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Keywords:

  • carbon cycle;
  • Duke FACE;
  • FACTS-1;
  • global climate change;
  • net ecosystem exchange;
  • soil respiration

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES

Forest ecosystems release large amounts of carbon to the atmosphere from fine-root respiration (Rr), but the control of this flux and its temperature sensitivity (Q10) are poorly understood. We attempted to: (1) identify the factors limiting this flux using additions of glucose and an electron transport uncoupler (carbonyl cyanide m-chlorophenylhydrazone); and (2) improve yearly estimates of Rr by directly measuring its Q10in situ using temperature-controlled cuvettes buried around intact, attached roots. The proximal limits of Rr of loblolly pine (Pinus taeda L.) trees exposed to free-air CO2 enrichment (FACE) and N fertilization were seasonally variable; enzyme capacity limited Rr in the winter, and a combination of substrate supply and adenylate availability limited Rr in summer months. The limiting factors of Rr were not affected by elevated CO2 or N fertilization. Elevated CO2 increased annual stand-level Rr by 34% whereas the combination of elevated CO2 and N fertilization reduced Rr by 40%. Measurements of in situ Rr with high temporal resolution detected diel patterns that were correlated with canopy photosynthesis with a lag of 1 d or less as measured by eddy covariance, indicating a dynamic link between canopy photosynthesis and root respiration. These results suggest that Rr is coupled to daily canopy photosynthesis and increases with carbon allocation below ground.


INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES

Plant (autotrophic) respiration (Ra) is globally important and releases about 60 Gt C to the atmosphere each year (Prentice et al. 2001), roughly eight times the flux of C from fossil fuels (7.2 Gt C year−1 from 2000 to 2005; IPCC 2007). As forest ecosystems comprise the largest portion of the terrestrial C flux (Prentice et al. 2001), small changes in Ra from forests could have a large effect on the global carbon cycle. Although Ra is typically predicted to increase with climate change because of its positive correlation with temperature (Boone et al. 1998; Friedlingstein et al. 2006), ecosystem responses to global change factors can influence the amount of respiring biomass or its temperature sensitivity, leading to a more complex relationship between global change and Ra (Luo 2007). Thus, it is important to understand how major aspects of global change such as increases in atmospheric CO2 (IPCC 2007) and nitrogen deposition (Galloway et al. 1995) affect Ra in forest ecosystems.

Because of the lack of a rigorous mechanistic model equivalent to that for photosynthesis (Farquhar, Caemmerer & Berry 1980), carbon cycling models are forced to make simplifying assumptions to incorporate Ra. Whereas some models assume Ra is a constant fraction of gross primary production (DeLucia et al. 2007), many physiological models (Aber & Federer 1992; Thornton et al. 2002) make the assumption that Ra increases exponentially with temperature with a constant Q10 of ∼2 (Q10: multiplicative change in Ra with a 10 °C change in temperature). However, the Q10 of vegetation varies by species, tissue type, temperature and environmental conditions (Tjoelker, Reich & Oleksyn 1999; Atkin, Holly & Ball 2000; Atkin et al. 2005; Bernhardt et al. 2006), with substantial impacts ecosystem carbon cycling. For example, allowing Q10 to acclimate to air temperature reduced modeled leaf respiration by 31–41% and increased above-ground net primary production by 18–38% in a boreal coniferous forest (Wythers et al. 2005).

Despite its importance, few field studies have estimated the Q10 of below-ground processes directly. Some studies have used seasonal changes in temperature to develop a temperature function for respiration (Lloyd & Taylor 1994; Zha et al. 2004; Rodeghiero & Cescatti 2005), but this method confounds other variables with temperature (e.g. phenology), and is not capable of detecting seasonality in Q10 (Davidson, Janssens & Luo 2006). One objective of this study was to improve annual estimates of fine-root respiration (Rr) in a loblolly pine (Pinus taeda L.) forest by directly measuring the temperature dependence of respiration (Q10) throughout the year. Fine-root respiration was investigated because it is the largest component of Ra in this ecosystem, comprising ∼40% of the total flux (Hamilton et al. 2002).

Atkin & Tjoelker (2003) proposed a tripartite mechanistic model of regulation, where Ra is proximally limited by enzyme capacity, substrate availability or negative feedbacks on the tricarboxylic acid (TCA) cycle by ATP production (i.e. limited by adenylate availability). Enzyme capacity generally limits Ra at low temperatures (Covey-Crump, Attwood & Atkin 2002), whereas substrate or adenylate limitations are common at moderate to high temperatures (Noguchi & Terashima 1997; Covey-Crump et al. 2002). The realized rate of respiration is determined by the minimum of these limiting factors.

To our knowledge, this model of respiratory control has not been applied in the field or on trees where the seasonality of C allocation and elements of global change could alter these proximal limits of Ra. We hypothesize that elevated atmospheric CO2 will alleviate substrate limitation of root respiration (Rr) by increasing tissue carbohydrate supply (Ainsworth & Long 2005), wheras simulated nitrogen deposition will increase respiratory capacity by providing more N for protein synthesis. Understanding how global change influences the limitations of Ra would lend confidence to future predictions of this important flux.

This study had two specific objectives: (1) identify the limits of fine-root respiration (Rr) of loblolly pine trees over a seasonal cycle, and to investigate modifications of these limitations by N fertilization and elevated CO2; and (2) improve annual estimates of Rr by directly measuring the Q10 multiple times throughout the season.

METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES

Site description

This research was conducted at the Duke free-air carbon dioxide enrichment (FACE) experiment (Orange County, NC, USA; 35°58′N 79°05′W) comprised of six 30-m-diameter plots within a continuous, unmanaged loblolly pine (Pinus taeda) plantation. Three fully instrumented control plots receive ambient air, and three treatment plots maintain atmospheric CO2 concentration at ambient plus 200 µmol mol−1 to simulate conditions expected in the year 2050 (IPCC 2007). The experimental design has been expanded to include the FACE prototype and reference plots, but this study was conducted in the original six plots only. In 2006, the year of this study, average daytime CO2 concentration was ∼383 µmol mol−1 in the ambient plots, and ∼577 µmol mol−1 in the elevated plots. The CO2 concentrations at night were similar in both treatment and control plots (∼410 µmol mol−1; Keith Lewin, Robert Nettles personal communication). Soils are of the Enon Series derived from mafic bedrock (fine, mixed, active, thermic Ultic Hapludalfs) and are slightly acidic (0.1 M CaCl2 pH 5.5). Detailed descriptions of the FACE technology (Hendrey et al. 1999) and soils at this site are available (Oh & Richter 2005).

A nitrogen fertilizer treatment was added to the FACE experiment in 2005. Each year, ammonium nitrate was hand-broadcasted to half of each plot at a rate of 11.2 gN m−2 year−1 in two applications (half in March, half in April). The unfertilized half of each plot was separated from this treatment by a 70-cm-deep impenetrable tarp; 95% of fine roots are <15 cm deep and nearly 0% are >30 cm (Matamala & Schlesinger 2000). The experimental design at the time of this study was a split-plot in a randomized complete block design with three replicates; CO2 treatment was the whole-plot factor and N treatment was the subplot factor.

Net ecosystem exchange of CO2 (NEE) was measured with an eddy covariance system (EC) comprised of a triaxial sonic anemometer (CSAT3, Campbell Scientific, Logan, UT, USA) coupled with an open-path infrared gas analyzer (LI-7500, Li-Cor, Lincoln, NE, USA) positioned 20.2 m above an upwind ambient CO2 plot. The Webb–Pearman–Leuning correction for the effects of air density fluctuations on flux measurements was applied to scalar fluxes measurements (Webb, Pearman & Leuning 1980), and a 1/2 h averaging interval was chosen. More information about these measurements and subsequent data analyses are available (Katul et al. 1997; Stoy et al. 2006a,b).

Oxygen electrode measurements

The rate of oxygen consumption before and after the addition of exogenous glucose or an electron transport uncoupler (carbonyl cyanide m-chlorophenylhydrazone, CCCP) was measured on excised roots three times during the year. Two subsamples were averaged per subplot in May and three subsamples were averaged per subplot in July and January. These sampling dates were chosen to capture variation in C allocation, as maximum wood growth is in May (Moore et al. 2006), needle and fine-root growth peaks are in July (Schafer et al. 2003), and little growth occurs in January. Roots were sampled between 0900 and 1200 h to minimize potential time-of-day effects. Attached P. taeda fine roots (≤1.5 mm diameter) were excavated by removing the litter layer and gradually exposing roots with paint brushes. Roots were excised with a razor blade and stored in 1 mm CaCl2 buffered to pH 5.50 with 2-Morpholinoethanesulfonic acid (MES) during transport to an on-site laboratory (∼5 min). Approximately 0.5 g fresh weight (FW) of fine roots were cut into 3 cm segments and divided into three subsamples. One subsample was stored in liquid N2 for analysis of carbohydrates. The other subsamples were incubated for 20 min in buffer or buffer plus glucose (50 mm) at a controlled temperature that approximated ambient soil temperature (20 °C in May and July, 10 °C in January). Increased Rr in the glucose-saturated sample relative to the subsample without added glucose (hereafter the ‘basal sample’) would indicate that the availability of sugar substrates limited basal Rr.

Respiration of the paired subsamples (basal and glucose-saturated) was measured concurrently at incubation temperature in Clark-type oxygen electrodes (Dual Digital Model 20; Rank Brothers, Cambridge, UK). The respiration rate was measured over a period of 10 min following 5 min of equilibrium in the electrode. CCCP was then injected to the basal sample to a final concentration of 15 µL and the uncoupled respiration rate was measured over the next 10 min following a 10 min equilibration period. CCCP dissipates the H+ gradient across the inner mitochondrial membrane, uncoupling proton transport from ATP synthesis. An increase in Ra upon addition of CCCP would indicate that the H+ gradient across the mitochondrial membrane limited O2-consumption (Lambers, Robinson & Ribas-Carbo 2005; Papa, Lorusso & Di Paola 2006). All measurements were completed <90 min after root excision. Rates of oxygen consumption were converted to CO2 efflux to facilitate comparisons with gas exchange measurements assuming a respiratory quotient of 1.25 (Penning de Vries, Brunsting & Van Laar 1974; Matamala & Schlesinger 2000). One measurement was taken per subplot of a single block per day. The concentrations of glucose and CCCP used in this study saturated the stimulation of O2 consumption in these fine roots (Drake, unpublished).

Tissue chemistry

Each frozen root subsample was ground using mortar and pestle in liquid N2 and immediately subjected to three extractions in 80% ethanol and 2 mm Hepes (pH 7.8), and one extraction in 50% ethanol and 2 mm Hepes (pH 7.8; all at 80 °C for 20 min). Concentrations of glucose, fructose and sucrose were quantified spectrophotometrically (Jones, Outlaw & Lowry 1977; Hendrix 1993) at 340 nm with a 96 well plate reader (Powerwave HT; Biotek, Winooski, VT, USA). Starch was degraded to glucose by overnight incubation with amyloclucosidase and α-amylase at 37 °C, and quantified as glucose equivalents in the plate reader. The other root subsamples were dried and combusted in an elemental analyzer to determine C and N contents (ECS 4010; Costech, Valencia, CA, USA).

Gas exchange measurements

The in situ rate of CO2 evolution was measured in July and January by enclosing intact, attached fine roots in buried gas exchange cuvettes. About 0.3 g FW of attached fine root tissue was excavated from 0 to 5 cm below the litter layer, washed as described previously, patted dry and placed into a custom polycarbonate cuvette with a type-E thermocouple. The leaf litter and disturbed soil was replaced to allow the cuvettes to reach thermal equilibrium with the soil. Four cuvettes were prepared in this way per subplot.

Root CO2 efflux was measured using a custom open-path automated sampling system built around a closed-path infrared gas analyzer (Li 6262; Li-Cor). Ambient air that passed through two 122 L buffer volumes was used as the input gas in the ambient plots; high temporal variation in [CO2] necessitated the use of standard air tanks (400 µmol CO2 mol−1) in elevated CO2 plots. Air was humidified as much as possible (∼80% relative humidity) using a series of water bubblers and traps. Gas manifolds containing five solenoid valves (Mac Valves, Wixom, MI, USA) allowed the air flow to be directed to a reference line or one of four cuvettes. The system was controlled by a data logger (CR10X; Campbell Scientific, Logan, UT, USA). The flow rate was measured with a mass flow meter (Hastings ST-1K; Teledyne Hastings, Hampton, VA, USA) upstream of the manifolds. A single measurement consisted of passing air through a cuvette for 4 min and then through the reference line for 1 min. The data logger recorded 10 s averages of cuvette temperatures and CO2 concentration and computed a difference measurement as ΔCO2 = [COs]cuvette − [CO2]reference (Long et al. 1993). A measurement cycle of all four chambers was achieved every 20 min. A 24 h diel cycle of Rr was measured on all sampling dates after roots acclimated to the cuvettes for 5 h. Q10 values were calculated from diel variation according to the following equation: inline image; where T1 and R1 denote temperature and Rr at the daily minimum temperature (0600 to 0800 h), and T2 and R2 denote temperature and Rr at the subsequent maximum temperature (1300 to 1500 h). Soil temperature and Rr were relatively constant between 0400 and 0700 h; values during this period were averaged to calculate basal Rr for each plot.

Temperature response of Rr

The temperature sensitivity of respiration (Q10) was measured following 24 h of in situ measurements by modulating cuvette temperatures with an external water bath that circulated water through the base of each cuvette. Rr was measured at five temperatures from 5 to 40 °C; two measurements per cuvette were averaged per temperature. Roots were excised, dried and analyzed for C and N content as described previously. Subplots of each main plot were sampled on successive days. Sampling of all plots was completed over 2 weeks in July 2006 and January 2007. The temperature response was not measured in N-fertilized subplots because of time constraints.

Scaling Rr to the stand-level

The basal in situ respiration rates were scaled to yearly estimates using soil temperature at a depth of 10 cm, the measured temperature sensitivity of Rr and plot-specific measurements of fine-root biomass. Fine-root biomass was measured every 3 months with soil cores (4.75 cm diameter, 15 cm deep, n = 3 per subplot). Roots were picked by hand, dried and weighed (Jackson, unpublished). Monthly fine-root biomass was estimated by interpolating plot averages with a linear spline function (Proc Expand, SAS v9.1; SAS Institute, Cary, NC, USA). In 2006, annually averaged fine-root biomass in ambient CO2 × ambient N, elevated CO2 × ambient N, ambient CO2 × N fertilized and elevated CO2 × N fertilized were 250, 374, 291 and 347 g m−2, respectively (data not shown). Thirty-minute averages of soil temperature were measured at 10 cm depth in four locations in the ambient plot associated with the eddy covariance system. These measurements were averaged by month to coincide with the biomass estimates. Values of Rr measured in situ were converted to monthly estimates using the measured temperature response. The summer rates and temperature response were used for May–November whereas the winter values were used for December–April, following the growing and dormant seasons at this site (Moore et al. 2006). Applying these rates and functions to different combinations of months altered the yearly Rr estimates by less than 5%.

Data analysis

Statistical analyses followed a repeated measures split-plot design and were computed using SAS (v9.1; SAS Institute). Repeated-measures mixed-model analyses of variance (Proc Mixed) were used in all analyses except for the regressions and temperature-response curves, where least squares regressions were used (Proc Reg). The apparent Q10 curves were fit in Sigmaplot 10.0 (Systat, San Jose, CA, USA). The lag analysis was performed as in Ekblad & Hogberg (2001). Covariance structures in the repeated-measures analyses were modeled as autoregessive-1, as this minimized the fit statistics based on the -2 res log likelihood parameter (Littell, Henry & Ammerman 1998). All analyses were checked to ensure homoscedasticity and normality of residuals; transformations were applied where appropriate. Unless otherwise stated, all data are presented as least-squares means and error bars are ±1 SE as estimated within a mixed model [i.e. least squares (LS)–standard errors].

RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES

Respiratory control and tissue chemistry

Low rates of Rr and the absence of a response to exogenous glucose or CCCP suggest that Rr was limited by enzyme capacity during January (Fig. 1). In contrast, Rr was limited by a combination of substrate supply and ATP utilization during May and July, as Rr was significantly increased by the addition of glucose and CCCP. There were no significant main effects or interactions of elevated CO2 or N on Rr, or its stimulation by substrate or uncoupler (P > 0.2), so the LS means of sampling date were presented for clarity (Fig. 1). Uncoupler stimulated Rr by 31.0 and 16.6% in May and July, whereas glucose additions stimulated Rr by 21.6 and 19.9%, respectively.

image

Figure 1. Basal respiration (Rr) of excised fine roots and the rates following addition of glucose or a mitochondrial uncoupler (carbonyl cyanide m-chlorophenylhydrazone) in liquid-phase oxygen electrodes assuming a respiratory quotient of 1.25. Categories that do not share a letter are significantly different (Tukey adjusted P < 0.05). Measurement temperatures approximated soil temperatures: 20 °C in May and July and 10 °C in January.

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Fine root carbohydrate contents were seasonally variable (Table 1). Concentrations of glucose increased in the winter, whereas fructose concentrations decreased. Starch decreased in July, the period of maximal root production (Pritchard et al. 2008), suggesting that this starch was used for growth. Notably, there were no direct effects of elevated CO2 or N fertilization on fine-root carbohydrates. Nitrogen fertilization caused an average 22.8% increase in fine-root N but did not increase Rr.

Table 1.  Chemistry of Pinus taeda fine roots grown in the field under elevated CO2 and N fertilization
CO2NMonthGlucoseFructoseSucroseTSCStarchC %N %C : N
  1. Statistically significant main effects and interactions (P < 0.05) are shown in the bottom row.

  2. Least-squares means are shown by treatment and sampling date.

  3. Standard errors estimated from repeated-measures mixed-model analyses of variance are as follows: glucose, 0.9; fructose, 0.9; sucrose, 2.5; total soluble carbohydrates (TSC; glucose + fructose + sucrose), 3.7; starch, 19.4, C %, 0.6; N %, 0.05, C : N, 1.4.

  4. Units for glucose, fructose, sucrose and TSC are µmol g−1 DW. Starch values are in µmol glucose equivalents g−1 DW.

  5. D = sampling date, C = CO2 treatment, N = nitrogen treatment (interactions are shown as combinations of these letters).

ControlControlMay3.67.821.733.264.050.91.2243.2
July3.86.116.025.942.951.71.2043.5
January8.71.610.721.062.551.71.1147.1
ControlFertilizedMay1.98.421.031.360.452.01.6432.4
July3.34.815.223.341.451.91.4635.8
January6.81.48.516.759.551.91.3838.2
ElevatedControlMay2.53.611.517.572.050.81.1743.7
July3.76.117.427.229.252.11.1844.7
January8.91.611.321.846.751.81.0649.1
ElevatedFertilizedMay4.88.118.131.089.152.31.4537.1
July2.97.017.727.612.952.71.3838.6
January8.51.410.820.755.751.91.2143.3
Significant effects DD, D × CDDDND, ND, C, N

Fine-root O2 consumption rates were positively correlated with tissue sucrose and N concentrations, but the degree of substrate or adenylate restriction was not related to any measured aspect of tissue chemistry. Sucrose was positively correlated with basal, glucose-saturated and CCCP-uncoupled respiration rates (data not shown, log–log plots, respective slopes = 0.027, 0.031, 0.036; respective r2 = 0.29, 0.26, 0.32; P < 0.01), but sucrose concentrations could not explain the differences between these rates. Root N content was positively correlated with basal respiration rate, but the slope was significantly decreased by N fertilization [analysis of covariance (ancova), data not shown, log-log plots, slope in ambient N = 0.086 ± 0.003; N-fertilized = 0.0718 ± 0.003, r2 = 0.26 and 0.12, respectively, ancovaP < 0.01].

In situ Rr: CO2 efflux

Basal Rr measured by gas exchange on attached roots in the field (Fig. 2) varied with sampling date (P < 0.01), and was reduced by the combination of elevated CO2 and N fertilization in July (Tukey adjusted P < 0.05). No treatment effects were observed in January (P > 0.5). Averaged across treatments, Rr was 8.60 and 1.87 nmol CO2 g−1 dry weight (DW) s−1 in July and January, respectively.

image

Figure 2. In situ respiration of attached loblolly pine fine roots. Treatments are: c, ambient [CO2]; C, elevated [CO2]; n, ambient nitrogen; N, nitrogen fertilized. Four subreplicates were averaged per plot (n = 3). Categories that do not share a letter are significantly different (Tukey adjusted P < 0.05). Rr was measured at ambient soil temperature: 20 °C in July and 10 °C in January.

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Temperature sensitivity of Rr

The relationship between Rr and temperature (Fig. 3a) was best described by a linear regression, although a small but significant second-order term was present in July (July: y = −7.25 + 1.17x – 0.011x2, P < 0.01, r2 = 0.61; January: y = −0.0115 + 0.173x, P < 0.01, r2 = 0.68). The observed temperature sensitivity of Rr (slope) was significantly higher in July than in January (ancova, P < 0.01). The observed data could only be described by the traditional exponential function if the Q10 declined with temperature (Fig. 3b). There was no observable difference in the temperature sensitivity of respiration by roots grown at different CO2 concentrations (ancova, P > 0.4).

image

Figure 3. (a) Temperature sensitivity of fine-root respiration of adult loblolly pine trees. Summer data: solid symbols and line: y = −7.25 + 1.17x– 0.011x2, P < 0.01, r2 = 0.61). Winter data: open symbols and dashed line: y = −0.0115 + 0.173x, P < 0.01, r2 = 0.68. Circles are ambient CO2; triangles are elevated CO2. Q10 values were generated from these data (b) using the following equation: Q10 = 10(10* slope). The slope was calculated as the derivative of the second-order polynomial describing log10 respiration versus temperature plots.

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Diel Rr variation

Diel variation in Rr was relatively small in the winter, but large diel variation was observed on some days during the summer (Fig. 4; examples of low and high diel variations in Rr during summer). Diel cycles of Rr were strongly related to temperature on all days (Pearson's r between temperature and Rr = 0.64 ± 0.03). Q10 values calculated from diel patterns of Rr (hereafter ‘apparent Q10’) were relatively low and constant in the winter, ranging from 1.07 to 5.0 with a mean of 3.0. However, apparent Q10 values calculated in this way for summer data were variable and extremely large, ranging from 2.5 to 104.8, with a mean of 24.2. The variation and magnitude of these values suggest that a process beyond simple temperature sensitivity was operating during the summer.

image

Figure 4. Examples of diel variation in loblolly pine fine-root respiration (Rr; ●, solid line) and temperature (○, dotted line). Each point indicates an hourly average of four subsamples, with three measurements per subsample. Light background indicates day; shaded background, night. The variation of Rr was small on some days [(a) 14 July 2006] with reasonable apparent Q10 values (Q10 = 3.3). Rr was highly variable on other days [(b) 5 July 2008] with very large apparent Q10 values (Q10 = 79.6).

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We hypothesized that day-to-day variation in the diel pattern of Rr was influenced by substrate supply, as Rr responded to additions of exogenous glucose in the summer (Fig. 1). Therefore, we investigated the relationship between carbon assimilation [daytime NEE measured by eddy covariance, when photosynthetically active radiation (PAR) > 0] and the apparent Q10 calculated from diel cycles. Variation in the apparent Q10 was correlated with NEE (Fig. 5a; apparent Q10 = 0.044 × 0.0992*e(0.185 × NEE), P < 0.01, r2 = 0.83). Furthermore, lag analysis indicated that recent carbon assimilation explained the observed apparent Q10 values; NEE from more than 1 d prior to measurements of Rr were not significantly correlated with apparent Q10 (Fig. 5b). It appears that NEE affected the temperature sensitivity of Rr instead of affecting Rr directly, as increasing NEE only slightly reduced the correlation between temperature and Rr (Pearson's r between temperature and Rr = 0.726 – 0.005*NEE, P < 0.05, r2 = 0.19).

image

Figure 5. Relationship between apparent temperature sensitivity of fine-root respiration (Q10) and simultaneous daytime net ecosystem exchange [NEE; (a)] Data are from diel gas exchange measurements as in Fig. 4 during July (●) and January (○). y = 0.044* 0.0992*e(0.185x) P < 0.001, r2 = 0.83. (b) Regressions as in (a) fit using lagged daytime NEE values. ** indicates significance at P < 0.01; *** at P < 0.001.

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Yearly stand-level Rr

The yearly quantity of carbon respired by fine roots varied with elevated CO2 and N fertilization (Fig. 6), and this variation was driven largely by the standing biomass of fine roots and tissue-specific rate of respiration. Rr released 645 ± 74 g C m−2 year−1 in ambient conditions, and this was not significantly affected by N fertilization alone (546 ± 74 g C m−2 year−1, P > 0.2). Elevated CO2 increased the amount of carbon released by Rr to 869.7 ± 74 g C m−2 year−1 (P < 0.05), primarily because of increased standing fine-root biomass in the elevated CO2 plots. The combination of elevated CO2 and N fertilization reduced Rr to 389 ± 73 g C m−2 year−1, but this decrease was not statistically significant after the Tukey adjustment for multiple comparisons (P > 0.05). This reduction was driven by the 61% decrease in the tissue-specific Rr (Fig. 2) despite an increase in the standing root biomass relative to ambient conditions.

image

Figure 6. Total annual loss of carbon caused by fine-root respiration (Rr) in a loblolly pine forest exposed to elevated [CO2] and nitrogen fertilization. Treatments are as follows: c, ambient [CO2]; C, elevated [CO2]; n, ambient nitrogen; N, nitrogen fertilized. Values are the mean of three experimental plots per treatment (n = 3). Treatments that do not share a letter are significantly different at P < 0.05 (Tukey adjusted P value).

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DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES

Fine-root respiration is a complex process best understood at multiple levels of organization in space and time. At the tissue level, instantaneous Rr was partially determined by substrate availability and ATP utilization (Fig. 1), and daily Rr was influenced by the temperature sensitivity of respiration, which was affected by recent canopy carbon assimilation (Figs 4 & 5). At the ecosystem scale, Rr was determined primarily by the standing crop of fine roots, which was likely governed by plant allocation to nutrient or water acquisition. Rr was reduced by the combination of elevated CO2 and N fertilization (Fig. 2, trend in Fig. 6), but no changes in tissue chemistry (Table 1) or respiratory control (Fig. 1) were found that might explain this observation. This may be explained by increased above-ground net primary production (ANPP) in elevated CO2 and N-fertilized plots, as ANPP has been shown to be inversely related to total below-ground allocation at this site (Palmroth et al. 2006). Perhaps less C is transported below-ground in these plots, reducing the C available for Rr.

The proximal limits to Rr varied seasonally but were not affected by elevated CO2 or N fertilization. The limitation of Rr by enzyme capacity in the winter (Fig. 1) was likely caused by the reductions of enzyme activity in cold temperatures (Ryan 1991; Atkin, Edwards & Loveys 2000). Reduced respiratory capacity in the winter is consistent with the significant reduction in fine root N (Table 1) and temperature sensitivity (Fig. 3a). This response is the opposite of temperature acclimation as it is generally understood (Atkin et al. 2005), suggesting that fine roots at this site enter a relatively dormant state during the winter (Alvarez-Uria & Korner 2007).

Yearly estimates of stand-level Rr were less sensitive to the temperature response function than expected. We recalculated yearly Rr assuming Q10 = 2 (George et al. 2003), and found that this overestimated Rr by 9.5%. Similarly, we found that applying the summer temperature response (Fig. 3a) to summer and winter tissue-specific rates overestimated Rr by only 0.7%. The reduced temperature sensitivity of Rr in the winter was thus unimportant to stand C balance at this site because the flux during these months was small, and soil temperatures were in a range where the temperature response functions converged (Fig. 2).

The combination of methods used in this study shed light on the mechanisms that caused previous estimates of Rr at this site to differ. Using the O2 electrode method on roots obtained from soil cores, a method similar to that used in this study (Fig. 1), Matamala & Schlesinger (2000) estimated Rr to be 4.08 and 4.42 nmol CO2 g−1 DW s−1 in ambient and elevated CO2, respectively, whereas George et al. (2003), using gas exchange techniques on intact roots as in Fig. 2, estimated Rr to be 8.93 and 6.91 nmol CO2 g−1 DW s−1 in ambient and elevated CO2. These methods lead to estimates of annual Rr that varied by more than 100%. Although this could arise from interannual variation in Rr, results presented here suggested that much of the disparity is methodological. We estimated Rr using both methods in the same forest at the same time and found little correspondence (compare Fig. 1 and 2). A treatment effect of CO2 × N was detected using in situ gas exchange (Fig. 1), but no treatment effects were detected using measurements of O2 consumption (Fig. 2). It is possible that the damage response to excision and immersion in buffer for the O2 consumption measurements overwhelmed the treatment differences. In addition, Rr measured by in situ gas exchange were higher than those of O2 consumption in July, but the opposite occurred in January. These results highlight the disparate results obtained with different methods. Although both techniques disrupt the root–microbe–soil matrix, we believe the in situ method is more reflective of in situ fluxes as roots are left intact.

The yearly estimates of Rr reported here correspond with soil respiration (Rsoil) measurements from this site. Compared with a 7 year mean of Rsoil (Bernhardt et al. 2006), Rr as reported here comprised 43% of Rsoil in ambient CO2 and 50% of Rsoil in elevated CO2. These values are close to the average of 55% for all temperate coniferous forests, and are consistent with the trend of increasing Rr/Rsoil with increasing Rsoil (Subke, Inglima & Cotrufo 2006). N fertilization reduced Rsoil by 20% when combined with elevated CO2 but only 8.5% in ambient CO2 (Oren et al. unpublished). The reduction of Rr by the combination of elevated CO2 and N fertilization (Figs 2 & 6) could explain this 20% reduction in Rsoil. Additionally, the observation that the Q10 of Rr declines with temperature (Fig. 3b) is supported by previous observations that the Q10 of Rsoil declines with temperature at this site (Bernhardt et al. 2006). This correspondence with Rsoil at this site increases our confidence in the accuracy of in situ gas exchange measurements of Rr.

The close correlation between NEE and the apparent Q10 of Rr (Fig. 4) suggests that the rate of root respiration is tightly and immediately coupled to canopy photosynthesis. Stoy et al. (2007) demonstrated a 1–3 d time lag between carbon uptake and Rsoil in this forest, but overlapping lag times in the biological (plant and mycorrhizae) and physical (soil matrix) components in the ecosystem complicated efforts to definitively attribute this lag time to biotic or abiotic factors. Automated soil respiration measurements have documented temperature-independent diel cycles that follow light availability and photosynthesis in a deciduous forest (Liu et al. 2006) as well as an oak–grass savannah (Tang, Baldocchi & Xu 2005). Similarly, strong coupling between photosynthesis and Rsoil has been observed in a Pinus ponderosa forest (Irvine, Law & Kurpius 2005). These studies and results from girdling experiments (e.g. Hogberg et al. 2001), suggest that there is a direct link between canopy photosynthesis and Rr. It is also possible that this coupling involved respiration by ectomycorrhizal fungi at the root surface, as it was not possible to separate mycorrhizae and fine roots without causing considerable damage. Thus, we are unable to determine if canopy photosynthesis is directly coupled with Rr, indirectly coupled to rhizosphere respiration via root exudation or both.

The coupling of canopy C assimilation and Rr suggests that elevated CO2 should increase Rr by increasing canopy photosynthesis (Schafer et al. 2003), but we did not detect such an increase in Rr (Fig. 2). This is because we estimated in situ Rr using measurements in the morning from 0400 to 0700 h to minimize between-day variance in temperature; it is reasonable to expect that coupling with canopy photosynthesis was absent in these early morning hours. We lacked the sampling intensity to investigate treatment level variation in the NEE–Rr coupling. Future work on Rr could investigate the implications of the NEE–Rr coupling for yearly stand C balance.

The timescale of the observed coupling (1 d or less) is shorter than the 3–4 d lag between fixation and soil efflux inferred from isotope data in this forest (Andrews et al. 1999; Mortazavi et al. 2005). The longer lag times may reflect the physical lag associated with CO2 movement through the soil before it is measured as surface efflux (Stoy et al. 2007). It is also possible that the process linking photosynthesis and Rr operates at a shorter timescale than actual carbohydrate transport between needles and fine roots. Models of phloem transport indicate that pressure-concentration waves propagate through a plant more quickly than the transport of individual sugar molecules (Thompson & Holbrook 2003; Thompson 2006). This indicates that high rates of photosynthesis could rapidly deliver sugars to distant tissues such as fine roots even if the delivered molecules were not fixed that day. Such an influx of sugar would likely stimulate Rr because of substrate limitation during the summer (Fig. 1).

We estimated the time it would take for sucrose loading into needle phloem at the top of the canopy to increase the sucrose concentration in the phloem of fine roots (propagation time: τp) using a theoretical model of phloem transport (Ferrier 1976; Thompson & Holbrook 2004) according to the equation τp = 0.5(µL2Ψπ−1k−1) where µ is viscosity, L is path length, Ψπ is sap osmotic potential, and k is specific conductivity. We estimated µ to be 1.5e−9 MPa·s, Ψπ to be 1.5 MPa (Thompson, personal communication), L to be 25 m (canopy height is 19 m), and k to be 4.4e−12 m2 (Thompson & Holbrook 2003), leading to an estimate of 20 h for τp. This value is consistent with our results (Figs 4 & 5). Furthermore, varying µ, L,Ψπ and k within reasonable limits lead to estimates of τp between 10 and 30 h, which is within the timeframe of the observed coupling (Fig. 5). Similarly, a maximum phloem transport rate on the order of 1 m h−1 (Peuke et al. 2001) results in similar lag times using the earlier mentioned assumptions.

CONCLUSIONS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES

Fine-root respiration is a complex process with controls that operate on different timescales and levels of organization. Elevated CO2 and N fertilization did not alter the regulation of Rr, but elevated CO2 increased stand-level Rr by increasing the amount of respiring tissue. The combination of elevated CO2 and N showed a trend of reduced Rr. The mechanism for this is unknown, but could be caused by reduced C allocation below ground. Measurements of Rr with high temporal resolution detected a dynamic coupling between canopy C assimilation and the temperature dependence of Rr, suggesting that carbohydrate transport can increase ecosystem C loss on short timescales, although the effects of this coupling on stand C balance is not yet known. With further research it may be possible to predict rhizospheric respiration from eddy covariance measurements of ecosystem fluxes given accurate models of phloem wave propagation and mass transport.

ACKNOWLEDGMENTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES

We gratefully acknowledge G. Hendrey, R. Nettles and D. Cooley (Brookhaven National Laboratory) and the staff of Duke Forest for operation of the FACTS-1 experiment. We thank Jeff Pippin for much needed support on-site. Chris Oishi's help in interpreting soil respiration data is appreciated. We thank Will Cook for providing root biomass data. Comments by members of the DeLucia laboratory and two anonymous reviewers improved the quality of this manuscript. This research was supported by the Office of Science (BER), US Department of Energy, Grant No. DE-FG02-95ER62083 and through its Southeast Regional Center (SERC) of the National Institute for Global Environmental Change (NIGEC) under Cooperative Agreement No. DE-FC02-03ER63613. Additional support was provided by DOE (BER) Grant No. DE-FG02-04ERG384.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES