does the temperature sensitivity of decomposition … the temperature sensitivity of decomposition...

Does the temperature sensitivity of decomposition of soilorganic matter depend upon water content, soil horizon,or incubation time?

M A R K U S R E I C H S T E I N 1 , J E N S - A R N E S U B K E 2 , A N D R E W C . A N G E L I 3 and

J O H N D . T E N H U N E N 1

Department of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany

Abstract

Several studies have shown multiple confounding factors influencing soil respiration in

the field, which often hampers a correct separation and interpretation of the different

environmental effects on respiration. Here, we present a controlled laboratory

experiment on undisturbed organic and mineral soil cores separating the effects of

temperature, drying–rewetting and decomposition dynamics on soil respiration.

Specifically, we address the following questions:

(1) Is the temperature sensitivity of soil respiration (Q10) dependent on soil moisture or

soil organic matter age (incubation time) and does it differ for organic and mineral

soil as suggested by recent field studies.

(2) How much do organic and mineral soil layers contribute to total soil respiration?

(3) Is there potential to improve soil flux models of soil introducing a multilayer source

model for soil respiration?

Eight organic soil and eight mineral soil cores were taken from a Norway spruce (Piceaabies) stand in southern Germany, and incubated for 90 days in a climate chamber with a

diurnal temperature regime between 7 and 23 1C. Half of the samples were rewetted

daily, while the other half were left to dry and rewetted thereafter. Soil respiration was

measured with a continuously operating open dynamic soil respiration chamber system.

The Q10 was stable at around 2.7, independent of soil horizon and incubation time,

decreasing only slightly when the soil dried. We suggest that recent findings of the Q10

dependency on several factors are emergent properties at the ecosystem level, that

should be analysed further e.g. with regard to rhizosphere effects. Most of the soil CO2

efflux was released from the organic samples. Initially, it averaged 4.0 lmol m�2 s�1 and

declined to 1.8 lmol m�2 s�1 at the end of the experiment. In terms of the third question,

we show that models using only one temperature as predictor of soil respiration fail to

explain more than 80% of the diurnal variability, are biased with a hysteresis effect, and

slightly underestimate the temperature sensitivity of respiration. In contrast, consis-

tently more than 95% of the diurnal variability is explained by a dual-source model,

with one CO2 source related to the surface temperature and another CO2 source related to

the central temperature, highlighting the role of soil surface processes for ecosystem

carbon balances.

Keywords: drying–rewetting, dual-source model, incubation experiment, Q10 , soil moisture, soil

respiration

Received 22 July 2004; received in revised form 4 March 2005; accepted 9 March 2005

1Present address: Department of Forest Environment and Resources, University of Tuscia, Via S. Camillo de Lellis 01100 Viterbo, Italy and

Potsdam Institute of Climate Impact Research, Telegrafenberg C4, D-14473 Potsdam, Germany.

Correspondence: Markus Reichstein, [email protected] and Jens-Arne Subke, e-mail: [email protected]

2Present address: Stockholm Environment Institute at York, Department of Biology, University of York, York, YO10 5DD, UK.3Present address: University of Wisconsin-Madison, Madison, WI 53706, USA.

Global Change Biology (2005) 11, 1754–1767, doi: 10.1111/j.1365-2486.2005.01010.x

1754 r 2005 Blackwell Publishing Ltd

Introduction

Considering that terrestrial ecosystems take up about

one-third of the CO2 emissions from anthropogenic

fossil fuel burning and cement manufacturing (Schimel

et al., 2001), it is critical to improve our knowledge and

understanding of the carbon exchange between terres-

trial ecosystems and the atmosphere. At 68–80 Pg C yr�1,

soil respiration represents the second largest global

carbon flux between ecosystems and the atmosphere

(Raich & Schlesinger, 1992; Raich & Potter, 1995; Raich

et al., 2002). This amount is more than 10 times the

current rate of fossil fuel combustion and indicates that

each year around 10% of the atmosphere’s CO2 cycles

through the soil. Thus, even a small change in soil

respiration could significantly intensify – or mitigate –

current atmospheric increases of CO2, with potential

feedbacks to climate change. Despite this global

significance as well as considerable scientific commit-

ment to its study over the last decades, there is still

only limited understanding of the factors controll-

ing temporal and interecosystem variability of soil

respiration.

It is clear that the most important factors influencing

soil respiration are soil temperature, soil water avail-

ability, and substrate quality and availability, the latter

being influenced by supply strength through vegetation

(photosynthate transport, root growth and exudation).

There are, however, ambiguous results showing the

extent to which soil respiration factors interact. This

lack of understanding partly stems from the fact that

many studies were performed under uncontrolled field

conditions where several co-varying factors are difficult

to isolate. Controlled laboratory experiments, on the

other hand, have often been performed on disturbed

soil samples (mixed and/or sieved, roots removed),

substantially altering the environment such that a

transfer of results to the ecosystem level seems dubious

(e.g. Winkler et al., 1996; Lomander et al., 1998; Reich-

stein et al., 2000). Moreover, soils are often incubated at

different and constant temperatures, which introduces

the confounding effect that with time substrate avail-

ability (and potentially microbial community) changes

among samples, as labile pool sizes decline more

rapidly in the higher temperature treatment (see

Reichstein et al., 2000 for a detailed discussion). For

example, Fang & Moncrieff (2001) form an exception in

that they tested the temperature dependence on

undisturbed soil samples, and exposed all samples to

identical temperature regimes. However, their investi-

gation into interdependencies with soil moisture only

considered broad moisture classes with ‘relatively dry’

as the driest of three categories, and accordingly no

moisture effects were found.

In the context of global warming, it is particularly

important to understand the temperature sensitivity of

soil respiration, as it is anticipated that in a warmer

world ecosystems provide a positive feedback to the

greenhouse effect because of the stronger response of

respiratory processes to temperature, compared with

assimilatory processes, which are easily limited by light

and nutrient supply (e.g. Kirschbaum, 2000). Recently

developed coupled climate-vegetation models predict

that the current biospheric CO2 sink will cease during

this century, partly because of enhanced respiration in a

warmer climate (Cox et al., 2000), assuming an

exponential relationship between temperature and

respiratory processes with a doubling per 10 1C (Q10

relationship). On the contrary, a number of field studies

at varying scales have cast some doubt on the

correctness of such an invariable Q10 relationship. For

instance, Giardina & Ryan (2000) did not find a clear

trend of turnover rates with mean annual temperature,

Liski et al. (1999) observed decomposition rates of old

organic matter to be temperature insensitive, and a

number of studies found a varying temperature

sensitivity of soil and ecosystem respiration (Q10

between 1 and 4), depending on the soil water

availability (e.g. Carlyle & Ba Than, 1988; Xu & Qi,

2001; Reichstein et al., 2002, 2003).

To better understand this critical issue, we continu-

ously observed CO2 efflux in a controlled experiment

from minimally disturbed forest soil monoliths. We

specifically addressed the following questions concern-

ing the temperature sensitivity of soil respiration. (1) Is

the temperature sensitivity of soil respiration (Q10)

dependent on soil moisture, drying/rewetting events,

or soil organic matter (SOM) age? (2) How much do

organic and mineral soil layers contribute to total

soil respiration and do their temperature sensitivities

differ? (3) How do estimates of the temperature

sensitivity change when fluxes from two layers are

accounted for compared with single-layer models?

Materials and methods

Soil sampling

Intact soil monoliths, each from the organic and the

upper mineral horizon, were sampled randomly in a

mature spruce forest (‘Weidenbrunnen’, Picea abies; 114

years old) in the Fichtelgebirge, a small mountain range

in SE Germany in April 2001. The local soil type is a

cambic podzol over granitic bedrock with low pH

values in the mineral (2.9–4.3) and organic (2.6–3.6)

layers. The average depth of the organic layer (includ-

ing surface litter) was 11 � 1 cm, and forest ground

vegetation at the sampling locations was dominated by

T E M P E R AT U R E S E N S I T I V I T Y O F D E C O M P O S I T I O N 1755

r 2005 Blackwell Publishing Ltd, Global Change Biology, 11, 1754–1767

the two grass species Deschampsia flexuosa and Calama-

grostis villosa. The main properties of the soil horizons

are described in Table 1. Field measurements of soil

CO2 efflux have been carried out during the growing

season in 1999. A more complete site description, as

well as, analytical results and flux observations are

reported in Subke et al. (2003).

The sampling took place 2 days after a period of

considerable rainfall, implying that the soils were near

field capacity. Cylindrical soil cores were taken of the

organic layer using a custom built steel cutter of 30 cm

diameter. After inserting the cutter between 15 and

20 cm into the forest floor, the monolith was carefully

retrieved from the cutter (having removed remaining

mineral soil from the base of the sample), and

transferred to custom-built polyvinyl chloride (PVC)

containers. Mineral soil samples were taken after

removing the organic horizons by inserting the cutter

into the mineral soil to a similar depth to the organic

soil samples in close proximity to the location where the

organic layer sample was taken. The strong build of the

cutter allowed it to be forced into the soil using a

sledgehammer. It was, therefore, readily possible to cut

through roots of up to approximately 2 cm in diameter,

while sampling attempts were aborted if larger roots

were encountered, as it would have resulted in

considerable sample disturbance to force the cutter

under these conditions. Mean sample volumes of both

mineral and organic soil were 8.1 � 1.6 dm3 with no

significant difference between mean volumes of the two

soil layers. The properties of the soil samples harvested

after the incubation are shown in Table 1.

The cylindrical sample containers were built from

large PVC pipes (30 cm diameter, 1 cm wall thickness),

and measured 25 cm in height (Fig. 1a). The base of the

containers was formed by a 1 cm PVC disk, which was

perforated with about 20 holes of 1 cm diameter

allowing excess water to drain during the course of

Table 1 Description of the soil profile and the soil samples

Object

Thickness

(cm)

Total organic

C (g kg�1)

C/N ratio

(g g�1)

Texture

(sand/silt/clay)

(g 100 g�1)

Bulk density

(g cm�3)

pH

(CaCl2)

Soil horizons*

L 1 478 24.8 nd nd 3.6

Of 5 372 20.7 nd nd 2.9

Oh 7 376 22.6 nd nd 2.6

Ahe 10 38.9 22.9 52/38/10 0.97 2.9

Bh 2 90.5 22.6 34/50/16 nd 3.3

Bhs 18 53.6 14.1 45/45/10 0.73 3.9

Bv-Cv 25 8.4 16.8 46/43/11 1.36 4.3

Incubated samplesw

Organic

0–2 cm 2 488 (34) 27.7 nd 0.11 (0.02) nd

2–6 cm 4 381 (23) 22.3 nd 0.41 (0.12) nd

6–12 cm 6 371 (21) 21.7 nd 0.52 (0.11) nd

Mineral 12 45.2 (4.2) 22.6 nd 0.92 (0.21) nd

Values in parentheses represent one standard deviation (n 5 8).

*(Subke et al., 2004a), (Kalbitz, 2004, neighbouring stand with same soil type).wThis study.

nd, not determined.

25 cm

30 cm

Sample container (PVC)

Chamber lid adapter (PVC)

Sample airReference air

Ambient air inlet

Chamber lid (Perspex)

to IRGA

Fig. 1 Cross section and dimensions of soil sample containers.

The base of the containers was perforated and covered with a

1 mm mesh gauze to allow water drainage. The containers were

placed on PVC saucers to ensure a gas seal around the base. All

containers were fitted with adapters to accommodate the

difference in size between the container and the Perspex

chamber lids (20 cm diameter).

1756 M . R E I C H S T E I N et al.


the experiment, preventing water logging. The perfo-

rated base was covered with 1 mm mesh nylon gauze to

contain soil samples. The container base was raised by

approximately 3 cm above the lower end of the cylinder

to provide drainage space for excess water, and

containers were placed in PVC saucers in the lab to

create a gas seal.

Laboratory set-up, soil moisture and temperaturetreatments

After about 4 h of sampling in the field, the soil cores

were immediately taken to a climate chamber (1.5 h

transport). The eight samples of each layer were

randomly split into two different water treatments,

one with a constant moisture treatment and one with a

drying–rewetting treatment, resulting in four types of

soil cores with four replicates each: Organic soil/

constant moisture (OC), organic soil/dry (OD), mineral

soil/constant moisture (MC), and mineral soil, dry

(MD). For conciseness, the ‘constant moisture’ and the

‘drying–rewetting’ treatment are called wet and dry

treatment, respectively. The samples were set up within

the climate chamber in a randomized design to

eliminate possible (undetected) gradients in tempera-

ture or air circulation within the chamber. Above-

ground plant parts of the ground vegetation were

removed at the beginning of the experiment, as was any

regrowth during the course of the experiment.

All soil samples were weighed upon arrival in the

lab, and the recorded mass was considered as reference

conditions for soil moisture, under which no limitations

of soil CO2 production exist. Over an initial period of 10

days, the mass of all monoliths was recorded daily.

Based on the loss of mass in 24 h, water was added to

each of the samples in the wet treatments by spraying

the given amount of water onto the surface of each

sample. After this initial period of 10 days, the wet

treatments were sprayed with water every day accord-

ing to the average loss previously experienced and the

mass of all containers was controlled every 3–4 days.

The samples of the dry treatments were watered to

initial water status within 24 h towards the end of the

experiment (OD samples on day 76, MD samples on

day 86). At the end of the experiment, the dry weight

and the volume of the samples was determined, so that

volumetric soil moisture could be calculated from the

recorded masses. To allow for comparison between the

different soil horizons, these water contents were

converted into soil water matric potential using Van

Genuchten parameters (Van Genuchten, 1980). The

parameters were optimized to describe the observed

field capacity and permanent wilting point of the

retention curve well and were previously used success-

fully to model the soil water dynamics at the site (Subke

et al., 2003).

Air temperature within the climate chamber cycled

between 7 and 23 1C every day, while the dew point

within each chamber was kept constant at 4 1C.

Following a ‘cold’ period, the temperature was set to

23 1C for 6 h, before moderation to 20 1C for another 6 h.

Similarly, temperatures were reduced drastically to 7 1C

following the warm period, before moderation to 10 1C.

As one part of this study involved the analysis of the

soil respiration dynamics in relation to vertical tem-

perature gradients (with the dual-source model) we

executed a prior experiment to understand if the

temperature conduction in our set up was mainly in

the vertical direction as in field conditions. Here, we

installed each five temperature sensors 2 cm below the

surface, at the centre and 2 cm above the bottom of the

soil core in a radial symmetric manner in the form of a

cross (each one sensor at the center and four sensors at

2 cm from the container wall). This prior experiment

showed that radial temperature gradients were negli-

gible, an order of magnitude lower than vertical

gradients (Fig. 2). We explain this quasiradial insulation

of the soil core as a combination of an insulation effect

of the containers, and much stronger convective heat

transport at the surface of the core because of

continually circulating air in the headspace of the open

dynamic chamber and a self-insulation effect, as the

containers were placed densely next to each other. The

results show further that changes in temperature are

attenuated within the first few centimetres of a

monolith. Therefore, we regard the central temperature

as a reasonable approximation of the bulk monolith

temperature, while the temperature of the surface layer

−10−8−6−4−202468

10

0 5 10 15 20 25 30Time (h)

Radial difference: 2 cm–central

Vertical difference: 2 cm–central

Vertical difference: surface–central

Tem

pera

ture

(°C

)

Fig. 2 Analysis of vertical and radial temperature differences

in an organic soil core during the pre-experiment. Time series of

temperature differences between the central temperature and

the surface temperature as well as between central temperature

and the average of temperatures 2 cm below the surface or 2 cm

inside the container (at the depth of the central temperature),

respectively, are shown.



is best described by direct measurements. For the CO2

efflux experiments, temperature probes were hence

installed at the centre of each of the soil monoliths, and

each of the five chamber lids used for soil CO2 efflux

also carried a temperature probe that made contact

with the soil surface of the monolith where efflux

measurements were conducted.

CO2 efflux measurements

A soil CO2 efflux system designed for continuous

measurements from soil chambers in the field (Subke

et al., 2003) was used to monitor CO2 evolution from the

monoliths. A simple PVC collar allowed the field

chamber lids to fit onto the monolith container ensuring

air-tight seals at all connecting points (Fig. 1). The

measuring system allows sequential CO2 flux measure-

ments from five collars and an additional intake for

zero calibration, with sampling intervals set to 5 min

between collars, so that two readings were obtained per

collar per hour. The lids were moved between soil

containers every day at about the same time (between

13:00 and 14:00 hours), in a way that at any time, each

soil treatment was included in the measuring cycle.

Upon restoring the initial water status of the samples in

the dry treatments, the allocation of lids was altered

with four lids monitoring only rewatered samples, for

10 days, while one lid continued to rotate between

other treatments.

Field measurements

Parallel to the lab incubation (May–June), we measured

soil CO2 efflux in the field at the same forest stand

where soil samples had been taken. The field set-up

was the same as described in detail for measurements

in 1999 in the same forest stand (Subke et al., 2003).

Briefly, 15 soil collars were installed in five groups of

three collars. These five groups were located within the

same part of the forest stand where samples had been

taken, but a minimum distance of 2 m was kept

between sampling sites and collar locations. Soil CO2

efflux was measured sequentially from one of five

chamber lids which were placed on one collar in each

group, forming an open dynamic soil CO2 efflux

chamber. The automated set-up produced hourly efflux

measurements from each of the lids. Lids were moved

between collars in each group every 2–3 days to prevent

measuring artefacts from prolonged measuring peri-

ods. Hourly averages of measurements from all collars

were used to estimate the spatial mean of the stand soil

CO2 efflux.

Data analysis

Each diurnal cycle of soil CO2 efflux was analysed by

two different models relating CO2 efflux to soil temper-

ature. Model I was the commonly applied approach

where soil respiration is modelled as a function of soil

temperature at a certain location in the soil:

Rsoil ¼ Rref QðTsoil�TrefÞ=10�C10 : ð1Þ

Rsoil is the instantaneous soil CO2 efflux (mmol m�2 s�1),

Tsoil is the soil temperature ( 1C) at the centre of the soil

core, Rref is the reference efflux (mmol m�2 s�1) at

Tref 5 15 1C, and Q10 the parameter that determines

the temperature response of the soil CO2 efflux (i.e. the

factor by which efflux increases for an increase in

temperature by 10 1C). We also analysed the data with

the model by Lloyd & Taylor (1994), but results were

indistinguishable within the temperature range of this

study, in agreement with (Katterer et al., 1998). The

Lloyd-and-Taylor and the Q10 models only differ

substantially, when larger temperature ranges are

considered. We present here the results from the Q10

model for the intuitiveness of the Q10 parameter and

because a majority of ecosystem models are (still) using

this function (cf. reviews by Rodrigo et al., 1997; Cramer

et al., 2001). For a given temperature, the Q10 and E0 (the

temperature sensitivity parameter of the Lloyd-and-

Taylor model) can be converted into each other (see

Reichstein et al. (2003) for details).

While still very simplified, model II partly accounts

for the fact that the soil temperature distribution cannot

be represented by a single temperature measurement. It

is assumed that the total soil respiration can be

attributed to two major sources, a surface layer and

the bulk soil (Fig. 2), expressed as:

Rsoil ¼ Rsurf;ref QðTsurf�TrefÞ=10�C10

þ Rcent;ref QðTcent�TrefÞ=10�C10 ; ð2Þ

where symbols are as in the previous equation, but the

subscripts ‘surf’ and ‘cent’ mean ‘of the surface

compartment’ and ‘of the bulk soil compartment’,

characterized by the temperature at the soil surface

and at the centre of soil core, respectively. For clarity,

Tcent in model II and Tsoil in model I, are physically the

same; we only differentiate for conceptual reasons.

Mathematically, model II represents a rough discre-

tization of the volume integral

RsoilðtÞ ¼1

A

Z Z Z

x;y;z

Rðx; y; z; tÞdx dy dz; ð3Þ

where R(x, y, z, t) is the respiration density (per volume;

mmol m�3 s�1) at the specified location and time, and A

is the area over which the flux is observed, neglecting

physicochemical processes (diffusion, solution, etc.).



Apart from the discretization another simplification of

the model formulation is that the Q10 is assumed to be

the same in both soil compartments. This model

described the data equally as well as more complex

formulations (e.g. with different Q10 by compartment,

and/or more layers), which were found to be over-

parameterized (high parameter correlations, unidentifi-

able parameters). In the following, models I and II will

be referred to as ‘single-source’ and ‘dual-source’

models, respectively.

The model parameters were estimated by a common

nonlinear regression algorithm using the least-sum-of-

residual-squares criterion (Levenberg–Marquardt algo-

rithm, implemented in the PV-WAVE 7.0 advantage

package, function nlinlsq, Visual Numerics Inc., 2001).

Standard errors of parameter estimates were calculated

according to Draper & Smith (1981), using standard

assumptions (e.g. normality and independence of the

residuals).

Results

Soil temperature and moisture

While the surface temperature responded fastest to

changes in air temperature, the temperature at the

centre of a sample never reached the value of the air

temperature, even at the end of respective ‘cold’ and

‘warm’ periods (Fig. 3). Soil water availability de-

creased in all samples of the dry treatment, as is shown

by the decrease in matric potential throughout the

experiment (Fig. 4). As water was primarily lost via

passive evaporation (no water extraction by transpira-

tion) the samples did not dry down near the permanent

wilting point but rather to matric potentials around

�1000 to �2000 hPa, even after 80 days in relatively dry

air (dew point 4 1C).

Soil CO2 efflux in relation to temperature

Soil CO2 efflux was positively related to central

soil temperature, but with a clear hysteresis effect

(Fig. 5a–c), (i.e. at the same central soil temperature the

observed soil efflux is higher during the warming

phase than during the cooling phase). This occurs

because the central temperature lags behind the

temperature at a more superficial layer (cf. Fig. 3).

Consequently, the single-source model typically leaves

10–30% of the efflux variance unexplained (Fig. 5d). In

contrast, the dual-source model, which used Tsurf and

Tcent as predictors, consistently explained more than

95% of the variance in the organic samples leaving

less than 5% unexplained (cf. also Table 2). Also in

the mineral soils the dual-source model worked

−3−2−10123456

133 134 135 136 137 138 139 140 141Julian day

Rso

il (µ

mol

m−2

s−1

)

5

10

15

20

25

30

35

Tem

pera

ture

(°C

)

RsoilTsurfTcent

Fig. 3 Time series for soil CO2 efflux (open circles, left axis), as

well as surface and central temperature (diamonds and crosses,

right axis) of one of the organic wet samples.

−2500

−2000

−1500

−1000

−500

0

100 120 140 160 180 200 220 240Julian day

So

il w

ater

mat

ric

po

ten

tial

(h

Pa)

MC

OC

MD

OD

Fig. 4 Average soil water matric potential during the drying–rewetting experiment for each treatment. Error bars indicate one standard

deviation. OC, OD, organic constantly wet, organic drying/rewetting; MC, MD, mineral constantly wet, mineral drying/rewetting.



5 10 15 20 250

2

4

Rso

il (µ

mo

l m−2

s−1

)

Rso

il (µ

mo

l m−2

s−1

)

Rso

il (µ

mo

l m−2

s−1

)

25 120 140 160 180 200Julian day

Dual-source modelSingle-source model

Tsurf (°C)Tcent (°C) 5

1015

202525

2015

105

4

2

6

50

2

4

6

0.5

0.6

0.7

0.8

0.9

1

r2

10

8

6

(a) (c)

(b)

(d)

20Tsurf (°C)

Tcent (°C)

10 15

Fig. 5 Scatter plots of soil CO2 efflux vs. (a) central soil temperature and (b) soil surface temperature for the measurement cycles of

Fig. 3. Labels indicate the integer hour of the respective day. (c) Soil CO2 efflux as a function of surface and central temperature. The

mesh shows the prediction from the dual-source model (fitted to these 3 days of data), data points (label as in a, b) are observed

data (Rsoil, ref 5 1.22 � 0.05 mmol m�2 s�1; Rcent, ref 5 1.03 � 0.04 mmol m�2 s�1; Q10 5 2.72 � 0.06; r2 5 0.96, RMSE 5 0.25). Arrows in a–c

underline the trajectory with time showing the hysteresis effect. (d) Time series of coefficients of determination obtained with the dual-

and the single-source model fitted to each diurnal cycle for one replicate of the organic wet treatment (OC). RMSE, root mean

square error.

Table 2 Average coefficient of determination and NRMSE when modelling the diurnal courses of soil chamber CO2 efflux using

the single- or dual-source model for each treatment at the beginning and at the end of the experiment as well after the rewetting

OC OD MC MD Mean

Single Dual Single Dual Single Dual Single Dual Single Dual

Coefficient of determination (r2)

Begin 0.65 0.97 0.80 0.96 0.51 0.77 0.43 0.63 0.60 0.83

End 0.83 0.98 0.84 0.98 0.73 0.91 0.29 0.66 0.67 0.88

Rewet na na 0.76 0.88 na na 0.67 0.83 0.71 0.85

NRMSE

Begin 0.124 0.048 0.070 0.030 0.124 0.082 0.121 0.070 0.110 0.057

End 0.051 0.017 0.062 0.031 0.079 0.040 0.106 0.064 0.074 0.038

Rewet na na 0.088 0.079 na na 0.195 0.032 0.142 0.055

OC, OD: organic constantly wet, organic drying/rewetting; MC, MD: mineral constantly wet, mineral drying/rewetting; NRMSE,

normalized root mean squared error. The column ‘mean’ contains the mean quantity over all treatments.



considerably better explaining around 20% more of the

variance than the single-source model (Table 2). In both

soil types, the dual-source model also roughly halved

the model error compared with the single-source model

to below 10% in the mineral soils and to below 5% in

the organic soils (except after rewetting).

Standardized respiration rates (Rref)

The time courses of the soil CO2 efflux standardized to

15 1C (Rref), that were yielded from the regression

analysis performed for each diurnal cycle are depicted

in Fig. 6. Soil CO2 efflux decreased throughout the

experiment for all treatments. The decrease in the wet

treatments indicates the effect of incubation time

during which substrate for decomposition diminishes.

Initial rates of CO2 efflux were around 3.5–

4.5mmol m�2 s�1 for organic soil, and 1–2 mmol m�2 s�1

for mineral soil. Soil CO2 efflux rates for all treatments

decreased rapidly during the first week, but subse-

quently declined at a slower rate. This behaviour is

indicative of labile pool depletion, and was particularly

pronounced in the mineral soil. Consequently, the

proportion mineral Rref to organic Rref declined rapidly

and stabilized at approximately 20% after 2 weeks of

incubation.

A difference in efflux rates between wet and dry

treatments developed during the first 25 days of the

experiment, but did not increase (in absolute terms)

after this period, although soil moisture continued to

decline. The rapid rewetting of the dry samples at the

end of the experiment led to an immediate increase in

soil CO2 efflux, which was initially higher than that of

the corresponding wet samples, but lower than the

MC

MD

0

1

2

3

4

5

120 140 160 180 200 220 240Julian day

Rre

f (µm

ol m

−2 s

−1)

Rre

f (µm

ol m

−2 s

−1)

0

1

2

3

4

5

120 140 160 180 200 220 240Julian day

OCOD

Organic layer

Mineral layer

Beg End Rewet

Beg End Rewet

(a)

(b)

Fig. 6 Time series of Rref as fitted to each diurnal cycle in the

laboratory incubation, for the organic layer (a) and mineral layer

(b) soil cores. Filled symbols denote the constantly wet

treatment; open symbols the drying–rewetting treatment. For

clarity, estimation errors are summarized as � 1 average

standard error of estimate for each treatment (OC, OD: organic

constantly wet, organic drying/rewetting; MC, MD: mineral

constantly wet, mineral drying/rewetting). Rref is derived from

the dual-source model as Rref 5 Rsurf,ref 1 Rcent,ref (cf. Eqn (2)).

All, the incubation time effect, the drying–rewetting effect and

the effect of the soil horizon are significant.

0

1

2

3

4

5

Rre

f (µm

ol m

−2 s

−1)

"Surface"

Organic

Mineral0

1

2

3

4

5

FieldBeg End Beg End Rewet

Const. wet Drying–rewetting

(a) (b)

Fig. 7 (a) Partitioning of fluxes at reference temperature (Rref; 15 1C) between three different compartments in the constantly wet

treatment (left) and the drying–rewetting treatment (right) at the beginning (Beg), end (End) of the drying period and after the rewetting

(Rewet; see shaded areas in Fig. 6). (b) Rref estimate from parallel field measurements. The difference in respiration between the two

treatments at the beginning of the incubation is because of the fact that the soil respiration was already reduced in the dry treatment over

the averaging period. Error bars denote standard errors.



efflux observed in the wet samples at the beginning of

the treatment.

The dual-source model allowed us to tentatively

separate the organic layer into a ‘surface’ layer and a

‘bulk organic’ layer, that could be added to fluxes

measured on the mineral soil monoliths, resulting in

three compartments (Fig. 7). At the beginning of the

experiment, the Rref summed up for the three compart-

ments in the wet treatment compared favourably to the

respective Rref estimate from the field measurements,

both at around 4 mmol m�2 s�1, with overlapping con-

fidence intervals. However, after the 60–80 days of

incubation, the total soil CO2 efflux was nearly halved,

and the relative contribution of the surface compart-

ment declined slightly (from 36% to 29%), while the

bulk organic compartments relative contribution in-

creased (from 45% to 55%).

Total CO2 efflux in the dry treatment was already

reduced within the first 10 days of incubation, and by

the end of the dry period total CO2 efflux was reduced

to about one-third of initial (wet) values. The surface

compartment experienced a more than proportional

decrease over this period, and eventually only con-

stituted of 0.25mmol m�2 s�1, or 20% of the total efflux.

The difference between the two columns labelled ‘End’

in Fig. 7 document the effect of soil drying alone. Soil

drying reduced the surface flux in absolute, but also

relative terms. The reduction in flux from the bulk

organic compartment is less severe, resulting in a larger

relative contribution of 70% in the dry treatment,

compared with 55% in the wet treatment. Rewetting

approximately doubled the total flux, reaching slightly

higher rates than the wet treatment suggesting a small

rewetting effect. Here the surface compartment re-

sponded most strongly, raising its relative contribution

from 20% to 36%.

Temperature sensitivity (Q10)

While the overall level of the soil CO2 efflux (expressed

as Rref) exhibited clear treatment effects (incubation

time effect, drying effect, mineral vs. organic soil effect),

the temperature sensitivity (expressed as Q10) was

nearly invariant, regardless of the treatment (Fig. 8).

Because of overall higher fluxes, the Q10 of organic

samples could be determined with less uncertainty,

fluctuating randomly between 2.5 and 3.0, while

mineral sample Q10 values fluctuated around a similar

mean but with higher variance. For both organic and

mineral soils, no pure incubation time effect on the Q10

of soil CO2 efflux could be detected. For both soils, a

minimal but significant (organic soils: P 5 0.049, miner-

al soils: P 5 0.006, t-test) effect of the dry treatment is

visible, resulting in slightly lower Q10s in the dry

treatment (Fig. 9). At the end of the dry period, the Q10

seems to drop more substantially, and significantly

(Po0.05) recovers after the rewetting. However, those

changes remain within 0.4 Q10 units. The situation

is summarized and compared with field estimates in

Fig. 9. The dual-source model consistently estimates

slightly higher Q10 values than the single-source model

(non-hatched vs. hatched bars). The Q10 value for the

field was derived from the same regression model and

parameters as described for Rref, and calculated as the

increase in CO2 efflux for a temperature change from

10 1C to 20 1C to allow the comparison with the lab data.

The Q10 estimates from the lab compare well with the

field estimate of Q10 that was derived with a single-

source model.

Discussion

A number of field and laboratory studies analysing the

temperature dependence of soil respiration suffered

1

2

3

4

120 140 160 180 200 220 240Julian day

Q10

Q10

1

2

3

4

120 140 160 180 200 220 240Julian day

OC

OD

MC

MD

(a)

(b)

Fig. 8 Time series of Q10 of soil CO2 efflux as fitted to each

diurnal cycle in the laboratory incubation, for the organic layer

(a) and mineral layer (b) soil cores. Filled symbols denote the

constantly wet treatment; open symbols the drying–rewetting

treatment. For clarity, estimation errors are summarized as � 1

average standard error of estimate for each treatment (OC, OD,

organic constantly wet, organic drying–rewetting; MC, MD,

mineral constantly wet, mineral drying–rewetting).



from the problem that the respiration response to

temperature is easily confounded by other factors

(Davidson et al., 1998, 2000; Russell & Voroney, 1998;

Koizumi et al., 1999; Moren & Lindroth, 2000; Savage

& Davidson, 2001; Reichstein et al., 2002). This is

most evident under uncontrolled field conditions,

where soil water availability, radiation, vegetation

production, and other factors co-vary with temper-

ature. This problem has most recently been shown by

Baath & Wallander (2003) who demonstrated that the

results by Boone et al. (1998) – a higher temperature

sensitivity of root compared the soil microbial respira-

tion – were partly caused by interaction with vegetation

activity.

In order to avoid confounding effects, laboratory

experiments need to be carefully designed, as relative

substrate availability changes during the experiment in

different temperature treatments as discussed in detail

by Reichstein et al. (2000). This problem can be

circumvented by using a decomposition model where

the rate constants are temperature dependent (e.g.

Katterer et al., 1998). However, decomposition models

already contain a number of assumptions, which one

might want to test independently. For instance, the

temperature sensitivity of decomposition is usually

assumed to be independent of SOM quality. This latter

assumption of invariant temperature sensitivity has

been criticized from very different perspectives. While

Liski et al. (1999) empirically estimated that deep SOM

(old low stabile carbon) decomposition is independent

of soil temperature, Bosatta & Agren (1999) argued that

the temperature sensitivity should theoretically in-

crease with decreasing carbon quality because the

activation energy increases. It has also been found that

the Q10 of soil and ecosystem respiration empirically

decreases with soil water deficit (Carlyle & Ba Than,

1988; Kutsch & Kappen, 1997; Reichstein et al., 2002).

Our laboratory experiment allowed these problems to

be studied on minimally disturbed soil cores under

controlled conditions without confounding effects by

varying the temperature at a much higher frequency

than other factors change, while continuously obser-

ving the soil CO2 efflux.

The observed decrease in soil respiration rate with

increasing incubation time is in accordance with earlier

studies, indicating a depletion of available substrate

(Witkamp, 1966; O’connell, 1990; Cotrufo et al., 1995;

Bottner et al., 1998; Bottner et al., 2000). Initial respira-

tion rates were very similar to those in the field, which

supports the fact that the internal structure of the

samples remained largely unaltered (minimally dis-

turbed samples). However, soil CO2 efflux in the field

includes root derived CO2, as well as truly hetero-

trophic respiration (i.e. CO2 derived from the decom-

position of litter and SOM). The soil cores in the lab did

contain roots, but as there was no new supply of

photosynthates from aboveground by trees or grasses,

this proportion soon diminished. The drop in respira-

tion rates within the first week of incubation observed

for all treatments is likely to be partly caused by the

rapid decline of CO2 flux from roots and labile root

derived organic material. Field experiments where the

supply of photosynthates to the roots has been

suppressed by forest girdling (Hogberg et al., 2001;

Subke et al., 2004b) have shown that under these

conditions, roots continue to respire starch reserves for

several weeks. Our samples only included roots of

diameters smaller than 2 cm, resulting in a smaller

starch store compared with field conditions, and it is

also likely that root growth and phloem transport had

(a) (b)

1

1.5

2

2.5

3

3.5

Q10

1

1.5

2

2.5

3

3.5

FieldBeg End Beg End Beg EndRewet Beg End Rewet

Organic Mineral

Fig. 9 (a) Average estimates of Q10 in the organic layer (left) and the mineral layer (right), at the beginning (beg), end (end) of the

drying period and after the rewetting (Rewet). Filled bars are from constantly wet treatment, open bars from drying–rewetting

treatment. Hatched or striped bars represent results from the dual-source model, solid bars from the single-source model. (b) Q10

estimate from parallel field measurements using the single-source model. Error bars denote standard errors. Only the effect of the dry

treatment is significant.



still not reached high values in this montane forest at

the time of sampling.

The effect of the soil drying is expected and confirms

the results of earlier studies (Orchard & Cook, 1983;

Howard & Howard, 1993; Lomander et al., 1998). The

drying was not extreme (e.g. not up to the permanent

wilting point), but nevertheless respiration was clearly

reduced by approximately 50%. Such reduction is

expected by common models that relate soil microbial

activity to the logarithm of soil water potential (pF-

value) with a maximum at field capacity (�250 hPa; pF

2.3) and zero respiration at the permanent wilting point

(�15 000 hPa; pF 4.2) (e.g., Andren et al., 1992; Rodrigo

et al., 1997) where in our study the soil water potential

dropped to between �1000 and 2000 hPa (pF 3–3.3). It is

noteworthy that the difference in soil respiration

between wet and dry treatments hardly increased after

30 days of incubation. Although speculative, this may

indicate that after a certain time substrate limitation

becomes more and more co-limiting to respiration, so

that the sensitivity to water availability declines.

Rewetting has been reported to cause soil CO2 efflux

rates that exceed those rates before drying (Redeker

et al., 2004; Xu & Baldocchi, 2004). This so-called Birch

effect (Birch, 1958) is explained by remineralization of

dead microbes and release of easily decomposable

substrate through rewetting. In our experiment, this

effect was confirmed, as CO2 efflux rates after rewetting

exceeded those of the wet samples at the same time. In

addition to the pool of labile C supplied from dead

microbial biomass, a portion of the extra respiration

may be caused by decomposition of SOM which had

been ‘saved’ under drought conditions, when microbial

activity was limited by water rather than substrate.

That a labile carbon fraction had mobilized is sup-

ported by the fact, that respiration rate dropped within

10 days of the rewetting, even though samples were

kept constantly wet thereafter.

According to our analysis with the dual-source

model, the surface layer showed the largest variation

with incubation time and the most pronounced

responses to drying–rewetting. Thus, our study calls

attention to the importance of fluxes from the soil

surface. In particular, superficial rewetting events that

trigger surface respiration may not be accounted for by

models that are driven by observations at 5 or 10 cm

depth or that treat the soil as monolayer ‘bucket’.

Reichstein et al. (2003) and Xu & Baldocchi (2004)

experienced these problems with rain events after

drought, when fluxes were higher than expected

according to soil moisture at 10 cm. Changes in relative

contributions from different soil compartments may

well change the apparent Q10 of soil respiration, when

CO2 efflux data are correlated with a temperature at a

fixed position (discussed in Subke et al., 2003). Such a

correlation may result in a wide range of observed

apparent Q10 values (e.g. Janssens & Pilegaard, 2003),

which have to be treated with caution before conclu-

sions about soil physiological responses to temperature

are drawn. Also with respect to results from Irvine &

Law (2002), we propose that ecosystem models operat-

ing at subdaily to daily time steps should comprise a

surface, a main root zone and – at least in forests – a

deep soil layer to account for different dynamics in

different compartments.

Our results from the dual-source model are some-

what exploratory and we were not able to provide a

final validation of the surface flux estimate. But we

show that with such a still simple approach, the

descriptive power is clearly enhanced, and the results

we obtained concerning relative contributions are quite

plausible: (1) The surface layer consisting of fresh grass

and needle litter, very likely contains the highest

proportion of labile SOM, and thus our result that

surface flux declines more strongly than the flux from

the bulk soil is reasonable. (2) Similarly, our observation

(derived from the dual-source model) that the surface

layer responds most sensitively to the drying–rewetting

treatment is conceivable, because the surface layer dries

up most severely. Two field studies conducted at the

same site showed conflicting results concerning flux

contributions from the organic and mineral layers.

While Buchmann (2000) concluded from direct mea-

surements following the removal of organic horizons

that the majority of respired CO2 originates from the

mineral layer, Subke et al. (2003) argued that the

correlation between temperature and surface efflux

suggest that the organic layer has the larger contribu-

tion to the total soil CO2 efflux. (The conflict between

the results of both field studies is discussed in Subke

et al., 2003). Thus, we suggest that such inverse estima-

tion of flux partitioning between different compartments

should be further investigated and could be validated

nondestructively (e.g. with isotope-labelling studies).

One of the main questions posed in the current study

was about the commonly assumed invariance of the

temperature sensitivity of soil respiration processes,

which has recently been criticized. The effect of organic

matter age on the temperature sensitivity of soil

respiration was inferred indirectly only, but from two

perspectives: (a) With incubation time the relative

contribution of young SOM pools declines and the

contribution of older pools increases, thus if older pools

had a different temperature sensitivity the Q10 should

change with incubation time. (b) The mineral-SOM is

older than the organic layer carbon, thus the mineral

soil should have had a different Q10 than the organic

layer, as (e.g. suggested by Liski et al. 1999). As virtually



no effects of incubation time or soil layer on the Q10 of

soil respiration were detected, our study suggests no or

only minor effects of SOM age on the Q10 of soil

respiration. A limitation to this conclusion is that we

did not explicitly determine the age of the carbon

respired and we cannot infer the temperature sensitiv-

ity of very old pools, if their contribution to the overall

flux is very small.

The effect of soil water availability on the tempera-

ture sensitivity of decomposition of SOM was statisti-

cally significant, but modest (o0.4 Q10 units on

average) at the levels of drying in this experiment and

much less drastic than suggested by recent field studies

(Xu & Qi, 2001; Reichstein et al., 2003). However, in this

case the results are somewhat equivocal, as (a) there

might be a threshold type of relationship between Q10

and soil water availability and (b) the drying might not

have been advanced enough to pass such a threshold.

The drop of Q10 at the end of the drying period is

indicative of this, but is too singular, as we did not

continue the drying past this point, and this possibility

should be investigated further.

Conclusions

Our study analysed the temperature sensitivity of soil

respiration in the laboratory under minimization of

confounding effects that often occur under field

conditions and separated the effects of soil moisture,

soil horizon and decomposition dynamics (incubation

time). Under our conditions we did not find evidence of

important changes of the direct sensitivity of soil

respiration to temperature in response to the mentioned

factors. This contrasts recent results from field studies

and shows that one must be careful with interpretation

of statistical results from ecosystem level studies, as

these results can be ‘emergent properties’ that do not

hold at the lower organizational level (e.g. Q10 of

soil respiration vs. Q10 of decomposition rate constants

in a process model). From our experiment we cannot

conclude that in process models the temperature

sensitivity of rate constants should either differ

between organic and mineral layer carbon, or should

change along ongoing decomposition of SOM. Our

results demonstrate that laboratory experiments, which

are commonly criticized for being unnatural, should be

realized as a successful complementary tool to field

measurements, as they allow a more elaborate elucida-

tion of confounded factors. The disturbance caused by

sampling of soil cores can be minimized if the structure

of the soil is retained, and is more than compensated by

the possibility of controlling abiotic conditions in the

laboratory.

Acknowledgements

The authors would like to thank A. Suske, D. Otieno and A.Bobeva for help with field and laboratory work during thisexperiment and Riccardo Valentini for valuable discussions. Weappreciate two anonymous reviewers’ and E. Davidson’s helpfulcomments on an earlier draft. The current study was performedwithin the CARBODATA and CARBOEUROFLUX projects.During data analysis and interpretation MR was supported bythe EU Marie-Curie fellowship INTERMODE (MEIF-CT-2003-500696).

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