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Exploring taxonomic, functional, and structural diversity can provide additional insights into our understanding of diversity responses to environment. Using altitude, slope, and relative radiation index as well as floristic and functional data from a South Africa Afromontane forest, we examined how taxonomic, structural, and functional diversity varied with local environmental variation. Taxonomic and structural diversity were quantified through species richness- and diameter class-based Shannon index and evenness, respectively. Skewness and coefficient of variation of diameter distribution were additionally computed for structural diversity. As for functional diversity, we used functional richness, evenness, divergence, and dispersion based on functional traits. Data were analyzed using multimodel inference and subset regression. We found little evidence of environmental effects on local-scale taxonomic diversity patterns. In contrast, structural and functional diversity metrics varied significantly along environmental gradients. Accordingly, diameter class-based Shannon evenness declined with increasing slope while skewness and coefficient of variation of diameter distribution increased with increasing slope. Functional evenness and divergence decreased with increasing altitude and radiation, respectively, while functional richness and dispersion increased with increasing slope. The results showed that taxonomic diversity patterns were less responsive to local-scale topographical variation than structural and functional diversity. Lower functional diversity on lower slope sites suggests weak environmental filtering effect promoting competitive exclusion and dominance of species with acquisitive traits. On higher slope sites, environmental filtering associated with slope gradient seems to favor coexistence of species with conservative traits and adapted to harsh conditions.
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Research Article
Differential Responses of Taxonomic,
Structural, and Functional Diversity to
Local-Scale Environmental Variation in
Afromontane Forests in South Africa
Sylvanus Mensah
, Vale
`re K. Salako
, Achille E. Assogbadjo
, and
Romain Gle
Exploring taxonomic, functional, and structural diversity can provide additional insights into our understanding of diversity
responses to environment. Using altitude, slope, and relative radiation index as well as floristic and functional data from a
South Africa Afromontane forest, we examined how taxonomic, structural, and functional diversity varied with local envir-
onmental variation. Taxonomic and structural diversity were quantified through species richness- and diameter class-based
Shannon index and evenness, respectively. Skewness and coefficient of variation of diameter distribution were additionally
computed for structural diversity. As for functional diversity, we used functional richness, evenness, divergence, and disper-
sion based on functional traits. Data were analyzed using multimodel inference and subset regression. We found little
evidence of environmental effects on local-scale taxonomic diversity patterns. In contrast, structural and functional diversity
metrics varied significantly along environmental gradients. Accordingly, diameter class-based Shannon evenness declined with
increasing slope while skewness and coefficient of variation of diameter distribution increased with increasing slope.
Functional evenness and divergence decreased with increasing altitude and radiation, respectively, while functional richness
and dispersion increased with increasing slope. The results showed that taxonomic diversity patterns were less responsive to
local-scale topographical variation than structural and functional diversity. Lower functional diversity on lower slope sites
suggests weak environmental filtering effect promoting competitive exclusion and dominance of species with acquisitive
traits. On higher slope sites, environmental filtering associated with slope gradient seems to favor coexistence of species with
conservative traits and adapted to harsh conditions.
diversity patterns, environmental filtering, radiation index, species coexistence, slope
The structures of natural forest ecosystems are not only
outcomes of natural processes (tree growth, mortality,
recruitment, and disturbances such as fire and wind
damage) and human disturbance (clear-felling, afforest-
ation, etc.) but are also codetermined by environmental
constraints (Assogbadjo, Mensah, & Gle
¨, 2017;
Foley et al., 2007; Seydack, Durrheim, & Louw, 2012).
Environmental effects may vary according to the scale of
interest. Regional and global scales diversity and struc-
tural patterns are regulated by species tolerance ranges
which are well related to climate and environmental
Laboratoire de Biomathe
´matiques et d’Estimations Forestie
`res, Universite
d’Abomey-Calavi, Benin
Department of Forest and Wood Science, Stellenbosch University, South
Regional Universities Forum for Capacity Building in Agriculture, Kampala,
Laboratory of Applied Ecology, University of Abomey-Calavi, Benin
Corresponding Author:
Sylvanus Mensah, Laboratoire de Biomathe
´matiques et d’Estimations
`res, 03 BP 2819 Benin, Abomey-Calavi, Benin.
Received 3 November 2017; Revised 24 January 2018; Accepted 2 February
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0
License ( which permits non-commercial use, reproduction and distribution of the work without further
permission provided the original work is attributed as specified on the SAGE and Open Access pages (
Tropical Conservation Science
Volume 11: 1–13
!The Author(s) 2018
Reprints and permissions:
DOI: 10.1177/1940082918762372
factors (Woodward & Williams, 1987). At local scale,
spatial variations in precipitation and temperature
might not be apparent, and environmental variation
would most likely be induced by other factors—edaphic
or topographic (elevation, aspect, slope, etc.)—which can
potentially influence species distribution, structure, and
dominance patterns (Baldeck et al., 2013; Laurance
et al., 2010; Sharma, Baduni, Gairola, Ghildiyal, &
Suyal, 2010; Zhang et al., 2016). In addition, local-scale
topography may be an influencing factor for soil nutri-
ents availability and soil moisture (Engelbrecht et al.,
2007; John et al., 2007) and thus may act as a key com-
ponent of environment filtering facilitating or preventing
species establishment and growth (Kraft et al., 2015).
There are insights that forest structure and community
assembly relate with local environmental variables such
as altitude and slope (Gallardo-Cruz, Pe
´a, &
Meave, 2009). For instance, basal area and forest bio-
mass are typically reported to be highest at lower slope
sites (Sefidi, Esfandiary Darabad, & Azarian, 2016;
Takyu, Aiba, & Kitayama, 2003; Vila
`et al., 2007). The
influence of slope or topographical variables can be
mediated by light radiation, temperature, moisture,
runoff, infiltration, soil properties, and resources avail-
ability (Tsui, Chen, & Hsieh, 2004; Yirdaw, Starr,
Negash, & Yimer, 2015). As a result, one might expect
tree size variability (structural diversity) to relate with
slope or altitude, even at a local scale.
In South Africa, Afromontane Mistbelt forests are one
of the few natural forests that, due to the modification of
fire regime, have developed in areas that were not covered
by forests historically (Geldenhuys, 2000; Geldenhuys &
Venter 2002). These forests persisted in fire-prone envir-
onment, and then expanded into potentially suitable
areas with protection of the larger landscape against
fire, for timber plantations and intensive agriculture.
One of the most striking characteristics in these
Afromontane forests is the tall moist evergreen vegetation
occurring at higher altitudes and varying slope aspect.
Lower water availability on slopes and hilltops as com-
pared to valleys, as a result of topography effects
(Gallardo-Cruz et al., 2009; Lebrija-Trejos, Pe
´a, Meave, Bongers, & Poorter, 2010), may govern
the structure and functional organization of these forest
communities. Most studies in Afromontane Mistbelt for-
ests in South Africa lack information on how diversity
relates to local-scale environmental variation, and only
few studies paid attention to variability in species func-
tional traits (Mensah, Gle
¨, & Seifert, 2016;
Mensah, Veldtman, Assogbadjo, Gle
¨, & Seifert,
2016). In the current ecological research context where
functional diversity—the value and range of functional
traits of the organisms present in a given ecosystem
´az & Cabido, 2001)—is being seen as complementary
and promising component to assess diversity effects on
ecosystem functioning, it is important to quantify
changes in functional diversity as response to environ-
mental filtering (Dyderski, Czapiewska, Zajdler,
Tyborski, & Jagodzinski, 2016; Lohbeck et al., 2012).
New species—with different functional traits—added to
an ecosystem would likely contribute differently to the
physiological processes (Mensah, Veldtman, et al.,
2016), as response to environmental conditions and avail-
able resources. Therefore, functional diversity can also be
used as an additional diversity component in assessing
biological diversity responses to environmental variation.
In addition to insights from structural diversity measures,
a functional traits-based analysis of diversity can further
our understanding of forest structure and diversity
responses to environmental variation.
Combined information on taxonomic, functional, and
structural diversity could shed light on our understanding
of processes and mechanisms behind community assem-
bly. However, it is unclear how each specific diversity
component would respond to local environmental vari-
ation. Therefore, in this study, we used taxonomy-, struc-
ture-, and functional trait-based diversity to explore how
environmental filtering imposed by local environmental
factors such as altitude, slope, and radiation influence
community assembly. We scrutinized species diversity,
separating taxonomic diversity from functional traits-
based (wood density, leaf area, and maximum height)
diversity and structural diversity (tree size variability),
and determined how they would respond individually to
local-scale environmental variation. We asked the follow-
ing questions: (a) How do taxonomic diversity and struc-
tural diversity (tree size variability) vary with radiation,
slope, and altitudinal gradient? (b) How do different
functional diversity metrics respond to local variation in
radiation, slope, and altitude? Because species distribu-
tions are governed by climatic and edaphic tolerance
range and adaptations to physical conditions of the envir-
onment, we expect taxonomic diversity patterns to be less
responsive to local-scale topographical variation.
Conversely, we assumed that functional and structural
diversity would relate with slope or altitude, even at a
local scale due to gravity-driven processes, mechanical
stability constraints and growth challenges faced by
trees growing on steeper slopes.
Study Area and Data
The study was carried out in the Woodbush—De Hoek
natural forest (Figure 1), near Magoebaskloof in the
Limpopo province, South Africa. The Woodbush—De
Hoek natural forest is part of the Limpopo Mistbelt for-
ests (Mucina & Rutherford, 2006), which belong to the
Northern Mistbelt Forests group, considered as part
2Tropical Conservation Science
of the Afromontane Archipelago in Africa (White, 1983).
These forests are composed of tall moist evergreen vege-
tation occurring at higher altitudes and varying slope
aspect (Geldenhuys, 2000; Geldenhuys & Venter, 2002;
Mensah, Veldtman, & Seifert, 2017). The data were col-
lected in Summer 2015 and consisted of floristic and
structural data and additional wood density data for
selected species (Mensah et al., 2017). The floristic and
structural data were collected by means of forest inven-
tories in a 709 ha forest block in the Woodbush—De
Hoek forest. Thirty circular plots of 500 m
were ran-
domly laid out in stratified compartments obtained
from subdivision of the research area on the basis of (a)
three classes of slope: flat, gentle, and steep; (b) three
classes of elevation: low, medium, and high; and (c)
four classes of aspect which were North, South, West,
and East. Diameter at breast height (dbh) and species
name were recorded for all trees with dbh=5cm.
Additional data on leaf and wood traits (specific wood
density, specific leaf area, and maximum plant height;
Mensah et al., 2016) were obtained from publicly avail-
able sources. Additional data on species wood density
were obtained from the Global Wood Density Database
(Zanne et al., 2009). Specific leaf area and maximum
plant height data were extracted from TRY database
(; Kattge et al., 2011).
Diversity Metrics
We used plot-level taxonomic diversity metrics (species
richness, Shannon–Wiener diversity index, and Shannon
evenness), structural diversity (tree size variability), and
functional diversity (functional traits-based diversity)
metrics. Structural diversity was assessed at plot level
by calculating four metrics: (a) Shannon diversity
Index; (b) Shannon evenness based on the relative pro-
portion of trees in dbh classes of 5 cm width, with 19
classes in total; (c) skewness of dbh distribution, and
34°0.0'S 32°0.0'S 30°0.0'S 28°0.0'S 26°0.0'S 24°0.0'S 22°0.0'S
16°0.0' E 18°0.0'E 20° 0.0'E 22° 0.0'E 24°0. 0'E 26°0. 0'E 28°0. 0'E 30°0. 0'E 32°0. 0'E
Figure 1. Location of South Africa map in Africa and of the Woodbush-De Hoek natural forest in South Africa.
Mensah et al. 3
(d) coefficient of variation of dbh (Da
˘nescu, Albrecht, &
Bauhus, 2016). Functional diversity was quantified using
specific leaf area, specific wood density, and maximum
plant height. We estimated functional richness, functional
evenness, functional divergence, functional dispersion
(Fdis), and Rao quadratic entropy (RaoQ) at plot level
´ger, Mason, & Mouillot, 2008) using the values of
the functional traits (specific wood density, specific leaf
area, and maximum plant height) with the ‘‘FD’’ package
in R (Laliberte
´, Legendre, & Shipley, 2014). These func-
tional diversity indices are multitrait metrics that com-
bine both the relative weight of each species and the
pairwise functional difference between species (Mensah,
Veldtman, et al., 2016). Because RaoQ and Fdis are
strongly related indices (Laliberte
´& Legendre, 2010),
we considered retaining only Fdis.
Environmental Variables
Slope, aspect, and altitude values were obtained from
digital elevation model available for the site, using rec-
orded coordinates and QGIS version 2.2.0. A convenient
way to explore the effect of such environmental factors
on the diversity metrics would be a three-way analysis of
variance (ANOVA), as this would allow for evaluation of
interactions effects among factors besides individual
effects. However, given the low number of plots, combin-
ations of elevation–slope–aspect in multiple-way
ANOVA is impracticable and may produce unreliable
results. In addition, aspect as simple variable may not
be biologically relevant as compared to radiation index
which combines aspect, slope, and latitude degrees
(Austin, Cunningham, & Fleming, 1984). Therefore, in
addition to slope and altitude which were used as inde-
pendent variables, we also calculated relative radiation
index (RRI), which is a relative measure of the slope
exposure to radiation at noon at specific location
(Vetaas, 1992). RRI was estimated from the following
formula: RRI ¼cos (180)sin (b)sin ()þcos
(b)cos (), where ¼aspect (slope azimuth), ¼lati-
tude, and b¼slope inclination (see Paudel & Vetaas,
Statistical Analysis
Statistical analyses were done in the R statistical software
package, version 3.3.2 (R Core Team, 2016). We first
explored the relationships between environmental vari-
ables and diversity metrics using Pearson correlations.
Correlation matrix heatmaps were built for each diversity
component using package ‘‘ggplot2’’ (Wickham, 2009).
We next tested for effects of slope, altitude, and radiation
on taxonomic, functional, and structural diversity using
multiple linear models. The linear models were fitted
to assess (a) combined effects of environmental variables
on taxonomic diversity metrics, (b) combined effects of
environmental variables on functional diversity metrics,
and (c) combined effects of environmental variables on
structural diversity metrics. Models were fitted using mul-
timodel inference and subset regression analysis of the
package ‘‘MuMIn’’ (Barton, 2017). Multimodal inference
is a powerful method to determine which model best fits
the data. The optimal models were selected based on the
AICc (Akaike Information Criterion, adjusted for small
sample sizes). Small difference (<2) in AICc between two
subset models indicates that these models are equally sup-
ported. To avoid autocorrelation and independence in
case two or more models were equally supported, the
most parsimonious model was selected by considering
the lowest number of uncorrelated predictors.
Significance fits were additionally used to determine
environmental variables that are relevant for each diver-
sity metric. For interpretation of the results, bivariate
relationships between response variables and predictors
were plotted using package ‘‘ggplot2.’’ Prior to fitting the
models, response variables were checked for normality
using the Shapiro–Wilk normality test.
Overall Diversity Patterns and Correlations
Between Variables
Fifty tree species and 33 botanical families were enumer-
ated. Overall Shannon–Wiener diversity was 2.84. Most
diversified families were Rutaceae and Rubiaceae, with
five and four species, respectively. Results of Pearson cor-
relations showed that slope, altitude, and radiation index
were more strongly related with functional and structural
diversity metrics than taxonomic diversity metrics
(Figure 2). Pearson correlations between environmental
variables and diversity metrics ranged from r¼.35 to
r¼.40 for taxonomic diversity metrics (Figure 2(a));
from r¼.72 to r¼.85 for structural diversity metrics
(Figure 2(b)); and from r¼.58 to r¼.69 for functional
diversity metrics (Figure 2(c)).
Diversity Responses to Local-Scale Environmental
Results from the model selection process as summarized
in Tables 1 to 3 indicate differential responses of taxo-
nomic diversity, structural diversity, and functional diver-
sity metrics, respectively, to local-scale environmental
Among taxonomic diversity metrics, only Shannon
evenness responded significantly to local environmental
variations (Table 1); and the effects were shown by sig-
nificantly higher values of Shannon evenness on steeper
sites (R
¼13%; p¼.031; Table 1; Figure 3). As for
4Tropical Conservation Science
species richness and Shannon diversity, there was no stat-
istical influence of local-scale environmental factors,
probability values being .301 and .112, respectively.
We found no significant environmental effects on
diameter class-based Shannon index, with all environ-
mental factors being left out in the finally selected
model (Table 2). However, diameter class-based
Shannon evenness declined significantly with increasing
slope (R
¼26%; p¼.002; Table 2; Figure 4) while skew-
ness and coefficient of variation of diameter distribution
increased (21% and 17% of variance explained, respect-
ively) with increasing slope (Table 2; Figure 4). On the
other hand, skewness of diameter distribution also
decreased with higher altitude (R
¼16%; p¼.016;
Figure 4).
When assessing fitted models for functional diversity
metrics, slope was retained as potentially influencing pre-
dictor of functional richness and Fdis (Table 3). The
effect of slope was shown by increasing functional rich-
ness and dispersion on steeper sites, with 16% (p¼.036)
and 45% (p<.001) of explained variance respectively
(Figure 5). On the other hand, altitude and RRI were
final selected predictors for functional evenness and func-
tional divergence, respectively (Table 3). Functional even-
ness decreased significantly with increasing altitude (12%
of explained variance; p¼.031) while functional diver-
gence declined significantly with increasing RRI (11%
of explained variance; p¼.042) (Figure 5). Overall,
results consistently suggest that taxonomic diversity pat-
terns are not easily identifiable at local scale and are less
responsive to topographical variation than functional
and structural diversity measures.
Diversity patterns at global, regional, and local scales are
still being increasingly debated in recent studies (Allen &
Gillooly, 2006; Ricklefs, 2004; Ricklefs & He, 2016). We
analyzed the patterns of taxonomic diversity, functional
diversity, and structural diversity, in relation to environ-
mental factors such as slope, altitude, and radiation in the
Woodbush—De Hoek natural forest, a Northern
Mistbelt forest type under strict and legal protection
and with relatively limited human disturbances. From
the results, (a) only Shannon evenness among taxonomic
diversity metrics showed significant and positive response
to increasing slope; (b) structural and functional diver-
sity, unlike taxonomic diversity metrics, varied along
environmental gradients; (c) diameter class-based
Shannon evenness declined with increasing slope while
skewness and coefficient of variation of diameter distri-
bution increased with increasing slope; (d) functional
evenness and divergence decreased with increasing alti-
tude and radiation, respectively; (e) functional richness
and dispersion increased with increasing slope; and (f)
taxonomic diversity measures were less responsive to
topographical variation than functional and structural
(Pears on)
Figure 2. Pearson correlations between environmental variables
and (a) taxonomic diversity, (b) structural diversity, and (c) func-
tional diversity. RRI ¼relative radiation index; Alt ¼altitude;
Ric ¼species richness; H ¼Shannon diversity index; Eq ¼Pielou
evenness; VarD ¼coefficient of variation of diameter;
SkwD ¼skewness of diameter distribution; Hd ¼size class-based
Shannon index; Ed ¼size class-based Shannon evenness;
Fric ¼functional richness; Feve ¼functional evenness;
Fdiv ¼functional divergence; Fdis ¼functional dispersion; blue cells
indicate positive relationships while red cells are negative rela-
tionships; white cells indicate no established correlation.
Mensah et al. 5
Table 1. Model Selection Table Resulting From Multiple Regression Models of Altitude, Slope, and Relative Radiation Index
Effects on Taxonomic Diversity.
Subset model Altitude RRI Slope df logLik AICc Delta Weight
Species richness
1274.728 153.9 0.00 0.337
5 0.195 3 74.144 155.2 1.31 0.175
7 0.398 0.483 4 72.908 155.4 1.52 0.158
20.090 3 74.605 156.1 2.23 0.110
3 0.050 3 74.690 156.3 2.40 0.101
6 0.045 0.223 4 74.124 157.8 3.95 0.047
8 0.015 0.397 0.491 5 72.906 158.3 4.41 0.037
40.150 0.123 4 74.429 158.5 4.56 0.035
Shannon diversity
5 0.297 3 15.00 36.9 0.00 0.287
1216.38 37.2 0.28 0.250
7 0.275 0.495 4 14.39 38.4 1.46 0.139
20.161 3 15.99 38.9 1.97 0.107
30.082 3 16.27 39.5 2.56 0.080
6 0.031 0.315 4 14.99 39.6 2.66 0.076
8 0.010 0.274 0.501 5 14.38 41.3 4.35 0.033
40.159 0.004 4 15.99 41.6 4.65 0.028
Shannon evenness
5 0.396 3 36.575 66.2 0.00 0.343
30.347 3 35.947 65.0 1.26 0.183
70.129 0.303 4 36.716 63.8 2.39 0.104
60.067 0.355 4 36.626 63.7 2.58 0.095
20.283 3 35.277 63.6 2.60 0.094
1 2 34.023 63.6 2.62 0.092
40.149 0.274 4 36.242 62.9 3.34 0.064
80.058 0.123 0.272 5 36.754 61.0 5.22 0.025
Note. RRI ¼Relative Radiation Index; df ¼degree of freedom; logLik ¼log-likelihood; AICc ¼second-order Akaike Information Criterion.
Bold values indicate selected optimal models.
Table 2. Model Selection Table Resulting From Multiple Regression Models of Altitude, Slope, and Relative Radiation Index Effects on
Structural Diversity.
Subset Model Altitude RRI Slope df logLik AICc Delta Weight
Diameter class-based Shannon diversity
1 2 18.119 31.8 0.00 0.429
50.128 3 18.365 29.8 1.99 0.159
20.038 3 18.142 29.4 2.43 0.127
3 0.011 3 18.121 29.3 2.47 0.125
60.185 0.240 4 18.698 27.8 4.00 0.058
70.168 0.249 4 18.574 27.5 4.25 0.051
40.058 0.039 4 18.160 26.7 5.07 0.034
80.173 0.151 0.343 5 18.869 25.2 6.56 0.016
Diameter class-based Shannon evenness
30.554 3 46.983 87.0 0.00 0.294
50.536 3 46.555 86.2 0.86 0.191
7 0.350 0.284 4 47.843 86.1 0.96 0.182
6Tropical Conservation Science
Table 2. Continued
Subset Model Altitude RRI Slope df logLik AICc Delta Weight
80.265 0.376 0.426 5 48.884 85.3 1.78 0.121
60.237 0.680 4 47.315 85.0 2.01 0.107
40.121 0.613 4 47.227 84.9 2.19 0.098
1 2 41.474 78.5 8.54 0.004
2 0.177 3 41.954 77.0 10.06 0.002
Skewness of tree diameter distribution
5 0.484 3 33.044 73.0 0.00 0.381
60.225 0.347 4 32.409 74.4 1.41 0.188
20.436 3 33.882 74.7 1.68 0.165
7 0.096 0.553 4 32.958 75.5 2.51 0.109
40.378 0.119 4 33.679 77.0 3.95 0.053
80.234 0.119 0.427 5 32.272 77.0 4.03 0.051
30.303 3 35.594 78.1 5.10 0.030
1237.040 78.5 5.51 0.024
Coefficient of variation of tree diameter
5 0.444 3 127.638 262.2 0.00 0.470
6 0.107 0.509 4 127.504 264.6 2.41 0.141
70.037 0.417 4 127.626 264.9 2.65 0.125
30.338 3 129.108 265.1 2.94 0.108
12130.926 266.3 4.10 0.061
8 0.110 0.048 0.476 5 127.483 267.5 5.27 0.034
20.203 3 130.295 267.5 5.31 0.033
40.050 0.313 4 129.075 267.8 5.55 0.029
Note. RRI ¼Relative Radiation Index; df ¼degree of freedom; logLik ¼log-likelihood; AICc ¼second-order Akaike Information Criterion. Bold values
indicate selected optimal models.
Table 3. Model Selection Table Resulting From Multiple Regression Models of Altitude, Slope, and Relative Radiation Index Effects on
Functional Diversity.
Subset Model Altitude RRI Slope df logLik AICc Delta Weight
Functional richness
7 0.423 0.668 4 161.075 312.5 0.00 0.343
5 0.364 3 159.517 312.1 0.44 0.276
1 2 157.391 310.3 2.21 0.114
80.003 0.423 0.667 5 161.075 309.6 2.90 0.081
6 0.028 0.381 4 159.526 309.5 3.10 0.073
20.203 3 158.025 309.1 3.42 0.062
30.059 3 157.445 308.0 4.58 0.035
40.228 0.052 4 158.056 306.5 6.04 0.017
Functional evenness
20.393 3 30.084 53.2 0.00 0.391
40.333 0.124 4 30.294 51.0 2.26 0.126
60.464 0.116 4 30.236 50.9 2.37 0.119
30.286 3 28.842 50.8 2.48 0.113
1 2 27.562 50.7 2.57 0.108
80.441 0.303 0.321 5 31.038 49.6 3.67 0.062
5 0.166 3 27.983 49.0 4.20 0.048
70.346 0.083 4 28.897 48.2 5.05 0.031
Mensah et al. 7
Our analyses revealed little evidence of environmental
effects on local-scale taxonomic diversity patterns.
A plausible reason might be that species distributions
are rather most likely governed by large-scale climatic
and edaphic tolerance range and adaptations to physical
conditions of the environment. Local species richness
reportedly reflects, to a significant extent, regional char-
acteristics, including geographical and geological history,
that influence evolutionary diversification and regional
extinction (Ricklefs & He, 2016). The variation of
Shannon evenness with increasing slope supports the
view that local taxonomic diversity patterns are outcome
of complex interactions of local and regional processes.
The local diversity of the studied forest trees species is
presumably codetermined by local processes (human
interventions against fire, competition among species
for limiting resources) and regional processes including
large-scale dispersal mechanism and climatic variation.
The result of significant association between Shannon
evenness and slope gradient is in line with report from
past studies (Cui & Zheng, 2016; Homeier, Breckle,
Gunter, Rollenbeck, & Leuschner, 2010; Takyu, Aiba,
& Kitayama, 2002). For instance, Cui and Zheng (2016)
and Homeier et al. (2010) observed significantly higher
tree species diversity at lower slope position in compari-
son with upper slope sites. However, a study by Zhang
et al. (2016) in mountain forest in China showed such
relationships nonsignificant. These outcomes suggest
that the effects of slope on taxonomic diversity may
partly depend on other factors such as the scale of the
study and the magnitude of the slope gradient.
Unlike Shannon evenness, species richness and
Shannon index did not vary with slope, and neither did
they respond to variations in altitude and RRI. Thus,
these outcomes may also reflect the specific dimension
of the taxonomic diversity measure used. Accordingly,
it is important to mention that Shannon evenness is a
standardized version of the Shannon index and is less
affected by range and quantifies the degree of evenness
Table 3. Continued
Subset Model Altitude RRI Slope df logLik AICc Delta Weight
Functional divergence
30.372 3 23.514 40.1 0.00 0.256
20.366 3 23.435 39.9 0.16 0.237
40.242 0.254 4 24.314 39.0 1.08 0.150
1 2 21.281 38.1 1.99 0.095
5 0.273 3 22.443 38 2.14 0.088
70.365 0.010 4 23.515 37.4 2.68 0.067
60.317 0.080 4 23.505 37.4 2.70 0.067
80.293 0.336 0.147 5 24.470 36.4 3.67 0.041
Functional dispersion
5 0.686 3 55.241 103.6 0.00 0.416
60.252 0.532 4 56.420 103.2 0.32 0.354
70.043 0.655 4 55.266 100.9 2.63 0.112
80.251 0.018 0.519 5 56.425 100.3 3.21 0.083
40.426 0.308 4 53.485 97.4 6.19 0.019
20.576 3 51.770 96.6 6.94 0.013
30.515 3 50.346 93.8 9.79 0.003
1 2 45.722 87.0 16.56 0.000
Note. RRI ¼Relative Radiation Index; df ¼degree of freedom; logLik ¼log-likelihood; AICc ¼second-order Akaike Information Criterion. Bold values
indicate selected optimal models.
Figure 3. Bivariate relationship between taxonomic diversity
metrics and variables retained by the final model; statistics are
obtained from the results of the subset regression analysis pre-
sented in Table 1. Graphs were not displayed for species richness
and Shannon–Wiener diversity as they were not significant.
8Tropical Conservation Science
in taxonomic diversity. As such, Shannon evenness could
stand as a more appropriate measure not only for com-
parison purposes in plots with varying species pool
(Pretzsch, 2009) but also for testing local environmental
effects on taxonomic diversity.
Failure to detect taxonomic diversity patterns can also
partly be attributed to spatial coverage of the data and
methodological caveats such as usage of samples or plot
sizes that are too small to adequately characterize the
diversity of local assemblages (Baraloto et al., 2013),
given that smaller plot sizes would tend to reflect homo-
geneity and may obscure the effects of environmental
variables on taxonomic diversity patterns.
Most structural diversity metrics varied with environ-
mental factors; diameter class-based Shannon evenness
declined with increasing slope while skewness and coeffi-
cient of variation of diameter distribution increased with
increasing slope. Ultimately, because tree size is inher-
ently related to tree growth and resources availability,
local environmental effect can be mediated through
R² = 0.28; p = 0.001
−1.0 −0.5 0.0 0.5 1.0
Relative radiation index
Diameter based
Shannon evenness
R² = 0.26; p = 0.002
10 15 20
Diameter based
Shannon evenness
R² = 0.16; p = 0.016
1400 1450 1500 1550 1600
Skewness of tree
diameter distribution
R² = 0.21; p = 0.007
10 15 20
Skewness of tree
diameter distribution
R² = 0.17; p = 0.014
10 15 20
Coefficient of variation
of tree diameter
Figure 4. Bivariate relationship between structural diversity metrics and variables retained by the final model; statistics are obtained from
the results of the subset regression analysis presented in Table 2.
Mensah et al. 9
light radiation (which is partly dependent on radiation
index), temperature, moisture, runoff, infiltration, and
soil properties (Tsui et al., 2004; Yirdaw et al., 2015).
For instance, in a lowland rain forest of southern
Taiwan, Tsui et al. (2004) observed higher organic
carbon, available N and K, extractable Fe and exchange-
able Na at upper slope position, and highest pH, avail-
able P, exchangeable Ca and Mg on the footslope.
Further, a negative correlation between basal area and
the percent of slope is expected because of the stability
of trees that grow on steeper slopes and gravity-driven
processes (Sefidi et al., 2016). Thus, water exigent species
would likely be more abundant and dominant on flat and
gentle slope sites, while steeper slopes (with limited water
availability and soil nutrients) would tend to challenge
tree growth and size. Our results corroborate with the
expectations on environment-structured tree size variabil-
ity and suggest that local environmental variation played
substantial role in tree size variability in the studied area.
The large values of coefficient of variation and skewness
of diameter distribution on higher slope sites suggest that
environmental filtering associated with increasing slope
favors highly structured stands. This is presumably a
result of efficient use of limited resources by competing
species characterized by conservative traits associated
with high water stress tolerance on higher slope.
Functional diversity metrics also responded better to
local-scale environmental variation compared to taxo-
nomic diversity indices. More specifically, higher slope
was significantly associated with higher functional rich-
ness and dispersion, while functional evenness and diver-
gence decreased with increasing altitude and radiation,
respectively. Our results suggest that functional trait com-
position is also likely dictated by small-scale topograph-
ical variables such as radiation index and altitude.
In addition, incoming radiation is often considered a
surrogate for moisture availability (Stohlgren &
Bachand, 1997).
Harsh conditions (higher slopes, lower water-retention
capacity, increased soil erosion, etc.) will tend to reduce
the range of functional and growth strategies that lead
species to persist, thus leading to functional clustering
and low functional diversity (Asefa et al., 2017).
Contrary to this, we found an opposite pattern, which
can also be explained by shift in plant species compos-
ition as a result of environmental filtering associated with
R² = 0.16; p = 0.036
10 15 20
Functional richness
R² = 0.12; p = 0.031
1400 1450 1500 1550 1600
Functional evenness
R² = 0.11; p = 0.042
−1.0 −0.5 0.0 0.5 1.0
Relative radiation index
Functional divergence
R² = 0.45; p < 0.001
10 15 20
Functional dispersion
Figure 5. Bivariate relationship between functional diversity metrics and variables retained by the final models; statistics are obtained
from the results of the subset regression analysis presented in Table 3.
10 Tropical Conservation Science
slope variation. On lower or gentle slope, environmental
filtering effect may be weak, and lower functional diver-
sity could result from competitive exclusion and domin-
ance of phenotypically similar species with acquisitive
traits, which is in line with the limiting similarity theory
(Abrams, 1983). As such, milder conditions in lower
slopes may promote the proliferation of a few fast-grow-
ing competitive and phenotypically similar species, lead-
ing to functional clustering and lower functional
diversity. On the other hand, high values of functional
diversity at upper slope position suggest that environmen-
tal filtering seems to promote coexistence of species with
conservative traits, probably because these species are
best adapted to harsh conditions.
Concluding Remarks, Limitations,
and Perspectives
We assessed the patterns of species diversity, in relation
to environmental factors such as slope, altitude, and RRI
in the Northern Mistbelt forests in South Africa. At local
scale, there was little evidence of environmental effects on
taxonomic diversity patterns. Nevertheless, differences in
functional and structural diversity seemed to be caused
by topological constraints, particularly difference in
slope, altitude, and radiation. Thus, it was concluded
that taxonomic diversity was less responsive to topo-
graphical variation than structural and functional diver-
sity. Both structural diversity and functional diversity
result from environmental filtering and exclusion based
on competitive interaction between species. The results
further suggest that functional diversity metrics especially
functional evenness, divergence, and dispersion are
good species diversity proxies to be considered in
studies addressing diversity response to local-scale
This study addressed an important aspect of the diver-
sity–environment relationship, yet there is need to
acknowledge the small plot size and subsequent sampling
effects, which may overrule the effect of the extent of
species occurrence patterns and range of taxonomic
diversity. Structural diversity metrics in contrast might
have better reflected both positive and negative inter-
actions between and within species. It is also important
to note that only few environmental variables (slope,
aspect, and topography) were considered while others
(e.g., edaphic, soil nutrients) might also be of significance;
factors such as soil depth, for example, shallower on steep
slopes versus deeper on flat or undulating slopes may well
influence tree size and species distribution. Similarly, soil
moisture content or plant available water might also be of
significance. Research investigating how edaphic factors
and soil fertility measures such as cation exchange cap-
acity in interaction with topography influence diversity
variables would contribute additional insights into our
understanding of the patterns and processes behind diver-
sity and environment relationship.
Implications for Conservation
Northern Mistbelt forests are one of the few natural vege-
tation habitats in South Africa that have expanded into
potentially suitable areas due to protection of the larger
landscape against fire, for timber plantations and inten-
sive agriculture. Today, these forests despite their limited
extent are important habitats for wild animals, pollin-
ators, and many tree species. Taking into account the
historical fire disturbance, the present study suggests
that the relatively strict conservation of these forests
and fire management interventions (protection of sur-
rounding land uses against fire) have reduced the
human disturbance impacts and contributed to a recovery
of these forests and more particularly to predominance of
functionally similar species on lower slope sites. Our
study showed higher functional diversity (coexistence of
species with functionally divergent traits) on higher slope
sites, suggesting that these environmental characteristics
are suitable for conservation areas. However, in addition
to the biodiversity component, a better understanding of
the structures of these forests is important to develop
further conservation strategies for enhancing their service
The authors are grateful to Mr. Otto Pienaar and Mr. Andrew
Perkins for their assistance during the fieldwork in the
Woodbush—De Hoek natural forest. The authors also thank Prof.
Coert Geldenhuys for the comments on the first draft of the manu-
script. The appreciation is extended to the two anonymous
reviewers for the constructive comments on the earlier version of
this article.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
Sylvanus Mensah
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... As pointed out in some recent small-scale studies, plant diversity and structure partly depend on habitat quality and climaterelated factors Mensah, Houe´hanou, et al., 2016). At larger scales, climatic conditions may influence an organism's life cycle and performance, whereas, at smaller scales, local environmental variation in edaphic or topographic factors (elevation, aspect, slope, etc.), resources availability, and species competitive abilities would likely codetermine species structural and dominance patterns (Mensah, Salako, Assogbadjo, & Gle`le`Kakaı¨, 2018;Zhang, Chen, Liu, & Pei, 2016). For example, environmental factors such as soil physical properties and slope were pointed out as potential drivers of vegetation structure and distribution (Gonc¸alves, Filho, Vendrame, & Telles, 2013). ...
... There are two important components in the silt-sand-rocky soils: the rocks that form the undulating relief and the silt resulting from a soil degradation-transport-deposit process. Thus, our results align with the ideas that species structures are environment structured (Mensah, Salako, et al., 2018) and indicate that local environmental variation played a substantial role in A. africana recruitment and growth. Gambiza (2001) reported that the patchy distribution of the soil types and topographic features modify plant-available moisture and available nutrients, thereby influencing plant structure. ...
... Yang, Fan, Li, and Ko (2018) highlighted that slope influenced tree growth through displacement of eroded soil material, water, and plant debris. Our finding is partly supported by Mohammed (2014) and Mensah, Salako, et al. (2018) who showed that topographical variation mainly slope can affect tree structure. Typically, steeper slopes and gravity-driven processes would challenge tree mechanical stability and growth. ...
Full-text available
Information on how abiotic and biotic factors affect species population structures and regeneration are critical for understanding plant growth in natural habitats. Here, we used data from three spatially distinct populations of Afzelia africana Sm. in the Pendjari Biosphere Reserve in Benin, to determine how the species population structures respond to abiotic and biotic factors. Afzelia africana population structures were studied using several parameters including basal area, tree height, density of successive diameter classes, and size class slope. We tested for individual effects of abiotic (mound density, soil type, terrain slope) and biotic (heterospecific tree density) factors on the species population structure. We also tested for similarity of species composition among studied A. africana population stands. Results revealed a tree density structure with mature individuals, and size class distribution indicating a recruitment bottleneck at the juvenile stage (10-20 cm diameter), possibly due to mammal browsing, natural and artificial fires. Heterospecific tree density was positively associated with A. africana adult density, but negatively related to the species growth parameters (mean diameter, basal area and tree height). These results indicate some degrees of niche overlap between A. africana and coexisting species, but also partly reflect A. africana tolerance and adaptation to limited resources environment. Soil type significantly influenced both basal area and regeneration density, greater values being observed on silt-sand-rocky soils. Basal area was higher on steeper slope, probably a result of species conservative strategies. These findings were discussed in line with management and restoration action needs in the Pendjari Biosphere Reserve.
... There has also been evidence of neutral patterns in natural forests and human-modified ecosystems (Mensah et al., 2020a;Sullivan et al., 2017). This lack of consistency across biogeographical areas suggests that biodiversity and ecosystem functioning (BEF) relationships are controlled by an interplay of complex ecological processes, which may vary with environmental gradients, stand developmental stages, ecosystem type and structure and spatial scale (Grace et al., 2016;Hao et al., 2020;Mensah et al., 2018bMensah et al., , 2020dTeixeira et al., 2020). For example, the positive effects of diversity on productivity have been reported to be stronger in environments unfavorable for growth such as dry climate and poor soil quality sites (Mori, 2018;Ouyang et al., 2019;Paquette and Messier, 2011). ...
... AGC was analysed as a continuous response variable using a GLMM with Gaussian distribution, after log-transformation to meet the assumption of normal distribution of residual errors. Species richness was modelled as a count data using a GLMM with Poisson distribution (Mensah et al., 2018b;Zuur et al., 2009). The marginal (R 2 m) and conditional (R 2 c) coefficients of determination were calculated to estimate the variance explained by the fixed and random factors (Nakagawa et al., 2013). ...
Grazing exclosures have been promoted as an effective and low-cost land management strategy to recover vegetation and associated functions in degraded landscapes in the tropics. While grazing exclosures can be important reservoirs of biodiversity and carbon, their potential in playing a dual role of conservation of biodiversity and mitigation of climate change effects is not yet established. To address this gap, we assessed the effect of diversity on aboveground carbon (AGC) and the relative importance of the driving biotic (functional diversity, functional composition and structural diversity) and abiotic (climate, topography and soil) mechanisms. We used a dataset from 133 inventory plots across three altitudinal zones, i.e., highland, midland and lowland, in northern Ethiopia, which allowed local- (within altitudinal zone) and broad- (across altitudinal zones) environmental scale analysis of diversity-AGC relationships. We found that species richness-AGC relationship shifted from neutral in highlands to positive in mid- and lowlands as well as across the altitudinal zones. Structural diversity was consistently the strongest mediator of the positive effects of species richness on AGC within and across altitudinal zones, whereas functional composition linked species richness to AGC at the broad environmental scale only. Abiotic factors had direct and indirect effects via biotic factors on AGC, but their relative importance varied with altitudinal zones. Our results indicate that the effect of species diversity on AGC was altitude-dependent and operated more strongly through structural diversity (representing niche complementarity effect) than functional composition (representing selection effect). Our study suggests that maintaining high structural diversity and managing functionally important species while promoting favourable climatic and soil conditions can enhance carbon storage in grazing exclosures.
... Identifying relationships between plant communities and environmental factors, specifically, the soil properties can help to understand ecological process and rehabilitation operations in natural ecosystems (Mensah et al., 2018). For example, land use changes, vegetation succession, and abandoned fields may have direct impacts on soil quality, biodiversity and hydrological connectivity (Van Hall et al., 2017). ...
... For all the traits and indices, topographic-climatic factors showed the largest part of the variability. The importance of these factors on functional traits was illustrated by (Pakeman et al., 2009;Rossier, 2011;Mensah et al., 2018). This was due to the high elevation gradient of our study area, which imposed different climatic conditions between high and low elevations and a high variety of environments for the growth of plant species. ...
By modeling different indices that describe functional diversity in communities as well as the mean, maximum and minimum of trait values – as a function of abiotic gradients–the impact of environmental changes on the functional components of biodiversity could be predicted. Moreover, functional traits provide the possibility to understand and predict the structure of plant communities as well as ecosystem performance better than species-specific identity-based approaches. Therefore, the main aim of this research was to assess the response of the plant functional diversity to environmental gradients in rangeland areas. As study case, we selected Iran which has 90 million hectares of rangelands that occupy nearly 54.6% of the total area and 65% of natural resources with 8000 plant species showing a richness genetic storage. Specifically, this research was carried out in Lasem, a mountain rangeland of the Mazandaran province. However, this area suffers from unsuitable conditions because of mismanagement as well as the high livestock intensity that cause a decreasing trend in species diversity. To achieve the above-mentioned goals, we applied three functional traits (specific leaf area, leaf dry matter content, and height) for 114 of the most common species. A set of functional indices were then calculated for these traits within 350 experimental plots including Community Weighted Mean (CWM), Functional Regularity (FRo), Functional Richness (FRic), Functional Divergence (FDiv) and Functional Evenness (FEve). Together with the functional indices, we also considered the mean, 5th and 95th quartiles of the trait values. The main results showed that the models of functional traits can provide a powerful tool to assess ecological patterns in the landscape. The most powerful models were the Community Weighted Mean index and Functional Divergence index, while the predictive power of the functional evenness models was consistently the weakest in our study. The results indicated that single-trait diversity indices were better predictor rather than multiple- trait diversity. We conclude that aggregated traits at the community level can give new insights into the ecosystem processes, services, and resilience.
... Several theories have been developed to explain the relationship between biodiversity and carbon, of which niche complementarity and selection theories are mostly supported by the results of biodiversity-carbon studies in different habitats [2,18,19]. In a study on whether the species richness (count of species in a plot or community), functional diversity (variation of different traits between species, e.g., maximum canopy height, wood density) or structural diversity (variation of DBH in a plot or community) has a positive effect carbon storage, the niche complementarity effect is assumed to be effective in carbon storage [2,7,20]. The explanation behind this assumption is that niche complementary theory assumes that the ecosystem carbon capacity is mostly determined by the diverse coexisting species, with their greater trait variation in a community [2,21]. ...
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Exploration of the biodiversity–environmental factors–carbon storage relationships have been a central research question in the changing global climate over the last few decades. However, in comparison to other forest ecosystems, very few studies have been conducted in homegarden agroforestry plantations, which have a tremendous capacity to battle global climate change sustainably. We hypothesized that (i) soil organic matter content has both a direct and indirect effect on aboveground carbon storage through species richness, structural diversity, functional diversity (FD) and functional composition (FC); (ii) some facets of diversity (structural diversity, FD and FC) would be more important in linking species richness to aboveground carbon; (iii) species richness, FC, structural diversity and FD would have a positive impact on aboveground carbon storage (AGC) after considering the effect of soil fertility; and (iv) FC would have a greater effect on AGC than the other three components of biodiversity. These hypotheses were tested using structural equation modeling with field data obtained from 40 homesteads in southwestern Bangladesh. We observed that species richness, FC of maximum canopy height and structural diversity had significant effects on AGC, while soil organic matter and FD of wood density had an insignificant effect. Among the four biodiversity components, the structural diversity had a greater influence on AGC. Contrary to our hypothesis, soil fertility and species richness did not have a significant indirect effect on AGC through their mediators. These four components of biodiversity, along with soil organic matter together explained 49% of the variance in AGC. Our findings indicate that both niche complementarity and selection effects regulate AGC in homegardens, where the former theory had stronger control of AGC in homegardens. Therefore, we need to maintain not only the species diversity but also structural diversity (DBH) and functional composition (canopy height) for enhancing aboveground carbon storage on a sustainable basis in homegardens and other restoration programs under nature-based solution.
... Indeed, abiotic factors such as habitat heterogeneity due to topography, climate, or disturbance at large scale (Zavaleta et al. 2007;Gomez-Aparicio 2008), soil conditions at a smaller scale (Farley and Fitter 1999;Hutchings et al. 2003), have impacts on seedling survival (Oshima et al. 2015;Yan et al. 2015). For example, low survival of seedling may be caused by low light availability due to mature forest canopies (Amusa 2011;Devaney et al. 2018), high temperature, drought and warming (Traore et al. 2011;Massad et al. 2015;Fagundes et al. 2018;Wright et al. 2018), and poor soil physical (type, slope and altitude) and chemical characteristics (organic matter, pH, N-P-K content) (Davis et al. 2018;Mensah et al. 2018;Shoemaker 2018;Yang et al. 2018). ...
Understanding abiotic and biotic factors affecting the survival of seedlings of threatened species such as Afzelia africana is fundamental for restoration and sustainable management purposes. This study used seedling individual-level morphological data and plot-level data to assess the effect of abiotic (season, elevation, soil type and terrain slope) and biotic (seedling initial density, basal diameter, height and number of leaves, insect and fungal infection, insect herbivory, mammal herbivory, vegetation type, adult conspecific density and diameter, and heterospecific density and diameter) factors on the survival probability (at individual level) and survival rate (at plot level) of seedlings of A. africana in the Pendjari Biosphere Reserve. Generalized Linear Mixed Models (GLMMs) were used for data analyses. At individual level, we found that the survival probability of A. africana seedlings increased with initial height, but decreased from wet to dry season. At plot level, the survival rate of A. africana seedlings also decreased from the wet season (0.72 ± 0.05) to the dry season (0.18 ± 0.04) and was inversely proportional to seedling basal diameter (P = 0.024) and density of conspecific adults (P = 0.016). There were also positive effects of seedling initial height (P = 0.026) and mean diameter of conspecific adults (P = 0.037) on survival rate. Among abiotic factors, only terrain slope showed significant and negative effect (P = 0.028) on the survival rate, suggesting higher survival rate on flat terrain. Our findings suggest that sustainably managing seedlings of A. africana would require accounting for conspecific neighboring effect, terrain slope and season-specific actions. Practical aspects of these factors were further discussed.
... Species richness i.e. the number of distinct species, Shannon diversity, and Pielou evenness indices (Daget 1976) were used as measures of taxonomic diversity (see Table 1). As measures of structural diversity, we computed Shannon diversity and Pielou evenness indices based on dbh and height classes (Dȃnescu et al. 2016;Mensah et al. 2018). The dbh and height data Permutation index, P P ¼ P n i¼1 j i À i j j;j i ¼ 1:::n J i is the rank of size class i (i = 1 for the smallest class) with the top rank J i = 1 ...
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Detailed understanding of interactions between humans and their surrounding ecosystems is essential for designing sustainable use and management of these ecosystems. Mangroves are one of the most productive ecosystems worldwide, yet among the most threatened. This study (1) explored main activities of local communities in relationship to mangroves and variation across geographical locations, gender, and age categories, (2) investigated plants and animals used and collected from mangroves and their adjacent areas, and (3) assessed local perception on the impacts of their activities on the degradation of mangroves and potential effects of this degradation on their life attributes (security, income, health and culture). The study was conducted in Grand-Popo Municipality, a hotspot of mangroves and the only one coastal Municipality embedded in the Mono Transboundary Biosphere Reserve in Benin. Data were collected through individual interviews (n = 360) in nine villages of the municipality. Results showed that local communities of Grand-Popo practice nine income generating activities within mangroves and fishing (31.65%), wood collection (22.73%), Cyperus articulatus collection (21.67%), medicinal plant collection (8.98%), and salt production (5.56%) were frequent. There were important differences across geographical locations, gender, and age categories with regard to used mangrove resources and socio-economic activities. Respondents reported twenty-three fish species, two shrimp species, two crab species and one oyster species as fishery resources commonly collected from mangroves. Most interviewees (58.33%) believed that their activities do not negatively impact mangroves despite popular recognition of mangroves’ depletion (75% of respondents). Our findings provide important information on resources collected and used in mangrove ecosystems and highlight strong geographical locations, gender, and age categories variation which have implications for their sustainable participative management.
... Species richness i.e. the number of distinct species, Shannon diversity, and Pielou evenness indices (Daget 1976) were used as measures of taxonomic diversity (see Table 1). As measures of structural diversity, we computed Shannon diversity and Pielou evenness indices based on dbh and height classes (Dȃnescu et al. 2016;Mensah et al. 2018). The dbh and height data Permutation index, P P ¼ P n i¼1 j i À i j j;j i ¼ 1:::n J i is the rank of size class i (i = 1 for the smallest class) with the top rank J i = 1 ...
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Understanding the impacts of wood harvesting intensity on the diversity and structure of ecosystems such as mangroves is essential for defining actions for their sustainable management. We compared tree taxonomic diversity, structural diversity and dominance patterns, density, growth characteristics, size class distribution-SCD and stand stability in West African mangroves subject to low vs. high wood harvesting intensity. Data on tree species identity, total height, diameter (dbh), and conditions (logged, topped or pruned) were collected from ten mangrove sites per harvesting intensity. We found seven species of which two true mangroves species (Rhizophora racemosa and Avicennia germinans) that were dominant across all sites. As expected, there were significantly 3–4, 3–7, and 2–4 times more logged, topped and pruned trees respectively in high-harvesting sites than in lowharvesting sites. Taxonomic diversity was less affected than structural diversity (dbh and heightbased diversity metrics). Tree density was significantly 1.3–5 times higher in low-harvesting sites than in high-harvesting sites for the whole stand and each of the dominant species. Total regeneration density was also low in high-harvesting sites. However, regeneration density was relatively higher in high-harvesting sites for R. racemosa contrary to A. germinans. Trees were also significantly smaller and shorter in high-harvesting sites. The SCD indicated inverse J-shaped distributions, irrespective of the harvesting intensity and showed that tree harvesting targeted mostly dbh classes 10–30 cm. The density of this class was 2.6–6.2 times lower in high-harvesting sites. This study provides important information on impacts of wood harvesting in a marginally studied mangroves’ area.
... The GLMMs were fitted by maximum likelihood (ML) method (Bates et al., 2015), and for each census (Supplementary Information SI2). Due to the high number of independent variables and the likelihood of autocorrelation, a multimodel inference followed by a full averaging procedure using the package ''MuMIn'' (Barton, 2018), was performed on the negative binomial GLMMs, in order to determine which model best fits the data (Mensah et al., 2018b). The optimal models were selected based on the Akaike Information Criterion (AICc). ...
Biotic and abiotic drivers of seedling establishment and survival are fundamental not only for elucidating processes occurring at plant early life stages, but also for assisting species natural regeneration. Keystone, multipurpose and economically important tree species such as Afzelia africana Sm. are reportedly facing recruitment constraints, yet little is known about how abiotic and biotic factors shape the species seedling dynamics. Here, we monitored the species seedlings over one year across three seasons in West Africa savannahs to determine how conspecific and heterospecific biotic neighborhood and habitat heterogeneity correlate with initial seedling density, leaves’ fungal infection and herbivory and how all these factors combined, influence the species seedling survival. Seedling densities increased with increasing conspecific adult densities, and were highest in tree savannahs and on sandy-silt soils. Leaves’ fungal infection and herbivory were also positively associated with conspecific adult density, but were more abundantly observed in tree savannahs than in shrub savannahs. Seedling survival was constrained on higher slope, and negatively affected by conspecific adult density, especially in shrub savannahs. There was a strong evidence for negative density-dependence effects of conspecific adults on seedling survival, which operated through negative effects of herbivory and fungal infection. Habitat heterogeneity was also an important driver, which modulated biotic factors’ effects on seedling survival: tree savannahs promote positive conspecific density-dependence of seedling fungal infection and herbivory more than shrub savannahs. Nonetheless, seedlings were more sensitive to natural enemies in shrub savannahs, suggesting increased negative conspecific density-dependence effects on seedling survival in less dense vegetation, possibly as a result of enhanced specialization of predators and pathogens on a limited set of species. The study brings important insights into the mechanisms that drive the establishment and survival of the species seedling, which should be considered in the design of management activities aiming at the conservation of this endangered species.
... Further, exploring the structural components along climatic gradient is guided by the need to accommodate plant growth responses to varying environments. Several previous studies in Benin showed that species population structures varied with climatic specificities (Mensah et al. 2014;Assogbadjo et al. 2017) because structural patterns are regulated by species tolerance ranges, which in turn are well related to climate and environmental factors (Woodward and Williams 1987;Mensah et al. 2018). These factors act as a key component of environment filtering, facilitating or preventing species establishment and growth (Kraft et al. 2015). ...
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Smallholder farmers make intensive use of anthelmintic plant species in the traditional treatment of animal parasitic infections. As a result, populations of these plant species are exposed to increased disturbances such as plant harvesting, threatening their stability. Information on population structure of threatened plant species is important not only for understanding their ecological status but also for conservation and restoration purposes. Using floris-tic and structural data from 61 plots of 0.09 ha each, we assessed the population structures of the three anthelmin-tic species (Bridelia ferruginea, Mitragyna inermis, and Combretum glutinosum) along the climatic gradient (Guinean, Sudano-Guinean and Sudanian climatic zones) in Benin. Structural characteristics (tree density, basal area, mean diameter, tree height), and species-specific diameter and height distribution were assessed. Results showed that B. ferruginea was found in all three climatic zones, but more prominent in the Sudano-Guinean zone with a scarcity index of less than one per cent. Mitragyna inermis and C. glutinosum were only observed in the Guinean zone and Sudanian zone, respectively. Bridelia ferruginea population structures, especially density and basal area, varied significantly among climatic zones. Diameter-and height-class distributions for the three species exhibited a bell shape with a tendency to right skewness, indicating a predominance of younger trees. These results suggest that the three species are not currently threatened in Benin; however, it would be necessary to prevent overexploitation to guarantee future sustainability.
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Environmental filtering consistently shapes the functional and phylogenetic structure of species across space within diverse forests. However, poor descriptions of community functional and lineage distributions across space hamper the accurate understanding of coexistence mechanisms. We combined environmental variables and geographic space to explore how traits and lineages are filtered by environmental factors using extended RLQ and fourth-corner analyses across different spatial scales. The dispersion patterns of traits and lineages were also examined in a 20-ha tropical rainforest dynamics plot in southwest China. We found that environmental filtering was detected across all spatial scales except the largest scale (100 × 100 m). Generally, the associations between functional traits and environmental variables were more or less consistent across spatial scales. Species with high resource acquisition-related traits were associated with the resource-rich part of the plot across the different spatial scales, whereas resource-conserving functional traits were distributed in limited-resource environments. Furthermore, we found phylogenetic and functional clustering at all spatial scales. Similar functional strategies were also detected among distantly related species, suggesting that phylogenetic distance is not necessarily a proxy for functional distance. In summary, environmental filtering considerably structured the trait and lineage assemblages in this species-rich tropical rainforest.
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The study aimed to investigate the relative significance of effects of climatic variability and human disturbance on the population structure of the threatened species Afzelia africana Sm. ex Pers. in the Republic of Benin in West Africa. Forest inventory data such as regeneration density, tree diameter and total height were compiled from A. africana forest stands under different disturbance regimes in the three climatic zones of Benin. Multiple generalised linear models and non-linear diameter–height equations were fitted to contrast the individual effects of categorical variables, such as climatic zone and disturbance level. Results revealed significantly higher scaling coefficients in less drier regions and low-disturbance stands. The diameter–height relationship was more controlled by the climatic zone than by the disturbance level. Accordingly, the disturbance level contributed only to the intercept of the diameter–height model, whereas the climatic zone significantly influenced both intercept and slope. In addition, when climatic zone and disturbance level were considered as sources of variation in the diameter–height model, the former explained the greater marginal variance. It was concluded that climate has the greater effect on population structure of A. africana in natural stands.
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We investigated community structure and tree species diversity of six subtropical mountain forests in relation to 11 topographical and edaphic factors in Lower Lancang River Basin, Yunnan Province, China, based on a census of all trees with diameter at breast height ≥5 cm in 45 0.06-ha plots. The forests were as follows: a river valley monsoon forest, semi-humid evergreen broad-leaved forest, monsoon evergreen broad-leaved forest, mid-mountain humid evergreen broad-leaved forest, summit mossy dwarf forest, and warm needle-leaved forest. Owing to the variation in microenvironment, forest structure (tree density, mean height, mean diameter at breast height, mean basal area at breast height) and tree diversity indices (the number of species, Margalef richness, Shannon-Wiener diversity, Simpson's index, and Pielou's evenness) differed significantly among forest types but did not differ among sites. We recorded a total of 5155 canopy trees belonging to 204 tree species, 104 genera, and 50 families at three sites, and the co-occurrence of tree species between adjacent communities was higher. A clear forest community distribution along an altitudinal gradient suggested that elevation was important in tree species distribution. Ordination identified elevation, slope degree, slope position, soil pH, organic matter, total nitrogen, and available nitrogen as significant explanatory variables of tree species distribution and showed that elevation was more important than the rest of the environmental variables in affecting local woody plant distribution. Understanding relationships between tree species distribution and environmental factors in subtropical mountain forests of the Lower Lancang River Basin would enable us to apply these findings to forest management and vegetation restoration.
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The classical environmental control model assumes that species diversity is primarily determined by environmental conditions (e.g., microclimate and soil) on the local scale. This assumption has been challenged by the neutral theory that assumes that the maintenance of biodiversity mainly depends on the ecological drift and dispersal limitation. Understanding the mechanisms that maintain biodiversity depends on decomposing the variation of species diversity into the contributions from the various components that affect it. We investigated and partitioned the effects of the biotic component (productivity, forest spatial structure) and the environmental component (topography and soil fertility) on the distribution of tree species richness jointly (the combined effect of environment and biotic process) and separately (the effect of environment or biotic process alone) in 25 permanent plots of 600 m² in a subtropical evergreen broadleaf secondary forest in southern China. The analysis was also completed for trees at different growth stages based on diameter breast height (young trees: 5 cm ≤ DBH < 10 cm, mature trees: 10 cm < DBH ≤ 20 cm, old trees: DBH > 20 cm) within each plot. Our results indicated that (1) tree species richness had significant negative relationship with productivity and a unimodal relationship with its spatially structured distribution; (2) biotic and environmental factors both have significant influence on species richness and jointly explain ~60% of the variation for the overall tree assemblage, and the variation explained by the two components jointly increased across growth stages (34%, 44%, and 75%, respectively); (3) additive variation partitioning revealed that the tree species richness was dominantly controlled by environmental factors (32%), while the biotic component also independently contributed a non-negligible effect (16%); and (4) the dominant fraction changed from the biotic component to the environmental component across growth stages. Results suggest that the tree species richness may be governed from neutral process to environmental control during tree life span in subtropical evergreen broadleaf secondary forests.
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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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Novel species-specific equations for the estimation of height and aboveground biomass were established for four dominant tree species (Syzygium gerrardii Burtt Davy, Combretum kraussii Hochst., Trichilia dregeana Sond. and Croton sylvaticus Hochst.), in the Northern Mistbelt Forests of South Africa. A non-destructive sampling methodology was applied, which was based on measuring standing trees, selecting smaller branches and taking core samples. The species-specific aboveground biomass equations were fitted using predictor variables such as diameter at breast height (DBH) and total height (H). The relative error of estimation was used to examine the accuracy of a pantropical biomass equation versus our established specific model. Biomass values were afterwards up-scaled from tree to stand level for each species, based on the selected models and the forest inventory data. As expected, the DBH–height relationship varied among studied species. The incorporation of both DBH and H in the biomass models significantly improved their precision. A model with DBH² × H as a single variable was suitable for three out of the four studied species, with more than 98% of explained variance. An existing pantropical biomass equation for moist forests showed larger relative error of estimation, especially in the upper range of tree diameter. The estimated aboveground biomass density varied significantly among studied species, with the highest values recorded for S. gerrardii (87.7 ± 15.4 Mg ha⁻¹), followed by T. dregeana (29.4 ± 14.7 Mg ha⁻¹), C. sylvaticus (24.3 ± 11.5 Mg ha⁻¹) and C. kraussii (20.1 ± 6.7 Mg ha⁻¹). It is also found that species-specific production of biomass at the tree level is not always sufficient to reflect the stand-level biomass density. The results from this study contribute to accurately predict aboveground biomass, thereby improving the reliability of the estimates of forest biomass and carbon balance.
Tools for performing model selection and model averaging. Automated model selection through subsetting the maximum model, with optional constraints for model inclusion. Model parameter and prediction averaging based on model weights derived from information criteria (AICc and alike) or custom model weighting schemes. [Please do not request the full text - it is an R package. The up-to-date manual is available from CRAN].