Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 May 2021 | 13(6): 18588–18597
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.5820.13.6.18588-18597
#5820 | Received 10 March 2020 | Final
received 18 March 2021 | Finally accepted 20 May 2021
An assessment of genetic
variation in vulnerable Borneo Ironwood Eusideroxylon
zwageri Teijsm. & Binn. in Sarawak using SSR markers
Siti Fatimah Md.-Isa 1,
Christina Seok Yien Yong 2, Mohd Nazre Saleh 3 & Rusea Go
4
1,2,4 Universiti Putra Malaysia, Faculty Science,
Department of Biology, 43400 Serdang, Selangor, Malaysia.
3 Universiti Putra Malaysia, Faculty of
Forestry and Environment, Department of Forestry Science and Biodiversity,
43400 Serdang, Selangor, Malaysia.
1 sitifatimahmdisa@gmail.com, 2
chrisyong@upm.edu.my, 3 nazre@upm.edu.my, 4 rusea@upm.edu.my
(corresponding author)
Editor: Ritesh Kumar Choudhary, Agharkar Research Institute, Pune, India. Date
of publication: 26 May 2021 (online & print)
Citation: Md.-Isa, S.F., C.S.Y. Yong, M.N.
Saleh & R. Go (2021). An assessment of genetic
variation in vulnerable Borneo Ironwood Eusideroxylon
zwageri Teijsm. & Binn. in Sarawak using SSR markers. Journal of Threatened Taxa 13(6): 18588–18597. https://doi.org/10.11609/jott.5820.13.6.18588-18597
Copyright: © Isa et al. 2021. Creative Commons Attribution
4.0 International License. JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: Financial support was provided by the
Ministry of Higher Education Malaysia for the Fundamental Research Grants Scheme project no. FRGS/1/2015/WAB13/UPM/02/3. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors
declare no competing interests.
Author details: SITI FATIMAH MD.-ISA earned her
PhD from Universiti Putra Malaysia in biodiversity
and conservation of natural resources. CHRISTINA SEOK YIEN YONG is a lecturer
at Department of Biology, Faculty of Science, Universiti
Putra Malaysia. Her research interest is in plant molecular genetics. MOHD
NAZRE SALEH is an Associate Professor at Department of Forestry Science and
Biodiversity, Faculty of Forestry and Environement, Universiti Putra Malaysia. Specialization in botany and
forest ecology. RUSEA GO is a Professor
at Department of Biology, Faculty of Science, Universiti
Putra Malaysia. She is an orchid expert with specialization in plant taxonomy
and conservation.
Author contributions: MISF collected, performed the experiments, analyzed data and
contributed in giving ideas. CSYY, MNS-assisted in analyzing the data, reviewed
and suggested some comments on the article.
RG-Principal investigator of the research grant and assisted in
designing and planning the research. MISF, RG-Wrote the manuscript. All authors
gave final approval for publication.
Acknowledgements: We are grateful to the Forest Department of Sarawak
(FDS) and Sarawak Forestry Co-operation (SFC) for the permit to conduct the
study and permission to collect samples from the sampling sites. We are thankful to the Ministry of Higher
Education Malaysia for the award of the Fundamental Research Grants Scheme
project no. FRGS/1/2015/WAB13/UPM/02/4 made available to the Universiti Putra Malaysia. We are thankful to Mohd. Akmal Raffi for his assistance during the fieldwork.
Abstract: Borneo Ironwood Eusideroxylon zwageri
Teijsm. & Binn. has
high market value for its valuable and durable timber, which has put it at risk
due to illegal logging. This study
analysed E. zwageri genetic variation
using four microsatellite markers in populations at Nirwana
Rehabilitation Forest (NRF), and Tatau, Sarawak. We found that 20.1% of total genetic
variation corresponded to differences between populations, while 79.9% was
attributed to differences among individuals from the same population. The Tatau population had lower genetic
diversity compared to NRF, and both populations showed depressed heterozygosity
indicative of inbreeding. Allelic data
were also used to confirm variety level differences proposed by earlier
workers, and three informal varieties: zwageri,
grandis, and exilis
were recognized in the study area.
It is expected that the results from this study could serve as baseline
data for conservation of this vulnerable species.
Keywords: Allelic data, Belian, DARwin, GenAlex, IUCN Red List, Malaysia.
Introduction
The Borneo Ironwood Eusideroxylon zwageri Teijsm. & Binn. is one of the
most treasured and crucial commercial timber trees endemic to the Asian forest
(Malaysia, Indonesia, Brunei, and The Philippines). It is known locally as ‘Belian’
in Malaysia, ‘Ulin’ in Indonesia, and ‘Tambulan’ in The Philippines. The species belongs to the Lauraceae family that includes the avocado, bay laurel and
cinnamon tree. It has been listed as
‘Vulnerable’ on the IUCN Red List of Threatened Species due to
over-exploitation and habitat destruction (Asian Regional Workshop 1998). This species will remain endangered unless
circumstances threatening its survival and reproduction improve. Slow growth rates (mean radial growth rate is
0.058cm per year; Kurokawa et al. 2003) and slow
regeneration in logging areas also contribute to lower numbers of forest trees.
Many
taxonomists have reported variety level morphological differences in E. zwageri (Teijsmann 1858; Teijsmann & Binnendijk 1863;
Koopman & Verhoef 1938; Kostermans
et al. 1994;), however, no valid taxonomic treatment for these varieties has
been proposed so far. Some of the
vernacular names given to the varieties were ‘Belian telor’, ‘Belian kapur’, ‘Belian sirap’, ‘Belian tanduk’, ‘Belian tembaga’, and ‘Belian lilin’.
Nevertheless, Irawan et al. (2016) tried to
confirm the presence of these varieties using amplified fragment length
polymorphism (AFLPs) studies. The study
showed promising results, with 98% out of a total of 50 samples clustered
according to the varieties recognized by the local people in Indonesia. The four varieties were informally recognized
as var. zwageri, var. exilis,
var. grandis, and var. ovoidus
(Irawan 2005a,b).
Local people in Sarawak also recognized these varieties based on the
differences in fruit’s form, bark or wood structure (Marzuki
pers. comm. 18.ix.2017), however, no valid taxonomic treatment has been
published.
This tree can
reach a height of up to 50m and may live over 1,000 years (Global Trees
Campaign 2020). Mature trees produce
large fruits that, although poisonous to humans, are important food sources for
foraging animals. The species is also
valued for cultural reasons. The wood is dense (0.85–1.1 g/cm3) (Irawan 2016), strong and resistant to decay, making it
preferred by indigenous people of Borneo for building houses. The Dayak people of Borneo believe the
tree protects them from dangerous animals, while ‘Murut’
(Borneo headhunter) use it to make blowpipes and Dusun
ancestors used it to create coffins. The
black pepper industry in Borneo has traditionally used Belian
wood as a support to the creeping herbs.
In the famous Murut Cultural Center of Malaysia, Belian wood
pillars are used.
The revised status of E. zwageri in Sarawak under Criteria B of IUCN
Red List (Md-Isa et al. 2021) indicates the need to formulate conservation
plans to protect this species from extinction.
In addition to economic or social information, occurrences and
distribution patterns of species, genetic information of the species is another
important aspect that needs to be considered in conservation action plans. Habitat fragmentation can contribute to the
reduction of genetic diversity of this species.
Although transplantation of E. zwageri
from other locations is a practical conservation strategy, an accurate
understanding of the genetic structure of natural population of E. zwageri is necessary for conserving
them. This is because relocation of the
species or reduction in size for other reasons will cause the loss of genetic
diversity in the new population through genetic drift (Lowe et al. 2005; Finlay
et al. 2017).
Little is known about the
genetics of E. zwageri, especially
in Sarawak. Two studies in Indonesia using
randomized amplified polymorphic DNA (RAPD) markers revealed 96% genetic
diversity of E. zwageri within
populations, and the remaining attributed to population differences (Harkingto et al. 2006; Rimbawanto
et al. 2006). This was probably due to
the samples that originated from the same population. Nurtjahjaningsih et
al. (2017a,b) showed high genetic diversity for E. zwageri
in Indonesia, and suggested transplantation among different populations
should be conducted with careful consideration.
A few studies on genotyping of E. zwageri
using direct amplified minisatellite DNA (DAMD)
marker (Yoon 2006), M13 universal marker (Siew 2005) and RAPD marker (Hong
2005) were mainly aimed at identifying and genotyping the two genera (Eusideroxylon and Potoxylon)
known by similar common names Belian in Sarawak.
In this study, two different
habitats were chosen to study the genetic variation of E. zwageri in Sarawak. One was a restoration forest, Nirwana Rehabilitation Forest (NRF) located in Universiti Putra Malaysia (UPM) Bintulu Campus. The other was a fragmented forest in Tatau,
Bintulu, Sarawak. This study was
conducted to assess the genetic variation of E. zwageri
by using four highly polymorphic microsatellite markers recently developed
for the species (Kurokochi et al. 2014). We compared genetic variation between the two
populations and within each population, and determined the level and pattern of
genetic variation in both areas. Allelic
data were also analyzed for the presence of variety
level differences in collected samples.
Materials
and methods
Sample collection
Two sampling sites were selected
as model habitats for this study; 1) Nirwana
Rehabilitation Forest (NRF) in UPM Bintulu Campus, and 2) fragmented forest
area in Tatau, Bintulu, Sarawak (Figure 1).
Samplings were conducted in April 2016, August 2016, and September 2017,
over the periods of two weeks each.
Leaves were collected from 52 trees, of which 39 from NRF and 13 from
Tatau forest. A single leaf was collected
per tree. The leaf materials were kept
in silica gel prior to DNA extraction.
DNA extraction
Total genomic DNA was isolated
using conventional Cetyl-Trimethyl Ammonium Bromide
(CTAB) method (Doyle & Doyle 1987) with some modifications. The leaf materials were ground with CTAB
buffer until fine paste and transferred into sterilized 1.5mL micro-centrifuge
tube. In the fume hood, 500μL of 2X preheated (at 65oC) CTAB
extraction buffer [2% (w/v) hexadecyltrimethylammonium bromide (CTAB); 1.4 M
sodium chloride (NaCl); 100 mM
Tris-HCl, pH 8.0; 20 mM
ethylenediamine tetra-acetic acid (EDTA), pH 8.0; 1–2% (w/v) polyvinyl-pyrrolidone (PVP-40T)] and 2μL of 1% β-mercaptoethanol
were added to each sample in the 1.5mL micro-centrifuge tube and vortex gently
until mixed well. The homogenized
mixture was then incubated for 20–30 minutes at 50oC in a water bath
and inverted every five minutes (Md-Isa 2020).
The tubes were then transferred to another water bath and incubated at
65oC for another 15 minutes.
The samples were allowed to cool slightly before adding 500μL of
chloroform: isoamyl alcohol (24 : 1) and inverted to mix. At room temperature, the samples were gently
shaken for 15 minutes and centrifuged for 10 minutes at 12,500rpm. About 400μL of supernatant was transferred
into a new sterile 2.0mL screw cap micro-centrifuge tube (Md-Isa 2020).
The DNA was precipitated by
adding 800–1,000 μL of 95% cold ethanol and inverted
gently, and allowed to precipitate up to three hours or longer in -20oC
freezer. The tubes were then centrifuged
for 10 minutes at 12,500rpm to pellet the DNA. The supernatant was discarded and the pellet
was washed with 500μL of 80% cold ethanol and gently mixed for 10 minutes. Then, the pellet was centrifuged for five
minutes at 12,500rpm and the supernatant was discarded again. The DNA pellet was allowed to air dry at room
temperature before re-suspending with 100μL of TE buffer or distilled
water. The DNA samples were then kept at
-20oC for further usage.
Afterward, the DNA quantity was
estimated through electrophoresis on 1.0% (w/v) agarose gel. The gel was run in tris-boric acid-EDTA (TBE)
buffer with EtBr “Out” Staining Solution (YEASTERN BIOTECH Co. Ltd) at 90V for
30–45 minutes and quantified in comparison to the 1kb DNA ladder (Promega,
Madison, WI, USA) with known concentrations.
The band was visualized using UV transilluminator. The band intensity of the DNA product was
quantified to the intensity in the ladder (Promega 2020). The images were captured with DOC PRINT system
(Vilber Lourmat, USA).
Polymerase chain reaction (PCR)
Polymerase chain reactions (PCR)
were performed in a volume of 25µL using 2µL of DNA template (5–10 ng), 10µM of
each primer, 5X GoTaq buffer, 10mM dNTPs, and 25mM MgCl using an Eppendort AG 22331 Mastercycler. PCR
cycling conditions were as follows: a single cycle of pre-denaturation for two
min a 95oC, followed by 40 cycles, each consisting of 30 sec
denaturing at 95oC, 30 sec at annealing temperature 50oC,
and 30 sec elongation at 72oC, the last cycle ending with a single
cycle of final extension at 72oC for five min.
The PCR amplification of the DNA
sample was carried out using four highly polymorphic microsatellites developed
for E. zwageri (Kurokochi et al. 2014).
Forward primer of each marker was labeled with
fluorescent dye (Table 1). One
representative of PCR product from each primer was subjected to one direction
sequencing done by First Base Laboratories Sdn. Bhd.
(Seri Kembangan, Selangor, Malaysia) to confirm the amplification of the
microsatellite repeat region. The
products were then subjected to bi-directional sequencing for fragment analysis
(FA). FA were carried out to detect
changes in the length of a specific DNA sequence to indicate the presence or
absence of the microsatellite marker through detection of fluorescent label in
the PCR product. The size standard
(500-ROX) was combined with the sample of interest and co-injected on the
capillary electrophoresis system (ABI3730XL Applied Biosystems Genetic
Analyzer).
Data analysis
For data analysis, a total of 52
leaves samples were distributed into two populations with Tatau (13 samples) as
one population and NRF (39 samples) as one population. The scoring of allele sizes were performed
using GeneMapper® version 4.0 analysis software using
the service provided by First Base Laboratories Sdn.
Bhd. The alleles nearest to the expected
PCR product size were recorded; while the non-specific products, which were out
of range, were ignored. Allele
frequencies per locus and per population were analyzed
by FSTAT version 2.9.3 (Goudet 1995). Number of alleles (Na), expected
heterozygosity (He), observed heterozygosity (Ho) and
polymorphism information content (PIC) were also estimated using Cervus version 3.0. F-statistics, including inbreeding
coefficient of an individual relative to the subpopulations (FIS),
inbreeding coefficient of an individual relative to the total population (FIT),
and genetic differentiation index between population (FST) were
calculated using GenAlex version 6.501 (Peakall & Smouse 2012). The software was also employed to determine
genetic diversity within each population (Na, Ho, He,
and FIS).
Further, the allelic data of the
52 samples were subjected to estimation of genetic distance among genotypes
using simple matching coefficients by bootstrapping 1,000 times and then were
clustered using unweighted neighbor-joining method by
using Dissimilarity Analysis and Representation for Windows (DARwin) version 6.0.21 (Perrier & Jacquemoud-Collet
2006). This analysis was done to see if
there are any variety level differences in the collected samples. The names of
varieties used in the present study were adopted based on earlier studies (Irawan 2005a,b; Irawan et al.
2016).
Results
The 52 samples from E. zwageri trees were scored for all four
microsatellite DNA loci. The
amplification of the microsatellite repeats was confirmed by sequencing, where
specific repeat motifs were successfully identified. The PCR reactions which failed to produce
sufficient product for genotyping were recorded as missing data for all
analyses, however, locus Ez-04 yielded less than 20% amplification and was
discarded for the subsequent analysis.
Genetic variations among three
markers tested for all 52 individuals are summarized in Table 2. In total, 25 alleles were detected at these
three loci in 52 individuals, with the number of alleles per locus ranging from
3 (Ez-09) to 12 (Ez-05), with an average of 8.33 alleles per locus. Ez-09 showed lowest number of alleles being
amplified but it was detected in 50 individuals with 38 individuals from NRF
and 12 individuals from Tatau compared to Ez-05 detected in 42 individuals with
35 individuals from NRF and seven individuals from Tatau (Appendix 1).
Meanwhile, observed and expected
heterozygosity values of all three loci ranged 0.511–0.720 and 0.664–0.867,
respectively. Whereas, the average
observed heterozygosity for both populations (Ho=
0.593) was lower than the average expected heterozygosity (He= 0.791), which
may indicate moderate levels of genetic variation in the Belian
populations studied. While, the
polymorphic information content (PIC) value for all three loci were higher than
0.5 ranging from 0.583 to 0.841, which suggested all three loci used in this
study were highly polymorphic.
Besides, locus Ez-05 showed the
highest value of allelic richness (AR= 7.117) among the three
markers with total of 12 alleles being amplified from both populations;
however, among the 12 alleles amplified, only six alleles were identified in
Tatau compared to 10 alleles in NRF. The
distributions and allele frequencies of the three loci in both populations are
shown in Figure 2A–2C and listed in Appendix 1.
Furthermore, F-statistics were
estimated in a fixation index as shown in Table 2. The average inbreeding coefficient of the
individuals to the total population (FIT) was 0.295 while the
average inbreeding coefficient of the individuals to the subpopulation (FIS)
was 0.048. Whereas, the average genetic
differentiation (FST) of the subpopulation compared to the total
populations was 0.201. The average value
of FST indicated that about 20.1% of total genetic variation
corresponded to differences between populations, while 79.9% was explained by
differences between individuals of the same populations. And the lower FST value, which is
less than 0.25, may suggest that there is gene flow between the two
populations.
The genetic indicators within
each population are summarized in Table 3.
The data showed higher number of alleles (Na= 7.333) being amplified in
36 individuals in NRF populations. The
observed heterozygosity value (Ho= 0.659), however,
was lower than the expected heterozygosity (He= 0.739), which indicated
moderate level of genetic variation in NRF populations. Comparatively, in Tatau, lower number of
alleles (Na= 4.000) was found in nine individuals. Moderate level of genetic variation was also
observed in Tatau population by the lower number of observed heterozygosity (Ho= 3.99) than the expected heterozygosity (He=
0.563).
Generally, the lower number of
observed heterozygosity than the expected heterozygosity was also evidence that
both populations deviated from Hardy-Weinberg equilibrium. Both populations also showed a deficiency of
heterozygosity, indicated by positive FIS values (NRF= 0.054; Tatau=
0.165).
Additionally, the allelic data
for 52 samples were analyzed based on the estimation
of genetic distance among genotypes to segregate the individuals according to
their varieties. The unweighted neighbor-joining dendogram
grouped the 52 samples of the two populations into three varieties (Figure
3). Of the 52 samples, 15 samples were
grouped together as variety exilis, 16 samples
as variety grandis and 21 samples as variety zwageri.
Discussion
The informativeness of observed
loci across the two populations was measured based on polymorphic information
content (PIC). Theoretically, PIC values
range 0–1 (Hilderbrand et al. 1994). At a PIC of 0, the marker has only one
allele, and at a PIC of 1 the marker would have an infinite number of
alleles. Thus values of PIC greater than
0.5 are considered to be highly informative.
Data from the current study resulted in an average value of PIC= 0.746. Therefore, all the three loci used in this
study can be classified as highly informative loci (PIC >0.5) and
appropriate for assessing genetic variation.
Based on the average value of
expected heterozygosity (He= 0.791), a moderate level of genetic variation
among the 52 individuals studied was obtained.
Nevertheless, when compared the genetic variation between NRF and Tatau,
NRF showed higher genetic variation within population compared to Tatau, where
theoretically, the wild population should have higher genetic variation (Pandey
et al. 2004; Gauli et al. 2009). This may be due to the source of NRF trees,
which originated from several places. It
may not only limit to Bintulu area but from several places, which contribute to
the high level of genetic variation in the populations, However, inbreeding depression may still
occur because of the small size population.
As for wild population of Tatau, the genetic variation was lower than
expected, probably due to the small population size in an island forest
fragment within palm oil plantation, which may contribute to the low genetic
variation in the population.
In addition, heterozygote
deficiency was detected, which was depicted by the lower average value of
observed compared to expected heterozygosity (Ho <
He). It suggested that both populations
might be inbred. This was also evidenced
by the positive average value of FIS (Table 2), which observed a
stronger inbreeding in the Tatau population than the NRF population. Heterozygote deficit can be explained by
various factors such as non-random mating, unamplified alleles (null alleles)
and inappropriate sampling (population admixture Wahlund’s
effects) (Borsa et al. 1991; Castric
et al. 2002; Dharmarajan et al. 2012; Waples 2015).
Inbreeding often results from a
population bottleneck (genetic drift) due to anthropogenic or environmental
events. In this study, higher inbreeding
depression was observed in Tatau. The small population size and limited number
of standing trees in the area might be the main cause of the inbreeding
depression. Other factors could be
founder events, where a population has reduced genetic variation compared to
the original population and thus produces an apparent high level of
inbreeding. This phenomenon may happen
in the NRF population where seeds from several unknown populations in Bintulu
and outside Bintulu added the small size of the NRF population. The different source of seeds, however, may
contribute to the high genetic variation in NRF compared to Tatau.
Furthermore, based on Wright
(1978), FST= 0–0.05 indicates little population differentiation, FST=
0.05–0.15 indicates moderate differentiation, FST= 0.15–0.25
indicates high differentiation, and FST >0.25 indicates highest
differentiation. Current study revealed
high genetic differentiation between the two populations (FST=
0.201). The genetic differences might be
due to the extensive geographic range (population isolation) of the species and
the small population size.
This fixation index (FST)
may also provide approach for estimating inter-population gene flow (Nm) (Avise 2004). Average
gene flow (Nm= 1.826, Table 2) in the current study may indicate one incoming
migrant per generation in each population when FST= 0.201 (Wright
1931). This indicates some migration
(gene flow) at low rate between the two populations and low levels of
interbreeding.
Additionally, both populations
showed a possible deviation from Hardy-Weinberg equilibrium (HWE), which can be
observed through the lower number of observed heterozygosity (Ho) than expected heterozygosity (He). Another possible parameter to look for the
deviation from HWE in this study is the F-statistics. Populations are in Hardy-Weinberg equilibrium
if FIS= 0 and FIT= FST (Guries
& Ledig 1981).
The value of FIS ranges between -1 and +1. Negative FIS values indicate
heterozygote excess (outbreeding) and positive values indicate heterozygote
deficiency (inbreeding) compared with HWE expectations. Result from this study showed a positive FIS
value, (NRF= 0.054; Tatau= 0.165) which indicates deficiency of heterozygosity,
hence, a possible deviation from HWE.
The deviation from HWE can be
caused by several factors such as mutation, migration, random mating, selection
and small size population (Keats & Sherman 2013; Johnston et al.
2019). In this study, the disequilibrium
was probably caused by the small population size of both study sites. Consequently, sampling error is unavoidable
in this study. Small sample size
contributes to the violations of the HWE principles. Other factors such as migration or seed
dispersal by human or animal were observed in NRF, which also contribute to the
deviation from HWE. While in Tatau,
dispersal of seed was observed mainly through the river where the population is
situated. Inbreeding and population
isolation as discussed above may also contribute to the deviation from HWE.
Overall, results from this study
should be used with caution, as the number of marker and samples tested in this
particular study are not sufficient to make any conclusive statement. The PIC
of all the three markers, however, is very high, in this case the average is
0.746, which is more than 0.5 and suitable to use for the analysis. Nonetheless, future study where more samples
and more markers can be included should be carried out to have more comprehensive
understanding on the genetic variation of E. zwageri
especially in Sarawak.
The allelic data obtained in this
study were also used to segregate the samples according to their varieties
based on estimation of genetic distance among genotypes. The result showed that the 52 samples were
grouped into three varieties.
Morphological study conducted in the same sampling area documented
similar variations (Md-Isa 2020). The present
result demonstrates the existence of variety level differences in E. zwageri. Nevertheless,
further study on using more markers, samples and DNA barcoding will verify and
give more concrete answer for this finding.
Thorough work on taxonomy classification is ongoing to validate the
taxonomic status of the varieties in E. zwageri.
Conclusion
Genetic analysis of the two
populations of E. zwageri in
Bintulu, Sarawak shows that 20.1% of total genetic variation corresponded to
differences between populations. The
Tatau population was observed to have relatively lower genetic diversity
compared to NRF area. Therefore, it was suggested that establishment of the
restored population (NRF) from a limited number of individuals originated from
unknown places has higher level of genetic variation compared to the wild
population (Tatau). This occurrence,
however, resulted in the inbreeding due to genetic drift (population bottleneck
and founder effect). Furthermore,
inbreeding can lead to fixation of deleterious alleles and may lead to a
decrease in the genetic variation of the population.
Consequently, it is important to
obtain more detailed information on the genetic variation of this species in
order to form an effective conservation strategy. We suggest further conservation efforts
focused on ensuring suitable habitat for the continued recovery of this
species. Effort can be made to identify
locations of E. zwageri as
protected forest areas. This will
facilitate natural regeneration without disturbance. Alternatively, sprouting and cutting
techniques can be proposed, as the sprouts tend to grow faster and may
reach mature stage faster than regenerating them from seedling (Mostacedo et al. 2009).
In addition, the findings on the
segregation of the species into three varieties based on the allelic data are
promising. Validation of the names of
the varieties can now be proposed with effective taxonomic publication. It would also be interesting to establish DNA
barcodes for this species to confirm our observations. More samples from different locations and
different genetic markers are suggested to be included to support future study
on this topic.
Table 1. Characteristics of four
polymorphic microsatellite loci in Eusideroxylon zwageri (Kurokochi et al.
2014) used in the current study.
|
Marker |
Sequence 5’ – 3’ with
fluorescent label |
(Repeat motif)n |
Size range (bp) |
Na |
Ho |
He |
Tm (ºC) |
|
Ez-04 |
F04 (56FAM)
TTGAAGTGGACGTCCTCTAG R04 CCAAAGAAGCGAAGTAAGG |
(AC)16 |
205–234 |
10 |
0.74 |
0.74 |
58 |
|
Ez-05 |
F05 (5HEX)
TCCTCTTGGTGAAATCTTCTC R05 CAGTTTTCTTCTCCTCCCATTC |
(GA)15 |
255–282 |
14 |
0.86 |
0.91 |
58 |
|
Ez-07 |
F07 (5HEX)
CTTGCGGAATCAATGAGAACT R07 GTAGGTAGGTCCAACTGGAAG |
(TC)12 |
132–177 |
19 |
0.69 |
0.90 |
58 |
|
Ez-09 |
F09 (5HEX)
CGCTAAATTTAAGAAAACCGTCTC R09 CCAGTCCTGCAGTAGGCTC |
(TAC)12 |
275–299 |
10 |
0.74 |
0.76 |
58 |
Na—number of allele | Ho—observed heterozygosity | He—expected heterozygosity |
Tm—melting temperature.
Table 2. Genetic diversity
analyses over all three loci and populations based on number of alleles per
locus (Na), observed (Ho) and expected heterozygosity
(He), allelic richness (AR), polymorphism information content (PIC),
and F--statistic (FIT, FIS and FST).
|
Locus |
Na |
N |
Ho |
He |
AR |
PIC |
FIT |
FIS |
FST |
Nm |
|
Ez-05 |
12 |
42 |
0.548 |
0.867 |
7.117 |
0.841 |
0.299 |
0.242 |
0.076 |
3.054 |
|
Ez-07 |
10 |
45 |
0.511 |
0.842 |
6.618 |
0.815 |
0.438 |
0.371 |
0.107 |
2.078 |
|
Ez-09 |
3 |
50 |
0.720 |
0.664 |
2.988 |
0.583 |
0.146 |
-0.470 |
0.419 |
0.346 |
|
Total |
25 |
|
|
|
|
|
|
|
|
|
|
Average |
8.333 |
|
0.593 |
0.791 |
5.574 |
0.746 |
0.295 |
0.048 |
0.201 |
1.826 |
N—number of individuals | FIT—global
heterozygote deficit among populations | FIS—heterozygote deficit
within populations | FST—fixation index as genetic differentiation |
Nm—gene flow.
Table 3. Genetic diversity
analyses within populations.
|
Population |
N |
Na |
Ho |
He |
FIS |
|
NRF |
36 |
7.333 |
0.659 |
0.739 |
0.054 |
|
Tatau |
9 |
4.000 |
0.399 |
0.563 |
0.165 |
|
Average |
|
4.759 |
0.529 |
1.020 |
0.109 |
N—number of individuals analyzed | Na—number of alleles | Ho—observed
heterozygosity | He—expected heterozygosity | FIS—heterozygote
deficit within populations.
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Appendix 1. Weighted (W) and
unweighted (UW) allele frequencies per locus and per population. N is allele
size (base pair).
|
Locus |
Pop |
NRF, UPM |
Tatau, Bintulu |
All_W |
All_UW |
|
|
N |
35 |
7 |
|
|
|
Ez-05 |
228 |
0.029 |
0.071 |
0.036 |
0.050 |
|
|
234 |
0.114 |
0.071 |
0.107 |
0.093 |
|
|
238 |
0.029 |
0.143 |
0.048 |
0.086 |
|
|
240 |
|
0.214 |
0.036 |
0.107 |
|
|
242 |
- |
0.286 |
0.048 |
0.143 |
|
|
248 |
0.143 |
- |
0.119 |
0.071 |
|
|
250 |
0.286 |
- |
0.238 |
0.143 |
|
|
252 |
0.200 |
0.214 |
0.202 |
0.207 |
|
|
254 |
0.014 |
- |
0.012 |
0.007 |
|
|
256 |
0.129 |
- |
0.107 |
0.064 |
|
|
260 |
0.043 |
- |
0.036 |
0.021 |
|
|
264 |
0.014 |
- |
0.012 |
0.007 |
|
Locus |
Pop |
NRF, UPM |
Tatau, Bintulu |
All_W |
All_UW |
|
|
N |
35 |
10 |
|
|
|
Ez-07 |
115 |
0.371 |
- |
0.289 |
0.186 |
|
|
117 |
0.086 |
- |
0.067 |
0.043 |
|
|
121 |
0.057 |
0.150 |
0.078 |
0.104 |
|
|
123 |
0.014 |
0.150 |
0.044 |
0.082 |
|
|
125 |
0.171 |
0.350 |
0.211 |
0.261 |
|
|
127 |
- |
0.350 |
0.078 |
0.175 |
|
|
131 |
0.157 |
- |
0.122 |
0.079 |
|
|
133 |
0.086 |
- |
0.067 |
0.043 |
|
|
139 |
0.029 |
- |
0.022 |
0.014 |
|
|
147 |
0.029 |
- |
0.022 |
0.014 |
|
Locus |
Pop |
NRF, UPM |
Tatau, Bintulu |
All_W |
All_UW |
|
|
N |
38 |
12 |
|
|
|
Ez-09 |
255 |
0.158 |
0.958 |
0.350 |
0.558 |
|
|
261 |
0.329 |
0.042 |
0.260 |
0.185 |
|
|
270 |
0.513 |
- |
0.390 |
0.257 |