Genome-wide SNP analysis: adaptation and population structure of Syagrus romanzoffiana (Cham.) Glassman (Arecaceae) in South America

Genome-wide SNP analysis: adaptation and population structure of Syagrus romanzoffiana (Cham.) 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Glassman (Arecaceae) in South America Kauanne Karolline Moreno Martins, Matheus Scaketti, Ana Flávia Francisconi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7208849/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 Palms play a crucial ecological and economic role, with Syagrus romanzoffiana being a prominent species in South America. Despite its widespread distribution and economic potential, genetic studies on this species remain limited. This study assessed the genetic diversity and population structure of S. romanzoffiana across distinct vegetation types in Brazil and Paraguay using single nucleotide polymorphisms (SNPs) derived from genotyping-by-sequencing (GBS). A total of 91 individuals from eight populations were analyzed, revealing significant genetic diversity and differentiation. Observed heterozygosity was generally high, with some populations exhibiting excess heterozygosity, indicating potential adaptive significance. Pairwise F ST and AMOVA analysis suggested that most genetic variation is maintained within populations, although moderate differentiation among populations was detected. Genetic structure analysis highlighted the influence of vegetation type on genetic differentiation, with restinga populations displaying unique genetic signatures. Despite geographical proximity, some populations exhibited greater genetic divergence due to local environmental adaptations. These findings underscore the importance of conserving S. romanzoffiana’s genetic diversity and highlight the role of vegetation type in shaping population structure. Understanding these patterns is essential for the sustainable management and conservation of this species, particularly in the face of habitat fragmentation and climate change. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Evolution Biological sciences/Genetics Biological sciences/Plant sciences Arecaceae1 Genomic diversity2 Population genomics3 Molecular markers4 Atlantic forest5 Adaptive loci6 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introdution Palm species (Arecaceae) stand out for its versatility and diversity, with more than 2,600 recognized species worldwide. Over centuries, palms have provided a wide range of services to humankind [1] ; [2] ; [3] . Many palms are considered ecological keystone species because large numbers of animals depend on their fruit and flower resources to survive [4] . Moreover, palms have been used for centuries by different communities around the world because their products have different biological properties that are frequently effective against numerous diseases [5] including chronic diseases, such as diabetes and Alzheimer’s disease [6] . In addition, palms are also used in other applications, such as the production of biodiesel and energy [7] . Syagrus romanzoffiana (Cham.) Glassman, from the Aracaceae family, commonly known in Brazil as jerivá, is an endemic palm distributed throughout South America [8] . S. romanzoffiana play a crucial role in the subsistence of many traditional and rural communities, providing a wide range of non-timber forest products. These include food and medicinal uses from fruits, cosmetics, fiber from leaves, building materials from leaves and stems, and handicraft from seeds [9 ];[ 10] . Furthermore, previous studies performed by Moreira et al., [11] , identified S. romanzoffiana as a promising resource for sustainable large-scale production of vegetable oil. The same authors also stated that the oil is a viable alternative to produce biodiesel. Species delimitation studies are key to understanding genetic diversity and population structure at both intra and interspecific levels. Such insights support informed decisions for resource management, sustainable use, and conservation planning [12] ; [13] . Analyzing the genetic diversity of S. romanzoffiana is crucial for guiding the selection of the most promising materials for cultivation, maximizing genetic gains, and facilitating the development of commercially viable cultivars, according to local adaptation. In this context, molecular markers have become essential tools in plant research for examining genetic diversity across ecological, phylogenetic, and evolutionary studies [14] . Furthermore, they have been extensively employed for direct management, conservation, and genetic breeding across various species [14] . Advances in next-generation sequencing (NGS) technologies have greatly enhanced the discovery of single nucleotide polymorphisms (SNPs). These markers are now among the most widely applied in molecular ecology because they are numerous, cover large portions of the genome, and can be used to assess both neutral and adaptive variation. NGS enables efficient, high-throughput SNP genotyping with low error rates, even in the absence of a reference genome [12] ; [15] ; [16] . SNP markers are diverse, spanning multiple scientific fields, particularly in the development of high-density panels for genomic selection, conservation genetics, and breeding programs [12] ; [17] ; [18] . The focus of this study is to evaluate and compare the genetic diversity and population structure of the palm Syagrus romanzoffiana across different regions in southern Brazil and Paraguay. Specifically, our objectives were to (i) sample individuals from contrasting vegetation types, (ii) assess genetic diversity and structure within and among these populations using SNP markers obtained through genotyping-by-sequencing (GBS), and (iii) investigate patterns of adaptive genetic variation in relation to environmental variables such as temperature and precipitation. We hypothesized that populations occurring in distinct vegetation types would exhibit differentiated genetic structures and that climatic variables would help explain signals of local adaptation. This is the first study to use SNP markers for S. romanzoffiana , highlighting the importance of conserving its genetic diversity and understanding its population structure to inform effective management and conservation strategies. Results Genetic diversity in Southern America After data filtering, a total of 24.859 SNP markers were retained for the S. romanzoffiana . The average percentage of missing data per individual was 6.11%, with a maximum of 14% (Supplementary Figure 1A). The average sequencing coverage per individual was 15.25x (Supplementary Figure 1B), and the mean coverage per locus, was 13.68x, with no coverage exceeding 100x or falling below 10x per locus (Supplementary Figure 2A). The SNP data also revealed a higher number of transitions compared to transversions (Supplementary Figure 2B). Observed heterozygosity ( H o ) and expected ( H s ) heterozygosity were high across populations, with H o exceeding H s in six sites. Lucena-Cecília was an exception, showing similar H o and H s values (0.169), while Travessa had a lower H o than H s . The highest H o values were in San Ramón (0.205) and Gravatá (0.174), with the highest H s also in San Ramón (0.203), followed by Lucena Cecilia (0.169). San Ramón and Lucena-Cecilia had the greatest number of alleles (A, Table 1), with San Ramón and Lucena-Cecilia showing the highest allelic richness ( Ar ) at 1.567 and 1.492, respectively, followed by Gravatá, which also had a notable number of private alleles ( Ap , Table 1). The highest fixation index ( f ) was computed from Travessa (0.014), Lucena-Cecilia (0.006), and Condomínio-Marítimo (0.004), while it was negative in Gravatá, Herval, and Pixirica, indicating an excess of heterozygotes (Table 1). Table 1. Population Genomic Diversity of Syagrus romanzoffiana based on 24,859 SNPs. H o = Observed heterozygosity, H s = Expected heterozygosity, A = Number of alleles, Ar = Allelic richness, Ap = Private alleles, f = Fixation index (bootstraps). Locations in Brazil; Borrussia – RS; CondMaritmo – RS, Herval – RS, Lucena Cecilia – RS, Pixirica – RS, Travessa – RS and Gravata – SC. Location in Paraguay: San Ramón – Concepción. Location H O H S A Ar Ap f Borrussia 0.144 0.143 35479 1.415 245 0.010 (0.0012,0.0218) CondMaritmo 0.140 0.138 36125 1.387 653 -0.004 (-0.0221,-0.0021) Gravata 0.174 0.167 38196 1.473 3291 -0.013 (-0.0354,-0.0175) Herval 0.162 0.159 36631 1.452 364 0.259 (0.3303,0.3469) LucenaCecilia 0.169 0.169 37555 1.492 329 -0.031 (-0.0527,-0.0343) Pixirica 0.138 0.132 34935 1.372 476 0.013 (0.0231,0.0524) San Ramón 0.205 0.203 40079 1.567 17426 -0.028 (-0.0534,-0.0341) Travessa 0.160 0.163 35518 1.467 98 0.449 (0.385,0.4055) Population genetic structure in Southern America Based on 24,859 SNPs, Wright's F statistics revealed significant differentiation among populations. The F statistics analysis of the eight sampled Syagrus romanzoffiana populations showed the following values: total inbreeding coefficient ( F IT = 0.139), within-population inbreeding coefficient ( F IS = 0.021), and genetic differentiation coefficient between populations ( F ST = 0.122). Pairwise F ST analysis indicated that the San Ramón location was the most divergent, with an F ST of 0.371 compared to the Pixirica. Despite being further apart geographically, Borrússia and Pixirica (~ 90 km apart), both in Atlantic Rain Forest, were more genetically similar, with an F ST of 0.079, than Borrússia and Condomínio Marítimo (~ 20 km apart, F ST of 0.081), the latter in an area of restinga (Figure 1). The AMOVA results indicated significant differentiation among populations (29.7% of the total variation), but the majority of genetic variation (70.3%) occurred within populations. These findings suggest a genetic structure in which most of genetic diversity is maintained within populations. This pattern may result from substantial gene flow within populations and moderate barriers to gene flow between them (Table 2). However, the Mantel test showed a very weak and non-significant correlation between genetic and geographic distances (r = -0.009, p = 0.141), indicating no clear pattern of isolation by distance. Table 2. Analysis of molecular variance (AMOVA) performed for eight populations of Syagrus romanzoffiana collected in the in Brazil and Paraguay. Source of Variation df MS Est. Var. PV (%) Among populations (Among Locations) 7 6617.983 483.8844 29.7% Within populations (Within Locations) 83 1145.607 1145.6073 70.3% Total 90 1571.237 1629.4917 100% p-value = 0.001 (Estimated based on 20000 permutations). Df = Degrees of Freedom; MS = Mean Squares. Est. Var. = Variance component. PV = Percentage of Variation. For the DAPC with populations, one principal component was retained, followed by one discriminant. The DAPC with predefined populations explained 19.22% of the variation. As observed with the genetic diversity estimates and the pairwise F ST , the San Ramón was the most distinct among the sampled S. romanzoffiana locations. The other sites showed some degree of overlap along the LD1 axis, reflecting more subtle genetic differentiation among them (Figure 2). Detection of adaptive loci and environmental associations LFMM identified 3,768 SNPs significantly associated with bio06 and bio19 (Supplementary Figures 5 and 6). A new sNMF analysis using only the candidate SNPs resulted in a population structure with K = 4 clusters (Figure 3), suggesting that environmental factors are influencing population differentiation beyond neutral processes. The RDA based on these candidates SNPs confirmed strong genotype–environment associations. The global model constrained by bio19 (precipitation of the coldest quarter) and bio6 (minimum temperature of the coldest month) was highly significant ( p < 0.001), with an adjusted R² of 20%. Both variables contributed significantly to the explained genetic variance, and the first two RDA axes were also significant ( p < 0.001), indicating that climatic factors are important drivers of adaptive genetic variation in S. romanzoffiana . The first two constrained axes explained 99.9% of the total variance (Figure 4). Individuals from San Ramón were positively associated with bio06, indicating adaptation to colder minimum temperatures, while Cond. Marítimo, Pixirica, Borrussia, and SC_Gravatá were strongly associated with bio19, suggesting adaptive responses to precipitation seasonality in the southern Atlantic Forest and restinga environments. Discussion The populations of S. romanzoffiana analyzed in this study revealed substantial genetic diversity. The high values of observed ( H o ) and expected ( H s ) heterozygosity found in various sites, with H o exceeding H s , except in Lucena Cecília and Travessa, indicates a potential excess of heterozygotes. This phenomenon can be interpreted as an adaptive response to local environmental conditions or as a result of a population structure that favors allogamy among genetically distinct individuals [19] . Populations with high heterozygosity are generally more resilient to environmental pressures, which may be crucial for survival in altered habitats [20] . Allelic richness ( Ar ) was also notable, especially when compared to other locations. This richness suggests a high genetic potential, particularly in the populations of San Ramón and Lucena Cecília, which are essential for the adaptation and evolution of populations [21] . Additionally, the high number of private alleles ( Ap ) found in Cond. Marítimo and Gravatá also highlights the genetic uniqueness of these populations, which may represent specific local adaptations or genetic variations not detected in other populations. In fact, these two sites are located in restinga vegetation domains, which are coastal vegetation formations with strong influence of maritime conditions, such as constant wind, sandy and saline soils, alternating with high water supply after heavy rains or even drought episodes [22] . Vetö et al., [23] found signatures of local adaptation for a Myrtaceae species, Eugenia uniflora , where populations located in the coastal plain or in restinga areas showed distinct alleles in comparison to inland populations. The positive fixation indexes ( f ) observed in Travessa, Lucena Cecília, and Cond. Marítimo suggest that these populations have a tendency towards homozygosity, which may be concerning in terms of the loss of genetic diversity. In fact, all of these sites are located in a severely fragmented landscape, that may have already led to some crosses among related individuals. In contrast, negative fixation indices in Borrusia, Gravatá, Herval, and Pixirica indicate Hardy-Weinberg equilibrium. These populations are located in larger and more conserved forest fragments than the previous ones. This may reflect a mating dynamic that is favorable for maintaining genetic diversity, essential for the adaptability of populations in constantly changing environments [24] ; [19] . Wright's F-statistics revealed moderate to high differentiation among S. romanzoffiana sites. The values of the genetic differentiation coefficient (F ST ) indicates that, despite gene flow, populations are sufficiently isolated to maintain genetic differences [25] . AMOVA results showed that most genetic variation occurs within populations, reinforce the idea that most of the genetic diversity is maintained at the intrapopulation level. This suggests that populations possess a genetic structure with a considerable variety of alleles [26] ; [27] . This pattern may result from barriers to gene flow among populations, combined with a complex population structure. The Mantel test, which did not find a significant correlation between genetic distance and geographic distance, suggests that the geographic separation of populations is not the sole determining factor for genetic differentiation. This may indicate that other elements, such as ecological barriers (as pointed out), local adaptations, or interactions with other species, are at play and should be considered in future investigations [28] ; [29] . In fact, when comparing the genetic differentiation among Pixirica, Borrússia and Cond. Marítimo, Pixirica and Borrússia were more genetically similar to each other despite their higher geographic distance (~90km), in comparison to Borrússia and Cond. Marítimo, that are ~20km apart only but are genetically more distinct than the previous pair. As previously stated, the vegetation type and local climate seem to be important drivers of the genetic differentiation in spite of the geographic proximity. Cond. Marítimo is located in a restinga fragment approximately 5km from the Atlantic Ocean. The area has sandy soils and frequent and strong winds that bring salt particles along with them in the air. Borrússia is located in an elevated area (approximately 200 to 300m in altitude) with soils originated from volcanic depositions, and a vegetation type that belongs with the range of the Atlantic rain forest. Therefore, local adaptations probably explain the genetic differentiation among the studied sites. The environmental association provided compelling evidence for local adaptation in Syagrus romanzoffiana . The identification of SNPs associated with climatic variables (particularly minimum temperature and precipitation) highlighted the selective pressures shaping genetic structure across distinct vegetation types and environmental conditions. Interestingly, the adaptive genetic structure emphasizes that selection may be acting independently of geographic distance or historical demography. This is consistent with findings in other tropical tree species, where environment-driven divergence occurs even at small spatial scales [30] . Populations from San Ramón showed strong signals of adaptation to colder winters, while southern Brazilian populations, particularly those in restinga and coastal forest habitats, aligned with wetter and more variable rainfall regimes. These results reinforce the importance of integrating adaptive genetic variation into conservation planning, especially for species distributed across ecotonal gradients or fragmented landscapes. Material and methods Plant material We sampled 91 leaf tissues from adult and reproductive individuals of Syagrus romanzoffiana from different locations representing three vegetation types in which they are found in South America: Atlantic Rain Forest, Atlantic Seasonal Forest, and restingas [31] ; [32] . The following sites were sampled: Linha Lucena-Cecília, Travessa and Herval, in the municipality of Venâncio Aires, in Rio Grande do Sul state, belonging with the domain of Atlantic Seasonal Forest. The site collected in San Ramón, Paraguay, also represents an area with seasonal forests. Pixirica, in the municipality of Morrinhos do Sul, and Borrússia, in Osório, both in Rio Grande do Sul state belong are part of Atlantic Rain Forest domain. Condomínio Marítimo represents a restinga fragment within the municipality of Tramandaí, also in Rio Grande do Sul. Another restinga site is Gravatá, located in the municipality of Laguna, in Santa Catarina (Figure 5). The samples were collected in Santa Catarina – Brazil, Rio Grande do Sul – Brazil and Concepción – Paraguay and placed in plastic bags containing silica-gel, duly identified, packed in a cardboard box, transported to the Genetics Department at Luiz de Queiroz College of Agriculture/University of São Paulo. Accordingly, this study received approval from Ethics Committee of University of Campinas and was carried out in accordance with the regulations of Brazilian Ministry of the Environment (MMA) and the National System for Genetic Heritage and Associated Traditional Knowledge (SisGen) under the number A3A9281. DNA extraction DNA extraction from S. romanzoffiana leaf tissues was performed according to the Doyle and Doyle [33] protocol with modifications for palms. Specifically, we applied a modified CTAB method; leaf samples were grounded and placed in a 1.5mL microtube containing 700 μL 2% CTAB plus 2 μL β-mercaptoethanol then incubated at 65°C for 40 min. The microtubes were mixed by inversion every 15 min. Next, 700μL chloroform-isoamylalcohol (24:1) were added and the tubes were gently mixed for 1 min, followed by centrifugation for 15 min at 13,000 rpm. Immediately after centrifuging, 500μL of the supernatant of each tube was transferred to a fresh and identified 1.5 mL tube with 600μL cold isopropanol (−20°C) plus 55μL ammonium acetate 7.5M 1:20. Samples were gently mixed by inversion and conditionate at −20°C for at least 2 hours, followed by centrifugation at 13,000 rpm for 15 min. After centrifugation, the supernatants were removed, and the DNA pellets were washed with 400μL of 70% ethanol and mixed by inversion for 30 seconds followed by centrifugation at 13,000 rpm for 5 min. The ethanol was discarded and 400μL 100% ethanol was added and mixed by inversion for 30 seconds plus centrifuged at 13,000 rpm for 5 min, the ethanol discarded, and the tubes were set to dry for at least 40 minutes. The DNA pellets were suspended in 30μL TE buffer (10 mM Tris–HCl pH 7.6, 1 mM EDTA pH 7.6) plus 2μL ribonuclease (RNAse 20 mg/mL), incubated at 37°C for 30 minutes, and stored at −20°C until quantification. The quantification and quality of the samples were evaluated on a 1% agarose gel. Based on the obtained reading, we standardized the DNA to a concentration of 30ng.μl-1. The final quantification was made using Qubit. Genotyping by sequencing (GBS) library preparation A genomic library was developed to obtain SNPs markers by the GBS double digestion technique [34] with modifications. We digested 7 μl of the genomic DNA [30ng.μl-1] from each sample with the enzymes Pst1 and Mse1. The first enzyme was used to fragment the DNA into small sites and the second one to fragment the DNA again, but now into several sites. After that, 25μM of specific adapters (barcodes) from Illumina technology were ligated to these digested fragments. Ligation was performed at the ends of the fragments with T4 DNA ligase. After adapter ligation, we purified the samples using QIAquick PCR Purification Kit (Qiagen). Library enrichment was carried out by PCR using primers matching the adapters. Additionally, eight replicates were performed, each PCR reaction contained 10 μL of the purified and amplified ligation product, 12.5 μL of Phusion1 High-Fidelity PCR Master Mix (NEB, New England Biolabs Inc.), and 2 μL of Illumina forward and reverse primers (10 μM), totaling 25 μL. The amplification conditions were: initial denaturation at 95°C for 30 s, followed by 16 cycles at 95°C for 10 s, 62°C for 20 s, 72°C for 30 s, and a final extension at 72°C for 5 min. Libraries were then purified with the QIAquick PCR Purification Kit (Qiagen). Fragment size distribution was assessed using the DNA 12,000 kit on the 2100 Bioanalyzer (Agilent Technologies). The libraries were quantified using quantitative PCR (qPCR) on a CFX 384 real-time thermocycler (Bio-Rad) to ensure accurate assessment of DNA concentration prior to sequencing. PCR sequencing was performed by Illumina NextSeq 1000/2000 platform at Luiz de Queiroz College of Agriculture – University of São Paulo – (ESALQ-USP). SNP identification SNP quality control was conducted through fastqc (https://github.com/s-andrews/FastQC). The DNA sequences (reads) obtained by Illumina sequencing were filtered to obtain SNP markers. Filtering was conducted with Stacks v. 2.62 pipeline [35] . We then used the process_radtags module to demultiplex the samples and remove the low-quality reads (reads with Phred scores lower than 10, or that contained Illumina adapters, uncalled bases - “Ns” - or without restriction sites). Next, the de novo assembly was conducted in Stacks, beginning with the ustacks module to cluster putatively homologous reads into loci. For each sample, we set a minimum stack depth (-m) of 4, allowed a maximum of 5 mismatches between stacks (-M), and a maximum of 5 mismatches between primary and secondary reads [15] . After that, using the cstacks module, we assembled a catalog of loci, setting the tolerance for sequence variation to a maximum of four mismatches between stacks originating from different individuals [15] . A conversion of the tvs2bam to gstacks was performed. Minor allele frequency ( p = t – 1 ), and minimum occurrence in 75% of individuals in each location/population ( r = 0.75). Filtering quality was checked out with vcftools (https://vcftools.github.io/man_latest.html). Population genetic diversity To characterize the genetic diversity of the collection sites, we estimated expected heterozygosity ( H s ), observed heterozygosity ( H o ), allelic richness ( Ar ) and fixation index ( F IS ), all calculated, through hierfstat package [36] . The number of alleles was estimated using the adegenet package [37] , and the number of private alleles was obtained through the poppr package [38] . All analysis were performed in R version 4.3.0 [39] . Population genetic structure Wright’s F statistics ( F ST , F IT , F IS ) and pairwise estimates of F ST were calculated using the hierfstat [36] . Pairwise estimates of F ST were computed using Nei’s distance [40] , and a heatmap was constructed with heatmaply package [41] . To further investigate the genetic structure and inbreeding levels of S. romanzoffiana , we conducted an Analysis of Molecular Variance (AMOVA) following Excoffier et al., [42] . The hierarchical structure among populations was defined based on a grouping file, and loci with more than 5% missing data were excluded. The analysis was performed using the poppr package [38] , and the significance of the AMOVA was tested through 20,000 permutations. A Mantel test [28] was conducted to assess the correlation between genetic and geographic distances among populations, to test for isolation by distance. Genetic distances were estimated using Nei’s method, and geographic distances were calculated from population coordinates. The analysis was performed with 10,000 permutations using the ade4 package [43] . All analysis were conducted in R version 4.3.0 [39] . Finally, we used discriminant analysis of principal components (DAPC) performed by the adegenet package [37] , to explore patterns of genetic differentiation and potential admixture among populations. The analysis was conducted using the collection sites as pre-defined genetic clusters. The optimal number of principal components to retain was determined using the α-score optimization procedure. We then generated both scatterplots and barplots to visualize population structure and individual assignment. Environmental association and detection of candidate adaptive loci To identify loci potentially under natural selection and associated with environmental gradients, we performed environmental association analysis using LatentFactor Mixed Models (LFMM). A total of 19 bioclimatic variables from the WorldClim v2 dataset were extracted, in addition to elevation data [44] . To reduce collinearity among predictors, a Principal Component Analysis (PCA) was conducted on the entire set of variables. The first two principal components explained approximately 85% of the total environmental variance (Supplementary Figure 3). Based on this, the two variables most correlated with these components were retained: bio06 (minimum temperature of the coldest month) and bio19 (precipitation of the coldest quarter), which are commonly identified as drivers of local adaptation in tropical plants [45] ; [46] ; [47] . LFMM analysis were implemented using the LEA package [48] . The number of latent factors (K = 5) was defined using the cross-entropy criterion from a preliminary population structure analysis withaparse non-negative matrix factorization – sNMF, which was conducted with 10 repetitions and 200,000 iterations (Supplementary Figure 4). LFMM was run for 10,000 iterations with a burn-in of 5,000 and replicated ten times per environmental variable. To control for false positives, p-values were adjusted using the genomic inflation factor (λ) and corrected with the Benjamini–Hochberg procedure for false discovery rate control ( q < 0.05) [49] ; [50] . The SNPs significantly associated with the selected bioclimatic variables were retained as candidate loci under selection. These loci were used for two downstream analysis: (i) a second sNMF analysis to investigate adaptive genetic structure, and (ii) a Redundancy Analysis (RDA) to test for genotype–environment associations. Prior to the RDA, missing genotypes (up to 20%) were imputed using the impute function from LEA based on individual population assignments [48] . The RDA was then performed using the vegan package [51] , and constrained axes were extracted to visualize the influence of climatic variables on population genetic composition. All analyses were again conducted in R version 4.3.0 [39] . Conservation Implications The results obtained have significant implications for the conservation of the genetic diversity of Syagrus romanzoffiana populations. Recognizing that most genetic diversity is maintained within populations underscores the importance of conservation strategies that prioritize the protection of local habitats and promote practices that favor gene flow. The observed differentiation also suggests that, although populations exhibit genetic variation, conservation should be adapted to the local context, considering the particularities of each population. Moreover, the vegetation type and the terrains that populations are distributed seem to input local adaptations worth being considered in endeavors toward conservation of the species. Conclusion In summary, the analysis of the genetic structure of Syagrus romanzoffiana populations reveals a complex mosaic of genetic diversity, with considerable differentiation among populations, but also great diversity within them. The vegetation type seems to be an important component for the genetic structure of the populations, in spite of their geographic distance in some cases. The interaction between environmental factors, evolutionary history, and gene flow dynamics remains a rich area for future exploration, especially regarding their implications for the conservation and sustainable management of species in changing environments. Continued research will be vital to better understand these interactions and to implement effective preservation strategies. Declarations Data availability The plant material used in this study was taxonomically identified by Dr. Enéas Ricardo Konzen, an expert in the taxonomy of palms of the genus Syagrus . No voucher specimens were deposited in a public herbarium; however, the corresponding genetic data generated from these samples are publicly available in NCBI’s GenBank repository (https://www.ncbi.nlm.nih.gov/) under the accession number Syagrus romanzoffiana (PRJNA1295587; http://www.ncbi.nlm.nih.gov/bioproject/1295587). Acknowledgements We thank Prof. Guilherme Dubal dos Santos Seger and Ms. Francisco Agustin Vergara for his assistance in guiding and harvesting leaf samples from populations on a few sites of this study. Authors' contributions K.K.M.M. First author, conceptualization, performed the experiment, analyzed the results, data curation, original draft writing, review & editing; M.S. Performed the experiment, analyzed the results; A.F.F. Analyzed the results, review & editing; I.A.S.de C. Developing of GBS plates & DNA quantification; C.B.G. Developing of GBS plates; T.D.L. Responsible for submitting the data in the NCBI’s GenBank repository and preparing the data availability information; E.R.K. Collected the samples, responsible for the taxonomic identification of the plant material, data curation, review & editing; M.I.Z. Funding, conceptualization, data curation, review & editing. Competing interests The authors declare that they have no conflict of interest. Funding We want to thank CAPES (Coordination for the Improvement of Higher Education Personnel) for the promotion granted to those involved. This study was supported by grants, including São Paulo Research Foundation (2011/50296-8), (2021/10319-0) and the National Council for Scientific and Technological Development (CNPq—313417/2023-7). The authors declare that they have no conflict of interest. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Additional information Supplementary Information The online version contains supplementary material available at: Correspondence and requests for materials should be addressed to K.K.M.M. and M.I.Z. References Cámara-Leret, R. et al. Fundamental species traits explain provisioning services of tropical American palms. Nat. Plants 3 , 16220 (2017). Eiserhardt, W. L., Svenning, J.-C., Kissling, W. D. & Balslev, H. Geographical ecology of the palms (Arecaceae): determinants of diversity and distributions across spatial scales. Ann. Bot. 108 , 1391–1416 (2011). Levis, C. et al. Persistent effects of pre-Columbian plant domestication on Amazonian forest composition. Science 355 , 925–931 (2017). Onstein, R. E. et al. Frugivory-related traits promote speciation of tropical palms. Nat. Ecol. Evol. 1 , 1903–1911 (2017). Sen, T. & Samanta, S. K. 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LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6 , 925–929 (2015). François, O., Martins, H., Caye, K. & Schoville, S. D. Controlling false discoveries in genome scans for selection. Mol. Ecol. 25 , 454–469 (2016). Ahrens, C. W., Byrne, M. & Rymer, P. D. Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species. Mol. Ecol. 28 , 2502–2516 (2019). Oksanen, J. et al. vegan: Community Ecology Package. (2024). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialSyagrus.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7208849","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":506003120,"identity":"a7f2f40c-4829-4ff2-86de-7cec29a5336f","order_by":0,"name":"Kauanne Karolline Moreno Martins","email":"","orcid":"","institution":"State University of Campinas – UNICAMP","correspondingAuthor":false,"prefix":"","firstName":"Kauanne","middleName":"Karolline Moreno","lastName":"Martins","suffix":""},{"id":506003121,"identity":"ca143e00-e2a8-42ff-8a3f-111c1250b048","order_by":1,"name":"Matheus Scaketti","email":"","orcid":"","institution":"University of São Paulo – ESALQ/USP","correspondingAuthor":false,"prefix":"","firstName":"Matheus","middleName":"","lastName":"Scaketti","suffix":""},{"id":506003123,"identity":"3f76f246-7c47-49d7-a8be-ee1cec99a037","order_by":2,"name":"Ana Flávia Francisconi","email":"","orcid":"","institution":"University of São Paulo – ESALQ/USP","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Flávia","lastName":"Francisconi","suffix":""},{"id":506003125,"identity":"401c6314-9144-4419-9603-5a7b3b7778e7","order_by":3,"name":"Igor Araújo Santos de Carvalho","email":"","orcid":"","institution":"University of São Paulo – ESALQ/USP","correspondingAuthor":false,"prefix":"","firstName":"Igor","middleName":"Araújo Santos","lastName":"de Carvalho","suffix":""},{"id":506003127,"identity":"bec5304f-fea6-4e39-9eab-95eeedb58256","order_by":4,"name":"Caroline Bertocco Garcia","email":"","orcid":"","institution":"University of São Paulo – ESALQ/USP","correspondingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"Bertocco","lastName":"Garcia","suffix":""},{"id":506003129,"identity":"a24368ad-f68a-4073-9013-25455a046b51","order_by":5,"name":"Thiago Deomar Ludwig","email":"","orcid":"","institution":"University of São Paulo – ESALQ/USP","correspondingAuthor":false,"prefix":"","firstName":"Thiago","middleName":"Deomar","lastName":"Ludwig","suffix":""},{"id":506003131,"identity":"a4d63cd5-a96e-413d-80e0-3e90c5c0e8df","order_by":6,"name":"Enéas Ricardo Konzen","email":"","orcid":"","institution":"Federal University of Rio Grande do Sul – UFRGS","correspondingAuthor":false,"prefix":"","firstName":"Enéas","middleName":"Ricardo","lastName":"Konzen","suffix":""},{"id":506003133,"identity":"af9f9601-b471-4101-b8cf-b998ff367682","order_by":7,"name":"Maria Imaculada Zucchi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACZjB5gIcfJmBAtBbJBqK1QMABBoMDxGrRbWd/JvGj5o6M8fmzBxi/VBxmMJc+gF+L2WGGNMmeY894zG7kJTDLnDnMYNmXQFDLMWnGhsNALTwGzJJtaQwGZwg4zOwwYxtYi3H/GaK1MLOBtRgw5BgwfmyzIUYLG7Nlz7HDPBJAvxxmOGPDY9lDSMv54w9v/Kg5bM/ff/bgwx8VEnLmPAS0IAEehsM8IJIEwMPA+IMU9aNgFIyCUTBiAADxsD6LwTEX/QAAAABJRU5ErkJggg==","orcid":"","institution":"APTA, UPDR-Piracicaba","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"Imaculada","lastName":"Zucchi","suffix":""}],"badges":[],"createdAt":"2025-07-24 22:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7208849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7208849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90265009,"identity":"c5e50604-9c11-4566-8cdd-b9c9c0455a5c","added_by":"auto","created_at":"2025-08-31 16:34:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160500,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of pairwise F\u003csub\u003eST\u003c/sub\u003e values using 91 individuals of \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e from different cities. Calculation based on Nei’s distance (1987), using 24,859 SNPs.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7208849/v1/626954ce484baf61937c43d8.png"},{"id":90265012,"identity":"b402f7ab-c647-44f6-b7f2-391ed2a3106a","added_by":"auto","created_at":"2025-08-31 16:34:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3932379,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Scatter Plot of Discriminant Analysis of Principal Components for 91 individuals from 8 \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e populations.\u003cstrong\u003e \u003c/strong\u003e(B) Comomplot with genetic groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7208849/v1/edfd2ec64d1aded89b92cc28.png"},{"id":90265013,"identity":"61e0a2ea-f48f-4f8f-b467-965339b7908a","added_by":"auto","created_at":"2025-08-31 16:34:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":227245,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic structure inferred by sNMF using only candidate adaptive SNPs identified by LFMM analysis (q \u0026lt; 0.05), with K = 4. Each bar represents an individual, and the colors indicate ancestry proportions associated with adaptive genetic clusters, suggesting patterns of local adaptation influenced by environmental factors.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7208849/v1/8f38ca5c9b2f2046b2d30ac7.png"},{"id":90265017,"identity":"086159a5-a487-4fee-ae11-52110c06f2ed","added_by":"auto","created_at":"2025-08-31 16:34:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":433677,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis (RDA) based on candidate SNPs for selection identified with LFMM. The arrows represent the vectors of the bioclimatic variables bio06 (minimum temperature in the coldest month) and bio19 (rainfall in the coldest quarter), which explain the genotypic variation between \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e populations. The colors represent the different sites sampled. The first two restricted axes explain more than 99% of the total adaptive variance.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7208849/v1/82129935847c3ecbfc8076ed.png"},{"id":90265020,"identity":"c78d2b6e-b92f-4adf-88bc-f50105223ba7","added_by":"auto","created_at":"2025-08-31 16:34:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":673236,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the sampled populations of \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e in Brazil and Paraguay. Each marked site corresponds to a sampled population, with dots representing the sampling sites. Populations in Brazil (Light Blue); Borrussia – RS (Dark Green); Cond. Maritimo – RS (Orange), Herval – RS (Pink), Lucena Cecilia – RS (Light Green), Pixirica – RS (Yellow), Travessa – RS (Gray), and Gravata – SC (Pale Blue). Population in Paraguay (Pale Green): San Ramón – Concepción (Light Brown). Light Pink represents South America and Bright Green, Atlantic Forest biome.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7208849/v1/de5ce81206cea0ada5385bcf.png"},{"id":91901467,"identity":"a28a4f81-4630-464d-829d-1b8d3deeb473","added_by":"auto","created_at":"2025-09-22 21:31:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6172062,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7208849/v1/b3b94251-c691-41bc-bbdb-d289f9028503.pdf"},{"id":90265352,"identity":"ffe9bf14-4962-4bdf-9016-62c65d66fb3b","added_by":"auto","created_at":"2025-08-31 16:42:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8004905,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialSyagrus.docx","url":"https://assets-eu.researchsquare.com/files/rs-7208849/v1/8fa03c0ccd7da1af3da0b758.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide SNP analysis: adaptation and population structure of Syagrus romanzoffiana (Cham.) Glassman (Arecaceae) in South America","fulltext":[{"header":"Introdution ","content":"\u003cp\u003ePalm species (Arecaceae) stand out for its versatility and diversity, with more than 2,600 recognized species worldwide. Over centuries, palms have provided a wide range of services to humankind \u003csup\u003e[1]\u003c/sup\u003e;\u003csup\u003e[2]\u003c/sup\u003e;\u003csup\u003e[3]\u003c/sup\u003e. Many palms are considered ecological keystone species because large numbers of animals depend on their fruit and flower resources to survive \u003csup\u003e[4]\u003c/sup\u003e. Moreover, palms have been used for centuries by different communities around the world because their products have different biological properties that are frequently effective against numerous diseases \u003csup\u003e[5]\u003c/sup\u003e including chronic diseases, such as diabetes and Alzheimer’s disease \u003csup\u003e[6]\u003c/sup\u003e. In addition, palms are also used in other applications, such as the production of biodiesel and energy \u003csup\u003e[7]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e (Cham.) Glassman, from the Aracaceae family, commonly known in Brazil as jerivá, is an endemic palm distributed throughout South America \u003csup\u003e[8]\u003c/sup\u003e. \u003cem\u003eS. romanzoffiana\u003c/em\u003e play a crucial role in the subsistence of many traditional and rural communities, providing a wide range of non-timber forest products. These include food and medicinal uses from fruits, cosmetics, fiber from leaves, building materials from leaves and stems, and handicraft from seeds \u003csup\u003e[9\u003c/sup\u003e\u003cstrong\u003e];[\u003c/strong\u003e\u003csup\u003e10]\u003c/sup\u003e. Furthermore, previous studies performed by Moreira et al., \u003csup\u003e[11]\u003c/sup\u003e,\u0026nbsp;identified \u003cem\u003eS. romanzoffiana\u003c/em\u003e as a promising resource for sustainable large-scale production of vegetable oil. The same authors also stated that the oil is a viable alternative to produce biodiesel.\u003c/p\u003e\n\u003cp\u003eSpecies delimitation studies are key to understanding genetic diversity and population structure at both intra and interspecific levels. Such insights support informed decisions for resource management, sustainable use, and conservation planning \u003csup\u003e[12]\u003c/sup\u003e;\u003csup\u003e[13]\u003c/sup\u003e. Analyzing the genetic diversity of \u003cem\u003eS. romanzoffiana\u003c/em\u003e is crucial for guiding the selection of the most promising materials for cultivation, maximizing genetic gains, and facilitating the development of commercially viable cultivars, according to local adaptation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this context, molecular markers have become essential tools in plant research for examining genetic diversity across ecological, phylogenetic, and evolutionary studies \u003csup\u003e[14]\u003c/sup\u003e. Furthermore, they have been extensively employed for direct management, conservation, and genetic breeding across various species \u003csup\u003e[14]\u003c/sup\u003e. Advances in next-generation sequencing (NGS) technologies have greatly enhanced the discovery of single nucleotide polymorphisms (SNPs). These markers are now among the most widely applied in molecular ecology because they are numerous, cover large portions of the genome, and can be used to assess both neutral and adaptive variation. NGS enables efficient, high-throughput SNP genotyping with low error rates, even in the absence of a reference genome \u003csup\u003e[12]\u003c/sup\u003e;\u003csup\u003e[15]\u003c/sup\u003e;\u003csup\u003e[16]\u003c/sup\u003e. SNP markers are diverse, spanning multiple scientific fields, particularly in the development of high-density panels for genomic selection, conservation genetics, and breeding programs \u003csup\u003e[12]\u003c/sup\u003e;\u003csup\u003e[17]\u003c/sup\u003e;\u003csup\u003e[18]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe focus of this study is to evaluate and compare the genetic diversity and population structure of the palm \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e across different regions in southern Brazil and Paraguay. Specifically, our objectives were to (i) sample individuals from contrasting vegetation types, (ii) assess genetic diversity and structure within and among these populations using SNP markers obtained through genotyping-by-sequencing (GBS), and (iii) investigate patterns of adaptive genetic variation in relation to environmental variables such as temperature and precipitation. We hypothesized that populations occurring in distinct vegetation types would exhibit differentiated genetic structures and that climatic variables would help explain signals of local adaptation. This is the first study to use SNP markers for \u003cem\u003eS. romanzoffiana\u003c/em\u003e, highlighting the importance of conserving its genetic diversity and understanding its population structure to inform effective management and conservation strategies.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003eGenetic diversity in Southern America\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter data filtering, a total of 24.859 SNP markers were retained for the \u003cem\u003eS. romanzoffiana\u003c/em\u003e. The average percentage of missing data per individual was 6.11%, with a maximum of 14% (Supplementary Figure 1A). The average sequencing coverage per individual was 15.25x (Supplementary Figure 1B), and the mean coverage per locus, was 13.68x, with no coverage exceeding 100x or falling below 10x per locus (Supplementary Figure 2A). The SNP data also revealed a higher number of transitions compared to transversions (Supplementary Figure 2B).\u003c/p\u003e\n\u003cp\u003eObserved heterozygosity (\u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e) and expected (\u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e) heterozygosity were high across populations, with \u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e exceeding \u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e in six sites. Lucena-Cec\u0026iacute;lia was an exception, showing similar \u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e and \u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e values (0.169), while Travessa had a lower \u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e than \u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e. The highest \u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e values were in San Ram\u0026oacute;n (0.205) and Gravat\u0026aacute; (0.174), with the highest \u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e also in San Ram\u0026oacute;n (0.203), followed by Lucena Cecilia (0.169). San Ram\u0026oacute;n and Lucena-Cecilia had the greatest number of alleles (A, Table 1), with San Ram\u0026oacute;n and Lucena-Cecilia showing the highest allelic richness (\u003cem\u003eAr\u003c/em\u003e) at 1.567 and 1.492, respectively, followed by Gravat\u0026aacute;, which also had a notable number of private alleles (\u003cem\u003eAp\u003c/em\u003e, Table 1). The highest fixation index (\u003cem\u003ef\u003c/em\u003e) was computed from Travessa (0.014), Lucena-Cecilia (0.006), and Condom\u0026iacute;nio-Mar\u0026iacute;timo (0.004), while it was negative in Gravat\u0026aacute;, Herval, and Pixirica, indicating an excess of heterozygotes (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003ePopulation Genomic Diversity of \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e based on 24,859 SNPs. \u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e = Observed heterozygosity, \u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = Expected heterozygosity, A = Number of alleles, \u003cem\u003eAr\u003c/em\u003e = Allelic richness, \u003cem\u003eAp\u003c/em\u003e = Private alleles, \u003cem\u003ef\u003c/em\u003e = Fixation index (bootstraps). Locations in Brazil; Borrussia \u0026ndash; RS; CondMaritmo \u0026ndash; RS, Herval \u0026ndash; RS, Lucena Cecilia \u0026ndash; RS, Pixirica \u0026ndash; RS, Travessa \u0026ndash; RS and Gravata \u0026ndash; SC. Location in Paraguay: San Ram\u0026oacute;n \u0026ndash; Concepci\u0026oacute;n.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"572\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003csub\u003eS\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAr\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAp\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ef\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eBorrussia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e35479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(0.0012,0.0218)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eCondMaritmo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e36125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(-0.0221,-0.0021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eGravata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e38196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(-0.0354,-0.0175)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eHerval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e36631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(0.3303,0.3469)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eLucenaCecilia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e37555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(-0.0527,-0.0343)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003ePixirica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e34935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(0.0231,0.0524)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSan Ram\u0026oacute;n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e40079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e17426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(-0.0534,-0.0341)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eTravessa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e35518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e(0.385,0.4055)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation genetic structure in Southern America\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on 24,859 SNPs, Wright\u0026apos;s \u003cem\u003eF\u003c/em\u003e statistics revealed significant differentiation among populations. The \u003cem\u003eF\u003c/em\u003e statistics analysis of the eight sampled \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e populations showed the following values: total inbreeding coefficient (\u003cem\u003eF\u003csub\u003eIT\u003c/sub\u003e\u003c/em\u003e = 0.139), within-population inbreeding coefficient (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e = 0.021), and genetic differentiation coefficient between populations (\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e = 0.122). Pairwise \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e analysis indicated that the San Ram\u0026oacute;n location was the most divergent, with an \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e of 0.371 compared to the Pixirica. Despite being further apart geographically, Borr\u0026uacute;ssia and Pixirica (~ 90 km apart), both in Atlantic Rain Forest, were more genetically similar, with an \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e of 0.079, than Borr\u0026uacute;ssia and Condom\u0026iacute;nio Mar\u0026iacute;timo (~ 20 km apart, \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e of 0.081), the latter in an area of restinga (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe AMOVA results indicated significant differentiation among populations (29.7% of the total variation), but the majority of genetic variation (70.3%) occurred within populations. These findings suggest a genetic structure in which most of genetic diversity is maintained within populations. This pattern may result from substantial gene flow within populations and moderate barriers to gene flow between them (Table 2). However, the Mantel test showed a very weak and non-significant correlation between genetic and geographic distances (r = -0.009, p = 0.141), indicating no clear pattern of isolation by distance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Analysis of molecular variance (AMOVA) performed for eight populations of \u003cem\u003eSyagrus romanzoffiana\u0026nbsp;\u003c/em\u003ecollected in the in Brazil and Paraguay.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eEst. Var.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ePV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eAmong populations (Among Locations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6617.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e483.8844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e29.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eWithin populations (Within Locations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1145.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1145.6073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e70.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1571.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1629.4917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ep-value = 0.001 (Estimated based on 20000 permutations). Df = Degrees of Freedom; MS = Mean Squares. Est. Var. = Variance component. PV = Percentage of Variation.\u003c/p\u003e\n\u003cp\u003eFor the DAPC with populations, one principal component was retained, followed by one discriminant. The DAPC with predefined populations explained 19.22% of the variation. As observed with the genetic diversity estimates and the pairwise \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e, the San Ram\u0026oacute;n was the most distinct among the sampled \u003cem\u003eS. romanzoffiana\u003c/em\u003e locations. The other sites showed some degree of overlap along the LD1 axis, reflecting more subtle genetic differentiation among them (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of adaptive loci and environmental associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLFMM identified 3,768 SNPs significantly associated with bio06 and bio19 (Supplementary Figures 5 and 6). A new sNMF analysis using only the candidate SNPs resulted in a population structure with K = 4 clusters (Figure 3), suggesting that environmental factors are influencing population differentiation beyond neutral processes.\u003c/p\u003e\n\u003cp\u003eThe RDA based on these candidates SNPs confirmed strong genotype\u0026ndash;environment associations.\u0026nbsp;The global model constrained by \u003cem\u003ebio19\u003c/em\u003e (precipitation of the coldest quarter) and \u003cem\u003ebio6\u003c/em\u003e (minimum temperature of the coldest month) was highly significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), with an adjusted R\u0026sup2; of 20%. Both variables contributed significantly to the explained genetic variance, and the first two RDA axes were also significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), indicating that climatic factors are important drivers of adaptive genetic variation in \u003cem\u003eS. romanzoffiana\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe first two constrained axes explained 99.9% of the total variance (Figure 4). Individuals from San Ram\u0026oacute;n were positively associated with bio06, indicating adaptation to colder minimum temperatures, while Cond. Mar\u0026iacute;timo, Pixirica, Borrussia, and SC_Gravat\u0026aacute; were strongly associated with bio19, suggesting adaptive responses to precipitation seasonality in the southern Atlantic Forest and restinga environments.\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eThe populations of \u003cem\u003eS. romanzoffiana\u003c/em\u003e analyzed in this study revealed substantial genetic diversity. The high values of observed (\u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e) and expected (\u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e) heterozygosity found in various sites, with \u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e exceeding \u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e, except in Lucena Cec\u0026iacute;lia and Travessa, indicates a potential excess of heterozygotes. This phenomenon can be interpreted as an adaptive response to local environmental conditions or as a result of a population structure that favors allogamy among genetically distinct individuals \u003csup\u003e[19]\u003c/sup\u003e. Populations with high heterozygosity are generally more resilient to environmental pressures, which may be crucial for survival in altered habitats \u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAllelic richness (\u003cem\u003eAr\u003c/em\u003e) was also notable, especially when compared to other locations. This richness suggests a high genetic potential, particularly in the populations of San Ram\u0026oacute;n and Lucena Cec\u0026iacute;lia, which are essential for the adaptation and evolution of populations \u003csup\u003e[21]\u003c/sup\u003e. Additionally, the high number of private alleles (\u003cem\u003eAp\u003c/em\u003e) found in Cond. Mar\u0026iacute;timo and Gravat\u0026aacute; also highlights the genetic uniqueness of these populations, which may represent specific local adaptations or genetic variations not detected in other populations. In fact, these two sites are located in restinga vegetation domains, which are coastal vegetation formations with strong influence of maritime conditions, such as constant wind, sandy and saline soils, alternating with high water supply after heavy rains or even drought episodes \u003csup\u003e[22]\u003c/sup\u003e. Vet\u0026ouml;\u0026nbsp;et al., \u003csup\u003e[23]\u003c/sup\u003e found signatures of local adaptation for a Myrtaceae species, \u003cem\u003eEugenia uniflora\u003c/em\u003e, where populations located in the coastal plain or in restinga areas showed distinct alleles in comparison to inland populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe positive fixation indexes (\u003cem\u003ef\u003c/em\u003e) observed in Travessa, Lucena Cec\u0026iacute;lia, and Cond. Mar\u0026iacute;timo suggest that these populations have a tendency towards homozygosity, which may be concerning in terms of the loss of genetic diversity. In fact, all of these sites are located in a severely fragmented landscape, that may have already led to some crosses among related individuals. In contrast, negative fixation indices in Borrusia, Gravat\u0026aacute;, Herval, and Pixirica indicate Hardy-Weinberg equilibrium. These populations are located in larger and more conserved forest fragments than the previous ones. This may reflect a mating dynamic that is favorable for maintaining genetic diversity, essential for the adaptability of populations in constantly changing environments \u003csup\u003e[24]\u003c/sup\u003e;\u003csup\u003e[19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWright\u0026apos;s F-statistics revealed moderate to high differentiation among \u003cem\u003eS. romanzoffiana\u003c/em\u003e sites. The values of the genetic differentiation coefficient (F\u003csub\u003eST\u003c/sub\u003e) indicates that, despite gene flow, populations are sufficiently isolated to maintain genetic differences \u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAMOVA results showed that most genetic variation occurs within populations, reinforce the idea that most of the genetic diversity is maintained at the intrapopulation level. This suggests that populations possess a genetic structure with a considerable variety of alleles \u003csup\u003e[26]\u003c/sup\u003e;\u003csup\u003e[27]\u003c/sup\u003e. This pattern may result from barriers to gene flow among populations, combined with a complex population structure.\u003c/p\u003e\n\u003cp\u003eThe Mantel test, which did not find a significant correlation between genetic distance and geographic distance, suggests that the geographic separation of populations is not the sole determining factor for genetic differentiation. This may indicate that other elements, such as ecological barriers (as pointed out), local adaptations, or interactions with other species, are at play and should be considered in future investigations \u003csup\u003e[28]\u003c/sup\u003e;\u003csup\u003e[29]\u003c/sup\u003e. In fact, when comparing the genetic differentiation among Pixirica, Borr\u0026uacute;ssia and Cond. Mar\u0026iacute;timo, Pixirica and Borr\u0026uacute;ssia were more genetically similar to each other despite their higher geographic distance (~90km), in comparison to Borr\u0026uacute;ssia and Cond. Mar\u0026iacute;timo, that are ~20km apart only but are genetically more distinct than the previous pair. As previously stated, the vegetation type and local climate seem to be important drivers of the genetic differentiation in spite of the geographic proximity. Cond. Mar\u0026iacute;timo is located in a restinga fragment approximately 5km from the Atlantic Ocean. The area has sandy soils and frequent and strong winds that bring salt particles along with them in the air. Borr\u0026uacute;ssia is located in an elevated area (approximately 200 to 300m in altitude) with soils originated from volcanic depositions, and a vegetation type that belongs with the range of the Atlantic rain forest. Therefore, local adaptations probably explain the genetic differentiation among the studied sites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe environmental association provided compelling evidence for local adaptation in \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e. The identification of SNPs associated with climatic variables (particularly minimum temperature and precipitation) highlighted the selective pressures shaping genetic structure across distinct vegetation types and environmental conditions. Interestingly, the adaptive genetic structure emphasizes that selection may be acting independently of geographic distance or historical demography. This is consistent with findings in other tropical tree species, where environment-driven divergence occurs even at small spatial scales \u003csup\u003e[30]\u003c/sup\u003e. Populations from San Ram\u0026oacute;n showed strong signals of adaptation to colder winters, while southern Brazilian populations, particularly those in restinga and coastal forest habitats, aligned with wetter and more variable rainfall regimes. These results reinforce the importance of integrating adaptive genetic variation into conservation planning, especially for species distributed across ecotonal gradients or fragmented landscapes.\u003c/p\u003e"},{"header":"Material and methods ","content":"\u003cp\u003e\u003cstrong\u003ePlant material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sampled 91 leaf tissues from adult and reproductive individuals of \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e from different locations representing three vegetation types in which they are found in South America: Atlantic Rain Forest, Atlantic Seasonal Forest, and restingas \u003csup\u003e[31]\u003c/sup\u003e;\u003csup\u003e[32]\u003c/sup\u003e. The following sites were sampled: Linha Lucena-Cec\u0026iacute;lia, Travessa and Herval, in the municipality of Ven\u0026acirc;ncio Aires, in Rio Grande do Sul state, belonging with the domain of Atlantic Seasonal Forest. The site collected in San Ram\u0026oacute;n, Paraguay, also represents an area with seasonal forests. Pixirica, in the municipality of Morrinhos do Sul, and Borr\u0026uacute;ssia, in Os\u0026oacute;rio, both in Rio Grande do Sul state belong are part of Atlantic Rain Forest domain. Condom\u0026iacute;nio Mar\u0026iacute;timo represents a restinga fragment within the municipality of Tramanda\u0026iacute;, also in Rio Grande do Sul. Another restinga site is Gravat\u0026aacute;, located in the municipality of Laguna, in Santa Catarina (Figure 5). The samples were collected in Santa Catarina \u0026ndash; Brazil, Rio Grande do Sul \u0026ndash; Brazil and Concepci\u0026oacute;n \u0026ndash; Paraguay and placed in plastic bags containing silica-gel, duly identified, packed in a cardboard box, transported to the Genetics Department at Luiz de Queiroz College of Agriculture/University of S\u0026atilde;o Paulo. Accordingly, this study received approval from Ethics Committee of University of Campinas and was carried out in accordance with the regulations of Brazilian Ministry of the Environment (MMA) and the National System for Genetic Heritage and Associated Traditional Knowledge (SisGen) under the number A3A9281.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA extraction from \u003cem\u003eS. romanzoffiana\u003c/em\u003e leaf tissues was performed according to the Doyle and Doyle \u003csup\u003e[33]\u003c/sup\u003e protocol with modifications for palms. Specifically, we applied a modified CTAB method; leaf samples were grounded and placed in a 1.5mL microtube containing 700 \u0026mu;L 2% CTAB plus 2 \u0026mu;L\u0026nbsp;\u0026beta;-mercaptoethanol then incubated at 65\u0026deg;C for 40 min. The microtubes were mixed by inversion every 15 min. Next, 700\u0026mu;L chloroform-isoamylalcohol (24:1) were added and the tubes were gently mixed for 1 min, followed by centrifugation for 15 min at 13,000 rpm. Immediately after centrifuging, 500\u0026mu;L of the supernatant of each tube was transferred to a fresh and identified 1.5 mL tube with 600\u0026mu;L cold isopropanol (\u0026minus;20\u0026deg;C) plus 55\u0026mu;L ammonium acetate 7.5M 1:20. Samples were gently mixed by inversion and conditionate at \u0026minus;20\u0026deg;C for at least 2 hours, followed by centrifugation at 13,000 rpm for 15 min. After centrifugation, the supernatants were removed, and the DNA pellets were washed with 400\u0026mu;L of 70% ethanol and mixed by inversion for 30 seconds followed by centrifugation at 13,000 rpm for 5 min. The ethanol was discarded and 400\u0026mu;L 100% ethanol was added and mixed by inversion for 30 seconds plus centrifuged at 13,000 rpm for 5 min, the ethanol discarded, and the tubes were set to dry for at least 40 minutes. The DNA pellets were suspended in 30\u0026mu;L TE buffer (10 mM Tris\u0026ndash;HCl pH 7.6, 1 mM EDTA pH 7.6) plus 2\u0026mu;L ribonuclease (RNAse 20 mg/mL), incubated at 37\u0026deg;C for 30 minutes, and stored at \u0026minus;20\u0026deg;C until quantification. The quantification and quality of the samples were evaluated on a 1% agarose gel. Based on the obtained reading, we standardized the DNA to a concentration of 30ng.\u0026mu;l-1. The final quantification was made using Qubit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenotyping by sequencing\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(GBS) library preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA genomic library was developed to obtain SNPs markers by the GBS double digestion technique \u003csup\u003e[34]\u003c/sup\u003e with modifications.\u0026nbsp;We digested 7\u0026nbsp;\u0026mu;l of the genomic DNA [30ng.\u0026mu;l-1] from each sample with the enzymes\u0026nbsp;Pst1 and Mse1. The first enzyme was used to fragment the DNA into small sites and the second one to fragment the DNA again, but now into several sites. After that,\u0026nbsp;25\u0026mu;M of specific adapters (barcodes) from Illumina technology were ligated to these digested fragments. Ligation was performed\u0026nbsp;at the ends of the fragments with T4 DNA ligase.\u0026nbsp;After adapter ligation, we purified the samples using QIAquick PCR Purification Kit (Qiagen).\u0026nbsp;Library enrichment was carried out by PCR using primers matching the adapters. Additionally,\u0026nbsp;eight replicates were performed, each PCR reaction contained 10\u0026nbsp;\u0026mu;L of the purified and amplified ligation product, 12.5\u0026nbsp;\u0026mu;L of Phusion1 High-Fidelity PCR Master Mix (NEB, New England Biolabs Inc.), and 2\u0026nbsp;\u0026mu;L of Illumina forward and reverse primers (10\u0026nbsp;\u0026mu;M), totaling 25\u0026nbsp;\u0026mu;L. The amplification conditions were: initial denaturation at 95\u0026deg;C for 30 s, followed by 16 cycles at 95\u0026deg;C for 10 s, 62\u0026deg;C for 20 s, 72\u0026deg;C for 30 s, and a final extension at 72\u0026deg;C for 5 min. Libraries were then purified with the QIAquick PCR Purification Kit (Qiagen). Fragment size distribution was assessed using the DNA 12,000 kit on the 2100 Bioanalyzer (Agilent Technologies). The libraries were quantified using quantitative PCR (qPCR) on a CFX 384 real-time thermocycler (Bio-Rad) to ensure accurate assessment of DNA concentration prior to sequencing.\u0026nbsp;PCR sequencing was performed by Illumina NextSeq 1000/2000 platform at Luiz de Queiroz College of Agriculture\u0026nbsp;\u0026ndash; University of S\u0026atilde;o Paulo \u0026ndash; (ESALQ-USP).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSNP quality control was conducted through \u003cem\u003efastqc\u003c/em\u003e (https://github.com/s-andrews/FastQC). The DNA sequences (reads) obtained by Illumina sequencing were filtered to obtain SNP markers. Filtering was conducted with Stacks v. 2.62 pipeline \u003csup\u003e[35]\u003c/sup\u003e. We then used the \u003cem\u003eprocess_radtags\u003c/em\u003e module to demultiplex the samples and remove the low-quality reads (reads with Phred scores lower than 10, or that contained Illumina adapters, uncalled bases - \u0026ldquo;Ns\u0026rdquo; - or without restriction sites). Next, the \u003cem\u003ede novo\u003c/em\u003e assembly was conducted in Stacks, beginning with the \u003cem\u003eustacks\u003c/em\u003e module to cluster putatively homologous reads into loci. For each sample, we set a minimum stack depth (-m) of 4, allowed a maximum of 5 mismatches between stacks (-M), and a maximum of 5 mismatches between primary and secondary reads\u0026nbsp;\u003csup\u003e[15]\u003c/sup\u003e. After that, using the \u003cem\u003ecstacks\u003c/em\u003e module, we assembled a catalog of loci, setting the tolerance for sequence variation to a maximum of four mismatches between stacks originating from different individuals \u003csup\u003e[15]\u003c/sup\u003e. A conversion of the \u003cem\u003etvs2bam\u003c/em\u003e to \u003cem\u003egstacks\u003c/em\u003e was performed. Minor allele frequency (\u003cem\u003ep = t \u0026ndash; 1\u003c/em\u003e), and minimum occurrence in 75% of individuals in each location/population (\u003cem\u003er\u003c/em\u003e = 0.75). Filtering quality was checked out with \u003cem\u003evcftools\u003c/em\u003e (https://vcftools.github.io/man_latest.html).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation genetic diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the genetic diversity of the collection sites, we estimated expected heterozygosity (\u003cem\u003eH\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e), observed heterozygosity (\u003cem\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/em\u003e), allelic richness (\u003cem\u003eAr\u003c/em\u003e) and fixation index (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e), all calculated, through \u003cem\u003ehierfstat\u0026nbsp;\u003c/em\u003epackage\u003csup\u003e[36]\u003c/sup\u003e. The number of alleles was estimated using the \u003cem\u003eadegenet\u003c/em\u003e package \u003csup\u003e[37]\u003c/sup\u003e, and the number of private alleles was obtained through the \u003cem\u003epoppr\u003c/em\u003e package \u003csup\u003e[38]\u003c/sup\u003e. All analysis were performed in R version 4.3.0 \u003csup\u003e[39]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation genetic structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWright\u0026rsquo;s F statistics (\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003eF\u003csub\u003eIT\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e) and pairwise estimates of \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e were calculated using the \u003cem\u003ehierfstat\u003c/em\u003e \u003csup\u003e[36]\u003c/sup\u003e. Pairwise estimates of \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e were computed using Nei\u0026rsquo;s distance \u003csup\u003e[40]\u003c/sup\u003e, and a heatmap was constructed with \u003cem\u003eheatmaply\u003c/em\u003e package \u003csup\u003e[41]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further investigate the genetic structure and inbreeding levels of \u003cem\u003eS. romanzoffiana\u003c/em\u003e, we conducted an Analysis of Molecular Variance (AMOVA) following Excoffier et al., \u003csup\u003e[42]\u003c/sup\u003e. The hierarchical structure among populations was defined based on a grouping file, and loci with more than 5% missing data were excluded. The analysis was performed using the\u0026nbsp;\u003ccode\u003e\u003cem\u003epoppr\u003c/em\u003e\u003c/code\u003e package \u003csup\u003e[38]\u003c/sup\u003e, and the significance of the AMOVA was tested through 20,000 permutations. A Mantel test \u003csup\u003e[28]\u003c/sup\u003e was conducted to assess the correlation between genetic and geographic distances among populations, to test for isolation by distance. Genetic distances were estimated using Nei\u0026rsquo;s method, and geographic distances were calculated from population coordinates. The analysis was performed with 10,000 permutations using the\u0026nbsp;\u003ccode\u003eade4\u003c/code\u003e package \u003csup\u003e[43]\u003c/sup\u003e.\u0026nbsp;All analysis were conducted in R version 4.3.0 \u003csup\u003e[39]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, we used discriminant analysis of principal components (DAPC) performed by the \u003cem\u003eadegenet\u003c/em\u003e package \u003csup\u003e[37]\u003c/sup\u003e, to explore patterns of genetic differentiation and potential admixture among populations. The analysis was conducted using the collection sites as pre-defined genetic clusters.\u0026nbsp;The optimal number of principal components to retain was determined using the \u0026alpha;-score optimization procedure. We then generated both scatterplots and barplots to visualize population structure and individual assignment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental association and detection of candidate adaptive loci\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify loci potentially under natural selection and associated with environmental gradients, we performed environmental association analysis using LatentFactor Mixed Models (LFMM). A total of 19 bioclimatic variables from the WorldClim v2 dataset were extracted, in addition to elevation data \u003csup\u003e[44]\u003c/sup\u003e. To reduce collinearity among predictors, a Principal Component Analysis (PCA) was conducted on the entire set of variables. The first two principal components explained approximately 85% of the total environmental variance (Supplementary Figure 3). Based on this, the two variables most correlated with these components were retained: \u003cem\u003ebio06\u003c/em\u003e (minimum temperature of the coldest month) and \u003cem\u003ebio19\u003c/em\u003e (precipitation of the coldest quarter), which are commonly identified as drivers of local adaptation in tropical plants \u003csup\u003e[45]\u003c/sup\u003e;\u003csup\u003e[46]\u003c/sup\u003e;\u003csup\u003e[47]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLFMM analysis were implemented using the \u003cem\u003eLEA\u003c/em\u003e package \u003csup\u003e[48]\u003c/sup\u003e. The number of latent factors (K = 5) was defined using the cross-entropy criterion from a preliminary population structure analysis withaparse non-negative matrix factorization \u0026ndash; sNMF, which was conducted with 10 repetitions and 200,000 iterations (Supplementary Figure 4). LFMM was run for 10,000 iterations with a burn-in of 5,000 and replicated ten times per environmental variable. To control for false positives, p-values were adjusted using the genomic inflation factor (\u0026lambda;) and corrected with the Benjamini\u0026ndash;Hochberg procedure for false discovery rate control (\u003cem\u003eq\u003c/em\u003e \u0026lt; 0.05) \u003csup\u003e[49]\u003c/sup\u003e;\u003csup\u003e[50]\u003c/sup\u003e. The SNPs significantly associated with the selected bioclimatic variables were retained as candidate loci under selection. These loci were used for two downstream analysis: \u003cem\u003e(i)\u003c/em\u003e a second sNMF analysis to investigate adaptive genetic structure, and \u003cem\u003e(ii)\u003c/em\u003e a Redundancy Analysis (RDA) to test for genotype\u0026ndash;environment associations. Prior to the RDA, missing genotypes (up to 20%) were imputed using the impute function from LEA based on individual population assignments \u003csup\u003e[48]\u003c/sup\u003e. The RDA was then performed using the \u003cem\u003evegan\u003c/em\u003e package \u003csup\u003e[51]\u003c/sup\u003e, and constrained axes were extracted to visualize the influence of climatic variables on population genetic composition.\u0026nbsp;All analyses were again conducted in R version 4.3.0 \u003csup\u003e[39]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConservation Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results obtained have significant implications for the conservation of the genetic diversity of \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e populations. Recognizing that most genetic diversity is maintained within populations underscores the importance of conservation strategies that prioritize the protection of local habitats and promote practices that favor gene flow. The observed differentiation also suggests that, although populations exhibit genetic variation, conservation should be adapted to the local context, considering the particularities of each population. Moreover, the vegetation type and the terrains that populations are distributed seem to input local adaptations worth being considered in endeavors toward conservation of the species.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the analysis of the genetic structure of \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e populations reveals a complex mosaic of genetic diversity, with considerable differentiation among populations, but also great diversity within them. The vegetation type seems to be an important component for the genetic structure of the populations, in spite of their geographic distance in some cases. The interaction between environmental factors, evolutionary history, and gene flow dynamics remains a rich area for future exploration, especially regarding their implications for the conservation and sustainable management of species in changing environments. Continued research will be vital to better understand these interactions and to implement effective preservation strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant material used in this study was taxonomically identified by Dr. En\u0026eacute;as Ricardo Konzen, an expert in the taxonomy of palms of the genus \u003cem\u003eSyagrus\u003c/em\u003e. No voucher specimens were deposited in a public herbarium; however, the corresponding genetic data generated from these samples are publicly available in NCBI\u0026rsquo;s GenBank repository (https://www.ncbi.nlm.nih.gov/) under the accession number \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e (PRJNA1295587; http://www.ncbi.nlm.nih.gov/bioproject/1295587).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Prof. Guilherme Dubal dos Santos Seger and Ms. Francisco Agustin Vergara for his assistance in guiding and harvesting leaf samples from populations on a few sites of this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.K.M.M. First author, conceptualization, performed the experiment, analyzed the results, data curation, original draft writing, review \u0026amp; editing; M.S. Performed the experiment, analyzed the results; A.F.F. Analyzed the results, review \u0026amp; editing; I.A.S.de C. Developing of GBS plates \u0026amp; DNA quantification; C.B.G. Developing of GBS plates; T.D.L. Responsible for submitting the data in the NCBI’s GenBank repository and preparing the data availability information; E.R.K. Collected the samples, responsible for the taxonomic identification of the plant material, data curation, review \u0026amp; editing; M.I.Z. Funding, conceptualization, data curation, review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to thank CAPES (Coordination for the Improvement of Higher Education Personnel) for the promotion granted to those involved. This study was supported by grants, including São Paulo Research Foundation (2011/50296-8), (2021/10319-0) and the National Council for Scientific and Technological Development (CNPq—313417/2023-7). The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e The online version contains supplementary material available at:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to K.K.M.M. and M.I.Z.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC\u0026aacute;mara-Leret, R. \u003cem\u003eet al.\u003c/em\u003e Fundamental species traits explain provisioning services of tropical American palms. \u003cem\u003eNat. 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(2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Arecaceae1, Genomic diversity2, Population genomics3, Molecular markers4, Atlantic forest5, Adaptive loci6","lastPublishedDoi":"10.21203/rs.3.rs-7208849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7208849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePalms play a crucial ecological and economic role, with \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e being a prominent species in South America. Despite its widespread distribution and economic potential, genetic studies on this species remain limited. This study assessed the genetic diversity and population structure of \u003cem\u003eS. romanzoffiana\u003c/em\u003e across distinct vegetation types in Brazil and Paraguay using single nucleotide polymorphisms (SNPs) derived from genotyping-by-sequencing (GBS). A total of 91 individuals from eight populations were analyzed, revealing significant genetic diversity and differentiation. Observed heterozygosity was generally high, with some populations exhibiting excess heterozygosity, indicating potential adaptive significance. Pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e and AMOVA analysis suggested that most genetic variation is maintained within populations, although moderate differentiation among populations was detected. Genetic structure analysis highlighted the influence of vegetation type on genetic differentiation, with restinga populations displaying unique genetic signatures. Despite geographical proximity, some populations exhibited greater genetic divergence due to local environmental adaptations. These findings underscore the importance of conserving \u003cem\u003eS. romanzoffiana’s\u003c/em\u003e genetic diversity and highlight the role of vegetation type in shaping population structure. Understanding these patterns is essential for the sustainable management and conservation of this species, particularly in the face of habitat fragmentation and climate change.\u003c/p\u003e","manuscriptTitle":"Genome-wide SNP analysis: adaptation and population structure of Syagrus romanzoffiana (Cham.) Glassman (Arecaceae) in South America","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-31 16:34:31","doi":"10.21203/rs.3.rs-7208849/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":"2eb6c4eb-3589-4ece-ada1-83b3901c945a","owner":[],"postedDate":"August 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53749858,"name":"Biological sciences/Ecology"},{"id":53749859,"name":"Earth and environmental sciences/Ecology"},{"id":53749860,"name":"Biological sciences/Evolution"},{"id":53749861,"name":"Biological sciences/Genetics"},{"id":53749862,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-09-22T21:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-31 16:34:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7208849","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7208849","identity":"rs-7208849","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}