MEMC is a R package that allows users to explore various
representation of soil organic matter (SOM) flux dynamics within a
consistent SOM pool model structure. Popular microbial-explicit SOM
models such as MEND1, MIMICS2, and CORPSE3 vary significantly
in their formulations of microbial mechanisms and underlying pool
structure. MEMC provides a consistent pool structure with flexible
flux dynamics which allows modelers to easily explore the effects of the
different conceptualizations of microbial mechanisms.
Follow the download and installation instructions for R and R studio.
Use remotes to install MEMC as a built R package directory from
github.
# use install.packages("remotes") to install this package the first time.
library(remotes)
# Now build and install the R package on your local machine.
install_github('Microbial-Explicit-Model/MEMC')
Now load the MEMC as you would any other package.
# Load the installed MEMC package
library(MEMC)The package ships with several already defined model configurations. Use
help(configurations) to see a list of all the available configurations
that are ready for use. Here we will demonstrate how to complete a
simulation using the MEND_model configuration. For more examples and
package details, please checkout our online
documentation.
Alternatively users can take a look at the model_configs table to take
a look at all model configurations included in the package and the
various dynamics that are used.
print(model_configs)
#> model DOMuptake POMdecomp MBdecay
#> 1 MEND MM MM LM
#> 2 COMISSION MM RMM LM
#> 3 CORPSE RMM LM LM
#> 4 MEMS LM LM LM
#> 5 BAMS MM MM LM
#> 6 MIMCS MM MM DDTake a look at the pre-built MEND_model configuration (see
help("MEND_model") for more details).
# Printing this MEMC model configuration will return a list defining the run name, a table of the flux dynamics, parameter values, and initial SOM pool sizes.
print(MEND_model)
#> $name
#> [1] "MEND"
#>
#> $table
#> model DOMuptake POMdecomp MBdecay
#> 1 MEND MM MM LM
#>
#> $params
#> parameter description
#> 1 V_p maximum specific decomposition rate for POM by EP
#> 2 K_p half-saturation constant for decomposition of POM
#> 3 V_m maximum specific decomposition rate for MOM by EM
#> 4 K_m half-saturation constant for decomposition of MOM
#> 5 V_d maximum specific uptake rate of D for growth of MB
#> 6 K_d half-saturation constant of uptake of D for growth of MB
#> 7 f_d fraction of decomposed P allocated to DOM
#> 8 g_d fraction of dead B allocated to DOM
#> 9 p_ep fraction of mR for production of EP
#> 10 p_em fraction of mR for production of EM
#> 11 r_ep turnover rate of EP
#> 12 r_em turnover rate of EM
#> 13 Q_max maximum DOC sorption capacity
#> 14 K_ads specific adsorption rate
#> 15 K_des desorption rate
#> 16 dd_beta strength of density dependent microbial decay
#> 17 Input_POM POM input
#> 18 Input_DOM DOM input
#> 19 CUE carbon use efficiency
#> units value
#> 1 mgC mgC^-1 h^-1 14.000
#> 2 mgC / g soil 50.000
#> 3 mgC mgC^-1 h^-1 0.250
#> 4 mg C/g soil 250.000
#> 5 mgC mgC^-1 h^-1 3.000
#> 6 mg C/g soil 0.250
#> 7 <NA> 0.500
#> 8 <NA> 0.500
#> 9 <NA> 0.010
#> 10 <NA> 0.010
#> 11 mgC mgC^-1 h^-1 0.001
#> 12 mgC mgC^-1 h^-1 0.001
#> 13 mgC / g soil 3.400
#> 14 mgC mgC^-1 h^-1 0.006
#> 15 mgC mgC^-1 h^-1 0.001
#> 16 <NA> 1.000
#> 17 mg C 0.000
#> 18 mg C 0.000
#> 19 0.400
#>
#> $state
#> POM MOM QOM MB DOM EP EM IC
#> 10.00000 5.00000 0.10000 2.00000 1.00000 0.00001 0.00001 0.00000
#> Tot
#> 18.10002Complete a model run using one of the pre-built model configurations.
time <- seq(0, 36500, by=25)
mend_out <- solve_model(mod = MEND_model, time = time)ggplot(data = mend_out) +
geom_line(aes(time, value, color = name), linewidth = 0.75) +
facet_wrap("variable", scales = "free") +
labs(y = "mg C/g soil",
x = "time (hour)"
title = "MEND Run Results")
With MEMC users are able easily run simulations with the provide model
configurations and are able to build toy model of their own design by
selecting any combination of the supported flux dynamics. See here for
an example for how to use configure_model to build your own SOM model.
For this example we will use the default parameter and initial pool
values that are included as package data (see help("default_params)
and help("default_initial) for more information).
# Running configure_model will print a table describing the model configuration.
my_model <- configure_model(params = default_params,
state = default_initial,
name = "my model",
DOMuptake = "MM",
POMdecomp = "LM",
MBdecay = "LM")
#> |model |DOMuptake |POMdecomp |MBdecay |
#> |:--------|:---------|:---------|:-------|
#> |my model |MM |LM |LM |Complete the model run.
time <- seq(0, 36500, by=25)
my_out <- solve_model(mod = my_model, time = time)Compare our toy model results with the MEND model results.
By changing the POM and DOM decomposition flux dynamics affects model
behavior! The flexibility of the flux dynamics makes MEMC a powerful
tool for SOM model exploration. Additional features supported by MEMC
include the ability to change model parameters, perform sensitivity
analyses, and fit models with experimental/observational data (see
online documentation
for examples featuring capabilities).
Footnotes
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Wang, Gangsheng, Sindhu Jagadamma, Melanie A. Mayes, Christopher W. Schadt, J. Megan Steinweg, Lianhong Gu, and Wilfred M. Post. 2015. “Microbial Dormancy Improves Development and Experimental Validation of Ecosystem Model.” The ISME Journal 9 (1): 226–37. ↩
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.Wieder, William R., Steven D. Allison, Eric A. Davidson, Katerina Georgiou, Oleksandra Hararuk, Yujie He, Francesca Hopkins, et al. 2015. “Explicitly Representing Soil Microbial Processes in Earth System Models.” Global Biogeochemical Cycles 29 (10): 1782–1800. ↩
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Sulman, Benjamin N., Jessica A. M. Moore, Rose Abramoff, Colin Averill, Stephanie Kivlin, Katerina Georgiou, Bhavya Sridhar, et al. 2018. “Multiple Models and Experiments Underscore Large Uncertainty in Soil Carbon Dynamics.” Biogeochemistry 141 (2): 109–23. ↩