Data from: 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 rates or pool sizes,
to improve the underlying drivers of modeled leaf litter decomposition. We
then validated the calibrated model and evaluated the effects of
calibration under climate change using the SSP 3–7.0 climate change
scenario. We find that incorporating empirical drivers of litter
decomposition provides similar, and sometimes better (in terms of
goodness-of-fit metrics), predictions of leaf litter decomposition but
with different underlying ecological dynamics. For some sites, calibration
also increased climate change-induced leaf litter mass loss by up to 5%,
with implications for carbon cycle-climate feedbacks. Our work also
provides an example for integrating data on the relative abundance of
bacterial functional groups into an ecosystem model using a novel
calibration method to bridge empiricism and process-based modeling,
answering a call for the use of empirical microbial community data in
process-based ecosystem models. We highlight that incorporating
mechanistic information into models, as done in this study, is important
for improving confidence in model projections of ecological processes like
litter decomposition under climate change.