Contrasting nutrient retention in alpine soils: the role of soil microbiome in phosphorus and nitrogen mobility in scree and meadow environments.
Part of the results reproduced; minor but material deviations remained.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- 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
Described well enough to reproduce: YES for the amplicon pipeline. The repo (chomic-kbe/tatry_scree) ships the verbatim DADA2 scripts and records the resulting ASV counts as code comments; data is public (ENA PRJEB96838). Re-running the deposited ITS script faithfully on the paper's own raw reads (dada2 1.38.0 vs the paper's ~1.36; bioconda lacks 1.36) reproduced BOTH recorded fungal ASV counts to within a single ASV: 2044->2045 and 1835->1836 (within-tol, no fabrication signal). The bacterial 16S-V4 script was set up and running error-free (env, ENA data, SILVA 138.2 all in place; R1+R2 denoised) but had not reached its ASV table / taxonomy / phyloseq counts when the operator requested immediate finalization, so V4 targets (5818/5487/5424/3231/18114) are recorded as not-captured-at-finalize. NOT attempted (out of scope): downstream ecology stats (PERMANOVA, Chao1/Shannon, P/N chemistry, figures) and ITS taxonomy (needs an undeposited BLAST table). its_rarefy_depth (5508) differs (6012) because the paper rarefies a Fungi-only table requiring that same missing BLAST taxonomy. Overall: a clear, honest near-exact 1:1 on the ITS half; V4 in-progress.
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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
- 2026-06-16
- 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
- 2026-07-29
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: opusSoil microbial communities, beyond abiotic soil properties, actively control the contrasting nitrogen and phosphorus mobility/retention of alpine scree versus meadow soils; the study tests whether nutrient mobility differences are consistently accompanied by microbial traits theoretically promoting nutrient mobilization (scree) or retention (meadow).
- ★ Scree soils have high mobile nitrate and phosphate, low phosphate sorption ability, and significantly greater phosphorus leaching despite lower organic matter and microbial biomass finding
- ★ Scree soil microbiomes are distinct, enriched with pioneer taxa (lichenized fungi, oligotrophic bacteria such as AD3 and Eremiobacteria, necromass-recycling saprotrophic fungi) and show high biomass-specific activities for nutrient mobilization finding
- ★ Meadow soils support larger microbial communities dominated by plant-associated fungi with functional traits enhancing nutrient retention finding
- ★ Soil microbiota act as either accelerators (scree) or buffers (meadow) of N and P leaching from alpine soils mechanism
- In situ phosphate leaching can be quantified using Fe-based ion-exchange resin (IER) traps placed in field soils method
- Integrating soil chemistry with microbial taxonomic, genetic, and functional traits across four catchments reveals functional consistency between microbial traits and nutrient mobility method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| In situ phosphate leaching via Fe-based ion-exchange resin (IER) traps | scree and meadow alpine soils, four Tatra Mountains catchments | none (field measurement) | annual PO4-P flux per g soil | Purolite FerrIX A33E hybrid anion resin; SRP measured per Murphy and Riley 1962 |
| Total organic C, N (and δ13C, δ15N) elemental analysis | dried milled scree and meadow soils | none | total organic C, N concentration and isotopic composition | NC Elemental analyzer (ThermoQuest) coupled to IR-MS Delta X Plus (Finnigan) |
| Total P colorimetry (ammonium molybdate-ascorbic acid) | scree and meadow soils | perchloric acid digestion | total P concentration | flow injection analyzer (FIA Lachat QC8500) |
| Chloroform fumigation-extraction for microbial biomass C, N, P | fresh scree and meadow soils | chloroform fumigation; KH2PO4 internal standard for MBP | MBC, MBN, MBP and phosphate retention/sorption ability | TOC-L analyzer with TNM-L (Shimadzu); colorimetric reactive P |
| Water-extractable dissolved nutrients (DOC, DN, NH4-N, NO3-N, SRP) | scree and meadow soil water extracts | none | most mobile C, N, P forms | FIA Lachat QC8500; TOC-L/TNM-L Shimadzu |
| Basal soil respiration | fresh scree and meadow soils | pre-incubation 10 days at 15°C | CO2 accumulation in headspace at 15°C | gas chromatography (Agilent Technologies) |
| Hydrolytic exoenzyme activity (BG, CEL, PHO, CHIT, LAP) and oxidative activity | thawed scree and meadow soils | none | C/N/P mining proportions and recalcitrant-C degrading oxidative activity | INFINITE F200 microplate reader (TECAN); L-DOPA substrate |
| Microbial DNA extraction / functional gene quantification (N-fixation, nitrification) and P-solubilizing bacterial isolation | scree and meadow soils, stones from scree fields | none | taxonomic composition, N functional gene abundance, P-solubilizing bacteria relative abundance | — |
- ▲ Scree soils exhibited high concentrations of mobile nitrate and phosphate and low phosphate sorption ability
- ▲ Scree soils showed significantly greater phosphorus leaching than meadow soils
- ▼ Scree soils had lower organic matter content and lower microbial biomass than meadow soils
- ▲ Scree microbiomes enriched in pioneer taxa with high biomass-specific nutrient mobilization activities
- ▲ Meadow soils supported larger microbial communities dominated by plant-associated fungi enhancing retention
- other scree soil amount (<2 mm fraction) 4–16 kg m−2, average 9 kg m−2 (scree soil in upper 0.5 m layer)
- other >30% (proportion of study catchments covered by scree deposits)
- count 4 catchments; 3 composite samples per soil type per catchment (sampling design (VW, PU, LA, VH))
- other 10–15 cm deep organomineral A horizon (meadow soils (shallow Leptosols))
- count 10 IER trap replicates per catchment (phosphate leaching measurement)
- other extraction efficiency corrections kEC=0.38, kEN=0.45, kEP=0.4 (microbial biomass C, N, P calculation)
- other IER trap area 28.3 cm2, depth ~5 cm (in situ phosphate leaching capture)
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 chemical, microbial, and functional properties of scree versus alpine meadow soils sampled from four high-elevation catchments in the Tatra Mountains, using a paired blocked design (each catchment contributing both habitat types; 3 composite samples per habitat per catchment, n=12 per group for most endpoints; n=10 IER traps per catchment per year for in situ phosphate leaching). The statistical analysis section was not present in the provided text excerpt, so specific test names, p-value reporting conventions, effect sizes, and software cannot be confirmed from the supplied text alone.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| not determinable — statistical analysis section absent from provided text excerpt | all scree vs meadow comparisons | — | not stated |
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Samples were collected in a paired structure: each of the 4 catchments contributed both scree and meadow sample sets, forming natural matched pairs at the catchment level↳ Could also: A linear mixed-effects model with catchment as a random effect (or a paired Wilcoxon signed-rank test treating catchment as the pairing unit) would also explicitly account for this blocked structure — Treating catchment as a random/blocking factor reduces residual variance attributable to catchment-level differences in altitude, hydrology, and bedrock, yielding more precise estimates of the habitat effect
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A large number of chemical, microbial biomass, enzymatic, and functional-gene endpoints were compared simultaneously between the two habitat types↳ Could also: A multivariate approach such as PERMANOVA on a dissimilarity matrix of all response variables, followed by univariate tests only on variables that contribute most to the overall difference, would also address the simultaneous comparison problem — A global multivariate test controls the experiment-wise error rate before drilling into individual variables and is well-suited when variables are correlated, as chemical and microbial endpoints typically are
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The hierarchical sampling structure (subsamples pooled into composites, composites nested within catchments, catchments treated as replicates) means variation exists at multiple spatial scales↳ Could also: A nested or hierarchical linear model (e.g., lme4 in R) with sample nested within catchment and catchment as a random intercept would also partition within-catchment from among-catchment variance components explicitly — Samples from the same catchment share climate, parent material, and deposition history; a multilevel model prevents underestimating standard errors by treating all n=12 observations as fully independent
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In situ phosphate leaching was quantified with IER traps replicated 10 times per catchment (a different replication level than the 3 soil-biochemistry samples)↳ Could also: A hierarchical model with traps nested within catchments, or aggregation to catchment-level means with catchment as the unit of analysis (n=4 paired values), would also respect the multilevel structure of the IER trap data — Using the 10 within-catchment trap replicates as independent observations without accounting for the catchment-level clustering would artificially inflate degrees of freedom relative to the true number of independent spatial units
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Microbial community composition (amplicon sequencing implied by references to taxonomic groups and functional gene quantification) was compared between habitat types across four catchments↳ Could also: Beta-diversity analysis using Bray-Curtis or UniFrac dissimilarities with a PERMANOVA model that includes catchment as a strata argument (i.e., permutations restricted within catchment blocks) would also test whether community composition differs between habitats while controlling for catchment effects — Stratified permutation in PERMANOVA preserves the paired catchment structure and prevents the catchment-level spatial signal from inflating the habitat effect estimate
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Biomass-specific enzyme activities (activity normalized to microbial biomass) were used to compare functional intensity between habitat types↳ Could also: Analysis of covariance (ANCOVA) with raw enzyme activity as the response and microbial biomass as a continuous covariate would also compare activity adjusted for biomass differences, and additionally tests whether the biomass-activity relationship differs between habitats — ANCOVA preserves information about the relationship between biomass and activity rather than assuming proportionality, and can reveal whether the ratio-normalization approach adequately captures between-habitat differences
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
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.
Where it ran, the reproduction is faithful: ITS ASV counts match within an off-by-one attributable to dada2 1.38.0 vs ~1.36, and the discarded bacterial sample (s012, 420 reads) is confirmed (n=23). Two real gaps remain on availability/completeness, not authors' fraud: the fungal rarefaction depth 5508 is not derivable because the Fungi-only BLAST xlsx was never deposited (we got 6012 from the full table), and the entire 16S-V4 branch (5818/5487/5424/3231/18114) has no reproduced output because the watchdog-salvaged job did not finish. Severity is low-to-moderate — only counts/preprocessing params, no significance or direction tested — so this is a solid-but-partial reproduction with explainable deviations, not a substantive discrepancy.
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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