Role of Genetic Diversity in the Adaptive Success of Silverleaf Nightshade (Solanum Elaeagnifolium) Under Variable Environmental Pressures

Role of genetic diversity in the adaptive success of silverleaf nightshade (Solanum elaeagnifolium) under variable environmental pressures

Joshua Singleton, B.S.

A Thesis

Plant and Soil Science

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCES

Approved

Dr. Rosalyn Angeles-Shim Chair of Committee

Dr. Cade Coldren

Dr. Venugopal Mendu

Dr. Junping Chen

Mark Sheridan Dean of the Graduate School

August, 2019

ACKNOWLEDGMENTS I would like to thank everyone who has contributed to my personal development and success. Without every single person that has helped me along the way, this all would not have been possible. I would first like to thank my mother and father, who have pushed me to always pursue my dreams and aspirations. I would like to thank my two sisters, my family members, and close friends for their love and support. I would like to thank my lab mates Ritchel B. Gannaban and Puneet Kaur Mangat for their patience and support. I would like to thank Dr. Junping Chen and Dr. Venugopal Mendu for their time and contribution in my graduate committee. I would like to thank Dr. Cade Coldren for his time and patience in setting up the EDYS model for my use in this research, as wells as, helping me learn how to use the EDYS user interface properly. Finally, I would like to thank Dr. Rosalyn B. Angeles-Shim for her overwhelming support and guidance throughout my entire graduate study.

Texas Tech University, Joshua James Singleton, August 2019

TABLE OF CONTENTS ACKNOWLEDGMENTS ...... ii

ABSTRACT ...... v

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

I. REVIEW OF LITERATURE ...... 1

Biology of silverleaf nightshade ...... 1 Economic importance of silverleaf nightshade as a noxious weed and a non-host vector of important plant pests and diseases ...... 2 Methods of controlling silverleaf nightshade ...... 4 Chemical control ...... 4 Mechanical and cultural control ...... 5 Biological control...... 6 Molecular mechanisms underlying the adaptive success of the silverleaf nightshade ...... 7 Phenotypic plasticity………………………………………………………7 Genetic diversity…………………………………………………………..8 II. GENETIC DIVERSITY AND POPULATION STRUCTURE OF SILVERLEAF NIGHTSHADE (SOLANUM ELAEAGNIFOLIUM) FROM TEXAS, USA ...... 18

Introduction ...... 18 Materials and Methods ...... 21 Cross-species transferability of DNA markers for genotyping...... 21 Assessment of genetic diversity across different populations of silverleaf nightshade using morphological and DNA markers ...... 22 Results and Discussion ...... 23 Transferability of S. lycopersicum- and S. lycopersicoides-specific DNA markers to silverleaf nightshade ...... 23 Descriptive statistics of DNA markers used in genetic diversity assessment studies ...... 25 Genetic diversity and population structure of silverleaf nightshade ...... 26 Conclusion ...... 28 III. SIMULATION OF ABOVEGROUND BIOMASS ACCUMULATION OF SILVERLEAF NIGHTSHADE IN CROPLANDS IN RESPONSE TO HERBICIDE PRESSURE ...... 44

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Texas Tech University, Joshua James Singleton, August 2019

Introduction ...... 44 Materials and Methods ...... 45 Description of the study site ...... 45 Mapping of the study site for silverleaf nightshade density and presence of other vegetation ...... 46 Required parameters to run EDYS simulation ...... 47 EDYS simulation of silverleaf nightshade aboverground biomass production in the next 25 years under variable herbicide pressure ...... 48 Results and Discussion ...... 49 Species composition of the study site ...... 49 Simulation of aboveground biomass production of silverleaf nightshade in the next 25 years ...... 49 Conclusion ...... 51 BIBLIOGRAPHY ...... 59

Texas Tech University, Joshua James Singleton, August 2019

ABSTRACT The genetic diversity present within a plant population is a critical indicator of its capacity for adaptation to environmental variation and change. Solanum elaeagnifolium Cav (silverleaf nightshade; SLN) is an aggressive, highly persistent weed infesting agricultural croplands and rangelands in the US and abroad. The widespread geographical distribution of silverleaf nightshade indicates the presence of genetic mechanisms underlying fitness flexibility that allows the species to adapt to variable ecological niches. Based on DNA marker profiling and structure analysis, we were able to establish a significant degree of variation within all four of the tested SLN populations from different localities in Texas. We suggest from our study that the intracluster genetic variation found between the examined plant populations is entirely the product of obligate outcrossing that has resulted in an innate degree of variability.

A simulation of a hypothetical development of herbicide resistance in silverleaf nightshade would be a significant resource to utilize, assess, and reference to design early onset preventive measures as opposed to in-response mitigation measures. The EDYS Simulation Model was used to conduct five independent scenarios to observe the simulated outcomes of differential herbicide control rates. The simulated herbicide resistance control rate of 75% reached a point of no recovery in biomass production in response to persistent annual herbicide control, while the herbicide resistance control rate of 50% and 25% showed both recovery and further growth. The results indicate that there is a threshold at which the value of resistance leads to either the species population dying off or recovering.

Texas Tech University, Joshua James Singleton, August 2019

LIST OF TABLES 1.1 Geographical distribution of silverleaf nightshade ...... 13 1.2 Known host plants of silverleaf nightshade ...... 17 2.1 DNA markers tested for cross-transferability in silverleaf nightshade ...... 31 2.2 Sequences of cross-species markers that were used to genotype silverleaf nightshade populations ...... 38 2.3 PIC values of SSR markers that amplified polymorphic targets in silverleaf nightshade ...... 40 2.4 Summary statistics of markers used for the assessment of genetic diversity in silverleaf nightshade populations from Texas ...... 41 2.5 AMOVA analysis comparing genetic variation within and among individuals and among populations ...... 42 3.1 Number of individual plants per species for all plots in the study site ...... 56

Texas Tech University, Joshua James Singleton, August 2019

LIST OF FIGURES 1.1 Silverleaf nightshade in field. Morphological variation in leaf size and shape; trichome density; flower color, shape, size, and petal number; anther number; berry shape and size of silverleaf nightshade individuals from the same population ...... 10 1.2 Typical growth cycle of silverleaf nightshade ...... 11 1.2 Typical growth cycle of silverleaf nightshade ...... 11 1.3 Map of the US showing states with silverleaf nightshade infestation ...... 12 2.1 Mapping of the four individual collection sites in relation to each other. Littlefield, TX; Lubbock, TX (TTU Rangeland); Blackwell, TX; Lubbock, TX (Quaker Farm) ...... 30 2.2 UPGMA clustering of silverleaf nightshade from four localities in Texas based on Jaccard’s coefficient ...... 43 3.1 The location of the study site at the Texas Tech University Quaker farm research facility in Lubbock, Texas...... 53 3.2 Annual precipitation recorded over the last 25 years for use in the EDYS simulation model...... 54 3.3 Images of the four weed species found in the field at the study sites ...... 55 3.4 Twenty-five year EDYS projection of silverleaf nightshade aboveground biomass production without herbicide pressure ...... 57 3.5 Twenty-five-year EDYS projections of silverleaf nightshade aboveground biomass production at the study site under: (A) herbicide pressure with standard control rates throughout, or standard herbicide pressure with acquisition of herbicide resistance in silverleaf nightshade in season four onward causing herbicide control rates to be (B) 75%, (C) 50%, and (D) 25%. SLNMEGFM = Silverleaf Nightshade; AMRTPLMI = Palmer amaranth; SLOAIRCA = Russian Thistle; URLATXNA = Large Crabgrass...... 58

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Texas Tech University, Joshua James Singleton, August 2019

CHAPTER I

REVIEW OF LITEREATURE

Biology of silverleaf nightshade Silverleaf nightshade (Solanum elaeagnifolium Cav.; SLN) is a diploid, perennial plant with a chromosome number of 2n=2x=24 (Wagner et al, 1999). It belongs to the family Solanaceae and is considered an aggressive, poisonous weed. SLN can grow up to 1 m tall (Figure 1A), with the established roots extending as deep as 2.8 m belowground (Zhu et al., 2013). The leaves and stems of SLN are dense with trichomes and the flowers are typically bright blue to purple with yellow anthers (7-9 mm) (Figure 1B, 1C, 1D, 1E). SLN typically germinates and grows vegetatively during the spring, flowers from late spring through the summer, and bears fruit during the summer until fall. In the winter, the aerial portion of SLN dies back, with the plant relying on its rootstock for overwintering (Figure 2). SLN reproduces both sexually through seed production and asexually through the vegetative growth of new plants from root banks (Hardin et al., 1972). The ability of SLN to reproduce either way greatly contributes to its ability to colonize different ecological niches (Huenneke and Vitousek, 1990). A single SLN plant can produce approximately 40-60 fruits, with approximately 60-120 tomato-like seeds per fruit. The endosperm allows the seeds to persist for up to 10 years in the soil seed bank (Boyd and Murray 1982a). Young plants develop an extensive root system during the first months of physiological growth and development (De Beer, 1985). The buds that develop in both the vertical and horizontal roots can give rise to new shoots. Taproots of SLN can remain viable for up to 15 months (Molnar and McKenzie, 1976). SLN originated from Central America, particularly northeast Mexico and southwest USA. Native to the southern half of the western United States, SLN favors habitats such as prairies, plains, meadows, pastures, and savannahs. It is mainly found in cultivated lands with hot summers and low annual rainfall and can survive altitudes of up to 1200 m (Californian Department for Food and Agriculture, 2006). It is important to note that the preferred habitats of this species make up most of the landscape in the Southern High Plains of Lubbock, Texas.

Texas Tech University, Joshua James Singleton, August 2019

SLN has known tolerance to saline soils and a significant degree of resistance to drought (EPPO, 2007). Young plants can withstand low temperatures of –23 to –18°C but once established, they become susceptible/sensitive to frost and waterlogging (EPPO, 2007). In the later stages of development, mature SLN requires high temperatures of 20- 34°C for growth (EPPO, 2007). Seed contamination over long distances is the primary source of the widespread dispersal of SLN. The weed is also spread through unmediated contamination of livestock, machinery, and silage. For example, seeds can be spread through the long- distance trade of livestock that fed on grasses contaminated with SLN seeds. Washington State Noxious Weed Control Board (2006) states that 10% of SLN seeds are still viable even after passing through a sheep’s gastrointestinal tract. The far, spatial population distributions of SLN lead to genetically isolated individuals with the potential for genetic divergence. Any form of local adaptation by an isolated population often renders the use of broad-spectrum herbicides to control the weed insufficient.

Economic importance of SLN as a noxious weed and a non-host vector of important plant pests and diseases SLN is recognized as a noxious weed in 21 US states (Figure 3) and an alien invasive species in 42 countries throughout Asia, Africa, North/Central/South America, the Caribbean, and Australia (Table 1). SLN introductions adversely affect the biodiversity of a native ecosystem where it is introduced. It negatively impacts crop production worldwide by competing with crops for water and nutrients, excreting allelopathins that inhibit crop growth, and vectoring a diverse array of economically significant pest and pathogens. In the US, SLN infestation can cause yield losses of up to 4-10% in sorghum and 5-14% in cotton under optimum water conditions (Robinson et al., 1978). These losses increase with a shift to semi-arid environments which is a more preferred habitat of the weed. In the Southern High Plains of Texas, yield losses of up to 75% of the total net production in cotton has been attributed to infestation by SLN (Abernathy, 1979; EPPO, 2007). These loses can be surmised apropos to land use inefficiency, economic loss, and mounting production costs.

Texas Tech University, Joshua James Singleton, August 2019

In Morocco, yield losses of up to 64% in corn (Baye and Bouhache, 2007) and up to 78% in cotton production have been attributed to SLN infestation (EPPO, 2007). In addition to the negative impact of the weed on crop production, it can also reduce the value of infested fields by 25% (Gmira et al., 1998). In Australia, yield losses of up to 77% have been reported for cereal crops infested by SLN (Stanton et al., 2009). Given the current widespread distribution of SLN in Australia, this invasive species can potentially infest 398 million hectares more of farmers’ fields in the region (Feuerherdt, 2009). Significant yield losses due to SLN infestation has also been documented for alfalfa, corn, peanuts, wheat, sugarcane, cultivated pastures, and various fruit crops including olives (Olea europaea subsp. europaea), peach (Prunus persica), and grapes (Vitis vinifera) in several countries in Asia, Australia, Africa, Europe and North America (Cuthbertson, 1976; Molnar and McKenzie, 1976; Robinson et al., 1978; Abernathy and Keeling, 1979; Boyd et al., 1984; Tanji et al., 1984; Wassermann et al., 1988; Eleftherohorinos et al., 1993; Randall, 2012; EPPO Bulletin, 2007) (Table 2). Aside from competing directly with agronomically important crops, SLN is also a known vector of a plethora of pests and viruses. SLN has been reported to vector potato virus Y (PVY) (Boukhris et al., 2007), tomato yellow leaf curl virus (TYLCV) (Zammour et al., 2014) and lettuce chlorosis virus (McLain et al., 1998), and serve as a secondary host of several insect pests including the Colorado potato beetle (Leptinotarsa decemlineata) (Hare, 1990) and the pepper weevil (Anthonomus eugenii) (Patrock and Schuster, 1992). The Colorado potato beetle is a serious pest of potato and other crops in the Solanaceae family (Jacques, 2000). Pepper weevil, on the other hand, causes significant economic damages to all varieties of pepper, tomatillo and eggplant grown in close proximity to infested pepper plots (Capinera, 2002). The larvae and adults of pepper weevil can develop and feed on plants from different genera of the family Solanaceae. The reproductive adults however, can oviposit only on plants belonging to the genera Capsicum and Solanum of the same family. In 1992, Patrock and Schuster reported that pepper weevil is able to oviposit and complete its immature development on SLN, indicating that this weed is a suitable alternative host for the pest in regions that produce/grow Solanum crops and related Capsicum species.

Texas Tech University, Joshua James Singleton, August 2019

Methods of controlling SLN

Chemical Control SLN is generally very difficult to control with herbicides. Its deep root system is impervious to chemicals, ensuring a rapid recovery post-application of herbicides. The aerial growth of SLN can be controlled in-season but can easily grow back that same season to the next by utilizing its deep rooting structure. Picloram, 2,4-D, and glyphosate are the most common herbicides used to control SLN (McKenzie, 1980) and are usually applied via spot spraying or boom application (Ensbey, 2009). Pre-emergence herbicides are applied to 79% of cotton acres while post-emergence herbicides are applied to 87% of land planted to cotton infested by SLN (Matocha et al., 1999). In quantifying the effectiveness of herbicide control, a current standard is to keep SLN re-growth to 10-30% of the pre-treatment stem number for ‘good control’; 10% or less is considered ‘excellent control’ (Eleftherohorinos et al., 1993). Of the herbicides used on SLN, 2,4-D, aminopyralid, dicamba, and picloram have been reported to reduce pre-treatment stem number by 95%. Studies conducted in Australia showed evident control of SLN shoot growth and fruiting by 2,4-D, although the herbicide caused insufficient damage to the root system. From the results of Molnar (1982), it was concluded that 2,4-D applied at a rate of 1.2 kg ha-1 is the most effective for the short-term control of the weed due to its suppression of flowering and seed set. The practical use and application of picloram for SLN control in crop fields has been limited to Macedonia and southeastern California. Spot-spraying of picloram is highly effective in controlling the root bank of SLN with limited impact on cereal crops and pasture grasses. In Australia, the use of picloram has been restricted to small infestations in range or non-crop locations due to the high costs associated with its use, the sensitivity of major crops to the herbicide and its residual effects on broad-leaved crops and pasture species (Heap et al., 1997). In Greece, picloram was identified to be the most effective while glyphosate was determined inconsistent and triclopyr ineffective in controlling SLN (Eleftherohorinos et al., 1993). The combination of picloram and 2,4-D resulted in the most consistent long-term control of SLN (Molnar, 1982).

Texas Tech University, Joshua James Singleton, August 2019

Glyphosate has been deemed valuable in managing SLN as it does not pose significant danger to non-target crops while providing adequate control of the weed. Local studies (Dotray and Keeling, 1996; Joy et al., 2008) conducted at the Quaker farm of Texas Tech University showed that application of glyphosate at 1.5kg ha-1 controlled 97-100% of SLN throughout the growing season (Dotray and Keeling 1996). A small decline in the efficacy of glyphosate in controlling SLN that ranged from 90% and below was reported by Joy et al. in 2008. This small, yet significant change could indicate a developing glyphosate resistance in SLN within a relatively short period of time. In Australia, glyphosate is an unreliable means of pre-harvest control of the weed unless applied with ammonium sulphate at the flowering and fruiting stage (Zaki et al., 1995). In combination with sulfosate and amitrole, however, glyphosate was able to provide a good postharvest control of the weed (Baye et al., 2007).

Mechanical and Cultural Control Mechanical and cultural methods of weed control including crop rotation and hand hoeing (under low weed populations) can be used to supplement chemical control but should not be used as alternatives for herbicide use, as they can be time consuming and costly. Cultivation is used in weed control programs by 61% of cotton farmers (Matocha et al., 1999) when controlling common weeds such as SLN. Zhu et al. (2013) observed that delayed emergence of SLN results in a concomitant delay in fruit formation and reduction in fruit production, which effectively decreases the sexual reproductivity of the plant. Based on this observation, it is advised to control SLN at the flowering stage to avoid seed bank establishment and manage early emerging plants. Cultural practices that will facilitate a significant height differential between the crop and the competing weed to ensure that the crop can outgrow the weed, has been proposed as a means to reduce the need for herbicide spraying. Other forms of cultural control to prevent the spread of SLN include quarantine and exclusion of infested fields from both uninfected field and from the normal production cycle which is a common practice in Australia; quarantine of livestock from infested areas for 14 days to allow passage of all viable seeds through the digestive tract of the animals before returning them to uninfested fields (Heap et al., 1997); and

Texas Tech University, Joshua James Singleton, August 2019 thorough inspection and cleaning of vehicles and machinery that have had exposure to an infested field for fruit and rooting structures of SLN (Anon., 1923).

Biological Control Biological control of SLN has been recommended to be considered only as a component in a larger complex of integrated management practices (EPPO Bulletin, 2007). Even then, the biological control of most Solanum species has been cited to be unreliable due to the ability of plants belonging to this genus to synthesize extracts with known molluscicidal and nematocidal properties (EPPO Bulletin, 2007). Biological control is typically effective on asexually reproducing species (Burdon and Marshall, 1981) as it is easier to match a biological control agent to a specific host genotype. SLN however, is a naturally outcrossing species (Hardin et al., 1972), and therefore has access to genetic variability that can challenge the effectivity of certain biological control agents. Nematodes and insects have been used as biological control agents to reduce SLN populations. Nematodes are usually dispersed artificially via a foliar spray. Once introduced, the nematodes spread rapidly through prolific reproduction in the presence of the specific host. In the US, the non-host-specific, native, leaf-galling nematode Ditylenchus phyllobius (syn. Orrina phyllobia) has been used as an inoculant on infestations of invasive weed species (Northam and Orr, 1982; Wassermann, 1988; Olckers and Zimmermann, 1991). While able to kill a broad range of plants, D. phyllobius is unable to differentiate target species, rendering it inapplicable to control SLN. The west Texas native nematode species Nothanguina phyllobia Thorne causes large galls in the foliage of its host species, SLN. The pronounced specificity N. phyllobia to SLN due to the co-evolution of the two organisms minimizes the possibility of N. phyllobia switching to another target host (Robinson et al., 1978). Aside from nematodes, two leaf-feeding beetles, Leptinotarsa texana and L. defecta, have been used as biological control agents of SLN in South Africa, although only L. texana has been reported to cause considerable damage to SLN (Olckers and Zimmermann, 1991) and Australia (Wapshere,1988).

Texas Tech University, Joshua James Singleton, August 2019

Molecular mechanisms underlying the adaptive success of the silverleaf nightshade The current geographical distribution of SLN indicates the extensive ability of the species to adapt to diverse environments. A key feature of the adaptability of invasive species in response to variable environmental pressures is their ability to adjust their morphological, growth and developmental attributes via phenotypic plasticity and/or genetic variation.

Phenotypic plasticity Phenotypic plasticity is defined as the adaptive strategy of clonal plants to alter its physiology/morphology to express many different phenotypes correlating to the plant’s adaptability to distinct environmental conditions. As an important mechanism of clonal plants, inherent plasticity can allow the plant to invade a wide range of dynamic habitats across a vast geographical selection of non-native heterogenous landscapes. However, these dynamic environments eventually lead to developmental constraints on the genetic evolution of invasive plant species. It is believed that plasticity is initially favored by natural selection but succumbs to continuous directional selection later in the evolutionary development of invasive plant species (Geng et al., 2016; Si et al., 2014; Castillo et al., 2014) When observed in the field, SLN displays a wide range of morphological variation among different individual plants within the same population. A few easily distinguishable phenotypic variations include differences in leaf size and shape; trichome density; flower color, shape and size; anther number; and fruiting structure development (berry shape and size) (Figure 1B-F), indicating the presence of a certain degree of plasticity within populations. In other invasive species such as Spartina densiflora, most inter-population differences in plant traits disappear when the plants are grown in controlled and homogenous common garden conditions, indicating that the observed phenotypic variation in natural populations is due to phenotypic plasticity (Castillo et al., 2014, 2016; Grewell et al., 2016). The degree to which phenotypic plasticity is able to confer fitness flexibility to a species suggests that it may be enough to facilitate the adaptive success of invasive plant taxa to global climate change (Meyerson et al., 2016).

Texas Tech University, Joshua James Singleton, August 2019

Genetic diversity The rate of evolutionary change in response to natural selection is proportional to the amount of additive genetic variation present within the organism (Fisher, 1930). If the number of individuals in a population is too small or genetically homogenous, a detrimental bottleneck is likely to occur. Low genetic diversity can lead to inbreeding depression which can limit the persistence and evolutionary ability of populations (Sakai et al., 2001; Castillo et al., 2018). For invasive plant species, access to a degree of genetic variation is critical in their rapid adaptation to a new environment. Both the genetic and epigenetic divergence of invasive plant populations can increase the fitness and range expandability of plants in response to broad climatic gradients (Konarzewski, Murray, and Godfree, 2012). While some invasive species are able to maintain a high degree of genetic variation, some lack the sufficient genetic diversity levels for successful adaptation. In such cases, these species may take advantage of the minimal genetic differentiation from multiple introductions to maintain variation and incorporate new combinatorial variations within the population (Zhao et al., 2013). For example, the invasive European olive (Olea europea) in Australia is able to maintain a high degree of genetic diversity due to the introduction of multiple cultivars of the same species (Besnard et al., 2007). Another mechanism of introducing genetic variation in a population is through wide hybridization between a wild and cultivated species, such as in the case of the invasive orange wattle (Acacia saligna) in South Africa (Thompson et al., 2012). In extreme cases however, wide hybridization may produce invasive hybrids with better fitness compared to either parents (Ellstrand and Elam, 1993; Ellstrand and Schierenbeck, 2000). Regardless of the source of genetic variation, an increase in the level of genetic diversity present in populations of invasive species largely contributes to its adaptability and evolutionary development (Jogesh et al., 2015). Quantifying this genetic variation will be crucial in the prediction of potential evolutions in response to current and future control management practices (Barrett, 1992; Van Driesche and Bellows, 1996). The adaptive success of invasive weed species to a wide array of multifarious environments rely on their vested levels of phenotypic plasticity and genetic diversity. SLN is a persistent, economically important pest that can cause significant damages and

Texas Tech University, Joshua James Singleton, August 2019 losses to major agricultural crops worldwide. To date, the level of phenotypic plasticity and/or genetic variation that confers the successful establishment of SLN outside of its natural habitat remains unknown. To develop an effective and efficient control strategy for SLN, it is imperative that that contributory effects of phenotypic plasticity and genetic diversity on the adaptive success of the species is understood. Similarly, tracking the source of an invasive weed species and the routes of invasion it has utilized is an important component to the design of an effective weed management strategy (Schaal et al., 2003). This research aims to elucidate the relative importance of genetic variation in the successful adaptation of SLN to various environments, as well as to illustrate the population dynamics of SLN in response to herbicide pressure and changing environment. With the completion of this study, we will be able to take the first steps in designing an effective, long-term management strategy for SLN as well as similar invasive weed species. The objectives of this research are as follows: 1. Evaluate the cross-species transferability of SSR markers derived from tomato (Solanum lycopersicon) and S. lycopersicoides to SLN. 2. Assess the genetic diversity and characterize the population structure of SLN populations from various localities in West Texas using cross-species and S. elaeagnifolium-specific DNA markers. 3. Estimate the population dynamics of SLN under herbicide pressure and environmental changes using the EDYS model.

Texas Tech University, Joshua James Singleton, August 2019

Figure 1.1. Silverleaf nightshade in field (A). Morphological variation in leaf size and shape (B); trichome density (C); flower color, shape, size, and petal number (D); anther number (E); berry shape and size (F) of silverleaf nightshade individuals from the same population. Bar = 1cm

Texas Tech University, Joshua James Singleton, August 2019

Figure 1.2. Typical growth cycle of silverleaf nightshade (adapted from Washington, 2003)

Texas Tech University, Joshua James Singleton, August 2019

Figure 1.3. Map of the US showing states with silverleaf nightshade infestation.

Texas Tech University, Joshua James Singleton, August 2019

Table 1.1. Geographical distribution of silverleaf nightshade. Country/ Continent Distribution Origin Invasive Reference Region Asia restricted Holm et al., 1979; Boyd et al., 1984; India introduced invasive distribution EPPO, 2014 -Karnataka widespread introduced invasive Babu et al., 1995; EPPO, 2014 -Tamil Nadu present EPPO, 2014 Iraq present EPPO, 2014 Israel present introduced invasive Boyd et al., 1984; EPPO, 2014 present, Lebanon EPPO, 2014 few occurrences Pakistan present EPPO, 2014; Flora of Pakistan, 2014 restricted Syria EPPO, 2014 distribution restricted Taiwan EPPO, 2014 distribution Turkey present EPPO, 2014 Africa Algeria present introduced invasive Boyd et al., 1984; EPPO, 2014 Egypt present introduced invasive Boyd et al., 1984; EPPO, 2014 Lesotho present introduced invasive Henderson, 2001; EPPO, 2014 Boyd et al., 1984; Tanji et al., 1984; Morocco widespread introduced invasive EPPO, 2014 Wasserman et al., 1988; Holm et al., South Africa widespread introduced invasive 1979; Boyd et al., 1984; EPPO, 2014 restricted Tunisia EPPO, 2014 distribution Zimbabwe present introduced invasive Boyd et al., 1984; EPPO, 2014 North Goeden, 1971; Holm et al., 1979; America Mexico present native invasive Boyd et al., 1984; Wapshere, 1988; EPPO, 2014 restricted Holm et al., 1979; Boyd et al., 1984; USA native invasive distribution EPPO, 2014 -Alabama present EPPO, 2014; USDA-NRCS, 2014

Texas Tech University, Joshua James Singleton, August 2019

Table 1.1 Continued North Davis et al., 1945; Parsons, 1981; -Arizona widespread native invasive America EPPO, 2014 -Arkansas present EPPO, 2014; USDA-NRCS, 2014 Amor, 1978; Robinson et al., 1978; -California widespread introduced invasive Boyd et al., 1984; EPPO, 2014; USDA-NRCS, 2014 -Colorado present EPPO, 2014; USDA-NRCS, 2014 Patrock and Schuster, 1992; EPPO, -Florida present introduced invasive 2014; USDA-NRCS, 2014 -Georgia present EPPO, 2014; USDA-NRCS, 2014 -Hawaii present EPPO, 2014; USDA-NRCS, 2014 -Idaho present EPPO, 2014; USDA-NRCS, 2014 -Illinois present EPPO, 2014 -Indiana present EPPO, 2014; USDA-NRCS, 2014 -Kansas present EPPO, 2014; USDA-NRCS, 2014 -Kentucky present EPPO, 2014; USDA-NRCS, 2014 -Louisiana present EPPO, 2014; USDA-NRCS, 2014 -Maryland present EPPO, 2014; USDA-NRCS, 2014 -Michigan present native USDA-NRCS, 2014 -Mississippi present EPPO, 2014; USDA-NRCS, 2014 Parsons, 1981; EPPO, 2014; Flora of -Missouri present introduced invasive Missouri, 2014 -Nebraska present EPPO, 2014; USDA-NRCS, 2014 -Nevada present EPPO, 2014; USDA-NRCS, 2014 Goeden, 1971; EPPO, 2014; USDA- -New Mexico widespread native invasive NRCS, 2014 -North Carolina present EPPO, 2014; USDA-NRCS, 2014 -Ohio present EPPO, 2014 Gunn and Gaffney, 1974; Burrows et -Oklahoma widespread introduced invasive al., 1981; EPPO, 2014; USDA-NRCS, 2014 -Oregon present EPPO, 2014; USDA-NRCS, 2014 14

Texas Tech University, Joshua James Singleton, August 2019

Table 1.1 Continued North -South Carolina present EPPO, 2014; USDA-NRCS, 2014 America -Tennessee present EPPO, 2014; USDA-NRCS, 2014 Robinson et al., 1978; Boyd et al., -Texas widespread native invasive 1984; Stubblefield and Sosebee, 1984; EPPO, 2014; USDA-NRCS, 2014 -Utah present EPPO, 2014; USDA-NRCS, 2014 -Washington present EPPO, 2014; USDA-NRCS, 2014 Central Aruba present introduced Acevedo-Rodriguez and Strong, 2012 America and Bahamas present introduced Acevedo-Rodriguez and Strong, 2012 Caribbean British Virgin present Acevedo-Rodriguez and Strong, 2012 Islands Cuba present introduced invasive Oviedo Prieto et al., 2012 Curacao present introduced Acevedo-Rodriguez and Strong, 2012 EPPO, 2014; Flora Mesoamericana, Guatemala present 2014 Honduras present EPPO, 2014 Acevedo-Rodriguez and Strong, 2012; Puerto Rico present EPPO, 2014; USDA-NRCS, 2014 South restricted Morton, 1976; Amor, 1978; Holm et Argentina introduced invasive America distribution al., 1979; EPPO, 2014 Brazil present introduced invasive Duarte and de Carvalho, 1999 Amor, 1978; Holm et al., 1979; Boyd Chile present introduced invasive et al., 1984; EPPO, 2014 Paraguay present EPPO, 2014 Uruguay present introduced invasive Krstic et al., 2000; EPPO, 2014 Europe Pandza and Stancic, 1999; EPPO, Croatia widespread introduced invasive 2014 Cyprus present EPPO, 2014 absent, formerly Denmark EPPO, 2014 present present, France EPPO, 2014 few occurrences 15

Texas Tech University, Joshua James Singleton, August 2019

Table 1.1 Continued Europe Greece present introduced invasive Boyd et al., 1984; EPPO, 2014 Italy present introduced invasive Boyd et al., 1984; EPPO, 2014 -Sardinia present EPPO, 2014 -Sicily present EPPO, 2014 Eleftherohorinos et al., 1993; EPPO, Macedonia present introduced invasive 2014 restricted Serbia EPPO, 2014 distribution Spain present introduced invasive Carretero, 1989; EPPO, 2014 absent, Switzerland EPPO, 2014 formerly present absent, UK EPPO, 2014 formerly present Oceania Australia present introduced invasive Holm et al., 1979; EPPO, 2014 -Australian present introduced invasive Lazarides et al., 1997; EPPO, 2014 Northern Territory Leys and Cuthbertson, 1977; Boyd et -New South Wales widespread introduced invasive al., 1984; Heap et al., 1997; EPPO, 2014 restricted Boyd et al., 1984; Heap et al., 1997; -Queensland introduced invasive distribution EPPO, 2014 Parsons, 1981; Boyd et al., 1984; -South Australia widespread introduced invasive Heap et al., 1997; EPPO, 2014 -Tasmania present introduced invasive Lazarides et al., 1997; EPPO, 2014 restricted Parsons, 1981; Boyd et al., 1984; -Victoria introduced invasive distribution Heap et al., 1997; EPPO, 2014 restricted Boyd et al., 1984; Heap et al., 1997; -Western Australia introduced invasive distribution EPPO, 2014

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Table 1.2. Known host plants of silverleaf nightshade (EPPO Bulletin, 2007). Plant Name Family Arachis hypogaea (groundnut) Fabaceae Asparagus officinalis (asparagus) Liliaceae Beta vulgaris var. saccharifera (sugar beet) Chenopodiaceae Citrus spp. (citrus) Rutaceae Cucumis sativus (cucumber) Cucurbitaceae Cynodon dactylon (Bermuda grass) Poaceae Gossypium hirsutum (Bourbon cotton) Malvaceae Medicago sativa (lucerne) Fabaceae Olea europaea subsp. europaea (European olive) Oleaceae Prunus persica (peach) Rosaceae Solanum lycopersicum (tomato) Solanaceae Solanum tuberosum (potato) Solanaceae Sorghum bicolor (sorghum) Poaceae Sorghum sudanense (Sudan grass) Poaceae Triticum aestivum (wheat) Poaceae Vitis vinifera (grapevine) Vitaceae Zea mays (maize) Poaceae

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CHAPTER II GENETIC DIVERSITY AND POPULATION STRUCTURE OF SILVERLEAF NIGHTSHADE (Solanum elaeagnifolium) FROM TEXAS, USA

Introduction Solanum elaeagnifolium Cav (silverleaf nightshade; SLN) is an aggressive, highly persistent weed infesting agricultural croplands and rangelands in the US and abroad. While human activities have aided in the spread of SLN across 41 countries worldwide, the inherent adaptability of the weed to heterogeneous environments has allowed its successful establishment outside of its native range (Boyd et al., 1982; EPPO, 2007; Stanton et al., 2009). The successful adaptation of invasive species has been largely attributed to phenotypic plasticity and/or genetic diversity. While phenotypic plasticity has been known to confer short term fitness to a species in response to environmental stimuli, genetic diversity provides the basis for a long-term, evolutionary adaptation of a species (Linhart and Grant, 1996; Geng et al., 2016; Sakai et al., 2001). In SLN, the contributory roles of phenotypic plasticity and/or genetic diversity in the successful adaptation of the species is unknown. However, the widespread geographical distribution of SLN indicates the presence of either or both mechanisms underlying fitness flexibility that allows the species to adapt to variable ecological niches. Understanding the mechanisms that drive the long-term, evolutionary adaptation of SLN and its population structure will facilitate the design of effective methods to control this weed. In addition, tracing the origin and dispersal patterns of SLN in a given region will allow the accurate prediction and efficient prevention of the further spread of this species. To generate such knowledge, it is imperative to first establish the degree of genetic diversity in natural populations of SLN. Genetic diversity can be quantified based on the amount of genetic polymorphism or heterogeneity present among and within individuals of different populations (Geng et al., 2016; Sakai et al., 2001). Several molecular marker systems including amplified fragment length polymorphism (AFLP) (Vos et al., 1995), random amplified

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polymorphic DNA (RAPD) (Penner, 1996; Welsh and McClelland, 1990; Williams et al., 1990), insertion-deletions (Indels) (Jain et al., 2019; Becerra et al., 2017; Jamil et al., 2013; Tu et al., 2007), simple-sequence repeats (SSR) (McCouch et al., 1997; Powell et al., 1996; Taramino and Tingey, 1996) and single nucleotide polymorphisms (SNPs) (Govindaraj et al., 2015) have been used to assess the genetic diversity of various plant species. AFLP markers are based on the selective PCR amplification of restriction fragments from whole genomic DNA digestion (Vos et al., 1995). It identifies only one dominant allele at each locus, but has high reproducibility, generates a large number of markers and polymorphic loci, and produces very robust assays. AFLP analysis has been used to evaluate the genetic similarity among ecotypes of crabgrass (Digitaria spp.) from different agricultural regions of Rio Grande do Sul in Brazil (Fontana et al., 2015) and to examine whether genetic differentiation has taken place between spatially intermixed populations of creeping thistles (Cirsium arvense) (Bommarco et al., 2010). RAPDs are dominant markers that uses primers of arbitrary nucleotide sequence to access random segments of genomic DNA and reveal polymorphisms (Collard et al., 2005). Although RAPDs have been reported to detect high levels of genetic diversity, they are limited in terms of reproducibility and are less informative than AFLPs (Williams et al., 1990). SSRs or microsatellites are short, highly repetitive sequences (around 1-6 base pairs in length) that are ubiquitous in eukaryotic genomes. Different species differ in the number of repetitive nucleotide units that can be detected by polymerase chain reaction (PCR) using a pair of flanking primers (Powell et al., 1996). SSRs are co-dominant, highly reproducible and polymorphic, chromosome-specific, multi-allelic and evenly distributed throughout the genome (Jamil et al., 2013). SSRs have been used to evaluate the level of polymorphism and determine the genetic diversity and relationships in a wide range of species including rice (Oryza sativa L.) (Becerra et al., 2017; Jamil et al., 2013; Tu et al., 2007), crabgrass (Fontana et al., 2015), Johnsongrass (Fernandez et al., 2013), and Wedelia trilobata germplasm (Si et al., 2014). Indels are co-dominant, highly polymorphic markers that are inexpensive and are most commonly used in newly discovered species that have not been properly sequenced 19

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(Jain et al., 2019). They are products of short sequence repeats found within a genome that are prone to insertions and deletions due to misalignment or alignment shifts in the DNA during replication (Streisinger et al., 1966). Indels have been used in a variety of applications including population genetics in many different crops such as rice (Wu et al., 2013), Arabidopsis thaliana (Pacurar et al., 2012), and tomato (S. lycopersicum L.) (Yang et al., 2014). Lastly, SNPs are markers based on single nucleotide variations in the DNA. Among the DNA markers, SNPs are the most abundant in the genome (Agarwal et al., 2008). They can be identified using microarrays and other next generation sequencing technologies and have been widely used in genetic mapping and association genetics in plants (Govindaraj et al., 2015; Ong et al., 2015). To date, very little genetic research has been done on SLN and therefore, very limited genetic resources are available for the species. Previous studies to evaluate the transferability of SSR markers from related Solanum species to SLN identified 13 out of 35 SSRs that can amplify targets in SLN, although only six were polymorphic (Zhu et al. 2013). A high degree of genetic variation was established in natural populations of SLN in Australia using these six markers (Zhu et al., 2012). In a separate study, 26 SSR primer pairs were designed based on expressed sequence tag and genomic sequences of SLN (Zhu et al., 2013). Of the 26 SSR markers, only nine were polymorphic. These 26 SSRs represent the first set of SSR markers that has been developed specifically for SLN (Zhu et al., 2013). The accurate assessment of the genetic diversity in SLN populations requires a core set of molecular markers that can capture informative variation across the genome. While a few SSR markers that can be used for genetic diversity analysis have been developed for SLN, it is yet undetermined whether these markers are enough to capture the genetic variation within the SLN genome, nor if they will successfully transfer to SLN populations in the US. To expand the existing genetic marker resource for genetic studies in SLN, this study aims to establish the transferability to SLN of SSR and indel- based markers that are specific to a related nightshade species, S. lycopersicoides. S. lycopersicoides markers that amplified in SLN will be used to assess the genetic diversity of SLN populations from different localities in Texas, USA. The ability of SSR markers 20

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that have been reported to amplify targets in natural populations of SLN in Australia to genotype SLN populations from the US will also evaluated.

Materials and Methods

Cross-species transferability of DNA markers for genotyping DNA marker screening. A total of 177 DNA markers that included 98 genomic- and EST-based SSRs that are specific to tomato (S. lycopersicum) and 44 indel markers that are specific to S. lycopersicoides (Table 2.1) were screened for their ability to amplify target sequences in the SLN genome. The tomato SSRs were synthesized based on publicly available primer sequences that were sourced from the Kazusa Marker Database (http://marker.kazusa.or.jp/Tomato/), whereas the S. lycopersicoides-specific indel and SSR markers were designed following standard specifications for primer design and an in-house analysis of a draft assembly of the S. lycopersicoides genome. SLN- specific and other Solanum-based SSR markers that have been identified to amplify targets in SLN populations from Australia (Zhu et al. 2013; Zhu et al. 2012) were also screened for their applicability in genotyping populations of the noxious weed from the US (Table 1). Synthesis of all DNA markers were outsourced to Sigma, USA. DNA extraction. Genomic DNA was isolated from six SLN plants collected randomly from the Horticultural Gardens of Texas Tech University using a modified CTAB method (Doyle and Doyle, 1987; Healey et al., 2014). Briefly, clipped leaf samples in 2-ml tubes containing metal beads were flash frozen in liquid N and homogenized for two minutes using the 2010 Geno/Grinder SPEX. Homogenized tissues were suspended in 300 µl 2X CTAB buffer and incubated at 65°C for 30 minutes. An equal volume of 24:1 chloroform:isoamyl alcohol was added to the solution before placing the tubes in a Thermo MaxQ™ 2000 Digital Orbital Shaker for 20 minutes at room temperature. The solution was then centrifuged at 12,000 rpm for 10 minutes and the supernatant (100 μl) for all samples was transferred to 96-well plates. The DNA were precipitated by adding 100 μl isopropanol and centrifugation at 3000 rpm for 10 minutes. The pelleted DNAs were washed with 150 μl ethanol, air dried and re-suspended in 100 μl of TE with RNase. All DNA samples were stored at 4°C prior to use.

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Polymerase chain reaction (PCR) and gel electrophoresis. Optimization of the PCR protocol using the Biorad C1000 Touch™ Thermal Cycler was based on standard SSR amplification methods reported by Shim et al. (2015). Multiple optimization trials established the following PCR profile as optimum for genotyping the silverleaf nightshade populations: Step 1. 95°C for 5 minutes Step 2. 95°C for 30 seconds Step 3. 55°C for 30 seconds Step 4. 72°C for 30 seconds Step 5. Repeat steps 2-4 35X Step 6. 72°C for 7 minutes Step 7. 25°C for ∞ PCR was conducted in 10 µl reactions composed of 2 μl of template DNA (20 ng/μl), 1 μl of 10X PCR buffer, 0.1 μl Taq polymerase, 0.5 μl of forward primer, 0.5μl of reverse primer, 1 μl of 25mM dNTPs, 2 μl of glycerol and 2.9 μl sterile dH2O. The resulting amplified DNA was stained using SYBR™ Safe and resolved in a 3% agarose gel in 1X TBE.

Assessment of genetic diversity across different populations of silverleaf nightshade using morphological and DNA markers Plant materials and collection sites. SLN populations derived from seeds that were harvested in 2019 from natural populations of the noxious weed in Lubbock (TX; 33.58°N, 101.85°W), Littlefield (TX; 33.92°N, 102.3°W) and Blackwell, TX (32.09°N, 100.32°W) were included in the study (Figure 2.1). The collection sites in Lubbock cover croplands and rangelands. The Littlefield site is a rangeland maintained under a Conservation Reserve Program. The Blackwell site is maintained as a hunting reserve with interspersed wheat plantings. Seeds from each collection site were germinated in plastic flats lined with 2 mm polypropylene plastic sheet with 1 cm puncture holes to allow drainage of water. The seeds were germinated in two substrates i.e. a conventional potting media (composition here) and a mixture of sand and potting media (1:1 ratio) without any fertilizer input. When the seedlings reached the two-true leaf stage, leaf samples were collected from 22

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each individual seedling and placed into 2ml tubes that were brought back to the lab for later DNA extraction. Genotyping of SLN populations. Markers that were identified to amplify targets in the SLN genome (Table 2.2) were used to establish the genetic diversity of populations of the weed from the different collection sites. PCR and resolution of amplicons in 1X TBE were conducted as stated above. All amplified PCR fragments were scored based on their molecular weight. Genetic diversity and population structure analysis. Descriptive statistics for the single-locus SSR markers including the number of different alleles (Na), number of effective alleles (Ne) and expected heterozygosity (He), as well as the average pair-wise genetic distance between populations (Fst) were generated using GenAlEx v 6.5b3 (Peakall and Smouse, 2012). Polymorphism information content (PIC) of each individual 2 SSR allele was calculated using the formula PIC = ∑ Pi , where Pi is the frequency of the ith allele in the genotypes tested (Weir 1990). A genetic distance matrix based on the DNA marker profiles was generated using GenAlEx v 6.5b3 and was used to calculate similarity indices based on Jaccard’s coefficient. Genetic divergence among the experimental materials was determined by clustering analysis using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) subroutine in MEGA with 1000 bootstraps (Kumar et al., 1994). Molecular variance within collection sites was calculated using an analysis of molecular variance (AMOVA) that is integrated in GenAlEx v 6.5b3.

Results and Discussion

Transferability of S. lycopersicum- and S. lycopersicoides-specific DNA markers to silverleaf nightshade The polymorphism information content, allelic differences in terms of size and range and the ability for multiplexing are some of the important features that define the efficiency of DNA markers. Despite the recent, rapid advances in sequencing and genotyping platforms, SSRs remain the marker of choice for most research laboratories because of their abundance in the genome, degree of polymorphism, cost-efficiency and reproducibility between laboratories.

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Due to a high level of sequence conservation within Solanaceae, published SSR primer sequences based on other members of the Solanum species are readily available for genetic diversity analysis in other related species such as potato (S. tuberosum), tomato (S. lycopersicum), eggplant (S. melongena), and S. lycopersicoides. In the current study, we evaluated the transferability of S. lycopersicum and S. lycopersicoides DNA markers to SLN. Of the tomato-based SSRs screened for transferability to SLN, 19 genome-based SSRs (24.67%) and nine EST-based SSRs (42%) amplified targets in SLN. EST-based markers are specific to transcribed regions of the genome and therefore are more conserved. As was observed in the study, the interspecific conservation of EST-based SSRs facilitated their higher transferability across species belonging to the same family compared to genome-based SSRs (Kuleung et al., 2004; Wang et al., 2007). Of the 54 S. lycopersicoides indel-based markers that were screened for their transferability, only 18 amplified targets in S. elaeagnifolium using a 55°C annealing temperature. SLYD 52, which maps at chromosome 9 of S. lycopersicoides amplified multiple locus in SLN (Table 2). Fragment size generated by the 18 indel markers ranged from 143 to 380 bp, consistent with in silico predictions for amplicon size. S. lycopersicoides belongs to the Potato clade, whereas SLN belongs to the Leptostemonum clade of the genus Solanum (Stern et al., 2011). The high degree of genetic differentiation between the two Solanum species that separated them into different clades may account for the ≤30% transferability of S. lycopersicoides indel markers to SLN. Nevertheless, the results of the study indicate the presence of conserved indel marker binding sites between S. lycopersicoides and SLN. Solanum-based SSR markers that have been previously reported to amplify targets in natural populations of SLN from Australia (Zhu et al., 2012; Zhu et al., 2013) were screened for their ability to genotype SLN populations from the US. Except for SEA 13 and SEA 26, all the EST-based, SLN markers, along with the potato SSRs and six out of the seven eggplant SSRs were able to amplify targets in SLN. Markers that required a 58°C or 50-60°C annealing temperature to amplify targets in Australian SLN populations gave clear, consistent bands when amplified at an annealing temperature of 55°C using SLN samples from the US. The size of the amplified fragments ranged from 220 to 340 24

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bp, consistent with the range of amplicon size obtained from Australian SLN populations. SEA 3 and SEA 16, which did not amplify in Australian SLN populations, amplified multiple alleles in US SLN populations, whereas SEA 7 and SEA 22, which amplified multiple loci in Australian SLN populations amplified single locus in US SLN populations. Differences in the ability of the SEA SSRs to amplify targets in the Australian and US SLN populations, as well as in the number of target loci the markers can amplify indicate possible diversification of the Australian population following the species introduction from the US (Zhu et al., 2012).

Descriptive statistics of DNA markers used in genetic diversity assessment studies The number of alleles at a particular marker locus can reveal the genetic diversity within/between germplasm. The more alleles at a particular marker locus, the greater the level of genetic diversity that can distinguish between related lines (Geng et al., 2016). The amount of relative information that can be derived from the utilized markers is dependent on their individual polymorphism information content (PIC) value. PIC is the quantification of the number of alleles and their frequencies that reflect the levels of polymorphism within/between germplasms (Botstein et al., 1980). Of the 52 markers that were used for genotyping, only nine (Table 2.3) generated polymorphic bands. These included the S. lycopersicoides markers SLYD10, SLYD29, SLYD30; the S. elaeagnifolium-specific markers SEA 5, SEA 6 and SEA 19; the potato SSRs STU 1 and STU 2; and the eggplant SSR, SME 2. All polymorphic markers generated 2 alleles each. Nine loci that were generated by SEA 6, SLYD 10 and STU2 markers recorded a band frequency of ≤5%, indicating the presence of rare, informative bands that are unique to individual plants from the TTU Quaker Farm and to two individual plants from Blackwell. PIC values for each of the polymorphic markers ranged from 0.014 (SME 2) to 0.621 (SLYD 29), with an average of 0.245 (Table 2.3). The calculated mean PIC coincided with the mean He (0.241) established for the same set of markers (Table 2.4) SEA 5 and SLYD 10 recorded PIC values that were ≥0.40, whereas SLYD 29 recorded the highest PIC of 0.621. These results indicate that the applicability of these three

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markers in genetically differentiating among SLN individuals within or between populations. The PIC values obtained for the SEA markers used in this study were different to those obtained by Zhu et al. (2013) for the same markers when used in SLN populations in Australia. The results indicate the genetic divergence of the two populations which may be attributed to the fact that SLN in Australia is an introduced species. The possibility of multiple introductions are also likely to widen this genetic variability between the populations. Based on the mean and individual PIC values of the DNA markers used, a moderate to high degree of genetic diversity was established within individuals of the different populations. This was supported by results of the AMOVA analysis which showed that 74.00% of the total genetic variation observed was due to differences within individuals of the population studied, while 26% of the variation observed was due to differences among population (Fst = 0.317) (Table 2.5).

Genetic diversity and population structure of silverleaf nightshade SLN propagates clonally as well as by seed production through obligate outcrossing (Hardin et al., 1972). To capture the maximum genetic variation that is present within and between SLN populations, we sampled individual plants that were generated from SLN seeds that were harvested from mature plants in each collection site. Based on Jaccard’s coefficient, UPGMA analysis distinctly grouped the SLN individuals into 6 clusters i.e. I, II, III, IV, V and IV. The clusters diverged between 1.0 and 0.5 (~0.80) based on Jaccard’s dissimilarity index along the Y-axis (Figure 2.1). Clusters I, II and III were composed solely of individuals from the Texas Tech University rangeland in Lubbock. Cluster IV was composed of individuals collected from a hunting reserve in Blackwell. Cluster V was composed mostly of individuals from the rangeland in Littlefield, with the addition of a single line (individual 87) from the Texas Tech University rangeland. Finally, Cluster VI consisted primarily of individuals collected from the Texas Tech University Quaker Farm, and a few individual pants from the rangelands in Littlefield and Texas Tech University.

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Generally, individual plants coming from the same collection site clustered closely together, indicating the possible role of different ecologies on the genetic differentiation of individuals from each population. Seeds that were used in the study were collected from two rangeland sites (Littlefield and Lubbock), a private hunting reserve (Blackwell) and a cropland (Lubbock). Rangelands are generally uncultivated lands for animal grazing and browsing. They are characterized by limited water and nutrient availability and low annual production (Havstad et al., 2009). Hunting reserves, on the other hand, are fenced-in land areas where hunting is carefully controlled. Rangelands and hunting reserves would differ in its native vegetation, as well as in the degree by which anthropogenic activities impact the composition of flora. Croplands are agricultural farming lands that are utilized for the production of crops and are characterized by a high degree of anthropogenic disturbance. Unlike naturally maintained rangelands and hunting reserves, croplands are exposed to a range of conventional management practices including tillage, irrigation and agrochemical use (Kladivko, 2001; Rands, 1986; Andreasen et al., 1996; Haughton et al., 1999; Green and Stowe, 1993; Perkins et al., 2000; Moller, 200). Intensive agricultural practices such as these have direct impacts on the ecology of croplands, including the existing flora and fauna. More specifically, such practices present viable sources of selection pressures that can drive the differential adaptation of species within a given time frame. In the present study, individual SLN plants from the same collection site generally clustered together, with only a few exceptions. Over time, the multitude of variable environmental stimuli that are unique to a particular ecology can serve as barriers that constrict gene flow between populations, thus increasing genetic differentiation between closely related individuals (Linhart and Grant, 1996). For example, environmental modification vis-à-vis agricultural practices such as herbicide application represents a ‘current event’ that can drive the genetic differentiation of a plant population. In such a case, both the rates and the frequency of herbicide application constitute a selection pressure that can induce variation in a single plant population towards adaptation i.e. herbicide resistance. The observed intra-cluster variation, as well as variation among individual within populations may be attributed to the nature of SLN as an obligate out-crosser (Hardin et 27

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al., 1972). Plants that are strictly cross-pollinated have been observed to maintain a higher degree of genetic diversity compared to self-pollinated species due to a higher frequency of genetic recombination. The innate genetic variation in SLN that is maintained by cross pollination among individuals within the same population is important in defining the ability of the weed to adapt to a plethora of environments.

Conclusion The genetic diversity present within a plant population is a critical indicator of its capacity for adaptation to environmental variation and change. In the current study, we tested a large number of publicly available genetic markers that can differentiate within and between SLN individuals from different populations. This is an important step towards establishing a core set of genetic markers that can be used in the assessment of genetic variation in SLN in different laboratories. Based on DNA marker profiling, we were able to establish a significant degree of variation among individuals from all four of the tested SLN populations. The observed variation in the test germplasms can easily be identified as the product of the unique ecology. The directional selection pressure from environmental and/or anthropogenic factors that defines each ecology must have favored specific phenotypes that caused allele frequency to shift over time in the direction of that phenotype, consequently driving the differentiation among populations. Intraspecific variations that were observed among individuals of the same cluster are attributed to the nature of SLN as an obligate out-crosser. Cross pollination facilitates recombination that contributes to the innate degree of variability cross-pollinated plants. Such genetic variation is important in enhancing the ability of small plant populations to remain viable under fluctuating stresses and novel environmental conditions (Wise et al., 2002). Menchari (et al., 2007) suggests that large effective population sizes, such as the four populations used in the study can favor rapid genetic adaptation without detrimental loss of genome-wide variation. The data and information presented highlights the limitations of our study. Our study tested SLN in its native region/environment, but to gauge the range of variation that could potentially be observed in SLN we need to include populations that survives in

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drastically different environments compared to its native range and find out how it survives in those places (i.e. infested northern US states; area regimes with less than ideal cold and/or wet conditions). As only nine markers of the original marker set were identified as polymorphic, a large number of DNA markers need to be tested to establish a core set of markers for genetic diversity assessment in SLN.

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Figure 2.1. Mapping of the four individual collection sites in relation to each other. a) Littlefield, TX; b) Lubbock, TX (TTU Rangeland); c) Blackwell, TX; d) Lubbock, TX (Quaker Farm)

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Table 2.1. DNA markers tested for cross-transferability in silverleaf nightshade. No. Primer Name Source species Marker type Expected amplicon size (bp) Chr Map position (Mb) 1 TGSa 10927 S. lycopersicum SSR 256 1 3.15 2 TGS 7324* S. lycopersicum SSR 231 1 0.07 3 TGS 2984 S. lycopersicum SSR 278 1 9.23 4 TGS 2465 S. lycopersicum SSR 126 1 12.55 5 TGS 2380 S. lycopersicum SSR 201 1 15.48 6 TGS 11481* S. lycopersicum SSR 201 1 18.69 7 TGS 1790 S. lycopersicum SSR 293 1 21.48 8 TGS 0265* S. lycopersicum SSR 179 1 24.39 9 TGS 1565* S. lycopersicum SSR 219 1 30.73 10 TGS 0085 S. lycopersicum SSR 275 1 36.50 11 TGS 7203 S. lycopersicum SSR 266 1 39.69 12 TGS 8920 S. lycopersicum SSR 167 1 42.45 13 TGS 13266 S. lycopersicum SSR 199 1 45.63 14 TGS 9398 S. lycopersicum SSR 221 1 48.63 15 TGS 11095 S. lycopersicum SSR 188 1 51.67 16 TGS 4708 S. lycopersicum SSR 185 1 54.62 17 TGS 4535 S. lycopersicum SSR 163 1 57.53 18 TGS 2115 S. lycopersicum SSR 136 1 60.60 29 TGS 2656 S. lycopersicum SSR 185 1 63.57 20 TGS 8225 S. lycopersicum SSR 176 1 66.65 21 TGS 12143* S. lycopersicum SSR 172 1 75.61 22 TGS 11545 S. lycopersicum SSR 256 1 81.63 23 TGS 12372* S. lycopersicum SSR 156 1 84.62 24 TGS 10482* S. lycopersicum SSR 232 1 87.64 25 TGS 9583* S. lycopersicum SSR 214 1 90.25 26 TGS 10411 S. lycopersicum SSR 253 2 0.32 27 TGS 4406 S. lycopersicum SSR 232 2 3.48

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Table 2.1 Continued 28 TGS 7378 S. lycopersicum SSR 145 2 6.44 29 TGS 10096 S. lycopersicum SSR 224 2 12.40 30 TGS 3008 S. lycopersicum SSR 157 2 15.49 31 TGS 2428* S. lycopersicum SSR 215 2 18.54 32 TGS 5394* S. lycopersicum SSR 268 2 21.51 33 TGS 5835 S. lycopersicum SSR 155 2 24.43 34 TGS 0798 S. lycopersicum SSR 293 2 27.54 35 TGS 12581* S. lycopersicum SSR 192 2 36.59 36 TGS 10252* S. lycopersicum SSR 196 2 39.45 37 TGS 1838 S. lycopersicum SSR 217 2 42.48 38 TGS 5494 S. lycopersicum SSR 236 2 45.51 39 TGS 8877 S. lycopersicum SSR 195 3 3.02 40 TGS 11210 S. lycopersicum SSR 216 3 6.04 41 TGS 4954 S. lycopersicum SSR 186 3 9.01 42 TGS 1020* S. lycopersicum SSR 223 3 12.01 43 TGS 9879 S. lycopersicum SSR 199 3 15.01 44 TGS 3124 S. lycopersicum SSR 174 3 18.26 45 TGS 0420 S. lycopersicum SSR 235 3 21.06 46 TGS 12123 S. lycopersicum SSR 252 3 24.11 47 TGS 0705 S. lycopersicum SSR 131 3 27.26 48 TGS 11933 S. lycopersicum SSR 234 3 33.54 49 TGS 7221 S. lycopersicum SSR 268 3 36.46 50 TGS 12842 S. lycopersicum SSR 233 3 39.63 51 TGS 10259 S. lycopersicum SSR 239 3 42.44 52 TGS 6246* S. lycopersicum SSR 225 3 45.54 53 TGS 0430* S. lycopersicum SSR 188 3 48.52 54 TGS 0583* S. lycopersicum SSR 180 3 57.45 55 TGS 0728 S. lycopersicum SSR 257 4 3.12 56 TGS 11679 S. lycopersicum SSR 230 4 6.27

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Table 2.1 Continued 57 TGS 1418* S. lycopersicum SSR 283 4 9.25 58 TGS 2877 S. lycopersicum SSR 267 4 15.24 59 TGS 3806* S. lycopersicum SSR 172 4 18.22 60 TGS 1711 S. lycopersicum SSR 144 4 21.13 61 TGS 3052 S. lycopersicum SSR 226 4 24.60 62 TGS 11147 S. lycopersicum SSR 223 4 27.32 63 TGS 8373 S. lycopersicum SSR 217 4 33.57 64 TGS 6934 S. lycopersicum SSR 157 4 36.55 65 TGS 0406 S. lycopersicum SSR 269 4 39.24 66 TGS 1264* S. lycopersicum SSR 174 4 42.30 67 TGS 0048 S. lycopersicum SSR 137 4 45.29 68 TGS 11520 S. lycopersicum SSR 151 4 48.16 69 TGS 1595 S. lycopersicum SSR 205 4 51.26 70 TGS 1450 S. lycopersicum SSR 122 4 57.31 71 TGS 1069 S. lycopersicum SSR 260 4 63.24 72 TGS 1794 S. lycopersicum SSR 216 5 3.08 73 TGS 0595 S. lycopersicum SSR 155 5 9.16 74 TGS 12065 S. lycopersicum SSR 190 5 12.33 75 TGS 6490 S. lycopersicum SSR 151 5 15.41 76 TGS 0759 S. lycopersicum SSR 124 5 18.30 77 TGS 2398 S. lycopersicum SSR 194 5 21.48 78 TESb 7377 S. lycopersicum SSR 229 1 6.30 79 TES 7421 S. lycopersicum SSR 199 1 33.62 80 TES 4051* S. lycopersicum SSR 243 1 69.58 81 TES 0187* S. lycopersicum SSR 294 1 78.56 82 TES 4332* S. lycopersicum SSR 234 2 9.20 83 TES 5116* S. lycopersicum SSR 230 2 30.42 84 TES 1432* S. lycopersicum SSR 192 2 33.55 85 TES 1301* S. lycopersicum SSR 199 2 48.54

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Table 2.1 Continued 86 TES 2519* S. lycopersicum SSR 243 3 0.01 87 TES 5971* S. lycopersicum SSR 295 3 30.40 88 TES 2367 S. lycopersicum SSR 238 3 51.70 89 TES 0236* S. lycopersicum SSR 300 3 54.47 90 TES 5525 S. lycopersicum SSR 253 3 60.46 91 TES 1682 S. lycopersicum SSR 246 3 63.65 92 TES 1220 S. lycopersicum SSR 218 4 0.09 93 TES 1783 S. lycopersicum SSR 185 4 12.22 94 TES 4279 S. lycopersicum SSR 242 4 30.67 95 TES 4572 S. lycopersicum SSR 155 4 54.38 96 TES 0080 S. lycopersicum SSR 231 4 60.12 97 TES 4239 S. lycopersicum SSR 185 5 0.06 98 TES 2019 S. lycopersicum SSR 242 5 6.12 99 SLYD 01 S. lycopersicoides Indel 330 1 0.04 100 SLYD 02 S. lycopersicoides Indel 321 1 27.85 101 SLYD 03 S. lycopersicoides Indel 320 1 30.76 102 SLYD 04 S. lycopersicoides Indel 338 2 27.25 103 SLYD 05 S. lycopersicoides Indel 347 2 36.06 104 SLYD 06 S. lycopersicoides Indel 338 2 45.01 105 SLYD 07 S. lycopersicoides Indel 349 2 54.05 106 SLYD 08 S. lycopersicoides Indel 236 3 0.01 107 SLYD 09 S. lycopersicoides Indel 261 1 3.03 108 SLYD 10 S. lycopersicoides Indel 282 1 6.17 109 SLYD 11 S. lycopersicoides Indel 380 1 15.50 110 SLYD 12 S. lycopersicoides Indel 352 1 79.10 111 SLYD 13 S. lycopersicoides Indel 218 1 93.22 112 SLYD 14 S. lycopersicoides Indel 316 2 0.04 113 SLYD 15 S. lycopersicoides Indel 273 2 9.26 114 SLYD 16 S. lycopersicoides Indel 374 2 15.60

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Table 2.1 Continued 115 SLYD 17 S. lycopersicoides Indel 143 2 18.13 116 SLYD 18 S. lycopersicoides Indel 296 2 24.10 117 SLYD 19 S. lycopersicoides Indel 389 3 3.02 118 SLYD 20 S. lycopersicoides Indel 226 3 6.22 119 SLYD 21 S. lycopersicoides Indel 362 3 18.36 110 SLYD 22 S. lycopersicoides Indel 278 3 24.02 111 SLYD 23 S. lycopersicoides Indel 203 3 51.68 112 SLYD 24 S. lycopersicoides Indel 308 4 0.03 113 SLYD 25 S. lycopersicoides Indel 377 4 12.30 114 SLYD 26 S. lycopersicoides Indel 244 4 24.31 115 SLYD 27 S. lycopersicoides Indel 204 4 30.44 116 SLYD 28 S. lycopersicoides Indel 251 4 47.98 117 SLYD 29 S. lycopersicoides Indel 238 5 0.13 118 SLYD 30 S. lycopersicoides Indel 270 5 18.74 119 SLYD 31 S. lycopersicoides Indel 392 5 33.41 120 SLYD 32 S. lycopersicoides Indel 207 5 45.13 121 SLYD 33 S. lycopersicoides Indel 271 5 57.42 122 SLYD 34 S. lycopersicoides Indel 269 6 3.17 123 SLYD 35 S. lycopersicoides Indel 322 6 6.75 124 SLYD 36 S. lycopersicoides Indel 266 6 15.51 125 SLYD 37 S. lycopersicoides Indel 361 6 24.86 126 SLYD 38 S. lycopersicoides Indel 276 6 30.52 127 SLYD 39 S. lycopersicoides Indel 335 7 3.37 128 SLYD 40 S. lycopersicoides Indel 253 7 9.13 129 SLYD 41 S. lycopersicoides Indel 412 7 15.54 130 SLYD 42 S. lycopersicoides Indel 355 7 24.13 131 SLYD 43 S. lycopersicoides Indel 306 7 36.28 132 SLYD 44 S. lycopersicoides Indel 397 8 3.42 133 SLYD 45 S. lycopersicoides Indel 468 8 9.17

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Table 2.1 Continued 134 SLYD 46 S. lycopersicoides Indel 315 8 24.47 135 SLYD 47 S. lycopersicoides Indel 228 8 33.47 136 SLYD 48 S. lycopersicoides Indel 357 8 45.28 137 SLYD 49 S. lycopersicoides Indel 298 9 3.24 138 SLYD 50 S. lycopersicoides Indel 230 9 12.33 139 SLYD 51 S. lycopersicoides Indel 370 9 18.63 140 SLYD 52 S. lycopersicoides Indel 372 9 27.98 141 SLYD 53 S. lycopersicoides Indel 293 9 33.70 142 SLYD 54 S. lycopersicoides Indel 335 10 3.02 143 SEA 1 S. elaeagnifolium SSR 308 - - 144 SEA 2 S. elaeagnifolium SSR 221-232 - - 145 SEA 3 S. elaeagnifolium SSR - - - 146 SEA 4 S. elaeagnifolium SSR 328-331 - - 147 SEA 5 S. elaeagnifolium SSR 196-204 - - 148 SEA 6 S. elaeagnifolium SSR 256-278 - - 149 SEA 7 S. elaeagnifolium SSR 226-259 - - 150 SEA 8 S. elaeagnifolium SSR 195-202 - - 151 SEA 9 S. elaeagnifolium SSR 226-268 - - 152 SEA 10 S. elaeagnifolium SSR 213 - - 153 SEA 11 S. elaeagnifolium SSR 231 - - 154 SEA 12 S. elaeagnifolium SSR 238 - - 155 SEA 13 S. elaeagnifolium SSR - - - 156 SEA 14 S. elaeagnifolium SSR 299 - - 157 SEA 15 S. elaeagnifolium SSR 174-186 - - 158 SEA 16 S. elaeagnifolium SSR - - - 159 SEA 17 S. elaeagnifolium SSR 162-174 - - 160 SEA 18 S. elaeagnifolium SSR 185 - - 161 SEA 19 S. elaeagnifolium SSR 216 - - 162 SEA 20 S. elaeagnifolium SSR 218-242 - -

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Table 2.1 Continued 163 SEA 21 S. elaeagnifolium SSR 245-289 - - 164 SEA 22 S. elaeagnifolium SSR 202-257 - - 165 SEA 23 S. elaeagnifolium SSR 294 - - 166 SEA 24 S. elaeagnifolium SSR 353 - - 167 SEA 25 S. elaeagnifolium SSR 224 - - 168 SEA 26 S. elaeagnifolium SSR 123-160 - - 169 STU 1 S. tuberosum SSR 136-163 4 - 170 STU 2 S. tuberosum SSR 177-192 3 - 170 SME 1 S. melongena SSR 260 - - 172 SME 2 S. melongena SSR 268 - - 173 SME 3 S. melongena SSR 228 - - 174 SME 4 S. melongena SSR 123 - - 175 SME 5 S. melongena SSR 213 - - 176 SME 6 S. melongena SSR 248 - - 177 SME 7 S. melongena SSR 230-243 - - agenomic-based SSRs bexpressed sequence tag-based SSRs *tomato-based markers that amplified targets in silverleaf nightshade

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Table 2.2. Sequences of cross-species markers that were used to genotype silverleaf nightshade populations. No. Primer name Source Species Forward primer Reverse primer 1 SLYD 02 S. lycopersicoides ctcgttagtgcacccatttc tgtttcccatagaacctcgg 2 SLYD 03 S. lycopersicoides tcacacatgagaggtaaccc tgaatgtccagttctgccac 3 SLYD 05 S. lycopersicoides aacaacaaccctggctttgc gagcagatggacagaacatc 4 SLYD 10 S. lycopersicoides tagggctctgttagaatgcg tggaattgactgctggtgag 5 SLYD 12 S. lycopersicoides ggggttcttgtttccatctc cagtcgtcagacaactgtag 6 SLYD 13 S. lycopersicoides ttcagagacaccagcatcag tactgtttcggtttggggac 7 SLYD 15 S. lycopersicoides cagactttgcctaggaagag ggcttcaacggaagatcaag 8 SLYD 21 S. lycopersicoides tccattgtggtggctacaac tcactaatccaccagtcagc 9 SLYD 25 S. lycopersicoides cctggtttgtgtcaattggc ttacccttggcctaacgatc 10 SLYD 27 S. lycopersicoides ccttcaagagaagcaaggag ccttggtaaattctgggagg 11 SLYD 29 S. lycopersicoides agacatccctgcaagcaatg aaggcgaaatacccacttcc 12 SLYD 30 S. lycopersicoides gatgactgggtatctttcgc ccatctgttggttgaggaag 13 SLYD 42 S. lycopersicoides cattgtcggacatggaatgg gagcaagagtgcttgtgtac 14 SLYD 43 S. lycopersicoides ggagggatatggcctttatg caacacccgataggtaagtc 15 SLYD 50 S. lycopersicoides tttcagtgccacaattgggc aacaccacttgaagcttccc 16 SLYD 52 S. lycopersicoides atgcttactctccctgagtg cacgttcatcaagctctctc 17 SLYD 53 S. lycopersicoides ttaagtggatgtgccggaag gctagagtacttagaggagc 18 SLYD 54 S. lycopersicoides caaaaaagcaccaccggatc ttcaacagagctaacgcctc 19 SEA 1 S. elaeagnifolium actaataccttaccccgttcatct attcgttcaagaagggctcc 20 SEA 2 S. elaeagnifolium atagtacactcagcatccatcataag acaggaggaacagcaaggc 21 SEA 3 S. elaeagnifolium tcacaccactaaaggggggat atcaacaggaggaacagcaagg 22 SEA 4 S. elaeagnifolium atgtagggactagtgctcgagtt aataaagcaagggcaataggtc 23 SEA 5 S. elaeagnifolium tatggggcacatgggagag aacccccattctaaatccttgt 24 SEA 6 S. elaeagnifolium ctttgttcggagttgttgacc cctccatcgcaaaaccatc 25 SEA 7 S. elaeagnifolium agagtggagaggagaagtagaagg ggtaaattgaggatcttgggtg 26 SEA 8 S. elaeagnifolium ggaattaagggtccaaggc ctcacaagttactcgggctct 27 SEA 9 S. elaeagnifolium ttcataaatgagaacttacacggac tcttagcagcgaactgggac 28 SEA 10 S. elaeagnifolium ccaagcgaggaaatagcact gtgcttccgatttctccaac

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Table 2.2 Continued 29 SEA 11 S. elaeagnifolium ggtgtttgttggagaaatcgg tcttctacgatttccttggtgc 30 SEA 12 S. elaeagnifolium gaaatgaaagtcccatctcc tgacttcagaaccagttactcct 31 SEA 13 S. elaeagnifolium caatcacagtagaaagggtcgct ttaccattccctatgttgatccag 32 SEA 14 S. elaeagnifolium gcgaacgaataattgaccacc agtcgccaaactccacatctc 33 SEA 15 S. elaeagnifolium tcatcacgcaaacgcttactc atttaactatgtgctaattgttatcgc 34 SEA 16 S. elaeagnifolium caaagatacggaccgcacct ggtaaacgccagacgaacaag 35 SEA 17 S. elaeagnifolium ccaaggctcggaagaacc ccacgaaaacacaacctaactaac 36 SEA 18 S. elaeagnifolium ggctaagtgactaaacaaaaatgg agcagtggtatcaatttgtgtcg 37 SEA 19 S. elaeagnifolium tggtagaggcgaaggcat gcatcttcaggtcccaactt 38 SEA 20 S. elaeagnifolium cacttgcccctattcctgtcat cttgtatccttctcgctacctttc 39 SEA 21 S. elaeagnifolium gctgctactcccaatcctaactg aaatctccgacgaaagctactact 40 SEA 22 S. elaeagnifolium gcagaatcccgtgaaccatc cgccgagagagttgggttac 41 SEA 23 S. elaeagnifolium attggttgggctgtgttcct tgggcggatttagcaactg 42 SEA 24 S. elaeagnifolium tttagcctattccacaatgtctca tggcgaatacaaccaactatcat 43 SEA 25 S. elaeagnifolium tcactatctctatggggtaaaaacg gcatagtattgtccgattcataagg 44 SEA 26 S. elaeagnifolium ggcattggaaatactttttattac cctaaaagcggaggaatgtc 45 STU 1 S. tuberosum cagcaaaatcagaacccgat ggatcatcaaattcaccgct 46 STU 2 S. tuberosum cgatctctgctttgcaggta gttcatcactaccgccgact 47 SME 1 S. melongena atcctgttgctgctcattttcctc aggaggatccaagaggtttgttga 48 SME 2 S. melongena ccaaaacaatttccagtgactgtgc gaccagaatgcccctcaaattaaa 49 SME 4 S. melongena tctgcatcgaatgtctacaccaaa aaaagcgcttgcactacactgaat 50 SME 5 S. melongena gatcatcactggtttgggctacaa aggggagaggaaacttgattggac 51 SME 6 S. melongena cagacacaactgctgagccaaaat cggtttaatcatagcggtgacctt 52 SME 7 S. melongena caaaagataaaaagctgccggatg catgcgtgagttttggagagagag

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Table 2.3. PIC values of SSR markers that amplified polymorphic targets in silverleaf nightshade. Primer No. of Total no. of individuals in Allele frequency (pi) No. ∑pi2 PIC Name alleles the population A He* B 1 STU 1 2 147 0.884 0.116 0.00 0.795 0.204 2 STU 2 2 147 0.993 0.006 0.00 0.986 0.014 3 SME 2 2 147 0.925 0.075 0.00 0.862 0.138 4 SEA 05 2 147 0.728 0.265 0.007 0.600 0.400 5 SEA 06 2 147 0.980 0.020 0.00 0.960 0.040 6 SEA 19 2 147 0.905 0.095 0.00 0.828 0.172 7 SLYD 10 2 147 0.701 0.286 0.014 0.573 0.427 8 SLYD 29 2 147 0.388 0.449 0.163 0.379 0.621 9 SLYD 30 2 147 0.899 0.054 0.048 0.812 0.188 *He=heterozygous allele

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Table 2.4. Summary statistics of markers used for the assessment of genetic diversity in silverleaf nightshade populations from Texas Descriptive statistics Value Total no. of markers used 52 Total no. of markers that amplified in >50% of the experimental 52 materials Average number of observed alleles per SSR 2 Average number of effective alleles (Ne) 1.18±0.049 Average expected heterozygosity (He) 0.241±0.027 Number of polymorphic SSR markers 9 PIC range 0.014-0.621 Average calculated PIC 0.245

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Table 2.5. AMOVA analysis comparing genetic variation within and among individuals and among populations.

Estimated Percent F statistics Source df SS MS variation variation Fst Fis Fit Fst max F’st Among Pops 3 53.684 17.895 0.244 26% Among Indiv 143 53.269 0.373 0.000 0% 0.317 -0.292 0.117 0.883 0.358 Within Indiv 147 100.000 0.680 0.680 74% Total 293 206.952 0.924 100%

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Figure 2.2. UPGMA clustering of silverleaf nightshade from four localities in Texas based on Jaccard’s coefficient.

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CHAPTER III SIMULATION OF ABOVEGROUND BIOMASS ACCUMULATION OF SILVERLEAF NIGHTSHADE IN CROPLANDS IN RESPONSE TO HERBICIDE PRESSURE

Introduction Silverleaf nightshade (SLN) is an economically significant invasive species worldwide that we found in Chapter 2 to possess a high degree of genetic diversity. An increase in the level of genetic diversity present in populations of invasive species largely contributes to its adaptability and evolutionary development (Jogesh et al., 2015). The capacity for rapid evolutionary change is key in the adaptive success of invasive plant species. An important type of evolutionary change found in, but not exclusive to invasive plant introductions is stress-induced modification of the genome (Richardson and Pysek, 2006; Pimentel et al., 2005; Facon et al., 2006; Prentis et al., 2008). Invasive species are frequently exposed to novel biotic and abiotic stresses (i.e. herbicides) that can affect genome stability (Bond and Finnegan, 2007; Henikoff, 2005; Prentis et al., 2008). A decrease in the genome stability of an invasive species can lead to genome and transcriptome modifications in response to a significant environmental stimulus, that can be incorporated into the genomic evolutionary selection pool (Rando and Verstrepen, 2007; Henikoff, 2005; Prentis et al., 2008). The components of a plant species invasion (e.g. initial lag in population growth, rate of spread and its geographic features) can now be modeled to predict the rate and direction of its spread. This is a critical challenge in designing management programs using theoretical models of invasive species. The development of computer-generated simulation models that are capable of predicting the results of control measures on weed densities is important in designing practical weed management practices and methods (Mortimer and Firbank, 1983). Applications of population genetic models can also help in predicting the invasiveness of a species, as well as the efficacy of control strategies. Mortimer (1987) has defined and outlined the four primary parameters to consider in weed control management: 1) the likelihood of a weed invasion, 2) how quickly will the

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weed spread and what are the projected loses, 3) the quantity of each control measure required to reach containment or control of the weed, and 4) costs and risks involved with each individual control strategy implemented. To comprehend the response of competitive mechanisms harbored by SLN from their interactions with herbicidal/weed-control stressors, we will need to fabricate a simulation and/or model that can illustrate this compound of complexities comprehensively. Simulation models have significantly increased in usage to help alleviate limitations found in the practice of field experimentation. The goal for our use of a simulation model is to evaluate long-term effects of differential control methods and their interstitial/interconnected interactions between each other from the effects of a relatively large number of variable environmental conditions. The EDYS (Ecological Dynamics Simulation) model was chosen for our study, with the purpose being to elucidate/quantify/illuminate the population growth curve fluctuations of SLN in response to differential herbicide pressure (and other biotic and abiotic stresses). EDYS evaluates ecological responses spatially from dynamic temporal exposure to a broad range of natural and anthropogenic stressors (Childress and McLendon 1999, Childress et al., 1999a, 1999b). The components used in the EDYS input parameters includes Climate, Soil, Hydrologic, Plant, Animal, Stressor, Spatial, Landscape, and Management modules. An advantage in using EDYS is that the number of plant species inputted is flexible and specifiable. EDYS models the dynamics of plant communities using different parameters that were defined for each species. Changes in vegetation are modeled in EDYS on a plant species basis by simulating differential responses to changes in environmental factors (e.g. rainfall, herbicide application, climate, and initial biomass). The aboveground and belowground biomass change for each plant species and each plant part (e.g., fine roots, trunks, and leaves) depending on the species at each time step (daily) during an EDYS simulation.

Materials and Methods

Description of the study site Quaker farm is a 120-acre research facility in Lubbock, Texas (33° 41' 36.4596" N, -101° 54' 18.612"W) that sits at an elevation of 3,256 feet (992 m) above sea level and

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receives an average annual precipitation of 18.6 inches (472 mm). Within a cropping season, Quaker farm typically grows cotton, castor, canola, soybean, mustard, and safflower. These crops are irrigated at an average of 13.5 inches of water within the growing season using two groundwater wells for subsurface drip irrigation. A site at the Texas Tech University Quaker farm research facility representing variable selective pressure from intensive chemical management of weeds in the area was selected for the ecological simulations (Figure 3.1). The study site is within an area that receives a standard dose of herbicide application every cropping season. The standard cultural practices for the farm include deep ploughing at 12-14 inches belowground and application of nitrogen-rich fertilizer (32-0-0) at 80 lbs/acre to replenish the heavy soil N loss due to cotton production. Additionally, there is the conventional pre- and post-planting application of herbicides that include Trifluralin (a,a,a-trifluoro-2,6-dinitro-N,N-dipropyl-p-toluidine), Warrant™ (acetochlor), Staple® (Pyrithiobac Sodium) and Outlook® ((S)-2-chloro-N-[(1-methyl-2-methoxy)ethyl]-N- (2,4-dimethyl-thien-3-yl)-acetamide) to control annual and perennial broadleaf weeds, grasses and sedges, such as SLN. Trifluralin is a selective, pre-emergence herbicide used to control annual and broadleaf weeds and is typically applied to the fields of Quaker farm in late May. Warrant™ is also a pre-emergence herbicide (post-emergence in cotton) that provides residual control of annual grasses and small-seeded broadleaf weeds including Palmer amaranth (Amaranthus palmeri) and Russian thistle (Salsola iberica). Staple® LX is a post-emergence herbicide used to control annual broadleaf weeds including Palmer amaranth. Both Warrant™ and Staple® LX are typically applied to the Quaker farm fields in late June. Outlook® is a selective residual herbicide used in agricultural fields and row crops, including cotton, for controlling annual grasses, broadleaves and sedges during germination. It is typically applied to the fields in Quaker farm in late July.

Mapping of the study site for silverleaf nightshade density and presence of other vegetation In May 2019, a 3 m x 3 m plot was selected and mapped out to represent the overall vegetation in the study site. The identity of every plant present within the experimental plot was determined and the total number of plant species within the plot 46

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was counted, taking into account the growth stage of each individual plant. To determine the density of SLN within the site, the total number of individual SLN plants were counted from smaller quadrats (1 m x 1 m) that were set-up within the 3 m x 3 m plot. The density of SLN and accompanying vegetation within the plot from the experimental site was measured in terms of aboveground biomass. Ten individual plants per species were collected randomly from the study site and placed inside brown paper bags. The fresh weight of the aboveground biomass of each sample was recorded on the day of the collection before air drying them in the greenhouse. After five days of air- drying, the samples were brought to the laboratory and placed in an oven (Thermo Scientific™ Heratherm™) set at 60°C for two days of drying before measuring their dry weight. The average biomass for each species found within the study site was calculated using their measured dry weight values.

Required parameters to run EDYS simulation To simulate the aboveground biomass production of SLN and accompanying vegetation under variable herbicide pressure in the next 25 years, input parameters that are required to run the EDYS model were sourced from publicly available databases. Information on the soil profile (i.e. soil taxonomy, topography, texture, available moisture content, wilting point, organic matter content, nitrogen content, and field capacity and saturation) in the study site were retrieved from the USDA Web Soil Survey database, the USDA NRCS web soil survey data, and from additional data published in related literature. Data on the annual precipitation in the last 25 years (1993-2008) were sourced from the Lubbock International Airport rainfall records (Figure 3.2). Because the basic spatial unit used in the EDYS model is a cell, the collection site was divided into nine cells (3m x 3m) each for a total of nine cells. Based on the information from the USDA Web Soil Survey database regarding the soil profiles in Quaker, each cell was independently assigned two soil taxonomies i.e. Acuff series and Amarillo series. Acuff is more predominant in the study site, but since Acuff and Amarillo are relatively similar in their profiles and are characterized as deep, well- drained, moderately permeable soils, both of them were used in the study. Each of the two soil types were divided into 35 layers, with the physical and chemical characteristics

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of each layer varying among the types. During a simulation, EDYS sets a few soil parameters as constants during the simulations (e.g., soil texture) while others (e.g. soil moisture) are set to change by layer daily depending on environmental factors such as amount of rainfall received, water availability and nutrient availability. The input for soil topography which describes the average elevation for each cell did not vary significantly between sites since the Quaker farm research facility is relatively flat.

EDYS simulation of SLN aboveground biomass production in the next 25 years under variable herbicide pressure The aboveground biomass production of SLN in the study site for the next 25 years was simulated using the EDYS model. The simulations were run in the presence of variable herbicide pressure, as well as competition from other weed species namely Palmer amaranth (Amaranthus palmeri), large crabgrass (Digitaria sanguinalis) and Russian thistle (Salsola iberica) (Figure 3.3). Five independent scenarios were simulated using a specific set of inputs: (1) no herbicide pressure, (2) constant herbicide pressure with standard herbicide control rates, and constant herbicide pressure with standard herbicide control rates until herbicide resistance acquisition in SLN in the fourth season causing herbicide control rates of SLN to fall from 93% to (3) 75%, (4) 50%, (5) 25%. The first scenario represents the baseline conditions and hence no treatment or external activities were loaded in the simulation run for the study site. In the second scenario, ‘Chemical Control’ was applied to the simulation run for the study site, cells 1- 9 on both Acuff and Amarillo soil profiles. The ‘Chemical Control’ simulation activity is a program algorithm that factors in an herbicide control rate percentage for each individual species. The chemical control rate for SLN was set to 93% based on the published success rates of herbicide control for the species (Joy et al., 2008). For Palmer amaranth, large crabgrass and Russian thistle, the chemical control rates were set to 99%, (Joy et al., 2008), 91% (Johnson et al., 2002), and 98%, respectively (Young et al., 2008). In the third through fifth scenarios, the ‘chemical control’ was also applied to simulation runs for the study site, cells 1-9 on both the Acuff and Amarillo soil profiles. Similar to the second scenario, the values for the chemical control rates in scenarios three through five for SLN, Palmer amaranth, large crabgrass and Russian thistle were also assigned based on the published control rates of herbicide control on the four weed species. Unlike 48

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the second scenario however, a decrease in the control rates of SLN was factored in to simulate a hypothetical development of herbicide resistance in the SLN population. Loss rates in SLN were reduced to 75% in scenario 3, to 50% in scenario 4 and to 25% in scenario 5. The different loss rates were applied on the fourth year of the simulations based on previous reports of herbicide resistance acquisition in the weed species, Lolium rigidum, observed in three generations of low dose herbicide exposure (Yu and Powles, 2014; Yu et al., 2014).

Results and Discussion

Species composition of the study site The initial plant community found in the study site was composed of four different species of weed. The study site which is subjected to herbicide pressure every cropping season was composed mostly of SLN (79%), followed by Palmer amaranth (19%) and a few Russian thistle (2%). More than 60% of the SLN at the study site were at the early vegetative stage. The aboveground biomass of the plants was calculated into an average for each species at the study site. In the study site, Palmer amaranth and Russian thistle had an average aboveground biomass of 5.263 g/plant and 4.395 g/plant respectively, with SLN recording the largest average aboveground biomass of the study site at 10.544 g/plant.

Simulation of aboveground biomass production of silverleaf nightshade in the next 25 years A twenty-five-year simulation of the aboveground biomass production of SLN in scenario 1 is presented in Figure 3.4. A gradual increase in the SLN aboveground biomass was observed towards the end of year 1 followed by a significant increase up to the end of year 2. By year 3, the SLN aboveground biomass reached the maximum of 350 g/m2 after which, biomass accumulation plateaued up to year 25. The constant aboveground biomass accumulation may be attributed to limitations in water, nutrients, and space. Fluctuations in the biomass accumulation during the first 12 years was also observed and this may be due to single-year growth dynamics, particularly the green out/growth/dormancy annual sequence. In such cases, differences in precipitation drives the monthly dynamics of SLN growth. The simulated aboveground biomass

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accumulation of Palmer amaranth, large crabgrass and Russian thistle steadily declined until it reached 0 before the end of year 2. The rapid decline in the simulated aboveground biomass of these species may be attributed to being outcompeted by the more invasive SLN. Under cool early-season temperatures, SLN has been reported to germinate earlier and grow faster than other weeds, allowing it to outcompete not only other weeds but also crops such as cotton (Vargas et al., 1998). A similar 25-year simulation of the aboveground biomass of SLN in the study site is presented in Figure 3.5. This simulation, scenario 2, describes the effects of constant herbicide pressure on SLN populations during the cropping season with the assumption that the SLN population will not acquire any degree of herbicide resistance. A steady decrease in the aboveground biomass of SLN was observed from year 0 to year 4, as expected with the application of herbicides with >90% success rates. From year 4 onwards, the aboveground biomass of SLN was 0. The control rates and tandem competition with SLN for resources were too overwhelming for the establishment and subsequent generation of a significant number of future progeny. The lack of seed dispersal into the sample plots, as well as the depletion of seed banks in the model may also account for the non-reemergence of SLN populations. Figure 3.5 shows 25-year simulations of the aboveground biomass production of SLN in the study site where the population of the SLN is subjected to constant herbicide pressure. In these simulations, we assumed the population develops a resistance to the herbicides by year four based on previous reports on other weed species developing herbicide resistance after only three years of herbicide pressure (Yu and Powles, 2014; Yu et al., 2014). The three variations simulated a reduction in herbicide control rates of SLN in season four from 93% to 75%, 50%, and 25% due to the acquisition of herbicide resistance (scenarios 3, 4, and 5, respectively). For all 3 simulations, a reduction in the aboveground biomass of SLN was observed from year 0 towards the end of year 3. Early during year 4, the simulated development of herbicide resistance in the population led to a gradual increase in aboveground biomass production up to year 18. From the start of year 19 towards the end of year 20, a drastic decline in the SLN aboveground biomass was observed and this might be due to a significantly low precipitation during both years. In years 2011, 2012 and 2013 the annual precipitation was at 148.844 mm, 270.51 mm 50

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and 320.04 mm respectively, which is relatively low if you take into consideration the range of annual precipitation within the 25-year simulation which was from 148.844 mm to 782.06 mm. From year 21 onward, SLN aboveground biomass accumulation gradually increased until year 25. Fluctuations in the aboveground biomass, both within and between years, throughout the 25-year simulation may be attributed to the lag phase that is often observed in the population growth curves of invasive plants that are recovering from the effects of herbicide control (Kolar and Lodge, 2001). Fluctuations in the average annual precipitation that consequently affect soil properties may also account for the variability in the aboveground biomass production of SLN across a 25-year simulation.

Conclusion In conclusion, the EDYS Simulation Model was used to project five simulation scenarios to observe the simulated outcomes of differential herbicide control rates. A limitation in this study was the availability of publications containing SLN herbicide resistance data and/or applicable herbicide resistance evolution rates/curves for SLN. Being able to predict the rate at which an SLN population acquires herbicide resistance under the right conditions would be a huge component that would benefit the current simulation approach. What we found out in Chapter 1 and 2 is that SLN is a highly adaptive invasive weed species with a high degree of genetic diversity making it very a viable/conducive candidate for genetic mutations in response to abiotic stresses such as herbicide application (Richardson and Pysek, 2006; Pimentel et al., 2005; Facon et al., 2006; Prentis et al., 2008). Thus, a model simulation of a hypothetical resistance outbreak would be a significant resource to utilize, assess, and reference to design early onset preventive measures as opposed to in-response mitigation measures. It is important to note the different graph curves associated with varying herbicide resistance rates, when comparing the graphs. The fact that the herbicide resistance control rate of 75% reached a point of no recovery in aboveground biomass production in response to persistent annual herbicide control, while the herbicide resistance control rates of 50% and 25% showed both recovery and further growth, indicates that there is a

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threshold for resistance recovery at which the value of resistance in the invasive weed population either leads to complete population death or recovery.

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Figure 3.1. The location of the study site at the Texas Tech University Quaker farm research facility in Lubbock, Texas.

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900

800

700

600

500

400 AnnualPrecipitation (mm)

300

200

100

1996 2008 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 1993 Figure 3.2. Annual precipitation recorded over the last 25 years for use in the EDYS simulation model. Source is Lubbock International Airport, Lubbock, Texas.

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Figure 3.3. Images of the four weed species found in the field at the study site. A) Russian thistle (Salsola iberica), B) Palmer amaranth (Amaranthus palmeri), C) large crabgrass (Digitaria sanguinalis), and D) Silverleaf nightshade (Solanum elaeagnifolium).

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Table 3.1. Number of individual plants per species for all plots in the study site. Palmer amaranth Russian thistle Silverleaf nightshade Large crabgrass Site Early Stage Late Stage Early Stage Late Stage Early Stage Late Stage Early Stage Late Stage Site 1A 8 0 0 0 7 3 0 0 Site 1B 3 0 0 0 8 1 0 0 Site 1C 3 0 0 0 4 3 0 0 Site 1D 8 0 0 0 1 2 0 0 Site 1E 3 0 0 0 7 4 0 0 Site 1F 2 0 0 0 3 0 0 0 Site 1G 8 0 0 0 4 2 0 0 Site 1H 3 0 0 0 1 1 0 0 Site 1I 3 0 0 0 6 0 0 0

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400

350

300

250

200

150

100

SLNMEGFM AMRTPLMI URLATXNA SLOAIRCA

Figure 3.4. Twenty-five-year EDYS projection of silverleaf nightshade aboveground biomass production without herbicide pressure (scenario 1). SLNMEGFM = Silverleaf Nightshade; AMRTPLMI = Palmer amaranth; SLOAIRCA = Russian Thistle; URLATXNA = Large Crabgrass.

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A B 140 140 120 120 100 100 80 80 60 60 40 40 20 20 0 0

SLNMEGFM AMRTPLMI URLATXNA SLOAIRCA SLNMEGFM AMRTPLMI URLATXNA SLOAIRCA

C D 140 140 120 120 100 100 80 80 60 60 40 40 20 20 0 0

SLNMEGFM AMRTPLMI URLATXNA SLOAIRCA SLNMEGFM AMRTPLMI URLATXNA SLOAIRCA

Figure 3.5. Twenty-five-year EDYS projections of silverleaf nightshade aboveground biomass production at the study site under: (A) herbicide pressure with standard control rates throughout, or standard herbicide pressure with acquisition of herbicide resistance in silverleaf nightshade in season four onward causing herbicide control rates to be (B) 75%, (C) 50%, and (D) 25%. SLNMEGFM = Silverleaf Nightshade; AMRTPLMI = Palmer amaranth; SLOAIRCA = Russian Thistle; URLATXNA = Large Crabgrass.

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