ST), haplotype inference, and whole chromosome scanning for signals of positive or of balancing selection. Derived alleles were determined by comparison to archaic hominin and other primate genomes. The potential effect of common non-synonymous variants on protein-protein interaction was also assessed. IZUMO1 displays higher variability among human individuals than JUNO. Genetic differentiation between continental population pairs was within whole-genome estimates for all but the JUNO gene in the African population group with respect to the other 4 population groups . Tajima’s D values demonstrated deviation from neutrality for both genes in comparison to a group of genes identified in the literature as under balancing or positive selection. Tajima’s D for IZUMO1 aligns with values calculated for genes presumed to be under balancing selection, whereas JUNO’s value aligned with genes presumed to be under positive selection. These inferences on selection are both supported by SNP density, nucleotide diversity and haplotype analysis. A JUNO haplotype carrying 3 derived alleles out of 5, one of which is a missense mutation implicated in polyspermy, was found to be significant in a population of African ancestry. Polyspermy has a disadvantageous impact on fertility and its presence in approximately 30% of the population of African ancestry may be associated to a potentially beneficial role of this haplotype. This role has not been established and may be related to a non-reproductive role of JUNO. The high degree of conservation of the JUNO sequence combined with a dominant haplotype across multiple population groups supports JUNO as a potential target for the development of contraceptive treatments. In addition to providing a detailed account of human genetic diversity across these 2 important and related genes, this study also provides a framework for large population-based studies investigating protein-protein interactions at the genome level.Fertilization in mammals begins with the union of egg and sperm, an event that starts a cascade of cellular processes. The molecular-level understanding of these processes can guide the development of new strategies for controlling and/or promoting fertilization, and inform researchers and medical professional on the best choice of interventions. The proteins encoded by the IZUMO1 and JUNO genes form a ligand-receptor protein pair involved in the recognition of sperm and egg. Due to their role in the fertilization process, these proteins are potential targets for the development of novel anti-contraceptive, as well as infertility treatments. Here we present a comprehensive analysis of these gene sequences, with the objective of identifying evolutionary patterns that may support their relevance as targets for preventing or improving fertility among humans. JUNO and IZUMO1 gene sequences were identified within the genomes of over 2,000 humans sequenced in the 1000 Genomes Project. The human sequences were subjected to analyses of nucleotide diversity, deviation from neutrality of genetic variation, population-based differentiation (F Fertilization is a multistep process that includes sperm-egg recognition and sperm-egg fusion, resulting in a genetically distinct diploid organism . Fusion IZUMO1 is a protein located on the cell surface of capacitated sperm cells, named after a Japanese marriage shrine . It is eDue to their participation in the early steps of fertilization, it has been suggested that IZUMO1 and JUNO could play a significant role in infertility , 11. TheIn this work, the genetic sequences of both IZUMO1 and JUNO protein coding genes in 2,504 humans sequenced in the 1000 Genomes project  were retST for selected genes. Our comparisons of Tajima’s D, nucleotide diversity, and overall FST thus correspond to different genes present on the same individuals used in the IZUMO1 and JUNO analyzes. In addition, scanning for signals of selection was performed on the complete sequences of human chromosomes 11 and 19 from the 1000 Genomes project and it is, thus, based on the same samples as the other analyzes. To our knowledge, the 1000 Genomes dataset is one of the most comprehensive large population-based whole-genome dataset publicly available.All the analyses presented for human IZUMO1 and JUNO in this manuscript were based on whole-genome sequence data from the phase 3 of the 1000 Genomes project. The 1000 Genomes project dataset corresponds to full-length DNA sequences of 5,008 chromatids from 2504 human individuals belonging to 26 population groups clustered into 5 larger population groups (African (AFR), American (AMR), East Asian (EAS), European (EUR) and South Asian (SAS)). The number of individuals in each population group as well as the composition of the larger groups in terms of the 26 populations are described in The IZUMO1 and JUNO genetic sequences of 2504 individuals included in the 1000 Genomes project  were obtSNV density (number of SNVs per 100 bp) in African and non-African population groups for IZUMO1 and JUNO is compared to other genes in Single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) ≥1% or ≥5% are distinguished from total SNVs for each group of genes. SNV density is generally higher in non-African populations. This difference can be attributed to a larger number of rare alleles (MAF < 1%) in non-Africans, as indicated by the larger gap between total number of SNVs and number of SNPs at 1% and 5% MAF cutoffs. JUNO in non-Africans has a larger number of rare alleles than IZUMO1. In contrast, IZUMO1 has larger percent of variant sites with a MAF ≥ 1% in both African (56%) and non-African populations (28%) than in the JUNO gene (19% in African and 9% in non-African populations), indicating that JUNO is more conserved across humans than IZUMO1. Neither JUNO or IZUMO1 stand out from the other genes selected for comparison. Genes classified in the literature as under balancing selection trend, on average, towards higher SNV density than genes believed to be under positive selection.In SNPEff  indicateTajima’s D is a null hypothesis test of deviations from neutrality. Significant deviation from zero indicates that the null hypothesis of neutrality is rejected, and that a non-random process is driving the evolution of the sequence . GeneralNucleotide diversity (π) was estimated for the same genes and populations used for Tajima’s D as an additional characterization of selection . NucleotHaploview  was usedA haplotype analysis was carried out for IZUMO1 and JUNO using the variant sites passing MAF and HWE thresholds. The individual sequences were grouped according to sex , and larger groups  of the original 26 populations. In addition, population analysis was performed using sequences from only females for the JUNO gene and from only males for the IZUMO1 gene, since the role of these proteins in reproduction can only be fully understood in the gametes of their respective sex. The data collected from this analysis is presented in Tables Analysis of linkage disequilibrium can be heavily influenced by sample size and it is generally believed that relatively large samples are required for detection of disequilibrium –41. How The haplotypes reported all contain missense mutations, which are underlined in the haplotypes in Tables AGA. A secondary high-frequency haplotype is present in the AMR and SAS population groups at the same frequency, 29%, but with different allele combinations . Interestingly, EAS presents overwhelmingly as AGA (83%), whereas SAS is 54% AGA and 29% GGC indicating that these two populations do not form a homogeneous group together (they are often grouped as “Asian” in the literature). The haplotypes AGA (45%) and GGC (46%) are evenly distributed in the EUR population group. The presence of multiple haplotypes at considerable frequencies in IZUMO1 is consistent with balancing selection. Two of the three variant positions that are part of the haplotypes for IZUMO1 are found in NCBI’s SNP database (dbSNP) [For IZUMO1, there are 3 haplotypes that appear equally frequently in the AFR population, with no sex difference. Differences between 1% and 2% in the haplotype frequency of males only compared to both sexes and females only are seen in the other populations. However, these differences could be attributed to incorrect haplogroup assignment due to error in variant calling, which is estimated to be between 0.1% and 1%, incorrectly , 43. EAS (dbSNP) , however (dbSNP) . In the  (dbSNP) . This isypes AAG % and CGGCGTTG) with frequency at or above 97%, except AFR. A 9% sex bias is seen in AFR for this haplotype. Sex bias is not observed in any other population group. AFR shows more diversity than the other population groups with the second haplotype present in 26% of the population. A third haplotype that is only present in the AFR population was identified but at a low frequency . This haplotype is not observed in any other population group. This further supports the conclusion that the largest difference in the JUNO gene is seen in the AFR population. Both the r2 values and the D’ values for the JUNO haplotypes are high, indicating strong linkage between all the loci involved in the haplotypes. The dominance of one haplotype in JUNO is consistent with both neutral evolution and positive selection, which may be distinguished from one another by determining the ancestral alleles which we will discuss in later sections. All five positions in the haplotype appear in dbSNP [in-vitro fertilization [GACGA, is significantly present in the AFR population (26% frequency) and, interestingly, displays a sex bias with a lower prevalence in females (17%) compared to the group as a whole (26%). The lower frequency of this haplotype in AFR females may reflect impaired fertility in females carrying this mutation. The frequency of this allele in AFR is much higher than in the other populations, suggesting the existence of a genetic advantage that has yet to be identified for the carriers of the G allele. Further investigation of the role of JUNO in biological processes other than reproduction may provide insights on the evolutionary role of the GACGA JUNO haplotype.For JUNO, all population groups present one dominant haplotype . To test for indirect co-dependency between the 2 genes, we calculated the frequencies at which each JUNO haplotype is observed in combination to each IZUMO1 haplotype within the same chromatid. This data is shown in AG-CGTTGA over AG-GACGAA, likely due to the female bias found for CGTTG in AFR. However this difference is not significant, as determined by Wilcoxon matched-pairs test yielding a two-tailed p-value of 0.99. Overall, we find no indication of strong genetic linkage between the two genes, since their combined haplotypes are observed at the same frequencies they are expected to be observed by chance.IZUMO1 and JUNO are a receptor-ligand pair encoded in 2 autosome chromosomes. Due to their role in fertilization, their expression and function is well-documented in gametes but not in other cell types. IZUMO1 is overwhelmingly expressed in testes, with low levels of expression in lungs, stomach, and adrenal glands, among other organs , 48. JUN10 helices, and two short two-stranded antiparallel beta sheets, stabilized by eight conserved disulfide bonds [The structures of the IZUMO1 and JUNO proteins, as well as their complex have been determined experimentally –52. On ide bonds –52. JUNOThese proteins bind with high affinity in a one-to-one ratio via the N-terminal of IZUMO1, resulting in major conformational changes observed in the four-helix bundle (4HB) and hinge region of IZUMO1. IZUMO1 abandons its boomerang shape and adopts a stabilized and locked upright conformation  upon binNeither one of the non-synonymous variant positions that are part of our haplotypes for IZUMO1 and JUNO is found in the experimentally determined structures. The experimentally determined structures of IZUMO1 normally span from amino acids 22 to 255 (out of 355 amino acids), which represent the extracellular regions of the protein. The non-synonymous SNP in our haplotype (rs2307019) encodes for position 333 of the cytoplasmic domain of IZUMO1 protein, which is located outside the boundaries of the currently available 3D structures. This position it is not located near the interaction surface of these two proteins (residues 57–113)  and, theThe currently available experimentally determined structures of JUNO span from amino acid 20 to 228, encompassing the majority of the 250 amino acids of this protein. The SNP of interest in our haplotype (rs61742524) is part of the codon position 3 of the JUNO protein which is not included in any of the currently available experimentally determined structures. The fact that the missense mutation (Cys3Trp) occurs far away from the interaction surface of the two proteins (roughly from Lys42 to Tyr 147) . JUNO ishttp://genetics.bwh.harvard.edu/pph2/) [Due to the lack of experimentally determined structures with the mutations highlighted in this work  an additional approach was used to assess the effect of these substitutions. PolyPhen-2, a human nonsynonymous SNP prediction database  , was empu/pph2/) . The Alau/pph2/) .ST) values between different populations were calculated. FST is a comparative measure of the average number of pairwise differences between individuals sampled from different population groups and the average number of pairwise differences between individuals sampled from the same population group [ST value of 0.124. This number is an average of 3 human genome wide FST values reported in the literature. The first (0.123) was calculated for the entire human genome using 26,530 autosomal SNPs from 3 different populations  [ST value (0.119) was calculated by Cavalli et al. using 120 “classical” non-DNA polymorphisms in 42 worldwide populations [ST value of 0.130 [ST for the set of reference genes used in the Tajima’s D and nucleotide diversity analysis, and found the mean of these values (0.103 ± 0.063) to be consistent with the genome-wide literature-based value but carrying a much larger standard deviation. Thus, an FST value for the IZUMO1 and JUNO genetic sequences of the different population pairs that is equal or lower than 0.124 plus or minus two standard deviations is deemed to be consistent with genome wide FST values and in line with the amount of variation seen within the entire human genome. The pairwise FST values for each pair of population groups for IZUMO1 and for JUNO are shown in ST values between all populations are displayed in ST of 0.291 ± 0112. However, this divergence is short of statistical significance , indicating that the differentiation seen in this gene between all four continental populations and EAS is still in line with the differentiation seen within the entire human genome. The average FST value (0.266 ± 0.051) between all four continental populations for JUNO with respect to AFR is significantly higher than the genome wide FST average , indicating a significant degree of population divergence. This divergence likely results from a 26% prevalence of the JUNO haplotype GACGA in the AFR population, but only 3% or less prevalence of this haplotype in the EUR, SAS, AMR, and EAS population groups. The average FST values for EUR, SAS, AMR, and EAS population groups in the JUNO gene are in line with the benchmark genome-wide value.The Weir-Cockerham fixation index (Fon group . We selemerican) . The seculations . A thirdof 0.130 . These vST values of population groups for all individuals, and for just males or just females with respect to EAS for IZUMO1 and to AFR for JUNO are presented in ST values in the data sets.The pairwise FST between the males and the females of all four of the continental populations was 0.0008 for IZUMO1 and 0.0007 for JUNO, respectively. Both are close to zero, indicating that there are no significant differences between the female and male sequences of IZUMO1 and JUNO. These FST values were calculated using SNPs with a MAF of 1%.FST values associated to all 26 original populations that form the 5 continental groups EAS, EUR, SAS, AMR and AFR, considering only SNPs with a MAF of 5% when all individuals from all populations are grouped together. We used these values in a principal component analysis (PCA) to map genetic diversity between the 26 population groups represented in the 1000 Genomes dataset  cluster together and are separate from the other core populations for both IZUMO1 and JUNO. Populations belonging to the EAS group (circles) are separate from the others for IZUMO1, but overlap with populations belonging to the EUR (crosses) and SAS (stars) groups for JUNO. Similarly, core geographic populations in the AMR group (triangles) are separate for JUNO but overlap with other geographic groups for IZUMO1. For IZUMO1, the core populations GBR and CEU segregate from the other EUR groups and do not overlap with any other core populations. Similarly, the core population LWK (AFR) segregates from the other AFR core populations for JUNO. These results do not support a model of co-selection between IZUMO1 and JUNO, as the pattern of genetic diversity represented by the PCs is not as similar as it would have been expected if these 2 genes were co-selected.Homo were downloaded from the Max Planck Institute for Evolutionary Anthropology  in vcf format, and analyzed to identify alleles at locations corresponding to haplotypes for IZUMO1 (Genomes of archaic members of the genus r IZUMO1  and JUNOr IZUMO1  in conteGC. A C at rs2307018 and G at rs2307019 correspond to either a GAC or a GGC haplotype in GGC haplotype is most prominent in EUR (0.46). GAC is present at moderate frequencies in AFR (0.35) and SAS (0.29). Assuming the same pattern of heritance, the heterozygous Ust’-Ishim likely had one AGA parent. This haplotype is predominant in EAS (0.83) and AMR (0.54), and is prominently present in EUR (0.45).Two out of 3 DNA markers for IZUMO1 (rs2307018 and rs2307019) are present in the genome sequences of the Neanderthal from the Vindija Cave  and the CGTTG, which is the major haplotype in present-day humans analyzed in this report. This haplotype has a frequency greater than 0.97 in every population except the AFR population (0.69). This could indicate that the JUNO gene has diverged from its ancient genetic sequence through selective pressures in AFR, or that inter-breeding between modern humans, Neanderthal and Denisova brought this haplotype to higher frequencies in all but AFR populations. As we shall see in the next section, the later is the most likely to have happened.JUNO’s DNA markers rs61742524, rs55784852, rs16920146, rs7925833 and rs7935583 were present in the genomes of the Vindija cave Neanderthal , Altai NGGC in our analysis which is 1 of 2 equally represented haplotypes in EUR (0.46 frequency) and it is 1 of 3 equally represented haplotypes in AFR (0.33 frequency). The dominant haplotype in EAS, AGA (0.83 frequency), contains 2 derived alleles out of 3, whereas the third IZUMO1 haplotype (GAC) contains one derived allele at the last position. The AGA haplotype is well-represented in all 5 populations, albeit it is only dominant in EAS, and may have resulted from a founder effect or a selective sweep in this population. For JUNO, the ancient alleles at positions rs61742524, rs55784852, rs16920146, rs7925833 and rs7935583 are GGCTG, in this order. This allele combination does not correspond to either one of our haplotypes. The haplotype (CGTTG) that we identified as prevalent in all of the 5 populations analyzed here contain 2 derived alleles, whereas the second JUNO haplotype (GACGA), which is mostly present in AFR populations, contains 3 derived alleles. The existence of multiple high frequency derived alleles within a relatively small genome segment is consistent with positive selection [A pre-built genomic alignment of 37 mammals  was usedHomo sapiens IZUMO1 and JUNO amino acid sequences were used in this study. These sequences were aligned with ClustalX [Homo sapiens amino acid sequences for both JUNO and IZUMO1 can be found in There are many homologous mammalian species for IZUMO1 and JUNO protein sequences. However, only 29 mammalian species with available matches to both the ClustalX . The perThe nucleotide sequences of all the 2504 individuals from the 1000 Genomes Project for JUNO and IZUMO1 were aligned using ClustalX . The perWe used RAiSD  to scan As an independent verification that IZUMO1 is under balancing selection, we scanned chromosome 19 (where IZUMO1 is located) for signals of balancing selection using BetaScan2 . This coST values below a genome-wide FST average obtained from multiple studies. However, there were some pairwise FST values that indicated greater diversity between populations, such as all other continental populations with respect to EAS in the IZUMO1 gene, and with respect to AFR in the JUNO gene. The trend associated to the African population group in the JUNO gene is consistent with positive selection which, as we will discuss later, is supported by other metrics.The IZUMO1 and JUNO genes encode 2 protein partners that play an essential role in fertilization. In this manuscript we present the first large population genetics study on the IZUMO1 and JUNO genes, with some very interesting results. It was expected that the IZUMO1 and JUNO genes would be well-conserved between human populations, given their essential role in fertilization. In general, this held true with both genes demonstrating pairwise-FGACGA, that has very low frequency in all other populations has a frequency almost ten-fold higher in the AFR population, suggesting that a selective advantage that has yet to be identified is carried by this haplotype. All 3 archaic genomes we examined have the haplotype CGTTG, which is the major haplotype in present-day humans, and primates carry the C allele in the first position of this haplotype. This indicates that the G allele at locus rs61742524 is a derived allele. Since the G allele is associated to polyspermy, which is disadvantageous for female reproduction, its relatively high frequency in AFR is likely associated to a non-reproductive role of JUNO. Investigating the role of the JUNO protein in non-reproductive cells may, thus, shed light on functional effects associated to the rs61742524 G allele. We found no LD between intergenic alleles and no significant differences between observed and predicted combined IZUMO1-JUNO haplotype frequencies indicating that the two genes are not genetically linked within the same genome. Thus, cross-genes haplotype analysis failed to provide evidence of co-selection for this pair of interacting genes, and so did PCA genetic diversity mapping.Interestingly, a JUNO haplotype, Metrics such as SNV density, nucleotide diversity (π) and Tajima’s D, as well as haplotype analysis and derived allele determination, all support the notion that the IZUMO1 gene in humans is under balancing selection whereas the JUNO gene is under positive selection. These assertions are further supported by chromosome-level scanning for signals of positive and balancing selection on chromosomes 11 and 19, respectively. The indication that IZUMO1 is under balancing selection seems to contradict that in mammals, many male reproductive genes have been shown to evolve rapidly through positive selection –70 On thRegarding the impact of genetic diversity on fertility, a study using recombinant proteins showed that hamster JUNO can directly interact with mouse, pig and human IZUMO1, matching the ability of zona-free hamster eggs to fuse with human, mouse and pig spermatozoa . This inEven though these genes code proteins that directly interact in a key step of fertilization. A great deal of tolerance in diversity of the IZUMO1 sequence, matched by a great deal of conservation of the JUNO sequence, seems to be sufficient to establish some degree of genetic “checks and balance” for this pair. The high degree of conservation combined to a dominant haplotype across multiple population groups makes the protein encoded by JUNO a potential target for the development of contraceptive treatments (with the caveat that such treatments would have to pass the zona pellucida barrier). These contraceptive treatments could include small molecules designed to bind to the JUNO protein in the region of the binding interactions to the IZUMO1 protein, delivered systemically or locally.Currently, there is a demand for population-based analysis workflows due to the increasing availability of full genome sequences. This demand is compounded by the realization that, in order to fully understand how genomes work, we must extend traditional population analysis to an interacting network of genes context. This study attends that demand by providing a framework for comprehensive analysis of multiple interacting genes.ST) to investigate the genetic diversity seen in the IZUMO1 and JUNO genes between human populations [http://wesley.lakeheadu.ca:8800), which is only accessible within the University’s network, or through in-house developed R [There are a wide variety of population genetic approaches to analyze human genes, however, in this work, only a few were selected to best answer the question of how inter-dependent the IZUMO1 and JUNO are among human populations. A method based on population allele frequency differences was selected  or balaulations ) selectieloped R  Scripts https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes). We obtained vcf files for IZUMO1 and JUNO as well as 40 selected genes used as reference, and for chromosomes 11 and 19. All of these are from the same set of 2504 individuals whose genomes were fully sequenced by the 1000 Genomes Project. The genomic locations (chromosome:first-last genomic coordinate) for IZUMO1 and JUNO were, respectively, 19:49244073–49250166 and 11:94038803–94040858. The genomic locations of the other genes are reported in https://vcftools.github.io/index.html), a table containing all of the single nucleotide polymorphisms (SNPs) for each gene was produced and allele frequencies for all sites in each gene were calculated.Data from phase 3 of the 1000 Genome Project  was retrhttp://blast.ncbi.nlm.nih.gov/Blast.cgi) [Blast (ast.cgi)  was usedhttps://galaxyproject.org) was used to determine the number of SNPs and their effects. This application determines the number of SNPs, where the SNPs are located and the effects of the SNPs [The program SNPeff  implementhe SNPs , 38. Thethe SNPs . It shouFor population-based analysis, we used the original 1000 Genomes 26 populations, as well as “supergroups” of the original populations . These sST values between each of the regional populations, as well as each of the supergroup populations, were estimated after excluding rare alleles (MAF < 1%), as their inclusion may introduce ascertainment bias if the distribution of rare alleles is uneven across the individual populations [ST values were calculated using the R package HierFSTAT (https://github.com/jgx65/hierfstat). For PCA of FST values, we used the R package FactoMineR [http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_sample_info.xlsx. Supergroup membership was assigned according to ST values calculated between regional population pairs or their supergroups .An R script was developed to calculate population genetics parameters and perform Principal Components Analysis. The script reads vcf files as input and converts the data to the various formats required by individual R packages. The input vcf files are pre-filtered using vcftools  to elimiulations , which sulations . LD and ulations  was usedulations  FST valuctoMineR . GeograpThe program Haploview  was usedhttps://vcftools.github.io/index.html) to calculate r2 between pairs of SNPs where each SNP in the pair came from a different gene. We only considered SNPs with a MAF of 1% or greater. This set, thus, included all SNPs used in Haplotype inference of each gene.We used vcftools  (https:/http://genetics.bwh.harvard.edu/pph2/) [The PolyPhen-2, a human nonsynonymous SNP prediction database  , was empu/pph2/) . The amihttp://cdna.eva.mpg.de/neandertal/altai/) in vcf format. The segments corresponding to the IZUMO1 and JUNO genes were isolated using vcftools [The ancient genomes of Denisova, Vindija Neanderthal, Altai Neanderthal and Ust’-Ishim ancient modern human were downloaded from the Max Planck Institute for Evolutionary Anthropology Department of Genetics and Bioinformatics Group’s server . This set contains 3,865,304 SNPs identified from the sequences of 5008 chromatids. Based on recommendation from the authors [The package RAiSD version 2.9  was used authors  we omitthttps://github.com/ksiewert/BetaScan and installed on an in-house Linux computer cluster. The vcf files containing data from the 1000 Genomes project corresponding to human chromosome 19 was downloaded from NCBI (https://www.ncbi.nlm.nih.gov/genome/gdv/). This set contains 1,746,975 SNPs. Due to size-handling limitations in the vcf to BetaScan input file converter, glactools [The package BetaScan2  was usedlactools , we had S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 TableAfrican versus Non-African populations.(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S11 Table(PDF)Click here for additional data file.S12 Table(PDF)Click here for additional data file.S13 Table(PDF)Click here for additional data file.S1 References(PDF)Click here for additional data file.


Pseudotaxus chienii, belonging to the monotypic genus Pseudotaxus (Taxaceae), is a relict conifer endemic to China. Its populations are usually small and patchily distributed, having a low capacity of natural regeneration. To gain a clearer understanding of how landscape variables affect the local adaptation of P. chienii, we applied EST‐SSR markers in conjunction with landscape genetics methods: (a) to examine the population genetic pattern and spatial genetic structure; (b) to perform genome scan and selection scan to identify outlier loci and the associated landscape variables; and (c) to model the ecological niche under climate change. As a result, P. chienii was found to have a moderate level of genetic variation and a high level of genetic differentiation. Its populations displayed a significant positive relationship between the genetic and geographical distance  and a strong fine‐scale spatial genetic structure within 2 km. A putatively adaptive locus EMS6  was identified, which was found significantly associated with soil Cu, K, and Pb content and the combined effects of temperature and precipitation. Moreover, P. chienii was predicted to experience significant range contractions in future climate change scenarios. Our results highlight the potential of specific soil metal content and climate variables as the driving force of adaptive genetic differentiation in P. chienii. The data would also be useful to develop a conservation action plan for P. chienii. P. chienii. Adaptive gene indeed occurred in genome of P. chienii and made the plant to have adaptive potential in response to severe environmental stress. These factors related to adaptation should be specially considered in ex situ conservation of P. chienii.Landscape and environmental heterogeneity play important roles in the spatial genetic structure and the adaptive evolution of  Landscape features, range boundaries, or environmental characteristics are well known to influence both population genetic differentiation and spatial genetic structure. The theory of isolation by distance (IBD) Wright,  expects Landscape genetics is one of the most promising approaches to explore how landscape pattern, structure, and composition affect spatial genetic variation of populations, continuity of gene flow, and local adaptation  has been widely applied: (a) to predict species distributions  and nutrient‐rich soils, receiving an annual mean precipitation of 1,800–2,400 mm  have been widely applied to examine adaptive genetic variation and local adaptation in both model and nonmodel species  characterize the level and pattern of genetic variation, genetic differentiation, and spatial genetic structure of P. chienii populations across its entire distribution range, (b) identify candidate outlier loci as well as their association with environmental variables, and (c) model the distribution of P. chienii under future climate changes and estimate the major factors affecting the distribution. These investigations may provide new information to deepen our understanding of the adaptation of P. chienii populations and assist the development of conservation strategies.In this study, we used EST‐SSRs in junction with landscape genetics statistical methods to explore the possible roles of geographical location and environmental factors played in shaping the population genetic variation of 22.1P. chienii individuals were collected from 11 populations in Zhejiang, Guangxi, Jiangxi, and Hunan provinces  agarose gel electrophoresis and a NanoDrop 2000c spectrophotometer . DNA was diluted to 50 ng/μl and stored at −20°C for subsequent use.Total genomic DNA was extracted using a modified cetyltrimethylammonium bromide (CTAB) protocol , 1.6 μl dNTPs (2.5 mM), 0.5 μl of each forward and reverse primers (10 μM), and 0.2 μl Taq DNA polymerase (5 U/μl) . Negative control was set without genomic DNA. All forward primers were labeled with fluorescent dyes 5‐FAM . PCR amplifications were performed in a thermal cycler  as follows: initial denaturation at 94°C for 5 min; followed by 35 cycles at 94°C for 40 s, varied annealing temperatures ranging from 55 to 62°C for 40 s with different primer pairs . Allele sizes were determined with GeneMapper 4.0 software (Applied Biosystems). Each genotype was visually checked and scored, and unclear samples were re‐amplified. We randomly selected two individuals per population to test reproducibility through two random primers. Genotyping error rate was detected as 5.3% using the sample function in R , allelic richness (Ar), observed heterozygosity (Ho), expected heterozygosity (He), unbiased expected heterozygosity (uHe), inbreeding coefficient (FIS), and fixation index (F). Allele frequency was calculated by the makefreq function of R package adegenet 2.1.1 , gene diversity within population (Hs) and overall gene diversity (Ht). Multilocus linkage disequilibrium (LD) was assessed by index of association (Ia)   with reduced maximum‐likelihood estimation was used to assess the difference of mean Ar, Ho, He, uHe, and F) and population size (Ns) using the corr.tes function in the R package psych 1.8.12  and 1,000 bootstrap replicates. Analysis of molecular variance (AMOVA) was performed using the poppr.amova function in the R package poppr. And the following Φ indices were estimated: within individuals, ΦIT; among individuals within populations, ΦIS; and among populations, ΦST. The randtest function was used to assess the significance of the Φ indices.To investigate genetic differentiation, dist.genpop function in the R package adegenet. A heatmap with UPGMA clustering was constructed using the hclust and the heatmap.2 functions of the R package gplots 3.0.1.1  and define k clusters using the K‐means clustering algorithm. Bayesian information criterion (BIC) value was used to determine the optimal cluster k. As a more powerful method, discriminant analysis of principal components (DAPC)  to test landscape heterogeneity among 11 populations and four provinces using the 5 Dixon, . Euclide3 Hervé, .2.7P. chienii populations. The geographical and environmental variables with variance inflation factor (VIF) below 5 were selected for analysis in the vifstep function of the R package usdm  and isolation by environment (IBE) for FST) and Euclidean geographical or environmental distance using the mantel function in the R package vegan with 10,000 permutations. Secondly, a partial Mantel test was further used to distinguish which geographical or environmental variables may have affected genetic distance through controlling one of the two types of variables using the mantel.partial function of the R package vegan with 10,000 permutations. In the two tests, the association between variables was quantified by Pearson's correlation coefficient. Finally, we applied a multiple matrix regression with randomization analysis (MMRR) to investigate the effects of geographical and environmental distance on genetic distance in the R script MMRR (deposited in the Dryad Data Repository under https://doi.org/10.5061/dryad.kt71r) with 999 permutations  using the varpart function of the R package vegan. Hellinger transformation was used to transform genetic data into response variables of RDA models using the decostand function in R. Predictor variables included the geographical and environmental variables with VIF below 5. We used the anova.cca and rda functions to estimate the contribution of a single and all predictor variables to the genetic variation with 999 permutations.To quantify the contribution of IBD and IBE to the genetic differentiation of lmer function of the R package lmer4. Two geographical variables and 16 environmental variables with VIF < 5 were used for environmental association analysis. To preserve the diversity of environmental factors as much as possible, we applied the vifstep function for three categories of environmental variables. Sixteen selected environmental variables included five ecological variables ; enhanced vegetation index, EVI; leaf area index, LAI; and fraction of absorbed photosynthetically active radiation, fPAR), four bioclimate variables , and seven soil variables . A multiple univariate logistic regression approach was employed to test correlations between allele frequencies and environmental variables. We compared models with and without environmental variables, and the significance was determined based on both Wald and G scores with a false discovery rate (FDR) cutoff of 10–6. As for the allele frequencies of outliers, LMM was constructed using the lmer function with landscape variables as the fixed effect and provinces as the random effect. The significance of difference was determined through a likelihood‐ratio test using the ANOVA function in R.Associations between outliers and landscape variables were assessed using two methods: Samβada v.0.8.1   was acquired through Fij regressing on the natural logarithm of the spatial distance (ln(dij)). The Sp values were calculated using Sp = b/(F1−1), based on the Fij of the first distance class, to quantify the strength of the fine‐scale spatial genetic structure  using SPAGeDi v1.3  and observed heterozygosity (Ho) at mutation‐drift equilibrium. The analysis was conducted under two mutation models: the stepwise mutation model (SMM) and the two‐phase mutation model (TPM) which were suitable for microsatellite data  for the Euclidean distance of genetic data using the dudi.pco function in the R ade4 1.7‐13 package  and 1% FDR. Parameters were set as follows: critical frequency 0.99, level of differentiation (target average θ) 0.06, 5,000 resamplings, Zhivotovsky parameters 0.25, trimmed mean P 0.3, and smoothing proportion 0.04.BAYESCAN , near‐future at 2050 (2041–2060), and far‐future at 2070 (2061–2080) periods. Species occurrence records were collected from the fieldwork, literature, the Global Biodiversity Information Facility , and Chinese Virtual Herbarium . In total, 51 occurrence points were obtained after removing duplicate geographical records.We employed a maximum entropy model in MaxEnt 3.4.1  with a resolution of 2.5 arc‐minutes. We predicted future distributions based on four RCPs (representative concentration pathways) scenarios from the Community Climate System Model (CCSM4): RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios. RCP4.5 and RCP6.0 scenarios exhibit stable scenarios for the greenhouse gas emission, whereas RCP2.6 and RCP8.5 represent lower and higher greenhouse gas emission, respectively  was lower than expected heterozygosity (mean He = 0.370) across populations. The mean inbreeding coefficient (FIS = 0.076) of each population indicated slight homozygote excess. Except for populations LHS and YS, the other populations exhibited a heterozygote deficit based on FIS (Table Allelic richness in each population ranged from 2.329 (LHS) to 3.406 (LMD), with an average of 2.868 Table . We idenuHe per locus at the population or province level. Likewise, no significant correlation was detected between population size (Ns) and Ar , Ho , He , uHe , and F , respectively. Based on the multilocus LD, and Ia and rD, LD was detected among EST‐SSR loci across all populations ; particularly, in DMS  and ZJJ   into population BJS (K = 9) and formed a group themselves (K = 11).Using STRUCTURE, the optimal clusters were identified as three, nine, and eleven Figure . We seleo Figure . ClusterP. chienii populations were clustered into three groups based on UPGMA . Pairwise comparison of variables between provinces also revealed significant difference, such as between Zhejiang (ZJ) and Guangxi (GX), Zhejiang (ZJ) and Jiangxi (JX), Hunan (HN) and Jiangxi (JX), and Hunan (HN) and Guangxi (GX). Similarly, Pairwise comparisons of variables between populations also showed significant difference, except for populations DXG versus LM and BJS versus YS.Based on PERMANOVA, landscape variables were shown significant difference across the whole distribution of 3.4P. chienii populations conformed to the pattern of isolation by distance (IBD) but did not to that of isolation by environment (IBE). A significant positive relationship was found between geographical and genetic distance , but was not between environmental and genetic distance . Partial Mantel tests ; but such a significant correlation was not found with environmental distance when controlling for geographical distance . As for MMRR analysis  but not with environment . When considering geography and environment simultaneously, a similar result was obtained .Mantel test Table  showed t3.5RDA showed that both geographical and environmental factors had a significant impact on genetic variation, and the environmental factors were more important Table . The com3.6Pseudotaxus chienii populations displayed strong fine‐scale spatial genetic structure within 2 km . The Fij for pairwise individuals was greater or less than zero when geographical distance was less or greater than 4 km, respectively. A decline tendency of Fij indicated that the similarity between individuals became lower with the increase of distance , but became significantly negative within the fifth distance class .I had negative values in all other distance classes , the spatial autocorrelation among P. chienii populations may extend to 600 km.Except for having positive values in the first and the fifth distance class, Moran's s Figure . As show3.7P. chienii populations   Table . It is oP. chienii. Three potential spatial boundaries were identified between populations YS and ZZB, YS and ZJJ, and LHS and DMS, respectively , while the latter to OsCESA7  through BLASTN search.Loci EMS3 and EMS6 were identified as under balance selection and positive selection, respectively Figure . The forWe investigated associations between the two loci and 18 selected landscape variables Table . Eight lLongitude, Cu, and PTC were the most important variables, associated with the largest number of EMS3 and EMS 6 alleles. Overall, longitude had effects on both loci , while latitude only imposed effects on EMS6. PTC and Cu affected both loci. Altitude had strong effects on EMS3 but relatively weak effects on EMS6. There were four temperature‐ and precipitation‐related variables constituting two combinations having effects on each locus , respectively. Moreover, there were more environmental variables associated with EMS6  than EMS3.3.9P. chienii. Bio14 (the precipitation of driest month) and Bio10 (the mean temperature of warmest quarter) were found to be the key factors in determining the distribution  in P. chienii at the species level. In comparison with other coniferous species (Table Torreya grandis (He = 0.432)          of the populations have the estimated inbreeding levels ranging from 0.009 to 0.199  and geographically scattered and isolated  , and T. wallichiana (FST = 0.156)  was much higher than that of other conifers including T. baccata     and EM6 to OsCESA7 . Notably, EMS6 is inferred to be positively selected, whose alleles are significantly associated with (1) eight individual variables: longitude, latitude, PTC, K, Cu, Pb, Bio11 , and Bio14 (precipitation of the driest month), and (2) the combined effects of Bio11 and Bio14.Two (10%) EST‐SSR loci EMS3 and EMS6 were simultaneously identified as candidate outliers by running BAYESCAN and FDIST; and they have also been detected significantly associated with landscape variables. The consistent identification by three different methods strongly supports that EMS3 and EMS are loci potentially under selection. EMS3 shows sequence similarity to P. chienii populations. First, Cu was found to be crucial. Cu is an essential nutrient element, functioning as a cofactor in more than 100 metalloproteins  , Bio14 (precipitation of the driest month), and the combined effects of Bio11 and Bio14. Particularly, Bio14 has been identified as the factor contributing the most (64.4%) in determining the distribution of P. chienii predicts that P. chienii may experience significant range contractions under future climate change scenarios ; Writing‐original draft . Zhen Wang: Investigation ; Writing‐review & editing . Yingjuan Su: Project administration (lead); Writing‐review & editing (supporting). Ting Wang: Project administration ; Writing‐review & editing .Supplementary MaterialClick here for additional data file.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.


The genetic prehistory of human populations in Central America is largely unexplored leaving an important gap in our knowledge of the global expansion of humans. We report genome-wide ancient DNA data for a transect of twenty individuals from two Belize rock-shelters dating between 9,600-3,700 calibrated radiocarbon years before present . The oldest individuals  descend from an Early Holocene Native American lineage with only distant relatedness to present-day Mesoamericans, including Mayan-speaking populations. After ~5,600 cal. BP a previously unknown human dispersal from the south made a major demographic impact on the region, contributing more than 50% of the ancestry of all later individuals. This new ancestry derived from a source related to present-day Chibchan speakers living from Costa Rica to Colombia. Its arrival corresponds to the first clear evidence for forest clearing and maize horticulture in what later became the Maya region. The genetic prehistory of central America has not been well explored. Here, the authors find evidence from ancient DNA from twenty individuals who lived in Belize 9,600 to 3,700 years ago of a migration from the south that coincided with the first evidence for forest clearing and the spread of maize horticulture. Published early Holocene  individuals from Belize (N = 3) are consistent in deriving their ancestry from this same large-scale north-to-south movement of people, but they display only distant relatedness to present-day groups in Mexico and Central America, including local Maya-speaking populations2. Instead, Maya people today show the greatest affinities to both South Americans and Indigenous Mexicans7, suggesting the potential for further episodes of population movement and admixture in this region during the past 7300 years. The genetic history of the region is essential to understand the evolution of cultures, languages, and technologies, including domesticated plant crops that transformed the neotropics.Poor preservation of ancient skeletal material in the hot and humid neotropics means that little is known about Early and Middle Holocene  population history of southeastern Mexico and northern Central America, an area that later became the Maya region. Previous ancient DNA analyses have indicated that the earliest Central and South Americans, as well as present-day groups from the same regions, descend primarily from the more southerly of two founding Native American genetic lineages9, which provide an unparalleled transect of well-preserved skeletal material spanning the past 10,000 years  from these individuals show increases in the consumption of maize starting after 4700 cal. BP8, but it remains unclear if this dietary shift represents local adoption of more intensive maize cultivation or a new population of maize farmers moving into the region.We studied an assemblage of human remains recently excavated from two rock-shelters in Belize  by ~6200 cal. BP22.Maize domestication began in southwest Mexico ~9000 years agoZea mays), manioc (Manihot sp.) and chili peppers (Capsicum sp.) were being processed during the Middle Holocene, possibly as early as 6500 cal. BP23. Paleoecological data  from lake and wetland cores point to increases in burning and land clearance associated with early maize cultivation after ~5600 cal. BP25. Maize was grown at low levels after initial introduction, and there is greater evidence for forest clearing and maize-based horticulture after ~4700 cal. BP27. Increases in forest clearing and maize consumption coincide with the appearance of more productive varieties of maize regionally28, which have been argued based on genetic and morphological data to to be reintroduced to Central America from South America29.Starch grains and phytoliths recovered from stone tools from sites in the southeastern Yucatan near MHCP and ST indicate that maize  the adoption of maize and other domesticates by local forager-horticulturalists, 2) the intrusion of more horticulturally-oriented populations carrying new varieties of maize, or 3) a combination of the two. Dispersals of people with domesticated plants and animals are well documented with combined archeological and genome-wide ancient DNA studies in the Near East;All ancient skeletons from the MHCP and ST rock-shelters were excavated by the Bladen Paleoindian and Archaic Archaeological Project (BPAAP) under permits issued by the Belize Institute of Archaeology (IA) and the Belize Forest Department. Skeletons of ancient individuals were exported under permits issued by the IA in accordance with the laws of Belize and permission granted to conduct molecular analyses on bulk tissues extracted from skeletons of ancient individuals. Research was conducted in close collaboration with the Ya’axché Conservation Trust, an internationally recognized Belizean NGO that is the co-manager of the Bladen Nature Reserve (BNR) with the Government of Belize. Ya’axché is locally managed and largely staffed by members of descendent Maya communities. As part of this collaboration, BPAAP research proposals are annually reviewed by the Ya’axché administrative and scientific staff. In 2016 and 2018 K.M.P. gave presentations to the local staff of Ya’axché and other interested community members on our research. In 2020, in coordination with Ya’axché, we invited indigenous leaders and community members from villages proximate to the BNR to consult on this research . We provided advance invitations and arranged transportation for forty-six people from five villages to attend the public consultation. K.M.P. delivered a presentation detailing the fieldwork, laboratory work, and results of this study, and this was followed by a session answering questions and clarifying the data and interpretations. Community members requested future public consultations to update them on additional research results, as well as copies of all study results in English with translations into the Mopan and Q’eqchi’ languages. Additionally, in 2017 and 2018–2019 results of this research were presented by K.M.P. at the annual Belize Archaeology Symposium (BAS), a widely publicized venue sponsored by IA. The BAS affords the opportunity for both the presentation of research results and feedback from professional and public communities (including members of indigenous Mayan-speaking communities).41, and prepared Illumina sequencing libraries treated with uracil DNA glycosylase to reduce cytosine deamination associated with ancient DNA42. We enriched the libraries for ~1.2 million single-nucleotide polymorphism (SNP) targets43 and sequenced each individual to between 0.002 and 3.6× average depth of coverage (Supplementary Data 44. All ten display some long (>4 centiMorgans [cM]) ROH segments, indicative of small population sizes, but with a paucity of very long segments (>20 cM), suggesting avoidance of close-kin unions . The oldest ancient individuals  fall near the intersection of the main axes of variation, closest to ancient individuals from South America2. Individuals in the transect dating between 5600 and 3700 cal. BP shift in the direction of present-day speakers of the Chibchan family of languages45 (hereafter Chibchan populations) from northern Colombia and Venezuela and the Isthmian region of southern Central America  fall near the 5600 and 3700 cal. BP individuals but shift toward groups from western and northern Mexico. Alternative PCA plots with different populations used to compute the axes show similar patterns  to visualize the genetic structure of the ancient individuals in relation to a diverse set of ancient and present-day individuals from South, Central, and North America Fig. 6. All insee Fig. . Presentf-statistics to measure differential allele-sharing between the ancient Belize individuals and other ancient and present-day populations  (Supplementary Table Z > 5), with the strongest indicating excess allele-sharing between the 5600–3700 cal. BP individuals and either Chibchan populations or Kaqchikel    > 0, Z > 3; Supplementary Data To test directly for any asymmetrical relationships between the 9600–7300 cal. BP and 5600–3700 BP groups and present-day populations, we computed statistics of the form ng) Fig. , as wellf4 ~0, |Z| < 2.2; Supplementary Data f4 with several geographically and linguistically diverse South American groups in the “Present-day2” position, likely induced by shared ancestry between the South Americans and Chibchan populations  carried mtDNA haplogroup D (including D4h3a and D4h3a5 haplotypes rare in the region today), and the sixth carried haplogroup C1b. By 5600–3700 cal. BP, however, the matrilineal makeup of the sampled individuals was almost entirely different, dominated by haplogroups C1c (9 out of 15) and A2 (5 of 15), similar to distributions found broadly in Central America todayf4-statistics comparing them to other present-day groups. Consistent with the PCA results, we observe highly significant positive statistics of the form f4 (Supplementary Table Z > 3) with northern Mexicans (Pima Bajo) in place of highland Mexicans  for the 5600–3700 cal. BP individuals under a two-way admixture model, with inferred proportions of 31 ± 9% ancestry related to the 9600–7300 cal. BP individuals and 69 ± 9% related to ancestors of Chibchan populations (Supplementary Table p = 0.25) for the present-day Maya involving a mixture of ancestry related to the 5600–3700 cal. BP individuals  and to highland Mexican populations (25 ± 10%). We replicated this result using two separate (but overlapping) sets of Maya individuals (n = 17 and 18) with post-contact admixture, with the non-American ancestry either masked  or unmasked .We used t p = 0.9 for the 2, plausibly linked directly to the first southward human disperals through the Americas during the Late Pleistocene. A gap in the skeletal record between 7300 and 5600 cal. BP is followed by more persistent burials between 5600 and 3700 cal. BP8. Individuals in this later cluster trace a portion of their ancestry to the earlier residents but also a portion to a source related to Chibchan populations from southern Central America and northern South America. We interpret this new ancestry as most parsimoniously explained by a south-to-north movement into the southeastern Yucatan sometime between 7300 and 5600 cal. BP. Archeological evidence throughout Central America suggests that populations were small and residentially mobile during this interval51, so this movement need not have involved a large number of people moving all at once, and may instead have been an amalgamation of small populations with associated interchange of language and/or cultural knowledge. However, the net long-term effect was a substantial shift in ancestry (69 ± 9%) of local populations by ~5600 cal. BP.Our analysis of ancient genomes from southern Belize indicates that there were multiple phases of human migration into what became the Maya region. We confirm that the earliest sampled individuals from the region  constitute an early-splitting branch of ancestry8. Our results therefore do not support a displacement scenario of forager-horticulturalist populations by agriculturalists with an economy based on maize. However, the influx of new ancestry does correlate with evidence for regional forest disturbance and burning associated with the first small-scale maize cultivation in the region by 5600 cal. BP 23. Plant domestication in the Americas occurred over vast areas and involved many different species39, with certain plants dispersing from Mesoamerica through Central America and South America  and others moving in the opposite direction . Genetic analyses of maize have provided evidence of secondary improvements in South America17, where it was a staple grain in coastal Peru by 6000–5000 cal. BP21, followed by dispersal back into Central America29. Our results identify a movement of people that could have accompanied this process and been a vector for it. In particular, our results support a scenario in which Chibchan-related horticulturalists moved northward into the southeastern Yucatan carrying improved varieties of maize, and possibly also manioc and chili peppers, and mixed with local populations to create new horticultural traditions that ultimately led to more intensive forms of maize agriculture much later in time 8. A modest increase in effective population size corresponds with the appearance of maize and these new Chibchan-related horticulturalists, consistent with the amalgamation of local and non-local populations.This hypothesized northward movement of people related to the ancestors of Chibchan populations predates evidence for widespread maize agriculture and increases in the dietary importance of maize evident in the stable isotope record from MHCP and ST after 4,700 cal. BP BP Fig. 25, raisi45. The highest linguistic diversity of the Chibchan family occurs today in Costa Rica and Panama near the Isthmian land bridge to South America, and this is hypothesized to be the original homeland from which the languages diversified, starting roughly 5500 years ago45. We undertook a preliminary analysis of 25 phonologically and semantically comparable basic vocabulary items to study the linguistic evidence for interaction between early Chibchan and Mayan languages 54. This term is much more phonologically diverse and much more widely distributed  in Chibchan  between these two language groups, which may provide further clues about the movements of material culture and people we discuss here.The distribution and history of languages in Central America provides an independent line of evidence for our proposed historical interpretation. Chibchan is a family of 16 extant 7–8 extinct) languages spoken from northern Venezuela and Colombia to eastern Honduras Fig. 45. The h extinct These ancient DNA findings are also relevant to the ancestry of present-day Maya populations living throughout the tropical lowlands, including the Mopan and Q’eqchi’ communities in southern Belize with whom we worked in the course of this project. Around 75% of Indigenous ancestry of the Maya can be traced to ancient groups living in the region between 5600–3700 cal. BP. This ancestry is in turn a combination of two components. The first was related to individuals who were buried at MHCP and ST from 9600–7300 cal. BP, who likely represented descendents of an early phase of colonization into the Americas. The second was related genetically to the ancestors of Chibchan population, and we propose was derived from a later northward dispersal that may have been accompanied by improved varieties of maize and other domesticated plants, as well as elements of early Chibchan languages. Finally, we can model ~25% of the ancestry of present-day Mayan speakers as most closely related to highland Mexican populations. However, future research will be required to explore the full complexity of these more recent interactions in detail, including determining when such ancestry arrived in the southeastern Yucatan and how it may have related to further cultural and linguistic changes over the past several millenia.56 or drilling (other bones and teeth) after cleaning the exterior surface. From the resulting material, we extracted DNA58 and constructed uracil-DNA-glycosylase (UDG)-treated sequencing libraries  via published methods. We enriched the libraries for molecules overlapping the mitochondrial genome and ~1.2 million genome-wide target SNPs62 and sequenced on Illumina NextSeq 500 and HiSeqX10 instruments.We prepared ancient bone and tooth samples in clean room facilities at Harvard Medical School. We obtained powder either by sandblasting (petrous bones)https://github.com/DReichLab/ADNA-Tools). Merged reads were mapped to the mitochondrial reference genome RSRS63 and the human reference genome (version hg19)64 using bwa-v.0.6.165. We removed duplicate mapped reads, reads shorter than 30 bases, and reads with mapping quality scores less than 10. For use in analysis, we discarded the final 2 bases at each end of the reads (to eliminate most deamination damage-induced errors) and called pseudo-haploid genotypes at the targeted SNPs by selecting one allele at random per site.We processed the raw sequencing data by de-multiplexing (based on library-specific barcodes), trimming adapters and barcodes, and merging reads having at least 15 bases of overlap and at most one mismatch, using in-house software tools  apparent heterozygosity rates at variable sites on the X chromosome in males66, and (e) reduction in linkage disequilibrium67. Most individuals had minimal evidence of contamination (Supplementary Data x).We assessed the authenticity of the data by measuring (a) frequencies of damage-induced errors in terminal positions of sequenced molecules, (b) numbers of reads mapping to the X and Y chromosomes, (c) rates of matching of mtDNA-mapped sequences to the consensus haplogroup68. Mitochondrial haplogroups were assigned using quality scores calculated in HaploGrep 269 and diagnostic positions relative to the PhyloTree Build 1768. We determined Y-chromosome haplogroups using the tree from the International Society of Genetic Genealogy (http://www.isogg.org).For all individuals, we determined genetic sex by comparing the fractions of sequences mapping to the X and Y chromosomes, and we searched for close family relatives by computing proportions of genome-wide alleles matching from one individual to another via in-house scripts. Mitochondrial DNA consensus sequences were visually reviewed for ambiguities and known problematic nucleotide positions70—with present-day Native American genotype data6  and other present-day populations75 , we incorporated individuals (1–3 per population) from the Simons Genome Diversity Project (SGDP) dataset73, although we did not use SGDP data in the f4-statistics testing relatedness to present-day Maya or in qpAdm (aside from Maya themselves) to avoid possible batch-effect artifacts.We merged the ancient data—including published individuals76. We computed axes for our primary plot 74 as the outgroup for f4-statistics, except in cases where this would result in statistics of the form f4 , in which case we used the ancient Alaskans  and three eastern Eurasian groups , plus Cabecar and Waunana when modeling present-day individuals, and Russian and Dinka for the four-way model . When modeling the 5600–3700 cal. BP individuals with a mixture of ancestry related to the 9600–7300 cal. BP individuals and to the ancestors of Chibchan populations, we used a combination of nine individuals to represent the second source . Each of those populations individually yielded less precise but statistically consistent mixture proportion estimates (Supplementary Table We estimated mixture proportions using the qpAdm software14C) dated all newly reported individuals via accelerator mass spectrometry (AMS) (Supplementary Table 8). Crude gelatin yields were high enough for one sample (MHCP.17.1.7) to use a modified Longin method with ultrafiltration79. The preservation of extracted and purified collagen or amino acid samples was evaluated using crude gelatin yields (% wt) and stable carbon and nitrogen isotope mass spectrometry . C/N ratios between 3.22 and 3.40 indicate that all radiocarbon dated collagen and amino acid samples were well preserved. We directly dated enamel carbonate in four samples after multiple failed attempts at extracting collagen or amino acids. Carbonate samples were chemically cleaned using published procedures80, and sample integrity was evaluated using Fourier-transform infrared spectroscopy . Multiple radiocarbon dates on the same individual were combined using the R_Combine command in OxCal.We directly radiocarbon (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-8Supplementary Data 9Reporting Summary


Hippocampus guttulatus) is one of two seahorse species occurring in the North-East Atlantic. The population living in the Ria Formosa  declined dramatically between 2001 and 2008, prompting fears of greatly reduced genetic diversity and reduced effective population size, hallmarks of a genetic bottleneck. This study tests these hypotheses using samples from eight microsatellite loci taken from 2001 and 2013, on either side of the 2008 decline. The data suggest that the population has not lost its genetic diversity, and a genetic bottleneck was not detectable. However, overall relatedness increased between 2001 to 2013, leading to questions of future inbreeding. The effective population size has seemingly increased close to the threshold necessary for the population to retain its evolutionary potential, but whether these results have been affected by sample size is not clear. Several explanations are discussed for these unexpected results, such as gene flow, local decline due to dispersal to other areas of the Ria Formosa, and the potential that the duration of the demographic decline too short to record changes in the genetic diversity. Given the results presented here and recent evidence of a second population decline, the precise estimation of both gene flow and effective population size via more extensive genetic screening will be critical to effective population management.Genetic diversity is the raw foundation for evolutionary potential. When genetic diversity is significantly reduced, the risk of extinction is heightened considerably. The long-snouted seahorse ( It was once thought that marine fishes were too abundant and fecund to be faced with threats from overexploitation3. Fishing was a right, oceans were open access, and baseline data were, more or less, non-existent. More than a century of literature and observations later, it is now recognized that the paradigm of inexhaustible marine fishes and exploitation is unequivocally false6. Unfortunately, while the burden of proof often falls to scientists to demonstrate that anthropogenic activity is the root cause of environmental degradation, the lack of baseline data for comparison often hampers inferences on impact7. As a result, many marine environments have been subjected to degradation in the form of pollution, overexploitation and habitat loss. While the paradigm of inexhaustible ocean resources has eroded, many ongoing discussions continue in the field of conservation biology. One of which, relevant here, was proposed by Lande8, who suggested that species are often driven to extinction before genetic consequences have time to take effect. However, a bulk of empirical evidence indicates the contrary9, and more recently, reduced genetic diversity has been documented in threatened species10. Quantifying gene flow, metapopulation structure and demographic and stochastic events are necessary when assessing whether a reduction in population size is likely to lead to a reduction in genetic diversity11.The loss of genetic diversity is a primary concern in conservation biology and should be at the forefront of strategy to promote biodiversity conservation management14 that ca. 90% of studies have shown reduced fitness in inbred populations compared with non-inbred individuals15. For example, inbreeding depression was reported in 99 species of birds in which failed hatching rate increased with genetically similar parents16, as well as in fish, where inbred populations of Poeciliopsis monacha suffered from spinal curvature, deformation and reduced resistance to low oxygen17.Interestingly, just as there has been contention about the effects of genetic changes on threatened populations, a similar debate has been held over inbreeding in the wild. Despite many questioning the likelihood that inbreeding depression can lead to declines in wild populations18. This is why maintaining genetic diversity in a population is so important. If evolutionary potential reflects the capacity of a population to adapt to environmental change, then genetic diversity is the raw material that enables natural selection to select for adaptations that are beneficial for dealing with those environmental changes19. Additionally, population genetic theory predicts that populations with small effective population size (Ne) lose genetic diversity faster than populations with a larger Ne due to genetic drift20. Thus, genetic factors are important to consider when evaluating extinction risks because threatened species often have smaller and/or declining population sizes21. In such populations, the loss of genetic diversity and an increased probability of inbreeding is inevitable18.While inbreeding depression may not inevitably lead to population decline, its interaction with basic parameters of a population’s viability, such as population growth rate and variation in population size, may influence population persistence. A population may experience positive growth and reach its carrying capacity, but when hampered with sequential levels of inbreeding, may reach that carrying capacity at a slower rate. Should a population reach a relatively high level of inbreeding, the negative effect of inbreeding depression may cause population growth to become negative, setting it on a path to possible extinction22, suggesting that seahorse populations may rapidly recover from population declines23. However, their low mobility, monogamous mating pattern, relatively few offspring, mate fidelity and elaborate parental care22 could increase their vulnerability.Seahorses are marine fish with relatively fast growth rates, which mature at young ages, and have short generation timesHippocampus guttulatus Cuvier, 1829, is one of two seahorse species occurring in the north-east Atlantic Ocean from the North African coast to the coast of Shetland Isles, into the North Sea and in the United Kingdom, the Mediterranean and the Black Sea. Along with the sympatric H. hippocampus, both species are classified as ‘Data deficient’ at global and regional European levels and near-threatened within the Mediterranean, according to regional assessments from the IUCN25. The long-snouted seahorse displays a preference for shallow, sheltered and complex coastal habitats, with high seagrass density and extensive vegetation cover that provide holdfasts and abundant food37. It reaches its highest abundances in marine lagoons29. The species’ dependence on shallow and protected environments that are physically isolated from each other potentially limits its dispersal across unsuitable habitat stretches.H. guttulatus across its range is low 30. Previous genetic differentiation assessments revealed the existence of five cryptic lineages across the species distributional range32: Parapatric North and South European Atlantic lineages , which meet in Southwest France, where they coexist in sympatry; two lineages in the Mediterranean, associated with lagoon and marine habitats; and a Black Sea lineage32. More regional studies in the NW Iberian Peninsula based on 13 microsatellite loci found no evidence of reproductive isolation33. Recent work on the Mediterranean marine lagoons of Taranto in southern Italy and Thau in the Gulf of Lion, France, based on eight microsatellite loci and a mitochondrial DNA region (cytochrome b) revealed private mitochondrial haplotypes and unique genotypic profiles that make these populations distinct from marine coastal locations34. The populations’ low genetic diversities in those two lagoons are consistent with a severe or long bottleneck34.DNA sequence variation in H. guttulatus living in the Ria Formosa, South Portugal . Unfortunately there are no data available on the possible effect of these variations in density over this short temporal window on population dynamics of the species except that more recently only one large class size was observed .While the strong decline in 2008 has been attributed to habitat loss39. As genetic diversity provides the raw foundation for adaptation and evolutionary potential, acquiring estimates of genetic diversity of the Ria Formosa’s H. guttulatus population in the light of extreme fluctuation and continued anthropogenic environmental change would provide critical insights into patterns of demographic change in contemporary time, and suggest potential management responses.Extinction rates are estimated to be three to eight times higher for populations than for speciesH. guttulatus population in the Ria Formosa and declining population trends may lead to the suspicion that this population is facing a demographic bottleneck that may induce genetic erosion. Using two genetic snapshots of samples taken 12 years apart, this study aims to examine the effects of population fluctuations as inferred by census data on the genetic diversity of the Ria Formosa H. guttulatus population. Individuals were sampled in 2001 and 2013, and for both years estimates of observed and expected heterozygosity, allelic richness, private allele richness, effective population size and relatedness were calculated for comparison. This study hypothesizes that the severe population decline observed in the 2008 survey would likely reduce the effective population size of the following generations and contribute to a reduction in the genetic diversity of H. guttulatus.The nature and severity of the threats faced by the 41. The sampling procedures comply with the guidelines of the European Union Council (2010/63/EU). All protocols were approved by the ethical committee ORBEA of CCMar / University of Algarve and performed under a ‘‘Group-C” license from the Direcção-Geral de Veterinária, Ministério da Agricultura, do Desenvolvimento Rural e das Pescas, Portugal.Seahorses were individually sampled in 2001 (N = 76) and 2013 (N = 258) from Ria Formosa  was performed as described in Pardo et al.43 and van de Vliet et al.44. PCR reactions with labelled primers were performed using standard procedures, and amplified products were run on an ABI 3130XL (Applied Biosystems) automated sequencer. Genotype scoring was performed using STRand (https://vgl.ucdavis.edu/informatics/strand.php). It was assumed that a single panmictic population was being assessed31, and downstream analyses of the 2013 sample pooled all locations into a single year population. To filter for missing data, the missingno function of the poppr45 R-package46 was used, removing loci with more than 25%, and individuals with more than 10% missing data, respectively. Genotyping errors and null alleles were estimated using calculations by Summers and Amos47, Dempster et al.48, Brookfield49, and Chakraborty et al.50.DNA from filament samples was extracted by standard phenol–chloroform protocol, according to SambrookHo) and expected (He) heterozygosity, allelic richness (Ar) and the inbreeding coefficient (Gis) were made for each locus and across all loci to assess changes in genetic diversity. These statistics were computed using diveRsity package in R51. He differences between 2001 and 2013 were calculated using a Monte Carlo exact test based on 10,000 permutations from Adegenet52. Allelic richness was calculated with a 1000 re-sampling rarefaction technique with replacement using the smallest sample size in the data set to standardise for differing sample sizes . A measure of private allelic richness (PAr) was also calculated by HP-Rare 1.153.Calculations of observed  and putative relationships among individuals  using a maximum likelihood approach. Random genotype simulations were performed 1000 times for likelihood ratio tests and identified plausible relationships  based on a 99% confidence interval criterion54. Estimates of the percentage of sample pairs identified as full-siblings and half-siblings were calculated. ML-RELATE can accommodate the presence of null alleles in the estimates via a Monte-Carlo randomization test55 and the U-statistic of Rousset56. The test is one-tailed, i.e. it estimates the probability of obtaining the observed U statistic or greater value under Hardy–Weinberg conditions. If null alleles are present, ML-RELATE will use a maximum likelihood estimate of their frequency in all calculations which is considered to be more accurate than other estimators54.Relationships between individuals from the two sampling years were estimated with the program ML-RELATEFis) estimates57. This is especially true when sampling in small geographical areas in which inbred or closely related individuals occur59, as is the case for H. guttulatus from Ria Formosa. Therefore, in this study, simultaneous estimation of the inbreeding coefficient, null allele frequencies and random genotyping failures were conducted using the software INEST v2.259. This software implements a Bayesian approach comparing different combinations of parameters  and chooses the model with the lowest deviance information criterion (DIC) as the best fit. MCMC (Monte Carlo Markov Chain) used 500,000 cycles and a burn-in of 50,000, retaining parameters every 100th cycle.Estimates of inbreeding depression often rely on limited datasets that can be affected by the presence of null alleles, which have been shown to induce an upward bias in inbreeding coefficient  with the proportions of multi-step mutations (pg) set as 0.22 and the mean size of multi-step mutations (Dg) as 3.1 as recommended by Peery61. Stepwise mutation (SMM) and infinite allele models (IAM) were also carried out for comparison, as these represent the extremes of mutation models. The Wilcoxon signed-rank test was applied to test for the significance of heterozygosity excess for both mutation models using 106 permutations to approximate the exact value, as well as combined z-scores62.To explore the possibility of a demographic bottleneck in Ria Formosa seahorses, two tests were performed. First, we used a test for heterozygosity excess62. To test if the data conformed to the L-shaped null hypothesis of a stable population, Bottleneck v2.2.0163 was used.Second, a mode shift test was used to determine whether a shift has occurred in allele frequency classes. This test assumes that stable populations have a peak in allele numbers at the lowest frequency class, displaying an L-shaped distribution. Population bottlenecks tend to distort this distribution to the right, as the loss of alleles results in a rightward skew in the allelic distributionNe) may be obtained from single or temporal samples64. In this study, the former approach was used, because the samples from 2001 and 2013 covered a restricted time interval and there were no age estimates from the sampled individuals66. Ne was assessed from levels of linkage-disequilibrium (LD)67 with NeEstimator v2.168. Monogamy was assumed69 and 95% confidence intervals of Ne were estimated using the jackknife‐across samples method70. Sample sizes are an important consideration when estimating Ne, and small sample sizes can result in considerable biases66. Due to the sample size disparity between the 2001 and 2013 samples, genetic data for both years were rarefied by generating 1000 replicate files with 30 individuals in each. Alleles with low frequencies (< 0.02) were omitted to avoid bias because their inclusion can bias estimates of Ne68. Population-specific Ne was calculated by taking the harmonic mean of LDNe estimates from each year the population was sampled. As 95% confidence intervals of Ne based on the LD often included infinity, only 95% lower bounds are reported, as these are considered valuable indicators of changes in population size64.Estimates of contemporary effective population size . Four loci  and 135 genotypes were removed after filtering at 25% and 10% of missing data, respectively, resulting in 291 informative individuals across eight loci, 33 individuals in 2001 and 258 in 2013. Estimated null allele frequencies averaged across loci were low (< 9%) for both years, except for Hhip3 locus from the 2001 sample, ranging between 0.147 and 0.198 across all four methods. Samples from 2001 show a higher frequency of null alleles (< 9%) than in 2013 (< 3%), possibly due to the poor preservation of the older samples.A total of 434 individuals were screened across 12 loci (Ho) and expected (He) heterozygosity was over 0.72, showing an overall increase in time (Table p < 0.05), increasing from 15.8 in 2001 to 20.3 in 2013 . Inference of all 528 pairwise relationships for 33 individuals from 2001 identified 22 (4%) half-sibling pairs. Of the 31,878 pairwise relationships between the 258 individuals collected in 2013, ML RELATE classified 2482 (8%) as half-sibling pairs and 62 (0.2%) as full-sibling pairs.The analysis of genetic relationships between individuals, with or without the accommodation for the presence of null alleles, did not change the results significantly  parameter (Table p > 0.05) for heterozygosity excess for either the Wilcoxon signed-rank test (p = 0.96) or the z-test for combined z-scores (p = 1). Similar non-significant results (p > 0.05) were obtained for SMM and IAM, which represent opposite ends of the mutation spectrum . All loci except two (Hgut4 and Hhip4) showed a deficiency of heterozygotes. Similarly, no distortion to the typically L-shaped distribution of allele frequencies was found , consistent with a stable population size.Overall, both genetic bottleneck analyses do not reflect the drastic decrease observed in the census data. From the eight loci examined, the results from the TPM were not significant  showed any substantial decrease. Over this same period, although Ne has risen and Fis has seemingly declined, no evidence was found to support the occurrence of a genetic bottleneck, apart from the observation that the overall relatedness has increased. Before discussing the implications of these findings, it is important to highlight some of the caveats of this study.The initial hypothesis for this investigation was based on the premise that the drastic decline in census population numbers reported between 2001 and 2008 would be reflected in reduced genetic diversity in subsequent generations. Based on this prediction, lower genetic diversity was expected in the 2013 sample. In contrast to this expectation, the 71. Secondly, the genetic diversity of the 2001 sample was derived from a higher number of sites (15) than that sampled in 2013 (6)36. Thirdly, the presence of null alleles in the dataset may have affected the study, particularly as cross-species amplification was used72. While the frequency of null alleles in the 2001 dataset was higher than that inferred for 2013, likely due to poor preservation, no evidence of null alleles in previous studies has been detected using these markers44. Thus, we found no reason to alter allele frequencies or exclude loci, except when using INEST, in cases in which the inclusion of null alleles or genotyping errors was found to upwardly bias Fis estimates. Finally, it is important to note that neutral microsatellites, particularly a limited number of loci, do not represent genome-wide diversity, and may fail to detect changes in the genetic diversity of loci experiencing selection73.Firstly, it must be stressed that the sample size disparity between both years may have, to some extent, influenced the results presented here. Some diversity estimates are more prone to biases due to sample size than others, making them more useful in the context of a comparison between years. For instance, while the number of alleles per locus in a small sample is greatly biased relative to an estimate based on a large sample size, this can be overcome with rarefaction, which improves the ability to compare between samples differing in sizeNa: 19/37; Ar(30): 16/20; He: 0.85/0.87 for 2001 and 2013, respectively) is higher than that detected in Ria Formosa samples from 2004/2005 with values averaged across five sampling sites of He: 0.37; Ho: 0.3674. Although these differences may be due in part to the use of different markers, Hgut4 was used in both studies, and as both studies employed similarly large sample sizes, this is unlikely to be a sampling artefact. When all available demographic and genetic diversity results are taken into consideration 34. Other coastal locations in the Mediterranean typically display low expected heterozygosities ranging from 0.30 to 0.54 with sample sizes from 2 to 17 see Table 334. In H. guttulatus from the Galician coasts in NW Spain, 32 individuals scored for 12 microsatellite loci, yielded He of 0.50043. Another study with samples from the same area, but from a considerably larger number of individuals, 255 and 13 microsatellite loci, resulted in overall expected heterozygosities  lower than 0.62033. However, common to our study are four loci, Hgut4, Hgut6, Hgu-USC5 and Hgu-USC9. Except Hgu-USC9 which has very low heterozygosity (ranging from 0.29 to 0.40) the remaining loci display high expected heterozygosities (ranging from 0.77 to 0.97), in line with the present study . Therefore, in spite of the severe population decline, Ria Formosa displays higher heterozygosities than coastal or lagoon populations elsewhere in Europe.Genetic diversity indices were generally high and showed an overall increase between 2001 and 2013, rejecting our initial hypothesis of a reduction in genetic diversity due to census population declines over the study period. The genetic diversity observed in our study averaged across loci . This hypothesis is also supported by studies that have shown that South Iberian populations display low levels of microsatellite genetic differentiation74 and are panmictic with respect to 286 SNPs31. Despite suggestions that seahorses are capable of assisted long-distance translocations, likely through rafting on floating debris, carried by oceanic and coastal currents75, that this is implausible in the Ria Formosa, where H. guttulatus have high site-fidelity and very limited dispersal capacity38, and the lack of intermediate required habitat does not facilitate seahorse dispersal. The panmictic nature of the South Iberian populations remains to be fully explained even though a commonly cited rule of thumb is that one migrant per generation is needed to sufficiently minimise the loss of polymorphism and heterozygosity within subpopulations77.The apparent reduction in 60, corroborating other findings37. It is possible that the doubling of related individuals between 2001 and 2013 is an artefact of the demographic decline inferred in 2008. With fewer individuals in the period after the population reduction, the likelihood of sampling related individuals would be expected to be higher, due to the remaining individuals contributing to the recovery reported by 2012. Nevertheless, both our investigation and Woodall’s74study used the heterozygosity excess method60, which is expected to have more power to detect recent population bottlenecks relative to the M-ratio statistic78. With this in mind, we suggest that H. guttulatus in the Ria Formosa may be naturally prone to population fluctuations that may be exacerbated by anthropogenic activity, e.g. clam farming, fisheries, dredging80. Seahorse populations are vulnerable to fluctuations83, which makes the conservation of their preferred habitats critical41 and of great urgency if disturbances and habitat degradation are identified.While the present results are inconsistent with a genetic bottleneck related to the inferred population decline, heterozygote deficiency was noted at six out of the eight loci, which could be a result of recent population expansionNe point estimates were negative, indicating that the results can be entirely explained by sampling error rather than drift68. This situation usually occurs when Ne is large, which may suggest that the Ria Formosa population was substantial during both sampling periods. Linkage disequilibrium due to drift is low in large populations and difficult to detect, rendering the estimation of Ne in large populations  challenging, particularly with the small number of markers employed in this study64. More comprehensive genetic screening via high-throughput sequencing may offer an opportunity to overcome this limitation, and more precisely estimate Ne64. Reporting the lower limits of these estimates remains valuable as an indicator of changes in population size64. Here, lower bound Ne means remained relatively stable, with a slight increase, less than the increases inferred from harmonic means and point estimates. The interpretation of these Ne estimates must be made with caution84 because there is only weak genetic differentiation between the Ria Formosa and other South Iberian subpopulations74. This implies a level of historical and/or contemporary gene flow, which seems improbable given the species’ life-history traits and habitat preferences. Nevertheless, Ne estimates may be inflated and/or reflect Ne of a larger metapopulation rather than the local population. While this cannot be ruled out, even with an apparent increase in Ne, the estimates of harmonic means reported here centre around 2000 individuals, and 50% of the lower bounds are below 500 , particularly in light of unpublished data that has shown that another severe decline has taken place, indicating an overall decline over the last 20 years.Both 2001 and 2013 H. guttulatus population of the Ria Formosa has been able to retain relatively high genetic diversity despite evidence of a recent demographic decline. Perhaps the decline reported in 2008 was not sustained for long enough for drift to have had an effect and a sufficient number of individuals were left to retain high genetic diversity. According to empirical observations e.g. kit foxes88 and simulations89, changes in genetic diversity following a decline in population size may take a number of generations to become obvious, and that the short time span of our study was not enough to capture the full range of genetic parameter fluctuations.Overall, based on the results presented here and in previous literature, it seems that the H. guttulatus, which would provide a more extensive panel of markers for the analysis of neutral diversity90. If SNPs or more microsatellite loci reveal declines in genome-wide genetic diversity, Fis will become extremely critical, as small differences in Fis values can significantly alter extinction risk91. Furthermore, the estimation of Ne for the broader region may highlight if the Ne value reported here is inflated through gene flow. Considering the lower bound estimates of Ne, the precautionary principle must be taken. We recommend that active management actions for this population should be continued and enhanced to protect the diversity and numbers indicated here.Future studies using an increased number of loci compared to those used here  could help to test this prediction. Alternatively, a recently published survey of available seahorse microsatellites identified 18 highly polymorphic loci for 92. Among the main stressors affecting coastal lagoons are habitat loss and alteration; eutrophication; sewage and organic wastes; fisheries overexploitation; sea-level rise; chemical contaminants, sediment input/ turbidity and floatables and debris93. Despite Ria Formosa being a semi-protected lagoon, many activities that alter the environment are permitted, including legal fisheries, anchoring and dredging, which contribute to habitat loss in the lagoon94. Habitat loss was identified as the leading probable cause for seahorse decline38, which was to some extent confirmed by other studies37. The observed extreme fluctuations in seahorse populations are correlated to the availability of holdfasts95. However, we cannot discard the impact that other undetermined abiotic or biotic factors such as warmer temperatures, food availability, or abundance of predators36 or overfishing may have on local populations. There is empirical evidence of a rapid and somewhat uncontrollable unreported and unregulated fishing (IUU) activity which may be a significant factor contributing to population decline .Coastal lagoons such Ria Formosa rank among the most productive ecosystems on Earth, and provide a wide range of ecosystem services and resources. Anthropogenic impacts are escalating in many coastal lagoons worldwide because of increasing population growth and associated land-use alteration in adjoining coastal watersheds35, suggesting that short distance migrations outside the survey area may have influenced census results. Seahorses have been found to colonize previously degraded habitats after being enriched with artificial structures95. We discard the possibility that the seahorses might have moved outside Ria Formosa in search of partners, because of the lack of suitable habitats , both to the west and the east of the lagoon. It is perplexing not to find a genetic signature of the demographic bottleneck in terms of allelic loss and of heterozygote decrease. At this short time scale, it would be expected to see an effect in allele number if not a reduction in heterozygosities, as it is known that heterozygosity decay trails the allele number reduction96.It is difficult to explain the maintenance of the high levels of genetic diversity observed in Ria Formosa individuals when census data suggest such a dramatic decline. While we cannot discard the possibility that the individuals have moved to other areas, the lagoon has been visited regularly by divers experienced in seahorse detection. During 2020, the number of visited sites was increased, and although an overall decline in population size was observed, local increases were found at the microhabitat level. Expansions of individual home ranges in response to low densities of potential mates have been observed in previous yearsH. guttulatus habitats would help to retain the genetic diversity in the Ria Formosa. The use of artificial holdfasts has been shown to be a useful tool for repopulating areas in which habitats have been damaged or disappeared95. However, this does not resolve the issue of degrading habitat, which should be at the forefront of conservation in the Ria Formosa. Clam farming poses a significant threat to the seagrass Zostera noltii80, and other species of seagrass have been degraded by coastal construction and dredging79. H. guttulatus have a preference for complex habitats41, which are likely to be subject to human impacts, fluctuations and disturbances in the dynamic barrier island system that characterizes the Ria Formosa97. Identifying temporally stable areas and enhancing habitat may help to ensure the long-term viability of seahorses in the Ria Formosa98, in addition to the enhanced protection of important refuges. Finally, future studies will need to clarify the potential impact of gene flow between subpopulations of the South Iberian Peninsula, as previously suggested by other authors74 in order to determine the relevant conservation unit for seahorses in the region.This study showed that genetic diversity and effective population size of the Ria Formosa long-snouted seahorse was retained through a severe recent demographic decline as inferred from census data. However, this population has suffered an even steeper decline in recent years (2018–2020) . Therefore, despite the genetic results, conservation measures and demographic and genetic monitoring should continue to be implemented. Enhanced protection and restoration of H. guttulatus in the Ria Formosa, nor cause a genetic bottleneck, and that the effective size of this population has instead modestly increased. A more in-depth genomic study could help to illuminate genetic changes at the whole genome level and identify genetic regions experiencing differential selection, which may be especially prone to population fluctuations. Finally, although the goal of this study was to assess genetic changes in diversity, it must be acknowledged that the effective conservation of essential habitat is critically important100. Bearing in mind the observed pattern of population fluctuations, the use of artificial holdfasts may alleviate pressures from environmental variation. Active and enhanced monitoring and conservation of key habitats should remain at the forefront of conservation efforts in nearshore environments such as the Ria Formosa.Molecular data suggest that, contrary to our initial hypothesis, a severe demographic decline reported in 2008 did not reduce the genetic diversity of Supplementary Table.