View of Ex Situ Plant Conservation for Exceptional Species

The genetic consequences of ex situ conservation of exceptional plant species

A dissertation submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biological Sciences of the College of Arts and Sciences

Megan Philpott B.S. Biology, University of Cincinnati, September 2011

Committee Chair: Dr. Theresa Culley

Abstract

The preservation of biodiversity is a pressing concern for many global taxa due to threats like climate change, habitat loss, and anthropogenic pressures. While in situ preservation of species in their habitats is ideal, it is not enough as threats often outpace our ability to conserve, and ex situ conservation methods must be used as a supplement to preserve species and their genetic diversity outside of their natural habitat. For exceptional species, those species ineligible for conventional seed banking due to production of little to no seeds or recalcitrant seeds, ex situ conservation must take the form of tissue culture and cryopreservation, the storage of seeds and tissues in liquid nitrogen (LN). The present study investigates the genetic consequences of these methods on exceptional species, from the initial conservation collection, to their tenure in ex situ collections, to their ultimate use in restorations and reintroductions. For Hedeoma todsenii, a federally endangered exceptional species, collections had been focused around populations located in the San Andres mountain range. A three-year study of populations in this range and the geographically distant Sacramento mountain range indicated low genetic diversity and high inbreeding in the species. However, the Sacramento population had a higher diversity and was genetically distinct from the San Andres populations, indicating that future collection efforts should include a greater focus on the Sacramento locality. Next, the genetic effects of long-term tissue culture and cryopreservation were assessed for 18 different plant species stored in LN for up to 23 years. No association was found between the survival of species after LN storage and

RNA integrity, indicating that RNA may not be one of the key indicators of survival after storage. However, evidence of genetic change in DNA after cryopreservation and tissue culture was detected in three species, although this did not appear to have an effect on the survival and growth of species. It is recommended that species in long-term tissue culture or cryopreservation

be regularly monitored for genetic stability. To understand the genetic effects of reintroduction using micropropagation, Minuartia cumberlandensis, a federally endangered exceptional species, was used as a case study. Seven genotypes of M. cumberlandensis were propagated using tissue culture and planted out into a suitable habitat in Daniel Boone National Forest (KY) in 2005. In

2015, 10 years after the initial outplanting, the population had more than tripled in size to over

200 plants. A genetic analysis indicated that the outplanting was genetically very similar to the natural source population despite the genetic bottleneck of only seven founding genotypes. This is likely due to low genetic diversity and clonal growth in the species. This case study shows that micropropagated plants can be successfully used to facilitate reintroductions. In sum, the presented research indicates that the ex situ conservation methods of tissue culture and cryopreservation are suitable for preserving exceptional species. These methods are key to preserving the biodiversity of often-overlooked exceptional plants for future generations.

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Megan Philpott, 2018

Acknowledgements

This work would not have been possible without the unfailing support of my advisors, Dr.

Theresa Culley and Dr. Valerie Pence. During my undergraduate career, the idea of doing plant conservation research was just a dream, and your mentorship and support have turned it into my career. I am filled with gratitude for the time and experience you have given me, and hope I can build on the foundation of plant science research you have given me in years to come.

I also thank my committee for their generous contribution of time and expertise to guide me through my graduate studies: Dr. Eric Tepe, for being constantly available for me to bounce ideas and frustrations off of; Sarena Selbo, for grounding me in the critical perspective of applied conservation and management; Dr. Dennis Grogan, for teaching me everything I know about the mechanics of DNA and RNA; and Dr. Steven Rogstad, for giving me the initial spark of interest in plant science.

I am personally grateful to Dr. Susan Dunford, for giving me the confidence to pursue this endeavor; Dr. Denis Conover, for teaching me everything I know about native plants; Charles

Britt, for his expertise and assistance in the Hedeoma todsenii study; Dr. Mary Chaiken, for lending her expertise to the RNA integrity study; David Taylor, for his assistance with and dedication to Minuartia cumberlandensis; Kristine Lindsey, for unending research and emotional support; Dr. Herman Mays, for giving me my first experience working in a lab; and all the undergraduate students who dedicated their time to helping me complete this research.

My graduate studies were a time of great personal growth, and I could not have finished without the intellectual and emotional support of the members of the Culley Lab, past and present. For pep talks, research discussions, and countless coffee breaks, I am forever indebted to Rob

Tunison, Ben Merritt, Jack Stenger, José Barreiro, Becky Fehn, and Dr. Anne-Catherine

Vanhove.

Finally, I owe most of my success and persistence to my family and my husband, Madison.

Madison, I owe you one.

Table of Contents

Abstract ...... ii List of Tables ...... ix List of Figures ...... x Chapter One: Introduction: An overview of ex situ plant conservation for exceptional species .... 1 Works Cited...... 13 Chapter Two: Collecting for ex situ conservation: An investigation of the genetic diversity of Hedeoma todsenii Irving (Lamiaceae) in White Sands Missile Range and Lincoln National Forest, New Mexico ...... 20 Introduction ...... 21 Materials & Methods ...... 27 Results ...... 30 Discussion ...... 34 Works Cited...... 39 Figure Legends ...... 45 Tables ...... 46 Figures ...... 50 Chapter Three: Long-term stability of RNA and DNA in ex situ cryopreserved collections: a case study of shoot tips stored for up to 23 years ...... 57 Introduction ...... 58 Materials and Methods ...... 63 Results ...... 68 Discussion ...... 70 Works Cited...... 78 Figure Legends ...... 86 Tables ...... 87 Figures ...... 91 Chapter Four: Augmentation and reintroduction using ex situ collections: Genetic effects of small founding population size in an experimental outplanting of the endangered perennial Minuartia cumberlandensis (Wofford & Kral) McNeill (Caryophyllaceae) ...... 94 Introduction ...... 95 Materials & Methods ...... 100 Results ...... 104 vii

Discussion ...... 105 Works Cited...... 110 Figure Legends ...... 116 Tables ...... 117 Figures ...... 121 Chapter Five: General conclusions ...... 122 Works Cited...... 130

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List of Tables

Chapter 2

Table 2.1: Microsatellite loci used in the study as reported in Conradina (Edwards et al., 2008).

Table 2.2: Descriptive statistics for individual populations sampled in Lincoln National Forest

(LNF) in 2017 and White Sands (WS) in 2015, 2016, and 2017.

Table 2.3: Pairwise D & G’ST values across populations.

Table 2.4: Diversity statistics for the microsatellite analysis.

Chapter 3

Table 3.1: Species used in the RNA quality study.

Table 3.2: Samples used in the DNA stability tests.

Table 3.3: SRAP primer combinations used for the genetic stability test.

Chapter 4

Table 4.1: SRAP primers used in the present study.

Table 4.2: Microsatellite primers developed and used in the study.

Table 4.3: Descriptive statistics for the sampled populations.

Table 4.4: AMOVA results for partitioning variation based on SRAP markers.

List of Figures

Chapter 2

Figure 2.1: Map showing the general location of the White Sands populations (in green) in the

San Andres mountain range and the Lincoln National Forest populations (in red) in the

Sacramento mountain range.

Figure 2.2: Genotype accumulation curve for the analyzed dataset.

Figure 2.3: Heat map showing missing data across loci and populations surveyed.

Figure 2.4: A plot of deviations from Hardy-Weinberg equilibrium (HWE) across loci (y-axis)

and populations (x-axis).

Figure 2.5: FST vs. FIS across loci.

Figure 2.6: Principal coordinates analysis of the sampled populations.

Figure 2.7: Rarefaction curve showing genotypic richness in all sampled populations.

Chapter 3

Figure 3.1: Conium maculatum validation experiment. Shown is a boxplot of RIN values for

seeds stored at -20°C, 4°C, in LN, and freshly harvested seed.

Figure 3.2: Crotalaria avonensis validation experiment. Shown is a boxplot of RIN values for

freshly harvested shoot tips and shoot tips cultured on 1B maintenance medium, mannitol

cryo-protectant medium, or salt stress medium for 1 week.

Figure 3.3: Scatterplot of RIN vs. average survival for all species assessed, with the exception of

samples used for the validation experiments.

Chapter 4

Figure 4.1: Minuartia cumberlandensis growing in the Daniel Boone National Forest

outplanting. Figure 4.1A the floral morphology of the species, and figure 4.1B depicts the

outplanting habitat.

Chapter One

Introduction: An overview of ex situ plant conservation for exceptional species

Megan Philpott Department of Biological Sciences University of Cincinnati Cincinnati, OH 45221-0006

“Why not save a piece of your native country, your native state, in its original condition as a monument to the original beauty and grandeur of your forests, just as you save an historical shrine?”

Dr. E. Lucy Braun, from “Save the Big Trees,” a talk given at the spring meeting of the Garden

Club of Kentucky at Millersburg, March 29, 1935.

Biodiversity loss is occurring on a global scale, with less than one quarter of land remaining untouched by human impact (IPBES 2018). While habitat degradation and loss are major drivers of biodiversity loss for plants, climate change can negatively affect even those few remaining species in untouched habitat. With threats to plant biodiversity compounding, the need for comprehensive conservation and management plans becomes more pressing. Dr. E. Lucy Braun, a pioneer in the fields of Ecology and Conservation Biology, recognized in 1935 the importance and urgency of preserving our natural history with as much fervor as we preserve human history.

As the biodiversity crisis continues, it is imperative that we examine our current conservation strategies and evaluate their efficacy to ensure the protection of plant biodiversity for future generations.

Extinction Risk

As the field of conservation biology has developed, scientists have identified certain plant traits which may be associated with a species’ increased risk of extinction globally (Willis 2017).

Some traits are cryptically associated with extinction risk, such as zygomorphic flowers (Harper,

1979; Stefanaki et al., 2015) or diploidy (Pandit, 2006; Pandit Maharaj K. et al., 2011). Other traits, however, are more understandably associated with extinction risk, such as an epiphytic

lifestyle, where a species is non-parasitically dependent on another species for its growth and development (Sodhi et al., 2008; Leão et al., 2014). Similarly, plants dependent on biotic pollinators are often at increased risk as their survival depends not only on their own ability to resist threats but on their pollinators’ ability as well (Harper, 1979; Sakai et al., 2002; Farnsworth and Ogurcak, 2008; Sodhi et al., 2008; Gabrielová et al., 2013). Dioecious plants appear to have an elevated extinction risk in general, potentially due to their inability to fall back on self- pollination for reproduction (Vamosi and Vamosi, 2005). Shorter flowering periods can be associated with extinction risk, and this risk may be exacerbated by climate change effects on temperature and precipitation, which could further shorten flowering times for these species

(Anderson, 1980; Mehrhoff, 1983; Lahti et al., 1991; Walck et al., 2001; Burne et al., 2003;

Ames et al., 2017).

Perhaps unsurprisingly, low seed production has been associated with increased extinction risk as well (Meagher et al., 1978; Banks, 1980; Miller et al., 2004; Dorsey and Wilson, 2011; Combs et al., 2013), although the pattern is not universal (Pavlik et al., 1993; Witkowski and Lamont,

1997; Mattana et al., 2010; Faucon et al., 2012). Low seed production may be a natural facet of the life-history of a species, or it may be due to some other factor, such as pollinator decline, disease, or habitat loss. Some of the extinction risk factors listed above may contribute to low seed production, which in turn could be a primary cause for those species’ increased extinction risk.

The factors underlying extinction risk are complex and often related, making no single conservation strategy universal to all threatened species. In situ conservation of species is the

gold standard, allowing these complex ecological processes and systems to continue working on their own by preserving the habitat they thrive in. However, in situ conservation is also costly and unable to keep up with the pace of habitat loss, making ex situ conservation, the preservation of species outside of their natural habitat, a necessary complement to in situ efforts.

Ex situ Conservation of Exceptional Species

While seedbanks are usually the simplest, most cost-effective method of ex situ plant conservation, they are not a viable strategy for the subset of plants known as exceptional species, or for those species that produce recalcitrant seeds (Pence, 2013). Exceptional species include those which do not produce seeds or produce very little seed as described above (Pence, 2010).

This designation can include those species which naturally do not produce seed, such as ferns and other seedless plants, as well as flowering plants which do not produce seed or produce very little due to either life history or environmental stressors. Recalcitrant-seed species do produce seed, but the seeds are so desiccation-sensitive that they are ineligible for seed-banking (Pence,

2010). For these subsets, the best method of ex situ conservation is often in vitro conservation, including propagation using plant tissue culture and long-term preservation using cryopreservation.

Methods of ex situ exceptional plant conservation

Ex situ plant conservation has historically taken the form of living collections and seed banking.

While seed banking is by definition not possible for exceptional species, their maintenance in living collections is a viable option. However, to grow adequate numbers of individuals to preserve the genetic diversity of many species often requires limited resources such as money,

technical expertise, and space. Propagation methods for exceptional species can include tissue culture as an efficient way to propagate species using tissue instead of seeds. Tissue culture generally involves the propagation of tissues in a sealed, sterile environment on a gel- or agar- based medium containing carbohydrates, micro- and macronutrients, and plant growth regulators. The method requires only small, non-destructive amounts of tissue from the parent plant for culture initiation, and many clones can be produced from a small number of starting cultures in a relatively short amount of time. Tissue culture is a widely used technique in the field of agriculture, where specific cultivars must be bred and maintained. However, the time, money, and expertise involved in developing tissue culture protocols for individual species limits its use in conservation for those species for which no other options exist (Pence, 2010).

While tissue culture is an excellent method for the propagation of exceptional species, plantlets must be regularly transferred to new growth media as they exhaust their resources. Furthermore, studies have shown evidence that repeated rounds of tissue culture may induce genetic mutation in plants, known as somaclonal variation (Bairu et al., 2010). For long-term storage, the most stable method of preservation for exceptional species is cryopreservation, storage in liquid nitrogen at -196°C. While cryopreservation protocols still must be developed for individual species, actual storage in liquid nitrogen requires little-to-no monitoring or intervention as metabolic processes and cell division have ceased (Martinez-Montero and Harding, 2015). Many short-lived seeds can endure some amount of desiccation, and these seeds can simply be plunged into liquid nitrogen and preserved for much longer than at current best practice seed-banking temperatures (-20°C) (Ballesteros and Pence, 2014). Desiccation-intolerant seeds and other plant tissues must undergo pre-treatments and cryoprotective methods before freezing to ensure

survival. For these tissues, the ultimate goal of cryopreservation is to avoid damage by ice crystal formation, usually through some form of dehydration, although this must be balanced with the maintenance of cellular integrity during dehydration (Pence, 2014).

Although a variety of cryopreservation methods exist, the most reliably used modern methods include encapsulation-dehydration, encapsulation-vitrification, and droplet-vitrification. Briefly, encapsulation-dehydration involves encapsulating tissues into an alginate bead, cryoprotecting them in a concentrated sucrose solution, and desiccating the beads before they are plunged into liquid nitrogen (Fabre and Dereuddre 1990; Engelmann et al., 2008). The other two methods rely on the use of vitrifying solutions, wherein tissues are treated with a cryoprotectant consisting generally of glycerol, ethylene glycol, DMSO, and sucrose to encourage the formation of a glass state, rather than ice crystals, when tissues are frozen (Hirai and Sakai, 1990; Pence, 2014).

Encapsulation-vitrification (Matsumoto et al., 1995) involves encapsulating tissues into an alginate bead before cryoprotection and freezing, and droplet-vitrification involves cooling tissues directly in a droplet of vitrification solution on aluminum foil before liquid nitrogen exposure (Halmagyi et al., 2005). Cryopreservation protocols have successfully been developed for a variety of exceptional species, and tissues have survived liquid nitrogen storage for over 23 years (Chapter 3).

Global Strategy for Plant Conservation

“Without plants, there is no life. The functioning of the planet, and our survival, depends on plants. The Strategy seeks to halt the continuing loss of plant diversity."

Global Strategy for Plant Conservation 2011 – 2020 Vision

The turn of the 21st century brought about a new urgency to protect biodiversity globally amidst increasing habitat loss, anthropogenic threats, and climate change. A resolution developed at the

XVI International Botanical Congress in 1999 called for plant conservation to be recognized as a global priority, which led Botanic Gardens Conservation International (BGCI) to convene a working group in 2000 that called for a global plant conservation strategy (Wyse Jackson and

Kennedy, 2009). This strategy was realized in the Global Strategy for Plant Conservation

(GSPC) adopted by the United Nations’ Convention on Biological Diversity (CBD) in 2002

(Wyse Jackson and Kennedy, 2009). The GSPC focuses on genetic diversity, species and community diversity, and habitat and ecosystem protection for higher plants, bryophytes, and pteridophytes (Secretariat of the Convention on Biological Diversity, 2014). The GSPC is subdivided into five objectives:

(I) To understand, document, and recognize plant diversity

(II) To urgently and effectively conserve plant diversity

(III) To use plant diversity in a sustainable and equitable manner

(IV) To educate and spread awareness of plant diversity & its importance

(V) To develop the capacity and public engagement necessary to the implementation

of the strategy

To achieve these objectives, the GSPC contains a set of 16 targets for countries and conservation organizations to achieve by set deadlines. The initial GSPC set target deadlines for 2010, and a subsequent review of progress on the GSPC revised and updated targets for a 2020 deadline.

Even though the deadlines are not fixed and the targets flexible, this structure provides guidance, direction, and goals for practitioners. Nations are able to utilize their own expertise and tailor their approaches while still maintaining an efficient global strategy for plant biodiversity protection.

GSPC Implementation

BGCI and the CBD provide a variety of resources to countries and organizations for implementing the GSPC, including international meetings, workshops, and online resources.

Countries are encouraged to implement the GSPC at a national level by developing their own plant conservation strategies using the GSPC as a framework, reviewing their own national progress in achieving GSPC targets without developing a formal national strategy, or integrating the GSPC into their existing Biodiversity Strategies and Action Plans (Secretariat of the

Convention on Biological Diversity, 2012). In addition to national implementation, regional implementation of the strategy is also encouraged, with regional responses to the GSPC already developed in Europe, the Caribbean, Central America and the Spanish-speaking Caribbean, and

Southeast Asia (Plants2020.net). The global scope of the strategy and the uneven distribution of both plant biodiversity and economic resources necessitates a plan for capacity-building, particularly in developing nations. While the GSPC stresses the conservation of plant diversity in one’s own country or region, institutions with existing international programs are encouraged to collaborate with institutions lacking resources and to send representatives to international meetings & workshops to spread knowledge. Furthermore, initiatives are underway to help support universal access to the literature and to develop online, easily accessible resources for institutions to share and exchange information, plans, and strategies. However, financial and

technological resources may still be a barrier to some countries fully implementing the GSPC and represents a hurdle to the safeguarding of plant biodiversity in these areas.

Target 8

Of the 16 targets in the GSPC, Target 8 is the focus of the Cincinnati Zoo & Botanical Garden’s

Center for Conservation and Research of Endangered Wildlife (CREW) Plant Research Division

(PRD). Target 8 calls for “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” (Secretariat of the Convention on Biological Diversity, 2012). Ex situ conservation works as a complement to in situ strategies to ensure that back-up collections of diversity exist to protect against extinction. Ex situ conservation strategies generally take one of the following forms: conservation of seeds in seedbanks, conservation of (typically) agricultural species in field genebanks, living collections in botanical gardens, and cryopreservation (Sharrock, 2012).

Target 8 is most often carried out by botanic gardens, with 1,090 gardens reporting to BGCI’s

PlantSearch database as of 2018. These institutions maintain collections of over 100,000 plant species, which represents almost a third of known plant species (Sharrock, 2012).

The GSPC has undergone two progress reviews: one in 2006 to assess progress for the 2010 target deadline, and one in 2014 to assess progress on the updated goals for 2020. As of 2014,

29% of species listed as threatened on the IUCN Red List were protected either in cultivation or seed/tissue banks, although this number is likely overestimated due to the lack of assessed species on the Red List (Sharrock et al., 2014). In the US alone, 39% of threatened species are maintained in ex situ collections, although federal funding for these collections remains low

(Sharrock et al., 2014). While plants make up 57% of the US federal endangered species list, they received only 3.82% of state and federal endangered species expenditures combined in 2011

(US Fish & Wildlife Service, 2011; Sharrock et al., 2014). Furthermore, basic plant science research funding remains low, with only 2% of federal life sciences research funds awarded to plant sciences in 2005 (McCormick and Tjian, 2010). While the US is making steady progress towards achieving Target 8 on a national level, practitioners must be resourceful to maximize conservation outcomes for limited resources.

Genetic consequences of ex situ conservation

The recognition of the importance of maintenance of genetic diversity in conservation collections brought revised collection guidelines for ex situ conservation. Historically, collectors were more concerned with species diversity than genetic diversity within species. However, researchers have found that adequate diversity within a population can help populations better respond to threats and environmental change (Hoban and Strand, 2015). While the majority of guidelines for collecting diversity in species focus on seed collection protocols (Frankham et al., 2014;

Hoban and Strand, 2015), ex situ exceptional plant conservation brings a unique set of challenges in the collection of tissues rather than seeds. Without seed collection, the potential to collect multiple genotypes of seeds from a single mother plant is limited, as any tissue collections are clones of the mother. Since exceptional plant species are also often rare or threatened, the collected genetic diversity must be balanced with the health and stability of the population, as only healthy individuals can be sampled. Because ex situ collections may be used in the future for restoration or reintroduction of a species, the collection of adequate genetic diversity is paramount (Pence, 2013). Small population size and low genetic diversity are associated with

inbreeding depression and genetic drift, both of which may contribute to extinction by reducing the genetic variation with which a population can respond to threats (Angeloni et al., 2011).

While small population size and low diversity can often not be avoided in natural populations of rare and threatened plant species, it can be mitigated in ex situ collections. Having some knowledge of the genetic diversity and structure of a population before collection can ensure that ex situ collections accurately reflect the diversity of their natural counterparts, which in turn helps future restorations and reintroductions be successful.

Study objectives

The present research is an attempt to investigate the genetic effects of ex situ exceptional plant conservation using CREW’s collection of micropropagated and cryopreserved plant species. The full ex situ conservation process for exceptional plants involves the collection of individuals, the preservation of individuals ex situ, and the ultimate use of those individuals in restorations.

Genetic consequences of each of these steps were investigated using case studies of exceptional species. To better inform initial collecting efforts, the population genetic structure and diversity was investigated in a federally endangered exceptional species, Hedeoma todsenii (Chapter 2).

While the species was already maintained in the CREW facility, little was known about the genetic structure of the species overall. Results of the study will help inform future collections and restoration of the species. To understand the effects of long-term tissue culture and cryopreservation on species, an experiment was undertaken to investigate whether DNA or RNA damage occurred over a variety of species’ tenure in the ex situ collection (Chapter 3). This study informs what kind of ongoing monitoring efforts best ensure the genetic fidelity of preserved species. Finally, the federally endangered Minuartia cumberlandensis was used as a case study

to investigate the genetic consequences of a micropropagated reintroduction (Chapter 4). This study will help ensure the success of future micropropagated reintroductions. While the present research is by no means a comprehensive investigation of genetic consequences in ex situ exceptional plant conservation, the results contained herein will provide answers to several critical questions that will help future ex situ conservation efforts to preserve plant biodiversity for future generations.

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Chapter Two

Collecting for ex situ conservation: An investigation of the genetic diversity of Hedeoma todsenii Irving (Lamiaceae) in White Sands Missile Range and Lincoln National Forest, New Mexico

Megan Philpott Department of Biological Sciences University of Cincinnati Cincinnati, OH 45221-0006

Introduction

Conservation of rare plant species has become a global concern with the realization that factors like climate change and growing human encroachment into plant habitats are increasing the threat of extinction for rare species (Wyse Jackson and Kennedy, 2009). To better address these threats on a global scale, the United Nations’ Convention on Biological Diversity adopted the

Global Strategy for Plant Conservation (GSPC) in 2002 (Wyse Jackson and Kennedy, 2009;

Secretariat of the Convention on Biological Diversity, 2014). While a major focus of the GSPC is in situ species and habitat conservation, the authors realized the need for an ex situ conservation plan as well to supplement in situ work. In particular, Target 8 of the GSPC calls for at least 75% of threatened plant species globally to be protected in ex situ collections (with

100% protected eventually), and for at least 20% of those species to be available for restoration programs (Secretariat of the Convention on Biological Diversity, 2014). For most species, an ex situ collection can be stored cheaply and efficiently in a conventional seed bank. However, an increasing number of plant species, particularly many rare and threatened species, are being discovered to be unsuitable for conventional seed banking due to issues like lack of seed production or recalcitrant seeds (Pence, 2014). These species, known as exceptional species, must be preserved ex situ in alternative collections like botanic gardens (as living collections) or in liquid nitrogen (cryopreservation).

To effectively protect a species, an ex situ collection must capture adequate genetic diversity from the natural population(s). Botanic gardens are valuable and necessary facilitators of plant conservation through cultivation of living collections, but it can be difficult to cultivate the great number of individuals necessary to capture the genetic diversity of natural populations due to

both space and financial resource limitations. Cryopreservation, the long-term storage of tissue in liquid nitrogen (LN) is a vital tool for the ex situ conservation of exceptional species. While the development of cryopreservation protocols requires specialized knowledge and equipment, once cryopreserved, many samples can be maintained indefinitely in LN without the need for continuous maintenance (Pence, 2010). Furthermore, samples can be banked in small units

(seeds, shoot tips, spores, embryonic axes, etc.), meaning that many more genetic individuals can be conserved in a much smaller space than in traditional living collections. For exceptional species in which seed collection is not possible, ex situ collections are composed of genetic clones of individuals from natural populations. To effectively conserve the genetic diversity of these species, it is of vital importance to understand the genetic structure in natural population(s) before an ex situ collection is established.

Rare and threatened exceptional species that do not produce seeds are also at risk for low genetic diversity within populations. Low genetic diversity can arise naturally due to life-history traits such as clonal growth, inbreeding, or pollinator loss, but genetic diversity can also be lost in natural populations if there is any significant population decline (Barrett and Kohn 1991; Kramer and Havens, 2009). These issues are compounded when the species in question is potentially capable of sexual reproduction but is known to be reproducing asexually. In ex situ collections of threatened and endangered plants, low genetic diversity can arise from under-sampling of the original natural populations when the ex situ collection is established, sampling when the population number is already significantly diminished, or simply from low storage capacity in the managing organization. A lack of genetic diversity in any population can hobble the ability of that population to respond to environmental changes, threats, or diseases (Templeton 1991;

Pence et al., 2009; Pence et al., 2017). Often, threatened populations are targets for reintroduction (the establishment of plants into an area in which it formerly occurred but is now believed to be extinct) or population augmentation (the establishment of plants into an existing population to increase population size or diversity) (Godefroid et al., 2011). Lack of genetic diversity can clearly be an issue if a low-diversity ex situ collection is used to populate a reintroduction of or augment a threatened species. Inadequate initial collection of genetic diversity in an exceptional species would lead to an ex situ ‘stock’ depauperate of the naturally occurring genotypes, and use of this stock for any population augmentation or reintroduction could alter the population structure of the target population or species at best and endanger the population or species by swamping out natural diversity at worst.

The Center for Conservation and Research of Endangered Wildlife (CREW) at the Cincinnati

Zoo & Botanical Garden maintains ex situ collections of exceptional threatened and endangered plants for potential future use in augmentation and reintroduction. Due to the focus on exceptional species, ex situ collections are maintained either in vitro, in cryopreservation, or both

(Pence, 2010). Hedeoma todsenii is one such exceptional species that is currently being maintained in an ex situ collection at CREW due to its low seed viability and lack of seed production. CREW has developed both tissue culture and cryopreservation protocols for H. todsenii, and the ex situ collection was recently shown to have survived up to 13 years of LN storage (Pence et al., 2009; Pence et al., 2017). A preliminary genetic analysis of micropropagated plants originally collected from two populations in the Sacramento mountains was undertaken in 2009 using randomly amplified polymorphic DNA (RAPD) markers to understand the levels of genetic diversity within and among natural populations used for ex situ

collection. This study showed that while genetic diversity was low within populations, it increased among populations, consistent with low seed production (Pence et al. 2009).

In order to understand the genetic diversity of the species more fully, the present study used microsatellite markers developed for related species in the Lamiaceae to survey the population genetic structure of the species across its entire geographic range. Investigating within & among population genetic diversity and structure on a larger geographic scale than previously assessed allows us to better prepare for potential future augmentations and reintroduction events. The

Center for Plant Conservation (CPC) recommends undertaking a genetic study of a species before reintroduction when no viable seed set is occurring (Maschinski and Albrecht, 2017).

Although a reintroduction or augmentation is not an immediate concern for this species, this study is intended to serve as a guide for any potential future augmentations or reintroductions of

H. todsenii or similar species, so that management agencies will be able to respond quickly and effectively.

Study System

Hedeoma todsenii (common name: Todsen’s pennyroyal) is an herbaceous perennial of southern

New Mexico, found in sites in the San Andres and Sacramento Mountains. The species was named in 1979 by Dr. Robert S. Irving after being discovered by its namesake, Dr. Thomas K.

Todsen, during a flora survey of the San Andres Mountains in 1978 (Irving, 1979). Upon its discovery, H. todsenii was known from only two small populations, and the US Fish & Wildlife

Service moved quickly in 1981 to list the species as federally endangered (US Fish & Wildlife

Service 1981). Initial threats to the species included low population size, habitat disturbance by

off-road vehicles and grazing, and a general lack of population management (US Fish & Wildlife

Service 1981). By 2001, however, the US Fish & Wildlife Service amended the list of threats to include wildfires, low fecundity and dispersal ability, and limited suitable natural habitat (US

Fish & Wildlife Service 2001). The Hedeoma genus is primarily distributed in the southwestern

US and northern Mexico (Irving 1979). Although some populations of H. todsenii are found in

Lincoln National Forest and on Bureau of Land Management lands nearby in the Sacramento mountain range, the majority of known populations are found within the US Army’s White

Sands Missile Range of south-central New Mexico in the San Andres mountain range (Figure

2.1; US Fish & Wildlife Service 2001). Due to their mountain habitat, the White Sands and

Lincoln National Forest populations are separated by the Tularosa basin. Given the range of habitat, future potential threats to the species include military activities and changes in land management, making continuing management of the species necessary (US Fish & Wildlife

Service 2001).

Hedeoma todsenii is found primarily on steep slopes in Pinyon-Juniper woodlands (US Fish &

Wildlife Service 2001). It grows on loose gravel substrate composed of gypseous limestone

(Irving 1979). It has been hypothesized that H. todsenii may be a relict species which thrived under past cooler and moister climate conditions 10,000 years ago (US Fish & Wildlife Service

2001). Some H. todsenii populations do occur near trees characteristic of higher elevations like

Pinus ponderosa (Ponderosa pine) and Pseudotsuga menziesii (Douglas fir), supporting the relict species theory, but the association is still strictly hypothetical (US Fish & Wildlife Service

2001).

Plants grow to 10-18cm with red tubular flowers blooming spring through summer (Irving 1979).

While thousands of stems can be found in individual populations, rhizomatous spread allows genetically distinct individuals to grow in clumps of 1-20 stems, making population counts difficult (US Fish & Wildlife Service 2001). Despite populations numbering up to thousands of clumps, it is estimated that less than 20% of plants flower, and of those flowering plants, seed set occurs in only 25-27% (US Fish & Wildlife Service 2001). Although flowers are capable of producing four nutlets each, on average only 1.6 – 2.3 nutlets per flower are produced (US Fish

& Wildlife Service 2001). For those seeds produced, 5% germination was reported in one study

(US Fish & Wildlife Service 2001), and 33% of embryos were viable in another investigation

(US Fish & Wildlife Service 2001). Given these estimates, a hypothetical population of 1,000 individuals may produce 200 flowering individuals, with 54 of those actually setting seed.

However, only 124 seeds may be produced in total, and, assuming a germination rate of 5%, only

6 seedlings may be produced for the year from this total population of 1,000 individuals.

This very low flowering, seed set, and seed viability is a primary cause for H. todsenii’s designation as an exceptional species. For this reason, the conservation strategy for H. todsenii includes cryopreservation of shoot tips in lieu of seed banking. Like seed banks, cryopreserved collections can ultimately be used for augmentation and reintroduction, although species stored as shoot tips must be recovered and propagated using tissue culture. Given their potential use in restoration, it is vital to catalogue and understand the genetic diversity of natural H. todsenii populations.

Materials & Methods

Material Collection & DNA Extraction

Leaf samples were collected from six populations in New Mexico’s White Sands Missile Range in 2015, 2016, and 2017, and from one population Lincoln National forest in 2017. New sites were visited for every collection, and no site was re-sampled after initial collection. These seven sites were sampled for a total of 399 samples. For the present analysis, 13 microsatellite loci were analyzed in all samples collected from the seven populations: two White Sands (WS) populations sampled in 2017 (WS-17-1, N=105 and WS-17-2, N=105), one Lincoln National

Forest (LNF) population sampled in 2017 (LNF-17-3, N=38), two White Sands populations sampled in 2016 (WS-16-4, N=34 and WS-16-5, N=14), and two White Sands populations sampled in 2015 (WS-15-6, N=86 and WS-15-7, N=17). From North to South, the geographic order of the White Sands populations is WS-16-5, WS-17-2, WS-16-4, WS-15-7, WS-17-1, and

WS-15-6. At each site, single leaves were taken from individual healthy plants with at least two stems to avoid stressing the population. Population sizes at each site varied from less than 50 individuals to thousands of stems. To minimize re-sampling within clonal mats, individuals were only sampled if they were not visually continuous with any other sampled individual. Leaf samples were maintained in glassine wax bags on silica gel at 4°C until DNA extraction. DNA extractions were performed using a modified CTAB protocol (Doyle and Doyle 1987) and kept at -20°C until genetic analysis.

Microsatellite marker selection

Microsatellite markers have not yet been developed for H. todsenii, so a literature search was undertaken to identify microsatellites developed in related Lamiaceae genera. Microsatellites

from four related genera were chosen for testing: Conradina (Edwards et al., 2008), Suzukia

(Hung et al., 2007), Scutellaria (Hsu et al. 2009), and Rosmarinus (Segarra-Moragues and

Gleiser, 2009). Twenty-six microsatellite primers were chosen from the selected papers for testing in H. todsenii, and eight primers successfully amplified, all from Conradina (Table 2.1;

Edwards et al., 2008).

DNA Amplification

PCR reaction mixtures consisted of 10µl multiplexed reactions containing 5µl Promega 5X

Green GoTaq Reaction Buffer (Promega), 0.2µM of each reverse primer and each fluorescently tagged forward primer (Table 2.1), 0.3µl of genomic DNA, and 3.7µl of distilled water (Culley et al., 2013). Thermocycling conditions included an initial denaturation at 95°C for 5 minutes, followed by 35 cycles of denaturation at 94°C for 30 seconds, annealing at 52°C for 90 seconds, and elongation at 70°C for 60 seconds, and a final extension at 60°C for 30 minutes. PCR products were visualized using fragment analysis on a 3730xl DNA Analyzer (ABI) at Cornell

University’s Biotechnology Resource Center using a LIZ 500 size standard. Allele calls were made using Genemarker software ver. 1.85 (SoftGenetics; Hulce et al., 2011).

Data Analysis

Data clean-up and analysis was performed using GenAlEx ver. 6.5 (Peakall and Smouse, 2012) and R ver. 3.3.2 (R Core Team, 2016). GenAlEx was used to estimate private alleles, the sample size per locus, number of alleles per locus, number of expected alleles per locus, and observed and expected heterozygosity. The R package Poppr (Kamvar et al., 2014) was used to check for uninformative loci, generate a genotype accumulation curve, and estimate population

differentiation, genotypic diversity, and genotypic evenness. For analyses in Poppr, individuals with no amplification over any alleles were excluded from the dataset. Hedrick’s G’ST was used as the fixation index instead of GST due to the generally high heterozygosity observed in microsatellite markers. G’ST can range from 0 to 1, unlike GST which may have a maximum value <1 when heterozygosity in the molecular marker or population is high (Hedrick 2005).

G’ST will approach 1 when all populations are fixed for their respective alleles, while a G’ST near

0 indicates no fixation in the population (Jost et al. 2018). To estimate allelic differentiation among populations, Jost’s D was used (Jost 2008). D will approach 1 when all alleles are private to the sampled populations and will approach 0 if each population shares all alleles at the same frequency (Jost et al. 2018). These two measures of genetic differentiation together provide a more complete picture of population structure that is more useful for making conservation decisions than using either measure alone. Poppr was also used to generate a rarefaction curve to visualize genotypic richness using expected multilocus genotypes (MLGs). Genotypic diversity was estimated with MLGs using Simpson’s index, λ (Simpson, 1949), which is equal to one minus the sum of squared genotype frequencies (Grunwald et al. 2017). To account for uneven sample sizes, λ’ is calculated by multiplying λ by N/(N-1) (Grunwald et al. 2017). Null allele frequencies were estimated using the software program ML- Null Freq (Kalinowski and Taper,

2006). ML-Null Freq utilizes a maximum likelihood approach to test for the presence of null alleles due to homozygote excess.

Poppr was used to estimate linkage disequilibrium by estimating 푟̅푑, an index of association to assess if loci are linked while accounting for the number of loci that were sampled (Brown et al.,

1980). Loci are assumed to be in linkage disequilibrium if the variance in the number of

heterozygous loci is significantly higher than would be expected under random association. The software program INEST 2.0 (Chybicki and Burczyk, 2009) was used to estimate the inbreeding coefficient while accounting for null alleles. This program uses a Bayesian approach and an individual inbreeding model to estimate inbreeding in the population. Models were generated using the inclusion of null alleles, inbreeding, and genotyping failures to investigate which parameters best explained the variation in the data. Model selection was performed based on the lowest deviance information criterion (DIC). For each model, 500,000 MCMC cycles were run with a 10% burn-in. The inbreeding coefficient (defined in INEST as the probability that two alleles at a locus are identical by descent) was then estimated using a maximum likelihood approach.

Results

Locus Selection & Null Alleles

The eight primer pairs used in the study generated 15 consistently amplifying loci in Hedeoma todsenii (Table 2.1). Two of these loci were found to be monomorphic and excluded from the dataset. A genotype accumulation curve indicates that after 12 sampled loci, observed diversity will not appreciably increase with additional loci (Figure 2.2; Kamvar et al., 2015). From this, we can be assured that there are enough loci (13) in the dataset to discriminate unique genotypes.

After locus selection, the number of alleles per locus ranged from 2 to 14, with a mean of 5.54.

Null alleles were inferred to be present in some loci and populations due to homozygote excess.

However, homozygote excess may reflect factors other than the presence of null alleles, such as high inbreeding, rarity, or the Wahlund effect (Wahlund, 1928). For accurate interpretation of the results, however, the null alleles should be taken into account when interpreting the data, as a

high incidence of null alleles can artificially deflate rates of heterozygosity and inflate rates of population differentiation.

Descriptive Statistics

The average number of alleles found per locus was relatively low (mean = 2.43), and levels of observed heterozygosity (mean = 0.20) and expected heterozygosity (mean = 0.26) were similarly low (Table 2.2). Five of the seven populations contained at least one private allele, although the LNF population exhibited 17 private alleles across all loci, the highest number by far (Table 2.2). Some loci appear to be limited to expression in certain populations. For example,

CSR30-4-1 is nearly absent from any WS population but prevalent in the LNF population, and

GLA30-4-3 is nearly absent in the LNF population but present in every WS population (Figure

2.3).

None of the loci were found to be consistently in Hardy-Weinberg equilibrium across all populations, and no population was found to be in Hardy-Weinberg across all loci (Figure 2.4).

In particular, the LNF population is significantly out of Hardy-Weinberg equilibrium across all but one locus. Deviations from Hardy-Weinberg could be due to many factors, including inbreeding, low effective population size, presence of null alleles, or genotyping errors. Waples

(2015) suggests that deviations from Hardy-Weinberg causing an excess of observed homozygotes could be attributed to the Wahlund effect, an artificial inflation of homozygosity due to cryptic subpopulation structure. The Wahlund effect should produce a positive correlation between Wright’s F-statistics FST (the fixation index) and FIS (the inbreeding coefficient) across

loci. For the entire dataset, FST & FIS exhibited a weakly positive, but non-significant correlation

(Kendall’s rank correlation, τ = 0.21, p=0.37; Figure 2.5).

Inbreeding

A model including the null allele (n), inbreeding (f), and genotyping failures (b) parameters performed better than a null model including only the null allele and genotyping failure parameters (DIC, nfb = 14,678.80 vs. nb = 15,423.42), indicating that inbreeding is likely present in the populations despite the incidence of null alleles. The average inbreeding coefficient was

0.20 (95% highest posterior density interval 0.18-0.22), which can be interpreted as the probability that two alleles at a locus are identical by descent.

Linkage disequilibrium was also used to estimate inbreeding within populations. This was influenced by the fact that every population sample except WS-16-5 was found to contain clones.

Population WS-17-2 had the highest number of clonal individuals in the sampled population with only 25 multilocus genotypes (MLGs) in 105 sampled individuals. Even after removing the clones from the sample set, the population was still found to be in linkage disequilibrium

(푟̅푑=0.20, p=0.01). Population WS-15-6 was also found to be in linkage disequilibrium with a high number of clones (47 MLGs over 72 samples, 푟̅푑=0.16, p=0.001), but after correcting for clones, the population was back in linkage equilibrium (푟̅푑=0.09, p=0.23). No other population was found to be in linkage disequilibrium.

Population differentiation

The global estimate of G’ST was high (G’ST=0.64, 95% CI 0.62-0.66), suggesting that most populations are trending toward fixation. The global estimate of Jost’s D is lower (D=0.35, 95%

CI 0.34-0.36), suggesting that despite a high incidence of fixation, there is less allelic differentiation among populations. A pairwise estimation of D across populations indicates that the Lincoln National Forest population has the most allelic differentiation when compared to all

White Sands populations (D range 0.06-0.69, Table 3). Allelic differentiation is much lower between only the White Sands populations. Similarly, pairwise G’ST indicates population differentiation is highest between LNF and WS populations, but there are still fairly high levels of fixation even among the WS populations (Table 2.3). There is no apparent pattern between geographic distance and fixation or allelic differentiation (Table 2.3). A principal coordinates analysis (PCoA) generated using Nei’s genetic distance suggests that the Lincoln National Forest population segregates separately from the White Sands populations, although null alleles likely inflate the genetic distance observed (Figure 2.6).

Genotypic richness, diversity, and evenness

Genotypic richness was visualized by a rarefaction curve to account for differences in sample size. Richness was approximately equal in all White Sands populations, with the exception of

WS-17-2, which showed markedly lower richness (Figure 2.7). Richness was highest in the

Lincoln National Forest population. Lambda and λ’, the normalized probability that two genotypes randomly pulled from the population are different and its standardized form respectively, indicate there is genotypic diversity in the populations overall with the maximum in a 2016 sampled White Sands population (WS-16-5, λ’=1.00), and the Lincoln National Forest

population (LNF-17-3, λ’=1.00; Table 2.4). The lowest genotypic diversity was found in the

2017 White Sands population 2 (WS-17-2, λ’=0.69). Gene diversity as measured by Nei’s unbiased gene diversity was highest in the Lincoln National Forest population (LNF-17-3,

Hexp=0.52), and lowest in the 2017 White Sands population 2 (WS-17-2, Hexp=0.16; Table 2.4).

Genotypic evenness was highest in a 2016 White Sands population (WS-16-5, E5=1.00), and lowest in population WS-17-2 (E5=0.36; Table 2.4).

Discussion

The sampled populations of H. todsenii were not small, with most populations maintaining hundreds to thousands of stems. However, growth in clonal mats and the high number of genetic clones found within the dataset suggest that the actual number of genetically distinct individuals is likely much smaller than the number of stems in each population. Genetic drift and inbreeding are often observed in rare species with small population sizes. Drift can contribute to a decrease in variation within subpopulations concurrent with an increase in differentiation among subpopulations, while inbreeding can cause an increase in homozygosity (Ellstrand and Elam,

1993). Lack of seed production, observed clonal growth, and rarity are consistent with the low diversity within populations and high differentiation among them. Heterozygosity in the populations is markedly low, and the low average number of alleles per locus and gene diversity are also consistent with the rarity of H. todsenii and other species (Brzyski and Culley, 2011).

Together, these measures suggest that genetic drift may be one of the drivers of genetic structure in the species. Previous research has found relatively high rates of heterozygosity in clonally reproducing species, despite low genetic diversity (Brzyski and Culley, 2011; Meloni et al.,

2013). The low heterozygosity observed in H. todsenii suggests that inbreeding is likely persistent in the reproductive strategy of the species.

Inbreeding is likely playing a large role in the population structure in addition to genetic drift, driving individual populations toward fixation in addition to reducing heterozygosity. Most genetic differentiation seems to be partitioned between the White Sands and Lincoln National

Forest regions, rather than within the White Sands populations. This is reflected in the geographic distance between the two regions, as well as the apparent barrier to dispersal that is the Tularosa basin (Figure 2.1). Distance between populations can have a negative effect on outcrossing by pollinator activity, so the genetic distance between the White Sands populations on the San Andres mountain range and the Lincoln National Forest population on the

Sacramento mountain range has likely been increasing for longer than the genetic distance among White Sands populations (Devaux et al., 2008). Outcrossing populations partition an estimated 20% of diversity among populations, while selfing populations partition closer to 50%

(Hamrick and Godt, 1996; Bussell, 1999). Low allelic differentiation among White Sands populations suggests some outcrossing among populations either may still be occurring, or had occurred in the past, but high population differentiation even among White Sands populations suggests current gene flow is limited. These results are consistent with studies on related rare and endemic Lamiaceae species which found most diversity partitioned within populations (Mattner et al., 2002; Rodrigues et al., 2013). Little is known about the pollinator system of H. todsenii because of the remote locations of populations and low levels of flowering, although hummingbirds have been observed visiting the flowers (US Fish & Wildlife Service 2001).

Pollinator studies may help elucidate whether low outcrossing is still occurring within White

Sands, or whether low genetic differentiation among some populations is a relic of past pollinator activity that has since declined. Globally, defaunation and pollinator loss have been amply documented, and extinctions have cascading effects on ecosystems (Young et al., 2016).

Pollinator loss may be contributing to the population structure and low seed set of H. todsenii observed in this study.

Genotypic diversity and allelic diversity were highest in the Lincoln National Forest population, and at least one seedling was observed in this population, suggesting that pollinator activity in the Lincoln National Forest may be more frequent, compared to White Sands populations with their lack of seed set. The two southernmost sampled population sampled had the most diversity among the White Sands populations. Most White Sands populations looked similar to one another in terms of genotypic and allelic diversity, but one population in particular (WS-17-2) showed consistently lower genotypic diversity and allelic richness. This population also showed evidence of clonal reproduction through linkage disequilibrium, had the highest number of clones in collected samples, and had the lowest genotypic richness, evenness, and diversity. WS-

17-2 was one of the larger, healthier sampled populations, so the low diversity and high clonality is unlikely an artefact of low sample size. The population is not found on the extremes of the range of the species, and it relatively close to other sampled populations, so the reasons for low diversity are not immediately clear. Additional field research at this population may lend valuable insight into whether it is a naturally less diverse population or if it is undergoing some sort of exogenous disturbance or threat.

The observed deviations from Hardy-Weinberg equilibrium could be due to a variety of factors, including the Wahlund effect. Although all populations diverged from Hardy-Weinberg in at least one locus, the LNF population diverged in all but a single locus. No significant evidence was found for the Wahlund effect in the overall data; however, given the substantial divergence from Hardy-Weinberg and the consistently higher measures of genotypic diversity and evenness in the LNF population, it is possible that there could be cryptic population structure in this population alone. A lack of population structure in the six sampled WS populations could be overwhelming the evidence for the Wahlund effect in the overall data even if it is occurring in the single LNF population. Again, more field studies and pollinator studies in particular may shed light on an underlying population structure beyond just the geographical location, particularly for populations found outside of White Sands.

Conservation Implications

The majority of sampling for ex situ conservation at CREW has been focused on White Sands populations, but these results suggest that intensive sampling should include the Lincoln

National Forest as well, to preserve the maximum diversity in the species. The White Sands populations exhibit high levels of fixation, but the diversity of alleles among these populations is low. General conservation guidelines for ex situ collections suggest that at least 50 individuals should be sampled from each of 50 populations (Brown & Marshall 1995). However, recent evaluation of these guidelines for seed collection through modeling suggests that collection of 50 individuals is likely too low for populations with high rates of selfing and low dispersal (Hoban and Strand, 2015). Given the low genetic diversity, low seed production, and high inbreeding in most sampled H. todsenii populations, the most effective strategy for preservation of genetic

diversity should include sampling more than 50 individuals per target population, although special care should be taken to ensure that the same genetic individual is not sampled multiple times due to the clonal growth system. However, low allelic diversity among the White Sands populations suggests that intensive sampling in a few target populations may be more successful than taking a lower sample size from many populations, which should allow for the preservation of maximum diversity without significantly increasing resource input. Efforts should be undertaken to sample evenly across the entire range of the species, as geographic distance and environmental barriers appear to play a part in the partitioning of genetic diversity in the species.

Because Lincoln National Forest had the highest diversity and was genetically distinct from the sampled White Sands populations, sampling priority for ex situ collection should be focused here and in the surrounding Sacramento Mountain populations. However, the incidence of private alleles in individual White Sands populations and the evidence of clonal reproduction necessitate a continued sampling effort in the White Sands populations of the San Andres Mountains as well. Among the White Sands populations, the southern populations appear to be more diverse than the northern populations, suggesting that additional sampling within White Sands should focus on southern populations, although the first priority in the San Andres range should be to target remaining unsampled populations. Understanding the genetic structure of a species is key before undertaking any sort of augmentation or reintroduction. Using these guidelines and the genetic structure of the species, the conservation strategy for H. todsenii will effectively preserve the maximum genetic diversity and allow for the potential future use of the ex situ collection in a reintroduction or augmentation following CPC recommendations (Maschinski and Albrecht,

2017). An effective ex situ collection strategy will support in situ conservation efforts and preserve H. todsenii for generations to come.

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Figure Legends

Figure 2.1: Map showing the general location of the White Sands populations (in green) in the

San Andres mountain range and the Lincoln National Forest populations (in red) in the

Sacramento mountain range. Separating the two mountain habitats is the Tularosa basin. Exact locations are not shown due to the federally endangered status of the species. Base map is taken from Google Earth.

Figure 2.2: Genotype accumulation curve for the analyzed dataset.

Figure 2.3: Heat map showing missing data across loci and populations surveyed.

Figure 2.4: A plot of deviations from Hardy-Weinberg equilibrium (HWE) across loci (y-axis) and populations (x-axis). Populations and loci significantly out of HWE are depicted in fuchsia, and those in HWE are depicted in aqua. White boxes denote NA values due to missing data.

Figure 2.5: FST vs. FIS across loci. A positive correlation between FST & FIS could indicate a

Wahlund effect in the population.

Figure 2.6: Principal coordinates analysis of the sampled populations. Included are White Sands populations from 2017 (WS-17-1 & WS-17-2), 2016 (WS-16-4 & WS-16-5), and 2015 (WS-15-

6 & WS-15-7), and the Lincoln National Forest population from 2017 (LNF-17-3). Axis 1 explains 46.46% percent of the variation in the data, while axis 2 explains 21.29%, and axis 3 explains 14.66%.

Figure 2.7: Rarefaction curve showing genotypic richness in all sampled populations. Number of expected multilocus genotypes (eMLGs) is plotted against the sample size to avoid biasing genotypic richness by sample size.

Tables

Table 2.1: Microsatellite loci used in the study as reported in Conradina (Edwards et al., 2008). Several primer pairs each amplified multiple loci.

a b c Locus Sequence (5’ – 3’) Repeat Motif Size (bp) NA Label CSRC1 F: *TGAACGCACTTCGCCTAGTA (TG)24 120 – 122 2 6-Fam R: TCATGCTGGCAAATTAGCTG 198 1 (155 – 171) (8) CSRF F: *CACTTTTTCCCGGTACCTCA (TG)17 171 – 177 4 Pet R: CCCCATAGAGAATTGGCAAG (159 – 201) (7) GLA-G F: *GGTGAAAAGAGGCTGAGCTG (GT)8 227 – 241 4 6-Fam R: GGAATAAGGATTGAAGGGGAAG 492 1 (251 – 267) (11) GLA-H F: *ACCAGTGTATCAACATCTGTTCG (C)3(TATG)2(TG)4T 226 – 269 14 Vic R: AAGCCCATAGAATGAAGAGCA (TG)5(GA)7A(AG)4 280 – 301 9 (166 – 214) (7) CSR30-4 F: *GCTTGGTCGTTGCTACCTTC (GT)15(GA)15 156 – 168 7 6-Fam R: GCTGTTTCAGGCGCAATATAA 171 – 182 4 (175 – 219) (11) CSR50-7 F: *TTCTGATAAACCTGTCTCAACTT (TG)14 160 – 166 3 Pet R: (177 – 201) (5) AACCTGAAAATAAATCATACGGAGT BR50-2 F: *CGATGGCCATTTTTGTTCTT (GT)11 185 – 190 3 Ned R: TGCCTAACCCATTGCCTTTA 194 – 209 8 (205 – 237) (11) GLA30- F: (C)12(TA)3(C)2 188 – 195 4 6-Fam 4 *AAGCTCAATAATTAACATTGGCACTC 197 – 204 4 R: GGAAAGCATCAGATGTAGCCAGAG 236 – 251 6 (259 – 281) (9) aSize of the fragment. Multiple observed fragments are displayed on separate lines. Fragment sizes from the source paper are displayed in parentheses. bNumber of alleles observed for each locus. Multiple observed alleles are displayed on separate lines. Number of alleles observed in the source paper are displayed in parentheses. cFluorescent label used for microsatellite multiplexing. *M13 tag (CACGACGTTGTAAAACGAC) added to 5’ end of forward primers to facilitate fluorescent labeling.

Table 2.2: Descriptive statistics for individual populations sampled in Lincoln National Forest (LNF) in 2017 and White Sands (WS) in 2015, 2016, and 2017. White Sands populations are arranged from northernmost sampled (WS 2016-5) to southernmost sampled (WS 2015-6).

a b c d e f g h Population N NP NL NA NE HO HE LNF 2017 Mean 38 17 27.23 3.62 2.42 0.14 0.52 SE 3.43 0.45 0.33 0.04 0.06 WS 2016 - 5 Mean 14 0 3.23 1.23 1.11 0.16 0.17 SE 0.73 0.26 0.22 0.08 0.07 WS 2017 - 2 Mean 105 3 81.77 2.77 1.18 0.11 0.15 SE 9.33 0.51 0.14 0.07 0.05 WS 2016 - 4 Mean 34 1 16.62 1.92 1.42 0.23 0.25 SE 2.03 0.18 0.12 0.10 0.07 WS 2015 - 7 Mean 17 1 8.69 1.57 1.57 0.31 0.23 SE 1.03 0.31 0.31 0.12 0.09 WS 2017 - 1 Mean 105 0 79.31 3.15 1.91 0.24 0.31 SE 9.44 0.73 0.48 0.09 0.09 WS 2015 - 6 Mean 86 1 42.15 2.34 1.50 0.22 0.23 SE 5.03 0.46 0.19 0.08 0.07 Total Mean 399 23 37.00 2.43 1.59 0.20 0.26 SE 3.76 0.19 0.11 0.03 0.03 aSampled populations. bSample size cNumber of private alleles dMean sample size per locus eMean number of alleles per locus fMean effective population size per locus gMean level of observed heterozygosity hMean level of expected heterozyosity

Table 2.3: Pairwise D & G’ST values across populations. G’ST is indicated above the diagonal, and D is indicated below the diagonal. White Sands populations are arranged from northernmost sampled (WS-16-5) to southernmost sampled (WS-15-6).

LNF-17-3 WS-16-5 WS-17-2 WS-16-4 WS-15-7 WS-17-1 WS-15-6 LNF-17-3 0.85 0.87 0.85 0.80 0.82 0.85 WS-16-5 0.68 0.63 0.20 0.49 0.35 0.36 WS-17-2 0.68 0.23 0.67 0.66 0.67 0.66 WS-16-4 0.69 0.06 0.28 0.66 0.36 0.61 WS-15-7 0.62 0.23 0.30 0.35 0.37 0.23 WS-17-1 0.66 0.13 0.33 0.14 0.16 0.27 WS-15-6 0.68 0.12 0.29 0.27 0.07 0.10

Table 2.4: Diversity statistics for the microsatellite analysis. White Sands populations are arranged from northernmost sampled (WS-16-5) to southernmost sampled (WS-15-6).

a b c d e f g h Population N MLG eMLG SE λ λ' E5 Hexp LNF-17-3 38 37 9.94 0.25 0.97 1.00 0.99 0.52 WS-16-5 7 7 7.00 0.00 0.86 1.00 1.00 0.25 WS-17-2 104 25 4.92 1.33 0.68 0.69 0.36 0.16 WS-16-4 32 22 8.52 1.02 0.93 0.96 0.75 0.25 WS-15-7 14 11 8.14 0.79 0.87 0.93 0.78 0.26 WS-17-1 105 66 9.29 0.79 0.97 0.98 0.73 0.33 WS-15-6 72 47 9.12 0.88 0.96 0.98 0.73 0.23 Total 372 213 9.11 0.97 0.97 0.97 0.29 0.49 aSample size bNumber of observed multilocus genotypes cNumber of expected multilocus genotypes dStandard error for number of expected multilocus genotypes eSimpson’s index fSimpson’s index corrected for sample size (λ’= λ*(N/(N-1))) gEvenness hNei’s unbiased gene diversity

Figures

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 2.5

Figure 2.6

Principal Coordinates (PCoA)

WS_15_7

WS_15_6

WS_17_1 Coord.2 WS_17_2 WS_16_4 LNF_17_3

WS_16_5

Coord. 1

Figure 2.7

Chapter Three

Long-term stability of RNA and DNA in ex situ cryopreserved collections: a case study of shoot tips stored for up to 23 years

Megan Philpott Department of Biological Sciences University of Cincinnati Cincinnati, OH 45221-0006

Introduction

The ex situ conservation of plants serves as a fail-safe against complete extinction when a species becomes extirpated in the wild (Secretariat of the Convention on Biological Diversity,

2014). The international seed banks at Kew Gardens (the Millennium Seed Bank) and the

Svalbard Global Seed Vault serve as a testament to the importance of this endeavor. However, a subset of plant species are known as exceptional species, meaning they cannot be seed-banked using traditional methods. Exceptional plants require other means of ex situ conservation for a variety of reasons, such as lack of seed production or production of recalcitrant seeds (Pence,

2013). For these species, the alternative long-term storage method of cryopreservation is required.

Genetic integrity in ex situ collections

Cryopreservation is the long-term storage of plant seeds and tissues in liquid nitrogen (LN) at -

196°C, and is commonly coupled with micropropagation to prepare, recover, and propagate individuals. The successful preservation of individuals that maintain their viability and their ability to recover from LN storage and grow into normal, true-to-type organisms is a primary goal in both plant and vertebrate cryopreservation (Lin and Tsai, 2012). In addition to viability and morphology, the preservation of genetic information during cryopreservation is another concern (Harding, 2004). Particularly in conservation applications, cryopreservation must preserve a faithful representation of the original target species or population. Although the biological factors responsible for successful LN storage and recovery are diverse and complex, the stability of DNA and RNA in tissues throughout the process is a common concern (Hao et al., 2001; Harding, 2004; Mikuła et al., 2011; Ai et al., 2012; Heringer et al., 2013; Kaity et al.,

2013; Martín et al., 2015). The effects of cryopreservation on RNA are well studied in tissue samples preserved primarily for later downstream analyses in tissue banks (Ahmann et al., 2008).

Similarly, tests of DNA stability are fairly common in developed cryopreservation protocols, particularly for agricultural plant species (Harding, 2004). However, tests of the stability of both

RNA and DNA in cryopreserved plant tissues are less common, particularly for rare, threatened, and endangered species cryopreserved for conservation purposes. The present study is an attempt to understand the relationship between cryopreservation and DNA and RNA stability in exceptional species in order to better inform ex situ conservation of these species by cryopreservation and tissue culture.

RNA stability

Whereas DNA is a generally stable molecule, RNA has a shorter lifespan because it is prone to damage. The RNA integrity of banked tissue samples is an accepted measure to determine the quality and suitability of a tissue’s RNA for downstream genetic analyses (Schroeder et al.,

2006; Kap et al., 2014). The Agilent 2100 Bioanalyzer System provides an automated, high- throughput method to check RNA quality by calculating a RNA Integrity Number (RIN)

(Schroeder et al., 2006). RNA quality has historically been assessed using gel electrophoresis to visualize the relative ratio of 28S to 18S ribosomal RNA (rRNA), which should be around 2.0 in a high-quality sample (Schroeder et al., 2006). However, differences in the amount of rRNA isolated from different tissues, the differing banding patterns produced by degradation by various

RNases, and the subjectivity of assessing a ratio visually lead to this ratio having a dubious correlation to actual downstream analysis success (Schroeder et al., 2006). Although RIN is calculated partially using the ratio of 28S to 18S rRNA, it also integrates additional measures of

quality as well to increase the correlation between RIN and actual RNA quality for use in downstream analyses (Schroeder et al., 2006). The RIN is a simple 1 – 10 quality rating, with 1 indicating total degradation of RNA and 10 indicating totally intact RNA. A RIN value of 8 typically indicates a sample of high enough quality to be used in transcriptomic analyses. While

RNA is a stable molecule, it can quickly be degraded by ubiquitous RNase molecules which can, in turn, skew results of or even entirely preclude a downstream analysis (Schroeder et al., 2006).

There is evidence that some damaged RNA may be repaired, but it appears that most damaged

RNA is likely degraded completely since RNA molecules are generally quickly used for cellular processes and turned over (Wurtmann and Wolin, 2009). RNA degradation has been shown to be one of the early results of apoptosis, making overall RNA quality a potential indicator of cellular health (Del Prete et al., 2002).

RNA stability in cryopreserved plant tissues

Plant seeds and tissues undergoing cryopreservation are at particular risk for oxidative damage

(Ren et al., 2015). Stress in tissues can be induced by nearly every step of the cryopreservation process, leading to the formation of reactive oxygen species (ROS), including shoot tip isolation, treatment with cryoprotectants, and freezing (Ren et al., 2015). While ROS are utilized in normal plant cell signaling and stress response, excess accumulation can lead to oxidative damage and cell death (Sharma et al., 2012; Chmielowska-Bąk et al., 2015). The accumulation of ROS during or after cryopreservation has been observed in a number of species (Johnston et al., 2007;

Fang et al., 2008; Skyba et al., 2012), and oxidative damage has been linked to reduced recovery or growth following cryopreservation (Chen et al., 2015; Di et al., 2017). In seeds, which typically must undergo periods of dormancy prior to germination, oxidative damage has been

linked with aging and loss of viability (Chen et al., 2013). Decline in RNA quality and loss of viability have been correlated with increasing age of soybean seedlots, but there has not been a clear link established directly between loss of viability and decline in RNA quality (Fleming et al., 2017). However, given the similar relationships between viability and age and between RNA quality and age, it is reasonable to test whether there could be a correlation between RNA quality, age, and viability in cryopreserved shoot tips and seeds. In order to better understand this relationship, the present study examines the relationship between RNA integrity, age, and viability in plant seeds and tissues of 26 species stored at 4°C, -20°C, and -196°C for up to 23 years.

DNA stability

In addition to RNA degradation, DNA damage or mutation is a common concern for micropropagated and cryopreserved samples. Genetic variability generated during micropropagation (or vegetative propagation by tissue culture) was first defined as somaclonal variation by Larkin and Scowcroft in 1981. The spontaneous generation of genetic variability is not limited to tissue culture, as the fields of horticulture and agriculture have utilized this variation throughout history in plant breeding (Bairu et al., 2010). Research suggests that spontaneous variability may be more common in tissues propagated using tissue culture, and the phenomenon of somaclonal variation is still used as an efficient way to generate new cultivars in horticultural and agricultural species (Krishna et al., 2016). While generating interesting new morphologies may be beneficial to commercially grown species, it is detrimental when species are being preserved for conservation purposes (Harding, 2004). Although somaclonal variation is well-documented, the causes underlying its occurrence remain unclear. Somaclonal variation has

been linked to micropropagation method, starting tissue type, plant hormone use, duration of subculture, and even the propensity of certain genotypes to mutation (Bairu et al., 2010).

Whatever the reason, it is a common enough occurrence that many cryopreservation and tissue culture procedures test for genetic stability during protocol development (Galdiano et al., 2013;

Hazubska-Przybył et al., 2013; Soni and Kaur, 2013; Coelho et al., 2014; Plitta et al., 2014; San

José et al., 2015). These tests typically use short (<1 month) tenures in LN by necessity. In contrast, the present system used in this study represents a unique opportunity to assess genetic stability using molecular markers after long-term (>10 years) cryopreservation.

The purpose of this study was to investigate the effects of long-term cryopreservation on the

RNA & DNA stability of plant tissues. The Center for Conservation and Research of Endangered

Wildlife (CREW) at the Cincinnati Zoo & Botanical Garden maintains the CryoBioBankTM, one of the oldest and most diverse cryo-banks of threatened and endangered plants in the world, and individual plants have been successfully recovered and grown after up to 23 years of cryostorage. This collection presents an excellent opportunity to examine the effects of long-term cryopreservation on the genetic stability of species contained therein. Using CREW’s unique conservation collection of plant species, we used a subset of cryopreserved shoot tips and seeds to investigate the relationship between RIN and survival after long-term liquid nitrogen storage.

For the present study, RNA was extracted from seeds and shoot tips immediately after removal from liquid nitrogen to compare the RIN of samples to their survival after cryostorage. When possible, cryopreserved samples were compared to samples stored at 4°C, -20°C, and/or fresh tissues, and within-species comparisons between pre-treatments and cryopreservation methods

were assessed as well. In total, RNA was extracted and analyzed for seeds and shoot tips of 18 mostly exceptional species stored in the CryoBioBankTM.

Materials and Methods

RNA Stability Study Plant Material

Seeds of 6 species and shoot tips of 20 species were removed from LN storage and used for RNA extractions (Table 3.1). Seeds had originally been air-dried before being plunged in LN, or placed in -20°C or 4°C, for long-term storage. Shoot tips were cryopreserved using either the encapsulation dehydration (ED) (Fabre and Dereuddre, 1990) or encapsulation vitrification (EV)

(Hirai and Sakai, 1999) procedures. For seeds, samples were removed from LN and allowed to thaw on the bench for 5 minutes before RNA was extracted from individual seeds where possible. The exception was Cyrtopodium punctatum, in which seeds were pooled by weight into

1-2 mg groups due to their small size. For shoot tips cryopreserved by ED, cryovials were removed from LN and rewarmed at ambient lab temperature (approximately 22°C) for 15 minutes before the dried, encapsulated shoot tips were removed from the vial. For shoot tips cryopreserved by EV, cryovials were removed from LN and rewarmed by swirling the cryovial in a water bath of approximately 45°C for 2 minutes. After rewarming, shoot tips were pooled into replicates of 5-10 shoot tips to ensure adequate quantities of RNA for analysis, and RNA was extracted immediately.

Viability Tests

Unless otherwise noted, seeds were germinated on moist filter paper in a sealed petri dish at room temperature. Conium maculatum and Baptisia leucantha were stratified in the dark at 4°C

for 3 months in wet paper towels before germination. Cyrtopodium punctatum was germinated on 0.5X Murashige & Skoog (MS) medium. Viability was assessed as the percentage of seeds exhibiting germination or growth at 4 weeks post-sowing.

Shoot tip samples were removed from LN and grown on the recovery medium that was initially used for each species in pre-cryopreservation LN exposure trials. For shoot tips cryopreserved by

ED, cryovials were removed as previously described and plated on their respective recovery media. For shoot tips cryopreserved by EV, cryovials were removed as previously described, and encapsulated shoot tips were rinsed in a solution of 1.2M sucrose in MS medium before being transferred to their respective recovery media. Survival was assessed at 4 weeks post-recovery.

Samples were noted as surviving if they showed any green tissue at 4 weeks, and viability was assessed as the percentage of shoot tips exhibiting any survival.

RNA Extractions & RIN Calculation

RNA was extracted using a Qiagen RNeasy Plant Mini Kit from either pooled samples (shoot tips) or individual seeds, as described. After extraction, RNA was stored at -20°C until it could be analyzed. Prior to analysis, RNA concentration was assessed using a NanoDrop spectrophotometer, and samples were diluted to a 1ng/µl concentration to mitigate the effects of

RNA quantity on the analysis. The Agilent Bioanalyzer was used to calculate the RIN as a measure of RNA quality using an Agilent RNA 6000 Pico Kit (Agilent, CA, US). The relationship between RIN and survival after cryostorage was assessed using Spearman’s rank correlation. Where possible, the relationship between within-species treatment effects and RIN was assessed using the Wilcoxon rank sum test.

Validation Tests

Two validation experiments were initially run to determine if RIN could discriminate between treatments for seeds and shoot tips. For the seed validation, freshly collected Conium maculatum seeds and C. maculatum seeds that had been stored for approximately 22 years in LN, at -20°C, or at 4°C were used for RNA extraction. Twenty individual seeds from each treatment were used as replicates. For shoot tips, the validation experiment was run using Crotalaria avonensis shoot tips to determine if moderate stress could affect measured RNA quality. Treatments simulating typical micropropagation, cryoprotection pre-treatment, and salt stress were used. Shoot tips of three genotypes, approximately 1mm in length, were excised from in vitro cultures of shoots growing on a maintenance medium of MS salts and minimal organics (Linsmaier and Skoog,

1965) with 3% sucrose, 0.25% Gelzan (gellan gum, Caisson Labs), and 1mg/L benzylaminopurine (BAP) (1B medium) and placed onto 60x15mm plates containing either 1B medium (simulating typical micropropagation conditions), 1B medium plus 0.3M mannitol

(simulating a typical cryoprotectant), or 1B medium plus 0.15M NaCl (simulating mild stress) for 1 week. Cultures were incubated at 26°C under cool white fluorescent lights with a 16:8 hour light:dark cycle. After 1 week, RNA was extracted from individual shoot tips as previously described. Shoot tips were not pooled to determine variation in RNA quality among replicates. In addition, RNA was extracted immediately from shoot tips freshly isolated from the same stock culture to use as a baseline for micropropagated tips. Six replicates were run for each treatment and genotype. For both seeds and shoot tips, RIN was assessed using the Agilent Bioanalyzer.

Statistical Analyses

All statistical analyses were performed in R statistical programming software ver. 3.3.2 (R Core

Team 2016). Statistical analysis for differences in RIN among treatments was performed using a

Kruskal-Wallis rank sum test, and pairwise comparisons were assessed using a Tukey and

Kramer post-hoc test. Wilcoxon rank sum tests were used to test for significant differences in mean RIN between two groups. Kendall’s rank correlation was used to determine the overall correlation between survival and RIN. All graphs were generated using the GGplot2 package in

R (Wickham, 2009).

DNA Stability Studies Plant Material

Species were included in the DNA stability tests if the same genotype had been maintained both in long-term LN storage and in tissue culture (Table 3.2). Tissue culture samples had not previously been cryopreserved. For cryopreserved species, samples were removed from LN as described above. Samples were allowed to grow in tissue culture for at least four weeks. Once samples were large enough to subculture, tissue was taken for DNA extraction. DNA extractions were performed using a modified CTAB protocol (Doyle and Doyle, 1987). DNA extractions were checked for concentration and quality using a Nanodrop spectrophotometer before analysis.

Quality was assessed using the A260/A280 absorbance ratio of the sample.

DNA Amplification

Genetic stability was assessed using sequence-related amplified polymorphism (SRAP) markers

(Li and Quiros, 2001). SRAP markers are dominant, non-species-specific markers that are designed to target regions flanking coding regions. Twenty-four SRAP primer pairs were tested

in the four species, and six primer pairs were selected based on consistent, reproducible amplification of bands (Table 3.3). PCR was carried out in 10µl reactions containing 5µl

Promega 5X Green GoTaq Reaction Buffer (Promega), 0.4µM of each primer, 0.3µl of DNA, and 3.7µl distilled water. Thermocycling conditions included an initial denaturation at 95°C for 5 minutes, followed by 5 cycles of denaturation at 94°C for 60 seconds, annealing at 35°C for 60 seconds, and elongation at 72°C for 60 seconds; 35 cycles of denaturation at 94°C, annealing at

50°C, and elongation at 72°C; and a final extension at 72°C for 10 minutes. PCR products were visualized and scored using gel electrophoresis on 1.2% sodium borate agarose gels. Samples were run in duplicate to verify the observed band patterns.

Genetic Stability Tests

Amplified bands were scored as present (1) or absent (0). Within each genotype, the total number of bands that matched between samples was totaled. Bands that dropped out or shifted position were scored as band changes. Band changes could occur as a result of mutation in the priming sequence of the target DNA which prevents annealing of the primers and subsequent amplification, or as a result of additions or deletions within the amplified fragment which change the size of the resulting amplified band. Percent change was calculated per genotype, species, and overall by dividing the number of samples exhibiting any band change by the total number of samples in each subset. Because samples were not genotyped before cryopreservation or tissue culture initiation, it is impossible to know what the initial genotype of the starting material was.

Therefore, any band that did not match a band found in the same genotype cohort in the study was deemed a “band change.”

Results

Validation Tests

For Conium maculatum seeds (stored at 4°C, -20°C, in LN, or harvested fresh), seeds stored at -

20°C had a significantly higher RIN on average than seeds stored at 4°C (Kruskal-Wallis rank sum test, Χ2=9.06, df=3, p=0.03). However, average RIN values of other storage conditions did not differ significantly from one another (Figure 3.1).

For the freshly isolated shoot tips from in vitro grown shoots of C. avonensis compared with tips isolated and incubated on media for two days, the average RIN for 1B (maintenance medium) incubated shoot tips was 7.92, significantly higher than the average RIN for fresh shoot tips, 6.84

(Kruskal-Wallis rank sum test, Χ2=9.56, df=3, p-0.02). Again, no other treatments differed significantly from one another (Figure 3.2).

RNA Integrity and Survival Across Species

There was no overall correlation between RIN and survival for all species and tissue types assessed in this study (Kendall’s rank correlation, τ = 0.12, p=0.26, Figure 3.3).

RNA Integrity and Storage Method

There was no significant difference in RIN between Baptisia leucantha seeds stored at -20°C or in LN for 23.5 years (Wilcoxon rank sum test, W=46.5, p=0.59).

RNA Integrity and Survival

There was no significant correlation between RIN and survival of the accession for any species assessed with more than five samples. Cyrtopodium punctatum seeds were originally harvested from one immature pod and one mature pod. While post-LN storage survival was higher in the seeds harvested from the mature pod (92.0% vs 19.4% survival), average RIN was higher in the immature pod’s seeds. However, high variation in RIN among all C. punctatum samples contributed to only near-significance of this result (Wilcoxon rank sum test, W=2.5, p=0.05).

RNA Integrity and Pre-treatment

A subset of C. avonensis accessions were pre-treated with either mannitol or mannitol plus abscisic acid (ABA), a plant hormone used to prevent intracellular freezing during cryopreservation, before LN storage (Na and Kondo, 1996). Five shoot tips were pooled for each

RNA extraction, and six and eight replicates were assessed for mannitol and mannitol plus ABA respectively. A single genotype was used in an attempt to control for genetic effects. There was no significant difference in average RIN between the mannitol and mannitol plus ABA samples

(Wilcoxon rank sum test, W=16, p=0.52).

RNA Integrity & Cryopreservation Method

There was no significant difference within species in average RIN between samples cryopreserved using different methods. Specifically, there was no significant difference in RIN of Hedyotis purpurea shoot tips cryopreserved using ED or EV (Wilcoxon rank sum test, W=10, p=0.50). There was also no significant difference in RIN of Lobelia boykinii shoot tips cryopreserved using ED or EV (Wilcoxon rank sum test, W=18.5, p=0.45).

Genetic Stability

Overall, 57% (20/35) of tested samples exhibited some form of DNA band change, including

64% (9/14) of tissue culture samples and 52% (11/21) of cryopreserved samples. For cryopreserved samples, 38% (3/8) of tissue exhibiting normal growth from which leaf tissue was taken exhibited change, and 29% (2/7) of tissue growing as callus exhibited change. In

Deeringothamnus rugelii, 63% (5/8) exhibited change, consisting of 60% (3/5) of genotype 1 and 67% (2/3) of genotype 2. Of D. pulchellus, 33% (3/9) of samples exhibited change, with

43% (3/7) of genotype 1 and 0% (0/2) of genotype 2. In Asimina tetramera, 0% (0/2) exhibited change. In Minuartia cumberlandensis, 75% (12/16) of samples exhibited change, consisting of

100% (3/3) of genotype 10, 100% (2/2) of genotype 3, 67% (2/3) of genotype 5, 67% (2/3) of genotype 6, 33% (1/3) of genotype 1, and 100% (2/2) of genotype 11.

Discussion

Overall, long-term cryopreservation does not appear to have a clear effect on RNA stability in the surveyed plant tissues. However, somaclonal variation in the form of DNA changes does appear to occur after both cryostorage and micropropagation.

RNA Integrity

A previous study has shown that there is a correlation between RNA quality and storage time for conventionally stored seeds over 20 years (Fleming et al., 2017). The present study tested that relationship in exceptional species stored in long-term cryopreservation. Using CREW’s unique collection of cryopreserved shoot tips and seeds, we tested the correlation between RIN and

survival among species, storage types, pre-treatments, and cryopreservation methods. Across all measures, no significant relationship was found between RIN and survival after storage, suggesting that the link between RNA quality and viability is not reflected for cryopreserved species.

An initial test of the ability of RIN to detect non-fatal stress in shoot tips compared RNA quality in fresh shoot tips versus tips cultured on maintenance and stress media. This test showed no relationship between RIN and treatment of the shoot tips prior to RNA extraction. Media prepared with salt have been successfully used in a prior study to understand the effects of salt stress on micropropagated plants, and the NaCl stress medium was chosen here to act as a mild stressor to determine if non-fatal stress could be detected by the RIN analysis (Pérez-Clemente et al., 2008). However, shoot tips cultured on the salt-stress medium had significantly higher RINs than fresh shoot tips. This result may simply reflect the stress of initial shoot tip excision in the freshly excised tips, compared with tips that were allowed to incubate and likely undergo some recovery for 2 days on the test media. The lack of significant difference in RIN among the remaining groups could be attributable to salt-stress resistance in the test species, C. avonensis, but it also suggests that RIN may be incapable of detecting moderate stress in the tissue cultured shoot tips prior to the tipping point where major cell death starts to occur. This result is consistent with previous research on seeds suggesting that RNA quality declines when major losses in germination potential begin to occur, which is presumably consistent with cell death

(Fleming et al., 2017). Decline in RNA quality may be undetectable in shoot tips unless they undergo catastrophic damage.

Across the wide range of exceptional and conventional species preserved as shoot tips and seeds and included in this study, there was no relationship between RNA quality and survival. Previous research in soybeans has found that RNA quality can differ within a seed depending on which tissue the RNA is isolated from (seed coat, plumule, embryonic axis, or cotyledon), although

RNA extracted from all tissue types was found to decline with age (Fleming et al., 2017).

However, this discrepancy may partially explain the present results as here RNA was extracted from entire seeds and shoot tips due to their small size. For the present study, undamaged RNA in more protected tissues may be masking damaged RNA in delicate tissues, and vice versa.

Furthermore, since some seeds and tissues were pooled for individual RNA extractions to ensure adequate RNA isolation for analysis, intact RNA in healthy tips and seeds could be masking degraded RNA in non-viable tips and seeds. Degraded RNA pooled into the same sample as high-quality RNA could cause an over- or under-estimation of the true RNA quality. Variation in

RIN among replicates was high, indicating that RNA quality may vary between individual samples and tissues within a sample. For these reasons, the RIN may not be a sensitive enough measure to detect changes in RNA quality after LN storage in small-sized seeds and shoot tips.

DNA Stability

In addition to RNA quality, DNA quality was investigated to test for evidence of somaclonal variation after long-term cryopreservation. Changes in the form of band drop-outs or additions were detected in three of the four tested species. The rate of band changes was slightly higher in tissue culture samples than in cryopreserved samples, which is consistent with current hypotheses about the causes of somaclonal variation that mainly involve facets of the tissue culture process such as the use of plant growth regulators and subculture frequency and duration

(Bairu et al., 2010). The polymorphism observed in the present study is substantially more than observed in another Lamiaceae species, Thymus lotocephalus (Coelho et al., 2014). In T. lotocephalus, only 3/10 RAPD primers exhibited polymorphism between genetic clones, and band changes were observed in three individuals for a total rate of change of 0.06% (Coelho et al., 2014). Our findings are closer in line with those found in Picea abies and Picea omorika after somatic embryogenesis and cryopreservation (Hazubska-Przybył et al., 2013). In this study,

2/13 microsatellite loci exhibited mutation in embryogenic tissue after recovery from cryopreservation, although one genotype reverted back to its initial state after growth on a maturation medium (Hazubska-Przybył et al., 2013). It should be noted that in the present study and cited studies, the genetic markers used target only a small portion of the genome to assess genetic change. For this reason, the actual mutation rate following cryopreservation across the entire genome may be over- or under-estimated.

While the preparation and recovery steps for cryopreservation typically involve at least some form of tissue culture for non-seed tissues, LN storage itself should not be a substantial cause of genetic change as the storage temperature should halt cell activities completely. However, the act of freezing has been shown to induce oxidative damage as evidenced by the detection of the oxidative damage marker 8-hydroxy-2’-deoxyguanosine in Ribes meristems following LN exposure (Johnston et al., 2010). Therefore, it can be difficult to determine whether genetic changes observed in cryopreserved samples in the present study are due to cryopreservation preparation or recovery, freezing, or even post-cryopreservation micropropagation for growth.

However, the high rate of genetic changes observed in the tissue culture samples that had never undergone cryopreservation suggests that tissue culture-related processes are a more likely cause

than LN storage itself. Observed AFLP band changes in Vitis vinifera tissues have been correlated to PVS2 exposure, with variation increasing with increasing PVS2 exposure time even in non-cryopreserved samples (Marković et al., 2015). It’s possible that cryoprotectants are a source of the variation observed here in D. rugelli, D. pulchellus, and M. cumberlandensis.

The observation of genetic changes in the micropropagated and cryopreserved samples in this study is a prime example of why testing for genetic stability before and after cryopreservation, and before, during, and after tissue culture propagation, is extremely important when preserving exceptional species in ex situ collections. Evidence for genetic changes after short-term storage in LN is varied, ranging from no detected changes (Hazubska-Przybył et al., 2013; Soni and

Kaur, 2013; Wang et al., 2014) to varying amounts of detected change (Coelho et al., 2014;

Marković et al., 2015; Martín et al., 2015; Hazubska-Przybył and Dering, 2017). Comparing results of genetic stability experiments across studies is difficult due to the variety of methods used to assess stability. Commonly used methods range from broad measures of genetic change through the use of flow cytometry (Galdiano et al., 2013; San José et al., 2015) to finer-scale measures using molecular markers like RAPDs (Coelho et al., 2014; Cordeiro et al., 2015),

ISSRs (Li et al., 2014; Yin et al., 2014), SSRs (Agrawal et al., 2014; Zhang et al., 2015), AFLPs

(Maki et al., 2015; Marković et al., 2015), or a combination of markers (González-Benito et al.,

2016; Wang et al., 2017). While molecular markers are more likely to detect minor changes due to their ability to pick up on nucleotide changes, they necessarily sample a very small percentage of the genome (Martinez-Montero and Harding, 2015). Although more comprehensive and specific sequencing techniques (ranging from species-specific SSRs to whole genome sequencing) may be possible for labs working on a single or very small number of species, these

techniques are often cost- and resource-prohibitive for labs banking a wide variety of species.

This problem is compounded in exceptional plant conservation, where tissue banks are working with typically genetically uncharacterized species as well.

The use of SRAP markers to assess genetic stability is an excellent tool for labs working with a wide variety of species under resource limitations, at least until next-generation sequencing technologies become less expensive and reliant on specialized technical knowledge. SRAPs are more reproducible than RAPDs, easier to use than AFLPs, and less resource-intensive to develop than microsatellites (Li and Quiros, 2001). Although not widely utilized for this purpose, SRAPs have been used in Rabdosia rubescens, Tetrastigma hemsleyanum, and Distylium chinense to assess genetic stability following cryopreservation, slow-growth storage, and somatic embryogenesis, respectively (Ai et al., 2012; Li et al., 2014; Peng et al., 2015). All three species are used in traditional Chinese medicine but remain largely genetically uncharacterized, making

SRAPs an economical, reproducible choice for assessment of genetic stability. Different molecular markers and techniques can provide snapshots into different aspects of the genome, so wherever possible, different methods should be used in combination to gain a more complete picture of genetic stability following cryopreservation.

Conclusions

In summary, the seeds and tissues maintained for up to 24 years in LN showed no clear pattern in

RNA integrity in relationship to their ultimate survival after cryopreservation, the method of long-term storage, cryopreservation method, or pre-treatment conditions. Although RIN has been used successfully to discriminate between high and low viability in soybean seeds, the method

does not appear to be applicable to the present dataset and is not recommended as a method to monitor viability or survival during or after LN storage for ex situ conservation collections

(Fleming et al., 2017). The lack of correlation between RIN and survival here could be due to a combination of factors like the use of whole seeds and shoot tips rather than specific tissues within a seed that may have differentially preserved RNA, or species-related variation in RNA preservation mechanisms. RIN values were not consistently high in cryopreserved tissues, suggesting cryopreservation does not maintain higher RNA quality in all tissues. However, RIN also did not correlate with viability after cryostorage. This suggests that cryopreservation of shoot tips and seeds could facilitate recovery and growth after storage without a dependence on

RNA quality.

In addition, tissues preserved using long-term cryopreservation or tissue culture do show evidence of genetic change. Since the purpose of ex situ conservation is to preserve individuals true-to-type, any observed change can be an issue (Harding, 2004). However, based on previous studies, it is possible that changed genotypes would revert back to their initial genotype during other stages of development, such as additional tissue culture growth for cryopreserved samples or rooting and acclimatization for micropropagated samples (Hazubska-Przybył et al., 2013).

There is an immediate need for a more reliable test of genomic stability for species cryopreserved for conservation purposes, and SRAP markers are reproducible, easy to use and interpret, and low-cost, in addition to working in a wide variety of species that often have completely uncharacterized genomes. Despite DNA-level changes, however, the plants tested here were surviving and growing. A more long-term study should be undertaken to determine

whether genetic changes translate into abnormal growth or morphologies later in the lifetime of the plant to ensure the faithful preservation of genetic material for these species.

The present study represents the survival and growth of seeds and tissues after long-term cryostorage despite varying RNA quality. For those samples which did not recover after cryostorage, RNA quality did not appear to be a significant factor. Tissues do exhibit some somaclonal variation following long-term cryostorage and micropropagation, although the effect on growth and morphology is unknown. Future studies should further investigate the conditions underlying somaclonal variation after long-term storage, its effects on growth and morphology, and the ability of genotypes to revert to their original state after recovery. However, despite observed changes, cryopreservation is a worthwhile and useful tactic in the ex situ conservation of exceptional plant species, and represents the best way to preserve these species long-term.

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Figure Legends

Figure 3.1: Conium maculatum validation experiment. Shown is a boxplot of RIN values for seeds stored at -20°C, 4°C, in LN, and freshly harvested seed.

Figure 3.2: Crotalaria avonensis validation experiment. Shown is a boxplot of RIN values for freshly harvested shoot tips and shoot tips cultured on 1B maintenance medium, mannitol cryoprotectant medium, or salt stress medium for 1 week.

Figure 3.3: Scatterplot of RIN vs. average survival for all species assessed, with the exception of samples used for the validation experiments. All samples shown are shoot tips except for

Baptisia leucantha, which were banked as seeds.

Tables

Table 3.1: Species used in the RNA quality study. Shown is the species name, tissue type, storage conditions (liquid nitrogen [LN], fresh collected tissue, or storage at 4°C or -20°C) with cryopreservation procedure in parentheses if applicable (air-dried [AD], encapsulation [E], encapsulation-dehydration [ED], or encapsulation-vitrification [EV]), time in storage, and the number of tests performed for each experimental set. Number of tests refers to the replicate RIN analyses per sample group, and number of samples per test refers to the number seeds or shoot tips pooled for each RNA extraction and subsequent test.

Species Tissue Storage Time in Number of Average Average type conditions storage tests (number Viability RIN (pre- (years) of samples per treatment) test) Conium Seed Fresh 0.00 20 (1) 55.0% 8.5 maculatum L. 4°C (AD) 21.92 20 (1) 73.0% 8.5 -20°C (AD) 21.92 20 (1) 77.0% 8.8 LN (AD) 21.92 20 (1) 76.0% 8.6 Baptisia leucantha Seed -20°C (AD) 23.50 10 (1) 4.4% 7.7 Torr. & A. Gray LN (AD) 23.50 10 (1) 41.7% 7.8 Cyrtopodium Seed LN (AD) 7.00 10 (1-2 mg) 55.7% 7.2 punctatum (L.) Lindl. Selaginella Shoot Fresh 0.00 2 (2) N/A 7.6 uncinate (Desv. tip LN (E) 20.17 2 (5) 0.0% 7.5 Ex Poir.) Spring Crotalaria Shoot LN (EV) 16.25 14 (5) 44.1% 5.9 avonensis tip DeLaney & Wunderlin Amsonia sp. Shoot LN (EV) 14.92 2 (10) 43.5% 4.9 tip Gonzalagunia Shoot LN (EV) 14.92 1 (10) 80.0% 3.2 hirsuta (Jacq.) K. tip LN (EV) 15.67 1 (10) 87.3% 7.1 Schum. LN (EV) 15.75 1 (10) 91.2% 1.8 Kalanchoe sp. Shoot LN (EV) 14.50 1 (10) 0.0% 7.6 tip Saintpaulia Shoot LN (ED) 5.75 1 (10) 0.0% 1.0 ionantha subsp. tip (ID: Confusa)

Saintpaulia Shoot LN (ED) 5.75 1 (12) 0.0% 1.3 ionantha subsp. tip (ID: MWA) Rorippa sp. Shoot LN (ED) 5.42 1 (5) 37.5% 7.8 tip Mespilus Shoot LN (ED) 2.42 2 (10) 40.0% 4.6 canescens Phipps tip Rhexia aristosa Shoot LN (EV) 13.08 3 (10) 0.0% 5.5 Britton tip LN (EV) 13.75 2 (10) 34.3% 4.8 LN (EV) 15.08 1 (10) 58.9% 2.3 LN (EV) 15.25 1 (10) 46.7% 7.7 LN (EV) 15.42 4 (10) 41.7% 8.1 LN (EV) 16.33 3 (10) 65.0% 4.1 Hippobroma Shoot LN (ED) 16.92 2 (10) 39.9% 5.4 longiflora (L.) G. tip LN (ED) 17.00 1 (10) 24.3% 4.4 Don LN (ED) 17.92 1 (10) 0.0% 3.8 LN (ED) 18.00 2 (10) 72.4% 4.2 LN (ED) 18.17 1 (10) 65.7% 1.0 LN (ED) 18.50 1 (10) 90.0% 2.3 Lobelia boykinii Shoot LN (ED) 14.00 1 (10) 31.7% 4.7 Torr. & A. Gray tip LN (ED) 14.50 1 (10) 69.4% 5.7 ex A. DC. LN (ED) 14.67 1 (10) 56.0% 5.5 LN (ED) 14.75 1 (10) 30.2% 1.0 LN (EV) 14.83 3 (10) 16.7% 4.4 LN (ED) 15.33 3 (10) 50.0% 2.6 LN (ED) 16.17 1 (10) 55.2% 7.3 Hedyotis purpurea Shoot LN (ED) 14.42 1 (10) 92.9% 6.9 (L.) Torr. & A. tip LN (ED) 14.50 1 (10) 97.4% 8.1 Gray LN (ED, EV) 14.58 3 (10), 1 (15) 82.5% 6.6 LN (ED, ED, 14.67 2 (10), 1 (3), 1 83.2% 8.7 EV) (14) Eucephalus Shoot LN (ED) 18.33 1 (10) 81.1% 7.0 (Aster) vialis tip LN (ED) 18.50 1 (10) 80.6% 3.8 Bradshaw Spermacoce Shoot LN (ED) 16.83 2 (10), 1 (15) 54.0% 6.2 assurgens Ruiz & tip LN (ED) 17.00 3 (10) 89.3% 6.0 Pav. LN (ED) 17.08 1 (10) 75.2% 5.6

Table 3.2: Samples used in the DNA stability tests. Shown are the species and number of genotypes assessed. Within each genotype, the storage method (LN or tissue culture [TC]), tissue type for LN-stored samples at time of DNA extraction (leaf [L] or callus [C]), years in storage for LN-stored samples, and the total number of observed band changes is shown.

Species Genotype Storage Tissue Years in Total Band ID Method Type Storage Changes Deeringothamnus rugelii (B.L. 1 TC 9.7 – 12.6 1 Rob.) Small LN C 0 LN L 0 LN L 1 LN L 1 2 TC (agar) 1 TC (gel) 0 LN C 1 Deeringothamnus pulchellus 1 TC 9.6 – 11.5 1 Small LN C 3 LN L 1 LN C 0 LN L 0 LN L 0 LN C 0 2 LN L 0 LN C 0 Asimina tetramera Small 7 LN L 9.3 – 13.7 0 LN C 0 Minuartia cumberlandensis 10 TC 7.0 – 12.7 3 (Wofford & Kral) McNeill TC 1 LN L 3 3 TC 1 LN L 2 5 TC 1 TC 0 LN L 6 6 TC 1 TC 0 LN L 6 1 TC 0 TC 0 LN L 5 11 TC 3 TC 3

Table 3.3: SRAP primer combinations used for the genetic stability test. Shown are the primer pair used, the sequence of each primer, the range of the number of bands amplified across the four species (D. rugelii, D. pulchellus, A. tetramera, and M. cumberlandensis), and the average number of amplified bands.

Primer Sequence (5’ – 3’) No. Amplified Average No. Amplified Pair Bands Bands Me2 TGAGTCCAAACCGGAGC 1 – 8 3.36 Em3 GACTGCGTACGAATTGAC Me4 TGAGTCCAAACCGGACC 3 – 5 3.45 Em6 GACTGCGTACGAATTGCA Me3 TGAGTCCAAACCGGAAT 0 – 5 3.09 Em1 GACTGCGTACGAATTAAT Me2 TGAGTCCAAACCGGAGC 1 – 4 2.27 Em5 GACTGCGTACGAATTAAC Me1 TGAGTCCAAACCGGATA 1 – 4 2.73 Em4 GACTGCGTACGAATTTGA Me4 TGAGTCCAAACCGGACC 2 – 5 3.55 Em3 GACTGCGTACGAATTGAC

Figures

Figure 3.1

Figure 3.2

Figure 3.3

Chapter Four

Augmentation and reintroduction using ex situ collections: Genetic effects of small founding population size in an experimental outplanting of the endangered perennial Minuartia cumberlandensis (Wofford & Kral) McNeill (Caryophyllaceae)

Megan Philpott Department of Biological Sciences University of Cincinnati Cincinnati, OH 45221-0006

Introduction

The maintenance of genetic diversity and integrity in ex situ collections is of vital importance due to the ultimate goal of ex situ conservation: to supplement natural populations with genetic material to prevent extinction and promote recovery (Pence, 2010). While seed banking is the most widely used, cost-effective, and arguably simple method of ex situ conservation, it is not viable for all species. An estimated 5,000 endangered plant species are considered “exceptional,” meaning they produce few or no seeds or produce recalcitrant, desiccation-intolerant seeds, and these species must be propagated using techniques such as tissue culture or micropropagation

(Pence, 2014). All of ex situ conservation is undertaken to propagate plants for use in a potential reintroduction or restoration of a population, but in order for the cost and effort of ex situ conservation to be worthwhile, reintroductions and restorations must demonstrate success.

Restoration and reintroduction techniques have come under scrutiny for a failure of land managers and researchers to monitor the long-term health and success of these recovered populations, leading to dubious, unreliable benefits of the reintroduction process (Albrecht et al.,

2011; Godefroid et al., 2011). While the literature on the long-term outcomes of plant reintroductions in general is sparse, that of plant reintroductions utilizing micropropagated plants is even more scarce. A cursory review of ten micropropagated plant reintroduction studies shows a range of success rates from 52% to 99%, with plants monitored between 3 months and 3 years

(Gangaprasad et al., 1999; García-Rubio and Malda-Barrera, 2010; Martin, 2003; Misic et al.,

2005; Radha et al., 2013; Rublo et al., 1992; Seeni and Latha, 2000; Stewart et al., 2003;

Tasheva and Kosturkova, 2010; Wu et al., 2014). Godefroid et al. (2011) suggest that to be considered successful, a reintroduction must be monitored and persist for at least 10 years, so it’s

clear that longer investigations are needed. However, the relative brevity of monitoring in published studies does not necessarily indicate reintroduction failures, as the reintroduced populations could very well be surviving years or even decades longer than the initial publication. This gap in the literature indicates a pressing need for long-term studies of reintroduction outcomes.

For a reintroduction to be truly successful from a conservation perspective, it must not only exhibit long-term success, but also mimic the genetic diversity of a natural population by including enough genetic variation to facilitate adaptation to the environment and avoid inbreeding depression (Godefroid et al., 2011; Neale, 2012). Generally, ensuring adequate genetic diversity translates to starting a reintroduction with a large number of founding individuals, with a general rule advocating for an effective population size (Ne) of at least 50 to avoid inbreeding depression (Frankham et al., 2014). For most plant species, this seems reasonable to achieve using seeds, but for many rare and exceptional species, a Ne of 50 is unattainable and may even exceed that of natural populations. This study seeks to use the federally endangered exceptional species, Minuartia cumberlandensis, as a case study to investigate the effectiveness of micropropagation in facilitating reintroductions, particularly in regard to the maintenance of genetic diversity and integrity of the source populations. Using two different types of genetic markers, sequence-related amplified polymorphism (SRAP) and microsatellite markers, this study will serve as a model for future introductions of those overlooked threatened, rare, and exceptional species for which accepted conservation standards may be unreasonable.

Study Organism

Minuartia cumberlandensis (Wofford & Kral) McNeill is a federally endangered herbaceous perennial native to the Cumberland Plateau of northern Tennessee and southern Kentucky.

Although the species flowers regularly, it is considered rare and exceptional due to the low viability of its seeds (Pence et al., 2011). First described in 1979 by Wofford and Kral, M. cumberlandensis grows primarily in moist, shaded sandstone rockhouse floors and ledges of the

Cumberland Plateau of Tennessee and Kentucky. Delicate with opposite leaves, M. cumberlandensis produces white 5-petaled flowers in July and August (Figure 4.1). Minuartia cumberlandensis has a complex and potentially still-unresolved phylogeny. Although it was initially described as Arenaria cumberlandensis, related most closely to Arenaria groenlandica,

McNeill (1980) reclassified it into Minuartia based on its capsule dehiscence morphology.

Adding to the confusion, Dillenberger & Kadereit (2014) reclassified the Minuartia genus again on the basis of nuclear ribosomal internal transcribed spacer (nrITS) and plastid matK sequences, which placed M. cumberlandensis, along with eight other former Minuartia species, in the genus

Mononeuria. As with other large genera in Caryophyllaceae, Minuartia appeared to exhibit a large amount of polyphyly (Dillenberger & Kadereit 2014).

Legal Status

In 1988, the species was listed as federally endangered because it was known only from four populations in Tennessee and one in Kentucky. As of 2013, there were 64 known occurrences of the species in Kentucky and Tennessee, although only 34 were considered viable (US Fish &

Wildlife Service 2013). Furthermore, there are only 10 protected, viable occurrences outside of

Pickett County, TN, which includes a single population in Kentucky. The species grows in or

near sandstone rockhouses, which are a well-known and charismatic geological feature of the

Cumberland Plateau. For this reason, a primary threat to the species is trampling by visitors, especially in populations that occur near trails. Rockhouses have a reputation for harboring

Native American artifacts, and are also the site of some rappelling activities, which also contribute to trampling. In addition, timber harvesting upsets the delicate shading and moisture conditions that allow M. cumberlandensis to thrive, and feral hogs have caused destructive soil disturbances at three occurrences (US Fish & Wildlife Service, 2013). To mitigate human- mediated destruction, the eight occurrences near trails are currently protected by signs, fencing, and/or boardwalks that divert foot-traffic.

The species has been monitored regularly by the US Fish & Wildlife Service since 2006, and though changes in population size occur yearly from population to population, the overall status of the species has remained stable or has improved since monitoring began (US Fish & Wildlife

Service, 2013). This, along with increased protection for those populations on public lands, led to the species being recommended for downlisting to threatened status in 2013, although the status remains officially endangered (US Fish & Wildlife Service, 2013). Little data are available on seed production & germination rates; however, anecdotally, Winder (2004) observed high seed production and seedling growth during his sampling for a genetic diversity study. Likewise, we have also observed robust seedling growth in populations during sampling from 2013 – 2015.

However, more observational data is needed to confirm seed production and germination in the field.

Experimental Outplanting

Due to its classification as an exceptional species, the Center for Conservation and Research of

Endangered Wildlife (CREW), which focuses on the ex situ conservation and reproduction of exceptional plant species, germinated seeds collected from two populations (Hazard Cave and

Ladder Trail) in Tennessee in 1999 to develop a micropropagation and cryopreservation protocol for the species. Of the resulting seedlings, only seven from one population, Hazard Cave, survived the tissue culture initiation process. In 2005, in conjunction with the US Forest Service,

63 micropropagated clones of these 7 genotypes were planted out in a suitable habitat in Daniel

Boone National Forest in Kentucky as an experimental outplanting to test the potential use of micropropagated plants in restorations and reintroductions. The habitat had not previously been known to host M. cumberlandensis but was similar in habitat and found within the known range of the species. Over the course of 6 years of monitoring, the original micropropagated plants declined to 12 plants but new seedlings appeared starting in the second summer (2007), and the population was observed flowering and producing seedlings every year thereafter (Pence et al.,

2011). As of 2015, the population had increased to over 200 individuals, with mature plants flowering and producing seedlings. The apparent success of this reintroduction over 10 years runs counter to accepted guidelines because there were only 7 founders (less than 1/7 the recommended effective population size of 50) (Frankham et al., 2014). For this reason, understanding the genetic diversity of the source population and the outplanting is of primary importance.

Materials & Methods

Sample Collection

In July 2013, 139 leaf samples were collected from the Daniel Boone National Forest outplanting

(DBNF, N=35), the original Hazard Cave seed source population (HC, N=72), and another natural population, Ladder Trail (LT, N=32) populations for genetic analysis. Single leaves were taken from individual plants, and samples within 1.5m of other visually diagnosed clumps were labeled as such to avoid multiple sampling of clones. Samples were dried in glassine envelopes with silica gel and kept at 4°C for storage until DNA was extracted. The seven genotypes originally propagated for the outplanting had been maintained in some combination of tissue culture and/or cryopreservation at CREW, and were used for DNA extractions to approximate the genetic diversity in the original outplanted population. In addition, DNA had also been extracted in 2011 and kept at 4°C. To account for somatic mutations that may have been generated during the tissue culture or cryopreservation processes (also known as somaclonal variation), DNA from genotypes maintained in multiple storage methods (tissue culture, cryopreservation, or extracted DNA) was used whenever possible (TC, N=17).

SRAP Genetic Analysis

DNA extractions were performed following a modified CTAB protocol (Doyle & Doyle 1987).

DNA quantity & quality was checked using a Nanodrop spectrophotometer. SRAP analysis was performed following the protocol of Li & Quiros (Li and Quiros, 2001). In brief, 24 primer combinations were tested on one individual from each population (for a total of four individuals) to test for amplification and polymorphism in the resulting bands. Each reaction was also run in duplicate to test for reproducibility. Five primer combinations were ultimately chosen for their

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ability to produce reproducible, polymorphic bands (Table 4.1). PCR was carried out in 10µl reactions containing 5µl Promega 5X Green GoTaq Reaction Buffer (Promega, Madison, WI),

0.4µM of each primer, 0.3µl of DNA, and 3.7µl distilled water. Thermocycling conditions included an initial denaturation at 95°C for 5 minutes, followed by 5 cycles of denaturation at

94°C for 60 seconds, annealing at 35°C for 60 seconds, and elongation at 72°C for 60 seconds,

35 cycles of denaturation at 94°C, annealing at 50°C, and elongation at 72°C, and a final extension at 72°C for 10 minutes. PCR product was visualized and scored using gel electrophoresis on 1.2% sodium borate agarose gels (pH=8.0). Bands were scored as present (1) or absent (0).

Microsatellite Development and Analysis

Microsatellite markers were also used for the analysis in addition to SRAP markers to gain a finer-scale understanding of the population genetic structure. The codominant nature of microsatellites allows for their use in more accurately estimating heterozygosity and relatedness.

As no microsatellite markers had been previously developed in the species, markers were developed following the protocol of Glenn and Schable (2005). Briefly, tissues of two genotypes of M. cumberlandensis were harvested from tissue culture-propagated plants. DNA extractions were performed using a modified CTAB protocol to produce high concentration (approximately

2500ng/µl) samples for microsatellite development (Doyle & Doyle 1987). DNA was digested into approximately 500bp fragments using the restriction enzymes Rsa I and BstU I. Double- stranded SuperSNX24 linker was ligated onto the ends of each DNA fragment to facilitate primer-binding, and streptavidin-coated magnetic Dynabeads (Dynal, Oslo, Norway) were used enrich samples for fragments containing microsatellite regions. Enriched DNA was incorporated

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into a pCR™4-TOPO® TA cloning vector using a TOPO TA cloning kit (ThermoFisher,

Carlsbad, CA), and was incorporated in turn into ampicillin sensitive Escherichia coli bacteria.

The bacteria were plated on petri dishes containing LB broth and ampicillin, and colonies were allowed to grow for approximately 40 hours. The cloning vectors contained a gene conferring ampicillin resistance, allowing only those bacteria that had taken up the cloning vector to grow on the ampicillin plates. Surviving colonies were picked using sterilized plastic toothpicks and transferred to PCR plates, each well of which contained 5µl of Promega 5X Green GoTaq

Reaction Buffer (Promega), 0.4µM each of forward and reverse universal M13 primer, and 4µl distilled water. Plates were set on an orbital shaker for 1 hour to allow for the transfer of DNA from the toothpick to the reaction mixture before PCR was performed. Thermocycling conditions included a 5 minute initial denaturation at 95°C, followed by a touchdown protocol of two cycles each of denaturation at 95°C for 15 seconds, annealing for 15 seconds, and elongation at 72°C for 30 seconds. The annealing temperature started at 57°C and decreased by one degree every two cycles down to 51°C. The annealing temperature was then lowered to 50°C for the final 15 cycles and followed by a 10 minute extension at 72°C. PCR product was visualized on 1.2% sodium borate agarose gels using gel electrophoresis. Ninety-six potential primers were chosen due to their ability to produce clear bands in the range of 300-1000bp, and these fragments were sequenced on an ABI 3730xl DNA Analyzer (Genewiz, Cambridge MA).

Sequences were analyzed using the software Geneious 11.1.4 (http://www.geneious.com, Kearse et al., 2012), and 26 sequences containing microsatellite regions with adequate length in the flanking regions to design forward and reverse primers were chosen from the 96 sequences.

Primers were designed using the software Primer3 ver. 4.1.0 (Koressaar and Remm, 2007;

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Untergasser et al., 2012), and were multiplexed into two reactions using fluorescent tags (Table

4.2) (Culley et al., 2013). Labeled primers were tested for amplification on 1.2% sodium borate agarose gels following the touchdown protocol on reactions containing 5µl of Promega 5X

Green GoTaq Reaction Buffer (Promega), 0.3µl of DNA, 0.2µM of each reverse primer and each fluorescently tagged forward primer, and 3.7µl distilled water. Seventeen putative primers amplified bands and were chosen to estimate polymorphism, and these primers were amplified using the previously described reaction mix and thermocycling conditions. PCR products were visualized using fragment analysis on a 3730xl DNA Analyzer (ABI) at Cornell University’s

Biotechnology Resource Center with a LIZ 500 size standard. Allele calls were made using

Genemarker ver. 1.85 software (SoftGenetics; Hulce et al., 2011). None of the 17 primers were found to be polymorphic across the 48 tested samples and were therefore not informative for the purposes of the study, so microsatellite analysis was discontinued for the remainder of samples in the dataset.

Statistical Analysis

Descriptive statistics including the average number of alleles per locus (NA), effective number of alleles per locus (NE), Shannon’s information index (I), the expected level of heterozygosity

(HE), and percentage of polymorphic loci (P) were calculated using GenAlEx ver. 6.5 (Peakall and Smouse, 2012). Shannon’s information index was used as a measure of diversity, as it represents the uncertainty in the identity of any genotype pulled from the population (Shannon

1948). As a system becomes more complex, with more genotypes evenly distributed throughout, it becomes more difficult to predict the identity of any particular genotype, and Shannon’s I increases (Morris et al., 2014). Linkage disequilibrium was used as an indicator of asexual

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reproduction and clonal growth and calculated using the R program Poppr as 푟̅푑 (Kamvar et al.,

2014). The index of association (IA) was developed as a measure of linkage disequilibrium and tests the likelihood that individuals that share one locus share another locus more often than would be expected under random association (Brown et al., 1980). Because IA is highly dependent on the number of loci sampled, 푟̅푑 was developed to standardize the measure (Agapow and Burt, 2001). An analysis of molecular variance (AMOVA) was generated in GenAlEx to partition genetic variation within- and among populations. The AMOVA was based on ΦPT, an analogue to Fisher’s fixation index (FST), and utilized 999 permutations to partition variation based on genotypes.

Results

Population Structure and Diversity

All populations showed high polymorphism across the SRAP bands studied (mean=87.86%,

Table 4.3). The two naturally occurring populations had the highest polymorphism (LT, HC, both 91.43%), with the outplanting slightly lower (DBNF, 88.57%), and the ex situ sources the lowest (TC, 80.00%). Diversity was also highest in the natural seed source population (HC,

I=0.47), although the outplanting was higher than the Ladder Trail population (DBNF, I=0.46;

LT, I=0.45; Table 4.3). Again, diversity was lowest in the ex situ source population (TC, I=0.44).

However, diversity was roughly equal across all sampled populations (range 0.44 – 0.47). The ex situ sources (TC, 푟̅푑 = 0.14, p<0.01), experimental outplanting (DBNF, 푟̅푑 =0.02, p<0.01), and seed source population (HC, 푟̅푑 =0.01, p<0.01) were all significantly in linkage disequilibrium.

The Ladder Trail population was nearly but not significantly in linkage disequilibrium (LT, 푟̅푑

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=0.01, p=0.06). An AMOVA indicated that most variation was distributed within populations, rather than among them (83% within vs 17% among, Table 4.4).

Discussion

The use of reintroductions as a conservation method is contentious among scientists, and reviews on the subject have come to very different conclusions (Guerrant, 2013). While philosophical questions on the efficacy of reintroductions are endlessly debatable, the present study represents a potential step forward in reintroduction planning. Here, an experimental outplanting was planned and carried out before an immediate need or emergency arose. This allows us to draw on the collected data should an actual reintroduction ever be needed for the species. Furthermore, the investigation into the population genetics of the natural populations, reintroduction, and ex situ collection helps to inform the number of genotypes and source of genotypes that will best ensure the success of a future reintroduction.

Population Monitoring

A decade after the initial planting, the experimental outplanting at Daniel Boone National Forest has more than tripled in number from 63 initially ouplanted micropropagated plants to over 200 plants. Micropropagated plants have been found to occasionally exhibit abnormal flowers and reproductive morphology, making them ineligible for use in reintroductions (Ye et al., 2011).

However, the current outplanting has been observed to flower and occasionally produce seedlings consistently over years of monitoring.

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A common criticism of the efficacy of reintroductions is the argument that a reintroduction is declared successful after relatively short monitoring times, and any future failures of reintroduced populations are either not observed or reported (Godefroid et al., 2011; Dalrymple et al., 2012). This may be a true phenomenon, particularly if one looks to the literature where reports are generally published quickly. However, Guerrant (2013) has argued that this is more a perceived issue than true practice, as many reintroductions likely undergo continued monitoring long after the publication of a study describing outplanting efforts. The M. cumberlandensis experimental outplanting is continuing to be monitored, and monitoring will continue into the future to investigate the response of the population to any future threats or environmental changes.

Genetic Diversity and Population Differentiation

Using the population demographics and measures of genetic diversity, the experimental outplanting of M. cumberlandensis appears to have been successful over 10 years. While the genetic diversity in the initial outplanting was necessarily low due to the use of only 7 genotypes, the overall low diversity found in the species suggests that this did not significantly harm the health of the outplanting. As would be expected, Shannon diversity and percentage polymorphism were highest in the naturally occurring seed source population (HC), lower in the outplanting (DBNF), and lowest in the genotypes used for micropropagation (TC), but the range between the lowest and highest diversity was only 0.03. This indicates that genetic diversity is likely low in all sampled populations. The vast majority of genetic variation found in the species occurs within populations, indicating that populations do not significantly diverge from one another. This finding is expected, since both the ex situ collection was sourced from the Hazard

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Cave population, and the outplanting was sourced from the ex situ collection, so there should ideally be no difference between the three populations, given adequate sampling at HC.

However, the founder effect (the reduction in genetic diversity in new populations due to the small number of colonizers) is known to reduce genetic diversity and increase allele fixation

(Hedrick et al., 2001). The diversity measures and partitioning of variation both suggest that the use of <50 genotypes for the initial outplanting did not contribute to a founder effect or a significant decay in the observed diversity.

Similar results were found in a community of 5,000 American chestnut trees that grew out from an initial founding population of only 10 individuals (Pierson et al., 2007). Shannon diversity did not differ significantly between the founding population and the community, meaning the founder effect did not factor in to the population demographics. These results suggest that utilizing a small number of founding individuals may not always be catastrophic to the diversity of a reintroduction. However, loss of diversity has been observed in the establishment of ex situ collections relative to source populations, so caution should be exercised in any reintroduction

(Lauterbach et al., 2012).

Minuartia cumberlandensis is known to grow in clumps, and both the naturally occurring seed source at Hazard Cave and the experimental outplanting in Daniel Boone National Forest showed evidence of linkage disequilibrium, suggesting a clonal growth habit in both populations.

The Ladder Trail population showed less evidence for linkage disequilibrium, which may be due to the geographic range of the population. Hazard Cave is a relatively compact, contiguous population, while Ladder Trail encompasses a larger area with more environmental barriers

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between patches, which could be limiting the amount of clonal growth observed in the Ladder

Trail population. Evidence of linkage disequilibrium in the ex situ source for the initial outplanting should not be due to clonal growth, as micropropagated individuals were initially grown from seed. However, this result is likely an artefact of the multiple inclusions of DNA taken from the same genotype. Although this was done primarily to account for any somaclonal variation, similar or identical SRAP fingerprints taken from the same genotype are likely inflating evidence for linkage disequilibrium. While clonal growth is often a reproductive advantage for species that employ sexual reproduction and outcrossing, large mats of clones can also increase the probability that pollen from one ramet pollinate flowers of the same genet, causing an increase in inbreeding (Vallejo-Marín et al., 2010). A pollination study of M. cumberlandensis could elucidate whether clonality and low population differentiation are beneficial for the persistence of populations, or whether they are contributing to the species’ reduced seed viability.

Genetic Markers

Microsatellite markers were developed in the species to take advantage of their co-dominance and specificity, which, in contrast to dominant markers like SRAPs, allows for more in-depth population genetic analyses like measures of inbreeding, population differentiation, and relatedness. While microsatellite markers were successfully identified, there was no variation contained therein. The lack of variation likely reflects low diversity observed within the sampled populations. Although dominant markers like SRAP markers give less information about the genetics of populations, they can be indispensable for species when other marker types fail or are otherwise unavailable (Robarts and Wolfe, 2014). This is particularly important for species such

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as M. cumberlandensis which are rare and threatened, but often mostly or completely genetically uncharacterized. It is of vital importance to understand population dynamics in these populations before they are further threatened by environmental, anthropogenic, or climate effects, and particularly before any reintroduction efforts are undertaken (Frankham et al., 2014). While the dominant markers used here provide useful information about the population dynamics of this species, comprehensive approaches such as next-generation sequencing should be undertaken as time and money allows to understand more fine-scale genetic factors.

Conservation Implications

In conclusion, the experimental outplanting of M. cumberlandensis appears to have been successful over the course of 10 years in terms of both population demography and survival, and genetic similarity to extant natural populations. Although the outplanting is genetically similar to its natural source population, clonal growth in the species suggests that more genotypes should be collected prior to any future reintroductions to account for unmeasured factors like rare alleles. Furthermore, genotypes taken from separate populations should be maintained separately, again due to clonal growth. While population information data should be retained, reintroductions created from mixed source populations have shown increased resilience to threats

(Maschinski et al., 2013). Therefore, mixing of source populations may be beneficial to the survival of any future reintroductions. The present study shows that reintroduction by micropropagation can be a viable future method of conservation for exceptional plant species.

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Figure Legends

Figure 4.1: Minuartia cumberlandensis growing in the Daniel Boone National Forest outplanting. Figure 4.1 A depicts the floral morphology of the species, and figure 4.1 B depicts the outplanting habitat.

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Tables

Table 4.1: SRAP primers used in the present study. Shown are the primer names (taken from Li and Quiros [2001]), primer sequences, and number of amplified bands.

Primer Pair Sequence (5’ – 3’) No. Amplified Bands Me4 TGAGTCCAAACCGGACC 6 Em6 GACTGCGTACGAATTGCA

Me3 TGAGTCCAAACCGGAAT 7 Em1 GACTGCGTACGAATTAAT

Me2 TGAGTCCAAACCGGAGC 10 Em5 GACTGCGTACGAATTAAC

Me1 TGAGTCCAAACCGGATA 6 Em4 GACTGCGTACGAATTTGA

Me4 TGAGTCCAAACCGGACC 6 Em3 GACTGCGTACGAATTGAC

Total 35

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Table 4.2: Microsatellite primers developed and used in the study. Shown are the primer ID, repeat motif, forward and reverse sequences 5’ – 3’, fragment length, and the fluorescent tag used for multiplexing. M13 universal sequences (GTAAAACGACGGCCAG) were added to the 5’ end of each forward primer to facilitate fluorescent tagging.

Primer Repeat Motif Sequence Fragment Fluorescent ID Length Tag

AC-01 (GT)5 F: CGAAACATGAAAACGAGTTGCA 152 6-Fam R: CACCATCTTGTTCCCCTTGC

AC-05 (GT)5 F: CAAAGCTCCTATCATCATCACGT 168 6-Fam R: TGCTAGTGAACTCATGCATGT

AC-06 (TTG)5 F: GCTCATCCAAACGTCACTCC 174 6-Fam R: TTAAGCCTAACCCCGAACCC

AC-08 (GATTT)3 F: AATTGGTGCAAAGGGGTCAC 188 PET R: CACACACAAGCACACCGTAA

AC-09 (CCCT)3 F: AGGCAAGATTACAGCTCAGC 192 6-Fam R: TGCAGCACAAATGACATCGA

AC-10 (GA)4 F: GCCGCAATGAAACAAACTCG 198 NED R: TAACTCCCCGCCTCTCTACT

AC-11 (TCC)3(GCAAA)2 F: ACCCAGAATCCGATCCTTTCA 205 VIC R: CGAGGCGGAAATGAAAGGAA

AC-14 (GT)8 F: GACCTCACACATCCCAGGAC 213 6-Fam R: ATCCTGCATTGGGTCCTTGA

AC-15 (TGC)3 F: TGTGGATTGGTGGATTTCTCA 213 VIC R: CCAAAGCGCTCATGGTATCA

AC-16 (AC)5 F: TTTTCTCTCCCTCTGGCACC 217 PET R: TGCAAGAGAACTGGGCTTTG

AC-17 (GA)4(AAG)2 F: ATGGTCGGAAACAGAGGAGT 219 NED R: ACCCTAACGACCCAAGATCA

AC-18 (TTTA)2 F: TGGCTCGTTTTGCAATCCAA 223 NED R: TATCCCGACCCGATCAATGG

AC-21 (CAG)4 F: GCACATCACAAGCTGCAAAC 237 PET R: CTTCGGGCCTGTGTTATGAC

AC-22 (GCA)4 F: GGAATCCCGAAAACACATAGGA 240 6-Fam R: CGTTTGTTGGGTCTTGCAAT

AC-24 (AC)8 F: TGGATGCAAAACGAAGGAGG 248 PET R: CTGTTGGCCAAAACCGAGAG

AC-26 (ACC)6 F: GGTGAATGGCCAAATCCTACA 250 6-Fam R: TTATCGGCGGCACATGGT

AC-27 (GT)4C(TG)3 F: GGAATTGTGGCGATGTCTGT 250 VIC R: CTTCTGGTTGTTTGGGGTCC

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Table 4.3: Descriptive statistics for the sampled populations. Populations shown are the founding genotypes (TC), the experimental outplanting (DBNF), the natural population (LT), and the natural seed source population (HC). Shown are the sample size (N), Shannon information index (I), the level of expected heterozygosity (HE), and percentage of polymorphic loci (P) for SRAP markers. For averaged numbers, the standard error is displayed in parentheses.

Population N I HE P TC 17 0.44 0.30 80.00% (0.04) (0.03) DBNF 35 0.46 0.31 88.57% (0.04) (0.03) LT 32 0.45 0.30 91.43% (0.04) (0.03) HC 72 0.47 0.31 91.43% (0.04) (0.03) Total 156 0.46 0.30 87.86% (0.02) (0.02) (2.70%)

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Table 4.4: AMOVA results for partitioning variation based on SRAP markers. Shown are the degrees of freedom (df), sum of squares (SS), mean sum of squares (MS), estimated variation (Est. var.), and percentage of variation (%).

Source df SS MS Est. var. % Among pops 3 135.96 45.32 1.13 17% Within pops 152 808.62 5.32 5.32 83% Total 155 944.58 6.45 100%

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Figures

Figure 3.1

A B

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Chapter Five

General Conclusions

Megan Philpott Department of Biological Sciences University of Cincinnati Cincinnati, OH 45221-0006

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The overall goal of this research was to investigate the genetic consequences of ex situ conservation methods for exceptional species. Ex situ conservation is a complicated process, with varied paths to the overall goal of species preservation, but the techniques investigated here serve as a general overview of the process. Specifically, this research investigated the collection of genetic diversity from wild populations, the maintenance of genetic diversity in ex situ collections, and the use of ex situ collections in restoration of populations.

Maintaining appropriate levels of genetic diversity in ex situ collections is imperative, as these collections must be available for use in species reintroductions and population restorations in the future (Sharrock, 2012). The first step to ensuring an adequate collection of diversity is the collection itself, and recommendations for seed collections for ex situ conservation vary substantially (Frankham et al., 2014; Franklin, 1980; Hoban and Schlarbaum, 2014; Hoban and

Strand, 2015). The classical reference for collections suggests collecting for an effective population size (Ne) of 50 to avoid inbreeding depression in the short term and 500 to maintain evolutionary potential long-term (Franklin, 1980). However, accumulated evidence has begun to suggest that these numbers are too low to achieve their goals, and Frankham et al. (2014) suggest a Ne of at least 100 to avoid inbreeding depression and over 1,000 to maintain evolutionary potential. Hoban and Schlarbaum (2014), however, argue that such blanket generalizations may work for some species but will fall short for others depending on life history, and suggest an approach driven more by the natural history of the target species. They suggest optimizing sampling strategy and sampling numbers based on a species’ population size, genetic connectivity, and pollination mode to capture at least 75% of all alleles (Hoban and Schlarbaum,

2014). In addition, Hoban and Strand (2015) suggest that the spatial sampling strategy will have

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an effect on the diversity captured, moving guidelines further from the classic blanket rule of Ne

= 50 or 500 and more in line with specialized recommendations for each species. While the specialized approach may require more information about a species’ life history, the end result will be not only a more effective collecting strategy for each species, but an increased knowledge of the natural history of those target species.

While there is a healthy debate surrounding the numbers and spatial distribution of seed collections, the application of these recommendations to exceptional plants is less clear. Since exceptional species produce few to no seeds, tissue collections are often necessary, meaning that each collection is a clone of an individual in the source population. While most guidelines for ex situ collections assume seed collections, tissue collections of exceptional species are often more time-consuming and labor-intensive than seed collections, and require more resources to process and store as well. Exceptional plant populations are often rare or threatened, meaning that collection of diversity must be balanced with the health of the population and resource allocation of the collecting agency. With all these factors in play, Hedeoma todsenii, a federally endangered exceptional species, was used as a case study to investigate the underlying genetic diversity of populations as compared to the tissue collection strategy for its ex situ conservation (Chapter 2).

While most of the collections have been concentrated around the White Sands Missile Range

(WSMR) in the San Andres Mountains due to permitting and budgetary constraints, the less heavily sampled and geographically distant Lincoln National Forest (LNF) population in the

Sacramento Mountains was found to contain the most genetic diversity and was also the most genetically distinct population. The distinction between the WSMR and LNF populations indicates that collections from each region should be maintained separately, and that future

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collections should focus more acutely on under-sampled populations in the Sacramento

Mountains to ensure the future health of the species. While the populations look generally healthy and self-sustaining, genetic diversity in the species as a whole is low, which is to be expected for a rare species with low fecundity. Should a restoration or reintroduction be attempted in the future, the mixing of genotypes from the San Andres Mountain range populations and the Sacramento Mountain range populations could produce a more genetically diverse population that could withstand threats more readily (Maschinski et al., 2013).

Once a collection is complete, it is assumed that individuals maintained in ex situ collections do not undergo any sort of mutation or damage, as tissue culture should produce clones of individuals, and cellular processes should cease during cryopreservation. However, there has been evidence in the past of somatic mutations (somaclonal variation) occurring during tissue culture, and cellular damage has been observed during cryopreservation, particularly in the form of oxidative damage (Bairu et al., 2010; Johnston et al., 2010). The Cincinnati Zoo & Botanical

Garden’s Center for Conservation and Research of Endangered Wildlife’s (CREW)

CryoBioBankTM provided an excellent opportunity to investigate the occurrence of genetic damage and mutation during long-term tissue culture and cryopreservation of exceptional species

(Chapter 3). Somaclonal variation can be detected by a variety of methods, but the breadth of species investigated here necessitated a universal approach that could be replicated. For this purpose, sequence-related amplified polymorphism (SRAP) markers were used to sample sites throughout each sample’s genome to detect genetic changes (Li and Quiros, 2001). While somaclonal variation was detected in most of the samples, this variation did not appear to have any major effects on the growth and development of the species. However, a more long-term

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study is needed to confirm that these species experiencing somaclonal variation are capable of normal growth and producing true-to-type plants throughout their lifetime. RNA damage was also assayed in a number of seed and shoot tip species maintained in long-term cryopreservation to understand the importance of RNA integrity through the post-cryopreservation recovery process, and to test the use of the RNA integrity number (RIN) as a potential indicator of viability (Fleming et al., 2017; Schroeder et al., 2006).

Over all the species and tissue types assayed, there were no general associations between RNA integrity and the ultimate survival of an accession after cryopreservation. This finding could be due to a few possibilities: first, it is possible that RNA integrity has little to no effect on the survival of tissues, at least for these species. While RIN has been found to correlate with seed viability in conventionally stored soybean seed tissues, the cryopreservation process may allow

RNA to be more adequately preserved for later growth due to the low storage temperature.

Alternatively, the use of shoot tips and recalcitrant seeds may have played a role as well. RNA is generally a less stable molecule than DNA, as RNA is transcribed, used, and degraded more readily than DNA. In conventional seeds, the preservation of RNA may be necessary as seeds come out of dormancy to germinate (Fleming et al., 2017). However, recalcitrant seeds lose viability more quickly, and shoot tips are not adapted to preserve RNA at all, so the integrity of

RNA following storage may be less vital to their recovery. The second possibility for the lack of association between survival and RIN is technical issues related to the technique. Fleming et al.

(2017) assayed individual tissue types within a seed separately and found that embryonic axes had a different RIN response than seed coats, cotyledons, and plumules. Seeds and shoot tips in the present study were assayed whole necessarily due to their size, and differential responses of

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RNA in different tissues to cryopreservation and recovery may have masked any correlation between RIN and survival. Either way, the use of RIN to assess viability in cryopreserved exceptional plant seeds and tissues is not recommended. However, SRAP markers are recommended as a reliable, economical method of surveying genetic stability in cryopreserved tissues.

Ex situ collections are not meant to simply be preserved forever and left unused. The Global

Strategy for Plant Conservation’s Target 8 requires that species not only be preserved in ex situ collections, but they should also be available for use in potential restorations and reintroductions

(Sharrock, 2012). Again, while there is a large body of literature surrounding restorations and reintroductions using seeds, the literature is less extensive for micropropagated species. Here, we used an experimental outplanting of the federally endangered exceptional species Minuartia cumberlandensis as a case study to understand the genetic diversity of micropropagated reintroductions (Chapter 4). Because of the low fecundity of M. cumberlandensis seeds, only seven genotypes were available for propagation to create the outplanting. While this presumably could subject the outplanting population to detrimental founder effects, the outplanting had more than tripled in size by 10 years post-planting. Genetic diversity in the outplanting was approximately equal to diversity in the natural population from which seeds were sourced, and there was very little differentiation between the populations. Genetic diversity in the two natural populations was very low, and there was visual and genetic evidence of clonal growth. This suggests that the species may simply be adapted to low diversity, inbreeding, and clonal growth, and that the use of just a few genotypes to populate the outplanting was still an accurate representation of natural populations. It is worth noting that the plants used in the outplanting

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were initially started from seed and propagated using tissue culture, and since viability of the seeds is generally low in the species, the seedlings that germinated and survived tissue culture initiation may have been descended from particularly healthy or prevalent individuals capable of producing high-quality seed. This could partially explain the lack of founder effects and success of the reintroduction. In any case, this case study suggests that M. cumberlandensis is an excellent candidate for restoration and reintroduction using micropropagation. The success of future reintroductions and restorations could be ensured by the micropropagation of more genotypes from more distinct populations, in the hopes that a mixed source reintroduction with greater genetic variation could respond more readily to threats and potentially even have an increased fecundity.

The studies contained herein are intended to serve as guidance for the management of ex situ collections of exceptional plant species, and to assist management agencies in their conservation planning. However, peer-reviewed publications are not often the best method of dissemination for this information. Management agencies have limited time to constantly seek out new literature, and there is often a disconnect between academic scientists doing research on a species and management agencies planning its conservation strategy (Boedhihartono et al., 2018). In order to bridge this gap, scientists working in areas of conservation concern could create short, one-page ‘fact sheets’ to distill their conclusions into useable actions that can be easily shared with the agencies managing threatened and endangered species. A stronger connection between academic research and applied conservation and management will lead to more effective conservation plans for threatened species.

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In conclusion, this research demonstrates that the use of tissue culture and cryopreservation for the ex situ conservation of species is vital to the preservation of exceptional species. Because of the rarity and low genetic diversity found in many exceptional species, an increased number of sampled genotypes is recommended to ensure success of future restorations and reintroductions.

The two case studies investigated here present similar, but not identical, results, and underscore the need for individualized studies of genetic structure and diversity before collections and restorations are undertaken. Samples preserved in long-term cryopreservation do exhibit survival after up to 23 years of liquid nitrogen storage, but there is some evidence of somaclonal variation. Regular monitoring of the genetic stability of micropropagated and cryopreserved tissues is recommended, and plants exhibiting somaclonal variation should be monitored throughout rooting and their lifespan to ensure the production of true-to-type plants. While there is evidence of somaclonal variation, a more comprehensive sequencing approach such as next- generation sequencing is recommended as resources allow, which would allow us to understand more fully the types of changes occurring at a genetic level. While there is unfortunately no one- size-fits-all approach to ex situ conservation, this research is an indication that tissue culture and cryopreservation are currently preserving those often-overlooked species known as exceptional plants.

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