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The temperature response of soil microbial efficiency and its feedback to climate

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SUPPLEMENTARY INFORMATION 1

The Temperature Response of Soil Microbial Efficiency and its Feedback to Climate 2

Serita D. Frey, Juhwan Lee, Jerry M. Melillo, and Johan Six 3

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METHODS 5

Sites and sample collection. Soil samples (0-10 cm mineral soil) were collected in May 6

2009 from three replicate control and heated plots at two long-term soil warming experiments 7

located in the Prospect Hill Tract of the Harvard Forest Long Term Ecological Research (LTER) 8

Site in Petersham, Massachusetts, USA (42°50´ N, 72°18´ W). The samples were maintained at 9

the field sampling temperature (12 and 18°C for control and heated plots, respectively) until 10

analysis (within 10 days of sampling). At the site, the forest is comprised of even-aged, mixed 11

hardwoods, including red oak (Quercus rubra), black oak (Quercus velutina), red maple (Acer 12

rubrum), striped maple (Acer pensylvanicum), American beech (Fagus grandifolia), white birch 13

(Betula papyrifera), and American chestnut (Castanea dentata). Soils are of the Gloucester 14

series (fine loamy, mixed, mesic, Typic Dystrochrepts)31. Mean annual temperature and 15

precipitation at the site is 7°C and 1100 mm, respectively32. At the time of sampling, soils in the 16

heated plots had been continuously warmed for 2 or 18 years. In the heated plots, the average 17

soil temperature is continuously elevated 5°C above ambient using buried heating cables placed 18

at 10 cm depth below the soil surface and spaced 20 cm apart. Additional experimental and site 19

information can be found in refs 33 and 34. 20

Temperature response of microbial growth efficiency. We assessed microbial efficiency by 21

measuring 13C-substrate incorporation into microbial biomass and loss as 13CO2 following the 22

protocol of Brant and colleagues35. Our approach measures the carbon utilization efficiency of 23

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added 13C-labeled substrates and represents a proxy for microbial growth efficiency. Fresh soil 24

(25 g) collected from control and heated plots was amended with 50 µg C g-1 soil of universally 25

13C-labeled substrate (glucose, glutamic acid, oxalic acid, or phenol) in enough deionized water 26

to bring the samples to field capacity. This level of substrate addition was found to have no 27

effect on the total microbial biomass or microbial community composition35. The substrates 28

selected for this work are components of the SOM pool in most soils, either being produced by 29

soil microorganisms during decomposition or excreted by plant roots36. The solution 30

concentration and pH of each of the added substrates was as follows: 124 mM at pH 4.3 for 31

glucose; 15 mM at pH 5.2 for glutamic acid; 37 mM at pH 1.6 for oxalic acid; and 13 mM at pH 32

4.9 for phenol. The pH of the glucose, glutamic acid, and phenol solutions was close to that of 33

the soil solution pH (4.9), therefore, we did not adjust the solution pH before addition. For the 34

oxalic acid solution, which had a low pH (1.6), we determined that its addition to the soil at the 35

above concentration changed the soil solution pH over the incubation period by 0.2 pH units. 36

The soil pH before solution addition was 4.9 ± 0.02 and after (at 2, 6, 12, 24, and 48 hr after 37

addition) was 4.7 ± 08. The microbial uptake of oxalic acid was comparable to the other 38

substrates (~13 µg g-1 soil) and the pattern of utilization for this substrate is consistent with that 39

previously observed35. So it does not appear that the change in soil pH significantly altered the 40

observed utilization patterns for this substrate. 41

Ninety-nine atom% 13C substrates were diluted with unlabeled substrate to achieve a final 42

solution enrichment of ~25 atom%. One set of samples received deionized water only and 43

served as controls. The samples were sealed inside 250-ml Mason jars and incubated at 5, 15, or 44

25°C for 6 or 48 hr, depending on the substrate (6 hr for glucose; 48 hr for glutamic acid, oxalic 45

acid or phenol). Preliminary tests were run to determine the optimal incubation time for each 46


3

substrate (i.e., when at least 25% of the substrate had been utilized, but before all of the substrate 47

had been consumed and substrate recycling had commenced). The incubation time was much 48

shorter for glucose compared to the other substrates because of the speed with which the 49

microbial community uses this substrate. At the end of the incubation period, a 12-ml gas 50

sample was collected using an airtight syringe and injected into a pre-evacuated vial. Total CO2 51

concentration was measured by gas chromatography and δ13C by isotope ratio mass spectrometry 52

(PDZ Europa TGII trace gas analyzer and Geo 20–20 isotope ratio mass spectrometer, Cheshire 53

UK) at the UC-Davis Stable Isotope Facility. The percentage of CO2-C coming from the added 54

substrate was calculated as: %Csubstrate = [(δC – δT) / (δC – δS)] × 100, where δC is the δ13C value 55

of the respired CO2 from the control (no added substrate), δT is the δ13C of respired CO2 in the 56

soils with added substrate, and δS is the δ13C of the labeled substrate35. Substrate incorporation 57

into microbial biomass was measured by chloroform fumigation-extraction. Briefly, 10 g soil 58

was extracted with 0.05 M K2SO4 and filtered through a Whatman #40 filter paper. A second 59

sample was fumigated for 24 hr with ethanol-free chloroform and extracted the same way. Total 60

dissolved organic C (DOC) in the extracts was measured on a DOC analyzer (Shimadzu 61

TOC/TN analyzer), and microbial biomass C was calculated as the difference in DOC between 62

the fumigated and unfumigated samples. The δ13C of the extracted DOC was determined by 63

isotope ratio mass spectrometry of the liquid extract (O.I. Analytical Model 1010 TOC 64

Analyzer, College Station, TX, interfaced to a PDZ Europa 20–20 isotope ratio mass 65

spectrometer, Cheshire, UK) at the UC-Davis Stable Isotope Facility. 66

Microbial efficiency was calculated as dBC / (dBC + ΣCO2-C), where dBC is the amount 67

of substrate C incorporated into microbial biomass and ΣCO2-C is the cumulative substrate C 68

lost by respiration over time37. This calculation assumes that biomass C plus cumulative respired 69


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substrate-derived C equals the total amount of substrate C utilized and does not account for 70

metabolite production or for biomass production that is consumed by grazers37. 71

Model runs to examine the sensitivity of a soil organic matter model to altered 72

microbial efficiency. To assess how sensitive conventional ecosystem models might be to 73

changes in microbial efficiency and, in particular, to examine how these changes in microbial 74

efficiency might impact model predictions of soil organic C (SOC) storage, we used DAYCENT, 75

the daily time-step version of the CENTURY model. DAYCENT is a fully resolved 76

biogeochemical model that simulates the major processes determining the dynamics of C, N, and 77

other nutrients, soil temperature, and water38. Key model outputs include plant growth, SOC (0–78

20 cm layer), daily N gas flux (N2O, NOX, and N2), and CH4 oxidation. The ability of 79

DAYCENT to simulate net ecosystem production and SOC has been tested and validated with 80

data from various forest ecosystem types39, 40. 81

Model inputs. Daily precipitation and maximum and minimum temperature data from 82

1964 to 2001 were obtained from the Shaler Meteorological Station located at the Harvard Forest 83

(harvardforest.fas.harvard.edu/data/archive.html). The weather data from 2002 to 2008 were 84

obtained from the Fisher Meteorological Station, which replaced the Shaler Meteorological 85

Station in 2002. Additional weather drivers such as solar radiation, wind speed, and relative 86

humidity were also obtained from the Fisher Meteorological Station. Estimates of soil 87

parameters were obtained from the Soil Survey Geographic Database of the Natural Resources 88

Conservation Service. Percent distribution of tree roots was estimated from ref. 41. Land use 89

history of the Harvard Forest and information on natural disturbance or forest clearance were 90

obtained from ref. 40 and 41. Tree inventory, productivity and chemistry data were available on 91

the Harvard Forest website. 92


5

Parameterization. Optimum and maximum temperatures for tree production were 93

estimated from daily changes in photosynthetically active radiation intercepted by the canopy of 94

deciduous hardwoods at the Harvard Forest42 and the weather data (2002–2008). Litter chemistry 95

data (e.g., N and lignin contents), measured from 1989 to 2002, were used to calibrate the 96

allocation of net primary productivity among components and the C/N ratio of biomass in each 97

component. For all components, C content in biomass was assumed to be 49.8%43. Observed leaf 98

area index during the growing season can be up to 4.1 for deciduous hardwoods42. Thus, 99

maximum theoretical leaf area index was set to 5. Monthly death rates for all components were 100

further adjusted to match modeled wood and litterfall biomass with observed data only after 101

modeled tree indices and ratios were correct. Annual wet and dry N deposition was fixed at 0.8 g 102

m-2 yr-1 and soil N fixation at 1.3 g m-2 yr-1 (ref. 44). 103

Calibration. DAYCENT was run from 0 to 2001 to initialize SOC and nutrient pools. 104

During early European-settlement, the initial period of forest clearance (1751–1790) was 105

simulated by assuming 1–4% of total tree biomass being harvested annually. Between 1791 and 106

1850, the harvest rate further increased by 50% mainly due to small industry development, 107

resulting in a transition from hardwoods to pasture. From 1851 to 1943, the site was assumed to 108

be unimproved pasture (e.g. not seeded, limed, or manured) that was followed by natural 109

succession back to secondary hardwood forest. Approximately 5% of the plot was assumed to be 110

harvested in 1924. The entire hardwood site was clear-cut in 1944. The stand was allowed to 111

regenerate without further site preparation. The effect of major hurricanes at the Harvard Forest 112

in 1635, 1788, 1815, and 1938 was also considered40. We assumed that hurricane disturbance 113

caused 70–90% loss of aboveground biomass and root death increased by 30–50%. Since fires 114

were relatively infrequent, especially prior to European settlement, they were not considered in 115


6

the simulations. No tillage and no fertilization were assumed. The weather data from 1961 to 116

2001 were used for the historical simulation. 117

Simulations for the years 2002–2008 were performed by assuming temperate deciduous 118

hardwoods without any disturbance to the stand. Simulations, including model parameterization, 119

were performed until the model was able to reasonably simulate SOC content in the O horizon 120

(3014 ± 559 g m-2) and the 0–20 cm depth (6697 ± 307 g m-2) which were measured in 2008 at a 121

hardwood site located near the soil warming experiments (S.D. Frey, unpubl. data). Modeled 122

changes in large wood C content were projected over the period 1750–2002 to check how 123

reasonably the model simulated the effects of forest clearance and hurricane disturbance over 124

time. Model calibration was performed by comparison of annual live wood and litterfall biomass, 125

and green foliage N content data measured over the years 1988–2002 and fine root biomass and 126

chemistry measured in 199945, 46 at the same site where SOC contents were determined. 127

Modeled sensitivity of SOC stocks to changes in microbial growth efficiency under soil 128

warming conditions. For the years 2002–2100, changes in SOC content with varying levels of 129

microbial efficiency were simulated. The period 2002-2100 was simulated in order to attain 130

equilibrium levels. Model efficiency parameters controlling the decomposition of structural 131

residue and SOC pools with intermediate and slow turnovers were modified to change efficiency 132

by ±10–50%. The following parameters, with their default values, were modified: PS1CO2(2) = 133

0.55, controls the amount of CO2 loss when belowground structural C decomposes to SOM with 134

fast turnover; P2CO2 = 0.55, controls flow from SOM with intermediate turnover (slow pool) to 135

CO2 (fraction of C lost as CO2 during decomposition); and P3CO2 = 0.55, controls flow from 136

SOM with slow turnover rate (passive pool) to CO2 (fraction of C lost as CO2 during 137

decomposition)47. Each of these parameters were increased or decreased by 10-50% in the 138


7

simulations, resulting in parameter values ranging from 0.27-0.82 (27 to 82% of C lost as CO2). 139

This is equivalent to microbial efficiency values of 0.18-0.73 (18 to 73% of C retained in the 140

microbial biomass), the range of efficiency values we observed in our experimental work for 141

glutamic acid and phenol (both of which showed a significant response to temperature). Both 142

ambient (no warming) and chronic soil warming conditions were considered. The chronic soil 143

warming condition was achieved by increasing daily maximum and minimum temperatures by 144

5°C year round. Regardless of changes in the efficiency parameter, SOC content appeared to 145

reach equilibrium approximately 25 and 75 years under the ambient and the chronic soil 146

warming conditions, respectively. Therefore, we reported mean SOC contents over 2076–2100 147

and % changes in efficiency. 148

Effects of warming on soil organic carbon with changes in microbial efficiency. We 149

compared the levels of SOC across the range of efficiencies with and without warming 150

(Supplementary Fig. S3). Overall, soil warming of ~5°C above ambient further increased 151

microbial respiration and led to greater losses of SOC released as CO2. Levels of SOC were 152

substantially decreased due to warming over the first 10-15 years, followed by a gradual decline 153

during the rest of the simulation period. Nevertheless, trends in annual SOC gain or loss with 154

changes in microbial efficiency relative to the control were different for the soils with and 155

without warming. There was an initial response of SOC to changes in efficiency with and 156

without warming. However, the SOC declines in the soil with warming tend to be delayed longer 157

with increasing efficiency from 20% to 50%. This was due to the initial gain in SOC over the 158

first 5-10 years. In addition, there was a period of showing drastic year-to-year changes in the 159

rate of SOC changes as warming effects on respiration were partly counterbalanced, particularly 160

when increasing the efficiency parameter to 40% and 50%. This trend was not apparent in the 161


8

soils with decreasing efficiency. These results indicate that warming effects depend on soil 162

microbial efficiency. In the long-term (i.e., 2076-2100), the levels of SOC in the soil with 163

warming decreased by around 6% compared to the control soil when no efficiency change was 164

made. In comparison, the amount of SOC that was lost due to warming could be attenuated by 165

26%, 52%, and 77% with increasing efficiency of 10%, 20%, and 30%, respectively. Increasing 166

microbial efficiency by more than 40% was expected to sufficiently negate warming effects on 167

SOC levels or to result in a gain in SOC stock. 168

169


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SUPPLEMENTARY TABLES AND FIGURES 170

171

Supplementary Table S1. Incorporation of 13C-labeled substrate into microbial biomass, its 172

release as 13CO2, and the efficiency of substrate utilization for soils collected from control plots 173

at the Soil Warming × Nitrogen Addition Plots at the Harvard Forest Long-Term Ecological 174

Research (LTER) site in Petersham, MA, USA and incubated at three temperatures. The values 175

presented here represent the total amount of substrate C incorporated or respired over the 176

incubation period (6 hr for glucose; 48 hr for glutamic acid, oxalic acid and phenol). Values are 177

means of three replicates ± one standard error. 178

Substrate incorporated

into microbial biomass (µg 13C g-1 soil)

Substrate

respired (µg 13CO2 g-1

soil)

Microbial

growth efficiency

(%) Glucose 5°C 21.4 (4.1) 5.2 (0.5) 75.7 (4.6) 15°C 15.3 (1.6) 5.7 (0.5) 71.5 (3.8) 25°C 15.0 (1.8) 5.9 (0.6) 70.2 (4.4) Glutamic acid 5°C 11.5 (1.3) 5.8 (0.1) 66.0 (3.2) 15°C 9.1 (0.8) 6.3 (1.2) 59.8 (7.0) 25°C 7.0 (0.4) 8.1 (0.0) 46.2 (1.2) Oxalic acid 5°C 0.6 (0.0) 12.4 (0.2) 4.5 (0.3) 15°C 0.4 (0.0) 13.1 (0.1) 2.8 (0.3) 25°C 0.4 (0.0) 11.4 (0.2) 3.2 (0.2) Phenol 5°C 1.6 (0.1) 2.6 (0.4) 41.5 (5.2) 15°C 2.6 (0.3) 9.6 (0.7) 21.3 (2.8) 25°C 2.3 (0.2) 10.3 (0.9) 18.6 (2.8)

179


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180

181

Supplementary Figure S1 │ Effect of chronic soil warming on microbial efficiency in 182

control versus heated soils following amendment with glucose. The soils were collected from 183

control or heated plots warmed continuously to 5°C above ambient for two (a) or 18 (b) years. 184

Error bars represent one standard error. The data were analyzed by non-parametric analysis of 185

variance (ANOVA) using SAS 9.3 (SAS Institute, Inc., Gary, IN, USA). 186

187


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188

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Supplementary Figure S2 │ Difference in microbial efficiency between control and heated 190

soils at field temperatures. The temperature response function of microbial efficiency to 191

phenol utilization (data shown in Fig. 2 of main text) was used in conjunction with soil 192

temperature data measured continuously at the field site to estimate how microbial efficiency 193

varies across the growing season (Apr-Nov) in control and heated plots exposed to 18 years of 194

continuous warming to 5°C above ambient. The inset graph shows the difference in microbial 195

efficiency between control and heated plots as a function of soil temperature. 196

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198

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Supplementary Figure S3. Annual changes in soil organic carbon (SOC) stocks (g C m-2 for 200

the 0-20 cm depth increment) with and without warming by varying microbial growth efficiency 201

(MGE; blue = no warming; red = warming). 202

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