Tree Genetics & Genomes (2006) 2: 121–131
DOI 10.1007/s11295-006-0035-3
ORIGINA L PA PER
Kevin Kit Siong Ng . Soon Leong Lee .
Leng Guan Saw . Joshua B. Plotkin . Chong Lek Koh
Spatial structure and genetic diversity of three tropical tree
species with different habitat preferences within a natural forest
Received: 20 May 2005 / Revised: 24 January 2006 / Accepted: 30 January 2006 / Published online: 3 March 2006
# Springer-Verlag 2006
Abstract Analyses of the spatial distribution pattern,
spatial genetic structure and genetic diversity were carried
out using a 33-ha plot in a hill dipterocarp forest for three
dipterocarps with different habitat preferences, i.e. Shorea
curtisii on the ridges, Shorea leprosula in the valleys and
Shorea macroptera both on the ridges and in the valleys.
The significant spatial aggregation in small-diameter trees
of all the three species was explained by limited seed
dispersal. At the large-diameter trees, only S. macroptera
showed random distribution and this might further prove
that S. macroptera is habitat generalist, whilst S. curtisii
and S. leprosula are habitat specific. The levels of genetic
diversity estimated based on five microsatellite loci were
high and comparable in all the three studied species. As the
three studied species reproduced mainly through outcrossing, the observed high levels of genetic diversity might
support the fact that the plant mating system can be used as
guideline to infer the levels of genetic diversity, regardless
of whether the species is habitat specific or habitat
generalist. The lack of spatial genetic structure but
significant aggregation in the small-diameter trees of all
the three species might indicate limited seed dispersal but
extensive pollen flow. Hence, if seed dispersal is restricted
but pollen flow is extensive, significant spatial aggregation
but no spatial genetic structure will be observed at the
K. K. S. Ng . S. L. Lee (*) . L. G. Saw
Forest Research Institute Malaysia,
52109 Kepong, Selangor, Malaysia
e-mail: [email protected]
Tel.: +60-3-62797145
Fax: +60-3-62804614
J. B. Plotkin
Harvard Society of Fellows, Harvard University,
78 Mount Auburn St,
Cambridge, MA 02138, USA
C. L. Koh
DNA Centre, National Institute of Education,
Nanyang Technological University,
1, Nanyang Walk,
Singapore 637616, Singapore
small-diameter trees, regardless of whether the species is
habitat specific or habitat generalist. The inferred extensive
pollen flow might indicate that energetic pollinators are
involved in the pollination of Shorea species in the hill
dipterocarp forests.
Keywords Genetic diversity . Habitat specific
and generalist . Hill dipterocarp forest . Microsatellite .
Shorea . Spatial distribution pattern and spatial genetic
structure
Introduction
Plants share several common requirements in their
preferable habitats, such as adequate supply of resources
(e.g. light, water and nutrients) for growth and reproduction, availability of pollinators, dispersers and other
symbionts, and the relative absence of herbivores,
predators and pathogens. However, with such common
needs, competition among plants within a habitat can be
intense and this may necessitate generating habitat specialization (Bazaaz 1991). Within a natural forest, habitat
specialization between plant species causes some species to
occur almost everywhere (habitat generalist), whilst other
species are confined to well-defined abiotic conditions
(habitat specific). Habitat specialization of tropical tree
species can be determined by resource-based niche differentiation (Ashton 1969), in which different tree species
adapt to different habitats where they are completely
dominant and relatively more abundant (Hubbell and
Foster 1983). The relationship between the distribution of a
tropical tree species and topography has been studied in
many regions (Hubbell and Foster 1986, Bunyavejchewin
et al. 2003). Several studies, particularly in the aseasonal
lowland dipterocarp forests of Southeast Asia, suggest that
tropical tree species may be habitat specific for particular
edaphic or topographic conditions (Ashton and Hall 1992).
Nonetheless, the relative importance of spatial distribution
patterns and spatial genetic structure of tropical tree species
in relation to habitat specialization of species-rich diptero-
122
carp forests remains unclear, especially in the hill
dipterocarp forests.
The spatial distribution pattern in plant populations is
determined by many abiotic and biotic factors, such as seed
dispersal (Plotkin et al. 2000), gap recruitment (Itoh et al.
1997; Plotkin et al. 2000), distance-dependent mortality
(Itoh et al. 1997), density-dependent recruitment (Okuda et
al. 1997), topography (Plotkin et al. 2000), species density
(Condit et al. 2000), edaphic conditions (Clark et al. 1998),
soil water (Swaine 1996) and soil nutrients (Palmiotto et al.
2004), as well as response to environmental heterogeneity
(Barot et al. 1999). Many tropical tree species show spatial
aggregation at varying scales, generally from higher to
looser aggregation or random distribution with age increase
(Hubbell 1979; Itoh et al. 1997; Okuda et al. 1997; Condit
et al. 2000; Plotkin et al. 2000; Ng et al. 2004).
Spatial genetic structure of plants within a natural
population is primarily influenced by the pattern and
distance of pollen and seed dispersals (Ennos 1994). If
both pollen and seed dispersals are random within a
population, then neither inbreeding nor spatial genetic
structure will develop (Kalisz et al. 2001). However, when
both pollen and seed dispersals are restricted, inbreeding and
intense spatial genetic structuring will result within population, and genetic substructuring of population will evolve
over time as described in the isolation by distance model
(Sokal and Wartenberg 1983). In contrast, if seed dispersal is
random or widely dispersed, regardless of long- or shortdistance pollen dispersal, neither inbreeding nor spatial
genetic structure will develop, as seed dispersal will
eventually randomise the spatial genetic structure within
the population (Loiselle et al. 1995; Kalisz et al. 2001;
Chung et al. 2003).
Many spatial genetic structure statistics are available to
describe and quantify the spatial genetic structuring of
plants. Two commonly used measures are Moran’s I and
kinship coefficients (Sokal and Oden 1978; Loiselle et al.
1995; Kalisz et al. 2001; Chung et al. 2003; Erickson and
Hamrick 2003). Many studies have failed to detect spatial
genetic structure due to several reasons: (1) lack of
sensitivity of the statistical procedure, particularly using
Moran’s I coefficient without multilocus estimator, which
leads to the random effects of genetic drift across loci that
may increase the associated statistical variance (Smouse
and Peakall 1999); (2) utilization of low polymorphism loci
(e.g. allozymes), which limits their statistical power (Streiff
et al. 1998); (3) analysis of spatial genetic structure without
consideration of life stages or age (Kalisz et al. 2001); and
(4) utilization of small sample sizes (Cavers et al. 2005).
Simulation studies have shown that the spatial distribution pattern of trees and microhabitat selection can
influence the spatial genetic structure of tree populations
(Sokal and Wartenberg 1983; Doligez et al. 1998). In
addition, the ecological and evolutionary processes that
affect the spatial distribution pattern can also be contributing factors to the observed significant spatial genetic
structure (Ng et al. 2004). However, these findings were
correlative and might not provide a clear understanding of
the factors that influence the spatial genetic structure, in
particular for habitat-associated tree species within a
heterogeneous environment. The high number of trees
coexisting at a favourable habitat has important implications for selection and persistence of a species in heterogeneous environments. Heterogeneous environments cause
selection favouring either an array of specialist genotypes
or generalist genotypes, depending on the species and the
heterogeneity of the environment (Epperson 1992). Thus,
heterogeneous environments can offer an opportunity to
examine the correlation between habitat-specific species
and their spatial genetic structure. To date, very few studies
have evaluated the important consequences of spatial
genetic structure of tree species in their preferred habitats.
In Peninsular Malaysia, hill dipterocarp forests can be
found in inland forests with altitudes ranging between 300
and 800 m above sea level (Symington 1943). Hilly, uneven
terrain, steep slopes, sheltered valleys or high degree of
environmental heterogeneity are some of the common
characteristics of hill dipterocarp forests. The aim of this
study was to investigate the habitat-related spatial distribution patterns, spatial genetic structure and genetic diversity
at two different diameter classes (small- and large-diameter
classes) of three important dipterocarps with different
habitat preferences in a hill dipterocarp forest, i.e. Shorea
curtisii on the ridges, Shorea leprosula in the valleys and
Shorea macroptera both on the ridges and in the valleys.
The three species are taxonomically grouped under the
Mutica section (Symington 1943). Seed dispersal in these
species is mainly by gravity, seldom exceeding 50 m from
the mother tree (Burgess 1975; Chan 1980). S. leprosula,
although abundant in lowland dipterocarp forests
(Symington 1943; Ashton 1982), is less common in hill
dipterocarp forests and shows a distinctive habitat preference in the valleys. Previous study of S. leprosula in
lowland dipterocarp forest reported that the species
reproduced mainly through outcrossing (outcrossing rate:
83.7%; Lee et al. 2000a). Spatial structure study of
S. leprosula in lowland dipterocarp forest observed a decrease in the magnitude of spatial aggregation and spatial
genetic structure with age increase (Ng et al. 2004).
Population genetic structure study of S. leprosula throughout
Malaysia showed that the species exhibited high levels
of genetic diversity and the majority of the diversity
was partitioned within population (Lee et al. 2000b).
S. macroptera is a common species in both the hill and
lowland dipterocarp forests. In a controlled pollination study,
S. macroptera exhibited a mixed mating system (Chan
1981). Pollination studies in lowland dipterocarp forest
showed that both S. leprosula and S. macroptera are
pollinated by low energetic insects (Thysanoptera), mainly
of thrips and megalurothrips (Chan and Appanah 1980;
Appanah and Chan 1981). S. curtisii is the most common
and abundant canopy tree species in the hill dipterocarp
forests. It tends to be gregarious and shows a distinct habitat
preference for ridge tops (Wyatt-Smith 1963). The species
has been documented to reproduce mainly through outcrossing (outcrossing rate: 96.3%; Obayashi et al. 2002).
123
Materials and methods
Study site and sample collections
This study was conducted at a 33-ha research plot in Sungai
Lalang Forest Reserve (Selangor, 3°05′N, 101°52′E),
Peninsular Malaysia. This forest reserve is categorised as
hill dipterocarp forest, which covers an area of 17,722 ha
and is subdivided into several compartments. Between May
2000 and June 2001, a 33-ha research plot was set up within
Compartment 14 (Fig. 1). Three important dipterocarp tree
species with different habitat preferences were chosen for
this study: S. curtisii on the ridges, S. leprosula in the valleys
and S. macroptera both on the ridges and in the valleys.
Within the 33-ha area, all the individuals with stems ≥5.0 cm
diameter at breast height (dbh) for the three species were
mapped (Fig. 2). Leaves and inner bark tissues were
sampled from all the mapped individuals. The samples
were classified further according to dbh into two
diameter classes: large (BIG, dbh >30 cm) and small
(SMA, dbh = 5–10 cm). Of the 138 S. curtisii
individuals, 91 were classified as BIG and 47 were
classified as SMA. Of the 68 S. leprosula individuals, 35
were classified as BIG and 33 as SMA. For S.
macroptera, of the 171 individuals, 98 were classified
as BIG and 73 as SMA. The tree densities within the 33ha plot were 2.76 trees ha−1 (BIG) and 1.42 trees ha−1
(SMA) for S. curtisii, 1.06 trees ha−1 (BIG) and 1.00 tree
ha−1 (SMA) for S. leprosula and 2.97 trees ha−1 (BIG)
and 2.21 trees ha−1 (SMA) for S. macroptera.
Genetic analysis
Genomic DNA was extracted from leaves or inner bark
tissues using the procedure of Murray and Thompson
Fig. 1 Location of Sungai
Lalang Forest Reserve in
Peninsular Malaysia and the
33-ha study plot set-up within
the 192-ha Compartment 14
(1980) with modifications. The extracted DNAs were
purified further using High Pure PCR Template Preparation
Kit (Roche Diagnostics, Indianapolis, IN, USA). The
samples were genotyped for five microsatellite loci,
developed for S. curtisii (Ujino et al. 1998), i.e. Shc01,
Shc02, Shc03, Shc07 and Shc09. Microsatellites amplification was performed in a 25-μl reaction volume containing 10 ng DNA, 50 mM KCl, 20 mM Tris–HCl (pH 8.0),
1.5 mM MgCl2, 0.2 μM of each primer, 0.2 mM of each
dNTP and 1 U of Platinum Taq DNA polymerase (GIBCOBRL, Germany). The PCR was carried out on a GeneAmp
9700 thermal cycler (Applied Biosystems, USA), for an
initial denaturing step at 94°C for 4 min, followed by 35
cycles each at 94°C for 1 min, 52–54°C for 30 s and 72°C
for 45 s. A final extension step at 72°C for 30 min was
performed after the 35 cycles. Genotyping was done on 5%
denaturing (6 M urea) polyacrylamide gels. Electrophoresis was carried out with 1X Tris–borate–EDTA (TBE)
buffer on an ABI Prism 377 automated DNA sequencer
(Applied Biosystems, USA). Allele sizes were scored
against the internal size standard and the individuals were
genotyped using GeneScan Analysis 3.1 and Genotyper
2.1 software (Applied Biosystems, USA).
Analysis of genetic diversity and fixation index
The levels of genetic diversity were estimated for mean
number of alleles per locus (Aa), effective number of alleles
per locus (Ae; Crow and Kimura 1970), allelic richness
(Rs; Petit et al. 1998), observed heterozygosity (Ho) and
expected heterozygosity (He; Nei 1987) with the assistance
of programs BIOSYS-1 (Swofford and Selander 1981),
POPGENE version 1.31 (Yeh et al. 1999) and FSTAT
version 2.9.3.2 (Goudet 2002). Fixation index (Fis) was
calculated based on Weir and Cockerham’s (1984) estima-
124
a Shorea curtisii
550
500
500
450
450
400
400
350
350
300
300
(m)
(m)
550
250
b Shorea leprosula
250
200
200
150
150
100
100
50
50
0
0
0
100
200
300
400
500
600
0
100
200
(m)
300
400
500
600
(m)
550
c Shorea macroptera
500
450
400
(m)
350
300
250
200
150
100
50
0
0
100
200
300
400
500
600
(m)
Fig. 2 The distributions of the three studied species within a 33-ha
study plot (600×550 m) in Sungai Lalang Forest Reserve. Within
this study plot, a S. curtisii dominates the ridges, b S. leprosula is
present in the valleys and c S. macroptera is common both on the
ridges and in the valleys. The individuals were classified according
to diameter at breast height (dbh) into two diameter classes: = BIG
(dbh >30 cm) and ○ = SMA (dbh 5–10 cm)
tor using the program FSTAT. Significant positive or
negative Fis was tested using 200 randomisations (default
parameter in FSTAT) for each locus.
formed using the program SPATIAL POINT PATTERN
ANALYSIS (Haase 1995).
•
Analysis of spatial genetic structure
Analysis of spatial distribution pattern
The spatial distribution pattern was tested for clumping
using univariate second-order spatial pattern analysis based
on Ripley’s (1976) K-function (see Haase 1995). This
method considers all individuals within a given radius t of the
focal individual. The estimator of the function K(t) used is:
XX
K ðtÞ ¼ n2 A
w1
ij It uij ;
i6¼j
where n is the number of plants in the plot, A is the area of
the plot in meter square (m2), wij is a weighting factor to
correct for edge effects, It is a counter variable and uij is the
distance between trees i and j (Haase 1995). The K(t) was
calculated separately for each distance t (0–250 m in 50 m
increments). Results were displayed as a plot of √[K(t)/π]−t,
and then plot K(t) vs t to examine the spatial dispersion at
all distance classes t. To test the significant deviation from a
random distribution, Monte Carlo computer-generated data
were used. To construct a 95% confidence envelope, 95
simulations were run, and the sample statistic was compared with this envelope. These calculations were per-
Spatial genetic structure was analysed using two different
estimators, the Moran’s I coefficient and the kinship
coefficient. For Moran’s I, the correlograms were computed as an indication of spatial scale of genetic substructuring (Sokal and Oden 1978; Sokal and Wartenberg
1983). Alleles with a frequency >5% were included in the
analysis of the Moran’s I. Mean Moran’s I coefficients were
calculated for all alleles as a summary statistic. A permutation procedure using Monte Carlo simulations was
applied to test significant deviation from random spatial
distribution of each calculated measure (Manly 1997).
Each permutation consisted of a random redistribution of
multilocus genotypes over the spatial coordinate of the
sampled trees. For each of the spatial distance classes,
observed values were compared with the distribution
obtained after 1,000 permutations. A 95% confidence
interval for the parameters was constructed as an interval
(Streiff et al. 1998). All calculations and tests were
performed using the program SPATIAL GENETIC SOFT
WARE—SGS (Degen et al. 2001).
The kinship coefficient, a measure of coancestry (Fij),
can estimate relationship between pairs of mapped
125
Table 1 Summary of genetic diversity measures based on five microsatellite loci in two diameter classes (BIG and SMA) of S. curtisii, S.
leprosula and S. macroptera from Sungai Lalang Forest Reserve: total number of alleles (At), effective number of alleles per locus (Ae),
allelic richness (Rs) and expected heterozygosity (He)
Diameter class/locus
S. curtisii
At
BIG
Shc01
Shc02
Shc03
Shc07
Shc09
Mean
S.E.
SMA
Shc01
Shc02
Shc03
Shc07
Shc09
Mean
S.E.
S. leprosula
Ae
Rs
He
At
S. macroptera
Ae
Rs
He
At
Ae
Rs
He
29
8
3
25
14
15.8
4.9
13.06
3.33
2.55
6.71
8.31
6.79
0.46
23.75
7.32
3.00
20.19
12.80
13.41
0.94
0.93
0.70
0.61
0.86
0.89
0.80
0.06
17
6
4
11
8
9.2
0.6
9.84
1.90
2.33
5.22
5.03
4.86
0.39
16.60
5.76
4.00
11.00
8.00
9.07
0.87
0.91
0.48
0.58
0.82
0.81
0.72
0.08
15
8
2
13
9
9.4
0.5
3.83
2.37
1.98
6.52
4.30
3.80
0.19
14.95
7.59
2.00
13.00
8.70
9.25
0.75
0.75
0.59
0.50
0.86
0.78
0.70
0.07
18
6
3
17
12
11.2
3.0
10.77
2.78
2.49
5.26
8.87
6.03
0.55
17.73
5.93
3.00
16.59
12.00
11.05
0.96
0.92
0.65
0.60
0.82
0.90
0.78
0.06
20
6
5
18
13
12.4
0.8
12.03
2.71
2.18
5.93
7.61
6.09
0.49
19.33
5.81
4.90
17.33
12.75
12.02
1.14
0.93
0.64
0.55
0.84
0.88
0.77
0.07
22
7
3
21
9
12.4
0.8
6.89
2.47
1.97
7.59
5.82
4.95
0.24
19.85
6.67
3.00
18.63
8.68
11.36
0.99
0.86
0.60
0.50
0.88
0.84
0.73
0.08
individuals i and j or the probability that genes in different
individuals within subpopulations are identical by descent
(Cockerham 1969). This statistic was computed between
all pairs of individuals belonging to the same ploidal using
multilocus estimates obtained following Loiselle et al.
(1995). The average Fij over pairs of individuals was
computed for distance intervals of 50 m. The standard error
over loci was estimated using the jackknife method. The
absence of spatial genetic structure was tested within each
class using a permutation method (1,000 permutations);
spatial distances were randomly permuted among pairs of
individuals, and the estimated value of the average kinship
coefficient was compared with the distribution after
permutations. These calculations were performed using
the program SPAGeDi 1.1 (Hardy and Vekemans 2002).
Results
Genetic diversity and fixation index
The levels of genetic diversity estimated based on five
microsatellite loci are summarised in Table 1. The mean
number of alleles per locus observed for S. curtisii ranged
from 11.2 (SMA) to 15.8 (BIG), from 9.2 (BIG) to 12.4
(SMA) for S. leprosula and from 9.4 (BIG) to 12.4 (SMA)
for S. macroptera. The mean effective number of alleles
(Ae) and allelic richness (Rs) for S. curtisii were highest at
BIG (Ae=6.79 and Rs=13.41), followed by SMA (Ae=6.03
and Rs=11.05). However, the mean Ae and Rs for
S. macroptera and S. leprosula were observed to be
highest at SMA followed by BIG (Table 1). The mean
Table 2 Fixation index (Fis) according to Weir and Cockerham (1984) based on five microsatellite loci in two diameter classes (BIG and
SMA) of S. curtisii, S. leprosula and S. macroptera from Sungai Lalang Forest Reserve. Significant positive or negative Fis was tested using
200 randomisations
Locus
S. curtisii
BIG
Shc01
Shc02
Shc03
Shc07
Shc09
All
0.072*
−0.185**
0.057
0.141**
0.194**
0.066**
S. leprosula
SMA
0.124*
−0.348**
0.080
0.098
0.199**
0.026
*Significantly different from zero (P<005)
**Significantly different from zero (P<0.01)
BIG
0.033
−0.231*
−0.206
0.110
0.333**
0.045
S. macroptera
SMA
−0.009
−0.429**
0.050
0.068
0.080
−0.047
BIG
0.121
−0.200**
0.021
0.068
−0.068
−0.003
SMA
0.186**
−0.111
0.194
0.092*
0.161
0.111**
126
BIG
40
30
25
20
15
10
5
0
-5
-10
-15
30
10
0
-20
50
100
b
150
200
0
250
50
100
150
t (m)
t (m)
BIG
SMA
200
250
200
250
200
250
50
25
20
15
10
5
0
-5
-10
-15
-20
40
Ripley'sK
Ripley's K
20
-10
0
30
20
10
0
-10
-20
-30
0
50
100
c
150
200
0
250
50
100
150
t (m)
t (m)
BIG
SMA
10
8
6
4
2
0
-2
-4
-6
-8
15
10
Ripley'sK
Ripley'sK
SMA
Ripley'sK
Ripley'sK
a
5
0
-5
-10
0
50
100
150
200
250
t (m)
0
50
100
150
t (m)
Fig. 3 The spatial distribution pattern analysis using Ripley’s
K-function for each diameter class across the 33-ha study plot for
a S. curtisii (significant aggregation at BIG: t=0–200 m and SMA:
t=0–150 m), b S. leprosula (significant aggregation at BIG and
SMA: t=0–200 m) and c S. macroptera (significant aggregation at
SMA: t=0 to>250 m). Continuous lines represent the sample statistic
and dashed lines represent the upper and lower 95% confidence
envelope over t=0–250 m
expected heterozygosity was relatively similar across the
two diameter classes for all the three species (S. curtisii:
BIG=0.80 and SMA=0.78; S. leprosula: BIG=0.72 and
SMA=0.77; and S. macroptera: BIG=0.70 and
SMA=0.73).
The fixation indices (Fis) calculated for all the three
studied species showed positive or negative values
(Table 2). For S. curtisii, deviations from the Hardy–
Weinberg equilibrium were observed in four loci at BIG
(Shc01, Shc02, Shc07 and Shc09) and in three loci at SMA
(Shc01, Shc02 and Shc09). The Fis values calculated for
S. leprosula were found to be significantly different from
zero in two loci at BIG (Shc02 and Shc09) and in one locus
at SMA (Shc02). For S. macroptera, a significant departure
from zero was found in Shc02 at BIG and in Shc01 and
Shc07 at SMA. Over loci, significant positive Fis values
were observed for S. leprosula at BIG (P<0.01) and for
S. macroptera at SMA (P<0.01), which might indicate
deficiency of heterozygotes.
Spatial distribution pattern
The results of the spatial distribution pattern analysis are
shown in Fig. 3. For S. curtisii (habitat-specific species),
significant spatial aggregations were observed at SMA
(t=0–150 m) and BIG (t=0–200 m). Similarly, S. leprosula
(habitat-specific species) also showed significant spatial
Fig. 4 The estimated Moran’s I and coancestry (Fij) correlograms "
for two diameter classes (BIG and SMA) of a S. curtisii,
b S. leprosula and c S. macroptera from the 33-ha study plot
using five microsatellite loci. Dashed lines represent upper and lower
95% confidence limits around zero relationship
127
BIG
0.08
0.06
0.04
0.02
0
-0.02
-0.04
50
100
250
0.06
0.02
-0.02
-0.06
-0.1
50
150
200
Distance (m)
250
-0.05
-0.1
250
300
150
200
Distance (m)
250
300
250
300
250
300
250
300
250
300
BIG
0.07
0.02
-0.03
-0.08
50
100
150
200
Distance (m)
250
300
50
SMA
0.16
0.12
0.08
0.04
0
-0.04
-0.08
-0.12
-0.16
-0.2
100
150
200
Distance (m)
SMA
0.06
0.04
Coancestry
Moran's I
100
0.12
0.05
0
150
200
Distance (m)
SMA
50
BIG
0.2
0.15
0.1
100
0.05
0.04
0.03
0.02
0.01
0
-0.01
-0.02
-0.03
-0.04
-0.05
300
Coancestry
Moran's I
100
-0.15
-0.2
0.02
0
-0.02
-0.04
-0.06
50
100
0.08
150
200
Distance (m)
250
50
300
BIG
0.06
0.04
100
0.04
0.02
0
-0.02
-0.04
150
200
Distance (m)
BIG
0.06
Coancestry
Moran's I
-0.01
-0.02
50
-0.14
0.02
0
-0.02
-0.04
-0.06
-0.08
-0.06
50
100
0.08
150
200
Distance (m)
250
300
50
100
0.08
SMA
0.02
0
-0.02
-0.04
150
200
Distance (m)
SMA
0.06
Coancestry
0.06
0.04
Moran's I
0.01
0
300
Coancestry
0.1
Moran's I
150
200
Distance (m)
SMA
0.14
c
0.03
0.02
-0.03
-0.04
-0.06
-0.08
b
BIG
0.04
Coancestry
Moran's I
a
0.04
0.02
0
-0.02
-0.04
-0.06
-0.08
-0.06
50
100
150
200
Distance (m)
250
300
50
100
150
200
Distance (m)
128
aggregation at BIG (t=0–200 m) as well as at SMA (t=0–
200 m). The magnitudes of spatial aggregation for these
two habitat-specific species were observed from less
aggregation to intense aggregation, as diameter class
increased. For the habitat-generalist species (S. macroptera), however, significant spatial aggregation was only
observed at SMA (t=0 to >250 m).
diversity (Ng et al. 2004). Genetic diversity information
generated for a species can then be adapted to species that
have similar types of mating systems. Thus, for the
management of forest tree species, we might want to group
the species according to mating systems.
Spatial distribution pattern
Spatial genetic structure
The correlograms computed using Moran’s I and kinship
coefficients showed similar results, which might indicate
that both statistics were sensitive enough to describe and
quantify spatial genetic structure (Fig. 4). Spatial genetic
structure analyses showed significant spatial genetic structure only at BIG for all the three studied species (Fig. 4),
with significant distance of 0–100 m for S. curtisii, and of
0–50 m for S. leprosula and S. macroptera. In summary,
both the habitat-specific and habitat-generalist species
showed spatial genetic structure at BIG but no significant
spatial genetic structure at SMA.
Discussion
Genetic diversity
The levels of genetic diversity estimated based on five
microsatellite loci in all the three studied species were
comparable with those reported on other dipterocarps, i.e.
Neobalanocarpus heimii (Konuma et al. 2000), Dryobalanops aromatica (Lim et al. 2002) and Shorea ovalis ssp.
sericea (Ng et al. 2004). The observed mean effective
number of alleles (Ae) and expected heterozygosity (He) at
the large-diameter class of S. curtisii (Ae=6.79 and
He=0.80, based on 91 samples over five loci) were higher
in comparison with the results from the previous study of
the same species in Semangkok Forest Reserve (Ae=4.6
and He=0.769, based on 108 adult samples over three loci;
Obayashi et al. 2002). For S. leprosula, the mean Ae and He
at the large-diameter class (Ae=4.86 and He=0.72, based on
35 samples over five loci) were comparable with our
previous results on the same species in Pasoh Forest
Reserve (Ae=5.50 and He=0.70, based on 62 adult samples
over seven loci; Ng et al. 2004).
According to Hamrick et al. (1992), outcrossing woody
plant species tends to have more genetic diversity within
species and populations. Similarly, the present study
showed that both the habitat-specific and habitat-generalist
species harbour high levels of genetic diversity. As the
three studied species reproduce mainly through outcrossing, the high levels of genetic diversity observed in these
species might support the fact that the plant mating system
can be used as guideline to infer the levels of genetic
diversity, regardless of whether the species is habitat
specific or habitat generalist. Our previous study also
showed that autotetraploid species with apomictic mode of
reproduction displayed relatively high levels of genetic
The spatial distribution pattern analyses using Ripley’s Kfunction showed significant aggregation at the smalldiameter classes of all the three species and this further
proves that seed dispersal for all the three species is limited;
although the three studied species produce winged seeds,
seed dispersal is mainly via gravity, seldom exceeding
50 m from the mother tree (Burgess 1975; Chan 1980).
Intense aggregation of S. curtisii and S. leprosula, but
random distribution of S. macroptera at the large-diameter
classes, might further prove that S. curtisii and S. leprosula
are habitat specific, whilst S. macroptera is habitat
generalist within the study area.
For small-diameter classes, the evidence presented in
this study strongly supports the hypothesis that limited seed
dispersal directly contributes to aggregation in the smalldiameter classes of trees, regardless of whether the species
are habitat specific or habitat generalist. For large-diameter
classes, given the high degree of environmental heterogeneity in hill dipterocarp forest, and for the habitat-specific
species (S. curtisii and S. leprosula), the aggregation in
large-diameter trees can be explained by strong habitat
preference, i.e. S. curtisii is confined to ridges and
S. leprosula dominates valleys. Burgess (1969), in his
autecological study of S. curtisii, found considerable
evidence that the species is specialised and restricted to
dry sites. Ridge crests are dry owing to increased
evaporation as wind speed increases with increased
exposure at higher elevations, to rapid runoff and drainage.
The coarse-textured soil found especially over sedimentary
rocks and quartz-rich parts of granite rocks has relatively
low water-holding capacity. Other studies also indicated
that species distributions are also strongly aggregated with
respect to variation in topography, soil water and soil
nutrient status (Clark et al. 1998; Palmiotto et al. 2004). For
the habitat-generalist species (S. macroptera), the random
distribution observed for the large-diameter trees might be
due to the compensatory mortality due to intra- and interspecific competition, herbivores and plant diseases that
aggravate the thinning process at the small-diameter trees
so that only few individuals will be able to survive and
form the future adults in both the valleys and on the ridges.
Many tropical tree species exhibit spatial aggregation at
varying scales, normally from higher to looser aggregation
or random distribution with age increase (Condit et al.
2000). Our previous study on S. leprosula in a lowland
dipterocarp forest also showed a similar trend (Ng et al.
2004). However, the present study on S. leprosula in hill
dipterocarp forest gave a contrasting result; the magnitude
of spatial aggregation became more intense with age
increase. This contrasting result between hill and lowland
129
dipterocarp forests suggests that ecological and evolutionary processes might be operating differently at different
forest types to shape the spatial distribution pattern of
large-diameter trees, as the habitat of the lowland
dipterocarp forest is rather homogenous compared with
the high degree of environmental heterogeneity observed in
the hill dipterocarp forest. However, from the present
result, it is still unclear how and which processes exactly
might shape the spatial distribution pattern in different
forest types. Nonetheless, the observed differences of
spatial distribution pattern between lowland and hill
dipterocarp forests might be related to the degree of habitat
heterogeneity, as predicted by the niche specialization
hypothesis (Ashton 1969; Hubbell and Foster 1983).
Spatial genetic structure
Moran’s I has been used widely, but recently, many studies
have employed the kinship coefficient for spatial genetic
structure analyses (Loiselle et al. 1995; Kalisz et al. 2001;
Chung et al. 2003; Erickson and Hamrick 2003). Several
studies have reported that the kinship coefficient has a
well-developed foundation in population genetics theory.
The coefficient provides a natural means of combining
data, over multiple alleles, at a locus and over loci to obtain
a more powerful analysis of spatial genetic structure
compared with the traditional single-locus estimators
(Smouse and Peakall 1999). In this study, however, the
spatial genetic structure analyses using Moran’s I and
kinship coefficients revealed similar results for all the three
studied species, indicating both statistics are comparable
and sensitive to describe and quantify spatial genetic
structure.
For all the three studied species, significant spatial
genetic structure was observed at the large-diameter classes
but not at the small-diameter classes. Given the high degree
of environmental heterogeneity in hill dipterocarp forest,
the spatial genetic structure in the large-diameter classes for
the habitat-specific species (S. curtisii and S. leprosula) can
be explained by intense selection in favour of certain
genotypes in the small-diameter trees; seedlings with
suitable genotypes for a specific habitat will be selected
to exist continuously and subsequently to become adults.
For S. macroptera, the ecological and evolutionary
processes (e.g. limited seed dispersal, intra- and interspecific competition, herbivores and plant diseases) that
affect spatial aggregation patterns might also be responsible in shaping the spatial genetic structure observed in the
large-diameter classes. The lack of spatial genetic structure
but significant aggregation in the small-diameter classes of
all the three species might indicate limited seed dispersal
and extensive pollen flow. Therefore, if seed dispersal is
restricted but pollen flow is extensive, significant spatial
aggregation but no spatial genetic structure will be
observed at the small-diameter trees, regardless of whether
the species is habitat specific or habitat generalist.
The finding, however, contradicts the explanation given
by Hamrick and Nason 1996. Their study shows that when
pollen dispersal is random but seed dispersal is highly
localised, there will be no inbreeding but spatial aggregations of siblings will result in significant fine-scale genetic
structure. Nevertheless, we still believe that if seed
dispersal is restricted but pollen flow is extensive, significant spatial aggregation but no spatial genetic structure
will be observed at the small-diameter trees. This can be
explained that in the event of extensive pollen flow, a
mother tree will receive pollen from various paternal trees
and subsequently the mother tree will produce seeds that
consist of various genotypes. The restricted seed dispersal
entails the establishment of seedlings around the mother
tree that creates the observed spatial aggregation but it is
not necessary to cause significant spatial genetic structure
at the seedling stage if the seedlings are genetically not
similar. In addition, strong overlapping of seed shadow
from neighbouring mother trees will contribute to the
absence of the spatial genetic structure.
The inferred extensive pollen flow might indicate that
energetic pollinators are involved in the pollination of
Shorea species in the hill dipterocarp forests. However,
previous studies in lowland dipterocarp forest (Pasoh
Forest Reserve) reported that the low-energy flower thrips
(Thysanoptera), mainly thrips and megalurothrips, are the
primary pollinators for Shorea species (Chan and Appanah
1980; Appanah and Chan 1981). In addition, our recent
study on S. leprosula within a lowland dipterocarp forest
revealed significant spatial genetic structure and this was
explained by limited seed and pollen dispersals (Ng et al.
2004). Furthermore, an extensive gene flow study in Pasoh
Forest Reserve also showed a high frequency of shortdistance pollen flow among S. leprosula (Lee et al.
unpublished data). Conversely, Sakai et al. (1999), in a
study conducted in Sarawak, reported that small beetles
and social bees are the main pollinators of Shorea species.
Hence, a future study shall attempt to compare the spatial
structures of dipterocarps in lowland and hill dipterocarp
forests to verify the postulation that different pollinators
might be involved in the pollination of Shorea species in
different forest types.
Acknowledgements The authors thank Ghazali Jaafar, Yahya
Marhani, Mariam Din, Sharifah Talib, the late Baya Busu, Ramli
Ponyoh, Ayau Kanit and Mohd. Lan Musa for their assistance in the
field and laboratory. The Selangor State Forest Department is
acknowledged for granting the permission to work in the forest under
their tenure. KNKS was supported by a PhD fellowship from the
Forest Research Institute Malaysia. This study was supported in part
by the IRPA research grant (09-04-01-0006-EA001) and the Timber
Export Levy Fund under the Project A179 QIZZ.
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