Shifts from cooperative to individual-based predation defense determine microbial predator-prey dynamics.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- 🟡Reported values were only indirectly comparable
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). In scope = the authors' own deterministic ODE predator-prey model (R + rodeo tabular->Fortran codegen + deSolve; 3 experiments F0_50/F3_50/F5_50 x 37 daily transfers; all params shipped in repo @88a96e8). The model builds, compiles, and integrates, and reproduces every published Fig.5/abstract signature: predator (flagellate) near-extinction (~100x crash) then recovery, filamentous-phenotype dominance by day37 (fraction 0.978), predator-free control with F=0, and the cooperative(toxin)->individual(filament) defense shift. Beyond the 80% floor the simulated predator dynamics were cross-checked against the repo's independently-shipped observed data: the F5_50 crash minimum (model 1143 vs observed 1250, same day 14) and recovery agree. This is a FRESH re-run on «our HPC» (SLURM «job», node n093) that reproduces the earlier «job» bit-for-bit (deterministic, no RNG); the conda env + rodeo 0.7.7 + repo were rebuilt/re-staged after the janitor reclaimed the «infra» workdir. NO fabrication concern: fully deterministic, every value derivable from shipped tables and consistent with observed data. KEY ENV FIX: the repo pins no rodeo version; current CRAN rodeo changed compile() default to fortran=FALSE, breaking the shipped compile('functions.f95') (-> 'functions.f95:1:8: unexpected symbol'). Pinning the contemporaneous rodeo 0.7.7 (current on CRAN at the 2023-02-19 commit) makes it run unmodified. NOT attempted (out of scope): 16S amplicon reanalysis of SRA PRJNA830374 (no shipped pipeline), all wet-lab quantities, parameter re-estimation, pixel-level figure overlay.
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Assessment versions
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v1 current initial assessment Score 84assessed: 2026-06-16 ⛓ 01470f27e58d
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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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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 study investigates why and how bacterial predation-defense strategies succeed one another over many generations of co-culture with a bacterivorous flagellate, testing whether an initially dominant cooperative (toxin-based) defense is superseded by an individual-based defense due to maximization of individual rather than population-level benefits.
- ★ Across all 16 replicate co-cultures of P. putida and P. lacustris, bacteria show a consistent succession of defense strategies over five weeks (~35 predator generations). finding
- ★ An initial cooperative defense based on toxic secondary metabolites is highly effective, bringing flagellate predators close to extinction. finding
- ★ The cooperative toxin-based defense is consistently superseded by a second, individual-based defense (filamentation) that arises via de novo mutations. finding
- ★ The second defense is inferior to the first in terms of reducing predator abundance. finding
- ★ The succession of defenses is not caused by predator adaptation invalidating the original (metabolite-based) defense. finding
- ★ The succession from cooperative to individual defense is driven by maximization of individual rather than population-level fitness benefits, and rapid evolution undermines social cooperation. mechanism
- ★ An ODE-based semi-continuous mathematical model (7 state variables, 9 processes) reproduces and explains the observed predator-prey and defense-succession dynamics. method
- Data and model code are made publicly available via a git repository. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Semi-continuous co-culture with daily dilution/resource replenishment | Pseudomonas putida KT2440 + Poteriospumella lacustris JBM10 (16 replicates) | varying initial flagellate/bacteria densities (high, low, absent-control) | daily predator and prey cell densities | epifluorescence microscopy, NSI-elements AR 5.11.01 software, Neubauer improved counting chambers |
| Morphometric analysis of bacterial filaments (length/size distribution) | Pseudomonas putida (single-celled and filamentous forms), October 2020 replicates | none/other (grazing pressure over time) | filament length and size distribution of 100 individuals per sample/day | NSI-elements AR 5.11.01, SYBR Green I staining, epifluorescence microscopy |
| Whole-genome sequencing | 9 filamentous P. putida isolates + 1 non-filamentous control isolate | none (comparative genomics of evolved isolates) | nucleotide mismatches/mutations associated with coding sequences (synonymous vs non-synonymous) | Illumina NovaSeq 6000 (2x150 bp paired-end), BWA-MEM, Ococo, Prokka, EMBOSS Transseq, Galaxy pipeline |
| Flagellate growth inhibition/exposure assay using culture filtrates | axenically grown Poteriospumella lacustris flagellates | exposure to sterile-filtered filtrates from co-cultures vs bacteria-only control cultures, sampled at different experimental phases (day 2-3, 8-12, 27-29) | flagellate growth rate after 24 h exposure | syringe filtration (0.2 µm), Neubauer counting chambers |
- – Consistent succession of bacterial defense strategies observed in all 16 replicate co-cultures n=16
- ▼ Initial cooperative toxin-based defense brought flagellate predator populations close to extinction
- – Toxin-based defense was superseded by filamentation defense arising from de novo mutations, which was less effective at suppressing predators
- – Coefficient of variation among replicate observations never exceeded 0.5 for any time point after day six CV ≤ 0.5
- – Mutations identified in filamentous isolates relative to non-filamentous control, located in coding sequences
- ▼ Flagellate growth was inhibited by filtrates from co-cultures, verifying metabolite-mediated growth inhibition
- – Succession of defenses occurred independent of predator adaptation to the original toxin-based defense
- count n = 16 (number of replicate co-culture experiments)
- other coefficient of variation ≤ 0.5 (variability among replicate abundance observations after day 6)
- count ~35 predator generations (generations of P. lacustris over the 5-week experiment)
- count ~5 million paired reads, minimum 100-fold coverage (whole-genome sequencing depth for filamentous isolates)
- count 1 × 10^5 flagellates mL^-1 and 1 × 10^5 bacteria mL^-1 (initial densities, high predation pressure line)
- count 1 × 10^3 flagellates mL^-1 and 1 × 10^4 bacteria mL^-1 (initial densities, low predation pressure line)
- count 1 × 10^4 bacteria mL^-1, 0 flagellates (initial densities, predator-absent control line)
- other dilution rate 0.5 day^-1 (daily semi-continuous culture transfer rate)
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 paired a 5-week semi-continuous co-culture experiment (n = 16 replicate vials across three starting-date blocks and two initial-density conditions) with a deterministic ODE-based mathematical model. Analysis was primarily descriptive: daily cell abundances were tracked by microscopy, replicate consistency was characterised with the coefficient of variation, and flagellate growth rates were computed arithmetically from 24-hour exposure assays. No formal inferential hypothesis tests are reported; mechanistic conclusions are supported principally by the calibrated ODE model and the qualitative consistency of patterns across all replicates.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Coefficient of variation (descriptive summary statistic) | Assessment of between-replicate consistency of abundance time-series (Fig. 1 caption) | 16 co-culture replicates; 4-replicate subset for filament measurements | not stated |
| Arithmetic growth-rate computation from density measurements | Flagellate growth rates during 24-hour filtrate exposure assays (Methods: Verification of flagellate growth inhibition) | Triplicate 3-mL batches per condition | not stated |
| ODE numerical integration (deterministic mechanistic model) | Simulation of predator-prey dynamics across all experimental phases (Methods: Mathematical modeling) | — | not stated |
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Dispersion across replicates is shown only graphically (individual dots) and summarised with a single CV threshold; no numerical spread metric (SD, IQR, CI) is reported for abundance data at any time point↳ Could also: Report SD or IQR at each sampled time point, or add shaded uncertainty bands to time-series plots — Explicit dispersion metrics allow readers to numerically gauge between-replicate variability and facilitate comparison with other predator-prey studies reporting similar summaries
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Flagellate growth rates from the 24-hour filtrate exposure assays (triplicate batches per condition) are computed arithmetically without a formal inferential comparison between filtrate origins or time points↳ Could also: A one-way ANOVA or Kruskal-Wallis test with pairwise post-hoc comparisons (e.g., Tukey HSD or Dunn) could formally compare growth rates across filtrate types and harvest phases — Formal testing would yield effect-size estimates and p-values, making it easier to evaluate whether inhibition differences between co-culture and control filtrates, or across harvest time points, exceed what would be expected by chance with n = 3
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Replicate consistency is assessed qualitatively via a single CV threshold (≤ 0.5 after day 6) rather than a formal reproducibility metric↳ Could also: Intraclass correlation coefficient (ICC) computed across replicates for each daily time point would provide a bounded, confidence-interval-bearing measure of reproducibility — ICC separates within-replicate measurement error from between-replicate biological variability and is interpretable on a standardised 0–1 scale, supporting stronger reproducibility claims
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ODE model parameters are reported as fixed point estimates with no stated uncertainty quantification or formal goodness-of-fit statistic↳ Could also: Markov chain Monte Carlo (MCMC) sampling or a bootstrap resampling scheme could propagate parameter uncertainty into model trajectories and yield credible or confidence intervals around simulated dynamics — Reporting uncertainty bands around model predictions would allow readers to assess how robust the mechanistic conclusions are to the inherent uncertainty in parameter estimation from noisy experimental data
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Mutation identification relied on whole-genome sequencing of 9 individually isolated filamentous clones and 1 non-filamentous control, providing a snapshot of genotypic diversity at a single endpoint↳ Could also: Metagenomic (population-level) sequencing at multiple experimental time points could track allele frequencies longitudinally across the 5-week experiment — Longitudinal allele frequency data would provide direct empirical evidence of selective sweep dynamics and complement the ODE model's inference about mutation emergence timing
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The two initial-density conditions (high and low flagellate density) and the predator-absent control are treated descriptively, with no formal statistical test comparing trajectory shapes or endpoints across conditions↳ Could also: Linear mixed models or generalised additive mixed models (GAMMs) with condition as a fixed effect and replicate as a random effect could formally test whether abundance trajectories differed by initial condition — Such models accommodate the repeated-measures, non-linear nature of the time-series data and would quantify whether initial density systematically altered the timing or magnitude of the observed defense succession
Citation network
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Clean, faithful reproduction. The authors' deterministic ODE predator-prey model (R + rodeo->Fortran + deSolve, all params shipped @88a96e8) builds, integrates all 3 experiments x 37 transfers, and reproduces every Fig.5/abstract signature: ~100x flagellate crash (model min 1143 vs observed 1250, same day14), recovery to ~4.1e4 by day37, filament dominance (fraction 0.978), predator-free control (F≡0), and the cooperative->individual defense succession. The only deviation was a resolvable dependency-version drift (rodeo 0.9.2 vs era-correct 0.7.7) on our side — not a result discrepancy and not an authors' defect. The one honest limitation is q2: the paper's claims are qualitative and shipped as figures (no numeric table), so exact-value comparison was indirect/structural, backed by the repo's own observed data. No fabrication signal.
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