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Biomass and carbon stocks are key information criteria to understand the role of forests in regulating global climate. However, for a bio-rich continent like Africa, ground-based measurements for accurate estimation of carbon are scarce, and the variables affecting the forest carbon are not well understood. Here, we present the first biomass study conducted in South Africa Mistbelt forests. Using data from a non-destructive sampling of 59 trees of four species, we (1) evaluated the accuracy of multispecies aboveground biomass (AGB) models, using predictors such as diameter at breast height (DBH), total height (H) and wood density; (2) estimated the amount of biomass and carbon stored in the aboveground compartment of Mistbelt forests and (3) explored the variation of aboveground carbon (AGC) in relation to tree species diversity and structural variables. We found significant effects of species on wood density and AGB. Among the candidate models, the model that incorporated DBH and H as a compound variable (DBH2 × H) was the best fitting. AGB and AGC values were highly variable across all plots, with average values of 358.1 Mg· ha-1 and 179.0 Mg·C· ha-1, respectively. Few species contributed 80% of AGC stock, probably as a result of selection effect. Stand basal area, basal area of the ten most important species and basal area of the largest trees were the most influencing variables. Tree species richness was also positively correlated with AGC, but the basal area of smaller trees was not. These results enable insights into the role of biodiversity in maintaining carbon storage and the possibilities for sustainable strategies for timber harvesting without risk of significant biomass decline.
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Article
Aboveground Biomass and Carbon in a South African
Mistbelt Forest and the Relationships with Tree
Species Diversity and Forest Structures
Sylvanus Mensah 1, 2, *, Ruan Veldtman 3,4, Ben Du Toit 1, Romain Glèlè Kakaï 2and
Thomas Seifert 1
1Department of Forest and Wood Science, Stellenbosch University, Private Bag X1, Matieland 7602,
South Africa; ben@sun.ac.za (B.D.T.); seifert@sun.ac.za (T.S.)
2Laboratoire de Biomathématiques et d’Estimations Forestières, Faculté des Sciences Agronomiques,
Université d’Abomey Calavi, Cotonou 03 BP 2819, Benin; gleleromain@gmail.com
3South African National Biodiversity Institute, Kirstenbosch Research Centre, Private Bag X7,
Claremont 7735, South Africa; veldtman@sun.ac.za
4Department of Conservation Ecology and Entomology, Stellenbosch University, Private Bag X1,
Matieland 7602, South Africa
*Correspondence: sylvanus.m89@gmail.com; Tel.: +27-616-227-698
Academic Editor: Timothy A. Martin
Received: 15 January 2016; Accepted: 29 March 2016; Published: 8 April 2016
Abstract:
Biomass and carbon stocks are key information criteria to understand the role of forests in
regulating global climate. However, for a bio-rich continent like Africa, ground-based measurements
for accurate estimation of carbon are scarce, and the variables affecting the forest carbon are not
well understood. Here, we present the first biomass study conducted in South Africa Mistbelt
forests. Using data from a non-destructive sampling of 59 trees of four species, we (1) evaluated the
accuracy of multispecies aboveground biomass (AGB) models, using predictors such as diameter at
breast height (DBH), total height (H) and wood density; (2) estimated the amount of biomass and
carbon stored in the aboveground compartment of Mistbelt forests and (3) explored the variation of
aboveground carbon (AGC) in relation to tree species diversity and structural variables.
We found
significant effects of species on wood density and AGB. Among the candidate models, the model that
incorporated DBH and H as a compound variable (DBH
2ˆ
H) was the best fitting.
AGB and
AGC values were highly variable across all plots, with average values of 358.1 Mg
¨
ha
´1
and
179.0 Mg¨
C
¨
ha
´1
, respectively. Few species contributed 80% of AGC stock, probably as a result of
selection effect. Stand basal area, basal area of the ten most important species and basal area of the
largest trees were the most influencing variables. Tree species richness was also positively correlated
with AGC, but the basal area of smaller trees was not. These results enable insights into the role of
biodiversity in maintaining carbon storage and the possibilities for sustainable strategies for timber
harvesting without risk of significant biomass decline.
Keywords:
climate regulation; non-destructive sampling; allometric equations; wood density;
carbon density
1. Introduction
Tropical forests harbour a considerable number of plant species that underpin ecosystem
functioning [
1
3
], provide forage resources to insect pollinators [
4
6
], contribute to control biological
invasion, and help alleviate the effects of climate change by storing atmospheric carbon [
7
,
8
].
As pointed
out by Pan et al. [
8
], more than 40% of the global terrestrial carbon is contained in the
living biomass of these forests. Assuming that most of these functions and services are vital for human
Forests 2016,7, 79; doi:10.3390/f7040079 www.mdpi.com/journal/forests
Forests 2016,7, 79 2 of 17
beings, climate regulation services are particularly critical with regard to increasing anthropogenic
greenhouse gas emissions in the atmosphere and its subsequent adverse effect on climate [8,9].
Carbon accounting and climate change mitigation activities are central topics in the landscape
management debate [
10
], and as a result, accurate and reliable information on the contribution of
land use to alleviate the effects of changing climate is needed. However, for much of Africa, there
is still a lot of uncertainty about the amount of aboveground biomass (AGB), belowground biomass
and carbon stocks in indigenous forest ecosystems and in particular, in the tropical and sub-tropical
regions [11,12].
Indigenous forests in South Africa are not spatially significant (approximately 0.56% of the total
land area of the country), but they support a high proportion of the country’s floral diversity [
13
] and
contribute important ecosystem services to the local population. These indigenous forests have been
intensely fragmented and exposed to illegal timber harvesting in the past [
14
]. The modification of
fire regimes to protect the agricultural farms and plantations surrounding these forests has favoured
a natural successional development that has contributed to the conversion of some degraded forest
areas into forest vegetation [
14
]. Mistbelt forests are some of the indigenous forests that have recovered
from the modification of fire regimes. In the Limpopo province of South Africa, these forests occur as
fragmented patches in zones of low and highland, and with surrounding pine and eucalypt plantations
and commercial farming areas, form a succession of widely distributed landscapes with a great
potential to supply fibres and food.
With the large greenhouse gas emissions due to industrialisation, deforestation and forest
degradation, care should be given to management policies aiming to balance the production of
food, fibres and fuels with the protection of biodiversity and regulation of global climate change [15].
Accounting for the potential of biophysical units in a landscape to store atmospheric carbon is vital
for policy and management decisions. More specifically, the clear understanding of the relative role
of these forests in carbon sequestration and climate regulation would support the motives behind
land relocation and landscape management schemes. To date, many studies have addressed the
biomass and carbon stocks in common plantation genera such as Pinus,Eucalyptus and Acacia in South
Africa [
16
18
], while comparatively very little attention has been given to native species and natural
woody vegetation (see for example, the study by Colgan et al. [
19
], which is one of the rare biomass
studies in natural woody vegetation in South Africa). This results in a lack of precise information
about these forests, and appears as a drawback for accurate local and national carbon inventories. It is
also a drawback for economic incentives and in particular, the implementation of carbon credit market
mechanisms for the conserved forest areas [20].
From previous studies, the quantification of forest biomass relies on different methods, from
remote sensing techniques to tree-based allometric approaches [
11
,
21
24
]. Multispecies allometric
equations have extensively been studied and offer possibilities to accurately estimate forest biomass at
smaller scales, and to elucidate the relationships of forest biomass with stand variables.
Recent studies
have shed light on the influence of forest stand variables and tree species diversity on the forest biomass
and carbon stocks [
25
27
], and how these relationships can serve not only to suggest appropriate
management strategies to increase carbon storage [
20
], but also to test niche complementarity and
selection effect hypotheses [
28
,
29
]. The niche complementary hypothesis suggests that higher diversity
in forest ecosystems would allow a greater variety of functional traits and provide opportunities
for efficient resource utilisation, thereby increasing ecosystem functions (for example, carbon
storage).
The selection
effect hypothesis assumes that a highly diversified ecosystem would allow
higher probability of occurrence of dominant species or traits that would positively influence the
ecosystem function. Both hypotheses have been subject to intense debate about the processes behind
ecosystem functioning.
In this study, we aim to quantify the stand biomass and carbon stocks in a Mistbelt forest, a
typical multi-storey, multispecies forest in South Africa, and to understand their variation in relation
to the stand characteristics and biodiversity. To our knowledge, this is the first biomass study in these
Forests 2016,7, 79 3 of 17
Mistbelt forests in South Africa. We based our method on forest inventory, tree sampling, laboratory
processing and biomass modelling. Therefore, the objectives of this study were to:
(1)
Develop three multispecies AGB equations and compare their ability to accurately estimate AGB
at the tree level; to do so, we first determined whether wood density and AGB varied among
study species. We next tested whether the inclusion of tree height and wood density in biomass
equations reduced the estimation error.
(2)
Estimate the total amount of biomass and carbon stored in the aboveground compartment in
Mistbelt forests; we used the best multispecies AGB equation and the forest inventory data to
upscale AGB from the tree to the stand level; we next applied the carbon fraction commonly used
in natural forests.
(3)
Understand the aboveground carbon (AGC) variation in relation to the forest tree species diversity
(richness) and stand characteristics. We assumed that selection effects and dominance patterns
are the main drivers of carbon variation.
2. Materials and Methods
2.1. Study Area
This study was conducted in the northern Mistbelt forests of Limpopo Province in South
Africa [
13
], classified as part of the Afromontane Archipelago in Africa [
30
]. These Mistbelt forests are
found at the southern end of the Mpumalanga escarpment as small and fragmented patches, and along
the northern escarpment as a large forest complex [
14
,
31
]. Most of these forests occur at an altitudinal
belt spanning from 1050 to 1800 m above mean sea level. The area covered in this study (707 ha) is
located in the Woodbush–De Hoek State Forest (23
˝
50
1
S, 29
˝
59
1
E), near Tzaneen in the Limpopo
Province. Annual mean precipitation varies from 1800 mm at higher altitude to 600 mm at lower
altitudes [
14
]. The vegetation in the Woodbush–De Hoek State Forest is dominated by species such
as Xymalos monospora (Harv.) Baill., Podocarpus latifolius (Thunb.) R.Br. ex Mirb., Combretum kraussii
Hochst., Syzygium gerrardii (Harv. ex Hook.f.) Burtt Davy, Cryptocarya transvaalensis Burtt Davy in the
canopy and sub-canopy layers, and Oxyanthus speciosus DC., Peddiea Africana Harv., Oricia bachmannii
(Engl.) I. Verd., Kraussia floribunda Harv. in the understorey vegetation, while the herb layer is made
up of species genera such as Isoglossa,Plectranthus,Stachys,Galopina,etc. [13].
2.2. Forest Inventory and Biomass Data
The study area was stratified into compartments based on different classes of aspect, slope and
elevation based on a digital elevation model. A first phase forest inventory was carried out using
thirty replicates of a nested plot design, which consisted of a 0.025-ha (hectare) circular subplot
inside a 0.05-ha larger circular plot. These plots were established based on a stratified random
sampling design. Diameter at breast height (DBH) was measured with a diameter tape, inside each
0.025-ha plot for trees in the 5–10-cm DBH class, and inside the 0.05 ha plots for trees with DBH
greater than 10 cm.
Total height
(H) was additionally measured where possible, using a Vertex
hypsometer.
In total
, 50 species were enumerated, of which four species were selected, on the basis
of their greater relative contribution to stand basal area, for further sampling and quantification of
biomass. The four selected species, namely Combretum kraussii (Combretaceae), Croton sylvaticus Hochst.
(Euphorbiaceae), Syzygium gerrardii (Myrtaceae) and Trichilia dregeana Sond. (Meliaceae) contributed
42% of the stand basal area. Among the species we did not consider, Xymalos monospora (Monimiaceae)
and Cussonia sphaerocephala Strey (Araliaceae) were also dominant and covered 29% of the stand basal
area. The remaining species (n= 44) contributed 29% of the total stand basal area.
For each species selected, fourteen to sixteen individual trees (a total of 59 trees; Table 1) were
chosen across a wide range of DBH and measured for biomass quantification. Information on DBH
and H, and wood core samples were collected from all selected trees. Wood core samples were
taken at breast height level and crown base level (i.e., the level of insertion of the first branch of the
Forests 2016,7, 79 4 of 17
living crown). Diameter was measured on standing stems at 2-m intervals up to the crown base
with the help of a tree climber. On larger branches (basal diameter >15 cm), both thick- and thin-end
diameters, at the base and the end of a branch, respectively, and the distance between these two points
were determined.
On smaller
branches (basal diameter <15 cm), only the branch basal diameter was
measured.
In addition
, two to four branches per tree were sampled at different height levels (distance
from the ground) from eight individual trees for each species, for further determination of dry mass.
As a
result, 19, 18, 20 and 16 branches were sampled for C. kraussii,C. sylvaticus,S. gerrardii and
T. dregeana
, respectively. To obtain the dry mass, branch wood and leaf samples were oven-dried at
105 ˝C
until weight equilibrium was reached [
22
]. Data on branch dry mass were used to establish the
branch biomass equations based on branch basal diameter, which explained 94.5%, 93.6%, 94.2% and
95.3% of the variation of the branch dry mass for C. kraussii,C. sylvaticus,S. gerrardii and T. dregeana,
respectively. Wood density was determined by dividing the oven-dried mass of each wood core sample
by its green volume (obtained from the water displacement method [
22
]). The volume of the standing
stem plus larger branch sections was calculated by applying Smalian’s formula [
32
], and the average
values of wood density (based on the two wood core samples per tree) were used to calculate the wood
biomass of the stem plus larger branches. The total AGB of each individual tree was then obtained by
adding the biomass of the stem and larger branches to the branch biomass predicted from the branch
biomass regression equations. Table 1shows a descriptive summary of the sampled trees.
Table 1.
Descriptive summary (minimum and maximum values) of characteristics of measured
tree species.
Species Number
of Trees DBH (cm) Height (m) Wood Density
(g/cm3)AGB (Kg)
C. kraussii 16 1.5–91.0 3.1–24.2 0.51–0.66 0.26–4590.19
C. sylvaticus 14 4.8–64.0 5.4–28.0 0.38–0.50 4.17–5127.94
S. gerrardii 15 0.7–92.5 2.3–22.1 0.51–0.65 0.17–3423.33
T. dregeana 14 2.8–62.0 4.4–27.0 0.35–0.55 0.82–2357.97
2.3. Assessing the Effect of Species on Wood Density and AGB
We tested for differences in wood density among species using one-way analysis of variance
(species as factor). Shapiro Wilk’s statistic was used to test for the normality of the data. Because of
the significant effect of species, Student-Newman-Keuls test was performed to classify the species
according to their wood density values. We also assessed whether and how the biomass allocated to
the aboveground compartment varied with species. Because biomass allocation is size dependent, we
assessed the effects of species (categorical variable) and tree size (DBH, continuous variable) using
a Generalised Linear Model [33]. Additionally, we tested for interaction effects between tree size and
species to determine if the effects of tree size would vary by species.
2.4. Multispecies DBH-Height and Biomass Allometric Models
As result of the forest inventory, total tree height was measured for 461 individual trees, accounting
for 37 species. The tree diameter and height relationship was explored using scatter plots. Because the
power function fitted well with the observed data, we developed the allometric relationship between
tree DBH and height (H) for all species using the function in Equation (1):
Hβ0¨DBHβ1¨ε(1)
where His the response variable, DBH the predictor, and
ε
the random error. Equation (1) can
be linearised by applying the natural logarithm to Hand DBH to obtain its logarithmic form
(Equation (2)) [32,34]:
lnHlnβ0`β1lnDB H `ε1(2)
Forests 2016,7, 79 5 of 17
Three allometric Equations (3)–(5) taking into account DBH (cm), H (m) and wood density
ρ
(g/cm3), were used to fit the multispecies biomass models:
lnAGB lnβ0`β1lnρ`β2lnDBH `ε1(3)
lnAGB lnβ0`β1lnρ`β2lnDBH `β3lnH`ε1(4)
lnAGB lnβ0`β1lnρ`β2lnpDBH2ˆHq ` ε1(5)
where
β0
,
β1
,
β2
and
β3
are the regression coefficients, and
ε
’ is the additive error. The selection of the
best multispecies equation was based on the values of adjusted R
2
, root mean squared error (RMSE),
Akaike information criterion (AIC), percent relative standard errors (PRSE, %) and mean absolute
deviation (MAD, %), as suggested by Chave et al. [
11
], Sileshi [
34
] and Fayolle et al. [
35
]. PRSE is
defined as follows:
PRSE 100 ˆˆSE
|θ|˙(6)
where SE is the standard error of model parameter (
θ
) [
36
]. Valued of PRSE greater than 20% indicate
an unreliable parameter [
37
]. MAD is calculated using the deviation of the predicted vs. observed
response variable [11], as defined below:
δD100 ˆ|y´y|
y(7)
In Equation (7), y and
y
are the observed and predicted values, respectively, of the response
variable. All deviations were averaged based on the total number of observations. The use of
logarithmic transformation in Equations (2)–(5) induces a systematic bias in the final estimation of the
response variable [
11
]. To account for that bias, the predicted values were back-transformed into the
original values and corrected by applying the correction factor, as defined in Baskerville [38]:
CF epRS E2
2q(8)
where RSE is the Residual Standard Error of the regression.
2.5. Quantifying AGB and AGC at the Stand Level
The best multispecies biomass equation was used to predict AGB for the total pool of species, based
on DBH, total height (predicted from DBH-height models), and wood density. Since wood density was
determined only for the studied species, the values of wood density for the species not sampled were
obtained from the global publicly available wood density database [
39
,
40
].
Average wood
density was
used when multiple values were available for a single species. When the wood density value was
missing for a given species, the average genus wood density was used. Similarly, when genus data on
wood density were missing, the mean wood density at the family level was used. In the case a family
was missing, the average wood density of the plot was used as proposed by Stegen et al. [41].
To upscale from the tree level to the stand level, the predicted AGB was first calculated at the plot
scale and averaged, based on the total number of plots. The AGB density (kg
¨
ha
´1
) was upscaled to
the stand level by applying the surface expansion factor (area of hectare/area of plot).
Because a nested
plots design was employed during the forest inventory (0.025-ha circular subplot within a 0.05-ha
circular larger plot), the AGB was computed for each DBH size class, i.e., 5–10 cm DBH in small subplot
and >10 cm DBH in the large subplot. The calculated values were summed to obtain the total AGB at
the stand level. The AGC stock was then determined by applying the carbon fraction of 0.50 [20].
Forests 2016,7, 79 6 of 17
2.6. Assessing the Structural Variables Influencing AGC
Stand variables such as stem density, mean diameter and basal area have positive effects on
carbon stocks, probably because these variables are the stand level attributes that reflect the structures
of the plant communities. However, combined use of basal area, diameter and tree density can lead to
double accounting of tree size, as the stand basal area already integrates population density and tree
size. To determine the most important stand variables, we only focused on basal area and partitioned
the plot level basal area based on the contribution of small trees (5–30 cm DBH), medium sized trees
(30–60 cm) and large trees (>60 cm) [
42
,
43
]. The selection effect hypothesis assumes that dominance
patterns drive ecosystem functioning. Thus, we additionally quantified the basal area of the 10 most
important species in each plot. The ten most important species were identified on the basis of their
importance value index IVI [
44
]. This index was determined for each species by summing the species
relative frequency, relative density and relative dominance (basal area), as follows:
IVI ni
řs
i1ni
`fi
řs
i1fi
`ci
řs
i1ci
(9)
where n
i
,f
i
and c
i
are the density, frequency and basal area, respectively, of the ith species. As a result,
the stand basal area, basal area of smaller trees, basal area of medium sized trees, basal area of the
largest trees, basal area of the 10 most important species, and species richness were considered as
candidate variables influencing AGC at the plot level. We explored the bivariate relationships between
AGC and these variables using scatter plots and by fitting regressions. We additionally combined
the variables that significantly explained AGC storage in the bivariate analyses, in a multiple linear
regression model and performed a stepwise model selection procedure to select the best predictors.
All statistical analyses were performed using R statistical software.
3. Results
3.1. Effect of Species on Wood Density and AGB
Analysis of variance showed that wood density varied significantly among species (F= 40.34,
p< 0.001
), with 68.21% of the variation being explained. The highest values of wood density were
observed for C. kraussii (0.593 g/cm
3˘
0.011) and S. gerrardii (0.571 g/cm
3˘
0.011) while the
lowest mean wood density values (0.459 g/cm
3
and 0.445 g/cm
3
) were observed for C. sylvaticus
and
T. dregeana
(Figure 1). The results of the generalised linear model (Table 2) showed significant
interaction effects between DBH and species. For a given tree size, C. sylvaticus had a scaling
coefficient that was 0.31
˘
0.13, being significantly higher than the base line value (C. kraussii, Table 2).
These results
mean that, for the same values of DBH, C. sylvaticus would have significantly higher
mean AGB at the tree level, as compared to C. kraussii,S. gerrardii and T. dregeana, which would have
similar average AGB values at the tree level (p> 0.05).
Forests2016,7,796of17
cmDBH),mediumsizedtrees(30–60cm)andlargetrees(>60cm)[42,43].Theselectioneffect
hypothesisassumesthatdominancepatternsdriveecosystemfunctioning.Thus,weadditionally
quantifiedthebasalareaofthe10mostimportantspeciesineachplot.Thetenmostimportant
specieswereidentifiedonthebasisoftheirimportancevalueindexIVI[44].Thisindexwas
determinedforeachspeciesbysummingthespeciesrelativefrequency,relativedensityandrelative
dominance(basalarea),asfollows:
IVI 


 (9)
whereni,fiandciarethedensity,frequencyandbasalarea,respectively,oftheithspecies.Asa
result,thestandbasalarea,basalareaofsmallertrees,basalareaofmediumsizedtrees,basalareaof
thelargesttrees,basalareaofthe10mostimportantspecies,andspeciesrichnesswereconsideredas
candidatevariablesinfluencingAGCattheplotlevel.Weexploredthebivariaterelationships
betweenAGCandthesevariablesusingscatterplotsandbyfittingregressions.Weadditionally
combinedthevariablesthatsignificantlyexplainedAGCstorageinthebivariateanalyses,ina
multiplelinearregressionmodelandperformedastepwisemodelselectionproceduretoselectthe
bestpredictors.AllstatisticalanalyseswereperformedusingRstatisticalsoftware.
3.Results
3.1.EffectofSpeciesonWoodDensityandAGB
Analysisofvarianceshowedthatwooddensityvariedsignificantlyamongspecies(F=40.34,p
<0.001),with68.21%ofthevariationbeingexplained.Thehighestvaluesofwooddensitywere
observedforC.kraussii(0.593g/cm3±0.011)andS.gerrardii(0.571g/cm3±0.011)whilethelowest
meanwooddensityvalues(0.459g/cm3and0.445g/cm3)wereobservedforC.sylvaticusandT.
dregeana(Figure1).Theresultsofthegeneralisedlinearmodel(Table2)showedsignificant
interactioneffectsbetweenDBHandspecies.Foragiventreesize,C.sylvaticushadascaling
coefficientthatwas0.31±0.13,beingsignificantlyhigherthanthebaselinevalue(C.kraussii,Table
2).Theseresultsmeanthat,forthesamevaluesofDBH,C.sylvaticuswouldhavesignificantlyhigher
meanAGBatthetreelevel,ascomparedtoC.kraussii,S.gerrardiiandT.dregeana,whichwouldhave
similaraverageAGBvaluesatthetreelevel(p>0.05).
Figure1.Variationofwooddensity(g/cm3)amongstudyspecies.
C. kraus sii C. sy lvaticus S. gerrardii T. dregeana
0.35 0.45 0.55 0.65
Species
Wood density
gcm
3
Figure 1. Variation of wood density (g/cm3) among study species.
Forests 2016,7, 79 7 of 17
Table 2.
Results of the generalised linear models showing the effects of species and size on the
aboveground biomass (AGB).
Estimate Std. Error tValue Pr(>|t|)
(Intercept) ´1.963 0.252 ´7.791 0.000
Tree size ln (DBH) 2.365 0.079 30.036 <0.001
Species C. sylvaticus ´0.958 0.439 ´2.183 0.034
S. gerrardii 0.403 0.322 1.254 0.215
T. dregeana ´0.465 0.402 ´1.155 0.253
Tree size: Species ln (DBH): C. sylvaticus 0.309 0.135 2.288 0.026
ln (DBH): S. gerrardii ´0.132 0.101 ´1.305 0.198
ln (DBH): T. dregeana 0.081 0.125 0.644 0.523
3.2. Multispecies DBH-Height and AGB Allometric Models
DBH and height data fitted well with the power law model used (Figures 2and 3). The model
coefficients, indicators for goodness of fit and correction factors of Equation (2) are summarised in
Table 3. DBH explained 83.81% of the variation in total height, as shown by the adjusted R square value.
Model coefficients were highly significant (p< 0.001), indicating that tree diameter was a significant
predictor of tree height for all species.
The comparison of the three fitted equations (Equations (3)–(5) for estimating AGB is also shown
in Table 3. Equation (3) produced the poorest fits (non-existent effect of wood density, lowest R square,
highest AIC, and highest residual standard and root mean square errors), while Equations (4) and
(5) provided the best fits (highest variance explained and lowest residual errors and AIC), with an
additional significant effect of wood density (p< 0.05). Compared to Equation (5), Equation (4) proved
less satisfactory because it showed high variance inflation factors (VIFs), especially for the correlated
variables such as DBH (8.105) and height (8.086). High VIFs reflect collinearity between predictors
and unreliable coefficients. Based on that, we considered that Equation (5) (incorporating DBH
2ˆ
H
as a single predictor) provided the best multispecies model for estimating AGB. Taking that model
into account, 98.45% of the variation in AGB was explained by positive and significant effects of wood
density (p< 0.05) and DBH
2ˆ
H (p< 0.001), with an associated correction factor of 1.03. The scatter
plot of the regression residuals vs. predicted values did not show any heteroscedastic behaviour for
the selected model (Figure 4). In addition, the trend in the observed and estimated values of AGB
showed a very good coincidence with the linear equation (y = x) (Figure 4).
Forests2016,7,797of17
Table2.Resultsofthegeneralisedlinearmodelsshowingtheeffectsofspeciesandsizeonthe
abovegroundbiomass(AGB).
Estimate Std.Error tValuePr(>|t|)
(Intercept)−1.9630.252−7.7910.000
Treesizeln(DBH)2.3650.07930.036<0.001
SpeciesC.sylvaticus0.9580.439−2.1830.034
S.gerrardii0.4030.3221.2540.215
T.dregeana0.4650.402−1.1550.253
Treesize:Speciesln(DBH):C.sylvaticus0.3090.1352.2880.026
ln(DBH):S.gerrardii−0.1320.101−1.3050.198
ln(DBH):T.dregeana0.0810.1250.6440.523
3.2.MultispeciesDBHHeightandAGBAllometricModels
DBHandheightdatafittedwellwiththepowerlawmodelused(Figures2and3).Themodel
coefficients,indicatorsforgoodnessoffitandcorrectionfactorsofEquation(2)aresummarisedin
Table3.DBHexplained83.81%ofthevariationintotalheight,asshownbytheadjustedRsquare
value.Modelcoefficientswerehighlysignificant(p<0.001),indicatingthattreediameterwasa
significantpredictoroftreeheightforallspecies.
Thecomparisonofthethreefittedequations(Equations(3)–(5)forestimatingAGBisalso
showninTable3.Equation(3)producedthepoorestfits(nonexistenteffectofwooddensity,lowest
Rsquare,highestAIC,andhighestresidualstandardandrootmeansquareerrors),whileEquations
(4)and(5)providedthebestfits(highestvarianceexplainedandlowestresidualerrorsandAIC),
withanadditionalsignificanteffectofwooddensity(p<0.05).ComparedtoEquation(5),Equation
(4)provedlesssatisfactorybecauseitshowedhighvarianceinflationfactors(VIFs),especiallyforthe
correlatedvariablessuchasDBH(8.105)andheight(8.086).HighVIFsreflectcollinearitybetween
predictorsandunreliablecoefficients.Basedonthat,weconsideredthatEquation(5)(incorporating
DBH2×Hasasinglepredictor)providedthebestmultispeciesmodelforestimatingAGB.Taking
thatmodelintoaccount,98.45%ofthevariationinAGBwasexplainedbypositiveandsignificant
effectsofwooddensity(p<0.05)andDBH2×H(p<0.001),withanassociatedcorrectionfactorof
1.03.Thescatterplotoftheregressionresidualsvs.predictedvaluesdidnotshowany
heteroscedasticbehaviourfortheselectedmodel(Figure4).Inaddition,thetrendintheobserved
andestimatedvaluesofAGBshowedaverygoodcoincidencewiththelinearequation(y=x)
(Figure4).
Figure2.Variationofheightandabovegroundbiomass(AGB)accordingtotreediameter.
0 20406080100
5 10152025
DBH (cm)
Height (m)
0 20406080100
0 1000 3000 5000
DBH (cm)
Aboveground biomas s (kg)
Figure 2. Variation of height and aboveground biomass (AGB) according to tree diameter.
Forests 2016,7, 79 8 of 17
Table 3.
Multispecies DBH-height and aboveground biomass (AGB) equations with coefficient estimates and statistic fits. SE: Standard Error; R
2
: Adjusted R Square;
VIF: Variance Inflation Factor; RSE: Residual Standard Error; RMSE: Root Mean Squared Error; MAD: Mean Absolute Deviation; AIC: Akaike Information Criterion
and CF: Correction Factors.
Models Predictors Parameter Estimate SE pR2VIF RSE RMSE MAD AIC CF
Height
Equation (2) Intercept ln (β0) 1.01 0.03
<0.001
83.81 - 0.181 - - - 1.016
DBH β10.51 0.01
<0.001
AGB
Equation (3) Intercept ln (β0)´1.89 0.25
<0.001
97.60 - 0.304 0.296 6.78
30.490
1.047
Wood density β10.37 0.26 0.159 1.005
DBH β22.41 0.05
<0.001
1.005
Equation (4) Intercept ln (β0)´2.84 0.27
<0.001
98.44 0.245 0.236 4.75 7.230 1.030
Wood density β10.75 0.22 0.001 1.115
DBH β21.81 0.12
<0.001
8.105
Height β31.14 0.21
<0.001
8.086
Equation (5) Intercept ln (β0)´2.69 0.21
<0.001
98.45 0.244 0.237 3.32 6.074 1.030
Wood density β10.69 0.21 0.002 1.002
DBH2ˆHeight β20.95 0.02
<0.001
1.002
Height (m); DBH (cm); Wood density (g/cm3) and AGB (kg).
Forests 2016,7, 79 9 of 17
Forests2016,7,79 9 of 18
Figure3.Residualsvs.predictedvaluesandobservedvs.predictedvaluesoftotalheight(Equation
(2)).Originalunitsareinmetres.
Figure4.Residualsvs.predictedvaluesandobservedvs.predictedvaluesofabovegroundbiomass
(AGB).ValuesarepredictedfromEquation(5).Originalunitsareinkilogramsofdrymass.
3.3.AGBandAGCStocksattheStandLevel
ThetotalAGB,whenpoolingallenumeratedspeciestogether,wasestimatedas358.1±31.9
Mgha1witharangeof98.2–952.2Mgha1.Anapproximatecarbonstockof179.0±15.9MgCha1
wasestimatedfortheabovegroundcompartment.
WhenassessingthecontributionofspeciestothetotalpoolofAGBandAGC,wefoundthat
fewspecies(8outof50species)contributed80%.ThemainsubstantialcontributioncamefromS.
gerrardii(25.3%),Xymalosmonospora(15.4%),T.dregeana(12.8%)andC.kraussii(5.5%).C.sylvaticus,
whichisoneofthefocusspeciesinthisstudy,contributed4%ofthetotalAGBandAGCstocks.The
pattern,however,differedfromthatoftheforestunderstorylayer(5≤DBH<10cm),wheresome
newspeciessuchasCassipoureamalosana(Baker)Alston,KraussiafloribundaandOchnaarboreavar.
oconnorii(E.Phillips)DuToitcontributed30.2%oftheAGBandAGCstocks.Withinthatsamelayer,
S.gerrardiiandXymalosmonosporacontributed20.8%.
3.4.FactorsInfluencingAGCStocks
AGCrangedfrom49.1MgCha1to476.1MgCha1acrossplots.Analysesofthebivariate
relationshipsshowedthatstandbasalarea,basalareaofthetenmostimportantspeciesandbasal
areaofthelargesttrees(>60cmDBH)werethemostinfluencingstandvariables,with95%,77%and
59%variationexplained,respectively(Figure5).Thebasalareaofmediumtrees(30–60cmDBH)
andtreespeciesrichnesswerepositivelycorrelatedwiththeAGC(R2=0.24,p=0.006andR2=0.13,p
=0.04,respectivelyFigure5),butthebasalareaofsmallertrees(5–30cmDBH)wasnot(Figure5).
WhenallthestandvariablesthatweresignificantlycorrelatedwithAGCstoragewere
combinedinthemultiplelinearregressionmodel,onlystandbasalareaandthebasalareaofthe
1.0 1.5 2.0 2.5 3.0
-0.6 -0.2 0.2 0.4
Predic ted ln (height)
Residuals
1.01.52.02.53.0
1.0 1.5 2.0 2.5 3. 0
Predic ted ln (hei ght)
Observ ed ln (height)
02468
-0.4 0.0 0.4
Predicted ln(AGB)
Residuals
2468
02468
Predic ted ln(AGB)
Observ ed ln(AGB)
Figure 3.
Residuals vs. predicted values and observed vs. predicted values of total height (
Equation (2)
).
Original units are in metres.
Forests2016,7,79 9 of 18
Figure3.Residualsvs.predictedvaluesandobservedvs.predictedvaluesoftotalheight(Equation
(2)).Originalunitsareinmetres.
Figure4.Residualsvs.predictedvaluesandobservedvs.predictedvaluesofabovegroundbiomass
(AGB).ValuesarepredictedfromEquation(5).Originalunitsareinkilogramsofdrymass.
3.3.AGBandAGCStocksattheStandLevel
ThetotalAGB,whenpoolingallenumeratedspeciestogether,wasestimatedas358.1±31.9
Mgha1witharangeof98.2–952.2Mgha1.Anapproximatecarbonstockof179.0±15.9MgCha1
wasestimatedfortheabovegroundcompartment.
WhenassessingthecontributionofspeciestothetotalpoolofAGBandAGC,wefoundthat
fewspecies(8outof50species)contributed80%.ThemainsubstantialcontributioncamefromS.
gerrardii(25.3%),Xymalosmonospora(15.4%),T.dregeana(12.8%)andC.kraussii(5.5%).C.sylvaticus,
whichisoneofthefocusspeciesinthisstudy,contributed4%ofthetotalAGBandAGCstocks.The
pattern,however,differedfromthatoftheforestunderstorylayer(5≤DBH<10cm),wheresome
newspeciessuchasCassipoureamalosana(Baker)Alston,KraussiafloribundaandOchnaarboreavar.
oconnorii(E.Phillips)DuToitcontributed30.2%oftheAGBandAGCstocks.Withinthatsamelayer,
S.gerrardiiandXymalosmonosporacontributed20.8%.
3.4.FactorsInfluencingAGCStocks
AGCrangedfrom49.1MgCha1to476.1MgCha1acrossplots.Analysesofthebivariate
relationshipsshowedthatstandbasalarea,basalareaofthetenmostimportantspeciesandbasal
areaofthelargesttrees(>60cmDBH)werethemostinfluencingstandvariables,with95%,77%and
59%variationexplained,respectively(Figure5).Thebasalareaofmediumtrees(30–60cmDBH)
andtreespeciesrichnesswerepositivelycorrelatedwiththeAGC(R2=0.24,p=0.006andR2=0.13,p
=0.04,respectivelyFigure5),butthebasalareaofsmallertrees(5–30cmDBH)wasnot(Figure5).
WhenallthestandvariablesthatweresignificantlycorrelatedwithAGCstoragewere
combinedinthemultiplelinearregressionmodel,onlystandbasalareaandthebasalareaofthe
1.0 1.5 2.0 2.5 3.0
-0.6 -0.2 0.2 0.4
Predic ted ln (height)
Residuals
1.01.52.02.53.0
1.0 1.5 2.0 2.5 3. 0
Predic ted ln (hei ght)
Observ ed ln (height)
02468
-0.4 0.0 0.4
Predicted ln(AGB)
Residuals
2468
02468
Predic ted ln(AGB)
Observ ed ln(AGB)
Figure 4.
Residuals vs. predicted values and observed vs. predicted values of aboveground biomass
(AGB). Values are predicted from Equation (5). Original units are in kilograms of dry mass.
3.3. AGB and AGC Stocks at the Stand Level
The total AGB, when pooling all enumerated species together, was estimated as 358.1 ˘31.9 Mg¨ha´1
with a range of 98.2–952.2 Mg
¨
ha
´1
. An approximate carbon stock of 179.0
˘
15.9 Mg
¨
C
¨
ha
´1
was
estimated for the aboveground compartment.
When assessing the contribution of species to the total pool of AGB and AGC, we found that few
species (8 out of 50 species) contributed 80%. The main substantial contribution came from
S. gerrardii
(25.3%), Xymalos monospora (15.4%), T. dregeana (12.8%) and C. kraussii (5.5%). C. sylvaticus, which is
one of the focus species in this study, contributed 4% of the total AGB and AGC stocks.
The pattern
,
however, differed from that of the forest understory layer (5
ď
DBH < 10 cm), where some new
species such as Cassipourea malosana (Baker) Alston, Kraussia floribunda and Ochna arborea var. oconnorii
(
E. Phillips
) Du Toit contributed 30.2% of the AGB and AGC stocks. Within that same layer, S. gerrardii
and Xymalos monospora contributed 20.8%.
3.4. Factors Influencing AGC Stocks
AGC ranged from 49.1 Mg
¨
C
¨
ha
´1
to 476.1 Mg
¨
C
¨
ha
´1
across plots. Analyses of the bivariate
relationships showed that stand basal area, basal area of the ten most important species and basal area
of the largest trees (>60 cm DBH) were the most influencing stand variables, with 95%, 77% and 59%
variation explained, respectively (Figure 5). The basal area of medium trees (30–60 cm DBH) and tree
species richness were positively correlated with the AGC (R
2
= 0.24, p= 0.006 and R
2
= 0.13, p= 0.04,
respectively Figure 5), but the basal area of smaller trees (5–30 cm DBH) was not (Figure 5).
Forests 2016,7, 79 10 of 17
When all the stand variables that were significantly correlated with AGC storage were combined
in the multiple linear regression model, only stand basal area and the basal area of the largest trees
(>60 cm DBH) were retained in the final model and best (and positively) predicted AGC, overruling
the species richness effect.
Forests2016,7,7910of17
largesttrees(>60cmDBH)wereretainedinthefinalmodelandbest(andpositively)predictedAGC,
overrulingthespeciesrichnesseffect.
Figure5.Bivariaterelationshipsbetweenabovegroundcarbon(AGC)andstandvariables.
4.Discussion
4.1.EffectofSpeciesonWoodDensityandAGB
Thevaluesofwooddensityfoundinthisstudyareintherangeofpublishedvalues[40,45].The
betweenspeciesvariationofwooddensityindicatesthatthisvariableisadeterminantformultiple
speciesbiomassassessments[45].ByexaminingtheeffectsofspeciesonAGB,ourresultsshowed
thatspecieswithrelativelylowerwooddensityhadhighermeanaveragewoodbiomass.Thisisin
partbecausespecieswithlowerwooddensitytypicallygrowfasterthanspecieswithhigherwood
density[46].ThesignificanteffectsofspeciesonbothwooddensityandAGBdemonstratethat
speciesperformdifferentlyintermsofresourceacquisitionandbiomassproduction[47].Thefact
thatC.sylvaticushadhigherbiomassthanC.kraussii,S.gerrardiiandT.dregeanaresultsfromagreater
foliagebiomassproductionbyC.sylvaticus,whichseemstobeconsistentwiththespeciesleaftraits
[48].
20 40 60 80 100
1e+05 3e+05
Stand bas al area (m²/ha)
AGC (kg/ha)
R² = 0.95
P < 0. 001
20 40 60 80
1e+05 3e+05
Basal area of 10 most im portant spec ies (m²/ ha)
R² = 0.77
P < 0. 001
0 5 10 15 20 25
1e+05 3e+05
Basal area of largest trees (m²/ha)
AGC (kg/ha)
R² = 0.59
P < 0. 001
2.0 2.5 3.0 3.5
1e+05 3e+05
Basal area of medium siz ed trees (m ²/ha)
R² = 0.24
P = 0. 006
12345
1e+05 3e+0 5
Basal area of smaller trees (m²/ha)
AGC (kg/ha)
R² = 0.01
P = 0.625
6 8 10 12 14 16 18
1e+05 3e+0 5
Speci es richnes s
R² = 0.13
P = 0.04
Figure 5. Bivariate relationships between aboveground carbon (AGC) and stand variables.
4. Discussion
4.1. Effect of Species on Wood Density and AGB
The values of wood density found in this study are in the range of published values [
40
,
45
].
The between
species variation of wood density indicates that this variable is a determinant for multiple
species biomass assessments [
45
]. By examining the effects of species on AGB, our results showed
that species with relatively lower wood density had higher mean average wood biomass. This is in
part because species with lower wood density typically grow faster than species with higher wood
density [
46
]. The significant effects of species on both wood density and AGB demonstrate that
species perform differently in terms of resource acquisition and biomass production [
47
]. The fact that
C. sylvaticus
had higher biomass than C. kraussii,S. gerrardii and T. dregeana results from a greater foliage
biomass production by C. sylvaticus, which seems to be consistent with the species leaf traits [48].
4.2. Uncertainties in the Multispecies AGB Equations
The AGB equations used in this study were based on a non-destructive sampling of 59 trees from
four species. Commonly applied methods for accurate estimation of tree biomass rely on destructive tree
sampling and measurement of sample weights in the field [
19
,
22
,
23
]. Accordingly, the non-destructive
method used here might be a source of uncertainty in the allometric biomass prediction. However, in
Forests 2016,7, 79 11 of 17
conserved natural forests (where tree felling is not authorised), this particular non-destructive method
is the only available option.
Besides this, the representativeness of the selected species may be another source of uncertainty
for the multispecies biomass equations. Indeed, four out of fifty species might not be sufficient enough
to account for the variation in all species traits. Our methodological approach, which was based on
branch biomass modelling in order to reconstruct the crown biomass, required an acceptable number
and diameter range of branch samples; and due to the policy, time and resource constraints, it was
relatively difficult to explore many species. Nevertheless, to quantify the biomass stocks at the stand
level, we assumed that the inclusion of multiple predictors (e.g., diameter, height and wood density)
in the biomass equations would help to catch some variability in the characteristics of the species that
we could not sample in the field.
The actual sample size (59 trees) may not be entirely sufficient to account for the total variation
of the species characteristics, and thus can also constitute a caveat for the reliability of our results.
Larger sample data sets are often preferred, but are difficult to obtain especially for biomass studies
in natural forests, because of their status and the amount of work that is required for measuring
tree components [
49
]. For instance, Ebuy et al. [
49
], Deans et al. [
50
], Henry et al. [
51
] and Segura
and Kanninen [
52
] used 12, 14, 42 and 19 trees, respectively, to establish biomass allometric models.
Although our sample size is low, it is higher than most of the sample sizes that are used in tree biomass
studies in natural forests, with some exceptions, however [
35
,
53
,
54
]. In addition, most of the larger
data sets used in tree biomass studies in natural forests were obtained from a compilation of existing
smaller data sets [
12
]. To our knowledge, the functions presented here are the first functions published
for Mistbelt forests in South Africa.
4.3. Predictors for Multispecies AGB Equations
DBH, total height and wood density acted as potential predictors in the multispecies biomass
equations, consistent with what is expected, and as also revealed in many recent studies [
11
,
24
,
35
,
53
].
DBH appeared to be commonly used because it is the most familiar and easily measured variable
during national forest inventories. However, as pointed out by Fayolle et al. [
35
], tree diameter should
not be considered solely as a predictor for AGB, especially when dealing with several species or
a species that does not show a strong relationship between diameter and other tree characteristics
(e.g., tree height). Contrary to DBH, the main reason why total height is often left out in evergreen
tropical forests is because tree height is difficult to measure accurately within complex or closed canopy
forests [
35
]. For that reason, and given the lower marginal variance that is often explained by the
additional use of height, some authors advocate using DBH alone as a predictor of tree biomass [
35
,
52
].
In reality, if height is included in biomass models as an additional variable, then the implication
is that any error in measuring height will propagate to the tree level and further to stand level
estimates [
55
]. Even so, regarding the species-specific differences in DBH-height allometry, tree height
is an important factor for biomass estimation [
24
,
56
]. The inclusion of height as an additional variable
helps accounting for variation in AGB among trees with the same value of DBH [
24
], thus reducing
the estimate errors [
11
]. For instance, in this study, we found that the additional use of height reduced
the residual standard error by 19.73%. All being considered, for quantification of AGB stocks in
multispecies forests, we recommend that DBH and total height be measured for a reasonable number
of trees from different species in order to establish a multispecies DBH-height equation for estimation
of total height for each individual tree, and account for it in AGB models.
While the use of both DBH and height is expected to improve the statistical fits of biomass
equations, it is interesting to discuss how to allow for the simultaneous effects of DBH and height [
24
].
Depending on how diameter and height are correlated, models can give rise to collinearity [
24
,
34
],
which deserves particular attention. In line with this, the model that incorporated DBH
2ˆ
H as
a compound variable was the most parsimonious in that it helped accounting for within-species
variation of height for the same value of DBH while solving the problem of collinearity. DBH
2ˆ
H
Forests 2016,7, 79 12 of 17
is expected to be good predictor because it is directly proportional to the volume of a cylinder with
diameter (DBH) and height (H). Chave et al. [
11
] also found DBH
2ˆ
H was a suitable predictor for
tropical moist forest stand biomass. The results are also consistent with other recent studies that used
DBH2ˆH as a variable in biomass models [24,53].
Wood density was a good indicator of AGB, and thus for multispecies biomass equations [
12
].
The significant
effect of wood density indicates that within- and between-species wood density patterns
are important to explain biomass estimates. Between-species variation would, however, strongly
influence AGB because wood density affects tree specific growth and survival rate [
57
]. Accordingly, it
has been documented that lower wood density allows for faster growth in size [
46
,
58
], probably because
trees grow faster when the conductive tissue is less expensive (in terms of carbon) to construct [
45
,
59
].
The use of wood density is not common, although it is known to result in substantial improvement.
The availability of global and regional wood density data bases should help to promote the use of
that variable. However, the biomass estimates obtained by applying the values of wood density of
such data bases (as used in this study) are not exempt from uncertainty. Fayolle et al. [
35
] showed that
using wood density from the global data base slightly increased the estimation errors. Therefore, the
direct determination of wood density in the field is better for reliable biomass estimation, but is not
practical due to the considerable amount of field and laboratory work, especially when dealing with
several species.
4.4. AGB and AGC Stocks at the Stand Level
When pooling all enumerated species together, the total AGB and AGC values for a diameter
range above 5 cm across all plots were estimated as 358.1
˘
31.9 Mg
¨
ha
´1
and 179.0
˘
15.9 Mg
¨
C
¨
ha
´1
,
respectively. The average estimated AGB in the northern Mistbelt forests is higher than the biomass
estimates provided by Lung and Espira [
20
] for an African tropical forest (279
˘
32.78 Mg
¨
ha
´1
), but
relatively lower than AGB values in Amazonian forests (312–464 Mg
¨
ha
´1
) [
60
] and closed-canopy
tropical forests (395.7 Mg
¨
ha
´1
) [
61
]. Using the same diameter range, Fischer et al. [
62
] estimated
the total AGB as 385 Mg
¨
ha
´1
for a tropical forest on Mt. Kilimanjaro. These comparisons showed
that northern Mistbelt forests store substantially higher quantities of biomass and carbon stocks
than some montane and sub-montane tropical forests [
7
,
63
]. A simple explanation for this is that
across a wide range of their geological and altitudinal gradients [
13
], these northern Mistbelt forests
not only support a high floral diversity but also a structurally diverse horizontal and vertical forest
matrix [
14
,
64
].
As expected
, the (taxonomic) diversity of trees and the stand structural variables such
as basal area and percentage of large trees were found to explain a high variability of the estimated
biomass and carbon density, as also reported in recent studies. More specifically, it was found that
species richness was significantly and positively correlated with AGC. Indeed, it is a well-known
pattern, especially at the global scale, that carbon stocks increase with increasing diversity [
27
,
29
,
65
].
The positive relationships between tree species diversity and carbon stocks in this study supports
the idea that diversity-carbon patterns are also identifiable even at smaller spatial scales. In fact, tree
species diversity correlates positively with carbon storage because higher species richness probably
leads to higher stem density and higher forest productivity [
66
], i.e., basically, the more tree species
that are present in a plot, the more biomass is produced. While this seems to agree with the niche
complementarity hypothesis [
67
], the weak effect of tree species diversity on AGC suggests that
dominance patterns are likely to be stronger.
Despite the higher biomass production of C. sylvaticus at the tree level, our results showed that
S. gerrardii
was ranked first in terms of the relative contribution to AGB and AGC stocks. In addition,
few species (8 out of 50 species) proved to greatly contribute to the biomass and carbon stocks (80%).
These results reinforce the importance of other factors (e.g., stand related factors) influencing the
biomass partitioning among species. More specifically, we found an important contribution of stand
basal area, basal area of the ten most important species and basal area of the largest trees, in this
order. The greatest influence of dominant stems has been evidenced in some previous studies [
68
,
69
].
Forests 2016,7, 79 13 of 17
This information
corroborates the fact that dominance patterns greatly influence the AGC stocks, thus
supporting the selection effects hypothesis. In a recent study, Lung and Espira [
20
] showed that tree
stems larger than 50 cm have the greatest impact on forest biomass, and less than 16% of the species
pool accounted for over 62% of the AGB. Ruiz-Jaen and Potvin [
28
] and Cavanaugh et al. [
29
] also
showed that selection effects hypothesis contributes greatly to the carbon stocks.
4.5. Implications for Landscape Management and Further Perspectives
The lack of site-specific biomass equations limits accurate assessment of the carbon stocks.
Using the
best predictive model, we found that the estimated values of AGB in the northern Mistbelt
forests are comparable to values in other important tropical African forests, thus providing evidence of
their great potential for sequestration of CO
2
. Due to the necessity for prioritization of CO
2
mitigation
actions, it is important to consider the carbon storage potential of these Mistbelt forests in landscape
management planning. More specifically, the promotion of the conservation of Mistbelt forests will
reduce the risk of loss of biodiversity while enhancing carbon stocks and mitigating the impact of
global climate change, thus shedding light on the implications for REDD (Reducing Emissions from
Deforestation and Degradation) schemes. However, as taxonomic diversity only cannot elucidate
the mechanism behind biodiversity-carbon patterns, aspects of functional diversity and functional
dominance must be investigated. Evidence on this will help to determine the level of biodiversity
that is important for carbon storage. In addition, further research should regard how functional traits
influence the patterns of biomass allocation between plant organs and how interspecific competition
between trees for various resources [70] modifies the structure and hence ABG and AGC.
The highly variable biomass and carbon in the survey plots are supported by the strong role
of structural variables in the biomass and carbon partitioning, which could also be used to design
management and conservation plans. For instance, the conservation of dominant tree species or
trees with larger size (in terms of height or diameter) could be a strategy to increase carbon storage.
Further, the
unexpected lack of significant influence of smaller trees on the AGC can be used to
support the idea that optimal solutions exist for sustainable harvesting of stems in the Mistbelt
forests without risk for significant carbon decline. Because conserved lager trees will die over time,
an appropriate management strategy would consist of maintaining a balanced forest over time, by
imposing a harvest regime that would target both smaller and larger trees. This, however, calls for
more oriented research on long-term sustainability of stem and non-timber forest products harvesting,
which can result in
a win-win
strategy whereby local populations would benefit while conserving the
forests for biodiversity and their ecosystem services [71].
Few species principally contributed to the total AGB and AGC pool, probably as a result of
selection effects. These species likely dominate the forest stands, as revealed by the positive relationship
between dominant species and carbon stock. If protected from removal or conversion, such species
could provide a better long-term guarantee of ecosystem services. As these Mistbelt forests are
generally the most important natural components of the landscapes in the region, more research
studies should endeavour to elucidate the role of these species in the provision of other ecosystem
services. Due to the importance of pollination services for agricultural farms, particular aspects should
regard the potential of these forests to supply nesting and forage resources to wild pollinators and
managed honey bees.
Acknowledgments:
This research was financially supported by the National Research Foundation of South Africa
through the project “Catchman Letaba” in the RTF funding scheme and the African Forest Forum through the
research grant on “Land Use, Land Use Change and Forestry linked to Climate Change” provided to Sylvanus
Mensah. We acknowledge the contribution of the SHARE Intra-ACP project and the EU Marie Curie IRSES project
“Climate Fit Forests”. We are grateful to the environmental authorities for permission to carry out this research, to
Andrew Perkins for his assistance during the field work, and to the personnel of Komatiland forests and Hans
Merensky Holdings for logistical support. We also thank the two anonymous reviewers and the academic editor
for their valuable comments.
Forests 2016,7, 79 14 of 17
Author Contributions:
S.M. and T.S. conceived and designed the experiment; S.M. collected and analyzed the
data, and wrote the paper; R.G.K. advised and edited the text; T.S., R.V. and B.d.T. supervised the research project
and edited the text.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AGB
Aboveground biomass
AGC
Aboveground carbon
DBH
Diameter at breast height
H
Total height
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2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons by Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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... This study found significant positive effects of species richness on AGC stocks. A positive correlation between AGC and tree species richness in the LCF ecosystem is similar to other studies [44,98,100] who reported a positive association between species richness and woody dry biomass in temperate and tropical forests at small plot size (<1 ha). While this finding accords with some recent studies that controlled environmental variables [101,102], it also supports the commonly described pattern in the highly diverse natural forest: biomass and carbon stocks increases with an increase in diversity. ...
... Our carbon stock estimates can also be compared with those of other tropical montane forests (TMFs) ranging between 16.8 MgC/ha and 222.1 MgC/ha [113,114]. We found a significant correlation between woody plant species biodiversity and carbon storage, consistent with several other studies' findings [98,104,115,116]. ...
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... It is evident that carbon stock in all carbon pools in the forest tree plays a significant role in mitigating climate change, it is recommended that, plantation establishment, silvicultural treatment, regeneration and reforestation is a panacea for sustainable forest trees in removing carbon dioxide from atmosphere. Biomass and carbon stock are key information criteria to understanding the role of forest in regulating global climate [32]. A small fraction of carbon remaining in forest continuously accumulates in vegetation, detritus and soil [33]. ...
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... Nogueira et al. also reported that stand age has strong effects on AGB [64]. The continued decrease in AGB is strongly correlated with the continued growth of young stands in the forest [65]. ...
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The Combretum-Terminalia woodlands and wooded grasslands (CTW) are widely distributed in East Africa. While these landscapes may have the potential to act as key global carbon sinks, relatively little is known about their carbon storage capacity. Here we developed a set of novel aboveground biomass (AGB) models and tested for species and site variation effects to quantify the potential for CTW to store carbon. In total, 321 trees were sampled from 13 dominant tree species, across three sites in the Northwest lowlands of Ethiopia. Overall, fitted species-specific models performed the best, with diameter at breast height explaining 94–99% of the AGB variations. Interspecific tree allometry differences among species were more substantial than intraspecific tree allometry among sites. Incorporating wood density and height in the mixed-species models significantly improved the model performance relative mean absolute error (MAPE) of 2.4–8.0%, while site variation did not affect the model accuracy substantially. Large errors (MAPE%) were observed when using existing pantropical models, indicating that model selection remains an important source of uncertainty. Although the estimates of selected site-specific models were accurate for local sites, mixed-species and species-specific models performed better when validation data collated from different sites were incorporated together. We concluded that including site- and species-level data improved model estimates of AGB for the CTW of Ethiopia.
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Various studies have shown that plant species richness can promote ecosystem functions such as biomass storage. However, it is less well known whether this is mostly driven by the dominance of a few species and their associated traits (functional identity), or by complementarity among species that highly vary in their traits (functional diversity). The relative contribution of functional diversity and functional identity on biomass and carbon storage may in part depend on the type of functional traits that are considered, and on ecosystem type. Here, we used forest inventory data from West African semi‐arid environments, and functional traits (wood density and tree maximum height) to examine the effects of functional trait identity (FI or community weighted mean; CWM) and diversity (FD or single functional divergence; FDvar) on aboveground carbon (AGC) storage in both forests and savannas. We fitted simple linear and structural equation models to test the direct and indirect effects of functional traits on AGC, while accounting for potential effects of vegetation stand structure such as stand density and basal area. When evaluated independently, CWM of tree maximum height and FDvar of wood density correlated positively with AGC, in both forests and savannas, whereas species richness was unrelated to AGC. However, structural equation models indicated different mechanisms by which these biodiversity components drove AGC in forests and savannas. In forests, species richness had an indirect, positive effect on AGC via basal area, but also an indirect, negative effect, through a reduction in CWM of maximum height. In savannas, species richness had a direct, negative effect on AGC, while both CWM of maximum height (through an increase in basal area) and FDvar of wood density had positive effects. Our study suggests that integrative models are crucial for understanding the effects of species richness, functional trait diversity, and identity on AGC across forests. Furthermore, our study shows that relationships between biodiversity and AGC differ among ecosystem types. In both forests and savannas, FI played an important role, as AGC was maximized in communities dominated by species with a high maximum height. However, only in savannas a high FD additionally promoted AGC.
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Biomass allocation is closely related to species traits, resources avail- ability and competitive abilities, and therefore it is often used to capture resource utilisation within plants. In this study, we searched for patterns in biomass alloca- tion between foliage and wood (stem plus branch), and how they changed with tree size (diameter), species identity and functional traits (leaf area and specific wood density). Using data on the aboveground biomass of 89 trees from six species in a Mistbelt forest (South Africa), we evaluated the leaf to wood mass ratio (LWR). The effects of tree size, species identity and specific traits on LWR were tested using Generalised Linear Models. Tree size (diameter) was the main driver of bio- mass allocation, with 44.43 % of variance explained. As expected, LWR declined significantly with increasing tree diameter. Leaf area (30.17% explained variance) and wood density (12.61% explained variance) also showed significant effects, after size effect was accounted for. Results also showed clear differences among species and between groups of species. Per unit of wood mass, more biomass is allocated to the foliage in the species with the larger leaf area. Inversely, less bio- mass is allocated to the foliage in species with higher wood density. Moreover, with increasing diameter, lower wood density species tended to allocate more biomass to foliage and less biomass to stems and branches. Overall, our results emphasise the influence of plant size and functional traits on biomass allocation, but showed that neither tree diameter and species identity nor leaf area and wood density are the only important variables.
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In the water-scarce environment of South Africa, drought-tolerant eucalypt species have the potential to contribute to the timber and biomass resource. Biomass functions are a necessary prerequisite to predict yield and carbon sequestration. In this study preliminary biomass models for Eucalyptus cladocalyx, E. gomphocephala and E. grandis × E. camaldulensis from the dry West Coast of South Africa were developed. The study was based on 33 trees, which were destructively sampled for biomass components (branchwood, stems, bark and foliage). Simultaneous regression equations based on seemingly unrelated regression were fitted to estimate biomass while ensuring additivity. Models were of the classical allometric form, ln(Y) = a + x1ln(dbh) + x2ln(h), of which the best models explained between 70% and 98% of the variation of the predicted biomass quantities. A general model for the pooled data of all species showed a good fit as well as robust model behaviour. The average biomass proportions of the stemwood, bark, branches and foliage were 60%, 6%, 29% and 5%, respectively.
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The viability and effectiveness of forestry-based carbon projects depends on the accurate estimation of carbon stocks as well as understanding the variables that influence carbon stocks, including human activity. However, for much of Africa, accurate, ground-based measurements are few, forests have centuries of human use, and the variables that influence forest biomass and carbon stocks are not well understood. The objectives of this study were to use field-based sampling to estimate aboveground biomass (and associated carbon stocks), and explore AGB variation in relation to forest stand characteristics and human biomass removal in an African tropical forest that lies in a transitional ecological zone between lowland equatorial and moist montane forest. We quantified tree densities, estimated plot aboveground biomass (AGB) using pan-tropical allometric equations, and estimated human biomass removal in four regions of Kakamega Forest in western Kenya that varied in historical and current human use. Our carbon stock estimate (173.3 MgC ha−1) was above large biome averages for moist tropical forests. Diameter of the largest tree, stem densities of large trees, and wood density were the most important variables influencing biomass, accounting for over 75% of the variation. While historical disturbance influenced biomass removal, we did not find that this disturbance or biomass removal significantly influenced AGB. The reason was that most biomass removal involves trees in small size classes, which are insignificant contributors to biomass. Our findings provide a field based estimate of carbon stocks for a transitional tropical forest. We also conclude that processes (both natural and anthropogenic) which influence the presence and health of species which can attain large size will have the greatest impact on biomass, while processes that influence stem densities of small trees will have no measurable effect. The application of these findings for forestry-based carbon projects would translate to the need for special attention being paid to activities which prevent or minimize loss of large dense trees (e.g. charcoal production, selective logging) if the carbon stock of a forest is to be maintained. On the other hand, activities which limit subsistence use in terms cutting small stems (e.g. energy efficient stoves), might not have measureable immediate effects on carbon stocks and the financial outcomes associated with the generation and sale of the carbon credits.