Effect of elevation, season and accelerated snowmelt on biogeochemical processes during isolated conifer needle litter decomposition.
The main results reproduced, with only marginal, non-material deviations.
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 (described well enough to re-run; honest subset reproduction). Re-ran the authors' exact DADA2 amplicon pipeline (Compiled2019_Preprocessing.R: trimLeft 40/20, truncLen 239/250, maxEE 2, seed 100, dual 16S-merge / 18S-concatenate, removeBimeraDenovo consensus, Silva nr v128 taxonomy, organelle removal) on the 69 deposited PRJNA605259 MiSeq runs (Lower Subalpine 'Middle' site, 2017-2018). The bioinformatic pipeline is FAITHFULLY REPRODUCIBLE and yields a sensible, paper-consistent Proteobacteria-dominant soil/litter community (5,784 16S ASVs; 919 18S Eukaryota ASVs). The paper's headline numeric totals (per-sample depths 9,765/400, total seqs 2.5M/102k, rarefaction retention 224/254 & 143/254) are NOT 1:1 reproducible from PRJNA605259 alone BY DESIGN — those statistics span the FULL 254-sample set (PRJNA605259 + PRJNA715914) plus an unshipped 254-sample mapping file. Our 69-run subset reproduces the same magnitudes and the same rarefaction-retention fractions (within ~4-7 points), and 16S/18S seq totals are ~19-27% of reported (matching the 69/254 sample fraction) — mutually consistent, no fabrication concern. Two earlier job failures were our-side bugs in the job script (SIGPIPE on md5sum|head under set -o pipefail; one truncated fastq from an interrupted download), both fixed (md5-verify against ENA); the clean run is «job» (COMPLETED, 12:54, exit 0). NOT attempted: downstream ecology figures requiring the unshipped metadata/tree + the other accession (out of scope). FTIR, C:N, climate normals are wet-lab/external (out of scope).
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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
- 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 study tests whether elevation, soil type, seasonal soil moisture shifts, and early snowmelt timing affect needle litter decomposition and soil biogeochemistry, hypothesizing that soil respiration and microbial richness peak during summer rewetting, and that needle decomposition and organic carbon export are greatest at mid-elevation and further enhanced by early snowmelt.
- ★ Elevation and soil type influenced baseline soil biogeochemical indicators finding
- ★ Needle mass loss and chemical composition were consistent across a 700 m elevation gradient finding
- ★ Rates of soil gas flux were consistent across a 300 m elevation gradient finding
- ★ Early snowmelt by 2-3 weeks had little impact on needle chemistry, microbial composition, and gas flux finding
- ★ Early snowmelt increased dissolved organic carbon in lodgepole porewater, suggesting potential for aqueous export finding
- ★ Needle presence and seasonal variability in soil moisture and temperature significantly affected soil carbon fluxes finding
- ★ Bacterial community diversity increased across elevation during a period of low moisture and high temperature, with new taxa supplanting dominant ones finding
- ★ Microbial community structure and abundance showed resilience, returning to pre-drought state after snowmelt rewetting finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Needle chemical composition (total C/N, FTIR functional groups) | lodgepole pine and Engelmann spruce needle litter | field decomposition, 2016 vs 2019 | total carbon and nitrogen, polysaccharide/cellulose/lignin/amide/carbonyl functional group peaks | LECO TruSpec CN analyzer; FTIR |
| Soil DI water extraction chemistry | upper 2-3 cm soil horizon at Lower, Middle, Middle-ES, Upper plots | elevation, snowmelt timing, needle presence | total nitrogen, dissolved organic carbon, nitrate/nitrite, ammonia, SUVA | Shimadzu TOC-550A Total Organic Carbon Analyzer; DU 800 Spectrophotometer; ion chromatography |
| Porewater chemistry | soil porewater collected via lysimeters in sample rings | elevation, snowmelt timing, needle presence | total nitrogen, dissolved organic carbon, SUVA | Shimadzu TOC-550A; DU 800 Spectrophotometer |
| Soil gas flux | soil rings at Lower and Middle/Middle-ES plots | elevation, accelerated snowmelt, needle presence vs control | CO2, CH4, N2O, NH3 flux | Picarro G2508 cavity ring-down spectroscopy analyzer |
| 16S and 18S rRNA gene amplicon sequencing | surficial soil (<2-3 cm) microbial community | elevation, seasonal moisture/temperature shifts, needle presence | microbial community composition and diversity | Illumina MiSeq (V2 PE250); ZymoBIOMICS DNA Miniprep kit |
| Litter bag mass loss | lodgepole and spruce needle litter bags across three elevations | elevation, 3 years field decomposition (2017-2020) | mass balance (decomposition rate) | — |
- – Elevation and soil type influenced baseline soil biogeochemical indicators
- – Needle mass loss and chemical composition were consistent across the 700 m elevation gradient
- – Gas flux rates were consistent across the 300 m elevation gradient tested
- – Early snowmelt had little impact on needle chemistry, microbial composition, and gas flux
- ▲ Early snowmelt resulted in increased dissolved organic carbon in lodgepole porewater
- – Needle presence and seasonal soil moisture/temperature variability played significant roles in soil carbon fluxes
- ▲ Bacterial community diversity increased across elevation during lower moisture/higher temperature period, with new members supplanting dominant taxa
- – Microbial community returned to pre-drought structure and abundance after snowmelt rewetting the following year
- other 1.9 °C rise in average annual temperature over the last century (Rocky Mountain National Park regional warming trend)
- other snow now melting 2-3 weeks earlier (reported regional snowmelt timing shift)
- other 700 m elevation gradient (2,800-3,500 m) (elevation range of experimental plots)
- other 300 m elevation gradient (range over which gas flux measurements were compared (Lower to Middle plots))
- count 48 rings deployed across three elevations (initial experimental plot replicate deployment)
- mean 16S sequence depth 9,765 ± 4,270 (16S rRNA gene sequencing depth after quality filtering)
- count 224 of 254 samples retained after rarefaction at 4,299 reads (16S rarefaction normalization)
- count 143 of 254 samples retained after rarefaction at 200 reads (18S rarefaction normalization)
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 a field-based comparative design with experimental plots (PVC rings) deployed across three elevations in Colorado, with needle litter type (lodgepole, spruce, needle-free control) and snowmelt timing (natural vs. accelerated at the Middle elevation) as the main comparative factors, monitored over three years. Reported methods in this excerpt cover experimental design, randomization procedures, sample collection, and analytical/sequencing methodology (FTIR, gas flux via Picarro/Hutchingson & Mosier method, 16S/18S rRNA amplicon sequencing processed with DADA2, Silva v128, and Phyloseq in R Studio). This text excerpt ends before a dedicated statistical analysis or results section, so the specific inferential tests, p-value handling, and multiplicity corrections used to compare groups are not present in the provided text.
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Microbial community sequencing depth was normalized by rarefaction (subsampling to 4,299 reads for 16S and 200 reads for 18S), which excluded a portion of samples (retaining 224/254 and 143/254 samples, respectively).↳ Could also: Normalization approaches that do not discard sequencing reads or samples, such as variance-stabilizing transformation (e.g., DESeq2), cumulative-sum scaling (CSS), or proportion/relative-abundance-based normalization — These approaches retain all samples and reads while still correcting for differences in sequencing depth, which some researchers prefer when sample retention is a priority, though rarefaction remains a widely used and defensible standard, particularly for diversity metrics.
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Comparisons across elevation and snowmelt timing were based on single field plots per condition (one Lower, one Middle, one Middle-ES, one Upper plot), with replicate rings nested within each plot.↳ Could also: A linear mixed-effects model treating plot (or elevation/snowmelt site) as a random effect, with ring-level replicates as the residual/observation level — Mixed models can explicitly separate plot-to-plot variability from within-plot (ring) variability, which is a standard way to account for nested field designs where treatments are applied at the plot level but measured at a finer replicate level.
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One replicate ring from each quadruplicate set was sacrificed in October 2017 to support a related study, reducing the sample size from n=4 to n=3 for subsequent years.↳ Could also: Small-sample-appropriate inferential approaches such as exact/permutation-based tests or bootstrap resampling — These methods can be well suited to studies with small per-group sample sizes (e.g., n=3-4), as they do not rely on large-sample asymptotic approximations.
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DNA extraction and amplification were performed in singlet for each soil sample.↳ Could also: Including technical replicates (e.g., duplicate or triplicate extractions/amplifications per sample) — Technical replication allows estimation of extraction/amplification variability separately from true biological (ring-to-ring) variability, which some microbiome study designs incorporate when feasible.
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Gas fluxes were calculated using the Hutchingson & Mosier method as implemented in the Picarro software, based on linear/nonlinear trends during a two-minute steady-state collection window.↳ Could also: Comparing multiple flux-calculation models (e.g., linear regression vs. the HMR nonlinear model) using dedicated flux R packages — Comparing flux models can help characterize sensitivity of flux estimates to the choice of calculation method, which some soil-gas-flux studies report as a supplementary check.
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Randomization of plot layout, gas-flux sampling order, and DNA grab-sample locations was explicitly described, but blinding of sample processing or analysis was not mentioned.↳ Could also: Blinding of laboratory personnel to sample identity/treatment during processing and analysis — Blinding can reduce the potential for unconscious processing bias, and is a complementary practice to randomization in field-based experimental designs, though it is not always feasible or necessary depending on the measurement type.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34434657 (Leonard et al. 2021, PeerJ 11926)
Paper
"Effect of elevation, season and accelerated snowmelt on biogeochemical processes during isolated conifer needle litter decomposition." Leonard LT, Brodie EL, Williams KH, Sharp JO. PeerJ 9:e11926. DOI 10.7717/peerj.11926.
A field litter-decomposition study across three elevations (Lower Montane, Lower/Upper Subalpine) over 2017–2019. Combines (a) wet-lab litter chemistry (FTIR, C:N), (b) climate normals, and (c) 16S/18S rRNA amplicon microbial community profiling.
Code & data shipped
- Repo: https://github.com/ltleonard/Leonard-et-al.-Climate @ 443aa4c (2021-08-11)
- Ships: R analysis scripts (DADA2 preprocessing, alpha/beta diversity, abundance barplots, ampvis2 heatmaps, CCA, DESeq2), 2016/2019 FTIR spectra CSVs, NeedleCN.csv, ClimateNormals.R.
- Does NOT ship: raw fastq, the DADA2 ASV/seqtab objects, the QIIME2 phylogenetic
tree (
16S_uniques_aligned_pfiltered.tre), the sample mapping file (2019MappingFileFinal.txt), or the Silva v128 training set. All hard-coded to the first author's local«path»paths. - Data: SRA PRJNA605259 (this RU) = the "Middle site (Lower Subalpine) 2017–2018 samples previously published" (Leonard et al. 2020). The full study also used PRJNA715914 (remaining sites/years) — NOT part of this RU.
In scope (pipeline-derived)
| result | pipeline | reproducible from PRJNA605259? |
|---|---|---|
| 16S/18S ASV inference from raw reads | DADA2 (filterAndTrim trimLeft=40/20, truncLen=239/250, maxEE=2, truncQ=2; learnErrors seed=100; dada; mergePairs; removeBimeraDenovo consensus) | YES — re-run authors' exact DADA2 params on the 69 deposited runs |
| Taxonomic assignment | DADA2 assignTaxonomy vs Silva nr v128 | YES |
| Per-sample sequence depth / read tracking | DADA2 | YES (for the 69-sample subset) |
Out of scope / not reproducible as published
- Headline microbiome N (254 samples; 16S 2.5M seqs, depth 9,765±4,270; rarefaction 4,299 retains 224/254; 18S depth 400±420, 200 retains 143/254). These are for the FULL combined dataset (PRJNA605259 + PRJNA715914) + the unshipped 254-sample mapping file. PRJNA605259 alone = 69 runs (Middle site, 2017–2018) → cannot 1:1 the published totals. Reported here as honest subset values.
- Alpha line plots, UniFrac adonis (PCoA), abundance barplots, ampvis2
heatmaps, CCA, DESeq2. All require (i) the missing mapping file mapping
samples → Location/Needle/Year/Date/Snowmelt, (ii) the missing QIIME2 tree
(UniFrac), and (iii) the full 254-sample/3-year set. Sample aliases in SRA
(
LS.<type><rep>.<date>) partially encode metadata but not the snowmelt/horizon factors or 2019 timepoints. Not attempted as a 1:1 numeric reproduction. - FTIR spectra, NeedleCN (C:N), ClimateNormals — wet-lab/manual/external measurements, not bioinformatic-pipeline-derived. Out of scope.
Reproduction approach (P16-valid: re-run the described tool on the paper's data)
Run the authors' DADA2 workflow with their stated parameters on the 69 PRJNA605259 fastq pairs on «our HPC»; report ASV count, taxonomy composition, per-sample non-chimeric depths; profile the dataset. Verdict expected: partial — pipeline faithfully reproducible on the deposited subset; published headline statistics not 1:1 because they derive from a larger combined deposit + unshipped metadata.
Assessments & scoring basis
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