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439
Ann. For. Sci. 60 (2003) 439–448
© INRA, EDP Sciences, 2003
DOI: 10.1051/forest:2003036
Original article
Aboveground biomass relationships for beech (Fag us m oe si aca Cz.)
trees in Vermio Mountain, Northern Greece, and generalised
equations for Fagus sp.
Dimitris ZIANIS*, Maurizio MENCUCCINI
Institute of Ecology and Resource Management, Darwin Building, Mayfield Road, Edinburgh EH9 3JU, UK
(Received 5 July 2002; accepted 30 October 2002)
Abstract – Allometric equations describing tree size-shape relationships for beech (Fagus moesiaca Cz.) in the Vermio Mountains of Northern
Greece are presented. Diameter at breast height explained most of the variability in the dependent variables (total aboveground, stem, and
branch biomass), while tree height was the second most important regressor in estimating foliage mass. Equations developed in USA and
Europe for Fagus spp. were also reported and validation with the field data indicated that the American regressions closely predicted total tree
biomass for the study forest. In addition, the raw data were used to test a recent theoretical model and large deviations were found between
theoretical and empirical values. Finally, generalised equations for Fagus spp. were developed based on these data and several other published
equations. Validation of the generalised equations indicated that accurate predictions may be obtained when these regressions are applied over
a broad geographical area.
allometry / biomass / generalised equations / Fagus moesiaca Cz. / Northern Greece
Résumé – Relations concernant la biomasse aérienne pour le hêtre (Fagus moesiaca Cz.), dans le massif du Vermio au nord de la Grèce,
et équations génériques pour Fagus sp. Dans l’article suivant sont présentées des équations allométriques décrivant les relations taille-forme
pour une espèce particulière de hêtre, Fagus moesiaca (Cz.), dans le massif du Vermio, au nord de la Grèce. Le diamètre à hauteur de poitrine
explique en grande partie la variabilité des variables indépendantes (biomasse aérienne totale, biomasse des tiges, biomasse des branches),
tandis que la hauteur de l’arbre est la deuxième variable indépendante la plus importante lors de l’estimation de la masse du feuillage. Les
équations développées aux États-Unis et en Europe pour les différentes espèces de hêtre (Fagus spp.) sont également présentées. Leur
validation, obtenue avec les données recueillies sur le terrain, indique que les régressions américaines prédisent de manière précise la biomasse
totale de l’arbre dans la forêt étudiée. En outre, les données de terrain ont été utilisées pour tester un modèle théorique récent, ce qui a permis
de mettre en évidence de larges variations entre les valeurs théoriques et empiriques. Finalement, des équations génériques concernant Fagus
spp. ont été développées à partir de ces données et de plusieurs autres équations publiées. La validation de ces équations génériques indique que
des prédictions précises peuvent être obtenues quand les régressions sont appliquées à une large échelle géographique
allométrie / biomasse / équations génériques / Fagus moesiaca Cz. / nord de la Grèce
1. INTRODUCTION
Tree biomass plays a key role in sustainable management
and in estimating the stocks of carbon (C) that forests contain.
In addition to making estimates of C pools in forests, estima-
tion of biomass is relevant for studying biogeochemical cycles,
because the content of nutrient elements in forests is also
related to the quantity of biomass present [16, 23, 31]. The
most accurate way to determine values of wood biomass is to
cut down the trees under investigation and perform appropriate
measurements. However, destructively harvesting of forest
biomass in sample plots is a time-consuming procedure and
generates considerable uncertainty when the obtained results
are extrapolated to larger areas [13]. Undoubtedly, the most
common approach to obtain biomass estimates at stand level is
through regression equations that are fitted to morphometric
measurements taken from destructively sampling of individual
trees. Subsequently, these regressions (also known as size
allometry relationships) are used to estimate the biomass of
sample plots within which the diameters and heights for all the
* Corresponding author: Zianis.Dimitris@ed.ac.uk
440 D. Zianis, M. Mencuccini
trees have been measured. Researchers throughout the globe
have developed a plethora of allometric equations for different
species growing in a wide range of environmental conditions.
However, aboveground tree biomass values vary with species,
stand age, site quality, climate, and stocking density of stands
[1, 4, 11]. Implementing allometric equations beyond the spe-
cific site and the diameter range for which they were devel-
oped is anticipated with scepticism. To circumvent this problem,
Pastor et al. [21] developed generalised allometric equations
for several north-American species and tests against field data
indicated quite accurate predictions for some of them. Ketter-
ings et al. [9] supported that aboveground biomass could be
estimated without destructive measurements on sampled trees.
They suggested that the parameters of allometric equations in
biomass studies should depend on the average wood density,
and on the exponent of the tree height-diameter relationship
(see [9] for the detailed approach).
In a theoretical context, the allometric equations describing
tree size-shape relationships are believed to be affected by the
physiological requirements to conserve water and to support
large loadings against the influence of gravity and/or wind
forces [17]. With reference to foliage biomass, the underlying
principle supports the idea that a unit of evaporating leaves is
sustained by a unit of vascular pipes. This approach was devel-
oped by Shinozaki et al. [25] and is known as the “pipe model
theory”. Originally, it was thought that leaf mass was directly
proportional to the basal area of the stems and branches, but
several studies empirically demonstrated that the amount of
foliage mass was strongly correlated with sapwood area e.g.
[14]. McMahon and Kronauer [12] studied the scaling of tree
height with respect to stem diameter using the stress and the
elastic similarity models. Assuming a constant stem density,
predictions about the relation between trunk mass and diame-
ter are readily obtained [17]. Recently, West et al. [30] inte-
grated the biomechanical and hydraulical principles of tree
architecture, and developed a model which seems to predict
quite accurately several structural plant variables (tree height
and diameter, number of leaves, number of branches, etc.) in
relation to plant body size (i.e. plant biomass). They supported
that theoretical values obtained by the model are accurate
enough to predict aboveground forest biomass. Parde [19]
reviewed historical and methodological aspects of forest bio-
mass studies and Cannell [4] compiled data on biomass pro-
duction from studies conducted throughout the world.
The objective of this study is to develop biomass equations
for beech (Fagus moesiaca Cz.) trees growing in Vermio
Mountain, Northern Greece. Some preliminary results were
presented by Zianis and Mencuccini [35]; in this paper, a more
detailed analysis is attempted. The obtained equations are
compared with the allometric equations for beech trees found
in the literature and with theoretical model presented by West
et al. [30]. Finally, an approach similar to that of Pastor et al.
[21] was used in order to build and validate generalised allom-
etric equations for Fagus genus. The obtained allometric rela-
tionships will be used to investigate the primary productivity
of beech stands along an elevation gradient.
2. MATERIALS AND METHODS
2.1. Study area
The Vermio Mountain is situated in the central part of Northern
Greece, about 80 km West of Thessaloniki, with a North-South ori-
entation. The East-facing slopes are influenced by pluvial aerial
masses originating from the Aegean Sea resulting in highly produc-
tive ecosystems in comparison to West-facing sites. The study forest
(40o 32’ N, 21o 58’ E) is located on the Eastern slopes of Vermio
Mountain, spanning from 380 to 2052 m above sea level and belongs
to the Municipality of Naousa town. Several plant species (Pinus
nigra Arn., Abies borissi-regis Mattf., Castanea sativa Miller., Ilex
aquifolium L., Juniperus sp., Quercus sp., Salix sp., Populus sp., Pla-
tanus sp, Acer sp., Fraxinus sp., Buxus sempervirens L., Cornus sp.,
Prunus sp., Robus sp., etc.) could be found in this ecosystem. The cli-
mate of the forest can be classified as temperate Mediterranean with
rainy winters and warm summers and the total annual rainfall is
1500 mm [27]. Minimum rainfall occurs during the July-August
period, but the atmosphere is not totally dry due to the vicinity to
archipelagos.
The Balkan region and particularly Northern Greece, is the contact
zone of the European Beech (F. sylvatica subsp. sylvatica L.) and
Oriental beech (F. sylvatica subsp. orientalis Lipsky). Strid and Tan
[28] supported that F. sylvatica subsp. sylvatica and F. sylvatica
subsp. orientalis are typical geographical races of F. sylvatica and
several individuals have been recorded to be more or less intermedi-
ate between these subspecies. Such hybrids are referred to as F. syl-
vatica subsp. moesiaca Cz. (Moesian beech) with leaves character-
ised by 5–9 pairs of lateral veins and seeds often presenting
somewhat spathulate basal cupule scales. However, Moesian beech is
reported to be the prevalent species in Central and North-west Greece
[6, 8, 24] and the morphological characteristics of the study trees
resemble the Moesian type rather than the European form. Naturally
regenerated, pure beech (Fagus moesiaca Cz.) stands occupy a total
area of 2121 ha, stretching from 900 m to 1900 m and covering a
range of various topographical conditions (see Tab. I). Due to the dis-
turbed history of the forest (clearcut fellings during the 19th century
and several fire events during World War Two), it is usual to meet
cohorts within stands that belong to different age- and consequently
size-classes.
2.2. Tree level data
Sixteen trees were harvested from the stands described in Table I
for the parameterisation of the allometric equations and the diameter
Tab le I . Characteristics of the 4 stands sampled along an elevation gradient (as modified from Stefanidis [27]).
Stand ID Elevation
(m) Aspect (Slope) Density
(trees/ha)
Basal area
(m2/ha)
D range
(cm)
Mean height
(m)
Mean annual
increment (m3/ha)
20 1030 N–NE (20%) 630 29.0 10–32 23.0 5.98
16b 1310 N–E (30%) 667 20.1 10–32 14.0 4.17
12a 1513 N–W (31%) 744 19.3 10–36 17.4 7.52
10a 1820 N–E (60%) 820 22.4 10–60 13.9 2.68
Allometric equations for Fagus trees 441
(D) range of the felled trees spanned from 5.19 to 40.6 cm so as to
represent the diameter distribution reported in the forest management
plan.
The following variables related to tree dimensions were recorded
per sample tree: the diameter at 0.30 m above ground (DB), the diam-
eter at 1.30 m (D), the diameter at the base of the live crown (DC), the
total height (H), the height to the base of the live crown (HS), diame-
ters (DBR), lengths (LBR) and positions of the branches on the stem.
The tree bole was cut at 0.30 m and at 1m intervals thereafter up to
the base of the live crown (i.e. the point where the main stem bifur-
cated), and the part of the stem within the crown was separated from
branches. After felling the tree, the stump was also removed. The
leaves of each branch were collected and put into plastic bags. The
stem sections (including bark), the stump, the branches and the leaves
were transported to the laboratory and oven dried to constant weight
at 80 oC. Before felling, the horizontal projections of the 8 longest
branches (excluding epicormics) were pointed down and the horizon-
tal crown projection area (Pa) was determined assuming that it could
be compared to a circle or to an ellipse.
2.3. Regression analysis
Foresters and ecologists have used different models for estimating
forest biomass. Undoubtedly, the most commonly used mathematical
model is the allometric equation corresponding to the following
power form:
Y = aXb (1)
where a and b are the scaling coefficients that vary with the variables
under investigation; Y is the total biomass or one of its components
and X a tree dimension variable (i.e. D, D2, D2H, DH, etc.).
Payandeh [22] further classified model (Eq. (1)) into two types:
the “intrinsically linear” type which assumes a multiplicative error in
raw data and the “intrinsically nonlinear” type with an additive ran-
dom error. In the “intrinsically linear” model, the original data are
log-transformed and the least square method is applied in order to
estimate the parameters. In many cases, log-transformation of raw
data results in homoscedasticity of the dependent variable Y, a prereq-
uisite for the regression methods. However, even though the logarith-
mic equation is mathematically equivalent to equation (1), they are not
identical in a statistical sense [34]. Using the logarithmic form of
equation (1), produces a systematic underestimation of the dependent
variable Y when converting the estimated lnY back to the original
untransformed scale Y. Although this inherent bias has long been rec-
ognised [7], concern of its potential impact on estimates of biomass
is relatively recent [10, 15, 26]. Several procedures for correcting bias
in logarithmic regression estimates have been advocated [2, 3, 26,
33].
Let be the mean predicted value for a given X
in arithmetic units. According to [2], an approximation of the cor-
rected, unbiased estimate is
where CF = SEE2/2 is the correction factor and SEE =
is the standard error of the estimate of the
regression; n and p denote the number of the observations and the fit-
ted parameters, respectively.
Madgwick and Satoo [11] found from intensive simulated sam-
pling of actual weights that with some corrections, values tend to be
overestimated, and they suggested that, as the bias from re-transfor-
mation is generally small compared to the overall variation in the estimate
of biomass, the correction factor be ignored. In addition, Beauchamp
and Olson [3] reported that data on stem biomass of Liriodendron tul-
ipifera L. showed small bias (< 1%) in the predicted dry weight
obtained from the biased (uncorrected) estimate. For the purposes of
the present study, a and b values are reported for the biased regression
in conjunction with the correction factor CF as given by Sprugel [26].
Yandle and Wiant [33] reported that the bias , as a
percent of the unbiased estimate equals to
Percent bias = ((eCF –1)/eCF)100 (2)
and is constant over the range of X values. Wiant and Harner [32] sug-
gested that it is informative to express the standard error of for a
given X as a percent of . This becomes
Percent standard error = (eCF –1)
1/2100 (3)
which is also constant over the range X values.
Usually, the validity of the relationship is tested by the coefficient
of determination of the logarithmic regression, R2, and SEE is com-
puted for the entire dataset of the transformed data. However, high
values of R2 and small values of SEE (typically obtained in allometric
studies) do not guarantee precision of the estimate, when values are
back transformed to the linear scale. Thus, it is not unusual that, for a
specific diameter, the predicted biomass deviates by a relative
amount of 90% from the corresponding observed value.
On the other hand, the general linear regression procedure does
not apply to the “intrinsically nonlinear” model and iterative proce-
dures are required for estimating the allometric parameters. Payandeh
[22] reviewed and compared the log-transformed linear model with
the simple nonlinear form and pointed out that the latter model
resulted in better fit for two datasets of Betula alleghaniensis Britton
and Acer saccharum Marsh.
Another basic point about allometric studies is that researchers
rarely validate the obtained relationships with data other than the ones
that were used in regression analysis. Madgwick [10] pointed out that
if models are to be used for prediction purposes, they should be eval-
uated with new data. Moreover, it must be emphasised that allometric
relationships are only valid over a certain X interval of the independ-
ent variables, and extrapolation to either higher or lower values may
result in large deviations between real and predicted values.
Parresol [20] reported several statistics for evaluating goodness-
of-fit and for comparing alternative biomass models. The mean per-
centage difference (MPD) between the predictions and the raw data
was used to assess the performance of different models. This statistic
gives the average deviation of the regression, relative to the raw data,
and assesses the variability of the fitted equation. MPD is calculated
as the average of differences between observed and predicted values
divided by the observed [17, 22].
2.4. Generalised equations
Apart from estimating biomass values at stand level, predictions at
landscape scale are also needed, especially for C related studies.
Since it would be unrealistic to develop allometric equations for each
stand found in a region, Pastor et al. [21] built generalised equations
based on published allometric relationships. Quite a similar approach
is presented in this paper in order to obtain generalised equations for
beech trees, which were derived from American and Europeans stud-
ies (Tab. II).
The D range of each dataset was divided into four classes and the
mean value of each class was used to derive the total aboveground
biomass (MT) predictions from the original equations. These MT-D
pairs (28 in total) were log-transformed and a generalised equation
for aboveground dry biomass of beech trees was subsequently
obtained.
Y
ˆ
ce1n
ˆab
ˆ Xln+
=
Y
ˆ
ce1n
ˆab
ˆ Xln CF++
=
Yi
ln Yi
lnˆ
–()
2np–()¤
å
BY
ˆ
cY
ˆ
–=
Y
ˆc
Y
ˆc
Y
ˆ
442 D. Zianis, M. Mencuccini
3. RESULTS
3.1. Allometric equations
Scatter plots of the data indicated that biomass values for
different tree compartments, as well as for H, HS, DC and Pa,
were non-linearly related to D. Subsequently, the raw values
were transformed using the Napierian logarithmic function
and the least squares method was applied to estimate the
parameters of the models. The results are presented in Table III
and surprisingly strong relationships were obtained in almost
all cases.
In one case however, the mass of the leaves of the epicor-
mics branches (MFE) was not highly correlated to D as indi-
cated by the R2 of the log-transformed data. The MPD for MBE
was about 87% but D explained 75% of the variability of the
biomass of epicormic branches. Stronger relationships are
reported for total foliage biomass (MFT) and for the mass of
leaves found in the canopy (MFC). In the following equations,
H is introduced as the second independent variable and
slightly better predictions for MFT and MFC were obtained
than with the allometric equation including only D:
MFT = 0.0001997(0.000012)D2H+ 0.331(0.227), R2=0.9493
(4)
and
MFC = 0.0001663(0 .0000 13) D 2H + 0.224(0.243), R2 = 0 . 9 1 9 .
(5)
The standard errors of the estimates are presented in the paren-
theses; in both equations the slopes were statistically different
from zero but the 95% range of the intercepts included this
value.
However, the addition of H for predicting the biomass of
other tree compartments did not substantially contribute to the
Table II. Published equations for beech trees of the form Y = aXb used to develop generalised allometric relationships for aboveground dry
biomass.
Species ab
R2D (cm) N* Region Source
Fagus grandifolia 0.2013 2.2988 n/a 3–66 29 USA, Maine [29]
Fagus grandifolia 0.1958 2.2538 0.99 2–29 46 USA, New Brunswick [29]
Fagus grandifolia 0.1957 2.3916 0.99 1–60 14 USA, New Hampshire [29]
Fagus grandifolia 0.0842 2.5715 0.97 5–50 56 USA, West Virginia [29]
Fagus sylvatica L.0.0798 2.601 0.99 2–32 32 EU, Netherlands Centre [1]
Fagus sylvatica L.0.1326 2.4323 0.99 4–35 7 EU, Spain North [23]
Fagus moesiaca Cz. 0.2511 2.3485 0.99 5–41 16 EU, Greece North This study
* Number of harvested trees.
Table III. Regression equations of the form Y = lna + bX. The standard errors of the estimates (s.e.) are significant at the 5%-level. R2, SEE,
CF, and SSE denote respectively the coefficient of determination, the standard error of the estimate for 14 degrees of freedom, the correction
factor and the sum of squares for error in arithmetic units. Percent bias and Percent s.e. were computed with equation (2) and equation (3)
respectively. The number of sampled trees was 16.
YXlnabR2s.e. (a)s.e. (b)SEE CF Percent bias Percent s.e. SSE MPD (%)
lnMTlnD–1.3816 2.3485 0.99 0.2080 0.0724 0.1841 1.0171 1.6819 18.5762 139617.36 14
lnMs lnD–1.6015 2.3427 0.98 0.2358 0.0821 0.2088 1.0220 2.1563 21.1099 91511.54 16.06
lnMBT lnD–5.2898 2.9353 0.97 0.3686 0.1284 0.3264 1.0547 5.1902 33.5387 14925.35 25.38
lnMBC lnD–6.3807 3.1037 0.95 0.5573 0.1941 0.4936 1.1295 11.4707 52.5287 13605.48 37.82
lnMBE lnD–5.9523 2.7501 0.75 1.2032 0.4191 1.0657 1.7645 43.3271 145.379 12658.01 86.89
lnMFT lnD–4.1814 1.6645 0.90 0.4362 0.1519 0.3863 1.0774 7.19 40.1176 10.94 39.92
lnMFC lnD–5.5168 1.9979 0.87 0.5970 0.2079 0.5287 1.15 13.0474 56.7997 8.67 40.26
lnMFE lnD–3.5789 0.9021 0.40 0.8420 0.2933 0.7458 1.3206 24.2792 86.2607 3.25 59.36
lnMSP lnD–1.7716 1.0730 0.78 0.4398 0.1532 0.3895 1.0788 7.3068 40.4809 35.3 27.86
lnMCS lnD–4.0543 2.2116 0.87 0.6683 0.2328 0.5918 1.1914 16.068 64.7719 1328.3 44.47
lnMCW lnD–4.1293 2.6741 0.96 0.4038 0.1407 0.3576 1.0660 6.1942 36.9358 12433.56 34.6
lnHlnD1.4192 0.5358 0.89 0.1459 0.0508 0.1292 1.0083 0.8315 12.9768 112.13 9.5
lnHSlnD1.2238 0.4677 0.75 0.2052 0.0715 0.1818 1.0166 1.639 18.33 86.75 14.12
lnDc D 1.1544 0.0504 0.91 0.0949 0.0042 0.1869 1.0176 1.7328 18.862 121.69 14.37
MT: total aboveground biomass, MS: stem mass, MBT: branch mass (including epicormics), MBC: branch mass in crown, MBE: mass of epicormic bran-
ches, MFC: foliage mass in crown, MFE: foliage mass of epicormics branches, MFT = MFC + MFE, MSP: stump mass, MCS: the mass of the stem
within the crown, MCW = MBC +MCS. For D, Dc, and H see Section 2.2. Biomass is expressed in kg, diameter in cm, and height in m.
Allometric equations for Fagus trees 443
increase of R2 or to the decrease of SEE. The height of the stem
was also closely related to D according to HS=3.4001D0.4677
(as transformed from the logarithmic equation in Tab. III).
Stump mass generally increased with increasing D
(Tab. III) or DB (Fig. 1) but a large variability occurred, which
resulted in a rather low R2.
Subsequently, the stump shape was approximated as a cylinder
with diameter and height equal to DB and 0.3 m respectively,
and the standard volume formula was used for predicting MSP;
however, this approach did not significantly decreased the
SEE (data not shown).
A highly exponential relationship between lnDC and D was
obtained (Tab. III) which, after transformation of the coeffi-
cients to arithmetic scale reads as
Dc = 3.172e0.0504D (6)
where e is the base of Napierian logarithms. The horizontal
projection area of crown (Pa), was also non-linearly related to
D and the empirical relationship was
Pa = 1.2830(0.1121)lnD – 0.9004(0.321) (7a)
with R2 = 0.9034, and standard errors in parentheses.
The percent bias was computed according to equation (2)
and resulted in a rather low estimate of 3.98%. Thus, no pro-
cedures were adopted in order to eliminate the inherent bias
and the antilog of the logarithmic predicted values were used
to derive the power function
Pa = 0.4064D1.2830 (7b)
with sum of squared errors, SSE = 1021.99 (in linear scale);
the projection area was measured in m2 and D in cm.
Larger values of percent bias (43.43%) were obtained for
the equation that relates the biomass of epicormics branches
(MBE) to D; however, the SSE of the biased and corrected
equations was 12.658 and 12.657 respectively (in linear scale),
indicating that unbiased predictions do not significantly
reduce the residual error.
Finally, the pooled data for the branches were used to
derive the following relationship between branch biomass
MBR and branch diameter DBR:
lnMBR = 3.415 (0.062) + 2.818 (0.056) lnDBR (8)
with R2 = 0.889, SEE = 0.6871, and standard errors of param-
eters in parentheses.
The equations developed so far were assumed to comply
with the “intrinsically linear” model and the least squares method
was applied to log-transformed data in order to derive empiri-
cal values for the parameters of the allometric relationships.
However, if one assumes an additive error term in the original
data, then predictions should be based on nonlinear functions.
The underlying model requires iterative procedures [22] for
parameter estimation. Nonlinear equations were developed for
the major tree biomass compartments (Tab. IV).
3.2. Generalised equations
A generalised equation for the aboveground biomass was
developed, based on published allometric equations (see Tab. II)
and the obtained relationship in logarithmic form is following:
lnMT = 2.45(0.055)lnD – 2.004(0.168) (9)
with R2 = 0.987, SEE = 0.1876, standard errors in parentheses;
(see Fig. 2).
Raw data for MT-D pairs reported by Santa Regina and
Tarazona [23] were used, in conjunction with the new dataset
reported in this study, to validate the generalised equation. To
avoid confounding results, the Spanish and Greek equations
Table I V. Regression equations of the form Y = aXb. Symbols as in Table III.
YXab
R2s.e. (a)s.e. (b)SSE MPD (%)
MTD0.9402 1.9643 0.97 0.4897 0.1474 87058.6 25.77
Ms D0.7568 1.9536 0.97 0.4007 0.1498 54623.8 25.88
MBT D0.1097 2.0545 0.91 0.116 0.2985 8467.7 45.43
MFT D0.0057 1.9836 0.96 0.0035 0.1758 5.23 39.92
Figure 1. Stump biomass MSP in relation to DB.
The slope is statistically different from one.
444 D. Zianis, M. Mencuccini
were excluded for the development of the new generalised
equation which takes the form:
lnMT = 2.456(0.05)lnD – 2.073(0.156) (10)
with R2 = 0.992, SEE = 0.1551, (standard errors in parentheses).
The new generalised equation (10) very closely predicted
biomass values for the Spanish dataset and there was virtually
no difference between estimations made by the original and by
the generalised regression (Fig. 3a). On contrary, the general-
ised equation did not accurately fit the data collected from the
Greek stand (Fig. 3b).
For the original equation MPD = 13.54%, while the gener-
alised regression yielded a MPD of 31.17%. In addition, the
pooled data from the two datasets were used to validate the
Figure 2. Generalised equation (9) and original
equations for aboveground biomass MT).
Figure 3. Predicted values for aboveground biomass (MT)
from the generalised equation (10) and from original
equations for the (a) Spanish and (b) Greek datasets.
Allometric equations for Fagus trees 445
generalised relationship. A MPD of 23.82%, which denotes a
rather accurate estimate, considering that no adjustments were
introduced to take into account the different anatomical and
morphological characteristics of the harvested trees (stand
structure, wood density, tree age, tree height, etc.). General-
ised equations for other tree compartments (stem, branches,
foliage) were also developed for Fagus spp. and the results are
presented in Figure 4 (see also Tab. V).
Figure 4. Generalised equations for (a) stem
(b) branches and (c) foliage biomass together
with the pooled datasets from the Spanish
and Greek stand. The Spanish and Greek
original equations were excluded from the
development of the generalised function.
The MPD value refers to the pooled data.
446 D. Zianis, M. Mencuccini
To assess the variability of the generalised equations rela-
tively to the original regressions, Pastor et al. [21] reported
three statistics. In this paper, the MPD between the predictions
made by the generalised and the original equations was calcu-
lated for different biomass components and presented in Table V.
3.3. Comparison between equations
The American regression (developed in New Hampshire)
better predicted the Greek raw MT values (Fig. 5), than the
Spanish and Dutch equations which deviated by a large
amount in relation to the raw data, constantly underestimating
the aboveground dry biomass values.
This trend was also evident for the stem and foliage bio-
mass, but the most accurate prediction for branch mass was
obtained with the Dutch regression. In addition, it is illustrated
in Figure 5 that the equation developed from beech trees har-
vested in Netherlands accurately predicted MT from the Span-
ish dataset.
We also explored the applicability of a new theoretical
model at the stand and regional scale, by comparing the pre-
dictions against the Spanish and the Greek datasets. According
to West et al. [30], the theoretical exponent (b) in the power
function (Eq. (1)) equals 2.67 for the aboveground biomass,
independently of species or site under investigation, but no
estimation is given for the parameter a. However, Chambers
et al. [5] reported that a is ca. 0.1002, and this value was used
to derive theoretical predictions. The MPD between the theo-
retical and raw values for the Greek dataset was 23.65% with
the highest value amounted to 58% for the largest tree (D =
41.45 cm) and to 40% for the smallest tree (D = 5.39 cm). The
MPD was larger for the Spanish data (49%) and the highest
value was equal to 80% for a tree with a diameter equal to
34.5 cm.
4. DISCUSSION
4.1. Allometric relationships
Diameter at breast height explained much of the variability
in biomass values of different tree compartments. Adding H as
a second variable, improved predictions for foliage biomass
were obtained, in accordance with Bartelink [1]. However,
tree height did not substantially decrease the SSE for the
regressions of total, stem, and branch biomass. The high cor-
relation between D and H may explain the low gains in predic-
tions when the latter variable is included in allometric models
(multicolinearity). Mcmahon and Kronauer [12] examined the
scaling of tree height based on stress and elastic similarity
models and Niklas [17] reported that, for very old dicot trees,
H µ D0.474 implying that mature trees taper so as to maintain
a constant elasticity throughout the tree. In this study, the 95%
confidence intervals for the reduce major axis scaling exponent of
H against D are 0.45–0.66 and indicate that wind-pressure
dynamic loadings most likely affect the size-shape relation-
ship of the study trees. Strong scaling relationships were also
found between different tree dimensions (i.e. Pa, Dc, HS) and
D as well as between branch biomass and branch diameter. It
Tab l e V. Generalised equations for different tree compartments for Fagus spp. derived from the sources presented in Table II. MPD denotes
the average deviation of the generalised predictions in relation to the original equations.
Compartment abD range (cm) R2MPD
Tota l 0.1348 2.4500 1–66 0.99 14.86%
Stem 0.1321 2.3594 1–66 0.99 12.67%
Branches 0.0219 2.4208 1–66 0.77 58.11%
Foliage 0.0188 1.8169 1–66 0.86 32.29%
Figure 5. Comparisons between published
equations for aboveground biomass (MT)
and field data collected from the studied
forest in Greece.
Allometric equations for Fagus trees 447
is obvious that stump biomass is not so tightly related to either
D or DB as indicated by the R2 values (Tab. III and Fig. 1). An
explanation could be that the sample trees were located at sites
with different slopes, which in turn may influence the shape –
butteressness – of the lowest part of the stem. This variability
in shape could not be captured by DB alone and it is speculated
that other variables might be more useful in predicting stump
biomass. However, the information collected from the har-
vested trees could not be used to thoroughly test this hypothe-
sis; compared to total tree biomass, stump mass is a very small
proportion and any deviations from real values would be insig-
nificant when extrapolated to stand scale. In statistical terms,
the highest inherent bias recorded in the dependent variables
after log-transformation was for the biomass of epicormic
branches: the percentage bias value was equal to 43.32%
(Tab. III). Applying the appropriate formula to eliminate this
bias, the SSE was insignificantly reduced (from 12658.01 to
12657.9); this observation was also true for other tree com-
partments, in accordance with [3] and [11]. When nonlinear
models (Tab. IV) were compared with log-transformed regres-
sions (Tab. III) it appeared that the former fitted the field data
better than the latter as implied by the SSE. However, in terms
of MPD, the linear models appeared to deviate less than the
simple power functions, for total aboveground, stem and
branch biomass.
Since data were pooled from stands with different structural
and topographical characteristics (Tab. I), one could expect
that quite accurate estimates may be obtained if the presented
equations are to be applied throughout the study forest. Thus,
the established relationships may be quite useful for the sus-
tainable management of the investigated forest since no model
existed so far for biomass estimations and the planning activ-
ity was totally based on the experience of foresters rather than
on statistically sound methods.
4.2. Generalised equations
Aboveground biomass was estimated very accurately by
generalised equations in the case of Spanish stand (Fig. 3a).
On the other hand, the mean percentage difference (31.17%)
between the generalised equation and the Greek dataset was
relatively high (Fig. 3b), but within the range (10–35%)
reported for published regressions developed from field data
(Pastor et al. [21]). Mean variability between predicted and
pooled stem biomass data was less than 27% (Fig. 4a), and
was about 40% for the pooled branch biomass data (Fig. 4b).
Generalised regression for foliage mass failed to predict the
Greek data correctly (MPD = 123%) but reasonably approxi-
mated the values of the Spanish stand (MPD = 20%). The large
deviation for the Greek dataset may be explained by the fact
that foliage mass is strongly related to sapwood area rather
than to D, as documented in several studies [1, 12]. For the
pooled dataset, MPD = 92% (Fig. 4c).
The generalised regressions accounted for more than 98%
of the variation in the values predicted by the original pub-
lished equations, for total and stem biomass (Tab. V). The
mean percentage difference between values obtained by gen-
eralised equations and those predicted by individual regres-
sions for branch and foliage biomass was 58% and 32%
respectively (Tab. V). Pastor et al. [21] reported similar per-
centage difference values for branch mass, and assigned this
large deviation to the difficulties in separating stem and branches
in broadleaved trees. Differences in silvicultural treatments, or
site productivity or stand age may also explain the obtained
values.
In general, it is clear that inaccurate predictions may be
obtained from generalised equations when applied to any par-
ticular stand. However, over- and under-estimations from gen-
eralised predictions may cancel out when these are applied to
large geographical areas, but more data are needed to robustly
test this hypothesis. This method might prove useful in esti-
mating dry biomass values at a national level with minimum
cost, since it appears to provide a good balance between accu-
racy of predictions and low input requirements. It is obvious
that this method can also be applied for other tree variables or
life forms.
4.3. Comparison between equations
The sustained total aboveground biomass per tree is greater
for the study forest in Vermio mountain than for the Dutch or
the Spanish forests, as implied by the coefficients of the allo-
metric equations in Table II. Larger biomass values in Vermio
mountain in comparison to the Dutch study may be explained
by the fact that the wood density of beech trees growing in
Greece is generally larger than for northern European beeches
[18]. Alternatively, one could speculate that Greek trees sup-
port a larger biomass at any given D in comparison to Dutch
and Spanish trees. Surprisingly, American regressions more
accurately predicted raw data for the Greek forest than Spanish
or Dutch equations, contrary to speculation that trees growing
in similar environment may sustain quite similar aboveground
biomass. Stand structural characteristics or anatomical param-
eters of the study trees may also play an important role on bio-
mass production and allocation.
Finally, the theoretical model [30] did not perform so accu-
rately for the Spanish and the pooled dataset. Chambers et al.
[5] who compared several models for trees growing in the
tropical zone also reported the same conclusion. Thus, the
applicability of this theoretical model at the stand scale is
questioned and the use of constant a and b values for trees
growing in different environmental conditions should be
viewed as tentative; more work is required to test whether this
theoretical model may be used at the landscape scale.
Acknowledgements: Christos Kontonasios and Thomas Zianis
significantly contributed in collecting raw data. Dimitris Zianis is
supported by Scholarship State Foundation of Greece (IKY) and
Maurizio Mencuccini was supported by the EU-FUNDED CARBO-
AGE project (contract n. EVK2-CT-1999-00045).
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