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The relationship between biodiversity and ecosystem function has increasingly been debated as the cornerstone of the processes behind ecosystem services delivery. Experimental and natural field-based studies have come up with nonconsistent patterns of biodiversity–ecosystem function, supporting either niche complementarity or selection effects hypothesis. Here, we used aboveground carbon (AGC) storage as proxy for ecosystem function in a South African mistbelt forest, and analyzed its relationship with species diversity, through functional diversity and functional dominance. We hypothesized that (1) diversity influences AGC through functional diversity and functional dominance effects; and (2) effects of diversity on AGC would be greater for functional dominance than for functional diversity. Community weight mean (CWM) of functional traits (wood density, specific leaf area, and maximum plant height) were calculated to assess functional dominance (selection effects). As for functional diversity (complementarity effects), multitrait functional diversity indices were computed. The first hypothesis was tested using structural equation modeling. For the second hypothesis, effects of environmental variables such as slope and altitude were tested first, and separate linear mixed-effects models were fitted afterward for functional diversity, functional dominance, and both. Results showed that AGC varied significantly along the slope gradient, with lower values at steeper sites. Species diversity (richness) had positive relationship with AGC, even when slope effects were considered. As predicted, diversity effects on AGC were mediated through functional diversity and functional dominance, suggesting that both the niche complementarity and the selection effects are not exclusively affecting carbon storage. However, the effects were greater for functional diversity than for functional dominance. Furthermore, functional dominance effects were strongly transmitted by CWM of maximum plant height, reflecting the importance of forest vertical stratification for diversity–carbon relationship. We therefore argue for stronger complementary effects that would be induced also by complementary light-use efficiency of tree and species growing in the understory layer.
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Ecology and Evoluon 2016; 1–12 www.ecolevol.org
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1
© 2016 The Authors. Ecology and Evoluon
published by John Wiley & Sons Ltd.
Received: 22 April 2016 
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  Revised: 1 September 2016 
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  Accepted: 4 September 2016
DOI: 10.1002/ece3.2525
Abstract
The relaonship between biodiversity and ecosystem funcon has increasingly been
debated as the cornerstone of the processes behind ecosystem services delivery.
Experimental and natural eld- based studies have come up with nonconsistent pat-
terns of biodiversity–ecosystem funcon, supporng either niche complementarity or
selecon eects hypothesis. Here, we used aboveground carbon (AGC) storage as
proxy for ecosystem funcon in a South African mistbelt forest, and analyzed its rela-
onship with species diversity, through funconal diversity and funconal dominance.
We hypothesized that (1) diversity inuences AGC through funconal diversity and
funconal dominance eects; and (2) eects of diversity on AGC would be greater for
funconal dominance than for funconal diversity. Community weight mean (CWM) of
funconal traits (wood density, specic leaf area, and maximum plant height) were
calculated to assess funconal dominance (selecon eects). As for funconal diver-
sity (complementarity eects), multrait funconal diversity indices were computed.
The rst hypothesis was tested using structural equaon modeling. For the second
hypothesis, eects of environmental variables such as slope and altude were tested
rst, and separate linear mixed- eects models were ed aerward for funconal di-
versity, funconal dominance, and both. Results showed that AGC varied signicantly
along the slope gradient, with lower values at steeper sites. Species diversity (richness)
had posive relaonship with AGC, even when slope eects were considered. As pre-
dicted, diversity eects on AGC were mediated through funconal diversity and func-
onal dominance, suggesng that both the niche complementarity and the selecon
eects are not exclusively aecng carbon storage. However, the eects were greater
for funconal diversity than for funconal dominance. Furthermore, funconal domi-
nance eects were strongly transmied by CWM of maximum plant height, reecng
the importance of forest vercal stracaon for diversity–carbon relaonship. We
therefore argue for stronger complementary eects that would be induced also by
complementary light- use eciency of tree and species growing in the understory layer.
KEYWORDS
carbon stock, community weight mean, funconal richness, maximum plant height, niche
complementarity, structural equaon modeling
1Department of Forest and Wood
Science, Stellenbosch University, Maeland,
South Africa
2Laboratoire de Biomathémaques et
d’Esmaons Foresères, Université
d’Abomey-Calavi, Cotonou, Bénin
3South African Naonal Biodiversity
Instute, Kirstenbosch Research Centre,
Claremont, South Africa
4Department of Conservaon Ecology
and Entomology, Stellenbosch University,
Maeland, South Africa
5Laboratoire d’Ecologie
Appliquée, Université d’Abomey-Calavi,
Cotonou, Bénin
Correspondence
Sylvanus Mensah, Department of Forest
and Wood Science, Stellenbosch University,
Maeland, South Africa.
Email: sylvanus.m89@gmail.com
Funding informaon
SHARE INTRA-ACP project; Naonal
Research Foundaon of South Africa;
African Forest Forum
ORIGINAL RESEARCH
Tree species diversity promotes aboveground carbon storage
through funconal diversity and funconal dominance
Sylvanus Mensah1,2| Ruan Veldtman3,4| Achille E. Assogbadjo5|
Romain Glèlè Kakaï2| Thomas Seifert1
This is an open access arcle under the terms of the Creave Commons Aribuon License, which permits use, distribuon and reproducon in any medium,
provided the original work is properly cited.
2 
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   MENSAH ET AL.
1 | INTRODUCTION
The relaonship between biodiversity and carbon storage is being
debated as one of the current ecological topics (Cavanaugh et al., 2014;
Day, Baldauf, Rushauser, & Sunderland, 2014; Ruiz- Benito et al., 2014;
Ruiz- Jaen & Potvin, 2011), and some aspects of climate- related eects
have been well invesgated (Durán, Sánchez- Azofeifa, Rios, & Gianoli,
2015; Wu et al., 2015). Because biomass is an important component
of forest stand producvity, the relaonship between biomass carbon
and biodiversity can also be assimilated to the one of biodiversity and
ecosystem funcon (Lasky et al., 2014). Basically, two well- debated
mechanisms are commonly used to explain the role of plant diversity
in ecosystem resource dynamics, ecosystem processes, and funcons:
niche complementarity eects and selecon eects (Dı́az & Cabido,
2001; Tilman et al., 1997); the niche complementary eects hypoth-
esis assumes increasing diversity would promote greater variety of
funconal traits and provide opportunies to species to eciently use
the available resources, thereby increasing ecosystem funcon; the
selecon eects hypothesis suggests that in ecosystem with higher
diversity, there would be a higher probability of occurrence of domi-
nant species or traits that inuence ecosystem funconing. Currently,
great research eorts are made to elucidate how diversity components
(taxonomic diversity, funconal diversity, and funconal dominance)
drive biomass and carbon stocks, and the extent to which the ndings
support niche complementarity and selecon eects hypotheses.
Taxonomic diversity, expressed by species richness and alpha-
diversity indices, has been commonly used as a simple measure of
biodiversity (Mayeld et al., 2010; Tilman et al., 1997) and has been
shown to correlate posively with carbon stocks. However, because a
new species—with dierent funconal traits—added to an ecosystem
would likely contribute to the physiological processes, the eects of
taxonomic diversity on carbon storage could be treated as dierent
eects of funconal diversity (accounng for niche complementarity)
or/and funconal dominance (comprising selecon eects). The func-
onal diversity is known as “the value and range of funconal traits
of the organisms present in a given ecosystem”(Dı́az & Cabido, 2001,
pp 654) and therefore might be the starng point of elucidang the
mechanisms underlying the relaon between biodiversity and carbon
(Cadoe, Carscadden, & Mirotchnick, 2011; Naeem, 2002). Yet, some
recent reviews showed controversy in the relaonship between tax-
onomic and funconal diversity (Mayeld et al., 2010; Naeem, 2002;
Song, Wang, Li, & Zhou, 2014). On the one hand, following Tilman
et al. (1997) and Mouchet, Villéger, Mason, and Mouillot (2010), func-
onal diversity was posively correlated with species richness, and in
this case, taxonomic diversity can simply be used to replace funconal
diversity. On the other hand, it was pointed out that land use, the local
species pool, etc. could also inuence the relaonship between func-
onal and taxonomic diversity (Cadoe et al., 2011; Mayeld et al.,
2010). Consequently, whether diversity (species richness) eects on
ecosystem funcon are fully mediated by funconal diversity or code-
termined by selecon eects (dominance paerns) is sll well debated.
In tropical natural forests, where several species cohabit and fulll the
major ecosystem funcons, it is common to observe the abundance
and dominance of highly producve tree species, thus increasing the
chances that diversity–carbon relaonships are mediated by selec-
on eects. This was partly conrmed by our previous observaons
in South African mistbelt forests, especially the greater inuence of
the most dominant species on biomass stocks (Mensah, Veldtman,
du Toit, Glèlè Kakaï, & Seifert, 2016). More and more, research tends
to show how funconal diversity and/or funconal dominance play
a major role in ecosystem funconing (Baraloto et al., 2012; Clark,
Flynn, Buereld, & Reich, 2012; Ruiz- Jaen & Potvin, 2011; Song
et al., 2014). Understanding whether diversity eects on ecosystem
funcon are more likely mediated through funconal diversity than
funconal dominance, or vice versa, will bring substanal insights into
which mechanism is more relevant.
Very few studies have addressed the relaonships between diver-
sity and ecosystem funcon in natural mulspecies tropical forests.
Using aboveground tree carbon data in a northern mistbelt forest in
South Africa, we examined the relaonship between diversity and car-
bon stocks through the eects of funconal diversity and funconal
dominance. We hypothesized that (1) diversity inuences tree carbon
storage through both funconal diversity and funconal dominance
eects. However, there are insights that diversity and carbon relaon-
ships can be caused by covarying environmental factors (Cavanaugh
et al., 2014; Ouyang et al., 2016). Therefore, we considered altude
and slope as the most physical gradients in these forests, and tested
their eects on tree carbon storage. In addion, while accounng for
signicant environmental gradient eects, we also hypothesized that
(2) eects of diversity on carbon storage would be greater for func-
onal dominance than for funconal diversity.
2 | MATERIALS AND METHOD
2.1 | Study area
This study was carried out in the northern mistbelt forests in the
Limpopo Province, South Africa. These forests are generally found as
large patches on steep eastern slopes in the province (Geldenhuys,
1997, 2002). The site selected for this study was the Woodbush-De
Hoek nave forest complex (23°50′S, 30°03′E) near Tzaneen. The
area is characterized by an altudinal gradient from 1,050 to 1,800 m
above mean sea level and an annual rainfall ranging from 600 mm to
1,800 mm (Geldenhuys, 2002). Pine plantaons are established in the
surrounding environment by the State Department of Water Aairs
and Forestry and transferred to forest companies for commercial m-
ber producon. The main sectors for the management policy in the
landscape are mber producon, nature conservaon, and recrea-
on (hiking). The vegetaon in the Woodbush-De Hoek nave forest
is dominated by canopy and above canopy species such as Xymalos
monospora, Podocarpus lafolius, Syzygium gerrardii, and Cryptocarya
transvaalensis (Mensah, Glèlè Kakaï, & Seifert, 2016). The understory
vegetaon is represented by species such as Oxyanthus speciosus,
Peddiea africana, and Kraussia oribunda (Geldenhuys, 1997).
    
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 3
MENSAH ET AL.
2.2 | Forest sampling and aboveground carbon data
Stand data (species, canopy layer, tree density, basal area) were
obtained by means of a straed random sampling design set in a
707.612 ha (hectare) forest block in the Woodbush-De Hoek for-
est. The sampling design consisted of 30 replicates of 250 m2 circular
subplots, each inside a 500 m2 circular larger plot. These plots were
laid out in straed compartments obtained by subdividing the re-
search area on the basis of three classes of slope: at (1.5%–15.3%),
gentle (15.3%–29.19%), and steep (29.19%–43.1%); four classes of
aspect (North, South, West, and East); and three classes of eleva-
on: low (1,174–1,332 m a.s.l.), medium (1,332–1,490 m), and high
(1,490–1,648 m). Inside 250 m2 plots, species names, diameter at
breast height (dbh), and height of trees belonging to 5–10 cm dbh
were recorded, while only individuals having more than 10 cm dbh
were tagged and measured within the larger plots.
We used the mulspecies allometric biomass equaon developed
for the northern mistbelt forests (Mensah, Veldtman, du Toit, Glèlè
Kakaï, & Seifert, 2016) to calculate the aboveground biomass (AGB)
for all individual trees present in the plots. The allometric equaon
provided more accurate esmated biomass values, compared with the
exisng pantropical biomass equaon (Chave et al., 2005; Mensah,
Veldtman, & Seifert, 2016). The formula for the allometric biomass
equaon is as follows:
where AGB stands for the aboveground tree biomass in kilograms,
SWD the specic wood density (g/cm3), DBH the diameter at breast
height (cm), and H the total height (m). AGB was computed for each
individual tree, upscaled to plot and stand level for each diameter class
(i.e., for 5–10 cm dbh in the 30 smaller plots and for ≥10 cm dbh in the
30 larger plots), and summed up to obtain the values for dbh > 5cm.
Carbon values were determined aerward, by mulplying the abo-
veground biomass by a factor of 0.5 (Lung & Espira, 2015).
2.3 | Diversity and dominance metrics
Diversity was measured using taxonomic diversity, at each plot.
We used species richness to characterize the taxonomic diversity
(Magurran, 1988). Species richness at plot level is simply dened as
the number of disnct species enumerated inside each plot. To assess
funconal diversity, we considered the funconal traits that are rel-
evant to the ecosystem funcon of interest (i.e., biomass and carbon
storage). Because carbon storage is strongly dependent on wood and
foliage structures, we used traits such as specic wood density (WD),
specic leaf area (SLA), and maximum plant height (PHm). Data on
specic wood density were obtained from the Global Wood Density
Database (Zanne et al., 2009). In case mulple values were available
for a single species, the average wood density was used. When a par-
cular species was missing, we used the average genus wood density.
SLA and PHm were extracted from the TRY database (Kage et al.,
2011). As funconal diversity metrics, we esmated funconal rich-
ness (Fric), funconal evenness (Feve), funconal divergence (Fdiv),
funconal dispersion (Fdis), and Rao quadrac entropy (RaoQ) at each
plot (Baraloto et al., 2012; Cavanaugh et al., 2014; Finegan et al.,
2015; Villéger, Mason, & Mouillot, 2008), using the values of the
funconal traits with the “FD” package in R (Laliberté, Legendre, &
Shipley, 2015). These diversity indices are multrait funconal diver-
sity metrics that combine both the relave weight of each species and
the pairwise funconal dierence between species. A review of these
measures can be found in study by Mouchet et al. (2010).
Funconal dominance was assessed by esmang the plot- level com-
munity weight mean (CWM) for each funconal trait. CWM is the mean
of each species trait value weighted by the relave abundance (stem
number) of that species (Cavanaugh et al., 2014). The per- plot CWM was
esmated for WD, SLA, and PHm, again using the “FD” package in R.
2.4 | Data analyses
Here, we tested two hypotheses: (1) diversity eects on carbon stor-
age are mediated through both funconal diversity and funconal
dominance eects; and (2) eects of diversity on carbon storage are
greater for funconal dominance than for funconal diversity. The
rst hypothesis was tested using structural equaon models (SEM),
while the second hypothesis was tested using linear mixed- eects
models. For both SEM and linear mixed- eects models, AGC data
were log- transformed to meet the normality assumpon (Shapiro–
Wilk stasc = 0.97, p- value = .628).
2.4.1 | Structural equaon modeling
SEM oers the possibility to test hypothesized paerns of direct and
indirect relaonships among the measured variables. This is parcu-
larly important, as we assumed that the diversity eects would be
transmied through both funconal diversity and funconal domi-
nance. Therefore, we examined the indirect and direct eects of di-
versity (species richness) on aboveground carbon. We constructed
two separate structural equaon models represenng (1) full media-
on: Diversity eects are fully transmied by funconal diversity and
dominance metrics; and (2) paral mediaon: There are both direct
and indirect diversity eects through funconal diversity and func-
onal dominance metrics. Due to the presence of mulple measures
for funconal diversity, we used stepwise selecon techniques to
select the most relevant funconal diversity metrics for the above-
ground carbon data. As a result, only funconal richness (Fric) and
funconal evenness (Feve) were selected (p- value <.05). We did not
deny the potenal eects of environmental variables on the species
diversity and aboveground tree carbon. Nevertheless, we believe that
such eects could be beer assessed in mixed modeling approach (ad-
dressed in the next paragraph), thus simplifying the outputs of the
SEMs. The overall t of the SEMs was assessed using χ2 – square test
(a p- value >.05 would indicate an absence of signicant deviaons be-
tween data and model), the comparave t index (CFI), and the Akaike
informaon criterion (AIC) (Grace & Bollen, 2005). We used the stand-
ardized coecients to allow direct comparisons across paths (Grace &
Bollen, 2005). SEMs were ed in the R stascal soware package
AGB
=
1.03
×
exp(
2.69
+
0.69
ln(SWD)
+
0.95
ln(DBH2
H
))
4 
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   MENSAH ET AL.
(R Development Core Team 2015), using the “sem” funcons in the
“lavaan” package version 0.5–19 (Rosseel, 2012).
2.4.2 | Linear mixed- eects models
Prior to the mixed- eects modeling, we tested for potenal environ-
mental variables and species richness eects on aboveground carbon
storage. Environmental variables are expected to have eects on plant
structures, growth, and survival (Mensah, Houehanou, Sogbohossou,
Assogbadjo, & Glèlè Kakaï, 2014; Wang, Fang, Tang, & Zhu, 2006)
and hence on standing aboveground biomass and carbon stocks.
Here, we focused on the variables that are determinant and quan-
able in the area, that is, topography (slope and altude) (Geldenhuys,
1997, 2002). Other environmental variables such as temperature and
precipitaon have also been proved to have much inuence on pro-
ducvity, biomass, and carbon stocks (Cavanaugh et al., 2014; Durán
et al., 2015), but were not explored here mainly because of their una-
vailability at the small scale in this study. Topography was character-
ized by classifying the slope and elevaon in three categorical levels.
Slope was categorized as at (low slope), gentle (intermediate slope),
and steep (high slope). As for elevaon, low, medium, and high cat-
egories were considered. Simple linear models were performed to test
for slope and elevaon eects on aboveground carbon storage. As a
result, only the slope showed slightly signicant impact on the car-
bon stock and therefore was considered for further analyses. Mulple
linear regressions were also ed on slope and species richness to
test their eects on aboveground carbon storage. For both simple
and mulple linear models, Shapiro–Wilk tests were used to check
for the normality of the log- transformed AGC data and of the residu-
als. Addionally, Breusch–Pagan tests and Durbin–Watson stascs
were used to test for homoscedascity and autocorrelaon between
residuals, respecvely.
We next examined the relaonship of each diversity component
(i.e., funconal diversity and funconal dominance) with carbon stor-
age, by ng separate linear mixed- eects models (Zuur, Ieno, Walker,
Saveliev, & Smith, 2009). We considered species richness and slope
as random factors, and each measure of funconal diversity (i.e., Fric,
Feve, Fdiv, Fdis, and RaoQ) and of funconal dominance (i.e., CWM
of WD, SLA, and PHm) as xed eects. The mixed- eects models
were ed to assess (1) individual eect of each funconal diversity
and funconal dominance metric; (2) combined eects of funconal
diversity metrics; (3) combined eects of funconal dominance met-
rics; and (4) combined eects of funconal diversity and funconal
dominance metrics. The best models were selected by performing a
backward eliminaon of nonsignicant eects (p- value >.05). The lin-
ear mixed- eects models were performed using the “lmer” funcon of
the “lmerTest” package (Kuznetsova, Brockho, & Christensen, 2016)
of the R stascal soware. The p- values reported were calculated
from the F test based on Saerthwaite approximaons to the de-
grees of freedom, in the package “lmerTest” (Kuznetsova et al., 2016).
The signicance of the random eects was assessed using likelihood
rao (LR) test, again in the package “lmerTest”. The performance of
ed models was assessed based on the t stascs such as Akaike
informaon criterion (AIC) and the marginal R square, which indicates
the proporon of variance explained by xed eects (Nakagawa &
Schielzeth, 2013).
3 | RESULTS
A total of 50 plant species were enumerated, belonging to 46 genera
and 33 families. The dominant families were Rutaceae (ve species),
Rubiaceae (four species), Slbaceae (three species), and Celastraceae
(three species). The number of species per plot, for trees ≥5 cm dbh,
ranged from ve species to 18 species, with an average species rich-
ness of 11 species per plot. Tree number varied from 19 to 67 stems,
with an average value of 42 stems per plot. The amount of above-
ground carbon was highly variable across all plots, and ranged from
49.1 MgC/ha to 476.1 MgC/ha, with an esmated average value of
179 MgC/ha.
3.1 | Diversity eects mediated through funconal
diversity and funconal dominance
The outputs of the structural equaon models ed to assess the me-
diated eects of diversity (species richness) on AGC, through func-
onal diversity and funconal dominance, are summarized in Table 1
and Figure 1. The rst model “full mediaon” had chi- square value of
11.59 (df = 7; p = .115), indicang good t and absence of signicant
deviaons between data and model.
In the “full mediaon” model, species richness showed a signi-
cant posive direct eect on funconal richness (R2 = 0.47; β = 0.69;
p < .001; Table 1), which also showed posive and signicant eect
on AGC (β = 0.47; p = .002; Table 1). Therefore, species richness,
through funconal richness, had a posive signicant eect on AGC
(β = 0.69*0.47 = 0.32). There was a nonsignicant eect of spe-
cies richness on funconal evenness (β = 0.02; p = .926; Table 1);
the laer, however, exhibited a signicant negave eect on AGC.
In addion, we found no signicant correlaon between funconal
richness and funconal evenness (β = 0.29; p = .090; Table 1), which
would suggest that the mediated eects of species richness were
transmied by funconal richness only. Among the funconal dom-
inance metrics, the CWM of maximum plant height did not retain any
signicant path. Only the CWM of wood density showed signicant
responses to species richness (R2 = 0.15; β = 0.38; p = .028), but did
not signicantly inuence the AGC (p = .275). In contrast, the CWM
of SLA had a negave signicant eect (β = −0.37; p = .039; Table 1)
on AGC, although not signicantly inuenced by species richness
(p = .324). The signicant residual correlaon between CWM of wood
density and CWM of SLA (β = 0.45; p = .003; Table 1) suggests that
the mediated eects of species richness are also transmied by these
two factors.
Note that the “paral mediaon” model was ed by only adding a
direct path from species richness to AGC to the “full mediaon” model.
The chi- square value for the “paral mediaon” model was 7.57 with
6 degrees of freedom and a p value of .272, also indicang good t.
    
|
 5
MENSAH ET AL.
There are similaries between the two models in terms of signicant
and nonsignicant paths (Table 1), but the “paral mediaon” model
exhibited slightly beer ts (CFI = 0.932; R2 = 0.52; AIC = 304.2) than
the “full mediaon” model (CFI = 0.977; R2 = 0.45; AIC = 306.2). The
added causal path from species richness to AGC was slightly signif-
icant at 0.05, suggesng an exisng true direct eect of diversity
on AGC. Both models suggest that species richness eects on abo-
veground carbon are mediated through funconal diversity and func-
onal dominance.
3.2 | Eects of environmental variables, funconal
diversity, and funconal dominance on carbon storage
Not surprisingly, there were signicant eects of the environmental
variables, especially the slope which explained 14.05% of the varia-
on of the aboveground carbon (Table 2). Low slope showed regres-
sion coecient which was 0.53 signicantly higher than the baseline
(higher slope), whereas intermediate slope was not. This indicates that
carbon stock was signicantly higher at low slope sites than high slope
sites. Unlike slope, altude did not have any signicant inuence on
the aboveground tree carbon (F- stasc = 1.381; p = .268; Table 2).
Furthermore, while accounng for the eects of the slope, we also
found that species richness was signicant and showed a posive re-
laonship with AGC (β = 0.06; p = .016; Table 2).
The results of the separate linear mixed- eects models tesng the
individual eects of funconal diversity metrics revealed that only Feve
was signicant, and had a negave eect on AGC (β = −1.6; p = .037;
Table 3). Fdis, Fdiv, and RaoQ showed high values of probability (from
0.359 to 0.528), while Fric had a slightly signicant and posive eect
on AGC (p = .079; Table 3). While assessing the combined eects of
funconal diversity metrics, we found that Fdis, Fdiv, and RaoQ were
le out aer backward selecon for the nal model (Table 3). The ef-
fects of funconal diversity on AGC were thus shown by a signicant
posive eect of funconal richness (β = 135.6; p = .013; Table 3)
and a signicant negave eect of funconal evenness (β = −2.03;
p = .006; Table 3). Both funconal richness and evenness explained
27% of the variance of AGC.
All the three funconal dominance metrics used in this study
showed signicant eects on the aboveground carbon (Table 4).
Both CWM of SLA and CWM of WD showed negave eects, while
CWM of maximum plant height exhibited a posive eect (Table 4).
However, when assessing their combined eects on AGC, CWM of
SLA was not retained by the nal model, and the eects of funconal
dominance were only shown by posive and signicant eects of
TABLE1 Results of the structural equaon modeling carried out to test the eects of species richness on carbon stocks (AGC) via funconal
diversity and funconal dominance
Est.std SE Zp- value Est.std SE Zp- value
Full mediaon Paral mediaon
Regressions
Path from species richness to Fric 0.69 0.14 5.02 <.001 0.69 0.14 5.02 <.001
Path from species richness to Feve 0.02 0.19 0.09 .926 0.02 0.19 0.09 .926
Path from species richness to CWM (PHm) 0.06 0.19 0.32 .750 0.06 0.19 0.32 .750
Path from species richness to CWM (SLA) −0.18 0.19 −0.99 .324 −0.18 0.19 −0.99 .324
Path from species richness to CWM (WD) 0.38 0.18 2.20 .028 0.38 0.18 2.20 .028
Path from Fric to AGC 0.47 0.16 3.04 .002 0.24 0.19 1.27 .203
Path from Feve to AGC −0.39 0.14 −2.70 .007 −0.38 0.14 −2.75 .006
Path from CWM (PHm) to AGC −0.10 0.22 −0.46 .642 −0.16 0.21 −0.77 .440
Path from CWM (SLA) to AGC −0.37 0.18 −2.06 .039 −0.30 0.17 −1.74 .081
Path from CWM (WD) to AGC −0.21 0.19 −1.09 .275 −0.33 0.20 −1.66 .096
Path from species richness to AGC 0.41 0.20 2.00 .046
Residual correlaons
Path from CWM (WD) to CWM (SLA) 0.45 0.15 3.02 .003 0.45 0.15 3.02 .003
Path from CWM (WD) to CWM (PHm) −0.71 0.09 −7.50 <.001 −0.71 0.09 −7.50 <.001
Path from CWM (SLA) to CWM (PHm) −0.63 0.11 −5.54 <.001 −0.63 0.11 −5.54 <.001
Path from Feve to Fric 0.29 0.17 1.69 .090 0.29 0.17 1.69 .090
Model t stascs
AIC 306.2 304.2
p- value (chi- square) .115 .275
R20.45 0.52
Est.std, path standardized coecients; SE, standard error; Fric, funconal richness; Feve, funconal evenness; CWM, community weight mean; PHm, maxi-
mum plant height; SLA, specic leaf area; WD, wood density.
6 
|
   MENSAH ET AL.
CWM of maximum plant height and CWM of wood density, with 21%
explained variance (Table 4).
Examinaon of separate mixed- eects models for funconal di-
versity and funconal dominance revealed that the marginal R square
(variance explained by xed factors) in the diversity–AGC relaonship
was greater for funconal diversity (27%) than for funconal domi-
nance (21%). When considering funconal diversity and funconal
dominance measures in a same model, we found that 34% of the
variaons of AGC were explained by signicant eects of funconal
richness, funconal evenness, and CWM of maximum plant height
FIGURE1 Summary of the path model relating species diversity (species richness), and measures of functional diversity and of functional
dominance to the aboveground carbon (AGC); a: full mediation; b: partial mediation. CWM: community weight mean; PHm: maximum plant
height; SLA: specific leaf area; WD: wood density. The figures with parentheses are the coefficients of determination (R2), shown for dependent
variables. The figures without parentheses are the standardized path coefficients. The single- pointed arrows represent causal paths, while the
double- pointed arrows represent the residual correlations. The blue lines indicate the positive effects, while the red lines show negative effects;
Chisq, Chi- square statistic; DF, degree of freedom indicating the number of paths omitted from the model; Prob, probability of the data given
the model; Prob >.05 indicates the absence of significant discrepancy between the data and the model. CFI, comparative fit index; AIC, Akaike
information criterion. The significance of each path is given in Table 1
TABLE2 Results of simple and mulple linear models tesng the eects of elevaon, slope, and richness on aboveground carbon stock
Est. SE t value Pr (>|t|) SW BP DW
Elevaon (Intercept) 12.15 0.19 63.48 <0.001 0.849 0.240 1.68
Low −0.36 0.24 −1.48 0.152
Medium −0.09 0.23 −0.40 0.691
Adjusted R2 (%) 2.56
Slope (Intercept) 11.67 0.20 59.24 <0.001 0.927 0.211 1.69
Low 0.53 0.23 2.32 0.028
Medium 0.19 0.24 0.84 0.409
Adjusted R2 (%) 14.05
Slope + Species richness (Intercept) 10.98 0.32 34.19 <0.001 0.821 0.263 1.93
Low 0.51 0.21 2.45 0.021
Medium 0.16 0.22 0.72 0.479
Species richness 0.06 0.03 2.56 0.017
Adjusted R2 (%) 28.71
Est., esmates of regression coecients; SE, standard errors; SW, p- values for Shapiro–Wilk normality tests; BP, p- values for Breusch–Pagan tests; DW,
Durbin–Watson autocorrelaon stasc.
    
|
 7
MENSAH ET AL.
(Table 5). For all the selected models, species richness as random fac-
tor had much less variability than slope. The nonsignicant variabil-
ity due to species richness in the mixed- eects models suggests that
much of its inuence on AGC has been considered by funconal diver-
sity and funconal dominance, as conrmed by the SEM.
4 | DISCUSSION
Our study explored the paerns of diversity–carbon stock relaonship
in mistbelt forests in South Africa, nding that carbon stocks varied
greatly as responses to environmental gradients, taxonomic diversity,
funconal diversity, and funconal dominance. Specically, the study
revealed that (1) slope gradient signicantly inuenced aboveground
carbon, with lower carbon stock found at steeper sites; (2) increasing
species diversity (species richness) increased tree carbon stock; (3) di-
versity eects on tree carbon stock were mediated through funconal
diversity and funconal dominance; (4) funconal diversity eects on
tree carbon stock were greater than those of funconal dominance;
and (5) the specic eects of funconal diversity and funconal domi-
nance on carbon stock varied with metrics and funconal traits.
4.1 | Eects of environmental variables on tree
carbon storage
We did not detect any signicant eect of altude on tree carbon stock,
according to Cavanaugh et al. (2014) who also reported in a global
scale study, a lack of signicant relaonship between forest carbon and
TABLE3 Results of linear mixed- eects models tesng the eects of funconal diversity on aboveground carbon stock
Fixed eects Random eects (variance)
Est. SE df tPr (>|t|) Sp.rich. Slope Rsd. Marg. R2AIC
(Intercept) 11.76 0.16 2.98 71.90 <0.001 0.00 0.05 0.15 0.09 30.74
Fric 103.06 56.38 24.19 1.83 0.079
(Intercept) 12.92 0.48 25.97 27.11 <0.001 0.00 0.03 0.15 0.13 37.96
Feve −1.66 0.75 24.58 −2.21 0.037
(Intercept) 11.75 0.27 8.16 43.48 <0.001 0.00 0.05 0.17 0.01 40.77
Fdis 1.00 1.57 25.82 0.64 0.528
(Intercept) 12.30 0.446 22.51 27.577 <0.001 0.01 0.02 0.16 0.03 41.95
Fdiv −0.64 0.686 25.47 −0.935 0.359
(Intercept) 11.77 0.22 4.18 53.14 <0.001 0.00 0.06 0.17 0.02 38.38
RaoQ 3.82 4.66 25.80 0.82 0.42
(Intercept) 12.97 0.43 24.83 30.08 <0.001 0.00 0.04 0.12 0.27 23.83
Fric 135.59 50.64 23.15 2.68 0.013
Feve −2.03 0.68 23.32 −2.97 0.006
Est., coecient esmates; SE, standard errors; Sp.rich., species richness; Rsd., residual variance; Marg. R2, marginal R square; Fric, funconal richness; Feve,
funconal evenness; Fdis, funconal dispersion; Fdiv, funconal divergence; RaoQ, Rao quadrac entropy.
TABLE4 Results of linear mixed- eects models tesng the eects of funconal dominance on aboveground carbon stock
Fixed eects Random eects (variance)
Est. SE df tPr (>|t|) Sp.rich. Slope Rsd. Marg. R2AIC
(Intercept) 13.92 0.66 18.99 21.15 <0.001 0.03 0.03 0.10 0.20 44.18
CWM (SLA) −0.02 0.01 17.55 −3.14 0.006
(Intercept) 10.21 0.51 20.18 20.14 <0.001 0.08 0.11** 0.07 0.17 41.29
CWM (PHm) 0.07 0.02 18.45 3.66 0.002
(Intercept) 14.85 1.19 16.42 12.46 <0.001 0.15 0.05 0.09 0.10 38.39
CWM (WD) −4.86 1.94 15.37 −2.50 0.024
(Intercept) 6.06 2.06 24.64 2.95 0.007 0.00 0.16** 0.11 0.21 38.03
CWM (PHm) 0.11 0.03 24.35 3.63 0.001
CWM (WD) 5.35 2.44 23.96 2.19 0.038
**Signicant at 0.01.
Est., coecient esmates; SE, standard errors; Sp.rich., species richness; Rsd., residual variance; Marg. R2, marginal R square; CWM (SLA), community
weight mean of specic leaf area; CWM (WD), community weight mean of wood density; CWM (PHm), community weight mean of maximum plant height.
8 
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   MENSAH ET AL.
altude. Yet, this nding runs contrary to many previous studies that
examined the relaonships between altude and biomass or carbon
storage (de Caslho et al., 2006; Ensslin et al., 2015; Sharma, Baduni,
Gairola, Ghildiyal, & Suyal, 2010). On the one hand, some authors re-
ported that biomass and carbon stocks can decline with increasing al-
tude (de Caslho et al., 2006; Moser, Hertel, & Leuschner, 2007). On the
other hand, studies found posive correlaon between increasing tree
carbon and increasing altude (Gairola, Sharma, Ghildiyal, & Suyal, 2011;
Zhu et al., 2010). Furthermore, biomass and carbon stocks were found
to increase up to a certain altudinal limit (3,000 m a.s.l.) and aerward
decline sharply with higher altudinal values (Ensslin et al., 2015; Singh,
Adhikari, & Zobel, 1994). This lack of clarity on the relaonship between
altude and forest biomass may be partly due to the variaon in the al-
tudinal range among studies. For instance, most of the abovemenoned
studies that reported signicant eects of altude have covered greater
altudinal ranges well above 2,500 m a.s.l; the relaonship between al-
tude and carbon stocks in our study might have been hidden due to
the smaller altudinal range covered (1,000–1,800 mm), which might
have not been considerable enough to detect substanal variaon in
growth condions and hence biomass and carbon stock.
Unlike altude, slope showed signicant inuence, and accounted
for 14% of carbon variance, evidencing that dierences in carbon
stocks can result from topological constraints, parcularly dierence in
slope. Consistent with our results, slope has been idened as a poten-
al environmental variable that aects tree carbon (de Caslho et al.,
2006; Chave et al., 2003). Because aboveground carbon is intrinsically
related to wood and biomass producon, the inuence of slope can be
seen as prior impacts of environment on availability of resources (de
Caslho et al., 2006; Luizao et al., 2004), which in turn aect forest dy-
namics. For example, steeper slope will speed up nutrients and water
runo and constrain trees and will also favor highly water and nutri-
ent ecient species against others. Taking this into account, it follows
that tree growth and biomass producon can be potenally reduced
at higher slope sites, as results of moisture and nutrient stress (Clark,
Clark, & Oberbauer, 2010; Durán et al., 2015), whereas at and gen-
tle slope sites would allow for more water availability, to which plant
would likely respond posively. The signicant eect of slope supports
the fact that ecosystem funcons in general and biomass and carbon
storage in parcular are environment- structured (Wu et al., 2015).
4.2 | Increasing species diversity promotes tree
carbon storage
We found signicant and posive eects of species richness on
aboveground carbon, even when the eects of environmental fac-
tors (i.e., slope) were accounted for. While this nding accords with
some recent studies that controlled for the eects of environmen-
tal variables (Ouyang et al., 2016; Wu et al., 2015), it also supports
the commonly described paern in highly diverse natural forests;
that is, biomass and carbon stocks increase with increasing diversity.
Indeed, several local and global studies on forest ecosystems have
shown posive relaonship between species richness and forest bio-
mass or carbon (Cavanaugh et al., 2014; Con et al., 2013; Day et al.,
2014; Ruiz- Benito et al., 2014; Sharma et al., 2010; Wu et al., 2015).
In addion, studies in boreal (Paquee & Messier, 2011), temper-
ate (Paquee & Messier, 2011; Vilà et al., 2007), and tropical forests
(Barrufol et al., 2013) have also reported increases in producvity
with increasing diversity.
One can expect that increasing species diversity would increase
carbon storage because higher taxonomic diversity would lead to
higher stem density and forest producvity (Ruiz- Benito et al., 2014).
The posive eect of species diversity can also be explained through
the benets of plant–plant interacons such as facilitaon, where by
some species could enhance soil ferlity (by xing nitrogen) for the
producvity of other species. This fact is even oen used to support
the reason why mixed species communies of plantaons are far
more producve than monospecic stands. But it might also be well
possible that increasing species richness increases the chances of in-
clusion of highly producve and naturally favored dominant species
(Ruiz- Benito et al., 2014), as shown by our previous results on the in-
uence of most dominant species on carbon stocks in mistbelt forests
(Mensah, Veldtman, du Toit, Glèlè Kakaï, & Seifert, 2016).
TABLE5 Results of linear mixed- eects models tesng the combined eects of funconal diversity and funconal dominance on aboveground
carbon (AGC) stock
Model descripon Est. SE df tPr (>|t|)
Funconal diversity
+ Funconal dominance
Fixed eects (Intercept) 11.39 0.63 23.82 18.03 <0.001
Fric 143.50 42.65 21.99 3.37 0.003
Feve −1.72 0.58 22.15 −2.95 0.008
CWM (PHm) 0.06 0.02 22.80 3.10 0.005
Random eects
(variance)
Species richness 0.00
Slope** 0.09
Residual 0.08
Marginal R20.34
AIC 24.28
**Signicant at 0.01.
Est., coecient esmates; SE, standard errors; Fric, funconal richness; Feve, funconal evenness; CWM (PHm), community weight mean of maximum
plant height.
    
|
 9
MENSAH ET AL.
While our dataset in the mistbelt forests supports the posive spe-
cies richness–carbon relaonship, it must be noted that evidence of the
inverse eect also exists. For instance, studies by Ruiz- Jaen and Potvin
(2011) in natural forest of Barro Colorado Island in Central Panama and
Szwagrzyk and Gazda (2007) in natural forests of central Europe revealed
negave relaonship of species diversity with biomass and carbon
stocks. Furthermore, others studies found such relaonships nonsignif-
icant (see Gairola et al., 2011). These controversial outcomes suggest
that the eects of diversity on forest carbon may vary with other factors
such as the types and the successional stages of the forests (Lasky et al.,
2014; Wu et al., 2015), and also the specic dimension of the diversity
measure used (Con et al., 2013; Lasky et al., 2014; Ouyang et al., 2016).
4.3 | Diversity eects mediated through funconal
diversity and funconal dominance
The use of mulple diversity measures to provide addional insights
into the mechanisms behind diversity–producvity has gained in-
creasing interest in recent years (Cadoe et al., 2011; Con & Díaz,
2013; Finegan et al., 2015; Lasky et al., 2014; Ruiz- Benito et al., 2014;
Vance- Chalcra, Willig, Cox, Lugo, & Scatena, 2010; Ziter, Benne,
& Gonzalez, 2013). Accordingly, funconal diversity and dominance
metrics were also examined in this study. While most of these studies
tended to compare the relave eects of species richness and other
diversity measures, we have provided here an addional example of
exploring diversity eects on carbon stocks, by assuming that these
eects were mediated through funconal diversity and funconal
dominance. Our results on the structural equaon modeling conrm
this assumpon and therefore support the need to explore beyond
species richness to beer elucidate the mechanisms that govern di-
versity–producvity relaonship. The results further support the idea
that both complementarity and selecon eects are not exclusively
aecng carbon storage (Ruiz- Benito et al., 2014; Wu et al., 2015).
Diversity (species richness) promotes carbon stock through eects of
both funconal diversity and funconal dominance, partly because
these diversity components are based on specic funconal traits,
which would reect funconal dierences among the species (Dı ́az &
Cabido, 2001; Song et al., 2014). This nding can also be due to the
fact that increased species richness indirectly accounted for dier-
ences among species, in terms of ecological niche and resource use.
4.4 | Funconal diversity eects on tree
carbon storage
Of the ve funconal diversity indices used in this study, only funconal
richness and funconal evenness were found to explain variaon in car-
bon stock. There is a variety of evidence for funconal diversity eects
on biomass and carbon. A study by Finegan et al. (2015) in tropical rain
forests of Bolivia, Brazil, and Costa Rica found only funconal rich-
ness—among other funconal diversity indices—as signicant predictor
for biomass variaon. Yet, a study in unmanaged forest fragments in
Quebec revealed signicant and posive relaonships between func-
onal dispersion and AGC (Ziter et al., 2013). Similarly, Ouyang et al.
(2016) found signicant but negave eects of the Rao quadrac en-
tropy on stand biomass in subtropical forests in China. While we be-
lieve that these funconal diversity indices have their specic biological
meaning, in this study, the posive eect of funconal richness on the
AGC could be due to funconal richness being posively correlated
with species richness (SEM results; Villéger et al., 2008).
The funconal richness measures the amount of trait or niche
space lled by the species within a community (Clark et al., 2012;
Mason, Mouillot, Lee, & Wilson, 2005). It would increase carbon stor-
age because species with various traits would dier in resource use,
and would more eciently use the resources available within the com-
munity for higher growth and producvity, thus reecng the niche
complementarity eects (Finegan et al., 2015). Unlike funconal rich-
ness, funconal evenness did not show any relaonship with species
diversity; however, it did exhibit negave inuence on AGC. Following
Mason et al. (2005), the funconal evenness measures the evenness
of abundance distribuon in the lled niche space. Therefore, both
funconal richness and funconal evenness relate to the niche space
or secons of niche space, and funconal diversity as measured here
could reect some form of “niche dierences” (Carroll, Cardinale, &
Nisbet, 2011). Greater funconal diversity, that is, greater value and
range of funconal traits, would reect not only the magnitude of
“niche dierences”, but also the dierences in resource ulizaon by
species, thus promong diversity eects on ecosystem funconing.
This is in line with Carroll et al. (2011) who showed that increasing
niche dierence contributes to species coexistence and posive diver-
sity eects on biomass yield.
The unexpected lack of strong individual eect of funconal rich-
ness on aboveground carbon in this study might be due to the num-
ber of funconal traits used. In fact, only three funconal traits were
considered; although these traits were found to be crucial to explain
biomass allocaon paerns (Chave et al., 2009; Mensah, Glèlè Kakaï,
et al., 2016), they might not be as important as we thought for com-
plementary resource allocaon. Similarly, these funconal traits might
not be sucient enough to catch the enre variability needed to ex-
plain carbon variaon. Adding other funconal traits such as plant hy-
draulic conducvity, leaf mass per area, and nitrogen xing potenal
could have well captured the funconal variability.
4.5 | Funconal dominance eects on tree
carbon storage
The use of CWM values of funconal trait to predict funconal domi-
nance eects is supported by the understanding that CWM metric
reects dominance of traits and species within a given community,
and also in line with the fact that dominant species would induce
funconal shis in mean trait values (Ricoa & More, 2011). CWM
as funconal dominance metric could be used to elaborate on com-
peve dominance of species (Ricoa & More, 2011). Therefore,
funconal dominance could indicate some aspect of “relave tness
dierences” between competors (Carroll & Nisbet, 2015; Carroll
et al., 2011). Moreover, the nding that funconal dominance signi-
cantly inuenced tree carbon storage is consistent with the previous
10 
|
   MENSAH ET AL.
report that the magnitude of “relave tness dierences” strength-
ens the inuence of diversity on biomass yield (Carroll et al., 2011).
The funconal dominance eects, as measured in this study, varied
with the funconal trait. Specically, CWM of wood density revealed
negave and signicant eect on carbon stocks. It is not surprising
given that wood density is a potenal predictor of tree biomass, which
highly correlates with the carbon stock. There are some insights that
CWM of wood density is negavely related to the biomass incre-
ment, as being good predictor of individual tree diameter increments
(Finegan et al., 2015). However, aer examining biomass stocks in
tropical forests, Stegen, Swenson, Valencia, Enquist, and Thompson
(2009) pointed out that increasing wood density can decrease or in-
crease the carbon stock, regardless of whether trees have high or low
mean wood density. The authors therefore came to the conclusion
that no general relaonship exists between forest biomass and wood
density. The present nding about CWM of wood density means that
low wood density species grow faster and tend to store more bio-
mass; thus, it suggests that conserving and planng low wood density
species would likely help to increase the carbon stock.
Similarly, CWM of specic leaf area exhibited negave and signi-
cant eect on carbon stocks. This is consistent with other studies that
found negave relaonship between specic leaf area and plant biomass
(Finegan et al., 2015). Leaf area is important for the amount of radiant
energy intercepted by the plant. It is also generally known to facilitate the
transfer of CO2 and water between foliage and atmosphere. Therefore,
the signicant inuence of CWM of specic leaf area in this study sup-
ports the idea that leaf area captures a strategy of the plant for resource
consumpon, especially light (Mensah, Glèlè Kakaï, et al., 2016).
Community weight mean of maximum plant height showed
posive relaonship with carbon storage, as also reported in re-
cent studies (Con & Díaz, 2013; Finegan et al., 2015; Ruiz- Jaen &
Potvin, 2011). This is probably because tree height is a key variable
for species- specic or mulspecies biomass regressions. In addion,
maximal tree height is a potenal species trait, as it denes the lim-
its of compeon for light and thus for light consumpon (Poorter,
Bongers, & Bongers, 2006; Poorter, Bongers, Sterck, & Woll, 2005).
Examinaon of combined eects of funconal dominance metrics
revealed that only CWM of wood density and of maximum plant
height were retained in the nal model, with maximum plant height
being the most signicant predictor. Furthermore, only maximum
plant height was also retained among funconal dominance metrics
when we assessed the combined eects of funconal dominance and
funconal diversity. Tree height being closely related to tree diameter,
the posive and signicant relaonship between CWM of maximum
plant height and carbon stocks reects the potenal importance of
characteriscs of dominant and adult trees for ecosystem funcon-
ing and producvity, thus supporng the selecon eects hypothesis.
The important contribuon of dominant stems to forest biomass has
well been evidenced in some recent studies (Chave et al., 2003; Lung
& Espira, 2015). The study by Lung and Espira (2015) revealed that
tree stems larger than 50 cm have the greatest impact on forest bio-
mass, and <16% of the species pool accounted for over 62% of the
aboveground biomass.
4.6 | Funconal diversity eects greater than
those of funconal dominance
When examining the percentage of variance explained, we found that
funconal diversity explained more variance than funconal domi-
nance (Tables 2 and 4). This rejects our second hypothesis, and sug-
gests that complementarity eects seem to be more important than
selecon eects. This nding contradicts Finegan et al.’s (2015) and
Ruiz- Jaen and Potvin’s (2011) results that selecon eects were more
important for the aboveground biomass and carbon stock in tropi-
cal forests. For this study, funconal dominance metrics (community
weight mean of funconal traits) were calculated using species rela-
ve abundance, while Ruiz- Jaen and Potvin (2011) and Finegan et al.
(2015) used species relave basal area and species relave biomass,
respecvely, as weighng variable. The strength of relaonship be-
tween community weight mean of traits and the ecosystem funcon
of interest could depend on the weighng variable. Biomass- or basal
area- weighted communies mean values would likely show stronger
relaon with biomass and carbon than abundance- based communies
mean values. Further studies should elaborate on this and show the
extent to which weighng variable can inuence our understanding of
weighted mean values’ eects on ecosystem funcons.
All being considered, it is important to menon that our result
actually supports the idea that these two hypotheses (complemen-
tarity and selecon eects) are not exclusive, and can contribute to
ecosystem funconing. Previous evidence of both complementarity
and selecon eects on ecosystem funcon suggests they can also
contribute at dierent proporons at dierent mes of ecosystem
development (Fargione et al., 2007). Both complementarity and se-
lecon eects mutually promote species coexistence. As pointed
out by Carroll et al. (2011), these two hypotheses could even be the
outcome of interacons of the “relave tness dierences” and the
“niche dierences”, whereby some species’ populaons could be sup-
pressed by dominant competors, to allow eecve ulizaon of the
available resources. The selecon eects reported here are strongly
transmied through specic maximum plant height, which reects the
inuence of dominant species and suggests a possible compeve ex-
clusion in terms of ulizaon of resources (e.g., light). In mulspecies,
mulstory natural forests, chances are high to observe dominant and
taller species that increase stand producvity, probably by achieving
higher absorpvity of photosynthecally acve radiaon, thus re-
ducing (through compeve dominance) the level of photosynthec
photon ux density available for understory species. However, it must
be noted that, even for these dominant species, interacons within
ontogenic stages (for example, compeon for light between seed-
lings, juveniles, and adults) could dene an ecient complementary
use of light for greater producvity. Furthermore, an ecient use by
understory species (limited to the subcanopy layer) of the available
photosynthec photon ux density, and also of decomposed lier
(from canopy and dominant trees leaves) may likely reect some com-
plementary eects on stand producvity. Therefore, selecon eects
(dominant traits and species) on ecosystem funcon would be appar-
ent in natural forests as we predicted, but complementary eects and
    
|
 11
MENSAH ET AL.
ecient use of limited resources, especially by coexisng and under-
story species, could promote greater ecosystem funcon.
5 | CONCLUSION
This study examined the diversity–carbon stock relaonship in
mistbelt forests in South Africa and revealed that taxonomic diver-
sity (species richness) promotes carbon storage through funconal
diversity and funconal dominance. The study further highlighted that
both the niche complementarity and selecon hypotheses are impor-
tant for carbon storage. However, the eects of funconal diversity
(niche complementarity eects) were greater than funconal domi-
nance eects (selecon eects). Moreover, the eects of funconal
dominance were strongly transmied through the CWM of maximum
plant height, reecng the importance of forest vercal stracaon
for diversity–carbon relaonship. Therefore, complementary eects
would be induced also by complementary light- use eciency of spe-
cies and trees growing in the understory layer. We suggest that future
research on the relaon between diversity and forest carbon be ori-
ented toward a perspecve of forest canopy (or dominant species vs.
other species), to contribute addional insights into our understand-
ing of biodiversity–ecosystem funcon relaonship.
ACKNOWLEDGMENTS
We sincerely acknowledge the anonymous reviewers and the editor
for their cricism and their construcve comments on the early ver-
sion of this arcle. This study was nancially supported by the SHARE
INTRA- ACP project, the Naonal Research Foundaon of South
Africa through the project “Catchman Letaba” in the RTF funding
scheme, and the African Forest Forum.
CONFLICT OF INTEREST
None declared
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... However, the mean AGCS (t ha -1 ) reported in our study is lower than that from other moist evergreen Afromontane forests [77,78]. However, the AGCS value of our study report is within the range when compared with reports from some tropical rainforests (49.1-476.1 Mg·ha -1 ) [79]. On the other hand, the AGCS value from our study is higher when compared with (60.09 to 121.43 t·ha-1) [80]. ...
... Overall, upper canopy individuals of tropical forests have a significant contribution to aboveground carbon storage. Previous [79,85]. A few tree species contributed a significant amount of AGCS to a forest stand. ...
... In addition, plot species richness (PSR) has shown a positive linear relationship with aboveground carbon storage in our study. Species richness is the simplest measure of species diversity; a study has also verified the significant effect of species diversity on aboveground carbon storage through functional diversity and functional dominance of species [79]. A positive relationship was demonstrated between AGCS and SOCS, which is consistent with the previous study's report [80,97]. ...
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... & p ¼ .05). The result was different from Mensah et al. (2016), who stated that woody carbon stock increases with diversity in highly diverse natural forests and high productivity areas i.e. church forests (Mensah et al., 2016). The Pearson's correlation relation of biomass carbon stock had statistically significant positive relationship with basal area at 95% confidence interval (r ¼ .763* ...
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... Although different studies have evaluated the effects of tree community proprieties, environmental conditions, and human impacts on carbon stocks, the existing evidence is more concentrated in relatively undisturbed forests [see, e.g., (12,13)] and it is inconsistent [see, e.g., (4,5,14,15)]. Such inconsistency may be explained by differences in the role played by each driver across biogeographic contexts or in the methods used across studies, such as field protocols or carbon allometric equations (16). ...
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... resource capture; Huang et al., 2018;Liu et al., 2018;Morin et al., 2011). However, understanding how diversity affects C stock is still being debated as a core issue in ecological research Mensah et al., 2016;Palandrani & Alberti, 2020;Sabatini et al., 2019). ...
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... Similarly, slope influences gravity processes and the movement of eroded soil, water and plant debris, which are important for stem growth. A steeper slope could limit the mechanical stability and growth of stems (Mensah et al., 2016). There is also considerable evidence that heterospecific and conspecific individuals may have negative effects on seedling survival due to resource competition (Comita and Hubbell, 2009;Lin et al., 2014). ...
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