Directory UMM :Data Elmu:jurnal:T:Tree Physiology:vol17.1997:

Tree Physiology 17, 757--765
© 1997 Heron Publishing----Victoria, Canada

Gas exchange of the lowest branches of young Scots pine: a
cost--benefit analysis of seasonal branch carbon budget
JAN WITOWSKI
Forest Research Institute, Department of Ecology and Environmental Protection, 00-973 Warsaw, Bitwy Warszawskiej 3, Poland

Received December 18, 1996

Summary A cost--benefit approach was developed to analyze the carbon budget of the lowest Scots pine (Pinus
sylvestris L.) branches subject to abscission. In addition to
within-branch growth and respiratory costs, the budget included an estimation of a branch’s share of the maintenance
respiration of the stem and root. A branch was considered
productive if the budget was positive.
Foliar gas exchange and woody-tissue respiration were nondestructively measured at monthly intervals during the growing season on the six lowest branches of 10-year-old Scots pine
trees, to the moment when the branches died naturally. Photosynthetic light response and temperature response of respiration, together with measurements of canopy light conditions
and meteorological data, were used to calculate seasonal carbon budgets for the branches. Maintenance respiration of
stems and roots was estimated from published data.
All but one of the branches studied were found to be nonproductive over the growing season. Following a decrease in
photosynthetic capacity in July, the cumulative budget became

negative and the branches died, indicating that a negative
carbon budget corresponds with the onset of abscission of the
lowest branches.
Keywords: branch abscission, branch autonomy, Pinus
sylvestris, respiration.

Introduction
For a better understanding of the ecological significance of the
architectural patterns of woody plants, it is important to link
the photosynthetic benefits associated with different crown
geometries to the structural costs of supporting and supplying
the crown arrangements (Givnish 1986). Competition should
favor plants that maximize the difference between carbon gain
and the related costs. Thus, cost--benefit analyses are essential
for the interpretation of individual plant success (Küppers
1989). I applied the cost--benefit approach to the analysis of
carbon fixation by the lowest, shaded branches of young Scots
pine (Pinus sylvestris L.) trees.
In conifers, the lowest, shaded branches apparently contribute little carbohydrate to the rest of the tree and fix just enough
carbon to meet their own needs, thus being autonomous with

respect to carbon (Sprugel et al. 1991). This conclusion is

supported by the results of pruning studies showing that
growth does not increase after lower branches are removed
(e.g., Uotila and Mustonen 1994), and by defoliation experiments that provide evidence for a mechanism that prevents
carbohydrate compensation to damaged branches (Honkanen
and Haukioja 1994). Additional evidence is provided by studies of the movement of 14C-labeled carbon among branches
(for references see Sprugel et al. 1991). Theoretical considerations concerning the adaptive benefits of plant modularity
(Watson and Casper 1984, Hardwick 1986, Sachs et al. 1993)
may be applied to coniferous branches. However, there are few
published data on the contribution of particular branches (or
whorls) to the total carbon budget of Scots pine. Existing
studies (Ågren et al. 1980, Linder and Axelsson 1982) are
based on extrapolation of the photosynthetic measurement
from one whorl to the whole crown, which does not provide
the accuracy needed to assess whether the carbon budget of the
lowest branches is positive.
I have attempted to assess, from the whole-tree perspective,
the costs of branch maintenance versus branch photosynthetic
gain. A seasonal branch carbon budget was calculated for the

six lowest branches subject to abscission. The budget estimate
was based on seasonal gas exchange measurements, published
data on stem and root respiration, and meteorological data
recorded for the growing period.

Theory: determination of costs of carbon acquisition by
a branch
On an annual basis, it can be assumed that the carbon assimilated by a tree is entirely consumed in respiration and total
growth, with the latter including turnover rates of various tree
parts. Denoting annual total growth by G (definitions of symbols and their units are given in Table 1) and annual respiration
by R, the annual gross photosynthesis, A, is therefore:
A = R + G.

(1)

Respiration can be divided into growth (RG) and maintenance
(RM) components:
R = RG + RM .

(2)

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WITOWSKI

i.e., the branch does not drain carbohydrates from the stem and
roots. If the photosynthetic production of the branch exceeds
its carbohydrate utilization, the branch contributes carbohydrate to the rest of the tree. This does not mean, however, that
the branch is productive from the whole-plant perspective,
because a fraction of stem and root respiration can be attributed
to the supply of water and nutrients to the branch. It is the total
carbon budget of the tree, rather than of the branch, that must
balance, and if all of the branches of a tree were just autonomous the tree could not survive. In this study a branch is said
to be productive if:

Table 1. Definitions of symbols and their units.
Symbol

Definition

Unit

A, AN∗

Photosynthesis of tree or of a
particular branch
Total growth of tree including
turnover rates of tree parts
Growth of all branches or of
a particular branch
Growth of all needles or of
needles of a particular branch
Growth of stem or root
Needle biomass of tree or of
a particular branch
Respiration of tree, all branches
or a particular branch
Growth respiration or maintenance respiration of tree
Respiration of all needles or of
needles of a particular branch

Growth respiration of stem
or root
Maintenance respiration of
stem, root or stem part located
below a particular branch
Instantaneous photosynthesis
PAR reaching measured
branches
Regression parameter,
Equation (6)
Regression parameter,
Equation (6)
Regression parameter,
Equation (6)
Instantaneous respiration
Temperature
Instantaneous respiration
at T = 10 °C
Proportional change in respiration resulting from a 10 °C
increase in temperature

g C year −1

G
G B, G B∗
G N, G N∗
GS, GR
MN, M N∗
R, RB,
R B∗

RG, RM
RN, RN∗
G
RG
S , RR
M
RM
S , RR ,
M

R S∗

Ai
I
A max

α
Io
Ri (T)
T
R10
Q10

g C year −1
g C year −1
g C year −1
g C year −1
gC

M

AN∗ > RN∗ + G N∗ + RB∗ + G B∗ + (RM
S∗ + R R )M N∗ /M N , (5)

g C year −1
g C year −1
g C year −1
g C year −1
g C year −1
mg CO2 gDW−1 h −1
µmol photon m −2 s −1
mg CO2 gDW−1 h −1
m2 s µmol −1
µmol photon m −2 s −1
mg CO2 gDW−1 h −1
°C
mg CO2 gDW−1 h −1

where M N∗ denotes the needle biomass of the branch, MN the
total needle biomass of the tree and RM
S∗ maintenance respiration of the stem part located below the branch. Following the

pipe model theory (Shinozaki et al. 1964), it is assumed that
the unit amount of leaves (branch) is associated with the
downward continuation of non-photosynthetic tissue (a pipe)
serving both as a vascular passage and as mechanical support
to the branch. The productive branch is not only autonomous,
but pays the cost of maintenance respiration of the pipe. Moreover, the productive branch participates in tree growth; i.e.,
supplies photosynthate for some of the last four components in
Equation 3. The branch’s share in stem and root maintenance
respiration can be estimated as the relative biomass of a
branch’s needles to the total needle biomass of a tree.
Equation 5, characterizing a productive branch, involves the
maintenance respiration of the stem section located between
the branch and the ground, and it holds only for the lowermost
tree branches. If it is to be generalized to hold for any branch,
each internodal stem section would have to be treated separately, because each supports a different population of needles.

--

Materials and methods
Study site

Let R and G with subscripts N, B, S and R denote the respiration and growth of the corresponding tree functional parts:
needles, branches, stem and roots, respectively. For the whole
tree, we have:
A = RN + G N + RB + GB + RM
S
G
G
+ RM
R + RS + RR + G S + G R,

(3)

where growth and maintenance components of respiration
G
M
were distinguished only for stem (RGS , RM
S ) and roots (R R , R R ).
Let A, R and G with subscripts B* and N* denote the gross
photosynthesis, respiration and growth of a particular branch
and its needles. Following Sprugel et al. (1991) the branch is
defined as autonomous with respect to carbon if:
AN∗ ≥ RN∗ + G N∗ + RB∗ + G B∗,

(4)

The study site was a 10-year-old Pinus sylvestris (Scots pine)
stand located about 25 km south of Warsaw (central Poland).
The soil is rusty and podsolized, with the texture of weak
loamy sand on loose sand. No silvicultural treatments had been
carried out in the stand, which was planted after a clearcut. The
stand, which had a stocking of 8400 trees ha −1, had formed a
closed dense canopy comprising 4 to 5-m high Scots pine trees
with some birches and larches, and fairly low ground vegetation. Most of the Scots pine trees in the stand had two whorls
of dead branches when the study started in spring 1995.
Climatic data
Climatic data for the study site were collected at the Forest
Research Institute Station located within a mature Scots pine
stand 9 km from the study area. Incident photosynthetically
active radiation (PAR) was measured every minute by a point
quantum sensor placed above the stand canopy, and 5-min
averages were recorded with a data logger (Delta-T Devices,

TREE PHYSIOLOGY VOLUME 17, 1997

CARBON BUDGET OF LOWEST SCOTS PINE BRANCHES

Cambridge, U.K.). Air temperatures were recorded every hour
at heights of 0.05 and 6 m above the ground, and soil temperatures were measured at depths of 10 and 20 cm by an RC-10
logger (Trax, Cracow, Poland). For this study, soil temperature
at 10 and 20 cm depths, and air temperatures at 0.05 and 6 m
heights were averaged, and represent average soil temperature
in the root zone and air temperature, respectively. The climatic
data collected for the period April--August 1995 are presented
in Figure 1.
Gas exchange measurements
Six codominant trees were selected in the stand that had a live
branch satisfying the following criteria: (1) the branch is located on the lowermost live whorl; (2) the tree has at least one
dead whorl below the branch; (3) the branch does not have
reproductive organs; (4) the branch has at least one live bud;
and (5) the branch has no visible signs of injury by herbivores
or insects. The six branches chosen for gas exchange measurements (one on each tree) are denoted as MBs (measured
branches) in the text. Criteria (1) and (2) allowed for an
expectation that at least some of the MBs would be self-pruned
by the trees during the oncoming growing season; however, the
fates of the MBs were not definitely determined, because they
had an opportunity for further growth (4)--(5). Criterion (3)
was set to avoid involving non-photosynthetic benefits from
the branch.
In situ gas exchange of each MB was determined with a
closed, battery-operated portable LI-6200 photosynthesis system (Li-Cor, Inc., Lincoln, NE). The measuring equipment
included a quantum sensor attached outside an assimilation
chamber to monitor PAR during gas exchange measurements,
and a thermocouple to measure air temperature in the chamber.
The system was used with two Li-Cor 4-dm3 Plexiglas chambers, one of which was modified for thicker branches by
cutting notches in the chamber wall. The use of large chambers

Figure 1. Seasonal course of daily sum of irradiance, and minimum/maximum air and soil temperatures measured in a mature Scots
pine stand located 9 km from the study site.

759

allowed the 13-cm long shoot sections to be located within the
chamber without disturbance to the needles. Care was taken to
keep the shoots in their natural position during measurements.
Two locations were selected on each MB: one on the 1-yearold shoots and the second on the non-needle-bearing part of the
branch. Needle gas exchange rates reported in this study include the needle-bearing branch parts, and the branch respiration values refer to the non-needle-bearing branch parts only.
During the period April--August, the diurnal course of needle CO2 exchange was monitored once a month for each MB.
The diurnal course consisted of three measurements repeated
approximately every 2 h from dawn to sunset. The temperature
dependences of MB needle dark respiration and branch respiration were determined for each MB by making measurements
at predawn and midday, because ambient temperatures differed most between these two periods. Needle dark respiration
rates were measured 10 min after the chamber was positioned
in a cardboard box blacked inside (cf. Sprugel et al. 1995),
whereas the assimilation chamber was left uncovered during
the branch respiration measurements.
Harvest measurements
When all needles on an MB turned yellow or brown, the MB
was harvested and returned to the laboratory where the number
of needles, needle dry weight (48 h, 85 °C), and branch dry
weight were determined. Following the MB harvest, PAR
measurements were performed in the stand (see below) and
then, in late September and October, the six sample trees were
felled and returned to the laboratory where the dry weights of
needles and stems were determined.
Measurements of PAR
As in the case of forest floor vegetation, the utilization of
sunflecks by a shaded Scots pine shoot may substantially
contribute to its carbon gain. The amount of PAR reaching the
MB shoot at a given time of day was mainly influenced by
three factors: sun elevation, sky conditions and wind movement of the canopy. Each of these factors was included in the
model. However, no attempt was made to account for seasonal
changes in sun elevation and canopy dynamics. Measurements
of PAR in the stand were made once during the growing
season, within a week following the death of an MB. After an
MB was harvested, a point quantum sensor was placed horizontally above the forest floor exactly at the previous location
of the MB shoot section used for photosynthesis measurements. A reference sensor was located above the canopy, and
both sensors were connected to a Li-Cor LI-1000 data logger.
The sensor readings were recorded every 5 s, and 1-min averages were stored every minute from 0700 to 1800 h. The
measurements were carried out for at least 3 days for each MB
location. The measurements were then divided into two groups
depending on whether the sky was overcast or clear during the
measurements (see Messier and Puttonen 1995). As a result,
two daily curves of 1-min PAR percentage (%PAR) reaching
the MB were obtained for each MB location: one for clear skies
and one for overcast weather. The use of a 5-s sampling
interval allowed short-term light variations to be incorporated

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WITOWSKI

in the model, assuming that these variations are averaged for
the shoot and the photosynthetic rate achieved is at the mean
(1-min) irradiance (Gross 1982).
Simulation model
For a given month and MB, the recorded values of the daily
course of photosynthesis were plotted against simultaneously
recorded PAR and the regression parameters of the response
curve (Landsberg 1977) were fitted using the Marquardt iterative least-square method available in the Statgraphic software
package (STSC, Rockville, MD):
Ai = Amax (1 − exp[−α (I − Io)]),

(6)

where Ai = instantaneous photosynthesis, I = PAR reaching the
MBs, and Amax , α, Io are parameters. The measured values of
needle and branch respiration were used to find the regression
parameters of the standard exponential curve of temperature
dependence of respiration:
Ri(T) = R10(Q10 )(T − 10)/10 ,

data. The amount of PAR reaching an MB was then used to
calculate MB photosynthesis from Equation 6. Needle dark
respiration, branch respiration and maintenance respiration of
the stem were calculated from the air temperature records by
means of Equation 7 and the corresponding coefficients. Similarly, maintenance respiration of the root was calculated from
the record of root temperature by means of Equation 7. The
budget was calculated independently for each MB with time
steps of 1 min and 1 h for the calculation of photosynthesis and
respiration, respectively. From the six sets of MB data an
‘average branch’ was then constructed to illustrate the dynamics of the budget during the growing season.

(7)

where Ri(T) = instantaneous respiration, T = temperature,
R10 = respiration rate at T = 10 °C, and Q10 is the proportional
change in respiration resulting from a 10 °C increase in temperature.
To estimate the parameters in Equation 7 for the temperature
dependence of maintenance respiration of the stem and root of
the experimental trees, I used the data on the annual course of
Scots pine stem and root respiration published by Linder and
Troeng (1981). It was assumed that the late September measurements of Linder and Troeng (1981) at T = 10 °C represented
stem maintenance respiration rates, and that higher values
recorded earlier in the season included growth plus maintenance (the ‘subtraction’ method, Sprugel 1990). Similarly,
late-October measurements of root respiration at T = 10 °C
were used to estimate the R10 parameter for maintenance respiration of the root. The parameter Q10 was set at 2 during the
growing season (Linder and Troeng 1981). The data on root
biomass of young Scots pines provided by Vyskot (1983) were
used to estimate the root biomass of the investigated trees. It
was assumed that the ratio of aboveground biomass to root
biomass was the same for the trees as for those harvested by
Vyskot (1983).
The photosynthetic production of 2- and 3-year-old MB
shoots was calculated from the measured photosynthesis of a
1-year-old shoot of the same MB, by assuming the efficiencies
of the 2- and 3-year-old shoots to be 75 and 50% of the
efficiency of the 1-year-old shoot, respectively (Linder and
Troeng 1980).
To calculate the branch carbon budget from the data, a
simulation model was written in Turbo Pascal 6.0. The input
variables of the simulation model were incident PAR above the
canopy, air temperature and soil temperature, and the input
parameters were the coefficients of Equations 6 and 7. The data
on %PAR reaching an MB (Figure 2) were used to calculate
the amount of PAR reaching an MB from the incident PAR

Results
Dimensions and biomass of the sample trees and MBs are
presented in Table 2. All of the MBs died and were harvested
between July 27 and August 25. Three of the MBs did not
develop any current-year shoots during the growing season,
and the remainder had very small current-year shoot increments. No measurable annual growth ring production was
detected during the period of study for any of the MBs. This is
a commonly observed phenomenon for branches located in the
lower portions of coniferous crowns (Roberts 1994).
The %PAR reaching the MB locations varied more under
clear skies than under overcast skies (Figure 2). A similar result
was obtained by Messier and Puttonen (1995) for young Scots
pine stands. Representative examples of the relationship between irradiance and CO2 assimilation and the dependence of
branch and needle respiration rates on temperature are given in
Figures 3 and 4, respectively. For the period April--June, lightsaturated photosynthesis was relatively stable, but in July a
rapid decline in light-saturated photosynthesis occurred for all
MBs, and it decreased even further in August (Figure 3, Table 3).
For the calculation of a seasonal MB carbon budget (Table 4), it was assumed that, for each MB, current growth
respiration balanced its photosynthesis, the error introduced by
this assumption is relatively small because the total biomass of
current-year growth was very small. All MBs but one were

Figure 2. Daily course of incident PAR fraction reaching measured
branch (MB) No. 1 under clear and overcast sky conditions. Solid
horizontal lines represent missing data for overcast sky.

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761

Table 2. Characteristics of sample trees and measured branches. All biomass values are given in grams of dry weight.
Characteristic

Trees
Height (m)
Base diameter (cm)
Needle biomass
Stem biomass
Root biomass1
Measured branches
Number of needles
April 1
Number of needles
at harvest
Needle biomass
Branch biomass
Current-year twig biomass
(including needles)
Harvest date
1

Tree and branch no.
1

2

3

4

5

6

4.54
8.6
1550
4009
931

4.32
9.3
1602
3871
917

4.72
8.4
1258
3264
757

5.09
8.6
1311
3740
846

4.70
9.1
1585
4103
953

4.80
8.8
1286
3671
830

1078

714

1054

1066

701

1153

630

494

838

868

672

1065

7.23
44.45
0

6.48
29.48
0

11.09
29.51
0.120

15.97
38.46
0.642

13.51
27.08
0

14.99
69.96
0.244

Aug. 23

July 27

Aug. 21

Aug. 17

Aug. 25

Aug. 10

Estimated from Vyskot (1983).

Figure 4. Temperature dependence of needle dark respiration and
branch respiration for branch No. 1. Data taken during entire measurement period.
Figure 3. Relationship between irradiance and CO 2 assimilation for
the shoot of branch No.1 measured on May 5, 1995. Inset shows
relationship averaged for all branches in monthly steps.

found to be autonomous with respect to carbon. Branch No. 5,
which failed to be autonomous, had a high respiration rate per
branch biomass unit compared with the other MBs. For all
MBs, the branch respiration cost was the highest component of
the budget, and was as a result of the high branch/needle
biomass ratio (Table 2), rather than a high respiration rate per
MB biomass unit. Of the six MBs, MB No. 1 was the only
productive branch, but it contributed little carbon for tree
growth, because it was only just productive.
The cumulative budget of an ‘average branch’ (Figure 5)
peaked in late June and then decreased as daily respiratory
costs outbalanced photosynthetic gain. The budget became
negative in late July, shortly before the MBs began to die.

Discussion
Measurement procedure
Because the MB gas exchange measurements were made
solely for the purpose of MB carbon budget estimation, the
measurement procedure was set accordingly. As a consequence, the curves used to estimate MB seasonal photosynthesis (Figure 3) are not true ‘light response curves,’ because
factors other than light varied significantly during the day-long
measurements (e.g., temperature, ambient CO2 concentration,
and air humidity). Moreover, the PAR measured by the quantum sensor during the photosynthesis measurements was lower
than the light actually intercepted by the shoot, because the
sensor was set horizontally and did not track the sun. For
low-radiation regimes, when the share of dispersed light in

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Table 3. Parameters of photosynthetic response to irradiance (monthly steps), and respiration response to temperature (whole season). Values are
given as means ± SD for the six branches, notation as in Equations 6 and 7, units are given in Table 1.
Parameter

April

May

June

July

August

α
Io

A max

5.63 ± 0.61
0.0088 ± 0.004
12.82 ± 2.57

5.01 ± 0.68
0.0106 ± 0.005
10.58 ± 4.52

5.45 ± 1.17
0.0072 ± 0.004
11.13 ± 5.26

1.61 ± 0.44
0.0163 ± 0.015
16.56 ± 5.46

0.98 ± 0.10
0.0310 ± 0.026
12.18 ± 6.79

Tree part
Needles
Branches
Stem
Roots

R10
0.123 ± 0.049
0.073 ± 0.042
0.0201
0.0851

Q10
3.32 ± 0.45
1.58 ± 0.16
21
21

Photosynthetic response

1

Estimated from Linder and Troeng (1981), see text for details.

Table 4. Estimated seasonal carbon budget for the six measured branches (MB). All values are given in mg of carbon, notation as in Equation 5.
Branch no.

1

Photosynthesis (AN∗)
4135
Needle respiration (RN∗)
461
Current growth1 (G N∗)
0
Branch respiration (RB∗)
2573
Branch growth (G B∗)
0
Available for export2
1100
(Ex = AN∗ -- (RN∗ + G N∗ + RB∗ + G B∗)
Branch’s share of
217
maintenance respiration
of stem (RM
S∗ M N∗ /M N)
Branch’s share of
775
maintenance respiration
of root (RM
R M N∗ /M N )
Available for tree growth2
109
M
(T = Ex -- (RM
S∗ + R R ) M N∗ /MN )
1
2

2

3

4

5

6

2223
577
0
1070
0
575

3767
473
60
2932
0
303

5888
568
321
3773
0
1226

5274
1257
0
5395
0
0

4163
648
122
2262
0
1132

178

497

258

524

347

486

995

991

1152

1226

0

0

0

0

0

Assuming a 50% carbon content of biomass.
Zero stands for negative values.

total light is high, the measured light could underestimate the
radiation received by a shoot as well. Thus, the curve would
not be the most suitable for comparative purposes; e.g., for
calculation of quantum yield. However, it served well for the
prediction of photosynthesis because it involved changes in
ambient microclimate that most likely accompany changes in
irradiance. For instance, when PAR at the MB location was low
(morning or evening hours), the air temperatures corresponding to the photosynthesis measurements were low and CO2
concentration of the air was high.
Figure 5. Seasonal course of modeled branch carbon budget. All values
are means for six branches. Bars represent daily carbon fixation (left
axis, positive) and daily respiratory carbon consumption (left axis,
negative) including the branches’ share in maintenance respiration of
stem and root. Lines represent cumulative amounts of total branch
carbon budget (T, right axis, thick line), and of carbohydrates available
for export (Ex, right axis, fine line). Equations defining T and Ex are
given in Table 4. Thick line on time axis denotes period of branch
death.

Sources of error in the simulation model
The assumption that the temperature record from the weather
station is valid for the experimental stand could have introduced error to the respiration estimates in the simulation
model. This assumption was verified only for the days of gas
exchange measurements; however, the differences between air
temperatures recorded by the weather station and those in the
study stand were low (less than 2 °C for most of the measure-

TREE PHYSIOLOGY VOLUME 17, 1997

CARBON BUDGET OF LOWEST SCOTS PINE BRANCHES

ments, data not presented) on these days. In the case of root
respiration estimates, there was an additional error associated
with the use of literature-based estimations of root biomass.
The partition of stem and root respiration into growth and
maintenance components was made on the assumption that
maintenance respiration at a constant temperature remains
constant throughout the year. This assumption is necessary for
most methods of partitioning respiration (Sprugel et al. 1995),
but its validity has been questioned (Sprugel and Benecke
1991).
Measurements of %PAR reaching an MB were made once
during the growing season, after the MB had died, and thus did
not include seasonal canopy and sun angle dynamics. The
%PAR reaching an MB had been higher earlier in the season,
i.e., before new canopy shoots had fully expanded. Also higher
sun elevation earlier in the season allowed for better light
penetration in the canopy. As a result, the model calculations
underestimated MB photosynthesis, which means that more
than one branch could have been on the threshold of productivity (Table 4).
Although the MB photosynthesis model (Equation 7) lacks
a temperature component, within-day and seasonal temperature variations were included in the measurement design. Between-day variation in air temperature during a month could
have introduced an error to the MB photosynthesis estimate;
however, this error will be minor because the temperature
effect on photosynthesis was most pronounced at light saturation, which was seldom attained by the shaded shoots.
All of these sources of error in the simulation model resulted
from limitations of the collected data, and must be considered
when analyzing the budget. Nevertheless, the budget analysis
provides some insight into the carbon economy of self-pruning
in Scots pine trees, because it is unlikely that the physiological
mechanism underlying self-pruning responds to the minute
changes in amounts of exported carbohydrates that may turn a
productive branch into a nonproductive one. Therefore, even
though the numbers obtained are approximations, some conclusions can be drawn from the budget analysis.
The budget
Because the carbon exported by a Scots pine branch to the stem
and root is not redistributed to the branch during the next
growing season (Hansen and Beck 1990), a single growing
season is an appropriate time unit of reference for branch
carbon budget analysis. The modeled budget (Table 4) revealed that all but one MB failed to be productive. This means
that the branches did not provide an adequate carbon return for
the water and nutrient supply they received. That is, this supply, or part of it, could have been available for other branches.
In accordance with this observation, green pruning of the lower
part of the crown has been found to improve water (Margolis
et al. 1988) and mineral (Nuorteva and Kurkela 1993) status in
the remaining upper crown.
In Scots pine, current-year shoot growth is supplied by
photosynthates from the 1-year-old needles of the same branch
(Ericsson 1978, Hansen and Beck 1994). In the case of an MB,
the carbon investment to develop a current-year shoot of regu-

763

lar size (a few grams of C) would be made at the expense of
imported carbohydrates (Table 4). The MBs had poor currentyear growth, and in this way avoided becoming parasitic on the
tree, a result that confirms the theoretical predictions of Cannell and Morgan (1990).
This cost--benefit analysis of a branch carbon budget did not
include changes in carbon storage within the branch. For this
reason, the corresponding values in the budget are referred to
as carbon available for export and growth, rather than amounts
actually exported or utilized for tree growth.
The cumulative amount of carbon available for export decreased after the late June peak (Figure 5, fine line), which
means that, after late June, the MBs were using more carbon
than they fixed. This short-term negative carbon balance could
possibly lead by itself to later MB death, without invoking the
branch’s share in the maintenance respiration of stem and root.
However, the time span of MB death begins when the cumulative total branch carbon budget becomes negative, i.e., when
seasonal carbon export becomes lower than seasonal respiratory demands of stem and root tissue supporting the MB. Thus,
the branch’s share in the maintenance respiration of stem and
root (Figure 5, thick line) is of relevance when considering the
links between branch carbon economy (cost--benefit analysis)
and the observed self-pruning strategy of a tree. No attempts
were made to determine the actual causes of MB deaths.
Implications and improvements
The concept of branch productivity proposed here may lead to
a better understanding of the mechanisms determining leaf
area index (LAI, area of foliage per unit land surface) and leaf
area efficiency (E, stemwood volume increment per unit leaf
area). Leaf area index and E are important indices of canopy
structure and tree growth efficiency. The LAI of different
conifer species varies by a factor of more than five (Leverenz
and Hinckley 1990). Species differences have also been observed in the relationship between E and the leaf area of
individual trees within stands (Roberts et al. 1993). Because E
of a nonproductive branch equals zero, an increase in number
of nonproductive whorls of branches would decrease E and
increase LAI.
Among the crown architectural traits that are related to LAI,
two are of particular importance with respect to the lowest
branches: shade-shoot geometry and needle longevity. Shadeshoot geometry can be quantified by the maximum silhouette
area ratio (Rmax ), which is strongly correlated with LAI for
evergreen conifers (Leverenz and Hinckley 1990, Leverenz
1992). The ideal shade shoot should be flat (Rmax = 1) and
horizontally inclined (Stenberg 1996). The cylindrical needle
arrangement on Scots pine shade shoots (very low Rmax ) means
that they are not able to utilize efficiently the light that occurs
at the bottom of canopy, which shortens the life span of shaded
branches. Needle longevity also affects the life span of
branches: long needle retention is associated with delayed
self-pruning (Stenberg et al. 1994). Although needle retention
time tends to increase down the crown (Reich et al. 1995), the
branch/needle biomass ratio of the lowest Scots pine branches
is still so high that branch respiration is the largest component

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764

WITOWSKI

of the respiratory costs (Tables 2 and 4). Shade-shoot geometry
and needle longevity combined with self-pruning of nonproductive branches may determine the maximum LAI of Scots
pine.
To obtain more detailed information about the productivity
of a branch, the simulation model could be improved by
incorporating a more accurate simulation of photosynthesis.
The empirical model proposed by Küppers and Schulze (1985)
provides a diurnal course of net photosynthesis for Scots pine
using climatic factors as input, and taking into account temperature and stomatal limitations on photosynthesis. The
model parameterization would require measurements of various photosynthetic, stomatal and respiration response curves
in the field. As in the research reported here, seasonal changes
in the response parameters would have to be measured. The
input meteorological data (PAR, air temperature, air humidity,
air CO2 concentration) would also have to be recorded in the
study stand together with the soil temperature needed for the
calculation of root respiration.
The precise assessment of branch productivity also requires
a knowledge of belowground respiration costs, which are difficult to estimate, either directly or indirectly (Sprugel et al.
1995). However, for the purpose of developing satisfactory
process-based tree growth models, measurements of photosynthesis must be linked to the respiration and growth of tree parts
(Ford and Bassow 1989). Therefore, further studies are needed
to couple branch photosynthesis with carbohydrate utilization
by roots.

Conclusions
Because the simulation model contains several simplifying
assumptions, considerable error may exist in some components of the modeled MB carbon budgets. I have shown,
however, that MB photosynthetic gain approximates the corresponding respiratory costs (Table 4). The results suggest that
these lowest branches abscise when they are no longer photosynthetically useful to the tree. Further growth of these
branches would require a substantial import of carbohydrates
from the rest of the tree. Apparently, there is a physiological
mechanism in a Scots pine tree that prevents this import,
indicating that young Scots pine trees are able to optimize their
self-pruning strategy with respect to carbon acquisition.

Acknowledgments
Study supported by General Directorate of the State Forests (DGLP)
Grant No. BLP-654.
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