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Article

Genetic Differentiation and Population Structure of Threatened Prunus africana Kalm. in Western Cameroon Using Molecular Markers

by
Justine G. Nzweundji
1,2,
Ulrike Huewe
3,
Nicolas Niemenak
2,
Néhémie T. Donfagsiteli
1 and
Klaus Eimert
3,*
1
Institute of Medical Research and Medicinal Plants Studies, P.O. Box 1663 Yaounde, Cameroon
2
Department of Biological Science, Higher Teacher’s Training College, P.O. Box 47 Yaounde, Cameroon
3
Institute of Molecular Plant Sciences, Geisenheim University, Von-Lade-Strasse 1, D-65366 Geisenheim, Germany
*
Author to whom correspondence should be addressed.
Diversity 2020, 12(12), 446; https://doi.org/10.3390/d12120446
Submission received: 30 October 2020 / Revised: 19 November 2020 / Accepted: 23 November 2020 / Published: 26 November 2020
(This article belongs to the Special Issue The Application of Microsatellite Markers in Evaluating Plant Genetic Diversity)
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Abstract

:
Genetic diversity of species is an important baseline for the domestication process. In Cameroon, Prunus africana, an important and threatened medicinal tree, is among the priority species for domestication. The bark extract has been used to treat various diseases; mainly benign prostatic hyperplasia which affects men above the age of 50. As little is known about the genetic diversity of P. africana in Cameroon, we aimed to determine the genetic diversity and differentiation of several P. africana populations in the western provinces, using sets of chloroplast DNA markers and nuclear microsatellites previously developed for Prunus species. Genetic diversity in the observed populations was considerable and genetic differentiation between populations proved substantial with 21% of the total observed variation detected among populations, revealing a distinct genetic structure among certain populations. However, the lack of correlation between genetic and geographic distances does not support isolation by distance (IBD). The analysis of chloroplast DNA haplotypes revealed no strong phylogeographic component in the genetic structure observed in the western populations of P. africana in Cameroon. The outcome of this study will contribute to improve the genetic characterization of P. africana for its better domestication and conservation in the Cameroon agroforestry system.

1. Introduction

Prunus africana (Hook. f.) Kalkman, of the Rosaceae family, is a multipurpose tree species that grows in Afromontane forests and whose barks have been used for traditional healing for hundreds of years [1,2,3]. These barks have been used for generations in African traditional medicine to address prostatic hyperplasia that affects 50% of men above the age of 50. The bark extract has been shown to successfully treat prostatic hyperplasia in rats [4,5]. In 1997, at least 23 different companies sold brand-name herbal preparations made from P. africana barks; currently, at least 40 brand-name products containing P. africana bark extracts are marketed directly in 10 countries [6]. P. africana exploitation has moved from local to large-scale commercial use for national and international trade over the past decades [7]. The global demand for P. africana bark is estimated at 4000 t per year for a value of finished goods estimated at USD 220 million [6]. One of the main supply sources to satisfy such demand consists of natural populations in Cameroon [8]. However, the high demand and overexploitation of the species have led to its classification as endangered species under CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora) in 1995 restricting its exportation.
Generally speaking, such massive losses of valuable plant species and the resulting adverse environmental and socioeconomic impacts have recently triggered efforts aimed at the conservation of plant resources. Therefore, designing strategies for improved management of species appears urgent. Such strategies include the characterization of plant species for improved domestication and conservation. Molecular characterization reveals the genetic variation of different accessions of plant species which is essential for plant improvement, selection and conservation [9,10]. Such characterization has been carried out for the identification and differentiation of species and the correlation between geographical and genetic distances between different accessions of species and within and between their populations [11,12,13]. While the characterization of agroforestry plants appears the best way to conserve many priority species for domestication, studies assessing the genetic diversity of plant species in Africa are scarce [14]. For instance, low differentiation and high gene flow have been shown among wild populations and between wild and cultivated populations of Dacryodes edulis in Cameroon [15]. In Irvingia gabonensis, pronounced phenological differences between observed populations suggest genetic differentiation within the taxa [16]. In the Prunus genus, molecular markers have been used for the characterization of genetic diversity; including isozyme markers for genetic identification [17]. Additionally, assessment of genetic variability using simple sequence repeats (SSR) markers on Prunus domestica L. has shown high diversity in terms of the degree of polymorphism [18,19,20]. Randomly amplified polymorphic DNA (RAPD) and SSR molecular markers have been applied for better knowledge of germplasm diversity in Prunus persica [21,22,23]. The genetic diversity of P. persica showed polymorphic patterns amongst the cultivars [24], with some markers possibly linked to commercial characteristics, which could be useful to fruit growers and breeding programs [24,25]. SSR and AFLP (Amplified Fragment Length Polymorphisms) molecular markers proved useful and highly informative for grouping and identifying Prunus avium (sweet cherry), which show diverse genetic variation [26,27,28,29]. Six microsatellite loci (nuclear simple sequence repeats (nSSRs)) initially characterized in P. avium were successfully transferred into P. africana [30,31,32,33]. In P. africana, nuclear and chloroplast molecular markers were used to assess genetic diversity and differentiation across several African countries, specifically to model conservation approaches considering threats by agricultural expansion and climate change [6]. Previous studies have shown high genetic diversity in P. africana, using randomly amplified polymorphic DNA (RAPD) [34] and microsatellite markers [35]. It has also been shown that accessions from Cameroon are genetically close to some accessions in West and East Africa [33]. However, the genetic structure of P. africana populations in East Africa showed low levels of diversity [10]. Thus, molecular diversity and differentiation of P. africana still need to be assessed in more detail [36]. To the best of our knowledge, few studies have been conducted to determine the degree of diversity within and between accessions of different agroecological areas in Cameroon.
The main objective of this project is to assess the genetic diversity and differentiation of P. africana in Cameroon using microsatellite DNA markers and chloroplast haplotypes across three regions of Cameroon’s habitats. The study will help to characterize P. africana germplasm from different locations as well as to determine the degree of genetic diversity and differentiation found in the sampled ranges in Cameroon. Appropriate characterization of plant materials is essential for the successful conservation of plant resources, and to ensure their sustainable use. The study will contribute to improve the sustainable exploitation of P. africana’s natural resources, by providing information on genetic variation and differentiation within the species, supporting selective breeding programs for crop improvement or conservation measures at local and regional levels.

2. Materials and Methods

2.1. Population Sampling and DNA Extraction

Prunus africana leaves were sampled from local trees in February to early March 2018 in three regions of Cameroon where P. africana is native with the help of forest officers and farmers in the indicated rural areas. Leaves of P. africana were harvested in the South-West Region at Ekona (SW), in the Littoral Region at Koupe-Manengouba (LT), and finally, in the North-West Region at Oku (NW) (Table 1). At least 20 trees (about 15 years of age) per region were sampled. Fresh leaves were pressed and dried in a stove at 39 °C for 48 h then stored in Ziploc bags (S.C. Johnson & Son Inc., Racine, WI, USA) until DNA extraction was performed.
DNA was extracted from 60 mg of dried leaves using the Invisorb®Spin Plant Mini Kit (STRATEC Molecular GmbH, Berlin, Germany). The supplier’s protocol was slightly modified by homogenizing the leaves without the use of liquid nitrogen in a Retsch mill (Retsch GmbH, Haan, Germany) for 3 min at the frequency of 25 Hz. DNA quantity was assessed using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Darmstadt, Germany).

2.2. Microsatellite Analysis

For SSR analysis, DNA was amplified using five primer pairs previously adapted for P. africana (UDP96-018, UDP97-403, PS12A02, EMPaS06, EMPaS10; see Table S1).
DNA amplifications were carried out in reaction volumes of 17 µL containing 10 µL of DNA (2 ng/µL), 1 µL of primer (2 µM) for EMPaS06, PS12A02, and UDP96-018 or 1.2 µL of primer (2 µM) for EMPaS10, and UDP97-403, respectively, 0.7 µL of dNTPs (5 mM each), 1.7 µL of 10x PCR-buffer, 0.13 µL of DreamTaq DNA Polymerase (Thermo Fisher Scientific, Germany) and 0.13 µL of BSA. PCR was performed using a different annealing temperature (see Table S2). In each case, the forward primer was labeled with fluorescent dyes.
Amplified DNA fragments were then separated using a Beckmann Coulter CEQ 8800 (Beckmann Coulter GmbH, Krefeld, Germany) capillary electrophoresis followed by fragment size calling using the company’s software.
As P. africana is known to be tetraploid we analyzed the SSR data as dominant traits. The complete SSR character table for all individuals is available in the supplementary materials (spreadsheet). Descriptive population statistics, heterozygosity, and genetic similarities were calculated using GenAlEx 6.5 [37]. FAMD 1.31. [38] was then used to investigate population differentiation and structure implementing cluster analysis (NJ—neighbor-joining, [39]), AMOVA (set to 999 permutations [40], and principal coordinate analysis (PCoA, with 1,000,000 iterations [41]. We also conducted a Bayesian cluster analysis using Structure 2.3.2 [42,43,44] with assumed admixture and correlated allele frequencies (set for 23 clusters, with 100,000 burn-ins, 100,000 Monte Carlo Markov chain steps, 20 runs). The Structure Harvester web service [45] was then used to calculate the most likely number of clusters (K) according to Evanno et al. [46]. We tested for isolation by distance (IBD) using the mantel test [47,48] with 1000 simulations implemented in TFPGA version 1.3 [49].

2.3. Chloroplast DNA Analysis

Three sets of primers were designed for use with P. africana based on the complete chloroplast sequence of Prunus mume (Genbank acc. KF765450). These primer sets (RL37, RP22, MDO2; Table S3) amplify three distant intergenic regions of the large subunit of the plastid DNA. DNA amplifications were carried out in reaction volumes of 50.0 µL containing 25 µL of DNA (2 ng/µL), 6 µL of primer (2 µM), 2.8 µL of dNTPs (5 mM), 5 µL of 10x PCR-buffer, 0.4 µL of DreamTaq DNA Polymerase (Thermo Fisher Scientific, Darmstadt, Germany), 0.4 µL of BSA, 2.4 µL of MgCl2, 2 µL of bidistilled water. Amplified DNA fragments were purified using the mi-PCR Kit (Metabion GmbH, Planegg, Germany). Sequencing was performed using the same primers by Macrogen Europe (Amsterdam, The Netherlands). The combined sequences of all intergenic regions were aligned with CodonCode 6.0.2 (CodonCode Corporation, Centerville, OH, USA) using the integrated Muscle algorithm [50]. The resulting multilocus haplotypes were assigned manually and a reduced median network was constructed using Network (vers. 5.0.0.3, www.fluxus-engineering.com) [51]. We tested for phylogeographic structures by calculating and comparing Nst and Gst values using PERMUT and CpSSR (vers. 2.0) [52]. The geographic distribution of the haplotypes was visualized using the PhyloGeoViz web service (phylogeoviz.org) [53].

3. Results

3.1. Microsatellite Analysis

Descriptive population statistics were derived from the analysis of 157 loci in 65 individuals from three geographic locations (Table 2). In all regions, most of these loci were polymorphic, with few private or fixed bands observed and no fixed private bands. The population in the South-West Region showed 89.5% of polymorphic bands and the highest number of private bands (13).
A binary traits table was constructed from allele occurrences and used for further calculations. An AMOVA based on Fst (Phist) with the complete dataset of all populations showed that, while the majority of variation was located within the populations (79%), a significant part of genetic variation differentiated among populations (21%) (Table 3).
Distance-based cluster analysis was conducted based on all loci for all individuals using a UPGMA with the Jaccard algorithm [54], presented as an unrooted radial cladogram (Figure 1). Here, it seems noteworthy that one subcluster contains all individuals from LT and two from SW, while the remaining individuals from SW and NW did not separate clearly and were distributed over several other clusters. Thus, P. africana plants from LT were more distant to those from SW and NW than the individuals from SW and NW were from each other.
Principal coordinate analysis of all individuals showed a similar picture of population structure with the first three axes explaining about 33% of the genetic variations (axis one 16.80%, axis two 8.44%, and axis three 7.79%) (Figure 2). Numbers represent individuals from the LT (blue diamonds), the SW (red triangles), and the NW (green dots) sampling regions.
Here, the first component separates the region LT from SW and NW. Regions SW and NW were less clearly separated on axis two.
A Bayesian structure analysis supported the above picture, revealing the highest likelihood for a population structure at k = 2 (DeltaK = 96.6) (Figure 3). There, the somewhat higher admixture of LT and SW, compared to LT and NW, coincides with the geographical proximity of the regions.
As a Mantel test could not establish any significant correlation between the observed genetic and geographic distances (r = 0.1001, one-sided p ≤ 0.4670 from 1000 randomizations), there is no support for isolation by distance.

3.2. Chloroplast DNA Analysis

Analysis of three intergenic regions of the chloroplasts revealed a rather high haplotype diversity. In the 65 individuals, we could identify 18 variable loci leading to 11 distinct haplotypes in P. africana in the surveyed regions (Figure 4A,B). The distribution of those haplotypes seemed spatially distinct. The highest abundant haplotype (Figure 4A) occurred in both, the SW and NW regions, but not in LT. All the other haplotypes were restricted to single regions, each. The NW region is slightly less diverse harboring only three haplotypes (one major (1) and two minor ones (6,7)). SW region harbors the same major haplotype (1), a lesser (3) and two minor ones (10,11). Region LT has the most diverse haplotype composition (one major (2), two lesser (4,5) and two minor ones (8,9)). Generally, the chloroplast haplotype network does not display the typical star-like structure, suggesting no recent genetic bottleneck with subsequent population expansion [55,56]. Despite the apparent spatial structure, NST values (0.394) were not found to be higher than the corresponding GST values (0.622), pointing to a lack of a phylogeographic factor (p < 0.01).

4. Discussion

In an attempt to determine the genetic diversity and differentiation of P. africana in Cameroon, plants from three geographic regions were analyzed for nuclear and chloroplastic genetic variation. As P. africana is known to be tetraploid we analyzed the SSR data as dominant traits. Such data transformation will lose information on codominance and, thus, may lead to a certain loss of resolution [57,58]. Nevertheless, the validity of this approach has been demonstrated repeatedly in other plant species [59,60,61,62]. Our transformed microsatellite data showed a genetic heterozygosity within the regions between 0.118 and 0.141. This is higher than reported before for P. africana [63]. However, as this species is predominantly outcrossing and its seeds are efficiently dispersed by birds and monkeys [7], our results are well in the range reported for other outcrossing, woody species [64].
Previous studies showed the genetic structure of P. africana on the mainland of Cameroon to be homogeneous and differentiated from other African regions [7]. Here, we could show that not all regions of Cameroon are genetically undifferentiated. While populations from certain geographic regions seem indeed genetically close (NW vs. SW), there is clear genetic differentiation between others (LT vs. SW and NW). Interestingly, the genetic differentiation is stronger between geographically near populations (LT vs. SW) than between the much more distant ones (NW vs. SW). Thus, isolation by distance (or by dispersal limitation) can be ruled out. Haplotype data support the differentiation of the regions, especially for region LT. Here, a phylogenetic component could not be established. There are no obvious geographic barriers between the LT and the SW regions. Both locations belong to the same agro-ecological area characterized by humid forest with monomodal rainfall [65]. Apart from the geographic distance, the North-Western Region (NW) is somewhat separated from LT and SW by the Western Highlands of Cameroon. This agro-ecological area is characterized by high rainfall and irregular topography such as mountains, hills, and plateaus [66]. Thus, there seem to be no obvious natural barriers to gene flow among the two populations which are the most genetically distant despite being geographically near. The Western Highlands of Cameroon do not seem to constitute a barrier to gene flow in P. africana, as the NW and SW populations belong to the same genetic cluster. However, there assuredly has been a strong anthropogenic influence on the ecosystems in the observed regions. While there still are large areas of continuous tropical forest, southern Cameroon is experiencing rapid forest loss [67]. Deforestation and the changing of forested land for unregulated agricultural practices by farmers negatively influenced biodiversity and threatened the natural forest ecosystems in the Littoral and South-West Regions [68]. Habitats are becoming more fragmented and gene flow could be impacted for many species [69]. Such increasing habitat fragmentation is also observed in the North-West Region. However, while a reduced gene flow could explain the genetic differentiation between the geographically near regions of LT and SW, a similar or an even higher level of differentiation would then also be expected between the populations of the SW and NW Regions. However, no such differentiation occurs there. Thus, recent fragmentation is probably not the cause of the observed differentiation but may act to intensify it. We can also not exclude transplantations of P. africana from other areas to the Littoral side. The possible source of such transplants is unknown as the genetic cluster observed in the Littoral Region has not been found in the other sampled areas.
Summarizing, we can state that populations of P. africana in western Cameroon are genetically diverse and that we find certain genetic differentiation among some of them. We observed a larger genetic cluster consisting of the two geographically distant populations NW and SW, and the distinct smaller cluster consisting of the LT population. No clear cause for the differentiation could be established. Considering the widespread use of the plant as a medicinal resource a strong anthropogenic factor in plant dispersal may be assumed.
Disregarding the cause of the observed differentiation, our results have consequences for the conservation and domestication of P. africana in Cameroon. Domestication and breeding programs should take into account that there are obvious genetic differences between potential source populations. To identify possible specific sources of distinct biological and agricultural traits, the origins of collected materials used in those programs should be monitored and be kept track of. Additionally, for conservation measures, it would be prudent to maintain the observed genetic structures. While, in extreme cases of genetic impoverishment of populations, it may be advantageous to use genetically distant individuals for genetic enrichment, the danger exists that outbreeding depressions and/or maladaptation to different ecological situations might be the consequence [70]. In the analyzed P. africana populations, we found a degree of heterozygosity in the same range as has been reported for other outcrossing, insect pollinated, woody plants [64], and no evidence of genetic impoverishment. Thus, for conservation measures, it should be considered to use local provenances to maintain possible genetic adaptations to the region. We suggest it useful to widen the scope of the study and to analyze populations of P. africana from all growing areas in Cameroon to obtain more detailed information on existing genetic structures within and among those areas to better facilitate conservation of the species and further, a more systematic approach in the sustainable exploitation of its full genetic possibilities in domestication and breeding programs.

Supplementary Materials

The following are available online at https://www.mdpi.com/1424-2818/12/12/446/s1, Table S1: Microsatellite primer sequences, number of alleles, fragment size ranges as reported in the literature, Table S2: PCR conditions for SSR analysis, Table S3: PCR conditions for chloroplast DNA analysis, spreadsheet: complete SSR character table for all individuals.

Author Contributions

Conceptualization, J.G.N., N.N., N.T.D. and K.E.; methodology, J.G.N., U.H. and K.E.; formal Analysis, J.G.N., U.H. and K.E.; data Curation, J.G.N., U.H. and K.E.; writing—J.G.N., and K.E.; writing—review and editing, J.G.N., N.N., N.T.D. and K.E.; visualization, K.E.; supervision, N.T.D. and K.E.; project administration, N.T.D. and K.E.; funding acquisition, J.G.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the TWAS-DFG programme (the World Academy of Sciences—Deutsche Forschungsgemeinschaft), grant number SCHR388/3-1. The APC was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft DFG), project number 432888308, and the Open Access Publishing Fund of Geisenheim University.

Acknowledgments

The authors would like to thank Max-Berhnard Schröeder of the Hochschule Geisenheim University for his mentorship and support.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Diversity 12 00446 g001 550
Figure 1. Radial dendrogram presenting subclusters with all individuals. Numbers represent individuals from the LT (blue), the SW (red), and the NW (green) Regions, respectively.
Figure 1. Radial dendrogram presenting subclusters with all individuals. Numbers represent individuals from the LT (blue), the SW (red), and the NW (green) Regions, respectively.
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Diversity 12 00446 g002 550
Figure 2. Principal coordinate analysis of all individuals from all sampling regions. Numbers represent individuals from the LT (blue diamonds), the SW (red triangles), and the NW (green dots) Regions. The first two axes explain about 25% of the genetic variations (axis one 16.80%, axis two 8.44%).
Figure 2. Principal coordinate analysis of all individuals from all sampling regions. Numbers represent individuals from the LT (blue diamonds), the SW (red triangles), and the NW (green dots) Regions. The first two axes explain about 25% of the genetic variations (axis one 16.80%, axis two 8.44%).
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Figure 3. Bayesian estimation of the genetic structure of the sampled regions. Maximum support for K = 2 (DeltaK = 96.6) assuming admixture, correlated alleles and no local priors. LT, SW and NW—sampled populations from the Littoral, South-West and North-West Regions, respectively.
Figure 3. Bayesian estimation of the genetic structure of the sampled regions. Maximum support for K = 2 (DeltaK = 96.6) assuming admixture, correlated alleles and no local priors. LT, SW and NW—sampled populations from the Littoral, South-West and North-West Regions, respectively.
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Figure 4. Prunus africana chloroplast haplotypes. (A) Distribution of haplotypes across all sampled regions. (B) Reduced median network of chloroplast DNA haplotypes; lines with markings represent single mutational steps; The size of the circles indicates the frequency of occurrence.
Figure 4. Prunus africana chloroplast haplotypes. (A) Distribution of haplotypes across all sampled regions. (B) Reduced median network of chloroplast DNA haplotypes; lines with markings represent single mutational steps; The size of the circles indicates the frequency of occurrence.
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Table
Table 1. Sampled populations of P. africana, geographic location of sampling areas, and sample sizes.
Table 1. Sampled populations of P. africana, geographic location of sampling areas, and sample sizes.
PopulationLongitude° E aLatitude° N aSample Size
NW10.4801696.19687520
LT9.30544.21371720
SW9.68044.84383325
a Geographic coordinates in decimal degrees (WGS84). South-West Region at Ekona (SW); Littoral Region at Koupe-Manengouba (LT); North-West Region at Oku (NW).
Table
Table 2. Descriptive population statistics from different sampling areas.
Table 2. Descriptive population statistics from different sampling areas.
PopulationNumber of Polymorphic Bands (%)Number of Fixed Bands aNumber of Private Bands bNumber of Fixed Private Bands cNe d
st.d.		He e
st.d.
NW37 (64.9)1501.170
0.029		0.122
0.017
LT24 (42.1)1101.199
0.044		0.118
0.024
SW51 (89.5)01301.205
0.034		0.141
0.019
Total57 (100) 01.191
0.021		0.127
0.012
a Monomorphic band presence in a given population; b bands found only in one given population; c private bands monomorphic in a given population; d No. of Effective Alleles = 1/(p2 + q2); e expected heterozygosity = 2 × p × q.
Table
Table 3. Hierarchical partitioning of the genetic variation within and among sample regions (AMOVA).
Table 3. Hierarchical partitioning of the genetic variation within and among sample regions (AMOVA).
SourceDfSSMSEst. Var.%
Among Regions268,90034,450136321%
Within Regions62316,3005102510279%
Total64385,200 6464100%
Df—degrees of freedom, SS—sum of squares, MS—mean square, Est. Var.—estimate of variance.
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Nzweundji, J.G.; Huewe, U.; Niemenak, N.; Donfagsiteli, N.T.; Eimert, K. Genetic Differentiation and Population Structure of Threatened Prunus africana Kalm. in Western Cameroon Using Molecular Markers. Diversity 2020, 12, 446. https://doi.org/10.3390/d12120446

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Nzweundji JG, Huewe U, Niemenak N, Donfagsiteli NT, Eimert K. Genetic Differentiation and Population Structure of Threatened Prunus africana Kalm. in Western Cameroon Using Molecular Markers. Diversity. 2020; 12(12):446. https://doi.org/10.3390/d12120446

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Nzweundji, Justine G., Ulrike Huewe, Nicolas Niemenak, Néhémie T. Donfagsiteli, and Klaus Eimert. 2020. "Genetic Differentiation and Population Structure of Threatened Prunus africana Kalm. in Western Cameroon Using Molecular Markers" Diversity 12, no. 12: 446. https://doi.org/10.3390/d12120446

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