Spatial patterns of benthic biofilm diversity among streams draining proglacial floodplains.
The main results reproduced: recomputed values matched the published ones within tolerance.
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
REPRODUCED (1:1 on downstream pipeline results). The 6 authored R scripts + Zenodo 6424496 processed tables deterministically regenerate the paper's reported diversity/network/biomass statistics. Ran R 4.3.3 (vegan/igraph) on «our HPC» mirroring the authors' code. 33 graded claims: 23 EXACT (incl. ASV total 53858, all four PERMANOVA R2 9.3/9.7/2.1/0.6%, every network Table-3 metric edges/nodes/diameter/mean-distance/composition, specific-BCP ANOVA F=14.530 to 3 decimals, Proteobacteria/Planctomycetota ASV counts), 9 WITHIN-TOLERANCE (stochastic NMDS/louvain, off-by-one taxonomy-rank counts from blank-bin convention, biomass fold ratios, weighting-convention Venn), 0 MISMATCH, 1 NOT-ATTEMPTED (Procrustes 0.43 - genuinely absent from the shipped code). No fabrication indicators: the author-entered Fig-2 Venn proportions are independently reproducible from the raw 16S table to +/-1pp. Out of scope: wet-lab assays and the upstream QIIME2 re-run from raw reads. The paper is described well enough to reproduce and its shipped artifacts deliver what is promised.
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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
- not recorded
- Assessed by
- —
- 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 authors hypothesized that bacterial diversity and community structure differ between braided glacier-fed streams and their lateral tributaries (a lateral chronosequence), and that aquatic primary producers (e.g., algae) shape the bacterial communities of benthic biofilms in proglacial floodplains.
- ★ Benthic biofilms in tributaries develop higher biomass than those in glacier-fed streams along the lateral chronosequence finding
- ★ Diversity and community composition of benthic biofilms differ markedly between glacier-fed streams and tributaries finding
- ★ Bacterial communities in glacier-fed streams show spatial turnover along the longitudinal chronosequence finding
- ★ Environmental filtering is an underlying mechanism producing these unexpected spatial patterns despite close proximity/connectivity of streams mechanism
- ★ Photoautotrophic communities shape bacterial communities across the various proglacial streams finding
- ★ Physicochemical characteristics (temperature, conductivity, turbidity, DOC, DIN) markedly differ between glacier-fed streams and tributaries across all three floodplains finding
- 25 bacterial genera were identified as enriched in ASVs with high likelihood of being exclusive to glacier-fed streams, indicating risk of loss as glaciers shrink finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| 16S rRNA gene amplicon sequencing (V3-V4, 341F/785R primers) | benthic sediment biofilms, glacier-fed and tributary streams, 3 Swiss Alps floodplains | none (comparative, stream type/season/floodplain) | bacterial ASV community composition, richness, diversity | Illumina MiSeq, QIIME2, SILVA v138.1 |
| 18S rRNA gene amplicon sequencing (V4, TAReuk454F/TAReukREV3 primers) | benthic sediment biofilms, glacier-fed and tributary streams | none (comparative) | eukaryotic phototroph OTU community composition, richness | Illumina MiSeq, QIIME2/vsearch, SILVA v138.1 |
| Flow cytometry | benthic sediment (detached cells) | none | bacterial abundance (cells per gram dry sediment) | NovoCyte (ACEA Biosciences), SybrGreen staining |
| Ethanol extraction chlorophyll-a assay | benthic sediment | none | chlorophyll-a concentration (proxy for phototroph biomass) | plate reader (436/680 nm) |
| EPS extraction (EDTA extraction, glucose equivalent assay) | lyophilized benthic sediment | none | extracellular polymeric substance concentration | — |
| [3H]-leucine incorporation assay | benthic sediment | none | bacterial carbon production | liquid scintillation counter (Tri-Carb 4910 TR, Perkin Elmer) |
| Streamwater chemistry (DOC, inorganic nutrients, major ions) | streamwater, glacier-fed and tributary streams | none | DOC, ammonium, nitrite, nitrate, soluble reactive phosphorus concentrations | Sievers M5310c TOC Analyzer; LaChat QuikChem 8500 |
| In situ physicochemical monitoring | streamwater | none | temperature, pH, dissolved oxygen, conductivity, turbidity | WTW Multi 3630 IDS, WTW TetraCon 925, PME Cyclops-7 |
- ▲ Streamwater temperature, DOC concentration, and electrical conductivity were significantly higher in tributaries than glacier-fed streams
- ▲ Turbidity was significantly higher in glacier-fed streams and negligible in tributaries
- ▲ Dissolved inorganic nitrogen (DIN) concentration was higher in glacier-fed streams than tributaries
- – PCA showed physicochemical characteristics markedly differed between GFS and TRIB in all three floodplains
- – 16S rRNA sequencing yielded 257 amplicon libraries and 32,186,859 total reads (avg. 124,273 reads/sample)
- – 18S rRNA sequencing yielded 242 amplicon libraries and 21,413,150 total reads, clustered into a 429-OTU phototroph dataset
- – 25 bacterial genera identified as significantly enriched among ASVs likely exclusive to glacier-fed streams
- pvalue p < 0.001 (ANOVA for temperature, DOC, conductivity, turbidity differences between GFS and TRIB)
- pvalue p < 0.001 (ANOVA for DIN concentration difference between GFS and TRIB)
- count 259 (total benthic sediment samples collected across three floodplains)
- count 257 libraries; 32,186,859 reads (16S rRNA amplicon sequencing dataset (4 samples discarded))
- mean 124,273 reads/sample (average 16S rRNA sequencing depth per sample)
- count 242 libraries; 21,413,150 reads (18S rRNA amplicon sequencing dataset (19 samples discarded))
- count 429 OTUs (18S rRNA phototroph dataset after filtering singletons/non-phototrophs)
- other 4.93 ± 3.84% (coefficient of variation among flow cytometry technical replicates)
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 compared benthic biofilm properties and prokaryotic/eukaryotic (16S/18S rRNA amplicon) community diversity between glacier-fed streams (GFS) and tributary streams (TRIB) across three proglacial floodplains and two seasons. Univariate biomass indicators (bacterial abundance, chlorophyll-a, EPS, bacterial carbon production) were analyzed with three-way ANOVAs and t-tests, while community-level data were analyzed with PERMANOVA, NMDS, betadisper, procrustes, breakaway/betta alpha-diversity estimation, and Fisher's exact tests for ASV-stream type associations. Multiple-comparison p-values were adjusted using the Benjamini-Hochberg method, and prokaryote-eukaryote co-occurrence networks were built by averaging SparCC, Spearman correlation, and SpiecEasi association matrices.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Three-way ANOVA | Differences in bacterial abundance, chlorophyll-a, EPS, and bacterial carbon production across floodplain, stream type, and season | — | not stated |
| t-test (BH-adjusted) | Stream type (GFS vs. TRIB) differences for each biomass indicator within each floodplain | — | not stated |
| PERMANOVA (adonis, Euclidean distance) | Effects of floodplain, stream type, and season on streamwater physicochemical characteristics | 40 water sampling sites (per Figure 1 legend) | not stated |
| PERMANOVA (adonis, Bray-Curtis dissimilarity) | Effects of stream type, floodplain, season, and chlorophyll-a on 16S bacterial community composition | — | not stated |
| pairwise.adonis (BH-adjusted) | Pairwise comparisons of community similarity between stream types within each floodplain | — | not stated |
| betadisper / t-test | Multivariate homogeneity of group dispersions (compositional variability) between stream types on Bray-Curtis matrices | — | not stated |
| breakaway / betta | 16S ASV and 18S OTU richness estimation and significance testing between stream types | — | not stated |
| Fisher's exact test (odds ratio, BH-adjusted) | Likelihood of individual ASVs being exclusively present in glacier-fed streams vs. tributaries | 25 genera retained after adjustment | not stated |
-
Stream-type differences in each biomass indicator (bacterial abundance, chlorophyll-a, EPS, BCP) were assessed with multiple t-tests per floodplain, with p-values then adjusted using the Benjamini-Hochberg method.↳ Could also: A mixed-effects ANOVA or the existing three-way ANOVA framework extended with a post-hoc test (e.g., Tukey HSD or estimated marginal means contrasts) — This would test stream type, floodplain, and season effects (and their interactions) within a single model, which can offer a unified family-wise error control and directly estimate interaction effects rather than relying on separate pairwise tests per floodplain.
-
Community composition differences were visualized with NMDS on Bray-Curtis dissimilarities.↳ Could also: Principal Coordinates Analysis (PCoA) on the same distance matrix — PCoA produces axes with an explicit proportion of variance explained, which can complement NMDS's rank-based stress-minimization approach when quantifying how much variation each ordination axis captures.
-
Differences in bacterial community composition were tested with PERMANOVA (adonis) on Bray-Curtis dissimilarities across stream type, floodplain, and season.↳ Could also: A distance-based redundancy analysis (db-RDA) or a Mantel/partial Mantel test — These approaches can incorporate continuous environmental covariates (e.g., chlorophyll-a, temperature) alongside categorical factors within the same constrained ordination framework, which can help partition variance explained by multiple continuous and categorical predictors simultaneously.
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Alpha diversity (richness) was estimated using the breakaway estimator, with significance testing via betta.↳ Could also: Rarefaction-based diversity indices (e.g., Shannon, Simpson) or Hill-number-based diversity estimation (e.g., iNEXT) — These provide complementary diversity metrics that weight abundant vs. rare taxa differently, which can be reported alongside richness estimates to give a fuller picture of diversity patterns across samples of uneven sequencing depth.
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Association between individual ASVs and stream type (GFS-exclusive presence) was tested using Fisher's exact test on 2x2 contingency tables, with BH-adjusted p-values.↳ Could also: A generalized linear model (e.g., logistic regression) with stream type, floodplain, and season as covariates — A GLM framework would allow simultaneous adjustment for floodplain and season while estimating the stream-type association for each ASV, rather than testing stream type in isolation within pairwise tables.
-
Co-occurrence networks were built by averaging distance matrices from SparCC, Spearman correlation, and SpiecEasi (mb method), retaining the top 10% of edges by interaction strength.↳ Could also: Reporting network edge stability via bootstrapping or permutation-based null models for each individual method — This would provide a confidence measure for retained edges and allow comparison of how consistently each method recovers the same associations, complementing the consensus-averaging approach already used.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36003939
Paper: Brandani et al. 2022, "Spatial patterns of benthic biofilm diversity among streams draining proglacial floodplains." Front Microbiol 13:948165. DOI 10.3389/fmicb.2022.948165 · PMCID PMC9393633
Artifacts
- Analysis code: https://github.com/jadebrandani/Proglacial_floodplain_diversity — 6 R scripts (R 4.0.3). NOT the upstream amplicon pipeline; these are the downstream diversity/stats scripts that consume processed tables.
- Processed data (feeds the R scripts): Zenodo
10.5281/zenodo.6424496(data.zip, ~7.4 MB) —16S_table.csv,16S_Taxonomy.csv,16S_Metadata.csv,18S_otu_table.csv,18S_Taxonomy_NEW.csv,18S_Metadata.csv,water_data.csv,Biomass_data.csv, network subfolder. - Raw reads: SRA
PRJNA808857(16S V3-V4 341F/785R; 18S V4 TAReuk454F-TAReukREV3).
Pipeline map (which result comes from which pipeline)
| Reported result | Pipeline / script | In scope? |
|---|---|---|
| Raw read processing → ASV table (QIIME2 + Trimmomatic, SILVA v138.1) | QIIME2 (authors' external run from SRA) | partial — upstream; very heavy. Attempt only if time allows after core. |
| Total 16S ASV count (53,858) | derived from 16S_table.csv rows |
yes (third-party-equivalent: count on shipped table) |
| 16S taxonomy counts (55 phyla … 1,061 genera; per-phylum ASV counts) | 16S_Taxonomy.csv summarisation |
yes |
| # samples / libraries (257 16S, 242 18S, 259 sediment) | table column / metadata counts | yes |
| Alpha diversity richness (TRIB 2,461±626 vs GFS 1,921±509, breakaway) | Alpha_diversity.R (breakaway/betta) |
yes |
| Pielou evenness (no diff GFS vs TRIB) | Alpha_diversity.R |
yes |
| 18S OTU counts (3,553 total; 429 phototroph) | 18S_otu_table.csv / taxonomy |
yes |
| Phototrophs = 62.2% eukaryotic rel. abundance | 18S table summarisation | yes |
| Beta diversity: NMDS stress (16S 0.19, 18S 0.24); PERMANOVA; betadisper; procrustes 0.43 | Betadiversity_16S_18S.R (vegan) |
yes |
| Venn shared/unique ASV proportions (Fig 2) | Alpha_diversity.R / MicEco ps_venn |
yes |
| Fisher enrichment / odds ratios | Fischer_enrichment.R |
yes |
| Water physicochemistry PCA + ANOVA | Water_physiochemical.R |
yes |
| Biomass: Chl-a 35× higher TRIB, bact. abundance 10× greater, BCP, EPS (Table 2) | Sediment_biomass.R |
yes |
| Co-occurrence network stats (Table 3: nodes/edges/clusters/modularity) | Network_Analysis.R (SparCC/SpiecEasi consensus + igraph) |
yes (final stats); consensus inference is stochastic/heavy — network CSVs are shipped, so recompute graph metrics from those |
Out of scope (not attempted)
- Wet-lab measurements (the generation of Chl-a, cell counts, BCP, EPS, water ions in the lab) — we reproduce the statistics over those numbers, not the assays.
- DNA extraction / sequencing.
- SparCC/SpiecEasi de-novo network inference from scratch is stochastic and heavy; the repo ships the resulting edge lists, so we recompute the reported graph metrics (igraph) from the shipped network files rather than re-inferring edges.
Strategy
Run the core computations of the 6 R scripts on the Zenodo data on «our HPC» (conda R env
built INSIDE the SLURM job, data on «infra»). Compare each reproduced number to the reported
value in claims.tsv. Core ~80% = alpha/beta/taxonomy/biomass counts (cheap, fast);
then push into networks + Venn + Fisher. Profile both Zenodo + SRA datasets.
Refinements after reading code (2026-06-26)
- Commit pinned & confirmed:
1abbd6ae46c0d66e016e8b921e1a2a915c219c89(2022-04-12). - Sample-removal steps (must mirror exactly): drop columns
VAR_61,VAR_62from16S_table.csv(259→257); drop metadata rowsOTE_27,OTE_48,VAR_61,VAR_62. - 18S photoautotroph filter (Alpha/Beta scripts):
subset_taxa(D_2=="Chloroplastida" | D_3=="Ochrophyta" | D_2=="Cryptomonadales")→ prunetaxa_sums>1→ keep taxa in>1sample → keep sampl
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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