American Journal of Plant Sciences, 2011, 2, 396-407
doi:10.4236/ajps.2011.23045 Published Online September 2011 (http://www.SciRP.org/journal/ajps)
A Survey of Genetic Diversity of the Weedy
Species Ipomoea lacunosa L. in the USA Mid-South
Nilda R. Burgos1*, Daniel O. Stephenson2, Hesham A. Agrama3, Lawrence R. Oliver1, Jason A. Bond4
1Department of Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, USA; 2Dean Lee Research Station,
Louisiana State University Agricultural Center, Alexandria, USA; 3Rice Research & Extension Center, University of Arkansas and
Dale Bumpers National Rice Research Center, Stuttgart, USA; 4Mississippi State University, Delta Research and Extension Center,
Stoneville, USA.
Received April 17th, 2011; revised May 14th, 2011; accepted June 1st, 2011.
ABSTRACT
Morningglories (Ipomoea spp.) are among the most troublesome weedy species in agroecological environments. Ipo-
moea lacunosa is one of the most prevalent of these species. Localized adaptations resulted in the evolution of several I.
lacunosa ecotypes in North America, which could potentially impact its response to crop management practices. To
evaluate the genetic diversity and population structure of I. lacunosa populations, we amplified inter-simple sequence
repeats loci by polymerase chain reaction (ISSR-PCR) of 64 accessions using 14 ISSR primers for Ipomoea. Of these,
64 polymorphic fragments were scored. Analysis of Nei’s genetic distance (GD) values placed the accessions into four
genotypic clusters, two of which were composed primarily of accessions from Arkansas and Mississippi with GD be-
tween clusters of 0.318. The overall GD was 0.238, indicating a narrow genetic base. Population structure analysis
determined three ancestral subgroups, with the majority of Arkansas and Mississippi accessions separated into two
subgroups. The existence of various genotypes and ecotypes of I. lacunosa demonstrates the evolutionary diversification
of this weedy species as it adapts to new colonized environments and agricultural activities.
Keywords: DNA Fingerprinting, Genetic Diversity, Ipomoea, ISSR, Morningglory Species, Morningglory Genotypes,
Population Structure
1. Introduction
Plant communities in agroecosystems undergo rapid
changes in composition, phenology, or genetic makeup
because of exposure to strong selection factors as crop
growers implement measures to favor crop growth. Ex-
amples of such activities include tillage, irrigation, fertil-
izer application, and use of herbicides to remove or
minimize competition from weedy species. Thus, plant
population composition quickly shifts to species that can
thrive in these altered environments. The morningglory
(Convolvulaceae) plant family is among such plant fami-
lies that are well adapted to agroecosystems and are se-
rious weeds in many crops. This family is composed of
Ipomoea and Jacquemontia species, which are generally
viney annuals or perennials [1]. There are more than 500
Ipomoea species worldwide [2], 342 of which exist in the
Americas [3].
Annual morningglories of the genus Ipomoea are
among the most troublesome weeds in numerous crops
throughout the USA. Full-season interference of annual
morningglories with soybean [Glycine max (L.) Merr.]
causes crop lodging, reduces harvest efficiency, and re-
duces yield up to 75% [4]. Ipomoea lacunosa L. (pitted
morningglory) is one of the more prevalent annual
morningglories [5]. Its indigenous range encompasses the
southern Midwest to the southeastern regions of the USA
[6,7]. It is among the top 10 most troublesome weeds in
southern USA row crops [8] as well as in fruit trees, nuts,
and vegetable crops [9]. Full-season interference of I.
lacunosa with soybean could cause up to 50% economic
loss [10]. Morningglories have generally large seeds with
hard seed coat, which contributes to its seed dormancy
and persistence in the soil seed bank [1]. Being large-
seeded enables it to emerge from greater depths and tol-
erate more stress factors around germination time than
many other annual weedy species and crops; therefore,
morningglories can easily become a dominant species in
disturbed environments such as crop production areas.
The reliance on chemical weed control in large crop

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South397
production areas also favors the dominance of morning-
glories in cropping systems where the herbicides used are
not effective on this weedy species [11].
The large morningglory seeds contaminate harvested
crop seed or grain and allow long distance dispersal.
Morphological characterization of I. lacunosa accessions
in the southern USA revealed that localized morphologi-
cal adaptations had occurred throughout its range, pro-
ducing at least four distinct morphological ecotypes [12,
13]. Morningglories have complete flowers. Many Ipo-
moea species (including I. lacunosa) are self-pollinated
[14], but several are insect-pollinated. The potential for
hybridization between species, or populations within a
species, contributes to species diversification. Plant ge-
netic diversity emanates from accumulated genomic mu-
tations among individuals and species with time [15] and
genetic exchange between populations within a species
and across genetically compatible species. Plant diversity
is an inevitable consequence of interaction with other
plants and the impact of environment [16] as external
selectors favor the proliferation of certain individuals
carrying a particular set of mutations.
Ipomoea lacunosa is widely dispersed in the southern
USA. Species with wide geographic ranges, such as this,
almost always develop locally adapted populations that
harbor certain genetic traits allowing them to proliferate
in local conditions [17]; thus, the evolution of new bio-
types. Such biotypes may harbor different competitive
abilities with crops, different emergence patterns, or dif-
ferent responses to herbicides. It is already documented
that I. lacunosa accessions from southern USA have dif-
ferential tolerance to sublethal (one-half the recom-
mended) dose of glyphosate herbicide, wherein growth
reduction ranged from 0% - 40% [18]. We hypothesized
that the phenotypic diversity in I. lacunosa is indicative
of distinct subpopulation structure in this species. This
research aimed to survey the genetic variability within I.
lacunosa in agroecological environments in the southern
USA and analyze its population structure.
2. Materials and Methods
2.1. Sample Collection
Samples were obtained from 36 locations across southern
USA (Table 1, Figure 1). Seeds were primarily collected
(courtesy of several colleagues) in or near agricultural
fields, with each seed lot (hereafter referred to as “acces-
sions”) obtained from a single plant. Each accession was
verified as I. lacunosa based on distinguishing morpho-
logical traits i.e., simple leaf margins with or without
No r th
Figure 1. Geographical distribution of Ipomoea lacunosa (pitted morningglory) accessions from southern USA used in the
experiment.

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South
398
Table 1. Origin of pitted morningglory (Ipomoea lacunosa L.) accessions used in the experiment and the approximate coor-
dinates of origination sites.
Accession Location no. State Region1 County Latitude
N
Longitude
W
AL1 1 Alabama WC Pickens 33.42 –88.09
AR1 2 Arkansas NW Washington 36.17 –94.02
AR2 2 Arkansas NW Washington 36.17 –94.02
AR3 3 Arkansas NE Mississippi 35.83 –90.59
AR4 3 Arkansas NE Mississippi 35.83 –90.59
AR5 4 Arkansas NE Poinsett 35.56 –91.00
AR6 4 Arkansas NE Poinsett 35.56 –91.00
AR7 5 Arkansas NE Cross 35.28 –90.59
AR8 6 Arkansas NE Crittenden 35.26 –90.22
AR9 7 Arkansas WC Conway 35.19 –92.84
AR10 8 Arkansas WC Conway 35.12 –92.75
AR11 9 Arkansas WC Conway 35.10 –92.71
AR12 9 Arkansas WC Conway 35.10 –92.71
AR13 10 Arkansas EC St. Francis 34.99 –90.43
AR14 11 Arkansas EC Lonoke 34.85 –91.88
AR15 11 Arkansas EC Lonoke 34.85 –91.88
AR16 12 Arkansas EC Lonoke 34.68 –92.09
AR17 13 Arkansas SE Desha 33.88 –91.26
AR18 14 Arkansas SW Miller 33.48 –93.91
AR19 15 Arkansas SW Miller 33.47 –93.87
AR20 15
Arkansas SW Miller 33.47 –93.87
AR21 16 Arkansas SW Miller 33.47 –93.76
AR22 16 Arkansas SW Miller 33.47 –93.76
AR23 17 Arkansas SW Miller 33.38 –93.73
AR24 17 Arkansas SW Miller 33.38 –93.73
AR25 18 Arkansas SE Ashley 33.34 –91.47
DE1 19 Delaware SC Sussex 38.66 –75.42
GA1 20 Georgia SC Colquitt 31.30 –83.73
KY1 21 Kentucky NW Daviess 37.72 –87.16
LA1 22 Louisiana NE Tensas 31.96 –91.27
LA2 22 Louisiana NE Tensas 31.96 –91.27
LA3 22 Louisiana NE Tensas 31.96 –91.27
LA4 23 Louisiana SE West Baton Rouge30.41 –91.18
MO1 24 Missouri NE Knox 40.16 –92.21
MO2 25 Missouri SE Dunklin 36.23 –90.10

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South
399
Accession Location no. State Region1 County Latitude
N
Longitude
W
MS1 26 Mississippi EC Noxubee 33.47 –88.74
MS2 27 Mississippi WC Washington 33.52 –90.85
MS3 27 Mississippi WC Washington 33.52 –90.85
MS4 27 Mississippi WC Washington 33.52 –90.85
MS5 28 Mississippi WC Washington 33.33 –90.91
MS6 28 Mississippi WC Washington 33.33 –90.91
MS7 28 Mississippi WC Washington 33.33 –90.91
MS8 28 Mississippi WC Washington 33.33 –90.91
MS9 29 Mississippi WC Washington 33.24 –90.78
MS10 30 Mississippi WC Washington 33.24 –90.97
MS11 31 Mississippi WC Washington 33.21 –90.91
MS12 32 Mississippi WC Washington 33.16 –90.82
MS13 32 Mississippi WC Washington 33.10 –90.92
MS14 32 Mississippi WC Washington 33.10 –90.92
MS15 32 Mississippi WC Washington 33.10 –90.92
MS16 32 Mississippi WC Washington 33.10 –90.92
MS17 32 Mississippi WC Washington 33.10 –90.92
NC1 33 North Carolina NE Edgecombe 35.92 –77.74
NC2 34 North Carolina C Johnston 35.64 –78.44
OK1 35 Oklahoma EC Sequoyah 35.27 –94.50
OK2 35
Oklahoma EC Sequoyah 35.27 –94.50
OK3 35 Oklahoma EC Sequoyah 35.27 –94.50
OK4 35 Oklahoma EC Sequoyah 35.27 –94.50
OK5 35 Oklahoma EC Sequoyah 35.27 –94.50
OK6 35 Oklahoma EC Sequoyah 35.27 –94.50
OK7 35 Oklahoma EC Sequoyah 35.27 –94.50
OK8 35 Oklahoma EC Sequoyah 35.27 –94.50
OK9 35 Oklahoma EC Sequoyah 35.27 –94.50
TN1 36 Tennessee WC Madison 35.55 –88.82
1Abbreviations: C, central; EC, east-central; NE, northeast; NW, northwest; SE, southeast; SW, southwest; WC, west-central.
Sharp points on the sides, relatively glabrous on both leaf
surfaces, while flowers. The study included 64 single-
plant accessions from 33 collection sites in 10 states of
the southern USA. The majority of accessions were col-
lected from the mid-south specifically, Arkansas and
Mississippi. Seed from the original, single-plant acces-
sions were planted and characterized in a common gar-
den at the University of Arkansas Agricultural Research
Center, Fayetteville [12]. Young leaf tissues were col-
lected from plants grown in a common garden and used
for DNA fingerprinting.
2.2. DNA Extraction
Approximately 5 cm2 of young leaf tissues were har-
vested per plant, with three biological replicates per ac-
cession. Tissues were stored in microcentrifuge tubes at
–80˚C until processed. DNA was extracted using a
hexadecyltrimethylammonium bromide (CTAB) method

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South
400
[19] with some modifications. Frozen leaf tissue was
placed in liquid N2 for 30 s, ground with a mortar and
pestle, and transferred to a chilled 1.5-ml microcentrifuge
tube. Extraction buffer (100 mM Tris-HCl, pH 8.0; 20 mM
EDTA, pH 8.0; 1 M NaCl; 2% CTAB; 2% polyvinylpyrrli-
done-40; 1 mM phenanthroline; 0.2% β-mercaptoethanol),
500 μl, was added; the samples were vortexed (Vor-
tex-Genie2, Scientific Industries Inc., 70 Orville Dr., Bohe-
mia, NY 11716, USA) briefly and incubated at 65˚C for 45
min, occasionally inverting the tubes to mix the sample.
The ground tissue was extracted with 500 μl of phe-
nol:chloroform:isoamylalcohol (25:25:1) and centrifuged
at 10,000 rpm at 4˚C for 10 min. The aqueous fraction
was recovered, 500 μl of isopropanol was added, mixed
gently by inverting the tubes, and centrifuged at 12,000
rpm at 4˚C for 10 min. The DNA pellet was washed with
500 μl 100% EtOH, dried in a vacufuge, and redissolved
in 50 μl of TE buffer (10 mM Tris-HCl, pH 8.0, with 1
mM EDTA). The DNA concentration was quantified
with a fluorometer (DyNA Quant 200, Amersham Phar-
macia Biotech AB, 654 Minnesota St., San Francisco,
CA 94107, USA) using calf thymus DNA (Sigma-Al-
drich, 3050 Spruce St., St. Louis, MO 63103, USA) as
standard. DNA concentrations were then adjusted to 20
ng·μl–1 with deionized water for subsequent amplification
reactions using the ISSR (inter-simple sequence repeats)
primers.
2.3. PCR-Amplification of ISSR Loci
Primers designed to amplify ISSR in the cultivated rela-
tive Ipomoea batatas L. (sweet potato) were used [20].
Simple sequence repeats are tandemly repeated, noncod-
ing short segments of genomic DNA [21,22]. These re-
petitive segments are ubiquitous, hypervariable, and
widely distributed in the genome [15,23,24]. For SSR-
DNA fingerprinting, primers are designed for regions
flanking the repetitive segment; therefore, sequence in-
formation of the flanking region is required [15,25]. To
circumvent this hurdle, Zietkiewicz et al. developed the
alternative ISSR approach [22], which has been success-
fully used in various species [23,26-28]. These primers
are degenerate and are designed to anneal to the repeti-
tive segments. This approach revealed sufficient varia-
tion within the Ipomoea genus to allow for analysis of
intraspecific diversity [20].
The reaction mixture consisted of 1 μl DNA template
plus reagents in the PCR kit (HotStarTaq PCR Handbook,
Qiagen Inc., 28159 Avenue Stanford, Valencia, CA
91355, USA). The PCR reagents included 2 μl 10X PCR
buffer; 4 μl 5X Q-solution, 0.5 μl (2.5 units) HotStar Taq
polymerase, 25 mM MgCl2, 5 μl (10 mM) dNTPs (Pro-
mega Corporation, 2800 Woods Hollow Road, Madison,
WI 53711, USA), 6.5 μl deionized water, and 1 μl (10
pmol) ISSR primer in a final volume of 20 μl. Fifteen
ISSR primers (Sigma Genosys, 1442 Lake Front Circle,
The Woodlands, TX 77380, USA) were used (Table 2).
Reactions without DNA were included in each batch of
PCRs as negative control. Duplicate reactions were pre-
pared for all samples to monitor repeatability.
Amplification reactions were performed with a PTC-
200 Peltier Thermal Cycler (MJ Research Inc., 149
Grove St., Watertown, MA 02472, USA). After 15-min
incubation at 94˚C, the PCR mixtures were subjected to
45 cycles of 94˚C for 45 s, 50˚C - 55˚C (depending on
the primer used) for 45 s, 72˚C for 1.5 min, and a final 7
min extension at 72˚C. The reaction tubes were then held
at 4˚C until analyzed. The amplified fragments were re-
solved in 1.6% agarose gels (containing 3% ethidium
bromide) in 0.5X TBE buffer (45 mM Tris-borate, 1 mM
EDTA), run at 250 v for 45 min and visualized using a
UV transilluminator (Electronic Dual Light-Transillu-
minator, Ultra-Lum Inc., 1480 North Claremont Blvd.,
Claremont, CA 91711, USA). Reproducible amplified
DNA fragments were scored as binary data whether pre-
sent (1) or absent (0).
2.4. Analysis of Genetic Diversity and Population
Structure
The binary matrix of ISSR marker scores was analyzed
to generate a matrix of genetic distance (GD) and simi-
larity coefficients according to the procedure of Nei [29]
using the PowerMarker v3.25 software [30]. The soft-
ware NTSYS-pc 2.21 [31] was used to construct a den-
dogram using an Unweighted Pair Group Method with
Algorithmic Mean (UPGMA) based on Nei’s similarity
coefficients. NTSYS-pc 2.21 was also used to compute
Principal Component Analysis (PCA) for the 64 geno-
types using the marker data. Principal component analy-
sis (PCA) is widely used technique for dimensional re-
duction and data summary. It enables the identification of
key components of the structure within the data without
resorting to a model [32]. Because the markers used to
estimate the panel structures are chosen so as to be
physically distant and selectively neutral, the linkage
disequilibrium between them is due principally to the
panel stratification, the panel structure is thus the main
information that is summarized by the first components.
A Bayesian clustering procedure in the STRUCTURE
software [33,34] was used to determine the population
structure using multi-locus genotype data to identify
groups being characterized by specific sets of marker
allele frequencies. Bayesian methods [33] and PCA [35,
36] are widely used to elucidate genetic diversity and
population structure of cultivated, wild, and weedy rela-
tives of rice [37-43]. The Bayesian model-based cluster-
ing method assumes Hardy-Weinberg and linkage equi-

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South
401
librium between the loci within the subpopulations. Start-
ing with uniform priors, information about the origins of
the individuals (in the case of mixture) or about the ori-
gin of proportions of individual genomes (in the case of
admixture) is inferred. Approximations of posterior dis-
tributions are obtained using Markov chain Monte Carlo
(MCMC) methods.
This model-based procedure probabilistically assigns
accessions to an assumed number (K) of different sub-
groups aiming to minimize LD (linkage disequilibrium)
and maximize gametic-phase equilibrium within popula-
tions. K indicates the ancestries or populations discern-
able among the samples analyzed. The program was run
for K ranging from 2 to 12 with 20 independent replica-
tions per K. For this we used the admixture model with
uncorrelated allele frequencies setting a burn-in length of
20,000 iterations followed by 50,000 MCMC iterations.
For each value of K, STRUCTURE produces a Q-matrix
(QST) that lists the estimated membership coefficients
for each accession in each subgroup. An individual hav-
ing more than 70% of its genome fraction value under a
particular K subgroup was assigned to that subgroup. The
model choice criterion implemented in STRUCTURE, i.e.
LnP(D), which is the estimate for the posterior probabi-
lity of the data for a given K, frequently did not show a
clear trend (Figure 2(a)). To identify the most likely
number K of subgroups, we additionally used the ap-
proaches of Evanno et al. [44]. Evanno et al. [44] sug-
gested an ad hoc statistic ΔK determining the break in
the slope of the LnP(D) probability function (Figure 2(b))
provided by STRUCTURE. Analysis of molecular vari-
ance (AMOVA) was also conducted using Arlequin 3.11
software [45] to partition the contribution of within and
among population variation to the formation of subpopu-
lations. Accessions were considered admix if their ge-
nomic values indicated shared alleles between two or
more subpopulations.
3. Results
3.1. Polymorphism of I. Lacunosa ISSR Loci
Fourteen of the 15 I. batatas ISSR primers amplified
several hypervariable loci in I. lacunosa. The bands
scored ranged from 250 bp to 1500 bp in size. A total of
67 DNA fragments were amplified across all accessions,
64 of which were polymorphic (Table 2). Each primer
amplified 2 to 9 DNA fragments, with an average of 4.5
bands per primer. The 14 primers all together amplified
16 to 37 fragments per accession.
Table 2. DNA sequences of the primers used for ISSR-PCR, annealing temperatures, and the number of polymorphic bands
scored across all samples.
Primer Sequence (5' to 3')a Annealing temperature (˚C) Total bands scored Polymorphic bands
ISSR-1 AGAGAGAGAGAGAGAGT 50.5 4 4
ISSR-2 GAGAGAGAGAGAGAGAC 53.0 2 2
ISSR-3 CACACACACACACACAA 51.5 5 4
ISSR-4 GTGTGTGTGTGTGTGTA 52.5 4 4
ISSR-5 ACACACACACACACACT 52.5 3 3
ISSR-6 AGAGAGAGAGAGAGAGYC 53.5 3 2
ISSR-7 GAGAGAGAGAGAGAGAYC 51.5 6 6
ISSR-8 GTGTGTGTGTGTGTGTYC 55.0 4 4
ISSR-9 ACACACACACACACACYG 55.0 5 5
ISSR-10 ACCACCACCACCACCACC 51.5 8 8
ISSR-11 ATGATGATGATGATGATG 52.0 4 3
ISSR-12 GGATGGATGGATGGAT 50.5 5 5
ISSR-13 BDBCACACACACACACA 52.0 0 0
ISSR-14 VHVGTGTGTGTGTGTGT 50.0 9 9
ISSR-15 HVHTGTGTGTGTGTGTG 51.5 6 6
Total 67 64
aY = pyrimidine; B = C, G, or T; D = A, G, or T; H = A, C, or T; and V = A, C, or G.

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South
402
(a)
(b)
Figure 2. Analysis of optimum ancestral subgroups for Ipomoea lacunosa (pitted morningglory) accessions, mostly from the
southern USA using STRUCTURE software.
3.2. Genetic Diversity of I. Lacunosa from
Southern USA
In this study, Arkansas (AR) and Mississippi (MS) were
more intensively sampled than the other states at 25 and
17, respectively. The overall GD among AR accessions
was 0.206 and that of the MS accessions was 0.201. The
64 accessions separated into four genotypic clusters
(Figure 3) with an overall average GD of 0.238. This is
relatively low, similar to that of rice cultivars grown in
the USA with an overall GD of 0.26, indicating a narrow
genetic base [41]. The largest cluster (cluster 1) was
composed of 38 accessions with three subclusters. The
majority (83%) of accessions from AR fell in subcluster
1a. The one accession each from Alabama (AL1), Dela-
ware (DE1) and Kentucky (KY1), two accessions from
North Carolina (NC), and one accession from Mississippi
(MS3) also were in the large AR subcluster. Another
accession from MS (MS1) grouped with three AR acces-
sions in subcluster 1c. The remaining 15 (88%) MS ac-
cessions fell in cluster 4, where one AR accession (AR10)
also belonged. Thus, in general, AR and MS accessions
belonged to different genotypic groups with an average
GD of 0.318 between groups. All nine accessions from
East-central Oklahoma (OK), which were collected from
one field, composed subcluster 1b. The average GD
within the OK population was 0.146, indicating that
plants in this field were very highly similar. Genetic
similarity approaches 1.0 as GD approaches 0. Although
the OK accessions were collected from a field close to
the AR border, collectively the OK population belonged
to a different genotypic subgroup relative to the AR
population with an average GD of 0.269 among the two
populations. The two accessions from eastern Missouri

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South403
(MO) grouped with AR1 and AR5 from northern AR in
cluster 2. One accession from Tennessee (TN1) was in a
cluster by itself (cluster 3). The four Louisiana (LA) ac-
cessions were all in cluster 4, together with the majority
of MS accessions. The one accession from Georgia (GA1)
also fell in the large MS cluster, forming a subcluster
with three of the LA accessions.
Figure 3. UPGMA tree of 64 accessions of Ipomoea lacunosa
(pitted morningglory) mostly from the southern USA, based
on Nei’s [29] similarity coefficients using NTSYS-pc 2.21
software.
The LA accessions and GA1 originated from latitudes of
31.97˚N or further south (Table 1). In many cases in this
data set, accessions collected along the same latitude were
more genetically similar. The grouping of LA accessions
and GA1 within the same genotypic subcluster indicates that
longitude (within its habitat range) might not influence ge-
netic variation of I. lacunosa as much as latitude would. The
LA and AR accessions were from a longitude of –91.18˚W
to –91.27˚W, while GA1 was from a longitude of –83.73˚W.
However, to properly define the relationship between lati-
tude and species diversification of I. lacunosa, a follow-up
experiment need to be conducted involving intensive and
structured sampling along different latitudinal gradients.
Different ecotypes of this species [12] may also be dispersed
along latitudinal gradient depending on its photoperiodicity
[46].
3.3. Population Structure of I. Lacunosa
The most probable number of ancestral lineage (K) of
these accessions is three (Figure 2(a)). This is the num-
ber of subgrouping that optimizes ΔK (Figure 2(b)). The
partitioning of molecular variance within and among
subpopulations was significant (P < 0.0001) and the
greatest contribution to population genetic divergence
(73%) emanated from within the subpopulation (Figure
4). The majority (15 of 25) AR accessions, which con-
stituted a large proportion of genotypic cluster 1, fell
under subpopulation K1 (Figure 5). This subpopulation
also included the two accessions from MO, the two ac-
cessions from NC, and the single accessions from DE,
KY, and TN. Only the MS1 accession from MS belonged
to this group. Three accessions in this group were ad-
mixtures (AR14, AR12, AR4), sharing > 30% of its al-
leles with other subgroups (Table 3).
The second subpopulation (K2) included all accessions
from OK, seven accessions from AR, and the one acces-
sion from Alabama (AL1) (Figure 5). Specifically, this
included subclusters 1b and 1c, 32% of accessions from
subcluster 1a, and two (AR4 and NC2) of four accessions
from cluster 2 (Figure 3). Three accessions in this sub-
group were admixtures: OK2, AR16, and AL1 (Table 3).
The third subpopulation (K3) included all accessions
from MS (except MS1) and LA, two accessions from AR
(AR10, AR23), and the single accession from GA (Figure
5). Thus, this ancestral subgroup covered all accessions in
genotypic cluster 4 (Figure 3). Five accessions in this sub-
population were admixtures (Table 3), generally sharing
alleles with K1. The presence of admix accessions indicated
genetic introgression between subpopulations.
4. Discussion
ISSR markers have been used to study the genetic diversity
of crops including barley [47], rice [48], sweet potato [20],

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South
404
and wheat [49]. These markers are highly polymorphic and
can be used to study intraspecific population structure. The
polymorphism of ISSR markers in I. lacunosa was high
(95%), which makes this PCR-based approach a good
tool for studying the population genetics and evolution of
the Ipomoea species complex when coupled with some
gene sequence information. In a study of 10 cultivated
and weedy Ipomoea species using ISSR markers, the
highest level of ISSR polymorphism was observed in the
weedy species I. trifida at 78.1% [20]. Among various
Ipomoea species, intraspecific accessions almost always
clustered together [20]. In the present study involving
one Ipomoea species, we provided further evidence of
the sensitivity of ISSRs in detecting subpopulation dif-
ferentiations in that, accessions from proximal localities
(i.e. AR or MS accessions) generally clustered together.
Further, accessions from one field (i.e. OK accessions)
also clustered together.
of I. lacunosa where the majority of AR accessions
shared the same ancestry (K1) and all but one MS acces-
sions also shared the same ancestry (K3). Some AR ac-
cessions shared the same ancestry as the OK accessions
(K2) apparently due to short-distance dispersal; on the
other hand, accessions from distant states belonged to the
AR or MS subpopulation. This indicates that seeds have
been moved to distant locations at some point in the past
and thereafter had colonized new geographies. The direc-
tion of movement could not be resolved in this study. To
determine if the evolution of localized subpopulations
commonly occurs in other geographies and microcli-
mates, a follow-up study needs to be conducted with in-
tensive sampling in various geographic locales.
The presence of admix genotypes indicates genetic in-
trogression between subpopulations, which could be due
AmongPops
27%
WithinPops
73%
PartitioningofMolecularVariance
The clustering of LA accessions with those of MS or
the eastern MO accessions with northern AR accessions
was not surprising because of the geographical proximity
of the collection sites in these states and the high similar-
ity of their agricultural environments. The clustering of
the one GA accession with MS and LA accessions indi-
cated shared ancestry, which could be due to colonization
of new areas via seed movement. The large morningglory
seeds make it easy for the weed seed to be harvested, and
moved, with the crop. Interstate transport of crop grains
is a common means of spreading weed seeds. The same
principle applies to the clustering of AL, DE, KY, and
NC accessions with AR accessions. It is also possible
that the colonization of I. lacunosa in the southern USA
started with only one genotype and evolved with time
due to localized adaptations and hybridization with com-
patible species [13]. This premise is supported by STRU-
CTURE analysis, which determined three subpopulations
Figure 4. Partitioning of molecular variance among Ipo-
moea lacunosa (pitted morningglory) accessions from the
southern USA Analysis of molecular variance (AMOVA)
using Arlequin 3.11 software.
Table 3. Shared ancestry among some Ipomoea lac uno sa accessions from the southern USA.
Sample code Sampling location K group assignment Allele sharing (proportion)a
K1 K2 K3
AR14 East-central Arkansas 1 0.566 0.422 0.011
AR12 West-central Arkansas 1 0.696 0.287 0.017
AR4 Northeast Arkansas 1 0.423 0.267 0.310
OK2 East-central Oklahoma 2 0.380 0.606 0.014
AR16 East-central Arkansas 2 0.295 0.683 0.022
AL1 West-central Alabama 2 0.335 0.389 0.275
LA3 Northeast Louisiana 3 0.324 0.037 0.639
AR10 West-central Arkansas 3 0.432 0.127 0.441
MS3 West-central Mississippi 3 0.429 0.051 0.520
LA4 South Louisiana 3 0.396 0.009 0.595
AR23 Southwest Arkansas 3 0.083 0.328 0.590
aAn individual having > 70% of its genome fraction value fall within a particular K subpopulation was assigned to that group. Otherwise, if a substantial pro-
portion of alleles are shared between subpopulations, such individual is considered an admixture of different ancestries.

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South405
Figure 5. Principal component analysis (PCA) of ISSR markers using STRUCTURE software, showing the separation of
Ipomoea lacunosa (pitted morningglory) accessions into three subpopulations. Numbers in parenthesis are the proportions of
genomic variation explained by the c omponent.
to recent gene flow or shared alleles from the distant past.
The data set could not distinguish between these factors.
Similarly, admix genotypes of weedy rice had been de-
tected, which was principally attributed to natural hy-
bridization between weedy rice types [41]. The same
interaction could occur in I. lacunosa. Although there is
no data on the extent of cross pollination in this species,
pollination by insects is most likely, and hybrid Ipomoea
had been observed [13].
Just as four morphological ecotypes of I. lacunosa have
been identified [12], three genetic subpopulations have
been determined in the present study, using the same ac-
cessions previously used in the morphological diversity
characterization. These demonstrate the morphological and
genetic divergence of I. lacunosa in southern USA, which
impacts the efficacy of weed management strategies. For
example, Burke et al. [18] showed significant variation in
tolerance of I. lacunosa accessions to sublethal dose of
glyphosate herbicide. Glyphosate is a non-selective herbi-
cide used as the main tool to kill weeds in glyphosate-
resistant crops. The doses of glyphosate that would reduce
the growth of I. lacunosa accessions by 50% (GR50) ranged
from 0.65 kg·ha–1 to 1.23 kg·ha–1 [18]. Farmers find it dif-
ficult to control this weed. Genetic and morphological
diversity could also result in different emergence behav-
iors between genotypes. This would allow I. lacunosa to
escape weed management tactics that are usually imple-
mented early in the crop growing season.
5. Conclusions
The overall genetic diversity of I. lacunosa from the
southern USA is low, which is indicative of a predomi-
nantly selfing mating behavior or a narrow ancestral ge-
netic base. The species has diverged into different geno-
typic clusters and subpopulations in the mid-south. The
subpopulations were generally geographically delimited,
indicating the evolution of locally adapted weedy popu-
lations. Differential tolerance of I. lacunosa to herbicides,
as reported by other researchers, maybe correlated with
genetic subpopulation differentiation in this species.
6. Acknowledgements
We thank Drs. Shawn Askew, Jeffery W. Barnes, Jeffery

A Survey of Genetic Diversity of the Weedy Species Ipomoea lacunosa L. in the USA Mid-South
406
W. Edwards, James L. Griffin, Robert Hayes, William G.
Johnson, Donnie K. Miller, Edward C. Murdock, Don S.
Murray, Eric Palmer, Andrew Price, Jason C. Sanders,
Mark Van Gessel, William Vencill, Blaine J. Viator, and
the late Drs. William Barrentine and John W. Wilcut for
contributing seed samples for this research. We also
thank Dr. Satyendra N. Rajguru, former Postdoctoral
Associate, for his help in setting up the DNA finger-
printing process. They made this research possible.
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