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 (P250 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
