Controls on carbon dynamics by ecosystem structure and ... et al 2012.pdfEcological Monographs, 82(1), 2012, pp. 101–128 2012 by the Ecological Society of America Controls on carbon

Ecological Monographs, 82(1), 2012, pp. 101–128� 2012 by the Ecological Society of America

Controls on carbon dynamics by ecosystem structure and climate forsoutheastern U.S. slash pine plantations

ROSVEL BRACHO,1,6 GREGORY STARR,2 HENRY L. GHOLZ,3 TIMOTHY A. MARTIN,1 WENDELL P. CROPPER,1 AND

HENRY W. LOESCHER4,5

1School of Forest Resources and Conservation, University of Florida, P.O. Box 110410, Gainesville, Florida 32611 USA2Department of Biological Sciences, University of Alabama, Box 870206, Tuscaloosa, Alabama 35487 USA

3Division of Environmental Biology, National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia 22230 USA4National Ecological Observatory Network (NEON), Suite 100, 38th Street, Boulder, Colorado 80301 USA

5Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado 80301 USA

Abstract. Planted pine forests (plantations) in the southeastern United States are animportant component of the continent’s carbon balance. Forest carbon dynamics are affectedby a range of factors including climatic variability. Multiyear droughts have affected theregion in the past, and an increase in the frequency of drought events has been predicted. Howthis increased climatic variability will affect the capacity of the region’s pine plantations tosequester carbon is not known. We used eddy covariance and biometric approaches tomeasure carbon dynamics over nine years in two slash pine plantations (Pinus elliottii varelliottii Englm) in north Florida, consisting of a newly planted and a mid-rotation stand.During this time, the region experienced two multiyear droughts (1999–2002 and 2006–2008),separated by a three-year wet period. Net ecosystem carbon accumulation measured usingboth approaches showed the same trends and magnitudes during plantation development. Thenewly planted site released 15.6 Mg C/ha during the first three years after planting, beforebecoming a carbon sink in year 4. Increases in carbon uptake during the early stages of standdevelopment were driven by the aggrading leaf area index (LAI). After canopy closure, bothsites were continuous carbon sinks with net carbon uptake (NEE) fluctuating between 4 and;8 Mg C�ha�1�yr�1, depending on environmental conditions. Drought reduced NEE by ;25%through its negative effects on both LAI and radiation-use efficiency, which resulted in a largerimpact on gross ecosystem carbon exchange than on ecosystem respiration. While resultsindicate that responses to drought involved complex interactions among water availability,LAI, and radiation-use efficiency, these ecosystems remain carbon sinks under currentmanagement strategies and climatic variability. Variation within locations is primarily due tomajor disturbances, such as logging in the current study and, to a much lesser extent, localenvironmental fluctuations. When data from this study are compared to flux data from abroad range of forests worldwide, these ecosystems fill a data gap in the warm-temperate zoneand support a broad maximum NEE (for closed-canopy forests) between 88C and 208C meanannual temperature.

Key words: carbon balance; drought; eddy covariance; Florida; forests; LAI; Pinus elliottii var elliottiiEnglm; radiation-use efficiency; slash pine.

INTRODUCTION

Forests occupy 29% of the land area in the

conterminous United States and 60% of the land in the

south. About 30% of southern forests are dominated by

the genus Pinus and most are regenerated from planted

seedlings (Conner and Hartsell 2002). These plantations

contain . 6 Pg C and averaged close to 0.4 Tg C/yr in

net accumulation between 1990 and 2000 (Conner and

Hartsell 2002), equivalent to ;12% of annual U.S.

carbon emissions (Turner et al. 2003). The vast majority

of southern pine plantations lie within the southeastern

United States coastal plain and piedmont provinces, a

large region of relatively uniform temperature and flat-

to-gently rolling topography. The carbon dynamics of

these ecosystems are influenced by a variety of factors,

including stand age (time since last harvest), genetic

composition, understory composition and structure, site

quality, management history (e.g., fertilization and

herbicide application), and fire history, in addition to

variations in climate. In general, southern pine planta-

tions are managed by clear-cut harvesting every 20–25

years, followed by the planting and development of new

replacement stands. Timber harvesting periodically

reverses long-term trends in ecosystem carbon accumu-

lation as new stands develop (Gholz and Fisher 1982,

Thornton et al. 2002, Binford et al. 2006). This

management regime so dominates the carbon signature

Manuscript received 29 March 2011; revised 29 August 2011;accepted 1 September 2011. Corresponding Editor: A. M.Ellison.

6 E-mail: [email protected]

101

at the ecosystem level that the range of observed annual

net exchanges of carbon (NEE) measured in recent

clearcuts and adjacent mature plantations in north-

central Florida (Clark et al. 2004) spans the global range

in the literature (e.g., Baldocchi and Vogel 1996,

Goulden et al. 1996, Luyssaert et al. 2007). It also

dominates the regional carbon signature in most years,

although other disturbances (e.g., insect outbreaks or

wildfire) may also be important locally in space and time

(Binford et al. 2006).

Variability in climate refers to deviations from long-

term averages (Bates et al. 2008). The intensity,

frequency, and duration of such deviations define

extreme events and determine their impacts on ecosys-

tem processes. Understanding how climatic variability

influences NEE of pine plantations in the southeast

United States requires consideration of process-level

responses at shorter time steps in the context of longer

term stand development patterns (Sierra et al. 2009).

Impacts also depend on the relative physiological status,

process rates, and capacity of the dominant tree and

understory species to respond to extreme situations.

Drought is arguably the most widely studied environ-

mental anomaly and can produce a range of effects on

ecosystem carbon balance depending on severity. In the

short term, the usual response is reduced ecosystem

carbon gain due to reduced canopy conductance (e.g.,

Panek and Goldstein 2001, Krishnan et al. 2006). Lower

rates of net assimilation may also constrain labile

carbohydrate supply to roots, reducing fine-root respi-

ration and heterotrophic metabolism and at least

temporarily offsetting decreased canopy carbon gain

(Ewel et al. 1987b, Janssens et al. 2001, Mahecha et al.

2010). In the longer term, there may be differential

effects imposed by stomatal limitations on C gain

(Novick et al. 2004, Arain and Restrepo-Coupe 2005,

Ibrom et al. 2006, Jarvis et al. 2007, Chasmer et al. 2008,

Noormets et al. 2008, Yi et al. 2010), relative to changes

in ecosystem respiration (Re), which is generally reduced

by drier soils (Davidson et al. 2004, Tang and Baldocchi

2005, Cisneros-Dozal et al. 2007, Knorr et al. 2008,

Muhr and Borken 2009). Seasonal-to-interannual

drought effects may include reduced LAI and tree

growth (Law et al. 2002, Olano and Palmer 2003,

Saleska et al. 2003, Powell et al. 2008, Klos et al. 2009),

turning sites and regions into weak carbon sinks or even

carbon sources (Ciais et al. 2005, Leuning et al. 2005,

Krishnan et al. 2006, Barr et al. 2007, Falk et al. 2008,

Grant et al. 2009). Large-scale droughts in the southern

hemisphere during the decade 2000–2009 reduced

regional net primary production (NPP) to the extent

that global NPP was also reduced (Zhao and Running

2010).

Globally, interactions of water availability and mean

annual temperature control annual gross ecosystem

carbon gain (GEE) and NEE (Law et al. 2002, Yi et

al. 2010). Latitudinal gradients of GEE and Re are

principally controlled by mean annual temperature

(Hirata et al. 2007, Luyssaert et al. 2007, Kato and

Tang 2008, Wang et al. 2008). However, long-term

trends in specific locations as affected by climatic

variability are still relatively scarce.

In the southeastern United States, severe droughts

over the past century occurred with a frequency of ;15

years, with a second year of dry weather often following

the first (Gholz and Boring 1991). Projections for the

region, although uncertain, are that rainfall will

increase, but evaporation will also increase due to

atmospheric warming, producing a negative water

balance (Twilley et al. 2001, Held and Soden 2006,

Christensen et al. 2007, Bates et al. 2008, Seager et al.

2009). Climate change may also lead to increased

drought frequency (Breshears et al. 2005). Therefore,

understanding the response of carbon storage in pine

plantations to climate change must include an analysis

of drought effects.

However, much of the earlier research on the carbon

dynamics of slash pine plantations was conducted

during short (,3 year) periods with abundant precipi-

tation (e.g., Ewel et al. 1987a, b, Cropper and Gholz

1993, Fang et al. 1998, Clark et al. 1999, 2004). In fact,

Teskey et al. (1994) concluded that mature slash pine

trees in Florida showed no significant response of

canopy conductance to vapor pressure deficits associat-

ed with normal seasonal drought, and Neary et al.

(1990) found that young slash pine and loblolly pine (P.

taeda) stands showed no response to irrigation treat-

ments. Such limited responses to drought are likely the

result of the trees’ access to subsurface water tables,

which in most years are rarely deeper than 150 cm. This

also suggests that NEE may be more generally

controlled by the effects of soil moisture on heterotro-

phic respiration than on canopy carbon gain. But in any

case, historical inventory data from the southeast clearly

show growth reductions in pine trees during severe

droughts (Klos et al. 2009).

At the other extreme, how heavy rainfall, high water

tables, or flooding during and after major storms affect

NEE in these ecosystems is also largely unknown.

Inundation can lead to hypoxic soils that may signifi-

cantly alter plant physiological activity (Kozlowski

1997, Blodau et al. 2004, Polacek et al. 2006) and

radiation-use efficiency (Martin and Jokela 2004).

Enhancing our abilities to model carbon exchange for

this region must therefore include greater knowledge of

the effects and feedbacks on carbon-related processes

associated with water availability at both extremes.

In this paper we synthesize biometric, physiological,

and meteorological measurements collected simulta-

neously in two slash pine plantations in north-central

Florida over nine consecutive years. During this time, a

larger range in water availability occurred than recorded

over the previous 100 years, including two multiyear

droughts separated by three years of average to wetter

conditions.

ROSVEL BRACHO ET AL.102 Ecological MonographsVol. 82, No. 1

The use of two slash pine plantations, one represent-

ing the early stage of growth from site preparation aftera clearcut to mid-rotation (Mize tract, MT), and the

second from mid-rotation age to rotation age (Donald-son tract, DT) growing in the same area, with the same

climatic conditions and on similar soils allowed us toassess the dynamic of the carbon cycle during a completemanagement rotation for one of the most productive

managed ecosystems in the world. We provide measure-ments of carbon pools and fluxes, elucidated mecha-

nisms controlling exchanges between the forests and theatmosphere, and show how carbon dynamics respond to

changing climate, key issues in ecosystem carbon cyclescience. We also place our sites in a global context by

comparing carbon fluxes reported here with fluxes froma comprehensive set of forests, both natural and planted.

We hypothesized that different mechanisms controlcarbon dynamics before and after canopy closure,

although, in both cases, LAI plays a major role. Thefirst hypothesis is that, after replanting and before

canopy closure, annual carbon accrual is dependent onLAI development and intercepted radiation, and we

tested this by relating carbon fluxes to LAI and canopyintercepted radiation. The second hypothesis is that,

after canopy closure, subsequent variations in NEE arerelated to both fluctuations in LAI and physiologicalcontrols in response to changes in environmental

conditions. During early stand development, below-ground carbon is expected to be highly dynamic, but

these dynamics cannot be directly measured. However,an inference can be made by comparing mensurational

estimates of changes in ecosystem carbon with indepen-dent measurements of NEE, where deviations indicate

nonsteady state soil carbon.

MATERIALS AND METHODS

Study sites

The two sites used in this study are both commercial

slash pine plantations, located ;15 km northeast ofGainesville, Florida, USA, and managed for pulpwoodproduction (Clark et al. 1999, 2004, Gholz and Clark

2002). The first site (Mize Tract, MT; 29845.8880 N,82814.6890 W) was established following the stem-only

harvest in 1998 of a 25-year-old plantation (Clark et al.1999, 2004). The clearcut was bedded (treated with a

plow that produces raised planting beds, which improvesdrainage, reduces competition, and improves seedling

survival (VanderSchaaf and South 2004, Fox et al.2007), treated with a combination of imazapyr and

triclopyr herbicide to reduce competition from bothwoody and herbaceous weeds, and replanted at ;1800

trees/ha in December 1998–January 1999. It wasfertilized during fall 2002 with 40 KgN/ha and 45

KgP/ha. The second site (Donaldson Tract, DT;29845.286 0 N, 82809.797 0 W) was established in aplantation that was eight years old in 1998 (Clark et

al. 1999, 2004). One year following harvest (1988–1989),the site was replanted at ;2000 trees/ha and fertilized in

August 1993 (50 Kg N/ha and 56 Kg P/ha) and

December 2001 (151 Kg N/ha). We define ‘‘plantation

age’’ as the number of years since seedlings were planted

(e.g., years after planting).

Soils of both sites are ultic alaquods (sandy, siliceous,

thermic), poorly drained, acidic, and low in organic

matter and available nutrients. The distributions of

discontinuous subsurface spodic (organic) and argillic

(clay) horizons range between 30–70 cm and 100–200

cm depth, respectively (Gaston et al. 1990). Understory

vegetation consisted of native species reestablished after

site preparation, primarily Serenoa repens (W. Bartram)

Small, Ilex glabra (L.) A. Gray, andMorella cerifera (L.)

Small. Other common but less dominant species

included Gelsemium sempervirens, Galusaccia spp., and

Vaccinium spp.

Precipitation in this region is seasonal, with summers

typically wet and warm (;50% of the rainfall occurs

from June to September), winters dry and mild, and

springs mostly dry and warm. Long-term average

annual precipitation (1975–2008) was 1226 6 211 mm

and long-term precipitation during the main growing

season (March–September) was 856 6 180 mm (Na-

tional Climatic Data Center [NCDC], available online).7

Long-term mean minimum and maximum air tempera-

tures were 148C and 278C for January and July,

respectively (NOAA 2009). The Palmer Drought Sever-

ity Index (PDSI) was used to characterize dryness of the

area during the study (NOAA 2009). PDSI of �2 is

considered a moderate drought, �3 a severe drought,

and �4 an extreme drought.

Meteorological measurements

Standard meteorological data were collected at both

sites. Photosynthetically active radiation (PAR; 190SA

quantum sensor, LI-COR 190SA, LI-COR, Lincoln,

Nebraska, USA), incident shortwave radiation (LI-

COR 200SA pyranometer), net radiation (Rn; REBS

Q*7.1 Net Radiometer, REBS, Seattle, Washington,

USA), relative humidity, and air temperature (RH and

Ta, respectively; HMP45c temperature and humidity

probe, Vaisala, Helsinki, Finland), precipitation

(TE525MM-L rain gage, Texas Electronics, Dallas,

Texas, USA), and wind speed and direction (12–002

R. M. Young wind/direction sensor, R. M. Young

Company, Traverse City, Michigan, USA) were mea-

sured on an antenna tower 4 m above each canopy.

Depth to the surficial water table (zone of saturated soil

water) was measured continuously using Stevens water

depth gages (F-68 water level recorder, Leupold and

Stevens, Beaverton, Oregon, USA).

For comparative purposes and to complete the age-

range coverage for local slash pine plantations, we also

used data from Clark et al. (2004) for measurements

made in a mature (24- and 25-year-old) plantation

7 http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwDI;StnSrch;StnID;20004544#ONLINE

February 2012 103C DYNAMICS IN SLASH PINE PLANTATIONS

present on the MT site before clear-cutting and for

which similar methods were used as in the present study.

Net ecosystem production (NEP) and net ecosystem

carbon exchange (NEE)

Net ecosystem production is defined as the net gain or

loss of carbon by an ecosystem in a time interval. NEP is

the difference between two large opposing processes:

carbon uptake by primary producers (gross primary

production, GPP) and respiration losses (autotrophic Ra

and heterotrophic Rh), and is shown by

NEP ¼ ðGPP� RaÞ � Rh: ð1Þ

The term (GPP� Ra) is net primary production (NPP).

We determined NEP using a biometric approach, which

requires measurements of carbon stored in different

pools over time, usually at one-year intervals (Clark et

al. 2001, Law et al. 2003, Loescher et al. 2006a). We use

the convention that positive values of NPP and NEP

indicate net carbon gain by the ecosystem.

Net ecosystem carbon exchange (NEE) is commonly

measured using eddy covariance at a time resolution of

one hour or less (Aubinet et al. 2000, Baldocchi 2003).

We use the convention here that positive values of NEE

indicate carbon uptake from the atmosphere by the

ecosystem. Annual NEE is the sum of measured and

gap-filled half hourly NEE. Cumulative annual NEE

should in theory be equivalent to NEP, such that

NEP ’ NEE ð2aÞ

and

GPP ’ GEE ¼ NEE� Re: ð2bÞ

But in reality this is not usually the case because

simplifying assumptions may not be valid (e.g., that

mineral soil carbon and/or fine roots are in equilibrium

in the case of NEP), and because each method is subject

to a variety of different measurement uncertainties

(Loescher et al. 2006a, Loescher and Munger 2006).

Also, short-term NEE cannot account for the time lag

between photosynthesis and tree growth related to

internal storage of carbon (Barford et al. 2001, Gough

et al. 2008). Nevertheless, simultaneous meteorological

and biometric estimations of NEE and NEP, with some

assumptions, allow independent estimates of ecosystem

carbon storage and exchange to be made (Curtis et al.

2002, Ehman et al. 2002, Gough et al. 2008).

NEE estimated using eddy covariance

NEE was measured using the eddy covariance

approach (Moncrieff et al. 1997, Clark et al. 1999,

Ocheltree and Loescher 2007) following protocols

outlined by AmeriFlux (Loescher and Munger 2006).

The system was comprised of a three-dimensional sonic

anemometer (R3A, Gill Instruments, Lymington, UK)

mounted on an antenna tower 4 m above the canopy

and a closed-path infrared gas analyzer (IRGA; LI-

COR 6262 or LI-COR 7000). Air was drawn by an air

pump from the inlet placed between the upper and lower

transducers of the anemometer, through a 30 m long, 0.4

cm internal diameter (ID) Teflon tube connected to an

air pump, and through the IRGA. A flow rate of 7.5 L/

min was used to maintain the information found in the

ambient turbulent atmosphere, minimize shear mixing

within the tubing (Massman 1991), and to attenuate

temperature fluctuations. The IRGA was calibrated

three times a week using a zero CO2 air source, a

traceable (61%) standard for CO2 concentration in the

measurement range, and a dew point generator (LI-

COR 610) for water vapor.

Flux calculation software (EdiRe; Clement 2007)

carried out coordinate rotation of the horizontal wind

velocities to obtain turbulence statistics perpendicular to

the local streamline. The covariance between turbulence

and scalar concentrations was maximized through the

examination of 0.1-s intervals on both sides of a fixed lag

time (in this case, a. 6 s). The turbulent fluctuations were

estimated from the deviations between instantaneous

measures of vertical wind speed (w0) and a block average

(Moncrieff et al. 2004). Because of the relatively short

roughness lengths and uniform canopy structure at these

sites, we assumed that the influence of coherent

structures and high frequency effects were captured by

this approach (Loescher et al. 2006b). Fluxes were

calculated in half-hourly intervals and then corrected for

the frequency attenuation on turbulent structure in the

sampling tube and nonideal frequency response of the

IRGA using transfer functions (Massman 2004). Local

barometric pressure data were used to correct the fluxes

to standard atmospheric pressure. Our flux processing

methodology was validated by comparisons with flux

calculations made using the closed-path AmeriFlux

‘‘gold files’’ (Powell et al. 2008).

Data were filtered to eliminate half-hourly fluxes

resulting from systematic errors and irrelevant environ-

mental influences, such as: (1) rainfall and condensation

in the sampling line; (2) incomplete half-hour data sets

during system calibration or maintenance; (3) when the

canopy was de-coupled with the external atmospheric

conditions, as defined by the friction velocity, u* (0.1

ms�1 before canopy closure for MT, and 0.2 ms�1 for

MT and DT; these thresholds were the asymptotic

values obtained by plotting nighttime NEE against u*

[Goulden et al. 1996, Clark et al. 1999]); and (4)

excessive variation in the half-hourly means based on an

analysis of standard deviations for u, v, and w wind and

CO2 statistics, where u and v are the orthogonal

horizontal wind speed components and w is the vertical

wind speed. On average, 55% and 61.2% of the NEE

values remained at MT and DT, respectively, after

applying u* filter. Quality assurance of the flux data was

maintained using plausibility tests and stationary criteria

(Foken and Wichura 1996, Foken et al. 2004).

Gaps in the flux data that remained after quality

checking or filtering were filled as follows. First, if PAR


was .10 lmol�m�2�s�1, daytime conditions were as-

sumed, and gaps in the data were filled using monthly

parameters obtained by fitting half-hourly NEE to PAR

using a non-rectangular hyperbola (Falge et al. 2001):

NEEday¼aPARFsat

Fsat þ aPAR� Rd ð3Þ

where a is the ecosystem quantum yield (lmol

CO2�m�2�s�1/lmol quantum�m�2�s�1), Fsat (lmol

CO2�m�2�s�1) is the net CO2 exchange at light satura-

tion, and Rd is the ecosystem dark respiration (e.g., NEE

at PAR ¼ 0). Second, if PAR was ,10 lmol�m�2�s�1,nighttime conditions were assumed and gaps were filled

using monthly parameters relating half-hourly nighttime

CO2 exchange (NEEnight) to air temperature:

NEEnight ¼ A expbTa ð4Þ

where A and b are regression coefficients and Ta (8C) is

half-hourly air temperature. Parameters for .400

monthly equations used for gap-filling were estimated

(P � 0.05) using a nonlinear regression procedure in

SAS (SAS Institute 2010).

Half-hourly GEE was calculated as the difference

between NEE and ecosystem respiration:

GEE ¼ NEE� Re ð5Þ

with daytime values of Re estimated from a monthly

parameterization of an exponential relationship between

Re and Ta. Annual NEE is then the sum of measured

and gap-filled half-hourly NEE.

Biometric net ecosystem production (NEP)

We estimated NEP as the sum of NPP and the annual

changes of detritus on the surface of the soil (wood,

forest floor), assuming that: (1) carbon losses due to

herbivory, emissions of volatile organic compounds,

dissolved organic carbon from the root zone are

negligible (Gholz et al. 1986, Curtis et al. 2002, Ehman

et al. 2002, Kominami et al. 2008); and (2) changes in

soil carbon and fine-root contribution to NPP were not

considered. We acknowledge that soil carbon decreases

at early stages in stand development, but it stabilizes

after canopy closure (Gholz et al. 1986). We did,

however, test for nonsteady state conditions by com-

paring NEP against NEE according to hypothesis two.

In the present case, annual increment in aboveground

detritus was determined as the difference between

measured detrital mass and its loss to decomposition

(assumed to be 15% per year after Gholz et al. [1985,

1986]).

Carbon stored in tree biomass was estimated following

protocols established by earlier studies (Clark et al. 2004,

Powell et al. 2008). Tree density, stem diameter at breast

height (dbh in cm at 1.37 m height), and tree height were

measured in four 625-m2 inventory plots located inside

the fetch of the flux towers at each site during the winter

of each year. Tree biomass was estimated from allometric

equations developed locally. Standing dead trees were

also inventoried (Clark et al. 2001) and their biomass

estimated. Understory biomass was measured on 20 1-m2

plots randomly distributed at each site during the same

winter period as tree inventories. All biomass was

clipped, sorted, and oven dried to a constant mass.

Aboveground understory NPP was determined accord-

ing to (Gholz and Fisher 1982). Tree foliage production

and LAI were estimated from litterfall (Gholz et al. 1991,

Martin and Jokela 2004) collected each month from 10 1-

m2 traps randomly located inside the inventory plots;

samples were sorted and dried to a constant mass.

Seasonal dynamic in LAI was estimated from monthly

needle fall values using a logistic model (Kinerson et al.

1974, Dougherty et al. 1995). The application of this

model assumes that current year needle accrual starts on

1 March (beginning of the phenological year), and that

needles formed in a given year are fallen by the end of the

second year (needle retention time is two years; Gholz

and Boring 1991, Gholz et al. 1991). Calculations

incorporated anomalous early needlefall pulses due to

drought and windstorms observed in some years. Total

needle fall for a given year represents needle production

during the previous year (Dallatea and Jokela 1991,

Gholz et al. 1991). Collected needles were corrected for

loss of carbon during senescence and foliar biomass was

converted to area using the specific leaf area, with LAI

expressed on all-sided basis (Liu et al. 1997). This

approach was validated using a combination of destruc-

tive sampling, measurements of needles elongation and

cumulative needlefall in successive years, and also by

measurements of canopy light absorption (Gholz et al.

1991, Liu et al. 1997). Tree coarse-root (.1 cm) biomass

was estimated using an allometric equation based on dbh

developed for several conifer species by (Santantonio et

al. 1977). Woody and understory increments in biomass

were added to foliage production to obtain aboveground

plus coarse root NPP. Biomass was assumed to be 50%carbon (H. L. Gholz, unpublished data)

Intercepted photosynthetically active radiation (I-

PAR) for each plot was estimated from measured

above-canopy radiation and stand LAI using the Beer-

Lambert law (after Martin and Jokela 2004). This

methodology was tested previously by comparing

monthly estimations of I-PAR at plot level with field

measurements over a year (Martin and Jokela 2004).

Aboveground radiation-use efficiency (RUE) was cal-

culated by dividing aboveground NPP by I-PAR, and

gross RUE (RUEG) was calculated by dividing GEE by

I-PAR. Canopy conductance (gc) was calculated by

inverting the Penman-Monteith equation (Kelliher et al.

1995).

Random measurement errors for NEE in our systems

were previously estimated by Powell et al. (2008) using a

daily differencing approach. Regression analysis was

used to detect dependence of C fluxes on environmental

or biological drivers on monthly and annual basis.

Significance of the regressions (P value) was estimated


using the F test at a significance level of 0.05 (P � 0.05).

Linear regressions were tested for independence of the

errors, homoscedasticity (constant variance) of the

errors and normality of the error distribution.

RESULTS AND DISCUSSION

Environmental conditions during the study

Annual precipitation varied widely over the course of

our measurement period (Fig. 1a). Annual precipitation

from 1996 through 1998 was near the long-term average

of 1226 mm/yr (NOAA 2009). A drought began at the

end of 1998 and continued through early summer of

2002. Deficits of 200 mm and 160 mm, as compared with

long-term average precipitation (856 mm), accumulated

during the growing seasons of 1999 and 2000, respec-

tively. Although annual precipitation was below the

long-term average in 2001, excess precipitation (.120

mm) accumulated during the growing season. After that,

average precipitation occurred for the next four years

(2002 through 2005). That hiatus was followed by a

second drought, during which growing seasons precip-

itation deficits of 300 mm and 175 mm occurred in 2006

and 2007, respectively.

Mean annual depth to the surficial water table (zone

of saturated soil water) increased to near 3 m at DT

during the first drought (Fig. 1b), reaching a maximum

of 3.4 m in May 2002. In contrast, the water table during

the same period at MT stayed within 1 m of the surface,

a result of the low LAI in this younger stand (four years

old in 2002). This pattern is typical (water tables

remaining close to the surface for a few years after

harvesting and regeneration), while evapotranspiration

is low compared to closed-canopy stands (Sun et al.

2000, Bliss and Comerford 2002, Gholz and Clark 2002).

The water table recovered at DT in 2003 and fluctuated

between 0.5 m and 1 m depth during the following three

years with near average precipitation. The water table

depths at both sites were similar and increased as the

second drought developed in 2006–2007.

Although the second drought (2006–2007) was

shorter, its intensity was greater compared with the

first, with severe to extreme drought conditions (PDSI

less than �3) recorded for 14 consecutive months from

August 2006 through September 2007. Five of those

months had PDSI less than�4, reaching a minimum of

�4.31 in November 2006, the lowest recorded monthly

value since May 1932 (NOAA 2009). In contrast, these

sites experienced only four consecutive months of PDSI

less than�3 during the first drought (May–August 2002)

and only one month less than �4. Between 1990 and

2007, six years had a mean annual PDSI less than �2,including 2007 that had the lowest mean annual PDSI

ever recorded (�3.51; Fig. 1a). In summary, this study

was carried out during a decade with the greatest

moisture variability recorded in .100 years.

LAI and carbon partitioning during stand development

LAI increased rapidly after planting (Fig. 2, Table 1),

its development no doubt hastened by the fertilization in

the fall of year four. Canopy closure was reached

FIG. 1. (a) Mean annual precipitation, long-term average (33 years) annual precipitation, and annual average of PalmerDrought Severity Index (PDSI); and (b) mean annual depth to the surficial water table for two slash pine plantations (Mize tract[MT] and Donaldson tract [DT]) in north Florida, USA.


between ages five and six at a value of about 6.0,

although a transient higher LAI (7.0 m2/m2) was

reached at about seven years. LAI at DT fluctuated

annually between 4.2 m2/m2 and 7.0 m2/m2. The

multiple tropical storms in 2004 reduced LAI by 15%at DT, which was 15 years old at the time, while an

effect at the much younger MT was not noticeable. The

second drought in 2006–2007 had a larger affect on LAI

than the first (1998–1902), leading to reductions of

.20% at both sites (Fig. 2).

Carbon partitioning during the development of the

two stands from a recently planted site (MT), to a mid-

rotation stand (DT), to a rotation-aged stand (from

Clark et al. 2004) is shown in Fig. 3. Total ecosystem

FIG. 2. All-sided annual leaf area index (LAI) during the development of two slash pine (Pinus elliottii var elliottii Englm.)plantations: (a) Mize tract (MT), (b) Donaldson tract (DT).

TABLE 1. Annual carbon fluxes (NEE, GEE, and Re), as measured by eddy covariance, all-sided leaf area index (LAI), andannually intercepted photosynthetically active radiation (I-PAR) during plantation development in north Florida, USA.

Plantation and yearAge(yr)

NEE(Mg C�ha�1�yr�1)

GEE(Mg C�ha�1�yr�1)

Re

(Mg C�ha�1�yr�1)LAI

(m2/m2)I-PAR

(TJ�ha�1�yr�1) PDSI

Mize tract (MT)

1998� 0 �12.68 7.05 �19.74 nd nd �0.381999 1 �8.85 14.9 �23.75 nd nd �2.582000 2 �5.28 13.72 �19 nd nd �2.952001 3 �2.37 24.58 �26.95 0.99 5.74 �2.42002 4 0.97 24.48 �23.52 2.22 11.59 �0.592003 5 4.58 29.67 �25.1 4.02 17.73 1.22004 6 5.27 29.36 �24.09 6.03 22.14 0.62005 7 7.18 29.27 �22.22 7.05 23.78 1.852006 8 4 29.31 �25.31 4.79 18.37 �2.092007 9 3.84 24.65 �20.8 4.58 17.96 �3.51

Donaldson tract (DT)

1999 10 7 26.67 �19.67 5.46 nd �2.582000 11 6.63 25.59 �18.96 5.79 21.86 �2.952001 12 6.4 27.03 �20.63 6.49 23.04 �2.42002 13 5.69 24.49 �18.8 7.07 23.76 �0.592003 14 8.18 25.12 �16.94 7.07 23.93 1.22004 15 7.75 24.36 �16.61 6.06 22.4 0.62005 16 7.35 23.2 �15.85 6.1 22.51 1.852006 17 4.91 22.65 �17.54 4.61 19.49 �2.092007 18 nd nd nd 4.17 18.92 �3.512008 19 6.13 24.99 �18.86 nd nd �1.38

Rotation aged�1996 24 7.57 27.13 �19.56 6.29 nd 0.21997 25 6.22 25.29 �19.07 6.17 nd 0.79

Note: Abbreviations are: NEE, net carbon uptake; GEE, gross ecosystem carbon exchange; Re, ecosystem respiration; PDSI,Palmer Drought Severity Index; nd, no data.

� From Clark et al. (2004).


carbon was 52.3 Mg C/ha at the time the trees were

planted at MT. This total decreased over the next four

years as the detritus remaining after harvest was

consumed by decomposers at a faster rate than carbon

accumulated via photosynthesis. Positive carbon accu-

mulation began in the fourth year and reached 64 Mg C/

ha at MT by age nine. DT had 55.7 Mg C/ha at the time

measurements were begun in year nine and increased to

92 Mg C/ha by year 18. Detritus was the largest carbon

pool in the early years (99% of the total in year one),

decreasing to a minimum (;23 Mg C/ha) by year nine.

More than 7 Mg/ha (at DT) then accumulated over the

next 10 years as the forest floor developed. The largest

single annual increment in carbon in woody biomass of

living trees (6.64 to 15.82Mg C/ha from year four to five)

at MT resulted from the fertilization in the fall of year

four (2002). Live wood reached 35.76 Mg C/ha (55% of

total ecosystem carbon) by year nine. Carbon content in

woody biomass at DT was 8.5 Mg C/ha lower than at

MT at the same age (nine years) and reached a maximum

of 52.96 Mg C/ha (59% of the total) at 18 years (Fig. 3).

These values are similar to those of other plantations in

the vicinity from an earlier chronosequence study (Gholz

and Fisher 1982) and to stands across the region subject

to a range of silvicultural treatments, including fertiliza-

tion and/or chemical control of competing vegetation

(Jokela and Martin 2000, Shan et al. 2001).

Carbon cycle dynamics after harvest

Removal of half the ecosystem carbon by harvesting

at MT changed the magnitude of NEE by a factor of

three so that the ecosystem shifted from being a net

carbon sink (.6 Mg C�ha�1�yr�1) to a strong carbon

source (less than�12 Mg C�ha�1�yr�1) (Table 1; Clark et

al. 2004). Harvesting affected GEE much more than Re,

compared to values of the rotation-aged stand (Clark et

al. 2004), with GEE reduced by .70%, while Re

remained virtually unchanged (;4%). Low GEE during

the year after harvesting (1998) is explained by the

carbon fixation of residual non-tree vegetation that

remained on the site. Re varied, but showed no trend

over time until after about year eight, declining steadily

through age 17, a trend likely related to a similar decline

in total NPP (Table 2). Re dominated the carbon

balance during the first three years after clear-cutting

(Table 1), owing to the low LAIs and decomposition of

fresh detritus left after harvesting and site preparation

(Table 1, Fig. 3). MT released a total of 28.34 Mg C/ha

during the four years after the harvest, before once again

becoming a net carbon sink (Table 1). Strong distur-

bances generally convert forests from carbon sinks into

carbon sources for varying periods of years (Amiro

2001, Kowalski et al. 2003, 2004, Kolari et al. 2004,

Misson et al. 2005, Noormets et al. 2007, Zha et al. 2009,

Amiro et al. 2010). However, annual carbon losses after

harvesting and site manipulation in these ecosystems are

the largest yet reported after any type of disturbance to

forests in the United States (Amiro et al. 2010).

Disturbances that impact LAI consistently decrease

GEE, but the reported response of Re to disturbance is

less consistent. Increases in Re after disturbance are

generally related to the intensity of disturbance, soil

FIG. 3. Carbon partitioning in different compartments during the development of slash pine plantations in north Florida: (a)Mize tract (MT), (b) Donaldson tract (DT), and (c) rotation age (RA; from Clark et al. 2004).


warming due to overstory removal, increased soil water

content, increases in substrate availability (e.g., residues

after disturbance), and more live root and microflora

activity associated with residual or new post-harvest

vegetation growth (Ewel et al. 1987a, b, Londo et al.

1999, Pangle and Seiler 2002, Concilio et al. 2006, Kim

2008, Selmants et al. 2008). On the other hand, soil and

ecosystem respiration could be less compared with

undisturbed plots if decreases in autotrophic respiration

are larger than the stimulation of heterotrophic respira-

tion, anoxic conditions persist, or perennial vegetation

with stored carbon does not survive the disturbance (as

after very hot fires; Londo et al. 1999, Kowalski et al.

2003, 2004, Sullivan et al. 2008). This situation makes

clear the need for long-term mechanistic studies of the

carbon dynamics of forests in relation to disturbances.

The strength of the MT carbon source decreased as

the planted trees rapidly expanded stand LAI and,

subsequently, I-PAR. LAI development controlled

increases in ecosystem carbon uptake (GEE, NPP)

and, consequently, net ecosystem carbon balance

(NEE and NEP). Increases in GEE and NPP through

year four were positively related to LAI (Fig. 4). GEE

more than doubled from 13.7 Mg C�ha�1�yr�1 at age twoto 29.7 Mg C�ha�1�yr�1 at age five (Table 1, Fig. 4),

while I-PAR increased more than three times, driving

NPP to its corresponding threefold increase and leading

the site to become a carbon sink (Table 2). These results

support our hypothesis of carbon accrual driven by

development of LAI at early developmental stages in

pine plantations. These results are similar to other

studies that show LAI development controlling carbon

uptake early in forest development after disturbances

(Law et al. 2003, Humphreys et al. 2006, Grant et al.

2007). RUE increased with stand development to a

maximum of 0.54 g C/MJ five years after planting

(Table 2), but then decreased after that to a constant of

;0.25 g C/MJ, similar to results from other studies of

early forest development after clear-cutting (Martin and

Jokela 2004).

The number of years that MT was a carbon source

after harvest is consistent with estimations from a

chronosequence (Gholz and Fisher 1982) and simula-

tions using the Biome-BGC model (Thornton et al.

2002). However, the total carbon released before the

ecosystems again became a carbon sink was twice that

estimated using the model. When compared with other

ecosystems, either replanted or naturally regenerated

after major disturbances such as clear-cutting or fire, the

slash pine plantations shifted back to a carbon sink in a

shorter amount of time (Rannik et al. 2002, Law et al.

2003, Kolari et al. 2004, Gough et al. 2007, Dore et al.

2008, Zha et al. 2009, Amiro et al. 2010).

Interannual variability after canopy closure

Leaf area index.—LAI fluctuated between 4 and 7

after canopy closure at both sites during the period of

measurements. Mean values during average precipita-

tion-wet years and drought years were significantly

different (6.54 6 0.18, 4.69 6 0.30, respectively; [mean 6

SE]; t ¼ �5.35, P , 0.001). LAI, and consequently,

annual I-PAR (Fig. 5), were controlled by precipitation

during the period of needle elongation (March to

September; r2 ¼ 0.61, P , 0.01, F ¼ 10.70 and r2 ¼0.65, P , 0.01, F ¼ 11.15 for LAI and I-PAR,

respectively; Fig. 5). LAI decreased as the deficit

exceeded 100 mm, with an effect of 3 LAI units over

the range of 0 to 300 mm precipitation deficit. Low LAI

and I-PAR during at the extreme wet periods were the

result of premature leaf loss during hurricanes and

tropical storms in 2004. A negative effect of water

deficits on LAI seems to be general across broad ranges

of ecosystems (Grier and Running 1977, Law et al. 2002,

Garbulsky and Paruelo 2004), although reports of

changes over time within individual ecosystems are rare.

Water deficits during the growing season actually had

two effects on LAI: (1) early needle drop as compared

with wet years and (2) a reduction in needle growth.

Two pulses of needle fall were recorded during drought

years (data not shown), a smaller one in late spring and

early summer, in addition to the normal more major

event in the late fall, as observed for similar stands by

Gholz et al. (1991). A larger fraction of older needles

(first cohort) are dropped in the first event under more

severe spring drought conditions. However, if low

precipitation extends through the growing season during

severe and extreme drought conditions, elongation of

newly formed needles is also reduced, as observed for

Pinus radiata by (Linder et al. 1987) and (Sands and

Correll 1976) and in our stands in 2006–2007. Early

needle fall pulses were also reported for a natural pine

stand in the same area in 2001(Powell et al. 2005), as

well as for various other pine ecosystems around the

world subject to drought (Vose and Allen 1988,

Hennessey et al. 1992, Borghetti et al. 1998).

Annual carbon fluxes.—After canopy closure, both

sites were continuous carbon sinks through the end of

the measurements (Tables 1 and 2). Annual NEE over

this period was significantly higher (P , 0.05) at DT

than at MT, with averages of 6.71 6 0.65 Mg

C�ha�1�yr�1 and 5.04 6 0.29 Mg C�ha�1�yr�1, respec-

tively; Table 1). These averages compare well with

simulated NEE of slash pine plantations in years with

average precipitation (Cropper 2000) and are also

similar to the range reported for a loblolly pine

plantation in North Carolina (Noormets et al. 2010).

However, they are more than four times higher than

those for an older, naturally regenerated pine forest in

the same area (Powell et al. 2008) and are in the top of

the range of other terrestrial ecosystems globally (Law et

al. 2002, Hirata et al. 2008, Kato and Tang 2008, Wang

et al. 2008). This high capacity of slash pine plantations

to sequester carbon results from the combination of

improved genetic stock and modern silvicultural prac-

tices (Fox et al. 2007) and ambient environmental


conditions that enable year-round carbon uptake (Clark

et al. 2004).

It is interesting to note that, while management of the

DT and MT stands is intensive compared to most forest

ecosystems, the level of intervention in these stands is

still relatively low. For example, nearby experimental

stands on similar soils, but managed with even greater

intensity (i.e., with multiple fertilizer applications per

rotation coupled with more aggressive control of

competing understory vegetation), had levels of carbon

accumulation 50% higher than the DT stand at age 18

(Vogel et al. 2011) and 25 (Jokela et al. 2010), showing

the degree to which carbon accrual can be manipulated

through silviculture (Sampson et al. 2006, Gonzalez-

Benecke et al. 2010). However, Vogel et al. (2011) also

showed that complete or near complete understory

vegetation removal can adversely impact carbon accu-

mulation in deeper soil horizons. The observed reduc-

tion in fine-root biomass in this case could explain the

negative impact on deep soil carbon. Removal of

understory vegetation reduces belowground carbon

allocation and NPP (Shan et al. 2001); additionally,

removal of understory vegetation may result in lower

TABLE 2. Compartmental carbon fluxes measured by the biometric approach during the development of slash pine plantations innorth Florida.

Location and year Age (yr)

Carbon flux

Woody biomass(Mg C�ha�1�yr�1)

Foliage(Mg C�ha�1�yr�1)

Aboveground tree NPP(Mg C�ha�1�yr�1)

Understory biomass(Mg C�ha�1�yr�1)

Mize tract (MT)

1998 0 0 0 0 0.621999 1 0 0 0 1.392000 2 0.26 nd 0.26 0.642001 3 1.26 0.52 1.78 0.82002 4 3.54 1.23 4.77 1.852003 5 7.55 1.99 9.55 1.232004 6 6.18 2.73 8.91 0.972005 7 4.05 2.5 6.55 0.32006 8 3.96 0.99 4.95 0.672007 9 3.62 1.28 4.9 0.67

Donaldson tract (DT)

1999 10 3.01 2.3 5.31 0.672000 11 2.45 2.65 5.1 0.672001 12 3.48 2.57 6.05 0.672002 13 3.54 2.95 6.49 0.672003 14 2.86 2.38 5.24 0.682004 15 2.93 2.64 5.57 0.62005 16 3.38 1.85 5.23 0.422006 17 2.35 1.83 4.18 0.232007 18 2.14 1.74 3.88 0.24

Rotation aged�1997 25 3.53 1.9 5.43 0.25

Notes: Abbreviations are: NPP, net primary production; NEP, net ecosystem production; RUE, radiation-use efficiency; nd, nodata. RUE was estimated using aboveground tree NPP.

� From Clark et al. (2004).

FIG. 4. Annual gross ecosystem carbon exchange (GEE)and net primary production (NPP) as a function of annual leafarea index (LAI) during the first years of development of a slashpine plantation (Mize tract) in north Florida.

FIG. 5. Interannual variation in LAI (thick line) andintercepted photosynthetically active radiation (I-PAR; thinline), as related to deviation of growing-season (March–September) precipitation from the long-term average (DPrecip.).


nutrient retention with possible impacts on ecosystem C

sequestration potential. This situation reinforces the

need for further research on the interactions between

silviculture and ecosystem carbon management.

Annual GEE and Re after canopy closure at MT

averaged 28.17 6 1.18 Mg C�ha�1�yr�1 and �23.15 6

0.98 Mg C�ha�1�yr�1, respectively, while at DT fluxes

averaged 24.93 6 0.48 Mg C�ha�1�yr�1 and �18.22 6

0.52Mg C�ha�1�yr�1 for GEE andRe, respectively (Table

1). Average values of both GEE and Re were significantly

higher at MT than at DT (P , 0.05). Maximum annual

values of GEE at MT were recorded during wet years.

The lowest annual GEE were recorded during the

extreme drought years at both sites (Table 1). Maximum

annual GEE at DT was recorded in 2001, even though

annual precipitation was below the long-term average;

however, water was not a limiting factor during the

growing season of 2001, as precipitation was þ120 mm

compared to the average for this period (856 mm) and

LAI remained high. However, GEE at DT was higher

during the wet years as compared with the second

drought, and although we did not complete measure-

ments in 2007, cumulative values until August 2007

indicate a decrease in GEE as compared with 2006 (data

not shown). Annual GEE values at these stands are at the

top of the range for ecosystems in the United States as

reported by Xiao et al. (2010) and are also among the

largest maximum GEE values reported globally for

forests (Hirata et al. 2008, Kato and Tang 2008, Wang et

al. 2008). Xiao et al. (2010) also reported a reduction in

GEE of .3 Mg C�ha�1�yr�1 for the southeastern United

States during a drought in 2006, similar to our observed

reduction of 5 Mg C�ha�1�yr�1. An extreme drought in

Europe in 2003 produced a 30% reduction in GEE,

leading the continent to become a net carbon source of

0.5 Pg C that year (Ciais et al. 2005, Granier et al. 2007).

Annual Re was affected by both very wet and very dry

conditions (Table 1). The lowest Re values during the wet

years (2003–2005) were recorded at both sites in 2005

(PDSI ¼ 1.85), with Re decreased .2 Mg C�ha�1�yr�1from the average. The extended periods of shallow water

table in 2005 may have produced anoxic conditions that

limited heterotrophic respiration. Re fluctuated around

the average between wet and dry conditions and

decreased again .2 Mg C�ha�1�yr�1 during extreme dry

conditions at MT in 2007 (PDSI ¼�3.51). We did not

complete measurements in 2007 for DT, but Re at both

sites showed the same trends since 2004. Additionally,

cumulative Re for the first six months at DT in 2007

showed a decrease as compared with 2006 and followed

the same decreasing trend as the MT. The highest annual

Re was recorded in 2001 at both sites under a PDSI

indicating drought. However, precipitation during the

growing season of that year actually exceeded the long-

term average enabling high Re. Considering that more

than half of Re in these ecosystems is soil respiration (Rs;

Clark et al. 2004) and half of that is heterotrophic

respiration (Fang et al. 1998), any change in factors

controllingRs will impactRe. Although patterns inRs are

mainly related to soil temperature, the relative magni-

tudes of Rs at any temperature are also affected by soil

water contents. At these sites, soil moisture contents 15%

TABLE 2. Extended.

Carbon flux

Coarse root(Mg C�ha�1�yr�1)

NPP(Mg C�ha�1�yr�1)

Detritus(Mg C�ha�1�yr�1)

NEP(Mg C�ha�1�yr�1)

RUE(g C/MJ)

0 0.62 �8.7 �8.08 nd0 1.85 �7.19 �5.35 nd0.64 1.54 �6.16 �4.62 nd1.03 3.6 �5.07 �1.46 0.311.07 7.68 �4.02 3.67 0.411.43 12.2 �2.89 9.32 0.541.57 11.46 �1.57 9.89 0.41.08 7.93 �1.15 6.77 0.281.09 6.71 0.45 7.16 0.271.19 6.76 �0.51 6.25 0.27

0.66 6.64 0.79 7.43 0.240.57 6.33 1.37 7.71 0.230.88 7.59 1.22 8.81 0.260.8 7.96 0.76 8.72 0.280.74 6.66 0.86 7.52 0.230.84 7.01 1.83 8.84 0.250.81 6.45 0.57 7.03 0.230.54 4.96 1 5.95 0.210.42 4.8 0.44 5.24 0.21


or 35% reduced Rs by limiting biological processes

directly at the low end, or reducing oxygen supply and

gas transport at the high end (Fang and Moncrieff 1999,

Moncrieff and Fang 1999).

In general, drought impacted GEE to a greater extent

than Re in lowering net carbon uptake, e.g., as

conditions changed from wet to drought (2005–2006),

GEE at both sites stay similar, while Re increased .2.5

Mg C/ha and 1.7 Mg C/ha at MT and DT, respectively.

These patterns seem general and explain interannual

variability in NEE in loblolly pine, Mediterranean and

boreal forests, as well as other pine forests (Barr et al.

2007, Thomas et al. 2009, Misson et al. 2010, Noormets

et al. 2010, Wen et al. 2010).

Trends in NEP were similar to NEE and also

indicated that the sites were continuous carbon sinks

after canopy closure (Table 2, Fig. 6). NEP averaged

7.88 6 1.62 Mg C�ha�1�yr�1 and 7.47 6 1.26 Mg

C�ha�1�yr�1 at MT and DT, respectively. NPP domi-

nated carbon fluxes at MT after year four (Table 2, Fig.

6), exceeding NEP for most of the measurement years

due to the efflux of carbon from the detrital pool.

Maximum NPP was 12.2 Mg C�ha�1�yr�1 at year five atMT. Aboveground tree growth accounted for .44% of

total NPP at both sites, with understory contributions

averaging ,10% (Table 2). Total NPP averaged 87% of

NEP at DT. Even while a new forest floor was forming

and accumulating, the detrital pool lost carbon through

age nine, after which it contributed up to 20% of NEP.

Annual radiation-use efficiency (RUE; Table 2)

generally decreased over time, although its maximum

value, 0.54 g C/MJ, was achieved as the canopy closed,

leading to the corresponding maximum tree NPP, and

consequently, the highest annual NPP we observed

(Table 2). Fertilization at the end of year three stimulated

LAI and increased RUE; however, fertilization at DT

during fall of 2001 did not have a significant effect on

RUE (Table 2), probably due to persistent drought

impacts on RUE. From age seven onward, RUE

averaged 0.24 6 0.02 g C/MJ. Maximum aboveground

tree NPP was reached earlier at MT than for other slash

and loblolly pine plantations in the area under intensive

silvicultural practices, as reported by Martin and Jokela

(2004). In the more intensive management cases, stands

reached an average maximum of 10.92 Mg C�ha�1�yr�1between the ages of 6–9 years. Since understory

vegetation contributed 1.23 Mg C�ha�1�yr�1 to the total

at MT, aboveground tree NPP was actually very similar

in these two cases. High levels of NPP at MT were short

lived, decreasing rapidly to a lower range of 4.8–7.9 Mg

C�ha�1�yr�1 for both sites through the end of the study.

Similar patterns of NPP have been reported for other

pine plantations in the southeastern United States

(Swindel et al. 1988, Colbert et al. 1990, Jokela and

Martin 2000, Jokela et al. 2000, Adegbidi et al. 2002,

Burkes et al. 2003, Samuelson et al. 2004). Values for

maximum RUE, the trend with age at both sites, and the

average of 0.25 for the older stands were all similar to

other slash and loblolly pine plantations across the

region (Gholz et al. 1991, Martin and Jokela 2004).

After canopy closure, the annual variability in NEE of

these sites was most closely related to the departure of

FIG. 6. Annual net ecosystem production (NEP), total net primary production (NPP), and detritus carbon flux during thedevelopment of two slash pine (Pinus elliottii var elliottii Englm.) plantations: (a) Mize tract (MT), (b) Donaldson tract (DT).Negative values for detritus C flux indicate net loss from detrital C pool due to decomposition.


growing season precipitation from long-term averages

(r2 ¼ 0.54, P , 0.01; Fig. 7a). A significant intercept

(6.45; P , 0.001) was not different from average NEE.

NPP, although highly variable, was also positively

related to growing season precipitation, changing by

.4 Mg C/ha over 600 mm range (r2¼ 0.52, F¼ 13.14, P

, 0.05; Fig. 7b). This suggests that drought effects on

annual carbon fluxes resulted not only from decreased

annual precipitation, but even more so by changes in its

seasonal distribution; particularly, below average pre-

cipitation during the growing season induced lower

carbon uptake. Other studies have shown that growth of

slash pine stands is positively correlated with water

balance during the current growing season (Ford and

Brooks 2003) and aboveground NPP in a longleaf pine–

wiregrass ecosystem was also positively correlated with

seasonal water availability (Mitchell et al. 1999). Similar

to our results, water deficit during the growing season

explained the interannual variability of tree growth in a

young beech plantation and also in older boreal

deciduous forests (Granier et al. 2008, Grant et al.

2009). Extreme droughts have been observed to sub-

stantially reduce the annual NEE of a range of global

forests, leading them to become weak carbon sinks or

even carbon sources in some cases (Leuning et al. 2005,

Humphreys et al. 2006, Kljun et al. 2007, Pereira et al.

2007, Falk et al. 2008, Noormets et al. 2010). Likewise,

annual NEE for a range of ecosystems in different

climatic zones is similarly affected by water availability

and drought (Goldstein et al. 2000, Arain et al. 2002,

Barr et al. 2007, Dunn et al. 2007, Allard et al. 2008, Yu

et al. 2008, Chen et al. 2009, Grant et al. 2009, Noormets

et al. 2010). Moreover, according to Zhao and Running

(2010), large-scale droughts over the southern hemi-

sphere during the decade 2000–2009 reduced regional

FIG. 7. (a) Annual net ecosystem carbon exchange (NEE) and (b) deviation from mean annual NPP (DAnnual NPP) at bothsites as related to deviation of growing-season (March–September) precipitation from the long-term average. (c) Annual NEE anddeviation form mean annual aboveground trees (At) NPP (DAt-NPP) as a function of annual intercepted PAR (I-PAR) aftercanopy closure at two slash pine plantations in north Florida.


NPP and offset increases in the northern hemisphere

that lead to a reduction in global NPP.

Water availability impacts on carbon uptake in slash

pine stands can be explained through its effect on LAI

development and I-PAR (Fig. 5). I-PAR controlled 68%(P , 0.01) of interannual variability in NEE at both MT

and DT, and 51% (P , 0.01) of interannual variability

in aboveground tree NPP (Fig. 7c), with a mean annual

RUE of 0.24 g C/MJ. The lowest RUE values were

recorded during extreme drought years at both sites

(Table 2). Average RUE at DT during drought years

(0.22 6 0.003 [mean 6 SE]) was slightly lower (P¼ 0.09)

compared with the average for wet years (0.25 6 0.005),

although the site coincidentally also received a high

amount of nitrogen (150 kg/ha) at the end of the first

drought. Similarly, RUE decreased during drought

years for a jack pine (P. banksiana) chronosequence, a

eucalyptus plantation (E. globulus), and an evergreen

oak forest (in a Mediterranean climate) (Pereira et al.

2007, Allard et al. 2008, Chasmer et al. 2008).

Seasonal carbon fluxes (NEE, GEE, and Re)

In order to better understand the factors affecting

interannual variability in carbon fluxes after canopy

closure, we analyzed monthly NEE, GEE, and Re during

each drought and the wet periods using typical years for

each condition (e.g., 2000, 2005, and 2006 for the first

drought, wet years, and second drought, respectively;

Fig. 8).

First drought (2000).—NEE at DT (Fig. 8a) de-

creased from winter to early summer during the first

drought, when the ecosystem became a weak carbon

sink or a source (May and June). This decrease is

attributable to the decoupling of GEE and Re during the

first six months of the year (Fig. 8b, c). GEE did not

have a significant increase, while Re increased .1 Mg C/

ha (twofold) over winter baseline conditions. Low

growth in LAI early in the year (Fig. 8d), a cumulative

precipitation deficit .200 mm through June, and high

mid-afternoon air vapor pressure deficit (VPD; .1.8

KPa; Fig. 8e), inducing to lower canopy conductance (gcdecreased from average 7.6 mm March–April to ,3.2

mm in May–June), affected GEE, while temperature

increases from winter through summer certainly pro-

duced the observed rise in Re. The reduction in canopy

conductance was followed by an early needle drop in

June and July (61 g/m2 above the average), which may

have limited maximum yearly LAI and net carbon

uptake. An increase in GEE with the onset of the rainy

season and decrease in Re after September then led the

site to be a carbon sink through the end of the year. The

lack of correspondence between monthly GEE and Re

during the first drought contrasts with the pattern

reported for a nearby naturally regenerated pine forest

(Powell et al. 2008), as well as for temperate coniferous

forests in general as derived from FLUXNET measure-

ments, where GEE and Re are generally in phase and

affected similarly (Falge et al. 2002, Baldocchi 2008).

Wet years (2005).—During a typical wet year (2005),

both plantations were carbon sinks year round. NEE

showed a different pattern compared with the first

drought: It increased continuously from winter, reaching

maximum during the spring and maintained high values

(0.6 Mg C�ha�1�month�1 through early fall; Fig. 8f ).

GEE was maintained high and constant from early

spring to early fall at both sites (Fig. 8g) and exceeded

Re (Fig. 8h), so that both sites had high annual NEE.

MT had both higher GEE and Re than DT. LAI showed

a rapid increase (Fig. 8i ) and double its value from

winter through summer, precipitation exceeded average

during the growing season, and VPD did not limit gc,

which was sustained above 7 mm/s all year round.

Second drought (2006).—Patterns of monthly NEE

during the second drought (Fig. 8k) differed from those

during the first. NEE started at low values as a

continuation of 2005, showed a small increase during

the spring, and maintained values around 0.4 Mg

C�ha�1�month�1 from spring through midsummer, when

both sites started a steady decrease in NEE toward the

end of the year, a time when the plantations were a

carbon source. NEE was basically driven by GEE (Fig.

8l), which, after reaching maximum at midsummer,

started decreasing three months earlier than the previous

year, while Re (Fig. 8m) stayed relatively high over the

year. LAI (Fig. 8n) showed only a small increase from

the beginning of the growing season toward the summer,

while it doubled over that interval in the previous year.

A precipitation deficit of 96 mm in March (long-term

average ¼ 100 mm) at the beginning of the growing

season (Fig. 8o), alongside high air VPD (.1.8 in April)

sustained through the growing season most likely

affected the growth of the new needle cohort. In

addition, an early pulse in needle drop was registered

in July at both sites, reducing drastically net ecosystem

carbon uptake at both sites. Severe drought conditions

that strengthened from the summer through the end of

the year (e.g., PDSI decreased from �2.85 in July to

�4.31 in November, 2006), associated with high VPD

(monthly average maximum VPD was 1.9 6 0.2 KPa

from April to October; Fig. 8o), combined to decrease

GEE and the sink strength for carbon of these

ecosystems during the fall of 2006 and into the summer

in 2007. By that time, drought conditions were extreme

(PDSI ¼�4.27) and DT had become a carbon source,

releasing close to 0.3 Mg C/ha between July and August

2007. The extreme drought conditions in 2006 and 2007

made the second drought stronger than the first one as

represented by year 2000, clearly affecting net ecosystem

carbon uptake. Goldstein et al. (2000) and Pereira et al.

(2007) also showed that drought conditions and high

VPD decreased GEE with decreased NEE as a

consequence.

In general, monthly NEE at both sites during drought

years was directly related to the depth of the water table

(r2¼ 0.29 to 0.52, P , 0.05; Fig. 9a, Table 3). However,

higher NEE at DT during the first drought occurred at a


similar water table depth compared with the second

drought or at MT during the same drought. Higher LAI

during the first drought enabled higher NEE at DT.

NEE during wet years was better explained by I-PAR

(r2¼0.41 and 0.43 for DT and MT, respectively; Fig. 9b,

Table 3).

Influences on monthly NEE were assessed by sepa-

rating responses of GEE and Re. GEE was limited when

average monthly maximum VPD exceeded 1.5 KPa

during drought (data not shown), indicating that

physiological controls on carbon exchange were opera-

ble. Nevertheless, I-PAR alone explained 38–57% of the

variance in GEE during drought years and 73–83%during wet years (Fig. 10a, b); the Fmax parameter was

significantly different between drought and wet years at

both sites (P , 0.05). Similar results were reported for

temperate eucalyptus and savanna sites in Australia,

ecosystems also subject to regular droughts (Leuning et

al. 2005).

Physiological controls on C gain.—Photosynthetic

capacity at light saturation (Fmax; Table 4) was

significantly higher at both sites only during wet years

compared with dry years (P , 0.05), indicating changes

in radiation use efficiency with water availability.

FIG. 8. Monthly carbon fluxes (NEE, GEE, and ecosystem respiration [Re]) and related monthly LAI, precipitation deficit, andaverage of maximum air vapor pressure deficit (VPD), for typical years during the (a–e) first drought (DT, 2000), (f–j) wet period(2005, MT and DT), and (k–o) second drought (2006, MT and DT) in slash pine plantations, Florida. Annual sums (R, Mg C/ha)for each flux by stand are indicated on each panel.


Similarly, Yu et al. (2008) reported that the response of

GEE to increased light was depressed by drought stress

in slash pine. Considering that RUE incorporates

physiological processes, we also estimated monthly

gross RUE (RUEG ¼ GEE/I-PAR) and related it to

monthly canopy conductance (gc) normalized by

monthly LAI (gc/LAI). This allowed us to assess

physiological controls on carbon uptake under different

climatic conditions. No differences in the relationships

were found between droughts and sites during drought

conditions (P . 0.1), nor during average-wet conditions

(P . 0.25), leading us to compare stomatal control on

RUEG between drought and average-wet years (Fig.

10c, d), with significant relationships found in each case:

RUEG ¼ 0:568ðgc=LAIÞ þ 0:517

ðr2 ¼ 0:61; P , 0:0001; drought; Fig 10cÞ;

RUEG ¼ 0:313ðgc=LAIÞ þ 0:818

ðr2 ¼ 0:42; P , 0:0001; average-wet; Fig 10dÞ:

Conductance per unit leaf area explained a higher

percentage of RUEG during drought conditions. Slopes

of the relationships were different (P , 0.001; SE¼ 0.05

and 0.04 for slope values during drought and average-

wet years, respectively). This indicates stronger physio-

logical control through stomatal closure was imposed on

carbon exchange during drought years. Although there

was higher radiation use efficiency per leaf area unit,

high VPD (.1.5 Kpa) during the droughts decreased

stomatal conductance and limited the conversion of

absorbed energy into biomass. Although these planta-

tions are more efficient in carbon uptake per unit of leaf

area during drought conditions, higher LAI developed

during average-wet conditions leads to larger carbon

uptake. This behavior is similar to that of many other

ecosystems (e.g., Turner et al. 2003, Allen et al. 2005,

Schwalm et al. 2006, Pereira et al. 2007, Chasmer et al.

2008, Yu et al. 2008, Noormets et al. 2010). Tree

hydraulic conductivity decreases with water limitations,

which restrains gc and consequently carbon uptake

(Domec et al. 2009, Gonzalez-Benecke and Martin 2010,

Noormets et al. 2010). Additionally, integrated biophys-

ical parameters, such as actual evapotranspiration,

usually explain most of the variation in seasonal RUE

across the global range of terrestrial ecosystems

(Garbulsky et al. 2010).

TABLE 3. Parameters and statistics for equations describing the relationship between monthly NEE and (a) depth to the watertable (DWT) in drought years and (b) intercepted PAR (I-PAR) in wet years at Donaldson tract (DT) and Mize tract (MT).

Relation/parameter Location a b r2 P

a) NEE vs. DWT

1999–2001 DT 1.49 6 0.30 �0.33 6 0.11 0.29 ,0.012006–2007 DT 1.37 6 0.21 �0.46 6 0.10 0.52 ,0.0012006–2007 MT 0.94 6 0.15 �0.36 6 0.09 0.39 ,0.001

b) NEE vs. I-PAR

2003–2005 DT 0.24 6 0.09 2 3 10�4 6 4 3 105 0.41 ,0.00012003–2005 MT 0.04 6 0.08 3 3 10�4 6 5 3 10�5 0.44 ,0.001

Notes: The relationship (a) NEE (Mg C�ha�1�month�1) vs. DWT (m) or (b) NEE vs. I-PAR (GJ�ha�1�month�1) is given by NEE¼ aþ bx, coefficient 6 SE, where a is the intercept, b is the slope of the relationship, and x is DWT or I-PAR, respectively. Valuesare means 6 SE.

FIG. 9. Dependence of net ecosystem exchange (NEE) after canopy closure on (a) depth to the water table during drought years(the thick and dashed lines represent the fit during first and second drought at DT, respectively, and the thin line represents the fitfor MT data) and (b) intercepted PAR (I-PAR) during wet years in Donaldson tract (thick line) and Mize tract (thin line).


RUE is commonly reported to respond to increases in

nutrient availability (Balster and Marshall 2000, Martin

and Jokela 2004). Fmax was probably improved by

increased foliage nutritional status due to the fertiliza-

tion events at the end of the first drought and relief of

water limitations between 2003 and 2005. RUE at DT

did not increase in 2002 after the fertilization in 2001.

This was probably due to the increasing precipitation

deficit (.250 mm) that developed through the subse-

quent 2002 growing season. All of these support the

notion that canopy processes responding to water

limitations controlled RUE during the droughts.

FIG. 10. Monthly gross ecosystem carbon exchange (GEE) as a function of intercepted PAR (I-PAR) during (a) drought yearsand (b) wet years for both sites. (c, d) Relationship between monthly radiation-use efficiency (RUE) and the average of normalizedmaximum canopy conductance (gc/LAI). In panel (a), the thick and dashed lines correspond to DT first and second drought,respectively, and the thin line corresponds to MT drought (as DT second drought) during drought years. In panel (b), the thick andthin lines correspond to DT and MT, respectively, during wet years.

TABLE 4. Parameters and statistics for the equations describing the relationship between monthly GEE and intercepted PAR (I-PAR) for drought and wet years.

Period/parameters Location Fmax q r2 P

Drought years

1999–2001 DT 3.71 6 0.57 0.003 6 7 3 10�4 0.38 ,0.00012006–2007 DT 2.86 6 0.33 0.003 6 8 3 10�4 0.55 ,0.00012006–2007 MT 3.39 6 0.38 0.005 6 0.001 0.50 ,0.0001

Wet years

2003�2005 DT 4.72 6 0.46 0.002 6 2 3 10�4 0.83 ,0.00012003�2005 MT 5.61 6 0.76 0.003 6 3 3 10�4 0.73 ,0.0001

Notes: The relationship between GEE (Mg C�ha�1�month�1) and I-PAR (GJ�ha�1�month�1) is given by GEE¼ q 3 (I-PAR) 3Fmax/[q3 (I-PAR)þFmax], where q is the quantum yield, and Fmax is the photosynthetic capacity at light saturation. Coefficients 6SE are shown.


Re was related to air temperature (Ta) for both sites

during both dry and wet years (Fig. 11), with Ta

explaining 53–94% of its monthly variation (Table 5).

Comparisons of respiration between dry and wet years

and between sites were performed using the responses of

monthly respiration to air temperature (respiration

coefficient, RC) and respiration at 108C (R10; Table 5).

RC at DT was significantly higher during the first

drought compared to the second (P , 0.01), but similar

between the first drought and wet years; R10 at DT was

similar between droughts and significantly lower in the

wet years. RC at MT was significantly higher in wet years

compared with dry ones, while R10 was not different (P

¼ 0.27). When the two sites are compared during the

same drought (2006–2007), no differences were found

for RC (P¼ 0.54); however, R10 was higher at MT (P ,

0.05). These results are similar to those found for other

slash pine stands (Yu et al. 2008). Higher amounts of

detritus at MT, both on top of the soil, as well as soil

organic carbon in the mineral soil itself, associated with

higher GEE, produced the observed differences in

ecosystem respiration between the two sites. This

coupling of Re and GEE has been observed in a range

of other studies (Ekblad and Hogberg 2001, Knohl et al.

2005, Richardson et al. 2007, Wen et al. 2010).

Comparisons of the eddy covariance and biometric

methods (NEE and NEP)

Both NEE and NEP showed the same trends and

magnitudes over time, from a net release of carbon

during the first three years after planting to a net carbon

uptake . 7 Mg C�ha�1�yr�1 thereafter (Tables 2 and 3),

with a similar duration of net carbon release following

stand establishment. Mean annual NEP after canopy

closure at MT (7.88 6 1.62 Mg C�ha�1�yr�1) was ,40%

higher than mean annual NEE (4.97 6 1.35 Mg

C�ha�1�yr�1). However, mean annual NEP and NEE

agreed within 10% at DT (7.47 6 1.27 Mg C�ha�1�yr�1and 6.67 61.02 Mg C�ha�1�yr�1, respectively). In other

locations, NEP and NEE were reported to be similar for

a tropical forest (Miller et al. 2004) and agreed within

25% of each other in a mature temperate deciduous

forest (Ohtsuka et al. 2007), although differences

between NEP and NEE ranged from 55% to 105%

during six continuous years in one deciduous broadleaf

forest (Ohtsuka et al. 2009). When our 18 years of NEP

measurements from the two sites are plotted against

NEE, most of the points lie above the 1:1 line (Fig. 12a).

Differences between approaches were reduced after

canopy closure at MT and were generally lower at DT.

FIG. 11. Monthly ecosystem respiration (Re) as a functionof air temperature (Ta) during (a) drought years (the thick anddashed lines correspond to DT first and second drought,respectively; and the thin line corresponds to MT drought), and(b) wet years for two slash pine plantations in northern Florida.

TABLE 5. Parameters, statistics, respiration coefficient (RC), and respiration at 108C (R10) describing the relationship betweenmonthly ecosystem respiration (Re) and air temperature at Donaldson tract (DT) and Mize tract (MT).

Period/parameters Location a b r2 P RC R10

Drought years

1999–2001 DT 0.65 6 0.02 0.047 6 0.002 0.94 ,0.0001 1.59 1.032006–2007 DT 0.69 6 0.05 0.036 6 0.003 0.87 ,0.0001 1.44 0.992006–2007 MT 0.94 6 0.13 0.030 6 0.006 0.53 ,0.0001 1.35 1.27

Wet years

2003–2005 DT 0.55 6 0.04 0.044 6 0.003 0.89 ,0.0001 1.55 0.852003–2005 MT 0.80 6 0.07 0.045 6 0.00 0.83 ,0.0001 1.57 1.26

Notes: Relationship between ecosystem respiration (Re; Mg C�ha�1�month�1) and monthly average air temperature (Ta; 8C) isgiven by Re ¼ a 3 exp(b 3 Ta). RC ¼ exp(10 3 b), and R10 ¼ a 3 RC.


We presume that deviations from 1:1 line in the

relationship NEP–NEE can be used to infer nonsteady

state transfers associated with soil carbon dynamics.

Significant detrital carbon (primarily former forest floor

and logging residues) is incorporated into the mineral

soil during site preparation, which on these sites

included mechanical tillage, and so carbon losses early

in stand development can be attributed to the relatively

rapid decomposition of this new ‘‘soil organic matter.’’

These dynamics are not detected by our NEP estimates,

but are included in the NEE measurement. A departure

from 1:1 in the NEP vs. NEE line in Fig. 12a could then

be interpreted as divergence from steady state in

belowground processes early in stand development. This

contribution from fine roots and soil organic matter of

;2.3 Mg C�ha�1�yr�1 (the intercept of the relationship),

a value similar to that of .1.5 Mg C�ha�1�yr�1 as

inferred from the chronosequence study of Gholz and

Fisher (1982). Additionally, we assumed a constant

annual decay rate in estimating carbon flux from the

detrital pool for all slash left after harvesting. Higher

decomposition rates and higher substrate temperature

may occur in recently planted and young plantations,

where more debris is found on the ground surface

compared with older stands. Silvicultural practices, such

as fertilization and understory vegetation control, can

also increase decomposition rates up to 30% (Polglase et

al. 1992a, b, c). All of these fluxes are not included in the

biometric approach, but the net results are included in

the NEE.

Comparing different components of NEP with eddy

covariance measurements of carbon exchange, we found

that GEE could explain 50% (P¼ 0.005) and 58% (P ,

0.001) of total and woody NPP, respectively (Fig. 12b).

FIG. 12. (a) Comparison between annual NEP and NEE and (b) total NPP (NPPt) and woody NPP (NPPw) as related toannual GEE in two stands of slash pine plantations in Florida. The thick line corresponds to NPPt vs. GEE, and the thin linecorresponds to NPPw vs. GEE.


The NPP/GEE ratios (regression slopes) are consistent

with values reported for a comparative analysis of eddy

covariance and biometric estimates of ecosystem carbon

balance by Luyssaert et al. (2009), as well as values

reported for other individual ecosystems (Law et al.

2001, Litton et al. 2007, Navarro et al. 2008).

After canopy closure, both sites showed similar

responses to environmental variability, with drought

and excess precipitation affecting each of the carbon

fluxes in a similar way (e.g., LAI, I-PAR, and RUE were

similarly affected by dry and wet conditions). Variability

of NEE and NPP were also similarly controlled by water

availability at both sites (Fig. 7), through controls on

LAI development, and the amount of fixed carbon

partitioned to NPP was similar (Fig. 12b). Higher GEE

and Re was observed at MT; however, NEE was similar

at both sites after canopy closure, with compensations

between GEE and Re producing similar values of NEE.

Increases in woody biomass at MT were also higher than

at DT. The differences between sites may be the result of

a number of factors. MT is a younger stand. We also

estimated site index as an indicator of site quality (SI25,

defined as mean height of dominant trees at age 25) for

the stands that previously occupied the MT and DT

using historic records and found a greater SI25 for MT

(19.2 m) than for DT (17.2 m). Extrapolating the

observed stand heights to 25 years for both stands leads

to SI25 values of 26.67 m and 20.77 m for MT and DT,

respectively. These results indicate an inherently higher

site quality for MT as compared with DT. This may be

related to average higher soil water availability (shal-

lower water table) or higher soil nutrient status. MT was

planted almost a decade after DT and likely included

seedlings with improved genotypes, so that improved

genetics and better silvicultural practices may also have

contributed to the differences observed between these

two ecosystems.

Florida pine plantations in a global context

In order to place the Florida ecosystems in a global

context, we compared carbon fluxes from our study with

those from other forests around the world. We used

carbon fluxes obtained by eddy covariance from 43

forests for a total of 220 site-years published to the end

of 2010 (Appendix, Fig. 13). The AmeriFlux database

was used to obtain annual precipitation and mean

annual temperature when values were not included in

publications. The data covered a large latitudinal range

FIG. 13. Relationships between (a) GEE, (b) Re, and (c) NEE and mean annual air temperature (Ta), and (d) between GEE andannual precipitation for forests at different latitudes. Open triangles represent sites at latitudes ,238 N, solid squares represent sitesfrom 238 N to 458 N, open squares represent slash pine plantations in Florida (latitude 298 N), and open circles represent sites atlatitudes .458 N.


where mean annual temperature increased from less

than�58C close to the edge of the Arctic Circle (boreal

forests) to .278C at the tropical sites (Appendix). The

two sites from the current study were included for the

closed-canopy years only (e.g., all of DT and MT after

year six).

Annual GEE increased linearly with mean annual air

temperature (r2 ¼ 0.68, P , 0.001; Fig. 13a) from less

than �58C to .278C, with maximum rates .30 Mg

C�ha�1�yr�1in the tropics. Ecosystem respiration also

increased with mean annual air temperature, but

exponentially (r2 ¼ 0.66, P , 0.001; Fig. 13b), reaching

values similar or even higher than annual GEE. These

different responses to temperature determine the mag-

nitude of NEE, making high GEE values in subtropical

sites possible and constrain Re more than GEE during

mild winters. Respiration appears more sensitive to

year-to-year changes in air temperature at tropical sites,

and because GEE ’ Re, small changes in their abiotic

controls determine the net carbon balance. This

supports Wang et al.’s (2008) notion that forests are

more efficient in their capture of carbon within a middle

range of temperatures.

Most of the forests in this review were net carbon

sinks (Fig. 13c). NEE increased with temperature to

values .4 Mg C�ha�1�yr�1 from 88C to 208C (Fig. 13c,

Appendix). However, interannual NEE was highly

variable inside this range, where managed and/or

relatively young stands sequestered .4 Mg C�ha�1�yr�1(Appendix). In several cases (as for the Florida

plantations), interannual variations in NEE could also

be explained by deviations from mean conditions

(Urbanski et al. 2007, Granier et al. 2008, Thomas et

al. 2009).

The apparent decrease in NEE above 208C may be a

real consequence of global patterns in GEE and Re, or

may be due to the limited number of studies conducted

in the tropics. Most tropical eddy covariance sites have

been mature forests, where NEE values near zero are

expected. However, NEE values . 4 Mg C�ha�1�yr�1 insome old tropical forests (Malhi et al. 1998, Loescher et

al. 2003, Hirata et al. 2008) suggest that tropical forests

have the potential to reach NEE values as high as

intermediate-latitude forests.

In earlier reviews, Law et al. (2002) and Luyssaert et

al. (2007) found NEE independent of climate and

explained high values in the subtropics as a result of

management. However, more recent results indicate that

less intensively managed forests can also reach high

NEE (Appendix): mixed hardwood–conifer forest (Har-

vard forest; Urbanski et al. 2007), ponderosa pine

(Thomas et al. 2009), and young beech (Granier et al.

2008). In these cases, climatic fluctuations and stand age

together determined carbon sink strength. This trend of

increasing NEE with temperature from high latitudes to

subtropics, and perhaps even to the tropics, was also

suggested in a broader survey of eddy covariance data

(Wang et al. 2008) and in a latitudinal transect of Asian

forests (Hirata et al. 2008). Wang et al. (2008) also

suggested a threshold of mean annual temperature 208C

above which the sensitivity of NEE to temperature

would decrease.

GEE, while more variable, also increased with

precipitation, showing an apparent optimum at ;1500

mm (Fig. 13d). Low average temperatures may con-

strain GEE at the two sites with low GEE, but with high

(1500–3000 mm) annual precipitation. Schuur (2003)

also reported decreases in NPP at precipitation .1500

mm.

Although temperature explained much of the vari-

ability in GEE and Re across sites in this review, large

variability was also very common within a site.

Deviations from average conditions can produce a

differential response in both uptake and efflux, changing

the direction in NEE in large areas from carbon sinks to

carbon sources (Ciais et al. 2005, Granier et al. 2007,

Xiao et al. 2010). Schwalm et al. (2010) reported that, at

the global scale, GEE is 50% more sensitive to drought

than Re, consequently reducing the terrestrial carbon

sink.

It is noticeable that plantations in the subtropics

(slash pine as seen in this study; loblolly pine, Stoy et al.

2006a, b, Noormets et al. 2010; cypress in Japan,

Takanashi et al. 2005, Ohkubo et al. 2007) can attain

annual GEE comparable with values reached by mature

tropical forests. However, lower annual respiration rates

(than in the tropics) leads these ecosystems to be more

efficient in carbon accumulation than natural, old-

growth tropical forests, indicating that, regardless of

the climatic variations, silvicultural practices can greatly

increase carbon sequestration potentials (Liski et al.

2001, Harmon and Marks 2002). Additionally, silvicul-

tural practices that promote production of long-lasting

forest products have a high impact on carbon seques-

tration (Gonzalez-Benecke et al. 2010); the use of these

products is proposed as a major strategy to sequester

carbon (Canadell and Raupach 2008). Recent studies

demonstrate that net carbon accumulation rates can be

doubled, compared to the maximum rates reported here,

using more aggressive silvicultural practices, such as

multiple fertilizations during the rotation cycle and

understory control (Sampson et al. 2006, Gonzalez-

Benecke et al. 2010), although there may be limits that

we are just exploring (Vogel et al. 2011). Although

silvicultural practices can boost the potential for carbon

sequestration, we demonstrated here that water-limiting

conditions can mitigate this potential. With predictions

of more frequent droughts, the capacity of managed

forests in the southern United States to sequester carbon

could be significantly impacted.

Beyond subtropical plantations, more data concern-

ing the impacts of climate variability on tropical forests

are clearly needed, as contrasting research results cannot

currently be rationalized (Saleska et al. 2007, Phillips et

al. 2009) and the range of potential NEE is huge. In

addition, little is known about ecosystem carbon


balances for the extensive tropical plantations charac-

terized by very short rotations and rapid tree growth

(Evans 2003, Gomez et al. 2008) that can be affecting the

carbon signal for extensive areas of the tropics.

While this paper focuses on the importance of water

balance and tree water relations in controlling the

carbon dynamics of slash pine plantations, soil nutrient

availability likely remains the dominant controller of the

productivity, and therefore, carbon balance of most

southern pine forests, at least in non-drought years.

Numerous studies have shown relatively small responses

of southern pines to irrigation in the absence of nutrient

amendments (Neary et al. 1990, Albaugh et al. 2004,

Allen et al. 2005, Samuelson et al. 2008). Southern pines

evolved on nutrient-poor soils, tend to be found on

marginally productive sites relative to other forest types,

and as a result, often have dramatic growth responses to

added nutrients (Jokela et al. 2004). Carbon balance

studies focusing on nutritional effects have shown large

impacts of nutrient additions on LAI and rates of

carbon sequestration (Gholz et al. 1991, Maier and

Kress 2000, Sampson et al. 2006, 2008, Vogel et al.

2011).

Pine plantations in the southeastern United States are

one of the most productive terrestrial ecosystems in the

world, contributing to a large offset of carbon emissions.

Their NEE can reach up to 8 Mg C�ha�1�yr�1 (See

appendix; Stoy et al. 2006b, Noormets et al. 2010), with

landscape level estimations of around 1–2 Mg

C�ha�1�yr�1 (Barnett and Sheffield 2004, Binford et al.

2006). Early estimations of 0.4 Tg C/yr between 1990

and 2000 for southern pine plantations (Conner and

Hartsell 2002) may well be higher now, considering

better silvicultural practices (Fox et al. 2007) and the

increase in planted area since 2000. Furthermore, if

carbon sequestered in long-lasting forest products

(Gonzalez-Benecke et al. 2010) are considered, regional

net sequestration may be even larger. This study

establishes a basis for more complex models by

synthesizing extensive measurements made through a

complete cycle of disturbance through recovery, and by

examining the biophysical mechanisms underlying car-

bon balance under changing climate, both key issues in

terrestrial carbon cycle science. Integrated modeling and

management of southern pine carbon sequestration in

the future will need to accommodate the (increasingly

likely) interactions of nutrient availability with drought

frequency and severity.

CONCLUSIONS

This study used nine years of carbon dynamics as

measured simultaneously in two slash pine plantations

spanning 20 years of stand age and covering a harvest

cycle. During this study, the sites experienced major

climatic fluctuations that included two extreme multi-

year droughts separated by three wetter than normal

years.

The major disturbance produced by harvesting shifted

the previous plantation from being a carbon sink to a

strong carbon source due to the eliminationof treeLAIand

a very large consequent reduction in GEE. Early in stand

development, aggrading LAI and intercepted PAR were

the dominant controls on carbon accrual. After canopy

closure, water availabilitywas an important environmental

regulator of annual carbon uptake and ecosystem balance,

although over a much lower amplitude.

These plantations returned to being carbon sinks after

four years, with maximum net carbon uptake reached

before age 10. Aboveground tree NPP was the major

sink for carbon uptake, with .100 Mg C/ha accumu-

lated before the next harvest.

The timing and magnitude of droughts had differing

effects on the processes controlling ecosystem carbon

balance. Water availability regulated net carbon uptake

through its effect on both LAI and on radiation-use

efficiency. Drought impacted LAI by inducing early

needle drop and/or by restraining needle growth. Radia-

tion-use efficiency was controlled by physiological con-

trols on gas exchange (stomata opening) during severe

droughts. Drought had a much stronger impact on GEE

than on Re, resulting in a clear reduction in NEE.

Globally, these Florida data reinforce expected trends

of ecosystem carbon dynamics in relation to climate,

despite their history of major intermittent human

disturbance through harvesting.

Considering the responsiveness of low-latitude and

tropical forests to both management and climate and

their important role in the global carbon cycle, more

research is urgently needed on their interacting control-

ling factors.

ACKNOWLEDGMENTS

This research was supported by the Office of Science (BER),U.S. Department of Energy, through the SE Regional Center ofthe National Institute for Global Environmental Change, theNational Institute for Climatic Change Research, and theNational Institute of Food and Agriculture through the ClimateChange Coordinated Agricultural Project program. We thankthe School of Forest Resources and Conservation (SFRC),University of Florida staff for logistical support, and RayonierCorporation for providing long-term access to the study sites.This paper was partially based on work supported by theNational Science Foundation, while H. L. Gholz was workingat the Foundation. Any opinion, findings and conclusionsexpressed here are those of the authors and do not necessarilyreflect the views of the Foundation. The Metolious sitemeteorological data were provided by B. E. Law (DOE Grantnumber DE-FG02-06ER64318). Wind River site meteorologi-cal data were provided by the Wind River Canopy crane dataarchive.

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SUPPLEMENTAL MATERIAL

Appendix

Annual carbon fluxes for forests at different latitudes (Ecological Archives M082-004-A1).

Data Availability

Data associated with the results reported here are available at: http://public.ornl.gov/ameriflux/


Ecological Archives M082-004-A1

file:///C|/Documents and Settings/tamartin/Local Settings/Temporary Internet Files/Content.Outlook/W7XI0FI0/Bracho_appendix-A.htm[3/21/2012 1:39:23 PM]


Rosvel Bracho, Gregory Starr, Henry L. Gholz, Timothy A. Martin, Wendell P. Cropper, and Henry W. Loescher.2011. Controls on carbon dynamics by ecosystem structure and climate for southeastern U.S. slash pine plantations.Ecological Monographs 82:101–128.

Appendix A (TABLE A1). Annual carbon fluxes for forests at different latitudes. Lat (N) and long (E) indicate the north latitude and east longitude indecimal degrees, respectively. Carbon fluxes (GEE, Re, and NEE) are expressed in Mg C·ha-1·yr-1

Site Lat(N)

Long(E)

DominantVegetation Year Annual

precip (mm)Annual

Temp (°C) Age GEE Re NEE Reference

Sinop MatoGrosso-Brasil -11.41 -55.33 Mature

tropical forest 99-01 1928 24 20.62 20.57 0.05 Vourlitis et al. 2004

TapajosForest-Brasil -2.85 -54.97 Old growth

tropical forest

2002 2112 25.84

Oldgrowth

30.55 33.22 -2.67

Hutyra et al. 20072003 1740 25.87 31.71 32.62 -0.912004 2311 26.06 31.95 31.73 0.222005 2201 27.75 32.06 32.45 -0.39

Cuieiras -2.58 -60.10 Ever green 1999 2200 27.8 30.4 24.5 5.9 Malhi et al. 1999

Palangkaraya 2.34 114.04 Tropical forestdrained peat

2002 1852 26.7 32.46 38.48 -6.02Hirano et al. 20072003 2292 26.4 34.61 38.44 -3.82

2004 2560 25.9 35.94 39.07 -3.13

Pasoh Forest 2.97 102.30 EvergreenForest

2003 1895 25.9Old

growth

32.55 31.76 0.79Kosugi et al. 20082004 1655 26.6 32.77 31.3 1.47

2005 1649 26.5 31.98 30.52 1.46

La Selva 10.42 -84.02 Tropicalrain forest

1998 3495 24.23 28.41 28.46 -0.05Loescher et al. 20031999 3475 23.4 30.6 29.07 1.53

2000 4127 23.66 33.9 27.93 5.97

Menglun 21.93 101.27 Tropicalrain forest

2003 1247 20.1

Oldgrowth

27.48 26.20 1.28

Tan et al. 20102004 1428 20.1 26.11 24.88 1.232005 1284 20.1 23.98 23.31 0.672006 1328 20.1 26.14 24.59 1.55

DHS 23.17 112.57 Subtropicalevergreen

2003 1289 21 100 15.3 10.94 4.36Yu et al. 2008;Wen et al. 20062004 1297 21 15.12 10.12 4.99

2005 1423 21 13.98 10.30 3.68

Qianyanzhu,China 26.74 115.06 Slash pine

Plantation

2003 855 18.9 18 17.02 12.86 4.16

Wen et al. 20102004 1325 18.6 19 18.58 14.47 4.102005 1330 18 20 16.29 13.23 3.062006 1310 17.9 21 18.52 14.40 4.122007 1107 17.9 22 18.57 14.27 4.30

Pineflatwoods Scrubby

flatwoods

2002 1177 23.15 19.37 15.60 3.77 Hinkle, R.,Personal

communication2004 1395 22.03 19.97 14.94 5.022005 1345 21.68 21.28 15.40 5.88

Scrub oak(Florida, USA) 28.6 -80.70 Scrub oak

2000 993 22.61 19.06 18.00 1.07

Powell et al. 2006

2001 828 22.44 20.84 18.39 2.462002 1177 23.15 21.57 18.38 3.212003 1130 22.03 21.46 17.29 4.192004 1395 21.68 18.86 15.37 3.522005 1345 21.55 20.79 16.15 4.67



2006 968 21.51 15.03 11.96 3.09

ACMF(Florida, USA) 29.74 -82.22

Naturalregenerating

pine

2000 956 18.35 17.94 16.02 1.92

Powell et al. 2005;Powell et al. 2008

2001 812 19.6 80 18.71 17.15 1.592004 1373 20.34 15.68 13.83 1.852005 1185 20.12 17.85 16.31 1.54

Mize tract(Florida, USA) 29.76 -82.24 Slash pine

plantation

2003 1299 19.34 5 29.67 25.1 4.58

This work2004 1480 19.65 6 29.36 24.09 5.272005 1270 19.65 7 29.27 22.22 7.052006 943 19.6 8 29.31 25.31 4.002007 1120 20.24 9 24.65 20.8 3.84

Donaldsontract 29.76 -82.16 Slash pine

plantation

1999 959 19.65 10 26.67 19.67 7

This work

2000 872 18.35 11 25.59 18.96 6.632001 1070 19.6 12 27.03 20.63 6.42002 1319 20.34 13 24.49 18.8 5.692003 1299 20.12 14 25.12 16.94 8.182004 1480 20.25 15 24.36 16.61 7.752005 1270 19.98 16 23.2 15.85 7.352006 943 20.78 17 22.65 17.64 4.912007 1120 20.77 18 2008 1163 20.25 19 24.99 18.86 6.13

Akou,Japan 34.73 134.37

Mixedtemperate

forest

2001 1078 19.98 14 19.32 11.02 8.3Kosugi et al. 20052002 578 15.5 15 15.11 10.35 4.76

2003 1230 15.5 16 18.34 12.95 5.39Flagstaff 35.09 -111.76 Ponderosa 2006 695 8.80 87 8.58 7.10 1.64 Dore et al. 2008

North Carolina,USA 35.8 -76.67 Loblolly

pine plantation

2005 1470 15.76 24.82 21.21 3.61Noormets et al. 20102006 1272 15.87 29.11 20.74 8.35

2007 834 27.64 20.51 7.24

KiryuExperimental

Station34.97 136

Japanesecypress

plantation

2001 1438 13.7 42 18.2 13.2 4.9Takanashi et al. 2005;Ohkubo et al. 2007;

Hirata et al. 2008

2002 1179 14.4 43 20.1 15.8 4.42003 1971 12.9 44 20.7 16.4 4.32004 1797 13.4 45 22.3 16.8 5.5

Duke ForestPine plantation 35.98 -79.09 Loblolly

pine plantation

2001 947 14.5 18 19.5 13.4 6.1

Stoy et al. 20062002 1072 15.1 19 18.8 16.1 2.72003 1346 14.23 20 19.5 17.3 2.32004 983 14.73 21 21.8 17.6 4.22005 935 14.71 22 25.8 18.4 7.4

Duke ForestHardwood 35.98 -79.09 Hardwood

forest

2001 947 14.5 80–100 17.1 12 5.1

Stoy et al. 20062002 1072 15.1 90 17.1 13.2 3.92003 1346 14.23 90 16.5 12.5 42004 982 14.73 90 17.5 13.1 4.32005 935 14.71 90 17.2 12.3 4.9

TakayamaForest RS 36.13 137.42 Deciduous

broad-leaved

1998 11.48 8.19 3.29

Ohtsuka et al. 2005

1999 50 9.44 7.46 1.982000 1912 6.4 51 10.65 7.56 3.092001 1655 6.3 52 10.4 7.5 2.92002 1912 6.5 53 10.92 7.46 3.46



2003 2294 6.3 54 9.52 7.18 2.342004 2392 7.3 55 9.47 8 1.472005 10.04 7.44 2.62006 10.42 7.55 2.87

HarvardForest 42.54 -72.17 Temperate

forest

1992 1102 7.82 75–100 11.7 10.1 1.6

Urbanski et al. 2007

1993 1226 7.48 13.6 11.8 1.81994 1326 7.24 12.4 10.6 1.71995 1077 7.91 12.5 9.7 2.81996 1470 6.89 13.2 11.3 1.91997 933 7.26 14 12.4 1.61998 955 8.7 12.1 10.6 1.61999 1027 8.57 14 11.9 2.12000 1083 7.27 14.5 11.9 2.62001 797 7.54 16.4 12.1 4.32002 1000 8.61 15.1 12.4 2.72003 1313 7.15 15.4 13.2 2.12004 1175 7.58 17.1 12.5 4.6

CBS 42.40 128.08 Temperatemixed forest

2003 774 200 15.28 12.86 2.42Yu et al. 20082004 707 15.05 12.48 2.57

2005 690 13.27 10.48 2.79

Tomakomaiforest 44.73 141.52 Larch

plantation

2001 1132 5.8 45 16.42 14.78 1.64Hirata et al. 20072002 967 6.6 16.36 14.13 2.23

2003 1021 6.3 17.42 14.93 2.49

PuéchabonState Forest 43.74 3.59 Evergreen

Mediterranean

2001

13.86 10.94 2.91

Allard et al. 2008

2002 1172 13.8 14.24 10.71 3.522003 1310 14 13.02 10.28 2.742004 14.74 10.23 4.512005 10.53 9.03 1.52006 12.64 9.86 1.49

Metoliusyoung pine 44.44 -121.57 Ponderosa

pine

1999 411 8.3 22 5.73 5.9 -0.17

Schwarz et al. 20042000 381 7.23 6.43 5.45 0.982001 471 7.86 6.74 6.26 0.482002 355 7.83 8.28 6.36 1.92

Metoliusmid age pine 44.45 -121.56 Ponderosa

pine

2001 529 10.26 9.25 1.01

Schwarz et al. 2004;Thomas et al. 2009

2002 371 7.2 90 15.76 11.9 3.862003 455 12.53 10.03 2.52004 465 7.9 17.85 12.47 5.382005 584 7.26 15.78 11.06 4.722006 729 7.48 15.03 10.26 4.772007 539 7.42 16.81 11.1 5.722008 436 17.41 11.38 6.03

Metoliusold age pine 44.50 -121.63 Ponderosa

pine

1999 497 8.06 156 11.26 6.9 4.36

Schwarz et al. 20042000 379 11.01 7.81 3.22001 439 10.49 6.88 3.612002 302 11.17 7.18 3.99

Poplar2002 1020 12.5 12 15.91 8.38 7.53



Zerbolo 45.20 -9.06 plantation 2003 545 12.5 13.81 7.54 6.27 Migliavaca et al. 20092004 742 12.5 15.98 8.95 7.02

Wind River 45.82 -121.95 Douglas-fir

1999 2858 8.85 500 13.38 11.16 2.17

Falk et al. 2008

2000 2345 8.3 0.422001 1247 8.72 0.32002 2446 8.68 0.982003 2130 9.74 16.56 17.65 -12004 1857 9.55 0.09

Hesse Forest 48.01 7.07 Beech

1996 820 9.8 30 11.24 9.3 1.94

Granier et al. 2008

1997 871 9.7 31 12.89 9.63 3.261998 974 9.7 32 13.54 12.78 0.761999 1073 10.1 33 14.79 11.58 3.212000 1013 9.8 34 15.7 10.37 5.332001 1157 10 35 15.96 10.14 5.822002 1158 10.5 36 16.34 10.58 5.762003 660 10.7 37 13.61 8.84 4.762004 9.8 38 14.85 10.02 4.832005 9.8 39 10.77 7.86 2.91

NortheasternMongolia 48.35 108.65 Larch 2003 296 -2.7 70–150 5.25 4.4 0.85 Li et al. 2005

DF49 Site 49.87 -125.34 Douglas-fir

1998 1432 9.07 48 21.31 17.52 3.79

Chen et al. 2009

1999 1777 7.62 49 20.24 16.42 3.822000 1145 8.2 50 20.91 16.93 42001 935 8.06 51 20.77 16.67 4.12002 1249 8.45 52 19.52 16.76 2.772003 1277 8.44 53 20.78 17.25 3.532004 1349 8.75 54 23.38 20.71 2.672005 1352 8.28 55 23.1 19.55 3.552006 1699 8.36 56 21.12 17.27 3.86

Anchor StationTharandt 50.96 13.56 Spruce

1996 771 6.1 15.9 11.42 4.48

Grünwald and Bernhoefer 2007

1997 714 8.3 18.6 12.96 5.641998 909 8.5 17.95 12.21 5.731999 826 9 108 20.95 13.97 6.982000 803 9.6 20.34 13.84 6.52001 938 8.3 16.94 11.35 5.592002 1098 9 18.74 13.29 5.442003 501 9 16.71 12.76 3.952004 874 8.3 18.68 13.95 4.732005 898 8.4 19.7 13.73 5.97

SOA site 53.7 -106.2 Borealaspen forest

1994 466 1.2 79 13.23 11.17 2.06

Barr et al. 2004;Barr et al. 2007

1996 494 -0.1 12.16 11.62 0.551997 413 2.6 13.3 11.98 1.311998 547 3.3 13.98 11.37 2.611999 479 3 12.69 11.5 1.192000 484 1.4 12.57 10.99 1.582001 235 3.1 14.13 10.46 3.672002 285 0.9 10.32 8.88 1.44



2003 261 2 10.57 9.53 1.04

SOBS 54 -105.1 Black spruce

1999 408 2.4 120

Krishnan et al. 2008

2000 484 1.5 8.97 8.4 0.572001 408 3.8 8.95 8.17 0.782002 434 1.2 7.83 7.56 0.272003 289 2.3 8.42 7.62 0.82004 637 1.3 7.65 7.31 0.342005 517 2.8 9.01 8.56 0.452006 592 2.4 8.98 8.24 0.74

NOBS 55.88 -98.48 Black spruce

1995 160 7.82 8.26 -0.41

Dunn et al. 2007;Grant et al. 2009

1996 7.06 7.95 -0.841997 6.98 7.43 -0.391998 389 -0.5 7.73 7.68 0.071999 308 -0.16 7.46 7.46 0.072000 390 -1.75 6.93 7.05 0.032001 373 -0.18 7.28 7.07 0.232002 534 -2.09 6.1 5.86 0.272003 355 -0.54 6.98 6.4 0.582004 463 -2.34 6.22 6.11 0.212005 773 0.32 6.97 6.92 0.052006 415 1.81 7.83 7.1 0.73

Sorøe 55.49 11.65 Beech

2000 5.9 15.4 13.79 1.61

Largegren et al. 2008

2001 524 3.84 15.89 13.93 1.962002 309 4.3 83 14.56 12.23 2.332003 452 4.1 15.49 12.6 2.892004 17.85 16.28 1.572005 17.5 16.09 1.41

Norunda 60.09 17.50Scots pine,

Norwayspruce

1995 5.5 10.89 11.94 -1.05


1996 4.7 11.42 11.41 0.011997 6.4 10.56 11.33 -0.771998 5.5 11.29 11.96 -0.671999 6.3 10.48 10.48 02000 5.5 10.2 11.25 -1.052001 5.5 10.11 10.91 -0.82002 5.5 11.02 11.17 -0.15

Hyytiälä 61.85 24.30 Scots pine

2000 595 5.9 38 10.94 9.05 1.89


2001 530 3.8 9.91 8.12 1.792002 309 4.2 10.84 8.52 2.322003 452 4.1 9.74 8.38 1.362004 499 4.1 10.68 8.42 2.262005 491 4.5 10.7 8.4 2.3

Huhus 62.87 30.82 Scots Pine

1999 747 2.4 7.68 6.15 1.52

Zha et al. 20042000 780 3.6 6.92 5.91 1.012001 813 2 9.24 7.52 1.722002 832 2.1 10.84 8.79 2.05

Tura 64.27 100.2 Mature larch 2004 360 -8.9 95 2.1 1.5 0.77 Nakai et al. 2008



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