Climatic ChangeDOI 10.1007/s10584-010-9942-2

Modeling soil respiration and variations in sourcecomponents using a multi-factor global climatechange experiment

Xiongwen Chen · Wilfred M. Post ·Richard J. Norby · Aimée T. Classen

Received: 26 February 2008 / Accepted: 5 October 2010© Springer Science+Business Media B.V. 2010

Abstract Soil respiration is an important component of the global carbon cycle andis highly responsive to changes in soil temperature and moisture. Accurate predictionof soil respiration and its changes under future climatic conditions requires a clear un-derstanding of the processes involved. Most current empirical soil respiration modelsincorporate just few of the underlying mechanisms that may influence its response.In this study, a new partially process-based component model that separately treatedseveral source components of soil respiration was tested with data from a climatechange experiment that manipulated atmospheric [CO2], air temperature and soilmoisture. Results from this model were compared to results from other widely usedmodels with the parameters fitted using experimental data. Using the componentmodel, we were able to estimate the relative proportions of heterotrophic andautotrophic respiration in total soil respiration for each of the different treatments.The value of the Q10 parameters for temperature response component of all of themodels showed sensitivity to soil moisture. Estimated Q10 parameters were higherfor wet treatments and lower for dry treatments compared to the values estimatedusing either the data from all treatments or from only the control treatments. Ourresults suggest that process-based models provide a better understanding of soilrespiration dynamics under changing environmental conditions, but the extent andcontribution of different source components need to be included in mechanistic andprocess-based soil respiration models at corresponding scales.

X. Chen (B)Program of Forestry, Ecology & Wildlife, Alabama A & M University,PO Box 1927, Normal, AL 35762, USAe-mail: xiongwen.chen@aamu.edu

W. M. Post · R. J. Norby · A. T. ClassenEnvironmental Sciences Division, Oak Ridge National Laboratory,Oak Ridge, TN 37831, USA

A. T. ClassenDepartment of Ecology and Evolutionary Biology, University of Tennessee,Knoxville, TN 37996, USA

Climatic Change

AbbreviationsACAT Ambient atmospheric CO2 concentration and ambient temperatureACET Ambient atmospheric CO2 concentration and elevated temperatureECAT Elevated atmospheric CO2 concentration and ambient temperatureECET Elevated atmospheric CO2 concentration and elevated temperatureD Dry split-plotW Wet split-plot

1 Introduction

Soil respiration is estimated to contribute around 75 × 1015 g C year−1 to the globalcarbon (C) budget annually and is second only to oceans in the magnitude of thegross CO2 flux to the atmosphere (Schlesinger and Andrews 2000). Because soilrespiration is a major C flux between the biosphere and the atmosphere (Raichand Schlesinger 1992; Townsend et al. 1992; Davidson et al. 2006), small changesin its rate may alter the annual C sink of terrestrial ecosystems. For example, ifsoil CO2 efflux is greater than plant production, soil respiration will significantlyalter atmospheric CO2 concentrations (Cox et al. 2000). Soil respiration has recentlyreceived considerable attention in the literature, because it is not only a factor thatinfluences net ecosystem C budgets, but also an important component of globalchange (Ryan and Law 2005; Trumbore 2006). Researchers need to accurately modelsoil respiration to understand changes in ecosystem C storage or changes in netfluxes of C to the atmosphere under climatic change. Workshops and synthesiswork have indicated the need to develop and test models of soil respiration in closecollaboration with experimental results (Ryan and Law 2005; Hibbard et al. 2004,2005), but this has rarely been done.

Bulk soil respiration can be partitioned into two component fluxes—heterotrophicrespiration (Rh) resulting from the microbial breakdown of organic matter and au-totrophic respiration (Ra) from plant root production and root-associated organisms,such as mycorrhizae. Both of these components are highly responsive to changesin environmental conditions, such as (1) soil temperature (Lloyd and Taylor 1994;Boone et al. 1998; Rustad et al. 2001), (2) soil water content (Davidson et al.2000), (3) soil nutrient availability (Raich and Tufekcioglu 2000), and (4) plantphotosynthetic rates (Högberg et al. 2001). Elevated air temperature may increasesoil respiration rates and consequently alter the soil C sink or source strength underclimate change resulting from increasing atmospheric CO2. However, the expectedincrease in soil respiration with temperature is not always detected by measurementsbecause other factors may limit respiration (Davidson et al. 1998; Reichstein et al.2003; Hibbard et al. 2005). The CENTURY model (Parton et al. 1987) and theRothamsted model (Jenkinson and Rayner 1977) both use temperature functions tomodel the decomposition of soil organic matter (SOM). Some soil respiration modelsalso use a temperature function with a single value of Q10 (defined as the factor bywhich the rate of a chemical reaction increases when temperature increases by 10◦C)for estimating soil respiration under different environment conditions. However, Q10

values for soil respiration often vary depending on the nominal soil temperaturerange (Lomander et al. 1998; Holland et al. 2000; Xu and Qi 2001).

Climatic Change

Soil water may inhibit microbial decay of SOM at excessively high and lowwater contents. Many experimental studies have shown that drying of soils can limitheterotrophic respiration when water availability drops below a certain thresholdlevel (Orchard and Cook 1983; Skopp et al. 1990; Howard and Howard 1993). Whilerespiration by living roots and microbes within the rhizosphere can be affected bylow soil water content, the direct relationship between soil moisture and root and rhi-zosphere respiration can be decoupled due to water uptake by roots from deeper soillayers, thereby maintaining root-rhizosphere functioning (hydraulic redistribution).

Plant root respiration is also dependent on the supply of carbohydrate fromphotosynthesis to roots (Poorter et al. 1991; Millenaar et al. 2000). Up to 52% ofthe carbohydrate fixed in photosynthesis may be used for root respiration daily (vander Werf et al. 1988; Poorter et al. 1991; Lambers et al. 1996). Boone et al. (1998)indicated that respiration from roots and rhizosphere produce a large portion of totalsoil respiration in a mixed temperate forest, which could limit C sequestration byincreasing allocation of photosynthate to roots under an increased CO2 environment.Fine roots contributed about 28% to the variability in maximum soil respirationacross different biomes (Hibbard et al. 2005). Respiration rates are often linearlyrelated to relative growth rates in roots of many plants (Lambers 1979). In somestudies, high CO2 around roots has been shown to inhibit or alter the total rootrespiration rate (Burton et al. 1997) and maintenance respiration rate (Qi et al. 1994;McDowell et al. 1999). The influence of CO2 enrichment on root respiration hasyielded variable results (Davey et al. 2004).

Currently, experimental partitioning of soil respiration has been attempted butwith varying results using different methods. The partitioning under experimentaltreatments is further complicated by differential responses of the components toenvironmental factors related to climate change (Kirschbaum 1995; Trumbore et al.1996; Giardina and Ryan 2000), and strong covariation among factors (Davidsonet al. 1998). Thus, accurate partitioning of soil respiration remains to be developed.The few manipulative field experiments that investigate how climate change factorsinteract with one another to alter soil respiration (e.g., Wan et al. 2007) have not beenable to separate soil respiration into its components without significantly disruptingthe soil (Hanson et al. 2000).

Quantifying or modeling changes in soil respiration under different environmentalsettings is critical for further exploring the mechanisms underlying change in soilrespiration due to climate change. Soil environments are complex, thus to date,most studies rely on empirical models (e.g., Subke et al. 2006), which are basedon the strong correlations between temperature (some including soil moisture) andsoil respiration (Janssens and Pilegaard 2003). However, process-based models areneeded to advance our quantitative understanding of soil respiration by taking intoaccount additional factors, such as soil water content and root growth (e.g., Ryan andLaw 2005; Hibbard et al. 2005).

We developed and tested a component model that separates soil respiration intoan autotrophic component and a heterotrophic component using data collectedfrom a multifactor climate change experiment conducted in a constructed old-field ecosystem. Old-fields are common along the east coast of the US and areoften made up of a combination of grasses, forbs, and N-fixing plants. The Old-field Community Climate and Atmospheric Manipulation experiment (OCCAM)manipulated atmospheric [CO2](ambient, ambient + 350 ppm), air temperature

Climatic Change

(ambient, ambient + 3.5◦C) and soil moisture (wet, dry) in a randomized, completeblock, split plot design (see Garten et al. 2008). Data from this experiment providedus with the opportunity to test the validity of our model as well as how it compared toseven other models that are commonly used. Specifically, the aims of this study wereto (1) test our component model; (2) quantify the variations of soil respiration fromdifferent components under climate change treatments; (3) test other empirical soilrespiration models under climate change treatments; and (4) to test the widespread-hypothesis that Q10 values are altered under different environmental conditions andthat temperature is the major contributing factor to variation in soil respiration underclimate change.

2 Materials and methods

2.1 Site description

The measurements were made at the OCCAM experiment on the Oak RidgeNational Environmental Research Park in Oak Ridge, Tennessee, USA (35◦ 54′12′′ N, 84◦ 20′ 22′′ W). OCCAM was designed to test the interactive effects of elevatedatmospheric [CO2], atmospheric warming, and soil water content on the functioningof a constructed old-field ecosystem. The soil type at the study site is Captina siltloam Typic Fragiudult (Soil Conservation Service 1967; Edwards and Norby 1999).In this area, precipitation is generally distributed evenly over the year with an annualmean of 1,322 mm. The mean July maximum temperature is 31.2◦C, and the meanJanuary minimum temperature is −2.7◦C.

2.2 Experimental design, treatment application and monitoring

Atmospheric CO2 concentration, air temperature, and soil moisture were controlledthrough the use of open-top chambers (OTCs) surrounding 12 circular whole-plots (4 m diameter) arranged in a randomized block split-plot design. Split-plotswere assigned to either ‘wet’ or ‘dry’ soil water treatments. In August 2002, plotswere planted with seven plant species common to old-field communities in thesoutheastern United States. These species are Plantago lanceolata L., Andropogonvirginicus L., Festuca pretense L. syn F. elatior L., Dactylis glomerata L., Trifoliumpretense L., Solidago canadensis, and Lespedeza cuneata (Dum. Cours.). Plots werewatered and weeded to ensure seedling establishment until treatment initiation inMay 2003 (Engel et al. 2009; Kardol et al. 2010a, b).

Mean air temperatures over the observation time period were 15.9 ± 0.1◦C inambient-temperature chambers and 18.5 ± 0.3◦C in elevated temperature chambers.CO2 was introduced into the plenum to achieve a daytime CO2 concentration around695.8 ± 10.0 ppm in elevated CO2 chambers compared to 395.6 ± 2.8 ppm inambient CO2 concentration chambers. Precipitation was excluded over each OTC,but collected rainwater was used to irrigate the plots weekly with 2 or 25 mm ofwater for ‘dry’ and ‘wet’ treatments, respectively (Dermody et al. 2007). Detailedinformation about the experimental setup as well as plant and soil responses can befound in Wan et al. (2007), Garten et al. (2008, 2009), Engel et al. (2009), Castro et al.(2010), and Kardol et al. (2010a, b).

Climatic Change

2.3 Measurements of soil respiration, soil water content, roots, and total N content

For this study, we used monthly measurements of soil respiration, soil temperature(0–15 cm), soil water content (0–15 cm), and total root length and root productionfrom March 2005 to April 2006. Nitrogen (N) concentrations of roots collected fromsoil cores were measured once within each treatment, and these data were used inmodels to set the possible range of root N concentrations in the experiment (seeGarten et al. 2008, for more details).

Soil respiration was measured monthly using a LI6200 infrared gas analyzer (Li-Cor Inc., Lincoln, Nebraska, USA) with attached chamber. Respiration from theaboveground plants was excluded by removing living plants inside the collars oncea week and the dead plant material was removed from the collars (Wan et al.2007). Thus, soil respiration measured here did not include aboveground respirationfrom living plants and respiration from aboveground litter located on the soilsurface. Soil temperature (at 15 cm) was measured at the time as soil respirationmeasurement. Soil volumetric water content at each plot was monitored using time-domain reflectometry (see Dermody et al. 2007, for more details).

Root production was measured using a minirhizotron tube (Bartz TechnologyCorporation, Santa Barbara, CA) installed in each split plot. Digital images werecaptured in the field by the I-CAP system (Bartz Technology Corp, Santa Barbara,CA) and analyzed by RooTracker software (Duke University, Durham, NC). Lengthand width of each root segment were measured and the incremental growth ordisappearance (mortality) recorded. Fine-root production (mm) for a time periodwas calculated as the total length of new roots and segments of new growth onexisting roots for that date. The detailed information on how soil respiration androot length were measured can be found in Wan et al. (2007). These data were usedin the component model.

2.4 Models

We used eight models to model soil respiration at the OCCAM site. Some arepartial process based models and others are simple empirical models. Each modelconsiders a different set of processes and factors. Because all of these modelsutilized the same dataset collected from the climate change treatments at OCCAM,it is possible to compare these models’ performance at the ambient conditionsas well as the manipulated conditions. In addition each of these models includeddifferent environmental variables (e.g., temperature and soil water), which allow usto compare their relative importance. The eight models compared in this paper areoutlined below.

(1) The Component Model

The component model is defined as soil respiration (Rs) = Rs = a × Rh + Ra, whereRh is heterotrophic soil respiration, Ra is autotrophic soil respiration, and a is acoefficient. Rh was estimated using the model of Del Grosso et al. (2005). Ra =b × rm + c × rg, where rm is root maintenance respiration and rg is root growthrespiration, b and c are coefficients for respiration from a unit (gram) root biomass.rm = (0.058N + 0.622M) e0.098T , where N is the root nitrogen concentration (g kg−1),M is the soil matric water potential (MPa), and T is the soil temperature (◦C at 15 cm

Climatic Change

depth) (Burton et al. 1998; Pregitzer et al. 1998). Since relative soil water content wasmeasured, we estimated the soil matric water potential from the relative soil watercontent using soil texture (Saxton et al. 1986). Soil texture across the experimentalsite is silt loam and is composed of 20% sand and 15% clay. Fine root biomassproduction and average root biomass per unit root length were estimated from fieldroot length measurements using the average value of 0.03338 g m−1. The a values,relative proportions of heterotrophic respiration (Rh) and autotropic respiration(Ra), were estimated using optimization for each treatment and for all treatmentscombined.

(2) The heterotrophic model (Del Grosso et al. 2005)

Del Grosso et al. (2005) considered heterotrophic soil respiration to be a functionof soil temperature (F(t)) and soil water content (F(w)). The heterotrophic modelis defined as Rh = d × M × F(t) × F(w), where Rh is the heterotrophic soil respira-tion, d is a coefficient and M is the maximal soil respiration provided by Del Grossoet al. (2005) for different biomes, and F(t) and F(w) are temperature and waterlimitation functions, respectively.

F(t) and F(w) are calculated as:

F (t) = 0.56 + (1.46 × (arctan (π × 0.0309) × (t − 15.7)) /π) ,

and

F (w) = 5 × (0.287 + (arctan (π × 0.009 × (w − 17.47))) /π) ,

t is temperature in ◦C, and w is relative soil water content (%).

(3) The Linear Temperature Model (T)

The linear temperature model is defined as Rs = f × T, where T is temperature in◦C, and f is a coefficient.

(4) The Exponential Temperature Model (e.g., Nakane 1980; Silvola et al. 1985)

The exponential temperature model is defined as Rs = λeβt, where λ is defined assoil respiration at temperature of 0◦C (μmol CO2 m−2s−1) and it is estimated about0.9506 in this study, and β is a coefficient. Based on this model, the Q10 parametercan be derived as Q10 = eβ×10.

(5) The Soil Water Content Model

The soil water content model is defined as Rs = g × RSW, were RSW is the relativesoil water content and g is a coefficient.

(6) The Linear Combined Temperature and Water model

The linear combined temperature and water model is defined as Rs = i × T + j ×RSW, where T is temperature, RSW is relative soil water content, and i and j arecoefficients.

Climatic Change

(7) The Exponential Model with Temperature and Water

The exponential model with temperature and water is defined as Rs = λek×t+l×w,where λ is defined as soil respiration at a temperature of 0◦C (same as model 4), t istemperature, w is relative soil water content, and k and l are coefficients.

(8) The Seasonal Pattern Model (Hanson et al. 1993)

The seasonal pattern model was developed to integrate seasonal patterns of soilrespiration. This model has been used to estimate soil respiration componentsof net ecosystem exchange (Wilson et al. 2001). The seasonal pattern model isdefined as

Rs = Mresp + Gresp,

where

Mresp = Rb Q(T/10)

10

(1 − CF

100

)(ψmax − ψ35

ψmax

)

and

Gresp = RG × Gcos t

Where

Mresp the maintenance respiration rate of roots and soil microbes.Gresp the cost of growing new rootsRb the maintenance respiration of roots and soil microbes when temperatures

approach 0◦C.Q10 the rate of change in respiration for a 10◦C increase in soil temperature.T the soil temperature at a depth of 10–15 cm (in ◦C)CF the percent coarse fraction of the soil. In this study this value is 0.ψmax the soil matric potential (Mpa) corresponding to the complete inhibition of

soil respiration.ψ35 the observed soil matric potential of the upper 35 cm of soil (Mpa). Here the

observed soil matric potential of the upper 15 cm of soil is used.RG the rate of root growth (g dry matter as C) on a ground-area basis. In

this study the biomass of 1 m fine roots is about 0.03338 g based on fieldmeasurements.

Gcost the carbon cost for growing roots.

2.5 Parameters estimation, optimization and statistical analyses

For each soil respiration model, we estimated optimal parameters using data acrossall the treatments simultaneously as well as for each specific treatment separately.The parameter optimization was accomplished using a cost function that quantifiesthe model-data mismatch and employing the simplex method contained in the add-onsoftware, PopTools (Hood 2004) to adjust the model parameters until the mismatchis minimized. This method has been previously used for soil C and soil respirationmodel parameter identification (e.g., Del Grosso et al. 2005; McLauchlan et al.

Climatic Change

2006). ANOVA (SAS Institute Inc., Cary, NC, USA) was used to examine thetreatment effects on soil respiration and for the auto- and heterotrophic components.To compare the results among models, in addition to a mean correlation coefficientR2, the root mean squared error (RMSE) for penalized likelihood was calculated(Burnham and Anderson 2002).

3 Results

3.1 Comparing model results

Parameters for eight models using data from all treatments considered together wereestimated (Table 1). The Linear Temperature Model (T), the Exponential Tempera-ture Model, and the Exponential Model with Temperature and Water have relativelylow RMSE (493.29, 503.32, and 602.84 respectively); the Component model and DelGrosso et al. (2005) model had slightly higher RMSE (e.g., 799.22 and 806.00); andthe Linear Combined Temperature and Water model and Seasonal Pattern Modelhad a higher RMSE (e.g., 926.20 and 968.24); and the Soil Water Content Model hadthe highest RMSE (1733.29). For all eight models with parameters estimated usingdata across all treatments, the highest RMSE are for the ACET-W, ECAT-D andECAT-W treatments (Table 1).

There were several features of the data that even the models with the lowestRMSE did not capture across all the treatments. To examine whether the modelscould capture some of the detailed features we optimized model-data fits for eachtreatments separately (Table 2). These parameters are different from the model-data optimizations over all treatments. Out of the eight models, the componentmodel performed relatively well describing soil respiration across different climatechange treatments, with mean R2 values ranging between 0.66 and 0.86 and meanRMSE from 0.2397 to 1.0651 (Table 2). The optimal parameter values for thismodel varied among the treatments and were different than the parameter valueswhen all treatments were considered together. With the exception of the Soil WaterModel, which always had a higher RMSE, other models performed well underambient temperature and ambient CO2 concentrations (ACAT). However, somemodels, for example, the Del Grosso et al. (2005) model, did not perform wellwhen environmental conditions changed (warming and elevated CO2), as indicatedby low R2 and higher RMSE (Table 2). In contrast, temperature related mod-els (Linear Temperature Model (T) and Exponential Model with Temperature)consistently worked well under both ambient and altered conditions. The para-meter for Linear Temperature Model (T) varied from 0.11 to 0.26. The exponentcoefficient (β) of the Exponential Temperature Model ranged from 0.04 to 0.97,when λ was fixed at 0.9506, a nominal value for all the treatments in the OCCAMexperiment.

When temperature and soil water content are both considered in linear orexponential models for soil respiration (Linear Combined Temperature and WaterModel and Exponential Model with Temperature and Water), values of i and kwere always positive and values of j and l were always negative. These resultsindicate that increasing temperature contributes positively to soil respiration, whileincreasing soil water content contributes negatively to soil respiration. It appears that

Climatic Change

Tab

le1

Par

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nera

lfit

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mod

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acro

ssdi

ffer

entt

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tsan

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2)

betw

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the

mod

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Rs

and

obse

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Rs

for

over

allt

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dea

chtr

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Tre

atm

ents

Com

pone

ntm

odel

Del

Gro

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etal

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λeβ

tW

ater

T&

wat

erλ

ek×t+

l×w

Seas

onal

patt

ern

mod

el

Par

amet

ers

a=

0.49

0.65

0.16

70.

065

3.70

k 1=

0.07

5k

=0.

05R

b=

0.75

b=

0.00

7k 2

=1.

875

l=0.

25Q

=2.

00c

=0.

02R

G=

3.20

Ove

rall

RM

SE79

9.22

806.

0049

3.29

503.

3217

33.2

992

6.20

602.

8496

8.24

Ove

rall

R2

0.33

370.

3240

0.55

860.

5643

0.19

010.

1623

0.56

540.

1192

RM

SEat

AC

AT

-D44

.56

62.0

027

.29

33.1

519

4.88

79.8

640

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5451

0.48

870.

8226

0.83

870.

3218

0.10

460.

8321

0.58

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75.4

871

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45.8

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178.

0489

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57.6

791

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CA

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0.46

440.

4083

0.65

980.

6743

0.36

900.

2647

0.67

830.

1243

RM

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753

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74.4

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26.9

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2220

0.17

700.

7074

0.71

210.

2995

0.23

660.

7160

0.19

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MSE

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CE

T-W

89.1

811

1.76

73.2

761

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357.

4219

3.53

111.

1816

6.32

R2

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CE

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0.49

920.

3702

0.78

640.

8234

0.56

190.

1482

0.79

410.

1243

RM

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53.7

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0.64

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5067

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271.

4121

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118.

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9.83

263.

5418

1.05

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1.07

R2

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CA

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0.69

290.

6835

0.40

280.

4353

0.55

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0.36

630.

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61.5

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2008

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Climatic Change

Tab

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Com

pari

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mod

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sba

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212

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930.

1181

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323

i=0.

2569

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j=−2

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0.78

380.

8057

0.82

800.

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330.

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500.

5770

0.74

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8873

2.48

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a=

0.18

61.

3105

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630.

0584

2.59

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0.15

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0761

8b

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012

j=−0

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−0.7

448

c=

5.19

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ean

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0.85

80.

858

0.85

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8744

0.42

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8367

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4378

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810.

9012

0.90

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0.77

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4898

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290.

1829

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2114

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2182

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0.07

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=0.

1551

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−0.5

262

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0.00

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Climatic Change

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Fig. 1 Soil respiration patterns (μmol m−2s−1)

modeled by different models with higher R2 atthe dry condition of elevated CO2 concentra-tion and ambient temperature (ECAT-D sub-plot 7). a Component model; b Del Grosso

et al. model; and c T model. Observed observedtotal soil respiration; autotrophic modeled au-totrophic soil respiration, Modeled total mod-eled total soil respiration

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adding soil water content to the original temperature related models does not alwaysimprove R2 but does always decrease RSME values. In contrast, in some cases soilwater slightly decreased the model fit as measured by R2. Moreover, comparing thebehavior of some models that had higher R2 and lower RMSE, we could see whichmodel best matches the observed soil respiration. For example, under dry conditionswith elevated [CO2] and ambient temperature (ECAT-D subplot number 7), thecomponent model came closest to fitting all the peaks (Fig. 1). The Seasonal PatternModel worked well at the dry ambient [CO2] and ambient temperature conditions(ACAT-D). However, under changed environmental conditions, it did not fit thedata well unless the Q10 values were well out of the acceptable range 1.5∼3.0 (Lloydand Taylor 1994). Based on the biological knowledge, Q10 values can not be close to 0or very large (e.g., 312 and 1093). The higher RMSE values showed similar patternsto the models across all treatments, where the parameters were within reasonableranges. Additional information is required to constrain the optimization process forthe Seasonal Pattern Model.

3.2 Contribution of Rh and Ra to total soil respiration

Based on the parameter of the component model within each treatment (Table 2),the proportions of Rh in total soil respiration were estimated to vary from 0.33 to 0.85across treatments (Fig. 2). The proportion of Rh was significantly higher (p = 0.0458)under ECAT (0.75 ± 0.18) than under ACAT (0.51 ± 0.11). In the dry split plots, theestimated proportion of Rh was significantly higher (p = 0.03) at ECAT treatment(0.86 ± 0.12) than at ECET treatment (0.57 ± 0.07). Soil water manipulations didnot cause significant changes in the proportion of Rh except under ECAT, where theproportion of Rh was significantly higher (p = 0.045) under dry conditions (0.86 ±0.12) relative to wet condition (0.58 ± 0.06). There was no significant difference in theproportion of Rh in total soil respiration between ambient and elevated temperaturetreatments or between ambient and elevated [CO2] treatments.

There were large differences in the estimated ratios of root growth to rootmaintenance respiration across treatments, and there was high within treatmentvariation through time in the ratio of root growth respiration to root maintenancerespiration. The average ratio was lower under ECAT (0.06 ± 0.08) than un-der ACAT (0.56 ± 0.97) (p = 0.01), ACET (0.32 ± 0.56) (p = 0.03) and ECET(0.85 ± 0.97) (p < 0.01). There were significant differences between ratios underACET and ECET (p = 0.01). Within treatments, the ratio of root growth respirationto root maintenance respiration was significantly higher under dry relative to wetconditions with the exception of ECET treatments.

3.3 Q10 values

Based on the β values in Exponential Model with Temperature (Table 2), Q10 valueswere estimated for the different treatments. The Q10 value was significantly higher(p = 0.03) in wet treatments, Q10 = 1.983 ± 0.26, relative to dry treatments, Q10 =1.753 ± 0.21. Q10 values did not significantly differ between elevated CO2 (1.95 ±0.25) and ambient CO2 (1.79 ± 0.25) treatments nor between elevated temperature(1.87 ± 0.25) and ambient temperature (1.86 ± 0.27) treatments.

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a

b

Fig. 2 a The relative proportion of Rh in total Rs in different climate change treatments based onmodels at each treatment (wet and dry sub-treatments are mixed); and b the relative proportion ofRh in total Rs at dry sub-treatments. Dif ferent letters indicate significant different (p < 0.05), whilesame letter means no significant difference (p > 0.05)

4 Discussion

4.1 Testing the utility of the component model

When data from all treatments were considered together the Linear TemperatureModel (T), the Exponential Temperature Model, and the Exponential Model withTemperature and Water had relatively small values of RMSE. This suggests that

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soil respiration, from a cumulative perspective over all processes, may be consideredto be largely determined by soil temperature. This result confirms numerousfield observations that soil respiration is highly correlated with temperature (e.g.,Rochette et al. 1991; Alvarez et al. 1995; Luo et al. 2001). However, when individualtreatments are examined separately, these simple models do not perform as wellunless their parameters are modified. The disadvantage of these models is that theycould not provide the detailed information about possible changes in underlyingprocesses or the tradeoff among different source components of soil respirationunder altered environments. So while many of the processes that influence soilrespiration are themselves temperature sensitive, temperature alone is not sufficientfor characterizing the response of soil respiration to a combination of environmentalchanges.

In this study, the inclusion of other factors in a model (e.g., soil water content)did not always improve model fit (such as R2), and sometimes even decreased theoverall model fit. Hibbard et al. (2005) reported an analysis of soil respiration acrossnorthern hemisphere temperate ecosystem and found that including soil moisturecould improve correlation but not significantly. Zhou et al. (2007) indicated that thecombined function of soil temperature and moisture did not fit the data well undersevere water stress. Jassal et al. (2008) indicated that in an 18-year-old temperateDouglas-fir stand soil respiration was positively correlated to soil temperature atthe 2 cm depth if soil water content at the 4 cm depth >0.11 m3 m−3. If soilwater content was below this value, soil respiration was largely decoupled from soiltemperature.

This suggests that under changed environmental conditions, the appropriate rep-resentation of some specific soil respiration processes may be altered. For example,elevated CO2 can reduce transpiration and result in higher soil water content(Pendall et al. 2003). Results such as these can increase respiration in 2 wayscompletely unrelated to temperature, through (1) higher soil moisture content, and(2) through the production of a larger amount of organic matter inputs that thenfuel respiration. In studies with a high frequency soil respiration measurements it ispossible to find a large fraction of soil respiration that is directly related to the magni-tude of photosynthesis, which not only has a temperature component different thansoil temperature, but also has a component related the amount of photosyntheticallyactive radiation, that are independent of soil environmental factors (Davidson et al.2006; Liu et al. 2006; Gu et al. 2008). These analyses show that the photosyntheticinfluence reproduced a typical seasonal pattern of apparent temperature sensitivityreported in the literature: higher sensitivity in winter (dormant season) and lowersensitivity in summer (growing season). Such pattern has been incorrectly interpretedas an indication of temperature acclimation of soil respiration by previous studies.Extending the application of simple correlative models from ambient to changedclimate conditions is of limited utility.

Comparing the models with their parameters estimated independently for eachspecific treatment, the component model, which represents many of the possiblecomponents of soil respiration separately, worked relatively well under ambienttemperature and CO2 conditions, and it also worked well under changed conditionswith appropriate changes of the model parameters (Table 2). This indicates that thereare still additional processes need to be explicitly represented in order to capture allthe impacts of changed climate conditions.

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4.2 Quantifying the variation of soil respiration from Rh and Ra

under climate change treatments

Under each specific treatment the component model partitions soil respiration intoautotrophic and heterotrophic components. Unfortunately, data are not availablefor directly addressing this result, because we were unable to measure autotrophicand heterotrophic respiration at the experimental site (excavation would havecompromised the experiment and plant species were short-stature with intermingledroot systems that had a high turnover rate). In addition excavation may not havegiven us reliable data to use in our models for a number of reasons. For example,measuring the respiration of roots in situ directly after removing the associated soil,or by digging trenches around small areas to exclude roots, results in significant soildisturbance likely increasing soil CO2 flux (Hanson et al. 2000). Högberg et al. (2001)applied large scale girdling, in which the phloem was cut away killing roots to esti-mate root contribution to total soil respiration, but this decreases labile carbon inputsinto the soil, thus altering microbial activity. More recently, isotopic approacheshave been developed (e.g., Trumbore et al. 1996), but these methods require anisotopic tracer and analysis but these analyses were not done in this experiment.Experimental studies are intrinsically limited with either the sampling methodologyor by changes in soil conditions (Hanson et al. 2000). These pitfalls emphasizethe need of reliable process-based component models to estimate autotrophic andheterotrophic soil respiration in order to understand how these components mayrespond to climate changes differently.

Using our component model, we could estimate the relative proportion of Rh intotal soil respiration across CO2, temperature, and soil water treatments under eachspecific setting. Hanson et al. (2000) showed the mean contribution in non-forestecosystems is 63%. The magnitude of the estimated Rh at the ambient condition inthis study is slightly lower than the range of 60–88% in grasslands and croplands(Buyanovsky et al. 1987; Buyanovsky and Wagner 1995; Raich and Tufekcioglu2000). Also, root respiration (Ra) in trees has been found to peak in late spring,coinciding with high temperature and leaf flush, and to peak again in autumn priorto litterfall due to the change in root physiology and phenology (e.g., Dickmannet al. 1996). The component model was able to represent these detailed changes byaccounting for root growth respiration.

4.3 Limitations of soil respiration models under climate change treatments

It is well established that tree root elongation declines when soil water potentialdecreases (e.g., Larson 1980; Teskey and Hinckley 1981). Because of their limitedroot system, grasses and forbs may be more impacted by low soil water potential.This may be why root maintenance and growth contributed little to soil respirationin this study. When root maintenance and growth contributed to soil respiration,they contributed less to overall respiration than heterotrophic respiration, and as aresult the estimates of component model were similar to the Del Grosso et al. (2005)model.

Another limit to model predictions is the difficulty in obtaining accurate fieldmeasurements of root activity and respiration. Young, new roots can respire four

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times more C than old roots, but it is difficult to determine the age of roots or theirindividual contribution to soil respiration in the field (Lipp and Andersen 2003).Another challenge is in measuring root nutrient uptake. High soil respiration isassociated with new root nutrient uptake (Veen 1980; van der Werf et al. 1988).Accurate measurements of root nitrogen, a proxy for the amount of metabolicallyactive tissue, are challenging without severely disturbing the root system, and thussoil respiration. Due to limited measurements of root nitrogen concentration in thisstudy, we approximated root nitrogen concentration using the model with a basicbiological constraint (e.g., Ra ≥ 0 or R ≥ 0 and N ≥ 0). All these estimated valueswere in the range of measured values.

The relationship between the time lag of soil water change and soil respirationpresents a further challenge. It is often assumed that the soil respiration is closeto zero when there is severe water stress, but in our experiment, there was stillsoil respiratory activity happening under very dry conditions. During dry periods,soil respiration may be under-predicted by models because (1) the estimation ofsoil water potential (or matric potential) during severe stress may be miscalculateddue to high spatial heterogeneity of water distribution, or (2) as the result ofhydrological redistribution by roots (Caldwell and Richards 1989). This suggests thatthe current models for soil respiration, especially mechanistic models, may have ahigh uncertainty under severe water stress.

Root physiology and phenology are not considered in the non-component mod-els. Dickmann et al. (1996) found root respiration to peak in late spring, whichcoincides with rising temperatures and leaf flush, and peaks again in autumn priorto litterfall. Although the temperature related models (T and exponential model)can provide approximations, the fits cannot capture these respiration excursionsdue to the gradual change of temperature or soil water content. However, thecomponent model can accurately provide this information through the use of rootgrowth and maintenance respiration estimates. The temperature sensitivity of soilrespiration may not directly support the hypothesis that higher temperatures depletesoil C pools due to increased soil respiration (e.g., Jenkinson et al. 1991; Raichand Schlesinger 1992; Amundson 2001), because higher soil respiration may notnecessarily indicate faster decomposition of soil organic matter (Raich and Mora2005). Instead, it may represent increased rates of root and rhizosphere respiration(Kirschbaum 2000; Andrews and Schlesinger 2001). Hibbard et al. (2005) reportedthat lowest respiration rates were observed at the highest summer temperature due todrought.

Our study found that modeling soil respiration as a function only of soil moisturewas of limited utility. This indicates that soil moisture contributes little to soilrespiration until it reaches a threshold or it has to be considered with other factors,such as temperature and root growth. The Seasonal Pattern Model (Hanson et al.1993) did not work well in this study even with the parameter optimization. Possiblereasons might be that (1) the water potentials at 15 cm were used instead of35 cm; and (2) the root maintenance respiration and partial heterotrophic respi-ration were not included separately in the model. This study indicates that bothRb and Q10 may change under changed environments. In fact, Rb can be site-specific and a function of a variety of characteristics, such as quantity of soil organicmatter, root density, nutrient status, microbial population size and activity (Hansonet al. 1993).

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4.4 Testing the hypothesis that Q10 values are altered and that temperatureis the major contributing factor to variation in soil respirationunder climate change

Soil respiration models generally apply a fixed Q10 coefficient for the exponentialfunction between soil respiration and temperature. However, in this study, irre-spective of CO2 and T treatments, the fitted optimal Q10 values in wet treatmentswere larger than in dry treatments. Davidson et al. (1998) also showed larger valuesfor the wetter sites than for dry sites. However, Zhou et al. (2007) indicated thatwarming did not significantly change Q10 values, and even decreased this value ingrass ecosystems (e.g., McHale et al. 1998; Luo et al. 2001). Other studies indicatedthat Q10 varies among ecosystems and across temperature ranges, in part becausethe various components of soil respiration have different temperature sensitivities(Kirschbaum 1995; Trumbore et al. 1996). Boone et al. (1998) suggested differenttemperature sensitivities for live roots, associated mycorrhizae, and the oxidationof plant detritus (dead roots, leaves and wood input), root exudates, and humifiedorganic matter. Several have indicated that increased atmospheric [CO2] would leadto larger Q10 values by increasing below-ground C allocation (Canadell et al. 1997;Cardon 1997; Hungate et al. 1997). However, we did not find significant changesin Q10 values at treatments of elevated temperature or CO2 concentration whencomponents of respiration are considered individually.

5 Conclusions

Global climate change will alter bulk soil respiration. However, it is difficult topredict the direction and magnitude of changes in this important part of the global Ccycle without accounting for how the heterotrophic and autotrophic components willrespond independent of each other. In this study, based on short-term intensive ob-servations, a partially process-based component model was tested and compared withother models across CO2, temperature, and soil water treatments. It appears that aprocess-based model had relatively high accuracy and could provide more biologicaldetails. The further developing of this model requires additional algorithms basedon a better understanding of the mechanisms that alter soil respiration across spatialand temporal scales, such as the interactions among factors (e.g., soil temperatureand water content), especially near thresholds values. Some soil respiration modelsthat predict ambient condition or general models that work well across all differenttreatments may not accurately predict soil respiration under future climate regimesdue to changes in ecological processes and feedbacks. Changes in parameters (suchas those in component model) or including more processes at corresponding spatialand temporal scales may be necessary to apply these models under projected futureenvironments. Our results show that controlled experiments to quantify the effectsof single or combined factors (such as temperature, soil moisture, plant roots andmicrobial activity) on soil respiration under multiple environmental conditions arenecessary to accurately define the mechanisms that alter soil respiration. Since thesemechanisms include dynamic autotrophic processes including diurnal and seasonalpatterns of carbohydrate supply, phenology of root growth, active redistribution ofsoil moisture, as well as heterotrophic processes, soil respiration cannot be accurately

Climatic Change

modeled independently from aboveground carbon cycle processes. Considering theentire terrestrial ecosystem as a unified system will be required to predict soilrespiration responses in the future using models.

Acknowledgements This research was supported in part by a research appointment to the OakRidge National Laboratory/Oak Ridge Associated Universities Historically Black Colleges andUniversities and Minority Education Institutions Summer Faculty Research Program.

The OCCAM experiment was sponsored by the U.S. Department of Energy, Office of Science,Biological and Environmental Research under contract DE-AC05-00OR22725 with Oak RidgeNational Laboratory (ORNL), managed by UT-Battelle. This study was also partially supportedby USDA (Evan-Allen and Mc-Stennis) and Department of Energy under cooperative agreementNo. DE-FC26-06NT43029-005. Jake Weltzin was integral in establishing the OCCAM experimentand Katherine Sides, Joanne Childs, and Courtney Campany assisted with data collection. We thankPaul Kardol, George Byrd, and Lara Souza and anonymous reviewers for their helpful suggestionsand comments on earlier drafts.

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