jgewirtzman/tree-methanogens: Tree Microbiomes and Methane Exchange in Upland Forests — Code & Data

Code, data, and analysis pipeline for:

Gewirtzman, J. et al. Tree Microbiomes and Methane Emissions in Upland Forests. bioRxiv 10.1101/2025.09.30.679632v2

Contents

This archive contains the full analysis code and all datasets for reproducing the study of methane fluxes from tree stems and soils at Yale-Myers Forest (YMF), Connecticut, and their relationship to methanogenic and methanotrophic microbial communities.

Data files

• tree-methanogens_compiled.zip — 12 analysis-ready CSV datasets with documented units and column dictionaries (README_data.md), suitable for reanalysis and meta-analysis:

  • Semirigid and static chamber CH₄/CO₂ flux (1,098 measurements from 482 trees + 276 soil collars, 2020–2023)
  • ddPCR gene abundances (mcrA, pmoA, mmoX, 16S) from 564 wood and soil samples
  • 16S rRNA amplicon OTU table (79,769 ASVs × 596 samples), taxonomy key, and sample metadata
  • PICRUSt2 MetaCyc pathway associations
  • Integrated tree-level properties (235 trees × 73 variables)
  • ForestGEO forest inventory (7,225 stems across 25.6 ha)
  • Hourly/daily environmental timeseries (met tower)
  • Felled black oak vertical flux profile
  • Curated methanotroph taxonomy definitions

• tree-methanogens_raw.zip — Unprocessed instrument and field data (LGR analyzer trace gas files, iPad field datasheets, ddPCR raw plate exports, Picarro isotope data, GC internal gas concentrations, 16S amplicon tables, ForestGEO census, spatial/GIS shapefiles, weather station records)

• tree-methanogens_processed.zip — Intermediate pipeline outputs, regenerable from raw data + code (calculated fluxes, processed ddPCR, tree core wood properties, tree ID mappings, isotope data)

Code

The tree-methanogens-v0.1.0.zip source archive contains the full R analysis pipeline (7 stages). Key entry points:

• Rscript code/generate_all_figures.R — regenerates all 24 publication figures (9 main + 15 supplementary)
• Rscript code/compile_zenodo_datasets.R — regenerates the 12 compiled CSVs from raw data

Reproducing the analyses

1. Clone or download the GitHub repository
2. Download the three data zip files from this record
3. Extract each into the repository's data/ directory (paths are pre-configured via .gitkeep files)
4. Run pipeline scripts from the project root directory