Genome-wide SNPs reveal adaptation and population structure in Syagrus romanzoffiana (Arecaceae) from southern South America

Genome-wide SNPs reveal adaptation and population structure in Syagrus romanzoffiana (Arecaceae) from southern South America | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide SNPs reveal adaptation and population structure in Syagrus romanzoffiana (Arecaceae) from southern 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Kauanne Karolline Moreno Martins, Matheus Scaketti, Ana Flávia Francisconi, and 5 more This is a preprint; it has not been peer reviewed by a journal.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-7862000/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Palms play crucial ecological and economic roles, and Syagrus romanzoffiana is a widely distributed species in South America. Despite its ecological importance and economic potential, population-level genetic studies of this palm remain limited. Here, we assessed the genetic diversity and structure of S. romanzoffiana across distinct vegetation types in Brazil and Paraguay using 24,859 single nucleotide polymorphisms (SNPs) derived from genotyping-by-sequencing (GBS). We analyzed 91 individuals from eight populations, revealing high genetic diversity within populations and moderate differentiation among them. Observed heterozygosity was generally high, with some populations showing an excess of heterozygotes, consistent with outcrossing and gene flow. Pairwise F ST and AMOVA confirmed that most variation is maintained within populations, while genetic structure analyses highlighted the influence of vegetation type on differentiation, with restinga populations displaying unique signatures. Despite geographic proximity, some populations exhibited greater divergence due to local environmental conditions, supporting isolation by environment. Environmental association analyses (LFMM and RDA) further identified SNPs linked to temperature and precipitation, reinforcing the role of climate as a driver of adaptive differentiation. These findings emphasize the role of ecological factors in shaping genetic variation in S. romanzoffiana and illustrate how population genomics of widespread, non-threatened palms can provide critical baselines for conservation, restoration, and sustainable management under ongoing habitat fragmentation and climate change. Arecaceae1 Genetic diversity2 Population genomics3 Molecular markers4 Atlantic forest5 Local adaptation6 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Palm species (Arecaceae) stand out for their versatility and diversity, with more than 2,600 recognized species worldwide. Over centuries, palms have provided a wide range of services to humankind (Cámara-Leret et al. 2017 ; Eiserhardt et al. 2011 ; Levis et al. 2017 ). Many are ecological keystone species because large numbers of animals depend on their fruit and flower resources to survive (Onstein et al. 2017 ). Moreover, palms supply non-timber forest products such as food, medicine, fibers, oils, and building materials, supporting both traditional communities and modern industries (Sen and Samanta 2015; Gunjan et al. 2015; Colombo et al. 2018 ). Syagrus romanzoffiana (Cham.) Glassman, from the Arecaceae family, commonly known in Brazil as jerivá, is a widespread palm native to southern and eastern South America, occurring in Brazil, Paraguay, northern Argentina and Uruguay (Lorenzi et al. 2010; Falasca et al. 2012). It plays a crucial ecological and socioeconomic role: sustaining frugivorous birds and mammals, is widely cultivated in landscaping and urban forestry, and supporting rural communities through diverse products derived from fruits, leaves, stems, and seeds (Scariot 2015; Zona and Henderson 1989 ). Its distribution spans heterogeneous environments, from humid Atlantic Rain Forest to seasonal inland forests and coastal restinga systems, the latter characterized by sandy soils, high salinity and wind exposure (Lorenzi et al. 2010; Henderson et al. 1995 ; Scarano 2009 ). Such environmental variation provides a natural framework to test for local adaptation and isolation-by-environment (IBE) (Wang and Bradburd 2014 ). Understanding genetic diversity and population structure is fundamental for informed decisions on resource management, sustainable use, and conservation planning (Helyar et al. 2011 ; Brumfield et al. 2003 ). For S. romanzoffiana , analyzing genetic diversity is particularly relevant for guiding seed sourcing and restoration strategies in degraded Atlantic Forest and restinga habitats, while also providing baseline information that can be used in the future for breeding or domestication efforts according to local adaptation. To investigate such genetic patterns, molecular markers are essential tools, widely applied in ecological, phylogenetic, and evolutionary studies (Vinson et al. 2018). To investigate such genetic patterns, molecular markers are essential tools, widely applied in ecological, phylogenetic, and evolutionary studies (Helyar et al. 2011 ; Boutet et al. 2016 ; Hohenlohe et al. 2010; Morgil et al. 2020; Morin et al. 2004). In this study, we focused on the southern part of the distribution of S. romanzoffiana , sampling populations from southern Brazil and Paraguay to establish a regional genomic baseline. We hypothesized that (i) genetic structure would reflect not only geographic distance but also habitat type (e.g., restinga vs. inland forest), and (ii) climatic variables, particularly temperature and precipitation, would help explain adaptive genomic variation. Specifically, our objectives were to: (i) assess genetic diversity and structure using SNPs obtained through genotyping-by-sequencing (GBS), and (ii) investigate adaptive variation in relation to environmental variables. This is the first SNP-based population study of S. romanzoffiana , providing baseline information to support conservation and restoration planning in Atlantic Forest and restinga ecosystems. 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 southern South America: Atlantic Rain Forest, Atlantic Seasonal Forest, and restinga (Henderson et al. 1995 ; Lorenzi 2010 ). Sampling sites included Linha Lucena-Cecília, Travessa, and Herval (Venâncio Aires, RS, Brazil; Atlantic Seasonal Forest); San Ramón (Concepción, Paraguay; Seasonal Forest); Pixirica (Morrinhos do Sul, RS, Brazil) and Borrússia (Osório, RS, Brazil; Atlantic Rain Forest); and Condomínio Marítimo, (Cond. Maritimo) (Tramandaí, RS, Brazil) and Gravatá (Laguna, SC, Brazil; restinga ) (Fig. 1 ). Leaf samples were stored in silica gel and transported to the Department of Genetics at Luiz de Queiroz College of Agriculture – University of São Paulo – ESALQ/USP, for DNA extraction. This study was approved by the Ethics Committee of the University of Campinas and conducted in accordance with Brazilian Ministry of the Environment (MMA) regulations and the National System for Genetic Heritage and Associated Traditional Knowledge (SisGen; registration A3A9281). DNA extraction DNA was extracted from leaf tissue using a modified CTAB protocol (Doyle 1991 ) optimized for palms. DNA integrity was checked on 1% agarose gels, and concentration was standardized to 30 ng/µL after quantification with a Qubit fluorometer (Thermo Fisher Scientific). A detailed step-by-step description of the extraction protocol, including buffer composition, centrifugation conditions, and incubation times, is provided in Supplementary Information to facilitate reproducibility. Genotyping-by-sequencing (GBS) library preparation Genomic libraries were prepared using a double-digest GBS protocol (Poland et al. 2012 ) with modifications. Genomic DNA (30 ng/µL) was digested with PstI and MseI, followed by ligation of Illumina-compatible adapters with unique barcodes. Libraries were purified (QIAquick PCR Purification Kit, Qiagen), enriched by PCR with Illumina primers, and quantified by qPCR. Fragment size distribution was evaluated on a 2100 Bioanalyzer (Agilent Technologies), and sequencing was performed on the Illumina NextSeq 1000/2000 platform at Luiz de Queiroz College of Agriculture – University of São Paulo – (ESALQ-USP). A complete description of adapter design, PCR conditions, and purification steps is provided in Supplementary Information to ensure reproducibility. SNP discovery and filtering Raw read quality was first assessed with FastQC (Andrews 2010 ). Demultiplexing and initial filtering were conducted in Stacks v2.62 (Catchen et al. 2013 ) using the process_radtags module, to remove low-quality reads (Phred < 10), adapter contamination, uncalled bases (Ns), and sequences lacking restriction sites. De novo assembly was performed in Stacks : ustacks was run with a minimum depth of coverage of 4 (–m 4), allowing up to 5 mismatches between stacks within individuals (–M 5) (Boutet et al. 2016 ). A catalog of loci was built with cstacks (tolerance of 4 mismatches among individuals) (Boutet et al. 2016 ), followed by tsv2bam and genotyping in gstacks. Variant filtering was conducted in VCFtools (Danecek et al. 2011 ). To reduce linkage disequilibrium, one SNP per locus was retained for structure analyses. Filters applied were per-locus missingness ≤ 20%, per-individual missingness ≤ 20%, MAF ≥ 0.05, and Hardy–Weinberg equilibrium within populations (p < 1 × 10⁻⁶). Filtering choices followed the guidelines of Hemstrom et al. ( 2024 ). Population genetic diversity We estimated expected heterozygosity ( H s ), observed heterozygosity ( H o ), allelic richness ( Ar ) and fixation index ( F IS ) using the hierfstat package (Goudet 2005 ). The number of alleles was calculated with adegenet package (Jombart and Ahmed 2011 ), and private alleles were identified using poppr package (Kamvar et al. 2014 ). Population genetic structure Wright’s F statistics ( F ST , F IT , F IS ) and pairwise estimates of F ST were estimated using hierfstat (Goudet 2005 ). Pairwise genetic distances (Nei 1987) were visualized as a heatmap with heatmaply package (Galili et al. 2018 ). Analyses of Molecular Variance (Excoffier et al. 1992 ) were conducted in poppr package (Kamvar et al. 2014 ), and the significance of the AMOVA was tested through 20,000 permutations, using population identity as the hierarchical grouping factor. To minimize bias, loci with more than 5% missing data were excluded. A Mantel test was performed with ade4 (Mantel 1967 ) using 10,000 permutations testing for correlation between genetic and geographic distances (Thioulouse et al. 2018 ). Population structure was further explored with discriminant analysis of principal components (DAPC) using adegenet (Jombart and Ahmed 2011 ). The optimal number of PCs was determined using α-score optimization, and results were visualized with scatterplots and barplots. Environmental association and detection of candidate adaptive loci Environmental association analyses were conducted with Latent Factor Mixed Models (LFMM) implemented in the LEA package (Frichot and François 2015 ). 19 bioclimatic variables from WorldClim v2 (Fick and Hijmans 2017 ) and elevation were considered. To reduce collinearity among predictors, a Principal Component Analysis (PCA) was performed; the first two PCs explained ~ 85% of the variance (Figure S1 , methodological). The most correlated variables were retained: bio06 (minimum temperature of the coldest month) and bio19 (precipitation of the coldest quarter), both commonly reported as drivers of local adaptation in tropical plants (Eckert et al. 2009 ; Manel et al. 2010 ; De Kort et al. 2014 ). LFMM was run with K = 5 latent factors, defined by cross-entropy optimization from preliminary sNMF analyses (10 repetitions, 200,000 iterations; Figure S2, methodological), Each LFMM run used 10,000 iterations (burn-in = 5,000) and 10 replicates per variable. P-values were adjusted using genomic inflation (λ) and the Benjamini–Hochberg false discovery rate (q < 0.05) (François et al. 2016 ; Ahrens et al. 2019 ). Candidate SNPs were used for two downstream analyses: (i) a second sNMF restricted to candidate loci, and (ii) Redundancy Analysis (RDA) in vegan (Oksanen et al. 2024). In the RDA, SNP genotypes were response variables, with bio06 and bio19 as constraints. Missing data (≤ 20%) were imputed using the LEA impute function based on individual assignments (Frichot and François 2015 ). All analyses were performed in R v4.3.0 (R Core Team 2024 ). Results Genetic diversity in southern South 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% (Figure S3A). The average sequencing coverage per individual was 15.25x (Figure S3B), and the mean coverage per locus was 13.68x, with no coverage exceeding 100x or falling below 10x per locus (Figure S4A). The SNP data also revealed a higher number of transitions compared to transversions (Figure S4B). Genetic diversity varies across sites (Table 1 ). 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, Gravatá 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 ). Allelic richness was generally higher in inland forest populations compared to restinga sites, with the exception of Gravatá, which also displayed high Ar values (1.473). The highest fixation index ( f ) showed variation among populations, with the highest value observed in Travessa (0.449), while negative values in Lucena-Cecília (–0.031), San Ramón (–0.028), Gravatá (–0.013), and Cond. Maritmo (–0.004), indicated an excess of heterozygotes. 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 Cond.Maritimo 0.140 0.138 36125 1.387 653 -0.004 Gravata 0.174 0.167 38196 1.473 3291 -0.013 Herval 0.162 0.159 36631 1.452 364 0.259 Lucena-Cecilia 0.169 0.169 37555 1.492 329 -0.031 Pixirica 0.138 0.132 34935 1.372 476 0.013 San Ramón 0.205 0.203 40079 1.567 17426 -0.028 Travessa 0.160 0.163 35518 1.467 98 0.449 Population genetic structure in southern South America Based on 24,859 SNPs, Wright's F statistics revealed significant differentiation among populations. The estimates across the eight sampled S. romanzoffiana populations were: total inbreeding coefficient ( F IT = 0.139), within-population inbreeding coefficient ( F IS = 0.021), and genetic differentiation coefficient among populations ( F ST = 0.122). Pairwise F ST analyses indicated that the San Ramón was consistently the most divergent population, with values reaching 0.371 compared to Pixirica, while the lowest differentiation was observed between Borrússia and Pixirica F ST = 0.079, both located in Atlantic Rain Forest (Fig. 2 ). In contrast, Borrússia and Cond. Maritimo, despite being geographically closer (~ 20 km apart), showed higher differentiation ( F ST = 0.081), reflecting the influence of habitat type ( restinga vs. forest) on genetic structure (Fig. 2 ). These pairwise comparisons highlight both geographic and ecological influences on population structure. Consistently, AMOVA confirmed significant differentiation among populations, with 29.7% of the genetic variance distributed among populations and 70.3% within populations (Table 2 ). These findings indicate that most of the genetic diversity is maintained within populations, while moderate structure exists among them. This pattern may result from substantial gene flow within populations and ecological or geographic factors constraining gene flow between populations. However, the Mantel test showed a non-significant correlation between genetic and geographic distances (r = − 0.009, p = 0.141), indicating no evidence of isolation by distance. Table 2 Analyses 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. Although no isolation by distance was detected, multivariate approaches further clarified population structure. Discriminant Analysis of Principal Components (DAPC) identified clear population structure. The first discriminant function (LD1) explained 19.22% of the variation and separated San Ramón from all other sites (Fig. 3 A). The remaining populations showed partial overlap along LD1, reflecting more subtle differentiation. Membership probabilities (Fig. 3 B) further supported these results, with San Ramón forming a distinct cluster, while restinga populations (Cond. Maritimo and Gravatá) tended to group together and inland forest populations showed mixed assignment. Detection of adaptive loci and environmental associations LFMM identified 3,768 SNPs significantly associated with bio06 (minimum temperature of the coldest month) and bio19 (precipitation of the coldest quarter) (Figures S5–S6). A new sNMF analysis based on these candidates SNPs identified a population structure with K = 4 clusters (Fig. 4 ), indicating that environmental variables contribute to population differentiation beyond neutral processes. San Ramón and the two restinga sites formed distinct clusters, while inland forest populations showed partial admixture. Redundancy Analysis (RDA) based on candidate SNPs confirmed strong genotype–environment associations. The global model constrained by bio19 and bio06 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 constrained axes explained nearly all of the variance in candidate loci (99.9%, Fig. 5 ). Individuals from San Ramón were positively associated with bio06, indicating adaptation to colder minimum temperatures, while Cond. Maritimo, Pixirica, Borrússia, and Gravatá were strongly associated with bio19, suggesting adaptive responses to precipitation seasonality in the southern Atlantic Forest and restinga environments. These findings confirm that climatic variables related to temperature and precipitation are important drivers of adaptive differentiation in S. romanzoffiana . 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, with H o exceeding H s in most sites, indicate a potential excess of heterozygotes. This pattern may reflect adaptive responses to local environmental conditions or a mating system that favors outcrossing among genetically distinct individuals. S. romanzoffiana is pollinated mainly by nitidulid beetles and other insects and has its fruits dispersed by birds and mammals (Zona and Henderson 1989 ), which contributes to maintaining gene flow and elevated heterozygosity. Populations with high heterozygosity are generally more resilient to environmental pressures, which may be crucial for persistence in altered habitats (Hoffmann and Sgrò 2011 ). In addition to heterozygosity, allelic richness ( Ar ) was particularly notable in San Ramón and Lucena-Cecília, highlighting their genetic potential for adaptation and long-term evolutionary resilience (Pritchard et al. 2000 ). The high number of private alleles ( Ap ) found in Cond. Maritimo and Gravatá emphasizes the genetic uniqueness of restinga populations, likely reflecting specific local adaptations to coastal environments characterized by sandy soils, high salinity, and strong wind exposure (Zamith and Scarano 2006 ). Comparable results have been reported for other neotropical trees, such as Eugenia uniflora (Myrtaceae) (Vetö et al. 2023 ). Similar patterns of habitat-associated divergence have been suggested in palms. For instance, Euterpe edulis shows structuring of alleles and signals of selection across contrasting microhabitats (Conte et al. 2008 ). Patterns of fixation indices further support the influence of habitat context. High positive ( f ) values, as observed in Travessa and Herval, suggest increased homozygosity, potentially resulting from inbreeding in fragmented landscapes. Smaller but positive values in Borrússia and Pixirica also indicate a slight trend toward homozygosity. By contrast, negative values in Lucena-Cecília, San Ramón, Gravatá, and Cond. Maritimo indicate an excess of heterozygotes, which may arise from outcrossing and gene flow in larger or more environmentally heterogeneous populations (Allendorf et al. 2010 ). Together, these results illustrate how habitat fragmentation and population size shape mating dynamics in palms, with consequences for the maintenance of genetic diversity. Wright’s F-statistics revealed moderate differentiation (FST = 0.122), indicating that while gene flow persists, populations remain sufficiently isolated to maintain genetic differences (Wright 1931 , 1949 ). AMOVA corroborated this finding, showing that most genetic variation occurs within populations (70.3%). This high intrapopulational diversity is consistent with expectations for long-lived, outcrossing tropical trees (Anderson 2001 ; Griffiths and Tavare 1997), where large effective population sizes and extensive gene flow maintain diverse alleles within local gene pools. Interestingly, the lack of a significant correlation between genetic and geographic distances in the Mantel test indicates that geographic separation is not the main driver of differentiation. Instead, ecological factors appear to play a greater role. For instance, Borrússia and Pixirica, although ~ 90 km apart, were genetically more similar to each other than Borrússia and Cond. Maritimo, which are separated by only ~ 20 km but differ in vegetation type ( restinga vs. Atlantic Rain Forest). This contrast reinforces the importance of isolation by environment (IBE) over isolation by distance, highlighting the role of local ecological conditions and microclimates in shaping genetic divergence (Mantel 1967 ; Wang and Bradburd 2014 ). Consistent with this interpretation, environmental association analyses provided compelling evidence for local adaptation in S. romanzoffiana . SNPs associated with climatic variables (bio06 and bio19) highlight the role of temperature and precipitation as selective pressures across vegetation types. Notably, adaptive genetic structure emphasized that selection could act independently of geographic distance or historical demography. Similar patterns of environment-driven divergence have been documented in Euterpe edulis (Gaiotto et al. 2003 ), supporting the idea that neotropical palms frequently undergo local adaptation even across relatively small spatial scales. In this study, 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. Collectively, these results reinforce the importance of integrating adaptive genetic variation into conservation planning. For species distributed across ecotonal gradients and fragmented landscapes, such as S. romanzoffiana , management strategies should explicitly account for both neutral and adaptive processes to preserve evolutionary potential under ongoing environmental change. Although we did not implement a separate Multiple Matrix Regression (MMRR) test, the Redundancy Analysis (RDA) already fulfills this role by formally partitioning genetic variance explained by climatic variables while accounting for neutral structure. Thus, our approach provides a direct test of isolation by environment. We also acknowledge that demographic history (e.g., bottlenecks or drift) was not explicitly tested here, and future studies with broader sampling or whole-genome data will be necessary to disentangle the relative contributions of drift versus selection to the observed genetic patterns. Conservation Implications The results obtained provide important insights into the conservation and management of genetic diversity in Syagrus romanzoffiana . Although this palm is widespread and not considered threatened, discussing its genetic patterns in a conservation framework is still relevant. First, genetic variation is not homogeneously distributed across its range: most diversity is maintained within populations, underscoring the importance of protecting local habitats that sustain viable population sizes and gene flow. Second, the distinctiveness of populations from Paraguay and restinga habitats demonstrates the value of conserving ecologically unique areas that harbor locally adapted variants. Moreover, because S. romanzoffiana is ecologically important and widely used in restoration, landscaping, and local livelihoods, preserving its adaptive genetic variation has practical significance for long-term sustainability. Rather than representing a species-wide conservation concern, the relevance of our findings lies in integrating this genomic baseline into landscape management, restoration initiatives, and sustainable use programs, ensuring that adaptive variation is preserved and can contribute to resilience under ongoing environmental change. Conclusion In summary, the analyses of the genetic structure of S. romanzoffiana populations revealed moderate differentiation among populations and high diversity within them. The vegetation type appears to be an important factor shaping genetic structure, sometimes overriding geographic distance. These results suggest that local ecological conditions, together with evolutionary history and patterns of gene flow, influence population differentiation. Importantly, our study provides a regional genomic baseline for a widespread but ecologically and socioeconomically relevant palm. Although S. romanzoffiana is not threatened, understanding the distribution of its neutral and adaptive genetic variation is valuable for restoration programs, urban forestry, and the management of Atlantic Forest and restinga landscapes. Future research should aim to disentangle the relative roles of neutral and adaptive processes in shaping genetic variation, thereby strengthening the integration of genomic data into conservation and sustainable management of neotropical palms. These results illustrate the value of applying population genomics to widespread tropical palms, showing how even non-threatened species provide key baselines for conservation and management in changing environments. Declarations 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. Competing interests The authors declare that they have no conflict of interest. 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. 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). Ethics approval and consent to participate This study was approved by the Ethics Committee of the University of Campinas and conducted in accordance with Brazilian Ministry of the Environment (MMA) regulations and the National System for Genetic Heritage and Associated Traditional Knowledge (SisGen; registration A3A9281). Correspondence and requests for materials should be addressed to K.K.M.M. and M.I.Z. 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Supplementary Files SupplementaryInformationsSyagrus.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 18 Oct, 2025 Submission checks completed at journal 18 Oct, 2025 First submitted to journal 14 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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18:02:14","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115570,"visible":true,"origin":"","legend":"","description":"","filename":"499a0cbba76f474c9aee6a817186193c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/68e174fbc0300e1afbde7b23.xml"},{"id":94884966,"identity":"a2077015-90b6-4e2a-9ac3-b080d6f09d96","added_by":"auto","created_at":"2025-10-31 18:02:14","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128062,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/31071a038a7147f03886430d.html"},{"id":94987206,"identity":"5b2c5b5f-0dc1-4650-b53c-9505c5af9e24","added_by":"auto","created_at":"2025-11-03 07:01:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341183,"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":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/ec425c9acbcf512cec79e6fc.png"},{"id":94884956,"identity":"42787b0c-bb09-40f5-add8-3df28acedc34","added_by":"auto","created_at":"2025-10-31 18:02:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100319,"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":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/41b12d834b7224bf376d0fb0.png"},{"id":94884959,"identity":"21b1ebb8-d3d9-491a-b8f1-862ef2174906","added_by":"auto","created_at":"2025-10-31 18:02:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":228926,"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) Membership barplot/compoplotwith genetic groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/4ea94f9d7cbb694b4286507e.png"},{"id":94986907,"identity":"3f777e41-359c-4bc3-960e-03fa81d2046e","added_by":"auto","created_at":"2025-11-03 07:00:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44853,"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":"floatimage45.png","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/41000b94af341d846f601237.png"},{"id":94987007,"identity":"ec3f2ae0-7732-410e-aa66-e77c018ae218","added_by":"auto","created_at":"2025-11-03 07:01:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":191808,"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 nearly all 99% of the total adaptive variance.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/ba4526d2a6c7895c2a2a317b.png"},{"id":95000611,"identity":"f8cdbf53-6347-40da-9877-b9203f64f4a9","added_by":"auto","created_at":"2025-11-03 08:59:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1700372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/898b7a4d-088b-430e-bbd2-61de7acc36e8.pdf"},{"id":94884970,"identity":"5c6c44de-7365-4b37-afab-b458f78d415c","added_by":"auto","created_at":"2025-10-31 18:02:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8438707,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationsSyagrus.docx","url":"https://assets-eu.researchsquare.com/files/rs-7862000/v1/77514053824e57e9aa922e13.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide SNPs reveal adaptation and population structure in Syagrus romanzoffiana (Arecaceae) from southern South America","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePalm species (Arecaceae) stand out for their versatility and diversity, with more than 2,600 recognized species worldwide. Over centuries, palms have provided a wide range of services to humankind (C\u0026aacute;mara-Leret et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Eiserhardt et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Levis et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Many are ecological keystone species because large numbers of animals depend on their fruit and flower resources to survive (Onstein et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, palms supply non-timber forest products such as food, medicine, fibers, oils, and building materials, supporting both traditional communities and modern industries (Sen and Samanta 2015; Gunjan et al. 2015; Colombo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e (Cham.) Glassman, from the Arecaceae family, commonly known in Brazil as jeriv\u0026aacute;, is a widespread palm native to southern and eastern South America, occurring in Brazil, Paraguay, northern Argentina and Uruguay (Lorenzi et al. 2010; Falasca et al. 2012). It plays a crucial ecological and socioeconomic role: sustaining frugivorous birds and mammals, is widely cultivated in landscaping and urban forestry, and supporting rural communities through diverse products derived from fruits, leaves, stems, and seeds (Scariot 2015; Zona and Henderson \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIts distribution spans heterogeneous environments, from humid Atlantic Rain Forest to seasonal inland forests and coastal \u003cem\u003erestinga\u003c/em\u003e systems, the latter characterized by sandy soils, high salinity and wind exposure (Lorenzi et al. 2010; Henderson et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Scarano \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Such environmental variation provides a natural framework to test for local adaptation and isolation-by-environment (IBE) (Wang and Bradburd \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding genetic diversity and population structure is fundamental for informed decisions on resource management, sustainable use, and conservation planning (Helyar et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Brumfield et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For \u003cem\u003eS. romanzoffiana\u003c/em\u003e, analyzing genetic diversity is particularly relevant for guiding seed sourcing and restoration strategies in degraded Atlantic Forest and restinga habitats, while also providing baseline information that can be used in the future for breeding or domestication efforts according to local adaptation.\u003c/p\u003e\u003cp\u003eTo investigate such genetic patterns, molecular markers are essential tools, widely applied in ecological, phylogenetic, and evolutionary studies (Vinson et al. 2018). To investigate such genetic patterns, molecular markers are essential tools, widely applied in ecological, phylogenetic, and evolutionary studies (Helyar et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Boutet et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hohenlohe et al. 2010; Morgil et al. 2020; Morin et al. 2004).\u003c/p\u003e\u003cp\u003eIn this study, we focused on the southern part of the distribution of \u003cem\u003eS. romanzoffiana\u003c/em\u003e, sampling populations from southern Brazil and Paraguay to establish a regional genomic baseline. We hypothesized that (i) genetic structure would reflect not only geographic distance but also habitat type (e.g., restinga vs. inland forest), and (ii) climatic variables, particularly temperature and precipitation, would help explain adaptive genomic variation. Specifically, our objectives were to: (i) assess genetic diversity and structure using SNPs obtained through genotyping-by-sequencing (GBS), and (ii) investigate adaptive variation in relation to environmental variables. This is the first SNP-based population study of \u003cem\u003eS. romanzoffiana\u003c/em\u003e, providing baseline information to support conservation and restoration planning in Atlantic Forest and \u003cem\u003erestinga\u003c/em\u003e ecosystems.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant material\u003c/h2\u003e\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 southern South America: Atlantic Rain Forest, Atlantic Seasonal Forest, and \u003cem\u003erestinga\u003c/em\u003e (Henderson et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Lorenzi \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Sampling sites included Linha Lucena-Cec\u0026iacute;lia, Travessa, and Herval (Ven\u0026acirc;ncio Aires, RS, Brazil; Atlantic Seasonal Forest); San Ram\u0026oacute;n (Concepci\u0026oacute;n, Paraguay; Seasonal Forest); Pixirica (Morrinhos do Sul, RS, Brazil) and Borr\u0026uacute;ssia (Os\u0026oacute;rio, RS, Brazil; Atlantic Rain Forest); and Condom\u0026iacute;nio Mar\u0026iacute;timo, (Cond. Maritimo) (Tramanda\u0026iacute;, RS, Brazil) and Gravat\u0026aacute; (Laguna, SC, Brazil; \u003cem\u003erestinga\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Leaf samples were stored in silica gel and transported to the Department of Genetics at Luiz de Queiroz College of Agriculture \u0026ndash; University of S\u0026atilde;o Paulo \u0026ndash; ESALQ/USP, for DNA extraction.\u003c/p\u003e\u003cp\u003eThis study was approved by the Ethics Committee of the University of Campinas and conducted in accordance with Brazilian Ministry of the Environment (MMA) regulations and the National System for Genetic Heritage and Associated Traditional Knowledge (SisGen; registration A3A9281).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA extraction\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from leaf tissue using a modified CTAB protocol (Doyle \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) optimized for palms. DNA integrity was checked on 1% agarose gels, and concentration was standardized to 30 ng/\u0026micro;L after quantification with a Qubit fluorometer (Thermo Fisher Scientific).\u003c/p\u003e\u003cp\u003eA detailed step-by-step description of the extraction protocol, including buffer composition, centrifugation conditions, and incubation times, is provided in Supplementary Information to facilitate reproducibility.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenotyping-by-sequencing\u003c/b\u003e \u003cb\u003e(GBS) library preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenomic libraries were prepared using a double-digest GBS protocol (Poland et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with modifications. Genomic DNA (30 ng/\u0026micro;L) was digested with PstI and MseI, followed by ligation of Illumina-compatible adapters with unique barcodes. Libraries were purified (QIAquick PCR Purification Kit, Qiagen), enriched by PCR with Illumina primers, and quantified by qPCR. Fragment size distribution was evaluated on a 2100 Bioanalyzer (Agilent Technologies), and sequencing was performed on the Illumina NextSeq 1000/2000 platform at Luiz de Queiroz College of Agriculture \u0026ndash; University of S\u0026atilde;o Paulo \u0026ndash; (ESALQ-USP).\u003c/p\u003e\u003cp\u003eA complete description of adapter design, PCR conditions, and purification steps is provided in Supplementary Information to ensure reproducibility.\u003c/p\u003e\n\u003ch3\u003eSNP discovery and filtering\u003c/h3\u003e\n\u003cp\u003eRaw read quality was first assessed with \u003cem\u003eFastQC\u003c/em\u003e (Andrews \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Demultiplexing and initial filtering were conducted in \u003cem\u003eStacks v2.62\u003c/em\u003e (Catchen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) using the process_radtags module, to remove low-quality reads (Phred\u0026thinsp;\u0026lt;\u0026thinsp;10), adapter contamination, uncalled bases (Ns), and sequences lacking restriction sites. \u003cem\u003eDe novo\u003c/em\u003e assembly was performed in \u003cem\u003eStacks\u003c/em\u003e: \u003cem\u003eustacks\u003c/em\u003e was run with a minimum depth of coverage of 4 (\u0026ndash;m 4), allowing up to 5 mismatches between stacks within individuals (\u0026ndash;M 5) (Boutet et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A catalog of loci was built with cstacks (tolerance of 4 mismatches among individuals) (Boutet et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), followed by tsv2bam and genotyping in gstacks.\u003c/p\u003e\u003cp\u003eVariant filtering was conducted in \u003cem\u003eVCFtools\u003c/em\u003e (Danecek et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). To reduce linkage disequilibrium, one SNP per locus was retained for structure analyses. Filters applied were per-locus missingness\u0026thinsp;\u0026le;\u0026thinsp;20%, per-individual missingness\u0026thinsp;\u0026le;\u0026thinsp;20%, MAF\u0026thinsp;\u0026ge;\u0026thinsp;0.05, and Hardy\u0026ndash;Weinberg equilibrium within populations (p\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁶). Filtering choices followed the guidelines of Hemstrom et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePopulation genetic diversity\u003c/h3\u003e\n\u003cp\u003eWe estimated expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e), observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e), allelic richness (\u003cem\u003eAr\u003c/em\u003e) and fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e) using the \u003cem\u003ehierfstat\u003c/em\u003e package (Goudet \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The number of alleles was calculated with \u003cem\u003eadegenet\u003c/em\u003e package (Jombart and Ahmed \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and private alleles were identified using \u003cem\u003epoppr\u003c/em\u003e package (Kamvar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePopulation genetic structure\u003c/h3\u003e\n\u003cp\u003eWright\u0026rsquo;s F statistics (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIT\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e) and pairwise estimates of \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e were estimated using \u003cem\u003ehierfstat\u003c/em\u003e (Goudet \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Pairwise genetic distances (Nei 1987) were visualized as a heatmap with \u003cem\u003eheatmaply\u003c/em\u003e package (Galili et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Analyses of Molecular Variance (Excoffier et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) were conducted in \u003cem\u003epoppr\u003c/em\u003e package (Kamvar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and the significance of the AMOVA was tested through 20,000 permutations, using population identity as the hierarchical grouping factor. To minimize bias, loci with more than 5% missing data were excluded.\u003c/p\u003e\u003cp\u003eA Mantel test was performed with \u003cem\u003eade4\u003c/em\u003e (Mantel \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) using 10,000 permutations testing for correlation between genetic and geographic distances (Thioulouse et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Population structure was further explored with discriminant analysis of principal components (DAPC) using \u003cem\u003eadegenet\u003c/em\u003e (Jombart and Ahmed \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The optimal number of PCs was determined using α-score optimization, and results were visualized with scatterplots and barplots.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental association and detection of candidate adaptive loci\u003c/h2\u003e\u003cp\u003eEnvironmental association analyses were conducted with Latent Factor Mixed Models (LFMM) implemented in the \u003cem\u003eLEA\u003c/em\u003e package (Frichot and Fran\u0026ccedil;ois \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). 19 bioclimatic variables from WorldClim v2 (Fick and Hijmans \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and elevation were considered. To reduce collinearity among predictors, a Principal Component Analysis (PCA) was performed; the first two PCs explained\u0026thinsp;~\u0026thinsp;85% of the variance (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, methodological). The most correlated variables were retained: bio06 (minimum temperature of the coldest month) and bio19 (precipitation of the coldest quarter), both commonly reported as drivers of local adaptation in tropical plants (Eckert et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Manel et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; De Kort et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLFMM was run with K\u0026thinsp;=\u0026thinsp;5 latent factors, defined by cross-entropy optimization from preliminary sNMF analyses (10 repetitions, 200,000 iterations; Figure S2, methodological), Each LFMM run used 10,000 iterations (burn-in =\u0026thinsp;5,000) and 10 replicates per variable. P-values were adjusted using genomic inflation (λ) and the Benjamini\u0026ndash;Hochberg false discovery rate (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fran\u0026ccedil;ois et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ahrens et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCandidate SNPs were used for two downstream analyses: (i) a second sNMF restricted to candidate loci, and (ii) Redundancy Analysis (RDA) in \u003cem\u003evegan\u003c/em\u003e (Oksanen et al. 2024). In the RDA, SNP genotypes were response variables, with bio06 and bio19 as constraints. Missing data (\u0026le;\u0026thinsp;20%) were imputed using the LEA impute function based on individual assignments (Frichot and Fran\u0026ccedil;ois \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). All analyses were performed in \u003cem\u003eR v4.3.0\u003c/em\u003e (R Core Team \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eGenetic diversity in southern South America\u003c/h2\u003e\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% (Figure S3A). The average sequencing coverage per individual was 15.25x (Figure S3B), and the mean coverage per locus was 13.68x, with no coverage exceeding 100x or falling below 10x per locus (Figure S4A). The SNP data also revealed a higher number of transitions compared to transversions (Figure S4B).\u003c/p\u003e\u003cp\u003eGenetic diversity varies across sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e) and expected (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) heterozygosity were high across populations, with \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e exceeding \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e in six sites. Lucena-Cec\u0026iacute;lia was an exception, showing similar \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e values (0.169), while Travessa had a lower \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e than \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e. The highest \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e values were in San Ram\u0026oacute;n (0.205) and Gravat\u0026aacute; (0.174), with the highest \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e also in San Ram\u0026oacute;n (0.203), followed by Lucena-Cecilia (0.169). San Ram\u0026oacute;n, Gravat\u0026aacute; and Lucena-Cecilia had the greatest number of alleles (A, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Allelic richness was generally higher in inland forest populations compared to \u003cem\u003erestinga\u003c/em\u003e sites, with the exception of Gravat\u0026aacute;, which also displayed high \u003cem\u003eAr\u003c/em\u003e values (1.473). The highest fixation index (\u003cem\u003ef\u003c/em\u003e) showed variation among populations, with the highest value observed in Travessa (0.449), while negative values in Lucena-Cec\u0026iacute;lia (\u0026ndash;0.031), San Ram\u0026oacute;n (\u0026ndash;0.028), Gravat\u0026aacute; (\u0026ndash;0.013), and Cond. Maritmo (\u0026ndash;0.004), indicated an excess of heterozygotes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePopulation Genomic Diversity of \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e based on 24,859 SNPs. \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e = Observed heterozygosity, \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = Expected heterozygosity, A\u0026thinsp;=\u0026thinsp;Number of alleles, \u003cem\u003eAr\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Allelic richness, \u003cem\u003eAp\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Private alleles, \u003cem\u003ef\u003c/em\u003e\u0026thinsp;=\u0026thinsp;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\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eAr\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eAp\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorrussia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCond.Maritimo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGravata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHerval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLucena-Cecilia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePixirica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSan Ram\u0026oacute;n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTravessa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePopulation genetic structure in southern South America\u003c/h2\u003e\u003cp\u003eBased on 24,859 SNPs, Wright's \u003cem\u003eF\u003c/em\u003e statistics revealed significant differentiation among populations. The estimates across the eight sampled \u003cem\u003eS. romanzoffiana\u003c/em\u003e populations were: total inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIT\u003c/em\u003e\u003c/sub\u003e = 0.139), within-population inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e = 0.021), and genetic differentiation coefficient among populations (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.122). Pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e analyses indicated that the San Ram\u0026oacute;n was consistently the most divergent population, with values reaching 0.371 compared to Pixirica, while the lowest differentiation was observed between Borr\u0026uacute;ssia and Pixirica \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.079, both located in Atlantic Rain Forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, Borr\u0026uacute;ssia and Cond. Maritimo, despite being geographically closer (~\u0026thinsp;20 km apart), showed higher differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.081), reflecting the influence of habitat type (\u003cem\u003erestinga\u003c/em\u003e vs. forest) on genetic structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese pairwise comparisons highlight both geographic and ecological influences on population structure. Consistently, AMOVA confirmed significant differentiation among populations, with 29.7% of the genetic variance distributed among populations and 70.3% within populations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings indicate that most of the genetic diversity is maintained within populations, while moderate structure exists among them. This pattern may result from substantial gene flow within populations and ecological or geographic factors constraining gene flow between populations. However, the Mantel test showed a non-significant correlation between genetic and geographic distances (r = \u0026minus;\u0026thinsp;0.009, p\u0026thinsp;=\u0026thinsp;0.141), indicating no evidence of isolation by distance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalyses of molecular variance (AMOVA) performed for eight populations of \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e collected in the in Brazil and Paraguay.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of Variation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEst. Var.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePV (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmong populations (Among Locations)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6617.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e483.8844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin populations (Within Locations)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1145.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1145.6073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1571.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1629.4917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.001 (Estimated based on 20000 permutations). Df\u0026thinsp;=\u0026thinsp;Degrees of Freedom; MS\u0026thinsp;=\u0026thinsp;Mean Squares. Est. Var. = Variance component. PV\u0026thinsp;=\u0026thinsp;Percentage of Variation.\u003c/p\u003e\u003cp\u003eAlthough no isolation by distance was detected, multivariate approaches further clarified population structure. Discriminant Analysis of Principal Components (DAPC) identified clear population structure. The first discriminant function (LD1) explained 19.22% of the variation and separated San Ram\u0026oacute;n from all other sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The remaining populations showed partial overlap along LD1, reflecting more subtle differentiation. Membership probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) further supported these results, with San Ram\u0026oacute;n forming a distinct cluster, while \u003cem\u003erestinga\u003c/em\u003e populations (Cond. Maritimo and Gravat\u0026aacute;) tended to group together and inland forest populations showed mixed assignment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDetection of adaptive loci and environmental associations\u003c/h2\u003e\u003cp\u003eLFMM identified 3,768 SNPs significantly associated with bio06 (minimum temperature of the coldest month) and bio19 (precipitation of the coldest quarter) (Figures S5\u0026ndash;S6). A new sNMF analysis based on these candidates SNPs identified a population structure with K\u0026thinsp;=\u0026thinsp;4 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that environmental variables contribute to population differentiation beyond neutral processes. San Ram\u0026oacute;n and the two \u003cem\u003erestinga\u003c/em\u003e sites formed distinct clusters, while inland forest populations showed partial admixture.\u003c/p\u003e\u003cp\u003eRedundancy Analysis (RDA) based on candidate SNPs confirmed strong genotype\u0026ndash;environment associations. The global model constrained by bio19 and bio06 was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an adjusted R\u0026sup2; of 20%. Both variables contributed significantly to the explained genetic variance, and the first two constrained axes explained nearly all of the variance in candidate loci (99.9%, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIndividuals from San Ram\u0026oacute;n were positively associated with bio06, indicating adaptation to colder minimum temperatures, while Cond. Maritimo, Pixirica, Borr\u0026uacute;ssia, and Gravat\u0026aacute; were strongly associated with bio19, suggesting adaptive responses to precipitation seasonality in the southern Atlantic Forest and \u003cem\u003erestinga\u003c/em\u003e environments. These findings confirm that climatic variables related to temperature and precipitation are important drivers of adaptive differentiation in \u003cem\u003eS. romanzoffiana\u003c/em\u003e.\u003c/p\u003e\u003c/div\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\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e) and expected (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) heterozygosity, with \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e exceeding \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e in most sites, indicate a potential excess of heterozygotes. This pattern may reflect adaptive responses to local environmental conditions or a mating system that favors outcrossing among genetically distinct individuals. \u003cem\u003eS. romanzoffiana\u003c/em\u003e is pollinated mainly by nitidulid beetles and other insects and has its fruits dispersed by birds and mammals (Zona and Henderson \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), which contributes to maintaining gene flow and elevated heterozygosity. Populations with high heterozygosity are generally more resilient to environmental pressures, which may be crucial for persistence in altered habitats (Hoffmann and Sgr\u0026ograve; \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to heterozygosity, allelic richness (\u003cem\u003eAr\u003c/em\u003e) was particularly notable in San Ram\u0026oacute;n and Lucena-Cec\u0026iacute;lia, highlighting their genetic potential for adaptation and long-term evolutionary resilience (Pritchard et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The high number of private alleles (\u003cem\u003eAp\u003c/em\u003e) found in Cond. Maritimo and Gravat\u0026aacute; emphasizes the genetic uniqueness of \u003cem\u003erestinga\u003c/em\u003e populations, likely reflecting specific local adaptations to coastal environments characterized by sandy soils, high salinity, and strong wind exposure (Zamith and Scarano \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Comparable results have been reported for other neotropical trees, such as \u003cem\u003eEugenia uniflora\u003c/em\u003e (Myrtaceae) (Vet\u0026ouml; et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similar patterns of habitat-associated divergence have been suggested in palms. For instance, \u003cem\u003eEuterpe edulis\u003c/em\u003e shows structuring of alleles and signals of selection across contrasting microhabitats (Conte et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePatterns of fixation indices further support the influence of habitat context. High positive (\u003cem\u003ef\u003c/em\u003e) values, as observed in Travessa and Herval, suggest increased homozygosity, potentially resulting from inbreeding in fragmented landscapes. Smaller but positive values in Borr\u0026uacute;ssia and Pixirica also indicate a slight trend toward homozygosity. By contrast, negative values in Lucena-Cec\u0026iacute;lia, San Ram\u0026oacute;n, Gravat\u0026aacute;, and Cond. Maritimo indicate an excess of heterozygotes, which may arise from outcrossing and gene flow in larger or more environmentally heterogeneous populations (Allendorf et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Together, these results illustrate how habitat fragmentation and population size shape mating dynamics in palms, with consequences for the maintenance of genetic diversity.\u003c/p\u003e\u003cp\u003eWright\u0026rsquo;s F-statistics revealed moderate differentiation (FST\u0026thinsp;=\u0026thinsp;0.122), indicating that while gene flow persists, populations remain sufficiently isolated to maintain genetic differences (Wright \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1931\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1949\u003c/span\u003e). AMOVA corroborated this finding, showing that most genetic variation occurs within populations (70.3%). This high intrapopulational diversity is consistent with expectations for long-lived, outcrossing tropical trees (Anderson \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Griffiths and Tavare 1997), where large effective population sizes and extensive gene flow maintain diverse alleles within local gene pools.\u003c/p\u003e\u003cp\u003eInterestingly, the lack of a significant correlation between genetic and geographic distances in the Mantel test indicates that geographic separation is not the main driver of differentiation. Instead, ecological factors appear to play a greater role. For instance, Borr\u0026uacute;ssia and Pixirica, although ~\u0026thinsp;90 km apart, were genetically more similar to each other than Borr\u0026uacute;ssia and Cond. Maritimo, which are separated by only\u0026thinsp;~\u0026thinsp;20 km but differ in vegetation type (\u003cem\u003erestinga\u003c/em\u003e vs. Atlantic Rain Forest). This contrast reinforces the importance of isolation by environment (IBE) over isolation by distance, highlighting the role of local ecological conditions and microclimates in shaping genetic divergence (Mantel \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Wang and Bradburd \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsistent with this interpretation, environmental association analyses provided compelling evidence for local adaptation in \u003cem\u003eS. romanzoffiana\u003c/em\u003e. SNPs associated with climatic variables (bio06 and bio19) highlight the role of temperature and precipitation as selective pressures across vegetation types. Notably, adaptive genetic structure emphasized that selection could act independently of geographic distance or historical demography. Similar patterns of environment-driven divergence have been documented in \u003cem\u003eEuterpe edulis\u003c/em\u003e (Gaiotto et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), supporting the idea that neotropical palms frequently undergo local adaptation even across relatively small spatial scales. In this study, populations from San Ram\u0026oacute;n showed strong signals of adaptation to colder winters, while southern Brazilian populations, particularly those in \u003cem\u003erestinga\u003c/em\u003e and coastal forest habitats, aligned with wetter and more variable rainfall regimes.\u003c/p\u003e\u003cp\u003eCollectively, these results reinforce the importance of integrating adaptive genetic variation into conservation planning. For species distributed across ecotonal gradients and fragmented landscapes, such as \u003cem\u003eS. romanzoffiana\u003c/em\u003e, management strategies should explicitly account for both neutral and adaptive processes to preserve evolutionary potential under ongoing environmental change. Although we did not implement a separate Multiple Matrix Regression (MMRR) test, the Redundancy Analysis (RDA) already fulfills this role by formally partitioning genetic variance explained by climatic variables while accounting for neutral structure. Thus, our approach provides a direct test of isolation by environment. We also acknowledge that demographic history (e.g., bottlenecks or drift) was not explicitly tested here, and future studies with broader sampling or whole-genome data will be necessary to disentangle the relative contributions of drift versus selection to the observed genetic patterns.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eConservation Implications\u003c/h2\u003e\u003cp\u003eThe results obtained provide important insights into the conservation and management of genetic diversity in \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e. Although this palm is widespread and not considered threatened, discussing its genetic patterns in a conservation framework is still relevant. First, genetic variation is not homogeneously distributed across its range: most diversity is maintained within populations, underscoring the importance of protecting local habitats that sustain viable population sizes and gene flow. Second, the distinctiveness of populations from Paraguay and \u003cem\u003erestinga\u003c/em\u003e habitats demonstrates the value of conserving ecologically unique areas that harbor locally adapted variants. Moreover, because \u003cem\u003eS. romanzoffiana\u003c/em\u003e is ecologically important and widely used in restoration, landscaping, and local livelihoods, preserving its adaptive genetic variation has practical significance for long-term sustainability. Rather than representing a species-wide conservation concern, the relevance of our findings lies in integrating this genomic baseline into landscape management, restoration initiatives, and sustainable use programs, ensuring that adaptive variation is preserved and can contribute to resilience under ongoing environmental change.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the analyses of the genetic structure of \u003cem\u003eS. romanzoffiana\u003c/em\u003e populations revealed moderate differentiation among populations and high diversity within them. The vegetation type appears to be an important factor shaping genetic structure, sometimes overriding geographic distance. These results suggest that local ecological conditions, together with evolutionary history and patterns of gene flow, influence population differentiation. Importantly, our study provides a regional genomic baseline for a widespread but ecologically and socioeconomically relevant palm. Although \u003cem\u003eS. romanzoffiana\u003c/em\u003e is not threatened, understanding the distribution of its neutral and adaptive genetic variation is valuable for restoration programs, urban forestry, and the management of Atlantic Forest and \u003cem\u003erestinga\u003c/em\u003e landscapes. Future research should aim to disentangle the relative roles of neutral and adaptive processes in shaping genetic variation, thereby strengthening the integration of genomic data into conservation and sustainable management of neotropical palms. These results illustrate the value of applying population genomics to widespread tropical palms, showing how even non-threatened species provide key baselines for conservation and management in changing environments.\u003c/p\u003e"},{"header":"Declarations","content":"\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 S\u0026atilde;o Paulo Research Foundation (2011/50296-8), (2021/10319-0) and the National Council for Scientific and Technological Development (CNPq\u0026mdash;313417/2023-7). The authors declare that they have no conflict of interest.\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\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\u0026apos; 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\u0026rsquo;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\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\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the University of Campinas and conducted in accordance with Brazilian Ministry of the Environment (MMA) regulations and the National System for Genetic Heritage and Associated Traditional Knowledge (SisGen; registration A3A9281).\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\u003cli\u003e\u003cspan\u003eAhrens CW, Byrne M, Rymer PD (2019) Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species. 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Selbyana 11:6\u0026ndash;21\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Arecaceae1, Genetic diversity2, Population genomics3, Molecular markers4, Atlantic forest5, Local adaptation6","lastPublishedDoi":"10.21203/rs.3.rs-7862000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7862000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePalms play crucial ecological and economic roles, and \u003cem\u003eSyagrus romanzoffiana\u003c/em\u003e is a widely distributed species in South America. Despite its ecological importance and economic potential, population-level genetic studies of this palm remain limited. Here, we assessed the genetic diversity and structure of \u003cem\u003eS. romanzoffiana\u003c/em\u003e across distinct vegetation types in Brazil and Paraguay using 24,859 single nucleotide polymorphisms (SNPs) derived from genotyping-by-sequencing (GBS). We analyzed 91 individuals from eight populations, revealing high genetic diversity within populations and moderate differentiation among them. Observed heterozygosity was generally high, with some populations showing an excess of heterozygotes, consistent with outcrossing and gene flow. Pairwise F\u003csub\u003eST\u003c/sub\u003e and AMOVA confirmed that most variation is maintained within populations, while genetic structure analyses highlighted the influence of vegetation type on differentiation, with \u003cem\u003erestinga\u003c/em\u003e populations displaying unique signatures. Despite geographic proximity, some populations exhibited greater divergence due to local environmental conditions, supporting isolation by environment. Environmental association analyses (LFMM and RDA) further identified SNPs linked to temperature and precipitation, reinforcing the role of climate as a driver of adaptive differentiation. These findings emphasize the role of ecological factors in shaping genetic variation in \u003cem\u003eS. romanzoffiana\u003c/em\u003e and illustrate how population genomics of widespread, non-threatened palms can provide critical baselines for conservation, restoration, and sustainable management under ongoing habitat fragmentation and climate change.\u003c/p\u003e","manuscriptTitle":"Genome-wide SNPs reveal adaptation and population structure in Syagrus romanzoffiana (Arecaceae) from southern South America","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 18:02:09","doi":"10.21203/rs.3.rs-7862000/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T16:54:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T20:26:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T18:18:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37829696671727592964535787842729644459","date":"2025-10-30T11:38:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153880688212734827549250963297208373340","date":"2025-10-28T17:07:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5178872609483416369021064936560277337","date":"2025-10-23T14:37:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T14:32:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-18T13:44:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-18T13:43:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2025-10-14T21:24:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"74a5967b-8246-484c-aecd-423c1d1a546d","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T01:08:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 18:02:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7862000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7862000","identity":"rs-7862000","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}