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Biochemical Genetics ISSN 0006-2928 Biochem GenetDOI 10.1007/s10528-011-9451-7
Genetic Diversity and Differentiationin Hedychium spicatum, a ValuableMedicinal Plant of Indian Himalaya
Arun Jugran, Indra D. Bhatt, SandeepRawat, Lalit Giri, Ranbeer S. Rawal &Uppeandra Dhar
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Genetic Diversity and Differentiation in Hedychiumspicatum, a Valuable Medicinal Plant of IndianHimalaya
Arun Jugran • Indra D. Bhatt • Sandeep Rawat •
Lalit Giri • Ranbeer S. Rawal • Uppeandra Dhar
Received: 23 February 2010 / Accepted: 12 May 2011
� Springer Science+Business Media, LLC 2011
Abstract Hedychium spicatum, a perennial rhizomatous medicinal plant distributed
in subtropical and temperate parts, is considered nearly endemic to the Himalayan
region.In this study allozyme markers were utilized to assess genetic variations and
relationships among 12 distinct populations of this species from the West Himalaya of
India. A high level of genetic diversity was found among the populations. Of the 13
loci generated using eight enzymes, 12 (92%) were polymorphic. F-statistics sug-
gested a prevalence of a high heterozygote excess among populations (FIS = –0.51).
Gene flow estimates and geographic distances between populations did not correlate
significantly (r = –0.0258, P \ 0.3550). The excess heterozygosity may be attrib-
uted to high pollinator mobility and inbreeding coefficient within the subpopulation,
relative to the total FIS value. High frequencies of several alleles at different loci
probably reflect the breeding pattern, as the species propagates clonally as well as
through seeds. We also discuss conservation implications for the target species.
Keywords Allozyme � Conservation � Genetic diversity � Threatened � Vegetative
propagation
Introduction
Studies of genetic variability are gaining momentum toward setting priorities for
conservation, particularly for successful reintroduction in the wild. Genetic
variation, which represents evolutionary potential (Wright 1978), is most often
A. Jugran � I. D. Bhatt (&) � S. Rawat � L. Giri � R. S. Rawal
G. B. Pant Institute of Himalayan Environment and Development, Kosi-Katarmal, Almora,
Uttarakhand 263643, India
e-mail: [email protected]; [email protected]
U. Dhar
Department of Botany, Hamdard University (Jamia Hamdard), New Delhi 110 062, India
123
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DOI 10.1007/s10528-011-9451-7
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correlated with the geographic distribution of plant populations (Hamrick and Godt
1996). Such studies have become particularly important in plant species with low
population size and genetic drift, which are exposed to the effects of inbreeding
(Frankham 1995).
Isozyme markers are extensively used in diverse aspects of plant studies, including,
among others, systematics (Gottlieb 1977), population genetics (Wright 1951;
Hamrick and Godt 1989), characterization of breeding systems (Sun 1997), and
identification of clones (Rajora 1988). Studies on genetic diversity of endangered
species using molecular polymorphisms have gained momentum in recent years
(Bouza et al. 2002), especially considering their importance in planning in situ and
ex situ conservation strategies (Sosa 2001). The loss of genetic variability due to
reduction in the population size of predominantly out-crossing plants has been
reported to lead to both inbreeding depression and decreased fitness (Francisco-Ortega
et al. 2000). In particular, this could reduce the ability of endemic species to compete
with alien species while coping with various environmental stresses (Ellstrand and
Elam 1993). In spite of their proven importance, information on genetic variation and
population genetic structure are not available for most of the Himalayan plant species.
Hedychium spicatum (Zingiberaceae), commonly known as kapoorkachari or
vanhaldi, is a perennial rhizomatous herb that grows in subtropical and temperate
Himalayan regions up to 2800 m. The species is considered locally threatened and
nearly endemic to the Himalayan region (Samant et al. 1998). The rhizome is
stomachic, carminative, a bronchodilator, a stimulant and tonic, and traditionally
used in dyspepsia, nausea, vomiting, and liver complaints (Chopra et al. 1986). The
essential oil is reported to have antimicrobial and antioxidant activities (Bisht et al.
2006; Joshi et al. 2008). Realizing the general gap of information at the genetic level
and in view of the ecological and economic importance of this species, the present
study attempts to determine genetic variation within and among populations and to
assess the genetic structure of H. spicatum.
Materials and Methods
Plant Material
Mature seeds were collected from 12 distantly located populations of H. spicatum in
Uttarakhand state (West Himalaya) of India (Table 1). Collected seeds were brought
into the laboratory and dried at room temperature. Dried seeds were kept in paper bags
before experimentation. Ten seeds of each population were placed for germination in
petri plates (Borosil India Ltd.) containing Whatman No. 1 filter paper. Seedlings with
two fully expanded leaves from each population were used for analysis of the allozyme
variability following the standard procedure (Soltis et al. 1983).
Allozyme Electrophoresis
Leaf samples were ground using a chilled mortar and pestle with freshly prepared
extraction buffer consisting of 0.05 M Tris–HCl (pH 7.5), 0.3 M sucrose, ascorbic
Biochem Genet
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acid 25 mM, and 5–8% polyvinylpyrrolidone (PVP). The samples were transferred
to 1.5 ml microcentrifuge tubes, and bromophenol blue dye (0.01% w/v) was added.
The samples were vortexed for 2 min and centrifuged at 7500 rpm for 12 min at
–4�C (REMI C-30). The supernatant (50 ll) was loaded in the well for native
polyacrylamide gel electrophoresis (PAGE).
Nine enzymes were assayed: glutamate dehydrogenase (GDH, EC 1.4.1.3),
alcohol dehydrogenase (ADH, 1.1.1.1), superoxide dismutase (SOD, EC 1.15.1.1),
polyphenol oxidase (PPO, EC. 1.14.18.1), acid phosphatase (ACP, EC 3.1.1.2),
peroxidase (PRX, EC 1.11.1.7), malate dehydrogenase (MDH, EC 1.1.1.37),
esterase (EST, EC 3.1.1.72), and glutamate transaminase (GOT EC.2.6.1.1). The
assay mixture was acrylamide:bisacrylamide, ammonium persulphate, Tris–HCl
(pH 8.8), Tris–HCl (pH 6.8), TEMED, and distilled water. The enzymes were
separated using a vertical slab gel electrophoresis unit (ATTO Corp., Japan, Model
Ae-8400). After electrophoresis, gels were rinsed with distilled water and stained
with a specific staining procedure according to Wendel and Weeden (1989) with
slight modifications. Since GOT did not show a clear banding pattern, only eight
enzymes were considered for further analysis. Genetic interpretation of band
patterns and nomenclature of loci were according to Wendel and Weeden (1989).
Data Analysis
PopGene version 1.31 (Yeh et al. 1999) was used to calculate allele frequency,
percentage of polymorphic loci, mean number of alleles per polymorphic locus, and
the effective number of alleles. Observed heterozygosity (HO) and expected
heterozygosity (HE) were calculated for assessing genetic diversity. Loci were
considered polymorphic if more than one allele was detected. Wright fixation
indices (F), or inbreeding coefficient, for each population was calculated as F = 1
– HO/HE to determine deviation from random mating expectations. The partitioning
Table 1 Characteristics of 12 populations of Hedychium spicatum sampled in the west Himalaya of
India
Code Population Altitude (m) Latitude (�N) Longitude (�E) Habitat Aspect
H1 Mukteswar 1700 29� 280 590 0 79� 390 000 0 Oak forest Northwest
H2 Sitlakhet 1760 29� 350 410 0 79� 320 420 0 Mixed forest Southwest
H3 Kalika 1785 29� 470 510 0 79� 540 220 0 Pine forest East
H4 Choubatiya 1935 29� 360 560 0 79� 360 560 0 Pine forest West
H5 Shyamkhet 1975 29� 230 000 0 79� 320 300 0 Mixed forest Southwest
H6 Ramgarh 2185 29� 240 460 0 79� 320 570 0 Pine forest West
H7 Gaggarh 2215 29� 240 570 0 79� 320 330 0 Oak forest North
H8 Chinapeak 2225 29� 240 100 0 79� 260 270 0 Oak forest Southeast
H9 Kedarnath 2250 30� 310 280 0 79� 300 060 0 Mixed forest Southeast
H10 Kilburry 2310 29� 250 210 0 79� 260 120 0 Oak forest Northeast
H11 Pandukholi 2425 29� 500 240 0 79� 280 040 0 Oak forest North
H12 Thakurh 2450 29� 260 160 0 79� 300 170 0 Oak forest Northeast
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of genetic diversity among all the populations was analyzed using F-statistics (Weir
1990). Nei’s genetic differentiation (Nei 1972, 1978) between pairs of populations
was summarized in a UPGMA (unweighted pair group method with arithematic
mean) phenogram (Swofford and Olsen 1990) using TFPGA software (Tools for
Population Genetics Analysis) version 1.3 (Miller 1997). The amount of gene flow
was estimated using Wright’s formula Nm = (1 – FST)/4FST (Wright 1951; Nei
1987) and also from GST. Pairwise geographic distance was calculated by latitude
and longitude data using GeneAlEx (Peakall and Smouse 2006). Also, the
relationship between gene flow among population pairs (M) and geographic distance
(d) was examined. Population subdivision index (FST) values among all population
pairs were estimated using all loci. Based on these FST values, all the pairwise
effective rates of migration were calculated. A linear regression between log10M and
log10d was performed to determine a linear relationship between both variables. To
evaluate significance, a Mantel test (Mantel 1967) at 1000 permutations and reduced
major axis (RMA) regression (Sokal and Rohlf 1981) were conducted with the
software IBD (Bohonak 2002). A principal component analysis (PCA) of allele
frequencies was performed using Statistical version 5 (StatSoft Inc. 1985).
Results
Allele Frequency
Across the populations, a high level of genetic diversity was detected. Of 13 loci
detected in eight isozyme systems, all except MDH revealed polymorphism (12 loci,
92%). Remarkable variations were revealed in these 12 loci (average frequency of
the most common allele below 0.90) and especially at ADH-1 (average frequency of
the most common allele below 0.70). For the entire gene pool, the chi-square test
suggested that only ADH-1 (v2 = 16.931; P \ 0.001), ADH-2 (v2 = 22.897;
P \ 0.001), and SOD (v2 = 32.069; P \ 0.001) were in Hardy–Weinberg equilib-
rium. Two loci were identified for ADH, GDH, and ACP; three others (SOD, MDH,
EST) were coded by only one allele. One enzyme system (PRX) was characterized
by the presence of three alleles. The percentage of polymorphic loci was over 70%,
and the number of alleles at each polymorphic locus ranged from 2 to 2.14 (total of
27 alleles identified). Most of these alleles were well spread across the populations.
Of the total, 25 alleles were common to all populations; however, two alleles (ACP-
1C and ACP-ID) were unique to the H6, H11, H1, H2, and H8 populations
(Table 2).
Genetic Diversity
The average number of alleles per locus ranged from 1.62 to 1.85 in different
populations (mean = 1.76). The mean expected heterozygosity was 0.372, and the
percentage of polymorphic loci (0.95 criterion) was 73% (Table 3). Populations H3
and H4 showed the highest percentage of polymorphic loci (85%); the H2
population was the lowest (54%). H2 also exhibited the lowest effective number of
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Tab
le2
All
ele
freq
uen
cies
for
all
Hed
ych
ium
spic
atu
mp
op
ula
tio
ns
sam
ple
d
Po
p.
cod
eA
llel
eL
ocu
s
AD
H-1
AD
H-2
GD
H-1
GD
H-2
PP
OP
RX
-1P
RX
-2P
RX
-3A
CP
-1A
CP
-2E
ST
SO
DM
DH
H1
A0
.33
30
.500
0.5
00
0.5
00
0.6
67
0.5
00
0.5
00
1.0
00
0.6
67
0.8
33
1.0
00
0.5
00
1.0
00
B0
.66
70
.500
0.5
00
0.5
00
0.3
33
0.5
00
0.5
00
0.1
67
0.1
67
0.5
00
D0
.167
H2
A1
.00
01
.000
1.0
00
0.6
67
0.8
33
1.0
00
0.5
00
1.0
00
0.5
00
0.6
67
0.5
00
0.8
33
1.0
00
B0
.333
0.1
67
0.5
00
0.3
33
0.3
33
0.5
00
0.1
67
D0
.167
H3
A1
.00
00
.333
0.8
33
0.6
67
0.6
67
0.6
67
0.6
67
0.5
00
0.5
00
0.5
00
0.8
33
0.8
33
1.0
00
B0
.667
0.1
67
0.3
33
0.3
33
0.3
33
0.3
33
0.5
00
0.5
00
0.5
00
0.1
67
0.1
67
H4
A1
.00
00
.333
0.8
33
0.5
00
0.8
33
0.5
00
0.8
33
0.8
33
0.5
00
0.8
33
0.8
33
0.8
33
1.0
00
B0
.667
0.1
67
0.5
00
0.1
67
0.5
00
0.1
67
0.1
67
0.5
00
0.1
67
0.1
67
0.1
67
H5
A1
.00
00
.333
1.0
00
0.6
67
0.6
67
0.5
00
0.8
33
0.8
33
0.5
00
0.8
33
0.6
67
0.5
00
1.0
00
B0
.667
0.3
33
0.3
33
0.5
00
0.1
67
0.1
67
0.5
00
0.1
67
0.3
33
0.5
00
H6
A1
.00
00
0.5
00
0.8
33
0.6
67
0.6
67
0.6
67
0.6
67
0.8
33
0.1
67
0.6
67
0.5
00
1.0
00
1.0
00
B0
.500
0.1
67
0.3
33
0.3
33
0.3
33
0.3
33
0.3
33
0.6
67
0.3
33
0.5
00
C0
.167
H7
A1
.00
00
.667
1.0
00
0.6
67
0.6
67
0.6
67
0.6
67
1.0
00
0.3
33
0.8
33
0.3
33
0.5
00
1.0
00
B0
.333
0.3
33
0.3
33
0.3
33
0.3
33
0.6
67
0.1
67
0.6
67
0.5
00
H8
A1
.00
00
.500
0.6
67
0.6
67
0.6
67
0.5
00
0.5
00
1.0
00
0.5
00
0.6
67
0.6
67
0.5
00
1.0
00
B0
.500
0.3
33
0.3
33
0.3
33
0.5
00
0.5
00
0.3
33
0.3
33
0.3
33
0.5
00
D0
.167
H9
A1
.00
00
0.5
00
1.0
00
0.5
00
0.8
33
1.0
00
0.5
00
0.8
33
0.5
00
0.8
33
0.6
67
0.5
00
1.0
00
B0
.500
0.5
00
0.1
67
0.5
00
0.1
67
0.5
00
0.1
67
0.3
33
0.5
00
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Tab
le2
con
tin
ued
Po
p.
cod
eA
llel
eL
ocu
s
AD
H-1
AD
H-2
GD
H-1
GD
H-2
PP
OP
RX
-1P
RX
-2P
RX
-3A
CP
-1A
CP
-2E
ST
SO
DM
DH
H1
0A
1.0
00
0.5
00
1.0
00
0.6
66
0.6
67
0.5
00
0.6
67
1.0
00
0.8
33
0.6
67
0.6
67
0.5
00
1.0
00
B0
.500
0.3
33
0.3
33
0.5
00
0.3
33
0.1
67
0.3
33
0.3
33
0.5
00
H1
1A
1.0
00
0.8
33
1.0
00
1.0
00
0.6
67
0.6
67
0.6
67
1.0
00
0.1
67
0.5
00
0.5
00
0.6
67
1.0
00
B0
.167
0.3
33
0.3
33
0.3
33
0.6
67
0.5
00
0.5
00
0.3
33
C0
.167
H1
2A
0.6
67
0.5
00
1.0
00
0.5
00
0.8
33
0.8
33
0.8
33
0.8
33
0.6
67
1.0
00
0.8
33
0.5
00
1.0
00
B0
.33
30
.500
0.5
00
0.1
67
0.1
67
0.1
67
0.1
67
0.3
33
0.1
67
0.5
00
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alleles (1.46). The range of mean number of polymorphic alleles per polymorphic
loci was 2.0–2.14. The H8 population had the maximum genetic diversity among
populations, followed by H1 [ H3 [ H6 [ H5 [ H10 [ H4 [ H7 [ H12 [H9 [ H11 [ H2. No conclusive trend was revealed regarding the relationship
between altitude and genetic diversity; however, across habitat conditions (i.e.,
forest types), the populations associated with Pinus roxburghii (Chir-Pine) forest
showed higher genetic diversity than those under Quercus leucotricophora (Banj-
Oak) and mixed forests (Table 4).
Table 3 Genetic variation among populations based on 12 allozyme loci in Hedychium spicatum
Pop. code Polymorphic loci Number of alleles Heterozygosity Inbreeding
coefficient FN % Effectivea Mean Apb Expected Observed
H1 10 76.92 1.69 1.85 2.1 0.43 0.59 -0.36
H2 7 53.85 1.46 1.62 2.14 0.28 0.38 -0.36
H3 11 84.62 1.63 1.85 2 0.42 0.46 -0.09
H4 11 84.62 1.50 1.85 2 0.35 0.46 -0.28
H5 10 76.92 1.57 1.77 2 0.38 0.41 -0.08
H6 10 76.92 1.60 1.85 2.1 0.39 0.46 -0.17
H7 9 69.23 1.54 1.69 2 0.35 0.46 -0.28
H8 10 76.92 1.74 1.85 2.1 0.44 0.61 -0.37
H9 9 69.23 1.54 1.69 2 0.34 0.46 -0.32
H10 9 69.23 1.57 1.69 2 0.36 0.51 -0.39
H11 8 61.54 1.51 1.69 2.125 0.32 0.38 -0.17
H12 10 76.92 1.50 1.77 2 0.34 0.41 -0.17
Average 9.41 73.08 1.57 1.76 2.05 0.37 0.46
a Kimura and Crow (1964)b Mean number of polymorphic alleles per polymorphic locus
Table 4 Genetic variation among habitat conditions based on 12 allozyme loci in Hedychium spicatum
Habitat
type
Number of
populations
Polymorphic
loci
Number of alleles Heterozygosity Inbreeding
coefficient
FN % Effectivea Mean Apb Expected Observed
Pine
forest
3 10.67 82.05 1.57 1.85 2.03 0.39 0.46 -0.18
Oak
forest
6 9.33 71.79 1.59 1.75 2.05 0.38 0.49 -0.29
Mixed
forest
3 8.67 66.33 1.52 1.69 2.04 0.33 0.41 -0.25
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Genetic Structure and Isolation by Distance
F-statistics revealed varying fixation indices among loci (Table 5). The estimates of
FIS exhibited excess heterozygotes in all the loci except ADH-1. Genetic
differentiation among populations, as measured by FST, showed only 13.5% of
the total genetic variation because of the differences among populations; the
remaining 86.5% of the variation resided within populations. The range of the
coefficient of the hierarchical FST value was 0.031–0.515, with an average of 0.135.
The indirect estimates of gene flow (Nm) observed in all loci averaged 1.599, with a
range of 0.235 (ADH-1) to 7.875 (PPO). According to the Mantel test, the
relationship between the pairwise effective rate of migration and the geographic
distance matrices among population pairs was not significant (r = –0.0258,
P \ 0.355).
Genetic Relationship
The analysis revealed low genetic proximity among populations (Fig. 1). Popula-
tions H8 and H10 showed the lowest genetic distance (0.0192); the distance was
highest between H1 and H11 (0.1368). The UPGMA dendrogram produced from
Nei’s genetic distance shows two groups (A and B), and group B contains four
subgroups (Fig. 1). To some extent, this grouping provides insights into possible
correlations with the geographic range of the populations. Likewise, the results of
PCA are comparable to the cluster analysis, accounting for 84% of the total variance
(Fig. 2). In the multivariate space defined by PCA, the H4, H8, and H10 populations
are much closer to H5, H9, and H12. Populations H1 and H2 appear to be distinct
from the others in the PCA as well as in the UPGMA analysis.
Table 5 F-Statistics at 12 loci
for 12 populations of Hedychiumspicatum
Note: FIS, coefficient of
inbreeding of an individual
relative to the population; FIT,
coefficient of inbreeding of an
individual relative to the total
species; FST, population
subdivision index; Nm, gene
flow estimated from FST =
(1–FST)/4FST (Wright 1951; Nei
1987)
Locus FIS FIT FST Nm
ADH-1 1.000 1.000 0.515 0.235
ADH-2 -0.780 -0.511 0.152 1.400
GDH-1 -0.500 -0.125 0.250 0.750
GDH-2 -0.696 -0.565 0.077 3.000
PPO -0.429 -0.385 0.031 7.875
PRX-1 -0.610 -0.375 0.146 1.464
PRX-2 -0.648 -0.532 0.071 3.289
PRX-3 -0.059 0.156 0.203 0.981
ACP-1 -0.359 -0.183 0.129 1.681
ACP-2 -0.040 0.071 0.106 2.103
EST -0.610 -0.375 0.146 1.464
SOD -0.814 -0.565 0.137 1.573
Mean -0.510 -0.305 0.135 1.599
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Discussion
Compared with other temperate zone species, H. spicatum populations in the west
Himalaya (India) maintained higher levels of genetic diversity (Table 6). A high
level of genetic diversity is usually attributed to a wide range of geographic
distribution (Hamrick and Godt 1989). This may hold true for H. spicatum, which
covers an extensive horizontal (east to west Himalaya) range of distribution in the
region (Samant and Pant 2006), accompanied by a wide altitudinal amplitude
(1200–2800 m). The breeding system of a species is also an important determinant
of variability (Hamrick and Godt 1989). The high allozyme variation of H. spicatumcan also be attributed to its out-crossing (insect-pollinated) mechanism. Hedychiumspicatum reproduces through both sexual and asexual means. In this case, while the
sexual reproduction could lead to enhancement of genetic variation, the asexual
reproduction would help in maintaining a lack of genetic variation. Such a
combination is well known to be associated with a high level of allozyme variation
(Huh et al. 1998).
Chinapeak
Kilburry
Thakurh
Shyamkhet
Kedarnath
Choubatiya
Kalika
Ramgarh
Gaggarh
Pandukholi1
Sitlakhet
Mukteswar
B
A
Fig. 1 UPGMA tree of 12 Hedychium spicatum populations based on Nei’s (1972) original distance.The populations are identified in Table 1
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On the whole, none of the H. spicatum populations conformed to Hardy–
Weinberg expectations. The excess heterozygosity of the populations (except H2)
may possibly be the consequence of random stochastic events or a result of
balancing selection, which often promotes high heterozygosity (Eguiarte et al.
1992). Conclusive data are not available to discard either possibility, but it is highly
improbable that most H. spicatum populations have an excess of heterozygotes by
drift alone. Population H2 showed a heterozygosity deficit, which suggests an
inbreeding level or a selection against some heterozygote genotypes, possibly due to
ecological changes. A possible explanation is that natural selection might have
favored heterozygotes that could cope with environmental changes in a highly
fragmented population, while inbreeding might have resulted in excess homozy-
gotes. Small heterozygosity deficits in out-breeding species are often a consequence
of biparental inbreeding, especially in small populations or those exhibiting spatial
genetic structure (Sampson et al. 1988). Low FIS values indicate high pollinator
Fig. 2 Principal component analysis based on the correlation matrix of the allele frequencies of 12Hedychium spicatum populations. Population codes (H1–H12) as in Table 1
Table 6 Comparison of genetic diversity of Hedychium spicatum and other plant species
Species Genetic
diversity
(HE)
Polymorphic
loci (%)
Mean
alleles
per locus
Source
Temperate zone species 0.146 48.5 1.91 Hamrick et al. (1992),
Hamrick and Godt
(1989)Short-lived perennial
species
0.116 39.3 1.7
Widespread geographic
range
0.205 58.9 2.29
H. spicatum 0.372 73.08 1.76 Present study
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mobility and inbreeding coefficient within the subpopulation relative to the total FIS
value. This is the proportion of the total genetic diversity (heterozygosity) that
separates the populations. The overall genetic divergence among H. spicatumpopulations was moderate, with only 13.5% of the total diversity attributable to
interpopulation variation. The loci with higher FST among populations are ADH-1,
GDH-1, and PRX-3. The estimate of gene flow based on the mean FST value
suggests that substantial genetic exchange between natural populations is sufficient
to counteract genetic drift (Slatkin 1987). The overall gene flow among populations
was calculated as 1.60, which gives an estimate of the average number of migrants
among all the studied populations per generation. Although gene flow estimates
from FST are subject to the influence of various factors, including selection, drift,
and mutation, the high gene flow in this species could probably be attributed to seed
dispersal via wind and insects.
The gene flow among populations and the geographic distance did not show a
pattern of isolation by distance. This indicates that populations are at disequilibrium
between drift and migration, but other processes may lead to apparent isolation by
distance (Slatkin 1993). The dispersal mechanism needs to be investigated in detail
to confirm the estimates of present gene flow, which might reflect effective
dispersal. The ecological processes occurring in the Himalayan region might
increase the genetic differentiation among populations, which may obviously
disrupt the pattern of isolation by distance. Cluster and PCA revealed that
populations from comparable areas were more genetically similar than populations
from distant geographic areas. Population H1 in particular appears to be highly
distinct from other populations. This might be because of the presence of private
alleles in the population. The relationships between the matrices of geographic
distance and pairwise gene flow indicate that isolation by distance does not play a
role in the population divergence of the migratory locus. Similarly, the increase in
pairwise FST/1–FST values with geographic distance does not match with the
isolation-by-distance model by Mantel test. Higher genetic diversity in the samples
collected from the Chir-Pine forest compared with the Banj-Oak and mixed forest
samples indicates that habitat plays a significant role in maintaining genetic
diversity of H. spicatum. This requires, however, further intensive studies on the
target species covering diverse habitat conditions.
Conservation Implications
Genetic diversity studies provide an understanding of long-term survivability and
continued evolution of a population or species, which can have significant
conservation implications (Frankham et al. 2002). In this context, the information
gained on the level and distribution of allozyme variation in H. spicatum might help
in suggesting appropriate management strategies for the target taxa. The high level
of genetic diversity present in H. spicatum would be helpful in in situ conservation.
The majority of genetic variability in H. spicatum resides within populations;
therefore, interpopulation genetic exchange will be beneficial to reduce fragmen-
tation and prevent the loss of genetic variability. This is an important consideration
for defining in situ conservation strategies. Although we do not have extensive
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coverage of habitat occurrence in the region, it can be suggested that the propagules
need to be collected from pine (Pinus roxburghii) forests while attempting ex situ
conservation of the target species.
Acknowledgments The authors thank Dr. L. M. S. Palni, Director, GBPIHED, for providing the
facilities and encouragement. Thanks are also due to Prof. Pedro Garcia, Universidad de Leon, Spain for
the valuable input during experimental design and critical analysis of the data. UD thanks the National
Academy of Sciences, India, and Hamdard University, New Delhi, for their support. Financial support
from GBPIHED under Project 10 is gratefully acknowledged.
References
Bisht GS, Awasthi AK, Dhole TN (2006) Antimicrobial activity of Hedychium spicatum. Fitoter
77:240–242
Bohonak AJ (2002) Antimicrobial activity of Hedychium spicatum. J Hered 93:153–154
Bouza N, Caujape CJ, Gonza lez-Perez MA, Batista F, Sosa PA (2002) Population structure and genetic
diversity of two endangered endemic species of the Canarian laurel forest: Dorycnium spectabile(Fabaceae) and Isoplexis chalcantha (Scrophulariaceae). Int J Plant Sci 163:619–630
Chopra RN, Nayar SL, Chopra LC (1986) Glossary of Indian medicinal plants. CSIR, New Delhi,
pp 130–131
Eguiarte LE, Perez-Nasser N, Pinero D (1992) Genetic structure, out-crossing rate and heterosis in
Astrocaryum mexicanum (tropical palm): implications for evolution and conservation. Heredity
69:217–228
Ellstrand NC, Elam DR (1993) Population genetic consequences of small population size: implications
for plant conservation. Annu Rev Eco Syst 24:217–242
Francisco-Ortega J, Santos-Guerra A, Kim SC, Crawford DJ (2000) Plant genetic diversity in the Canary
Islands: a conservation perspective. Am J Bot 87:909–919
Frankham R (1995) Conservation genetics. Ann Rev Genet 29:305–327
Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conservation genetics. Cambridge University
Press, Cambridge
Gottlieb LD (1977) Electrophoretic evidence and plant systematics. Ann Mo Bot Gard
Hamrick JL, Godt MJW (1989) Allozyme diversity in plant species. In: Brown AHD, Clegg MT, Kahler
AL, Weir BS (eds) Plant population genetics, breeding and genetic resources. Sinauer Associates,
Sunderland, pp 43–63
Hamrick JL, Godt MJW (1996) Effect of life history traits on genetic diversity in plant species. Philos
Trans Roy Soc Lond B (Biol Sci) 351:1291–1298
Hamrick JL, Godt MJW, Sherman-Broyles SL (1992) Factors influencing levels of genetic diversity in
woody plant species. New For 6:95–124
Huh MK, Chung SD, Huh HW (1998) Allozyme variation and population structure of Pyrola japonica in
Korea. Bot Bull Acad Sin 39:107–112
Joshi S, Chanotiya CS, Agarwal G, Prakesh O, Pant AK, Methela CS (2008) Terpenoide compositions
and antioxidant and antimicrobial properties of the rhizome essential oil of different Hedychiumspecies. Chem Biodivers 5:299–309
Kimura M, Crow JF (1964) The number of alleles that can be maintained in a finite population. Genetics
49:725–738
Mantel NA (1967) The detection of disease clustering and a generalized regression approach. Cancer Res
27:209–220
Miller MP (1997) Tools for population genetic analyses (TFPGA) 1.3: a Windows program for the
analysis of allozyme and molecular population genetic data. Distributed by author
Nei M (1972) Genetic distance between populations. Am Nat 106:283–292
Nei M (1978) Estimation of average heterozygosity and distance from a small number of individuals.
Genetics 89:583–590
Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New York
Peakall R, Smouse PE (2006) Genalex 6: genetic analysis in excel. Population genetic software for
teaching and research. Mol Ecol Notes 6:288–295
Biochem Genet
123
Author's personal copy
Rajora OP (1988) Allozymes for identification and differentiation of some Populus maximowiczii Henry
clonal varieties. Biochem Syst Evol 16:635–640
Samant SS, Pant S (2006) Diversity, distribution pattern and conservation status of the plants used in liver
disease ailments in Indian Himalayan region. J Mt Sci 3:28–47
Samant SS, Dhar U, Palni LMS (1998) Medicinal plants of Indian Himalaya: diversity, distribution
potential values. Himavikas, Gyanodaya Prakashan, Nainital, publ. 13
Sampson JF, Hopper SD, Jones SH (1988) Genetic diversity and the conservation of Eucalyptus crucismaiden. Aust J Bot 36:447–460
Slatkin M (1987) Gene flow and the geographic structure of natural populations. Science 236:787–792
Slatkin M (1993) Isolation by distance in equilibrium and nonequilibrium populations. Evolution
47:264–279
Sokal RR, Rohlf FJ (1981) Biometry, 2nd edn. Freeman, New York
Soltis DE, Haufler CH, Darrow DC, Gastony GJ (1983) Starch gel electrophoresis of ferns: a compilation
of grinding buffers, gel and electrode buffer, and staining schedules. Am Fern J 73:9–27
Sosa PA (2001) Genes, poblaciones y especies. In: Martin-Esquivel J, Fernandez-Palacios JM (eds)
Naturaleza de las Islas Canarias. Santa Cruz de Tenerife, Editorial Turquesa, Canary Islands,
pp 151–155
StatSoft Inc (1985) Statistica for Windows, release 6.0. StatSoft Inc., Tulsa, OK
Sun M (1997) Genetic diversity in three colonizing orchards with contrasting mating systems. Am J Bot
84:224–232
Swofford DL, Olsen GJ (1990) Phylogeny reconstruction. In: Hillis DM, Moritz C (eds) Molecular
systematics. Sinaur Associates, Sunderland
Weir BS (1990) Genetic data analysis: methods for discrete population analysis. Sinauer Associates,
Sunderland
Wendel JF, Weeden NF (1989) Visualization and interpretation of plant isozymes. In: Soltis DE, Soltis
PS (eds) Isozymes in plant biology. Dioscorides Press, Portland, pp 5–45
Wright S (1951) The genetical structure of populations. Ann Eugen 15:323–354
Wright S (1978) Evolution and the genetics of populations, vol. 4: variability within and among natural
populations. University of Chicago Press, Chicago
Yeh FC, Yang RC, Boyle TBJ, Ye ZH, Mao JX (1999) PopGene version 1.32. The user-friendly
shareware for population genetic analysis. Molecular Biology and Biotechnology Centre, University
of Alberta, Canada
Biochem Genet
123
Author's personal copy
