Forest Ecology and Management 318 (2014) 318–325

Contents lists available at ScienceDirect

Forest Ecology and Management

journal homepage: www.elsevier .com/ locate/ foreco

The spatial genetic pattern of Castanopsis chinensis in a large forest plotwith complex topography

http://dx.doi.org/10.1016/j.foreco.2014.01.0420378-1127/� 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +86 20 37252996; fax: +86 20 37252615.E-mail address: why@scbg.ac.cn (W.-H. Ye).

Zheng-Feng Wang, Ju-Yu Lian, Wan-Hui Ye ⇑, Hong-Lin Cao, Zhang-Ming WangKey Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, PR China

a r t i c l e i n f o

Article history:Received 12 October 2013Received in revised form 25 January 2014Accepted 27 January 2014Available online 15 February 2014

Keywords:DHS plotGene flowSmall spatial scalesSpatial genetic structure

a b s t r a c t

Topography is an important environmental feature that affects the spatial genetic structures of plant spe-cies. However, its influence on such structures at small spatial scales has hardly been investigated for for-est tree species even though many of them are located in mountains with complex topographic features.Here we report the genetic structures of a pioneer species, Castanopsis chinensis (Fagaceae), in a 20-haforest plot in Dinghushan in lower subtropical China, which has complex topographic features, such asvalleys and ridges. C. chinensis typically undergoes wind pollination, but its seeds are dispersed by gravityand animals. Therefore, the mechanisms of transportation of its seeds may result in topography-dependent genetic structures, whereas wind-mediated pollen flow of C. chinensis may reduce such struc-tures through counteracting the influence of topography. Our results indicate that most of the geneticpatterns in C. chinensis in our study plot were attributable to wind-mediated pollen flow. However, wealso found both seed and pollen flows could be impeded by ridges, causing some varied genetic patternseven between locations separated by only small distances. We observed that owing to topographic posi-tions where they grew, the 13 old individuals that were probably the oldest in the southeast corner of theplot had not made major genetic contributions to the young individuals that had recently colonised pre-viously clear-cut land in the rest of the plot. Therefore, our results indicate that we should consider bothspecies life-history traits and topography when studying the genetic structures of plant populations inorder to design sound conservation and management programs for the areas with complex topography.

� 2014 Elsevier B.V. All rights reserved.

1. Introduction

Topography is an important environmental feature that influ-ences the distribution of species. In population genetics, differenttypes of topographic feature might act as barriers or corridorsand result in minor or strong environmental selection, whichcauses spatial variation of genetic diversity either among popula-tions or among individuals within populations. Recently, the useof neutral genetic markers, such as microsatellites and amplifiedfragment length polymorphisms (AFLP), and landscape geneticstools has provided remarkable insight into the effects of topogra-phy on such variation (Storfer et al., 2007; Holderegger and Wag-ner, 2008). However, these studies have generally been carriedout on a large spatial scale over hundreds or thousands of kilome-tres for animals (Epps et al., 2007; Giordano et al., 2007) and plants(Kramer et al., 2011). Few studies, if any, have used landscapegenetics to address the effect of topography on genetic diversityamong individuals within a plant species by dense sampling of

individuals along discontinuous topographic gradients at relativelysmall spatial scales.

In the case of plants, topographic features that differ on a localscale could include slopes, ridges, valleys, small hills, mounds,rocky outcrops, and unfavourable habitats. Each of these featuresmight limit or promote the distribution of individuals and, overtime, result in different local genetic structures. For example,Ohsawa et al. (2007) found that steep slopes increased the ten-dency of Quercus crispula seeds to roll downhill, which weakenedthe fine-scale genetic structures of seedling populations at or nearthe bottoms of slopes.

Early attempts to address genetic patterns on a fine spatial scalesuffered from the analysis of relatively small plots, in which ahomogeneous habitat and isotropic gene dispersal were assumed.This might have resulted in an incomplete understanding of the ge-netic structures in the studied species because, within these smallsampling areas, individuals could still constitute a mosaic ofdifferent genetic groups or have been influenced by particulartopographic features in their surroundings.

Therefore, we initiated this work to study the influence oftopography on the genetic structures in a pioneer species,

Z.-F. Wang et al. / Forest Ecology and Management 318 (2014) 318–325 319

Castanopsis chinensis Hance (Fagaceae), in a large 20-ha forest plotin Dinghushan (DHS), a part of lower subtropical China that exhib-its a complex landscape (Fig. S1).

Two biological features of C. chinensis could interact with the ef-fects of topography on its genetic structures. Firstly, C. chinensis is alight-demanding pioneer plant species (Du and Huang, 2008). Itless commonly inhabits valleys (Fig. 1c and d), where the intensityof light might be low. Therefore, valleys might cause discontinuityin the distribution and thus genetic structures in C. chinensis. Sec-ondly, seeds of C. chinensis are dispersed by gravity and then byanimals, with small mammals (in particular rodents) being impor-tant for their secondary dispersal at local scales. Therefore, if thepresence of topographic features, such as ridges, that limit themovement of rodents, and thus seeds, they might prevent seedsfrom dispersing equally in all directions. This would produce ananisotropic genetic structure in C. chinensis populations. However,C. chinensis is a wind-pollinated outcrossing species, and its seedscan also be dispersed by birds before and after they fall to theground. These two features could reduce the effects of topographyin limiting gene flow and homogenise the gene pool among areaswith different topographies. However, because wind flow is greatlyinfluenced by rough surfaces such as ridges with trees growing onthem (Yassin et al., 2012) and a southwesterly wind prevails in theflowering season of C. chinensis in our study plot from May to July,the ridges in the centre of the plot might serve as obstacle fence onthe wind flow and then influence the dispersal patterns of pollensat different places. In that case, we can still investigate non-ran-dom distributions of genetic variations in C. chinensis in the plot.

The plot studied in DHS has a history of forest disturbance,which might help us to understand the influence of topographyon C. chinensis. In this plot, the forest at the southeast corner hasbeen well protected and is estimated to be more than 400 years

Fig. 1. Maps showing the 20-ha (400 � 500 m) DHS plot and spatial distribution of Cseparates the gently and steeply sloping areas was added by the authors on the basis ofSpatial distribution of trees in different DBH ranges. Contour lines in all panels and the otypes in panels a–d are delineated by 20 � 20 m quadrats.

old, whereas the rest (mostly areas with gentle slopes, Fig. 1a)has frequently been managed. About 60 years ago, managed partsof the forest were clear-cut and then planted with Pinus massoni-ana. As a consequence, most of the large C. chinensis trees with adiameter at breast height (DBH) P40 cm are in the southeast part,and those with a smaller DBH are generally in the other areas ofthe plot (Fig. 1c and d). Given that the large C. chinensis trees inthe southeast corner are separated from those with a smallerDBH by a ridge and southeast valley V3 (Fig. 1c and d), if the influ-ences of topography on genetic structures are small, we would ex-pect the former to have made a large genetic contribution to thelatter. Then, we would expect to find some spatial genetic struc-tures established by chance and less related to old individuals.The determination of such a contribution is valuable because theprotected old forest at the southeast corner is considered to bethe major source of germplasm needed for reforestation, althoughno studies have been conducted to show whether this is appropri-ate from a conservation perspective. In fact, in a tropical forest inCosta Rica, Sezen et al. (2005) found that the adjacent old-growthindividuals of Iriartea deltoidea have great contribution to its sec-ond-growth population.

The two specific objectives of this study were: (1) to investigatethe spatial genetic structure of C. chinensis in the DHS plot and howit might be affected by topography, and (2) to assess the geneticcontributions of the old individuals, specifically those with aDBH P 100 cm in the southeast corner, to the individuals in therest of the plot (Fig. S2). Previously, we found that topographymay influence spatial autocorrelation analysis because of differenttypes of distances (projection vs. surface distance) betweenindividuals used (He et al., 2013). However, this study did not at-tempt to infer the genetic structure patterns and their possiblerelationship with different topographic types and only used part

astanopsis chinensis individuals in the plot. (a) Slope categorisation. The line thatthe topography. (b) Habitat types for the 20-ha (400 � 500 m) DHS plot. (c and d)ther figures represent elevations at 5-m intervals. Slope categorisation and habitat

320 Z.-F. Wang et al. / Forest Ecology and Management 318 (2014) 318–325

of individuals restricted to the south of the plot; therefore, at whatextend the topography influences the spatial genetic structure of C.chinensis is still unclear. We hope that the information on the ge-netic structure obtained in our present study using different anal-yses and more individuals in the plot will provide more valuableinsight to facilitate efficient management and conservation of C.chinensis.

2. Materials and methods

2.1. Study species

The evergreen tree C. chinensis is distributed from South Chinato Vietnam at altitudes below 1 500 m above sea level (Wu,2009). As a pioneer canopy tree, it can grow up to 20 m, and it usu-ally plays a key role in the ecosystems where it is found. It is amonoecious wind-pollinated species with unisexual staminateand pistillate flowers on the same plant. Its seeds are oval(1.50 ± 0.26 cm long and 0.98 ± 0.22 cm wide), enclosed by spinybracts (Yang et al., 2005), and can be dispersed by animals, suchas rodents, pigs, cats, and birds (Du and Huang, 2008). C. chinensiscan flower when its DBH reaches about 10–15 cm (Hong-Lin Cao,personal observation). It is a masting species and lacks a seed bank.Thus, current year’s seedlings come from the seeds produced in theprevious year.

2.2. Study site and field methods

This study was conducted in a 20-ha (400 � 500 m) DHS plotthat is in the 1 155-ha DHS National Nature Reserve on the south-ern verge of the Tropic of Cancer in the subtropical part of SouthChina (Wang et al., 2009). The elevation of the plot ranges from230 to 476.1 m with the lowest on the east and highest on thesouthwest corner. The The plot was divided into 500 quadratsmeasuring 20 � 20 m. Four topographic attributes—elevation,slope, aspect, and convexity—were measured for each quadrat, asdescribed by Wang et al. (2009). To facilitate illustrating theresults, the DHS plot can be artificially divided into areas with agentle or a steep slope on the basis of the inclination of slopes inthe quadrats (Fig. 1a).

The multivariate regression tree method (De’ath, 2002) wasused to divide the 500 quadrats into three habitat types (ridge, hill-side, and valley) (Wang et al., 2009). We further divided the quad-rats categorised into ridge and valley habitats into high and lowridges, and shallow and deep valley habitats, respectively, on thebasis of the median value of the convexities in each quadrat(Fig. 1b).

2.3. Sample collection and microsatellite analysis

Leaf samples of the 2 175 C. chinensis individuals in the plotwith DBH P 1 cm were collected between July and October 2008.

Table 1Locus name, GenBank accession, annealing temperature (Ta), allelic richness (A),observed (HO) and expected heterozygosity (HE), and fixation index (f) for sevenmicrosatellite loci analysed for 1 982 C. chinensis individuals.

Locus GenBank accession Ta (�C) A HO HE f

Cch11 EU846108 60 10 0.814 0.805 �0.011Cch12 EU846109 60 8 0.759 0.775 0.020Ccu62F15 AB092346 64 5 0.669 0.640 �0.044Ms04 GU097387 58 8 0.627 0.611 �0.025Ms06 GU097389 56 24 0.899 0.888 �0.013Ms09 GU097392 60 4 0.421 0.419 �0.003Cch40 HM123729 58.5 7 0.708 0.702 �0.009Total 0.700 0.691 �0.011

They were stored in sealed plastic bags that contained silica gel un-til the time of DNA extraction. Seven microsatellites were analysedaccording to He et al. (2013) (Table 1). A total of 193 individualscould not be genotyped due to inadequate quality of the DNA ex-tracts, and were excluded from subsequent data analysis.

2.4. Data analysis

Genetic diversity parameters, allelic richness (A), observed andunbiased expected heterozygosity (HO, HE), and fixation index (f),were estimated using GENETIX 4.05 (Belkhir et al., 1996–2004).Deviations from Hardy–Weinberg equilibrium (HWE) at each lo-cus, and genotypic linkage disequilibrium (LD) between all pairsof loci, were tested using GENEPOP 4.0.7 (Rousset, 2008), and thesignificance level was adjusted by Bonferroni correction.

2.5. Graphical representation of spatial genetic structure

Spatial genetic structure was visualised using ‘‘Genetic Land-scape Shapes’’ (GLS) in ALLELES IN SPACE (AIS 1.0; Miller, 2005).On the basis of the Delaunay triangulation network among individ-uals and genetic distances in the network, this method generatesthree-dimensional patterns of genetic distance across space, whichis analogous to geographical topography. Across the genetic land-scape, the peaks (illustrated in purple in Fig. 2a) and troughs(green) indicate large and small genetic distances between individ-uals, respectively. Given that the shape of the genetic landscapecould be influenced by the types of genetic distance, the grid sizes,and the distance weighting parameter (a), we initially used raw ge-netic distance, residual genetic distance (which takes isolation bydistance into account), various grid sizes (50 � 40 m, 100 � 80 m,and 200 � 160 m), and a values of 0.5, 1.0, 2.0, and 3.0 to analyseour genetic data. Our analyses showed that these different geneticdistances and grid sizes had little influence on GLS, whereas an avalue > 2 yielded a surface with too many peaks providing lessinformation. Therefore, we only reported GLS results with a = 1using a grid size of 100 � 80 m and raw genetic distance.

2.6. Boundary identification

Boundaries were identified using the Genetical BandwidthMapping (GBM) function in GENBMAP (Cercueil et al., 2007). Ituses a nonparametric method that is not dependent on particularmodel assumptions, and can be successfully applied if the fine-scale structure is strong. After several attempts, we found thatchanging the parameters only changed the results slightly; there-fore, we used the program’s default values for all parameters.The GENBMAP generates a two-dimensional matrix that containsthe bandwidth values computed at each point of the grid that cov-ers the study area. We represented these values graphically toshow the genetic structure in the C. chinensis population on theDHS plot map using colour gradients, ranging from white (areasof important boundaries), to brown, to yellow, and finally to darkgreen (homogeneous areas).

2.7. Spatial principal component analysis (SPCA)

The spatial distribution of genetic variability of C. chinensis wasfurther examined using SPCA (Jombart et al., 2008). Delaunay tri-angulation was used to define the networks that connect individu-als. A bar plot of eigenvalues of SPCA and a scree plot of theirvariance and Moran’s I (measuring spatial autocorrelation) compo-nents were used to determine the number of principal componentsto retain.

Fig. 2. Maps showing spatial genetic structures within the C. chinensis population in the DHS plot. (a) The genetic landscape using a grid of 100 � 80 m with a distanceweighting parameter (a) of 1. (b) Boundaries identified using the genetic bandwidth mapping method. Boxes in panels a and b show the ridge habitats based on 20 � 20 mquadrats. Thick line: high ridge; thin line: low ridge.

Z.-F. Wang et al. / Forest Ecology and Management 318 (2014) 318–325 321

2.8. Related individuals and their distribution along topographicalfeatures

We used the methods proposed by Frantz et al. (2010) to iden-tify related C. chinensis pairs, and plotted them in a map of the en-tire DHS plot to determine how these pairs are distributed amongthe different topographical features. We used KINGROUP 2.0(Konovalov et al., 2004) to simulate 10 000 unrelated individualsbased on allele frequencies of our collected data, and calculatedthe pairwise index of relatedness (R) (Queller and Goodnight,1989) for every pair of simulated individuals. We repeated thissimulation two more times. Then, we chose the highest R valueamong these three simulations as a threshold, and considered realindividual pairs to be related if their R values were higher than thisthreshold.

2.9. Genetic contribution of old individuals to recently colonised youngindividuals

The level of genetic contribution of the old individuals to theyoung individuals that had recently colonised previously clear-cut land in the gently sloping area was estimated using HWLER(Pella and Masuda, 2006). The HWLER algorithm analyses the ex-tent of genetic mixing, and can estimate the relative proportionsof samples from known sources (baseline populations or stocks)and unknown sources (extra-baseline populations or stocks), aswell as the possible number of unknown sources. In this study,we specifically focused on the level of genetic contribution of the13 oldest individuals (with DBH P 100 cm in the southeast cornerof the plot) to the 1 710 individuals with DBH 6 97 cm, which werefound in the gently sloping area (Fig. S2). For comparison, we in-cluded the two oldest individuals—one with DBH of 122 cm, andthe other with DBH of 103 cm—in the analysis of the gently slopingarea, and recalculated the level at which these 15 individuals con-tributed to the recently colonised young ones. The HWLER programwas run for 1,400,000 samples for each estimate. On the basis ofthe trace plot of the number of unknown contributing sources,

relative proportion analysis was then performed with a burn-indiscard of 700,000 samples.

3. Results

3.1. Genetic variation

The number of alleles per locus ranged from 4 to 24, and HE ran-ged from 0.419 to 0.888 among the loci (Table 1). Deviation fromHWE was significant only at locus Ccu62F15, and it remained sig-nificant at the 5% level, but not at the 1% level, after Bonferroni cor-rection. The negative f value (�0.044) (Table 1) indicated that thedeviation was caused by an excess of heterozygosity at the locus.Further analyses showed that the deviation at Ccu62F15 was sig-nificant (p < 0.05) only during a particular stage of the life history(15 6 DBH < 20 cm, n = 355). All locus pairs showed significantdeviation from LD at the 5% level after Bonferroni correction whenall 1982 individuals were analysed. However, they did not showsignificant deviation from LD at the 5% level after Bonferroni cor-rection if only individuals with DBH P 70 cm were analysed(n = 54).

3.2. Graphical representation of spatial genetic structure

The GLS results at a = 1 suggested that the individuals in thesteeply sloping area and on the NE hill were more genetically sim-ilar to each other than those in the gently sloping area (Fig. 2a),particularly those on the west low ridge (WLR).

3.3. Boundary identification

The GBM results showed two main boundaries. Whereas onelay along high ridges HR1, HR2, and upper HR3, the other ran fromthe NE hill through high ridge HR4 and two valleys (V1 and V2)close to the western edge of the DHS plot (Fig. 2b). Steeply andgently sloping areas are separated by these two boundaries.

1.00

0.00

-1.00

0.80

0.00

-0.80

0.77

0.00

-0.77

0.69

0.00

-0.69

345 m

(d) (c)

(b) (a)

HR1

290m

HR2

Fig. 3. Scatter plots for the first four global principal components analysed by SPCA. (a–d) SPC1–SPC4. Individual symbol sizes are proportional to the values of the componentscores shown beside each panel; thus, the differentiation between two individuals, one in red and the other in a blue circle, increases as the circle sizes increase. The dashedlines in panel b and c show a contrast in and around HR1 and HR2. Two dashed lines in panel d show the projected widths of the valley, which are 115.4 m and 121.1 m forupper and lower parts, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

322 Z.-F. Wang et al. / Forest Ecology and Management 318 (2014) 318–325

3.4. Spatial principal component analysis (SPCA)

On the basis of the eigenvalues and spatial autocorrelation(Fig. S3), we chose the first four global SPCA components (SPC1–SPC4), which accounted for 18.2%, 18.5%, 15.0%, and 17.3% of thetotal variance, respectively, to display the distributions of the indi-viduals in the entire DHS plot. In general, these four indicated lar-gely random distributions of genetic variations although differentcomponents revealed some specific patterns. The SPC1 component(Fig. 3a) separated the intermediate elevations from uphill anddownhill in the gently sloping areas. When used together, theSPC2 and SPC3 components (Fig. 3b and c) separated the east fromthe west areas in the plot, and both of them specifically indicated acontrast in and around high ridges HR1 and HR2. The SPC4 compo-nent (Fig. 3d) separated the individuals located downhill on thesoutheast hillside from those across valley V3 (at elevations be-tween 290 and 345 m).

3.5. Related individuals and their distributions

From three maximum relatedness values calculated by threeindependent sets of 10,000 simulations of unrelated individualpairs, we chose the highest value of 0.8133 as the minimum re-quired to consider two C. chinensis individuals as being related.Using this criterion, 484 pairs were identified. Among these pairs,the respective minimum and maximum separation distances were0.1 and 553.8 m between paired individuals. Individuals in 282pairs were separated by more than 100 m and, in 63 pairs, by lessthan 20 m.

For ridges, in the case of paired individuals separated by dis-tances less than 100 m, high ridge HR1 showed a particular patternof acting as a barrier to hinder nearby individuals from transferringgenetic material from its western side (Fig. 4a). For pairs separatedby distances greater than 100 m, the two individuals of many pairswere separated by ridges, indicating that genetic material had

crossed these ridges (Fig. 4b–d). There were more pairs associatedwith HR4 and its surrounding low ridge than with HR1–HR3 andthe low ridges between them, and most of the connections of theformer pairs were orientated in a west-east direction and on thewest part of the plot (Fig. 4e and f).

Valleys were generally found to be present between the twoindividuals within pairs, indicating that these topographic featuresdid not usually act as a barrier to the transfer of genetic material.

3.6. Genetic contribution of old individuals to recently colonised youngindividuals

The 13 individuals with DBH > 100 cm in the southeast cornerarea contributed 12.32% (2.5% and 97.5% quantile values: 10.23%and 14.74%) to the germplasm composition of the recently colon-ised young individuals in the gently sloping area. When the twoindividuals with DBH P 100 cm in the gently sloping area wereadded to these 13, the contribution of these 15 became 34.46%(2.5% and 97.5% quantile values: 31.11% and 38.19%) for the re-cently colonised ones.

4. Discussion

Unlike most previous studies, which used plots that were smalland either flat or assumed to be flat, to study the genetic structuresof plants over fine spatial scales, in this study, we investigated suchstructures in a large plot with a complex topography. Overall, ourresults indicate that most of the genetic patterns in C. chinensismight be caused by random distribution of genetic variationalthough topographic effects on them cannot be ignored. Becausethese genetic structures are influenced by pollen and seed dis-persal in plants, we first discuss these two kinds of dispersal andtheir relationships to spatial genetic structures in C. chinensis.

Most C. chinensis seeds are initially dispersed by gravity andconfined around the individual that produce them, but are then

Fig. 4. Related individual pairs and their distributions in the DHS plot. (a) 202 pairs with 100 P distance > 0 m. (b) 152 pairs with 200 P distance > 100 m. (c) 77 pairs with300 P distance > 200 m. (d) 53 pairs with distance > 300 m. (e) 50 pairs related to high ridge HR4 and its surrounding low ridge. The arrow in the upper left corner shows thedirection of the prevailing wind in the flowering season of C. chinensis. (f) 23 pairs related to HR1–HR3 ridges and their surrounding low ridge.

Z.-F. Wang et al. / Forest Ecology and Management 318 (2014) 318–325 323

distributed further afield by animals, mainly rodents (Du andHuang, 2008). Although the dispersal distances of C. chinensis seedsremain unknown, the largest seed dispersal distance by rodents intwo other Castanopsis species (C. indica and C. fargesii) was reportedto be about 20 m, with an average of < 10 m (Xiao et al., 2005; Chouet al., 2011). Roth (2001) found that seeds of C. sempervirens couldbe dispersed distances > 65 m, with an average of 35.8 m. Giventhat the seeds of C. chinensis are smaller than those of C. indica(1.7–3.2 cm long, 1.4–2.3 wide; Chou et al., 2011), and a littlelarger than those of C. sempervirens (1.15 ± 0.13 cm long,1.28 ± 0.21 cm wide; Roth, 2001) and C. fargesii (1.14 ± 0.11 cmlong, 0.98 ± 0.12 cm wide; Chen and Wang, 2003), we expect seeddispersal, even that by rodents, in C. chinensis to be local, contrib-uting little to long-distance gene flow and to large-scale geneticpatterns.

Birds are also important seed predators and secondary dispers-ers of C. chinensis seeds. However, in the context of Castanopsis spe-cies, the effects of birds on seed dispersal (in terms of quantity,distance, and direction) have been investigated less than those ofrodents. In our study, on the basis of analyses of related pairs, ifwe consider the pairs with DBH differences > 10 cm or > 15 cm be-tween the two individuals as having a maternal-offspring relation-ship, and then among this group we attribute the relationshipbetween the pair to seed dispersal by birds in cases with a longseparation distance between them, such as P 100 m (127 or 92pairs, respectively), we find that the directions of the lines connect-ing the two individuals in the 127 or 92 maternal-offspring pairsare randomly orientated in the DHS plot (Fig. S4b, d). This findingsuggests that, if the offspring are from seeds dispersed by birds,such bird-mediated dispersal is independent of topography and oc-curs in no particular direction. However, although birds might beimportant for long-distance dispersal of C. chinensis seeds andcan move freely regardless of the topography, their contributions

to overcoming topographic barriers and homogenising the genepool are expected to be limited because otherwise we would nothave found some topography-dependent and directional asymme-try genetic structures in the plot.

Pollen flow via the wind is generally considered extensive, tooccur over a long distance, and to be able to homogenise the genepool in Castanopsis species (Shi and Chen, 2012, and referencestherein). Therefore, most of the genetic patterns in C. chinensis inour study may be attributable to wind-mediated pollen flow with-in the plot. However, unlike the non-directional bird-mediatedseed flow discussed above, because a directional southwesterlywind prevails in this region during the pollination period of C. chin-ensis, pollen flow may not homogenise the genetic variationsamong all different places in the plot. Two pieces of our data sup-port this speculation. First, our data indicate separations of geneticgroups along elevations in the gently sloping area (SPC1; Fig. 3a).The reason for this could be C. chinensis individuals that are sepa-rated by a large distance and in the direction perpendicular to theprevailing wind cannot be easily homogenised into a single pollenpool. Second, the individuals within related pairs show typicalsouthwesterly connections in high ridge HR4 and its surroundinglow ridge (Fig. 4e), which is mostly likely attributable to directionalpollen flow.

In our study, one of the most worthwhile to note is that our GLSresults show two distinct patterns (Fig. 2a) between the east andwest sides of high ridges (HR1–HR4) in the centre of the DHS plot:genetic distances among neighbouring individuals are smaller onthe east than on the west side, especially on the west low ridgeWLR. Because the west side of the plot is clear-cut in history andthe individuals of C. chinensis are newly colonised, the contrast be-tween these two parts could be due to that during the colonisingprocesses the west part received more diversified genetic materialsthan the east part. As genetic contribution from the east by old

324 Z.-F. Wang et al. / Forest Ecology and Management 318 (2014) 318–325

individuals in the southeast corner area is limited (12.32%), themajority of such genetic materials are most likely from the westout of the plot caused by extensively southwesterly pollen flowduring C. chinensis flower seasons.

At large geographic scales, ridges constitute important topo-graphic barriers to gene flow for both plants and animals (Funket al., 2005; Tsuda et al., 2010). However, it is not known whetherthis is true for plants at local scales. In our plot, we found thatridges could act as barriers that limit gene flow through dispersalof either seeds or pollen of C. chinensis.

Our results indicate that only high ridge HR1 acted as an effec-tive barrier to seed flow by blocking the passage of genetic materialfrom nearby individuals. This is because HR1 is located on a largerocky outcrop (Fig. S5) that is unsuitable as a habitat for C. chinen-sis, and obstructs the free movement of animals that disperse seedsof C. chinensis.

Our results show that both high and low ridges can hinderpollen flow partially. As we have seen in high ridge HR4 and its sur-rounding low ridge, related pairs analysis reveals that southwest-erly gene flow is most typical but only restricted on the westpart of the plot. If the HR4 and its surrounding low ridge per sedid not impede some of the southwesterly gene flow, we wouldfind some similar related pairs patterns on the east part of theseridges. In addition, HR2 and HR3 are adjacent to HR4, but we donot find similar directional flow on them (Fig. 4e). Regarding thesouthwesterly prevailing wind in the flowering season of C. chinen-sis, the main low ridge WLR is directly upwind of HR2 and HR3,whereas the topographic feature upwind of HR4 is the WLR edge(see highlighted contour lines in Fig. 4e). Therefore, the absenceof clear westerly genetic patterns in HR2 and HR3 might be attrib-utable to pollen flow being blocked by the upwind low ridge WLR.However, such blocking may not be complete, and there might stillbe wind directly passing over or being diverted over the top of for-est canopy on the ridges. These winds can take pollens to travellong distance, which may be part of the reasons that many of ourrelated pairs showed crossing-ridge connections. Anyway, furtherwork is needed to confirm these inferences.

The effects of valleys as potential barriers to gene flow havebeen investigated mostly in the context of animals studied at largedistance scales, with the obtained results both supporting (Leh-mann et al., 1999; Hagerty et al., 2011) and refuting this role(Moore et al., 2011). The only study on plants was on Vellozia gigan-tea, a monocot species from Brazil; it failed to find that valleys re-stricted gene flow (Lousada et al., 2011). Although C. chinensisindividuals were in general less abundant in the valleys than onthe hillsides or ridges in our plot, it does not seem that the valleysimpose an effective resistance against gene flow. However, as theability of rodents to disperse seeds is limited, whether valleyscould restrict such seed flow warrants further studies.

5. Conclusion

Our results illustrate that, owing to the influence of topographicfeatures, the patterns of genetic structure vary among small areas,even those separated by only dozens of meters. Therefore, if onefails to consider topographic features and their influences, theunderstanding of spatial genetic patterns for individuals in smallplots in topographically complex areas might be incomplete.

Our results indicate that, for management purposes, seed col-lection zones for C. chinensis should be on windward ridges, suchas low ridge WLR, where individuals exhibit higher genetic diver-sity than in other parts of the plot. In contrast, both GLS and genet-ic relatedness analyses have indicated that individuals on thesoutheast hillside (where many C. chinensis individuals with largeDBH grow, and where people have generally collected seeds there)

display a high level of genetic relatedness (Figs. 2a and S6). Giventhat the southeast hillside is on the leeward side of the ridges,which block pollen flow from outside of the area, such high relat-edness is likely to be attributable to mating among neighbours.Therefore, seed collection from these large trees should be avoided.Furthermore, because these large trees have made limited contri-butions to those in the rest of the plot, if the purposes of conserva-tion efforts are to restore the C. chinensis population and tomaintain the plot, preservation of these large trees should not begiven a high priority.

Finally, because many plants are located on mountains withcomplex topographic features, the influence of such featuresshould not be limited to C. chinensis studied here. Similar studiesare warranted for species with different life-history traits in thisand other large forest plots.

Acknowledgements

We thank Lin-Fang Wu, Xiao-Yi Li, Lei Dong, Dan-Dan Gao,Wen-Ping Liu, Guo-Min Huang, Dan-Dan Zhang, Hong-Yu Niu,and Xin Zhang for assistance in collecting samples and performinglaboratory analyses. This study was funded by the National NaturalScience Foundation of China (31170352, 31100312), KnowledgeInnovation Program of the Chinese Academy of Sciences (KSCX2-EW-Z, KSCX2-EW-J-28), Foreign Exchange Program NationalFounder (31011120470) and the Chinese Forest BiodiversityMonitoring Network.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.foreco.2014.01.042.

References

Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N., Bonhomme, F., 1996–2004. GENETIX4.05, logiciel sous Windows TM pour la génétique des populations. –Laboratoire Génome, Populations, Interactions, CNRS UMR 5000, Université deMontpellier II, Montpellier (France).

Cercueil, A., Francois, O., Manel, S., 2007. The genetical bandwidth mapping: aspatial and graphical representation of population genetic structure based onthe Wombling method. Theor. Popul. Biol. 71, 332–341.

Chen, B., Wang, L., 2003. Analysis on fruit properties of Castanopsis fargesii inTiantong national forest park. J. Zhejiang Forestry Sci. Technol. 23 (6), 9–14.

Chou, F.-S., Lin, W.-C., Chen, Y.-H., Tsai, J.-B., 2011. Seed fate of Castanopsis indica(Fagaceae) in a subtropical evergreen broadleaved forest. Bot. Stud. 52, 321–326.

De’ath, G., 2002. Multivariate regression trees: a new technique for modelingspecies – environment relationships. Ecology 83, 1105–1117.

Du, Y., Huang, Z., 2008. Effects of seed mass and emergence time on seedlingperformance in Castanopsis chinensis. For. Ecol. Manage. 255, 2495–2501.

Epps, C.W., Wehausen, J.D., Bleich, V.C., Torres, S.G., Brashares, J.S., 2007. Optimizingdispersal and corridor models using landscape genetics. J. Appl. Ecol. 44, 714–724.

Frantz, A.C., Pope, L.C., Etherington, T.R., Wilson, G.J., Burke, T., 2010. Usingisolation-by-distance-based approaches to assess the barrier effect of linearlandscape elements on badger (Meles meles) dispersal. Mol. Ecol. 19, 1663–1674.

Funk, W.C., Blouin, M.S., Corn, P.S., Maxell, B.A., Pilliod, D.S., Amish, S., Allendorf,F.W., 2005. Population structure of Colombia spotted frogs (Rana luteiventris) isstrongly affected by the landscape. Mol. Ecol. 14, 483–496.

Giordano, A.R., Ridenhour, B.J., Storfer, A., 2007. The influence of altitude andtopography on genetic structure in the long-toed salamander (Ambystomamacrodactulym). Mol. Ecol. 16, 1625–1637.

Hagerty, B.E., Nussear, K.E., Esque, T.C., Tracy, C.R., 2011. Making molehills out ofmountain landscape genetics of the Mojave desert tortoise. Landscape Ecol. 26,267–280.

He, J., Li, X., Gao, D., Zhu, P., Wang, Z., Wang, Z., Ye, W., Cao, H., 2013. Topographiceffects on fine-scale spatial genetic structure in Castanopsis chinensis Hance(Fagaceae). Plant Spec. Biol. 28, 87–93.

Holderegger, R., Wagner, H.H., 2008. Landscape genetics. Bioscience 58, 199–208.Jombart, T., Devillard, S., Dufour, A.-B., Pontier, D., 2008. Revealing cryptic spatial

patterns in genetic variability by a new multivariate method. Heredity 101, 92–103.

Z.-F. Wang et al. / Forest Ecology and Management 318 (2014) 318–325 325

Konovalov, D.A., Manning, C., Henshaw, M.T., 2004. KINGROUP: a program forpedigree relationship reconstruction and kin group assignments using geneticmarkers. Mol. Ecol. Notes 4, 779–782.

Kramer, A.T., Fant, J.B., Ashley, M.V., 2011. Influences of landscape and pollinatorson population genetic structure: examples from three Penstemon(Plantaginaceae) species in the Great Basin. Amer. J. Bot. 98, 109–121.

Lehmann, T., Hawley, W.A., Grebert, H., Danga, M., Atieli, F., Collins, F.H., 1999. TheRift Valley complex as a barrier to gene flow for Anopheles gambiae in Kenya. J.Hered. 90, 613–621.

Lousada, J.M., Borba, E.L., Ribeiro, K.T., Ribeiro, L.C., Lovato, M.B., 2011. Geneticstructure and variability of the endemic and vulnerable Vellozia gigantea(Velloziaceae) associated with the landscape in the Espinhaco Range, insoutheastern Brazil: implications for conservation. Genetica 139, 431–440.

Miller, M.P., 2005. Alleles in space (AIS): computer software for the joint analysis ofinterindividual spatial and genetic information. J. Hered. 96, 722–724.

Moore, J.A., Tallmon, D., Nielsen, J., Pyare, S., 2011. Effects of the landscape on borealtoad gene flow: does the pattern–process relationship hold true across distinctlandscapes at the northern range margin? Mol. Ecol. 20, 4858–4869.

Ohsawa, T., Tsuda, Y., Saito, Y., Sawada, H., Ide, Y., 2007. Steep slopes promotedownhill dispersal of Quercus crispula seeds and weaken the fine-scale geneticstructure of seedling populations. Ann. For. Sci. 64, 405–412.

Pella, J., Masuda, M., 2006. The Gibbs and split-merge sampler for populationmixture analysis from genetic data with incomplete baselines. Can. J. FishAquat. Sci. 63, 576–596.

Queller, D.C., Goodnight, K.F., 1989. Estimating relatedness using genetic markers.Evolution 43, 258–275.

Roth, J.K., 2001. Dispersal of Castanopsis sempervirens by scatter hoarding rodents: aseed-fate model approach. MSc Thesis, University of Nevada.

Rousset, F., 2008. GENEPOP ‘007: a complete re-implementation of the GENEPOPsoftware for Windows and Linux. Mol. Ecol. Resour. 8, 103–106.

Sezen, U.U., Chazdon, R.L., Holsinger, K.E., 2005. Genetic consequences of tropicalsecond-growth forest regeneration. Science 307, 891.

Shi, M.M., Chen, X.Y., 2012. Leading-edge populations do not show low genetic diversityor high differentiation in a wind-pollinated tree. Popul. Ecol. 54, 591–600.

Storfer, A., Murphy, M.A., Evans, J.S., Goldberg, C.S., Robinson, S., Spear, S.F., Dezzani,R., Delmelle, E., Vierling, L., Waits, L.P., 2007. Putting the ‘‘landscape’’ inlandscape genetics. Heredity 98, 128–142.

Tsuda, Y., Sawada, H., Ohsawa, T., Nakao, K., Nishikawa, H., Ide, Y., 2010. Landscapegenetic structure of Betula maximowicziana in the Chichibu mountain range,central Japan. Tree Genet. Genomes 6, 377–387.

Wang, Z.G., Ye, W.H., Cao, H.L., Huang, Z.L., Lian, J.Y., Li, L., Wei, S.G., Sun, I.F., 2009.Species-topography association in a species-rich subtropical forest of China.Basic Appl. Ecol. 10, 648–655.

Wu, T., 2009. Flora of Guangdong (IX). Guangdong Science and Technology Press,Guangzhou, China.

Xiao, Z., Zhang, Z., Wang, Y., 2005. Effects of seed size on dispersal distance in fiverodent-dispersed fagaceous species. Acta. Oecol. 28, 221–229.

Yang, Q.H., Ye, W.H., Zhang, Y., Liao, F.L., 2005. Eco-physiological characteristics ofgermination and storage of Castanopsis chiensis seeds. J. Beijing Forestry Univ.27, 92–95.

Yassin, M.F., Takahashi, T., Kato, S., 2012. Experimental simulation of wind flow overthe ridge topography. Air Qual. Atmos. Health 5, 293–301.