Divergence and genetic parameters between Melia dubia genotypes based on morpho-anatomical stomatal descriptors

Divergence and genetic parameters between Melia dubia genotypes based on morpho-anatomical stomatal descriptors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Divergence and genetic parameters between Melia dubia genotypes based on morpho-anatomical stomatal descriptors Aghila Samji, Komal Eashwarlal, Kannan CS Warrier, Santhosh Kumar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3883484/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Stomata, a regulatory system in leaves, aids in identifying plant species to complete taxonomic data. This study investigated the stomatal descriptors of Melia dubia , a potential plywood species to distinguish within variation in the species to explain the diversity and diagnostic significance of these attributes. Twenty clones were selected to investigate nine stomatal characters related to stomatal type, length, width, density, and distribution. The results showed the presence of hypostomatic leaves with anomocytic stomata that falls under the category small. Stomatal clustering, an abnormal stomatal patterning formed by two or more stomata in the epidermis was also observed. The examined data were subjected to a numerical analysis using SPSS and R packages. A significant variation in observed parameters were obtained. Correlation analysis shows that stomatal length, width, and size were significantly correlated to pore length, pore width, pore perimeter and stomatal pore depth. Further hierarchical cluster analysis using average linkage between groups method clustered all the 20 clones into 5 clusters apportioning the variation among clones. Divergence analysis using Mahanalobis distance-based clustering detailed the dissimilarities and differences between the clones. The study highlights the diagnostic potential of stomatal features in identifying variations within the species. This report is the first detailed description of stomatal features in the genus Melia , implying its significant contribution to the knowledge in this area. This study underscores the potential of stomatal features as a diagnostic tool for plant species identification and taxonomic studies. Clonal variation stomatal clustering hypostomatic anomocytic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Stomata are microscopic active interface structures present on the aerial epidermis of land plants, exclusively on sporophytes and occasionally on gametophytes which act as a channel between plants and the environment for the exchange of water and gas (Franks and Beerling 2009; Rudall et al. 2013). They are essential for primary productivity and predominantly present on the CO2 impermeable, waterproof cuticle of central photosynthetic organ leaf and stem surfaces characterised by a central pore flanked by two symmetric guard cells (Caine et al. 2023; Chandra et al. 2023). Guard cells are specialised structures distinct from normal epidermal cells that control the diffusional gaseous flux by adjusting their turgor status followed by the size of the aperture (Kirkham 2014; Wall et al. 2022). However, guard cells also influence stomatal patterning in embryophytes. Stomata acquisition on the impervious cuticle was considered a fundamental novelty at the time of evolution in land plants (Raven 2002). Most vital plant physiological activities like photosynthesis, transpiration, and respiration correlate with stomata opening and closing to various internal and external stimuli. Plant stomata sensibly balance CO 2 uptake and retain photosynthetic carbon assimilation. Water loss through transpiration is also minimised by stomata, thereby helping drought avoidance mechanisms to enhance water use efficiency (WUE) and desiccation (Nilson and Assmann 2007; Songsri et al. 2013). A plant’s ability to sense and respond to fluctuating environmental conditions is related to photosynthesis and transpiration; hence stomata also influence adaptability and productivity (Hetherington and Woodward 2003; Muradoglu and Gundogdu 2011; Liu et al. 2018). Leaf morphology is an important character used for classifying and delimitating plant species, with stomata significantly contributing to the evolutionary and speciation aspects (Ma D 1987; Abdel-Hameed 2014). Adult stomatal characters have been used to draw interrelationships and natural taxonomic affinities between families (Van Cotthem, 1970) such as the classification of the clade Cryptostomata and Phanerostomata (Mast and Givnish 2002). Stomata are morphologically and mechanically diverse in different plants; within-species stomatal characters show variation in shape, size, type, position, and density (Franks and Farquhar 2007; Zhu et al. 2016; Hong et al. 2018). The anatomical features and pore width regulation of stomata introduced by growth conditions or genetic factors influence gas exchange. Another important stomatal trait, the pore area, is associated with leaf lamina hydraulic conductance (Sack et al. 2003). Melia dubia Cav. is a deciduous, perennial, short-rotation, money-spinning tree of the Melia ceace family with multifarious uses (Warrier 2020). M.dubia is distributed in India, Sri Lanka, Australia, China, and Africa. The anti-termite and fungus-resistant timber properties make M.dubia extremely valued in plywood industries (Samji et al. 2023). It is an excellent secondary timber and potential hardwood for pulp production. It is also identified as a suitable alternative species for different agro-farm forestry programs (Geetha et al. 2019). The presence of high variabilities to identify genotypes with higher genetic potential provides wide range of options for selection in plant breeding programs. The present work focuses on leaf stomatal characters and its morphological parameters of M. dubia clones, an important germplasm resource characterisation study, a pre-requisite for selecting and breeding different genotypes through trait evaluations. Twenty clones of Melia dubia were examined for stomatography and morphometric analysis described the variations in the species. Materials and methods L eaf sample collection and p reparation of leaf samples for microscopy Leaves samples were collected from 20 different clones of Melia dubia housed in the germplasm bank at the Institute of Forest Genetics and Tree Breeding (IFGTB) (11.0176°N, 76.9514°E) Coimbatore, India during 2021-22. We sampled mature, fully expanded, sun-exposed, similar-sized leaflets from ten individuals at the standard median level. These fresh leaves were directly used for microscopic studies without any chemical treatment. Leaves samples were collected from 20 different clones of Melia dubia housed in the germplasm bank at the Institute of Forest Genetics and Tree Breeding (IFGTB) (11.0176°N, 76.9514°E) Coimbatore, India during 2021-22. We sampled mature, fully expanded, sun-exposed, similar-sized leaflets from ten individuals at the standard. Leaf samples were prepared according to the common nail varnish imprint method by Miller and Ashby (1968). After a wash with distilled water, fully expanded leaves were coated with a thin layer of clear nail varnish, followed by applying adhesive tape over the dried polish, avoiding the major veins of leaves. The adhesive film was then peeled off carefully and placed on a clean microscopic slide. The imprints were observed under Nikon Eclipse Ei microscope equipped with a digital camera and photomicrographs at 40X were captured using Imageview x64. Measurements were taken with ImageJ 1.53a. Characters including stomata type, structure, and parameters like stomatal density-d (mm -2 ), stomatal index-SI (%), stomatal length-SL (µm), stomatal width-SW (µm), pore length-PL (µm), pore width-PW (µm), pore perimeter-PP (µm), stomatal size-S (µm 2 ) and stomatal pore depth-SPD (µm) were determined. Length, width, and perimeter were taken from 20 randomly selected stomata per leaflet, whereas density and index were recorded from ten slides per clone. Number of stomata per field of view was counted and recorded along with the area of each picture calculated using a micrometre. Micro measurements and calculations Stomatal index (SI) was calculated using the formula , where S is the no: of stomata per unit area and E is the no: of epidermal cell in same unit area (Salisbury 1928). Stomatal density (d) was calculated as . Stomatal size (S) in µm 2 was calculated as , where SL and SW is the stomatal length and stomatal width. Stomatal pore depth (SPD) equal to guard cell width was calculated using the formula (Fanourakis et al. 2015). Clustering was performed using hierarchical cluster analysis. Genetic analysis and Statistical Analysis Phenotypic and Genotypic variances were estimated as described by Johnson et al. (1955) while Phenotypic and Genotypic Coefficient of variation (PCV and GCV) were computed following Burton (1952). The genetic advance was calculated as described by Johnson et al. (1955) for all the characters studied. Genetic advance as per cent of mean was calculated following Burton and Devane (1953). For divergence analysis, the test of significance of difference with regard to the pooled effect of all the characters was estimated prior to subjecting the data to genetic divergence analysis. Group distance based on multiple characters was calculated using the method explained by Mahalanobis (1928). Wilks’ criterion was estimated using the formula by Wilks (1932) and Rao (1952) and tested using ‘V’ stat. The ‘V’ statistic was compared with the tabulated χ2 value and thus significance was tested. Clusters were determined following Tocher’s method (Singh and Choudhary 1976). Data was subjected to analysis of variance (ANOVA) at 5% significance level and post-hoc comparisons were done with Duncan’s Multiple Range Test (DMRT) when significant main effects were detected. Pearson’s correlation at 99% to identify significant correlations subsequently followed by hierarchical cluster analysis using average linkage between groups method and Euclidean distance as measurement. Mahalanobis D 2 analysis was used to determine genetic divergence among 20 genotypes using seven characters (Mahalanobis 1928). Matrices were generated in R version 4.2.1 Result Description of M.dubia stomata and it’s morphometric analysis Microscopic stomatography in 20 clones of M.dubia ( Fig. 1 ) revealed the occurrence of stomata in leaves as hypostomatic where stomata are restricted only to the abaxial epidermis. Abundant stomatal distribution was observed irregularly over the surface and on both sides of the veins. Stomata were present in veins occasionally. Each stoma appeared to be elliptical in outline. Peristomatal rim differentiated them. The uniformity of the anticlinal walls of the epidermis was smooth and regularly thickened, whereas the straightness of the anticlinal wall was undulated. The stomatal surface was in the shape of eyelids that protruded towards the slits of the stomata. Anomocytic stomata have also been observed in M.dubia, where each stoma is surrounded by more than five subsidiary cells ( Fig. 2 ). Based on the classification of Pataky (1969), the stomata of M.dubia falls under the category “Small” as the guard cells measure less than 15µm. Guard cells were reniform and symmetrical. Epidermal cells were long and quadrilateral. Morphometric analysis of the 20 clones of M.dubia (Table 1) showed a significant difference in stomatal parameters (α 0.05). The average stomatal index in M.dubia was 22%, ranging from 15 (MD18) to 29% (MD14). The lowest stomatal density was observed in MD02, MD10, and MD17 (333 mm -2 ) and the highest in MD04, MD09 and MD15 (533 mm -2 ) with an average of 443.25 µm 2 . The average stomatal length (SL) was 3.84 µm in M.dubia, with MD06 showing the lowest SL of 3.51 µm and MD16 showing the highest SL of 4.26 µm. MD 01 and MD14 showed the largest and smallest variation coefficients of 11.50 and 21.50%, respectively. MD05 had a minimum stomatal width of 2.56 µm, and MD17 showed a maximum stomatal width of 3.24 µm. The coefficient of variation of stomatal width varied from 9.70 to 18.60%. The average SW of the clones was 2.97 µm. The pore length of stomata from 20 clones ranged between 2.03 µm (MD14) and 2.65 µm (MD17) with an average of 2.32 µm whereas pore width ranged from 1.12 µm (MD07, MD14) to 1.43 µm (MD11) with an average of 1.24 µm. The CV of pore length ranged from 13.50 to 22.20%, and pore width ranged from 13.70 to 40.80%. The stomatal pore perimeter was lowest in MD09 (4.53 µm) and highest in MD17 (6.27 µm). The average stomatal perimeter was 5.29 µm. MD06 and MD16 showed the highest and lowest coefficient of variation. MD06 and MD16 revealed the smallest and largest stomatal sizes with a CV of 22.8 and 22.30%, respectively. Stomatal pore depth (SPD) recorded an average of 0.87 µm in M.dubia . MD05 showed the least, and MD16 showed the highest values in terms of SPD with a coefficient of variation of 28 and 21.80% (Supplementary Table 1). Correlation and hierarchical cluster analysis We obtained strong positive correlations (r >0.8) significant at P< 0.01 ( Fig. 3 ). Stomatal length was significantly correlated with pore length, pore width, pore perimeter and stomatal pore depth. Likewise, the correlation of stomatal width and size with pore length, pore width, pore perimeter, stomatal pore depth were significant. A strong positive correlation between stomatal width and stomatal pore depth (0.80) was observed (Supplementary Table 2). Hierarchical cluster analysis using average linkage between groups method and Euclidean distance set to five revealed five clusters( Fig. 4 ). Clones MD19, MD20, MD17, and MD18 formed first cluster. Second cluster included MD15, MD16, MD13, MD14. MD03, MD04, MD01, MD02 formed the third cluster whereas MD11, MD12, MD09, MD10 formed the fourth cluster. Fifth cluster included MD 07, MD08, MD05 and MD06. Genetic analysis and Divergence Analysis Environmental, genetic, and phenotypic variance ranged from 0.04-7.07, 0.01-2.46, 0.04-9.53 respectively (Table 2). Among all the parameters S shows highest EV, GV, and PV values. The EV, GV and PV were low for the trait SPD i.e., 0.04, 0.01, 0.04 respectively. GCV and PCV values ranged from 6.64% -13.44% and 14.86%-26.77%. GCV values were found to be low (<10%) for all the traits including SL (7.20%), SW (6.64%), PL (7.78%), PW (7.72%), PP (7.67%), SPD (8.62%) except S with 13.44% that noted as moderate. PCV was relatively higher than corresponding GCV in the present study. S (26.7%) and SPD (23.6%) exhibited high PCV (>20%) whereas moderate or medium PCV (10%-20%) were obtained for all the other traits such as SL (14.97%), SW (14.86%), PL (18.6%), PW (22.19%) and PP (17.44%). PCV and GCV values difference was high for all the tested parameters indicating environmental influence on these parameters. Low estimates of heritability were recorded for all the tested traits such as SL (23.99%), SW (20.38%), PL (19.41%), PW (12.9%), PP (20.22%), S (26.32%), SPD (14.41%). GA (Genetic advance) and GAM (Genetic advance as percentage of mean) at 1%, 5% and 10% was estimated for all the seven stomatal morphometric traits. SL was found to be with 0.37, 0.3, 0.23, SW with 0.23, 0.17,0.13, PL with 0.23, 0.17, 0.13, PW with 0.1, 0.07, 0.07, PP with 0.50, 0.37, 0.33, S with 2.10, 1.67, 1.40 and finally SPD with 0.07, 0.07, 0.07 was obtained for GA at 1%, 5% and 10% respectively. Range of GAM at 1%, 5% and 10% was 9.03.-18.55, 6.03-14.45, 5.16-12.36. Moderate genetic advance as percentage of mean (10-20%) was recorded for S (18.55%, 14.45%, 12.36%) at 1%, 5% and 10% correspondingly, whereas low GAM at 1%, 5% and 10% was obtained for the traits such as SL (9.46%,7.38%,6.31%), SW (7.94%,6.20%,5.29%), PL(9.71%,7.58%,6.47%),PW(7.73%,6.03%,5.16%), PP (9.57%, 7.47%, 6.38%), S (18.55%,14.45%,12.36%) and SPD (9.03%, 7.04%, 6.02%). Analysis on genetic divergence using Mahalanobis D 2 statistics and all the 20 genotypes were grouped using Tocher’s clustering method ( Fig. 5 ). A total of eight clusters were formed that grouped all the 20 genotypes based on their stomatal morphometric trait’s genetic divergence. The strength of clusters varied from one to five. The maximum numbers of clones contributed to the higher cluster strength in cluster six (MD12, MD13, MD14, MD15, MD18) and the minimum cluster strength was recorded for Cluster seven and eight which includes MD17 and MD19 respectively. Cluster three (MD01, MD02, MD03), 4 (MD04, MD09, MD16) and cluster five (MD07, MD08, MD11) contained three clones per clusters and MD05 and MD06 together formed cluster one, Similarly MD10 and MD20 formed cluster two (Table 3). Six out of seven stomatal morphometric traits contributed to the genetic divergence and among all the characters SPD (56.84%) contributed to the maximum followed by PL (14.21%). SW did not contribute to the divergence (Table 4). The mean values were calculated cluster wise and Cluster eight with MD19 was found to be with highest mean value for SL, SW, PP, S followed by cluster seven that includes MD17 (Table 5). The members of the cluster seven and one was observed with highest inter cluster distance (2.9716). D 2 distance between cluster seven and one was noted as 8.8306 followed by the members of cluster eight and five (inter cluster distance 2.8427) with D 2 distance 8.08097. The maximum intra cluster distance was noticed for cluster six followed by cluster four and three and it was minimum for clusters seven and eight (0.00) (Table 6) Discussion Morphometric analysis This is the first detailed description of the stomata in Melia genus. Similar description of the stomata has been provided for Khaya (Oyedapo et al. 2018). They report that scant stomata were observed on the adaxial surfaces of K. senegalensis and K. ivorensis (hypoamphistomatic) are scanty. This is in accordance with our observations, where poor to nil distribution of stomata were observed on the adaxial surface. Hexacytic stomata observed in the abaxial surface of K. grandifoliola is useful in distinguishing this species from K. senegalensis and K. ivorensis . Similarly, Isawumi (1986) established the use of stomata frequency and density in delineating taxa in Jatropha. Thus, the stomatal morphology and its distribution on the adaxial and abaxial surfaces can be used in delineating the species. Significant differences were observed by the F-test (p < 0.05) between the clones of Melia dubia for all stomatal characters evaluated. This evidences the distinctness of the evaluated clones, which is quite relevant for the genetic divergence analysis and for further breeding or tree improvement programmes aimed at identifying superior genotypes. The evaluated traits showed environmental coefficient variations (ECV) varying from 15.77% to 26.56%, with four traits falling below 20 per cent (Table 2). Pimentel-Gomes (2009) reports that most stomatal traits show less than 20 per cent ECV, except stomatal density. Any modifications have been attributed to precision in evaluation performed (Cruz et al. 2012). In our study, the values were higher suggesting both precision in the recording of data, and diversity in the clones evaluated. The genetic coefficient of variation (GCV) varied from 6.64% to 13.44 % for the traits, with greatest values observed for stomatal size (Table 2). However, excluding the maximum value, GCV had a narrow range. This measure being directly linked to genetic variability, it gives an idea of the relative magnitude of changes that could result from selection (Ferreira et al. 2016). The better the genetic variance, the greater is the scope to achieve genetic improvement (Oliveira et al. 2015). The heritability coefficient (h 2 ), expressing the relation between genotypic and phenotypic variance, showed values from 12.9% to 26.32%, which is considered low. The highest was observed for stomatal size. Reasons for such low values could be attributed to the narrow genetic base of the species, and clonal material used in the study. Crop cultivars that are clones, inbred lines, or hybrids are to a large extent genetically homogenous, hence not in Hardy–Weinberg equilibrium resulting in low heritability values (Schmidt et al. 2019). Amongst the evaluated genotypes, only MD1, MD4, MD5, MD6, MD8, MD9, MD11, MD12, MD13, MD14, MD15 and MD16 showed higher values for stomatal index (above 20), while stomatal density was highest (above 500) for clones MD4, MD9, MD15, MD11, MD14, MD19 and MD20. Ribeiro et al. (2012) suggests that stomatal index may vary differently from stomatal density, thus genotypes with smaller stomata and low stomatal densities such as MD5, MD6, MD13 and MD12 may show relatively high stomatal indices due to the bigger size of epidermal cells. Stomata development results from a network of genetic and cellular interactions, showing as well a high responsiveness to environmental changes (Xu et al. 2016). Stomatal size and density relate to maximum stomatal conductance of CO 2 . The smaller the stomata, better the stomatal conductance for the same total pore area due to shorter diffusion path length. Therefore, high densities of small stomata are the way to attain the highest conductance (Franks and Beerling 2009). This trait, consequently increases CO 2 influx and promotes photosynthesis (Batista et al. 2010). Our study revealed Melia stomata to be in the “small” category. Photosynthetic efficiency in Melia dubia has been reported to be high and shows minimal deviations with respect to short-term responses of stomatal conductance and photosynthetic responses when subjected to elevated CO 2 levels (Janani et al. 2016). This stability during short-term exposures to elevated CO 2 levels as an acclimation mechanism could be due to the well distributed and small stomata in the species. Usually, leaves with smaller stomata use water more efficiently and the differences in the size of stomatal opening affects more water than CO 2 diffusion (Abrams et al. 1994). This could also be a reason for Melia dubia to be able to acclimatise better. Cluster analysis is an efficient biometric tool for grouping data according to similarity. Data can be categorised into homogenous and distinct groups with cluster analysis. Clustering by the Tocher’s optimisation method and using the distance of Mahalanobis (D-square) as a genetic dissimilarity measure, using these seven stomatal morpho-anatomical traits, grouped the 20 genotypes into eight different groups ( Fig. 5 ). This reflects the broad genetic variability amongst the evaluated clones, as this method minimises intragroup distance and maximises inter-group distances. Phenotypic variation was observed to be higher suggesting the role of environment over the genetic component in response of Melia . Clustering by the UPGMA hierarchical method and D2 as a genetic dissimilarity measure, was possible to produce a dendogram which illustrates the genetic distance between the genotypes studied ( Fig. 5 ). These groups formed by the Hierarchial clustering showed many similarities with groups formed by the Tocher method. For instance, clones MD13 and MD14 were grouped together, clones MD5 and MD6; clones MD7 and MD8 and MD1 & MD2 remained together. This suggests strong similarities in their genetic make-up. Similarities between the optimisation methods of Tocher and hierarchic clustering has also been reported (Guedes et al. 2013; Covre et al. 2016), establishing high dissimilarity limit between the genotypes. The intra-cluster distances and with respect to stomatal traits were high, and clones within clusters were also adequately divergent suggesting their use for tree improvement programme. Information on genetic distance between genotypes helps in developing planting design, such that it can facilitate equal opportunity for hybridisation among the genotypes and obtaining quality seed with high vigour. In Melia dubia , genetic divergence studies in relation to fruit and stone characteristics did not show show any relation to geographic origins (Warrier et al. 2018) suggesting underlying factors playing a role behind the genetic differences among genotypes originating from the same areas. Stomatal Clustering Abnormal stomatal patterning, or stomatal cluster has been reported in certain species of Begonia, Crassulaceae, Sonneratiaceae, and Moraceae (Gan et al. 2010). These clusters have been frequently observed in plants living in arid, salty, or otherwise adverse environments (Chen 1996). A clusters having two (or more) stomata placed in direct contact (without intervening epidermal cells between neighboring guard cells), was observed during our study in Melia dubia ( Fig. 6 ). This is the first observation in the family Meliaceae . It is suggested to be a long-term adaptation to the changing environment keeping to the “one cell spacing rule.” Links between such stomatal clusters and environmental factors needs further investigation to understand the underlying mechanisms of adaptation. Conclusion This is the first detailed study on stomata in the genus Melia. This study forms the basis for identifying morphological descriptors to distinguish high yielding genotypes of Melia dubia , a fast growing short rotation tree favoured in the plywood industry. Release of DUS (Distinctness, Uniformity and Stability) guidelines has necessitated the need to identify clones / varieties / genotypes within the species for varietal identification, since only morphological traits can be used as identifiers. Hence, identifying simple and easy descriptors using simple microscopic measurements would enable quick differentiation of the genotypes, and support the ongoing systematic tree improvement programmes. Declarations Funding Partial financial support was received from the Protection of Plant Varieties and Farmers Rights Authority (PPVFRA) and Indian Council of Forestry Research and Education (ICFRE). Conflicts of interest/Competing interests Authors declare no conflict of interest. Authors' contributions AS was involved in methodology and laboratory work, KE supported laboratory work, SK, RT and KS supported the field / nursery work, KCS contributed to statistical analysis, RRW contributed to conceptualization, methodology, resources, supervision, funding acquisition, writing original draft and editing. All authors read and approved the final manuscript. Data archiving statement Information related to the clones is archived in the Fischer Herbarium, ICFRE-IFGTB, Coimbatore. Ethics statement Not Applicable References Abdel Hameed UK (2014) Delimitation of Azadirachta indica A. Juss. from Melia azedarach L. (Meliaceae Juss.) based on leaf morphology. Phyton Int J Exp Bot 83:363–367 http://dx.doi.org/10.32604/phyton.2014.83.363 Abrams MD, Kubiske ME, Mostoller SA (1994) Relating wet and dry year ecophysiology to leaf structure in contrasting temperate tree species. 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Cav and its comparative phylogenetic analysis with major Meliaceae members. 3 Biotech 13(1):30. https://doi.org/10.1007/s13205-022-03447-1 Schmidt P, Hartung J, Bennewitz J, Piepho HP (2019) Heritability in plant breeding on a genotype-difference basis. Genetics 212(4):991–1008. https://doi.org/10.1534/genetics.119.302134 Singh RK, Chaudhary BD (1977) Biometrical methods in quantitative genetic analysis. Kalyani, New Delhi Songsri P, Jogloy S, Junjittakarn J, Kesmala T, Vorasoot N, Holbrook CC, Patanothai A (2013) Association of stomatal conductance and root distribution with water use efficiency of peanut under different soil water regimes. Australian Journal of Crop Science 7(7):948–955 Van Cotthem W (1970) Comparative morphological study of the stomata in the Filicopsida. 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Frontiers in plant science 7:657. https://doi.org/10.3389/fpls.2016.00657 Zhu L, Wu J, Peng X, Zheng Y, Zhang Y (2016) Phenotypic difference among species and a variation type of Azadirachta. Forest Research Beijing 29(2):162–166 Tables Table 1. Stomatal morphometry of M.dubia . (d: density(mm -2 ); SI: Stomatal index(%); SL: Stomatal length(µm); SW: Stomatal width(µm); PL: Pore length(µm); PW: Pore width(µm); PP: Pore perimeter(µm); S: Stomatal size(µm 2 ); SPD: Stomatal pore depth(µm)) CLONE SI (%) d SL SW PL PW PP S SPD MD01 20.00 433 3.9abcde 2.95cdef 2.41efg 1.14ab 5.22bcde 12cdefg 0.91de MD02 17.00 333 3.72abc 2.99cdef 2.32bcdef 1.29abcd 5.47de 11abcde 0.85cde MD03 18.00 433 3.98cde 3.01cdef 2.5fg 1.29abcd 5.7e 12cdefg 0.86cde MD04 27.00 533 3.7abc 2.75abc 2.38defg 1.34bcd 5.41de 10abcd 0.71ab MD05 27.00 467 3.63abc 2.56a 2.17abcde 1.26abcd 5.18bcde 9ab 0.65a MD06 24.00 467 3.51a 2.62ab 2.07abc 1.15abc 4.74abc 9a 0.73abc MD07 18.00 367 3.52a 2.82abcd 2.11abcd 1.12a 4.72ab 10abc 0.85cde MD08 20.00 400 3.78abcd 2.9bcde 2.34cdef 1.26abcd 5.36cde 11abcde 0.82bcd MD09 28.00 533 3.54ab 2.82abcd 2.04ab 1.14ab 4.53a 10abc 0.84cde MD10 19.00 333 4.13de 3.13def 2.37defg 1.23abcd 5.34cde 13efg 0.95de MD11 24.00 500 3.78abcd 3.08def 2.26abcdef 1.43d 5.44de 12cdefg 0.83bcde MD12 24.00 433 3.82abcd 2.99cdef 2.39defg 1.19abc 5.38de 12cdefg 0.9de MD13 27.00 467 4.01cde 3.11def 2.34cdef 1.24abcd 5.24bcde 13efg 0.94de MD14 29.00 500 3.77abcd 2.93cdef 2.03a 1.12a 4.88abcd 11bcdef 0.9de MD15 26.00 533 3.84abcd 3.11def 2.3abcdef 1.29abcd 5.31bcde 12cdefg 0.91de MD16 22.00 400 4.26e 3.18ef 2.55fg 1.23abcd 5.46de 14g 0.97e MD17 18.00 333 4.16de 3.24f 2.65g 1.35cd 6.27f 14fg 0.94de MD18 15.00 400 3.99cde 3.09def 2.4defg 1.19abc 5.38de 12efg 0.95de MD19 18.00 500 3.82abcd 3.12def 2.41efg 1.27abcd 5.33cde 12cdefg 0.92de MD20 19.00 500 3.94bcde 3.12def 2.45efg 1.26abcd 5.38de 12defg 0.93de AVERAGE 22.00 443.25 3.84 2.97 2.32 1.24 5.29 11.55 0.87 SD 4.30 67.65 0.57 0.44 0.42 0.27 0.90 3.11 0.20 SEM 0.01 0.03 0.03 0.02 0.02 0.01 0.05 0.16 0.01 * duncan’s multiple test for comparison of means of stomatal parameters. Different letters denote significant difference (α 0.05) Table 2. Genetic variability among twenty genotypes of Melia dubia (GV: genotypic variance; PV: phenotypic variance; EV: environmental variance; GCV: genotypic coefficient of variation; PCV: phenotypic coefficient of variation; ECV: environmental coefficient of variation; h 2 : heritability; GA: genetic advance as percent of the mean) Parameters Stomatal Length Stomatal Width Pore Length Pore Width Pore Perimeter Stomatal Size Stomatal Pore Depth EV 0.25 0.16 0.15 0.07 0.67 7.07 0.04 GV 0.08 0.37 0.04 0.01 0.18 2.46 0.01 PV 0.33 0.20 0.19 0.08 0.85 9.53 0.04 PCV(%) 14.97 14.86 18.60 22.19 17.44 26.77 23.60 GCV(%) 7.20 6.64 7.78 7.72 7.67 13.44 8.62 ECV(%) 16.03 15.77 19.13 22.64 17.84 26.56 23.91 h 2 (%) 23.99 20.38 19.41 12.90 20.22 26.32 14.41 GA at 1% 0.37 0.23 0.23 0.10 0.50 2.10 0.07 GA at 5% 0.30 0.17 0.17 0.07 0.37 1.67 0.07 GA at 10% 0.23 0.13 0.13 0.07 0.33 1.40 0.07 GAM at 1% 9.46 7.94 9.71 7.73 9.57 18.55 9.03 GAM at 5% 7.38 6.20 7.58 6.03 7.47 14.45 7.04 GAM at 10% 6.31 5.29 6.47 5.16 6.38 12.36 6.02 Table 3. Cluster formation by D2 analysis using data on stomata of Melia dubia Cluster Number Clones of Melia dubia Cluster 1 MD05, MD06 Cluster 2 MD10, MD20 Cluster 3 MD01, MD02, MD03 Cluster 4 MD04, MD09, MD16 Cluster 5 MD07, MD08, MD11 Cluster 6 MD12, MD13, MD14, MD15, MD18 Cluster 7 MD17 Cluster 8 MD19 Table 4. Contribution of each stomatal character to divergence for Melia dubia Character No. of First Rank % Contribution Stomatal Length (SL) 14 7.37 Stomatal Width (SW) 0 0 Pore Length (PL) 27 14.21 Pore Width (PW) 9 4.74 Pore Perimeter (PP) 17 8.95 Stomatal Size (S) 15 7.90 Stomatal Pore Depth (SPD) 108 56.84 TOTAL 190 100 Table 5. Cluster means for stomatal parameters in Melia dubia Clusters Stomatal Length Stomatal Width Pore Length Pore Width Pore Perimeter 1 3.52 2.53 2.28 1.28 5.16 2 3.43 2.83 1.98 1.38 4.49 3 3.47 2.67 2.23 0.97 4.86 4 4.07 3.10 2.53 1.14 5.57 5 3.63 2.71 2.12 1.10 4.65 6 3.74 2.96 2.20 1.13 5.38 7 4.35 3.22 2.70 1.32 5.47 8 4.40 3.42 2.65 1.33 6.37 Table 6. Inter and intra cluster D-square values for stomatal parameters of Melia dubia 1 2 3 4 5 6 7 8 1 0.32015 3.98811 4.10114 4.07058 2.82193 4.35797 8.8306 8.0218 2 0.70513 4.47636 4.77607 2.7433 3.56583 6.05604 7.70717 3 2.4026 3.53558 2.92576 3.787 6.04846 7.98174 4 2.42654 3.18195 3.57443 3.36652 3.59442 5 1.96877 3.55075 5.92085 8.08097 6 2.67423 7.0801 4.92243 7 0 4.45526 8 0 Additional Declarations No competing interests reported. Supplementary Files 5.SupplementaryDataGRCE.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3883484","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269407177,"identity":"77aac828-3c3c-4171-952e-c59e79d5376b","order_by":0,"name":"Aghila Samji","email":"","orcid":"","institution":"Institute of Forest Genetics and Tree Breeding","correspondingAuthor":false,"prefix":"","firstName":"Aghila","middleName":"","lastName":"Samji","suffix":""},{"id":269407178,"identity":"90e66155-bdad-404d-a960-350163761618","order_by":1,"name":"Komal Eashwarlal","email":"","orcid":"","institution":"Institute of Forest Genetics and Tree 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07:12:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":642816,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics and morphometry of \u003cem\u003eM.dubia \u003c/em\u003estomata. a and b indicates stomatal length and stomatal width. c and d stands for pore width and pore length. e represents guard cells and f denotes stomata.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3883484/v1/c7146a1cefbd2d497a6f0435.png"},{"id":50287408,"identity":"5bb64ec0-ce44-48ae-a36b-b5c53c99369b","added_by":"auto","created_at":"2024-01-29 07:12:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183029,"visible":true,"origin":"","legend":"\u003cp\u003eCluster matrix of \u003cem\u003eM.dubia\u003c/em\u003e clones based on Pearson correlation\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3883484/v1/f7dcfb2e002b3a45bca25c8d.png"},{"id":50288516,"identity":"de17af50-5cb6-4744-9b89-19c5b760d3cc","added_by":"auto","created_at":"2024-01-29 07:28:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40135,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram showing hierarchical cluster analysis of \u003cem\u003eM.dubia\u003c/em\u003eclones. Average linkage between groups method and Euclidean distance as measurement was applied.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3883484/v1/251cd6a8c925a4b54ae7e7c9.png"},{"id":50287404,"identity":"ecbbb98d-0605-43d4-9a8e-ecbdedfc7d2e","added_by":"auto","created_at":"2024-01-29 07:12:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66919,"visible":true,"origin":"","legend":"\u003cp\u003eCluster matrix of \u003cem\u003eM.dubia\u003c/em\u003e clones based on Mahalanobis’ D2 statistics and Tocher’s clustering\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3883484/v1/61084daf591e64f35be7d31b.png"},{"id":50287406,"identity":"636bb740-805b-4186-ad25-e001727f1ea3","added_by":"auto","created_at":"2024-01-29 07:12:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":965538,"visible":true,"origin":"","legend":"\u003cp\u003eStomatalClustering observed in clones of \u003cem\u003eMelia dubia \u003c/em\u003eat 40X\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3883484/v1/f4c29369b977d1e75892149b.png"},{"id":57542372,"identity":"05e6b00b-8266-41b5-ba47-77f894d0fb4f","added_by":"auto","created_at":"2024-06-01 11:46:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4530593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3883484/v1/3bc8645d-ed72-4d50-b898-4b9b49fce110.pdf"},{"id":50288122,"identity":"48693585-d247-4763-9a9b-78d54657657f","added_by":"auto","created_at":"2024-01-29 07:20:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14618,"visible":true,"origin":"","legend":"","description":"","filename":"5.SupplementaryDataGRCE.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3883484/v1/1c02a34dff755d933564f05a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Divergence and genetic parameters between Melia dubia genotypes based on morpho-anatomical stomatal descriptors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStomata are microscopic active interface structures present on the aerial epidermis of land plants, exclusively on sporophytes and occasionally on gametophytes which act as a channel between plants and the environment for the exchange of water and gas (Franks and Beerling 2009; Rudall et al. 2013). They are essential for primary productivity and predominantly present on the CO2 impermeable, waterproof cuticle of central photosynthetic organ leaf and stem surfaces characterised by a central pore flanked by two symmetric guard cells (Caine et al. 2023; Chandra et al. 2023). Guard cells are specialised structures distinct from normal epidermal cells that control the diffusional gaseous flux by adjusting their turgor status followed by the size of the aperture (Kirkham 2014; Wall et al.\u0026nbsp;2022). However, guard cells also influence stomatal patterning in embryophytes. Stomata acquisition on the impervious cuticle was considered a fundamental novelty at the time of evolution in land plants (Raven 2002). Most vital plant physiological activities like photosynthesis, transpiration, and respiration correlate with stomata opening and closing to various internal and external stimuli. Plant stomata sensibly balance CO\u003csub\u003e2\u003c/sub\u003e uptake and retain photosynthetic carbon assimilation. Water loss through transpiration is also minimised by stomata, thereby helping drought avoidance mechanisms to enhance water use efficiency (WUE) and desiccation (Nilson and Assmann 2007; Songsri et al. 2013). A plant\u0026rsquo;s ability to sense and respond to fluctuating environmental conditions is related to photosynthesis and transpiration; hence stomata also influence adaptability and productivity (Hetherington and Woodward 2003; Muradoglu and Gundogdu 2011; Liu et al. 2018).\u003c/p\u003e\n\u003cp\u003eLeaf morphology is an important character used for classifying and delimitating plant species, with stomata significantly contributing to the evolutionary and speciation aspects (Ma D 1987; Abdel-Hameed 2014). Adult stomatal characters have been used to draw interrelationships and natural taxonomic affinities between families (Van Cotthem, 1970) such as\u0026nbsp;the classification of the clade Cryptostomata and Phanerostomata (Mast and Givnish 2002). Stomata are morphologically and mechanically diverse in different plants; within-species stomatal characters show variation in shape, size, type, position, and density (Franks and Farquhar 2007; Zhu et al. 2016; Hong et al. 2018). The anatomical features and pore width regulation of stomata introduced by growth conditions or genetic factors influence gas exchange. Another important stomatal trait, the pore area, is associated with leaf lamina hydraulic conductance (Sack et al. 2003).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMelia\u003c/em\u003e\u003cem\u003e\u0026nbsp;dubia\u0026nbsp;\u003c/em\u003eCav. is a deciduous, perennial, short-rotation, money-spinning tree of the \u003cem\u003eMelia\u003c/em\u003eceace family with multifarious uses (Warrier 2020). \u003cem\u003eM.dubia\u003c/em\u003e is distributed in India, Sri Lanka, Australia, China, and Africa. The anti-termite and fungus-resistant timber properties make \u003cem\u003eM.dubia\u0026nbsp;\u003c/em\u003eextremely valued in plywood industries (Samji et al. 2023). It is an excellent secondary timber and potential hardwood for pulp production. It is also identified as a suitable alternative species for different agro-farm forestry programs (Geetha et al. 2019).\u003c/p\u003e\n\u003cp\u003eThe presence of high variabilities to identify genotypes with higher genetic potential provides wide range of options for selection in plant breeding programs. The present work focuses on leaf stomatal characters and its morphological parameters of \u003cem\u003eM. dubia\u003c/em\u003e clones, an important germplasm resource characterisation study, a pre-requisite for selecting and breeding different genotypes through trait evaluations. Twenty clones of \u003cem\u003eMelia\u003c/em\u003e\u003cem\u003e\u0026nbsp;dubia\u0026nbsp;\u003c/em\u003ewere examined for stomatography and morphometric analysis described the variations in the species.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003cstrong\u003eeaf sample collection\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003ereparation of leaf samples for microscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeaves samples were collected from 20 different clones of \u003cem\u003eMelia\u003c/em\u003e\u003cem\u003e\u0026nbsp;dubia\u0026nbsp;\u003c/em\u003ehoused in the germplasm bank at the Institute of Forest Genetics and Tree Breeding (IFGTB) (11.0176\u0026deg;N, 76.9514\u0026deg;E) Coimbatore, India during 2021-22. We sampled mature, fully expanded, sun-exposed, similar-sized leaflets from ten individuals at the standard median level. These fresh leaves were directly used for microscopic studies without any chemical treatment. Leaves samples were collected from 20 different clones of \u003cem\u003eMelia\u003c/em\u003e\u003cem\u003e\u0026nbsp;dubia\u0026nbsp;\u003c/em\u003ehoused in the germplasm bank at the Institute of Forest Genetics and Tree Breeding (IFGTB) (11.0176\u0026deg;N, 76.9514\u0026deg;E) Coimbatore, India during 2021-22. We sampled mature, fully expanded, sun-exposed, similar-sized leaflets from ten individuals at the standard.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLeaf samples were prepared according to the common nail varnish imprint method by Miller and Ashby (1968). After a wash with distilled water, fully expanded leaves were coated with a thin layer of clear nail varnish, followed by applying adhesive tape over the dried polish, avoiding the major veins of leaves. The adhesive film was then peeled off carefully and placed on a clean microscopic slide. The imprints were observed under Nikon Eclipse Ei microscope equipped with a digital camera and photomicrographs at 40X were captured using Imageview x64. Measurements were taken with ImageJ 1.53a. Characters including stomata type, structure, and parameters like stomatal density-d (mm\u003csup\u003e-2\u003c/sup\u003e), stomatal index-SI (%), stomatal length-SL (\u0026micro;m), stomatal width-SW (\u0026micro;m), pore length-PL (\u0026micro;m), pore width-PW (\u0026micro;m), pore perimeter-PP (\u0026micro;m), stomatal size-S (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e) and stomatal pore depth-SPD (\u0026micro;m) were determined. Length, width, and perimeter were taken from 20 randomly selected stomata per leaflet, whereas density and index were recorded from ten slides per clone. Number of stomata per field of view was counted and recorded along with the area of each picture calculated using a micrometre.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicro\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emeasurements and calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStomatal index (SI) was calculated using the formula\u0026nbsp;\u0026nbsp;, where S is the no: of stomata per unit area and E is the no: of epidermal cell in same unit area (Salisbury 1928). Stomatal density (d) was calculated as\u0026nbsp;\u0026nbsp;. Stomatal size (S) in \u0026micro;m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ewas calculated as\u0026nbsp;\u0026nbsp;, where SL and SW is the stomatal length and stomatal width. Stomatal pore depth (SPD) equal to guard cell width was calculated using the formula\u0026nbsp;\u0026nbsp;\u0026nbsp;(Fanourakis et al. 2015). Clustering was performed using hierarchical cluster analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis and Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhenotypic and Genotypic variances were estimated as described by Johnson et al. (1955) while Phenotypic and Genotypic Coefficient of variation (PCV and GCV) were computed following Burton (1952). The genetic advance was calculated as described by Johnson et al. (1955) for all the characters studied. Genetic advance as per cent of mean was calculated following Burton and Devane (1953). For divergence analysis, the test of significance of difference with regard to the pooled effect of all the characters was estimated prior to subjecting the data to genetic divergence analysis. Group distance based on multiple characters was calculated using the method explained by Mahalanobis (1928). Wilks\u0026rsquo; criterion was estimated using the formula by Wilks (1932) and Rao (1952) and tested using \u0026lsquo;V\u0026rsquo; stat. The \u0026lsquo;V\u0026rsquo; statistic was compared with the tabulated \u0026chi;2 value and thus significance was tested. Clusters were determined following Tocher\u0026rsquo;s method (Singh and Choudhary 1976).\u003c/p\u003e\n\u003cp\u003eData was subjected to analysis of variance (ANOVA) at 5% significance level and post-hoc comparisons were done with Duncan\u0026rsquo;s Multiple Range Test (DMRT) when significant main effects were detected. Pearson\u0026rsquo;s correlation at 99% to identify significant correlations subsequently followed by hierarchical cluster analysis using\u0026nbsp;average linkage between groups method and Euclidean distance as measurement. Mahalanobis D\u003csup\u003e2\u003c/sup\u003e analysis was used to determine genetic divergence among 20 genotypes using seven characters (Mahalanobis 1928). Matrices were generated in R version 4.2.1\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eDescription of \u003cem\u003eM.dubia\u0026nbsp;\u003c/em\u003estomata and it\u0026rsquo;s \u0026nbsp;morphometric analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMicroscopic stomatography in 20 clones of \u003cem\u003eM.dubia\u003c/em\u003e(\u003cstrong\u003eFig. 1\u003c/strong\u003e) revealed the occurrence of stomata in leaves as hypostomatic where stomata are restricted only to the abaxial epidermis. Abundant stomatal distribution was observed irregularly over the surface and on both sides of the veins. Stomata were present in veins occasionally. Each stoma appeared to be elliptical in outline. Peristomatal rim differentiated them. The uniformity of the anticlinal walls of the epidermis was smooth and regularly thickened, whereas the straightness of the anticlinal wall was undulated. The stomatal surface was in the shape of eyelids that protruded towards the slits of the stomata. Anomocytic stomata have also been observed in \u003cem\u003eM.dubia,\u003c/em\u003e where each stoma is surrounded by more than five subsidiary cells (\u003cstrong\u003eFig. 2\u003c/strong\u003e). Based on the classification of Pataky (1969), the stomata of \u003cem\u003eM.dubia\u0026nbsp;\u003c/em\u003efalls under the category \u0026ldquo;Small\u0026rdquo; as the guard cells measure less than 15\u0026micro;m. Guard cells were reniform and symmetrical. Epidermal cells were long and quadrilateral.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMorphometric analysis of the 20 clones of \u003cem\u003eM.dubia\u0026nbsp;\u003c/em\u003e(Table 1)\u0026nbsp;showed a\u0026nbsp;significant difference in stomatal parameters (\u0026alpha; 0.05). The average stomatal index in \u003cem\u003eM.dubia\u0026nbsp;\u003c/em\u003ewas 22%, ranging from 15 (MD18) to 29% (MD14). The lowest stomatal density was observed in MD02, MD10, and MD17 (333 mm\u003csup\u003e-2\u003c/sup\u003e) and the highest in MD04, MD09 and MD15 (533 mm\u003csup\u003e-2\u003c/sup\u003e) with an average of 443.25 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e. The average stomatal length (SL) was 3.84 \u0026micro;m in \u003cem\u003eM.dubia,\u003c/em\u003e with MD06 showing the lowest SL of\u0026nbsp;3.51 \u0026micro;m and\u0026nbsp;MD16 showing the highest SL of\u0026nbsp;4.26 \u0026micro;m. MD 01 and MD14 showed the largest and smallest variation coefficients of 11.50 and 21.50%, respectively. MD05 had a minimum stomatal width of 2.56 \u0026micro;m, and MD17 showed a maximum stomatal width of 3.24 \u0026micro;m. The coefficient of variation of stomatal width varied from 9.70 to 18.60%. The average SW of the clones was 2.97 \u0026micro;m.\u003c/p\u003e\n\u003cp\u003eThe pore length of stomata from 20 clones ranged between 2.03 \u0026micro;m (MD14) and 2.65 \u0026micro;m (MD17) with an average of 2.32 \u0026micro;m whereas pore width ranged from 1.12 \u0026micro;m (MD07, MD14) to 1.43 \u0026micro;m (MD11) with an average of 1.24 \u0026micro;m. The CV of pore length ranged from 13.50 to 22.20%, and pore width ranged from 13.70 to 40.80%. The stomatal pore perimeter was lowest in MD09 (4.53 \u0026micro;m) and highest in MD17 (6.27 \u0026micro;m). The average stomatal perimeter was 5.29 \u0026micro;m. MD06 and MD16 showed the highest and lowest coefficient of variation. MD06 and MD16 revealed the smallest and largest stomatal sizes with a CV of 22.8 and 22.30%, respectively. Stomatal pore depth (SPD) recorded an average of 0.87 \u0026micro;m in\u0026nbsp;\u003cem\u003eM.dubia\u003c/em\u003e. MD05 showed the least, and MD16 showed the highest values in terms of SPD with a coefficient of variation of 28 and 21.80% (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ehierarchical cluster analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained strong positive correlations (r \u0026gt;0.8) significant at P\u0026lt; 0.01 (\u003cstrong\u003eFig. 3\u003c/strong\u003e). Stomatal length was significantly correlated with pore length, pore width, pore perimeter and stomatal pore depth. Likewise, the correlation of stomatal width and size with pore length, pore width, pore perimeter, stomatal pore depth were significant. A strong positive correlation between stomatal width and stomatal pore depth (0.80) was observed\u0026nbsp;(Supplementary Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHierarchical cluster analysis using\u0026nbsp;average linkage between groups method and Euclidean distance set to five revealed five clusters(\u003cstrong\u003eFig. 4\u003c/strong\u003e). Clones MD19, MD20, MD17, and MD18 formed first cluster. Second cluster included MD15, MD16, MD13, MD14. MD03, MD04, MD01, MD02 formed the third cluster whereas MD11, MD12, MD09, MD10 formed the fourth cluster. Fifth cluster included MD 07, MD08, MD05 and MD06.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic analysis and Divergence Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnvironmental, genetic, and phenotypic variance ranged from 0.04-7.07, 0.01-2.46, 0.04-9.53 respectively (Table 2). Among all the parameters S shows highest EV, GV, and PV values. The EV, GV and PV were low for the trait SPD i.e., 0.04, 0.01, 0.04 respectively. GCV and PCV values ranged from 6.64% -13.44% and 14.86%-26.77%. GCV values were found to be low (\u0026lt;10%) for all the traits including SL (7.20%), SW (6.64%), PL (7.78%), PW (7.72%), PP (7.67%), SPD (8.62%) except S with 13.44% that noted as moderate. PCV was relatively higher than corresponding GCV in the present study. S (26.7%) and SPD (23.6%) exhibited high PCV (\u0026gt;20%) whereas moderate or medium PCV (10%-20%) were obtained for all the other traits such as SL (14.97%), SW (14.86%), PL (18.6%), PW (22.19%) and PP (17.44%). PCV and GCV values difference was high for all the tested parameters indicating environmental influence on these parameters. Low estimates of heritability were recorded for all the tested traits such as SL (23.99%), SW (20.38%), PL (19.41%), PW (12.9%), PP (20.22%), S (26.32%), SPD (14.41%). GA (Genetic advance) and GAM (Genetic advance as percentage of mean) at 1%, 5% and 10% was estimated for all the seven stomatal morphometric traits. SL was found to be with 0.37, 0.3, 0.23, SW with 0.23, 0.17,0.13, PL with 0.23, 0.17, 0.13, PW with 0.1, 0.07, 0.07, PP with 0.50, 0.37, 0.33, S with 2.10, 1.67, 1.40 and finally SPD with 0.07, 0.07, 0.07 was obtained for GA at 1%, 5% and 10% respectively. Range of GAM at 1%, 5% and 10% was 9.03.-18.55, 6.03-14.45, 5.16-12.36. Moderate genetic advance as percentage of mean (10-20%) was recorded for S (18.55%, 14.45%, 12.36%) at 1%, 5% and 10% correspondingly, whereas low GAM at 1%, 5% and 10% was obtained for the traits such as SL (9.46%,7.38%,6.31%), SW (7.94%,6.20%,5.29%), PL(9.71%,7.58%,6.47%),PW(7.73%,6.03%,5.16%), PP (9.57%, 7.47%, 6.38%), S (18.55%,14.45%,12.36%) and SPD (9.03%, 7.04%, 6.02%).\u003c/p\u003e\n\u003cp\u003eAnalysis on genetic divergence using Mahalanobis D\u003csup\u003e2\u003c/sup\u003e statistics and all the 20 genotypes were grouped using Tocher\u0026rsquo;s clustering method (\u003cstrong\u003eFig. 5\u003c/strong\u003e). A total of eight clusters were formed that grouped all the 20 genotypes based on their stomatal morphometric trait\u0026rsquo;s genetic divergence. The strength of clusters varied from one to five. The maximum numbers of clones contributed to the higher cluster strength in cluster six (MD12, MD13, MD14, MD15, MD18) and the minimum cluster strength was recorded for Cluster seven and eight which includes MD17 and MD19 respectively. Cluster three (MD01, MD02, MD03), 4 (MD04, MD09, MD16) and cluster five (MD07, MD08, MD11) contained three clones per clusters and MD05 and MD06 together formed cluster one, Similarly MD10 and MD20 formed cluster two (Table 3). Six out of seven stomatal morphometric traits contributed to the genetic divergence and among all the characters SPD (56.84%) contributed to the maximum followed by PL (14.21%). SW did not contribute to the divergence (Table 4). The mean values were calculated cluster wise and Cluster eight with MD19 was found to be with highest mean value for SL, SW, PP, S followed by cluster seven that includes MD17 (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe members of the cluster seven and one was observed with highest inter cluster distance (2.9716). D\u003csup\u003e2\u0026nbsp;\u003c/sup\u003edistance between cluster seven and one was noted as 8.8306 followed by the members of cluster eight and five (inter cluster distance 2.8427) with D\u003csup\u003e2\u0026nbsp;\u003c/sup\u003edistance 8.08097. The maximum intra cluster distance was noticed for cluster six followed by cluster four and three and it was minimum for clusters seven and eight (0.00) (Table 6)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eMorphometric analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is the first detailed description of the stomata in \u003cem\u003eMelia\u003c/em\u003e genus. Similar description of the stomata has been provided for Khaya (Oyedapo et al. 2018). They report that scant stomata were observed on the adaxial surfaces of \u003cem\u003eK. senegalensis\u003c/em\u003e and \u003cem\u003eK. ivorensis\u003c/em\u003e (hypoamphistomatic) are scanty. This is in accordance with our observations, where poor to nil distribution of stomata were observed on the adaxial surface. Hexacytic stomata observed in the abaxial surface of \u003cem\u003eK. grandifoliola\u003c/em\u003e is useful in distinguishing this species from \u003cem\u003eK. senegalensis\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;K. ivorensis\u003c/em\u003e. Similarly, Isawumi (1986) established the use of stomata frequency and density in delineating taxa in Jatropha. Thus,\u0026nbsp;the stomatal morphology and its distribution on the adaxial and abaxial surfaces can be used in delineating the species.\u003c/p\u003e\n\u003cp\u003eSignificant differences were observed by the F-test (p \u0026lt; 0.05) between the clones of \u003cem\u003eMelia\u003c/em\u003e\u003cem\u003e\u0026nbsp;dubia\u003c/em\u003e for all stomatal characters evaluated. This evidences the distinctness of the evaluated clones, which is quite relevant for the genetic divergence analysis and for further breeding or tree improvement programmes aimed at identifying superior genotypes. The evaluated traits showed environmental coefficient variations (ECV) varying from 15.77% to 26.56%, with four traits falling below 20 per cent (Table 2). Pimentel-Gomes (2009) reports that most stomatal traits show less than 20 per cent ECV, except stomatal density. Any modifications have been attributed to precision in evaluation performed (Cruz et al. 2012). In our study, the values were higher suggesting both precision in the recording of data, and diversity in the clones evaluated.\u003c/p\u003e\n\u003cp\u003eThe genetic coefficient of variation (GCV) varied from 6.64% to 13.44 % for the traits, with greatest values observed for stomatal size (Table 2). However, excluding the maximum value, GCV had a narrow range. This measure being directly linked to genetic variability, it gives an idea of the relative magnitude of changes that could result from selection (Ferreira et al. 2016). The better the genetic variance, the greater is the scope to achieve genetic improvement (Oliveira et al. 2015). The heritability coefficient (h\u003csup\u003e2\u003c/sup\u003e), expressing the relation between genotypic and phenotypic variance, showed values from 12.9% to 26.32%, which is considered low. The highest was observed for stomatal size. Reasons for such low values could be attributed to the narrow genetic base of the species, and clonal material used in the study. Crop cultivars that are clones, inbred lines, or hybrids are to a large extent genetically homogenous, hence not in Hardy\u0026ndash;Weinberg equilibrium resulting in low heritability values (Schmidt et al. 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmongst the evaluated genotypes, only MD1, MD4, MD5, MD6, MD8, MD9, MD11, MD12, MD13, MD14, MD15 and MD16 showed higher values for stomatal index (above 20), while stomatal density was highest (above 500) for clones MD4, MD9, MD15, MD11, MD14, MD19 and MD20. Ribeiro et al. (2012) suggests that stomatal index may vary differently from stomatal density, thus genotypes with smaller stomata and low stomatal densities such as MD5, MD6, MD13 and MD12 may show relatively high stomatal indices due to the bigger size of epidermal cells. Stomata development results from a network of genetic and cellular interactions, showing as well a high responsiveness to environmental changes (Xu et al. 2016).\u003c/p\u003e\n\u003cp\u003eStomatal size and density relate to maximum stomatal conductance of CO\u003csub\u003e2\u003c/sub\u003e. The smaller the stomata, better the stomatal conductance for the same total pore area due to shorter diffusion path length. Therefore, high densities of small stomata are the way to attain the highest conductance (Franks and Beerling 2009). This trait, consequently increases CO\u003csub\u003e2\u003c/sub\u003e influx and promotes photosynthesis (Batista et al. 2010). Our study revealed \u003cem\u003eMelia\u003c/em\u003e stomata to be in the \u0026ldquo;small\u0026rdquo; category. Photosynthetic efficiency in \u003cem\u003eMelia\u003c/em\u003e\u003cem\u003e\u0026nbsp;dubia\u003c/em\u003e has been reported to be high and shows minimal deviations with respect to short-term responses of stomatal conductance and photosynthetic responses when subjected to elevated CO\u003csub\u003e2\u003c/sub\u003e levels (Janani et al. 2016). This stability during short-term exposures to elevated CO\u003csub\u003e2\u003c/sub\u003e levels as an acclimation mechanism could be due to the well distributed and small stomata in the species. Usually, leaves with smaller stomata use water more efficiently and the differences in the size of stomatal opening affects more water than CO\u003csub\u003e2\u003c/sub\u003e diffusion (Abrams et al. 1994). This could also be a reason for \u003cem\u003eMelia\u003c/em\u003e dubia to be able to acclimatise better.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCluster analysis is an efficient biometric tool for grouping data according to similarity. Data can be categorised into homogenous and distinct groups with cluster analysis.\u0026nbsp;Clustering by the Tocher\u0026rsquo;s optimisation method and using the distance of Mahalanobis (D-square) as a genetic dissimilarity measure, using these seven stomatal morpho-anatomical traits, grouped the 20 genotypes into eight different groups (\u003cstrong\u003eFig. 5\u003c/strong\u003e). This reflects the broad genetic variability amongst the evaluated clones, as this method minimises intragroup distance and maximises inter-group distances. Phenotypic variation was observed to be higher suggesting the role of environment over the genetic component in response of \u003cem\u003eMelia\u003c/em\u003e.\u0026nbsp;Clustering by the UPGMA hierarchical method and D2 as a genetic dissimilarity measure, was possible to produce a dendogram which illustrates the genetic distance between the genotypes studied (\u003cstrong\u003eFig. 5\u003c/strong\u003e). These groups formed by the Hierarchial clustering showed many similarities with groups formed by the Tocher method. For instance, clones MD13 and MD14 were grouped together, clones MD5 and MD6; clones MD7 and MD8 and MD1 \u0026amp; MD2 remained together. This suggests strong similarities in their genetic make-up. Similarities between the optimisation methods of Tocher and hierarchic clustering has also been reported (Guedes et al. 2013; Covre et al. 2016), establishing high dissimilarity limit between the genotypes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe intra-cluster distances and with respect to stomatal traits were high, and clones within clusters were also adequately divergent suggesting their use for tree improvement programme. Information on genetic distance between genotypes helps in developing planting design, such that it can facilitate equal opportunity for hybridisation among the genotypes and obtaining quality seed with high vigour. In \u003cem\u003eMelia\u003c/em\u003e\u003cem\u003e\u0026nbsp;dubia\u003c/em\u003e, genetic divergence studies in relation to fruit and stone characteristics did not show show any relation to geographic origins (Warrier et al. 2018) suggesting underlying factors playing a role behind the genetic differences among genotypes originating from the same areas. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStomatal Clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbnormal stomatal patterning, or stomatal cluster has been reported in certain species of Begonia, Crassulaceae, Sonneratiaceae, and Moraceae (Gan et al. 2010). These clusters have been frequently observed in plants living in arid, salty, or otherwise adverse environments (Chen 1996). A clusters having two (or more) stomata placed in direct contact (without intervening epidermal cells between neighboring guard cells), was observed during our study in \u003cem\u003eMelia\u003c/em\u003e dubia (\u003cstrong\u003eFig. 6\u003c/strong\u003e). This is the first observation in the family \u003cem\u003eMeliaceae\u003c/em\u003e. It is suggested to be a long-term adaptation to the changing environment keeping to the \u0026ldquo;one cell spacing rule.\u0026rdquo; Links between such stomatal clusters and environmental factors needs further investigation to understand the underlying mechanisms of adaptation.\u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThis is the first detailed study on stomata in the genus \u003cem\u003eMelia.\u0026nbsp;\u003c/em\u003eThis study\u003cem\u003e\u0026nbsp;\u003c/em\u003eforms the basis for identifying morphological descriptors to distinguish high yielding genotypes of \u003cem\u003eMelia dubia\u003c/em\u003e, a fast growing short rotation tree favoured in the plywood industry. Release of DUS (Distinctness, Uniformity and Stability) guidelines has necessitated the need to identify clones / varieties / genotypes within the species for varietal identification, since only morphological traits can be used as identifiers. Hence, identifying simple and easy descriptors using simple microscopic measurements would enable quick differentiation of the genotypes, and support the ongoing systematic tree improvement programmes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e Partial financial support was received from the Protection of Plant Varieties and Farmers Rights Authority (PPVFRA) and Indian Council of Forestry Research and Education (ICFRE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u0026nbsp;\u003c/strong\u003eAuthors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAS was involved in methodology and laboratory work, KE supported laboratory work, SK, RT and KS supported the field / nursery work, KCS contributed to statistical analysis, RRW contributed to conceptualization, methodology, resources, supervision, funding acquisition, writing original draft and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData archiving statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation related to the clones is archived in the Fischer Herbarium, ICFRE-IFGTB, Coimbatore.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdel Hameed UK (2014) Delimitation of \u003cem\u003eAzadirachta indica\u003c/em\u003e A. Juss. from \u003cem\u003eMelia azedarach\u003c/em\u003e L. (Meliaceae Juss.) based on leaf morphology. 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Stomatal morphometry of \u003cem\u003eM.dubia\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e(d: density(mm\u003csup\u003e-2\u003c/sup\u003e); SI: Stomatal index(%); SL: Stomatal length(\u0026micro;m); SW: Stomatal width(\u0026micro;m); PL: Pore length(\u0026micro;m); PW: Pore width(\u0026micro;m); PP: Pore perimeter(\u0026micro;m); S: Stomatal size(\u0026micro;m\u003csup\u003e2\u003c/sup\u003e); SPD: Stomatal pore depth(\u0026micro;m))\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCLONE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSI (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.9abcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2.95cdef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.41efg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.14ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.22bcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12cdefg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.91de\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.72abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2.99cdef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.32bcdef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.29abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.47de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e11abcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.85cde\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.98cde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.01cdef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.5fg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.29abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.7e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12cdefg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.86cde\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.7abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2.75abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.38defg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.34bcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.41de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e10abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.71ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.63abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.17abcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.26abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.18bcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e9ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.65a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.51a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2.62ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.07abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.15abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e4.74abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e9a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.73abc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.52a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2.82abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.11abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.12a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e4.72ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e10abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.85cde\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.78abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2.9bcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.34cdef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.26abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.36cde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e11abcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e10abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.84cde\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e4.13de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.13def\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.37defg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.23abcd\u003c/p\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e2.99cdef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.39defg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.19abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.38de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12cdefg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.9de\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003eMD15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.84abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.11def\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.3abcdef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.29abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.31bcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12cdefg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.91de\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e4.26e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.18ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.55fg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.23abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.46de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e14g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.97e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e4.16de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.24f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.65g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.35cd\u003c/p\u003e\n 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\u003cp\u003e2.4defg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.19abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.38de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12efg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.95de\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.82abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.12def\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.41efg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.27abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.33cde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12cdefg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.92de\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMD20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.94bcde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.12def\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.45efg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.26abcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.38de\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12defg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.93de\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eAVERAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e443.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e67.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u003csup\u003eduncan\u0026rsquo;s multiple test for comparison of means of stomatal parameters. Different letters denote significant difference (\u0026alpha; 0.05)\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Genetic\u0026nbsp;variability among twenty genotypes of \u003cem\u003eMelia dubia\u003c/em\u003e (GV: genotypic variance; PV: phenotypic variance; EV: environmental variance; GCV: genotypic coefficient of variation; PCV: phenotypic coefficient of variation; ECV: environmental coefficient of variation; h\u003csup\u003e2\u003c/sup\u003e: heritability; GA: genetic advance as percent of the mean)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStomatal Length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStomatal Width\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePore Length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePore Width\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePore Perimeter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStomatal Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStomatal Pore Depth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eEV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003ePV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003ePCV(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e14.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e14.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e22.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e17.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e26.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e23.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eGCV(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e13.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eECV(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e16.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e19.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e22.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e17.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e26.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e23.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e23.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e20.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e19.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e12.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e20.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e26.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e14.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eGA at 1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eGA at 5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eGA at 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eGAM at 1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e7.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e18.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eGAM at 5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.711598746081506%\" valign=\"top\"\u003e\n \u003cp\u003eGAM at 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.128526645768025%\" valign=\"top\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.658307210031348%\" valign=\"top\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.9717868338558%\" valign=\"top\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\" valign=\"top\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e12.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.598746081504702%\" valign=\"top\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Cluster formation by D2 analysis using data on stomata of \u003cem\u003eMelia dubia\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster Number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClones of \u003cem\u003eMelia dubia\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003eMD05, MD06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003eMD10, MD20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eCluster 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003eMD01, MD02, MD03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eCluster 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003eMD04, MD09, MD16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eCluster 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003eMD07, MD08, MD11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eCluster 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003eMD12, MD13, MD14, MD15, MD18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eCluster 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003eMD17 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eCluster 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.82352941176471%\" valign=\"top\"\u003e\n \u003cp\u003eMD19 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Contribution of each stomatal character to divergence for \u003cem\u003eMelia dubia\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"470\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of First Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Contribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003eStomatal Length (SL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003eStomatal Width (SW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003ePore Length (PL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003ePore Width (PW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003ePore Perimeter (PP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003eStomatal Size (S)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e7.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003eStomatal Pore Depth (SPD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e56.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.276595744680854%\" valign=\"top\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.27659574468085%\" valign=\"top\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.4468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Cluster means for stomatal parameters in \u003cem\u003eMelia dubia\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClusters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStomatal Length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStomatal Width\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePore Length\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePore Width\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePore Perimeter\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.582381729200652%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.575856443719413%\" valign=\"top\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.823817292006526%\" valign=\"top\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.760195758564436%\" valign=\"top\"\u003e\n \u003cp\u003e6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. Inter and intra cluster D-square values for stomatal parameters of \u003cem\u003eMelia dubia\u003c/em\u003e \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e0.32015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.98811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e4.10114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e4.07058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e2.82193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e4.35797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e8.8306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e8.0218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e0.70513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e4.47636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e4.77607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e2.7433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.56583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e6.05604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e7.70717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e2.4026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.53558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e2.92576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e6.04846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e7.98174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e2.42654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.18195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.57443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.36652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.59442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e1.96877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e3.55075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e5.92085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e8.08097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e2.67423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e7.0801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e4.92243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e4.45526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.14864864864865%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Clonal variation, stomatal clustering, hypostomatic, anomocytic","lastPublishedDoi":"10.21203/rs.3.rs-3883484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3883484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStomata, a regulatory system in leaves, aids in identifying plant species to complete taxonomic data. This study investigated the stomatal descriptors of \u003cem\u003eMelia dubia\u003c/em\u003e, a potential plywood species to distinguish within variation in the species to explain the diversity and diagnostic significance of these attributes. Twenty clones were selected to investigate nine stomatal characters related to stomatal type, length, width, density, and distribution. The results showed the presence of hypostomatic leaves with anomocytic stomata that falls under the category small. Stomatal clustering, an abnormal stomatal patterning formed by two or more stomata in the epidermis was also observed. The examined data were subjected to a numerical analysis using SPSS and R packages. A significant variation in observed parameters were obtained. Correlation analysis shows that stomatal length, width, and size were significantly correlated to pore length, pore width, pore perimeter and stomatal pore depth. Further hierarchical cluster analysis using average linkage between groups method clustered all the 20 clones into 5 clusters apportioning the variation among clones. Divergence analysis using Mahanalobis distance-based clustering detailed the dissimilarities and differences between the clones. The study highlights the diagnostic potential of stomatal features in identifying variations within the species. This report is the first detailed description of stomatal features in the genus \u003cem\u003eMelia\u003c/em\u003e, implying its significant contribution to the knowledge in this area. This study underscores the potential of stomatal features as a diagnostic tool for plant species identification and taxonomic studies.\u003c/p\u003e","manuscriptTitle":"Divergence and genetic parameters between Melia dubia genotypes based on morpho-anatomical stomatal descriptors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 07:12:19","doi":"10.21203/rs.3.rs-3883484/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58856ce7-dd9d-453a-839a-daee6943a9f1","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-01T11:38:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-29 07:12:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3883484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3883484","identity":"rs-3883484","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}