ORIGINAL PAPER

Demography of the upward-shifting temperate woody speciesof the Rhododendron pseudochrysanthum complexand ecologically relevant adaptive divergence in its trailingedge populations

Chia-Ying Chen & Bo-Kai Liang & Jeng-Der Chung & Chung-Te Chang &

Yi-Chiang Hsieh & Teng-Chiu Lin & Shih-Ying Hwang

Received: 20 August 2012 /Revised: 22 August 2013 /Accepted: 24 September 2013 /Published online: 5 October 2013# Springer-Verlag Berlin Heidelberg 2013

Abstract Trailing edge populations of upward migrating spe-cies induced by postglacial climatic warming can be evolu-tionarily significant in the face of global warming. We testedfor population size changes between lower- and higher-elevation populations located in the same mountainous re-gions of the upward-shifting temperate woody species of theRhododendron pseudochrysanthum complex in Taiwan. Wealso tested whether natural selection evoked adaptive diver-gence in trailing edge populations of this species complex.Wegenotyped 26 expressed sequence tag-simple sequence repeat(EST-SSR) loci of 185 individuals from nine populations of theR. pseudochrysanthum complex including Rhododendronrubropunctatum , Rhododendron hyperythrum , Rhododendronmorii , and R. pseudochrysanthum . R. rubropunctatum popu-lations in the R. pseudochrysanthum complex possessed thelowest estimates of genetic diversity and effective populationsize. Higher-elevation R. pseudochrysanthum populations hadlower effective population sizes than lower-elevation R. morii

populations in Hohuanshan and Tahsueshan, as revealed byestimates using both MIGRATE-N and approximate Bayesiancomputation (ABC). R. rubropunctatum populations divergedsignificantly from populations of other members of the R.pseudochrysanthum complex. An outlier potentially underpositive selection specific to R. rubropunctatum populationswas identified and strongly associated with ecologically rele-vant environmental variables. Postglacial climatic warminghas a significant influence on population isolation in the R.pseudochrysanthum complex. The results indicate adaptive evo-lution in the trailing edge populations, i.e., R. rubropunctatumpopulations of the upward shifting R. pseudochrysanthumcomplex.

Keywords Climate warming . Population isolation .

Rhododendron . Taiwan . Trailing edge population . Upwardrange shift

Introduction

Upward shifts in species distribution are common in responseto postglacial climatic warming (Lenoir et al. 2008). However,the ability to migrate upwards may be limited by increasinglyharsh environmental conditions (Köner 2007). When speciesmove upward in elevation, reductions in habitat area andeffective population size can occur, resulting in the loss ofrare, high-elevation species (Dirnböck et al. 2011). A discon-tinuous population distribution along elevation gradientscan influence the level of genetic diversity within and amongpopulations associated with climate change (Ohsawa andIde 2008). Biogeographical range shifts induced by upward

Communicated by P. Ingvarsson

Chen and Liang contributed equally to this work.

Electronic supplementary material The online version of this article(doi:10.1007/s11295-013-0669-x) contains supplementary material,which is available to authorized users.

C.<Y. Chen :B.<K. Liang :C.<T. Chang :Y.<C. Hsieh : T.<C. Lin :S.<Y. Hwang (*)Department of Life Science, National Taiwan Normal University, 88Tingchow Road, Section 4, Taipei 11677, Taiwan, Republic of Chinae-mail: hsy9347@ntnu.edu.tw

J.<D. ChungDivision of Silviculture, Taiwan Forestry Research Institute, 53Nanhai Road, Taipei 10066, Taiwan, Republic of China

Tree Genetics & Genomes (2014) 10:111–126DOI 10.1007/s11295-013-0669-x

migration can promote evolution, particularly at the leadingand trailing edges of a species’ range (Thuiller et al. 2008).Current genetic differentiation and diversity within speciesmay have been affected by long-term habitat isolation, whichcan promote local adaptation and eventually lead to speciation(Schluter 2000; Gavrilets and Vose 2007). The level of geneticvariation plays a key role in maintaining a population that isrobust to future environmental changes, thereby reducing theprobability of extinction (Booy et al. 2000).

Based on a palynological study of a lake core at an elevationof 745.5 m in central Taiwan, cold-temperate species may havedominated lowland vegetation because temperatures were 8.0to 11.0 °C cooler than today during the last glacial maximum(LGM; Tsukada 1966). Plant species, woody plants, in partic-ular, decrease in population sizes with narrow vertical distribu-tions after the LGM due to elevated temperature and cannotcompete with rapidly growing subtropical forest species thatcan inhabit broader altitudinal and ecological ranges in thelowland forests (Jump et al. 2009). A 1,500 to 1,600 m upwardmigration of forests occurring in Taiwan after the LGM hasbeen reported (Liew and Chung 2001). Moreover, Jump et al.(2012) found a rapid upward migration of ca. 3.6 m yr-1 ofmountain plants during the last century in Taiwan. Althoughnumerous species and genera continued to grow at the samelatitude, suitable habitats would have been reduced when forestspecies distributions shifted upward to a different range ofelevations in response to postglacial climatic warming(Tsukada 1966; Liew and Chung 2001). Moreover, accordingto an altitude-for-latitude model for the range retractions ofwoody species, a temperature increase of 1 °C is equivalentto range retractions of approximately 167 m in altitude andapproximately 145 km in latitude (Jump et al. 2009). Theinfluence of postglacial climatic warming on range retractionsin Taiwan may be substantial for island-endemic temperatewoody species (Tsukada 1966; Liew and Chung 2001), be-cause Taiwan covers only a narrow latitudinal range of 385 km.

Rhododendron hyperythrum Hay., Rhododendron moriiHay., Rhododendron pseudochrysanthum Hay., andRhododendron rubropunctatum Hay. are recognized as theR. pseudochrysanthum species complex because of their sub-stantial morphological similarities (Li et al. 1998). Molecularphylogenetic analyses also revealed close genetic relationshipsamong species of the R. pseudochrysanthum complex (Chunget al. 2007; Huang et al. 2011). R. morii and R. pseu-dochrysanthum populations of the R. pseudochrysanthumcomplex are presently distributed in mountain ranges at eleva-tions of 2400 to 3950 m, with R. morii distributed lower andR. pseudochrysanthum distributed higher (Li et al. 1998).Populations of these two species in the R. pseudochrysanthumcomplex typically overlap at elevations of approximately 3,000min mountains such as at Hohuanshan and Tahsueshan. TheR. pseudochrysanthum populations of theR. pseudochrysanthumcomplex grow on exposed gravelly slopes at the periphery of

cold-temperate coniferous forests above 3,000 m. The R.morii populations of the R. pseudochrysanthum complexgrow on exposed gravelly slopes at the periphery of warm-temperate evergreen broadleaf forests at elevations of 2,400to 3,000 m. The R. hyperythrum population of the R.pseudochrysanthum complex is only found on the peak ofNanhutashan, dominating the alpine tundra at an elevation of3,500 m. Only a few remnant R. rubropunctatum populationsof the R. pseudochrysanthum complex have been found insubtropical broadleaf woody forests on the northern tip ofTaiwan at elevations of 600 to 1,200 m (Li et al. 1998).

A previous study using chloroplast DNA (cpDNA) se-quence data (Chung et al. 2007) inferred that populations ofthe R. pseudochrysanthum complex experienced north-to-south expansion at middle elevations during the LGM. Thiswas based on significantly negative values of demographicneutrality test statistics, a star-like phylogeny, and the directionof cpDNA haplotype derivations from R. rubropunctatum(northern Taiwan) to other members (central and southernTaiwan) of the R. pseudochrysanthum complex. Moreover,nested clade analysis revealed population isolation with re-stricted gene flow within this complex. Therefore, a once-widespread distribution of theR. pseudochrysanthum complexat middle elevations followed by range contractions into highelevations or reduced habitat areas was suggested (Chung et al.2007) and led to genetic isolation among populations of the R.pseudochrysanthum complex (Chung et al. 2007; Huang et al.2011). Postglacial climatic warmingmay have caused the cold-adapted Rhododendron species in the R. pseudochrysanthumcomplex to shift upwards into a greatly confined space, subse-quently reducing effective population sizes. The postglacialupward migrations of R. hyperythrum , R. morii , and R.pseudochrysanthum populations of theR. pseudochrysanthumcomplex are verified by their recolonizations on high peaksof various mountainous areas. The persistence of R. rubro-punctatum populations in low-elevation habitats can be con-sidered the trailing edge populations of the R. pseudo-chrysanthum complex shifting up in elevation after theLGM. Therefore, populations of the R. pseudochrysanthumcomplex provide an opportunity to characterize the contempo-rary population isolation and adaptive divergence of islandendemic temperate woody species in response to postglacialclimatic warming.

This study collected multilocus genotypic data of 26expressed sequence tag-simple sequence repeat (EST-SSR)markers from 185 individuals of nine populations of the R.pseudochrysanthum complex. We estimated the level of genet-ic diversity, population structure, and recent population bottle-necks in natural populations of the R. pseudochrysanthumcomplex. We tested whether postglacial climatic warmingcaused upward migration and population size change by com-paring lower- and higher-elevation populations of the R.pseudochrysanthum complex located in the same mountainous

112 Tree Genetics & Genomes (2014) 10:111–126

regions. Genome scan approaches were used to test foroutliers potentially under selection in populations of the R.pseudochrysanthum complex. We also applied a logistic re-gression method to test for associations between genetic dataand environmental variables indicative of ecologically relevantlocal adaptation.

Materials and methods

Sampling and genotyping

Genomic DNA was extracted from the leaves (Doyle andDoyle 1987) of 185 individuals from nine populations of theR. pseudochrysanthum complex including one population(n =45) of R. hyperythrum, three populations (n =43) of R.morii , three populations (n =60) of R. pseudochrysanthum ,and two populations (n =37) of R. rubropunctatum (Table 1,Fig. 1). Samples were collected at a distance of 2.5 m at leastto avoid collecting clonemates. SSRs were identified in 1,245ESTs generated from R. catawbiense in the NCBI databasecontaining di-, tri-, tetra-, and penta-nucleotide repeats usingSSRIT (http://www.gramene.org/db/markers/ssrtool), andthen SSR primers were designed using Primer 3 (http://frodo.wi.mit.edu/primer3/input.htm). All samples weregenotyped at 26 EST-SSR loci (Electronic supplementarymaterial, Table S1). Polymerase chain reaction (PCR) wasperformed in a 10-μl reaction volume containing 20 ng tem-plate DNA, 75mMTris–HCl (pH 8.8), 20 mM (NH4)2SO4, 0.01 % (v/v) Tween 20, 2.5 mMMgCl2, 0.2 mM dNTP mix, 75nM of each primer, 0.8 μg bovine serum albumin, and 0.5 UTaq DNA polymerase. We conducted the PCR with the fol-lowing process: 94 °C for 10 min, followed by 38 cycles at94 °C for 30 s, 30 s at the optimal annealing temperatures(Electronic supplementary material, Table S1), and 30 s at72 °C, with a final extension of 5 min at 72 °C. AmplifiedPCR products were run on MegaBACE 1000, and allele sizeswere scored using Genetic Profiler software.

Genetic diversity

Possible genotyping errors (i.e., stuttering, large allele drop-outs and null alleles) were evaluated using MICRO-CHECKER v2.2.1 (Van Oosterhout et al. 2004) with 1,000randomizations, and errors were corrected. Deviation from theHardy–Weinberg equilibrium (HWE) was tested using a mod-ified Fisher’s exact test for each population at every locus(Guo and Thompson 1992), based on 9×105 Markov chainiterations with GENEPOP v4.0.10 (Raymond and Rouset1995). A total of 234 locus-by-population combinations ofHWE test were performed. GENEPOP was further used toestimate pairwise locus linkage disequilibrium (LD) based onthe permuted distribution of likelihood ratio statistics. The

Bonferroni correction was used for multiple comparisons.Allelic richness (AR) was estimated, accounting for differ-ences in sample-sizes, using the rarefaction procedure(Hurlbert 1971) for the smallest sample size (16 genes) asimplemented in HP-RARE v1.0 (Kalinowski 2005). Thenumber of alleles (AT), number of private alleles (AP), percentpolymorphisms (P), and observed (HO) and expected (HE)heterozygosity were analysed using MSA v4.05 (Dieringerand Schlötterer 2003). Inbreeding coefficient (F IS) was calcu-lated based on neutral loci (excluding outliers, see the“Results” section) using FSTAT v2.9.3 (Goudet 2001).

Identification of outliers based on FST-based neutrality tests

Various FST-based neutrality tests were developed to identifyloci potentially under selection using genetic data (Holdereggeret al. 2008). These methods perform coalescent simulations togenerate a null distribution of FST under neutral expectations.Loci that do not fit the neutral drift simulations because ofunusually high or low FST values are identified as outlierspotentially under positive or balancing selection, respectively.Four FST-based tests were used to identify outlier loci todetermine whether adaptive divergence had occurred in theinvestigated populations.

At first, we employed the global population comparisonmethods of FDIST2, BAYESFST, and hierarchical islandmodel test (HT) to identify outliers (Beaumont and Nichols1996; Beaumont and Balding 2004; Excoffier et al. 2009). InFDIST2, FSTwas calculated for each sampled locus based onan infinite island model. The expected neutral distribution ofFST conditioned on heterozygosity of each locus was gener-ated by 1×105 iterations of coalescent simulation (Beaumontand Nichols 1996; Beaumont and Balding 2004). The P-valuefor each locus was calculated at a 95 % confidence level. Lociwith unusually high or low FST values conditioned on hetero-zygosity were recognized as outliers potentially under positiveor balancing selection, respectively. BAYESFST, which dif-fers from FDIST2 by assuming unequal FST among the studypopulations, was also employed. With this method, FST wasmodeled as log(Fij/1-Fij)=α i+β j+γ ij, where α i is a locuseffect, β j is a population effect and γ ij is a locus-by-population effect (Beaumont and Balding 2004). Locus effectwas used to examine potential outliers based on 2,000 samplesof Markov chain Monte Carlo (MCMC) simulations extractedfrom the resulting posterior probability distributions. A locuswas considered to be under positive selection if its locus effect2.5 % quantile was positive and under balancing selection ifits locus effect 97.5 % quantile was negative. Three indepen-dent runswith different parameter values were performed untilconvergence. The HT was further applied to identify outliersusing a method similar to that of FDIST2 (Beaumont andNichols 1996) implemented in ARLEQUIN v3.5 (Excoffierand Lischer 2010). However, HT considers population

Tree Genetics & Genomes (2014) 10:111–126 113

Tab

le1

Species,populatio

ns(code),sam

plesize,locality,elevatio

n,andgenetic

diversity

estim

ates

foreach

localityacross

26EST-SSRlociin

theRhododendronpseudochrysanthum

complex

Species/population

Populationcode

Locality

(latitu

de°N

/longitu

de°E)

Elevatio

n(m

)N

AT

AAP

AR(SD)

HE(SD)

HO(SD)

PFIS

Rhododendronpseudochrysanthum

complex

185

275

10.6

3.194(2.25)

0.424(0.30)

0.330(0.33)

100

0.074

Rhododendronrubropunctatum

Tsaigongkeng

RTG

121.31/25.11

886

1481

3.1

12.617(1.96)

0.298(0.29)

0.330(0.33)

80.8

−0.157*

Tsankuanliao

RTK

121.51/25.05

738

2398

3.8

82.736(1.77)

0.301(0.28)

0.291(0.29)

80.8

0.047

R.pseudochrysanthum

Tahsueshan

PTH

121.07/24.19

3121

20104

4.0

72.967(2.29)

0.392(0.30)

0.411(0.31)

80.8

−0.055

Hohuanshan

PHH

121.20/24.10

3400

19107

4.1

73.152(2.24)

0.379(0.32)

0.367(0.31)

84.6

0.036

Lulinshan

PLL

120.52/23.27

2862

21131

5.0

133.464(2.10)

0.402(0.27)

0.404(0.28)

92.3

−0.031

Rhododendronmorii

Alishan

MAL

120.48/23.30

2100

19114

4.4

93.296(2.56)

0.382(0.33)

0.384(0.34)

69.2

0.008

Tahsueshan

MTH

121.07/24.19

3085

989

3.4

33.283(2.63)

0.389(0.33)

0.427(0.37)

73.1

−0.131*

Hohuanshan

MHH

121.15/24.07

2800

15126

4.8

163.776(2.32)

0.458(0.29)

0.478(0.34)

88.5

−0.066*

Rhododendronhyperythrum

Nanhutashan

HNH

121.26/24.21

3500

45162

6.2

183.456(2.42)

0.411(0.30)

0.420(0.30)

88.5

−0.019

The

FISstatistic

was

calculated

afterremovingselectiveoutlierspresentedin

Table2.

Follo

wingaBonferronicorrectio

n(α

=0.05),populatio

nsexhibitin

gsignificantd

eparturesfrom

Hardy–W

einberg

equilib

rium

areindicatedwith

anasterisk

Nsamplesize,A

Ttotalnum

bero

fallelesperp

opulation,Ameannumbero

fallelesperlocus,A

Pnumbero

fprivatealleles,ARmeannumbero

fstandardizedallelic

richnesswith

acorrectedsamplesize

of16,H

Emeanexpected

heterozygosity,H

Omeanobserved

heterozygosity,P

percentp

olym

orphicloci,F

ISinbreeding

coefficient

114 Tree Genetics & Genomes (2014) 10:111–126

subdivisions to identify loci with unusual levels of differenti-ation (Excoffier et al. 2009). Genetic differentiation calculatedfrom the empirical data set was integrated into the hierarchicalisland model to detect outliers across all populations; 95 %confidence limits were determined from 5×105 simulated loci.Bonferroni correction was applied to evaluate the significanceof selective outliers at P <0.025 for the FDIST2, BAYESFST,and HT methods.

We further employed DETSEL, a pairwise population com-parison method, to identify loci potentially under selection.DETSEL uses a model where a common ancestor populationsplit into two populations and only subsequently diverged byrandom drift after a possible bottleneck event (Vitalis et al.2001, 2003). We performed 10,000 coalescent simulations byconsidering a wide range of potential parameters. For eachpopulation pair (i , j), and for all loci, population-specific diver-gences of Fi and Fj were calculated. Expected joint distribu-tions of Fi and Fj were generated by the 10,000 coalescentsimulations. Null distributions were generated using nuisanceparameters including the locus mutation rate per generationfollowing an infinite allele model set to μ =1×10-4, 50 and500 generations before the bottleneck (T0), 50 and 500 indi-viduals during the bottleneck (N0), and ancestor populationsizes of 500 and 5,000 before the bottleneck (Ne). With variouscombinations of T0, N0, and Ne set for the coalescent simula-tions, outliers were identified as falling outside the 99.99 %confidence envelope. However, no estimates of the microsatel-lite mutation rates are available for Rhododendron species. Theaverage microsatellite mutation rate estimated for di-nucleotiderepeats in Arabidopsis thaliana was 8.87×10-4 (Marriage et al.2009). Therefore, because most markers in this study were di-nucleotide repeats, we adopted amore conservative value of 1×10-4 for the EST-SSRs. The empirical P value for each outlierof a population pair comparison was determined using 2Darrays of 50×50 square cells (Vitalis et al. 2001), and a com-bined P value for different comparisons was also calculated.

Association between environmental variables and EST-SSRalleles

We tested associations of EST-SSR alleles with 11 environ-mental variables obtained from the Central Weather Bureau ofTaiwan recorded in 1989–2009 from 390 meteorological sta-tions. The environmental variables were mean temperature(Tmean), maximum temperature (Tmax), minimum temperature(Tmin), mean wind speed (WSmean), precipitation (PRE), rela-tive humidity (RH), cloud cover (CLO), hours of sunshine(SunH), days of maximum temperature>30 °C (D30), days ofminimum temperature<10 °C (D10), and number of days with>0.1 mm rain per month (RainD). The selected environmentalvariables were primarily related to temperature, precipitation,and monsoons because of their roles in vegetation type differ-ences in Taiwan (Liew et al. 2006; Lee and Liew 2009). We

used a spherical model of the universal Kriging method inArcGIS to generate a distribution map, which was used tointerpolate values of environmental variables at an unobservedlocation from nearby meteorological observations (Carrera-Hernández and Gaskin 2007). Monthly mean values of the11 environmental variables at each sampling site wereextracted from their associated distribution maps/layers cover-ing a latitude/longitude grid within 1 km2. Since most envi-ronmental variables are likely correlated to some extent, weapplied a principal component analysis (PCA) to examinecorrelations between environmental variables.

To identify environmental variables that may have servedas selective forces, we used the spatial analysis method (SAM)of Joost et al. (2007, 2008). This method uses univariatelogistic regressions to determine the degree of associationbetween allelic frequencies at all marker loci and values ofenvironmental variables. With all possible pairwise combina-tions of allele vs. environmental variables, the significance ofan association was determined using both likelihood ratio andWald tests with Bonferroni correction at P <0.05.

Genetic structure

We used 22 loci not identified as outliers by the FST-basedneutrality tests (see the “Results” section) for genetic structureand recent bottleneck analyses. Pairwise FST values werecalculated and tested for significance with Fisher’s exact testafter 50,000 permutations using ARLEQUIN. Hierarchicalgenetic structure of the total genetic variation was partitionedinto among-group, within-group, and within-population com-ponents using analysis of molecular variance (AMOVA)implemented in ARLEQUIN with 50,000 permutations.Bayesian clustering was used for individual assignmentto different population clusters using STRUCTURE v2.3(Pritchard et al. 2000). STRUCTURE identifies distinct ge-netic populations and assigns individuals probabilistically topopulations. In practice, we applied no admixture model withallele frequencies correlated among populations assuming thatthe genotypes of individuals within a population are derivedcompletely from that population. If genetic admixtures be-tween different populations occur, they are probably causedby migration or shared ancestry. Prior population informationwas not incorporated in calculating the posterior probabilityfor individuals belonging to different clusters. For each clus-tering scenario (K =1–10), we performed 20 complete analy-ses each with a 50,000 burn-in period and a sampling periodof 5×105 iterations. Different clustering scenarios were eval-uated using the mean log probability, LnP(D ) (Pritchard et al.2000) and the change in log probability, ΔL (K ) (Evanno et al.2005). The symmetric similarity coefficients (SSCs) werecomputed with CLUMPP, as a measure of the similarityamong the 10 replicate STRUCTURE runs within each valueof K (Jakobsson and Rosenberg 2007). The mean pattern

Tree Genetics & Genomes (2014) 10:111–126 115

of STRUCTURE outputs for each value of K were thendisplayed graphically using the software DISTRUCT(Rosenberg 2004).

Demographic analyses

We used MIGRATE-N v3.1.3 (http://popgen.scs.fsu.edu/Migrate-n.html) applying a simple electrophoretic laddermodel to estimate long-term migration rates and confidenceintervals (CIs) of microsatellite alleles among populations.Long-term migration rates, M , 4N e generations in the past,were estimated based on a coalescent approach using themaximum likelihood mode. All possible combinations ofmigration that were either symmetrical or had no dispersalbetween populations were evaluated with the likelihood ratiotest. Long-term effective population size (Ne) of each popu-lation was estimated based on parameter θ , i.e., 4Neμ , whereμ is the mutation rate per site. Bidirectional M among popu-lations (M=m /μ , where m is the immigration rate per gener-ation) were also estimated. In the maximum likelihood runs ofMIGRATE-N, we sampled one of every 20 reconstructedgenealogies for each locus for ten short and three long chains.In the recorded 1,000 and 104 genealogies for short and longchains, respectively, the first 200 and 2,000 genealogies werediscarded as burn-in.

To determine whether decreases in population size oc-curred recently, we used BOTTLENECK v1.2.02 (Cornuetand Luikart 1996; Piry et al. 1999). This program identifies abottleneck within 0.5 to 5 Ne generations after the initiation ofa population reduction, where Ne is the effective bottleneckedpopulation size according to a Wilcoxon signed-rank test forsignificant deviation from heterozygosity excess, and as-sumed an infinite allele model, a stepwise mutation model,or a two-phase model of microsatellite mutation (Cornuet andLuikart 1996). A bottleneck within 2 to 4 N e generationsshould be detectable using a mode-shift test, where allelefrequency distribution deviates from an L-shape (Piry et al.1999).

We further tested whether higher-elevation populations ofthe R. pseudochrysanthum complex in Hohuanshan andTahsueshan had smaller population sizes than lower-elevation populations using the approximate Bayesian com-putation (ABC) implemented in DIYABC v.0.4.39 (Cornuetet al. 2008, 2010). Two competing scenarios were tested; oneassuming constant population size and the other allowing apopulation size change (Electronic supplementary material,Fig. S1). Four population samples (PHH, MHH, PTH, andMTH) containing 63 individuals genotyped at 22 neutral loci(excluding outlier loci, see the “Results” section) were includ-ed in ABC analysis. In the constant population size scenario,population sizes of the two Hohuanshan (PHH andMHH) andthe two Tahsueshan (PTH and MTH) populations were des-ignated N t1 and N t2, respectively. In the scenario allowing

population size change, each population was given its sizeestimator (N1 for MTH,N2 for PTH, N3 for MHH, and N4 forPHH), and the higher-elevation PHH and PTH populationswere constrained to have smaller population sizes than thelower-elevation MHH and MTH populations, respectively(N1>N2; N3>N4). Hohuanshan MHH and PHH populationsdiverged td1 generations in the past from an ancestral popula-tion size of NA1, and Tahsueshan MTH and PTH populationsdiverged td2 generations in the past from an ancestral popula-tion size of NA2. Looking backward in time at td generationsago, a common ancestral population size of NAwas given forthe four populations. The divergence times (in the number ofgenerations) were constrained in that td was larger comparedto td1 and td2. Simulated genetic data were obtained using ageneralized stepwise mutation model of microsatellite muta-tion (Estoup et al. 2002). The prior distributions of mutationrate, μ , and the parameter of geometric distribution, P, wereset at default values. Uniform priors for the population sizes(10≤N ≤104) and divergence times (1≤t ≤104) were used.Summary statistics including the mean number of alleles, themean genetic diversity, the mean size variance, the mean Mratio (Garza and Williamson 2001), the mean index of classi-fication (Rannala and Moutain 1997; Pascual et al. 2007), andFST values were calculated for the observed and 2×106 sim-ulated data sets. We generated posterior parameter distribu-tions from the 104 simulated data sets that had the smallestnormalized Euclidean distances using Locfit 2.0 in the Renvironment, based on the local linear regression method ofBeaumont et al. (2002). A logistic regression approach wasused to estimate the 95 % CIs for the posterior probability ofeach scenario (Fagundes et al. 2007; Cornuet et al. 2008).

Results

Genetic diversity and inbreeding coefficients

This study observed 234 alleles over 26 loci from nine popu-lations of the R. pseudochrysanthum complex (Table 1). Onaverage, R. rubropunctatum RTG and RTK populations hadthe lowest genetic diversity estimates for AT, AR,HE, andHO.In Hohuanshan and Tahsueshan, lower AR and HO valueswere found in the R. pseudochrysanthum PHH and PTHpopulations at higher elevations, respectively, compared tothe R. morii MHH and MTH populations at lower elevations.Higher HE was found for the R. morii MHH population atlower elevation compared to the R. pseudochrysanthum PHHpopulation at higher elevation in Hohuanshan, although thedifference was not significant due to overlapping standarddeviations. Moreover, a comparable level of HE was foundby comparing the R. pseudochrysanthum PTH population athigher elevation with the R. morii MTH population at lowerelevation in Tahsueshan. Population F IS values estimated

116 Tree Genetics & Genomes (2014) 10:111–126

using 22 neutral loci (excluding outliers) ranged from −0.157(RTG) to 0.047 (RTK) with significant heterozygote excessobserved in the MTH and MHH populations of R. morii andRTG population of R. rubropunctatum (Table 1).

After adjusting for multiple comparisons with Bonferronicorrection at P <0.05, significant departures from HWE werefound for loci 5557 and 6637 in the R. pseudochrysanthumPHH population. No LD was found after applying theBonferroni correction.

Ecologically associated outliers

Locus 5571 was identified as being an outlier potentiallyunder positive selection using FDIST2 and HT (Table 2).Three outliers (loci 1804, 1812, and 6637) were identifiedpotentially under balancing selection, of which loci 1812 and

6637 were detected by both FDIST2 and HT methods andlocus 1804 was identified by both BF and HT methods.DETSEL also identified locus 5571 as being an outlier poten-tially under positive selection in six of seven population paircomparisons of the RTG or RTK populations of R.rubropunctatum with populations of other members of theR. pseudochrysanthum complex under various conditions(combined empirical P <0.0001). Protein coding gene se-quences of the outlier 5571 potentially under positive selec-tion was functionally annotated to the RAB GTPaseholomlog, RABA1f, with an E -value of 2E -81 using theBlastX tool of NCBI (Electronic supplementary material,Table S1).

We further employed SAM to test the association of EST-SSR alleles at all loci with environmental variables in additionto the use of FST-based tests in identifying outliers. In the R.

Fig. 1 Map of sampling localitiesof nine populations of theRhododendronpseudochrysanthum complex

Tree Genetics & Genomes (2014) 10:111–126 117

rubropunctatum RTG and RTK populations, close associa-tions between allelic frequencies of alleles 263 and 265 of thelocus 5571 and values of a number of environmental variableswere found at a significance threshold of 2.73411E-07 corre-sponding to the 99.99 % CI (Table 2). However, no associationwas found between genetic data and environmental variables inpopulations of other members of the R. pseudochrysanthumcomplex.

The first two axes of the PCA used to investigate correla-tion between environmental variables explained 84.14 %(68.77 % and 15.37 %, respectively) of the total variation.The PC1 was derived by giving approximately equal weightto each of the 11 environmental variables except D30. Theseresults suggest that environmental variables examined arehighly correlated, and hence alleles 263 and 265 of the locus5571 potentially under positive selection had significant asso-ciations with most environmental variables as that revealed bySAM analysis (Table 2).

Genetic differentiation

Significantly higher levels of genetic differentiation for pairwisecomparisons between R. rubropunctatum populations and pop-ulations of other members of the R. pseudochrysanthum com-plex were found (average pairwise FST=0.188, combined P<0.0001, Table 3). This was based on 22 neutral loci (excludingoutliers) after applying Bonferroni correction at P <0.05. Theaverage pairwise FST was 0.109 for the R. pseudochrysanthum

complex, which declined to 0.056 when R. rubropunctatumpopulations were excluded. Significant genetic differentiationwas also found among species groups of the R. pseudo-chrysanthum complex (ΦCT=0.06431, P=0.011) based onAMOVA. The AMOVA also showed significant genetic dif-ferentiation (ΦCT=0.12492, P =0.028) between R. rubro-punctatum populations and populations of other members ofthe R. pseudochrysanthum complex (Table 4). Although theAMOVA showed no significant genetic differences amongspecies groups of R. pseudochrysanthum , R. hyperythrum ,and R. morii (ΦCT=0.01875, P=0.144), significant geneticdifferentiation among populations of these three species of theR. pseudochrysanthum complex was found (ΦST=0.06569,P <0.00001).

Population differentiation of the R. pseudochrysanthumcomplex was further analyzed by STRUCTURE (Fig. 2). TheSSCs computed with CLUMPP across ten independent runswere 0.9964, 0.9836, 0.9187, and 0.9558, respectively, for K =2, 3, 4, and 5, suggesting absence of genuine multimodalityacross runs. The largest ΔK was found at K =2 (Fig. 2a).However, STRUCTURE cannot distinguish individuals amongR. hyperythrum , R. morii , and R. pseudochrysanthum whenK =2. Although the maximum LnP(D) value was found whenK =3, LnP(D) value at K =3 was not significantly differentfrom LnP(D) values of K =4 and K =5 (Fig. 2a). Nonetheless,LnP (D) decreased significantly beyond K =5. When K =3was specified, STRUCTURE yielded the following clusters:(i) R. rubropunctatum RTG and RTK individuals; (ii) R.

Table 2 Outliers identified by paired and global comparison methods in the Rhododendron pseudochrysanthum complex

Globalcomparison

Pair comparison Environmental variables

FD BF HT DS SAM

R. pseudochrysanthum complex

Outlier potentially under positive selection

5571 (alleles 263 and 265) ** * RTG, RTK Tmean, Tmax, Tmin: all year

WSmean: January, February, April, May, June, October, November

PRE: January, February, March, September, October, November, December

CLO: January, February, April, May, June, October, November, December

SunH: January, February, March, April, May, June, October, November, December

D30: February, April, May, July, September, October

D10: all year

RainD: January, February, March, April, October, November, December

Outlier potentially under balancing selection

1804 ** *

1812 ** *

6637 ** *

The significant threshold for DETSEL was set to 99.99 %

FD FDIST2, BF BAYESFST, HT hierarchical test, DS DETSEL, SAM spatial analysis method

Significant values at *P<0.025; **P <0.001 (after applying the Bonferroni correction at α=0.05)

118 Tree Genetics & Genomes (2014) 10:111–126

pseudochrysanthum PTH individuals and R. hyperythrumHNH individuals; and (iii) R. pseudochrysanthum PHH andPLL individuals plus R. morii MAL, MTH, and MHH indi-viduals (Fig. 2b). When K =4 was specified, STRUCTUREyielded the following clusters: (i) R. rubropunctatum RTG andRTK individuals; (ii) R. pseudochrysanthum PTH individuals;(iii) R. pseudochrysanthum PHH and PLL individuals plus R.morii MAL, MTH, and MHH individuals; and (iv) R.hyperythrum HNH individuals (Fig. 2b). When K =5 wasspecified, no further increase in the number of geneticallydistinct groups was found.

Demography

No bottlenecks were detected in all examined populations up to5 Ne generations ago in the R. pseudochrysanthum complex

based on heterozygosity excess and allele frequency distributiontests (Electronic supplementary material, Table S2). Historicalmigration rates expressed as the number of migrants per gener-ation (Nm, the product of θ andM divided by 4) estimated withMIGRATE-N revealed asymmetrical migration among popula-tions (Table 5). Estimates of Nm among populations were lowand ranged from 0.072 to 0.527, with an average of 0.279.However, higher average dispersal rates (average Nm=0.323)were found among populations of R. pseudochrysanthum , R.morii , and R. hyperythrum compared to the averageNm of totalpopulations. In Hohuanshan and Tahsueshan, historical migra-tion rates were higher from higher-elevation to lower-elevationpopulations (from R. pseudochrysanthum PHH to R. moriiMHH in Hohuanshan and from R. pseudochrysanthum PTHto R. morii MTH in Tahsueshan, respectively) than the reverse.However, asymmetrical migration rates between the two

Table 3 Estimated population pairwise FST (below the diagonal) and corresponding P values (above the diagonal) in the Rhododendronpseudochrysanthum complex

Population code RTG RTK PTH PHH PLL MAL MTH MHH HNH

RTG – <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

RTK 0.0866* – <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

PTH 0.2149* 0.2229* – <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

PHH 0.1496* 0.2039* 0.0529* – 0.0006 0.0004 0.0387 0.0056 <0.0001

PLL 0.1577* 0.2221* 0.0938* 0.0322* – <0.0001 <0.0001 <0.0001 <0.0001

MAL 0.1494* 0.2099* 0.1108* 0.0433* 0.0400* – 0.0038 <0.0001 <0.0001

MTH 0.1650* 0.2209* 0.0788* 0.0202 0.0651* 0.0395 – <0.0001 <0.0001

MHH 0.1540* 0.1905* 0.0632* 0.0251 0.0529* 0.0469* 0.0459* – <0.0001

HNH 0.1775* 0.1990* 0.0521* 0.0428* 0.0952* 0.0775* 0.0507* 0.0542* –

*Significance after Bonferroni correction at α=0.05

Table 4 Summary of AMOVA results comparing genetic variation within and among different groupings

Source of variation Df Sum of squares Variance components Percentage of variation Φ statistics P value

Four species groups in the Rhododendron pseudochrysanthum complex

Among groups 3 135.080 0.30340 6.43 ΦCT=0.06431 0.01108

Among populations within 5 65.206 0.26186 5.55 ΦSC=0.05932 <0.00001

Groups

Within populations 361 1499.105 4.15265 88.02 ΦST=0.11981 <0.00001

Total 369 1699.392 4.71791

Two groups: Rhododendron rubropunctatum vs. other species in the R. pseudochrysanthum complex

Among groups 1 91.236 0.63382 12.49 ΦCT=0.12492 0.02818

Among populations within groups 7 109.050 0.28751 5.67 ΦSC=0.06475 <0.00001

Within populations 361 1499.105 4.15265 81.84 ΦST=0.18158 <0.00001

Total 369 1699.392 5.07397

Three species groups: R. pseudochrysanthum , Rhododendron morii , and Rhododendron hyperythrum

Among groups 2 46.835 0.08801 1.88 ΦCT=0.01875 0.14471

Among populations within groups 4 51.365 0.22026 4.69 ΦSC=0.04783 <0.00001

Within populations 289 1267.118 4.38449 93.43 ΦST=0.06569 <0.00001

Total 295 1362.318 4.69275

Significance was tested against a null distribution of 50,000 random permutations

Tree Genetics & Genomes (2014) 10:111–126 119

populations in Tahsueshan were not significantly different be-cause of overlapping 95 % CIs. Moreover, estimates of Nm

indicate that the mean rate of migration into R. rubropunctatumpopulations from populations of other members of the R.pseudochrysanthum complex were lower than the mean rateof migration from populations of other members of the R.pseudochrysanthum complex to R. rubropunctatum popula-tions (average Nm=0.135 vs. 0.305). Long-term effective pop-ulation size (θ) ranged from 0.5659 to 1.6338 with the smallestat the R. rubropunctatum RTG population and the highest at theR. hyperythrum HNH population. Both R. rubropunctatumRTG and RTK populations had relatively lower θ values com-pared to those of populations of other members of the R.pseudochrysanthum complex. Moreover, in the context ofcomparing populations in the same mountainous areas ofHohuanshan and Tahsueshan, effective population size wassignificantly higher in lower-elevation than in higher-elevationpopulation, i.e., R. morii MHH (θ=1.2160, 95 % CI: 1.1136–1.3116) vs. R. pseudochrysanthum PHH (θ =0.9214, 95 % CI:

0.8543–0.9778), and R. morii MTH (θ =0.9254, 95 % CI:0.8443–1.0200) vs. R. pseudochrysanthum PTH (θ= 0.8484,95 % CI: 0.7942–0.8974), respectively, according to non-overlapping (MHH vs. PHH) or slightly overlapping (MTHvs. PTH) 95 % CIs.

Population size reductions of higher-elevation Hohuanshanand Tahsueshan populations were also revealed by ABC, basedon a higher posterior probability of 0.89 (95 % CI: 0.866–0.923) obtained in the scenario allowing population size changecompared to a posterior probability of 0.11 (95 % CI: 0.078–0.134) in the constant population size scenario (Electronicsupplementary material, Fig. S1). In Hohuanshan, the medianof recent population sizes were estimated to be 8,030 (95 % CI:3,930–9,910) and 3,070 (95%CI: 948–7,040), respectively, forthe R. morii MHH population at lower elevation and R.pseudochrysanthum PHH population at higher elevation(Table 6). Moreover, the median of ancestral population sizewas estimated to be 6,830 (95 % CI: 1,160–9,830) for the R.morii MHH and R. pseudochrysanthum PHH populations in

Fig. 2 Bar plots ofSTRUCTURE analysisrepresenting assignments ofgenotypes to nine populations ofthe Rhododendronpseudochrysanthum complex.See Table 1 for population codes.(a) Log likelihood and changes inthe log likelihood for differentscenarios of groupings based on22 microsatellite loci derivedfrom expressed sequence tags(ESTs) of R. catawbiense . (b) Barplots represent assignments ofgenotypes for nine populations ofthe R. pseudochrysanthumcomplex

120 Tree Genetics & Genomes (2014) 10:111–126

Tab

le5

Mutation-scaled

effectivepopulatio

nsizes(θ=4N

eμ)andthenumberof

immigrantspergeneratio

nacross

nine

populatio

nsin

theRhododendronpsedochrysannthum

complex

Recipient

ΘSource

RTG

RTK

PTH

PHH

PLL

MAL

MTH

MHH

HNH

RTG

0.5659

(0.5324,

0.5991)

*0.1567

(0.1265,

0.1905)

0.1478

(0.1178,

0.1804)

0.1492

(0.1211,

0.1817)

0.0846

(0.0649,

0.1115)

0.1218

(0.0955,

0.1513)

0.1564

(0.1273,

0.1901)

0.0726

(0.0552,

0.0938)

0.1110

(0.0869,

0.1386)

RTK

0.6962

(0.6414,

0.7389)

0.2649

(0.2150,

0.3179)

*0.1824

(0.1462,

0.2232)

0.1077

(0.0806,

0.1378)

0.1187

(0.0913,

0.1545)

0.1414

(0.1107,

0.1788)

0.1887

(0.1471,

0.2308)

0.1276

(0.0968,

0.1624)

0.1794

(0.1395,

0.2258)

PTH

0.8484

(0.7942,

0.8974)

0.1768

(0.1418,

0.2188)

0.1122

(0.0861,

0.1453)

*0.3058

(0.2554,

0.3647)

0.2372

(0.1931,

0.2872)

0.2226

(0.1790,

0.2706)

0.1941

(0.1557,

0.2398)

0.1381

(0.1083,

0.1743)

0.2869

(0.2386,

0.3461)

PHH

0.9214

(0.8543,

0.9778)

0.3154

(0.2583,

0.3838)

0.3039

(0.2491,

0.3670)

0.3109

(0.2553,

0.3747)

*0.2434

(0.1943,

0.3027)

0.1621

(0.1262,

0.2049)

0.2948

(0.2399,

0.3578)

0.2359

(0.1889,

0.2903)

0.4349

(0.3579,

0.5156)

PLL

1.1123

(1.0350,

1.1894)

0.3468

(0.2805,

0.4404)

0.2735

(0.2175,

0.3428)

0.3717

(0.3015,

0.4664)

0.2698

(0.2141,

0.3387)

*0.5273

(0.4396,

0.6339)

0.3074

(0.2449,

0.3821)

0.3504

(0.2845,

0.4313)

0.3149

(0.2525,

0.4059)

MAL

1.4244

(1.3156,

1.5132)

0.3307

(0.2671,

0.4035)

0.3085

(0.2421,

0.3793)

0.2483

(0.1960,

0.3095)

0.3507

(0.2845,

0.4263)

0.3052

(0.2455,

0.3741)

*0.5062

(0.4191,

0.6016)

0.3665

(0.2844,

0.4462)

0.3865

(0.3151,

0.4756)

MTH

0.9254

(0.8443,

1.0200)

0.2091

(0.1644,

0.2692)

0.1882

(0.1464,

0.2442)

0.2182

(0.1720,

0.2802)

0.3531

(0.2884,

0.4408)

0.0905

(0.0644,

0.1315)

0.4063

(0.3349,

0.5006)

*0.2338

(0.1835,

0.2985)

0.2026

(0.1589,

0.2612)

MHH

1.2160

(1.1136,

1.3116)

0.4144

(0.3276,

0.5121)

0.3131

(0.2471,

0.3942)

0.3503

(0.2778,

0.4375)

0.5138

(0.4182,

0.6264)

0.3342

(0.2614,

0.4272)

0.3779

(0.1629,

0.2768)

0.2135

(0.1629,

0.2768)

*0.4391

(0.3555,

0.5407)

HNH

1.6338

(1.5516,

1.7016)

0.4633

(0.3920,

0.5433)

0.5122

(0.4320,

0.6000)

0.4362

(0.3674,

0.5140)

0.4667

(0.3942,

0.5478)

0.3824

(0.3184,

0.4583)

0.4451

(0.3404,

0.4874)

0.4122

(0.3404,

0.4874)

0.3321

(0.2744,

0.3983)

*

The

mutationrateisgivenas

aconstant

value(μ

=0.0001)in

each

populatio

n.Migratio

ndirectionfrom

thepopulatio

n;recipient:migratio

ndirectionto

thepopulatio

n.The

numberof

immigrantsper

generatio

niscalculated

byNe×m

ij=θj×M

ij/4.T

he95

%highestp

osterior

credibility

(HPC)values

oftheparameter

arein

parentheses

Tree Genetics & Genomes (2014) 10:111–126 121

Hohuanshan and the median of their divergence time wasestimated to be 256 (95 % CI: 35–1,060) generations ago,which is equivalent to 3,072 years ago based on the minimumgeneration time of 12 years in Rhododendron plants (Cross1975). In Tahsueshan, the median of recent population sizeswere estimated to be 5,330 (95 % CI: 1,540–9,680) and 2,930(95 % CI: 855–6,730), respectively, for the lower-elevation R.morii MTH population and higher-elevation R. pseudo-chrysanthum PTH population. The median of ancestral popu-lation size was estimated to be 5,780 (95 % CI: 751–9,760) forthe R. morii MTH and R. pseudochrysanthum PTH popula-tions in Tahsueshan, and the median of their divergence timewas estimated to be 305 (95 % CI: 35–1,280) generations ago(3,660 years ago). In addition, the median of divergence timefrom a common ancestor of all member populations of the R.pseudochrysanthum complex located in Hohuanshan andTahsueshan was estimated to be 400 (95 % CI: 89–3,050)generations ago (4,800 years ago), and the median of commonancestral population size was estimated to be 531 (95%CI: 29–3,140).

Discussion

Genetic diversity, population isolation, and inbreeding

A lower level of genetic variability is frequently found innarrowly distributed species, which face a greater threat ofextinction from stochastic ecological events. The level ofEST-SSR variation in the R. pseudochrysanthum complex(average HE=0.424) was comparable to those of endemicplant species on average (HE=0.420), based on genomic-derived SSRs (Nybom 2004). However, the average HE valuebased on EST-SSRs of the R. pseudochrysanthum complexwas significantly smaller than averageHE values of R. ripense(averageHE=0.800; Kondo et al. 2009) and R. brachycarpum(average HE=0.815; Hirao 2010), based on genomic-derived

SSRs. Although genomic-derived SSRs varymore, whichmaybe due simply to highmicrosatellitemutation rate, the variationobserved in EST-SSRs may represent functional significancein response to selection more effectively (Ellis and Burke2007; Kane and Rieseberg 2007).

The effects of postglacial climatic warming may havediffered among populations of the R. pseudochrysanthumcomplex with similar life histories. Although genetic diversitydecreases with increased elevation because of range contrac-tion associated with upward migration (e.g., Quiroga andPremoli 2007), no conclusive evidence was found in thisstudy that higher-elevation populations had lower levels ofgenetic diversity than lower-elevation populations of the R.pseudochrysanthum complex. Nevertheless, postglacial cli-matic warming may have had greater effects on low elevation(trailing edge) R. rubropunctatum populations because oftheir lower levels of genetic variation compared to populationsof other members of the R. pseudochrysanthum complex.This agrees with the central-marginal hypothesis (e.g.,Eckert et al. 2008; Herrera and Bazaga 2008).

The shallow genetic structuring estimated by the pairwiseFST and AMOVA analyses indicated high dispersal ratesamong species groups of R. hyperythrum , R. morii , and R.pseudochrysanthum probably because of historical geneticconnectivity. However, significant levels of genetic differen-tiation among populations of the R. pseudochrysanthum com-plex, especially among populations ofR. pseudochrysanthum ,R. morii , and R. hyperythrum , indicated genetic isolation inaccordance with the results of previous studies (Chung et al.2007; Huang et al. 2011). Genetic isolation wasmore apparentbetween trailing edge populations (R. rubropunctatum popu-lations) and populations of other members of the R. pseudo-chrysanthum complex, based on the deeper genetic structuringrevealed by pairwise FST, AMOVA, and STRUCTURE analyses.Moreover, genetic structuring revealed by STRUCTURE suggeststhat evolutionary histories of R. rubropunctatum RTG and RTK,R. hyperythrum HNH, and R. pseudochrysanthum PTH

Table 6 Mean, median, modeand quantile calculated from sim-ulation of population size changemodel of lower-elevation andhigher-elevation population sam-ples of Rhododendronpseudochrysanthum and Rhodo-dendron morii in Tahsueshan andHohuanshan using approximateBayesian computation

Scenario/parameter Mean Median Mode Quantile 2.5 % Quantile 97.5 %

Scenario 2 (posterior probability: 0.89; 95 % confidence interval: 0.866–0.923)

NA 789 531 43 29 3140

NA1 (MHH-PHH) 6440 6830 9110 1160 9830

NA2 (MTH-PTH) 5630 5780 6610 751 9760

N1 (MTH) 5500 5330 4830 1540 9680

N2 (PTH) 3140 2930 2840 855 6730

N3 (MHH) 7740 8030 8520 3930 9910

N4 (PHH) 3280 3070 2910 948 7040

Td 639 400 272 89 3050

td1 (MHH-PHH) 330 256 174 35 1060

td2 (MTH-PTH) 394 305 152 35 1280

122 Tree Genetics & Genomes (2014) 10:111–126

populations differed from those of other populations of the R.pseudochrysanthum complex.

Population genetics theory predicts that reduced local ge-netic variation in small populations may trigger negativeinbreeding effects (Frankham 1995). However, genetic signa-tures of inbreeding may not be apparent if a population re-mains small for an extended period. No significant positivevalues of population F IS were found in this study. Thus,substantial evidence of inbreeding or significant geneticsubstructuring within the study populations was not obtained.Significant negative values of population F IS can be found ifrates of gene flow among populations are high and can also beattributed to the predominantly insect-pollinated outcrossingin Rhododendron species (Ono et al. 2008; Hirao 2010).However, the F IS of most of the populations examined in thisstudy showed no significant negative values departing fromHWE toward an excess of heterozygotes, which may haveresulted from restricted gene flow among populations as re-vealed by MIGRATE-N analyses.

Reduced population sizes in the trailing-edgeR. rubropunctatum and high elevation R. pseudochrysanthumpopulations

It is proposed that postglacial climatic changes caused woodyspecies to shift to much-narrower vertical habitats due toecological niche conservation (Wiens and Graham 2005;Hardy et al. 2009). Climate changes are known to causespecies to shift their ranges upward and can lead to reducedpopulation sizes (Jump et al. 2009). No evidence of bottle-necks up to 5 N e generations ago was found based on analysiswith the BOTTLENECK program, suggesting that populationsize declines may have occurred for a prolonged period oftime as a result of long-term postglacial climatic warming.The statistical power of the bottleneck tests may be low fordetecting recent population declines given the limited samplesizes of individuals, and estimations are sensitive to violationsin the assumptions of the microsatellite mutation model(Girod et al. 2011; Peery et al. 2012). However, populationisolation caused by postglacial climatic warming is inevitableand may be severe for island endemic temperate woodyspecies in Taiwan (Liew and Chung 2001; Jump et al.2012). The post-glacial climate in Taiwan has been primarilyhumid and warm, which can facilitate the rapid growth ofsubtropical plant species that were limited by the cooler, drierclimate of the LGM (Tsukada 1966; Liew et al. 1998, 2006).The characteristics of a slow growth rate and long generationtime of temperate Rhododendron species may limit theirability to become established in this habitat. In addition,because of minimal latitudinal coverage and the numerousplant species accommodated within the limited areas of steepmountains, competition to survive may be intense (Jump et al.2012).

No conclusive evidence of upward migration was found inthis study using MIGRATE-N on populations of the R.pseudochrysanthum complex. A simulation study by Abdoet al. (2004) found that MIGRATE-N often yielded inaccurateestimates of migration using DNA sequence data, although itis not known whether this conclusion can be applied to mi-crosatellite data. Nevertheless, migration rates among popula-tions of the R. pseudochrysanthum complex were found to below, consistent with findings for other Rhododendron species(Escaravage et al. 1997; Kameyama et al. 2001), and can beattributed to the ineffective pollen flow and short seed dis-persal distance in Rhododendron (Kameyama et al. 2000;Marshall et al. 2010). Moreover, it is probable that the paceof climate change may have exceeded their ability to migrate,resulting in population losses. Abdo et al. (2004) also pointedout that MIGRATE-N performs well in estimating the θ value,and in this study, θ values were higher in lower-elevation thanin higher-elevation populations in Hohuansan andTahsueshan. This was further supported by ABC results,although the median value of recent population size for theR. morii MTH population slightly overlapped the 97.5 %quantile value of recent population size estimated for the R.pseudochrysanthum PTH population. This suggests there ishigher overall demographic stability in lower-elevation popu-lations than in those at higher-elevation in both Hohuanshanand Tahsueshan.

The effects of postglacial climate warming would be mostprofound in R. rubropunctaum because there are no suitablehabitats in higher elevations along its current distributionalranges for populations to grow and persist. This would alsoresult in much smaller populations compared to those of othermembers of the R. pseudochrysanthum complex, as revealedby MIGRATE-N analyses. MIGRATE-N results indicate thatsmall effective population sizes may be a result of low immi-gration rates into R. rubropunctatum populations from popu-lations of other members of the R. pseudochrysanthum com-plex. It is also likely that more severe declines in effectivepopulation sizes of R. rubropunctatum are a result of increas-ingly lower limits and restraints on upward migration and/or amore stringent confinement to suitable habitats.

Ecologically relevant adaptive divergence in the trailing edgeR. rubropunctatum populations of the R. pseudochrysanthumcomplex

Dispersal is restricted among small populations, facilitatinggenetic drift and/or genetic divergence because of spatiallyheterogeneous selection pressures (Mäkinen et al. 2008).Evolutionary adaptations can occur in natural populationsshifting their geographical distributions because of climatechange, especially in populations that persist at the trailingedges (Ackerly 2003; Hampe and Petit 2005; Thuiller et al.2008). For trailing edge R. rubropunctatum populations

Tree Genetics & Genomes (2014) 10:111–126 123

persisting in low-elevation environments despite climatechange, adaptive evolution is vital because no suitable habitatsexist at higher elevations. SAM and FST-based neutrality testresults suggest a correlation between probable selective forcesand outlier or nearby linked gene in the R. rubropunctatumpopulations. In trailing edge populations, the scarcity of suit-able habitat may be reflected in small population sizes anddecreased genetic diversity (Hampe and Petit 2005; Thuilleret al. 2008; Levin 2012) and may have resulted in the loss ofadaptive potential (Pearson et al. 2009). However, variationspecific to trailing edge populations may have evoked localadaptations in response to changing local environments(Parisod and Joost 2010; Savolainen et al. 2011; Hollidayet al. 2012). The trailing edge R. rubropunctatum populationswith relatively small population sizes and low immigrationrates are strongly isolated genetically from populations ofother members of the R. pseudochrysanthum complex, assuggested by pairwise FST, AMOVA, and STRUCTUREresults. Genetic isolation of trailing edge populations fromcore populations may have evoked selection against immi-grants (Nosil et al. 2008; Levin 2012) and also downplayedthe effects of maladapted gene flow from core populations(Garcia-Ramos and Kirkpatrick 1997; Kirkpatrick and Barton1997; Holliday et al. 2012). Moreover, long-term persistenceof R. rubropunctatum at low elevations may have promotedadaptive divergence in response to environmental changesdespite small population sizes (Hampe and Petit 2005;Thuiller et al. 2008; Levin 2012). Therefore, local adaptivedivergence of the trailing edge R. rubropunctatum popula-tions may have been invoked by ecologically relevant localselective forces. Moreover, natural selection produces similareffects on local adaptations in both R. rubropunctatum popu-lations, indicating the potential for allopatric speciation com-pared to the geographically distant populations of other mem-bers of the R. pseudochrysanthum complex.

Determining the function of the outlier identified potentiallyunder positive selection in this study would be challenging. Inthis study, protein gene coding sequences of the outlier poten-tially under positive selection had functional annotation corre-sponding to RAB GTPase homolog RABA1f. RAB GTPasesare small GTP-binding proteins and are central regulators ofprotein trafficking of cellular components like proteins be-tween cellular compartments (Vernoud et al. 2003; Woollardand Moore 2008). A plant-specific RAB GTPase, ARA5, wasshown to be involved in plant salt stress tolerance (Bolte et al.2000; Zhang et al. 2009). Recently, an ARA6 gene paralogousto ARA5 was also shown to be salt-responsive (Ebine et al.2011), which was thought to emerge in the common ancestorof land plants (Ebine et al. 2012). It is possible that plant-specific RAB GTPase protein genes play general roles inabiotic stress tolerance (Ebine et al. 2012). Our result suggeststhe putative role of RAB GTPase protein genes in local adap-tations of the trailing edge R. rubropunctatum populations of

the R. pseudochrysanthum complex. Future study on whetherspecific ecological factors drive adaptive genetic variation ofthe RAB GTPase protein genes may help in understandinghow populations of R. rubropunctatum respond to climaticwarming.

Conclusions

This study employed a combination of methods to explore theeffects of biogeographical range shifts prompted by postglacialclimatic warming on populations of closely related species inthe R. pseudochrysanthum complex. No evidence of geneticerosion was found for most populations. Historical populationconnectivity may have caused low genetic differentiationamong species groups of R. hyperythrum , R. morii , and R.pseudochrysanthum . However, population isolation was re-vealed by significant genetic differentiation among all hierar-chical population groups. R. rubropunctatum populations per-sist at the trailing edge under divergent or extreme environ-mental conditions; adaptive divergence can result from histor-ical and contemporary ecological and evolutionary forces.Trailing edge populations tended to diverge from core popula-tions of a species complex and displayed lower genetic diver-sity but greater local adaptation. These trailing edge popula-tions are significant as sources of evolutionary novelty becauseof the association between outlier and environmental variables.Thus, trailing edge populations should not be considered anevolutionary dead-end destined for extinction and should per-haps be the focus of conservation efforts.

Acknowledgments This work was supported by the National ScienceCouncil, Executive Yuan, Taiwan (grant number NSC97-2313-B-003-002-MY3) to SYH. The authors are grateful to Yushan National Park forallowing them to collect plant materials. Funding for a graduate student-ship to BKL and YCH and a postdoctoral associateship to CYC and CTCby the National Science Council is also acknowledged.

Data Archiving Statement EST-SSR genotyping data of this studywere deposited at Dryad: http://doi.org/10.5061/dryad.m33cb.

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