Spatial structure and genetic diversity of three tropical tree species with different habitat preferences within a natural forest

Spatial structure and genetic diversity of three tropical tree species with different habitat preferences within a natural forest
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  Tree Genetics & Genomes (2006) 2: 121  –  131DOI 10.1007/s11295-006-0035-3 ORIGINAL PAPER  Kevin Kit Siong Ng.Soon Leong Lee.Leng Guan Saw.Joshua B. Plotkin.Chong Lek Koh Spatial structure and genetic diversity of three tropical treespecies 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 carriedout using a 33-ha plot in a hill dipterocarp forest for threedipterocarps with different habitat preferences, i.e. Shoreacurtisii 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 treesof all the three species was explained by limited seeddispersal. At the large-diameter trees, only S. macroptera showed random distribution and this might further provethat  S. macroptera is habitat generalist, whilst  S. curtisii and S. leprosula are habitat specific. The levels of geneticdiversity estimated based on five microsatellite loci werehigh and comparable in all the three studied species. As thethree studied species reproduced mainly through outcross-ing, the observed high levels of genetic diversity might support the fact that the plant mating system can be used asguideline to infer the levels of genetic diversity, regardlessof whether the species is habitat specific or habitat generalist. The lack of spatial genetic structure but significant aggregation in the small-diameter trees of allthe 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 thesmall-diameter trees, regardless of whether the species ishabitat specific or habitat generalist. The inferred extensive pollen flow might indicate that energetic pollinators areinvolved in the pollination of  Shorea species in the hilldipterocarp forests. Keywords Genetic diversity.Habitat specificand generalist .Hill dipterocarp forest .Microsatellite. Shorea .Spatial distribution pattern and spatial geneticstructure 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 reproduc-tion, availability of pollinators, dispersers and other symbionts, and the relative absence of herbivores, predators and pathogens. However, with such commonneeds, competition among plants within a habitat can beintense and this may necessitate generating habitat spe-cialization (Bazaaz1991). Within a natural forest, habitat specialization between plant species causes some species tooccur almost everywhere (habitat generalist), whilst other species are confined to well-defined abiotic conditions(habitat specific). Habitat specialization of tropical treespecies can be determined by resource-based niche differ-entiation (Ashton1969), in which different tree speciesadapt to different habitats where they are completelydominant and relatively more abundant (Hubbell andFoster 1983). The relationship between the distribution of a tropical tree species and topography has been studied inmany regions (Hubbell and Foster 1986, Bunyavejchewinet al.2003). Several studies, particularly in the aseasonallowland dipterocarp forests of Southeast Asia, suggest that tropical tree species may be habitat specific for particular edaphic or topographic conditions (Ashton and Hall1992). Nonetheless, the relative importance of spatial distribution patterns and spatial genetic structure of tropical tree speciesin relation to habitat specialization of species-rich diptero- K. K. S. Ng.S. L. Lee ( * ). L. G. SawForest Research Institute Malaysia,52109 Kepong, Selangor, Malaysia e-mail: +60-3-62797145Fax: +60-3-62804614J. B. PlotkinHarvard Society of Fellows, Harvard University,78 Mount Auburn St,Cambridge, MA 02138, USAC. L. KohDNA Centre, National Institute of Education, Nanyang Technological University,1, Nanyang Walk,Singapore 637616, Singapore  carp forests remains unclear, especially in the hilldipterocarp forests.The spatial distribution pattern in plant populations isdetermined by many abiotic and biotic factors, such as seeddispersal (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 (Swaine1996) and soil nutrients (Palmiotto et al.2004), as well as response to environmental heterogeneity(Barot et al.1999). Many tropical tree species show spatialaggregation at varying scales, generally from higher tolooser aggregation or random distribution with age increase(Hubbell1979; 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 anddistance of pollen and seed dispersals (Ennos1994). If  both pollen and seed dispersals are random within a  population, then neither inbreeding nor spatial geneticstructure will develop (Kalisz et al.2001). However, when bothpollenandseeddispersalsarerestricted,inbreedingandintense spatial genetic structuring will result within popula-tion, and genetic substructuring of population will evolveover time as described in the isolation by distance model(SokalandWartenberg1983).Incontrast,ifseeddispersal israndom or widely dispersed, regardless of long- or short-distance pollen dispersal, neither inbreeding nor spatialgenetic structure will develop, as seed dispersal willeventually randomise the spatial genetic structure withinthe population (Loiselle et al.1995; Kalisz et al.2001; Chung et al.2003).Many spatial genetic structure statistics are available todescribe and quantify the spatial genetic structuring of  plants. Two commonly used measures are Moran ’ s I  andkinship coefficients (Sokal and Oden1978; Loiselle et al.1995; Kalisz et al.2001; Chung et al.2003; Erickson and Hamrick 2003). Many studies have failed to detect spatialgenetic structure due to several reasons: (1) lack of sensitivity of the statistical procedure, particularly usingMoran ’ s I  coefficient without multilocus estimator, whichleads to the random effects of genetic drift across loci that may increase the associated statistical variance (Smouseand Peakall1999); (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 distribu-tion pattern of trees and microhabitat selection caninfluence the spatial genetic structure of tree populations(Sokal and Wartenberg1983; Doligez et al.1998). In addition, the ecological and evolutionary processes that affect the spatial distribution pattern can also be contribut-ing factors to the observed significant spatial geneticstructure (Ng et al.2004). However, these findings werecorrelative 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 treescoexisting at a favourable habitat has important implica-tions for selection and persistence of a species in hetero-geneous environments. Heterogeneous environments causeselection favouring either an array of specialist genotypesor generalist genotypes, depending on the species and theheterogeneity of the environment (Epperson1992). Thus,heterogeneous environments can offer an opportunity toexamine the correlation between habitat-specific speciesand their spatial genetic structure. To date, very few studieshave evaluated the important consequences of spatialgenetic structure of tree species in their preferred habitats.In Peninsular Malaysia, hill dipterocarp forests can befound in inland forests with altitudes ranging between 300and 800 m above sea level (Symington1943). Hilly, uneventerrain, steep slopes, sheltered valleys or high degree of environmental heterogeneity are some of the commoncharacteristics of hill dipterocarp forests. The aim of thisstudy was to investigate the habitat-related spatial distribu-tion patterns, spatial genetic structure and genetic diversityat two different diameter classes (small- and large-diameter classes) of three important dipterocarps with different habitat preferences in a hill dipterocarp forest, i.e. Shoreacurtisii 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 (Symington1943). Seed dispersal in thesespecies is mainly by gravity, seldom exceeding 50 m fromthe mother tree (Burgess1975; Chan1980). S. leprosula ,although abundant in lowland dipterocarp forests(Symington1943; Ashton1982), is less common in hill dipterocarp forests and shows a distinctive habitat pref-erence in the valleys. Previous study of  S. leprosula inlowland dipterocarp forest reported that the speciesreproduced mainly through outcrossing (outcrossing rate:83.7%; Lee et al.2000a ). Spatial structure study of  S. leprosula in lowland dipterocarp forest observed a de-crease in the magnitude of spatial aggregation and spatialgenetic structure with age increase (Ng et al.2004).Populationgeneticstructurestudyof  S.leprosula throughout Malaysia showed that the species exhibited high levelsof genetic diversity and the majority of the diversitywas partitioned within population (Lee et al.2000b). S. macroptera is a common species in both the hill andlowlanddipterocarpforests.Inacontrolledpollinationstudy, S. macroptera exhibited a mixed mating system (Chan1981). Pollination studies in lowland dipterocarp forest showed that both S. leprosula and S. macroptera are pollinated by low energetic insects (Thysanoptera), mainlyof thrips and megalurothrips (Chan and Appanah1980;Appanah and Chan1981). S. curtisii is the most commonand abundant canopy tree species in the hill dipterocarpforests. It tends to be gregarious and shows a distinct habitat  preference for ridge tops (Wyatt-Smith1963). The specieshas been documented to reproduce mainly through out-crossing (outcrossing rate: 96.3%; Obayashi et al.2002). 122  Materials and methods Study site and sample collectionsThis study was conducted at a 33-ha research plot in SungaiLalang Forest Reserve (Selangor, 3°05 ′  N, 101°52 ′ E),Peninsular Malaysia. This forest reserve is categorised ashill dipterocarp forest, which covers an area of 17,722 ha and is subdivided into several compartments. Between May2000 and June 2001, a 33-ha research plot was set up withinCompartment 14 (Fig.1). Three important dipterocarp treespecies with different habitat preferences were chosen for thisstudy: S.curtisii ontheridges, S.leprosula inthevalleysand S. macroptera both on the ridges and in the valleys.Withinthe33-haarea,alltheindividuals withstems ≥ 5.0cmdiameter at breast height (dbh) for the three species weremapped (Fig.2). Leaves and inner bark tissues weresampled from all the mapped individuals. The sampleswere classified further according to dbh into twodiameter 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 wereclassified as SMA. Of the 68 S. leprosula individuals, 35were classified as BIG and 33 as SMA. For  S.macroptera , of the 171 individuals, 98 were classifiedas BIG and 73 as SMA. The tree densities within the 33-ha 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 treeha  − 1 (SMA) for  S. leprosula and 2.97 trees ha  − 1 (BIG)and 2.21 trees ha  − 1 (SMA) for  S. macroptera .Genetic analysisGenomic DNA was extracted from leaves or inner bark tissues using the procedure of Murray and Thompson(1980) with modifications. The extracted DNAs were purified further using High Pure PCRTemplate PreparationKit (Roche Diagnostics, Indianapolis, IN, USA). Thesamples were genotyped for five microsatellite loci,developed for  S. curtisii (Ujino et al.1998), i.e. Shc 01, Shc 02, Shc 03, Shc 07 and Shc 09. Microsatellites amplifi-cation was performed in a 25- μ  l reaction volume contain-ing 10 ng DNA, 50 mM KCl, 20 mM Tris  –  HCl (pH 8.0),1.5 mM MgCl 2 , 0.2 μ  M of each primer, 0.2 mM of eachdNTP and 1 U of Platinum Taq DNA polymerase (GIBCO-BRL, Germany). The PCR was carried out on a GeneAmp9700 thermal cycler (Applied Biosystems, USA), for aninitial denaturing step at 94°C for 4 min, followed by 35cycles each at 94°C for 1 min, 52  –  54°C for 30 s and 72°Cfor 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. Electrophore-sis was carried out with 1X Tris  –   borate  –  EDTA (TBE) buffer on an ABI Prism 377 automated DNA sequencer (Applied Biosystems, USA). Allele sizes were scoredagainst the internal size standard and the individuals weregenotyped using GeneScan Analysis 3.1 and Genotyper 2.1 software (Applied Biosystems, USA).Analysis of genetic diversity and fixation indexThe levels of genetic diversity were estimated for meannumber of alleles per locus (  A a  ), effective number of alleles per locus (  A e ; Crow and Kimura 1970), allelic richness(  R s ; Petit et al.1998), observed heterozygosity (  H  o ) andexpected heterozygosity (  H  e ; Nei1987) with the assistanceof programs BIOSYS-1 (Swofford and Selander 1981),POPGENE version 1.31 (Yeh et al.1999) and FSTATversion (Goudet 2002). Fixation index (  F  is ) wascalculated based on Weir and Cockerham ’ s(1984) estima- Fig. 1 Location of SungaiLalang Forest Reserve inPeninsular Malaysia and the33-ha study plot set-up withinthe 192-ha Compartment 14123  tor using the program FSTAT. Significant positive or negative F  is was tested using 200 randomisations (default  parameter in FSTAT) for each locus.Analysis of spatial distribution patternThe spatial distribution pattern was tested for clumpingusing univariate second-order spatial pattern analysis basedon Ripley ’ s(1976) K  -function (see Haase1995). Thismethodconsidersallindividualswithinagivenradius t  ofthefocal individual. The estimator of the function K  ( t  ) used is:  K t  ð Þ ¼ n À 2  A XX i 6¼  j  w À 1 ij  I  t  u ij  À Á ; where n is the number of plants in the plot, A is the area of the plot in meter square (m 2 ), w ij is a weighting factor tocorrect for edge effects, I  t  is a counter variable and u ij is thedistance between trees i and j  (Haase1995). The K  ( t  ) wascalculated separately for each distance t  (0  –  250 m in 50 mincrements). 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  .Totest the significant deviation from a random distribution, Monte Carlo computer-generated data were used. To construct a 95% confidence envelope, 95simulations were run, and the sample statistic was com- pared with this envelope. These calculations were per-formed using the program SPATIAL POINT PATTERNANALYSIS (Haase1995).Analysis of spatial genetic structureSpatial genetic structure was analysed using two different estimators, the Moran ’ s I  coefficient and the kinshipcoefficient. For Moran ’ s I  , the correlograms were com- puted as an indication of spatial scale of genetic sub-structuring (Sokal and Oden1978; Sokal and Wartenberg1983). Alleles with a frequency >5% were included in theanalysis of the Moran ’ s I  . Mean Moran ’ s I  coefficientswerecalculated for all alleles as a summary statistic. A per-mutation procedure using Monte Carlo simulations wasapplied to test significant deviation from random spatialdistribution of each calculated measure (Manly1997).Each permutation consisted of a random redistribution of multilocus genotypes over the spatial coordinate of thesampled trees. For each of the spatial distance classes,observed values were compared with the distributionobtained after 1,000 permutations. A 95% confidenceinterval for the parameters was constructed as an interval(Streiff et al.1998). All calculations and tests were performed using the program SPATIAL GENETIC SOFTWARE  —  SGS (Degen et al.2001).The kinship coefficient, a measure of coancestry (  F  ij ),can estimate relationship between pairs of mapped 0501001502002503003504004505005500 100 200 300 400 500 600 (m)    (  m   ) 0501001502002503003504004505005500 100 200 300 400 500 600 (m)    (  m   )   0501001502002503003504004505005500 100 200 300 400 500 600 (m)    (  m   ) a Shorea curtisii b Shorea leprosula c Shorea macroptera Fig. 2 The distributions of the three studied species within a 33-ha study plot (600×550 m) in Sungai Lalang Forest Reserve. Withinthis study plot, a S. curtisii dominates the ridges, b S. leprosula is present in the valleys and c S. macroptera is common both on theridges and in the valleys. The individuals were classified accordingto diameter at breast height (dbh) into two diameter classes: • = BIG(dbh >30 cm) and ○ = SMA (dbh 5  –  10 cm)124  individuals i and j  or the probability that genes in different individuals within subpopulations are identical by descent (Cockerham1969). This statistic was computed betweenall pairs of individuals belonging to the same ploidal usingmultilocus estimates obtained following Loiselle et al.(1995). The average F  ij over pairs of individuals wascomputed for distance intervals of 50 m. The standard error over loci was estimated using the jackknife method. Theabsence of spatial genetic structure was tested within eachclass using a permutation method (1,000 permutations);spatial distances were randomly permuted among pairs of individuals, and the estimated value of the average kinshipcoefficient was compared with the distribution after  permutations. These calculations were performed usingthe program SPAGeDi 1.1 (Hardy and Vekemans2002). Results Genetic diversity and fixation indexThe levels of genetic diversity estimated based on fivemicrosatellite loci are summarised in Table1. The meannumber of alleles per locus observed for  S. curtisii rangedfrom 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(  A e ) and allelic richness (  R s ) for  S. curtisii were highest at BIG (  A e =6.79 and R s =13.41), followed by SMA (  A e =6.03and R s =11.05). However, the mean A e and R s for  S. macroptera and S. leprosula were observed to behighest at SMA followed by BIG (Table1). The mean 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 (  A t  ), effective number of alleles per locus (  A e ),allelic richness (  R s ) and expected heterozygosity (  H  e )Diameter class/locus S. curtisii S. leprosula S. macroptera A t  A e R s H  e A t  A e R s H  e A t  A e R s H  e BIG Shc 01 29 13.06 23.75 0.93 17 9.84 16.60 0.91 15 3.83 14.95 0.75 Shc 02 8 3.33 7.32 0.70 6 1.90 5.76 0.48 8 2.37 7.59 0.59 Shc 03 3 2.55 3.00 0.61 4 2.33 4.00 0.58 2 1.98 2.00 0.50 Shc 07 25 6.71 20.19 0.86 11 5.22 11.00 0.82 13 6.52 13.00 0.86 Shc 09 14 8.31 12.80 0.89 8 5.03 8.00 0.81 9 4.30 8.70 0.78Mean 15.8 6.79 13.41 0.80 9.2 4.86 9.07 0.72 9.4 3.80 9.25 0.70S.E. 4.9 0.46 0.94 0.06 0.6 0.39 0.87 0.08 0.5 0.19 0.75 0.07SMA Shc 01 18 10.77 17.73 0.92 20 12.03 19.33 0.93 22 6.89 19.85 0.86 Shc 02 6 2.78 5.93 0.65 6 2.71 5.81 0.64 7 2.47 6.67 0.60 Shc 03 3 2.49 3.00 0.60 5 2.18 4.90 0.55 3 1.97 3.00 0.50 Shc 07 17 5.26 16.59 0.82 18 5.93 17.33 0.84 21 7.59 18.63 0.88 Shc 09 12 8.87 12.00 0.90 13 7.61 12.75 0.88 9 5.82 8.68 0.84Mean 11.2 6.03 11.05 0.78 12.4 6.09 12.02 0.77 12.4 4.95 11.36 0.73S.E. 3.0 0.55 0.96 0.06 0.8 0.49 1.14 0.07 0.8 0.24 0.99 0.08 Table 2 Fixation index (  F  is ) according to Weir and Cockerham (1984) based on five microsatellite loci in two diameter classes (BIG andSMA) of  S. curtisii , S. leprosula and S. macroptera from Sungai Lalang Forest Reserve. Significant positive or negative F  is was tested using200 randomisationsLocus S. curtisii S. leprosula S. macroptera BIG SMA BIG SMA BIG SMA Shc 01 0.072* 0.124* 0.033 − 0.009 0.121 0.186** Shc 02 − 0.185** − 0.348** − 0.231* − 0.429** − 0.200** − 0.111 Shc 03 0.057 0.080 − 0.206 0.050 0.021 0.194 Shc 07 0.141** 0.098 0.110 0.068 0.068 0.092* Shc 09 0.194** 0.199** 0.333** 0.080 − 0.068 0.161All 0.066** 0.026 0.045 − 0.047 − 0.003 0.111***Significantly different from zero (  P  <005)**Significantly different from zero (  P  <0.01)125
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