Teira dugesii lives in starkly contrasting shingle beach and inland habitats on the island of Madeira. We used a matched pairs sampling design to examine morphological and genomic divergence between four beach and adjacent (<1 km) inland areas. Beach populations are significantly darker than corresponding inland populations. Geometric morphometric analyses reveal divergence in head morphology: beach lizards have generally wider snouts. Genotyping-by-sequencing allows the rejection of the hypothesis that beach populations form a distinct lineage. Bayesian analyses provide strong support for models that incorporate gene flow, relative to those that do not, replicated at all pairs of matched sites. Madeiran lizards show morphological divergence between habitats in the face of gene flow, revealing how divergence may originate within small islands.Limited spatial separation within small islands suggests that observed population divergence may occur due to habitat differences without interruption to gene flow but strong evidence of this is scarce. The wall lizard  Analyses of colouration, head shape, and genetic markers across pairs of neighbouring beach and inland habitats reveal replicated patterns of morphological divergence in the Madeiran wall lizard, despite gene flow between habitats. One scenario is the existence of strong divergent selection across different environments. In principle, divergence could accumulate around loci under selection while neutral loci will be homogenized by gene flow4. There is empirical evidence that neutral gene flow continues following colonization of a novel environment5. However, it is also possible that gene flow could be reduced at all loci mediated by, for example, selection against migration when this leads to lower fitness in the new environment8. The number of clear examples is relatively small and the identification of new model organisms is needed to obtain greater insights into the population genomics of divergence between environments.Gene flow impedes divergence between populations by reducing differences in allele frequencies and facilitating the disruption of associations across loci. Nonetheless, detailed population genomic studies of some model organisms, including sticklebacks12, with volcanic events such as debris avalanches and major lava flows often being implicated14.Small oceanic islands have provided excellent models for studies of divergence and speciation, with lizards being one of the more frequently-studied vertebrates. Many island species occupy a greater variety of environments than continental counterparts and this can provide opportunities for examining divergence and gene flow between habitats. To date, there seem to be few examples that demonstrate within-island adaptive divergence with ongoing gene flow. Instead, many studies have shown that either historical or current interruptions to gene flow have contributed to population divergence and speciation16 although, as described above, interruption of gene flow by spatial isolation is also likely to have been important17. In addition to these historical approaches, population-level studies of species showing incipient divergence within a single island could provide better insights into whether population isolation is a prerequisite for within-island evolution. These studies are facilitated by methods that can examine historical and current gene-flow based on the coalescent20, with recent approaches being highly suitable for use with genomic data21. Here we examine habitat-associated divergence between populations and the degree of gene flow between them.A better understanding of how within-island divergence originates may also be important in explaining how island communities develop. Adaptive responses to different microhabitats seem to partially explain the existence of sets of species comprising distinct ecomorphs within some islands22. Divergence in dorsal colour was studied as this has been found to vary in a way that appears to enhance crypsis on different backgrounds in other small vertebrates, such as lizards and mice25. Morphological divergence is less well-known over such short distances but has been detected for this species26. We found consistent patterns of divergence in both of these groups of characters, replicated across sample locations, which provided a platform for testing our main hypothesis that this had occurred in the face of gene flow. We tested genomic divergence between matched pairs of adjacent habitats and detected ongoing gene flow in every case.The first aim was to test for morphological and colour divergence in a lizard between several pairs of similar adjacent habitats: parallel patterns of divergence at different locations can substantiate the hypothesis of divergent selection10-transformed R, G and B luminances of the 6 colour characters indicated that habitat, locality and habitat-locality interaction were all highly significant . The effect size for males was considerably greater for habitat  than for locality  and habitat-locality interaction . For females, habitat, locality and their interaction were also highly significant . Again, the effect size was much greater for habitat  than for locality  and habitat-locality interaction .Beach (B) individuals had lower luminance  on average than inland (I) individuals. A two-way MANOVA on logDFAs revealed that most of the variation across the eight locality/habitat groups was expressed by the first two discriminant functions (DFs). Concordant with the finding of a significant habitat effect (above), the individual DFA plots  showed that beach individuals were clearly divergent on male and female DF1 axes with largely negative scores at all localities , locality , and locality–habitat interaction . Analysis of male head size revealed greater within-group variation (due to larger size differences in samples) and so five small outlying males were removed prior to the two-way ANOVA to meet the assumptions of normality and homoscedasticity. The ANOVA revealed a habitat effect that was close to the 5% significance level  a significant difference between locations  and a large interaction effect . Although variation was significant, there were no consistent patterns of variation in mean size between habitats or between localities. Inland males from locality 2 , while locality  and interaction  were not significant. For the first 21 PCs analyzed for females, habitat again had the largest effect size and was highly significant , locality was significant , but the habitat-locality interaction was not significant . .For male head shape, two-way MANOVA of the first 23 PCs revealed that habitat had the largest effect size and was highly significant  illustrated how females were divergent between B and I sites at all localities and the direction of the divergence was the same in all cases, i.e., individuals at B localities had a broader snout than I individuals Fig. . A paral10 RGB values indicated significant effects for all terms in the model . Habitat showed a very large effect size  relative to locality  and the interaction . .As expected, B and I sites differed both in terms of the substrate RGB scores  and percentage vegetation cover . For luminance, a two-way MANOVA on logThe DFA on the three RGB values showed separation of B and I sites, irrespective of locality Fig.  with minNo vegetation was found in any of the B samples, while median vegetation cover was greater than 60% at all I samples  was obtained by removal of SNPs that showed patterns expected under selection , together with sampling of one SNP per tag.A total of 52 outlier SNPs were detected within the ALLSNPS data, after Bonferroni correction, using pcadapt. Of these outliers, only four SNPs showed a significant association with habitat type, but none of these were located on the same tag. Three of these were significant for 7/10 or fewer bayenv replicates. One SNP showed a significant association with habitat type for 9/10 replicates, although none of the other SNPs on the same tag were outliers.ST summary statistics are provided in Supplementary Table Pairwise FP = 0.0002), as well as significant global structuring .The sPCA on the thinned dataset revealed significant local structuring among neighbours within the localities  Table . The modTeira dugesii, it has been estimated at 2.1 years in another wall lizard27. Mean migration rate estimates were mostly higher from inland to beach than vice-versa.The favoured model (TWOGFLOW) indicated lower migration rates per individual immediately following divergence, followed by a more recent period of higher migration rates. This pattern was replicated at all four localities  but our findings differ in detail. The previous study described differences in perceived darkness of the lizards and relative digit and tail length, but not in relative head width (after adjustment for body length). Hence, the variation that we found in head shape was largely unexpected. Although the morphological divergence in colour and head morphology is highly statistically significant, it is also clear that there is considerable morphological overlap between habitats for both of these sets of traits. This would largely be expected under gene flow.Beach/inland morphological divergence had been reported previously at one of our localities . This was proceeded by a long period of lower gene flow, prior to higher gene flow during more recent times. High levels of allelic exchange between habitats seem unquestionable due to the extremely abundant and ubiquitous nature of this species, particularly in coastal areas. The only likely unsuitable habitat between our beach and inland sites were one or more narrow roads but these should not present a barrier, as we frequently saw lizards on/around roads.Genomic analyses of models of divergence showed similarities at all matched pairs of beach and inland sites. The same gene flow scenario was supported in each case: initial divergence following the assumed colonization of the beach from the inland. Beach-living is fairly unusual in this lizard group so it is assumed that 29 and/or reduced migration between beach and inland habitats. There is no obvious historical scenario that might explain this, although it would fit an island-mainland model, involving the creation of relatively isolated coastal demes by colonization from inland habitats. Migration rates could have subsequently increased after changes in coastal topography/sea-level. Sea-level fluctuations have had an impact on coastal communities30 with dramatic changes in habitat availability expected even during the recent 18,000–6000 years BP period, when ~130 m rises in sea level were evident31.The asymmetric gene flow with generally higher rates from inland to beach sites would be predicted between a large metapopulation (the main island) and a peripheral habitat . In contrast, the finding that historical gene flow is relatively lower than more recent gene flow is less easy to explain. Recent gene flow estimates were generally one or more orders of magnitude higher than equivalent estimates during the ancient period of gene flow and are again substantiated by being repeated across the four study areas. The expectation under ongoing divergence might be higher gene flow to begin with, as the beach habitat is colonized, followed by a decrease over time due to evolution of assortive mating at the ecotone32, although the inclusion of both gypsum and lava habitats in that study provided more divergent substrate colours than those described here . Other lizard studies have identified distinct alleles that appear to underpin geographic variation in dorsal luminance/melanism33. In other cases, while divergence in specific genes explained darker Utah lizards on dark volcanic lava flows, simulations also suggested that phenotypic plasticity might have facilitated differences in melanism prior to de novo mutations appearing34. The latter finding could be applicable to beach-living Teira dugesii, i.e., observed divergence represents only the phenotypic stage of this process. However, the considerable variance in dorsal luminance of beach-living populations seems to indicate incoming migration by ‘inland’ alleles (as supported by our simulations): a short term plastic response should more likely lead to dark coloration in all beach lizards. Specific mutations that influence melanin production and underpin variation in dorsal luminance have been identified in several other lizards34 giving weight to the hypothesis that allelic differences in relevant genes (such as MC1R) are responsible for the divergence in luminance here.The hypothesis of divergent selection obviously requires that variation in dorsal colour and head shape is underpinned by allelic differences. We also note that if underlying allelic differences are present then the replication of the beach-inland patterns across the four areas clearly favour selection rather than drift. Colour variation in three North American lizard species across extreme white gypsum, dark lava flows and more typical dark brown background colours does not seem to be explained by phenotypic plasticityPodarcis muralis is likely to be inherited35. Studies that identify potential genomic regions that might underpin these morphological characteristics are clearly needed.Genetic components of variation in head morphology are less well-established, although it has been reported that a substantial proportion  of the variation in head morphology of the wall lizard 24, and also different species of Peromyscus mice in Florida and Nebraska36. Tropidurus lizard species in Roraima, Brazil, also show morphological divergence between populations from rock outcrops and savannah habitats37. However, while high gene flow is inferred in these cases38, the current findings are quite novel because the absolute geographic separation is substantially lower. Nonetheless, differences in dewlap colour have recently been described in island Anolis lizards in different habitats that are separated by only a few kilometres and likely to experience high levels of gene flow39, while microgeographic divergence has been described in several other taxonomic groups41. The very close proximity of the distinct beach and inland populations facilitates very high gene flow which should in turn dilute the effects of selection. Little is known about dispersal rates although ranges of introduced populations of another wall lizard  seem to extend by approximately 40–70 m per year42 which is high relative to the separation between our sites. A non-vertebrate example shows how very divergent selection can cause divergence in the face of high gene flow. The marine gastropod Littorina saxatalis differs between intertidal shoreline habitats that can be as close as ~10 m apart40, although migration rates must be much lower than those in Teira dugesii.In vertebrates, between-population divergence in colour mediated by divergent selection has been reported for several lizards from different habitats such as three species that have colonized white gypsum substrates at White Sands in New Mexico43, other island squamates such as skinks44, Uta45, Microlophus46 and the well-known Galapagos marine iguana which is intertidal/subtidal47, but to our knowledge, morphologically divergent intertidal populations have not been described. Future studies will be useful in determining whether the same mutations underpin divergence at different localities or not. For example, it is feasible that some or all of the described divergence is due to changes in allele frequency at the same loci.There are occasional reports of other lizards that inhabit the shoreline, includingisland wall lizards50 indicate that these estimates of divergence times are long relative to the short times under which morphological divergence becomes detectable.Irrespective of the genetic basis, there is some variation between mean estimates of initial timing of divergence which suggests that beach colonization may have occurred at different times across the four localities despite the degree of beach-inland morphological variation being similar. Several recent studies of morphological differences between rural and urban environments26, while a brown/green coloration might be a better match to inland habitats. During fieldwork, Kestrels  were seen nesting and hunting by the coast. Predation by this species is thought to be quite intense52 and could be a potential driver of selection on dorsal colour. The fact that both males and females are darker on the beach suggests that sexual selection is not principally involved in determining colour differences between habitats. There are several possible explanations of divergent selection on head morphology, but it would be speculative to consider these until further data have been collected.At present we can only speculate on how divergent selection might operate in different habitats. Broadly, lower dorsal luminance (more melanic) could enhance crypsis on the darker beaches, as originally postulated by Davenport and Dellinger55. We show that environmental differences between beach and inland habitats may have a much greater impact on morphology than do other quite substantial environmental differences across inland sites56. We also more generally suggest that substantial within-island morphological divergence is most likely to arise when there is either (i) divergent selection that is strong enough to overcome gene flow and may originate following colonization of a novel environment (such as the shoreline) as shown here, or (ii) historical population fragmentation that has impeded gene flow, as shown by previous studies.Overall, this study shows that within-island divergence can originate from differences between habitats alone, without requiring interruption to gene flow. Island topographies, particularly elevations, can lead to extremely heterogeneous environments and this variation is often correlated with within-island variation in lizard morphologyTeira dugesii is endemic to the Madeira archipelago in the Atlantic and its high abundance has been well-documented57. It is found across most habitats in the island of Madeira  from sea-level to the highest peaks where it lives in rocky refuges. It is diurnal in habit and eats invertebrates and vegetable material58. Environment-correlated patterns of morphological variation are evident but appear quite weak relative to other oceanic island lizards56. Also, there is no evidence of strong within-island phylogeographical patterns59 unlike some other insular systems where distributions of divergent ancient lineages are concordant with morphological variation60, making interpretation more complex. This study builds on previous work that described a melanic population from a grey shingle beach in south-east Madeira, i.e., Caniço26. While there are a small number of reports of lizards that inhabit the seashore, e.g., ref. 61, the finding of Teira dugesii living in the intertidal zone was a fairly interesting observation. In addition, the described population displayed morphological characteristics that appeared to be adaptive, such as darker skin pigmentation26.The native lizard T. dugesii reaches very high densities62. All I sites contained loose stone walls, which provide refuges for the lizards. Traps were set along the sides of walls. Locality 1 (Caniço) was selected because it corresponded to the area originally described by Davenport and Dellinger26. Localities 2 (Porto da Cruz), 3 (Paul do Mar), and 4 (São Vicente) contained similar B and I habitats but were all quite distant (range: 13–39 km) from locality 1. Distances between B and I habitats within localities ranged from approximately 0.2 km between 4-I and 4-B, to 0.8 km between 2-I and 2-B.Animal ethics: the study was approved by Liverpool John Moores University Animal Ethics committee on 05/06/19 and fieldwork was authorized by the regional government of Madeira . A matched pairs design was used with B and adjacent I habitats being identified at four localities in different parts of the island  using upright plastic containers baited with fresh tomato. Sample sizes were similar for each of the eight sites . The six body characters on each lizard  with scale bar. From each photograph, overall luminances were determined for the six dorsal/head areas (see below) from the three RGB channels using the multispectral imaging plug-in Micatoolbox v. 1.2210-transformed and significance of variation between localities and habitats was tested using a two-way MANOVA (IBM SPSS Statistics v. 26), following analyses of normality and equality of covariance matrices. Pillai’s trace test statistic was used for both males and females because the residuals of one of the six characters appeared to deviate from normality for both sexes and there was evidence of inequality of covariance matrices. General divergence was also explored using Discriminant Function Analysis (DFA), with individuals grouped according to the eight locality/habitat sites . Overexposed photographs  were not used, so 214 males and 118 females were analyzed , which is defined as the square root of the squared distances between each landmark and the barycentre of landmark configuration66. Generalized Procrustes Analysis was applied following the established geometric morphometric protocol68 to standardize 2D coordinates, after translation, rotation and scaling to the unit centroid size. This generated two covariance matrices, corresponding to the symmetric and asymmetric components of shape69. The latter was discarded from further analyses while the symmetric covariance matrix, which explained the larger percentage of the biological variation within our sample, was used for Principal Components Analyses (PCA: see below).Unless stated otherwise, morphometric analyses of size and shape variables were performed using the programme MorphoJe transformed CS was tested for the main effects of habitat and locality and habitat-locality interaction using two-way factorial ANOVA with IBM SPSS Statistics v. 26. For shape, the PCAs were used to obtain Principal Component (PC) scores that were used for subsequent analyses of head morphology. A two-way MANOVA (IBM SPSS Statistics v. 26) tested for locality and habitat effects on the PCs that represented 95% of the total variation, i.e., we disregarded the PCs that represented least variation. MANOVA assumptions were examined, as for the dorsal luminance data. A DFA was applied to the shape data (as represented by Procrustes coordinates) grouped by the eight locality/habitat sample areas.For head size, log2). Variation in substrate colour was assessed by comparison of means of RGB channels across quadrats, with site and locality as factors, using two-way MANOVA. Percentage vegetation cover was also recorded and compared between sites and localities.Nine or ten standard photographs were taken at each locality/habitat to provide a simple assessment of differences in substrate luminance and vegetation cover. Each photograph was taken adjacent to a trap at which lizards were captured. Photographs contained a standard grey balance target (X-Rite ColorChecker Passport Photo 2) and a square wire quadrat  at 37 °C and digested DNA then recovered using magnet beads and the GBS library prepared using the NGS Fast DNA Library Prep Set . The library was purified and electrophoresed on a 2.5% agarose gel and DNA fragments of 350–450 bp were excised and diluted before paired-end sequencing on a NovaSeq 6000 platform . Quality filtration was carried out; adapters were removed using AdaptorRemoval v2 , and reads with low quality eliminated using FastQC v0.10.170.General genomic divergence between sites was established using genotype-by-sequencing (GBS), carried out as follows by Hangzhou Lianchuan Biotechnology Co., Ltd. Total genomic DNA was extracted from the lizard tail tips. The DNA was incubated with the restriction enzymes 71). A minimum phred score base call quality of 30 was specified. Due to the lack of a reference genome, a mock reference was created from the individual with the greatest number of reads72. Following production of the variant discovery matrix containing all SNPs, variants were filtered largely using the default options except for the following: (1) alternate allele strength parameter  = 0.95, (2) maximum average depth of an acceptable variant (-mxAvgDepth) = 30, (3) minimum average depth of an acceptable variant (-mnAvgDepth) = 3, 4) minimum acceptable proportion of genotyped individuals to retain a SNP position  = 0.90. SNP positions that also showed major heterozygote excess were also removed using VCFtools v. 0.1.1673: these were defined as SNP positions that showed a significant deviation from Hardy–Weinberg equilibrium at the 1% significance level. We also subsampled the full dataset (ALLSNPs) to obtain a thinned dataset with only one SNP per tag (to remove interdependence of SNPs in close proximity) and with any SNPs that appeared to be under selection removed (see later)SNPs were called from the reads that were aligned using a GBS SNP Calling Pipeline , using the ALLSNP data. This involved computing a PCA first (homozygous SNPs coded 0 or 2 and heterozygous SNPs coded 1). PCs with the largest eigenvalues were then input into a DFA. The number of PCs that were retained was determined from comparisons of Root Mean Squared Error (RMSE) and Mean Successful Assignment Rate (MSAR) of individuals to groups following cross-validation (100 training sets sampled from the data).Pairwise F77 on the ALLSNP data. Four groups were specified to capture the observed population genomic structure. In brief, this approach involves a PCA on the SNPs, regression of individual SNPs on the PCAs and then testing of whether each SNPs Mahalanobis D2 distance, derived from the regression coefficients, is significant or not (by comparison with a χ2 distribution). Outliers were defined as those with a minor allele frequency greater than 5% that had a Bonferroni-adjusted outlier p-value <0.1 (with the aim of including most outliers). In the second step, an association between these outlying SNPs and habitat variation  was tested using bayenv278. This analysis used a covariance matrix estimated by Bayesian MCMC analysis of the thinned dataset . The B/I environment at each locality/habitat sample was specified using a binary variable. Bayes factors were obtained for all SNPs. Due to the stochasticity of this MCMC analysis, ten independent runs  were carried out with 1000000 MCMC steps in each.Potentially divergent selection on all SNPs between B and I habitats was tested using a two-step process. We first detected outlying SNPs using the pcadapt package v. 4.3.3, within R79 (multispati command), using site latitudes and longitudes. PCA scores were obtained from the thinned data were used as input. Spatial information was supplied through a connection network of distances between sites, which allowed B/I individuals from the same locality to be specified as neighbours and those from different localities to be specified as non-neighbours. Significant local structuring occurs when genetic differences between neighbours are greater than those between randomly-selected individuals  while global structuring occurs when genetic distances between non-neighbours are greater . Eigenvalue tests (9999 randomizations) were used to test for local and global structuring80.Spatial structuring was also investigated using spatial PCA (sPCA), as implemented in the R package adespatial81 which estimates a tree representing historical population splits from population allele frequency data derived from genome-wide SNPs. No outgroup was available so historical migrations could not be inferred . The thinned dataset was used for this analysis and support for the observed splits was obtained using trees obtained from 1000 bootstrap replicates.The hypothesis that populations from B sites formed a separate lineage from the I sites was examined using Treemix21) which implements a maximum likelihood approach to predict the SFS under each scenario for subsequent comparison with the observed SFS. The scenarios that were modelled were: (i) divergence without subsequent gene flow (NOGFLOW), (ii) divergence followed by constant gene flow (ONEGFLOW), (iii) divergence followed by two different periods of gene flow (TWOGFLOW) to accommodate, say, higher gene flow after divergence but lower gene flow nearer to the present. All SFS were obtained from SNPs with no missing values for all individuals within the four B/I habitat pairs. To help decrease the number of SNPs that showed missing values, the three individuals with most missing SNPs were removed from each sampled habitat, except for site 4 where only two individuals were removed. Two sets of analyses were carried out for each B/I pair using: (i) within-locality datasets subsampled from the full dataset (these are referred to as ALLSNP datasets and used to obtain parameter estimates), (ii) within-locality datasets subsampled from the thinned dataset, excluding any outliers determined by the pcadapt analysis (referred to as INDSNP datasets and used for model comparisons). The greater number of SNPs in the ALLSNP datasets should provide better parameter estimation21, but non-independence of SNPs may affect the robustness of likelihood-based model comparisons. Another reason for using the ALLSNP datasets was that reasonable estimates of the number of monomorphic sites could be used, allowing a fixed mutation rate . The number of monomorphic sites was estimated by first calculating the reduction in the number of SNPs from the master matrix containing all potential SNPs to the final set of filtered SNPs. We then assumed that this reduction reflected the reduction from the total number of sites sequenced to the total number of sites used . Potential errors in inference arising from the estimation of monomorphic sites should be relatively small, because (i) the number of monomorphic sites hugely exceeded the number of SNPs and was similar for all matched pairs, and (ii) identification of the best gene flow model and relative comparison of parameter estimates between regions was more important than precise parameter estimation .Joint folded site frequency spectra (SFS) were used to compare three B/I scenarios of divergence at each of the four localities using the programme fastsimcoal2  was compared between models. We also assessed stochastic variation in likelihood estimation by rerunning the fastsimcoal2 analyses 100 times using the parameters obtained for our best model.5 coalescent simulations, 50 optimization cycles, and 40 replicates.Confidence intervals for the ALLSNP parameter estimates were obtained using the parametric bootstrap. For each locality, 100 SFS were generated to reflect the observed amount of genomic data structured as 300 bp contigs, reflecting our illumina reads. The parameters of the best model ALLSNP  for the locality analyzed were used to generate these bootstrap replicates. These SFS were individually analyzed using the observed SFS for each run starting from the values obtained from the best run with the real data, based on 1 × 10Statistical analyses were carried out using the programmes described above. Sample sizes per site for the morphological and genomics analyses are given in Supplementary Table Further information on research design is available in the Supplementary InformationReporting Summary


Castanea sativa Mill.) are planted within the natural distribution area of the species. Thus, we investigated the population structure of several orchards of sweet chestnuts. Furthermore, the genetic background of three toponymous clonal varieties was explored. Six genomic simple sequence repeat (gSSR) and nine EST-derived SSR (EST-SSR) loci were utilized in this research, and both grafted and non-grafted individuals were included in this study. Five closely related clones were identified, which represent a singular, polyclonal marron variety, found in all three cultivation areas. Furthermore, many hybrids, a result of breeding between cultivated and wild chestnuts, have been found. Analyzed semi-wild orchards defined by a diverse genetic structure, represent a hotspot for further selection and could result in creation of locally adapted, high-yielding varieties.Gene flow between cultivated and wild gene pools is common in the contact zone between agricultural lands and natural habitats and can be used to study the development of adaptations and selection of novel varieties. This is likely the case in the northern Adriatic region, where centuries-old cultivated orchards of sweet chestnut ( On the other hand, genes originating from cultivated populations have been shown to influence the genetic structure of wild populations in numerous species3. This gene flow between wild populations and cultivated varieties of the same or closely related species is well-known4 and has been observed when cultivated varieties are planted within the natural range of their wild relatives7, with spontaneous hybrids known to form their own distinct populations and persist in the wild9. While this introgression can lead to the extinction of the wild lineages10, alternatively it may cause increase of genetic variation and the creation of novel adaptations11.While the cultivated plants are characterized by limited genetic diversity, resulting from the process of domesticationCastanea sativa Mill.), which has spread both naturally and aided by humans throughout the Mediterranean region and Central Europe12, especially during the time of the Roman Empire13. Together with olive tree15 and grapevine17, this species played a crucial role in sustaining the Mediterranean and south-Alpine communities, particularly during the Middle Ages18. As a result, countries with natural populations of this tree have created and cultivated their own local varieties20. Although a multi-purpose species used for its fruits, wood, honey and tannins21, selection of the cultivated varieties of sweet chestnut primarily focused on traits pertaining to fruit22. As a result, varieties known as marrons, characterized by sweet fruit of high quality, were developed and are still subjected to the official standards in both France and Italy23.A species illustrating the complexity of the cultivated-to-wild gene flow is the sweet chestnut (25 and genic EST-derived (EST-SSRs)27 markers, as well as chloroplast DNA (cpDNA)28. Three main gene pools had been revealed, coinciding with major countries of production29 and genetic divergence between the eastern (Greek and Turkish) and western  populations was determined, coinciding with the glacial refugia of the south European flora30. In addition, a trend of genetic separation of populations from north to south was noted26, but general haplotypic diversity is low, most likely due to a strong human impact on the species28. Human influence on the species is also evident in the high number of marron varieties, with over 300 known in Italy alone31. The diversity of European marron varieties has been analyzed using gSSRs in Switzerland32, Italy35 and the Iberian Peninsula41, as well as using the EST-SSRs markers in Italy and Spain42. In addition, researchers looked into introgression and hybridization between cultivated and wild populations45.Due to the importance of chestnuts, the diversity of the wild chestnut germplasm in Europe has been extensively researched using the genomic (gSSRs)46. In addition, both wild and cultivated chestnuts have become an integral part of the local folklore and customs and festivals, such as “Lovranska marunada” (http://www.marunada-lovran.com) which take place every Autumn. Since family-owned orchards are located within the natural populations, both wild and cultivated trees can freely intermix. In addition, locally known “marušnjak” trees, hybrids between the two, are being cultivated in the same manner as the grafted clones, albeit on a smaller scale45. Nowadays, the production of marrons is being popularized and neglected or abandoned orchards are being revitalized19.One of the chestnut growing regions of Europe is the north-eastern coast of the Adriatic, with the majority of orchards in the regions of Istria and Kvarner, characterized by Sub-Mediterranean climate, which provides optimal conditions for both, cultivated orchards, as well as native stands of wild chestnuts. As a result, production thrives and marron exports from these regions have been recorded as early as the seventeenth century48. As a result, the dominance of one genotype was determined, but studies were conducted on a small number of individuals using a limited set of markers and thus revealed only a part of the genetic diversity. In addition, further research into morphological and chemical variability of marrons in the Lovran area was conducted, uncovering that the dominant genotype can be considered a marron variety, according to both French and Italian standards20. Unlike the orchards of Lovran, orchards in Lovrin and Cres have never been researched, thus opening the possibility for additional varieties to be confirmed. In addition, orchards in all three cultivation areas are located within or in the vicinity of wild chestnut populations, enabling gene flow between the wild and the cultivated trees. This introgression of genes from cultivated trees could alter the genetic structure of the wild populations and produce hybrids, which could spread through both wild and cultivated populations.This newly rekindled interest for marrons has brought back the traditional varieties into the focus of the producers. In Istria and Kvarner regions, three toponymic marron varieties are known locally: the ‘Lovran Marron’, the ‘Lovrin Marron’ and the ‘Cres Marron’. While ‘Lovran Marron’ is very well known and commonly grafted within the orchards, the remaining two varieties are largely overlooked, and their cultivation is on a much smaller scale. Although known by the local producers, data on the variability and characteristics of the marrons in this region is limited, with previous research only conducted in the Lovran areaDue to the unique combination of actively cultivated and neglected orchards as well as wild populations in a relatively small area, chestnut varieties from the northern Adriatic provide a unique opportunity to study the process of gene flow between populations of a partially cultivated tree species. Therefore, the aim of this research was to determine and describe three cultivation regions of sweet chestnuts in the regions of Lovran, Lovrin and island Cres as possible selection zones for traditional varieties in the past, with the possibility of determining and describing three varieties locally believed to exist. Six genomic simple sequence repeat (gSSR) and nine EST-derived SSR (EST-SSR) markers were used to explore the population structure of the orchards, with the aim of confirming the existence of three distinct varieties within the 219 sampled trees which included potential marron clones within 95 visibly grafted trees. Additionally, we analyzed potential hybrids between cultivated and wild trees, as well as ungrafted trees within orchards, which could have spontaneously spread into them from surrounding forests.From the total of 219 individuals included in the research (ALL), 141 were classified as non-redundant multi-locus genotypes (MLG). While the ALL group encompassed all sampled trees, the MLG group consisted of only those genotypes which were distinct. Microsatellite diversity was therefore analyzed on both levels, for all individuals (ALL) and non-redundant genotypes (MLG).Na) varied between gSSR and EST-SSR markers, from 12 (CsCAT6) to two (FIRO30). gSSR markers have shown to be significantly more variable, with average alleles number of 8.667, in comparison to 3.778 for EST-SSR markers (Table Ne) was also higher for gSSRs in both ALL and MLG, with mean values of 2.444 and 2.695, respectively, but has not shown significant differences between the two groups of markers. Number of minor alleles (Nma), with frequency below 5%, was significantly higher for the gSSRs, with average value of 5.500 for ALL and 5.167 for MLG, in comparison to the EST-SSR values of 0.778 and 0.667, respectively.All 15 gSSR and EST-SSR markers were polymorphic, with a total of 86 alleles revealed, 52 for gSSR and 34 for EST-SSR respectively  to 0.708 (CsCAT3), whereas the MLG demonstrated mostly higher PIC values, with range between 0.013 (EMCs15) and 0.785 (CsCAT3), with three markers showing lower PIC values for MLG than ALL  and marker FIRO30 having the same PIC value in both ALL and MLG (0.052). Most informative loci for the MLG were PIE228 and CsCAT3, with PIC values above 0.7, whereas eight markers  were moderately informative, with PIC values within the range 0.412–0.691. When observing all individuals (ALL), only CsCAT3 marker was highly informative (0.708), whereas moderately informative loci remained the same as in MLG. No statistically significant differences in PIC values between the EST-SSR and gSSR marker groups were determined.Nav) was determined for P1 (5.267), as well as the highest levels of allelic richness (Nar) (5.267). In addition, P1 was characterized by the largest number of private alleles (Npra) (15), compared to significantly lower numbers for P2 (three) and P3 (four).To observe the allelic diversity on the population level, three groups of individuals were considered: P1 (Lovrin), P2 (Lovran) and P3 (Cres), with 44, 83 and 92 individuals sampled in each group, respectively (Table Ng) were identified, with the highest number of distinct genotypes found in P3 (84), followed by P1 (44) and P2 (17). The most common genotypes, with all individuals showing visible graft joints, were those belonging to the M001–M005 group, with the prominent predominance of the M001 clone. This genotype was found only in P2 and P3 and accounted for 78.31% and 3.26% of the total sample size in these populations, respectively. Clones M002–M005 accounted for 2.27%, 6.02% and 9.78% of the total sample size in P1, P2 and P3, respectively. Due to the predominance and clear clonal origin, the clonal group M001–M005 was named the “Marron clones” and analyzed in depth. Number of unique genotypes (Nug) was 136, with the majority found in P3 (80), accounting for 86.96% of the total sample in that population. In addition, 43 unique genotypes in P1 represented 97.73% of the total sample size, signifying these two populations as highly diverse. In that population only one out of the 44 individuals, was a ramet of a clone found in the M002–M005 clone group, i.e., the remaining 43 were individuals not found anywhere else. On the other hand, P2 was characterized by having only 13 unique genotypes, representing 15.66% of the total sample size. In addition, this population was noted to have the highest number of grafted individuals. This is in agreement with the very high number of P2 individuals belonging to a single clone, M001, and further supported by values of genotypic richness (R) and Simpson’s complement index (D*), both of which demonstrated low values in P2, 0.195 and 0.388, respectively. In comparison, the most diverse population, P1, scored values of 1.000 for both parameters. In all three populations, expected heterozygosity (HE) was lower than the observed (HO), indicating high levels of genetic variability, in accordance with previously mentioned results.A total of 141 distinct multi-locus genotypes , and a similar FST value was noted between P2 and P3 . The lowest value of FST was between P1 and P3 . In addition, analysis of molecular variance (AMOVA) showed statistically significant differences among populations, with 10.83% of total variation attributed to inter-population variability and 89.17% of variability resulting from intra-population variability  was K = 3, determined both by calculating ΔKTo further investigate the population structure of the researched genotypes, an unrooted neighbour-joining tree was created Fig. . The resRelatedness between the most common genotype M001 and the remaining 140 unique multi-locus genotypes was analyzed and the number of differentiating alleles was determined  was calculated for all genotypes relative to genotype M001 , with 25 (58.14%) found in P1, one (7.69%) in P2, and 40 in P3 (50.00%).The coefficient of relatedness , half-siblings (HS), full-siblings (FS), and parent-offspring (PO) using ML-Relate Fig. . All fou51 and cultivated varieties42. The most and least informative gSSR loci were CsCAT3 (PIC = 0.785) and EMCs15 (PIC = 0.013), respectively, which was expected as the same was reported previously37. The Na values obtained in this research (8.67 for gSSR and 3.78 for EST-SSR) fall within the range of those previously reported in southern Europe for both gSSR and EST-SSR markers52, with ranges of 4.75–13.25 and 0.78–4.00, respectively. The data differences between gSSR and EST-SSR marker groups stem from the different regions of genomes they assess. While SSRs are located in introns or noncoding regions of the DNA, EST-SSRs are particularly associated with functional genes53. EST-SSRs are more conserved due to their linkage to a certain gene and are thus a cost-effective and labour-saving option in assessing the genetic diversity within or adjacent to a certain gene, whereas gSSRs' abundance in the genome, easy reproducibility, and high polymorphism makes them specifically suitable for germplasm characterization, variety identification and paternity analysis56.Our results confirmed the high effectiveness of both gSSR and EST-SSR markers in describing the genetic diversity of sweet chestnut, as was previously reported for wild populations51 and Switzerland25, and slightly lower than those reported in Bulgaria57, Italy30 and Greece26. In comparison to populations from Asia Minor and the Black Sea region, defined by the highest genetic variability of chestnut populations resulting from being at the meeting point of two glacial refugia58, values obtained in our research were understandably lower. As a result of land abandonment and depopulation in the twentieth century19, these orchards have been left unattended and are, to a large extent, overgrown by natural vegetation. The influx of wild chestnut genes has increased the diversity of these populations and nowadays populations P1 and P3 are genetically closer to wild chestnut stands than to cultivated orchards. Although clonal individuals were found in P3, the majority of noted grafted trees were dead and, together with the lack of newly grafted individuals, indicated these clones are likely the last individuals from the once cultivated orchard. Land abandonment to this degree has been shown to influence spontaneous landscape restoration and rewilding, particularly in the Mediterranean basin59 and, in synergy with the differences in the habitat conditions, certainly affected the levels of genetic diversity noted in P1 and P3.Although considered to be orchards at the beginning of our research, P1 and P3 demonstrated genetic diversity levels more akin to those of wild chestnut populations, with expected and observed levels of heterozygosity and genotypic richness values similar to the previously reported in Spain.29. Most of these orchards have been cultivated by local families in the Lovran area, with the knowledge of grafting techniques of the ‘Lovran Marron’ passed on for generations60. This is evident in the high number of clonal genotypes, with 70 individuals belonging to the M001 genotype and M002–M005 genotype group. Low allelic diversity of the population P2 is further illustrated by only 13 distinct genotypes found, as well as very low values of genotypic richness (0.195) and Simpson's complement index (0.388). This suggests the Lovran area is the original cultivation zone of marrons in the North Adriatic region, from which the culture of chestnut spread to Lovrin and Cres, albeit to a lesser degree.Unlike the naturalized state of populations P1 and P3, population P2 demonstrated low genetic diversity akin to an orchard. Pronounced human influence and longer periods of vegetative propagation present in P2 likely maintained the low genetic diversity of this population, as previously reported by Mattioni et al57. These values indicate a lack of intensive cultivation and high genetic diversity on population level, although great differences in genetic diversity and clonality between the populations exist, with population P2 clearly demonstrating the strongest influence of clonality on its genetic diversity. In addition, these high levels of clonality in P2 have led to significant differences to be noticeable between P2 and the remaining two populations. However, as P1 and P3 are characterized by a larger number of distinct genotypes, and similarly low degrees of human interference, these two populations are genetically statistically significantly different. Variable conditions in the environment could have influenced these differences since heterogeneity of habitat is known to play a major role in genetic differentiation62. Almost a complete lack of human influence on the P1 and limited human influence in P3 meant that the natural processes of selection, with alleles providing adaptation to specifics of the habitats of each population63 were far more important than the selection brought on by cultivation. Alternatively, P1 and P3 could be the result of two separately introduced gene pools, as opposed to a series of natural colonization events45. As pollen analysis in the eastern Adriatic revealed, anthropogenic influence on native species was significant, dating back to the times of Greek and Roman colonization65.The results of AMOVA indicate 89.17% of total variance to be assigned to variation within each population, whereas only 10.83% referred to the variation among populations in accordance with other research on sweet chestnut populations in Europe45. In this previous research, ten markers were used to explore the structure of local orchards and the predominance of one genotype was determined. Out of those ten markers, six were also used in this research and have revealed the most common genotype observed in both the research by Poljak et al.45 and this research to be the same. Male-sterile and unable to pollinate, M001 nonetheless had proven to differ in a single allele from the M002–M005 clones, demonstrating a full-sibling relationship to them. Such relationship can be the result of one of two possible events in cultivation history of the clones. The likelier theory is that of the shared ancestor, i.e., the existence of ancestral polyclonality. This theory is based on the highly probable cross-pollinations within the ancient marrons, present in the Lovran area, before the male-sterility arose. This would have led to small genetic differences, and ultimately to inbreeding, evident as the present-day-male-sterility, as previously proven for other plant species67, as well as animals68. The selection that must have occurred favored individuals with appreciable fruit traits and this, in addition to male-sterility, remains even today an important trait in discerning the traditional variety known as ‘Lovran Marron’. Considering all five genotypes have developed a common phenotype and are, due to common methods of vegetative propagation, cultivated in the same manner, they have continued to co-exist and can be considered a singular, polyclonal variety ‘Lovran Marron’. Similar results were reported for traditional Portuguese olive (Olea europaea L.) variety ‘Galega Vulgar’69 and Croatian varieties ‘Lastovka’, ‘Oblica’ and ‘Drobnica’70, with common polyclonal ancestry also revealing Croatian variety ‘Bjelica’ and Montenegrin variety ‘Žutica’ to be synonymous71. Based on these facts, ‘Lovrin Marron’ and ‘Cres Marron’ should not be considered different varieties; rather, all genotypes from M001–M005 should be considered as a singular, polyclonal variety ‘Lovran Marron’. Although not identical, these genotypes are cultivated in the same manner, further supporting the polyclonal theory. The alternative explanation of the relationship between the M001–M005 clones is the accumulation of somatic mutations, through which from one ancestral genotype (M001), other four MLGs would have developed. Such mutations can accumulate without affecting the phenotype since gSSR regions are neutrally evolving and intrinsically very variable72. However, this theory is less likely, due to the relatively short time the mutations could have accumulated, of only three centuries, as opposed to ancient varieties of olives, in which such occurrences are well-documented73. On the other hand, sexual propagation and subsequent grafting has been shown to influence intravarietal variability of Iberian chestnut varieties74. Here, chestnut growers planted seeds of known varieties and, when seedlings were found to produce nuts with identical parameters, cultivated them under the same name, increasing intravarietal variability along the way. Alternatively, seedlings that produced seeds with different traits were named as a new variety and further cultivated by clonal propagation. This was evidenced by a frequent parent–offspring relationship between the main variety and its generative offspring. However, in this study, this theory is less likely because clones M002–M005 differ from the most common clone (M001) in a single allele, which excludes the possibility that they are its offspring resulting from a cross with wild chestnut.The most common genotype overall was the genotype M001, found in populations P2 and P3, with the second most common genotype being M002, with all five individuals found in P3. Ten individuals were assigned to genotypes M003–M005 and were distributed in all three populations. Genotype M001 was predominantly found in the cultivation area of P2, which was previously investigated by Poljak et al.45 and within orchards and their presence created the opportunity for a second wave of human selection, particularly considering the intermediate values of both leaf and fruit morphometric traits and chemical composition of these “marušnjak” fruits20. In turn, they could prove to be a valuable source of genetic variability, resulting from possible hybrid vigor75. Unlike the population P2, populations P1 and P3 demonstrated lower relatedness to M001, with over 50% of all individuals in both populations demonstrating r value of 0.00 and less than 20% of individuals defined by parent–offspring relationship. These results could be attributed to the conditions noted in the field. In these populations, the orchards are mostly neglected, with surrounding forest vegetation and wild chestnut trees taking over. In addition, large numbers of visibly grafted but dead trees have been observed in P3. Overall, populations P1 and P3 can be considered as an intermediate state between a cultivated orchard and a wild population. In other words, characteristics of the genetic diversity in these populations bear witness to past cultivation efforts, in form of the grafted trees, whilst a large number of non-grafted trees represent the succession of the wild forest back into the neglected orchards. The long history of broadening the natural distribution of sweet chestnut and the domestication within the range of local population had influenced the germplasm diversity of Switzerland25, whereas intermixing of the cultivated genes with the surrounding wild populations influenced the populations of Spain51 and Italy29. In addition, in France41, Italy and Spain38, the interaction between the orchards and the wild populations can particularly be observed in the “instant domestication” cases, i.e., grafting of the spontaneously grown chestnuts, which diminishes the genetic differences between the orchard and the wild population.Alongside with grafted, now proven clonal individuals in P2, non-grafted individuals related to M001 were found within the P2. These individuals are locally known as “marušnjak” trees and represent the long history of breeding between the cultivated and wild individuals, i.e., M001 progeny pollinated by a wild parent. These individuals are found in both wild populations76, as well as genera Beta L.77, Malus Mill.10, Medicago L.78, Cichorium L.8, Lactuca L.79 and Solanum L.80. Although some authors express concerns about the negative impact of this gene flow on the extinction of the wild populations83, the occurrence of “pestification”85 and the spread of transgenes from the cultivated crops to wild populations79, hybridization may, in contrast, increase genetic variability of a population and promote the emergence of beneficial adaptations11. A similar occurrence has been previously reported for cultivated apple 6, as well as for cacao plant (Theobroma cacao L.)86.Similar genetic interactions between cultivated and wild populations are not uncommon and have previously been noted for 12 of the 13 most important food crops in the worldOverall, the genetic structure of the three cultivation areas we analyzed in this research did not confirm the existence of three locally known varieties. Rather, one polyclonal variety was found and can be considered as ‘Lovran Marron’ variety. The origin of ‘Lovran Marron’ is most likely the result of cross-pollination between ancestral genotypes, which led to male-sterility noticeable today. The possibility of somatic mutations causing or, at least, influencing the genetic variability of the variety cannot be excluded but is less likely to have occurred in such a short span of time. Likewise, it is unlikely the M002–M005 clones are the offspring of the predominant M001 genotype and a wild pollinator since they differ from the most common clone in a single allele. In addition, the prevalence of grafted individuals in the Lovran area indicates this to be the original cultivation area from which chestnut culture most likely spread to Lovrin and Cres. The cultivation-associated genes also spread spontaneously into the wild populations surrounding the orchards, through seeds of ‘Lovran Marron’. This offspring continues to be cultivated, thus broadening the gene pool of the populations, albeit to various degrees. Through this, valuable alleles from wild populations might be reintroduced into the cultivated genotypes and boost their response to changes in the environment, as well as possibly create new and improved chestnut varieties.The research was conducted in the three known marron producing areas of the Sub-Mediterranean zone: Lovran and Lovrin in Istria, and on the island Cres, a part of the Kvarner region Fig. . Three lhttp://dendherbarij.sumfak.unizg.hr/herbariumdend_en.html; Herbarium IDs: 05493-05509).For the DNA extraction and further analyses, fully developed buds were collected during the vegetation season of 2019 from well-lit branches in the crown of the trees. Buds were collected into filter bags and stored in plastic bags with silica gel to dry. Once in the laboratory, samples were kept inside the silica gel, at room temperature and in the dark until further analysis. The collection of plant material was carried out in accordance with relevant institutional, national, and international guidelines and legislation. Voucher specimens were identified by Igor Poljak and Antonio Vidaković and are deposited in the DEND Herbarium, Zagreb, Croatia  produced for Quercus petraea (Matt.) Liebl. and Q. robur L.88 were used. Polymerase chain reactions (PCRs) were performed in a total volume of 20 μL containing 1 × PCR buffer, 1.5 mM MgCl2, 200 μM of each dNTPs, 5 μM forward and reverse primers, 0.5 U TaqHS polymerase  and 5 ng of template DNA. The resulting products were analyzed by capillary electrophoresis on an ABI 3730xL DNA analyzer  provided by Macrogen DNA service , and alleles were scored using GeneMapper software version 4.0 .Dried buds were used to extract total genomic DNA using the DNeasy Plant Mini Kit  according to the manufacturer’s instructions with the addition of 1% PVP and 1% β-mercaptoethanol in lysis buffer. DNA concentration was determined with NanoDrop . For genetic analysis, six genomic microsatellite gSSR markers  developed for sweet chestnut89.The number of distinct multi-locus genotypes (MLGs) in three sweet chestnut populations was identified using GenClone 2.0Na), polymorphic information content (PIC), and probability of identity (PI) were calculated using Cervus v3.090. The effective number of alleles (Ne) was derived from the expected heterozygosity (HE) using the following formula: Ne = 1/(1 − HE). The alleles with a frequency lower than 0.05 (5%) were considered minor alleles. The parameters were calculated using all individual trees (n = 219) as well as non-redundant genotypes . To test the significance of the differences in Na, Nma , Ne and PIC between EST-SSRs and gSSRs, a nonparametric Wilcoxon signed-rank test was performed using SAS v9.391.For each microsatellite locus, the number of alleles per locus (Nav), allelic richness (Nar), and the number of private alleles (Npra). We used FSTAT v2.9.3.2 software92 to calculate allelic richness . Clonal diversity of populations was assessed by calculating the number of multi-locus genotypes (Nc), genotypic richness (R)93 and Simpson's complement index (D*)95 using GenClone v2.089. Genetic diversity of populations was assessed by estimating observed (HO) and expected (HE) heterozygosity using GENEPOP v4.096, excluding redundant genotypes from each population.Allelic diversity of populations was assessed by calculating the average number of alleles per locus 98. The Neighbor-joining tree was constructed using PHYLIP v3.69899. The tree was bootstrapped100 over 1000 replicates generated in MICROSAT and subsequently used in the NEIGHBOR and CONSENSE programs in PHYLIP.Genetic distances between multi-locus genotypes were calculated in MICROSATFST estimates in FSTAT. P values were calculated after 10,000 random permutations. ARLEQUIN v3.5.2.2101 was used for the analysis of molecular variance (AMOVA)102 by partitioning total microsatellite diversity among and within sweet chestnut populations. The significance level of φST was determined by a nonparametric randomization test with 10,000 permutations.Population differentiation was assessed by calculating pairwise 103. Thirty runs were performed with the number of clusters set from 1 to 11, on the Isabella computer cluster at the University of Zagreb (Croatia), University Computing Centre (SRCE). Each run consisted of a burn-in period of 200,000 steps followed by 106 MCMC replicates using the admixture model with correlated allele frequencies. The optimal number of clusters was determined by calculating ΔK49 and MedMeaK, MaxMeaK, MedMedK and MaxMedK50 as implemented in StructureSelector104, which integrates the CLUMPAK program105 used to cluster and merge data from independent runs.The genetic structure of sweet chestnut populations was assessed using STRUCTURE v2.3.4106 was used to calculate a pairwise estimate of the relatedness (r) of M001 to each tree across populations and to discriminate among four different pedigree relationships: unrelated (U), half-siblings (HS), full-siblings (FS), and parent-offspring (PO).A maximum-likelihood method implemented in ML-RelateThe collection of plant material was carried out in accordance with relevant institutional, national, and international guidelines and legislation.Supplementary Information.


Plasmodium malariae, a neglected human malaria parasite, contributes up to 10% of malaria infections in sub-Saharan Africa (sSA). Though P. malariae infection is considered clinically benign, it presents mostly as coinfections with the dominant P. falciparum. Completion of its reference genome has paved the way to further understand its biology and interactions with the human host, including responses to antimalarial interventions. We characterized 75 P. malariae isolates from seven endemic countries in sSA using highly divergent microsatellites. The P. malariae infections were highly diverse and five subpopulations from three ancestries (independent of origin of isolates) were determined. Sequences of 11 orthologous antimalarial resistance genes, identified low frequency single nucleotide polymorphisms (SNPs), strong linkage disequilibrium between loci that may be due to antimalarial drug selection. At least three sub-populations were detectable from a subset of denoised SNP data from mostly the mitochondrial cytochrome b coding region. This evidence of diversity and selection calls for including P. malariae in malaria genomic surveillance towards improved tools and strategies for malaria elimination. An understanding of the distribution of malaria parasites, particularly with respect to candidate drug resistance markers in endemic areas, will further inform the design of malaria control and elimination intervention strategies. Such knowledge has often targeted the two main human malaria species, Plasmodium falciparum and P. vivax, while others such as P. malariae have been largely neglected. A limited number of studies on genetic diversity of P. malariae in sub-Saharan Africa reported high diversity of the parasite species in different endemic regions, but no population structure or significant differentiation was found3. These studies included mostly infections from sites not very far apart, such as was reported for two endemic villages in Malawi that are about 100 km apart2. With additional sources for isolates across sub-Saharan Africa and more neutral genetic markers, a broader view of the diversity of this species could be provided.The genetic diversity and population structure of human pathogens, often derived from molecular genotyping, are useful for monitoring their local and regional transmission patterns and the effect of public health interventions. The population structure of major malaria parasite species are assumed to be driven by geographic isolation, human migration, malaria transmission intensity, and selective pressure by drugs and vector interventionsP. malariae populations. Genetic variations in P. malariae antigenic proteins thrombospondin-related anonymous protein—TRAP, apical membrane antigen 1—AMA1, and 6-cysteine protein—P48/45 were reported in Asia 4. These showed high mutational diversity in P. malariae trap and ama1 compared to p48/45, with mostly non-synonymous mutations that suggest genetic variation in these genes is maintained by positive diversifying selection4. Similarly, new non-synonymous mutations were observed in polymorphic sites of the dhfr gene, leading to the description of six new candidate DHFR alleles associated with sulfadoxine-pyrimethamine (SP) resistance5. This has been further shown in recent P. malariae whole genome sequence analyses that reported four synonymous and seven non-synonymous single nucleotide polymorphisms (SNPs) in some orthologs of drug resistance genes6. All these studies suggest high diversity in P. malariae parasite species as well as frequent recombination and gene flow between populations. To further investigate this, we explored the genetic diversity and population structure of P. malariae across seven endemic countries in sub-Saharan Africa using highly polymorphic microsatellite loci and describe the SNPs in orthologous genes associated with antimalarial resistance in P. falciparum.Targeted characterisation of gene loci has also been explored to describe P. malariae infected samples analyzed were from seven sub-Saharan countries with varying malaria transmission settings and antimalarial drug use and resistance profiles  were used for further genetic diversity analysis as shown in Table Archived blood samples at the Medical Research Council Unit The Gambia (MRCG) and those from collaborators within the Pathogen Diversity Network Africa (PDNA) were accessed following mutual consent and ethical permissions. The es Table . SamplesP. malariae was carried out using five highly divergent microsatellites  in a semi-nested PCR reaction as previously described2. The primary amplification contained 3 µL of P. malariae DNA in a 15 µL reaction, 1 × Thermopol buffer (New England Biolabs), MgCl2 at 1.5 mM, 0.6 U of Taq Polymerase (New England Biolabs), 0.8 mM of dNTPs and 0.05 μM each of forward and reverse primer. Same conditions applied for the semi-nested reaction using 2 μL of the primary amplification product in a 35 μL reaction with forward and reverse primer concentration at 0.1 μM. Cycling conditions for the primary amplification were—initial denaturation for 4 min at 94 °C followed by 35 cycles of denaturation for 30 s at 94 °C, annealing at 48 °C for 30 s, extension for 60 s at 68 °C and then a final extension for 2 min at 68 °C. The same cycling conditions were used for the semi-nested amplification except for annealing at 52 °C and a total of 20 cycles. Microsatellite PCR products were separated on a 3730 capillary sequencer (Applied Biosystems) following dilution (1:50–1:100) and post-amplification mixing of differently labelled and sized loci. Analysis of electropherograms was carried out using Geneious Prime Version 2022.0.1 (http://www.geneious.com) and alleles were scored automatically in comparison with size standard HD400 (Applied Biosystems).Multi-locus genotyping of P. malariae orthologues of P. falciparum antimalarial resistance genes were amplified and sequenced  in R software. In addition, the ‘find.clusters’ function in R’s adegenet package (version 2.0) was used to assign each individual into genetic clusters. Discriminant analysis of principal component (DAPC) was applied to describe clusters of genetically related individuals and displayed using scatter plots. DAPC transforms the data using principal component analysis (PCA), and then performs a discriminant analysis on the principal components retained using a cross-validation method. Ancestry was determined with the admixture model using STRUCTURE Version 2.3.415 with individuals assigned to K populations based on their multilocus genotype. The estimated K likelihood value was set to 1–9 and STRUCTURE was used with 100,000 MCMC Reps over 10,000 Burn-in period. The most appropriate value of K was determined using calculated ΔK implemented in Structure Harvester Web v0.6.9416. Ancestry proportions were then visualized as bar plots.Binned microsatellite alleles were imported as genind objects into R statistical software (Version 4.1.13) and used for population genetic analysis. We initially defined populations as the countries from which the isolates were collected. Population-level genetic diversity was assessed with expected heterozygosity and number of alleles per locus . Values for expected heterozygosity range from 0 to 1 . Diversity parameters such as observed and expected multilocus genotypes, Shannon–Wiener Index of multilocus genotype (MLG) diversityThe quality of Fastq files obtained was checked using FASTQC, then trimmed and filtered to remove poor quality segments and indices. The reads were processed as shown in Fig. P. malariae orthologous drug resistance SNPs by Nei’s genetic distance were visualised as a hierarchical heat map. SNP-based genetic complexity within samples (complexity or multiplicity of infection) was assessed by estimating Wright’s inbreeding co-efficient (Fws). The FWS metric  is a measure of within-host diversity that describes the relationship between the genetic diversity of an individual infection relative to the genetic diversity of the parasite population17. A low FWS indicates high within-host diversity relative to the population. The extent of linkage disequilibrium (LD) between SNPs of the orthologous drug resistance targets was also determined and displayed using the LDheatmap R package.Reads were mapped as merged or unmerged using both BWA and BOWTIE separately. Variant calling from each bam file was done using both BCFtools and freebayes. The resulting vcf files of variants were compared and concordant SNPs were identified for further analysis. To determine the structure of isolates from SNPs, DAPC was applied as described above and the first two coordinates visualised on a scatter plot. Pairwise population comparisons of the 18.All five microsatellite loci were highly polymorphic, with 8–12 alleles and a mean of 9.8 alleles per locus. Pm_09 was the most polymorphic microsatellite and Pm_11 was the least variable Table . The expAt the country population level, only two pairs of MLGs were found in the isolates from Burkina Faso and Nigeria. Genotypic diversity and richness were high as indicated by high lambda or Simpson’s Index and expected heterozygosity—Nei’s unbiased gene diversity Table . The leaP. malariae isolates was independent of their geographic origin  of differentiation between populations was biased by small sample sizes but were low overall, and indication of poor differentiation between country populations due to intrapopulation genetic variation. The SNP data however showed relatively high genetic distance between Cameroon  and Burkina Faso (FST = 0.437)  were identified in the SNP data from all populations, though most isolates had high Fws from the denoised data, suggesting a single dominant genotype  at position S452C aligned in close proximity with the S436A/F mutation in P. falciparum. This was also the case for the nonsynonymous SNP observed in the multi-drug resistance gene (Pmmdr1) at position V100L, lying in close proximity to the N86Y/F mutation in P. falciparum.Sequence alignment of the orthologous drug-resistance genes with BOWTIE identified five more mutations from the unmerged reads compared to the merged reads with both variant calling tools, whereas the merged reads identified significantly more variants with BWA sequence alignment  heat map revealed significant patterns of LD between SNPs on different orthologous drug resistance genes. High rset Fig. a. The hiset Fig. b,c.FigurP. falciparum and P. vivax, but other malaria parasites species such as P. malariae are co-transmitted in malaria endemic regions and warrant attention for achieving elimination. As knowledge of the diversity and effect of elimination tools on these minor species have not had much academic or public health focus, this study took advantage of the Pathogen Diversity Network Africa (PDNA), to collate a small but the widest sample set of P. malariae isolates across seven African countries and one from Asia, describing population structure, high genetic diversity, genotypic richness and evenness in the species of malaria parasite.Malaria elimination programs and tools are focused on eliminating the main malaria parasites P. malariae found here is similar to those previously reported in Kenya and Malawi3, despite the variable and small number of samples analysed from some of the countries. Malaria transmission intensity and history of interventions vary across the different countries represented in this study and could have affected the results. High transmission results in frequent heterologous recombination of the parasite in the mosquito vector, breaking down linkage disequilibrium between variable loci and increasing the genetic diversity within populations. In general, the probability of outcrossing in parasite populations vary from a high to low malaria transmission gradient in the West, Central and Eastly direction in sub-Saharan Africa20. This notwithstanding, heterozygosity was high and genetic distance was low between isolates despite the differences in malaria transmission intensity, except for the three isolates from Cameroon. The low genetic distance observed in this study require further validation since there are no studies on P. malariae for direct comparison. However, the results obtained are consistent with a recent P. falciparum study in Nigeria21 howbeit in sharp contrast with an older study in Senegal22, suggesting relatively high levels of panmixia in the current sampled populations despite the varying transmission patterns. Indeed, multi-locus genotypes were rare across all populations, an indication of recombination and absence of the clonal expansion observed in some P. falciparum populations with low or seasonal malaria transmission23.A high level of genetic diversity is essential for the long-term survival of populations, and the extent of variation determines the ability of the species to adapt to environmental challenges imposed by nature or control interventions. The high diversity in 3. This was evident in low microsatellite differentiation indices between countries, although the small number of isolates per country population limits the accuracy of the indices inferred. It is also possible that gene flow alone does not explain the high genetic diversity or lack of geographic differentiation observed in P. malariae, other factors such as a lack of bottleneck event or intervention to reduce local diversity could also be considered.Recurrent gene flow promotion between parasite populations across countries via human or vector migration, could have led to a lack of differentiation according to geographic originP. falciparum, where genetic clusters can be allocated to geographic populations in the west, central and eastern African regions. As P. malariae mostly occurs as coinfections with P. falciparum, the drivers of such independent substructure may therefore be different, or it is possible that this could have been established by an earlier event preceding any population drift due to demography or isolation. Using the admixture model in STRUCTURE, an optimum of three ancestral clusters were determined and this also showed all isolates with components of each ancestry irrespective of the country of origin. STRUCTURE implements a Bayesian algorithm to identify groups of individuals at Hardy–Weinberg and linkage equilibrium. However, its robustness was shown to be impacted by small uneven sample sizes between subpopulations and/or hierarchical levels of population structure25.Population structure analysis with neutral microsatellite loci (i.e SSRs) identified five clusters, each with isolates from different countries. This is further indication of high intrapopulation variability in these markers and absence of population specific selection of these loci that may drive population differentiation. This structure is not consistent with isolation by distance seen for P. malariae orthologues of P. falciparum drug resistance genes also clustered isolates into 5 subpopulations, although only 3 less distinct clusters were retained after stringent filtration processes, and membership of the subgroups did not overlap fully with those determined by microsatellites. The distribution between the SNP and microsatellite distance differed, with wider distances between smaller number of isolates using SSR data. This is expected as SSRs are multi-allelic, likely neutral, and more likely to differ between pairs of isolates. Thus, further investigation into the possible drivers of population differentiation for this parasite species will improve understanding of its complexity, particularly with regard to control and elimination strategies. While it appeared that most infections had mixed genomes (polygenomic) as indicated by Fws, this was affected by the denoising and filtration pipelines used for analysis. Thus, further investigation using appropriate sample size will be necessary to clarify if co-transmission of different clones and increased possibility of recombination exists for this Plasmodium species. High level of infection complexity is important to note, given that it is one of the indices for monitoring the effect of interventions. Unlike for P. falciparum, the complexity did not seem to be higher in relatively higher malaria transmission settings and may be part of the unique biology of this species that needs further investigation. As drugs and other interventions drive down populations, selection and changes in complexity should be monitored for the species.The SNP data from selected P. falciparum, with resistance associated with mutations in several genes and signatures of positive selection across the genomes. We identified 20 P. malariae mutations in the orthologous drug resistant genes by combining different sequence alignment and variant calling algorithms. These putative variants have not been described in previous targeted or genome scans, probably because of differences in isolates used or the methods applied. Here we retained only high-quality variants supported by combinations of two mapping and two SNP calling algorithms. Most of the candidate variants were synonymous but there were several nonsynonymous SNPs across seven genes, especially in Pmcytb, whose P. falciparum orthologue drive resistance against atovaquone. Atovaquone is a member of the quinolines, to which resistance in P. falciparum have been associated with mutations in multidrug resistance gene (Pfmdr1), chloroquine resistance transporter (Pfcrt) and an amino acid transporter (Pfaat1). Most of the LD observed with the unfiltered dataset were not reproducible with the denoised and filtered dataset, with the exception of the LD observed in the SNPs of the mitochondrial gene, either due to common ancestry or selection of dominant haplotype by drugs or other factors. Additional candidate variants were seen in Pmdhfr and Pmdhps, orthologues of antifolate resistance in P. falciparum. The antifolate antimalarials, sulfadoxine-pyrimethamine are still in wide use for chemoprevention against malaria in pregnancy and combination with amodiaquine for seasonal malaria chemoprevention in West Africa. These together could be selecting for the identified variants. While the nonsynonymous SNPs reported here occurred in low frequencies, further verification, characterization and association of these SNPs will require increased genomic surveillance and phenotype association studies from in vivo and ex vivo therapeutic efficacy tests.Drugs have been a major selective force on P. malariae with appropriate epidemiology or clinical data are required to validate the findings of smaller studies as reported here. Another limitation is the use of standard bioinformatics pipelines designed for P. falciparum. While these may be acceptable for preliminary analysis, custom pipelines that take possible amplification and sequencing errors into consideration may be better for P. malariae, particularly because of the scarcity of population data with high quality confirmed variants from this parasite species. The different sample types analyzed could also be a limitation to this study, the dried blood spot samples were more amenable to producing poor quality results, possibly due to the low prevalence and low parasite density of the non-falciparum species. Therefore, venous blood sampling and more robust molecular techniques that take these into consideration will be beneficial in future molecular surveillance of P. malariae.A limitation of this study, which warrants cautious interpretation of the results, is the small number of samples analyzed across the different countries and the lack of bio and clinical data of the samples. Larger population studies for Plasmodium species into malaria control and elimination programs in sub-Saharan Africa, learning from the experience with COVID-19, to refine approaches as new variants are identified and monitored. This study has established the relevance of this in P. malariae.The current drive for malaria elimination needs innovative strategies to target all malaria parasites. One approach can be by integrating genomic surveillance of all Supplementary Information.