ASSESSMENT OF GENETIC VARIATION AND POPULATION DIFFERENTIATION IN INVASIVE MULTIFLORA ROSE, ROSA MULTIFLORA THUNBERG (ROSACEAE) IN NORTHEASTERN OHIO
A thesis submitted to Kent State University in partial fulfilment of the requirements for the degree of Master of Science
By
Rajlakshmi Ghosh
August 2009
Thesis written by
Rajlakshmi Ghosh
B.S. University of Calcutta, 2001
M.B.A. West Bengal University of Technology, 2003
M.S. Kent State University, 2009
Approved by:
Dr. Oscar J. Rocha , Advisor
Dr. James Blank , Chair, Department of Biological Sciences
Dr. Timothy Moerland , Dean for Graduate Affairs College of Arts and Sciences
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Table of Contents
Chapter Page List of Figures...... iv List of Tables…………………………………………………… v List of Appendices……………………………………………… vi List of Abbreviations…………………………………………… vii Acknowledgements…………………………………………….. viii Summary……………………………………………………….. ix
1 Introduction……………………………………………………... 1 Impact of Biological Invasions……………………………... 1 Research Objectives and Rationale………………………..... 2 Microsatellites as Molecular Markers in Invasion Studies…. 4 Study Organism - Rosa multiflora Thunberg …………...... 5 History of Introduction ……………………………………... 7 Distribution, Current Invasion Status and Forms of Control.. 7
2 Methodology……………………………………………………. 11 Survey and Site Selection...... 11 Sample Collection...... 11 DNA Extraction...... 13 PCR Conditions...... 13 Detection of Microsatellite Polymorphism...... 14 Genetic Data Analysis...... 16
3 Results...... 17 Overview……………………………………………………. 17 Genetic Diversity…………………………………………… 1 9 Levels of Heterozygosity…………………………………... 20 Population Differentiation…………………………………... 22 Summary of F statistics…………………………………….. 22 Genetic Relationships………………………………………. 24
4 Discussion………………………………………………………. 26 Overview…………………………………………………….. 26 Genetic Diversity and Population Differentiation…………... 27
5 Future Research ………………………………………………... 34
References………………………………………………………. 35 Appendices……………………………………………………… 46
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List of Figures
Figure Page
1 Vegetation (1a), flower (1b) and leaves (1c) of multiflora rose. 6 2 State wide distribution of multiflora rose in U.S.A. 8 3 County wide distribution of multiflora rose in the state of Ohio. 9 4 Locations of the eight populations of multiflora rose in Portage 12 County (inset), Ohio sampled. 5 Image of amplified product of multiflora rose DNA in capillary 15 electrophoresis as observed with a homozygote (5a) and heterozygote sample (5b). 6 Number of loci polymorphic in the eight populations of multiflora 17 rose from northeastern Ohio (out of six microsatellites studied). 7 UPGMA tree based on genetic distances between eight populations 24 of multiflora rose from northeastern Ohio using six nuclear microsatellite loci.
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List of Tables
Table Page
1 Locus name, repeat motif, and allele size range for the six 14 microsatellite markers used in this study. 2 Allele frequencies for the six polymorphic loci in the eight 18 populations of multiflora rose from northeastern Ohio. 3 Mean number of alleles and mean effective number of alleles per 20 locus for the six polymorphic loci in the eight populations of multiflora rose from northeastern Ohio. 4 Mean values of observed heterozygosity and expected 21 heterozygosity per locus for the six polymorphic loci in eight populations of multiflora rose from northeastern Ohio. 5 F-statistics and gene flow calculated for the six polymorphic loci 23 of the eight populations of multiflora rose from northeastern Ohio. 6 Nei's unbiased estimates of genetic distances between eight 25 populations of multiflora rose from northeastern Ohio.
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List of Appendices
Appendix Page
1 Systematic classification of multiflora rose. 46 2 Geographic details for the eight populations of multiflora rose 47 from northeastern Ohio (study sites).
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List of Abbreviations
Full form Abbreviated term
Analysis of Molecular Variance AMOVA Base pair bp Bovine Serum Albumin BSA Celsius C Cetyltrimethyl Ammonium Bromide CTAB Deoxyribonucleic Acid DNA Deoxyribonucleotide triphosphate dNTP Global Positioning System GPS Hardy Weinberg Equilibrium HWE Kilometer km Magnesium chloride MgCl2 Meter m Microlitre µl Micromolar µM Milligram mg Molar concentration (Stoichiometric) M Multiflora Rose ROMU Northeastern Ohio NE Ohio Polymerase chain reaction PCR Polyvinylpolypyrolidone PVP Rose Rosette Disease RRD Rotations Per Minute rpm Sodium Acetate NaAC Sodium Chloride NaCl Tools for Population Genetic Analysis TFPGA Tris EDTA buffer TE buffer Tris Hydrochloric acid Tris HCl Unweighted Pair Group Method with Arithmetic mean UPGMA United States Department of Agriculture USDA
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Acknowledgements
I would like to express my sincere gratitude to my graduate advisor Dr. Oscar J.
Rocha for encouraging me into this exciting world of plant population genetics and supporting me throughout this project. I thank him for all the help he rendered me during my Master’s thesis research, including all funding and facilities he provided for the successful completion of the project. I thank my professors, Dr. Barbara K. Andreas and
Dr. Andrea L. Case, for their kindness to serve on my thesis committee and reviewing my work and answering my endless questions. I also thank my friends, Lisa Regula Meyer and John J. Voelkar for their help during site survey and sample collection. I am grateful to the Department of Biological Sciences, Kent State University for providing me with graduate assistantship to support my coursework and research. Finally, I would like to thank my parents and my husband Amiya Ghosh who gave me constant inspiration to finish this thesis. I know that this work would not have been possible without their help.
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SUMMARY
In this study, I examined genetic diversity and levels of population differentiation between eight populations of the invasive plant multiflora rose (Rosa multiflora
Thunberg Ex. Murray) located in Portage and Summit Counties, in northeastern Ohio.
I used six microsatellite marker loci to determine allelic diversity, percent polymorphic
loci, and expected and observed heterozygosity. My results show that multiflora rose
populations included have moderate levels of genetic variation. I found a total of twenty
five alleles in the six microsatellite loci that were examined. On an average, there were
4.16 alleles per locus, but the average number of alleles per locus observed in each
population ranged between 1.66 and 3.33. Effective number of alleles ranged between
1.38 and 2.17, indicating variation in allele frequencies among loci. Average observed
heterozygosity (HO = 0.15) was less than expected heterozygosity (HE = 0.43) across all
but one population. This trend of heterozygosity deficiency and F statistics results
indicate significant level of inbreeding in the studied populations. Most of the loci failed
to conform to Hardy-Weinberg expectations in all populations indicating forces of evolution interacting. Analysis of molecular variance revealed significant levels of genetic differentiation between populations. Nei’s unbiased estimators of genetic distance range from nearly 0 to 0.59, indicating high genetic differentiation. These findings are then discussed in the light of ecological and historical considerations.
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1. INTRODUCTION
Impact of Biological Invasions
Human societies have radically changed the distribution of plant and animal species by transporting them throughout the world (Drake et al., 1989; Williamson, 1996; Vitousek et
al., 1999). Most exotic species do not become established in places where they are introduced
(Ellstrand and Schierenbeck, 2000; Reichard and White, 2001). Only a small percentage of
introduced species establish themselves in their new habitat, and a smaller fraction of these
species increase in abundance at the expense of native populations, communities, and
ecosystems (Pattison et al., 1998; Ellstrand and Schierenbeck, 2000; Reichard and White,
2001). Exotic species that are capable of producing environmental or economic damage, or
that are hazardous to human health, are considered to be invasive (Rossman, 2001).
The invasion and spread of unwanted species impose a major threat to human and
animal health, agricultural production and biological diversity (Pimentel et al., 2000;
Rejmanek, 2000; Sandlund et al., 1999; Wade and McCauley, 1988). An example of such
invasions is the chestnut blight disease that nearly eliminated the economically most
important hardwood species in the eastern American forests, the chestnut tree. In addition to
the death of nearly one billion trees, the chestnut blight also led to profound ecosystem
changes in the eastern hardwood forests (McNeely, 1999; Sandlund et al., 1999). Another
example is the accidental introduction of zebra mussels (Dreissena polymorpha) that have
invaded the Great Lakes, altering its water quality (Nicholls and Hopkins, 1993; Leach,
1 2
1993; Maclsaac, 1996), threatening both urban water systems and freshwater fisheries
(Johnson and Padilla, 1996; Moller, 1996) and causing the impending extinction of North
American freshwater mussels (Ricciardi et al., 1998).
There is overwhelming evidence indicating that the introduction of alien species have negative effects on biological diversity at the local level (Mack, 1986; Vitousek, 1990). Such introductions can lead to severe disruption of ecological communities (Mooney and Drake,
1986). For example, many bottomlands of the southeastern United States are completely dominated by the perennial Japanese honeysuckle (Lonicera japonica). Fire ants and African honeybees have built up huge populations in the southern United States. Overall, it is estimated that the economic impact of invasive species on US agriculture, forestry, fisheries and human health, is at least $134 billion annually (US Congress, Office of Technology
Assessment, 1993; Pimentel et al., 2000; 2005).
Research Objectives and Rationale
Patterns of genetic diversity and population differentiation can indicate the evolutionary forces interacting in the invaded habitat and provide information that could possibly explain an invader’s colonization success. Study of genetic differentiation can help us to explore a challenging question as to ‘Why are some species invasive?’ Some believe that invasives already possess the traits that make them successful invaders; others believe that non-natives get established first and later develop traits that make them invasive. 3
Non natives in the new environment can face either of three fates. It has been well documented that invasive species can show an increase in genetic diversity following colonization process. This can happen due to hybridization with other related species, other ecotypes or populations of the same species otherwise not in contact. Hybridization can result in hybrid vigour that acts as a stimulus to the species invasion success. Increase in genetic diversity can happen as a result of multiple introduction events, a phenomenon often associated with several successful biological invasions. Alternately, non-native species might see a decrease in genetic diversity due to inbreeding, small number of founder members and drastic effect of genetic drift. These can prevent non natives to get established in the new environment. A third scenario is where there is not significant difference in genetic variation found between native and invaded environments.
Assessment of genetic variation and population differentiation might be successfully used to obtain valuable information about how populations are performing in their new habitat following colonization process.
To understand what is going on in the new environment from ecological and evolutionary context and to identify the impact of biological invasions, study of its genetic variation is important. In this project, I estimated the genetic structure of wild populations of multiflora rose that are found in NE Ohio, particularly focusing on populations in Portage and Summit counties, where the species is abundant. I conducted a study to (1) estimate the genetic variation of multiflora rose in this small geographical range, (2) determine the partitioning of genetic variation within and between populations, 4 and (3) examine the impact of different degrees of geographic isolation on the genetic diversity found in each population.
Microsatellites as molecular markers in invasion studies
I have used microsatellites as molecular markers in this study. Microsatellites are selectively neutral codominant molecular markers, successfully used to measure various population genetics parameters. They can tell the amount of genetic variation present at different population levels thereby helping one to study genetic structure within populations and levels of gene flow among populations (Slatkin, 1995). Due to their co- dominant nature, deviations from HWE can be assessed directly. They are also capable of detecting hybridization with natives, which is considered to provide stimulus to successful invasion process (Ellstrand and Scheirenbeck, 2000). Microsatellite variations identify shared alleles or common alleles that are capable of tracking down potential sources of spread and help one to trace history of invasion (Durka et al., 2005; Genton et al., 2005). They can be easily scored in PCR reactions followed by genotyping in an automated sequencer (Karp et al., 1997).
Microsatellites have been frequently used to investigate genetic diversity and population structure in native and non-native species, and have been used as an excellent tool to reconstruct the history of biological invasions (Davies et al., 1999; Milne and
Abbott, 2000; Novak and Mack, 2001; Tsutsui et al., 2000). They trace invasion history in invasive species and detect potential sources of spread and population genetic polymorphisms that can contribute to species invasiveness (Rejmanek, 1999). In this 5
project I conducted an assessment of genetic variation in multiflora rose populations in
NE Ohio using six microsatellite loci.
Study Organism – Multiflora rose (Rosa multiflora Thunberg)
Multiflora rose (Rosa multiflora Thunberg Ex. Murray) (Fig. 1) is a thorny perennial shrub with arching stems and 5-9 sharp-toothed, slightly oval leaflets in each leaf (Steavenson, 1946). It belongs to the rose family, Rosaceae (see Appendix 1 for detailed classification). The stipules at the base of the leaf petiole are feathery in nature and are characteristic of the plant. It has terminally borne white flowers and bright red fruits or the “rose hips” which develop during the summer and remain on the plant throughout the winter. They have tenacious growth, can tolerate a wide range of habitat and are capable of forming dense thickets (Steavenson, 1943; 1946). The plant is capable of performing asexual reproduction through its arching stems that can extend up to 20 feet in length and can start rooting when they hit ground (Klimstra, 1956; Doll, 2006) and rhizomes (Christen and Matlack, 2009). They are robust in growth and show high fecundity, producing thousands of small, easily dispersible seeds each year. Doll (2006) reported that potentially 500,000 seeds are produced per plant. A variety of insect visitors are found on the branch tips (Jesse et al., 2006a; 2006b). Birds and small mammals eat fruits and promote dispersal of seeds. 6
1a.
1b. 1c.
Fig.1: Vegetation (1a), flower (1b) and leaves (1c) of multiflora rose. Photographs by Rajlakshmi Ghosh. 7
History of Introduction
Multiflora rose was introduced to North America from Japan in the 1860s as a rootstock variety to ornamental roses (Rehder, 1936). Along with their rapid growth and the ability to form dense thickets, they showed high resistance to soil borne diseases and parasites that are typical traits of rootstock plants. Later, in the 1930s and 1940s, the
United States Soil Conservation Services actively promoted its growth for use in erosion control and as "living fences" (Klimstra, 1951; 1956; Bargeron et al., 2003). In addition, many state conservation departments recommended multiflora rose as cover for wildlife.
The species provided food and habitat to birds, rodents and small mammals (Steavenson,
1943; Klimstra, 1956). Multiflora rose was introduced in the east coast of the United
States from where it got transported elsewhere (Rehder, 1936). The species soon escaped from its confinement through prolific sexual and asexual reproduction and intense dispersal system and established itself as a successful invader, rapidly colonizing forest edges, fragment corridors, open fields, matured forests and land subjected to disturbances.
Distribution, Current Invasion Status and Forms of Control
Presently, Multiflora rose is found throughout the US, except the Rocky Mountains and the deserts of Nevada and Colorado (Fig. 2).
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Fig. 2: State wide distribution of multiflora rose (ROMU) in U.S.A. Source : Plant Database 2009, Natural Resources and Conservation Service, USDA.
Currently, multiflora rose infests more than 45 million acres throughout eastern
United States (Loux et al., 2005), and it is designated as a Category 2 noxious weed in many states, including the state of Ohio (Fig. 3), where its planting or purchase is prohibited (ODNR, 2000; Loux et al., 2005).
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Fig. 3: County wide distribution of multiflora rose (ROMU) in the state of Ohio. Source : Plant Database 2009, Natural Resources and Conservation Service, USDA. Note : County data is based primarily on the literature, herbarium specimens, and confirmed observations. However, not all populations have been documented, so some gaps in the distribution shown above may not be real.
Multiflora rose is known for its rapid, tenacious growth, aggressive root system and prolific reproduction through sexual and asexual mechanisms. The infestations can have significant impact on local economies (Loux et al., 2005). For example, cattle grazing can be severely reduced in pastures densely infested by this species. Moreover, multiflora rose infestations can lower the value of land for agricultural, recreational, and forestry use. The local governments spend a sizeable amount of funding trying to control invasions of multiflora rose using regular mechanical (rooting, cutting and mowing) and 10
chemical control (application of herbicides). However, successful eradication requires
repeated cutting or mowing at the rate of three to six times per growing season, for two to
four years (Loux et al., 2005). The plant is vulnerable to a mite-vectored rose rosette disease (Armine and Hindal, 1988). RRD being a viral disease, is associated with an uncertain aetiology (Epstein et al., 1997; Epstein and Hill, 1995; Rohozinski et al., 2001) and hence biological control of this invasive species using rose rosette disease is still questionable. Surprisingly, studies exploring the genetic diversity and population differentiation in multiflora are still scant. To my knowledge, there is no published study investigating the level of genetic diversity in invaded habitats.
2. METHODOLOGY
Survey and Site Selection
I conducted a survey of local parks, state parks, natural area preserves, and wetlands to determine locations of multiflora rose infestations in Portage and Summit counties in NE Ohio. I selected eight large, well established populations for this study
(with >100 adult individual plants) along the extensive bike trails of Portage and Summit counties, allowing a minimum distance of 1 km between populations (Fig. 4). Details of population size, distribution, site characteristics such as like soil condition, topography, temperature, precipitation range, vegetative cover, amount of human interference and
GPS coordinates were recorded for future reference (see Appendix 2 for geographic details).
Sample Collection
Young, green leaves (5-6 per plant) from at least ten adult individual plants from each of the eight sampling locations were collected in clean, dry sealable plastic bags.
Sampled individuals were separated from each other by at least 10m in order to avoid sample collections from identical vegetative clones. Leaf material was kept in a cooler with dry ice to preserve the integrity of the DNA. On return to the laboratory, all leaf material was stored in -40°C freezer until DNA extraction.
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Fig. 4: Locations of the eight populations of multiflora rose in Portage County (inset), Ohio sampled.
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DNA Extraction
Total genomic DNA was extracted from leaves of multiflora rose plant, using a
modification of the protocol described by Karp et al. (1997) and Lodhi et al. (1994).
Approximately 80 mg of leaf tissue was ground in liquid nitrogen with a mortar and pestle. The ground tissue was mixed with 600 µl of 0.1 M Tris-HCl (pH 8.0), 1.0 M
NaCl, 2% CTAB, 2% PVP, and 1% β mercaptoethanol extraction buffer. The mixture was incubated at 60°C for 40 minutes with agitation every 10 minutes. The samples were then centrifuged for 10 minutes at 12,000 rpm at 10°C. The supernatant solution was placed in another tube, and one volume of chloroform-octhanol (ratios 24:1) was added, mixed softly, and centrifuged for 10 minutes. The supernatant was transferred to another tube where 1/10 volume of 3 M NaAC (pH 5.2) and 0.6 volume of ice-cold isopropanol was added. The mixture was kept at 5°C for 10 to 20 hours. DNA was precipitated with
70% ethanol, air dried, and later resuspended in 100 µl of 1.0 M TE buffer.
PCR Conditions
I studied the levels of genetic variation using six microsatellite marker loci developed for Rosa hybrida by Esselink et al. (2003) (Table 1). The forward primers were labelled with a fluorochrome (D4) dye. PCR reactions were performed using a PCR engine (MJ Research) in a total volume of 20 µl containing 3.3 µl of 10x PCR buffer (50
µM Tris-HCl pH 8.3, 2.5 mg/ml of BSA and 10 µM MgCl2), 2 µl (0.3 units) Taq
polymerase (Promega), 2.6 µl 0.32 mM dNTP, 2 µl of template DNA and 9.7 µl DNA
free water. I also added 1.6 µl 0.4 µM solution of forward and reverse primers. PCR
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reaction conditions were as follows: 94°C for 1 minute, followed by 30 cycles of 5
seconds at 94°C, 10 seconds at 55°C and 35 seconds at 72°C. A final extension step of
four minutes at 72°C was added after the last cycle.
Table 1: Locus name, repeat motif, and allele size range for the six microsatellite markers used in this study.
Locus name Repeated motif Size range (base pairs)
RhE2b (TGT)20-6 151 - 195 RhD221 (TCT)21-1 163 - 233 RhO517 (GAC)7 249 - 265 RhP519 (TGA)11-1 198 - 232 RhEO506 (CAG)6(CAA)18-7(CAG)6 180 - 232 RhAB22 (GT)13(GA)13 150 - 178
Detection of Microsatellite Polymorphism
PCR products were first checked in 1.5% agarose gel to see if enough DNA is amplified (Fig 5). Four µl of the amplification product was mixed with 28 μl of formamide and 0.4 μl of 400 bp DNA size standard. Fluorescent samples were denatured at 95oC for 5 minutes and DNA fragments were separated using a capillary
electrophoresis on an automated genetic analysis system (Beckman Coulter CEQ8800).
Fragments were identified on basis of their size and according to their mobility in relation to the size standard using a cubic function (Fig 5).
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5a.
5b.
Fig. 5: Image of amplified product of multiflora rose DNA in capillary electrophoresis as observed with a homozygote (5a) and heterozygote sample (5b). Note: Locus 5 with allele size range of 267-283 bp observed.
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Genetic Data Analysis
Genetic diversity at each site was quantified by the mean number of alleles per
locus (Na), the effective number of alleles per locus (ne), observed heterozygosity (HO),
and Nei’s expected heterozygosity (HE) (Nei, 1973), for each locus and averaged over all
loci. The significance of allele frequency differences between locations was assessed
using the exact test. These analyses were conducted using the program POPGENE 1.31
(Yeh et al., 1999). In addition, genetic differentiation was determined using the infinite
allele model Fst (Weir and Cockerham, 1984). Analysis of molecular variance
(AMOVA) was also used to estimate the level of population differentiation using genetic
analysis software GENALEX 6 (Peakall and Smouse, 2006). The degree of relatedness
between populations, based on Nei’s genetic distances, was represented in a tree using
UPGMA (TFPGA ver. 1.3, Miller, 1997).
RESULTS
Overview
Six microsatellites yielded two to nine alleles in each locus: a total of twenty five
alleles were identified. On an average, 83% of the studied populations exhibited variable
levels of genetic variation (Fig. 6), indicating presence of more than one allele at the
studied locus. I found moderate levels of allele diversity and lower than expected levels
of heterozygosity in the studied populations. Overall, all six loci that I studied, exhibited
significant amounts of polymorphism. However, the proportion of polymorphic loci varied within and among populations (Table 2).
Number of loci polymorphic
7
6
5
4
3
2
1
0 Barlow Seasons Silver Silver Silver Peninsula Peninsula Towner's MEAN Road Road Lake 1 Lake 2 Lake 3 2 3 Woods
Fig. 6: Number of loci polymorphic in the eight populations of multiflora rose from northeastern Ohio (out of six microsatellites studied).
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Table 2: Allele frequencies for the six polymorphic loci in the eight populations of multiflora rose from northeastern Ohio. Asterisks indicate loci that failed to conform to Hardy-Weinberg equilibrium (* <0.05 P-value > 0.001, ** <0.001 P-value > 0.0001, and *** P-value <0.0001).
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Genetic Diversity
My results show that the studied populations of multiflora rose had significant allelic diversity. All loci were polymorphic at more than one population, but the level of polymorphism varied from one population to another. With six microsatellites, I found a total of twenty five alleles. The number of alleles per locus in each location ranged from
1 to 9 and frequency and distribution of the alleles among and between populations were variable (Table 2). Effective number of alleles (Ne) was reported to be less than observed number of alleles (Na), indicating variable contribution of alleles to measures of total genetic diversity. On an average, 83% of the populations were polymorphic – ranging from 50% polymorphism in Season’s Road population to 100% polymorphism in Silver
Lake 3 and Peninsula 3 and Towner’s Woods populations.
On an average, there were 4.16 alleles observed per locus, the effective number of alleles ranged between 1.38 in Silver Lake 3 and 2.17 in Silver Lake 1. The effective numbers were less than the mean number of alleles observed per locus which ranged between 1.66 in Silver Lake 3 and 3.33 in Peninsula 3 population (Table 3). All allele were present in at least two populations. Populations deviated from HWE at more than one locus.
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Table 3: Mean number of alleles and mean effective number of alleles per locus for the six polymorphic loci in eight populations of multiflora rose from northeastern Ohio. Numbers in parentheses indicate standard errors.
Population Mean sample No. of alleles Effective No. of alleles size (Na) (Ne*)
Barlow Road 20 2.16 1.50 (0.75) (0.32) Seasons Road 20 2.67 1.99 (3.14) (1.94) Silver Lake 1 18 3.00 2.17 (1.67) (1.49) Silver Lake 2 20 1.83 1.49 (0.41) (0.33) Silver Lake 3 20 1.66 1.38 (0.52) (0.37) Peninsula 2 20 2.16 1.78 (0.75) (0.46) Peninsula 3 20 3.33 1.93 (2.42) (1.06) Towner’s 20 3.00 2.03 Woods (1.26) (0.89)
All locations 158 4.16 2.07 (2.46) (1.19)
* Ne = Effective number of alleles (Kimura and Crow, 1964)
Levels of heterozygosity
Levels of heterozygosity, a common indicator of genetic diversity was measured
in terms of observed heterozygosity and expected heterozygosity as predicted under
HWE. Overall, mean observed heterozygosity across all loci (H0 = 0.15) was lower than
mean expected heterozygosity (HE = 0.43) (Table 4). This was true for seven populations.
Mean observed heterozygosity (H0) ranged from 0.11 to 0.33, in Barlow Road and Silver
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Lake 3 populations respectively. Expected heterozygosity (HE) ranged from 0.23 to 0.43, in Silver Lake 3 and Towner’s Woods respectively.
Table 4: Mean values of observed and expected heterozygosity per locus for the six polymorphic loci studied in eight populations of multiflora rose from northeastern Ohio. Numbers in parentheses indicate standard errors.
Population Mean Observed Expected heterozygosity sample heterozygosity (HO) (HE )** size Barlow Road 20 0.11 0.30 (0.13) (0.17) Seasons Road 20 0.18 0.24 (0.35) (0.35) Silver Lake 1 18 0.23 0.38 (0.29) (0.30) Silver Lake 2 20 0.18 0.25 (0.19) (0.16) Silver Lake 3 20 0.33 0.23 (0.05) (0.20) Peninsula 2 20 0.22 0.40 (0.33) (0.20) Peninsula 3 20 0.14 0.37 (0.16) (0.26) Towner’s Woods 20 0.17 0.43 (0.19) (0.24) All locations 158 0.15 0.43 (0.13) (0.13) ** HE = Nei's (1973) expected heterozygosity.
The trend of heterozygosity deficiency was not located in Silver Lake 3 population where observed heterozygosity was more than the expected levels. I found that at least one locus failed to conform to HWE in each population (χ2 test, P-value <0.05). In all cases, there is
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an excess of homozygotes among the individuals included in the samples, but some of
this deviation may result from the small sample in relation to the number of genotypes.
Population Differentiation
I performed an Analysis of Molecular Variance (AMOVA) with my data to
analyze partitioning of genetic variation. The results showed that 77% of the total genetic
variation is due to within-population differences and the rest 23% is due to among
population differences. Thus, a major portion of the total genetic diversity was
contributed by within population variance, indicating significant variation among
individuals within a population. All alleles were present in more than one population,
thus no private alleles were observed in any population, and no significant linkage disequilibrium among loci was detected.
Summary of F statistics
I further analyzed my data with F-statistics, which helps one to understand distribution of genetic variation within and between populations (Table 5). High values for Fit (mean Fit = 0.53) and Fis (mean Fis = 0.64) were obtained, suggesting significant
levels of inbreeding going on in the studied populations. The high values for Fit and Fis
could possibly explain the deficiency of heterozygotes observed in the populations. The
high values for mean Fis indicates significant levels of within population structure, which
might be due to the existence of inbreeding, founder effect, and or presence of Wahlund
effect.
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I obtained a mean Fst of 0.23. This value matches with the AMOVA results, indicating significant levels of genetic differentiation. 23% of genetic variation was distributed between populations – which is a high number for plants. The genetic analysis also generated the number of migrants per generation to be less than one (Nm = 0.79).
This explains that on an average, less than one individual in migrating into the populations per generation, suggesting that the gene flow is low.
Table 5: F-statistics and gene flow calculated for the six polymorphic loci of the eight populations of multiflora rose from northeastern Ohio.
Locus Sample Fis Fit Fst Nm * size
RhE2b 160 0.47 0.60 0.25 0.75
RhD221 158 0.95 0.97 0.45 0.31
RhO517 154 1.00 1.00 0.13 1.63
RhP519 160 0.30 0.45 0.22 0.89
RhEO506 158 0.10 0.20 0.10 2.13
RhAB22 156 0.46 0.58 0.22 0.89
Mean 158 0.53 0.64 0.23 0.79
* Nm = Gene flow estimated from Fst = 0.25(1 - Fst)/Fst.
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Genetic Relationships
Genetic differentiation among the eight populations was calculated using Nei’s
unbiased estimators of genetic distance (Nei, 1973). Genetic distance between populations ranged from nearly 0 to 0.60 and is represented in the dendrogram (Fig. 7).
Fig. 7: UPGMA tree based on genetic distances between eight populations of multiflora rose from northeastern Ohio using six nuclear microsatellite loci.
It is clear that there are three well defined clusters of populations. The first one includes the three populations found near the town of Silver Lake (Silver Lake 1, 2, 3) and Season Road. The second group with Peninsula region populations (Peninsula 2, 3) and Towner’s Woods are located in areas that are geographically distant from the first
25
group. Finally, the population from Barlow Road population was genetically most
distinct from the other seven populations and formed its own group. This was not due to
the presence of unique alleles in this population, but due to drastic differences in allele
frequencies with respect to the other populations (Table 2). I did not find any significant
correlation between geographic distance and genetic distance (Table 6). Barlow Road
population, located in the vicinity of the first group, was genetically most distinct from
the other seven populations and formed a separate group. In contrast, populations from
Silver Lake (Silver Lake 1 and Silver Lake 2) were most similar both geographically and genetically.
Table 6: Nei's unbiased estimates of genetic distances between eight populations of multiflora rose from northeastern Ohio.
Barlow Seasons Silver Silver Silver Peninsula Peninsula Towner’s Road Road Lake 1 Lake 2 Lake 3 2 3 Woods Barlow -- Road Seasons 0.45 -- Road Silver 0.43 0.05 -- Lake 1 Silver 0.45 0.10 0.08 -- Lake 2 Silver 0.42 0.43 0.09 0.00 -- Lake 3 Peninsula 0.60 0.29 0.20 0.15 0.15 -- 2 Peninsula 0.38 0.14 0.06 0.08 0.07 0.07 -- 3 Towner’s 0.28 0.17 0.08 0.09 0.09 0.08 0.01 -- Woods
4. DISCUSSION
Overview
In summary, I found moderate levels of allelic diversity and lower than expected levels of heterozygosity in the studied populations of multiflora rose. The levels heterozygosity and inbreeding reported here are not different from those predicted for species with similar life history traits and breeding behaviour in their native range
(Hamrick and Godt, 1990). Genetic diversity in multiflora rose was lower than that reported in other North American invasives like the garlic mustard [Alliaria petiolata (M.
Bieb.) Cavara and Grande] (Fst = 0.74 to 0.82) (Durka et al., 2005), European thistle
(Cirsium spp) (Fst = 0.64) (Solé et al., 2004), gorse (Ulex L.) (Fst = 0.64 to 0.84) (Cubas et al., 2005) and Canada thistle [Cirsium arvens] (Fst = 0.28) (Slotta et al., 2006) but was higher than ragwort ( Senecio gallicus Vill.) (Fst = 0.15) (Comes and Abbott, 2000).
Genetic differentiation was comparable to other potent invasive plants of Ohio like the
Japanese honeysuckle [Lonicera japonica (L.) Thunb.] and kudzu [Pueraria lobata
(Willd.) Ohwi] (Pappert et al., 2000) which also show high variance within populations.
However, allelic diversity in the eight populations that I studied was lower than that reported by Kimura et al. (2006). Overall, all six loci that we studied were polymorphic,
but the proportion of polymorphic loci varied between populations.
26
27
Genetic Diversity and Population Differentiation
Kimura et al. (2006) examined the level of genetic diversity in a sample of wild multiflora rose from Japan using ten microsatellites. They reported an average of 8.4 alleles per locus and observed and expected heterozygosity to be 0.62 and 0.82, respectively. My results showed lower levels of allelic diversity in the sample from NE
Ohio, which were only 4.16 alleles per locus. However, such level of genetic diversity is significant considering the relatively small geographical range sampled in this study area.
In addition, observed and expected heterozygosity reported here were also lower (HO =
0.15 and HE = 0.43) than those reported by Kimura et al. (2006). However, the results of both studies show a trend towards heterozygote deficiency. The high Fit values (mean Fit
= 0.64) reported in this study indicates the occurrence of significant inbreeding in the studied populations. The low level of genetic diversity conforms to the capability of the plant to perform vigorous vegetative propagation. Even though samples were selected
10m apart, the trend towards heterozygote deficiency points to the occurrence of asexual reproduction. However, high degree of within population variation suggests that I did not sample clones. My findings are comparable to a roadside succesional system study where most of the within-site spread of Rosa multiflora was reported through arching stems and rhizomes (Christen and Matlack, 2009). However, genetic variation is greater than expected for a plant that shows high vegetative propagation (Novak and Mack, 1993), indicating maintenance of sexual reproduction.
Successful biological invasions by alien plants are often associated with high levels of genetic diversity that act as a broad base upon which selection can act (Lee,
28
2002). High diversity is often coupled with multiple introductions in several successful invasive species distributed across the world. (Genton et al., 2005; Kolbe et al., 2004;
Therriault et al., 2005). The increased genetic diversity resulting from the hybridization of closely related species or ecotypes of the same species may provide raw materials for evolution that help the introduced species to adapt to its new environment. Ellstrand and
Schierenbeck (2000) proposed that crosses between species of the same genus or between disparate populations may provide a stimulus for the evolution of invasiveness.
Schierenbeck et al. (1995) compared the levels of isozyme variability between the invasive Lonicera japonica and the native L. sempervirens. They reported the occurrence of hybridization between these two species, and suggested that it may favour the acquisition of the physical attributes that characterize invasive species. Similarly,
Gaskin and Schaal (2002) studied the population genetics of invasive Tamarix species in the United States. They found that the largest Tamarix invasion in the USA consisted of
Tamarix chinensis and Tamarix ramosissima, two morphologically similar species. They also found that the most common plant in this U.S. invasion is a hybrid combination between two species-specific genotypes that were geographically isolated in their native
Eurasian range. Less extensive hybrids exist in the invasion, involving combinations of T. ramosissima and T. chinensis with Tamarix parviflora and Tamarix gallica. The presence of potentially novel hybrids in the U.S. illustrates how importation of exotics can alter the population structure of species and contribute to invasions. Future studies exploring hybridization in invasive multiflora rose could draw similar significant conclusions.
29
In some taxa, hybridization of cultivated plants plays a crucial role for the evolution of invasiveness. Many invasive plants belong to species with registered cultivars in the United States. For example, the Rem-Red cultivar of Amur Honeysuckle,
Lonicera maackii, developed by the USDA Soil Conservation Service, is still widely promoted in the ornamental plant trade. Some of the cultivars are the product of hybridization between different genes pools of a single species, others the product of hybridization between different species of the same genus (e.g., Lonicera mandarin, is an intentional garden hybrid between Lonicera tragophylla and Lonicera × brownii
‘Dropmore Scarlet’). Lonicera x bella is an interspecific hybrid (L. morrowii x L. tartarica) that has arisen spontaneously in multiple locations in North America, and is the basis of several cultivars (Green, 1966; Hauser, 1966). This hybrid’s invasiveness and broad ecological amplitude have been attributed to hybrid vigour (Barnes and Cottam,
1974).
Pappert et al. (2000) reported high levels of genetic variation among twenty populations of kudzu in southeastern United States. They claim that such levels are consistent with its history of multiple introductions over an extended period of time.
These works suggest that genetic variation accumulates in invasive species during their establishment, and that it might explain why some species turn invasive only after a long lag of time (Ellstrand and Schierenbeck, 2000). The patterns of genetic diversity of multiflora rose populations in the USA may also a result from multiple introduction events. Multiflora rose was deliberately introduced to the North American continent in the 1860’s as a rootstock variety for ornamental roses. It was known for its rapid growth,
30
deep rooting system and resistance to soil borne diseases. Later in the 1940’s, the USDA
promoted its propagation for soil conservation purposes and as a wildlife cover and
“living fences” (Anderson and Edminster, 1954). Thus, there is historical evidence for
one species being introduced at multiple times for multiple purposes, which was possibly
bringing different genotypes together.
Despite the lower levels of genetic diversity observed in these multiflora rose
populations with respect their Asian counterparts, the nature of the materials that were
introduced into North American may explain the invasive behaviour of this species.
Invasive populations may combine high resistance to soil borne diseases and parasites
that are typical of rootstock, with the rapid growth and the ability to form dense thickets
of the materials that were introduced by the USDA (Anderson and Edminster, 1954). In
addition, multiflora rose plants expand their new leaves a few weeks earlier in the spring
than native trees and shrubs, and drop them about 3 weeks later in the fall (O. Rocha,
personal communication). Because of these traits, R. multiflora plants grow aggressively forming thickets that exclude most native shrubs and herbs from establishing.
Genetic differentiation among populations of invasive plant species is typically high and there is a strong pattern of population structuring (for example see Marrs et al.,
2008). Partitioning of genetic variance of Linaria vulgaris in western USA using analysis of molecular variance revealed that only 1.7% of genetic variation was due groups of populations from four different regions, 29.1% was due to populations within regions, and 69.2% within populations. They argued that these findings are consistent with expectations for an outcrossing species but suggesting little geographic differentiation
31
(Ward et al., 2008). Similar partitioning of genetic variance was reported for the
imperfectly dioecious Canada thistle [Cirsium arvens] (Fst = 0.28) (Slotta et al., 2006), and ragwort (Senecio gallicus) (Fst = 0.15) (Comes and Abbott, 2000). In contrast, the distribution of genetic variation among populations of the self-compatible garlic mustard
(Alliaria petiolata) was also low between regions (6.79%), and most of the variation was found among populations within regions (71.42%) (Durka et al., 2005). Genetic differentiation among the eight populations of multiflora rose included in this study was comparatively lower than other North American invasive plants; however, the observed level of differentiation is considerably high given the restricted geographical range of the populations sampled. Overall, the results are consistent with relatively diverse initial founder populations, and a subsequent spread of the species in local founder populations.
The findings also suggest some level of inbreeding within local populations following population establishment. Uniqueness of genetic structure in the three groups is indicative of founding individuals being genetically different in these three population subgroups.
Genetic distance did not correlate with geographic distance and population structure did not reveal any patterns of isolation by distance. It can be inferred that the populations might have been established from seeds coming from different sources.
Seed dispersal is another ecological factor that can help explaining the genetic structure of multiflora rose in NE Ohio. Many invasive plant species are dispersed by birds (Gosper et al., 2005), and for these we can expect the landscape to influence colonization, both through its effects on disperser movements and via spatial patterns of suitable habitat for plant establishment and reproduction. For example, fencerows
32 enhance dispersal of seeds across agricultural landscapes by blue jays (Johnson and
Adkisson, 1985); invasion of Prunus serotina in Belgium was related to the movement of birds through hedgerows towards roost sites and structurally diverse areas of the hedgerows (Deckers et al., 2005). American robins disperse Lonicera maackii seeds to wooded corridors and forest edges where they can get established (Bartuszevige et al.,
2006). Seeds of multiflora rose are also dispersed by song birds, such as robins, mockingbirds, starlings, red-winged blackbirds, and other species that feed heavily on multiflora rose hips in fall and winter. Because of the numerous spicules in each hip, seeds pass rapidly through their digestive tracts and remain intact. Moreover, digestion also facilitates germination (Lincoln, 1978; Nalepa, 1989; Nalepa, 1994; Scott, 1965).
New populations may be established by only few seeds dropped by birds as they move from one location to another, and thus may experience significant founder effect. This mode of dispersal may explain the relative low levels of genetic diversity within each population, the high levels of inbreeding, and the deficiency of heterozygotes. Moreover, bird dispersal can also explain the abundance of multiflora rose along fencerows, wooded corridors, forest edges, and roads where canopy openness is higher (Christen and
Matlack, 2009).
Finally, selection might play at important role in contributing to the low levels of genetic diversity. An invasive in its new environment while trying to adapt it to the local surrounding, produces more the fittest genotype, leading to reduced genetic variation
(Kliber and Eckrel, 2005). Sample size in the study might not be enough to draw such conclusion. Also, there is a possibility of loss of a proportion of genetic diversity due to
33
human interference in the trail locations. This is consistent with the findings of an early succesional habitat study in northeastern United States where colonization of multiflora rose positively correlated with amount of human altered land in the surrounding landscape (Johnson et al., 2006). Multiflora rose tends to be a roadside weed that flourishes in disturbed habitat (Steavenson, 1943; Doll, 2005). It has been reportedly found in roadsides, forest edges and open fields. As a result, variation among populations might be low due to ability of the plant to flourish in roadsides, where population structuring is naturally less (Christen and Matlack, 2009). The structure that is observed study can be a result of the sampling effect or establishment of different genotypes following multiple introductions (Christen and Matlack, 2009).
5. FUTURE RESEARCH
Patterns of genetic diversity and population differentiation in eight populations of multiflora rose, as found in this study help us to identify the forces of evolution interacting in the small geographical range. My findings quantifies the amount of genetic variation found in this invasive plant for the first time, and helps me to identify forces of evolution acting in the invaded habitats. My results of trends towards heterozygosity deficiency, moderate genetic differentiation and low gene flow indicate significant inbreeding and population structuring in the studied populations. My results confirm that the plant reproduces vigorously through both sexual and asexual methods. Integrating the plants behavioural, ecological and genetic provenances can provide valuable insights for better management practices. For example, control measures should not be limited to removal of individual or groups of plants by repeated cutting or mowing or repeated application of herbicides, but should compliment with methods suppressing flowering and seed maturation. Management practices can be designed after careful analyses of genetic diversity along with other parameters like initial population size, number of introductions, dispersal patterns, biological characteristics and habitat compatibility studies as identified by Rejmanek (2000) in potential invasive species. There is vast scope for future research investigating mating system, breeding behaviour of this invasive plant or hybridization with natives or other cultivars as discussed in this study.
34
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Appendix 1 : Systematic Classification of Multiflora rose Source : Plant Database 2009, Natural Resources and Conservation Service, USDA.
Kingdom - Plantae
Subkingdom - Tracheobionta
Superdivision - Spermatophyta
Division - Magnoliophyta
Class - Magnoliopsida
Subclass - Rosidae
Order - Rosales
Family - Rosaceae
Genus - Rosa L.
Species - Rosa multiflora Thunb.
47
Appendix 2. Geographic information of study sites for eight populations of Multiflora rose from northeastern Ohio.
Population Population name Latitude Longitude Elevation Id 2 Barlow Road N41° 13’ 111” W81° 28’ 799” 1096 ft
3 Seasons Road N41° 12’ 040” W81° 28’ 553” 1053 ft
4 Silver Lake 1 N41° 12’ 927” W81° 28’ 014” 1036 ft
5 Silver Lake 2 N41° 12’ 260” W81° 26’ 898” 1055 ft
6 Silver Lake 3 N41° 11’ 870” W81° 25’ 837” 1055 ft
10 Peninsula 2 N41° 15’ 828” W81° 31’ 441” 939 ft
12 Peninsula 3 N41° 17’ 162” W81° 32’ 415” 945 ft
TW Towner’s Woods N41° 10’ 129” W81° 17’ 332” 1045 ft