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Citation: Chen, Z.; Wang, J.; Tang, J.;
Wang, Z.; Chai, S.; He, X.; Wei, X.
Conservation Genetics of the Rare
and Endangered Tree Species,
Camellia nitidissima (Theaceae),
Inferred from Microsatellite DNA
Data. Forests 2022,13, 1662. https://
doi.org/10.3390/f13101662
Academic Editors: Rusea Go and
Christina Seok Yien Yong
Received: 28 August 2022
Accepted: 7 October 2022
Published: 10 October 2022
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Article
Conservation Genetics of the Rare and Endangered Tree
Species, Camellia nitidissima (Theaceae), Inferred from
Microsatellite DNA Data
Zongyou Chen 1,2 , Junfang Wang 3, Jianmin Tang 2, Zhengfeng Wang 4, Shengfeng Chai 2, Xingjin He 1, *
and Xiao Wei 2,*
1Key Laboratory of Bio-Resources and Eco-Environment of Ministry of Education, College of Life Sciences,
Sichuan University, Chengdu 610065, China
2
Guangxi Institute of Botany, Guangxi Zhuangzu Autonomous Region and the Chinese Academy of Sciences,
Guilin 541006, China
3Nanning Survey and Design Institute Group Co., Ltd., Nanning 530022, China
4Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Key Laboratory of
Carbon Sequestration in Terrestrial Ecosystem, South China Botanical Garden, Chinese Academy of Sciences,
Guangzhou 510650, China
*Correspondence: xingjinhe@126.com (X.H.); weixiao@gxib.cn (X.W.)
Abstract:
Camellia nitidissima Chi, is a rare and endangered plant that is narrowly distributed in South
China and North Vietnam. In this study, seven polymorphic microsatellite markers were used to
investigate the genetic diversity, recent population bottlenecks as well as population structure of
twelve remnant populations of the plant. Our results indicated that, despite their severely fragmented
natural range, C. nitidissima remnants maintained a moderate level of genetic variability, and only a
bottlenecked population was detected by the clear evidences. No significant correlation was found
between genetic diversity and population size. Significantly high genetic differences among popula-
tions were found, and the twelve populations could be classified into two distinct genetic groups.
AMOVA indicated that 16.14% (16.73%, after one suspected artificial population was excluded) of
the molecular variation was attributable to regional divergences (between Nanning and Fangcheng),
and the majority of genetic variation existed within populations which were 69.24% (70.63%, after
one suspected artificial population was excluded). For conservation management plans, the genetic
resources of the two distinct groups are of equal importance for conservation, separate management
unit for each of them should be considered. Given that all remnant populations are small and isolated,
and many plants are illegally dug out for commercial purposes, management efforts in terms of
habitat protection and legal protection, as well as transplantations and reintroductions, would be
necessary for this species.
Keywords:
genetic diversity; population bottlenecks; population structure; conservation strategies;
management unit
1. Introduction
In nature, rare and/or endangered species typically exist in small and geographically
isolated populations [
1
]. Empirical studies have shown that, for a number of plant species,
being in isolation or in small populations reduced genetic variation and fitness [
2
–
8
]. In
principle, small population size and increased isolation tend to restrict the exchange of
pollen and seed, thereby reducing interpopulation gene flow. Additionally, small popula-
tions are more prone to inbreeding and genetic drifts [
9
–
11
]. It is believed that restricted
gene flow, inbreeding, and genetic drift can cause the loss of genetic diversity of popula-
tions and subsequently reduce a population’s ability to adapt to changing environments
while increasing its susceptibility to disease and pests [
12
]. Sometimes, however, small and
isolated populations may have normal or even enhanced gene flow without suffering from
Forests 2022,13, 1662. https://doi.org/10.3390/f13101662 https://www.mdpi.com/journal/forests
Forests 2022,13, 1662 2 of 17
genetic erosion [
13
,
14
]; hence, inferences regarding genetic variation in rare or endangered
species must be made with caution. Genetic diversity patterns are attributable to many
factors, such as a species’ mating system as well as its demographic or life history [
15
,
16
].
Therefore, understanding genetic factors that increase the risks of extinction for particular
species is critically important for their conservation [15,17].
Camellia nitidissima C. W. Chi (Theaceae) is a rare and endangered evergreen tree
that is narrowly distributed in both the Guangxi Province of South China, as well as in
North Vietnam [
18
]. It is a diploid tree (2 n = 30) [
19
] that grows in moist, shady habitats,
but it tends to avoid ones with strong direct sunlight. It also usually grows to 2 to 3 m
tall (Supplementary Figure S1a online), although it can reach up to 6 m [
20
]. It produces
large (diameter: 1.2–2.3 cm), scented single axillary flowers which are cup-shaped, with
many yellow petals and dozens of stamens with orange anthers (Supplementary Figure S1c
online). The flowers, which blossom from November to March [
21
], are insect-pollinated,
and pollination is primarily by bees [
22
]. It is likely that the plant relies on the bright yellow
color of its petals to attract insects in order to promote allogamy. In South China, fruits of
C. nitidissima set in the spring and ripen from October to December [21,23], while its large
and heavy seeds (1.73–2.16 cm long and 1.94–2.5 cm in diameter, 2.3–3.5 g in weight) [
21
]
have thick pericarps and seedcoats that help them avoid desiccation in dry climates and
insulate them from frost in cold winters [
23
]. These seeds are mainly dispersed by gravity
and occasionally by water (personal observation). This species has serious reproductive
disadvantages, such as low seed productivity and low germination rates (c. 30%, when
seeds are on the soil surface) [23].
C. nitidissima was first discovered in Fangcheng County in 1933, but, despite being
known to the public since 1948, it initially received no attention from the public or hor-
ticulturists until the early 1960s when it was again found in Yongning County [
24
]. It is
one of several camellias with yellow flowers, and, with its big size, golden color, and the
transparent waxy appearance of its flowers, this species was honored as “the queen of
camellia” [
25
]. As a result, it was introduced as an ornamental plant in many countries,
where it has attracted the attention of gardeners worldwide [26,27].
In China, natural populations of camellias, including C. nitidissima, have been ex-
tensively investigated for many years [
28
], and, so far, only two disjunctive areas with
C. nitidissima have been found in Guangxi, with the first being at the junction of Fushu,
Longan, and Fusui, near the city of Nanning, and the other one located in Fangcheng, to
the south of Mount Shiwan [
18
]. Most of the populations of C. nitidissima exist in residual
forests or secondary forests [
29
]. Although the historic distribution range and population
size of C. nitidissima remain unknown, it is clear that the species experienced a rapid de-
cline due to increasing anthropogenic pressures on its natural habitat over the last few
decades [
23
,
30
]. Moreover, despite its threatened status, C. nitidissima remains popular for
horticultural trades. As a result, large numbers of its seeds are illegally collected, and its
plants are also illegally dug out in the wild. Recently, C. nitidissima has been included in
the checklist of the State Protection Category I in China [31].
To support conservation and management programs for C. nitidissima, information
about its genetic variability and population structure in natural populations is necessary,
and it has led to several molecular marker-based genetic studies to investigate the ge-
netic variation of its remnant populations [
21
,
28
]. Combined analysis of RAPD and AFLP
markers showed that C. nitidissima populations could be classified into two major genetic
groups corresponding to the Nanning and Fangcheng areas, while Mantel tests revealed
significant correlations between the genetic and geographical distances of C. nitidissima
populations [
28
]. More recently, a population genetic study using ISSR markers indicated a
low level of genetic diversity at both the species and population levels but a relatively high
degree of differentiation among natural populations [
21
]. Detailed information on popula-
tion genetics (e.g., breeding system, genetic bottlenecks), therefore, remained unresolved
by these studies. Additionally, these previous studies may have underestimated the genetic
consequences of past demographic events as they used traditional standard approaches for
Forests 2022,13, 1662 3 of 17
assessing population structure [
32
,
33
]. In contrast, nuclear microsatellite markers (SSR) are
powerful tools for the study of population genetics because of their high polymorphism,
codominant transmission, and presumably neutral and extensive genome coverage [
34
].
The polymorphic microsatellite markers pre-selected for C. nitidissima by Wei et al. [
35
]
have now allowed us to conduct a detailed study of the population genetics of the species.
Here we use seven polymorphic microsatellite markers which have been pre-selected
for C. nitidissima by Wei et al. [
35
] to conduct the detailed study of the population genetics
of the species. The main objectives were as follows: (1) to examine the levels and patterns
of genetic diversity of the species. Given its small population size and restricted geographic
distribution, we expected C. nitidissima populations to be genetically impoverished; (2)
to test whether the species has experienced genetic bottleneck; (3) to assess the genetic
structure among populations in order to identify management units. Such information
can have important implications for assessing the suitability of current-available conserva-
tion and management programs for this endangered species as well as for devising new
conservation strategies.
2. Materials and Methods
2.1. Sample Collection
Leaf samples from 385 C. nitidissima individuals were collected between August and
October 2008 from twelve geographically isolated extant populations across the entire
distribution range of the species in Guangxi, China (Table 1and Figure 1a). The NZS
population consisted of three subpopulations which were distributed along the same slope
but at different altitudes of mount Nazi (Table 1). However, the geographical coordinates
(latitude and longitude locations) are not provided here because of the species’ threatened
status and economic value. Overall, these populations could be divided into two regions
separated by about 117–148 km [
21
], with four populations being to the west of Nanning
and the remaining eight located around Fangcheng (Table 1). Three of the twelve popula-
tions (Table 1) were located in the protected area where C. nitidissima is currently preserved,
and, within each region, the populations were separated from each other by 3 to 40 km.
Population sizes were estimated in the field by inspection, and, depending on the results as
well as accessibility to the areas, the sample sizes ranged from 11 for ZD and to 89 for NZS
(30, 29, and 30 individuals respectively sampled for NZS-1, NZS-2, and NZS-3) (Table 1).
After sample collection, all leaves were dried in silica gel in sealed polyethylene bags prior
to storage at room temperature until genomic DNA was extracted.
2.2. DNA Extraction and Microsatellite Analysis
Total DNA was extracted by following the CTAB method of Doyle [
36
]. The quality
and quantity of the DNA were determined by electrophoresis on 1% agarose gels, while
microsatellite genotyping was performed according to the method of Wei et al. [
35
] at
seven loci (CamsinM3,CamsinM4,CamsinM5,MSCjaF37,MSCjaH38,MSCjaH46, and P12).
Briefly, this involved polymerase chain reaction (PCR)-based amplifications which were
performed in 10
µ
L reaction mixtures consisting of 5 ng of template DNA, 50 mM KCl,
20 mM Tris-HCl (pH 8.0), 1.5 mM MgCl
2
, 0.5
µ
M of each primer, 0.2 mM of each dNTP,
and 1U of Taq DNA polymerase (Takara). The amplification procedure was carried out as
described by Wei et al. [
35
] (4 min at 94
◦
C, followed by 35 cycles of 94
◦
C for 30 s, 51–63
◦
C
depending on locus annealing temperature for 30 s, and 72
◦
C for 45 s, followed by 10 min
at 72
◦
C), and PCR was performed on a LabCycler Gradient thermocycler (SensoQuest,
Gottingen, Germany). PCR products were then resolved on 4% denaturing polyacrylamide
gels and visualized by silver staining. In each polyacrylamide gel electrophoresis (PAGE),
the same ladders plus the same 5 samples were used for all PAGEs as references. For the
samples whose alleles were different by one base pair and were not confirmed in a run,
they were chosen to be put together to run in the same PAGE.
Forests 2022,13, 1662 4 of 17
2.3. Data Analysis
All conversions of the format of the genetic data were performed using the software
CONVERT 1.3 [
37
] and GenALEx 6.3 [
38
]. The Ewens–Watterson test for neutrality of
polymorphic markers was then conducted using Manly’s algorithm [
39
] as implemented
in PopGene 1.31 [
40
] with 1000 simulated samples. The observed number of alleles (N
A
),
the number of effective alleles (N
E
), the number of private alleles (N
P
), the proportion of
polymorphic loci (P), the observed heterozygosity (H
O
), Nei’s unbiased expected heterozy-
gosity (UH
E
) [
41
], and Wright’s inbreeding coefficient (F
IS
) [
42
] per (sub)population were
subsequently calculated using the program GenALEx 6.3 [
38
] before testing the Hardy–
Weinberg equilibrium (HWE) per (sub)population by using FSTAT 2.9.3 [
43
]. The FSTAT
2.9.3 [
43
] was also used to estimate allelic richness (A
R
). In addition, F
IS
per population,
while considering the frequency of null alleles, was estimated with INEST 2.0 [
44
], ap-
plying the default Bayesian approach using 300,000 steps, sampling every 100 steps, and
discarding the first 30,000 steps as burn-in. Then, FREENA [
45
] was used to estimate null
allele frequencies for each population and locus while the (sub)population differentiation
was measured by F
ST
[
46
] for each pair of (sub)populations with the excluding null allele
(ENA) correction. The software was then also used to estimate the global F
ST
[
46
], both
with and without ENA correction, in order to determine the influence of null alleles. The
ENA correction method corrects for the positive bias introduced by the presence of null
alleles. Correlation analyses between population size and parameters of genetic variation
were eventually conducted using SPSS 19.0 for Windows (SPSS lnc., Chicago, IL, USA),
and two-tailed analyses of correlation were performed using Pearson’s tests. The software
was also used to test for significant differences between the mean Fst values within each
region and between them, and the data were analyzed by performing one-way analysis of
variance (ANOVA) followed by Duncan’s post-hoc test.
Table 1. Details of remnant populations of Camellia nitidissima in South China.
Region and
Population Location Altitude (m) Sample Size
(Count as Individual)
Estimated Population
Size (Count as Individual)
Nanning
ZZC Zhongzhencun, Gutan, Longan 230 36 170
BLS Boluoshan, Fushu, Xixiangtang 240 33 190
GMS Gengmaoshan, Fushu,
Xixiangtang 380 30 160
ZD Zhongdong, Fusui 280 11 140
Fangcheng
FL Fulong 170 31 39
BLC Bailicun, Dalu 20 31 180
NLP * Niulanping, Nasuo 178 20 63
PTC * Paotaicun, Nasuo 165 17 120
NZS *
NZS-1 Mount Nazi, Nasuo 120 30 70
NZS-2 Mount Nazi, Nasuo 250 29 100
NZS-3 Mount Nazi, Nasuo 306 30 40
total 89 210
DWJ Dawangjiang, fucheng 70 30 150
DYC Diaoyingcun, Malu, Dongxing 410 30 32
JDC Jiaodongcun, Jiangping,
Dongxing 130 27 37
*, located in the protected area.
Forests 2022,13, 1662 5 of 17
Forests 2022, 13, x FOR PEER REVIEW 5 of 19
Figure 1. Map showing the distribution of samples and Bayesian-based clusters for Camellia nitidis-
sima performed using STRUCTURE. (a) The geographical distributions of the 12 C. nitidissima pop-
ulation; (b) Bayesian clustering of all individuals in the 12 populations (Names are shown in Table
1). The color bars represent the probability of assigning an individual to a particular cluster. The
populations could be divided into two genetic groups (C1 and C2); (c) The assignment results for
the nine populations in C2.
Figure 1.
Map showing the distribution of samples and Bayesian-based clusters for Camellia nitidissima
performed using STRUCTURE. (
a
) The geographical distributions of the 12 C. nitidissima population;
(
b
) Bayesian clustering of all individuals in the 12 populations (Names are shown in Table 1). The
color bars represent the probability of assigning an individual to a particular cluster. The populations
could be divided into two genetic groups (C1 and C2); (
c
) The assignment results for the nine
populations in C2.
Forests 2022,13, 1662 6 of 17
In addition, using BOTTLENECK 1.2.02 [
47
], Wilcoxon tests (two-tailed) for heterozy-
gote excess were performed under the Infinite Allele Model (IAM), the Stepwise Mutation
Model (SMM), and the Two-Phase Model (TPM) in order to examine potential bottlenecks.
The IAM considers any point mutation along a stretch of DNA within a locus to constitute
a new allele, whereas the SMM counts new alleles along a stretch of DNA with respect to
the addition or subtraction of particular subsets of DNA motifs [
48
]. Moreover, the TPM
has been proposed as an “intermediate” model that provides a more realistic picture of
how some DNA sequences evolve [
49
]. Under the TPM, 70% and 30% of the mutations
were assumed to occur under the SMM and the IAM, respectively. For each mutational
model, 10,000 replicates were performed. By using the same software, the mode-shift
indicator test was also used for detecting potential bottlenecks. The non-bottlenecked
populations that are near mutation-drift equilibrium have a large proportion of alleles at
low frequencies. In this test, the microsatellite alleles are grouped into ten frequency classes
to investigate whether the distribution followed the normal L-shaped form, where alleles
with low frequencies are the most numerous.
To evaluate the relationships among populations, a Bayesian cluster analysis, imple-
mented in STRUCTURE 2.3.1 [
50
], was performed to assign individuals into clusters based
on their multilocus genotypes. Using an admixture model with correlated allele frequencies
among populations, 10 independent runs were performed for each K(putative cluster num-
bers, from 1 to 12), with 10
6
iterations after a burn-in period of 10
6
steps. However, since Ln
Pr(X|K) does not reliably identify the optimal number of clusters, another ad hoc criterion,
namely the
∆
K[
51
], was calculated to determine the optimal K. CLUMPP 1.1.2 [
52
] was
further used to calculate the average membership coefficient for each individual by aligning
and converging the results from the above 10 runs. As all individuals could be assigned
to two genetic groups (the red cluster and the green cluster in Figure 1b), as identified by
STRUTURE at the highest hierarchical level, and, at the same time, nine populations were
completely assigned to the green cluster (Figure 1b), we eventually repeated the above
analyses for this cluster with Kfrom 1 to 9 in order to understand the genetic structure
in detail.
Furthermore, hierarchical analysis of molecular variance (AMOVA) was carried out
with the software Arlequin 3.0 [
53
] to quantify the partitioning of genetic variance between
regional groups (Nanning and Fangcheng), among populations within regional groups, as
well as within populations.
Mantel tests were carried out using GenALEx 6.3 [
38
] to test for the significance of
isolation by distance. First, a matrix of pairwise F
ST
/(1
−
F
ST
) (F
ST
was corrected by the
ENA) values between populations was generated, and subsequently, it was compared
against a matrix of geographic distance [
54
]. In this case, geographical distances between
pairs of populations were calculated based on longitudes and latitudes using the Spheroidal
Distance function in the Mathematica software (Wolfram Research, Champaign, IL, USA).
This software generates the distance between two points on the earth (in km) based on
the spheroidal model of the planet. Evidence of significant isolation by distance was then
indicated by a significant positive correlation between pairwise F
ST
/(1
−
F
ST
) values and
the geographic distances.
It should, however, be noted that since the ZD population anomalously clustered with
the group from the Fangcheng region (see results) (Figure 1b), it was suspected to have
been artificially introduced. Thus, all of the above genetic parameters were also analyzed
after excluding the ZD population from the dataset.
3. Results
3.1. Test of Neutrality
The neutrality test for all loci showed no significant differences (data not shown), both
including or excluding the ZD population, hence indicating that the allele distribution was
in accordance with the assumption of selective neutrality.
Forests 2022,13, 1662 7 of 17
3.2. Genetic Diversity
A total of 130 alleles at 7 microsatellite loci were identified in the 385 C. nitidissima
individuals. The number of observed alleles per population ranged from 4.286 in PTC
to 11.857 in NZS, with a mean number of 6.583. Twenty-seven private alleles were also
detected in 10 of the 12 populations (Table 2), and about 20.24% of the null allele frequen-
cies were greater than 0.1 for each population and locus. The values of allelic richness
among populations ranged from 2.478 to 3.679, with an average value of 3.033 (Table 2).
It was also found that the value of genetic diversity was highest in the ZD population
(UH
E
= 0.757) but lowest in the BLS population (UH
E
= 0.482), while three small popula-
tions, namely FL, DYC and JDC (Table 1), showed moderate UH
E
values when comparing
with other populations (Table 2). In terms of the inbreeding coefficient, F
IS
values calculated
in GenALEx 6.3 [
38
] (F
IS-GenALEx
) for the populations ranged from
−
0.031 to 0.324, with an
average value of 0.126 (Table 2). In particular, eight of the twelve populations, along with
three subpopulations of NZS, deviated significantly from HWE, and the F
IS-GenALEx
values
of all these deviations were positive (Table 2), indicating heterozygote deficiencies. F
IS
estimates in INEST 2.0 [
44
] (F
IS-INest
) varied from 0.064 to 0.289, and the values of their 95%
HDPI were positive and did not include zero (Table 2). Furthermore, when the ZD popula-
tion was excluded, the mean values for H
O
,UH
E
, and F
IS-GenALEx
were 0.539, 0.608, and
0.107, respectively (Table 2). Correlation analyses also found that observed heterozygosity,
unbiased expected heterozygosity, and inbreeding coefficient, both with and without the
ZD population, were not related to the population size (with the ZD population: R= 0.286,
p= 0.368 for H
O
,R= 0.192, p= 0.551 for UH
E
,R=
−
0.032, p= 0.922 for F
IS-GenALEx
; after
excluding the ZD population: R= 0.249, p= 0.461 for H
O
,R= 0.506, p= 0.112 for UH
E
,
R= 0.204, p= 0.547 for F
IS-GenALEx
. NZS population was not divided into three subpopula-
tions in both cases).
3.3. Test for Bottleneck Effects
A significant excess of heterozygosity was detected in five and four (sub)populations
under the assumptions of the IAM and SMM models, respectively, while similar results
were obtained in only one population under that of the TPM model (Table 2). None of the
(sub)populations simultaneously displayed significant excess of heterozygosity in all three
models (Table 2).
As a second method to detect potential bottlenecks, the mode-shift indicator test
indicated the abundance of low frequency (<0.10) alleles for every (sub)population (Sup-
plementary Table S1 and Figure S2 online) along with a normal L-shaped graph for every
(sub)population but PTC (Supplementary Figure S2 online).
3.4. Genetic Structure
The global measure of F
ST
with and without the ZD population changed from 0.199 to
0.190 and 0.203 to 0.194, respectively, after excluding null allele (ENA) correction (Table 2).
The corrected pairwise F
ST
values were all significantly high (p< 0.05, Table 3). More
specifically, three populations (ZZC, BLS, and GMS) from the Nanning region showed
higher genetic differentiation when compared with each other and other populations.
Similarly, significantly higher genetic differentiation was also found between populations
from Nanning and Fangcheng regions, especially after excluding the ZD population, unlike
the case when populations within the regions were compared (Table 3).
With increasing Knumbers from 1 to 12 in STRUCTURE, the value of ln Pr(X|K)
increased continuously without forming a plateau, but
∆
Kshowed a large peak at K= 2
(Supplementary Figure S3 online). At this value of K(K= 2), most individuals from the
Nanning region, as well as several ones from the DYC population, were assigned to one
cluster (C1, red), while those from the region of Fangcheng except several individuals,
along with the ZD population and several individuals of BLS population from Nanning
region, clustered separately (C2, green) (Figure 1b). Furthermore, within the BLC and NZS
populations, some individuals showed a certain degree of genetic admixture (Figure 1b).
Forests 2022,13, 1662 8 of 17
Further analyses indicated that the nine populations within the C2 could be further divided
into four clusters (optimal K= 4, Supplementary Figure S4 online), namely the green
cluster (C2-I), the blue cluster (C2-II), the red cluster (C2-III) and the yellow cluster (C2-IV),
and genotypes from different populations are merged together in the NZS population
(Figure 1c).
AMOVA results, summarized in Table 4, indicated that 16.14% and 16.73% of molec-
ular variation (after testing with and without the ZD population, respectively) could be
attributed to regional differences between the regions of Nanning and Fangcheng. How-
ever, most of the molecular variance (69.24 and 70.63% corresponding to test results with
and without the ZD population, respectively) occurred within populations. In addition,
results of Mantel tests, both with and without the ZD population, indicated that there was a
significant “isolation by distance” pattern when all the populations were analyzed together
(R= 0.525, p= 0.040 with the ZD population and R= 0.818, p= 0.010 after excluding the
ZD population) (Figure S5a,b online). A similar pattern was observed when the analysis
was performed for the populations of Fangcheng region (R= 0.473, p= 0.030) (Figure S5c
online); however, it was not observed for the populations of the Nanning region (R= 0.485,
p= 0.180 with the ZD population and R= 0.038, p= 0.470 after excluding the ZD population)
(Figure S5d,e online).
Forests 2022,13, 1662 9 of 17
Table 2. Genetic variation and test of bottleneck effects for Camellia nitidissima.
Population NANENPP(%) ARHOUHEFIS-GenALEx p-Value of
HWE Test FIS-INest
FIS-INest
95% HDPI FST FST
(ENA)
BOTTLENECK Test (pValue)
IAM SMM TPM
ZZC 5.857 2.888 3 100.00 2.871 0.606 0.603 −0.031 0.4673 0.121 0.083–0.162 0.05469 0.03906 *a0.68750
BLS 4.857 2.335 0 100.00 2.478 0.353 0.482 0.307 0.0005 * 0.289 0.224–0.352 1.00000 0.03906 *a0.10938
GMS 7.000 4.065 5 85.714 3.235 0.589 0.649 0.069 0.0071 * 0.087 0.063–0.127 0.04688 *a1.00000 0.43750
ZD 6.857 4.783 2 100.00 3.679 0.494 0.757 0.324 0.0005 * 0.188 0.049–0.414 0.37500 0.57813 1.00000
FL 6.429 3.638 1 85.714 2.932 0.498 0.603 0.148 0.0005 * 0.066 0.004–0.137 0.01563 *a0.07813 0.43750
BLC 8.429 4.137 2 100.00 3.409 0.608 0.684 0.077 0.0026 * 0.064 0.002–0.144 0.81250 0.01563 *a0.10938
NLP 5.000 2.813 0 85.714 2.749 0.513 0.560 0.054 0.0658 0.115 0.038–0.192 0.68750 0.15625 0.56250
PTC 4.286 2.779 2 85.714 2.662 0.485 0.533 0.098 0.0709 0.135 0.051–0.221 0.01563 *a1.00000 0.01563 *a
NZS
NZS-1 7.143 3.929 100.00 3.194 0.604 0.667 0.108 0.0077 * 0.00781 *a0.07813 0.93750
NZS-2 8.571 4.486 100.00 3.492 0.608 0.691 0.130 0.0005 * 0.37500 0.03906 *a0.68750
NZS-3 8.429 4.983 100.00 3.415 0.560 0.645 0.192 0.0005 * 0.10938 0.29688 0.93750
Population
level 11.857 6.056 5 100.00 3.588 0.590 0.694 0.169 0.0006 * 0.129 0.102–0.157 0.29688 0.07813 0.93750
DWJ 6.571 3.922 3 100.00 3.081 0.656 0.657 −0.028 0.5046 0.073 0.032–0.111 0.02344 *a0.10938 0.29688
DYC 6.143 2.736 1 100.00 2.797 0.545 0.614 0.110 0.0087 * 0.128 0.068–0.205 0.93750 0.05469 0.68750
JDC 5.714 3.145 3 100.00 2.920 0.490 0.609 0.200 0.0005 * 0.143 0.083–0.206 0.29688 0.46875 0.93750
Mean bMean c6.583 3.608 95.238 3.033 0.536 0.620 0.126
Mean d6.558 3.501 94.805 2.975 0.539 0.608 0.107
Species c0.199 0.190
Species d0.203 0.194
N
A
, number of observed alleles; N
E
, number of effective alleles; N
P
, private alleles per population over seven loci; P, Percentage of polymorphic loci; A
R
, allelic richness; H
O
, observed
heterozygosty; UH
E
, unbiased expected heterozygosy; F
IS-GenALEx
, inbreeding coefficient over polymorphic loci calculated with the software GenALEx 6.3 [
38
]; F
IS- INest
, inbreeding
coefficient over polymorphic loci calculated with the software INEST 2.0 [
44
]; F
ST
, genetic differentiation among populations; HPDI, highest posterior density interval; ENA, excluding
null alleles; *, Significant at p< 0.05, denoted significant deviation from HWE; *
a
, Significant at p< 0.05, rejection of null hypothesis/bottleneck;
b
, NZS, population not divided into three
subpopulations; c, with ZD population; d, after excluding ZD population.
Forests 2022,13, 1662 10 of 17
Table 3. Pairwise estimated values of FST among (sub)populations of Camellia nitidissima.
Nanning Group Population Mean Fangcheng Group Population
ZZC BLS GMS ZD FL BLC NLP PTC NZS DWJ DYC JDC
NZS-1 NZS-2 NZS-3 Population
level
Nanning
group
population
ZZC —
0.2002 B
BLS 0.2332 * —
GMS 0.1598 * 0.2076 * —
ZD 0.2360 * 0.2822 * 0.2046 * —
Mean 0.2206 a
Fangcheng
group
population
FL 0.2598 * 0.3129 * 0.2583 * 0.0793 *
0.2304 a
—
BLC 0.2124 * 0.2546 * 0.2024 * 0.0760 * 0.0886 * —
NLP 0.3463 * 0.3919 * 0.3000 * 0.0781 * 0.2099 * 0.2012 * —
PTC 0.3290 * 0.3845 * 0.3002 * 0.1066 * 0.2034 * 0.1852 * 0.0745 * —
NZS
NZS-1 0.2163 * 0.2946 * 0.1895 * 0.0670 * 0.1510 * 0.1248 * 0.1043 * 0.0901 * —
NZS-2 0.2071 * 0.2581 * 0.1965 * 0.0534 * 0.1153 * 0.1083 * 0.0888 * 0.0878 * 0.0656 * —
NZS-3 0.2298 * 0.2878 * 0.2139 * 0.0779 * 0.1336 * 0.1353 * 0.0999 * 0.0646 * 0.0539 * 0.0282 * —
Popu-
lation
level
0.2000 * 0.2509 * 0.1844 * 0.0507 * 0.1163 * 0.1114 * 0.0769 * 0.0597 * — — — —
DWJ 0.2795 * 0.2886 * 0.2694 * 0.0489 * 0.1514 * 0.1523 * 0.1412 * 0.1428 * 0.1258 * 0.1126 * 0.1199 * 0.1034 * —
DYC 0.2733 * 0.3087 * 0.2579 * 0.0944 * 0.1761 * 0.1972 * 0.1345 * 0.1608 * 0.1508 * 0.1097 * 0.1433 * 0.1153 * 0.1580 * —
JDC 0.2901 * 0.3224 * 0.2740 * 0.0860 * 0.1686 * 0.1605 * 0.1243 * 0.1064 * 0.1237 * 0.1022 * 0.0996 * 0.0904 * 0.1103 * 0.1511 * —
Mean 0.2813 A0.1383 bC
*, p< 0.05; Numbers in grey shading indicate three populations in the northern region that show higher genetic differentiation with each other and other populations; Different
superscript small letters indicate significant differences among the mean F
ST
(NZS population not divided into three subpopulations) in each region and between them (p< 0.05);
Different superscript capital letters indicate significant differences among the mean F
ST
(NZS population not divided into three subpopulations) in each region and between them after
excluded ZD population (p< 0.05).
Forests 2022,13, 1662 11 of 17
Table 4. Results of hierarchical AMOVA testing for Camellia nitidissima populations.
Source of Variation d.f. Sum of Squares Variance
Components
Percentage of
Variation
with ZD
population
Between regional groups 1 105.083 0.27423 16.14 **
Among populations within
regional groups 10 163.984 0. 24829 14.61 **
Within populations 758 891.677 1.17636 69.24 **
Total 769 1160.744 1.69887
after excluding ZD
population
Between regional groups 1 129.538 0.36877 16.73 **
Among populations within
regional groups 9 177.470 0.27863 12.64 **
Within populations 737 1147.375 1.55682 70.63 **
Total 747 1454.383 2.20422
d.f., degree of freedom; **, p≤0.01 (1000 permutation).
4. Discussion
4.1. Genetic Variation of C. nitidissima
Some studies have demonstrated that threatened and endangered species tend to
possess low levels of genetic diversity [
33
,
55
–
58
], while others did not support similar
conclusions [
9
,
17
,
59
–
61
]. In the case of this study, results from microsatellite analysis
showed that C. nitidissima maintained a moderate level of genetic diversity, with a mean
UH
E
value of 0.620 (0.608 after the ZD population was excluded) and more than 69% (70%,
after the ZD population was excluded) of the genetic variation occurring among individuals
within populations. Previously reported genetic diversities in C. nitidissima have been based
on dominant molecular markers (RAPD, AFLP, ISSR), which yielded mean H
E
values of
0.1069, 0.1288, and 0.0831 for RAPD, AFLP, and ISSR markers, respectively [
21
,
28
]. These
values were much lower in comparison to our results, but such inconsistencies based on
microsatellites and dominant markers were also reported for another endangered species,
Changiostyrax dolichocarpa, where its mean H
E
was 0.64 for microsatellites [
62
] and 0.13
for ISSR markers [
63
]. Fundamental differences between microsatellites and dominant
markers likely account for these different estimates. To overcome such differences due to
markers, comparative studies using the same molecular markers are therefore necessary to
study genetic variations in endangered species. In the present study, our data showed that
the genetic diversity in C. nitidissima (mean UH
E
= 0.620 (0.608, after the ZD population
was excluded) Table 2) was relatively higher than that of many gravity-dispersed (mean
H
E
= 0.500) [
64
] and narrowly distributed species (mean H
E
= 0.560) [
64
] but similar to that
of its widespread congener C.sinensis (H
E
= 0.620) [
65
] which was studied using the same
molecular markers. Thus, it is reasonable to believe that, despite severe fragmentation,
C. nitidissima remnants maintained a moderate level of genetic diversity even though this
result was not consistent with the hypothesis that C. nitidissima is genetically impoverished.
In fact, many tree species are probably resilient to habitat fragmentation within one or two
generations after fragmentation because they already contain high genetic diversity [
62
,
66
–
68
].
Although the natural population size of C. nitidissima has declined greatly in recent decades,
the current fragmented populations are probably remnants of the old generation of the
population. Long generation time actually helps to keep these ancient genetic variations
for long time periods. This is why for the small populations (FL, BLC, and JDC), we did not
find reduced genetic variations compared to the larger ones (Tables 1and 2). However, since
for small-sized populations the likelihood of inbreeding and genetic drift increases [
69
],
plant species with small and isolated populations tend to be vulnerable to demographic,
environmental, and genetic stochasticity [
9
]. To support this view, it would be interesting
to point out that during our field survey, poor fruit set was found in most individuals of
this species, with this being indicative of potential issues with inbreeding depression.
Mutations in microsatellites generally do not appear consistently with either the
IAM or the SMM [
70
], and the TPM model fits better for most of the microsatellites [
71
].
Forests 2022,13, 1662 12 of 17
Hence, the results from the TPM should be considered to be more reliable for this study
and revealed that only the PTC population had undergone bottleneck (Table 2). It was
also confirmed by the mode-shift indicator test that all (sub) populations but PTC have a
normal L-shaped graph (Supplementary Figure S2 online), which showed that only the
PTC population is bottlenecked population. In addition, allelic richness is an alternative
criterion for measuring genetic diversity, and it is more sensitive to bottlenecks than
expected heterozygosity [
72
,
73
]. In this study, the allelic richness of PTC population (2.662)
was somewhat lower than those of all (sub) populations (2.749–3.588), but BLS (2.478)
(Table 2), it, therefore, reflected the possibility of the bottleneck of the PTC population.
Heterozygote deficiencies were also found in several C. nitidissima populations (Table 2).
Heterozygote deficits can arise from a number of factors, especially through null alleles,
biparental inbreeding, or population substructure (i.e., the Wahlund effect [
74
]) [
75
,
76
].
In the present study, we found that the global F
ST
value showed a negligible change
after correcting for the occurrence of null alleles, thus indicating that the influence of null
alleles was negligible in our data. Furthermore, the fact that the F
IS-GenALEx
values of
4 of the 12 populations along with one subpopulation of NZS were higher than that of
species in mixed breeding system (mean F
IS
= 0.15, it was calculated with the formula
(H
E−
H
O
)/H
E
, and H
O
and H
E
from Nybom [
64
]) suggested possible inbreeding in C.
nitidissima. Then, the F
IS
-
INest
values of their 95% HDPI for all populations were positive
and did not include zero (Table 2), which indicated significant inbreeding in all these
populations. In addition, results from pairwise comparisons of F
ST
values, AMOVA, and
bayesian cluster analyses indicated the presence of significant population structuring within
the sampling regions of C. nitidissima (Table 3, Table 4, and Figure 1), and significant genetic
structure within the fragmented populations can cause a potential Wahlund effect [
77
].
Therefore, we suspected that these deficiencies were primarily due to biparental inbreeding
or population substructures.
4.2. Genetic Differentiation and Population Genetic Structure
The corrected pairwise F
ST
values were all significant at p< 0.05 (Table 3), indicating
high genetic differences among populations. While the overall F
ST
of 0.190 (0.194, after the
ZD population was excluded) was below the average value normally observed for plants in
general (0.26) [
64
], narrow (0.23) [
64
], and outcrossing (0.22) [
64
], it still indicated a possible
geographically-restricted gene flow among the remnant populations. A similar conclusion
could be drawn from the fact that significant genetic structure was found in the sampling
regions (Figure 1), and the private alleles were detected in 10 of the 12 populations. These
observations could be linked to the geographical distances that were no less than 3 km
between the populations, while mountain ranges (400–1400 m in elevation) also contributed
to the separation. Moreover, C. nitidissima was found to be mainly pollinated by bees [22],
with eighteen bee species reported as having a maximum flight distance from 540 to 2050
m [
78
]. Although there was no information about the flight range of other bee species, it
is likely that the presence of the above geographical barriers could have hampered the
movement of bees from one population to another, thus promoting pollen exchange only
between nearby aggregations. In addition, with its large and heavy seeds, interpopulation
seed dispersal was unlikely for C. nitidissima.
Significant divergences between the two regions were also observed after the ZD
population was excluded. This genetic divergence has been interpreted as either the result
of localized selection processes [
79
–
84
] or due to seeds/pollen dispersal [
85
,
86
]. However,
in the present study, the neutrality tests showed that there was no evidence of selection. In
fact, these two regions were separated by more than 100 km of low mountains (400–1400 m
in elevation), and as such, it was almost impossible to exchange seed/pollen between them.
Simulation studies previously showed that population substructures develop rapidly under
“isolation by distance” models without spatial heterogeneous selection [
87
,
88
]. Therefore,
the geographically-restricted gene flow between the two regions could have been the cause
of the observed regional divergence of this species. However, it is noteworthy that the
Forests 2022,13, 1662 13 of 17
ZD population was the most distinct from the other populations of the Nanning region as
it clustered with those of the Fangcheng region irrespective of its geographical isolation
(Figure 1) (which is in accordance with the previous finding by Wei et al. [
21
]; it was
named L population in that paper). Moreover, a few individuals that were genetically
clustered with populations from other regions, irrespective of geographical isolation, were
also found in BLS and DYC (Figure 1b). There was a high probability that all individuals
from the ZD population, as well as several ones from the BLS population (green), could be
migrants or offspring of migrants from the Fangcheng region, while several individuals
from DYC population (red) were opposite, they could be migrants or offspring of migrants
from the Nanning region (Figure 1b). Furthermore, the geographic distances between ZD
and other populations of the Nanning region are less than 16 km, and these populations
were located in the same climate zone. We, therefore, suspected that other factors, such as
human-mediated gene flow, could have probably influenced the overall genetic structure of
C. nitidissima. As an ornamental plant, C. nitidissima dispersal pathways were unavoidably
influenced by humans, with the anomalous clustering likely to have been caused by the
artificial introduction of some individual plants.
Interestingly, the NZS population harbor all genotypes of different populations of the
Fangcheng region (Figure 1c), as well as low genetic differentiation between NZS and these
populations were found (Table 2), suggesting a possibility that the NZS population was
of hybridogenous origin and shared genetic stock with other populations in Fangcheng
region, and therefore NZS could be a local “genetic melting-pot”.
4.3. Conservation Implications for C. nitidissima
The present results indicated that C. nitidissima remnants maintained a moderate level
of genetic variability despite their severely fragmented natural range. However, since
all extant populations are small and isolated, large numbers of their seeds are illegally
collected, and many plants are illegally dug out for commercial purposes, the species face
an uncertain future.
In 1986, in order to protect this valuable genetic resource, one natural reserve was
established in Fangcheng, which is one of the primary habitats of the species in China, yet
germplasm of the other nine populations (Table 1), especially ZZC, GMS, ZD, FL, BLC, DWJ,
DYC, and JDC which harbor private alleles were never included. Since the unprotected
populations can be quite vulnerable, the protection of their habitats is particularly urgent,
and for this purpose, given the significant genetic divergence between the regions of
Nanning and Fangcheng, a separate management unit could be considered for each of
them. Moreover, as the populations of the Fangcheng region were divided into four
genetically distinct groups, for each of these four groups, a separate management subunit
based on genetic differentiation, different population sizes, as well as the presence of
private alleles could also be considered. Finally, the NZS population needs to be prioritized
for conservation in the Fangcheng region, as almost all regional genotypes were present in
this population (Figure 1c).
An increased legal framework will undoubtedly be required to protect the species
against the illegal harvesting of seeds and the digging up of plants from natural populations,
especially to allow the plants to persist. In addition, management efforts in the form
of transplantations and reintroductions may also play an important role in recovering
threatened populations [
89
]. Given that the present population size of C. nitidissima is
generally small and that seedling recruitment is halted in some populations, augmentation
of each population by artificially propagated progenies of local individuals is recommended
to increase effective population size. It is also worth noting that plants recruited from local
seed sources are more likely to exhibit increased fitness over non-local genotypes in specific
environments. Genetic theory predicts that the transfer of individuals from one population
to another could result in outbreeding depression and reduced fitness [
9
,
12
,
90
]. However,
for some populations that lack seed sources, artificially increasing the number of individuals
by using the closest genetic neighbors could be justified. This could be the case for the FL,
Forests 2022,13, 1662 14 of 17
DYC, and JDC populations that not only consist of an extremely low number of individuals
(39, 32, and 37, respectively) but also lack seed sources. Thus, reinforcement by this method
could be considered. Finally, given that significant genetic divergences exist between the
populations of Nanning and Fangcheng, the transfer of individuals from one region to
another should be carried out with caution.
In the 1980s, an ex situ conservation program for this species was started at Guilin
Botanical Garden, in which C. nitidissima seeds and seedlings collected from Yongning,
Fangcheng, Dongxing, and Fusui counties were introduced. Today, there are about 800
C. nitidissima individuals in the garden, and most of them blossom and set fruits [
21
];
however, according to the sampling record, the germplasm of the Long’an County (ZZC)
has not been included. Therefore, the collection of seeds from ZZC populations, along with
the enhancement of current ex situ conservation, should be conducted. For C. nitidissima,
the extant twelve populations of the two regions may have evolved into locally adapted
ecotypes. Hence, care should be taken not to cross the Nanning stock and the Fangcheng
stock until it is known whether outbreeding depression could be a problem.
Supplementary Materials:
The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/f13101662/s1, Table S1. The proportion of alleles in differ-
ent allelic frequency classes for different (sub)populations of Camellia nitidissima; Figure S1. Plant
photos of Camellia nitidissima; Figure S2. Graphic representation of proportion of alleles in different
allelic frequency classes for different (sub)populations of Camellia nitidissima; Figure S3. Criteria for
selecting the optimal Kfor assignment of all individuals during the Bayesian cluster analysis; Figure
S4. Criteria for selecting the optimal Kfor assigning individuals in C2 during the Bayesian cluster
analysis; Figure S5. Relationship between pairwise F
ST
/(1
−
F
ST
) and geographic distance among
populations of Camellia nitidissima.
Author Contributions:
Conceptualization, Z.C., X.H. and X.W.; data processing and formal analyses,
Z.C., J.W., J.T. and Z.W.; writing—original draft, Z.C., X.H. and X.W.; review and editing, Z.C., J.W.,
J.T. and Z.W.; funding acquisition, S.C. and X.W.; overall investigation, Z.C., Z.W. and X.H. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the National Natural Science Foundation of China (NO:
32060248), the Key Research and Development Project of Guangxi (NO: GuiKeAB22080044), and the
Central Guidance on Local Science and Technology Development Fund (NO: ZY21195035).
Institutional Review Board Statement:
The sample collection was permitted by the Guangxi Fangcheng
Management Division of Yellow Camellia National Nature Reserve, and the sampling process was
followed by the Regulations of the People’s Republic of China on the Protection of Wild Plants.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The raw data is available from the first author for any further
information/queries.
Conflicts of Interest: The authors declare no conflict of interest.
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