Molecular Evolutionary and Macroecological Investigations of the Eastern Asian - Eastern North American Floristic Disjunction

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MOLECULAR EVOLUTIONARY AND MACROECOLOGICAL INVESTIGATIONS OF THE EASTERN ASIAN - EASTERN NORTH AMERICAN FLORISTIC DISJUNCTION

ANTHONY ELI MELTON

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2020

To Maddie

ACKNOWLEDGMENTS

I would like to thank my advisors, Doug and Pam Soltis, for letting me be a part of their academic family for the last five years. They have been incredible in their support and I would not be where I am without them. I would like to thank my committee members, Robert P.

Guralnick and Ethan P. White, for their assistance and guidance. I would like to thank everyone, including past members, in the Soltis lab who helped so often on just about everything. I would like to thank my parents for being supportive for all these years. And I would like to thank

Maddie McClinton for making all the grad school problems seem insignificant and making every day so much better. I owe many thanks to all the people, from the professors at Montevallo and

Auburn to everyone at the University of Florida, who have helped lift me up to where I am today

- I wouldn’t be here without your support. During my time at UF, I had the pleasure of working with several amazing undergraduate students whom I would like to thank for all their work. I would like to thank the National Science Foundation, the American Society of Plant

Taxonomists, the Botanical Society of America, and UF Biology Department and Graduate

Student Association for funding for research and travel.

I would also like to acknowledge and thank the many collaborators I have worked with during my time at the University of Florida. Shichao Chen, Yunpeng Zhao, Chengxin Fu, Qiu-

Yun (Jenny) Xiang, Shifeng Cheng, and Gane K.-S. Wong helped collect leaf samples, perform

RNA extractions, and sequencing and assembly of transcriptomes used for analyses in Chapter 2.

I would like to thank Matthew H. Clinton, Donald N. Wasoff, Limin Lu, Haihua Hu, Zhiduan

Chen, and Keping Ma for assistance in acquiring occurrence data and preliminary analyses using ecological niche modeling for Chapter 3. I would like to thank Hanyang Lin, Miao Sun, William

M. Whitten, Michelle Mack, Stephanie Bohlman, Sarah Graves, Yuhnpeng Zhou, Chengxin Fu,

Jeremy Lichstein, and Robert P. Guralnick for collecting plant samples and functional trait data and helping develop the functional diversity analyses used in Chapter 4.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 10

ABSTRACT ...... 11

CHAPTER

1 INTRODUCTION ...... 13

2 COMPARISONS OF RATES OF MOLECULAR EVOLUTION IN EASTERN ASIAN AND EASTERN NORTH AMERICAN DISJUNCT PLANT SPECIES ...... 17

Background ...... 17 Methods ...... 19 Sample Selection, Tissue Processing, Transcriptome Assembly, and Ortholog Selection ...... 19 Codeml Analyses ...... 21 Post-Codeml Statistical Analyses ...... 22 Results...... 23 Pipeline Output ...... 23 Pairwise Analyses ...... 24 Per-branch Analyses ...... 24 GO Categories ...... 25 Discussion ...... 26

3 CLIMATIC NICHE COMPARISONS OF EASTERN ASIAN AND EASTERN NORTH AMERICAN DISJUNCT PLANT SPECIES ...... 40

Background ...... 40 Methods ...... 42 Occurrence Data Processing ...... 42 Climatic Data Processing ...... 43 Ecological Niche Model Development ...... 44 Analyses ...... 45 Results...... 46 Discussion ...... 48

4 NICHE FILLING DYNAMICS OF EASTERN ASIAN AND EASTERN NORTH AMERICAN WOODY PLANT COMMUNITIES ...... 57

Background ...... 57

Methods ...... 59 Sampling ...... 59 Trait Analyses ...... 60 Functional Diversity Calculations ...... 61 Results...... 62 Discussion ...... 64

5 CONCLUSIONS ...... 71

APPENDIX

A CHAPTER TWO SUPPLEMENTARY MATERIAL ...... 74

B CHAPTER THREE SUPPLEMENTARY MATERIAL ...... 112

C CHAPTER FOUR SUPPLEMENTARY MATERIAL ...... 126

LIST OF REFERENCES ...... 128

BIOGRAPHICAL SKETCH ...... 143

LIST OF TABLES

Table page

2-1 This table includes all species included in the study, the region in which it occurs, and mean for dN, dS, and dN/dS () scores...... 33

3-1 Regional means per genus for estimated distribution size, niche breadth (B1 and B2), hypervolumes (HV), and mean climatic niche widths (mean annual temperature and annual precipitation) per genus...... 52

4-1 Summary of diversity statistics for whole PAM functional diversity analyses...... 70

A-1 Voucher information for all plant specimens used in this study...... 76

A-2 Gene number and mean, median, and standard deviation (SD) for scores for each species included...... 77

A-3 t-test statistics, including 95% confidence intervals (CI), for pairwise comparisons (excluding Liriodendron) of the log of dN, dS, and dN/dS scores for entire gene sets...... 80

A-4 ANOVA results for Liriodendron...... 81

A-5 Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) for Calycanthus...... 82

A-6 Results for Dunn’s pairwise test for all Cornus branch comparisons...... 83

A-7 Results for Dunn’s pairwise test for all Hamamelis branch comparisons...... 87

A-8 Median scores, standard deviation (SD), and P-values for t-tests on GO category dN/dS results...... 91

A-9 Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) of Calycanthus Biological Process genes...... 94

A-10 Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) of Calycanthus Molecular Function genes...... 95

A-11 Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) of Cornus Biological Process genes...... 96

A-12 Results of Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) for Cornus Molecular Function genes...... 100

A-13 Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) for Hamamelis Biological Process genes...... 104

A-14 Results of Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) for Hamamelis Molecular Function genes...... 108

B-1 Estimated distribution size, raster breadth metrics (B1 and B2), Hypervolume (HV), and niche widths (Tw and Pw) per species...... 112

B-2 Results of asymmestric ecospat background tests...... 116

B-3 Means, medians, and standard deviations (SD) for asymmetric ecospat background tests per genus...... 122

C-1 Site information for plants sampled functional trait analyses...... 127

LIST OF FIGURES

Figure page

2-1 Topologies used for the analyses of Calycanthus, Cornus, and Hamamelis genes...... 34

2-2 Boxplots and bar graphs for dN, dS, and dN/dS scores for pairwise comparisons...... 36

2-3 Boxplots for log-transformed dN, dS, and  scores for Calycanthus analyses...... 37

2-4 Boxplots for log-transformed dN, dS, and  scores for Cornus analyses...... 38

2-5 Boxplots for log-transformed dN, dS, and  scores for Hamamelis analyses...... 39

3-1 Violin plot for A) estimated distribution sizes, B) niche breadth metrics, B1 and B2, C) climatic niche hypervolumes, D) and climatic niche widths, Tw (mean annual temperature, C°) and Pw (annual precipitation, mm)...... 55

3-2 Violin plots for empirical Schoener’s D per asymmetric ecospat background tests...... 56

4-1 Map of the 11 sampling sites in the eastern United States and eastern China highlighted in green...... 68

4-2 Barplots for non-rarefied PAM analyses (A) and rarefied PAM analyses (B). Bars represent standard deviation of FRicSES in replicate runs...... 69

A-1 Bar plots for dN, dS, and dN/dS scores of genes falling under Biological Process categories for pairwise comparisons...... 74

A-2 Bar plots for dN, dS, and dN/dS scores of genes falling under Molecular Functions categories for pairwise comparisons...... 75

C-1 Density plots for replicate analyses of A) FRicSES, B) MPDSES, and C) MNTDSES...... 126

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

MOLECULAR EVOLUTIONARY AND MACROECOLOGICAL INVESTIGATIONS OF THE EASTERN ASIAN - EASTERN NORTH AMERICAN FLORISTIC DISJUNCTION

Anthony Eli Melton

May 2020

Chair: Douglas E. Soltis Cochair: Pamela S. Soltis Major: Botany

Eastern Asia and eastern North America (EA-ENA) share a complex biogeographic history with numerous clades of organisms, from rodents to flowering plants, shared between the regions. The flora has been a particularly well-studied component of the disjunction. While the two floras are very similar in composition, they vary strikingly in size, with EA containing ~1.6 times as many species as ENA. My dissertation research focused on understanding possible causes of this species richness anomaly. First, I utilized transcriptomics to evaluate rates of molecular evolution in disjunct species pairs and genera. This work showed that the genes selected for study have evolved at similar rates within both regions. Thus, higher rates of molecular evolution in species from EA are not generally responsible for the species richness anomaly. Then, using ecological niche modeling methods, I showed that distributions of EA species often contain greater amounts of climatic niche space than that found in sister species from ENA, potentially providing greater ecological opportunity in which to diversify; furthermore, disjunct species pairs often occupy climatic niche space that is more similar than expected by chance. Finally, I used functional trait data to assess niche filling dynamics in woody plant communities in EA and ENA. Diversity analyses revealed that ENA temperate

forest communities have assembled through niche packing (i.e., communities occupy less functional trait space than expected given their species richness), while EA community assembly has been more variable. These results support several long-standing hypotheses regarding the floristic disjunction. This work: 1) demonstrated that while rates of molecular variation may vary from gene to gene, in general, there is not an increase in species richness related to rates, suggesting that other factors, such as the increased environmental heterogeneity of EA, have contributed to the species richness anomaly, 2) demonstrated that climatic heterogeneity within the distributions of disjunct congeneric species is greater in EA, and 3) EA woody plant communities tend to be more diverse in functional traits and experience less niche packing than

ENA communities. This work has helped paint a more complete, yet still highly complex, picture of the evolution of these two floras.

CHAPTER 1 INTRODUCTION

The eastern Asian - eastern North American (EA-ENA) floristic disjunction has been a prominent focus of botanical research for over 250 years, with recognition of the similarities between the floras beginning with a dissertation by Linnaeus’ student Jonas P. Halenius in 1750

(Boufford and Spongberg, 1983; Gray, 1859; Graham, 1966; Li, 1952; Wen, 1999). The work of

Asa Gray (1859) subsequently made the disjunction well known to a global audience of biologists. Halenius’s dissertation included nine species showing the disjunction, but this list has grown to include species in approximately 65 genera of seed plants (Boufford and Spongberg,

1983; Graham, 1966; Wen, 1999; Zhengyi, 1983).

Evidence suggests that these two disjunct floras are remnants of a once-contiguous temperate/subtropical forest that was present across the Northern Hemisphere during the

Miocene and Pliocene. Fragmentation of that widespread forest over the last 30 million years resulted in the disjunct species that are present today (Donoghue and Smith, 2004; Graham,

1993; Manos and Meireles, 2015; Wen et al., 2010). Clades that exhibit this disjunction have a complex history, with numerous expansion and fragmentation events occurring at different times leading to pseudocongruent biogeographic patterns (Lee et al., 1996; Nie et al., 2006a, 2006b;

Xiang et al., 1998, 2000, 2006). Given this complex history, this disjunction offers an excellent opportunity to study macroecological, biogeographical, and evolutionary processes.

Many EA-ENA genera contain sister species or sister clades that exhibit the disjunction, with one species or clade per region, but in other genera the pattern is more complex (Manos and

Meireles, 2015) with one region home to many more species of a genus than the other. Although

EA and ENA are of roughly the same geographic size (9.6 x 106 km2 vs. 9.4 x 106 km2, respectively; Qian, 2002), the flora of eastern Asia is generally more species-rich, with genera

that exhibit the disjunction containing more species in EA than in ENA (~29,200 vs. ~18,000 species, respectively; Qian and Ricklefs, 1999). This species richness anomaly has led to the formation of several ecological hypotheses that aim to explain the higher species richness in EA:

1) the tropical forests, an ecosystem known to be high in species richness with high speciation rates, of China contribute to its higher species richness, 2) China’s vegetative communities span tropical, temperate, and boreal habitats, which has led to rich plant associations, and 3) greater topographical heterogeneity in eastern China has led to isolation of lineages, speciation, and formation of local endemics (Axelrod et al., 1998; Guo et al., 1998; Kubitzki and Krutzch, 1998;

Lu, 1999; Mittelbach et al., 2007). Large disjunctions in species distributions provide excellent opportunities to study processes that shape distributional patterns. The goals of the research presented here were to assess possible causes of the species richness anomaly, including molecular and macroecological evolution, as well as biogeographic causes.

In Chapter 2, I investigated whether differences in rates of molecular evolution could have contributed to the EA-ENA species richness anomaly. Longer phylogenetic branch lengths have been found in some EA clades, relative to their ENA sister clades, suggesting that higher molecular evolutionary rates in the EA lineages may have led to higher rates of speciation

(Xiang et al., 2004). To evaluate whether rates of molecular evolution are elevated in EA relative to ENA sister species and clades, we used transcriptomes to estimate rates of molecular evolution from species in 11 genera displaying this disjunction. No statistically significant differences were identified between EA and ENA sister species, suggesting equal rates of molecular evolution and similar selection pressures for both species within pairs. For larger genera, no strong evidence was detected to suggest that more species-rich clades have higher molecular evolutionary rates. Our results suggest that genes across multiple gene ontology

categories are under strong purifying selection in both EA and ENA species. The data also support the hypothesis that greater species richness in EA than ENA is due to factors other than an overall increase in rates of molecular evolution in one region, such as increased habitat heterogeneity in EA.

In Chapter 3, I generated ecological niche models (ENMs) for species in genera exhibiting the disjunction to assess for differences in breadth in geographic space, multidimensional climatic niche space, and one-dimensional climatic niche width, as well as to evaluate niche overlap. I tested the following hypotheses: 1) EA species will exhibit narrower distributions of suitable climatic niche space, 2) ENMs for species from EA will predict lesser suitability smoothness across the distributions in geographic and niche space due to greater climatic heterogeneity than ENA, and 3) the disjunct lineages will exhibit greater climatic niche similarity than predicted by null models. ENMs were developed for 64 species across 19 genera showing the EA-ENA floristic disjunction. ENMs were assessed for niche breadth, and binary predicted occurrence maps were used to estimate the geographic area of suitable climate, climatic and elevational niche width, and occupied climatic niche space hypervolume.

Asymmetric ecospat background tests were conducted using ENMTools to determine whether disjunct congeneric species occupy more or less similar climatic niche space than predicted by null models (Broennimann et al., 2012; Warren et al., 2017). Analyses of ENMs show that while disjunct species tended to occupy similarly sized distributions, ENA species tended to have larger raster.breath scores and EA species have greater niche widths and hypervolumes. Results of background tests show that EA-ENA congeners occupy similar areas within these broader climatic regimes and exhibit general patterns of elevated niche similarity. It is likely that niche retention and climatic heterogeneity have influenced how EA-ENA disjunct species partition

themselves within their respective geographies. Greater environmental heterogeneity in EA has likely allowed species to occupy greater climatic niche space in a geographic area that is similar in size to the ENA distributions. These lineages also often occupy similar niche space within their respective environments, highlighting how climatic niche conservatism may influence the distributions of disjunct lineages.

In Chapter 4, I tested whether niche filling processes differ for EA and ENA woody plant communities. Niche packing, the process of communities assembling into shared and increasingly partitioned niche space, has been found to play an important role in species richness anomalies along environmental gradients or complex biomes (Kruk et al., 2017; Pigot et al.,

2016; Ricklefs and Marquis, 2012). Functional trait data were collected from plants within 11 temperate forest communities: five in EA and six in ENA. Functional diversity metrics were calculated using the R packages picante and FD (Kembel et al., 2010; Villéger et al., 2008).

Results suggest ENA communities have assembled through niche packing processes, while EA communities have assembled through varied processes.

CHAPTER 2 COMPARISONS OF RATES OF MOLECULAR EVOLUTION IN EASTERN ASIAN AND EASTERN NORTH AMERICAN DISJUNCT PLANT SPECIES

Background

The eastern Asian – eastern North American (EA-ENA) floristic disjunction represents a large distributional disjunction of approximately 65 seed plant genera (Boufford and Spongberg,

1983; Graham, 1966; Wen, 1999; Zhengyi, 1983). These two disjunct floras are hypothesized to be remnants of a once-contiguous temperate/subtropical forest that became disrupted during the late Miocene and Pliocene. Genera that exhibit this disjunction have a complex history, with numerous expansion and fragmentation events (Lee et al., 1996; Nie et al., 2006a, 2006b; Xiang et al., 1998, 2000, 2006). The complex history of this disjunction offers an excellent opportunity to study biogeographical and evolutionary processes.

Many genera exhibiting this disjunct distributional pattern contain sister species or sister clades, with one species or clade per region, but other genera exhibit more complex species richness and biogeographic patterns. (Manos and Meireles, 2015; Xiang et al., 1998). Although

EA and ENA are of roughly the same geographic size (9.6 x 106 km2 vs. 9.4 x 106 km2, respectively; Qian, 2002), the flora of eastern Asia is generally more species-rich, with genera that exhibit the disjunction containing more species in EA than in ENA (~29,200 vs. ~18,000 species, respectively; Qian and Ricklefs, 1999). This species richness anomaly has led to the formation of several ecological hypotheses that aim to explain the higher species richness in EA, including: 1) the tropical rain forests of China contribute to its higher species richness, and 2) greater topographical heterogeneity in eastern China has led to isolation of lineages, speciation, and formation of local endemics (Axelrod et al., 1998; Guo et al., 1998; Kubitzki and Krutzch,

1998; Lu, 1999). Moreover, lower rates of extinction in EA than in ENA may also have contributed to the species richness anomaly (Qian, 2002); greater species richness in EA than

ENA in older clades, such as magnoliids, supports this hypothesis, as does recent support for eastern China as a ‘floristic museum’, comprising significantly more ancient lineages than predicted by chance (Lu et al., 2018). The combined effects of higher speciation and/or lower extinction rates in EA than ENA result in the ‘net diversification’ hypothesis: higher species richness in EA may be due to higher rates of net diversification (speciation - extinction) than in

ENA (Guo and Ricklefs, 2000; Qian and Ricklefs, 2000).

An alternative to ecological hypotheses is that higher rates of molecular evolution in species from EA may have generated greater species diversity (Xiang et al., 2004). Phylogenetic studies of some genera exhibiting the disjunction have shown that species in EA tend to have longer branch lengths than their ENA congeners, which may lead to a higher rate of speciation and species richness within EA (Xiang et al., 1998, 2004). Cornus, for example, has two clades that have been studied in this context: subgenus Mesomora, which comprises two species (one in

EA and one in ENA), and the Big-Bracted clade. Cornus controversa from EA has a longer branch length in phylogenies reconstructed from ITS sequence data than its ENA congener, C. alternifolia (Xiang et al., 2004). These results, showing higher species diversity and longer branch lengths in species from EA clades, support the hypothesis that EA species have higher rates of molecular evolution, and thus potentially higher rates of diversification, than their ENA congeners (Qian and Ricklefs, 2000; Xiang et al., 2004). Rates of molecular evolution have often been found to be positively correlated with speciation rates (Barraclough and Savolainen, 2001;

Duchene and Bromham, 2013; Ezard et al., 2013; Harvey et al., 2017; Mindell et al., 1990;

Webster et al., 2003). Moreover, environmental factors may lead to regional effects and drive differential rates of molecular evolution; for example, higher temperatures have been associated with higher nucleotide substitution rates (Davies et al., 2004; Gillooly et al., 2005). The positive

associations between accelerated evolutionary rates and increased diversification and between an environmental cue and molecular evolutionary rates could lead to a species richness anomaly across clades, such as that observed between EA and ENA.

The goal of this study was to use a large-scale genetic analysis of species representing these disjunct EA-ENA floras to investigate rates of molecular evolution in EA-ENA disjuncts across a range of genera using transcriptome data for up to several hundred single- and low-copy nuclear genes (77-385 loci). We test the hypotheses that molecular evolutionary rates (1) differ between species in EA and ENA and (2) are related to species richness. We also evaluate the strength and direction of selection on these genes and determine whether certain gene ontology

(GO) categories are evolving differently within species of a specific region (ENA versus EA).

Methods

Sample Selection, Tissue Processing, Transcriptome Assembly, and Ortholog Selection

The genera studied here were chosen based on previous research supporting their monophyly and status as EA-ENA disjuncts (Nie et al., 2006a; Parks and Wendel, 1990; Wen and Shi, 1999; Xie et al., 2010; Xiang et al. 2006; Xue et al., 2012; Zhou et al., 2006). All species pairs are sister species (with the exception of Gelsemium, which has two sister ENA species; Jiao and Li, 2007), and the larger genera (Calycanthus, Cornus, and Hamamelis) each form a clade. These species represent both herbaceous (Phyrma, Penthorum, Nelumbo, and

Saururus) and woody (Calycanthus, Campsis, Cornus, Cotinus, Gelsemium, Hamamelis,

Liriodendron) perennials. All species sampled occur in temperate forest habitats, except for the aquatic Nelumbo species. Ability to obtain fresh leaf tissue was also a factor in deciding which genera were included, as transcriptomes were needed to obtain numerous loci for analyses. Other genera were initially considered, but were not included due to potential polyphyly or restrictions on our ability to obtain fresh leaf tissue.

Fresh leaf tissue for species of 11 genera that exhibit the EA-ENA floristic disjunction was collected from natural populations and botanical gardens (see Table A-1 for accession and voucher information), placed in liquid nitrogen, and stored at -80C for subsequent RNA extraction. RNA extractions were performed per option 2 of the Jordon-Thaden et al. (2015) protocol, with the addition of 20% sarkosyl. RNA library preparation and sequencing were performed by BGI (Shenzhen, China). Transcriptome assemblies were performed following

Wickett et al. (2014). For each genus investigated, an appropriate outgroup was selected; we chose the closest relative of each genus represented in the OneKP project (excluding Cornus, for which a novel sample of Nyssa sinensis was used; see Supplementary Table A-1 for voucher information), and transcriptomes of outgroups were downloaded from OneKP (onekp.com). The outgroup species and OneKP accession codes are: Idiospermum australiense (BSVG, outgroup for Calycanthus), Kigelia africana (QKEI, outgroup for Campsis), Rhus radicans (YUOM, outgroup for Cotinus), Rauvolfia tetraphylla (QEHE, outgroup for Gelsemium), Loropetalum chinense (HQRJ, outgroup for Hamamelis), Magnolia grandiflora (WBOD, outgroup for

Liriodendron), Hakea prostrata (OBOJ, outgroup for Nelumbo), Aphanapetalum resinosum

(TOKV, outgroup for Penthorum), Teucrium chamaedrys (LRRR, outgroup for Phryma), and

Houttuynia cordata (CSSK, outgroup for Saururus).

MarkerMiner (Chamala et al., 2015) was used to identify and extract orthologous nuclear loci from transcriptome data, with Arabidopsis thaliana (L.) Heynh. as the reference genome.

MarkerMiner uses reference databases of several thousand orthologous genes identified from 20 angiosperm genomes (De Smet et al., 2013). We chose to analyze nuclear loci rather than organellar genes because of their higher substitution rate than plastid and mitochondrial genes and thus their increased utility for this study (Curtis and Clegg, 1984; Strand et al., 2003; Wolfe

et al., 1987; Zimmer and Wen, 2015). Given variation in transcriptome content, gene copy number, and other aspects specific to each clade, the number of orthologous loci identified for each genus varied. A pipeline consisting of GetOrf (EMBOSS v6.5.7; Rice et al., 2000), Muscle v3.8.31 (Edgar, 2004), and a Perl script to convert fasta files to phylip format was created to prepare the resulting alignments for Codeml (PAML v4.9a; Yang, 1997) analyses. Minimum

Open Reading Frame (ORF) size was optimized in two categories: 700 bases for taxa in pairwise comparisons and 750 bases for Calycanthus, Cornus, and Hamamelis, genera that comprise more than two species. Default settings were used for Muscle alignments. Locus identification, extraction, and alignment were conducted at the generic level to increase accuracy of alignments and site homology assessment.

Codeml Analyses

To assess differentiation of molecular evolutionary rates between species, we used

Codeml to estimate the rate of non-synonymous substitutions (dN), the rate of synonymous substitutions (dS), and their ratio, dN/dS ( for per-branch analyses), which is influenced by the strength and direction of selection. dS (also known as Ks) is a common statistic for inferences of molecular evolutionary rates, as it is less influenced by natural selection than dN and represents a rate of neutral evolution in protein-coding genes.

Codeml analyses (PAML v4.9a; Yang, 1997) were conducted either as (1) pairwise comparisons (parameters: model = 0, ratio = 1) for species pairs or (2) using a phylogeny with topologies based on previously reported results for the larger genera (Calycanthus = Xiang et al.,

1998; Zhou et al., 2006, Cornus = Xiang et al., 2006; Xiang et al., 2008, Hamamelis = Wen and

Shi, 1999; Xie et al., 2010; parameters: model = 1, nsites = 0; see Figure 2-1A-C for phylogenies used and how branches relate to named groups). When a published phylogeny was available, dN,

dS, and  were estimated per branch using a maximum likelihood framework. Results for per- branch estimates are reported as species names for terminal branches, clade names for branches that subtend named clades, or North America for branches that subtend clades that contain only

North American species. Both pairwise and per-branch analyses used an estimated codon table

(parameter: codon table = F3x4). Two pairwise comparisons were conducted for each species pair (one EA versus outgroup and one ENA versus outgroup, except for Liriodendron, for which we had two samples of L. tulipifera L., so two ENA versus outgroup analyses were conducted) so that we could compare relative rates for taxa in each region.

Post-Codeml Statistical Analyses

Custom Perl scripts were used to extract results from Codeml output files and output them to a csv file (all scripts and examples of required files (e.g., Codeml control files) have been uploaded to GitHub at https://github.com/meltonae/EA_ENA_Transcriptomics). GO category classifications were obtained from the Gene Ontology Consortium (Ashburner et al.,

2000; The Gene Ontology Consortium, 2015) and were uploaded to an SQL database with the resulting files. These GO categories were then associated with the appropriate single-copy orthologous loci by their gene IDs. SQL was used to identify and extract Codeml results for genes that met certain a priori criteria: 1) the gene was present in the dataset for all taxa in pairwise comparisons and by-branch analyses, 2) only one ORF was extracted, and 3) dN/dS and

 did not equal 99 or 999, respectively. (A dN/dS of 99 or  of 999 designates a score that could not be calculated and should not be reported, per the PAML author’s recommendation; https://groups.google.com/forum/#!forum/pamlsoftware.) This was the case for dN/dS and  comparisons only; scores for dN and dS were used in statistical comparisons of by-branch results, but not for pairwise comparisons due to a lack of genes that met criteria 1 and 2. These

strict loci selection criteria were set to control for variation in transcriptome assembly size and content, enabled direct comparison of results between sister species and among members of clades, and allowed us to assess patterns on a per-gene basis between EA and ENA taxa.

Welch’s t-tests were used to test for significance between the pairwise comparison datasets. An ANOVA followed by a Tukey multiple comparison test was used for Liriodendron due to the inclusion of two samples of L. tulipifera. All t-tests and ANOVAs were performed on log-transformed Codeml results due to a left-skewed distribution. Kruskal-Wallis and Dunn’s multiple comparison tests were used to identify branches that experience higher rates of evolution over the large gene sets (all statistical analyses conducted in R v.3.3.2 (R Core Team,

2016); Dunn’s test performed using dunnTest from the R package FSA v0.8.13 (Ogle, 2017).

Results

Pipeline Output

The pipeline to identify and select genes provided dN/dS data from between 77 genes

(Saururus) and 385 genes (Cotinus). The average number of genes represented in each genus plus the outgroup used for analysis was 168 (Supplemental Table A-2). The disparity in gene number between species sets was likely due to differences in the size, quality, and properties of the transcriptomes; together, these factors may have altered the overlap in the detected orthologues between ingroup and outgroup species in each species set, resulting in varying numbers of genes among species sets. In addition, the number of single-copy genes may vary among species sets. The combination of strict loci selection criteria and disparity in gene number between species ultimately resulted in a much smaller number of usable loci than expected from a transcriptome study or identified as single-copy by MarkerMiner.

Pairwise Analyses

Relative rates of molecular evolution were similar between samples from both geographic regions in pairwise comparisons (see Table 2-1 for mean statistics for each species,

Figure 2-2 for boxplots of pairwise results, Supplemental Table 2-3 for t-test statistics, and

Supplemental Table 2-4 for ANOVA results for Liriodendron). Although some differences in rates between sister species in EA and ENA were identified, none were statistically significant.

Liriodendron chinense had the highest mean dN and dS (mean dN = 0.0316; mean dS = 0.1478), as well as median dN (0.0226), while one sample of L. tulipifera had the highest median dS

(0.1176), but none of these differences were statistically significant.

For dN/dS scores, Cotinus obovatus had the highest mean score (0.2845), and Penthorum sedoides had the smallest mean score (0.1460). All dN/dS scores, except for those of Cotinus spp. (C. coggyira = 0.2805, C. obovatus = 0.2845), ranged from 0.1460 (Penthorum sedoides) to

0.1603 (Nelumbo lutea). Individual dN/dS scores for genes ranged from 0.0122 (AT4G20940 in

Gelsemium sempervirens) to 2.2790 (AT3G17810 in Phryma leptostachya var. asiatica). Of the species pairs, Nelumbo had the most genes with dN/dS ratios greater than one, including

AT2G15230, AT2G21860, and AT2G33610. Both species of Nelumbo had elevated dN/dS ratios for these genes.

The greatest disparity of dN/dS ratios between EA-ENA species pairs was found in

Cotinus (dN/dS = 0.004). Liriodendron chinense had the highest dN/dS (0.1440). No statistically significant differences for dN/dS ratios were identified between Liriodendron datasets.

Per-branch Analyses

For Calycanthus, the two North American species (C. floridus of ENA and C. occidentalis of western North America (WNA)) generally had higher mean and median dN and

dS scores than their Asian congener, C. chinensis (C. chinensis had higher median dN, but was lower for other scores; Figure 2-3 and Supplemental Table A-5 for Dunn test statistics). Both

North American species had statistically significant differences from C. chinensis for dS, although not for dN. No statistically significant differences were identified between species for dN, while EA-ENA comparisons resulted in statistical significance for dS. No statistical significance was found for C. floridus – C. occidentalis comparisons.

For Cornus, the highest mean and median dN and dS scores were for branches leading to nodes for clades recognized as subgenera (Figure 2-4 and Supplemental Table A-6 for Dunn test statistics). This genus includes a pair of sister species that exhibit the disjunction: C. alternifolia

(a North American species) and C. controversa (an eastern Asian species). No statistically significant differences were identified between these taxa. Within the broader context of the genus, C. florida had higher rates than other Cornus species included in this study, and those of

C. kousa were next highest. The highest rates per branch occurred for the branches leading to the

Big-Bracted Cornus clade and subgenus Mesomora.

Hamamelis vernalis had the highest mean and median dS and dN scores within the genus

(Figure 2-5 and Supplemental Table A-7 for Dunn test statistics). All comparisons with H. vernalis were statistically significant, with H. vernalis having higher dN and dS values than other species and branches. Hamamelis japonica also had significantly higher rates than the ENA taxa

(except H. vernalis).

GO Categories

Tests of significance for GO categories in species pairs showed no statistical significance for either region. Some per-branch results were statistically significant, but the trends followed those of the whole gene sets (see Supplementary Tables A-8 through A-14 for medians, standard deviations, and t-test/Kruskal-Wallis test results and Supplemental Figures A-1 and A-2 for

barplots of pairwise comparisons). For pairwise comparisons, an elevated score for a GO category in one region was generally matched in the other. Median dS scores for Biological

Process categories ranged from 0.0721 (Cotinus obovatus) to 0.8700 (Nelumbo nucifera).

Cotinus coggyira and N. nucifera also had the most extreme median dN scores for Biological

Process (0.0182 and 0.1096, respectively) and Molecular Function categories (0.0198 and

0.1062, respectively). Almost all genes with identified GO categories were under purifying selection (dN/dS or  < 1), and all median ratios were <0.2650. The highest median dN/dS ratios were found in Cotinus obovatus, which had a median of 0.2650 for Biological Process genes and

0.2622 for Molecular Function genes.

Discussion

The results of our analyses suggest that rates of evolution are not intrinsically higher in the geographic region with greater species richness for each clade, whether EA or ENA. Results of pairwise comparisons show statistically equivalent relative rates of molecular evolution.

While some variation was found for individual loci across taxa, the variation did not appear to be related to geographic region. Values of dS represent rates of neutral evolution within genes and thus are a good tool to evaluate rates of molecular evolution. Scores of dN are influenced by natural selection and are informative, but do not represent intrinsic rates of evolution as well as dS. There was a tendency for the EA species in a pairwise comparison to have a slightly higher mean dS score than its ENA counterpart (6/8), but none of these differences was significant, and neither region had a tendency for higher medians (4/8 for both EA and ENA species). Means of

EA species were likely inflated due to larger standard deviations and outlier results of some genes. Similarly, no patterns relating species from either geographic region to higher dS and dN scores were identified in the larger genera, although some terminal branches from EA had

significantly higher rates of molecular evolution. Both North American species of Calycanthus had higher mean and median dN and dS scores than their EA congener, C. chinensis, although only dS comparisons were statistically significant. Similarly, Cornus florida and Hamamelis vernalis (both ENA species) exhibited the highest mean and median rates in their respective genera. Within Cornus, there were no statistically significant differences between the two species of subgenus Mesomora, which are sister species separated by the disjunction.

One mechanism proposed to explain the ‘net-diversification’ hypothesis is that molecular divergence is correlated with species richness. Our results for some genera tend to support this hypothesis, but few of the differences in evolutionary rates are statistically significant. For example, Calycanthus has both higher rates of molecular evolution and greatest species richness in the same region (North America). However, given that only one species occurs in ENA and one in western North America, Calycanthus does not truly represent a strong test case for this hypothesis. In fact, of all genera analyzed in this study, only Cornus exhibits the typical pattern of higher species richness in EA than ENA. However, the results for most of the genera in this study are consistent with the hypothesis of species richness being positively correlated with molecular evolutionary rates (per Xiang et al., 2004) in that genera comprising EA-ENA sister species have equivalent species richness (one species each) and equivalent molecular evolutionary rates. For larger genera (i.e., Cornus, Hamamelis), no strong evidence supporting this hypothesis was identified, as both regions had species with elevated dN and dS scores:

Cornus florida (ENA) and C. kousa (EA), and Hamamelis vernalis (ENA) and H. japonica (EA).

Further tests of the relationship between species richness and molecular rates should involve additional genera that show greater disparity in species richness between EA and ENA.

For example, Carya, a genus that is more diverse in ENA (11 species) than EA (four species),

exhibited longer branch lengths in ENA than in EA congeners, supporting the molecular evolution hypothesis (Zhang et al., 2013). Unfortunately, our study was limited to those genera for which we could obtain fresh tissue for transcriptome analysis. Thus, several genera that would provide opportunities to test the relationship between molecular evolutionary rates and species richness were not included here, but we recommend further analyses that include these genera, whether with greater species diversity in EA or ENA.

While some species were shown to have higher rates of evolution than close relatives, they did not always occur in the region with higher species richness for that genus. For example,

Cornus florida has generally higher dN and dS values than other species of Cornus included in this study, but it is the only Big-Bracted dogwood species in this analysis to occur in ENA.

These results suggest that there is not a specific relationship between rates of molecular evolution and a species’ respective geographic region. However, the higher rates in Cornus florida may, in part, be due to missing Big-Bracted taxa, including Cornus nuttallii (a WNA species that is the most probable sister to C. florida) and C. disciflora of Mexico (Xiang et al.,

2008). Without C. nuttalli, the branch length for C. florida relative to those of the EA Big-

Bracted species may have been artificially increased in these analyses, leading to inflated dN and dS scores.

The intercontinental EA-ENA separation of many congeneric species has occurred within the last 30 million years (Donoghue and Smith, 2004; Graham, 1993; Manos and Meireles, 2015;

Wen et al., 2010). Published molecular-based median divergence dates for species pairs investigated here range from 1.5 mya for Nelumbo (95% highest posterior density = 0.3 – 4.2 mya; Xue et al., 2012) to 14.15 mya for Liriodendron (95% intervals = 8.69 - 21.01 my; Nie et al., 2008), suggesting that the disjunction arose at different times in different lineages (Xiang et

al., 1998), although these studies used different methods and different loci. Here, species pairs isolated for long periods of time have retained similar rates of molecular evolution, even in distinct regions. Given the generally low dN/dS ratios and , it could be interpreted that strong purifying selection has acted on species in the disjunct lineages in similar ways, perhaps due to very similar environmental conditions, leading to similar relative rates of evolution.

The dS, dN, and  scores for Cornus were highest on the branches leading to the two major subclades, the Big-Bracted clade and subgenus Mesomora. The diversification of the major lineages of Cornus likely occurred approximately 70.33 - 72.77 mya, with the Big-Bracted clade further diversifying approximately 44.55 mya (Yu et al., 2017). The diversification of these clades followed a whole-genome duplication (WGD) event, which occurred approximately 76.10 mya (SD = 2.70; Yu et al., 2017). It has been well established that rates of molecular evolution are often elevated following a WGD event as genome restructuring and changes in gene function may occur (Lim et al., 2008; Akhunov et al., 2013). However, it is unlikely that this event would have influenced the dS and dN scores and their ratio of the branch leading to the Big-Bracted clade, as it occurred ~35 mya before the divergence of the clade. Although Mesomora did separate from other Cornus lineages ~78 mya, there are other lineages absent from this phylogeny that are more closely related to Mesomora than the Big-Bracted clade (Yu et al.,

2017). Both key divergences in the phylogeny (the split into major clades and the origin of the

Big-Bracted clade) occurred following increases in temperature of the Northern Hemisphere

(Late Cretaceous, ~72.8 mya, and the Eocene Optimum, ~49 mya). These climatic events may have acted as driving forces behind the elevated rates of molecular evolution in Cornus estimated in our analyses.

Most of the genes investigated here were generally under strong purifying selection in both regions. This is likely due to the nature of single-copy orthologous genes serving essential functions within the cells, such as ion transport and DNA replication. With few exceptions, genes under directional selection had approximately equivalent ratios. This implies that natural selection has acted upon these genes in equivalent ways in both geographic regions. Dong et al.

(2019) found a similar pattern to that reported here of genes being under purifying selection with low rates of molecular evolution. However, Dong et al. (2019) aimed to answer fundamentally different questions about molecular evolution in allopatric species using species pairs and allopatric non-sister members of disjunct genera to evaluate patterns in Ks and to estimate the timing of disjunction events. The research described here focuses explicitly on the disjunction to address a specific hypothesis about the relationship between rates of molecular evolution and species diversity within genera, following a previously posed hypothesis as a possible explanation for higher species richness in eastern Asia than eastern North America, despite the shared presence of many genera in both regions. Other than the different questions addressed, different methods to characterize rates of molecular evolution in this paper and Dong et al.

(2019). Dong et al. (2019) conducted simple pairwise comparisons of one Asian and one North

American species, even when the genus included additional species. In contrast, we utilized multiple comparisons to outgroups and tree-based analyses to enable comparisons of molecular rates within and between continental regions. While Dong et al. (2019) analyzed a larger number of genes than employed in this study, the analyses described here used a more stringent ortholog assessment pipeline that is more likely to have returned true orthologs and required that orthologs be present in all species per comparison for inclusion in analyses. While most genes included here were under purifying selection, some genes were identified that were under

directional selection. For example, Nelumbo had the most genes under directional selection:

AT2G15230, AT2G21860, and AT2G33610. Phryma also exhibited a high dN/dS for one gene:

AT3G17810. These genes function in a broad range of processes and are expressed in numerous tissues throughout the plant life cycle.

Finally, the results of our analyses may have been influenced by the choice of genes. We included only orthologous nuclear loci identified by MarkerMiner and subject to strict selection criteria for orthology. Many of these genes, and presumably others, serve important functions in various cellular processes. Given the diverse roles of these genes, it is not surprising that many exhibited strong purifying selection. The use of these genes may have biased our results towards lower rates of molecular evolution, more purifying selection, and potentially less genetic differentiation. Given the similarity of rates and dN/dS ratios, we conclude that differences in the molecular evolutionary rates of these genes have not contributed to differences in species richness per region for the clades investigated. While this research provides an important step forward in our understanding of the species richness anomaly, future research should build upon these findings. For example, methods of locus selection that incorporate a broader range of loci from transcriptomes and comparable dN/dS analyses on plastid and mitochondrial loci should be a focus of future research. Furthermore, investigations of clades that exhibit a greater disparity in species richness in the two regions should be conducted. The work presented here used mostly species pairs and smaller clades due to limitations in availability of fresh tissue required for the successful analysis of transcriptomes. However, there are clades that have large disparities in species number between EA and ENA (e.g., Illicium, which has 27 species in EA and two in

ENA, and tribe Lysimachieae of Primulaceae, which has 171 species in EA and <30 in other regions, including ENA). These clades with larger disparities in species richness between regions

will be ideal models for more rigorous analyses of molecular evolution and how rates of molecular evolution may affect species richness across disjunct distributions.

Table 2-1. This table includes all species included in the study, the region in which it occurs, and mean for dN, dS, and dN/dS () scores. Mean Mean Mean Species Region dN dS dN/dS Calycanthus chinensis EA 0.0024 0.0102 5.4071 Calycanthus floridus ENA 0.0045 0.0226 0.7408 Calycanthus occidentalis WNA 0.0039 0.0236 0.6454 Campsis radicans ENA 0.0451 0.2881 0.1471 Campsis grandiflora EA 0.0403 0.2878 0.1462 Cornus alternifolia ENA 0.0046 0.0478 0.3362 Cornus capitata EA 0.0055 0.4370 1.9630 Cornus controversa EA 0.0027 0.0145 2.3391 Cornus elliptica EA 0.0040 0.4613 1.9907 Cornus florida ENA 0.0039 0.0179 0.2943 Cornus kousa EA 0.0032 0.0160 0.2238 Cotinus coggyria EA 0.0199 0.0753 0.2805 Cotinus obovatus ENA 0.0199 0.0753 0.2845 Gelsemium elegans EA 0.0907 0.6420 0.1535 Gelsemium sempervirens ENA 0.0913 0.6407 0.1557 Hamamelis japonica EA 0.0018 0.0068 0.7419 Hamamelis mollis EA 0.0009 0.0037 6.2917 Hamamelis ovalis ENA 0.0006 0.0026 20.0503 Hamamelis vernalis ENA 0.0077 0.0348 0.2473 Hamamelis virginiana (A) ENA 0.0006 0.0036 16.4061 Hamamelis virginiana (B) ENA 0.0008 0.0032 2.0461 Liriodendron tulipifera (A) ENA 0.0298 0.1397 0.2123 Liriodendron tulipifera (B) ENA 0.0282 0.1311 0.2161 Liriodendron chinense EA 0.0316 0.1478 0.2141 Nelumbo lutea ENA 0.1155 0.8721 0.1603 Nelumbo nucifera EA 0.1156 0.8775 0.1601 Penthorum chinense EA 0.0802 0.5582 0.1479 Penthorum sedoides ENA 0.0757 0.5531 0.1460 Phryma leptostachya ENA 0.0757 0.5749 0.1470 Phryma leptostachya EA 0.0760 0.5771 0.1470 Saururus cernuus ENA 0.0447 0.2850 0.1577 Saururus chinensis EA 0.0408 0.2676 0.1576

Figure 2-1. A) Topology used for the analyses of Calycanthus genes. The branch labels represent the median dS value for that branch. The ENA branch is in light blue, WNA branch in dark blue, and EA branch in pink. No statistically significant differences were identified for dN comparisons, while EA- North American comparisons were statistically significant for dS. B) Topology used for the analyses of Cornus genes. The branch labels represent the median dS value for that branch. ENA branches are in light blue, and EA branches are in pink. The highest median dS scores are for the interior branches leading to the major clades: the Big-Bracted clade (median dS = 0.0663), subgenus Syncarpea (0.0121), and subgenus Mesomora (0.047). The highest median dS for a terminal branch was that of C. florida (0.0178). C) Topology used for the analyses of Hamamelis genes. The branch labels represent the median dS value for that branch. ENA branches are in light blue, and EA branches are in pink. The highest median dS score was for the terminal branch of H. vernalis (ENA).

Figure 2-2. Boxplots and bar graphs for dN, dS, and dN/dS scores for pairwise comparisons. Median values for all three metrics were found to be very similar for EA (pink) and ENA (light blue) species.

Figure 2-3. Boxplots for log-transformed dN, dS, and  scores for Calycanthus analyses. The two North American species had higher median dS scores, while C. chinensis had higher median dN.

Figure 2-4. Boxplots for log-transformed dN, dS, and  scores for Cornus analyses. The ENA species C. florida had the highest median dN and dS scores, and the EA species C. kousa had the second highest scores. These are the only two taxa that have statistically significant deviations from all other included Cornus species. The highest scores overall occurred on the branches leading to the Big-Bracted and subgenus Mesomora clades.

Figure 2-5. Boxplots for log-transformed dN, dS, and  scores for Hamamelis analyses. The ENA species H. vernalis had the highest median scores and was statistically significant in its differences. The EA species, H. japonica and H. mollis, had higher medians than ENA species except H. vernalis.

CHAPTER 3 CLIMATIC NICHE COMPARISONS OF EASTERN ASIAN AND EASTERN NORTH AMERICAN DISJUNCT PLANT SPECIES

Background

Species richness anomalies in a diverse array of clades spanning eukaryotes have been found to occur along altitudinal, ecological, and latitudinal gradients, as well as across distribution disjunctions (van Hooindonk et al., 2013; Kerkhoff et al., 2014; Rahbek, 1995; Rex et al., 2000; Willig and Lyons, 1998;). One of the most well-known species richness anomalies is the eastern Asian – eastern North American (EA-ENA) floristic disjunction. This disjunction has a complex history, with numerous range shift and migration events, during the Miocene and

Pliocene (Xiang et al., 2000, 2006).

Many lineages that exhibit this EA-ENA disjunction possess sister species or sister clades occurring in each region, although the disjunctions may have originated at different times (i.e., a pseudocongruent pattern; Manos and Meireles, 2015; Xiang et al., 1998). Approximately 65 seed plant genera are shared between the two regions, most of which are generally more species-rich in EA than in ENA (Wen, 1999). In fact, the flora of EA contains approximately 1.6 times as many species as the ENA flora (Boufford and Spongberg, 1983; Graham, 1966; Ricklefs et al.,

2004; Qian and Ricklefs, 1999; Wen, 1999). The ‘net diversification’ hypothesis states that species richness is related to the rate of speciation and extinction within each region since the formation of the disjunction, with greater diversification and less extinction in EA than in ENA, and may explain this disparity (Guo and Ricklefs, 2000).

Climatic heterogeneity has also been found to influence species richness (Jimenez and

Ricklefs, 2014; Tripathi et al., 2019). This finding has led some researchers to hypothesize that

‘regional effects’ have led to increased net speciation within one region of a disjunction, leading to the species richness anomaly (Ricklefs, 2004). This pattern of increased species richness

coupled with climatic heterogeneity is evident in the EA-ENA floristic disjunction. While the two general environments of these regions are highly similar (both are temperate and continental climate zones per the Köppen climate classification; Beck et al., 2018), EA is more heterogeneous for numerous climatological parameters, such as mean annual temperature and mean annual precipitation (Jimenez and Ricklefs, 2014). This increased climatic heterogeneity in

EA has been linked to its high topographical heterogeneity (Liu and Yin, 2002; Zhang et al.,

2018). Seasonal and annual climatic heterogeneity have been hypothesized to influence species richness and contribute to species richness anomalies, such as the EA-ENA anomaly (Chesson,

2000; Jimenez and Ricklefs, 2014; Mathias and Chesson, 2013; de Souza et al., 2014; Wiens,

2000).

Niche conservatism, the tendency of novel lineages to retain aspects of the ancestral fundamental niche, has been found to be an important aspect of allopatric speciation (Wiens,

2004; Wiens and Graham, 2005). One hypothesis for the origin of the EA-ENA species richness anomaly is that the elevational and physiographical heterogeneity of EA, in conjunction with climate and sea-level change events, has led to increased ecological opportunity and thus higher rates of allopatric speciation per unit area (Qian and Ricklefs, 2000). While EA exhibits greater environmental heterogeneity than ENA, it has been noted that congeneric species spanning the

EA-ENA disjunction often occur in highly similar habitats and have conserved morphologies

(Li, 1952).

The goal of this study was to assess the similarity and breadths of the climatic niches of congeneric species that are disjunct between EA and ENA. I aimed to test the hypotheses that: 1)

EA species will exhibit narrower geographic distributions of suitable climatic niche space, 2) ecological niche models of EA species will predict lesser suitability smoothness across the

distributions in geographic space due to greater climatic heterogeneity than those for ENA species, and 3) disjunct species will exhibit elevated climatic niche similarity relative to null expectations. These hypotheses are tested using a large sampling of species exhibiting disjunction (64 species from 19 genera). This study represents the first effort to evaluate the climatic niches of these disjunct species at this broad scale.

Methods

Occurrence Data Processing

Sixty-four species representing 19 genera exhibiting the EA-ENA disjunction were selected for this study (Wen, 1999). These 19 genera include: Aesculus, Amphicarpaea, Campsis,

Castanea, Catalpa, Cercis, Cornus subgenus Mesomora, Corylus, Gelsemium, Gymnocladus,

Liriodendron, Mitchella, Nelumbo, Pethorum, Pieris, Sassafras, Saururus, Torreya, and

Wisteria. Ten of these genera (Amphicarpaea, Campsis, Cornus subgenus Mesomora,

Gelsemium, Gymnocladus, Liriodendron, Mitchella, Nelumbo, Penthorum, and Saururus) consist of EA-ENA species pairs (with the exception of Gelsemium, which has two sister ENA species though is represented here by one EA and one ENA species; Jiao and Li, 2007). For other genera containing EA-ENA sister clades, all EA and ENA species with sufficient occurrence data were included for analysis, while any non-EA-ENA species (e.g., Aesculus hippocastanum of Europe) were excluded. Criteria for use in analyses included: 1) support of monophyly, 2) status as sister

EA-ENA species or clades, and 3) sufficient occurrence data available for analyses (Bremer and

Eriksson, 2009; Davis et al., 2002; Du et al., 2020; Jiao and Jianhua, 2007; Lang et al., 2007; Li et al., 2009, 2014; Meng et al., 2002; Nie et al., 2008; Ohashi and Ohashi, 2018; Olmstead et al.,

2009; Whitcher and Wen, 2001; Xiang et al, 1998, 2000, 2006; Xue et al., 2012).

Occurrence data for all species were downloaded from iDigBio

(https://www.idigbio.org/) and the Global Biodiversity Information Facility (GBIF;

https://www.gbif.org/) via the ‘spocc’ V.0.7.0 package in R (Chamberlain, 2017). Downloaded data points were cleaned to exclude: 1) those without complete GPS coordinates, 2) those with unlikely coordinates (e.g., 0,0), 3) duplicate localities and dates, and 4) those lacking environmental data (1 and 2 were performed via ‘scrubr’ V.0.1.1 package in R; Chamberlain,

2016). Remaining data points were reduced in resolution to the precision of the environmental data. Points occurring outside of natural distributions (per Flora of North America and Flora of

China via eFloras; Brach and Song, 2006) were excluded as well. Occurrence data were collected from the Chinese Virtual Herbarium (http://www.cvh.ac.cn/) for EA taxa without sufficient data in iDigBio and GBIF. The gridSample command in the R package ‘dismo’ was used to ensure that only one occurrence point per 2.5 arc minute grid cell was present (Hijmans et al., 2017). Dan Warren’s thin_max.R script (available for download at http://enmtools.blogspot.com/) was used to rarefy occurrence point sets to a maximum of 100 points per species for model development. This latter analysis was performed to reduce collection biases and maximize geographic evenness of occurrence data for species with large quantities of publicly available data. Spatial biases in collection data have been found to reduce predictive power of models, and rarefying data has been found to reduce the negative effects of biases (Beck et al., 2014; Boria et al., 2014).

Climatic Data Processing

Climatic data (BioClim version 2 layers at a 2.5 arc minute resolution) were downloaded from WorldClim (Hijmans et al., 2005; Fick and Hijmans, 2017). To delimit the training regions for model development, a convex hull polygon was constructed around occurrence points for each species using ‘rgeos’ V.0.4-2 in R (Bivand and Rundel, 2018). A buffer of 0.25 degrees was then added around the convex hull to delimit the training region via ‘rgeos’ V.0.4-2 in R. Layers were masked and cropped to the extent of the buffered convex hull for each species via the mask

and crop functions in the ‘raster’ package V.2.6-7 in R (Hijmans, 2017). All scripts used for this research have been deposited in the GitHub repository https://github.com/meltonae/ENM.

Ecological Niche Model Development

Ecological niche models (ENMs) were developed using Maxent V.3.3.3k (Phillips et al.,

2018) through ‘ENMEval’ V0.3.0 (Muscarella et al., 2014) in R using the cleaned occurrence data, cropped BioClim layers, and a maximum of 10,000 background points randomly sampled throughout each species respective training as input data. Model development was performed with eight regularization multiplier (RM) values, ranging from 0.5 to 4.0, increasing in increments of 0.5, and five feature-class (FC) combinations (“Linear”, “Hinge”, “Linear +

Quadratic”, “Linear + Hinge + Quadratic”, “Linear + Quadratic + Product”). This combination of model parameters generated 40 models for testing and evaluation for each species. Model testing was performed using the “checkerboard2” method (aggregation factors equal to two and two), which geographically partitions occurrence data into four bins, at a coarse and a fine scale.

This method of model testing results in testing data bins that approximately equally sample geographic and ecological space (Muscarella et al., 2014). While the Receiver Operating

Characteristic Curve, also known as the AUC, scores are commonly used to assess niche models, they can be misleading as they are easily biased, can often be inflated by over-fit models, and are not ideal for species distribution estimation (Jiménez‐Valverde, 2012; Lobo et al., 2008).

Therefore, models were assessed using ∆AICc scores, with the model score of zero being converted to a logistic output format, projected onto the training region, and used for downstream analyses. Features of the best-fit models are listed in the supplemental file

Lambdas.xlsx. Binary predicted presence/absence maps were constructed from ENMs by transforming raster cells that contained a predicted suitability score that met the minimum

predicted suitability threshold, equal to the lowest suitability score of a cell containing an occurrence point for a given model, into a predicted presence and those that did not meet the threshold into a predicted absence. Binary predicted occurrence maps were used to estimate the geographic area occupied by each species.

Analyses

The raster.breadth function of ‘ENMTools’ V.0.2 in R (Warren et al., 2017) was used to assess models for how much of the available niche space within the training region of each species is being utilized. These metrics are standardized versions of metrics for niche breadth

(Levins, 1968; referred to as B1 and B2). These metrics range from zero (no suitability in the raster) to one (all cells in the raster have high suitability) and represent the smoothness of suitability values across the landscape onto which the model is projected. These metrics are both equal to N for N uniformly used resources, which are, in this case, components of climatic niche space as projected onto the geographic training region for a given species (Levins, 1968).

Climatic niche widths (parameter maximum – parameter minimum) were calculated for mean annual temperature and mean annual precipitation. To calculate climatic niche widths (Tw = temperature, and Pw = precipitation), values were extracted from 2.5 arc minute BioClim layers

(mean annual temperature (MAT), mean annual precipitation (MAP)) at occurrence points and the difference of the maximum and the minimum for each parameter was taken. The kernel density estimation method implemented in the R package ‘hypervolume’ V.2.0.8 (Blonder and

Harris, 2017) was used to estimate climatic niche breadth for each species within a five- dimensional niche space comprising the top five principal component layers for the training region of each species, derived from all 19 BioClim layers using the rasterPCA command in the

R package ‘RStoolbox’ V.0.2.4 (Leutner et al., 2019). The principal component layers were used to reduce dimensionality and correlations within the climatic data. To estimate the occupied

niche space hypervolume, points were generated from within estimated distributions using the

RastertoPoints function of the ‘raster’ package in R. Niche space hypervolumes were calculated using the box kernel density estimation method. An unpaired Mann-Whitney test via the wilcox.test function in the R package ‘stats’ V.3.4.3 (R Core Team, 2017) was used to assess statistically significant differences between the estimated distribution sizes, raster.breadth scores, niche widths, and climatic niche hypervolumes for EA species versus ENA species.

Asymmetric ecospat background tests were conducted for each possible pairwise species comparison within each genus via ‘ENMTools’ using the top two principal component layers of the training region of each species, derived within ‘ENMTools’ from all 19 BioClim layers, and

1000 replicates. The asymmetric background test samples environmental data at occurrence points for one species to compare to environmental data at random background points from the training region of another species. This is repeated n times to develop a distribution curve of overlap scores to which the empirical overlap of e-space of each species is compared. Because the test is asymmetric, two tests must be conducted per species pair to determine statistical significance for both comparisons. The ecospat version of this test uses kernel density smoothing to evaluate density and overlap in e-space (Broennimann et al., 2012).

Results

Overall, estimated sizes of suitable climate distributions ranged from 15,871 km2 to

7,328,562 km2 (SD ± 1,631,036 km2) for the 64 species analyzed here, with ten genera having larger average estimated distributions for their ENA species than EA species (Aesculus, Campsis,

Catalpa, Cercis, Corylus, Gymnocladus, Mitchella, Penthorum, Sassafras, and Wisteria) and nine genera having larger average estimated distributions for their EA species (Amphicarpaea,

Castanea, Liriodendron, Nelumbo, Pieris, Saururus, and Torreya). For EA species, the mean of the estimated distributions was 1,911,248 km2 (SD ± 1,599,542 km2), while for ENA species, the

mean was 2,384,568 km2 (SD ± 1,694,490 km2; regional results for all geographic space metrics per genus are reported in Table 3-1, and all results for species are listed in Table A-1; Figure 3-

1). Results for the Mann-Whitney test for EA-ENA comparison of estimated distribution size were not statistically significant (P-value = 0.2281).

Raster breadth metrics ranged from 0.920509 to 0.999947 (SD ± 0.016531) and 0.34431 to 0.998742 (SD ± 0.166602), for B1 and B2, respectively. For both metrics, Gymnocladus was the only genus to have higher mean scores for EA species than ENA species. The EA species had means of 0.9830953 (SD ± 0.01709871) and 0.768542 (SD ± 0.1744313) for B1 and B2, respectively. For ENA, B1 and B2 means were 0.9907589 (SD ± 0.01528102) and 0.8684513

(SD ± 0.1424184), respectively. Results for Mann-Whitney tests for both B1 and B2 were statistically significant, with P-values of 0.007391 and 0.01075, respectively.

Climatic niche space hypervolumes ranged from 57.320796 to 327.664033 (SD ±

52.11487371). The mean niche hypervolumes were 138.6532 (SD ± 48.43493) and 101.8 (SD ±

51.20381) for EA and ENA species, respectively, with only Pieris and Torreya having higher means for ENA species than EA species. The Mann-Whitney test for hypervolume comparisons were statistically significant, with a P-value of 3.62e-05. Regional results for all ecological space metrics per genus are reported in Table 3-2.

Climatic niche widths ranged from 2.520917 to 45.02167 (SD ± 8.571903) for MAT

(°C/year) and 318 to 6482 (SD ± 1307.135) for MAP (mm/year). At the generic level, all but two genera, Sassafras and Wisteria, had broader mean Tw for their EA species and 14 genera had broader Pw for their EA species, with Cercis, Corylus, Gymnocladus, Mitchella, and Nelumbo having higher mean Pw for ENA species. For EA species, mean climatic niche widths were

24.93971(SD ± 8.281703) and 2593.324 (SD ± 1250.321), for Tw and Pw, respectively. For ENA

species, mean climatic niche widths for individual climatic variables were 16.31461(SD ±

6.421791) and 1725.185 (SD ± 1260.455) for Tw and Pw, respectively. Mann-Whitney tests for climatic niche width parameters were statistically significant, with P-values for Tw and Pw being

4.54e-05 and 0.001431318, respectively.

The asymmetric ecospat background test comparison set with the highest mean empirical overlap was EA-EA (i.e., the occurrence data of species 1, an EA species, being compared to the background points of species 2, an EA species), with a mean Schoener’s D of 0.3733653 (SD ±

0.149732). Mean empirical Schoener’s D values were 0.2866196 (SD ± 0.2343286), 0.3632442

(SD ± 0.1710659), and 0.3632442 (SD ± 0.1710659) for ENA-ENA, ENA-EA, and EA-ENA comparisons, respectively. Eleven EA-EA, five ENA-ENA, 19 ENA-EA, and 21 EA-ENA comparisons were statistically significant for Schoener’s D value comparisons. All statistically significant comparisons showed that occupied niche space had higher overlap than the null expectation (all comparisons are listed in Supplementary Table 3-2; overlap averaged by genus is listed in Supplementary Table 3-3; Figure 3-2).

Discussion

These comparisons of climatic niche suggest that ENA and EA species generally have high levels of niche overlap while highlighting differences in the distribution of suitable climate and how much of the available climatic niche space these species occupy. While the investigated

ENA and EA species had similarly sized predicted geographic distributions, ENA species had smaller hypervolumes of climatic niche space than their EA relatives, most likely due to relatively greater environmental homogeneity in the region. The ENA species were found to have higher raster breadth scores, indicating that they occupy more of the available climatic niche space available to them within their distributions, as more of the g-space is of suitable climate.

While EA species tended to have much greater hypervolumes than their ENA congeners, there were two exceptions: Pieris and Torreya. These genera both have species in ENA that narrow endemics, Torreya taxifolia and Pieris phillyreifolia. The kernel density estimation method of calculating hypervolumes performs well with regards to sensitivity when using small sample sizes, though may not perform as well as others in regards to specificity, potentially leading to overestimation of hypervolumes (Qiao et al., 2017). The EA species examined here tended to have much broader niche widths for all parameters. All but two genera, Sassafras and

Wisteria, had greater average Tw scores for EA species than ENA species. Both of these genera have one ENA species each that is more broadly distributed than their EA congeners, increasing the range of mean annual temperature extremes within their distributions. The greater climatic heterogeneity and resolution of the climate layers (2.5 arc minute, ~5-km2 cells) may also explain these broader climatic niche widths of the EA species, as these data may be too coarse to capture the true values at a given occurrence point. Given the more heterogeneous topography and climate of EA, there should be greater opportunity for species to partition themselves in microhabitats that are more similar to those of the ENA species.

The asymmetric ecospat background showed that many species across the disjunction share greater niche overlap than expected given similarity in the background environments.

While the overlap in background environmental data was variable and sometimes quite low, many congeneric species showed statistically significant overlap in occupied e-space. EA-ENA and ENA-EA comparisons had the most statistically significant comparisons, with 21 and 19, respectively. Species separated by the disjunction likely have some phylogenetic signal for niche parameters, although this was not tested here, and these sister species and members of sister clades are likely partitioning themselves in the most similar climatic niche space possible within

these different areas. While the phylogenetic relationships of many disjunct genera have been investigated, many relationships remain unresolved (Han et al., 2010; Li et al., 2014; Manzanilla et al., 2018; Morris et al., 2007; Schnabel et al., 2003; Wen, 1999; Zuo et al., 2017). Increasing resolution of phylogenetic relationships within disjunct genera will allow for more testing of niche conservatism and divergence using methods such as age-overlap correlation tests and reconstructions of ancestral niche states. In a study of Aesculus L., Du et al. (2020) found evidence of phylogenetic signal for several bioclimatic variables, though they analyzed each bioclimatic layer individually, as opposed to an overall bioclimatic niche space, and did not take geography into account.

The topography of Asia plays important roles in influencing the climate of Asia, as well as species distributions. The climate of East Asia has been heavily influenced by mountain ranges, especially the Himalayas and Qinghai-Tibetan Plateau (QTP), since the Eocene (Zhang et al., 2018). The Himalayas and QTP act as barriers to the incoming monsoons, leading to stalls over land and increased precipitation; these highlands even influence winter snowfall and summer rainfall in East Asia (Wu and Qian, 2003). The East Asian Monsoon is particularly sensitive to these mountain ranges (Liu and Yin, 2002). The Hengduan Shan, at the southeastern edge of the QTP, are also biological hotspots, harboring numerous endemic and endangered species (Antonelli et al., 2018; Lei et al., 2003; Lockwood et al., 2013; Matuszak et al., 2016;

Zhang, et al., 2014; Zhang and Ma, 2008).

Species in EA have likely experienced greater allopatric speciation, and these results are consistent with hypotheses that species in EA exhibit niche conservatism and may have speciated due to greater topographical heterogeneity of EA. This interpretation of our results is consistent with results described by Qian and Ricklefs (2000), who found that allopatric speciation due to

topographical heterogeneity was the most probable mode of speciation. Qian and Ricklefs (2004) used analyses of distribution maps to show that disjunct genera generally occupied larger geographic distributions in EA than their ENA counterparts. While their results differ from those described here, they are not necessarily contradictory. The investigation by Qian and Ricklefs analyzed lineages at the generic level, while those described here were conducted at the species level. While EA and ENA species may occupy similarly sized geographic areas, the combined total distribution of a genus with more species in EA may be larger than its ENA counterpart.

While sizes of estimated geographic distributions of suitable climatic niche space were not statistically significantly different between the two regions, ecological niche models of EA species did predict lesser suitability smoothness across these distributions in geographic space than those for ENA species. This is likely due to increased environmental heterogeneity, which is demonstrated by greater hypervolumes and niche widths for ENMS for EA species. Many comparisons of disjunct congeneric species also demonstrated that these species exhibit greater climatic niche similarity than by chance, highlighting that niche conservatism via retention may be an important aspect of allopatric speciation within these genera. While our study included species from numerous genera that exhibit the EA-ENA disjunction, future studies including investigations using other disjunct genera, as well comparisons including non-disjunct genera, other ecological factors, such as soil data, should be conducted. Analyses informed by phylogenies will also be useful, as they will allow us to disentangle biogeographic and ecological processes, and how they have influenced the evolution of species within this disjunct flora.

Table 3-1. Regional means per genus for estimated distribution size, niche breadth (B1 and B2), hypervolumes (HV), and mean climatic niche widths (mean annual temperature and annual precipitation) per genus. Statistics were averaged by region. Statistics per species are listed in Table B-1.

Genus Region km2 B1 B2 EA 1179667.087 0.9616711 0.588970225 Aesculus ENA 934469.6113 0.9748679 0.7184252 EA 5019907.573 0.9737322 0.578727 Amphicarpaea ENA 4017483.462 0.9975996 0.9531993 EA 3241116.747 0.9908785 0.8280334 Campsis ENA 3404901.125 0.9987991 0.9704227 EA 1860070.107 0.99450115 0.90205585 Castanea ENA 1582187.622 0.99612775 0.92006485 EA 2055159.138 0.9917224 0.857271033 Catalpa ENA 3101676.626 0.9979559 0.9558385 EA 1371690.575 0.975860725 0.70299465 Cercis ENA 3585849.547 0.9999472 0.998742 EA 3438950.032 0.9847701 0.7123821 Cornus ENA 4031087.827 0.9941339 0.880648 EA 2265081.916 0.987092867 0.793322367 Corylus ENA 5263568.278 0.9890598 0.8160042 EA 1844880.06 0.9741882 0.6636191 Gelsemium ENA 1378841.036 0.9925171 0.8689206 EA 1280078.023 0.9986364 0.9704702 Gymnocladus ENA 1507246.248 0.9730235 0.6232162 EA 2275195.853 0.9628968 0.496357 Liriodendron ENA 1659291.539 0.9916691 0.8527061 EA 330420.4706 0.9760057 0.7142776 Mitchella ENA 3714256.302 0.9972064 0.9375332 EA 6304696.933 0.9902036 0.8049597 Nelumbo ENA 4348457.871 0.9947665 0.8982399 EA 3171308.339 0.9983548 0.9617917 Penthorum ENA 3922398.253 0.998467 0.9684721 EA 1854465.706 0.9803717 0.7515055 Pieris ENA 265737.3701 0.99190675 0.8579036 EA 886060.4986 0.9959797 0.93236565 Sassafras ENA 2639545.535 0.9996181 0.9910199 EA 3429621.915 0.98108 0.7077611 Saururus ENA 2604353.185 0.9978365 0.9512256

Table 3-1. Continued. Genus Region km2 B1 B2 EA 885488.4201 0.981386467 0.7326409 Torreya ENA 15871.44267 0.9946997 0.9541883 EA 818020.5399 0.9911699 0.8520739 Wisteria ENA 2455056.966 0.9957676 0.9079029

Table 3-1. Continued.

Genus Region HV Tw Pw EA 163.1794473 24.765125 2668.25 Aesculus ENA 144.5562848 10.9901334 1013.8 EA 96.075283 33.45109 3454 Amphicarpaea ENA 69.005889 21.00842 1747 EA 122.143033 29.87333 3051 Campsis ENA 57.320796 20.44167 1601 EA 131.6565658 22.3418775 2803.5 Castanea ENA 109.715361 16.715445 1320.5 EA 104.6021803 29.29866333 2438 Catalpa ENA 67.5279015 19.095695 1533 EA 151.6588865 21.0372075 1731.75 Cercis ENA 64.236995 18.421 2577 EA 120.151877 32.19617 3916 Cornus ENA 74.320265 22.38367 1747 EA 141.1193547 25.74705667 1779.666667 Corylus ENA 81.3720145 19.50125 2593.5 EA 98.008235 19.7665 3313 Gelsemium ENA 85.112917 16.23933 1349 EA 154.824988 20.0015 1536 Gymnocladus ENA 74.527996 11.73683 1601 EA 93.10747 18.95383 2085 Liriodendron ENA 88.533823 16.48833 1342 EA 178.398313 28.63167 3225 Mitchella ENA 71.981349 26.73742 4332 EA 83.735752 45.02167 4297 Nelumbo ENA 79.213174 28.24383 6482 EA 115.939961 25.35917 3275 Penthorum ENA 71.189904 20.64617 1747 EA 136.8536715 28.93833 4600 Pieris ENA 155.4732105 7.184668 899 EA 225.107598 18.79517 2236 Sassafras ENA 76.05414 19.19222 1390 EA 120.991211 27.13961 4220 Saururus ENA 71.705107 21.97767 1124 EA 139.774 27.24616333 1778.333333 Torreya ENA 236.60956 2.520917 318 EA 140.2018405 13.72764 1553.5 Wisteria ENA 78.009678 14.51208 1462

Figure 3-1. Violin plot for A) estimated distribution sizes (km2), B) niche breadth metrics, B1 and B2, C) climatic niche hypervolumes, D) and climatic niche widths, Tw (mean annual temperature, C°) and Pw, (annual precipitation, mm). The black dot represents the mean of a given metric.

Figure 3-2. Violin plots for empirical Schoener’s D per asymmetric ecospat background tests. The black dot represents the mean of a given metric. The first region listed in each comparison represents the region of species 1 being projected into the region of species 2 (e.g., EA-ENA is the comparison of an EA species occurrence data with background data from an ENA species training region).

CHAPTER 4 NICHE FILLING DYNAMICS OF EASTERN ASIAN AND EASTERN NORTH AMERICAN WOODY PLANT COMMUNITIES

Background

Species richness anomalies occur when regions share lineages of flora or fauna, but are not equivalent in species richness. These anomalies often occur along altitudinal, ecological, and latitudinal gradients, as well as across distribution disjunctions (van Hooindonk et al., 2013;

Kerkhoff et al., 2014; Mittelbach et al., 2007; Rahbek, 1995; Rex et al., 2000; Willig and Lyons,

1998). While the causes of species richness anomalies remain unclear, research has shown that speciation is higher at lower latitudes, as in tropical regions (Allen and Gillooly, 2006; Cardillo,

1999; Cardillo et al., 2005; Mittelbach et al., 2007; Rolland et al., 2014). While species richness tends to be greatest near the equator, there are notable exceptions (Kouki, 1999). Marine species, including cartilaginous (Class Chondrichthyes) and bony fishes (Superclass Osteichthyes), algae

(Phyla Chlorophyta and Rhodophyta), and invertebrates (Phyla Arthropoda, Mollusca, and

Porifera), tend to have bimodal species richness distributions with regards to latitude, with large dips in richness near the equator (Chaudhary et al., 2016; Macpherson and Duarte, 1994). Some plant lineages, such as the Poaceae and rosids, have also been found to have greater species richness outside of the tropics (Folk et al., 2018; Visser et al., 2014).

Niche filling dynamics, including niche packing and niche expansion, can create differences in community species richness. Under niche packing, communities assemble and species diversify within a given niche space and must further partition themselves along niche axes. This pattern of niche packing in more species-rich communities has been observed in a variety of organisms, including phytoplankton (subclass Copepoda, class Branchoposa), passerine birds (Class Aves), and lepidopteran foliavores (Kruk et al., 2017; Pigot et al., 2016;

Ricklefs and Marquis, 2012). While niche packing has been observed in some species-rich

communities, utilization of novel niche space (i.e., niche expansion) may also lead to increased species richness and diversification. Niche expansion has been found to have spurred increased diversification in gall-inducing insects and Anolis lizards, as well as in the assembly of successional grassland and island communities (Blondel et al.,1988; Joy, 2013; Lister, 1976;

Roscher et al., 2009).

Niche filling processes can be strongly related to the bound versus unbound hypotheses.

The bound hypothesis suggests that communities are bound within a given niche space and must continue to partition themselves as they diversify (Rabosky and Hurlbert, 2015; Wright, 1983).

This limit in niche space has been found to play a role in limiting diversification in some clades, such as butterflies in the genera Erebia and Parnassius, and in the assembly of Culicidae larvae communities (Condamine, 2018; Gilbert et al., 2008; Peña et al., 2015). Conversely, the unbound hypothesis suggests that there are no limits to species richness in the niche space of a community, or even a continent (Harmon and Harrison, 2015). Thus, as lineages diversify, they will develop novel traits and inhabit novel niche space via niche expansion processes.

The eastern Asian – eastern North American (EA-ENA) floristic disjunction has a notable species richness anomaly, with EA having ~1.6 times as many plant species as ENA, even though they occupy areas of approximately the same size (Qian and Ricklefs, 1999). EA features much more heterogeneous topography and climates, leading some to hypothesize that this elevated environmental heterogeneity has spurred speciation in the region, leading to the EA-

ENA species richness anomaly (Boufford and Spongberg, 1983; Guo and Ricklefs, 2000;

Graham, 1966; Qian and Ricklefs, 1999, 2000; Ricklefs, 2004; Ricklefs et al., 2004; Wen, 1999).

In this study, I assessed niche filling dynamics among 11 plant communities (six in ENA and five in EA; Figure 4-1) to determine if the communities in these two regions occupy

differing amounts of functional trait space and have assembled under different niche filling processes. This work represents the first investigation of how niche filling dynamics may differ between the EA and ENA floras and affect the species richness anomaly.

Methods

Sampling

Plant samples for vouchers and leaf trait analyses were collected from 11 communities in

EA and ENA (Figure 4-1). Five sites belonging to the National Ecological Observatory Network

(NEON; https://www.neonscience.org/) were sampled in the eastern United States, including:

Bartlett Experimental Forest (BART; Bartlett, NH), Harvard Forest (HARV; Petersham, MA),

Mountain Lake Biological Station (MLBS; Pembroke, VA), Ordway-Swisher Biological Station

(OSBS; Hawthorne, FL), and Talladega National Forest (TALL; Talladega, AL). One

USDA/USFS Southern Research Station site in the eastern United States was sampled: Coweeta

Hydrologic Laboratory (CWHL; Otto, NC). Five sites, with all except Tianmushan belonging to the Chinese Ecological Research Network (CERN; www.cern.ac.cn/), were sampled in China, including: Changbai Mountain Forest Ecosystem Research Station (CBS; Antu County, Jilin

Province), Beijing Forest Ecological Station (DLS; Mentougou District, Beijing), Gutianshan

National Nature Reserve (GTS; Zhejiang and Jiangxi Provinces), Shennongjia Nature Reserve

(SNJ; Hubai Province), and Tianmushan Nature Reserve (TMS; Zhejiang Province). For more information about these sites, see appendix C-1.

Species sampling had two primary foci: species of disjunct genera and non-disjunct dominant canopy trees, for which leaf tissue from three to 10 individuals of each species were sampled. Sampling was also conducted for all woody species, ranging from subshrub to tree growth forms, in the communities, with at least one individual of each of these species being

sampled. In all, 1,641 samples representing 553 taxa were included for analysis. All trait data have been deposited in the TRY database (Fraser, 2020).

Trait Analyses

Leaf area, leaf dry mass, C, N, and P content were measured for intensively sampled species; all but P content were also measured for extensively sampled species. Digital images of freshly collected leaf tissue were collected on-site for calculation of leaf area. Samples were dried at 60°C within 24 hours of collection, and ground to a fine powder in the lab for chemical analysis. Leaves were analyzed for %C and %N using a Costech elemental analyzer (Valencia,

CA, USA) coupled to a Thermo Fisher Scientific Delta V Advantage continuous flow isotope ratio mass spectrometer (Thermo Fisher Scientific Inc., Bremen, Germany). To measure foliar P, subsamples of foliar tissue (0.2g) were ashed at 500°C in a muffle furnace and fully digested with 1ml of 6N HCl. Digests were run for %P using the ascorbate colorimetric method

[(modified from Murphy and Riley (1962) and described in Alvarez-Clare et al. (2013)] on a microplate reader (PowerWave XS Microplate Reader, Bio-Tek Instruments Inc., Winooski, VA,

USA). These measurements were then used to calculate leaf mass per area (LMA), %C, %N,

%P, N/area, N:P, C:P, and C:N. A spearman’s correlation test was performed on all variables, to assess for collinearity. %N and %P were dropped due to a correlation coefficients of > |0.85| with C:N (correlation coefficient = -0.95) and C:P (correlation coefficient = -0.86), respectively.

These traits are related to different ecological strategies regarding leaf lifespan and resource allocation and, in some cases, have been found to be related to environment (Westoby et al.,

2002; Wright et al., 2004). The scores function from the R package outliers was used to identify outliers via a chi-squared test with 95% confidence intervals for each functional trait variable

(Komsta, 2011). Many outlier samples were the only representatives collected for a given species; thus, these were not removed from analyses. All samples of Pinus L. and one sample

each of Populus davidiana Dode and Picea koraiensis Nakai from CBS were excluded from all analyses due to being significant outliers with high deviations in LMA, likely due to analysis error.

Functional Diversity Calculations

Means of trait variables were taken for species with more than one specimen. Trait data were scaled using the scale command in the R package ‘stats’, and a distance matrix using

Euclidean distances via the vegdist command from the R package ‘vegan’ was generated

(Oksanen et al., 2019; R Core Team 2019). The distance matrix was used to generate a dendrogram via centroid-based hierarchal clustering using the hclust command in R (R Core

Team 2019). To account for effect sizes due to species richness, we used the standardized effect size (SES) of several metrics as described in Swenson (2014). The metrics used were Functional

Richness (FRic) and the Functional Diversity (FD) equivalents to Mean Pairwise Distance

(MPD) and Mean Nearest Taxon Distance (MNTD). FRic represents the amount of functional trait space occupied by a community determined by delimiting the community in N-dimensional space using a convex hull (Villéger et al., 2008). Comparing FRic to null models allows us to determine if a community is more or less functionally diverse than expected given the species richness and has been used in similar studies of niche filling dynamics (Pigot et al., 2016). FD statistics can be calculated in the same way as Faith’s PD measure, while using a dendrogram derived from trait distances (Faith, 1992; Petchey and Gaston, 2002). MPDSES is the mean distance between members of a community and is equivalent to –NRI; it uses a null distribution of expected MPD values to assess the deviation in observed values (Webb, 2000; Webb et al.,

2002). MNTDSES, equal to –NTI, for functional traits shows the functional distances between species closest to each other in trait space (Webb et al., 2002).

FRic was calculated using the R package FD using the maximum number of traits that allows the number of species to be greater than or equal to two times the calculated number of principal components for analysis (Laliberté and Legendre, 2010; Laliberté et al., 2014). R code by Plass-Johnson et al. (2016; DOI: 10.6084/m9.figshare.2009331) was used to generate null distributions of expected FRic, calculate FricSES for each community, and test for significance for

FRicSES statistics. A statistically significant negative FricSES indicates that the species occupy less functional trait space than expected given the species richness (i.e., they have assembled under niche packing), while a statistically significant positive FricSES indicates that species occupy greater than expected functional trait space given the species richness (i.e., they have assembled under niche expansion). FD equivalents to MPD and MNTD were calculated for each community in the R package ‘picante’ V.1.8 with a null distribution developed using the independent swap method and default settings of 999 runs, 1000 iterations per run, and abundance weights equals false (Kembel et al., 2010). The independent swap method for null distribution development has been found to be the most reliable and best for finding patterns due to niche-based processes (Kembel, 2009). To account for variation in sampling effort and site size, analyses were also replicated 10 times using randomly rarefied presence-absence matrices

(PAM) generated using the rrarefy command in the R package ‘vegan’. The rarecurve function of vegan was used to estimate the most appropriate subsample size for PAMs (N = 45).

Results

Fifty-nine species were sampled at BART. Means for trait data for BART samples were:

LMA = 59.57 (SD = 42.60), %C = 46.98 (SD = 2.25), N:P = 15.46 (SD = 3.92), C:N = 855.62

(SD = 467.33), C:P = 413.71 (SD = 430.20), and N/area = 107.57 (SD = 48.86). Seventy-two species were sampled at CBS. Means for trait data for CBS samples were: LMA =48.33 (SD =

22.31), %C = 46.41 (SD = 1.85), N:P = 9.15 (SD = 2.72), C:N = 703.74 (SD = 410.59), C:P =

262.00 (SD = 313.58), and N/area = 105.61 (SD = 40.39). Seventy-seven species were sampled at CWHL. Means for trait data for CWHL samples were: LMA = 42.97 (SD = 22.94), %C =

45.24 (SD = 1.88), N:P = 16.89 (SD = 3.80), C:N = 556.76 (SD = 417.17), C:P = 338.98 (SD =

331.82), and N/area = 99.13 (SD = 38.95). Fifty species were sampled at DLS. Means for trait data for DLS samples were: LMA = 48.04 (SD = 15.89), %C = 45.16 (SD = 1.80), N:P = 12.77

(SD = 3.60), C:N = 473.46 (SD = 358.82), C:P = 191.91 (SD = 243.08), and N/area = 123.12

(SD = 41.78). Seventy-one species were sampled at GTS. Means for trait data for GTS samples were: LMA = 68.84 (SD = 35.47), %C = 46.19 (SD = 3.04), N:P = 22.18 (SD = 7.45), C:N =

1119.72 (SD = 445.11), C:P = 956.72 (SD = 374.89), and N/area = 109.52 (SD = 46.17). Sixty- eight species were sampled at HARV. Means for trait data for HARV samples were: LMA =

53.35 (SD = 33.72), %C = 47.14 (SD = 2.29), N:P = 14.62 (SD = 3.98), C:N = 872.42 (SD =

419.05), C:P = 376.73 (SD = 402.43), and N/area = 101.51 (SD = 45.29). Sixty-eight species were sampled at MLBS. Means for trait data for MLBS samples were: LMA = 68.71 (SD =

27.64), %C = 46.57 (SD = 2.02), N:P = 14.23 (SD = 3.16), C:N = 564.14 (SD = 426.12), C:P =

278.16 (SD = 254.68), and N/area = 160.32 (60.41). Eighty species were sampled at OSBS.

Means for trait data for OSBS samples were: LMA = 96.68 (SD = 41.17), %C = 49.35 (SD =

2.62), N:P = 16.51 (SD = 3.90), C:N = 1049.04 (SD = 445.13), C:P = 570.82 (SD = 485.15), and

N/area = 185.28 (SD = 79.81). Forty-five species were sampled at SNJ. Means for trait data for

SNJ samples were: LMA = 43.64 (SD = 21.05), %C = 45.25 (SD = 2.50), N:P = 11.65 (SD =

6.77), C:N = 862.80 (SD = 447.97), C:P = 391.54 (SD = 293.61), and N/area = 85.29 (SD =

38.05). Ninety-five species were sampled at TALL. Means for trait data for TALL samples were:

LMA = 73.11 (SD = 25.31), %C = 48.15 (SD = 3.70), N:P = 15.98 (SD = 4.02), C:N = 1173.76

(SD = 391.81), C:P =661.65 (SD = 507.04), and N/area = 124.29 (SD = 40.91). One hundred and

four species were sampled at TMS. Means for trait data for TMS samples were: LMA = 55.13

(SD = 20.87), %C = 45.95 (SD = 2.35), N:P = 17.92 (SD = 4.33), C:N = 676.27 (SD = 414.91),

C:P = 721.52 (SD = 259.22), and N/area = 122.53 (SD = 44.61).

For non-rarefied community analyses, all ENA communities, except OSBS (FRicSES =

-0.86088), were statistically significantly negative for FRicSES, with values ranging from -

1.48592 (TALL; P-value = 0.014) to -1.96394 (MLBS; P-value = 0). Three EA communities were found to be statistically significantly negative for FRicSES: CBS (FRicSES = -1.4608; P- value = 0.005), DLS (FRicSES = -1.72590; P-value = 0), and TMS (FRicSES = -1.9877; P-value =

0). No communities were found to have statistically significantly positive values for FRicSES.

Seven communities also had statistically significant FD MPDSES scores: four ENA communities

(CWHL, HARV, MLBS, and OSBS), and three EA communities (CBS, DLS, and TMS). All statistically significant FD MPDSES scores, except for that of OSBS, were negative, suggesting clustering of functional traits among species within the traits dendrogram. Eight communities had statistically significant FD MNTDSES scores (BART, CWHL, DLS, GTS, HARV, MLBS,

TALL, and TMS), with all but GTS being functionally clustered among functionally similar species. Diversity metrics for each community are listed in Table 4-1. FRic replicate analysis results were similar to those of the whole PAM analyses. GTS and OSBS had the highest mean

FRicSES within their respective regions, with means of -0.8108 and -0.08128, respectively.

Means for other communities ranged from -1.02294 (SNJ) to -1.6415 (DLS). Barplots for non- rarefied PAM and replicate analyses are shown in Figure 4-2. Density plots for replicate results are shown in Figure C-1.

Discussion

While results for most communities under investigation (eight of 11) were statistically significantly negative for FricSES, suggesting niche packing was a primary niche filling process,

niche filling dynamics did vary across the communities. ENA communities tended be less functionally rich than the null expectation, suggesting that they tend to assemble through niche packing processes. An exception to this was the OSBS, which is the most southerly community in ENA. Functional diversity tends to increase towards the equator and is often lower than null expectations in temperate plant communities (Lamanna et al., 2014; Swenson et al., 2012).

Results for EA communities revealed fewer instances of significant deviations from the null expectation for FRic than for ENA communities, with two communities (GTS and SNJ) assembling through neutral niche filling processes. FRic replicates also suggested that the ENA communities tend to be less functionally diverse given their species richness than EA communities.

Ricklefs (2015) found that ENA tree communities were not saturated in climatic niche space and were open to increases in species richness, based on gridded climatic data rather than functional trait data, as presented here. Functional traits, which reflect strategies in light and nutrient acquisition and leaf lifespan in this dataset, can heavily influence species distributions at various ecological scales, as well as ability to compete (Elser et al., 2000; Hessen et al., 2004;

Kunstler et al., 2016; Westoby et al., 2002; Wright et al., 2004). Similar to the finding of

Ricklefs (2015), our results suggest that competition is not a primary assembly process. Instead, our results show that environmental filtering and niche packing are the predominant assembly processes in ENA and EA, as indicated by negative scores for diversity metrics. Using United

States Forest Service Forest Inventory and Analysis (FIA) plot data and functional trait data

(seed mass, maximum height, wood density, and leaf nitrogen), Swenson and Weiser (2014) found that ENA tree communities assembled via niche packing. Other studies using functional traits have found similar patterns of niche packing in communities of various taxa, such as

understory birds (Class Aves) in the Amazon and zooplankton (subclass Copepoda, class

Branchoposa), and successional status (Hidasi‐Neto et al., 2012; Vogt et al.,2013).

Most communities investigated here were functionally clustered per FD MPDSES and FD

MNTDSES scores (MPDSES: four ENA and two EA; MNTDSES: five ENA and two EA). The FD

MPDSES metric is informative of the distribution of trait distances for relationships deeper in the trait dendrogram, while FD MNTDSES is informative for shallower relationships. So, while these communities had less functional diversity than expected, this diversity was also clustered within trait space, both along deeper and shallower nodes of the trait dendrogram. While most ENA communities examined here were functionally clustered per FD MNTDSES scores, only two EA communities, DLS and TMS, were functionally clustered, while others were neutral. This result indicates that the most functionally similar members of the EA communities typically overlap less in functional trait space than in ENA communities. This finding suggests that these EA communities, which tend to be more species rich, maximize the functional trait distances between similar species and are more likely to increase their species richness. A similar pattern of more species-rich tree communities having a higher mean nearest neighbor distances (MNND; similar to MNTD) was also found in FIA plots in ENA (Swenson and Weiser, 2014).

A higher proportion of ENA communities tended to have less functional diversity than expected than observed for EA communities (5/6 versus 3/5, respectively), suggesting that niche filling processes may be different between the two regions and may affect, or be influenced by, the species richness anomaly. EA has greater environmental heterogeneity, which is correlated with species richness, than ENA (Stein et al., 2014). Species richness has been found to be correlated with available niche and filling of novel niche space (Ricklefs and Marquis, 2014;

Title and Burns, 2015). Therefore, the greater environmental heterogeneity in EA may have allowed for greater diversification due to greater available niche space.

The results discussed above demonstrate that niche packing, along at least some important ecological strategy axes, may have played an important role in the assembly of ENA communities, while the EA communities investigated here have experienced less niche packing and more neutral niche filling processes for community assembly. While evidence of niche packing was identified for all ENA communities except OSBS, only three EA communities,

CBS, DLS, and TMS, had statistically significantly negative FRicSES. Results for other EA communities, GTS and SNJ, suggest that they assemble through more neutral niche filling processes. While more studies of EA and ENA communities should be conducted, these results support the hypothesis that communities within each region (EA and ENA) assemble through different niche filling processes and provide a first step towards understanding differences between functional trait space and niche filling processes between the two regions.

Figure 4-1. Map of the 11 sampling sites in the eastern United States and eastern China highlighted in green. Collection sites included: Bartlett Experimental Forest (BART; Bartlett, NH), Harvard Forest (HARV; Petersham, MA), Mountain Lake Biological Station (MLBS; Pembroke, VA), Ordway-Swisher Biological Station (OSBS; Hawthorne, FL), and Talladega National Forest (TALL; Talladega, AL), Coweeta Hydrologic Laboratory (CWHL; Otto, NC), Changbai Mountain Forest Ecosystem Research Station (CBS; Antu County, Jilin Province), Beijing Forest Ecological Station (DLS; Mentougou District, Beijing), Gutianshan National Nature Reserve (GTS; Zhejiang and Jiangxi Provinces), Shennongjia Nature Reserve (SNJ; Hubai Province), and Tianmushan Nature Reserve (TMS; Zhejiang Province). Information for sites is included in Table C-1.

Figure 4-2. Barplots for non-rarefied PAM analyses (A) and rarefied PAM analyses (B). Bars represent standard deviation of FRicSES in replicate runs. Collection sites included: Bartlett Experimental Forest (BART; Bartlett, NH), Harvard Forest (HARV; Petersham, MA), Mountain Lake Biological Station (MLBS; Pembroke, VA), Ordway-Swisher Biological Station (OSBS; Hawthorne, FL), and Talladega National Forest (TALL; Talladega, AL), Coweeta Hydrologic Laboratory (CWHL; Otto, NC), Changbai Mountain Forest Ecosystem Research Station (CBS; Antu County, Jilin Province), Beijing Forest Ecological Station (DLS; Mentougou District, Beijing), Gutianshan National Nature Reserve (GTS; Zhejiang and Jiangxi Provinces), Shennongjia Nature Reserve (SNJ; Hubai Province), and Tianmushan Nature Reserve (TMS; Zhejiang Province).

Table 4-1. Summary of diversity statistics for functional richness analyses. N represents the number of species sampled per site. *P-value ≤ 0.05, **P-value ≤ 0.005, ***P-value = 0, +P-value ≥ 0.95.

Site N FRicSES MPDSES MNTDSES BART 59 -1.579869262** -1.331117139 -3.151946848** CBS 72 -1.460769722** -2.72440618** -0.94729398 CWHL 77 -1.898143869*** -2.786417655** -3.245772509** DLS 50 -1.725902173*** -3.804734326** -2.411362359** GTS 71 -1.027026822 1.51759157 1.814130689+ HARV 68 -1.789708454*** -3.297738551** -3.790050377** MLBS 68 -1.963936273*** -3.335056093** -3.296073008** OSBS 80 -0.860880529 3.38750993+ -0.943891549 SNJ 45 -1.040622844 -1.399120772 -0.33064635 TALL 95 -1.485916856* -0.714805762 -3.556670657** TMS 104 -1.987660135*** -2.326936186** -2.025942018**

CHAPTER 5 CONCLUSIONS

The EA-ENA floristic disjunction has been identified in 65 seed plant genera and features a prominent species richness anomaly. That is, the flora of EA has been found to contain ~1.6 times as many species as the flora of ENA. This has made the disjunction of great interest to biogeographers and biodiversity scientists. Numerous hypotheses have been proposed to explain this species richness anomaly, from increased topographical heterogeneity to regional effects increasing rates of molecular evolution in EA. This research utilized both molecular and macroecological methods to investigate differences in the environments and flora of EA and

ENA to further clarify possible causes of the species richness anomaly between the two regions.

In Chapter 2, transcriptomes from species of 11 disjunct genera were used to investigate molecular evolution in disjunct species. By comparing relative rates of molecular evolution and reconstructing rates of evolution over phylogenies, I demonstrated that relative rates of molecular evolution in the disjunct lineages are approximately equivalent. This would suggest that different rates of molecular evolution are not likely the cause of higher net diversification in

EA and points towards an ecological cause of the species richness anomaly.

In Chapter 3, ecological niche modeling methods were used to investigate the climatic differences in the distributions of species of disjunct genera. This work demonstrated that many lineages of the disjunction occupy highly similar climatic niche space, suggesting that niche conservatism via niche may be a promninet pattern associated with speciation for these genera investigated. The species of EA generally occupied greater niche space hypervolumes than their

ENA congeners, while occupying similarly sized geographic distributions. This finding suggests that environmental heterogeneity in EA has allowed the EA species to occupy more niche space

in approximately the same size area of geographic space. These results are congruent with hypotheses stating that environmental heterogeneity has influenced the species richness anomaly.

In Chapter 4, functional trait and niche filling analyses were utilized to assess niche filling dynamics in the woody plant communities of EA and ENA. This functional trait work demonstrated that EA communities tend to occupy greater amounts of functional trait space than

ENA communities. Niche packing was found to be a primary pattern of niche filling dynamics within ENA communities, while EA communities have assembled under niche packing to niche neutral processes.

Overall, the work of this dissertation highlights the effects of environmental heterogeneity in EA. No evidence of regional effects on rates of molecular evolution was identified, pointing to ecological causes of the differences in regional species richness. The effects of the greater environmental heterogeneity in EA than ENA can be seen in Chapters 3 and

4: distributions of EA species tended to contain greater amounts of niche space, and communities of EA tended to have greater functional trait diversity. It is therefore likely that the EA communities have been able to assemble with less overlap in functional trait space due to increased niche space within their respective geographies. This work further provides evidence that ecology has greatly influenced the species richness anomaly and lays the groundwork for future work. Further investigations into molecular evolution should be conducted in larger clades that exhibit greater disparities in regional species richness. In addition, future investigations should include phylogenetics and niche evolution, as well as reconstruction of rates of molecular evolution and rates of species diversification. Ecological analyses should include functional trait and diversity analyses that include a greater variety of traits. These community-based studies should also be conducted at varying spatial extents, from small plots to large grid cells similar in

resolution to those used in ecological niche modeling (30 arc seconds to 10 arc minutes). Finally, studies should be conducted to assess possible correlations between various environmental factors, such as topography or climatic stability, and species richness. Those types of investigations would further clarify the differences between EA and ENA and how the species richness anomaly may have formed.

APPENDIX A CHAPTER TWO SUPPLEMENTARY MATERIAL

Figure A-1. Bar plots for dN, dS, and dN/dS scores of genes falling under Biological Process categories for pairwise comparisons. Genera are as follows, from top to bottom: Campsis, Cotinus, Gelsemium, Liriodendron, Nelumbo, Penthorum, Phryma, and Saururus.

Figure A-2. Bar plots for dN, dS, and dN/dS scores of genes falling under Molecular Functions categories for pairwise comparisons. Genera are as follows, from top to bottom: Campsis, Cotinus, Gelsemium, Liriodendron, Nelumbo, Penthorum, Phryma, and Saururus.

Table A-1. Voucher information for all plant specimens used in this study. Asterisks indicate electronic vouchers available from Qiu-Yun Xiang. All other vouchers are specimens deposited at the indicated herbarium. Species Voucher Calycanthus chinensis Xianglab312, NCSC Calycanthus floridus Xianglab333 Calycanthus occidentalis Xianglab313, NCSC Campsis grandiflora Xianglab325* Campsis radicans Doug&Chen3029, FMNH Cornus alternifolia Xianglab327* Cornus capitata subsp. emeiensis Xianglab180 Cornus controversa Xianglab152, NCSC Cornus elliptica Xianglab105, NCSC Cornus florida subsp. urbiniana Xianglab128, NCSC Cornus kousa Xianglab102 Cotinus coggygria Xianglab324, NCSC Cotinus obovatus Xianglab148, NCSC Gelsemium elegans Fu ZJU Gelsemium sempervirens Doug&Chen3028, FMNH Hamamelis japonica Xianglab316, NCSC Hamamelis mollis Xianglab197 Hamamellis ovalis Xianglab212 Hamamelis vernalis Xianglab317, NCSC Hamamelis virginiana Xianglab335 Hamamelis virginiana var. henryi Xianglab336 Liriodendron chinense Xianglab320, NCSC Liriodendron tulipifera (Liriodendron floridanum) Xianglab334 Liriodendron tulipifera Xianglab321, NCSC Nelumbo nucifera Xianglab330* Nelumbo lutea Doug&Chen3030, FMNH Penthorum chinense Fu120203, ZJU Penthorum sedoides Xianglab331* Phryma leptostachya subsp. asiatica CF15040, ZJU Phryma leptostachya subsp. leptostachya Xianglab332* Saururus chinensis Mobot # 1998-0613, FMNH Saururus cernuus Xianglab158, NCSC Nyssa sinensis Fu s.n., ZJU

Table A-2. Gene number and mean, median, and standard deviation (SD) for scores for each species included. Species Region Genes per Genus Mean Median SD dN Calycanthus chinensis EA 119 0.0024 0.0023 0.0025 Calycanthus floridus ENA 0.0045 0.0018 0.0178 Calycanthus occidentalis ENA 0.0039 0.0020 0.0122 Campsis radicans ENA 153 0.0451 0.0350 0.0731 Campsis grandiflora EA 0.0403 0.0353 0.0230 Cornus alternifolia ENA 124 0.0046 0.0012 0.0251 Cornus capitata EA 0.0055 0.0015 0.0288 Cornus controversa EA 0.0027 0.0015 0.0072 Cornus elliptica EA 0.0040 0.0013 0.0278 Cornus florida ENA 0.0039 0.0035 0.0029 Cornus kousa EA 0.0032 0.0027 0.0026 Cotinus coggyria EA 385 0.0199 0.0193 0.0104 Cotinus obovatus ENA 0.0199 0.0187 0.0104 Gelsemium elegans EA 120 0.0907 0.0837 0.0601 Gelsemium sempervirens ENA 0.0913 0.0827 0.0592 Hamamelis japonica EA 151 0.0018 0.0012 0.0019 Hamamelis mollis EA 0.0009 0.0007 0.0011 Hamamelis ovalis ENA 0.0006 0.0000 0.0008 Hamamelis vernalis ENA 0.0077 0.0070 0.0043 Hamamelis virginiana (A) ENA 0.0006 0.0000 0.0009 Hamamelis virginiana (B) ENA 0.0008 0.0000 0.0016 Liriodendron tulipifera (A) ENA 176 0.0298 0.0224 0.0514 Liriodendron tulipifera (B) ENA 0.0282 0.0224 0.0464 Liriodendron chinense EA 0.0316 0.0226 0.0667 Nelumbo lutea ENA 161 0.1155 0.1100 0.0498 Nelumbo nucifera EA 0.1156 0.1085 0.0499 Penthorum chinense EA 162 0.0802 0.0743 0.0688 Penthorum sedoides ENA 0.0757 0.0737 0.0302 Phryma leptostachya ENA 219 0.0757 0.0743 0.0289 Phryma leptostachya EA 0.0760 0.0753 0.0297 Saururus cernuus ENA 77 0.0447 0.0404 0.0179 Saururus chinensis EA 0.0408 0.0414 0.0145

Table A-2. Continued. Species Region Genes per Genus Mean Median SD dS Calycanthus chinensis 0.0102 0.0068 0.0227 Calycanthus floridus 0.0226 0.0096 0.1061 Calycanthus occidentalis 0.0236 0.0088 0.1147 Campsis radicans 0.2881 0.2656 0.1410 Campsis grandiflora 0.2878 0.2618 0.1731 Cornus alternifolia 0.0478 0.0081 0.3792 Cornus capitata 0.4370 0.0071 4.7181 Cornus controversa 0.0145 0.0085 0.0386 Cornus elliptica 0.4613 0.0070 5.0443 Cornus florida 0.0179 0.0178 0.0087 Cornus kousa 0.0160 0.0132 0.0112 Cotinus coggyria 0.0753 0.0726 0.0345 Cotinus obovatus 0.0753 0.0717 0.0411 Gelsemium elegans 0.6420 0.6045 0.1702 Gelsemium sempervirens 0.6407 0.6082 0.1663 Hamamelis japonica 0.0068 0.0053 0.0058 Hamamelis mollis 0.0037 0.0032 0.0043 Hamamelis ovalis 0.0026 0.0019 0.0031 Hamamelis vernalis 0.0348 0.0335 0.0121 Hamamelis virginiana (A) 0.0036 0.0026 0.0049 Hamamelis virginiana (B) 0.0032 0.0026 0.0039 Liriodendron tulipifera (A) 0.1397 0.1176 0.1142 Liriodendron tulipifera (B) 0.1311 0.1163 0.0661 Liriodendron chinense 0.1478 0.1172 0.1895 Nelumbo lutea 0.8721 0.8355 0.2065 Nelumbo nucifera 0.8775 0.8373 0.2310 Penthorum chinense 0.5582 0.5193 0.1800 Penthorum sedoides 0.5531 0.5125 0.1789 Phryma leptostachya 0.5749 0.5395 0.1754 Phryma leptostachya 0.5771 0.5461 0.1708 Saururus cernuus 0.2850 0.2586 0.1813 Saururus chinensis 0.2676 0.2478 0.0649

Table A-2. Continued. Species Region Genes per Genus Mean Median SD dN/dS, ω Calycanthus chinensis 5.4071 0.1726 35.0167 Calycanthus floridus 0.7408 0.1963 5.0393 Calycanthus occidentalis 0.6454 0.2225 3.8751 Campsis radicans 0.1471 0.1294 0.0688 Campsis grandiflora 0.1462 0.1302 0.0626 Cornus alternifolia 0.3362 0.1508 1.4064 Cornus capitata 1.9630 0.1905 12.9042 Cornus controversa 2.3391 0.1567 17.5726 Cornus elliptica 1.9907 0.1810 15.8539 Cornus florida 0.2943 0.1974 0.3211 Cornus kousa 0.2238 0.1752 0.2073 Cotinus coggyria 0.2805 0.2607 0.1322 Cotinus obovatus 0.2845 0.2632 0.1290 Gelsemium elegans 0.1535 0.1332 0.1557 Gelsemium sempervirens 0.1557 0.1315 0.1604 Hamamelis japonica 0.7419 0.1729 3.7941 Hamamelis mollis 6.2917 0.1006 38.8971 Hamamelis ovalis 20.0503 0.0001 107.2883 Hamamelis vernalis 0.2473 0.2082 0.1560 Hamamelis virginiana (A) 16.4061 0.0001 82.4253 Hamamelis virginiana (B) 2.0461 0.0001 17.6089 Liriodendron tulipifera (A) 0.2123 0.1940 0.1278 Liriodendron tulipifera (B) 0.2161 0.1898 0.1393 Liriodendron chinense 0.2141 0.1961 0.1443 Nelumbo lutea 0.1603 0.1258 0.2458 Nelumbo nucifera 0.1601 0.1250 0.2498 Penthorum chinense 0.1479 0.1371 0.0879 Penthorum sedoides 0.1460 0.1382 0.0713 Phryma leptostachya 0.1470 0.1357 0.1429 Phryma leptostachya 0.1470 0.1372 0.1560 Saururus cernuus 0.1577 0.1508 0.0578 Saururus chinensis 0.1576 0.1523 0.0563

Table A-3. t-test statistics, including 95% confidence intervals (CI), for pairwise comparisons (excluding Liriodendron) of the log of dN, dS, and dN/dS scores for entire gene sets. No statistical significance was found for any comparison. Genus t df P-value lower CI upper CI dN Campsis 0.056579 297.55 0.9549 -0.1115922 0.1181987 Cotinus 0.15291 767.56 0.8785 -0.07745575 0.09054179 Gelsemium 0.12505 238 0.9006 -0.1086354 0.1233617 Nelumbo -0.019406 319.95 0.9845 -0.09252677 0.09071931 Penthorum -0.21257 319.9 0.8318 -0.11367143 0.09150334 Phryma -0.010457 435.96 0.9917 -0.08196099 0.08109345 Saururus -0.16181 148.58 0.8717 -0.1518305 0.1288473 dS Campsis 0.26733 303.82 0.7894 -0.05690577 0.07479768 Cotinus -0.55565 764.23 0.5786 -0.05262601 0.02940672 Gelsemium -0.038638 237.98 0.9692 -0.06368319 0.06123314 Nelumbo 0.072961 319.38 0.9419 -0.0601514 0.06478459 Penthorum -0.34689 321.85 0.7289 -0.06729456 0.0471205 Phryma -0.18991 435.97 0.8495 -0.05764149 0.04748351 Saururus -0.40325 133.35 0.6874 -0.10098614 0.06678242 dN/dS Campsis -0.10898 302.51 0.9133 -0.111648 0.0999306 Cotinus 0.60434 766.38 0.5458 -0.0489518 0.09249757 Gelsemium 0.13226 237.94 0.8949 -0.1191135 0.1362585 Nelumbo -0.14701 317.77 0.8832 -0.1294962 0.1114893 Penthorum -0.019579 321.87 0.9844 -0.1123227 0.110109 Phryma 0.095167 436 0.9242 -0.09312821 0.10260577 Saururus 0.080242 149.58 0.9362 -0.1291618 0.1400962

Table A-4. ANOVA results for Liriodendron. No statistical significance was found for any comparison. Statistic F value Pr(>F) dN 0.038 0.963 dS 0.007 0.993 dN/dS 0.052 0.949

Table A-5. Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) for Calycanthus. Statistically significant results for EA-ENA comparisons for dS, as well as all comparisons to the branch leading to the North American clade. Branches Z P-value dN C. chinensis - C. floridus 0.3497657 1.00E+00 C. chinensis - C. occidentalis -0.2653313 7.91E-01 C. floridus - C. occidentalis -0.615097 1.00E+00 C. chinensis - North America 7.7769202 3.72E-14 C. floridus - North America 7.4271545 4.44E-13 C. occidentalis - North America 8.0422515 5.29E-15 dS C. chinensis - C. floridus -2.70521005 2.05E-02 C. chinensis - C. occidentalis -2.6474769 1.62E-02 C. floridus - C. occidentalis 0.05773314 9.54E-01 C. chinensis - North America 4.92758251 3.33E-06 C. floridus - North America 7.63279256 1.38E-13 C. occidentalis - North America 7.57505942 1.79E-13 ω C. chinensis - C. floridus -0.4591049 6.46E-01 C. chinensis - C. occidentalis -0.949134 1.00E+00 C. floridus - C. occidentalis -0.5169524 1.00E+00 C. chinensis - North America 3.8175175 5.39E-04 C. floridus - North America 4.4655878 3.99E-05 C. occidentalis - North America 4.9556169 4.33E-06

Table A-6. Results for Dunn’s pairwise test for all Cornus branch comparisons. All comparisons including the Big-Bracted clade and the subgenus Mesomora were found to be statistically significant, except their direct comparison with each other. Branches Z P-value dN C. alternifolia - Big-Bracted -13.7437157 2.33E-41 C. alternifolia - C. capitata -0.8751019 1.00E+00 Big-Bracted - C. capitata 12.8686138 2.64E-36 C. alternifolia - C. controversa -0.6143464 1.00E+00 Big-Bracted - C. controversa 13.1293693 8.94E-38 C. capitata - C. controversa 0.2607555 7.94E-01 C. alternifolia - C. elliptica 0.5680175 1.00E+00 Big-Bracted - C. elliptica 14.3117332 7.95E-45 C. capitata - C. elliptica 1.4431194 1.00E+00 C. controversa - C. elliptica 1.1823639 1.00E+00 C. alternifolia - C. florida -5.1692254 4.94E-06 Big-Bracted - C. florida 8.5744903 2.79E-16 C. capitata - C. florida -4.2941235 3.33E-04 C. controversa - C. florida -4.554879 1.05E-04 C. elliptica - C. florida -5.7372429 2.12E-07 C. alternifolia - C. kousa -3.6681505 4.15E-03 Big-Bracted - C. kousa 10.0755653 2.27E-22 C. capitata - C. kousa -2.7930485 6.79E-02 C. controversa - C. kousa -3.053804 3.39E-02 C. elliptica - C. kousa -4.236168 4.09E-04 C. florida - C. kousa 1.501075 1.00E+00 C. alternifolia - C. kousa + C. elliptica 6.5618446 1.28E-09 Big-Bracted - C. kousa + C. elliptica 20.3055603 5.17E-90 C. capitata - C. kousa + C. elliptica 7.4369465 2.78E-12 C. controversa - C. kousa + C. elliptica 7.176191 1.79E-11 C. elliptica - C. kousa + C. elliptica 5.9938271 4.71E-08 C. florida - C. kousa + C. elliptica 11.73107 3.18E-30 C. kousa - C. kousa + C. elliptica 10.229995 4.80E-23 C. alternifolia - Mesomora -12.5869126 9.47E-35 Big-Bracted - Mesomora 1.1568031 1.00E+00 C. capitata - Mesomora -11.7118107 3.88E-30 C. controversa - Mesomora -11.9725662 1.83E-31 C. elliptica - Mesomora -13.1549301 6.54E-38 C. florida - Mesomora -7.4176872 3.10E-12

Table A-6. Continued. Branches Z P-value dN C. kousa - Mesomora -8.9187621 1.37E-17 C. kousa + C. elliptica - Mesomora -19.1487572 4.36E-80 C. alternifolia - Syncarpea -2.8139054 6.85E-02 Big-Bracted - Syncarpea 10.9298103 2.82E-26 C. capitata - Syncarpea -1.9388035 5.25E-01 C. controversa - Syncarpea -2.199559 3.06E-01 C. elliptica - Syncarpea -3.3819229 1.15E-02 C. florida - Syncarpea 2.35532 2.22E-01 C. kousa - Syncarpea 0.8542451 1.00E+00 C. kousa + C. elliptica - Syncarpea -9.37575 2.06E-19 Mesomora - Syncarpea 9.7730072 4.56E-21 dS C. alternifolia - Big-Bracted -13.95966194 1.13E-42 C. alternifolia - C. capitata 0.31221079 1.00E+00 Big-Bracted - C. capitata 14.27187274 1.38E-44 C. alternifolia - C. controversa -0.07971528 9.36E-01 Big-Bracted - C. controversa 13.87994666 3.35E-42 C. capitata - C. controversa -0.39192607 1.00E+00 C. alternifolia - C. elliptica 1.19227104 1.00E+00 Big-Bracted - C. elliptica 15.15193298 3.16E-50 C. capitata - C. elliptica 0.88006025 1.00E+00 C. controversa - C. elliptica 1.27198632 1.00E+00 C. alternifolia - C. florida -5.3890722 1.42E-06 Big-Bracted - C. florida 8.57058975 2.99E-16 C. capitata - C. florida -5.70128299 2.50E-07 C. controversa - C. florida -5.30935692 2.09E-06 C. elliptica - C. florida -6.58134324 1.12E-09 C. alternifolia - C. kousa -3.68907068 3.60E-03 Big-Bracted - C. kousa 10.27059126 3.06E-23 C. capitata - C. kousa -4.00128148 1.07E-03 C. controversa - C. kousa -3.6093554 4.60E-03 C. elliptica - C. kousa -4.88134172 1.90E-05 C. florida - C. kousa 1.70000152 7.13E-01 C. alternifolia - C. kousa + C. elliptica 7.73459901 2.70E-13 Big-Bracted - C. kousa + C. elliptica 21.69426096 1.05E-102 C. capitata - C. kousa + C. elliptica 7.42238822 2.88E-12 C. controversa - C. kousa + C. elliptica 7.8143143 1.49E-13

Table A-6. Continued. Branches Z P-value dS C. elliptica - C. kousa + C. elliptica 6.54232797 1.39E-09 C. florida - C. kousa + C. elliptica 13.12367121 9.40E-38 C. kousa - C. kousa + C. elliptica 11.4236697 1.05E-28 C. alternifolia - Mesomora -11.80167441 1.38E-30 Big-Bracted - Mesomora 2.15798753 2.78E-01 C. capitata - Mesomora -12.11388521 3.30E-32 C. controversa - Mesomora -11.72195913 3.44E-30 C. elliptica - Mesomora -12.99394546 5.03E-37 C. florida - Mesomora -6.41260222 3.15E-09 C. kousa - Mesomora -8.11260373 1.39E-14 C. kousa + C. elliptica - Mesomora -19.53627343 2.38E-83 C. alternifolia - Syncarpea -2.23974224 2.76E-01 Big-Bracted - Syncarpea 11.7199197 3.43E-30 C. capitata - Syncarpea -2.55195304 1.29E-01 C. controversa - Syncarpea -2.16002696 3.08E-01 C. elliptica - Syncarpea -3.43201329 8.39E-03 C. florida - Syncarpea 3.14932995 2.13E-02 C. kousa - Syncarpea 1.44932844 1.00E+00 C. kousa + C. elliptica - Syncarpea -9.97434126 6.12E-22 Mesomora - Syncarpea 9.56193217 3.47E-20 ω C. alternifolia - Big-Bracted -2.95670824 9.64E-02 C. alternifolia - C. capitata -1.29878179 1.00E+00 Big-Bracted - C. capitata 1.62755076 1.00E+00 C. alternifolia - C. controversa -0.7133651 1.00E+00 Big-Bracted - C. controversa 2.21532133 7.22E-01 C. capitata - C. controversa 0.58055838 1.00E+00 C. alternifolia - C. elliptica -0.67656202 1.00E+00 Big-Bracted - C. elliptica 2.25864754 6.69E-01 C. capitata - C. elliptica 0.61981754 1.00E+00 C. controversa - C. elliptica 0.03802524 9.70E-01 C. alternifolia - C. florida -2.11637623 8.92E-01 Big-Bracted - C. florida 0.84379731 1.00E+00 C. capitata - C. florida -0.79436767 1.00E+00 C. controversa - C. florida -1.38213824 1.00E+00 C. elliptica - C. florida -1.42364919 1.00E+00 C. alternifolia - C. kousa -0.95740849 1.00E+00

Table A-6. Continued. Branches Z P-value ω Big-Bracted - C. kousa 1.98297084 1.00E+00 C. capitata - C. kousa 0.34405402 1.00E+00 C. controversa - C. kousa -0.23895916 1.00E+00 C. elliptica - C. kousa -0.27765808 1.00E+00 C. florida - C. kousa 1.14617611 1.00E+00 C. alternifolia - C. kousa + C. elliptica 2.77792348 1.64E-01 Big-Bracted - C. kousa + C. elliptica 5.70092851 5.24E-07 C. capitata - C. kousa + C. elliptica 4.03478275 2.24E-03 C. controversa - C. kousa + C. elliptica 3.45800655 1.90E-02 C. elliptica - C. kousa + C. elliptica 3.42748397 2.07E-02 C. florida - C. kousa + C. elliptica 4.87330772 4.72E-05 C. kousa - C. kousa + C. elliptica 3.70982647 7.67E-03 C. alternifolia - Mesomora -4.54797524 2.27E-04 Big-Bracted - Mesomora -1.59782896 1.00E+00 C. capitata - Mesomora -3.20528044 4.32E-02 C. controversa - Mesomora -3.79305101 5.65E-03 C. elliptica - Mesomora -3.83981464 4.80E-03 C. florida - Mesomora -2.44162627 4.24E-01 C. kousa - Mesomora -3.56753958 1.30E-02 C. kousa + C. elliptica - Mesomora -7.26812532 1.64E-11 C. alternifolia - Syncarpea -1.13202942 1.00E+00 Big-Bracted - Syncarpea 1.80134081 1.00E+00 C. capitata - Syncarpea 0.16814618 1.00E+00 C. controversa - Syncarpea -0.41364612 1.00E+00 C. elliptica - Syncarpea -0.45263545 1.00E+00 C. florida - Syncarpea 0.96634246 1.00E+00 C. kousa - Syncarpea -0.17593129 1.00E+00 C. kousa + C. elliptica - Syncarpea -3.87620042 4.24E-03 Mesomora - Syncarpea 3.3825079 2.37E-02

Table A-7. Results for Dunn’s pairwise test for all Hamamelis branch comparisons. Hamamelis vernalis was found to have the highest rates of molecular evolution, followed by H. japonica. Branches Z P-value dN H. japonica - H. mollis 3.80692082 2.39E-03 H. japonica - H. japonica + North America 4.00562115 1.11E-03 H. mollis - H. japonica + North America 0.19870033 1.00E+00 H. japonica - H. ovalis 5.51731415 7.57E-07 H. mollis - H. ovalis 1.71039333 7.85E-01 H. japonica + North America - H. ovalis 1.511693 9.14E-01 H. japonica - H. vernalis -8.46757024 8.02E-16 H. mollis - H. vernalis -12.27449106 4.59E-33 H. japonica + North America - H. vernalis -12.47319139 3.97E-34 H. ovalis - H. vernalis -13.98488439 7.90E-43 H. japonica - H. virginiana 11.24070308 9.26E-28 H. mollis - H. virginiana 7.43378226 3.27E-12 H. japonica + North America - H. virginiana 7.23508193 1.40E-11 H. ovalis - H. virginiana 5.72338893 2.61E-07 H. vernalis - H. virginiana 19.70827332 8.06E-85 H. japonica - H. virginiana + H. ovalis 8.78569562 5.44E-17 H. mollis - H. virginiana + H. ovalis 4.9787748 1.28E-05 H. japonica + North America - H. virginiana + H. ovalis 4.78007447 3.33E-05 H. ovalis - H. virginiana + H. ovalis 3.26838147 1.51E-02 H. vernalis - H. virginiana + H. ovalis 17.25326586 4.55E-65 H. virginiana - H. virginiana + H. ovalis -2.45500746 1.69E-01 H. japonica - H. virginiana A 5.46248378 9.86E-07 H. mollis - H. virginiana A 1.65556297 7.82E-01 H. japonica + North America - H. virginiana A 1.45686264 8.71E-01 H. ovalis - H. virginiana A -0.05483037 9.56E-01 H. vernalis - H. virginiana A 13.93005402 1.66E-42 H. virginiana - H. virginiana A -5.77821929 1.96E-07 H. virginiana + H. ovalis - H. virginiana A -3.32321183 1.33E-02 H. japonica - H. virginiana B 5.72209157 2.53E-07 H. mollis - H. virginiana B 1.91517075 6.10E-01 H. japonica + North America - H. virginiana B 1.71647042 8.61E-01 H. ovalis - H. virginiana B 0.20477742 1.00E+00 H. vernalis - H. virginiana B 14.18966181 4.46E-44 H. virginiana - H. virginiana B -5.51861151 7.86E-07 H. virginiana + H. ovalis - H. virginiana B -3.06360404 2.84E-02

Table A-7. Continued. Branches Z P-value dN H. virginiana A - H. virginiana B 0.25960779 1.00E+00 H. japonica - North America 12.54686756 1.61E-34 H. mollis - North America 8.73994674 7.93E-17 H. japonica + North America - North America 8.54124641 4.38E-16 H. ovalis - North America 7.02955341 5.80E-11 H. vernalis - North America 21.0144378 2.18E-96 H. virginiana - North America 1.30616448 9.57E-01 H. virginiana + H. ovalis - North America 3.76117194 2.71E-03 H. virginiana A - North America 7.08438377 4.05E-11 H. virginiana B - North America 6.82477599 2.38E-10 dS H. japonica - H. mollis 4.4429657 1.60E-04 H. japonica - H. japonica + North America 3.1511423 2.11E-02 H. mollis - H. japonica + North America -1.2918234 1.00E+00 H. japonica - H. ovalis 6.4239478 3.98E-09 H. mollis - H. ovalis 1.9809821 5.24E-01 H. japonica + North America - H. ovalis 3.2728055 1.49E-02 H. japonica - H. vernalis -8.8455766 3.10E-17 H. mollis - H. vernalis -13.2885423 1.05E-38 H. japonica + North America - H. vernalis -11.9967189 1.40E-31 H. ovalis - H. vernalis -15.2695244 5.13E-51 H. japonica - H. virginiana 10.2133231 6.05E-23 H. mollis - H. virginiana 5.7703574 2.06E-07 H. japonica + North America - H. virginiana 7.0621808 5.08E-11 H. ovalis - H. virginiana 3.7893753 2.27E-03 H. vernalis - H. virginiana 19.0588997 2.38E-79 H. japonica - H. virginiana + H. ovalis 10.4922176 3.38E-24 H. mollis - H. virginiana + H. ovalis 6.0492519 4.07E-08 H. japonica + North America - H. virginiana + H. ovalis 7.3410753 6.78E-12 H. ovalis - H. virginiana + H. ovalis 4.0682698 7.58E-04 H. vernalis - H. virginiana + H. ovalis 19.3377942 1.14E-81 H. virginiana - H. virginiana + H. ovalis 0.2788945 1.00E+00 H. japonica - H. virginiana A 5.0704285 7.94E-06 H. mollis - H. virginiana A 0.6274628 1.00E+00 H. japonica + North America - H. virginiana A 1.9192862 5.49E-01 H. ovalis - H. virginiana A -1.3535193 1.00E+00 H. vernalis - H. virginiana A 13.9160051 2.03E-42

Table A-7. Continued. Branches Z P-value dS H. virginiana - H. virginiana A -5.1428946 5.68E-06 H. virginiana + H. ovalis - H. virginiana A -5.4217891 1.42E-06 H. japonica - H. virginiana B 5.1952829 4.50E-06 H. mollis - H. virginiana B 0.7523171 1.00E+00 H. japonica + North America - H. virginiana B 2.0441405 4.91E-01 H. ovalis - H. virginiana B -1.228665 1.00E+00 H. vernalis - H. virginiana B 14.0408594 3.59E-43 H. virginiana - H. virginiana B -5.0180402 9.92E-06 H. virginiana + H. ovalis - H. virginiana B -5.2969348 2.71E-06 H. virginiana A - H. virginiana B 0.1248543 9.01E-01 H. japonica - North America 10.8554786 6.95E-26 H. mollis - North America 6.4125128 4.15E-09 H. japonica + North America - North America 7.7043362 4.34E-13 H. ovalis - North America 4.4315307 1.59E-04 H. vernalis - North America 19.7010551 9.50E-85 H. virginiana - North America 0.6421554 1.00E+00 H. virginiana + H. ovalis - North America 0.3632609 1.00E+00 H. virginiana A - North America 5.78505 1.96E-07 H. virginiana B - North America 5.6601957 3.78E-07 ω H. japonica - H. mollis 2.14591351 5.42E-01 H. japonica - H. japonica + North America 3.38862872 1.62E-02 H. mollis - H. japonica + North America 1.19907457 1.00E+00 H. japonica - H. ovalis 3.18134708 2.93E-02 H. mollis - H. ovalis 1.02880662 1.00E+00 H. japonica + North America - H. ovalis -0.15381044 1.00E+00 H. japonica - H. vernalis -2.52299921 2.09E-01 H. mollis - H. vernalis -4.68204888 9.66E-05 H. japonica + North America - H. vernalis -5.98853227 8.47E-08 H. ovalis - H. vernalis -5.73209204 3.77E-07 H. japonica - H. virginiana 6.86002762 2.89E-10 H. mollis - H. virginiana 4.58088035 1.44E-04 H. japonica + North America - H. virginiana 3.40338158 1.60E-02 H. ovalis - H. virginiana 3.50515032 1.19E-02 H. vernalis - H. virginiana 9.59776207 3.59E-20 H. japonica - H. virginiana + H. ovalis 3.32194757 1.88E-02 H. mollis - H. virginiana + H. ovalis 1.12132544 1.00E+00

Table A-7. Continued. Branches Z P-value ω H. japonica + North America - H. virginiana + H. ovalis -0.08520698 1.00E+00 H. ovalis - H. virginiana + H. ovalis 0.07076153 1.00E+00 H. vernalis - H. virginiana + H. ovalis 5.93583656 1.14E-07 H. virginiana - H. virginiana + H. ovalis -3.50937897 1.21E-02 H. japonica - H. virginiana A 3.10087316 3.67E-02 H. mollis - H. virginiana A 0.91977034 1.00E+00 H. japonica + North America - H. virginiana A -0.28032453 1.00E+00 H. ovalis - H. virginiana A -0.12246307 1.00E+00 H. vernalis - H. virginiana A 5.68810793 4.62E-07 H. virginiana - H. virginiana A -3.68162037 6.49E-03 H. virginiana + H. ovalis - H. virginiana A -0.19682052 1.00E+00 H. japonica - H. virginiana B 3.43657634 1.47E-02 H. mollis - H. virginiana B 1.25455116 1.00E+00 H. japonica + North America - H. virginiana B 0.06058259 9.52E-01 H. ovalis - H. virginiana B 0.21289901 1.00E+00 H. vernalis - H. virginiana B 6.02698841 6.85E-08 H. virginiana - H. virginiana B -3.32859731 1.92E-02 H. virginiana + H. ovalis - H. virginiana B 0.14579698 1.00E+00 H. virginiana A - H. virginiana B 0.3397425 1.00E+00 H. japonica - North America 8.03250021 4.11E-14 H. mollis - North America 5.7231551 3.87E-07 H. japonica + North America - North America 4.55390947 1.58E-04 H. ovalis - North America 4.63731718 1.13E-04 H. vernalis - North America 10.81710766 1.29E-25 H. virginiana - North America 1.15255521 1.00E+00 H. virginiana + H. ovalis - North America 4.66716718 1.01E-04 H. virginiana A - North America 4.83125429 4.75E-05 H. virginiana B - North America 4.47416923 2.22E-04

Table A-8. Median scores, standard deviation (SD), and P-values for t-tests on GO category dN/dS results. Liriodendron tulipifera samples were combined for calculating median scores and for t-tests. Biological Process Species Region dN SD P-value Campsis elegans EA 0.0353 0.016373475 0.8601 Campsis radicans ENA 0.0336 0.017297647 Cotinus coggygria EA 0.01818889 0.006044307 0.6909 Cotinus obovatus ENA 0.01859444 0.006548999 Gelsemium elegans EA 0.07887 0.099082678 0.893 Gelsemium sempervirens ENA 0.08075 0.096759974 Liriodendron chinense EA 0.02055 0.010740659 0.8416 Liriodendron tulipifera ENA 0.0206 0.006726988 Nelumbo nucifera EA 0.1096 0.038827115 0.8651 Nelumbo lutea ENA 0.1017 0.038815109 Penthorun chinensis EA 0.0657 0.030023169 0.7621 Penthorun sedoides ENA 0.0665 0.025505064 Phryma leptostachya EA 0.0787 0.022987201 0.9562 Phryma leptostachya ENA 0.077 0.023029522 Saururus chinensis EA 0.0419 0.012232382 0.6291 Saururus cernuus ENA 0.0403 0.014027078 dS Campsis elegans EA 0.24663333 0.06394696 0.9019 Campsis radicans ENA 0.24703333 0.06365891 Cotinus coggygria EA 0.07381667 0.01358479 0.7286 Cotinus obovatus ENA 0.07206071 0.04692062 Gelsemium elegans EA 0.5973 0.12057718 0.9493 Gelsemium sempervirens ENA 0.60305 0.11782444 Liriodendron chinense EA 0.1053 0.03131935 0.9165 Liriodendron tulipifera ENA 0.10585 0.02230231 Nelumbo nucifera EA 0.87 0.13165196 0.3582 Nelumbo lutea ENA 0.854475 0.09211556 Penthorun chinensis EA 0.52034 0.12042081 0.7084 Penthorun sedoides ENA 0.52159 0.11467095 Phryma leptostachya EA 0.53717857 0.16726317 0.9039 Phryma leptostachya ENA 0.5298 0.16474288 Saururus chinensis EA 0.2526 0.03498751 0.8766 Saururus cernuus ENA 0.2496 0.04498199

Table A-8. Continued. Species Region dN/dS SD P-value Campsis elegans EA 0.1435461 0.05571212 0.7924 Campsis radicans ENA 0.1373371 0.0553727 Cotinus coggygria EA 0.2515947 0.08599813 0.5792 Cotinus obovatus ENA 0.2650038 0.09272236 Gelsemium elegans EA 0.1451427 0.25101175 0.9249 Gelsemium sempervirens ENA 0.1374093 0.2526772 Liriodendron chinense EA 0.1963788 0.05916468 0.908 Liriodendron tulipifera ENA 0.1906776 0.0608204 Nelumbo nucifera EA 0.120505 0.08822042 0.977 Nelumbo lutea ENA 0.1189265 0.08766155 Penthorun chinensis EA 0.1337254 0.05151473 0.9912 Penthorun sedoides ENA 0.1318835 0.05174278 Phryma leptostachya EA 0.1459518 0.125831 0.9362 Phryma leptostachya ENA 0.1446132 0.11489209 Saururus chinensis EA 0.1584634 0.04685175 0.5362 Saururus cernuus ENA 0.1593798 0.04800718 Molecular Function Species Region dN SD P-value Campsis elegans EA 0.03665 0.011936885 0.8761 Campsis radicans ENA 0.036975 0.012155831 Cotinus coggygria EA 0.01975 0.005469025 0.6731 Cotinus obovatus ENA 0.02015 0.005510437 Gelsemium elegans EA 0.076625 0.027790523 0.7948 Gelsemium sempervirens ENA 0.080525 0.02694616 Liriodendron chinense EA 0.02315 0.008068809 0.9596 Liriodendron tulipifera ENA 0.02282778 0.007898975 Nelumbo nucifera EA 0.1062 0.058740599 0.9275 Nelumbo lutea ENA 0.10369 0.060028093 Penthorun chinensis EA 0.072975 0.060004481 0.3967 Penthorun sedoides ENA 0.07323125 0.016553211 Phryma leptostachya EA 0.07546667 0.022591446 0.9423 Phryma leptostachya ENA 0.07543333 0.02216778 Saururus chinensis EA 0.04085 0.011939834 0.9225 Saururus cernuus ENA 0.03945 0.013342612

Table A-8. Continued. Species Region dS SD P-value Campsis elegans EA 0.26016667 0.04604051 0.8816 Campsis radicans ENA 0.26075 0.04920624 Cotinus coggygria EA 0.0792 0.01420663 0.468 Cotinus obovatus ENA 0.07532857 0.01411893 Gelsemium elegans EA 0.6056 0.13480566 1 Gelsemium sempervirens ENA 0.61073333 0.12368562 Liriodendron chinense EA 0.1127 0.02044231 0.9683 Liriodendron tulipifera ENA 0.1131 0.01835389 Nelumbo nucifera EA 0.8993 0.18373435 0.922 Nelumbo lutea ENA 0.8911 0.17958829 Penthorun chinensis EA 0.5109 0.15592432 0.6007 Penthorun sedoides ENA 0.50278333 0.14481503 Phryma leptostachya EA 0.5479 0.09887978 0.829 Phryma leptostachya ENA 0.546 0.1017318 Saururus chinensis EA 0.259425 0.0419873 0.8416 Saururus cernuus ENA 0.263125 0.04383481 dN/dS SD P-value Campsis elegans EA 0.1419256 0.04616499 0.7965 Campsis radicans ENA 0.1376652 0.04457969 Cotinus coggygria EA 0.259629 0.08474068 0.3969 Cotinus obovatus ENA 0.2621686 0.09271421 Gelsemium elegans EA 0.1411672 0.05154566 0.8163 Gelsemium sempervirens ENA 0.1376856 0.05022732 Liriodendron chinense EA 0.1947836 0.07079889 0.9737 Liriodendron tulipifera ENA 0.1897642 0.07255764 Nelumbo nucifera EA 0.1189691 0.34033967 0.9233 Nelumbo lutea ENA 0.1209304 0.33053919 Penthorun chinensis EA 0.139831 0.06589242 0.8945 Penthorun sedoides ENA 0.1368987 0.04449887 Phryma leptostachya EA 0.136258 0.04425543 0.9722 Phryma leptostachya ENA 0.1356802 0.04515923 Saururus chinensis EA 0.1624336 0.05067268 0.8267 Saururus cernuus ENA 0.1575776 0.04971104

Table A-9. Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) of Calycanthus Biological Process genes. No statistically significant differences were identified between species. Branches Z P-value dN C. chinensis - C. floridus -0.05439905 1.00E+00 C. chinensis - C. occidentalis -0.03466606 1.00E+00 C. floridus - C. occidentalis 0.01973299 9.84E-01 C. chinensis - North America 8.09319266 2.32E-15 C. floridus - North America 8.14759172 2.23E-15 C. occidentalis - North America 8.12785873 2.18E-15 dS C. chinensis - C. floridus -1.6248369 2.08E-01 C. chinensis - C. occidentalis -2.6038984 2.77E-02 C. floridus - C. occidentalis -0.9790615 3.28E-01 C. chinensis - North America 6.2849779 1.31E-09 C. floridus - North America 7.9098148 1.29E-14 C. occidentalis - North America 8.8888763 3.70E-18 ω C. chinensis - C. floridus -0.4222746 1.00E+00 C. chinensis - C. occidentalis 0.1571351 8.75E-01 C. floridus - C. occidentalis 0.5899732 1.00E+00 C. chinensis - North America 5.4130245 3.10E-07 C. floridus - North America 5.8991375 2.19E-08 C. occidentalis - North America 5.3554916 3.41E-07

Table A-10. Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) of Calycanthus Molecular Function genes. No statistically significant differences were identified between species. Branches Z P-value dN C. chinensis - C. floridus 1.4657476 4.28E-01 C. chinensis - C. occidentalis 0.8165799 8.28E-01 C. floridus - C. occidentalis -0.6491677 5.16E-01 C. chinensis - North America 7.1711211 4.46E-12 C. floridus - North America 5.7053735 4.64E-08 C. occidentalis - North America 6.3545412 1.05E-09 dS C. chinensis - C. floridus -2.0819422 7.47E-02 C. chinensis - C. occidentalis -2.2760669 6.85E-02 C. floridus - C. occidentalis -0.1941246 8.46E-01 C. chinensis - North America 3.3713573 2.99E-03 C. floridus - North America 5.4532995 2.47E-07 C. occidentalis - North America 5.6474241 9.77E-08 ω C. chinensis - C. floridus 1.9503949 1.53E-01 C. chinensis - C. occidentalis 1.1574505 4.94E-01 C. floridus - C. occidentalis -0.8264966 4.09E-01 C. chinensis - North America 4.9910784 3.60E-06 C. floridus - North America 3.1911516 5.67E-03 C. occidentalis - North America 4.0013276 3.15E-04

Table A-11. Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) of Cornus Biological Process genes. The highest rates were identified in the branches leading to the Big-Bracted clade and the subgenus Mesomora. Branches Z P-value dN C. alternifolia - Big-Bracted -8.8206297 4.10E-01 C. alternifolia - C. capitata 0.409644 1.00E+00 Big-Bracted - C. capitata 9.2302737 1.08E-18 C. alternifolia - C. controversa 0.8717493 1.00E+00 Big-Bracted - C. controversa 9.692379 1.36E-20 C. capitata - C. controversa 0.4621053 1.00E+00 C. alternifolia - C. elliptica 1.5890171 1.00E+00 Big-Bracted - C. elliptica 10.4096467 9.63E-24 C. capitata - C. elliptica 1.179373 1.00E+00 C. controversa - C. elliptica 0.7172677 1.00E+00 C. alternifolia - C. florida -2.104923 5.29E-01 Big-Bracted - C. florida 6.7157067 5.61E-10 C. capitata - C. florida -2.514567 2.03E-01 C. controversa - C. florida -2.9766723 5.83E-02 C. elliptica - C. florida -3.69394 4.86E-03 C. alternifolia - C. kousa -1.9926336 6.02E-01 Big-Bracted - C. kousa 6.827996 2.67E-10 C. capitata - C. kousa -2.4022777 2.61E-01 C. controversa - C. kousa -2.864383 7.94E-02 C. elliptica - C. kousa -3.5816507 7.17E-03 C. florida - C. kousa 0.1122893 9.11E-01 C. alternifolia - C. kousa + C. elliptica 6.0692058 3.60E-08 Big-Bracted - C. kousa + C. elliptica 14.8898354 1.73E-48 C. capitata - C. kousa + C. elliptica 5.6595617 3.79E-07 C. controversa - C. kousa + C. elliptica 5.1974565 4.85E-06 C. elliptica - C. kousa + C. elliptica 4.4801887 1.72E-04 C. florida - C. kousa + C. elliptica 8.1741288 1.07E-14 C. kousa - C. kousa + C. elliptica 8.0618394 2.63E-14 C. alternifolia - Mesomora -7.8481994 1.43E-13 Big-Bracted - Mesomora 0.9724302 1.00E+00 C. capitata - Mesomora -8.2578435 5.49E-15 C. controversa - Mesomora -8.7199488 1.06E-16 C. elliptica - Mesomora -9.4372165 1.57E-19 C. florida - Mesomora -5.7432765 2.41E-07 C. kousa - Mesomora -5.8555658 1.28E-07

Table A-11. Continued. Branches Z P-value dN C. kousa + C. elliptica - Mesomora -13.9174052 2.19E-42 C. alternifolia - Syncarpea -1.163523 1.00E+00 Big-Bracted - Syncarpea 7.6571066 6.28E-13 C. capitata - Syncarpea -1.5731671 1.00E+00 C. controversa - Syncarpea -2.0352723 5.86E-01 C. elliptica - Syncarpea -2.7525401 1.06E-01 C. florida - Syncarpea 0.9414 1.00E+00 C. kousa - Syncarpea 0.8291106 1.00E+00 C. kousa + C. elliptica - Syncarpea -7.2327288 1.51E-11 Mesomora - Syncarpea 6.6846764 6.71E-10 dS C. alternifolia - Big-Bracted -9.99195696 6.61E-22 C. alternifolia - C. capitata 0.06114564 9.51E-01 Big-Bracted - C. capitata 10.0531026 3.65E-22 C. alternifolia - C. controversa 0.93414476 1.00E+00 Big-Bracted - C. controversa 10.92610172 3.72E-26 C. capitata - C. controversa 0.87299912 1.00E+00 C. alternifolia - C. elliptica 0.55946033 1.00E+00 Big-Bracted - C. elliptica 10.55141728 2.10E-24 C. capitata - C. elliptica 0.49831468 1.00E+00 C. controversa - C. elliptica -0.37468444 1.00E+00 C. alternifolia - C. florida -4.0103955 1.09E-03 Big-Bracted - C. florida 5.98156146 6.63E-08 C. capitata - C. florida -4.07154114 8.87E-04 C. controversa - C. florida -4.94454026 1.83E-05 C. elliptica - C. florida -4.56985583 1.12E-04 C. alternifolia - C. kousa -3.5730033 5.29E-03 Big-Bracted - C. kousa 6.41895366 4.25E-09 C. capitata - C. kousa -3.63414895 4.46E-03 C. controversa - C. kousa -4.50714807 1.45E-04 C. elliptica - C. kousa -4.13246363 7.18E-04 C. florida - C. kousa 0.4373922 1.00E+00 C. alternifolia - C. kousa + C. elliptica 5.97375088 6.72E-08 Big-Bracted - C. kousa + C. elliptica 15.96570784 9.97E-56 C. capitata - C. kousa + C. elliptica 5.91260524 9.43E-08 C. controversa - C. kousa + C. elliptica 5.03960612 1.17E-05 C. elliptica - C. kousa + C. elliptica 5.41429056 1.60E-06

Table A-11. Continued. Branches Z P-value dS C. florida - C. kousa + C. elliptica 9.98414638 6.97E-22 C. kousa - C. kousa + C. elliptica 9.54675419 5.09E-20 C. alternifolia - Mesomora -7.80276438 1.94E-13 Big-Bracted - Mesomora 2.18919258 3.43E-01 C. capitata - Mesomora -7.86391002 1.23E-13 C. controversa - Mesomora -8.73690914 8.86E-17 C. elliptica - Mesomora -8.3622247 2.22E-15 C. florida - Mesomora -3.79236888 2.54E-03 C. kousa - Mesomora -4.22976108 4.91E-04 C. kousa + C. elliptica - Mesomora -13.77651526 1.55E-41 C. alternifolia - Syncarpea -1.9758523 4.34E-01 Big-Bracted - Syncarpea 8.01610465 3.82E-14 C. capitata - Syncarpea -2.03699795 4.58E-01 C. controversa - Syncarpea -2.90999707 5.06E-02 C. elliptica - Syncarpea -2.53531263 1.46E-01 C. florida - Syncarpea 2.0345432 4.19E-01 C. kousa - Syncarpea 1.597151 8.82E-01 C. kousa + C. elliptica - Syncarpea -7.94960319 6.36E-14 Mesomora - Syncarpea 5.82691207 1.52E-07 ω C. alternifolia - Big-Bracted 0.34339314 1.00E+00 C. alternifolia - C. capitata -0.80770531 1.00E+00 Big-Bracted - C. capitata -1.14679526 1.00E+00 C. alternifolia - C. controversa 0.01545557 0.987668728 Big-Bracted - C. controversa -0.32270886 1.00E+00 C. apitate - C. controversa 0.81067313 1.00E+00 C. alternifolia - C. elliptica 0.13560821 1.00E+00 Big-Bracted - C. elliptica -0.20829569 1.00E+00 C. apitate - C. elliptica 0.94280275 1.00E+00 C. controversa - C. elliptica 0.11805006 1.00E+00 C. alternifolia - C. florida 0.05514734 1.00E+00 Big-Bracted - C. florida -0.28845351 1.00E+00 C. apitate - C. florida 0.86264494 1.00E+00 C. controversa - C. florida 0.03883672 1.00E+00 C. elliptica - C. florida -0.08046087 1.00E+00 C. alternifolia - C. kousa 0.825854 1.00E+00 Big-Bracted - C. kousa 0.47935029 1.00E+00

Table A-11. Continued. Branches Z P-value ω C. capitata - C. kousa 1.63044874 1.00E+00 C. controversa - C. kousa 0.79759371 1.00E+00 C. elliptica - C. kousa 0.69024579 1.00E+00 C. florida - C. kousa 0.77070666 1.00E+00 C. alternifolia - C. kousa + C. elliptica -0.77477491 1.00E+00 Big-Bracted - C. kousa + C. elliptica -1.11524986 1.00E+00 C. capitata - C. kousa + C. elliptica 0.03584858 1.00E+00 C. controversa - C. kousa + C. elliptica -0.77821774 1.00E+00 C. elliptica - C. kousa + C. elliptica -0.91038312 1.00E+00 C. florida - C. kousa + C. elliptica -0.82992225 1.00E+00 C. kousa - C. kousa + C. elliptica -1.60062891 1.00E+00 C. alternifolia - Mesomora -2.95354682 0.125657981 Big-Bracted - Mesomora -3.28581545 0.044742549 C. capitata - Mesomora -2.134717 1.00E+00 C. controversa - Mesomora -2.92320819 0.135113466 C. elliptica - Mesomora -3.08915503 0.086312467 C. florida - Mesomora -3.00869416 0.107572969 C. kousa - Mesomora -3.77940082 0.007074279 C. kousa + C. elliptica - Mesomora -2.17877191 1.00E+00 C. alternifolia - Syncarpea 0.111162 1.00E+00 Big-Bracted - Syncarpea -0.23136298 1.00E+00 C. capitata - Syncarpea 0.91543228 1.00E+00 C. controversa - Syncarpea 0.09405197 1.00E+00 C. elliptica - Syncarpea -0.02393545 1.00E+00 C. florida - Syncarpea 0.05622237 1.00E+00 C. kousa - Syncarpea -0.71158143 1.00E+00 C. kousa + C. elliptica - Syncarpea 0.88301872 1.00E+00 Mesomora - Syncarpea 3.05358431 0.094972574

Table A-12. Results of Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) for Cornus Molecular Function genes. The highest rates were identified in the branches leading to the Big-Bracted clade and the subgenus Mesomora. Branches Z P-value dN C. alternifolia - Big-Bracted -8.35441153 2.43E-15 C. alternifolia - C. capitata 0.62077982 1.00E+00 Big-Bracted - C. capitata 8.97519135 1.16E-17 C. alternifolia - C. controversa 0.56884804 1.00E+00 Big-Bracted - C. controversa 8.92325957 1.81E-17 C. capitata - C. controversa -0.05193177 9.59E-01 C. alternifolia - C. elliptica 1.02452684 1.00E+00 Big-Bracted - C. elliptica 9.37893838 2.87E-19 C. capitata - C. elliptica 0.40374703 1.00E+00 C. controversa - C. elliptica 0.4556788 1.00E+00 C. alternifolia - C. florida -2.55936591 1.89E-01 Big-Bracted - C. florida 5.79504562 1.91E-07 C. capitata - C. florida -3.18014573 3.09E-02 C. controversa - C. florida -3.12821396 3.52E-02 C. elliptica - C. florida -3.58389276 7.45E-03 C. alternifolia - C. kousa -1.47270105 1.00E+00 Big-Bracted - C. kousa 6.88171048 2.01E-10 C. capitata - C. kousa -2.09348087 5.08E-01 C. controversa - C. kousa -2.04154909 5.36E-01 C. elliptica - C. kousa -2.49722789 2.13E-01 C. florida - C. kousa 1.08666486 1.00E+00 C. alternifolia - C. kousa + C. elliptica 4.64294072 8.93E-05 Big-Bracted - C. kousa + C. elliptica 12.99735225 5.70E-37 C. capitata - C. kousa + C. elliptica 4.0221609 1.38E-03 C. controversa - C. kousa + C. elliptica 4.07409268 1.15E-03 C. elliptica - C. kousa + C. elliptica 3.61841388 6.82E-03 C. florida - C. kousa + C. elliptica 7.20230663 2.07E-11 C. kousa - C. kousa + C. elliptica 6.11564177 2.79E-08 C. alternifolia - Mesomora -8.02360914 3.70E-14 Big-Bracted - Mesomora 0.3308024 1.00E+00 C. capitata - Mesomora -8.64438895 2.11E-16 C. controversa - Mesomora -8.59245718 3.23E-16 C. elliptica - Mesomora -9.04813598 6.11E-18 C. florida - Mesomora -5.46424322 1.26E-06 C. kousa - Mesomora -6.55090808 1.83E-09

100

Table A-12. Continued. Branches Z P-value dN C. kousa + C. elliptica - Mesomora -12.66654986 3.99E-35 C. alternifolia - Syncarpea -1.68433053 1.00E+00 Big-Bracted - Syncarpea 6.670081 8.44E-10 C. capitata - Syncarpea -2.30511035 3.39E-01 C. controversa - Syncarpea -2.25317858 3.64E-01 C. elliptica - Syncarpea -2.70885738 1.28E-01 C. florida - Syncarpea 0.87503538 1.00E+00 C. kousa - Syncarpea -0.21162948 1.00E+00 C. kousa + C. elliptica - Syncarpea -6.32727125 7.49E-09 Mesomora - Syncarpea 6.3392786 7.16E-09 dS C. alternifolia - Big-Bracted -8.8297068 4.20E-17 C. alternifolia - C. capitata 0.77310935 1.00E+00 Big-Bracted - C. capitata 9.60281615 3.19E-20 C. alternifolia - C. controversa 0.96481167 1.00E+00 Big-Bracted - C. controversa 9.79451847 4.99E-21 C. capitata - C. controversa 0.19170232 1.00E+00 C. alternifolia - C. elliptica 1.00681218 1.00E+00 Big-Bracted - C. elliptica 9.83651897 3.37E-21 C. capitata - C. elliptica 0.23370283 1.00E+00 C. controversa - C. elliptica 0.04200051 9.66E-01 C. alternifolia - C. florida -2.12852574 5.33E-01 Big-Bracted - C. florida 6.70118105 6.20E-10 C. capitata - C. florida -2.9016351 7.42E-02 C. controversa - C. florida -3.09333741 4.16E-02 C. elliptica - C. florida -3.13533792 3.78E-02 C. alternifolia - C. kousa -1.16041404 1.00E+00 Big-Bracted - C. kousa 7.66929276 6.05E-13 C. capitata - C. kousa -1.93352339 6.91E-01 C. controversa - C. kousa -2.1252257 5.04E-01 C. elliptica - C. kousa -2.16722621 5.14E-01 C. florida - C. kousa 0.96811171 1.00E+00 C. alternifolia - C. kousa + C. elliptica 5.79307007 1.87E-07 Big-Bracted - C. kousa + C. elliptica 14.62277686 9.05E-47 C. capitata - C. kousa + C. elliptica 5.01996072 1.34E-05 C. controversa - C. kousa + C. elliptica 4.8282584 3.31E-05 C. elliptica - C. kousa + C. elliptica 4.78625789 3.91E-05

101

Table A-12. Continued. Branches Z P-value dN C. florida - C. kousa + C. elliptica 7.92159581 8.68E-14 C. kousa - C. kousa + C. elliptica 6.9534841 1.10E-10 C. alternifolia - Mesomora -7.05068528 5.88E-11 Big-Bracted - Mesomora 1.77902152 9.03E-01 C. capitata - Mesomora -7.82379463 1.85E-13 C. controversa - Mesomora -8.01549695 4.17E-14 C. elliptica - Mesomora -8.05749746 3.04E-14 C. florida - Mesomora -4.92215953 2.14E-05 C. kousa - Mesomora -5.89027124 1.12E-07 C. kousa + C. elliptica - Mesomora -12.84375535 4.10E-36 C. alternifolia - Syncarpea -1.22761485 1.00E+00 Big-Bracted - Syncarpea 7.60209195 9.91E-13 C. capitata - Syncarpea -2.0007242 6.36E-01 C. controversa - Syncarpea -2.19242652 5.10E-01 C. elliptica - Syncarpea -2.23442703 4.84E-01 C. florida - Syncarpea 0.9009109 1.00E+00 C. kousa - Syncarpea -0.06720081 1.00E+00 C. kousa + C. elliptica - Syncarpea -7.02068492 7.07E-11 Mesomora - Syncarpea 5.82307043 1.62E-07 ω C. alternifolia - Big-Bracted -1.07940491 1.00E+00 C. alternifolia - C. capitata -1.37017839 1.00E+00 Big-Bracted - C. capitata -0.29077348 1.00E+00 C. alternifolia - C. controversa -0.83073772 1.00E+00 Big-Bracted - C. controversa 0.23862599 1.00E+00 C. apitate - C. controversa 0.52669454 1.00E+00 C. alternifolia - C. elliptica -1.12216571 1.00E+00 Big-Bracted - C. elliptica -0.04276081 9.66E-01 C. apitate - C. elliptica 0.24801267 1.00E+00 C. controversa - C. elliptica -0.28098902 1.00E+00 C. alternifolia - C. florida -1.29926667 1.00E+00 Big-Bracted - C. florida -0.22482457 1.00E+00 C. apitate - C. florida 0.06461201 1.00E+00 C. controversa - C. florida -0.46030109 1.00E+00 C. elliptica - C. florida -0.18226037 1.00E+00 C. alternifolia - C. kousa -0.12107146 1.00E+00 Big-Bracted - C. kousa 0.94829225 1.00E+00

102

Table A-12. Continued. Branches Z P-value ω C. capitata - C. kousa 1.2363608 1.00E+00 C. controversa - C. kousa 0.70318515 1.00E+00 C. elliptica - C. kousa 0.99065527 1.00E+00 C. florida - C. kousa 1.16676452 1.00E+00 C. alternifolia - C. kousa + C. elliptica -1.83526089 1.00E+00 Big-Bracted - C. kousa + C. elliptica -0.76589718 1.00E+00 C. capitata - C. kousa + C. elliptica -0.47782863 1.00E+00 C. controversa - C. kousa + C. elliptica -0.99534925 1.00E+00 C. elliptica - C. kousa + C. elliptica -0.72353415 1.00E+00 C. florida - C. kousa + C. elliptica -0.5396885 1.00E+00 C. kousa - C. kousa + C. elliptica -1.6985344 1.00E+00 C. alternifolia - Mesomora -3.94498975 0.003591153 Big-Bracted - Mesomora -2.86558485 0.17482068 C. capitata - Mesomora -2.57481137 3.91E-01 C. controversa - Mesomora -3.07755366 0.089744123 C. elliptica - Mesomora -2.82282404 0.195171029 C. florida - Mesomora -2.6275851 0.343973316 C. kousa - Mesomora -3.78721991 0.006703059 C. kousa + C. elliptica - Mesomora -2.07303049 1.00E+00 C. alternifolia - Syncarpea -1.62719683 1.00E+00 Big-Bracted - Syncarpea -0.55275473 1.00E+00 C. capitata - Syncarpea -0.26331815 1.00E+00 C. controversa - Syncarpea -0.78520828 1.00E+00 C. elliptica - Syncarpea -0.51019053 1.00E+00 C. florida - Syncarpea -0.32643617 1.00E+00 C. kousa - Syncarpea -1.4916717 1.00E+00 C. kousa + C. elliptica - Syncarpea 0.21478132 1.00E+00 Mesomora - Syncarpea 2.29965494 0.815775519

103

Table A-13. Results for Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) for Hamamelis Biological Process genes. Hamamelis vernalis and H. japonica were found to have the highest rates in these categories. Branches Z P-value dN H. japonica - H. mollis 3.5666081 5.42E-03 H. japonica - H. japonica + North America 3.9044569 1.51E-03 H. mollis - H. japonica + North America 0.3378488 1.00E+00 H. japonica - H. ovalis 5.4247764 1.57E-06 H. mollis - H. ovalis 1.8581683 7.58E-01 H. japonica + North America - H. ovalis 1.5203195 1.00E+00 H. japonica - H. ovalis + H. virginiana 8.5010651 6.58E-16 H. mollis - H. ovalis + H. virginiana 4.9344569 1.93E-05 H. japonica + North America - H. ovalis + H. virginiana 4.5966082 9.45E-05 H. ovalis - H. ovalis + H. virginiana 3.0762887 2.93E-02 H. japonica - H. vernalis -5.0390292 1.17E-05 H. mollis - H. vernalis -8.6056373 2.73E-16 H. japonica + North America - H. vernalis -8.9434861 1.40E-17 H. ovalis - H. vernalis -10.4638056 5.19E-24 H. ovalis + H. virginiana - H. vernalis -13.5400943 3.90E-40 H. japonica - H. virginiana 9.350806 3.39E-19 H. mollis - H. virginiana 5.7841978 2.19E-07 H. japonica + North America - H. virginiana 5.446349 1.44E-06 H. ovalis - H. virginiana 3.9260295 1.47E-03 H. ovalis + H. virginiana - H. virginiana 0.8497409 1.00E+00 H. vernalis - H. virginiana 14.3898352 2.64E-45 H. japonica - H. virginiana A 4.4317058 1.96E-04 H. mollis - H. virginiana A 0.8650976 1.00E+00 H. japonica + North America - H. virginiana A 0.5272489 1.00E+00 H. ovalis - H. virginiana A -0.9930707 1.00E+00 H. ovalis + H. virginiana - H. virginiana A -4.0693593 8.49E-04 H. vernalis - H. virginiana A 9.470735 1.11E-19 H. virginiana - H. virginiana A -4.9191002 2.00E-05 H. japonica - H. virginiana B 4.0850817 8.37E-04 H. mollis - H. virginiana B 0.5184736 1.00E+00 H. japonica + North America - H. virginiana B 0.1806248 8.57E-01 H. ovalis - H. virginiana B -1.3396947 1.00E+00 H. ovalis + H. virginiana - H. virginiana B -4.4159834 2.01E-04 H. vernalis - H. virginiana B 9.1241109 2.75E-18 H. virginiana - H. virginiana B -5.2657243 3.63E-06

104

Table A-13. Continued. Branches Z P-value dN H. virginiana A - H. virginiana B -0.3466241 1.00E+00 H. japonica - North America 11.0477283 9.44E-27 H. mollis - North America 7.4811201 2.51E-12 H. japonica + North America - North America 7.1432713 3.01E-11 H. ovalis - North America 5.6229518 5.44E-07 H. ovalis + H. virginiana - North America 2.5466632 1.41E-01 H. vernalis - North America 16.0867574 1.42E-56 H. virginiana - North America 1.6969223 9.87E-01 H. virginiana A - North America 6.6160225 1.14E-09 H. virginiana B - North America 6.9626465 1.07E-10 dS H. japonica - H. mollis 4.4256671 1.63E-04 H. japonica - H. japonica + North America 1.8780453 6.04E-01 H. mollis - H. japonica + North America -2.5476219 1.19E-01 H. japonica - H. ovalis 6.1370897 2.52E-08 H. mollis - H. ovalis 1.7114225 7.83E-01 H. japonica + North America - H. ovalis 4.2590444 3.28E-04 H. japonica - H. ovalis + H. virginiana 9.7966189 4.42E-21 H. mollis - H. ovalis + H. virginiana 5.3709517 2.04E-06 H. japonica + North America - H. ovalis + H. virginiana 7.9185736 7.93E-14 H. ovalis - H. ovalis + H. virginiana 3.6595292 3.54E-03 H. japonica - H. vernalis -4.7748852 3.42E-05 H. mollis - H. vernalis -9.2005523 1.28E-18 H. japonica + North America - H. vernalis -6.6529305 9.19E-10 H. ovalis - H. vernalis -10.9119749 4.14E-26 H. ovalis + H. virginiana - H. vernalis -14.5715041 1.83E-46 H. japonica - H. virginiana 10.1625355 1.17E-22 H. mollis - H. virginiana 5.7368684 2.70E-07 H. japonica + North America - H. virginiana 8.2844902 4.03E-15 H. ovalis - H. virginiana 4.0254458 8.53E-04 H. ovalis + H. virginiana - H. virginiana 0.3659166 1.00E+00 H. vernalis - H. virginiana 14.9374207 8.28E-49 H. japonica - H. virginiana A 4.9298915 1.81E-05 H. mollis - H. virginiana A 0.5042244 1.00E+00 H. japonica + North America - H. virginiana A 3.0518463 2.73E-02 H. ovalis - H. virginiana A -1.2071981 1.00E+00 H. ovalis + H. virginiana - H. virginiana A -4.8667274 2.38E-05

105

Table A-13. Continued. Branches Z P-value dS H. vernalis - H. virginiana A 9.7047767 1.06E-20 H. virginiana - H. virginiana A -5.232644 4.18E-06 H. japonica - H. virginiana B 5.2181235 4.34E-06 H. mollis - H. virginiana B 0.7924563 1.00E+00 H. japonica + North America - H. virginiana B 3.3400782 1.09E-02 H. ovalis - H. virginiana B -0.9189662 1.00E+00 H. ovalis + H. virginiana - H. virginiana B -4.5784954 8.43E-05 H. vernalis - H. virginiana B 9.9930087 6.38E-22 H. virginiana - H. virginiana B -4.944412 1.76E-05 H. virginiana A - H. virginiana B 0.2882319 7.73E-01 H. japonica - North America 10.9250433 3.67E-26 H. mollis - North America 6.4993762 2.50E-09 H. japonica + North America - North America 9.046998 5.14E-18 H. ovalis - North America 4.7879536 3.37E-05 H. ovalis + H. virginiana - North America 1.1284244 1.00E+00 H. vernalis - North America 15.6999285 6.81E-54 H. virginiana - North America 0.7625078 1.00E+00 H. virginiana A - North America 5.9951518 5.90E-08 H. virginiana B - North America 5.7069198 3.11E-07 ω H. japonica - H. mollis 0.72910426 1.00E+00 H. japonica - H. japonica + North America 2.40059791 5.57E-01 H. mollis - H. japonica + North America 1.64969988 1.00E+00 H. japonica - H. ovalis 0.92120372 1.00E+00 H. mollis - H. ovalis 0.18319545 1.00E+00 H. japonica + North America - H. ovalis -1.48141028 1.00E+00 H. japonica - H. ovalis + H. virginiana 0.46897713 1.00E+00 H. mollis - H. ovalis + H. virginiana -0.26585849 1.00E+00 H. japonica + North America - H. ovalis + H. virginiana -1.93670313 1.00E+00 H. ovalis - H. ovalis + H. virginiana -0.45377267 1.00E+00 H. japonica - H. vernalis -1.41468995 1.00E+00 H. mollis - H. vernalis -2.1431962 9.95E-01 H. japonica + North America - H. vernalis -3.85730695 4.70E-03 H. ovalis - H. vernalis -2.35311273 6.14E-01 H. ovalis + H. virginiana - H. vernalis -1.89485456 1.00E+00 H. japonica - H. virginiana 2.75993254 2.08E-01 H. mollis - H. virginiana 2.00401148 1.00E+00

106

Table A-13. Continued. Branches Z P-value ω H. japonica + North America - H. virginiana 0.35392174 1.00E+00 H. ovalis - H. virginiana 1.83899873 1.00E+00 H. ovalis + H. virginiana - H. virginiana 2.29578654 6.94E-01 H. vernalis - H. virginiana 4.22648513 1.04E-03 H. japonica - H. virginiana A 1.75424681 1.00E+00 H. mollis - H. virginiana A 1.02346034 1.00E+00 H. japonica + North America - H. virginiana A -0.60037084 1.00E+00 H. ovalis - H. virginiana A 0.85198556 1.00E+00 H. ovalis + H. virginiana - H. virginiana A 1.29775455 1.00E+00 H. vernalis - H. virginiana A 3.17091456 5.93E-02 H. virginiana - H. virginiana A -0.94869458 1.00E+00 H. japonica - H. virginiana B 2.45319603 4.96E-01 H. mollis - H. virginiana B 1.7122422 1.00E+00 H. japonica + North America - H. virginiana B 0.08583919 9.32E-01 H. ovalis - H. virginiana B 1.54666892 1.00E+00 H. ovalis + H. virginiana - H. virginiana B 1.99572285 1.00E+00 H. vernalis - H. virginiana B 3.8898012 4.21E-03 H. virginiana - H. virginiana B -0.26289302 1.00E+00 H. virginiana A - H. virginiana B 0.67650756 1.00E+00 H. japonica - North America 3.92414125 3.74E-03 H. mollis - North America 3.15994553 6.00E-02 H. japonica + North America - North America 1.52612981 1.00E+00 H. ovalis - North America 3.00724063 9.75E-02 H. ovalis + H. virginiana - North America 3.46402844 2.13E-02 H. vernalis - North America 5.4064313 2.89E-06 H. virginiana - North America 1.17610894 1.00E+00 H. virginiana A - North America 2.09606353 1.00E+00 H. virginiana B - North America 1.41882706 1.00E+00

107

Table A-14. Results of Dunn’s pairwise test (ad-hoc test for Kruskal-Wallis test) for Hamamelis Molecular Function genes. Hamamelis vernalis and H. japonica were found to have the highest rates in these categories. Branches Z p-value dN H. japonica - H. mollis 3.086836919 3.64E-02 H. japonica - H. japonica + North America 2.118113141 3.42E-01 H. mollis - H. japonica + North America -0.968723778 1.00E+00 H. japonica - H. ovalis 4.425034541 2.31E-04 H. mollis - H. ovalis 1.338197622 1.00E+00 H. japonica + North America - H. ovalis 2.3069214 2.53E-01 H. japonica - H. ovalis + H. virginiana 7.090233608 4.69E-11 H. mollis - H. ovalis + H. virginiana 4.00339669 1.37E-03 H. japonica + North America - H. ovalis + H. virginiana 4.972120467 1.85E-05 H. ovalis - H. ovalis + H. virginiana 2.665199067 1.23E-01 H. japonica - H. vernalis -4.778477495 4.59E-05 H. mollis - H. vernalis -7.865314414 1.36E-13 H. japonica + North America - H. vernalis -6.896590636 1.81E-10 H. ovalis - H. vernalis -9.203512036 1.39E-18 H. ovalis + H. virginiana - H. vernalis -11.8687111 7.40E-31 H. japonica - H. virginiana 8.406293335 1.61E-15 H. mollis - H. virginiana 5.319456416 3.23E-06 H. japonica + North America - H. virginiana 6.288180194 1.03E-08 H. ovalis - H. virginiana 3.981258794 1.37E-03 H. ovalis + H. virginiana - H. virginiana 1.316059727 1.00E+00 H. vernalis - H. virginiana 13.18477083 4.72E-38 H. japonica - H. virginiana A 4.418164159 2.29E-04 H. mollis - H. virginiana A 1.331327241 1.00E+00 H. japonica + North America - H. virginiana A 2.300051019 2.36E-01 H. ovalis - H. virginiana A -0.006870381 9.95E-01 H. ovalis + H. virginiana - H. virginiana A -2.672069449 1.28E-01 H. vernalis - H. virginiana A 9.196641655 1.44E-18 H. virginiana - H. virginiana A -3.988129175 1.40E-03 H. japonica - H. virginiana B 4.511041538 1.61E-04 H. mollis - H. virginiana B 1.424204619 1.00E+00 H. japonica + North America - H. virginiana B 2.392928397 2.34E-01 H. ovalis - H. virginiana B 0.086006997 1.00E+00 H. ovalis + H. virginiana - H. virginiana B -2.579192071 1.49E-01 H. vernalis - H. virginiana B 9.289519033 6.35E-19 H. virginiana - H. virginiana B -3.895251797 1.86E-03

108

Table A-14. Continued. Branches Z p-value dN H. virginiana A - H. virginiana B 0.092877378 1.00E+00 H. japonica - North America 9.438631755 1.59E-19 H. mollis - North America 6.351794837 7.02E-09 H. japonica + North America - North America 7.320518615 8.89E-12 H. ovalis - North America 5.013597214 1.55E-05 H. ovalis + H. virginiana - North America 2.348398147 2.45E-01 H. vernalis - North America 14.21710925 3.23E-44 H. virginiana - North America 1.03233842 1.00E+00 H. virginiana A - North America 5.020467596 1.55E-05 H. virginiana B - North America 4.927590218 2.25E-05 dS H. japonica - H. mollis 4.3196515 3.44E-04 H. japonica - H. japonica + North America 1.4199869 1.00E+00 H. mollis - H. japonica + North America -2.8996646 4.86E-02 H. japonica - H. ovalis 5.430858 1.68E-06 H. mollis - H. ovalis 1.1112065 1.00E+00 H. japonica + North America - H. ovalis 4.0108711 1.15E-03 H. japonica - H. ovalis + H. virginiana 8.3332931 2.99E-15 H. mollis - H. ovalis + H. virginiana 4.0136416 1.20E-03 H. japonica + North America - H. ovalis + H. virginiana 6.9133062 1.56E-10 H. ovalis - H. ovalis + H. virginiana 2.9024351 5.18E-02 H. japonica - H. vernalis -4.7463024 4.97E-05 H. mollis - H. vernalis -9.065954 5.06E-18 H. japonica + North America - H. vernalis -6.1662894 2.24E-08 H. ovalis - H. vernalis -10.1771605 1.05E-22 H. ovalis + H. virginiana - H. vernalis -13.0795956 1.85E-37 H. japonica - H. virginiana 8.5987536 3.14E-16 H. mollis - H. virginiana 4.2791021 3.94E-04 H. japonica + North America - H. virginiana 7.1787667 2.39E-11 H. ovalis - H. virginiana 3.1678956 2.30E-02 H. ovalis + H. virginiana - H. virginiana 0.2654605 1.00E+00 H. vernalis - H. virginiana 13.3450561 5.57E-39 H. japonica - H. virginiana A 3.1968595 2.22E-02 H. mollis - H. virginiana A -1.1227921 1.00E+00 H. japonica + North America - H. virginiana A 1.7768726 6.80E-01 H. ovalis - H. virginiana A -2.2339986 3.06E-01 H. ovalis + H. virginiana - H. virginiana A -5.1364336 7.56E-06

109

Table A-14. Continued. Branches Z p-value dS H. vernalis - H. virginiana A 7.9431619 7.10E-14 H. virginiana - H. virginiana A -5.4018941 1.91E-06 H. japonica - H. virginiana B 3.5686049 6.46E-03 H. mollis - H. virginiana B -0.7510466 1.00E+00 H. japonica + North America - H. virginiana B 2.148618 3.48E-01 H. ovalis - H. virginiana B -1.8622531 6.26E-01 H. ovalis + H. virginiana - H. virginiana B -4.7646882 4.73E-05 H. vernalis - H. virginiana B 8.3149074 3.40E-15 H. virginiana - H. virginiana B -5.0301487 1.27E-05 H. virginiana A - H. virginiana B 0.3717454 1.00E+00 H. japonica - North America 8.727706 1.04E-16 H. mollis - North America 4.4080544 2.40E-04 H. japonica + North America - North America 7.3077191 9.51E-12 H. ovalis - North America 3.2968479 1.66E-02 H. ovalis + H. virginiana - North America 0.3944128 1.00E+00 H. vernalis - North America 13.4740084 1.00E-39 H. virginiana - North America 0.1289523 8.97E-01 H. virginiana A - North America 5.5308465 9.88E-07 H. virginiana B - North America 5.1591011 6.95E-06 ω H. japonica - H. mollis 0.1710551 8.64E-01 H. japonica - H. japonica + North America 1.2528536 1.00E+00 H. mollis - H. japonica + North America 1.0614244 1.00E+00 H. japonica - H. ovalis 1.0539946 1.00E+00 H. mollis - H. ovalis 0.8651455 1.00E+00 H. japonica + North America - H. ovalis -0.202634 1.00E+00 H. japonica - H. ovalis + H. virginiana 0.7936209 1.00E+00 H. mollis - H. ovalis + H. virginiana 0.6085182 1.00E+00 H. japonica + North America - H. ovalis + H. virginiana -0.466104 1.00E+00 H. ovalis - H. ovalis + H. virginiana -0.2637726 1.00E+00 H. japonica - H. vernalis -1.7755152 1.00E+00 H. mollis - H. vernalis -1.916417 1.00E+00 H. japonica + North America - H. vernalis -3.0277851 8.87E-02 H. ovalis - H. vernalis -2.8345916 1.61E-01 H. ovalis + H. virginiana - H. vernalis -2.5789141 3.27E-01 H. japonica - H. virginiana 2.7704667 1.90E-01 H. mollis - H. virginiana 2.5471576 3.48E-01

110

Table A-14. Continued. Branches Z p-value ω H. japonica + North America - H. virginiana 1.477874 1.00E+00 H. ovalis - H. virginiana 1.6914418 1.00E+00 H. ovalis + H. virginiana - H. virginiana 1.9670678 1.00E+00 H. vernalis - H. virginiana 4.6034071 1.83E-04 H. japonica - H. virginiana A 2.383884 5.14E-01 H. mollis - H. virginiana A 2.1685794 8.73E-01 H. japonica + North America - H. virginiana A 1.0998351 1.00E+00 H. ovalis - H. virginiana A 1.3108397 1.00E+00 H. ovalis + H. virginiana - H. virginiana A 1.583677 1.00E+00 H. vernalis - H. virginiana A 4.2043742 1.10E-03 H. virginiana - H. virginiana A -0.3802817 1.00E+00 H. japonica - H. virginiana B 1.9872298 1.00E+00 H. mollis - H. virginiana B 1.7781188 1.00E+00 H. japonica + North America - H. virginiana B 0.7015843 1.00E+00 H. ovalis - H. virginiana B 0.9116418 1.00E+00 H. ovalis + H. virginiana - H. virginiana B 1.1838309 1.00E+00 H. vernalis - H. virginiana B 3.8102763 5.55E-03 H. virginiana - H. virginiana B -0.7931308 1.00E+00 H. virginiana A - H. virginiana B -0.4096184 1.00E+00 H. japonica - North America 4.3623806 5.53E-04 H. mollis - North America 4.1114875 1.61E-03 H. japonica + North America - North America 3.0609197 8.16E-02 H. ovalis - North America 3.2807227 3.93E-02 H. ovalis + H. virginiana - North America 3.5621736 1.43E-02 H. vernalis - North America 6.2076613 2.42E-08 H. virginiana - North America 1.6230055 1.00E+00 H. virginiana A - North America 1.995193 1.00E+00 H. virginiana B - North America 2.4129056 4.91E-01

111

APPENDIX B CHAPTER THREE SUPPLEMENTARY MATERIAL

Table B-1. Estimated distribution size, raster breadth metrics (B1 and B2), Hypervolume (HV), and niche widths (Tw and Pw) per species. Species Region Est. Dist. (km2) B1 B2 Aesculus assamica EA 1487823.246 0.9558313 0.4688178 Aesculus chinensis EA 2760300.474 0.976017 0.6362105 Aesculus flava ENA 409612.7073 0.9817696 0.7117698 Aesculus glabra ENA 2141879.007 0.9925116 0.8573046 Aesculus indica EA 170069.1407 0.9205087 0.3443095 Aesculus parviflora ENA 77435.71941 0.9871198 0.8179448 Aesculus pavia ENA 1711073.046 0.9912631 0.8569334 Aesculus sylvativa ENA 332347.5769 0.9216755 0.3481735 Aesculus turbinata EA 300475.4868 0.9943274 0.9065431 Amphicarpaea bracteata ENA 4017483.462 0.9975996 0.9531993 Amphicarpaea edgeworthii EA 5019907.573 0.9737322 0.578727 Campsis grandiflora EA 3241116.747 0.9908785 0.8280334 Campsis radicans ENA 3404901.125 0.9987991 0.9704227 Castanea crenata EA 478130.2742 0.9954052 0.9234636 Castanea dentata ENA 1302998.377 0.9961628 0.9212855 Castanea henryi EA 2159592.674 0.9935949 0.8824561 Castanea mollissima EA 3144498.018 0.998872 0.9742843 Castanea pumila ENA 1861376.867 0.9960927 0.9188442 Castanea seguinii EA 1658059.462 0.9901325 0.8280194 Catalpa bignonioides ENA 2761360.271 0.9993153 0.9836168 Catalpa bungei EA 441406.4897 0.999699 0.9924979 Catalpa fargesii EA 2069098.212 0.9824404 0.7123716 Catalpa ovata EA 3654972.713 0.9930278 0.8669436 Catalpa speciosa ENA 3441992.981 0.9965965 0.9280602 Cercis canadensis ENA 3585849.547 0.9999472 0.998742 Cercis chinensis EA 3189397.63 0.9984327 0.9650396 Cercis chingii EA 248749.5413 0.9916189 0.856635 Cercis chuniana EA 300909.4891 0.9705119 0.6011905 Cercis glabra EA 1747705.639 0.9428794 0.3891135 Cornus alternifolia ENA 4031087.827 0.9941339 0.880648 Cornus controversa EA 3438950.032 0.9847701 0.7123821 Corylus americana ENA 3198574.406 0.9989616 0.9790007 Corylus chinensis EA 1059855.834 0.9933725 0.8710435 Corylus cornuta ENA 7328562.149 0.979158 0.6530077 Corylus heterophylla EA 5204676.628 0.9924582 0.8480945 Corylus yunnanensis EA 530713.2864 0.9754479 0.6608291 Gelsemium elegans EA 1844880.06 0.9741882 0.6636191 Gelsemium sempervirens ENA 1378841.036 0.9925171 0.8689206 Gymnocladus chinensis EA 1280078.023 0.9986364 0.9704702 Gymnocladus dioicus ENA 1507246.248 0.9730235 0.6232162

112

Table B-1. Continued. Species Region Est. Dist. (km2) B1 B2 Liriodendron chinense EA 2275195.853 0.9628968 0.496357 Liriodendron tulipifera ENA 1659291.539 0.9916691 0.8527061 Mitchella repens ENA 3714256.302 0.9972064 0.9375332 Mitchella undulata EA 330420.4706 0.9760057 0.7142776 Nelumbo lutea ENA 4348457.871 0.9947665 0.8982399 Nelumbo nucifera EA 6304696.933 0.9902036 0.8049597 Penthorum chinense EA 3171308.339 0.9983548 0.9617917 Penthorum sedoides ENA 3922398.253 0.998467 0.9684721 Pieris floribunda ENA 388968.9437 0.9887483 0.7997382 Pieris formosa EA 3467500.741 0.9644616 0.567063 Pieris japonica EA 241430.6719 0.9962818 0.935948 Pieris phillyreifolia ENA 142505.7964 0.9950652 0.916069 Sassafras albidum ENA 2639545.535 0.9996181 0.9910199 Sassafras randaiense EA 20029.34514 0.9962254 0.9503038 Sassafras tzumu EA 1752091.652 0.995734 0.9144275 Saururus cernuus ENA 2604353.185 0.9978365 0.9512256 Saururus chinensis EA 3429621.915 0.98108 0.7077611 Torreya fargesii EA 614685.677 0.9816935 0.7228687 Torreya grandis EA 1722789.963 0.9718112 0.6140211 Torreya nucifera EA 318989.6203 0.9906547 0.8610329 Torreya taxifolia ENA 15871.44267 0.9946997 0.9541883 Wisteria floribunda EA 222648.7608 0.9950783 0.9263999 Wisteria frutescens ENA 2455056.966 0.9957676 0.9079029 Wisteria sinensis EA 1413392.319 0.9872615 0.7777479

113

Table B-1. Continued.

Species Region HV Tw Pw Aesculus assamica EA 196.076124 26.50617 5311 Aesculus chinensis EA 91.536192 30.40967 2020 Aesculus flava ENA 168.017552 10.4035 1166 Aesculus glabra ENA 66.763946 16.07683 1286 Aesculus indica EA 199.188675 23.44683 928 Aesculus parviflora ENA 244.284332 4.2295 322 Aesculus pavia ENA 84.213628 14.47067 1523 Aesculus sylvativa ENA 159.501966 9.770167 772 Aesculus turbinata EA 165.916798 18.69783 2414 Amphicarpaea bracteata ENA 69.005889 21.00842 1747 Amphicarpaea edgeworthii EA 96.075283 33.45109 3454 Campsis grandiflora EA 122.143033 29.87333 3051 Campsis radicans ENA 57.320796 20.44167 1601 Castanea crenata EA 171.273227 25.70967 3426 Castanea dentata ENA 119.716146 15.4925 1377 Castanea henryi EA 128.870055 27.20367 2059 Castanea mollissima EA 107.142686 22.03067 4045 Castanea pumila ENA 99.714576 17.93839 1264 Castanea seguinii EA 119.340295 14.4235 1684 Catalpa bignonioides ENA 66.761922 18.88572 1447 Catalpa bungei EA 94.456543 37.98033 748 Catalpa fargesii EA 107.947292 25.57333 2311 Catalpa ovata EA 111.402706 24.34233 4255 Catalpa speciosa ENA 68.293881 19.30567 1619 Cercis canadensis ENA 64.236995 18.421 2577 Cercis chinensis EA 116.165803 30.2605 2965 Cercis chingii EA 165.33927 6.946833 1013 Cercis chuniana EA 212.17393 8.961167 1282 Cercis glabra EA 112.956543 37.98033 1667 Cornus alternifolia ENA 74.320265 22.38367 1747 Cornus controversa EA 120.151877 32.19617 3916 Corylus americana ENA 77.502603 17.62433 1747 Corylus chinensis EA 132.17774 26.58067 1166 Corylus cornuta ENA 85.241426 21.37817 3440 Corylus heterophylla EA 111.120501 28.77283 2411 Corylus yunnanensis EA 180.059823 21.88767 1762 Gelsemium elegans EA 98.008235 19.7665 3313 Gelsemium sempervirens ENA 85.112917 16.23933 1349 Gymnocladus chinensis EA 154.824988 20.0015 1536 Gymnocladus dioicus ENA 74.527996 11.73683 1601 Liriodendron chinense EA 93.10747 18.95383 2085 Liriodendron tulipifera ENA 88.533823 16.48833 1342 Mitchella repens ENA 71.802667 26.73742 4332 Mitchella undulata EA 178.398313 28.63167 3225

114

Table B-1. Continued.

Species Region HV Tw Pw Nelumbo lutea ENA 79.213174 28.24383 6482 Nelumbo nucifera EA 83.735752 45.02167 4297 Penthorum chinense EA 115.939961 25.35917 3275 Penthorum sedoides ENA 71.189904 20.64617 1747 Pieris floribunda ENA 166.417481 10.86667 1184 Pieris formosa EA 83.52797 34.21133 5768 Pieris japonica EA 190.179373 23.66533 3432 Pieris phillyreifolia ENA 144.52894 3.502666 614 Sassafras albidum ENA 76.05414 19.19222 1390 Sassafras randaiense EA 327.664033 18.39017 2921 Sassafras tzumu EA 122.551163 19.20017 1551 Saururus cernuus ENA 71.705107 21.97767 1124 Saururus chinensis EA 120.991211 27.13961 4220 Torreya fargesii EA 120.019383 23.50733 1397 Torreya grandis EA 120.389367 38.71183 1604 Torreya nucifera EA 178.91325 19.51933 2334 Torreya taxifolia ENA 236.60956 2.520917 318 Wisteria floribunda EA 177.23254 12.8505 1513 Wisteria frutescens ENA 78.009678 14.51208 1462 Wisteria sinensis EA 103.171141 14.60478 1594

115

Table B-2. Results of asymmetric ecospat background tests. Genus Species Regions D P-value Aesculus assamica-chinensis ea-ea 0.4194885 0.1118881 Aesculus assamica-flava ea-ena 0.2885719 0.3496503 Aesculus assamica-glabra ea-ena 0.3328696 0.07192807 Aesculus assamica-indica ea-ea 0.1593508 0.6313686 Aesculus assamica-parviflora ea-ena 0.09102921 0.3976024 Aesculus assamica-pavia ea-ena 0.5460135 0.07592408 Aesculus assamica-sylvatica ea-ena 0.2092996 0.4375624 Aesculus assamica-turbinata ea-ea 0.236647 0.6413586 Aesculus chinensis-assamica ea-ea 0.4194885 0.2737263 Aesculus chinensis-flava ea-ena 0.3000678 0.1858142 Aesculus chinensis-glabra ea-ena 0.5672308 0.06593407 Aesculus chinensis-indica ea-ea 0.428169 0.5274725 Aesculus chinensis-parviflora ea-ena 0.1021271 0.3956044 Aesculus chinensis-pavia ea-ena 0.3957453 0.05594406 Aesculus chinensis-sylvatica ea-ena 0.3282381 0.4535465 Aesculus chinensis-turbinata ea-ea 0.2519267 0.4295704 Aesculus flava-assamica ena-ea 0.2885719 0.1518482 Aesculus flava-chinensis ena-ea 0.3000678 0.1018981 Aesculus flava-glabra ena-ena 0.3224869 0.06993007 Aesculus flava-indica ena-ea 0.2345037 0.4655345 Aesculus flava-parviflora ena-ena 0.04797035 0.3996004 Aesculus flava-pavia ena-ena 0.2817382 0.1358641 Aesculus flava-turbinata ena-ea 0.5540588 0.03196803 Aesculus glabra-assamica ena-ea 0.3328696 0.2357642 Aesculus glabra-chinensis ena-ea 0.5672308 0.03596404 Aesculus glabra-flava ena-ena 0.3224869 0.05194805 Aesculus glabra-indica ena-ea 0.5960941 0.1998002 Aesculus glabra-parviflora ena-ena 0.05711148 0.4195804 Aesculus glabra-pavia ena-ena 0.4351195 0.07392607 Aesculus glabra-sylvatica ena-ena 0.2337981 0.4375624 Aesculus glabra-turbinata ena-ea 0.2979343 0.07992008 Aesculus indica-assamica ea-ea 0.1593508 0.4135864 Aesculus indica-chinensis ea-ea 0.428169 0.08791209 Aesculus indica-flava ea-ena 0.2345037 0.03996004 Aesculus indica-glabra ea-ena 0.5960941 0.06393606 Aesculus indica-parviflora ea-ena 0.01846068 0.3956044 Aesculus indica-pavia ea-ena 0.3312228 0.05594406 Aesculus indica-turbinata ea-ea 0.2151918 0.2117882 Aesculus parviflora-assamica ena-ea 0.09102921 0.08591409

116

Table B-2. Continued. Genus Species Regions D P-value Aesculus parviflora-chinensis ena-ea 0.1021271 0.2097902 Aesculus parviflora-flava ena-ena 0.04797035 0.4715285 Aesculus parviflora-glabra ena-ena 0.05711148 0.3996004 Aesculus parviflora-indica ena-ea 0.01846068 0.7212787 Aesculus parviflora-pavia ena-ena 0.07764805 0.11788212 Aesculus parviflora-sylvatica ena-ena 0.09193215 0.4335664 Aesculus parviflora-turbinata ena-ea 0.03254114 0.7952048 Aesculus pavia-assamica ena-ea 0.5460135 0.06993007 Aesculus pavia-chinensis ena-ea 0.3957453 0.03796204 Aesculus pavia-flava ena-ena 0.2817382 0.03796204 Aesculus pavia-glabra ena-ena 0.4351195 0.06593407 Aesculus pavia-indica ena-ea 0.3312228 0.6093906 Aesculus pavia-parviflora ena-ena 0.07764805 0.3436563 Aesculus pavia-sylvatica ena-ena 0.1786743 0.4135864 Aesculus pavia-turbinata ena-ea 0.2508272 0.08191808 Aesculus sylvatica-assamica ena-ea 0.2092996 0.3296703 Aesculus sylvatica-chinensis ena-ea 0.3282381 0.1358641 Aesculus sylvatica-glabra ena-ena 0.2337981 0.3196803 Aesculus sylvatica-parviflora ena-ena 0.09193215 0.4035964 Aesculus sylvatica-pavia ena-ena 0.1786743 0.1598402 Aesculus sylvatica-turbinata ena-ea 0.1977238 0.9250749 Aesculus turbinata-assamica ea-ea 0.236647 0.2417582 Aesculus turbinata-chinensis ea-ea 0.2519267 0.1098901 Aesculus turbinata-flava ea-ena 0.5540588 0.04995005 Aesculus turbinata-glabra ea-ena 0.2979343 0.05594406 Aesculus turbinata-indica ea-ea 0.2151918 0.4355644 Aesculus turbinata-parviflora ea-ena 0.03254114 0.4195804 Aesculus turbinata-pavia ea-ena 0.2508272 0.1078921 Aesculus turbinata-sylvatica ea-ena 0.1977238 0.4335664 Amphicarpaea bracteata-edgeworthii ena-ea 0.5807152 0.06593407 Amphicarpaea edgeworthii-bracteata ea-ena 0.5807152 0.02197802 Campsis grandiflora-radicans ea-ena 0.6038397 0.01598402 Campsis radicans-grandiflora ena-ea 0.6038397 0.02997003 Castanea crenata-dentata ea-ena 0.4631881 0.08591409 Castanea crenata-henryi ea-ea 0.3200948 0.07592408 Castanea crenata-mollissima ea-ea 0.3532259 0.07192807 Castanea crenata-pumila ea-ena 0.4868093 0.05394605 Castanea crenata-seguinii ea-ea 0.3141371 0.1178821 Castanea dentata-crenata ena-ea 0.4631881 0.07992008

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Table B-2. Continued. Genus Species Regions D P-value Castanea dentata-henryi ena-ea 0.2866084 0.2957043 Castanea dentata-mollissima ena-ea 0.2683993 0.4775225 Castanea dentata-pumila ena-ena 0.5281126 0.07192807 Castanea dentata-seguinii ena-ea 0.2957669 0.09390609 Castanea henryi-crenata ea-ea 0.3200948 0.07592408 Castanea henryi-dentata ea-ena 0.2866084 0.1918082 Castanea henryi-mollissima ea-ea 0.5978588 0.03196803 Castanea henryi-pumila ea-ena 0.4894783 0.06193806 Castanea henryi-seguinii ea-ea 0.5928952 0.1078921 Castanea mollissima-crenata ea-ea 0.3532259 0.05394605 Castanea mollissima-dentata ea-ena 0.2683993 0.1338661 Castanea mollissima-henryi ea-ea 0.5978588 0.06393606 Castanea mollissima-pumila ea-ena 0.4420238 0.04395604 Castanea mollissima-seguinii ea-ea 0.5235007 0.0999001 Castanea pumila-crenata ena-ea 0.4868093 0.02997003 Castanea pumila-dentata ena-ena 0.5281126 0.08991009 Castanea pumila-henryi ena-ea 0.4894783 0.06393606 Castanea pumila-mollissima ena-ea 0.4420238 0.1358641 Castanea pumila-seguinii ena-ea 0.4249776 0.1018981 Castanea seguinii-crenata ea-ea 0.3141371 0.1278721 Castanea seguinii-dentata ea-ena 0.2957669 0.4515485 Castanea seguinii-henryi ea-ea 0.5928952 0.06193806 Castanea seguinii-mollissima ea-ea 0.5235007 0.02397602 Castanea seguinii-pumila ea-ena 0.4249776 0.05594406 Catalpa bignonioides-bungei ena-ea 0.4409091 0.04795205 Catalpa bignonioides-fargesii ena-ea 0.5504444 0.04595405 Catalpa bignonioides-ovata ena-ea 0.5430599 0.02997003 Catalpa bignonioides-speciosa ena-ena 0.7583105 0.03396603 Catalpa bungei-bignonioides ea-ena 0.4409091 0.1778222 Catalpa bungei-fargesii ea-ea 0.6389244 0.06193806 Catalpa bungei-ovata ea-ea 0.3979341 0.1518482 Catalpa bungei-speciosa ea-ena 0.4411733 0.08591409 Catalpa fargesii-bignonioides ea-ena 0.5504444 0.02597403 Catalpa fargesii-bungei ea-ea 0.6389244 0.03196803 Catalpa fargesii-ovata ea-ea 0.4886243 0.02197802 Catalpa fargesii-speciosa ea-ena 0.5099287 0.04595405 Catalpa ovata-bignonioides ea-ena 0.5430599 0.01798202 Catalpa ovata-bungei ea-ea 0.3979341 0.02997003 Catalpa ovata-fargesii ea-ea 0.4886243 0.04795205

118

Table B-2. Continued. Genus Species Regions D P-value Catalpa ovata-speciosa ea-ena 0.555689 0.03796204 Catalpa speciosa-bignonioides ena-ena 0.7583105 0.03196803 Catalpa speciosa-bungei ena-ea 0.4411733 0.03396603 Catalpa speciosa-fargesii ena-ea 0.5099287 0.05394605 Catalpa speciosa-ovata ena-ea 0.555689 0.07792208 Cercis canadensis-chinensis ena-ea 0.5054769 0.02597403 Cercis canadensis-chingii ena-ea 0.1636988 0.04795205 Cercis canadensis-chuniana ena-ea 0.1596859 0.05194805 Cercis canadensis-glabra ena-ea 0.4053699 0.04595405 Cercis chinensis-canadensis ea-ena 0.5054769 0.02397602 Cercis chinensis-chingii ea-ea 0.2758352 0.06193806 Cercis chinensis-chuniana ea-ea 0.2639616 0.07992008 Cercis chinensis-glabra ea-ea 0.6401724 0.03796204 Cercis chingii-canadensis ea-ena 0.1636988 0.1698302 Cercis chingii-chinensis ea-ea 0.2758352 0.1738262 Cercis chingii-chuniana ea-ea 0.4776193 0.2217782 Cercis chingii-glabra ea-ea 0.2944838 0.3336663 Cercis chuniana-canadensis ea-ena 0.1596859 0.2937063 Cercis chuniana-chinensis ea-ea 0.2639616 0.3096903 Cercis chuniana-chingii ea-ea 0.4776193 0.1318681 Cercis chuniana-glabra ea-ea 0.2389953 0.5054945 Cercis glabra-canadensis ea-ena 0.4053699 0.1058941 Cercis glabra-chinensis ea-ea 0.6401724 0.03796204 Cercis glabra-chingii ea-ea 0.2944838 0.04795205 Cercis glabra-chuniana ea-ea 0.2389953 0.06793207 Cornus alternifolia-controversa ena-ea 0.4880885 0.2257742 Cornus controversa-alternifolia ea-ena 0.4880885 0.03596404 Corylus americana-chinensis ena-ea 0.6005637 0.02197802 Corylus americana-cornuta ena-ena 0.6453408 0.02397602 Corylus americana-heterophylla ena-ea 0.4811186 0.05594406 Corylus americana-yunnanensis ena-ea 0.4121131 0.04595405 Corylus chinensis-americana ea-ena 0.6005637 0.04195804 Corylus chinensis-cornuta ea-ena 0.4852648 0.1758242 Corylus chinensis-heterophylla ea-ea 0.3609227 0.4655345 Corylus chinensis-yunnanensis ea-ea 0.6266876 0.05594406 Corylus cornuta-americana ena-ena 0.6453408 0.03396603 Corylus cornuta-chinensis ena-ea 0.4852648 0.05194805 Corylus cornuta-heterophylla ena-ea 0.6117333 0.01198801 Corylus cornuta-yunnanensis ena-ea 0.3227269 0.1238761

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Table B-2. Continued. Genus Species Regions D P-value Corylus heterophylla-americana ea-ena 0.4811186 0.03796204 Corylus heterophylla-chinensis ea-ea 0.3609227 0.09190809 Corylus heterophylla-cornuta ea-ena 0.6117333 0.01798202 Corylus heterophylla-yunnanensis ea-ea 0.2311036 0.0999001 Corylus yunnanensis-americana ea-ena 0.4121131 0.05394605 Corylus yunnanensis-chinensis ea-ea 0.6266876 0.02397602 Corylus yunnanensis-cornuta ea-ena 0.3227269 0.4775225 Corylus yunnanensis-heterophylla ea-ea 0.2311036 0.7132867 Gelsemium elegans-sempervirens ea-ena 0.4260171 0.06993007 Gelsemium sempervirens-elegans ena-ea 0.4260171 0.2397602 Gymnocladus chinensis-dioicus ea-ena 0.2481598 0.4895105 Gymnocladus dioicus-chinensis ena-ea 0.2481598 0.1158841 Liriodendron chinense-tulipifera ea-ena 0.4990616 0.07792208 Liriodendron tulipifera-chinense ena-ea 0.4990616 0.03796204 Mitchella repens-undulata ena-ea 0.5256545 0.02197802 Mitchella undulata-repens ea-ena 0.5256545 0.02197802 Nelumbo lutea-nucifera ena-ea 0.3999808 0.3716284 Nelumbo nucifera-lutea ea-ena 0.3999808 0.04995005 Penthorum chinense-sedoides ea-ena 0.6531032 0.00999001 Penthorum sedoides-chinense ena-ea 0.6531032 0.03596404 Pieris floribunda-formosa ena-ea 0.4087834 0.3236763 Pieris floribunda-japonica ena-ea 0.282499 0.4495504 Pieris floribunda-phillyreifolia ena-ena 0.06781192 0.1298701 Pieris formosa-floribunda ea-ena 0.4087834 0.04795205 Pieris formosa-japonica ea-ea 0.3544812 0.1698302 Pieris formosa-phillyreifolia ea-ena 0.1743299 0.1558442 Pieris japonica-floribunda ea-ena 0.282499 0.04795205 Pieris japonica-formosa ea-ea 0.3544812 0.05594406 Pieris japonica-phillyreifolia ea-ena 0.1183257 0.1878122 Pieris phillyreifolia-floribunda ena-ena 0.06781192 0.5774226 Pieris phillyreifolia-formosa ena-ea 0.1743299 0.7892108 Pieris phillyreifolia-japonica ena-ea 0.1183257 0.4075924 Sassafras albidum-randaiense ena-ea 0.1682783 0.6713287 Sassafras albidum-tzumu ena-ea 0.4704434 0.05994006 Sassafras randaiense-albidum ea-ena 0.1682783 0.04795205 Sassafras randaiense-tzumu ea-ea 0.06825899 0.1738262 Sassafras tzumu-albidum ea-ena 0.4704434 0.1198801 Sassafras tzumu-randaiense ea-ea 0.06825899 0.8571429 Saururus cernuus-chinensis ena-ea 0.4107415 0.07192807

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Table B-2. Continued. Genus Species Regions D P-value Saururus chinensis-cernuus ea-ena 0.4107415 0.02997003 Torreya fargesii-grandis ea-ea 0.4873614 0.08791209 Torreya fargesii-nucifera ea-ea 0.2926941 0.1358641 Torreya fargesii-taxifolia ea-ena 0.0243658 0.8231768 Torreya grandis-fargesii ea-ea 0.4873614 0.05194805 Torreya grandis-nucifera ea-ea 0.394954 0.02397602 Torreya grandis-taxifolia ea-ena 0.1001427 0.4215784 Torreya nucifera-fargesii ea-ea 0.2926941 0.1638362 Torreya nucifera-grandis ea-ea 0.394954 0.1858142 Torreya nucifera-taxifolia ea-ena 0 2 Torreya taxifolia-fargesii ena-ea 0.0243658 0.4695305 Torreya taxifolia-grandis ena-ea 0.1001427 0.2177822 Torreya taxifolia-nucifera ena-ea 0 2 Wisteria floribunda-frutescens ea-ena 0.3589749 0.07792208 Wisteria floribunda-sinensis ea-ea 0.2554597 0.07792208 Wisteria frutescens-floribunda ena-ea 0.3589749 0.04395604 Wisteria frutescens-sinensis ena-ea 0.2853552 0.08991009 Wisteria sinensis-floribunda ea-ea 0.2554597 0.2317682 Wisteria sinensis-frutescens ea-ena 0.2853552 0.11988012

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Table B-3. Means, medians, and standard deviations (SD) for asymmetric ecospat background tests per genus. EA-EA Genus mean median SD Aesculus 0.28512897 0.24428685 0.10675135 Amphicarpaea Campsis Castanea 0.45028542 0.4383633 0.1296025 Catalpa 0.50849427 0.4886243 0.1088676 Cercis 0.36517793 0.2851595 0.15220298 Cornus Corylus 0.40623797 0.3609227 0.18035915 Gelsemium Gymnocladus Liriodendron Mitchella Nelumbo Penthorum Pieris 0.3544812 0.3544812 Sassafras 0.06825899 0.06825899 Saururus Torreya 0.39166983 0.394954 0.08709502 Wisteria 0.2554597 0.2554597

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Table B-3. Continued. ENA-ENA Genus mean median SD Aesculus 0.191831 0.1786743 0.1320163 Amphicarpaea Campsis Castanea 0.5281126 0.5281126 Catalpa 0.7583105 0.7583105 Cercis Cornus Corylus 0.6453408 0.6453408 Gelsemium Gymnocladus Liriodendron Mitchella Nelumbo Penthorum Pieris 0.06781192 0.06781192 Sassafras Saururus Torreya Wisteria

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Table B-3. Continued. EA-ENA Genus mean median SD Aesculus 0.29866102 0.2979343 0.17563994 Amphicarpaea 0.5807152 0.5807152 Campsis 0.6038397 0.6038397 Castanea 0.39465646 0.4335007 0.09467037 Catalpa 0.5068674 0.5264943 0.05341907 Cercis 0.30855787 0.2845343 0.17444836 Cornus 0.4880885 0.4880885 Corylus 0.48558673 0.4831917 0.11052097 Gelsemium 0.4260171 0.4260171 Gymnocladus 0.2481598 0.2481598 Liriodendron 0.4990616 0.4990616 Mitchella 0.5256545 0.5256545 Nelumbo 0.3999808 0.3999808 Penthorum 0.6531032 0.6531032 Pieris 0.2459845 0.2284144 0.12815088 Sassafras 0.31936085 0.3193609 0.21366299 Saururus 0.4107415 0.4107415 Torreya 0.04150283 0.0243658 0.0522245 Wisteria 0.32216505 0.322165 0.05205699

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Table B-3. Continued. ENA-EA Genus mean median SD Aesculus 0.29866102 0.2979343 0.17563994 Amphicarpaea 0.5807152 0.5807152 Campsis 0.6038397 0.6038397 Castanea 0.39465646 0.4335007 0.09467037 Catalpa 0.5068674 0.5264943 0.05341907 Cercis 0.30855787 0.2845343 0.17444836 Cornus 0.4880885 0.4880885 Corylus 0.48558673 0.4831917 0.11052097 Gelsemium 0.4260171 0.4260171 Gymnocladus 0.2481598 0.2481598 Liriodendron 0.4990616 0.4990616 Mitchella 0.5256545 0.5256545 Nelumbo 0.3999808 0.3999808 Penthorum 0.6531032 0.6531032 Pieris 0.2459845 0.2284144 0.12815088 Sassafras 0.31936085 0.3193609 0.21366299 Saururus 0.4107415 0.4107415 Torreya 0.04150283 0.0243658 0.0522245 Wisteria 0.32216505 0.322165 0.05205699

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APPENDIX C CHAPTER FOUR SUPPLEMENTARY MATERIAL

A BART CBS CWHL DLS GTS HARV MLBS OSBS SNJ TALL TMS 6

y 4

d 2

0 −3 0 3 −3 0 3 −3 0 3 −3 0 3 −3 0 3 −3 0 3 −3 0 3 −3 0 3 −3 0 3 −3 0 3 −3 0 3 FRic

B BART CBS CWHL DLS GTS HARV MLBS OSBS SNJ TALL TMS 3

d 1

0 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 MPD

C BART CBS CWHL DLS GTS HARV MLBS OSBS SNJ TALL TMS

s 2

d 1

0 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 −5 0 5 MNTD

Figure C-1. Density plots for replicate analyses of A) FRicSES, B) MPDSES, and C) MNTDSES.

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Table C-1. Site information for plants sampled for functional trait analyses. Centroid Centroid Area Site Code Longitude Latitude (km2) Bartlett Experimental Forest BART -71.29 44.06 15.72 Harvard Forest HARV -72.17 42.54 11.08 Mountain Lake Biological Station MLBS -80.52 37.38 2.32 Ordway-Swisher Biological Station OSBS -81.99 29.69 37.89 Talladega National Forest TALL -87.42 32.92 1330.64 Coweeta Hydrologic Laboratory CWHL -83.45 35.05 16.25 Changbai Mountain Forest Ecosystem Research Station CBS 128 42.06 1962.19 Beijing Forest Ecological Station DLS 115.5 39.93 301.73 Gutianshan National Nature Reserve GTS 118.13 29.24 96.87 Shennongjia Nature Reserve SNJ 110.31 31.49 732.4 Tianmushan Nature Reserve TMS 119.44 30.37 43.33

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BIOGRAPHICAL SKETCH

Anthony E. Melton grew up in Hanceville, Alabama. He began his undergraduate studies at Wallace State Community College (WSCC) of Hanceville, AL in 2008. From WSCC, he transferred to the University of Montevallo, Montevallo, AL, in 2010 to major in biology and minor in chemistry. Anthony chose to focus on biodiversity and ecological courses. It was during his senior year that Anthony got his first exposure to botanical sciences during field botany and plant systematics courses. Anthony graduated from the University of Montevallo cum laude in the spring of 2013. Anthony then began his graduate studies at Auburn University, Auburn, AL, under the supervision of Leslie R. Goertzen. Anthony’s thesis was focused on genetics of a genus of southeastern wildflowers, Marshallia. It was during this time that Anthony began to be increasingly interested in plant biodiversity, particularly members of the Asteraceae. He completed his MS in the spring of 2015 and moved to Gainesville, Florida to begin his doctoral training at the University of Florida with Drs. Douglas E. and Pamela S. Soltis in the fall of

2015. Anthony then shifted focus to learning about processes that affect distributional and diversity patterns in disjunct flora. Anthony completed his PhD in 2020.

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