Does Autopolyploidy Contribute to Range Size in the Giant Goldenrod Solidago Gigantea?

DOES AUTOPOLYPLOIDY CONTRIBUTE TO RANGE SIZE IN THE GIANT GOLDENROD SOLIDAGO GIGANTEA?

A Thesis by

Maria Martino

Bachelor of Science, Wichita State University, 1985

Submitted to the Department of Biological Sciences and the faculty of the Graduate School of Wichita State University in partial fulfillment of the requirements for the degree of Master of Science

May 2020

DOES AUTOPOLYPLOIDY CONTRIBUTE TO RANGE SIZE IN THE GIANT GOLDENROD SOLIDAGO GIGANTEA?

The following faculty members have examined the final copy of this thesis for form and content, and recommend that it be accepted in partial fulfillment of the requirements for the degree of Master of Science with a major in Biological Sciences.

James Beck, Committee Chair

Mary Liz Jameson, Committee Member

Susan Sterrett, Committee Member

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DEDICATION

To my mom and dad, Patricia and Michael Martino who made all this possible. To all the lifetime learners, keep pushing through the pain, it is worth the effort!

ACKNOWLDEGMENTS

Oh, I hate this part as I do not want to miss anyone who has been on this crazy journey with me. James Beck has been an amazing mentor on this long journey. Being a part-time graduate student when you have a full-time job is a challenge. We sampled, we PCR’d, we laughed, we cried, we traveled, we were slow, but thanks to James, we were unstoppable.

Finally, James, here we are at the end or is it the beginning? I would also like thank Leland

Russell for help with statistical analyses, John Semple and other researchers for many chromosome counts, the Beck Lab group; Jacob Hadle, David Wickell, and especially Nikki

Schmaltz and Lauren Kondrade for help with sampling herbarium tissue, as well as the curators of ISC, BUT, WIS, MICH, F, VDB, SMU, BRIT, MO, KANU, NY, CHRB, PH, US, NCU,

ILLS, MISS, MU, STAR, NLU, WICH for permission to sample from herbarium specimens.

Special thanks to my committee members who hung in while I finished this work: Susan

Sterrett, a lifelong learner for bringing a different viewpoint to this work and Mary Liz Jameson, an amazing colleague and all around magnificent teacher, mentor, and friend. I appreciate the time and energy you put into being on my committee.

A major shout out to my sons Keeler and Brennan and my sisters; Tia, Adria, and Lucia for being my cheerleaders. Also, thanks to Bill Hendry and Marcia Norton, my fellow graduate students as well all of my extended WSU family for patience and understanding while I juggled flaming axes. To my dearest friends; Bobbie, Ames, Izze, and Sanda, thank you for being supportive when my schedule was upside down. If I forgot you (Kirby and Gomez), please insert your name here and pat yourself on the back for helping me on this journey. This was a massive team effort and I thank you from the bottom of my heart for everything.

ABSTRACT

Autopolyploidy is an under-recognized evolutionary phenomenon in angiosperms, creating cryptic patterns of reproductive isolation and phenotypic divergence within a single species. Abundant evidence shows that autopolyploid cytotypes can exhibit different phenotypes, which could lead to subtle niche differentiation. In this way a species comprising an autopolyploid series could exhibit a broad geographic distribution that encompasses vast abiotic/biotic differences due to the collective ranges of its cytotypes. We aim to test this hypothesis in the giant goldenrod Solidago gigantea (Ait), an abundant plant species found throughout much of eastern North America. Diploid (2n=18), tetraploid (2n=36), and hexaploid

(2n=54) cytotypes are known, and previous studies suggest that they are non-randomly distributed across the species' range. Previous work also suggests that these cytotypes are morphologically distinguishable based on leaf vestiture and dimensions. We evaluate these claims by combining two datasets: genotype-based estimates of the cytotype of herbarium specimens and a large set of previous chromosome counts. Together these provided a larger sample size, and one spanning the extensive range of S. gigantea. Our results show that S. gigantea cytotypes are non-randomly distributed and that their abiotic niches are not equivalent.

This suggests that genome doubling and/or subsequent natural selection confer greater ecological amplitude on S. gigantea polyploid cytotypes. This pattern was more evident in the tetraploid to hexaploid transition. Finally, while morphological trends are apparent, leaf vestiture and dimensions are not sufficient to consistently discriminate cytotypes.

TABLE OF CONTENTS

Page Chapter

1. INTRODUCTION 1

2. MATERIALS AND METHODS 12

2.1 Obtaining Samples 12 2.2 Molecular and Cytological Methods 12 2.3 Microsatellite Ploidy Estimation 13 2.4 Morphological Analysis 14 2.5 Genetic Analysis 14 2.6 Geographic and Ecological Comparison of Cytotypes 14

3. RESULTS 17

3.1 Sampling, Cytology, and Cytotype Estimation 17 3.2 Morphology 18 3.3 Genetic Distances Among Cytotypes 19 3.4 Cytogeography 19 3.5 Cytotype Ecological Differentiation: Diploid and Tetraploid 21 3.6 Cytotype Ecological Differentiation: Tetraploid and Hexaploid 24

4. DISCUSSION 27

4.1 Do Cytotypes Differ Morphologically? 27 4.2 Do cytotypes Differ Geographically and Ecologically? 27 4.3 Further Study 29

REFERENCES 31

APPENDIX 38

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LIST OF TABLES

Table Page

1. Zero-abundance study plots and associated treatments and ecoregions 18

2. Significant treatment parameter estimates from univariate analyses 19

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LIST OF FIGURES

Figure Page

1. Solidago gigantea distribution 8

2. Box and whisker plots of leaf measurements 18

3. Solidago gigantea cytogeography 20

4. Ecospat-generated niches of diploid/tetraploid 22

5. Abiotic variability of diploid/tetraploid 23

6. Ecospat-generated niches of tetraploid/hexaploid 25

7. Abiotic variability of tetraploid/hexaploid 26

CHAPTER 1

Introduction

Understanding the factors underlying the size, shape, and position of a species’ geographic range has been a fundamental biological question since Darwin hypothesized declines in species distributions south to north or from dry to wet climates within the geographic range (Darwin 1859). Many authors view range limitation as due to some combination of dispersal and adaptive limitations, but mechanisms have not been fully evaluated (Sexton et al.

2009, Wiens 2011, Hargreaves et al. 2014, Lew-Yaw 2016). Adaptive limitations include varying levels of fitness as both biotic (inter- or intraspecific interactions) and abiotic (climate, soil, etc.) conditions change (Sexton et al. 2009). Here we consider dispersal as the movement of individuals or gametes from one population to another location. Species exhibit differing dispersal modes and efficiency (Sexton et al. 2009); for example, seed number, size, shape, and germination requirements all affect the likelihood that a species will be successful from site to site.

To fully understand adaptive limits, both biotic and abiotic interactions must be considered. The Species Interaction-Abiotic Stress Hypothesis (SIASH) predicts that abiotic factors, specifically climate, drive range limits when abiotic stress levels are high, but in the absence of strong abiotic stress, range limits are more heavily influenced by biotic factors

(competition, mutualism, predation, etc.) (Louthan et al. 2015). The SIASH has not been rigorously tested because evaluating biotic factors along a full abiotic stress gradient is difficult.

In contrast, the role of abiotic factors in influencing range limits has been more fully studied, both through experiments and comparative studies. Abiotic conditions can be more easily re-

created in a controlled environment, and major abiotic features for any given location

(temperature, precipitation, soil, etc.) can be extracted from publicly available databases.

Hargreaves et al. (2014) summarized 111 “over-the-edge transplant experiments,” in which plant fitness from within-range populations was compared to that of populations transplanted outside the range. In 75% of these experiments a decline in fitness was detected in populations outside the normal range, suggesting that limited adaptation to abiotic conditions frequently affects species’ ranges (Hargreaves et al. 2014). In order to better separate adaptive versus dispersal limitation, Lee-Yaw et al. (2016) synthesized data from transplant experiments (TE) with ecological niche modeling (ENM). They proposed that if adaptive limits drive range attributes, both fitness (TE) and “suitability” (as measured by ENM) should decline outside the range. Alternatively, neither fitness nor suitability should decline outside the range if dispersal ability determines range limits (Lee-Yaw et al. 2016). In 78% of the 40 cases examined, the data were fully consistent with range limits being driven by adaptation rather than dispersal (Lee-Yaw et al. 2016).

One key to overcoming adaptive limits is to develop a large number of genotypes, and thus phenotypes, on which natural selection may act. One way that plant species can quickly generate a set of divergent genotypes is through polyploidy. Polyploidy, also known as whole genome duplication, is the presence of more than two copies of the genome in an individual. This typically results from fertilization involving non-reduced (non-haploid) gametes. Although most prominent in plants, polyploidy, is observed in many organisms including amphibians, lizards, and fish (Stöck et al. 2009). Polyploidy can involve genomes from two different species

(allopolyploidy) or from a single species (autopolyploidy). Immediately following duplication; polyploid genomes are unstable and undergo

numerous changes (Comai 2005). These include meiotic alterations, multivalent pairing, additional crossing over, epigenetic changes, neofunctionalization of duplicated genes, increased transposable element activity, cytonuclear functions, increased heterozygosity, and decreased linkage (te Beest et al. 2012, Segraves and Anneberg 2016, Laport and Ng 2017). The presence of >2 homologous chromosomes creates the possibility of multivalent pairing, increasing both opportunities for crossing over and meiotic instability (Madlung and Wendel 2013, Hollister

2014, Baduel et al. 2018). Although multivalent pairing can create aneuoploid gametes, and thus reduced fertility, heterozygosity is typically greatly enhanced (Comai 2005, Otto 2007, Parisod et al., 2010, te Beest et al. 2012, Hollister 2014, Baduel et al. 2018). Although transposable element

(TEs) activity is seen in all genomes, polyploidy initiates a burst of transposition activity, scattering TEs across the genome (Parisod et al. 2012, Baduel et al. 2018). Increased TEs activity caused by the disruption of epigenetic silencing likely stabilizes the meiotic process as well gene dosage compensation (Otto 2007, Parisod et al. 2012, Baduel et al. 2018). Whole genome duplication also modifies gene expression (Comai 2015, Visger 2019, Baduel et al.

2018). In a comparison of gene expression between Tolmiea diplomenziessi Judd, Soltis, & P.S.

Soltis (diploid) and its tetraploid offspring [T. menziesii (Pursh) Torr. & A. Gray], ca. 17% of loci were found to be differentially expressed (Visger et al. 2019). Epigenetics can also alter gene expression as methylation is increased near TEs (Comai 2005, Parisod et al. 2010, Madlung and Wendel 2013). Polyploid cells also exhibit an altered nuclear/organelle gene stoichiometry due to both nuclear genome duplication and the increased number organelles due to larger cell size (Sharbrough et al. 2017). Potential compensatory mechanisms include reduced nuclear gene expression, particularly for genes targeting organelles, and increased cytoplasmic gene expression (Sharbrough et al. 2017).

Genomic changes resulting from polypoidy would be expected to influence phenotype.

Indeed, whether the immediate result of whole genome duplication (“neo-polyploids”) and/or strengthened by later selection, multiple investigators have demonstrated that cytotypes express different phenotypes (Thompson and Merg 2008, Hollister 2014, Baduel et al. 2018). Phenotypic differences extend from cells (increased cell size) to macro-features (changes in seed morphology/physiology, flowering phenology, stress responses/tolerances, growth rate, and interspecies interactions) (te Beest et al. 2012, Baduel et al. 2018).

In Chamerion angustifolium (L.) Holub, phenotypic changes in water stress response relative to the diploid progenitor were seen in both neo-tetraploids and tetraploids (Maherali et al. 2009). Water stress decreased in neo-tetraploids (87%) and tetraploids (32%), and tetraploids took 22% to 30% longer to wilt (Maherali et al. 2009). A transplant experiment involving Achillea borealis Bong. showed that neo-hexaploids had a 70% increase in survivorship as well as an intermediate flowering phenology between tetraploids and hexaploids (Ramsey 2011). Although diploid and tetraploid Ranunculus adoneus A. Gray seedling emergence time and germination rates did not differ in a field experiment, tetraploid plants were 30% larger after 30 days growth and had cotyledons 12% larger than diploids in a subsequent greenhouse growth experiment (Baack and Stanton 2005). A study involved diploid, tetraploid, and hexaploid cytotypes of Solidago canadensis L. involved genotypes from both the native range in North America and the introduced range in East Asia (Cheng et al. 2020). Each

“geo-cytotype” was grown along with a set of native Chinese species, and various measures of community composition and plant performance were assessed over a 5-year period. Introduced range (Chinese) tetraploids and hexaploids were more competitive than native tetraploids/hexaploids and diploids from either range. These introduced polyploids displayed

increased plant height and rhizome spread, and more dramatically changed the communities in which they were planted. Interspecific interactions were also affected by polyploidy. Sudová et al. (2014) examined the relationship between symbiotic mycorrhizal fungi and diploid/hexaploid cytotypes of Aster amellus L. Although cytotype had no effect on mycorrhizal colonization of roots in the field, in a greenhouse experiment cytotypes differed for numerous growth characteristics, and interactive effects on growth between cytotype and mycorrhizal inoculation were seen (Sudová et al. 2014). Tĕštilová et al. (2013) examined mycorrhizal fungi interactions with diploid and tetraploid cytotypes of Gymnadenia conopsea (L.) R. Br. and found different operational taxonomic units (OTUs) of fungal symbionts associate with different cytotypes, distinctions that were most pronounced where cytotypes were closely associated in a mixed ploidy site (Tĕštilová et al. 2013). Forrester and Ashman (2019) inoculated diploid and lab-produced neo-tetraploid Medicago sativa L. individuals with 2 strains of Sinorizobium to examine root nodule features. Neo-tetraploid individuals exhibited a trend towards larger nodules and “N-fixation zones,” although these differences were not significant. However, a clear significant increase of 36% in symbiosomes was observed in the neo-tetraploids. Pollinator communities may also be affected by differences in ploidy within a species. Two sympatric populations of diploid and tetraploid Heuchera grossularifolia Rydb. cytotypes in northern

Idaho were evaluated for pollinator visitation (Thompson and Merg 2008). Most notably,

Bombus centralis Cresson queens preferentially visited tetraploid flowers at both sites.

Preferential cytotype visitation patterns were observed for other pollinators, but these differed in direction and intensity between sites.

Whether the immediate result of genome-duplication or the consequence of later selection, the phenotypic differences between autopolyploid cytotypes suggest that many such

cytotypes may occupy different ecological niches relative to their diploid progenitors. Indeed, the niche shift hypothesis (NSH) states that the establishment and persistence of polyploids requires usage of a different ecological niche relative to their diploid progenitor (Glennon 2014).

However, results of empirical studies of autopolyploid cytotype (abiotic) niche differentiation have been mixed (Baack 2004, Godsoe et al. 2013, Laport et al. 2013, Thompson et al. 2014,

McAllister et al. 2015, Visger et al. 2016). Baack (2004) compared Ranunculus adoneus cytotypes (2x and 4x) from sites in Wyoming, Utah and Colorado for spatial segregation.

Almost all sites harbored a single cytotype, cytotype-specific populations were geographically clustered, and cytotypes were spatially segregated in a few mixed-ploidy populations. However, the diploid and tetraploid populations did not differ significantly for either elevation or soil parent material (Baack 2004). Godsoe et al. (2013) used flow cytometry to determine ploidy in

Heuchera cylindrica Douglas 43 populations from the Pacific Northwest. Each population comprised a single cytotype and appeared geographically segregated, but species distribution modeling using the BIOCLIM climatic variables (Hijmans et al. 2005) suggested that the tetraploid and diploid cytotypes occurred in similar climatic conditions (Godsoe et al. 2013).

Thompson et al. (2014) evaluated climatic and soil-moisture differentiation between previously documented pure diploid, pure tetraploid, and mixed-ploidy Chamerion angustifolium populations. Pure diploid and pure tetraploid sites differed significantly for all seven environmental variables, and cytotypes appeared segregated on a plot of principal components extracted from the combined data. Visger et al. (2016) analyzed the ranges and abiotic conditions of Tolmiea diplomenziesii (diploid) and Tolmiea menziessi (tetraploid). The cytotypes were highly geographically segregated, and a test of abiotic niche equivalency (Warren et al.

2008) rejected the hypothesis that the two cytotype niches were identical. In addition, a test of

niche similarity (Warren et al. 2008) showed that the abiotic niches of the two cytotypes were not less similar than would be expected at random sites in each cytotype’s range (Visger et al.

2016), providing further evidence of their distinctiveness. Although populations of Andropogon gerardi Vitman in historical tallgrass prairies were found to frequently (47%) harbor both enneaploid (9x) and hexaploid cytotypes (McAllister et al. 2015), principal component analysis of climatic data determined that enneaploids were more frequent than hexaploids in locations with drier summers and higher temperature fluctuations (McAllister et al. 2015). Laport et al.

(2013) compared the abiotic niches and soil conditions of Larrea tridentata (D.C.) Coville cytotypes (diploid, tetraploid, and hexaploid), which are highly geographically segregated in the southwestern U.S. and northern deserts of Mexico. Niche equivalency tests showed that the abiotic niche of three cytotype were each distinct, although niche similarity tests indicated that these niches were more similar than would be expected at random sites in each cytotype’s range.

Both multivariate and univariate tests of soil attributes, however, established significant differences among cytotypes. Collectively, these studies establish that autopolyploid cytotypes are typically distributed non-randomly, and that these distributions typically experience real but perhaps subtle macro-climatic differences. Furthermore, fine-scale examination of other abiotic features (e.g. soil type and moisture) typically reveal significant differences among cytotypes.

The research discussed above collectively establishes that auto-polyploidization typically creates new genotypes and phenotypes. This diversity is available for adaptation and may allow an autopolyploid species to occupy a larger range than the diploid cytotype alone. Our research investigates the degree of cytotype geographic and abiotic niche differentiation in the giant goldenrod (Solidago gigantea Ait.), a wide-ranging North American species that harbors several autopolyploid cytotypes. Solidago gigantea is a perennial species in the sunflower family

(Asteraceae) that blooms from August to October. It is pollinated by generalist insects, and its abundant yellow flowers are a common sight along roadsides and in old fields. The giant goldenrod occupies a large section of eastern North America (Fig. 1), found in open, mesic to wet areas in a variety of soil types. Introduced in Europe in 1758, it is believed that only the tetraploid cytotype of S. gigantea is invasive there (Schlaepfer 2008, Nagy et al. 2017).

Solidago gigantea

FIGURE 1. Solidago gigantea distribution in North America Map by John Semple.

This cytotype is also invasive in many Asian countries, including Japan, New Zealand, and

Australia (Auld et al. 2003, Howell and Sawyer 2006). Solidago gigantea is a member of

Solidago subsection Triplinerviae (Torrey & A. Gray) G. L. Nesom (Semple and Cook 2006), the so-called “tri-nerved” goldenrods. Although the morphological boundaries between many species in this group are difficult to consistently define (Semple and Cook 2006), S. gigantea has been shown to be a genetically distinct species by both microsatellite (Beck et al. 2013) and multi-locus

SNP (Beck and Semple 2015) data. Semple et al. (2017) stated that some giant goldenrod species from the western portion of the range may be hybrids between S. gigantea and Solidago lepida

DC. and/or Solidago elongata Nuttall. Solidago gigantea comprises three cytotypes (diploid 2n =

18; tetraploid 2n = 36; hexaploid 2n = 56), and these groups do appear to be non-randomly distributed geographically. To understand giant goldenrod cytotype distribution, Schlaepfer et al.

(2008) combined data from flow cytometry, chromosome counts from co-authors, and other published counts. The geographic distribution of cytotypes at 336 locations suggested that each giant goldenrod cytotype is most commonly observed in a subset of the total species range.

Diploids were more common in the eastern and southern thirds of the range, tetraploids were common in the range center, and hexaploids were found almost exclusively in the western third of the range (Schlaepfer et al. 2008).

Solidago gigantea has long been recognized as morphologically variable taxon, and

Beaudry (1970, 1974) recognized three species that he suggested corresponded to the three cytotypes: the diploid S. gigantea, the tetraploid Solidago serotina Ait., and the hexaploid

Solidago shinnersii (Beaudry) Beaudry. The diploid cytotype was distinguished by the presence of hairs on the abaxial (lower) leaf surface, with both polyploids glabrous in this area. Among the two polyploids, Beaudry (1974) suggested that the tetraploid cytotype exhibited narrower leaves relative to the hexaploid. Morton (1984) assessed the fit of these morphological categories to cytotype and found an imperfect relationship. Diploid samples (n=9) exhibited at least some hairs on the abaxial leaf surface, hexaploid (n=5) samples were completely glabrous, and tetraploids exhibited both hairy (n=2) and glabrous leaves (n=2). Leaf width did not discriminate between tetraploids and hexaploids, as all three cytotypes exhibited broadly overlapping leaf widths. It should be noted that besides Morton's (1984) modest sample size (n = 18), four of the nine polyploid samples analyzed originated from Oregon and Washington, and populations formerly considered S. gigantea in these states are now typically assigned to S. lepida DC. or S. elongata

(Semple et al. 2017). These complications aside, Morton suggested that no subspecies should be

recognized within S. gigantea. Semple et al. (2017) found statistical support for a wide-leaved morphology that corresponded to Beaudry’s concept of S. shinnersii and suggested that the name

Solidago gigantea var. shinnersii Beaudry could be applied to these plants. However, as with the

Morton (1984) study, Semple et al. (2017) reported an imperfect relationship between morphology and cytotype. Not all “variety shinnersii” morphotypes could be documented as hexaploids (some specimens lacked a corresponding chromosome count), and some known hexaploids did not exhibit this morphology.

Although imperfect, both Morton (1984) and Semple et al. (2017), document a link between S. gigantea cytotype and morphology, suggesting a relationship between giant goldenrod cytotype and phenotype. Multiple researchers have, in fact, reported phenotypic differences among S. gigantea cytotypes (Schlaepfer et al. 2010, Nagy et al. 2017). Schlaepfer et al. (2010) identified numerous phenotypic differences with a common garden experiment of diploids and tetraploids, including biomass, shoot number, rhizome number, flowering duration, and specific leaf area. They suggested diploids had a “fast growing” strategy (higher leaf nitrogen and specific leaf area), with tetraploids following a “longer-lived” strategy (larger rhizome system, more biomass as the experiment progressed) (Schlaepfer et al. 2010). Nagy et al. (2017) compared

North American hexaploids to invasive-range (European) tetraploids in greenhouse and common garden experiments, observing more stems, taller stems, and an increased number of leaves in tetraploids. Additionally, herbivory rate was significantly higher in hexaploids. Although potentially confounded by geography (tetraploids were from the invaded European range and hexaploids from native North America range) this study suggested that there are phenotypic differences between S. gigantea cytotypes. Considering that previous work has identified non- random cytotype distributions and cytotype phenotypic differentiation, it is possible that

autopolyploidy contributes to the giant goldenrod's large range. In other words, the total range of the species could be due to the collective ranges of three regionally adapted cytotypes. This hypothesis has yet to be rigorously tested, however. The Schlaepfer et al. (2008) cytogeographic study was based on a geographically biased dataset, with samples largely confined to two regions;

1) the southern Appalachians in Tennessee and North Carolina; and 2) New England and adjacent southern Ontario. There was modest to no sampling in over 20 states throughout S. gigantea’s distribution. The geographic bias is understandable given the time needed to field-collect samples across the broad range of S. gigantea (figure 1). In this study, we aim to overcome this basic limitation by obtaining tissue from herbarium specimens associated with all portions of the range in North America. These data combined with a large set of chromosome counts and phenotypic characters across the species’ range allowed us to rigorously address three basic questions: 1) Are giant goldenrod cytotypes non-randomly distributed throughout its range? 2) Do S. gigantea cytotypes occupy different abiotic niches? 3) Do S. gigantea cytotypes exhibit consistent morphological differences?

CHAPTER 2

Materials and Methods

2.1 Obtaining Samples

We sampled 246 specimens from 21 herbarium collections, choosing a single specimen per county across S. gigantea's expansive range (Fig. 1). Specimen identity was confirmed by documenting the combination of a glabrous stem (below the inflorescence) along with strongly tri- veined leaves (Semple and Cook 2006). A single mid-stem leaf or 2-3 smaller leaf fragments were removed, placed in a coin envelope, and stored in silica gel desiccant. Length and width of a single mid-stem leaf were measured, along with an assessment of abaxial leaf surface vestiture: 0

= abaxial leaf surface completely glabrous; 1 = hairs present only on the veins of the abaxial leaf surface; 2 = hairs on both veins and abaxial leaf surface. Label data were recorded and specimen locality was manually georeferenced using Earth Point (2015) and Google Earth (Google,

Mountain View, CA). Specimens were field-collected in order to provide material for meiotic counts (n=11). For each population young, floral bud tissue was collected and stored in Farmer’s fixative (Windham et al. in press), leaf tissue from a single individual was stored in silica gel dessicant, and material from that same individual sufficient for a voucher specimen was obtained.

All such vouchers are archived at the Arthur Youngman Herbarium at Wichita State University

(WICH). Finally, we incorporated previous S. gigantea cytological knowledge by georeferencing

371 chromosome count voucher specimens from Morton et al. (2020) and references cited therein.

2.2 Molecular and cytological methods

DNA extractions followed a standard CTAB protocol modified for 96 well plates (Beck et al. 2012). A Qubit fluorometer (Life Technologies, Eugene, OR) was used to establish DNA

concentration. 10 samples per plate, including the oldest sample, were diluted 1:10 and 1:20 to determine which dilution provided DNA concentrations between 5-80 ng/µl. The 1:10 dilution samples all fell in this range, and all plates were diluted 1:10 to for subsequent PCR. Twelve microsatellite (simple sequence repeat, SSR) loci developed for Solidago (Beck et al. 2014) were

PCR-amplified under the conditions outlined in that paper. Amplified fragments were genotyped at the University of Chicago Comprehensive Cancer Center DNA Sequencing and Genotyping facility (Chicago, IL, USA).

Immature anthers were isolated, macerated, stained, and flattened following the aceto- carmine squash detailed in Windham et al. (in press). Stained, flattened cells were examined with brightfield microscopy using a Nikon Eclipse E800 microscope (Nikon, Tokyo, Japan) and photographed at 1000× with a Nikon DS-Fi1 camera.

2.3 Microsatellite ploidy estimation

Fragments were sized and alleles scored with GeneMarker (SoftGenetics, State College,

PA). In all analyses discussed below, only samples which amplified at a minimum of 9 loci were included. A “maximum allele approach” (Beck et al. 2012, Servick et al. 2015) was used to estimate the cytotype of all herbarium and field-collected samples. The maximum allele approach is simple: a sample with 1-2 alleles at any locus is estimated to be diploid, a sample with 3-4 alleles at any locus is estimated to be tetraploid, and a sample with 5-6 alleles at any locus is estimated to be hexaploid. A standard meiotic chromosome count (Windham et al. in press) was conducted on all field collected samples, the results of which were compared to the

SSR-based cytotype estimation for these samples. Our SSR-based cytotype estimation was also compared to 9 herbarium specimens which were vouchers of chromosome counts performed by other researchers.

2.4 Morphological analyses

All workflows here and below were conducted in R version 3.5.1 (R Core Team, 2017).

In order test for the association between abaxial leaf surface pubescence and S. gigantea cyotype, a Chi-squared test was performed on the set of samples for which pubescence and cytotype were both scored. In order to test the hypothesis that leaf length/width differ among S. gigantea cyotypes, analyses of variance (ANOVAs) were performed on the same set of samples.

2.5 Genetic analyses

The Lynch genetic distance was calculated among the 258 individuals for which sufficient SSR information was obtained using the R package 'polysat' (Clark and Jasieniuk, 2011). This distance matrix was used to perform nearest-neighbor identification and Mantel testing (Mantel,

1967). The nearest genetic neighbor to each sample was identified with the “dist_to_knn” function of the scanstatistics v1.0.1 package (Allévius, 2018). Chi-square analysis was performed in R to determine if the identity of nearest-neighbors was non-randomly associated with cytotype. The relationship between inter-individual Lynch genetic and geographic distances was assessed with a Mantel test. Geographic distances were calculated with the R package 'geosphere' (Hijmans et al., 2017a), and Mantel tests were conducted with the 'vegan' package (Oksanen et al., 2018). Diploid, tetraploid- and hexaploid-only datasets were then constructed, Lynch genetic distance were calculated, and Mantel tests conducted on these single- cytotype datasets.

2.6 Geographic and ecological comparison of cytotypes

To evaluate whether the three cytotypes are non-randomly distributed, a MANOVA was performed with cytotype as the independent variable and both latitude and longitude as dependent variables. Subsequent ANOVAs were then performed with longitude or latitude as

single dependent variables. For ecological comparisons, we assumed that the most frequent polypoid events involved tetraploids forming from diploids [i.e. “autotetraploid, one step” or

“autotetraploid, triploid bridge” (Ramsey and Schemseke 1998)] and hexaploids forming from tetraploids [“higher ploidy, one step” (Ramsey and Schemseke 1998)]. These assumptions were supported by nearest neighbor analyses (see Results), and ecological comparisons (diploid vs. tetraploid and tetraploid vs. hexaploid) assume these evolutionary transitions. Two general statistical approaches (termed “ecospat” and “MANOVA”] were employed to determine if the two cytotypes being compared are found in areas with different environmental conditions (Hadle et al. 2019). Both are based on the set of 19 WorldClim version 2 climatic variables (Fick and

Hijmans, 2017).

Two workflows (termed “ecospat” and “MANOVA”) were employed to determine if two cytotypes are distributed in areas with different abiotic conditions (Hadle et al., 2019). Both are based on the set of 19 WorldClim version 2 climatic variables (Fick and Hijmans, 2017). The ecospat approach involved construction and comparison of abiotic niches in the R package

'ecospat' (Di Cola et al., 2017). Abiotic data at occurrence and 1000 background sites for each cytotype were obtained using the workflow presented in Hadle et al. (2019). A set of WorldClim variables were identified that were highly correlated (Pearson correlations ≥ 0.8) in a combined diploid/tetraploid/hexaploid dataset. Variables were removed to eliminate these strong correlations, choosing variables to retain that exhibited the fewest strong correlations, and/or that displayed stronger loadings in the principal components analysis (PCA) described below. A

PCA was performed on the occurrence/background abiotic datasets of both cytotypes in ecospat.

The niche of each cytotype was then projected as a smoothed density function onto a grid representing the combined abiotic PCA space of both cytotype study areas. Measures of niche

unfilling and expansion (Petitpierre et al., 2012) were evaluated and niche equivalency/similarity tests were performed (Broennimann et al., 2012; Warren et al., 2008). One thousand replications were performed, with both cytotype niches randomly shifted during the niche similarity test

(rand.type = 1).

The MANOVA workflow (Rushworth et al., 2018; Hadle et al., 2019) involved a comparison of cytotype multivariate and univariate means. A discriminant function analysis was performed with the 'adegenet' package to identify variables best discriminating the two cytotypes

(Jombart, 2008). This package was then used to perform PCA on standardized environmental variables, followed by multivariate analysis of variance (MANOVA) on cytotype scores on PCs

1–4. Cytotype means at each PC were subsequently compared with multiple-test corrected

(Holm, 1979) ANOVA tests.

CHAPTER 3

RESULTS

3.1 Sampling, cytology, and cytotype estimation

After excluding samples for which fewer than 9 loci were amplified, 258 samples with SSR genotypes remained. These SSR-genotyped samples included 11 field-collected samples for which we obtained a chromosome count in this study. All of these 11 new chromosome counts identified diploids (2 samples), tetraploids (2 samples), or hexaploids (7 samples). In addition, nine SSR-genotyped herbarium specimens were vouchers of chromosome counts performed by other researchers. Of these 20 samples for which cytotype was previously established with a chromosome count, our SSR-based estimation procedure accurately identified cytotype 17 times

(85%). In the three mis-diagnosed cases, cytotype was over estimated by one level (two instances of diploids estimated as tetraploids and a single instance of a tetraploid estimated as a hexaploid). Of the remaining 238 SSR-genotyped samples, 54 were inferred as diploids, 105 as tetraploids, and 79 as hexaploids. After adding the 371 previously counted specimens (Morton et al. 2020 and references cited therein) our dataset comprised 629 locations for which the cytotype of a S. gigantea specimen was known or estimated (Appendix).

3.2 Morphology

Both morphological and cytotype data was available for 264 individuals. Table 1 shows the distribution of abaxial leaf pubescence character states for the three cytotypes. Diploid individuals rarely exhibited glabrous abaxial leaf surfaces, while hexaploids rarely exhibited hairy surfaces. A Chi-squared test indicated these trends were highly significant (P < 2.2 x 10-16). Box and whisker plots (Fig. 2) of mid-stem leaf length and width illustrate that the three cytotypes

overlap broadly at these characters, and ANOVAs establish cytotype means are not significantly different (length P = 0.06403; width P = 0.3932).

TABLE 1. DISTRIBUTION OF ABAXIAL LEAF SURFACE PUBESCENCE CHARACTER STATES IN 264 SOLIDAGO GIGANTEA INDIVIDUALS OF KNOWN CYTOTYPE

Hairs on Hairs on Glabrous Veins veins/surface Diploid 6 35 19 Tetraploid 76 24 16 Hexaploid 81 5 2

FIGURE 2. Box and whisker plots showing mid-stem (A) width and (B) length for a single mid-stem leaf in 264 Solidago gigantea individuals of known cytotype.

3.3 Genetic distances among cytotypes

Table 2 shows the results of the nearest neighbor analysis among the SSR-genotyped samples. Diploid individuals were most frequently most genetically similar to other diploids, and rarely with hexaploids. Conversely, hexaploids were most frequently most genetically similar to tetraploids and other hexaploids, rarely with diploids. A Chi-squared test indicated these trends were highly significant (P < 2.2 x 10-16).

TABLE 2. RESULTS OF THE NEAREST NEIGHBOR ANALYSIS AMONG THE SSR- GENOTYPED SAMPLES

Diploid Tetraploid Hexaploid Diploid 41 13 5 Tetraploid 14 60 37 Hexaploid 2 40 45

3.4 Cytogeography

Figure 3 shows the geographic distribution of the 629 locations for which the cytotype of a

S. gigantea specimen was known or estimated. The geographic centroid of the 151 diploid samples is in Tyler Co., WV (39.56314 -80.99652). The geographic centroid of the 340 tetraploid samples is in Essex Co., Ontario (42.3091 -82.68999). The geographic centroid of the 138 hexaploid samples is in Howard Co., NE (41.14312 -98.33364). The two-dimensional spatial position (latitude/longitude) of three cytotypes are different (P < 2.2 x 10-16), and subsequent

ANOVA comparisons of cytotype latitude (P = 0.003022) and longitude (P < 2.2 x 10-16) were also significant.

FIGURE 3. Solidago gigantea cytogeography. A) Location of 629 S. gigantea specimens for which cytotype is known or estimated. Diploids are shown in black, tetraploids in blue, and hexaploids in red. B) Location of each cytotype’s geographic centroid.

3.5 Cytotype ecological differentiation: Diploid and tetraploid

Elimination of highly correlated variables resulted in a reduced dataset of eight

WorldClim variables (mean diurnal range, maximum temperature of the warmest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, precipitation seasonality, precipitation of the wettest quarter, precipitation of the warmest quarter). The test of niche similarity established that the diploid and tetraploid niches were similar, as the empirical diploid/tetraploid Schoener's D value (0.62) was higher than those observed in 977/1000 replications (P = 0.98). However, the test of niche equivalency established that the diploid and tetraploid abiotic niches were not equivalent, as the empirical diploid/tetraploid Schoener's D value was lower than those observed in 998/1000 replications (P

= 0.001). Niche unfilling and expansion measures (Petitpierre et al. 2012) indicated that the tetraploid niche has left 6% of the diploid niche unfilled and expanded on the diploid niche by

4% (Fig. 4).

FIGURE 4. Relative position of the ecospat-generated niches of diploid (black) and tetraploid (blue) Solidago gigantea cytotypes. Solid lines show the extent of each cytotype’s study area (known = background sites). Purple indicates niche overlap. A) Plot showing density of the diploid cytotype. B) Plot showing density of the tetraploid cytotype

The first four PCs recovered by the adegenet PCA explained >91% of the dataset variation (PC1: 48.1%; PC2: 24.3%; PC3: 10.3%; PC4: 8.4%). The MANOVA indicated that the multivariate means of the tetraploid and hexaploid cytotypes for the first four PCAs were different (P = 1.59 x 10-13). Subsequent univariate tests indicated that the cytotype means were different at the first (P = 1.02 x 10-13) and fourth (P = 0.01) individual PCs, but not PCs two and three (PC2: P = 0.10; PC3: P = 0.54). Individual environmental variables loading highly on the discriminant function included temperature annual range and precipitation seasonality.

Smoothed cytotype occurrence densities for these variables (generated with ecospat) indicate a general abiotic niche shift towards stronger temperature and precipitation seasonality in tetraploids (Fig. 5).

FIGURE 5. Two individual environmental variables loading most highly on the discriminant function separating diploid and tetraploid individuals. In both graphs black indicates the smoothed distribution of diploid values, blue indicates the smoothed distribution of tetraploid values, grey indicates overlap. A) Temperature annual range (°C) (maximum temperature of the warmest month – minimum temperature of the coldest month). B) Precipitation seasonality, a measure (%) of the variation in monthly precipitation totals (ratio of the standard deviation of the monthly total precipitation to the mean monthly total).

3.6 Cytotype ecological differentiation: Tetraploid and hexaploid

The test of niche similarity established that the tetraploid and hexaploid niches were similar, as the empirical tetraploid/hexaploid Schoener's D value (0.41) was higher than those observed in 943/1000 replications (P = 0.94). However, the test of niche equivalency established that the tetraploid and hexaploid abiotic niches were not equivalent, as the empirical tetraploid/hexaploid Schoener's D value was lower than those observed in all 1000 replications

(P = 0.001). Niche unfilling and expansion measures (Petitpierre et al. 2012) indicated that the hexaploid niche has left 22% of the tetraploid niche unfilled, and expanded on the tetraploid niche by 27% (Fig. 6).

FIGURE 6. Relative position of the ecospat-generated niches of tetraploid (blue) and hexaploid (red) Solidago gigantea cytotypes. Solid lines show the extent of each cytotype’s study area (known + background sites). Purple indicates niche overlap. A) Plot showing density of the diploid cytotype. B) Plot showing density of the tetraploid cytotype.

The first four PCs recovered by the adegenet PCA explained 92% of the dataset variation (PC1:

38.1%; PC2: 30.6%; PC3: 16.2%; PC4: 6.9%). The MANOVA indicated that the multivariate means of the tetraploid and hexaploid cytotypes for the first four PCAs were different (P < 2.2 x

10-16). Subsequent univariate tests indicated that the cytotype means were different at the first (P

< 2.2 x 10-16), second (P < 2.2 x 10-16) and third (P = 0.02) individual PCs, but not at PC4 (P =

0.123). Individual environmental variables loading highly on the discriminant function included mean diurnal temperature range and precipitation seasonality. Smoothed cytotype occurrence

densities for these variables (generated with ecospat) indicate a general abiotic niche shift towards stronger temperature and precipitation seasonality in hexaploids (Fig. 7).

FIGURE 7. Two individual environmental variables loading most highly on the discriminant function separating tetraploid and hexaploid individuals. In both graphs, blue indicates the smoothed distribution of tetraploid values, red indicates the smoothed distribution of hexaploid values, rose indicates overlap. A) Mean diurnal temperature range (°C) (mean of the monthly temperature ranges). B) Precipitation seasonality, a measure (%) of the variation in monthly precipitation totals (ratio of the standard deviation of the monthly total precipitation to the mean monthly total).

CHAPTER 4

DISCUSSION

4.1 Do cytotypes differ morphologically?

Similar to Morton (1984), our examination of leaf dimension and vestiture showed clear and statistically significant trends, but no absolute differences among cytotypes. Most (92%) hexaploids exhibited glabrous abaxial leaf surfaces, while most (90%) diploids exhibited hairs on this surface.

However, the tetraploid cytotype exhibited both character states. This is not surprising because the tetraploid cytotype is thought to be the intermediate stage between diploidy and hexaploidy (Ramsey and Schemseke 1998). Indeed, the nearest genetic neighbor of diploids were other diploid or tetraploids 92% of the time, while the nearest genetic neighbor of hexaploids were tetraploids or other hexaploids 98% of the time. Cytotype leaf length and width measurements overlapped broadly with no significant differences among cytotypes (Fig. 2).

Combining these phenotypic characters, our data show that the tetraploid cytotype blurs otherwise fairly clear boundaries between S. gigantea diploids and hexaploids, with neither leaf vestiture nor leaf dimensions allowing for a consistent identification of cytotype. Although we examined only three character states, we recommend that cytotype specific names [S. serotina, S. shinnersii, Solidago gigantea var. serotina (Kuntze) McNeill, Solidago gigantea var. shinnersii] should not be applied until fully diagnostic morphological differences are identified.

4.2 Do cytotypes differ geographically and ecologically?

Solidago gigantea cytotypes are non-randomly distributed (Fig 3), with diploids most common in the Gulf Coastal Plain and Atlantic Coast regions, tetraploids most common in the upper Midwest, and hexaploids most common on the Great Plains (Fig 3). These geographic

trends underlie ecological differentiation, as both MANOVA/ANOVA and ecospat approaches identified abiotic distinctions between the pairs of cytotypes we examined (2x vs. 4x, 4x vs. 6x).

Interestingly, variables that describe the range of abiotic conditions over a month or a year (mean diurnal range, precipitation seasonality, temperature annual range) loaded most strongly on both the diploid versus tetraploid and the tetraploid versus hexaploid tdiscriminant functions. In both cases the higher-ploidy cytotype occurred in environments exhibiting a broader range of conditions (Fig. 5, 7). This suggests that genome doubling and/or subsequent natural selection confer greater ecological amplitude on S. gigantea polyploid cytotypes. Our results therefore support the niche shift hypothesis (Glennon et al. 2014) and indicate that some genome-doubling events are more consequential than others depending on environmental variability. The distance between the tetraploid and hexaploid centroids (1303 km) was ca. four times farther removed than that between the diploid and tetraploid centroids (337 km), and measures of niche unfilling and expansion indicate that the 4x to 6x transition was more ecologically consequential than the transition from 2x to 4x.

This study of 629 sites is the most extensive ecological comparison of autopolyploid cytotypes performed, surpassing Hadle et al. (2019). Comparison with an earlier study (Glennon et al. 2014) suggests that sampling can affect the results of comparative cytological studies.

Glennon et al. (2014) looked for climate niche shifts in 20 polyploid species, among them S. gigantea. Although not specifically addressed, their data presumably came from Schlaepfer et al. (2008), a study featured a large but geographically biased S. gigantea dataset, with modest to no sampling in over 20 states throughout the species’ distribution. Interestingly, Glennon et al.

(2014) reported that diploid and tetraploid S. gigantea cytotypes had equivalent niches whereas we found that these cytotypes do not have equivalent niches. Our near doubling of sites (336 vs.

629) compared to Schlaepfer et al. (2008), with notable sampling in several areas sparsely sampled in that study (Gulf Coastal Plain, upper Midwest, southern Great Plains) contributed to a more robust comparison of S. gigantea niches.

4.3 Further study

To our knowledge this is only the second study (Laport 2013) to examine the ecological consequences of two separate transitions within an autopolyploid species. Our finding that transitions between cytotypes can have ecological consequences of different scale suggests that further study of multiple cytological transitions within autopolyploid series is warranted.

Further study should also include additional abiotic (elevation, soil, etc.) and biotic variables.

Our study relied on comparative data, and we established that S. gigantea cytotypes tend to be found in different locations. Establishing that these cytotypes are actually adapted to differing environments requires field and greenhouse experiments (Ramsey 2011). The clonal nature of S. gigantea is ideal – a single genotype can be separated into multiple individuals and placed into different environments. A final avenue for further study concerns the S. gigantea invasion, predominantly in Europe and Asia. Flow cytometry and chromosome counts suggest that only tetraploids occur in the invasive range (Schlaepfer et al. 2008). It is unclear, however, if this involved the transition from diploid to tetraploid in the invasive range or if tetraploids were transported directly. Microsatellite-based cytotype estimation of historical invasive-range S. gigantea specimens would allow these two scenarios to be evaluated. Our study highlights the invasive potential of the hexaploid cytotype, particularly given that the temperate grasslands of

Central Asia mirror the abiotic conditions seen in the North American Great Plains. Nagy et al.

(2017) discounted the invasive potential of hexaploid S. gigantea in Europe due to the fact that it was outperformed by invasive-range tetraploids in a garden experiment. This assertion should be

re-considered for two reasons. The invasive-range tetraploids were sourced from the same area as the garden in southern Hungary, and thus perhaps represented locally adapted genotypes.

Secondly, the abiotic conditions of the garden may have been favorable to the tetraploid cytotype. Given the strong ecological shift that we demonstrated between tetraploids and hexaploids in this study, and the potential for hexaploids to be produced within the native range, the possibility of colonization of the temperate grasslands of Central Asia by invasive-range hexaploids should be considered.

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APPENDIX

Sample information. Vestiture refers to 0 = abaxial leaf surface completely glabrous; 1 = hairs present only on the veins of the abaxial leaf surface; 2 = hairs on both veins and abaxial leaf surface. Data source refers to SSR inference = cytotype inferred from SSR data only; previous count = chromosome count from a previous study; new count/SSR inference = chromosome count obtained in this study compared to an SSR inference; previous count/SSR inference = chromosome count obtained in a previous study compared to an SSR inference.

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Alabama C.F. Nixon 2575 2-Aug-86 2 112x15 33.256936 -87.772364 2x inference SSR Alabama A.R. Diamond 13583 12-Aug-02 1 78x12 32.222022 -85.501608 2x inference SSR Alabama Crouch 1163 25-Aug-94 1 32.205658 -88.014219 2x inference SSR Alabama D.D. Spaulidng 10818 19-Aug-00 1 106x22 35.302842 -92.678575 2x inference SSR Alabama DiPietro 279A 17-Aug-93 1 87x15 34.651986 -89.560908 2x inference previous Alabama G. Morton M4460 34.759982 -85.68139 2x count Harold D. SSR Alabama 748 20-Aug-70 1 90x12 31.577658 -86.737736 2x Moore inference SSR Alabama R. Kral 37054 19-Sep-69 1 66x15 33.432547 -85.634839 2x inference SSR Alabama Sessler 445 31-Aug-76 0 70x13 32.443919 -87.179256 4x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Alabama Spaulding 11523 4-Aug-02 1 111x15 33.778742 -85.5639 4x inference previous Alberta G.H.Turner 58-286-1 53.70072 -113.2095 2x count previous Alberta G.H.Turner 58-299-1 14-Jul-61 53.70072 -113.2095 6x count previous Alberta G.H.Turner 58-300-2 53.70072 -113.2095 6x count previous Alberta G.H.Turner 58-301-1 53.70072 -113.2095 6x count J.K. Morton & - previous Alberta NA14169 13-Jul-82 53.736614 6x J. Venn 114.363552 count J.K. Morton & - previous Alberta NA14181 29-Jul-82 53.451551 6x J. Venn 111.868489 count - previous Alberta Morton s.n. 11-Aug-90 53.700716 6x 113.209466 count - previous Alberta Morton & Venn NA14155 22-Aug-80 55.232034 6x 118.294643 count Semple & previous Alberta 4287 29-Aug-79 53.57005 -113.0663 6x Brouillet count SSR Arkansas D. Demaree 9887 20-Oct-32 2 61x9 33.967944 -94.1866 4x inference SSR Arkansas D. Demaree 56905 2-Sep-67 0 130x23 33.586314 -91.586864 4x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Arkansas D. Demaree 64110 18-Aug-71 0 78x12 33.868892 -91.441081 4x inference SSR Arkansas D. Demaree 65848 11-Oct-72 0 90x12 35.263108 -94.255997 6x inference SSR Arkansas Demaree 26449 0 112x19 36.31583 -91.4825 4x inference SSR Arkansas Demaree 31369 8-Sep-51 0 99x19 36.2025 -91.17444 4x inference previous Arkansas Morton & Venn NA16246 13-Oct-85 36.474477 -91.624049 4x count SSR Arkansas R. D. Thomas 162862 17-Sep-99 2 78x12 33.869603 -83.425578 2x inference R. D. Thomas SSR Arkansas 137742 16-Sep-93 2 52x11 33.307633 -92.564839 2x & C. Amason inference R.D. & B.G. SSR Arkansas 120720 16-Aug-90 2 83x15 33.080953 -93.90955 2x Thomas inference SSR Arkansas R.D. Thomas 137008 10-Sep-93 2 86x16 33.363825 -93.157608 2x inference R.D. Thomas & SSR Arkansas 162146 19-Aug-99 2 67x8 33.373608 -93.410803 2x H. Young inference SSR Colorado Freeman 16516 24-Aug-00 0 73x10 39.262779 -103.68 6x inference - SSR Colorado King 10798 7/28/1998 0 133 x 26 40.581944 6x 105.376944 inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) - SSR Colorado King 14007 8/15/2005 0 77 x 20 40.254939 6x 103.649184 inference - previous Colorado Morton & Venn NA15965 21-Sep-85 38.101265 6x 103.135814 count - previous Colorado Morton & Venn NA15966 21-Sep-85 38.063755 6x 102.346924 count SSR Colorado Osterhout 4119 20-Aug-09 0 113x15 38.035 -103.045 6x inference Semple & previous Colorado 7269 9-Sep-83 38.85769 -104.8951 6x Brouillet count Semple & previous Colorado 7292 10-Sep-83 38.06046 -103.2763 6x Brouillet count Semple & previous Connecticut 3616 41.99884 -72.90523 4x Brouillet count SSR Delaware W.A. McAvoy 4922 17-Aug-00 1 117x24 39.728215 -75.758934 4x inference SSR Georgia A. Cronquist 4601 24-Aug-47 1 77x11 33.953497 -83.114839 2x inference SSR Georgia D. Demaree 51222 6-Sep-64 0 86x17 33.869603 -83.425578 4x inference previous Georgia G. Morton 8591 34.1147 -83.46306 2x count SSR Georgia G.H. Morton 3355 11-Sep-68 1 97x17 34.893706 -84.261411 2x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) Semple & previous Idaho 4396 4396 46.44024 -116.8968 6x Brouillet count Semple & previous Idaho 4403 4-Sep-79 46.13602 -115.9476 6x Brouillet count SSR Illinois Chase 9055 4-Sep-47 0 115x23 40.74722 -89.57389 4x inference previous Illinois Cook & Parks C-423 4-Sep-01 41.309186 -88.999479 4x count G.S. SSR Illinois 5134 29-Aug-50 2 97x16 40.660628 -90.425639 4x Winterringer inference SSR Illinois J.A. Moe 349 26-Aug-01 0 180x16 38.817053 -89.974111 4x inference J.C. Semple & SSR Illinois 7690 5-Sep-85 0 99x11 41.394175 -89.571672 4x S. Heard inference J.K. Morton & J. previous Illinois NA18719 27-Sep-95 39.362586 -87.781996 4x Venn count J.K. Morton & J. previous Illinois NA18722 27-Sep-95 39.278137 -88.058489 2x Venn count J.K. Morton & J. previous Illinois NA18725 27-Sep-95 39.121469 -88.542993 2x Venn count J.K. Morton & J. previous Illinois NA18727 27-Sep-95 38.916783 -89.085474 4x Venn count J.K. Morton & J. previous Illinois NA18732 27-Sep-95 38.581145 -89.372576 4x Venn count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) J.K. Morton & previous Illinois NA18737 27-Sep-95 37.497861 -89.315711 4x J. Venn count M. Bohlke, S. Totura, SW SSR Illinois Yang, S. 449 18-Aug-93 1 87X11 41.633 -88.689775 4x inference Maloney & H. Kahn SSR Illinois N. Pliml 78 9-Sep-83 1 109x21 39.462361 -88.192669 4x inference SSR Illinois P.J. Reich 518 11-Aug-60 0 123x20 41.730403 -87.889714 4x inference SSR Illinois R. Kral 48506 20-Sep-72 1 87x15 42.436281 -87.818778 4x inference Semple & previous Illinois 7373 13-Sep-83 38.78402 -90.00051 4x Brouillet count Semple & previous Illinois 7379 1983 40.30363 -90.13023 2x Brouillet count Semple & previous Illinois 9424 8 Oct 1991 0 90x15 37.965896 -88.328638 4x Suripto count Semple & previous Illinois 9871 4-Oct-91 41.199009 -87.975918 4x Suripto count SSR Illinois T.G. Lammers 9204 25-Aug-94 0 63x7 41.290189 -88.178872 4x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) 31-Aug- SSR Illinois V.H. Chase 116-1897 2 97x14 41.019975 -89.682344 4x 1897 inference SSR Indiana C. Buhl F540 27-Aug-33 0 100x12 41.605208 -87.306153 6x inference SSR Indiana Deam 54513 15-Sep-33 2 87x9 41.462379 -85.263369 4x inference SSR Indiana Friesner 3594 5-Sep-31 0 114x18 40.192056 -87.023953 4x inference SSR Indiana Friesner 14827 14-Sep-40 0 80x9 41.274732 -85.940773 4x inference SSR Iowa A. Hayden 10664 19-Aug-34 0 109x33 43.166258 -94.920622 6x inference SSR Iowa B. Shimek s.n. 22-Aug-26 0 73x13 41.508811 -91.154578 4x inference previous Iowa Cook & Parks 456 4-Sep-01 41.889726 -91.517741 2x count SSR Iowa E. Whitehouse 23635 15-Aug-50 0 96x25 43.437989 -95.369806 6x inference - SSR Iowa Freeman 21081 17-Aug-05 0 59x14 41.610298 6x 96.1119995 inference J.K. Morton & previous Iowa s.n. 27-Aug-91 41.700285 -92.668449 4x J. Venn count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Iowa Malone 353 12-Aug-67 0 96x18 42.09216 -93.278665 4x inference SSR Iowa Monson 1198 1-Sep-55 2 79x9 43.402825 -93.530227 4x inference SSR Iowa Monson 1244 2-Sep-55 0 57x8 42.67039 -93.49945 6x inference previous Iowa Morton & Venn NA15997 22-Sep-85 41.432168 -95.899685 4x count previous Iowa Morton & Venn NA15999 22-Sep-85 41.424696 -95.060068 4x count previous Iowa Morton & Venn NA16005 23-Sep-85 41.689837 -94.587594 2x count previous Iowa Morton & Venn NA16006 23-Sep-85 41.689126 -94.587523 6x count previous Iowa Morton & Venn NA16011 23-Sep-85 41.678896 -93.620018 4x count previous Iowa Morton & Venn NA16020 23-Sep-85 41.754721 -92.183479 4x count previous Iowa Morton & Venn s.n. 24-Sep-92 41.469993 -95.746298 6x count previous Iowa Morton & Venn s.n. 28-Aug-92 41.538642 -94.012947 6x count previous Iowa Morton & Venn s.n. 23-Aug-90 41.67115 -91.36644 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Iowa Rosburg s.n. 24-Aug-93 2 60x8 43.37056 -93.178877 4x inference Semple & previous Iowa 4507 18-Aug-83 41.49694 -94.52054 6x Brouillet count Semple & previous Iowa 4514 8-Sep-79 41.702641 -92.357814 4x Brouillet count T.S. SSR Iowa 2738 16-Aug-56 1 86x15 42.022856 -91.124686 4x Cooperrider inference T.S. SSR Iowa 2871 22-Aug-56 0 95x20 41.91185 -90.830144 4x Cooperrider inference T.S. SSR Iowa 3375 11-Sep-56 0 100x18 42.098469 -90.188458 4x Cooperrider inference SSR Iowa T.Van Bruggen 3849 19-Aug-57 0 62x8 41.523417 -93.494325 4x inference SSR Kansas Barker 4461 23-Aug-67 0 111x16 38.36866 -96.57879 6x inference new Kansas Beck 1384 1-Sep-13 0 126x16 38.609722 -97.961111 count/SSR 6x inference new Kansas Beck 1388 8-Sep-13 1 104x21 37.648056 -98.255 count/SSR 6x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) new Kansas Beck 1389 22-Aug-11 0 73x17 37.645278 -98.645 count/SSR 6x inference new Kansas Beck 1390 8-Sep-13 0 56x10 37.420833 -99.063889 count/SSR 6x inference new Kansas Beck 1391 8-Sep-13 0 84x14 37.258611 -99.348333 count/SSR 6x inference new Kansas Beck 1392 8-Sep-13 0 63x12 37.676111 -97.946389 count/SSR 6x inference SSR Kansas Brooks 12695 22-Sep-76 0 95x15 39.87938 -95.18101 4x inference s.n. SSR Kansas Burgan, B Oct-66 0 90x13 37.706369 -97.350464 6x (03240) inference SSR Kansas Croat 2573A 28-Jul-66 2 83x13 39.458556 -95.03895 4x inference - SSR Kansas Freeman 8431 10/9/1996 0 101x18 38.868009 6x 101.016551 inference SSR Kansas Freeman 8450 9-Oct-96 0 115x19 39.352191 -99.844961 6x inference - SSR Kansas Freeman 11812 31-Aug-98 0 72x14 39.738822 6x 101.876107 inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) - SSR Kansas Freeman 13985 26-Aug-99 0 115x15 39.099998 6x 96.7069473 inference - SSR Kansas Freeman 19268 8/29/2002 0 78x12 39.103333 4x 96.7936096 inference previous Kansas G. Morton M5100 39.192397 -96.537695 4x count SSR Kansas Holland 9219 8/22/1997 0 65x14 37.695404 -95.506722 6x inference SSR Kansas McGregor 10873 8-Aug-55 0 60x12 37.89186 -99.44898 6x inference SSR Kansas McGregor 15125 1-Sep-59 0 61x13 37.34016 -98.58015 6x inference SSR Kansas McGregor 24097 8/18/1971 0 90x13 37.95231 -101.24029 6x inference SSR Kansas McGregor 29743 9/15/1976 0 68x9 39.24995 -95.66874 6x inference SSR Kansas McGregor 29816 9/20/1976 0 103x17 39.84894 -96.64237 6x inference SSR Kansas McGregor 29823 9/20/1976 0 99x15 39.76161 -96.0227 6x inference SSR Kansas McGregor 29874 21-Sep-76 0 76x8 39.67876 -95.53028 6x inference SSR Kansas McGregor 30536 15-Aug-77 0 64x11 38.46954 -98.65437 6x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Kansas McGregor 33791 4-Sep-82 0 116x21 37.91587 -95.11627 6x inference - SSR Kansas McGregor 38409 8/19/1987 0 94x14 37.148855 6x 101.821853 inference SSR Kansas McGregor 39385 8/25/1988 0 90x13 37.159732 -95.6831 6x inference SSR Kansas McGregor 39439 6-Sep-88 0 61x10 37.787121 -95.074166 6x inference SSR Kansas McGregor 40857 8-Sep-92 0 82x10 38.47512 -95.357654 6x inference SSR Kansas McGregor 41162 10/12/1995 0 99x17 39.070765 -95.432223 6x inference - SSR Kansas Morse 5209 10/5/2000 2 118x19 38.587502 6x 94.7458344 inference previous Kansas Morton & Venn NA15968 21-Sep-85 38.627096 -100.9046 6x count - previous Kansas Morton & Venn NA15972 21-Sep-85 39.601931 6x 100.535801 count SSR Kansas R. Brooks 14397 8-Aug-79 0 94x15 39.126633 -95.143692 6x inference - SSR Kansas R.L. McGregor 30619 17-Aug-77 0 70x10 37.111392 6x 100.790725 inference Semple & previous Kansas 7308 1-Sep-83 38.06426 -99.25572 6x Brouillet count

APPENDIX (continued)

Length Collector Collection Data State/province Collector Vestiture x width Latitude Longitude Cytotype number date source (mm) SSR Kansas Stephens 50343 3-Aug-71 0 87x13 37.97003 -101.78186 6x inference - SSR Kansas Stephens 50401 4-Aug-71 0 101x16 37.799176 6x 100.351514 inference SSR Kansas Stephens 51025 8/13/1971 0 77x13 38.13377 -94.78591 6x inference SSR Kansas Stephens 56490 8-Jul-72 0 97x19 37.45466 -94.70474 6x inference SSR Kansas Stephens 59117 8/16/1972 0 80x14 39.10658 -97.5749 4x inference SSR Kansas Stephens 59353 8/19/1972 0 114x23 39.59614 -99.96454 4x inference SSR Kansas W. Cook 642802 13-Sep-37 0 66x14 38.864389 -99.335389 6x inference SSR Kansas Wagenknecht 3477 10/20/1956 0 92x19 38.83365 -94.64042 6x inference SSR Kansas Weedon 5183 22-Oct-68 0 71x13 37.07693 -94.704 4x inference new Kentucky Beck 1476 25-Aug-15 1 105x14 36.59924 -88.9091 count/SSR 4x inference Geroge F SSR Kentucky 2308 1 102x17 38.792154 -84.178012 6x Buddell II inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) J.K. Morton & previous Kentucky NA18748 28-Sep-95 36.777672 -88.065013 4x J. Venn count J.K. Morton & previous Kentucky NA18752 29-Sep-95 36.801072 -87.011357 4x J. Venn count J.K. Morton & previous Kentucky NA18754 30-Sep-95 37.085104 -85.35994 4x J. Venn count J.K. Morton & previous Kentucky NA18758 29-Sep-95 37.145111 -84.163363 2x J. Venn count J.K. Morton & previous Kentucky NA18759 29-Sep-95 36.791173 -83.259947 2x J. Venn count James SSR Kentucky 596 Aug-65 2 98x15 37.582603 -84.244834 2x Grossman inference previous Kentucky Morton & Venn NA16223 12-Oct-85 36.919749 -87.850652 4x count SSR Kentucky R. Athey 1465 26-Aug-71 0 113x20 36.935878 -88.916406 4x inference previous Kentucky Semple 11559 37.152918 -84.288738 4x count Semple & previous Kentucky 9620 14-Sep-91 37.088923 -83.395718 2x Suripto count SSR Louisiana R. D. Thomas 151387 23-Aug-96 1 74x11 32.8822 -92.970797 2x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) R. Dale Thomas SSR Louisiana 157728 4-Sep-98 1 46x8 32.179489 -92.510828 2x w/ A. Dunn inference R.D. Thomas w/ SSR Louisiana 157689 4-Sep-98 2 114x21 31.980061 -92.866361 2x A. Dunn inference SSR Maine F.C. Seymour 26258 22-Aug-67 2 87x12 45.830367 -67.984878 4x inference J.K. Morton & previous Maine NA11957 1-Aug-78 44.607236 -67.925835 4x J. Venn count J.K. Morton & previous Maine NA17620 45.485091 -67.607264 4x J. Venn count J.K. Morton & previous Maine NA17627 2-Sep-88 45.18542 -69.232184 4x J. Venn count J.K. Morton & previous Maine NA17630 2-Sep-88 45.436483 -70.054168 4x J. Venn count J.K. Morton & previous Maine NA17631 2-Sep-88 45.233455 -69.990317 4x J. Venn count J.K. Morton & previous Maine NA17634 2-Sep-88 44.998683 -69.859257 4x J. Venn count J.K. Morton & previous Maine NA17636 2-Sep-88 44.316751 -69.770993 4x J. Venn count J.K. Morton & previous Maine NA17646 43.479777 -70.486352 4x J. Venn count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Maine Morton s.n. 20-Jul-86 45.513644 -68.354495 4x count previous Maine Morton & Venn NA11945 1-Aug-78 44.765037 -69.71938 4x count previous Maine Morton & Venn NA17639 2-Sep-88 44.230646 -69.766655 4x count previous Manitoba J.K. Morton s.n. 13-Jul-82 50.19861 -98.20472 6x count previous Manitoba J.K. Morton s.n. 13-Jul-82 50.26507 -98.08139 6x count previous Manitoba J.K. Morton s.n. 16-Aug-84 49.62856 -95.70208 6x count J.K. Morton & - previous Manitoba NA14214 24-Aug-80 50.965937 6x J. Venn 101.392766 count J.K. Morton & - previous Manitoba NA14215 25-Aug-80 50.965937 6x J. Venn 101.392766 count J.K. Morton & previous Manitoba NA14236 26-Aug-80 49.663119 -96.499893 6x J. Venn count J.K. Morton & previous Manitoba NA14241 26 Agu 1980 49.694241 -96.631206 4x J. Venn count J.K. Morton & previous Manitoba NA14254 26-Aug-80 49.738888 -95.159949 6x J. Venn count previous Manitoba Löve & Löve 7327 49.493685 -97.032522 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) Morton & previous Manitoba NA14248 26-Aug-80 49.921613 -95.831705 6x Venn count Morton & previous Manitoba NA14262 26-Aug-80 49.325553 -96.293068 6x Venn count Morton & previous Manitoba s.n. 13-Jul-82 50.224998 -98.950765 6x Venn count Morton & previous Manitoba s.n. 29-Jul-82 49.972341 -98.290378 6x Venn count Semple & previous Manitoba 4173 27-Aug-79 49.97272 -98.62908 6x Brouillet count Morton & previous Maryland NA16575 3-Nov-85 39.476099 -76.408403 2x Venn count SSR Massachusetts C.C. Curtiss s.n. x-xxx-1892 1 78x10 41.380528 -70.645219 2x inference SSR Massachusetts E.L. Morris s.n. X-Aug-1897 1 53x9 42.118064 -72.430594 2x inference 28-VIII- previous Massachusetts F. C. Seymour 57-21 3 42.105652 -71.552286 2x 1961 count previous J. C. Semple & - Massachusetts 3538 25-Aug-78 2 98x11 42.245833 count/SSR 2x L. Brouillet 71.2912217 inference Jean R. previous Massachusetts 57-21-2 5-IX-61 42.105652 -71.552286 2x Beaudry count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Massachusetts R.G. Poland s.n. 1-Aug-70 2 125x18 42.544572 -72.605667 4x inference previous Massachusetts Semple 11024 19-Aug-01 42.252029 -71.287748 2x count previous Massachusetts Seymour 57-21-1 42.10565 -71.55229 2x count previous Massachusetts Seymour 57-21-1-2 42.10565 -71.55229 2x count previous Massachusetts Seymour 57-21-1-3 42.10565 -71.55229 2x count SSR Michigan Cusick 32680 16-Aug-95 0 113x18 41.867268 -84.596837 6x inference previous Michigan G. Morton 6677 43.43136 -85.42329 4x count SSR Michigan Garlitz 895 28-Jul-89 0 123x18 44.380388 -83.551991 4x inference SSR Michigan Garlitz 989 27-Aug-89 0 116x30 45.0661 -83.632036 4x inference J.K. Morton & previous Michigan NA10959 2-Sep-77 41.702493 -86.933136 4x J. Venn count J.K. Morton & previous Michigan NA15146 6-Aug-83 46.485823 -88.894038 4x J. Venn count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) J.K. Morton & previous Michigan s.n. 16-Aug-84 47.458109 -88.162322 4x J. Venn count J.R. Rastorfer & SSR Michigan 597 13-Oct-89 0 26x4 43.568233 -84.507683 4x G.D. VanDyke inference SSR Michigan L.A. Schram 889 7-Aug-74 0 91x17 46.231722 -86.037453 4x inference previous Michigan Morton & Venn NA10945 1-Sep-77 45.581861 -87.242882 4x count previous Michigan Morton & Venn NA15143 6-Aug-83 46.332425 -85.145208 4x count SSR Michigan Pringle s.n. 22-Sep-69 0 83x10 42.25913 -83.900575 4x inference SSR Michigan Smith 1031 28-Jul-85 1 114x14 41.886356 -84.110225 2x inference previous Minnesota Morton & Venn NA15159 7-Aug-83 47.750454 -96.243178 4x count previous Minnesota Morton & Venn NA15181 11-Aug-83 45.079909 -94.301766 4x count previous Minnesota Morton & Venn NA15658 30-Aug-85 43.895519 -91.343206 4x count previous Minnesota Morton & Venn NA15662 30-Aug-85 43.673721 -92.794193 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Minnesota Morton & Venn NA15668 30-Aug-85 43.663451 -94.117561 4x count SSR Minnesota O. Lakela 1835 6-Sep-36 1 53x11 46.760083 -92.079147 4x inference SSR Minnesota S.R. Ziegler 384 29-Jul-74 0 86x12 43.682608 -91.272161 4x inference Semple & previous Minnesota 6948 23-Aug-83 45.90475 -95.52651 6x Brouillet count Semple & previous Minnesota 5135 1980 0 81x15 44.2488 -96.28957 6x Chmielewski count SSR Mississippi J. D. Ray, Jr. 7095 19-Jul-56 0 108x13 32.620981 -88.750575 2x inference SSR Mississippi J. E. Duncan 187 5-Sep-87 0 80x16 33.583869 -88.933253 6x inference previous Mississippi J.Beaudry 57-591 30.45464 -89.08837 2x count SSR Mississippi J.D. Smith 588 11-Sep-1885 1 84x18 31.41925 -91.442683 2x inference SSR Mississippi Jones 17993 10-Sep-69 1 137,24 32.519952 -89.972532 2x inference SSR Mississippi L. C. Temple 6862 28-Aug-67 0 56x7 33.508833 -90.158606 4x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Mississippi L. C. Temple 6179 3-Aug-67 2 46x13 33.983203 -89.697119 4x inference SSR Mississippi R. D. Thomas 136719 23-Aug-93 1 66x9 32.427575 -90.823914 2x inference SSR Mississippi S. McDaniel 31314 29-Sep-91 1 78x21 33.400294 -88.715531 4x inference Semple & previous Mississippi 10165 33.550185 -88.433182 2x Suripto count SSR Mississippi Temple 3797 4-Aug-66 2 60,10 31.925554 -90.203719 4x inference SSR Mississippi Temple 4128 21-Aug-66 2 34,7 31.059683 -88.733239 4x inference SSR Missouri A.E. Brant 3076 24-Aug-94 0 65x13 36.823122 -90.374664 4x inference SSR Missouri B. Summers 6268 15-Aug-93 1 97x16 36.771231 -91.697456 4x inference SSR Missouri B.F. Bush 13158 10-Oct-33 1 126x20 39.513272 -93.018147 4x inference SSR Missouri B.F. Bush 13857 4-Sep-34 0 115x12 38.679369 -93.337961 4x inference Caroline SSR Missouri 6 6-Oct-93 2 150x20 39.064994 -93.680586 4x Amrein inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Missouri Christ s.n. 10/13/1972 0 92x11 38.580025 -90.773244 4x inference SSR Missouri D Laplante 310 15-Sep-85 2 85x14 39.494247 -95.012494 4x inference SSR Missouri Francis Dimer s.n. 16-Aug-33 0 170x18 38.987108 -92.351342 4x inference SSR Missouri J Kellogg s.n. 21-Sep-30 0 95x16 38.984822 -94.311331 6x inference SSR Missouri J. H. Kellogg 1315 21-Aug-27 0 180x15 38.393439 -90.346131 4x inference J. Stone & K. SSR Missouri 1383 29-Jul-98 0 79x9 36.684167 -93.284167 4x Sikes inference SSR Missouri J.H. Kellogg 2068 2-Sep-28 0 100x23 37.925958 -91.977656 4x inference J.K. Morton & previous Missouri NA16236 12-Oct-85 36.841779 -90.534927 4x J. Venn count SSR Missouri Miller, James S. 1994 14-Aug-84 0 120x25 38.626617 -90.706722 4x inference SSR Missouri N.C. Henderson 67-1340 28-Jul-67 0 69x11 37.624203 -94.010317 4x inference SSR Missouri N.C. Henderson 68-630 26-Jul-68 0 88x10 38.446186 -93.955847 6x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Missouri N.C. Henderson 68-706 1-Aug-68 0 122x14 38.373475 -94.583578 6x inference SSR Missouri N.C. Henderson 95-485 18-Aug-95 0 51x8 39.449017 -94.020667 4x inference SSR Missouri N.C. Henderson 95-946 9/30/1995 0 45x7 37.989081 -94.368383 4x inference SSR Missouri P.C. Walker 144 16-Aug-95 0 65x11 36.678619 -92.672931 6x inference P.L. Redfearn, SSR Missouri 12738 12-Jul-63 0 84x13 37.0552 -91.683392 4x Jr. inference SSR Missouri R.D. Noyes 906 17-Aug-91 0 60x10 40.439556 -92.217133 4x inference SSR Missouri Rev. John Davis 3231 21-Jul-11 0 180x25 40.368353 -91.754042 4x inference SSR Missouri Rev. John Davis 6027 9-Aug-16 0 108x19 39.683262 -91.317791 6x inference Semple & previous Missouri 9928 36.81024 -92.146231 4x Suripto count Semple & previous Missouri 9945 37.947361 -93.377615 4x Suripto count - SSR Nebraska Backer 30 9/11/1991 0 85x20 41.592805 6x 103.176109 inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Nebraska Croat 2873 8/7/1966 0 89x13 40.887217 -96.943085 4x inference SSR Nebraska Croat 3454 8/4/1966 0 83x18 40.28705 -96.846062 6x inference SSR Nebraska D. Demaree 54170 30-Aug-66 0 72x18 40.880708 -96.144572 6x inference SSR Nebraska J.B.E. Menecke s.n. 14-Aug-31 0 104x20 42.333389 -98.12665 6x inference - previous Nebraska Morton & Venn NA15974 22-Sep-85 40.196658 6x 100.624861 count previous Nebraska Morton & Venn NA15987 22-Sep-85 40.697822 -99.603973 6x count previous Nebraska Morton & Venn NA15990 22-Sep-85 40.841022 -99.10135 2x count previous Nebraska Morton & Venn s.n. 26-Aug-90 40.691138 -99.54017 6x count previous Nebraska Morton & Venn s.n. 22-Aug-92 40.977666 -96.388466 6x count SSR Nebraska S. Winter 1706 12-Aug-06 0 94x18 40.850869 -96.899525 2x inference Semple & B. previous Nebraska 11414 42.564129 -102.46485 6x Semple count Semple & previous Nebraska 4504 1979 41.53894 -96.45347 4x Brouillet count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) Semple, Suripto - previous Nebraska 9200 5-Aug-90 42.068577 6x & Ahmed 101.040025 count - SSR Nebraska T.B. Croat 2839 7-Aug-66 0 109x21 42.705625 6x 103.018458 inference SSR Nebraska T.B. Croat 2909 29-Aug-66 0 51x9 41.643656 -96.639711 6x inference SSR Nebraska T.B. Croat 2917 29-Aug-66 0 59x14 42.409275 -97.073442 4x inference - SSR Nebraska T.B. Croat 3372 3-Sep-66 0 96x17 41.1599 6x 101.029883 inference SSR Nebraska unknown unknown 0 88x14 40.916661 -98.046561 6x inference - SSR Nebraska W. Kiener 22583 16-Aug-47 0 67x14 41.153444 6x 102.692383 inference New previous J.K. Morton s.n. 7-Jul-85 45.82584 -67.48054 4x Brunswick count New J.K. Morton & previous NA12520 28-Aug-78 46.080821 -65.416683 4x Brunswick J. Venn count New J.K. Morton & previous NA12521 28-Aug-78 46.080821 -65.416683 4x Brunswick J. Venn count New J.K. Morton & previous NA17585 30-Aug-88 48.039943 -66.417431 4x Brunswick J. Venn count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) New J.K. Morton & previous NA17590 30-Aug-88 47.03051 -65.46906 4x Brunswick J. Venn count New J.K. Morton & previous NA17608 31-Aug-88 46.07893 -64.786975 4x Brunswick J. Venn count New J.K. Morton & previous NA17613 31-Aug-88 45.952364 -65.815842 4x Brunswick J. Venn count New previous Semple 11517 5-Sep-06 45.890756 -66.904169 4x Brunswick count New previous Semple 11519 45.896554 -66.899158 4x Brunswick count New previous Semple 11521 45.942018 -66.866213 4x Brunswick count New Semple & B. previous 11489 2-Sep-06 46.136015 -63.845742 2x Brunswick Semple count New Semple & B. previous 11536 2006 46.792241 -67.487273 4x Brunswick Semple count New previous Semple & Keir 4668 23-Aug-80 46.50321 -67.58437 4x Brunswick count New SSR D.E. Boufford 8518 17-Sep-72 2 127x28 43.024453 -72.321169 2x Hampshire inference previous New J.C. Semple 3453 22-Aug-78 0 99x10 44.15271 -71.979715 count/SSR 4x Hampshire and L. Brouillet inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) New J.K. Morton & previous NA17654 3-Sep-88 43.696689 -71.631934 4x Hampshire J. Venn count New previous Morton & Venn NA17649 2-Sep-88 43.493843 -71.255563 4x Hampshire count New previous Morton & Venn NA17662 3-Sep-88 43.812872 -71.671306 4x Hampshire count New previous Morton & Venn NA17665 3-Sep-88 44.595312 -71.510168 4x Hampshire count New previous Morton & Venn NA17668 3-Sep-88 44.654457 -71.558039 4x Hampshire count New previous Morton & Venn s.n. 3-Aug-86 43.756738 -71.948694 4x Hampshire count New previous unknown unknown 44.05159 -71.437407 4x Hampshire count previous New Jersey G. Morton 5186 40.63347 -74.65224 4x count previous New Jersey G. Morton 6169 40.86317 -75.0481 4x count previous New Jersey G. Morton 6204 40.89283 -75.06612 4x count previous New Jersey G. Morton 6219 40.86367 -74.41756 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous New Jersey G. Morton 6234 40.79617 -74.97773 4x count previous New Jersey G. Morton 6235 40.79617 -74.97773 4x count previous New Jersey G. Morton 6302 40.9466 -74.27334 4x count previous New Jersey G. Morton 6303 40.9466 -74.27334 4x count previous New Jersey G. Morton M5874 40.853972 -74.829067 4x count previous New Jersey G. Morton M5888 40.853972 -74.829067 4x count - SSR New Mexico Taylor 16664 8/1/1974 1 111x18 36.536881 6x 105.223159 inference SSR New York E.P. Killip 7147 3-Aug-20 1 59x9 43.765558 -74.761292 2x inference SSR New York H. D. House 8701 13-Sep-21 1 53x5 43.150548 -75.729398 2x inference Howard J. SSR New York 1404 23-Aug-11 1 91x14 42.89457 -73.62993 2x Baker inference previous New York J.C. Semple 3377 2-Aug-78 0 93x17 43.108586 -75.171775 count/SSR 4x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous New York J.C. Semple 6808 30-Sep-82 0 71x11 42.095701 -75.75446 count/SSR 4x inference J.K. Morton & previous New York NA18671 24-Sep-95 42.501116 -78.06091 4x J. Venn count J.K. Morton & previous New York NA18676 24-Sep-95 42.500007 -78.067728 4x J. Venn count previous New York M. Nee 54556 21-Aug-06 1 101x16 41.969928 -73.498097 count/SSR 4x inference previous New York Morton NA18679 24-Sep-95 42.101113 -78.522531 4x count Morton & previous New York NA16063 3-Oct-85 42.28171 -76.696849 4x Venn count Morton & previous New York NA17674 4-Sep-88 44.986427 -73.446538 4x Venn count Morton & previous New York NA17678 4-Sep-88 44.978506 -74.738497 4x Venn count Morton & previous New York s.n. 3-Aug-86 44.986427 -73.446538 4x Venn count previous New York Ringius 1541 44.38861 -73.81544 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) Semple previous New York Chmielewski & 6495 41.57288 -74.47057 4x count Ringius 14-Aug- SSR New York T.F. Lucy 6256 1 67x9 42.088222 -76.787911 2x 1897 inference SSR New York T.G. Lammers 10227 2-Aug-97 1 85x8 43.205614 -76.702147 4x inference C. Peng De. E. North SSR Boufford & E. 3727 21-Aug-79 0 79x11 35.470481 -78.187272 2x Carolina inference Wood North SSR D.E. Boufford 15072 28-Jul-74 2 120x18 36.046636 -79.010506 2x Carolina inference North SSR D.E. Boufford 15083 3-Aug-74 2 95x16 36.243561 -79.257128 2x Carolina inference North previous G. Morton 3742 34.98161 -80.46487 2x Carolina count North previous G. Morton M3743 34.982457 -80.453762 2x Carolina count North previous G. Morton M3882 35.935265 -82.732851 2x Carolina count North previous G. Morton M3883 35.935265 -82.732851 2x Carolina count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) North J. Stone & D. SSR 1463 13-Aug-98 2 104x18 35.016667 -83.548889 4x Carolina Holland inference North J.K. Morton & previous NA18764 30-Sep-95 36.106053 -81.670585 4x Carolina J. Venn count North J.K. Morton & previous NA18766 30-Sep-95 36.121058 -81.670962 4x Carolina J. Venn count North J.K. Morton & previous NA18768 30-Sep-95 35.221305 -80.839337 4x Carolina J. Venn count North J.K. Small & 12to18-Aug- SSR 410 1 44x9 35.691119 -80.413133 2x Carolina A.A. Heller 1891 inference North SSR Martin 564 23-Aug-72 1 120x15 35.025264 -77.850743 4x Carolina inference North previous Morton & Venn NA16171 10-Oct-85 35.42043 -83.458679 4x Carolina count North previous Morton & Venn NA16567 2-Nov-85 35.262664 -77.581635 2x Carolina count North previous Semple 10798 36.259871 -81.871834 4x Carolina count North previous Semple 10842 35.175304 -82.736235 2x Carolina count North previous Semple 11594 13-Oct-06 35.647031 -82.03459 4x Carolina count North Semple & previous 9732 36.0926 -78.124032 2x Carolina Suripto count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) North Semple & previous 9748 18-Sep-91 2 57x14 35.932418 -76.508044 2x Carolina Suripto count North SSR T.B. Croat 17079 2-Sep-71 0 58x8 35.190831 -83.360558 4x Carolina inference previous North Dakota Morton & Venn NA15161 10-Aug-83 47.925257 -97.032855 4x count previous North Dakota Morton & Venn NA15165 11-Aug-83 46.979411 -96.880637 4x count previous North Dakota Semple 6686 28-Aug-82 47.99332 -98.98395 6x count previous North Dakota Semple et al. 6670 46.27905 -100.2451 6x count J.K. Morton & previous Nova Scotia NA12492 27-Aug-78 45.78318 -60.733356 4x J. Venn count J.K. Morton & previous Nova Scotia NA12510 28-Aug-78 45.565134 -62.643252 4x J. Venn count J.K. Morton & previous Nova Scotia NA12514 28-Aug-78 45.722512 -63.880338 4x J. Venn count previous Nova Scotia Morton & Venn NA12500 28-Aug-78 45.672788 -61.508276 2x count previous Nova Scotia Semple & Keir 4721 24-Aug-80 45.622509 -63.50288 2x count previous Nova Scotia Semple & Keir 4883 28-Aug-80 44.87005 -65.2052 2x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Ohio A. Cronquist 4048 17-Aug-45 1 96x15 39.109792 -82.635381 2x inference SSR Ohio A. Cronquist 4058 19-Aug-45 1 92x12 39.456567 -82.939878 4x inference J.K. Morton & previous Ohio NA18700 27-Sep-95 40.798275 -81.544893 4x J. Venn count J.K. Morton & previous Ohio NA18706 26-Sep-95 40.705044 -82.82261 4x J. Venn count SSR Ohio R.M. Lowden 4290 24-Aug-89 1 84x12 40.052008 -83.030583 4x inference SSR Ohio W.C. Werner 550 Aug 1887 0 79x9 41.719406 -81.227956 4x inference C. Lawson & G. SSR Oklahoma 520 14-Aug-72 0 87x13 35.339342 -98.130728 4x Musselman inference - SSR Oklahoma Freeman 18346 10/3/2001 0 88x12 35.545277 6x 99.7616653 inference - SSR Oklahoma Freeman 18507 10/4/2001 0 78x13 36.376945 6x 99.6180573 inference SSR Oklahoma G. W. Stevens 2200 22-Aug-13 0 120x18 36.800994 -95.065333 4x inference SSR Oklahoma J. & C. Taylor 32159 19-Oct-83 0 77x10 34.246842 -97.964456 6x inference SSR Oklahoma J. & C. Taylor 32714 22-Aug-84 0 94x13 36.775219 -99.900361 4x inference

APPENDIX (continued)

Length Collector Collection Data State/province Collector Vestiture x width Latitude Longitude Cytotype number date source (mm) previous J. C. Semple & Oklahoma 2743 8-Jul-77 0 92x9 34.954381 -96.34655 count/SSR 6x R. Brammall inference - SSR Oklahoma J. Taylor 25260 11-Aug-77 0 94x15 36.588314 6x 102.551631 inference M. Hopkins & SSR Oklahoma 679 30-Sep-44 0 44x8 34.395225 -97.065406 2x A & R Nelson inference previous Oklahoma Morton & Venn NA16292 15-Oct-85 35.454078 -95.678278 6x count SSR Oklahoma P. Nighswonger 1986 29-Aug-82 0 99x11 36.906614 -98.071722 6x inference SSR Oklahoma Stephens 8411 8/8/1966 0 71x15 34.83247 -96.67806 2x inference SSR Oklahoma Stephens 27370 8/15/1968 0 107x13 34.84053 -97.95667 6x inference SSR Oklahoma T.B. Croat 3805 26-Aug-67 0 53x8 36.924961 -94.743833 4x inference SSR Oklahoma Wallis 5512 9/8/1957 0 65x9 35.797488 -95.196633 6x inference R. Cook & previous Ontario 2-Sep-99 43.469478 -80.544235 4x Semple count SSR Ontario Garton 1690 28-Aug-51 0 96x20 48.215599 -90.262827 6x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) J.K. Morton & J. previous Ontario NA10784 24-Aug-77 44.614232 -81.26546 4x Venn count previous Ontario J.K.Morton NA10833 26-Aug-77 45.050621 -81.373252 4x count previous Ontario J.K.Morton NA10856 27-Aug-77 45.973953 -81.929399 4x count J.K.Morton and previous Ontario NA10866 27-Aug-77 45.899953 -81.916673 4x J. Venn count previous Ontario Melville 52 8-Aug-77 44.018552 -81.657211 4x count previous Ontario Melville 53 8-Aug-77 44.018552 -81.657211 4x count previous Ontario Melville 58 8-Aug-77 44.018552 -81.657211 4x count previous Ontario Melville 136 11-Aug-77 44.160439 -79.693865 4x count previous Ontario Melville 683 26-Aug-77 46.64201 -83.42218 4x count previous Ontario Melville 684 26-Aug-77 46.64201 -83.42218 4x count previous Ontario Melville 869 1-Sep-77 46.205159 -82.139034 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Melville 870 1-Sep-77 46.205159 -82.139034 4x count previous Ontario Melville 974 3-Sep-77 49.63058 -87.91892 4x count previous Ontario Melville 984 3-Sep-77 49.70784 -87.36516 4x count previous Ontario Melville 985 3-Sep-77 49.70784 -87.36516 4x count previous Ontario Melville 1035 3-Sep-77 48.72179 -91.57596 4x count previous Ontario Melville 1054 4-Sep-77 48.72591 -92.016564 4x count previous Ontario Melville 1079 4-Sep-77 48.60908 -93.32574 4x count previous Ontario Melville 1080 4-Sep-77 48.580454 -93.439875 4x count previous Ontario Melville 1086 4-Sep-77 48.609276 -93.334223 4x count previous Ontario Melville 1140 6-Sep-77 49.78009 -92.83696 4x count previous Ontario Melville 1274 20-Aug-78 45.275349 -75.275492 4x count previous Ontario Melville 1276 18-Sep-77 45.31325 -75.24197 2x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Melville 1277 18-Sep-77 45.31325 -75.24197 2x count previous Ontario Melville 1278 18-Sep-77 45.31325 -75.24197 2x count previous Ontario Melville 1281 18-Sep-77 45.31325 -75.24197 2x count previous Ontario Melville 1285 18-Sep-77 45.31325 -75.24197 2x count previous Ontario Melville 1286 18-Sep-77 45.31325 -75.24197 2x count previous Ontario Melville 1294 18-Sep-77 45.31325 -75.24197 2x count previous Ontario Melville 1295 18-Sep-77 45.31325 -75.24197 2x count 29 Jul 1982 previous Ontario Melville 1306 (voucher 44.865562 -76.496169 2x count cult.) previous Ontario Melville 1310 18-Sep-77 44.87431 -76.32116 2x count previous Ontario Melville 1311 18-Sep-77 44.87431 -76.32116 2x count previous Ontario Melville 1321 18-Sep-77 44.87431 -76.32116 2x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Melville 1322 18-Sep-77 44.87431 -76.32116 2x count previous Ontario Melville 1392 21-Sep-77 43.469989 -80.543536 4x count previous Ontario Melville 1395 21-Sep-77 43.469989 -80.543536 4x count previous Ontario Melville 1441 21-Sep-77 43.469989 -80.543536 4x count previous Ontario Melville 1442 21-Sep-77 43.469989 -80.543536 4x count previous Ontario Melville 1481 24-Sep-77 42.822643 -82.416492 4x count previous Ontario Melville 1482 24-Sep-77 42.822643 -82.416492 4x count previous Ontario Melville 1483 24-Sep-77 42.822643 -82.416492 4x count previous Ontario Melville 1485 24-Sep-77 42.822643 -82.416492 4x count previous Ontario Melville 1503 25-Sep-77 42.265644 -83.090906 4x count previous Ontario Melville 1509 25-Sep-77 42.265644 -83.090906 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Melville 1512 25-Sep-77 42.265644 -83.090906 4x count previous Ontario Melville 1513 42.308875 -83.035136 4x count previous Ontario Melville 1608 9-Aug-78 46.783333 -81.633333 4x count previous Ontario Melville 1631 52.53333 -103.5 4x count previous Ontario Melville 1666 11-Aug-78 51.27457 -80.64679 4x count previous Ontario Melville 1671 11-Aug-78 51.262449 -80.592952 4x count previous Ontario Melville 1702 44.189189 -79.031974 4x count previous Ontario Melville 1707 18-Aug-78 44.24151 -78.403134 4x count previous Ontario Melville 1711 18-Sep-78 44.038672 -78.218877 4x count previous Ontario Melville 1725 19-Aug-78 44.475634 -77.311165 4x count previous Ontario Melville 1731 19-Aug-78 44.162587 -77.383291 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Melville 1743 19-Aug-78 44.73574 -76.84545 2x count previous Ontario Melville 1747 19-Aug-78 44.542234 -76.679246 4x count previous Ontario Melville 1753 19-Aug-78 44.414822 -76.313528 4x count previous Ontario Melville 1756 19-Aug-78 44.537474 -76.198107 4x count previous Ontario Melville 1763 19-Aug-78 44.61799 -75.75599 2x count previous Ontario Melville 1765 19-Aug-78 44.841463 -75.54943 2x count previous Ontario Melville 1771 20-Aug-78 45.059004 -75.51714 2x count previous Ontario Melville 1774 20-Aug-78 45.09225 -75.22425 2x count previous Ontario Melville 1777 20-Aug-77 45.117371 -75.012052 2x count previous Ontario Melville 1783 20-Aug-78 45.288254 -74.8557 2x count previous Ontario Melville 1785 21-Aug-78 45.311026 -74.63676 4x count previous Ontario Melville 1807 21-Aug-78 45.225228 -76.234824 2x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Melville 1813 21-Aug-78 44.99233 -76.88891 2x count previous Ontario Melville 1815 21-Aug-78 44.842318 -77.121038 4x count previous Ontario Melville 1819 21-Aug-78 44.99527 -77.27854 2x count previous Ontario Melville 1824 21-Aug-78 45.474722 -76.687822 4x count previous Ontario Melville 1831 21-Aug-78 45.512332 -77.559599 4x count previous Ontario Melville 1843 22-Aug-78 45.046188 -78.406484 4x count previous Ontario Melville 1846 22-Aug-78 44.972793 -78.151336 4x count previous Ontario Melville 1847 22-Aug-78 45.251391 -77.912162 4x count previous Ontario Melville 1852 22-Aug-78 44.7531 -78.08824 4x count previous Ontario Melville 1855 44.429155 -78.137421 4x count previous Ontario Melville 1858 22-Aug-78 44.659729 -77.603126 4x count previous Ontario Melville 1866 29-Aug-78 42.99124 -79.249143 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Melville 1870 29-Aug-78 42.948807 -79.914578 4x count previous Ontario Melville 1872 42.950395 -79.855176 4x count previous Ontario Melville 1878 29-Aug-78 43.035945 -80.803321 4x count previous Ontario Melville 1885 30-Aug-78 42.693101 -81.389379 4x count previous Ontario Melville 1889 30-Aug-78 42.636822 -81.707017 4x count previous Ontario Melville 1893 30-Aug-78 42.308897 -81.999596 4x count previous Ontario Melville 1898 30-Aug-78 42.130811 -82.744729 4x count previous Ontario Melville 1903 31-Aug-78 42.446019 -82.140034 4x count previous Ontario Melville 1909 30-Aug-78 42.978918 -82.118208 4x count previous Ontario Melville 1917 31-Aug-78 43.019267 -81.501192 4x count previous Ontario Melville 1921 31-Aug-78 43.08937 -80.86902 4x count previous Ontario Melville 1926 31-Aug-78 43.260935 -80.390763 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Melville 1950 7-Sep-78 43.504972 -79.814395 4x count previous Ontario Melville 1736b 19-Aug-78 44.468669 -77.035802 2x count previous Ontario Melville 672 b 26-Aug-77 46.64201 -83.42218 4x count previous Ontario Melville 682b 46.64201 -83.42218 4x count previous Ontario Melville M1666 51.27309 -80.64005 4x count previous Ontario Melville M1671 1978 51.274546 -80.639084 4x count previous Ontario Melville & Blok 1634 10-Aug-78 49.27599 -81.68756 4x count Melville & previous Ontario 1308 18-Sep-77 44.891027 -76.300563 2x Brouillet count Melville & previous Ontario 1316 18-Sep-77 44.891027 -76.300563 2x Brouillet count Melville & previous Ontario 1318 18-Sep-77 44.891027 -76.300563 2x Brouillet count previous Ontario Ringius 1505 43.22797 -81.89618 4x count previous Ontario Ringius 1506 0 116x15 43.31304 -81.75623 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Ontario Semple 2936 1-Sep-77 0 79x8 46.02676 -83.73199 4x count Semple & B. previous Ontario 6718 29-Aug-82 49.81031 -95.12825 6x Semple count Semple & B. previous Ontario 11055 28-Aug-01 44.660287 -78.132612 4x Semple count Semple & previous Ontario 2807 44.05311 -81.74275 4x Brammall count Semple & previous Ontario 2867 0 79x18 46.92728 -84.60533 4x Brammall count Semple & previous Ontario 2952 44.03016 -77.72739 4x Brammall count Semple & D. previous Ontario Schlaepfer & R. 11340 24-Sep-05 44.656161 -77.103967 4x count Billeter previous Ontario Semple et al. 6717 29-Aug-82 49.81031 -95.12825 6x count Semple, D. previous Ontario Schlaepfer & R. 11347 25-Sep-05 45.333758 -75.254296 2x count Billeter previous Ontario unknown 1794 20-Aug-78 45.275356 -75.275489 4x count 10-Aug- SSR Pennsylvania A.A. Heller (sn) s.n. 0 71x9 41.053978 -76.233275 4x 1889 inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) A.A. Heller & SSR Pennsylvania s.n. 9-Sep-1893 0 51x7 39.820542 -76.366681 6x E. G. Halbach inference SSR Pennsylvania B.F. Donley 129 6-Aug-48 0 64x9 39.733203 -80.076456 4x inference previous Pennsylvania G. Morton 6495 40.48558 -76.30326 2x count SSR Pennsylvania G.H. Morton 381 18-Aug-65 1 65x12 40.186017 -74.992906 2x inference SSR Pennsylvania G.H.Morton 752 5-Aug-66 1 78x13 41.817644 -76.715017 2x inference SSR Pennsylvania G.H.Morton 7363 25-Jul-78 0 118x13 41.294189 -74.827876 4x inference SSR Pennsylvania H.A. Wahl 1327 15-Aug-42 2 96x13 40.881589 -77.969828 2x inference SSR Pennsylvania H.A. Wahl 148300 7-Aug-54 1 106x16 40.761761 -80.357503 4x inference SSR Pennsylvania J. Bright 7337 21-Aug-32 1 84x14 41.052847 -80.142936 2x inference J.K. Morton & J. previous Pennsylvania NA18691 25-Sep-95 41.176984 -79.708443 4x Venn count SSR Pennsylvania J.K. Small (sn) s.n. 1-Sep-1890 0 76x8 40.127378 -76.712247 4x inference J.K. Small SSR Pennsylvania s.n. 20-Sep-1890 0 107x13 41.290128 -75.701458 4x &A.A. Heller inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Pennsylvania L.K. Henry 545 9-Aug-36 1 95x14 40.830442 -80.061394 4x inference previous Pennsylvania Morton & Venn NA16072 4-Oct-85 41.533351 -75.948397 4x count previous Pennsylvania Morton & Venn NA16128 7-Oct-85 39.98215 -79.157576 2x count R.L. Schaeffer, SSR Pennsylvania 10987 23-Aug-40 1 107x21 40.579083 -75.345094 4x Jr. inference previous Pennsylvania Semple 11802 17-Sep-09 41.17908 -75.398802 2x count Prince J.K. Morton & previous Edward NA12046 4-Aug-78 47.028398 -64.019119 4x J. Venn count Island Prince J.K. Morton & previous Edward NA12055 4-Aug-78 46.675904 -64.407995 4x J. Venn count Island Prince previous Edward Morton & Venn NA12052 4-Aug-78 47.033296 -64.000044 4x count Island Prince previous Edward Morton & Venn s.n. 12-Sep-81 46.950479 -64.033072 4x count Island Denise 3 octobre previous Quebec 55-126 45.559944 -73.562993 2x Lafontaine 1955 count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) J.-R. Beaudry, Frère Rolland- 20 Juillet previous Quebec Germain, 55-U-83 45.560001 -73.563008 4x 1955 count Denise Lafontaine previous Quebec J.Beaudry 55-212 45.50169 -73.56726 4x count previous Quebec J.Beaudry 56-278-1 45.49703 -73.40715 4x count previous Quebec J.Beaudry 56-278-2 45.49703 -73.40715 4x count J.Beaudry & previous Quebec 56-299-1 45.28313 -72.97481 2x Cinq-Mars count J.Beaudry & previous Quebec 56-299-2 45.28313 -72.97481 2x Cinq-Mars count J.Beaudry & previous Quebec 56-299-3 45.28313 -72.97481 2x Cinq-Mars count J.Beaudry & 56-303-1, previous Quebec 45.28313 -72.97481 2x Cinq-Mars 56-303-1-2 count J.Beaudry & previous Quebec 55-226 45.46514 -74.07987 4x Louis-Marie count J.K. Morton & previous Quebec NA11929 1-Aug-78 45.364484 -71.846694 4x J. Venn count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) J.K. Morton & previous Quebec NA15372 30-Aug-83 47.330578 -79.48806 4x J. Venn count J.K. Morton & previous Quebec NA17505 26-Aug-88 46.169394 -70.947946 4x J. Venn count J.K. Morton & previous Quebec NA17577 29-Aug-88 48.198367 -65.844891 4x J. Venn count J.K. Morton & previous Quebec NA17581 30-Aug-88 48.175249 -66.188536 4x J. Venn count J.K. Morton & previous Quebec NA17597 30-Aug-88 48.831395 -64.508564 4x J. Venn count previous Quebec Jean R. Beaudry 56-303-1 14 aout 1958 45.283142 -72.974816 2x count previous Quebec Jean R. Beaudry 56-303-2 5 aout 1958 45.283142 -72.974816 2x count previous Quebec Morton & Venn NA11926 1-Aug-78 45.364543 -71.846713 4x count previous Quebec Morton & Venn NA12547 30-Aug-78 45.880291 -72.484283 4x count previous Quebec Morton & Venn NA15234 23-Aug-83 45.202606 -74.36283 2x count previous Quebec Morton & Venn NA15235 23-Aug-83 45.202606 -74.36283 2x count previous Quebec Morton & Venn NA15236 23-Aug-83 45.441342 -73.954872 2x count

APPENDIX (continued)

Length Collector Collection Data State/province Collector Vestiture x width Latitude Longitude Cytotype number date source (mm) previous Quebec Morton & Venn NA15237 23-Aug-83 45.501689 -73.567256 2x count previous Quebec Morton & Venn NA15349 29-Aug-83 49.91323 -74.369199 2x count previous Quebec Semple 3387 8/13/1978 1 65 x 10 45.271504 -72.973791 count/SSR 2x inference previous Quebec Semple 3415 8/20/1978 1 55 x 9 45.569061 -72.526041 count/SSR 2x inference - SSR Saskatchewan Ledingham 2293 7/29/1956 0 113 x 22 50.739124 4x 104.713786 inference - previous Saskatchewan Morton & Venn NA14184 23-Aug-80 53.087744 6x 109.292116 count South previous Morton & Venn NA16550 1-Nov-85 33.159006 -80.621037 2x Carolina count South previous Morton & Venn NA16551 1-Nov-85 33.159524 -80.619899 2x Carolina count South SSR Nelson 9578 12-Aug-90 2 72x12 34.053892 -80.96226 2x Carolina inference South Semple & previous 9819 21-Sep-91 33.929554 -82.331259 2x Carolina Suripto count - previous South Dakota Morton & Venn s.n. 11-Aug-90 44.668076 6x 103.852489 count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Tennessee Beck 1256 22-Aug-11 0 119x23 35.6275 -89.860278 6x inference SSR Tennessee Beck 1257 22-Aug-11 0 104x20 35.400556 -88.375833 6x inference new Tennessee Beck 1258 22-Aug-11 2 91x13 35.388889 -88.780278 count/SSR 2x inference SSR Tennessee Beck 1262 23-Aug-11 2 100x21 35.453611 -87.614722 2x inference new Tennessee Beck 1263 24-Aug-11 0 91x15 35.595278 -86.693333 count/SSR 4x inference new Tennessee Beck 1266 24-Aug-11 2 62x10 36.516111 -87.99 count/SSR 2x inference SSR Tennessee Carter 2212 30-Sep-79 1 100x17 36.521672 -86.232572 4x inference previous Tennessee G. Morton 8500 35.04563 -85.30968 4x count previous Tennessee G. Morton 8537 35.29869 -84.95606 4x count previous Tennessee G. Morton 8538 35.29869 -84.95606 4x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Tennessee G. Morton M2920 36.451812 -83.569332 4x count previous Tennessee G. Morton M2921 36.451812 -83.569332 4x count previous Tennessee G. Morton M2923 36.451812 -83.569332 4x count previous Tennessee G. Morton M2998 35.993323 -84.220075 2x count previous Tennessee G. Morton M3408 35.993323 -84.220075 2x count previous Tennessee G. Morton M3879 36.208034 -82.473791 4x count previous Tennessee G. Morton M3880 36.208034 -82.473791 4x count previous Tennessee G. Morton M3912 35.968109 -87.752152 2x count previous Tennessee G. Morton M3913 35.968109 -87.752152 2x count previous Tennessee G. Morton M4443 35.105939 -85.153194 2x count previous Tennessee G. Morton M4445 35.105939 -85.153194 2x count SSR Tennessee Gerry Moore 3741 22-Aug-98 1 91x17 35.905183 -84.98575 2x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) J.K. Morton & J. previous Tennessee NA18762 30-Sep-95 36.343333 -82.207234 4x Venn count SSR Tennessee K.H. Souza 86-699 28-Aug-86 0 117x20 36.022617 -87.228606 4x inference SSR Tennessee P. Somers 1562 11-Oct-78 1 69x12 36.540806 -87.381578 2x inference SSR Tennessee R. Kral 32963 7-Sep-68 1 121x25 35.435536 -87.282083 2x inference SSR Tennessee R. Kral 33522 3-Oct-68 0 92x18 36.10985 -86.773972 4x inference SSR Tennessee R. Kral 53803 27-Jul-74 1 85x15 35.609189 -85.920783 2x inference SSR Tennessee R. Kral 81592B 11-Sep-92 1 141x22 36.147475 -85.475692 2x inference SSR Tennessee R.L. Jones 3131 10-Sep-80 0 96x17 35.221458 -88.257194 4x inference Semple & previous Tennessee 9118 35.58908 -86.69894 2x Chmielewski count SSR Tennessee T.S. Patrick 366 21-Aug-77 1 81x9 35.430431 -85.630747 2x inference SSR Tennessee V.E. McNeilus 91-669 27-Jul-91 1 81x15 36.540497 -85.791422 4x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Tennessee 35.739689 -83.523613 2x count new Texas Beck 1380 23-Aug-11 0 86x10 34.933639 -101.65365 count/SSR 6x inference previous Texas G. Morton M3966 32.458988 -95.312528 2x count previous Texas G. Morton M3967 32.458988 -95.312528 2x count previous Texas G. Morton M3969 32.458988 -95.312528 2x count SSR Texas J.W. Kessler 5086 18-Sep-81 2 38x8 30.607811 -96.341478 6x inference previous Texas Morton s.n. 4-Sep-96 34.919541 -102.11139 6x count Morton & - previous Texas NA16305 16-Oct-85 34.979914 6x Venn 101.919666 count Morton & previous Texas NA16405 22-Oct-85 28.80432 -96.99856 6x Venn count SSR Texas R. Kral 87883 25-Oct-98 0 81x12 32.731406 -97.358086 6x inference 22-May to SSR Texas R.A. Dixon 617 0 97x9 29.763447 -95.384486 6x 12-Jun 1910 inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) - SSR Texas Stephens 71804 8/26/1973 0 59x11 35.896181 6x 100.863019 inference SSR Texas Stephens 82557 8/7/1974 0 90x14 36.44611 -100.30641 6x inference previous Vermont J.C. Semple 6888 3-Oct-82 0 83x18 43.199765 -72.50784 count/SSR 4x inference previous Vermont Morton s.n. 20-Jul-86 43.637358 -72.408525 4x count Semple & previous Vermont 3441 44.73192 -72.48152 4x Brouillet count Semple & previous Vermont 3449 44.18607 -72.48495 4x Brouillet count Semple & previous Vermont 3506 23-Aug-78 0 114x10 43.95344 -72.6366 4x Brouillet count previous Vermont Semple & Keir 4960 21-Aug-80 42.85888 -72.80123 2x count Willard W 8-11 Sept- SSR Vermont 1405 1 68x11 42.792164 -73.225929 2x Eggleston 1899 inference SSR Virginia B. Mikula 4438 10-Sep-49 0 107x11 37.36285 -77.569572 2x inference SSR Virginia B. Mikula 7089 27-Jul-50 1 120x15 36.781322 -80.103414 4x inference

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Virginia B. Mikula 7789 15-Aug-50 1 87x15 36.655708 -77.121525 2x inference previous Virginia Morton & Venn NA16151 8-Oct-85 37.125294 -80.130357 4x count SSR Virginia R. D. Thomas 173909 6-Aug-04 2 142x16 36.697417 -82.001247 2x inference SSR Virginia S.F. Blake 10647 17-Aug-28 1 113x17 38.849756 -77.133803 6x inference SSR West Virginia Allard 21700 24-Aug-53 0 132x11 39.01862 -79.449749 4x inference SSR West Virginia E.E. Berkley s.n. 1-Aug-30 1 95x13 38.235917 -80.079019 6x inference SSR Wisconsin Fewless 7408 15-Aug-92 0 127x13 45.08783 -87.12636 4x inference J.K. Morton & previous Wisconsin NA15654 29-Aug-85 43.719076 -89.893451 4x J. Venn count previous Wisconsin Morton & Venn NA15149 6-Aug-83 46.500511 -90.454368 4x count previous Wisconsin Morton & Venn NA15189 11-Aug-83 44.936919 -91.392935 4x count previous Wisconsin Morton & Venn NA15194 12-Aug-83 44.959062 -90.800528 2x count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) previous Wisconsin Morton & Venn NA15197 12-Aug-83 44.959062 -90.800528 4x count previous Wisconsin Morton & Venn NA15201 12-Aug-83 45.141605 -89.152377 4x count previous Wisconsin Morton & Venn NA15208 12-Aug-83 45.184591 -88.473846 4x count previous Wisconsin Morton & Venn NA15649 28-Aug-85 42.627538 -89.253162 4x count SSR Wisconsin N.A. Harriman 3815 11-Aug-68 0 110x20 44.251544 -88.445858 6x inference SSR Wisconsin N.C. Fassett 162 28-Sep-35 0 119x15 43.286664 -89.712853 4x inference SSR Wisconsin N.V. Haynie 2004 18-Aug-20 2 81x11 42.833578 -89.075364 4x inference SSR Wisconsin N.V. Haynie 2067 24-Aug-30 1 95x15 42.881631 -89.071269 4x inference R.A., A.E. & SSR Wisconsin 1111 8-Aug-59 1 114x19 45.283658 -88.565794 4x V.E. Schlising inference S.R. Ziegler & SSR Wisconsin 2760 18-Sep-75 0 78x17 43.666544 -91.254833 4x D.F. Sefton inference Semple & previous Wisconsin 6931 42.79577 -88.62227 4x Brouillet count

APPENDIX (continued)

Length x Collector Collection Data State/province Collector Vestiture width Latitude Longitude Cytotype number date source (mm) SSR Wisconsin Smith 3820 0 79x16 44.319 -87.815 6x inference SSR Wisconsin T. G. Hartley 3215 20-Sep-56 0 64x10 44.058178 -91.282975 4x inference T.G. & D.L. SSR Wisconsin 13150 2-Oct-10 0 77x15 44.227761 -91.228825 6x Lammers inference