EX SITU CONSERVATION of OAK (QUERCUS L.) in BOTANIC GARDENS: a NORTH AMERICAN PERSPECTIVE by Raakel Toppila a Thesis Submitted

Home , Batumi Botanical Garden, Cosmos atrosanguineus, Encephalartos latifrons, Outcrossing, Quercus acerifolia, Quercus arkansana

EX SITU CONSERVATION OF OAK (QUERCUS L.)

IN BOTANIC GARDENS:

A NORTH AMERICAN PERSPECTIVE

Raakel Toppila

A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Public Horticulture

Summer 2012

EX SITU CONSERVATION OF OAK (QUERCUS L.)

IN BOTANIC GARDENS:

A NORTH AMERICAN PERSPECTIVE

by Raakel Toppila

Approved: ______Robert E. Lyons, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee

Approved: ______Blake E. Meyers, Ph.D. Chair of the Department of Plant and Soil Sciences

Approved: ______Robin W. Morgan, Ph.D. Dean of the College of Agriculture and Natural Resources

Approved: ______Charles G. Riordan, Ph.D. Vice Provost for Graduate and Professional Education

ACKNOWLEDGMENTS

Thank you to those who supported and guided me throughout the research and writing of this thesis. To my thesis committee members, Dr. Robert Lyons, Dr. Andrea Kramer and Dr. Randy Wisser, thank you for your mentorship and enthusiasm throughout the process. Thank you to the Longwood Foundation, Longwood Gardens and the University of Delaware for providing the support needed to carry out this work. Additional funding came from a U.S. Forest Service Challenge Cost Share Grant to Botanic Gardens Conservation International U.S.

Thank you to the many herbaria and botanists who provide provenance data for the study species and the botanic gardens that provided information about their collections. Particular thanks to the ten botanic gardens that provided leaf tissue for genetic analysis, and the site managers who provided information and access to natural populations of Quercus georgiana for leaf tissue collection.

Thank you to Dr. Larry Stritch, National Botanist for the U.S. Forest Service, who suggested and supported ex situ research on these rare oak species, as well as the volunteers and employees at the Chicago Botanic Garden, especially Jeremie Fant, John Keller, Sonia Doshi, Jairus Rossi and Andrea Kramer who spent countless hours in the lab to collect the data used for this study.

Finally, thank you to my family and friends who have supported me through my academic pursuits and especially to my husband, Matthew Pelletier, who stood beside me through it all. iii

TABLE OF CONTENTS

LIST OF TABLES ...... vi LIST OF FIGURES ...... viii ABSTRACT ...... ix

Chapter

1. INTRODUCTION ...... 1

1.1 Loss of Biodiversity and Role of Plant Conservation at Botanic Gardens ...... 1 1.2 Why Oaks? ...... 3

2. LITERATURE REVIEW ...... 6

2.1 What is Ex Situ Conservation? ...... 6

2.1.1 Seed Banks ...... 6 2.1.2 Cryopreservation ...... 7 2.1.3 Tissue Culture ...... 8 2.1.4 Cultivation of Living Collections ...... 10

2.2 Ex situ Plant Conservation at Botanic Gardens ...... 10

2.2.1 Oaks at Botanic Gardens ...... 12

2.3 The Role of Genetics in Ex Situ Conservation ...... 13

2.3.1 Genetic Drift and Inbreeding Depression ...... 15 2.3.2 Outbreeding Depression and Genetic Swamping ...... 16 2.3.3 Quantitative Genetics ...... 17

3. SURVEY OF EX SITU LIVING COLLECTIONS OF NORTH AMERICAN NATIVE OAK TAXA ...... 20

3.1 Methods ...... 20 3.2 Results ...... 22

4. DETAILED SURVEY OF EX SITU LIVING COLLECTIONS OF FOUR OAK TAXA OF CONSERVATION CONCERN ...... 24 iv

4.1 Methods ...... 24 4.2 Results ...... 26

4.2.1 Quality of Collection Data and Representation of Wild Populations ...... 27

5. MOLECULAR GENETIC DIVERISTY OF QUERCUS GEORGIANA IN WILD POPULATIONS AND EX SITU LIVING COLLECTIONS ...... 30

5.1 Materials and Methods ...... 30

5.1.1 Study Species ...... 30 5.1.2 Study Sites ...... 31 5.1.3 Collections ...... 33 5.1.4 Molecular Data ...... 35 5.1.5 Analysis ...... 36

5.2 Results ...... 38

5.2.1 Descriptive Statistics of Loci ...... 38 5.2.2 Descriptive Statistics of Populations ...... 39 5.2.3 Population Genetic Structure ...... 40

6. PHENOTYPIC DIVERSITY IN QUERCUS GEORGIANA ...... 50

6.1 Methods ...... 50 6.2 Results ...... 50

7. DISCUSSION ...... 52

7.1 Survey of Ex Situ Living Collections of North American Native Oak Taxa and Four Oaks of Conservation Concern ...... 52 7.2 Molecular Genetic Diversity of Quercus georgiana in Wild Populations and Ex Situ Living Collections ...... 55 7.3 Comparison of Genetic Diversity in Ex Situ Living Collections of Quercus georgiana to Genetic Diversity in Wild Populations ...... 60 7.4 Phenotypic Diversity in Quercus georgiana ...... 64 7.5 Limitations of the Study and Future Research...... 66 7.6 Conclusions ...... 68

REFERENCES ...... 70

Appendix

A. NUMBER OF BOTANIC GARDENS GROWING 97 OAK TAXA NATIVE NORTH OF MEXICO AND CORRESPONDING NATURESERVE CONSERVATION STATUS RANKS, NATURESERVE CONSERVATION STATUS ADJUSTED RANKS AND IUCN RED-LIST RANKS………...... 85

B. 277 BOTANIC GARDENS GROWING 97 OAK TAXA NATIVE NORTH OF MEXICO…… ...... 89

LIST OF TABLES

Table 1. NatureServe conservation status ranks...... 21

Table 2. Representation of NatureServe ranked North American native oak taxa versus North American native oak taxa in living collections...... 23

Table 3. Summary of four oak taxa of conservation concern, status in the wild, distribution, conservation ranks, and habitat descriptions...... 25

Table 4. Summary of living collections of four oak taxa of conservation concern...... 27

Table 5. Quercus georgiana study populations...... 33

Table 6. Living collections of Quercus georgiana within ten U.S. botanic gardens that were used for comparison to wild populations...... 34

Table 7. Summary of eight oak microsatellite loci derived from both wild and living collections ...... 36

Table 8. Summary statistics for Quercus georgiana by locus ...... 39

Table 9. Summary statistics for Quercus georgiana by population ...... 40

Table 10. Results from Analysis of Molecular Variance (AMOVA) for Quercus georgiana study populations...... 41

Table 11. Pairwise genetic and geographic distance for Quercus georgiana populations ...... 42

Table 12. Summary statistics for comparisons between wild populations and and living collections of Quercus georgiana...... 47

Table 13. Putative assignment of individuals from living collections to Quercus geogiana wild populations……………………...……………49

vii

LIST OF FIGURES

Figure 1. Number of accessions in living collections of four oak taxa of conservation concern ...... 29

Figure 2. Quercus georgiana study population sites in Georgia and Alabama...... 32

Figure 3. Isolation by distance for Quercus georgiana ...... 43

Figure 4. UPGMA clustering of Quercus georgiana using Nei’s unbiased genetic distance...... 44

Figure 5. Bayesian admixture proportions for Quercus georgiana populations...... 45

Figure 6. Bayesian admixture proportions for Quercus georgiana living collections ...... 46

Figure 7. Differences between mean leaf length, mean long lobe length and mean short lobe length by population ...... 51

viii

ABSTRACT

Oaks have significant ecological, economic and aesthetic importance, but are threatened by habitat loss, invasive species, shifting climates, and pests and pathogens like sudden oak death. Recalcitrant acorns lose viability when dried, rendering long-term seed bank storage futile. Conservation through living collections is currently the only ex situ conservation option. This research investigated the representation of North American threatened oaks in living collections. Using BGCI’s PlantSearch database a survey of ex situ living collections of North American native oak taxa was conducted to determine their prevalence in botanic gardens. A detailed survey of four oak taxa, Quercus acerifolia (E. J. Palmer) Stoynoff & W. J. Hess, Q. arkansana Sarg., Q. boyntonii Beadle and Q. georgiana M.A. Curtis, was conducted to gain insight into the ex situ conservation of wild-collected plants of known provenance in living collections. Botanic gardens growing the four taxa were contacted individually to gain information about the number of accessions, number of individuals and provenance. Among the 97 North American oak taxa presently recognized by the Flora of North America (north of Mexico), six were not represented in botanic gardens and only 2% of records represent the most threatened taxa. However, the four oaks studied here were relatively well represented in collections, and the most threatened oaks (Q. acerifolia and Q. boyntonii) had a higher percent representation of wild collections than less threatened oaks (Q. arkansana and Q. georgiana).

Q. georgiana is highlighted as a case study by asking whether oak genetic diversity in living collections represents genetic diversity in the wild through use of DNA fingerprinting techniques. Twenty-four individuals from living collections were

compared to 224 individuals from nine populations across the range of the species using microsatellite markers developed for Q. rubra. Genetic diversity was high in wild populations, while less than 63% the genetic variation identified in wild populations was present in known ex situ collections.

Results from this study can be used to assist in making management decisions about ex situ conservation of oaks such that genetic diversity within species is maximized in living collections, which has long-term implications for restoration and reintroduction efforts.

Chapter 1

INTRODUCTION

1.1 Loss of Biodiversity and Role of Plant Conservation at Botanic Gardens

The loss of biodiversity is occurring at an unprecedented rate (Pimm et al. 1995) igniting a worldwide response aimed at halting further loss (UNEP 1992). The International Union for Convention of Nature (IUCN) estimates that at least 380 plant extinctions have occurred since 1500 (Walter, Gillett 1998). With staggering statistics predicting extinction rates at 100 to 1000 times their pre-human levels, this number is expected to increase dramatically in the future (Pimm et al. 1995). Occasionally, plants on the brink of extinction are captured in ex situ (off-site) collections. They are safeguarded in gene banks, tissue culture facilities or other dedicated conservation facilities (Guerrant, Havens & Maunder 2004). For some species such as Franklinia alatamaha, Tulipa sprengeri and Cosmos atrosanguineus, botanic gardens (herein referring to both gardens and arboreta) remain their only refuge (Maunder, Higgens & Culham 1998). However, Maunder (1994) asserts,

“In a hundred years botanic gardens will be judged not by the number of relictual species maintained as botanical ‘living dead’ but by the number of viable species and habitats surviving as a result of botanic garden intervention and importantly by the contribution to economic and social development.”

There is an important role for botanical gardens to play a role in the conservation of the world’s disappearing flora. The Global Strategy for Plant Conservation aims to have “at least 75 per cent of threatened plant species in ex situ collections, preferably in the country of origin, and at least 20 per cent available for recovery and restoration programmes” (CBD 2011). For species with recalcitrant seeds, ex situ conservation in seed banks is not an option, rather plants must be secured in cryopreserved or living collections.

The primary purpose of ex situ conservation is to support in situ (on-site) conservation and the augmentation and reintroduction of plants into the wild (Guerrant, Havens & Maunder 2004; CBD 2002). Germplasm serves as a source for restoration and a backup for genetic variation that may otherwise be lost in wild populations through local extinction (Millar, Libby 1991). Additionally, ex situ collections alleviate pressures on wild populations by providing germplasm for research and monitoring (Millar, Libby 1991).

Genetic diversity is essential to the adaptability and survival of a population (Reed, Frankham 2003), especially in the face of rapid climate change (Jump, Marchant & Peņuelas 2009; Jump, Peñuelas 2005; Barrett, Schluter 2008). Therefore, the foundation for any effective ex situ conservation program begins with the collection of a genetically appropriate and representative sample (Guerrant, Havens & Maunder 2004), such that reestablishment efforts using ex situ germplasm are more likely to succeed. However, botanic garden collections are falling short. The most threatened taxa are not well represented in living collections and those that are, are represented by a small number of individuals (Kramer et al. 2011; Sharrock, Jones

2009). Additionally, living collections are dominated by accessions of non-wild origin that are inadequately documented (Maunder, Higgens & Culham 2001).

1.2 Why Oaks?

Oaks (Quercus L.) have significant ecological (Tallamy, Shropshire 2009), economic (Varela, Eriksson 1995; Campos-Palacìn et al. 2002) and aesthetic importance, but are threatened by habitat loss (Hannah, Carr & Lankerani 1995), invasive species (Pimentel, Zuniga & Morrison 2005), shifting climates (Gomez- Mendoza, Arriaga 2007), and pests and pathogens like sudden oak death caused by the fungus Phytophthora ramorum S. Werres & A.W.A.M. deCock (Rizzo, Garbelotto 2003). These threats highlight a need to take conservation action, however, ex situ conservation of oak has challenges. Recalcitrant acorns lose viability when dried, making their long-term storage in seed bank facilities futile (Connor 2004). Tissue culture and cryopreservation are challenging for most oaks due to the presence of tannins (Kramer, Pence 2012), though this has been carried out successfully in some species. Conservation through living collections is currently the only ex situ conservation option for threatened oak species.

Prioritization is difficult as the conservation status of nearly 70% of oak taxa is unknown. In 2007, The Global Trees Specialist Group attempted to assess the status of the estimated 500 known oak taxa using the IUCN Red List Categories and

Criteria (version 3.1) (Oldfield et al. 2007). At least 333 taxa lacked the information required for assessment (data deficient and not evaluated) and 56 of the 208 taxa evaluated were found to be threatened by extinction (critically endangered, endangered and vulnerable) (Oldfield et al. 2007). The report called for action to ensure that taxa 3

lacking information are evaluated, and that threatened taxa are conserved both in situ and ex situ.

Four North American red-listed oak taxa of conservation concern include Quercus acerifolia (E.J. Palmer) Stoynoff & Hess, Quercus arkansana Sarg, Quercus boyntonii Beadle, and Quercus georgiana M.A. Curtis (G11, G3, G1, and G3 respectively). All four taxa have restricted populations in the southern United States where they are vulnerable to extirpation by sudden oak death (Kliejunas 2010; Kluza et al. 2007). Priority for their conservation includes developing genetically diverse ex situ collections not exposed to Phytophthora ramorum (Kramer, Pence 2012).

For botanic gardens to effectively contribute to their conservation, oak taxa of conservation concern must be present in gardens, represented by genetically diverse, well-documented, wild collections of known provenance. This research investigated the capacity of botanic gardens to support in situ conservation of oaks by investigating ex situ living collections of North American oak taxa in botanic gardens. Focusing on the above-mentioned four taxa of conservation concern, the representation of wild collected plants and the quality of collection data was examined. Finally, Q. georgiana is highlighted as a case study by asking whether genetic diversity in botanic garden living collections represents genetic diversity in the wild, using DNA fingerprinting techniques. Results from this study can be used by botanic gardens to assist in making management decisions about creating conservation-based,

1 NatureServe Conservation Status Ranks; G1 – critically imperiled, G3 – vulnerable

living collections that are genetically-diverse and representative of the species, and which can therefore support restoration and reintroduction efforts. Results may be applicable to other species of conservation concern with similar life history traits.

Chapter 2

LITERATURE REVIEW

What is Ex Situ Conservation?

Two strategies are employed for the conservation of biological diversity, ex situ and in situ conservation. Ex situ conservation is the “conservation of components of biological diversity outside their natural habitats” (UNEP 1992). In situ conservation is “the conservation of ecosystems and natural habitats and the maintenance and recovery of viable populations of species in their natural surroundings” (UNEP 1992). Ex situ conservation involves a suite of complementary tools used to strengthen in situ conservation efforts and the persistence of species in the wild.

Four approaches to ex situ conservation are used: seed banking, cryopreservation, tissue culture and cultivation in living collections. These four are introduced below and discussed in terms of their usefulness for the conservation of oaks.

2.1.1 Seed Banks

Seed banking is one of the easiest and most cost effective ex situ conservation options for plants with orthodox seed; meaning seed that can be dehydrated to low moisture content and stored at low temperatures (approximately

20°C) and low humidity (approximately 15%) for long periods of time (Roberts 1973). It can cost as little as 1% the cost of in situ conservation, seeds are relatively easy to collect, take up little space and can represent the genetic diversity of the species (Li, Pritchard 2009). Depending on the species, seed viability ranges anywhere from many years to centuries (Berjak, Pammenter 2008). Pre-storage dehydration is necessary to prevent physical and chemical reactions that cause seed deterioration (Walters 2004). However, not all seeds tolerate the required low moisture levels for long-term, seed bank storage. Contrary to orthodox seeds, which are quiescent when mature, recalcitrant seeds are metabolically active, and intolerant of desiccation (Berjak, Pammenter 2008). Approximately 3% of the 7,000 taxa for which seed storage behavior has been published are recalcitrant (Hong 1996). Li and Prichard (2009) regard the problem of how to conserve recalcitrant species ex situ as “the greatest conservation science challenge of the 21st century.”

Eighty percent of genera of the Fagaceae evaluated (8.2% of species) have recalcitrant seeds and all but one oak species (Q. emoryi Torr.) are desiccation- intolerant, making their long-term storage in seed banks impracticable (Dickie, Pritchard 2002). In general, germination rates decline 25% - 40% after five to eight months in traditional seed bank storage and seeds lose viability after three and a half years (Gonzalez-Benito, Martin 2002).

2.1.2 Cryopreservation

When seed storage in conventional seed banks is not an option, cryopreservation offers an alternative solution. This process involves the storage of seeds, pollen and vegetative tissue in liquid nitrogen at ultralow temperatures (often to 7

-196°C) (Engelmann 2004), causing no changes in cell metabolism or structure such that they are recoverable after a long period of time (Fuller 2004). Cryopreservation is particularly useful for the storage of unorthodox seed and vegetatively propagated plants, however, its success is based on knowledge of tissue culture protocols, seed biology, storage behavior and propagation protocols (Engelmann 2004). Additionally, large recalcitrant seeds cannot be cryopreserved because of their high water content and intolerance of the dehydration; instead, removal of the embryo axes is required for storage (Berjak, Pammenter 2008).

Cryopreservation of oak pollen, embryo axes and immature embryos has been successful (Gonzalez-Benito, Martin 2002) but not entire oak seeds. The embryo axes must be stored and subsequently recovered using in vitro techniques, which has been successful for a number of oak taxa (Gonzalez-Benito, Martin 2002; Pence 1992; Gonzalez-Benito et al. 2002; Valladares et al. 2004; Martinez, Ballester & Vieitez 2003; Fernandes et al. 2008). While this demonstrates potential for conserving other oak taxa using this method, significant research must be done and protocols developed before cryopreservation is considered a viable ex situ conservation technique for oaks.

2.1.3 Tissue Culture

Tissue culture, or micropropagation in vitro, is a clonal propagation technique in which cells are isolated from a parent plant and grown on artificial media

(George, Hall & Klerk 2008). This method is important for threatened taxa when few seeds are available and cuttings are difficult. In vitro techniques can be used as a source for embryo and shoot tip cultures for cryopreservation and are necessary for the recovery of tissues and isolated embryo axes following cryopreservation (Pence 2010). 8

In vitro storage in temperature and light controlled conditions that encourage slow growth is another ex situ conservation option (Engelmann, Engels 2002) but its use is limited by the high cost for equipment and the need for trained staff (Engelmann, Engels 2002). Each species responds differently to the various stages of in vitro propagation and requires research for the development of protocols (Pence 2010). Somaclonal variation, mutation or epigenetic changes in plants maintained in culture, is also a concern and must be considered on an individual basis

(Pence 2010; Fay 1992).

In vitro propagation of oaks has been successful with selected economically important taxa, but little research has considered endangered taxa, with the exception of Q. euboica (Kartsonas, Papafotiou 2007) and Q. acerifolia, Q. arkansana, Q. boyntonii and Q. georgiana (Kramer, Pence 2012). Juvenile seedling material (Vengadesan, Pijut 2009; Tamta et al. 2008), stump sprouts, epicormic shoots and forced branch segments (Vieitez et al. 2009) have been successfully cultured.

Somatic embryogenesis (Wilhelm 2000) and propagation from dormant buds (Pijut, Lawson & Michler 2011) is reportedly successful for several oak taxa. Q. suber L. has been stored for one year without subculturing (Gonzalez-Benito, Martin 2002). Given that plant tissue cultures in related taxa often respond similarly (Pence 2010) this method of ex situ conservation shows some promise, though it is only effective for short periods of time, and should be considered secondary to cryopreservation (Guerrant, Havens & Maunder 2004).

2.1.4 Cultivation of Living Collections

The curation of living collections for the purpose of ex situ conservation refers to the cultivation of plants in field gene banks, controlled environments (e.g. glasshouses), and display or reference collections herein referred to as living collections. Living collections can be especially valuable for taxa with recalcitrant seeds (Oldfield 2009). The challenges of maintaining living collections are numerous, however, and for some taxa this remains the only viable ex situ conservation option to reduce the risk of extinction. Maintenance of living collections is costly, requiring land, materials, labor and technical expertise (Engelmann, Engels 2002). Living collections are susceptible to natural disasters (Bergquist 2009), pest and pathogens (Gapinski 2010) and vandalism. The 2009 Jesusita Fire that destroyed part of the Santa Barbara Botanic Garden is a reminder of the vulnerability of living collections. Sample decline, maladaptation, hybridization, population and quantitative genetic issues are important considerations (Guerrant, Fiedler 2004).

2.2 Ex situ Plant Conservation at Botanic Gardens

Biodiversity is measured at a number of levels: ecosystems, species, populations and genes (Humphries, Williams & Vane-Wright 1995). Conservation planning typically targets one or more of these levels but which level to target continues to be a focus of debate (Myers et al. 2000, Walker 1995, Franklin 1993). While the Convention on Biological Diversity (CBD) emphasizes conservation at the ecosystem level, the core strength of botanic gardens is in living collections and herbaria (Donaldson 2009), as well as in their traditional use in plant taxonomy and whole-organism botanical studies, and increasingly, molecular systematics (Marris

2006). Accordingly, conservation of biodiversity at the species-level has historically been the primary focus for botanic gardens. However, there is an opportunity for botanic gardens to build on their core strengths to contribute to ecological restoration (Hardwick et al. 2011), and conservation of species at the population and genetic level.

Botanic institutions are an undeniable force with over 3000 worldwide (BGCI 2012) reaching an estimated 200 million visitors each year (Oldfield 2010). They are predicted to hold between 80,000 and 100,000 of the world’s plants species in their living collections (Oldfield 2010). Despite this enormous number, their direct conservation value has been questioned because the majority of taxa are held in a small number of collections, dominated by inadequately documented, non-wild accessions that are not representative of the genetic diversity of their wild counterparts (Hurka 1994; Maunder, Higgens & Culham 2001). Recent reports support this (Kramer et al. 2011; Sharrock, Jones 2009), however, research on the genetic representativeness of species in living collections is sparse. Despite this, a number of studies have been enlightening.

Metasequoia glyptostroboides Hu & W.C. Cheng, is arguably the most successfully recovered critically threatened species thanks to its representation in living collections. However, living collections showed low genetic variation compared to wild populations, likely attributed to sampling bias, vegetative propagation and mixing of propagules (Li et al. 2005). Living collections of the critically endangered Encephalartos latifrons Lehm. (Silva et al. 2012), Brighamia insignis A. Gray

(Gemmill et al. 1998) and vulnerable Malus sieversii (Ledeb.) M. Roem. (Volk et al. 2005) have been shown to be genetically representative of the wild populations of the species. Namoff et al. (2010) demonstrated that use of ex situ conservation protocols 11

designed to maximize genetic diversity at the population level can capture a great deal of the diversity of the wild population. These studies exhibit the potential for living collections to contribute to in situ conservation of threatened taxa by providing genetically representative source material for reintroduction and restoration.

2.2.1 Oaks at Botanic Gardens

The majority of ex situ oak collections are found in botanic gardens, with very few collections cryopreserved or held in vitro, mainly due to lack of protocols for most taxa (Kramer, Pence 2012). To provide an initial assessment of the value of existing living collections of oak, BGCI conducted a global survey in 2009 that identified 3,796 oak records from 198 institutions in 39 countries (BGCI 2009). Only 91 records represented 13 of the 29 critically endangered and endangered taxa identified by The Global Trees Specialist Group (Oldfield et al. 2007).

The North American Plant Collections Consortium (NAPCC), under the auspices of the American Public Gardens Association (APGA), manages an ex situ Quercus multisite collection consisting of 19 gardens across the U.S. who seek to “maintain their oak collections to high curatorial and horticultural standards, and to contribute to group goals to expand and improve the collection” (Kramer et al. 2011). The 2010 NAPCC oak collection inventory identified 3,646 accessions, 1,647 (45%) of which are wild-collected.

The Center for Plant Conservation (CPC) is a network of 38 U.S. botanical institutions dedicated to preventing the extinction of U.S. native plants through use of in situ and ex situ conservation approaches (Center for Plant

Conservation N.D.). Of the 750 imperiled plants that they manage, Q. hinckleyi C.H. Mull is the only oak species represented.

2.3 The Role of Genetics in Ex Situ Conservation

Genetic diversity is essential to the adaptability of a population (Barrett, Kohn 1991). Consequently, the value of a conservation collection is directly related to the amount of diversity it contains (Falk, Holsinger 1991). Given that the ultimate goal of ex situ conservation is for the reestablishment of wild populations, collections should maintain the maximum amount of genetic diversity feasible (Guerrant, Havens & Maunder 2004).

Two types of genetic diversity, neutral and adaptive, must be considered. Neutral genetic diversity is measured with neutral molecular markers such as microsatellites and restriction fragment length polymorphisms (RFLPs), and is useful in conservation biology for inferring information about taxonomy, hybridization, genetic diversity patterns, inbreeding and gene flow within and among populations (Kramer, Havens 2009). However, neutral molecular markers do not undergo selection; consequently they do not allow for inference about the adaptive or evolutionary potential of a populations or species (Holderegger, Kamm & Gugerli 2006). Adaptive diversity underlies the variability in traits such as stem length, flowering time or response to light, and is affected by natural and artificial selection

(Storfer 1996). Molecular measures of genetic diversity using neutral markers have limited ability to predict adaptive genetic variability (Reed, Frankham 2003), though the correlation between molecular heterozygosity and fitness is moderate (Reed, Frankham 2005). Correlations between neutral and adaptive genetic differentiation 13

have shown variable results among species (Merilä, Crnokrak 2001) indicating that inference from neutral genetic variation should not be used as a proxy for adaptive genetic variation (Holderegger, Kamm & Gugerli 2006).

The establishment of an ex situ conservation collection is comprised of the basic elements of a colonizing or founder event (Templeton 1991); the sampling from a finite number of individuals that forms the basis for a new population occupying a new environment (Husband, Campbell 2004). The founding population retains a sample of the original genepool (Mayr 1963). If the restriction in number of individuals is accompanied by a loss of genetic diversity, it is called a genetic bottleneck (Barrett, Kohn 1991). However, genetic diversity, measured by heterozygosity or quantitative variation is not significantly reduced when sampling small numbers because only rare alleles are lost (Nei 1975; Lande 1980). Rather, founder events are accompanied by a drastic reduction in the number of alleles, especially rare alleles (Barrett, Kohn 1991).

The effects of genetic drift and inbreeding on genetic diversity in small population samples results in the loss of genetic variation and fixation of mildly deleterious mutations (Husband, Campbell 2004). The degree to which genetic variation is reduced is determined by the number and genetic diversity of founders, and if ex situ collections are allowed to interbreed and reproduce, the mating system and the population growth rate following the bottleneck (Hufford, Mazer 2003),

Candidate species for ex situ conservation collections, by definition, have small or declining populations (Guerrant, Havens & Maunder 2004). Similarly, owing to the finite size of botanic gardens, ex situ living collections of any one species are

constrained in size (Oldfield 2009). Thus, the genetic consequences of a small population size, both in the source population and ex situ collections are important to consider in the development of a conservation scheme (Schall, Leverich 2004).

2.3.1 Genetic Drift and Inbreeding Depression

Four key evolutionary forces account for the way genetic variation is distributed among regions, populations and individuals: mutation, natural selection, random genetic drift and gene flow (Barrett, Kohn 1991). Mutation, natural selection and genetic drift drive population differentiation, while gene flow, or migration, through movement of gametes, individuals and entire populations, opposes population differentiation (Slatkin 1993; Slatkin 1987). In small populations (<100), genetic drift, the random fluctuation in allele frequencies across generations, has dominant influence on population genetic structure (Barrett, Kohn 1991). It results in decreased genetic variation within populations through the loss of alleles, decreased heterozygosity and increased genetic differentiation between populations through fixation of alleles (Schall, Leverich 2004).

The occurrence of inbreeding (the mating of related individuals) increases in small populations compared to large populations. Inbreeding occurs through selfing in the most extreme case, and biparental mating (Ellstrand, Elam 1993). It causes inbreeding depression, which is a reduction in fitness caused by the unmasking of deleterious recessive alleles due to increases in homozygosity (Charlesworth and Willis 2009).

The consequences of genetic drift and inbreeding in small populations are thought to reduce a population’s ability to adapt to change (Barrett, Kohn 1991), thus making it more vulnerable to extinction. While this is difficult to show, a significant reduction in genetic variation has been observed in rare plants when compared to more common plants (Cole 2003) and there is compelling evidence that inbreeding depression and loss of genetic diversity increases extinction risk (Frankham 2005).

2.3.2 Outbreeding Depression and Genetic Swamping

Intra- and interspecific hybridization has important implications for conservation. It has been shown to increase gene diversity, increase fitness and adaptation to new environments (Rieseberg 1991). Conversely, hybridization can result in loss of genetic diversity through genetic assimilation of a small population by a larger one (genetic swamping) and outbreeding depression (Rieseberg 1991). The impact of hybridization deserves special consideration for taxa brought together in artificial sympatry, which is often the case of living collections, as it can compromise the genetic makeup, integrity and value of ex situ living collections if allowed to interbreed and reproduce (Maunder et al. 2004). Oaks, in particular, are a concern because of their tendency to hybridize making them vulnerable to “genetic contamination” in ex situ living collections (Maunder et al. 2004).

The detrimental effects of inbreeding depression can be countered with the introduction of just a few individuals (Tallmon, Luikart & Waples 2004). However, the benefits of reducing these effects must be weighed against the potentially negative impact of outbreeding depression (Edmands 2007). Outbreeding depression is manifested as a fitness decline resulting from hybridization between genetically 16

differentiated taxa (Rieseberg 1991). A reduction in fitness occurs through the disruption of local adaptation, underdominance or epistatic interactions (Edmands 2007). Edmands (2007) suggests that the detrimental effects of outbreeding depression are just as severe as inbreeding depression.

Genetic swamping is the increase in frequency of an introduced genotype or allele that may lead to replacement of local genotypes and can be caused by a numerical and/or fitness advantage (Hufford, Mazer 2003). Genetic swamping is reportedly attributed to the rapid invasion of Phragmites australis (Cav.) Trin. ex Steudel due to the introduction of the Eurasian genotype into North American native populations (Saltonstall 2002). In other cases, genetic swamping has been shown to put endangered species at greater risk of extinction (Rieseberg 1991).

The negative effects of intra- and interspecific hybridization through outbreeding depression and genetic swamping highlight the need to be sensitive to sampling protocols and provenance data when building an ex situ living collection.

2.3.3 Quantitative Genetics

Conservation programs designed around findings of neutral molecular genetic studies are unlikely to be successful because they may fail to target populations that contain important adaptive genetic variation (Kramer, Havens 2009). Despite this, research on neutral genetic variation dominates the literature, especially for endangered plants, likely owing to labor, time and cost associated with common garden experiments that are necessary for evaluating quantitative genetic variation (Kramer, Havens 2009; Reed, Frankham 2001).

The work of Clausen, Keck and Hiesey in the 1940’s demonstrated geographical and altitudinal adaptation in some plant species and further showed reduced viability and mortality in plants moved from their native environment (Clausen, Keck & Hiesey 1941). Their work and the work of others (Joshi et al. 2001; Dudley 1996; Sambatti, Rice 2006) demonstrate that local adaptation plays an important role in the performance of plants and the potential detrimental effects of moving plants to a site ex situ (Montalvo, Ellstrand 2001). Furthermore, the use of non-local plants for augmentation could introduce maladapted genes to the detriment of the existing population and reduce the survivability of the introduced plants (Vitt, Havens 2004).

Ex situ collections must seek to capture the most adaptive genetic diversity possible by sampling from a wide range of individuals (Vitt, Havens 2004) across a diversity of microhabitats across time (Guerrant, Havens & Maunder 2004). Transferring collections to an ex situ site has particular challenges since the new environment is likely quite different from the plant’s native habitat. Once in a new environment, plants may experience maladaptation, or the displacement of a phenotypic distribution for a given trait from the optimal phenotypic distribution for the new environment (Husband, Campbell 2004). This may have a short-term impact on survival and reproduction, and long-term impact on population growth and persistence, determined by how well the plants respond to the new environment (Husband, Campbell 2004). Selection pressures in the new environment will cause a genetic change in the living collection over time, by favoring adapted genotypes over maladapted ones (Husband, Campbell 2004).

In order for ex situ collections to contribute to the long-term persistence of species in the wild, they must maintain high levels of genetic diversity in well- documented, representative collections. Molecular and quantitative genetic studies to inform conservation decisions are rarely feasible prior to the establishment of an ex situ collection. This has prompted the development of sampling guidelines for building ex situ collections based on scientific literature (Guerrant, Havens & Maunder 2004; Falk, Holsinger 1991; Brown, Marshall 1995). These publications are useful tools when genetic information about a species is unavailable.

Chapter 3

SURVEY OF EX SITU LIVING COLLECTIONS OF NORTH AMERICAN NATIVE OAK TAXA

3.1 Methods

A survey of ex situ living collections of 97 oak taxa native to North America north of Mexico (herein referred to as North America) was conducted using BGCI’s PlantSearch database (BGCI 2011) to determine the representation of North American oaks of conservation concern in living collections. Data was obtained from the PlantSearch database on December 13, 2011. The Flora of North America (FNA) was used for nomenclature (Missouri Botanical Garden & Harvard University

Herbaria 2008) and NatureServe’s conservation status ranks were used (Table 1) (NatureServe 2010). Ninety-six taxa and infra-specific taxa recognized by the FNA and Quercus cedrosensis C.H. Mull, an imperiled species not treated in FNA, were included in the survey. Quercus robur L., a non-native, naturalized species treated in FNA was not included in the survey. Infraspecific taxa recognized by NatureServe but not FNA were recognized at the species level here.

Table 1. NatureServe conservation status ranks.

Rank Description G1 Critically imperiled - At very high risk of extinction due to extreme rarity (often 5 or fewer populations), very steep declines, or other factors. G2 Imperiled - At high risk of extinction or elimination due to very restricted range, very few populations, steep declines, or other factors. G3 Vulnerable - At moderate risk of extinction or elimination due to a restricted range, relatively few populations, recent and widespread declines, or other factors. G4 Apparently secure - Uncommon but not rare; some cause for long-term concern due to declines or other factors. G5 Secure - Common; widespread and abundant. NR Global rank not yet assessed. G#G# Range rank indicates the range of uncertainty about the exact status of a taxon. Adapted from (NatureServe 2010)

A chi-square test for homogeneity was used to test the null hypothesis that the conservation status rank distribution of North American native oak taxa (null distribution) was equal to that of ex situ living collections. For this comparison, range ranks for 199 records2 (6%) (14 species and infra-specific taxa) were assigned to the lower end of the range (e.g. G2G4 assigned to G2) (see Appendix A for taxon list and adjusted ranks).

2 A record is the presence of a single Quercus taxon within a collection and may include multiple accessions and/or individuals.

3.2 Results

Of 344 botanic gardens growing oak taxa world-wide, 277 in 36 countries were found to grow taxa native to North America (Appendix B). Ninety-one of 97 taxa were grown in botanic gardens with six taxa not represented: Q. robusta (G1), Q. tardifolia (G1), Q. carmenensis (G2), Q. similis (G4) and Q. ajoensis (G2G4)3, Q. viminea (G4G5)2. Of the 3192 oak records, 2058 (64%) were grown in botanic gardens in their country of origin.

There was a significant difference in the representation of NatureServe ranked North American native oak taxa compared to that in gardens (P<0.0001 by Fisher’s exact test) (Table 2). Expected values from the contingency table were higher for G1, G2, G3, G4 and GNR ranked taxa and lower for G5 ranked taxa. Just 67 of 3192 records (2%) represented critically imperiled and imperiled taxa, while this group made up 11% of the flora. By contrast, 2622 of 3192 records (82%) represented secure taxa (G5), while this group represented just 51% of the flora.

3 Range rank indicates the range of uncertainty about the exact status of a taxon.

Table 2. Representation of NatureServe ranked North American native oak taxa versus North American native oak taxa in living collections.

North No. North NatureServe American oak American oak conservation status % % taxa in each in living rank rank collections G1 6 6.2 60 1.9 G2 5 5.2 7 0.2 G3 11 11.3 142 4.4 G4 23 23.7 303 9.5 G5 49 50.5 2622 82.1 GNR 3 3.1 58 1.8 TOTAL 97 100 3192 100

Chapter 4

DETAILED SURVEY OF EX SITU LIVING COLLECTIONS OF FOUR OAK TAXA OF CONSERVATION CONCERN

4.1 Methods

A survey of four oak taxa of conservation concern, Quercus acerifolia (G1), Q. arkansana (G3), Q. boyntonii (G1) and Q. georgiana (G3) (Table 3), was conducted to investigate the representation of wild-collected plants of known provenance and the number of individuals in each botanic garden living collection. Botanic gardens growing these taxa were identified using a number of sources including BGCI’s PlantSearch database (BGCI 2011), the multisite BG‐BASE search facility maintained by Royal Botanic Garden Edinburgh (Royal Botanic Garden

Edinburgh n.d.), PLANTCOL maintained by The Association of Botanical Gardens and Arboreta in Belgium (The Association of Botanical Gardens and Arboreta n.d.), data from the Global Survey of Ex situ Oak Collections (BGCI 2009), and the International Oak Society’s Oak Names Checklist (Trehane 2011). Gardens growing the aforementioned four taxa were contacted individually to gain information about the number of accessions, number of individuals and provenance. For records lacking data including number of accessions, number of individuals and/or provenance data, a single accession of a single individual of unknown origin was assumed.

Table 3. Summary of four oak taxa of conservation concern, status in the wild, distribution, conservation status ranks, and habitat descriptions.

Common Nature IUCN Species Status in wild States Habitat name -Serve RedList Open woods, 6 populations, ledges and cliff Quercus Mapleleaf only a few 100's Arkansas G1 Endangered edges, and the acerifolia Oak of individuals rocky edges of each. plateaus.

Sandy or sandy Alabama, Over 200 clay uplands or Arkansas, populations, upper ravine Quercus Arkansas Florida, most healthy G3 Vulnerable slopes near arkansana Oak Georgia, populations are heads of streams Louisiana, in FL. in deciduous Texas woods.

Only few Shrub layer of populations in pine-oak forests one county in on deep sandy Boynton's Quercus Alabama, likely Alabama, Critically soils in creek Sand Post G1 boyntonii extirpated in Texas Endangered bottoms. Oak Texas. Once Possibly also considered shallower soils of extinct. upland prairies.

Scattered in Alabama, Alabama, more Granite outcrops; Quercus Georgia Georgia, populations in 14 G3 Endangered dry slopes over georgiana Oak North counties in granite Carolina Georgia. G1 – critically imperiled; G3 – vulnerable. From (Kramer, Pence 2012).

4.2 Results

Eighty-seven records (130 accessions, 253 individuals) of Quercus acerifolia, Q. arkansana, Q. boyntonii and Q. georgiana were identified in 52 botanic gardens in 11 countries (Table 4). This was a 43% increase in the number of records for the same species as found in BGCI’s PlantSearch database, indicating the need for botanic gardens to contribute collection data to the PlantSearch database. Of the 87 records, 22 lacked information about the number of accessions. Of the 130 accessions,

29 lacked data about the number of individuals assigned to the accession and 18 lacked data about provenance. Thirty-nine of the 87 records (45%) represented taxa grown in its country of origin (U.S.). Q. acerifolia and Q. boyntonii were represented by a higher percentage of wild collected accessions, (57% and 81% respectively) compared to Q. arkansana and Q. georgiana (39% and 50% respectively) (Figure 1). However, both species had fewer accessions and individuals represented in botanic gardens.

Table 4. Summary of living collections of four oak taxa of conservation concern.

No. of No. of No. of No. of No. of accessions of accessions records accessions individuals horticultural Species of wild (% from (% from (% from or unknown origin U.S.) U.S.) U.S.) source (% of total) (% of total) Q. acerifolia 16 (44) 21 (57) 43 (74) 12 (57) 9 (43) Q. arkansana 29 (24) 41 (29) 45 (27) 16 (39) 25 (61) Q. boyntonii 12 (83) 16 (81) 29 (90) 13 (81) 3 (19) Q. georgiana 30 (50) 52 (52) 133 (75) 26 (50) 26 (50) TOTAL 87 (45) 130 (64) 253 (68) 68 (52) 62 (48)

4.2.1 Quality of Collection Data and Representation of Wild Populations

Q. acerifolia is documented from four localities in Arkansas: Magazine Mountain, Logan County; Porter Mountain, Polk County; Pryor Mountain, Montgomery County; and Sugarloaf Mountain, Sebastian County (Missouri Botanical

Garden & Harvard University Herbaria 2008). Twelve accessions of Q. acerifolia were of wild provenance, while nine were of horticultural or unknown source (Figure 1). Of these, three accessions were documented to the county level and six had more detailed locality information. Two accessions were collected from each of three mountains, while no accessions represented Porter Mountain or Polk County.

Q. arkansana is more widespread, occurring in Alabama, Arkansas,

Florida, Georgia, Louisiana and Texas in small, intermittent populations (Missouri Botanical Garden & Harvard University Herbaria 2008, NatureServe 2011). Sixteen of 41 accessions were of wild provenance. Nine accessions were documented to the

county level and six had more detailed collection data. Of the 14 accessions with documented provenance, seven were from Louisiana, six from Georgia and a single accession was from Arkansas.

Q. boyntonii naturally occurs in five to twelve populations in northeastern Alabama and thought to be extirpated from Texas (NatureServe 2011). Thirteen of 16 accessions were of wild provenance. Three accessions were documented at the county level, two of which were from counties otherwise not collected from. Ten accessions were collected from the same locality.

Q. georgiana occurs in Alabama, Georgia and North Carolina. It is thought to be extirpated from South Carolina (NatureServe 2011) and further inquiries into its status did not support the contrary. Of 52 accessions, 26 were of known wild provenance. Three were documented to the county level and 12 had more detailed documentation. Nine of 12 accessions were from a single population on Stone

Mountain and three were from Pine Mountain.

No. of accessionsNo. 20

0 Q. acerifolia Q. arkansana Q. boyntonii Q. georgiana (43 individuals) (45 individuals) (29 individuals) (133 individuals)

Figure 1. Number of accessions in living collections of four oak taxa of conservation concern. White – horticultural or unknown provenance; light grey – wild collected, state known or unknown provenance; dark grey – wild collected, county known; black – wild collected, detailed provenance.

Chapter 5

MOLECULAR GENETIC DIVERSITY OF QUERCUS GEORGIANA IN WILD POPULATIONS AND EX SITU LIVING COLLECTIONS

5.1 Materials and Methods

5.1.1 Study Species

This study focused on the Quercus georgiana, a rare, multi-stemmed oak found on soil islands and margins of granite outcrops, and dry slopes and knolls in the Piedmont region of Georgia, Alabama, and North Carolina. Historically, the species has been found in South Carolina, however, it is reported to be extirpated from the state (Oldfield et al. 2007). The first record for the species in North Carolina was recorded in 2010 (The Herbarium of the University of North Carolina at Chapel Hill 2005). Q. georgiana is listed as endangered by the International Union for Conservation of Nature (Nixon 1998) and vulnerable (G3) by NatureServe (NatureServe 2011). Threats include the impact from tourism, especially on Stone

Mountain in Georgia, soil erosion, poor regeneration and soil compaction (Nixon 1998). Q. georgiana is an out-crossing, long-lived perennial tree. Seed is consumed by woodpeckers, yellow-bellied sapsucker, tufted titmouse, Carolina wren, deer and small mammals such as squirrels (Tarner 2010). Gene flow among populations is expected to occur through both seed dispersal and wind-mediated pollen movement.

5.1.2 Study Sites

Nine populations of Q. georgiana were sampled from sites in Georgia and Alabama (Figure 2, Table 5). Camp Meeting Rock in Heard County, Georgia and Panola Mountain, on the boundary between Henry County and Rockdale County, Georgia were visited, however no collections were made as Q. georgiana was infrequent and/or trees encountered appeared to be hybrids. Sampling occurred across the known range of the species, which was determined through use of herbarium records, collection data from botanic garden records, and USDA PLANTS Database

(USDA 2012). Populations used in this study were located using detailed collection data from herbarium specimens and living collections. Sampled populations were separated by at least 15 kilometers. Voucher specimens for each site were deposited at Longwood Gardens Herbarium (KEN) and University of Georgia Herbarium (GA).

Figure 2. Quercus georgiana study population sites (dark dots) in Georgia and Alabama. Open dots indicate sites visited but where no collections were made. Cross hatching indicates all known counties where Q. georgiana is reported to be present. Simple hatching shows historical locations (USDA 2012). See Table 5 for population code information.

Table 5. Quercus georgiana study populations.

Pop. Latitude Longitude Locality County State Code °N °W Davidson-Arabia Mountain DeKalb GA AM 33.667 -84.125 Nature Preserve Chattahoochee Bend Coweta GA CB 33.421 -84.962 State Park Concord Road Pike GA CR 33.147 -84.464 Dowdell's Knob, F.D. Harris GA DK 32.840 -84.746 Roosevelt State Park Eden St. Claire AL ED 33.640 -86.369 Moss Rock Preserve Jefferson AL MR 33.384 -86.841 Penton Chambers AL PN 33.026 -85.483 Stone Mountain DeKalb GA SM 33.809 -84.152 Walnut Grove Walton GA WG 33.751 -83.832 Living Collections1 LC 1 Refer to Table 6 for details; Pop. – population.

5.1.3 Collections

Leaves were collected from wild populations in June, 2011. At least 24 individual trees were randomly sampled from each site, and sampled plants were at least five meters apart. GPS coordinates were recorded for each plant. Fresh leaves were stored on ice until they were transferred to a -80°C freezer.

Fresh leaf material was shipped overnight May through June, 2011 from ten gardens in the United States holding 24 individuals of Q. georgiana (Table 6). Leaf material was immediately transferred to a -80°C freezer upon arrival at the Chicago Botanic Garden Laboratories.

Table 6. Living collections of Quercus georgiana within ten U.S. botanic gardens that were used for comparison to wild populations.

Accession Garden City State Accession origin No. Bartlett Tree Research Charlotte NC 2004-624 horticultural source Laboratories Arboretum 2008-0001 horticultural source 93-2058 horticultural source 93-2059 horticultural source 94-2122 horticultural source 94-2123 horticultural source Charles R. Keith Arboretum Chapel Hill NC N/A horticultural source Donald E. Davis Arboretum Auburn AL 2008156 unknown source 2009114A Harris Co., GA 2009114B Harris Co., GA The Morton Arboretum Lisle IL 49-2002*1 Stone Mountain, GA 49-2002*3 Stone Mountain, GA 49-2002*4 Stone Mountain, GA 40-2003*5 Stone Mountain, GA Sarah P. Duke Gardens Durham NC unknown Gwinnett Co., GA Schoenike Arboretum at South Carolina Botanical Clemson SC SCBG.13M3 unknown source Garden SCBG.13N2 unknown source Starhill Forest Arboretum of Petersburg IL 1994-030 Stone Mountain, GA Illinois College 1994-029 Pine Mountain, GA State Arboretum of Virginia Boyce VA 1532 9253.41 Stone Mountain, GA The State Botanical Garden wild collected, Athens GA 98-0221.1 of Georgia unknown location wild collected, 98-0221.2 unknown location wild collected, 98-0221.3 unknown location

Taltree Arboretum & Valparaiso IN 2007.036 Stone Mountain, GA Gardens

5.1.4 Molecular Data

Genomic DNA was extracted from fresh leaf tissue stored at -80°C using Qiagen miniprep extraction protocol. Polymorphic nuclear microsatellite markers were identified based on their successful use in Q. rubra L. and other red-oak group species

(Table 7) (Aldrich et al. 2002; Aldrich et al. 2003b). DNA was amplified using a polymerase chain reaction (PCR) using M13-labelled forward primers. Genotypes were scored using a CEQ 8000 Genetic Analysis System and CEQ Fragment Analysis software (Beckman Coulter).

Table 7. Summary of eight oak microsatellite loci derived from both wild and living collections of Quercus georgiana.

Locus Repeat Primer Sequence (5’-3’) Size range (bp) F: ATATGGTCCCGATTAATTC QU.i21 (GA) 189-219 16 R: GGCAACATTCAAATGTATCTA F: TTTAGCATCACATTTCCGTT QU.M07 (GA) 201-229 19 R: TTTTGTGTCATCCGGTATTA F: CCAATCCACCCTTCCAAGTTCC QU.F02 (GA) 165-205 15 R: TGGTTGTTTTGCTTTATTCAGCC F: TTAGCTTTTACGCAGTGTCG QU.C19 (GA) 234-264 18 R: CGGCTTCGGTTTCGTC F: TGCCATCCCTATACACAACCA QU.E09 (GA) 192-232 16 G: CCTCCATCACAAAGTTGCC F: TCCCAATCGATGTTTGATAAGG QU.C08 (GA) 271-309 29 R: GGGCTCTTGAGAGGATGTAGG F: GCCAACAAATTTAACTATCCAT QU.G07 (GA) 224-244 23 R: TAACTGGGCTAGATAATCAG F: GCTTGGGCTTGTTCCTACT QU.H14 (GA) 279-325 22 R: CAACACTTCTCATGGATTAGAGA

5.1.5 Analysis

Microsatellite genotype data was formatted for analysis using CREATE (Coombs, Letcher & Nislow 2008) and CONVERT (Glaubitz 2004). Descriptive parameters for loci were estimated using FSTAT (2.9.3.2) (Goudet 2001) including: A, total number of alleles; HE, expected heterozygosity; HO, observed heterozygosity; and

Weir and Cockerham’s (Weir, Cockerham 1984) estimate of Wright’s F-statistic FIS (f; within population inbreeding coefficient). Descriptive parameters for each population were calculated in GDA (Lewis, Zaykin 2002) including: P, proportion of polymorphic loci; n, mean sample size; A, mean number of alleles per locus; AP, total number of private alleles; HE, expected heterozygosity; HO, observed heterozygosity and Weir

and Cockerham’s estimate of Wright’s F-statistic FIS (f; within population inbreeding coefficient). Departures from Hardy Weinberg equilibrium for each locus and population were tested using exact Hardy Weinberg test in GENEPOP (Haldane

1954). Pairwise comparisons of population differentiation in FST ( ; correlation of gene frequencies) were calculated for all populations using FSTAT.

Several techniques were used to analyze population genetic structure. To partition genetic variation among and within populations, analysis of molecular variance (AMOVA) was carried out in GENALEX (Peakall, Smouse 2006). Significance for each F-statistic was carried out through 999 permutations. Geodesic distance between populations was measured in kilometers using ArcMap (ESRI 1999- 2010). Isolation by distance (Slatkin 1993) was measured by computing a regression of linearized genetic distance (FST/(1-FST)) to ln(distance) in GENEPOP using Mantel (Mantel 1967) tests (104 permutations). To visualize genetic distance between populations, Nei’s unbiased estimate of genetic distance (Nei 1978) was used for unweighted pair-group clustering based on arithmetic averages (UPGMA) using TFPGA (Miller 1997). Baysian clustering analysis software, STRUCTURE (2.2) (Pritchard, Stephens & Donnelly 2000) was used to infer population structure and assign individuals from both wild and living collections to populations without prior knowledge of assignment. The most likely number of clusters (K) was determined following the techniques described by Evanno et al. (2005). To infer the number of genetic clusters, 20 independent runs were conducted, using a burn-in period of 10,000 runs and collected data for 10,000 iterations.

To compare wild populations and living collections, estimates of mean genetic diversity (HE) and pairwise FST values were calculated in FSTAT. Nei’s 37

unbiased genetic distance was calculated in GENALEX. To determine the genetic variability of wild populations present in living collections, allelic capture was measured by proportion of wild alleles present in living collections.

To assign individuals from living collections to their putative provenance based on molecular marker data, pairwise correlation coefficients were calculated between the probability vector of each individual from living collections and the average of the probability vectors among individuals from each collection site of this study. The probability vectors, which describe the proportional association of an individual to a subpopulation, were estimated using the software STRUCTURE at

K=∆7 (see section 5.2.3).

5.2 Results

5.2.1 Descriptive Statistics of Loci

All eight microsatellite primer pairs amplified consistently and were highly polymorphic at all loci (Table 8). The number of alleles per locus ranged from nine to 20. Significant deviations from expected heterozygosity were observed for the M07, C19 and G07 loci after Bonferroni correction in the direction of heterozygosity deficiency.

Table 8. Summary statistics for Quercus georgiana by locus. Living collections excluded.

Allele No. of Exact HW test Locus N H H f size (bp) alleles E O P-value i21 217 189-219 16 0.777 0.740 0.047 0.1572 M07 214 201-229 14 0.757 0.677 0.104 0.0034* F02 222 165-205 15 0.806 0.820 -0.018 0.8191 C19 216 234-264 15 0.808 0.788 0.025 0.0059* E09 220 192-232 18 0.607 0.590 0.031 0.4582 C08 224 271-309 17 0.807 0.808 -0.001 0.2102 G07 214 224-244 9 0.355 0.326 0.073 0.0036* H14 223 279-325 20 0.835 0.763 0.087 0.0115

HE – expected heterozygosity; Ho – observed heterozygosity; f – Weir and Cockerham’s estimate of within population inbreeding coefficient; HW – Hardy Weinberg; asterisks (*) denote statistical significance after Bonferroni correction.

5.2.2 Descriptive Statistics of Populations

No statistically significant deviation from expected heterozygosity was observed within populations after Bonferroni correction (Table 9). AM and WG harbored the largest number of private alleles, 5 and 4 respectively, MR and PN each harbored 2 private alleles and ED and CR harbored a single private allele.

Table 9. Summary statistics for Quercus georgiana by population.

Mean Mean Proportion Exact HW alleles Private Pop. n sample polymorphic H HO f test per alleles E size loci P-value locus AM 25 24.000 1.000 10.000 5 0.803 0.724 0.100 0.0083 CB 24 23.750 1.000 7.000 0 0.709 0.671 0.055 0.0126 CR 24 23.875 1.000 6.750 1 0.677 0.673 0.006 0.0476 DK 25 24.000 0.875 7.625 0 0.677 0.639 0.056 0.0513 ED 25 24.375 1.000 8.000 1 0.624 0.625 -0.002 0.5554 LC 24 20.750 1.000 9.750 0 0.812 0.783 0.036 0.1846 MR 24 23.875 1.000 7.500 2 0.738 0.708 0.043 0.3847 PN 26 25.750 1.000 7.750 2 0.736 0.704 0.044 0.0334 SM 26 24.625 1.000 8.375 0 0.732 0.698 0.048 0.0529 WG 25 24.500 1.000 9.250 4 0.769 0.758 0.014 0.2615

Mean 25 23.950 0.988 8.213 1.500 0.728 0.698 0.041 -

HE – expected heterozygosity; Ho – observed heterozygosity; f – Weir and Cockerham’s estimate of within population inbreeding coefficient; HW – Hardy Weinberg; No statistical significance difference between expected and observed heterozygosity after Bonferroni correction.

5.2.3 Population Genetic Structure

Results from AMOVA indicated that 93% of genetic variance was partitioned within populations and 7% found among populations (Table 10).

Table 10. Results from Analysis of Molecular Variance (AMOVA) for Quercus georgiana study populations.

Variance Total variation Source of variation Df SS MS P component (%) Among populations 8 113.823 14.228 0.227 7 <0.001 Within populations 439 1285.914 2.929 2.929 93 <0.001 df – degrees of freedom; SS – sum of squares; MS – mean square

Pairwise FST ranged from 0.0072 (AM and LC) to 0.1275 (ED and PN) (Table 11). All pairwise comparisons were significant at P=0.0011 with the exception of two; AM and MR, which was significant at P=0.0144 and SM and MR, which was significant at P=0.0033. Mantel tests did not reveal a strong pattern of isolation by distance (R2 =0.046, P=0.21) (Figure 3).

Table 11. Pairwise genetica and geographic distanceb for Quercus georgiana populations. Living collections represented by ten botanic gardens in various locations and excluded from pairwise geographic distance.

AM CB CR DK ED LC MR PN SM WG AM 82.34 65.71 108.42 208.11 254.23 144.98 15.935 28.75 CB 0.0432 55.41 67.453 132.93 174.89 65.38 86.59 111.09 CR 0.0472 0.1003 43.047 185.41 223.02 96.06 78.925 89.13 DK 0.0681 0.0732 0.0653 175.27 204.61 71.94 120.84 132.13 ED 0.0749 0.0801 0.1039 0.0684 52.25 106.87 206.3 235.51 LC 0.0072 0.0364 0.0511 0.0424 0.0634 MR 0.0591* 0.0872 0.0813 0.0525 0.0735 0.0455 132.69 254.01 282.35 PN 0.0745 0.0861 0.1137 0.1153 0.1275 0.0447 0.1249 151.17 173.37 SM 0.0471 0.0543 0.0855 0.0476 0.0636 0.0259 0.0648* 0.0759 30.33 WG 0.0403 0.0609 0.0819 0.0572 0.0949 0.0319 0.0691 0.0654 0.0377 a b lower left matix: pairwise FST values for each population comparison; upper right matrix: pairwise geographic distance in kilometers between each population; all pairwise FST values significant at P=0.05 after Bonferroni correction, except the asterisks (*) which denote insignificance after Bonferroni correction.

0.16

0.14

0.12

0.10 ST

0.08

0.06 Linearized F Linearized

0.04

0.02

0.00 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 ln (distance)

Figure 3. Linearized genetic distance (FST/1-FST) versus log-transformed geographic distance (originally recorded in kilometers) for pairwise comparisons of nine populations of Quercus georgiana. While the graph shows a trend of increased genetic distance with geographic distance, this trend was not significant (R2=0.046, P=0.21).

UPGMA results (Figure 4) indicated PN had the largest genetic distance from other populations. AM and LC clustered together and had the shortest genetic distance compared to other groups. AM and WG clustered and ED and MR clustered, which was consistent with their geographic proximity. Bayesian analysis via STRUCTURE estimated the number of genetic clusters (K) or populations in the least

possible genetic disequilibrium (Figure 5). The results demonstrated pronounced genetic structure among populations of Q. georgiana. The value of the distribution of true K identified a major peak at ∆K=4 and a minor peak at ∆K=7. However, the clusters identified in the dendrogram appear to be more consistent with the graphical representation of ∆K=7.

Figure 4. Unweighted pair group method with arithmetic mean (UPGMA) showing genetic distance relationships among populations of Quercus georgiana based on Nei’s unbiased genetic distance (Nei 1978).

K=4

Walnut Stone Arabia Concord Chattahoo- Dowdell’s Penton, Eden, Moss Rock, Living Grove, Mountain, Mountain, Road, chee Bend, Knob, AL AL AL Collections GA GA GA GA GA GA 1 2 3 4 K=7

Walnut Stone Arabia Concord Chattahoo- Dowdell’s Penton, Eden, Moss Rock, Living Grove, Mountain, Mountain, Road, chee Bend, Knob, AL AL AL Collections GA GA GA GA GA GA 1 2 3 4 5 6 7

Figure 5. Identified genetic clusters and Bayesian admixture proportions (Q) for individual plants (represented by each vertical bar) and populations of Quercus georgiana. Results shown for models of structure at two levels: top K=4; bottom K=7.

624

029 030

- -

0001

Duke

2059 2122 2058 2123

- - - -

0221.3

0221.1 0221.2

2002*3 2003*5 2002*1 2002*4

- -

- - - -

SCBG.13N2

SCBG.13M3

Charles Keith Charles

SBC SBC 98

Davis 2008156Davis

SBG 98SBG 98SBG

Davis 2009114BDavis

Davis 2009114ADavis

Bartlett 93Bartlett 94Bartlett Bartlett Bartlett 93 Bartlett 94

Taltree 2007.036 Taltree

Starhill 1994Starhill Starhill 1994Starhill

Bartlett 2004Bartlett

SAV 15329253.41 SAV

Bartlett Bartlett 2008

Morton 49 Morton 40 Morton Morton 49Morton Morton 49Morton

Figure 6. Identified genetic clusters and Bayesian admixture proportions (Q) for individual plants (represented by each vertical bar) in living collections of Quercus georgiana. Results shown for model of structure at K=7. Refer to Table 6 for provenance details for each accession.

In comparing wild populations to the living collections, measures of

genetic variation were similar; gene diversity (HE) was 0.771 for wild populations and 0.812 for living collections and loci for living collections were highly polymorphic (Table 12). One hundred twenty-four alleles were recovered in wild populations, 15 of which were private alleles (private to single populations) not recovered in living

collections. Seventy-eight alleles were recovered in living collections, representing 63% of the 124 alleles in wild populations. Of the 124 alleles recovered in wild populations, 79 were low frequency alleles (<0.05). Thirty-three of the 79 (42%) low

frequency alleles were recovered in living collections. Mean alleles per locus was 9.75 for living collections compared to 15.5 for wild populations. Nei’s unbiased genetic

distance was 0.019. Pairwise FST between living collections and wild populations was 46

0.0057 (Table 12) and pairwise FST between living collections and each population was low to moderate, ranging from 0.0072 (LC and AM) up to 0.0634 (LC and ED) (Table 11).

Table 12. Summary statistics for comparisons between wild populations and and living collections of Quercus georgiana

Living Wild

Collections Populations Sample size 24 224 Proportion polymorphic loci 1.00 1.00 Alleles recovered 78 124 Private alleles1 0 15 Mean alleles per locus 9.75 15.5

Mean gene diversity (HE) 0.812 0.771 Nei’s unbiased genetic distance 0.019

Pairwise FST 0.0057 1 refers to alleles found in single populations

Based on molecular marker inference, 10 of 24 individuals were found to have been putatively collected from Arabia Mountain (Table 12). Of these ten, none had records from gardens indicating their provenance was Arabia Mountain. Instead, two of the ten records indicated they were collected from Stone Mountain and Gwinnett County, sites in close proximity to Arabia Mountain. Another two accessions conflicted with the records kept by gardens. Results indicated they were putatively collected from Arabia Mountain, DeKalb County, while provenance data indicated they were collected in Harris County. The remaining six accessions were either wild

collected or horticultural source and lacked specific records of provenance that would allow for comparison.

Of the remaining 14 accessions, two were not correlated with any population, three were correlated with two populations (Walnut Grove and Stone Mountain; Chattahoochee Bend and Eden; Chattahoochee Bend and Eden respectively), one was correlated with Walnut Grove, one with Stone Mountain, two with Concord Road, one with Dowdell’s Knob, two with Eden, one with Pine

Mountain and none with Penton. Nine of these 14 accessions had provenance data associated with them, seven of which were collected from Stone Mountain. Only two of the nine accessions had correlation coefficients that corresponded with provenance data (both collected from Stone Mountain).

Table 13. Putative assignment of individuals from living collections to Quercus geogiana wild populations. The table shows pairwise correlation coefficients between the probability vectors of subpopulation assignment, comparing those for each individual from living collections (rows in table) versus the average among individuals belonging to each of nine different collection sites (columns in table). High correlations suggest that an individual living collection accession was sampled from the corresponding location; correlation coefficients greater than 0.80 are highlighted. Refer to Table 6 for provenance details for each accession.

Stone Arabia Chata- Dow- Walnut Concord Moss Moun- Moun- hoochee dell's Penton Eden Grove Road Rock tain tain Bend Knob Morton 49-2002*3 0.99 0.82 0.02 -0.18 -0.28 -0.13 -0.19 -0.24 -0.20 Morton 49-2002*1 -0.21 0.36 -0.24 -0.11 0.85 -0.27 -0.06 0.99 -0.19 Morton 49-2002*4 0.50 0.56 -0.57 -0.21 0.17 -0.37 -0.18 0.15 0.40 Morton 40-2003*5 0.67 0.37 0.64 0.35 -0.30 -0.25 -0.32 -0.52 -0.16 Starhill 1994-030 0.00 0.02 0.98 -0.11 -0.27 -0.12 -0.15 -0.15 0.00 SAV 1532 9253.41 0.36 0.81 -0.21 -0.28 0.56 -0.16 -0.25 0.76 -0.30 Taltree 2007.036 -0.28 -0.45 -0.08 0.90 0.31 -0.30 -0.20 -0.22 0.21 Duke 0.50 0.40 0.88 -0.21 -0.45 -0.05 -0.20 -0.31 -0.18 Starhill 1994-029 -0.28 -0.30 -0.20 -0.24 -0.17 -0.22 -0.13 -0.17 0.99 Davis 2009114A -0.07 -0.07 0.95 -0.16 -0.32 -0.07 -0.07 -0.19 0.02 Davis 2009114B -0.06 -0.07 0.93 -0.21 -0.37 -0.07 -0.11 -0.22 0.13 SBG 98-0221.1 -0.18 0.40 -0.21 -0.14 0.82 -0.22 -0.16 0.97 -0.16 SBG 98-0221.2 -0.11 -0.16 0.97 0.20 -0.22 -0.02 -0.20 -0.25 -0.13 SBC 98-0221.3 -0.07 -0.08 0.99 0.04 -0.23 -0.09 -0.17 -0.19 -0.06 Bartlett 2004-624 -0.24 -0.36 0.37 -0.35 -0.30 -0.32 0.69 -0.15 0.25 Bartlett 2008-0001 -0.06 -0.05 0.96 -0.14 -0.31 -0.04 -0.14 -0.18 0.00 Bartlett 93-2058 -0.15 -0.02 0.93 0.17 0.06 -0.17 -0.23 0.06 -0.16 Bartlett 93-2059 -0.26 0.16 -0.04 -0.21 0.67 -0.38 0.51 0.87 -0.35 Bartlett 94-2122 0.99 0.80 -0.01 -0.12 -0.26 -0.12 -0.20 -0.26 -0.21 Bartlett 94-2123 -0.13 -0.23 0.46 -0.11 -0.58 0.81 -0.31 -0.42 -0.27 Charles Keith 0.03 0.02 0.97 -0.14 -0.34 -0.01 -0.13 -0.20 -0.09 Davis 2008156 -0.07 -0.07 0.96 -0.12 -0.30 -0.03 -0.04 -0.16 -0.10 SCBG.13N2 -0.30 -0.42 0.29 0.89 0.34 -0.43 -0.14 -0.14 0.10 SCBG.13M3 -0.14 0.38 -0.35 -0.25 0.63 0.13 -0.13 0.87 -0.34

Chapter 6

PHENOTYPIC DIVERSITY IN QUERCUS GEORGIANA

6.1 Methods

To test for morphological differences among populations, three leaves from eight individuals from each population were digitally scanned; the number of lobes and bristles, leaf length, short lobe length and long lobe length were recorded. Mean differences between characteristics were examined using a one-way analysis of variance (ANOVA) in JMP Pro 9.0.0 statistical analysis software. Leaf length, long lobe length and short lobe length were either square root or log transformed to create a normal distribution of the data. One outlier was removed from the data to satisfy this assumption. Results were reverse transformed for visualization in figures. Tukey- Kramer HSD tests were used to compare study populations that showed significant difference in the morphological measurements.

6.2 Results

There were significant, site-dependent differences for leaf length (F- ratio=6.693, P<0.0001), mean long lobe length (F-ratio=4.040, P=0.0002) and mean short lobe length by population (F-ratio=9.740, P<0.0001) (Figure 7). Despite data transformations, distributions of number of lobes and number of bristles did not meet assumptions of normality and were not further analyzed.

11 A 10

AB 9 ABC

8 BCD BCD BCD CD CD D 7

6 Leaf length 5 Long lobe length

Mean length Mean (cm) Short lobe length 4

3 A A AB AB AB B B AB B 2

A A 1 BC C C C AB C C

0 DK WG AM CR PN SM CB MR ED Population code

Figure 7. Analysis of Variance (ANOVA) and Tukey Kramer results for significant differences between mean leaf length (F-ratio=6.693, P<0.0001), mean long lobe length (F-ratio=4.040, P=0.0002) and mean short lobe length by population (F-ratio=9.740, P<0.0001). Levels not connected by the same letter are significantly different from each other (α=0.05). For population code descriptions refer to Table 5.

Chapter 7

DISCUSSION

7.1 Survey of Ex Situ Living Collections of North American Native Oak Taxa and Four Oaks of Conservation Concern

Botanic gardens are positioned to contribute to ex situ conservation of the world’s disappearing flora. When seed banking and cryopreservation are not an option, living collections can serve as an alternative ex situ conservation option. For living collections to contribute to in situ conservation efforts and the long-term persistence of species in the wild, they must be present in living collections, of known wild provenance and genetically representative of the species.

Despite an urgent call to action, the survey of ex situ North American oak taxa showed that living collections are biased towards taxa that are globally secure and taxa of greatest conservation concern are underrepresented in living collections. Although sample size prevents broad inference on the representation of wild collections of known provenance, it is notable that of the four taxa of conservation concern, more than half were represented by accessions of wild origin rather than accessions of horticultural or unknown source. The value of these living collections, however, is compromised by a lack of provenance data, which is crucial knowledge for breeding and reintroduction programs. Using provenance data as a proxy for the

genetic representativeness of the species, living collections tended to be from a limited number of geographic locations with many populations unrepresented.

The usefulness of ex situ living collections of North American native oak for in situ restoration efforts is currently questionable owing to the lack of representation of the most threatened taxa, and lack of provenance data for wild collected individuals. Constraint in source material is a noticeable trend in plant reintroduction programs. In one review of 50 plant reintroductions, 79% of all plantings were based on propagules from a single source population and 50% of plantings were based on fewer than 10 source individuals (Husband, Campbell 2004). While a number of those reintroductions were deemed successful in the short term, the ability for the populations to persist over long time period is uncertain. In order for ex situ living collections to contribute to the in situ conservation and restoration, this trend must change.

There are many possible reasons for the observed patterns. Traditionally botanic gardens have focused on taxonomic diversity to serve the functions of taxonomic research, teaching and public display (Maunder, Higgens & Culham 2001). The enactment of the U.S. Endangered Species Act in 1973 and the ratification of the Convention on Biological Diversity (CBD) in 1992 were perhaps the greatest stimuli for ex situ conservation (Guerrant, Havens & Maunder 2004). More recently, the Global Strategy for Plant Conservation provided tangible targets for ex situ conservation (UNEP 2002). However, botanic gardens have been slow to respond to this call to action. Numerous publications have demonstrated the poor representation of plants of conservation concern in living collections (Kramer et al. 2011; Sharrock, Jones 2009; Maunder, Higgens & Culham 2001). It is perhaps the deep-rooted 53

tradition in taxonomic research, teaching and display that restrain progress towards the development of ex situ living collections that can contribute to the conservation of species.

Threatened taxa are less likely to be represented in botanic gardens that do not have conservation as part of their mission. A 2005 survey of 167 U.S. botanic gardens identified only 55% of gardens with conservation as part of their mission (Headen 2005). It is expected that taxa of conservation concern are less likely to be found in the 45% of botanic gardens that do not have conservation as part of their mission compared to the 55% that do, however, this remains to be tested. Nevertheless, this represents a large percentage of botanic gardens that may less inclined to contribute to the ex situ conservation of species.

Propagules of threatened taxa are likely a great deal more difficult to obtain compared to common species. Wild collections require permits which may be difficult to acquire, especially for critically imperiled taxa. Additionally, funding is required for collecting trips and the maintenance of added accessions. The ratification of the CBD in 1992 made the movement of plants between countries more rigorously regulated. It requires that access to genetic resources must be on mutually agreed terms between the country seeking access and the source country (UNEP 1992). There is evidence that the bureaucracy, delay, and expense of compliance with access laws has deterred scientists and unintentionally stifled conservation work (ten Kate 2002). These aspects present obstacles for botanic gardens seeking to conserve threatened taxa in gardens.

The survey of ex situ North American oak taxa is limited because it cannot predict the exact number of individuals ex situ living collections. A record is defined here as the presence of a single Quercus taxon within a living collection which may include multiple accessions and/or individuals. For example, the 87 records identified in living collections for the four study oaks corresponded to 252 individual trees. The number of records is not necessarily correlated with the number of accessions and/or number of individuals within living collections. However, data from the survey of four oaks showed a strong relationship between number of records and number of accessions (R2=0.99, P=0.0003) and number of records and number of individuals (R2=0.89, P=0.01). It is expected that a similar pattern would be observed for other taxa.

Finally, other factors which prevent an accurate depiction of the status of North American oaks in botanic gardens include the number of institutions uploading data into BGCI’s PlantSearch database, the dynamic nature of living collections, the quality of the data being uploaded into the database (i.e. based on up-to-date inventories and taxanomic ambiguities) (Kramer et al. 2011).

7.2 Molecular Genetic Diversity of Quercus georgiana in Wild Populations and Ex Situ Living Collections

Quercus georgiana has a relatively restricted global range, raising concerns over the genetic consequences of a small population size such as losses of genetic variation through drift, fewer polymorphic loci and low gene diversities. Indeed, Hamrick et al. (1992) showed that geographic range is the best predictor of allozyme variation in woody plants, with endemic woody plants maintaining less

diversity compared to more widespread species. Additionally, Q. georgiana occurs primarily on granite outcrops, which are discontinuously distributed in the southeast U.S., and have been compared to islands in a sea of Piedmont oak-hickory-pine forest (Wyatt 1984). Theory predicts that these populations may also experience loss of genetic diversity, increased genetic drift within fragments, and increased genetic differentiation among fragments, although empirical evidence demonstrates that different species respond differently to fragmentation (Young, Boyle & Brown 1996,

Kramer et al. 2008). Genetic variation, measured by mean heterozygosity, was overall high in Q. georgiana and is comparable to heterozygosity in other members of the red oak group with more widespread distributions (Aldrich et al. 2003a, Peñaloza-Ramírez et al. 2010). No populations showed evidence for heterozygosity deficiency or excess, suggestive of genetic drift. The majority of genetic variation was distributed within populations (93%), as compared to among populations (7%), and analysis with the program STRUCTURE indicated several subpopulation groups with evidence of admixture between them (Figure 5). Taken together, this suggests that gene flow is occurring or has historically occurred between populations.

Q. georgiana is discontinuously distributed throughout the landscape. It is unknown if or to what extent populations have been further isolated through habitat loss and fragmentation. However, the findings of this study suggest little genetic isolation among populations. This could be due to either historic or contemporary gene flow between populations owing to the connectivity of populations throughout the landscape through pollen and seed dispersal. Evidence for long distance pollen movement in oak is substantial (Craft, Ashley 2007; Streiff et al. 1999; Muir et al. 2004) and it is thought that wind pollinated trees may be less susceptible to the

negative genetic consequences of habitat fragmentation (Craft, Ashley 2007). For example, effective population size may be larger in species with long-distance pollination or dispersal abilities owing to gene flow among multiple fragments (Kramer, Havens 2009). Indeed, discontinuity among populations is thought to increase gene flow (Young, Boyle & Brown 1996; White, Boshier & Powell 2002). These results may support this theory, however, it is unknown if the patterns shown here are artifacts of historic gene flow among populations and the negative genetic consequences of isolation remain to be seen. The genetic effects of fragmentation are thought to be especially difficult to detect in long-lived plants that have been recently fragmented, especially if there is little recruitment (Schall, Leverich 2004).

Knowledge of partitioning of genetic variation within and among populations may give some indication of the risk of outbreeding depression involved in mixing plants from different sources in ex situ living collections. That is, if there is little genetic differentiation between populations there is smaller risk in mixing sources. Conversely, if there is great differentiation between populations, mixing of sources should be cautioned against (Schall, Leverich 2004). Results from AMOVA indicated that the majority of the variation was found within populations (93%). That is, most of the genetic diversity within the species results from difference between individuals rather than between populations (Hamrick et al. 1991). Results from STRUCTURE also identified some gene flow between populations, suggesting that mixing of sources for the purpose of building of ex situ living collections will not result in the detrimental effects associated with mixing genotypes across populations (Schall, Leverich 2004). However, results from STRUCTURE also caution against

universally applying this assumption by demonstrating a conservative estimate of at least four genetic clusters.

While most of the genetic variation was partitioned within populations, some level of population subdivision was apparent. Pairwise FST values indicated significant population differentiation between all but two populations (Table 11). The Bayesian clustering approach implemented by STRUCTURE (Pritchard, Stephens & Donnelly 2000), which searches for underlying genetic structure among populations based on multilocus genotype, found the highest probability for four genetic clusters using the methods described by Evanno et. al. (2005). However, a minor peak was also identified at ∆K=7. Results from STRUCTURE may be limited by the small number of markers used and the small sample size. In simulation studies Evanno et. al. (2005) found that STRUCTURE failed to detect the true K when partial samplings of 20 individuals and five microsatellite markers were used. However, the results presented here, which identify seven genetic clusters, appear to be consistent with the UPGMA tree (Figure 4). For example, Walnut Grove and Stone Mountain clustered together and Dowdell’s Knob and Eden clustered together on both the UPGMA dendrogram and the STRUCTURE diagram.

Genetic differentiation (measured by pairwise FST) and genetic clustering shown by STRUCTURE and UPGMA appeared to be consistent with population proximity in at least some cases. For example, Walnut Grove and Stone Mountain, which are separated by 30 miles, clustered together and pairwise FST showed little genetic differentiation between the two populations. However, it is more difficult to explain low genetic differentiation between Dowdell’s Knob and Eden, which also clustered together, but are separated by 175 miles. Penton was the most genetically 58

differentiated population but not the most isolated by distance. These apparent disparities and lack of evidence for isolation by distance (Figure 4) may be explained be the way distance is measured. For example, in one study, the relationship between pairwise FST and distance was investigated under three alternative models of geographic interpopulation distance in the wetland species, Hibiscus moscheutos L.: Euclidean distance, distance along river drainages or proximity to tidal stream (Kudoh, Whigham 1997). Genetic differences between populations were explained primarily by a gene flow model that accounted for the location of populations relative to the tidal stream because of the species’ dependence on water dispersal of seeds. In this case, combining pairwise estimates of gene flow with information about the landscape allowed for a more accurate assessment of the historical patterns of dispersal (Sork et al. 1999; Sork, Stowe & Hochwender 1993). Taking into account landscape characteristics may help to explain the large genetic distance between Penton and other populations. It is plausible that Penton is isolated, not by distance, but lack of connectivity to other populations due to landscape features. The apparent relationship between Dowdell’s Knob and Eden could be explained by one population serving as the source for an intentional planting. Alternatively, the two populations may have a similar history, both originating from the same population.

Low pairwise FST, and clustering of Arabia Mountain and living collections on the UPGMA dendrogram and STRUCTURE, indicate a relationship between living collections and the population at Arabia Mountain. There are several possible reasons for this. Firstly, of living collections of known provenance, there is a biased towards collections from Stone Mountain (29%). It is possible that there is significant gene flow between Stone Mountain and Arabia Mountain, which are only

16 miles apart; and paternity tests on Stone Mountain would reveal pollen donors from Arabia Mountain or other nearby populations. In considering the provenance data for the living collections, 54% were from an unknown location or source. Evidence for living collection’s relationship with Arabia Mountain may indicate a bias for acquiring accessions close to the major city of Atlanta, which is approximately 20 miles from Arabia Mountain. It may also indicate that the majority of horticultural sourced accessions come from the vicinity. Inference from this data is limited by the small sample size, and it is possible the use of a larger sample could reveal different relationships among populations.

These findings suggest that when building ex situ collections at least four populations should be sampled, representing the four genetic clusters identified in STRUCTURE, in addition to the disjunct populations in North Carolina and populations on South Carolina if they are discovered as they are expected to harbor genetic diversity not found in the populations sampled here. Current ex situ living collections are representative of localities in the vicinity of Stone Mountain and Arabia Mountain. Future collections should target populations in the vicinity of Penton.

7.3 Comparison of Genetic Diversity in Ex Situ Living Collections of Quercus georgiana to Genetic Diversity in Wild Populations

The primary objective of conservation genetics is to preserve the genetic diversity of species such that they are capable of evolving and adapting to environmental change. In order for ex situ collections to be useful for the augmentation and restoration of species in the wild they must maintain high levels of diversity and be genetically representative of the species they seek to conserve.

Genetic diversity within living collections of Quercus georgiana was high as evidenced by mean gene diversity (HE), and proportion of polymorphic loci. Mean alleles per locus in living collections was low compared to the mean for all wild populations and allelic capture in living collections was 63% of the alleles in the wild populations. Fifty eight percent of low frequency alleles (<0.05) in wild populations were not recovered in living collections. These findings are consistent with findings that heterozygosity and proportion polymorphic loci are not particularly sensitive to sampling error. Rather, a reduction in the number of alleles, especially rare alleles, tends to accompany a population bottleneck (Barrett, Kohn 1991; Nei 1975; Spencer, Neigel & Leberg 2000). When considering the ability of living collections to capture alleles of wild populations, living collections sampled here are falling short.

Heterozygosity is thought to provide a good measure of the capability of a population to respond to selection immediately following a bottleneck because it is directly proportional to genetic variance and heritability (Allendorf 1986). However, it has been suggested that allelic richness should be given high priority as a measure of genetic diversity in the context of genetic conservation (Petit, El Mousadik & Pons 1998) because it is thought to be important for the long-term response of a population to selection and survival of populations and species (Allendorf 1986). This highlights the importance for ex situ living collections to not only maintain high levels of genetic diversity, measured by heterozygosity and proportion polymorphism, for example, but also the importance of maintaining allelic richness comparable to wild populations.

However, the maintenance of allelic richness is dependent on sample size. The finite size of botanic gardens and limited resources prevent the maintenance of large living collections in botanic gardens. To capture all allelic diversity is an unrealistic goal.

However, Namoff et al. (2010) demonstrated that 80% of alleles could be captured in just ten individuals of Leucothrinax morrisii (H. Wendl.) C. Lewis and Zona, a long- lived, monoecious palm. They also showed that percent allelic capture leveled off, with no appreciable increase in alleles beyond 30 individuals (85% allelic capture). It should be noted that the wild population studied represented only a subset of the entire range of the species. It is possible that the reason the palm collection captured high levels of allelic diversity was due to a rigorous collection policy that provided guidelines to maximize genetic diversity within the living collection. A direct comparison cannot be made between living collections of Q. georgiana and L. morrisii. However, it expected that collection guidelines based on the findings presented here would significantly increase allelic capture in living collections of Q. georgiana, especially considering living collections appear to have been developed through ad hoc collection and exchange, with little regard for targeting genetic diversity. This provides an opportunity for botanic gardens. The present study could serve as a baseline for comparison against a more rigorous approach to building living collections that target genetic diversity in Q. georgiana.

The maintenance of living collections of known wild provenance is another important aspect of building ex situ collections. The success of reintroduction programs is dependent on a genetically appropriate stock, such that genetic swamping, outbreeding depression, and maladaptation are minimized (Hufford, Mazer 2003). This can be determined, at least in part, through access to detailed provenance data.

Fifty-four percent of the individuals used for this analysis were of unknown provenance or horticultural source. Though population structure was evident, in some

subpopulations admixture was substantial (Figure 5). This suggests that it would be difficult to assign individuals of unknown provenance to specific subpopulations.

Pairwise correlation coefficients used to assign individuals from living collections to wild populations enabled for comparison between population assignment and provenance data associated with each accession. Eleven of the 24 accessions had detailed provenance data, of which two had corresponding population assignments and provenance data. Nine accessions with provenance data did not have matching population assignments and provenance data. The remaining thirteen accessions did not have detailed provenance data that enabled for comparison. There are a number of possible reasons for these seeming incongruities. There may not be enough power in the statistics to assign plants correctly given the small number of loci used. Additionally, some loci did not amplify well in living collections preventing their use in the analysis. It is also possible that collection records for some gardens are not accurate. Finally, it is possible that the pollen donor for accessions grown in gardens was from a different population than where the acorn was collected. Alternatively, the pollen donor may be another species such as Q. nigra, which is known to hybridize with Q. georgiana. These scenarios would result in different genetic profiles than expected for a particular population and skew the results of the probability vectors for subpopulation assignment. These findings highlight the importance of record keeping in gardens, as population assignment is difficult using the technique presented here.

7.4 Phenotypic Diversity in Quercus georgiana

Molecular markers are useful for inferring information about hybridization, genetic diversity patterns, inbreeding and gene flow within and among populations (Kramer, Havens 2009), however, they do not serve as a good proxy for quantitative trait variation (Lynch 1996). For example, in one study of a wind- pollinated outcrossing tree, Pinus sylvestris L., neutral genetic markers showed little differentiation between northern and southern populations of the species, however, adaptive variation, measured by the date of bud set in first-year seedlings, was significantly different between the two populations (Karhu et al. 1996). To explore the potential presence of quantitative trait variation in Q. georgiana, leaf morphology was measured across populations. While this study was limited in its ability to detect local adaptation because it does not involve a common garden study, it is highlighted here to demonstrate that populations of Q. georgiana potentially harbor adaptive variation, though most neutral genetic variation was partitioned within populations. Despite the outcrossing nature of oak, evidence for local adaptive variation has been demonstrated on a very small geographic scale. Sork et al. (1993) found that red oak (Quercus rubra L.) showed evidence for adaptive variation as measured by herbivory, on a four- hectare plot. Oaks were reciprocally transplanted from adjacent subpopulations on north-, southwest- and west-facing slopes. Seedlings showed the least herbivore damage when planted on the site of the maternal plants.

Results shown here demonstrated that potentially adaptive variation does exist among populations of Q. georgiana, as measured by leaf morphology. The F- ratio values showed a relatively low but significant correlation between the three leaf morphology measurements and site. Some populations stood out as being significantly

different than others. Dowdell’s Knob, for example, had significantly different mean leaf length compared to all populations of Q. georgiana. Mean long lobe length and mean short lobe length was significantly different from all populations except one. It is also notable that all three leaf morphology measurements were significantly different in Dowdell’s Knob compared to Eden, though these populations clustered together on the UPGMA dendrogram and STRUCTURE diagram, and pairwise Fst was relatively low. This suggests that though there is relatively little differentiation in neutral genetic variation between the two populations, important adaptive genetic variation, as measured by leaf morphology, may be significant. It is possible that the difference observed in the leaves at Dowdell’s Knob is entirely due to environmental factors at the site. Indeed, this population grew on Hollis quartzite, a metamorphic rock, as opposed to granite, an igneous rock, on which many of the other populations were growing. It is possible that this feature alone accounts for the difference in leaf morphology. Common garden experiments should be used to determine the degree to which quantitative measures, such as leaf morphology, are site dependent in this species. However, this study provides at least some indication that potentially quantitative adaptive variation is present in the species. The reliance on neutral genetic studies for making conservation decisions is cautioned against (Kramer, Havens 2009) and thus these findings are intended to provide some insight into the population genetic structure of Q. georgiana with respect to quantitative variation. Additional research on the quantitative genetics of the species is needed as other factors such as phenotypic plasticity and environmental conditions are expected to play a role in the variation seen between populations. In the absence of a common garden experiment, this data is limiting.

7.5 Limitations of the Study and Future Research

Many of the limitations of this study have been discussed throughout the text; however, limitations in the use of microsatellites are discussed herein. While there are numerous advantages to using microsatellites, there are also disadvantages. When using size-based identification of alleles, some allelic diversity may not be captured because though two alleles may be the same size, they may have different lineages (homoplasy) (Selkoe, Toonen 2006). This can be partially overcome by sequencing alleles however this was not practical for this study.

Another potential problem is allele scoring error which may be attributed to poor amplification, misprinting an artifact peak as a true microsatellite allele, incorrect interpretation of stutter patterns or artifact peaks (Selkoe, Toonen 2006). Every effort was made to avoid such errors by re-genotyping poorly amplified alleles or alleles that had stutter patterns. Where alleles could not be scored with confidence, they were not included in the dataset.

Heterozygote deficiency in loci can be attributed to null alleles or large allelic dropout. Null alleles are alleles that fail to amplify during PCR due to a mutation in the primer region or when PCR conditions are not ideal for amplification (Selkoe, Toonen 2006). Null alleles can be difficult to detect because mutations may not occur in all populations or they may occur only in rare alleles. One indication of the presence of a null allele is if individuals fail to amplify at one locus, where others amplify normally. Large allelic dropout occurs when a large allele in a heterozygote fails to amplify as well as a shorter allele, preventing the detection of the longer allele (Selkoe, Toonen 2006). Micro-Checker software was used to detect null alleles, allelic dropout and stutter patterns (Van Oosterhout et al. 2004). Some data suggested null 66

alleles were present due to homozygote excess, however, these occurred in the same population (AM), across three loci and is thought to be due to genetic structuring within the population.

Linkage disequilibrium, or gametic disequilibrium, occurs when two loci are very close together on a chromosome and as a result they may not assort independently and will be transmitted to offspring as a pair. This can also happen when two loci are functionally linked, though this is not common in microsatellites

(Selkoe, Toonen 2006). Linkage disequilibrium increases the likelihood of a Type I error, but can be avoided through tests using a number of genetic-based software. Data used in this study was checked for linkage disequilibrium using GENEPOP (Raymond, Rousset 1995). Evidence for linkage disequilibrium was found in three pairs of loci.

Finally, Mendelian inheritance must be assumed for population genetic analysis. While the majority of studies have shown that microsatellites have Mendelian inheritance, there is some evidence for microsatellites demonstrating non-Mendelian inheritance (Selkoe, Toonen 2006). However, the only way to test for Mendelian inheritance is through controlled crosses that were not possible in this study due to time constraints. Despite these caveats, and those discussed previously, this research provides some useful insight into ex situ oak conservation at botanic gardens and the population genetic structure of a rare oak with a restricted range.

While this study focuses on oaks native to North America north of

Mexico, there is a need to focus on oak conservation on a global scale, especially in Mexico, the center of diversity for the genus, with approximately 160 taxa, 109 of which are endemic to the country (Oldfield et al. 2007).

There is an apparent need to expand ex situ conservation options for oak. Micropropagation and cryopreservation protocols have been developed primarily for oaks of economic importance. However, similar technology protocols must be developed for rare oaks for conservation, in addition to building ex situ living collections.

Botanic gardens have a tremendous opportunity to contribute to the conservation of oaks through the development of propagation protocols, reintroduction trials, phenology studies and reproductive biology studies. There is also an opportunity for botanic gardens to contribute to the understanding of adaptive genetic variation. Quantitative variation studies within common garden experiments are a natural fit for botanic gardens.

This thesis focuses on the genetic aspects of ex situ plant conservation in botanic gardens. However, numerous other factors contribute to the success of ex situ living collections and the reintroduction of species, including seed collection and extinction risk (Menges, Guerrant & Hamze 2004), sample decline (Guerrant Jr, Fiedler 2004) and demographic considerations (Lande 1988), for example.

7.6 Conclusions

This research originally set out to determine the capacity of botanic gardens to support in situ conservation of oaks by investigating ex situ living collections of North American oak taxa in botanic gardens. Findings here show that there is a bias in ex situ living collections towards oak taxa that are of least conservation concern. Living collections of the four oak species of conservation

concern were fairly well represented by accessions of wild provenance, however, lack of detailed provenance data compromised the conservation value of the living collections.

Q. georgiana was used as a case study to examine population structure and determine whether genetic diversity in botanic garden represents genetic diversity in the wild though use of microsatellites. Genetic variation was partitioned primarily within wild populations of Q. georgiana; however, other measures demonstrated population structure as measured by both molecular markers and quantitative traits. Ex situ living collections were genetically representative of the species in terms of heterozygosity and proportion polymorphism, however allelic capture was poor, especially for rare alleles. It is anticipated that this research may be useful for helping practitioners make decisions about building ex situ living collections of oaks for the conservation of threatened species. The present study could serve as a baseline for comparison against a more rigorous approach to building living collections that target genetic diversity in Q. georgiana.

REFERENCES

Aldrich, P.R., C.H. Michler, W. Sun, and J. Romero‐Severson. 2002. Microsatellite markers for northern red oak (Fagaceae: Quercus rubra). Molecular Ecology Notes. 2(4):472-474.

Aldrich, P.R., G.R. Parker, C.H. Michler, and J. Romero-Severson. 2003a. Whole-tree silvic identifications and the microsatellite genetic structure of a red oak species complex in an Indiana old-growth forest. Canadian Journal of Forest Research. 33(11):2228-2237.

Aldrich, P.R., M. Jagtap, C.H. Michler, and J. Romero-Severson. 2003b. Amplification of North American red oak microsatellite markers in European white oaks and Chinese chestnut. Silvae Genetica. 52(3-4):176-179.

Allendorf, F.W. 1986. Genetic drift and the loss of alleles versus heterozygosity. Zoo Biology. 5(2):181-190.

Barrett, R.D.H. and D. Schluter. 2008. Adaptation from standing genetic variation. Trends in Ecology and Evolution. 23(1): 38-44.

Barrett, S.C.H. and J.R. Kohn. 1991. Genetic and evolutionary consequences of small population size in plants: implications for conservation, p. 3-37. In: D. Falk, and K.E. Holsinger (eds.). Genetics and Conservation of Rare Plants. Oxford University Press, New York, N.Y.

Bergquist, J. 2009. The Development of a Natural Disaster Planning Template for use in Plant Collections Management. University of Delaware. Newark, Delaware. MSc. Thesis.

Berjak, P. and N.W. Pammenter. 2008. From Avicennia to Zizania: seed recalcitrance in perspective. Annals of Botany. 101(2):213-228.

BGCI. 2012. GardenSearch online database. 1 April 2012. .

BGCI. 2011. PlantSearch online database. 13 December 2011.

BGCI. 2009. Global survey of ex situ oak collections. Botanical Gardens Conservation International. London, U.K.

Brown, A.H.D., and A. R. Marshall. 1995. A basic sampling strategy: theory and practice, collecting plant genetic diversity: technical guidelines, p. 75-91. In: L. Guarino, V. Ramanatha Rao., R.Reid. (eds.). Collecting plant genetic diversity. Technical guidelines. CAB International Publishing. Wallingford, U.K.

Pacific Southwest Research Station, Forest Service. U.S. Department of Agriculture. 2002. Proc. of the fifth symposium on oak woodlands: oaks in California’s changing landscape. Albany, California. 22-25 Oct. 2001.

Center for Plant Conservation. n.d. Mission. 4 February 2012.

Charlesworth, D., and J.H. Willis. 2009. The genetics of inbreeding depression. Nature Reviews: Genetics. 10:783-796.

Clausen, J., D.D. Keck, and W.M. Hiesey. 1941. Regional differentiation in plant species. The American Naturalist. 75(758)231-250.

USDA Forest Service. Rocky Mountain Research Station. 2003. National Proc., forestry and conservation nursery associations. Ogden, Utah. 2003.

Cole, 2003. Genetic variation in rare and common plants. Annual review of ecology, evolution, and systematics. 34:213-237.

Coombs, J., B. Letcher, and K. Nislow. 2008. CREATE: a software to create input files from diploid genotypic data for 52 genetic software programs. Molecular Ecology Resources. 8(3):578-580.

Craft, K.J., and M.V. Ashley. 2007. Landscape genetic structure of bur oak (Quercus macrocarpa) savannas in Illinois. Forest Ecology and Management. 239(1-3):13- 20.

Dickie, J.B. and H.W. Pritchard. 2002. Systematic and evolutionary aspects of desiccation tolerance in seeds, p. 239-259. In: M. Black and H.W. Pritchard (eds.). Desiccation and survival in plants: Drying without dying. CABI Publishing. Wallingford, U.K.

Dudley, S.A. 1996. The response to differing selection on plant physiological traits: evidence for local adaptation. Evolution. 50(1):103-110.

Edmands, S. 2007. Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Molecular Ecology. 16(3):463-475.

Ellstrand, N.C. and D.R. Elam. 1993. Population genetic consequences of small population size: implications for plant conservation. Annual Review of Ecology and Systematics. 24:217-242.

Engelmann, F. 2004. Plant cryopreservation: progress and prospects. In Vitro Cellular and Developmental Biology-Plant. 40(5):427-433.

Engelmann, F. and J. Engels. 2002. Technologies and strategies for ex situ conservation. Managing Plant Genetic Diversity, p. 89-103. In: J. Engels, V. Ramanatha Rao, A.H.D. Brown, and M.T. Jackson (eds.). CAB International Wallingford. UK.

ESRI. 1999-2010. ArcMap. Version 10.0 edn. Environmental Systems Research Institute.

Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular ecology. 14(8):2611-2620.

Falk, D.A., and K.E. Holsinger. 1991. Genetics and conservation of rare plants. Oxford University Press. New York. N.Y.

Fay, M.F. 1992. Conservation of rare and endangered plants using in vitro methods. In Vitro Cellular and Developmental Biology-Plant. 28(1):1-4.

Fernandes, P., E. Rodriguez, G. Pinto, I. Roldán-Ruiz, M. De Loose, and C. Santos. 2008. Cryopreservation of Quercus suber somatic embryos by encapsulation- dehydration and evaluation of genetic stability. Tree Physiology. 28(12):1841- 1850.

Frankham, R. 2005. Genetics and extinction. Biological Conservation. 126(2):131- 140.

Franklin, J.F. 1993. Preserving biodiversity: species, ecosystems, or landscapes?. Ecological Applications. 3(2):202-205.

Fuller, B.J. 2004. Cryoprotectants: the essential antifreezes to protect life in the frozen state. Cryoletters. 25(6):375-388.

Gapinski, A. 2010. Preparing plant collections for biological invasion: a study of the effect of emerald ash borer (Agrilus planipennnis Fairmaire) through case study analysis. University of Delaware. Newark, Delaware. MSc. thesis.

Gemmill, C.E.C., T.A. Ranker, D. Ragone, S.P. Perlman, and K.R. Wood. 1998. Conservation genetics of the endangered endemic Hawaiian genus Brighamia (Campanulaceae). American Journal of Botany. 85(4):528-528.

George, E.F., M.A. Hall, and G.J.D. Klerk. 2008. Plant tissue culture procedure- background, p. 1-28. In: E.F. George, M.A. Hall, G. De Klerk (eds.). Plant propagation by tissue culture. 3rd Ed. Springer. Dordrecht, Holland.

Glaubitz. J.C. 2004. CONVERT: a user friendly program to reformat diploid genotypic data for commonly used population genetic software packages. Molecular Ecology Notes. 4:309-310.

Gomez-Mendoza, L., and L. Arriaga. 2007. Modeling the effect of climate change on the distribution of oak and pine species of Mexico. Conservation Biology. 21(6):1545-1555.

Gonzalez-Benito, M.E. and C. Martin. 2002. Cryopreservation of Quercus (Oak) species, p. 312. In: L.E. Towill and Y.P.S. Bajaj (eds.). Cryopreservation of plant germplasm II. Springer. Berlin, Germany.

Gonzalez-Benito, M.E., R.M. Prieto, E. Herradon, and C. Martin. 2002. Cryopreservation of Quercus suber and Quercus ilex embryonic axes: In vitro culture, desiccation and cooling factors. CryoLetters. 23(5):283-290.

Goudet, J. 2001. FSTAT: a program to estimate and test gene diversities and fixation indices (version 2.9.3).

Guerrant, E.O. and P.L. Fiedler. 2004. Accounting for sample decline during ex situ storage and reintroduction, p. 365-385. In: E.O. Guerrant, K. Havens, and M.

Maunder (eds.). Ex situ plant conservation: supporting species survival in the wild. Island Press. Washington, D.C.

Guerrant, E.O., K. Havens, and M. Maunder (eds.). 2004. Ex situ plant conservation: supporting species survival in the wild. Island Press. Washington, D.C.

Haldane, J. B. S., 1954. An exact test for randomness of mating. Journal of Genetics. 52:631-635.

Hamrick, J.L., M.J.W. Godt, and S.L. Sherman-Broyles. 1992. Factors influencing levels of genetic diversity in woody plant species. New Forests. 6(1):95-124.

Hamrick, J., M. Godt, D. Murawski, and M. Loveless. 1991. Correlations between species, genetics and conservation of rare plants, p. 75. In: D. Falk and K.E. Holsinger (eds.). Genetics and Conservation of Rare Plants. Oxford University Press. New York, N.Y.

Hannah, L., J.L. Carr, and A. Lankerani. 1995. Human disturbance and natural habitat: a biome level analysis of a global data set. Biodiversity and Conservation. 4(2):128-155.

Hardwick, K.A., P. Fiedler, L.C. Lee, B. Pavlik, R.J. Hobbs, J. Aronson, M. Bidartondo, E. Black, D. Coates, M.E. Daws, K. Dixon, S. Elliot, K. Ewing, G. Gann, D. Gibbons, J. Gratzfeld, M. Hamilton, D. Hardman, J. Harris, P.M. Holmes, M. Jones, D. Mabberley, A. Mackenzie, C. Magdalena, R. Marrs, W. Milliken, A. Mills, E.N. Lughadha, M. Ramsay, P. Smith, N. Taylor, C. Trivendi, M. Way, O. Whaley and S.D. Hopper. 2011. The Role of Botanic Gardens in the Science and Practice of Ecological Restoration. Conservation Biology. 25(2):265- 275.

Headen, S. 2005. Conservation. as part of a garden's mission, promotes organizational practices that conserve biodiversity. University of Delaware. Newark, Delaware. MSc. Thesis.

Holderegger, R., U. Kamm, and F. Gugerli. 2006. Adaptive vs. neutral genetic diversity: implications for landscape genetics. Landscape Ecology. 21(6):797-807.

Hong, T.D., S. Linnington, and R.H. Ellis. 1996. Seed storage behaviour: a compendium. Handbooks for Genebanks No. 4. International Plant Genetic Resources Institute. Rome, Italy. 74

Hufford, K.M. and S.J. Mazer. 2003. Plant ecotypes: genetic differentiation in the age of ecological restoration. Trends in Ecology and Evolution. 18(3):147-155.

Humphries, C.J., P.H. Williams, and R.I. Vane-Wright. 1995. Measuring biodiversity value for conservation. Annual Review of Ecology and Systematics. 26:93-111.

Hurka, H. 1994. Conservation genetics and the role of botanical gardens. Experientia Basel Supplementum. 68:371-371.

Husband, B.C. and L.G. Campbell. 2004. Population responses to novel environments: implications for ex situ plant conservation, p. 231-266. In: E.O. Guerrant. K. Havens. and M. Maunder (eds.). Ex situ plant conservation: supporting species survival in the wild. Island Press. Washington, D.C.

Donaldson, J.S. 2009. Botanic gardens science for conservation and global change. Trends in Plant Science. 14(11):608-613.

Joshi, J., B. Schmid, M. Caldeira, P. Dimitrakopoulos, J. Good, R. Harris, A. Hector, K. Huss‐Danell, A. Jumpponen, and A. Minns. 2001. Local adaptation enhances performance of common plant species. Ecology Letters. 4(6):536-544.

Jump, A.S., R. Marchant, and J. Peņuelas. 2009. Environmental change and the option value of genetic diversity. Trends in Plant Science. 14(1):51-58.

Jump, A.S., and J. Peñuelas. 2005. Running to stand still: adaptation and the response of plants to rapid climate change. Ecology Letters. 8(9):1010-1020.

Karhu, A., P. Hurme, M. Karjalainen, P. Karvonen, K. Kärkkäinen, D. Neale, and O. Savolainen. 1996. Do molecular markers reflect patterns of differentiation in adaptive traits of conifers? TAG Theoretical and Applied Genetics. 93(1):215- 221.

Kartsonas, E. and M. Papafotiou. 2009. Micropropagation of Quercus euboica Pap., a rare endemic oak species in Greece. Acta Hort. (ISHS). 813:485-490.

Kliejunas, J.T. 2010. Sudden oak death and Phytophthora ramorum: a summary of the literature. U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station. Albany. CA.

Kluza. D., D. Vieglais, J. Andreasen, and A. Peterson. 2007. Sudden oak death: geographic risk estimates and predictions of origins. Plant Pathology. 56(4):580- 587.

Kramer, A.T., and K. Havens. 2009. Plant conservation genetics in a changing world. Trends in Plant Science. 14(11):599-607.

Kramer. A.T., A. Hird, K. Shaw, M. Dosmann, and R. Mims. 2011. Conserving North America's Threatened Plants: Progress Towards Target 8 of the Global Strategy for Plant Conservation. Botanic Gardens Conservation International U.S.

Kramer. A.T., J.L. Ison, M.V. Ashley, and H.F. Howe. 2008. The paradox of forest fragmentation genetics. Conservation Biology. 22(4):878-885.

Kramer, A.T., and V.C. Pence. 2012. The challenges of ex situ conservation for oaks. Journal of the International Oak Society. International Oaks. 23:90-108.

Kudoh, H., and D.F. Whigham. 1997. Microgeographic genetic structure and gene flow in Hibiscus moscheutos (Malvaceae) populations. American Journal of Botany. 84(9):1285-1285.

Lande, R. 1988. Genetics and demography in biological conservation. Science. 241(4872):1455-1460.

Lande, R. 1980. Genetic variation and phenotypic evolution during allopatric speciation. American Naturalist. 16(4):463-479.

Lewis, P. and D. Zaykin. 2002. Genetic data analysis (GDA): computer program for the analysis of allelic data (software) (version 1.1).

Li. Y.Y., X.Y. Chen, X. Zhang, T.Y. Wu, H.P. Lu, and Y.W. Cai. 2005. Genetic differences between wild and artificial populations of Metasequoia glyptostroboides: implications for species recovery. Conservation Biology. 19(1):224-231.

Li. D. and H.W. Pritchard. 2009. The science and economics of ex situ plant conservation. Trends in Plant Science. 14(11):614-621.

Lynch, M. 1996. A quantitative-genetic perspective on conservation issues, p. 471- 501. In: J.C. Avise and J.L. Hamrick (eds.). Conservation genetics: case histories from nature. Kluwer Academic Publishers Group. Dordrecht, Netherlands. 76

Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Research. 27(2):209-220.

Marris, E. 2006. Plant science: Gardens in full bloom. Nature. 440(7086):860-863.

Martinez, M., A. Ballester, and A. Vieitez. 2003. Cryopreservation of embryogenic cultures of Quercus robur using desiccation and vitrification procedures. Cryobiology. 46(2):182-189.

Maunder, M., S. Higgens, and A. Culham. 2001. The effectiveness of botanic garden collections in supporting plant conservation: a European case study. Biodiversity and Conservation. 10(3):383-401.

Maunder, M., C. Hughes, J.A. Hawkins, and A. Culham. 2004. Hybridization in ex situ plant collections: conservation concerns, liabilities, and opportunities, p. 325- 362. In: E.O. Guerrant, K. Havens, and M. Maunder (eds.). Ex situ plant conservation: supporting species survival in the wild. Island Press. Washington, D.C.

Maunder, M., S. Higgens, and A. Culham. 1998. Neither common nor garden: The garden as a refuge for threatened plant species. Curtis's Botanical Magazine. 15(2):124-132.

Mayr, E. 1963. Animal species and evolution. Belknap Press. Cambridge, U.K.

Menges, E.S., E.O. Guerrant, and S. Hamze. 2004. Effects of seed collection on the extinction risk of perennial plants, p. 305-324. In: E.O. Guerrant, K. Havens, and M. Maunder (eds.). Ex situ plant conservation: supporting species survival in the wild. Island Press. Washington, D.C.

Merilä, J., and P. Crnokrak. 2001. Comparison of genetic differentiation at marker loci and quantitative traits. Journal of Evolutionary Biology. 14(60):892-903.

Millar, C.I. and W.J. Libby. 1991. Strategies for conserving clinal, ecotypic, and disjunct population diversity in widespread species, p. 149-170. In: D. Falk, and K.E. Holsinger (eds.). Genetics and Conservation of Rare Plants. Oxford University Press. New York, N.Y.

Miller, M.P. 1997. Tools for population genetic analyses (TFPGA) Version 1.3: A Windows program for the analysis of allozyme and molecular population genetic

data. Department of Biological Sciences. Northern Arizona University. Arizona, U.S.A.

Missouri Botanical Garden and Harvard University Herbaria. 2008. eFloras. 8 February 2012. .

Montalvo, A.M. and N.C. Ellstrand. 2001. Nonlocal transplantation and outbreeding depression in the subshrub Lotus scoparius (Fabaceae). American Journal of Botany. 88(2):258-269.

Muir, G., A.J. Lowe, C.C. Fleming, and C. Vogl. 2004. High nuclear genetic diversity, high levels of outcrossing and low differentiation among remnant populations of Quercus petraea at the margin of its range in Ireland. Annals of Botany. 93(6):691-697.

Myers, N., R.A. Mittermeier, C.G. Mittermeier, G.A.B. da Fonseca, and J. Kent. 2000. Biodiversity hotspots for conservation priorities. Nature. 403(6772):853-858.

Namoff, S., C.E. Husby, J. Francisco-Ortega, L.R. Noblick, C.E. Lewis, and M.P. Griffith. 2010. How well does a botanical garden collection of a rare palm capture the genetic variation in a wild population? Biological Conservation. 143(5):1110- 1117.

NatureServe. 2011. NatureServe Explorer: An online encyclopedia of life. 7 February 2012. .

NatureServe. 2010. NatureServe Conservation Status. 24 February 2012. .

Nei, M. 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 89(3):583-590.

Nei, M. 1975. Molecular population genetics and evolution. North-Holland Publishing Company. Amsterdam, Netherlands.

Nixon, K.E.A. 1998. Quercus georgiana. IUCN Red List of Threatened Species. (version 2011.2). 8 February 2012. .

Oldfield, S.F. 2010. 4th Global Botanic Gardens Congress. Plant conservation, botanic gardens and the International Agenda. Botanic Gardens Conservation International. June, 2010. 78

Oldfield, S.F. 2009. Botanic gardens and the conservation of tree species. Trends in Plant Science. 14(11):581-583.

Oldfield, S.F. and A. Eastwood. 2007. The red list of oaks. Fauna & Flora International, Cambridge, UK.

Peakall, R., and P.E. Smouse. 2006. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes. 6(1):288-295.

Peñaloza-Ramírez, J.M., A. González-Rodríguez, L. Mendoza-Cuenca, H. Caron, A. Kremer, and K. Oyama. 2010. Interspecific gene flow in a multispecies oak hybrid zone in the Sierra Tarahumara of Mexico. Annals of Botany. 105(3):389.

Pence, V.C. 2010. The possibilities and challenges of in vitro methods for plant conservation. Kew Bulletin. 65(4):1-9.

Pence, V.C. 1992. Desiccation and the survival of Aesculus, Castanea, and Quercus embryo axes through cryopreservation. Cryobiology. 29(3):391-399.

Petit, R.J., A. El Mousadik, and O. Pons. 1998. Identifying populations for conservation on the basis of genetic markers. Conservation Biology. 12(4):844- 855.

Pijut, P.M., S.S. Lawson, and C.H. Michler. 2011. Biotechnological efforts for preserving and enhancing temperate hardwood tree biodiversity, health, and productivity. In Vitro Cellular and Developmental Biology-Plant. 47(1):123-147.

Pimentel, D., R. Zuniga, and D. Morrison. 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics. 52(3):273-288.

Pimm, S.L., G.J. Russell, J.L. Gittleman, and T.M. Brooks. 1995. The future of biodiversity. Science. 269(5222):347-350.

Pritchard, J.K., M. Stephens, and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics. 155(2):945-959.

Raymond, M., and F. Rousset. 1995. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. Journal of Heredity. 86(3):248-249.

Reed, D.H., and R. Frankham. 2003. Correlation between fitness and genetic diversity. Conservation Biology. 17(1):230-237.

Reed, D.H., and R. Frankham. 2001. How closely correlated are molecular and quantitative measures of genetic variation? A meta‐analysis. Evolution. 55(6):1095-1103.

Rieseberg, L.H. 1991. Hybridization in rare plants: insights from case studies in Cercocarpus and Helianthus, p.171-181. In: D. Falk and K.E. Holsinger (eds.). Genetics and Conservation of Rare Plants. Oxford University Press. New York, N.Y.

Rizzo, D.M. and M. Garbelotto. 2003. Sudden oak death: endangering California and Oregon forest ecosystems. Frontiers in Ecology and the Environment. 1(4):197- 204.

Roberts, H. 1973. Predicting the viability of seeds. Seed Science and Technology. 1:499-514.

Royal Botanic Garden Edinburgh. n.d. Multisite Searches. 2 February 2012. .

Saltonstall, K. 2002. Cryptic invasion by a non-native genotype of the common reed. Phragmites australis, into North America. Proceedings of the National Academy of Sciences. 99(4):2445-2449.

Sambatti, J. and K.J. Rice. 2006. Local adaptation. patterns of selection, and gene flow in the Californian serpentine sunflower (Helianthus exilis). Evolution. 60(4):696- 710.

Schall, B. and W.J. Leverich. 2004. Population genetic issues in ex situ plant conservation, p. 267-285. In: E.O. Guerrant. K. Havens. and M. Maunder (eds.). Ex situ plant conservation: supporting species survival in the wild. Island Press. Washington. D.C.

CBD. 2011. Global Strategy for Plant Conservation 2011-2020. The Secretariat of the Convention on Biological Diversity, Montreal, Canada.

CBD. 2002. Global Strategy for Plant Conservation. The Secretariat of the Convention on Biological Diversity, Montreal, Canada.

UNEP. Secretariat of the Convention on Biological Diversity. 1992. Convention on Biological Diversity. VNEP No. 92-8314. United Nations Environment Programme, Nairobi, Kenya.

Selkoe, K.A. and R.J. Toonen. 2006. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecology Letters. 9(5):615-629.

Sharrock, S.L. and M. Jones. 2009. Conserving Europe's threatened plants: progress towards Target 8 of the Global Strategy for Plant Conservation. Botanic Gardens Conservation International, Richmond, U.K.

Silva, D.A., J.S. Donaldson, G. Reeves, and T.A. Hedderson. 2012. Population genetics and conservation of critically small cycad populations: a case study of the Albany Cycad, Encephalartos latifrons (Lehmann). Biological Journal of the Linnean Society. 105:293–308.

Slatkin, M. 1993. Gene flow and population structure. Ecological Genetics. 16:3-18.

Slatkin, M. 1987. Gene flow and the geographic structure of natural populations. Science. 236(4803):787-792.

Sork. V.L., J. Nason, D.R. Campbell, and J.F. Fernandez. 1999. Landscape approaches to historical and contemporary gene flow in plants. Trends in Ecology and Evolution. 14(6):219-224.

Sork, V.L., K.A. Stowe, and C. Hochwender. 1993. Evidence for local adaptation in closely adjacent subpopulations of northern red oak (Quercus rubra L.) expressed as resistance to leaf herbivores. The American Naturalist. 142(6):928-936.

Spencer, C., J. Neigel, and P. Leberg. 2000. Experimental evaluation of the usefulness of microsatellite DNA for detecting demographic bottlenecks. Molecular Ecology. 9(10):1517-1528.

Storfer, A. 1996. Quantitative genetics: a promising approach for the assessment of genetic variation in endangered species. Trends in Ecology and Evolution. 11(8):343-348.

Streiff, R., A. Ducousso, C. Lexer, H. Steinkellner, J. Gloessl, and A. Kremer. 1999. Pollen dispersal inferred from paternity analysis in a mixed oak stand of Quercus robur L. and Q. petraea (Matt.) Liebl.. Molecular Ecology. 8(5):831-841.

Tallamy, D.W. and K.J. Shropshire. 2009. Ranking lepidopteran use of native versus introduced plants. Conservation Biology. 23(4):941-947.

Tallmon, D.A., G. Luikart, and R.S. Waples. 2004. The alluring simplicity and complex reality of genetic rescue. Trends in Ecology and Evolution. 19(9):489- 496.

Tamta, S., L.M.S. Palni, V.K. Purohit, and S.K. Nandi. 2008. In vitro propagation of brown oak (Quercus semecarpifolia Sm.) from seedling explants. In Vitro Cellular and Developmental Biology-Plant. 44(2):136-141.

Tarner, K. 2010. Georgia Oak: Quercus georgiana. 8 February 2012. .

Templeton. A.R. 1991. Off-site breeding of animals and implications for plant conservation strategies, p. 182-194. In: D. Falk and K.E. Holsinger (eds.). Genetics and Conservation of Rare Plants. Oxford University Press. New York, N.Y. ten Kate, K. 2002. Science and the convention on biological diversity. Science. 295(5564):2371-2372.

The Association of Botanical Gardens and Arboreta. n.d. PLANTCOL. 24 February 2012. .

The Herbarium of the University of North Carolina at Chapel Hill. 2005. Flora of the Southeast. 7 February 2012.

Trehane, P. 2011. Oak Name Checklist. 18 February 2012.

USDA. 2012. The PLANTS Database. 7 February 2012. .

Valladares. S., M. Toribio, C. Celestino, and A.M. Vieitez. 2004. Cryopreservation of embryogenic cultures from mature Quercus suber trees using vitrification. CryoLetters. 25(3):177-186.

Van Oosterhout, C., W.F. Hutchinson, D.P.M. Wills, and P. Shipley. 2004. Micro‐checker: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes. 4(3):535-538.

Varela, M. and G. Eriksson. 1995. Multipurpose gene conservation in Quercus suber-a Portuguese example. Silvae Genetica. 44(1):28-36.

Vengadesan, G. and P.M. Pijut. 2009. In vitro propagation of northern red oak (Quercus rubra L.). In Vitro Cellular and Developmental Biology-Plant. 45(4):474-482.

Vieitez, A., E. Corredoira, A. Ballester, F. Munoz, J. Durán, and M. Ibarra. 2009. In vitro regeneration of the important North American oak species Quercus alba, Quercus bicolor and Quercus rubra. Plant Cell, Tissue and Organ Culture. 98(2):135-145.

Vitt, P. and K. Havens. 2004. Integrating quantitative genetics into ex situ conservation and restoration practices, p. 286-304. In: E.O. Guerrant. K. Havens. and M. Maunder (eds.). Ex situ plant conservation: supporting species survival in the wild. Island Press. Washington, D.C

Volk, G.M., C.M. Richards, A.A. Reilley, A.D. Henk, P.L. Forsline, and H.S. Aldwinckle. 2005. Ex situ conservation of vegetatively propagated species: Development of a seed-based core collection for Malus sieversii. Journal of the American Society for Horticultural Science. 130(2):203-210.

Walker, B. 1995. Conserving biological diversity through ecosystem resilience. Conservation Biology. 9(4):747-752.

Walter, K.S. and H.J. Gillett. 1998. 1997 IUCN red list of threatened plants. World Conservation Union. Gland, Switzerland.

Walters, C. 2004. Principles for preserving germplasm in gene banks, p. 113-138. In: E.O. Guerrant. K. Havens, and M. Maunder (eds.). Ex situ plant conservation: supporting species survival in the wild. Island Press. Washington. D.C

Weir, B. and C.C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution. 38(6):1358-1370

White, G.M., D.H. Boshier, and W. Powell. 2002. Increased pollen flow counteracts fragmentation in a tropical dry forest: An example from Swietenia humilis Zaccarini. Proceedings of the National Academy of Sciences of the United States of America. 99(4):2038-2042.

Wilhelm, E. 2000. Somatic embryogenesis in oak (Quercus spp.). In Vitro Cellular and Developmental Biology-Plant. 36(5):349-357.

Wyatt, R. 1984. The evolution of self-pollination in granite outcrop species of Arenaria (Caryophyllaceae). I. Morphological correlates. Evolution. 38(4):804- 816.

Young, A., T. Boyle, and T. Brown. 1996. The population genetic consequences of habitat fragmentation for plants. Trends in Ecology and Evolution. 11(10):413- 418.

Appendix A

NUMBER OF BOTANIC GARDENS GROWING 97 OAK TAXA NATIVE NORTH OF MEXICO AND CORRESPONDING NATURESERVE CONSERVATION STATUS RANKS, NATURESERVE CONSERVATION STATUS ADJUSTED RANKS USED IN TABLE 2 AND IUCN RED-LIST RANKS

No. botanic NatureServe Nature- IUCN gardens conservation Serve Red Species growing status conservation List species adjusted status rank rank (records) rank Q. ajoensis 0 G2G4 G2 VU Q. carmenensis 0 G2? G2 - Q. robusta 0 G1Q G1 DD Q. similis 0 G4 G4 - Q. tardifolia 0 G1 G1 CR Q. viminea 0 G4G5 G4 - Q. chihuahuensis 1 G3G5 G3 - Q. depressipes 1 G2G4 G2 LC Q. durata var. gabrielensis 1 G4T3 G3 - Q. garryana var. garryana 1 G5T5 G5 - Q. inopina 1 G4 G4 - Q. intricata 1 G5 G5 - Q. sinuata var. sinuata 1 G4G5T4 G4 - Q. cedrosensis 2 G2? G2 VU Q. durata var. durata 2 G4T4? G4 - Q. toumeyi 3 G4 G4 LC Q. chapmanii 4 G4G5 G4 - Q. garryana var. semota 4 G5T4 G4 - Q. hinckleyi 4 G2 G2 CR Q. havardii 5 G4 G4 - Q. minima 5 G5 G5 LC Q. cornelius-mulleri 6 G4 G4 - Q. john-tuckeri 6 G4? G4 - Q. mohriana 6 G4 G4 LC 85

Q. myrtifolia 7 G5 G5 LC Q. sinuata var. breviloba 7 G4G5T4T5 G4 - Q. boyntonii 8 G1 G1 CR Q. geminata 8 G5 G5 LC Q. laevis 8 G5 G5 LC Q. austrina 9 G4? G4 LC Q. pacifica 9 G3 G3 VU Q. palmeri (syn. Q. dunnii) 10 G3G5 G3 - Q. laceyi 11 G4 G4 - Q. oblongifolia 11 G5 G5 - Q. buckleyi 12 G5 G5 LC Q. pungens 12 GNR GNR - Q. sinuata 12 G4G5 G4 LC Q. acerifolia 13 G1 G1 EN Q. arizonica 13 G5 G5 LC Q. gravesii 13 G5 G5 LC Q. pumila 13 G3G5 G3 LC Q. emoryi 14 G5 G5 LC Q. garryana var. breweri 14 G5T3 G3 - Q. margarettae 15 G5 G5 LC Q. tomentella 15 G3 G3 VU Q. dumosa 16 G1G2 G1 EN Q. durata 16 G4 G4 - Q. engelmannii 16 G3 G3 VU Q. sadleriana 16 GNR GNR - Q. berberidifolia 17 G5 G5 LC Q. vaseyana 17 G4G5 G4 - Q. fusiformis 18 G5 G5 LC Q. georgiana 18 G3 G3 EN Q. vaccinifolia 18 G4G5 G4 - Q. grisea 19 G5 G5 - Q. hemisphaerica 19 G5 G5 LC Q. incana 19 G5 G5 LC Q. hypoleucoides 20 G5 G5 - Q. arkansana 22 G3 G3 VU Q. graciliformis 23 G1 G1 CR Q. oglethorpensis 23 G3 G3 EN Q. polymorpha 24 G5 G5 NT 86

Q. turbinella 28 G5 G5 LC Q. douglasii 30 G4? G4 - Q. montana 30 - GNR LC Q. pagoda 31 G5 G5 LC Q. chrysolepis 34 G5 G5 LC Q. prinoides 34 G5 G5 LC Q. rugosa 35 G4G5 G4 LC Q. kelloggii 38 G5 G5 - Q. garryana 45 G5 G5 LC Q. lobata 45 G4 G4 - Q. wislizenii (syn. Q. 47 G5 G5 EN parvula) Q. gambelii 49 G5 G5 LC Q. laurifolia 49 G5 G5 LC Q. agrifolia 50 G5 G5 LC Q. lyrata 56 G5 G5 LC Q. virginiana 56 G5 G5 LC Q. stellata 57 G5 G5 LC Q. ilicifolia 58 G5 G5 LC Q. michauxii 58 G5 G5 LC Q. falcata 61 G5 G5 LC Q. ellipsoidalis 62 G5 G5 LC Q. marilandica 64 G5 G5 LC Q. texana 65 G4G5 G4 LC Q. nigra 67 G5 G5 LC Q. phellos 100 G5 G5 LC Q. shumardii 102 G5 G5 LC Q. imbricaria 105 G5 G5 LC Q. muehlenbergii 106 G5 G5 LC Q. velutina 112 G5 G5 LC Q. coccinea 123 G5 G5 LC Q. bicolor 133 G5 G5 LC Q. palustris 141 G5 G5 LC Q. alba 150 G5 G5 LC Q. macrocarpa 160 G5 G5 LC Q. rubra 202 G5 G5 LC NatureServe conservation status ranks: G1 – critically imperiled; G2 – imperiled; G3 – vulnerable; G4 – apparently secure; G5 – secure; ? – inexact numeric rank; Q –

questionable taxonomy; G#G# – range rank indicates the range of uncertainty about the exact status of a taxon. IUCN Red List ranks: CR – critically endangered; EN – endangered; VU – vulnerable; NT – near threatened; LC – least concern.

APPENDIX B

277 BOTANIC GARDENS GROWING 97 OAK TAXA NATIVE NORTH OF MEXICO (BGCI PLANTSEARCH, 2011)

Botanic Gardens of Adelaide, Australia; Brisbane Botanic Gardens, Australia; Royal Botanic Gardens Sydney, Australia; Royal Botanic Gardens, Melbourne, Australia; St.Kilda Botanic Garden, Australia; Botanical Garden of the University of Vienna, Austria; Arboretum Groenendaal - Flemish Forest Department - Houtvesterij Groenendaal, Belgium; Arboretum Wespelaar, Belgium; Bokrijk Arboretum, Belgium; Ghent University Botanic Garden, Belgium; Hof ter Saksen Arboretum, Belgium; Kalmthout Arboretum, Belgium; Leuven Botanic Garden, Belgium; National Botanic Garden of Belgium, Belgium; Annapolis Royal Historic Gardens, Canada; Arboretum at the University of Guelph, The, Canada; Biodôme de Montréal - Botanical Garden, Canada; Cowichan Lake Research Station Arboretum, Canada; Dominion Arboretum, Canada; Gardens of Fanshawe College and A.M. Cuddy Gardens, Canada; Great Lakes Forestry Centre Arboretum, Canada; Harriet Irving Botanical Gardens, Canada; Montreal Botanical Garden / Jardin botanique de Montréal, Canada; Morden Arboretum Research Station, Canada; New Brunswick Botanical Garden, Canada; Niagara Parks Botanical Gardens and School of Horticulture, The, Canada; Patterson Gardens, Canada; Riverview Horticultural Centre Society, The, Canada; Royal Botanical Gardens, Ontario, Canada; Royal Roads University Botanical Gardens, Canada; Sherwood Fox Arboretum, Canada; Toronto Zoo, Canada University of British Columbia Botanical Garden, Canada; VanDusen Botanical Garden, Canada; Jardin Botanico Nacional, Chile; Hunan Forest Botanical Garden, China; Lushan Botanical Garden, China; Wuhan Botanic Garden, China; Arboretum St?ední lesnické školy Czech Republic Zoological and Botanical Garden of the Plzen Town Czech Republic Københavns Universitets Botaniske Have, Denmark; Royal Veterinary and Agricultural University Arboretum, Denmark; Botanical Garden of Tartu University, Estonia; Botanical Gardens, Finland; Helsinki University Botanic Garden, Finland; Conservatoire Botanique National du Brest, France; Conservatoire et Jardins Botaniques de Nancy, France; Jardin Botanique Alpin de la Jaysinia, France; Jardin Botanique de la Ville de Lyon, France; Jardin botanique de la Ville de Paris, France; Jardin Botanique de l'Universite Louis Pasteur, France; Jardin des Plantes de Paris et Arboretum de Chevreloup, France; Jardin des Serres d' Auteuil, France; Sentier de Decouverte, France; Batumi Botanical Garden, Georgia; Arboretum Freiburg- Guenterstal im Staedtischen Forstamt Freiburg, Germany; Deutschland Botanic Garden of Rostock University, Germany; Deutschland Botanische Gärten der Universität Bonn, Germany; Deutschland Botanischer Garten, Germany; Deutschland 89

Botanischer Garten der Carl von Ossietzky-Universitat Oldenburg, Germany; Deutschland Botanischer Garten der Friedrich-Schiller-Universitaet, Germany; Deutschland Botanischer Garten der J.W. Goethe-Universitat, Germany; Deutschland Botanischer Garten der Justus-Liebig Universität Giessen, Germany; Deutschland Botanischer Garten der Philipps-Universität Marburg, Germany; Deutschland Botanischer Garten der Ruhr-Universität Bochum, Germany; Deutschland Botanischer Garten der Technischen Universitaet, Germany; Deutschland Botanischer Garten der Universitaet des Saarlandes, Germany; Deutschland Botanischer Garten der Universität Freiburg, Germany; Deutschland Botanischer Garten der Universitat Osnabruck, Germany; Deutschland Botanischer Garten der Universität Ulm, Germany; Deutschland Botanischer Versuchs- und Lehrgarten, Germany; Deutschland Forstbotanischer Garten, Germany; Deutschland Forstbotanischer Garten der Technischen Universitaet Dresden, Germany; Deutschland Forstbotanischer Garten und Arboretum, Germany; Deutschland Grugapark und Botanischer Garten der Stadt Essen, Germany; Deutschland Neuer Botanischer Garten der Universität Göttingen, Germany; Deutschland Oekologisch- Botanischer Garten Universitaet Bayreuth, Germany; Eötvös Loránd University Botanic Garden, Hungary; Hortus Botanicus ReykjavikensisIcel and Center for Plant Conservation; Bogor Botanic Gardens Indonesia National Botanic Gardens, Glasnevin, Ireland; Università di Camerino, Italy; Orto Botanico di Perugia, Italy; Orto Botanico Università degli Studi di Padova, Italy; National Botanic Garden of Latvia, Latvia; Botanical Garden of Vilnius University, Lithuania; Arboretum Kirchberg, Luxembourg; Ecojardin del CIEco, Mexico; Fundación Xochitla A.C., Mexico; Jardin Botanico - Ignacio Rodriguez Alconedo - BUAP, Mexico; Jardin Botanico - Louise Wardle de Camacho, Mexico; Jardin Botanico Benjamin F. Johnston, Mexico; Jardin Botanico de la Facultad de Estudios Superiores, Mexico; Arboretum Oudenbosch, Netherlands; Botanic Garden, Delft University of Technology, Netherlands; Botanical Gardens Wageningen UR, Netherlands; Hortus Botanicus Amsterdam, Netherlands; Rotterdam Zoological and Botanical Gardens, Netherlands; Stichting Botanische Tuin Kerkrade, Netherlands; Utrecht University Botanic Gardens, Netherlands; Dunedin Botanic Garden, New Zealand; University of Oslo Botanical Garden, Norway; Rogów Arboretum of Warsaw University of Life Sciences, Poland; Parque Botânico da Tapada da Ajuda, Portugal; Botanic Garden of Tver State University, Russian Federation; Botanical Garden of Chelyabinsk State University, Russian Federation; Botanical Garden of Mari State University, Russian Federation; Botanical Garden of the V.L. Komarov Botanical Institute, Russian Federation; Botanical Garden-Institute, Ufa Research Center, Russian Federation; Main Botanical Garden, Russian Academy of Science, Russian Federation; Main Botanical Garden, Russian Academy of Science, Russian Federation; Moscow State University Botanical Garden, Russian Federation Mountain; Botanical Garden of the Dagestan Scientific Centre, Russian Federation;

Novosibirsk Dendropark, Russian Federation; The B.M. Kozo-Polyansky Botanical Garden of Voronezh State University, Russian Federation; Bukavu Arboretum/Garden, Rwanda; Arboretum Mlyany SAS, Slovakia; Maribor University Botanic Garden, Slovenia; Dr. P. Font i Quer Arboretum of Lleida Botanic Garden, Spain; Jardí Botànic de la Universitat de València, Spain; University of Uppsala Botanic Garden, Sweden; Catalogue of Medicinal Plants of Ukrainian Botanic Gardens and Parks, Ukraine; Benmore Botanic Garden, United Kingdom; Bristol Zoological Gardens, United Kingdom; Cambridge University Botanic Garden, United Kingdom; High Beeches Gardens Conservation Trust, United Kingdom; Paignton Zoo Environmental Park, United Kingdom; Royal Botanic Garden, Edinburgh, United Kingdom; Royal Botanic Gardens Kew (Wakehurst), United Kingdom; Royal Botanic Gardens, Kew, United Kingdom; Royal Horticultural Society‘s Garden, Wisley, United Kingdom; Royal Horticultural Society's Garden, Harlow Carr, United Kingdom; Royal Horticultural Society's Garden, Hyde Hall, United Kingdom; Royal Horticultural Society's Garden, Rosemoor, United Kingdom; Sheffield Botanical Gardens, United Kingdom; St. Andrews Botanic Garden, United Kingdom; Tatton Garden Society/Quinta Arboretum, United Kingdom; The National Arboretum – Westonbirt, United Kingdom; The Sir Harold Hillier Gardens, United Kingdom; Adkins Arboretum, USA; Arboretum at Kutztown University, USA; Arboretum at Penn State, The, USA; Arboretum at the University of California, Santa Cruz, USA; Arboretum of The Barnes Foundation, USA; Arnold Arboretum of Harvard University, The, USA; Atlanta Botanical Garden, USA; Bartlett Tree Research Laboratories Arboretum, USA; Bayard Cutting Arboretum, USA; Berkshire Botanical Garden, USA; Betty Ford Alpine Gardens, USA; Bickelhaupt Arboretum, USA; Botanic Garden of Smith College, The, USA; Botanic Gardens of the Heard Natural Science Museum, USA; Bowman‘s Hill Wildflower Preserve, USA; Boyce Thompson Arboretum, USA; Brenton Arboretum, The, USA; Brooklyn Botanic Garden, USA; Brookside Gardens, USA; C. M. Goethe Arboretum, USA; Cape Fear Botanical Garden, USA; Center for Plant Conservation (, USA; ), USA; Chanticleer Foundation, USA; Charles R. Keith Arboretum, The, USA; Chester M. Alter Arboretum, USA; Chicago Botanic Garden, USA; Chihuahuan Desert Gardens at the Centennial Museum, USA; Cincinnati Zoo and Botanical Gardens - CryoBioBank, USA; Cleveland Botanical Garden, USA; Coastal Maine Botanical Gardens, USA; Columbus Botanical Garden, USA; Connecticut College Arboretum, USA; Cornell Plantations, USA; Crosby Arboretum, The, USA; Dawes Arboretum, The, USA; Denver Botanic Gardens, USA; Desert Botanical Garden, USA; Desert Botanical Garden - Seed Bank, USA; Dixon Gallery and Gardens, The, USA; Donald E. Davis Arboretum, USA; Duke Farms, USA; DuPage Forest: Forest Preserve District of DuPage County, USA; Eddy Arboretum - Pacific Southwest Research Station, USA; Edison and Ford Winter Estates, USA; Elisabeth C. Miller Botanical Garden, USA; Eloise Butler Wildflower Garden & Bird Sanctuary, USA; Fairchild Tropical Botanic

Garden, USA; Fellows Riverside Gardens, USA; Fernwood Botanical Garden and Nature Preserve, USA; Florida Botanical Gardens, USA; Foellinger-Freimann Botanical Conservatory, USA; Forrest Deaner Native Plant Botanic Garden, USA; Fort Worth Botanic Garden, USA; Frederik Meijer Gardens & Sculpture Park, USA; Frelinghuysen Arboretum, USA; Ganna Walska Lotusland, USA; Gardens at SIUE, The, USA; Garvan Woodland Gardens, USA; Green Bay Botanical Garden, USA; Green Spring Gardens, USA; Greenwood Gardens, USA; Henry Schmieder Arboretum, USA; Hershey Gardens, USA; Hidden Lake Gardens, USA; Holden Arboretum, The, USA; Hoyt Arboretum, USA; Huntington Botanical Gardens, USA; Huntsville Botanical Garden, USA; JC Raulston Arboretum, USA; John C. Gifford Arboretum, USA; Lady Bird Johnson Wildflower Center, USA; Landis Arboretum, USA; Lauritzen Gardens, USA; Living Desert Zoo & Gardens State Park, USA; Living Desert Zoo and Gardens, USA; Longwood Gardens, USA; Los Angeles County Arboretum and Botanic Garden, USA; Marie Selby Botanical Gardens, USA; Matthaei Botanical Gardens & Nichols Arboretum, USA; Maymont Foundation, USA; Mead Garden, USA; Memphis Botanic Garden, USA; Mercer Arboretum & Botanic Gardens, USA; Minnesota Landscape Arboretum, USA; Missouri Botanical Garden, USA; Missouri State Arboretum, USA; Mitchell Park Horticultural Conservatory (The Domes")", USA; Montgomery Botanical Center, USA; Morris Arboretum, The, USA; Morton Arboretum, The, USA; Mount Auburn Cemetery, USA; Mountain Top Arboretum, USA; Mt. Cuba Center, USA; Naples Botanical Garden, USA; Naples Zoo at Caribbean Gardens, USA; National Plant Germplasm System - USDA-ARS-NGRL, USA; Nebraska Statewide Arboretum, USA; New England Wild Flower Society - Garden in the Woods, USA; New York Botanical Garden, The, USA; Norfolk Botanical Garden, USA; North Carolina Arboretum, The, USA; Northwest Trek Wildlife Park, USA; Oklahoma City Zoo and Botanical Gardens, USA; Polly Hill Arboretum, The, USA; Queens Botanical Garden, USA; Rancho Santa Ana Botanic Garden, USA; Reading Public Museum and Arboretum, The, USA; Red Butte Garden and Arboretum, USA; Reiman Gardens, USA; Rio Grande Botanic Garden, USA; San Diego Botanic Garden, USA; San Diego Zoo Botanical Gardens, USA; San Diego Zoo Safari Park, USA; San Francisco Botanical Garden (formerly Strybing Arboretum), USA; San Luis Obispo Botanical Garden, USA; Santa Barbara Botanic Garden, USA; Sarah P. Duke Gardens, USA; Scott Arboretum of Swarthmore College, The, USA; Shaw Nature Reserve of the Missouri Botanical Garden, USA; Sister Mary Grace Burns Arboretum, USA; Smith-Gilbert Gardens, USA; Smithsonian Gardens, USA; Starhill Forest Arboretum, USA; Taltree Arboretum & Gardens, USA; Toledo Botanical Garden, USA; Trees Atlanta, USA; Tyler Arboretum, USA; UC Davis Arboretum, USA; United States Botanic Garden, USA; United States National Arboretum, USA; University of California Botanical Garden at Berkeley, USA; University of Delaware Botanic Gardens, USA; University of Idaho Arboretum &

Botanical Garden, USA; University of Kentucky-Lexington Arboretum, USA; University of Washington Botanic Gardens, USA; Vanderbilt University Arboretum, USA; W. J. Beal Botanical Garden, USA; Wallace Desert Gardens, USA; Willowwood Arboretum, USA; Yew Dell Botanical Gardens, USA