Assessment of Genetic Variablity of Spigelia Marilandica and S

ASSESSMENT OF GENETIC VARIABLITY OF SPIGELIA MARILANDICA AND S.

GENTIANOIDES USING AMPLIFIED FRAGMENT LENGTH POLYMORPHISM (AFLP)

MARKERS AND CLONAL PROPAGATION OF STEM CUTTINGS OF S. MARILANDICA,

S. GENTIANOIDES VAR. ALABAMENSIS, AND S. MARILANDICA × S. GENTIANOIDES

VAR. ALABAMENSIS F2 AND F3 HYBRIDS

AMANDA JANNETTE HERSHBERGER

(Under the Direction of Carol Robacker)

ABSTRACT

Despite the ecological and ornamental potential of southeastern U.S. native Spigelia, little is known about the intraspecific or the interpopulation genetic variation. Spigelia habitat is becoming fragmented due to human activity, making it imperative to gain an understanding of natural genetic variation among and within species and populations. This study used amplified fragment length polymorphism (AFLP) analysis to determine interspecific and intraspecific genetic variation and to evaluate gene flow. Thirteen populations of two species of native

Spigelia, S. marilandica (L.) (SM), S. gentianoides Chapm. ex A. DC. var. alabamensis K.

Gould (SGA), and S. gentianoides var. gentianoides (SGG), were analyzed. Based on analysis of molecular variance (AMOVA) and estimates of Nei’s coefficients of gene diversity (HS, HT, and

GST), the majority of variation found in Spigelia occurs within populations. Among species and among population variation were low, likely the effect of common ancestry as well as relatively frequent introgression among individuals (and populations) of Spigelia. An unrooted UPGMA

i phenogram delineated three clades. The significance of these results is discussed in relation to breeding in Spigelia. In addition to the lack of information concerning genetic variation in

Spigelia, little is known concerning clonal propagation strategies of SM and SGA. The effects of cutting date, indole-3-butyric acid (IBA) level, and genotype on rooting percentage, root number, and root length were evaluated. Stem cuttings were obtained from five genotypes of SM, one genotype of SGA, three genotypes of SM× SGA F2 hybrids, and two genotypes of SM × SGA F3 hybrids. IBA level significantly affected rooting percentage and root number, but not root length.

The SM × SGA hybrids successfully rooted through all months evaluated, while SM and SGA genotypes exhibited a decline in rooting in cuttings taken in Sept. Results suggest that SM and

SGA may be successfully propagated by treating stem cuttings taken in May, June, July, or Aug. with 0.3% IBA. Cuttings of SM × SGA hybrids can be taken through Sept. These protocols provide a basis for rapid propagation of Spigelia.

INDEX WORDS: AFLP markers, Spigelia spp., native plants, endangered species, among population diversity, within population diversity, AMOVA, clonal propagation, bottom heat,

IBA

ASSESSMENT OF GENETIC VARIABLITY OF SPIGELIA MARILANDICA AND S.

GENTIANOIDES USING AMPLIFIED FRAGMENT LENGTH POLYMORPHISM (AFLP)

MARKERS AND CLONAL PROPAGATION OF STEM CUTTINGS OF S. MARILANDICA,

S. GENTIANOIDES VAR. ALABAMENSIS, AND S. MARILANDICA × S. GENTIANOIDES

VAR. ALABAMENSIS F2 AND F3 HYBRIDS

AMANDA JANNETTE HERSHBERGER

B.S., Purdue University, 2006

M.S., University of Georgia, 2008

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements of the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GA

2012

iii

Amanda Jannette Hershberger

ASSESSMENT OF CLONAL PROPAGATION AND GENETIC VARIABILITY OF

SPIGELIA MARILANDICA AND S. GENTIANOIDES USING AMPLIFIED FRAGMENT

LENGTH POLYMORPHISM (AFLP) MARKERS

AMANDA JANNETTE HERSHBERGER

Major Professor: Carol Robacker

Committee: James Affolter Allan Armitage Melanie Harrison-Dunn Tracie Jenkins

Electronic Version Approved:

Maureen Grasso Dean of the Graduate School The University of Georgia August 2012

DEDICATION Writing this part is always the most difficult. It’s tempting to allow this dedication to become either too personal or completely devoid of any human component. This is my attempt at a happy medium. I have attended the University of Georgia for six years for both my M.S. and

Ph.D. degrees and have met a number of people along the way that have taught me a lot about myself, for better or for worse.

Many people have helped me so much that I wouldn’t even have enough room to thank each and every one of them here. Thank you to Lee Ann Kelly for all of your help in the beginning. I didn’t know you for very long, but thank you. Thanks to Suzzanne Tate for your patience and all of your help with my first project. Thank you to Sherrod Baden and Allen Byous for maintaining all of the Griffin material. Thanks to Noelle Barkley, Zhenbang Chen, Tyler

Eaton, and Tracie Jenkins for all of your assistance with the DNA portion of my research. Thank you to all my grad student comrades for helping with various projects and for being entertaining.

Thanks to Jim Gegogeine for being there through it all.

Thank you to my husband, Luke, for becoming the best friend I in no way deserve and being the calm element in my life. Without you I am absolutely sure I never would have finished.

This wouldn’t be a dedication from me if I didn’t also thank my dogs for being alive.

Gordita, my curmudgeon of a dog, and Professor Finworth Solomon Jones (Jonesy), the playful and sweet counterpart to Gordita, share best friend status with Luke. I was so happy to come home from campus to your wagging tails.

ACKNOWLEDGEMENTS

There were many parts of this research that were not limited to execution and understanding of project plans. One of those factors is writing, an important portion with which I have always struggled. Dr. Robacker spent a considerable amount of time working with me on this without showing too much irritation. I hope that her influence is evident in this work.

Enjoy!

TABLE OF CONTENTS

Page ACKNOWLEDGEMENTS…………………………………………………………………..v

LIST OF TABLES……………………………………………………...…………………….vii

LIST OF FIGURES…………………………………………………………………………...x

CHAPTER

1 INTRODUCTION AND LITERATURE REVIEW………………………….1

Purpose of studies……………………………………………………..1

Botanical and geographical description of Spigelia…………………..2

Molecular studies in Spigelia species……………….………………...7

AFLP background and technology……………………………………9

Spigelia propagation………………………………………………….15

Literature cited………………………………………………………..18

2 ASSESSMENT OF GENETIC VARIABILITY OF SPIGELIA

MARILANDICA AND S. GENTIANOIDES USING AMPLIFIED

FRAGMENT LENGTH POLYMORPHISM (AFLP) MARKERS………….26

Abstract……………………………………………………………….27

Introduction…………………………………………………………...28

Materials and Methods……………………………….……………….32

Results and Discussion………………………………………………..36

Literature cited……….……………………………………………….45

3 CLONAL PROPAGATION OF STEM CUTTINGS OF SPIGELIA

MARILANDICA, S. GENTIANOIDES VAR. ALABAMENSIS, AND S.

MARILANDICA × S. GENTIANOIDES VAR. ALABAMENSIS F2

AND F3 HYBRIDS…………………………………………………………...60

Abstract……………………………………………………...………..61

Introduction…………………………………………………………...61

Materials and Methods………………………………………………..64

Results and Discussion………………………………………………..65

Literature cited………..………………………………………………69

4 CONCLUSIONS……………………………………………………………...80

Literature cited………………………………………………………..84

vii

LIST OF TABLES

Page

Table 2.1. Phenotypic differences between S. marilandica, S. gentianoides var.

gentianoides, and S. gentianoides var. alabamensis...... 49

Table 2.2. Locations of populations collected, grouped by species. Each population is

numbered in succession and includes four to 11 individual plant samples……….………50

Table 2.3. List of adaptors and primers screened and used in this study to characterize the

amplification fragment length polymorphism (AFLP) band patterns in two Spigelia

species…………………………………………………………………………………….51

Table 2.4. Percentage of polymorphic loci, average genetic diversity within populations

(HS), average genetic diversity within species (HT), and proportion of species genetic

diversity attributed to among population variation (GST) for species of

Spigelia…………………………………………………………………………………....52

Table 2.5. Percentage of polymorphic loci in all populations of Spigelia evaluated in this

study…………… ………………………………………………………………………...53

Table 2.6. Analysis of molecular variation (AMOVA) for three Spigelia spp./varieties

included in this study………… ……………………………………….…………………54

Table 2.7. Nei's unbiased measures of genetic distance (Nei, 1978) below diagonal and

geographic distance (km) above diagonal……………………..………………………….55

Table 3.1. Spigelia genotypes and numbers of cuttings taken throughout duration of

project………………………………………………………………………………………...72

viii

Table 3.2. Levels of significance of analysis of variance (ANOVA) effects for rooting

percentage, root number, and root length for individual genotypes of Spigelia………….73

Table 3.3. Effect of IBA level on rooting percentage, root number, and root length of

Spigelia with all genotypes analyzed separately……………………………………….…74

Table 3.4. Effect of IBA level and cutting date on rooting percentage, root number, and

root length of Spigelia over all months…………………………………………………...75

Table 3.5. Levels of significance of analysis of variance (ANOVA) effects for rooting

percentage, root number, and root length for grouped genotypes of Spigelia (SM, SGA,

and SM × SGA hybrids within their respective categories)……………………….……...76

Table 3.6. Effect of grouped genotype and cutting date on rooting percentage, root number,

and root length of Spigelia over all months…………………………………….………...77

LIST OF FIGURES

Page

Figure 2.1. Spigelia collection sites for current project……………………………………...56

Figure 2.2. The unrooted UPGMA phenogram of Nei’s unbiased genetic distance matrix

(Nei, 1978) over all 13 populations of Spigelia………….…………………………………57

Figure 2.3. Inferred population structure based on 116 individuals and 269 markers,

assuming correlations among allele frequencies across clusters-arranged by individual...58

Figure 2.4. Inferred population structure based on 116 individuals and 269 markers,

assuming correlations among allele frequencies across clusters-arranged by allelic

distribution in each cluster………….………..…………………………………………...59

Figure 3.1. Spigelia marilandica (left) and S. gentianoides var. alabamensis (right)……….78

Figure 3.2. Rooting percentage of grouped genotypes evaluated over all months…………..79

CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

Purpose of studies

Spigelia (L.), (Division: Magnoliophyta; Class: Magnoliopsida; Subclass Asteridae;

Order: Gentianales; Family: Loganiaceae) is a genus of approximately 50 species that has distribution from the southeastern U.S. to Central America and south to temperate areas of South

America. The species inhabit mid-elevation to lowland areas. Most of the species, approximately

36, are located in South America. Those found in South America typically exist along the

Atlantic coast in the rainforest. Of the remaining approximately 14 species, only six are considered to be present in the southeastern U.S. and in northern Mexico. One of these species,

S. anthelmia, exists in the tropics. The other five species are considered endemic species in the

U.S. The five endemic temperate species are S. marilandica, S. gentianoides, S. hedyotidea, S. loganioides, and S. texana (Gould, 1997). This study will focus primarily on the characteristics of two species endemic to the southeastern United States, specifically S. marilandica and S. gentianoides. They are of particular interest due to their ornamental potential as well as potential of genetic variability present in their respective populations.

AFLP technology was used to distinguish genetic similarities and differences within and among populations of two Spigelia species indigenous to the southeastern U.S., S. marilandica and two rare varieties thought to be separate species, S. gentianoides, var. gentianoides and var. alabamensis. These findings will facilitate the efforts of conservationists and plant breeders.

Observations of physical traits of Spigelia species revealed morphological variation among the species, populations within a species, and between plants of the same population. Though these 1

morphological differences have been observed, the degree of genetic difference in these two species has yet to be determined. Previous research has utilized cpDNA, ITS sequencing, and allozyme studies, yet to date there have been no assessments of within species diversity in S. marilandica and S. gentianoides using AFLP technology. Additionally, since new clonally propagated ornamental cultivars must display high levels of rooting success in order to be commercially viable this research evaluated the effects of varying IBA levels, cutting date, and genotype on rooting characteristics of clonally propagated S. marilandica, S. gentianoides var. alabamensis, and S. marilandica × S. gentianoides var. alabamensis F2 and F3 hybrids. These species have had limited research focusing on their genetic variability and clonal propagation although they have apparent economic value.

Research objectives were:

1. To define a protocol for genomic DNA extraction and AFLP analysis in Spigelia

marilandica, S. gentianoides var. alabamensis, and S. gentianoides var. gentianoides.

2. To determine the genetic diversity among wild populations of Spigelia marilandica, S.

gentianoides var. alabamensis, and S. gentianoides var. gentianoides using the AFLP

marker system.

3. To assess genetic diversity within the aforementioned populations.

4. To increase the effectiveness of clonal propagation of S. marilandica, S. gentianoides var.

alabamensis, and S. marilandica × S. gentianoides var. alabamensis F2 and F3 hybrids.

Botanical and geographical description of Spigelia

Spigelia gentianoides. Chapm. Ex A. DC. is an upright, perennial herbaceous plant growing 25-40 cm high. Plants can be solitary or clump-forming and rarely branch near the base.

Inflorescences are solitary and terminal, although rarely are axillary with three-eight flowers per

cyme. Corollas are light pink outside, sometimes with two dark pink vertical lines on each lobe.

Filaments are approximately two mm long, anthers two-three mm long, connivent around the style, and are one-two mm below the stigma. Pistils are 17-27 mm long. Two disjunct varieties occur in northern Florida, southeastern Alabama, or central Alabama (Gould, 1997). Both varieties are federally listed endangered species (Gould, 1997). Threats to this species include

“logging practices, lack of natural disturbance regimes, and over collection” (USFWS, 1990).

Spigelia gentianoides Chapm. ex A. DC. var. gentianoides (hereafter, var. gentianoides) has leaves that are broadly ovate with rounded bases. The corollas have 25-30 mm lobes with those lobes not reflexing. Inflorescences have up to eight flowers per cyme. Corollas grow between 25-30 mm long with throats seven-eight mm wide and lobes five-seven mm long.

Flowers are barely opening and not reflexed at anthesis. Flowering is from May to July.

Spigelia gentianoides Chapm. ex A. DC. var. alabamensis K. Gould. (hereafter, var. alabamensis) has leaves that are typically lanceolate to elliptic with cuneate to rounded bases.

The corollas are from 36-50 mm long and their lobes reflex at anthesis. Inflorescences have two to six flowers per cyme. Corollas are funnelform, growing 36-50 mm long with throats eight to

15 mm wide. Pistils are approximately 28 mm long, five to six mm above the ovary. In the bud stage of var. alabamensis, five connivent anthers closely surround the style and dehisce while the corolla is still sealed. Anther dehiscence occurs during the morning of the first day of anthesis.

The yellow pollen adheres to short bristles on the style, forming a “pollen pack”. As the style elongates the pollen pack is raised above the level of the anthers, a characteristic referred to as

“secondary pollen presentation”. The top of the pollen pack is usually 0.5-2.0 mm below the base of the stigma, but in some cases pollen touches or actually overtops the stigma. This elongation results in the pollen being presented two to four mm above the top of the anthers. By the time the

corolla opens on the first day of anthesis, this elongation of the style is complete. The corolla color changes as the flower bud matures. Young buds are relatively deeply pigmented, while corollas in open flowers are white-pink to white with darker pink “nectar guides” in the interior of the corolla tube (Affolter, 2005). Flowering occurs from May to June.

Comparatively var. alabamensis or SGA has longer corollas with broader throats, longer lobes, as well as longer sepals than var. gentianoides. The style and stigma remain included within the corolla, in contrast to the exerted style of S. marilandica. In addition to these differences, the flowers of SGA completely open at anthesis, whereas the flowers of var. gentianoides remain closed (Gould, 1997). Differences between SGA and var. gentianoides are stable when grown in a common greenhouse environment, indicating that their characteristics have a genetic basis, which supports Alabama populations being listed as a distinct variety

(Allison and Stevens, 2001).

Variety gentianoides is restricted to five locations within three counties in the Florida

Panhandle and southeastern Alabama. Variety alabamensis is found only in Bibb County,

Alabama. USFWS/Negrón-Ortiz (2009) has suggested that fire is necessary for the maintenance of both of these varieties and that fire suppression in the Florida locations may explain the previous decline in population size in that area. Variety gentianoides occurs in Florida in the counties of Calhoun, Jackson, and Washington, in the sandy loam of upland oak-pine woods of north-central Florida. A population in Jackson County, Florida has more than 1,700 individuals.

A large population is located in Jackson County, Florida at Three Rivers State Park. Population size of over 1,000 was estimated in 2005. The recent use of prescribed fire in this area promoted an increase in population size to 2,000 individuals in 2008. There are approximately 400 plants in the Geneva State Forest located in Geneva County, Alabama. Containing approximately 100

individuals, a fourth population is found in Calhoun County, Florida, on property owned by the

Nature Conservancy. The last known site has only three plants and is found on a piece of personal property north of Jackson County, Florida (USFWS/Negron-Ortiz, 2009).

In 1992 and 1993, Allison and Stevens found var. alabamensis growing in approximately

17 Ketona Glades, which is about 200 miles from the Florida variety (Allison, 1994; Allison and

Stevens, 2001). The Ketona Glades are made up of dolomitic limestone, a type of sedimentary carbonate rock and mineral combination that consists of calcium magnesium carbonate. Standard limestone contains only calcium carbonate. All of the Bibb County glades are close to the Little

Cahaba River in Alabama. Variety alabamensis occurs in an area covering approximately three miles. These glades are of particular interest due to the recent discovery of eight previously unknown plant species including var. alabamensis. Additionally, 60 rare species occur on these glades (Garland, 2008), which has aided in labelling this unique location as a “botanical lost world” (Allison, 1994). Elevated levels of magnesium, in addition to the high levels of calcium expected in limestome, may explain why there are so many rare and endemic plant species in these glades. Another factor which may explain the diversity is that much of the county is rural and undeveloped. Surrounding areas have become heavily developed, rendering Bibb County largely isolated. However, there is concern that an interest in the glades’ ability to supply iron and steel may bring in developers (Garland, 2008). The varietal labeling as opposed to species designation of SGA by Katherine Gould Mathews was conservative. Subsequent assessment of

S. gentianoides and its two varieties suggests the possibility that the two S. gentianoides varieties warrant specific rank (USFWS 2009). Gould (1997) lists copious and distinct morphological trait differences between the two varieties. It was determined that by reassessment of the appropriate ranks of the varieties and by guidelines of taxonomic rank normally applied to taxa in Spigelia,

specific rank of var. alabamensis is justified. Additional molecular and morphological studies were also suggested in order to corroborate this rank change (Weakley et al., 2011).

Spigelia marilandica (L.) L. is an upright perennial herbaceous plant growing from 30-60 cm high. This species has several stems that grow from a large rhizome. The glossy, opposite leaves add to the ornamental value of the plant (Cullina, 2000). Inflorescences are solitary, terminal, and 4-17 flowered (Gould, 1997), with an average of 13 on a one-sided cyme

(Dunwell, 2003). Corollas are bright red outside, yellow to greenish-yellow inside. In Kentucky, the flowering period starts in late May through June; occasionally scattered blooms will occur in the fall (Dunwell, 2003). Spigelia marilandica will re-flower if cut back after their June bloom period (Darke, 2002). It is thought to be pollinated by the ruby-throated hummingbird (Cullina,

2000; Glick, 2002) as well as by insects (Affolter, 2005; Rogers, 1988). The yellow pollen adheres to short bristles on the style, forming a “pollen pack”. Spigelia marilandica seed is found in a two-sided capsule. The seeds are grouped into small balls of four to seven seeds that separate readily.

Distribution is from eastern South Carolina north to Kentucky, south to north-central and northwestern Florida, and westward to southeastern Oklahoma and far-eastern Texas (Duncan and Duncan, 2005; Gould, 1997). Typical growing environments for S. marilandica are in calcareous wooded areas rich in organic matter. Soils in which this species grows are typically moist, yet well drained. Full to part shade exposure is typically found in the wild; however, S. marilandica is sold as a full sun to partial shade plant to be grown in moist to very dry soils and is marketed for USDA zones 5b-9 (Tony Avent, personal communication).

Dr. James Affolter obtained hybrids between S. marilandica and var. alabamensis in

1997. These hybrids were morphologically intermediate between the parent species. The interior

of the hybrid flower is white (as in SGA) rather than yellow, although the exterior displays much of the red pigment of S. marilandica. The leaves are intermediate in size and shape in the hybrid.

Subsequently, F2 and F3 hybrids were created that have pink flowers with white throats. The ability of these species to hybridize increases the potential variation that breeding will be able to introduce.

Molecular studies in Spigelia species

Limited research has been conducted to determine genetic diversity in Spigelia. Gould

(1997) utilized internal transcribed spacer (ITS) sequencing to assess relationships of 14 species of Spigelia including species indigenous to Mexico, South America, and all of the north- temperate species. The ITS region is fragment of RNA situated between structural ribosomal

RNAs (rRNA). This region was shown to be informative at the interspecific level in many groups of angiosperms (Baldwin et al., 1995; Kim and Jansen, 1994). Findings revealed the non- monophyly of the north-temperate species of Spigelia due the presence of two tropical species, S. coelostylioides and S. paraguariensis, within the north-temperate clade. Two north-temperate lineages were represented, one with the morphologically similar species, S. hedyotidea A. DC.,

S. loganioides (Torr. & A. Gray) A. DC., and S. texana (Torr. & A. Gray) A. DC. and the other with morphologically distinctive species, S. marilandica and S. gentianoides.

Gould and Jansen (1999) conducted research on a disjunct group of Spigelia in the Gulf

Coast. Using restriction sites on predominately maternally inherited chloroplast DNA (cpDNA) and biparentally inherited ITS sequences, the phylogeny of three species of Spigelia, S. loganioides from Florida, S. texana from western Texas, and S. hedyotidea from central to western Texas, were reconstructed. They found that the pattern of relationships was not based on morphological characteristics, but on geographical distance. Based on these genetic studies, S.

texana is actually more closely related to S. hedyotidea than to S. loganioides. This is surprising since S. texana looks almost identical to S. loganioides; the only morphological difference between the two is that corollas are 8-13 mm long in S. texana and 10-17 mm long in S. loganioides. Additionally, using cpDNA, Gould and Jansen (1999) determined that there were no polymorphisms within population samples. Fourteen out of 16 haplotypes sampled had distinct haplotypes, while no intrapopulation variation was found.

Genetic variability research in S. gentianoides and S. marilandica

Affolter (2005) used allozymes to evaluate genetic variability in both varieties of S. gentianoides as well as in S. marilandica. High levels of within population variation and extremely low levels of among population variation were observed in var. alabamensis in the Alabama Ketona glades.

This finding suggests that there are relatively high levels of gene flow among populations. Since the different glade populations are terrestrial islands separated by habitat unsuitable for this species, it is difficult to account for high levels of gene flow among them via known pollinators or seed dispersal mechanisms. The most variable trait was leaf shape ranging from narrowly lanceolate to broadly ovate. Leaf apices varied from acute to obtuse, and the leaf surfaces from flat to folded upward from the midrib or boat-shaped. Stems also varied from plant to plant, with some narrow while others were much broader in diameter. As these traits were maintained when placed in a common greenhouse environment, the observed morphological variation is likely genetic (Affolter, 2005).

Analysis of 43 ex situ plants of var. gentianoides located at Bok Tower Gardens in Lake

Wales, Florida showed that the proportion of polymorphic loci and heterozygosity was much lower in the Bok Tower ex situ collection compared to the natural populations of var. alabamensis. No unique alleles were observed in the Bok Tower collection when compared with

Alabama populations. Genetic identity, which estimates the proportion of genes that are identical in two populations, was high. On the basis of the loci studied, the Bok Tower sample was composed of a relatively narrow subset of the genetic variation observed in the Alabama populations (Affolter, 2005).

When comparing six populations of S. marilandica to one population of var. alabamensis, Affolter (2005) found that 12 of the 18 loci scored were polymorphic between species. Also, the mean number of alleles per polymorphic locus was 2.33 in S. marilandica, which is comparable to the 2.4 for var. alabamensis (Affolter, 2005). According to ITS sequence data, S. gentianoides is the sister species of S. marilandica, which overlaps its range in northern

Florida (Gould, 1997). In this study, Gould evaluated only the Florida variety.

AFLP background and technology

Studies in reproductive biology, geographic distribution, and morphology have provided valuable information on relatedness in Spigelia, yet none survey the entire genome of a single individual, population, or species. Amplified fragment length polymorphism (AFLP) technology has been used to characterize genetic diversity within many genera of plants including horticultural crops such as Chrysanthemum spp. (Zhang et al., 2010), Ipomoea batatas (sweet potato) (Cervantes-Flores et al., 2008), Carya illinoinensis (pecan) (Beedanagari et al., 2005),

Rhododendron spp. (Chappell and Robacker, 2007), Berberis thunbergii (barberry) (Lubell et al.,

2009), Prunus spp. (Vilanova et al., 2003), and Lactuca spp. (Koopman et al., 2001). AFLP analysis is a method of discerning genetic relationships based upon genetic sequence without the limitations of the previous analytical methods used on Spigelia. AFLP is the amplification of restriction-digested DNA using Polymerase Chain Reaction (PCR) technology with directed primers (Vos et al., 1995). Most genetic analyses rely on PCR-based methods including

randomly amplified polymorphic DNA (RAPD), simple sequence repeats (SSRs or microsatellites), and AFLPs (Kriegner et al., 2003; Malay, 2005; Nathalie et al., 2008) as well as a non-PCR based method, restriction fragment length polymorphism (RFLP). AFLPs have several advantages over these methods of evaluating genetic variability. AFLP technology will therefore be used to assess genetic diversity of Spigelia spp.

Allozymes were developed in the 1960’s and have been useful in the assessment of population variability. This system does not require DNA sequence characterization which translates into savings in time and cost. Both alleles in a diploid are usually clearly detectable, and heterozygotes can be distinguished from homozygotes. There are disadvantages, however, to the use of allozymes. Detection of a new allele will only occur if it alters the electrophoretic movement of the molecule, which means not all genetic variation will be observed.

Approximately 30% of nucleotide substitutions result in polymorphic fragment patterns.

Allozyme analysis therefore downplays the genetic variability (Weising et al., 2005).

Additionally, they are functional proteins and therefore not “selectively neutral”, making their use inappropriate for assessment of within population variability (Freeland, 2005; Lemaire et al.,

2000).

AFLPs evaluate the entire genome and their markers are not under direct selection pressure (Jones et al., 1997; McGregor et al., 2000; Russell et al. 1997). They also have high levels of reproducibility unlike RAPDs and RFLPs (Demeke et al., 1997; Depypere et al., 2009;

Karp et al. 1997; Palacios et al., 1999). RAPD technology was introduced in 1990 by Williams et al. These markers are DNA fragments created by using a primer with an arbitrary nucleotide sequence. They have been used to differentiate between genetically different materials. RFLP analysis is a method that uses restriction enzymes to generate data and cuts at specific short

sequences (approximately four to six bp). In this marker system, differentiation between species is determined by evaluation of fragment sizes on a gel. If fragment number and sizes are different there are differences between individuals. SSRs are often used due to their abundance, reproducibility, high levels of polymorphism detection, and good genome coverage (Becker et al., 1995; McCouch et al., 2002; Morgante et al., 2002; Powell et al., 1996; Salimath et al., 1995;

Wu and Tanksley, 1993), but the positive attributes of this marker system are limited to specific chromosomal segments (Lu et al., 2010). SSRs are also considered to be more informative than

RAPDs and RFLPs due to their co-dominant nature (Russell et al., 1997), however AFLPs offers an advantage over SSRs in that AFLPs cover a wide range of the genome (Pejic, 1998). SSRs are core repeats of one to six base pairs. This is a PCR-based marker system and their mutation is caused by gain or loss of repeat units. The abundance of microsatellites is high. Humans, for example, have 50,000 in their genome. Their mutation rate is high as well ranging from 10-2 to

10-4 (Jin et al., 1996). This molecular marker system is categorized as ‘codominant’ meaning that it allows for the analysis of one locus per experiment and can distinguish heterozygotes from homozygotes due the distinguishing ability of allelic variations of a locus (Freeland, 2005).

AFLP analysis can be used to assess genetic diversity when no sequence information is available, as is the case in Spigelia. RAPDs and AFLPs are both PCR-based dominant markers that share the advantages of not needing prior sequence information and allow good detection of polymorphism in many species. RFLPs and SSRs don’t have this advantage as they require considerable sequencing or cloning efforts before markers can be developed. The number of species that have had SSRs developed grows exponentially each year. These sequences are often conserved between species that are closely related and can therefore be used to provide information from multiple species. Assessment of variation between plant species using the same

microsatellite markers has been conducted in genera ranging from coconut, (Cocos nucifera L.)

(Perera et al., 2000), quinoa (Chenopodium quinoa Willd.) (Mason et al., 2005), species of peanut (Arachis) (Bravo et al., 2006), and poplar (Populus) (Khasa et al., 2005), to name a few.

Unfortunately, microsatellite markers have not been developed for Spigelia or related species.

AFLP markers have a very high diversity index (a measure of evaluation efficiency that combines the effective number of alleles identified per locus and the number of polymorphic bands in each assay) (Philips and Vasil, 2001). A study by Russell et al. (1997) compared the use of AFLPs, RFLPs, RAPDs and SSRs in evaluating the genetic relatedness of 18 barley accessions. All of the marker types were able to differentiate each of the accessions; however

AFLPs had the highest diversity index (0.937). RAPDs and SSRs were intermediate (0.521 and

0.566, respectively) and RFLPs were the lowest (0.322). The average number of bands per lane or per PCR for AFLPs was 49.5, compared to 1.0 band per lane or PCR for SSRs. RFLPs were useful in the assessment of genetic relationships in the barley accessions, but due to their requirement of several probe and enzyme combinations used to find differences between accessions their use for this Spigelia study is inappropriate. Although AFLPs did not offer the highest levels of polymorphism, they were considered to be the most efficient because they had the ability to display many polymorphic bands in a single lane (Russell et al., 1997). The high diversity index of AFLPs suggests that the ability of this marker system to analyze a large number of bands rather than the levels of polymorphism (Powell et al., 1996) makes this marker system appropriate for use in the evaluation of genetic variability in Spigelia.

Another study comparing AFLPs, RAPDs, SSRs, and RFLPs found similar results when evaluating maize inbreds. This index was ten times higher for AFLPs compared to the other methods used. AFLPs and SSRs could replace RFLPs because of their similar accuracy in

genotyping (Pejic et al., 1998). AFLPs revealed more phylogeographic structuring than the microsatellites in arctic Draba spp. (Skrede et al., 2009). Additionally, it was found that the amount of polymorphisms found using RAPDs and ISSRs (inter-simple sequence repeat) in chrysanthemum populations was higher than that of AFLP. However, the total informative bands amplified by AFLP were 44.1 fragments on average per primer pair compared to the 2.7 bands using RAPDs and 2.5 using ISSRs (Zhang et al., 2010). Similar results showing the usefulness of

AFLPs for evaluating a large number of loci, as compared to RAPDs and ISSRs, has been reported in other plant species as well (Beedanagari et al., 2005; Kriegner et al., 2003; Kunkeaw et al., 2010; Martin et al., 2006).

Cost comparisons

AFLPs have an additional benefit of evaluation in a shorter time with a lower cost than other marker systems (Philips and Vasil, 2001; Russell et al., 1997). The use of AFLPs requires considerably more in initial costs and equipment than RAPDs, but AFLPs do generate a larger number of markers per primer pair than RAPDs. Also, AFLPs have been considered to be more efficient in terms of the time spent per marker produced; however, AFLPs are more difficult to clone and sequence than are RAPDs (Beedanagari et al., 2005; Paran and Michelmore, 1993).

Although these drawbacks are present in AFLPs, the lack of reproducibility in RAPDs makes them unacceptable for use in this project.

Although the use of SSRs has experienced a recent popularity, the initial development of

SSR markers can take copious amounts of time and money if using a previously unstudied species such as Spigelia. The Savannah River Ecology Laboratory group will currently develop microsatellites with a price range of $9,250 for one species to $33,700 for five species at one time (T. Glenn, pers. comm.). Many steps are involved in the development of SSR markers

beginning with the isolation regions of genomic DNA that contain microsatellite loci. This is followed by the development of strategies for screening each locus, which requires designing primers for amplification, optimizing reaction conditions, and screening for variation. Lastly, individuals are genotyped. The traits that make them useful for this project make them undesirable for the assessment of changes in the relatively distant past. Their rapid rate of mutation and their tendency to either increase or decrease in size means that size homoplasy may often occur. Size homoplasy refers to the formation of alleles that are the same size, but are not the result of common ancestry; they arose independently in different ancestors by parallel or convergent mutations (Freeland, 2005). All of these reasons make microsatellites inappropriate for use in this study.

Overcoming obstacles in AFLP analysis

The issue of determining appropriate restriction enzyme combinations and primers is an inherent component of AFLP analysis. As the plant genome is AT rich, the use of AT poor primers may reduce polymorphism detection. To remedy this issue, [adapter] + primer sets will be sampled to determine the optimum combinations. The majority of published protocols use the restriction enzyme combination of EcoRI/MseI and primer +3, as this method has been effective in a majority of plant species studied. EcoRI cuts at the GAATTC nucleic acid sequence and

MseI cuts at TTAA.

The structure of each adapter is:

EcoRI

5’- CTCGTAGACTGCGTACC

CATCTGACGCATGGTTAA- 5’

MseI

5’- GACGATGAGTCCTGAG

TACTCAGGACTCAT- 5’

The structure of each primer is (CORE=Core Sequence; RE= Restriction Endonuclease Site;

SN=Selective Nucleotide Site):

CORE RE SN

EcoRI 5’- GACTGCGTACC AATTC NNN – 3’

MseI 5’- GATGAGTCCTGAG TAA NNN – 3’

Major corporations such as Applied Biosystems and Qiagen as well as many public institutions use this protocol. This, however, does not mean that this will always be the method used. For example, in a study evaluating genetic relationships in Chrysanthemum morifolium, cultivars 48 selective primers of EcoRI+3/PstI+3 were used successfully to evaluate polymorphisms (Zhang, 2010).

Solutions to overcome the inability of AFLPs to distinguish homozygotes from heterozygotes are inherently a component of AFLP analysis. Since AFLPs produce a high multiplex ratio, the issue of dominance can be ameliorated. Also, increasing sample sizes two to tenfold assist in this (Castiglioni et al., 1999). Lastly, employing fluorescent tags and setting a threshold value to use as a definition of presence or absence of bands is effective (ABI, 2004).

Spigelia propagation

New clonally propagated ornamental cultivars must display high levels of rooting success in order to be commercially viable. Little is known concerning clonal propagation strategies of S. marilandica, var. alabamensis, and their hybrids. Both of these species are native to the southeastern U.S. and var. alabamensis is rare and endangered. Currently, the commercial

availability of both S. marilandica and SGA is limited. The rarity of SGA has contributed to its lack of production in the nursery trade. Vegetative propagation has, until recently, been a major factor in reducing the presence of S. marilandica in nurseries. Schmid (2002) stated in reference to S. marilandica that “this dazzling native is still uncommon in gardens and deserves to be more widely grown”. Research by Foster and Kitto (2001) and Bir and Barnes (2000) showed that timing and environmental conditions impacted the rate of successful rooting of S. marilandica.

They found that cuttings taken prior to flowering as well as from greenhouse-grown plants increased the likelihood of success. Also, greenhouse-grown plants enabled cuttings to be obtained throughout the growing season as opposed to only prior to flowering. More recently, work by Pill and Goldberger (2010) determined that the combination of bottom heat at 27°C and submersion in 3000 ppm IBA for one minute increased rooting percentage (average 76.6%) compared to the control (46.9%). They additionally evaluated the 3000 ppm IBA application methods of solution dip, powder dip (Hormodin 2), solution submersion (one minute), and control. Their results showed that the solution dip had the highest percentage of rooting at 60.1% in winter of 2007. Data collected during the summer of 2009 showed no difference in rooting percentages in greenhouse-grown plants given the varying application methods of 3000 ppm IBA

(average 54.1%); however, field-grown plants had the highest rooting percentages in the solution submersion treatment (80.1%). Cuttings taken from greenhouse-grown plants from either terminal versus subterminal growth had rooting percentages of 73.3% and 34.8%, respectively.

This effect was not seen in field-grown plants. The authors did not make clear whether the plant material that was used in this study were clones or varying genotypes. If the plant material used were clones, it is possible that the use of another genotype may give different results. Also, data collection occurred only 28 days after cuttings were obtained. The rooting percentages may have

increased had they waited an additional four weeks to remove plants for data collection. The research described in this paper investigates effects of IBA levels, cutting date, and genotype on rooting characteristics of S. marilandica, S. gentianoides var. alabamensis, and S. marilandica ×

S. gentianoides var. alabamensis F2 and F3 hybrids to increase the effectiveness of clonal propagation in these species.

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CHAPTER 2

ASSESSMENT OF GENETIC VARIABILITY OF SPIGELIA MARILANDICA AND S.

GENTIANOIDES USING AMPLIFIED FRAGMENT LENGTH POLYMORPHISM (AFLP)

MARKERS1

1 Hershberger Ward, A.J., C.D. Robacker, T.J. Jenkins, and J. Affolter. To be submitted to the Journal of the American Society of Horticultural Science.

Abstract

Despite the ecologic and ornamental potential of southeastern U.S. native Spigelia, little is known about the intraspecific or the interpopulation genetic variation. Southeastern U.S. native Spigelia habitat is becoming more and more fragmented due to human activity, making it imperative to gain an understanding of natural genetic variation among and within species and populations. The objective of this study, therefore, was to use amplified fragment length polymorphism (AFLP) analysis to determine interspecific and intraspecific genetic variation and to evaluate gene flow. Thirteen populations of two species of native Spigelia, S. marilandica

(SM), S. gentianoides var. gentianoides (SGG), and S. gentianoides var. alabamensis (SGA), were analyzed using four primer pairs that amplified a total of 269 bands. Based on analysis of molecular variance (AMOVA) and estimates of Nei’s coefficients of gene diversity (HS, HT, and

GST), the majority of variation found in Spigelia occurs within populations. Both among species and among population variation was low, likely the effect of common ancestry as well as relatively frequent introgression among individuals (and populations) of Spigelia. When all individuals were evaluated using Nei’s unbiased genetic distances and viewed as a unweighted pair group method with arithmetic mean (UPGMA) phenogram, three main clades were shown, one with two samples of SGG from one population, one with 13 individuals from both SGG populations used in this study, and one with all of the SM, SGA, and remaining SGG individuals.

Further evaluation using STRUCTURE software showed introgression between populations and species, though all allele clusters have not entirely introgressed into all populations. The significance of these results is discussed in relation to breeding in Spigelia.

Introduction

Spigelia (L.), (Division: Magnoliophyta; Class: Magnoliopsida; Subclass Asteridae;

Order: Gentianales; Family: Loganiaceae) is a genus of approximately 50 species that ranges from the southeastern U.S. to Central America and south to temperate areas of South America.

Only five species are considered endemic to the U.S.: S. marilandica, S. gentianoides, S. hedyotidea, S. loganioides, and S. texana (Gould, 1997). Two of these species are indigenous to the southeastern U.S., S. marilandica (L.) L. and the endangered Spigelia gentianoides Chapm. ex A. DC. They are of particular interest due to their ornamental and pharmacological potential, as well as their ecological importance. Assessment of genetic variability present in their respective populations would be useful to conservation efforts as well as to plant breeders.

Observation of physical traits of Spigelia species has shown morphological variation among species, populations within a species, and between plants of the same population. Though these morphological differences have been observed, the degree of genetic difference in these two species has yet to be determined. Previous research has utilized chloroplast DNA (cpDNA), internal transcribed spacer (ITS) sequencing, and allozyme studies, yet to date there have been no assessments of within species diversity in S. marilandica and S. gentianoides using AFLP technology. These species have had limited research focusing on their genetic variability although they have apparent economic value.

Spigelia gentianoides (SG) is an upright, perennial herbaceous species growing 25-40 cm high. Threats to this species include logging practices, lack of natural disturbance regimes, and over collection (USFWS, 1990). This species has two disjunct varieties occurring in either northern Florida and southeastern Alabama or central Alabama (Gould, 1997). Both varieties are considered endangered species (Gould, 1997).

Spigelia gentianoides var. alabamensis K. Gould (SGA) has pink corollas from 36-50 mm long and their lobes reflex (open) at anthesis. Flowering occurs from May to June. This variety is endemic to a ~3 mile area in the Ketona dolomite glades of Bibb County, AL. Spigelia gentianoides var. gentianoides (SGG) has pink flowers that are barely opening and not reflexed at anthesis (Table 1). Flowering is from May to July. SGA differs from SGG in that it has longer corollas with broader throats, longer lobes, as well as longer sepals (Table 1) (Gould, 1997).

Phenotypic differences between SGA and SGG are stable when grown in a common greenhouse environment, indicating that their characteristics have a genetic basis, and supporting Alabama populations being listed as a distinct variety (Affolter, 2005; Allison and Stevens, 2001). SGG is restricted to five locations within three counties in the Florida Panhandle and southeastern

Alabama, and SGA is found only in Bibb County, Alabama. It has been suggested that fire is necessary for the maintenance of both of these varieties and that fire suppression in the Florida locations may be the cause of the previous decline in population size in that area (USFWS,

2011).

The varietal labeling as opposed to species designation of SGA by Katherine Gould

Mathews was conservative. Subsequent conservation assessment of SG and its two varieties suggests the possibility that the two SG varieties warrant specific rank (USFWS, 2011). Gould

(1997) lists copious and distinct trait differences between the two varieties. Weakley et al. (2011) determined that by reassessment of the appropriate ranks of the varieties and by guidelines of taxonomic rank normally applied to taxa in Spigelia, specific rank of SGA is justified; however, additional molecular and morphological studies were suggested to corroborate this rank change.

Spigelia marilandica (SM) is an upright perennial herbaceous plant growing from 30-60 cm high. Corollas are bright red outside, yellow to greenish-yellow inside (Table 1). In

Kentucky, the flowering period starts in late May through June; occasionally scattered blooms will occur in the fall (Dunwell, 2003). It is thought to be pollinated by the ruby-throated hummingbird (Cullina, 2000; Glick, 2002) as well as by insects (Affolter, 2005; Rogers, 1988).

Limited research has been conducted to determine genetic diversity in Spigelia. Gould

(1997) utilized ITS sequencing to assess relationships of 14 species of Spigelia including species indigenous to Mexico, South America, and all of the north-temperate species. The ITS region is known to be informative at the interspecific level in many groups of angiosperms (Baldwin et al.,

1995; Kim and Jansen, 1994). Findings revealed that the non-monophyly of the north-temperate species of Spigelia is due to the presence of two tropical species, S. coelostylioides and S. paraguariensis, within the north-temperate clade. Two north-temperate lineages were represented, one with the morphologically similar species, S. hedyotidea A. DC., S. loganioides

(Torr. & A. Gray) A. DC., and S. texana (Torr. & A. Gray) A. DC. and the other with morphologically distinctive species, SM and SG.

Gould and Jansen (1999) conducted research on a disjunct group of Spigelia in the Gulf

Coast. Using restriction sites on maternally inherited chloroplast DNA (cpDNA) and biparentally inherited ITS sequences, the phylogeny of three species of Spigelia, S. loganioides from Florida,

S. texana from western Texas, and S. hedyotidea from central to western Texas, were reconstructed. They found that the pattern of relationships was not based on morphological characteristics, but on geographical distance.

Affolter (2005) used allozymes to evaluate genetic variability in both varieties of SG as well as in SM. High levels of within population variation and extremely low levels of among population variation were observed in SGA from the Alabama Ketona glades. This finding suggests that there are relatively high levels of gene flow among populations. Since the different glade populations are terrestrial islands separated by habitat unsuitable for this species, it is difficult to account for high levels of gene flow among them via known pollinators or seed dispersal mechanisms. The most variable trait was leaf shape ranging from narrowly lanceolate to broadly ovate. Leaf apices varied from acute to obtuse, and the leaf surfaces from flat to folded upward from the midrib or boat-shaped. Stems also varied from plant to plant, with some narrow while others were much broader in diameter. As these traits were maintained when placed in a common environment, the observed morphological variation is likely genetic

(Affolter, 2005).

Analysis of 43 ex situ plants of SGG located at Bok Tower Gardens in Lake Wales,

Florida showed that the proportion of polymorphic loci and heterozygosity was much lower in the Bok Tower ex situ collection compared to the natural populations of SGA. Genetic identity, an estimate of the proportion of genes that are identical in two populations, was high (Affolter,

2005).

When comparing six populations of SM to one population of SGA, Affolter (2005) found that 12 of the 18 loci scored were polymorphic between species. Also, the mean number of alleles per polymorphic locus in SM was comparable to that found in SGA (Affolter, 2005).

According to ITS sequence data, SG is the sister species of SM, which overlaps its range in northern Florida (Gould, 1997). In this study, Gould evaluated only the Florida variety.

AFLP analysis can be used to assess genetic diversity when no sequence information is available, as is the case in Spigelia. AFLP markers have a very high diversity index (a measure of evaluation efficiency that combines the effective number of alleles identified per locus and the number of polymorphic bands in each assay) (Russell et al., 1997; Philips and Vasil, 2001) and is therefore appropriate for use in the evaluation of genetic variability in Spigelia. We used AFLP technology to distinguish genetic similarities and differences within and among populations of two Spigelia species indigenous to the southeastern U.S.

Materials and Methods

Plant material

Ten wild populations of SM, two of SGG, and one of SGA were used in genetic analysis

(Table 2 and Fig. 1). The number of plants evaluated per population ranged from 4 to11 (Table

2). Locations of individual plants collected in each population were recorded using GPS coordinates and were evenly distributed throughout their respective populations. Approximately

100 mg of immature leaves of each sample were collected on site, placed on ice in a cooler, and transported to the laboratory for immediate extraction of DNA.

DNA extraction protocol and quantification

DNA extraction was carried out using the E.Z.N.A. plant DNA kit (Omega Bio-Tek) with slight modifications including the addition of 26 µl ß-Mercaptoethanol to step one, an increase of the incubation time at 65°C for 30 minutes in step two, the use of isopropanol stored at -20°C in step four, and the use of 0.9 µl carrier RNA in the initial column step. DNA was tested for quantity and quality (shearing) using a standard 1.5% agarose gel with Low DNA Mass

LadderTM (InvitrogenTM, Carlsbad, CA). Subsequently, genomic DNA was stored at 4°C until

AFLP analysis was performed.

AFLP procedure

Generation of restriction fragments was accomplished using two restriction endonucleases, EcoRI and MseI, to fragment the genome, following the protocol described by

Vos et al. (1995). Restriction-digestion, ligation, and pre-selective amplification of genomic

DNA were carried out using the Li-Cor, Inc. (Lincoln, NE) IRDyeTM AFLP Template

Preparation Kit. All polymerase chain reactions (PCR) were carried out in a Perkin-Elmer Model

9600 Thermal Cycler® (Wellesley, MA). Thirty-two primer combinations containing both 700 and 800 wavelength IRDye labeled EcoRI selective primers (Table 3) were screened on four individuals, two samples from separate SM populations, one sample from a SGG population, and one sample from a SGA population. After initial screening of the four samples, nine primer combinations were selected on the basis of number of polymorphic bands visualized on a gel.

These were used to screen nine samples: five from separate SM populations, two from the same

SGG population, one from a separate SGG population, and one from a SGA population. This screening of nine primer combinations included the same four samples from the initial screening.

Based on the number of polymorphic bands and their repeatability on these nine samples, four primer pairs were selected for this study (Table 3). Selective amplification was carried out on all individuals with each of the four selected primer pairs. In a 1.5ml microcentrifuge tube (Fisher

Scientific Company L.L.C.), the following was combined: 2.15μl sterile deionized water, 2.0μl

MgCl2 (Promega Corp., Madison, WI), 0.05μl GOTaq® DNA polymerase (Promega), 2μl 5x

GOTaq buffer (Promega), 0.8μl 100mM dNTP (Promega), 0.5μl MseI primer, and 0.5μl EcoRI primer. In each well of a Fisherbrand 96-well PCR plate (Fisher Scientific Company L.L.C.), 8μl of the aforementioned mix was combined with 4μl of template DNA from the preselective amplification stage. PCR conditions for selective amplification were set based on the Li-Cor

AFLP protocol. Following completion of the selective amplification PCR program, 5μl of Blue

Stop Solution® (Li-Cor® Biosciences) were added to each well and samples denatured at 94°C for four min. Samples were then cooled to 4°C using the PCR machine.

Gels were cast using Li-Cor 25cm glass plates with 0.25mm spacers. Twenty ml of 6.5%

KB Plus acrylamide gel solution was combined with 150μl APS (Fisher Scientific Company

L.L.C.) and 15μl TEMED (Fisher Scientific Company L.L.C.). DNA was loaded at a volume of

0.5 μl per well. Each gel included individuals representing both species in this study. Each gel also included three standards, four test samples, and one blank lane to enable efficient and reliable gel comparison in the scoring process. The first standard to be employed was a 50:50 mixture of Li-Cor IRDye700 and IRDye800 50-700 bp size standards, placed on the outside two lanes of each gel and in the middle. The second standard was four test samples representing two populations of SM, one population of SGG, and one from a SGA population placed in the same locations in each gel. Extraction and analysis was repeated in 10% of the individuals to ensure repeatability of banding patterns.

Gels were run on a Li-Cor Model 4300S DNA Analysis System using the SagaLite® software package (Li-Cor Biosciences Inc.) with laser focus adjusted on a run-by-run basis to optimize performance. Run length was set to four and a half hours with KBplus standard electrophoresis conditions. Standard power and temperature settings were utilized with the exception of voltage that was reduced to 1000 to allow for low base pair band separation. Gel images produced by SagaLite® were graphically adjusted within the program and exported to

GelBuddy (Zerr and Henikoff, 2005) where gels were graphically aligned using monomorphic banding patterns. Image files were then exported to Adobe Photoshop® CS2 (Adobe Systems

Inc., San Jose, CA) and all gel images from a single primer pair merged into a single graphics

file. Individual gel images were aligned using three standards: the Li-Cor IRDye® 50-700 bp size standard, four test samples, and monomorphic bands that were shared by all individuals. The resulting single graphics file was utilized in the scoring of gels.

Data analysis

Bands were manually scored in binary format as present (1) or absent (0) and values were recorded in Microsoft® Excel, including monomorphic bands. PopGene v. 1.32 (Yeh and Boyle,

1997) was used to calculate Nei’s genetic diversity (Nei, 1987) and percentage of polymorphic loci. Settings for analysis included a significance level of p≤0.05, 3 groups (one for each species and variety) when comparing species and varieties, 13 groups (one for each population) when evaluating all populations, and 10,000 simulations. A matrix of Nei’s unbiased genetic distances

(Nei, 1978) was calculated with PopGene v.3.2 using all markers, including monomorphs. An unrooted UPGMA phenogram based on Nei’s unbiased genetic distance matrix over all individuals (Nei, 1978) was produced using PHYLIP v. 3.69 software (Felsenstein, 2009).

Within PHYLIP, the programs utilized were seqboot to bootstrap sequences, restdist to obtain distances of the restriction sites, neighbor to create 1,000 Unweighted Pair Group Method with

Arithmetic Mead (UPGMA) phenograms, and consense to create one single UPGMA consensus tree using the majority rule function of the 1,000 trees created in neighbor. TreeView 1.6.6

(Page, 1996) was used to view phenograms. Bootstrap support of less than 50% for nodes was considered collapsed in the phenogram and colored red. Analysis of molecular variance

(AMOVA) was calculated among species using Arlequin v.3.5.1.3 (Excoffier and Lischer, 2010) to determine the hierarchical partitioning of genetic variability among all species, populations within a single species, and within each population. Population structure was evaluated using

STRUCTURE software v. 2.3.2.1 (Pritchard et al., 2000; Falush et al. 2003). This method uses a

Markov Chain Monte Carlo (MCMC) algorithm to cluster individuals into populations on the basis of multilocus genotype data. Parameters of STRUCTURE were set to use the admixture model and correlated alleles frequencies model as is recommended in cases of subtle population structure (Falush et al, 2003). The degree of admixture alpha was inferred from the data. The number of population clusters (K) was estimated by performing ten independent runs for each K

(from 1 to 15). Each run was performed using 5,000 replicates for burn-in and 5,000 during the analysis. The highest likelihood was used to select K after ten runs at each K. On this basis, K=6 in this study. Ten independent runs were performed at K=6 to assess convergence of the data to verify that estimates were consistent across runs (Jonathan Pritchard, personal communication, 5

Mar. 2012).

Results and Discussion

Level of polymorphism

AFLPs among and within populations of each species were analyzed to determine the genetic similarities and/or differences. The same AFLP data set obtained in the analysis of among species differences was employed to evaluate among population differences/similarities and within population differences/similarities. The ability to use the same data set for multiple analyses is facilitated by scoring each individual plant separately. Four AFLP primer combinations (Table 3) amplified a total of 269 scorable bands. The average repeatability of

AFLP fragments across two replications was 97.9% (data not shown).

The numbers of amplified bands used per primer pair are as follows:

 MseI-CTC/ EcoRI-ACC amplified 60 bands

 MseI-CTC/ EcoRI-ACG amplified 50 bands

 MseI-CTG/ EcoRI-ACG amplified 66 bands

 MseI-CTT/ EcoRI-ACT amplified 93 bands

The percentage of polymorphic loci across all species was 97.7% (Table 4). Within species, polymorphic band percentages ranged from 59.1% in SGG to 88.9% in SM. The high degree of polymorphism is due primarily to SM; however, while combined values for both S. gentianoides varieties were 30% lower than SM, the polymorphisms found were still moderate in this species.

When evaluated by individual populations, the polymorphic loci percentage ranged from 35.3% in population 4 to 55.8% in population 1, both SM populations (Table 5). Individual populations of SGG had 42.8% and 47.7% polymorphic loci in populations 13 and 12, respectively. The percentage of polymorphic loci found within population 11 (SGA) was 50.2. There was no relationship between population size and polymorphic loci present in individual populations.

Diversity among species

Based on AMOVA results, the proportion of variation among species is low (15.3%)

(Table 6). This low variability contrasts with the considerable phenotypic differences between

SM and SG (Table 1). Similar results were found by Affolter (2005) when comparing genetic variability between an ex situ population of SGG at Bok Tower Gardens (Lake Wales, FL) to populations of SGA using allozymes. On the basis of the loci studied, the Bok Tower sample was composed of a relatively narrow subset of the genetic variation observed in the SGA populations

(Affolter, 2005). According to ITS sequence data, SG is the sister species of SM, which overlaps its range in northern Florida (Gould, 1997). The ITS region is known to be informative at the interspecific level in many groups of angiosperms (Baldwin et al., 1995; Kim and Jansen, 1994).

Gould (1997) utilized ITS sequencing to assess relationships of 14 species of Spigelia including species indigenous to Mexico, South America, and all of the north-temperate species. Findings revealed the non-monophyly of the north-temperate species of Spigelia due the presence of two

tropical species, S. coelostylioides and S. paraguariensis, within the north-temperate clade. Two north-temperate lineages were represented, one with the morphologically similar species, S. hedyotidea, S. loganioides, and S. texana and the other with the morphologically distinctive species evaluated in this study, SM and SG. The agreement between ITS data and AFLP data from this study indicates that members of Spigelia species used in this study are highly related and possibly derived from a common ancestor. Further research, such as chloroplast DNA studies in addition to previous research conducted on three other southeastern U.S. native

Spigelia species (S. texana, S. loganioides, and S. hedyotidea) (Gould and Jansen, 1999), is needed to conclusively determine the ancestry of SM and SG.

Diversity among populations

The proportion of species genetic diversity attributed to among population variation (GsT

=1− Hs/HT, where HS is the average genetic diversity within populations and HT is the average genetic diversity within species) is a critical indicator of genetic diversity at the population level.

The proportion of total genetic diversity that occurred among populations over all species and loci (GST) was 0.23, and among populations within each species ranged from 0.12 in SGG to 0.20 in SM (Table 4). The relatively low overall GST indicates that a low proportion of diversity is observed among populations as opposed to a high level of diversity observed within populations using AMOVA (80.6%) (Table 6). Low GST values also indicate a high level of gene flow among populations, which tends to homogenize a species’ genetic structure. SM, SGA, and SGG are pollinated by insects. Insect pollination leads to populations with a high level of genetic variation while individuals within the population share a similar complement of alleles in similar frequencies (Falk et al., 2001; Hamrick and Godt, 1996). Rogers (1988) proposed that the flowers of SGG, though remaining nearly closed, are pollinated by moths inserting their

proboscis through the five slits in the petals to find nectar. Additionally, he observed small

Halictidae bees (sweat bees) entering and exiting the flower of the SGG plants in population 12 of our study. The 25 insect visitors observed by Affolter (2005) on SGA included a pipevine swallowtail butterfly (Battus philenor L.) and a large bee fly (Bombylius spp. L.). Rogers (1988) recorded visitors such as bumblebee, ants, beetles, and a moth. Hummingbirds are pollinators of

SM (Cullina, 2000; Glick, 2002) as well as a variety of insects (Affolter, 2005; Rogers, 1988).

The relatively low GST value combined with low proportion of variation among populations from

AMOVA further indicates that individuals within populations are likely to be genetically distinct; however, each population contains a similar complement of alleles in similar frequencies. Thus, from a breeding standpoint, the low percentage of among population variation is important in the selection of parents. Within each species, individuals obtained from geographically isolated populations may not substantially increase allelic diversity in a breeding program.

Diversity within populations

AMOVA and GST values correspond to the proportion (percentage) of genetic variation partitioned among species, among populations, and/or within populations. HS and HT values, conversely, are a direct measure of diversity within populations and within species, respectively

(Falk et al., 2001). HS and HT values offer insight into the actual level, rather than proportion, of genetic variation within populations and within each species. The average genetic diversity within populations (HS) was low for all species, ranging from 0.14 in SM to 0.16 in SG (Table

4). These findings deviated from the expectation that diversity within populations of SG would be lower than in SM due to the comparatively narrower geographic area in which SG inhabits.

Within species diversity

Genetic diversity within species (HT) was low, ranging from 0.17 in SGG to 0.19 when both SG varieties’ data were combined (Table 4). While there were large geographic distances between SM populations collected in this study, many populations of Spigelia are scattered throughout the southeastern U.S. that likely intercrossed via insect pollination. This may, in part, explain why diversity estimates deviated from the expectation that SM populations separated by large geographic distances would exhibit an increase in genetic variability. Additionally, SM,

SGG, and SGA do not naturally exist in a broad range of ecological environments within their respective species/varieties. Population genetic theory predicts that less variable environments will result in a more narrow range of genetic variation within species (Chesson, 1985; Cohen,

1966; Tilman, 1999; Tuljapurkar, 1989). Typical growing environments for SM are in calcareous wooded areas rich in organic matter. Soils in which SGG grow are in the sandy loam of upland oak-pine woods of north-central Florida that are typically moist, yet well drained. SGA occurs in an area of dolomitic limestone covering approximately three miles in Bibb County, AL. The narrow range of ecological niches within species may explain low within species diversity estimates.

Evidence of introgression based on genetic distance comparisons

Nei’s unbiased genetic distances (Nei, 1978) among populations within species were similar to among species (Table 7), indicating that all populations evaluated are highly related, likely due to high levels of gene flow. Genetic distances were very low for all populations compared. Values ranged from 0.02 when comparing population 1 to population 3 (SM populations) to 0.10 when comparing population 9 to population 12 (SM and SGA populations).

When genetic distances were visualized as a UPGMA phenogram (Fig. 2), three clades were shown. Two individuals of population 12 (SGG) were placed within a clade (hereafter, clade 1) at the base of the tree 100% of the time. Thirteen individuals from both populations 12 and 13 (SGG) were separated (hereafter, clade 2) from all of the SM and SGA samples, as well as from six of the other SGG samples (hereafter, clade 3) with 91.8% confidence. Nodes with bootstrap values below 50% were collapsed and yielded polytomies, which prevents the non- parsimonious addition of both duplications and deletions (Perea et al., 2010). Bootstrap values of the nodes directly following the separation of clade 2 and clade 3 were 32.2% and 19.7%, respectively. These nodes, therefore, were collapsed and colored red indicating unresolved branches. This data suggests high levels of interspecific gene flow; however, many of the external branches have high levels of branch support. For example, while the node following clade 3 had only 32.2% bootstrap support, six individuals from population 12 were clustered together with 92.3% confidence. High levels of bootstrap values were found on some of the outermost leaves of the tree (all following values are separate locations within the tree) including two individuals from population 4 (91.0%), two samples of population 8 (75.4%), three samples of population 2 at (73.6%), and two samples of population 3 (98.9%). While much of SGG makes up two separate clades, there are six SGG samples within clade 3, where all of the SM and

SGA samples are located, indicating the occurrence of gene flow among all varieties. However,

SGG individuals appear to be diverging from SGA and SM.

Population structure analysis

Further illustration of gene flow among these populations is shone in Figs. 3 and 4 from

Structure analysis. Six clusters (K) were chosen for analysis of individuals on the basis of highest likelihood values and convergence of alpha. These values show individual allelic contributions

similar to that in PHYLIP, whereas previous PopGene analyses depict variability levels in accordance to populations as a whole.

Cluster 1 contained samples from populations 5, 8, and 10. Within this cluster, two samples contained 4% and 13% of their alleles from cluster 6. Both of these samples are from population 10 (SM) located in Wakulla Springs in the Florida panhandle (Fig. 1). Cluster 6 contains only SGG samples from the Florida Panhandle and southeastern Alabama. These data suggest introgression from SGG populations into samples from population 10. Additionally, cluster 1 also contains alleles from clusters 2, 3, 4, 5.

Cluster 2 contained samples from populations 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, and 13, which further indicates gene flow between all varieties used in this study. This cluster additionally contained alleles from clusters 1, 3, 4, 5, and 6.

Cluster 3 contained samples from populations 2 and 7 (SM populations) indicating particularly high levels of gene flow between those two populations. This cluster additionally contained alleles from clusters 1, 2, 4, 5, and 6.

Cluster 4 contained samples from populations 1, 3, 5, 7, 8, 11, and 12. Eight out of 10 samples of population 11 (SGA) were placed in this cluster, all but one of which contained greater than 81.5% of its alleles from cluster 4. The other sample contained 62.7%. None of the

SGA samples contained any alleles from cluster 6, a cluster that contains only SGG samples

(populations 12 and 13). Furthermore, only one individual from population 12 and no individuals from population 13 were found in this cluster. This supports taxonomic separation of SGA from

SGG, Samples from populations 1, 3, and 8 (SM populations) had an allelic distribution similar to many of the samples from population 11 (SGA), indicating that gene flow between these

populations is exceptionally high. This cluster additionally contained alleles from clusters 1, 2, 3,

5, and 12.

Cluster 5 contained samples from populations 1, 3, 5, 6, 7, 9, 10, and 11. This cluster additionally contained alleles from clusters 1, 2, 3, 4, and 6. It should be noted that, while cluster

6 was represented, only one sample from the aforementioned population 10 (SM; Wakulla

Springs, FL) contained any alleles from this cluster.

Cluster 6 contained samples from populations 12 and 13, both SGG populations. No samples from SM or SGA were located in this cluster, indicating the unique alleles of SGG. This data provides support for taxonomic separation of SGG from SGA and SM (Weakley et. al,

2011). Nine out of 15 samples in this cluster had greater than 89.8% of allelic contribution from cluster 6. The other individuals ranged from 40% to 72% allelic contribution from cluster 6.

In summary, AFLP markers exhibited a high level of efficiency in detecting genetic variation within Spigelia. While AFLP analysis evaluates multilocus fragments, the sequences and locations of genes responsible for such distinctive phenotypic differences between these species as flower color, flower number, or leaf shape in Spigelia are not yet known. The results of this study indicate that members of Spigelia species, SM and SG, are genetically similar both among and within species, despite occupying differing ecological environments. Assessing the actual (not proportional) variation of individual species demonstrates that within-population and among population variation is relatively low. The proportion of variation among species and among populations of each species is also low, with the greatest proportion of genetic variation residing within individual populations. Therefore, for purposes of obtaining variability for plant breeding or to preserve the genetic variability in Spigelia, collection or preservation of plant material from multiple geographically dispersed populations may not be critical. However,

population structure analysis revealed that all allele clusters have not entirely introgressed into all populations, and some populations have more allelic diversity than others, such that breeding success in Spigelia may be impacted by individual collections.

Evaluation of population structure revealed subtle population differences and delineated how gene flow occurs through the populations. Some samples in this study were shown to be almost entirely composed of alleles from specific clusters. For example, every sample from SGG contained alleles from cluster 6 with some samples having nearly 90% of their allelic makeup from this cluster. No samples from SGA and only three populations of SM had alleles from cluster 6, indicating considerable genetic differences between SGG and both SGA and SM. The levels of genetic diversity, coupled with its entomophilous outcrossing nature, suggest that gene flow both among populations and among species (introgression) is important in maintaining the heterosis of populations and/or species. Fragmentation or isolation of populations due to human activity could have a significant and abrupt negative effect on the adaptability and success of individual populations and/or species.

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Table 2.1. Phenotypic differences between S. marilandica, S. gentianoides var. gentianoides, and S. gentianoides var. alabamensis (Partially from USFWS, 2011).

Height Leaf # flw Corolla Corolla lobes Flower color (cm) shape per inf. length at anthesis

S. gentianoides var. gentianoides (SGG) 10-30 Broadly 3-8 25-30 mm Not reflexed Pink ovate S. gentianoides var. alabamensis (SGA) 10-30 Lanceolate to 2-4 36-50 mm Reflexed Pink elliptic S. marilandica (SM) 30-60 Lanceolate 4-17 1-1.5 cm Reflexed Red outside/ to elliptic greenish yellow inside

Table 2.2. Locations of populations collected, grouped by species. Each population is numbered in succession and includes four to 11 individual plant samples.

Population Species Location GPS Coordinates Sample Size 1 SMz Hamilton Co., TN N35°06.123 W85°16.995 10 2 SM DeKalb Co., GA N33°40.288 W84°07.017 10 3 SM Marianna Co., FL N30°48.659 W85°13.572 9 4 SM Edgefield Co., SC N33°43.842 W82°02.644 7 5 SM Oconee Co., SC N34°47.747 W83°08.530 10 6 SM Houston Co., GA N32°30.002 W83°31.692 5 7 SM Houston Co., GA N32°34.063 W83°36.100 10 8 SM Murray Co., GA N34°45.355 W84°41.589 10 9 SM Charleston Co., SC N33°10.660 W79°29.800 4 10 SM Wakulla Co., FL N30°14.074 W84°18.242 10 11 SGA Bibb Co., AL N33°03.520 W87°02.308 10 12 SGG Geneva Co., AL N31°08.545 W86°11.128 10 13 SGG Jackson Co., FL N30°44.656 W84°55.884 11 SM = Spigelia marilandica; SGA = S. gentianoides var. alabamensis; SGG = S. gentianoides var. gentianoides.

Table 2.3. List of adaptors and primers screened and used in this study to characterize the amplification fragment length polymorphism (AFLP) band patterns in two Spigelia species.

MseI site EcoRI site Adaptors 5' TACTCAGGACTCAT 3' 5’CTCGTAGACTGCGTACC 3' 5' GACGATGAGTCCTGAG3' 5'AATTGGTACGCAGTCTAC 3’ Pre-selective amplification primers 5' GATGAGTCCTGAGTAAC3' 5' GACTGCGTACCAATTCA 3' Selective primers screened Mse I-CAA Eco RI-ACT 800 z Mse I-CAA Eco RI-ACA 700 Mse I-CAA Eco RI-AAG 700 Mse I-CAA Eco RI-AAC 700 Mse I-CAC Eco RI-AGG 800 Mse I-CAC Eco RI-ACG 800 Mse I-CAC Eco RI-AGC 800 Mse I-CAC Eco RI-ACC 700 Mse I-CAG Eco RI-ACC 700 Mse I-CAG Eco RI-AGG 800 Mse I-CAG Eco RI-ACG 800 Mse I-CAG Eco RI-AGC 800 Mse I-CAT Eco RI-ACT 800 Mse I-CAT Eco RI-ACA 700 Mse I-CAT Eco RI-AAG 700 Mse I-CAT Eco RI-AAC 700 Mse I-CTA Eco RI-ACT 800 Mse I-CTA Eco RI-ACA 700 Mse I-CTA Eco RI-AAG 700 Mse I-CTA Eco RI-AAC 700 Mse I-CTC Eco RI-AGG 800 Mse I-CTC Eco RI-ACG 800 Mse I-CTC Eco RI-AGC 800 Mse I-CTC Eco RI-ACC 700 Mse I-CTG Eco RI-AGG 800 Mse I-CTG Eco RI-ACG 800 Mse I-CTG Eco RI-AGC 800 Mse I-CTG Eco RI-ACC 700 Mse I-CTT Eco RI-ACT 800 Mse I-CTT Eco RI-ACA 700 Mse I-CTT Eco RI-AAG 700 Mse I-CTT Eco RI-AAC 700 Selective primer sets used y Mse I-CTC Eco RI-ACC 700 Mse I-CTC Eco RI-ACG 800 Mse I-CTT Eco RI-ACT 800 Mse I-CTG Eco RI-ACG 800 z EcoRI primers were labeled with one of two Li-Cor fluorescent tags with peak fluorescence at 700 or 800 nm. y Four primer sets selected from 32 screened primer pairs.

Table 2.4. Percentage of polymorphic loci, average genetic diversity within populations (HS), average genetic diversity within species (HT), and proportion of species genetic diversity attributed to among population variation (GST) for species of Spigelia.

Polymorphic Loci Percentage HS HT GST OVERALL 97.77 0.144 0.186 0.226

S. marilandica (SM) 88.85 0.140 0.175 0.199 S. gentianoides (SGA and SGG) 72.86 0.156 0.191 0.186 S. gentianoides var. gentianoides (SGG) 59.11 0.152 0.173 0.125

Table 2.5. Percentage of polymorphic loci in all populations of Spigelia evaluated in this study.

Population Species Population Polymorphic Number Loci Percentage OVERALL 97.77 1 Spigelia marilandica (SM) Hamilton Co., TN 55.76 2 S. marilandica DeKalb Co., GA 50.93 3 S. marilandica Marianna Co., FL 44.98 4 S. marilandica Edgefield Co., SC 35.32 5 S. marilandica Oconee Co., SC 49.44 6 S. marilandica Houston Co., GA (Kathleen, GA) 34.57 7 S. marilandica Houston Co., GA (Bonaire, GA) 52.79 8 S. marilandica Murray Co., GA 55.39 9 S. marilandica Charleston Co., SC 31.97 10 S. marilandica Wakulla Co., FL 44.24 11 S. gentianoides var. alabamensis (SGA) Bibb Co., AL 50.19 12 S. gentianoides var. gentianoides (SGA) Geneva Co., AL 47.69 13 S. gentianoides var. gentianoides (SGG) Jackson Co., FL 42.75

Table 2.6. Analysis of molecular variation (AMOVA) for three Spigelia spp./varieties included in this study.

Source of df Sum of Variance Variance Probability variation squares components percentage P Among all populations 12 957.77 6.16 19.43 <0.05 Within all populations 102 2607.04 25.56 80.57 <0.05 Total 114 3564.80 31.72

Among species/variety 2 315.31 5.26 15.34 <0.05 Within species/variety 112 3250.56 29.02 84.66 <0.05 Total 114 3656.88 34.28

Table 2.7. Nei's unbiased measures of genetic distance (Nei, 1978) below diagonal and geographic distance (km) above diagonal. 1 2 3 4 5 6 7 8 9 10 11 12 13 1z *** 227 536 406 259 373 364 88 676 615 303 504 546 2 0.052 *** 381 225 182 156 145 166 483 422 325 394 380 3 0.020 0.053 *** 504 562 288 290 505 697 129 341 116 36 4 0.035 0.051 0.035 *** 179 240 232 323 285 496 547 557 494 5 0.031 0.052 0.031 0.035 *** 303 291 192 441 576 475 575 560 6 0.030 0.048 0.032 0.018 0.037 *** 13 320 452 291 388 343 282 7 0.035 0.030 0.029 0.043 0.034 0.039 *** 309 449 295 380 341 282 8 0.029 0.051 0.023 0.039 0.028 0.044 0.033 *** 594 571 333 501 515 9 0.053 0.083 0.068 0.082 0.050 0.067 0.078 0.072 *** 639 805 768 676 10 0.044 0.072 0.044 0.076 0.043 0.071 0.050 0.039 0.068 *** 460 235 101 11 0.052 0.073 0.048 0.087 0.063 0.074 0.063 0.049 0.076 0.061 *** 267 364 12 0.06 0.084 0.059 0.079 0.066 0.081 0.065 0.047 0.104 0.062 0.076 *** 142 13 0.056 0.060 0.043 0.076 0.057 0.068 0.050 0.057 0.091 0.066 0.068 0.052 *** z Population numbers correspond to the following species; Spigelia marilandica (SM)(1-10), S. gentianoides var. alabamensis (SGA)(11) S. gentianoides var. gentianoides (SGG)(12-13). Population numbers correspond to those listed in Table 2.

Figure 2.1. Spigelia collection sites for current project. S. marilandica (SM) = ; S. gentianoides var. gentianoides (SGG) = ; S. gentianoides var. alabamensis (SGA) = . Numbers within stars indicate assigned population numbers described in Table 2.2.

1 5 8

4 2

9 11 7 6

3 13

Figure 2.2. The unrooted UPGMA phenogram of Nei’s unbiased genetic distance matrix (Nei, 1978) over all 13 populations of Spigelia. Surveyed with an indication of bootstrap values of 1000. Population numbers are denoted to the right of species identificationZ.

zBranches colored red indicated bootstrap values below 50%.

Figure 2.3. Inferred population structure based on 116 individuals and 269 markers, assuming correlations among allele frequencies across clusters-arranged by individual. K=6.

Each vertical line represents one individual and the colors represent the membership coefficients to the K clusters. K=6 clusters. Cluster 1=red; cluster 2=green; cluster 3=blue, cluster 4=yellow; cluster 5=pink; 6=aqua. Numbers preceding those in parentheses = individual sample order. Numbers in parentheses = population site.Populations 1-10 = S. marilandica (SM); population 11= var. alabamensis (SGA), population 12 and 13 = var. gentianoides (SGG).

Figure 2.4. Inferred population structure based on 116 individuals and 269 markers, assuming correlations among allele frequencies across clusters-arranged by allelic distribution in each cluster (K=6).

Each vertical line represents one individual and the colors represent the membership coefficients to the K clusters. K=6 clusters. Cluster 1=red; cluster 2=green; cluster 3=blue, cluster 4=yellow; cluster 5=pink; 6=aqua. Numbers preceding those in parentheses = individual sample order. Numbers in parentheses = population site.Populations 1-10 = S. marilandica (SM); population 11= S. gentianoides var. alabamensis (SGA), population 12 and 13 = S. gentianoides var. gentianoides (SGG).

CHAPTER 3

CLONAL PROPAGATION OF STEM CUTTINGS OF SPIGELIA MARILANDICA, S.

GENTIANOIDES VAR. ALABAMENSIS, AND S. MARILANDICA × S. GENTIANOIDES

2 VAR. ALABAMENSIS F2 AND F3 HYBRIDS

2 Hershberger Ward, A.J. and C.D. Robacker. To be submitted to HortScience.

Abstract

New clonally propagated ornamental cultivars must display high levels of rooting success in order to be commercially viable. Spigelia marilandica (L.) and S. gentianoides

Chapm. ex A. DC. var. alabamensis K. Gould are species endemic to the southeastern

U.S. that show potential for development of cultivars for landscape use. The objectives of this study were to determine whether cutting date, indole-3-butyric acid (IBA) level, and genotype impacted rooting percentage, root number, and root length in Spigelia. Stem cuttings were obtained from five genotypes of S. marilandica (SM), one genotype of S. gentianoides var. alabamensis (SGA), three genotypes of SM× SGA F2 hybrids, and two genotypes of SM × SGA F3 hybrids. IBA level significantly affected rooting percentage and root number, but not root length of the genotypes. The SM × SGA F2 and F3 hybrids successfully rooted through all months evaluated, while SM and SGA genotypes exhibited a decline in rooting in cuttings taken in Sept. Results suggest that SM and SGA may be successfully propagated by treating stem cuttings taken in May, June, July, or

Aug. with 0.3% IBA. Cuttings of SM × SGA hybrids can be taken through Sept. These protocols provide a basis for rapid propagation of Spigelia and may provide a foundation for other species and hybrids within the genus.

Introduction

Spigelia L., (Division: Magnoliophyta; Class: Magnoliopsida; Subclass Asteridae;

Order: Gentianales; Family: Loganiaceae) is a genus of approximately 50 species that has distribution from the southeastern United States to Central America and south to temperate areas of South America. The species inhabit mid-elevation to lowland areas.

Most of the species, approximately 36, are located in South America. Five species are

61 considered endemic species in the U.S.: S. marilandica, S. gentianoides, S. hedyotidea, S. loganioides, and S. texana (Gould, 1997). This study will focus on clonal propagation of two herbaceous perennial species endemic to the southeastern U.S., S. marilandica (L.)

L. and the endangered Spigelia gentianoides Chapm. ex A. DC. var. alabamensis K.

Gould. They are of particular interest to breeders due to their ornamental and pharmacological potential.

Spigelia gentianoides var. alabamensis (SGA) has pink corollas from 36-50 mm long (Fig 1). Flowering occurs from May to June. This variety is endemic to an approximately three-mile area in Bibb County, AL. S. marilandica (SM) is an upright herbaceous perennial plant growing from 30-60 cm high. Corollas are bright red outside, yellow to greenish-yellow inside (Fig. 1). Flowering occurs from May through June

(Dunwell, 2003). Distribution is from eastern South Carolina north to Kentucky, south to north-central and northwestern Florida, and westward to southeastern Oklahoma and far- eastern Texas (Duncan and Duncan, 2005; Gould, 1997).

SM × SGA hybrids were created in 1997 by the State Botanical Garden of

Georgia (Athens, GA). The F1 hybrids were morphologically intermediate between the parent species. The interior of the hybrid flower is white rather than yellow, although the exterior displays much of the red color of SM. The leaves are intermediate in size and shape in the hybrid. Subsequently, F2 (Affolter, 2005) and F3 hybrids (created by the authors) were created that have pink flowers with white throats (Affolter, 2005). The ability of these species to hybridize increases the potential variation that breeding will be able to introduce.

Though a few studies have been conducted on clonal propagation of SM, no work has been reported on propagation of SGA. Currently, the commercial availability of both

SM and SGA is limited. Vegetative propagation has, until recently, been a major factor in reducing the presence of SM in nurseries. The rarity of SGA has contributed to its lack of production in the nursery trade. Research by Foster and Kitto (2001) and Bir and Barnes

(2000) showed that timing and environmental conditions impacted the rate of successful rooting of SM. They found that cuttings taken prior to flowering as well as from greenhouse-grown plants increased the likelihood of success. Also, use of greenhouse- grown plants enabled cuttings to be obtained throughout the growing season as opposed to only prior to flowering. More recently, work by Pill and Goldberger (2010) determined that the combination of bottom heat at 27°C and submersion in 0.3% IBA for one minute increased rooting percentage of SM compared to the control. They additionally evaluated several methods of applying IBA (0.3%): solution dip, powder dip, solution submersion

(one minute), and a control. The authors did not provide information on the genotypes in their study, so genotypic effect is unknown. The purpose of our work was to formulate a system for successful propagation of Spigelia from stem cuttings in a greenhouse setting.

Three factors were studied: cutting date, IBA concentration, and genotype. With development and introduction of new selections of Spigelia, it would be advantageous to have efficient clonal propagation protocols to promote production and commercialization of these plants. Therefore, the objective of this research was to develop an efficient protocol for clonal propagation of stem cuttings of SM, SGA, and their F2 and F3 hybrids.

Materials and Methods

Plant material

Cuttings were obtained from five genotypes of SM provided by North Creek

Nurseries (Landenberg, PA), one genotype of SGA provided by the State Botanical

Garden of Georgia (Athens, GA), three genotypes of SM × SGA F2 hybrids, provided by the State Botanical Garden of Georgia (Athens, GA), and two genotypes of SM × SGA F3 hybrids developed by the authors. Specific parents of the hybrids were not used in this study. Plants were maintained in an unheated greenhouse at the University of Georgia

Griffin campus. All cuttings were of the terminal tips of stock plants and included three nodes (Bir and Barnes, 2000).

Procedures

Stem cuttings of greenhouse material were given no treatment (control) or dipped into Hormodin 2 powder (0.3% IBA) or Hormodin 3 (0.8% IBA) powder. The rooting media used was a 50:50 mix of Metro Mix 830 (Sun Gro Horticulture, Canada): perlite.

Plants were placed under a reflective shade cloth with 12 seconds of mist at eight-minute intervals in a greenhouse for eight weeks. All material received bottom heat at 27°C. The total number of cuttings obtained per genotype was dependent upon availability of plant material, and ranged from 31 to 223 (Table 1). Cuttings were taken on 14 May, 16 June,

18 July, 20 Aug., and 19 Sept. 2011. Stem cutting data was collected for all cutting dates and genotypes except for root number and root length of SGA cuttings taken 14 May due to insufficient plant material. Cuttings from one of the F2 plants (labeled F2-c) taken 14

May were missing from the data due to human error.

Plant measurements and data analysis

Eight weeks after taking cuttings, rooting percentage, root number, and root length were determined. Media was rinsed off the roots with water. Only roots attached to the main stem were counted. Root length was measured on the longest root on each cutting. Data were analyzed with a three-way analysis of variance (ANOVA) using the

SAS general linear model procedure. Main effects were genotype (G), IBA levels (I), and cutting date (C). G×I interaction, G×C interaction, and I×C interaction were calculated.

Data were calculated with genotypes listed individually as well as with genotypes grouped into three categories for further analysis: hybrids (three F2 and two F3 genotypes), SM (five genotypes) and SGA (one genotype). Additional ANOVA were conducted for individual cutting dates. Means were separated using Tukey’s honestly significant difference test (P ≤ 0.05).

Results and Discussion

Genotype, IBA level, and cutting date all significantly affected rooting percentage and root number, and genotype and date significantly affected root length as well (Table

2) when data were analyzed as individual genotypes. IBA levels of 0%, 0.3% and 0.8% yielded rooting percentages of 76.9%, 82.3%, and 83.1%, respectively, and root numbers of 6.6, 7.5, and 7.7 respectively, when averaged over all genotypes and dates (Table 3).

Pill and Goldberger’s study (2010) found that SM cuttings rooted at a rate of 46.9% when no growth regulators were used. The higher rate of rooting that we observed may be due to genotypic effects. Also, Pill and Goldberger evaluated rooting four weeks after taking the cuttings, while our data was collected at eight weeks. Increasing concentration of IBA from 0.3% to 0.8% did not significantly increase rooting percentage in our study, similar

65 to the findings of Pill and Goldberger (2010), nor did it increase root length. However, root number was significantly higher in both IBA treatments than in the control (Table 3).

In some species, increasing the concentration of IBA did yield a higher percentage of rooting (Griffin and Lasseigne, 2005; Husen, 2004; Husen and Pal, 2007). We observed a significant interaction of IBA level × date (I × D) for root number (Table 2). When rooting was evaluated by individual cutting date, results showed that IBA concentration had a significant effect on root number only in June and July (Table 4). Genotype × IBA

(G × I) interaction was significant for root number and root length (Table 2) when evaluated by individual genotype. Genotypes were, therefore, grouped into three categories for further analysis: hybrids (three F2 and two F3 genotypes), SM (five genotypes) and SGA (one genotype). IBA did not significantly affect rooting percentage and root length of these grouped genotypes; however, IBA did significantly affect root number (Table 5). Grouped genotype data did not have any significant genotype × IBA interaction for any of the rooting traits.

Genotype by date (G × D) interaction was significant for rooting percentage, root number and length when genotypes were evaluated individually and for rooting percentage when grouped (Tables 2 and 5). All stock plants had begun flowering in an unheated greenhouse prior to taking cuttings. Foster and Kitto (2001) found a greater rooting percentage in SM when cuttings were taken prior to flowering. In our study, stock plants were too small to provide cuttings prior to flowering so this factor could not be evaluated. Timing is known to have an effect on rooting success and has been documented in many species including Actinidia deliciosa (kiwifruit) (Ercisli et al.,

2003), Styrax spp. (snowbells) (Griffin and Lasseigne, 2005), and Populus nigra (poplar)

(Nanda and Anand, 1970). Genotype has also been shown to have an effect on rooting traits in many species such as oak (Quercus spp.) (Drew III and Dirr, 1989); Dalbergia sissoo (Husen, 2004), jojoba (Simmondsia chinensis) (Llorente and Apóstolo, 1998), western hemlock (Tsuga heterophylla) (Pounders and Foster, 1992), snowbells (Styrax spp.) (Griffin and Lasseigne, 2005), eastern red cedar (Juniperus virginiana) (Henry et al., 1992), and silver maple (Acer saccharinum) (Preece et al., 1991); however, the causes for genotypic variation are not fully understood, and may (Nanda and Anand, 1970) or may not (Greenwood et al., 1976) be associated with different concentrations of endogenous auxins. In our study, grouped genotypes had similar rooting percentages in all individual months except Sept. (Table 6 and Fig. 2). Rooting percentage of Sept. cuttings was 89.7%, 50.0 and 6.7% for hybrids, SGA and SM genotypes, respectively.

Spigelia used in this study undergoes a dormant period wherein plant material dies back overwinter and reemerges in spring. The SM and SGA used in this study ceased growing in Sept., while the hybrids continued producing new growth during this time (personal observation). This delay in dormancy likely explains higher rooting percentages of hybrids in Sept (Chen, 2010; Romagnoli et al., 1990; Vilà and D'antonio, 1998).

Evaluation of rooting percentages of individual genotypes over each cutting date showed that all F2 and F3 hybrids used in this study maintained high percentages of rooting through all cutting dates. One of the SM genotypes, SM-b, exhibited lower rooting percentages when cut in Aug. compared to previous months. In Sept., three of the five

SM genotypes did not root and of the remaining two, only 16.7% of the cuttings rooted

(data not shown).

G × D interaction was significant for root number when genotypes were analyzed separately (Table 2); however, it was not significant when genotypes were grouped

(Table 5). This was due, primarily, to the high root numbers of SM-d in individual genotypic analyses, as compared to all other genotypes. The mean root number of SM-d averaged from May to Sept. was 20.4, while the mean for all other genotypes was 7.1.

Furthermore, the rooting percentage for SM-d was 100% for May through Aug. and 0% for Sept. This variation among genotypes for rooting provides an opportunity for breeders to develop cultivars with high rooting potential. G × D interaction was also significant for root length when genotypes were analyzed separately. SGA exhibited a shorter average root length (2.9) than both hybrid (5.6) and SM (6.3) genotypes in all months measured except in Sept. when all genotypes had similar root lengths. All genotypes that rooted exhibited higher root lengths in Sept. cuttings compared to other months (Table 6).

In summary, IBA levels had a limited effect on rooting percentage and root length in this study, though a greater root number was found in both Hormodin treatments compared to the control. Therefore, application of 0.3% IBA to stem cuttings is recommended. While genotype and cutting date significantly affected rooting, most genotypes were successfully propagated from greenhouse cuttings from early through mid-summer. These protocols provide a basis for rapid propagation of Spigelia and may provide a foundation for other species and hybrids within the genus.

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genetic variation, reproduction biology, and propagation. Final project report to

the Georgia Cooperative Fish and Wildlife Research Unit.

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Ercisli, S., A. Esitken, R. Cangi, and F. Sahin. 2003. Adventitious root formation of

kiwifruit in relation to sampling date, IBA and Agrobacterium rubi inoculation.

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Gould, K. 1997. Systematic studies in Spigelia. University of Texas at Austin.

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28:53-57.

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Table 3.1. Spigelia genotypes and numbers of cuttings taken throughout duration of project.

Genotype N SM-a 66 SM-b 35 SM-c 69 SM-d 31 SM-e 51 SGA 49 F2-a 100 F2-b 223 F2-c 172 F3-a 220 F3-b 95 SM=Spigelia marilandica genotype; letters following SM indicate assigned genotype code, SGA=S. gentianoides var. alabamensis, F2=F2 hybrid of S. marilandica × S. gentianoides var. alabamensis cross; letters following F2 indicate assigned genotype code, F3= F3 hybrid of S. marilandica × S. gentianoides var. alabamensis cross; letters following F3 indicate assigned genotype code.

Table 3.2. Levels of significance of analysis of variance (ANOVA) effects for rooting percentage, root number, and root length for individual genotypes of Spigelia. Source Rooting Root Root of variation percent number length Genotype *** *** *** IBA * *** NS Date *** *** *** G × I NS *** *** G × D *** *** *** I × D NS * NS * = Pr ≤ 0.05, ** = Pr ≤ 0.01, *** = Pr ≤ 0.001.

Table 3.3. Effect of IBA level on rooting percentage, root number, and root length of Spigelia with all genotypes analyzed separately. Rt % Rt. num. Rt. lgth (cm) 0% IBA 76.9bZ 6.6b 6.5a 0.3% IBA 82.3ab 7.5a 6.6a 0.8% IBA 83.1a 7.7a 6.5a

ZMeans in columns followed by different letters are significantly different based on Tukey’s mean separation test at P≤0.05. Rt. = root, lgth = length, num. =number.

Table 3.4. Effect of IBA level and cutting date on rooting percentage, root number, and root length of Spigelia over all months. Rooting % Root number Root length (cm) May June Jul. Aug. Sept. May June Jul. Aug. Sept. May June Jul. Aug. Sept. 0.0% IBA 89.3az 87.4a 91.9a 75.4a 42.9a 8.8a 6.2b 6.0b 5.4a 6.8a 5.8a 7.1a 4.9a 4.4a 10.1a 0.3% IBA 95.2a 90.6a 86.1a 90.3a 53.0a 9.3a 8.6a 6.5ab 6.1a 7.0a 5.5a 7.0a 5.3a 4.9a 10.5a 0.8% IBA 92.1a 95.0a 94.2a 86.3a 49.2a 9.9a 8.6a 7.6a 5.7a 6.7a 5.6a 6.7a 5.3a 5.0a 10.1a

ZMeans in columns followed by different letters are significantly different based on Tukey’s mean separation test at P≤0.05.

Table 3.5. Levels of significance of analysis of variance (ANOVA) effects for rooting percentage, root number, and root length for grouped genotypes of Spigelia (SM, SGA, and SM × SGA hybrids within their respective categories). Source Rooting Root Root of variation percent number length Genotype *** NS *** IBA NS *** NS Date *** *** *** G × I NS NS NS G × D *** NS NS I × D NS NS NS * = Pr ≤ 0.05, ** = Pr ≤ 0.01, *** = Pr ≤ 0.001.

Table 3.6. Effect of grouped genotype and cutting date on rooting percentage, root number, and root length of Spigelia over all months. Rooting % Root number Root length (cm) May June Jul. Aug. Sept. May June Jul. Aug. Sept. May June Jul. Aug. Sept. SM 91.3az 92.6a 89.4a 79.7a 6.7c 8.8a 8.5a 7.4a 5.7a 5.0a 5.7a 8.2a 5.5a 5.8a 9.5a SGA 100a 86.7a 84.6a 88.9a 50b NA 6.2a 8.2a 6.7a 4.1a NA 3.8b 3b 2.1b 7.6a Hybrids 92.9a 90.3a 91.1a 87.4a 89.7a 9.8a 7.8a 6.6a 5.6a 7.2a 5.5a 6.8a 5.2a 4.7a 10.5a

ZMeans in columns followed by different letters are significantly different based on Tukey’s mean separation test at P≤0.05.

Figure 3.1. Spigelia marilandica (left) and S. gentianoides var. alabamensis (right).

Figure 3.2. Rooting percentage of grouped genotypes evaluated over all months.

A A 100 \ A A A A A A A A 90 A A A 80

60 Hybrids B 50 SGA SM 40

10 C

0 May June July Aug. Sept. ZMeans in columns followed by different letters are significantly different based on Tukey’s mean separation test at P≤0.05. Mean separation was conducted separately for each month.

CHAPTER 4

CONCLUSIONS

Previous genetic analyses of Spigelia have utilized chloroplast DNA (cpDNA)

(Gould and Jansen ,1999), internal transcribed spacer (ITS) sequencing(Gould and

Jansen, 1999), and allozyme studies (Affolter, 2005), yet to date there have been no assessments of within species diversity in S. marilandica (SM), S. gentianoides var. gentianoides (SGG), and S. gentianoides var. alabamensis (SGA) using AFLP technology. These southeastern U.S. native species have had limited research focusing on their genetic variability although they have apparent ecological and ornamental value.

There has been recent interest in the phenotypic differences between SGA and SGG.

These differences are stable when grown in a common greenhouse environment, indicating that their characteristics have a genetic basis, and supporting Alabama populations being listed as a distinct variety (Affolter, 2005; Allison and Stevens,

2001).This has led researchers to believe these two varieties’ differences warrant specific rank (USFWS, 2011). Weakley et al. (2011) determined that by reassessment of the appropriate ranks of the varieties and by guidelines of taxonomic rank normally applied to taxa in Spigelia, specific rank of SGA is justified; however, additional molecular and morphological studies were suggested to corroborate this rank change. SM encompasses a broader geographical range than both SGA and SGG. Additional interest in the genetic variability of SM is due to both the ornamental breeding potential of populations of SM and the effect that human activity has on the conservation of this species.

The present study addressed the matter of genetic diversity among and within two species and thirteen populations of southeastern U.S. native Spigelia through the use of amplified fragment length polymorphism (AFLP) analysis. Utilizing four primer pairs,

269 polymorphic bands were obtained that allowed for high resolution of genetic differentiation among and within species and populations. Based on analysis of molecular variance (AMOVA) and estimates of Nei’s coefficients of gene diversity (HS, HT, and

GST), the majority of variation in Spigelia occurs within populations. Both among species and among population variation was low, likely the effect of common ancestry as well as frequent introgression among members (and populations) of Spigelia. When all individuals were evaluated using Nei’s unbiased genetic distances and viewed as a

UPGMA phenogram, three main clades were shown, one with two samples of SGG from one population, one with 13 individuals from both SGG populations used in this study, and one with all of the SM, SGA, and remaining SGG individuals. Further evaluation using STRUCTURE software showed introgression between populations and species, though all allele clusters have not entirely introgressed into all populations. For example,

STRUCTURE results showed that no samples from SM or SGA were located in a cluster group containing only SGG, indicating the unique alleles of SGG. This data provides support for taxonomic separation of SGG from SGA and SM (Weakley et. al, 2011).

Results indicated that some individuals in this study were almost entirely composed of alleles from specific clusters suggesting that breeding success in Spigelia may be impacted by individual collections. The low levels of genetic diversity, coupled with its insect (Cullina, 2000; Glick, 2002) and hummingbird (Affolter, 2005; Rogers, 1988) pollinated status, suggest that gene flow both among populations and among species is

81 important in maintaining the heterosis of populations and/or species. Insect pollination leads to populations with a high level of genetic variation while individuals within the population share a similar complement of alleles in similar frequencies (Falk et al., 2001;

Hamrick and Godt, 1996). Fragmentation or isolation of populations due to human activity could have a significant and abrupt negative effect on the vigor and adaptability of species and/or individual populations. Studies should be conducted to identify those species that may be at risk either due to small geographic range as is that case in both varieties of S. gentianoides or habitat preferences that collide with human activities such as housing or mining development (S. marilandica and S. gentianoides).

The commercial availability of SM and SGA is limited. Vegetative propagation has been a major factor in reducing the presence of S. marilandica in nurseries. The rarity of S. gentianoides var. alabamensis has contributed to its lack of production in the nursery trade. Though a few studies have been conducted on clonal propagation of SM, no work had previously been reported on propagation of SGA. Research by Foster and

Kitto (2001) and Bir and Barnes (2000) showed that timing and environmental conditions impacted the rate of successful rooting of SM. They found that cuttings taken prior to flowering as well as from greenhouse-grown plants increased the likelihood of success.

Also, greenhouse-grown plants enabled cuttings to be obtained throughout the growing season as opposed to only prior to flowering. More recently, work by Pill and Goldberger

(2010) determined that the combination of bottom heat at 27°C and submersion in 3000 ppm IBA for one minute increased rooting percentage compared to the control in SM.

This study’s objectives were to determine whether cutting date, indole-3-butyric acid

(IBA) level, and genotype impacted rooting percentage, root number, and root length in

Spigelia. Stem cuttings were obtained from five genotypes of SM, one genotype of SGA, three genotypes of SM× SGA F2 hybrids, and two genotypes of SM × SGA F3 hybrids.

IBA level significantly affected rooting percentage and root number, but not root length of the genotypes. The SM × SGA F2 and F3 hybrids successfully rooted through all months evaluated, while SM and SGA genotypes exhibited a decline in rooting in cuttings taken in Sept. Results suggest that SM and SGA can be successfully propagated by treating stem cuttings taken in May, June, July, or Aug. with 0.3% IBA powder.

Cuttings of SM × SGA hybrids can be taken through Sept. These protocols provide a basis for rapid propagation of Spigelia and may provide a foundation for other species and hybrids within the genus.

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