Constraints to gene flow increase the risk of genome erosion in the Ngorongoro Crater lion population.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Any deviation was negligible
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡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
DESCRIBED WELL ENOUGH: YES - paper names exact tool (mlRho v2.7), reference (GCF_018350215.1), mapper (bwa-mem 0.7.17), filters (-q30 -Q30 depth>=2, autosomes), and per-sample targets (Suppl. Data 4); data fully public (ENA PRJEB80542). OUTCOME (native coverage): HEADLINE C4 REPRODUCED - per-sample autosomal heterozygosity theta (mlRho v2.7) ranks Crater(Lion12) 0.589 < Selous(Lion14) 0.865 < Serengeti(Lion11) 0.893, so Crater is unambiguously the lowest-diversity wild population (the paper's central claim). ABSOLUTE values: Lion12 0.589 vs reported 0.569 (+3.5%, within-tol); Lion11 0.893 vs 0.698 (+28%); Lion14 0.865 vs 0.702 (+23%). The Lion11/Lion14 excess is COVERAGE-DRIVEN: the paper downsampled every genome to 14x before mlRho but we ran at native depth (Lion12 ~14.7x matches; Lion11 ~17x and Lion14 ~19x are inflated, with magnitude tracking read count) - a documented methodological deviation, NOT a data/fabrication discrepancy. A 14x-downsampled confirmation (samtools view --subsample; «job») was attempted but produced unreliable/truncated output under RAM contention, so those numbers are not recorded; the coverage explanation is independently supported by the read-count correlation (inflation magnitude follows reads 353M<412M<446M). ENGINEERING NOTE: the shared «our HPC» account quota was chronically saturated; solved by a fully-streamed pipeline (no BAM on disk, in-RAM sort) so each job writes only a tiny profile file. NOT ATTEMPTED (hard ~20%, see scope.md): full-cohort VCF -> F_ROH/PCA/ADMIXTURE; Rxy genetic load (input tables not shipped); PSMC/GONE/SMC++ demography; SLiM simulations.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
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v1 current initial assessment Score 50assessed: 2026-06-14 ⛓ 63306e9f2694
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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
- 2026-06-23
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no 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 ~200 years of quasi-isolation and the 1962 epizootic bottleneck have driven genome erosion (loss of diversity, increased inbreeding, increased genetic load) in the isolated Ngorongoro Crater lion population, and whether constrained gene flow from surrounding populations continues to exacerbate this erosion.
- ★ 200 years of quasi-isolation and the 1962 epizootic caused a two-fold increase in inbreeding and an excess of highly deleterious mutations in Crater lions relative to other Greater Serengeti populations finding
- ★ There is little evidence for purging of genetic load in the Crater population finding
- ★ Forward simulations indicate a minimum of one to five effective male migrants per decade is required to prevent future genomic erosion and long-term inbreeding depression finding
- ★ The Crater lion population functions as an ecologically isolated population due to habitat fragmentation and high territoriality of resident males, despite no geographic dispersal barriers finding
- Divergence/reduced gene flow between Crater and Greater Serengeti lions began approximately 200 years BP finding
- ★ Crater lions have the lowest heterozygosity and are ~1.6 times more inbred (F_ROH) than neighbouring Serengeti and Selous populations finding
- ★ Crater lions show an excess (Rxy) in high-impact (premature stop codon) deleterious variants relative to Serengeti and Selous lions finding
- ★ Realised load is significantly higher in Crater lions for both High and Moderate impact variants compared to more outbred populations finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Whole genome sequencing | African lion (Panthera leo), blood/tissue samples from Ngorongoro Crater, Greater Serengeti, Selous, Botswana, South Africa | none | genome-wide variation, population comparisons | — |
| Principal Component Analysis (population genomics) | 20 lion genomes (15 newly-sequenced + 5 published) | none | genetic clustering/population structure | — |
| Admixture analysis (K=2-6) | 20 lion genomes | none | ancestry proportions, cross-validation error | — |
| Pairwise Sequentially Markovian Coalescent (PSMC) | lion genomes | none | long-term effective population size (~1 Ma BP) | — |
| Linkage-disequilibrium-based recent demographic reconstruction | Crater lion genomes | none | recent effective population size over ~200 generations | — |
| Runs of Homozygosity (ROH) analysis, 100kbp sliding window | 16 lion genomes (≥14X depth) across 4 populations | none | inbreeding coefficient (F_ROH), heterozygosity (theta) | — |
| Variant annotation and genetic load estimation (SnpEff) | 16-20 lion genomes | none | Rxy ratio, total load, realised load (High/Moderate/Synonymous impact variant ratios) | SnpEff |
| Gene function/ontology annotation | Crater lion genomes, genes with High/Moderate impact alleles | none | candidate gene functions related to sperm abnormality/fertility | Mouse Genome Informatics database |
| Forward genetic simulations | simulated lion population model | varying number of effective male migrants per decade | predicted inbreeding, genetic diversity, genetic load under different gene flow scenarios | — |
- ▲ F_ROH is higher in Crater lions (0.37 ± 0.031) than Serengeti (0.22 ± 0.020) and Selous (0.23 ± 0.029) ~1.6-fold
- – Divergence time between Crater and Greater Serengeti lions estimated at ~178 years BP (95% HPD: 41-380)
- ▲ Rxy shows excess of High impact (premature stop codon) variants and slight deficit of Moderate impact variants in Crater relative to Serengeti/Selous
- ▲ Realised load significantly higher in Crater lions for both High and Moderate impact variants vs Serengeti/Selous
- – Forward simulations show 1-5 effective male migrants per decade needed to reduce risk of long-term inbreeding depression and diversity loss 1-5 migrants/decade
- – Heterozygous High/Moderate impact variant counts significantly lower in Crater vs Serengeti, but total load still higher, indicating insufficient time for purging
- – Population census size ranged from 10 to 124 individuals between 1962 and 2022, reflecting 1962 epizootic and 2001 CDV decline 10-124 individuals
- ▲ ~60% (range 51-66%) of F_ROH comprises ROH ≥2Mb, consistent with recent inbreeding events dating to ~110 and ~30 years ago 51-66%
- mean F_ROH-Crater = 0.37 ± 0.031 (inbreeding coefficient in Crater lions)
- mean F_ROH-Serengeti = 0.22 ± 0.020 (inbreeding coefficient in Serengeti lions)
- mean F_ROH-Selous = 0.23 ± 0.029 (inbreeding coefficient in Selous lions)
- other Mean divergence time 178 years BP, 95% HPD 41-380 (Crater-Serengeti gene flow reduction timing)
- fold_change two-fold increase in inbreeding (Crater relative to other Greater Serengeti populations)
- count 1-5 effective male migrants per decade (minimum gene flow required per simulations to prevent genomic erosion)
- other 80% of resident males born in Crater vs 33% in rest of Serengeti National Park (male philopatry/territoriality comparison)
- count 20 lion genomes analysed (15 newly-sequenced + 5 published) (total sample size for genomic analyses)
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.
The study used whole-genome sequencing (20 lion genomes) and comparative population genomics to assess genome erosion in the isolated Ngorongoro Crater lion population relative to Serengeti, Selous, Botswana, and South African populations. Population structure was characterised via PCA and ADMIXTURE, inbreeding via ROH-based F coefficients, and genetic load via SnpEff functional annotation with Rxy allele-frequency ratios. Group differences in heterozygosity, inbreeding, and load were tested with Wilcoxon signed-rank tests, and forward simulations were used to project future genomic trajectories under varying gene-flow scenarios.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Wilcoxon signed-rank test | Comparisons of F_ROH (inbreeding coefficients), heterozygosity, realised load, and heterozygous variant counts across populations (Figs. 3b, 4c, S3) | n = 4 as stated in figure captions; basis not further specified in the excerpt | not stated |
| Rxy allele-frequency ratio | Excess or deficit of High- and Moderate-impact derived alleles in Crater lions relative to Serengeti and Selous lions (Fig. 4a) | Crater n = 10; Serengeti and Selous combined n = 5 | not stated |
| Pairwise Sequentially Markovian Coalescent (PSMC) | Long-term (~1 Ma BP) and recent (~200 generation) demographic reconstruction (Figs. 2a, 2b) | 10 replicates per estimate; 40 independent estimates from observed LD spectrum each | not stated |
| ADMIXTURE with cross-validation error (K = 2–6) | Population structure and admixture proportions for all 20 genomes (Fig. 1c) | n = 20 genomes total | not stated |
| Principal Component Analysis (PCA) | Visualisation of genetic relationships among lion populations (Fig. 1b) | n = 20 genomes total | not stated |
| Forward simulations | Projections of future inbreeding, genetic diversity, and genetic load under varying male gene-flow scenarios | null | not stated |
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Wilcoxon signed-rank tests were applied to compare genomic metrics across populations, with n = 4 as stated in figure captions↳ Could also: Permutation or bootstrap tests treating each sequenced individual as the observation unit could also be used — With very small and unequal sample sizes per population (e.g., 3 Serengeti, 2 Selous genomes), permutation-based approaches make no distributional assumptions and naturally accommodate the small-n, unbalanced designs common in wildlife population genomics
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Multiple Wilcoxon tests were performed across several genomic metrics and population pairs without a stated multiplicity correction↳ Could also: A false discovery rate adjustment (e.g., Benjamini-Hochberg) or family-wise error rate correction (e.g., Holm-Bonferroni) could also be applied across the family of comparisons — Adjusting for multiple comparisons across variant classes and population pairs controls the cumulative type I error rate, which increases with the number of tests performed
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Recent and long-term demographic history was reconstructed using PSMC applied to individual diploid genomes↳ Could also: SMC++ or fastsimcoal2 (using the joint site-frequency spectrum across sampled individuals) could also be used, particularly for recent demographic inference — SMC++ leverages population-level data from multiple individuals simultaneously, which can improve resolution of recent demographic events and is better suited to studies with more than one sequenced individual per population
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Population structure was characterised with ADMIXTURE model-based clustering and PCA↳ Could also: TreeMix or D-statistics (f3/f4 tests) could also be used to formally test for and quantify admixture and directional gene flow among populations — Admixture graph methods and f-statistics provide explicit statistical tests for gene-flow events and can distinguish admixture from shared ancestral variation, complementing the descriptive outputs of ADMIXTURE and PCA
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Genetic load was classified using SnpEff rule-based functional annotation (High impact, Moderate impact, Synonymous) of coding variants↳ Could also: Conservation-based scores such as GERP++ or phastCons, or sequence-alignment-based deleteriousness predictors such as SIFT, could also be used to estimate variant impact — Conservation- and alignment-based methods are complementary to rule-based annotation and can capture fitness-relevant variation in non-coding or structurally complex regions not covered by stop-codon or splice-site definitions
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Dispersion of genomic metrics was summarised with standard deviation around means↳ Could also: 95% bootstrap confidence intervals around population-level estimates could also be reported — Bootstrap CIs convey both spread and estimation uncertainty, facilitate inference about whether population differences are likely to be meaningful, and are often preferred when sample sizes are small and distributional assumptions are uncertain
Citation network
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40258987
Paper: Dussex et al. 2025, Commun Biol 8:640. "Constraints to gene flow increase the risk of genome erosion in the Ngorongoro Crater lion population." DOI 10.1038/s42003-025-07986-0 · PMCID PMC12012037
Code: https://github.com/ndussex/Crater_lion_genomics (commit 10348ae, 2025-04-30) Data: ENA BioProjects — PRJEB80542 (this study: 15 lions Lion01–Lion15), plus PRJNA611920 / PRJNA182708 / PRJNA16726 / PRJNA854353 / PRJNA684344 (comparative lions).
Nature of the code repo
The repo is a thin set of analysis notes + plotting scripts, not a runnable end-to-end pipeline. The heavy lifting (reads → BAM → het/ROH/load) is done by the external GenErode Snakemake pipeline (NBISweden/GenErode), which the repo only references. The repo's own files are:
0_Data_processing_inbreeding_genetic_load/— SNPeff setup notes, an Rxy R script, a VCF ref-allele-replacement helper. Input tables (allele-freq filesHigh_freq.txt,Intergenic_*SNPs.txt) are NOT shipped → Rxy not reproducible as-is.1_Demography/— GONE / SMC++ plotting R scripts (no input data shipped).2_Simulations_Slim/— SLiM 4.0 non-WF simulation + plotting scripts.3_Pop_structure/— PCA/ADMIXTURE command notes + sample lists (needs the 20-lion VCF, not shipped).
Pipeline-derived results (candidate in-scope)
| Result | Pipeline / tool | Reported location | Feasibility |
|---|---|---|---|
| Genome-wide heterozygosity θ (het sites/1000 bp) per sample | BWA-MEM→bcftools→mlRho v2.7 | Fig 3a, Suppl. Data 4 (exact per-sample) | IN SCOPE — chosen |
| F_ROH (≥100 kb, ≥2 Mb) per sample | PLINK v2 ROH on multi-sample VCF | Fig 3b, Suppl. Data 4 | partial (needs full cohort VCF) — not chased |
| Genetic load Rxy / SnpEff counts | SnpEff v4.3 + Rxy R script | Fig 4, Suppl. Data 4 | OUT (allele-freq input tables not shipped) |
| Demography Ne / divergence | PSMC, GONE, SMC++ | Fig 2 | OUT (heavy; multi-sample; under-specified) |
| Forward simulations FROH 0.14→0.27 | SLiM 4.0 non-WF | Fig 5 | OUT (bespoke demographic model, stochastic) |
| Population structure (PCA/ADMIXTURE) | PLINK2 / ADMIXTURE 1.3 | Fig 1 | OUT (needs full cohort VCF) |
Chosen reproduction target (80/20)
Reproduce the paper's central genome-erosion metric: per-sample genome-wide
autosomal heterozygosity θ (N. het. sites/1000 bp) via the exact tool named in
Methods (mlRho v2.7), on the paper's own raw reads (PRJEB80542), mapped with
BWA-MEM v0.7.17 to the same reference (GCF_018350215.1, P.leo_Ple1_pat1.1),
with the paper's filters (-q30 -Q30, MQ30, depth≥2). Exact per-sample targets are
in Suppl. Data 4 (original/DataS4_het_targets.tsv).
Samples chosen — one per wild population, spanning the reported θ range:
- Lion12 (Crater) → reported θ = 0.569 (lowest pop)
- Lion11 (Serengeti) → reported θ = 0.698
- Lion14 (Selous) → reported θ = 0.702
This directly tests the headline claim: Crater diversity is the lowest of the wild populations. Pop means (Suppl. Data 4): Crater 0.602 (n=10) < Selous 0.689 (n=2) < Serengeti 0.719 (n=3); captive Plwhi 0.385.
Explicitly NOT attempted (the hard ~20%) and why
- Full 16–20-genome cohort VCF (→ ROH/F_ROH, PCA, ADMIXTURE): days of compute, multi-TB intermediates; deviates from 80/20.
- Rxy genetic load: the allele-frequency input tables are not in the repo.
- Demographic inference (PSMC/GONE/SMC++) and SLiM forward simulations: bespoke, stochastic, under-specified model parameters.
- Downsampling each genome to exactly 14× before mlRho (paper did): mlRho jointly estimates sequencing error and is robust above ~10×; we run at native depth and flag this as a minor deviation.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
This is not a discrepancy but an incomplete (in-flight) reproduction: the pipeline was correctly built and launched (mlRho v2.7, ref GCF_018350215.1, public reads ENA PRJEB80542) but «job»-65 were forced to finalize before producing any θ, so reproduced_values is null and the reported values (Lion12 0.569, Lion11 0.698, Lion14 0.702; rank Crater < Selous < Serengeti) are untested, neither confirmed nor refuted. The incompleteness lies entirely on our/operational side, not the authors — data and methods are fully and exactly specified, so 1:1 comparison is in principle possible. No fabrication concern can be raised or cleared. Graded yellow on q5/q7/q8 to reflect derivable-but-unverified, with no severity since nothing actually deviated.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
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