Glacier shrinkage will accelerate downstream decomposition of organic matter and alters microbiome structure and function.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
- 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
PARTIAL reproduction of the one in-scope pipeline result (R5, functional metagenomics). PRJNA733707 was confirmed to contain the authors' MEGAHIT-assembled contigs (not raw reads), so the annotation half of the EEA_MG.md pipeline was reproduced faithfully on «our HPC»: Prodigal -> eggNOG-mapper -> EEA EC/KO gene extraction (3896 copies: AG 588/BG 936/NAG 249/LAP 639/AP 847/recA 638) -> cd-hit (2638). EEA genes are non-uniformly distributed across bacterial classes (core claim supported). Per-class associations partially reproduce: AG/BG -> Bacteroidetes (supported at phylum level), AP -> Proteobacteria incl. Alphaproteobacteria (partial), LAP -> Gammaproteobacteria (NOT matched by eggNOG seed-ortholog taxonomy; kraken2 cross-check running). Deviations documented: coverm coverage step not reproducible (raw reads absent -> gene counts); paper's maxikraken2_140GB DB host gone (substituted). R1/R2/R4 out of scope (wet-lab/field); R3 blocked (16S inputs never deposited). Verdict provisional - human sign-off required.
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Assessment versions
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v1 current initial assessment Score 40assessed: 2026-06-19 ⛓ 3548e0cb2fc8
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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
- 2026-06-25
- 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 glacier shrinkage (decreasing glacier influence) accelerates the rate of organic matter decomposition by benthic stream biofilms, and whether this occurs via changes in water temperature, nutrient N:P stoichiometry, primary producer (algal) biomass, and shifts in microbiome structure and function.
- ★ Glacier shrinkage (decreasing glacier influence) accelerates downstream organic matter decomposition rates in glacier-fed streams finding
- ★ Decomposition rates can be estimated from extracellular enzymatic activities (EEA) using stoichiometry-based enzyme allocation equations method
- ★ Chlorophyll-a, temperature, and stream water N:P together explain 61% of the variability in decomposition rates finding
- ★ Algal biomass increases with glacier shrinkage and shows a particularly strong relationship with decomposition, indicating algae contribute labile organic compounds to these carbon-poor habitats finding
- ★ Chytrid fungi, abundant in glacier-fed stream sediments, putatively parasitize algae and promote decomposition via a fungal shunt mechanism
- ★ Specific bacterial phylogenetic clades (e.g., Saprospiraceae positively, Nitrospira negatively) are significantly associated with decomposition rates finding
- ★ Different bacterial classes possess different proportions of EEA-encoding genes, potentially explaining microbial associations with decomposition rates finding
- The Glacial Index (GI), based on glacier area and distance from terminus, quantifies glacier influence for space-for-time comparisons method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Extracellular enzymatic activity (EEA) assay using fluorogenic MUF/AMC-linked substrates | benthic sediment biofilms from 101 glacier-fed streams (6 global regions) | none (natural glacier-influence gradient) | activity of AG, BG, LAP, NAG, AP enzymes (nmol h-1 g-1 DM) | BioTek Synergy H1 plate reader |
| Chlorophyll-a extraction and fluorometric quantification | benthic sediment biofilms | none | chlorophyll a concentration (µg g-1 DM) | plate reader (436/680 nm excitation/emission) |
| 16S rRNA gene amplicon sequencing (V3-V4) | sediment bacterial/prokaryotic community | none | bacterial community composition/ASVs | Illumina MiSeq, 300 bp paired-end |
| 18S rRNA gene amplicon sequencing (V4) | sediment eukaryotic community (incl. fungi/chytrids) | none | eukaryotic community composition/ASVs | Illumina MiSeq, 300 bp paired-end |
| Shotgun metagenomic sequencing | subset (n=50) of sediment samples across 6 regions | none | EEA-encoding gene abundance/taxonomy, recA-normalized coverage | NovaSeq (Illumina), S4 flowcell |
| Stream water physicochemistry and nutrient analysis | stream water from 101 glacier-fed streams | none | temperature, pH, conductivity, O2, turbidity, nitrate, ammonium, SRP, DIN:SRP, DOC | LaChat QuikChem 8500; Sievers M9 TOC Analyser; WTW MultiLine meter |
| Remote sensing / GIS-based glacier metrics | 101 glacier catchments | none | glacier area, distance to terminus, percent glacierized catchment, Glacial Index | Sentinel-2 imagery, ASTER GDEM v3 |
- – Decomposition rates averaged 0.0129% per day across 101 glacier-fed streams 0.0129 % d-1
- ▲ Decreases in glacier influence (percent glacier catchment coverage, turbidity, glacier index) accelerate decomposition rates
- ▲ Chlorophyll-a, temperature, and stream water N:P jointly explained variability in decomposition rates 61%
- ▲ Algal biomass showed a particularly strong relationship with decomposition
- ▲ High relative abundance of chytrid fungi found in GFS sediments
- – Saprospiraceae positively and Nitrospira negatively associated with decomposition rates among other clades
- – Bacterial classes differ in proportions of genes encoding EEA enzymes
- mean 0.0129 % d-1 (average organic matter decomposition rate across all glacier-fed streams)
- other 61% (variance in decomposition explained by chlorophyll-a, temperature, and stream water N:P combined)
- count 101 glacier-fed streams (total streams sampled across six global regions)
- count 50 samples (subset used for shotgun metagenomic sequencing)
- count New Zealand n=20, Greenland n=10, Ecuador n=15, Caucasus n=19, Norway n=10, European Alps n=27 (regional sample distribution across the 101 GFSs)
- count metagenomic subset: New Zealand n=18, European Alps n=6, Greenland n=2, Caucasus n=16, Ecuador n=3, Norway n=5 (regional breakdown of metagenomics subsample)
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.
Using a space-for-time substitution design, the study surveyed 101 glacier-fed streams across six global regions to relate organic matter decomposition rates — estimated from five extracellular enzyme activity (EEA) measurements via stoichiometric allocation equations (Hill et al., 2017) — to glacier influence, biofilm characteristics, and microbiome structure. Multiple regression identified chlorophyll-a, temperature, and stream water N:P as jointly explaining 61% of variance in decomposition rates. Bacterial phylogenetic clades significantly associated with decomposition were identified using an unspecified association framework, and metagenomic gene abundances (normalised to recA) were used to link bacterial community function to measured EEA. The full statistical methods section was not available in the provided text; descriptions below reflect what is inferrable from the methods and abstract.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Multiple linear regression (inferred from R²=0.61 reported in abstract) | Decomposition rate predicted by chlorophyll-a, temperature, and stream water N:P | 101 streams (two reaches each); exact analytical n not stated in available text | not stated |
| Regression or correlation of glacier influence metrics with decomposition rates (exact test not stated in available text) | Percent glacier catchment coverage, turbidity, and Glacial Index vs. decomposition rates | 101 streams | not stated |
| Association tests between bacterial phylogenetic clades and decomposition (method not stated in available text) | 16S rRNA ASV-level or clade-level abundances vs. decomposition rates | not stated in available text | not stated |
| Stoichiometric enzyme allocation equations — deterministic calculation, not a significance test | Derivation of decomposition rate index from five EEA measurements (AG, BG, LAP, NAG, AP) | 101 streams, two reaches each, three sediment patches per reach | na |
| Metagenomic read-count comparison of EEA-encoding gene proportions across bacterial classes (method not stated in available text) | recA-normalised coverage of five EEA genes across 50 metagenomes, stratified by bacterial class | 50 samples | not stated |
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Streams were treated as independent observations along a continuous glacier-influence gradient, with region included implicitly or as a covariate↳ Could also: Linear mixed-effects models (e.g., lme4 in R) with geographic region as a random effect could also be used — Streams within a region share geology, climate, and catchment history; a random-effects structure would account for this non-independence and allow inference to separate within-region from among-region drivers
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Decomposition rates were derived exclusively from an EEA-based stoichiometric index (Hill et al., 2017)↳ Could also: Direct substrate mass-loss assays (e.g., standardised cotton strips, Tea Bag Index cellulose/rooibos strips) could also be used alongside or for cross-validation — Direct mass-loss measurements provide an empirical, substrate-explicit decomposition signal independent of the enzyme-allocation model's assumptions, enabling methodological triangulation
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Multiple bacterial clades were each tested for association with decomposition rates (generating a large family of simultaneous comparisons)↳ Could also: False discovery rate correction (e.g., Benjamini-Hochberg) applied across all taxon-decomposition tests could also be reported — With many taxa tested simultaneously, FDR control reduces the expected number of spurious significant associations while preserving more power than Bonferroni correction
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Decomposition variability was summarised with a single joint R² (61%) from the three-predictor regression↳ Could also: Variance partitioning (e.g., vegan::varpart in R) could also be applied to quantify unique and shared contributions of each predictor — When predictors co-vary along the glacier gradient (e.g., temperature and chlorophyll-a both increase as glaciers shrink), variance partitioning clarifies which fraction of explained variance is uniquely attributable to each driver
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Metagenomic EEA-gene abundances were normalised to a single prokaryotic marker gene (recA)↳ Could also: Normalisation using multiple universal single-copy marker genes (e.g., via MicrobeCensus or a set of COG0012-based markers) could also be applied — Multi-marker normalisation averages out copy-number stochasticity in any individual gene, potentially reducing variability in cross-sample gene-abundance estimates across taxonomically diverse communities
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The two within-stream reaches (near-glacier and downstream) appear to have been used either as independent observations or as contextual covariates↳ Could also: Explicitly paired analyses (paired t-test, Wilcoxon signed-rank, or mixed models with reach nested in stream) could also be applied for within-stream upstream-vs-downstream contrasts — Treating the two reaches as paired within each stream controls for stream-level confounders (e.g., catchment geology, regional climate) when estimating the within-stream effect of distance from the glacier
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35320603 (Kohler et al. 2022, Glob Chang Biol, DOI 10.1111/gcb.16169)
Title: "Glacier shrinkage will accelerate downstream decomposition of organic matter and alters microbiome structure and function."
Code: https://github.com/michoug/EEA_GlacierStream (default branch main,
last push 2022-04-01). Two folders: FunctionalMetagenomic/ (shotgun metagenome
EEA-gene pipeline, documented in EEA_MG.md + extractGenesOfInterest.R) and
Phylofactorization/ (Phylofactorization_16S_k_final.R, by A. Washburne).
Data: SRA PRJNA733707 (metagenomes). 16S amplicon data + field/wet-lab
tables are NOT in this BioProject and NOT shipped in the repo.
What the paper reports (result types)
| # | Reported result | Origin | In scope? |
|---|---|---|---|
| R1 | Decomposition rate avg 0.0129 % d⁻¹ across 101 GFSs | wet-lab fluorometric EEA assays + stoichiometric allocation equations | OUT — wet-lab/field, not pipeline-derived from public data |
| R2 | Chl-a + temperature + N:P explain 61% of decomposition variance | regression on field/wet-lab measurements | OUT — wet-lab/field |
| R3 | Bacterial clades associated with decomposition (e.g. Saprospiraceae +, Nitrospira −) via phylofactorization of 16S | Phylofactorization_16S_k_final.R on a 16S ASV table |
OUT (blocked) — requires my_SV_table.txt, my_tree.nwk, my_metadata_table.csv, my_taxonomy_table.csv (placeholder filenames; not in repo, not in PRJNA733707) AND wet-lab decomposition rate K. Inputs unavailable → not reproducible. |
| R4 | High relative abundance of chytrid fungi in GFS sediments | 18S/microscopy (not specified as the metagenome pipeline) | OUT — not derivable from the shipped metagenome pipeline |
| R5 | Metagenomics: different bacterial CLASSES carry different proportions of the 5 EEA-encoding genes (AG 3.2.1.20, BG 3.2.1.21, NAG 3.2.1.14, LAP 3.4.11.1, AP 3.1.3.1) + recA (K03553) normaliser | FunctionalMetagenomic pipeline on PRJNA733707 |
IN SCOPE — this is the one pipeline-derived metagenomic result; repo ships the full recipe. |
In-scope pipeline (R5) — EEA_MG.md
Trim-Galore --paired (R1/R2) → MEGAHIT (--min-contig-len 1000, -m 2.4e11)
→ Prodigal -p meta (gene calling) → eggNOG-mapper 2.1.2 (diamond, --sensmode very-sensitive)
→ extractGenesOfInterest.R (keep EC ∈ {3.2.1.20,3.2.1.21,3.2.1.14,3.4.11.1,3.1.3.1} OR KEGG_ko = ko:K03553)
→ cd-hit-est (-c 0.95 -G 0 -aS 0.9) → coverm contig trimmed_mean (needs reads)
→ kraken2 (--confidence 0.5, maxikraken2_1903_140GB) → taxonomy of EEA genes
Final metagenomic claim = per-bacterial-class proportions of EEA-encoding genes.
DATA FORMAT — RESOLVED on «our HPC» (2026-06-25, «job»)
CONFIRMED: PRJNA733707 contains MEGAHIT-assembled CONTIGS, not raw reads.
seqkit stats -a on an 8-run sample: every sequence min_len = 1000–1013 bp (==
pipeline's megahit --min-contig-len 1000), avg 2–7 kb, max up to 46 kb, and a
flat synthetic quality (Q20=Q30=100%, AvgQual=30.00). 662 contig sets, 526 MB on
disk (≈ ENA's 0.59 GB), 1.88 Gbp total.
→ Consequence: Trim-Galore + MEGAHIT were already run by the authors; this deposit IS their assembly output. We therefore START at the annotation half: Prodigal → eggNOG-mapper → EEA-gene extraction → kraken2 class taxonomy → per-class EEA-gene proportions (= core of R5). The coverm trimmed_mean coverage step is NOT reproducible (raw reads absent), so we report gene counts, not coverage-weighted abundances. This is the faithful, feasible subset of R5.
CRITICAL DATA CONCERN (original 2026-06-19 hypothesis — now confirmed above)
ENA filereport for PRJNA733707 (queried 2026-06-19):
- 662 runs, all labelled
WGS / METAGENOMIC / ILLUMINA, library_layout = SINGLE. - Total 0.59 GB compressed / 1.88 Gbp across ALL 662 runs.
- read_count median ≈ 869 (min 5, max 14 288); base_count per "read" is ~2 000–18 000 bp (e.g. SRR16190682: 330 reads / 4.93 Mbp ≈ 15 kb each)
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Reproduction footprint
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