Genetic diversity and population structure of Trichilia emetica Vahl in western Kenya using ISSR markers

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Trichilia emetica , a versatile tree of ecological and economic importance in Kenya, remains uncharacterized at the genetic level. This study determined the genetic diversity and population structure of T. emetica in western Kenya using 15 Inter-Simple Sequence Repeat (ISSR) markers. A total of 171 DNA fragments were amplified, of which 94.65% were polymorphic, revealing substantial genomic variability. Overall, gene diversity was moderate (He = 0.15; I = 0.22), with Nandi (0.24) and Kakamega (0.18) populations showing the highest diversity while Kisumu and Siaya had the least (0.10). Analysis of molecular variance (AMOVA) partitioned 65% of total variation within populations and 35% among populations (ΦST = 0.35, p < 0.001), indicating moderate differentiation and restricted but ongoing gene flow (Nm = 2.39). The six populations were clustered into three major genetic groups consistent with ecological gradients. This study establishes the first molecular baseline for T. emetica in Kenya and demonstrate that ecological heterogeneity may play a role in influencing its genetic structure. Conservation work should preserve the populations in Nandi and Kakamega while implementing enrichment and assisted regeneration in Siaya and Kisumu populations to reduce loss of genetic diversity. Conservation genetics Forest restoration Genetic diversity ISSR markers Population structure Trichilia emetica Figures Figure 1 Figure 2 Introduction Most terrestrial biodiversity of the world is found in the forests that help provide ecological stability by regulating climate conditions, nutrients cycles, and supporting livelihoods. Nevertheless, uncontrolled, habitat fragmentation, logging, and unsustainable use of resources have caused extensive loss of genetic variation of trees, particularly in the tropics (Soares et al. 2019 ). Reduction in genetic diversity of intra-species undermines the adaptive ability of the population to environmental change and the resilience of forest ecosystems (Konrad et al. 2025 ). In such a way, the conservation of forest genetic resources has been on the global climate and biodiversity agendas, following the restoration objectives of the Bonn Challenge (International Union of Conservation of Nature [IUCN] 2011), Sustainable Development Goal 15 (Life on Land), and the United Nations Decade on Ecosystem Restoration (2021–2030), which put emphasis on the restoration and sustainable use of biodiversity to improve ecosystem resilience and human well-being (Forest and Trees, Agroforestry Flagship 1 2025; FAO 2025 ).Despite African forest ecosystems having ecological and sio-economic importance, the level of genetic research of native trees is underrepresented in comparison to Asia and Latin America. In recent genomic research, widely distributed species like Afzelia africana and A. quanzensis are only beginning to be studied at the population level (Donkpegan et al. 2020 ). The lowland humidity forests and the arid woodlands, are fragmenting at a high pace being by other land uses. Such pressures interfere with gene flow and can result in or cause inbreeding, genetic diversity loss, and reduced evolutionary capacity, such as studies of Acacia Senegal where human activities predominantly define spatial genetic structure (Omondi et al. 2023 ). Thus, it is important to comprehend genetic diversity patterns and population structure in the context of evidence-based conservation, restoration, and sustainable management of African forest trees, as exemplified by the recent genetic analysis of Vitellaria paradoxa to inform West Africa conservation planning (Attikora, et al. 2024 ). Trichilia emetica Vahl (Meliaceae), also known as Natal mahogany, is a multi-purpose tree species found widely in sub-Saharan Africa, from Senegal to the Red Sea, and moving eastwards and centrally through Africa to Congo and South Africa (PROTA 2025 ). It plays a significant ecological role in dry and moist forest ecosystems where it helps to establish the canopy structure, nutrient cycling, and habitat stability (Kenya Forestry Research Institute [KEFRI] 2021). It offers good-quality timber, which is valued to make furniture and construction, and its bark, seeds and leaves are also used in traditional ethnomedicine to treat fever, gastrointestinal disorders, and infection of the skins (Oyedeji-Amusa et al. 2021 ; Chebii et al. 2022 ). T. emetica also contains bioactive metabolites like limonoids, triterpenoids, and flavonoids, which have antimicrobial, antioxidant, and anti-inflammatory effects (Tsomele et al. 2021 ; Aldholmi et al. 2024 ). Furthermore, its seed oil possess good lipid profile with excellent oxidative stability, and could be a future nutraceutical and cosmetic ingredient source (Oyedeji-Amusa et al. 2021 ; Mabaso et al. 2025 ), and is proving to have an emerging economic payoff as a pharmaceutical and cosmetic industry feedstock.Morphological and phenological variation between the populations of T. emetica , such as leaf form, canopy architecture, fruit and seed size, and flowering time, has been reported in different ecological zones, which may be evidence of local adaptation and phenotypic plasticity (Akweni et al. 2021 ; KEFRI 2021; Tsomele et al. 2021 ). Although such variation indicates possible genetic differentiation, no population-level molecular analysis of T. emetica has yet been carried out to confirm it. Existing studies are restricted to morphological and ethnobotanical value, whereas molecular genetics has not been investigated in the populations of T. emetica in East Africa. In contrast, other genera of Meliaceae, e.g Khaya (Bouka et al. 2022 ), Swietenia (Alcalá et al. 2015 ; Limongi Andrade et al. 2022 ), Cedrela (Finch et al. 2022 ), Azadirachta and Melia (Cui et al. 2023), and Toona (Nie et al. 2025 ), have been analyzed using molecular and morphological markers, unveiling cryptic species boundaries, population differentiation, and adaptive divergence, of which were influenced by ecological and anthropogenic pressures. These findings demonstrate how habitat fragmentation, selective logging and ecological gradients can cause loss of genetic diversity and increase differentiation among populations. The absence of baseline molecular data for T. emetica is a critical knowledge gap in the evaluation of its intraspecific population structure and genetic diversity and limiting evidence-based conservation and restoration planning. With its ecological functions, commercial value, and extensive distribution, population structure and genetic diversity in T. emetica is required to form grounds for conservation and planning sustainable utilization. Molecular markers give a precise means of revealing genetic diversity and population structure in forest trees, in particular, when the phenotypic variation is influenced by environmental factors. Co-dominant markers like simple sequence repeats (SSRs) and single-nucleotide polymorphisms (SNPs) offer high and accuracy resolution of alleles but are expensive to carry out and require relatively large amount of genomic resources (Alicandri et al. 2022 ; Faria et al. 2024 ). On the contrary, dominant multilocus markers like Inter Simple Sequence Repeat (ISSR) remain affordable and applicable to non-model and under-studied species (Viswanathan et al. 2018 ; Borah et al. 2021 ; Le and Le 2024 ). Specifically, ISSR prove to be invaluable since they are reproducible, do not need prior sequence information, and produce high polymorphic loci that enable one to make precise estimation of the genetic variation and differentiation. They were shown to be able to identify population structure and gene flow in Meliaceae species, such as Melia dubia (Rawat et al. 2018 ). Even though ISSRs are not able to differentiate between homozygous and heterozygous loci, which is not advantageous as co-dominant markers, their high reproducibility and applicability in low-cost genotyping make them a reasonable genomic tool in genetic conservation and management (Alicandri et al. 2022 ). Without genomic resources in T. emetica , the ISSR markers will prove useful and helpful as a means to the end of creating a baseline level of knowledge about its genetic diversity and population structure. Western Kenya is ecologically heterogenous with lowland, mid-altitude, and highland agroecological zones where rainfall, land-use, soil fertility differs (Jaetzold et al. 2006 ). The vast farming practices in region have led to land fragmentation which results in the disappearance of forests (Kogo et al. 2019 ; Rotich and Ojwang 2021 ; Osewe et al. 2022 ). Such landscape shifts may interfere with pollen and seed dispersal pathways, gene flow, population connectivity, and local tree species adaptation (Cheptou et al. 2017 ; Soares et al. 2019 ). T. emetica is a common in the ecological environments of natural forests and agroforestry in the region, which means its ecological strength and the intervention of mankind in in the spread of this tree species (KEFRI 2021). Such perseverance in diverse habitats where they exposed to anthropogenic activities is a challenge that can be exploited to unravel genetic traits of a species of conservation and socio-economicsignificance. As such, the current study aimed to determine genetic diversity and population structure of T. emetica in western Kenya using ISSR markers. Materials and methods Description of the study area This study was carried out on six natural stands of T . emetica collected in Bungoma, Kakamega, Kisumu, Nandi, Siaya, and Vihiga in western Kenya, representing ecological and altitudinal variation in distribution of the species (Table 1). This region has varied agroecological zones, ranging from the lower midlands to upper highlands, with high variation in rainfall, temperature, and land use intensity (Jaetzold et al. 2006). The forest and woodland cover in western Kenya have been fragmented through agricultural land use and settlement (Kogo et al. 2019; Rotich and Ojwang 2021; Osewe et al. 2022), but T. emetica still occurs in both natural and modified environments. The locations were selected to capture such environmental diversity and to cover contrasting moisture and elevation, from the semi-arid Kisumu lowlands to the humid highlands of Nandi and Kakamega. Mean annual rainfall in the locations ranges from 1,100 mm in Bungoma to over 2,100 mm in Kakamega, while mean annual temperatures ranging from 20°C to 24°C (Jaetzold et al. 2006). Geographic coordinates, climatic conditions, and agroecological zones are presented in Table 1. Table 1 Geographic and ecological characteristics of T . emetica populations sampled in western Kenya Population Altitude (m) Latitude Longitude Temperature ( °C) Rainfall (mm) Agroecological zones Bungoma 1,700 0˚34'10.29"N 34˚33’30.15"E 20.3 1102 Lower highland Kakamega 1,950 0˚17'0.00"N 34˚45’0.00"E 22.0 2100 Mid highland Kisumu 1,131 0˚5'30.12" S 34˚46’4.64"E 23.0 1250 Lower midland Siaya 1,525 0˚3'45.46" N 34˚17’16.11"E 23.4 2155 Lower midland Vihiga 1,800 0˚3'0.00" N 34˚43’30.00"E 23.9 1900 Lower midland Nandi 2,047 0˚10'0.00" N 35˚09’0.00"E 22.0 2000 Upper highland Sampling d esign and l eaf c ollection A systematic random sampling approach was used in each population to minimize the chance of sampling genetically related individuals. Trees were sampled 50 m apart on 300–500 m transects traversing representative patches of habitat. Only healthy and mature trees with a diameter at breast height (DBH) ≥10 cm was sampled. For each tree sampled, fresh young leaves were in the middle-canopy were harvested and placed in sterile bags with silica gel and stored at −20°C before DNA extraction. Twenty individuals from each population were sampled in Bungoma, Kakamega, Kisumu, Siaya, Vihiga, and Nandi. DNA e xtraction and p urification Genomic DNA was extracted from 0.5 g of leaf tissue by using the CTAB protocol optimized for phenolic-rich tree species (Doyle 1991). Leaf tissues were homogenized in liquid nitrogen and powder dispensed in 1.5% CTAB buffer (100 mM Tris-HCl pH 7.5; 1.4 M NaCl; 20 mM EDTA) supplemented with 0.75 μl β-mercaptoethanol and 100 mg polyvinylpolypyrrolidone (PVPP) to remove polyphenolic impurities. The homogenate was incubated at 60°C for 20 min, mixed with chloroform: isoamyl alcohol (24:1), and centrifuged at 13,000 rpm for 25 min. The aqueous phase was harvested, re-extracted with 10% CTAB, and precipitated with cold isopropanol. Nucleic acid was precipitated in ice-cold isopropanol and pellets washed in 70% ethanol, air-dried, and resuspended in 100 μl TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0). RNA was removed by treatment with RNase A (10 mg/mL) at 65°C for 3 h. Further purification was achieved by ethanol precipitation and re-suspension in 50 μl TE buffer. DNA quality and quantity were ascertained by electrophoresis on 1.5% agarose gels using unmethylated lambda (λ)DNA markers and spectrophotometry (A260/A280 ratio). DNA was diluted to approximately 30 ng/μl for PCR amplification. ISSR m arker s election and PCR a mplification Fifteen ISSR primers (Table 2) previously optimized for other Meliaceae species like Melia dubia was used for polymorphsism screening (Rawat et al. 2018). Those primers that gave reproducible clear fragments duplicate reactions were selected for genetic diversity and population structure assessment. PCR amplifications were carried out in 25 μl reactions containing: 30 ng template DNA, 0.2 mM of each dNTP, 1.5 mM MgCl₂,1× PCR buffer (with (NH₄)₂ SO₄), 2 U Taq DNA polymerase (Thermo Fisher Scientific), and 0.5 μM of each primer (Rawat et al., 2018). Amplifications were performed in an Eppendorf Master cycler (Germany) under the following conditions: initial denaturation at 94°C for 4 min; 39 cycles of 94°C for 30 s, primer-specific annealing (45–61°C) for 1 min, and extension at 72°C for 2 min; followed by a final extension at 72°C for 5 min. Amplicons were mixed with 1× bromophenol blue loading dye and electrophoresed on 2% agarose gels in 1× TAE buffer at 50 V for 2.5 h. DNA fragments were visualized under UV light after staining with ethidium bromide and gel image captured by a Herolab Gel Documentation System (Germany). Table 2 ISSR primer sequences and amplification conditions used for PCR analysis of T . emetica populations in western Kenya Marker code Sequence (5’-3’) Tm (⁰ C) Ta (⁰ C) UBC -809 AGAGAGAGAGAGAGAGG 46.6 50.0 UBC -810 GAGAGAGAGAGAGAGAT 42.9 45.0 UBC -811 GAGAGAGAGAGAGAGAC 43.3 45.0 UBC -813 CTCTCTCTCTCTCTCTT 45.0 50.4 UBC -823 TCTCTCTCTCTCTCTCC 47.5 50.0 UBC -840 GAGAGAGAGAGAGAGAYT 45.8 47.0 UBC -845 CTCTCTCTCTCTCTCTRG 43.4 47.0 UBC -847 CACACACACACACACARC 54.2 53.0 UBC -855 ACACACACACACACACYT 60.2 61.0 UBC -857 ACACACACACACACACYG 57.1 58.0 UBC -864 ATGATGATGATGATGATG 51.2 52.0 UBC -880 GGAGAGGAGAGGAGA 49.0 44.7 UBC -888 BDBCACACACACACACA 52.3 55.4 UBC -890 VHVTGTGTGTGTGTGTG 51.8 52.0 UBC -891 VHVGTGTGTGTGTGTGT 51.8 55.0 Note: Tm = melting temperature, Ta = annealing temperature, B =C, G or T; R= A or G; Y = C or T; V = A, C or G and H = A, C or T. Molecular d ata a nalysis Only clear, well-resolved, and reproducible ISSR fragments were scored. Each distinct fragment was treated as a single locus and was scored manually for presence (1) or absence (0) in all individuals, following the binary data scoring approach of Rawat et al. (2018) and Borah et al. (2021). Consistently amplifying and polymorphic fragments were included in the final dataset. The resultant binary matrix was utilized for genetic diversity and population structure analyses. P ercentage of polymorphic loci ( % P) , number of effective alleles (Ne) , Shannon’s Information Index (I) , and the coefficient of genetic differentiation (Gst) were estimated using PopGene version 1.32 (Yeh et al. 2000). These genetic indices provide complementary perspectives on within- and among-population genetic diversity and allow comparisons of allelic richness and differentiation (Nei 1973). Analysis of Molecular Variance (AMOVA) was carried out using GenAlEx version 6.5 (Peakall and Smouse 2012) to partition total genetic variation within and among populations. AMOVA was based on Φ-statistics, analogs of Wright’s F-statistics, to quantify the amount of population differentiation and gene flow (Excoffier et al. 1992). Nei’s unbiased genetic distance was used to compute pairwise genetic distances among individuals and populations (Nei, 1978). The gene flow (Nm) between populations was estimated indirectly from Gst using the formula Nm = 0.5 (1−Gst)/GstNm = 0.5(1 - Gst)/GstNm=0.5(1−Gst)/Gst (Slatkin 1987). This index was used to infer the degree of genetic exchange and isolation among the populations of T. emetica . Genetic relationships among populations were estimated using a Principal Coordinates Analysis (PCoA) using GenAlEx 6.5 t (Peakall and Smouse 2012). A dendrogram was constructed based on Nei’s genetic distance matrix using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA ) to infer relatedness among populations (Tamura et al. 2013). The robustness of cluster nodes was evaluated through 1,000 bootstrap replicates . Results Genetic diversity within and among T, emetica populations Fifteen ISSR primers produced a total of 171 scorable fragments , 162 (94.65%) of which were polymorphic (Table 3). The number of amplicons per primer varied from 7 (UBC-857) to 19 (UBC-810) . Primers UBC-809, UBC-845, UBC-847, UBC-857, UBC-864, UBC-880, and UBC-891 displayed 100% polymorphism. Polymorphism was 94.74%, 94.12%, and 93.75% for UBC-810, UBC- 840, and UBC-888, respectively. On other hand, the lowest values of polymorphism were detected in UBC-811 (84.62%), UBC-813 (87.50%), UBC-823 (87.50%), UBC-855 (90.00%), and UBC-890 (87.50%). The size of amplicons differed among primers and ranged from 169 bp in UBC-855 to 2,060 bp in UBC-880. A high fragment size range was observed with UBC-880 (178-2,060 bp), while UBC-809 produced the narrowest range of 315-389 bp. T able 3 Polymorphism profile of 15 ISSR primers used for the genetic analysis of T . emetica populations in western Kenya Marker code Total fragments Polymorphic fragments Polymorphism (%) Fragment size range (bp) UBC -809 10 10 100.00 315-389 UBC -810 19 18 94.74 224-1420 UBC -811 13 11 84.62 183-1388 UBC -813 8 7 87.50 190-1265 UBC -823 8 7 87.50 213-1705 UBC -840 17 16 94.12 175-1390 UBC -845 8 8 100.00 470-1155 UBC -847 9 9 100.00 275-1367 UBC -855 10 9 90.00 169-1368 UBC -857 7 7 100.00 276-1895 UBC -864 12 12 100.00 290-1487 UBC -880 15 15 100.00 178-2060 UBC -888 16 15 93.75 201-1654 UBC -890 8 7 87.50 221-679 UBC -891 11 11 100.00 216-1672 Total 171 162 94.65 Genetic diversity within populations Table 4 shows the genetic diversity indices derived from ISSR marker analysis of six T. emetica populations in western Kenya. The number of observed alleles (Na) differed between populations from 0.66 in Siaya and Kisumu to 1.6 3 in Nandi with a mean of 1.02 ± 0.1 7 Kakamega and Vihiga populations had intermediate Na values of 1.53 and 0.88, respectively, and Bungoma displayed a moderate value of 0.78. The number of effective alleles (Ne) ranged from 1.1 7 in Siaya to 1.40 in Nandi, with a mean of 1.24 ± 0.06 . Kakamega and Bungoma populations registered intermediate Ne values of 1.29 and 1.25, respectively, while Kisumu (1.18) and Vihiga (1.17) had lowest values. Shannon Information Index (I) displayed similar pattern of variation among populations, ranging from 0.1 5 in Siaya to 0.3 6 in Nandi, with a mean value of 0.22 ± 0.0 5 . Intermediate index values of 0.29 and 0.21, were observed in Kakamega and Bungoma, respectively, while lowest values were found in Kisumu (0.16) and Vihiga (0.16). The Nei’s gene diversity (He) also differed among populations with a mean of 0.15 ± 0.03. The lowest He values were found in Siaya and Vihiga at 0. 1 0, followed by Kisumu ( 0.1 1) and Bungoma (0.14), which registered relatively high gene diversity. Kakamega (0.1 8 ) was moderate for the He value, while Nandi (0.24) had the highest gene diversity among all the populations. The percentage of polymorphic loci (%P) varied among the six T. emetica populations. It ranged from 31.25% in both Siaya and Kisumu populations to 78.31% in Nandi, amean of 48.96% ± 8.94 . Kakamega population had a relatively higher l percentage of polymorphism (75.00%) like that that in Nandi (78.31%), but lower percentage of polymorphism were observed in Siaya and Kisumu at 31.25%. Bungoma and Vihiga populations had 34.38 and 43.75%, respectively. Table 4 Genetic diversity parameters based on ISSR markers across six T . emetica populations in western Kenya Population Na Ne I He %P Bungoma 0.78 ± 0.17 1.25 ± 0.07 0.21 ± 0.05 0.14 ± 0.04 34.38 Kakamega 1.53 ± 0.15 1.29 ± 0.06 0.29 ± 0.04 0.18 ± 0.03 75.00 Kisumu 0.66 ± 0.17 1.18 ± 0.06 0.16 ± 0.05 0.11 ± 0.03 31.25 Siaya 0.66 ± 0.17 1.17 ± 0.07 0.15 ± 0.04 0.10 ± 0.03 31.25 Vihiga 0.88 ± 0.18 1.17 ± 0.06 0.16 ± 0.04 0.10 ± 0.03 43.75 Nandi 1.63 ± 0.13 1.40 ± 0.07 0.36 ± 0.05 0.24 ± 0.04 78.31 Mean ± SE 1.02 ± 0.17 1.24 ± 0.06 0.22 ± 0.05 0.15 ± 0.03 48.96 ± 8.94 Note: Na = number of observed alleles; Ne = number of effective alleles; I = Shannon’s Information Index; He = Nei’s gene diversity; %P = percentage of polymorphic loci. Molecular g e netic differentiation Sixty five percent of total genetic variation was found within populations and e 35% was observed among populations (Table 5). The fixation index (ΦST) was estimated to be 0.35 , confirming that approximately 35% of the total genetic diversity was attributed to differences between populations. Table 5 Analysis of molecular variance showing partitioning of genetic variation among and within six T . emetica populations in western Kenya based on ISSR marker data Source of variation Df SS MS Estimated variance Percentage of total variation (%) ΦST p-value Among populations 5 140.72 28.143 1.29 35 0.35 <0.001 Within populations 114 272.40 2.389 2.39 65 — — Note : df = degrees of freedom; SS = sum of squares; MS = mean square; ΦST = fixation index representing the proportion of total genetic variation among populations. The ΦST value was calculated as 1.288 ÷ 3.677 = 0.35. Significance of variance components was tested using 999 random permutations (p < 0.001). The overall genetic differentiation coefficient (Gst) of all loci was 0.27 ( p = 0.001), as analyzed through 15 ISSR loci (Table 6). Uneven genetic differentiation was observed in each locus, with Gst ranging from 0.04 (UBC-811) to 0.4 6 (UBC-888) . Intermediate Gst values were found for some primers like UBC-809 (0.35) , UBC-810 (0.3 1 ) , UBC-845 (0.3 5 ) , UBC-864 (0.34) , and UBC-891 (0.36) . Lower differentiation estimates were obtained for UBC-847 (0.08) , UBC-855 (0.05) , UBC-857 (0.16) , and UBC-880 (0.08) . The corresponding total genetic diversity (Ht) among loci ranged from 0.0 2 (UBC-811) to 0. 50 (UBC-864) , while different primers such as UBC-810 (0.4 9 ) , UBC-840 (0. 50 ) , UBC-888 (0. 50 ) , UBC-890 (0. 50 ) , and UBC-891 (0.4 9 ) had relatively high total heterozygosity. The within-population genetic diversity (Hs) values ranged from 0.0 2 (UBC-811) to 0.36 (UBC-840) , averaging 0.24 across loci. G ene flow estimates (Nm) , whose relationship is inversely correlated with Gst, were variable between loci, ranging from 0.59 (UBC-888) to 11.40 (UBC-811) and averaging 2.39. Loci like UBC-847 (Nm = 5. 60 ) , UBC-855 (Nm = 9.1 2 ) , and UBC-880 (Nm = 5.6 7 ) displayed relatively higher Nm values, while UBC-809 (Nm = 0.9 3 ) , UBC-845 (Nm = 0.9 3 ) , UBC-864 (Nm = 0.96) , and UBC-890 (Nm = 0.6 4 ) had lower estimates. Table 6 Coefficients of genetic differentiation across 15 ISSR loci in six T . emetica populations from western Kenya Marker code Ht Hs Gst Nm* UBC-809 0.29 0.19 0.35 0.93 UBC-810 0.49 0.34 0.31 1.13 UBC-811 0.02 0.02 0.04 11.40 UBC-813 0.24 0.14 0.41 0.71 UBC-823 0.22 0.16 0.29 1.24 UBC-840 0.50 0.36 0.29 1.25 UBC-845 0.45 0.29 0.35 0.93 UBC-847 0.28 0.26 0.08 5.60 UBC-855 0.28 0.26 0.05 9.12 UBC-857 0.15 0.13 0.16 2.63 UBC-864 0.50 0.33 0.34 0.96 UBC-880 0.14 0.13 0.08 5.67 UBC-888 0.50 0.27 0.46 0.59 UBC-890 0.50 0.28 0.44 0.64 UBC-891 0.49 0.31 0.36 0.89 Mean 0.34 0.21 0.27 2.39 Note: Ht = total genetic diversity; Hs = within-population genetic diversity; Gst = coefficient of genetic differentiation; Nm = estimate of gene flow, calculated as Nm=0.5(1−Gst)/GstNm = 0.5(1 - Gst)/GstNm=0.5(1−Gst)/Gst. Genetic relationships among populations A clear spatial distribution of genetic relationships among the six T. emetica populations in western Kenya (Fig 1). The first two principal coordinates accounted for the maximum percentage of overall genetic variance and produced a two-dimensional arrangement of genetic relationships among populations. The biplot showed distinct clustering with respect to population origin, and each grouping displayed differences in the level of dispersion. The Nandi population were plotted separately from the other populations, forming a distinct cluster along the first coordinate axis. The Vihiga population was positioned near the Nandi cluster but showed partial overlap with it and hence intermediate placement in the coordinate space. Siaya and Kisumu populations clustered in proximity to one another, while Bungoma and Kakamega formed a separate distinct cluster on the biplot. Pairwise Nei’s genetic distances among the six populations of T . emetica varied from 0.02 between Vihiga and Kisumu to 0.2 4 between Bungoma and Nandi , while the genetic identities differed from 0.7 9 to 0.9 8 (Table 7). Low genetic distances were reported for several population pairs, such as Kisumu–Siaya (0.0 3 1) , Siaya–Vihiga (0.0 3 ) , and Kisumu–Vihiga (0.02) , all of which also showed high genetic identity values exceeding 0.97 . Moderate distances were observed for Kakamega–Siaya (0.09) , Kakamega–Vihiga (0.10) , and Bungoma–Kakamega (0.0 3 ) , with identity values of 0.9 1 , 0.90 , and 0.97 , respectively. Larger genetic distances were found in the pairs that involved the Nandi population, especially Bungoma–Nandi (0.2 4 ) , Siaya–Nandi (0.1 5 ) , Vihiga–Nandi (0.1 5 ) , and Kisumu–Nandi (0.13) , and these had relatively low genetic identities of 0.7 9 and 0.8 8, respectively . Comparison involving Bungoma –Siaya (0.16) and Bungoma–Vihiga (0.18) also involved relatively large distances. Table 7 Pairwise Nei’s genetic identity and genetic distance among six T . emetica populations from western Kenya based on ISSR marker data Population Bungoma Kakamega Kisumu Siaya Vihiga Nandi Bungoma **** 0.9742 0.8793 0.8487 0.8330 0.7867 Kakamega 0.0262 **** 0.9417 0.9097 0.9015 0.8734 Kisumu 0.1286 0.0601 **** 0.9714 0.9767 0.8759 Siaya 0.1640 0.0946 0.0291 **** 0.9744 0.8612 Vihiga 0.1827 0.1037 0.0236 0.0259 **** 0.8612 Nandi 0.2399 0.1354 0.1325 0.1494 0.1494 **** Note: Values above the diagonal represent Nei’s genetic identity; values below the diagonal represent Nei’s genetic distance. Fig 2 shows the genetic relationships among the six populations of T. emetica as defined by the Nei’s unbiased genetic distances . The dendrogram produced three clusters, indicating discrete levels of genetic relatedness among T. emetica populations. Siaya, Vihiga, and Kisumu were grouped in the same cluster and showed minimum separation in branch length within the cluster. The second cluster consisted of Bungoma and Kakamega , a closely related group different from Siaya-Vihiga-Kisumu cluster. On other hand, Nandi population was grouped in the third cluster, demonstrating its discreteness from the rest of the populations. Discussion Genetic diversity of T. emetica populations The populations of T. emetica from western Kenya showed high mean percentage of polymorphic loci (94.65%), demonstrating discriminative power of ISSR markers to detect genetic variation. The high level of polymorphism observed in this study is line with 91.7% reported in Melia dubia (Rawat et al. 2018 ), 95–97% in Pinus sylvestris (Sheikina and Romanov 2024 ), 80–100% in Osyris lanceolata (Mugula et al. 2023 ) and higher than 63.8% and 46–76% in Parashorea chinensis and Robinia pseudoacacia (Li et al. 2024 ; Uras et al. 2024 ), respectively, which shows the potential of ISSR in dissecting genetic diversity in tree species. The 100% polymorphism observed in several ISSR primers such as UBC-809, UBC-845, UBC-847, UBC-857, UBC-864, UBC-880, and UBC-891 indicates that ISSR tool is highly effective in detecting genetic variation in the T. emetica populations. Borah et al. ( 2021 ) also reported ssimilar reliability of ISSR markers in showing high levels of genetic polymorphism in Illicium griffithii , further confirming their effectiveness in analyzing intra-specific variation in tree species. The fragment sizes of amplicons ranged from 169 to 2060 bp further reinforcing the suitability of ISSR markers for assessement of genetic diversity in tree species and demonstrates wide genome variability among the T. emetica populations. Such high variation in fragment size shows the presence of several polymorphic loci distributed across the genome of T. emetica populations, indicating high allelic diversity, in line with its wide ecological distribution over lowland, mid-altitude, and highland habitats (Chebii et al. 2022 ; Osewe et al. 2022 ). Similar findings have been reported in Khaya anthotheca (Bouka et al. 2022 ) and Cedrela odorata (Finch et al. 2022 ), where wide ecological distribution was associated with high intra-specific polymorphism, indicating that tropical tree species with extensive habitats have large effective population sizes and diverse genetic backgrounds. The variation between primers in the percentage of polymorphism and the sizes of amplicons may indicate possible underlying differences in genome structure or localized selection pressures operating throughout T. emetica populations. These results are in line with recent findings in other forest trees such as Acacia Senegal, Parashorea chinensis , Juniperus spp., and Anadenanthera colubrina , where variation among ISSR primers in the percentage of polymorphism and fragment sizes was attributed to differences in genome structure and localized environmental influences (Omondi et al. 2023 a; Xu et al. 2024 ; Al-Yasi and Al-Qthanin 2024 ). The diversity indices in this study show the existence of genetic heterogeneity among the populations of T. emetica in western Kenya. Nandi population displayed the highest diversity indices (Na = 1.63; Ne = 1.40; He = 0.24; I = 0.36; %P = 78.31%), indicating substantial allelic richness, evenness, and heterozygosity. These indices demonstrate that the Nandi population may be maintaining a large effective size and experiences high gene flow, with reduced human activities, which allow preservation of higher genetic variability. These findings are line with the previous ISSR and SSR-studies showing relatively high within-population diversity attributed to large, continuous populations, extensive gene flow, and open pollination systems that facilitate allelic exchange and reduced genetic drift in Meliaceae trees species such as Melia dubia (Rawat et al., 2018 ), Swietenia macrophylla (Limongi Andrade et al. 2022 ) and Khaya anthotheca (Bouka et al. 2022 ). Congruent with the results in this study, populations of Memecylon subcordatum (Viswanathan et al. 2018 ), Osyris lanceolata (Mugula et al. 2023 ), Pinus sylvestris (Sheikina and Romanov 2024 ) also had both high Shannon’s diversity indices and percentage of polymorphic loci, demonstrating the influence of population size and ecological integrity in determining within-population genetic variation. In contrast, Siaya and Kisumu T. emetica populations showed a lower diversity index (Na ≈ 0.66; He ≈ 0.10; %P = 31.25%), indicating reduced allelic variation and possible genetic erosion. The reduced allelic richness and heterozygosity in Siaya and Kisumu correspond with documented pressures of deforestation, agricultural expansion, and unsustainable resource extraction in western Kenya (Kogo et al. 2019 ; Rotich and Ojwang 2021 ; Chebii et al. 2022 ; Osewe et al. 2022 ). Similar trend where habitat fragmentation and land-use change contributed to reduction in genetic diversity and gene flow in tropical tress species (Soares et al. 2019 ). A reduced gene diversity in isolated or heavily exploited populations has also been reported in Melia dubia (Rawat et al. 2018 ), Khaya anthotheca (Bouka et al. 202) and Swietenia macrophylla ( Limongi Andrade et al. 2022 ). Bungoma, Kakamega, and Vihiga populations had moderate diversity indices (He = 0.14–0.19; I = 0.21–0.29; %P = 43–75%), indicating partial preservation of genetic diversity, reflecting their position along a continuum of anthropogenic distance and ecological connectivity. Therefore, the Bungoma-Kakamega-Vihiga populations may still be exchanging genes through fragmented corridors or remnant trees in agricultural landscapes, a pattern observed in Tabebuia rosea (Ruiz-González et al. 2023 ) and Acacia senegal (Omondi et al. 2023 ), where exchange of genes among isolated or remnant populations has been maintained despite fragmentation of the habitats. Population differentiation and structure of T. emetica The populations of T. emetica in western Kenya showed a structured yet genetically diverse pattern, influenced by limited gene flow. Sixty-five percent of the total genetic variation was observed within populations and 35% among the populations (ΦST = 0.35), showing moderate to high genetic differentiation. The Gst value (0.27, p = 0.001) reinforced this pattern, indicating that while diversity of alleles is preserved within the populations, a significant percentage of variation is spread among them. In comparison with other Meliaceae species, T. emetica displayed higher population differentiation than Khaya anthotheca (FST = 0.12) and Swietenia macrophylla (ΦST = 0.28), meaning that gene flow is restricted across its populations in western Kenya (Alcalá et al. 2015 ; Bouka et al. 2022 ). Similar reduction in gene flow due to habitat fragmentation has been reported in plant populations (Cheptou et al. 2017 ). The differentiation indices in this study (ΦST ≈ 0.35; Gst ≈ 0.27) corresponded well with a mean gene flow estimate of Nm = 2.39, reflecting moderate connectivity, sufficient to reduce genetic drift but insufficient to homogenize populations completely. This balance demonstrates that the populations of T. emetica may be undergoing semi-independent evolutionary processes, with geographic isolation and environmental gradients acting as filters to exchange of genes. Rajarajan et al. ( 2024 ) reported ccomparable trends of genetic differentiation and population structuring that are influenced by environmental heterogeneity in Azadirachta indica and Melia azedarach , reinforcing the view that ecological variation drives genetic structuring within the Meliaceae. Similar levels of gene flow and spatial structuring have been reported in Acacia senegal (Omondi et al. 2023 ), Khaya anthotheca (Bouka et al. 2022 ), and Swietenia macrophylla (Limongi Andrade et al. 2022 ), where fragmentation and ecological discontinuities have produced moderate differentiation despite ongoing genetic connectivity.Variation in Gst values (0.04–0.46) across ISSR loci further demonstrated unevenness of genetic differentiation across the genome, differing levels of mutation, selection, or gene exchange among loci. Primers such as UBC-809 and UBC-864, which had intermediate Gst values, may have captured genomic regions undergoing partial isolation, while highly differentiated loci like UBC-888 likely reflect regions of genome under selection or low recombination. This trend is in line with findings in Melia dubia (Rawat et al. 2018 ) and Juniperus excelsa (Al-Yasi and Al-Qthanin 2024 ), where ISSR markers effectively revealed localized allelic divergence in response to ecological isolation and restricted pollen flow. The PCoA and the UPGMA analyses supports these molecular patterns. These analyses showed three different genetic clusters: (i) Siaya–Vihiga–Kisumu, (ii) Bungoma–Kakamega, and (iii) Nandi. The similarity between UPGMA and PCoA underline the strength of these relationships and their ecological basis. The first two principal coordinates accounted for the majority of total variance and delineated populations along ecological gradients from lowland to highland zones, reflecting the regional topography and rainfall patterns of western Kenya. The T. emetica population from Nandi occupied the first coordinate axis and had the highest Nei’s genetic distances, ranging from 0.13 to 0.24, and had the least genetic distances of between 0.79 and 0.88 relative to other populations. This variation in classification can be linked to its ecological and geographical isolation in the highlands, where reduced pollen and seed dispersal, coupled with differences in climatic conditions, support genetic divergence. Previous studies showing distinct genetic differentiation connected to topographic and ecological discontinuities has been reported in Swietenia macrophylla from fragmented Mexican forests (Alcalá et al. 2015 ), Osyris lanceolata in East Africa (Mugula et al. 2023 ), and Parashorea chinensis across Southeast Asia (Li et al. 2024 ; Xu et al. 2024 ). In contrast, the populations of T. emetica from Siaya, Kisumu, and Vihig) displayed very low genetic distances, which ranged from 0.02 to 0.03, and high identity values greater than 0.97, pointing a recent common ancestry or ongoing gene flow preserved through landscape connectivity such as riverine corridors or agricultural mosaics. These findings mirror the weak spatial structuring reported in Cedrela odorata across Neotropical lowlands (Finch et al. 2022 ) and Sonneratia caseolaris in coastal Vietnam (Le and Le 2024 ), where gene flow remains relatively unrestricted in connected habitats. On other hand, Bungoma and Kakamega clustered in a moderately distinct category, likely sustained by partial connectivity through remnant mid-altitude forest corridors. The small Nei’s distance of 0.03 observed between these two populations and their close grouping on the dendrogram support the presence of ongoing but reduced gene flow across fragmented forest patches. Conservation implications of genetic diversity patterns in T. emetica The ISSR genetic patterns in this study have important conservation and sustainable management implications for T. emetica populations in western Kenya. The high level of polymorphism (94.65%) and the finding that 65% of the total variation occurs within populations indicate that substantial local genetic diversity still exists. This within-population diversity is vital for maintaining adaptive potential and ensuring long-term evolutionary resilience, particularly under increasing environmental and anthropogenic pressures. Given projections of shifting climatic zones and increased habitat stress, maintaining such genetic diversity is crucial for forest resilience and adaptive response (Konrad et al. 2025 ). However, the relatively high population differentiation (ΦST = 0.35; Gst = 0.27) shows that genetic resources are unevenly distributed, highlighting the need for population-specific conservation strategies rather than a uniform management approach. Similar to patterns reported in fragmented alpine and tropical plant systems, where restricted gene flow and local adaptation demand site-specific conservation actions (Cheptou et al. 2017 ; Soares et al. 2019 ), these findings emphasize that the management of T. emetica should account for regional ecological and genetic distinctiveness. Populations such as Nandi and Kakamega, which exhibited the highest gene diversity (He = 0.24 and 0.18, respectively) and the greatest percentage of polymorphic loci, represent genetic reservoirs essential for the species’ survival. These populations should be prioritized as core units for in situ and ex situ conservation, serving as key sources for seed collection and the establishment of genetic resource banks. Similar conservation initiatives intergrating molecular diversity and population structure have been conducted for Vitellaria paradoxa in Côte d’Ivoire (Attikora et al. 2024 ), highlighting the importance of conserving genetically diverse populations as reservoirs for breeding and restoration. In contrast, populations from Siaya and Kisumu, which recorded low gene diversity (He = 0.10–0.11) and only 31.25% polymorphic loci, are vulnerable to genetic erosion and stochastic loss. These populations require genetic enrichment through assisted regeneration, enrichment planting, and controlled exchange of germplasm from genetically richer populations to prevent further decline in adaptive capacity (Donkpegan et al. 2020 ). The moderate fixation index (ΦST = 0.35) and variable Gst values among ISSR loci (0.04–0.46) reflect limited genetic exchange between populations. From a conservation perspective, this underscores the importance of preserving and restoring gene flow corridors between fragmented forest remnants. Conservation planning should prioritize connectivity conservation, linking genetically related populations such as Bungoma-Kakamega and Kisumu -Vihiga -Siaya to sustain pollen and seed dispersal. Establishing community-based forest restoration zones that utilize genetically diverse and locally adapted materials can help stabilize population structures and mitigate ongoing isolation. Furthermore, the clear delineation of populations into three genetic groups: (i) Siaya-Vihiga-Kisumu, (ii) Bungoma-Kakamega, and (iii) Nandi, provides a strong foundation for conservation and seed zoning. Such genetic zoning supports Kenya’s forest seed production and certification systems, ensuring that restoration initiatives use materials compatible with the adaptive traits of each ecological region (FAO 2025 ). Integrating this molecular data into forest restoration policy will enhance seed sourcing, prevent maladaptation, and strengthen reforestation programs aligned with the Bonn Challenge (IUCN 2011), the UN Decade on Ecosystem Restoration (2021–2030), and Sustainable Development Goal 15 (Life on Land) (FAO 2025 ). Such integration is needed, given evidence that hunan activities are eroding genetic diversity and threatening species persistence (Soares et al. 2019 ; Omondi et al., 2023 ). Limitations of the study The present study reports the first molecular data on the genetic diversity and population structure of T. emetica in Kenya. The fifteen ISSR markers revealed over 94% level of polymorphism and delineated the T. emetica populations into distinct clusters that corresponded with ecological stratification. The sample size of 20 individuals per population was adequate to detect genetic diversity but may have been insufficient to fully capture intra and inter population variation. The dominant nature of ISSR markers does not allow distinction between homozygous and heterozygous loci, reducing precise estimation of heterozygosity, inbreeding coefficients, and allelic richness. Since ISSRs target non-coding regions of the DNA, they do not accurately dissect adaptive loci. However, chloroplast genomic studies in Meliaceae have revealed interspecific variation useful for phylogenetic and adaptive inference (Nie et al. 2025 ), indicating that integrating plastid and nuclear genomic tools could enhance understanding of T. emetica ’s evolutionary history. In this context, combining chloroplast genomic data with co-dominant nuclear markers like SSRs and SNPs would enable precise estimation of heterozygosity, inbreeding, and adaptive loci (Bouka et al. 2022 ; Limongi Andrade et al. 2022 ; Faria et al. 2024 ), thereby providing a comprehensive view of genetic structure and adaptive potential. Hence, while ISSRs produced valuable baseline molecular data for assessing genetic variation and population differentiation,, integrating plastid and co-dominant markers can help refine the adaptive diversity, evolutionary dynamics, and conservation of T. emetica . Conclusion The present study dissected the genetic diversity and population structure of T. emetica . The molecular analysis uncovered over 94% genome-wide polymorphism, with 65% of total variation occurring within and 35% among populations. The populations of T. emetica from Nandi and Kakamega displayed the highest genetic diversity, whereas Siaya and Kisumu had reduced variability. The population differentiation (ΦST = 0.35) and clustering patterns revealed restricted but ongoing gene flow among populations. These findings provide molecular evidence that T. emetica maintains considerable intra-population variation. The enhanced genetic structuring across ecological zones shows the influence of topography, climate, and habitat degradation on gene exchange and adaptive potential. Cconservation efforts should prioritize Nandi population as core genetic reservoirs, while enhancing connectivity and regeneration in Siaya and Kisumu populations through assisted natural regeneration and targeted enrichment planting. Despite the contributions made in this study, it used fewer individuals to represent each population and relied on dominant ISSR markers that do not distinguish heterozygotes from homozygotes, reducing the estimation of heterozygosity, inbreeding, and adaptive variation. Future studies should increase the number of individuals sampled per population and incorporate co-dominant or high-resolution genomic tools to capture both neutral and adaptive diversity. Therefore, preserving the genetic diversity of T. emetica is critical for sustaining forest ecosystem resilience, supporting local livelihoods, and ensuring adaptive responses under climate change. Declarations Compliance with Ethical Standards This study was exempted from ethical review for human clinical trials or animal experiments, as it involved only collection of plant leaves. The Ethics and Review Committee of the University of Eldoret reviewed and approved the study. Clinical trial number not applicable Consent for publication: All authors have reviewed and approved the manuscript for submission to the New Forests Competing interests: The authors declare no competing interests. Funding The authors did not receive funding from any organization the submitted work. Author Contribution The study was carried out as part of the first author’s Master of Science thesis in Plant Genetics at the University of Eldoret. E.K.S: conceptualized the study, designed the methodology, and conducted data collection and analysis, drafted original manuscript. B .O.N and O.G.D: supervised the first author, critically reviewed the manuscript and approved version for submission. K.P.A: Supported the first author in sample collections and reviewed the manuscript. Acknowledgement The authors are grateful to Kenya Agricutlural and Livestock Research Organization (KARLO) at Njoro for providing laboratory facility to undertake this study. We would like to thank Martin Lagat and Cyrus Kimani from KALRO-Njoro for technical support during the study. We would like to thank Ms. Samantha Cynthia Akinyi for editing the manuscript. 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Nyongesa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACAziLvQFIFDCDmMxEauE5AOKSpEUigUgt5tI9ho8LftnY8898Y/jgg4E1A3/7AWaDD3i0WM45Y2w8sy8tccbtHGPDGQbpDBJnEpgTZ+Bz2I0cM2nensMJDLeBDB6DwwwMNxiYD/MQ1vLfXv7mGTPpP0At8iAtfwhp4flxgHHDDR4zaQagFgOglmR83reckVZszNuQnLjxTFqxYY9BOo/hmcRmwx48Wswlkjc+5vljZy93/PDGBz8qrOWAjMMSP/BZw8BhwMDYhuACPc7YgFcDMKE8YGDA59tRMApGwSgYBQAt+koZTP0yAgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Eldoret","correspondingAuthor":true,"prefix":"","firstName":"Benson","middleName":"Ouma","lastName":"Nyongesa","suffix":""},{"id":538499678,"identity":"f437d267-56a6-45b3-8f00-27db72435a66","order_by":2,"name":"Otto George Dangasuk","email":"","orcid":"","institution":"University of 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09:21:36","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184157,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7887580/v1/56f305f8ff6631e086a9636b.html"},{"id":95004208,"identity":"634304ec-61d6-4f37-920a-d9964d833f6a","added_by":"auto","created_at":"2025-11-03 09:21:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35328,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinates analysis showing genetic relationships among 120 individuals representing six \u003cem\u003eT. emetica\u003c/em\u003e populations based on ISSR data\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7887580/v1/5e90b506c1c9fcee9992d1d8.jpeg"},{"id":95004209,"identity":"1b52f29b-0d11-4909-87e9-ce68bd6ecb1b","added_by":"auto","created_at":"2025-11-03 09:21:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50471,"visible":true,"origin":"","legend":"\u003cp\u003eUPGMA dendrogram constructed from Nei’s unbiased genetic distances illustrating genetic relationships among six \u003cem\u003eT. emetica\u003c/em\u003epopulations in western Kenya based on ISSR marker data\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7887580/v1/7ae2ac1637f783f88ef8b0d4.jpeg"},{"id":99172857,"identity":"491bd748-4544-4545-9c54-6da6ab62cde6","added_by":"auto","created_at":"2025-12-29 16:11:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2296903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7887580/v1/721e5dea-371a-44fa-bb1a-9fcfac09d54f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic diversity and population structure of Trichilia emetica Vahl in western Kenya using ISSR markers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMost terrestrial biodiversity of the world is found in the forests that help provide ecological stability by regulating climate conditions, nutrients cycles, and supporting livelihoods. Nevertheless, uncontrolled, habitat fragmentation, logging, and unsustainable use of resources have caused extensive loss of genetic variation of trees, particularly in the tropics (Soares et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Reduction in genetic diversity of intra-species undermines the adaptive ability of the population to environmental change and the resilience of forest ecosystems (Konrad et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In such a way, the conservation of forest genetic resources has been on the global climate and biodiversity agendas, following the restoration objectives of the Bonn Challenge (International Union of Conservation of Nature [IUCN] 2011), Sustainable Development Goal 15 (Life on Land), and the United Nations Decade on Ecosystem Restoration (2021\u0026ndash;2030), which put emphasis on the restoration and sustainable use of biodiversity to improve ecosystem resilience and human well-being (Forest and Trees, Agroforestry Flagship 1 2025; FAO \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).Despite African forest ecosystems having ecological and sio-economic importance, the level of genetic research of native trees is underrepresented in comparison to Asia and Latin America. In recent genomic research, widely distributed species like \u003cem\u003eAfzelia africana\u003c/em\u003e and \u003cem\u003eA. quanzensis\u003c/em\u003e are only beginning to be studied at the population level (Donkpegan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The lowland humidity forests and the arid woodlands, are fragmenting at a high pace being by other land uses. Such pressures interfere with gene flow and can result in or cause inbreeding, genetic diversity loss, and reduced evolutionary capacity, such as studies of \u003cem\u003eAcacia Senegal\u003c/em\u003e where human activities predominantly define spatial genetic structure (Omondi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, it is important to comprehend genetic diversity patterns and population structure in the context of evidence-based conservation, restoration, and sustainable management of African forest trees, as exemplified by the recent genetic analysis of \u003cem\u003eVitellaria paradoxa\u003c/em\u003e to inform West Africa conservation planning (Attikora, et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003cem\u003eTrichilia emetica\u003c/em\u003e Vahl (Meliaceae), also known as Natal mahogany, is a multi-purpose tree species found widely in sub-Saharan Africa, from Senegal to the Red Sea, and moving eastwards and centrally through Africa to Congo and South Africa (PROTA \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It plays a significant ecological role in dry and moist forest ecosystems where it helps to establish the canopy structure, nutrient cycling, and habitat stability (Kenya Forestry Research Institute [KEFRI] 2021). It offers good-quality timber, which is valued to make furniture and construction, and its bark, seeds and leaves are also used in traditional ethnomedicine to treat fever, gastrointestinal disorders, and infection of the skins (Oyedeji-Amusa et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chebii et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eT. emetica\u003c/em\u003e also contains bioactive metabolites like limonoids, triterpenoids, and flavonoids, which have antimicrobial, antioxidant, and anti-inflammatory effects (Tsomele et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aldholmi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, its seed oil possess good lipid profile with excellent oxidative stability, and could be a future nutraceutical and cosmetic ingredient source (Oyedeji-Amusa et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mabaso et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and is proving to have an emerging economic payoff as a pharmaceutical and cosmetic industry feedstock.Morphological and phenological variation between the populations of \u003cem\u003eT. emetica\u003c/em\u003e, such as leaf form, canopy architecture, fruit and seed size, and flowering time, has been reported in different ecological zones, which may be evidence of local adaptation and phenotypic plasticity (Akweni et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; KEFRI 2021; Tsomele et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although such variation indicates possible genetic differentiation, no population-level molecular analysis of \u003cem\u003eT. emetica\u003c/em\u003e has yet been carried out to confirm it. Existing studies are restricted to morphological and ethnobotanical value, whereas molecular genetics has not been investigated in the populations of \u003cem\u003eT. emetica\u003c/em\u003e in East Africa. In contrast, other genera of Meliaceae, e.g \u003cem\u003eKhaya\u003c/em\u003e (Bouka et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003eSwietenia\u003c/em\u003e (Alcal\u0026aacute; et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Limongi Andrade et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003eCedrela\u003c/em\u003e (Finch et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003eAzadirachta\u003c/em\u003e and \u003cem\u003eMelia\u003c/em\u003e (Cui et al. 2023), and \u003cem\u003eToona\u003c/em\u003e (Nie et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), have been analyzed using molecular and morphological markers, unveiling cryptic species boundaries, population differentiation, and adaptive divergence, of which were influenced by ecological and anthropogenic pressures. These findings demonstrate how habitat fragmentation, selective logging and ecological gradients can cause loss of genetic diversity and increase differentiation among populations. The absence of baseline molecular data for \u003cem\u003eT. emetica\u003c/em\u003e is a critical knowledge gap in the evaluation of its intraspecific population structure and genetic diversity and limiting evidence-based conservation and restoration planning. With its ecological functions, commercial value, and extensive distribution, population structure and genetic diversity in \u003cem\u003eT. emetica\u003c/em\u003e is required to form grounds for conservation and planning sustainable utilization.\u003c/p\u003e\u003cp\u003eMolecular markers give a precise means of revealing genetic diversity and population structure in forest trees, in particular, when the phenotypic variation is influenced by environmental factors. Co-dominant markers like simple sequence repeats (SSRs) and single-nucleotide polymorphisms (SNPs) offer high and accuracy resolution of alleles but are expensive to carry out and require relatively large amount of genomic resources (Alicandri et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Faria et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the contrary, dominant multilocus markers like Inter Simple Sequence Repeat (ISSR) remain affordable and applicable to non-model and under-studied species (Viswanathan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Borah et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Le and Le \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, ISSR prove to be invaluable since they are reproducible, do not need prior sequence information, and produce high polymorphic loci that enable one to make precise estimation of the genetic variation and differentiation. They were shown to be able to identify population structure and gene flow in Meliaceae species, such as \u003cem\u003eMelia dubia\u003c/em\u003e (Rawat et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Even though ISSRs are not able to differentiate between homozygous and heterozygous loci, which is not advantageous as co-dominant markers, their high reproducibility and applicability in low-cost genotyping make them a reasonable genomic tool in genetic conservation and management (Alicandri et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Without genomic resources in \u003cem\u003eT. emetica\u003c/em\u003e, the ISSR markers will prove useful and helpful as a means to the end of creating a baseline level of knowledge about its genetic diversity and population structure.\u003c/p\u003e\u003cp\u003eWestern Kenya is ecologically heterogenous with lowland, mid-altitude, and highland agroecological zones where rainfall, land-use, soil fertility differs (Jaetzold et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The vast farming practices in region have led to land fragmentation which results in the disappearance of forests (Kogo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rotich and Ojwang \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osewe et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such landscape shifts may interfere with pollen and seed dispersal pathways, gene flow, population connectivity, and local tree species adaptation (Cheptou et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Soares et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eT. emetica\u003c/em\u003e is a common in the ecological environments of natural forests and agroforestry in the region, which means its ecological strength and the intervention of mankind in in the spread of this tree species (KEFRI 2021). Such perseverance in diverse habitats where they exposed to anthropogenic activities is a challenge that can be exploited to unravel genetic traits of a species of conservation and socio-economicsignificance. As such, the current study aimed to determine genetic diversity and population structure of \u003cem\u003eT. emetica\u003c/em\u003e in western Kenya using ISSR markers.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eDescription of the study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out on six natural stands of \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;emetica\u003c/em\u003e collected in Bungoma, Kakamega, Kisumu, Nandi, Siaya, and Vihiga in western Kenya, representing ecological and altitudinal variation in distribution of the species (Table 1). This region has varied agroecological zones, ranging from the lower midlands to upper highlands, with high variation in rainfall, temperature, and land use intensity (Jaetzold et al. 2006). The forest and woodland cover in western Kenya have been fragmented through agricultural land use and settlement (Kogo et al. 2019; Rotich and Ojwang 2021; Osewe et al. 2022), but \u003cem\u003eT. emetica\u003c/em\u003e still occurs in both natural and modified environments. The locations were selected to capture such environmental diversity and to cover contrasting moisture and elevation, from the semi-arid Kisumu lowlands to the humid highlands of Nandi and Kakamega. Mean annual rainfall in the locations ranges from 1,100 mm in Bungoma to over 2,100 mm in Kakamega, while mean annual temperatures ranging from 20\u0026deg;C to 24\u0026deg;C (Jaetzold et al. 2006). Geographic coordinates, climatic conditions, and agroecological zones are presented in Table 1.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Geographic and ecological characteristics of \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;emetica\u003c/em\u003e populations sampled in western Kenya\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltitude (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature (\u003c/strong\u003e\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRainfall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgroecological zones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eBungoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1,700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0˚34\u0026apos;10.29\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e34˚33\u0026rsquo;30.15\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLower highland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eKakamega\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1,950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0˚17\u0026apos;0.00\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e34˚45\u0026rsquo;0.00\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eMid highland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eKisumu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1,131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0˚5\u0026apos;30.12\u0026quot; S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e34˚46\u0026rsquo;4.64\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLower midland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eSiaya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1,525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0˚3\u0026apos;45.46\u0026quot; N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e34˚17\u0026rsquo;16.11\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLower midland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eVihiga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1,800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0˚3\u0026apos;0.00\u0026quot;\u0026nbsp;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e34˚43\u0026rsquo;30.00\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eLower midland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eNandi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2,047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0˚10\u0026apos;0.00\u0026quot;\u0026nbsp;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e35˚09\u0026rsquo;0.00\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eUpper highland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSampling\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eesign and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003el\u003c/strong\u003e\u003cstrong\u003eeaf\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eollection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA systematic random sampling approach was used in each population to minimize the chance of sampling genetically related individuals. Trees were sampled 50 m apart on 300\u0026ndash;500 m transects traversing representative patches of habitat. Only healthy and mature trees with a diameter at breast height (DBH) \u0026ge;10 cm was sampled. For each tree sampled, fresh young leaves were in the middle-canopy were harvested and placed in sterile bags with silica gel and stored at \u0026minus;20\u0026deg;C before DNA extraction. Twenty individuals from each population were sampled in Bungoma, Kakamega, Kisumu, Siaya, Vihiga, and Nandi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003cstrong\u003extraction and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eurification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from 0.5 g of leaf tissue by using the CTAB protocol optimized for phenolic-rich tree species (Doyle 1991). Leaf tissues were homogenized in liquid nitrogen and powder dispensed in 1.5% CTAB buffer (100 mM Tris-HCl pH 7.5; 1.4 M NaCl; 20 mM EDTA) supplemented with 0.75 \u0026mu;l \u0026beta;-mercaptoethanol and 100 mg polyvinylpolypyrrolidone (PVPP) to remove polyphenolic impurities. The homogenate was incubated at 60\u0026deg;C for 20 min, mixed with chloroform: isoamyl alcohol (24:1), and centrifuged at 13,000 rpm for 25 min. The aqueous phase was harvested, re-extracted with 10% CTAB, and precipitated with cold isopropanol. Nucleic acid was precipitated in ice-cold isopropanol and pellets washed in 70% ethanol, air-dried, and resuspended in 100 \u0026mu;l TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0). RNA was removed by treatment with RNase A (10 mg/mL) at 65\u0026deg;C for 3 h. Further purification was achieved by ethanol precipitation and re-suspension in 50 \u0026mu;l TE buffer. DNA quality and quantity were ascertained by electrophoresis on 1.5% agarose gels using unmethylated lambda (\u0026lambda;)DNA markers and spectrophotometry (A260/A280 ratio). DNA was diluted to approximately 30 ng/\u0026mu;l for PCR amplification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;ISSR\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003earker\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003eelection and PCR\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003emplification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifteen \u003cstrong\u003eISSR\u003c/strong\u003e primers (Table 2) previously optimized for other Meliaceae species like \u003cem\u003eMelia dubia\u0026nbsp;\u003c/em\u003ewas used for polymorphsism screening (Rawat et al. 2018). Those primers that gave reproducible clear fragments duplicate reactions were selected for genetic diversity and population structure assessment. PCR amplifications were carried out in 25 \u0026mu;l reactions containing: 30 ng template DNA, 0.2 mM of each dNTP, 1.5 mM MgCl₂,1\u0026times; PCR buffer (with (NH₄)₂ SO₄), 2 U Taq DNA polymerase (Thermo Fisher Scientific), and 0.5 \u0026mu;M of each primer (Rawat et al., 2018). Amplifications were performed in an \u003cstrong\u003eEppendorf Master\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecycler\u0026nbsp;\u003c/strong\u003e (Germany) under the following conditions: initial denaturation at 94\u0026deg;C for 4 min; 39 cycles of 94\u0026deg;C for 30 s, primer-specific annealing (45\u0026ndash;61\u0026deg;C) for 1 min, and extension at 72\u0026deg;C for 2 min; followed by a final extension at 72\u0026deg;C for 5 min.\u0026nbsp;Amplicons\u0026nbsp;\u0026nbsp;were mixed with 1\u0026times; bromophenol blue loading dye and electrophoresed on 2% agarose gels in 1\u0026times; TAE buffer at 50 V for 2.5 h. DNA fragments were visualized under UV\u0026nbsp;light\u0026nbsp;\u0026nbsp;after\u0026nbsp;staining with\u0026nbsp;ethidium bromide and\u0026nbsp;gel image captured by\u0026nbsp;a Herolab Gel Documentation System (Germany).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e ISSR primer sequences and amplification conditions used for PCR analysis of \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eemetica\u003c/em\u003e populations in western Kenya\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"528\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence (5\u0026rsquo;-3\u0026rsquo;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTm (⁰ C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTa (⁰ C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAGAGAGAGAGAGAGAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eGAGAGAGAGAGAGAGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eGAGAGAGAGAGAGAGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eCTCTCTCTCTCTCTCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e50.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eTCTCTCTCTCTCTCTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eGAGAGAGAGAGAGAGAYT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eCTCTCTCTCTCTCTCTRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eCACACACACACACACARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eACACACACACACACACYT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e61.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eACACACACACACACACYG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eATGATGATGATGATGATG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eGGAGAGGAGAGGAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e49.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eBDBCACACACACACACA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eVHVTGTGTGTGTGTGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUBC -891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eVHVGTGTGTGTGTGTGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eTm = melting temperature, Ta = annealing temperature, B =C, G or T; R= A or G; Y = C or T; V = A, C or G and H = A, C or T.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eata\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly clear, well-resolved, and reproducible ISSR fragments were scored. Each distinct fragment was treated as a single locus and was scored manually for presence (1) or absence (0) in all individuals, following the binary data scoring approach of Rawat et al. (2018) and Borah et al. (2021). Consistently amplifying and polymorphic fragments were included in the final dataset. The resultant binary matrix was utilized for genetic diversity and population structure analyses. \u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003eercentage of polymorphic loci (\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003eP)\u003c/strong\u003e, \u003cstrong\u003enumber of effective alleles (Ne)\u003c/strong\u003e, \u003cstrong\u003eShannon\u0026rsquo;s Information Index (I)\u003c/strong\u003e, and the \u003cstrong\u003ecoefficient of genetic differentiation (Gst)\u003c/strong\u003e were estimated using \u003cstrong\u003ePopGene version 1.32\u003c/strong\u003e (Yeh\u0026nbsp;et al.\u0026nbsp;2000). These\u0026nbsp;genetic\u0026nbsp;indices provide complementary perspectives on within- and among-population genetic diversity\u0026nbsp;and allow comparisons of allelic richness and differentiation (Nei 1973).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Molecular Variance (AMOVA)\u003c/strong\u003e was carried out using \u003cstrong\u003eGenAlEx version 6.5\u003c/strong\u003e (Peakall and Smouse 2012) to partition total genetic variation within and among populations. AMOVA was based on \u0026Phi;-statistics, analogs of Wright\u0026rsquo;s F-statistics, to quantify the amount of population differentiation and gene flow (Excoffier et al. 1992). Nei\u0026rsquo;s unbiased genetic distance was used to compute pairwise genetic distances among individuals and populations (Nei, 1978). The \u003cstrong\u003egene flow (Nm)\u003c/strong\u003e between populations was estimated indirectly from Gst using the formula Nm = 0.5 (1\u0026minus;Gst)/GstNm = 0.5(1 - Gst)/GstNm=0.5(1\u0026minus;Gst)/Gst\u0026nbsp;(Slatkin 1987). This index\u0026nbsp;was used to infer\u0026nbsp;the degree of genetic exchange and isolation among\u0026nbsp;the populations of\u0026nbsp;\u003cem\u003eT. \u003cem\u003eemetica\u003c/em\u003e\u003c/em\u003e.\u0026nbsp;Genetic relationships among populations were estimated using a\u0026nbsp;\u003cstrong\u003ePrincipal Coordinates Analysis (PCoA)\u003c/strong\u003e using\u0026nbsp;\u003cstrong\u003eGenAlEx 6.5\u003c/strong\u003e t (Peakall\u0026nbsp;and\u0026nbsp;Smouse 2012).\u0026nbsp;A dendrogram was constructed based on Nei\u0026rsquo;s genetic distance matrix using\u0026nbsp;the \u003cstrong\u003eUnweighted Pair Group Method with Arithmetic Mean (UPGMA\u003c/strong\u003e\u003cstrong\u003e) to\u0026nbsp;\u003c/strong\u003einfer relatedness among populations (Tamura et al. 2013). The robustness of cluster nodes was evaluated through \u003cstrong\u003e1,000 bootstrap replicates\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGenetic diversity within and among \u003cem\u003eT, emetica\u0026nbsp;\u003c/em\u003epopulations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifteen ISSR primers produced a total of \u003cstrong\u003e171 scorable fragments\u003c/strong\u003e, \u003cstrong\u003e162 (94.65%)\u003c/strong\u003e of which were polymorphic (Table 3). The number of amplicons per primer varied from \u003cstrong\u003e7 (UBC-857)\u003c/strong\u003e to \u003cstrong\u003e19 (UBC-810)\u003c/strong\u003e. Primers \u003cstrong\u003eUBC-809, UBC-845, UBC-847, UBC-857, UBC-864, UBC-880, and UBC-891\u003c/strong\u003e displayed 100% polymorphism. Polymorphism was 94.74%, 94.12%, and 93.75% for UBC-810, UBC- 840, and UBC-888, respectively. On other hand, the lowest values of polymorphism were detected in \u003cstrong\u003eUBC-811 (84.62%), UBC-813 (87.50%), UBC-823 (87.50%), UBC-855 (90.00%), and UBC-890 (87.50%).\u003c/strong\u003e The size of amplicons differed among primers and ranged from 169 bp in UBC-855 to 2,060 bp in UBC-880. A high fragment size range was observed with UBC-880 (178-2,060 bp), while UBC-809 produced the narrowest range of 315-389 bp.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e Polymorphism profile of 15 ISSR primers used for the genetic analysis of \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;emetica\u003c/em\u003e populations in western Kenya\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal fragments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphic fragments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphism (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFragment size range (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e315-389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e94.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e224-1420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e84.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e183-1388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e87.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e190-1265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e87.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e213-1705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e94.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e175-1390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e470-1155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e275-1367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e90.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e169-1368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e276-1895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e290-1487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e178-2060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e93.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e201-1654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e87.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e221-679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eUBC -891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e216-1672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e171\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e162\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e94.65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic diversity within populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 shows the genetic diversity indices derived from ISSR marker analysis of six \u003cem\u003eT. emetica\u003c/em\u003e populations in western Kenya. The \u003cstrong\u003enumber of observed alleles (Na)\u003c/strong\u003e differed between populations from \u003cstrong\u003e0.66\u003c/strong\u003e in Siaya and Kisumu to \u003cstrong\u003e1.6\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e in Nandi with a mean of \u003cstrong\u003e1.02 \u0026plusmn; 0.1\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e Kakamega and Vihiga populations had intermediate Na values of 1.53 and 0.88, respectively, and Bungoma displayed a moderate value of 0.78. The \u003cstrong\u003enumber of effective alleles (Ne)\u003c/strong\u003e ranged from \u003cstrong\u003e1.1\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e in Siaya to \u003cstrong\u003e1.40\u003c/strong\u003e in Nandi, with a mean of 1.24\u003cstrong\u003e\u0026nbsp;\u0026plusmn; 0.06\u003c/strong\u003e. Kakamega and Bungoma populations registered intermediate Ne values of 1.29 and 1.25, respectively, while Kisumu (1.18) and Vihiga (1.17) had lowest values. \u003cstrong\u003eShannon Information Index (I)\u003c/strong\u003e displayed similar pattern of variation among populations, ranging from \u003cstrong\u003e0.1\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e in Siaya to \u003cstrong\u003e0.3\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e in Nandi, with a mean value of \u003cstrong\u003e0.22 \u0026plusmn; 0.0\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e. Intermediate index values of 0.29 and 0.21, were observed in Kakamega and Bungoma, respectively, while lowest values were found in Kisumu (0.16) and Vihiga (0.16). The \u003cstrong\u003eNei\u0026rsquo;s gene diversity (He)\u003c/strong\u003e also differed among populations with a mean of \u003cstrong\u003e0.15 \u0026plusmn; 0.03. The lowest He values were\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efound\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein Siaya\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and Vihiga at\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e0, followed by\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eKisumu (\u003c/strong\u003e\u003cstrong\u003e0.1\u003c/strong\u003e\u003cstrong\u003e1)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand Bungoma (0.14), which\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eregistered relatively\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ehigh\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egene\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ediversity. Kakamega (0.1\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003cstrong\u003e)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewas\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emoderate\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efor the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHe value, while\u0026nbsp;\u003c/strong\u003eNandi (0.24) had the highest gene diversity among all the populations.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003epercentage of polymorphic loci (%P)\u003c/strong\u003e varied among the six \u003cem\u003eT. emetica\u003c/em\u003e populations. It ranged from \u003cstrong\u003e31.25%\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein both\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSiaya and Kisumu populations to \u003cstrong\u003e78.31%\u003c/strong\u003e in Nandi, amean of \u003cstrong\u003e48.96% \u0026plusmn; 8.94\u003c/strong\u003e. Kakamega population had a relatively higher l percentage of polymorphism (75.00%) like that that in Nandi (78.31%), but lower percentage of polymorphism were observed in Siaya and Kisumu at 31.25%. Bungoma and Vihiga populations had 34.38 and 43.75%, respectively.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Genetic diversity parameters based on ISSR markers across six \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eemetica\u003c/em\u003e populations in western Kenya\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"545\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eBungoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.78 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.25 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.21 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.14 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e34.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eKakamega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.53 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.29 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.29 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.18 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eKisumu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.66 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.18 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.16 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.11 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eSiaya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.66 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.17 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.15 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.10 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eVihiga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.88 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.17 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.16 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.10 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e43.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eNandi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.63 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.40 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.36 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.24 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e78.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.02 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.24 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.22 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.15 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e48.96 \u0026plusmn; 8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e \u003cem\u003eNa\u003c/em\u003e = number of observed alleles; \u003cem\u003eNe\u003c/em\u003e = number of effective alleles; \u003cem\u003eI\u003c/em\u003e = Shannon\u0026rsquo;s Information Index; \u003cem\u003eHe\u003c/em\u003e = Nei\u0026rsquo;s gene diversity; \u003cem\u003e%P\u003c/em\u003e = percentage of polymorphic loci.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMolecular g\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003cstrong\u003enetic differentiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSixty five percent of total genetic variation was found \u003cstrong\u003ewithin populations\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003ee \u003cstrong\u003e35%\u003c/strong\u003e was observed \u003cstrong\u003eamong populations\u003c/strong\u003e (Table 5). The \u003cstrong\u003efixation index (\u0026Phi;ST)\u003c/strong\u003e was estimated to be \u003cstrong\u003e0.35\u003c/strong\u003e, confirming that approximately 35% of the total genetic diversity was attributed to differences between populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e Analysis of molecular variance showing partitioning of genetic variation among and within six \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eemetica\u003c/em\u003e populations in western Kenya based on ISSR marker data\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eSource of variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eEstimated variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePercentage of total variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026Phi;ST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eAmong populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e140.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e28.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eWithin populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e272.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: df = degrees of freedom; SS = sum of squares; MS = mean square; \u0026Phi;ST = fixation index representing the proportion of total genetic variation among populations. The \u0026Phi;ST value was calculated as 1.288 \u0026divide; 3.677 = 0.35. Significance of variance components was tested using 999 random permutations (p \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe overall \u003cstrong\u003egenetic differentiation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecoefficient\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(Gst)\u003c/strong\u003e of all loci was \u003cstrong\u003e0.27\u003c/strong\u003e (\u003cem\u003ep\u003c/em\u003e = 0.001), as analyzed through 15 ISSR loci (Table 6). Uneven genetic differentiation was observed in each locus, with \u003cstrong\u003eGst\u003c/strong\u003e ranging from \u003cstrong\u003e0.04 (UBC-811)\u003c/strong\u003e to \u003cstrong\u003e0.4\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(UBC-888)\u003c/strong\u003e. Intermediate Gst values were found for some primers like \u003cstrong\u003eUBC-809 (0.35)\u003c/strong\u003e, \u003cstrong\u003eUBC-810 (0.3\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eUBC-845 (0.3\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eUBC-864 (0.34)\u003c/strong\u003e, and \u003cstrong\u003eUBC-891 (0.36)\u003c/strong\u003e. Lower differentiation estimates were obtained for \u003cstrong\u003eUBC-847 (0.08)\u003c/strong\u003e, \u003cstrong\u003eUBC-855 (0.05)\u003c/strong\u003e, \u003cstrong\u003eUBC-857 (0.16)\u003c/strong\u003e, and \u003cstrong\u003eUBC-880 (0.08)\u003c/strong\u003e. The corresponding \u003cstrong\u003etotal genetic diversity (Ht)\u003c/strong\u003e among loci ranged from \u003cstrong\u003e0.0\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(UBC-811)\u003c/strong\u003e to \u003cstrong\u003e0.\u003c/strong\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(UBC-864)\u003c/strong\u003e, while different primers such as \u003cstrong\u003eUBC-810 (0.4\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eUBC-840 (0.\u003c/strong\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eUBC-888 (0.\u003c/strong\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eUBC-890 (0.\u003c/strong\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, and \u003cstrong\u003eUBC-891 (0.4\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e had relatively high total heterozygosity. The \u003cstrong\u003ewithin-population genetic diversity (Hs)\u003c/strong\u003e values ranged from \u003cstrong\u003e0.0\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(UBC-811)\u003c/strong\u003e to \u003cstrong\u003e0.36 (UBC-840)\u003c/strong\u003e, averaging \u003cstrong\u003e0.24\u003c/strong\u003e across loci. \u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003eene flow\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eestimates\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(Nm)\u003c/strong\u003e, whose relationship is inversely correlated with Gst, were variable between loci, ranging from \u003cstrong\u003e0.59 (UBC-888)\u003c/strong\u003e to \u003cstrong\u003e11.40 (UBC-811)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and averaging\u0026nbsp;\u003c/strong\u003e2.39. Loci like \u003cstrong\u003eUBC-847 (Nm = 5.\u003c/strong\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eUBC-855 (Nm = 9.1\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, and \u003cstrong\u003eUBC-880 (Nm = 5.6\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e displayed relatively higher Nm values, while \u003cstrong\u003eUBC-809 (Nm = 0.9\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eUBC-845 (Nm = 0.9\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eUBC-864 (Nm = 0.96)\u003c/strong\u003e, and \u003cstrong\u003eUBC-890 (Nm = 0.6\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e had lower estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e Coefficients of genetic differentiation across 15 ISSR loci in six \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eemetica\u003c/em\u003e populations from western Kenya\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"480\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMarker code\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;Ht\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;Hs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eGst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eNm*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e11.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e9.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eUBC-891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e \u003cem\u003eHt\u003c/em\u003e = total genetic diversity; \u003cem\u003eHs\u003c/em\u003e = within-population genetic diversity; \u003cem\u003eGst\u003c/em\u003e = coefficient of genetic differentiation; \u003cem\u003eNm\u003c/em\u003e = estimate of gene flow, calculated as Nm=0.5(1\u0026minus;Gst)/GstNm = 0.5(1 - Gst)/GstNm=0.5(1\u0026minus;Gst)/Gst.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic relationships among populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA clear spatial distribution of genetic relationships among the six \u003cem\u003eT. emetica\u003c/em\u003e populations in western Kenya (Fig 1). The first two principal coordinates accounted for the maximum percentage of overall genetic variance and produced a two-dimensional arrangement of genetic relationships among populations. The biplot showed distinct clustering with respect to population origin, and each grouping displayed differences in the level of dispersion. The \u003cstrong\u003eNandi\u003c/strong\u003e population were plotted separately from the other populations, forming a distinct cluster along the first coordinate axis. The \u003cstrong\u003eVihiga\u003c/strong\u003e population was positioned near the Nandi cluster but showed partial overlap with it and hence intermediate placement in the coordinate space. \u003cstrong\u003eSiaya\u003c/strong\u003e and \u003cstrong\u003eKisumu\u003c/strong\u003e populations clustered in proximity to one another, while \u003cstrong\u003eBungoma\u003c/strong\u003e and \u003cstrong\u003eKakamega\u003c/strong\u003e formed a separate distinct cluster on the biplot.\u003c/p\u003e\n\u003cp\u003ePairwise \u003cstrong\u003eNei\u0026rsquo;s genetic distances\u003c/strong\u003e among the six populations of \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eemetica\u003c/em\u003e varied from \u003cstrong\u003e0.02\u003c/strong\u003e between \u003cstrong\u003eVihiga and Kisumu\u003c/strong\u003e to \u003cstrong\u003e0.2\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e between \u003cstrong\u003eBungoma and Nandi\u003c/strong\u003e, while the \u003cstrong\u003egenetic identities\u003c/strong\u003e differed from \u003cstrong\u003e0.7\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e to \u003cstrong\u003e0.9\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e (Table 7). Low genetic distances were reported for several population pairs, such as \u003cstrong\u003eKisumu\u0026ndash;Siaya (0.0\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e1)\u003c/strong\u003e, \u003cstrong\u003eSiaya\u0026ndash;Vihiga (0.0\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, and \u003cstrong\u003eKisumu\u0026ndash;Vihiga (0.02)\u003c/strong\u003e, all of which also showed high genetic identity values exceeding \u003cstrong\u003e0.97\u003c/strong\u003e. Moderate distances were observed for \u003cstrong\u003eKakamega\u0026ndash;Siaya (0.09)\u003c/strong\u003e, \u003cstrong\u003eKakamega\u0026ndash;Vihiga (0.10)\u003c/strong\u003e, and \u003cstrong\u003eBungoma\u0026ndash;Kakamega (0.0\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, with identity values of \u003cstrong\u003e0.9\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e, \u003cstrong\u003e0.90\u003c/strong\u003e, and \u003cstrong\u003e0.97\u003c/strong\u003e, respectively. Larger genetic distances were found in the pairs that involved the Nandi population, especially \u003cstrong\u003eBungoma\u0026ndash;Nandi (0.2\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eSiaya\u0026ndash;Nandi (0.1\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, \u003cstrong\u003eVihiga\u0026ndash;Nandi (0.1\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e, and \u003cstrong\u003eKisumu\u0026ndash;Nandi (0.13)\u003c/strong\u003e, and these had relatively low genetic identities of \u003cstrong\u003e0.7\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e and \u003cstrong\u003e0.8\u003c/strong\u003e\u003cstrong\u003e8, respectively\u003c/strong\u003e. Comparison involving Bungoma\u003cstrong\u003e\u0026ndash;Siaya (0.16)\u003c/strong\u003e and \u003cstrong\u003eBungoma\u0026ndash;Vihiga (0.18)\u003c/strong\u003e also involved relatively large distances.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e Pairwise Nei\u0026rsquo;s genetic identity and genetic distance among six \u003cem\u003eT\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;emetica\u003c/em\u003e populations from western Kenya based on ISSR marker data\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eBungoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eKakamega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eKisumu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eSiaya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eVihiga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNandi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBungoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.9742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.8793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.8487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.8330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.7867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eKakamega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.0262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.9417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.9097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.9015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eKisumu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.1286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.0601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.9714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.9767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSiaya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.1640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.0946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.9744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eVihiga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.1827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.1037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNandi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.2399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.1354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.1325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.1494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.1494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values above the diagonal represent Nei\u0026rsquo;s genetic identity; values below the diagonal represent Nei\u0026rsquo;s genetic distance.\u003c/p\u003e\n\u003cp\u003eFig 2 shows the genetic relationships among the six populations of \u003cem\u003eT. emetica\u003c/em\u003e as defined by the \u003cstrong\u003eNei\u0026rsquo;s unbiased genetic distances\u003c/strong\u003e. The dendrogram produced three clusters, indicating discrete levels of genetic relatedness among \u003cem\u003eT. emetica\u0026nbsp;\u003c/em\u003epopulations. Siaya, Vihiga, and Kisumu were grouped in the same cluster and showed minimum separation in branch length within the cluster. The second cluster consisted of \u003cstrong\u003eBungoma\u003c/strong\u003e and \u003cstrong\u003eKakamega\u003c/strong\u003e, a closely related group different from Siaya-Vihiga-Kisumu cluster. On other hand, Nandi population was grouped in the third cluster, demonstrating \u0026nbsp; \u0026nbsp;its discreteness from the rest of the populations.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eGenetic diversity of\u003c/b\u003e \u003cb\u003eT. emetica\u003c/b\u003e \u003cb\u003epopulations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe populations of \u003cem\u003eT. emetica\u003c/em\u003e from western Kenya showed high mean percentage of polymorphic loci (94.65%), demonstrating discriminative power of ISSR markers to detect genetic variation. The high level of polymorphism observed in this study is line with 91.7% reported in \u003cem\u003eMelia dubia\u003c/em\u003e (Rawat et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), 95\u0026ndash;97% in \u003cem\u003ePinus sylvestris\u003c/em\u003e (Sheikina and Romanov \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), 80\u0026ndash;100% in \u003cem\u003eOsyris lanceolata\u003c/em\u003e (Mugula et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and higher than 63.8% and 46\u0026ndash;76% in \u003cem\u003eParashorea chinensis\u003c/em\u003e and \u003cem\u003eRobinia pseudoacacia\u003c/em\u003e (Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Uras et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), respectively, which shows the potential of ISSR in dissecting genetic diversity in tree species. The 100% polymorphism observed in several ISSR primers such as UBC-809, UBC-845, UBC-847, UBC-857, UBC-864, UBC-880, and UBC-891 indicates that ISSR tool is highly effective in detecting genetic variation in the \u003cem\u003eT. emetica\u003c/em\u003e populations. Borah et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also reported ssimilar reliability of ISSR markers in showing high levels of genetic polymorphism in \u003cem\u003eIllicium griffithii\u003c/em\u003e, further confirming their effectiveness in analyzing intra-specific variation in tree species.\u003c/p\u003e\u003cp\u003eThe fragment sizes of amplicons ranged from 169 to 2060 bp further reinforcing the suitability of ISSR markers for assessement of genetic diversity in tree species and demonstrates wide genome variability among the \u003cem\u003eT. emetica\u003c/em\u003e populations. Such high variation in fragment size shows the presence of several polymorphic loci distributed across the genome of \u003cem\u003eT. emetica\u003c/em\u003e populations, indicating high allelic diversity, in line with its wide ecological distribution over lowland, mid-altitude, and highland habitats (Chebii et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Osewe et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similar findings have been reported in \u003cem\u003eKhaya anthotheca\u003c/em\u003e (Bouka et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cem\u003eCedrela odorata\u003c/em\u003e (Finch et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where wide ecological distribution was associated with high intra-specific polymorphism, indicating that tropical tree species with extensive habitats have large effective population sizes and diverse genetic backgrounds. The variation between primers in the percentage of polymorphism and the sizes of amplicons may indicate possible underlying differences in genome structure or localized selection pressures operating throughout \u003cem\u003eT. emetica\u003c/em\u003e populations. These results are in line with recent findings in other forest trees such as \u003cem\u003eAcacia Senegal, Parashorea chinensis\u003c/em\u003e, \u003cem\u003eJuniperus\u003c/em\u003e spp., and \u003cem\u003eAnadenanthera colubrina\u003c/em\u003e, where variation among ISSR primers in the percentage of polymorphism and fragment sizes was attributed to differences in genome structure and localized environmental influences (Omondi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003ea; Xu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Al-Yasi and Al-Qthanin \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe diversity indices in this study show the existence of genetic heterogeneity among the populations of \u003cem\u003eT. emetica\u003c/em\u003e in western Kenya. Nandi population displayed the highest diversity indices (Na\u0026thinsp;=\u0026thinsp;1.63; Ne\u0026thinsp;=\u0026thinsp;1.40; He\u0026thinsp;=\u0026thinsp;0.24; I\u0026thinsp;=\u0026thinsp;0.36; %P\u0026thinsp;=\u0026thinsp;78.31%), indicating substantial allelic richness, evenness, and heterozygosity. These indices demonstrate that the Nandi population may be maintaining a large effective size and experiences high gene flow, with reduced human activities, which allow preservation of higher genetic variability. These findings are line with the previous ISSR and SSR-studies showing relatively high within-population diversity attributed to large, continuous populations, extensive gene flow, and open pollination systems that facilitate allelic exchange and reduced genetic drift in Meliaceae trees species such as \u003cem\u003eMelia dubia\u003c/em\u003e (Rawat et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eSwietenia macrophylla\u003c/em\u003e (Limongi Andrade et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cem\u003eKhaya anthotheca\u003c/em\u003e (Bouka et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Congruent with the results in this study, populations of \u003cem\u003eMemecylon subcordatum\u003c/em\u003e (Viswanathan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eOsyris lanceolata\u003c/em\u003e (Mugula et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003ePinus sylvestris\u003c/em\u003e (Sheikina and Romanov \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also had both high Shannon\u0026rsquo;s diversity indices and percentage of polymorphic loci, demonstrating the influence of population size and ecological integrity in determining within-population genetic variation.\u003c/p\u003e\u003cp\u003eIn contrast, Siaya and Kisumu \u003cem\u003eT. emetica\u003c/em\u003e populations showed a lower diversity index (Na\u0026thinsp;\u0026asymp;\u0026thinsp;0.66; He\u0026thinsp;\u0026asymp;\u0026thinsp;0.10; %P\u0026thinsp;=\u0026thinsp;31.25%), indicating reduced allelic variation and possible genetic erosion. The reduced allelic richness and heterozygosity in Siaya and Kisumu correspond with documented pressures of deforestation, agricultural expansion, and unsustainable resource extraction in western Kenya (Kogo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rotich and Ojwang \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chebii et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Osewe et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similar trend where habitat fragmentation and land-use change contributed to reduction in genetic diversity and gene flow in tropical tress species (Soares et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A reduced gene diversity in isolated or heavily exploited populations has also been reported in \u003cem\u003eMelia dubia\u003c/em\u003e (Rawat et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eKhaya anthotheca\u003c/em\u003e (Bouka et al. 202) and \u003cem\u003eSwietenia macrophylla\u003c/em\u003e ( Limongi Andrade et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bungoma, Kakamega, and Vihiga populations had moderate diversity indices (He\u0026thinsp;=\u0026thinsp;0.14\u0026ndash;0.19; I\u0026thinsp;=\u0026thinsp;0.21\u0026ndash;0.29; %P\u0026thinsp;=\u0026thinsp;43\u0026ndash;75%), indicating partial preservation of genetic diversity, reflecting their position along a continuum of anthropogenic distance and ecological connectivity. Therefore, the Bungoma-Kakamega-Vihiga populations may still be exchanging genes through fragmented corridors or remnant trees in agricultural landscapes, a pattern observed in \u003cem\u003eTabebuia rosea\u003c/em\u003e (Ruiz-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and \u003cem\u003eAcacia senegal\u003c/em\u003e (Omondi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), where exchange of genes among isolated or remnant populations has been maintained despite fragmentation of the habitats.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation differentiation and structure of\u003c/b\u003e \u003cb\u003eT. emetica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe populations of \u003cem\u003eT. emetica\u003c/em\u003e in western Kenya showed a structured yet genetically diverse pattern, influenced by limited gene flow. Sixty-five percent of the total genetic variation was observed within populations and 35% among the populations (ΦST\u0026thinsp;=\u0026thinsp;0.35), showing moderate to high genetic differentiation. The Gst value (0.27, p\u0026thinsp;=\u0026thinsp;0.001) reinforced this pattern, indicating that while diversity of alleles is preserved within the populations, a significant percentage of variation is spread among them. In comparison with other Meliaceae species, \u003cem\u003eT. emetica\u003c/em\u003e displayed higher population differentiation than \u003cem\u003eKhaya anthotheca\u003c/em\u003e (FST\u0026thinsp;=\u0026thinsp;0.12) and \u003cem\u003eSwietenia macrophylla\u003c/em\u003e (ΦST\u0026thinsp;=\u0026thinsp;0.28), meaning that gene flow is restricted across its populations in western Kenya (Alcal\u0026aacute; et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bouka et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similar reduction in gene flow due to habitat fragmentation has been reported in plant populations (Cheptou et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe differentiation indices in this study (ΦST\u0026thinsp;\u0026asymp;\u0026thinsp;0.35; Gst\u0026thinsp;\u0026asymp;\u0026thinsp;0.27) corresponded well with a mean gene flow estimate of Nm\u0026thinsp;=\u0026thinsp;2.39, reflecting moderate connectivity, sufficient to reduce genetic drift but insufficient to homogenize populations completely. This balance demonstrates that the populations of \u003cem\u003eT. emetica\u003c/em\u003e may be undergoing semi-independent evolutionary processes, with geographic isolation and environmental gradients acting as filters to exchange of genes. Rajarajan et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported ccomparable trends of genetic differentiation and population structuring that are influenced by environmental heterogeneity in \u003cem\u003eAzadirachta indica\u003c/em\u003e and \u003cem\u003eMelia azedarach\u003c/em\u003e, reinforcing the view that ecological variation drives genetic structuring within the Meliaceae. Similar levels of gene flow and spatial structuring have been reported in \u003cem\u003eAcacia senegal\u003c/em\u003e (Omondi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003eKhaya anthotheca\u003c/em\u003e (Bouka et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and \u003cem\u003eSwietenia macrophylla\u003c/em\u003e (Limongi Andrade et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where fragmentation and ecological discontinuities have produced moderate differentiation despite ongoing genetic connectivity.Variation in Gst values (0.04\u0026ndash;0.46) across ISSR loci further demonstrated unevenness of genetic differentiation across the genome, differing levels of mutation, selection, or gene exchange among loci. Primers such as UBC-809 and UBC-864, which had intermediate Gst values, may have captured genomic regions undergoing partial isolation, while highly differentiated loci like UBC-888 likely reflect regions of genome under selection or low recombination. This trend is in line with findings in \u003cem\u003eMelia dubia\u003c/em\u003e (Rawat et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and \u003cem\u003eJuniperus excelsa\u003c/em\u003e (Al-Yasi and Al-Qthanin \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where ISSR markers effectively revealed localized allelic divergence in response to ecological isolation and restricted pollen flow.\u003c/p\u003e\u003cp\u003eThe PCoA and the UPGMA analyses supports these molecular patterns. These analyses showed three different genetic clusters: (i) Siaya\u0026ndash;Vihiga\u0026ndash;Kisumu, (ii) Bungoma\u0026ndash;Kakamega, and (iii) Nandi. The similarity between UPGMA and PCoA underline the strength of these relationships and their ecological basis. The first two principal coordinates accounted for the majority of total variance and delineated populations along ecological gradients from lowland to highland zones, reflecting the regional topography and rainfall patterns of western Kenya.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eT. emetica\u003c/em\u003e population from Nandi occupied the first coordinate axis and had the highest Nei\u0026rsquo;s genetic distances, ranging from 0.13 to 0.24, and had the least genetic distances of between 0.79 and 0.88 relative to other populations. This variation in classification can be linked to its ecological and geographical isolation in the highlands, where reduced pollen and seed dispersal, coupled with differences in climatic conditions, support genetic divergence. Previous studies showing distinct genetic differentiation connected to topographic and ecological discontinuities has been reported in \u003cem\u003eSwietenia macrophylla\u003c/em\u003e from fragmented Mexican forests (Alcal\u0026aacute; et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), \u003cem\u003eOsyris lanceolata\u003c/em\u003e in East Africa (Mugula et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and \u003cem\u003eParashorea chinensis\u003c/em\u003e across Southeast Asia (Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, the populations of \u003cem\u003eT. emetica\u003c/em\u003e from Siaya, Kisumu, and Vihig) displayed very low genetic distances, which ranged from 0.02 to 0.03, and high identity values greater than 0.97, pointing a recent common ancestry or ongoing gene flow preserved through landscape connectivity such as riverine corridors or agricultural mosaics. These findings mirror the weak spatial structuring reported in \u003cem\u003eCedrela odorata\u003c/em\u003e across Neotropical lowlands (Finch et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cem\u003eSonneratia caseolaris\u003c/em\u003e in coastal Vietnam (Le and Le \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where gene flow remains relatively unrestricted in connected habitats. On other hand, Bungoma and Kakamega clustered in a moderately distinct category, likely sustained by partial connectivity through remnant mid-altitude forest corridors. The small Nei\u0026rsquo;s distance of 0.03 observed between these two populations and their close grouping on the dendrogram support the presence of ongoing but reduced gene flow across fragmented forest patches.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConservation implications of genetic diversity patterns in\u003c/b\u003e \u003cb\u003eT. emetica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ISSR genetic patterns in this study have important conservation and sustainable management implications for \u003cem\u003eT. emetica\u003c/em\u003e populations in western Kenya. The high level of polymorphism (94.65%) and the finding that 65% of the total variation occurs within populations indicate that substantial local genetic diversity still exists. This within-population diversity is vital for maintaining adaptive potential and ensuring long-term evolutionary resilience, particularly under increasing environmental and anthropogenic pressures. Given projections of shifting climatic zones and increased habitat stress, maintaining such genetic diversity is crucial for forest resilience and adaptive response (Konrad et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the relatively high population differentiation (ΦST\u0026thinsp;=\u0026thinsp;0.35; Gst\u0026thinsp;=\u0026thinsp;0.27) shows that genetic resources are unevenly distributed, highlighting the need for population-specific conservation strategies rather than a uniform management approach. Similar to patterns reported in fragmented alpine and tropical plant systems, where restricted gene flow and local adaptation demand site-specific conservation actions (Cheptou et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Soares et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), these findings emphasize that the management of \u003cem\u003eT. emetica\u003c/em\u003e should account for regional ecological and genetic distinctiveness.\u003c/p\u003e\u003cp\u003ePopulations such as Nandi and Kakamega, which exhibited the highest gene diversity (He\u0026thinsp;=\u0026thinsp;0.24 and 0.18, respectively) and the greatest percentage of polymorphic loci, represent genetic reservoirs essential for the species\u0026rsquo; survival. These populations should be prioritized as core units for in situ and ex situ conservation, serving as key sources for seed collection and the establishment of genetic resource banks. Similar conservation initiatives intergrating molecular diversity and population structure have been conducted for \u003cem\u003eVitellaria paradoxa\u003c/em\u003e in C\u0026ocirc;te d\u0026rsquo;Ivoire (Attikora et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), highlighting the importance of conserving genetically diverse populations as reservoirs for breeding and restoration. In contrast, populations from Siaya and Kisumu, which recorded low gene diversity (He\u0026thinsp;=\u0026thinsp;0.10\u0026ndash;0.11) and only 31.25% polymorphic loci, are vulnerable to genetic erosion and stochastic loss. These populations require genetic enrichment through assisted regeneration, enrichment planting, and controlled exchange of germplasm from genetically richer populations to prevent further decline in adaptive capacity (Donkpegan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The moderate fixation index (ΦST\u0026thinsp;=\u0026thinsp;0.35) and variable Gst values among ISSR loci (0.04\u0026ndash;0.46) reflect limited genetic exchange between populations. From a conservation perspective, this underscores the importance of preserving and restoring gene flow corridors between fragmented forest remnants. Conservation planning should prioritize connectivity conservation, linking genetically related populations such as Bungoma-Kakamega and Kisumu -Vihiga -Siaya to sustain pollen and seed dispersal. Establishing community-based forest restoration zones that utilize genetically diverse and locally adapted materials can help stabilize population structures and mitigate ongoing isolation. Furthermore, the clear delineation of populations into three genetic groups: (i) Siaya-Vihiga-Kisumu, (ii) Bungoma-Kakamega, and (iii) Nandi, provides a strong foundation for conservation and seed zoning. Such genetic zoning supports Kenya\u0026rsquo;s forest seed production and certification systems, ensuring that restoration initiatives use materials compatible with the adaptive traits of each ecological region (FAO \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Integrating this molecular data into forest restoration policy will enhance seed sourcing, prevent maladaptation, and strengthen reforestation programs aligned with the Bonn Challenge (IUCN 2011), the UN Decade on Ecosystem Restoration (2021\u0026ndash;2030), and Sustainable Development Goal 15 (Life on Land) (FAO \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such integration is needed, given evidence that hunan activities are eroding genetic diversity and threatening species persistence (Soares et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Omondi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLimitations of the study\u003c/h2\u003e\u003cp\u003eThe present study reports the first molecular data on the genetic diversity and population structure of \u003cem\u003eT. emetica\u003c/em\u003e in Kenya. The fifteen ISSR markers revealed over 94% level of polymorphism and delineated the \u003cem\u003eT. emetica\u003c/em\u003e populations into distinct clusters that corresponded with ecological stratification. The sample size of 20 individuals per population was adequate to detect genetic diversity but may have been insufficient to fully capture intra and inter population variation. The dominant nature of ISSR markers does not allow distinction between homozygous and heterozygous loci, reducing precise estimation of heterozygosity, inbreeding coefficients, and allelic richness. Since ISSRs target non-coding regions of the DNA, they do not accurately dissect adaptive loci. However, chloroplast genomic studies in Meliaceae have revealed interspecific variation useful for phylogenetic and adaptive inference (Nie et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), indicating that integrating plastid and nuclear genomic tools could enhance understanding of \u003cem\u003eT. emetica\u003c/em\u003e\u0026rsquo;s evolutionary history. In this context, combining chloroplast genomic data with co-dominant nuclear markers like SSRs and SNPs would enable precise estimation of heterozygosity, inbreeding, and adaptive loci (Bouka et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Limongi Andrade et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Faria et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), thereby providing a comprehensive view of genetic structure and adaptive potential. Hence, while ISSRs produced valuable baseline molecular data for assessing genetic variation and population differentiation,, integrating plastid and co-dominant markers can help refine the adaptive diversity, evolutionary dynamics, and conservation of \u003cem\u003eT. emetica\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study dissected the genetic diversity and population structure of \u003cem\u003eT. emetica\u003c/em\u003e. The molecular analysis uncovered over 94% genome-wide polymorphism, with 65% of total variation occurring within and 35% among populations. The populations of \u003cem\u003eT. emetica\u003c/em\u003e from Nandi and Kakamega displayed the highest genetic diversity, whereas Siaya and Kisumu had reduced variability. The population differentiation (ΦST\u0026thinsp;=\u0026thinsp;0.35) and clustering patterns revealed restricted but ongoing gene flow among populations. These findings provide molecular evidence that \u003cem\u003eT. emetica\u003c/em\u003e maintains considerable intra-population variation. The enhanced genetic structuring across ecological zones shows the influence of topography, climate, and habitat degradation on gene exchange and adaptive potential. Cconservation efforts should prioritize Nandi population as core genetic reservoirs, while enhancing connectivity and regeneration in Siaya and Kisumu populations through assisted natural regeneration and targeted enrichment planting. Despite the contributions made in this study, it used fewer individuals to represent each population and relied on dominant ISSR markers that do not distinguish heterozygotes from homozygotes, reducing the estimation of heterozygosity, inbreeding, and adaptive variation. Future studies should increase the number of individuals sampled per population and incorporate co-dominant or high-resolution genomic tools to capture both neutral and adaptive diversity. Therefore, preserving the genetic diversity of \u003cem\u003eT. emetica\u003c/em\u003e is critical for sustaining forest ecosystem resilience, supporting local livelihoods, and ensuring adaptive responses under climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompliance with Ethical Standards\u003c/h2\u003e\n\u003cp\u003eThis study was exempted from ethical review for human clinical trials or animal experiments, as it involved only collection of plant leaves. The Ethics and Review Committee of the University of Eldoret reviewed and approved the study.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003ch2\u003eConsent for publication:\u003c/h2\u003e\n\u003cp\u003eAll authors have reviewed and approved the manuscript for submission to the \u003cem\u003eNew Forests\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eCompeting interests:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors did not receive funding from any organization the submitted work.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eThe study was carried out as part of the first author\u0026rsquo;s Master of Science thesis in Plant Genetics at the University of Eldoret. E.K.S: conceptualized the study, designed the methodology, and conducted data collection and analysis, drafted original manuscript. B .O.N and O.G.D: supervised the first author, critically reviewed the manuscript and approved version for submission. K.P.A: Supported the first author in sample collections and reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors are grateful to Kenya Agricutlural and Livestock Research Organization (KARLO) at Njoro for providing laboratory facility to undertake this study. We would like to thank Martin Lagat and Cyrus Kimani from KALRO-Njoro for technical support during the study. We would like to thank Ms. Samantha Cynthia Akinyi for editing the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and raw data can be obtained upon request from the first author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Yasi HM, Al-Qthanin R (2024) Comparing genetic differentiation and variation using ISSR and SCoT among \u003cem\u003eJuniper\u003c/em\u003e plant markers in Saudi Arabia. \u003cem\u003eFront Plant Sci\u003c/em\u003e 15:1356917. https://doi.org/10.3389/fpls.2024.1356917\u003c/li\u003e\n\u003cli\u003eAldholmi M, Althomali E, Aljishi F, Ahmad R, Alqathama A, Alaswad D (2024) A comparative study on the antidiabetic activity, cytotoxicity and lipid profile of \u003cem\u003eTrichilia emetica\u003c/em\u003e oils. \u003cem\u003ePlants, 13\u003c/em\u003e(16), 2234. https://doi.org/10.3390/plants13162234\u003c/li\u003e\n\u003cli\u003eAkweni AL, Zharare GE, Zimudzi C (2021) Predicting the number of fruits and the seed biomass of \u003cem\u003eTrichilia emetica\u003c/em\u003e (Vahl.) in the eastern coastal region of South Africa. \u003cem\u003eTrees For. 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Accessed 11 Jun 2025\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"new-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nefo","sideBox":"Learn more about [New Forests](http://link.springer.com/journal/11056)","snPcode":"11056","submissionUrl":"https://submission.nature.com/new-submission/11056/3","title":"New Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Conservation genetics, Forest restoration, Genetic diversity, ISSR markers, Population structure, Trichilia emetica","lastPublishedDoi":"10.21203/rs.3.rs-7887580/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7887580/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand-use changes can reduce genetic variation and limit the ability of tropical trees to adapt to climate change. \u003cem\u003eTrichilia emetica\u003c/em\u003e, a versatile tree of ecological and economic importance in Kenya, remains uncharacterized at the genetic level. This study determined the genetic diversity and population structure of \u003cem\u003eT. emetica\u003c/em\u003e in western Kenya using 15 Inter-Simple Sequence Repeat (ISSR) markers. A total of 171 DNA fragments were amplified, of which 94.65% were polymorphic, revealing substantial genomic variability. Overall, gene diversity was moderate (He\u0026thinsp;=\u0026thinsp;0.15; I\u0026thinsp;=\u0026thinsp;0.22), with Nandi (0.24) and Kakamega (0.18) populations showing the highest diversity while Kisumu and Siaya had the least (0.10). Analysis of molecular variance (AMOVA) partitioned 65% of total variation within populations and 35% among populations (ΦST\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating moderate differentiation and restricted but ongoing gene flow (Nm\u0026thinsp;=\u0026thinsp;2.39). The six populations were clustered into three major genetic groups consistent with ecological gradients. This study establishes the first molecular baseline for \u003cem\u003eT. emetica\u003c/em\u003e in Kenya and demonstrate that ecological heterogeneity may play a role in influencing its genetic structure. Conservation work should preserve the populations in Nandi and Kakamega while implementing enrichment and assisted regeneration in Siaya and Kisumu populations to reduce loss of genetic diversity.\u003c/p\u003e","manuscriptTitle":"Genetic diversity and population structure of Trichilia emetica Vahl in western Kenya using ISSR markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 09:21:23","doi":"10.21203/rs.3.rs-7887580/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-15T16:11:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T13:16:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T15:14:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T16:57:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T13:51:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147911983884621014977570566957285265103","date":"2025-10-24T11:59:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160974066438547016647989753987370417665","date":"2025-10-24T11:02:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204633681258335497415405929209767174534","date":"2025-10-23T09:19:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47150338163740908837615546411984004448","date":"2025-10-23T08:00:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89017460091810040403752359326980733860","date":"2025-10-22T20:42:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T16:04:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-18T02:46:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-18T02:46:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"New Forests","date":"2025-10-17T14:09:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"new-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nefo","sideBox":"Learn more about [New Forests](http://link.springer.com/journal/11056)","snPcode":"11056","submissionUrl":"https://submission.nature.com/new-submission/11056/3","title":"New Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b5593122-7465-444d-b897-de01cc1de532","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:09:48+00:00","versionOfRecord":{"articleIdentity":"rs-7887580","link":"https://doi.org/10.1007/s11056-025-10154-1","journal":{"identity":"new-forests","isVorOnly":false,"title":"New Forests"},"publishedOn":"2025-12-24 15:57:59","publishedOnDateReadable":"December 24th, 2025"},"versionCreatedAt":"2025-11-03 09:21:23","video":"","vorDoi":"10.1007/s11056-025-10154-1","vorDoiUrl":"https://doi.org/10.1007/s11056-025-10154-1","workflowStages":[]},"version":"v1","identity":"rs-7887580","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7887580","identity":"rs-7887580","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}