Carettacaretta). In this study, we performed a multidisciplinary investigation into levels of hybridization in sea turtles and their reproductive output in the Abrolhos beaches. Genetic data from mitochondrial DNA (mtDNA) and six autosomal markers showed that there are first-generation hybrid females nesting in Abrolhos, resulting from crossings between hawksbill males (Eretmochelysimbricata) and loggerhead females, and backcrossed hatchlings from both parental species. The type and extent of hybridization were characterized using genomic data obtained with the 3RAD method, which confirmed backcrossing between F1 hybrids and loggerhead turtles. The reproductive output data of Abrolhos nests suggests a disadvantage of hybrids when compared to loggerheads. For the first time, we have shown the association between hybridization and low reproductive success, which may represent a threat to sea turtle conservation.Hybridization between sea turtle species occurs with particularly high frequency at two adjacent nesting areas in northeastern Brazil. To understand the outcomes of hybridization and their consequences for sea turtle conservation, we need to evaluate the extent of hybridization occurrence and possible deleterious effects in the hybrid progeny. Thus, we investigated the hypothesis of the existence of a new hybrid spot offshore of Brazil’s northeastern coast. The Abrolhos Archipelago is surrounded by the largest and richest coral reefs in the South Atlantic and is known to be a nesting site for loggerhead turtles ( However, predicting the outcomes of crossings between genetically differentiated species or populations is extremely hard, as it depends on various factors that shape each particular case, such as the divergence between the species involved in hybridization, the extent of the process and the fitness impact on hybrids, which is determined by complex genetic mechanisms2. A reduction in hybrid fitness—also known as outbreeding depression—happens when hybridization leads to lower reproductive success in hybrids relative to the parental species. When significant outbreeding depression is concurrent with continual hybridization events, this can lead to a decrease in the population growth rate of the rarer species to a level below that required for replacement, a phenomenon known as demographic swamping3. In extreme cases, the complete loss of reproductive potential for one of the species may occur, leading to localized extinction events4, which have the potential to impact neighboring populations in a cascading fashion. Alternatively, when hybrid fitness decrease is not significant, multiple generations of hybrids can occur, resulting in the loss of genetically pure individuals for one of the species while preserving the genetic material from that same species in introgressed individuals, which is known as genetic swamping2.Hybridization may play an important role in the evolutionary history of species1, it becomes a matter of concern when it is a consequence of anthropogenic actions and/or involves endangered species5. Human-mediated environmental change, including habitat modification and exotic species introductions, may favor interspecific crossings and lead to demographic or genetic swamping, threatening species integrity, especially in small populations or rare species6. Considering the large-scale declines in many animal populations, resulting in the increasing scarcity of conspecific mates, the early recognition of hybrids should be a priority in the conservation management of small populations7.Although hybridization is generally considered a natural phenomenon happening in about 25% of plants and 10% of animals8. Occasional hybridization has been detected between very divergent species, such as between Carettini and Chelonini turtles, which have 63 million years of separation9. However, hybridization has been shown to occur more frequently in two northeastern spots along the Brazilian coast between sea turtle species with separation times of up to 29 million years, involving mainly three Carettini species: hawksbill (Eretmochelysimbricata), loggerhead (Carettacaretta) and olive ridley (Lepidochelysolivacea)10. In both areas, loggerhead turtles—which have an increasing population trend in the South Atlantic—hybridize with either hawksbill (in northern Bahia state)11 or olive ridley turtles (in Sergipe state)12.According to the World Conservation Union (IUCN) Red List of Threatened Species, six out of the seven species of sea turtles are considered globally as either endangered, critically endangered or vulnerable. Sea turtles comprise a group with deep divergence times, reaching up to 110 million years13. Backcrossed hatchlings have also been reported, confirming that F1 hybrids are fertile and introgression may be possible13. Despite the confirmed viability of hybrid F1 females, it has been shown that their nests in northern Bahia have a lower success rate of hatching emergence, whilst other reproductive output parameters, such as hatchling production per clutch, clutch frequency and breeding frequency remained similar to the parental species14. Furthermore, no negative effect of hybridization was found on hatchling viability15. Nonetheless, little is known about sea turtle hybrids in other life stages in terms of survivorship, migratory behavior, feeding preferences, reproductive fitness and mate choice, such that the temporal and spatial extent of hybridization consequences for sea turtle populations remains unclear13.Hybrid females found nesting in both Brazilian populations are first generation (F1) hybrids, which suggests that the hybridization process is recent and probably associated with environmental changes and global population declines in sea turtles17, is located 70 km off shore from Bahia state and 600 km south of Praia do Forte, where loggerhead and hawksbill hybrids reach up to 42% of the identified hawksbill nesting population11. Besides being an important feeding area for hawksbill and green turtles (Cheloniamydas), respectively attracted by the richness of invertebrates and seagrasses20, Abrolhos is also a nesting area for loggerhead turtles, which lay their eggs on the short sandy beaches of Redonda and Santa Bárbara islands21. Recent nesting monitoring on the islands has identified a mean of 34 nests per reproductive season between the years of 2015 and 201822. In 2015, hawksbill turtles were also reported to be nesting in the archipelago, but the mixed morphology of the nesting females suggested that they could in fact be hybrids22. However, no genetic analysis has ever been carried out to test the hypothesis of the existence of hybrids in Abrolhos. Furthermore, no hybrids were detected in the only population genetics study of hawksbill juveniles (N = 65) hand-captured by divers in the Abrolhos Archipelago23.Abrolhos Archipelago, which supports the largest and richest coral reefs in the South Atlantic and the world's largest rhodolith beds22 to 46.8% in 2016/2017 in Abrolhos20. The annual average HS of loggerhead turtles in Espírito Santo, Bahia and Rio de Janeiro states in Brazil was 79.9%24, 73.1%25 and 76.74%26, respectively, which demonstrated that the HS in Abrolhos is relatively low. This could be related to the features of Abrolhos’ beaches, which are very short, covered by boulders and grasses, frequently disrupted by tides, and composed of a mixture of carbonate debris and rock fragments27. The difficulty finding appropriate nesting sites in Abrolhos is confirmed by the low success rate of nesting attempts, with 40% of sea turtles emerging from the ocean onto the beach during the reproductive season of 2017/2018 returning to the ocean without nesting20. Alternatively, other biotic factors associated with the sea turtle population of Abrolhos may also affect the HS, such as the still unverified presence of interspecific hybridization28.The proportion of eggs that produced live hatchlings, referred to as hatching success (HS), ranged from 25.4% in 2015/2016In this paper, we tested the hypothesis of the existence of sea turtle hybrids in the Abrolhos Archipelago through different levels of genetic analysis. We used a combination of mtDNA and six species-diagnostic autosomal loci to identify hybrids, backcrosses and pure parental species. To confirm the power of our molecular assay in establishing the generation and type of crosses of later hybrids, we used genomic data obtained through the 3RAD (Restriction site Associated DNA) method. We also assessed the reproductive output of the Abrolhos population and tested whether interspecific hybridization is associated with low hatching success in Abrolhos. These analyses provide new insights into the conservation of sea turtle species, particularly the Abrolhos population.30. The archipelago is part of the Abrolhos National Marine Park, which is responsible for the conservation of its unique ecosystem.This study was performed in the Abrolhos Archipelago (17° 20′–18° 10′ S and 38° 35′–39° 20′ W), a set of five small volcanic islands situated on the Abrolhos Bank, 70 km offshore from Bahia state . Nesting females encountered were double tagged with Inconel metal tags on the front flippers and their curved carapace length (CCL) and curved carapace width (CCW) measured31. During the 2015/2016 reproductive season, the females (N = 6) found during oviposition were sampled through a small piece of skin taken from either the anterior flipper or the neck region using a sterilized scalpel. These samples were stored in EtOH 70% at − 20 °C and deposited in the Taxonomic Collections Center of the Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.Sampling and data collection was performed by the Abrolhos National Marine Park team applying the methodology established by Projeto TAMARPatrols during the morning were conducted daily on the beaches of Santa Bárbara and every 2 days in Redonda. Based on the tracks and nesting signs, nests were identified by carefully probing with a stick and then digging by hand to locate the eggs. All nests were marked with a unique number and monitored until hatchling emergence, indicative of a successful nesting event. Once hatchling tracks emerging from the nest were detected, the hatched nests were excavated, the species of hatchlings were morphologically identified whenever possible, and the number of dead hatchlings and unhatched eggs was quantified. The hatched eggshells were counted to estimate the number of live hatchlings produced in each nest. The date of each nesting event was recorded to verify the temporal nesting distribution in the Abrolhos Archipelago. During the 2017/2018 reproductive season, samples were taken from between one and three dead hatchlings from each of 20 distinct nests by collecting anterior flipper fragments as described above.32. The hawksbill turtle has a medium-sized head, strong horny beak, depressed oval carapace, shell-imbricated scutes, and four pairs of lateral scutes on the carapace with the anterior pair not touching the pre-central scute. First generation hybrids usually exhibit a different pattern of scutes on the shell, and have a medium-sized head, pointy beak and depressed oval carapace, similar to hawksbill turtles  was calculated as the period between oviposition and hatchling emergence. The clutch size (CS) was determined by nest excavation after emergence, counting the total number of unhatched eggs, dead hatchlings and the potential number of live hatchlings  per nest. The hatching success (HS) was calculated through the proportion of eggs that produced live hatchlings reaching the beach surface compared to the total number of laid eggs. The female body size was calculated using the first measurement of CCL and CCW recorded for each animal (N = 18). We compared these parameters between two different groups: (1) loggerhead turtles and (2) hybrids, including individuals for which species assignment was confirmed either by genetic analysis (N = 32) or based on the morphology of females encountered during nesting (N = 15). To test for differences between groups, statistical analysis was performed with nonparametric t test (Mann–Whitney U test) implemented in GraphPad Prism 5.0 software.33 or the DNeasy Blood and Tissue kit (Qiagen). In order to determine the type of hybrid, we used a multilocus sequencing approach developed by Arantes et al.13, in which 14 loci presenting species-specific diagnostic alleles/haplotypes  were selected from double-digest RADseq (ddRADseq) data produced the same way for both loggerhead and hawksbill turtles. Since the primers developed were based on data generated for both species, the reproducibility was very high. For the present work, we selected the six nuclear DNA (nDNA) loci  presenting the highest species assignment accuracy in the previous study13. Based on other cases of hybridization in Brazil, we expected to find hybrids of up to two generations (F1 and backcrossing). Considering the 50% chance of detecting homospecific or heterospecific loci in a backcrossed individual, we estimated an error rate of assignment of a hybrid individual with single nucleotide polymorphism (SNP) data from these six markers of 1.56%. Posterior genomic analysis confirmed the hybrid categorization of some individuals. We conducted polymerase chain reactions (PCRs) as described in Arantes et al.13. Amplicons were sequenced on an ABI 3130xl DNA sequencer (Applied Biosystems).We extracted genomic DNA from the tissue samples with either a modified phenol–chloroform protocol34. We included in the alignment for each marker all known loggerhead and hawksbill haplotypes previously determined by Arantes et al.13. Gametic phases for nDNA markers were resolved with the PHASE algorithm35 and the haplotypes inference was done with the DnaSP v5 program36.High-quality consensus sequences were used in the alignments performed with the ClustalW algorithm in the MEGA 7 software37, which calculates the proportion of allele copies coming from parental reference sets using a Bayesian algorithm39. As references for parental species, we included five loggerhead and five hawksbill turtles from different populations along the Brazilian coast analyzed by Arantes et al.13. We ran HI estimations using 50,000 Markov Chain Monte Carlo (MCMC) iterations after a 10,000 MCMC burn-in period.Diagnostic sites (SNPs) were used to test the hybrid index (HI) in the R package gghybrid40. The PCR included 200 μM dNTP, 0.3 units (U) Platinum Taq DNA polymerase (Invitrogen by Life Technologies), 1.5 mM of MgCl2, 0.5 mg/mL of Bovine Serum Albumin (BSA), 0.5 μM forward and reverse primers and 15 ng genomic DNA in 1X reaction buffer in a final volume of 10 μl. PCR cycling conditions were performed with one initial denaturation cycle of 95 °C for 5 min, 35 cycles of denaturation at 95 °C for 30 s, annealing at 50 °C for 30 s, extension at 72 °C for 1 min, and a final extension at 72 °C for 7 min. mtDNA haplotypes were identified running a BLAST search as implemented in the NCBI database (https://www.ncbi.nlm.nih.gov). Haplotype identification was also checked against the database of The Archie Carr Center for Sea Turtle Research (https://accstr.ufl.edu/resources/mtdna-sequences) and haplotypes were named as assigned by Shamblin et al41.We also analyzed the control region of mtDNA using the primers LCM 15,382 and H95042 to analyze hatchlings from six different nests collected at Abrolhos. Briefly, we digested 100 ng of genomic DNA with the enzymes MseI, EcoRI and CviQI (New England Biolabs) at 10 U/μL and the specific combination of adapters for each sample for 1 h at 37 °C. We proceeded with the ligation of adapters containing in-line barcodes on both P5 and P7 adapters42, immediately after the digestion. Barcoded samples were equimolarly pooled and cleaned with 0.8X CleanPCR magnetic beads (GC biotech). Subsequently, we performed the size selection of fragments between 350 and 450 bp with Blue Pippin using a 1.5% cassette and the R2 marker (Sage Science). This range was chosen based on an in silico digestion analysis of the ~ 2.2 Gb green sea turtle draft genome currently available (GenBank accession number GCF_000344595.1). Using a dedicated python script, we have estimated ~ 21,300 loci based on the three enzymes and size range used to build our 3RAD libraries.We used the 3RAD protocol as described by Bayona-Vásquez et al.43, followed by a ten-cycle indexing PCR, in which we used a P5 short primer and P7 indexing primer to complete the library construction. The final library was characterized with qPCR using the KAPA Library Quantification Kit (Kapa Biosystems) and checked with the Agilent 2100 Bioanalyzer High Sensitivity DNA. The fragments were sequenced using 150 bp paired end reads in one partial lane of the HiSeq 4000 platform (Illumina) with the TruSeq 300-cycle Kit, aiming at a minimum of 20 × coverage per individual sequenced.The libraries were submitted to a single-cycle PCR to add the Tru5-8N primer44 and Multiqc45. The software FLEXBAR46 was used to demultiplex the P5 in-line barcodes. To remove PCR replicates, we used the python script Filter_PCR_duplicates.py, which recognizes identical combinations of i5 sequence tags and the first 100 bp of each read pair sequence and keeps only a single representative copy of the pair in the output. We used the software PEAR47 to merge reads pairs with overlapping regions of at least 30 bp. All merged reads less than 240 bp were considered as short fragments (i.e. out of the size selection range) and were removed from the subsequent analysis. The unassembled reads were kept in the analysis as paired-end reads, filtered for quality (Q > 30) and trimmed to a maximum length of 130 bp using Trimmomatic48. The reverse read presented low quality in the first 6 bp, which were therefore removed from the sequences for subsequent analysis. This probably occurred due to low sequence diversity at the EcoRI recognition site, which was identical for virtually all reads. We used the Check_Restriction_Site.py script to select only read pairs digested by MseI, filtering out any fragment digested by CviQI. Finally, read pairs containing an internal complete restriction site of MseI, EcoRI or CviQI were removed using the Filter_Reads.py script. The number of reads filtered out in each step is shown in Supplementary Figure Illumina reads were demultiplexed based on P7 indexes and the raw sequence quality was checked with Fastqc49. Reads were mapped using Bowtie2 software50 with default parameters and the flags "-no-mixed" and "-no-discordant" to ensure that only paired reads aligned to the same locus would be present in the SAM files. Mapped reads were then analyzed with the Stacks reference-based pipeline51. The gstacks.pl module was used to call variant sites within the population for each locus and genotype each individual at each identified SNP. The populations.pl module was used to filter population parameters, allowing the inclusion of a locus in the final data set only if the locus was genotyped in at least 40% of individuals within a population and was present in all 3 populations.3RAD sequences were analyzed using the green turtle draft genome as a reference52, we used the Create_Haplotype_Structure.py script to code the haplotypes as multi-allelic loci for the Bayesian clustering analysis performed in the software STRUCTURE53. The script uses Stacks output files as well as the alignments in SAM format to filter the set of loci, based on a given read minimum coverage (– readCOV), a minimum number of populations covering a locus (– popCOV), as well as a percentage of individuals within a population to cover a locus (– intraCOV). Additionally, reads with undefined positions were filtered out. We used the following parameters: readCOV = 6, popCOV = 3, and intraCOV = 0.7.Since haplotypes increase the power to detect population structure regarding SNPs54.The haplotype data was used to test the assignment of individuals to populations assuming the admixture model in STRUCTURE. We included individuals of loggerhead (N = 5) and hawksbill (N = 5) turtles as a control for species-specific haplotypes and hybrid diagnostics. Using independent allele frequencies, ten independent runs for each K value (from K = 1 to K = 5) were performed with 500,000 MCMC repeats after a 100,000 burn-in period. We summarized the replicates and visualized the STRUCTURE-estimated membership coefficients using CLUMPAK55. The analysis was done using a burn-in period of 10,000 followed by 50,000 MCMC iterations with Jeffrey option and no priors. We restricted the dataset to the first 300 loci, due to the NewHybrids’ limited processing capacity. We also tested NewHybrids with a different set of 300 loci that were selected randomly to ensure the consistency of the results. The R package HybridDetective was used to plot the NewHybrids analysis56.The parental species and the hybrid categories  of each hybrid individual were determined with the program NewHybrids v. 1.1 Beta3A total of 47 nests were analyzed in this study and classified into two groups: loggerhead turtles and hybrids. For 28 nests, 17 different females were encountered while nesting  and were therefore classified based on the female’s morphology. The remaining 19 nests, plus one nest with assigned mother, were tested genetically with a combination of mtDNA control region and autosomal multilocus analyses. Six of the 19 remaining nests were also analyzed using 3RAD genomic data. A representation of all nests analyzed in this study and the types of species and hybrid identification performed is shown in Supplementary Figure Eleven females (found in 20 nests) were classified as loggerhead turtles and six females (found in eight nests) as hybrids. Six females were also tested molecularly with the multilocus approach and in all cases the morphological classification was confirmed (five loggerhead turtles and one hybrid). For the 19 nests with no assigned mother, female classification was performed based on hatchling genetic analysis (between one and three tested per nest). Eleven nests were classified as loggerheads and eight nests as hybrids. The classification of the mothers as hybrids was based on the identification of hatchlings as backcrosses with either loggerhead turtles or hawksbill turtles (see below for more details). Nests were assumed to be properly represented by one hatchling because our main goal was to distinguish nests from hybrid versus pure loggerhead females. Once a purely loggerhead hatchling is found, the mother can be assumed to be pure and once a second generation hybrid is found, the mother can be assumed to be a hybrid. First generation hybrids would also indicate a pure parental species mother, but these were not found among the hatchlings .41. It is therefore not possible to distinguish the Abrolhos population from other Brazilian rookeries based on the mtDNA control region.The analysis of the mtDNA control region indicated that all analyzed sea turtles from Abrolhos . The genetic analyses confirmed the morphology of only 12 individuals (five loggerheads and seven putative hybrids), while four hatchlings morphologically identified as putative hybrids were in fact genetically classified as pure loggerhead turtles.3RAD data was generated for ten control individuals (five loggerhead and five hawksbill turtles) as well as for 14 hatchlings from six different nests  and CCL measures (Mann–Whitney U test p = 0.2548), despite the fact that hybrids appear to be slightly larger and longer than pure loggerhead turtles 13.Hybridization between sea turtles has been previously reported at two nesting areas in Brazil with an extremely high frequency, reaching up to 42% of the hawksbill population of Bahia13, reinforcing the gender bias in crosses between loggerhead and hawksbill turtles.Based on the mtDNA haplotype species assignment, our results indicate that the nesting hybrid female analyzed is the progeny of a female loggerhead and a male hawksbill. The same pattern was reported for the F1 hybrids from Bahia13 showed that F1 hybrids can reproduce with males of both parental species, indicating that the introgression process may be established in both directions, i.e., hybridization may potentially result in gene flow towards both parental species. It is well known that the Abrolhos Archipelago is a breeding area for loggerhead turtles, as well as a feeding, resting and cleaning symbiosis area for hawksbill turtles, which are attracted by the shallow fringing reefs22. Thus, opportunities for interspecific mating with both species are possible in Abrolhos—as our data indicate—assuming that nesting females are mating locally. Our results also suggest that hybrid females are not selective of parental species in their mate choice.Backcrossed hatchlings identified via a low throughput multilocus approach42 confirmed the introgression level of some backcrossed hatchlings using 2,405 3RAD loci. This is the first time that backcrosses with loggerhead turtles are confirmed by genome-wide sequencing analysis. Previously, only backcrosses with hawksbills had been confirmed by multilocus analyses13, while confirmation of backcrosses with loggerheads had been suggested using only one nuclear marker15.The reduced-representation genomic data obtained with a 3RAD approach57. This latter finding was reached by satellite tracking, which is a powerful tool for the understanding of sea turtle behavior and highly recommended to investigate the migratory, feeding and use of habitats of hybrids.The first hybrid crossing that generated F1 female hybrids currently nesting in Abrolhos beaches may have originated between individuals from the Abrolhos population, if philopatric behavior is also expected for hybrids. Alternatively, these nesting hybrids could originally be from other neighboring nesting sites . This is supported by the fact that nesting hybrids were recently reported (and confirmed by our investigation) in Abrolhos, and a hybrid female from Praia do Forte was shown to forage in habitats associated with coral reefs around Abrolhos21, is also an atypical behavior, since their nesting beaches are typically wide and open beaches fronted by a flat approach from the sea and backed by low dunes59. The nesting monitoring of sea turtles in Abrolhos’ beaches has been performed since 2015, with the aim of clarifying important behavioral aspects such as the natal fidelity of females.This is the first documentation of sea turtle hybrids nesting on islands. Indeed, the nesting of loggerhead turtles in the Abrolhos Archipelago, first reported in 198410. Considering the future projections of the deleterious effects of climate change and other anthropogenic pressures that may destabilize sea turtle populations60, interspecific hybridization may increase in nesting areas used by different species. Thus, if hybridization is indeed a consequence of human-induced population decline and confirmed to be detrimental, as suggested by our results, targeted conservation management strategies, such as separating the rookeries of different species, will need to be developed. Additional populations should be analyzed in order to investigate the hybridization phenomenon between sea turtle species worldwide and its consequences for species conservation.The high frequency of interspecific hybrids occurring in nesting sites in Brazil is an atypical event when compared to rookeries worldwide and is likely connected to the decreased chance of conspecific mating encounters due to the decline of sea turtle populations at the end of the twentieth century61. While we did not find significant differences in body size between hybrids and loggerheads, the previous analyses of Marcovaldi et al.57 and Soares et al.14 found that HxL hybrid turtles are larger than hawksbill and loggerhead turtles. Soares et al.14 hypothesized that either hybrids reach sexual maturity later or have faster growth rates than the parental species.The hybrids presented larger clutch sizes relative to loggerhead turtles, which is usually correlated with body size, as the volume of eggs in a clutch is constrained by the volume within the hard shell of the female26. The landscape features of Abrolhos’ beaches help to explain lower rates of reproductive success in the archipelago20. Abrolhos’ beaches consist of narrow and short stretches of coral sand with extensive rock and grass coverage , Bahia 73.1%) and Rio de Janeiro (76.74%)% and Rio65.The average HS for hybrids in Abrolhos was 27%, even lower than for loggerhead turtles. This demonstrated that the low overall reproductive success in Abrolhos is likely also related to hybridization, with a reproductive disadvantage for hybrids. This outcome is related to the greater number of unhatched eggs in the nests of F1 HxL hybrids in comparison to loggerhead turtles’ nests. However, the presence of embryos in the unhatched eggs was not checked. Future research should evaluate whether the low HS is associated with low egg fertility or embryonic mortality, two factors that can be responsible for the low success rate68, however, the underlying mechanisms have rarely been elucidated. Outbreeding depression usually occurs due to the disruption of local adaptation, the breakup of coadapted gene complexes, and/or the expression of hybrid incompatibilities2. The effect of outbreeding depression may be strongest in F1 hybrids, especially if the two hybridizing species have different karyotypes28. However, all sea turtle species have the same karyotype69 and therefore F1 hybrids must carry a haploid set of homologous chromosomes from each parental lineage. In such cases, the effect of outbreeding depression is often delayed until the F2 generation or later, when segregation and recombination begin to break apart coadapted genes from a single parental lineage70. In this work, the low hatching success was found for backcrossed hybrid nests, which suggests that outbreeding depression in sea turtles probably occurs starting from the second generation of hybrids. Fitness declines in F2 hybrids has been shown for other taxa, such as the tidepool copepod 71 and the largemouth bass 72.Previous studies have shown reduction in the fitness of hybrids73. High conservation at the genomic level combined with deep phylogenetic splits make sea turtles a particularly interesting example of hybridization. Unhatched eggs from a backcrossed hybrid nest are likely to carry some type of genetic incompatibility across the genome, likely due to rearrangement of chromosomes and sets of incompatible genes present within or across chromosomes. Living hatchlings from the same nests are not necessarily viable for reproduction but have already been shown not to carry lethal combinations of genetic material. These individuals are valuable to the study of species-specific sea turtle genomic features, as they can provide insights into the potential for introgression between highly divergent sea turtle species and the areas of the genomes that are more prone to introgression. Such information is especially important for sea turtles, as their long generation times (20–40 years for loggerhead and hawksbill turtles75) would require vast lengths of time to determine population changes and track hybridization outcomes in nature.Apart from carrying the same number of chromosomes, sea turtles are expected to have high levels of homology, largely conserved syntenic blocks and relatively low divergence in DNA sequences14. In addition, the clutch size, incubation period, observed clutch frequency and observed breeding frequency were similar among all groups, suggesting no reproductive advantage or disadvantage of hybrids relative to their parental species. The proportion of viable hybrid hatchlings was also similar to ‘pure’ hatchlings15. These varying outcomes in different populations should be further investigated in order to elucidate the impact of hybrid fitness and inform conservation management.Future impacts of hybridization in sea turtles will also depend on the establishment of the introgression process over generations, the onset of which was observed to be occurring in both directions of parental species. The continuous introduction of maladaptive alleles into threatened populations may lead to demographic and/or genetic swamping. As it will depend on the replacement rate, hybridization is a bigger threat to hawksbill turtles, which is the rarest species involved in hybridization events in Bahia. Previous studies analyzing the reproductive output of F1 HxL hybrid females compared to the parental species at Praia do Forte in Bahia, revealed that while emergence success was lowest for hybrids, hatchling production (the product of clutch size and emergence success) per clutch was similar among all groups14 indicating that loggerhead females nest earlier than both hybrids and hawksbills, and that hybrids have a temporal nesting distribution that overlaps with both parental species. Hawksbill males arrive in the reproductive area of Bahia (Praia do Forte) around the loggerhead nesting peak (November and December), thus having the opportunity to mate with conspecific females as well as loggerhead females, which is the most abundant species on the Brazilian coast and in Abrolhos23. The timing of reproductive seasons of both species and the abundance of loggerheads along the Brazilian coast appear to favor interspecific crossings in Bahia14.Analyzing all 47 nests included in this study, we found that nesting events for loggerhead females finish earlier than for hybrids, since no loggerheads were found nesting in February and March, unlike hybrids Fig. . Our temThe different temporal distribution of nesting for loggerheads and hybrids likely influences the incubation period (IP), which was significantly longer for hybrids compared to loggerheads. IP is determined by the temperature at which a clutch develops, which varies during the reproductive season. Our study highlights the importance of combining ecological and genetic data to provide important insights into the effects of hybridization on sea turtle populations.77 and that the average number of nesting events per reproductive season in Abrolhos is 3420, we were able to infer that the number of females nesting in Abrolhos per season is about nine. The low density of the nesting population may reflect a very low number of potential conspecific mates, favoring interspecific crossings. Despite its small population size, the Abrolhos population deserves thorough and continued study in order to understand the likely association between the hybridization process and reproductive success, which may impact sea turtle conservation.Considering that the estimated nesting frequency per female per season for loggerhead turtles is around four events78. More recently, a rupture of a tailings dam in Mariana  caused a hazardous environmental catastrophe in the Doce River and estuary, reaching the nearby Abrolhos protection area and potentially contaminating the coral reef ecosystem79. These environmental stressors synergistically reduce the resilience of the environment and associated species. Here we present another important concern regarding sea turtle conservation at the Abrolhos Archipelago, where reduced hatching success is likely dependent on both environmental and genetic (interspecific outbreeding effects) influences.The Abrolhos National Marine Park was created in 1983 with the aim to protect an area home to the richest marine biodiversity in Brazil. However, the conservation of this unique ecosystem is threatened by several factors, such as the illegal fishing activity, overfishing around the protected area, expanding tourism, dredging, invasive species, oil drilling and othersSupplementary file1Supplementary file2Supplementary file3


Using a genetically informative design (about 2000 twin pairs), we investigated the phenotypic and genetic and environmental architecture of a broad construct of conscientiousness . These four different measures were substantially correlated; the coefficients ranged from 0.74 (0.72–0.76) to 0.79 (0.76–0.80). Univariate genetic analyses revealed that individual differences in conscientiousness measures were moderately attributable to additive genetic factors, to an extent ranging from 62 (58–65) to 64% (61–67%); we obtained no evidence that shared environmental influences were observed. Multivariate genetic analyses showed that for the four measures used to assess conscientiousness, genetic correlations were stronger than the corresponding non-shared environmental correlations, and that a latent common factor accounted for over 84% of the genetic variance. Our findings suggest that individual differences in the four measures of conscientiousness are not distinguishable at both the phenotypic and behavioural genetic levels, and that the overlap was substantially attributable to genetic factors. In recent years, these concepts have attracted increasing attention in the fields of education, economics and health care3. Grit, in particular, is a useful construct explaining for why some individuals perform better than others even after controlling for cognitive abilities (i.e. intelligence)4. Although immense efforts have been devoted to establishing novel measures of specific psychological constructs, criticisms include the “jangle fallacy,” a term first coined by Kelley5 to describe how different measures with apparently dissimilar labels might measure similar constructs; thus, the supposedly related traits may have a common core6. Thus, it is important to evaluate newly developed measures using widely accepted measurement systems, such as the Big Five7, to explore whether the new constructs indeed add unique theoretical information, and whether the constructs are informative when developing policy.Individuals differ in a variety of psychological features, such as socio-emotional skills, that affect how they move through the educational system. Socio-emotional skills reflect personality traits, non-cognitive ability, twenty-first century skills, soft skills, and character strength8. Grit has been incorporated into educational policies, with the aim of children developing “non-cognitive” skills9, although the construct validity of grit and its relationship to other constructs has not really been addressed.Grit is defined as perseverance and passion in those with long-term goals, strenuous efforts to address challenges, and maintenance of effort and interest for years despite failure, adversity, and plateaus in progress. Of the various supposed socio-emotional skills, grit has emerged as a significant predictor of life success and educational achievement10. One recent meta-analysis found that the grit scale was only marginally more useful than pooled conscientiousness measures11. A second study found that grit and conscientiousness were highly correlated when the estimates were greater than 0.512. The relationship between grit and other aspects of conscientiousness (such as self-control) has been explored13. Although Duckworth and Gross14 discussed both grit and self-control, the capacity to change one’s responses, bringing them into line with the ideals, values, morals, and social expectations of others, and using them to pursue long-term goals15, are related but distinct determinants of success, and are usually treated as conceptually interchangeable10. Likewise, effortful control (or regulation of effort) and the ability to inhibit prepotent responses16 have been considered to be developmental predecessors of conscientiousness17. In college students, Muenks et al.13 found that grit correlated highly with both of these constructs, with coefficients of 0.67 for self-control and 0.41 for effortful control. Together, these data suggest that although slight differences are apparent, similar psychological constructs have been defined by different names within a segmented professional discipline. Given such commonalities among them, it seems likely that a common factor may underlie several aspects of conscientiousness, conscientiousness per se from personality psychology, effortful control from developmental psychology, self-control from social psychology, and grit from positive psychology. However, no prior study has explored whether a single common factor captures the interrelationships among the four constructs.However, by definition, grit is arguably similar to a relatively narrow aspect of conscientiousness and is derived using a global construct involving the Big Five: self-controlled, responsible to others, hardworking, orderly behaviour, and a rule-abiding nature9, it is essential to understand the relationship between this trait and other similar constructs. It is also important to examine the genetic and environmental structure of various constructs, including grit, to inform policy and interventions effectively. As Duckworth commented, “Science says grit comes from both nature and nurture18”; it is thus essential to explore whether the construct validity of the grit concept occurs through a genetic or environmental pathway. In this respect, behavioural genetic approaches such as twin studies can be most useful. Apart from exploring correlations between measures of observed phenotypes, a twin design which uses the differences between monozygotic (MZ) and dizygotic (DZ) twin siblings allow estimation of how variations in the observed results are associated with genetic differences. Using a behavioural genetic design, we can investigate not only how measures are affected by genetic differences but also the genetic and environmental architectures of the associations among measures21.Given the potential impact of grit on educational policy in advanced countries such as the United States and the United Kingdom23; even less is known about the genetic and environmental architecture of conscientiousness in young subjects. Tucker-Drob et al.24 found that grit was 48% heritable, and that about 50% of the genetic variance was shared with that of conscientiousness. However, to date, no effort has been made to explore the genetic and environmental associations among various measures of conscientiousness when assessing individual differences within a broader conscientiousness construct , or whether there is a common dimension accounting for their overlap.Few studies have used genetically informative designs to explore the genetic and environmental influence of grit or any associations of grit with other related conceptsTherefore, we addressed the following three core questions regarding the phenotypic and genetic and environmental relationships between conscientiousness, effortful control, self-control, and grit. First, how are these four measures associated at the phenotypic level? We expected that substantial correlations would be evident, given their conceptual similarities. Second, to what extent are such measures heritable? We estimated heritabilities, and the genetic and environmental origins of individual differences in the four conscientiousness-related measures, using a large Japanese adolescent twin sample (about 2000 pairs). Third, are the constructs genetically coherent, or do they exhibit independent genetic and environmental dimensions? In other words, if it is assumed that several psychological constructs are mutually phenotypically correlated, the coherence (or incoherence) of the genetic and environmental architectures should be empirically investigated to determine the source(s) of covariance. We explored the extent to which the four conscientiousness-related measures were attributable to common genetic and environmental factors, to test, in particular, whether grit might be associated with unique factors. If grit, which has attracted widespread enthusiasm, is indeed a novel independent determinant of life outcomes, grit should be genetically and/or environmentally independent of any broader construct of conscientiousness. Alternatively, if grit is essentially one component of the broad construct of conscientiousness, genetic and environmental influences should be largely attributable to a common factor, with a little unique genetic or environmental variance. Based on our interpretations of the results of previous studies, we predicted that the phenotypic correlations among the four conscientiousness measures would be primarily genetic, and that their architecture would be genetically and environmentally coherent.Table In the phenotypic correlation matrix, numbers below the diagonal are simple correlations, and those above the diagonal partial correlations after controlling for age and sex.r values with 95% confidence intervals [CIs] 0.79 ; 0.75 ; and 0.74 , respectively). Additionally, the MZ twin correlations were higher than the DZ twin correlations for all four variables, indicating genetic influences. We next performed confirmatory factor analysis using the R package lavaan25. The single-factor model fitted the data reasonably well , indicating that the four measures were phenotypically coherent.The grit score was highly (and positively) associated with the conscientiousness, effortful control, and self-control scores  for grit to 0.64  for conscientiousness in the absence of any common environmental influence.Given that the MZ correlations were less than twice those of the DZ correlations for all phenotypes, it was more likely that a shared environmental effect  were stronger than the non-shared environmental correlations ; the rG values ranged from 0.82  to 0.91  and the rE values from 0.56  to 0.62 . A genetic  correlation indicates the extent to which a genetic  influence on one phenotype overlaps with those on other phenotypes, regardless of heritability (or environmentability).As we improved model fit by constraining all shared environmental influences to zero, all subsequent multivariate genetic analyses were performed assuming that only A and E influenced the four phenotypes. The extent to which these factors contributed to the observed phenotypic correlations were calculated. Table 2 × 0.852)/(0.902 × 0.852 + 0.222)] ; self-control (84.07%); and grit (85.35%), whereas specific genetic factors accounted for small proportions of the genetic variance (7.52 to 15.93%). A common non-shared environmental factor explained over half of the non-shared environmental variance, ranging from 55.98% for effortful control to 63.19% for conscientiousness; non-shared environmental variance accounted for less than half of these proportions (36.81 to 44.02%). Thus, the phenotypic covariation between each pair of conscientiousness measures was attributable primarily to genetic influences.To establish the genetic and environmental architecture of the overlap, and the distinctive features of the four conscientiousness measures, we compared correlated-factors, independent pathway, and common pathway models see the “. As show28 ; effortful control items from the Early Adolescent Temperament Questionnaire Revised29 ; self-control items from the Brief Self-Control scale30 ; and grit items from the Grit scale8 (positive psychology).In this study, we examined phenotypic, and genetic and environmental influences on interrelationships among four psychological constructs related to conscientiousness in adolescent Japanese twins. This is the first study to reveal the genetic and environmental architecture of conscientiousness-related constructs assessed using four different measures from four different psychological disciplines: conscientiousness items from the Chernyshenko Conscientiousness scale (short form)11, the four conscientiousness measures were strongly phenotypically related, and such relationships held even after controlling for demographic variables  relationships among individual differences in a broader construct of conscientiousness in several significant ways. First, in line with previous findings31. These findings are typical of those of behavioural genetic studies on individual differences in human traits ; shared environmental influences are of minimal significance33. Although conscientiousness measures are moderately heritable, it is important to note that heritability does not mean immutability34. Behavioural genetic studies seek to partition the variance components of variables of interest at a particular time point in a particular sample. Therefore, our results do not imply that the mean scores of conscientiousness measures do not increase or decrease through development or after intervention.Second, we found that all four conscientiousness measures were moderately heritable; additive genetic effects explained over half of the phenotypic variances, and a negligible shared environmental effect was found Table . The her35 and non-clinical mindfulness programs increase the conscientiousness of medical students36.Additionally, we found a high level of genetic correlations among conscientiousness measures, providing further evidence for genetic influence among all four variables; the correlations ranged from 0.82 (0.79–0.84) to 0.92 0.89–0.93) and individual differences in the propensity to be conscientious stem principally from the same set of hundreds (probably thousands) of variants across the genome .Most importantly, our findings demonstrated that a latent common factor substantially explains the covariance among four different measures, and that individual differences in the broad construct of conscientiousness are substantially explained by the genetic influence. Although previous meta-analyses have identified several genetic loci significantly associated with conscientiousnessWe obtained moderate non-shared environmental correlations ~ 0.62; Table ; Table 2Notably, our analyses revealed that conscientiousness-related constructs were both phenotypically and behavioural-genetically similar. Even grit, which was recently listed first among the socio-emotional skills, was highly loaded to the common latent factor of conscientiousness, along with the three other constructs. This finding indicates that grit did not have a greater proportion of variance due to specific genetic and environmental influences. We doubt that these four conscientiousness-related constructs are fully psychologically distinct; the large degree of overlap suggests that they represent a jangle fallacy.42, suggesting a downward estimation bias for shared environmental effects. Indeed, a previous meta-analysis found that, for individual differences in personality traits, the AE model provided the best model fit33. According to model comparisons in univariate genetic analyses, shown in Table 46. Third, our data were cross-sectional in nature; the development of the genetic and environmental architecture of conscientiousness throughout the lifespan remains unclear and requires a longitudinal study. Fourth, the lack of convergence of model selection methods should be mentioned. In terms of BIC, the common pathway model fit best to the data, whereas in terms of the likelihood-ratio chi-square test, all the models fit worse than the baseline model (i.e. correlated factors model), which is another limitation. However, because the BIC performs better with regard to model selection in the context of complex multivariate models47, the BIC was preferentially interpreted in this study.This study has several limitations. First, the research design with twin siblings who are reared together tends to show smaller shared environmental effects than do adoption study design48 interestingly suggested that shared and non-shared environmental influences moderate the significance of genetic influences, and that a genetic predisposition can be shaped in part by our environment, such that formal testing of the effects of gene by environmental interactions would be a promising approach for improving our understanding of the developmental and behavioural genetic bases of conscientiousness.In summary, we found that conscientiousness-related constructs assessed using four different scales strongly correlated both phenotypically and behavioural-genetically, and that the architectures were genetically coherent. Conscientiousness in Japanese adolescents is best conceptualised as a common latent factor with additive genetic and non-shared environmental factors specific for each measure. However, compared to the effects of the common latent factor, the effects of specific genetic and non-shared environmental factors were weaker. Conscientiousness as a latent personality trait factor was genetically accounting for 54–59% of the phenotypic variance, whereas specific additive genetic factors accounted for only 5–10% of the phenotypic variance. This finding indicates that over 84% of the genetic variance was explained by a common genetic factor, suggesting that individual differences within broad conscientiousness constructs are primarily attributable to genetics. Additionally, we found little indication that any shared environmental influence was the source of the phenotypic variance; non-shared environmental influences do exist but exerted smaller effects than the additive genetic influences. However, this does not imply that environmental influences are always irrelevant in terms of the variance/covariance of individual differences in personality traits. Recently, Dick et al.We newly recruited mothers of adolescent twins; we asked them (and the twins) to engage in an online survey run by a Japanese marketing research company. To identify relevant mothers, we initially asked: (a) Are you female? (b) Do you have children? (c) Are your children twins? and, (d) Are your twins aged between 9 and 18 years? If all answers were yes, mothers proceeded to an informed consent screen. Valid informed consent was obtained from all participants prior to administering the online survey. Participants (i.e. twins’ mothers) were compensated with online shopping points after the completion of the online questionnaire. Internet-based survey respondents may engage in careless or inattentive responding. For data cleaning, a marketing research company excluded possible suspected inappropriate responses in terms of the percentage of missing data, too-short completion times, and clearly suspicious response patterns  prior to data delivery.49. Both same- and opposite-sex DZ twin pairs were included. As the results barely changed when we removed the opposite-sex DZ twins, we decided to retain them to enhance the statistical power of our work. The study was approved by the institutional ethics committee for experimental psychology research at the Graduate School of Education, Kyoto University, and all research was performed in full accordance with the relevant guidelines and regulations.Valid completed questionnaires were obtained from a total of 1958 families with adolescent twins . The twins ranged in age from 9 to 18 years , and the mothers from 25 to 68 years . Zygosity was determined by assessing the extent of physical similarity between twins28; the effortful control items of the Early Adolescent Temperament Questionnaire Revised 29; the Brief Self-Control measure 30; and the Grit scale 8. Twin mothers were asked to rate their children on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). The Cronbach’s alphas were 0.79 for conscientiousness, 0.86 for effortful control, 0.78 for self-control, and 0.70 for grit; all values were acceptable.We measured conscientiousness using four scales: the Chernyshenko Conscientiousness scale (short form) In preliminary phenotypic analyses, we calculated means, standard deviations, correlations, and partial correlations after controlling for twin age and sex, for all four conscientiousness-related scales. Additionally, all four scales were subjected to maximum likelihood factor analysis, using individual twins randomly selected from all twin pairs.50, were performed by exploiting the differences in the genetic relatedness of MZ and DZ twin pairs. MZ twins share all genes and the family environment; DZ twins share (on average) half of their genes and the family environment. Given such differences in genetic relatedness, univariate genetic analyses can decompose variance components into additive genetic, non-additive genetic, common (or shared) environmental, and nonshared environmental influences (the latter includes measurement error). The effect of additive genetic factors (A) is assumed to be the sum of contributions from multiple genes that sum to form a quantitative phenotype. If the MZ intraclass correlation is larger than the DZ intraclass correlation, a genetic influence is inferred. If the MZ intraclass correlation is more than double the DZ intraclass correlation, a non-additive genetic (D) influence  is inferred. If the MZ intraclass correlation is less than double the DZ twin correlation, a common environmental (C) influence (not a genetic influence) rendering family members alike is inferred. A nonshared environmental (E) influence reflects differences among family members even when they live together. However, a full ADCE model is not identified if data are available for twins reared together, due to limitations of the model assumptions. If intraclass correlations did not suggest non-additive genetic influences, model fitting results were given for ACE and reduced models ; similarly if intraclass correlations did not suggest shared environmental influences, model fitting results were given for ADE and reduced models . Note that the DE model was not tested because genetic dominance in the absence of genetic additivity is biologically implausible50. As we expected that the four variables would be moderately correlated, we next performed multivariate genetic modelling; this decomposes the covariance between variables, and the variance of variables, into genetic and environmental influences. We tested three models: (A) a correlated-factors, (B) an independent pathway, and (C) a common pathway model  extended from a univariate model was initially employed to decompose the covariance between the given traits into genetic and environmental sources of variance, to estimate genetic and environmental correlations. The least restrictive model included A, C (or D), and E effects on each variable, and the extents of all effects were correlated. This model explores the extent to which phenotypic correlations among variables are attributable to correlations between individual, latent genetic and environmental factors.The second model (the independent pathway model) is more restricted. This model uses a single set of common A, C (or D), and E factors that affect all observed variables directly, and also specific A, C (or D), and E residual factors for all variables. This model explores the extent to which the same genes and similar environments are implicated in covariation among all traits included in the model; common genetic and environmental factors directly influence the observed variables, without an intermediate higher-order factor. As shown in Fig. The third model tested, the common pathway model, is the most restrictive. This model includes the effects of a common set of A, C (or D), and E factors that influence an underlying phenotypic factor, and measure-specific residual factors influencing each variable. This model explores whether common-factor effects on the latent psychometric factor provide reliable estimates of the genetic and environmental influences on conscientiousness. If the common latent factor is included in the model, each observed variable is an exemplar of that factor. If the common pathway model provides the best fit with the data then the latent phenotype in the model is considered to be a statistically and psychologically valid construct, as it serves to mediate the genetic and environmental covariance among the variables.χ2); the χ2 difference test (Δχ2) can also be used as a measure of model fit. However, because log-likelihood ratio testing is problematic in its application to structural equation modelling51, and because BIC26 has been found to outperform the AIC52 calculated for the log-likelihood test and degrees of freedom, in the context of complex multivariate models in larger samples47, the BIC was mainly used during model selection in this study.When fitting models to raw data, variances, covariances, and means are first freely estimated to obtain a baseline index of fit . The − 2lnL under this unrestricted baseline (saturated) model is then subtracted from the − 2lnL under more restrictive biometric models. This result is a likelihood-ratio chi-square test of goodness of fit for the model (OpenMx 2.053 and lavaan25.All behavioural genetic analyses in this study were performed using raw data with the full information maximum likelihood estimation implemented in the R packages Supplementary Information.


Polistes paper wasps are primitively eusocial insects, with a cooperative breeding system in which females often found nests in cooperative associations. Prior mark-recapture studies of Polistes have documented extreme female philopatry, although genetic studies frequently fail to detect the strong population structure expected for highly philopatric species. Together these findings have led to lack of consensus on the degree of dispersal and population structure in these species. This study assessed population structure of female Polistes fuscatus wasps at three scales: within a single site, throughout Central New York, and across the Northeastern United States. Patterns of spatial genetic clustering and isolation by distance were observed in nuclear and mitochondrial genomes at the continental scale. Remarkably, population structure was evident even at fine spatial scales within a single collection site. However, P. fuscatus had low levels of genetic differentiation across long distances. These results suggest that P. fuscatus wasps may employ multiple dispersal strategies, including extreme natal philopatry as well as longer-distance dispersal. We observed greater genetic differentiation in mitochondrial genes than in the nuclear genome, indicative of increased dispersal distances in males. Our findings support the hypothesis that limited female dispersal contributes toward population structure in paper wasps.Relatively little is known about the processes shaping population structure in cooperatively breeding insect species, despite the long-hypothesized importance of population structure in shaping patterns of cooperative breeding.  The genetic structure of populations is shaped by selection, genetic drift, and gene flow . LocalizCooperative breeding occurs when individuals work together to raise offspring, with nonbreeding individuals often sacrificing direct fitness for indirect fitness . IncreasPolistes are primitively eusocial insects and are an emerging model system for testing theoretical predictions of social evolution , whereas the remaining studies detected no significant IBD   (rp = 240 km)  . The two 0.2 km) , but not 240 km) . Taken tPolistes. First, authors have defined subpopulations using a variety of spatial scales, with a lack of cross-scale analyses. Dispersal rates are typically measured at a single spatial scale within a given study, and these scales vary widely across studies  to detect population structure have found that the three type of loci yield similar estimates of genetic divergence  in 2015 and 2016 , Massachusetts (N = 11), Maryland (N = 8) and North Carolina (N = 7) to measure genetic differentiation across longer distances. We approximated the size of each region by using the Google Earth measurement tool (earth.google.com) to trace a polygon around sampling sites and estimate the radii of this polygon. Only one individual per nest was sequenced in order to avoid confounding the effects of nestmate relatedness with patterns of broad population structure. To account for related individuals caught on the wing, we removed one individual from each pair of samples with kinship coefficients >0.1 (see below). We removed eight individuals from Central New York and one individual from North Carolina resulting in a final data set of N = 174 individuals and N = 6 individuals for these populations, respectively.Female and 2016  online. P. fuscatus reference genome, which included the mtDNA reference genome  in combination with the HaplotypeCaller tool in GATK (v3.8) . Library sizes were quantified using a bioanalyzer  prior to being sequenced on the Illumina HiSeq 2000. Raw reads were processed with Trimmomatic (v0.36) to remove adaptors and poor-quality sequence. Trimmed reads were mapped to the e genome  using the genome . VariantK (v3.8) . VariantK (v3.8)  online. K (v3.8)  and remoK (v3.8) . To accoK (v3.8) . This alπ), inbreeding coefficients (FIS), expected heterozygosity (He) and observed heterozygosity (Ho) were calculated for the entire Eastern US population and each of the regionally defined subpopulations in 10,000-bp windows with VCFtools (Mean whole-genome nucleotide diversity (VCFtools  and vcflπmt) by concatenating the mitochondrial genes COI, COII, ATP6, ATP8, ND4, ND5, 12S, 16S, and CytB and identifying variants within the concatenated sequence. Concatenated mitochondrial genes have been shown to be as effective as whole mitochondrial genome sequences for detecting population structure using a similar set of mitochondrial genes in Apis mellifera . We used multidimensional scaling (MDS) to plot genetic relationships based on estimates of pairwise genetic differentiation among all 204 individuals from the Eastern United States. MDS plots were generated using plink (v1.07), specifying only the top ten axes (–mds-plot 10). Additionally, for comparisons across the Eastern United States, to avoid overrepresentation of individuals from highly sampled areas, we used the same data set of subsampled individuals from Central New York and Massachusetts described above. For comparisons within Central New York, we similarly randomly subsampled individuals from Freeville and Arnot Forest.K = 1–6 demes and determined the optimal K value with the “chooseK” script included with the program. We iterated the program 50 times and merged the runs with CLUMPP (v1.1.2) (Genetic structure across the Eastern United States was examined by using fastSTRUCTURE (v1.0) to determine the optimal number of demes within the data . To avoi(v1.1.2) .K = 1–6 using a burnin period of 100,000 steps followed by 200,000 sampling steps implemented with Structure_threader (v1.2.11)  . Due to v1.2.11) . Repeatev1.2.11)  and mergv1.2.11) .FST/(1 – FST) on geographical distance :De is the effective population density of breeders. The only formal assessment of Polistes nest density (a good approximation of De) is 2,500 nests/km2, calculated for P. chinensis from Japan ) and mitochondrial (FST(mito)) genetic diversity statistics. Under equal male and female dispersal, FST(mito) and FST(nuc) have the following relationship  values are greater than expected values.Male-based dispersal is supported when observed FST(mito) and FST(nuc) between subpopulations were calculated using VCFtools. The strength and significance of observed patterns of IBD were analyzed using linear models.Pairwise To visualize mitochondrial population structure at multiple scales, haplotype networks were constructed using the haploNet function from the R package pegas . MitochoFIS) significantly >0. The average FIS was 0.296  was similar across populations (range: 0.0005–0.0008). Central New York and Massachusetts showed a slight decrease in mean nucleotide diversity of mitochondrial genes (πmt) relative to the other populations. All population genetic statistics were similar between the full and the downsampled data sets.Across the Eastern United States, all populations had inbreeding coefficients  grouped individuals broadly by region, with some overlap among regions  but a poor correlation between these values in Central New York . The mean pairwise divergence across all subpopulation comparisons was FST = 0.0525  and a weaker correlation within Central New York . The mean pairwise mitochondrial divergence across all comparisons was FST = 0.24  was estimated as 0.24. This value was greater than the average observed value of 0.172  indicating likely male-biased dispersal in these populations.To look for evidence of differences in male and female dispersal distances, we compared nuclear and mitochondrial genetic diversity statistics. We calculated the difference between the observed and expected values for each pairwise comparison across the Eastern United States. The average expected value of FST/(1 − FST) against pairwise distance. This yields an estimated dispersal distance of σ = 761 m using the measurements of nest density from Arnot Forest and σ = 501 m using nest density estimates from σ = 188 m based on the nest density from Arnot Forest and σ = 124 m using the nest density from We estimated the mean parent–offspring axial dispersal distance using the slope of the regression line for the Eastern US comparison of P. fuscatus individuals. Upper level MDS axes (2–10) were driven by variation within Central New York samples, likely caused by the overrepresentation of individuals from this region relative to the other regions in the analysis. These 24 samples were geographically widespread, and largely, but not entirely, corresponded with samples assigned >10% membership probability in group 2 or group 3 in the fastSTRUCTURE analysis  and a similar poor correlation between linearized genetic and geographic distance in Central New York   or in Central New York . Calculating mean parent–offspring axial dispersal using the slope of regression line for the Eastern US comparison yields an estimate of σ = 578–879 m.To test the contribution of these 24 samples to our findings, we repeated our calculations of IBD across the Eastern United States and within Central New York without these samples. Due to the smaller sample sizes of some populations, comparisons between Maryland and North Carolina were dropped from the Eastern US analysis, and all comparisons with Erin New York were dropped from the Central New York analysis. We find significant IBD in the nuclear genome across Eastern United States ( = 0.32)  online. P. fuscatus populations. All studied populations had inbreeding coefficients >0. From the MDS clustering analysis, spatial genetic structure was evident across the Eastern United States, within Central New York, and at a site-specific scale in Arnot Forest, albeit with some overlap among populations. Notably, at the site-specific scale, individuals from Arnot Forest showed fine-scale differentiation across multiple years, indicating year-to-year fidelity of natal nest locations within a few meters at this site. The distribution of mitochondrial haplotypes additionally supports population structure across multiple scales. We detected distinct mitochondrial haplotypes in each regional subpopulation sampled across the Eastern United States. At the regional scale, haplotypes were shared only between neighboring sites, and at the site-specific scale, haplotype divergence was evident among buildings in some cases. Dispersal limitation is further supported by significant patterns of IBD detected in the nuclear and mitochondrial genomes across the Eastern United States and a trend toward IBD within Central New York. This fine-tuned degree of natal philopatry is consistent with previous mark-recapture findings  values in Central New York  how far do males and females travel from their natal nest before mating? 2) Do males choose a single mating tactic or change courtship strategy over the season? 3) Do males stay at a single location or move among sites?Another explanation for the seemingly contradictory population structure findings across multiple scales could be alternate dispersal strategies between males and females. Longer dispersal distances in males have been reported in other Hymenopteran species e.g., . Adding stically . Courtshstically . Future FST between populations may be the result of mechanisms unrelated to contemporary dispersal. Within the last 10,000 years, P. fuscatus has undergone a northern range expansion into New York and Massachusetts due to melting of glaciers in these regions following the end of the last ice age. Prior to this time, P. fuscatus populations may have been smaller and more panmictic. Genetic divergence of contemporary populations could result from incomplete lineage sorting of ancestral alleles, or plausibly historical population structure following secondary contact of populations from different glacial refugia, although the use of multiple refugia has not been tested in this species. Additionally, the value of FST depends on both the variation within subpopulations and the total variation (FST (P. fuscatus over the entire range of the species to generate the observed pattern of low genetic divergence. Further investigation of genomic processes in paper wasps will help address the potential role of alternative mechanisms in generating low genetic divergence coupled with fine-scale population structure.A third explanation for our results is that the low overall genetic divergence in ariation . Mechaniion (FST . HoweverFindings from this study have important implications for theoretical models of the evolution of cooperation. Theories of social evolution predict that cooperation should tend to favor limited dispersal  and promPolistes wasps that vary in their degree of cooperative nesting.Our findings are consistent with the predictions made by the hypothesis that cooperative breeding is linked to fine-scale population structure. Specifically, we detect highly structured philopatry at the scale of a single site. However, this study has not explicitly tested this hypothesis as we lack information about cooperation rates in all sampled locations. Future comparative analyses are necessary to further probe evident fine-scale structure to more fully understand how cooperative nesting behavior relates to spatial genetic structure. If cooperative nesters have a greater tendency toward philopatry, we expect to find a pattern of greater fine-scale genetic structure between cooperatively nesting versus solitary nesting individuals. Similarly, if cooperatively breeding systems lead to a higher degree of natal philopatry, we expect to find correlations between foundress rates and population structure in future comparative studies between species of evaa070_Supplementary_DataClick here for additional data file.


Petrosia ficiformis, with highly isolated populations (low gene flow), showed a stronger influence of the host genetic distance on the microbial composition than the spatial distance. Host‐specificity was therefore detected at the genotypic level, with individuals belonging to the same host genetic cluster harbouring more similar microbiomes than distant ones. On the contrary, the microbiome of Ircinia fasciculata and Dendrilla antarctica ‐ both with weak population structure (high gene flow) ‐ seemed influenced by location rather than by host genetic distance. Our results suggest that in sponge species with high population structure, the host genetic cluster influence the microbial community more than the geographic location.Most animals, including sponges (Porifera), have species‐specific microbiomes. Which genetic or environmental factors play major roles structuring the microbial community at the intraspecific level in sponges is, however, largely unknown. In this study, we tested whether geographic location or genetic structure of conspecific sponges influences their microbial assembly. For that, we used three sponge species with different rates of gene flow, and collected samples along their entire distribution range (two from the Mediterranean and one from the Southern Ocean) yielding a total of 393 samples. These three sponge species have been previously analysed by microsatellites or single nucleotide polymorphisms, and here we investigate their microbiomes by amplicon sequencing of the microbial 16S rRNA gene. The sponge  DespiteHost‐specificity patterns below species level are even less understood, because in almost all cases, research has focused on describing the intraspecific variability of sponges with respect to environmental drivers of variation, and among these studies, results have been inconsistent. In some studies, intraspecific microbial communities differed over spatial and temporal scales, location, nutrient concentration or habitat  and one Antarctic demosponge (Dendrilla antarctica), which present different levels of gene flow and a priori microbiome acquisition strategies. The connectivity of the sponge hosts had previously been assessed with microsatellite markers for the Mediterranean sponges  on the microbial community structure and composition of three marine sponge species. We characterized the microbiome compositions of two Mediterranean demosponges  sponge that displays exclusively horizontal transmission of symbionts  sponge with horizontal transmission . Significance of FST values was analysed with 20,000 permutations.Genetic clusters were assigned to individuals based on microsatellite data sets for 2.3P. ficiformis and I. fasciculata, we targeted the V3–V4 hypervariable regions of the 16S rRNA gene, while the V4 hypervariable region was used for D. antarctica. The V3V4 region was amplified using a one‐step PCR with the following conditions: 98°C for 30 s, followed by 30 cycles of 98°C for 9 s, 55°C for 1 min, 72°C for 1.5 min, and a final elongation at 72°C for 10 min. We used the primer pair 341F .For https://www.nhm.ac.uk/) on an Illumina MiSeq device using v3 chemistry (2 × 300 bp).The V4 region of the 16S rRNA gene was amplified using general bacterial primers 515F‐Y , and an adaptation of the MiSeq SOP protocol was followed , and a minimum of 11,000 sequences for D. antarctica (two samples were excluded). The core microbiome was determined on the rarefied data sets using two definitions: ASVs that were present in 100% of the samples at any abundance, and in 80% of samples at any abundance.Description of the microbial community was done using the total number of ASVs transformed to relative abundances within each individual. Furthermore, for alpha diversity analyses, samples were rarefied to 5,000 sequences for 2.5To analyse the influence of genetic and geographic effect on the microbial community, both continuous and discrete variables were included for statistical analyses. Continuous genetic distances among hosts were calculated as Euclidean genetic distances. Samples with 0 Euclidean distance  were discarded from further analyses. Continuous geographic distances were calculated as kilometres between sampling sites based on the GPS coordinates of each site. Discrete genetic groups were based on the host genetic clusters defined previously  and both continuous distances (i.e. host Euclidean distance and geographic distances) were tested using Mantel and partial Mantel tests implemented in the R package vegan. The Mantel approach computes Pearson correlation between continuous distances, with significance based on 999 permutations of the distance matrix. Correlations were analysed globally , using partial Mantel test controlling for the effect of a third variable, and also on each location separately and each genetic cluster.P. ficiformis, I. fasciculata and D. antarctica respectively. The relative abundances were then log2 transformed prior to calculation of Bray‐Curtis dissimilarities. These dissimilarity matrices were visualised using Principal coordinates analysis (PCoA) using “cmdscale” in vegan v. 2.5–6 , and at genus level (ASVs abundances belonging to the same genera were aggregated), to see the effect on alternative data sets.Measures of ASV richness, Shannon index, and inverse Simpson's index were calculated using the rarefied samples in R v.3.6.1. These metrics were compared among genetic clusters using analyses of variance (ANOVA). Pairwise comparisons were conducted using TukeyHSD. Beta diversity was calculated using the Bray‐Curtis dissimilarity coefficient. ASVs were filtered by a relative abundance >0.01% in at least 5% of samples, leaving 1,557, 2,032 and 1,059 ASVs for P. ficiformis and I. fasciculata) were considered. In D. antarctica we reduced the number to two replicates due to the lower number of samples in the data set. This resulted in the informative locations BLA, LIG and NAP for P. ficiformis; CALA, CRO, NAP, TOSS, CAB and ESC for I. fasciculata; and CIE, KG, ADE and HM for D. antarctica. For the analysis of each host genetic cluster separately, we included only genetic clusters present in more than one location with 2 or 3 replicates, similarly as before. These were Pf3, Pf4, Pf5, Pf6, Pf8 for P. ficiformis; If1, If2, If4, If5 for I. fasciculata; and Da1, Da2, Da3, Da4 for D. antarctica. Moreover, to disentangle genetic and geographic effects, these selected locations and genetic clusters were also analysed together.Beta diversity analyses were initially performed on the entire data sets, however, since genetic, geographic and year effect may be confounding, we repeated analyses for each independent location, genetic cluster and year of sampling. In this case, locations with insufficient host genetic variation were excluded, meaning that only locations with more than one identified genetic cluster and with at least three replicates  was conducted using the R package 33.1Petrosia ficiformis and Ircinia fasciculata were collected across the Mediterranean Sea, including 168 and 163 samples, respectively. Dendrilla antarctica included 62 samples from the Southern Ocean  between genetic clusters were the largest in P. ficiformis ranging from 0.048 to 0.276, followed by D. antarctica (from 0 to 0.156) and I. fasciculata , genetic groups Pf4 and Pf8 were found to be more closely related than with the rest, presenting overlapping clustering. The PCA in D. antarctica explained 26% of the total variation , Proteobacteria (25.4% mra) and Acidobacteria , Cyanobacteria (19.4% mra) and Chloroflexi , Bacteroidetes (16.0% mra) and Verrucomicrobia  to look at general differences between the species knowing the limitations that this approach represented. P. ficiformis and I. fasciculata shared most of the genera, while D. antarctica hosted different ones  > .30, with p < .01) between the microbial community Bray‐Curtis dissimilarity and host genetic distance, these were BLA_2013, LIG_2008 and NAP_2013, and marginally BLA_2006  had high significant correlation (mr(MH) > .40, p < .01) between the microbial dissimilarity and the host genetic distance. Three other locations, CIE, KG and HM, presented multiple genetic clusters but did not show significant positive correlation  had high correlations (mr(MH) > .50, p = .001; Figure To remove the bias of the geographical location and year, we analysed the effect of phylosymbiosis in each site independently and each sampling year. In n Figure . For I. P. ficiformis, while for D. antarctica only Da1 showed correlation (mr(MD) = .30, p = .002), and for I. fasciculata two of the three genetic clusters including multiple locations showed high correlation ; and I. fasciculata .The Shannon index was used to unravel whether the genetic cluster or the locations exhibited different diversity patterns. The alpha diversity ranged between 4.42–6.22, 2.14–5.66, and 2.16–4.98 for ly Table . Diversi3.4.2P. ficiformis that appeared separated from the rest of the samples , and there was a clear grouping of samples associated with their host genetic cluster  except for the pair Pf4–Pf8 . This means that for 8 Figure . Both, l8 Figure b, but thD. antarctica , and genetic clusters If1 to If4 . However, in these last two locations, samples from either genetic cluster were collected in different years , but it was by location (p = .001), which explained 10.7% of variability.In 4 Figure a. Samplers Table  preventi3.5P. ficiformis was the sponge species with the strongest specificity between microbiome and host genetic cluster, our goal was to identify taxa that were more likely to be found in a given genetic cluster than in others. The R package indcspecies identified 725 indicator ASVs among single specific host genetic clusters of P. ficiformis, and 373 ASVs whose patterns of abundance were associated to two genetic clusters , which can be taken as a measure of high population differentiation or early speciation  than distant ones across the distribution area of the sponge. It is remarkable that mechanisms to acquire and maintain variable specific host‐symbiont associations are displayed within the same sponge species and genetic clusters, and even more importantly, that those are acting over the same bacterioplankton community  that is being filtered and acquired through horizontal strategies. However, P. ficiformis microbial communities were less similar across basins (Eastern and Western basins of the Mediterranean) even when the same genetic cluster appeared in both basins. The availability of the specific microbial variants in the environment could have affected the fidelity of the sponge microbiomes in such distant locations, since the planktonic communities are affected by very particular ocean circulation patterns in both basins , samples from Italy and Israel harboured highly different communities  and therefore strong speciation constraints, their allele content and frequencies in host loci responsible for symbiont selection are not fixed. Moreover, site‐specific environmental conditions could amplify or mask the effects of related host genes on the microbiome composition, giving variable results in different locations, as previously noted for plant species  of the surrounding water in the microbial community of this species. Interestingly, studies on the microbiome composition and stability in Ircinia species  but these also presented a latitudinal distribution, not allowing to discriminate the effect of each factor in the microbiome dissimilarity  was again the focus of one of them, and the authors reported that microbiomes significantly differed in composition between locations, and that within individual locations variability was correlated with host genetic distance , populations were also confined to specific sampling locations, preventing the decomposition of genetic and geographic effects, however, an analysis restricted to the two populations present in more than one site showed lack of spatial correlation but a significant correlation with host genetic distance  sponges  sponge based on our results , were not shared with the Mediterranean sponges.s Figure  and micrs Figure . The parP. ficiformis, D. antarctica and I. fasciculata, respectively. The relative abundance that these core ASVs represented was usually high, up to 43% and 49.8% of the whole microbiome for D. antarctica and I. fasciculata, respectively. In P. ficiformis, the values were generally lower, with a maximum relative abundance of 33%, already showing that its microbiome was more variable than the other two species.In global analyses of the microbiome in Porifera, symbiont taxa found in many species (core microbiome) account for a small part of the community, while a much larger portion of the community is usually host species‐specific  as well as indicator ASVs . Core ASVs accounted for a high percentage of the community, while indicator ASVs usually comprised low abundances. These results indicate that ASVs with opposite behaviour , are very closely‐related microbial variants, instead of distant taxa. It has been proposed previously that different sponges contain different bacterial species, however, these bacteria are still closely related to each other explaining the co‐evolution patterns observed in bacterial communities of sponges , which may contribute to hide species‐specific patterns in microbial species. The genus level analysis in our 4.3Two of the sponge species studied here presented exclusive horizontal transmission of symbionts (larvae devoid of microbes), however their microbiome was still very specific to the hosts, suggesting that high species‐specificity is not dependent of the vertical transmission of symbionts to offspring in sponges. Recent advances to understand the role of vertical transmission on the specificity of the microbiome have shown that the vertical transmission of the microbiome from the parents to the larvae is incomplete, with larvae harbouring less than 50% of the microbes from their parents, and siblings sharing only 17% of microbes among them . When gene flow is restricted, the effect of host genetic cluster is larger than when gene flow allows relatively homogenous genomic pools across locations. Our study provides fundamental insight to understand when microbiomes become specific in sponges, highlighting the necessity of taking into account the complex evolutionary history of each species.C.D.V., and A.R. conceived the study. A.R, S.T, and C.L. performed the fieldwork. C.D.V., K.B., and C.L. performed the laboratory work, and C.D.V. analysed the data. C.D.V., and A.R. drafted the first versions of the manuscript. All authors commented on and approved of later versions of the paper.Fig S1‐S7Click here for additional data file.Table S1‐S14Click here for additional data file.