Volume 59(2):127-137, 2015
Acta Biologica Szegediensis
http://www2.sci.u-szeged.hu/ABS
ARTICLE
Genetic and morphological diversity in Chara vulgaris L.
(Characeae)
Fariba Noedoost*, Masoud Sheidai, Hossein Riahi, Akram Ahmadi
Faculty of Biological Sciences, Shahid Beheshti University, Tehran, Iran
Chara vulgaris L. (Characeae) is a highly polymorphic species that plays an important ecological role in aquatic ecosystems. It grows in different regions of Iran and forms several
geographical populations. Genetic diversity studies are very limited in algal taxa of the country
and there is no detailed information about the genetic diversity present in C. vulgaris. Therefore,
the present investigation was performed to study the population structure of 89 plant specimen
collected from 11 geographical populations of C. vulgaris in Iran. Genetic diversity parameters
were determined in each population based on ISSR molecular markers. AMOVA test revealed
significant genetic difference among the studied populations. Mantel test revealed significant
correlation between genetic distance and geographical distance of the studied populations.
However, STRUCTURE analysis revealed that some common ancestral alleles exist among these
populations. ANOVA test revealed significant differences in quantitative morphological characters among the studied populations. UPGMA tree and PCoA plot revealed morphological
variability of these populations as the members of each population were scattered in different groups. Therefore, in spite of genetic differences of the studied populations, they are not
morphologically differentiated.
Acta Biol Szeged 59(2):127-137 (2015)
ABSTRACT
Introduction
The Charales (Characeae), commonly called stoneworts is a
group of highly complex green algae that comprises six genera (Wood 1965). They have a close evolutionary history to
land plants (Karol et al. 2001; McCourt et al. 2004) and play
an important ecological role in aquatic ecosystems throughout
the world except Antarctica (Wood 1965). The presence of
Characeae indicates a pristine aquatic ecosystem. They support the other biological components of the water ecosystems
(Carpenter and Lodge 1986; Noordhuis et al. 2002) and make
water clean with filtering mud particles between the whorls of
their branchlets. Charophytes have been used for fish-culture,
polishing-paste, mud-bathing, therapy, clarification of sugar
and luring noxious insects (Scheffer 1998). Charophytes are
sensitive to environmental changes such as eutrophication
(Blindow 1992), therefore, many Charophytes become rare
or endangered in recent decades (Baastrup-Spohr et al. 2013;
Auderset and Rey-Boissezon 2015).
High morphological variability has been reported in
Chara species (Wood and Imahori 1965; Corillion 1972)
due to variation in their habitats (Blindow and Schutte 2006;
Submitted May 19, 2015; Accepted November 11, 2015
*Corresponding author. E-mail: fariba.noedoost@gmail.com
KEy WoRdS
gene flow
ISSR molecular markers
population structure
Schneider et al. 2006) and the genetic alterations inside species (Mannschreck et al. 2002).
Chara vulgaris L. has worldwide distribution from South
America, Africa, Asia to Europe (Caisova and Gabka 2009).
It is also a highly polymorphic species with many forms and
varieties (Wood and Imahory 1965; Caisova and Gabka 2009).
This species grows under different environmental conditions
in many geographical areas of Iran. C. vulgaris is the most
commonly found taxon and probably the most abundant
Charophytes in Iran and can be collected in a wide variety of
habitats from most of the provinces of the country. Specimens
show variability in morphological characters. They typically
grow on sandy or sandy-mud substrates with relatively low
organic content. This species occurs at nearly every altitude
and latitude and can be found in streams, river channels, in
artificial, natural, permanent and temporal small water bodies
between 0.1-2 m depth. It also roots at the bottom of artificial
basins covered with a thin film of silt, nonetheless, it has a
higher abundance in running water.
Previous genetic diversity investigations have been performed in different Chara species (e.g., Mannschreck et al.
2002; Schaible et al. 2009). However, there is no detailed
study about the degree of genetic variability within and
among geographical populations of C. vulgaris in Iran.
Therefore, in the present study we investigated the genetic
diversity and the population structure of C. vulgaris in 11
127
Noedoost et al.
Table 1. C. vulgaris populations studied, their localities and voucher numbers.
Populations
Number of samples
Altitude (m)
Longitude
Latitude
Voucher No.
Razavi Khorasan
South Khorasan
Yazd
Kerman
Isfahan
Mazandaran
Gilan
Tehran
Golestan
Khuzestan
Semnan
43
2
7
5
4
9
4
3
2
9
1
1201
777
1645
2024
1606
1294
625
1881
1306
353
1157
36°48’33”
33°47’57”
31°43’36”
29°56’54”
33°36’51”
36°11’44”
36°40’24”
35°44’34”
37°25’46”
30°25’53”
35°34’18”
58°50’47”
56°49’21”
54°09’25”
56°33’43”
51°43’32”
52°10’17”
49°31’31”
52°40’29”
56°34’41”
50°19’37”
53°22’27”
2011405
2011413
2011514
2011482
2011402
2011510
2011493
2011409
2011490
2011464
2011476
geographical populations by using morphological and intersimple sequence repeat molecular markers (ISSRs). These
molecular markers are easy to use, simple and cost effective
Figure 1. Distribution map of C. vulgaris populations.
128
along with high degree of reproducibility (Sheidai et al. 2012,
2013, 2014; Azizi et al. 2014).
Genetic and morphological diversity in C. vulgaris L.
Figure 2. ISSR marker profiles of 15 individuals of C. vulgaris population generated by primer (AGC)5GT in 2% agarose gel. N: negative control;
1-15: individuals; L: 100 kb molecular-weight size marker (Fermentas, Germany).
Materials and Methods
Plant materials
Eighty-nine samples were collected from 11 different geographical populations (Razavi Khorasan, South Khorasan,
Yazd, Kerman, Isfahan, Mazandaran, Gilan, Tehran, Golestan,
Khuzestan, and Semnan). Details of localities are provided in
Table 1 and Figure 1. Specimens are deposited in Herbarium
of Shahid Beheshti University (HSBU).
DNA extraction and ISSR assay
Fresh thalli were collected randomly from 10 plants derived
from each of the studied populations and mixed, then dried in
silica gel powder. These thalli were used for DNA extraction.
Genomic DNA was extracted using CTAB activated charcoal
protocol (Sheidai et al. 2013). The quality of extracted DNA
was examined electrophoretically by running on a 0.8%
agarose gel.
Ten ISSR primers were used: (AGC)5GT, (GA)9C,
UBC807, UBC811, (CA)7GT, (GA)9A, (GA)9T, UBC834,
UBC810, and UBC823. They were commercialized by UBC
(the University of British Columbia). Polymerase chain reactions (PCR) were performed in a volume of 25 μl containing:
10 mM Tris-HCl buffer (pH 8); 50 mM KCl; 1.5 mM MgCl2;
0.2 mM of each dNTP (Bioron, Germany); 0.2 μM of a single
primer; 20 ng of genomic DNA and 3 U of Taq DNA polymerase (Bioron, Germany). The reactions were performed in
Techne thermocycler (Germany) using the following cycling
conditions: 5 min initial denaturation step at 94 °C, 45 cycles
of 30 s at 94 °C; 30 s at 50 °C/52.6 °C/53.3 °C/55.3 °C/58.2
°C/ and 1min at 72 °C. The reaction was completed by final
extension step of 10 min at 72 °C. Five different annealing
temperatures were used as follows: 58.2 °C for the primer
((AGC)5GT); 55.3 °C for ((GA)9C and (GA)9T); 53.3 °C
for (UBC807), 52.6°C for (UBC811) and 50 °C for the other
primers.
The amplicons were visualized electrophoretically by running on a 2% agarose gel, followed by the ethidium bromide
staining. The fragment size was estimated by using a 100 bp
molecular-weight size marker (Fermentas, Germany).
Morphological study
Specimens (5-10) were collected randomly in each location
for morphological studies. In total, 24 characters (quantitative and qualitative) were studied and coded accordingly for
multivariate statistical analyses (Table 4).
Data analyses
ISSR bands obtained (Fig. 2) were coded as binary characters
(presence = 1, absence = 0). Genetic diversity parameters
were determined for dominant molecular markers in each
129
Noedoost et al.
Table 2. Genetic diversity parameters in the studied populations.
Population
Number of
samples
Ne
I
He
UHe
%P
Razavi Khorasan
South Khorasan
Yazd
Kerman
Isfahan
Mazandaran
Gilan
Tehran
Golestan
Khuzestan
Semnan
43
2
7
5
4
9
4
3
2
9
1
1.324
1.164
1.334
1.187
1.308
1.318
1.333
1.244
1.061
1.316
1.000
0.333
0.140
0.314
0.166
0.266
0.287
0.278
0.216
0.053
0.312
0.000
0.209
0.096
0.203
0.110
0.179
0.190
0.189
0.145
0.036
0.200
0.000
0.211
0.128
0.219
0.123
0.205
0.201
0.216
0.173
0.048
0.212
0.000
85.51%
23.19%
68.12%
31.88%
47.83%
57.97%
49.28%
39.13%
8.70%
66.67%
0.00%
Ne: number of effective alleles; I: Shannon’s Information Index; He: gene diversity; UHe: unbiased gene diversity; %P: percentage of polymorphic loci.
population. These parameters were Nei’s gene diversity (H),
Shannon information index (I), number of effective alleles
and percentage of polymorphism (Weising et al. 2005; Freeland et al. 2011).
Nei’s genetic distance was determined among the studied
populations and used for clustering. For grouping specimens,
Neighbor Joining (NJ) clustering methods as well as Neighbor Net method of networking were performed after 100 times
of bootstrapping (Huson and Bryant 2006; Freeland et al.
2011). DARwin (ver. 5; 2012) was used for clustering, while
SplitsTree4 (V4.6; 2006) was used for network analysis.
Mantel test was performed to check correlation between
geographical distance and genetic distance of the studied
populations (Podani 2000). PAST (ver. 2.17; Hamer et al.
2012) program was used for Mantel test.
Significant genetic difference among the studied populations and provinces were determined by AMOVA (Analysis
of molecular variance) test (with 1000 permutations) for
dominant molecular markers as implemented in GenAlex 6.4
(Peakall and Smouse 2006). Furthermore, Nei’s Gst analysis
of dominant markers as implemented in GenoDive (ver.2)
(Meirmans and Van Tienderen 2004) was also carried out.
Finally, genetic differentiation of the populations was also
studied by G’st-est (standardized measure of genetic differentiation, Hedrick 2005), and D-st (Jost measure of differentiation, Jost 2008). These parameters were determined in case if
the studied populations do not follow normal distribution.
In order to overcome potential problems caused by the
dominance of ISSR markers, a Bayesian program, Hickory
(ver. 1.0; Holsinger and Lewis 2003), was used to estimate
parameters related to genetic structure (Theta B value).
The genetic structure of geographical populations and
provinces were studied by structure analysis (Pritchard et al.
2000) for dominant markers (Falush et al. 2007).
Model-based clustering was carried out to group the
130
studied populations based on genetic affinity using STRUCTURE software (ver. 2.3; Pritchard et al. 2000). This program
was also used to reveal the genetic admixture of the studied
populations. For this analysis, the admixture ancestry model
under the correlated allele frequency model was used. The
Markov chain Monte Carlo simulation was run 20 times
for each value of K (2-11) for 20 iterations after a burn-in
period of 105. All other parameters were set at their default
values. Data were scored as dominant markers and analyzed
according to the method suggested by Falush et al. (2007).
STRUCTURE Harvester web site (Earl and von Holdt 2012)
was used to visualize the STRUCTURE results and also to
perform Evanno method to identify the proper number of K
(Evanno et al. 2005).
The occurrence of gene flow among populations was
checked by different methods. First, we performed indirect
Nm analysis using POPGENE (ver. 2) for ISSR loci studied
according to the following formulae:
Nm = estimate of gene flow from Gst, Nm = 0.5(1 - Gst)/
Gst.
Then we used reticulation (Legendre and Makarenko
2002) and NeighborNet analyses (Huson and Bryant 2006).
Finally, the population, assignment test was performed by
using maximum likelihood method as implemented in GenoDive (ver.2; 2013) (Meirmans and Van Tienderen 2004).
Morphological data were standardized (mean = 0, variance = 1) and used to estimate Euclidean distance among the
studied populations. UPGMA (unweighted group mean using
average) and PCoA (principal coordinate analysis) as well as
PCA (principal components analysis) were used for grouping
the populations and for the identification of the most variable
morphological characters among the studied populations (Podani 2000). Mantel test was used to determine the correlation
between genetic distance and morphological distance.
Genetic and morphological diversity in C. vulgaris L.
Table 3. Nei’s genetic identity (above diagonal) and genetic distance (below diagonal) among the studied populations.
Population
RK
SK
Yz
Kr
Is
Mz
Gi
Th
Gl
Kh
Sm
RK
SK
Yz
Kr
Is
Mz
Gi
Th
Gl
Kh
Sm
0.1460
0.0301
0.0420
0.0197
0.0347
0.0358
0.0994
0.0580
0.0271
0.1471
0.8642
0.1300
0.2290
0.1554
0.1545
0.1603
0.2255
0.1750
0.1761
0.2819
0.9704
0.8781
0.0683
0.0323
0.0600
0.0592
0.1126
0.0685
0.0661
0.1971
0.9588
0.7953
0.9340
0.0453
0.0569
0.0743
0.1534
0.0470
0.0734
0.2088
0.9805*
0.8561
0.9683
0.9557
0.0428
0.0422
0.0922
0.0648
0.0459
0.1756
0.9658
0.8569
0.9417
0.9447
0.9581
0.0494
0.0988
0.0634
0.0612
0.1899
0.9648
0.8519
0.9425
0.9284
0.9587
0.9518
0.0968
0.0796
0.0700
0.1721
0.9054
0.7981
0.8935
0.8577
0.9119
0.9059
0.9078
0.1668
0.1505
0.2649
0.9436
0.8395
0.9338
0.9541
0.9372
0.9386
0.9235
0.8464
0.1008
0.2377
0.9732
0.8386
0.9360
0.9292
0.9551
0.9406
0.9324
0.8603
0.9041
0.1567
0.8632
0.7543
0.8211
0.8115
0.8389
0.8270
0.8419
0.7673
0.7884
0.8550
-
*
bold numbers indicate significant values RK: Razavi Khorasan; SK: South Khorasan; Yz: Yazd; Kr: Kerman; Is: Isfahan; Mz: Mazandaran; Gi: Gilan; Th: Tehran; Gl:
Golestan; Kh: Khuzesta; Sm: Semnan.
Results
Populations’ genetic diversity and structuring
In total, 69 ISSR bands were obtained, from which all were
polymorphic (Fig. 2). Genetic diversity parameters determined
in 11 geographical populations of C. vulgaris are presented
in Table 2. The highest value for polymorphism percentage
(85.51%), gene diversity (0.209) and Shanon’ information
index (0.333) occurred in Razavi Khorasan population. The
highest value for polymorphism percentage (85.51%), gene
diversity (0.209) and Shanon’ information index (0.333) occurred in Razavi Khorasan population. Golestan and Semnan
populations had the lowest value for the same parameters:
8.700, 0.053, 0.036, and 0.00, 0.00, 0.00, respectively.
AMOVA test revealed the presence of significant molecular difference among the studied populations (P = 0.01). It
also revealed that 11% of total genetic variability occurred
among the studied populations, while 89% occurred within
these populations. These results indicate the presence of high
level of genetic variability within C. vulgaris populations. Gst
analysis (0.148, P = 0.001) and Hickory test (Theta B = 0.40)
also supported the AMOVA test results and revealed signifi-
Figure 3. NJ tree of populations based on genetic data.
131
Noedoost et al.
Figure 4. STRUCTURE plot of C. vulgaris populations studied. RK: Razavi Khorasan; SK: South Khorasan; Yz: Yazd; Kr: Kerman; Is: Isfahan; Mz:
Mazandaran; Gi: Gilan; Th: Tehran; Gl: Golestan; Kh: Khuzestan; Sm: Semnan.
cant genetic differences among the studied populations.
Hedrick’s standardized fixation index (G’st = 0.161, P =
0.001) and Jost’s differentiation index (D-est = 0.062, P =
0.001) revealed that the studied geographical populations of
C. vulgaris are genetically differentiated.
Nei’s genetic identity and genetic distance of the studied
populations are presented in Table 3. The highest value for
genetic identity (0.9805) occurred between Razavi Khorasan
and Isfahan populations, while the lowest value of the same
(0.7543) occurred between South Khorasan and Semnan
populations.
The NJ tree of ISSR data is presented in Fig. 3. It produced 3 major clusters. Population numbers 1, 3, 5, 6, 7 and
10 (Razavi Khorasan, Yazd, Isfahan, Mazandaran, Gilan and
Khuzestan Province, respectively) comprised the first major
cluster. In this cluster, Razavi Khorasan and Isfahan populations (1 and 5) showed higher genetic similarity. Yazd, Mazandaran, Gilan and Khuzestan Province populations (3, 6, 7
and 10, respectively) joined them with some distance. Kerman
and Golestan populations (4 and 9) formed the second major
cluster. Populations Tehran, South Khorasan, and Semnan (8,
2 and 11, respectively) formed the third major cluster.
Pair-wise AMOVA revealed that all paired populations
differed significantly from each other.
Mantel test performed between populations’ genetic distance and their geographical distance produced significant
positive correlation (r=0.20, P=0.05). Therefore, C. vulgaris
populations showed isolation by distance (IBD) phenomenon,
and with increase in geographical distance, a lower degree of
gene flow occurred between them.
The STRUCTURE plot (Fig. 4) revealed some degree of
genetic admixture in the studied populations. This is due to
132
shared ancestral alleles, or ongoing gene flow. These results
showed high degree of genetic variability both within and
among the studied populations supporting our results obtained
from AMOVA.
The Neighbor Net diagram (Fig. 5) produced similar
grouping to NJ tree and The STRUCTURE plot. It also revealed some degree of gene flow between populations, and
also showed intra-population genetic diversity of populations.
Members of many populations were placed intermixed with
other populations due to genetic variability possibly caused
by inter-population gene flow. This is supported by the mean
Nm = 0.85 value obtained.
Evanno method produced K=8 genetic groups. Eight out
of 11 studied populations revealed almost complete lack of
genetic fragmentation and the occurrence of genetic continuity among the studied populations. This is well supported by
the STRUCTURE plot based on K=8. High degree of intrapopulation genetic variability and inter-population genetic
admixture was observed in this plot too. For example, members of Khorasan Razavi population varied in their genetic
structure (differently colored segments). This also held true
for Khorasan and Mazandaran populations.
Some members of these populations contained alleles
from the other populations (similarly colored segments). For
example, members of Khorasan Razavi population contained
similar alleles (colored segments) from both Khorasan and
Mazandaran populations.
Morphometry
The mean of morphological characters of the studied populations is provided in Table 4. The studied populations varied
Genetic and morphological diversity in C. vulgaris L.
Table 4. Mean values of morphological characters studied in C. vulgaris populations.
Population
Mz
Gi
Morphological characters
RK
RS
Yz
Kr
Is
Th
Gl
Kh
Sm
P value
Length of bract cell inside
Length of bract cell outside
Number of cells in end segment
Length of end segment of branchlet (mm)
Length of first segment of branchlet (mm)
Length of end cell in end segment
(mm)
Length of branchlet (mm)
Length of tips of the axis (mm)
Internode length (μm)
Diameter of antheridium (μm)
Oogonium wide (μm)
Oogonium length (μm)
Corona wide (μm)
Corona length (μm)
Number of corticate segment
Number of ecorticate segment
Internode diameter (mm)
Number of branchlets in each
node
Oospore length (μm)
Oospore wide (μm)
Oospore length/wide ratio (μm)
Internode diameter (mm)
Number of branchlets in node
Oospore length (μm)
Oospore wide (μm)
Oospore length/wide ratio (μm)
Fossa breath (μm)
Number of striae
Length of plant (cm)
3.02
4.47
3.20
13.89
1.54
2.38
3.00
15.75
2.79
5.06
3.57
15.34
3.68
6.18
3.20
12.50
2.64
5.00
3.00
11.64
2.43
4.65
3.00
11.80
2.89
4.33
2.75
10.43
3.67
5.59
3.00
10.51
3.75
6.13
3.00
10.71
1.87
3.11
3.00
9.93
1.97
4.20
3.00
8.50
0.01
0.01
0.01
0.01
1.78
1.75
2.21
2.49
1.91
1.98
1.49
3.00
3.00
2.18
2.25
0.01
2.33
3.40
2.02
1.84
1.53
1.94
2.26
1.83
1.78
1.86
2.66
0.01
20.33
14.88
15.05
392
440.2
670.2
190.6
128.4
2.40
3.20
0.63
10.00
20.75
25
35
370
450
712
200
150
3.00
4.00
0.65
9.00
21.69
16.19
17.83
393.1
422.3
691.4
177.3
107.2
2.72
3.57
0.72
10.14
20.01
15.43
20.05
437
409
663.2
193.2
107.5
2.20
3.20
0.82
10.00
16.63
16.83
13.79
446.7
383.7
598.5
197.5
105
2.50
3.00
0.87
10.25
18.53
18.17
19.36
430.6
380.8
635.2
186.7
110
3.60
2.60
0.75
10.20
15.42
14.99
14.57
424
457
715.7
216.5
135.2
3.00
2.50
0.72
10.00
20.45
18.16
26.86
471
421.3
712
236.6
128.6
3.00
3.00
1.06
10.00
22.11
22.42
29.52
440
399.7
718.7
199.5
133.5
3.50
3.00
0.81
10.00
17.41
17.74
16.87
444.8
423.8
694.8
194.9
153.9
3.40
3.00
0.71
10.20
14
18.2
24.2
391
425
762
200
125
3.00
3.00
0.75
10.0
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
518.6
330.6
1.56
0.63
10.00
518.6
330.6
1.56
51.29
11.20
18.80
738.2
370.1
1.99
0.65
9.00
738.1
370.0
1.99
67.24
11.00
60.00
493.1
328.5
1.51
0.72
10.14
493.1
328.5
1.51
51.12
11.57
35.28
514.8
344.3
1.49
0.82
10.00
514.8
344.3
1.49
57.66
10.40
35.00
543.2
329.9
1.65
0.86
10.25
543.2
329.9
1.65
49.03
11.75
17.25
471.8
301.9
1.56
0.75
10.20
471.8
301.9
1.56
45.30
11.00
36.00
557.9
383.9
1.45
0.71
10.00
557.9
383.9
1.45
56.84
11.50
23.75
547.4
346.9
1.57
1.06
10.00
547.3
346.9
1.57
50.94
12.00
66.66
440.3
278.1
1.58
0.81
10.00
440.3
278.1
1.58
42.14
10.00
40.00
509.1
329.3
1.54
0.70
10.20
509.1
329.3
1.54
54.33
10.60
22.00
596.3
324.2
1.84
0.75
10.00
596.3
324.1
1.83
54.19
11.00
25.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
RK: Razavi Khorasan; SK: South Khorasan; Yz: Yazd; Kr: Kerman; Is: Isfahan; Mz: Mazandaran; Gi: Gilan; Th: Tehran; Gl: Golestan; Kh: Khuzestan; Sm: Semnan
in the studied quantitative morphological characters. For example, Golestan and Tehran populations had the highest value
for length of bract cell inside (3.75 and 3.65, respectively).
Similarly, Kerman and Golestan populations had the highest
value for length of bract cell outside (6.16 and 6.13, respectively). South Khorasan populations and Yazd population had
the highest value for Length of end segment of branchlet (15.8
and 15.3, respectively). ANOVA (analysis of variance) test
revealed significant difference for quantitative morphological
characters among the studied populations (P<0.01).
UPGMA dendrogram of morphological characters and
PCoA plot (Fig. 6) produced similar results. Therefore, only
PCoA plot is given and discussed here. PCoA plot revealed
morphological variability within the studied populations. The
members of each population were scattered in the plot and
did not form a separate group. These populations differed in
degree of morphological variability; a higher degree of variability was observed among plant specimens of populations
Razavi Khorasan and Yazd (1 and 3).
In general, some agreement occurred between genetic
similarity and morphological similarity among the studied
populations. Plant specimens of populations Razavi Khorasan,
Yazd, Kerman and Khuzestan (1, 3, 4 and 10, respectively) are
in many places close to each other in both analyses. However,
we did not get a complete agreement between the two types
of data. In fact, Mantel test did not show significant correlation between morphological distance and genetic distance in
these populations (r = 0.04, P = 0.3).
PCA analysis of morphological data revealed that the first
3 PCA components comprised about 70% of total variation
among the studied populations. It showed that three morphological characters (length of the bract cell from inside and
133
Noedoost et al.
Figure 5. Neighbor Net diagram of ISSR data. Populations Razavi Khorasan, South Khorasan, Yazd, Kerman, Isfahan, Mazandaran, Gilan, Tehran,
Golestan, Khuzestan, and Semnan, are marked with numbers 1-11, respectively.
from outside as well as the length of branchlet) possessed r=
>0.80 with the first axis and are the most variable characters
among the studied populations.
discussion
Plant species that grow in different environmental conditions
diversify in their genetic and morphological features due to
local adaptations, genetic drift and species expansion (Sheidai
et al. 2012, 2013). According to Knaus (2008), if we take
the species to be the unit of distinction, the infra-taxa (the
subspecies, the variety and the ecotype) are consequently nondistinct. The process by which a group of organisms diverge
from being one cohesive group to becoming two or more
distinct groups is the process of speciation. Stebbins (1993)
also included the idea that species are systems of populations,
which resemble each other, yet contain genetically different
ecotypes that could be arranged in a continuous series. These
134
allopatric infra-specific categories are usually recognized as
infra-taxa.
The extent of polymorphism detected in the populations
investigated in this study (up to 85.51%) suggests high intraspecific genetic diversity within C. vulgaris populations,
which is also reflected in high morphological variation. This
study is in agreement with previous reports finding very
high levels of genetic diversity between Chara populations
of a single taxon. Allozyme studies by Grant and Proctor
(1980) and molecular marker studies by Mannschreck et al.
(2002) and O’Reilly et al. (2007) found both high inter- and
intraspecific genetic diversity in Chara.
Mannschreck et al. (2002) reported 99% AFLP band polymorphism among Chara species, and 91% variation between
populations of a single taxon. Genetic variation in Chara
populations may result from gene duplication via polyploidy,
as presumed in Grant & Proctor (1980). Polyploidy is only
widespread amongst monoecious species of Chara (Proctor
1976), such as C. vulgaris. Reported chromosome counts for
C. vulgaris are n = 14, 18, 28, 42 (Sato 1959; Guerlesquin
Genetic and morphological diversity in C. vulgaris L.
Figure 6. PCoA plot of morphological characters in C. vulgaris populations. Populations Razavi Khorasan, South Khorasan, Yazd, Kerman,
Isfahan, Mazandaran, Gilan, Tehran, Golestan, Khuzestan, and Semnan, are marked with numbers 1-11, respectively.
1966, 1967; Mirasidov 1971; Grant and Proctor 1972; Khatun
et al. 2009).
The present study showed genetic divergence of the
studied C. vulgaris populations, but did not show their morphological divergence. A Mantel test showed no significant
correlation between the genetic data and the morphological
data, supporting the hypothesis that phenotypic variability in
Chara L. is either some extent environmentally induced or
represents developmental stages. Absence of association between the genetic data and the morphological data within and
between the populations of C. curta and C. aspera was also
observed by O’Reilly et al. (2007). They suggest that genetic
variation in Chara populations may result from polyploidy.
Variation in ISSR bands results in sequence changes due to
either insertion/deletion or sequence rearrangements (Sheidai
et al. 2012, 2013; Noormohammadi et al. 2012). It seems that
gene flow/presence of ancestral alleles in the studied C. vulgaris populations resulted in both genetic and morphological
overlap/similarities among them and we cannot completely
differentiate these populations from each other. Studies of
putative phenotypic plasticity in other algae have shown that
morphological variation may be at least partly genetically
and partly environmentally controlled (Guiry 1992). Very
few experimental investigations of phenotypic plasticity
or developmental differentiation in the Charales have been
published, despite plasticity having long been hypothesized
for this group (Willdenow 1805; Wood and Imahori 1965;
Proctor 1975). Therefore, in spite of significant genetic difference among the studied populations we do not attempt to
consider them as separate ecotypes or varieties that are known
to exist in C. vulgaris.
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