How switches and lags in biophysical regulators affect ...nature.berkeley.edu/biometlab/pdf/DDB et al 2006 JGR Biogeoscience.pdf2 exchange at two scales (soil and canopy) and with

How switches and lags in biophysical regulators

affect spatial-temporal variation of soil

respiration in an oak-grass savanna

Dennis Baldocchi,1 Jianwu Tang,1,2 and Liukang Xu1,3

Received 9 June 2005; revised 19 December 2005; accepted 3 February 2006; published 1 June 2006.

[1] Complex behavior, associated with soil respiration of an oak-grass savanna ecosystemin California, was quantified with continuous measurements of CO2 exchange at twoscales (soil and canopy) and with three methods (overstory and understory eddycovariance systems, soil respiration chambers, and a below-ground CO2 flux gradientsystem). To partition soil respiration into its autotrophic and heterotrophic components, weexploited spatial gradients in the landscape and seasonal variations in rainfall. During thedry summer, heterotrophic respiration was dominant in the senesced grassland area,whereas autotrophic respiration by roots and the feeding of microbes by root exudates wasdominant under the trees. A temporal switch in soil respiration occurred in the spring. Butthe stimulation of root respiration lagged the timing of leaf-out by the trees. Anothertemporal switch in soil respiration occurred at the start of autumn rains. This switch wasinduced by the rapid germination of grass seed and new grass growth. Isolated summerrain storms caused a pulse in soil respiration. Such rain events stimulated microbialrespiration only; the rain was not sufficient to replenish soil moisture in the root zone or togerminate grass seed. Soil respiration lagged photosynthetic activity on hourly scales. Thelikely mechanism is the slow translocation of photosynthate to the roots and associatedmicrobes. Another lag occurred on daily scales because of modulations in photosynthesisand stomatal conductance by the passage of dry and humid air masses.

Citation: Baldocchi, D., J. Tang, and L. Xu (2006), How switches and lags in biophysical regulators affect spatial-temporal variation

of soil respiration in an oak-grass savanna, J. Geophys. Res., 111, G02008, doi:10.1029/2005JG000063.

1. Introduction

[2] How soil respiration will respond to changes in tem-perature, soil moisture and carbon pools remains a criticalquestion for assessing the carbon balance of ecosystems[Gower, 2003; Granier et al., 2000; Hanson et al., 2004;Law et al., 2003] and for quantifying feedbacks on theclimate system [Cox et al., 2000; Friedlingstein et al.,2003; Sitch et al., 2005]. In general, rates of soil respirationare modulated by a large set of edaphic (soil texture,temperature and moisture, carbon and nitrogen content),physiological (photosynthesis), functional (plant functionaltype, phenological and reproductive stage) and structural(leaf area index) factors [Hanson et al., 2000; Hogberg etal., 2001; Norman et al., 1992; Ryan and Law, 2005].Additional complications in predicting rates of soil respira-tion are introduced by the differential and nonlinearresponses to environmental perturbations by the soil’s auto-trophic and heterotrophic respiratory components [Hanson et

al., 2000; Raich and Schlesinger, 1992], biophysicalswitches (leaf-out, senescence, snow, drought) [Granier etal., 2000; Wilson and Baldocchi, 2001] and pulses (rain)[Borken et al., 2003; Huxman et al., 2004; Irvine and Law,2002; Jassal et al., 2005; Lee et al., 2004; Xu et al., 2004].[3] In principle, heterotrophic soil respiration increases

exponentially with soil temperature and its basal rate, at areference temperature, is inhibited by extremely dry orwet soilconditions [Hanson et al., 2000;Kelliher et al., 2004;Orchardand Cook, 1983]. Basal rates of heterotrophic respiration alsovary as a function of the age and type (e.g., digestibility) of theorganic matter in the soil [Gleixner et al., 2005; Grayston etal., 1997; Kelliher et al., 2004; Trumbore, 2000].[4] Autotrophic soil respiration is a direct consequence of

root respiration, so it is coupled to rates of photosynthesis.In practice, it is difficult to disentangle and measure theeffect of photosynthesis on autotrophic respiration becauseroot exudates feed microbes, influencing the relative frac-tion of heterotrophic respiration [Bowling et al., 2002;Gleixner et al., 2005; Grayston et al., 1997; Tang et al.,2005]. Consequently, most relevant studies examine linksbetween soil respiration and photosynthesis, together[Kuzyakov and Larionova, 2005].[5] The coupling between soil respiration and photosyn-

thesis depends on the timescale at which the two variablesare correlated with one another. On annual timescales, soilrespiration correlates with gross primary productivity (GPP)

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, G02008, doi:10.1029/2005JG000063, 2006

1Ecosystem Sciences Division, Department of Environmental Science,Policy and Management, University of California, Berkeley, California,USA.

2Now at Department of Forest Resources, University of Minnesota, St.Paul, Minnesota, USA.

3Now at LICOR, Lincoln, Nebraska, USA.

Copyright 2006 by the American Geophysical Union.0148-0227/06/2005JG000063$09.00

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[Janssens et al., 2001; Raich and Tufekciogul, 2000]. Ondaily to weekly timescales, soil respiration is sensitive toantecedent rates of photosynthesis. Hogberg et al. [2001],for example, girdled a cohort of trees to disrupt the supplyof photosynthate to their roots. They reported that a signif-icant reduction in soil respiration occurred five days afterthe cessation of photosynthesis. Bowling et al. [2002] andMcDowell et al. [2004] examined the linkage between soilrespiration and natural modulations in photosynthesis—theywere imposed by temporal variations in humidity deficits.These cited authors found that daily to weekly variations inphotosynthesis were followed by a 5 to 10 day lag in theisotopic signature of soil respiration. More recently, Tang etal. [2005] measured photosynthesis and soil respirationsimultaneously, on hourly timescales, and found that soilrespiration lagged variations in carbon supply by severalhours.[6] Horizontal and vertical gradients in biophysical driv-

ers of soil respiration will impose spatial variation insoil respiration [Curiel-Yuste et al., 2004; Davidson et al.,1998; Irvine and Law, 2002; Qi and Xu, 2001; Tang andBaldocchi, 2005]. Horizontal and vertical variations in soilrespiration are contingent upon how the population of roots,fungi and microbes, and their controlling edaphic variablesvary in space [Belnap et al., 2003; Gleixner et al., 2005].Small-scale variance is produced by respiratory ‘hot spots’that are associated with clusters of microbes on soil grains,along soil pores, by roots and associated mychorrhizae, andchannels formed by vertebrates and invertebrates [Belnap etal., 2003]. At the landscape-scale geographical position ofplants and the transition of ensembles of plants into com-munities and ecosystems causes spatial variation in soilrespiration. Spatial switches in the controls and magnitudeof soil respiration can be expected as one moves across alandscape between open spaces and the vicinity of plants[Tang and Baldocchi, 2005]. Vertical variations in theproduction of CO2 will occur because root [Jackson et al.,1996] and microbial presence and activity, soil temperatureand moisture vary with soil depth [Hirsch et al., 2004;Jassal et al., 2004, 2005].[7] The overarching goal of this paper is to quantify how

switches and lags in biophysical regulators affect spatial-temporal variations of soil respiration in an oak-grasssavanna ecosystem in California. Our experimental ap-proach involves continuous measurements of CO2 exchangeat two scales (soil and canopy) and with three methods(overstory and understory eddy covariance systems, soilrespiration chambers and a below-ground, CO2 flux gradi-ent system).[8] We use natural spatial gradients in canopy structure

and the landscape and seasonal variations in rainfall topartition soil respiration into its autotrophic and heterotro-phic components. Californian savanna ecosystems are ame-nable to this approach. They comprise widely spaced trees,separated by isolated grass patches that are minimallyaffected by neighboring tree roots [Tang and Baldocchi,2005]. The natural gradient approach is most capable ofseparating autotrophic and heterotrophic respiration duringthe long dry summers when the annual grasses are dead.Then, the predominant source of respiration in open spacescomes from microbial respiration of decaying organicmatter. In contrast respiration under the photosynthesizing

trees represents the sum of autotrophic respiration by rootsand heterotrophic respiration associated with the microbialconsumption of root exudates, mychorrhizae, invertebratemetabolism and the decomposition of dead organic matter.To a first-order approximation, we can evaluate the dynam-ics and magnitude of root respiration by subtracting soilrespiration measurements made under a tree from those inthe open area. The advantage of the natural gradientapproach is its minimal disturbance of the system, asopposed to girdling and trenching studies [Kuzyakov andLarionova, 2005]. Albeit, potential artifacts will exist be-cause of differences in stores of carbon, soil moisture,temperature and texture under the trees and in the open.However, they are minor [Tang and Baldocchi, 2005] andthey should not affect the dynamical responses that wereport.[9] With this integrated and continuous measurement

approach we have the unique ability to quantify lagsbetween autotrophic respiration and canopy photosynthesisusing inverse Fourier transforms [Press et al., 1988]. We arealso able to observe and infer switches in the partitioning ofsoil respiration by autotrophic or heterotrophic respirationthat are associated with the landscape and with seasonalchanges in soil moisture and phenology and by episodic rainpulses. Information on the pulse dynamics of ecosystemrespiration have been assessed in a previous paper [Xu et al.,2004].

2. Site Description

[10] The study was conducted at a pair of field siteslocated on the lower foothills of the Sierra Nevada Moun-tains near Ione, California. The study sites are members ofthe AmeriFlux and FLUXNET networks. The oak savannafield site (Tonzi Ranch) is located at 38.4311�N,120.966�W. The altitude of the site is 177 m and the terrainis relatively flat. The companion site (Vaira Ranch) is anannual grassland, that is 2 km away (latitude: 38.4133�N;longitude: 120.9508�W; altitude: 129 m). The grasslandstudy started in October, 2000 and the savanna study startedin April, 2001.

2.1. Vegetation Characteristics

[11] The woodland overstory consists of scattered blueoak trees (Quercus douglasii) and occasional grey pine trees(Pinus sabiniana). The understory landscape is managed;the rancher removes brush and cattle graze the grass andherbs. The understory consists of exotic annual grassesand herbs; the species include Brachypodium distachyon,Hypochaeris glabra, Bromus madritensis, and Cynosurusechinatus.[12] A demographic survey on stand structure of the oak

woodland was conducted using a multireturn laser altimetersystem; the survey extended for a kilometer area centeredon the meteorological tower [Chen et al., 2006]. The meantree height was 9.41 ± 4.33 m, the mean trunk height was1.75 ± 1.35 m, the mean crown radius was 3.18 ± 1.54 mand the mean basal area was 0.074 ± 0.0839 m2 (Q. Chen,personal communication, 2005). The trees covered 40% ofthe landscape. The mean leaf area index of the savannawoodland has been evaluated using a combination ofremote sensing information (CASI, IKONOS, LIDAR)

G02008 BALDOCCHI ET AL.: SWITCHES, LAGS, AND SOIL RESPIRATION

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and allometric relations [Karlik and McKay, 2002]. The leafarea index for this heterogeneous canopy equals 0.706 ±0.408. This new value is slightly greater than our previouslypublished estimate of projected leaf area index (0.6) thatwas derived from a 200 m transect using a LICOR-2000[Kiang, 2002].[13] Leaf area index of the grasses was measured

throughout the growing season using the direct clippingmethod on 3 to 5 samples, 0.0625 m2 in area. The leaf areaof each sample was determined with an area meter (LICORLI 3100 C). The grasses growing at the savanna (TonziRanch) and open grassland (Vaira Ranch) sites exhibitedconsiderable seasonal dynamics and site differences in theirleaf area indices (Figure 1). Growth started in the autumnwith commencement of the rainy season, but it remainedmodest during the cold winter season. Grass growth accel-erated during spring and peaked around day 100. Afterward,the grass canopy senesced as moisture in the soil reservoirwas depleted. No live leaves existed after day 160 in 2003and after day 125 in 2004. The amount of grass growthdiffered among the two sites, too. Greater grass productionoccurred at the open grassland site than in association withthe oak trees. These differences are consistent with the

observation of greater soil carbon in the open grasslandthan in the understory at the savanna site (see below).

2.2. Climate

[14] Annual temperature at a nearby weather station withsimilar altitude and vegetation (Pardee, California) is16.3�C. The mean annual precipitation is about 559 mmper year (from weather station in Ione, California, thatoperated between 1959 and 1977). During the 2003 and2004 study period, the mean air temperature was 16.31�Cand 16.18�C, respectively. More precipitation fell during the2003 study year (616 mm) than during the 2004 study year(485 mm) and essentially no rain falls during the summermonths of this Mediterranean climate region. Seasonaltrends in sunlight, temperature and humidity are reportedelsewhere [Baldocchi et al., 2004].

2.3. Soil Characteristics

[15] The soil of the oak-grass savanna is classified asAuburn rocky silt loam (Lithic haploxerepts; soil survey ofAmador Area, California, 1965, USDA, Soil ConservationService). Physical properties are reported by Baldocchi etal. [2004]. The chemical properties of the soil were sur-veyed in November, 2003 for the 0.15 m layer below thesurface. Under the trees, the percentages of organic matter,organic carbon and nitrogen were, on average, 4.82, 2.80and 0.286%, respectively. In the space between trees, thesoil contained 1.85% organic matter, 1.07% carbon and0.10% nitrogen. At the nearby annual grassland, the soilcontained 1.39% carbon and 0.14% nitrogen.[16] Seasonal variations in mean soil moisture (between 0

and 0.60 m) and daily mean soil temperature (at 0.08 m) aredepicted in Figures 2a and 2b, respectively, for the TonziRanch savanna during 2003. Together, Figures 2a and 2bshow that the roots and microbes experience a very widerange of soil moisture and temperature conditions over thecourse of the year, a fact we exploit to examine responses ofautotrophic and heterotrophic respiration components tochanges in the environment. Soil moisture was near fieldcapacity during the wet period and experienced minor dry-wet cycles between rain events. In general soil moistureranged between 30 and 50% at all depths during the wetwinter. With the cessation of rainfall (around day 125 in2003) there was an immediate and dramatic reduction in soilmoisture. During the summer dry period, soil moisture atdepth below 0.15 m was near 10% and soil moisture in thefirst layer was below 5%. Daily mean soil temperature, at0.08 m, experienced a 20�C range over the course of theyear. Lowest temperatures, near 10�C, occurred during thewinter wet season. During the dry summer, mean daily soiltemperature reach exceeded 30�C because latent heat ex-change was low [Baldocchi et al., 2004].

3. Methods

3.1. Environmental Measurements

[17] Air temperature and relative humidity were measuredwith a platinum resistance thermometer and solid-statehumicap, respectively (model HMP-45A, Vaisala, Helsinki,Finland). Static pressure was measured with a capacitanceanalog barometer (model PTB101B, Vaisala, Helsinki, Fin-land). Soil temperatures were measured with multilevel

Figure 1. Seasonal variation in leaf area index of thegrass during 2003 and 2004. Data are from the oak savannafield site (Tonzi Ranch) and for the open grassland site(Vaira Ranch).


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thermocouple probes. Volumetric soil moisture content wasmeasured continuously in the field at several depths in thesoil with frequency domain reflectometry sensors (ThetaProbe model ML2-X, Delta-T Devices, Cambridge, UnitedKingdom). Sensors were placed at various depths in the soil(5, 10, 20 and 50 cm) and were calibrated using thegravimetric method. Profiles of soil moisture (0–15, 15–30, 30–45 and 45–60 cm) were made periodically andmanually using a time domain, reflectometer (MoisturePoint, model 917, E.S.I Environmental Sensors, Inc, Victo-ria, British Columbia).[18] Ancillary meteorological and soil physics data were

acquired and logged on CR-23x and CR-10x data loggers(Campbell Scientific Inc., Logan, Utah). The sensors were

sampled every second, and half-hour averages were com-puted and stored on a computer, to coincide with the fluxmeasurements.[19] Fluxes of carbon dioxide, water vapor and heat

between the land and the atmosphere were measured withthe eddy covariance method [Baldocchi, 2003]. Wind ve-locity and virtual temperature fluctuations were measuredwith a three-dimensional ultrasonic anemometer (Windmas-ter Pro, Gill Instruments, Lymington, United Kingdom).Carbon dioxide and water vapor fluctuations were measuredwith an open-path, infrared absorption gas analyzer (modelLI-7500, LICOR, Lincoln, Nebraska). The micrometeoro-logical sensors were sampled and digitized ten times persecond.

Figure 2. (a) Seasonal variation in soil moisture for four layers at the oak savanna field site. The datawere sampled weekly with a segmented time domain reflectometer. (b) Seasonal variation in soiltemperature at 0.08 m. Both data sets were acquired in 2003 at the oak savanna field site.


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[20] At the savanna site, a set of micrometeorologicalinstruments was supported 23 m above the ground (�10 mover the forest) on a walk-up scaffold tower. The gasanalyzer was mounted 0.35 m below the sonic and 0.25 mto the side of the anemometer. Two other sets of fluxmeasurement instrumentation were mounted about 2 mabove the ground in the understory and over the opengrassland. Each tower was protected from the cows withan electrical fence.[21] In-house software was used to process the measure-

ments into flux densities. The software computed covarian-ces between velocity and scalar fluctuations over half-hourintervals. Turbulent fluctuations were calculated using theReynolds decomposition technique by taking the differencebetween instantaneous and mean quantities. Mean velocityand scalar values were determined using 30-min records.The computer program also removes electrical spikes androtates the coordinate system to force the mean verticalvelocity to zero. Corrections for the effect of densityfluctuations were applied to the scalar covariances that weremeasured with the open-path sensor using theory developedby Webb et al. [1980].[22] The fast response CO2/water vapor sensors were

calibrated every three to four weeks against gas standards.The calibration standards for CO2 were traceable to thoseprepared by NOAA’s Climate Monitoring and DiagnosticLaboratory. The output of the water vapor channel wasreferenced to a dew point generator (LI-610, Licor, Lincoln,Nebraska). Over the past three years the calibration zerosand spans have shown negligible drift.[23] Prior to the experimental set up computations of the

flux covariance transfer functions [Moore, 1986] were madeto guide the positioning of sensors in the field. Overalltransfer correction factors were less than a few percent.Considering uncertainties with applying the transfer func-tions, we chose not to apply them to our data. We also havereported near closure of the surface energy balance with ourinstrument setup [Baldocchi et al., 2004].[24] Data gaps are inevitable and must be filled to

compute daily and annual sums of fluxes. We filled datagaps with the mean diurnal average method [Falge et al.,2001]. The diurnal means were computed for consecutive26-day windows to account for seasonal trends in phenol-ogy and soil moisture. The 26-day window correspondswell with a spectral gap in energy fluxes, suggesting thatthis time window was nearly optimal.

3.2. Soil CO2 Profile Measurements

[25] Soil CO2 concentrations were measured at depths of0.02, 0.08, 0.16 and 0.24 m and at two locations. Onestation was under a mature oak tree and the other wasmidway between two widely separated trees. The systemunder the tree was 1m from a tree; the tree had a diameter atbreast height (DBH) of 0.716 m and an average of crowndiameter of 13.05 m. The system in the open was 24.5 maway from the above tree and 18 m away from anothersmaller tree with an average of crown diameter of 6.05 m.CO2 concentrations measured in the open had negligibleinfluence from trees on the basis of transect measurementsof soil respiration using a manual chamber system [Tangand Baldocchi, 2005].

[26] CO2 concentrations in the soil air were measured bysolid-state infrared gas analyzers (Vaisala CarboCap sen-sors, series GMT 220). The sensors were enshrouded inGortex, to keep them dry, and were installed in the soil insealed aluminum tubes that were installed vertically andopen only at the sampling depth. In the shallow depths andat the open location we used model GMT 222. Thesesensors measure CO2 across the range between 0 and10000 ppm. Their accuracy is ±(20 ppm + 2% of reading).Under the tree and at depth we used model GMT 221. Thesesensors operate in the 0 to 2% range and have an accuracyof ±(0.2% + 2% of reading). The concentration readingsfrom the CO2 sensors were corrected for variations intemperature and pressure using empirical formulae reportedby Tang et al. [2003] and CO2 mole density (Cmol, mmolm�3) was computed using the universal gas law:

Cmol ¼CvP

RT; ð1Þ

where Cv is the CO2 concentration (mmol mol�1), P is theair pressure (Pa), T is the soil absolute temperature (K), andR is the universal gas constant (8.3144 J mol�1 K�1).Calibrations were performed against standard gases trace-able to the world standards produced at NOAA’s ClimateMonitoring and Diagnostics Laboratory.[27] The deployment of solid-state CO2 sensors in the soil

has many advantages over the periodic and manual sam-pling of ports inserted into the soil with syringes, a methodmany former flux gradient studies used [Hirsch et al., 2004;Jassal et al., 2004; Moncrieff and Fang, 1999; Risk et al.,2002; Takle et al., 2004]. Sampling a finite volume of soilair with a syringe has the potential to smear CO2 gradientmeasurements—sampling pulls a volume of air that issubject to a very strong concentration gradients. In rareinstances, this sampling problem has been circumvented byinserting long porous tubes horizontally in the soil whosevolume exceeds that of the sampling syringe [Jassal et al.,2004]. However, the insertion of this horizontal samplingtube stills disturbs the soil profile. If the air sample isinjected into a stream of air that is carried to a gaschromatograph or an infrared gas analyzer, error can beintroduced by how well one evaluates the area under thecurve in the time signal. Relying on manual samplingprevents one from taking continuous and long-term meas-urements, as we are able to do with these sensors. Incomparison, measuring CO2 concentrations continuouslyand at multiple levels enables us to examine the productionof CO2 at depth [Fang and Moncrieff, 1999; Risk et al.,2002] and correlate it with the local and representativetemperature and moisture.[28] One negative aspect of using the solid-state CO2

sensors is associated with its heating of the soil when thesensor is activated constantly [Hirano et al., 2003; Jassal etal., 2005]. To assess this error, we conducted a set oflaboratory studies. We discovered that continual operationof the CO2 sensors caused a 2�C elevation in temperature atthe mouth of the sampling tube relative to a column of soilnot heated by the CO2 probe. This degree of warming hasthe potential to increase soil respiration rates in the vicinityof the heated probe by about 14%, assuming a Q10 of 2.


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3.3. Soil CO2 Efflux

[29] Soil respiration efflux rates were computed usingflux gradient theory [Amundson et al., 1998; Hirano et al.,2003; Jassal et al., 2005; Liang et al., 2004; Tang et al.,2003]. In principle, the time rate of change of CO2 in thesoil is a function of the diffusive flux divergence and thesource production, as defined by the conservation of massequation:

@eCmol zð Þ@t

¼ � @F zð Þ@z

þ S zð Þ: ð2Þ

In equation (2), Cmol is the CO2 molar density, F is the CO2

efflux (mmol m�2 s�1), e is the volumetric air content (air-filled porosity), S is the source strength (mmol m�3 s�1) andz is the depth (m). Using Fick’s law of diffusion we cancompute the flux density, where F is positive when the fluxis directed toward the atmosphere, as a function of the molardensity gradient and the molecular diffusivity, Ds:

F ¼ Ds

@Cmol

@z: ð3Þ

[30] At steady state conditions, the change in storage isnil (LHS of Equation 2), causing the diffusive flux diver-gence to be in balance with the source production term:

@F

@z¼ S zð Þ: ð4Þ

If the CO2 production and molecular diffusivity are constantwith depth, the local source production is proportional to thesecond derivative in the change of CO2 concentration withdepth (Ds

@2Cmol

@z2 = S(z)) and the concentration field within the

soil is a second-order function of soil depth (z) [Amundsonet al., 1998]:

Cv ¼S

DLz� z2

2

� �þ Ca: ð5Þ

We tested this assumption by examining soil CO2 profilesduring the winter, spring and summer, under the tree and inthe open. Figure 3 presents data from one case, day 343 atnoon. These data, and those not shown, indicate that sourceprofile was generally constant. This assumption allows us toapproximate and integrate equation (4) in finite differenceform, thereby estimating CO2 efflux density at the soilsurface, F(0) as

F 0ð Þ ¼ z2F1 � z1F2

z2 � z1: ð6Þ

We estimated F1 between 0.02 and 0.08 m and F2 between0.08 and 0.16 m.[31] The CO2 diffusion coefficient in the soil, Ds, is equal

to the product of CO2 diffusion coefficient in the free air,Da, and the gas tortuosity factor, x. The diffusion coefficientin free air, Da, was computed using

Da ¼ Da;ref T=293:15ð Þ1:75 101:3=Pð Þ: ð7Þ

In equation (7), T is temperature, P is pressure and Da,ref is areference value of Da at 20�C (293.15 K) and a pressure of101.3 kPa; it equals 14.7 mm2 s�1. Soil tortuosity wasevaluated as a function of volumetric water content and soiltexture [Moldrup et al., 1999]:

Ds

Da

¼ x ¼ f2 ef

� �b�g; ð8Þ

where g is the percentage of mineral soil with size >2 mm, bis a constant (2.9), f is the porosity, defined as the sum ofthe volumetric air content, e, and the volumetric watercontent, q. The diffusivity of CO2 through liquid water wasignored as it is insignificant compared to diffusion throughthe air spaces [Fang and Moncrieff, 1999; Welsch andHornberger, 2004].

3.4. Validation

[32] Application of the flux gradient method is notwithout pitfalls and needs validation before we can use itto interpret differences between soil respiration that wasmeasured at different components of the landscape. Errorsin evaluating soil respiration can be introduced by (1) themeasurement of soil CO2 concentration gradients, (2) thetheoretical assessment of soil diffusivity in terms of soilmoisture and soil porosity, and (3) from the numericalassessment of CO2 gradients at a discrete and limitednumber of depths. We assessed potential for error bycomparing measurements of soil respiration, using theLICOR 6400 soil respiration system, and fluxes computedwith the soil CO2 flux gradient system (Figure 4). Thecorrespondence between the two methods was very high(r2 = 0.90), but on average the flux gradient effluxes wereabout 77% of the chamber measurements. The agreement

Figure 3. Relationship between CO2 concentration anddepth. These data are from under the tree and in the opengrassland to support the assumption that the source profilewas relatively constant with depth.


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between the flux profile and soil chamber system is lowerthan what we reported in our earlier study [Tang et al.,2003], which was confined to the dry season when soilmoisture and the diffusivity was more conservative. On theother hand, this level of agreement falls within the boundsreported by Pumpanen et al. [2004], who compared theperformance of 20 soil respiration systems over wet sandand Jassal et al. [2005], who compared profile effluxes withsoil chambers. In contrast, Liang et al. [2004] report thatflux gradient estimate of soil CO2 effluxes, using the samesolid-state CO2 sensors were 45% greater than estimatesmade with an automatic chamber system. In summary, CO2

effluxes, produced by the flux gradient system, are sensitiveto soil diffusivity models through their dependence on soilmoisture and the measurements of soil moisture. However,the method is robust enough to study spatial and temporaldifferences with confidence.[33] While we were unable to replicate the flux profile

system in a desired manner because of the cost of sensors,we were able to explore the representiveness of the array bycomparing area-weighted averages against eddy covariancemeasurements made in the forest understory (Figure 5). Weweighted efflux measurements made by the flux gradientsystem in proportion with its representative cover (60% forthe system in the open and 40% for system under the tree).Except for a period between days 130 and 200, we observedvery close agreement between the two measurement meth-ods as the soil dried. We also saw good correspondence inhow the two systems responded to the summer rain eventand the commencement of autumnal rainfall. The bias, afterday 130, may be explained by the fact that we moved oneprofile system on day 128 because gophers were churningsoil and altering nearby soil properties. Application of theeddy covariance method in the understory is susceptible to

its own bias errors. However, several studies have shown itcan produce reliable results in open canopies [Baldocchi etal., 2000; Blanken et al., 1998; Janssens et al., 2001; Law etal., 1999; Subke and Tenhunen, 2004].

3.5. Canopy Photosynthesis

[34] Net canopy photosynthesis was measured by takingthe difference between simultaneous eddy covariance meas-urements over and under the canopy [Baldocchi and Vogel,1996; Baldocchi et al., 1997]. The overstory eddy covari-ance system measured the difference between gross canopyphotosynthesis (Pg) and leaf (Rl), bole (Rb), root (Rr) andmicrobial respiration (Rm):

Fc � �Pg þ Rl þ Rb þ Rr þ Rm: ð9Þ

The understory eddy covariance system measured the sumof root and microbial respiration, when the grass was dead:

Fu � Rr þ Rm: ð10Þ

Taking the difference between the two systems yields anapproximate measure of net canopy photosynthesis:

Ac � �Pg þ Rl þ Rb: ð11Þ

[35] The sign convention is with respect to the atmo-sphere, so losses of CO2 from the atmosphere reflect gainsby the ecosystem. Consequently, Ac has a negative value forcarbon uptake.

4. Results and Discussion

4.1. Switches: CO2 Concentration Profiles

[36] CO2 concentrations in the soil exhibited stronghorizontal and vertical gradients that varied with time

Figure 4. Comparison between CO2 effluxes measured with a portable chamber system and the fluxgradient method.


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(Figure 6). First we consider measurements in the opengrassland. Starting in spring we observed a rapid diminutionof CO2 as the soil dried, the grass senesced and microbialactivity was inhibited. Through the spring/summer dry-down (days 120 to 200), we observed a large and significantgradient in CO2 between 0.02 and 0.16 m. Then, CO2 at0.02 ranged between 10000 and 500 ppm and CO2 at 0.16 mranged between 20000 and 2000 ppm. During the driestperiod of the summer/autumn period (to day 290), CO2 wasabove 400 ppm at 0.02 m and increased in steps of about200 and 500 ppm down to depths of 0.08 and 0.16 m. Thecommencement of autumn rains caused an immediate jumpin soil CO2 concentration as porosity diminished andmicrobial activity resumed. During the wet winter, CO2

concentrations above 5000 ppm were common at depths of0.08 and 0.16 m.[37] CO2 levels under the tree were more than double

those measured in the open grassland, during the spring tosummer dry period. However, CO2 concentrations under thetree diminished linearly with time, as drought inhibitedphotosynthesis and soil respiration. The commencement ofwinter rains stimulated CO2 production in shallow layer bygrass and deeper layers by tree roots. In general, theobserved range and the seasonal dynamics of CO2 concen-trations in the soil were consistent with recent measure-ments and model simulations for forests [Jassal et al., 2004;Welsch and Hornberger, 2004].

4.2. Switches: CO2 Effluxes

[38] Switches in soil CO2 effluxes were observed andattributed to both spatial and temporal origins. A spatialswitch was associated with landscape class. Here, weobserved very large and distinct differences whether the

Figure 5. Upscaled comparison between soil CO2 efflux measured in the forest understory with theeddy covariance method and the soil CO2 probes. The contributions by the probes were weighted by theirfractional area (40% understory, 60% open).

Figure 6. Daily averaged CO2 concentrations in the soilover the course of the year. Data are shown for the wet anddry seasons and for locations in the open grassland andunder an active tree.


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effluxes were measured under a tree or in the open grassland(Figure 7). CO2 effluxes from the open grassland, whichprimarily consisted of heterotrophic respiration, remained ata low basal level, below 0.5 mmol m�2 s�1, throughout thedry period after the grass had died. In contrast, soil CO2

effluxes from under the tree were both very dynamic andalways larger than soil respiration rates measured in theopen space between trees. Effluxes under the tree exceeded5 mmol m�2 s�1 during the spring/summer transition whenphotosynthesis was most active (Figure 8). Afterward agradual reduction in CO2 efflux occurred as the summerdrought reduced photosynthesis. The commencement ofwinter rains (after day 300) caused another temporal switchin soil respiration under the tree and in the open grassland.This response reflects an enhancement in respiration by thenewly wetted microbes and the new grass growth in thesavanna understory. However, soil respiration rates under

the tree remained greater despite the cessation of photosyn-thesis by day 300 (Figure 8).[39] Significant diurnal patterns in soil CO2 efflux were

superimposed on the seasonal courses at both locations.They reflect changes in microbial respiration as soil temper-atures in the layers under investigation ranged over 15�C ona daily basis [Tang et al., 2005] and diurnal modulations inphotosynthesis. A pulse in soil CO2 efflux, near day 215,was associated with a rain event (Figure 7). A ten dayrelaxation period followed as the soil dried and as readilydigestible carbon sources were consumed. Detailed quanti-fication of the temporal dynamics associated with ecosys-tem respiration after rain pulses is described elsewhere [Xuet al., 2004] and will not be repeated here.[40] A question about temporal switches that remains

involves when root activity under the trees commenced?Did it occur during the dormant period (prior to leaf-out),simultaneously with the initiation of photosynthesis or afterleaf expansion? Photosynthetic activity commenced afterday 70 in year 2003 (Figure 8). Observations at this site, andacross the deciduous forest biome, indicate a close corre-spondence between the date when canopy photosynthesisinitiates and when soil temperature equals mean air temper-ature [Baldocchi et al., 2005].[41] A first-order attempt to examine the seasonal varia-

tion in autotrophic respiration is produced by comparingnocturnal eddy flux measurements in the savanna understoryand at the nearby open grassland (Figure 9). We assume thatnighttime respiration from the dry grassland representsheterotrophic respiration associated with the decompositionof organic matter. In comparison, respiration measured withthe understory eddy covariance system represents the sumof autotrophic and heterotrophic respiration from a grove oftrees in its flux footprint. This CO2 efflux is associated withlabile root exudates and more recalcitrant organic matter.During the wet winter, respiration from the grasslandexceeded that from the forest understory. This offset isexpected because greater grass growth occurred at the open

Figure 7. Comparison between measurements of soil CO2

efflux from under a physiologically active oak tree and froman open spot consisting of senescent grass.

Figure 8. Seasonal pattern in net canopy photosynthesisintegrated over a day by the oak woodland, as measured byan eddy covariance system over and under the woodland.

Figure 9. Comparison between nighttime respiration inthe understory of the oak savanna and at a nearby opengrassland. Measurements were made with the eddycovariance technique.


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site (Figure 1) and the trees were leafless and dormant.These results suggest that root respiration from the oak treesduring the dormant period was low. An acceleration inunderstory soil respiration occurred after day 80 about tendays after when leaf out occurred. However, respiration inthe understory of the oak woodland did not exceed that fromthe open grassland until after day 100. Thus we infer fromour data that a delay in autotrophic root respiration occurredabout three weeks after the initiation of photosynthesis inthe spring. This result suggests that the priority for newcarbohydrates went to leaves rather than roots. We alsoobserved that the estimate of autotrophic respiration peakedaround day 120, which led the date of peak of photosyn-thesis, which was near day 150. By day 300, when photo-synthetic activity was close to nil, the spatially integratedefflux from the dry grassland and the forest understory weresimilar.

4.3. Lags

[42] Having CO2 flux measurements with high temporalresolution enables us to reexamine the coupling of soilrespiration to diurnal variations in photosynthesis and to testthe hypotheses derived from the analyses of Hogberg et al.[2001] and Bowling et al. [2002] that detect day to weeklags between photosynthesis and respiration. Other note-worthy lag responses involve dark respiration of leaves andprevious days’ photosynthesis [Whitehead et al., 2004], butthese are not evaluated here.[43] We evaluated lag correlations between soil respira-

tion and canopy photosynthesis at hourly temporal resolu-tion using an inverse Fourier transform [Press et al., 1988].Our lag analysis was based on measurements of net canopyphotosynthesis (Ac) rather than gross canopy photosynthesisbecause we could measure Ac directly and because it isimpossible to evaluate hourly and daily values of grossphotosynthesis without modeling of the partitioning of

autotrophic and heterotrophic respiration. Because photo-synthesis was assigned a negative value, negative correla-tions are indicative of respiration increasing with morephotosynthesis.[44] We discovered that soil respiration in July was

best correlated, |r = 0.8|, with rates of photosynthesis thatwere lagged by ten 30-min averaging periods, or 5 hours(Figure 10). These data are consistent with the fact that ittakes a finite amount of time for pulses of carbohydrates toflow from the leaves, through the phloem and to the roots[Thompson and Holbrook, 2003]. Nevertheless, direct mod-eling and measurement studies will be needed to prove thisassertion.[45] Over the course of the summer growing season, the

field site experienced an alternating succession of air masseswith high and low vapor pressure deficits and air temper-ature; winds from the southwest were of marine origins andhad lower vapor pressure deficits [Zhong et al., 2004], whilethose coming from the north were continental in origin andwere hotter and drier. The pattern of synoptic meteorologymodulated canopy photosynthesis (Figure 11) and gave usthe opportunity to examine how repeated modulation inphotosynthesis altered soil respiration (Rs).[46] Using daily integrated data we observed that the

highest degree of correlation (r = j0.6j) between soilrespiration and canopy photosynthesis occurred with a zerolag (Figure 12). A second and less pronounced lag (r =j0.25j) was quantified with a time delay of 14 days. What isdeduced from Figure 12 is a correspondence between short(daily) and long (seasonal) trends in photosynthesis and soilrespiration. The peak correlation with zero lag is consistentwith the intraday lag found in Figure 10. The 14 day lagreflects effect of vapor pressure deficits on photosynthesis(Figure 11) and the low correlation coefficient reflects theeffect of diminishing rates of photosynthesis (Figure 8) and

Figure 10. Lag correlation between soil respiration andcanopy photosynthesis for July 2003 (days 182–212). Thenegative correlation states that an increase in soil respirationis associated with a more negative value in canopyphotosynthesis (we adopt an atmospheric framework wherea loss from the atmosphere is a gain by the ecosystem).

Figure 11. Correlation between canopy photosynthesisand daily averaged vapor pressure deficit (vpd). Data wereconfined to the period when photosynthesis was mostactive. The negative correlation states that an increase invpd is associated with a more negative value in canopyphotosynthesis (we adopt an atmospheric framework wherea loss from the atmosphere is a gain by the ecosystem.


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its lower power to modulate soil respiration, as the summerdrought proceeds.

5. Conclusions

[47] We have reported how soil respiration of an oak-grass savanna is affected by switches and lags in itsautotrophic and heterotrophic components. Such an assess-ment was possible by combining several continuous fluxmeasurement techniques (the eddy flux covariance and soilflux gradient measurements) across natural gradients in thelandscape.[48] Switches in soil respiration were associated with

spatial and temporal factors. A large switch in respirationoccurred during the summer as one transcended from anopen grasslands (where heterotrophic respiration was dom-inant) to the woodland understory (where rates were ele-vated because of the presence of roots and the feeding ofmicrobes by root exudates). A temporal switch in respira-tion occurred at the commencement in autumnal rains, withrapid germination of grass seed and new growth. Anothertemporal switch was associated with leaf out by the treesand the triggering of renewed root activity.[49] Pulses in soil respiration were associated with infre-

quent and isolated summer rain storms. These events onlystimulated microbial respiration, since rainfall only wettedthe upper soil surface and the annual grasses were senesced.Furthermore, summer rains were typically not sufficient toreplenish soil moisture in the root zone to stimulate phys-iological activity by the trees.[50] Lags were associated between variations in photo-

synthetic activity and soil respiration. The lags were ob-served on hourly scales because of the slow translocation ofphotosynthate to the roots and associated microbes, and ondaily scales because of modulations in photosynthesis bythe passage of dry and humid air masses.[51] The quantification of nonlinear and hysteretic

responses, associated with physiological activity of treesprovide a foundation for developing a new generation of soilrespiration models that are coupled with well formulated

models of photosynthesis [Farquhar et al., 1980] andphoelem transport [Thompson and Holbrook, 2003] andthe soil environment. Such data sets may also be used totest new dynamic theories such as the threshold delay model[Ogle and Reynolds, 2004].[52] Finally, we suggest additional studies that may stem

from this research. There is a need to examine the seasonalvariation in the depth of maximal CO2 production (S(z) =@F@z ). This can be accomplished with a denser and deepernetwork of soil CO2 sensors. Direct and continuous meas-urements of respired 13CO2 with new tunable diode laserspectrometers will provide additional information whetherphotosynthate translocation is stimulating soil respirationdirectly, or indirectly by supplying carbohydrates tomicrobes.

[53] Acknowledgments. This research was supported in part by theOffice of Science (BER), U.S. Department of Energy, grant DE-FG02-03ER63638, and through the Western Regional Center of the NationalInstitute for Global Environmental Change under cooperative agreementDE-FC02-03ER63613. Other sources of support included the Kearney SoilScience Foundation, the National Science Foundation, and the CalifornianAgricultural Experiment Station. These sites are members of the AmeriFluxand Fluxnet networks. We thank Ted Hehn for technical assistance duringthis experiment and to Siyan Ma for assistance in reprocessing the soilmoisture data and for reviewing this manuscript. We thank Qi Chen forprocessing the LIDAR data on canopy structure. We are grateful to RussellTonzi and Fran Vaira for use of their ranches for this research.

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��D. Baldocchi, Ecosystem Sciences Division, Department of Environ-

mental Science, Policy and Management, University of California,Berkeley, CA 94720, USA. ([email protected])J. Tang, Department of Forest Resources University of Minnesota, 1530

Cleveland Ave. N, St. Paul, MN 55108, USA.L. Xu, LICOR, 4421 Superior St., Lincoln, NE 68504-0425, USA.


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