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Running Head: Soil organic matter and N addition
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Article Type: Original research
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Title: Nitrogen addition changes grassland soil organic matter decomposition
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Charlotte E. Riggs1*, Sarah E. Hobbie1, Elizabeth M. Bach2,3, Kirsten S. Hofmockel2, Clare E. Kazanski1
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1
Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, Minnesota 55108
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2
Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa 50011
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3
Present affiliation: Illinois Natural History Survey, Champaign, Illinois 61820
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*Corresponding Author: charlotte.e.riggs@gmail.com; Phone: 612-625-5700; Fax: 612-624-6777
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Appendices
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Contents
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Appendix A: Soil sampling and analysis
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Appendix B: Evaluation of equifinality from maximum-likelihood estimation (MLE)
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Appendix C: ANOVA tables
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Appendix A: Soil sampling and analysis
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Table 1 Experimental plots sampled for each analysis performed in this study
Analysis
All plots a
Microbial respiration
X
Total soil C and N
Microbial biomass C and
N
X
POM C and N
b
Control and +N plots
Control plots only
X
X
Soil pH
X
Net N mineralization
Water-stable soil
aggregates
X
X
Root biomass
Mycorrhizal colonization
of root biomass
X
X
Soil texture
X
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a
Full factorial nutrient experiment: control, +N, +P, +K, +NP, +NK, +PK, and +NPK plots.
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b
POM: particulate organic matter.
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Table 2 Number of experimental plots included in the statistical analysis of each variable measured a
Cedar Creek,
Minnesota
Ambient
Nb
Added N b
Analysis
Cedar Point,
Nebraska
Ambient
Nb
Added N b
Chichaqua Bottoms,
Iowa
Ambient
Nb
Added N b
Konza Prairie, Kansas
Ambient
Nb
Added N b
Shortgrass Steppe,
Colorado
Ambient
Nb
Added N b
Microbial respiration
20
20
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12
24
24
12
12
12
12
Total soil C and N
Microbial biomass C
and N
20
20
12
12
24
24
12
12
12
12
20
20
12
12
24
24
12
12
12
12
17
20
12
12
24
24
12
12
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12
POM C and N
c
Soil pH
20
20
12
12
24
24
12
12
12
12
Net N mineralization
Water-stable soil
aggregates
20
20
12
12
24
24
12
12
12
12
5
5
3
3
6
6
3
3
3
3
Root biomass
Mycorrhizal
colonization of root
biomass
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5
3
3
6
6
3
3
3
3
3
5
3
0
6
5
3
3
2
3
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a
Plots were excluded from statistical analyses because the sample was missing or there was sample contamination during lab analyses.
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b
Treatment codes: For the analyses where the full nutrient factorial was sampled, ambient N includes all plots where N was not added (control, +P, +K, +PK
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plots); added N includes all N addition plots (+N, +NP, +NK, +NPK). For the analyses where only control and +N plots were sampled, ambient N = control plots
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and added N = +N plots.
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c
POM: particulate organic matter.
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Appendix B: Evaluation of equifinality from maximum-likelihood estimation (MLE)
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Parameter estimates fit using maximum-likelihood estimation (MLE) can result in equifinality: multiple
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combinations of parameters that produce equally good model fits (Beven 2006). We evaluated whether equifinality
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in parameter estimates was possible in the parameter space of our decomposition parameter estimates by randomly
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generating 50,000 parameter combinations for each sample. The parameters were randomly selected from a defined
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parameter space that spanned from 0.33x to 3x of the parameter values fit with MLE (using the bbmle package in
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R). We fit both the one-pool and two-pool models (see Methods for model details) with these randomly generated
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parameters for each sample. We then compared the predicted C respiration rate (mg C g soil-1 day-1) to the actual C
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respiration rate of each sample to generate an R2 value for each randomly generated parameter combination.
In all cases, the best R2 of the randomly generated parameter combinations were no better than, but similar
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to, those selected using MLE. Furthermore, the best-fit models from the randomly generated parameter set
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converged on one area of parameter space, indicating that there are not multiple combinations of parameters that
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result in equally good model fits. See Appendix B, Figure 1 for illustrative examples of three samples for which we
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randomly generated 1,000,000 parameter combinations and evaluated model fit (R 2) against our MLE parameter
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values.
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Figure Legend
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Figure 1 Model fits (R2) of randomly generated parameter combinations versus model fit (R2) of MLE parameter
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values from three soil samples. In each panel, the graphs show R2 (color coded) of 1,000,000 randomly generated
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combinations of the two-pool model parameters (from left to right): fast pool decay (kf) versus fast pool size (Cf);
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slow pool decay (ks) versus fast pool decay (kf); and slow pool decay (ks) versus fast pool size (Cf). Grey triangles
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are the top ten best parameter combinations (based on R2) from the randomly generated parameters. The black
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triangle shows the parameter values selected with MLE. The average R 2 from the top ten best parameter
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combinations (“Manual Pred”) was always less than the R2 from the MLE parameters (“Fit Pred”). Panel a:
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Chichaqua Bottoms (Iowa) control plot, sample number 24. Panel b: Cedar Creek (Minnesota) +K plot, sample
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number 57. Panel c: Cedar Point (Nebraska) +PK plot, sample number 14.
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a
b
c
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Figure 1
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Appendix C: ANOVA tables
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Table 1 ANOVA table: decomposition parameters and cumulative respiration from the microbial respiration incubation
Effect
Site
N
P
K
NxP
NxK
PxK
NxPxK
Site x N
Marginal R2 a
Conditional R2 b
kf
***
*
ks
****
*
Cf
*
†
Cs
****
Cumulative C respired
****
***
*
**
0.3614
0.3614
*
0.3865
0.3958
NA
0.1723
0.2006
NA
0.7887
0.8023
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† p ≤0.10, * p ≤0.05, ** p ≤0.01, *** p ≤0.001, **** p ≤0.0001, NA = non-significant interaction term removed from model.
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a
Marginal R2 represents the variance that is explained by fixed effects only.
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b
Conditional R2 represents the variance that is explained by both fixed and random effects.
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*
0.6226
0.6593
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Table 2 ANOVA table: aggregate-occluded and mineral-associated soil fractions
Effect
Site
N
Site x N
Marginal R2 a
Conditional R2 b
Large macroaggregate fraction
****
†
NA
0.7862
0.7862
Small macroaggregate fraction
†
Micro-aggregate
fraction
****
Mineral-associated
fraction
****
NA
0.4900
0.7663
NA
0.8784
0.8842
NA
0.8315
0.9130
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† p ≤0.10, * p ≤0.05, ** p ≤0.01, *** p ≤0.001, **** p ≤0.0001, NA = non-significant interaction term removed from model.
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a
Marginal R2 represents the variance that is explained by fixed effects only.
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b
Conditional R2 represents the variance that is explained by both fixed and random effects.
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Table 3 ANOVA table: additional soil variables
Effect
Site
N
P
K
NxP
NxK
PxK
NxPxK
Site x N
Marginal R2 a
Conditional R2 b
Total soil C
****
Total soil N
****
*
Soil C:N ratio
****
***
*
*
NA
0.7902
0.8042
NA
0.7966
0.8143
NA
0.6829
0.7081
Effect
Site
N
P
K
NxP
NxK
PxK
NxPxK
Site x N
Marginal R2 a
Conditional R2 b
Microbial C
****
Microbial N
****
Microbial C:N ratio
*
NA
0.7003
0.7115
*
0.6057
0.6057
*
0.2023
0.2023
Effect
Site
N
P
K
NxP
NxK
PxK
NxPxK
Site x N
Marginal R2 a
Conditional R2 b
POM C c
**
*
POM N c
***
**
NA
0.3586
0.4607
NA
0.3954
0.5113
Effect
Site
N
P
K
NxP
NxK
PxK
NxPxK
Site x N
Marginal R2 a
Soil pH
****
****
Net N mineralization
****
****
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†
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**
POM C:N ratio c
**
**
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*
*
NA
0.6439
****
0.6495
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NA
0.2879
0.3921
Conditional R2 b
0.7318
0.6952
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† p ≤0.10, * p ≤0.05, ** p ≤0.01, *** p ≤0.001, **** p ≤0.0001, NA = non-significant interaction term removed
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from model.
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a
Marginal R2 represents the variance that is explained by fixed effects only.
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b
Conditional R2 represents the variance that is explained by both fixed and random effects.
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c
POM: particulate organic matter.
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Table 4 ANOVA table: root variables
Effect
Site
N
Site x N
Marginal R2 a
Conditional R2 a
Root biomass
****
NA
0.7338
0.7338
Mycorrhizal colonized
root biomass
(absolute)
****
*
NA
0.8057
0.8133
Mycorrhizal colonized
root biomass (%)
†
†
NA
0.3759
0.3759
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† p ≤0.10, * p ≤0.05, ** p ≤0.01, *** p ≤0.001, **** p ≤0.0001, NA = non-significant interaction term removed
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from model.
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a
Marginal R2 represents the variance that is explained by fixed effects only.
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b
Conditional R2 represents the variance that is explained by both fixed and random effects.
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