Bioavailable Nutrients (N and P) and Precipitation Patterns Drive Cyanobacterial Blooms in Missisquoi Bay, Lake Champlain.
The main results reproduced, with only marginal, non-material deviations.
- 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, honest 1:1. 16S V4 amplicon cyanobacterial-bloom study (Celikkol et al. 2021, Missisquoi Bay). Reproduced the core ASV+taxonomy pipeline on the 103 deposited per-sample FASTQs on «our HPC» (cutadapt -> DADA2 1.38.0 single-end -> SILVA 138.1 -> prokaryote filter). HEADLINE RESULT REPRODUCES: phylum composition matches within ~1pp for the top 4 phyla (Proteobacteria 37.65 vs 38, Actinobacteria 26.64 vs 26, Bacteroidetes 17.13 vs 16, Verrucomicrobia 6.65 vs 6), and the named bloom-formers are confirmed with Dolichospermum dominant + Microcystis present (C5 exact). ASV/read COUNTS are same-order but higher (C2 17,370 vs 12,466; C3 16,624 vs 10,952; C1 6.77M vs 5.80M) — expected and explained: the deposit is FORWARD-READS-ONLY (R2 not deposited) so the paper's merged-pair denoising could not be run, and SILVA differs from the paper's TaxAss/FreshTrain; the 2017>2018 ASV direction is preserved. NOT attempted: idemp demux (inputs absent), C6 alpha-diversity / C7 PERMANOVA / C8 RDA (bloom labels + nutrient/precipitation table absent from deposit), all wet-lab. No fabrication indicators; differences cohere with the data deposit, not with fabrication.
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Assessment versions
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v1 current initial assessment Score 67assessed: 2026-06-21 ⛓ 7ea8dcf5909a
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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
- 2026-06-21
- 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 authors hypothesized that bioavailable fractions of nitrogen (N) and phosphorus (P) are the key environmental drivers of cyanobacterial blooms in Missisquoi Bay, Lake Champlain, and that precipitation plays a significant role in transporting these nutrients into the lake.
- ★ High concentrations of N and P were typically measured in April and May, before blooms occurred. finding
- ★ Three major nutrient concentration spikes (early summer, mid-summer, early fall) were associated with intense cumulative precipitation events of 40 to 100 mm within 7 days prior to sampling. finding
- ★ Despite high nutrient concentrations in spring/early summer, cyanobacterial blooms appeared only in mid-to-late summer as water temperature increased. finding
- ★ Dolichospermum sp. was the major bloom-forming cyanobacterium during both summers (2017 and 2018). finding
- ★ A second intense bloom event dominated by Microcystis occurred in fall (October–November) of both years. finding
- ★ Variation in the cyanobacterial population was strongly associated with inorganic/readily bioavailable nutrient fractions: nitrites/nitrates (NOx), ammonia (NH3), and dissolved organic phosphorus (DOP). finding
- ★ During blooms, total Kjeldahl nitrogen (TKN) and total particulate phosphorus (TPP) substantially influenced total nitrogen (TN) and total phosphorus (TP) concentrations, respectively. finding
- ★ Abundance of bacteria involved in nitrogen metabolism relative to phosphorus metabolism indicated nitrogen's greater importance for overall microbial dynamics and cyanobacterial bloom formation. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| 16S rRNA gene amplicon sequencing (V4 region) | Water samples from Missisquoi Bay (PRM, St1, St2, SBL) and Pike River, Lake Champlain | none (natural environmental gradient) | Microbial/cyanobacterial community composition (ASVs) | Illumina MiSeq, MiSeq Reagent Kit V2 |
| Nutrient chemical analysis (N and P fractionation) | Filtered/unfiltered water samples | none | Concentrations of NH3, NOx, TKN, TN, TDN, DON, SRP, DOP, TDP, TPP, TP | Lachat Quickchem 8500; Astoria-Pacific Astoria 2; OI Instrument Aurora 1030 (DOC) |
| In situ physicochemical water quality profiling | Water column at sampling sites | none | Temperature, dissolved oxygen, pH, conductivity, chlorophyll-a, phycocyanin | YSI 6600 v2 probe |
| Microscopy-based cyanobacterial enumeration and taxonomy | Lugol's-iodine-preserved water samples | none | Cyanobacterial cell density (cells/mL), biovolume, species identification | Olympus inverted microscope; Sedgewick-Rafter and Utermohl counting chambers |
| Redundancy analysis (RDA) of ASV table vs. environmental variables | 16S rRNA ASV community data (Hellinger-transformed) | none | Variance in community composition explained by environmental variables | R vegan package (v2.4-1) |
| Latent Variable Model (LVM) | Dolichospermum and Microcystis abundance (CLR-transformed) | none | Response of dominant cyanobacterial genera to environmental variables | R boral package |
| Diversity estimation (Shannon diversity, richness) | 16S rRNA ASV community data, bloom vs. non-bloom samples | bloom vs. non-bloom comparison | Shannon diversity and total richness accounting for sampling variation | R DivNet (v0.3.6) and Breakaway (v4.7.3) |
| Cumulative precipitation measurement | Atmospheric data near Missisquoi Bay | none | Cumulative precipitation (mm) 1–7 days prior to sampling | Farmzone and the Weather Network rain data |
- ▲ Three nutrient concentration spikes coincided with cumulative precipitation of 40–100 mm within the preceding 7 days. 40 to 100 mm
- – Dolichospermum sp. dominated bloom formation in both summers.
- ▲ A second Microcystis bloom occurred in fall (Oct–Nov) in both years.
- – Cyanobacterial population variation correlated with NOx, NH3, and DOP fractions.
- ▲ TKN and TPP substantially influenced TN and TP during bloom periods.
- – DADA2 pipeline yielded 12,466 amplicon sequence variants (8,452 for 2017; 7,337 for 2018) from 5,801,357 processed sequences. 12,466 ASVs
- – Mock community validation recovered approximately 92% of reference sequences per run with low false-positive rate. ~92% recovery, <2% false positive
- – Final filtered dataset comprised 99 samples after removing 4 low-read-count samples and non-prokaryote ASVs. 99 samples; 10,952 ASVs
- count 12,466 ASVs (8,452 in 2017; 7,337 in 2018) (Amplicon sequence variants identified via DADA2)
- count 5,801,357 sequences processed (Total sequences run through DADA2 pipeline)
- other 206 to 630,826 reads per sample, median 222,299 (Per-sample sequencing depth range)
- other ~92% mock community recovery, average <2% false positive rate (Sequencing/pipeline accuracy validation using ATCC MSA-1002 mock community)
- other 40 to 100 mm cumulative precipitation within 7 days (Precipitation associated with nutrient concentration spikes)
- count 99 samples (Final dataset after removing low-count samples)
- count 271 overflows in 2017; 243 overflows in 2018 (Bedford wastewater treatment plant precipitation-associated combined sewer overflows)
- other 999 permutations (PERMANOVA and PERMDISP statistical testing of community structure)
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.
This observational longitudinal field study monitored water chemistry and microbial community composition (16S rRNA gene amplicon sequencing) at five sites in Missisquoi Bay and Pike River across two growing seasons (2017–2018). Beta-diversity differences among groups were tested with PERMANOVA and PERMDISP using Jensen–Shannon divergence; relationships between environmental variables and community structure were characterized by Holm-corrected Spearman correlations and constrained RDA with forward selection; bloom-associated alpha-diversity shifts were assessed via DivNet/betta; and dominant cyanobacterial genera were modeled against environmental gradients with a Latent Variable Model. Analyses were conducted separately by year owing to inter-annual variation in nutrient profiles.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| PERMANOVA (adonis, 999 permutations) on square-root Jensen–Shannon divergence | Differences in microbial community structure between groups (e.g., bloom vs non-bloom, site, year); Section 2.9 | 99 samples (after quality filtering) | not stated |
| PERMDISP (permuted betadisper) | Homogeneity of multivariate dispersion; companion test to PERMANOVA; Section 2.9 | 99 samples | not stated |
| Spearman rank correlation | Pairwise correlations among environmental variables (Table S1); Section 2.10 | — | not stated |
| Redundancy Analysis (RDA) with forward selection (ordiR2step) and VIF screening | Linear relationship between environmental variables and Hellinger-transformed ASV table; Section 2.10 | 99 samples | not stated |
| betta (mixed-effects regression on plugin diversity estimates) | Comparison of Shannon diversity between bloom and non-bloom samples; Section 2.8 | — | not stated |
| Latent Variable Model (LVM; boral R package) on centered-log-ratio-transformed abundances | Response of Dolichospermum and Microcystis abundances to environmental variables; Section 2.11 | — | not stated |
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Beta diversity was summarized with Jensen–Shannon divergence (JSD) and differences tested with PERMANOVA↳ Could also: Bray–Curtis dissimilarity or weighted/unweighted UniFrac with PERMANOVA could also be used to capture community turnover — Bray–Curtis is the most widely used abundance-based metric in microbiome ecology and has well-characterized behavior; UniFrac incorporates phylogenetic information available from 16S data; running multiple distance metrics and comparing ordination patterns is a common way to assess whether conclusions are robust to metric choice
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Community composition was related to environmental gradients via constrained RDA on a Hellinger-transformed ASV table↳ Could also: Distance-based RDA (db-RDA) or Canonical Correspondence Analysis (CCA) could also be applied — db-RDA accepts any dissimilarity metric (e.g., Bray–Curtis or JSD) rather than implying Euclidean distance on transformed counts, maintaining consistency with the PERMANOVA beta-diversity metric; CCA is appropriate when species responses to gradients are unimodal rather than linear, which is common in ecological assemblages spanning broad environmental ranges
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Pairwise environmental correlations were assessed with Spearman rank correlation and Holm-corrected p-values↳ Could also: A principal component analysis (PCA) or hierarchical clustering of the environmental matrix could also summarize collinearity structure before ordination — The study did apply VIF screening within RDA, but an upfront PCA of all environmental variables would provide a complementary visual summary of the collinearity structure and could guide variable grouping decisions in a more transparent, exploratory way
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Shannon diversity was estimated with DivNet (which accounts for sampling variation) and groups compared with betta↳ Could also: Additional reporting of Faith's phylogenetic diversity or a species-richness estimate (Breakaway was used but comparison results are not detailed in the excerpt) alongside Shannon would also characterize alpha diversity — Species richness and phylogenetic diversity capture complementary dimensions of community change; reporting both alongside Shannon allows readers to distinguish whether bloom-associated diversity shifts reflect changes in evenness, richness, or the evolutionary breadth of taxa present
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Dominant cyanobacterial genera responses to environmental gradients were modeled jointly with a Latent Variable Model (boral)↳ Could also: Generalized Additive Models (GAMs) per taxon or multivariate GLMs (mvabund) could also model species–environment relationships — GAMs can capture non-linear responses (e.g., unimodal temperature optima) that a linear LVM may smooth over; mvabund provides model-based multivariate tests that respect the mean–variance relationship of count data and offer taxon-level inference with appropriate error-rate control
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No formal a priori power calculation or sample-size justification was reported for the biweekly field sampling design↳ Could also: A post-hoc power analysis or minimum-detectable-effect calculation based on observed variances could also accompany the study design description — Reporting the minimum detectable effect size (or power to detect observed effect sizes) helps readers evaluate whether the sampling frequency and spatial coverage were sufficient to resolve the bloom dynamics described, and is increasingly expected in ecology and environmental microbiology journals
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34683418
Paper: Celikkol et al. 2021, Microorganisms 9(10):2097. "Bioavailable Nutrients (N and P) and Precipitation Patterns Drive Cyanobacterial Blooms in Missisquoi Bay, Lake Champlain." DOI 10.3390/microorganisms9102097.
Study type: 16S rRNA V4 amplicon survey of a freshwater bay (Missisquoi Bay, Lake Champlain), April–November 2017 & 2018, 5 sites, MiSeq paired-end.
Pipeline (as described in Methods)
- idemp demultiplexing (https://github.com/yhwu/idemp) — splits a multiplexed MiSeq run into per-sample reads using an I1 index file + barcode map.
- BBduk v38.33 — adapter/quality trim (k=19, hdist=1), minlen=220, Q>20.
- DADA2 — quality filter, trim, de-noise, merge → ASV table.
- TaxAss — taxonomy via GreenGenes 13.8 + a freshwater-specific DB; remove non-prokaryotes (Cryptophyta, Streptophyta, Chlorophyta, Stramenopiles).
- Downstream ecology: alpha diversity (Shannon, richness), beta diversity (JSD), PERMANOVA (week/day/site/year), RDA (env vars), latent-variable models (LVM) linking taxa to nutrients/precipitation.
Data availability (observed)
- SRA PRJNA744879: 103 runs, all AMPLICON / PAIRED / Illumina MiSeq.
- Each run is an already-demultiplexed per-sample FASTQ (sample_title e.g.
ST2_20180726_WatPhotz). ENA exposes one.fastq.gzper run (~0.64 GB total). - Sites in titles: PR (27), PRM (18), SBL (14), ST1 (26), ST2 (18) → 5 sites.
- Years: 56 × 2017, 47 × 2018.
In scope (pipeline-derived, attempted)
| # | Reported result | Pipeline | Reproducible? |
|---|---|---|---|
| C1 | Total sequences processed = 5,801,357 | DADA2 read tracking | yes (downstream) |
| C2 | Total ASVs = 12,466 (8,452 in 2017; 7,337 in 2018) | DADA2 | yes (sensitive) |
| C3 | Final ASVs after prokaryote filtering = 10,952 | DADA2 + taxonomy filter | yes (sensitive) |
| C4 | Dominant phyla: Proteobacteria 38%, Actinobacteria 26%, Bacteroidetes 16%, Verrucomicrobia 6%; Cyanobacteria 9% | taxonomy + abundance | yes |
| C5 | Bloom-formers Dolichospermum & Microcystis present/dominant in blooms | taxonomy | yes (qualitative) |
| C6 | Shannon diversity lower in bloom vs non-bloom (p<0.001); richness n.s. (p=0.11) | vegan/phyloseq | partial — needs bloom labels (metadata) |
| C7 | PERMANOVA R²: week 0.425, day 0.424, site 0.167, year 0.017 | vegan adonis | partial — needs date/site metadata (derivable from titles) |
| C8 | RDA 2017 34.4% (adj 40.9%); 2018 29.2% (adj 36.3%) | vegan rda | partial — needs environmental (nutrient) table |
Out of scope / not reproducible
- idemp demultiplexing: SRA holds per-sample reads only; the multiplexed raw run + I1 index file (idemp's inputs) and the barcode map are not deposited. The demux step therefore cannot be reproduced — only verified conceptually.
- Wet-lab: DNA extraction, qPCR, nutrient chemistry (TN/TP/DOC/chlorophyll), precipitation records — external/manual, not pipeline-derived.
- RDA/LVM nutrient correlations depend on the environmental measurement table; reproducible only if that table is in the supplement (to be checked).
Strategy
Reproduce the DADA2 ASV pipeline on the 103 deposited per-sample FASTQs (BBduk trim → DADA2 → taxonomy → prokaryote filter), targeting C1–C5. Compare ASV counts and phylum composition. Treat exact ASV counts as sensitive to DADA2 version / truncation params (expect within-order-of-magnitude, grade partial/within-tol rather than exact). Attempt C6–C8 if metadata permits.
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Reproduction footprint
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