Genetic Differentiation and Population Structure of Threatened Prunus

diversity

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

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; [email protected] (J.G.N.); [email protected] (N.T.D.) 2 Department of Biological Science, Higher Teacher’s Training College, P.O. Box 47 Yaounde, Cameroon; [email protected] 3 Institute of Molecular Plant Sciences, Geisenheim University, Von-Lade-Strasse 1, D-65366 Geisenheim, Germany; [email protected] * Correspondence: [email protected]

Received: 30 October 2020; Accepted: 23 November 2020; Published: 26 November 2020

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.

Keywords: microsatellite; SSR; chloroplast DNA; haplotypes; genetic diversity; Prunus africana

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 [1–3]. 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

Diversity 2020, 12, 446; doi:10.3390/d12120446 www.mdpi.com/journal/diversity Diversity 2020, 12, 446 2 of 11 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 [11–13]. 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 [18–20]. Randomly amplified polymorphic DNA (RAPD) and SSR molecular markers have been applied for better knowledge of germplasm diversity in Prunus persica [21–23]. 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 [26–29]. Six microsatellite loci (nuclear simple sequence repeats (nSSRs)) initially characterized in P. avium were successfully transferred into P. africana [30–33]. 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. Diversity 2020, 12, 446 3 of 11

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) (Table1). 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.

Table 1. Sampled populations of P. africana, geographic location of sampling areas, and sample sizes.

a a Population Longitude◦ E Latitude◦ N Sample Size NW 10.480169 6.196875 20 LT 9.3054 4.213717 20 SW 9.6804 4.843833 25 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).

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 [42–44] 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]. Diversity 2020, 12, 446 4 of 11

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 (Table2). 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).

Table 2. Descriptive population statistics from diﬀerent sampling areas.

Number of Number Number of Number of Ne d He e Population Polymorphic of Fixed Private Fixed Private st.d. st.d. Bands (%) Bands a Bands b Bands c 1.170 0.122 NW 37 (64.9) 1 5 0 0.029 0.017 1.199 0.118 LT 24 (42.1) 1 1 0 0.044 0.024 1.205 0.141 SW 51 (89.5) 0 13 0 0.034 0.019 1.191 0.127 Total 57 (100) 0 0.021 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. × × 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%) (Table3). 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 (Figure1). 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. DiversityDiversity2020 2020, 12, 12, 446, x FOR PEER REVIEW 55 of of 11 12 Diversity 2020, 12, x FOR PEER REVIEW 5 of 12

Df—degrees of freedom, SS—sum of squares, MS—mean square, Est. Var.—estimate of variance. TableDf—degrees 3. Hierarchical of freedom, partitioning SS—sum of the of geneticsquares, variation MS—mean within square, and amongEst. Var.—e samplestimate regions of (AMOVA).variance.

Distance-basedDistance-basedSource clustercluster analysisanalysis Df waswas conductedconducted SS basedbased MS onon allall Est. lociloci Var. forfor allall individualsindividuals % usingusing aa UPGMA with the Jaccard algorithm [54], presented as an unrooted radial cladogram (Figure 1). UPGMA withAmong the Jaccard Regions algorithm 2 [54], presented 68,900 as 34,450an unrooted 1363 radial cladogram 21% (Figure 1). Here, it seems noteworthy that one subcluster contains all individuals from LT and two from SW, Here, it seemsWithin noteworthy Regions that one 62 subcluster 316,300 contains all 5102 individuals 5102 from LT 79%and two from SW, whilewhile thethe remainingremaining individualsindividuals fromfrom SWSW andand NWNW diddid notnot separateseparate clearlyclearly andand werewere distributeddistributed Total 64 385,200 6464 100% overover several several other other clusters. clusters. Thus, Thus, P. P. africana africana plants plants from from LT LT were were more more distant distant to to those those from from SW SW and and Df—degrees of freedom, SS—sum of squares, MS—mean square, Est. Var.—estimate of variance. NWNW than than the the individuals individuals from from SW SW and and NW NW were were from from each each other. other.

FigureFigure 1. 1.Radial Radial dendrogram dendrogram presenting presenting subclusters subclusters with all individuals.with all individuals. Numbers representNumbers individuals represent Figure 1. Radial dendrogram presenting subclusters with all individuals. Numbers represent fromindividuals the LT (blue), from the the LT SW (blue) (red),, andthe SW the (red), NW (green) and the Regions, NW (green) respectively. Regions, respectively. individuals from the LT (blue), the SW (red), and the NW (green) Regions, respectively. Principal coordinate analysis of all individuals showed a similar picture of population structure PrincipalPrincipal coordinate coordinate analysis analysis of of all all individuals individuals showed showed a a similar similar picture picture of of population population structure structure with the ﬁrst three axes explaining about 33% of the genetic variations (axis one 16.80%, axis two 8.44%, withwith thethe firstfirst threethree axesaxes explainingexplaining aboutabout 33%33% ofof thethe geneticgenetic variationsvariations (axis(axis oneone 16.80%,16.80%, axisaxis twotwo and axis three 7.79%) (Figure2). Numbers represent individuals from the LT (blue diamonds), the SW 8.44%,8.44%, andand axisaxis threethree 7.79%)7.79%) (Figure(Figure 2).2). NumbersNumbers representrepresent individualsindividuals fromfrom thethe LTLT (blue(blue (red triangles), and the NW (green dots) sampling regions. diamonds),diamonds), the the SW SW (red (red triangles), triangles), and and the the NW NW (green (green dots) dots) sampling sampling regions. regions.

Figure 2. Principal coordinate analysis of all individuals from all sampling regions. Numbers FigureFigure 2.2.Principal Principal coordinate coordinate analysis analysis of all of individuals all individuals from all from sampling all sampling regions. Numbersregions. representNumbers represent individuals from the LT (blue diamonds), the SW (red triangles), and the NW (green dots) individualsrepresent individuals from the LT from (blue the diamonds), LT (blue diamonds), the SW (red the triangles), SW (red triangles), and the NW and (green the NW dots) (green Regions. dots) Regions. The first two axes explain about 25% of the genetic variations (axis one 16.80%, axis two TheRegions. ﬁrst two The axes first explain two axes about explain 25% ofabout the genetic25% of variationsthe genetic (axis variations one 16.80%, (axis axisone two16.80%, 8.44%). axis two 8.44%). 8.44%).

Diversity 2020, 12, 446 6 of 11

Here,Diversity the 2020 ﬁrst, 12 component, x FOR PEER REVIEW separates the region LT from SW and NW. Regions SW and6 of NW 12 were less clearly separated on axis two. A BayesianHere, structurethe first component analysis separates supported the region the above LT from picture, SW and revealing NW. Regions the highestSW and NW likelihood were for a less clearly separated on axis two. population structure at k = 2 (DeltaK = 96.6) (Figure3). There, the somewhat higher admixture of LT DiversityA Bayesian2020, 12, x FOR structure PEER REVIEW analysis supported the above picture, revealing the highest likelihood6 of for 12 and SW,a comparedpopulation structure to LT and at NW,k = 2 (DeltaK coincides = 96.6) with (Fig theure geographical 3). There, the somewhat proximity higher of the admixture regions. of Here, the first component separates the region LT from SW and NW. Regions SW and NW were AsLT a Mantel and SW, test compared could to not LT establish and NW, coincides any signiﬁcant with the correlation geographical between proximity the of the observed regions.genetic and less clearly separated on axis two. geographic distancesAs a Mantel (r test= 0.1001, could not one-sided establish pany 0.4670significant from correlation 1000 randomizations), between the observed there genetic is no support A Bayesian structure analysis supported the above picture, revealing the highest likelihood for and geographic distances (r = 0.1001, one-sided≤ p ≤ 0.4670 from 1000 randomizations), there is no for isolationa population by distance. structure at k = 2 (DeltaK = 96.6) (Figure 3). There, the somewhat higher admixture of support for isolation by distance. 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.

Figure 3.FigureBayesian 3. Bayesian estimation estimation of of the the geneticgenetic structure structure of the of thesampled sampled regions. regions. Maximum Maximum support for support K = 2 (DeltaK = 96.6) assuming admixture, correlated alleles and no local priors. LT, SW and 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.. NW—sampled populations from the Littoral, South-West and North-West Regions, respectively. 3.2. ChloroplastFigure 3. Bayesian DNA Analysis estimation of the genetic structure of the sampled regions. Maximum support for 3.2. ChloroplastK = DNA2 (DeltaK Analysis = 96.6) assuming admixture, correlated alleles and no local priors. LT, SW and NW—sampledAnalysis of three populations intergenic from thregionse Littoral, of South-West the chloroplasts and North-West revealed Regions, a rather respectively. high haplotype Analysisdiversity. of In three the 65 intergenic individuals, regions we could of theidentify chloroplasts 18 variable revealed loci leading a ratherto 11 distinct high haplotypehaplotypes in diversity. In the 653.2.P. individuals,africana Chloroplast in theDNA we surveyed couldAnalysis identify regions (Figure 18 variable 4A,B). loci The leading distribution to 11 of distinct those haplotypes haplotypes seemed in P. africana spatially distinct. The highest abundant haplotype (Figure 4A) occurred in both, the SW and NW in the surveyedAnalysis regions of three (Figure intergenic4A,B). regions The distribution of the chloroplasts of those revealed haplotypes a rather seemed high spatiallyhaplotype distinct. regions, but not in LT. All the other haplotypes were restricted to single regions, each. The NW The highestdiversity. abundant In the 65 haplotype individuals, (Figure we could4A) identify occurred 18 variable in both, loci the leading SW andto 11 NWdistinct regions, haplotypes but notin in LT. region is slightly less diverse harboring only three haplotypes (one major (1) and two minor ones All the otherP. africana haplotypes in the surveyed were restricted regions (Figure to single 4A,B). regions, The distribution each. The NWof thos regione haplotypes is slightly seemed less diverse (6,7)). SW region harbors the same major haplotype (1), a lesser (3) and two minor ones (10, 11). spatially distinct. The highest abundant haplotype (Figure 4A) occurred in both, the SW and NW harboringRegion only LT three has haplotypesthe most diverse (one haplotype major (1) composition and two minor (one onesmajor (6,7)). (2), two SW lesser region (4,5) harbors and two the same regions, but not in LT. All the other haplotypes were restricted to single regions, each. The NW major haplotypeminor ones (1), (8,9)). a lesser Generally, (3) and the chloroplast two minor haplot onesype (10,11). network Region does not LT display has the the most typical diverse star-like haplotype region is slightly less diverse harboring only three haplotypes (one major (1) and two minor ones compositionstructure, (one suggesting major (2), no tworecent lesser genetic (4,5) bottleneck and two with minor subsequent ones (8,9)).populati Generally,on expansion the [55,56]. chloroplast (6,7)). SW region harbors the same major haplotype (1), a lesser (3) and two minor ones (10, 11). Despite the apparent spatial structure, NST values (0.394) were not found to be higher than the haplotypeRegion network LT has doesthe most not diverse display haplotype the typical composition star-like (one structure, major (2), suggestingtwo lesser (4,5) no and recent two genetic corresponding GST values (0.622), pointing to a lack of a phylogeographic factor (p < 0.01). bottleneckminor with ones subsequent (8,9)). Generally, population the chloroplast expansion haplotype [55 network,56]. Despite does not thedisplay apparent the typical spatial star-like structure, NST valuesstructure, (0.394) suggesting were not no found recent to genetic be higher bottleneck than thewith corresponding subsequent populati GSTonvalues expansion (0.622), [55,56]. pointing to a lack ofDespite a phylogeographic the apparent spatial factor structure, (p < 0.01). 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).

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. Diversity 2020, 12, 446 7 of 11

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 [59–62]. 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 Diversity 2020, 12, 446 8 of 11 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 http://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.

References

1. Kalkman, C. The Old-World Species of Prunus Subg. Laurocerasus Including Those Formerly Referred to Pygeum. Blumea 1965, 13, 1–174. Available online: http://www.repository.naturalis.nl/document/566657 (accessed on 25 November 2020). 2. Hall, J.; O’Brien, M.E.; Sinclair, F. Prunus Africana, a Monograph; University of Wales Bangor, Mount Cameroon Project, ICRAF; University of Wales Bangor, School of Agricultural and Forest Sciences: Bangor, UK, 2000; 104p, ISBN 1-84220-048-8. 3. Jimu, L. Threats and conservation strategies for the African Cherry (Prunus africana) in its natural range-a review. J. Ecol. Nat. Environ. 2011, 3, 118–130. [CrossRef] 4. Jena, A.K.; Vasisht, K.; Sharma, N.; Kaur, R.; Dhingra, M.S.; Karan, M. Amelioration of testosterone induced benign prostatic hyperplasia by Prunus species. J. Ethnopharmacol. 2016, 190, 33–45. [CrossRef] 5. Nyamai, D.W.; Arika, W.M.; Rachuonyo, H.O.; Wambani, J.R.; Ngugi, M.P. Herbal management of benign prostatic hyperplasia. J. Cancer Sci. Ther. 2016, 8, 130–134. [CrossRef] 6. Cunningham, A.B.; Ayuk, E.; Franzel, S.; Duguma, B.; Asanga, C. An Economic Evaluation of Medical Tree Cultivation; Prunus Africana in Cameroon. People and Plants Working Paper 10.UNESCO. 2002. Available online: http://www.worldagroforestry.org/publication/economic-evaluation-medicinal-tree-cultivation- prunus-africana-cameroon (accessed on 25 November 2020). 7. Vinceti, B.; Loo, J.; Gaisberger, H.; Van Zonneveld, M.J.; Schueler, S.; Konrad, H.; Kadu, C.A.C.; Geburek, T. Conservation Priorities for Prunus africana Deﬁned with the Aid of Spatial Analysis of Genetic Data and Climatic Variables. PLoS ONE 2013, 8, e59987. [CrossRef] 8. Nsawir, A.T.; Ingram, V. Prunus africana: Money growing on trees? A plant that can boost rural economies in Cameroon highland. FAO Nat. Faunal J. Value Biodivers. 2007, 22, 1–6. Diversity 2020, 12, 446 9 of 11

9. Mbatude, M.; Mwanjololo, M.; Kakudidi, E.K.; Dalitz, H. Modelling the potential distribution of endangered Prunus africana (Hook. F.) Kalkm. in East Africa. Afr. J. Ecol. 2011, 50, 393–403. [CrossRef] 10. Mbatude, M.; Nyakaana, S.; Plobe, S.; Delitz, H. Genetic structure of Prunus africana Rosaceae (Hook. f.) Kalman. In East Africa, as inferred from nuclear and chloroplast DNA. Afr. J. Plant Sci. Biotechnol. 2013, 7, 9–14. 11. Ben Mustapha, S.; Ben Tamarzizt,H.; Baraket, G.; Abdallah, D.; Salhi, A.H. Genetic diversity and differentiation in Prunus species (Rosaceae) using chloroplast and mitochondrial DNA CAPS markers. Genet. Mol. Res. 2015, 14, 4177–4188. [CrossRef] 12. Dias, E.F.; Moura, M.; Schaefer, H.; Silva, L. Geographical distance and barriers explain population genetic patterns in an endangered island perennial. AoB Plants 2016, 8, plw072. [CrossRef] 13. Bao, W.; Wuyun, T.; Li, T.; Liu, H.; Jiang, Z.; Zhu, X.; Du, H.; Bai, Y.-E. Genetic diversity and population structure of Prunus mira (Koehne) from the Tibet plateau in China and recommended conservation strategies. PLoS ONE 2017, 12, e0188685. [CrossRef][PubMed] 14. Kadu, C.A.C.; Schüler, S.; Konrad, H.; Muluvi, G.M.M.; Eyog-Matig, O.; Muchugi, A.; Williams, V.L.; Ramamonjisoa, L.; Kapinga, C.; Foahom, B.; et al. Phylogeography of the Afromontane Prunus africana reveals a former migration corridor between East and West African highlands. Mol. Ecol. 2010, 20, 165–178. [CrossRef][PubMed] 15. Tchinda, N.D.; Wanjala, B.W.; Muchugi, A.; Fotso, F.; Nzweundjl, G.; Ndoumou, D.O.; Skilton, R. Genetic diversity and gene flow revealed by microsatellite DNA markers in some accessions of African Plum (Dacryodes edulis) in Cameroon. Afr. J. Biotechnol. 2016, 15, 511–517. [CrossRef] 16. Vihotogbé, R.; Berg, R.G.V.D.; Bongers, F.; Sinsin, B.; Sosef, M.S.M. Does phenology distinguish bitter and sweet African bush mango trees (Irvingia spp., Irvingiaceae)? Trees 2014, 28, 1777–1791. [CrossRef] 17. Martínez-Gómez, P.; Dandekar, A.M.; Gradziel, T.M.; Lopez, M.; Batlle, I.; Alonso, J.M.; Ortega, E.; Sánchez-Pérez, R.; Dicenta, F. Identification of Self-Incompatibility Alleles in Almond and Related Prunus Species Using PCR. Acta Hortic. 2003, 128, 397–401. [CrossRef] 18. Decroocq, V.; Hagen, L.S.; Favé, M.G.; Eyquard, J.P.; Pierronnet, A. Microsatellite markers in the hexaploid Prunus domestica species and parentage lineage of three European plum cultivars using nuclear and chloroplast simple-sequence repeats. Mol. Breed. 2004, 13, 135–142. [CrossRef] 19. Xuan, H.; Ding, Y.; Spann, D.; Möller, O.; Büchele, M.; Neumuller, M. Microsatellite markers (srr) as a tool to assist in identification of european plum (prunus domestica). Acta Hortic. 2011, 918, 689–692. [CrossRef] 20. Urrestarazu, J.; Pina, A.; Errea, P. Diversity and genetic structure of European plum in mountainous areas of Northeastern Spain. Acta Hortic. 2017, 1172, 129–132. [CrossRef] 21. Wünsch, A.; Hormaza, J.I. Molecular characterisation of sweet cherry (Prunus avium L.) genotypes using peach [Prunus persica (L.) Batsch SSR sequences. Heredity 2002, 89, 56–63. [CrossRef] 22. Hong, J.-H.; Yi, S.-I.; Kwon, Y.-S.; Kim, Y.; Choi, K.-J. Genetic Diversity Analysis of Peach [Prunus persica (L.) Batsch] Varieties Using SSR Markers. Korean J. Breed. Sci. 2013, 45, 201–211. [CrossRef] 23. Sharma, P.; Sharma, R.; Rana, M.; Sharma, H. Molecular characterization of peach (Prunus persica) germplasm to study genetic diversity using RAPD molecular markers. Green Farming 2015, 6, 1246–1249. 24. Nagaty, M.A.; El-Assal, S.E.-D.; Rifaat, M.M. Characterization of the Genetic Diversity of Peach Cultivars in Taif by RAPD-PCR. Am. J. Appl. Sci. 2011, 8, 708–715. [CrossRef] 25. Trivedi, P.; He, Z.; Van Nostrand, J.D.; Albrigo, G.; Zhou, J.; Wang, N. Huanglongbing alters the structure and functional diversity of microbial communities associated with citrus rhizosphere. ISME J. 2012, 6, 363–383. [CrossRef] 26. Struss, D.; Ahmad, R.; Southwick, S.M.; Boritzki, M. Analysis of Sweet Cherry (Prunus avium L.) Cultivars Using SSR and AFLP Markers. J. Am. Soc. Hortic. Sci. 2003, 128, 904–909. [CrossRef] 27. Turet-Sayar, M.; Turkec, A.; Demir, T. Identification of Sweet Cherry Cultivars (Prunus avium L.) and Analysis of Their Genetic Relationship Using Microsatellite DNA Fingerprinting. J. Agric. Sci. 2012, 4, 134. [CrossRef] 28. Stanys, V.; Frercks, B.; Šikšnianiene,˙ J.B.; Stepulaitiene,˙ I.; Gelvonauskiene,˙ D.; Staniene,˙ G.; Bobinaset, C.ˇ Identification of sweet cherry (Prunus avium L.) cultivars using AFLP and SSR markers. Žemdirbyste˙ 2012, 99, 437–444. Available online: https://pdfs.semanticscholar.org/2c5d/ebb9fb5e1d6aed9786d82a6765d8d6b7950f. pdf?_ga=2.204826192.1928654254.1584445211-1813343098.1584445211 (accessed on 25 November 2020). 29. Ivanovych, Y.; Volkov, R. Genetic relatedness of sweet cherry (Prunus avium L.) cultivars from Ukraine determined by microsatellite markers. J. Hortic. Sci. Biotechnol. 2017, 93, 64–72. [CrossRef] Diversity 2020, 12, 446 10 of 11

30. Cipriani, G.; Lot, G.; Huang, W.-G.; Marrazzo, M.T.; Peterlunger, E.; Testolin, R. AC/GT and AG/CT microsatellite repeats in peach [Prunus persica (L) Batsch]: Isolation, characterisation and cross-species amplification in Prunus. Theor. Appl. Genet. 1999, 99, 65–72. [CrossRef] 31. Sosinski, B.; Gannavarapu, M.; Hager, L.D.; Beck, L.E.; King, G.; Ryder, C.D.; Rajapakse, S.; Baird, W.V.; Ballard, R.E.; Abbott, A.G. Characterization of microsatellite markers in peach [Prunus persica (L.) Batsch]. Theor. Appl. Genet. 2000, 101, 421–428. [CrossRef] 32. Vaughan, S.P.; Russell, K. Characterization of novel microsatellites and development of multiplex PCR for large-scale population studies in wild cherry, Prunus avium. Mol. Ecol. Notes 2004, 4, 429–431. [CrossRef] 33. Kadu, C.A.C.; Konrad, H.; Schüler, S.; Muluvi, G.M.; Eyog-Matig, O.; Muchugi, A.; Williams, V.L.; Ramamonjisoa, L.; Kapinga, C.; Foahom, B.; et al. Divergent pattern of nuclear genetic diversity across the range of the Afromontane Prunus africana mirrors variable climate of African highlands. Ann. Bot. 2012, 111, 47–60. [CrossRef] 34. Muchugi, A.; Lengkeek, A.; Kadu, C.; Muluvi, G.; Njagi, E.; Dawson, I.K. Genetic variation in the threatened medicinal tree Prunus africana in Cameroon and Kenya: Implications for current management and evolutionary history. South Afr. J. Bot. 2006, 72, 498–506. [CrossRef] 35. Avana, M.L.; Tchoundjeu, Z.; Bell, M.J.; Vaillant, A.; Chevalier, M.H. Diversité génétique de Prunus africana (Hook. f.) Kalkman au Cameroun. Bois For. Trop. 2004, 282, 41–49. [CrossRef] 36. Mihretie, Z.; Schüler, S.; Konrad, H.; Bekele, E.; Geburek, T. Patterns of genetic diversity of Prunus africana in Ethiopia: Hot spot but not point of origin for range-wide diversity. Tree Genet. Genomes 2015, 11, 1–13. [CrossRef] 37. Peakall, R.; Smouse, P.E. genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 2006, 6, 288–295. [CrossRef] 38. Schlüter, P.M.; Harris, S.A. Analysis of multilocus fingerprinting data sets containing missing data. Mol. Ecol. Notes 2006, 6, 569–572. [CrossRef] 39. Saitou, N.; Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 1987, 4, 406–425. [CrossRef] 40. Excoffier, L.; Smouse, P.E.; Quattro, J.M. Analysis of Molecular Variance Inferred from Metric Distances among DNA Haplotypes: Application to Human Mitochondrial DNA Restriction Data. Genetics 1992, 131, 479–491. 41. Gower, J.C. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 1966, 53, 325–338. [CrossRef] 42. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 2000, 155, 945–959. 43. Falush, D.; Stephens, M.; Pritchard, J.K. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes 2007, 7, 574–578. [CrossRef][PubMed] 44. Hubisz, M.J.; Falush, D.; Stephens, M.; Pritchard, J.K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 2009, 9, 1322–1332. [CrossRef][PubMed] 45. Earl, D.A.; Vonholdt, B.M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 2012, 4, 359–361. [CrossRef] 46. Evanno, G.; Regnaut, S.; Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 2005, 14, 2611–2620. [CrossRef][PubMed] 47. Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967, 27, 209–220. [PubMed] 48. Sokal, R. Testing statistical significance of geographic variation patterns. Syst. Zool. 1979, 28, 227. [CrossRef] 49. Miller, M.P. Tools for Population Genetic Analyses (TFPGA) 1.3: A Windows Program for the Analysis of Allozyme and Molecular Population Genetic Data. 1997. Available online: http://www.marksgeneticsoftware. net/tfpga.htm (accessed on 2 March 2018). 50. Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [CrossRef] 51. Bandelt, H.J.; Forster, P.; Sykes, B.C.; Richards, M.B. Mitochondrial Portraits of Human Populations Using Median Networks. Genetics 1995, 141, 743–753. Diversity 2020, 12, 446 11 of 11

52. Burban, C.; Petit, R.J.; Carcreff, E.; Jactel, H. Rangewide variation of the maritime pine bast scale Matsucoccus feytaudi Duc. (Homoptera: Matsucoccidae) in relation to the genetic structure of its host. Mol. Ecol. 1999, 8, 1593–1602. [CrossRef] 53. Tsai, Y.-H.E. PhyloGeoViz: A web-based program that visualizes genetic data on maps. Mol. Ecol. Resour. 2010, 11, 557–561. [CrossRef] 54. Jaccard, P. Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull. Soc. Vaud. Sci. Nat. 1901, 37, 241–272. [CrossRef] 55. Stefenon, V.M.; Klabunde, G.; Lemos, R.P.M.; Rogalski, M.; Nodari, R.O. Phylogeography of plastid DNA sequences suggests post-glacial southward demographic expansion and the existence of several glacial refugia for Araucaria angustifolia. Sci. Rep. 2019, 9, 2752. [CrossRef][PubMed] 56. Doorenweerd, C.; Jose, M.S.; Barr, N.; Leblanc, L.; Rubinoff, D. Highly variable COI haplotype diversity between three species of invasive pest fruit fly reflects remarkably incongruent demographic histories. Sci. Rep. 2020, 10, 6887. [CrossRef] 57. Dufresne, F.; Stift, M.; Vergilino, R.; Mable, B.K. Recent progress and challenges in population genetics of polyploid organisms: An overview of current state-of-the-art molecular and statistical tools. Mol. Ecol. 2014, 23, 40–69. [CrossRef][PubMed] 58. Wang, J.; Scribner, K.T. Parentage and sibship inference from markers in polyploids. Mol. Ecol. Resour. 2014, 14, 541–553. [CrossRef][PubMed] 59. Mengoni, A.; Gori, A.; Bazzicalupo, M. Use of RAPD and microsatellite (SSR) variation to assess genetic relationships among populations of tetraploid alfalfa, Medicago sativa. Plant Breed. 2000, 119, 311–317. [CrossRef] 60. Lian, C.; Oishi, R.; Miyashita, N.; Nara, K.; Nakaya, H.; Wu, B.; Zhou, Z.; Hogetsu, T. Genetic structure and reproduction dynamics of Salix reinii during primary succession on Mount Fuji, as revealed by nuclear and chloroplast microsatellite analysis. Mol. Ecol. 2003, 12, 609–618. [CrossRef] 61. Pfeiffer, T.; Roschanski, A.M.; Pannell, J.R.; Korbecka, G.; Schnittler, M. Characterization of Microsatellite Loci and Reliable Genotyping in a Polyploid Plant, Mercurialis perennis (Euphorbiaceae). J. Hered. 2011, 102, 479–488. [CrossRef] 62.L ópez-Vinyallonga, S.; Soriano, I.; Susanna, A.; Montserra, J.M.; Roquet, C.; Garcia-Jacas, N. The Polyploid Series of the Achillea millefolium Aggregate in the Iberian Peninsula Investigated Using Microsatellites. PLoS ONE 2015, 10, e0129861. [CrossRef] 63. Dawson, I.K.; Powell, W. Genetic variation in the Afromontane tree Prunus africana, an endangered medicinal species. Mol. Ecol. 1999, 8, 151–156. [CrossRef] 64. Hamrick, J.L.; Godt, M.J.W.; Sherman-Broyles, S.L. Factors influencing levels of genetic diversity in woody plant species. New For. 1992, 6, 95–124. [CrossRef] 65. Nembungwe, R.M.; Tamfuh, P.A.; Bonguen, S.M.N. Morphology, Physicochemical Characteristics and Land Suitability in the Western Highlands of Cameroon. Int. J. Plant Soil Sci. 2015, 7, 29–44. [CrossRef] 66. Nembungwe, R.M.; Nghonda, J.P.; Tematio, P.; Basga, S.D. Changes in soil properties and soil organic carbon stocks along an elevation gradient at Mount Bambouto, Central Africa. Catena 2019, 175, 251–262. [CrossRef] 67. Ingram, V.; Ewane, M.; Ndumbe, L.N.; Awono, A. Challenges to governing sustainable forest food: Irvingia spp. from southern Cameroon. For. Policy Econ. 2017, 84, 29–37. [CrossRef] 68. Mahmoud, M.I.; Campbell, M.J.; Sloan, S.; Alamgir, M.; Laurance, W.F. Land-cover change threatens tropical forests and biodiversity in the Littoral Region, Cameroon. Oryx 2020, 54, 882–891. [CrossRef] 69. Cheek, M.; Cable, S.; Hepper, F.N.; Ndam, N.; Watts, J. Mapping plant biodiversity on mount cameroon. In The Biodiversity of African Plants; van der Maesen, L.J.G., van der Burgt, X.M., van Medenbach de Rooy, J.M., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1996; pp. 110–120. [CrossRef] 70. McKay, J.K.; Christian, C.E.; Harrison, S.; Rice, K.J. “How Local Is Local?”—A Review of Practical and Conceptual Issues in the Genetics of Restoration. Restor. Ecol. 2005, 13, 432–440. [CrossRef]

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