Chiloscyllium punctatum is a major component of sharks landed in Malaysia. However, little is known about their population structure and the effect of high fishing pressure on these weak swimming sharks. Both mitochondrial DNA control region (1072 bp) and NADH dehydrogenase subunit 2 (1044 bp) were used to elucidate the genetic structure and connectivity of C. punctatum among five major areas within the Sundaland region. Our findings revealed (i) strong genetic structure with little present day mixing between the major areas, (ii) high intra-population genetic diversity with unique haplotypes, (iii) significant correlation between genetic differentiation and geographical distance coupled with detectable presence of fine scale geographical barriers (i.e. the South China Sea), (iv) historical directional gene flow from the east coast of Peninsular Malaysia towards the west coast and Borneo, and (v) no detectable genetic differentiation along the coastline of east Peninsular Malaysia. Genetic patterns inferred from the mitochondrial DNA loci were consistent with the strong coastal shelf association in this species, the presence of contemporary barriers shaped by benthic features, and limited current-driven egg dispersal. Fine scale population structure of C. punctatum highlights the need to improve genetic understanding for fishery management and conservation of other small-sized sharks.The demersal brown banded bamboo shark  Overexploitation has been shown to reduce genetic diversity and increase extinction risk especially for small populations6; therefore understanding the genetic structure and migration patterns or gene flow of shark species are essential to inform effective management and conservation plans. Earlier shark genetic studies had been largely focused on marine neritic shark species, that are charismatic, economically important and of conservation interest5, e.g. blacktip shark C. limbatus7, hammerhead shark Sphyrna lewini8, whale shark Rhincodon typus9 and great white shark Carcharodon carcharias15. Studies on small-sized benthic coastal sharks have increased in recent years in part due to their catch prominence and importance in coastal fisheries, both globally and in Southeast Asia, e.g. whitespotted bambooshark Chiloscyllium plagiosum16, nurse shark Ginglymostoma cirratum17, whitetip reef shark Triaenodon obesus18, leopard shark Triakis semifasciata19 and common smoothhound Mustelus mustelus20.Sharks are generally highly vulnerable to overexploitation due to their life history strategies, such as late maturity and low fecundityHemiscyllium Müller & Henle, 1838 (nine species) which is confined to the Australia-New Guinea region and Indonesia, and the Indo-Pacific genus of Chiloscyllium Müller & Henle, 1837 (eight species)21. These bottom-living sharks are known to be weak swimmers22 with great camouflage ability, allowing them to adapt to demersal habitat by hiding around crevices23. They live either solitarily or within a group and appear to be strongly territorial. They usually deposit egg cases on the sea floor (sheltered or deserted), with some cases attached to benthic marine plants23. Currently, their fecundity in the wild is still unknown, but studies on captive individuals suggested that they produce annually less than 30 egg cases that require a three-month incubation period27. To the best of our knowledge, no movement study on this animal exists. Dispersal is likely very restricted due to their oviparous nature28, thus rendering them highly vulnerable to localized exploitation and habitat destruction.One such group of sharks is the longtail carpet shark or bamboo shark from the family Hemiscyllidae (order Orectolobiformes). The family Hemiscyllidae comprises two genera: Chiloscyllium are found in Malaysia: Indonesian bamboo shark C. hasseltii Bleeker, 1852; slender bamboo shark C. indicum ; whitespotted bamboo shark C. plagiosum ; and brown banded bamboo shark C. punctatum Müller & Henle, 1838. A previous record of grey bamboo shark C. griseum Müller & Henle, 1838 in Malaysian waters was a misidentification of C. hasseltiiAhmad, personal communication. The brown banded bamboo shark C. punctatum has been classified as Near Threatened under the IUCN Red List but may be uplisted to Vulnerable in the near future in Southeast Asia due to high fishing pressure29. It is the top ten shark species landed by numbers in commercial fisheries across Malaysia30. This species makes up almost half of all shark individuals caught using bottom trawl fishing vessels in the west coast of Peninsular Malaysia31. Approximately 40% of C. punctatum landed in commercial fishing gears comprised of immature individuals, with higher percentage in the west than the east coasts of Peninsular Malaysia32,Lim, unpublished data. Among similar-sized sharks, C. punctatum has relatively lower commercial value, fetching an ex-vessel price of USD 0.20–0.60 per kg. Many artisanal fishers in Malaysia generally do not discard any of their non-trash fish catches; when they do, bamboo sharks are among those selectively discardedThen, unpublished data. No formal fishery assessment exists for this species (or other sharks) in Malaysia, and the effect of present fishing level on the animal is unknown.Presently, four species of C. punctatum includes clarifying the population substructure due to concerns of population fragmentation29 likely due to being a weak swimmer and limited potential for dispersal. Genetic tools have been used to identify population vulnerability or risk of local extinction based on their genetic diversity and structure as well as connectivity among populations35. Intra-specific genetic studies on small-sized coastal sharks and rays with limited vagility showed that they display high genetic population differentiation across ocean basins due to environmental barriers (nurse shark Ginglymostoma cirratum17), vicariance and dispersal events (maskray Neotrygon kuhlii36), or a combination of both . On the other hand, studies of these small coastal species along continuous continental coastlines either displayed clear genetic differentiation  or lack of genetic structure (milk shark Rhizoprionodon acutus39); reasons for these pattern variations included differences in habitat preferences, site fidelity, geographical distance, and physical barriers to movement. Despite growing concerns of shark exploitation in Malaysia as one of the top shark-fishing nation globally40, genetic studies of local shark populations, including C. punctatum are almost non-existent .One of the priorities for 46. Sundaland, a shelf within the biodiverse Indo-West Pacific region that extends from Thailand southwards primarily covering Malaysia and Indonesia, has undergone dramatic plate tectonic evolution forming episodic submergence and exposure of the shelf48. These series of events had affected distributions of local organisms  in multiple ways due to different population dispersal patterns . In relation to elasmobranch populations, the Sunda Shelf appeared to be acting as biogeographic barrier for wedgefish Rhynchobatus australiae55 but not for spottail shark Carcharhinus sorrah56. However finer-scale seascape features that may affect genetic structuring of sharks and rays within the Sundaland region had not been investigated previously.The genetic structure of any organism is influenced not only by the ecological processes but also the geological historyC. punctatum in Malaysia, specifically along coastal Peninsular Malaysia and northern Malaysian Borneo coastline. Based on the biogeographic history of the Sundaland region, we hypothesized that there are at least two distinct local C. punctatum populations, with little genetic mixing between populations from either side of the peninsular (northern end of the Sunda Shelf barrier) and across the Karimata Strait that separates the peninsular from Borneo. Furthermore, we tested to see if observed population genetic differentiation can be explained by simple geographical distance or otherwise. In addition, we also assessed the gene flow directionality between distinctive populations to infer putative dispersal pattern. Knowledge of fine scale genetic structuring of C. punctatum provides the spatially relevant information for appropriate national fisheries management and conservation of this highly fished shark and likely of other sharks sharing similar life history strategies.The aim of the present study is therefore to assess the degree of genetic diversity and differentiation of C. punctatum samples. A total of 70 unique haplotypes and 63 polymorphic sites were detected, in which 40 sites were found in the CR region and the rest were found in the ND2 region , with a broad range of pairwise genetic distances at all geographical distance concentrated mostly along the regression line   among the a priori subpopulations along the coastline of EP. No significant genetic differentiation was found among subpopulations p > 0.05)  strong genetic differentiation with little present day mixing between the five areas (populations) sampled, (ii) high intra-population genetic diversity with unique haplotypes, (iii) significant correlation between genetic differentiation and geographical distance coupled with detectable presence of fine scale geographical barriers (i.e. the South China Sea), (iv) historical directional gene flow from EP bifurcating towards WP and towards Borneo (from the western end of SR to Sabah), and (v) no detectable genetic differentiation along the EP coastline. The high genetic structure observed in C. punctatum within the central area of its distribution range is also consistent with their strong coastal association and life history strategies. We discuss these results in relation to biological and geographical barriers that shape the animal’s phylogeography and implications for fishery management of this small-sized shark.Using mitochondrial DNA markers for the bamboo shark C. punctatum found within a small region had exceeded those reported for other elasmobranchs sampled across broader geographical range, e.g. 26 haplotypes in Negaprion brevirostris58, 39 in Carcharhinus sorrah56, and 12 in Ginglymostoma cirratum17. The exception is a report of 67 haplotypes found in C. plumbeus59. Analysis using cumulative haplotype curves suggested that there are many haplotypes remaining to be discovered for C. punctatum .The large number of unique mtDNA haplotypes in C. punctatum samples underscores the need for careful fisheries management to maintain the natural diversity of this highly exploited species and the role of the Malaysian stock as a source population. To date, there is no study that directly assess the relationship between overfishing and genetic diversity in shark and ray species5. Overharvesting of marine fish species has been shown to result in reduced mean body size, faster growth rate, and earlier sexual maturity63, as well as lower genetic diversity, especially allelic richness64. On the other hand, the level of genetic diversity in fishes was surprisingly not found to be lower for threatened species relative to non-threatened ones65. Our findings thus provide an important baseline data for key genetic diversity metrics that can be used as indicators of population health and overall conservation status of C. punctatum.The high haplotype diversity and directional population expansion detected in our C. punctatum genetic information could occur as far as 400 km, the longest distance between two known catch locations of the trawl survey conducted. The coastwide genetic mixing can either be facilitated by adult movement or dispersal of egg cases. No mobility study had been conducted on C. punctatum or other congeners to date. However, a closely related species with similar behaviour, the nurse shark G. cirratum  is able to travel at least 541 km66 and genetic studies on the species indicated possibility of longer distance travel along a ~ 5000 km coastline17. Another continental shelf species, South African catshark Poroderma africanum (TL = 101 cm) could travel as far as 1964 km66.The lack of genetic structure along the coastline of EP, absence of IBD and sharing of haplotype H18 among EP subpopulations suggests high gene flow and that mixing of C. punctatum movement, ancient and present day bathymetry maps of this region suggest that the species should be able to travel along the shallow waters of coastal EP connected to WP along the Johore Strait  or Singapore Strait and through the Karimata Strait to the Borneo side69. However significant genetic differentiation and weak support for IBD in C. punctatum across the five sampling areas indicates that benthic seascape features may form substantial barriers for adult shark movement across EP-WP and EP-SR. The barrier between EP and WP could result from intensive anthropogenic activities at the Johor and Singapore Straits. These straits, though only approximately 50 km in length, are well known for major reclamation and busy shipping traffic since 181970. Furthermore, industrial activities within the Straits are likely to impact benthic habitat availability and deter adult movement between either coasts of Peninsular Malaysia. The Karimata Strait between EP and SR had been reported to have strong unpredictable currents72, thus potentially limiting adult movement across the Strait between EP and SR at the western end of Borneo. Study on movement and benthic habitat use for C. punctatum will be useful to corroborate the role of distance and bottom features that shape present day genetic structure.Since coastal depth is probably one major restriction for adult 73; this would be most likely consistent with a pelagic stage dispersal. Egg cases for various elasmobranch species had been recorded to have holding abilities, adhesive fibre in the case of C. punctatum, to prevent drifting, exposure to predation or damage by wave and tidal action76. However, ROV video footage capturing movement of egg cases along with water currents lend support for the possibility of egg case dispersal as a mode of limited population connectivity across these areas77. The gene flow directionality implies that population in EP could act as a source for populations in Borneo and in WP.The westward gene flow directionality observed from EP to WP and eastward flow from EP to Borneo Fig.  could beC. punctatum could also be observed along the northern Borneo coastline of about 385 km. Detection of a biogeographical barrier between Sarawak and Sabah suggests that benthic seascape features coupled with differing water bodies (South China Sea and Sulu Sulawesi Sea) are important in shaping the genetic structure for C. punctatum of Borneo and possible other similar sharks. This physical delineation is sufficiently significant that Scoliodon macrorhynchos, a small sized spadenose shark, could be found along the coastline of the Sarawak Borneo but not of SabahThen, unpublished data. In relation to the weak genetic structuring observed between eastern and western Sabah, further investigation is warranted. Discussions with local fishers and other researchers suggest that sharks landed in Sabah, especially by trawlers, could have been caught from the waters of the Philippines (from the Sulu-Sulawesi waters) Thus, the genetically differentiated Sabah populations and their transboundary relationship with putative populations from the Philippines should be jointly considered.Unexpectedly, significant genetic differentiation in Nerita albicilla78 and Echinolittorina spp.49, echinoderm Diadema setosum54, small bony fishes 54, to larger and more mobile species like wedgefish Rhynchobatus australiae55. The congruency between our data and these past studies indicated the importance of the Sunda Shelf barrier in shaping the phylogeography of marine species in this region.The genetic differentiation and possible directional population expansion observed in this study could reflect historical vicariance events in the Sunda Shelf region. Other marine organisms in the Indo-Malaya archipelago showed genetic separation between the east and west peninsular populations. These range from smaller and less mobile species such as gastropods C. punctatum inferred from the mitochondrial DNA loci would reflect signatures that occurred over historical time scale rather than during contemporary ecological time scale. However, an additional possibility to the strong genetic structure observed can be due to behavioural traits, for instance philopatry or site fidelity which has been shown for diverse species of sharks79; thus this cannot be ruled out for the strongly coastal-associated C. punctatum. The joint use of nuclear and mitochondrial markers will be important to discern whether the observed patterns of genetic structure in C. punctatum could be attributed to philopatric tendencies or due to the presence of biogeographic barriers, or both.Overall, the genetic patterns and differentiation observed for Chiloscyllium punctatum experiences high fishing pressure despite being caught primarily as non-target species (bycatch). In 2017 alone, we estimated that approximately 670,000 individuals were caught and landed at the four major shark landing states in Malaysia , based on estimated landings of 750 metric tonnes of C. punctatum and average weight of C. punctatum landed in trawl fisheries of 1.12 kg80, Lim unpublished data. The national demersal resources were estimated to have decreased by 80% relative to assumed virgin (unexploited) levels during 1960s and this decrease was attributed to overfishing that had reportedly been ongoing for the past two decades81. As this species make up almost half of all sharks caught from trawlers, and almost half of the individuals landed were juveniles, impact of fishing pressure remains a great concern in the long run.C. punctatum across Malaysian waters coupled with low levels of inter-region connectivity strongly suggests the need to consider separate fishery management stocks and conservation plans for the west coast of Peninsular, east coast of Peninsular and Borneo. These separate populations appeared to rely almost exclusively on self-recruitment (with FST mostly > 0.25 indicating low migration rate among areas)82; migrants would also likely find difficulty transmitting their genes locally given the territorial nature of the species. Localized population extinctions may be possible if present fishing pressure is not curbed.High level of genetic structuring in C. punctatum, unique biological traits likely confer considerable resilience to this species. Chiloscyllium punctatum has shown the ability for long term sperm storage83, is extremely hardy and physiologically adapted to inhabiting environments that undergo cyclical hypoxic conditions . However, this is likely not the case for many other coastal-associated shark and ray species within the highly fished waters of Malaysia and other neighbouring countries. In addition to clarifying the genetic structuring of these exploited species, efforts to reduce present fishing effort should be prioritized. The proposed move towards a total ban of bottom trawl fishing within Malaysian waters in the next few years would substantially reduce fishing pressure on sharks and rays.Despite ongoing high fishing pressure on highly differentiated genetic stocks of C. punctatum within the Indo-Malaya Archipelago. Observed high genetic differentiation between the five sampled areas could be attributed to the Sunda Shelf formation acting as ancient barrier for the animals. This genetic pattern persists in the present day due to their strong coastal shelf association and likely limited adult movement, apart from limited directional gene flow between areas. Presence of contemporary barriers for population connectivity is shaped by both distance and benthic features and limited current-driven egg dispersal. Although present high fishing pressure do not appear to pose immediate concerns for C. punctatum, findings of strong genetic structure within the small geographical location considered raises the concern of the population health of other exploited small sized sharks in the area.We present novel evidence of high genetic structure and diversity of The studied shark species is not legally protected in Malaysia. All sharks were dead when encountered at the landing sites. Permission was obtained from the Department of Fisheries Malaysia (DOFM) for sampling onboard the demersal trawl survey in the east coast of Malaysia, in accordance with the DOFM sampling protocol. Collection permits and sampling protocol for samples in Sabah was approved by the Sabah Biodiversity Council [Access License Reference No: JKM/MBS.1000-2/2 JLD.9 (21–23) and Transfer License Reference No: JKM/MBS. 1000-2/3 JLD.4 (18)].85 as well as the distribution range of C. punctatum86. It is surrounded by three major water bodies namely the Strait of Malacca, southern South China Sea and the Sulu-Celebes Sea , depth and distance barrier (South China Sea) between EP and SR (B2), and distance barrier between SR and Sabah (B3).Malaysia is located in the centre of the Indo-West Pacific biogeographic region88. Samples from EP were obtained through a fisheries-independent demersal trawl survey within the Exclusive Economic Zone (EEZ) organised by the Department of Fisheries Malaysia from July 2015 to July 2016. This invaluable opportunity allowed us to examine finer-scale genetic structure and connectivity of C. punctatum along the 724 km-long EP coastline. The EP samples were further divided into five subpopulations (EP1 to EP5) along the coastline . Two mitochondrial DNA (mtDNA) markers were employed to analyse the genetic diversity and structure of C. punctatum: Control region (CR) and NADH dehydrogenase subunit 2 (ND2). The primer sets used for the targeted regions were CR-F 5′ GAC CTT GTA AGT CGA AGA 3′ and CR-R 5′ TCT TAG CAT CTT CAG TGC 3′ for CR90, and Ilem-Mustelus 5'- AAG GAC CAC TTT GAT AGA GT -3' and Asn-Mustelus 5'- AAC GCT TAG CTG TTA ATT AA -3' for ND291. The PCR amplification was performed using a 20 µL reaction mix containing 2 µL of 10 × PCR buffer, 0.5 µL of dNTPs mixture (2.5 mM each), 1 µL of 10 pmol primer (both primers), 1.25 unit of Taq DNA polymerase , 1 µL of 50 pg to 1.0 µg DNA templates, and molecular-grade water. The PCR cycles for both markers comprised of 2 min initial denaturation at 94 °C, followed by 30 cycles of 20 s at 94 °C, 20 s at 52 °C and 1 min at 72 °C, and subsequently a final extension of 5 min at 72 °C. All PCR products were examined using 1% agarose in TAE buffer prior to Sanger sequencing service at Apical Scientific Sdn Bhd .The DNA of each sample was extracted using 10% Chelex resin incubated for 2 min at 60 °C, followed by 25 min at 103 °C , polymorphic sites (k) and, haplotype diversity (ha) and nucleotide diversity (π). Haplotype or gene diversity is the probability of getting two different alleles from random sampling while nucleotide diversity is the average differences in number of nucleotides per site among DNA sequences96. The NETWORK v5.0 software  that covered both CR and ND2 was included in the analysis to observe potential connection between samples in the South China Sea. Intra and inter-population genetic diversity by individual and concatenated markers (ND2CR) was characterized using D97 with 10,000 permutations to identify the hierarchical partitioning of genetic variation under the influence of various combinations of biogeographical barriers stated earlier . The best model for each marker was selected using jModelTest2; since the best models identified were not implemented in ARLEQUIN, we used the next best available models, i.e., Tamura and Nei correction for CR, Kimura 2 Parameter for ND2 and Kimura 2 Parameter with Gamma 0.656 for ND2CR. Pairwise comparisons of population differentiation using FST values among (1) subpopulations in EP, and (2) all populations were determined and significant probability values were reported after correction for false discovery rate (FDR)98.AMOVA analysis was conducted in ARLEQUIN v3.5.2.2ST/(1 − FST))99. Pairwise geographical distance among populations was estimated as the mean value of the shortest straight-line distance between two points at the sea among the pairs adapting two turning point around southern tip of peninsular to reach WP  and one turning point at northern tip of Sabah to ES . For EP, individual-based IBD analysis was performed given that individual catch locations were known and distances for some catch locations within a subpopulation were further than those between subpopulations; uncorrected p-distance calculated in PAUP* 4.0b10 software100. The IBD analysis was performed using Adegenet version 2.1.1 package in R version 3.5.1 software101.Isolation-by-distance (IBD) i.e., correlation between genetic distance and log (base 10) of geographical distances, between populations was calculated and their significance was tested using the Mantel test with 9999 randomizations. Genetic distance between populations was defined as . Migrate-n allows for the estimation of relative effective population size, θNe = (Neμ), and asymmetric gene flow, M (m/μ), among populations over longer periods of time (> 1000 years)106 with the assumption of constant population size, random mating within population, constant mutation rate, constant immigration rate and genetic materials can only exchange through migrants 1. Results from both pairwise testing and haplotype network were used to guide the selection of a priori population pairs for statistical testing and to reduce the number of models considered in Migrate-n. Five population pairs  were subjected to four migration models  under 10,000 and 50,000 recorded Markov chain Monte Carlo (MCMC) steps; additional analyses using 100,000 steps were done when results did not agree between runs of 10,000 and 50,000 steps. Four-chain heating at temperatures of 1, 1.5, 3 and 1,000,000 was applied to increase the efficiency of the MCMC. Other analysis criteria followed the default settings Modelling protocol and convergence detection followed the guidelines by Beerli et al.107. Comparison of models was performed using the Bayes factors based on guidelines by Kass and Raftery108.Historical demographic expansion was inspected using Fu’sSupplementary Information.


Eleusine coracana (L.) Geartn.) is a self-pollinating amphidiploid crop cultivated with minimal input for food and feed, as well as a source of income for small-scale farmers. To efficiently assess its genetic diversity for conservation and use in breeding programs, polymorphic DNA markers that represent its complex tetraploid genome have to be developed and used. In this study, 13 new expressed sequence tag-derived simple sequence repeat (EST-SSR) markers were developed based on publicly available finger millet ESTs. Using 10 polymorphic SSR markers (3 genomic and 7 novel EST-derived), the genetic diversity of 55 landrace accessions and 5 cultivars of finger millet representing its major growing areas in Ethiopia was assessed. In total, 26 alleles were detected across the 10 loci, and the average observed number of alleles per locus was 5.6. The polymorphic information content (PIC) of the loci ranged from 0.045 (Elco-48) to 0.71 (UGEP-66). The level of genetic diversity did not differ much between the accessions with the mean gene diversity estimates ranging only from 0.44 (accession 216054) to 0.68 (accession 237443). Similarly, a narrow range of variation was recorded at the level of regional states ranging from 0.54 (Oromia) to 0.59 (Amhara and Tigray). Interestingly, the average gene diversity of the landrace accessions (0.57) was similar to that of the cultivars (0.58). The analysis of molecular variance (AMOVA) revealed significant genetic variation both within and among accessions. The variation among the accessions accounted for 18.8% of the total variation . Similarly, significant genetic variation was obtained among the geographic regions, accounting for 6.9% of the total variation (P < 0.001). The results of the cluster, principal coordinate, and population structure analyses suggest a poor correlation between the genetic makeups of finger millet landrace populations and their geographic regions of origin, which in turn suggests strong gene flow between populations within and across geographic regions. This study contributed novel EST-SSR markers for their various applications, and those that were monomorphic should be tested in more diverse finger millet genetic resources.Finger millet ( Eleusine coracana G.), which is commonly called “Ragi” in India and “Dagusa” in Ethiopia, belongs to the subfamily Chloridoideae in the family Poaceae. It is a self-pollinated (95%) amphidiploid  cereal crop cultivated in tropical and subtropical parts of Africa and Asia  have become popular due to their various advantages such as high transferability among closely related taxa, relative ease, and cost-effectiveness to develop  whereas the remaining five were improved cultivars. The landrace accessions and cultivars were obtained from the Ethiopian Biodiversity Institute (EBI) and the Bako Agricultural Research Center (BARC), respectively . Seeds owww.eurofins.com) using a mixture of 2 μl of 10 μM of a sequencing primer and 15 μl of a purified PCR product. Each amplified product was sequenced using both forward and reverse primers. The comparative analysis of the EST sequences containing these target EST-SSRs through the Basic Local Alignment Search Tool (BLAST) was also performed to evaluate the transferability of the repeat motif across the Poaceae species and to determine the position of the SSRs within the corresponding genes.A total of 1,956 finger millet EST sequences downloaded from the National Center for Biotechnology Information (NCBI) were analyzed for the presence of SSRs within the EST sequences using a web-based software WebSat . S1000™ Thermal Cycler  was used for the amplification of the target loci using the following temperature profile: initial denaturation at 95°C for 5 min followed by 35 cycles of 30-s denaturation at 95°C, 30-s primer annealing at optimized temperature for each primer pair (ranging from 55°C to 59°C), and 1 min primer extension at 72°C, with a final primer extension at 72°C for 10 min.A total of 10 genomic SSRs reported to be highly polymorphic in the previous research  triggered through the Rcmd command button added to Arlequin version 3.5.2.2 toolbar. The Numerical Taxonomy System (NTSYS) statistical program version 2.1  was used to calculate Nei's standard genetic distance (GD) as described by Rohlf  based on different approaches, the median of medians (MedMedK), median means (MedMeaK), maximum of medians (MaxMedK), and maximum of means (MaxMeaK)  software with default settings was used for peak identification at recommended threshold intensity. The determination of the fragment size was based on the GS600 size standard. Each peak was treated as an allele at a codominant locus, and the genotype of each individual/pool at each locus was recorded. Polymorphic information content (PIC)  showed that, on the one hand, the SSRs of Elco-33, Elco-37, Elco-39, and Elco-41 are located within the coding sequences. On the other hand, the SSRs of Elco-27 and Elco-40 are located in the 5′UTR whereas those of Elco-42, Elco-43, and Elco-48 are located in the 3′UTR  were monomorphic across the 60 accessions studied. The remaining seven were polymorphic and used for the analyses of population genetics together with the three polymorphic genomic SSRs (Thirteen new EST-SSR markers were developed in this study. The list of these EST-SSRs is given in he 3′UTR . For examic SSRs . The thrmic SSRs .Na) per locus ranged from three (in Elco-37 and Elco-42) to nine (Elco-39) with an average value of 5.62  of each locus across all populations ranged from 0.004 in locus Elco-48 to 0.19 in locus UPEG-66 with an average value of 0.09. Among the 29 indiv-accessions, Elco-48 was polymorphic only in two accessions (242622 and 243623), which were originally collected from western Tigray. Similarly, two accessions that were polymorphic only for Elco-48 (accessions 215908 and 215929) were identified among the pooled accessions. These accessions were originally collected from Agew Awi and Gojam, respectively. The within-population gene diversity (HS) of each locus varied from 0.004 to 0.16 with an average value of 0.08. The observed heterozygosity (HO) for each locus varied from 0.30 (Elco-48) to 0.81 (UPEG-24). The estimates of population differentiation (GST) at each locus varied from 0 (Elco-48) to 0.27 (Elco-37) with a mean of 0.12. Similarly, the lowest (0.045) and highest (0.71) PIC values were recorded for Elco-48 and UPEG-66, respectively, with a mean of 0.51  to 0.68 (accession 237443) . SimilarP < 0.0001). Of the total genetic variation, 18.8% accounted for the differentiation among the accessions (FST = 0.18) whereas 81.2% was varied within the accessions. Furthermore, when the accessions were pooled to their respective regions, variation among groups (regions) and among accessions within groups were 6.9 and 13.2%, respectively.The AMOVA based on allele frequency of the 60 accessions (combined data set of indiv-accessions and pool-accessions) was conducted to determine the genetic differentiation at accession and higher hierarchical levels . AMOVA tFST values demonstrated in FST = 0.18; P < 0.0001) among all accessions studied.The Nei's pairwise GD among the 29 indiv-accessions ranged from 0 indicating high genetic similarity  to slightly over 0.5 showing low genetic similarity . Accession 216046 from Oromia is the most genetically distinct accession with a mean GD of 0.32 from the other 28 accessions. The highest GD between accessions within a region was 0.42 for Amhara (215929 vs. 215943), 0.38 for Oromia (216046 vs. 237969), and 0.31 for Tigray (238321 vs. 242623). The highest GD within the cultivar group (0.41) was recorded for Boneya vs. Addis-01 cultivar . The meaThe Nei's standard GD-based NJ dendrogram revealed six clusters with each cluster comprising accessions from different groups  except Cluster-4, which comprised only two accessions from Oromia. Cluster-1 was the largest comprising eight accessions composed of three accessions from Amhara, one accession from Tigray, and four cultivars. Cluster-2 was composed of four accessions from Tigray, two from Oromia, and one from Amhara. Cluster-3 comprised one accession from Oromia and a cultivar. Cluster-5 comprised two accessions from Tigray and one from Oromia, whereas four accessions from Amhara, two from Oromia, and one from Tigray were placed in Cluster-6 .The PCoA was used to visualize the differentiation among the 29 indiv-accessions . The anaK = 3 , indicating that the individual genotypes of the 29 indiv-accessions most likely came from three genetic populations  to the aquaporin gene in maize and rice. The aquaporin gene is responsible for water stress-induced chilling tolerance  whereas four were located in either 3′UTR or 5′UTR. Interestingly, those in the CDS were more informative (mean PIC = 0.40) than the others (mean PIC = 0.24). The three SSRs in the CDS were trinucleotide repeat SSRs and hence do not cause frameshift mutations, which might have contributed to their higher polymorphism. Among the new EST-SSRs, Bharathi  and BabuBharathi  who repoHO, HT, HS, percentage of polymorphic loci (PPL), and Nei's gene diversity (HE). The PIC of an SSR locus indicates the extent of its usefulness in the determination of population genetic diversity. Similar to other population genetics parameters, the PIC of a locus is dependent on the number and frequency of alleles at that locus. The higher the PIC value of a locus, the more informative it is. In this study, a broad range of PIC values (0.05–0.71) was obtained. Interestingly, the average PIC of the seven EST-SSRs (0.31) was lower than the mean PIC of the three previously published SSRs (0.69)  was detected within accessions compared with that of among accessions (18.77%), which is in line with the fact that finger millet is a predominantly self-pollinating species. Babu et al.  reportedThe NJ cluster analysis of the 29 indiv-accessions revealed a weak clustering pattern of the accessions according to the geographic regions of origin of the landrace accessions, indicating that the genetic makeup of the accessions does not have a strong correlation with their geographic origin. The clustering together of accessions from different regions, as well as the placement of accessions from geographically close areas in different clusters in this study, agreed with the results of previous studies. For instance, Pandian et al.  reportedK = 3), which were not based on the geographical origin of the accessions. Some individual genotypes from different accessions showed highly similar genetic profiles and hence have a close relationship. Previous studies, using SSR markers on different finger millet accessions, also produced three genetic clusters having week grouping based on their geographic origin .This study was supported by the Swedish International Development Cooperation Agency (Sida) Research and Training Grant awarded to the Addis Ababa University and the Swedish University of Agricultural Sciences (AAU-SLU Biotech; The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


These outcomes provide evidence in favor of a shared genetic etiology between MDD and the lifestyle factors.In recent years, evidence has accumulated with regard to the ubiquity of pleiotropy across the genome, and shared genetic etiology is thought to play a large role in the widespread comorbidity among psychiatric disorders and risk factors. Recent methods investigate pleiotropy by estimating genetic correlation from genome-wide association summary statistics. More comprehensive estimates can be derived from the known relatedness between genetic relatives. Analysis of extended twin pedigree data allows for the estimation of genetic correlation for additive and non-additive genetic effects, as well as a shared household effect. Here we conduct a series of bivariate genetic analyses in extended twin pedigree data on lifetime major depressive disorder (MDD) and three indicators of lifestyle, namely smoking behavior, physical inactivity, and obesity, decomposing phenotypic variance and covariance into genetic and environmental components. We analyze lifetime MDD and lifestyle data in a large multigenerational dataset of 19,496 individuals by variance component analysis in the ‘Mendel’ software. We find genetic correlations for MDD and smoking behavior ( It is widely observed that multiple complex human traits tend to co-occur at the population-level. Klein and Riso 1993) presented a series of models explaining the causes of such comorbidity, which were extended by Neale and Kendler 1995) 993 prese,3. Expla 993 presIn behavior genetics and genetic epidemiology, pleiotropy can be investigated by estimating the genetic correlation between traits. Similar to how the heritability of a trait encompasses the relative proportion of phenotypic variation in a population at a given time that is due to variation at the genetic level , the genMajor depressive disorder (MDD) is a complex, prevalent and burdensome condition with a well-established link with multiple lifestyle factors ,15, inclThe models proposed by Klein and Riso (1993) provide a range of explanations for these associations. In this paper we focus on exploring the shared genetic and environmental etiology of MDD and lifestyle traits, applying a bivariate biometrical approach to a large extended twin pedigree dataset of lifetime MDD and smoking behavior, physical inactivity, and obesity. We aimed to decompose phenotypic variance of these traits and their covariance with MDD into genetic and environmental components, thereby quantifying the degree to which comorbidity is explained by genetic and non-genetic contributions.We collected data on Major Depressive Disorder (MDD) and lifestyle variables in multi-generation twin families that are registered with the Netherlands Twin Register (NTR). Over the past three decades, the NTR recruited twins and their families for the study of human health and behavior. Methods of data collection include survey studies, experimental studies and biological sampling, as described in detail elsewhere . Every tThe survey was collected in twins and their relatives. Opposite-sex twins are always dizygotic. Zygosity in same-sex twins was determined from genotype data (55.5%), or information from self-, parent-, or co-twin report. With this information, zygosity could be determined with certainty for 66.3% of twins. For the remaining twin pairs, zygosity was estimated from items on physical similarity, which we showed to capture DNA-confirmed zygosity 93% of the time . InformaBIONIC developed the Lifetime Depression Assessment Survey (LIDAS), an online self-report instrument for lifetime MDD ascertainment . LIDAS in = 1747) were excluded, together with individuals with unknown sex (n = 7), age < 16 (n = 3), and insufficient symptom data for diagnosis (n = 570).Lifetime major depressive disorder (MDD) status was determined in accordance with DSM-5 criteria from LIDAS data on nine symptoms . Diagnos> 30), in accordance with WHO criteria.Smoking behavior was recorded with three answers  and was dichotomized . Physical inactivity was recorded as the number of times per week respondents engaged in physical activities in their leisure time that caused sweating, with responses ranging from 0  to 1 , 2 , 3 , and 4 . Answers were recoded into three levels of physical inactivity, so that 0 = ‘three or four times per week or more’, 1 = ‘once or twice per week’, and 2 = ‘zero times per week’. Body-mass index (BMI) was defined as weight in kg divided by length in meters squared. Extreme BMI values were excluded if weight < 45 kg or > 200 kg, height < 150 cm or > 220 cm, or BMI < 15 or > 50. BMI data of 157 participants were retrieved from earlier surveys. For obesity, continuous BMI data were binned into categories so that 0 = unaffected (BMI < 30) and 1 = obese relations in large irregular pedigrees, at a cost of freedom in model specification. The specification of familial relations is achieved in a single input pedigree file, where one row corresponds to one individual, and where the first six columns contain all the required information to specify genetic relations: Family ID, Person ID, Father ID, Mother ID, Sex, and Twincode (to accommodate MZ twin pairs). All members of an (extended) pedigree share a family ID, while each individual in the pedigree has a unique personal identifier. Mendel requires either both parents or neither of them to be specified, and dummy parents were created to complete nuclear families where one parent was missing . An addi2 were added to the linear regression model as fixed effects. Age2 was included to account for non-linear effects of age on MDD. Age was standardized before its quadratic term was computed, effectively reducing their correlation to approximately zero [The Mendel software treats all phenotypes as quantitative. Categorical variables were scaled in such a way that higher categories reflected a higher score. In the genetic analyses, we consider obesity both as a dichotomous measure (BMI < 30 vs. BMI ≥ 30) and a continuous variable (BMI). BMI was log transformed to eliminate skewness. In all analyses, sex, age, and ageely zero .The residual phenotypic (co)variances were decomposed into four variance components: additive genetic variance (A), non-additive genetic variance (D), household variance (H), and unique environmental variance (E). The bivariate model is illustrated in Box 1  The bivariate model represented for an individual for two phenotypes (P1 and P2):  P1 = G1 + NG1 = A1+ D1 + H1 + E1  P2 = G2 + NG2 = A2+ D2 + H2 + E2  Var (P1) = Var(G1) + Var (NG1)  Var (P2) = Var(G2) + Var (NG2)H2  Broad-sense heritability (P1) = Var(G1)/Var (P1) = h2  Narrow-sense heritability (P1) = Var(A1)/Var (P1) =   Covar  = Covar  + Covar r  = Covar /SD(P1) × SD(P2)  Phenotypic correlation: r  = Covar /SD(G1) × SD(G2);  Genetic correlation: where P is an individual’s phenotypic value , G isgenotypic value and NG stands for non-genetic value. Var(P) is the varianceof the phenotype ; var(G) and var(NG) stand forgenetic and non-genetic variance components (assuming no covariance of G andNG). G can be decomposed into additive genetic (A) and non-additive values; non-genetic influences can be distinguished into thosethat are common to members from the same household  in Mendel) and all other  environmental effects.  The covariance between two phenotypes, here labeled P1and P2  likewise can be decomposed into genetic andnon-genetic covariance. The correlation of P1 and P2 is obtained by scalingthe phenotypic covariance by the product of the standard deviations of P1 andP2. Likewise, the genetic correlation is obtained by dividing the geneticcovariance by the standard deviations of G1 and G2.SD = 16.61). Females were on average 40.60 (SD = 15.98) years old, and males were on average 43.81 (SD = 15.98) years old. There were 10,799 nuclear families with at least one individual with phenotype data. The sample contained 9261 twins and 10,235 non-twin individuals. The 9261 twins  comprised 2375 complete twin pairs and 4482 unpaired twin individuals.The sample throughout the analyses consisted of 19,496 individuals , for which a descriptive overview is provided in r = 0.046), smoking behavior and BMI (r = 0.163), physical inactivity and BMI (r = 0.126).r = 0.439 and r = 0.299 in MZ males and females and ranged between r = 0.072 and r = 0.303 in DZ and sibling pairs, suggesting that a genetic component contributed to familial resemblance of MDD. r = 0.105 vs. r = 0.050). The largest difference between the two age groups for BMI (r = 0.272 in twins and their spouses vs. r = 0.185 in parents of twins). Cross-trait correlations between MDD and smoking showed a pattern where MZ twin correlations were larger than those in first-degree relatives, suggestive of a genetic contribution to their comorbidity. Such patterns were less evident for the other trait combinations.We estimated within- and cross-trait kinship correlations for various relative pairings, listed in H2), that is, the sum of the additive and non-additive genetic variance components divided by the total phenotypic variance, were H2 = 0.335 in MDD, H2 = 0.550 in smoking behavior, H2 = 0.318 in physical inactivity, H2 = 0.725 in BMI and H2 = 0.647 in obesity. Thus, these broad-sense estimates combine the influence of additive and non-additive genetic effects. The magnitude of non-additive genetic effects  varied across traits. The magnitude of household effects (H) was consistently low but significant across traits, suggesting that shared household effects play a minor role in the phenotypic resemblance among relatives who live together. A large proportion of phenotypic variance was due to unique environment i.e., individual-specific environmental factors (E) and measurement error, particularly in lifetime MDD (E = 0.618) and physical inactivity (E = 0.574).We first conducted a series of univariate variance component analyses, decomposing the phenotypic variances of lifetime MDD, smoking behavior, physical inactivity, BMI and obesity into four variance components: additive genetic variance (A), non-additive genetic variance (D), household variance (H), and unique environmental variance (E). Maximum likelihood estimates of variance components and their standard errors are listed in rG) between lifetime MDD and smoking behavior (rG = 0.249), physical inactivity (rG = 0.161), BMI (rG = 0.081), and obesity (rG = 0.155). The positive directions of these genetic correlations indicate that the genetic factors that are shared between traits tend to influence both trait values in the same direction; the shared genetic etiology contributes either to an increase or a decrease in both traits, but not an increase in one and a decrease in the other.Next, bivariate analyses were conducted for MDD and each of the lifestyle factors. Covariance component and correlation estimates are listed in rA) tended to be larger than non-additive genetic correlations (rD), and most non-additive genetic covariances included zero in their 95% confidence interval (10.96 times the standard error). Additive and non-additive genetic components were typically correlated in the same (positive) direction. The genetic correlation with lifetime MDD was stronger for obesity (rG = 0.155) than for BMI (rG = 0.081), suggesting that the genetic correlation of body weight and lifetime MDD may be stronger when differentiating between normal and more extreme cases of body weight.Unique to an extended twin pedigree design, we were able to delineate genetic correlations into additive and non-additive genetic components. Estimates of additive genetic correlations , and MDD and BMI (rH = 0.146), were larger than others. However, the main effects of household, defined as the contribution of non-genetic factors that increase familial resemblance, were very modest to begin with. Finally, unique environmental effects explained considerable trait variance, but correlations between unique environmental components (rE) were close to zero, ranging from rE = 0.005 in BMI to rE = –0.043 in obesity. This suggests that although unique environmental effects can have a large effect on individual differences in both lifetime MDD and lifestyle variables, few of these effects are shared across the traits considered here. Estimates of E incorporate non-systematic effects and measurement error, which likely are uncorrelated, and so the low estimates of rE are not unexpected.Household components for lifetime MDD and lifestyle were not correlated, as household covariances did not reach significance for any of the trait combinations. A correlation between household components would suggest that some aspects of sharing a household can cause similar changes in two traits. Some estimates of rA) for all MDD-lifestyle combinations.We sought to quantify the genetic and non-genetic contributions to comorbidity between lifetime Major Depressive Disorder (MDD) and three indicators of lifestyle: smoking behavior, physical inactivity, and obesity. We conducted bivariate variance component analyses in data of twins and extended family relations from the Netherlands Twin Register, decomposing phenotypic variance and covariance into additive genetic (A), non-additive genetic (D), household (H), and unique environmental (E) components. Covariance estimates between trait components were scaled by their respective variance estimates to obtain estimates of genetic and environmental correlations. We found genetic correlations between MDD and all lifestyle factors. In contrast, there was little evidence for correlations between household or unique environmental effects. That is, phenotypic relations between MDD and the lifestyle traits were primarily driven by genetic effects, with considerable additive genetic correlations , equals 8.9% [We found a broad-sense heritability for MDD of 33.5%, in line with previous estimates from twin data (34–37%) ,52, and als 8.9% ,56. Envials 8.9% ,57,58), als 8.9% .rG = 0.25–0.56 [h2, genetic correlation estimates can be derived from molecular data (SNP-rG) [rG estimate between MDD and ‘ever vs. never smoked’ equals SNP-rG = 0.314 (95% CI: 0.242–0.385) [rG = 0.249) lies at the low end of this confidence interval. For physical inactivity, findings from an earlier bivariate twin study suggest a moderate genetic correlation between depressive symptoms and exercise behavior (rG = –0.230) [rG between MDD and physical activity of SNP-rG = –0.100 [rG = 0.161). Finally, twin studies provide some evidence for a shared genetic etiology between MDD and BMI or obesity. Afari et al. (2010) applied a bivariate twin method to depression and obesity data and found a genetic correlation of rG = 0.120 [rG estimate for depression and BMI equals SNP-rG = 0.076, and those between depression and obesity equal SNP-rG = 0.086, 0.082, and 0.168 for obesity class 1, 2, and 3, respectively [rG estimates, with rG = 0.081 for BMI and rG = 0.155 for obesity.Turning to the bivariate analyses of MDD and lifestyle factors, we may also compare our results of modest genetic correlations with findings from twin and SNP-based studies. Findings from twin studies vary with regard to the genetic correlation between MDD and smoking behavior. Some studies report moderate to large genetic correlations in the range of .25–0.56 ,60,61, w.25–0.56 ,63, or i.25–0.56 . Similar(SNP-rG) . The mos2–0.385) . Our est –0.230) , and a m= –0.100 . We find = 0.120 . However = 0.120 . The mosectively . Our estThese results should be viewed in light of some limitations. First, the extended twin pedigree design relies on a number of assumptions which, when violated, could bias parameter estimates . HoweverInterpreting these findings requires a consideration of the mechanisms that can underlie phenotypic relations at the population level, and the mechanisms that can underlie correlations at the genetic level. Well-established associations exist for MDD and smoking , physicaHere, we find genetic correlations, but environmental correlations of nearly zero. Together, these findings suggest that insomuch as the associations between lifetime MDD and lifestyle are explained by causal effects, they also reflect a partially shared genetic etiology. Distinguishing between etiological and pleiotropic mechanisms, which need not be mutually exclusive, is no easy task, and caution should be applied in inferring causality or the absence thereof. However, we do note that under causality, we would have expected both genetic and environmental effects to translate from one trait to the other .In summary, we find that phenotypic relations in the Dutch population between lifetime MDD and smoking behavior, physical inactivity, and obesity are partly driven by a shared genetic etiology. We demonstrate how estimates of genetic and environmental correlation can be derived in an extended twin pedigree design, with analyses conducted in the Mendel software. We show how pedigree analyses can serve as an alternative and feasible means to studying the shared etiology of disease and potential risk factors, and how genetic correlation estimates from extended twin pedigree data triangulate with similar estimates from molecular genetic data to benchmark pleiotropic effects.


Our data recovered 49 haplotypes that showed a significant phylogeographic structure in three regions: north, central, and south. Bayesian Phylogeographic and Ecological Clustering (BPEC) analysis also supported the divergence in three lineages and highlighted the role of environmental factors (temperature and precipitation) in genetic differentiation. Our historical demography analyses revealed demographic expansions predating the Last Interglacial , while Approximate Bayesian Computation (ABC) simulations equally supported two contrasting demographic scenarios. The BPEC and haplotype network analyses suggested that ancestral haplotypes were geographically found in central Veracruz. Our paleodistributions modeling showed evidence of range shifts and expansions-contractions from the LIG to the present, which suggested the complex evolutionary dynamics associated to the climatic oscillations of the Pleistocene. Habitat management of remnant forest fragments where large and genetically diverse populations occur in the three TMCF regions analyzed would be key for the conservation of these magnolia populations.The Tropical Montane Cloud Forest (TMCF) is a highly dynamic ecosystem that has undergone frequent spatial changes in response to the interglacial-glacial cycles of the Pleistocene. These climatic fluctuations between cold and warm cycles have led to species range shifts and contractions-expansions, resulting in complex patterns of genetic structure and lineage divergence in forest tree species. In this study, we sequenced four regions of the chloroplast DNA ( Examining population genetic structure over historical spatial and temporal scales and its relationship with environmental changes is crucial for understanding species distributions and adaptations to the ongoing climatic changes . This inThe TMCF is a highly dynamic ecosystem, which has undergone frequent spatial changes in response to physical and climatic phenomena over geological time scales since the Neogene , and witThe most relevant genetic signatures according to the dry refugia are the genetic differentiation of populations from separate refugia due to limited gene flow during the LGM, a star-shaped allele genealogy of post-glacial expanding lineages and thus evidence of demographic expansions. Decline of genetic diversity away from refugial regions might also be expected by the occurrence of founder effects, whereas rare alleles would occur in high frequency in refugial populations . For the). In Mexico, magnolias diversified with approximately 40 recognized species occurring in humid forests, such as the TMCF and other ecosystems , central (M. schiedeana), and south (M. schiedeana pop. Oaxaca)  our aims were to: (i) perform a phylogeographic approach to evaluate the historical signatures of the genetic divergence between M. pedrazae, M. schiedeana and M. schiedeana pop. Oaxaca, and (ii) evaluate whether demographic changes and past species distributions were influenced by the climatic oscillations of the Pleistocene.In this study we analyzed 20 populations and 96 individuals of  Oaxaca) . SpecifiMagnolia schiedeana is a perennial tree (>200 years) up to 25 m in height , trnK5-matk (789 bp), rpl32-trnL (624 bp) (trnS-trnG (702 bp) (www.ncbi.nlm.nih.gov) . Additionally, we used four cpDNA sequences from eight M. pedrazae individuals from San Luis Potosí and Querétaro and three M. schiedeana from Veracruz (CP population) obtained from Genomic DNA from 20 mg of dried tissue was extracted following the CTAB extraction protocol of (624 bp) , and trn(702 bp) . PCR reaMagnolia species available from GENBANK: Liriodendron chinese (KU170538), M. mexicana, Section Talauma (MN700657), M. ovata Section Talauma (MT293605), M. tripetala Section Rhytidospermum , M. acuminata Section Tulipastrum (MN990595), M. virginiana Section Magnolia , M. iltisiana Section Magnolia , and one sequence obtained in this study from M. pacifica Section Magnolia . These species were chosen based on their closely phylogenetic relationships and L. chinese as the sister group of Magnolia , M. schiedeana (n = 74) and M. schiedeana pop. Oaxaca (n = 5). All subsequent analyses were carried for this 96 individual’s data set.Genealogic relationships were obtained by constructing a median-joining (MJ) haplotype network in POPART  for the Hd) and nucleotide diversity (π) using DNASP v6.12.03 (FST) found in the following groupings: (1) no predefined groups, (2) populations in two lineages, M. pedrazae + M. schiedeana and M. schiedeana pop. Oaxaca, and (3) populations in three lineages, M. pedrazae, M. schiedeana and M. schiedeana pop. Oaxaca. AMOVAs were performed using the Tamura and Nei genetic distance and 1,000 permutations to determine the statistical significance of each partition as implemented in ARLEQUIN v3.5.2.2  and three independent runs, we explored the likely number of genetic clusters from K = 2 to 10. By looking at the posterior probabilities and likelihood values, we observed that the most likely number of clusters was between 3 and 5 and thus the final analysis was carried out with two independent runs for K = 2 to 5 with 10 replicates each. The most likely number of K clusters was determined by its higher posterior probability and likelihood value. Additionally, pairwise FST comparisons were calculated in ARLEQUIN v3.5.2.2 with 1,000 permutations to test for statistically significant differences between lineages. Mantel correlations to test the effect of isolation by geographical distance (IBD) on GST genetic and Euclidean distances was examined for the overall data set. Significance of the Mantel correlation was tested by permuting observations 1,000 times using the R library vegan 2.5.7 (We used BAPS v5.3  to deteran 2.5.7 .https://chelsa-climate.org).We discarded correlated bioclimatic variables  and use the first two synthetic axes from Principal Components Analysis (PCA) as covariates. After several initial short runs, the final analysis was carried out with two maximum number of migrations and relaxation of the parsimony criterion not allowed (zero) to reach convergence. MCMC chains were run for 10 million steps with 10,000 posterior samples saved.We implemented a Bayesian Phylogeographic and Ecological Clustering analysis (BPEC) in the BPEC v1.3.1 R package to reveal the geographical distribution of genetic clusters by considering genetic, geographical, and environmental data . This anpost hoc Tukey-HSD pairwise comparisons on each of the PC axes  using the Pillai’s trace as the test statistic . Moreove PC axes  using thR2  indicate deviations from the sudden expansion model. Third, we used Bayesian skyline plots in BEAST v2.4.2 (Ne) changes across time. Two independent analyses were run, one for M. schiedeana (n = 74) and one for M. pedrazae (n = 17). We used the substitution model GTR+I as selected using AIC in JMODELTEST v2.1.10 , all scenarios had the three genetic lineages. The first scenario predicts that the three lineages diverge at the same time from a common ancestor (Na) at time t2 and remained without demographic changes until time t0 (stable model). Scenarios 2, 3, and 4, predict divergence at time t1 between two of the lineages to subsequently merge the three lineages at time t2 (divergence model). The difference between these three scenarios is the lineage diverging at time t2, and subsequently the pair of lineages diverging at time t1 (see results). Lastly, the fifth scenario predicts a split between M. schiedeana and M. schiedeana pop. Oaxaca at time t2, then both lineages merge with M. pedrazae at time t1 (admixture model). We considered these five scenarios as the most plausible given the observed relationships of divergence and admixture among the three lineages. Prior to the final run, we performed six runs to compare different parameter conditions. The run that we present in the results (see below) was the run with best adjusted parameters  framework implemented in DIYABC v2.1 , 2014 torameters . Each ruNa, N1, N2 and N3 with a uniform prior distribution with a minimum of 10 and a maximum of 100,000 values for effective population sizes; the prior distribution of the timing of events was for t1 and t2 100–100,000 generations respectively and considering a 10-year generation time (2 ≥ t1) on the prior time distribution was added up to avoid incongruences in the simulated genealogies. For the final run, we used the following summary statistics: (1) For the three groups: Number of segregating sites, mean pairwise differences, and mean number of the rarest nucleotide at segregation sites; (2) For the 1–2, 1–3 and 2–3 groups: Number of segregating sites and FST. We used a prior distribution of mean mutation rates of 1.59 × 10–9 for the chloroplast , but due to the likely uncertainty of some of these records, we also included localities reported in previous studies, such as herbaria specimens of M. schiedeana (https://www.epa.gov/); the ecoregions were: Mexican high plateau, eastern Sierra Madre Oriental, west humid coastal plains and hills of the Gulf of Mexico, southern Sierra Madre del Sur, east of the Mexican Plateau, east of the Trans Mexican Volcanic Belt, and south of the North American deserts. These areas were chosen as likely accessible areas.Due to the taxonomic uncertainty for is study . We usedis study . To avoiis study . Additiohiedeana , 2017. Shiedeana . The envValues from the 19 current bioclimatic layers were extracted from individual occurrences and Pearson’s correlations threshold of 0.8 and 0.7 were performed with the occurrences sites to minimize variable redundancy using NTBOX v0.1.4.5 R package . The finhttp://www.worldclim.com), early-Holocene  at 2.5 min resolution , Last Glacial Maximum  at 30 s downloaded from CHELSA ) at 30 s downloaded from WORLDCLIM  with the four past scenarios  by quantifying the extrapolation due to covariate range (NT1) and correlation change (NT2) with the ExDet method  in each past scenario, we compare the calibration area . Samples from M. pedrazae and M. schiedeana were not monophyletic, but samples from M. schiedeana pop. Oaxaca were grouped within well-supported subclade (PP = 1) (The phylogenetic cpDNA concatenated matrix including sequences from other (PP = 1) .M. pedrazae (north) and M. schiedeana , and none with M. schiedeana pop. Oaxaca (south). Overall, few haplotypes were shared among populations, but most of them diverged by one mutational change (max 5 mutation steps between H02 and H13 from M. schiedeana). The most frequent haplotypes (H02 and H03) were present in 7 out of 15 M. schiedeana populations, and present only (H02) in one population from M. pedrazae in San Luis Potosí. Haplotype connections between M. pedrazae and M. schiedeana occurred through haplotypes from Veracruz populations (MI (H36), FXC (H22 and H23)), while the connection of the three M. schiedeana pop. Oaxaca haplotypes were through a missing haplotype that tied a haplotype from M. schiedeana in Hidalgo (GO) and M. pedrazae from San Luis Potosí and Querétaro   .M. schiedeana  and M. pedrazae  and moderate for M. schiedeana pop. Oaxaca . At the level of populations, the most genetically diverse were MY and AC in Veracruz for M. schiedeana, and CS in San Luis Potosí for M. pedrazae . When populations were grouped in two lineages , a much higher proportion of the genetic variance and thus genetic differentiation was observed , whereas when three lineages were considered , a slightly lower proportion of the genetic variance and genetic differentiation were observed relative to the two lineages grouping  , and the lowest between M. schiedeana and M. pedrazae  , but which significant effect disappeared when M. schiedeana pop. Oaxaca was excluded from the analysis . Results from PERMUT revealed a significant phylogeographical structure .Genetic diversity was high for pedrazae . The AMO< 0.001) . Pairwis= 0.001) . We founK = 4, log marginal likelihood = −201.9484, PP = 0.72): cluster one was mostly formed by M. pedrazae, cluster 2 mostly with M. schiedeana, cluster 3 with only one individual from GO, and cluster 4 with M. schiedeana pop. Oaxaca and MI population; these observed clusters thus corresponded to the three TMCF regions  from Veracruz (except H10 from Puebla) were assigned to M. schiedeana pop. Oaxaca . Tukey-HSD revealed statistically significant pairwise differences between the three lineages along the PC 1 (38.3% P < 0.001); variable contribution was: temperature annual range (BIO7) = 27.3%, precipitation seasonality (BIO15) = 24.2%, temperature seasonality (BIO4) = 21.3% and precipitation of the driest month (BIO14) = 20.3%). For the PC 2 (28.4%) statistically significant differences were observed between M. schiedeana and M. pedrazae (P < 0.001) and between M. schiedeana and M. schiedeana pop. Oaxaca (P < 0.001); variable contribution was: annual precipitation (BIO12) = 34.6%, precipitation of the warmest quarter (BIO18) = 34.2%, and precipitation of the driest month (BIO14) = 13.2%  of M. schiedeana pop. Oaxaca from M. schiedeana occurred 82,300 (CI [37.7–99.2]) and 84,800 (CI [41.7–99.4]) years ago, respectively, which fits the LIG. Under scenario 2, the mean estimated divergence time of M. schiedeana and M. pedrazae was 35,000 (CI [3.5–81.3]) years ago before the LGM, while for scenario 5, the admixture among the tree lineages was estimated around 22,900 (CI [1.7–72.7]) years ago, during the LGM. However, confidence intervals were large for both scenarios, going from the LIG until very recent times  that performed better than the other three scenarios. Confidence estimates for scenario choice indicated that Type I errors for the best-supported scenarios were high (scenario 2: 0.62 and scenario 5: 0.64) but Type II errors were higher for scenario 5 (0.73) relative to the scenario 2 (0.51). As statistically there is no way to discriminate which of the two is the best scenario when both are equally likely , we consnt times .P < 0.001), 0.22 OR, and 1617.9 AICc with the LQP features and 1.0 RM. This niche model was constructed with the following variables and permutation importance: BIO14 (39%), BIO1 (34%), BIO3 (10.7%), BIO7 (6.2%), BIO18 (4.8%), BIO12 (2.7%), and BIO15 (2.7%).The evaluation showed that the best niche model has a significant pROC  , whereasMagnolia trees of the largest remaining TMCF strip in Mexico. Based on four cpDNA sequences, we found evidence of three genetic lineages and signatures of demographic expansions, while past species distributions showed range expansions and contractions during the interglacial-glacial cycles of the Pleistocene, which suggest the strong role of climatic and/or environmental factors influencing the complex evolutionary dynamics of these Magnolia lineages.In this study we employed analysis of phylogeography, historical demography and paleodistributions to understand how climatic oscillations of the Pleistocene have shaped geographical patterns of genetic diversity in endemic M. pedrazae), 34 haplotypes to the central (M. schiedeana), and three to the south (M. schiedeana pop. Oaxaca) of the TMCF. Only two haplotypes were shared between the north and central lineages, one of which was widespread in the central region but not in the north. None of the haplotypes were shared between M. schiedeana pop. Oaxaca. Similarly, AMOVAs showed that the amount of genetic variance explained among these three lineages was moderate (48%), but it was considerable higher (58%) when only two lineages (M. pedrazae + M. schiedeana and M. schiedeana pop. Oaxaca) were considered. In contrast to these results, the Bayesian phylogenetic tree did not resolve the monophyly of the three lineages as all samples formed a polytomy with other closely related Magnolia species (M. iltisiana and M. pacifica). The lack of resolution of the phylogenetic relationships based on the cpDNA may be due to its slow-evolving mutation rate  and ecological data and broader population sampling is needed to further clarify species delimitation of the Magnolia section.The geographical circumscription of aracters , while m biology  and the  biology . HoweverLiquidambar styraciflua, Palicourea padifolia, Magnolia section has been estimated around 32.4 Mya at the early Oligocene, while the divergence of M. schiedeana can be placed at ~10 Mya during the late Miocene  and M. pedrazae , both predating the LIG. Demographic growth was also suggested by the ABC simulations, which showed an increase of population sizes for both scenarios after the divergence from a common ancestor around the LIG  the increase in temperature and humidity could have facilitated the range expansion from the south-central towards the northern periphery. Colonization of the northern periphery may have occurred by long-distance dispersal as suggested by the lack of IBD between M. pedrazae and M. schiedeana, and the retention of ancestral polymorphism in the cpDNA. The star-like network topology, the high haplotype diversity, but the low nucleotide diversity suggests the most recent recolonization of the northern periphery of the TMCF. The range contraction-expansion from the mid-Holocene (~8.3 ya) to the present suggested by the climatic models, may explain the weaker genetic divergence between M. pedrazae and M. schiedeana as there is no major geographical barriers that could explain their genetic divergence. Moreover, the BPEC analysis suggested that genetic divergence can also be shaped by environmental factors. Specifically, the environmental PCAs revealed significant differences among the three lineages, which mainly can be attributed to annual temperature range and precipitation seasonality. The TMCF of Mexico are composed of highly heterogenous forest that can vary markedly in environmental conditions  are large populations , while most of them are small . In fact, we found localities where the species might be locally extinct, such as TLA in Puebla and MI in Veracruz, as we found a single remaining individual. The TMCF of San Luis Potosí and Veracruz are under strong anthropogenic pressures from ongoing land-conversion to agriculture and livestock farming , and where local communities host ecotourist destinations and agreed the protection of their well-conserved forests forbidding commercial wood extraction.The TMCF is one of the most threatened ecosystems due to the strong anthropogenic pressures for land conversion and the predicted changes in temperature and precipitation regimes due to climate change . In Mexihiedeana . Genetic farming . Moreove species . This caM. schiedeana suitable habitat by 2080 (M. schiedeana is abundant in some localities; the same applies for the northern periphery in San Luis Potosí. Monitoring genetic diversity and evaluating the role of geographical and environmental factors on genetic diversity and gene flow would also be key for the in-situ species management in the long-term.On the other hand, our niche species distributions suggest that magnolias are sensitive to climatic events since historical times, which lead to range shifts, expansions, and contractions. Future climatic scenarios suggest a decrease of  by 2080 . Conserv by 2080 . Accordi10.7717/peerj.12181/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.12181/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.12181/supp-3Supplemental Information 3Concatenated cpDNA matrix for 96 individuals analyzed in this study.Click here for additional data file.


Ne). In real populations, multiple factors that modulate the variance in reproductive success among individuals cause Ne to differ from the total number of individuals (N). Among these, age‐specific mortality and fecundity rates are known to have a direct impact on the Ne/N ratio. However, the extent to which vital rates account for differences in genetic diversity among species remains unknown. Here, we addressed this question by comparing genome‐wide genetic diversity across 16 marine fish species with similar geographic distributions but contrasted lifespan and age‐specific survivorship and fecundity curves. We sequenced the whole genome of 300 individuals to high coverage and assessed their genome‐wide heterozygosity with a reference‐free approach. Genetic diversity varied from 0.2% to 1.4% among species, and showed a negative correlation with adult lifespan, with a large negative effect  that was further increased when brooding species providing intense parental care were removed from the dataset . Using published vital rates for each species, we showed that the Ne/N ratio resulting simply from life tables parameters can predict the observed differences in genetic diversity among species. Using simulations, we further found that the extent of reduction in Ne/N with increasing adult lifespan is particularly strong under Type III survivorship curves  and increasing fecundity with age, a typical characteristic of marine fishes. Our study highlights the importance of vital rates as key determinants of species genetic diversity levels in nature.Genetic diversity varies among species due to a range of eco‐evolutionary processes that are not fully understood. The neutral theory predicts that the amount of variation in the genome sequence between different individuals of the same species should increase with its effective population size ( Lophius budegassa) to 1.4% in the European pilchard (Sardina pilchardus). Using life tables summarizing age‐specific mortality and fecundity rates for each species, we showed that the variance in lifetime reproductive success resulting from age structure, iteroparity, and overlapping generations can predict the range of observed differences in genetic diversity among marine fish species. We then used computer simulations to explore how combinations of vital rates characterizing different life histories affect the relationship between adult lifespan and genetic diversity. We found that marine fishes that display high juvenile but low adult mortality, and increasing fecundity with age, are typically expected to show reduced genetic diversity with increased adult lifespan. However, the impact of adult lifespan vanished using bird and mammal‐like vital rates. Our study shows that variance in lifetime reproductive success can have a major impact on species genetic diversity and explains why this effect varies widely across taxonomic groups.Understanding how and why genetic diversity varies across species has important implications for evolutionary and conservation biology. Although genomics has vastly improved our ability to document intraspecific DNA sequence variation at the genome level, the range and determinants of genetic diversity remain partially understood. At a broad taxonomic scale in eukaryotes, the main determinants of diversity are reproductive strategies distributed along a trade‐off between the quantity and the size of offspring, which likely affect the long‐term effective population size. Long‐lived species also tend to show lower genetic diversity, a result that has however not been reported by comparative studies of genetic diversity at lower taxonomic scales. Here, we compared genetic diversity across 16 European marine fish species showing marked differences in longevity. Adult lifespan was the best predictor of genetic diversity, with genome‐wide average heterozygosity ranging from 0.2% in the black anglerfish  of a stable randomly mating population is equal to 4Neμ , also called the census population size. Comparative genomic studies in mammals and birds have shown that current species abundance correlates with the long‐term coalescence Ne, despite a potential deviation from long‐term population stability in several of the species studied  would be more resilient to low population size episodes compared to species with high fecundity and small propagule size (r‐stategists), which would go extinct if they reach such population sizes , which reduces Ne compared to N following Ne=4N−4Vk+2  effect, a variant of the “sweepstakes reproductive success” hypothesis  and increasing fecundity with age. Consequently, it has been suggested that marine fish species show a marked discrepancy between adult census size and effective population size, resulting in s Waples . The reas Waples , fecundis Waples , and oves Waples . Howevers Waples . They als Waples .GenomeScope . Raw reads were preprocessed with fastp version 0.20.0 . To perform this test, we used the sea bass (Dicentrarchus labrax) and the European pilchard (Sardina pilchardus), two species that represent the lower and upper limits of the range of genetic diversity in our dataset  or nest‐guarding . Detailed information on data collection is available in Supporting Information.We collected seven simple quantitative variables describing various aspects of the biology and ecology of the 16 species: body size, trophic level, fecundity, propagule size, age at maturity, lifespan, and adult lifespan , although some studies also used an exponential function (F=αeβL) or a linear function (F=α+Lβ). We collected empirical estimates of α and β and determined age‐specific fecundity from the age‐specific length and the fecundity‐length function reported in the literature for each species. Fecundity was set to zero before the age at first maturity.We collected age‐specific length from empirical data and estimated Appendix . When diVk and the ensuing ratio Ne/N using the analytic framework developed in AgeNe . We ran AgeNe and estimated Ne/N for each species.To understand how differences in life tables drive differences in genetic diversity between species, we estimated the variance in lifetime reproductive success, Ne/N between species: age at maturity, age‐specific survival rates, age‐fecundity relationships and sex‐related differences in these components. To determine the role that each parameter plays in shaping levels of genetic diversity among species, we built 16 alternative life tables where the effect of each component was added one after the other, while the others were kept constant across species. Thus, in our null model, age at maturity was set at 1 year for all species, fecundity and survival did not vary with age  and 100 (maximum age) within each species. In the simulations, each individual first reproduces and then either survives to the next year or dies following a probability determined by its age and the corresponding life table. We kept population size constant and estimated the mean genetic diversity  over the last 10,000 years of the simulation after the mutation‐drift equilibrium was reached and using 50 replicates (see Supporting Information for further information).A complementary analysis of the contribution of life table properties on genetic diversity was performed using forward simulations in As previously, we evaluated the contribution of each component among 8 alternative life tables by comparing scaled observed and simulated genetic diversity.c defines the form of the survivorship curve, with c>1, c=1 and c<1 defining respectively Type I , Type II , and Type III  survival curves. We took values of c from 0.01 to 30 1/c to scale survivorship curves in such a way that 1% of the initial population remains at maximum age.To generalize our understanding of the influence of life tables on genetic diversity beyond the species analyzed in this study, we simulated a wide range of age‐specific survival and fecundity curves and explored their effect on the relationship between adult lifespan and variance in reproductive success. To this end, we defined 16 theoretical species with age at first maturity and lifespan equal to that of our real species and then introduced variation in survival and fecundity curves. First, age‐specific mortality was simulated following Pinder et al. :(3)M.For each combination of z‐transformation of individual genetic diversity within each species to put spatial differences in within‐species diversity on the same scale. To detect similar spatial patterns of genetic diversity among species, we finally performed a hierarchical clustering analysis of the matrix of z‐transformed genetic diversity values with the pheatmap function available in pheatmap v1.0.12 R package.We addressed the potential effects of population structure, demography, and historical contingencies on genetic diversity by examining the extent of spatial variation in genetic diversity between the four populations within each species. First, we evaluated the relative amount of intraspecific compared to interspecific variation in genetic diversity. Then, we applied a R‐3.6.1 . We fitted beta regression models between genetic diversity and any covariate with the R‐package betareg version 3.1‐3  and moderate duplication rates (10.8%) Fig. . GC contGenomeScope model successfully converged for all of the 297 individual genomes retained  and most of the variability in genetic diversity was observed between species  showed a surprisingly high genetic diversity (more than twice the average level of their species), indicating possible issues in the estimation of genome‐wide heterozygosity. Therefore we removed these individuals from subsequent analysis, although their estimated genome size and GC content matched their average species values (therefore excluding contamination as a cause of genetic diversity estimation failures).The ned Fig. . The aveies Fig. . Estimates Fig.  . Two indLophius budegassa to 1.415% for Sardina pilchardus. We found no correlation between species genetic diversity and genome size . The estimation of genetic diversity was robust to the choice for k‐mer lengths ranging from 21 to 25, suggesting a low sensitivity of GenomeScope regarding this parameter  as in Romiguier et al. , or the logarithm of fecundity  and genetic diversity  and adult lifespan  were significantly negatively correlated with genetic diversity , adult lifespan , and body size  on genetic diversity. Brooding species  had systematically lower genetic diversity than nonbrooding species with similar adult lifespan.We found no significant interaction between hermaphroditism and any of the previous variables on genetic diversity. By contrast, parental care showed a significant interaction with lifespan  and adult lifespan  and the same was found for pseudo‐R2 . Furthermore, considering nonbrooding species only, there was still no significant correlation between genetic diversity and trophic level , propagule size , and fecundity , but genetic diversity appeared significantly negatively correlated to body size . We did not detect any significant correlation between any trait variable and genetic diversity within the sub‐dataset of brooding species. However, this should be taken with caution given the very low number of brooding species (n=5) in our dataset.When considering only nonbrooding species, we found steeper negative correlations and higher percentages of between‐species variance in genetic diversity explained by lifespan , lifespan (−0.095), and adult lifespan (−0.129), which rule out body size as a major determinant of diversity in our dataset.Body size and lifespan were highly positively correlated traits in our dataset . However, four out of five brooding species had low genetic diversity despite high Ne/N ratios .Nongenetic estimates of ios Fig. . As prevNe/N ratio ranged from 0.558 to 0.733, a variance much lower than that of observed genetic diversities. Then, adding separately age at maturity . It was reduced to 0.070% in the species with the least effect of age‐specific vital rates , and down to 0.010% in the species with the greatest effect (L. budegassa). Again, simulated genetic diversity was not correlated to genetic diversity considering all 16 species .We simulated a population of 2000 individuals with age‐specific survival and fecundity. As expected, including age‐specific vital rates decreased the equilibrium level of genetic diversity compared to expectations under the classical Wright‐Fisher model  but not for species having an age‐specific survivorship curve constant, c, superior to 2, including type I species . When we included this exponential increase of fecundity with age, the percentage of variation explained was superior for approximately all values of c, and the abrupt drop of the percentage of variation explained shifted toward higher c values, around c=3. Interestingly, we found significant positive relationships associated with low slope values when c became superior to 10 (type I species).Then, we added an exponential increase in fecundity with age, first taking ies Fig. . The sloR2 for all c values and for f ranging from −1 to 1 , and exponential constant, f, between 0.18 and 0.31. For type III and type II species (c<1), both the slope and the percentage of variation explained first increased with increasing exponential constant and then decreased. Significant negative relationships were found for c<1 for any values of f, except some extreme values near −1, whereas no significant relationship was found for c>1 when f is negative except for values of c near 1 and values of f near 0. The steepest slope and the highest percentage of variation explained were obtained for type III species with intermediate values of f (0.1<f<0.5) and for type II species (1<c<5) for positive values of f. For type I species, as c values increased, higher values of f are needed to obtain a significant negative relationship between adult lifespan and the Ne/N ratio. Above c>20, no significant negative relationship was found for any values of f. Again, we found significant positive relationships and low slopes for c>15 and intermediate positive values of f.Then, we compared values of slope and o 1 Fig. . The steNe/N and adult lifespan, and no significant correlations for extreme positive values of f and extreme low values of c  was also found in teleost fishes , summarized in life tables, naturally predict the empirical correlation between adult lifespan and genetic diversity in marine fishes.In this study, we used whole‐genome high‐coverage sequencing data to estimate the genetic diversity of 16 marine teleost fish with similar geographic distribution ranges. We found that adult lifespan was the best predictor of genetic diversity, species with long reproductive lifespans generally having lower genetic diversities Fig. . LongeviNe/N estimated from life tables can be explained with only two life history traits: age at maturity and adult lifespan. Therefore, the effect of adult lifespan on genetic diversity should reflect variations in age‐specific fecundity and survival across species. If the species vital rates used to derive Ne/N ratios are relatively stable over time, the reduction in Ne due to lifetime variance in reproductive success should not only apply to contemporary time scales but more generally throughout the coalescent time. Thus, a direct impact of life tables on genetic diversity can be expected for iteroparous species with overlapping generations.On a broad taxonomic scale including plants and animals, Waples et al.  showed tAgeNe) and a simulation‐based (with SLiM) approach, we showed that age‐specific survival and fecundity rates alone can explain a significant fraction of the variance in genetic diversity among species  for our 16 species while keeping their true adult lifespans, we found that Ne/N can decrease by at most 0.05 per year of lifespan , meaning than lifespan and variance in reproductive success may have limited influence in other taxonomic groups, such as birds or mammals.Age‐specific survivorship curves are one of the main biological components of life tables. Three main types of survivorship curves are classically distinguished: type I curves are characterized by low juvenile and adult mortality combined with an abrupt decrease of survival when approaching the maximum age ; in type II curves, survival is relatively constant during lifetime  while type III curves are characterized by high juvenile mortality followed by low adult mortality . Type III survivorship curves favor the disproportionate contribution of a few lucky winners that survive to old age, compared to type I survivorship curves, where individuals have more equal contributions to reproduction, generating a lower variance in reproductive success. Thus, in type III species, higher lifetime variance in reproductive success is expected as the lifespan of a species increases. By simulating extreme type III survivorship curves  or power‐law (F=aAb) function. By adding an exponential increase in fecundity with age to our simulations, we found that Ne/N decreases even more strongly with increasing adult lifespan (Ne/N decreases by up to 0.07 per extra year of reproductive life). Using both type III survivorship and exponentially increasing fecundity with age, we could thus predict up to 84% of the variance in genetic diversity between species with the shortest and longest lifespans.Another important component of life tables is age‐specific fecundity. In marine fishes, fecundity is positively correlated to female ovary size, and the relationship between fecundity and age is usually well approximated with an exponential  between species. Population census sizes can be huge and are notoriously difficult to estimate in marine fishes. For that reason, abundance data remain largely unavailable for the 16 species of this study. We nevertheless expect long‐lived species to have lower abundance compared to short‐lived species because in marine fishes N is generally negatively correlated to body size  showed only little variance compared to their range of variation across metazoans, and none of them were correlated to adult lifespan. However, we found that the five brooding species of our dataset, which are typical K‐strategists, displayed lower genetic diversities with respect to their adult lifespan  by a very low mutation rate of 2×109 per base per generation estimated from pedigree analysis. Although the herring is one of the most abundant and fecund pelagic species in the North Atlantic Ocean, its genetic diversity appears approximately 80% lower than that of the European pilchard S. pilchardus, another member of the Clupeidae family that shows the highest diversity in our study. Even if C. harengus has a larger body size , percentage of reads with quality superior to Q30, duplication rate and GC content after filtering, correcting and trimming steps carried out with fastp v.0.20.0 , and the high standard reference‐based approach in GATK (y‐axis), for european sea bass  and european pilchard .Figure S7: Correlation matrix between genetic diversity and all quantitative life history traits.Figure S8: Relationship between species median genetic diversity (%) and 5 covariables.Figure S9: Effect of population structure on genetic diversity estimates.Figure S10: Relationship between relative species genetic diversity and relative Ne/N estimated by AgeNe for 16 sets of life tables.Figure S11: Relationship between relative species genetic diversity and simulated genetic diversity with forward‐in‐time simulations for 16 sets of life tables.Figure S12: Residuals of the linear model between genetic diversity and variance in reproductive success estimated from various combinations of life tables from a model with slope equals 1 and intercept 0.Figure S13: Distribution of slope and pseudo R2 of the beta regression between adult lifespan and genetic diversity for random subsets of 11 species.Figure S14: Distribution of slope and pseudo R2 of the beta regression between Ne/N and genetic diversity for random subsets of 11 species.Figure S15: Distribution of slope and pseudo R2 of the beta regression between simulated, with SLiM v.3.3.1, and observed genetic diversity for random subsets of 11 species.Figure S16: Slope of and proportion of variance explained by linear models between adult lifespan and Ne/N estimated with AgeNe for different combinations of age‐specific survival and fecundity for three fecundity‐age models: linear, polynomial and power‐law.Figure S17: Population size count for the 50 iterations of the 16 species for set 1 of life tables .Figure S18: Population size count for the 50 iterations of the 16 species for set 2 of life tables .Figure S19: Population size count for the 50 iterations of the 16 species for set 3 of life tables .Figure S20: Population size count for the 50 iterations of the 16 species for set 4 of life tables .Figure S21: Population size count for the 50 iterations of the 16 species for set 5 of life tables .Figure S22: Population size count for the 50 iterations of the 16 species for set 6 of life tables .Figure S23: Population size count for the 50 iterations of the 16 species for set 7 of life tables .Figure S24: Population size count for the 50 iterations of the 16 species for set 8 of life tables .Figure S25: Genetic diversity simulated for each of the 16 species for set 1 of life tables .Figure S26: Genetic diversity simulated for each of the 16 species for set 2 of life tables .Figure S27: Genetic diversity simulated for each of the 16 species for set 3 of life tables .Figure S28: Genetic diversity simulated for each of the 16 species for set 4 of life tables .Figure S29: Genetic diversity simulated for each of the 16 species for set 5 of life tables .Figure S30: Genetic diversity simulated for each of the 16 species for set 6 of life tables .Figure S31: Genetic diversity simulated for each of the 16 species for set 7 of life tables .Figure S32: Genetic diversity simulated for each of the 16 species for set 8 of life tables .Click here for additional data file.Data S1Click here for additional data file.Data S2Click here for additional data file.