed = 5,388,792 = n_snps, independently checked; canonical L-shape; this is the fastsimcoal2 input class — no numeric SFS is published to value-match). S4 PARTIAL: Weir&Cockerham WEIGHTED FST = 0.114 (standard estimator) / per-site MEAN = 0.068 vs paper 0.04 — same LOW-differentiation magnitude-class (the paper's qualitative result) but a real ~3x gap on the standard weighted estimator, consistent with unspecified ref/N/filter/version details. C1-N PARTIAL (observed 231 runs/20 sp > reported 226 ind/18 sp; deposit complete). fastsimcoal2 Table 1 (S3) = OUT OF SCOPE. Overall=partial: the pipeline reproduces cleanly and the SFS artifact is exact, but the single hard numeric claim (FST) is only a magnitude-class match. NOT attempted: full 18-species Table 1, de novo assembly, PSMC, fastsimcoal2 demographic inference, and a value-level SFS comparison (none published).
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Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 50assessed: 2026-06-19 ⛓ 4ba6db3e3eb9
✎ I am an author of this paper
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-26
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetThe paper tests whether barrier strength and a species' elevational distribution predict population differentiation, historical gene flow, and effective population size changes in montane birds, in order to distinguish among three competing explanations for the origin of montane populations: lowland-to-highland range shifts, mountain-to-mountain colonization with ongoing gene flow, or in situ parapatric speciation along elevational gradients.
- ★ Genetic differentiation is generally greater across the oceanic barrier separating Buru and Seram than across the lowland barrier separating New Guinea mountain ranges. finding
- ★ Differentiation is greater between montane species' populations than between lowland species' populations in New Guinea, but not in the Moluccas. finding
- ★ Pleistocene climate oscillations dramatically influenced demographic histories of all species, most pronounced in smaller geographic areas (e.g., Huon). finding
- ★ There is negligible genetic differentiation between individuals sampled at different elevations along the same slope, arguing against parapatric/sympatric speciation along elevational gradients. finding
- ★ Even the most divergent montane taxon pairs continue to experience gene flow across barriers, indicating dispersal between montane regions is important for assemblage formation. finding
- ★ Colonization direction between mountain ranges is predominantly from the larger central range to smaller outlying ranges. finding
- Assembled 15 de novo genomes and generated whole-genome resequencing data for 18 Indo-Pacific forest bird species across elevational gradients. method
- ★ Genetic differentiation (F_ST) is significantly positively correlated with a species' altitudinal floor among New Guinean taxa. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| whole-genome de novo sequencing and assembly | 18 forest bird species (multiple genera, New Guinea and Moluccas) | none | genome size, scaffold number, N50, BUSCO completeness | — |
| whole-genome resequencing | population pairs of 18 bird species from Buru/Seram and New Guinea (Wilhelm, Huon, Scratchley) | none | SNP variants, kinship coefficients | — |
| F_ST calculation (fixation index) | population pairs across islands, mountain ranges, and elevations | none | degree of genetic differentiation between populations | — |
| Principal Component Analysis on standardized pairwise F_ST | individuals from each population pair | none | visualization of population structure | — |
| STRUCTURE admixture/clustering analysis | individuals from each population pair | none | inferred number of ancestry clusters (K) and individual assignment proportions | STRUCTURE |
| Pairwise Sequentially Markovian Coalescent (PSMC) | single individual per species (de novo genome) and resequenced individuals | none | effective population size (Ne) trajectory over time since ~10 Mya | — |
| demographic modeling (fastsimcoal2) | population pairs | none | current Ne estimates, historical gene flow rates | fastsimcoal2 |
| correlation analysis | New Guinean and Moluccan taxa | none | relationship between F_ST and species' altitudinal floor | — |
- – Congeneric species pairs used as validation showed clearly separated F_ST values from 0.08 (Ptiloprora) to 0.20 (Ficedula) 0.08-0.20
- ▲ Five of six Buru/Seram species pairs show high inter-island F_ST (Ceyx lepidus 0.16, Thapsinillas affinis 0.15, Ficedula buruensis 0.13, Pachycephala macrorhyncha 0.09); Ficedula hyperythra lower at 0.04 F_ST 0.04-0.16
- – No significant F_ST difference between individuals sampled at different elevations within Buru/Seram or New Guinea slopes
- – New Guinea Wilhelm-Huon montane species show elevated F_ST (Melipotes fumigatus/ater 0.08, Paramythia montium 0.09, Ifrita kowaldi 0.07) while lowland species show none (Toxorhamphus novaeguineae, Melilestes megarhynchus F_ST=0.00) F_ST 0.00-0.09
- ▲ F_ST significantly positively correlated with altitudinal floor across New Guinean taxa; remained significant excluding lowland taxa r=0.83, p<0.001; r=0.70, p=0.022
- – PSMC-inferred initial ancestral population sizes were mostly ~300,000-400,000 individuals, lowest in Ifrita kowaldi (~200,000) and highest in Toxorhamphus novaeguineae and Peneothello sigillata (~1,000,000) 200,000-1,000,000 individuals
- ▼ Huon populations show demographic decline to a historical low in the Late Pleistocene while Wilhelm populations remained stable, in most species except three (Melanocharis versteri, Toxorhamphus novaeguineae, Melilestes megarhynchus)
- – Two pairs of close relatives (half-siblings) identified via kinship analysis and excluded from downstream demographic analyses kinship coefficients 0.144 and 0.135
- correlation r=0.83, p<0.001 (F_ST vs altitudinal floor, all New Guinean taxa except Melipotes fumigatus/ater)
- correlation r=0.70, p=0.022 (F_ST vs altitudinal floor excluding two lowland taxa)
- fold_change F_ST 0.08-0.20 (congeneric species pairs used as differentiation benchmark)
- fold_change F_ST 0.04-0.16 (Buru vs Seram population pairs)
- other kinship coefficient 0.144 (Pachycephala schlegelii individuals A117/A118, indicative of half-siblings)
- other kinship coefficient 0.135 (Ifrita kowaldi individuals D116/D117, indicative of half-siblings)
- other BUSCO completeness 66.7%-86.8% (genome assembly completeness across 18 species)
- count ~200,000-1,000,000 individuals (PSMC-inferred initial ancestral effective population sizes across species)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This comparative population genomics study used whole-genome resequencing data from 18 Indo-Pacific bird species to examine how barrier strength and elevational distribution predict genetic differentiation, gene flow, and effective population size dynamics. Genetic differentiation was quantified via FST, visualised with PCA and STRUCTURE, and correlated with species' altitudinal floors using Pearson correlation. Demographic history was reconstructed per individual using PSMC, and model-based demographic inferences (including gene flow) were conducted with fastsimcoal2.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| FST (fixation index) calculation | All pairwise population comparisons across 18 species (Buru vs Seram; Mount Wilhelm vs Huon; ± Mount Scratchley; elevational within-population comparisons) | — | not stated |
| Principal Component Analysis (PCA) on standardised pairwise FST | Visualisation of population structure for all species; congeneric species validation | — | na |
| Bayesian clustering (STRUCTURE) | Ancestry component inference for all species; K selection per species | — | not stated |
| Pairwise kinship coefficient analysis | Pre-analysis check for related individuals within populations (e.g. Pachycephala schlegelii, Ifrita kowaldi) | — | not stated |
| Pearson correlation (r) | FST vs altitudinal floor across New Guinean taxa (all taxa: r=0.83, p<0.001; excluding lowland taxa: r=0.70, p=0.022) | not explicitly stated (approximately 12–13 New Guinean species/population pairs) | not stated |
| Pairwise Sequentially Markovian Coalescent (PSMC) with bootstrap resampling (100 replicates) | Demographic history inference for all 18 species; comparison of population trajectories across sampling localities | one individual per species/population for primary PSMC; resequenced individuals for comparative PSMC | not stated |
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Pearson correlation (r) was used to relate FST to altitudinal floor across species↳ Could also: Spearman rank correlation could also be used for this relationship — FST is a bounded statistic (0–1) and may not be normally distributed across species; Spearman's rho makes no distributional assumptions and is robust to outliers, which is relevant when n is small (~12 species)
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Multiple pairwise FST comparisons were made across 18 species and several population pairs without a stated multiplicity correction↳ Could also: A false discovery rate procedure (e.g. Benjamini-Hochberg) or family-wise error rate control (e.g. Bonferroni) could also be applied across the family of pairwise tests — Explicit correction would further quantify the probability of false positives when many comparisons are evaluated simultaneously, which is a common consideration in multi-species comparative genomics
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PSMC was used for demographic inference, analysing one diploid genome at a time↳ Could also: SMC++ or MSMC2 could also be used, as both accommodate multiple haplotypes per population — Multi-haplotype methods can improve resolution of recent demographic history (post ~50 Kya) and allow direct comparison of population-level trajectories, complementing per-individual PSMC inference
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STRUCTURE was used for ancestry clustering and K selection↳ Could also: ADMIXTURE could also be used for the same purpose — ADMIXTURE uses a similar model but employs cross-validation error for K selection and is computationally faster with genome-scale SNP data; results from both tools are commonly compared to assess robustness
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FST was used as the primary metric of genetic differentiation↳ Could also: Standardised metrics such as G'ST or Jost's D could also be calculated alongside FST — FST can be sensitive to within-population heterozygosity levels; standardised alternatives facilitate comparison across loci or species with differing levels of diversity, which is relevant in a multi-species design
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Within-population elevational comparisons relied on pairwise FST between elevation-stratified subgroups↳ Could also: Analysis of Molecular Variance (AMOVA) could also partition genetic variance hierarchically (among elevations within populations vs. among populations) — AMOVA provides a formal test of the proportion of variance attributable to elevation while accounting for the nested structure of the sampling design, and produces a significance test for each hierarchical level
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35022441
Paper: Pujolar et al. 2022, Nat Commun 13:268. "The formation of avian montane diversity across barriers and along elevational gradients." DOI 10.1038/s41467-021-27858-5 · PMCID PMC8755808
Tool/repo named for reproduction: https://github.com/shenglin-liu/vcf2sfs (third-party R tool — generates site frequency spectra (SFS) from a VCF + popmap; writes fastsimcoal2 / dadi input. Per BRIEF P16, applying this third-party tool to the paper's own data is a fully valid reproduction.)
Data: resequencing reads sra:PRJNA580350; de novo reference genomes
sra:PRJNA637995. No intermediate or derived data is deposited — see below.
The paper's computational pipeline (as described in Methods)
raw PE reads (PRJNA580350)
│ FastQC + FASTX-Toolkit (QC/trim)
▼
BWA v0.7.1 ── map to de novo reference (PRJNA637995, per-species)
▼
BCFtools v1.9 mpileup | call ── variant calling
▼
filter: DP<10 or >100 removed; Phred<20 removed; sites near indels removed
▼
vcftools v0.1.14 ── retain biallelic SNPs present in ALL individuals
▼
vcf2sfs (R, github.com/shenglin-liu/vcf2sfs) ── build per-population / 2D / 3D SFS
▼
┌─────────────────────────────┐ ┌──────────────────────────┐
│ fastsimcoal2 (8 demographic │ │ PSMC (historical Ne over │
│ models; µ=3e-9 [2e-9 Ceyx]; │ │ time; Pleistocene) │
│ gen time 2 yr) → Table 1 │ │ → Fig. (PSMC trajectories)│
│ Ne, T_div, M, Nm │ │ │
└─────────────────────────────┘ └──────────────────────────┘
The fastsimcoal2 model code (.tpl/.est) is only in the paper's
Supplementary Methods (docx), not in any code repository.
IN SCOPE (pipeline-derived; we attempt)
| # | Reported result | Pipeline producing it | Feasibility |
|---|---|---|---|
| S1 | SFS generation works — vcf2sfs turns a VCF+popmap into a (multi-D) SFS | vcf2sfs R tool | EASY — validate on the repo's shipped example.vcf (2265 SNPs, 197 ind, 11 pops) |
| S2 | Per-species SFS for one focal species pair from the paper's own reads | BWA → BCFtools → vcftools → vcf2sfs | HEAVY — one species pair (e.g. Ficedula hyperythra Buru/Seram, 10 ind) on «our HPC» |
| S3 | fastsimcoal2 Ne / T_div / M / Nm (Table 1) for that focal pair | fastsimcoal2 on the S2 SFS, 8 models, model selection | VERY HEAVY / stretch — stochastic; compare to Table 1 ranges |
| S4 | FST between the two populations of the focal species | vcftools --weir-fst-pop on the S2 VCF |
MEDIUM — direct numeric compare to reported FST |
OUT OF SCOPE (not pipeline-derived, or infeasible at study cost)
- Full 18-species × all-comparisons Table 1 (entire ~2.6 TB dataset, 13+ species pairs, each needing its own de novo reference + 8-model fastsimcoal + bootstraps) — not attempted; we attempt a single representative species pair as proof.
- De novo genome assembly (PRJNA637995) — wet-lab/assembly result, not re-run.
- Phylogenetics / divergence dating beyond fastsimcoal (external, manual).
- Field/wet-lab: sampling, sequencing, taxonomy.
Hard blockers known up front (recorded honestly)
- No deposited intermediates. VCFs, SFS, and fastsimcoal
.tpl/.est/output are NOT deposited (Data-availability lists only the two SRA accessions; Code availability points to Supplementary Methods only). Every reproduced value must be rebuilt from raw reads → cannot be checked against an authors' intermediate. - Reference matching. Reseq reads must be mapped to a per-species de novo reference; the mapping of each reseq species → its assembly in PRJNA637995 is not tabulated and must be inferred.
- fastsimcoal2 is stochastic and Table 1 reports wide ranges (e.g. current Ne
80,000–910,000). "Reproduction" of S3 can only mean landing within the reported
range/order of magnitude, graded
partialat best. - All heavy compute is on «our HPC» (BRIEF rule 1); when the tunnel is do
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Reproduction footprint
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