Integrating microbial community data into an ecosystem-scale model to predict litter decomposition in the face of climate change

Litter decomposition is an important ecosystem process and global carbon flux that has been shown to be controlled by climate, litter quality, and microbial communities. Process-based ecosystem models are used to predict responses of litter decomposition to climate change. While these models represent climate and litter quality effects on litter decomposition, they have yet to integrate empirical microbial community data into their parameterizations for predicting litter decomposition. To fill this gap, our research used a comprehensive leaf litterbag decomposition experiment at 10 temperate forest U.S. National Ecological Observatory Network (NEON) sites to calibrate (7 sites) and validate (3 sites) the MIcrobial-MIneral Carbon Stabilization (MIMICS) model. MIMICS was calibrated to empirical decomposition rates and to their empirical drivers, including the microbial community (represented as the copiotroph-to-oligotroph ratio). We calibrate to empirical drivers, rather than solely rate..., We leveraged 16S rRNA gene amplicon sequence data from soil samples at experiment initiation (temporally matching the litter lignin and N measurements) in 5–12 plots (depending on sampling extent and data quality) at each of the seven sites to obtain bacterial copiotroph:oligotroph ratios to be used in our statistical model (Polussa and Oliverio 2025). Five plots were used at GRSM and HARV, 8 at BART, 10 at TREE, 11 at LENO and TALL, and 12 at SERC. We use copiotroph and oligotroph groupings to represent the microbial community because these groups are represented in the process-based model used in this study but acknowledge there are multiple ways to represent functional traits of microbial communities. In brief, DNA was extracted from 200 to 700 mg soil using the Zymo Quick-DNA Fecal/Soil Microbe DNA Miniprep Kit and then amplified using a 250-bp fragment of the V4–V5 region of the 16S rRNA gene. Sample concentrations were normalized and sequenced on the Illumina MiSeq platform w..., , # Data from: Integrating microbial community data into an ecosystem-scale model to predict litter decomposition in the face of climate change

Dataset DOI: [10.5061/dryad.5hqbzkhg6](10.5061/dryad.5hqbzkhg6)

## Description of the data and file structure

#### File: metadata_soilT0_subset.csv

##### Variables

* **sample.id**: Unique ID used in library assembly for sequence data for bioproject submission
* **site**: NEON site ID from which soils are sourced
* **plot**: Unique plot number
* **time.point**: All at 0 for when samples were sampled and decomposition experiment was initiated
* **material**: soil
* **species**: USDA plant ID code for tree under which soils were sampled (unitless)
* **soil.vwc**: Soil volumetric water content measured by TDR probe (m³/m³)
* **soil.pH**: Soil pH
* **moisturePercent.whc**: Soil water holding capacity (%)
* **moisturePercent.gwc**: Soil gravimetric water content (%)
* **MAT**: Mean annual temperature from WorldClim for each site (°C)
* **MAP**: Mea..., ,