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Keywords:

  • net ecosystem exchange;
  • soil respiration;
  • carbon flux;
  • eddy covariance;
  • jack pine (Pinus banksiana);
  • disturbance

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[1] Within the FLUXNET network of tower stations for performing long-term measurements of CO2 exchange between forest ecosystems and the atmosphere, most research has focused on mature forests that are strong carbon sinks. Nevertheless, it is just as valuable to quantify fluxes from recently disturbed forests so that we can recognize and predict the impact of disturbance on carbon fluxes. We measured carbon fluxes and microclimatic variables within a naturally regenerating, young (12–14 years of age) jack pine ecosystem in northern Michigan. During the months June to October of 2001–2003, this ecosystem exhibited a low net uptake of approximately 17.8–18.3 g C m−2 5 months−1. Soil respiration was independently measured and then modeled on the basis of soil temperature and soil moisture. Model estimates of soil respiration were 627, 583, and 681 g C m−2 5 months−1 from June to October in 2001, 2002, and 2003, respectively. Net ecosystem exchange (NEE) and soil respiration were inversely correlated in midsummer (r = −0.6, p = 0.001) during the period of lowest NEE (greatest uptake) and highest soil respiration rates. In the spring, NEE and soil respiration were positively correlated (r = 0.4, p = 0.01). During the fall, when soil temperatures remained fairly steady and air temperatures fluctuated, this coefficient between NEE and soil respiration declined to an average −0.25 (p = 0.2). Our results indicate that 12–14 years following disturbance this ecosystem displays a small net uptake during the June to October months but respiratory losses during the snow season (mid-October to April) could possibly counterbalance this carbon gain.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[2] In recent years, much research has focused on long-term tower-based measurements of CO2 exchange between forests and the atmosphere. In this effort to determine the role of terrestrial ecosystems in the global carbon budget, most studies that utilize the eddy covariance technique have focused on mature forests that are demonstrable carbon (C) sinks (e.g., see the reviews of Law et al. [2002] and Baldocchi et al. [2001]). Equally valuable, and as a basis for comparison, are measurements within young, recently disturbed ecosystems.

[3] Generally, net ecosystem productivity (NEP, or equivalently, net ecosystem exchange of carbon, NEE = –NEP; where a positive value of NEP indicates a C uptake, or equivalently, a C sink, and a negative value of NEP indicates a C loss, or equivalently, a C source) changes over the course of succession. NEP is believed to exhibit negative to slightly positive values in young ecosystems, increase to a maximum value as the ecosystem reaches maturity, and then decline slightly as the ecosystem ages [Odum, 1969; Ryan et al., 1997; Pregitzer and Euskirchen, 2004]. Consequently, modifications in land use can play a dominant role in carbon cycling. The sequestering of carbon in temperate regions during recent decades may be associated with forest regrowth in disturbed landscapes [Houghton et al., 1999; Houghton, 2003; Schimel et al., 2000]. In order to better understand carbon cycling in complex landscapes, it is important to consider ecosystems over a diverse array of developmental stages, site conditions, and disturbance regimes [Litvak et al., 2003; Thornton et al., 2002; Chen et al., 2002, 2004].

[4] The disturbance history of the tree species jack pine (Pinus banksiana Lamb.) includes timber harvesting at 50-year intervals and frequent fires, resulting in numerous young (e.g., <20 years) jack pine ecosystems within the landscapes of the northern Great Lakes region of the United States. Jack pine is one of nine tree species that are widespread and dominant in the North American boreal forest [Payette, 1992], and it is also prevalent within the northern limit of the temperate biome [Barnes and Wagner, 1996]. It commonly grows on drier, less fertile soils than other native tree species in the Great Lakes region. The species is of both ecological and commercial importance in the United States, serving as habitat for unique plant assemblages and threatened bird species [Houseman and Anderson, 2002], and a source of timber production [Vasievich and Webster, 1997]. The pervasiveness of this species combined with its commercial and ecological roles suggest that quantification of its ability to sequester carbon over a range of successional stages, and how this relates to biophysical constraints, is important both in terms of global climate change and international science treaties such as the Kyoto Protocol, which require quantification of carbon sinks across forested ecosystems worldwide for the trading of carbon credits [IGBP Terrestrial Carbon Working Group, 1998; Schulze et al., 2002].

[5] Although other researchers have examined NEE within jack pine ecosystems using the eddy covariance technique, these studies have taken place in mature (30–32 years) and old (65–71 years) jack pine forests [Baldocchi et al., 1997; Joiner et al., 1999; Griffis et al., 2003]. On a daily, seasonal, and interannual basis, one would expect the carbon fluxes (including both NEE and soil respiration) within a young (12–14 years of age) jack pine ecosystem to vary because of fluctuations in air temperature, soil temperature, solar radiation, precipitation, soil moisture, vapor pressure deficit, phenology of the understory, and photosynthetic capacity. All the same, we do not know exactly how and why the fluxes in this type of ecosystem vary. Therefore the study described in this paper focuses on the carbon fluxes over a young jack pine ecosystem located in northern Michigan. Over the months of April to November of 2001–2003, the specific objectives of this study were to (i) define the daily, seasonal, and interannual patterns of NEE, (ii) investigate possible biophysical controls of NEE, (iii) examine soil respiration within this ecosystem, while relating this flux back to NEE, and (iv) compare our measured NEE estimates to published estimates of similar pine forests in different age classes.

2. Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

2.1. Site Description

[6] The experimental site is located in the upper peninsula of Michigan, about 16 km southeast of Lake Superior near the town of Alberta (46°N, 88°W). The naturally regenerated jack pine (averaging 2.5 m in height) are growing on a site that was clear-cut and left with large piles of slash in 1988. The slash piles measured approximately 1 m in height, 2 m in width, 3 m in length, and were distributed at 20–60 m intervals throughout the site. Also growing at the site are the occasional black cherry (Prunus serotina; averaging 1.5 m in height) and red oak (Quercus rubra; averaging 1.5 m in height). The stand density is 1,158 stems ha−1, and the average diameter at breast height (dbh) is 13.4 ± 2.2 cm with an estimated aboveground biomass of 2.1 (±0.5) Mg C ha−1 [Alban and Laidly, 1982]. The leaf area index is 0.93 ± 0.30 m2 m−2. The understory is extensive and dominated by blueberry (Vaccinium spp.), bracken fern (Pteridium aquilinum) and assorted graminoids.

[7] The terrain is level with a fetch to height ratio >1:100. The soils are excessively drained, dry, drought-prone sands of the Rubicon series located on glacial outwash [U.S. Department of Agriculture Natural Resources Conservation Service, 2003] (available at http://soils.usda.gov/technical/handbook/). They consist of 1.5% C and 0.07% N in the A/E horizon to a depth of 10 cm. The climate is strongly influenced by Lake Superior, with an average annual snowfall of 400–500 cm, or a snow water equivalent of ∼125–150 cm, and average annual rainfall of 75–90 cm [Albert, 1995].

2.2. Instrumentation and Measurements

2.2.1. Eddy Covariance and Microclimatic Measurements

[8] Because of the remote location of the site and the absence of line power, the eddy covariance equipment was driven by 12-V deep-cycle marine batteries connected to three 100-W solar panels. This setup hindered measurements during winter periods when the snowpack was deep and cloud cover obscured solar radiation. Consequently, depending on the climatic conditions for a given year, all eddy covariance and meteorological data were typically collected from April or May to October or November, with the precise measurement periods for each set of variables noted in more detail below.

[9] The eddy covariance system for computing fluxes of carbon, water, and energy was placed on a triangular, 30-cm-wide, 10-m-high, communication tower in the center of the site. This instrumentation consisted of a 3-D sonic anemometer (CSAT-3; Campbell Scientific Instruments, Logan, Utah, USA) and an open-path infrared gas analyzer (LI-7500 IRGA; LI-COR, Inc., Lincoln, Nebraska, USA) mounted at a height of 5.4 m. The LI-7500 IRGA was calibrated following the instructions detailed in the manual [LI-COR Inc., 2000]. The main axis of the LI-7500 IRGA was tilted by 30° with respect to the horizontal axis to aid in draining condensation and precipitation from the optical windows. The LI-7500 IRGA and CSAT-3 were both mounted on a shared horizontal bar and were laterally separated by 20 cm to reduce flux loss and flow distortion. The differing time delays in signals for the CSAT-3 and LI-7500 IRGA were properly taken into account by shifting the CSAT-3 data by one scan (at 10 Hz) to match the fixed 302.369 ms delay (or three scans at 10 Hz) that is programmed into the LI-7500 IRGA [LI-COR Inc., 2000]. This instrumentation was connected to a digital system (CR23X; Campbell Scientific Instruments, Logan, Utah, USA) to log data at 10 Hz intervals with the online computation of 30-minute block averages. Raw data and the 30-minute block averaged data were collected at least once a week from a laptop computer that was linked to the CR23X data logger. The “WPL” terms were applied off-line to the flux measurements to account for changes in mass flow caused by changes in air density [Webb et al., 1980; Leuning and Moncrieff, 1991]. In addition, analytical corrections were applied to account for frequency attenuation of the eddy covariance fluxes [Massman, 2001, Table 1; Massman, 2000]. In 2001, collection of data began in May and ended in mid-November, while in 2002, the measurements began in April and ended in late October. During 2003, measurements began in April and ended in early November.

[10] Basic microclimatic information was also collected, including: photosynthetically active radiation (PAR; 6 m above the ground; LI190SB, LI-COR, Inc., Lincoln, Nebraska, USA), air temperature (Ta) and relative humidity (Rh; at 1 and 4 m above the ground; HMP45C, Vaisala, Helsinki, Finland), soil water matric potential (MSW; Watermark #257 Campbell Scientific Instruments, Logan, Utah, USA), soil heat flux (G; three replicates at 5 cm below the soil surface, HFT3, Radiation Energy Balance Systems, Seattle, Washington, USA), net radiation (Rn; at 3.5 m above the canopy, Q*7.1, Radiation Energy Balance Systems, Seattle, Washington, USA), precipitation (P; at 3 m above the ground; TE525MM, Texas Electronics, Dallas, Texas, USA), and barometric pressure (Bp; PB105, Vaisala, Helsinki, Finland). These variables were measured at 15-second intervals with the computation of 30-minute averages and stored on two data loggers (CR10X; Campbell Scientific Instruments, Logan, Utah, USA). Soil temperature (Ts; at depths of 0, 5, and 20 cm) data were recorded at hourly to half-hourly intervals continuously from late May 2001 to early November of 2003 (HOBO four channel external data loggers; Onset Corp., Pocasset, Massachusetts, USA). During each measurement year, the dates by when 100% of the snowpack had melted and of leaf out in the understory were recorded.

2.2.2. Soil Respiration Measurements

[11] Beginning in June 2001, soil respiration (SR) measurements were taken using an infrared gas analysis system attached to a cylindrical chamber of known volume (EGM3 and SRC1; PP Systems, Amesbury, Massachusetts) at least once every two weeks during the growing season. The instrumentation was calibrated before each use following the instructions provided in the PP Systems manual [PP Systems, 1998]. Prior to taking measurements in the spring, soil respiration collars constructed from polyvinyl chloride tubing 10 cm in diameter were installed to create a tight seal between the soil respiration chamber and the ground. The collars were installed in the ground at distances of 5, 10, 20, 30, 40, 60, 80, 120, and 200 m from the base of the eddy flux tower and extending in the north, northeast, northwest, south, southeast, southwest, east and west directions, yielding 56 points of measurement. Simultaneous measurements of Ts (5 cm depth) were taken alongside each collar at the time of each soil respiration measurement using a digital thermometer (Checktemp; Hanna Instruments, Bedfordshire, UK). Soil samples were obtained to a 10 cm depth in the A horizon from four randomly selected points within the ecosystem at the time of each SR measurement. These samples were then oven-dried for 48 hours at 105°C to determine gravimetric moisture contents (MSG, % dry weight).

2.3. Data Treatment

2.3.1. Assessment of Data Quality

[12] Two quality analysis methodologies were employed. In the first, the degree of energy budget closure was assessed. In the second, the magnitudes of the eddy covariance measurement were considered as a function of friction velocity (u*), after removing flux values that were out of range or constant (because of low battery power), to examine if a critical threshold existed below which the errors in the eddy covariance were readily apparent.

[13] One measure of the accuracy of an eddy covariance system is to examine if an energy budget, which includes eddy covariance measurements of sensible and latent heat losses, closes. Prior to computing the energy budget closure, the sensible and latent heat storage term (M) in the air column was calculated [Campbell and Norman, 1998]. The energy budget closure during dry conditions was then computed by summing the daytime sensible (H) and latent (LE) heat fluxes with the inclusion of the storage term (H + LE + M) and plotting them against net radiation minus soil heat flux (Rn – G). We then used to methodology of Twine et al. [2000] to correct the CO2 flux values on the basis of the forced energy balance closure measurements.

[14] In terms of the u* threshold, the flux community generally recognizes that the eddy covariance technique may underestimate NEE (e.g., indicating a greater carbon uptake than may actually be occurring) under calm conditions at night because of weak vertical exchange [Gu et al., 2005]. Therefore the data were screened by comparing nighttime NEE (2200–0500 local time (LT)) to u* and visually determining the u* threshold below which NEE declined. The NEE data below this u* threshold were then estimated on the basis of seasonal linear regression models formulated from nighttime NEE data above the u* threshold (see section 2.4.1).

2.3.2. Gap Filling

[15] Data gaps occurred because of either instrument malfunction or power outages. Gaps in meteorological data were filled using values from a nearby (≈4 km from the site) weather station. As a pretreatment measure prior to filling large data gaps in the NEE data, small, 2–3 half-hourly data gaps, were filled via linear interpolation of the adjacent missing values [Falge et al., 2001]. These short gaps in the eddy covariance data were usually related to instrumental errors during times of precipitation.

[16] For larger data gaps in NEE, typically 1–6 days, our gap-filling methods consisted of calculations of mean diurnal variation (MDV). We followed the methodology of Falge et al. [2001], who reported that the mean diurnal method of gap-filling provided stable approximations of missing data using 7-day independent windows during the nighttime hours (2200–0500 LT), and 14-day windows for the daytime hours (530–2130). An equipment failure during July 2003 resulted in a near complete loss of eddy covariance data for the month. We based our estimate of NEE during this month on results obtained from empirical models, as described below (section 2.4).

2.4. Empirical Modeling

2.4.1. Modeling NEE

[17] We examined the relation of daytime NEE to PAR with the Landsberg model,

  • equation image

Pmax is the maximum rate of photosynthesis (μmol CO2 m−2 s−1), α is a shape parameter representing apparent quantum yield (e.g., an indication of the rate of change of daytime NEE per unit of PAR, or, the slope of the curve; μmol CO2/μmol photons), and Icomp is the light compensation point (e.g., the flux density at which photosynthesis is zero; μmol CO2 m−2 s−1). NEEday (μmol CO2 m−2 s−1) are the daytime NEE values, and includes data from the hours between 0530 and 2130 LT. Although this model was originally developed for leaf-level photosynthesis, other studies have successfully applied this model to examine ecosystem-level trends [e.g., Hollinger et al., 1994; Chen et al., 2002]. We implemented this model to examine the NEEday-PAR relation on a seasonal basis and for all seasons combined.

[18] After fitting this model to the NEEday and PAR data, we investigated the residuals (measured NEEday – predicted NEEday) of the model, a technique that is useful in assessing the direct effect of each forcing variable. We first determined that the residuals of the Landsberg model were independent of PAR by visually examining plots of the residuals versus PAR to check for white noise [Rice, 1995]. Following this affirmation, we looked for significant relations between the residuals of the fitted Landsberg models and other biophysical variables such as vapor pressure deficit (VPD; kPa), Ta (at 3 m), Ts (5 cm depth), Msw, Rh, P, LE, and H. To find the best fit between the residuals and the biophysical variables, we analyzed a number of statistical regression models, including linear, power, polynomial, and logarithmic power functions.

[19] To model nighttime (2200–0500 LT) NEE, we implemented the multiple regression stepwise procedure in SAS (SAS Institute, Version 8.01) and identified statistically significant (p < 0.01) variables related to nocturnal NEE fluxes above the u* threshold (see section 2.3.1) for each season and for all seasons combined. The variables we included in the stepwise multiple regressions were Ta (at 3 m), Ts (5 cm depth), the hour of the day, LE, H, Msw, VPD, and Rh. After identifying the significant variables, we tested these variables in different model forms (e.g., exponential, linear) to find the best fit on a seasonal basis.

[20] To determine how well the models might perform in determining NEE on a monthly basis at this site, and to estimate the eddy covariance fluxes that were lost during July 2003 (see section 2.3.2), we incorporated the measured PAR data into equation (1) and the relevant data into the corresponding seasonal model for nighttime NEE and then summed the values obtained over each month,

  • equation image
2.4.2. Modeling Soil Respiration

[21] The chamber-measured soil respiration data (SR; μmol CO2 m−2 s−1, see section 2.2.2) were modeled on the basis of two exponential models. One model examined the relation with soil temperature (°C, at a 5 cm depth) as the single predictor variable and two fitted parameters. In this model, β0, (μmol CO2 m−2 s−1) is a scaling factor and β1 is a parameter that represents the shape of the curve,

  • equation image

The other model incorporated both soil temperature and gravimetrically measured soil moisture where the parameters β0 and β1 are as described above, with β2 (% moisture) also representing the shape of the curve and β3 representing the interaction between temperature and moisture,

  • equation image

The models were fit to the data using a Gauss-Newton estimation method with the SAS software (SAS Version 8.02). The estimated regression coefficients equation (4) were used in conjunction with the continually collected soil temperature data (see section 2.2.1) to calculate total amounts of soil respiration over the measurement period, and to estimate SR during the winter months. On the basis of equation (4), we determined a Q10 value, the rate of change in soil respiration given a 10°C change in soil temperature,

  • equation image

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

3.1. Energy Budget Closure and u* Thresholds

[22] Following the removal of data during wet periods and the calculations of the sensible and latent heat storage terms in the air column, the energy budget closure was decent, as indicated by the R2 (0.98) and slope (0.94) for the no-intercept model (Figure 1). The calculations of the sensible and latent heat storage terms in the air column added marginal closure (an increase of 2%) while the removal of data during wet periods had a larger impact on the closure (an increase of 6%). Adjustment of CO2 flux based on forced energy balance closure yielded a small (1–2%) underestimate of NEE. We determined a u* threshold of 0.3 during nocturnal periods (2200–0500 LT; Figure 2) and an underestimate of NEEnight by ∼5–10% following corrections using predicted values based on the models presented in Table 3 (see section 3.3.3).

image

Figure 1. Latent plus sensible heat flux plus heat storage (LE + H + M) versus net radiation minus soil heat flux (Rn – G, or available energy) using half-hourly averages during dry conditions from the measurement periods, as described in section 2.2.1. The solid lines represent the 1:1 line (thick line) and the fitted line (thin line). The linear no-intercept model yielded a slope of 0.94 and R2 of 0.95.

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image

Figure 2. Half-hourly, quality-controlled net ecosystem exchange (NEE) of CO2 plotted as a function of friction velocity (u*) for nocturnal periods (2200–0500 LT).

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3.2. Local Weather and Climatic Anomalies

[23] Over the 3-year measurement period, 2002 was the warmest and wettest while 2003 was the coolest and driest. For example, monthly air temperatures averaged over the June to October months varied by 1.1°C, with 2002 having the warmest average of 15.2°C, 2003 being the coolest with an average of 14.1°C, and 2001 falling in the middle with an average of 14.6°C. On a daily basis, air temperature was most variable during April 2002, ranging from −18.0°C on 4 April (the lowest air temperature recorded during the measurements of NEE) to an anomalous 30.0°C that occurred 13 days later, on 17 April (Figure 3b). The highest air temperature (35.4°C) occurred on 1 July 2002.

image

Figure 3. Time series of (a) daily total NEE, (b) average daily air (3 m height) and soil (5 cm depth) temperatures, and (c) daily total precipitation (thin vertical lines) and soil water matric potential (thick lines) with the precipitation amounts summed over each April-year. Negative NEE values indicate a C sink while positive NEE values indicate a C loss.

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[24] The day by which all the snow had melted in the spring varied by eight days over the 3 years. In 2002, all the snow had melted by 18 April, while in 2003, all the snow had melted by 15 April. Although we did not collect flux data in April 2001, we did note that all the snow had melted by 23 April. On a year-to-year basis, there was a large degree of variability as to when the air temperature first fell below 0°C in the early fall: this occurred on 25 September 2001, 5 October 2002, and 4 September 2003. From April to October, total precipitation in 2002 (143.6 cm) was nearly twice that of total precipitation in 2003 (78.7 cm), and about 40 cm greater than that in 2001 (98.0 cm; Figure 3c). One rainy period in May of 2003 contributed over 170 mm of rain in a day, equating to about 22% of the total rainfall received in 2003 (Figure 3c). Soil water matric potential varied from about −0.1 to −1.1 bar, with the smallest amounts occurring directly after the heavy rains (Figure 3c). As evidence of the sandy, excessively drained soils, soil water matric potential remained low for a short period of time following a rain event and then increased rapidly in the absence of rain (Figure 3c). Averages of soil temperature (5 cm depth) for the June to October time frame mirrored that of air temperature, with the lowest soil temperatures occurring in 2003 (14.5°C), the highest in 2002 (15.4°C), and 2001 falling in between (15.2°C; Figure 3b).

3.3. Net Ecosystem Exchanges of Carbon

3.3.1. Monthly NEE

[25] On a monthly basis, the ecosystem was a net carbon sink with the strongest measured uptake occurring between May and August, reaching a maximum measured fixation of approximately 7.6 g C m−2 in July 2002 (Table 1). During the month of April and early fall (September to October), the ecosystem accumulated about half as much carbon as it did during the peak months, with a minimum of 0.5 g C m−2 taken up in October of 2002. There was less C uptake in June 2002 than either June 2001 or 2003 because of enhanced respiration. Combined, the corrections for frequency and u* fell between ∼5% and ∼15% (Table 1).

Table 1. Monthly and Cumulative Values of Net Ecosystem Exchange for the 2001–2003 Measurement Periodsa
Month200120022003
MeasuredModeledMeasuredModeledMeasuredModeled
PrecorrectionPostcorrectionPrecorrectionPostcorrectionPrecorrectionPostcorrection
  • a

    Values are given in g C m−2. NEE, net ecosystem exchange. The measurement periods were 20 May to 11 November 2001, 1 April to 31 October 2002, and 11 April to 4 November 2003. The modeled values are derived from the equations presented in Tables 2 and 3. Both the precorrection and postcorrection estimates incorporate the gap-filled, quality-controlled eddy covariance data. The postcorrection data incorporate the corrections for (1) frequency, (2) underestimated nocturnal fluxes during low wind conditions, and (3) forced energy balance closure, as mentioned in sections 2.2.1 and 2.3.1.

  • b

    The cumulative values refer to the total summed NEE over the various measurement periods for each year and for the comparable measurement period of June to October.

  • c

    These sums take into account the modeled estimate for July 2003.

April---−2.7−2.5−2.2−2.3−2.0−1.5
May−1.4−1.2−1.3−5.1−4.7−4.4−5.8−5.4−4.6
June−5.4−5.2−4.9−4.0−3.6−4.2−5.8−5.0−4.7
July−5.4−5.1−5.3−8.0−7.6−6.1--−4.9
August−4.5−4.2−4.6−5.5−5.2−4.2−4.8−4.2−4.6
September−2.2−2.1−2.0−1.6−1.4−0.4−3.3−3.0−1.4
October−1.1−1.1−0.4−0.6−0.5−0.5−1.4−1.3−1.2
November−0.2−0.20.1---−0.1−0.10.0
Cumulativeb
NEE total−20.2−19.1−18.4−27.5−25.5−21.8−28.4c−25.9c−22.9
NEE June-October−18.6−17.7−17.2−19.7−18.2−14.8−20.2c−18.3c−16.7
3.3.2. Day-to-Day NEE

[26] On a day-to-day basis, the ecosystem usually behaved as a weak C sink, but there were some days when the ecosystem acted as a C source: most of these days occurred in spring and fall (Figure 3a). The ecosystem reached a maximum value of daily NEE (−0.6 g C m−2 d−1; Figure 3a) in April 2002 during a period of anomalously high temperatures and well before bud break in the understory (Figures 3a and 3b). Also during 2002, the ecosystem reached the minimum measured daily NEE (+0.2 g C m−2 d−1), an event that occurred directly following warm temperatures in late September (Figure 3b).

3.3.3. Daytime and Nighttime NEE

[27] On a seasonal basis, the Landsberg model was a significant predictor of NEEday during the middle to late summer periods (R2 = 0.72–0.77, p < 0.001), but not as reliable a predictor (R2 = 0.32–0.55, p < 0.0001; Table 2) during the early to late spring and fall (Table 2; Figures 4a–4c). The residuals of the Landsberg model were weakly, albeit consistently and significantly, correlated to VPD and H, illustrating that multiple factors controlled NEE (Figure 4). In each case, the linear regression model provided the best fit to the residuals and the biophysical variables (p < 0.0001; Figure 4). When all the daytime NEE data were combined across the seasons, the residuals did not show a clear correlation with any other single variable.

image

Figure 4. (a–c) Seasonal relation between daytime carbon flux (NEEday) and photosynthetically active radiation (PAR) as modeled with equation (1), and the relation between the residuals of NEEday from equation (1) for (d–f) vapor pressure deficit (VPD) and (g–i) sensible heat (H). The parameters of the fitted models are given in Table 2, and the residuals are defined as measured NEEday – predicted NEEday. The solid lines in Figures 4d–4i represent best fit linear regression models with a coefficient b, and its significant deviation from zero based on a t-test (p = 0.05). Although the models were fit to the full range of data, in order to more clearly depict the trends in carbon flux and de-emphasize outliers, the y-axes in graphs were truncated at −1 and 1 μmol m−2 s−1.

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Table 2. Parameters (Plus or Minus Standard Errors) and R2 Values of the Landsberg Model (Equation (1)) Fitted to the Daytime NEE Data Based on PARa
SeasonJulian DaysParameter (± Standard Error)b
PmaxαIcompR2
  • a

    NEEday, daytime NEE (μmol CO2 m−2 s−1). Data are based on PAR (μmol m−2 s−1). Models were formulated separately for five subsets of the seasons based on all 3 years and for all years of data combined (2001–2003). All models are significant at p < 0.0001.

  • b

    The parameters of the Landsberg model, NEEday = Pmax(1 − exp−α(PAR – Icomp)), are as follows: Pmax, the maximum rate of photosynthesis (negative indicates carbon fixation or uptake); α, a shape parameter; and Icomp, the light compensation point.

  • c

    “All” refers to the early spring–fall seasons combined across all years of data.

Early spring91–151−1.185 (0.175)7.76 × 10−4 (1.51 × 10−4)35.945 (5.874)0.60
Late spring152–181−0.662 (0.027)2.27 × 10−3 (1.76 × 10−4)59.420 (3.836)0.64
Summer182–230−0.789 (0.037)1.99 × 10−3 (1.69 × 10−4)41.046 (4.132)0.75
Late summer231–273−0.723 (0.033)2.20 × 10−3 (1.80 × 10−4)72.964 (3.71)0.85
Fall274–315−0.609 (0.069)2.21 × 10−3 (3.72 × 10−4)54.880 (4.067)0.79
Allc91–315−0.951 (0.036)1.56 × 10−3 (1.34 × 10−4)53.851 (6.325)0.65

[28] There were generally two consistent predictors of NEEnight: Msw and H (Table 3). Msw was a significant predictor during early spring, late summer, and fall, but was not a significant predictor when data were combined across all seasons. Sensible heat flux was a significant predictor during late spring, summer, fall, and across all seasons combined.

Table 3. Parameters (Plus or Minus Standard Errors) and R2 Values of the Fitted Nighttime NEE Data Soil Water Matric Potential, Sensible Heat, and Latent Heata
SeasonJulian DaysModelβ0β1β2R2
  • a

    NEEnight, nighttime NEE (μmol CO2 m−2 s−1). MSW, soil water matric potential (bar); H, sensible heat (W m−2); L, latent heat (W m−2). Models were formulated separately for five subsets of the seasons based on all 3 years and for all measurement periods combined (2001–2003). All models are significant at p < 0.0001.

  • b

    “All” refers to the early spring–fall seasons combined across all years of data.

Early spring91–151β0 + β1MSW1.237 (0.531)−1.474 (0.689)-0.46
Late spring152–181β0 + β1H0.032 (0.021)−0.004 (0.001)-0.67
Summer182–230β0 + β1H0.064 (0.042)−0.003 (0.001)-0.42
Late summer231–273β0 + β1MSW + β2L1.179 (0.504)−1.110 (0.572)−1.197 × 10−4 (4.719 × 10−5)0.33
Fall274–315β0 + β1MSW + β3H−1.235 (0.558)1.911 (0.821)−0.002 (0.001)0.47
Allb91–315β0 + β1H0.025 (0.032)−0.003 (0.001)-0.55

3.4. Soil Respiration (SR)

[29] Measured rates of SR reached a maximum in August during all 3 years of measurements in association with the highest soil temperatures (Figures 3b and 5) . Soil respiration was generally lowest in the late fall and early spring (≤0.2 g CO2 m−2 hr−1), but by late May, SR sharply increased (∼0.8–1.0 g CO2 m−2 hr−1) during all 3 years (Figure 5).

image

Figure 5. Comparison between actual (±1 standard deviation) soil respiration (SR) measurements and modeled SR estimates for the years 2001–2003. The modeled SR estimates are based on equation (3), with the coefficients presented in Table 2. The breaks in the lines represent measurement gaps between the years.

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[30] Equation (3) provided a statistically significant (p < 0.0001) fit to the SR-Ts relation, explaining between 68% and 77% of the variability in the SR rates (Figure 5 and Table 3). The associated Q10 values (equation (5)) were 1.1 in 2001, 2.3 in 2002, 1.9 in 2003, and 2.2 over all years of data combined. On the basis of equation (4), our estimates of SR were 627, 583, and 681 g C m−2 over the June to October months of 2001, 2002, and 2003, respectively.

[31] Equation (4) explained between 75% and 88% of the variability in the SR rates, and was also statistically significant (p < 0.0001). In particular, in comparing the performance of equations (3) and (4), the data collected in 2002, the year with the greatest amount of precipitation and lowest predicted soil respiration rates, showed the most improvement when the moisture term was included (Table 3). That is, there was generally sufficient moisture at this site, with the extremely wet soils that directly followed the heavy rain events (Figure 3c), possibly inhibiting soil respiration.

3.5. Coupling Between NEE and SR

[32] The association between SR and NEE fluctuated over the April to November time period (Figure 6). The inverse relation between the two fluxes was generally most significant during the period of lowest NEE (e.g., greatest uptake) and highest rates of SR in the summer months from June to August, when the average Pearson correlation coefficient was −0.6 (p = 0.001) over the 3 years. In the spring months of April and May this correlation coefficient was not as significant, but was positive, at around an average of 0.4 (p = 0.01) over the 3 years. The Pearson correlation coefficient declined to an average −0.25 (p = 0.2) during September to early November. While soil temperatures remained fairly steady during these times of the year, air temperatures showed greater variation (Figure 3b).

image

Figure 6. Time series comparison (using 5-day backward moving averages) for years (a) 2001, (b) 2002, and (c) 2003 of daily net ecosystem exchange (NEE) and daily soil respiration (SR) rates computed from equation (3), with the coefficients presented in Table 3.

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4. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

4.1. Energy Balance Closure

[33] We have confidence in our eddy covariance measurements of NEE from the perspective that adjustments based on forced energy balance closure did not substantially alter our estimates. Our lack of complete energy balance closure may be due to the use of a single radiometer, which has a much smaller footprint than that of carbon, sensible, and latent heat fluxes. Similarly, the footprint for soil heat flux is also extremely small (<10 cm). The more critical adjustment at this site is that for weak vertical exchange at night following the determination of a critical u* threshold.

4.2. Seasonal and Interannual Behavior of NEE and SR

[34] Both the measured and modeled NEE data indicate moderate seasonal variability in this jack pine forest during the growing season (Table 1). However, our estimated values of NEE are a weak point of this study since we lost data during July 2003, and all the gap-filled values in the data set were likely sensitive to the postprocessing scheme [Falge et al., 2001]. Nevertheless, seasonal variation might be attributable to the behavior of the ecosystem during the spring recovery of jack pine photosynthesis at the onset of the growing season and the entrance into winter dormancy in the fall. Relatively high rates of early season C uptake, as indicated by high values of Pmax (−1.19 μmol CO2 m−2 s−1), were observed in the early spring. Pmax then declined in the late spring (−0.66 μmol CO2 m−2 s−1), rose again in summer (−0.79 μmol CO2 m−2 s−1) and fell off again in the fall (−0.61 μmol CO2 m−2 s−1; Table 2).

[35] Lower values of Pmax in late spring compared to early spring (Table 2) may have been caused by either nutrient or water limitations. Soil water matric potential was generally high (∼−0.5 bar) during the later spring months, particularly in 2002 when most of the heavier rains occurred in midsummer (Figure 3c). This increase in soil water matric potential may have contributed to water stress and a reduction in photosynthesis during the late spring in this ecosystem. It is also possible that while the trees have considerable photosynthetic capacity in the early spring, before bud break and the development of new shoots, they retranslocate nitrogen to the developing needles during the late spring, resulting in nutrient limitations for a period of time. We did observe appreciable needle yellowing in the late spring, a characteristic of water-stressed and/or nutrient-limited trees.

[36] It is also possible that during the early spring in April and May, bud break and full leaf out in the dense understory of ferns, blueberry bushes, and grasses would contribute to a steep rise in photosynthetic capacity and a greater net C uptake in this ecosystem. Although we did not measure LAI in early spring, we did note first bud break in both the deciduous understory and the jack pine overstory occurred about three weeks before full leaf out of the understory. The full leaf out of the understory occurred earliest in the warm year of 2002 (15 May), and latest in 2003 (23 May). Correspondingly, the cumulative net C uptake from 1 May to 15 May was greater in 2002 (∼0.11 g C m−2 d−1, or 1.7 g C m−2 over the 15 day period) than in 2003 (∼0.08 g C m−2 d−1, or 1.2 g C m−2 over the 15 day period), with cumulative net C uptake from April to May being 7.2 g C m−2 in 2002 and 6.4 g C m−2 in 2003. Future studies in this ecosystem would benefit from a comprehensive partitioning of NEE into overstory and understory fluxes.

[37] The effect of subzero air temperatures in the spring and early fall had a much different impact on net C uptake than they did in the late fall. The last frosts in the spring, which occurred during periods of long photoperiods in late May or early June, seemingly did not decrease the net C uptake during this time (Figures 3a and 3b and Table 2). While the first frost occurred earlier in 2003 (4 September; daily minimum of −0.6°C) than in 2001 (25 September; daily minimum of −2.9°C) or 2002 (5 October; daily minimum of −8.2°C), this early fall frost did not strongly decrease C uptake in the following weeks (Figure 3a). In fact, net C uptake was higher in September and October of 2003 than in September and October of 2001 and 2002 (Table 1), indicating that the plants easily recovered from early frosts if temperatures were not substantially below 0°C, but did not recover from colder frosts later in the season [Havranek and Tranquillini, 1995; Lamontagne et al., 1998; Monson et al., 2002]. That is, during the late fall there appear to be temperature thresholds that initiate a decline in stomatal conductance and gas exchange rates, effectively ending the growing season. These thresholds may also be related to the decreases in light intensity and photoperiod at this time of year [Havranek and Tranquillini, 1995].

[38] The overall low temperatures during September and October of 2002 that resulted in little net C uptake also resulted in generally low values of SR, except for a brief period in late September 2002 when soil temperatures and SR increased, and NEE decreased substantially (Figures 3b, 5, and 6). In 2001, the warmest of the three measurement years, C losses from soil respiration were high from June to August (Figure 6a), and consequently, total net carbon uptake was reduced below that observed during 2002 and 2003 (Table 1). Thus it appears that soil respiratory losses had a large impact on net C uptake in this ecosystem. However, to gain a more complete understanding of the influence of soil respiration in this system, it would be useful to obtain predictions of total soil respiration based not just on temperature, but also on moisture since moisture appeared to have an influence on soil respiration rates (Table 4).

Table 4. Parameters and R2 Values of the Exponential Models Fitted to the Soil Respiration Data Based on Soil Temperature and Soil Moisturea
Year(s)ModelbParameter (± Standard Error)R2
β0β1β2β3
  • a

    SR, soil respiration (μmol CO2 m−2 s−1); Ts, soil temperature (°C to 5 cm depth); Ms, soil moisture (percent). Models were formulated separately for each of the 3 years and for all years combined (2001–2003). All models are significant at p < 0.0001.

  • b

    Ts refers to the simple exponential model based on soil temperature, SR = β0 * eβ1* Ts, and Ts * Ms refers to the exponential model with both soil temperature and soil moisture, SR = β0 * eβ1* Ts * eβ2* Ms * β3* Ts * Ms.

2001Ts0.759 (0.257)0.102 (0.017)--0.76
2001Ts * Ms0.330 (2.036)0.088 (0.057)−0.683 (5.885)0.166 (0.841)0.78
2002Ts0.868 (0.351)0.085 (0.020)--0.68
2002Ts * Ms0.014 (0.0018)0.098 (0.018)−0.037 (0.088)0.728 (0.284)0.87
2003Ts1.554 (0.0493)0.066 (0.010)--0.77
2003Ts * Ms0.091 (0.0198)0.045 (0.013)−0.504 (3.095)0.613 (0.270)0.88
AllTs1.114 (0.0313)0.079 (0.009)--0.69
AllTs * Ms0.181 (0.0181)0.075 (0.028)−0.075 (0.593)0.210 (0.431)0.75

[39] The SR rates we measured were comparable to those of others taken in coniferous ecosystems of similar age. For example, estimates of SR in jack pine forests during one growing season, with a length that was similar to the one in this study, were 415.2 g C m−2 at an 8-year-old ecosystem and 378 g C m−2 at a 20-year forest in Saskatchewan, Canada [Striegl and Wickland, 1998]. A decline in SR between the 8- and 20-year forests may be caused by a decrease in the amount of material available for decomposition as the large amounts of microbial substrate due to the previous disturbance are exhausted. In particular, the forest in this study contained large piles of slash left behind from logging practices that were probably a major source of substrate for heterotrophs, which tend to favor the less resilient organic mater fractions [Alexander, 1977], and a reason for relatively high SR rates. Older jack pine forests growing on outwash sands with low soil C content have been shown to exhibit lower SR (e.g., 300 g C m−2 over the growing season, with a length that was similar to the one in this study, at a 60–75-year-old jack pine ecosystem [Striegl and Wickland, 1998]) than younger forests: a finding that is generally attributable to an absence of large pools of labile litter that are associated with disturbance events.

[40] Nevertheless, the day-to-day activities of the soil microorganisms are highly temperature dependent and even with large amounts of labile substrate, their activities decline during low temperatures. Consequently, SR contributed less to NEE during the low temperatures in the spring (Figure 6). When the soils remained cool, (≈1°–3°C), SR stabilized at around 2.0 g C m−2 day−1, but the overall carbon balance of the ecosystem still fluctuated between ±0.05 g C m−2 day−1 in concert with fluctuations in air temperatures (Figures 3a, 3b, and 6). From roughly mid-October to November, SR and NEE were poorly correlated. We hypothesize that at this point, the soils were still warm and the microbes still responsive, but the trees began to enter winter dormancy (Figure 6).

4.3. Annual NEE and Nongrowing Season C Losses

[41] At the annual scale, it is possible that the 12–14-year-old ecosystem in this study has recently switched from a source to slight sink of CO2. All the same, the weak growing season sink strength measured in this young jack pine forest is likely an overestimation of the annual C uptake. For instance, Griffis et al. [2003] found that nongrowing season C losses accounted for 46% of the summertime NEE in an old jack pine ecosystem in Saskatchewan, Canada.

[42] Moreover, although we did not consistently measure SR in the winter, we did find that even during periods of near freezing soil temperatures some carbon efflux was occurring, the sum of which could amount to significant carbon losses at the site. Empirically based studies of winter SR have measured highly temperature-dependent rates between 40 and 132 g C m−2, with soil moisture having little to no effect [McDowell et al., 2000; Winston et al., 1997]. Projections of climate change forecast warmer winters within the latitude of this forest. Such warming could elicit greater respiratory losses from the soil during the nongrowing season, and consequently affect the C balance of these young jack pine forests. Alternatively, warming may also lengthen the growing season and thereby increase the cumulative NEE in this forest [Myneni et al., 1997].

4.4. Comparison With Other Direct Measurements of Ecosystem C Flux

[43] Although we know of no studies of direct measurements of NEE in younger jack pine ecosystems over an entire growing season, Amiro [2001] used eddy covariance techniques to measure NEE in a 1-year-old burned jack pine ecosystem for nine days in July 1998, during the height of the growing season. This ecosystem was a consistent C source at roughly 0.8 g C m−2 day−1. Independently, Pypker and Fredeen [2002] measured C fluxes in a 5–6-year-old subboreal clear-cut composed of white spruce and lodgepole pine, with a net C loss of 1.0 to 1.4 Mg C ha−1 during the growing season (Figure 7).

image

Figure 7. Summary of NEE during the growing season (Mg C ha−1 growing season−1) for three comparable pine ecosystems of various age classes. The 5- to 6-year, recent clear-cut is located in British Columbia, Canada (54°N) [Pypker and Fredeen, 2002]. The 30- to 32-year forest is located in Manitoba, Canada (56°N) [Joiner et al., 1999], and the 65- to 71-year forest is in Saskatchewan, Canada (53°N) [Baldocchi et al., 1997; Griffis et al., 2003]. The solid line is drawn by hand to indicate a general trend in ecosystem carbon flux across the age classes.

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[44] Mature jack pine ecosystems are likely to sequester more C than the young ecosystem described in the current study with variability in these ecosystems being generally attributable to prior land use and latitude. Prior land use differences may be related to soil type, stand density, reseeding, and the amount of slash left to decay following harvest [Pregitzer and Euskirchen, 2004]. It is also possible that jack pine growing in the harsher climates of the northern limit of the species range are less productive than those in the southern limits. Using a similar measurement period as the one reported in this study, Joiner et al. [1999] reported a net uptake of 2.1 and 2.7 Mg C ha−1 growing season−1 for a 30–32-year-old jack pine ecosystem in Manitoba, Canada. These estimates and those from this study suggest that jack pine ecosystems switch from acting as a source to sink of C at around 10 to 20 years (Figure 7). Meanwhile, older (e.g., >50 year) jack pine ecosystems may sequester less carbon than mature jack pine ecosystems. For example, a jack pine forest measured during two growing seasons at 65 and 71 years took up −0.47 and −0.36 Mg C ha−1 growing season−1, respectively [Baldocchi et al., 1997; Griffis et al., 2003] (Figure 7).

5. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[45] We measured carbon fluxes over three growing seasons in a young jack pine ecosystem in northern Michigan, USA, and found the following:

[46] 1. The ecosystem is a slight C sink (∼17.2–18.3 g C m−2 5 months−1) during the months June to October, but winter data would presumably decrease the sink strength of our measurements.

[47] 2. Soil respiration was a major C flux from this ecosystem. On the basis of empirical models, approximately 583–681 g C m−2 5 months−1 may have been respired between June and October.

[48] 3. NEE and soil respiration showed a strong inverse correlation during the summer, but during the spring and fall months this relation was weaker.

[49] 4. On the basis of a comparison of our data to that of other studies of NEE in similar pine ecosystems, but of different ages, jack pine ecosystems may switch from acting as a C source to a C sink after between 10 and 20 years. That is, forest age is a key factor in determining net carbon uptake at the decadal timescale.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[50] We received valuable field assistance and logistical coordination from numerous individuals, including Kim Armington, Jennifer Ashby, Andrew Burton, Jennifer Eikenberry, Adam Gagnon, Christian Giardina, Evan Kane, Kimberly Larsen, Jim LeMoine, Wendy Loya, Lindsey Moritz, Asko Noormets, and Chris Seck. Funding for this study was provided from the National Science Foundation (NSF), the Ecological Circuitry Collaboratory of the NSF, and the Research Excellence Funds of Michigan. We thank the Forestry Center Management Committee of Michigan Technological University for permission to use this study site and the anonymous reviewers, who provided valuable comments on an earlier draft of this manuscript.

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  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods
  5. 3. Results
  6. 4. Discussion
  7. 5. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
jgrd12077-sup-0001-t01.txtplain text document2KTab-delimited Table 1.
jgrd12077-sup-0002-t02.txtplain text document1KTab-delimited Table 2.
jgrd12077-sup-0003-t03.txtplain text document1KTab-delimited Table 3.
jgrd12077-sup-0004-t04.txtplain text document1KTab-delimited Table 4.

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