Erymnochelys madagascariensis is a Critically Endangered turtle endemic to Madagascar. Anthropogenic activity has depleted the wild population by 70% in the last century, and effective conservation management is essential to ensuring its persistence. Captive breeding was implemented to augment depleted populations in the southern part of Ankarafantsika National Park (ANP), when no genetic data were available for E. madagascariensis. It is unknown how much of the natural population’s diversity is encapsulated in captivity. We used eight microsatellite loci and fragments of two mitochondrial genes to identify the genetic structure of E. madagascariensis in the wild. Captive bred turtles were compared with wild populations in order to assess the representativeness of this ex situ conservation strategy for ANP. Six microsatellite clusters, ten cytochrome b, and nine COI haplotypes were identified across wild populations, with high genetic divergence found between populations in two groups of watersheds. Captive bred individuals represent three out of six sampled microsatellite clusters found in the wild and just one mitochondrial haplotype, possibly due to genetic drift. To improve genetic representation, the strategy of frequent interchange between captive and wild breeders within ANP should be revitalised and, as originally planned, hatchlings or juveniles should not be released beyond ANP. Madagascar’s separation from Africa occurred over 150 million years ago, and its most recent split occurred with India ~ 90 million years ago1. As a result, Madagascar’s biota has undergone unique evolutionary pathways in isolation and is largely endemic2. In an analysis of 25 regions with especially high levels of endemism endangered by habitat loss—‘biodiversity hotspots’—Madagascar is listed as the ‘hottest hotspot’ of all3. Malagasy biota is also characterised by ‘micro endemism’, meaning that many of the species are only found across a small geographic range within Madagascar4. Madagascar’s biodiversity is severely threatened by anthropogenic activity: agriculture, deforestation, illegal poaching and the wildlife trade, and the introduction of non-native species5. Of the 327 reptile species on the island, 92% are endemic and 39% of these are at risk of extinction6.Madagascar is an island, separated from nearby land for many millions of yearsErymnochelys madagascariensis, the Rere, or Madagascar side-necked turtle8. Erymnochelys madagascariensis is the only member of the Erymnochelys genus and also the only existing old world member of the family Podocnemididae10. Erymnochelys madagascariensis is endemic to western Madagascar7. An estimated 10,000 individuals are distributed in subpopulations along a stretch of eight lowland watersheds8. The species is mainly sedentary but individuals will migrate into seasonally flooded areas to find better sources of food, or in dry conditions to reach new refuge wetlands11. Being limited to a specific freshwater aquatic habitat, E. madagascariensis is vulnerable to anthropogenic disturbance in this type of environment8, for example, when wetlands are drained for conversion to rice paddies12. Of all the endemic Malagasy chelonians, E. madagascariensis has the oldest age of sexual maturity at 18–25 years old making the species especially susceptible to population decline if individuals are removed from the population before reaching maturity13. Illegal trade and consumption of Reres and their eggs is a major threat to the survival of E. madagascariensis. Turtles may reach ~ 26 cm in length before they are able to breed and due to their large size, they are an attractive food source even when sexually immature13. Additionally, Rere habitat loss and degradation is being precipitated by deforestation, soil erosion and river siltation14. As a result of human activity, E. madagascariensis is now only present in 7.6% of its historical geographical range, and on the verge of extinction in a quarter of this remaining area7.One such threatened endemic reptile is the Critically Endangered Madagascar big-headed turtle E. madagascariensis as Endangered in 1996 and conservation of the species began in 19988. The species was reclassified as Critically Endangered in 200815. The long-term goal for E. madagascariensis conservation is to have at least one viable population in each of the eight watersheds inhabited across the turtle’s entire range7. Conservation involves research and monitoring of wild populations, the engagement of local communities, population management through captive breeding, and head starting of juveniles between populations16. Hatchlings collected from wild nests in Ankarafantsika National Park (ANP) are head started for three to ten years in the captive breeding facility in Ampijoroa (set up in 1999) in parallel with captive bred hatchlings prior to release into the wild to augment depleted populations at ANP7. To date, captive breeding in this facility has contributed 114 hatchlings to the population of E. madagascariensis at lake Ravelobe . However, at the time of the facility’s start in 1999, no information existed on the genetic structure of E. madagascariensis7. As the population genetic structure was unknown, a representative sample from the whole range of E. madagascariensis—as recommended when starting ex situ breeding programmes for the long-term securing of a species in captivity17—was not used in the ex situ strategy, as this could risk the loss of unique genetic variation that may have existed within different isolated subpopulations. As a result, the captive population was created from nine turtles within ~ 30 km of the southern part of Ankarafantsika National Park (ANP) to conserve any local genetic diversity from turtles in that area7. Analysing the genetics of founder populations before the initiation of ex situ conservation breeding is recommended to avoid the loss of genetic diversity17, but is not always possible, and many conservation programs now face the task of reconstructing the genetic history and representativeness of captive populations post hoc in order to better manage genetic diversity in the present population and for future viability, see18. Little published information exists on the genetic structure of E. madagascariensis in the wild. Velosoa et al.7 mention an unpublished report which describes a divergence between northern and southern subpopulations, and a potential but as yet unidentified hybrid zone. In light of this, Velosoa et al. recommend that future research focus on understanding the genetic structure that exists across the range of E. madagascariensis. It is important to provide a published description of the genetic structure of E. madagascariensis, based on clearly defined sampling, locations, and genetic markers.IUCN classified 19. In all cases, genetically distinct subpopulations have arisen either side of rivers, as a result of the reptiles’ inability to cross water. Erymnochelys madagascariensis, however, is in principle, easily able to move along and across waterways, although it is unclear as to what extent it does so in practice. An example of a water-dependent species that also exhibits genetic differentiation due to bodies of water in Madagascar is the tomato frog Dyscophus guineti, in which genetic variation arises due to its limited dispersal beyond rivers20. If water-dependent amphibians with low vagility in Madagascar exhibit genetic differentiation in different geographical areas20, it is possible that water-dependent reptiles known to be largely sedentary may also exhibit divergent genotypes at different geographically isolated areas—i.e. different watersheds. Similarly, a phenomenon originally described by Wilmé et al.2 and termed the ‘watershed hypothesis’ by Pearson and Raxworthy21 suggests that watersheds, combined with historic climatic shifts, drive patterns of endemism and speciation in Madagascar. Wilmé et al.2 describe ‘retreat-dispersion watersheds’ (RDW) which maintained cooler, moister climates at higher elevations during periods of lowland aridity. The higher elevations of RDWs may have served as a retreat from inhospitable lowlands during the Quaternary. The initial retreat of some populations, coupled with their eventual dispersal back into lower elevations, may have served to isolate populations and drive allopatric speciation21. Indeed, the position of RDWs significantly correlate with the distribution of many species in Madagascar21. Understanding the genetic variation across the range of wild turtles may help to direct conservation goals towards maintaining specific levels and/or elements of diversity in the wild (e.g. avoid inbreeding). Secondly, this information may help conservationists to better implement the full range of genetic variation in additional captive populations, and in doing so decrease the risk of inbreeding.Rivers have been shown to act as barriers to gene flow in several Malagasy reptiles including tree boas, geckos and chameleonsE. madagascariensis individuals from across the species’ range in north-western Madagascar, using eight microsatellite markers and partial sequences for the mitochondrial genes cytochrome b and cytochrome oxidase subunit I (COI). These data were used to analyse E. madagascariensis samples from 22 locations and test the hypothesis that genetic structure exists between different watersheds. We also assessed the genetic variation between captive bred individuals and wild individuals, to establish the genetic representation of the captive population. As the ex situ operation has always been limited to providing translocation stock to augment depleted populations in the southern part of ANP, we hypothesise that this captive population would only be genetically representative of ANP, rather than all of the wild diversity available throughout the range of the species. Our findings are then contextualised within their possible implications for future conservation of E. madagascariensis.Here we present the results from an analysis of genetic variation in 467 A total of 467 blood or tissue samples were taken from individuals across 23 locations, and seven watersheds across north-western Madagascar Fig. . Tissue 22. Microsatellite primers9 26. DNA amplification was conducted using 5 µL of QIAGEN Multiplex solution, 3.8 µL of water and 0.1 µL of each primer, per 1 µL of DNA. The PCR thermal profile consisted of an initial denaturation of 95 °C for 15 min, followed by 30 cycles of 94 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, with a final extension of 72 °C for 10 min. Cytochrome oxidase subunit 1 (COI) primers used to amplify a 300 bp fragment were as follows: M72L (5′-TGATTCTTCGGTCACCCAGAAGTGTA -3′) M73H (5′-CCTATTGATAGGACGTAGTGGAAG -3′). PCR reaction volumes of 20 µL were used consisting of: 13.58 µL H2O, 2.5 µL 5 × Go Taq Flexi Buffer, 0.2 µL 25 Mm MgCl2, 0.32 µL 10 p/mol dNTPs, 0.2 µL Go Taq Flexi DNA polymerase, 0.2 µL of each primer and 1 µL of DNA. PCR conditions consisted of 40 cycles of an initial 95 °C for 15 min, denaturation at 94 °C for 1 min, annealing at 54 °C for 1 min, extension at 72 °C for 1 min, and final extension at 72 °C for 10 min. PCR products were purified using 1.25 µL of 10 × SAP buffer, 0.5 µL of Thermosensitive Alkaline Phosphate and 0.25 µL of Exonuclease I per 10 µL of PCR product. The purified product was then sequenced by Eurofins Genomics.A 342 bp fragment of the cytochrome b gene primers was produced using the following primer sequences: Forward (CB-J-10933) = 5′-TATGTTCTACCATGAGGACAAATATC-3′, Reverse (CytbC) = 5′-CTACTGGTTGTCCTCCGATTCATGT-3′27. To understand how the Rere’s population size has changed throughout history, an analysis of demography was carried out. Sampling locations with n ≥ 10 were analysed for evidence of a genetic bottleneck, population expansion or stability using Msvar v1.328. For sampling locations with n ≥ 20, 20 individuals were randomly subsampled for the analysis to restrict the computation time. A standard vertebrate mutation rate was used (10−3–10−5) as no specific value was available for E. madagascariensis29. Generation time was set at 25 years13. 4 × 109 iterations were performed with 20% discarded as burn in. A Gelman & Rubin test30 from the CODA library31 was used to identify convergence of runs under three different prior scenarios in R statistical software32. Priors assumed a stable population, a population expansion, and a population bottleneck, allowing confirmation that the posterior distributions resulting from Msvar analysis were not biased by these priors. MtDNA samples were not included in demography analysis, because they only partially represent the whole dataset, and mtDNA can only give an insight to maternal lineage demography.To aid in the interpretation of genetic structure and diversity results, the demographic history of the Rere was investigated. Linked microsatellite loci can be an indication of non-random mating in populations, and Arlequin was used to perform tests for linkage disequilibrium between microsatellite locus pairs. To assess the presence and extent of gene flow between sampling locations, the number of migrants (Nm) based on the private allele method was estimated in Genepop33. To avoid biasing the determination of genetic clusters with missing data, all microsatellite samples with missing data for ≥ 1 loci were removed from the dataset . Similarly, to minimise the chance of incorrectly inferring K (number of genetic clusters), the magnitude of uneven sampling in the dataset was reduced34. This was achieved by randomly subsampling all populations of n ≥ 30, without replacement, to include only 30 individuals (deemed appropriate by35), before performing Structure analysis. Three different resampled datasets were analysed to confirm that 30 was an appropriate number, and that differently subsampled populations did not generate largely different results in Structure. Structure was run using 500,000 MCMC iterations with 100,000 discarded as burn in and K set between 1 and 8. Three iterations of each analysis were run to assess convergence between results. POPHELPER Structure Web App v.1.0.1036 was used to generate bar plots from Structure results. To determine the most likely K, three methods were used. Two Evanno plots were used—Mean L(K) and ΔK37. Especially in the instance of uneven sampling, inaccuracies in the estimation of K using Evanno methods have been identified34. Consequently, a third, independent set of methods was used and calculated manually—termed MedMeaK, MaxMeaK, MedMedK and MaxMedK (MMMM test)34. A threshold of 0.8 was used as the most stringent estimator of K. Consensus sequences for mtDNA were produced in Geneious v.6.0638. MEGA was used to create Maximum Likelihood (ML) trees for cytochrome b and COI with a bootstrap value of 100 for sampling locations and haplotypes.To examine how many distinct ‘clusters’ of individuals with similar genetic variation exist within the dataset, and whether these clusters associate with sampling locations, a Structure (v.2.3.4) analysis was run39 to estimate the observed and expected heterozygosity, allelic richness, average number of alleles per locus, and pairwise FST. This was performed using the full microsatellite dataset, grouped according to sampling localities and by genetic cluster. For FST significance was assessed with 10,000 permutations and applying Benjamini and Yekutieli false discovery rate (FDR) correction40. Inbreeding coefficient (FIS) values and significance were calculated using FSTAT v2.9.341. To assess the partition of genetic diversity within and between sampling locations, genetic clusters, or phylogenetic clade (for mtDNA), an AMOVA was performed in Arlequin v3.542 using 1000 permutations. If genetic clusters or clades can better distinguish genetic lineages than individual sampling locations, this is important to guide conservation management. Genepop v4.627 was used to perform an exact test to determine whether the sampling locations and genetic clusters were in Hardy–Weinberg equilibrium. As the standard errors of the Genepop results were small (less than the order of P) and the number of switches high (> 1000), the Markov chain run parameters were suitable and left as default27. Heterozygote deficiency and excess were also calculated to explain any deviation from the Hardy–Weinberg equilibrium. To compare mtDNA diversity between populations, the number and diversity of haplotypes for both cytochrome b and COI were calculated using DNAsp v5.10.143. In order to compare the genetic representativeness of the captive population with the genetic diversity expected from a wild population of the same size, a bootstrap resampling method was employed for microsatellite and mtDNA. One hundred random datasets of 47 individuals  were generated from the 67 wild individuals with available COI sequences. The same was done for cytochrome b, resampling the 50 wild individuals in sets of six. Haplotype diversity and pi, generated using DNAsp for all 100 datasets and the captive population, were used to create expected distribution plots for the wild population’s mtDNA diversity distribution compared to the captive population. The same process was repeated for microsatellite data, resampling the 354 wild individuals to replicate the captive sample size of 88 individuals. Distributions of heterozygosity and average number of alleles per locus were generated. TempNet44 was used to illustrate the distribution of mitochondrial haplotypes between the overall wild population and the captive bred hatchlings.Summary statistics were calculated to estimate the populations’ genetic variation and divergence. We used Microsatellite Analyser v4.05Sampling was carried out by a veterinarian according to blood and tissue sampling protocols approved by Durrell Wildlife Conservation Trust and the Madagascar Ministry of the Environment, Ecology, Sea and Forests (MEEMF) under permit number M6/15/MEEMF/SG/DGF/DCB.SAPT/SCBT. All samples were exported with CITES permit no. 617C-EA07/MG15 and imported with CITES permit nos. 537437/01, 537437/02, 537437/03.ST when null alleles were removed from the analysis . Subsequently no loci were excluded due to the presence of null alleles. Sequences of sufficient quality were produced for 56 individuals for cytochrome b, and 114 for COI.Twenty-six samples were discarded due to missing data, leaving 442 samples for the remaining microsatellite analyses. Null alleles were detected at two loci in sampling location populations with more than 15 individuals or at a frequency > 0.8%. T-tests revealed no significant effect on heterozygosity or Fp value < 0.05). More private alleles were detected amongst sampling locations (0.065) than genetic clusters (0.02). When corrected for sample size, 1.79 migrants per generation were detected between sampling locations, compared to 4.24 between genetic clusters. With a mean generation time of ~ 40 years, this equates to ~ 9 individuals moving between locations over 200 years. To search for demographic processes that may underlie some of these observations, an Msvar analysis was performed. Three different combinations of priors were used and the convergence of the Msvar MCMC was determined using Gelman & Rubin statistics that were < 1.2 (indicating convergence of Msvar runs). Priors and hyperpriors used are shown in Supplementary Table Only the captive bred population had loci which were not independent of each other, with 13 out of 28 pairs exhibiting linkage disequilibrium  to 0.30 (cluster 3–5) and were all significant . Cluster 6 had the highest average observed heterozygosity while cluster 5 had the lowest . The highest observed heterozygosity amongst the wild locations with n > 1 was found at Bemangaoka (n = 2). The highest heterozygosity in a location with > 5 individuals was at Amboromalandy  and Pi 0.034. COI data revealed 9 haplotypes present across five locations from 114 sequences. Lower COI haplotype diversity of 0.244  was observed, along with by Pi = 0.0095. The captive bred population from Ampijoroa was represented by one haplotype for each mitochondrial marker  thus providing evidence of their genetic divergence. The geographic distance between Lakes Sariaka and Ambondrobe, and the population most genetically diverged from them (at Andranomiditra River) is ~ 300 km. The maximum FST values between populations of another Malagasy testudine (Geochelone radiata) over a similar range were smaller than those identified for populations of E. madagascariensis45. This suggests that individuals from Lake Sariaka exhibit a notably large genetic divergence with respect to other Rere populations. Furthermore, of the sampling locations with > 5 individuals, Lake Sariaka had the lowest heterozygosity (0.51) which may be indicative of genetic drift or inbreeding. Cytochrome b data revealed a divide between individuals in the most north-westerly point of the sampled area (Lake Sariaka and Akilolio), and the south-west of the sampling area (Manambolo and Tsiribihina watersheds). COI data supported this divide, as far as the limited data for this marker could show. As no data were collected for individuals in north-westerly Maningoza watershed or Lake Sariaka, the additional division between north and south westerly populations, as seen with cytochrome b data, cannot be assessed. Microsatellite data did not reveal such distinct geographical separation of genotypes, but cluster 2 was restricted just to Lake Sariaka.This study has identified key divisions of genetic variation and patterns of diversity across the sampled range 46. Watersheds in Fig. Podocnemis expansa48. As mitochondria are maternally inherited, female preference for one watershed could limit the spread of haplotypes outside of that watershed, in contrast to microsatellites which may be inherited from either sex49. Erymnochelys madagascariensis’ genetic structure could be better explained by a retreat-dispersion watershed pattern, as suggested for many other patterns of genetic diversity found throughout Madagascar21. Betsiboka and Mahajamba watersheds, and the unique genotypes they contain, lie in the 9th centre of endemism depicted by21. Other unique genotypes from Sariaka and Ambondrobe lie in the 8th centre of endemism21. Historical charcoal records suggest that Madagascar experienced a pronounced period of drought and wildfires approximately 5200–5800 years ago51. Msvar analysis also estimated that E. madagascariensis passed through a large bottleneck ~ 5600 years ago, reducing the ancestral population size by ~ 62%. The coincidence of a bottleneck, climate extreme, and RDWs could explain the presence of populations that are distinct on either side of the RDW separating the 8th and 9th centres of endemism21. It is likely that after this pronounced drought and population decline, the remnant ancestral E. madagascariensis population was located a climate refugia at higher, colder elevations of the RDW. Following the watershed hypothesis, E. madagascariensis may have dispersed down either side of the watershed peak as lowland climates become more agreeable. Rere populations separated by this peak would undergo evolution in isolation from one another and undergo genetic drift, becoming the genetically unique populations revealed by our study. Local adaptations to the RDW, developed under natural selection, may have conferred differing fitness to individuals in the areas colonised after the RDW, which have varied climates21. This geographic variation in fitness and survival rate may also have contributed to the population divergence observed in this study.Watershed boundaries can structure the genetic similarity between freshwater turtles, for example the Wood turtle7. The founding populations  have different allele frequencies and are in Hardy–Weinberg equilibrium. When such populations are interbred or analysed as one, as is the case for Ampijoroa, an excess of heterozygotes are observed due to the Wahlund effect52. Despite the high level of heterozygosity in the captive population compared with the wild metapopulation, the ANAPL for the captive population was too low to be representative of the wild population. This indicates that the captive population does not have sufficient diversity to maintain a high level of heterozygosity if breeding continues for several generations without new breeding individuals. The captive population was never intended to maintain genetic diversity for the entire range of E. madagascariensis, but as originally planned may be suitable to maintain the genetic diversity of ANP as it has a higher ANAPL than all populations within the reserve, except Antsilomba.When comparing wild and captive populations, microsatellites revealed a heterozygote excess and a higher average number of alleles per locus in the captive bred turtles compared to the averages for each wild population. The captive breeding population at Ampijoroa was founded from nine individuals from three different localities within ANP53. In this study, the number of individuals sampled for mtDNA was also smaller than microsatellites, and this sampling bias can reduce the number of haplotypes detected. In a review of 18 captive bred species, Witzenberger and Hochkirch17 estimated that captive populations founded from < 15 individuals will fail to maintain a desired 90% of natural genetic diversity after 100 years. Long-term maintenance of the species over several generations is not part of the ex situ strategy in Ampijoroa. The low overall genetic variety observed in the captive bred hatchlings at Ampijoroa 21 years after its creation is due to the small number of breeding females, the three original founders. The founder effect may have been exacerbated if the founding females were related and held similar or the same haplotypes, if only one female introduced was capable of breeding, or if one female dominated the breeding. However, no data exists for COI from founding populations—and this would be important to verify. Captive bred individuals from Ampijoroa were observed to have loci in linkage disequilibrium, which can be a sign of non-random and consanguineous mating54. Structure plots reveal that the principal cluster of captive bred Reres is only the principal cluster for three out of nine clusters held by the founder individuals from Lake Ravelobe, indicating that these individuals could be mating more often than the others, resulting in genetic drift. Given that turtles are purposefully placed together during breeding season at Ampijoroa7, an effort to randomise turtle pairing for breeding may reduce the effects of drift implied by linkage disequilibrium. Enabling multiple pairings may also increase microsatellite diversity, as female turtles may store sperm from multiple males, and high proportions of clutches have been observed to have multiple paternities in the side-necked turtle Podocnemis expansa55.A higher proportion of the wild populations’ nuclear (microsatellite) diversity was represented by the captive bred hatchlings when compared with mitochondrial diversity. A single haplotype was present in the captive born individuals, while nine cytochrome b and ten COI haplotypes were found in the wild samples. It is expected that after a bottleneck event (such as the founding of a captive population) that mitochondrial diversity may be lost faster than nuclear diversity due to the mitochondrial DNA having a four-fold smaller effective population size compared to the nuclear DNA57. MUs will show signs of significant reproductive isolation from other conspecific populations—namely divergent nuclear allele frequencies and mtDNA—both observed in this study57. The separate management of populations in the Betsiboka and Mahajamba watersheds from other populations would be supported from this study’s findings. Furthermore, Lakes Ambondrobe and Sariaka should be prioritised as separate MUs, due to their large genetic divergence. Fortunately, Lakes Sariaka and Ambondrobe already exist within protected areas. Lake Sariaka lies within Baly Bay National Park, while Lake Ambondrobe became the first area in Madagascar to be protected for the sake of a single species (the Rere) in 2015. However, there is a large discrepancy in the number of individuals sampled between locations. Over 40 individuals were sampled from Lakes Sariaka and Ambondrobe, while fewer than five were sampled from all other locations along the Maningoza, Manambaho, Manambolo, and Tsiribihina watersheds. In addition, 324 more samples exist from Betsiboka and Mahajamba watersheds, further east, than from across the other watersheds. Consequently, more samples are needed to confidently assign the separate MUs of Betsiboka/Mahajamba, and other watersheds. An analysis of a higher number of markers (e.g. whole genome data or RAD-seq SNPs) would help to confirm this decision. Applying these methods may have the power to detect local adaptation at a finer scale than observed in this study, which would also aid in more precise designation of MUs22.These findings allow certain wild populations to be prioritised for in situ conservation efforts. The identification of management units (MUs) is an important process in conserving intraspecific genetic diversity58). Ne and associated levels of neutral genetic diversity can be predictors of a population’s adaptive potential58. For example, in the case of genetic rescue, increasing Ne also increases evolutionary potential59. However, neutral genetic diversity is not always correlated with a population’s fitness, as discussed by60. There are other important aspects of diversity to consider, such as that held in functionally important genes (e.g. the MHC), when assessing captive population’s viability. The negative effects of captive breeding are well documented in captive and reintroduced species, and a low mitochondrial diversity was observed within captive bred hatchlings61. If the same individuals are repeatedly bred, over time the turtles released into ANP will all be of a similar genotype, and this could lead to inbreeding and/or genetic drift in the wild. Additionally, lakes within ANP that contain genotypes not found in captive hatchlings (Ankomakoma for example) may have their genetic distinctness diluted by the hatchlings. Ideally, adult breeders from Lakes Ankomakoma, Antsilomba and Ankorovoka, as well as the three original founding localities, would be temporarily taken into the captive colony to broaden the genetic founder basis to encompass all four genetic clusters, three cytochrome b haplotypes, and five COI haplotypes found in ANP. Realistically, this would require a much larger captive facility than is currently present . As already assumed at the start of this breeding project7, turtles bred at Ampijoroa will not be suitable for release outside of ANP, as they may begin to dilute the genetic uniqueness found outside the park—for example at Lakes Sariaka and Ambondrobe. For ex situ conservation to b e applicable for these genetically distinct populations, separately managed breeding programs would be recommended for both. However, as long as the protected status of these populat ions can ensure that the Lakes hold a constant or growing number of Reres, ex situ management may not be necessary.In relation to ex situ conservation at Ampijoroa, captive bred hatchlings hold higher microsatellite genetic diversity than the rest of the wild metapopulation. Higher neutral genetic diversity can be indicative of a higher effective population size (Ne) and lower levels of inbreeding (reviewed in García-Dorado and CaballeroE. madagasc ariensis to date. Genetic structure is observed in the wild and relates to a division between the Betsiboka and Mahajamba watershed populations from other sampled populations, as well as Lakes Sariaka and Ambondrobe, from which management units have been putatively identified. This structure is hypothesised to originate from a dispersal out of a retreat dispersion watershed after a climate-induced, historic bottleneck event. The captive bred hatchling population at Ampijoroa has maintained high levels of heterozygosity but, as planned from the beginning, does not encapsulate the full scope of genetic variation from the sampled range in the wild. However, the genetic variation of its founding populations is currently also not fully represented. The breeding project would benefit from a temporary integration of additional wild breeders from ANP to broaden the diversity of the captive bred translocation stock. Increased sampling effort to achieve ≥ 25 samples from each locality would help to more accurately resolve population structure35. Further sampling from the entire range of the species would be beneficial to provide a complete g enetic analysis with which to direct and implement con servation for all remnant subpopulations. Northern and southern populations of E. madagascariensis might be divergent enough to be considered subspecies7 and verifying the existence of a subspecies would be fundamental to successful delegation of management units for conservation.To conclude, thi s study provides the most comprehensive genetic analysis of Supplementary Information.


Pecten maximus) across its natural range in the northeastern Atlantic Ocean, using a recent chromosome-level genome assembly. We report the presence of at least three large (12–22 Mb), putative chromosomal inversions associated with sea surface temperature and whose frequencies are in contrast to neutral population structure. These results highlight a potentially large role for recombination-suppressing chromosomal inversions in local adaptation and suggest a hypothesis to explain the maintenance of differences in reproductive timing found at relatively small spatial scales across king scallop populations.The genomic landscape of divergence—the distribution of differences among populations or species across the genome—is increasingly characterized to understand the role that microevolutionary forces such as natural selection and recombination play in causing and maintaining genetic divergence. This line of inquiry has also revealed chromosome structure variation to be an important factor shaping the landscape of adaptive genetic variation. Owing to a high prevalence of chromosome structure variation and the strong pressure for local adaptation necessitated by their sessile nature, bivalve molluscs are an ideal taxon for exploring the relationship between chromosome structure variation and local adaptation. Here, we report a population genomic survey of king scallop ( A commoet al. [Increasingly, chromosomal architecture is being implicated in the process of adaptation and formation of genomic islands of divergence ,8, in laet al. ), may pret al. ). Chromoet al. , birds [et al. ,14, inseet al. ,16 and fet al. –20.Pecten maximus), also known as great scallop, is a high-value mollusc that supports a large fishery in the eastern North Atlantic ocean, and for which attempts to describe genetic population structure span decades [Bivalve molluscs are a useful model system for investigating the relationship between genomic architecture and adaptation, as there is ample evidence of local adaptation across heterogeneous environments –23, as w decades –29. The  decades ) in the  decades ,31. A reUsing a recent chromosome-level genome assembly , a popul. 2n = 15), northwest , northeast  and southeast  Scotland, and the Shetland Islands . Individuals from Norway were obtained from fish markets in the Hordaland  and Trøndelag  regions and individuals from Galicia, Spain  were obtained by diving. Further information, including details of sampling location, is presented in the electronic supplementary material, table S1.King scallops were sampled from eight localities in European waters of the eastern North Atlantic Ocean . Individet al. [process_radtags from the Stacks (v. 1.47) software package [dDocent (v. 2.6) pipeline [et al. [tools v. 0.1.16 [vcflib v. 1.0.0-rc1 (https://github.com/vcflib/vcflib) and the R package vcfR v. 1.8.0 [https://www.github.com/chollenbeck/king_scallop_popgen_2022.Double-digest restriction-site-associated DNA libraries were prepared following Peterson et al.  for 225  package . Demultipipeline , which ppipeline . The res [et al.  using th. 0.1.16 , vcflib v. 1.8.0 . R scripeagle [snp_autoSVD in the R package bigsnpR [To facilitate linkage disequilibrium (LD) pruning and later haplotype-based tests for selection, the SNP genotypes were phased using the program Beagle ,40. SNPs bigsnpR . This pr bigsnpR .ayescan [pcadapt [LEA [vegan [http://www.ifremer.fr/erddap/griddap/CORA.html). Temperature values for each grid point were averaged across the entire time period to obtain a single estimate for each point on the grid, and the temperature estimate at the grid point nearest to the approximate geographical location of each sampling locality was used in the association analyses. In addition, phased genotypes were used to calculate two haplotype-based selection statistics: iES, a single-population measure of haplotype homozygosity (in this form the average length of shared haplotypes in a particular genomic region) indicative of positive selection [rehh [Four genome scan methods were applied to identify loci potentially under the influence of natural selection: (i) a Bayesian differentiation outlier method implemented in the program Bayescan , (ii) a [pcadapt , (iii) aapt [LEA , and , based on the results of the outlier PCA.Results of the selection tests were used to separate the genotype data into two datasets: one containing loci that were identified as being putatively under directional selection by at least one of the genome scan methods and one containing the remainder of the putatively neutral loci. Population genetic structure was evaluated for both datasets separately using PCA, implemented in the R package adegenet ,52. Estiierfstat . Pairwisadegenet. The first CA axis (corresponding to outlier PCA axis 1) was used as the dependent variable in a multiple linear regression with sea surface temperature as the independent variable and neutral genetic group (Norway versus Scotland/Spain) and latitude as covariates.To further test for an association between sea surface temperature and genotype, allele frequencies for outlier loci in each locality were decomposed into a set of composite synthetic variables with correspondence analysis (CA), as implemented in gaston [LDna [gaston, as above, with individuals at all localities grouped together. Single outlier clusters (SOCs) of loci linked together by LD were then identified, and the resulting LD network was visualized with LDna and the R package ggnetwork [find.clusters function in adegenet was used to assign individuals to one of three clusters . For each putative inversion, frequency of each inversion genotype, heterozygosity and conformance to Hardy–Weinberg equilibrium within individual localities were then calculated using adegenet.In order to test for the presence of putative chromosomal inversions or other regions of low recombination, pairwise LD for all loci within each locality and region  was calculated using the R package gaston . LD netwon [LDna , was usegnetwork . To explgnetwork ). To idehttps://www.github.com/chollenbeck/king_scallop_popgen_2022.Using the reference genome annotation, genes in the vicinity of each outlier region were extracted by selecting genes falling within 50 kb (25 kb upstream and downstream) of each outlier locus. In the case of large outlier clusters, all genes located within the bounds of the region were selected as candidate genes, whether or not they were within 50 kb of an outlier locus. Candidate genes were further refined by assigning outlier loci to two separate groups based on contribution to the principal components in the outlier PCA. Further details regarding methods used, including specific parameters, can be found in the electronic supplementary material, methods and in R scripts provided at . 3The raw sequencing data contained 562.9 million read pairs, with a total of 514.5 million read pairs retained after demultiplexing. Following read mapping and variant calling, a total of 747 758 putative raw variants were discovered. Stringent filtering produced a set of 1920 SNPs that were used in all subsequent analyses.FST, reduced heterozygosity, and increased levels of haplotype homozygosity (iES) in Spain . The approximately 17 Mb region on chromosome 8 exhibited generally high estimates of pairwise FST and showed allele frequency differences similar to the other two regions but did not exhibit a reduction in heterozygosity or an increase in iES across the entire 17 Mb region in Spain . For chromosome 8, the distribution of Rsb  contained several smaller peaks rather than a single large peak.Sixty-eight loci putatively under the influence of selection were identified by at least one of the four methods. Eleven loci were identified with all four methods. Fifty-three loci were significantly associated with sea surface temperature, based on at least one environmental-association test, and 30 loci were significantly associated with both environmental-association methods. The genomic distribution of loci putatively under directional selection was non-random, with most loci grouped into one of three large regions (ranging from 12 to 22 Mb) on chromosomes 2, 8 and 12 . The rega). Estimates of pairwise FST based on the neutral dataset were at least three times larger for comparisons between localities in Norway and localities in Scotland or Spain (ranging from 0.036 to 0.045) than comparisons between localities in Scotland/Spain (ranging from 0.008 to 0.010). Fine-scale substructure was also detected between Scottish localities, with SW Scotland differing significantly at neutral loci (FST = 0.0043–0.0049) from NE Scotland and the Shetland Islands, and there was a small, but significant difference (FST = 0.005) between the two Norwegian localities .PCA revealed that individuals grouped into three distinct ‘regional’ groupings: Norway, Scotland and Spain. The PCA involving only neutral loci revealed a primary component of variation  that differentiated Norway from Scotland and Spain, and a secondary component of variation  that differentiated Spain from Scotland and Norway a. Estimab). SNPs contributing most to each outlier PC tended to group together in the genome, with loci contributing a larger effect to PC1  were located on chromosomes 3, 10, 13 and 19. A comparison of regional pairwise FST confirmed this pattern, showing that SNPs which strongly differentiated Spain from the other localities (high pairwise FST) tended to be located in the same regions, while SNPs that differentiated Norway from the other localities also tended to group together in the genome .The PCA conducted using outlier loci revealed a contrasting pattern. The primary component of variation  differentiated Spain from Norway and Scotland, and the secondary component of variation  differentiated Norway from Scotland and Spain b. SNPs cR2 = 0.877; p < 0.001) and after correcting for the effects of neutral genetic group and latitude . Visualization of the CA and allele frequencies from the SNPs contributing most to CA axis 1 showed a north/south gradient in allele frequencies, with alleles in SW Scotland often intermediate to Spain and other Scottish localities .The regression-based test for genotype-environment association with outlier loci was significant, both with sea surface temperature as the sole independent variable , suggesting a reduction in recombination over a large segment of each of the chromosomes. For chromosome 12, LD was strongest in Spain, with a block of LD (r2 > 0.99) spanning at least 10 Mb (figure 3g). The same block of LD was apparent in chromosome 12 in Scotland and Norway, but at reduced levels of LD, as measured by r2 . For chromosome 2, the LD block was more apparent in Scotland and Norway, but largely because it was not possible to measure LD in Spain owing to fixation of many of the SNPs in the LD block. The LD block on chromosome 8 was largest (greater than 17 Mb) and most clearly defined in Norway , although elevated LD could still be seen in the same chromosomal region in Scotland , and LD was not able to be estimated at all loci owing to fixation of several loci in Spain. LDna identified five SOCs containing more than three loci on chromosomes 2  and 12  . Three of these SOCs (one on each chromosome) corresponded to regions containing LD blocks identified with previous analyses. In addition, two overlapping SOCs with relatively lower median 2r (containing four and six loci) were identified adjacent to the major SOC on chromosome 2.Visualization of pairwise LD revealed that the three clusters of outlier loci on chromosomes 2, 8 and 12 fell into well-defined blocks of extended LD (figure 3α = 0.1) was found in northern Norway  and also in the SW Scotland locality . The LD block on chromosome 8 revealed a more complex pattern of divergence than the putative inversions on chromosomes 2 and 12, with a component of variation differentiating Spain from the other localities and a component where Norway was differentiated from all other localities .In general, SNP loci within the three LD blocks showed similar patterns of allele frequency differences among localities , but the frequency of putative inversion genotypes across localities differed among the three LD blocks. For chromosomes 2 and 12, local PCA grouped individuals into three genotype clusters , while 66 genes were in close proximity to genes contributing more to outlier PC2 .. 4A chromosome-level reference genome and a genotyping-by-sequencing approach was used to explore the genomic landscape of divergence in king scallop in the NE Atlantic. Neutral population genetic structure was in concordance with the results of previous studies ,31, supp (a)e), which would occur if these loci are adjacent in particular inversion genotypes or if there are multiple small inversions present. The hypothesis of the existence of inversions can be tested in future studies using alternative methods to those presented here, including genetic mapping [While localized reduction in recombination can be caused by several possible aspects of chromosome architecture, including proximity to centromeres  and strufigure 3 mapping , whole-g mapping  or polym mapping .et al. [Chromosomal inversions linked to adaptation have recently been described in several marine systems, notably in Atlantic cod ,62 and tet al.  recentlyet al.  revealed (b)One key result of the present study that highlights the benefits of establishing a genomic position for loci in a population genomics context is the finding that patterns of divergence revealed by each of the two primary outlier PCs tended to be driven by loci grouped in separate regions of the genome. The loci contributing to the weaker, secondary pattern of adaptive genetic variation  were found as singletons or pairs of outlier SNPs on chromosomes 3, 10, 13 and 19, and potentially represent loci that promote local adaptation across larger regional populations (Norwegian and Atlantic). However, the fact that allele frequency patterns within these loci are congruent with the overall neutral signal make it difficult to rule out the effects of purely demographic processes . One obsDrosophila melanogaster, where allele frequencies in some populations have been shown to fluctuate seasonally in response to variables such as temperature, independently of neutral population structure [Drosophila subobscura [Anopheles mosquitos [The second observed pattern of adaptive genetic variation involved significant differences in frequencies of temperature-associated alleles within putative inversions, which was in contrast to neutral population structure. Reduction in heterozygosity and elevated iES for putative inversions located on chromosomes 2 and 12 observed in Spain are evidence for strong positive selection (i.e. selective sweeps) in these genomic regions . Signifitructure . Balancibobscura  and Anoposquitos . Furtherosquitos . However (c)Genomic islands of divergence are hypothesized to arise under a variety of conditions, and these mechanisms can be broadly classified by whether zero (selection is not involved), one, or multiple loci within an island are targets of selection . ChromosP. maximus [P. maximus to be roughly May through to August [et al. [Littorina saxatilis, in which ecotypes characterized by differences in the frequencies of inversion polymorphisms have been observed along intertidal transects on the scale of tens of metres [Littorina system have been largely morphological, similar observations have been made involving Atlantic cod and Pacific herring, where genetic variation associated with reproductive timing and strategy has also been associated with putative chromosomal inversions [Connecting the genomic landscape revealed here to adaptive mechanisms will require further work; however, one hypothesis relates to the fact that temperature is known to be among the most important factors influencing timing of gametogenesis and spawning in bivalve molluscs, including  maximus ,76. Prevo August , with soo August . It has o August , and popo August  and Atlao August ,81 populo August ,81,82. Ho August ,30,31, co August . The evif metres ,83. Whilversions ,84.The size of the LD blocks and the large number of genes contained within them make it difficult to identify specific candidate genes that may be the targets of selection, particularly because of the possibility that a single gene under selection within the inversion could influence allele frequencies within the entire region. However, a number of genes previously linked to gonad-specific expression in scallops are present in the putative inversions. These include several serine/threonine protein kinases and phosphatases (two tandem serine/threonine kinases on chromosome 2 and four serine/threonine protein phosphatases on chromosome 12) and two adenosine deaminase-like genes on chromosome 8, all of which have been shown to be differentially expressed in male and female scallop gonads ,66. In a. 5Observing and understanding the genomic landscape of divergence, which is now possible owing to ever-improving genome sequencing and assembly techniques, allows for a more sophisticated view of microevolutionary processes because it incorporates the effects of local recombination, in addition to migration, drift and selection. The results presented here demonstrate an association between sea temperature and genetic variation in specific regions of the genome characterized by local reductions in recombination and highlight the importance of establishing genomic context in disentangling the effects of microevolutionary forces. These results suggest a mechanism by which broadcast spawning species with a high degree of connectivity can maintain genetic differences that allow for local adaptation. Further work in king scallops and other taxa to better characterize these systems will help to improve our understanding of how chromosome structure variation contributes to evolutionary change.


Anopheles stephensi, an invasive malaria vector, has been reported to have three biological forms identifiable mainly based on the number of ridges present on the egg’s floats. Recently, the first intron of the odorant-binding protein-1 (AsteObp1) has been introduced as a molecular marker for the identification of these forms, and based on this marker, the presence of three putative sibling species  has been proposed. However, there is no data on the association of proposed markers with biological form or putative species on field populations.An. stephensi were characterized for biological forms based on the number of ridges on the egg’s float. DNA sequencing of the partial AsteObp1 gene of An. stephensi individuals were performed by Sanger’s method, either directly or after cloning with a plasmid vector. Additionally, AsteObp1 sequences of various laboratory lines of An. stephensi were retrieved from a public sequence database.Field collected and laboratory-reared AsteObp1 intron-1 in Indian An. stephensi populations are highly polymorphic with the presence of more than 13 haplotypes exhibiting nucleotides as well as length-polymorphism (90-to-121 bp). No specific haplotype or a group of closely related haplotypes of intron-1 was found associated with any biological form identified morphologically. High heterozygosity for this marker with a low inbreeding coefficient in field and laboratory populations indicates that this marker is not suitable for the delimitation of putative sibling species, at least in Indian populations.AsteObp1 cannot serve as a marker for identifying biological forms of An. stephensi or putative sibling species in Indian populations. Anopheles stephensi is an efficient malaria vector responsible for malaria, mainly in urban areas. Earlier, this species was reported to be distributed mainly in countries of the Middle East and South Asia [uth Asia . During uth Asia , Sri Lanuth Asia , countriuth Asia –8 and thuth Asia , 9, and uth Asia , 7, 10. uth Asia , which puth Asia .An. stephensi in India is predominantly found in urban areas due to the presence of favourable breeding habitats in urban settings and is considered an urban malaria vector. In the 1940’s, Sweet and Rao [var. mysorensis’ in the An. stephensi based on the dimension of eggs, length of eggs-floats and numbers of ridges on the float. The ‘type form’ was reported mainly from the urban areas and ‘var. mysorensis’ from the rural areas [var. mysorensis’. and Rao  and Rao  and Rao  describeal areas , 14. Inial areas  but subsal areas . Later, var. mysorensis’ is considered to be poor in malaria transmission [An. stephensi involves morphometrics of eggs, which is a cumbersome process; for which, live female mosquitoes are to be transported to the laboratory and their eggs have to be obtained for the counting of ridges on the egg’s floats. Nagpal et al. [An. stephensi can be used for the discrimination of biological forms in the adult mosquitoes, which was higher in ‘type form’ as compared to ‘var. mysorensis’ in Rajasthan, India (an arid zone). They also reported that the two forms of An. stephensi, i.e., ‘type form’ and ‘var. mysorensis’, occupy different ecological niches where ‘var. mysorensis’ prefers outdoor habitat and ‘type form’ indoors [AsteObp1) gene of An. stephensi from four laboratory strains, two strains representing ‘var. mysorensis’ and one of each representing ‘type form’ and ‘intermediate’. Based on sequences of a total of 20 samples from all of the four colonies, they demonstrated fixed differences (no heterozygotes were reported) in the first intron of the gene among three biological forms. This prompted them to propose the AsteObp1 intron-1 as a molecular marker for the identification of biological forms. However, this marker was not tested on field populations. Subsequently, the same research group [var. mysorensis’, respectively. However, they didn’t establish an association of the AsteObp1 intron-1 haplotypes with the eggs’ morphology. In the absence of any study showing the association of AsteObp1 haplotypes with biological form in field populations, we investigated the extent of polymorphism in AsteObp1 intron-1 in Indian An. stephensi (field populations as well as laboratory colonies) and validated their association with biological forms or proposed sibling species.The ‘type form’ is reported to be an efficient malaria parasite vector, while ‘smission , though smission , 18. Labsmission , 20. Duel et al.  reported indoors . The ‘sp indoors  for the ch group , speculaAnopheles stephensi samples were collected from urban, peri-urban and rural localities. The list of localities and their geographical coordinates is provided in An. stephensi were transferred inside separate paper cups containing a small amount of water and having the inner side lined with blotting paper. The mosquitoes were kept in the cup overnight to allow them to lay their eggs. The next morning, female mosquitoes which laid their eggs were removed, anesthetized with diethyl ether, and confirmatory identification of An. stephensi was performed under a binocular microscope following keys by Christophers [An. stephensi originating from New Delhi (NIMR strain) and being maintained since the year 2011, was also characterized for the number of ridges present on egg’s float. DNA was isolated from individual mosquitoes manually following methods by Black and Duteau [An. stephensi individuals from a laboratory colony (F5) originating from Chennai (urban area) and An. stephensi strain STE2  were also used for molecular studies. A total of 143 samples were included in this study, out of which 137 samples were sequenced successfully and 119 samples were successfully characterized for the number of ridges on eggs, of which 27 were characterized as ‘var. mysorensis’, 30 as ‘intermediate’ and 62 as ‘type form’.stophers . The mosstophers  from 10 d Duteau . BesidesAsteObp1 was performed using primers OBP1F1 (5’-CGT AGG TGG AAT ATA GGT GG-3’) and OBP1R2  and sequenced on both strands of DNA using ABI BigDye Terminator v3.2 (ThermoFisher Scientific) following the manufacturer’s protocol.Initially, the amplification of partial TTG C-3’  which co2 and 200 mM of each dNTP), 0.5 μM of each primer and 0.5 μL of PCR product. PCR thermal cycling conditions were: initial denaturation at 98 °C for 30 s, 35 cycles, each of denaturation at 98 °C for 10 s; annealing at 65 °C for 30 s and extension at 72 °C for 45 s, followed by a final extension at 72 °C for 2 min. Five μL of PCR product was loaded onto 2% agarose gel and visualized under UV illumination. The remaining PCR products were purified using the QIAquick PCR purification kit (Qiagen Inc) following the vendor’s protocol. The purified PCR product was ‘A’-tailed using dATP in the presence of Taq polymerase. The reaction mixture (50 μL) contained 0.25 μL of Taq polymerase , 5μL of 10X buffer containing 1.5 mM MgCl2, 5μL of purified PCR product, 1μL of 10mM dATP and incubated at 70 °C for 20 min. Three μL of purified A-tailed PCR product was then ligated into pGemT®-T vector (Promega Corporation) in a 1:3 vector to insert ratio following the recommended protocol by the manufacturer. Then 5 μL of the ligated product was transformed into DH5-alpha competent E. coli (New England Biolab) and 90 μL of the transformed product was spread onto Ampicillin-IPTG/X-Gal LB agar plates and incubated overnight at 37 °C for blue/white colony screening. Plasmid DNA from five clones from each mosquito was PCR amplified and sequenced from both directions of the DNA strand as described above.To phase out haplotypes in heterozygotes, the PCR products of 12 heterozygous individuals exhibiting different patterns of mixed bases in sequence chromatograms were selected for cloning followed by sequencing. For cloning, PCR products were amplified using a high-fidelity Taq DNA polymerase to avoid or minimize PCR errors. Briefly, PCR reactions were performed in a total volume of 25 μL containing 0.5 units of Phusion (New England Biolabs) Taq DNA polymerase, Phusion HF reaction buffer (containing 1.5 mM MgCl5’-CGC CGT GAT GCC GAA TA-3’ and OBP_INTR: 5’-ATT GTC GTC CAC CAC CTT G-3’) from flanking exons. The PCR conditions were similar to what we used for the PCR amplification for direct sequencing using primers OBP1F1 and OBP1R2, except for the duration of extension time in PCR-cycling, which was reduced to 30 s. A total of 137 samples, which includes 109 samples characterized for egg ridges count were successfully sequenced for AsteObp1-intron-1.For subsequent sequencing reactions for the characterization of full intron-1 (short-PCR), we designed a different primer set  were aligned using MUSCLE implemented in the application MEGA7 [An. stephensi was treated as an outgroup. The genetic distances among all haplotypes of An. stephensi and phylogenetic analyses were performed using the T92 model (Tamura 3-parameter), the best model as determined by MEGA7 based on the lowest Bayesian Information Criterion (BIC) scores. The bootstrap consensus tree inferred from 500 replicates was taken to represent evolutionary history. Haplotype network was constructed using NETWORK 10 (https://www.fluxus-engineering.com/sharenet.htm). The pairwise differences between and within biological forms, and FST between biological forms, based on the AsteObp1 haplotypes were estimated using software Arlequin [All the haplotypes of intron-1 of on MEGA7 . The seqArlequin .AsteObp1 were retrieved from publicly available genome sequence databases of various strains of An. stephensi, i.e., AsteI2 strain (origin: India), SDA500 strain (origin: Pakistan) Hor strain (origin: India) and UCISS2018 strain (probably Indian origin), along with information on their biological forms, if available.Sequences of the intron-1 of A total of nine haplotypes (H1 to H9) were identified through sequencing of cloned PCR products from heterozygous individuals . Direct AsteObp1 intron-1 exhibited extensive polymorphism in field populations. A high degree of heterozygosity, as per expectations, was observed in field populations, i.e., in Karamchand Pur , Jaisingh Pur  and Bengaluru  . In all cases, the inbreeding coefficient was close to zero (-0.05 to 0.00), suggesting random breeding. In populations with a sample size of less than 10, we didn’t calculate heterozygosity and inbreeding coefficient, but the majority of the individuals in such populations were heterozygous (The distribution of haplotypes in laboratory colonies and field populations is shown in E = 79%) . The labrozygous .AsteObp1 intron-1 haplotypes identified by Gholizadeh et al. [To infer genetic relatedness among different haplotypes, haplotype clustering was performed based on (i) pair-wise genetic distance; (ii) phylogenetic tree, and (iii) minimum spanning haplotype network. For haplotype clustering, 13 haplotypes obtained in this study were analyzed together with five h et al.  and Firoh et al.   nucleotide substitution model, has been displayed in Phylogenetic clustering of 13 haplotypes showed a similar pattern as seen above. Trees constructed using three different methods showed similar topology . The hapHaplotype network also showed similar patterns of clustering . Thirtee’var. mysorensis’ (H8 and H14) from an independent cluster.None of the above analyses could partition haplotypes designated as markers for ’type form’ (H4) and ’intermediate’ (H2 and H10), which fall within the same cluster. However, the haplotypes designated as markers for var. mysorensis’, H2 and H10 for the ‘intermediate’ and H4 for the ‘type form’  haplotype group-I (HG-I) for ‘intermediate’, comprising H2, H3, H12, H10 and H1, (iii) haplotype-T (H-T) for ‘type form’, i.e., H4, and (iv) other haplotypes . The frequency distribution of different haplotype groups, categorized above, among phenotypically determined biological forms based on the number of floats on eggs, has been shown in var. mysorensis’, ≥16 as ‘type form’ and 14–15 as ‘intermediate’. There was no significant difference in the frequencies of different haplotype groups in three biological forms among field populations , which is equivalent to zero, and were not significantly different (p = 0.65), which means there is no genetic subdivision between the populations . The laboratory colony of Delhi (NIMR strain) was characterized as a typical ‘type form’ based on floats on egg-ridges. The mode number of ridges ranged between 16 and 20 (average: 18.29). However, the expected haplotype H4 (marker for ‘type form’) was absent. Instead, two different haplotypes, H8 (marker for var. mysorensis) and H9 were found with frequencies of 0.34 and 0.66, respectively. These two haplotypes are far distantly related to the designated haplotype for ‘type form’ (H4) with T-3-P distances of 17 and 18%, respectively . The mode number of ridges ranged between 16 and 18 (mean:16.55) which can be categorized as ‘type form’. The expected haplotype H4, the marker for ‘type form’, was not found in this population. The Bengaluru population had haplotypes belonging to HG-I (50%), HG-M (25%) and ‘others’ (25%). Haplotype H-T was absent.var. mysorensis)Village Chilawali , which can be categorized as ‘var. mysorensis’. Out of a total of 18 haplotypes identified, eight belonged to HG-I, one to H-T and nine to ‘others’. Expected haplotypes (HG-M) were not found.var. mysorensis and intermediate)Village Karamchand Pur (. The mode number of ridges in the Karamchand Pur population ranged between 12 and 16 (average: 14.85) which can be categorized as a mixture of ‘intermediate’ and ‘var. mysorensis’ forms. The Karamchand Pur population had all the haplotypes identified in this study . The mode number of ridges ranged between 11 and 16 (average 13.69). All forms were present in the village. Majority of haplotypes were belonging to HG-I (66%), followed by HG-M and the ‘others’ group (each 16%) and H-T(2%).AsteObp1 and their biological forms in different laboratory strains, all originating from the Indian subcontinent, has been listed in var. mysorensis’, irrespective of their biological forms determined phenotypically. Delhi strain, which is a relatively new strain (5 years old) had additional haplotype H9, which is not assigned to any biological form. Among all strains, only the Hor strain was characterized as ‘var. mysorensis’ morphologically. Two strains; AsteI2 [AsteObp1 haplotypes failed to identify biological forms of laboratory colonies.The list of haplotypes of intron-1 of ; AsteI2  and NIMR; AsteI2  as ‘inteAn. stephensi are ecological variants exhibiting differences in their biological characteristics including vectorial competence [var. mysorensis’ based on ribosomal DNA (ITS2 and 28S rDNA) [AsteObp1 intron-1 haplotype and proposed it as a molecular marker for their identification. The observation, by Gholizadeh et al. [var. mysorensis) was shown by the same research group [Evidence suggests that the biological forms of mpetence , 20. It 8S rDNA) , 31 and 8S rDNA) , 33 were8S rDNA)  reportedh et al. , that fich group  in a higch group . A similIn another study by the same group , the bioE _Max = 1-(1/n); see In the present study, we observed a high degree of heterozygosity in Indian field populations with low inbreeding coefficients indicating unrestricted gene flow. The observed high degree of heterozygosity (>80%) in the field population is not unusual considering the number of haplotypes present in the population. The heterozygosity is expected to increase with the increase in the number of haplotype alleles. With the presence of 13 haplotype alleles, theoretically, we expect maximum heterozygosity up to 0.9 when alleles are equally frequent  in a laboratory colony morphologically characterized as ‘type form’. Similarly, HG-M (H8 and H14) was absent in a population , characterized morphologically as ‘var. mysorensis’. Inconsistency in the association of haplotype with biological form was also noted in several laboratory strains, originating from India and Pakistan and being maintained for long (more than 30 years). Interestingly, all such laboratory colonies had H8 haplotype (designated for mysorensis) irrespective of their biological forms  based on the AsteObp1 intron-1 sequence indicate lack of genetic subdivision. However, a single marker is not sufficient to draw any conclusion on population structure; but the use of this AsteObp1 marker for the identification of biological forms or putative sibling species may lead to serious consequences.In this study, we also found low inbreeding coefficients (close to zero) in both, laboratory as well as field populations, based on the AsteObp1 intron-1 is not a suitable marker for the identification of biological forms or putative species but does not rule out the presence of putative sibling species or genetic constraints in gene flow between the forms. Previous genetic analyses have shown conflicting reports on gene flow between different biological forms occupying different ecological niches. A population genetic analysis on Indian An. stephensi populations using microsatellite markers indicated high genetic differentiation (FST) and negligible gene flow between the three variants [An. stephensi  have been reported in Iran based on mitochondrial genes cytochrome oxidase 1 and cytochrome oxidase 2 (COI and COII) [An. stephensi have been found to be different from all other populations, viz., India, Pakistan, Djibouti, Ethiopia and derived colony strains based on COI sequence [An. stephensi from Saudi Arabia [An. pseudopictus and An. hyrcanus [The present study provides a cautionary note that the variants . Howevernd COII) , 33. Irrsequence , 38. Howi Arabia  is no dii Arabia , 39, 40,i Arabia . Though hyrcanus . So far,hyrcanus , 31. TheAsteObp1 as a marker to identify biological forms of An. stephensi but by no means is indented to study the population structure or evolutionary aspects, which remains to be studied using multiple markers. Therefore, there is an urgent need to perform a robust population genetic analysis using multiple molecular markers on morphologically characterized individuals to establish genetic diversity, population structure, and extent of gene flow between different biological forms. Next generation sequencing, on the other hand, may not only provide genetic differentiation among different biological forms; but can also reveal the underlying genetic mechanisms of ecological adaptation of different biological forms.The objective of this study was to evaluate the utility of AsteObp1 intron-1 is not a suitable marker for the identification of biological forms, at least in Indian populations. No specific haplotype was found associated with biological forms. The presence of a high degree of heterozygosity and low inbreeding coefficients in Indian populations refutes the probable existence of sibling species in An. stephensi based on AsteObp1 marker, at least in India.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableValues above diagonal are pairwise differences between populations (PiXY), diagonal elements are pairwise differences within population (PiX) and below diagonal are corrected pairwise difference (PiXY-(PiX+PiY)/2).(DOCX)Click here for additional data file.S1 FigThe numbers shown above diagonal represent base differences and below diagonal represent genetic distances.(PDF)Click here for additional data file.S2 FigAn. gambiae was taken as outgroup.The value on each node represent bootstrap value. The haplotypes labelled with red colour font are markers assigned for the identification of haplotypes by earlier workers. The corresponding intron of (PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.


Soybean has a recognized narrow genetic base that often makes it difficult to visualize available genetic and phenotypic variability and identify superior genotypes during the selection process. However, the phenotypic expression of soybean plants is highly affected by photoperiod and the cultivation of a given variety is performed in the latitude range that presents ideal conditions for its development based on its relative maturity group (RMG) for the optimization of the phenotypic expression of its genotype. Based on the above, this study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group during the population selection process with the aim of optimizing the phenotypic performance of these selected genotypes. For this purpose, three biparental populations were synthesized, one with a wide genetic variability for the RMG character obtained from the hybridization between genitors of maturity groups RMG 5  × RMG 9.4  and two populations with a narrow variability obtained between genitors RMG 7.3  × RMG 9.4 and RMG 5.3 × RMG 6.7, respectively. Criteria for comparing the developed ANN architecture with Fisher’s linear and Anderson’s quadratic parametric discriminant methodologies were applied to the data for the discrimination and classification of the genotypes. ANN showed an apparent error rate of less than 8.16% as well as a low influence of environmental factors, correctly classifying the genotypes in the populations even in cases of reduced genetic variability such as in the RMG 5 × RMG 6 population. In contrast, the discriminant functions were inefficient in correctly classifying the genotypes in the populations with genealogical similarity (RMG 5 × RMG 6) and wide genetic variability, with an error rate of more than 50%. Based on the results of this study, ANN can be used for the discrimination of genotypes in the initial generations of selection in breeding programs for the development of high performance cultivars for wide and reduced photoperiod amplitudes, even with fewer selection environments, more efficiently, and with fewer time and resources applied. As a result of similarity between the parents, ANN can correctly classify genotypes from populations with a narrow genetic base, in addition to pure lines and genotypes with a high degree of inbreeding. Soybean is the most important oilseed in the world and whose genetic improvement plays a significant role in the continuous growth of the crop. Soybean is highly sensitive to the photoperiod and considered a short-day species. The day length at the location of cultivation directly affects plant growth. In addition, the photoperiod influences the change from vegetative to reproductive stage, and consequently, the flowering, total cycle, and grain yield , consequE. Thus far, 11 major loci (E1–E11 and J) involved in the control of these characteristics have been identified in soybean  have a high capacity in predicting, recognizing, discriminating, and classifying patterns. Moreover, different from parametric statistical approaches, they capture the complex characteristics of a dataset in addition to being slightly susceptible to noise and outliers and being suitable for nonlinearly separable problems common to agricultural experimentation . CurrentBased on the above, the objective of the present study was to evaluate the efficiency of ANNs as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group during the population selection process, with the aim of optimization of the phenotypic performance of these selected genotypes. The possible application of this analysis is expected to obtain high performance cultivars for a wide range of photoperiods in soybean growing regions in Brazil.Three soybean populations with different ranges of genetic variability were synthesized and evaluated for their RMG characteristics. Hybridizations were performed at the Department of Agricultural Production Sciences, Faculty of Agricultural Sciences, São Paulo University - UNESP/FCAV located at latitude 21°14′58″S and longitude 48°17′08″W, in Jaboticabal, São Paulo, Brazil. The population with a wide genetic variability for the RMG character was obtained from the hybridization between the genitors BMX Veloz  × BRS 278 RR , called the Brazil population. The Northern and Southern populations, characterized by the most restricted variability for the character group of relative maturity, were established from crosses between the cultivars BRS 245 RR  and BRS 278 RR (RMG 9.4) and between cultivars BMX Energia (RMG 5.3) and BMX Potência (RMG 6.7), respectively. The controls of each population were their respective genitors, in addition to the cultivars TMG 7262 RR (RMG 6.2), TMG 1174 RR (RMG 7.4), and TMG 1179 RR (RMG 7.9).Xanthomonas axonopodis, Cercospora sojina, and Diaporthe phaseolorum, were susceptible to common soybean mosaic, cyst nematode, Meloidogyne incognita, and Meloidogyne javanica, and tolerant to stem necrosis virus (CpMMV). BMX Veloz was of medium size, had an average cycle of 120 days, indeterminate growth habit, light brown pubescence color, purple flower color, RMG of 5.0, was resistant to D. phaseolorum and Phytophthora sojae (RPS1k gene), and was moderately resistant to C. sojina and X. axonopodis. BMX Energia was of medium size, had an indeterminate growth habit, gray pubescence color, purple flower color, RMG of 5.3, resistance to D. phaseolorum, and moderately resistant to C. sojina and X. axonopodis. BMX Potência had a height of 90 to 100 cm, indeterminate growth habit, gray pubescence color, white flower color, RMG of 6.7, was susceptible to the nematode M. incognita and moderately resistant to the nematode Meloidogyne javanica, and resistant to lodging, D. phaseolorum, C. sojina, and Phytophthora sojae. BRS 245 RR had a height of 70 to 94 cm depending on the planting region, determined growth habit, brown pubescence color, white flower color, RMG of 7.3, was resistant to D. phaseolorum, C. sojina, and common soybean mosaic, tolerant to stem necrosis virus, moderately susceptible to powdery mildew, and susceptible to cyst nematodes, M. incognita, and M. javanica.The parent cultivar BRS 278 RR was approximately 73 cm tall, had an average cycle of 115–127 days, determined growth habit, brown pubescence, purple flower color, RMG of 9.4, and resistance to lodging, All genitor cultivars were transgenic, with resistance to the herbicide glyphosate.To perform the analyses, the controls were considered as distinct populations and there were 11 populations in total .3 to F6 for the Brazil population and F4 to F7 for the Northern and Southern populations. The three populations were conducted in the three agricultural years with a variable number of progenies, in addition to four commercial cultivars as controls within each population. The Brazil population consisted of 220 progenies in 2017/2018, 252 in 2018/2019 and 252 in 2019/2020. The Southern population consisted of 120 progenies in 2017/2018, 168 in 2018/2019 and 168 in 2019/2020. The Northern population consisted of 60 progenies in 2017/2018, 60 in 2018/2019 and 104 in 2019/2020. The agronomic characteristics evaluated were: number of days to flowering (NDF), number of days to maturity (NDM), total crop cycle (CYCLE), first pod insertion height (AIV), plant height at maturity (APM), lodging (Ac), agronomic value (VA), and grain yield (PG). Ac was evaluated based on a visual rating scale ranging from 1  to 5 , and VA was evaluated with a visual grading scale ranging from 1 (plants with poor agronomic characteristics) to 5 (plants with excellent agronomic characteristics). All experiments (three populations in three crop years) were conducted in Federer’s augmented block design  to April (autumn), allowing the cultivation of soybean cultivars with a cycle of up to 150 days. Field evaluation data were obtained from years 2017/2018, 2018/2019, and 2019/2020, corresponding to the filial generations from Fk design , with raFisher and Anderson discriminant analyses are linear combinations of the observed characteristics that present the best discrimination power among all possible linear combinations of the same characteristics . In thesk-fold cross-validation, which divided the dataset into k partitions, where k−1 were the data for training and k was the set used for model testing. Thus, k-models were created, where the data for training and testing were changed for each iteration  as a criterion.After several experiments on the best architecture for the multilayer perceptron network type, neural network architecture 12–64–128-11 built in Python 3.6 using Keras as the frontend, TensorFlow 2.3.0 as the backend, and Scikit-learn 0.22.2 was adopted. It was necessary to convert the categorical variable (year) to one-hot representation. Thus, the number of input neurons was 12, corresponding to the POP, NDF, NDM, CYCLE, AIV, APM, Ac, VA, and PG populations and three agricultural years 2017/2018, 2018/2019, and 2019/2020. In the output layer, the number of neurons corresponded to the number of defined classes, that is, 11, and the hidden layers had 64 and 128 neurons, respectively. The dataset had 7,287 examples. The algorithm used to train ANN was stochastic backpropagation (stochastic gradient descent). Adam optimizer was used. The number of training cycles was set as 600 epochs to prevent training from becoming excessive, which could lead to loss of generalization power. The ANN architecture evaluation was based on the evaluation metrics for classifiers that are mostly derived from the confusion matrix generated by the Scikit-learn package. The matrix was obtained from the test data and used to analyze the quality of predictions of the models. The other metrics used were accuracy (hit rates for positive and negative examples), precision (hit rate for positive examples), recall (coverage of correct positive examples), and F1-score . Two procedures were implemented to validate the ANN architecture. The first was the hold-out procedure, which divided the dataset into two random bases: one for the training set with 80% of the data, and the other for testing with 20% of the data. The second procedure was the teration . The fink-fold approaches. According to Fisher’s analysis, of the total of 1,517 classifications, a total of 889 were considered as erroneous classifications, which makes an AER of 58.6%. For Anderson’s methodology, in turn, for a total of 1,517 classifications, the number of erroneous classifications was 769, leading to an error rate of 50.59%, which is lower than the AER observed in Fisher. Despite this, both methods presented AER above 50%. In turn, it was observed that the validations of the ANN analyses indicated that of the 1,458 classifications performed, only 119 were considered erroneous for the hold-out approach, leading to an AER of only 8.16%, while, of the 729 classifications for the k-fold approach, only 41 were considered erroneous, constituting an even lower AER of only 5.62%.k-fold cross-validation to the hold-out procedure can also be observed from k-fold and hold-out ANN approaches. k-Fold showed accuracy of 93.36%, precision of 93.49%, recall of 93.23%, and F1-score of 93.36%, which was greater than the corresponding hold-out scores by at least 1.30% for all metrics. In turn, the highest loss value was presented by the hold-out model (34.10%) when compared to the k-fold model (26.39%), in a difference of 7.71% between the two approaches.The superiority of the k-fold cross-validation, are presented in Considering the best parametric and nonparametric approaches, the confusion matrices generated from the validation dataset based on the classification by the Anderson’s discriminant analysis and the k-fold cross-validation approach. The reciprocal case of the genotypes belonging to the Brazil population being allocated to the southern population was also highlighted by the presence of errors. In addition to these pairs of populations, classification errors occurred between the Brazil and southern; GS1 and southern; southern, TGM6 and the reciprocal; and TGM6, Brazil and the reciprocal populations. The GBN1 population did not receive erroneous allocations from any other population and its genotypes were not classified as belonging to other populations.From k-fold cross-validation, differences in the classifications by the two procedures were observed. In the latter, in addition to the GBN1 population, the GS2, GN2, and TGM8 populations did not receive any incorrect genotypes. Moreover, the genotypes of the Northern, GS2, TGM7, and TGM8 populations were correctly classified.In addition to the reduction in Anderson’s errors for the k-fold approach. From the increase in the bars of the graph that indicate the number of incorrect classifications, an increase in classification errors can be observed from the year 2018 to 2019 and 2019 to 2020, reaching a total increase of approximately 40%, considering the interval between the first and last years of assessment.k-fold cross-validation is considered an accurate and suitable approach for small datasets , precision (93.49%), recall (93.23%), and f1-score (93.36%) than the latter. The higher loss presented by the hold-out approach (34.10%) compared to k-fold (26.39%) confirms the latter’s higher classification efficiency. In terms of percentage, the less efficient ANN validation approach correctly classified 40% more genotypes than the more efficient discriminant function.The failure to discriminate between genotypes and classify them into their populations correctly is caused by four main factors. First, the populations can be very similar in their origin and genealogy. Second, the number of evaluated variables may be insufficient, in addition to having low discriminatory quality, as a third factor. The fourth cause is the use of an inadequate statistical approach . In quaddatasets . The evaThe proximity of the origin and genealogy of genotypes belonging to different populations largely contributes to the inefficiency of their discrimination, as also observed by The Brazil population presents a high level of genetic variability in the relative maturity, resulting in the presence of many allelic combinations of genes that control the timing of flowering and maturity . This waWhen considering the genitors of the populations and the other three witnesses, it was noted that in the Northern population, fewer misclassifications occurred compared with that in the other populations; however, the errors of classification, in general, were always large, both in absolute values and percentages. There was a tendency for no differentiation of the genitor with the derived population, as in the case of GS1, Brazil and Southern populations and GN2, Brazil and Northern populations. Witnesses, even being pure lines, were erroneously classified as belonging to different populations, mainly for plants of the Southern population in the TGM6 witness. Thus, the statistical technique is inefficient in classifying pure lineage genotypes or those with a narrow genetic base and is partially effective even in populations with a broad genetic base, such as the Brazil population.The only case in which no error occurred using Anderson’s methodology was the GBN1 population, corresponding to the BRS 278 RR cultivar with RMG 9.4, which did not receive incorrect genotypes from any other population. The RMG of this population, considered late for the evaluation region, induces elongation of the vegetative stage, resulting in extremely tall plants with low grain production, contributing to an atypical phenotype of this cultivar in Jaboticabal, the ideal RMGs for which are between 6 and 8.k-fold cross-validation, the wide variability of the Brazil population also led to misclassifications of the genotypes from the Southern population. However, the number of errors by this method was 11 compared to 96 errors by Anderson’s discriminant analysis for the above pair of populations. The other k-fold misclassifications comprising the 5.62% errors occurred between Brazil and southern; GS1 and Southern; southern, TGM6 and the reciprocal; and TGM6, Brazil and the reciprocal populations. However, the number of errors was always equal to or less than 4. The non-occurrence of misclassification errors in the GBN1, GS2, GN2, and TGM8 populations as well as the absence of incorrect allocations in the Northern, GBN1, GS2, TGM7, and TGM8 populations demonstrate that, even with a relationship between the genitor and its derived population, ANN is very efficient for broad-based populations. Furthermore, the ANN correctly classifies the narrow-based populations and pure lines, learning to differentiate genotypes. Although the classification scheme of the ANN was affected by the genotype, it was to a lesser extent than Anderson’s discriminant method.Based on the confusion matrix obtained by the 6 and F7 generations.The mechanical evaluations based on the traditional characteristics in the three considered years at the same location did not impair the performance of the ANN; however, it shows the poor influence of noise and data loss on the quality of the information obtained . Further5 or F6 were evaluated in the target region according to their photoperiod. The dataset also leads to the conclusion that lineage development in an intermediate region (RMG 7) is suitable for advancing the lineages for extreme photoperiods, as for RMGs 5 and 9.The built ANN identified the genotypes that were incorrectly classified by their corresponding numbers in the data table. From this information, it was possible to identify the generation of self-fertilization of each of these genotypes based on the year of evaluation. As the generations advance in homozygosity and selections are made within the populations, mainly based on productivity, they become uniform and similar to each other because they are generally intended for cultivation where they are located, as also identified by In a conventional soybean breeding program, many resources are used to obtain a successful cultivar. Time and money are invested in researching, testing, selecting, and developing promising genotypes. After the selection stage, it takes at least 5 years for the implementation of tests in the intended cultivation regions for the commercialization of the cultivar and it can be extended up to 10 years until the launch of the final product  . This stThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.LA: planning, implementation, and experimental conduction, field evaluation, analysis and data collection, and text writing. GM: analysis and data collection, text writing, and correction and guidance. BV: implementation and experimental conduction, and analysis and data collection. AS: implementation and experimental conduction, field evaluation, and analysis and data collection. AM: implementation and experimental conduction, field evaluation, and analysis and data collection. SU-T: planning, analysis and data collection, text writing; correction and guidance, and work leader. All authors contributed to the article and approved the submitted version.This work was supported by the Pro-rectory of Postgraduation (PROPG), São Paulo State University - UNESP/FCAV Jaboticabal.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.