Behind the Skeleton: Unraveling the Genetic Basis of Skeletal Variation in the Coral Platygyra daedalea

Behind the Skeleton: Unraveling the Genetic Basis of Skeletal Variation in the Coral Platygyra daedalea | 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 Behind the Skeleton: Unraveling the Genetic Basis of Skeletal Variation in the Coral Platygyra daedalea Shoug Alguthmi, Sebastian Schmidt-Roach, Marcelle Muniz-Barreto, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9092160/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 Environmental factors have long been recognized as the primary drivers of intraspecific morphological variation in corals, as demonstrated in numerous species. However, coral calcification is a process that depends on both environmental and biological factors. Understanding the extent to which genetics contributes to morphological variation in corals remains lacking, particularly in corals like Platygyra daedalea , a species with complex morphological variation that has been found to be neither induced environmentally nor driven by genetic divergence. To address this gap, we conducted a genome-wide association study using single-nucleotide polymorphism and phenotype data of eight skeletal traits, obtained through restriction enzyme site-associated DNA sequencing and micro-computed tomography, respectively. Here, we demonstrate that genetics contributes to the variation of specific Platygyra daedalea skeletal traits, particularly porosity ratio, interseptal distance, and septal thickness. Associated variants were located near genes involved in cell cycle regulation, ciliary function, cytoskeletal rearrangement, and skeletal protein formation. We also found some of these traits to correlate significantly with larger-scale morphological features such as valley width and valley depth, suggesting a potential influence of genetically shaped traits on broader skeletal structure. Population Biology Evolutionary Biology Molecular Genetics Corals Morphological variation Platygyra daedalea Red Sea Population genetics Genome-wide association study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Coral reefs are among the most biodiverse ecosystems, supporting at least 25% of marine life while providing us with coastal protection 1 , 2 . These coral reef frameworks are mainly, albeit not exclusively, built by stony corals, which are increasingly threatened by climate change and ocean acidification 1 , 3 – 7 . Understanding how corals build and maintain their skeletons, and how these processes respond to environmental change, is critical for predicting the future of reef ecosystems. Scleractinian corals generally form their skeletons through the biomineralization of calcium carbonate, facilitated by the calicoblastic epithelium 8 – 10 . This tissue transfers inorganic and organic components into the extracellular calcifying medium (ECM). The organic matrix (OM) and minerals within the ECM interact to build the skeleton 8 – 10 along two main axes: lateral thickening and linear extension 4 . Yet, coral skeletal structures vary widely across species, from their microstructure and colony structure to overall growth forms 11 . Skeletal growth rates and characteristics can be influenced by environmental factors such as depth 12 , light spectra 13 , current intensity 14 , and seawater pH 4,5,15,16 . For instance, ocean acidification, driven by elevated atmospheric CO₂ levels, lowers seawater pH and reduces carbonate ion availability 17 , decreasing the saturation state of calcium carbonate 8 , 17 . In response, corals may exhibit reduced calcification rates 15 , increased skeletal porosity 4 , 16 , and changes at the molecular level, such as altered DNA methylation patterns 16 . Lateral skeletal thickening in Porites , for example, has been found to be sensitive to carbonate ion concentrations 4 . Moreover, Stylophora pistillata has shown increases in skeletal porosity under acidified conditions while maintaining linear extension, possibly through enlarged corallite calyces 15 , 16 induced by altered cell cycle regulation 16 . Platygyra daedalea is a common reef-building coral throughout the Indo-Pacific region, recognized for its characteristic maze-like skeletal structure 11 . This species exhibits remarkable morphological variation 18 , 19 . Some coral species with immense morphological variability have long complicated the delineation of taxonomic boundaries 20 , 21 . Previous studies have sought to determine the drivers of such variation: for example, Miller 18 found that environmental factors along gradients at the Davies Reef in the Great Barrier Reef did not influence P. daedalea morphology. Mangubhai et al 19 did distinguish two morphotypes in Kenyan P. daedalea populations and developed a mathematical equation for field assignment. Additionally, a genetic variance analysis (AMOVA) of microsatellite and internal transcribed spacer (ITS) sequences revealed significant genetic differences between the two morphotypes 19 . However, the morphotypes had no phylogenetic divergence detected using ITS sequences 19 . Moreover, distinguishing between the morphotypes was only possible when both morphological and genetic data were considered together 19 . These findings suggest that P. daedalea retains species cohesion despite showing morphological variability. The genetic basis of such intraspecific skeletal variation remains largely unexplored. Here, we investigated the skeletal variation of Platygyra daedalea in the Red Sea, a region that harbors 3.8% of the world’s coral reefs and presents steep environmental gradients 22 . We used micro-computed tomography (micro-CT), a high-resolution imaging technique, to quantify skeletal features beyond the limits of traditional methods 23 – 27 . Additionally, we applied ezRAD sequencing, a cost-effective reduced-representation genomic approach targeting restriction enzyme sites 28 , which has been successfully used in coral population studies 29 – 31 . By integrating these datasets, we examined the genetic basis of skeletal traits, assessed trait correlations, and explored how environmental gradients shape intraspecific skeletal diversity. MATERIALS AND METHODS Sample Collection A total of 90 fragments of Platygyra daedalea were collected from five Red Sea locations as follows: Duba (N = 21), Al Wajh (N = 16), Yanbu (N = 18), Al Lith (N = 20), and Southern Farasan Banks (N = 15), see Fig. 1 . The sampling sites spanned a range of habitats, including reef slope, reef crest, and seagrass environments (Table 1 ). Tissue samples were preserved for genetic analysis, and skeletal fragments were bleached using a 20% chlorine solution to remove tissue and dried for morphological examination. Table 1 Coordinates and habitat types (reef or seagrass) of sample collection sites from five locations along the Red Sea. Location Site Habitat Latitude (°N) Longitude (°E) Duba Reef 2 Crest 27.29778 35.64389 Reef 3 Crest 27.27417 35.63722 Reef 4 Crest 27.30166 35.63361 Seagrass Lagoon (onshore) 27.14583 35.73528 Al Wajh Reef 1 Wall, Crest 25.64139 36.47806 Reef 2 Wall, Crest 25.50194 36.61833 Reef 3 Crest 25.30028 36.94972 Seagrass Lagoon (offshore) 25.36444 36.90972 Yanbu Reef 1 Crest 23.79694 37.95528 Reef 2 Crest 23.76806 37.95667 Reef 3 Crest 23.65167 38.03361 Al Lith Reef 1 Wall, Crest 19.77083 39.88972 Reef 2 Wall, Crest 19.74639 39.90583 Reef 3 Wall 19.76111 39.95806 Southern Farasan Banks Reef 1 Wall, Crest 18.27389 40.73028 Reef 2 Lagoon (offshore) 18.25083 40.73028 Sample Preparation for X-Ray CT Scanning Skeletal morphological phenotyping was conducted utilizing high-resolution X-ray micro-computed tomography (micro-CT). A subset of 82 P. daedalea specimens was phenotyped for skeletal traits: Duba (N = 14), Al Wajh (N = 15), Yanbu (N = 18), Al Lith (N = 20), and Southern Farasan Banks (N = 15). Eight of the ninety collected fragments were unavailable for micro-CT scanning. The bare skeletons were fixed in packaging foam and then inserted into plastic measuring cylinders 32 , 33 (Figure S1A). Micro-CT scanning was performed using a TESCAN CoreTOM™ CT scanner at 150 kV and 60 W. The images were acquired at a voxel size of 60 µm x 60 µm x 60 µm. Micro-CT Image Analysis All image analysis was performed with the software Avizo 3D (Thermo Fisher Scientific Inc., Berlin; v2021.2). The TIFF image stack for each scanned cylinder was cropped into individual files for each coral fragment. These cropped fragments were filtered using Non-local Means and Unsharp Masking (Avizo module names are italicized in this section). A total of 8 skeletal traits were recorded, as described by Miller 18 : valley width (VW), valley depth (VD), columella width (CW), theca thickness (TT), and septal thickness (ST). In addition, we measured interseptal distance (SS) as the distance between two adjacent septa across an interseptal cavity, measured from the center of the structure, septa teeth count, and porosity ratio. The macroscopic skeletal traits (CW, TT, ST, and SS) were measured from 2D cross-sections (Fig. 2 A), while topological traits (VD and VW) were measured using both 3D reconstructions and cross-sections (Fig. 2 C). Septa teeth were counted along one side of a wall in 3D reconstruction (Fig. 2 B). Each trait was measured in ten technical replicates and averaged per individual. All measurements, except for the septa teeth count, were made using the Avizo ruler tool. To enhance visibility during analysis, the colormap settings of the Ortho-slice and Volume-Rendering tools were adjusted (Figure S1B). Porosity ratio was calculated by segmenting each image, using Auto-Thresholding , into two regions: skeletal area (high-threshold) and void area (low-threshold). The skeletal segment was further processed using the Fill Holes tool to fill internal skeletal voids. To isolate internal voids, the background was removed from the void segment using the Border Kill tool. The porosity ratio was then calculated with the Volume Fraction tool by dividing the volume of the void segment (set as the input image) by the volume of the filled skeletal segment (set as the input image mask), providing the ratio of void to skeleton (Figure S2). Boring organisms, including Pyrgomatidae and Dendropoma, were found in the skeletal specimens. Segmentation methods excluded the voids created by these organisms and thus do not affect the calculation of the porosity ratio, as illustrated in Figure S3. Statistical Analysis of Phenotypic Data The variance of each trait was compared between the five collection locations in RStudio 34 (v2023.06.0). The assumptions for ANOVA, including normality and equal variance, were tested using the Shapiro-Wilk test and the Bartlett test, respectively. If the null hypotheses were not rejected (p > 0.05), then parametric tests of ANOVA with Tukey's post hoc test were applied. If the assumptions were rejected, non-parametric Kruskal-Wallis tests with Dunn’s post hoc test were used for the comparison. To evaluate trait correlations, we calculated either Pearson’s or Spearman’s correlation coefficients, depending on the normality of each trait (Shapiro-Wilk test, p > 0.05). DNA Extraction and ezRAD Library Preparation Genomic DNA was extracted using a salting out method, commonly known as “Wayne's Method” 35 . DNA concentration was measured using the DNA Broad Range Qubit kit, and the DNA quality was assessed using 0.8% agarose gel electrophoresis. For the preparation of ezRAD DNA libraries, 100ng of DNA was used following the method of Toonen et al. 28 . The DNA was digested with MboI and Sau3AI enzymes for 6 hours at 37˚C. After digestion, DNA libraries were prepared using the NEB Ultra II DNA library preparation kit for Illumina. Library size selection was then performed using a 2% agarose gel, where a band of DNA was cut from the gel at 300-500bp. The DNA was recovered from the agarose gel using the QIAGEN MinElute Gel extraction kit following the manufacturer's guidelines. The libraries were then assessed using the Bioanalyzer High sensitivity chip and Qubit. Finally, the libraries were pooled in equimolar ratios and sequenced on the Illumina HiSeq 4000 using the 2 X 150bp chemistry. Genetic Variant Calling The primary genetic dataset consisted of 78 individuals: Duba (N = 14), Al Wajh (N = 16), Yanbu (N = 15), Al Lith (N = 19), and Southern Farasan Banks (N = 14). Genetic data for twelve fragments were unavailable, likely due to data loss, DNA extraction failure, or unsuccessful library preparation. A reduced genetic dataset was prepared for the association analysis, including only samples with both genetic and phenotypic data. This reduced dataset comprised 69 individuals: Duba (N = 7), Al Wajh (N = 14), Yanbu (N = 15), Al Lith (N = 19), and Southern Farasan Banks (N = 14). Genetic variants were obtained first by trimming library adapters and removing low-quality reads from raw reads sequencing data using Fastp 36 (v0.23.2). The quality of reads was assessed throughout the workflow using FASTQC 37 (v0.12.0). Clean reads were then mapped to the P. daedalea reference genome 38 v1.0 with BWA 39 (v0.7.17). Then, Samtools 40 (v1.16.1) fixmate was used to pair complementary reads, and with Samtools markup , duplicate reads were removed. Bcftools 40 (v1.16) mpileup was used to call genetic variants into a raw variant file (VCF). Variants were filtered with VCFtools 41 (v0.1.16) using the following parameters: a minor allele frequency of 0.1, a minimum quality of 20, a minimum depth of 10, a maximum depth of 335, and a missing data threshold of 80%. To prune linkage loci, PLINK 42 (v2.0) was used with the indep-pairwise function, setting the window size to 50 kb, the window step size to 10, and an r 2 threshold of 0.1. Population Genetics To estimate population structure, first, a principal component analysis (PCA) was performed using PLINK pca . The resulting eigenvalues and eigenvectors were used to plot the PCA in RStudio with the tidyverse package 43 . A maximum likelihood phylogenetic tree was estimated from the genetic variants (in PHYLIP format obtained by vcf2phylip 44 (v2.9)) using IQTREE 45 (v2.2.6) model finder 46 to identify the most suitable substitution model with 1000 bootstrap replications. An additional tree was constructed for the reduced genetic dataset. The resulting trees were visualized in iTOL 47 (v6). Admixture analysis was performed using the sparse non-negative matrix factorization (sNMF) model in the R package LEA 48 . The snmf function was used to compute cross-entropy values for ancestral populations (K sNMF ) ranging from 1 to 10, with ten replicates for each value. The cross-entropy values were visualized, and the K sNMF corresponding to the lowest cross-entropy was selected as the optimal value of K. The admixture coefficients of the runs with the minimum cross-entropy values were visualized as bar charts and admixture pie charts. F ST population differentiation statistics were calculated using VCFtools , which uses the Weir and Cockerham approach 49 . Pairwise F ST was computed between the three genetic clusters identified from the phylogenetic tree and admixture coefficients of the reduced dataset (Figure S4). SNPs with an F ST ≥ 0.3 were considered significant as being under selection. Additionally, F ST values were averaged for each pairwise test between the clusters to assess population structure and divergence. Genome-Wide Association Study The genome-wide association analysis was conducted using the Latent Factor Mixed Model ( LFMM2 ) 48 function from the LEA package in RStudio , with the number of latent factors (K) set to three based on the estimated population structure. The significance of the results was computed using lfmm2.test function for each trait, and the results were visualized in Manhattan plots. To determine the significance of the associations, we applied a Bonferroni-corrected threshold of p = 0.05. SNP annotation was performed using SnpEff 50 (v5.2c) to predict the potential functional effects of associated variants. A custom SnpEff database for P. daedalea was built using the reference genome, gene annotations files in the format of ‘GFF’, and protein sequence 38 . The VCF file was annotated using this custom database. RESULTS Morphological Variation Along the Red Sea To assess the effect of environmental factors on phenotypic variation, we analyzed trait variance for eight skeletal traits across five collection sites along the Red Sea: Duba, Al Wajh, Yanbu, Al Lith, and Southern Farasan Banks (SFB). A one-way ANOVA was applied to traits that met the assumptions of normality and equal variance: theca, thickness (TT), valley width (VW), and septa teeth (Table 2 ). For traits that violated these assumptions, i.e., porosity ratio, interseptal distance (SS), columella width (CW), and septal thickness (ST), we used Kruskal-Wallis tests (Table 3 ). One-way ANOVA showed no significant differences for the traits TT, VW, and septa teeth (p > 0.05). Kruskal-Wallis tests showed significant differences for three traits: porosity ratio (χ2(4) = 18.8, p = 0.00087); CW (χ2(4) = 15.8, p = 0.0033); and ST (χ2(4) = 15.9, p = 0.0032). Dunn's post hoc pairwise comparisons indicated significant differences in porosity between Duba and Al Wajh (p = 0.011) as well as between Duba and Yanbu (p = 0.00373) (Fig. 3 A). CW showed significant differences between Duba and Al Lith (p = 0.00204) and Duba and Yanbu (p = 0.0307) (Fig. 3 B). Lastly, ST was significantly different between SFB and Al Lith (p = 0.0011) and SFB and Yanbu (p = 0.048) (Fig. 3 C). No significant differences were found for SS and VD (p > 0.05). Table 2 One-way ANOVA of variation in morphological traits (TT, VW, septa teeth) among samples from the five locations. Variable Condition N Average S F P TT Duba 14 2.18 0.609 1.145 0.342 Al Wajh 15 1.95 0.446 Yanbu 18 1.82 0.4 Al Lith 20 1.85 0.512 SFB 15 1.94 0.593 VW Duba 14 5.84 0.796 2.275 0.0688 Al Wajh 15 5.24 0.563 Yanbu 18 5.08 0.63 Al Lith 20 5.23 0.786 SFB 15 5.3 0.899 Septa teeth Duba 14 4.51 1.02 0.716 0.584 Al Wajh 15 4.94 1.01 Yanbu 18 4.75 0.797 Al Lith 20 4.46 0.917 SFB 15 4.57 0.974 Table 3 Kruskal–Wallis H test results for variation in morphological traits (SS, ST, porosity ratio, VD, CW) among samples from the five locations. Variable Condition N Mean Rank Df χ2 P SS Duba 14 47.39 4 1.57 0.814 Al Wajh 15 40.13 Yanbu 18 39.69 Al Lith 20 38.15 SFB 15 44.00 ST Duba 14 38.86 4 15.9 0.00319 Al Wajh 15 43.33 Yanbu 18 38.06 Al Lith 20 30.05 SFB 15 61.53 Porosity Duba 14 58.14 4 18.8 0.000867 Al Wajh 15 29.20 Yanbu 18 27.94 Al Lith 20 48.60 SFB 15 45.07 VD Duba 14 57.43 4 7.96 0.0932 Al Wajh 15 39.47 Yanbu 18 35.67 Al Lith 20 40.20 SFB 15 37.40 CW Duba 14 60.07 4 15.8 0.00327 Al Wajh 15 44.20 Yanbu 18 34.94 Al Lith 20 29.25 SFB 15 45.67 Correlation Between Morphological Traits Pearson correlation was applied to VW, CW, TT, VD, and septa teeth, as the Shapiro-Wilk normality test indicated normal distributions with a p-value > 0.05 and an average of W = 0.98. For ST (W = 0.90), SS (W = 0.90), and porosity ratio (W = 0.67), where normality was rejected (p < 0.05), Spearman’s correlation was used (Table 4 ). Correlation coefficients were computed to assess the linear relationship among the eight traits. Very high positive correlations were found between VD and VW (r(80) = 0.93, p < 0.001), as well as VD and TT (r(80) = 0.80, p < 0.001). Additionally, VW was highly positively correlated with TT (r(80) = 0.71, p < 0.001). Several traits exhibited moderate positive correlations, all statistically significant: porosity with VD (r(80) = 0.48, p < 0.001), and VW (r(80) = 0.41, p < 0.001); CW with VW (r(80) = 0.51, p < 0.001), and VD (r(80) = 0.58, p < 0.001); VD with SS (r(80) = 0.43, p < 0.001). Table 4 Pearson and Spearman* correlation pairwise traits results, with the method chosen based on the Shapiro–Wilk normality test results. VW CW TT ST* SS* Porosity ratio* VD Septa teeth VW - 0.51(80) 0.71(80) 0.32(80) 0.31(80) 0.41(80) 0.93(80) 0.02(80) p < 0.001 p < 0.001 p = 0.003 p = 0.005 p < 0.001 p < 0.001 p = 0.87 CW - 0.28(80) 0.34(80) 0.36(80) 0.18(80) 0.58(80) 0.02(80) p = 0.009 p = 0.002 p < 0.001 p = 0.11 p < 0.001 p = 0.85 TT - 0.34(80) 0.29(80) 0.37(80) 0.80(80) 0.04(80) p = 0.002 p = 0.008 p = 0.001 p < 0.001 p = 0.72 ST* - 0.25(80) 0.11(80) 0.36(80) -0.13(80) p = 0.026 p = 0.33 p < 0.001 p = 0.23 SS* - 0.16(80) 0.43(80) -0.07(80) p = 0.16 p < 0.001 p = 0.52 Porosity ratio* - 0.48(80) -0.05(80) p < 0.001 p = 0.66 VD - 0.01(80) p = 0.89 Septa teeth - Population Structure of Red Sea Platygyra daedalea Using an ezRAD sequencing approach, we identified 20,290 high-quality single-nucleotide polymorphisms (SNPs) from 78 individuals. The reduced secondary data set, consisting of 69 individuals, had a total of 20,140 SNPs. Population structure was assessed using admixture and principal component analyses (PCA) with the primary dataset of 20,290 (N = 78). For the admixture analysis, we evaluated clustering for up to ten groups (K = 1–10) using LEA sNMF cross-entropy, which indicated that the optimal K sNMF value is either K = 2 or K = 3. Admixture coefficients at K sNMF = 2, 3, and 4 were visualized alongside a phylogenetic tree (Fig. 4 ). The samples did not separate based on collection location, and most individuals appeared admixed. However, at K sNMF = 3 and K sNMF = 4, a stable cluster (yellow) emerged, primarily composed of individuals from Duba and SFB. This cluster was supported by a 94% bootstrap value, suggesting an ancestral relationship among these individuals. When the clustering coefficients were plotted as pie charts for each location sample, Duba and SFB showed a greater proportion of the yellow cluster in comparison to other locations (Fig. 4 B). PCA further revealed that individuals from Duba and SFB exhibited greater genetic variation than individuals from other locations (Fig. 4 C and 3 D). In PC1 vs. PC2 and PC2 vs. PC3, a subset of individuals from Duba and SFB separated along PC2. Additionally, PC2 versus PC3 showed a separation of a subset of individuals from Al Lith from the larger cluster. These PCA results are consistent with the admixture analysis and phylogenetic tree, supporting K sNMF = 3 as the most likely population structure. To assess whether the genetic clusters belong to a single species, we calculated pairwise F ST , which measures allele frequency differences. The pairwise F ST ​ values were low: F ST = 0.027 between populations 1 and 2, F ST = 0.018 between populations 1 and 3, and F ST = 0.017 between populations 3 and 2. These values indicate minimal genetic differentiation, suggesting that despite some population structure, these groups likely belong to a single species with ongoing gene flow. Phenotype to Genotype Association The association between genetic variation and morphological variation in P. daedalea was investigated for eight traits, using a Bonferroni-corrected threshold of p = 2.5 × 10 − 6 . Significant associations were identified for three traits: 35 SNPs were associated with SS, 32 SNPs with ST, and 27 SNPs with porosity ratio (Fig. 5 ). Among the 35 SNPs associated with SS, a total of 45 functional annotations were assigned: 91% had a modifying effect, 55.5% were intergenic, and 15.5% were located upstream of genes. The 32 SNPs associated with ST had 45 annotations, 89% had a modifying effect, 55.5% were intergenic, and 20% were downstream of genes. The 27 SNPs associated with porosity ratio had a total of 38 annotations, of which 89% had a modifying effect, 45% were intergenic, and 18% were upstream of genes. The SNPs associated with phenotypic traits determined by LFMM2 were compared to F ST outliers, and no overlaps were found. Genes located near the associated SNPs had various functions, including roles in processes such as the cell cycle, regulation of cell shape, cilium assembly, and transport. Details of the SNPs associated with porosity ratio, SS, and ST, along with their corresponding gene annotations, are provided in Tables S1, S2, and S3, respectively. However, the exact causal SNPs remain to be identified through further sequencing and analysis, and the precise functions of the associated genes in corals are yet to be determined. DISCUSSION Slight Effect of Environmental Gradients on Platygyra daedalea Morphology Here, we analyzed the skeletal variation of Platygyra daedalea across the pronounced environmental gradients of the Red Sea, although pinpointing exact causal factors remains challenging due to the lack of microenvironmental data. Nevertheless, leveraging natural environmental gradients offers a powerful approach, as it mirrors the varied conditions typically explored in laboratory experiments. Several environmental gradients, including alkalinity 51 , 52 , salinity, and annual maximum temperature, occur along the Red Sea, shaped largely by its limited freshwater input and regional climate 22 . The most significant results were observed for porosity, with Duba corals exhibiting significantly higher variation in their porosity ratio compared to nearby locations of Al Wajh and Yanbu. Under lower pH conditions, corals can become more porous 4 , 5 , 15 , 16 . Interestingly, we found that the northernmost corals accounted for most of the study’s highly porous corals, contrasting with the Red Sea alkalinity gradient, where northern regions are generally more alkaline than southern regions 51 , 52 . Corals from other locations followed a more predictable pattern consistent with the alkalinity gradient 51 , 52 , except for a decrease in porosity variation in SFB corals. Columella width displayed a declining trend across the study locations, aside from SFB corals, with a decrease in variation, likely influenced by their proximity to a renewing water source 22 . Septal thickness variation was stable over four locations, with an increase in thickness for SFB corals. Environmental and depth gradients at Davies Reef in the central Great Barrier Reef had no significant association with P. daedalea VW, CW, ST, TT, and VD variation 18 . This further confirms that environmental differences are not the primary driver of most P. daedalea morphological variation 18 . However, porosity was not investigated at different depths for this species. The Mediterranean Balanophyllia europaea showed a slight decrease in porosity with depth, measured at 1, 11, and 21 meters 12 . However, species-specific sensitivities to environmental conditions and their effects on skeletal morphology were demonstrated in an ex-situ light spectra study 13 . In this experiment, Acropora formosa and Stylophora pistillata each exhibited distinct morphological changes under three light spectra on a macro and microstructure level 13 . These included variations in theca thickness, septal length, distance among corallites, and their diameter, while porosity remained unchanged 13 . Red Sea Platygyra daedalea Populations are Genetically Connected with Slight Structure Given the skeletal trait differences observed across collection sites, we next examined whether underlying genetic structure could account for the skeletal patterns along the Red Sea. For that, we analyzed the genetic population structure of Red Sea P. daedalea using admixture and principal component analyses. Our results indicated widespread genetic admixture among individuals, suggesting a lack of reproductive barriers within this species across the Red Sea. Confirming that the species harbors genetic variation and connectivity throughout the Red Sea. In contrast, P. daedalea populations in the Arabian Gulf are highly structured and exhibit limited connectivity 31 . This pronounced structure is likely driven by thermal isolation formed by temperature gradients in the Arabian Gulf, which increase towards the center of the water body 31 . Our findings align with observed genetic connectivity patterns in Red Sea Pocillopora favosa (previously P. verrucosa 53 ), which are influenced by the species reproductive mode 54 . Like P. favosa , P. daedalea employs broadcast spawning, releasing eggs and sperm into the water column for external fertilization 54 – 56 . However, individuals from Duba and SFB exhibit higher genetic diversity, potentially influenced by specific environmental gradients 22 , 51 or genetic gradients 22 specific to P. daedalea. The extreme environmental conditions in Duba and SFB do not seem to reduce the species’ genetic diversity through selective pressures. Conversely, the northern region exhibits increased salinity, alkalinity, and lower temperatures 22 . The latter has established the region as a coral refugia, contributing to significantly lower bleaching rates compared to corals in the southern Red Sea 22 , 57 . These favorable conditions may enhance coral growth and resilience to climate stress 22 , 57 , potentially contributing to Duba maintaining a higher genetic diversity as observed. Moreover, gene flow may have increased the gene pool diversity of SFB, given its proximity to Indian Ocean water inputs. This genetic pattern bears some resemblance to that observed in a genetic clustering of Red Sea Stylophora pistillata , which was only present in the southern and northern regions of the Red Sea 54 . However, for P. daedalea , the genetic cluster is also present among individuals in the central region, albeit with lower abundance. Genetic Influence on Coral Morphological Traits Association analysis of eight quantitative traits revealed strong genetic associations for porosity ratio, septal thickness, and interseptal distance. While previous studies using microsatellite and internal transcribed spacer (ITS) sequences have differentiated two P. daedalea morphotypes, these molecular biomarkers required integration with morphological data to effectively distinguish morphotypes 19 . Notably, Mangubhai et al 19 excluded 88 out of 133 P. daedalea samples due to intermediate morphologies. In contrast, our study identified traits distributed along a continuum of phenotypes. The quantitative nature of our measurements suggests that, if genetically associated, these traits may be polygenic 58 . We refrained from categorizing corals into morphotypes or genetic groups to enable independent association testing of each trait. By leveraging ezRAD sequencing, which provides high-density single-nucleotide polymorphism (SNP) data 59 , we provide a deeper understanding of the relationship between genetic and morphological variation. However, the specific associated genetic variants remain to be identified through higher-resolution sequencing and analyses. Porosity: The Most Complex of All Skeletal Traits We identified 27 genetic associations with porosity ratio. The most significantly associated SNP was located 1.41kb downstream of a gene encoding a THAP domain-containing protein 2; the protein localizes in the nucleolus and has DNA and metal ion binding activities 60 . The THAP domain has sequence-specific DNA binding activity 61 , 62 , and it may be involved in various cellular processes, including “proliferation, apoptosis, cell cycle, chromosome segregation, chromatin modification, and transcriptional regulation” 62 . The THAP protein family, which has the THAP domain in common 63 , remains largely understudied, except for THAP1, which plays a fundamental role in cell proliferation and cell-cycle pathways in human endothelial cells 64 . A synonymous variant was identified in a gene encoding a probable DNA polymerase, along with an intronic variant in the gene encoding a translation initiation factor eIF-2B subunit gamma. Additionally, three intergenic variants were found near genes encoding a WW domain-containing oxidoreductase and Tetratricopeptide repeat protein 28, located 7 kb upstream of the latter. WW domain-containing oxidoreductase is essential for normal bone development 65 , 66 and Tetratricopeptide repeat protein 28 functions during mitosis 67 . Coral skeletons become more porous due to a decrease in skeletal density 24 , a response strongly associated with lower pH levels 4 , 5 , 15 , 16 , 24 . Liew et al. 16 showed that pH induced porosity increase was associated with DNA methylation changes affecting pathways regulating body size and cell cycle 16 . Morphological plasticity has been linked with this induced porosity 15 , 16 , observed to result from initial changes within the coral polyps 16 . We found porosity to have significant moderate positive correlations with valley depth and width, which in turn showed highly significant correlations with other traits. Although porosity has been shown to arise environmentally 4 , 5 , 15 , 16 , 24 , we also found underlying genetic associations. While the exact functions of the associated proteins remain to be investigated in corals, they are known to have sequence-specific DNA-binding abilities 61 , 62 , functions in the cell cycle 64 , 67 , as a DNA polymerase, and as a translation initiation factor. We hypothesize that the associated SNPs may influence the regulation of the gene expression, potentially causing subtle changes in cell cycle pathways that may affect cell and polyp sizes, ultimately leading to increased porosity. Linear growth is crucial for corals to compete for light availability required for the coral- Symbiodiniaceae symbiosis 68 . Porosity and its associated phenotypes allow maintaining linear extension even with reduced calcification rates under pH stress 4 , 15 , 16 . However, P. daedalea is not a columnar coral, yet we think, based on the transversal sections (Figure S5), that some void patterns may support linear extension. As P. daedalea colonies are mostly massive and hemispherical 11 , we hypothesize that wider and deeper valleys may develop as the wall's linear extension increases. However, compared to the high positive correlations between VD, VW, and others, porosity only showed moderate correlations with each VD and VW. This suggests that, in addition to porosity, other factors also play significant roles in VW and VD phenotypic plasticity. Increased coral porosity can exert both negative 5 , 69 and positive 5 , 15 , 16 impacts on the corals’ fitness. Even healthy corals are susceptible to damage and breakage during severe cyclonic events, which are anticipated to increase with climate change 69 . Ocean acidification-driven increases in porosity 4 , may lead to more fragile colonies 69 . On the other hand, high porosity acquired by corals can be regarded as a phenotypic adaptation to sea-level rise 16 , 68 , 69 . Through this phenotypic plasticity, corals may have the potential to overcome the effects of climate change driven ocean acidification. Platygyra daedalea is a slow-growing coral with a lower porosity ratio than branching corals 15 . If the genetic associations identified in this study are true positives, this may suggest that Platygyra daedalea is exhibiting phenotypic plasticity, potentially adopting characteristics of faster-growing coral structures. The Surprisingly Significant Variation of Septa We found two septal traits, thickness and distance, to have significant genetic associations. Septa determine the arrangement of mesenteries, which are internal folds of tissue that divide the coelenteron, the body cavity of the coral polyp 70 , 71 . As Veron et al. 70 describe, “mesenteries give the gastrodermis a large surface area for digestion, photosynthesis, and respiration, and also contain the reproductive organs”. They also form mesenterial filaments that can extend outside the coelenteron for feeding, defense, and wound cleaning 70 – 72 . Since porosity was unexpectedly linked to cell and polyp phenotypes 16 , we hypothesize that variation of septal traits may similarly be driven by mesenterial phenotypes. To our knowledge, no studies have explored these aspects; however, the genetic variants identified in this study suggest a potential link that requires further investigation. Interseptal distance Interseptal distance exhibited a strong genetic association with 35 SNPs. Five SNPs were located within genes with distinct functional roles, while two SNPs were positioned near genes with functional annotations in cnidarians. To our knowledge, no prior studies have investigated interseptal distance as a distinct trait in corals. Nevertheless, we hypothesize that certain variants may directly influence variations in biomineralization, while others may impact coral polyp structures. Skeletal organic matrix protein 5 (SOMP5) is a known component of the coral skeletal proteome, although its specific function remains unclear within the organic matrix 73 . A variant downstream of SOMP5 may contribute to biomineralization variation, influencing the phenotypic variation of P. daedalea interseptal distance. Tetratricopeptide repeat protein 21B is part of the intraflagellar transport (IFT) complex, which is essential for the assembly of cilia and flagella 60 , 74 , 75 . This protein may affect primary cilia found in the coral ectoderm 76 . Primary cilia are short, non-motile cilia with functions in detecting signals from the surrounding microenvironments 76 . Their presence in the aboral calicoblastic ectoderm suggests that they may serve as sensors of the extracellular calcifying medium environment 76 . Additionally, we speculate that variants within genes encoding E3 ubiquitin-protein ligase (TRIM71) and deoxynucleotide monophosphate kinase (dNMP) may influence variation in reproduction, given their roles in development 77 , 78 and nucleotide synthesis 79 , respectively. Both TRIM71 and dNMP kinase genes exhibited synonymous SNPs associated with interseptal distance. While synonymous mutations are generally considered neutral, they can reduce an organism’s fitness by disrupting binding to regulatory sequences, splicing, and mRNA structure 80 – 84 . These changes may affect codon bias, gene expression levels, protein structure, translation efficiency, and RNA stability 80 – 84 . If synonymous mutations identified in this study have resulted in any phenotypic changes, we presume they would only lead to phenotypic variation rather than gene function changes. We found intronic variants in the genes coding for Myoferlin 85 and NDRG1 86 to be associated with interseptal distance phenotypes. Myoferlin and NDRG1 are known to play roles in muscle cells and in regulating microtubule dynamics 87 , respectively. Specifically, Myoferlin aids in rapid repair and growth by operating in cell division, cell migration, regulation of signaling, and organization of actin dynamics, which promote cytoskeletal rearrangements 85 . As previously mentioned, alterations in cell size and shape have been shown to affect skeletal phenotypes, as does the increase in polyp cell size with larger skeletal calyces 10 . Changes driven by the identified variants may affect cells within the interseptal cavity, potentially altering the distance between adjacent septa. These changes might involve the coral mesenteries, packed between septa, which can elongate their ends to form mesenterial filaments 70 , 71 , driven by cells with muscular activity 88 . Additionally, mesenterial filaments are characterized by ciliation and the presence of stinging cells (nematocysts) 89 , 90 . We also identified intronic variants in Tetratricopeptide repeat protein 21B and DELTA-alicitoxin-Pse2b, which have been reported to function in cilia assembly and toxin production, respectively 74 , 91 . While intronic mutations are often considered functionally neutral, they have been found, for example, to influence gene expression and translation efficiency, suggesting that the intronic variants we identified could have similar effects 92 , 93 . As outlined earlier, based on the functions of the associated genes and the structural roles of septa in relation to mesenteries 70 , we believe the variation in septa morphology may be correlated with mesenteries and their filaments. While there is limited knowledge on the cellular and molecular biology of these filaments, we do know they act as defense mechanisms and may confer survival advantages 94 , 95 . As to retain light availability, larger, slow-growing coral colonies like P. daedalea have been observed to use their mesenterial filaments to compete with faster-growing species that try to overshadow them 94 , 95 . Septal thickness We found septal thickness (ST) to be genetically associated with 32 SNPs. While ST is a widely examined trait 18 , 19 , 96 , its connection to mesenteries remains vague, unlike interseptal distance (SS). Our analysis revealed only a weak correlation between ST and SS, suggesting that ST may not be directly influenced by mesenteries. We speculate that as septa become thicker, it may drive mesenteries further apart and provide additional gastrovascular area. However, this will depend largely on additional polyp characteristics such as size and septa number. Among the identified SNPs, we detected an intronic variant in the gene encoding the BBSome complex member BBS7 protein, with functions in cilium biogenesis 97 , 98 . In humans, mutations in BBS proteins have been linked to ciliary dysfunction, leading to various features, including skeletal abnormalities 60 . Given that primary cilia may contribute to coral calcification 76 , this variant could influence skeletal variation in P. daedalea . Several SNPs were intergenic, including those near genes encoding PAX-interacting protein 1 and Histone lysine acetyltransferase CREBBP, both of which have functions in transcription regulation 60 . Specifically, CREBBP is found to acetylate histones and non-histone proteins 60 . Additionally, RRAGC, which encodes Ras-related GTP-binding protein C, functions as a hydrolase with a crucial role in regulating the mTORC1 signaling cascade 60 , which regulates protein synthesis and cell growth 99 . Moreover, Tetratricopeptide repeat protein 28 functions during mitosis 67 . As these proteins play roles in transcriptional regulation, cell growth, and division, we speculate that variations in their genes may potentially affect polyp characteristics and size, much like SNPs associated with porosity ratio. Especially since we found two genes expressing a Tetratricopeptide repeat protein 28 to have genetic associations with both porosity ratio and ST. However, porosity ratio and ST have no correlations based on our results. Thus, the specific relationship between polyp traits and septal thickness remains unclear and will require further investigation, particularly with polyp traits included. Conclusions Our study suggests that morphological variation in Platygyra daedalea has a genetic basis, indicating that genetics can contribute to species-level morphological variation in corals. While we identified genetic associations for three traits, it is possible that detecting associations for other traits will require a larger sample size or that these traits are more strongly shaped by environmental or epigenetic factors. Notably, the traits with genetic associations were not large structural features, but the latter did correlate with porosity ratio, reinforcing previous findings 15 , 16 . Annotation of significant SNPs suggests that these variations may impact coral calcification or polyp and mesenterial characteristics, ultimately affecting the skeleton. However, validating these associations will require whole-genome sequencing of individuals to precisely pinpoint potential causal variants through linkage disequilibrium analyses. The observed morphological variation of P. daedalea across nearby reefs, combined with our findings and previous studies 18 , 19 , suggest that environmental factors are not the only influences shaping these phenotypic variations. The lack of significant associations for five traits with environmental gradients indicates that environmental influences vary across traits. Further studies are necessary, particularly through controlled ex situ experiments testing the effects of multiple environmental factors on P. daedalea. Overall, our study underscores the intricate relationship between genetic factors and environmental gradients in shaping coral morphology. The observed phenotype variations suggest that corals develop a range of phenotypes that may enhance their resilience to diverse environmental conditions. These findings provide a clearer understanding of skeletal variation in P. daedalea and serve as a starting point for future research on genotype-phenotype-environment associations of coral morphology. Declarations Author Contributions C.M. and M.A. designed the research. S.S.-R. and M.M.-B. collected the samples. C.M. and S.S.-R. performed laboratory work. V.C. scanned corals via Micro-CT. 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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-9092160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604293445,"identity":"bf51a970-35d1-48c0-bb9f-ef3d142c7cd5","order_by":0,"name":"Shoug Alguthmi","email":"","orcid":"https://orcid.org/0009-0006-7219-1995","institution":"Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Shoug","middleName":"","lastName":"Alguthmi","suffix":""},{"id":604293446,"identity":"c941c45b-c50e-4d82-8cf2-3638997aefb2","order_by":1,"name":"Sebastian Schmidt-Roach","email":"","orcid":"","institution":"Ocean Revive, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Schmidt-Roach","suffix":""},{"id":604293447,"identity":"c601fda5-378f-436e-98bf-0d2c2ce919e9","order_by":2,"name":"Marcelle Muniz-Barreto","email":"","orcid":"","institution":"Department of Environmental Protection and Regeneration, Red Sea Global, Umluj, Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Marcelle","middleName":"","lastName":"Muniz-Barreto","suffix":""},{"id":604293448,"identity":"487a90d1-f4b5-469e-a5fb-e1f080a0797d","order_by":3,"name":"Viswasanthi Chandra","email":"","orcid":"","institution":"Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Viswasanthi","middleName":"","lastName":"Chandra","suffix":""},{"id":604293449,"identity":"bb44c4fd-98ec-47fd-9b41-9e81c5dc7989","order_by":4,"name":"Ronell Sicat","email":"","orcid":"","institution":"Visualization Core Lab, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Ronell","middleName":"","lastName":"Sicat","suffix":""},{"id":604293450,"identity":"e3ae5d3a-4cf9-4612-ba87-ee32b6f6c8b0","order_by":5,"name":"Thomas Theussl","email":"","orcid":"","institution":"Visualization Core Lab, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Theussl","suffix":""},{"id":604293451,"identity":"34f7e7ed-2b06-4da1-9b41-c43d97da409a","order_by":6,"name":"Craig T. Michell","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie3RMUvDQBTA8Xcc2OUk6w3FfIXnLNKvknAQl4BOJYODINSl2LVS8TMEhM4vHHQKyXpQh+QbRHDIInihneRaOzrcH7Lc8cu74wB8vn8a2U+ABEZNFu3Xzk4jQFE5EP432TWQeHYCCVa6Lb5gPQ5Wj0TxWxJiXRN0Uw3hE7n/XSWox7AV8mMTUbxOL3OjgC0rDVhGToICUEtLUKZoScZyw4GfzyyBQ2TU7cltR/FrNslrDfzbknDRHCACi243xV7/IY1zUsCZJWDcU2Qp7uwZ7F1MghRtEvViFBbz6kagcU8J5qP3zz7bToKlapvuXl0/10Xb9NOri3DhnjLEBf5aIRge90isP7br8/l8vh8kIGX6UgQjGQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4706-7256","institution":"Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia","correspondingAuthor":true,"prefix":"","firstName":"Craig","middleName":"T.","lastName":"Michell","suffix":""},{"id":604293452,"identity":"c98eb850-6337-43a9-9d4f-fa7b9a931401","order_by":7,"name":"Manuel Aranda","email":"","orcid":"","institution":"Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Aranda","suffix":""}],"badges":[],"createdAt":"2026-03-11 08:42:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9092160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9092160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104466111,"identity":"cc74e402-8e63-443c-bcc0-9c1ef1800b92","added_by":"auto","created_at":"2026-03-12 06:11:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePlatygyra daedalea\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e sampling locations. \u003c/strong\u003eSampling locations along the Red Sea (North to South): Duba, Al Wajh, Yanbu, Al Lith, and Southern Farasan Banks.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/39d8eeb9d09173a12537b628.png"},{"id":104466085,"identity":"47f2778f-e126-42f1-bc40-5198e090ce4d","added_by":"auto","created_at":"2026-03-12 06:11:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":655365,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSkeletal trait measurements. \u003c/strong\u003eA. Cross-section showing trait measurements: columella width (CW), theca thickness (TT), septal thickness (ST), interseptal distance (SS). B. Septa teeth counted along one side of the wall. C. Valley depth and width indicated by vertical and horizontal lines, respectively. Scale bar = 10 mm.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/486c4bc902a060157fca7986.png"},{"id":104466112,"identity":"216751fa-b7ae-4bea-a871-abe10e3416b1","added_by":"auto","created_at":"2026-03-12 06:11:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":320661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSkeletal traits variation across five Red Sea locations. \u003c/strong\u003eThe locations from north to south of the Red Sea: Duba N = 14 (green), Al Wajh N = 15 (beige), Yanbu N = 18 (blue), Al Lith N = 20 (red), and Southern Farasan Banks N = 15 (purple). Kruskal-Wallis for A. porosity ratio, B. columella width average (mm), and C. septal thickness average (mm).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/05d6a8df8449c749424e558a.png"},{"id":104466126,"identity":"eaf4a503-91fa-45f6-bfe1-6d81dcbcd16c","added_by":"auto","created_at":"2026-03-12 06:11:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1040766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation structure of Red Sea \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePlatygyra daedalea\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e A. maximum likelihood nuclear tree with admixture proportions for each individual in barcharts at each K = 2,3,4;\u003cstrong\u003e \u003c/strong\u003eB. LEA analyses indicating admixture proportions for each population in a pie chart on the Red Sea map, with Duba (N = 14), Al Wajh (N = 16), Yanbu (N = 15), Al Lith (N = 19), and Southern Farasan Banks (N = 14). C., D. Principal components analysis plots of PC1 vs. PC2 and PC2 vs. PC3.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/2ae625a9817b8ab236776946.png"},{"id":104466110,"identity":"e1014e8d-f93e-40c1-8b72-ac7d1f2ed93b","added_by":"auto","created_at":"2026-03-12 06:11:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":457669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide association analysis for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePlatygyra daedalea \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eskeletal traits. \u003c/strong\u003eA.\u003cstrong\u003e \u003c/strong\u003eporosity ratio, B. interseptal distance, and C. septal thickness. The X-axis shows the positions of the genetic variants. The Y-axis shows -Log10 of the p-values. The dotted line indicates the threshold used of Bonferroni-corrected alpha of 0.05.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/b020e2c013c0cbe3b71029ec.png"},{"id":104781046,"identity":"611cec02-a61e-4647-a55e-cdc271fade77","added_by":"auto","created_at":"2026-03-17 07:54:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3748122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/c78b628c-cabf-48c6-9652-9f2d38e0fa1a.pdf"},{"id":104466120,"identity":"2144d3a7-e160-4618-a100-f77d89882933","added_by":"auto","created_at":"2026-03-12 06:11:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2814629,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Figures\u003c/p\u003e","description":"","filename":"SupplementalfiguresofBehindtheSkeleton.docx","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/869d8ced00887edd4d267dae.docx"},{"id":104466108,"identity":"1e1da390-d439-4a4c-8049-1c0caa9add74","added_by":"auto","created_at":"2026-03-12 06:11:08","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11592,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table S1\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/ca5ce0f587b627adf34fa0ad.xlsx"},{"id":104466128,"identity":"5a691670-833f-450a-a0e3-cf71e9bd9f08","added_by":"auto","created_at":"2026-03-12 06:11:12","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12321,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table S2\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/675fd62b50e08be276243ef7.xlsx"},{"id":104466127,"identity":"2ee0f9a8-3519-4f9b-8e44-7de85a12fe55","added_by":"auto","created_at":"2026-03-12 06:11:11","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11319,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table S3\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9092160/v1/27db4773e9e4ddc31dda2f01.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBehind the Skeleton: Unraveling the Genetic Basis of Skeletal Variation in the Coral \u003cem\u003ePlatygyra daedalea\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCoral reefs are among the most biodiverse ecosystems, supporting at least 25% of marine life while providing us with coastal protection\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These coral reef frameworks are mainly, albeit not exclusively, built by stony corals, which are increasingly threatened by climate change and ocean acidification\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Understanding how corals build and maintain their skeletons, and how these processes respond to environmental change, is critical for predicting the future of reef ecosystems.\u003c/p\u003e \u003cp\u003eScleractinian corals generally form their skeletons through the biomineralization of calcium carbonate, facilitated by the calicoblastic epithelium\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This tissue transfers inorganic and organic components into the extracellular calcifying medium (ECM). The organic matrix (OM) and minerals within the ECM interact to build the skeleton\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e along two main axes: lateral thickening and linear extension\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Yet, coral skeletal structures vary widely across species, from their microstructure and colony structure to overall growth forms\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSkeletal growth rates and characteristics can be influenced by environmental factors such as depth\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, light spectra\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, current intensity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and seawater pH\u003csup\u003e4,5,15,16\u003c/sup\u003e. For instance, ocean acidification, driven by elevated atmospheric CO₂ levels, lowers seawater pH and reduces carbonate ion availability\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, decreasing the saturation state of calcium carbonate\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In response, corals may exhibit reduced calcification rates\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, increased skeletal porosity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and changes at the molecular level, such as altered DNA methylation patterns\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Lateral skeletal thickening in \u003cem\u003ePorites\u003c/em\u003e, for example, has been found to be sensitive to carbonate ion concentrations\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Moreover, \u003cem\u003eStylophora pistillata\u003c/em\u003e has shown increases in skeletal porosity under acidified conditions while maintaining linear extension, possibly through enlarged corallite calyces\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e induced by altered cell cycle regulation\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePlatygyra daedalea\u003c/em\u003e is a common reef-building coral throughout the Indo-Pacific region, recognized for its characteristic maze-like skeletal structure\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This species exhibits remarkable morphological variation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Some coral species with immense morphological variability have long complicated the delineation of taxonomic boundaries\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Previous studies have sought to determine the drivers of such variation: for example, Miller\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e found that environmental factors along gradients at the Davies Reef in the Great Barrier Reef did not influence \u003cem\u003eP. daedalea\u003c/em\u003e morphology. Mangubhai \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e19\u003c/sup\u003e did distinguish two morphotypes in Kenyan \u003cem\u003eP. daedalea\u003c/em\u003e populations and developed a mathematical equation for field assignment. Additionally, a genetic variance analysis (AMOVA) of microsatellite and internal transcribed spacer (ITS) sequences revealed significant genetic differences between the two morphotypes\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, the morphotypes had no phylogenetic divergence detected using ITS sequences\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Moreover, distinguishing between the morphotypes was only possible when both morphological and genetic data were considered together\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These findings suggest that \u003cem\u003eP. daedalea\u003c/em\u003e retains species cohesion despite showing morphological variability. The genetic basis of such intraspecific skeletal variation remains largely unexplored.\u003c/p\u003e \u003cp\u003eHere, we investigated the skeletal variation of \u003cem\u003ePlatygyra daedalea\u003c/em\u003e in the Red Sea, a region that harbors 3.8% of the world\u0026rsquo;s coral reefs and presents steep environmental gradients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. We used micro-computed tomography (micro-CT), a high-resolution imaging technique, to quantify skeletal features beyond the limits of traditional methods\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Additionally, we applied ezRAD sequencing, a cost-effective reduced-representation genomic approach targeting restriction enzyme sites\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which has been successfully used in coral population studies\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. By integrating these datasets, we examined the genetic basis of skeletal traits, assessed trait correlations, and explored how environmental gradients shape intraspecific skeletal diversity.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eSample Collection\u003c/p\u003e \u003cp\u003eA total of 90 fragments of \u003cem\u003ePlatygyra daedalea\u003c/em\u003e were collected from five Red Sea locations as follows: Duba (N\u0026thinsp;=\u0026thinsp;21), Al Wajh (N\u0026thinsp;=\u0026thinsp;16), Yanbu (N\u0026thinsp;=\u0026thinsp;18), Al Lith (N\u0026thinsp;=\u0026thinsp;20), and Southern Farasan Banks (N\u0026thinsp;=\u0026thinsp;15), see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The sampling sites spanned a range of habitats, including reef slope, reef crest, and seagrass environments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Tissue samples were preserved for genetic analysis, and skeletal fragments were bleached using a 20% chlorine solution to remove tissue and dried for morphological examination.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoordinates and habitat types (reef or seagrass) of sample collection sites from five locations along the Red Sea.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHabitat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatitude (\u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLongitude (\u0026deg;E)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.29778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.64389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.27417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.63722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.30166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.63361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeagrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLagoon (onshore)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.14583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.73528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWall, Crest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.64139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.47806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWall, Crest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.50194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.61833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.30028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.94972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeagrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLagoon (offshore)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.36444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.90972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.79694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.95528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.76806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.95667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.65167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.03361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWall, Crest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.77083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.88972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWall, Crest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.74639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.90583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.76111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.95806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSouthern Farasan Banks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWall, Crest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.27389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.73028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReef 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLagoon (offshore)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.25083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.73028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSample Preparation for X-Ray CT Scanning\u003c/p\u003e \u003cp\u003eSkeletal morphological phenotyping was conducted utilizing high-resolution X-ray micro-computed tomography (micro-CT). A subset of 82 \u003cem\u003eP. daedalea\u003c/em\u003e specimens was phenotyped for skeletal traits: Duba (N\u0026thinsp;=\u0026thinsp;14), Al Wajh (N\u0026thinsp;=\u0026thinsp;15), Yanbu (N\u0026thinsp;=\u0026thinsp;18), Al Lith (N\u0026thinsp;=\u0026thinsp;20), and Southern Farasan Banks (N\u0026thinsp;=\u0026thinsp;15). Eight of the ninety collected fragments were unavailable for micro-CT scanning. The bare skeletons were fixed in packaging foam and then inserted into plastic measuring cylinders\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e (Figure S1A). Micro-CT scanning was performed using a TESCAN CoreTOM\u0026trade; CT scanner at 150 kV and 60 W. The images were acquired at a voxel size of 60 \u0026micro;m x 60 \u0026micro;m x 60 \u0026micro;m.\u003c/p\u003e \u003cp\u003eMicro-CT Image Analysis\u003c/p\u003e \u003cp\u003eAll image analysis was performed with the software \u003cem\u003eAvizo 3D\u003c/em\u003e (Thermo Fisher Scientific Inc., Berlin; v2021.2). The TIFF image stack for each scanned cylinder was cropped into individual files for each coral fragment. These cropped fragments were filtered using \u003cem\u003eNon-local Means\u003c/em\u003e and \u003cem\u003eUnsharp Masking\u003c/em\u003e (Avizo module names are italicized in this section). A total of 8 skeletal traits were recorded, as described by Miller\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e: valley width (VW), valley depth (VD), columella width (CW), theca thickness (TT), and septal thickness (ST). In addition, we measured interseptal distance (SS) as the distance between two adjacent septa across an interseptal cavity, measured from the center of the structure, septa teeth count, and porosity ratio.\u003c/p\u003e \u003cp\u003eThe macroscopic skeletal traits (CW, TT, ST, and SS) were measured from 2D cross-sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), while topological traits (VD and VW) were measured using both 3D reconstructions and cross-sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Septa teeth were counted along one side of a wall in 3D reconstruction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Each trait was measured in ten technical replicates and averaged per individual. All measurements, except for the septa teeth count, were made using the Avizo ruler tool. To enhance visibility during analysis, the colormap settings of the \u003cem\u003eOrtho-slice\u003c/em\u003e and \u003cem\u003eVolume-Rendering\u003c/em\u003e tools were adjusted (Figure S1B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePorosity ratio was calculated by segmenting each image, using \u003cem\u003eAuto-Thresholding\u003c/em\u003e, into two regions: skeletal area (high-threshold) and void area (low-threshold). The skeletal segment was further processed using the \u003cem\u003eFill Holes\u003c/em\u003e tool to fill internal skeletal voids. To isolate internal voids, the background was removed from the void segment using the \u003cem\u003eBorder Kill\u003c/em\u003e tool. The porosity ratio was then calculated with the \u003cem\u003eVolume Fraction\u003c/em\u003e tool by dividing the volume of the void segment (set as the input image) by the volume of the filled skeletal segment (set as the input image mask), providing the ratio of void to skeleton (Figure S2). Boring organisms, including Pyrgomatidae and Dendropoma, were found in the skeletal specimens. Segmentation methods excluded the voids created by these organisms and thus do not affect the calculation of the porosity ratio, as illustrated in Figure S3.\u003c/p\u003e \u003cp\u003eStatistical Analysis of Phenotypic Data\u003c/p\u003e \u003cp\u003eThe variance of each trait was compared between the five collection locations in \u003cem\u003eRStudio\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e (v2023.06.0). The assumptions for ANOVA, including normality and equal variance, were tested using the Shapiro-Wilk test and the Bartlett test, respectively. If the null hypotheses were not rejected (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), then parametric tests of ANOVA with Tukey's \u003cem\u003epost hoc\u003c/em\u003e test were applied. If the assumptions were rejected, non-parametric Kruskal-Wallis tests with Dunn\u0026rsquo;s \u003cem\u003epost hoc\u003c/em\u003e test were used for the comparison. To evaluate trait correlations, we calculated either Pearson\u0026rsquo;s or Spearman\u0026rsquo;s correlation coefficients, depending on the normality of each trait (Shapiro-Wilk test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eDNA Extraction and ezRAD Library Preparation\u003c/p\u003e \u003cp\u003eGenomic DNA was extracted using a salting out method, commonly known as \u0026ldquo;Wayne's Method\u0026rdquo;\u003csup\u003e35\u003c/sup\u003e. DNA concentration was measured using the DNA Broad Range Qubit kit, and the DNA quality was assessed using 0.8% agarose gel electrophoresis.\u003c/p\u003e \u003cp\u003eFor the preparation of ezRAD DNA libraries, 100ng of DNA was used following the method of Toonen et al.\u003csup\u003e28\u003c/sup\u003e. The DNA was digested with MboI and Sau3AI enzymes for 6 hours at 37˚C. After digestion, DNA libraries were prepared using the NEB Ultra II DNA library preparation kit for Illumina. Library size selection was then performed using a 2% agarose gel, where a band of DNA was cut from the gel at 300-500bp. The DNA was recovered from the agarose gel using the QIAGEN MinElute Gel extraction kit following the manufacturer's guidelines. The libraries were then assessed using the Bioanalyzer High sensitivity chip and Qubit. Finally, the libraries were pooled in equimolar ratios and sequenced on the Illumina HiSeq 4000 using the 2 X 150bp chemistry.\u003c/p\u003e \u003cp\u003eGenetic Variant Calling\u003c/p\u003e \u003cp\u003eThe primary genetic dataset consisted of 78 individuals: Duba (N\u0026thinsp;=\u0026thinsp;14), Al Wajh (N\u0026thinsp;=\u0026thinsp;16), Yanbu (N\u0026thinsp;=\u0026thinsp;15), Al Lith (N\u0026thinsp;=\u0026thinsp;19), and Southern Farasan Banks (N\u0026thinsp;=\u0026thinsp;14). Genetic data for twelve fragments were unavailable, likely due to data loss, DNA extraction failure, or unsuccessful library preparation. A reduced genetic dataset was prepared for the association analysis, including only samples with both genetic and phenotypic data. This reduced dataset comprised 69 individuals: Duba (N\u0026thinsp;=\u0026thinsp;7), Al Wajh (N\u0026thinsp;=\u0026thinsp;14), Yanbu (N\u0026thinsp;=\u0026thinsp;15), Al Lith (N\u0026thinsp;=\u0026thinsp;19), and Southern Farasan Banks (N\u0026thinsp;=\u0026thinsp;14).\u003c/p\u003e \u003cp\u003eGenetic variants were obtained first by trimming library adapters and removing low-quality reads from raw reads sequencing data using \u003cem\u003eFastp\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (v0.23.2). The quality of reads was assessed throughout the workflow using \u003cem\u003eFASTQC\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e (v0.12.0). Clean reads were then mapped to the \u003cem\u003eP. daedalea\u003c/em\u003e reference genome\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e v1.0 with \u003cem\u003eBWA\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e (v0.7.17). Then, \u003cem\u003eSamtools\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e (v1.16.1) \u003cem\u003efixmate\u003c/em\u003e was used to pair complementary reads, and with \u003cem\u003eSamtools markup\u003c/em\u003e, duplicate reads were removed. \u003cem\u003eBcftools\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e (v1.16) \u003cem\u003empileup\u003c/em\u003e was used to call genetic variants into a raw variant file (VCF). Variants were filtered with \u003cem\u003eVCFtools\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e (v0.1.16) using the following parameters: a minor allele frequency of 0.1, a minimum quality of 20, a minimum depth of 10, a maximum depth of 335, and a missing data threshold of 80%. To prune linkage loci, \u003cem\u003ePLINK\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e (v2.0) was used with the \u003cem\u003eindep-pairwise\u003c/em\u003e function, setting the window size to 50 kb, the window step size to 10, and an r\u003csup\u003e2\u003c/sup\u003e threshold of 0.1.\u003c/p\u003e \u003cp\u003ePopulation Genetics\u003c/p\u003e \u003cp\u003eTo estimate population structure, first, a principal component analysis (PCA) was performed using \u003cem\u003ePLINK pca\u003c/em\u003e. The resulting eigenvalues and eigenvectors were used to plot the PCA in \u003cem\u003eRStudio\u003c/em\u003e with the \u003cem\u003etidyverse\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA maximum likelihood phylogenetic tree was estimated from the genetic variants (in PHYLIP format obtained by vcf2phylip\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (v2.9)) using IQTREE\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (v2.2.6) \u003cem\u003emodel finder\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e to identify the most suitable substitution model with 1000 bootstrap replications. An additional tree was constructed for the reduced genetic dataset. The resulting trees were visualized in \u003cem\u003eiTOL\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e (v6).\u003c/p\u003e \u003cp\u003eAdmixture analysis was performed using the sparse non-negative matrix factorization (sNMF) model in the R package \u003cem\u003eLEA\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003esnmf\u003c/em\u003e function was used to compute cross-entropy values for ancestral populations (K\u003csub\u003esNMF\u003c/sub\u003e) ranging from 1 to 10, with ten replicates for each value. The cross-entropy values were visualized, and the K\u003csub\u003esNMF\u003c/sub\u003e corresponding to the lowest cross-entropy was selected as the optimal value of K. The admixture coefficients of the runs with the minimum cross-entropy values were visualized as bar charts and admixture pie charts.\u003c/p\u003e \u003cp\u003e \u003cem\u003eF\u003c/em\u003e \u003csub\u003eST\u003c/sub\u003e population differentiation statistics were calculated using \u003cem\u003eVCFtools\u003c/em\u003e, which uses the Weir and Cockerham approach\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e was computed between the three genetic clusters identified from the phylogenetic tree and admixture coefficients of the reduced dataset (Figure S4). SNPs with an \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e \u0026ge; 0.3 were considered significant as being under selection. Additionally, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values were averaged for each pairwise test between the clusters to assess population structure and divergence.\u003c/p\u003e \u003cp\u003eGenome-Wide Association Study\u003c/p\u003e \u003cp\u003eThe genome-wide association analysis was conducted using the Latent Factor Mixed Model (\u003cem\u003eLFMM2\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e function from the LEA package in \u003cem\u003eRStudio\u003c/em\u003e, with the number of latent factors (K) set to three based on the estimated population structure. The significance of the results was computed using \u003cem\u003elfmm2.test\u003c/em\u003e function for each trait, and the results were visualized in Manhattan plots. To determine the significance of the associations, we applied a Bonferroni-corrected threshold of p\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eSNP annotation was performed using \u003cem\u003eSnpEff\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e (v5.2c) to predict the potential functional effects of associated variants. A custom \u003cem\u003eSnpEff\u003c/em\u003e database for \u003cem\u003eP. daedalea\u003c/em\u003e was built using the reference genome, gene annotations files in the format of \u0026lsquo;GFF\u0026rsquo;, and protein sequence\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The VCF file was annotated using this custom database.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eMorphological Variation Along the Red Sea\u003c/p\u003e \u003cp\u003eTo assess the effect of environmental factors on phenotypic variation, we analyzed trait variance for eight skeletal traits across five collection sites along the Red Sea: Duba, Al Wajh, Yanbu, Al Lith, and Southern Farasan Banks (SFB). A one-way ANOVA was applied to traits that met the assumptions of normality and equal variance: theca, thickness (TT), valley width (VW), and septa teeth (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For traits that violated these assumptions, i.e., porosity ratio, interseptal distance (SS), columella width (CW), and septal thickness (ST), we used Kruskal-Wallis tests (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne-way ANOVA showed no significant differences for the traits TT, VW, and septa teeth (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Kruskal-Wallis tests showed significant differences for three traits: porosity ratio (χ2(4)\u0026thinsp;=\u0026thinsp;18.8, p\u0026thinsp;=\u0026thinsp;0.00087); CW (χ2(4)\u0026thinsp;=\u0026thinsp;15.8, p\u0026thinsp;=\u0026thinsp;0.0033); and ST (χ2(4)\u0026thinsp;=\u0026thinsp;15.9, p\u0026thinsp;=\u0026thinsp;0.0032). Dunn's post hoc pairwise comparisons indicated significant differences in porosity between Duba and Al Wajh (p\u0026thinsp;=\u0026thinsp;0.011) as well as between Duba and Yanbu (p\u0026thinsp;=\u0026thinsp;0.00373) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). CW showed significant differences between Duba and Al Lith (p\u0026thinsp;=\u0026thinsp;0.00204) and Duba and Yanbu (p\u0026thinsp;=\u0026thinsp;0.0307) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Lastly, ST was significantly different between SFB and Al Lith (p\u0026thinsp;=\u0026thinsp;0.0011) and SFB and Yanbu (p\u0026thinsp;=\u0026thinsp;0.048) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). No significant differences were found for SS and VD (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way ANOVA of variation in morphological traits (TT, VW, septa teeth) among samples from the five locations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.0688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSepta teeth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKruskal\u0026ndash;Wallis H test results for variation in morphological traits (SS, ST, porosity ratio, VD, CW) among samples from the five locations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e0.00319\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePorosity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e0.000867\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.0932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e0.00327\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Wajh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYanbu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl Lith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelation Between Morphological Traits\u003c/p\u003e \u003cp\u003ePearson correlation was applied to VW, CW, TT, VD, and septa teeth, as the Shapiro-Wilk normality test indicated normal distributions with a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and an average of W\u0026thinsp;=\u0026thinsp;0.98. For ST (W\u0026thinsp;=\u0026thinsp;0.90), SS (W\u0026thinsp;=\u0026thinsp;0.90), and porosity ratio (W\u0026thinsp;=\u0026thinsp;0.67), where normality was rejected (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Spearman\u0026rsquo;s correlation was used (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelation coefficients were computed to assess the linear relationship among the eight traits. Very high positive correlations were found between VD and VW (r(80)\u0026thinsp;=\u0026thinsp;0.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as VD and TT (r(80)\u0026thinsp;=\u0026thinsp;0.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, VW was highly positively correlated with TT (r(80)\u0026thinsp;=\u0026thinsp;0.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Several traits exhibited moderate positive correlations, all statistically significant: porosity with VD (r(80)\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and VW (r(80)\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); CW with VW (r(80)\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and VD (r(80)\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); VD with SS (r(80)\u0026thinsp;=\u0026thinsp;0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson and Spearman* correlation pairwise traits results, with the method chosen based on the Shapiro\u0026ndash;Wilk normality test results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSS*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePorosity ratio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSepta teeth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02(80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02(80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04(80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eST*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.13(80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSS*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.43(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.07(80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePorosity ratio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.05(80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01(80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepta teeth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePopulation Structure of Red Sea \u003cem\u003ePlatygyra daedalea\u003c/em\u003e\u003c/p\u003e \u003cp\u003eUsing an ezRAD sequencing approach, we identified 20,290 high-quality single-nucleotide polymorphisms (SNPs) from 78 individuals. The reduced secondary data set, consisting of 69 individuals, had a total of 20,140 SNPs. Population structure was assessed using admixture and principal component analyses (PCA) with the primary dataset of 20,290 (N\u0026thinsp;=\u0026thinsp;78). For the admixture analysis, we evaluated clustering for up to ten groups (K\u0026thinsp;=\u0026thinsp;1\u0026ndash;10) using LEA sNMF cross-entropy, which indicated that the optimal K\u003csub\u003esNMF\u003c/sub\u003e value is either K\u0026thinsp;=\u0026thinsp;2 or K\u0026thinsp;=\u0026thinsp;3. Admixture coefficients at K\u003csub\u003esNMF\u003c/sub\u003e = 2, 3, and 4 were visualized alongside a phylogenetic tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The samples did not separate based on collection location, and most individuals appeared admixed. However, at K\u003csub\u003esNMF\u003c/sub\u003e = 3 and K\u003csub\u003esNMF\u003c/sub\u003e = 4, a stable cluster (yellow) emerged, primarily composed of individuals from Duba and SFB. This cluster was supported by a 94% bootstrap value, suggesting an ancestral relationship among these individuals. When the clustering coefficients were plotted as pie charts for each location sample, Duba and SFB showed a greater proportion of the yellow cluster in comparison to other locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003ePCA further revealed that individuals from Duba and SFB exhibited greater genetic variation than individuals from other locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In PC1 vs. PC2 and PC2 vs. PC3, a subset of individuals from Duba and SFB separated along PC2. Additionally, PC2 versus PC3 showed a separation of a subset of individuals from Al Lith from the larger cluster. These PCA results are consistent with the admixture analysis and phylogenetic tree, supporting K\u003csub\u003esNMF\u003c/sub\u003e = 3 as the most likely population structure.\u003c/p\u003e \u003cp\u003eTo assess whether the genetic clusters belong to a single species, we calculated pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, which measures allele frequency differences. The pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e​ values were low: \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.027 between populations 1 and 2, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.018 between populations 1 and 3, and \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.017 between populations 3 and 2. These values indicate minimal genetic differentiation, suggesting that despite some population structure, these groups likely belong to a single species with ongoing gene flow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePhenotype to Genotype Association\u003c/p\u003e \u003cp\u003eThe association between genetic variation and morphological variation in \u003cem\u003eP. daedalea\u003c/em\u003e was investigated for eight traits, using a Bonferroni-corrected threshold of p\u0026thinsp;=\u0026thinsp;2.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e. Significant associations were identified for three traits: 35 SNPs were associated with SS, 32 SNPs with ST, and 27 SNPs with porosity ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the 35 SNPs associated with SS, a total of 45 functional annotations were assigned: 91% had a modifying effect, 55.5% were intergenic, and 15.5% were located upstream of genes. The 32 SNPs associated with ST had 45 annotations, 89% had a modifying effect, 55.5% were intergenic, and 20% were downstream of genes. The 27 SNPs associated with porosity ratio had a total of 38 annotations, of which 89% had a modifying effect, 45% were intergenic, and 18% were upstream of genes. The SNPs associated with phenotypic traits determined by LFMM2 were compared to \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e outliers, and no overlaps were found. Genes located near the associated SNPs had various functions, including roles in processes such as the cell cycle, regulation of cell shape, cilium assembly, and transport. Details of the SNPs associated with porosity ratio, SS, and ST, along with their corresponding gene annotations, are provided in Tables S1, S2, and S3, respectively. However, the exact causal SNPs remain to be identified through further sequencing and analysis, and the precise functions of the associated genes in corals are yet to be determined.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSlight Effect of Environmental Gradients on \u003cem\u003ePlatygyra daedalea\u003c/em\u003e Morphology\u003c/p\u003e \u003cp\u003eHere, we analyzed the skeletal variation of \u003cem\u003ePlatygyra daedalea\u003c/em\u003e across the pronounced environmental gradients of the Red Sea, although pinpointing exact causal factors remains challenging due to the lack of microenvironmental data. Nevertheless, leveraging natural environmental gradients offers a powerful approach, as it mirrors the varied conditions typically explored in laboratory experiments. Several environmental gradients, including alkalinity\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, salinity, and annual maximum temperature, occur along the Red Sea, shaped largely by its limited freshwater input and regional climate\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe most significant results were observed for porosity, with Duba corals exhibiting significantly higher variation in their porosity ratio compared to nearby locations of Al Wajh and Yanbu. Under lower pH conditions, corals can become more porous\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Interestingly, we found that the northernmost corals accounted for most of the study\u0026rsquo;s highly porous corals, contrasting with the Red Sea alkalinity gradient, where northern regions are generally more alkaline than southern regions\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Corals from other locations followed a more predictable pattern consistent with the alkalinity gradient\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, except for a decrease in porosity variation in SFB corals. Columella width displayed a declining trend across the study locations, aside from SFB corals, with a decrease in variation, likely influenced by their proximity to a renewing water source\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Septal thickness variation was stable over four locations, with an increase in thickness for SFB corals.\u003c/p\u003e \u003cp\u003eEnvironmental and depth gradients at Davies Reef in the central Great Barrier Reef had no significant association with \u003cem\u003eP. daedalea\u003c/em\u003e VW, CW, ST, TT, and VD variation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This further confirms that environmental differences are not the primary driver of most \u003cem\u003eP. daedalea\u003c/em\u003e morphological variation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, porosity was not investigated at different depths for this species. The Mediterranean \u003cem\u003eBalanophyllia europaea\u003c/em\u003e showed a slight decrease in porosity with depth, measured at 1, 11, and 21 meters\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, species-specific sensitivities to environmental conditions and their effects on skeletal morphology were demonstrated in an ex-situ light spectra study\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In this experiment, \u003cem\u003eAcropora formosa\u003c/em\u003e and \u003cem\u003eStylophora pistillata\u003c/em\u003e each exhibited distinct morphological changes under three light spectra on a macro and microstructure level\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These included variations in theca thickness, septal length, distance among corallites, and their diameter, while porosity remained unchanged\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRed Sea \u003cem\u003ePlatygyra daedalea\u003c/em\u003e Populations are Genetically Connected with Slight Structure\u003c/p\u003e \u003cp\u003eGiven the skeletal trait differences observed across collection sites, we next examined whether underlying genetic structure could account for the skeletal patterns along the Red Sea. For that, we analyzed the genetic population structure of Red Sea \u003cem\u003eP. daedalea\u003c/em\u003e using admixture and principal component analyses. Our results indicated widespread genetic admixture among individuals, suggesting a lack of reproductive barriers within this species across the Red Sea. Confirming that the species harbors genetic variation and connectivity throughout the Red Sea. In contrast, \u003cem\u003eP. daedalea\u003c/em\u003e populations in the Arabian Gulf are highly structured and exhibit limited connectivity\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This pronounced structure is likely driven by thermal isolation formed by temperature gradients in the Arabian Gulf, which increase towards the center of the water body\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our findings align with observed genetic connectivity patterns in Red Sea \u003cem\u003ePocillopora favosa\u003c/em\u003e (previously \u003cem\u003eP. verrucosa\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e), which are influenced by the species reproductive mode\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Like \u003cem\u003eP. favosa\u003c/em\u003e, \u003cem\u003eP. daedalea\u003c/em\u003e employs broadcast spawning, releasing eggs and sperm into the water column for external fertilization\u003csup\u003e\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, individuals from Duba and SFB exhibit higher genetic diversity, potentially influenced by specific environmental gradients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e or genetic gradients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e specific to \u003cem\u003eP. daedalea.\u003c/em\u003e The extreme environmental conditions in Duba and SFB do not seem to reduce the species\u0026rsquo; genetic diversity through selective pressures. Conversely, the northern region exhibits increased salinity, alkalinity, and lower temperatures\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The latter has established the region as a coral refugia, contributing to significantly lower bleaching rates compared to corals in the southern Red Sea\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. These favorable conditions may enhance coral growth and resilience to climate stress\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, potentially contributing to Duba maintaining a higher genetic diversity as observed. Moreover, gene flow may have increased the gene pool diversity of SFB, given its proximity to Indian Ocean water inputs. This genetic pattern bears some resemblance to that observed in a genetic clustering of Red Sea \u003cem\u003eStylophora pistillata\u003c/em\u003e, which was only present in the southern and northern regions of the Red Sea\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. However, for \u003cem\u003eP. daedalea\u003c/em\u003e, the genetic cluster is also present among individuals in the central region, albeit with lower abundance.\u003c/p\u003e \u003cp\u003eGenetic Influence on Coral Morphological Traits\u003c/p\u003e \u003cp\u003eAssociation analysis of eight quantitative traits revealed strong genetic associations for porosity ratio, septal thickness, and interseptal distance. While previous studies using microsatellite and internal transcribed spacer (ITS) sequences have differentiated two \u003cem\u003eP. daedalea\u003c/em\u003e morphotypes, these molecular biomarkers required integration with morphological data to effectively distinguish morphotypes\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Notably, Mangubhai et al\u003csup\u003e19\u003c/sup\u003e excluded 88 out of 133 \u003cem\u003eP. daedalea\u003c/em\u003e samples due to intermediate morphologies. In contrast, our study identified traits distributed along a continuum of phenotypes. The quantitative nature of our measurements suggests that, if genetically associated, these traits may be polygenic\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. We refrained from categorizing corals into morphotypes or genetic groups to enable independent association testing of each trait. By leveraging ezRAD sequencing, which provides high-density single-nucleotide polymorphism (SNP) data\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, we provide a deeper understanding of the relationship between genetic and morphological variation. However, the specific associated genetic variants remain to be identified through higher-resolution sequencing and analyses.\u003c/p\u003e \u003cp\u003ePorosity: The Most Complex of All Skeletal Traits\u003c/p\u003e \u003cp\u003eWe identified 27 genetic associations with porosity ratio. The most significantly associated SNP was located 1.41kb downstream of a gene encoding a THAP domain-containing protein 2; the protein localizes in the nucleolus and has DNA and metal ion binding activities\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. The THAP domain has sequence-specific DNA binding activity\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, and it may be involved in various cellular processes, including \u0026ldquo;proliferation, apoptosis, cell cycle, chromosome segregation, chromatin modification, and transcriptional regulation\u0026rdquo;\u003csup\u003e62\u003c/sup\u003e. The THAP protein family, which has the THAP domain in common\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, remains largely understudied, except for THAP1, which plays a fundamental role in cell proliferation and cell-cycle pathways in human endothelial cells\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA synonymous variant was identified in a gene encoding a probable DNA polymerase, along with an intronic variant in the gene encoding a translation initiation factor eIF-2B subunit gamma. Additionally, three intergenic variants were found near genes encoding a WW domain-containing oxidoreductase and Tetratricopeptide repeat protein 28, located 7 kb upstream of the latter. WW domain-containing oxidoreductase is essential for normal bone development\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e and Tetratricopeptide repeat protein 28 functions during mitosis\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCoral skeletons become more porous due to a decrease in skeletal density\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, a response strongly associated with lower pH levels\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Liew et al.\u003csup\u003e16\u003c/sup\u003e showed that pH induced porosity increase was associated with DNA methylation changes affecting pathways regulating body size and cell cycle\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Morphological plasticity has been linked with this induced porosity\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, observed to result from initial changes within the coral polyps\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. We found porosity to have significant moderate positive correlations with valley depth and width, which in turn showed highly significant correlations with other traits.\u003c/p\u003e \u003cp\u003eAlthough porosity has been shown to arise environmentally\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, we also found underlying genetic associations. While the exact functions of the associated proteins remain to be investigated in corals, they are known to have sequence-specific DNA-binding abilities\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, functions in the cell cycle\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, as a DNA polymerase, and as a translation initiation factor. We hypothesize that the associated SNPs may influence the regulation of the gene expression, potentially causing subtle changes in cell cycle pathways that may affect cell and polyp sizes, ultimately leading to increased porosity.\u003c/p\u003e \u003cp\u003eLinear growth is crucial for corals to compete for light availability required for the coral-\u003cem\u003eSymbiodiniaceae\u003c/em\u003e symbiosis\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Porosity and its associated phenotypes allow maintaining linear extension even with reduced calcification rates under pH stress\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, \u003cem\u003eP. daedalea\u003c/em\u003e is not a columnar coral, yet we think, based on the transversal sections (Figure S5), that some void patterns may support linear extension. As \u003cem\u003eP. daedalea\u003c/em\u003e colonies are mostly massive and hemispherical\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, we hypothesize that wider and deeper valleys may develop as the wall's linear extension increases. However, compared to the high positive correlations between VD, VW, and others, porosity only showed moderate correlations with each VD and VW. This suggests that, in addition to porosity, other factors also play significant roles in VW and VD phenotypic plasticity.\u003c/p\u003e \u003cp\u003eIncreased coral porosity can exert both negative\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and positive\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e impacts on the corals\u0026rsquo; fitness. Even healthy corals are susceptible to damage and breakage during severe cyclonic events, which are anticipated to increase with climate change\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Ocean acidification-driven increases in porosity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, may lead to more fragile colonies\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. On the other hand, high porosity acquired by corals can be regarded as a phenotypic adaptation to sea-level rise\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Through this phenotypic plasticity, corals may have the potential to overcome the effects of climate change driven ocean acidification. \u003cem\u003ePlatygyra daedalea\u003c/em\u003e is a slow-growing coral with a lower porosity ratio than branching corals\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. If the genetic associations identified in this study are true positives, this may suggest that \u003cem\u003ePlatygyra daedalea\u003c/em\u003e is exhibiting phenotypic plasticity, potentially adopting characteristics of faster-growing coral structures.\u003c/p\u003e \u003cp\u003eThe Surprisingly Significant Variation of Septa\u003c/p\u003e \u003cp\u003eWe found two septal traits, thickness and distance, to have significant genetic associations. Septa determine the arrangement of mesenteries, which are internal folds of tissue that divide the coelenteron, the body cavity of the coral polyp\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. As Veron et al.\u003csup\u003e70\u003c/sup\u003e describe, \u0026ldquo;mesenteries give the gastrodermis a large surface area for digestion, photosynthesis, and respiration, and also contain the reproductive organs\u0026rdquo;. They also form mesenterial filaments that can extend outside the coelenteron for feeding, defense, and wound cleaning\u003csup\u003e\u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince porosity was unexpectedly linked to cell and polyp phenotypes\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, we hypothesize that variation of septal traits may similarly be driven by mesenterial phenotypes. To our knowledge, no studies have explored these aspects; however, the genetic variants identified in this study suggest a potential link that requires further investigation.\u003c/p\u003e \u003cp\u003eInterseptal distance\u003c/p\u003e \u003cp\u003eInterseptal distance exhibited a strong genetic association with 35 SNPs. Five SNPs were located within genes with distinct functional roles, while two SNPs were positioned near genes with functional annotations in cnidarians. To our knowledge, no prior studies have investigated interseptal distance as a distinct trait in corals. Nevertheless, we hypothesize that certain variants may directly influence variations in biomineralization, while others may impact coral polyp structures.\u003c/p\u003e \u003cp\u003eSkeletal organic matrix protein 5 (SOMP5) is a known component of the coral skeletal proteome, although its specific function remains unclear within the organic matrix\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. A variant downstream of SOMP5 may contribute to biomineralization variation, influencing the phenotypic variation of \u003cem\u003eP. daedalea\u003c/em\u003e interseptal distance. Tetratricopeptide repeat protein 21B is part of the intraflagellar transport (IFT) complex, which is essential for the assembly of cilia and flagella\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. This protein may affect primary cilia found in the coral ectoderm\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Primary cilia are short, non-motile cilia with functions in detecting signals from the surrounding microenvironments\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Their presence in the aboral calicoblastic ectoderm suggests that they may serve as sensors of the extracellular calcifying medium environment\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, we speculate that variants within genes encoding E3 ubiquitin-protein ligase (TRIM71) and deoxynucleotide monophosphate kinase (dNMP) may influence variation in reproduction, given their roles in development\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e and nucleotide synthesis\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e, respectively. Both TRIM71 and dNMP kinase genes exhibited synonymous SNPs associated with interseptal distance. While synonymous mutations are generally considered neutral, they can reduce an organism\u0026rsquo;s fitness by disrupting binding to regulatory sequences, splicing, and mRNA structure\u003csup\u003e\u003cspan additionalcitationids=\"CR81 CR82 CR83\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. These changes may affect codon bias, gene expression levels, protein structure, translation efficiency, and RNA stability\u003csup\u003e\u003cspan additionalcitationids=\"CR81 CR82 CR83\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. If synonymous mutations identified in this study have resulted in any phenotypic changes, we presume they would only lead to phenotypic variation rather than gene function changes.\u003c/p\u003e \u003cp\u003eWe found intronic variants in the genes coding for Myoferlin\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e and NDRG1\u003csup\u003e86\u003c/sup\u003e to be associated with interseptal distance phenotypes. Myoferlin and NDRG1 are known to play roles in muscle cells and in regulating microtubule dynamics\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, respectively. Specifically, Myoferlin aids in rapid repair and growth by operating in cell division, cell migration, regulation of signaling, and organization of actin dynamics, which promote cytoskeletal rearrangements\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. As previously mentioned, alterations in cell size and shape have been shown to affect skeletal phenotypes, as does the increase in polyp cell size with larger skeletal calyces\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Changes driven by the identified variants may affect cells within the interseptal cavity, potentially altering the distance between adjacent septa. These changes might involve the coral mesenteries, packed between septa, which can elongate their ends to form mesenterial filaments\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, driven by cells with muscular activity\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Additionally, mesenterial filaments are characterized by ciliation and the presence of stinging cells (nematocysts)\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. We also identified intronic variants in Tetratricopeptide repeat protein 21B and DELTA-alicitoxin-Pse2b, which have been reported to function in cilia assembly and toxin production, respectively\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. While intronic mutations are often considered functionally neutral, they have been found, for example, to influence gene expression and translation efficiency, suggesting that the intronic variants we identified could have similar effects\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs outlined earlier, based on the functions of the associated genes and the structural roles of septa in relation to mesenteries\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, we believe the variation in septa morphology may be correlated with mesenteries and their filaments. While there is limited knowledge on the cellular and molecular biology of these filaments, we do know they act as defense mechanisms and may confer survival advantages\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. As to retain light availability, larger, slow-growing coral colonies like \u003cem\u003eP. daedalea\u003c/em\u003e have been observed to use their mesenterial filaments to compete with faster-growing species that try to overshadow them\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeptal thickness\u003c/p\u003e \u003cp\u003eWe found septal thickness (ST) to be genetically associated with 32 SNPs. While ST is a widely examined trait\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e, its connection to mesenteries remains vague, unlike interseptal distance (SS). Our analysis revealed only a weak correlation between ST and SS, suggesting that ST may not be directly influenced by mesenteries. We speculate that as septa become thicker, it may drive mesenteries further apart and provide additional gastrovascular area. However, this will depend largely on additional polyp characteristics such as size and septa number.\u003c/p\u003e \u003cp\u003eAmong the identified SNPs, we detected an intronic variant in the gene encoding the BBSome complex member BBS7 protein, with functions in cilium biogenesis \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e,\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. In humans, mutations in BBS proteins have been linked to ciliary dysfunction, leading to various features, including skeletal abnormalities\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Given that primary cilia may contribute to coral calcification\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, this variant could influence skeletal variation in \u003cem\u003eP. daedalea\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eSeveral SNPs were intergenic, including those near genes encoding PAX-interacting protein 1 and Histone lysine acetyltransferase CREBBP, both of which have functions in transcription regulation\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Specifically, CREBBP is found to acetylate histones and non-histone proteins\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Additionally, RRAGC, which encodes Ras-related GTP-binding protein C, functions as a hydrolase with a crucial role in regulating the mTORC1 signaling cascade\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, which regulates protein synthesis and cell growth\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. Moreover, Tetratricopeptide repeat protein 28 functions during mitosis\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. As these proteins play roles in transcriptional regulation, cell growth, and division, we speculate that variations in their genes may potentially affect polyp characteristics and size, much like SNPs associated with porosity ratio. Especially since we found two genes expressing a Tetratricopeptide repeat protein 28 to have genetic associations with both porosity ratio and ST. However, porosity ratio and ST have no correlations based on our results. Thus, the specific relationship between polyp traits and septal thickness remains unclear and will require further investigation, particularly with polyp traits included.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study suggests that morphological variation in \u003cem\u003ePlatygyra daedalea\u003c/em\u003e has a genetic basis, indicating that genetics can contribute to species-level morphological variation in corals. While we identified genetic associations for three traits, it is possible that detecting associations for other traits will require a larger sample size or that these traits are more strongly shaped by environmental or epigenetic factors. Notably, the traits with genetic associations were not large structural features, but the latter did correlate with porosity ratio, reinforcing previous findings\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Annotation of significant SNPs suggests that these variations may impact coral calcification or polyp and mesenterial characteristics, ultimately affecting the skeleton. However, validating these associations will require whole-genome sequencing of individuals to precisely pinpoint potential causal variants through linkage disequilibrium analyses.\u003c/p\u003e \u003cp\u003eThe observed morphological variation of \u003cem\u003eP. daedalea\u003c/em\u003e across nearby reefs, combined with our findings and previous studies\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, suggest that environmental factors are not the only influences shaping these phenotypic variations. The lack of significant associations for five traits with environmental gradients indicates that environmental influences vary across traits. Further studies are necessary, particularly through controlled ex situ experiments testing the effects of multiple environmental factors on \u003cem\u003eP. daedalea.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eOverall, our study underscores the intricate relationship between genetic factors and environmental gradients in shaping coral morphology. The observed phenotype variations suggest that corals develop a range of phenotypes that may enhance their resilience to diverse environmental conditions. These findings provide a clearer understanding of skeletal variation in \u003cem\u003eP. daedalea\u003c/em\u003e and serve as a starting point for future research on genotype-phenotype-environment associations of coral morphology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eC.M. and M.A. designed the research. S.S.-R. and M.M.-B. collected the samples. C.M. and S.S.-R. performed laboratory work. V.C. scanned corals via Micro-CT. R.S. and T.T. advised on proper image analysis for porosity ratio. S.A. and C.M. did image analysis. S.A. performed bioinformatics analyses and wrote the paper with significant input from C.M. and M.A. All co-authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank Prof. Francesca Benzoni for her significant revision and suggestions regarding skeletal features. This research was supported by a King Abdullah University of Science and Technology Competitive Research Grant URF/1/4697-01-01 to Aranda, M.\u003c/p\u003e\u003ch3\u003eData Accessibility\u003c/h3\u003e\n\u003cp\u003eRaw sequencing data of ezRAD have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1321291. Micro-CT data, skeletal measurements, and coding script have been deposited in the Dryad repository (Dataset DOI: https://doi.org/10.5061/dryad.9s4mw6mvv).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoegh-Guldberg O, Poloczanska ES, Skirving W, Dove S (2017) Coral Reef Ecosystems under Climate Change and Ocean Acidification. Front Mar Sci 4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReguero BG, Beck MW, Agostini VN, Kramer P, Hancock B (2018) Coral reefs for coastal protection: A new methodological approach and engineering case study in Grenada. J Environ Manage 210:146\u0026ndash;161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson HR, Kuempel CD, Altieri AH (2016) The resilience of reef invertebrate biodiversity to coral mortality. 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Nat Rev Neurol 12:379\u0026ndash;392\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"e24bec14-ad35-4ebb-8075-1072f3656b85","identifier":"10.13039/501100004052","name":"King Abdullah University of Science and Technology","awardNumber":"URF/1/4697-01-01 ","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"King Abdullah University of Science and Technology","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":"Corals, Morphological variation, Platygyra daedalea, Red Sea, Population genetics, Genome-wide association study","lastPublishedDoi":"10.21203/rs.3.rs-9092160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9092160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnvironmental factors have long been recognized as the primary drivers of intraspecific morphological variation in corals, as demonstrated in numerous species. However, coral calcification is a process that depends on both environmental and biological factors. Understanding the extent to which genetics contributes to morphological variation in corals remains lacking, particularly in corals like \u003cem\u003ePlatygyra daedalea\u003c/em\u003e, a species with complex morphological variation that has been found to be neither induced environmentally nor driven by genetic divergence. To address this gap, we conducted a genome-wide association study using single-nucleotide polymorphism and phenotype data of eight skeletal traits, obtained through restriction enzyme site-associated DNA sequencing and micro-computed tomography, respectively. Here, we demonstrate that genetics contributes to the variation of specific \u003cem\u003ePlatygyra daedalea\u003c/em\u003e skeletal traits, particularly porosity ratio, interseptal distance, and septal thickness. Associated variants were located near genes involved in cell cycle regulation, ciliary function, cytoskeletal rearrangement, and skeletal protein formation. We also found some of these traits to correlate significantly with larger-scale morphological features such as valley width and valley depth, suggesting a potential influence of genetically shaped traits on broader skeletal structure.\u003c/p\u003e","manuscriptTitle":"Behind the Skeleton: Unraveling the Genetic Basis of Skeletal Variation in the Coral Platygyra daedalea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 06:10:52","doi":"10.21203/rs.3.rs-9092160/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":"4d9566e3-7322-4b98-9c86-68bf23bef060","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64303151,"name":"Population Biology"},{"id":64303152,"name":"Evolutionary Biology"},{"id":64303154,"name":"Molecular Genetics"}],"tags":[],"updatedAt":"2026-03-12T06:10:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 06:10:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9092160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9092160","identity":"rs-9092160","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}