BIOCHEMICAL AND COLORIMETRIC STUDY OF FLOWER COLOR IN PHLOX SPECIES
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University
By
Andres Bohorquez-Restrepo, B.S
Graduate Program in Horticulture and Crop Science
The Ohio State University
2015
Thesis Committee:
Dr. Pablo Jourdan, Co-Advisor
Dr. Joseph Scheerens, Co-Advisor
Dr. Michelle Jones
Copyrighted by
Andres Bohorquez-Restrepo
2015
ABSTRACT
Flower color is arguably the most important phenotypic feature of ornamental plants and
extensive selection and breeding is done to develop new colors. Variation in flower color can be caused by different factors such as the composition and concentration of pigments, vacuolar pH and the presence of cofactors. The same color in two flowers may be the result of different mechanisms. Yet quantifying color variation can be challenging.
Digital imaging coupled with analytic software is a powerful tool that can transform qualitative measurements of phenotypic characters like color and shape into quantitative data. Such a tool permits both a more objective analysis of traits that can be difficult to measure, as well as integration with molecular and biochemical data. In this study, a germplasm collection of Phlox was examined both for flower color variation as well as for anthocyanidin pigment composition. Phlox is a genus native to North America that includes an array of colorful flowers often described as purple, lilac, pink, red, orange and blue. The principal pigments of Phlox flowers are anthocyanins, but little is known about pigment variation in this genus. Tomato Analyzer (TAn) software was used to examine the variation in color from digital images of flowers of 89 Phlox accessions from the
Ornamental Plant Germplasm Center. These accessions included representatives of 17
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species distributed across the three major sections of the genus. Hierarchical cluster
analysis was used to classify the flower color in the sampled population into ten different clusters based on colorimetric data. These clusters provide an objective color category system that can be used to describe the variation in flowers within Phlox. The most frequent color in the germplasm collection was the commonly described “phlox purple” or “pink”, here named Red-Purple, that could be separated into three clusters that distinguished subtle variations in lightness within this group. The Red-Purple group accounted for around 80% of the accessions and was widely distributed across the three sections of the genus. Another color cluster, named Lavender, was primarily found in numerous samples within a single section. The species with most variation in color were
P.subulata, P.pilosa, P.carolina, P.ovata and P.paniculata. To complement the colorimetric analysis, anthocyanidin pigments were extracted from the flowers and the
relationship between pigments and colorimetric parameters was analysed. All six main
anthocyanidins (delphinidin, cyanidin, petunidin, malvidin, pelargonidin and peonidin)
were found in the population. Delphinidin and cyanidin were the major anthocyanidins in
most of the samples; petunidin and malvidin often co-appeared; malvidin and delphinidin
were often found in an inverse relationship. Pelargonidin and peonidin were absent in the
majority of samples, but the presence of pelargonidin resulted in color that identified a
different cluster. Variation in lightness within the Red-Purple group of clusters coincided
with fluctuations in petunidin and malvidin content. Linear regressions suggested
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petunidin may be an important component of variation in colorimetric parameters,
possibly due to its relationship to malvidin and delphinidin. While general trends in
anthocyanidin pattern and color were observed, different pigment composition could
result in the same color, and nearly identical pigment composition could yield different
colors. Thus, flower color in Phlox is only partly influenced by anthocyanidin composition and other factors play an important role. This work presents the most extensive color
survey in Phlox to date, from both a biochemical and colorimetric perspective. The
defined color clusters provide a useful objective system to describe flower color in the
genus and facilitate more extensive characterization of an expanding germplasm
collection. The anthocyanidin patterns provide a tool to identify potentially interesting
parents in crosses aimed at modifying flower color. The findings provide information
relevant for the study of anthocyanin biology in Phlox and for breeding improved cultivars
of this important ornamental genus.
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Dedication
A los Abicaru
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ACKNOWLEDGEMENTS
First and most important my parents Rodrigo and Amanda, for their eternal support.
There are not enough words to explain how thankful I am. I love you.
Ian, I also fall short in words to express my gratitude. You have been on this trip with me since the beginning, thank you for your never ending patience, love and unconditional support.
Joe and Pablo, your guidance through this journey is invaluable. It warms my heart to see you guys teaching with so much passion after all these years.
My colleagues and friends from the Grotewold lab: Isa, Katherine, Katja and Bettina. I guess you are all my big sisters now, thank you for your friendship and continuous support, I’ve learned quite a lot from all of you! Erich, I thank you as well for introducing me to the extraordinary world of the anthocyanins.
OPGC members, you guys rock! It has been very entertaining sharing my work space and all those lunches with you guys. Special thanks to Peter (Phlox wizard), Steven, Michelle
(Third committee member), Dominic and all the unsung heroes that contributed one way or another to this process. Muchisimas gracias.
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VITA
March 23, 1985 ...... Born, Medellin, Colombia
2009 ...... B.S. Biology, Universidad de Antioquia
2010-2011 ...... Research Assistant, Grotewold Lab The Ohio State University
2011 to present ...... Graduate Teaching Assistant/Graduate Research Assistant, Department of Horticulture and Crop Science, The Ohio State University
PUBLICATIONS
Pourcel L., Irani N., Bohorquez-Restrepo A. & Grotewold E. (2012) Anthocyanin Biosynthesis, Transport and Regulation: New Insights from Model Species. In Recent Advances in Polyphenol Research, Volume 3. Wiley-Blackwell, UK. Pp. 143-154.
Pourcel L., Irani N., Koo A., Bohorquez-Restrepo A., Howe G. & Grotewold E. (2013). A chemical complementation approach reveals novel genes and interactions of flavonoids with other pathways. Plant J. 74:383-397.
FIELDS OF STUDY
Major Field: Horticulture and Crop Science
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TABLE OF CONTENTS
ABSTRACT ...... ii
DEDICATION ...... v
ACKNOWLEDGEMENTS ...... vi
VITA ...... vii
LIST OF FIGURES ...... x
LIST OF TABLES ...... xiii
Chapters:
1. Digital Imaging to Classify Phlox Flowers by Colorimetric Parameters ...... 1
Introduction ...... 1
Materials and Methods ...... 10
Results ...... 12
Discussion...... 15
2. Anthocyanidins in Phlox and Their Relationship to Flower Color...... 31
Introduction ...... 31
Materials and Methods ...... 37
Results ...... 40
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Discussion...... 45
Conclusions and Future Prospects ...... 72
Appendices:
Appendix A Colorimetric Analysis Data and Scans ...... 76
Appendix B Chromatography Data ...... 122
Appendix C Multiple Linear Regressions in R ...... 127
Appendix D Relative Abundance and Concentration of Anthocyanidins in Minor
Species ...... 131
References ...... 135
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LIST OF FIGURES
1.1 Representative flower color variation in Phlox. Left: range of flower shapes and colors among some of the accessions held at the Ornamental Plant Germplasm Center. Right: colors described by Locklear (2011) among the species of Phlox in this study (a complete table listing the common colors for all Phlox species can be found Appendix A)...... 21
1.2 Typical interface of Tomato Analyzer software showing, at left, the scanned flowers of a sample where each flower area to be analyzed for color is delineated with a yellow line, and at right, an enlarged flower showing the exclusion of the ‘eye’ from color analysis using the boundary function. The colorimetric values for each of the flowers can be seen on the lower right pane.…...... 22
1.3 Hierarchical clustering of flower color found within the sampled Phlox germplasm. The dendrogram was produced using L*, a* and b* as clustering parameters. The flower images are representative of the different clusters. Nodes (N1-6) indicated distinguish major group separations at each level. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple- red, R: red, L: lavender, W: white, Y: yellow ...... 23
1.4 Distribution of color clusters under color parameters a* and b*. RP: red- purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Y: yellow ...... 24
1.5 Correlation between color parameters L* and Chroma with linear regression equations and R2. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white ...... 25
2.1 Structure of anthocyanidins present in Phlox flowers. Arrows indicate biosynthetic relationships. The images accompanying each structure show flowers where the corresponding anthocyanindin was dominant or highly abundant. ... 55
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2.2 Example of a chromatogram obtained for a sample (P.paniculata; PZ12-032p1) containing all six anthocyanidins. Average retention times with standard error is shown for each. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin...... 56
2.3 Anthocyanidin concentration in samples within different color clusters. Each sample is identified by a different bar color for easy identification of the anthocyanidins in that sample. Clusters are grouped based on the main nodes established on Fig. 1.3. RP: red-purple, DRP: dark red-purple, LRP: light red- purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin. Note the different Y axis scale for the R cluster (max 5 instead of 3.5)...... 57
2.4 Total anthocyanidins and color clusters. Clusters with only one sample can be seen as a single black horizontal line. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white ...... 59
2.5 Relationship between total anthocyanidins (TA) and the colorimetric parameter L* of the five major color clusters. The remaining clusters had less than 6 samples and were thus not included in this analysis. Fitted lines for each cluster are shown. Linear regression equations are included for two clusters with the highest R2: Lavender (L) and dark red-purple (DRP)...... 60
2.6 Relationship between total anthocyanidins (TA) and the colorimetric parameter Chroma of the five major color clusters. The remaining clusters had less than 6 samples and were thus not included in this analysis. Fitted lines for each cluster are shown. Linear regression equation is shown for highest R2 cluster, Lavender (L)...... 61
2.7 Presence of anthocyanidins within species in the sampled germplasm, organized by phylogeny as described by Zale (2014). Representative flowers for each of the species are shown. Anthocyanidins on the same biosynthetic branch are identified by different shading. S= number of samples, A = number of accessions. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin ...... 62
2.8 Distribution of individual anthocyanidins among Phlox species. Anthocyanidin relative abundance (left, expressed as peak area percentage) and concentration
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(right, in µg anthocyanidin/mg tissue) in species with ≥ 5 samples, organized by section. Each sample is identified by a different bar color for easy identification of the anthocyanidins in that sample. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv:malvidin...... 63
2.9 Chromatograms of two samples with very similar anthocyanidin profile but different color in the flowers. Picture of representative flower for each sample on upper right. Top: P.divaricata (PZ11-008 p1), bottom: P.divaricata (PZ10- 100pw3)...... 66
2.10 Chromatograms of two samples with very similar flower color but different anthocyanidin profile. Picture of representative flower for each sample and the total anthocyanidins (TA, in µg anthocyanidin/mg tissue) on upper left. Top: P.amoena (PZ12-057p2), bottom: P.carolina (PZ12-047p2) ...... 67
A.1 Flower scans used with the Tomato Analyzer software to obtain the colorimetric data. Accession identifier of each sample is shown at the bottom left corner ...... 77
D.1 Distribution of individual anthocyanidins among Phlox species with underrepresented samples (n <5) in section Annuae. Anthocyanidin relative abundance (left, expressed as peak area percentage) and concentration (right, in µg anthocyanidin/mg tissue) Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv:malvidin ...... 132
D.2 Distribution of individual anthocyanidins among Phlox species with underrepresented samples (n <5) in section Occidentales. Anthocyanidin relative abundance (left, expressed as peak area percentage) and concentration (right, in µg anthocyanidin/mg tissue) Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv:malvidin ...... 133
D.3 Distribution of individual anthocyanidins among Phlox species with underrepresented samples (n <5) in section Phlox. Anthocyanidin relative abundance (left, expressed as peak area percentage) and concentration (right, in µg anthocyanidin/mg tissue) Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin ...... 134
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LIST OF TABLES
1.1 Phlox accessions used for this research with assigned species identification according to the germplasm resources information network (GRIN). A: hybrid produced at the OPGC; B: commercial variety...... 26
1.2 Summary of colorimetric parameters and sample size for each of the color clusters. Values are presented with standard deviation. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Y: yellow...... 29
1.3 Distribution of color clusters among species in the sampled germplasm, organized by phylogeny as described by Zale (2014). Representative flowers for each of the species are shown; the eye of each flower is covered with a black circle to highlight the petals used for the analysis. S= number of samples, A = number of accessions, Sc= Section, SubSc = Subsection. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Y: yellow ...... 30
2.1 Anthocyanidins identified among species of Phlox...... 68
2.2 Average relative abundance and total anthocyanidins (TA, µg anthocyanidins/mg tissue) for 131 flower samples of Phlox spp. divided in different color clusters. Values represent average and standard deviation. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin ...... 69
2.3 Summary of the step-wise multiple linear regressions using the pigment concentration values and total anthocyanidins to explain colorimetric parameters… ...... 70
2.4 Pearson correlation coefficients (r) for the different color parameters with anthocyanidin content. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin ...... 71
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A.1 Table with all Phlox spp. indicating its color as described by Locklear (2011) ...... 113
A.2 Average and standard error of colorimetric parameters obtained in the tomato analyzer software. Values are average of five flowers per scan ...... 115
B.1 Anthocyanidin concentration and total anthocyanidin (TA) of 131 samples of Phlox flowers. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin ...... 123
C.1 R output for a step-wise multiple linear regression using the colorimetric parameter a*as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables ...... 128
C.2 R output for a step-wise multiple linear regression using the colorimetric parameter b* as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables ...... 128
C.3 R output for a step-wise multiple linear regression using the colorimetric parameter L* as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables ...... 129
C.4 R output for a step-wise multiple linear regression using the colorimetric parameter Chroma as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables ...... 129
C.5 R output for a step-wise multiple linear regression using the colorimetric parameter Hue as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables ...... 130
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CHAPTER 1
DIGITAL IMAGING TO CLASSIFY PHLOX FLOWERS BY COLORIMETRIC PARAMETERS
Introduction
Color plays a pivotal role in nature for the many living organisms that can perceive it. In plants, flower color is an important feature in pollination and reproductive success; it can be used to attract pollinators that are specific to the plant species or repel unwanted ones. In humans, color plays a role in many forms of communication and we are able to relate color to feelings (Lewis et al., 2013) and to social customs or concerns (e.g., light blue and pink to male and female infants respectively, red to love, green to recycling/nature, etc.). Color, as we perceive it, is the result of the absorption/reflection of visible wavelengths (390 to 700 nm) of the light coming from the sun or other luminous objects. Perception of color can also be affected by the observer’s point of view, the surface of the reflecting object or differences in light receptors within the observers (Lee, 2007). There are also rare cases where genetically controlled modifications or deficiencies, primarily in cone receptors within the retina
(e.g. various forms of color blindness), are involved in modifying color perception.
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In ornamental horticulture, color is the key feature that breeders look to modify
or reinvent, particularly in flowers. The search for novelty is so exhaustive that, for
example, blue flowers have reached the status of a “Holy Grail” for species that do not
possess them such as roses. Yet after several decades of intensive breeding and many
attempts at genetic transformation it still remains unattainable (Sasaki and Nakayama,
2015). Efforts to develop novel flower colors have involved the modification
(suppression/activation) of specific genes of biosynthetic pathways, promoters,
insertion of foreign genes, alteration of transcription, and metabolic engineering
(Nishihara and Nakatsuka, 2010).
Parallel to the development of new varieties and color combination in flowers,
methods have also been designed to define color as numeric data using instruments
that measure color, along with the establishment of color standards that provide a
reference system. The Munsell color system, developed in the early Twentieth Century
by Albert H. Munsell, placed color into a three-dimensional scale comprised of hue,
lightness and chroma (Lee, 2007). Many widely-used measuring systems such as the
CIELab® (Commision Internationale de l’Eclairage) color system retains the three- dimensional nature of Munsell scaling , but aims to simplify the mathematics of color definition into easily computed coordinates that create a more uniform color space
(Gulrajani, 2010). Since the introduction of these systems, intensive research in color modeling has opened the door for the application and integration of numeric color data
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into horticulture allowing researchers to add new layers of information to their studies
(Lee, 2007).
In recent years, digitalization of biological data, including color, has been
possible thanks to the rapid development of high throughput biological methodologies
in combination with advanced computational tools. Digital capture of data permits the study and characterization of phenomena on a large scale as well as the statistical analysis of results for improved accuracy. Techniques like RNA-seq (for molecular
biology) or digital imaging (for morphological and other phenotypic traits) are some of
the many examples of these methodologies (Darrigues et al., 2008a; van Dijk et al.,
2014). Digital imaging, defined as ‘…the acquisition and processing of visual information by computer’ (Umbaugh, 2011), comprises the procuring or digitalization of images and the use of informatics software permitting the normalization, production and
management of data according to the needs of the user. The application of digital
imaging for the acquisition of color data is very diverse, including psychology research
for relating perception of color with memory (Lewis et al., 2013), or medical research in
pathology for quantification of color in stained tissues (Prasad et al., 2012).
The application of digital imaging in horticulture facilitates the analysis of
biological and morphological patterns in plants with better precision than the
conventional manual inspection methodologies. This group of methodologies have been
widely used in diverse fields: In seed science, to calculate yield based on corn ear
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photometry (DuPont Pioneer, 2014; visited December 2014). In plant pathology, to
characterize changes in color and lesion area due to infection and to develop disease
keys which identify progress of disease severity in the field (Kwack et al., 2005;
Wijekoon et al., 2008). In fruit science, to asses fruit quality and to study factors
associated with ripening such as carotenoid content in tomato, surface defects on
apples or fruit shape variations in tomato (Darrigues et al., 2008b; Li et al., 2002; Brewer
et al., 2006). In floriculture, to study petal/sepal color patterns (e.g. in Lisianthus;
Yoshioka et al., 2006) or color itself (e.g. in Begonia; Lootens and Carlier, 2007).
Digital imaging is characterized by reproducibility (computational tools are easy to calibrate or standardize), objectivity (it is not subject to the observer's point of view) and applicability to statistical analysis, as in the case of color studies where the color of the pixels can be translated into numeric data. Various software packages have been developed to interpret digital information derived from different organs of plants.
LEAFPROCESSOR® (Backhaus et al., 2010), uses single metrics and principal component analysis to analyze an array of leaf-shape parameters for identification of subtle differences between seemingly similar leaf mutants in Arabidopsis. Ka-me® (Khiripet et al., 2012), can analyze Voronoi-like layouts which are mathematical structures or diagrams that resemble the layout of epidermal cells, among others. LeafAnalyser®
(Weight et al., 2008) can identify all leaves in an image, detect leaf margins and estimate the position of the leaf tip to generate a large dataset for mathematical, computational
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or statistical analysis. EZ-Rhizo®, serves as “the gateway to root architecture analysis”
according to its developer, Armengaud (2009); it can be used in diverse studies of root
growth and development.
There is no flower-focused software for digital imaging, but some authors have
adapted or modified existing software for application in flower research. Juenger et al.
(2005) used Scion Image® (Scion Corporation, MD, USA) to measure floral and leaf
characters to study the genetic correlation between these two organs. Chacon et al.
(2013) developed a workflow to do flower phenotyping using altered algorithms of
Image J® (http://rsb.info.nih.gov.ij/), MATLAB®
(http://www.mathworks.es/products/matlab) and LeafAnalyser® but focused more in morphological parameters rather than color. Garcia et al. (2014) used digital imaging to map flower colors and study the perception of color by potential pollinators.
The study of color through digital means and its quantitation has been a
common practice since the 1990s. Colorimeters, spectrophotometers and
spectrocolorimeters can be used to transform color parameters into numerical data
using color scales like CIELab. Such tools have been applied in food science studies to
color stability and co-pigmentation of anthocyanin pigments in solution, allowing for the integration of this digitalized data with chemical information (Gonnet, 1998;
Torskangerpoll and Andersen, 2004). It has also been possible to integrate digital color data with biochemical data for better chemotaxonomic assumptions and better
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relations between pigments and color through multivariate analysis (Wang et al., 2004;
Zhang et al., 2007). Darrigues et al. (2008b) used Tomato Analyzer (TAn) software to
study carotenoid trait variation in tomatoes to assess the effects of sampling and of ripening disorders on pigment content in the fruit. TAn is a public domain software that is simple to use and efficient, as it can process batches of images. Its most recent version (v.3.0) includes a ‘Color Test’ feature that collects numeric color data based on the CIELab color space scale. This scale quantifies a specific color within a 3-dimensional sphere using three types of coordinates (L*, a* and b*) that can match human color perception with accuracy. In this scheme, L* represents lightness or darkness ranging from white (L*=100) to black (L*=0); while a* and b* represent coordinates for color
(negative a*=green, positive a*=red, negative b*=blue, positive b*=yellow) (Brewer et al., 2006; Darrigues et al., 2008a). From these values two other important color components are derived: Chroma, which can be defined as the intensity or purity of a color (how vivid it is); and Hue which describes the color attribute (red, green, etc.) independent of intensity or lightness. Hue ranges in value between 0-360 degrees
(Strecker et al., 2010).
In a recent study, Robarts (2013) was the first to use the ‘Color Test’ feature of
Tomato Analyzer to examine flower morphology and color of violets (Viola spp.), integrating color and morphometric data into more comprehensive analyses including clustering, principal component (PCA), and quantitative trait loci (QTL). This study
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demonstrated the applicability of TAn to flower color studies, particularly for
phenotyping germplasm collections of ornamental plants such as those found for Viola,
Phlox, Coreopsis, Rudbeckia and Begonia at the Ornamental Plant Germplasm Center
(OPGC). Analysis of flower color by digital imaging and colorimetric assessment using
TAn to generate objective descriptors of color can be a very useful tool in any characterization of ornamental germplasm. Such objective color characterization can be further coupled with analysis of flower pigment composition by HPLC in the germplasm collection to provide information that may useful in breeding programs seeking to alter flower color. The combination of colorimetric analysis and pigment composition in the
Phlox germplasm collection of the OPGC is a primary goal of this study.
The genus Phlox L. is native to North America and contains 60+ species of predominantly herbaceous plants distributed in two major geographic centers, one
Western and another Eastern (Locklear, 2011; Wherry, 1955). The genus varies widely in horticulturally-relevant attributes such as life cycle (annual or perennial); habit
(upright, mounding, clumping, stoloniferous); leaf morphology (needle-like or broad); flower color (white, pink, purple, red); and disease susceptibility (e.g. to powdery mildew, Erysiphe cichoraceaum). Phlox species are popular garden plants and innumerable cultivars have been developed that vary primarily in flower-related characteristics like color, inflorescence shape and size, and flower shape and size
(Hawke, 2011; Locklear, 2011). The genus has also been important in studies that aim to
7 understand basic biological processes such as plant-pollinator interactions involving color (Levin, 1972) or scent (Majectic et al., 2014); barriers and dynamics for intra- and inter-specific hybridization (Levin, 1974: 1977; 1983; Levin and Schaal, 1970; Levin and
Schlichting, 1986); and polyploidization (Levy, 1976; Levy and Levin, 1971). More recent studies in evolutionary biology have used Phlox flower color as a model system for analysis of general concepts such as reinforcement (Hopkins et al., 2012; Hopkins and
Rausher, 2011; Matute and Ortiz-Barrientos, 2014), making Phlox not only an important horticultural crop but also an interesting model to study genetic and evolutionary processes in angiosperms.
Phlox is a priority genus at the Ornamental Plant Germplasm Center whose activities involve not only collection, conservation and distribution of germplasm, but also research on controlled pollinations, seed biology, interspecific hybridization, and polyploidization (Zale, 2014). Characterization of flower color in the collection would be a valuable addition to our knowledge of this genus and its potential for further manipulation and breeding.
The colors of Phlox flowers have been described variously as purple, lilac, violet, lavender, pink, red, or white, with modifiers for intensity and combinations (Figure 1.1;
Locklear, 2011). There is a need for a more objective color classification system that can be used for describing color in Phlox so that characterization of germplasm for this trait can rely less on observers making visual comparisons with color cards (e.g. RHS Color
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scale) or other subjective methods. While some color variation (eg. pink and white)
occurs in wild populations of the various Phlox species, a much greater variation in
flower color has been achieved in cultivars of P. drummondii, P. paniculata, and P.
subulata through many years of selection and breeding (Benstsen, 2009; Locklear, 2011;
Wherry, 1955). Phlox flowers are described as “…salverform, occasionally funnel-
shaped, each with a narrow, tubular base opening to 5 flat, ovate petal lobes,
sometimes in a star-shaped arrangement” (Brickell and Zuk, 1996). This salverform
shape (having a slender tube and an abruptly expanded tip) of Phlox flowers results in a flattened arrangement of 5 petal lobes that give a rounded outline to the flower (Figure
1.1). The petal lobes are most commonly entire, but in some species (e.g., P. bifida), there is a distinct notch at the tip of each lobe. The flower may consist of a single dominant color or it may display significant variation in color pattern around the central portion where the petal lobes come together. There is also variation in overall flower size as well as the degree to which the petals overlap in the flattened, distal region.
The purpose of this chapter is to present the first step of an integrated digital and biochemical characterization of flower color in Phlox. This includes the collection of the plant material, scanning of flowers, generation and analysis of the color data with
TAn using the CIELab color scale. Flower color data can be used to identify the evident and subtle differences in some of the Phlox accessions using hierarchical clustering that will lay the ground for integration with the biochemical data (Chapter 2).
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Materials and Methods
Plant material
Accessions of Phlox were maintained at the Ornamental Plant Germplasm Center
(Columbus, Ohio) either under greenhouse or field conditions, as described by Zale
(2014). The collection consists of 17 species (Table 1.1), predominantly from the Eastern
distribution center of the genus. Plants for this study were grown in the greenhouse under 16hr photoperiod and temperatures at 18-24°C between 0-12hrs and 26-22°C
between 12-24 hrs year-round. Relative humidity fluctuated between 20-80%, and
photon flux was ~1200 µmol/m2/s during summer and ~250 µmol/m2/s during winter.
Plants were fertilized as needed (Osmocote® 15N-9P-12K; Scotts Co., Marysville, OH; or
Greencare® 17N-4P-17K; Green Fertilizers, Kankakee, IL) and grown from seeds or
cuttings. When plants bloomed, the first 3 to 5 flowers were observed to determine the
optimal time for flower collection (ideally 2-3 days after flower opening). Subsequently,
corollas were collected from each plant and five representative flowers from the harvest
were immediately scanned. Collection was done in the mornings, before noon.
Harvested flowers were kept for no more than 20 minutes at room temperature prior to
scanning, so only 3 accessions could be processed at a time. A sample is represented by
a scan of 5 flowers from an accession; some accessions had multiple samples of flowers
when different colors were present between plants.
10
After scanning, corollas were pooled and placed in 15mL plastic conical tubes
and either lyophilized immediately or stored at -20°C for up to two days prior to lyophilization, then kept at -20°C in sealed tubes until pigment extraction (Chapter 2).
Imaging
Collected flowers were placed in 5 holes of a black, 18cm x 18cm polycarbonate
plate that was then inverted on the flatbed scanner surface (CanoScan 5600F, Canon
Inc.); the plate gently pressed the open flowers against the surface, holding them in
place and providing a standardized background color for each scan. Scans were done at
100dpi and 200dpi depending on the size of the flowers as recommended by the TAn
Manual (100dpi for 1-8cm and 200dpi for 0.5-1cm; Rodriguez et al., 2010). The images
were edited with the TAn software ver. 3.0 (Brewer et al., 2006) using the boundary
function to delimit the area for analysis, often leaving out the center of the flower or
“eye” for a more accurate measurement of the predominant petal color (Figure 1.2).
After proper editing, the color of the petals was analyzed with the Color Test function.
The L*, a*, b*, Chroma and Hue values obtained for each of the 5 flowers scanned were
averaged for statistical analysis. Data for the analysis is presented in Appendix A.
Hierarchical clustering and statistics
Colorimetric data was organized and analyzed for clustering using the MultiBase
add-in for Microsoft®Excel (Numerical Dynamics, 2014). Squared euclidean distances
under the centroid clustering method were used to obtain a dendrogram grouping all
11
samples using the L*, a* and b* values. Simple linear regressions, equations and R2
values were calculated using Microsoft®Excel.
Results
A total of 142 scans or samples (approximately 710 flowers) were obtained and
analyzed with the TAn color test for L*, a*, b*, Hue and Chroma. To simplify analysis, the
central portion of each flower, where significant contrast in color can occur with the rest
of the petal lobes, was not included in the color assessment. These scans represent a
total of 89 OPGC accessions that include 17 species of Phlox from the 3 sections of the
genus (Annuae, Occidentales and Phlox). The species in the collection are not represented by equal number of accessions; there were 11 species with 1 to 4 accessions and the remaining 6 species had 6 to 21 accessions. Data from all scans, including the images and the calculated values can be found in Appendix A.
The values for the color coordinates obtained for the entire collection had a wide range, given that the collection included flowers that were white, pale yellow and various shades of pink, red and purple. The variation in values is as follows (Table 1.2):
L* from 39.64 (obtained from the only intense red accession) to 83.99 (obtained from one of the white accessions); a* from -12.11 (obtained from one of the two individuals in the only yellow accession) to 44.47 (obtained from one of the dark red-purple
accessions), and b* from -25.09 (obtained from a dark red-purple accession) to 16.9
12
(obtained from one of the two individuals in the only yellow accession). Most of the
Hue values were around 330, but ranged about 2-fold, from 165 to 340; whereas
Chroma varied nearly 6-fold, from 9.2 to 46.7.
Through hierarchical clustering it was possible to group accessions using the three main parameters L*, a* and b* (Fig. 1.3; Table 1.2). The analysis resulted in 10 color clusters, that could be named loosely based on the Munsell scale: Red-Purple (RP),
Light Red-Purple (LRP), Dark Red-Purple (DRP), Red-ish (RR), Light Red (LR), Red (R),
Purple-Red (VP), Lavender (L), White (W) and Yellow (Y). The 10 clusters had hierarchical nodal relationships and could be grouped in different ways. For example, clusters RP,
RR, LRP and DRP shared one node whereas LR, PR, and R shared a different one; these 7 clusters shared a further node that distinguished them from another one that grouped
L, W, and Y. Four of the clusters included 12-52 samples whereas the remaining 6 clusters were based on 1-6 samples (Table 1.2). Overall, hierarchical clustering based predominantly on variability in a* separated the clusters into two main clades (one with
7 clusters and the other with 3), where the predominant trait separating them was the presence or lack of the red color. Thus the red/purple clusters had a stronger relationship with each other than with the lavender/white clusters.
To confirm that the clustering was representative of color variation, a distribution plot between a* and b* was done to observe how the clusters behaved on the plane that both of these two color parameters determine (Fig. 1.4). All clusters could
13
be distinguished easily and although the red-purple clusters are close to each other
there are no data points from one crossing into the other. The relationship between L*
and Chroma for the color clusters with minimum sample size of n≥6 was established
(Fig. 1.5). In all cases these two parameters negatively correlated, with the exception of
the W cluster. The interaction was stronger and very significant in L followed by LRP (R2 values of 0.92 and 0.61 respectively).
The pattern of color clusters found in the Phlox collection organized by current phylogenetic consensus is summarized in Table 1.3. The predominant color in the collection is represented by the group of clusters in the ‘red-purple’ category; at least one of the color clusters in this group was found in 15 of the 17 species. Only
P.drummondii and P.bifida did not include accessions in this group, but the sampling of these two species is minimal (n=3 and n=2 respectively). The species present in most of the clusters were P.pilosa, P.subulata, P.ovata, P.carolina and P.paniculata (4 color clusters each) while P.kelseyi, P.stolonifera, P.amplifolia, P.nivalis, P.pattersonii and
P.floridana were only present in 1 (all small sample size). Perhaps as a consequence of small sample sizes, the least common clusters were found in P.paniculata (n=5) and
P.drummondii (n=3) (Table 1.3).
14
Discussion
Flower color variability within Phlox has been a driving force behind the proliferation of this ornamental in American gardens and elsewhere, and the breeding efforts to improve commercially relevant species over the past 200 years. Locklear
(2011) describes the flower color of each of the 61 species (including the 17 reviewed in this study) and the overall range in floral hues is quite large (Figure 1.1; Appendix A), yet there is still much opportunity to further modify this color range. The most extensively bred species of Phlox (P. drummondii, P. paniculata and P. subulata) exhibit a much wider range of color than the other species in the genus, most likely as a consequence of selection and breeding, but it is possible that a similar breeding effort in other species characterized by different habits, flowering time, and adaptability, may result in novel forms of interest to the ornamentals industry. The development of a germplasm collection at the OPGC (Zale, 2014) serves this purpose, and my efforts at examining flower color in the collection is meant to contribute information that may ultimately assist in selection of parents for hybridization.
The use of flower colorimetric data to generate color clusters that can be identified across the various Phlox species facilitates a more objective characterization of the germplasm collection. The color test tool of TAn proved useful in quantifying color parameters among the 89 Phlox accessions that resulted in data that could be subjected to cluster analysis. This analysis resulted in 10 distinct color clusters although
15
the clusters had unequal representation. Interaction between the colorimetric
components L*, a* and b* provided the basis for clustering that resulted in the dendrogram represented in Figure 1.3. The 10 clusters can be traced back to individual
branches in the dendrogram. At the first level, nodes N1 and N2 separate into 2
branches based mostly on the level of a*, indicating that factors contributing to red
color are most significant in defining major color groups in this population of Phlox. The
first level nodes branched into a clade that included the L, W and Y color clusters and
another clade containing the remaining 7 clusters (RP, DRP, RR, LRP, LR, PR, and R). This
major clade was originally named “pink” based on a quick view of the general population of flowers, but the objective clustering reveals that these “pink” flowers really consisted of different groups (color clusters) involving more red and purple components. At the next hierarchical level, the value of b* was the basis for separating
into two branches (nodes N3 and N4), one leading to a clade containing LR, PR and R,
and the other containing mainly the ‘red-purple’ clusters. The branches arising from
node N3 are represented by a single accession in each case, but 3 clusters can be
identified in this clade. The first of the three, PR, is a very high a*, almost 0 b* and mid lightness accession; the second, R, is similar to PR but has a higher b* value. Despite the fact that the length of each of the three branches in the clade is high (indicating greater differences and less relationship), the third accession (LR) of the clade differs mostly in having a higher lightness value.
16
The branches arising from node N4 represent the largest number of samples and the most species in the collection. Two branches arise from N4, in this case separated mainly by differences in values of both a* and L*. These latter two branches further separated into four distinct clusters (DRP, RP and RR for N6; LRP for N5) that occupied different positions in the tridimensional color-space. Two of the clusters, RP and DRP, showed similar lightness but DRP exhibited a greater mean for a* (more intensely red) and b* that go further on the respective scales making this group darker than RP. Both these clusters have lower averages of L* than LRP. The LRP cluster showed significant reduction on a* and b*, and increased lightness which explains why this cluster is not included in the RR-RP-DRP group but is a sibling to it. Interestingly, the small cluster with three samples (RR) aligns more closely with the RP-DRP clusters than the LRP cluster aligns with the latter. RR is characterized by low b* and high a* making its color lean more towards red (hence the name red-dish) but it also has a high L* more similar to LRP, which altogether will most likely be the numeric range of what is called pink.
The branches that arise from node N1 are all characterized by low a* (values close to zero or negative). The 3 clusters all have more lightness than any of the N2 group. The lavender (L) cluster lays on the Y axis (Fig 1.4) around small negative values of b* and the white cluster does the same but on the X axis, while the yellow cluster has higher positive values for b*. While these 3 clusters are represented by relatively few accessions, they clearly identify very distinct clusters. The color scale system predicts
17
that white flowers should locate close to the origin for both a* and b* (Fig 1.4) and will
have more lightness, but because white petals are often somewhat translucent, it is
likely that the color test of TAn captures some of the black background used in the
scanning. Similar results have been obtained in other studies, where the white flowers
have slightly negative a* and slightly positive b* values (Jia et al., 2008; Jie et al., 2008).
The initial thought of naming the main clusters as variations of pink came from
the perception of those colors by human observation. This is a common problem in
floriculture where the color is described subjectively, depending on the observer, even
when using color charts such as the Royal Horticultural Society Colour Chart (Grayer,
2009). This subjectivity is often a problem when describing specific components of color
such as lightness (L*) or saturation (Chroma). For example, red, purple and orange
flowers often show an inverse relationship between lightness and saturation; as the
brightness of a color is reduced, the saturation or intensity of the main color becomes
more evident (Fig. 1.3 this study; He et al., 2011). In contrast, for yellow and white
flowers the opposite relationship is observed, lightness and saturation are proportional
(Fig. 1.3 this study; Jia et al., 2008). This behavior could be easily explained by the
concentration of pigments as observed by Hopkins and Rausher (2011), where dark flowers for blue and red varieties had three times or more the relative amount of anthocyanidin pigment compared to the light varieties.
18
The distribution of color clusters across the 17 Phlox species represented in this germplasm collection shows some distinctive patterns, but because of the very unequal representation of samples for the different color clusters, interpretation must remain somewhat speculative until a more exhaustive sampling of other species is completed.
Nevertheless, the collection provides some valuable insights into color patterns in Phlox.
It is evident that the ‘red-purple’ clade dominates the flowers in this collection (Table
1.3). This broad group probably fits the common description of ‘phlox purple’ as a color that is named in popular literature; it is one of those identifiable terms that evokes distinct images of phlox in people’s minds. Since the majority of samples used to develop the color clusters are from wild-collected material, it is probably safe to say that this color represents the ‘wild type’ for most species of Eastern North America Phlox.
Not surprisingly, the white color cluster is found across various species as it has been reported for many of the Phlox species in nature (Locklear, 2011). The yellow cluster is confined to P. drummondii and there are only two other species where yellow of any type has been described in the genus: P. roemeriana, the golden-eye phlox, has a distinct yellow ring in the center of the flower; P. nana (syn P. mesoleuca) includes a rare population from Mexico where vibrant yellow flowers have been described
(Locklear, 2011). Similarly, the most vivid red color has thus far only been found in P. drummondii, but some cultivars of P. paniculata and P. subulata do approach a more distinct red tone (Bendtsen, 2009; Locklear, 2011). It is interesting to speculate whether
19
two of the more red clusters (LR, PR) identified in P. paniculata, arose from natural
variation within this species or whether it was introduced through hybridization with P.
drummondii.
The distinctive Lavender cluster was found mainly in P. divaricata and P. pilosa of
the Divaricatae subsection and in P. bifida and P. subulata of the Subulatae subsection.
These two subsections are in different sections of the genus. Lavender (L) color cluster
was not found in section Phlox, but more sampling is needed to establish whether this color is truly phylogenetically distinct.
The 10 color clusters identified in this study can form the basis of a color designation scheme for characterization of the germplasm collection and for products of any germplasm enhancement efforts. The Germplasm Resources Information Network
(GRIN) database is a great example where the information obtained through this research can be made available for stakeholders interested in color diversity in Phlox.
The description of color in this study is equivalent to color classification systems such as those of the RHS color chart, but because they are based on specific numeric data that can be routinely acquired, it provides a more consistent and robust system for categorizing flower color in our germplasm. One can envision the routine use of flower scanning and TAn analysis for flowers of all accessions at the OPGC.
20
Figure 1.1: Representative flower color variation in Phlox. Left: range of flower shapes and colors among some of the accessions held at the Ornamental Plant Germplasm Center. Right: colors described by Locklear (2011) among the species of Phlox in this study (a complete table listing the common colors for all Phlox species can be found Appendix A).
21
Figure 1.2: Typical interface of Tomato Analyzer software showing, at left, the scanned flowers of a sample where each flower area to be analyzed for color is delineated with a yellow line, and at right, an enlarged flower showing the exclusion of the ‘eye’ from color analysis using the boundary function. The colorimetric values for each of the flowers can be seen on the lower right pane
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Figure 1.3: Hierarchical clustering of flower color found within the sampled Phlox germplasm. The dendrogram was produced using L*, a* and b* as clustering parameters. The flower images are representative of the different clusters. Nodes (N1-6) indicated distinguish major group separations at each level. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Y: yellow 23
a* vs. b* 20 15 RP 10 DRP 5 LRP 0 L -20 -10 0 10 20 30 40 50 W
b* -5 -10 Y -15 LR -20 PR -25 R -30 RR a*
Figure 1.4: Distribution of color clusters under color parameters a* and b*. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Y: yellow
24
L* vs. Chroma y = 0.4313x + 75.485 90 R² = 0.2088
80 y = -0.5006x + 71.032 70 R² = 0.6065
60 y = -0.514x + 71.923 R² = 0.1627 RP 50 y = -1.2753x + 80.792 R² = 0.9241
L* DRP 40 y = -0.34x + 66.104 LRP R² = 0.1548 30 L W 20
10
0 0 10 20 30 40 50 60 Chroma
Figure 1.5: Correlation between color parameters L* and Chroma with linear regression equations and R2. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white
25
ACCESSIONS SPECIES PZ10-034 Phlox carolina L. PZ10-048 Phlox subulata L. PZ10-060 Phlox subulata L. PZ10-071 Phlox paniculata L. PZ10-100 Phlox divaricata subsp. divaricata PZ10-105B Phlox nivalis hort. Lodd. ex Sweet ‘Eco Brilliant’ PZ10-115 Phlox subulata L. PZ10-119 Phlox subulata L. PZ10-145 Phlox divaricata subsp. divaricata PZ10-149 Phlox bifida L. C. Beck PZ10-161 Phlox drummondii subsp. wilcoxiana PZ10-173 Phlox subulata L. PZ10-174 Phlox subulata L. PZ10-187 Phlox ‘Chattahoochee’ (P.divaricata) PZ10-192 Phlox pilosa subsp. pilosa PZ10-199 Phlox pilosa subsp. dentonsa (A. Gray) Wherry PZ10-208 Phlox maculata L. PZ10-209 Phlox paniculata L PZ10-231 Phlox paniculata L. PZ10-235 Phlox maculata L. PZ10-239B Phlox glaberrima L. ‘N3 Hvtke Lmkte’ PZ10-247 Phlox pattersonii Prather PZ10-249 Phlox glaberrima L. PZ11-002 Phlox kelseyi Britton PZ11-008 Phlox divaricata L. PZ11-012B Phlox ‘Minnie Pearl’ PZ11-013 Phlox pilosa subsp. pilosa PZ11-017 Phlox carolina L. PZ11-023 Phlox divaricata L. PZ11-026 Phlox pilosa L. PZ11-032 Phlox amoena Sims PZ11-034 Phlox pilosa L. PZ11-036 Phlox carolina L. PZ11-045 Phlox carolina L. PZ11-049 Phlox carolina L. Table 1.1: Phlox accessions used for this research with assigned species identification according to the germplasm resources information network (grin). A: hybrid produced at the opgc; b: commercial variety. (continued) 26
Table 1.1 continued PZ11-051 Phlox nivalis hort. Lodd. ex Sweet PZ11-057 Phlox carolina L. PZ11-064A Phlox carolina x Phlox carolina subsp. angusta PZ11-066 Phlox pilosa subsp. fulgida (Wherry) Wherry PZ11-067 Phlox carolina L. PZ11-068 Phlox amoena Sims PZ11-072 Phlox carolina L. PZ12-032B Phlox paniculata L.’Spitfire’ PZ12-039 Phlox divaricata subsp. laphamii (Alph. Wood) Wherry PZ12-040 Phlox pilosa subsp. pilosa PZ12-042 Phlox amoena Sims PZ12-047 Phlox carolina L. PZ12-054 Phlox pilosa L. PZ12-057 Phlox amoena Sims PZ12-065 Phlox subulata L. PZ12-066 Phlox subulata L. PZ12-071 Phlox ovata L. PZ12-076 Phlox ovata L. PZ12-077 Phlox ovata L. PZ12-080 Phlox ovata L. PZ12-082 Phlox ovata L. PZ12-092 Phlox pilosa subsp. fulgida (Wherry) Wherry PZ12-093 Phlox carolina L. PZ12-094 Phlox amoena Sims PZ12-102 Phlox amoena Sims PZ12-106 Phlox amplifolia Britton PZ12-111 Phlox ovata L. PZ12-113 Phlox divaricata L PZ12-128B Phlox nivalis hort. Lodd. ex Sweet ‘A2GA-005 EX’ PZ12-130 Phlox stolonifera Sims PZ12-131 Phlox glaberrima L. PZ13-005 Phlox pilosa subsp. pilosa PZ13-007 Phlox subulata L. PZSH2011-001 Phlox amoena Sims PZSH2011-004 Phlox divaricata subsp. laphamii (Alph. Wood) Wherry PZSH2011-005 Phlox carolina L. PZSH2011-007 Phlox divaricata subsp. laphamii (Alph. Wood) Wherry (continued)
27
Table 1.1 continued PZSH2011-008 Phlox divaricata subsp. laphamii (Alph. Wood) Wherry PZSH2011-009 Phlox floridana Benth. PZSH2011-013 Phlox nivalis hort. Lodd. ex Sweet PZSH2011-017 Phlox pilosa subsp. pilosa PZSH2011-018 Phlox divaricata subsp. laphamii (Alph. Wood) Wherry PZSH2011-020 Phlox pilosa subsp. pilosa PZSH2011-021 Phlox pilosa subsp. pilosa PZSH2011-022 Phlox divaricata subsp. laphamii (Alph. Wood) Wherry PZSH2011-023 Phlox pilosa subsp. pilosa PZSH2011-030 Phlox pilosa subsp. pilosa PZSH2011-031 Phlox pilosa subsp. pilosa PZSH2011-034 Phlox pilosa subsp. pulcherrima Lundell PZSH2011-038 Phlox pilosa subsp. latisepala Wherry PZSH2011-039 Phlox pilosa subsp. latisepala Wherry PZSH2011-040 Phlox pilosa subsp. latisepala Wherry PZSH2011-042 Phlox pilosa subsp. riparia Wherry YELLOWB Phlox drummondii ‘nana compacta beauty yellow’
28
Color #Samples L a* b* Hue Chroma cluster RP 52 53.4 ± 0.3 31.6 ± 0.4 -19.7 ± 0.2 327.9 ± 0.5 37.3 ± 0.4 DRP 20 48.2 ± 0.5 40.0 ± 0.5 -22.9 ± 0.3 330.1 ± 0.5 46.2 ± 0.4 RR 3 53.2 ± 2.3 36.5 ± 2.4 -12.8 ± 0.2 340.5 ± 1.4 38.7 ± 2.2 LRP 44 57.8 ± 0.5 20.7 ± 0.7 -16.3 ± 0.4 321.1 ± 0.9 26.5 ± 0.7 LR 1 55.7 36.0 2.5 93.9 36.1 PR 1 41.9 41.5 -2.3 356.8 41.6 R 1 39.6 39.1 11.7 16.6 41.0 L 12 67.1 ± 1.1 -29.8 ± 0.8 - 10.3 ± 0.9 265.3 ± 4.5 9.2 ± 1.0 W 6 79.6 ± 0.9 -9.5 ± 1.0 0.6 ± 0.3 177.9 ± 2.2 9.5 ± 1.0 Y 2 77.5 ± 1.7 -11.5 ± 0.6 13.6 ± 3.3 131.5 ± 8.4 18.0 ± 2.2
Table 1.2: Summary of colorimetric parameters and sample size for each of the color clusters. Values are presented with standard deviation. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Y: yellow
29
ghlight the purple, DRP: -
s, Sc=s, Section, SubSc Subsection. = RP: red red, R: red, L: lavender, W: white, yellow Y: - purple, RR: reddish, LR: light red, PR: purple PR: red, light LR: reddish, RR: purple, - S= number of samples, A = number of accession
Distribution of color clusters by Zale among species in described the as sampled by phylogeny germplasm, organized
purple, LRP: light red -
dark red petals used for the analysis. the for used petals (2014). Representative flowers for each of the species are shown; the eye of each flower is covered with a black circle to hi Figure 1.3:
30
CHAPTER 2
ANTHOCYANIDINS IN PHLOX AND THEIR RELATIONSHIP TO FLOWER COLOR
Introduction
Phlox's ornamental importance resides mostly in its diversity of floral colors
among and within species and within individual flowers, prompting Linnaeus in Genera
Plantarum (1737) to describe Phlox in Latin as "floris flammeo igneoque colore" (which
translates flowers the color of glowing flame) (Locklear, 2011). This variety of colors
arises primarily from the anthocyanin pigments present in Phlox flowers. Anthocyanins
are phytochemicals that belong to the plant metabolite family of flavonoids and include
the red-purple-blue pigments that can be present in flowers, fruits, stems, leaves and tubers. These compounds occur naturally during plant development and can be synthesized as a consequence of biotic and abiotic stress conditions. The pigments in the plant serve as signaling cue for pollinators (flowers) and seed dispersers (fruits), UV screen (leaves and shoots) and plant defense mechanisms (in various organs). The biosynthesis and accumulation of anthocyanins has been extensively studied in many species (Cheynier et al., 2012). Aside from their biological importance in flowers and fruit, they also have proven to be excellent natural pigments (Giusti and Wallace, 2009)
31
and possess strong antioxidant properties (Cheynier et al., 2012). In general, a specific
anthocyanin core can be associated with certain range of colors. The cores are called
anthocyanidins or anthocyanin aglycones, and are basically the typical C6-C3-C6 ring
structure of flavonoids without any sugar or acyl group moeties (Figure 2.1). The most
common anthocyanidins are cyanidin, pelargonidin and delphinidin; slight variations
(e.g., addition of methoxy groups to the B ring) can give rise to other common variants, namely, peonidin, petunidin and malvidin. Individual anthocyanidins and corresponding anthocyanins have been associated with specific colors: cyanidins to red (peonidin and malvidin yield fairly similar colors), pelargonidin to orange-red, petunidin to dark red- purple and delphinidin to violet-blue. However, only in rare cases there is a single anthocyanin responsible for the color of a flower and multiple anthocyanins in a tissue is more typical (Lee, 2007).
It is important to consider the biology and biochemistry of anthocyanins in order to appreciate how flower color can be affected by these pigments. When anthocyanidins and anthocyanins are synthesized in the endoplasmic reticulum and transported to the vacuole, they remain colorless; it is the acidic conditions in the vacuole that cause coloration to appear (Cheynier et al., 2012). The pH of the vacuole is a very important factor for anthocyanin color; its variation alters the structure of the anthocyanidin chromophore leading to a bathochromic shift that reflects a change in color (Yoshida et al., 2009). Some petunia mutants that have altered flower color and
32 pigment stability have a defective transcription factor (PH4) that interacts with a regulatory element of vacuole acidification (AN1 for ANTHOCYANIN1) and results in higher vacuolar pH and bluer flowers than the wild type (Quattrocchio et al., 2006). Co- pigmentation is another important factor influencing the color of anthocyanins. Co- pigments are formed by the addition of sugars, amino acids, other flavonoids and even metal ions that stabilize the anthocyanin structure in the vacuole. Anthocyanin co- pigmentation can be viewed as two different mechanisms of molecular interaction, self- association and metal complex formation (or simply metalloanthocyanin formation).
While self-association and intramolecular stacking describe the interaction of anthocyanins with other flavonoids (including other anthocyanins), metalloanthocyanin formation describes the interaction of single molecules (or self-assembled structures) with metal ions (i.e Mg, Fe, Mn, Al) (Yoshida et al., 2009). However, regardless of the mechanism, the occurrence of co-pigmentation has been proposed as the basis for blue color in flowers (Yoshida et al., 2009). Floral pigmentation can also be affected by the over-accumulation of anthocyanins in anthocyanin-rich structures called anthocyanoplasts or anthocyanin vacuolar inclusions (AVIs) (Zhao and Dixon, 2010).
Anthocyanoplasts have been described as cytoplasmic, membrane-bound organelles filled exclusively by anthocyanins while AVIs are considered intravacuolar structures composed mainly of anthocyanins located predominantly in the darkest areas of floral
33
petals from different species (Zhao and Dixon, 2010). The ultrastructure of AVIs has yet to be determined.
Another interesting but poorly understood phenomenon is the change of flower color in response to molecular signals emitted by prospective pollinators (Weiss, 1991,
1995). Physiological and biochemical pollinator-triggered cues for color change in Viola cornuta, affecting floral pigment deposition, have been described (Farzad, 2002).
The mechanisms explained above account for a great portion of floral color
variability; modifying the genetic control of them via genetic engineering or traditional
breeding techniques is the avenue by which floriculturists develop unique and
potentially valuable new phenotypes (e.g., uncommon flowers that are blue or blue-
green in color). Almost 20 years ago, the first genetically modified (GM) commercially-
available floricultural crop, the deep violet-colored FLORIGENE®MoondustTM carnation
(Tanaka et al., 2009), was developed by the insertion of DFR and F3’5’H petunia genes
(for dihydroflavonol-4-reductase and flavonoid 3’5’ hydroxylase respectively). After its
introduction, many other GM floricultural crops were produced but governmental
regulation, high costs and consumer acceptance still hinder their commercial
proliferation (Mol et al., 1999; Tanaka et al., 2009).
Genetic engineering can be employed to introgress genes for new pigment
combinations into floricultural crops, but for genera or species with adequate to
plentiful genetic diversity, conventional breeding is a very effective method to develop
34
new flower color patterns. Such is the case for Phlox, which has been bred for around
200 years with new cultivars exhibiting unique floral characteristics being released
annually (Bendtsen, 2009; Locklear, 2011). In Phlox, the anthocyanins can produce
shades of purple, lilac, pink, red, orange and blue, suggesting ample variation in pigment concentrations or profiles, and in mechanisms that modify coloration (See table 1.1). A
few authors have extracted, described and discussed the anthocyanins in flower
extracts of Phlox (Beale et al., 1941; Harborne and Smith, 1978; Hopkins and Rausher,
2011; Hopkins et al., 2012; Robinson and Robinson, 1931; 1932; 1934) (Table 2.1), or
have discussed the role of these compounds on their effect on phenotype (Gilbert,
1915). Nevertheless we know that throughout the genus all 6 main anthocyanidins can
be found, which could explain the large variation of color within and between species.
Comparative analysis of non-anthocyanin flavonoids in Phlox has established
phylogenetic relationships through diversity in flavonoid biochemistry (Levy and Levin,
1975), and in some cases the production of novel flavonoids has been reported in newly
formed Phlox allotetraploids (Levy and Levin, 1971). From these studies it has been
suggested that white and yellow Phlox flowers are albino cases for anthocyanins; dark
and light red flowers have a mixture of cyanidin and peonidin; and dark and light blue
flowers a mixture of cyanidin, peonidin, malvidin and delphinidin (Hopkins and Rausher,
2011). Crimson-colored flowers are a result of petunidin while bluish-crimson flowers
contain petunidin mixed with malvidin and delphinidin derivatives (Robinson and
35
Robinson, 1932). Pelargonidin is also found in Phlox flowers, but interestingly, this pigment appears to be more common in genera of tropical origin, rather than in those adapted to temperate climates like Phlox (Beale et al, 1941; Seigler, 1998). Although informative, these studies only represent the observations on few Phlox species, reported without the benefit of a colorimetric scale, without using uniform color descriptors, and without a photographic record. As such, their value is limited.
Currently, it is possible to integrate biochemical and colorimetric data for quantitative and correlation analyses, facilitating the identification of primary factors underlying observable color. Parameters like lightness, hue and saturation can now be explained by the presence of specific metabolites, total pigments or even the presence of possible co- pigments (He et al., 2011; Jia et al., 2008).
This research aims to fill the gap currently existing in the area of Phlox flower color using two different approaches: digital imaging and biochemical analysis of pigments. While digital imaging provides a fast approach to classify and group the different accessions analyzed based on color, the biochemical data offer a deeper insight about the biochemistry and biology of pigments present in Phlox. The integration of colorimetric parameters, anthocyanidin presence and total anthocyanin content establishes the color-pigment relationship, corroborating or disputing claims that specific pigment patterns correspond to specific colors. The value of this work is supported by the ornamental importance of Phlox and the need to clearly define color-
36
pigment relationships in order to facilitate breeding commercially successful new
cultivars with unique or visually exciting flower colors and patterns.
Materials and Methods
Anthocyanidin extraction
Extraction of anthocyanidins was accomplished by the method of Hopkins and
Rausher (2011). Two 20 mg extraction replicates (“a” and “b”) from each pool (see plant
material collection in Chapter 1) of lyophilized flowers were placed in 1.5 ml tubes and
incubated overnight at room temperature in 1 ml of 2 N HCl. Following the initial
extraction, samples were centrifuged at 14000 rpm to remove particulates and 800 µl of
supernatant was transferred into a 2 ml screw-cap tube. The samples were placed in a
boiling water bath for 1 hour to hydrolyze glycosidic bonds then cooled in an ice bath
between 5-10 minutes. After cooling, approximately 400 µl of ethyl acetate (EA) was
added to remove non polar contaminants; samples were vortexed and again
centrifuged. Supernatants were discarded by pipetting. The treatment with ethyl
acetate was repeated twice. To extract the pigments from the aqueous phase, 400 µl of
iso-amyl alcohol (IA) was added, samples were vortexed and then centrifuged again for
2 min at 10000 rpm to form two distinct liquid phases. The upper IA layer containing pigments was transferred to a 1.5 ml tube. The IA phase separation steps were repeated until the aqueous phase was relatively free of pigment. The pigments were
37 precipitated by centrifugation at 8000 rpm under vacuum to evaporate the IA solvent.
After 3-4 hrs the pellet was re-suspended in 400 µl of methanol:HCl (99:1) solution then transferred to a 1.5 ml tube and sealed. Samples were stored at -20°C until used for
HPLC.
Anthocyanidin quantification
Chromatographic analysis of the flower extract was performed on a System Gold
HPLC equipped with a model 508 autosampler, model 126 binary pump and model 168 diode array detector (Beckman Coulter®, Fullerton, CA) controlled by an IBM Lenovo®
ThinkCentre computer with 32 Karat® software. The column heater was a model 631 from Alltech Associates (Deerfield, IL) and the column was a C-18 type Prodigy® from
Phenomenex (Torrance, CA). Before the HPLC run, samples were passed through a
45µm nylon syringe filter (Thermo Fisher, Rockwood, TN) using a polyethylene syringe
(3ml luerlock, Becton-Dickinson, Franklin Lakes, NJ) and a precision-glide needle (18 gauge, Becton-Dickinson, Franklin Lakes, NJ). The sample was then transferred to a poly- spring glass insert (150 µl, Thermo-Fisher/National, Rockwood, TN) and placed into a target vial (12 X 32 mm, rubber septum cap, Thermo-Fisher/National, Rockwood, TN).
The loaded target vials were placed in the autosampler and a sequence program was written to control sample analysis order and injection volume (30 µl for most samples;
50 µl for very pale samples from white flowers). A binary solvent system delivered at a flow rate of 0.8ml/min was used to elute sample anthocyanins from the column;
38
solvents A and B were 5% formic acid and methanol, respectively. The running program
was as follows: hold of initial conditions (5% B) for 2 min, ramp up to 60% B in 30min
then hold for 5min, ramp up to 100% B in 5 min and hold for 2min, ramp down to 5% B
in 5min and hold for 3min and end run at 53 min. Throughout the run, the column was kept at 25ºC. Quantification was carried out at 520 nm wavelength. Aglycone identification was based on retention time and comparable UV-visible spectra
characteristics to those of pure standards. Anthocyanidin standards used were
delphinidin, cyanidin, petunidin, peonidin and malvidin (acquired from ExtraSynthase,
Genay, France); pelargonidin was purchased from Sigma Aldrich Co. (St. Louis, MO). Raw
data was expressed as megapixels and then transformed to µg delphinidin
equivalents/mg dry floral tissue using a delphinidin standard curve. When
chromatographic peaks corresponding to specific aglycones were not visible, or when
they were present at trace levels (i.e., visible but not quantifiable and unverifiable by
spectral analysis), values of zero were assigned. All extractions were done in duplicate.
An example of a chromatogram with all anthocyanidins (including average retention
time and standard error for all the samples) can be seen in figure 2.2.
Statistical analysis
Multiple linear regression (MLR) (R 3.1.3 software) was carried out to correlate
colorimetric parameters and anthocyanidin composition. The color parameters L*, a*,
b*, Chroma and Hue were the dependent variables while the concentration of each of
39
the six different anthocyanidins (µg anthocyanidin/mg tissue) and total anthocyanidins
(TA) were set as the independent variables. Due to the excessive amount of data points equal to zero for pelargonidin and peonidin (129 and 109 respectively) these factors were not included for the regression. Simple linear regressions within figures and
Pearson correlation coefficients were calculated with Microsoft®Excel.
Results
The Phlox germplasm collection surveyed in this study showed the presence of all 6 anthocyanidins. Some samples had readily detectable levels of all six (Fig 2.2) whereas in most samples, a few of the anthocyanidins were found only in trace amounts. It is possible that all the major anthocyanidins are present in all species of phlox, but some are in such low levels that for practical purposes were considered as absent in this study. Anthocyanidin analysis was possible for only 9 of the 10 color clusters because there was not enough tissue to perform extraction from the samples in the yellow cluster (Y).
Anthocyanidins associated with color clusters.
The distribution of each of the anthocyanidins within the different color clusters is presented in Figure 2.3 and summarized in Table 2.2. Out of the 142 samples collected for scans it was possible to do extractions from 131. The 6 different anthocyanidins found (confirmed with standards) showed wide variation in
40
concentration and could be detected either as the only pigment (delphinidin, cyanidin)
or co-appearing with others (delphinidin, cyanidin, petunidin, pelargonidin, peonidin
and malvidin). Delphinidin and cyanidin are present in approximately 95% of the
samples (absent in only 5 and 6 samples respectively); petunidin and malvidin in close to
50% of the samples, while peonidin and pelargonidin are present in 17% and <1% of the samples respectively.
Each graph in Fig 2.3 combines the anthocyanidin profile of each sample for the corresponding color cluster to give an overall view of the pigments identified in flowers
assigned to that color cluster. The samples within a color cluster are organized
sequentially so it is possible to identify the profile for an individual sample by matching
the bar color in the different anthocyanidin categories. The ‘red-purple’ color clusters
DRP and RP share a common overall pattern where cyanidin and petunidin have similar
levels, but delphinidin is lower, and malvidin is higher in DRP, while the reverse is true for RP. The red purple cluster LRP, on the other hand, has higher levels of delphinidin and cyanidin, with fewer samples containing petunidin and malvidin. These three clusters are the most similar in terms of anthocyanidin presence throughout the samples, and they are also the ones with the larger samples size.
The ‘red’ color clusters RR and PR showed high cyanidin and low delphinidin, but
PR also included the other, less abundant anthocyanidins (petunidin, pelargonidin, peonidin, malvidin). LR had overall low levels of anthocyanidins, but pelargonidin was
41
the predominant pigment. R had the second highest concentration of TA, after PR, but
the highest concentration of a single anthocyanidin, delphinidin. Similarly, the lavender
color cluster L showed delphinidin as the major anthocyanidin but its concentration was
only half that of flowers in the R cluster; it also had a low cyanidin content. The white
color cluster W included two samples with an unexpectedly high level of cyanidin
although the concentration was still much lower than those found in the other color
clusters. The other samples in W had barely detectable dephinidin, peonidin and
malvidin. The total anthocyanidin level in these two samples within W were similar to
some samples associated with the color clusters DRP, RP, LRP and L.
For the germplasm collection surveyed, the total concentration of
anthocyanidins in the sample (TA) could not be used to differentiate color clusters
except for W (Fig 2.4). The W cluster had the lowest overall anthocyanidin concentration at 0.2 µg/mg (Table 2.2). The clusters defined by larger number of samples all had anthocyanidin concentrations between 1 and 2 µg/mg. The only clusters that had significantly higher TA (> 5 µg/mg) were R and PR, each represented by
a single sample.
Relationship between anthocyanidins and colorimetric parameters
Limited sample size for the 4 color clusters identified by red components (RR, PR,
LR, R; Table 2.2) prevented statistical analysis of relationships between anthocyanidin
and colorimetric parameters. Nevertheless, it was possible to examine this relationship
42
in the other clusters to determine if patterns could be identified that may give insights
into the influence of specific anthocyanidins on flower color.
TA and L*. There was a negative correlation between the total anthocyanidin
concentration and lightness (Fig. 2.5). Simple linear regression showed higher (negative)
coefficient values for color clusters L (-3.61) and W (-3.65). The highest R2 was for DRP
and L (0.46 and 0.71 respectively) while the others fell below 0.35 indicating a poor
correlation.
TA and Chroma. The correlation between total anthocyanidin and intensity of
color (Chroma) yielded very low R2values (below 0.1) except in L (0.77) (Fig 2.6). DRP
and RP had a negative (but not significant) correlation while the coefficient values for W
(3.0) and L (2.8) were positive and considerably higher than the others (<0.7), but only L
was significant.
Multiple linear regressions. To model the relationship between each of the
colorimetric parameters and the concentration of individual anthocyanidins and total
anthocyanidin, multiple linear regressions were carried out. Table 2.3 summarizes
regressions and p-values for each model; complete R output for stepwise multiple linear
regression can be found on Appendix C. All models obtained through this method fell
below the 0.05 significance level. Though the models work, they do not explain all the
variation in the dependent variable. The R2 did not surpass 0.3. For two colorimetric parameters, a* and Chroma, petunidin was positively correlated and the only variable
43
significant for each of the models at the 0.001 level. For b* and Hue all variables were significant for the model at the 0.001 and 0.01 levels respectively. For b*, the correlation was negative for individual anthocyanidins and positive for total anthocyanidin, while for Hue, the reverse was true. In both of these models petunidin had the most weight of all the independent variables. Delphinidin, petunidin and malvidin were significant for the model, explaining L* (delphinidin and petunidin at
0.001 and malvidin at 0.05). All the major anthocyanidins were negatively correlated to
L*.
Anthocyanidin presence among Phlox species
The presence of anthocyanidins among the Phlox species surveyed is presented in Figure 2.7. The species are grouped phylogenetically into sections and subsections as described by Zale (2014). In this survey of 16 of the 17 species in the collection
(P.pattersonii was part of the color clustering but was not included in the anthocyanidin analysis), delphinidin and cyanidin are the most dominant anthocyanidins, present in all species and also at the highest concentrations (Fig 2.8). The next most common anthocyanidin is malvidin. The least common anthocyanidin is pelargonidin, identified only in P.amplifolia, and P.paniculata, although this pigment has been reported in
P.drummondii as well (Table 2.1).
The relative abundance as well as the concentration of the individual pigments in species with 5 or more samples is presented in Figure 2.8. For species with less than 5
44 samples, the data is presented in Appendix D. The species from section Annuae that were surveyed lacked pelargonidin and had little or no petunidin and peonidin. P.pilosa was the exception in having malvidin in more samples (similar to cyanidin and delphinidin) and also lower levels of petunidin. The anthocyanidin profiles in section
Occidentales, represented only by P.nivalis (n=5), P.subulata (n=13) and P.bifida (n=2), were restricted to dephinidin and cyanidin with only 2 examples having low levels of petunidin (1-5% of TA), one in P. bifida. Species in section Phlox had the most diverse anthocyanidin profile, including the presence of pelargonidin in P.paniculata and P. amplifolia, and the presence of low levels of peonidin in various samples of P.paniculata and P.carolina. The other anthocyanidins are present in this section as well but more sporadically.
Discussion
The ability to identify color clusters among the species in the germplasm collection as well as the identification of anthocyanidins in each accession provides a potentially useful combination to assess the relationship between cluster analysis and anthocyanidin pattern. In considering this relationship, it is important to note that clustering was based on the color of petal lobes, excluding the often variable center or
‘eye’ of each flower (Fig 2.7), whereas anthocyanidin extraction included the entire flower. In some cases the eye is merely a darker or lighter version of the main color of
45 the petals; in others, it is a contrasting color (see samples PZ10-161 p1, PZ10-187 p2,
PZ10-048 in Appendix A). The assumption made is that the eye pigments will not have a major effect on the total anthocyanidins, and this was true for P. bifida (PZ10-149p1), for example, where dark eye patterns did not yield high TA; but it was not true for
P.carolina ‘Minnie Pearl’ (PZ11-012 p1, p2) where although the eye was no different from the white petals, TA was relatively high. These ‘Minnie Pearl’ samples showed up to ten times more total anthocyanidins compared to the rest of its cluster. It is likely these anthocyanidins were found in the corolla tube, a part of the flower that does not contribute to the color clustering analysis. Small areas of flowers can contain higher levels of anthocyanins, clustered in AVIs, even surpassing in content the larger flower area (Markham et al., 2000). With this in mind, statements and assumptions made about the relationship of colorimetric patterns and anthocyanidins content of the samples studied must be taken with due caution, but it is also an opportunity for an interesting course of action in future research on Phlox flower color.
Anthocyanidins in color clusters
My observations suggest a complex relationship between anthocyanidins and color clusters. Total anthocyanidin, which was expected to have an overall impact on coloration, was not a determinant for major color differences other than for the W cluster (Figure 2.4). In general, samples within a cluster showed similar patterns (Figure
2.3), but contrary to what appears in the literature, the presence of specific
46
anthocyanidins did not predict the color of the flowers in the population studied in all
cases (van Raamsdonk, 1993; Giusti, 2009). It is apparent that, as is the case in many
other species, and described earlier, additional factors besides anthocyanidins are involved in the definition of flower color in Phlox. This lack of linearity between color and anthocyanidins can be demonstrated in cases where flowers located in different clusters and with a clear difference in color, show very similar chromatograms and TA
(Figure 2.9); or in cases where flowers clustered together have noticeably different chromatograms and TA (Figure 2.10).
Though the definition of color based solely on anthocyanin presence is unlikely, some trends were noticeable between the anthocyanidins and color clusters.
Delphinidin and cyanidin were consistently associated with the ‘red-purple’ group of color clusters: DRP, LRP and RP. A decrease in delphinidin was linked with an increase in malvidin, which also coincided with the darkening of the group. High levels of delphinidin, with mid- to low levels of cyanidin and no other anthocyanidin, was typical
of the L color cluster. Interestingly, similar concentrations of delphinidin and cyanidin
were also associated with LRP and RP, but the presence of additional anthocyanidins,
even in small amounts, appears to make enough of a difference to identify different
clusters. The concentration of delphinidin seemed sufficient to account for the
difference between the rather light L cluster and the much darker R cluster. The
combination of a higher delphinidin to cyanidin ratio as well as a higher overall
47 concentration of both anthocyanidins is linked to a more red color rather than blue, which contrast with the common assumption that delphinidin is often found in more bluish flowers. However, the R color cluster is based on a single accession, so it may be premature to make strong conclusions about the relationship of anthocyanidins and this color. The color cluster RR has a high level of cyanidin similar to PR, but the presence of small amounts of pelargonidin, peonidin, petunidin and malvidin in the latter makes for a more vivid color compared to RR.
Of the six major anthocyanidins, pelargonidin was the least frequent, present in only 3 of 131 samples. High levels of this pigment were found only in P. paniculata and represented in two distinct color clusters (PR, LR; Fig.1.4; Table 2.2), which may not be too surprising given that the latter species has been extensively bred for unique color variants (Bendsten, 2009). Peonidin, the other less frequent anthocyanidin in the collection (present in only 22 samples), was never among the most relatively abundant anthocyanidins and did not show a consistent pattern for cluster distribution or have a striking effect on color, like pelargonidin (Fig. 2.4).
Total anthocyanin content is often used to explain the intensity of variables like
L* and Chroma (He et al., 2011; Jia et al., 2008) such that higher levels of anthocyanins are associated with darker colors. My results support this association: the two highest values for total anthocyanidins are also the lowest for L*. The general negative correlation between these two colorimetric parameters was more evident for clusters L
48
and DRP. On the other hand, the positive correlation between total anthocyanidins and
Chroma was only observed for L, W and LRP, contrasting with DRP and RP (very low R2).
Both of these relationships were better explained in L where the patterning of the eye
was less intense than in the other groups, suggesting that the unaccounted contribution
of the pigments in the eye to the TA might be the reason why this correlation is not as
significant as in the other clusters.
The individual colorimetric parameters that describe color may be only partly
defined by the anthocyanidins in the flower. When examined by themselves, both the total and individual anthocyanidin often have a stronger influence in the components of color - as evidence by relatively high Pearson correlation coefficients (Table 2.4). When combined in multiple linear regression analysis, the interaction between the anthocyanidins reduces each others’ significance. This results in a weaker effect of the biochemical components on the model (Table 2.4). Overall the delphinidin-petunidin- malvidin biosynthetic branch was the most influential in the regression models obtained. Petunidin was the most influential factor. According to the multiple linear regression, petunidin alone can explain 20% of the variation in a* and 24% in Chroma while the other variables were not significant. A small increase in petunidin concentrations appears to intensify the colors on the clusters having a purple component. While not alone in the other models, petunidin was still the factor with the most weight. The fact that delphinidin, petunidin and malvidin were the only factors in
49
the model for L* reinforces the role of this pigment biosynthetic branch in the dark/light perception of color in Phlox flowers. Total anthocyanidin as a whole cannot explain L*
in the clusters in Figure 2.5; instead, the interaction of individual anthocyanidins is
important and explains in part the poor correlation between TA and L*.
Given the relationship between a* and b* in our population, the high positive
correlation of petunidin with a* shows an opposite, high negative correlation with b*.
However, the multiple linear regression showed that all independent variables were
relevant for the model, and that all the anthocyanidins were negatively correlated while
TA was positively correlated. The simplest explanation is that petunidin is the major
determinant in the red color axis, while the interaction between all anthocyanidins and
TA influence the effect of blue, leading to a purple color. It appears that pelargonidin
has a strong positive effect on b* (r =0.28) but because of low sample size they were not
used in the multiple linear regressions and thus it is not possible to generalize its effect.
The positive correlation with TA can be explained by what this biochemical parameter
represents when in low numbers. Very low values for TA represent very low amounts of
pigments and lack of color as a consequence; while the colors get closer to white, a*
and b* get closer to 0.
In general, all biochemical parameters were negatively correlated with L*. TA has
been shown in the past to have a strong relationship with values of lightness (He et al.,
2011; Jia et al., 2008). Petunidin was the second highest r value, close to the one for TA. 50
As explained earlier, petunidin levels parallel the changes from dark to light on the clusters DRP, RP and LRP, which explains why L* is affected as well by this anthocyanidin. The multiple linear regression did not have TA as relevant for the model to explain L*. Delphinidin, petunidin and malvidin were the most relevant factors in the model to explain lightness. The fluctuation of petunidin and malvidin follow a direction from light to dark in similar groups of clusters. The strong influence of these anthocyanidins on L* and the close biosynthetic relationship with delphinidin (these are
3 of the 4 most dominant anthocyanidins in Phlox) altogether can account for a large fraction of TA, but not all.
Hue is based on a* and b* and is intended to represent changes in color that are not related to intensity, as a consequence, it showed very low correlation coefficient for all anthocyanidins, because its values represent the interaction of all the different pigments present. The multiple linear regression model to explain Hue agrees with this statement; all anthocyanidins are relevant to the model and are positively correlated, while TA has a negative correlation. Petunidin is the variable with more weight.
However, the model only represents 12% of the variation, emphasizing that there are more important factors that determine the final color of a flower and it cannot be solely explained by presence or absence of pigments.
The variability observed highlights the important role that cellular physiology
(e.g., vacuolar pH) and morphology (e.g. cell stacking and orientation) plays in flower 51
color and supports the notion that we cannot rely solely on pigment composition to determine color, especially for practical purposes (e.g breeding). The influence of additional complex factors on color most likely explains the poor significance observed in the multiple linear regression analysis.
The identification of anthocyanidin pigments and the categorization of petal lobe colors into different clusters adds new levels of information to the current knowledge of flower color in Phlox. My work provides only a preliminary assessment of anthocyanidins and color in this genus, and is based on limited sampling of the color range that occurs in it. A more exhaustive survey of color is needed in order to validate the colors that were represented by only one or a few samples in this study. The particular germplasm collection that was sampled included mostly wild-collected accessions of the eastern perennial Phlox species. At least for the broadly red-purple and lavender flower colors, these defined color clusters can be useful in assigning color categories to other accessions. Additional accessions of Phlox at the OPGC, especially cultivars, have yet to be surveyed, and species like P.drummondii, P.subulata and
P.paniculata are often found to be the most variable due to intensive breeding, but these were underrepresented in the study.
Anthocyanidins in species of Phlox
The distribution of anthocyanidins within taxonomically related species appears to have an identifiable pattern. As seen on table 2.1, the few previous studies of 52 anthocyanidin presence in Phlox indicate that cyanidin and delphinidin are found among different species and that P.drummondii is capable of producing all six anthocyanidins.
Because I only examined one accession of P.drummondii, I am not able to verify that all anthocyanidins are present in this species, although it appears quite likely. As expected, cyanidin and delphinidin are the dominant anthocyanidins in the population sampled
(Fig. 2.8). Petunidin and malvidin coexisted in 56 samples (42%) and both were not present at the same time in 63 samples (48%), showing a very strong correlation between these two anthocyanidins. This not surprising given that both share delphinidin as a common precursor (Fig. 2.1). The absence or presence of both of these anthocyanidins at the same time correlates to a certain extent with the taxonomy. All
P.nivalis and most of P.subulata, P.amoena, and P.divaricata showed absence of both
(the first two are from section Occidentales while the second pair from section Annuae).
Phlox bifida and P.kelseyi, also from section Occidentales, lacked both anthocyanidins but their sample size was too small to draw conclusions. In contrast, petunidin and malvidin appeared together very frequently in species of section Phlox and in P.pilosa, of section Annuae. When petunidin and malvidin co-occured, they tended to follow different patterns. When delphinidin decreased, so did petunidin, but malvidin increased (e.g., P.pilosa, P.ovata, P.carolina) (Fig. 2.8). My study is a small reflection of the distribution of the main anthocyanidins in one genus of the Polemoniaceae family:
53 delphinidin and cyanidin are the most common while pelargonidin is occasionally present throughout the family (Harborne and Smith, 1978).
54
Figure 2.1: Structure of anthocyanidins present in Phlox flowers. Arrows indicate biosynthetic relationships. The images accompanying each structure show flowers where the corresponding anthocyanindin was dominant or highly abundant 55
Figure 2.2: Example of a chromatogram obtained for a sample (P.paniculata; PZ12-032p1) containing all six anthocyanidins. Average retention times with standard error is shown for each. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin
56
N2
Figure 2.3: Anthocyanidin concentration in samples within different color clusters. Each sample is identified by a different bar color for easy identification of the anthocyanidins in that sample. Clusters are grouped based on the main nodes established on Fig. 1.3. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white, Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin. Note the different Y axis scale for the R cluster (max 5 instead of 3.5).. (continued)
57
Figure 2.3 continued N1
58
Figure 2.4: Total anthocyanidins and color clusters. Clusters with only one sample can be seen as a single black horizontal line. RP: red-purple, DRP: dark red-purple, LRP: light red-purple, RR: reddish, LR: light red, PR: purple-red, R: red, L: lavender, W: white
59
TA vs. L* 90
80 y = -3.6179x + 72.715 R² = 0.7087 70
60 DRP 50
L* L 40 LRP y = -1.2862x + 50.483 30 RP R² = 0.4562 20 W
10
0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 TA(µgAnt/mgTissue)
Figure 2.5: Relationship between total anthocyanidins (TA) and the colorimetric parameter L* of the five major color clusters. The remaining clusters had less than 6 samples and were thus not included in this analysis. Fitted lines for each cluster are shown. Linear regression equations are included for two clusters with the highest R2: Lavender (L) and dark red-purple (DRP).
60
TA vs. Chroma 60
50
40 DRP 30 L
Chroma LRP 20 RP W 10 y = 2.835x + 6.3369 R² = 0.7658 0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 TA(µgAnt/mgTissue)
Figure 2.6: Relationship between total anthocyanidins (TA) and the colorimetric parameter Chroma of the five major color clusters. The remaining clusters had less than 6 samples and were thus not included in this analysis. Fitted lines for each cluster are shown. Linear regression equation is shown for highest R2 cluster, Lavender (L).
61
ent shading.
Presenceof anthocyanidinswithin speciesin the sampled germplasm, organized
2.7: e Figur are species the of each flowers for (2014). Representative by Zale described by as phylogeny shown. Anthocyanidinstheon same biosynthetic branch are identified differ by S= numberofsamples, number =of A accessions.delphinidin, Dp: Cy: cyanidin, Pt: petunidin,
62
Section: Annuae
Figure 2.8: Distribution of individual anthocyanidins among Phlox species. Anthocyanidin relative abundance (left, expressed as peak area percentage) and concentration (right, in µg anthocyanidin/mg tissue) in species with ≥ 5 samples, organized by section. Each sample is identified by a different bar color for easy identification of the anthocyanidins in that sample. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv:malvidin. (continued)
63
Figure 2.8 continued Section: Occidentales
(continued)
64
Figure 2.8 continued
Section: Phlox
65
Figure 2.9: Chromatograms of two samples with very similar anthocyanidin profile but different color in the flowers. Picture of representative flower for each sample on upper right. Top: P.divaricata (PZ11-008 p1), bottom: P.divaricata (PZ10-100pw3).
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TA = 1.3
TA = 2.5
Figure 2.10: Chromatograms of two samples with very similar flower color but different anthocyanidin profile. Picture of representative flower for each sample and the total anthocyanidins (TA, in µg anthocyanidin/mg tissue) on upper left. Top: P.amoena (PZ12-057p2), bottom: P.carolina (PZ12-047p2)
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Anthocyanidin Species Delphinidin P.drummondii 2,4, P.divaricata3, P.austromontana4, P.diffusa4, P.dolicantha4 Cyanidin P.drummondii4,5 , P.paniculata1, P.austromontana4, P.diffusa4, P.dolicantha4,P.superba4 Pelargonidin P.drummondii4, P.paniculata1 Petunidin P.drummondii2 Peonidin P.drummondii5 Malvidin P.drummondii2,5 1: Robinson and Robinson (1931). 2: Robinson and Robinson (1932). 3: Robinson and Robinson (1934). 4: Harborne and Smith (1978). 5: Hopkins and Rausher (2011)
Table 2.1: Anthocyanidins identified among species of Phlox.
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69
Coef. Sig. Parameter R2 Equation <0.05 a* Pt 0.2 20.6 + 61.2Pt Dp, Cy, Pt, 0.3 -14.3 - 15.6Dp - 14.7Cy - 40.4Pt - 18.9Mv + 15.2TA b* Mv, TA L* Dp, Pt, Mv 0.29 62.1 - 3.5Dp - 29.7Pt - 3.9Mv Chroma Pt 0.24 27.8 + 54.5Pt Dp, Cy, Pt, 0.12 301.8 + 66.4Dp + 93.4Cy + 232.5Pt + 71.2Mv - Hue Mv, TA 75.433TA
Table 2.3: Summary of the step-wise multiple linear regressions using the pigment concentration values and total anthocyanidins to explain colorimetric parameters
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TA Dp Cy Pt Pl Pn Mv a* 0.167394 -0.09076 0.108911 0.452862 0.12795 0.165751 0.334148 b* -0.02747 0.082824 0.118616 -0.35998 0.280862 0.076329 -0.38365 L* -0.42762 -0.18064 -0.2606 -0.41815 -0.15645 -0.15797 -0.33365 Chroma 0.152449 -0.12382 0.081099 0.49385 0.087838 0.177084 0.391098 Hue 0.009803 -0.14007 0.049976 0.270764 -0.07251 0.031441 0.195196
Table 2.4: Pearson correlation coefficients (r) for the different color parameters with anthocyanidin content. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin
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Conclusions and Future Prospects
This study shows that much pigment variability occurs across and within color
clusters defined with the data obtained from digital imaging. This variability helps
pinpoint key pigments and branches of the anthocyanin pathway that probably have a
more substantial influence in the expression of certain flower colors. At the same time,
it is clear that a given color can be achieved by different patterns of anthocyanidins
suggesting multiple approaches to manipulating flower color by breeding. The
identification of pigment patterns at the taxonomic level helps identify the diversity of
the anthocyanin pathway that could be used for prospective crosses between species
that have sexual compatibility. Thus, breeders may be able to more deliberately select
parents for crosses based on the presence or absence of specific anthocyanidins or
pathways even though the color of the progeny cannot be predicted from this data. For
example, pelargonidin appears to be an uncommon pigment within different species of
Phlox, yet it may present a lot of potential to generate major changes in color, as it may
be the responsible anthocyanidin for two distinct color clusters. Thus, breeders seeking to develop new colors in, for example, P.carolina, would do well to focus on crosses with
72
compatible species that have high levels of pelargonidin, such as P.drummondii or
P.paniculata.
The relationships between canonical colors and the different anthocyanidins that are often presented in the literature were challenged by the findings in this study (as discussed on the introduction of this chapter; Giusti, 2009). The mixture of cyanidin and delphinidin or delphinidin derivatives in similar proportions was responsible for different shades of red-purple (commonly called purple or mauve). When flowers had almost only cyanidin and little delphinidin red colors were observed, while the opposite situation caused lavender. Both these colors were very pale. Intense red color was a consequence of very high delphinidin with little cyanidin, while intense purple-red was due to high cyanidin and low levels of the other 5 anthocyanidins. Finally, orange tones could be observed when pelargonidin was dominant. Some of these results contrast to what is normally said about color of specific anthocyanidins, but other studies have found that the assumptions on these relationships are not necessarily linear. When dominant, delphinidin can be responsible for red (Anagallis monelli, Quintana et al.,
2007), purple (Tulip, van Raamsdonk, 1993.), violet and blue (Clitoria ternatea, Kazuma et al., 2003). Cyanidin also has a wide range of colors when alone or dominant, from red
(Tulip, Raamsdonk, 1993; Lycoris longituba He et al., 2011) and pink (Tulip, van
Raamsdonk, 1993; Lycoris longituba He et al., 2011; Peony, Jia, 2008) to purple (Lycoris longituba He et al., 2001) and in some cases, orange (Lycoris longituba He et al., 2011).
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My research demonstrates a practical and easy approach to study flower color in the genus Phlox. Flower color in this genus has not been studied using the two methodologies (color scans and anthocyanidin patterns) or with such a diverse set of species. I was able to confirm the anthocyanidins that had been reported previously in a few species of Phlox. However, in the majority of species sampled, this study provides the first report for the anthocyanidin pigment composition. Additional and routine surveys of flower color in Phlox should be to provide a more comprehensive picture of flower pigments in the genus, particularly for species with great diversity in color achieved by breeding, e.g. P. drummondii and P. paniculata.
The primary purpose of this research was to characterize the variation in flower color within a germplasm collection of Phlox so that the information can become part of the data housed in GRIN, a database available to general users. Both the digital imaging and color analysis system using Tomato Analyzer coupled with the anthocyanidin profile(s) for each accession can provide valuable information about this collection.
Given the relatively straightforward methodology for colorimetric and biochemical analysis, it is logical to suggest that the remaining samples of the Phlox collection at the
OPGC be surveyed to generate a more complete picture of color/anthocyanidin variation in this genus. Of particular interest would be to survey the variously colored cultivars in the trade to provide a more robust color clustering scheme. The presence of all anthocyanidins in Phlox opens the doors for perhaps a wider set of colors that we
74
could possibly observe. Knowing that color can also vary with the same anthocyanidin
patterns increases the possibilities even more and in species that have not been bred
extensively (or that have limited pigment pools) this opens the door to color variation.
Identifying color clusters based on objective colorimetric parameters is an
efficient way to identify subtle differences between colors in flowers. The effectiveness of this method was confirmed by the similarities on the anthocyanidin patterns for each of the clusters. For future analysis a more detailed tissue separation, perhaps removing the corolla tube and even the ‘eye’ is recommended to ensure a more accurate relationship between colorimetric and biochemical characters. This is particularly important since Phlox has great variety of pigmentation patterns in the center of the flower (eye). In addition, it may be worthwhile to examine the presence of other flavonoids in the flowers to see of relationships could be explained by possible co- pigmentation between, for example, flavonols and anthocyanins.
In summary, this research provides one of the most detailed studies of flower color in a population of Phlox that can be accessible to breeders interested in using the results as a tool for manipulation of flower color. Ideally, these methodologies can be further used with other priority genera within the OPGC, such as Begonia, Coreopsis and
Rudbeckia. This study also provides valuable information about anthocyanin biology that could be of use for researchers interested in the biology of color.
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APPENDIX A: COLORIMETRIC ANALYSIS DATA AND SCANS
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PZ10-034 P3 PZ10-034 P8
PZ10-048 P5 PZ10-048 P6
Figure A.1: Flower scans used on the Tomato Analyzer® software to obtain the colorimetric data. Identifier of each at the bottom left corner. (continued)
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Figure A.1 continued
PZ10-048 P7 PZ10-060 P1
PZ10-060 P2 PZ10-071 P1 (continued)
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Figure A.1 continued
PZ10-100 PW3 PZ10-105 P1
PZ10-115 P1 PZ10-119 P1 (continued)
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Figure A.1 continued
PZ10-145 P1 PZ10-145 PW1
PZ10-149 P1 PZ10-149 P2 (continued)
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Figure A.1 continued
PZ10-161 P1 PZ10-173 P2
PZ10-173 P5 PZ10-174 P1 (continued)
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Figure A.1 continued
PZ10-187 P1 PZ10-187 P2
PZ10-192 P1 PZ10-192 P2 (continued)
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Figure A.1 continued
PZ10-199 P3 PZ10 199 P6
PZ10-208 P1 PZ10-208 P2 (continued)
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Figure A.1 continued
PZ10-209 P1 PZ10-231 P1
PZ10-231 P2 PZ10-235 P1 (continued)
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Figure A.1 continued
PZ10-239 P1 PZ10-239 P2
PZ10-239 P3 PZ10-247 P1 (continued)
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Figure A.1 continued
PZ10-249 P1 PZ11-002 P1
PZ11-002 P2 PZ11-008 P1 (continued)
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Figure A.1 continued
PZ11-012 P1 PZ11-012 P2
PZ11-013 P1 PZ11-013 P2 (continued)
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Figure A.1 continued
PZ11-017 P1 PZ11-017 P2
PZ11-023 P1 PZ11-026 P1 (continued)
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Figure A.1 continued
PZ11-032 P2 PZ11-032 PW2
PZ11-034 P1 PZ11-036 P1 (continued)
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Figure A.1 continued
PZ11-036 P2 PZ11-045 P1
PZ11-045 P2 PZ11-049 P1 (continued)
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Figure A.1 continued
PZ11-051 P4 PZ11-051 P5
PZ11-057 P1 PZ11-064 P1 (continued)
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Figure A.1 continued
PZ11-064 P2 PZ11-066 P1
PZ11-067 P1 PZ11-068 P1 (continued)
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Figure A.1 continued
PZ11-072 P1 PZ11-072 P2
PZ12-032 P1 PZ12-039 P1 (continued)
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Figure A.1 continued
PZ12-040 P1 PZ12-040 P2
PZ12-042 P1 PZ12-047 P1 (continued)
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Figure A.1 continued
PZ12-047 P2 PZ12-054 P1
PZ12-057 P1 PZ12-057 P2 (continued)
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Figure A.1 continued
PZ12-065 PW1 PZ12-065 PW2
PZ12-066 P1 PZ12-071 P1 (continued)
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Figure A.1 continued
PZ12-071 P2 PZ12-076 P1
PZ12-077 P1 PZ12-077 P2 (continued)
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Figure A.1 continued
PZ12-080 P1 PZ12-080 P2
PZ12-082 P1 PZ12-092 P1 (continued)
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Figure A.1 continued
PZ12-092 P2 PZ12-092 P3
PZ12-093 P1 PZ12-093 P2 (continued)
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Figure A.1 continued
PZ12-094 P1 PZ12-102 P1
PZ12-102 P2 PZ12-106 P1 (continued)
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Figure A.1 continued
PZ12-111 P1 PZ12-111 P2
PZ12-113 P1 PZ12-128 P1 (continued)
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Figure A.1 continued
PZ12-130 P1 PZ12-131 P1
PZ12-131 P2 PZ13-005 P1 (continued)
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Figure A.1 continued
PZ13-007 P1 PZ13-007 P2
PZSH11-001 P2 PZSH11-001 P3 (continued)
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Figure A.1 continued
PZSH11-004 P1 PZSH11-004 P3
PZSH11-005 P1 PZSH11-007 PW1 (continued)
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Figure A.1 continued
PZSH11-007 PW2 PZSH11-008 P1
PZSH11-009 P1 PZSH11-009 P2 (continued)
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Figure A.1 continued
PZSH11-013 P1 PZSH11-013 P2
PZSH11-017 P1 PZSH11-017 P2 (continued)
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Figure A.1 continued
PZSH11-018 P1 PZSH11-018 P2
PZSH11-020 P1 PZSH11-021 P1 (continued)
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Figure A.1 continued
PZSH11-021 P2 PZSH11-022 PW1
PZSH11-022 PW2 PZSH11-023 P1 (continued)
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Figure A.1 continued
PZSH11-030 P1 PZSH11-030 P2
PZSH11-031 P1 PZSH11-031 P2 (continued)
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Figure A.1 continued
PZSH11-034 P1 PZSH11-038 P1
PZSH11-039 P1 PZSH11-039 P2 (continued)
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Figure A.1 continued
PZSH11-040 P1 PZSH11-040 P2
PZSH11-042 P1 PZSH11-042 P2 (continued)
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Figure A.1 continued
YELLOW® P1 YELLOW® P2
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Phlox spp. Color P.aculeata Purple to deep pink, lighter-white P.adsurgens Purple, lilac, pink P.albomarginata Purplish to pink, or white P.alyssifolia Purple to pink, rarely white P.amabilis Purple to bright pink P.amoena Purple to pink, rarely white or lavender P.amplifolia Pink to white P.andicola Pale lavender to white, sometimes yellowish or purplish P.austromontana Pink, lavender or white, sometimes yellowish or bright pink P.bifida Lavender, sometimes reddish purple or white P.buckleyi Bright purple to pink P.caespitosa Lavender to white P.carolina Purple to pink, rarely lilac or white P.caryophylla Purple to pink P.cluteana Purple P.colubrina Pink to white P.condensata White or lavender P.cuspidata Purple to lilac P.diffusa Purple, lilac, pink, lavender or white, sometimes deep purple P.dispersa White P.divaricata Light violet, lavender to near-white P.dolichantha Bright pink P.douglasii Lavender , pink or white P.drummondii Purple to lavender to lilac to pink P.floridana Purple to pink P.glaberrima Light purple to pink, sometimes white P.gladiflormis Pale lilac to lavender, sometimes white with a blue hue P.gliseola Light purple, pink or white P.hendersonii White P.hirsuta Bright rose-pink to white P.hoodii Pale lavender to white P.idahonis Lilac to lavender, sometimes white Table A.1: Table with all Phlox spp. indicating its color as described by Locklear (2011)
(continued)
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Table A.1 continued
P.kelseyi Lilac to lavender to pink to white with a blue hue P.longifolia Light purple, lilac, pink or white P.maculata Purple, pink or white P.missoulensis Light blue to white, sometimes pink P.multiflora Lilac to pink to white P.muscoides White, sometimes pale lavender P.nana Purple to lilac, pink or white, rarely light yellow P.nivalis Deep purple to lilac, pink, sometimes white P.oklahomensis Lilac pink or lavender, sometimes white P.opalensis White, sometimes pink P.ovata Bright or pale purple, pink, sometimes white P.paniculata Pale to bright purple, pink, sometimes white P.pattersonii Blue or lavender P.pilosa Purple to pink to white P.pulcherrima Rose-pink P.pulchra Lilac, lavender, pink, sometimes white P.pulvinata Lavender to white with blue hue P.pungens White to bluish P.richardsonii Lilac, lavender or white P.roemeriana Purple to lilac, sometimes white P.sibirica Pink P.speciosa Purple to pink to white P.stansburyi Purple to pink or white P.stolonifera Violet to lavender, purple to lilac P.subulata Purple to lavender, sometimes reddish, bluish P.tenuifolia White, creamy, sometimes lavender hue P.villosissima Purple to pink P.viscida Purple, pink or white P.woodhousei Purple to pink
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APPENDIX B: CHROMATOGRAPHY DATA
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µg anthocyanidin/mg tissue Identifier Spp. Cluster TA Dp Cy Pt Pl Pn Mv PZ10-034 p3 carolina RP 1.03 0.09 0.27 0.10 0.00 0.00 0.57 PZ10-034 p8 carolina LRP 0.90 0.07 0.15 0.09 0.00 0.00 0.59 PZ10-048 p5 subulata LRP 1.08 0.82 0.26 0.00 0.00 0.00 0.00 PZ10-048 p6 subulata LRP 1.08 0.79 0.29 0.00 0.00 0.00 0.00 PZ10-060 p1 subulata RR 2.49 0.00 2.49 0.00 0.00 0.00 0.00 PZ10-060 p2 subulata RR 1.91 0.09 1.82 0.00 0.00 0.00 0.00 PZ10-071 p1 paniculata LR 1.04 0.04 0.33 0.04 0.48 0.08 0.06 PZ10-100 pw3 divaricata LRP 1.57 1.35 0.21 0.00 0.00 0.00 0.00 PZ10-105 p1 nivalis LRP 2.33 1.25 1.08 0.00 0.00 0.00 0.00 PZ10-115 p1 subulata LRP 1.47 1.08 0.39 0.00 0.00 0.00 0.00 PZ10-119 p1 subulata RP 1.81 1.34 0.47 0.00 0.00 0.00 0.00 PZ10-145 p1 divaricata L 2.04 1.57 0.47 0.00 0.00 0.00 0.00 PZ10-145 pw1 divaricata L 2.02 1.75 0.27 0.00 0.00 0.00 0.00 PZ10-149 p1 bifida W 0.17 0.12 0.05 0.00 0.00 0.00 0.00 PZ10-149 p2 bifida L 0.31 0.24 0.05 0.02 0.00 0.00 0.00 PZ10-161 p1 drummondii R 5.45 4.41 1.03 0.00 0.00 0.00 0.00 PZ10-173 p2 subulata L 0.93 0.78 0.14 0.00 0.00 0.00 0.00 PZ10-173 p5 subulata L 0.93 0.79 0.14 0.00 0.00 0.00 0.00 PZ10-174 p1 subulata LRP 1.73 1.29 0.44 0.00 0.00 0.00 0.00 PZ10-187 p1 divaricata L 2.90 1.96 0.93 0.00 0.00 0.00 0.00 PZ10-187 p2 divaricata L 3.08 2.17 0.91 0.00 0.00 0.00 0.00 PZ10-192 p1 pilosa LRP 2.36 1.91 0.46 0.00 0.00 0.00 0.00 PZ10-192 p2 pilosa LRP 1.82 1.35 0.47 0.00 0.00 0.00 0.00 PZ10-199 p3 pilosa DRP 3.07 0.49 0.57 0.21 0.00 0.00 1.80 PZ10-199 p6 pilosa LRP 2.10 0.41 0.42 0.11 0.00 0.00 1.17 PZ10-208 p1 maculata RP 1.68 1.41 0.27 0.00 0.00 0.00 0.00 PZ10-208 p2 maculata RP 1.38 1.12 0.26 0.00 0.00 0.00 0.00 PZ10-209 p1 paniculata DRP 1.55 0.07 0.34 0.12 0.00 0.22 0.79 PZ10-231 p1 paniculata DRP 1.83 0.24 0.76 0.25 0.00 0.19 0.39 PZ10-231 p2 paniculata RP 1.77 0.22 0.77 0.21 0.00 0.27 0.29 PZ10-235 p1 maculata W 0.06 0.02 0.01 0.00 0.00 0.01 0.02 PZ10-239 p1 glaberrima LRP 1.21 0.29 0.24 0.20 0.00 0.00 0.48 PZ10-239 p2 glaberrima W 0.08 0.01 0.03 0.01 0.00 0.01 0.02
Table B.1: Anthocyanidin concentration and total anthocyanidin (TA) of 131 samples of Phlox flowers. Dp: delphinidin, Cy: cyanidin, Pt:petunidin, Pl: pelargonidin, Pn:peonidin, Mv: malvidin. (continued)
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Table B.1 continued PZ10-239 p3 glaberrima RP 1.84 0.07 0.38 0.13 0.00 0.23 1.04 PZ10-249 p1 glaberrima RP 1.55 1.31 0.13 0.11 0.00 0.00 0.00 PZ11-002 p1 kelseyi LRP 0.65 0.58 0.07 0.00 0.00 0.00 0.00 PZ11-002 p2 kelseyi LRP 0.79 0.71 0.08 0.00 0.00 0.00 0.00 PZ11-008 p1 divaricata L 1.55 1.36 0.19 0.00 0.00 0.00 0.00 PZ11-012 p1 carolina W 0.64 0.00 0.48 0.00 0.00 0.08 0.08 PZ11-012 p2 carolina W 0.52 0.00 0.38 0.00 0.00 0.07 0.07 PZ11-013 p1 pilosa LRP 1.95 1.47 0.48 0.00 0.00 0.00 0.00 PZ11-013 p2 pilosa LRP 2.26 1.83 0.43 0.00 0.00 0.00 0.00 PZ11-017 p1 carolina LRP 1.01 0.34 0.12 0.20 0.00 0.04 0.31 PZ11-017 p2 carolina LRP 0.96 0.32 0.09 0.19 0.00 0.03 0.33 PZ11-023 p1 divaricata L 1.76 1.30 0.33 0.04 0.00 0.03 0.06 PZ11-026 p1 pilosa LRP 2.97 1.69 0.95 0.22 0.00 0.05 0.06 PZ11-032 p2 amoena RP 0.91 0.79 0.12 0.00 0.00 0.00 0.00 PZ11-032 pw2 amoena RP 2.00 1.90 0.11 0.00 0.00 0.00 0.00 PZ11-034 p1 pilosa LRP 1.81 0.33 0.27 0.16 0.00 0.00 1.04 PZ11-045 p1 carolina LRP 1.71 0.83 0.42 0.20 0.00 0.00 0.26 PZ11-045 p2 carolina RP 1.12 0.40 0.30 0.23 0.00 0.00 0.20 PZ11-049 p1 carolina RP 1.32 0.28 0.33 0.22 0.00 0.00 0.49 PZ11-051 p4 nivalis LRP 1.27 0.60 0.67 0.00 0.00 0.00 0.00 PZ11-051 p5 nivalis LRP 1.60 0.77 0.82 0.00 0.00 0.00 0.00 PZ11-057 p1 carolina DRP 1.48 1.28 0.20 0.00 0.00 0.00 0.00 PZ11-064 p1 carolina RP 1.37 0.07 0.24 0.09 0.00 0.07 0.90 PZ11-064 p2 carolina RP 1.96 0.19 0.66 0.20 0.00 0.09 0.83 PZ11-066 p1 pilosa RP 1.52 1.26 0.26 0.00 0.00 0.00 0.00 PZ11-067 p1 carolina DRP 2.71 0.34 0.53 0.18 0.00 0.20 1.46 PZ11-068 p1 amoena LRP 0.86 0.72 0.14 0.00 0.00 0.00 0.00 PZ11-072 p1 carolina RP 0.87 0.08 0.38 0.05 0.00 0.06 0.30 PZ11-072 p2 carolina RP 1.48 0.45 0.41 0.20 0.00 0.00 0.41 PZ12-032 p1 paniculata PR 6.20 0.62 2.94 0.46 1.16 0.52 0.50 PZ12-039 p1 divaricata LRP 4.64 2.79 1.85 0.00 0.00 0.00 0.00 PZ12-040 p1 pilosa RP 1.97 1.75 0.22 0.00 0.00 0.00 0.00 PZ12-040 p2 pilosa LRP 1.14 0.80 0.34 0.00 0.00 0.00 0.00 PZ12-042 p1 amoena RP 0.97 0.97 0.00 0.00 0.00 0.00 0.00 PZ12-047 p1 carolina DRP 1.53 1.06 0.13 0.34 0.00 0.00 0.00 PZ12-047 p2 carolina DRP 1.26 0.84 0.11 0.30 0.00 0.00 0.00 (continued)
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Table B.1 continued PZ12-054 p1 pilosa RP 1.04 0.58 0.08 0.17 0.00 0.00 0.20 PZ12-057 p1 amoena LRP 3.22 0.77 0.72 0.29 0.00 0.08 1.37 PZ12-057 p2 amoena DRP 2.50 0.47 0.65 0.20 0.00 0.07 1.11 PZ12-065 pw1 subulata RP 2.71 2.13 0.55 0.04 0.00 0.00 0.00 PZ12-065 pw2 subulata RP 2.05 1.63 0.42 0.00 0.00 0.00 0.00 PZ12-071 p1 ovata RP 0.53 0.30 0.00 0.12 0.00 0.00 0.11 PZ12-071 p2 ovata RP 0.26 0.14 0.03 0.05 0.00 0.00 0.05 PZ12-076 p1 ovata RP 0.46 0.13 0.03 0.07 0.00 0.00 0.23 PZ12-077 p1 ovata W 0.05 0.00 0.05 0.00 0.00 0.00 0.00 PZ12-077 p2 ovata RR 0.40 0.40 0.00 0.00 0.00 0.00 0.00 PZ12-080 p2 ovata RP 0.04 0.00 0.02 0.00 0.00 0.00 0.02 PZ12-082 p1 ovata RP 0.13 0.01 0.02 0.01 0.00 0.00 0.08 PZ12-092 p1 pilosa RP 0.80 0.63 0.18 0.00 0.00 0.00 0.00 PZ12-092 p2 pilosa L 0.12 0.08 0.04 0.00 0.00 0.00 0.00 PZ12-092 p3 pilosa RP 1.21 0.97 0.24 0.00 0.00 0.00 0.00 PZ12-093 p1 carolina DRP 0.76 0.48 0.09 0.18 0.00 0.00 0.00 PZ12-093 p2 carolina DRP 0.62 0.41 0.08 0.13 0.00 0.00 0.00 PZ12-094 p1 amoena RP 0.93 0.93 0.00 0.00 0.00 0.00 0.00 PZ12-102 p1 amoena RP 0.53 0.48 0.05 0.00 0.00 0.00 0.00 PZ12-102 p2 amoena RP 0.83 0.78 0.05 0.00 0.00 0.00 0.00 PZ12-106 p1 amplifolia DRP 0.86 0.20 0.24 0.16 0.03 0.03 0.20 PZ12-111 p1 ovata RP 1.27 0.73 0.18 0.19 0.00 0.00 0.17 PZ12-111 p2 ovata RP 0.27 0.11 0.03 0.04 0.00 0.00 0.09 PZ12-113 p1 divaricata L 1.87 1.55 0.31 0.00 0.00 0.00 0.00 PZ12-128 p1 nivalis LRP 1.06 0.48 0.57 0.00 0.00 0.00 0.00 PZ12-130 p1 stolonifera LRP 1.47 1.30 0.18 0.00 0.00 0.00 0.00 PZ12-131 p1 glaberrima RP 1.13 0.39 0.23 0.18 0.00 0.00 0.34 PZ12-131 p2 glaberrima RP 0.99 0.25 0.17 0.20 0.00 0.00 0.37 PZ13-005 p1 pilosa LRP 0.69 0.61 0.09 0.00 0.00 0.00 0.00 PZ13-007 p1 subulata LRP 0.34 0.34 0.00 0.00 0.00 0.00 0.00 PZ13-007 p2 subulata LRP 0.30 0.30 0.00 0.00 0.00 0.00 0.00 PZSH11-001 p2 amoena RP 0.98 0.82 0.16 0.00 0.00 0.00 0.00 PZSH11-001 p3 amoena RP 0.91 0.71 0.20 0.00 0.00 0.00 0.00 PZSH11-004 p1 divaricata L 1.17 0.65 0.52 0.00 0.00 0.00 0.00 PZSH11-004 p3 divaricata LRP 1.34 0.84 0.51 0.00 0.00 0.00 0.00 PZSH11-005 p1 carolina RP 1.36 0.25 0.40 0.13 0.00 0.05 0.52 (continued)
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Table B.1 continued PZSH11-007 pw1 divaricata LRP 2.62 2.05 0.56 0.00 0.00 0.00 0.00 PZSH11-008 p1 divaricata RP 2.92 2.23 0.69 0.00 0.00 0.00 0.00 PZSH11-009 p1 floridana RP 0.35 0.07 0.06 0.03 0.00 0.00 0.18 PZSH11-009 p2 floridana RP 0.53 0.12 0.10 0.04 0.00 0.00 0.28 PZSH11-013 p1 nivalis LRP 2.43 1.17 1.27 0.00 0.00 0.00 0.00 PZSH11-017 p1 pilosa RP 0.63 0.11 0.32 0.00 0.00 0.00 0.19 PZSH11-018 p1 divaricata LRP 3.89 3.09 0.80 0.00 0.00 0.00 0.00 PZSH11-018 p2 divaricata LRP 2.84 2.40 0.44 0.00 0.00 0.00 0.00 PZSH11-020 p1 pilosa DRP 3.71 1.25 0.59 0.36 0.00 0.00 1.51 PZSH11-021 p1 pilosa DRP 2.16 0.55 0.36 0.22 0.00 0.00 1.03 PZSH11-021 p2 pilosa DRP 2.64 0.70 0.60 0.26 0.00 0.00 1.07 PZSH11-022 pw1 divaricata LRP 3.94 2.86 1.09 0.00 0.00 0.00 0.00 PZSH11-022 pw2 divaricata LRP 2.70 2.09 0.60 0.00 0.00 0.00 0.00 PZSH11-023 p1 pilosa RP 1.58 0.33 0.52 0.08 0.00 0.00 0.65 PZSH11-030 p1 pilosa RP 1.71 0.57 0.58 0.18 0.00 0.00 0.37 PZSH11-030 p2 pilosa RP 2.02 0.66 0.60 0.21 0.00 0.00 0.54 PZSH11-031 p1 pilosa RP 1.79 0.64 0.48 0.22 0.00 0.00 0.45 PZSH11-031 p2 pilosa RP 1.48 0.48 0.41 0.18 0.00 0.00 0.41 PZSH11-034 p1 pilosa LRP 0.62 0.22 0.09 0.09 0.00 0.00 0.23 PZSH11-038 p1 pilosa RP 0.94 0.34 0.15 0.12 0.00 0.00 0.33 PZSH11-039 p1 pilosa DRP 1.27 0.25 0.25 0.13 0.00 0.00 0.63 PZSH11-039 p2 pilosa DRP 1.06 0.26 0.28 0.10 0.00 0.00 0.41 PZSH11-040 p1 pilosa DRP 0.97 0.34 0.19 0.11 0.00 0.00 0.33 PZSH11-040 p2 pilosa DRP 1.25 0.37 0.30 0.12 0.00 0.00 0.46 PZSH11-042 p1 pilosa RP 1.55 0.75 0.18 0.19 0.00 0.00 0.43 PZSH11-042 p2 pilosa RP 1.05 0.43 0.09 0.15 0.00 0.00 0.38
126
APPENDIX C: MULTIPLE LINEAR REGRESSIONS IN R
127
Call: lm(formula = a ~ Cy + Pt + Mv + TA)
Residuals: Min 1Q Median 3Q Max -32.129 -5.590 1.852 7.768 24.041
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 16.71896 2.66943 6.263 5.46e-09 *** Cy -0.08845 0.06089 -1.453 0.148817 Pt 0.69842 0.17822 3.919 0.000145 *** Mv 0.09316 0.05889 1.582 0.116156 TA 3.35544 1.02375 3.278 0.001353 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11.82 on 126 degrees of freedom Multiple R-squared: 0.2601, Adjusted R-squared: 0.2366 F-statistic: 11.07 on 4 and 126 DF, p-value: 9.939e-08
Table C.1: R output for a step-wise multiple linear regression using the colorimetric parameter a* as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables. Call: lm(formula = b ~ Dp + Cy + Pt + Mv + TA)
Residuals: Min 1Q Median 3Q Max -8.677 -3.193 -1.222 2.529 30.288
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 24.51688 7.61742 3.219 0.001642 ** Dp -0.39386 0.07441 -5.293 5.22e-07 *** Cy -0.32835 0.08398 -3.910 0.000151 *** Pt -0.61573 0.10861 -5.669 9.41e-08 *** Mv -0.48643 0.08063 -6.033 1.69e-08 *** TA -0.90902 0.43860 -2.073 0.040269 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.055 on 125 degrees of freedom Multiple R-squared: 0.4064, Adjusted R-squared: 0.3826 F-statistic: 17.11 on 5 and 125 DF, p-value: 7.176e-13
Table C.2: R output for a step-wise multiple linear regression using the colorimetric parameter b* as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables.
128
Call: lm(formula = L ~ Dp + Pt + Mv + TA)
Residuals: Min 1Q Median 3Q Max -12.1114 -4.1160 -0.9818 2.8879 20.8575
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 70.15569 2.40839 29.130 < 2e-16 *** Dp -0.08038 0.02827 -2.843 0.00521 ** Pt -0.37158 0.09174 -4.050 8.88e-05 *** Mv -0.12138 0.04355 -2.787 0.00614 ** TA -3.77299 0.53723 -7.023 1.20e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.195 on 126 degrees of freedom Multiple R-squared: 0.3591, Adjusted R-squared: 0.3388 F-statistic: 17.65 on 4 and 126 DF, p-value: 1.587e-11
Table C.3: R output for a step-wise multiple linear regression using the colorimetric parameter L* as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables. Call: lm(formula = Chroma ~ Pt + Mv + TA)
Residuals: Min 1Q Median 3Q Max -20.7987 -6.0618 0.8512 7.5712 20.0969
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 22.55760 1.73902 12.971 < 2e-16 *** Pt 0.63573 0.13714 4.636 8.71e-06 *** Mv 0.10861 0.04634 2.344 0.02064 * TA 2.57868 0.80382 3.208 0.00169 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 9.317 on 127 degrees of freedom Multiple R-squared: 0.3047, Adjusted R-squared: 0.2883 F-statistic: 18.55 on 3 and 127 DF, p-value: 4.832e-10
Table C.4: R output for a step-wise multiple linear regression using the colorimetric parameter Chroma as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables.
129
Call: lm(formula = Hue ~ Dp + Cy + Pt + Mv, data = data)
Residuals: Min 1Q Median 3Q Max -289.256 -5.941 6.845 20.190 133.048
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -15.1173 63.0797 -0.240 0.811 Dp 3.2475 0.6219 5.222 7.09e-07 *** Cy 3.0505 0.7029 4.340 2.90e-05 *** Pt 4.5186 0.9038 5.000 1.88e-06 *** Mv 3.4877 0.6734 5.179 8.58e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 42.33 on 126 degrees of freedom Multiple R-squared: 0.2471, Adjusted R-squared: 0.2232 F-statistic: 10.34 on 4 and 126 DF, p-value: 2.842e-07
Table C.5: R output for a step-wise multiple linear regression using the colorimetric parameter Hue as dependent variable and cyanidin (Cy), delphinidin (Dp), petunidin (Pt), malvidin (Mv) and total anthocyanidins (TA) as independent variables.
130
APPENDIX D: RELATIVE ABUNDANCE AND ANTHOCYANIDIN CONCENTRATION IN MINOR
SPECIES
131
Section: Annuae
Figure D.1: Relative anthocyanidin abundance (Left, in peak area %) and absolute anthocyanidin concentration (Right, in µg anthocyanidin/mg tissue) of underrepresented species (n ≤ 5) in section Annuae. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv:malvidin.
132
Section: Occidentales
Figure D.2: Relative anthocyanidin abundance (Left, in peak area %) and absolute anthocyanidin concentration (Right, in µg anthocyanidin/mg tissue) of underrepresented species (n ≤ 5) in section Occidentales. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv:malvidin.
133
Section: Phlox
Figure D.3: Relative anthocyanidin abundance (Left, in peak area %) and absolute anthocyanidin concentration (Right, in µg anthocyanidin/mg tissue) of underrepresented species (n ≤ 5) in section Phlox. Dp: delphinidin, Cy: cyanidin, Pt: petunidin, Pl: pelargonidin, Pn: peonidin, Mv: malvidin.
134
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