University of Florida Thesis Or Dissertation Formatting

ESTIMATION OF PLOIDY LEVELS AND GENETIC PARAMETERS FOR A BERMUDAGRASS COLLECTION

ALEXANDRA RUCKER

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2016

To my mother, without whom none of this would be possible

ACKNOWLEDGMENTS

I would like to sincerely thank my main advisor, Dr. Patricio Munoz, for his constant supervision and support. I truly appreciate all of the time he invested in me, and my project. I would like to add additional thanks to Esteban Rios, Lin Xing and

Yolanda Lopez for all of the aid they provided in the field and in the lab.

I would also like to thank my mother and siblings for their emotional support.

Completing this project would have been much harder without them. This has been a long journey, but a worthwhile one.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 9

ABSTRACT ...... 10

CHAPTER

1 INTRODUCTION ...... 12

Importance ...... 12 Bermudagrass Cultivars and Forage Breeding ...... 12 New Threat ...... 14 Objectives ...... 15 Cynodon Ploidy ...... 15 Cynodon Genetic Parameters for Breeding ...... 17

2 DETERMINATIONS OF BERMUDAGRASS PLOIDY ...... 19

Materials and Methods...... 19 Germplasm ...... 19 Field Trial ...... 19 Phenotypes ...... 20 Flow Cytometry ...... 21 Ploidy-Traits Association Analysis ...... 23 Results and Discussion...... 23 Data Overview ...... 23 Ploidy Effect on phenotypic traits ...... 29

3 ESTIMATION OF GENETIC PARAMETERS OF BERMUDAGRASS ...... 45

Materials and Methods...... 45 Germplasm ...... 45 Field Trial ...... 45 Phenotypes ...... 46 Genetic Parameter Estimation ...... 47 Results and Discussion...... 49 Heritability ...... 49 Genotype-by-measurement correlation ...... 51 Traits Genetic Correlations ...... 52

Genotype-by-Environment Interaction ...... 54

4 CONCLUSION ...... 63

APPENDIX: SUPPLEMENTARY MATERIAL ...... 67

LIST OF REFERENCES ...... 74

BIOGRAPHICAL SKETCH ...... 79

LIST OF TABLES

Table page 2-1 Dates of Data Collection ...... 35

2-2 Ploidy level by genome size determined by flow cytometry ...... 36

2-3 Mean and standard error (in parenthesis) of all phenotypic traits collected in the field experiment by measurement...... 37

2-4 P-values of ploidy level effect on dry matter yield for six harvests for all Cynodon species present in collection and for C. dactylon specifically...... 38

2-5 P-values provided for all species in collection and for C. dactylon only ...... 39

2-6 Cultivars with the lowest and highest values for measured traits, as well as the average values for the control cultivars...... 40

2-7 Lowest and highest yields (kg/ha) per harvest in Citra 2015, as well as the average yields for the control cultivars. Cultivar with highest and lowest yield in parenthesis ...... 40

2-8 Lowest and highest yields (kg/plot) per harvest in Tifton 2015, as well as the average yields for the control cultivars. Cultivar with highest and lowest yield in parenthesis ...... 41

3-1 Trait classification, description and date of its data recorded from the field experiment ...... 55

3-2 Mean and standard error (in parenthesis) of all phenotypic traits collected in the field experiment by measurement...... 56

3-3 Broad-sense heritability of traits measured in the bermudagrass field trial ...... 57

3-4 Genetic correlations (below diagonal) among traits measured from the field trial and its respective standard errors (above diagonal)...... 58

3-5 Genetic correlations (below diagonal) between Citra yield measurement dates and its respective standard errors (above diagonal) ...... 59

3-6 Genetic correlations (below diagonal) between Tifton yield measurement dates and its respective standard errors (above diagonal) ...... 59

A-1 Estimated genome size and ploidy level for whole bermudagrass collection ..... 67

LIST OF FIGURES

Figure page 2-1 Histograms showing fluorescence intensity...... 42

2-2 Bar graphs comparing the average environmental damage ...... 43

2-3 Bar graphs comparing the average physiological traits ...... 44

3-1 Broad-sense heritability for yield by harvest measurement in two location of the southern USA (Citra, FL and Tifton, GA)...... 60

3-2 Broad-sense heritability accounting for measurement effect for traits measured more than once in the field experiment...... 61

3-3 Genotype-by-measurement correlation for traits measured more than once in the field trials ...... 62

LIST OF ABBREVIATIONS

ADF Acid detergent fiber (ADF)

BSM Bermudagrass stem maggot (Atherigona reversura Villeneuve), a relatively new bermudagrass pest in the United States. Originally from Asia, the fly was first discovered in Tifton, GA in 2010 (Hancock, 2012).

GxE Genotype-by-Environment interaction

GxM Genotype-by-Measurement interaction

IVTD In vitro true digestibility (IVTD)

NDF Neutral detergent fiber (NDF)

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

ESTIMATION OF PLOIDY LEVELS AND GENETIC PARAMETERS FOR A BERMUDAGRASS COLLECTION

Alexandra Rucker

December 2016 Chair: Patricio R. Munoz Major: Agronomy

Bermudagrass is the most widely used warm-season perennial forage in the

Southeastern United States. There is increased market demand due to the introduction of a new invasive pest, Bermudagrass Stem Maggot (BSM), and the desire for higher quality forage. However in recent years, the development of new cultivars has decreased. The objectives of this study were to determine the ploidy level of a large collection of bermudagrass accessions and to determine the genetic parameters for economically important traits. The 287 bermudagrass accessions used in the experiment were obtained from the USDA-ARS’s CORE collection in Tifton, GA and the

Germplasm Resources Information Network (GRIN). Data for the observed traits (yield, flowering percentage, BSM damage, plot coverage percentage, canopy height, stolon length, leaf width, frost damage, and percent nitrogen in leaf tissue) was gathered from the bermudagrass accessions grown in Citra, FL, while only yield data were gathered from Tifton, GA. The collection was found to be composed of 28 diploids, 111 triploids,

119 tetraploids, 14 pentaploids and one hexaploid individual(s). These results will aid in parent selection in order to improve genetic gains for a bermudagrass breeding program. Additionally, ploidy level affected percentage plot coverage, flowering

percentage, frost damage, bermudagrass stem maggot (BSM), and leaf width. When broad sense heritability was calculated the phenotypic traits measured at Citra ranged from 0.37 for BSM damage to 0.79 for canopy height. Yield for Citra and Tifton varied throughout the growing season, but generally Tifton had lower heritability than Citra.

Measurement dates were found to have an effect on broad sense heritability. Genetic correlations varied greatly for all traits and for yield measurements. The genotype by environment interaction between yield harvests in Citra and Tifton was low, at 0.11, which indicates unstable yields across different locations. The ploidy level estimation will aid in parental selection, since sterility can be inferred with triploids and pentaploids, as well as determining the ploidy level of the offspring. Additionally, the high genotype by environment interactions indicate that multiple locations and years of data collection will be needed in order to produce stable cultivars.

CHAPTER 1 INTRODUCTION

Importance

Bermudagrass (Cynodon dactylon (L.) Pers.) is a genetically diverse, warm- season perennial grass. It is a member of the Cynodon genus, which is made up of ten different grass species. Of the ten Cynodon species, C. dactylon is the most prevalent

(Horowitz, 1996). In the past it was believed that bermudagrass originated in Asia, more specifically India (Burton. 1948), but now it is believed to have originated in Africa

(Caetano-Anolles et al., 1997). It can be found all across Europe, Asia and the

Americas. It is believed that bermudagrass was introduced to the United States around the 1750s, though the exact circumstances of its introduction are unknown (Hanna et al., 2008; Caetano-Anolles et al., 1997). Since its introduction, bermudagrass has become the most important warm season hay and pasture crop in the Southeastern

United States for the past several decades. Similarly, bermudagrass is becoming the most popular turf species in warm climatic regions (Marcum and Pessarakli, 2006).

Bermudagrass has gained high popularity due to the presence of many desirable traits such as: high yield, dramatic response to nitrogen fertilization, quick drying for hay production, salt tolerance, and drought tolerance (Ball, 2007; Marcum and Pessarakli.

2006). Bermudagrass has an incredibly prolific growth habit due to the presence of above ground stolons and underground rhizomes, and thus it is considered a weed in many parts of the world (Horowitz, 1996; Kovács et al., 2002).

Bermudagrass Cultivars and Forage Breeding

There have been extensive efforts in breeding both forage and turf bermudagrass, which have resulted in the release of highly improved cultivars (Bade,

2000; Burton, 1972; Burton et al., 1967; Burton et al., 1993; Marcum and Pessarakli,

2006). The most popular forage cultivars on the market were developed decades ago and were made through hybridization of C. dactylon with C. nlemfuensis (e.g. ‘Tifton

85’). Generally, hybrid cultivars have higher yields and better digestibility than seeded bermudagrass cultivars (Corriher and Redmon, 2009). A study performed by Mandebvu et al. (1999), found that Tifton 85 had significantly higher IVDMD, NDF, and ADF when compared to ‘Coastal’. Coastal is an F1 hybrid between South African and American species, was developed back in 1943 and is still one of the most popular cultivars grown

(Burton, 1943; Stichler and Bade, 2003). In 2010, Coastal was grown on more than 15 million acres (Lee et al., 2010). ‘Alicia’ released in 1967, though not as popular as

Coastal, was another cultivar selected for its rapid establishment but generally presented poor to average performance (Corriher and Redmon, 2009). The cultivar

Coastcross-1, released by Burton in 1972, was developed with higher digestibility than commercial cultivars of the time (Burton, 1972). One of the most popular cultivars today is ‘Tifton 85’, officially released in 1992, which is a cross between a plant introduced from Africa and ‘Tifton 68’ (Burton et al., 1993; Bade, 2000). Tifton 85 has a higher acid detergent fiber (ADF) than coastal, as well as a higher neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) than Coastcross II, and Jiggs (Vendramini, 2010).

The bermudagrass cultivar ‘Jiggs’ was a unique release by a private owner so there is no clear consensus as to when and how it was originally distributed (Bade, 2000).

The performance of bermudagrass cultivars varies considerably depending on the area they were tested (Hanna and Anderson, 2008). For example, Jiggs has been reported to be one of the few bermudagrass cultivars that thrives in poorly drained soils,

making it more suited for production in South Florida (Aguiar et al., 2014; Bade, 2000;

Vendramini, 2010). For a three-year study, Silveira et al. (2013) concluded that Jiggs significantly out yielded Tifton 85 in Southern Florida for the first year, had a higher, although not statistically different, yield for the second year, and virtually no difference in yield for the third year. Multiple studies in Oklahoma found that bermudagrass cultivars varied significantly with environmental location, and year (Avis et al., 1980; Rose et al.,

2008). Avis et al (1980) concluded that it was necessary to use multiple testing sites when measuring yield for bermudagrass. Even though bermudagrass is a vital forage crop, development of new cultivars has slowed in the last decades. Producers are always looking for a cultivar that provides the highest yield, high-quality, along with high levels of disease and pest resistance. This is even more relevant since the discovery of a new pest in the southern United States, Bermudagrass Stem Maggot (Anderson et al.,

2016; Baxter et al., 2013; Baxter et al., 2014; Hancock, 2012).

New Threat

Bermudagrass stem maggot (BSM), Atherigona reversura Villeneuve, is believed to be native to Asia and was first discovered in the United States around 2009 in Los

Angeles, California. Though the exact circumstances surrounding its introduction to the

United States is unclear. Since its discovery in America, BSM has spread to Georgia and throughout the southeast. However, until its discovery in Georgia, BSM had only been regarded as a problem in turf. BSM has spread across the United States so rapidly that it has been reported in any areas that have large acres of Bermudagrass.

The insect causes chlorosis and death to the upper two or three leaves, the damage has often been described as having a similar appearance to frost or drought damage.

Frost damage is ruled out as the reason for the damage because BSM avoids cold 14

temperatures and is most active in warm, humid areas (Baxter et al., 2013; Baxter et al.,

2014). Details about the BSM life cycle are still mostly a mystery, some information has been gathered from field work and comparisons to a well-known relative, the sorghum shoot fly. So far, cultural control and chemical control have proven to be minimally effective (Baxter et al., 2014; Hancock, 2012). A recent study has shown that BSM can have a yield reduction from 12% to 50% depending on the bermudagrass cultivar used

(Anderson et al., 2016). Since this percentage of damage can result in considerable financial loss for bermudagrass forage production, it is imperative to evaluate potential sources of resistance to BSM as well as to determine its genetic control.

Objectives

The goal of this study was to develop the information necessary to create a modern breeding program of bermudagrass for Florida and the Southeast. Specifically, two objectives were defined: 1) determine the ploidy level of a large collection of bermudagrass accessions, which will allow for the selection of parental lines for the breeding program, and 2) determine the genetic parameters (broad-sense heritability and genetic parameters) for key traits such as yield, needed to develop a modern breeding strategy.

Cynodon Ploidy

Bermudagrass is a polyploid species, however there is not a consensus in the scientific community on if it is an autopolyploid or an allopolyloid, with multiple possible ploidies varying from diploid to hexaploid (Gulsen et al., 2009; Zeven, 1979). Common bermudagrass is usually either tetraploid, 2n=36, or diploid, 2n=18 (Caetano-Anolles at al., 1997). However, the introduction of hybridization sterile triploids, 2n=27, have become more commonplace (Caetano-Anolles et al., 1997). A collection that has

multiple ploidy levels tend to extend an additional level of difficulty to breeding programs when it comes to identifying appropriate parents for crosses and whether the progeny of these crosses will be fertile. However, polyploid species have become popular in both food and ornamental plant production since traits, like vigorous growth, are more often observed in higher ploidy levels, such as tetraploids compared to diploids (Ramanna and Jacobsen 2003; Rogalska et al., 2007). Studies have shown that increasing ploidy level has a direct correlation with certain morphological changes in crops, the most well- known being increased stomata size and increased cell size (Anssour et al., 2009; Rios et al., 2015; Stupar et al., 2007).

A general decrease in fertility is another trait that is shared by many polyploid species. The decrease in fertility is usually from the decrease in pollen production or the absence of working ovaries. Usually this is caused by chromosomal imbalances that occur during replication. This imbalance in meiosis often produces sterile individuals if the chromosome number is odd; such as in triploids and pentaploids. This sterility has been a desirable trait with fruit production (which creates seedless fruit) or production that focuses on biomass yield (Acquaah. 2009). However, sterility or general decrease in fertility makes it functionally impossible to obtain offspring. Meaning that replicating a triploid individual would rely on clonal or vegetative propagation. So knowing the ploidy level of crossing parents becomes vital if a breeder wants to know whether a cross will produce viable seeds that could be used in the improvement process or infertile progeny that could only be tested for final cultivars.

In the past, cytogenetic techniques (i.e. chromosome counting) were used to determine the ploidy of an organism. This method, while still used today, is time-

consuming and it is a prohibitive task given the size of the collection in the present experiment (Rios et al., 2015). Flow cytometry is currently the preferred methodology as it is a fast, cost effective method to determine ploidy and estimate genome size with high accuracy (Wang et al., 2009; Pang et al., 2010). Once ploidy is determined, the association of ploidy with phenotypic traits could also be established (Rios et al., 2015).

Cynodon Genetic Parameters for Breeding

Chapter 3 discusses the procedures and results to determine the genetic parameters of the entire bermudagrass collection. Since bermudagrass is a perennial crop, each stage of a breeding program can take several years. However, with genetic information and use of advanced selection methods, the breeding process can be reduced significantly (Serba et al., 2015). One of the initial expectations of this project is to use the data gathered to form a bermudagrass breeding program. In order to do this, a deeper understanding of the potential breeding material is needed. The genetic parameters studied include the broad-sense heritability (H2) and genotype x environment (G x E) interaction. Broad-sense heritability is essential information that can help provide plant breeders with an understanding of the transmission of economically important traits from one generation to another (Ajmal et al., 2009). With this information a breeder can determine how to allocate resources to the different traits of interest when parental crosses begin, as well as determine the clear expectations for the breeding efforts. While discovering the genetic determination (broad-sense heritability) of a given trait is important, a breeding program usually improves more than one trait at a time. Thus, determining whether a genetic relationship between traits exists, as well as the strength of such relationships is relevant for a modern breeding program (Fernandez et al., 2009). Another important factor is the relevance that the 17

different environments, within the target areas, will have in the expression of the genotype’s traits. The full influence a genotype has on the expression of a trait cannot be easily understood if environment plays a large role in phenotypic expression

(Annicchiarico, 2002). For this reason two locations in the Southeast were used.

In Chapter 4, the conclusions and implications of this research are described.

Additionally, a modern breeding strategy for bermudagrass is proposed.

CHAPTER 2 DETERMINATIONS OF BERMUDAGRASS PLOIDY

Materials and Methods

Germplasm

A set of 287 bermudagrass accessions was established from combining two collections: 146 Cynodon clonal accessions from the Tifton (GA) USDA-ARS’s CORE collection and 137 accessions from the Germplasm Resources Information Network

(GRIN) collection maintained at the Plant Genetic Resources Conservation Unit, Griffin,

GA by the USDA. The CORE collection was selected based on 22 phenotypic traits and

AFLP markers from a larger population maintained at Tifton, GA (Anderson et al.,

2009). While the USDA GRIN collection (npgsweb.ars-grin.gov/gringlobal), provides little genetic information (such as parental crosses and ploidy) on its bermudagrass accessions.

Field Trial

The morphological and yield data were measured on the above bermudagrass germplasm from a field trial planted at the Plant Science Research and Education Unit located at Citra, Florida. A soil test performed in 2013, determined that the pH of the soil was 6.9, the P2O5 content was high, the K2O was low, the S was low and the Mg was low. The experiment was established July 02, 2014, using a single 5 x 5 cm plug planted in the center of the plot and allowed to spread to a plot size of 1.8 x 3.0 m. The field experiment was a row-column design with two replicates and augmented representation of three controls: ‘Tifton 85’, ‘Jiggs’ and ‘Coastal’. The field had access to an overhead pivot irrigation system, which was used to aid in establishment, but mainly relied on rainfall irrigation.

Phenotypes

The traits measured in this experiment included: yield, flowering percentage,

BSM damage, plot coverage percentage, canopy height (cm), stolon length (cm), leaf width (mm) forage yield (kg/ha), frost damage, and percent nitrogen in leaf tissue (Table

2-1). Data for these traits was collected from the start of the experiment in 2014 to 2016.

In 2015, yield was measured every five weeks during the growing season, ranging from April to November (Table 2-1). Staging was done in early March and involved cutting the plots to a stubble height of 10.2 cm and fertilized. A 1.2 x 3.0 m area was harvested in each plot, using a small flail-chopper attached to a small Kubota tractor. After each harvesting event, excluding the last of the season, the field was

-1 fertilized with 90 kg ha of nitrogen and 45 kg/ha of K2O. Subsamples (approx. 450 g) were taken from the fresh weight yield of the plot and placed in 30.5 x 17.8 x 43.2 cm brown paper bags. Once the samples were collected, they were placed in forced air dryers and kept at 55°C for at least one week to dry to a constant weight. Due to the harvesting method used, a significant amount of sand was collected with the samples.

Thus, sand weight was excluded from the third harvest. In total for the Citra location, yield was measured six times during the 2015-growing season.

Flowering plot percentage was measured using a scale from 1 to 9, with 1 representing a limited number of seed heads (up to 5), while 9 represents presence of flowering heads in the entire plot. BSM and frost damage was measured on a scale of 1 to 9, where 1= less than 10% of the area of the plot is flowering and 9= greater than

90% flowering. Plot coverage was also measured on a scale of 1 to 9, with 1= less than

10% plot coverage and 9= greater than 90% plot coverage. Leaf width was measured using a ruler and averaging the width of five mature leaves, in a position at least three 20

nodes below the youngest leaf on a stem, and the full length of the midsection was recorded in centimeters. Canopy height was measured in centimeters using a meter stick, the tallest point of the canopy was measured in each plot. Stolon length was measured in centimeters, and data was taken by selecting the longest extended stolon of each plot. Percent nitrogen in leaf tissue was gathered from of all bermudagrass accessions ground leaf tissue collected from Citra, FL. Nitrogen percentage was measured in the Forage Lab at Marianna, Florida; using the Vario Micro cube (CHNS analyzer using the Dumas dry combustion method) interfaced with ISOPRIME 100

(isotope ratio mass spectrometer).

Flow Cytometry

In order to have access to fresh, young leaf tissue samples each accession was kept in a 5.5x5.5 cm pot. The samples were kept in a greenhouse adjacent to the lab where samples were processed (2202 SW 23rd St, Gainesville, FL). The protocol to prepare bermudagrass for flow cytometry analysis was described by Rios et al. (2009) and Bennett and Leitch (2011). Specifically, between 50-100 mg of young leaf tissue was chopped with a razor blade inside a petri dish along with 500 μl of extraction buffer

(CyStain PI absolute P, Partec GmbH, Münster) for approximately 45 s. Any diseased tissue was removed a priori because, as speculated by Pang et al. (2010), the DNA of the sample may be contaminated by the pathogen. The tissue was allowed to rest for approximately 30 s in the buffer to facilitate the extraction of nuclei. The mixture was then filtered through Partec 50 μm CellTrics (Partec GmbH, Münster) and 1.6 ml of staining solution containing propidium iodide and RNase (CyStain PI absolute P, Partec

GmbH, Münster) was added to stain the extracted nuclei. Samples were incubated on ice until sample processing was completed and were analyzed with the BD Accuri C6 21

Flow Cytometer using the FL2 channel (Accuri Cytometers, Ann Arbor, MI) at the

University of Florida Interdisciplinary Center for Biotechnology Research, Gainesville,

FL.

For each sample at least 5,000 nuclei were counted and analyzed using the BD

Accuri CFlow software and only samples with histograms showing peaks with coefficients of variation smaller than 10% were considered for analysis (Rios et al.,

2015). Only the DNA that was counted within the G1 peak was used in the analysis.

Since bermudagrass has yet to have its genome sequenced, an outside standard was used to help estimate the genome size of each accession. Barley, Hordeum vulgare, was chosen as the standard that was run with each batch of bermudagrass samples for later comparison as described below. Barley was selected for the flow cytometry standard because it has already had its genome sequenced and its genome size is known. Barley is a diploid species that has a genome size of 5.1 gigabases (Gb) (Wise et. al., 2012). Additionally, two bermudagrass standards were used to further improve the accuracy of the results, Tifton 85 a known pentaploid and an African bermudagrass

(referred to as African Bermuda 42) that was a known diploid.

The output of the Accuri C6 Flow Cytometer was a histogram that provided the

DNA count on the x-axis and the cell count on the y-axis (Figure 2-1). Ploidy was determined by comparing the G1 peaks obtained for the Cynodon accessions with the

G1 peak from barley. Then, the genome size of each bermudagrass was estimated by the equation below (Rios et. al., 2015):

(2-1)

Once the genome size had been estimated in pictograms (pg), ploidy was determined utilizing the criteria proposed by Pang et al. (2010) (Table 2-2).

Ploidy-Traits Association Analysis

To determine if ploidy had an effect on the expression of the different characteristics collected in this study, all phenotypic traits were analyzed with the following linear mixed model:

Y= µ + Rep + Ploidy + Rep.row + Rep.column + entry + error (2-2)

Where Y is the response variable being analyzed. The fixed effects consisted of

Rep and ploidy. While the random effects consisted of Rep.row, Rep.column, entry and

2 error, all under the assumption of normal distribution with zero mean and variances σ r,

2 2 2 σ c, σ g, and σ , respectively. The main purpose of this model was to test if ploidy level had a significant impact on a trait of interest, while accounting for all other experimental factors. Data were analyzed using the ‘lsmeans’, ‘lmerTest’, and ‘lme4’ packages in R statistical software (www.r-project.org).

Results and Discussion

Data Overview

The joint collection of 287 bermudagrass samples had ploidy levels ranging from diploid to hexaploid. A total of 24 diploids, 113 triploids, 119 tetraploids, 16 pentaploids and one hexaploid individual(s) were found along with 14 that were unable to be identified (Table S1-1). Due to the low number of hexaploid individuals they were not factored into any of the following ploidy analyses performed.

The collection used in the experiment is made up of 11 Cynodon species, of which C. dactylon makes up the majority, 210 accessions. Out of the 210 C. dactylon accessions 24 are diploids, 85 are triploids, 92 are tetraploids, nine are pentaploids and

one is hexaploid. The remaining 26 accessions consist of C. arcuatus, C. barberi, C. bradleyi, C. coursii, C. hirsutus, C. incompletus, C. plectostachyus, C. polevansii, C. magennisii, and C. nlemfuensis. This group of ten species is made up of 12 triploids, 13 tetraploids and one pentaploid. The remaining 34 accessions are of unknown Cynodon species of which three are diploid, 13 are triploid, 14 are tetraploid, and four are pentaploid. Some of the best yielding entries are tetraploid, such as entry 322, which means they can be selected as parent for breeding. Many of the released cultivars in the collection are triploid and pentaploid, which indicated prevalence for hybridization, either natural or man-made.

Part of the collection that made up this experiment was gathered from the Tifton

Core collection, which ploidy levels were first analyzed by Anderson (2005). Table S-1 shows a list of the entire bermudagrass collection from both the USDA and the Tifton

Core collection. The Core collection makes up the entries numbered from 200-343.

After estimating the ploidy levels for the Tifton Core collection, there was a 40% matching rate between Anderson finding and the most recent findings (Anderson,

2005). One possible reason that could have caused the differences between the two analyses is the level of accuracy of the flow cytometry technology used at the time they completed their original experiment, which took place more than a decade ago

(Anderson, 2005). Another possible reason is the nature of the internal standards used; when the ploidy of the Tifton core collection was characterized only bermudagrass tissue was used to locate the G1 peak. The cultivars used were Tifton 617 for diploids,

TifSport for triploids, 93-166 (Common) for tetraploids, Tifton 85 for pentaploids, and

Tifton 10 for hexaploids (Anderson, 2005). The use of same species standards can help

add accuracy to an analysis, however, it can wrongly assign the ploidy in cases where the sample does not run and it is mistakenly estimated as the same ploidy as the standard (assuming the peaks overlap). Also, utilizing a species with a sequenced genome is an important factor so that the location of the G1 peak for the crop of interest can be properly estimated (Wang et al., 2009). To help with the final estimation of genome size, previously used criteria to determine bermudagrass ploidy helped to establish a set standard for this experiment (Pang et. al., 2010, Table 2-2).

The complete collection was characterized using ten different traits that were classified into five groups: establishment, morphology, quality, biotic and abiotic stresses, as well as yield. The establishment trait stolon length was measured just two months after the 5x5 cm plug was put into the plot (Table 2-1). The average stolon length was 41 and 37 cm for all species and C. dactylon, respectively (Table 2-3). Entry

308 had the largest stolon length, at 100 centimeters, and since it is a tetraploid it is an ideal candidate to use in order to improve this trait. Out of the control cultivars used,

Jiggs had the largest average stolon length, just under 86 centimeters, which was larger than Tifton 85 stolon length and more than double the stolon lengths of Coastal (Table

2-6). Jiggs was able to establish easily compared to Tifton 85, which took longer to completely fill the plot.

Later when coverage percentage was measured two months after establishment, the average plot coverage was found to be 5.12 on a scale from 1 to 9 for all species, and 4.83 for C. dactylon (9 indicates full coverage, Table 2-3). At the first measurement for coverage percentage, all Jiggs entries had completely covered their respective plots, while Tifton 85 and Coastal had lower average plot coverage (Table 2-6). Since Jiggs

had longer average stolon length and more rapid plot coverage, it supports the idea that longer stolons aid in plant establishment. Also, these results indicate that the findings of

Silveira et al. (2013), that found Jiggs to produce more than Tofton85 in the first year, could be due to the rapid establishment of Jiggs compared to Tifotn85. Both stolon length and percent plot coverage indicate the capacity of bermudagrass, and other

Cynodon species, to rapidly establish under Florida conditions. Rapid establishment is very important for producers and thus, a potential trait for breeding cultivars with this capacity.

The morphological trait group included flowering percentage, leaf width and canopy height. Flowering percentage had a low average for all species as well as for C. dactylon species, however there is a significant variability in the collection. The range in flowering percentage varied from zero to nine (more than 90 % plot flowering), showing that there is a high genetic variability within the collection (Table 2-3). This was also the case for canopy height and leaf width. The average canopy height for all species and C. dactylon was 23 cm and 21 cm respectively, but the standard deviation was 13 for all species and 12 for C. dactylon (Table 2-3). Similarly, the average leaf width for all species was 2.7 cm for all species and 2.5 cm for C. dactylon with both groups having a standard deviation of slightly more than 1 cm (Table 2-3). The morphological traits observed for all species and C. dactylon had a large standard deviation indicating that the collection has highly variable morphological traits. There is a specialized market for finer leaf varieties, so this trait can be important for breeding. Also, for the production of hybrids, that will be commercialized vegetatively, the lower flowering number is

beneficial, as these parents should produce cultivars that invest less energy in flowering structures and more in vegetative biomass.

Percent nitrogen content in leaf tissue was an interesting quality trait that was gathered. The average percent nitrogen for all species was 2.71%, while C. dactylon accessions had an average of 2.74. All species and C. dactylon shared the same standard deviation of 0.53 (Table 2-3). Bermudagrass is desirable forage because it usually is higher quality than other warm season grasses (e.g. bahiagrass). Since the

Nitrogen analysis was performed during the beginning of the growing season, the tissue used would not have been too old. The presence of variation from 1.52% to 4.78% indicates the high potential for this trait to be improved.

Bermudagrass stem maggot damage was measured throughout the harvest season of 2015. The highest average BSM damage was recorded in August of 2015, with all species and C. dactylon having around 40% damage (Table 2-3). August is one of the warmest months in Florida and usually provides preferred conditions for insect reproduction (Baxter et al., 2014). These results are useful in targeting when BSM can be expected to have the largest impact on production and also when to carry selection in the breeding process for this important pest (Table 2-3). The fact that BSM damage had a higher rate during the warmest time of the year enforces the idea that time of year does affects BSM damage. This information can help growers decide when to harvest, since this is one of the few cultural control methods suggested to suppress BSM (Baxter et al., 2014; Hancock, 2012). The entry 25 was found to have lowest average BSM damage compared to all of the entries in the collection, but since it is triploid there is a high chance it is sterile and cannot be used as a parent for crossing (Table 2-6), also

his biomass production was not over the controls used in this experiment. Out of the controls used in the experiment, Tifton85 had the lowest average BSM damage, this information can be utilized immediately by growers who wish to reduce economic loss due to the pest (Table 2-6).

Frost damage was the abiotic trait measured in 2015. Many Cynodon species are not cold tolerant, which restricts the areas it can be grown. Frost damage was measured in January and February of 2015, which are the colder months in Florida. In

January the average frost damage per plot was 40% and 42% for all species and C. dactylon accordingly. By February the average increased to around 60% for all species and C. dactylon (Table 2-3). The significant increase in average damage after one month shows that frost does have a significant impact on bermudagrass performance and it is a trait that should be closely observed when making recommendations for newly developed cultivars. Several entries, including 333, which had high yields through the growing season, had the lowest recorded frost damage. These entries could be used to breed for cold tolerance (Table 2-6).

Yield is often the trait that is given the largest consideration when working with a forage species. In 2015 the Citra, FL, location was harvested a total of six times starting from April to November. For all species and C. dactylon, April had the lowest average dry matter yield, while yield peaked in July. Average yield tripled from April to July for all species, and almost quadrupled for C. dactylon (Table 2-3). For the entire growing season, an average yield of 2000 to 1800 Kg/Ha was recorded for all species and C. dactylon, respectively (Table 2-3). The lowest and highest yields at Citra usually differed by thousand kilograms per harvest. The highest yielding entries at Citra remained fairly

constant throughout the season: 275, 286, 322 and 272 (Table 2-7). These entries have already been noted as possible parents for future crosses. For the controls Tifton 85 had a higher average yield than Jiggs or Coastal in Citra, FL and Tifton, GA (Table 2-7,

2-8). Entry ranking for yield at Tifton was slightly different, but 275 and 322 still had some of the highest yields during parts of the growing season. North Florida climate provides an environment that allows bermudagrass to have a high average yield for the entire growing season. A high potential for selection of early spring and late fall production was found in the collection, however, in order to confirm this trend, more years of field experiments are required.

Ploidy Effect on phenotypic traits

The advantage of having different ploidy levels within the same species gives us the opportunity to determine if ploidy does make a difference for the different traits.

Thus, we estimated the effect of ploidy for all traits measured in this experiment considering all Cynodon species, but also only C. dactylon. Ploidy level had no significant effect (P-value>0.05) on dry matter yield (kg/ha) for any of the harvests in

2015 for all Cynodon species within the collection as well as specifically C. dactylon

(Table 2-4). However, for both groups, ploidy was significant associated to yield at harvest 6, when relaxing the threshold (P-value<0.10). Additionally, C. dactylon accessions also had a significant ploidy effect (P-value=0.06) for harvests 3 (Table 2-4).

The final harvest of 2015, in November, was done with the intention to measure the extent of yield decrease when bermudagrass is exposed to colder conditions. Ploidy level was also found to have a significant effect on the degree in which an accession is affected by frost damage. These findings support the data shown in Figure 2-2, in which

diploids experienced higher average frost damage than pentaploids, which result in decreased yields.

Coverage percentage, flowering percentage, frost damage, and BSM damage had significant ploidy effects. These results were found to vary when observing all

Cynodon species as well as C. dactylon accessions alone (Table 2-5). However, not all of these traits were found to have significant ploidy effect when all Cynodon species were considered. This is the case for one measurement in coverage percentage, and one measurement for BSM damage. While there was one measurement for frost damage in which all Cynodon species within the collection had a significant effect, but the C. dactylon accessions did not have a significant ploidy level effect (Table 2-5). This can suggest that ploidy level does have a larger effect on specific C. dactylon accessions, but the presence of many additional Cynodon species could be confounding these results. So additional research would be required to be sure.

For the entire collection, with all of the species of Cynodon, the traits that had significant ploidy effects were coverage percentage, flowering percentage, frost damage, BSM, and leaf width. For coverage percentage, only the second and third measuring dates had a significant ploidy effect. This could have occurred since the third date, in August, is nearing the end of the bermudagrass growing season, and with less optimal growing conditions the differences in ploidy may be more easily observed (Ball et al., 2007). Additionally, we could have been measuring the ploidy effects of plant regrowth, since several harvests had already taken place by August.

Frost damage was significant in regards to ploidy level for every date that the data was recorded as well as the combined dates (Figures 2-2). For the first

measurement of average frost damage in all species, diploid was significantly higher than pentaploid. The same trend was observed for C. dactylon analysis. The second measurement also found that diploids had a significantly higher average frost damage than pentaploids, was also observed for C. dactylon. When considering average frost damage for all species during both measurements, diploids were significantly higher than pentaploids, and triploids have a significantly lower average than tetraploids

(Figure 2-2A, B). For instance, in C. dactylon, triploid was significantly lower than both diploid and tetraploid. (Figure 2-2B). Cold tolerance is an important factor when deciding where to grow bermudagrass, for both forage and turf varieties (Anderson et al., 1993;

Anderson and Taliaferro, 1995; Gilbert and Davis, 1971). These results suggest that increasing the ploidy level of bermudagrass, above diploid, decreases average frost damage. Coastal, a hybrid cultivar, has been historically known for its cold tolerance, which supports the idea that increased ploidy level decreases frost damage (Burton.

1948). There is currently collaboration with several institutions in more northern parts of the United States that is assessing this collection. From these studies more dramatic frost damage and regrowth data will be gathered and analyzed.

The severity of BSM was lower than other bermudagrass production systems because of the scheduled harvests, which removes the affected portion of the plant.

BSM, as with many diptera, have a short life cycle which can be witnessed several times in one season, but it is possible to miss the peak population as another harvest occurs (Hancock, 2012). The third measurement date (Table 2-1) for BSM was found to have a significant ploidy effect (Table 2-5). Average BSM damage for measurement one and two was not significant different at the 0.95 confidence level for all species or C.

dactylon. The third BSM measurement for all species in the collection showed no significance at the 0.95 confidence level (Figure 2-2C). However, pentaploids had significantly higher average damage than triploids and tetraploids for C. dactylon (Figure

2-2D). This value could be inflated due to the small number of pentaploid C. dactylon individuals. Some past researchers have suggested that BSM adult females are selective in where they lay their eggs. The theory is that leaf coarseness or the thickness of the stem/leaf influences where eggs will be laid (Baxter et al., 2014;

Hancock, 2012). The presence of a significant pentaploid effect in regards to BSM and leaf width could help support this theory, since many studies have found a connection with hybrid bermudagrass and a change in vegetative growth (Stebbins, 1971;

Sugiyama. 2005; Tal, 1980). Closer examination of leaf morphology, BSM damage and ploidy level would help determine if a concrete relationship exists.

Percentage coverage was found to only have significant difference between ploidy levels in the second and third measurement dates. The following trends were found for all species within the collection and C. dactylon alone. On the second measurement date tetraploids had a significantly higher coverage percentage than diploids, but did not differ significantly from triploids and pentaploids (Table 2-5). For the third measurement date tetraploids and pentaploids had a higher average coverage percentage than diploids, but did not differ significantly from each other or from triploids.

(Table 2-5). Higher ploidy individuals, especially hybrid individuals, could have higher coverage percentage due to heterosis (e.g. hybrid vigor), a phenomenon that results in higher biomass production in hybrid offspring (Miller et al., 2012).

Both flowering percentage and leaf width are the traits that have the highest significance in regards to ploidy level. Flowering percentage for all species and C. dactylon diploids were found to have significantly higher average than the other ploidy levels. (Figure 2-3C, D). Furthermore, for both, all species and C. dactylon, there is linear decrease in flowering percentage with an increase in ploidy level. In the case of pentaploids, their flowering percentage average was not significantly different from zero.

This could have occurred because of the small population of pentaploids within the collection, 16 with the entire collection and only ten when narrowed down to C. dactylon.

It has been recorded by multiple other studies that bermudagrass hybrids are self- incompatible to varying degrees (Burton and Hart. 1967; Hanna and Burton. 1977). Self- incompatibility has even been noted in tetraploid hybrids (Hanna and Burton. 1977).

Leaf width, for diploid and pentaploid were significantly higher than for triploid and tetraploid levels, while triploid and tetraploid levels are not significantly different from one another for all species (Figure 2-3 E). For C. dactylon, pentaploid individuals had a significantly higher leaf width than triploid and tetraploid levels but was not significantly different than diploid; at the same time diploid, triploid, and tetraploid were not found to have significantly different leaf width averages (Figure 2-3 F). Pentaploid individuals could have larger average leaf width due to heterosis as mentioned previously (Miller et al., 2012). It has been documented in many plant species that an increase in leaf size is observed when ploidy level increases (Stebbins, 1971; Sugiyama. 2005; Tal, 1980).

The main difference between all Cynodon species and C. dactylon alone are the significance of the measurement dates in which data was collected. This difference could be attributed to the closer genetic relationships that all of the C. dactylon entries

have, so variation amongst ploidy levels may be more apparent (Harlan, 1970:

Taliaferro et al., 2004). The presence of turf type bermudagrass within the collection could also be affecting the data analysis, especially in the case of yield. For example, the mean yield for the entire bermudagrass collection during the first Citra, FL harvest was 1112 kg/ha with a standard error of 1009 kg/ha (Table 2-3). However, when the turf type grasses are factored out the average yield is found to be 1283 kg/ha with a standard error of 723.

Table 2-1. Dates of Data Collection Classification Trait Date of Data Collection October 27, 2014 June 2-3, 2015 Establishment Coverage % August 11-12, 2015 August 10-11, 2016

Stolon Length August 8, 2014

Flowering % January 25, 2016 Morphological Canopy height August 4, 2015 Leaf Width June 29 -July 5, 2016

Quality Nitrogen % April 15, 2015

April 28-29, 2015 June 2-3, 2015 Biotic BSM Scale August 11-12, 2015 September 15-16, 2015

January 25, 2016 Abiotic Frost Damage Feburary 19, 2016

April 28-29, 2015, June 2-3, 2015 July 7-8, 2015 Citra Harvest August 11-12 September 15-16, 2015 Yield November 3-4,2015 June 1, 2015 July 1, 2015 Tifton Harvest August 5, 2015 September 9, 2015 October 20, 2015

Table 2-2. Ploidy level by genome size determined by flow cytometry Ploidy Level Genome Size (pg) 2x 1.25 or less 3x 1.30-1.75 4x 1.80-2.25 5x 2.30-2.55 6x 2.60-2.75

Table 2-3. Mean and standard error (in parenthesis) of all phenotypic traits collected in the field experiment by measurement. Trait All Cynodon Cynodon Measurement Classification Trait Name Species dactylon 1 5.12(2.44) 4.83(2.42) 2 7.16(1.81) 6.99(1.84) Establishment Coverage % 3 7.74(1.50) 7.64(1.58) 4 8.13(1.60) 8.02(1.68) All 7.03(2.20) 6.87(2.28) Stolon Length 1 41.56(0.98) 37.74(21.13)

Flowering % 1 0.82(2.16) 0.76(2.11) Morphological Canopy height 1 23.74(13.06) 21.78(12.26) Leaf Width 1 2.77(1.21) 2.52(1.05)

Quality Nitrogen % 1 2.71(0.53) 2.74(0.53)

BSM Scale 1 3.47(1.66) 3.35(1.60) 2 2.41(1.80) 2.35(1.83) Biotic 3 4.07(1.75) 3.92(1.76) 4 3.21(1.68) 3.03(1.62) All 3.29(1.82) 3.16(1.79)

Frost Damage 1 4.04(2.46) 4.22(2.54) Abiotic 2 5.99(1.67) 6.09(1.68) All 5.02(2.32) 5.15(2.34)

1 1112(1009) 820(911) 2 1630(1064) 1472(962) 3 3235(1556) 3001(1489) Dry Matter 4 2535(1087) 2345(1027) Yield (kg/ha) 5 2027(859) 1925(856) 6 1844(943) 1752(943) All 2064(1299) 1885(1254)

Table 2-4. P-values of ploidy level effect on dry matter yield for six harvests for all Cynodon species present in collection and for C. dactylon specifically. Harvest Number All species C. dactylon 1 0.26 0.13 2 0.89 0.97 3 0.11 0.06 4 0.87 0.91 5 0.68 0.77 6 0.07 0.06 All 0.50 0.42

Table 2-5. P-values provided for all species in collection and for C. dactylon only C. Trait Collection All Species Classification Trait Name dactylon

Coverage % October 27, 2014 0.46 0.35

June 2-3, 2015 0.15 . 0.05. Establishment August 11-12, 2015 0.003** 0.003** August 10-11, 2016 0.11 . 0.08 All Dates 0.23 0.12 Stolon August 4, 2014 0.26 0.28 Length (cm)

Flowering % January 25, 2016 1.0*10-5*** 0.001***

Canopy Morphological August 4, 2015 0.10 0.27 height (cm) Leaf Width June 29 -July 5, 2016 4.67e-06 *** 0.002** (mm)

Quality Nitrogen % April 15, 2015 0.577 0.818

BSM April 28-29, 2015 0.16 0.07 June 2-3, 2015 0.14 0.23 Biotic August 11-12, 2015 0.1 0.05*

September 15-16, 2015 0.18 0.15

All Dates 0.31 0.22

Frost January 25, 2016 0.05. 0.09 Damage Abiotic February 19, 2016 0.01* 0.01* All Dates 0.02* 0.04* * significant at p<0.05; ** significant at p<0.005; *** significant at p<0.001

Table 2-6. Cultivars with the lowest and highest values for measured traits, as well as the average values for the control cultivars.

Trait Min (Cultivar) Max (Cultivar) Tifton 85 Coastal Jiggs

Cover % 0 (135) 9 (Many) 8.75 4.42 9 BSM 0 (25) 6.88 (39) 4 5.88 5.5 Frost D. 2 (115,208,333) 8.5 (25) 3 3 4.5 Stolon 7.5 (33) 100 (308) 68 34 86 Flower 0 (Many) 9 (Many) 0 0 1 Canopy Height 0 (135) 91.44 (323) 51.5 27.3 45 L. Width 1 (37/54) 7.5 (296) 4.88 2.65 3.90 % N 1.75 (296) 4.42 (301) 2.42 3 2.51

Table 2-7. Lowest and highest yields (kg/ha) per harvest in Citra 2015, as well as the average yields for the control cultivars. Cultivar with highest and lowest yield in parenthesis Trait April June July August September November Lowest 13.5 (30) 84 (4) 17 (46) 542 (86) 228 (3) 168 (3) Highest 4924 (T) 5120 (275) 7280 (286) 5211 (286) 3143 (322) 3760 (272) Coastal 1327 2032 3512 2981 2324 1675 Jiggs 2223 2559 3950 3625 2330 2169 T85 2771 3455 5436 4368 2924 2807

Table 2-8. Lowest and highest yields (kg/plot) per harvest in Tifton 2015, as well as the average yields for the control cultivars. Cultivar with highest and lowest yield in parenthesis

Trait June July August September November

Lowest 0.07 (C) 0.01 (26) 0.04 (86) 0.02 (297) 0.15 (24)

Highest 0.64 (80) 1.57 (216) 2.16 (323&322) 1.12 (275) 0.84 (240)

Coastal 0.07 0.26 0.85 0.39 0.27 Jiggs 0.16 0.51 1.1 0.49 0.40 T85 0.34 0.86 1.45 0.82 0.44

B A

Figure 2-1. Histograms showing fluorescence intensity of propidium iodide (PI) bound to cell nuclei extracted from samples of (A) diploid (AB42) African bermudagrassand compared to the internal standard (B) diploid barley.

Figure 2-2. Bar graphs comparing the average environmental damage recorded for A) frost damage for all species within the collection, B) frost damage for only C. dactylon individuals, C) BSM scale for all species within the collection, and D) BSM scale for only C. dactylon individuals.

Figure 2-3. Bar graphs comparing the average physiological traits recorded for A) plot coverage percentage for all species in the collection, B) plot coverage percentage for only C. dactylon individuals C) plot flowering percentage for all species in the collection, D) plot flowering percentage for only C. dactylon individuals, E) leaf width in centimeters for all species in the collection, F) leaf width in centimeters for only C. dactylon individuals.

CHAPTER 3 ESTIMATION OF GENETIC PARAMETERS OF BERMUDAGRASS

Materials and Methods

Germplasm

A set of 287 bermudagrass accessions was established from combining two plant collections: 146 Cynodon clonal accessions from the Tifton (GA) USDA-ARS’s

CORE collection and 137 accessions from the Germplasm Resources Information

Network (GRIN) collection maintained at the Plant Genetic Resources Conservation

Unit, Griffin, GA by the USDA. The CORE collection was selected based on 22 phenotypic traits and AFLP markers from a larger population maintained at Tifton, GA

(Anderson et al., 2009). While the USDA GRIN collection (npgsweb.ars- grin.gov/gringlobal), provides little genetic information (such as parental crosses and ploidy) on its bermudagrass accessions.

Field Trial

The two field trials were planted at the Plant Science Research and Education

Unit located at Citra, Florida, and at the Tifton campus of the University of Georgia,

Tifton, Georgia. A soil test performed in 2013 at Citra, FL, determined that the pH of the soil was 6.9, the P2O5 content was high, the K2O was low, the S was low and the Mg was low. No soil test was available for the Tifton, GA experiment location. The experiments were established in the summer of 2014 using a single 5x5 cm plug planted in the center of the plot and allowed to propagate to a plot of 1.83x3.05 m at

Citra, FL and 1.83x1.83 m at Tifton, GA. The experiments for both Citra and Tifton were set as a row-column design with two replicates and augmented representation of three commercial controls: ‘Tifton 85’, ‘Jiggs’ and ‘Coastal’. The field at Citra, FL, had access

to an overhead pivot irrigation system, which was used to aid in establishment, but mainly relied on rainfall irrigation. No irrigation system was provided at the Tifton, GA field.

Phenotypes

The traits measured in this experiment included: yield, flowering percentage,

BSM damage, plot coverage percentage, canopy height (cm), stolon length (cm), leaf width (mm), forage yield (kg/ha), frost damage, and percent nitrogen in leaf tissue

(Table 2-1). Data for these traits was collected from the start of the experiment in 2014 to 2016.

In Citra, FL, yield was measured every five weeks during the growing season, ranging from April to November (Table 2-1). Staging was done in early March and involved cutting the plots to a stubble height of 10.16 cm and fertilized. An area of 1.22 x 3.05 m area was harvested in each plot, using a small flail-chopper attached to a small Kubota tractor. After each harvesting event, excluding the last of the season, plots were fertilized with 89.9 kg/ha of nitrogen and 44.9 kg/ha of K2O (80 lbs/A of N and 40 lbs/A K2O). Subsamples were taken from the fresh harvested forage and placed into

30.5 x 17.8 x 43.2 cm brown paper bags. The aim was to have the subsamples weight between 300-600 grams. Samples were placed in dryers at the Citra location and kept at 55°C for at least one week. Due to the harvesting method used, significant amount of sand was collected with the sample in the Citra location. Thus, sand weight was excluded since the third harvest. In total for the Citra location, yield was measured six times in 2015 and will be measured six times in 2016, but due to time constraints, only harvests made during 2015 will be utilized in this study. In the Tifton location 5 harvest were performed in 2015. 46

Plot flowering percentage was measured using a scale from 1 to 9, with 1 representing a limited number of seed heads (up to 5), while 9 represents presence of flowering heads in the entire plot. BSM and frost damage was measured on a scale of 1 to 9, where 1= less than 10% incidence and 9= greater than 90% incidence. Plot coverage was also measured on a scale of 1 to 9, with 1= less than 10% plot coverage and 9= greater than 90% plot coverage. Leaf width was measured using an average of five mature leaves, in a position at least three nodes below the youngest leaf on a stem, and the full length of the midsection was recorded in centimeters. Canopy height was measured in centimeters using a meter stick, the tallest point of the canopy was measured in each plot. Stolon length was measured in centimeters, and data was taken by selecting the longest extended stolon of each plot. Percent nitrogen in leaf tissue of all bermudagrass accessions was measured in the Forage Lab at Marianna, Florida; using the Vario Micro cube (CHNS analyzer using the Dumas dry combustion method) interfaced with ISOPRIME 100 (isotope ratio mass spectrometer).

Genetic Parameter Estimation

For the estimation of heritability and genetic correlations only C. dactylon accessions were used. In order to obtain the heritability for all recorded traits in the Citra location and the yield measurements from Tifton the statistical software ASReml was used (Butler et al., 2007). ASReml uses linear mixed models in order to perform a multitude of linear mixed model analyses (Butler et al., 2007). The analysis for heritability was done using the following linear mixed model:

Y= µ + Rep + Ploidy + Rep.row + Rep.column + entry + error (3-1)

Where Y is the response variable being analyzed. The fixed effects consisted of

Rep and Ploidy. Ploidy has been used as a fixed effect when determining covariance in

past research (Petit et al., 1996). While the random effects were Rep.row, Rep.column, entry and error, all under the assumption of normal distribution with zero mean and

2 2 2 2 variances σ r, σ c, σ g, and σ , respectively. The main purpose of these analyses is to estimate the genetic variances, while accounting for all other experimental factors and ploidy. These genetic variances were used to estimate the broad-sense heritability or clonal repeatability for each trait as

2 2 2 σg σg H = 2 = 2 2 2 2 (3-2) σp σg+σr +σc+σ

Genetic correlations among measured traits were estimated using a bivariate linear mixed model in ASReml (Gilmour et al., 2009). The bivariate model, with the same factor as described in Eq 3-1, allowed the estimation of the covariance among pair of traits (σy1,y2), and thus the genetic correlation was calculated as

σy1,y2 ρy1,y2 = (3-3) 2 2 √σy1∗σy2

An estimation of genotype by environment interaction was obtained by a combined analysis of yield from Citra, FL and Tifton, GA. A linear mixed model using ASReml including the site effect was as follows:

Y = µ + Site + Site.Rep + Site.Rep.row + Site.Rep.column + entry + Site.entry +error (3-4)

Where Y is the yield vector of both sites. All effects are as described as in Eq 3-1.

Additionally, site is the fixed effect of location, Site.Rep, Site.Rep.row, and

Site.Rep.column are as described above, but in this case nested within site. Site.entry is

2 the random effect of site by entry interaction ~N(0, σ SxE). Two error effects, one for each site, where used to account for the difference in precision of each location. The genotype- by-environment was calculated as:

2 σg ρGxE = 2 2 (3-5) σg+σSxE

The statistical software R (www.r-project.org), with the package ggplot2

(http://ggplot2.org/) were used to create the figures used in this Chapter

Results and Discussion

Heritability

Yield. The heritability of each yield measurement in both sites has a range of values

(Figure 3-1). The heritability values of yield ranged from a low of 0.27 in second harvest of Tifton to a high of 0.75 in 5th harvest of Citra. Yield is usually difficult to breed for because it is quantitative trait (Serba et al., 2015), and thus many genes control it, each with a small effect (Carlborg and Haley, 2004). In general, the heritability values for

Citra were relatively more stable, ranged between 0.40 and 0.70 (Figure 3-1), with the lowest values estimated in the last two harvests of the season. The broad-sense heritability for the harvests in June and July in Tifton was significantly lower than the

Citra harvests during the same months. During the third (August) yield measurement in

Tifton, the broad-sense heritability increased dramatically (Figure 3-1), and was not significantly different than the fourth (August) measurement in Citra. Tifton’s fourth harvest had the highest broad-sense heritability, but it was not significantly higher than the first measurement of Citra. The heritability estimation of the last harvest of the two sites was not significantly different for both locations. These variations of heritability are an indication of the changes in expression of the genes controlling these traits due to change in environmental conditions (Hoffmann and Merilä, 1999), but also could indicate variation in the precision of the data collection.

Other Phenotypic Traits. The morphological traits, flower coverage percentage, canopy height, and leaf width, had the highest heritability’s out of all the measured traits with calculated valued of 0.75, 0.79 and 0.76, respectively (Table 3-3). These estimations indicate these traits in this population are mostly controlled by genetic factors. In fact, for all traits measured, excluding yield, the overall broad sense heritability did not fall below 0.37 (Table 3-3), which is considered intermediate or moderate genetic control level (Annicchiarico et al., 1999;Ketata et al., 1976). BSM had two of the lowest heritabilities in this set of traits, indicating the second more difficult trait to improve behind yield.

Heritability was also found to vary along with dates of data collection, as expected for these quantitative traits, partially influenced by the environment. While the estimation was small for coverage percentage, and for frost damage, this variation was larger for BSM. The establishment trait coverage percentage had heritability values of

0.59, 0.59 and 0.60 for the three measurements in 2015; a year later, in August of 2016, the heritability was estimated decreased to 0.50 (Table 3-3). The high heritability levels indicate that breeding for this important trait would be possible. This trait, as well as stolon length, is measuring the capacity of the forage grass to establish and thus an indicator of early production after establishment. The traits coverage percentage, frost damage and BSM, were measured multiple times, thus offer an opportunity to estimate a heritability accounting for the measurement date effect, and thus free of the genotype- by-measurement interaction. The values of coverage percentage, frost damage and

BSM decreased to 0.46, 0.53, and 0.42, respectively when multiple measurements were

accounted in the model (Figure 3-2). This decrease from the original values is an indicator of the GxM effect acting over the traits expression.

Genotype-by-measurement correlation

Genotype-by-measurement (GxM) correlation was estimated for all traits that were measured on more than one occasion, these traits included: coverage percentage,

BSM scale, and frost damage. In all cases, genotype-by-measurement correlation was relatively large, with values of 0.80, 0.89, and 0.85 for coverage percentage, BSM scale, and frost damage respectively (Figure 3-3). These values indicate that the environment

(season variation) has a small influence on the ranking of accessions across measurements for C. dactylon accessions. And thus, that one measurement will be needed to make selection in these traits.

Yield in Citra and Tifton was also measured multiple times. In these cases bivariate analyses by pair of measurements were used. These analyses provide a correlation of the ranking of accessions across pair of measurement. Correlations among measurements in the Citra locations were moderated to high, from a 0.51 between measurements 1 and 5, to a 0.93 between measurements 4 and 5. In general the further the measurements the lower the correlation (Table 3-5).

Correlations between different Tifton harvest dates varied greatly (Table 3-6), with values ranging from -0.30 between the measurements 1 and 5 to a 0.97 between the measurements 1 and 2 (or 3 and 4). The lowest correlation, between harvest 2 and harvest 5 however was no significant different from zero (Table 3-6). Harvests five and four generally saw a dramatic drop in correlation values (Table 3-6). This big variation of

GxM correlation among measurements and sites, suggests that improvement of this

trait, while not impossible, will be more difficult and will require evaluation in multiple years (Comstock and Moll, 1963).

Traits Genetic Correlations

Genetic correlations were estimated using bivariate analysis between all recorded traits. The genetic correlations for the traits were found to have a large range, from a non-significant 0.02 to a significant 0.76 (Table 3-4). As expected, a moderate- high negative correlation (-0.67) was found for yield and frost damage (Table 3-4). This suggests that the more frost damage an accession experiences, the lowest the average yield. This fact is especially important because the production area of bermudagrass has been historically limited to where it can survive due to poor cold tolerance

(Anderson et al., 1993; Anderson and Taliaferro, 1995; Gilbert and Davis, 1971).

High correlations indicate that when selecting a trait highly correlated to another the other traits will be indirectly selected as well. The efficiency of indirect selection thus depends partially on the level of the correlation between the two traits. With the breeding efforts that have gone into improving bermudagrass, increase in biomass yield has always been of important consideration (Burton, 1943; Burton, 1972; Burton et al.,

1993). The highest genetic correlation with yield was found for canopy height, 0.76

(Table 3-4). The notion that canopy height is directly related to biomass yield for forage plants is supported by multiple studies (Serba et al., 2015; Fernandez et al., 2009). Leaf width was also found highly correlated with canopy height, 0.74 (Table 3-4). Others have found that leaf width is not significantly correlated with canopy height in other forage grasses (Redfearn et al.1997). This difference could be caused by the different species being studied. It has been postulated that leaf morphology could affect the feeding preference of cattle, or other grazing animals, however a study in ryegrass 52

concluded that leaf morphology did not have a significant effect on grazing habits (Smit et al., 2006). Yield had significant genetic correlations with all other traits, confirming that yield is highly affected by environment and is affected by the performance of many other traits (Table 3-4).

Smit et al. (2006) also concluded that the chemical composition of leaf tissue

(forage quality) did affect the grazing preferences of cattle. Crude protein content, dry matter digestibility, nutrient content, and palatability of forages are all factors used to define the broad term ‘Forage Quality’. Quality improvement has been an important factor in forage breeding for several decades (Burton et al., 1967). Out of all of the traits, percent leaf nitrogen has the fewest significant correlations with other traits.

Nitrogen percentage was only found significantly correlated with frost damage (0.63) and negatively correlated to yield (-0.40) (Table 3-4). Other researchers have also found negative correlations between other forage quality traits and yield (Julier and Huyghe,

1997). The negative correlation between yield and percent leaf nitrogen indicate that improving one trait would negatively impact the other. Most modern breeding programs are focusing in improving quality, but it is important to understand that overall yield could be negatively affected in the process.

Studies focusing on BSM, using a limited number of cultivars, have indicated that cultivars with wider leaves were more significantly affected by BSM than ones with thinner leaves (Baxter, 2014). Using more than 200 accessions, we found a non- significant correlation between these two traits. However, we found that BSM had a low- moderated positive correlation with establishment traits, coverage % (0.44), and stolon

length (0.31), and growth traits, canopy height (0.24), and yield (0.26). This means that selection for BSM tolerance could decrease yield if selection is not carefully performed.

Genotype-by-Environment Interaction

High GxE has been a reported problem in bermudagrass yield, which reduces the ability to improve this trait (Avis et al. 1980, Chakroun et al. 1990). When the Citra and Tifton data was combined to estimate a single heritability, unbiased for GxE, the broad sense heritability was estimated at 0.03 with a standard error of 0.02, a value that is not significant different from zero. This is an indication that for most cases the performance of the accession was highly affected by the environment. Additionally, the

GxE correlation between both locations was estimated as 0.11 with a standard error of

0.08, non-significantly different from zero. The low GxE correlation estimation represents that the overall yields for the population were unstable stable across both locations (Rose et al., 2008), and thus multiple location will be needed across multiple years in order to perform selections for yield in bermudagrass. Also, these values indicate that larger populations will be needed in order to select improved individuals.

Table 3-1. Trait classification, description and date of its data recorded from the field experiment Trait Classification Trait Name Date of Data Collection October 27, 2014 June 2-3, 2015 Establishment Coverage % August 11-12, 2015 August 10-11, 2016

Stolon Length August 8, 2014

Flowering % January 25, 2016 Morphological Canopy height August 4, 2015 Leaf Width June 29 -July 5, 2016

Quality Nitrogen % April 15, 2015

April 28-29, 2015 June 2-3, 2015 Biotic BSM Scale August 11-12, 2015 September 15-16, 2015

January 25, 2016 Abiotic Frost Damage February 19, 2016

April 28-29, 2015, June 2-3, 2015 July 7-8, 2015 Yield, Citra August 11-12 September 15-16, 2015 Growth November 3-4,2015

June 1, 2015 July 1, 2015 Yield, Tifton August 5, 2015 September 9, 2015 October 20, 2015

Table 3-2. Mean and standard error (in parenthesis) of all phenotypic traits collected in the field experiment by measurement. Trait All Cynodon Cynodon Measurement Classification Trait Name Species dactylon 1 5.12(2.44) 4.83(2.42) 2 7.16(1.81) 6.99(1.84) Establishment Coverage % 3 7.74(1.50) 7.64(1.58) 4 8.13(1.60) 8.02(1.68) All 7.03(2.20) 6.87(2.28) Stolon Length 1 41.56(0.98) 37.74(21.13)

Flowering % 1 0.82(2.16) 0.76(2.11) Morphological Canopy height 1 23.74(13.06) 21.78(12.26) Leaf Width 1 2.77(1.21) 2.52(1.05)

Quality Nitrogen % 1 2.71(0.53) 2.74(0.53)

BSM Scale 1 3.47(1.66) 3.35(1.60) 2 2.41(1.80) 2.35(1.83) Biotic 3 4.07(1.75) 3.92(1.76) 4 3.21(1.68) 3.03(1.62) All 3.29(1.82) 3.16(1.79)

Frost Damage 1 4.04(2.46) 4.22(2.54) Abiotic 2 5.99(1.67) 6.09(1.68) All 5.02(2.32) 5.15(2.34)

1 1112(1009) 820(911) 2 1630(1064) 1472(962) 3 3235(1556) 3001(1489) Dry Matter 4 2535(1087) 2345(1027) Yield (kg/ha) 5 2027(859) 1925(856) 6 1844(943) 1752(943) All 2064(1299) 1885(1254)

Table 3-3. Broad-sense heritability of traits measured in the bermudagrass field trial Trait Classification Trait Name Measurement H2 1 0.59(0.04) Coverage % 2 0.59(0.04) Establishment 3 0.60(0.04) 4 0.50(0.05)

Stolon Length 1 0.53(0.05)

Flowering % 1 0.75(0.03)

Morphological Canopy height 1 0.79(0.03)

Leaf Width 1 0.76(0.03)

Quality Nitrogen % 1 0.47(0.06)

1 0.37(0.06) 2 0.46(0.05) Biotic Stress BSM Scale 3 0.64(0.04) 4 0.59(0.04)

Frost 1 0.68(0.04) Abiotic Stress Damage 2 0.64(0.04) .

Table 3-4. Genetic correlations (below diagonal) among traits measured from the field trial and its respective standard errors (above diagonal). Leaf Canopy Trait width Nitrogen % Coverage % Flowering % height F. Damage BSM Stolon L. Yield Leaf Width 0.10 0.09 0.06 0.04 0.09 0.08 0.07 0.06 Nitrogen % 0.08 0.10 0.10 0.09 0.08 0.10 0.11 0.09 Coverage % 0.20 0.17 0.09 0.07 0.09 0.07 0.08 0.06 Flowering % 0.61 0.11 0.06 0.07 0.09 0.08 0.09 0.08 Canopy height 0.74 -0.21 0.44 0.34 0.08 0.08 0.06 0.04 F. Damage -0.18 0.63 0.28 -0.14 -0.37 0.08 0.10 0.06 BSM 0.02 -0.03 0.44 -0.16 0.24 -0.16 0.09 0.08 Stolon L. 0.65 0.03 0.59 0.31 0.74 -0.15 0.31 0.06 Yield 0.57 -0.40 0.56 0.19 0.76 -0.67 0.26 0.69

F. Damage = Frost Damage, Stolon L.=Stolon Length

Table 3-5. Genetic correlations (below diagonal) between Citra yield measurement dates and its respective standard errors (above diagonal) Trait Harvest 1 Harvest 2 Harvest 3 Harvest 4 Harvest 5 Harvest 6 Harvest 1 0.02 0.03 0.05 0.08 0.07 Harvest 2 0.89 0.02 0.05 0.07 0.06 Harvest 3 0.78 0.88 0.03 0.04 0.04 Harvest 4 0.75 0.82 0.92 0.06 0.05 Harvest 5 0.51 0.67 0.84 0.93 0.04 Harvest 6 0.55 0.62 0.82 0.83 0.92

Table 3-6. Genetic correlations (below diagonal) between Tifton yield measurement dates and its respective standard errors (above diagonal) Trait Harvest 1 Harvest 2 Harvest 3 Harvest 4 Harvest 5 Harvest 1 0.09 0.09 0.1 0.20 Harvest 2 0.97 0.06 0.08 0.19 Harvest 3 0.64 0.94 0.02 0.12 Harvest 4 0.43 0.68 0.97 0.09 Harvest 5 -0.30 0.16 0.58 0.68

Figure 3-1. Broad-sense heritability for yield by harvest measurement in two location of the southern USA (Citra, FL and Tifton, GA).

Figure 3-2. Broad-sense heritability accounting for measurement effect for traits measured more than once in the field experiment.

Figure 3-3. Genotype-by-measurement correlation for traits measured more than once in the field trials

CHAPTER 4 CONCLUSION

The ploidy level of the bermudagrass collection, in addition to commercial cultivars were successfully determined through the use of flow cytometry. Most accessions in the germplasm collection were characterized as either tetraploids or triploids. In addition, diploids, pentaploids and a singular hexaploid were identified. The complete collection of bermudagrass samples had ploidy levels assessed as 24 diploids, 113 triploids, 119 tetraploids, 16 pentaploids and one hexaploid individual(s) along with 14 accessions that were unable to be identified due to lack of material. The use of flow cytometry provided and effective and efficient method for determining ploidy level for C. dactylon and other Cynodon species.

Ploidy level was found to significantly affect the expression of some of the measured traits: frost damage, BSM damage, coverage percentage, flowering percentage, and leaf width. However, ploidy level significance was not consistent when comparing different measurement dates for the same trait or when comparing the analysis of all of the Cynodon species within the collection with only the C. dactylon.

Yield was only significantly different when comparing ploidy levels at the 0.10 significant level. Flowering percentage and leaf width had the highest significance in regards to ploidy level. Diploids had a significantly higher flowering percentage than triploids, tetraploids, and pentaploids. Flowering is usually a trait that is not desirable in forage production, but in order to produce new offspring flowers that produce viable seed are necessary. Diploids and pentaploids were found to have significantly larger average leaf width than both triploids and tetraploids. Pentaploids had significantly higher average

BSM damage than triploids and tetraploids for C. dactylon. Tetraploids and pentaploids

were found to have significantly higher average coverage percentage than diploids for all species. Diploids were found to have significantly higher average frost damage than triploids. This ploidy level data will be vital information when performing parental selection for breeding improved bermudagrass cultivars either for further breeding or for generation of non-fertile hybrids for cultivar development. Additionally, genome doubling could be one strategy to improve the traits found to be associated with higher ploidies in desirable genetic backgrounds (e.g. elite lines lacking good levels for this specific trait).

The levels of heritability varied greatly from trait to trait. The broad sense heritability values of all traits ranged from a low of 0.27 for yield in Tifton to a high of

0.79, while the heritability for yield ranged from 0.70 to 0.40 and 0.27 to 0.76 for Citra and Tifton respectively. These results show that genetic improvement is possible for all economically important traits, but the rate of trait improvement will vary if the same resources are distributed for all traits. Alternatively, more resources could be assigned to lower heritability traits and thus the rate of improvement could be similar among traits.

Bivariate analysis was performed to determine the genetic correlation between traits. The highest correlation was found to be between leaf height and yield. Leaf width was also found to be highly correlated (0.74) with leaf height. The presence of high positive genetic correlation between morphological traits makes it easier to improve both traits simultaneously. However, nitrogen percentage, a desirable quality trait, in leaf tissue was the trait with some of the strongest negative correlations with other important economically important traits. Nitrogen percentage was positively correlated with frost damage (higher N more damage) and negatively correlated with yield. Yield and frost damage had the largest negative correlation with a value of -0.67. Having a

strong negative correlation with yield, increasing nitrogen percentage in leaf tissue will be more difficult than breeding for morphological traits. When comparing Citra yield across different evaluation, the genetic correlation remained high, with no value below

0.51. However, Tifton yield measurement comparisons presented a much larger range, with a non-significant correlation of -0.30 and a strong positive correlation of 0.97 between pair of measurements.

With the ploidy level information gathered in this study breeders will be able to make more informed crosses in breeding programs. The entire germplasm collection is publically available, which means that all of the accessions that were studied are freely available for all breeders across the country. Additionally, the data collected on BSM could help researchers reduce the economic impact of the pest. We have begun to recognize a lifecycle trend for the BSM that can be utilized immediately by growers, and we have found some genetic correlations that could help breeders develop highly tolerant cultivar.

With the results of this study the selection of viable crosses for breeding is possible. A recurrent genotypic selection should be implemented. This breeding strategy should incorporate advanced experimental design to test the experimental accessions in the breeding program. These field experiments must be established in multiple locations across the southeast, and measured multiple times for yield, must be used to estimate breeding values for each accession and thus improve the accuracy of selection and finally accelerate the breeding process. All other traits showed relatively high correlation among measures dates and thus can be measure fewer times per season in each location. However, the genotype-by-environment interaction for these

other traits should be estimated in a future study. Plant canopy height is a relatively easy and high-throughput trait that could be used to select for plant yield indirectly. This way will allow to phenotype large populations, increasing the selection intensity and the probability to develop improved individuals. An experiment testing the performance of non-fertile hybrids (e.g. triploids, and pentaploids) against fertile crosses (e.g. diploids and tetraploids) should be performed. This experiment will allow the evaluation of developing hybrids among elite fertile selections to improve biomass yield. Other important quality traits should be and are currently under evaluation in the entire population, forage digestibility and fiber content for example. In this way, the digestible dry matter could be calculated and used to make selections.

Several individuals have already been selected, using breeding values, for their exceptional characteristics, which may lead to parental crossing or even immediate cultivar release if they prove to yield better than Tifton85 or to have better forage seasonal distribution than Tifton85. Quick progress is expected in morphological traits with less effort required. While yield, as expected, would require multiple years and locations to determine stable genotypes that could create an improved cultivar.

APPENDIX A SUPPLEMENTARY MATERIAL

Table A-1. Estimated genome size and ploidy level for whole bermudagrass collection ID Species/cultivar Est.Genome Ploidy 93 C. dactylon 0.81 2 102 C. dactylon 0.88 2 207 C. dactylon 0.94 2 100 C. dactylon 0.94 2 119 C. dactylon 0.95 2 42 C. dactylon 0.95 2 19 C. dactylon 0.99 2 226 C. dactylon 1 2 270 Cynodon spp. 1 2 118 C. dactylon 1.01 2 225 Cynodon spp. 1.03 2 24 C. dactylon 1.04 2 23 C. dactylon 1.06 2 54 C. dactylon 1.07 2 117 C. dactylon 1.11 2 269 C. dactylon 1.13 2 44 C. dactylon 1.15 2 200 C. dactylon 1.16 2 41 C. dactylon 1.16 2 219 C. dactylon 1.18 2 94 C. dactylon 1.25 2 287 C. sp. 1.26 2 10 C. dactylon 1.27 2 79 C. dactylon 1.24 2 259 C. dactylon 1.28 3 62 C. dactylon 1.28 3 36 C. dactylon 1.3 3 55 C. dactylon 1.31 3 47 C. dactylon 1.31 3 334 C. dactylon 1.33 3 216 C. dactylon 1.34 3 35 C. dactylon 1.35 3 99 C. dactylon 1.35 3 25 C. dactylon 1.37 3 309 C. plectostachyus 1.38 3 224 C. dactylon 1.38 3

ID Species/cultivar Est.Genome Ploidy 98 C. dactylon 1.38 3 211 C. dactylon 1.39 3 206 C. dactylon 1.4 3 290 C. dactylon 1.41 3 96 C. dactylon 1.42 3 121 C. dactylon 1.42 3 281 C. hirsutus 1.42 3 258 C. dactylon 1.43 3 251 C. dactylon 1.43 3 338 C. sp. 1.45 3 212 C. dactylon 1.45 3 106 C. dactylon 1.46 3 80 C. dactylon 1.46 3 123 C. dactylon 1.47 3 59 C. dactylon 1.47 3 264 C. dactylon 1.48 3 83 C. dactylon 1.48 3 306 C. dactylon 1.48 3 205 C. dactylon 1.48 3 48 C. dactylon 1.49 3 249 C. dactylon 1.49 3 52 C. dactylon 1.49 3 330 C. dactylon 1.5 3 116 C. dactylon 1.5 3 61 C. dactylon 1.51 3 70 C. dactylon 1.51 3 312 C. dactylon 1.52 3 285 C. dactylon 1.52 3 227 C. arcuatus 1.53 3 234 C. dactylon 1.53 3 253 Cynodon spp. 1.53 3 333 C. sp. 1.54 3 29 C. dactylon 1.54 3 17 C. dactylon 1.54 3 319 C. dactylon 1.54 3 124 C. dactylon 1.55 3 272 C. dactylon 1.55 3 222 C. dactylon 1.56 3 63 C. dactylon 1.56 3 273 C. dactylon 1.57 3 257 C. sp. 1.57 3 223 Cynodon spp. 1.58 3

ID Species/cultivar Est.Genome Ploidy 129 C. dactylon 1.58 3 317 C. dactylon 1.58 3 115 C. dactylon 1.59 3 282 C. dactylon 1.59 3 103 C. dactylon 1.59 3 78 C. dactylon 1.59 3 108 C. dactylon 1.61 3 65 C. dactylon 1.62 3 111 C. dactylon 1.62 3 318 C. dactylon 1.62 3 325 C. dactylon 1.63 3 91 C. dactylon 1.63 3 107 C. dactylon 1.63 3 84 C. dactylon 1.63 3 82 C. dactylon 1.63 3 77 C. dactylon 1.64 3 340 C. sp. 1.64 3 262 C. dactylon 1.64 3 303 C. arcuatus 1.64 3 135 C. dactylon 1.64 3 343 C. dactylon 1.65 3 237 C. polevansii 1.65 3 326 C. dactylon 1.65 3 302 C. species 1.65 3 310 C. plectostachyus 1.65 3 110 C. dactylon 1.65 3 215 C. dactylon 1.65 3 316 C. dactylon 1.66 3 214 C. dactylon 1.66 3 229 Cynodon spp. 1.67 3 280 C. nlemfuensis 1.67 3 245 C. dactylon 1.67 3 92 C. dactylon 1.67 3 120 C. dactylon 1.67 3 32 C. dactylon 1.67 3 97 C. dactylon 1.68 3 305 C. incompletus 1.68 3 49 C. dactylon 1.68 3 31 C. dactylon 1.68 3 242 C. nlemfuensis 1.68 3 243 C. bradleyi 1.68 3 112 C. dactylon 1.72 3

ID Species/cultivar Est.Genome Ploidy 136 C. dactylon 1.72 3 34 C. dactylon 1.72 3 213 C. dactylon 1.72 3 13 C. dactylon 1.72 3 298 Cynodon spp. 1.72 3 126 C. dactylon 1.72 3 288 C. sp. 1.72 3 210 C. dactylon 1.74 3 67 C. dactylon 1.74 3 240 C. sp. 1.74 3 87 C. dactylon 1.74 3 314 C. dactylon 1.74 3 Fl44 C. sp. 1.74 3 C C. sp. 1.68 3 241 C. barberi 1.52 3 220 C. barberi 1.43 3 33 C. dactylon 1.84 4 B2000 C. sp. 1.76 4 68 C. dactylon 1.76 4 315 C. dactylon 1.77 4 265 C. sp. 1.77 4 39 C. dactylon 1.78 4 230 Cynodon spp. 1.78 4 40 C. dactylon 1.78 4 323 C. dactylon 1.79 4 248 C. dactylon 1.79 4 18 C. dactylon 1.79 4 132 C. dactylon 1.8 4 37 C. dactylon 1.8 4 131 C. dactylon 1.8 4 277 C. dactylon 1.81 4 292 C. dactylon 1.81 4 46 C. dactylon 1.81 4 21 C. dactylon 1.82 4 60 C. dactylon 1.82 4 3 C. dactylon 1.82 4 339 C. sp. 1.83 4 113 C. dactylon 1.83 4 76 C. dactylon 1.84 4 250 Cynodon spp. 1.84 4 38 C. dactylon 1.85 4

263 C. dactylon 1.85 4 ID Species/cultivar Est.Genome Ploidy 81 C. dactylon 1.85 4 286 C. nlemfuensis 1.86 4 209 C. dactylon 1.86 4 89 C. dactylon 1.86 4 71 C. dactylon 1.86 4 88 C. dactylon 1.86 4 27 C. dactylon 1.87 4 336 C. sp. 1.88 4 218 C. dactylon 1.88 4 320 C. dactylon 1.88 4 J C. sp. 1.89 4 56 C. dactylon 1.89 4 254 C. polevansii 1.89 4 105 C. dactylon 1.9 4 276 C. dactylon 1.9 4 66 C. dactylon 1.9 4 26 C. dactylon 1.9 4 114 C. dactylon 1.91 4 4 C. dactylon 1.91 4 322 C. dactylon 1.92 4 1 C. dactylon 1.92 4 247 C. coursii 1.92 4 244 C. dactylon 1.92 4 20 C. dactylon 1.93 4 295 C. dactylon 1.94 4 300 C. dactylon 1.94 4 45 C. dactylon 1.94 4 304 C. incompletus 1.95 4 233 C. dactylon 1.95 4 327 C. dactylon 1.96 4 235 C. sp. 1.96 4 58 C. dactylon 1.96 4 221 Cynodon spp. 1.96 4 75 C. dactylon 1.96 4 232 C. polevansii 1.97 4 299 Cynodon spp. 1.97 4 128 C. dactylon 1.98 4 271 C. coursii 1.98 4 12 C. dactylon 1.99 4 328 C. dactylon 1.99 4 252 . 2 4 71

260 Cynodon spp. 2 4 ID Species/cultivar Est.Genome Ploidy 101 C. dactylon 2 4 228 C. dactylon 2 4 308 C. plectostachyus 2 4 16 C. dactylon 2.01 4 6 C. dactylon 2.01 4 22 C. dactylon 2.02 4 335 C. dactylon 2.03 4 122 C. dactylon 2.04 4 85 C. dactylon 2.04 4 5 C. dactylon 2.05 4 313 C. dactylon 2.05 4 217 C. magennisii 2.05 4 231 C. dactylon 2.06 4 279 C. nlemfuensis 2.07 4 73 C. dactylon 2.07 4 72 C. dactylon 2.07 4 266 C. nlemfuensis 2.08 4 125 C. dactylon 2.08 4 50 C. dactylon 2.08 4 134 C. dactylon 2.08 4 43 C. dactylon 2.09 4 297 C. dactylon 2.1 4 109 C. dactylon 2.1 4 332 C. dactylon 2.11 4 104 C. dactylon 2.12 4 275 C. dactylon 2.12 4 11 C. dactylon 2.13 4 255 C. dactylon 2.13 4 C. dactylon X C. 201 2.13 4 nlemfuensis 2 C. dactylon 2.14 4 51 C. dactylon 2.15 4 337 C. sp. 2.16 4 30 C. dactylon 2.17 4 90 C. dactylon 2.17 4 57 C. dactylon 2.18 4 283 C. dactylon 2.19 4 130 C. dactylon 2.2 4 236 Cynodon spp. 2.22 4 289 C. dactylon 2.22 4 261 Cynodon spp. 2.22 4

238 C. dactylon 2.24 4 ID Species/cultivar Est.Genome Ploidy 53 C. dactylon 2.24 4 86 C. dactylon 2.25 4 321 C. dactylon 2.25 4 284 C. dactylon 2.25 4 7 Cynodon spp. 2.26 4 307 C. plectostachyus 2.27 4 331 C. dactylon 2.27 4 74 C. dactylon 2.29 4 301 C. dactylon 2.31 4 274 C. dactylon 2.31 4 342 C. dactylon 2.38 5 69 C. dactylon 2.38 5 341 C. dactylon 2.39 5 9 C. dactylon 2.41 5 202 Cynodon spp. 2.41 5 28 C. dactylon 2.42 5 15 C. dactylon 2.42 5 296 C. nlemfuensis 2.43 5 C. dactylon X C. T85 2.43 5 nlemfuensis 95 C. dactylon 2.49 5 8 C. dactylon 2.49 5 267 C. dactylon 2.53 5 239 C. sp. 2.53 5 291 C. sp. 2.54 5 311 Breeding line 2.55 5 137 C. dactylon 2.56 5 293 C. dactylon 2.6 6

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BIOGRAPHICAL SKETCH

Alexandra Rucker graduated from the University of Florida with her bachelor’s in plant science in the spring of 2014. With a year and a half of lab experience gained from working under Dr. Jim Olmstead she decided to pursue a master’s degree with Dr.

Patricio Munoz. With her degree coming to a close, Alexandra Rucker is eager to gain experience through professional work. She would like to continue her education to a

PhD within the next several years.