Bouteloua gracilis (blue grama) is a wind‐dispersed, perennial grass consisting of several cytotypes (2n = 2×–6×) with a widespread distribution in western North America. The species is locally dominant and used regularly in restoration treatments. Using amplified fragment length polymorphism (AFLP) and cpDNA analyses, we assessed the genetic variability and adaptive genetic structure of blue grama within and among 44 sampling sites that are representative of the species’ environmental and habitat diversity in the southwestern United States. Five cultivars were also included to investigate genetic diversity and differentiation in natural versus cultivated populations. Three main findings resulted from this study: (a) Ninety‐four polymorphic AFLP markers distinguished two population clusters defined largely by samples on and off the Colorado Plateau; (b) substructure of samples on the Colorado Plateau was indicated by genetic divergence between boundary and interior regions, and was supported by cytotype distribution and cpDNA analysis; and (c) six AFLP markers were identified as “outliers,” consistent with being under selection. These loci were significantly correlated to mean annual temperature, mean annual precipitation, precipitation of driest quarter, and precipitation of wettest quarter in natural populations, but not in cultivated samples. Marker × environment relationships were found to be largely influenced by cytotype and cultivar development. Our results demonstrate that blue grama is genetically variable, and exhibits genetic structure, which is shaped, in part, by environmental variability across the Colorado Plateau. Information from our study can be used to guide the selection of seed source populations for commercial development and long‐term conservation management of B. gracilis, which could include genetic assessments of diversity and the adaptive potential of both natural and cultivated populations for wildland restoration.With the increasing frequency of large‐scale restoration efforts, the need to understand the adaptive genetic structure of natural plant populations and their relation to heavily utilized cultivars is critical.  One hundred and thirteen samples were obtained from 17 natural sites. One hundred and ten samples were obtained from a common garden representing 20 natural sites. Twelve samples from two natural sites were obtained from living specimens growing at the Northern Arizona University greenhouse in October of 2015. Forty‐four samples from six natural sites were grown from seed in December of 2015. Eighty‐one samples from five cultivated varieties were also grown from seed (~16 samples/cultivar). All materials were harvested while still green, desiccated upon collection in science‐grade silica and stored at room temperature.As indicated above, some samples were obtained from specimens grown from seed collected by the Seeds of Success (SOS) program following their standardized protocol and distributed by the SOS program directly or the Germplasm Resources Information Network. All cultivar seeds were obtained from the Granite Seed Company distribution center in Lehi, UT. Granite Seed is a heavily utilized distributor of native seed for large‐scale restoration purposes. While the specific agricultural production site of the seeds remains undisclosed by Granite Seed, the varieties and state of origin include Hatchita (CO), Lovington (CO), Alma (CO), Bird's Eye (WY), and Bad River (MN), with the first three being the primary varieties utilized on the Colorado Plateau.2.4B. gracilis  using the MagJET reagents and protocol as outlined in “The User Guide: MagJET Plant Genomic DNA Kit” (Thermo Fisher Scientific). Five no‐template controls (NTCs) and 25 replicated samples were included. Replicates consisted of same‐plate and different‐plate individuals to identify any possible plate effects on subsequent analysis.2.62O (4.8 μl), BSA (0.2 μl of 20 mg/ml), 500 units of T4 ligase , T4 buffer (2 μL of 10× concentration), EcoRI adapter (1 μl of 5 pM/μl concentration), MseI adapter (1 μl of 50 pM/μl concentration), and 10 units each of EcoRI (0.25 μl of 40 units/μl concentration) and MseI (0.5 μl of 20 units/μl concentration). Reactions remained at room temperature overnight for approximately 18 hr and were diluted 1:10 in Tris‐Cl, pH 8.0.All extracted samples were analyzed using AFLP genotyping using a protocol slightly modified from Gray et al. . Restric2 (0.3 μl of 50 mM), ddH2O (3.45 μl), a primer combination (0.25 μl of 20 μM concentration), and DNA template (1 μl). Preselection product was diluted 1:20 in Tris‐Cl, pH 8.0 prior to selective PCR. Selective PCR product was diluted 1:10 in ddH2O and run on a 3730 DNA Analyzer (Thermo Fisher).Preselective and selective polymerase chain reactions (PCRs) were run in 10 μl reaction volumes and were composed of 2× Phusion Green HSII High‐Fidelity PCR Master Mix by Thermo Fisher (5 μl), MgClThermal cycler conditions were as follows for the preselective PCRs:20°C for 5 s; ramp from 20 to 70°C (0.2°C/s); 70°C for 2 min; 94°C for 1 min; then 30 cycles of 94°C for 30 s, 56°C for 1 min; 72°C for 1 min; followed by 72°C for 10 min; 15°C for 5 min; and a hold at 4°C.Thermal cycler conditions for the selective PCRs were as follows: 95°C for 2 min; 13 cycles of 65°C for 30 s (−0.7°C/cycle), 72°C for 90 s, and 94°C for 30 s; then 23 cycles of 56°C for 30 s, 72°C for 90 s, and 94°C for 30 s; followed by 72°C for 10 min, 15°C for 5 min, and a hold at 4°C.A total of 12 selective AFLP primer combinations were screened, with a panel of six geographically representative samples, including replicates of all six samples. Of these, three primer combinations were chosen based on replicability and consistency: M49/E35, M50/E35, and M62/E35  software. Panels were manually created, and loci were automatically scored and manually confirmed as present (1) or absent (0) for each sample. GeneMarker parameters included smoothing, inclusion of fragment lengths of 50–500 base pairs, a peak height threshold of 50, local and global detection percentages set to off, and the stutter peak filter set to off. Percent error rates were calculated by comparing reproducibility and consistency among replicates as a whole and at each locus.2.7To provide a framework for examining patterns detected by the AFLP analysis and to investigate colonization history, chloroplast DNA (cpDNA) sequences were acquired from select samples using the Sanger‐sequencing method. Seventeen natural sites and all five cultivars, each represented by a single individual, were used for the cpDNA analysis. Eight primer combinations , ddH2O (3.6 μl), a primer combination (0.1 μl at 200 nM concentration), and template (1 μl). Thermal cycler conditions were as follows: 95°C for 1 min; then 35 cycles of 95°C for 1 min, 54°C for 30 s, and 65°C for 2 min; followed by 72°C for 5 min and a hold at 4°C. PCR product was purified using a 1:1 18% PEG magnetic bead clean‐up and rinsed twice with 70% ethanol.DNA templates were suspended in Tris‐Cl pH 8.0 and standardized to 5 ng/μl. Polymerase chain reactions (PCRs) were run in 10 μl reaction volumes and were composed of 2× Phusion Green HSII High‐Fidelity PCR Master Mix by Thermo Fisher (5 μl), MgCl2 (0.3 μl at 50 mM), ddH2O (to volume), a single primer (2 μl at 15 mM concentration), and template . Both forward and reverse primers were cycled independently to create consensus sequences. Thermal cycler conditions were as follows: 95°C for 2 min; then 50 cycles of 95°C for 10 s, 50°C for 10 s, and 60°C for 2 min; followed by a 10°C hold. Cycle‐sequencing products were purified using a 1:3 25% PEG magnetic bead clean‐up, rinsed twice with 70% ethanol, and sequenced on a 3730 Genetic Analyzer. Electropherograms were imported into Staden , MgCl2.8K  were performed in GenAlEx  between 0 and 1  was 0.174 and ranged from 0.003 to 0.302; for cultivated varieties, He averaged 0.258 and ranged from 0.232 to 0.286. Half of the populations’ average He fell close to or near (±0.03) the average He. Sites with above‐average He were found within three of the four natural sampling sites for the off‐Colorado Plateau region. Four additional above‐average He sites were identified across the central portion of the Plateau. Eleven sample sites had below‐average He, and all occurred within the Colorado Plateau region. Six of these sites dominated the northern region of the Plateau, with particularly high occurrence near the boundary of the Plateau, while the remainder occupied the more central portions of the Plateau's boundary.Collectively, the three AFLP primer combinations yielded 94 polymorphic loci. Each primer combination yielded an average of 32 polymorphic loci, an average error rate of 4.2% for individual loci, and an average error rate of 2.9% across replicates. Although the number of polymorphic loci is relatively low for the AFLP method, our results are consistent with other Two genetically distinct populations were identified using the program Structure: one largely on the Colorado Plateau and the other off the Colorado Plateau Figure . All fivWhen all 44 natural populations were analyzed together, the AMOVA showed that 17% of genetic variation was attributed to differentiation between the two regions: on and off the Colorado Plateau (Table p < 0.05) differed between these two groups, ranging from 0.11 and 0.19, with an average of 0.15. The differentiation between the Colorado Plateau population from both the cultivars and natural off‐Plateau population was even more extreme, with high significance (p < 0.006) in regard to outlier loci frequency across all six markers, with a change in locus frequency ranging from 0.27 to 0.55, with an average of 0.4. Furthermore, three of the six loci were present in the cultivar individuals at a frequency of 100% , while none of the natural populations demonstrated genetic fixation of any identified outlier loci for any of the environmental variables examined.When assessing cultivars independently, 11% of their genetic variation was due to differences among individual varieties, with 6% of genetic variation due to differences between the two Northern Plains varieties  and the three southwestern varieties . Although the AMOVA analysis showed nominal differentiation between the off‐Plateau group and cultivars (2%), the frequencies of three of the six outlier loci  significantly  produced similar relationships and bootstrap values. Thus, only the neighbor‐joining tree is presented here Figure . Only onThe cpDNA phylogeny suggests a weak relationship  between two natural sampling sites on the far northern and western edge of the Colorado Plateau  and two cultivars: one from the north (Bird's Eye sourced from Wyoming) and one from the south (Lovington sourced from New Mexico). Two of the four populations have cytotype identified, with one containing only diploid individuals (0C) and the other containing only tetraploid individuals (LOV).3.4r‐value between the Mantel and partial‐Mantel tests was 0.04, as was the case with the regression of locus 378 and MAT (0.25 vs. 0.21).Six loci (described above) were identified by BayeScan as outliers and showed significant correlations with one or more of the six nonredundant environmental variables Table . GeneticR‐squared values ranging from 0.09 to 0.33 (Supporting Information Table p = 0.026) and off (p = 0.009) Plateau populations, with R‐squared values of 0.13 and 0.95, respectively .With respect to linear regressions, cultivation, population, and cytotype were all influential in shaping the genetic response to environmental variation. Across all six environmental variables included, the genetic variability of natural populations was significantly correlated with at least one outlier locus per environmental variable, though moderately so with All cultivars were originally sourced from off the Colorado Plateau and genetically grouped with the natural off‐Plateau sampling sites Figures  and 3. Hp = 0.066) and nearly 40% drier than their diploid counterparts . These climatic differences likely drive adaptive divergence between the ploidy races, which is suggested in five genetic × environmental correlations: locus 378 × MAT, locus 436 × MAT, locus 286 × PS, locus 378 × PDQ, and locus 407 × precipitation of the coldest quarter (PCQ). In all cases, loci from diploid cytotypes were highly and significantly (0.004 < p < 0.020) correlated to the environmental variable with an adjusted R‐squared value between 0.72 and 0.89. Conversely, tetraploid cytotypes were not significantly (0.369 > p > 0.946) correlated to any of the aforementioned environmental variables. The diploid and tetraploid correlations described above were significantly independent from one another (0.001 < p < 0.043). Across all loci and all six environmental variables, diploids demonstrated the strongest genetic correlations to environmental variables than any other population groupings, with an average significant R‐squared value twice that of tetraploids and nearly fivefold that of natural populations assessed collectively .The different cytotypes demonstrated the most pronounced variance in response to environment Figure . Tetrapl44.1B. gracilis’ range. Above‐average expected heterozygosity occurs both on and off the Colorado Plateau, primarily in the central region, while below‐average expected heterozygosity is predominant on the north, east, and western boundary. While the level of within‐population diversity is quite high (70%), it is consistent with patterns of genetic diversity observed in other dominant marker studies of natural B. gracilis populations   may also play a role in determining how genetic variation is shaped and ultimately the detection of loci that may be under divergent selection  were distributed along an aridity gradient and tetraploids were found to have greater water use efficiency than diploids under water‐restricted growing conditions. In B. gracilis, temperature was shown to significantly affect physiologically driven traits, including Net CO2 assimilation and Rubisco activity in plants collected from high and low elevation sites in the Rocky Mountains  were suggested to have an evolutionary advantage over their diploid counterparts when it came to colonizing and surviving on the arid Qinghai‐Tibetan Plateau in Central and East Asia . This level of within‐population genetic diversity has been documented in other wind‐pollinated species, even within populations that occupy fragmented habitat , subtle though significant shifts in marker frequency in cultivated ecotypes was evident after only a single generation of breeding (G1). The shift was most pronounced in the ecotype developed from fewer local sources with the fixation of some polymorphic markers arising within only a single generation , these studies highlight that any population in cultivation, regardless of a multisource origin, is susceptible to the loss of genetic diversity. In this regard, marker identification, as was used in this study and others, could be developed to be an affordable and rapid test for recurrent genetic monitoring of cultivated varieties. If such ill‐effects are recognized early, corrective measures could be more efficiently and effectively applied  and a field site colonized by tetraploids (interior) could substantiate differences observed between cytotypes. A diploid and tetraploid site at off‐Plateau locations could further bring to light the differences observed in this study between populations on and off the Colorado Plateau. The inclusion of currently available cultivars across sites could test their performance across different environments in direct comparison with native genotypes. Seed viability and germination as well as mortality would be important measurements to include to more fully investigate restoration success of different seed sources. A common garden study of this nature could strengthen efforts to develop seed transfer guidelines as well as assist in population selection for cultivated ecotype development.Bouteloua gracilis plays a crucial and expanding role in the restoration of the Colorado Plateau. This study confirms that this species is genetically variable and structurally distinct on and off the Colorado Plateau and is genetically divergent across this area due to environmental and cytotypic variation. Because genetic, cytotypic, and functional trait data have all been acquired for this species, along with methodologies for spatially delineating this information (Butterfield & Wood, None declared.KT performed all of the molecular genetic experiments and data analysis and wrote the first draft of the manuscript. GJA oversaw the laboratory work and analyses and edited the final version of the manuscript.http://data/dryad.org. The regression summary of outlier loci and environmental variables is included as a Supporting Information Table The AFLP data generated for this study will be made available for public use on  Click here for additional data file.


There is currently no oversight for canine clinical genetic testing laboratories. We published an initial set of standards and guidelines with the goal of providing a basis for which canine testing laboratories could evaluate their quality assurance programs. To further those standards and guidelines, we have developed a checklist that can be used as a self-evaluation to identify gaps in their programs for continual quality improvement over time. Because there is currently no organization willing to oversee an external proficiency program, the checklist provides the first step toward an internal, self-assessment that can be used periodically to monitor improvements. In addition, we attempt to address concerns from the canine community regarding rare or private mutations, genetic screening using array-based technologies, non-peer reviewed tests that are being offered, and the clinical validity of certain mutations in particular breeds. Through coordination, conversation and hard work, the canine genetic testing community can strive to organize to improve testing and to provide more transparency to consumers and better outcomes for dogs.The online version of this article (10.1007/s00439-019-02013-9) contains supplementary material, which is available to authorized users. The role and responsibility of the clinical laboratory are to translate new genetic discoveries into clinical tests to make them available to breeders and dog owners with the goal of improving breeding programs and the overall health of dogs. Providing quality testing for companion animals goes beyond just good laboratory practices. Information contained on websites must be presented in a way that the consumer can understand what they are ordering prior to testing. Genetic counseling should be available to the owner or breeder prior to ordering their testing and at the time that they receive their results. The results from the tests offered should have medically actionable outcomes, even if it is just simply to inform the breeder on how to pair dogs for breeding to avoid producing puppies with genetic diseases. To help laboratories in this mission, we published the first set of standards and guidelines for clinical canine genetic testing laboratories . Again, taking experience from the ACMG and the College of American Pathologists, external proficiency involves performing surveys 2–3 times per year with laboratory challenges that are graded against a consensus of laboratories participating in that specific survey    in the Doberman Pinscher Below is the link to the electronic supplementary material.


Tenualosa ilisha) live in the Bay of Bengal and migrate to the estuaries and freshwater rivers for spawning and nursing of the juveniles. This has led to two pertinent questions: (i) do all Hilsa shad that migrate from marine to freshwater rivers come from the same population? and (ii) is there any relationship between adults and juveniles of a particular habitat? To address these questions, NextRAD sequencing was applied to genotype 31,276 single nucleotide polymorphism (SNP) loci for 180 individuals collected from six strategic locations of riverine, estuarine and marine habitats. FST OutFLANK approach identified 14,815 SNP loci as putatively neutral and 79 SNP loci as putatively adaptive. We observed that divergent local adaptations in differing environmental habitats have divided Hilsa shad into three genetically structured ecotypes: turbid freshwater (Western Riverine), clear freshwater (Eastern Riverine) and brackish-saline (Southern Estuarine-Marine). Our results also revealed that genes involved in neuronal activity may have facilitated the juveniles’ Hilsa shad in returning to their respective natal rivers for spawning. This study emphasized the application of fundamental population genomics information in strategizing conservation and management of anadromous fish such as Hilsa shad that intersect diverse ecotypes during their life-history stages.The anadromous Hilsa shad ( The Hilsa shad  is an anadromous clupeid species with a diverse range of distribution extending from freshwater rivers, estuaries, foreshore areas and seas of the Indo-Pacific region. It is the most important commercial trans-boundary species of which Bangladesh has the major share (86%), followed by India (8%), Myanmar (4%) and other countries within the Bay of Bengal region4. Hilsa provides the nutrition of 260 million Bengali people living in Bangladesh, some states of India and others living around the world, and it also support the livelihood of 2.5 million fishers and related people in the value chain alone in Bangladesh5. It is the single-most dominating food fish in the Bay of Bengal region with an average of 44% contribution to capture fisheries in Bangladesh6.Anadromous fish migrate from a freshwater breeding habitat to a marine feeding habitat and back to freshwater for spawning and their level of genetic diversity and population divergence is intermediate to that of marine and freshwater species7. There are some exceptions to this behavior as two other subtypes of the species -a marine type and a fluvial potamodromous type have also been reported10. The marine subtype inhabited nearshore coastal and/or sea habitats and relies on downstream estuarine waters for spawning without migrating to freshwater11. The potamodromous types appear to stay in the middle reaches of the major river system and complete its life cycle within freshwater without migrating to the sea10. However, it is not truly known whether the anadromous T. ilisha mix and breed with two others minor subtypes during migration or whether they pass each other spatially and temporally. Therefore, understanding of stock structure and divergence in Hilsa shad population across its range of different habitats  is still in dispute.The Hilsa shad mainly inhabits in the Bay of Bengal including lower regions of the estuaries and foreshore areas, but migrates to the upstream rivers, mainly the Ganges-Brahmaputra-Meghna river systems during the spawning season and returns to the original habitat after spawning12. This is mainly because of the highly connected diverged population with large effective population size of anadromous fish often show very weak genetic differentiation, thus decreasing the power of genetic tools in assigning individuals to their origins and defining the management units. However, the recent advances in next-generation sequencing (NGS) genotyping methods have expanded the prospects for exploring adaptive genetic markers to finely define weakly structured population, which ultimately improve our understanding of the genetic basis of fitness traits13.Understanding the genetic background and population structure of a species is crucial for planning and implementing conservation and management schemes, and needs to be explicitly considered for long-term sustainability. It is highly essential for fishery managers to know whether they are dealing with single or multiple spawning populations to design a sustainable management plan. However, revealing the genetic structure of an anadromous fish is challenging for conservation and management purposes16, Restriction Fragment Length Polymorphism (RFLP)18, Random Amplification of Polymorphic DNA (RAPD) markers22, and mitochondrial DNA cytochrome b gene nucleotide sequencing24. However, results from all of these studies are contradictory and inconclusive. The allozyme marker based studies reported that there is only one overall panmictic population of Hilsa shad in the Gangetic river systems15 and the Bay of Bengal region including Bangladesh, India and Myanmar16, but the Hilsa shad of the Bay of Bengal was genetically distinct from Kuwait and Indonesian Hilsa shad population16. Similarly, study-using mitochondrial DNA cytochrome b region also reported that the Hilsa shad population of the Ganga and the Hoogly rivers are the same population23. In contrast, studies using RAPD markers indicated that there is more than one gene pool of Hilsa shad in Bangladesh waters20. Similarly, it was reported that Hilsa shad has genetically two different populations from two major inland rivers of Bangladesh, the Padma and the Meghna20. PCR-RFLP analysis of the mitochondrial DNA D-loop region provided the same conclusion, suggesting that the Hilsa shad population has at least two differentiated populations in Bangladesh waters, which were subsequently divided into three, corresponding to the riverine, estuarine and marine populations18.Past research on the population genetic structure of this Hilsa shad mostly focused to investigate whether or not the species belongs to a single stock that uses rivers, brackish water estuaries and marine waters. The previous studies were mostly conducted through the use of allozyme markers25. NGS-based restriction-site-associated DNA (NextRAD) techniques is a modern approach of high-throughput sequencing techniques to study population genetic structures that simultaneously facilitate both genotyping-by sequencing and discovering a large number of single-nucleotide polymorphisms (SNPs) across the genome26. The NextRAD approach includes surveys of both neutral and adaptive panels of SNP loci that can be used to determine the levels of genetic differentiation and genetic diversity. This approach has already been successfully applied in several fish species and has proven to be effective at identifying the genomic basis of fitness traits29. Our application of nextRAD sequencing alleviates the problem of absent reference genome sequence of this non-model species and facilitates the development of SNP markers, with the aim of identification of candidate adaptive loci. The FST OutFLANK approach has been proven as a popular means for identifying the adaptive markers of a fish species in which annotated reference genome is not readily available31. As Hilsa shad is a highly dispersive, anadromous fish, represents an ideal candidate for identifying putatively adaptive panel of SNP loci with genome scan and outlier test.The inconclusive results of the past research about the population structure of the Hilsa shad can be addressed by using the genome-wide approach, which allows the genotyping of thousands of markers to detect genetic structure of this species at a finer spatial scaleJatka) of a particular habitat return to their respective natal rivers (rivers where they were born/nursed) for spawning as adults? To unravel the answers of the above two questions, the present study was conducted by identifying a set of neutral and adaptive genetic markers. The NextRAD sequencing technique was employed to genotype 31,276 SNP loci for 180 individuals of the species collected from six strategic aquatic habitats for revealing parental assignment and stock structure of the Hilsa shad population in Bangladesh waters. We applied the FST OutFLANK approach to determine adaptive vs. neutral SNP loci, and additional analysis were employed to verify the significance of these putatively adaptive loci under selection in the context with different migratory routes and habitats.Until today, two important questions about the population genetic structure and parental assignment remained unclear: (1) What is the degree of genetic differences among the Hilsa shad populations available in different migratory routes of the species? (2) Are the juveniles of Hilsa shad . All samples were collected under Bangladesh’s government permit and in accordance with animal care protocol (CVASU20160422) as approved by the Chattogram Veterinary and Animal Sciences University’s Animal Care and Biosafety Committee.Juveniles and adults of tes Fig. ; Table 1Genomic DNA was converted into nextRAD genotyping-by-sequencing libraries . Genomic DNA was first fragmented with Nextera DNA Flex Library Prep Kit , which also ligates short adapter sequences to the ends of the fragments. The Nextera reaction was scaled for fragmenting 10 ng of genomic DNA, although 20 ng of genomic DNA was used for input to compensate for the amount of degraded DNA in the samples and to increase fragment sizes. Fragmented DNA was then amplified for 25 cycles at 72 °C, with one of the primers matching the adapter and extending 8 nucleotides into the genomic DNA with the selective sequence GTGTAGAG. Thus, only fragments starting with a sequence that can be hybridized by the selective sequence of the primer will be efficiently amplified. The nextRAD libraries were sequenced on a HiSeq 4000 with eight lanes of 150 bp reads . The resulting fragments are fixed at the selective end, and have random lengths depending on the initial Nextera fragmentation.http://sourceforge.net/projects/bbmap/):bbmap/bbduk.shin = reads/run_1612/1612_GTAGAGGACTAAGCCT_S483_L006_R1_001_subset.fastq.gzout = reads/run_1612/1612_GTAGAGGACTAAGCCT_S483_L006_R1_001_t.fastq.gz ktrim = r k = 17 hdist = 1 mink = 8 ref = bbmap/resources/nextera.fa.gz minlen = 100 ow = t qtrim = r trimq = 10. The original shadstringent.vcf file contained data for 31, 276 SNP loci existing within a catalog of 92, 721 consensus NextRAD tagged sequences of 150 bases each. A column containing a unique ID for each SNP locus was added to the unfiltered vcf file using a custom perl script to remove redundancy among loci. Further filtering steps included the removal of complex SNPs with more than two alleles, less than 5% overall minor allele frequency, and less than 80% completeness of data among samples. Likewise, samples containing less than 80% completeness of data among the remaining loci were removed from the dataset. Additionally, where there were multiple SNPs located in a single NextRAD sequence tag only the first cataloged SNP was kept in the dataset so as to avoid the possibility of a single NextRAD locus having a disproportionate effect on the analyses. The unique IDs for SNP loci passing quality control standards were passed to a whitelist, which was used to filter the original vcf file using a custom perl script. After all filtering steps a total of 14, 894 individual SNP loci remained in the dataset. An additional custom perl script was used to convert the filtered vcf file to genepop format.Reads were trimmed using bbduk in custom scripts of SNPsaurus, LLC . To reduce annotations to repetitive sequences in the database, each locus in the vcf file had the associated sequence annotated by blast, using: blastn -db ncbi-blast-2.4.0+/db/nt and filtering for those blast hits with a significance of e-15 or better.A de novo reference was created by collecting 10 million reads in total, evenly from the samples, and excluding reads that had counts of <7 or >1000. The remaining loci were then aligned to each other to identify allelic loci and collapse allelic haplotypes to a single representative. All reads were mapped to the reference with an alignment identity threshold of 0.95 using bbmap (BBMap tools). Genotype calling was done using Samtools and bcftools . The vcf was filtered to remove alleles with a population frequency of <3%. Loci that were heterozygous in all samples or had more than 2 alleles in a sample  were removed. The absence of artifacts was checked by counting SNPs at each read nucleotide position and determining that SNP number did not increase with reduced base quality at the end of the read. In addition, each haplotype from all nextRAD-tags that contained putatively adaptive loci were subject to a BLASTn34. After a first look revealed little variation among collections, an outlier locus approach was taken using the R package OUTFLANK35 which calculates a neutral distribution of Fst values and then uses this distribution to assign q-values to each locus to detect adaptive loci which are putatively influenced by selection. Default parameters were used for OUTFLANK analysis and the “number_of_samples” parameter was set to 6 . Output from this analysis was used to create a neutral loci dataset and an adaptive loci dataset for further analysis. The complete, outlier, and neutral datasets were further analyzed using the GenePop R package36. Significance testing for deviation from Hardy-Weinberg Equilibrium (HWE) test was conducted on the complete dataset using default settings. This data was not used for filtering of loci from either dataset since the collections don’t represent single populations and loci under divergent selection are expected to violate HWE. Neighbor-joining trees were generated using both the neutral and outlier datasets using Nei’s genetic distance method.The filtered data was imported as a genind object into R and analyzed largely using the adegenet package37. The GenePop R package was also used for significance testing of pairwise FST to determine the genetic differences between collection sites using the neutral and outlier datasets using default settings with the samples grouped by collection site. Isolation by Distance analysis using the outlier dataset was done using the ‘adegenet’ R package using a pairwise distance matrix for all collection sites. Significance testing for isolation by distance was done by Mantel test using an Edward’s genetic distance matrix and a physical distance matrix between the collection sites with 9,999 iterations .The R package ‘poppr’ was used to conduct Analysis of Molecular Variance (AMOVA) on both datasets38 was used to genetically assign individuals to clusters. Simulations were run for 100,000 steps following a burn-in period of 100,000 steps, considering values of K (number of clusters) from one to 15, with 10 replications for each value of K. The analysis was performed using admixture, correlated allele frequencies and no prior information regarding sampling location or morphological species. For each individual the program identifies the fraction of the genome that belongs to each one of the clusters. The rate of change in the log likelihood between successive K values39 was also estimated. The calculations were performed with STRUCTURE HARVESTER40. The clusters of the estimated population structure were visualized by using CLUMPAK41. Clustering analysis using Discriminate Analysis of Principal Components (DAPC) using the outlier dataset was also done using ‘adegenet’ in order to reveal possible genetic clustering among samples without grouping by collection sites.Principal Component Analysis (PCA) was done using the adegenet R package on both neutral dataset  and outlier dataset (79 SNPs). The Bayesian clustering method implemented in the STRUCTURE software v. 2.3.4https://www.ncbi.nlm.nih.gov/sra/PRJNA503852, data publicly released on 31st December 2018).Our raw data with SRA accession number PRJNA503852 are available online in the NCBI sequence read archive  of the contigs returned matching coding regions.On average, 2 million reads of 150 bp per individual were generated from the 180 nextRAD-genotyped ST outliers and candidates for positive selection loci (P > 0.995)  tests of the 14,894 loci revealed that an approximately 15% of these loci were significantly deviated from the HWE expectations. Some of the loci with a significant heterozygote deficit may be linked to adaptive variations and population structuring between sampling sites. Outlier analysis run in R Package OUTFLANK identified 79 putatively adaptive SNPs as being F95) Fig. . NeutralFrom the 14,894 loci being analyzed, only 1,814 loci (0.06%) were significantly matched to known genes in the public Nr databases and has been categorized into 51 groups with general functions of biological processes, molecular functions, cellular components and systemic functions Fig. . 27 grouNeighbor-joining (NJ) analysis of 14,815 putatively neutral loci Fig.  revealedST of 0.1826 (P = 0.001). The largest component of genetic variability was explained at the individual level (63.8%). The portion of variation captured among the six populations was 18.6% (P = 0.0001), while the remaining variation among individuals within population was 17.6% (P = 0.0001). An AMOVA analysis based on these neutral loci dataset yielded a mean FST of 0.0029 (P = 0.001). The highest genetic variation was observed among individuals (86.8%), with little genetic variation among individuals within population  and even significantly smaller variation between populations .For the AMOVA analysis, 27 loci were dropped from the outlier dataset and 5,593 loci were dropped from the neutral dataset (approximately 1/3 of the loci in each set) Table . In bothST values for the adaptive loci dataset were markedly higher than the FST values for the neutral loci dataset, supporting genetic distinctiveness between the Hilsa shad populations  and Jamuna River (JR) were observed to have the highest pairwise FST values  and geographic distance. Isolation-by-Distance (IBD) analysis of neutral loci generated a regression slope of 0.0565, and the Mantel test was insignificant (P > 0.05). We found a statistically significant relationship between increasing degree of geographic separation and increasingly large FST for the putatively adaptive loci, in which the IBD slope was significantly higher at R2 = 0.1722 (P = 0.0134)  and juveniles from Surma-Kushiara (SKA-J) was also found to be zero . However, juveniles from these three sites showed a significant higher genetic differentiation with the adults of the other collection sites. In contrast, small, but significant distinction was seen between adults and juveniles in Meghna River at Chandpur  and sea at Kuakata .We have also conducted a pairwise comparison of Fla Table . NotablyST values. On the other hand, PCA based on the adaptive SNPs showed three major clusters  without clear separation of the populations according to the collection sites, which is found to be consistent with the NJ clustering, AMOVA analysis and FST values. Similarly, STRUCTURE analysis  and spatial analyses  generated by small panel of highly differentiated loci (79 putatively adaptive loci) provided strong evidence for three major genetically separated groups of Hilsa shad in Bangladesh: Marine and Estuary (Kuakata and Bhola), Western Riverine (Upper Padma and Jamuna), and Eastern Riverine (Surma-Kushiara and Meghna). This is consistent with the findings of19 based on RAPD markers, of17 based on RFLP of mitochondrial DNA, and of18 based on PCR-RFLP of mitochondrial D-loop region. These studies came to a consensus that Hilsa shad populations were genetically diverged by three different environments. This broad structuring in three independent groups in turn mirrors the spatial structuring which is likely maintained by environmental differences such as turbidity and salinity levels in the marine or brackish, muddy/turbid freshwater (Western Riverine) and clear freshwater (Eastern Riverine) habitat43. Fishes of Western Riverine habitats are of bright silvery in color with thicker structure that could be the influence of the whitish silty turbid water, while fishes of Eastern Riverine are thinner, a bit darker and elongated that could be the influence of dark colored water43. Likewise, biometrical comparisons have also delineated Hilsa shad into three stocks according to different ecotypes46. Our spatial analysis and low FST value (0.02888) did reveal that population of Kuakata and Bhola appear to be intermediate which may be indicative of gene flow and admixture events, as both locations seems to be adjacently located to each other in the further southern coastal regions  may likely represent two separate breeding grounds within the same riverine system, which is also congruent with previous studies48. Besides, the presence of population structuring in Hilsa shad was also reported by studies conducted using allozymes markers50 and RAPD analysis21.Our AMOVA and F51, otolith microchemistry and morphometrics8, mitochondrial cytochrome b23 and mitochondrial control region52. The lack of detectable population structural differences among the Hilsa shad in their distributed regions in Bangladesh suggests that there was extensive movement and mixing of Hilsa shad throughout Bangladesh for breeding purpose, given its anadromous nature53. Indeed, reproductive isolation may be likely impeded by the influx of large quantities of freshwater during monsoon, seasonal water circulation and continuous migration of Hilsa shad54. Therefore, these populations should be considered as belonging to a single gene pool and managed as a panmictic population8.On the other hand, there was no evidence of genetic differentiation between all studied populations based on multiple analyses of 14,815 neutral loci dataset in this study. Our results were consistent with findings from other studies using various markers and techniques such as allozymes11. Previous meristic and morphometric studies have also revealed some morphological differences among the Hilsa stocks from different environments55. Owing to the distinctive differences observed in the body height, the Hilsa stocks were divided into Western Riverine’s ‘Broad type’ and Eastern Riverine’s ‘Slender type’58. The different seasonal spawning between these two morphotypes: the “broad type” during monsoon and “slender type” during winter, and each with different fecundity level, may have also contributed to the limited natal and breeding dispersal, and thus population divergence of Hilsa shad59.However, the lack of population genetic structure does not preclude a genetic basis for the presence of genetic differentiation as postulated by the presence of putatively adaptive loci, which have clearly separated the populations. It is highly possible that the neutral loci and previously used methods were unable to detect the smaller-scale genetic difference between the three ecotypes identified by putatively adaptive loci. Moreover, we also hypothesized that the lower water discharge from the upstream river flow from Farakka barrage  with constant heavy siltation may have disrupted the migration routes of the anadromous Hilsa shad ascending tributaries in the Western Riverine habitats60. Isoforms are messenger RNAs resulted from transcription of the same gene locus but at a different transcription start sites (TSSs) or untranslated regions (UTRs), which results in alteration of gene functions61. This molecular complexity may lead to phenotypic differences and genetic variation between individuals at cellular level and thus deliberating the potential of population divergence in Hilsa shad63. From the match analysis of putatively adaptive loci, gene from the 51,360_15 locus encodes cleavage and polyadenylation specificity factor (CPSF), which is involved in the cleavage of the 3’ signaling region from a newly synthesized pre-messenger RNA (pre-mRNA) molecule in the process of gene transcription , enzymatic activity, immunity, signaling, nervous system and neuronal activity  associates with the mitochondrial DNA, function and mitochondrial cytochrome oxidase subunit 1 , in which adults from Meghna River in Chandpur (CA) were genetically similar to juveniles from Surma-Kushiara, which likely reflect the migratory behavior of some of the Chandpur River’s adults to the spawning ground in the upper stream of Surma-Kushiara River to breed. This finding was consistent with the major groupings in the above-mentioned analyses, in which individuals of Chandpur and Surma-Kushiara River were clustered as Eastern Riverine group. Unexpectedly, similar trend was not clearly depicted in both Chandpur (CA) and Kuakata (K), in which the adults and juveniles showed low genetic differentiation, but not sharing the same genetic structure as Surma-Kushiara, Upper Padma and Jamuna. This might be because that these two sites are the common routes of Hilsa shad to migrate from downstream to the geographically separated upstream rivers, thereby frequent admixture events may likely happen, and some adults may have been mixed during sampling activities. However, these observations may still allow us to conclude that Hilsa shad do return to their respective natal grounds for spawning, in which they are geographically separated. Fascinatingly, some putatively adaptive SNP loci  encode the genes, which are involved in neuronal function such as neural communication, memory formation and learning, in which lead to us to suggest that these genes might have played an important role in facilitating Hilsa shad to reach their natal ground in subsequent years for spawning.While Hilsa shad populations were found to be genetically differentiated by three major groups due to geographical separation, juveniles and adults from each populations seems to be sharing the same genetic structure, except for individuals from Kuakata and Chandpur. The significant weak genetic structuring in three sampling locations  as revealed by zero FAlthough neutral loci did not reveal much genetic structural differences among the Hilsa shad populations, by using 79 adaptive loci, we were still able to observe genetic differences which are so distinctive that they warrant the division of Hilsa shad into three separate ecotypes in Bangladesh waters: (1) marine/brackish represented by two closest relatives at Kuakata and the Meghna Estuary, Bhola, (2) muddy freshwater represented by two closest relatives in the Upper Padma and the Jamuna, and (3) clear freshwater types represented by the Meghna River and its upstream tributaries, Surma-Kushiara. In a conservation management context, it is important to maintain the genetic diversity of Hilsa shad in each location, given that our results have suggested high levels of gene flow within populations but lower variation across most of their range. The most important phenomenon revealed from this study that has great management implications is the fact that Hilsa shad returns to their natal rivers for spawning. Hilsa fishers try to catch juveniles before they move to the sea for maturation, because of the fear that they might not be able to uphold their catch yield. This finding will help in motivating fishers not to catch the juveniles, as they will get the assurance of getting back the juvenile as adult in the respective rivers. Our findings also indicated the importance of incentive-based juvenile protection activities involving all stakeholders, including the fishers for each of the rivers where Hilsa shad spawn and juveniles spend their early life. We note that empirically testing these hypotheses will require further characterization of the remaining putatively adaptive loci based on a more complete whole-genome sequence and at a larger regional level constituting the native distribution of Hilsa shad. Ultimately, isolating mechanisms, environmental factors, and the temporal stability of these patterns in structuring these groups in Bangladesh will require further study to help fishery managers in formulating ecotypes oriented management strategies for sustainable fisheries of two million fishers who participate in the Hilsa shad fishery. We suggest that this innovative work provides a model that may be of value in other aquatic species, especially in anadromous fish species that intersect diverse ecotypes during their life-history stages.Supplementary Dataset (STRUCTURE)Supplementary Info


Octopus cyanea and O. vulgaris populations in order to obtain baseline data for management plans. A fragment of the cytochrome C oxidase subunit 1 (COI) gene was sequenced in 275 octopus individuals from Madagascar, Kenya and Tanzania. In addition, 41 sequences of O. vulgaris from South Africa, Brazil, Amsterdam Island, Tristan da Cunha, Senegal and Galicia were retrieved from databases and included in this study. Five different species were identified using DNA barcoding, with first records for O. oliveri and Callistoctopus luteus in the WIO. For O. cyanea , 22 haplotypes were found, forming one haplogroup. AMOVA revealed shallow but significant genetic population structure among all sites , with significant differentiation among: (1) Kanamai, (2) southern Kenya, Tanzania, North and West Madagascar, (3) Southwest Madagascar and (4) East Madagascar . For O. vulgaris , 15 haplotypes were identified, forming three haplogroups. A significant genetic population structure was found among all sites . Based on pairwise ϕST-values and hierarchical AMOVAs, populations of O. vulgaris could be grouped as follows: (1) Brazil, (2) Madagascar and (3) all other sites. A significant increase in genetic distance with increasing geographic distance was found . These results indicate that for O. cyanea four regions should be considered as separate management units in the WIO. The very divergent haplogroups in O. vulgaris from Brazil and Madagascar might be evolving towards speciation and therefore should be considered as separate species in FAO statistics.The market demand for octopus grows each year, but landings are decreasing, and prices are rising. The present study investigated (1) diversity of Octopodidae in the Western Indian Ocean (WIO) and (2) connectivity and genetic structure of  Octopus vulgaris , O. maya , Eledone cirrhosa  and E. moschata , while the remaining of the approximately 100 octopus species being landed is classified as ‘unidentified’ [O. vulgaris, was thought to be a cosmopolitan species, first described from the Mediterranean Sea and the Eastern North Atlantic and later reported in the subtropical and temperate East and West Atlantic Ocean, the Indian Ocean and West Pacific Ocean [O. vulgaris comprise a group of morphologically similar but genetically distinct vulgaris-like species, the O. vulgaris species complex [O. vulgaris sensu stricto (s.s.), occurring in the Mediterranean and Eastern North Atlantic, and several Octopus “vulgaris” types that have been defined based on geographic distances, temperature boundaries, as well as the lack of plausible gene flow mechanisms [Octopus “vulgaris” type I occurs in the tropical Western and Central Atlantic Ocean (the Caribbean and Gulf of Mexico). Octopus “vulgaris” type II inhabits the subtropical waters along the coast of Brazil in the Southwest Atlantic Ocean. Octopus “vulgaris” type III occurs in the temperate Southeast Atlantic, along the coast of South Africa and the South Indian Ocean. Octopus “vulgaris” type IV inhabits the temperate to subtropical waters of East Asia [O. vulgaris species complex [O. vulgaris is commercially the most valuable octopus species worldwide [While the global market demand for octopus grows year after year, the supplies are becoming scarcer, and the prices are rising . In 2017ntified’ . If the ntified’ . The comic Ocean ,6. Howev complex ,8–12. Thorldwide .O. vulgaris, the majority of the octopus catch in the WIO consists of the big blue octopus, O. cyanea  [O. vulgaris) or 700,000 eggs (O. cyanea) in a single clutch [O. cyanea and O. vulgaris have a pelagic larval duration (PLD) of one to two months [While octopus farming might be an option for the future , to datey, 1849) , 17–19. e clutch . The feme clutch , 22. In o months , 22 and o months . After ro months , 22.To maintain the sustainability of these socio-economic important octopus fisheries, management actions must be taken, such as the establishment of permanent Marine Protected Areas (MPAs) . In thesO. cyanea and O. vulgaris populations, using a fragment of the mitochondrial COI gene as marker. To the knowledge of the authors, this will be the first study dealing with population genetics of O. cyanea. Therefore, findings from this study are pioneering and can be used to further expand the knowledge base available for marine management decisions in the WIO.In order to obtain useful indications for the formulation of effective management plans, the present study investigated (1) the catch composition and diversity of Octopodidae in the Western Indian Ocean and (2) the connectivity and genetic structure of O. cyanea, the study focussed on the Western Indian Ocean. Because connectivity relies on dispersal of pelagic larvae, patterns are likely to be influenced by sea surface currents, moving larvae in a specific direction. In the WIO, the South Equatorial Current (SEC) flows from East to West across the Indian Ocean. Near the East coast of Madagascar, one component splits into the North-East Madagascar Current (NEMC) and South-East Madagascar Current (SEMC), while another one joins the NEMC at the Northern tip of Madagascar  and GenBank  and included in the genetic population structure analysis of this study. The complete list of used COI sequences in this study can be found in Between 2014 and 2017, arm tips of 275 octopus individuals were collected in the WIO at landing sites and/or fish markets in Madagascar , Tanzania (Stone Town) and Kenya (Shimoni and Kanamai)  S1 Tabl. The sam5’-TAA ACT TCA GGG TGA CCA AAA AAT CA-3’) and LCO1490 (5’-GGT CAA CAA ATC ATA AAG ATA TTG G-3’), described by Folmer et al. [2, 1 μl (10 mg/ml) BSA and 0.2 μL of (5 U/μL) Taq DNA polymerase, using a UNO96 VWR Thermal cycler. The following temperature profile was used: 5 min at 94 °C, followed by 40 cycles of 30 sec at 94 °C, 1 min at 51 °C and 1 min at 72 °C. Final extension was conducted at 72 °C for 10 min. After checking if the amplification was successful with gel electrophoresis on a 2% TBE agarose gel, fragment sequencing was carried out using an ABI 3730XL sequencer.A DNA extraction kit  was used for extracting DNA, following the manufacturer’s protocol. Success of DNA extraction was measured with a NanoDrop 2000 spectrophotometer (Thermo Scientific). The universal primers HCO2198 . In addition, a Neighbour Joining (NJ) tree [The sequences were edited using the software CHROMASPRO , i.e. the correct representation of nucleotides was checked based on the chromatogram peaks. DNA barcoding for species identification was performed using the Identification System (IDS) for COI from BOLD systems  tree  includinNJ) tree  was consNJ) tree . For thehttp://users-birc.au.dk/biopv/php-/fabox/dnacollapser.php#, last consulted on 12/07/2018) was used to reduce the sequence dataset to haplotypes. Nucleotide and haplotype diversities for all populations were calculated according to Nei [Multiple and pairwise alignment of the sequences was carried out using Clustal W  as impleg to Nei  with theg to Nei .S [Unless otherwise stated, all following analyses were carried out with Arlequin. To insure suitability for population genetic analyses, the null hypothesis of neutral evolution of the marker was evaluated on the basis of Tajima’s D  and Fu ‘S  tests, wS  and the S  and HarpS  were calS .ST)  were determined. The pairwise ϕST P-values were adjusted using Holm’s sequential Bonferroni correction [http://ibdws.sdsu.edu, last consulted on 14/07/2018) was then used to assess the correlation between geographic and genetic distances  of sampled populations by applying a Mantel test [An analysis of molecular variance was performed to investigate the overall genetic population structure in the dataset  and pairrection . Groups rrection ; http://tel test . A haploO. cyanea, 30 as O. vulgaris, ten as Callistoctopus luteus, five as C. ornatus and one as O. oliveri , of which 20 samples were O. vulgaris and one sample being O. oliveri, and Fort Dauphin , of which two samples were O. cyanea, ten samples were O. vulgaris, ten samples were C. luteus and five samples were C. ornatus.COI sequences of 275 octopus individuals, from 15 locations, were successfully amplified and deposited in Genbank (accession numbers: MK593176 –MK593450). Of those 275 samples, 229 were identified as  oliveri . At all O. cyanea COI sequences with a length of 563 base pairs, 22 haplotypes were defined. Haplotype diversity (h) within each population ranged from 0 in Nosy Be (No) and Morondava (Mo) to 0.71 in Beheloke (Be). Nucleotide diversity (π) ranged from 0 in No and Mo to 0.15 in Be , rejecting the null hypothesis of panmixia. Pairwise ϕST-values between sample sites showed homogeneity among most of them, with significant differentiation visible in only two out of the 91 pairwise population comparisons, after applying sequential Bonferroni correction . Analysis of isolation by distance revealed no significant increase in genetic distance with increasing geographic distance .The evolutionary relationships among 22 plogroup . AMOVA rrrection . A hierarrection . HoweverO. vulgaris COI sequences with a length of 482 base pairs, 15 haplotypes were defined. Haplotype diversity (h) within each population ranged from 0 in Ga, Se, Tc, Pe, and AI to 0.83 in NBr and Sb. Nucleotide diversity (π) ranged from 0 in Ga, Se, Tc, Pe, and AI to 0.013 in NBr , rejecting the hypothesis of panmixia. The genetic differentiation between Brazilian, Malagasy and all other sites was confirmed by very high and significant pairwise ϕST-values . Since the population structure seems to be mainly driven by the samples from Brazil, with the blue haplogroup only occurring in that region and a minimum of nine mutation steps between the red and blue haplogroup . Other hierarchical AMOVAs were carried out, with all tested groupings being significant . Furthermore, analysis of isolation by distance revealed a significant increase in genetic distance with increasing geographic distance .The evolutionary relationships among 15 logroups . The disT-values . Based oT-values . Howeverplogroup , the ananificant . HoweverO. cyanea, O. vulgaris and C. ornatus are known to occur in the waters of the Western Indian Ocean [C. luteus and O. oliveri in these waters. This is a substantial range expansion because until now, O. oliveri and C. luteus are only known from the Western Pacific Ocean [C. luteus being found in India . Therefore, the actual distribution range (e.g. in World Register of Marine Species (WoRMS) database) should be updated.While an Ocean –19, 55, ic Ocean , 55–56, O. cyanea, except in the two most southern located sites in Madagascar, Lavanono (La) and Fort Dauphin (Fd). In the former, samples consisted of O. vulgaris and O. oliveri, with O. cyanea not represented, while in the later samples consisted of O. vulgaris, C. luteus, C. ornatus and only a small fraction of O. cyanea. A possible explanation could be the difference in Sea Surface Temperature (SST). All sample sites are located in the tropics, with SST ranging between 26.0 °C and 28.2 °C. However, La and Fd are located more south, in sub-tropical waters, with SST below 25.0 °C [C. luteus [O. vulgaris [O. oliveri [O. cyanea, with most rapid development at temperatures above 27.1 °C [C. ornatus no such data was available. From these experiments it seems that, even if ranges might overlap [O. cyanea in the southern located sites, while temperature might be too warm for the other octopus species in more northern located sites. Another explanation could be the differences in fishing time period. Traditionally, women and children catch octopus by gleaning at low spring tides on the exposed reef flats and probe octopus dens with spears [O. cyanea is active during the day [C. ornatus [C. luteus [O. oliveri [O. vulgaris [At all sample sites all collected individuals were identified as  25.0 °C . Egg lay. luteus , at 21.2vulgaris , at an a oliveri  and at a overlap , 60–61, h spears . Dependi the day , while C ornatus , C. lute oliveri  and O. vvulgaris  are noctvulgaris  might beO. cyanea and O. vulgaris showed a moderate level of haplotype diversity (h) and a low level of nucleotide diversity (π). This pattern has also been reported in other studies in the WIO [h and low π might indicate a genetic bottleneck that has caused stochastic extinction of most haplotypes, followed by population expansion [Scylla serrata [Amphiprion akallopisos [Terebralia palustris [All populations of  the WIO , 66. Thexpansion . This isllopisos  and the alustris .O. cyanea is characterised by a dominant haplotype that is present at all sample sites . Based on a hierarchical AMOVA, restricted gene flow could be detected between O. cyanea populations from (1) Kanamai (Ka), (2) southern Kenya (Sh), Tanzania (St), North and West Madagascar , (3) Southwest Madagascar  and (4) East Madagascar . Similar findings have been reported for Terebralia palustris, for which restricted gene flow was detected between (1) northern Kenya, (2) Kenya, Tanzania and western Madagascar, (3) northeastern Madagascar and (4) southern Madagascar [Scylla serrata a significant genetic divergence was found between sites in eastern Madagascar and sites on the West coast of Madagascar, mainland East Africa, as well as the Seychelles [Acropora tenuis in Tanzania and Kenya [The results from this study indicate that the WIO population of le sites . The hapdagascar . For theychelles . At a smnd Kenya . On the nd Kenya , 71. ThiAmphiprion akallopisos, for which the population from Sm was significantly differentiated from the other WIO populations [Terebralia palustris [No significant correlation was found between genetic differentiation and geographical distance. Therefore, the main factor that restricts gene flow and thus dispersal seems to be the direction and strength of ocean currents. During the Northeast Monsoon, the East African Coast Current (EACC) and the Somali Current (SC) converge into the eastward South Equatorial Counter Current (SECC) , which mulations . The soualustris . Howeveralustris .O. cyanea in the WIO region due to the long-distance dispersal capacity of O. cyanea paralarvae. However, currents are one of the main factors responsible for restricted gene flow to a certain extent between the central part , Kanamai (convergence of EACC and SC into SECC), eastern Madagascar (NEMC and SEMC) and southwestern Madagascar (EACC retroflection and MCE). It is important to remark that the small number of samples for certain sites may have influenced the results that were found. Furthermore, we hypothesise that the contribution of adult migration to population connectivity is negligible. Adult O. cyanea may use one den for three to five weeks [O. cyanea, it is known for other octopus species that this distance is around 15 m [O. cyanea then the species appears to be relatively resident and migration of adult O. cyanea is negligible. However, research needs to be done to confirm this hypothesis.The results of this study suggest there is high genetic exchange of ve weeks , 73. Eveund 15 m . If thesST = 0.82, p ≤ 0.01), was found among all sample sites. Based on very high and significant pairwise ϕST-values and hierarchical AMOVA, restricted gene flow could be detected between populations from Brazil, Madagascar and all other sampling sites. The strong genetic differentiation of the Brazilian and Malagasy sites can be attributed to the exclusive occurrence of haplotypes from the blue (Brazil) and yellow (Madagascar) haplogroup  populations from South Africa and the West Coast of Madagascar did not show significant genetic differentiation among each other, but a significant genetic break was observed with populations from East Madagascar [Scylla serrata [A strong and significant population structure  and more samples of Galicia (Accession numbers: JX500655—JX50065577) . Since wOver the past couple of years, concern was raised about the exclusive use of mtDNA markers, since they only reflect one locus, only showing the maternal genetic history  and theyO. cyanea populations in the WIO, with low but significant differentiation between the central part , Kanamai, eastern Madagascar and southwestern Madagascar. For O. vulgaris highly restricted gene flow could be detected between populations from Brazil, Madagascar and all other sampling sites due to the large geographical distance. Therefore, this study suggests that four separate management units should be considered for O. cyanea in the WIO. The very divergent haplogroups in O. vulgaris from Brazil and Madagascar might be evolving towards speciation and therefore should be considered as separate species in FAO statistics.More species of the family Octopodidae occur in the WIO than previously thought. Furthermore, this study revealed high levels of connectivity among S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Euglossa dilemma and E. viridissima. Male orchid bees acquire chemical compounds from their environment to concoct species-specific perfumes to later expose during courtship. We find that the two lineages acquire chemically distinct perfumes and are reproductively isolated despite low levels of genome-wide differentiation. Remarkably, variation in perfume chemistry coincides with rapid divergence in few odorant receptor (OR) genes. Using functional assays, we demonstrate that the derived variant of Or41 in E. dilemma is specific towards its species-specific major perfume compound, whereas the ancestral variant in E. viridissima is broadly tuned to multiple odorants. Our results show that OR evolution likely played a role in the divergence of sexual communication in natural populations.Sexual signaling is an important reproductive barrier known to evolve early during the formation of new species, but the genetic mechanisms that facilitate the divergence of sexual signals remain elusive. Here we isolate a gene linked to the rapid evolution of a signaling trait in a pair of nascent neotropical orchid bee lineages,  Male orchid bees collect scents from the environment to attract females for mating. Here, Brand et al. combine population genomic, perfume chemistry, and functional analyses to show how divergence in odorant receptor genes may be driving reproductive divergence between two orchid bee species. Minute differences in sexual signals can lead to reproductive isolation, enforcing species boundaries through the interplay of highly specific and reciprocally tuned signals, and sensory organs5. However, notwithstanding the important role that sexual signaling plays in the origin and maintenance of species boundaries, the genetic mechanisms underlying their evolution remain poorly understood. Chemical signaling—despite being the most ancient and widespread form of intersexual communication—has received limited attention3.Sexual signaling allows organisms to detect, identify, and choose appropriate mating partners. The remarkable diversity of sexual communication systems found across the animal tree of life reflects the important role that pre-zygotic reproductive barriers have played in lineage diversification6. Similar to other pre-zygotic reproductive barriers, chemical signals are characterized by elevated rates of differentiation, especially among co-occurring sympatric lineages7. In addition, several gene families involved in the sensory perception of chemical signals have been shown to mirror these evolutionary dynamics9. For example, olfactory receptor genes tend to differentiate rapidly in disparate groups of animals including lemurs10, rodents11, and moths12. However, it remains unclear how sexual chemical signaling evolves in a genome-wide context and contributes to the early diversification of species.Many animal species use chemical signals to identify and discriminate potential mates14. Accordingly, population-level analyses of lineages during the early stages of speciation provide excellent opportunities to determine the genetic basis of sexual communication and its role in the formation of new species. However, identifying loci associated with the evolution of sexual signaling and/or reproductive isolation is not a straightforward process. Genome-wide analyses of divergence often produce large numbers of candidate genes with unknown function17, which are seldom functionally linked to relevant traits. To understand how genetic variation relates to the evolution of sexual signaling, it is necessary to establish a link between genotype and phenotype. Here we take this approach in a pair of incipient orchid bee lineages.Speciation theory predicts that genetic loci associated with the evolution of reproductive isolation should exhibit pronounced divergence relative to background genome-wide variation18. Male orchid bees acquire chemical compounds from various environmental sources including orchid flowers, fungi, and rotten vegetation, and store them in highly specialized pouches in their hind tibiae19. Male bees release the resulting “perfume” bouquet in elaborate courtship displays at perching sites where mating takes place20. Although the exact function of perfume communication has not been demonstrated, behavioral21, sensory23, and macroevolutionary evidence24 suggest that perfumes are important sexual signaling traits involved in pre-zygotic reproductive isolation, presumably by enabling species recognition. Recent broad-scale phylogenetic analyses revealed that perfume signals diverge rapidly, especially among species of relatively recent origin that co-occur in sympatry7, a common signature of reproductive barrier traits. This is corroborated by species-specific neurophysiological responses and behavioral attraction towards conspecific perfume compounds21, suggesting a highly specialized signaling function—the hallmark of sexual communication systems6. In this unique chemical communication system, the sense of smell is crucial for both perfume acquisition by males and perfume detection by females. Therefore, any genetic changes that modify the olfactory sensory perception (e.g. mutations in a single chemosensory gene) might have the potential to simultaneously introduce changes to male perfume signals and female perfume preferences21, a scenario that can favor the rapid evolution of assortative mating25.Orchid bees are one of the most important insect pollinators in the neotropical regionEuglossa dilemma and Euglossa viridissima constitute a pair of recently diverged orchid bee lineages that diverged ~150,000 years ago26. Previous work conducted on the overlapping (sympatric) populations of these two lineages revealed discrete differences in male perfume collection behavior and perfume chemistry26, in addition to rapid divergence in chemosensory receptor genes involved in olfactory detection28. Although previous work27 showed that divergence in chemosensory genes is correlated with perfume differentiation, it remains unclear to what extent the evolution of specific chemosensory genes facilitated the evolution of perfume communication and reproductive isolation among these lineages. This species pair provides an excellent opportunity to identify the genetic basis and evolutionary mechanisms underlying the origin of reproductive isolation.E. dilemma and E. viridissima, coupled with functional assays, to identify genetic loci associated with the evolution of perfume signaling in orchid bees. We find that the two lineages collect chemically distinct perfumes and are reproductively isolated, despite exhibiting low levels of genome-wide differentiation. We then show that variation in perfume chemistry between E. dilemma and E. viridissima coincides with two species-specific selective sweeps in different regions of the genome that likely evolved via strong positive selection. These two regions harbor two different tandem arrays of odorant receptor (OR) genes jointly containing 43 OR genes. Although most of the ORs in these regions exhibited low levels of divergence, we identified one receptor (Or41) that is significantly enriched with amino acid changes in E. dilemma but conserved in E. viridissima. Using functional assays, we demonstrate that the derived variant of Or41 in E. dilemma is specific towards its major species-specific perfume compound, whereas the ancestral variant in E. viridissima is broadly tuned to multiple odorant compounds. Taken together, our results suggest that divergence in chemical tuning in a key OR gene contributed to the evolution of pre-mating reproductive barriers in natural populations of these orchid bee lineages. This raises the intriguing possibility that genes controlling sensory perception might simultaneously drive the evolution of male traits and female preferences used in mate recognition, highlighting the importance of rapid pre-zygotic reproductive isolation in the formation of new species.Here we use a population-level analysis of perfume chemistry and genome-wide genetic divergence throughout the entire geographic range of E. dilemma and E. viridissima are isolated genetic lineages, we first genotyped 232 males sampled across their geographic range in Central America , a southern E. dilemma population (Edsouth), and a northern E. dilemma population (Ednorth). These results indicate that E. dilemma and E. viridissima form genetically distinct lineages with a complex evolutionary history.To assess whether ica Fig. . A princica Fig. . E. dileE. dilemma and E. viridissima, we further analyzed the above admixture patterns between Ev, Edsouth, and Ednorth. Several individuals drew ancestry from more than one genetic lineage, especially in sympatry : 0.001, z: 2.7, p-value = 0.007; Supplementary Table E. viridissima evolved from within E. dilemma with Ev diverging from Edsouth following the split of Ednorth and Edsouth  of 384 individuals  was only present in perfume blends of E. dilemma and L97 (linoleic acid lactone-derivative29) was only present in perfume blends of E. viridissima , which is calculated by subtracting the z-transformed intraspecific FST (FST’) from the interspecific FST’, for non-overlapping 50 kb windows across the genome31. The resulting windows of elevated ∆FST’ (>99th percentile) were clustered into seven distinct outlier peaks of varying size , nucleotide diversity , absolute sequence divergence , and linkage disequilibrium , divergent (positive) selection, and combinations thereof37. To isolate potential outlier windows evolving under divergent selection between E. dilemma and E. viridissima, we scanned for patterns of selective sweeps. Notwithstanding the general trend, we identified three outlier windows with elevated values of both ∆FST’ and Dxy  and increased Dxy values , and, in the E. dilemma-specific sweep, reduced nucleotide diversity (π) in E. dilemma .Close inspection of the two 50 kb windows with evidence of a selective sweep Fig.  revealeddow Fig.  and 4 ges28 Fig. . ORs bels27 Fig. . Indeed,ous Fig. . A compaOr41 and the observed specificity in perfume chemistry, we heterologously expressed the two Or41 variants present in E. dilemma and E. viridissima in the Drosophila olfactory T1 neuron lacking endogenous ORs38 and measured electrophysiological responses to a diverse array of perfume compounds and odor blends , but not to other odors , IAH-D , and CLYO . We dissected males in the field directly following collection and stored both hindlegs in 500 µL hexane for perfume extraction, whereas the rest of the body was preserved in 95% ethanol for subsequent morphological and genetic analyses. Perfume extracts and body tissue were stored at −20 °C until analyzed.We sampled males of two orchid bee species 44. Briefly, the DNA of each individual was digested using the EcoT22I restriction enzyme, followed by barcode ligation. Ninety-five individually barcoded samples were then pooled and PCR amplified, and finally size selected for ~300 bp using AMPure bead cleanup . Libraries were then validated on a Bioanalyzer high-sensitivity DNA chip  and sequenced on a HiSeq 2100  in 150 bp single-end mode. Three individuals were run on each of the three lanes to correct for potential batch effects.DNA was extracted from dissected flight musculature of tissue stored in 95% ethanol using the Blood and Tissue Kit (Qiagen). The extracted DNA was used for genotype-by-sequencing (GBS)E. dilemma reference genome30. As male Hymenoptera are haploid, we discarded all reads that mapped to more than one region of the genome to exclude repetitive loci. Nucleotides were called when a locus was sequenced in at least 50% of all individuals to a minimum read depth of four reads. SNPs were pruned if they were in LD of r2 ≥ 0.2 or had more than two alleles using plink45. After initial filtering, we excluded all individuals with <5000 called SNPs and repeated the filtering steps above on the final set of 232 individuals. The resulting pre-processed SNP set was then used for all downstream analyses.Sequencing reads were mapped to the 46. Independent PCAs were performed on the entire set of individuals as well as on subsets of all sympatric sampling sites. In addition, we performed ancestry estimation in ADMIXTURE47. For both analyses, the SNP set was pruned to a minimum allele frequency (MAF) of 5%. To estimate admixture proportions for each individual in the dataset, we used ADMIXTURE based on SNPs called in at least 75% of individuals. We estimated levels of genetic structure for k = 1 to 10 jointly for all individuals. Each run was repeated ten times with random seeds including cross-validation (CV) error estimation for each level of k. We inferred the estimated number of genetic clusters using the mean CV error. We inferred the fixation index (FST) between genetic clusters in SNPRelate.To visualize genetic structure among populations and species, we performed a PCA in the R package SNPRelateE. dilemma and E. viridissima, we used the f4-test48 as provided as part of the treemix software package49. The SNP set used was pruned to unlinked SNPs called in at least 75% of individuals with a MAF of 0.05 .To test for “treeness” of the phylogeny including sympatric and allopatric populations of Ev, Ednorth, and Edsouth) using Moments50 based on the GBS dataset. The 96 demographic models tested included 16 different variations of bidirectional gene flow between all branches of six three-population models. As the phylogenetic relationships between the E. dilemma and E. viridissima lineages are unclear, we included models of all possible branching patterns . We kept the basic model as simple as possible with a single static population size per population  and two divergence times (t1 and t2) leading to a minimum of five estimated parameters (no migration/no gene flow). Models allowing for migration were incremental from single bidirectional vertices to up to four vertices  added to the basic model leading to a maximum of nine estimated parameters. Parameter bounds were set to a minimum of 0.001 and a maximum of 100 for population size, 0 and 10 for divergence times, and 0 and 20 for migration rates. The likelihood of the empirical allele frequency spectrum (AFS) given the model AFS was estimated five times for each model. As likelihoods did not differ much between runs, we took the mean log likelihood for model comparison. To compare models, we used the AIC based on the log likelihoods of the empirical AFS given each of the 96 model AFS calculated in Moments. AIC scores, ∆AIC, and Aikaike weights were calculated in base R and used to identify the model with the best fit.We estimated the likelihood of 96 different demographic models of the 3 genetic lineages identified . GC–MS data were processed using the MassHunter GC/MS Acquisition software vB.07.00 (Agilent Technologies) and analyzed in OpenChrom .We analyzed the perfume composition of 384 individuals  to a minimum genome-wide read depth of 5×.E. dilemma reference genome30 and SNPs were called with GATK54. Genome differentiation was analyzed in R using PopGenome55. We used a non-overlapping 50 kb sliding window approach to estimate pairwise relative genetic differentiation (FST), absolute sequence divergence (Dxy), and nucleotide diversity (π) for each window between all three genetic lineages. In addition, we calculated LD (r2) within the same 50 kb windows using vcftools. Therefore, we considered only SNPs of at least 1 kb distance present in 90% of all individuals. Additional descriptive statistics were produced using plink45 and base R.Reads were mapped to the z-transformed net interspecific differentiation (∆FST’31 by subtracting intraspecific diversity within E. dilemma from interspecific diversity between E. dilemma and E. viridissima (∆FST’ = FST‘ [Ev vs. Ed] − FST‘ [Ednorth vs. Edsouth]). We then filtered the >99th percentile ∆FST‘ regions as outliers of interspecific differentiation. To identify windows with π-values biased towards one species, we contrasted π between species by subtracting πEd from πEv to calculate the net differential in intraspecific nucleotide diversity (∆π) between the two species. If ∆π equals 0, π is indifferent between species in the corresponding genomic window.We calculated the FST outlier windows using (1) SweeD56 and (2) hapFLK57. These two methods identify selective sweeps based on two different types of data including either the allele frequency spectra (SweeD) or haplotype information (hapFLK) of genomic regions. We ran hapFLK with k = 5 clusters and the default of 20 model fits (–nfit) on all scaffolds carrying ∆FST outlier windows. The optimal number of clusters was calculated using the fastPHASE CV method58 as implemented in the imputeqc R package. SweeD was run on the folded site frequency spectra using a sliding window size of 1000 bp. The ∆FST outlier window that revealed a species-specific sweep pattern was then analyzed with a 10 kb non-overlapping sliding window in SNPrelate as described above. The significance value of the CLR statistic was calculated by simulating 50 kb regions with the evolutionary simulation framework SLiM59 under the inferred demographic model. We simulated a total of one thousand 50 kb regions assuming a mutation rate of 3.5e−9 (mean of bumble bee60 and honey bee61) and a recombination rate between 4.7e−8 (bumble bee62) and 2.6e−7 (honey bee63), which was randomly drawn from a uniform distribution throughout the simulated region. The simulation was performed for 600,000 generations with an assumed NE of 5,000 in the x-chromosome mode in SLiM, to simulate haplodiploid molecular population dynamics, and 10 male  individuals were drawn randomly at the end of each simulation. For each simulated dataset, we computed CLR test statistics in the same way as with the empirical dataset. We then defined the significance threshold as the top CLR value for each lineage. Hapflk p-values were obtained by fitting a linear model to the distribution of estimated hapflk statistics using an M estimator as implemented in the R function “rlm”57. The resulting p-values were then adjusted for multiple testing using the Bonferroni method. We then performed permutation tests for ∆π, Dxy, and FST with 10,000 permutations in 50 kb windows across the two scaffolds with a selective sweep identified (scaffold_45 and scaffold_61) to test whether these significantly deviated from randomized population subsamples.We performed two independent tests for selective sweep signatures in all ∆64 of OR genes of the two tandem arrays with selective sweep patterns were performed by counting the number of polymorphic and fixed synonymous and non-synonymous of open reading frame (ORF) alignments of all individuals with whole-genome information and applying a four-field Fisher’s exact test. Polymorphic sites were scored when present in more than one individual.McDonald–Kreitman testsOr41 gene using tiled Sanger sequencing. We designed a set of four tiled PCR primer pairs spanning all 6 exons and 5 introns .We re-sequenced the Or41 genotypes, we produced a multi-sequence alignment including all individuals in mafft and analyzed it in MEGA66. We estimated π within each species for the entire gene. Subsequently, substitutions in the ORF were identified visually and defined as fixed between species when all individuals of one species had a nucleotide different from all individuals of the other species. Otherwise, a substitution was defined polymorphic. Similarly, we visually identified substitutions as non-synonymous or synonymous based on the ORF. We then mapped the substitutions to the predicted membrane topology27.After the reconstruction of Or41 evolutionary history, we inferred a phylogenetic tree based on the entire ORFs of the 47 sequenced individuals and the publicly available ORFs of Eacles imperialis, Eilema flammea, and the more distantly related orchid bee Eufriesea mexicana28 as outgroup. For tree inference, we estimated a maximum likelihood tree in RaxML67, including 1000 bootstraps.To reconstruct the E. imperialis, E. flammea, and E. mexicana as outgroup. The tree was used for a dN/dS test using codeml in PAML68. Therefore, we estimated the likelihood of a model allowing for two or more dN/dS values over branches in the tree with E. dilemma as foreground branch (M1), and a null model allowing only a single dN/dS for all branches (M0). We conducted a likelihood ratio test, to test whether the model with branch variation is more likely than the null model (∆ = 2(ln(M1) − ln(M0)) with ∆ approximating a χ2 distribution with one degree of freedom). Only fixed differences between E. dilemma and E. viridissima were taken into account to prevent overestimation of dN/dS values.To test for selection along the branches leading to the respective species, we produced a gene phylogeny based on the consensus sequence for each of the two species together with Ev or Edsouth) using the standard Trizol protocol  followed by first-strand cDNA synthesis with the SuperScript III kit . EdOr41 and EgOrco  were amplified from Edsouth cDNA. EvOr41 was amplified from Ev cDNA. PCR was conducted with the high-fidelity Phusion DNA polymerase system (New England Biolabs). The gel extracted PCR product was then ligated to the pUAST-attB vector69. Correct insertion and sequence identity was confirmed using Sanger sequencing and the final constructs were sent to BestGene  for injection in Drosophila melanogaster eggs (attP40 stock) for targeted insertion on the second chromosome. Successful transformation was verified by a white marker gene on the injected construct, Sanger sequencing of attP target sites, and PCR of cDNA based on RNA extracted from 50 fly heads carrying the construct.To amplify the target receptor alleles, we extracted antennal RNA from pools of 10–20 males of the same genotype  leading to flies with the genotype w-; UAS-EgOrco/UAS-ExOr41; DmelOR67d-Gal4, co-expressing EgOrco and Or41 of either E. dilemma or E. viridissima (“ExOr41”). Co-expression of EgOrco with ExOr41 resulted in spontaneous activity and responsiveness to odors in an ExOr41-specific manner.To drive the targeted expression of our UAS-receptor constructs in the olfactory sensory neuron of the T1 sensillum, we used the DmelOr67d-Gal4 empty neuron system−1, CS-55, Ockenfels Syntech, Buchenbach, Germany). Test odors were loaded on a flint glass Pasteur pipet  and heated for ~1 s with a handheld butane lighter to enable evaporation of chemicals70, followed by delivery in 1 s puffs. Each odor was tested in 5–15 replicate sensillae with no more than three sensillae per fly. For dose–response curves, dilutions of a compound were tested on a single sensillum per individual beginning with the solvent followed by the test compound in decreasing dilutions. Responses of T1 sensillae heterologously co-expressing EdOr41, EvOr41, or EgOrco with EgOrco were assessed by counting the number of spikes in the first 0.5 s following the onset of a puff and subtracting the number of spikes in the first 0.5 s preceding the onset of a puff and multiplied by 2 to calculate the corrected number of spikes per second in response to a given odor. T1 sensillae were identified by size, location, the presence of a single spike amplitude, and the absence of T4-specific response to methyl laurate. The DmelOR67d-Gal4/DmelOR67d-Gal4 genotype was confirmed by the absence of wild-type T1 response to cis-vaccenyl acetate.For SSR, 2- to 8-day-old female flies were trapped inside a 200 µl pipette tip so that the antennae and part of the eyes protruded from its narrow end. The tip was placed on a microscope slide using dental wax and the antenna was fixed on a glass slide covered with double-sided tape using a glass capillary. Using a micromanipulator , an electrolysis-sharpened tungsten recording electrode was introduced at the base of a T1 sensillum using ×1000 magnification under an upright fixed-stage microscope  and the reference electrode was inserted into the eye or head of the fly. The recording electrode was connected to an amplifier  and the signals were fed through a Hum Bug noise eliminator (A-M Systems) into an analog–digital signal converter . The signals were then visualized and recorded on a computer using WinEDR (Strathclyde). Odors and odor mixtures were delivered to the mounted fly via a humidified charcoal-filtered air stream  with the exception of HNDB and L97 (≥99% purity), which were isolated from crude tibial hexane extracts using preparative GC. Analytes were trapped inside short pieces of megabore column (DB-1) connected to the end of the chromatographic column . Trapping success, concentration, and purity of isolated compounds was confirmed by GC–MS using a HP 5890 II GC fitted with a 30 m nonpolar DB-5 column and a HP 5972 mass selective detector . Body washes were produced by individually introducing the head, thorax , and abdomen of three freshly killed E. dilemma individuals of each sex into 500 µl hexane. After incubating for at least 10 min extracts were pooled to produce one male and one female whole-body wash. Nesting materials and waxes were purchased from various sources  with the exception of nesting material, which was gathered freshly from an active E. dilemma nest. Pure compounds commonly found in natural waxes were purchased individually . For SSR, all single compounds were diluted 1:100 in mineral oil (Sigma Aldrich) with the exception of 1,4-dimethoxybenzene and the fatty acids, which were diluted in pure ethanol (Sigma Aldrich) to a concentration of 0.005 M. Control chemicals were purchased in ≥95% purity .The chemical compounds and compound mixtures that we tested were obtained from multiple sources. Perfumes mixtures were extracted from hindlegs of wild-caught males using 500 µl of hexane per leg, which we then pooled from 10 to 20 individuals. We tested a total of five species all of which occur in sympatry with All experiments in this study were conducted in compliance with all relevant ethical regulations for animal testing and research.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1