Population genetic variation, geographic structure, and multiple ...

AJB Advance Article published on June 12, 2015, as 10.3732/ajb.1400554. The latest version is at http://www.amjbot.org/cgi/doi/10.3732/ajb.1400554 RESEARCH ARTICLE

A M E R I C A N J O U R N A L O F B OTA N Y

POPULATION GENETIC VARIATION, GEOGRAPHIC STRUCTURE, AND MULTIPLE ORIGINS OF AUTOPOLYPLOIDY IN GALAX URCEOLATA1 STEIN SERVICK2, CLAYTON J. VISGER2, MATTHEW A. GITZENDANNER2, PAMELA S. SOLTIS3,4, AND DOUGLAS E. SOLTIS2–5 2Department

of Biology, University of Florida, Gainesville, Florida 32611 USA; 3Florida Museum of Natural History, University of Florida, Gainesville, Florida 32611 USA; and 4Genetics Institute, University of Florida, Gainesville, Florida 32610 USA

• Premise of the study: Whereas population genetic studies have examined allopolyploids, comparable studies of naturally occurring autopolyploids remain rare. To address fundamental questions regarding autopolyploidy, we undertook a detailed population genetic study of one of the classic examples of autopolyploidy, Galax urceolata (Diapensiaceae), which comprises diploid, triploid, and autotetraploid cytotypes. Galax is endemic to the Appalachian Mountains, the adjacent piedmont, sandhills, and coastal plain and represents perhaps the most widely known example of autopolyploidy in nature. • Methods: Flow cytometry was used to diagnose ploidal level of ~1000 individuals across 71 populations. We used 10 microsatellite markers to examine genetic variation across the geographic range of Galax and assessed multiple origins though comparisons of diploid, triploid, and tetraploid accessions using multiple analytical approaches. • Key results: Tetraploids had higher levels of heterozygosity than diploids did. Genetic variation in diploid and tetraploid Galax is geographically structured among the ecoregions of the southeastern United States. Autotetraploidy in Galax urceolata has occurred independently at least 46 times, with triploidy having occurred a minimum of 31 times. • Conclusions: Genetic differentiation among ecoregions suggests historical patterns of local adaptation. The numerous independent origins of tetraploid Galax reported here are among the highest frequencies of independent polyploidizations ever reported for any polyploid (auto- or allopolyploid). Key words: autopolyploidy; Diapensiaceae; Galax urceolata; geographic structure; multiple polyploid origins; population genetics, southeastern United States.

Polyploidy is an important force in the evolution and diversification of many plant lineages, particularly angiosperms (e.g., Müntzing, 1936; Clausen et al., 1945; Löve and Löve, 1949; Stebbins, 1950; Lewis, 1980a, b; Grant, 1981). Historical estimates of polyploidy in angiosperms range from 30–70%. However, recent genomic analyses indicate that all living angiosperms are of polyploid origin and identify many other whole-genome duplications (WGD) throughout angiosperm history (reviewed by Jiao et al., 2011, 2012; Soltis et al., 2014). Autopolyploids have long been grossly under-investigated compared with allopolyploids. In part, this neglect reflects history in that successful autopolyploids were thought to be extremely 1 Manuscript received 21 December 2014; revision accepted 29 April 2015. C.J.V. and S.S. contributed equally to this project and are co-first authors. This work was supported by U.S. National Science Foundation Grant DEB-0910113, and C.J.V. was funded by NSF Fellowship grant DGE1315138. The authors thank B. Barringer for information on the breeding system of Galax. 5 Author for correspondence (e-mail: [email protected])

doi:10.3732/ajb.1400554

rare in nature due to hypothesized chromosome pairing problems and a limited capacity for adaptation (Stebbins, 1950; Grant, 1981; Soltis and Soltis, 1993, 1999; Ramsey and Schemske, 1998, 2002; Tate et al., 2005; Soltis et al., 2014). In fact, Stebbins (1950) and Grant (1981) recognized only a few unambiguous autopolyploids: Galax urceolata, Sedum ternatum, Sedum pulchellum, Fritillaria camschatcensis, Biscutella laevigata, Dactylis glomerata, and Solanum tuberosum. In addition, Grant (1981) considered Vaccinium uliginosum, Eragrostis pallescens, Galium mollugo, and Galium verum as “probable” autopolyploids. Over the past 25 years, the widespread use of molecular markers has revealed that autopolyploidy is more common than previously believed (e.g., Soltis and Soltis, 1993; Husband and Schemske, 1998; Ramsey and Schemske, 1998, 2002; Soltis et al., 2007; Parisod et al., 2010). In contrast to the traditional paradigm, genetic data has also revealed important reasons for the success of autopolyploids, including greater genetic variability, allelic diversity, and enzyme multiplicity than their diploid progenitors (e.g., Levin, 1983; Moody et al., 1993; Soltis and Soltis, 1993, 1999; Segraves et al., 1999; Soltis et al., 2009). Despite great advances in our understanding of the genetic consequences of polyploidy, we still know very little

American Journal of Botany 102(6): 1–10, 2015; http://www.amjbot.org/ © 2015 Botanical Society of America

1 Copyright 2015 by the Botanical Society of America

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about the origin and population genetic attributes of autopolyploids. Whereas many allopolyploids have now been examined at the population level (e.g., Symonds et al., 2010; Sampson and Byrne, 2012), genetic studies of naturally occurring autopolyploids remain rare (Soltis et al., 2014). We therefore addressed fundamental questions regarding the origins and genetic variability in an autopolyploid by undertaking a detailed population genetic study of one the classic examples of autopolyploidy, Galax urceolata. Galax urceolata is endemic to the Appalachian Mountains and adjacent piedmont, sandhills, and coastal plains (Nesom, 1983; Soltis et al., 1983; Burton and Husband, 1999; Johnson et al., 2003) and comprises diploid, triploid, and tetraploid individuals. Plants of G. urceolata are herbaceous perennials that spread rhizomatously, forming dense populations. The pollination system, reproductive strategy, and seed dispersal mechanisms of G. urceolata are largely unknown, although recent evidence has emerged suggesting self-incompatibility (B. Barringer, University of Wisconsin–Stevens Point, personal communication). Galax urceolata has long been considered one of the clearest examples of autopolyploidy in nature because Galax comprises a single described species (G. urceolata) that has both diploid and polyploid cytotypes that are morphologically indistinguishable. Galax has no close relatives in Diapensiaceae (Stebbins, 1950; Lewis, 1962; Grant, 1981; Rönblom and Anderberg, 2002), so there are no other extant entities with which diploid Galax could hybridize to yield the tetraploid. Cytotypes have indistinguishable flavonoid profiles, supporting the hypothesis of autopolyploidy (Soltis et al., 1983). Allozyme data also match the genetic expectations of autopolyploidy. There was no evidence for fixed heterozygosity in tetraploid Galax as would be expected following an allopolyploid origin; furthermore, diploid and tetraploid populations exhibited the same suite of allozymes (Epes and Soltis, 1984). The diploid cytotype occurs over most of the range of G. urceolata; the tetraploid cytotype inhabits a relatively smaller portion of the range (Nesom, 1983). Diploids and tetraploids often occur in sympatry. However, several populations from the Virginia coastal plain and the north-central piedmont of North Carolina are exclusively tetraploid (Nesom, 1983). In areas

Fig. 1.

of diploid and tetraploid sympatry, the triploid cytotype is commonly intermixed or immediately adjacent to either diploid or tetraploid plants. The presence of habitat differentiation between cytotypes has remained uncertain. Nesom (1983) and Johnson et al. (2003) reported major overlaps in habitat preference between cytotypes, although Nesom (1983) suggested that diploids and autotetraploids occupy more xeric and mesic habitats, respectively. Johnson et al. (2003) suggested that subtle habitat differentiation may be related to changes in relative species abundance. Other than preliminary allozyme data, no genetic studies have been undertaken in G. urceolata. We employed microsatellite markers to (1) investigate the genetic diversity of Galax across cytotypes, (2) assess whether genetic variation of Galax is geographically structured, and (3) estimate the number of independent origins of the tetraploid cytotype.

MATERIALS AND METHODS Sampling—Leaf samples from 1000 individuals representing 71 populations were collected across the geographic distribution of G. urceolata (Fig. 1, Table 1), ranging from central West Virginia south to northwestern Georgia, eastern Tennessee, and eastern Virginia to coastal Virginia and North Carolina. Sampling was primarily based on locales identified by Nesom (1983) and Burton and Husband (1999). Leaf tissue from up to 24 individuals was collected in silica gel from each population when population sizes were large enough to permit. Due to the rhizomatous nature of G. urceolata (Scott, 2004), distinct genets were impossible to identify in most populations. Hence, in most cases, leaves were sampled at regular intervals (e.g., every 3 m) across the population to increase the probability of sampling the genetic variation within the population. Where populations consisted of multiple distinct individuals, all such clusters were sampled. Ploidy determination—Chromosome counts were obtained for several plants collected in the field and grown in the greenhouse. Root tip collection and tissue squashes followed Soltis (1980). We used flow cytometry (FCM) following a modification of Suda and Trávnícek (2006), with values calibrated using the chromosome counts to obtain ploidy estimates from dried leaf tissue of all 1000 plants sampled. Approximately 3 cm2 of silica-dried leaf tissue of G. urceolata was cochopped with approximately 1 cm2 fresh leaf tissue of Pisum sativum in 1 mL

Localities and cytotype composition of sampled populations of Galax urceolata. Blue = tetraploid; yellow = diploid; green = triploid.

S E R V I C K E T A L . — G E N E T I C VA R I AT I O N I N G A L A X

TABLE 1.

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Galax urceolata, locales, population composition, and basic population genetic parameters.

State Georgia

North Carolina

Tennessee Virginia

West Virginia

County

Latitude

Longitude

Population ID

N (2x, 3x, 4x)

P (%)

HO

HE

FIS

Bartow Rabun Rabun Rabun Rabun

34.1603 34.9694 34.96827 34.96528 34.9642

−84.74575 −83.3002 −83.29995 −83.29955 −83.30147

48 34 36 40 43

10 (13, 0, 0) 9 (1, 0, 8) 11 (0, 5, 6) 5 (0, 0, 5) 7 (0, 0, 7)

90 100 100 100 100

0.457 n/a

0.57 n/a

0.208 n/a

Alleghany Alleghany Ashe Ashe Ashe Ashe Avery Brunswick Burke Gaston Gates Harnett Harnett Haywood Haywood Jackson Macon Macon Macon Macon Macon Macon Martin McDowell McDowell McDowell Mitchell Moore Stokes Stokes Stokes Surry Surry Surry Surry Swain Watauga Watauga Wayne Wayne Wayne Wilkes Yancey Polk Alleghany Appomattox Bedford Bland Botetourt Botetourt Buckingham Carroll Carroll Craig Craig Floyd Giles James City James City Monroe Roanoke Scott Tazewell Washington Fayette

36.4294 36.48367 36.4027 36.40058 36.28854 36.37598 36.007 34.35273 35.6039 35.23702 36.43697 35.46907 35.46952 35.31159 35.41201 35.45404 35.06282 35.06137 35.04043 35.0402 35.03905 35.06053 35.8117 35.7302 35.72937 35.73228 35.89074 35.1536 36.39497 36.39378 36.39152 36.3417 36.3407 36.34045 36.3407 35.51771 36.11956 36.21867 35.23673 35.2346 35.23667 36.39767 35.81527 35.08442 37.7306 37.3652 37.55638 37.24574 37.39029 37.48539 37.63573 36.64357 36.65405 37.53681 37.49196 36.97984 37.32416 37.3651 37.3645 37.45066 37.17827 36.8358 37.00909 36.86373 38.06231

−81.12439 −80.9789 −81.46415 −81.46065 −81.37406 −81.28292 −81.88986 −78.14822 −81.63835 −81.27293 −76.69247 −78.89987 −78.90148 −82.87809 −82.74983 −83.09449 −83.1918 −83.19262 −83.19445 −83.19348 −83.19292 −83.18428 −76.89163 −81.90382 −81.90393 −81.90722 −81.98064 −79.3685 −80.2581 −80.26053 −80.26482 −80.47857 −80.4786 −80.4751 −80.47388 −83.28895 −81.78275 −81.59031 −77.88337 −77.88137 −77.8826 −81.03955 −82.14535 −84.51472 −80.04087 −78.8421 −79.36642 −81.10095 −79.83796 −79.66859 −78.78125 −80.72741 −80.52318 −80.26176 −80.25901 −80.15676 −80.52316 −76.8252 −76.8424 −80.4897 −80.08293 −82.62553 −81.52156 −81.94535 −81.0608

140 141 121 122 138 139 135 101 126 127 112 107 108 131 132 130 7 10 14 16 18 26 102 123 124 125 134 106 117 118 119 113 114 115 116 129 136 137 109 110 111 120 133 128 150 403 149 155 147 148 103 142 143 151 152 145 153 201 202 154 146 157 156 158 404

24 (0, 0, 24) 24 (0, 0, 24) 10 (10, 0, 0) 4 (4, 0, 0) 15 (15, 0, 0) 24 (24, 0, 0) 24 (0, 0, 24) 6 (6, 0, 0) 7 (0, 0, 7) 24 (24, 0, 0) 24 (1, 0, 23) 5 (5, 0, 0) 18 (18, 0, 0) 24 (0, 0, 24) 16 (0, 0, 16) 11 (10, 0, 1) 3 (0, 0, 3) 10 (8, 2, 0) 6 (3, 3, 0) 7 (1, 3, 3) 3 (0, 3, 0) 8 (0, 0, 8) 4 (4, 0, 0) 4 (4, 0, 0) 23 (23, 0, 0) 6 (6, 0, 0) 20 (13, 7, 0) 24 (24, 0, 0) 24 (0, 0, 24) 24 (0, 0, 24) 7 (0, 0, 7) 24 (0, 0, 24) 6 (0, 0, 6) 11 (0, 0, 11) 5 (0, 0, 5) 19 (19, 0, 0) 10 (0, 0, 10) 21 (5, 16, 0) 24 (23, 1, 0) 12 (11, 1, 0) 10 (10, 0, 0) 7 (0, 0, 7) 22 (4, 16, 2) 6 (6, 0, 0) 18 (18, 0, 0) 32 (0, 0, 32) 3 (3, 0, 0) 24 (24, 0, 0) 24 (24, 0, 0) 24 (24, 0, 0) 9 (9, 0, 0) 15 (5, 9, 1) 21 (0, 0, 21) 19 (19, 0, 0) 24 (24, 0, 0) 5 (5, 0, 0) 24 (24, 0, 0) 24 (0, 0, 24) 24 (0, 0, 24) 24 (24, 0, 0) 10 (10, 0, 0) 24 (24, 0, 0) 12 (12, 0, 0) 24 (24, 0, 0) 3 (3, 0, 0)

100 100 90 70 90 90 100 90 100 100 90 60 100 100 100 80 100 90 100 90 60 100 80 80 100 90 100 40 60 100 100 100 100 100 100 100 100 100 100 90 80 100 100 100 100 100 40 90 90 90 80 100 100 80 100 60 100 100 100 90 70 100 90 100 50

0.484 0.475 0.414 0.542

0.435 0.406 0.365 0.514

−0.133 −0.176 −0.125 −0.033

0.492

0.511

0.022

0.549 n/a 0.56 0.573

0.656 n/a 0.338 0.587

0.183 n/a −0.674 0.007

0.34

0.282

−0.103

0.75 0.5 n/a

0.506 0.4 n/a

−0.474 −0.2 n/a

0.567 0.475 0.451 0.38 0.522 0.354

0.401 0.359 0.494 0.472 0.474 0.212

−0.415 −0.298 0.062 0.173 −0.069 −0.678

0.558

0.509

−0.086

0.48 0.545 0.582 0.539

0.426 0.524 0.462 0.441

−0.118 −0.056 −0.231 −0.189

0.575 0.647 0.378

0.509 0.516 0.419

−0.135 −0.256 0.074

0.367 0.5 0.429 0.469 0.422 0.3

0.194 0.425 0.45 0.462 0.327 0.316

−0.875 −0.185 0.093 −0.017 −0.25 −0.011

0.46 0.488 0.24 0.49

0.384 0.482 0.242 0.509

−0.208 −0.001 −0.048 0.027

0.578 0.3 0.55 0.467 0.507 0.5

0.505 0.272 0.538 0.402 0.479 0.25

−0.142 0.017 −0.021 −0.167 −0.054 −1


ice-cold Otto 1 buffer (100 mM citric acid and 0.5% Tween 20). The raw suspension was filtered through a 35-µm cell filter tube (Becton Dickinson, Franklin Lakes, New Jersey, USA) and centrifuged at 800 rpm for 5 min. The supernatant was aspirated, and the pellet resuspended in 100 µL Otto 1 buffer and incubated at room temperature for 30 min. One milliliter of room-temperature Otto 2 buffer (100 mM Na2HPO4·12 H2O, 2 µg/mL β-mercaptoethanol, and 4 µg/mL 4′,6-diamidino-2-phenylindole [DAPI]) was added to the cell suspension and allowed to incubate for a minimum of 10 min before analysis. Nuclei were stable at this stage at 4°C for up to 4 d. Isolated nuclei were analyzed on an LSR-II flow cytometer with BD FACSdiva version 6.1.2 software (Becton Dickinson). Events of interest were isolated from noninformative cellular debris by gating the scatter plots; at least 5000 events were collected from the gated region. Peak identification, coefficient of variation, and geometric means were generated using FCSexpress version 3.1 (De Novo Software, Thornhill, Ontario, Canada). Ploidy was estimated using MATLAB version R2010a (Mathworks, Natick, Massachusetts, USA). Due to variation inherently found in FCM analyses, as well as to genome size variation between individuals, cutoff values between diploid, triploid, and tetraploid cytotypes were determined by an agglomerative clustering algorithm implementing a Euclidean distance metric and a maximum number of clusters of 3. Clustering quality was evaluated using a Kruskal– Wallis analysis of variance between identified clusters. Ploidy was estimated for 970 of the 1000 sampled individuals. The remaining 30 samples did not yield results, likely due to tissue degradation. Ploidy for these 30 samples was estimated based on microsatellite allele configuration (see below) and information gathered for other plants in the same populations. If four alleles were present at a locus in a sample, then that sample was inferred to be a tetraploid. To minimize the misidentification of polyploid plants, we inferred samples to be diploid if only two alleles per locus were identified, even though a tetraploid could have only two alleles, and triploidy was inferred if three alleles were identified at a locus and the population contained FCM-identified triploids. Even with this conservative approach, misinterpretation is possible in populations of mixed ploidy, particularly with respect to tetraploids that harbor only two or three alleles/locus and co-occur with diploids or triploids. Marker development and screening—Genomic DNA was extracted from silicadried tissue using a modified CTAB protocol (Doyle and Doyle, 1987). Microsatellite loci for G. urceolata were previously developed and reported (Servick et al., 2011) and were used here: Glx1, Glx6, Glx7, Glx14, Glx19, Glx20, Glx21, Glx23, Glx104, and Glx2tri. PCR was performed in 10-µL reaction volumes containing 1 M betaine, 1.5 mM MgCl2, 0.1 µM dNTPs, 0.5 µM forward and reverse primers, 0.5 µM of either 6FAM–, VIC–, NED–, or PET–M13-labeled primer (Applied Biosystems, Carlsbad, CA, USA), and 0.2 unit Taq polymerase. PCR cycling conditions for most loci were 94°C for 5 min; 34 cycles of 94°C for 30 s, 52°C (Glx19, Glx20, Glx23 annealing temperature adjusted to 55°C) for 30 s, and 72°C for 45 s; and a final extension of 72°C for 20 min. Labeled amplicons and GS600LIZ size standard were analyzed using an ABI 3730 DNA Analyzer (Applied Biosystems). Data were scored using GeneMarker version 1.6 (Soft Genetics, State College, Pennsylvania, USA) and confirmed manually. Genetic diversity—Genetic variation was characterized by calculating the percentage polymorphic loci (P), expected heterozygosity (HE) for diploid G. urceolata, and observed heterozygosity (HO) for all sampled diploid and tetraploid populations as implemented in the program SPAGeDi version 1.3 (Hardy and Vekemans, 2002). Additionally, the number of alleles per locus was characterized by ploidal level. Tests for Hardy–Weinberg equilibrium (HWE), linkage disequilibrium, and global HO and HE were previously reported by Servick et al. (2011) for 44 diploid populations of G. urceolata. Genetic structure—The model of microsatellite evolution (stepwise mutation model vs. infinite allele model) was determined by permutation test (20 000 permutations) in SPAGeDi version 1.3. FST (Weir and Cockerham, 1984) was used for analyses among the exclusively diploid populations of G. urceolata. FST values may be inappropriate for analyses of autopolyploids due to the difficulty in determining dosage as well as the potential production of multivalents in meiosis, leading to polysomic inheritance, and the possibility of double reduction, both of which violate the assumptions of FST (Bever and Felber, 1992; Ronfort et al., 1998). As a result, mixed ploidy populations and exclusively triploid or tetraploid populations were analyzed using rho (Ronfort et al., 1998). Additionally, rho is unbiased with respect to inheritance pattern (e.g., disomic vs. polysomic) and hence the best metric for this system. We further examined genetic differentiation using analysis of molecular variance (AMOVA; Excoffier et al., 1992) as performed in the program GenAlEx

version 6.41 (Peakall and Smouse, 2006). To achieve an unbiased measure of genetic distance among individuals, we treated microsatellite loci as dominant markers by conversion to a binary matrix containing all defined alleles. Pairwise genetic distances were calculated such that only dissimilarities between states were compared (Huff et al., 1993; Peakall and Smouse, 2006). Significance of genetic partitioning estimates within and among populations, as well as within and between ploidy, was estimated by 9999 AMOVA permutations. Nested hierarchical AMOVA was performed using the program GenoDive (Meirmans and van Tienderen, 2004). Population structure for diploid G. urceolata was assessed using the program TESS version 2.3 (Chen et al., 2007). For each value of K (K = 2–20), 100 replicates of 12 000 sweeps were collected with a burn-in of 2000. Mean deviance information criterion and likelihood estimates for the top 20% of the iterations at each K were plotted to assess possible K models using deviance information criterion (DIC) (Spiegelhalter et al., 2002) and ln[Pr(X|K)] (based on the rate of change in the log probability of the data) (Evanno et al., 2005). The best estimate of K as well as the immediately adjacent estimates were reanalyzed, each consisting of 400 iterations of 50 000 sweeps with a burn-in of 25 000. The parameter set used for all analyses was as follows: the level of spatial influence was set to admixture, implementing the CAR model (Durand et al., 2009); degree of trend was set to linear; change in spatial parameter, change in variance, and parameter of allele frequency model were left at default values; however, the model was set to update the spatial interaction parameter and variance values throughout the analyses. The first sweeps of all analyses were initialized from a neighbor-joining tree; all following sweeps were continued from the lowest DIC run. The top 20% of the iterations for each K value were permuted in the program CLUMPP (Jakobsson and Rosenberg, 2007). CLUMPP settings were as follows: the algorithm used for aligning runs was set to use the Greedy algorithm and set to test the specified number of random input orders of the runs; the pairwise similarity matrix statistic used was set to G’; and the number of repeat input orders to be tested was set to 20. CLUMPP results were visualized using the program DISTRUCT (Rosenberg, 2004). Spatial display of admixture interpolation was performed using population Q-matrices [indiv (Q)]. Population genetic structuring in the tetraploid was initially assessed using STRUCTURE (Pritchard et al., 2000), which has been suggested to be compatible with the polysomic inheritance of autopolyploids (Dufresne et al., 2014). However, given the difficulty in assigning microsatellite allele dosage to autopolyploids, there is a large volume of missing data for tetraploid Galax, which was analyzed both “as is” and filled in using population allele frequencies to infer genotype (De Silva et al., 2005; Meirmans and van Tienderen, 2004). (For full methods, see Appendix S1 in Supplemental Data with the online version of this article.) Isolation by distance—For all diploid and tetraploid populations with N > 1, we calculated pairwise geographic distances from latitude/longitude coordinates using great circle distance measures. FST was calculated under an AMOVA framework for diploids using SPAGeDi; FST was calculated as FST / (1 − FST) as the statistic is expected to vary linearly. Rho was calculated for all populations (multiple cytotypes) and for tetraploids only; Rho is presented as Rho / (1 − Rho). Mantel tests (9999 permutations) were performed independently for all populations, diploid populations, and tetraploid populations to test whether neighboring populations have a higher degree of similarity than more distant populations independent of ploidy, as well as in relation to ploidy, using GenAlEx. Assessing multiple origins—Genetic distance matrices were generated in the package Polysat (Clark and Jasieniuk, 2011) implemented in R version 1.35 (R Development Core Team, 2010), under the Lynch distance metric. The Lynch distance metric was chosen because it is based on allele frequency and implements a band-sharing dissimilarity index (Lynch, 1990; Clark and Jasieniuk, 2011). Neighbor-joining analyses were performed in the program PAUP* version 4.0b10 (Swofford, 2003). Because determining the true dosage of microsatellite alleles is not feasible in polyploids, we expected a loss of power due to the fact that we were missing complete tetraploid genotypes. To alleviate this issue, we first ran a neighbor-joining analysis using a diploid only data set to recover the diploid topology. Next, we conducted a second neighbor-joining analysis using the full genotype data set (including diploids, triploids, and tetraploids), which was constrained to the diploid consensus tree topology and rooted at the midpoint. We estimated changes in chromosome number under a likelihood framework using the program ChromEvol version 1.2 (Mayrose et al., 2010). Eight


models of chromosome evolution were evaluated: four models each with either constant or linear rates of evolution. The four models consisted of rate estimates of the following chromosomal evolution parameter sets: (1) increase or decrease by single chromosomes (λ and δ, respectively), (2) also including whole-genome duplication (WGD) (λ, δ, and μ), (3) also including demi-polyploidization, defined by Mayrose et al. (2010) as a 1.5× increase in chromosome number (triploids in our study), where rates of WGD and demi-polyploidization are assumed to be equal (λ, δ, μ = ρ), and (4) where demi-polyploidization is estimated separately ((λ, δ, μ, and ρ). Linear rate models allow for the increase and/or decrease in ploidy to be dependent on the current chromosome number at a given node, while constant rate models hold the rate across the tree. Parameters for each model were estimated and optimized in ChromEvol with 10 000 simulations. The best model of chromosome evolution, including events of WGD, was chosen by the lowest Akaike information criterion (AIC). The number of polyploid origins was inferred by visual inspection of the ChromEvol results using the best model determined by AIC.

RESULTS Cytotype identification—Of the 30 samples for which ploidy could not be accurately estimated through FCM analysis, 17, 11, and two were inferred to be diploid, tetraploid, and triploid, respectively. Mis-scorings were unlikely, because no tetraploids identified via FCM contained only two alleles at all loci. Four individuals contained at most three alleles per locus, making triploidy vs. tetraploidy ambiguous; however, of those four ambiguous individuals, two were inferred to be tetraploid, and two were inferred to be triploid based on the population configurations. Furthermore, this set of 30 individuals represents only 3% of all samples and is unlikely to bias the results of any analysis. For the remaining 970 individuals, the median (min–max) picogram values for diploid, triploid, and tetraploid genome sizes were 1.61 (1.43–1.93), 2.34 (2.04–2.60), and 3.07 (2.70– 3.44), respectively (Appendix S2, see online Supplemental Data). Fifty-five populations were composed of a single cytotype (32 diploids, 1 triploid, and 22 tetraploids), whereas 14 had multiple ploidies (see Table 1 and Fig. 1). Genetic diversity— Servick et al. (2011) found no significant linkage disequilibrium (LD) or deviations from HWE in the diploids. Previous LD and HWE tests encompassed all of the loci and diploid populations used here, and therefore have not been recalculated. Our sample of 1000 individuals yielded 234 alleles (12–41 per locus) across 10 loci, with diploids, triploids, and tetraploids exhibiting 146, 121, and 217 alleles, respectively, of which 10, TABLE 2.

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5, and 65 were unique to that cytotype. Among ploidal levels, HO followed the expectations for autopolyploids; that is, diploids showed the lowest levels of observed heterozygosity (0.3– 0.647), whereas tetraploids showed the greatest levels of observed heterozygosity, ranging from 0.641–0.787 (Table 2). The partitioning of genetic variation among populations of G. urceolata through AMOVA analysis supports moderate genetic differentiation among populations; 66–67% of the variation in diploid, triploid, and tetraploid cytotypes is found among populations (Table 2). When G. urceolata was analyzed with respect to ploidal level, 91–94% of the variation was found within cytotypes as opposed to among cytotypes. Genetic and geographic structure— Genetic and geographic structure of diploid populations was assessed using TESS (Durand et al., 2009). Initial analyses estimated K = 6: both DIC and ln[Pr(X|K)] methodologies for model selection support these estimates for K (Spiegelhalter et al., 2002; Evanno et al., 2005) (online Appendix S3). The DIC and ln[Pr(X|K)] for K = 6 were 2.642 × 104 and 1.291 × 104. In addition, detailed inspection of the K = 6 bar plot was used to ascertain the optimal value for K. The maximum population membership coefficient [pop(Q)] for each cluster for K = 6 ranged from 0.8589–0.9945. Given that TESS uses spatial data as priors for the MCMC algorithm, it eliminates spurious clusters automatically (i.e., populations will not subdivide horizontally for only minor increases in likelihood scores). When individuals are partitioned into K = 6, there is strong differentiation between populations from the coastal plain (Fig. 2A, green), piedmont (Fig. 2A, pink), sandhills (Fig. 2A, blue), ridge, and valley (Fig. 2A, yellow), and Blue Ridge Mountains (Fig. 2A, red), which correspond geographically with the established USDA Forest Service (2004) ecoregions of the southeastern United States (distinguished hierarchically on the basis of climatic and vegetative characteristics, including precipitation, temperature, vegetation or other natural land cover, elevation and terrain). The additional cluster (Fig. 2A, light blue) arises via the subdivision of the ridge and valley ecoregions into two distinct clusters, based on high individual membership coefficient [indiv(Q)] values within populations 153 [indiv(Q) = 0.7653] and 155 [indiv(Q) = 0.8589]. Visual examination of the partitioning reveals some admixture among Appalachian Mountain clusters (red, yellow, and light blue clusters, Fig. 3). These clusters remain largely distinct from the piedmont, sandhills, and coastal populations.

Cytotype genetic differentiation and AMOVA results for populations of Galax urceolata.

Source of genetic variation Among G. urceolata populations Within G. urceolata populations Among 2x populations Within 2x populations Among 3x populations Within 3x populations Among 4x populations Within 4x populations Among 2x and 3x populations Within 2x and 3x populations Among 2x and 4x populations Within 2x and 4x populations Among 3x and 4x populations Within 3x and 4x populations

P (%)

HO

FST

100

0.723

100

0.641

100

0.712

0.406

100

0.787

0.467

100

0.652

0.046

100

0.722

0.12

100

0.783

0.088

0.271

Rho

df

Sum of squares

Estimated variance

Percentage of variation

0.103

70 929 42 513 8 54 26 346 1 617 1 932 1 437

7297.515 11 440.215 7920.15 10 611.63 1105.5 1673.93 10 011.42 13 545.09 138.247 8421.833 802.264 16 776.805 176.439 10 298.196

6.559 12.315 13.107 20.685 16.361 30.999 25.422 39.148 138.247 13.65 1.746 18.001 1.417 23.566

35 65 38.8 61.2 34.5 65.5 39.4 60.6 7 93 9 91 6 94

0.438


Fig. 2. Standard bar plots for population structuring (K = 6) in diploid and tetraploid Gaxlax urceolata. Each column represents the individual membership coefficient; black lines separate populations. Inferred clusters of individuals are represented by different colors. (A) Diploid results, (B) tetraploid results using the uncorrected missing allele data set (see text), and (C) corrected allele dosage data set.

Our preliminary analysis of the population structure of the tetraploid revealed a pattern similar to that of the diploid. The most likely number of clusters for both tetraploid data sets (missing alleles untreated and missing alleles filled in) was K = 6, inferred using the Evanno et al. (2005) method. The results from the final analysis yielded nearly identical results for both data sets (Fig. 2B, C), with the inferred genotype data set resulting in a more uniform representation of admixture within populations. Due to all sampled individuals from population 403 sharing one clonal genotype, both data sets showed that population to belong to a cluster distinct from any other tetraploid population. Population assignments mapped geographically showed considerable agreement between diploid and tetraploid data sets (online Appendices S4 and S5).

Isolation by distance— Mantel tests for diploid G. urceolata indicated significant, albeit weak, correlation between geographic distance and genetic distance (FST: R2 = 0.14767, P = 0.013) (Fig. 4A). In contrast, genetic differentiation in tetraploids was not significantly correlated with geographic distance (Rho/(1 − Rho): R2 = 0.00372, P = 0.199) (Fig. 4B). Multiple origins of autopolyploidy— The best model of chromosomal evolution in G. urceolata was the constant rate with independent estimation of WGD rates and demi-polyploidization rates (λ = 1.814 × 10−10, δ = 0.686, µ = 11.076, ρ = 15.422) (Table 3). These parameter estimates are consistent with visual inspection of the tree in suggesting a minimum of 47 WGD (diploid to tetraploid) events and 31 triploidy (demipolyploidization sensu Mayrose et al., 2010) events (Fig. 5).

Fig. 3. Geographic structuring of diploid Galax urceaolata using Bayesian clustering (K = 6) membership coefficients averaged across populations. Colors represent the six population clusters (green: coastal plain, pink: Piedmont, blue: sandhills, yellow/light blue: ridge and valley, red: Blue Ridge Mountains), with the ratio of colors reflecting the inferred mixture of population membership.


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individuals. Therefore, it is possible that more origins may have occurred, as it is likely that polyploidy has occurred multiple times within populations. The estimates obtained through ChromEvol are directly tied to the input tree. We did not investigate topological support or the effects of different distance measures, rootings, or other assumptions. However, the inference of repeated origins of polyploidy is entirely consistent with patterns of genetic diversity reported above with greater similarity among different ploidies within a geographic region compared with the same ploidy across regions. DISCUSSION

Fig. 4. Pairwise isolation-by-distance scatterplots of Galax urceolata. (A) Diploid genetic distance is weakly but significantly correlated with geographic distance. (B) Tetraploid genetic distance is not significantly correlated with geographic distance.

The latter could arise from either crosses between diploid and tetraploid plants or true demi-polyploidization (diploid to triploid) events; however, we cannot distinguish between these alternatives. We took a conservative approach and reconciled single deep WGD events to be the most inclusive of polyploid TABLE 3.

Using microsatellite markers, we examined the genetic diversity and geographical structure of G. urceolata and demonstrated numerous independent origins of the triploid and tetraploid cytotypes. These results are consistent with those reported for most other polyploids in that multiple independent origins of both autopolyploids and allopolyploids have become the dominant paradigm of polyploidization for plants as well as animals (Soltis and Soltis, 1989, 1993, 1999; Parisod and Besnard, 2007; Parisod et al., 2010). However, the number of origins of polyploidy estimated here is much higher than values in any other study of polyploid formation (auto- or allopolyploid). For example, 11 and 21 allopolyploid origins were inferred for Tragopogon mirus and T. miscellus, respectively (Soltis and Soltis, 2000); these are among the highest such values known. Ten separate origins were found in the allopolyploid fern Astrolepis integerrima (Beck et al., 2012), 2–7 tetraploid origins were reported in Heuchera grossulariifolia (Segraves et al., 1999), and four polyploid origins were inferred in the Asplenium complex (Perrie et al., 2010). In part, the high value we obtained for Galax could reflect the sensitivity of microsatellite markers (Morgante and Olivieri, 1993). In the two recently formed allotetraploid Tragopogon species, microsatellite markers revealed more origins than suggested by other markers (e.g., allozymes; DNA restriction site data) and also documented origins on a very fine geographic scale (Symonds et al., 2010). However, Galax may simply be prone to the frequent establishment of autopolyploids. Ramsey and Schemske (1998) have postulated high rates of autopolyploid formation in nature, suggesting that most polyploids do not persist at the population level. With its propensity for clonal propagation, Galax can circumvent the need for sexual reproduction to ensure establishment of a nascent autopolyploid. Coupled with the similar ecological preferences of diploids and polyploids, Galax autopolyploids may achieve establishment at higher frequencies than many other nonclonal polyploids.

Log likelihood, AIC scores, and final model parameters for multiple origin estimates of Galax urceolata. Final model parameters

Model Constant Constant, polyploidy = demi Constant, polyploidy, demi Linear rate Linear, polyploidy = demi Linear, polyploidy, demi

Log likelihood

AIC

λ

δ

−635.078 −404.442 -403.309 −624.252 −410.3 −406.936

1276.16 814.885 814.617 1258.5 830.6 825.872

2.221 × 10−10 1.251 × 10−10 1.814 × 10−10 2.391 × 10−10 1.814 × 10−10 1.814 × 10−10

28.457 0.659 0.686 1.987 × 10−10 5.057 3.794

λl

1.669 × 10−10 1.716 × 10−10 1.716 × 10−10

δl

4.303 −0.186 −0.146

ρ

11.076 14.336 12.785 11.453

μ 14.266 13.501 15.422 15.422

Notes: λ = gains in single chromosomes, δ = losses in single chromosomes, λl = linear gains in single chromosomes, δl = linear losses in single chromosomes, ρ = full genome duplication, μ = half genome duplication. Boldface type indicates the best model.


Fig. 5. Neighbor-joining tree of diploid, triploid, and tetraploid Galax urceolata using a Lynch distance matrix and constrained to a diploid-only generated topology. Duplication estimates were inferred from maximum likelihood analysis using ChromEvol. Black stars indicate tetraploid events. Red stars indicate triploid formation events. Shading corresponds to Bayesian clustering (K = 6).

Distribution and genetic diversity— Diploids and their autopolyploid derivatives often exhibit geographically distinct distributions. Examples include Tolmiea diplomenziesii/T. menziesii, Chamerion angustifolium, Heuchera cylindrica, and Vaccinium corymbosum (Soltis et al., 2007). However, no distinct regional pattern of cytotype distribution is apparent in G. urceolata. Our study shows similar cytotype distributions to those reported by Nesom (1983); the diploids and tetraploids are more clinally distributed with diploids most common in the north and decreasing

toward the southern part of the range (see Burton and Husband, 1999) (Fig. 1). Our results parallel those of Burton and Husband (1999) in showing that populations are primarily composed of a single predominant cytotype (Fig. 1, Table 1), suggesting that the distribution of diploid and autotetraploid cytotypes reflects a geographical pattern or environmental heterogeneity. Relative changes in cytotype composition within populations may be associated with differentiation and could indicate an environmental-dependent


mechanism for tetraploid establishment and present distribution (Johnson et al., 2003). Our data support the hypothesis that environmental interactions and cytotype fitness may play a role in the composition of cytotypes within populations. Variation in fitness among ploidies, as well as recurrent formation and clonal expansion, may alleviate the demographic disadvantage imposed by minority cytotype exclusion and increase the likelihood that a cytotype may become established (Levin, 1975; Johnson et al., 2003). Autopolyploids, as well as allopolyploids, are generally thought to form through the union of unreduced gametes (de Wet, 1980; Ramsey and Schemske, 1998, 2002); this process can take place either instantly through the fusion of two unreduced gametes, immediately generating an autotetraploid, or through the fusion of a single unreduced gamete and normal reduced gamete to produce an intermediate triploid (Harlan and de Wet, 1975; reviewed by Ramsey and Schemske, 1998). Triploids may represent the hybridization of tetraploid and diploid individuals and/or function as a triploid bridge in the twostep formation of tetraploids. Regardless of the mode of formation, genetic diversity of autopolyploids is driven by the recurrent formation of autopolyploids from disparate progenitors. Multiple independent origins of polyploids allow for genetic diversity in the diploid progenitors to be transferred to the autopolyploid gene pool: in effect, multiple origins may increase the genetic diversity in an autopolyploid and facilitate long-term survival and adaptation (Soltis and Soltis, 1993, 1999; Husband, 2004; Parisod et al., 2010). However, it remains unclear as to the actual consequences that multiple independent autopolyploid origins have on the successful establishment and persistence of an autopolyploid. One potential disadvantage of recurrent formation is the influx into tetraploid populations of diploid alleles that may be maladaptive for tetraploids that have undergone local adaptation. It does not appear, however, based on the mixture of cytotypes found in many locations, that in Galax there has been a dramatic shift in habitat preference of tetraploid Galax from diploids. However, the tetraploid is often found surrounded by more herbaceous plants than the diploid, although this ploidydependent association with plant communities may in fact reflect correlations with open and distributed habitats that typically facilitate establishment of a nascent cytotype (Johnson et al., 2003). Conclusions— Our study of Galax provides new insights into the dynamic nature of autopolyploid evolution. The numerous independent origins of tetraploid Galax reported here are the highest values of independent polyploidizations ever reported for any polyploid (auto- or allopolyploid). However, the high frequency of autopolyploidization estimated for Galax matches predictions (Ramsey and Schemske, 1998) that autopolyploid events are frequent. Furthermore, the propensity for clonal reproduction in Galax may facilitate a high survival rate of nascent autopolyploids. Frequent origins of autopolyploid Galax also provide additional evidence for the potential success of autopolyploids; recurring tetraploid formation from genetically distinct parents results in increased genetic diversity and successful establishment of autopolyploid lineages. Populations of Galax show a clear pattern of genetic differentiation among the different ecoregions of the southeastern United States, regardless of ploidy. This observation, coupled with previous studies (Johnson et al., 2003), suggests that geographic

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