Respiratory carbon losses in a managed oak forest ecosystem

Forest Ecology and Management 279 (2012) 1–10

Contents lists available at SciVerse ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Respiratory carbon losses in a managed oak forest ecosystem Qinglin Li a,b,⇑, Jiquan Chen b, Daryl L. Moorhead b a b

Forest Analysis and Inventory Branch, Ministry of Forests, Lands, and Natural Resource Operations, Victoria, British Columbia, Canada V8W 9C2 Department of Environmental Sciences, University of Toledo, Toledo, OH 43606, USA

a r t i c l e

i n f o

Article history: Received 9 January 2012 Received in revised form 22 March 2012 Accepted 3 May 2012 Available online 23 June 2012 Keywords: Ecosystem respiration Missouri Ozarks Temperature Timber harvest

a b s t r a c t Respiratory carbon losses from a mixed oak forest ecosystem following experimental manipulations were examined for their magnitudes and biophysical regulations. To quantify these losses, respiration measurements from chamber-based ecosystem components of sapwood, snags, down-logs, and soil, using chamber-based methods, were collected from experimental stands 8 yr after the manipulations of: non-harvest (NHM), uneven-aged (UAM), and even-aged (EAM) managements. Temperature and respiration relationships (R = R0  ebT) were used to estimate annual ecosystem respiration. The annual respiration rates were 1684 g C m2 yr1 in the NHM, 1787 g C m2 yr1 in the UAM, and 1231 g C m2 yr1 in the EAM stands. Harvesting reduced annual ecosystem respiration in the EAM stands by 28% compared to the NHM stands. Soil respiration was the largest component and contributed from 72% to 85% of the total respiration. The sapwood and leaf respiration were the second largest components of ecosystem respiration in both NHM and UAM stands, but down-logs were the second largest component in the EAM stands. Harvest significantly affected ecosystem respiration, with intensity driving changes in component respiration. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction The net ecosystem carbon (C) gain or loss is a small difference between two large fluxes: photosynthesis and respiration. In general, respiration is nearly equal to photosynthesis, and is more important than photosynthesis in determining the variability of net ecosystem productivity (Valentini et al., 2000; Yuan et al., 2009). Recent studies pointed out considerable variability regarding the role of forests in the global C budget (Houghton, 1999; Xiao et al., 2007). Also, natural and human disturbances have been proposed as a major factor causing great uncertainties in C budget estimates (Kurz et al., 2008). For forest ecosystems, the magnitudes of and changes in C fluxes vary with not only vegetation type, age, species composition, and microclimate (Tang and Baldocchi, 2005; Vogel et al., 2005; Wu et al., 2006), but also management practices such as harvesting and prescribed burning (Chen et al., 2002; Amiro et al., 2006; Noormets et al., 2008a). This study was designed to examine the changes in ecosystem respiration and its contributing components (e.g., live and dead trees) using a longterm experimental site at the Missouri Ozark Forest Ecosystem Project (MOFEP) where controlled manipulations were established in 1989 to mimic management practices for Ozark forests. Our

⇑ Corresponding author at: Forest Analysis and Inventory Branch, Ministry of Forests, Lands, and Natural Resource Operations, Victoria, British Columbia, Canada V8W 9C2. Tel.: +1 250 387 9355; fax: +1 250 953 3838. E-mail addresses: [email protected], [email protected] (Q. Li). 0378-1127/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.foreco.2012.05.011

critical question is: How do different disturbances alter the magnitude and composition of ecosystem respiration? So far, only a small number of studies have examined the contributions of these components over time (Law et al., 1999; Curtis et al., 2005; Reichstein et al., 2005). Ecosystem respiration is regulated, and thus might be predicted, by several biophysical variables including temperature, moisture, biomass, photosynthesis, precipitation, and others. For example, soil moisture was not only an important regulator of soil respiration (Noormets et al., 2008b), but also changed the respiration–temperature relationship in the Sierra Nevada/Madre forests of California, USA (Xu and Qi, 2001b; Tang et al., 2005). Several other recent studies found that leaf photosynthesis as well as environmental variables (Högberg et al., 2001; Tang et al., 2005; Xu et al., 2011), such as precipitation frequency and duration may affect soil respiration during and after drought (Xu et al., 2004). Leaf respiration varies by species in part due to nitrogen content differences (Bolstad et al., 1999). Snowfall in the previous winter can predict total respiration in the subsequent growing season in semi-arid forests (Concilio et al., 2009). Finally, contributions by snags and down-logs may be driven by temperature, moisture, and decay class (Pyle and Brown, 1998; Wilcke et al., 2005). The overall objective of this study was to understand how management alternatives alter ecosystem C losses by quantifying various components of ecosystem respiration for the Ozark forests, including soil, sapwood, leaf, snag, and down-log respiration. Specifically, our research tasks included: (1) conducting in situ

2

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

respiration measurements, (2) estimating the changes in ecosystem respiration and the portions contributed by each component at multiple temporal scales and under different experimental treatments, and (3) exploring the underlying mechanisms of component respiration dynamics by relating them to forest stand structure and species composition. 2. Materials and methods 2.1. Study site The study was conducted at the Missouri Ozark Forest Ecosystem Project (MOFEP), which was designed to understand the consequences of different management practices such as clear-cut and thinning on ecosystem functions and services (Brookshire and Shifley, 1997; Shifley and Brookshire, 2000; Shifley and Kabrick, 2002). MOFEP is located in the southeastern Missouri Ozarks (91°120 W and 37°060 N). This area is primarily mature (>70 yrs old) upland oak, oak-hickory and oak-pine communities; the mean canopy height is 16 m, and the mean DBH by species ranges from 4.5 to 22.8 cm (Brookshire and Shifley, 1997; Xu et al., 2004). Dominant overstory species include white oak (Quercus alba), black oak (Quercus velutina), scarlet oak (Quercus cocinea), shortleaf pine (Pinus echinnata), and hickory (Carya spp). MOFEP has an annual average of 1120 mm precipitation and a mean annual air temperature of 13.3 °C (Guyette and Larsen, 2000). The soils are mostly Alfisols and Ultisols (Kabrick et al., 2000). 2.2. Experimental treatments The MOFEP sites were treated in the fall of 1996 according to Missouri Department of Conservation (MDC) forest land management guidelines for even-aged management (EAM), uneven-aged management (UAM), and no-harvest management (NHM) treatments (Missouri Department of Conservation, 1986). The three treatments were randomly assigned to nine sites, ranging in size from 266 to 527 ha, using a randomized complete block design (Brookshire and Shifley, 1997; Sheriff and He, 1997). Each site was subdivided into stands, averaging 4 ha in size, of similar ecological land types (ELTs) defined by slope, aspect, vegetation composition, and soil type. A system of 648 permanent forest vegetation plots (0.2 ha) was distributed across the nine MOFEP sites to document forest vegetation response to treatments (Li et al., 2007). These plots were allocated among stands based on stand size with the constraint that each stand receives at least one plot (Brookshire and Shifley, 1997). In this study, twelve replicates were sampled of each treatment type (EAM, UAM, and NHM) among the permanent forest vegetation plots with similar soil types, species composition, and ELT for a total of 36 plots. Although EAM included a combination of clear-cutting and intermediate thinning, our sampling points were only located within the clear-cut area. UAM consisted of harvesting by both single-tree selection and group selection, but our plots were all located in areas of singletree selection. 2.3. Field data collection Soil respiration (Rsoil, g CO2 m2 h1) was measured using an EGM4 (PP Systems, Amesbury, MA, USA) at each of 36 plots. Six soil collars, each with a height of 4.4 cm and a diameter of 10 cm, were inserted into the soil at each plot in three random clusters, and each cluster had two collars. Surface efflux was measured during the summer and fall 2003, summer 2004, and early spring and summer 2005. Soil temperature at 5 cm was measured adjacent to each respiration collar with a portable temperature probe (Fig. 1). Soil water

content was measured from TDR rods that were installed near each cluster of soil collars by a time domain reflectometer (TDR100, Campbell Scientific In., Logan, UT). Both temperature and soil water measurements were made every 2–4 weeks in the 2003–2005 growing season, late fall of 2003, and early spring of 2005. However, the soil water content did not appear to be a constraint to soils and plants throughout the growing season (see data analysis) so that further details are not presented in this study. In addition to the periodic measurements of soil temperature coinciding with respiration measurements, continuous soil temperature was measured at 5 cm by three HOBO thermostats per plot (Fig. 1) and averaged every hour using a HOBO data-logger (Onset computer Corporation, Pocasset, MA, USA), starting at November 11, 2002. The following exponential equation was used to develop an empirical model to predict respiration from soil temperature:

R ¼ R0  ebT

ð1Þ

where R is component respiration, T is temperature, R0 and b are empirically estimated coefficients for each component model (soil, down dead wood, snag, sapwood, or leaf; Table 1). The Q10 can be derived from Q10 = exp(10  b). Estimated coefficients were used to predict respiration at an hourly scale over the 3-yr study period. Down-log decay classes were defined according to Shifley and Brookshire (2000) as: (I) recently downed material with tissue and bark intact throughout; (II) sapwood beginning to decay or mostly present, bark beginning to crack, and heartwood tissue intact; and (III) sapwood and bark mostly gone or gone, heartwood beginning to decay or with substantial decay. Down-log respiration was measured on three down-logs for each decay class for a total of nine down-logs per plot and for a total of 108 down-logs per treatment. The collars were inserted into randomly selected, largediameter down-logs and sealed with silicon. We used the same measurements protocols as for soil respiration. Three plots per treatment were randomly selected for continuous temperature measurement. Each measured log in a selected plot had a HOBO thermostat installed near the collar at 5 cm depth for a total of 27 down-logs per treatment; hourly mean temperature was recorded during the study period. The volume of down-logs was estimated according to (Wagner, 1964; Martin, 1976). Basically, we laid out a 100-m transect in each plot for a total of 36 transects. Along each transect, every down-log greater than 5 cm in diameter touching the transect line was recorded by decay classes, length, and diameter. The volume of the total down-logs was estimated as:

V¼

p2

P

2

d

8L

ð2Þ

where V is volume per unit area (m3 ha1), d is diameter of the log intersected with transect (cm), and L is the length of the transect (m). Snag decay classes were defined according to Shifley and Brookshire (2000) as: (I) dead trees with most of the bark and branches present; (II) sapwood and bark completely gone. Snag respiration was measured on three snags of each decay class for a total of six snags per plot and for a total of 72 snags per treatment. The measurement collars were mounted on each snag with silicon at approximately 137 cm height above ground and at random azimuths. These respiration measurements were also taken at 2– 4 week intervals. The chamber measurements were scaled up to the stand level by estimating snag surface area/volume based on (Martin et al., 1998):

log10 Y ¼ a þ b  log10 x

ð3Þ

where x is the stem DBH (cm), Y is the snag surface area/volume, and a and b are species specific parameters. The mean surface area

3

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

40 Snag & Sapwood

Direct Measurements

Soil & Down-log

Temperature (oC)

30 20 10 0 -10 -20 Jan/03

May/03 Aug/03

Dec/03

Apr/04 Aug/04 Date

Dec/04

Apr/05

Aug/05

Dec/05

Fig. 1. Direct field temperature measurements (dots) and hourly averages of data-loggers records for snags and sapwood (dotted line) and soil and down-log (solid line) during study periods.

Table 1 Parameters in the temperature response function (R = R0  ebT) for soil respiration (Rsoil, g CO2 m2 h1) from three experimental treatments (non-harvest management: NHM, uneven age management: UAM, and even age management: EAM), down-log respiration (Rdown-log, g CO2 m2 h1) from three decay classes, snags (Rsnag, g CO2 m2 h1) from two decay classes, stem (Rstem, g CO2 m3 h1), and leaf (Rleaf, g CO2 kg mass1 h1) from four species. The unit of temperature is °C. R0

b

Q10

R2

Rsoil

NHM UAM EAM

0.2658 0.2537 0.1747

0.0549 0.0570 0.0785

1.732 1.768 2.192

0.68 0.80 0.76

Rdown-log

Decay I Decay II Decay III

0.0312 0.0251 0.0907

0.1321 0.1523 0.1067

3.747 4.658 2.907

0.75 0.57 0.45

Rsnag

Decay I Decay II

0.0821 0.0896

0.0797 0.0911

2.219 2.487

0.59 0.68

Rstem

White oak Hickory Black oak Pine

1.4379 0.5348 0.9485 0.4618

0.0395 0.0768 0.0474 0.0541

1.484 2.155 1.606 1.718

0.46 0.48 0.39 0.29

White oak Hickory Black oak Pine

1.1991 0.8883 0.9575 1.0154

0.0282 0.0394 0.0343 0.0218

1.221 1.470 1.410 1.239

Rleaf

of each class was projected to ground area. Three plots per treatment were randomly selected for continuous temperature measurements. Each selected snag had a HOBO thermostat installed to a 5 cm depth into the snag near each collar for a total of 18 snags per treatment, and hourly mean snag temperature was recorded during the study period. Sapwood respiration was measured on four trees (white oak, hickory, black oak and short-leaf pine) at or near each plot (note that trees were not limited within the plot) for a total of 48 trees per treatment. The collars were mounted on each tree with silicon sealant at approximately 137 cm height and random azimuths. The measurements and equipment and frequency were the same as for soil respiration. Three plots were randomly selected for continuous temperature measurement. Each tree had a HOBO thermostat installed into the stem at 5 cm depth for a total of 12 trees per treatment, and hourly mean sapwood temperature was recorded during the study period. Sapwood volume was calculated using the same allometric equation as snags (Eq. (3)) by species (Li et al., 2007). Measured stem respiration rates per unit area were converted to

rates per unit of sapwood volume based on tree DBH, assuming a wedge-shape volume. Eq. (1) was used to predict stem respiration per unit of sapwood volume by species from stem temperature. The total sapwood volume per unit of ground area in each stand was estimated following the methods of Bolstad et al. (2004). Leaf respiration was estimated from exponential response function (Eq. (1)) by developing species-specific parameters adopted from Bolstad et al. (1999). Continuous leaf respiration over time was estimated with Eq. (1) based on canopy temperature and leaf biomass per unit land area. Canopy temperature was approximated by air temperature in the canopy at 2 m. Ground-based leaf biomass of each species was estimated using allometric Eq. (3). The time for leaf expansion and leaf senescence for deciduous trees at MOFEP were approximately between the Julian days of 100 and 285. 2.4. Data analysis Pearson correlation was performed to scrutinize the relationship between soil respiration and soil water content by treatments and years, because it was a driver for soil respiration (Concilio et al., 2005; Xu et al., 2011). However, we did not observe a significant correlation between soil water content and soil respiration during our study period; even though higher soil water content was observed in the EAM stands (Li et al., 2007). Thus, temperature was used as the sole driver for developing respiration relationships in this study (Table 1). Temperature and respiration relationship were estimated by nonlinear regression fitting (NLIN procedure) in the following steps: 1. Model development a. Simple model per component: Rs ¼ a0  eb0 T þ e b. Complex model per component:

Rs ¼ ða0 þ a1  d1 þ a2  d2 þ a3  d3 Þ  eðb0 þb1 d1 þb2 d2 þb3 d3 ÞT þe where Rs is respiration, ai and bi are fitted parameters, di are binary indicators, and e is an error term randomly selected from the normal distribution with mean of zero and standard deviation matching the actual temperature data set.

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

Annual soil respiration was significantly lower at the intensively disturbed EAM stands, but significantly higher in the intermediately disturbed UAM stands (Table 2). For example, annual soil respiration in the UAM stands was 16% and 9% (q < 0.01) higher than that in the EAM and NHM stands, respectively. Cumulative annual soil respiration was 1193, 1309, and 1097 g C m2 yr1 in the NHM, UAM, and EAM stands, respectively. The average of annual soil respiration over the three stands was 1199 g C m2 yr1 (Fig. 4). 3.2. Down-log respiration Annual down-log respiration per unit ground area in the EAM stands was significantly (q < 0.01) higher than for UAM (29%) and NHM (63%) stands (Table 2 and Fig. 4), while the annual respiration per volume down-log in the UAM stands was 29% (q < 0.01) higher than for EAM stands, and there were no difference between the UAM and NHM stands (Fig. 5). The volume was estimated at 54, 107, and 177 m3 ha1 in the NHM, UAM, and EAM stands, respectively, for diameters greater than 5 cm (Table 3). The annual respiration per unit ground area varied significantly among decay classes (q = 0.03), with decay class II having the highest annual respiration in every treatment. Annual respiration per unit of ground area was 40, 75, and 106 g C m2 yr1 for the NHM, UAM, and EAM stands, respectively (Table 3).

Decay I Decay II Decay III Decay I Decay II Decay III

(a) NHM EAM UAM NHM EAM UAM

1.2

0.8

(b)

1.6

1.2

0.8

0.4

0.4

0.0 0.8 Snag respiration (g CO2 m-2 hr-1)

3.1. Soil respiration

(c)

Black oak White oak Hickory Short-leaf Pine Black oak White oak Hickory Short-leaf Pine

Decay I Decay II Decay I Decay II

0.6

0.4

(d)

0.0 8 -

Soil respiration (g CO2 m-2 hr-1)

1.6

3. Results

6

4

0.2

0.0

2

0

10

20 Temperature (o C)

30

40/0

10

Down-log respriation (g CO2 m-2 hr-1 )

2. Significance tests: we then computed the F and P values to test the two models for each component, if they were significantly different (q < 0.05), a complex model should be developed for that component. Our tests suggested that all the components except soil respiration needed a complex curve fitting. Although we could not obtain a complex model for soil respiration, the soil temperature was significantly different between the NHM and UAM (Li et al., 2007), so we felt that it was reasonable to keep NHM and UAM curve fits separate. Hourly respiration rates of each component (i.e., soil, down-log, snag, sapwood, and leaf) were estimated by the temperature and respiration relationship (Figs. 2 and 3). Hourly respiration rates of each component were summed to obtain component annual respiration. To quantify the treatment effects and inter-annual variability for each component respiration, we used mixed model analysis of variance (ANOVA) with repeated measures (Table 2). There was no significant inter-annual variation within treatment; thus, averaged annual respiration rates over the study period were used for further analyses (the average data presented). Twoway ANOVA (mixed model with repeated measures) by treatments and species with comparison between means by adjusted Tukey’s test was used to compare leaf and sapwood respiration, while two-way ANOVA (mixed model with repeated measures) by treatments and decay classes with comparison between means by adjusted Tukey’s test was used to compare down-log and snags. Significance was determined with an a of 0.05. All statistical analyses were performed with SAS 9.1 (2003, Cary, NC, USA).

Sapwood respiration (g CO2 m-3 hr 1 )

4

20

30

40

0

Temperature (oC)

Fig. 2. Component respiration as a function temperature. A. Soil respiration versus soil temperature at 5 cm in the NHM (non-harvesting management), UAM (uneven-aged management), and EAM (even-aged management) stands. Exponential fitted lines for EAM (short dashed line), NHM (solid dark line), and UAM (gray solid line). B. Down-log respiration per log surface area for decay classes I–III versus down-log temperature. Exponential fitted lines for decay class I (solid dark line), class II (dashed line), and class III (doted line). C. Snag respiration per surface area for decay class I and II versus snag wood temperature. Exponential fitted lines for classes I (solid line) and class II (dashed line). D. Sapwood respiration per sapwood surface area for white oak, black oak, hickory, and short-leaf pine versus sapwood temperature. Exponential fitted lines for black oak (short dashed line), hickory (long dashed line), short-leaf pine (gray solid line), and white oak (dark solid line).

5

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

0.30

0.04 Down-log

Snag

Sapwood

Leaf 0.03

0.25 Ecosystem and soil respiration (g C m-2 hr -1)

Soil

ecosystem

leaf 0.03

0.20

sapwood 0.02

0.15 0.02

soil 0.10

0.01 snag 0.05

0.00 Jan/03

down-log

May/03 Aug/03

Dec/03

Apr/04

Aug/04 Date

Dec/04

Apr/05

0.01

Aug/05

Down-log, snag, sapwood, and leaf (g C m-2 hr -1)

Ecosystem

0.00 Dec/05

Fig. 3. Estimated daily mean soil, stem, leaf, down-log, snag, and total ecosystem respiration (g C m2 h1) during the study period (2003–2005).

Table 2 Statistical analyses results of repeated-measures ANOVA (mixed model) for soil, down-log, snag, sapwood, leaf during the study period. d.f.

F

P

Soil

Treatment

2

8.4

<0.01

Down-log (density)

Treatment Decay class

2 2

9.4 1.6

<0.01 0.04

Down-log (respiration rate)

Treatment Decay class

2 2

5.1 5.8

0.04 0.03

Snag (density)

Treatment Decay class

2 1

11.8 4.3

<0.01 0.03

Snag (respiration rate)

Treatment Decay class

2 1

31.8 8.3

<0.01 0.02

Sapwood (density)

Treatment Species

2 3

21.5 11.3

<0.01 <0.01

Sapwood (respiration rate)

Treatment Species

2 3

31.5 21.3

<0.01 <0.01

Leaf mass density

Treatment Species

2 3

10.3 8.2

<0.01 <0.01

Leaf respiration rate

Treatment Species

2 3

17.6 9.4

<0.01 0.01

Component respiration rate per unit mass

Treatment Component

2 3

77.6 107.6

<0.01 <0.01

3.3. Snag respiration

3.4. Sapwood respiration

The annual snag respiration per unit ground area in the UAM stands was 65% higher (q < 0.01) than that in the NHM stands, and there were no snags in the EAM stands (Fig. 4). The annual respiration per unit snag volume in the UAM stands was similar to the NHM stands (Fig. 5). Snag respiration per unit ground area varied significantly among decay classes (q = 0.02; Table 2). The annual snag respiration per unit of ground area was 37 and 105 g C m2 yr1 in the NHM and UAM stands, respectively. The total snag density was 19 and 63 m3 ha1 in the NHM and UAM stands, respectively (Table 4).

The treatment intensity significantly (q < 0.01) decreased annual sapwood respiration per unit ground area (Fig. 4). For example, this component of respiration in the EAM stands was 90% (q < 0.01) lower than in the UAM stands, and this respiration value in the UAM stands was 22% (q < 0.01) lower than in the NHM stands. The annual respiration per unit sapwood volume in the EAM stands was 22% higher (q < 0.01) than that in the NHM stands and there was no difference between NHM and UAM stands (Fig. 5). The annual respiration per ground unit area also varied significantly (q < 0.01) among species. The annual sapwood

6

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

2000

Respiration (g C m-2 yr-1)

NHM

UAM

b

EAM

a

1500 b

c

a c 1000

500 a a b c 0

Soil

a

Down-log

b

b

a

b

c

Snag Components

Sapwood

Leaf

c Ecosystem

Fig. 4. Annual ecosystem and component respiration (g C m2 yr1) in a managed forest landscape (mean ± one standard error; NHM: non-harvesting management; UAM: uneven-aged management; and EAM: even-aged management). The same lowercase letters represent statistically similar (q > 0.05) means between treatments.

25

0.8

Down-log

Snag

ACd

Respiration rate (kg C

BCa ABa ABd

m-3

yr -1)

Ac Ad

20

15

Leaf 0.6

Ac

Aa

0.5

10

0.3 Bb

ABb

ACb

5

0.2

0

Leaf respriation rate (g C g-1 yr -1)

Sapwood

0.0 NHM

UAM

EAM

Management regimes Fig. 5. Annual ecosystem component respiration rate per unit mass (kg C m3 yr1 or g C g1 yr1 for leaves) in a managed forest landscape (mean ± one standard error; NHM: non-harvesting management; UAM: uneven-aged management; and EAM: even-aged management). The same uppercase letters represent statistically similar (q > 0.05) means between treatments, while the same lowercase letters represent statistically similar (q > 0.05) means between components within treatment.

Table 3 Total amount of down-log (mean ± one standard error; m3 ha1) and annual respiration per ground area (mean ± one standard error; g C m2 yr1) for three decay classes in three treatments (NHM: non-harvest management; UAM: uneven-aged management; and EAM: even-aged management). The same uppercase letters represent statistically similar (q > 0.05) means between treatments, while same lowercase letters represent statistically similar (q > 0.05) means between decay classes within treatment. Decay classes

Down-log density (m3 ha1)

Respiration rates (g C m2 yr1)

NHM

UAM

EAM

NHM

UAM

EAM

I

0.9Aa (0.3)

20.8Ba (3.6)

18.5Ba (5.5)

0.3Aa (0.1)

13.4Ba (0.1)

8.1Ca (0.2)

II

28.4Ab (1.2)

74.4Bb (4.9)

144.2Cb (9.7)

24.8Ab (0.1)

53.8Bb (0.1)

91.5Cb (0.8)

III

24.9Ac (1.4)

12.2Bc (1.6)

14.2Ba (5.7)

14.5Ac (0.3)

7.4Bc (0.1)

6.1Ba (0.1)

Sum

54.2

107.4

176.9

39.6

74.6

105.7

respiration per unit ground area was 251, 196, and 20 g C m2 yr1 in the NHM, UAM, and EAM stands, respectively. Treatment inten-

sity directly reduced sapwood volume from 181 m3 ha1 in NHM to 11 m3 ha1 in EAM (Table 5).

7

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

Table 4 Total amount of snag density (mean ± one standard error; m3 ha1) and annual respiration per ground area (mean ± one standard error; g C m2 yr1) for two decay classes in three treatments (NHM: non-harvest management; UAM: uneven-aged management; and EAM: even-aged management). The same uppercase letters represent statistically similar (q > 0.05) means between treatments, while the same lowercase letters represent statistically similar (q > 0.05) means between decay classes within treatment. Snag density (m3 ha1)

Decay classes

Respiration rate (g C m2 yr1)

NHM

UAM

EAM

NHM

UAM

EAM

I

11.5Aa (1.5)

50.3Ba (2.0)

0Ca

21.5Aa (8.4)

82.0Ba (9.7)

0Ca

II

7.4Ab (0.8)

12.4Bb (1.1)

0Ca

15.2Ab (1.2)

23.2Bb (2.2)

0Ca

Sum

18.9

62.7

0

36.7

105.2

0

Table 5 Total amount of sapwood (mean ± one standard error; m3 ha1) and cumulative respiration per ground area (mean ± one standard error; g C m2 yr1) for the four dominant species (WO: white oak; HC: hickory; BO: black oak; and PS: short-leaf pine) in three treatments (NHM: no-harvest management; UAM: uneven-aged management; and EAM: even-aged management). The same uppercase letters represent statistically similar (q > 0.05) means between treatments, while the same lowercase letters represent statistically similar (q > 0.05) means between species within treatment. Sapwood density (m3 ha1)

Species

NHM

Respiration rate (g C m2 yr1)

UAM

Aa

Ba

EAM

NHM

Ca

UAM

55.5 (2.0)

33.4 (1.7)

2.5 (0.2)

84.3 (0.3)

47.6 (0.1)

4.7Ca (0.1)

HC

22.1Ab (2.4)

23.1Ab (1.4)

0.5Bb (0.1)

36.1Ab (0.1)

41.9 Bb (0.1)

0.9Cb (0.2)

BO

68.9Ac (2.9)

46.8Bc (2.0)

2.8Ca (0.3)

92.6Ac (0.6)

82.2Bc (0.2)

5.1Ca (0.2)

PS

34.7Ad (2.2)

15.5Bb (2.2)

4.9Cc (1.0)

37.7Ab (0.4)

24.0Bd (0.1)

9.0Cc (0.3)

Sum

181.2

118.8

10.7

250.7

195.7

19.7

Respiration rate (g C m2 yr1)

Ba

EAM

WO

Table 6 Leaf dry mass (mean ± one standard error; g m2) and cumulative respiration per ground area (mean ± one standard error; g C m2 yr1), for the four dominant species (WO: white oak; HC: hickory; BO: black oak; and PS: short-leaf pine) in three treatments (NHM: non-harvest management; UAM: uneven-aged management; and EAM: even-aged management). The same uppercase letters represent statistically similar (q > 0.05) means between treatments, while the same lowercase letters represent statistically similar (q > 0.05) means between species within treatment.

Aa

3.6. Ecosystem respiration The annual ecosystem respiration was 1684, 1787, and 1231 g C m2 yr1 in the NHM, UAM, and EAM, respectively (Fig. 4). The annual ecosystem respiration in the EAM stands was 37% (q < 0.01) to 45% (q < 0.01) lower than the NHM and UAM stands. Soil respiration was the largest component of ecosystem respiration and contributed between 71% and 89% of the total. Aboveground autotrophic respiration (i.e., stem + leaf respiration) comprised 25%, 17%, and 2% in the NHM, UAM, and EAM stands, respectively. Snags and down-logs contributed 5%, 10%, and 9% in the NHM, UAM, and EAM stands, respectively (Fig. 4).

Species

Dry leaf mass (g m2) NHM

UAM

EAM

NHM

UAM

EAM

WO

93.5Aa (3.9)

50.6Ba (4.3)

2.9Ca (0.4)

62.0Aa (2.1)

30.0Ba (9.6)

1.9Ca (0.1)

HC

27.0Ab (1.2)

31.3Ab (3.5)

0.5Cb (0.1)

13.3Ab (4.6)

13.7Bb (4.5)

0.3Cb (0.1)

BO

130.8Ac (6.5)

102.1Bc (8.7)

3.3Ca (0.4)

69.4Ac (9.4)

48.1Bc (5.6)

1.9Cc (0.1)

4. Discussion

PS

33.8Ab (2.3)

20.8Bd (2.3)

6.2Cc (1.1)

18.9Ad (5.6)

10.5Bb (3.4)

3.5Cd (0.1)

Sum

285.1

204.8

12.9

163.6

102.3

7.6

We found significant effects of management on all measures of respiration in Ozark forests (Fig. 4), but these effects varied by treatment. For example, selective thinning (UAM) increased both soil and ecosystem respiration but clearcuts (EAM) decreased both measures. Soil respiration was the largest component of ecosystem respiration and the increase in soil respiration with UAM may result from a number of changes to the soil environment, including higher soil temperature (Li et al., 2007), increased radiation and reduced evapotranspiration (Gordon et al., 1987), higher decomposition of leaves (Li et al., 2009), roots or aboveground litter (Rustad et al., 2000), and modified litter chemistry (Li et al., 2009). In addition, logging slash has been found to promote productivity of soil microflora, presumably through an increase in soil moisture and microbial biomass (Sohlenius, 1982), and higher soil organic carbon has been reported in the UAM sites (Li et al., 2007), all of which factors may increase soil respiration (Mattson et al., 1987). In contrast, the EAM treatment decreased soil respiration rates demonstrating a qualitative difference in the soil respiration response to

3.5. Leaf respiration The annual leaf respiration per unit ground area in the NHM stands was significantly (q < 0.01) higher than in both UAM and EAM stands (Fig. 4). The annual leaf respiration per unit mass in the EAM stands was significantly (q < 0.01) higher than in the UAM stands and there were no significant differences between NHM and UAM stands, or between NHM and EAM stands (Fig. 5). The annual leaf respiration per unit ground area was 164 g C m2 yr1 in NHM stands, 102 g C m2 yr1 in UAM stands, and 8 g C m2 yr1 in EAM stands. The total leaf dry mass was highest in the NHM (285 g m2) stands, followed by UAM (205 g m2) and EAM (13 g m2) stands (Table 6).

8

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

treatment intensity. The state of stand recovery might be the main factor determining this because net primary production, litterfall, foliage biomass, nutrient accumulation, and fine root biomass reach a maximum at canopy closure when stands start self-thinning (Vogt et al., 1987; Fahey and Hughes, 1994). The 8-year-old EAM stands in our study probably need another decade or two to reach this stage. Additionally, the photosynthetic capacity of EAM stands is probably lower than other treatment types due to a reduced vertical canopy structure, also reducing autotrophic soil respiration rates driven by photosynthesis (Högberg et al., 2001). In contrast, the vigorous stump sprouts (Dey and Jensen, 2000) and significant re-growth of canopy and ground cover after thinning (Grabner and Zenner, 2002) at UAM stands might further explain the increased soil respiration rates on these sites (Xu et al., 2011). The various components (down-log, snag, sapwood, and leaf) of cumulative annual respiration per unit ground area were also sensitive to the forest management regime and in predictable ways (Fig. 4). In general, the amount of down-log and snag mass and respiration increased with harvest intensity, with the exception of no snags being left on EMA sites (Tables 3 and 4). In contrast, the amount of sapwood and leaf mass and respiration per unit ground area was decreased with management intensity, from no harvest to uneven-aged to even-aged management (Tables 5 and 6). We also found that the stand age, species, and decay classes of down-logs and snags were important in determining the magnitude and proportion of component respiration. Previous studies also found that both the stand age and species composition had critical impacts on component respiration in northern temperate hardwood forests (Tang et al., 2008). For example, stand age correlated with succession pattern of respiration in the northern forests, peaking in mature forests and declining in the old-growth forests (Curtis et al., 2005). The decline in ecosystem respiration on NHM stands in Ozark forests compared to the UAM stands, may be due to decreased gross primary production, changes in C pool sixes and allocation within the system (Ryan et al., 2004), decreased soil nutrient availability and/or increased stomatal/hydraulic limitation that can decrease photosynthetic rates (Gower et al., 1996). The species effects likely vary with the physiology of each species. In general, the deciduous species have a higher photosynthetic capacity than coniferous species, and oaks are higher than hickory (Tang et al., 2005). Lastly, respiration rates are also affected by decay classes of down-logs and snags because the decay class is a measure of the substrate quality, with the lesser the decay the higher the substrate quality for micro-organisms to consume; consequently, higher respiration rates would be expected in fresher materials (Mattson et al., 1987). The component (down-log, snag, sapwood, and leaf) cumulative respiration rate per unit biomass/necromass is another index of component metabolism (Fig. 5). The down-log respiration in the UAM stand is higher per unit mass than that in the EAM stand. This may result from the higher temperature in the UAM stands (Li et al., 2007). In contrast, the sapwood and leaf respiration per unit mass in the EAM stand is higher than that in the UAM stand, probably because young tissue has higher potential photosynthetic rates per unit mass (Tang et al., 2008). Respiration rate per unit biomass/necromass is a common way of quantifying mass respiratory fluxes in species (Sprugel and Benecke, 1991), stands (Lavigne et al., 1996), and ecosystems (Ryan and Waring, 1992). Some researchers even argue that sapwood volume is a better proxy of living tissue better than stem surface area (Ryan, 1990), although surface area has been used more often (Kinerson, 1975; Lavigne, 1988; Matyssek and Schulze, 1998). Other researchers found that sapwood volume and stem surface area were equally useful for expressing sapwood respiration rates in terms of the amount of sampling required to obtain precise estimates for balsam fir stands in eastern Canadian provinces (Lavigne et al., 1996).

Regardless of treatment, species or ecosystem component, temperature is the primary controlling factor affecting respiration in our Ozark oak-hickory forest ecosystem, and an exponential function explained most of the observed temporal variation (Chimner and Welker, 2005). Temperature sensitivity (Q10) to respiration is usually regarded as temperature-dependent (Lloyd and Taylor, 1994; Fang and Moncrieff, 2001), and fixed Q10 values over study periods (2003–2005) provided useful estimates for ecosystem respiration and its components at our study site. However, recent research found that moisture content also had significant impacts on respiration, especially at arid sites (Ma et al., 2005). In arid or semiarid ecosystems, water content is one of the major factors limiting ecosystem activities (i.e., respiration), particularly in the summer (Xu and Qi, 2001a). Moreover, a summer drought was found to reduce the annual soil respiration in a mature forest in Northern Wisconsin (Martin and Bolstad, 2005), hardly an arid system, although the impact was less than 8% of the total annual respiration (Noormets et al., 2008b). Nevertheless, soil water content averaged 16% at our site and appears to be sufficient to maintain microbial activity and plant physiology. Thus, temperature remained the major determinant for component respiration at our study site. Another potential controlling factor for down-log and snag respiration is decay status (Robertson and Daniel, 1989; Harmon et al., 1995; Kruys et al., 2002). We observed that respiration per unit mass was highest for class II materials for both down-logs and snags, and that class II materials made the greatest contributions to total respiration of these components for all management treatments (Table 3 and Fig. 5). Also, these patterns did not vary much between treatments. However, the factors controlling respiration rates in down-logs and snags are not clear. Dead wood density is reported to significantly affected respiratory flux, but the results are not consistent among studies. Some researchers report an increase in respiratory flux with decreasing dead wood density, which may result from higher water absorption in down-logs stimulating respiration (Bond-Lamberty et al., 2003; Gough et al., 2008). However, the opposite trend was reported for dead wood in a coniferous forest of the Pacific Northwest, in which the less decayed wood appeared to have an elevated respiratory flux because of high labile C content (Marra and Edmonds, 1996). Thus interactions between substrate quality, which correlates to stage of decay, and water holding capacity, which may also vary with stage of decay, may affect respiration rate in antagnoistic ways. An important advantage our simple temperature-based model is its broader application in both scientific research (e.g., cross-site comparisons) and management practices because temperature data are readily available (Raich and Schlesinger, 1992). Of course we were fortunate that little variation existed in either moisture characteristics of our management treatments or proportional distribution of decay classes of down-logs and snags between treatments. We could not find other comparative studies in similar ecological zones that used chamber methods to estimate total ecosystem respiration from second growth forests. However, a recent study in a mature US northern hardwood forest using chamber and biometric measurement methods, reported 1425, 1003, 166, and 256 g C m2 yr1 of ecosystem respiration, soil respiration, stem respiration, and leaf respiration, respectively (Curtis et al., 2005). These rates were somewhat higher than our second growth oakhickory stands, but the magnitude of our cumulative respiration in our study is comparable to studies in other forest ecosystems. For example, Ryan et al. (1997) used a biometric approach in several Canadian forests and estimated that annual autotrophic respiration ranged from a low of 535 g C m2 yr1 in Pinus banksiana forests to a high of 908 g C m2 yr1 in Populus tremuloides stands. Law et al. (1999) reported ecosystem respiration at 894 g C m2 yr1 in a mixed age Pinus ponderosa forest and Bolstad

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

et al. (1999) reported a comparatively high annual ecosystem respiration of 1469 g C m2 yr1 in a mature Pinus tremuloides stand. Wang et al. (2004) estimated annual ecosystem respiration in a Finnish Pinus sylvestris forest using both modeling and meteorological approaches, averaging 611 g C m2 yr1, which did not differ significantly from meteorological estimates. Ecosystem respiration from our site was also much lower than that of mature Amazon tropical forests, estimated at 2338 g C m2 yr1 (Grace et al., 1996), and of 3070 g C m2 yr1 (Carswell et al., 2002). Of course the higher temperature, longer growing season, and higher photosynthesis and growth rates in tropical forests may explain the higher respiration. 5. Conclusions Our chamber-based flux measurements, coupled with spatial and temporal scaling and equation fitting methods, allowed us to estimate ecosystem respiration and its components. Harvesting regimes altering ecosystem component respiration and temperature were the major factors affecting respiration in the temperate oak forest ecosystem. Exponential functions between respiration and temperature explained most of the observed spatial and temporal variation for ecosystem and component respiration. The annual ecosystem respiration was 1684, 1787, and 1231 g C m2 yr1 in the NHM, UAM, and EAM stands, respectively. Autotrophic respiration (sapwood + leaf respiration) – the cost for tree growth and maintenance in stems and leaves – is about 414 g C m2 yr1 in the NHM stands, and decreases to 298 and 27 g C m2 yr1 in the UAM and EAM stands, respectively. The heterotrophic respiration (soil + down-log + snag) varies greatly among treatments and is 1270, 1489, and 1203 g C m2 yr1 for NHM, UAM, and EAM, respectively. Soil respiration was the largest component, which accounted for between 71% and 89% of total ecosystem respiration. Our data indicated that harvesting method affected ecosystem processes and local climatic conditions such as temperature can be used to predict variation of the ecosystem functions within different harvesting regimes. Acknowledgments This research was funded by the Missouri Department of Conservation (MDC) through its MOFEP Project. We offer special thanks to the Landscape Ecology and Ecosystem Sciences (LEESs) research group at The University of Toledo for critical comments. Special thanks to Rachel Henderson, who not only fully supported the research during her master studies at the University of Toledo, but also reviewed and edited the manuscript. Randy Jensen provided much logistical support for our field operation. The following people helped with the field logistics: Mark Johanson, Charity Bernard, and Lori Schmitz. References Amiro, B.D., Barr, A.G., Black, T.A., Iwashita, H., Kljun, N., McCaughey, J.H., Morgenstern, K., Murayama, S., Nesic, Z., Orchansky, A.L., Saigusa, N., 2006. Carbon, energy and water fluxes at mature and disturbed forest sites, Saskatchewan, Canada. Agricultural and Forest Meteorology 136, 237–251. Bolstad, P.V., Mitchell, K., Vose, J.M., 1999. Foliar temperature–respiration response functions for broad-leaved tree species in the southern Appalachians. Tree Physiology 19, 871–878. Bolstad, P.V., Davis, K.J., Martin, J., Cook, B.D., Wang, W., 2004. Component and whole-system respiration fluxes in northern deciduous forests. Tree Physiology 24, 493–504. Bond-Lamberty, B., Wang, C., Gower, S.T., 2003. Annual carbon flux from woody debris for a boreal black spruce fire chronosequence. Journal of Geophysical Research 108, 8220. Brookshire, B.L., Shifley, S.R. (Eds.), 1997. Proceedings of the Missouri Ozark Forest Ecosystem Project: An Experimental Approach to Landscape Research. USDA, For. Ser., North Central Res. Station, St. Paul, MN.

9

Carswell, F.E., Costa, A.L., Palheta, M., Malhi, Y., Meir, P., Costa, J.D.R., Ruivo, M.D., Leal, L.D.M., Costa, J.M.N., Clement, R.J., Grace, J., 2002. Seasonality in CO2 and H2O flux at an eastern Amazonian rain forest. Journal of Geophysical Research: Atmospheres 107 (D20), 8076. Chen, J., Falk, M., Euskirchen, E., Paw U, K.T., Suchanek, T.H., Ustin, S.L., Bond, B.J., Brosofske, K.D., Phillips, N., Bi, R., 2002. Biophysical control of carbon flows in three successional Douglas-fir stands based on eddy-covariance measurements. Tree Physiology 22, 169–177. Chimner, R.A., Welker, J.M., 2005. Ecosystem respiration responses to experimental manipulations of winter and summer precipitation in a mixed grass prairie, WY, USA. Biogeochemistry 73, 257–270. Concilio, A., Ma, S., Li, Q., LeMoine, J., Chen, J., North, M., Moorhead, D., Jenson, R., 2005. Soil respiration response to prescribed burning and thinning in mixedconifer and hardwood forests. Canadian Journal of Forest Research 35, 1581– 1591. Concilio, A., Chen, J., Ma, S., North, M., 2009. Precipitation drives interannual variation in soil respiration in an experimental forest. Climate Change 92, 109– 122. Curtis, P.S., Vogel, C.S., Gough, C.M., Schmid, H.P., Su, H.B., Dovard, B.D., 2005. Respiratory carbon losses and the carbon use efficiency of a northern hardwood forest, 1999–2003. New Phytologist 167, 437–455. Dey, D.C., Jensen, R.G., 2000. Stump sprouting potential of oaks in Missouri Ozark Forests managed by even- and uneven aged silviculture. In: Shifley, S.R., Kabrick, J.M. (Eds.), Proceedings of the Second Missouri Ozark Forest Ecosystem Project Symposium: Post-Treatment Results of the Landscape Experiment. USDA For. Ser., St. Louis, MI, P227, pp. 102–113. Fahey, T.J., Hughes, J.W., 1994. Fine root dynamics in a northern hardwood forest ecosystem, Hubbard Brook Experimental Forest, NH. Journal of Ecology 82. Fang, C., Moncrieff, J.B., 2001. The dependence of soil CO2 efflux on temperature. Soil Biology and Biochemistry 33, 155–165. Gordon, A.M., Schlentner, R.E., Cleve, K.V., 1987. Seasonal pattern of soil respiration and CO2 evolution following harvesting in the white spruce forests of interior Alaska. Canadian Journal of Forest Research 17, 304–310. Gough, C.M., Vogel, C.S., Schmid, H.P., Su, H.-B., Curtis, P.S., 2008. Multi-year convergence of biometric and meteorological estimates of forest carbon storage. Agricultural and Forest Meteorology 148, 158–170. Gower, S.T., McMurtrie, R.E., Murty, D., 1996. Aboveground net primary production decline with stand age: potential causes. Trends in Ecology and Evolution 11, 378–382. Grabner, J.K., Zenner, E.K., 2002. Changes in ground layer vegetation following timber harvest on the Missouri Ozark forest ecosystem project. In: Shifley, S.R., Kabrick, J.M. (Eds.), Proceedings of the Second Missouri Ozark Forest Ecosystem Project Symposium: Post-treatment Results of the Landscape Experiment. USDA Forest Service General Technical Report, St. Louis, MO, pp. 66–83. Grace, J., Lloyd, J., McIntyre, J., Meir, P., Miranda, A.C., Malhi, Y., Miranda, H.S., 1996. The use of eddy covariance to infer the net carbon dioxide uptake of Brazilian rain forest. Global Change Biology 2, 209–217. Guyette, R., Larsen, D., 2000. A history of Anthropogenic and Natural Disturbances in the Area of the Missouri Ozark Forest Ecosystem Project. GTR NC-208, pp. 19– 40. Harmon, M.E., Whigham, D.F., Sexton, J., Olmsted, I., 1995. Decomposition and mass of woody detritus in the dry tropical forests of the northeastern Yucatan Peninsula, Mexico. Biotropica 27, 305–316. Högberg, P., Nordgren, A., Buchmann, N., Taylor, A.F.S., Ekblad, A., Hogberg, M.N., Nyberg, G., Ottosson-Lofvenius, M., Read, D.J., 2001. Large scale forest girdling shows that current photosynthesis drives soil respiration. Nature 411, 789–792. Houghton, R.A., 1999. The annual net flux of C to the atmosphere from changes in land use 1850–1990. Tellus 51B, 298–313. Kabrick, J., Meinert, D., Nigh, T., 2000. Physical plot characteristics with landform, soil, and site quality. In: Shifley, S.R., Brookshire, B.L. (Eds.), Missouri Ozark Forest Ecosystem Project: Site History, Soils, Landforms, Woody and Herbaceous Vegetation, Down Wood, and Inventory Methods for the Landscape Experiment. USDA For. Ser., St. Paul, MN, P314, pp. 149–172. Kinerson, R.S., 1975. Relationships between plant surface area and respiration in loblolly pine. Journal of Applied Ecology 12, 965–971. Kruys, N., Jonsson, B.G., Stahl, G., 2002. A stage-based matrix model for decay-class dynamics of woody debris. Ecological Applications 12, 773–781. Kurz, W.A., Stinson, G., Rampley, G.J., Dymond, C.C., Neilson, E.T., 2008. Risk of natural disturbances makes future contribution of Canada’s forests to the global carbon cycle highly uncertain. Proceedings of the National Academy of Sciences of the United States of America 105, 1551–1555. Lavigne, M.B., 1988. Stem growth and respiration of young balsam fir trees in thinned and un-thinned stands. Canadian Journal of Forest Research 18, 483– 489. Lavigne, M.B., Franklin, S.E., Hunt Jr., E.R., 1996. Estimating stem maintenance respiration rates of dissimilar balsam fir stands. Tree Physiology 16, 687–695. Law, B.E., Ryan, M.G., Anthoni, P.M., 1999. Seasonal and annual respiration of a ponderosa pine ecosystem. Global Change Biology 5, 169–182. Li, Q., Chen, J., Moorhead, D.L., DeForest, J.L., Jensen, R., Henderson, R., 2007. Effects of timber harvest on carbon pools in Ozark forests. Canadian Journal of Forest Research 37, 2337–2348, doi: 2310.1139/x2307-2086. Li, Q., Moorhead, D.L., DeForest, J.L., Henderson, R., Chen, J., Jensen, R., 2009. Mixed litter decomposition in a managed Missouri Ozark forest ecosystem. Forest Ecology and Management 257, 688–694. Lloyd, J., Taylor, J.A., 1994. On the temperature dependence of soil respiration. Functional Ecology 8, 315–323.

10

Q. Li et al. / Forest Ecology and Management 279 (2012) 1–10

Ma, S., Chen, J., Butnor, J.R., North, M., Euskirchen, E.S., Oakley, B., 2005. Biophysical controls on soil respiration in the dominant patch types of an old-growth, mixed-conifer forest. Forest Science 51, 221–232. Marra, J.L., Edmonds, R.L., 1996. Coarse woody debris and soil respiration in a clearcut on the Olympic Peninsula, Washington, USA. Canadian Journal of Forest Research 26, 1337–1345. Martin, A.J., 1976. Suitability of the Line Intersect Method for Sampling Hardwood Logging Residues. USDA Forest Service General Technical Report, NE-339. Martin, J.G., Bolstad, P.V., 2005. Annual soil respiration in broadleaf forests of northern Wisconsin: influence of moisture and site biological, chemical, and physical characteristics. Biogeochemistry 73, 149–182. Martin, J.G., Kloeppel, B.D., Schaefer, T.L., Kimbler, D.L., McNulty, S.G., 1998. Aboveground biomass and nitrogen allocation of ten deciduous southern Appalachian tree species. Canadian Journal of Forest Research 28, 1648–1659. Mattson, K., Swank, W., Waide, J., 1987. Decomposition of woody debris in a regenerating clear-cut forest in the Southern Appalachians. Canadian Journal of Forest Research 17, 712–721. Matyssek, R.E., Schulze, D., 1998. Carbon uptake and respiration in above-ground parts of a Larix decidua x leptolepis tree. Trees 2, 233–241. Missouri Department of Conservation, 1986. Forest Land Management Guidelines. Missouri Department of Conservation, Jefferson City, MO. Noormets, A., Desai, A., Ricciuto, D., Cook, B.D., Davis, K.J., Bolstad, P.V., Schmid, H.P., Curtis, P.S., Carey, E.V., Su, H.B., Chen, J., 2008a. Moisture sensitivity of ecosystem respiration: comparison of 14 forest ecosystems in the Upper Great Lakes Region, USA. Agricultural and Forest Meteorology 148, 216–230. Noormets, A., McNulty, S.G., DeForest, J.L., Sun, G., Li, Q., Chen, J., 2008b. Drought during canopy development has lasting effect on annual carbon balance in a deciduous temperate forest. New Phytologist 179, 818–828. Pyle, C., Brown, M.M., 1998. A rapid system of decay classification for hardwood logs of the eastern deciduous forest floor. Journal of the Torrey Botanical Society 125, 237–245. Raich, J.W., Schlesinger, W.H., 1992. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus 44B, 81–99. Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Granwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., Valentini, R., 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology 11, 1424–1439. Robertson, A.I., Daniel, P.A., 1989. Decomposition and the annual flux of detritus from fallen timber in tropical mangrove forests. Limnology and Oceanography 34, 640–646. Rustad, L.E., Huntington, T.G., Boone, R.D., 2000. Controls on soil respiration: implications for climate change. Biogeochemistry 48, 1–6. Ryan, M.G., 1990. Growth and maintenance respiration in stems of Pinus contorta and Picea engelmanii. Canadian Journal of Forest Research 20, 48–57. Ryan, M.G., Waring, R.H., 1992. Maintenance respiration and stand development in a subalpine lodgepole pine forest. Ecology 73, 2100–2108. Ryan, M.G., Lavigne, M.B., Gower, S.T., 1997. Annual carbon cost of autotrophic respiration in boreal forest ecosystems in relation to species and climate. Journal of Geophysical Research 102, 28871–28884. Ryan, M.G., Binkley, D., Fownes, J.H., Giardina, C.P., Senock, R.S., 2004. An experimental test of the causes of forest growth decline with stand age. Ecological Monographs 74, 393–414. Sheriff, S.L., He, Z., 1997. The experimental design of the Missouri Ozark forest ecosystem project. In: Brookshire, B.L., Shifley, S.R. (Eds.), Proceedings of the Missouri Ozark Forest Ecosystem Project Symposium: An Experimental Approach to Landscape Research. USDA Forest Service, North Central Forest Experiment Station, St. Louis, MO, pp. 26–40. Shifley, S.R., Brookshire, B.L., 2000. Missouri Ozark Forest Ecosystem Project Site History, Soils, Landforms, Woody, and Herbaceous Vegetation, Down Wood, and Inventory Methods for the Landscape Experiment. USDA, For. Ser., Nort. Cen. Res. Stat., St. Paul, MN. Shifley, S.R., Kabrick, J.M., 2002. Proceedings of the Second Missouri Ozark Forest Ecosystem Project Symposium: Post-Treatment Results of the Landscape Experiment. USDA, For. Ser., Nort. Cent. Res. Stat., St. Paul, MN.

Sohlenius, B., 1982. Short-term influence of clear-cutting on abundance of soilmicrofauna (nematoda, rotatiotia, and tardigrada) in a Swedish pine forest soil. Journal of Applied Ecology 19, 88–89. Sprugel, D.G., Benecke, U. (Eds.), 1991. Measuring Wood-Tissue Respiration and Photosynthesis. CRC Press, Boca Raton. Tang, J., Baldocchi, D.D., 2005. Spatial-temporal variation in soil respiration in an oak-grass savanna ecosystem in California and its partitioning into autotrophic and heterotrophic components. Biogeochemistry 73, 183–207. Tang, J., Baldocchi, D.D., Xu, L., 2005. Tree photosynthesis modulates soil respiration on a diurnal time scale. Global Change Biology 11, 1298–1304. Tang, J., Bolstad, P.V., Desai, A.R., Martin, J.G., Cook, B.D., Davis, K.J., Carey, E.V., 2008. Ecosystem respiration and its components in an old-growth forest in the Great Lakes region of the United States. Agricultural and Forest Meteorology 148, 171–185. Valentini, R., Matteucci, G., Dolman, A.J., Schulze, E.D., Rebmann, C., Moors, E.J., Granier, A., Gross, P., Jensen, N.O., Pilegaard, K., Lindroth, A., Grelle, A., Bernhofer, C., Grunwald, T., Aubinet, M., Ceulemans, R., Kowalski, A.S., Vesala, T., Rannik, U., Berbigier, P., Loustau, D., Guomundsson, J., Thorgeirsson, H., Ibrom, A., Morgenstern, K., Clement, R., Moncrieff, J., Montagnani, L., Minerbi, S., Jarvis, P.G., 2000. Respiration as the main determinant of carbon balance in European forests. Nature 404, 861–865. Vogel, J.G., Valentine, D.W., Ruess, R.W., 2005. Soil and root respiration in mature Alaskan black spruce forests that vary in soil organic matter decomposition rates. Canadian Journal of Forest Research 35, 161–174. Vogt, K.A., Vogt, D.J., Moore, E.E., Fatuga, B.A., Redlin, M.R., Edmonds, R.L., 1987. Conifer and angiosperm fine-root biomass in relation to stand age and site productivity in Douglas-fir forests. Journal of Ecology 75, 857–870. Wagner, C.E.v., 1964. The line intersect method in forest fuel sampling. Forest Science 10, 267–276. Wang, K.-Y., KellomÃki, S., Zha, T., Peltola, H., 2004. Seasonal variation in energy and water fluxes in a pine forest: an analysis based on eddy covariance and an integrated model. Ecological Modelling 179, 259–279. Wilcke, W., Hess, T., Bengel, C., Homeier, J.r., Valarezo, C., Zech, W., 2005. Coarse woody debris in a montane forest in Ecuador: mass, C and nutrient stock, and turnover. Forest Ecology and Management 205, 139–147. Wu, J., Guan, D., Wang, M., Pei, T., Han, S., Jin, C., 2006. Year-round soil and ecosystem respiration in a temperate broad-leaved Korean pine forest. Forest Ecology and Management 223, 35–44. Xiao, J., Zhuang, Q., Baldocchi, D.D., Law, B.E., Richardson, A.D., Chen, J., Oren, R., Starr, G., Noormets, A., Ma, S., Verma, S.B., Wharton, S., Wofsy, S.C., Bolstad, P.V., Burns, S.P., Cook, D.R., Curtis, P.S., Drake, B.G., Falk, M., Fischer, M.L., Foster, D.R., Gu, L., Hadley, J.L., Hollinger, D.Y., Katul, G.G., Litvak, M., Martin, T.A., Matamala, R., McNulty, S., Meyers, T.P., Monson, R.K., Munger, J.W., Oechel, W.C., Paw U, K.T., Schmid, H.P., Scott, R.L., Sun, G., Suker, A.E., Torn, M.S., 2007. Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data. Agricultural and Forest Meteorology 148, 1827–1847. Xu, M., Qi, Y., 2001a. Soil -surface CO2 efflux and its spatial and temporal variations in a young ponderosa pine plantation in northern California. Global Change Biology 7, 667–677. Xu, M., Qi, Y., 2001b. Spatial and seasonal variations of Q10 determined by soil respiration measurements at a Sierra Nevadan forest. Global Biogeochemical Cycles 15, 687–696. Xu, M., Qi, Y., Chen, J., Song, B., 2004. Scale-dependent relationship between landscape structure and microclimate. Plant Ecology 173, 39–57. Xu, J., Chen, J., Brosofske, K., Li, Q., Weintraub, M., Henderson, R., Wilske, B., John, R., Jensen, R., Li, H., Shao, C., 2011. Influence of timber harvesting alternatives on forest soil respiration and its biophysical regulatory factors over a five-year period in the Missouri Ozarks. Ecosystems 14, 1310–1327. Yuan, W., Lou, Y., Richardson, A.D., Oren, R., Luyssaert, S., Janssens, I.A., Ceulemans, R., Grünwald, T., Aubinet, M., Bernhofer, C., Baldocchi, D.D., Chen, J., Dunn, A.L., Deforest, J., Goldstein, A.H., Moors, E., Munger, J.W., Monson, R.K., Suyker, A.E., Starr, G., Scott, R.L., Tenhunen, J., Verma, S.B., Vesala, T., Wofsy, S.C., 2009. Latitudinal patterns of interannual variability in net ecosystem exchange. Global Change Biology 15, 2905–2920.