![(A): Results from STRUCTURE for K = 2 for populations of Sorbus aucuparia, (B): the optimal number of clusters (K) for STRUCTURE estimated by method from Evanno et al. (2005) [50], (C): results from DAPC for K = 3, (D): best number of cluster determined by find cluster in R. For abbreviations of the populations, see Table 1.](https://www.researchgate.net/profile/Gregor-Kozlowski/publication/353331309/figure/fig2/AS:11431281413231757@1745966431820/A-Results-from-STRUCTURE-for-K-2-for-populations-of-Sorbus-aucuparia-B-the_Q320.jpg)
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Article
Genetic Diversity and Structure of Rear Edge Populations of
Sorbus aucuparia (Rosaceae) in the Hyrcanian Forest
Hamed Yousefzadeh 1,*, Shahla Raeisi 2, Omid Esmailzadeh 2, Gholamali Jalali 2, Malek Nasiri 3,
Łukasz Walas 4and Gregor Kozlowski 5,6,7
Citation: Yousefzadeh, H.; Raeisi, S.;
Esmailzadeh, O.; Jalali, G.; Nasiri, M.;
Walas, Ł.; Kozlowski, G. Genetic
Diversity and Structure of Rear Edge
Populations of Sorbus aucuparia
(Rosaceae) in the Hyrcanian Forest.
Plants 2021,10, 1471. https://
doi.org/10.3390/plants10071471
Academic Editor: Calvin O. Qualset
Received: 27 May 2021
Accepted: 23 June 2021
Published: 19 July 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Department of Environmental Science, Faculty of Natural Resources, Tarbiat Modares University (TMU),
Mazandaran 14115-111, Iran
2Department of Forest Science and Engineering, Faculty of Natural Resources,
Tarbiat Modares University (TMU), Mazandaran 14115-111, Iran; reisi.shahla@yahoo.com (S.R.);
oesmailzadeh@modares.ac.ir (O.E.); jalali_g@modares.ac.ir (G.J.)
3Department of Forestry, Faculty of Natural Resources, Tehran University (TU), Tehran 31587-77871, Iran;
nasiri.malek@gmail.com
4Department of Biogeography and Systematics, Institute of Dendrology, Polish Academy of Sciences,
Parkowa 5, PL-62-035 Kornik, Poland; lukaswalas@man.poznan.pl
5Department of Biology and Botanic Garden, University of Fribourg, Chemin du Musée 10,
CH-1700 Fribourg, Switzerland; gregor.kozlowski@unifr.ch
6Natural History Museum Fribourg, Chemin du Musée 6, CH-1700 Fribourg, Switzerland
7Eastern China Conservation Centre for Wild Endangered Plant Resources,
Shanghai Chenshan Botanical Garden, 3888 Chenhua Road, Songjiang, Shanghai 201602, China
*Correspondence: h.yousefzadeh@modares.ac.ir; Tel.: +98-445-531-013; Fax: +98-4455-3499
Abstract:
Sorbus aucuparia (Rosaceae) is a small tree species widely distributed in Eurasia. The
Hyrcanian forest is the southernmost distribution limit of this species. Severe habitat degradation
and inadequate human interventions have endangered the long-term survival of this species in this
region, and it is necessary to develop and apply appropriate management methods to prevent the loss
of its genetic diversity. In this study, we used 10 SSR markers in order to evaluate the genetic diversity
of this taxon. Leaf samples were collected from five known populations of S. aucuparia throughout its
distribution area in the Hyrcanian forest. Expected heterozygosity ranged from 0.61 (ASH) to 0.73,
and according to the M-ratio, all populations showed a significant reduction in effective population
size, indicating a genetic bottleneck. Global F
ST
was not statistically significant and attained the
same values with and without excluding null alleles (ENA) correction (F
ST
= 0.12). Bayesian analysis
performed with STRUCTURE defined two genetic clusters among the five known populations, while
the results of discriminant analysis of principal components (DAPC) identified three distinct groups.
The average proportion of migrants was 22. In general, the gene flow was asymmetrical, with the
biggest differences between immigration and emigration in Barzekoh and Asbehriseh. The Mantel
test showed that there was no significant correlation between genetic distance (F
ST
) and geographic
distance in S. aucuparia. The best pathway for theoretical gene flow is located across the coast of the
Caspian Sea and significant spatial autocorrelation was observed in only one population. In order to
reduce the extinction risk of very small and scattered populations of S. aucuparia in the Hyrcanian
forest, it is very important to establish and/or enhance the connectivity through habitat restoration
or genetic exchange.
Keywords:
conservation genetics; inbreeding depression; range-edge populations; rowan tree;
Hyrcanian forest
1. Introduction
The Hyrcanian forest, located along the southern coast of the Caspian Sea in Iran and
Azerbaijan, is one of the most important biodiversity centers on our planet [
1
]. The area
possesses a remarkable amount of nearly 150 woody species, among them numerous relict
Plants 2021,10, 1471. https://doi.org/10.3390/plants10071471 https://www.mdpi.com/journal/plants
Plants 2021,10, 1471 2 of 12
trees [
2
,
3
]. The main reason of this impressive tree and shrub diversity lies in the fact that
this region was never covered by glaciers during the Pleistocene [4,5].
The rowan tree (Sorbus aucuparia L.) is one of the most important species of the genus
Sorbus, which has a medicinal value, and has a wide natural range in areas with low
and high altitudes from the Atlantic coasts of Europe to the Kamchatka Peninsula and
East China in Asia [
6
–
8
]. The Hyrcanian forest is the southernmost distribution limit of
S. aucuparia
, with only a few and small populations of this species remaining in this region.
The rowan tree is distributed in the Hyrcanian forest at higher altitudes in mountainous
regions, reaching the upper forest limit (1800–2800 m a.s.l.) and often growing on rocky
slopes [
9
]. Iranian occurrences of this species are typical rear-edge populations, isolated
from each other, and occurring in a scattered distribution. Hence, this species in the
Hyrcanian region is vulnerable and highly sensitive to climate change.
Future climate change may alter the genetic diversity within species [
10
] through
the reduction of species distribution and increase of habitat fragmentation [
11
]. It has
been reported by many researchers that rising temperatures and drought stress over the
last half-century, increased mortality and decreased the growth of plants. This effect is
especially strong in the case of edge populations [12].
Marginal populations are potentially important for conservation, since they may
preserve rare alleles and gene combinations important for adaptation to extreme environ-
mental conditions [
13
,
14
]. However, the assessment of genetic diversity and evolution of
peripheral populations is still insufficient [
15
]. Hoffmann et al. [
16
] showed that decreasing
adaptation potential to severe conditions is often encountered at range edges [
17
]. This is
connected with increased genetic drift, which leads to a reduction in gene diversity. On the
other hand, Sáenz-Romero et al. [
18
] mentioned that in some cases, the migratory fluxes
from core populations may improve genetic diversity in peripheral populations [18].
Severe habitat degradation and inadequate human interventions have endangered the
survival of many plant species in the Hyrcanian forest [
19
] and this is even more worrying
for marginal species with very low density and abundance. Additionally, the severe habitat
conditions (rocky sites with shallow soil and harsh habitat conditions) of S. aucuparia in the
Hyrcanian forest are responsible for weak regeneration, as there is a strong relationship
between habitat quality and genetic diversity [
20
]. Thus, the long-term survival of this
species in the Hyrcanian forest is uncertain. Moreover, due to the rapid degradation of the
Hyrcanian forest, it is necessary to apply appropriate management methods to prevent
the decline of plant populations, and consequently, the genetic richness of many species,
especially those present in the upper forest border due to their higher vulnerability [14].
Knowledge about the levels and patterns of genetic diversity within and between
populations is crucial to adopt a good conservation strategy for potentially threatened
species [
21
]. Several molecular techniques have been used as efficient methods for con-
sidering the genetic diversity of the genus Sorbus [22–27]. Simple sequence repeat marker
(SSR) is a cross-selective marker and a powerful tool in evaluating diversity levels, phy-
logenetic relationships, and genetic structure of the genus Sorbus [
23
,
28
]. This type of
marker has been frequently used in the last years due to its co-dominant character and
abundance in the plant’s genome [
29
], and due to the high transferability to the closely
related species [29,30].
This study was designated to investigate the genetic diversity and population genetic
structure of S. aucuparia in its southernmost distribution area, using a set of 10 SSR markers
and using plant material covering the whole known natural distribution of this species in
Iran. More specifically, we aimed to answer the following specific questions: (1) What is
the genetic diversity of S. aucuparia within and between its natural populations in Iran?
(2) What is the spatial genetic structure of natural populations of S. aucuparia in the study
area? (3) What is the migration rate and gene flow between the populations of this species?
Finally, based on our results, we are discussing the conservation implications and measures
needed for the long-term conservation of this species in the Hyrcanian forest.
Plants 2021,10, 1471 3 of 12
2. Materials and Methods
2.1. Sampling, DNA Extraction, and SSR Amplification
Leaf samples were collected from five known populations of S. aucuparia throughout
its distribution area in the Hyrcanian forest (Table 1).
Table 1. Geographical characteristics of the studied populations.
Population
Name Sample Code Longitude Latitude Altitude Sample Size Associate Species
Khalkhal KH 373929.7 483540.9 2100–2500 28 Quercus macranthera-Sorbus graeca
Olsehposht NAV 373936.3 484015.2 1650–2100 12 Fagus orientalis, Acer spp.
Asbehriseh ASH 373741.4 484416.2 1500–1900 12 Carpinus betulus, Acer spp.
Barzekoh LOM 373231.5 484634.4 1450–1850 11 Fagus orientalis, Carpinus orientalis, Acer
mazandaranicum
Sangedeh BAN 360601.6 531232.5 1900–2400 15 Betula pendula-Acer hyrcanum
Hoebee et al. [
23
] concluded that trees are very unlikely to be clones if the minimum
distance between trees is 30 m. Hence, depending on the population size, 10–28 mature
trees were chosen from each population with at least a 50–100 m distance between trees to
avoid recurring genotypes [23]. In total, 78 trees were sampled.
DNA was extracted from the leaf tissue using the CTAB methods [
31
,
32
] with some
modifications [
33
]. The quantity and quality of the extracted DNA were determined by
loading the samples on agarose 1% gel and using spectrophotometry, respectively. In total,
a set of 10 polymorphic SSR markers from 15 initially screened SSR markers were selected
to detect the genetic variation among populations (Table S1). Markers were amplified using
a DNA Engine Thermal Cycler (Bio-rad, Hercules, CA, USA). The reaction mixtures of
10
µ
L contained 1
×
buffer, 0.2 mM dNTPs, 2.5 mM MgCl
2
, 0.2
µ
M each SSR forward and
reverse primer, 30 ng of genomic DNA, and 1 U of Taq polymerase (Thermo Scientific). The
PCR program involved an initial denaturation step of 5 min at 94
◦
C, followed by 30 cycles
at 94
◦
C for 30 s, the appropriate annealing temperature for 30 s, 72
◦
C for 40 s, and an
extension cycle of 1 min at 72
◦
C. PCR product was run on 8% polyacrylamide gel and
dyed with silver nitrate protocol [
34
]. The multimode bands were coded in the Gel-Pro
analyzer 32 software.
2.2. Genetic Diversity
The null allele frequencies of each locus were assessed using Microchecker 2.2.3 soft-
ware [
35
]. The average number of alleles (A), number of private alleles [
36
], and the
effective number of alleles (Ae) were calculated using the GENEALEX 6.501 software [
37
],
INEst v. 2.0 [
38
] was used to estimate the expected heterozygosity (He), observed heterozy-
gosity (Ho), and the inbreeding coefficient (FIS), as well as for a bottleneck test [
39
]. FSTAT
was used to estimate allelic richness (Ar). Global and pairwise F
ST
were estimated using
FREENA and tested with bootstrapping over loci [
36
]. The significance of a deviation from
the Hardy–Weinberg equilibrium, including a Bonferroni correction and the estimated
frequency of null alleles, were estimated using CERVUS software.
2.3. Genetic Structure
Analysis of molecular variance (AMOVA) among and within populations was per-
formed using GenAlex [
37
,
40
]. From AMOVA, the fixation index (F
ST
) and Nm (haploid
number of migrants) within the population were obtained. The Bayesian algorithm im-
plemented in STRUCTURE [
41
] was used to clustering individuals, whereas discriminant
analysis of principal components (DAPC; [
42
] provided an independent, non-Bayesian
method. STRUCTURE procedure included 10
5
MCMC iterations, 10
4
burn-in, and 10 in-
dependent runs with the maximum number of clusters set to K = 6. Evanno’s delta K
method from CLUMPAK software was used to choose the best K. Function ‘find.cluster,’
implemented in the adegenet package in R, was used to estimate the optimal number of
Plants 2021,10, 1471 4 of 12
clusters for the DAPC. Next, the ‘dapc’ function was used to perform this analysis. To
estimate the contemporary dispersal patterns and determining the degree of connectivity
in populations under study, assignment analysis was done by GENEALEX 6.501 software.
In order to infer historical gene flow (Nm) patterns, MIGRATE-N v3.6 [
43
] was used to
estimate the effective population sizes (
θ
) and mutation-scaled immigration (M) among the
stands [
44
,
45
]. Four independent runs with different initial seeds were performed and the
Bezier approximation for the marginal likelihood was used to test which run has the best
fit for the data. Each run consisted of 50,000 sampled parameter values and 5000 recorded
steps after a burn-in of 1000 steps. A static heating scheme was used (chains set at 1, 1.5,
3, 10
5
). The software CIRCUITSAPE [
46
,
47
] was used for testing how topography could
shape the gene flow between populations. The altitude raster was a resistance surface with
analyzed populations as nodes.
2.4. Mantel Test
Patterns of isolation by distance (IBD; [
48
]) were investigated using function ‘Mantel
test’ with 9999 iterations implemented in R. The matrix of the genetic distance (pairwise F
ST
with ENA correction) was tested against the matrix of spatial distance between populations
created using the program QGIS.
2.5. Spatial Autocorrelation
Spatial autocorrelation analysis [
49
] was performed in GenAlEx [
37
]. The spatial
autocorrelation coefficient (r) was computed using the multilocus genetic distance and the
Euclidean distance between individuals.
3. Results
3.1. Genetic Diversity
Analysis of 10 microsatellite loci in 78 individuals (genets) showed 41 different alleles.
The number of different alleles per locus ranged from 3 (MSS5) to 5.83 (MSS9). The values
of He and Ho per locus varied from 0.47 (MSS5) to 0.73 (MSS9, SA08) and from 0.23 (MSS9)
to 0.82 (MSS1), respectively. The highest and lowest frequency values of null alleles were
in MSS9 (0.28) and MSS16 (0.003), respectively. The mean null allele frequency for all
examined populations was 0.10 (Table S2).
Genetic diversity estimates obtained for each population at the genet level are summa-
rized in Table 2. The expected heterozygosity ranged from 0.61 (ASH) to 0.73 (KH), while
Ho ranged from 0.45 (NAV) to 0.56 (BAN and KH). The highest Ar value was in KH (4.56)
and the lowest in ASH (3.48). Private alleles were observed in the eastern and western
populations (BAN and KH).
Table 2. Parameters of the genetic diversity of the studied populations. For abbreviations of the populations, see Table 1.
Pop Lat Long n A Ae Ar Ap Null Ho He Fis M-
Ratio
BAN 36.06 53.124 15 4.56 3.29 4.12 2.00 0.102 0.56 0.70 0.16 0.006
KH 37.393 48.354 28 5.78 3.64 4.56 5.00 0.125 0.56 0.73 0.21 0.050
NAV 37.394 48.401 12 4.56 3.12 4.08 0.00 0.134 0.45 0.69 0.31 0.037
ASH 37.374 48.442 12 3.67 2.79 3.48 0.00 0.082 0.49 0.61 0.17 0.004
LOM 37.323 48.463 11 3.89 2.89 3.61 0.00 0.123 0.46 0.63 0.29 0.000
n—total number of individuals, A—the average number of alleles, Ae—effective number of alleles, Ar—allelic richness, Ap—number of
private alleles, Null—frequency of null alleles, Ho—observed heterozygosity, He—expected heterozygosity, F
Is
—fixation index, M-ratio—p-
value of Wilcox sign-rank test after 10,000 permutations.
According to the M-ratio, all populations showed a significant reduction in the effec-
tive population size, indicating a genetic bottleneck (Table 2). The spatial pattern of He and
Ar is demonstrated in Figure 1; the highest values were observed in the eastern population.
Plants 2021,10, 1471 5 of 12
F
IS
ranged from 0.16 to 0.31; according to the DIC in all populations under study, inbreeding
was not the likely factor of the deviation in the Hardy–Weinberg equilibrium (Table 2).
Figure 1.
Maps of the genetic diversity of Sorbus aucuparia populations visualized by QGIS software. (
A
): expected
heterozygosity (Ho), (B): allelic richness (Ar), (C): number of private alleles (Ap).
Global F
ST
was statistically insignificant and attained the same values with and
without ENA correction (F
ST
= 0.12). This result suggests that the presence of null alleles
does not influence the level of differentiation. The pairwise F
ST
ranged from 0.005 (between
BAN and LOM) to 0.08 (between BAN and ASH), indicating a varied level of differentiation
among populations (Table 3).
3.2. Spatial Genetic Structure and Gene Flow
Bayesian analysis of a genetic structure, performed in STRUCTURE, defined two
genetic clusters among the five analyzed populations (Figure 2a). Three populations of
BAN, KH, and NAV were grouped as a single cluster, whereas two populations of ASH and
LOM were assigned to the second cluster. We used DAPC analysis to increase the validation
and support the output of Bayesian clustering. The results of DAPC for K = 3—best K for
Plants 2021,10, 1471 6 of 12
STRUCTURE—were relatively dissimilar to those obtained with STRUCTURE. The results
of DAPC for K = 3 revealed that the BAN population from the eastern part and KH from
the western part of the Hyrcanian forest comprised a separated group. However, three
populations (NAV, ASH, and LOM) were not assigned to either of the two detected clusters
and presented a relatively high admixture (Figure 2b). This result was also confirmed by
the population assignment test (Figure S1).
Table 3.
Matrix of the genetic distance between populations. For abbreviations of the populations,
see Table 1.
BAN KH NAV ASH LOM
BAN 0.0337 0.0326 0.0831 0.0057
KH 0.0330 0.0162 0.0655 0.0411
NAV 0.0332 0.0126 0.0779 0.0532
ASH 0.0777 0.0597 0.0732 0.0078
LOM 0.0064 0.0366 0.0527 0.0108
FST with ENA correction above the diagonal, FST without ENA correction below the diagonal.
Figure 2.
(
A
): Results from STRUCTURE for K = 2 for populations of Sorbus aucuparia, (
B
): the optimal number of clusters
(K) for STRUCTURE estimated by method from Evanno et al. (2005) [
50
], (
C
): results from DAPC for K = 3, (
D
): best number
of cluster determined by find cluster in R. For abbreviations of the populations, see Table 1.
The results of recent migration rates are shown in Figure 3and Table S3. The average
proportion of migrants was 22.02, suggesting that more individuals than 20 per population
may be migrants. However, differences between populations are very strong—in LOM,
the number of migrants was 34.9, whereas in BAN and ASH, this value was lower than 20
(15.9 and 14.0, respectively). In general, the gene flow was asymmetrical, with the biggest
differences between immigration and emigration in LOM (strong immigration) and ASH
(strong emigration). The intensity of gene flow between populations from the western and
eastern parts of the Hyrcanian forests was rather low.
Plants 2021,10, 1471 7 of 12
Figure 3.
Theoretical gene flow between populations of Sorbus aucuparia estimated with MIGRATE-N.
For abbreviations of the populations, see Table 1.
Figure 4.
Theoretical gene flow between populations of Sorbus aucuparia in relation to the topography
determined using CIRCUITSCAPE.
Plants 2021,10, 1471 8 of 12
The Mantel test showed that there was no significant correlation between genetic
distance (F
ST
) and geographic distance in S. aucuparia (r =
−
0.282, p= 0.7). However, a
resistance analysis made in CIRCUITSCAPE, with elevation as a matrix of resistance against
the F
ST
matrix, indicated that elevation could be a significant barrier for gene flow across
the eastern and western range of the species. CIRCUITSCAPE models the connectivity
between stands as a landscape resistance distance (isolation-by-resistance). In our analysis,
altitude was used as a resistance raster, and paths without topographical barriers were
estimated as best ways for a gene flow. The map generated by CIRCUITSCAPE showed
the path of high conductance that represents possible pathways of gene flow among
populations; theoretical conductance is presented in Figure 4.
The best pathway for theoretical gene flow is located across the coast of the Caspian
Sea. Theoretical southern path across the mountains is less probable, because of topo-
graphic complexity. Significant spatial autocorrelation was observed only in population
KH
(Figure 5)
. Lack of spatial autocorrelation confirms the Mantel test result and suggests
that IBD is irrelevant in the studied populations.
Figure 5.
Correlograms illustrating spatial autocorrelation for all analyzed populations. Upper and lower error bars bound
the 95% confidence interval to r, as determined by bootstrap resampling.
4. Discussion
Our study revealed that the Hyrcanian populations of S. aucuparia in the southern
Caspian Sea have higher genetic diversity compared with reported results for other species
of the genus Sorbus [
23
,
51
,
52
] and even compared with the populations of. S. aucuparia in
refugial regions of Europe [
52
]. This is not surprising because the Hyrcanian region was a
potential refugium during the last glacial maximum for a wide range of woody taxa [
2
,
53
].
An interesting result is the increase in Ho, Ar, and Ap (except the KH population) from the
western to the eastern limit distribution of the S. aucuparia in Iran. The western population
of S. aucuparia in the Hyrcanian forest (KH) with a small area and high tree density that is
Plants 2021,10, 1471 9 of 12
separated about 100 km from the main region of the Hyrcanian forest, showed the highest
heterozygosity, allelic richness, and number of private alleles. An appropriate interpretation
is that this population is the nearest to the European populations, and it may has acted as a
receiver of genes from western and south-eastern Europe, especially from countries with
access to the Black Sea (Turkey and Georgia). On the other hand, the BAN population, as the
easternmost population of S. aucuparia in the Hyrcanian forest, showed high heterozygosity
and private alleles. There are several examples where genetic diversity within populations
showed an increase towards species distribution margins [
54
,
55
]. Kucerova et al. [
56
] found
higher differentiation over central-European populations than those located in southern
locations for S. torminalis. Additionally, Jankowska-Wroblewska et al. demonstrated that
peripheral populations of S. torminalis have relatively high levels of genetic diversity [26].
On a global scale, the S. aucuparia populations in the Hyrcanian forest are considered
as a range-edge population of this species in the Northern Hemisphere. These range-edge
populations are isolated from the populations in Europe and are vulnerable to genetic
drift [
57
]. Inbreeding depression, genetic drift, and differentiation of peripheral populations
are all exacerbated by persistent reductions in gene flow among small isolated and less
dense populations [
57
,
58
]. These interpretations contrast strongly with the high levels
of individual heterozygosity, suggesting a heavy selection against selfed offspring [
59
].
All five stands studied had higher expected heterozygosity than observed heterozygosity,
resulting in positive inbreeding coefficients. This is contrary to the gametophytic self-
incompatibility system of woody Rosaceae [
23
]. Given that the size and/or density of
a population can influence the outcrossing rate of self-compatible plants [
60
], it seems
that harsh habitat conditions, small size, and low tree density, as well as a severe human
intervention, has caused, contrary to expectations for the genus Sorbus [
26
], positive
inbreeding in S. aucuparia populations in the Hyrcanian forest. Additionally, because of
their often high levels of heterozygosity, outcrossing trees such as S. aucuparia can be
disproportionately vulnerable to a reduction in pollen-mediated gene flow, which can
mask deleterious recessive alleles that, if expressed, can lead to a reduction in population’s
fitness [61].
A bottleneck was detected in the Hyrcanian populations of S. aucuparia using the M-
ratio with positive inbreeding. Genetic drift is inversely related to the effective population
size (1/2Ne; [
62
]) and typically occurs in small populations, where rare and private alleles
face a greater chance of being lost. The current populations of S. aucuparia in the north of
Iran may be the remnants of a large population from the past, which over time, due to low
competitiveness with other species, has decreased their density and nested in harsh sites
with steep slopes and rocky outcrops. Additionally, habitat disturbance, such as forest fires
or logging, could lead to fragmented habitat and influence genetic patterns and structures,
local extinctions, and subsequent colonization.
In the periphery of a species range, abiotic and biotic environments may differ from
those in the center, and there are likely less suitable habitats [
59
]. Habitat suitability, the
historical colonization, migration pattern, and geographical distance among populations
shaped the genetic structure of a species [
63
]. In this study, the habitat conditions of KH
population were completely different from the other sites. Sorbus aucuparia is usually found
above the timberline and in the rocky and steep habitat of the Hyrcanian forest, while
the KH habitat is a dune forest with relatively suitable soil and a much smaller habitat
slope than other habitats. This could be the reason for the higher genetic diversity and
completely different genetic structure of S. aucuparia in this habitat. Due to the distance
of at least 500 km of the population of BAN from the other four Hyrcanian populations
and possibly the complete cessation of gene flow over time, it has been differentiated from
other populations.
5. Conclusions
The results of our study demonstrate a positive inbreeding in S. aucuparia populations
in the Hyrcanian forest, showing evidence of a past bottleneck. To reduce the extinc-
Plants 2021,10, 1471 10 of 12
tion risk of very small and isolated populations of S. aucuparia in this region, there is a
need to establish and/or enhance the connectivity between isolated populations through
habitat restoration or genetic exchange. In fact, gene movement via seedling could pro-
vide a pathway for dispersal and, as a result, greater genetic diversity retention through
increased effective population size, reducing the effects of drift [
62
,
64
]. To achieve the
above-mentioned goals, suitable new areas for afforestation with S. aucuparia should be
identified to reduce the geographical distance for gene flow among the main populations.
Additionally, improving the habitat quality and increasing the density of trees by planting
additional seedlings should be used as another alternative to increase connectivity among
neighboring trees and reduce the inbreeding depression within the populations.
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/10
.3390/plants10071471/s1, Table S1: Repeat motif, primer sequence, fragment size and Tm information
for 15 under study microsatellite loci, Table S2: Null allele’s analysis results by Freena; Table S3.
Migration with correction on the sink population (theta*M)/4.
Author Contributions:
Plant material collection and preparation, H.Y., S.R., O.E. and G.J.; experi-
ments and data analysis, H.Y., M.N. and Ł.W.; writing—original draft preparation, H.Y; writing—
review and editing, H.Y., Ł.W. and G.K.; supervision, H.Y. and G.K. All authors agreed to be
accountable for all aspects of the work. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research was funded by the Tarbiat Modares University, the Iran National Sci-
ence Foundation (INSF, Grant Number: 96000370), the Fondation Franklinia and University of
Fribourg, Switzerland.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data is attached in Supplementary Materials.
Acknowledgments:
We thank Mansour Pouramin and Mohsen Yousefzadeh for the assistance during
field data collection. We also wish to thank the two anonymous reviewers for the useful comments.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Bayat, M.; Burkhart, H.; Namiranian, M.; Hamidi, S.K.; Heidari, S.; Hassani, M. Assessing biotic and abiotic effects on biodiversity
index using machine learning. Forests 2021,12, 461. [CrossRef]
2. Leestmans, R. Le refuge caspiens et son importance en biogéographie. Linneana Belg. 2005,20, 97–102.
3.
Zohary, M. Geobotanical Foundations of the Middle East; Gustav Fischer Verlag Press: Stuttgart, Germany; Swets & Zeitlinger:
Amsterdam, The Netherlands, 1973; Volume 1.
4.
Kozlowski, G.; Gibbs, D.; Huan, F.; Frey, D.; Gratzfeld, J. Conservation of threatened relict trees through living ex situ collections:
Lessons from the global survey of the genus Zelkova (Ulmaceae). Biodivers. Conserv. 2012,21, 671–685. [CrossRef]
5.
Kozlowski, G.; Song, Y.-G.; Bétrisey, S.; Alvarado, E.V.; Bétrisey, S. Wingnuts (Pterocarya) & Walnut Family: Relict Trees: Linking the
Past, Present and Future; Natural History Museum: Fribourg, Switzerland, 2018.
6.
Hultén, E.; Fries, M. Atlas of North European Vascular Plants. North of the Tropic of Cancer; Koeltz Scientific Books: Königstein,
Germany, 1986.
7.
Raspé, O.; Findlay, C.; Jacquemart, A. Sorbus aucuparia L.: Biological flora of the British Isles, list br. Vasc. Pl. 232, 1. J. Ecol.
2000
,
88, 910–930. [CrossRef]
8.
˙
Zywiec, M.; Ledwo´n, M. Spatial and temporal patterns of rowan (Sorbus aucuparia L.) regeneration in west Carpathian subalpine
spruce forest. Plant Ecol. 2008,194, 283–291. [CrossRef]
9. Sabeti, H. Forest, Trees and Shrubs of Iran; Yazd University Publishers: Yazd, Iran, 1997; 810p.
10.
McInerny, G.; Turner, J.; Wong, H.; Travis, J.; Benton, T. How range shifts induced by climate change affect neutral evolution. Proc.
R. Soc. B Biol. Sci. 2009,276, 1527–1534. [CrossRef]
11.
Wróblewska, A.; Mirski, P. From past to future: Impact of climate change on range shifts and genetic diversity patterns of
circumboreal plants. Reg. Environ. Change 2018,18, 409–424. [CrossRef]
12.
Jump, A.S.; Hunt, J.M.; Penuelas, J. Rapid climate change-related growth decline at the southern range edge of Fagus sylvatica.
Glob. Change Biol. 2006,12, 2163–2174. [CrossRef]
13.
Lesica, P.; Allendorf, F.W. When are peripheral populations valuable for conservation? Conserv. Biol.
1995
,9, 753–760. [CrossRef]
Plants 2021,10, 1471 11 of 12
14.
Hampe, A.; Petit, R.J. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett.
2005
,8, 461–467. [CrossRef]
15.
Arnaud-Haond, S.; Teixeira, S.; Massa, S.I.; Billot, C.; Saenger, P.; Coupland, G.; Duarte, C.M.; Serrao, E. Genetic structure at
range edge: Low diversity and high inbreeding in southeast Asian mangrove (Avicennia marina) populations. Mol. Ecol.
2006
,15,
3515–3525. [CrossRef] [PubMed]
16.
Hoffmann, A.A.; Blows, M.W. Species borders: Ecological and evolutionary perspectives. Trends Ecol. Evol.
1994
,9, 223–227.
[CrossRef]
17.
Bradshaw, A.D. The croonian lecture, 1991. Genostasis and the limits to evolution. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci.
1991
,
333, 289–305.
18.
Sáenz-Romero, C.; O’Neill, G.; Aitken, S.N.; Lindig-Cisneros, R. Assisted migration field tests in Canada and Mexico: Lessons,
limitations, and challenges. Forests 2021,12, 9. [CrossRef]
19.
Yosefzadeh, H.; Tabari, M.; Akbarinia, M.; Akbarian, M.R.; Bussotti, F. Morphological plasticity of Parrotia persica leaves in eastern
Hyrcanian forests (Iran) is related to altitude. Nord. J. Bot. 2010,28, 344–349. [CrossRef]
20.
Gaitán-Espitia, J.D.; Hobday, A.J. Evolutionary principles and genetic considerations for guiding conservation interventions
under climate change. Glob. Change Biol. 2021,27, 475–488. [CrossRef]
21.
Rischkowsky, B.; Pilling, D. The State of the World’s Animal Genetic Resources for Food and Agriculture; Food & Agriculture Org.:
Rome, Italy, 2007.
22. Robertson, A.; Newton, A.; Ennos, R. Breeding systems and continuing evolution in the endemic Sorbus taxa on Arran. Heredity
2004,93, 487–495. [CrossRef]
23.
Hoebee, S.E.; Menn, C.; Rotach, P.; Finkeldey, R.; Holderegger, R. Spatial genetic structure of Sorbus torminalis: The extent of clonal
reproduction in natural stands of a rare tree species with a scattered distribution. For. Ecol. Manag. 2006,226, 1–8. [CrossRef]
24.
Angelone, S.; Hilfiker, K.; Holderegger, R.; Bergamini, A.; Hoebee, S. Regional population dynamics define the local genetic
structure in Sorbus torminalis.Mol. Ecol. 2007,16, 1291–1301. [CrossRef]
25.
Rasmussen, K.K.; Kollmann, J. Low genetic diversity in small peripheral populations of a rare European tree (Sorbus torminalis)
dominated by clonal reproduction. Conserv. Genet. 2008,9, 1533–1539. [CrossRef]
26.
Jankowska-Wroblewska, S.; Meyza, K.; Sztupecka, E.; Kubera, L.; Burczyk, J. Clonal structure and high genetic diversity at
peripheral populations of Sorbus torminalis (L.) Crantz. iForest-Biogeosciences For. 2016,9, 892. [CrossRef]
27.
Kavaliauskas, D.; Šeho, M.; Baier, R.; Fussi, B. Genetic variability to assist in the delineation of provenance regions and selection
of seed stands and gene conservation units of wild service tree (Sorbus torminalis (L.) Crantz) in southern Germany. Eur. J. For.
Res. 2021,140, 1–15. [CrossRef]
28.
Liu, C.; Dou, Y.; Guan, X.; Fu, Q.; Zhang, Z.; Hu, Z.; Zheng, J.; Lu, Y.; Li, W. De novo transcriptomic analysis and development of
est-ssrs for Sorbus pohuashanensis (Hance) hedl. PLoS ONE 2017,12. [CrossRef]
29.
Abid, M.; Scheffran, J.; Schneider, U.A.; Ashfaq, M. Farmers’ perceptions of and adaptation strategies to climate change and their
determinants: The case of Punjab province, Pakistan. Earth Syst. Dyn. 2015,6, 225–243. [CrossRef]
30. Ellis, J.; Burke, J. Est-SSRs as a resource for population genetic analyses. Heredity 2007,99, 125–132. [CrossRef]
31. Doyle, J.J.; Doyle, J.L. Isolation of plant DNA from fresh tissue. Focus 1990,12, 39–40.
32. Dellaporta, S.L.; Wood, J.; Hicks, J.B. A plant DNA minipreparation: Version ii. Plant Mol. Biol. Rep. 1983,1, 19–21. [CrossRef]
33.
Janfaza, S.; Nasr, S.M.H.; Yousefzadeh, H.; Botta, R. Phylogenetic relationships of the genus Castanea based on chloroplast rbcl
with focusing Iranian chestnut. JBES 2015,5, 312–323.
34.
Bassam, B.J.; Caetano-Anollés, G.; Gresshoff, P.M. Fast and sensitive silver staining of DNA in polyacrylamide gels. Anal. Biochem.
1991,196, 80–83. [CrossRef]
35.
Van Oosterhout, C.; Hutchinson, W.F.; Wills, D.P.; Shipley, P. Micro-checker: Software for identifying and correcting genotyping
errors in microsatellite data. Mol. Ecol. Notes 2004,4, 535–538. [CrossRef]
36.
Chapuis, M.-P.; Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol.
2007
,24,
621–631. [CrossRef]
37.
Peakall, R.; Smouse, P.E. Genalex 6: Genetic analysis in excel. Population genetic software for teaching and research. Mol. Ecol.
Notes 2006,6, 288–295. [CrossRef]
38.
Chybicki, I.J.; INEST 2.2. The User Manual. 2017. Available online: https://www.ukw.edu.pl/pracownicy/strona/igor_chybicki/
software_ukw (accessed on 12 October 2018).
39.
Garza, J.; Williamson, E. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol.
2001
,10, 305–318.
[CrossRef]
40.
Excoffier, L.; Smouse, P.E.; Quattro, J.M. Analysis of molecular variance inferred from metric distances among DNA haplotypes:
Application to human mitochondrial DNA restriction data. Genetics 1992,131, 479–491. [CrossRef] [PubMed]
41.
Wilson, G.A.; Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics
2003
,163, 1177–1191.
[CrossRef] [PubMed]
42.
Jombart, T.; Devillard, S.; Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically
structured populations. BMC Genet. 2010,11, 1–15. [CrossRef] [PubMed]
43.
Beerli, P. Comparison of bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics
2006
,22,
341–345. [CrossRef]
Plants 2021,10, 1471 12 of 12
44.
Beerli, P.; Felsenstein, J. Maximum-likelihood estimation of migration rates and effective population numbers in two populations
using a coalescent approach. Genetics 1999,152, 763–773. [CrossRef]
45.
Beerli, P.; Palczewski, M. Unified framework to evaluate panmixia and migration direction among multiple sampling locations.
Genetics 2010,185, 313–326. [CrossRef]
46. McRae, B.H. Isolation by resistance. Evolution 2006,60, 1551–1561. [CrossRef]
47.
McRae, B.H.; Beier, P. Circuit theory predicts gene flow in plant and animal populations. Proc. Natl. Acad. Sci. USA
2007
,104,
19885–19890. [CrossRef]
48. Wright, S. Isolation by distance. Genetics 1943,28, 114. [CrossRef]
49.
Smouse, P.E.; Peakall, R. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity
1999
,
82, 561–573. [CrossRef]
50.
Evanno, G.; Regnaut, S.; Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation
study. Mol. Ecol. 2005,14, 2611–2620. [CrossRef]
51.
Bednorz, L.; Myczko, L.; Kosinski, P. Genetic variability and structure of the wild service tree (Sorbus torminalis (L.) Crantz) in
poland. Silvae Genet. 2006,55, 197–201. [CrossRef]
52.
George, J.-P.; Konrad, H.; Collin, E.; Thevenet, J.; Ballian, D.; Idzojtic, M.; Kamm, U.; Zhelev, P.; Geburek, T. High molecular
diversity in the true service tree (Sorbus domestica) despite rareness: Data from Europe with special reference to the Austrian
occurrence. Ann. Bot. 2015,115, 1105–1115. [CrossRef]
53.
Alavi, S.J.; Veiskarami, R.; Esmailzadeh, O.; Gadow, K.V. Analyzing the biological and structural diversity of Hyrcanian forests
dominated by Taxus baccata L. Forests 2020,11, 701. [CrossRef]
54.
Yakimowski, S.B.; Eckert, C.G. Populations do not become less genetically diverse or more differentiated towards the northern
limit of the geographical range in clonal Vaccinium stamineum (Ericaceae). New Phytol. 2008,180, 534–544. [CrossRef] [PubMed]
55.
Assis, J.; Coelho, N.C.; Alberto, F.; Valero, M.; Raimondi, P.; Reed, D.; Serrão, E.A. High and distinct range-edge genetic diversity
despite local bottlenecks. PLoS ONE 2013,8, e68646. [CrossRef]
56.
Kucerova, E.; Clifton, S.W.; Xia, X.-Q.; Long, F.; Porwollik, S.; Fulton, L.; Fronick, C.; Minx, P.; Kyung, K.; Warren, W. Genome
sequence of Cronobacter sakazakii baa-894 and comparative genomic hybridization analysis with other Cronobacter species. PLoS
ONE 2010,5, e9556. [CrossRef] [PubMed]
57.
Kottler, E.J.; Dickman, E.E.; Sexton, J.P.; Emery, N.C.; Franks, S.J. Draining the Swamping Hypothesis: Little Evidence that Gene
Flow Reduces Fitness at Range Edges. Trends Ecol. Evol. 2021,36, 533–544. [CrossRef]
58.
Ouborg, N.; Vergeer, P.; Mix, C. The rough edges of the conservation genetics paradigm for plants. J. Ecol.
2006
,94, 1233–1248.
[CrossRef]
59.
Michalski, S.G.; Durka, W. High selfing and high inbreeding depression in peripheral populations of juncus atratus. Mol. Ecol.
2007,16, 4715–4727. [CrossRef] [PubMed]
60.
DeSilva, R.; Dodd, R.S. Patterns of Fine-Scale Spatial Genetic Structure and Pollen Dispersal in Giant Sequoia (Sequoiadendron
giganteum). Forests 2021,12, 61. [CrossRef]
61.
Bacles, C.F.; Jump, A.S. Taking a tree’s perspective on forest fragmentation genetics. Trends Plant Sci.
2011
,16, 13–18. [CrossRef]
[PubMed]
62.
Frankham, R. Challenges and opportunities of genetic approaches to biological conservation. Biol. Conserv.
2010
,143, 1919–1927.
[CrossRef]
63.
Pannell, J.R.; Dorken, M.E. Colonisation as a common denominator in plant metapopulations and range expansions: Effects on
genetic diversity and sexual systems. Landsc. Ecol. 2006,21, 837–848. [CrossRef]
64.
Jangjoo, M.; Matter, S.F.; Roland, J.; Keyghobadi, N. Connectivity rescues genetic diversity after a demographic bottleneck in a
butterfly population network. Proc. Natl. Acad. Sci. USA 2016,113, 10914–10919. [CrossRef]
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