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Botanical Journal of the Linnean Society, 2012, 170, 40–49. With 2 figures
How genetically variable are Neottia ovata
(Orchidaceae) populations in northeast Poland?
EMILIA BRZOSKO* and ADA WRÓBLEWSKA
Institute of Biology, University of Białystok, Świerkowa 20B, 15-950 Białystok, Poland
Received 17 November 2011; revised 7 March 2012; accepted for publication 27 April 2012
Using 32 allozyme loci, we examined the genetic diversity of ten populations of Neottia ovata differing in size
and located in different regions of northeast Poland. Within-population genetic variation was low (Ppl = 9.4–31.2%,
A = 1.09–1.34 and HE = 0.044–0.128) relative to taxa with similar life histories. In the majority of N. ovata
populations (excluding those in Biebrza), we noted high genotypic diversity (0.49-1.00) and high proportions of
unique genotypes (86–100%). Neottia ovata shows a strong pattern of genetic differentiation among populations in
northeast Poland, reflected in the high overall FST value (0.409). There was a weak, but significant, relationship
between genetic and geographical distance (r = 0.09, P < 0.05). We discuss the breeding system, small population
sizes and population subdivision as the most important factors affecting the genetic diversity of this species. © 2012
The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
ADDITIONAL KEYWORDS: allozymes – genotypic diversity – small populations.
INTRODUCTION
The genetic diversity of plants can be distributed in
space in different ways. In some species, it is structured both across and within geographically distinct
populations; in others, the pattern is more homogeneous (Loveless & Hamrick, 1984). In some cases,
relationships between genetic and geographical distance have been documented; in others, no such correlation has been noted. Geographically restricted
species usually have lower levels of genetic variation
within populations (Karron, 1987; Hamrick & Godt,
1989; Frankham, 1997).
A knowledge of the spatial distribution of the
genetic resources of species is needed for a proper
consideration of phylogeographical patterns and their
future evolutionary consequences, and for conservation planning. Many genetic diversity estimates are
restricted to a small geographical scale, reporting
fragments of the genetic potential of a given species.
For example, in earlier work, we documented low
genetic variation in a study of only two Neottia ovata
*Corresponding author. E-mail: [email protected].
40
Bluff & Fingerh. populations from the Biebrza
National Park (northeast Poland) (Brzosko &
Wróblewska, 2003). These populations were genetically similar, despite having different habitats and
different ratios of sexual to asexual reproduction. Our
analysis of the causes of low genetic diversity in
N. ovata indicated the importance of the small population size in that case. The relationship between
population size and genetic variability parameters
has been documented in many studies (Frankel &
Soulé, 1981; Honney et al., 2006; Leimu et al., 2006;
Brzosko et al., 2011). Two consequences of a small
population size have been highlighted: increased
genetic drift and inbreeding (Ellstrand & Ellam,
1993; Whitlock, 2000; Tremblay & Ackerman, 2001).
Increasing genetic drift reduces heterozygosity within
populations and increases differentiation between
them. However, increased inbreeding (through selfing
or biparental mating) may lower the fitness of individuals and populations, reflected in a lower fecundity
of individuals, lower recruitment and, finally, declining population size. Higher levels of genetic drift
and inbreeding promote genetic erosion at both population and species level. Population size should be
considered in the context of the minimum effective
© 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
GENETIC DIVERSITY OF NEOTTIA OVATA
population size required to retain sufficient allelic
richness, to counteract the effects of genetic drift
and to permit evolutionary change (Frankel, Brown &
Burdon, 1995). A certain minimum population size
is needed to attract the specific pollinators so that
the population can be maintained by seed set (Brys,
Jacquemyn & Hermy, 2008).
Small population size and isolation can result from
habitat fragmentation and, recently, a wide range of
human activities have produced habitat fragmentation
by altering the environment. Natural fragmentation
is a result of the natural heterogeneity of the environments in which populations exist, influencing their
demographic and genetic characteristics. Habitat
fragmentation is considered to be an important factor
leading to decreasing genetic variation within populations and increasing differentiation between them; this
particularly threatens rare and endangered species,
which are usually represented by small and isolated
populations (Loveless & Hamrick, 1984; Gibbs, 2001).
Karron (1987) reported lower genetic variation in
rare and endangered species than in their widespread
congeners; other authors have found genetic variation
to be similar in the two groups (Gitzendanner & Soltis,
2000; Ellis et al., 2006). Such differences in findings
have also been observed in studies of the largest
angiosperm family, Orchidaceae, representing a wide
spectrum of genetic variability (Forrest et al., 2004;
Tremblay et al., 2005). Diversity in this family is
closely connected with the huge variety of life history
traits and, especially, with the variety of floral structures and their adaptations to pollination, producing
many different breeding mechanisms (Scacchi, De
Angelis & Corbo, 1991; Wong & Sun, 1999; Sun &
Wong, 2001; Brzosko & Wróblewska, 2003; Tremblay
et al., 2005; Tałałaj & Brzosko, 2008).
We questioned whether the particularly low genetic
diversity found in N. ovata in the Biebrza region
(Brzosko & Wróblewska, 2003) is characteristic of
other populations of this species. Despite the profusion of new data on the genetic variation of orchid
species, to our knowledge, the literature provides
no such information for this species. We resolved to fill
this gap with a study which forms part of a broader
investigation of the genetic diversity of orchid species,
mainly in northeast Poland. It provides the possibility
to compare the genetic diversity of orchid species with
different life histories. Moreover, many of the populations studied are localized in protected areas. Thus,
a knowledge about their genetic resources should be
helpful for more effective conservation planning. The
aims of this investigation were: (1) to estimate genetic
diversity within and among N. ovata populations from
different regions in northeast Poland; and (2) to investigate the factors influencing genetic diversity in this
species.
41
MATERIAL AND METHODS
PLANT MATERIAL
Neottia ovata is one of the most common European
orchids. Its range covers all of Europe and reaches
Asia. In Poland, this species is rather widespread,
but populations do not usually exceed 100 shoots
(Brzosko, 2002; Vakhrameeva et al., 2009), although
Brys et al. (2008) found much larger ones. It is
a shade-tolerant forest herb (Tamm, 1972), but can
occur in open areas (Vakhrameeva et al., 2009).
According to Hutchings (1989), the average half-life
of this long-lived species is 80 years. The yellow–
green flowers are loosely arranged on the flowering
stalk. The characteristics of N. ovata individuals and
populations can vary greatly depending on habitat
conditions and location within the geographical range
(Brzosko, 2002; Blinova, Willems & van Reenen,
2003; Blinova, 2008; Brys et al., 2008). Brzosko
(2002) recorded 7.7–48.8% of flowering plants in different populations and consecutive years, whereas
Vakhrameeva et al. (2009) found only 2.5% to be generative plants. Although apparently all species of
Neottia Guett. secrete nectar onto the surface of the
labellum, not all species are odoriferous. Van der Pijl
& Dodson (1966) reported that N. ovata is scentless.
Nilsson (1981) and Brys et al. (2008) found that,
although N. ovata is a self-compatible species, spontaneous autogamy is not possible. Many different
insects are known to visit N. ovata flowers, but ichneumonid wasps are the most common and effective
pollinators (Ackerman & Mesler, 1979; Nilsson, 1981;
Brys et al., 2008). Different sources give different
numbers of seeds in the fruits of this species. Willems
& Melser (1998) found, on average, c. 1240 seeds per
capsule, whereas Nazarov (1995) reported from 324 to
1471 (average 825) seeds. Tamm (1972) noted that ‘a
few of the new individuals may originate from seedlings, but according to the observations, the normal
propagation is vegetative’.
STUDY
AREAS AND SAMPLING PROCEDURE
We collected samples from ten N. ovata populations
distributed within the continuous geographical range
(northeast Poland, Fig. 1, Table 1). We also incorporated allozyme data from the Biebrza Valley gathered
in earlier work (Brzosko & Wróblewska, 2003). This
made it possible to compare genetic diversity for
N. ovata across different regions in northeast Poland.
All of the studied populations were within natural,
semi-natural and anthropogenic communities of
national and landscape parks, reserves and protected
areas, including Natura 2000 sites. Not all shoots
that appeared above ground in the year were taken.
Some were damaged as a result of animal pressure
© 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
42
E. BRZOSKO and A. WRÓBLEWSKA
Baltic Sea
Vis
tu
TW
JA
DS JW
SUWA£KI
la
O
RP
de
r
AUGUSTÓW
100 km
a
brz
Bie
OPA
ZAB WK
RB
SK
BIA£YSTOK
Figure 1. Locations of Neottia ovata populations in northeast Poland. See Table 1 for site codes.
Table 1. Characteristics of Neottia ovata populations in northeast Poland
Location
Code
N
NS
Ppl
A
AP
HO
HE
FIS
G
G/NS
GU
GU (%)
Jurowce
Perkuć Reserve
Czarna Wieś
Jaczno Lake 1
Szeszupa Valley
Wodziłki Lake
Jaczno Lake 2
Budzisk Reserve
Oparzelisko**
Zabudnik**
Species level
SK
RP
WK
JA
DS
JW
TW
RB
OPA
ZAB
9
52
24
12
58
107
83
54
153
89
9
49
19
7
47
97
64
36
69
89
15.6
16.1
18.7
15.2
25.8
28.1
31.2
31.2
9.4
9.4
34.4
1.12
1.12
1.19
1.16
1.25
1.28
1.28
1.34
1.09
1.09
1.16
2.0
2.2
2.2
2.2
2.0
1.9
2.0
2.1
2.0
2.0
2.4
0.056
0.040
0.058
0.067
0.057
0.079
0.081
0.111
0.057
0.059
0.069
0.051
0.044
0.064
0.080
0.044
0.075
0.084
0.128
0.058
0.045
0.063
-0.137
0.147*
0.126
0.213†
0.146†
-0.057*
0.186‡/§
0.106‡/¶
0.064
0.094
0.069‡
9
24
17
7
29
53
53
35
9
9
229#
1.00
0.49
0.89
1.00
0.62
0.52
0.81
0.97
0.13
0.10
0.47
9
24
15
6
28
50
50
33
0
0
215#
100
100
88
86
96.5
94.3
94.3
94
0
0
93.8
A, mean number of alleles per locus; AP, mean number of alleles per polymorphic locus; FIS, inbreeding coefficient;
G, number of genotypes; G/NS, clonal diversity; GU, number of unique genotypes; GU (%), percentage of unique genotypes;
HE, expected heterozygosity; HO, observed heterozygosity; N, population size; NS, number of analysed samples; Ppl,
percentage of polymorphic loci (Fischer’s exact test: *P < 0.05, †P < 0.01, ‡P < 0.001; randomization test: §P < 0.05,
¶P < 0.01, #sum of parameters).
**Brzosko & Wróblewska (2003).
© 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
GENETIC DIVERSITY OF NEOTTIA OVATA
and/or drought. The smallest individuals were
also excluded because their leaves were too small
to provide fragments sufficiently large for electrophoretic analyses, and removing the leaves would
probably have killed the plants. From each plant
sampled, one fresh leaf tip (c. 2 cm long) was removed
and placed in a 1.5-mL tube. The samples were then
frozen in liquid nitrogen. In total, 486 N. ovata leaf
samples were analysed.
ALLOZYME
DIVERSITY
Leaf tissue was ground in extraction buffer containing 0.1 M Tris-HCl (pH 7.5), 10 mM KCl, 10 mM
MgCl2.6H2O, 1 mM ethylenediaminetetraacetic acid
(EDTA) (disodium salt), 0.1% Triton X-100 and
28 mM 2-mercaptoethanol. Tissue was homogenized
in water only for extracts prepared for the peroxidase
(PRX) enzyme system (Szweykowski & Odrzykowski,
1990). Enzymes were separated on 11% starch gel
(Sigma-Aldrich, Germany) in two discontinuous
buffer systems: tris–citrate–lithium borate buffer
system, pH 8.3 (SC), and histidine–citrate buffer
system, pH 7.0 (HC) (Szweykowski & Odrzykowski,
1990). The first system (SC) was used for aconitase
(Aco), alcohol dehydrogenase (Adh), aldolase (Ald),
diaphorase (Dia-1, Dia-2), esterase (Est-1, Est-2),
glutamate dehydrogenase (Gdh-1, Gdh-2), glutamate
oxaloacetate transaminase (Got), hexokinase (Hex),
lactate dehydrogenase (Ldh), malate dehydrogenase
NADP+ (Me), mannose phosphate isomerase (Mpi),
peptidase (Pep-1, Pep-2), peroxidase (Prx-1, Prx-2,
Prx-3, Prx-4), 6-phosphogluconate dehydrogenase
(6Pgd), phosphoglucose isomerase (Pgi), superoxide
dismutase (Sod-1, Sod-2) and triose-phosphate
isomerase (Tpi). The second (HC) discontinuous
buffer was used for isocitric dehydrogenase (Idh-1,
Idh-2), malate dehydrogenase NAD+ (Mdh-1, Mdh-2),
phosphoglucomutase (Pgm) and shikimic dehydrogenase (Skd-1, Skd-2). Stain formulations were taken
from Soltis & Soltis (1989) and Szweykowski &
Odrzykowski (1990). Modifications mainly involved
the amounts of components used. These formulations
can be obtained on request from the corresponding
author.
GENETIC
VARIATION AND GENOTYPIC DIVERSITY
WITHIN POPULATIONS
The following diversity measures were calculated
using Tools for Population Genetic Analyses (TFPGA)
software (Miller, 1997): percentage of polymorphic
loci (Ppl), mean number of alleles per locus (A) and
per polymorphic locus (AP), and average observed
(HO) and expected (HE) heterozygosity. Fixation
indices, FIS (inbreeding within an individual in a
43
population; an inbreeding coefficient) and FST (an
indicator of the degree of differentiation among populations), were calculated based on the estimators of
Weir & Cockerham (1984) using GENEPOP ver. 3.2a
(Raymond & Rousset, 1995). Deviations from Hardy–
Weinberg expectations were tested for the population
by the Markov chain method (GENEPOP) and by
randomizations using FSTAT ver. 2.9.3 (Goudet,
2001).
Parameters of within-population genotypic diversity
were also estimated. All sampled ramets were
sorted by multilocus genotype based on polymorphic
loci. Each distinct multilocus genotype detected was
assumed to be a distinct genet. Three different measures of clonal diversity were used: number of observed
genotypes (G), number of genotypes unique to a single
population (GU) and the probability that the next
ramet sampled would be a different genotype (G/NS;
NS, number of samples).
The relationships between parameters of genetic
(Ppl, A, HE, HO) and genotypic (G, G/NS, GU) diversity
and the number of ramets sampled were tested with
pairwise Spearman’s rank correlations (Statsoft,
2001).
GENETIC
DIFFERENTIATION AMONG POPULATIONS
F statistics were calculated to quantify the levels of
genetic differentiation between pairs of populations
(FST) and to assess population subdivision (Weir &
Cockerham, 1984; GENEPOP). We used the Mantel
test to examine the pairwise relationships between
FST/(1 – FST) and logarithms of geographical distance
between all populations using GENALEX ver. 6
(Peakall & Smouse, 2005). To investigate spatial
patterns of genetic variation, distinct groups were
separated by principal component analysis (PCA) of
allozyme gene frequency data, using PCAGEN ver. 1.2
(Goudet, 2001). An important characteristic of this
program is that it tests the significance of total inertia
and individual PCA axis inertia with a randomization
procedure (Manly, 1997), avoiding the interpretation of
nonsignificant axes. We ran 1000 randomizations
of genotypes to test the significance of the inertia of
individual axes. The genotypes were permuted among
the samples and a PCA was run for each permuted
dataset. The proportion of values greater than or equal
to that observed was the unbiased estimate of the test
P value (J. Goudet, pers. comm.).
RESULTS
GENETIC
VARIATION
Thirty-two loci were resolved in N. ovata, 11 of
which were variable (Dia-2, Gdh-2, Mdh-1, Mdh-2,
6Pgd, Pgi-1, Pgi-2, Skd, Sod, Tpi-2 and Prx-1).
© 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
E. BRZOSKO and A. WRÓBLEWSKA
All polymorphic loci were observed in only two
of the ten investigated N. ovata populations. Two
unique alleles, 6Pgdc and Tpi-2c, were found in
population RB, and one unique allele, Pgi-2a, was
found in population JW. Allele frequencies at different loci varied greatly between the populations
studied (Appendix 1).
The percentage of polymorphic loci (Ppl) in N. ovata
populations ranged from 9.4% in the two populations
from Biebrza National Park (OPA and ZAB) to 31.2%
in populations TW and RB. Population-level estimates of the mean number of alleles per locus were
lowest for populations OPA and ZAB (A = 1.09) and
highest for population RB (A = 1.34), whereas estimates of the mean number of alleles per polymorphic
locus were lowest for population JW (AP = 1.9) and
highest for populations RP, WK and JA (AP = 2.2).
Expected levels of heterozygosity (HE) ranged from
0.044 to 0.128 and, in most cases, were similar to
the observed heterozygosity (HO) (Table 1). Deviations
from Hardy–Weinberg equilibrium were found for
six populations, where mainly a significant overabundance of homozygotes was detected (Table 1). In populations TW and RB, deviation from Hardy–Weinberg
equilibrium was shown by randomization and Markov
tests. No relationship between genetic variation and
population size was found (P > 0.05).
CLONAL
DIVERSITY
We detected 229 genotypes among the 486 ramets
sampled from ten N. ovata populations. Most of the
multilocus genotypes were unique. The proportion of
unique genotypes ranged from 86% to 100%, except
in the two populations from Biebrza (OPA, ZAB).
The probability of finding a new genet was also high
(G/NS = 0.49–1.00), except in the two populations from
Biebrza (OPA, G/NS = 0.13; ZAB, G/NS = 0.10). In SK,
the smallest population, and in population JA, each
ramet sampled represented a distinct multilocus genotype (G/NS = 1.00), and almost all were unique. Duplicated genotypes were noted exceptionally in eight
N. ovata populations. In two populations only (RP,
JW) and in the case of two genotypes, more than ten
ramets belonged to the same multilocus genotype. The
highest number of populations with common genotypes was three. Population size was correlated with
G/NS (r = -0.79, P < 0.05) and with the proportion of
unique genotypes (r = -0.63, P < 0.05).
GENETIC
DIFFERENTIATION AMONG POPULATIONS
The overall FST value for all N. ovata populations
was moderate: 0.409 (P < 0.001). Pairwise comparison
of FST values revealed significant differentiation
between all population pairs, ranging from 0.068
DS
PC2 11.2% (P = 0.950)
44
JA
RB
OPA
WK
ZAB
JW
SK
TW
RP
PC1 74.5% (P = 0.009)
Figure 2. Principal component analysis (PCA) plot
showing genetic differences among ten Neottia ovata populations in northeast Poland. P values for PC1 and PC2
axes were obtained by randomization (1000 replicates).
between OPA and ZAB (P < 0.05) to 0.695 between
JA and OPA (P < 0.001, Appendix 2). Genetic and
geographical distances were weakly, but significantly,
related (r = 0.09, P < 0.05). A two-dimensional representation of PCA computed from allele frequencies
of allozymes clearly separated the Biebrza groups
from the rest of the populations (Fig. 2). The first two
components explained 74.5% (P = 0.009) and 11.2%
(P = 0.950) of the total variance among populations.
The ordination diagrams show that the majority of
geographically close populations are genetically
similar.
DISCUSSION
Genetic variation in the studied N. ovata populations
was lower (Ppl = 9.4–31.2%, A = 1.09–1.34, HE = 0.044–
0.128) than in other orchids and animal-pollinated
plants and in long-lived perennials (Loveless &
Hamrick, 1984; Hamrick & Godt, 1989). The level of
genetic variation estimated in eight N. ovata populations distributed in different regions of northeast
Poland was higher than the levels found previously
(Brzosko & Wróblewska, 2003) in the two populations
from Biebrza National Park.
Studies on many plants, including orchids, indicate
that the breeding system is the most significant factor
shaping genetic variation (Tremblay et al., 2005). The
pollination mechanism of N. ovata ensures gene
exchange between different individuals, as the flower
requires at least two pollinator visits for effective
pollination, increasing the probability that pollinia
belonging to other individuals will be deposited
(Nilsson, 1981; Brys et al., 2008; E. Brzosko & I.
Tałałaj, unpubl. data). Gene exchange between
© 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
GENETIC DIVERSITY OF NEOTTIA OVATA
different shoots is also facilitated by sequential development of the flowers on the inflorescence (Nilsson,
1981; Brys et al., 2008, E. Brzosko & I. Tałałaj,
unpubl. data). Despite the operation of these mechanisms promoting gene exchange between different
shoots, genetic variation was low in the N. ovata
populations examined. We can exclude autogamy or
geitonogamy as a cause of this, because, in pollination
exclusion experiments, we did not observe these
processes in N. ovata (E. Brzosko & I. Tałałaj, unpubl.
data). The absence of spontaneous autogamy
has been noted in other European populations
of N. ovata (Nilsson, 1981; Brys et al., 2008), and so
we can assume that this trait is species specific.
We suggest that the low level of genetic variation
in isolated Polish populations of this species can be
explained by biparental inbreeding. This phenomenon
has been noted in almost all populations in which
significant inbreeding is observed. It can be strengthened by subdivision into isolated groups (Allendorf &
Luikart, 2007). In the Biebrza populations, individuals flowering in one year also flowered the next
year (Brzosko, 2002), and intensification of mating
between the same individuals can result in the
production of closely related progeny. In addition,
the genetically similar progeny (seeds) are dispersed
mainly in the vicinity of the mother plants, enhancing
reproductive isolation between particular groups of
plants (Loveless & Hamrick, 1984). Genetic variation
in N. ovata populations in northeast Poland may also
be reduced by the exceptionally low or absent recruitment from seeds (Brzosko, 2002). Two explanations of
this poor generative reproduction should be considered. First, seeds originating from related individuals
were not able to germinate if they were homozygous
and possessed harmful or lethal recessive alleles; as
suggested by Kang, Jiang & Huang (2005) for Berchemiella wilsonii (C.K.Schneid.) Nakai, they may have
lower germination rates and higher mortality than do
heterozygous individuals. Second, the absence of juveniles could reflect an insufficiency of safe sites for
germination. In N. ovata and other orchids, a safe
site provides accessible mycorrhizal components and
appropriate soil moisture. We have no clear evidence
for the presence or absence of fungi required for
N. ovata seedling development, but the availability of
mycorrhizae is known to be one of the most important
determinants of orchid recruitment (Rasmussen,
1995; Diez, 2007). Jacquemyn et al. (2007; and references cited therein) found that the germination of
orchid seeds decreased with increasing distance from
adult plants. Even when reproduction from seeds was
relatively high in some years/populations, mortality
in this life stage was high (E. Brzosko, pers. observ.).
High mortality of seedlings and juveniles has been
observed in orchids and other perennials, especially
45
in unfavourable and altered environmental conditions
(Tamm, 1991; Batygina, Bragina & Vasilyeva, 2003;
E. Brzosko, pers. observ.), pointing to the importance
of environmental factors in shaping the demographic
and genetic structure of populations. In our survey,
we observed that juveniles emerged in populations
growing in wetter habitats. The high proportion of
unique genotypes within populations (excluding the
Biebrza populations) suggests the effect of specific
features of habitats on population-level mechanisms
and processes. Despite the low effectiveness of reproduction from seeds, decreasing the level of genetic
variation in N. ovata populations, the high levels
of genotypic variation within populations and the
number of unique genotypes emphasize the role of
sexual reproduction in N. ovata.
Why do the N. ovata populations from Biebrza show
distinctly lower genetic and genotypic variation than
other populations in northeast Poland? In population
ZAB, the almost complete lack of sexual reproduction
could explain the low level of polymorphism. During
long-term monitoring, new individuals did not arise
in this population, and changes in population size
were dependent on the level of dormancy (Brzosko,
2002). In the other population from this region
(OPA), high effectiveness of reproduction from seeds
was noted in some years, but with low survival to the
next year. The low proportion of generative shoots
could promote the above-mentioned mating between
relatives, and gene exchange between a small number
of individuals may yield genetically similar progeny.
Low genotypic diversity, especially in the ZAB population, might be partly an effect of vegetative propagation (Brzosko, 2002), although this seems in conflict
with Tamm’s (1972) finding that the role of vegetative
spread is marginal in this species. The small number
of genotypes observed for OPA and ZAB may also
have been an artefact of the inability to observe
different genotypes because of the small number of
polymorphic loci. In other words, there is insufficient
genetic variation to separate individuals that are
sexually produced from those that are the result of
vegetative spread.
The influence of mating between relatives on the
genetic variation of N. ovata populations is strictly
connected with their small size, especially in the
context of effective population size (Whitlock, 2000).
Many empirical studies have shown that smaller
populations have lower genetic variation (Young,
Boyle & Brown, 1996; Frankham, Ballou & Briscoe,
2003; Brzosko et al., 2011), and the main factor behind
this is genetic drift. Small population size is a common
characteristic of the populations studied and, presumably, it was an important factor in their decreased
levels of genetic and genotypic variation. We found a
relationship between population size and genotypic,
© 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
46
E. BRZOSKO and A. WRÓBLEWSKA
but not genetic, diversity parameters. In Narcissus
longispathus Pugsley, Medrano & Herrera (2008) also
found no relationship between population size and
genetic variation. We assessed population size during
one year only; in long-lived species, the number of
plants varies from year to year, and a single-year
estimation of population size does not reflect the
long-term average for effective population size. This
is especially true for orchids, in which flowering varies
greatly between years (Kindlmann & Balounová,
2001; Brzosko, 2002). Moreover, the range of sizes of
the studied populations is probably too narrow to
assess the relations between their size and genetic
variation. All were below the effective size of 500
individuals, considered to be sufficient to counteract
the effects of genetic drift, and the majority had an
effective size of < 50 (the minimum capable of retaining sufficient allelic richness; Frankham et al., 2003).
Small populations of rare species are often spatially
isolated. They become increasingly differentiated, as
reflected in our results indicating moderate genetic
differences between the mostly small N. ovata populations studied (FST = 0.409). The FST value is considerably higher than the mean for orchids (0.087) given
by Hamrick & Godt (1996), and the estimate of population differentiation in a more comprehensive review
of orchids (GST = 0.187; Forrest et al., 2004). According
to McCauley, Raveill & Antonovics (1995), FST values
should be greater among younger, recently established populations than among older populations.
Here, we note that, after excluding the Biebrza populations from the dataset, genetic differentiation
among the rest of the Polish N. ovata populations
was halved (FST = 0.205, data not shown in Results).
Significant isolation by distance is the cause of
high genetic differentiation between sites, especially
between distant sites (Rousset, 1997; Hutchinson &
Templeton, 1999; Garnier et al., 2004). Similar conclusions can be drawn from PCA. The postglacial
history of northeast Poland suggests that N. ovata
populations were established recently, and that colonization proceeded according to the stepping stone
model. The Biebrza valley is isolated from adjacent
regions as a result of its geological history. The
similarity of the two Biebrza populations suggests
their common origin and ongoing gene exchange.
Other orchids investigated in northeast Poland
present a similar situation (Brzosko, Wróblewska &
Tałałaj, 2004; Brzosko et al., 2009, 2011).
According to Sun & Wong (2001), gene flow in
orchids is much more restricted than in other plants.
In an experiment on N. ovata seed dispersal, its seeds
dispersed < 10 m, and the majority were found close
to the fruiting plants (E. Brzosko, unpubl. data). In
addition, pollen transport from one population to
another would be precluded by the behaviour of
pollinators; small insects, such as the ichneumonids
that pollinate Neottia spp., generally have restricted
movement patterns (Heinrich & Raven, 1972; after
Loveless & Hamrick, 1984). All this suggests that gene
exchange, occurring within populations but rarely
between them, is not sufficient to prevent genetic drift,
and the probability of gene flow by means of other
vectors (animal or human) is low, especially between
distant regions. We cannot, however, rule out longdistance gene transfer via seeds through, for example,
intensive tourism or forest management. Theoretical
studies show that only a small amount of longdistance gene flow is needed to prevent population
differentiation for neutral alleles (Wright, 1951;
Slatkin & Maruyama, 1975).
The present-day levels of genetic differentiation
among N. ovata populations are a result of natural
and anthropogenic isolation of habitats. Suitable
environments for this species are rare and spatially
fragmented in northeast Poland. Habitat availability
is known to influence genetic divergence between
populations (Gibbs, 2001; Gonzales & Hamrick, 2005;
Medrano & Herrera, 2008). Human activity has
increased the isolation of populations by reducing the
habitats suitable for N. ovata. Artificial fragmentation is considered to be an important factor increasing
differentiation between populations (Templeton et al.,
1990; Young et al., 1996; Tomimatsu & Ohara, 2003;
Kang et al., 2005; Leimu & Mutikainen, 2005).
Although we considered different factors which may
have led to the low level of within-population variation and high between-population genetic differentiation of N. ovata, we were not able to indicate the
major cause of this pattern of population genetic
structure. Often a combination of a few factors is
responsible for genetic diversity. Further research is
needed to resolve this problem, especially concerning
the biological properties of the species, how they are
realized in a given habitat and demographic dynamics. A knowledge of the genetic diversity of N. ovata
has predictive value for the development of more
effective conservation strategies for this species and
other orchid species in northeast Poland, especially in
protected areas.
ACKNOWLEDGEMENTS
We thank Edyta Jermakowicz, Teresa Świerubska,
Izabela Tałałaj, Dan Wołkowycki and Agnieszka
Zalewska who helped to collect samples in northeastern Poland. We also thank Michael Jacobs and
two anonymous referees for insightful comments on
an earlier version of the manuscript. This research
was funded by a grant from the Polish Ministry of
Science and Higher Education (2P04C 048 30).
© 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
GENETIC DIVERSITY OF NEOTTIA OVATA
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0.000
NA
0.211
0.000
0.000
0.448
0.016
0.153
0.000
0.000
1.000
NA
0.789
1.000
1.000
0.552
0.984
0.847
0.000
0.000
1.000
1.000
1.000
0.571
0.894
0.675
0.508
0.375
0.000
0.000
0.000
0.000
0.000
0.429
0.106
0.325
0.492
0.625
0.000
0.000
b
RP
WK
JA
DS
JW
TW
RB
OPA
ZAB
0.078
0.373
0.428
0.134
0.233
0.228
0.238
0.685
0.684
SK
0.000
0.000
1.000
1.000
0.340
0.356
0.383
0.389
0.000
0.000
b
1.000
1.000
0.000
0.000
0.660
0.536
0.617
0.611
0.000
0.000
c
NEOTTIA
0.889
1.000
1.000
1.000
0.979
1.000
0.914
0.611
0.000
0.000
a
6Pgd
0
0.407
0.483
0.122
0.149
0.214
0.376
0.644
0.616
RP
0
0.138
0.230
0.198
0.223
0.300
0.657
0.638
WK
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.083
0.000
0.000
c
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.014
0.000
0.000
c
1.000
1.000
1.000
1.000
0.926
0.985
0.984
0.583
0.000
0.000
a
APPENDIX 2
0.222
0.265
0.368
0.500
0.043
0.253
0.367
0.208
0.000
0.000
b
Mdh-1
0
0.285
0.275
0.157
0.238
0.695
0.680
JA
0
0.150
0.105
0.237
0.621
0.601
DS
F ST
0.000
0.000
0.000
0.000
0.074
0.015
0.016
0.417
0.000
0.000
b
OVATA POPULATIONS (ALL
0.778
0.735
0.632
0.500
0.957
0.747
0.633
0.778
0.000
0.000
a
NEOTTIA
0.111
0.000
0.000
0.000
0.021
0.000
0.086
0.306
0.000
0.000
b
Tpi-2
0.944
0.949
0.816
0.571
0.681
0.969
0.539
0.403
0.000
0.000
b
0.444
0.245
0.132
0.429
0.266
0.119
0.109
0.194
0.588
0.354
a
Prx-1
0.556
0.755
0.868
0.571
0.734
0.881
0.891
0.806
0.412
0.646
b
1.000
0.878
0.789
0.571
0.979
0.969
0.930
0.847
0.000
0.000
a
Skd
0.000
0.051
0.053
0.214
0.021
0.031
0.070
0.153
0.000
0.000
b
0.000
0.071
0.158
0.214
0.000
0.000
0.000
0.000
0.000
0.000
c
0
0.141
0.294
0.583
0.554
JW
0
0.164
0.604
0.583
TW
0
0.552
0.560
RB
0.500
0.173
0.263
0.000
NA
0.077
0.125
0.611
0.654
0.410
a
Sod-2
0.500
0.827
0.737
1.000
NA
0.923
0.875
0.389
0.346
0.590
b
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.500
0.500
a
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.500
0.500
b
0
0.068
OPA
Gdh-2
LOCI NOT RESOLVED IN ELECTROPHORESIS; BOLD,
VALUES ARE STATISTICALLY SIGNIFICANT)
0.056
0.051
0.184
0.429
0.319
0.031
0.461
0.597
0.000
0.000
a
Mdh-2
APPENDIX 1
POLAND (NA,
UNIQUE ALLELE)
OVATA POPULATIONS IN NORTHEAST
STATISTICS AMONG TEN
0.000
0.000
0.000
0.000
0.000
0.108
0.000
0.000
0.000
0.000
a
Pgi-2
WEIR & COCKERHAM’S (1984) F ST
SK
RP
WK
JA
DS
JW
TW
RB
OPA
ZAB
a
a
b
Pgi-1
FREQUENCIES IN TEN
Dia-2
ALLELE
GENETIC DIVERSITY OF NEOTTIA OVATA
© 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, 170, 40–49
49