Canis latrans), to investigate patterns of genomic diversity and identify variants that may have been under selection during range expansion. Using a restriction‐associated DNA sequencing (RADseq), we genotyped 394 coyotes at 22,935 SNPs and found that overall population structure corresponded to their 19th century historical range and two distinct populations that expanded during the 20th century. Counter to theoretical expectations for populations to bottleneck during range expansions, we observed minimal evidence for decreased genomic diversity across coyotes sampled along either expansion front, which is likely due to hybridization with other Canis species. Furthermore, we identified 12 SNPs, located either within genes or putative regulatory regions, that were consistently associated with range expansion. Of these 12 genes, three  have putative functions related to dispersal, including habituation to novel environments and spatial learning, consistent with the expectations for traits under selection during range expansion. Although coyote colonization of eastern North America is well‐publicized, this study provides novel insights by identifying genes associated with dispersal capabilities in coyotes on the two eastern expansion fronts.Range expansion is a widespread biological process, with well‐described theoretical expectations associated with the colonization of novel ranges. However, comparatively few empirical studies address the genomic outcomes accompanying the genome‐wide consequences associated with the range expansion process, particularly in recent or ongoing expansions. Here, we assess two recent and distinct eastward expansion fronts of a highly mobile carnivore, the coyote ( Confined to the western and central regions of North America prior to 1900  and/or gray wolves (C. lupus) has been documented along the northeastern expansion route  in the southeastern expansion front  from state management programs (Princeton IACUC #1961A‐13), government organization archives , or museum archives . In all cases, state or province of origin was documented Figure  Table S1For downstream analyses, we considered samples collected from AZ, CA, ID, MN, MO, NE, NM, NV, OK, SK, TX, WA, and WY, to be part of the historical range  and randomly sheared to 400 bp in a Covaris LE220. Following shearing, we used a Dynabeads M‐280 streptavidin bead binding assay to enrich for adapter‐ligated fragments. Final sequencing libraries were then prepared using either the NEBNext Ultra DNA Library Prep Kit or the NEBNext UltraII DNA Library Prep Kit. Size selection was made for 300–400 bp insert with Agencourt AMPure XP magnetic beads, which were also used for library purification. Libraries were standardized to 10 nM and sequenced (2X150nt) on two lanes on the Illumina HiSeq 2500.We prepared genomic libraries following a modified restriction‐associated DNA sequencing (RADseq) protocol described by Ali et al. . BrieflysbfI cut site and one of the possible barcodes were aligned in a single file, while the matching read pairs that lacked the cut site were aligned in a separate file, and all remaining reads were discarded. This was accomplished using a custom Perl script  within a sliding window of 0.15. Next, PCR duplicates were removed with the paired‐end sequencing filtering option in clone_filter. We excluded all samples with low read count  after removal of PCR duplicates from further analysis. Remaining samples were then mapped to the dog genome CanFam3.1 assembly . Cstacks and sstacks were run as recommended by Catchen et al.  to be reported but did not apply any missing data thresholds. We then evaluated the per‐individual genotyping success, as measured by missingness per individual in Plink v 1.90b3i , and again restricted analysis to only the first SNP per locus. Only this latter dataset was used for subsequent analyses. Following SNP calling, we filtered for statistical linkage disequilibrium in Plink with the argument ‐‐indep‐pairwise 50 5 0.5.We completed SNP calling in STACKS following the recommended pipeline for reference mapped data , within a promoter , or int2.4flashPCA . This individual was removed from further analyses.To visualize clustering in our data and identify strong outliers, we conducted a Principal Component Analysis using our full SNP dataset with K = 1–10, with the K value with the lowest cross‐validation (cv) score indicative of the best fit K. ADMIXTURE is similar in principle to the classic Bayesian software STRUCTURE , we determined the most likely number of genomic clusters represented by the data, by conducting an analysis of population structure in ADMIXTURE v1.3.  and expected (eH) heterozygosity, were calculated in STACKS across all loci. To determine whether trends of heterozygosity differed based on which part of the genome was surveyed, we recalculated both oH and eH across putatively neutral loci and genic loci. Allelic richness (rA) and private allelic richness (Apr) were calculated using a rarefaction approach implemented in ADZE v 1.0  within the coyote historical range, as well as within each recently expanded eastern population with a series of Mantel tests implemented in ade4 v1.7‐11 . Additionally, to prevent biases resulting from long‐distance dispersers, we removed three individuals that had high cluster assignments to different populations from which they were sampled . Though restricting the analyses to individuals with high cluster assignments may inflate the genetic distinction between the historical range and the recently expanded coyote populations, we believe the inferred historical range population to be the best available representation of pre‐range expansion allele frequencies, as there is likely ongoing contemporary gene flow between coyotes in the historical range and both recently expanded populations.To identify loci as candidates for selection during range expansion, we restricted analyses to individuals with high cluster assignments as identified in the ADMIXTURE analysis  in the case of range expansion  and PCadapt is expected to perform well under a variety of complex demographic scenarios, including range expansion  of 5%. This was implemented in the R package qvalue v2.6 , as PC1 primarily captured the major axis of range expansion and therefore corresponded to the level of population structure relevant for this study , 24,139 of which contained at least one variable site in 394 coyotes. Following LD filtering, our full dataset consisted of 22,935 biallelic SNP loci, with a total genotyping rate of 93.3% , with 14,108 SNPs found within genes . These annotations sum to >22,935 as SNPs may have multiple annotations . Additionally, our putatively neutral dataset, which consisted of intergenic SNPs in HWE, retained 11,518 SNPs. Our genic data included 10,259 SNPs within introns, exons, and promoters.Overall, we primarily captured rare variation, with an average global minor allele frequency of 1.73%. Further, within each of the three regions sampled, minor allele frequencies were typically ≤5%  separating samples originating from the historical coyote range from either recently expanded eastern population Figure a. AccordQHistorical = 0.868; Supporting information Table QNortheast = 0.943, and southeast average QSoutheast = 0.933  and northeastern expansion front (Average EH = 0.0268) and slightly elevated in the southeastern expansion front (Average EH = 0.0300). These relative trends were similar when analysis was restricted to either putatively neutral loci  and lower values observed in the southeastern front  and northeastern expansion front .Genome‐wide heterozygosity was approximately equivalent across the historical range , or in either recently expanded eastern population .We found no evidence of isolation by distance in the historical coyote range (Mantel 3.4nintron = 45), with six exonic SNPs, and two located in putative promoter regions  and 37 were intergenic  for the northeast and historical range analysis and 13.10–130,670,000  for the southeast and historical range analysis Table . A totalns Table . With thic Table . Though ic Table .WDR17, the minor allele was lost in both of the recently expanded eastern populations , the minor allele was lost in one of the recently expanded populations and substantially reduced in frequency in the other  where the minor allele was absent from the historical range, but present at appreciable frequencies, in both recently expanded populations  and damselflies (Coenagrion scitulum) in expansion fronts relative to their source populations  to directly determine the impact of hybridization on the genome‐wide trends of diversity in eastern coyote populations. Interestingly, we note that if hybridization is responsible for the observed heterozygosity trends, our results suggest that interspecific hybridization is most prevalent in the southeastern expansion front, which has received comparatively less attention than coyote/wolf hybridization in the northeastern expansion front, especially on a genome‐wide scale. Accordingly, red wolf/coyote hybridization, particularly outside of the red wolf recovery area in North Carolina , is an intriguing area for future research.The maintenance of relatively high heterozygosity in recently expanded populations could be the result of a selective process, such as balancing selection along the expansion axes. However, it is perhaps more likely that this pattern results from extensive gene flow, either due to long‐distance dispersal by coyotes originating from the historical range or due to interbreeding with other E H= 0.028) are approximately one order of magnitude lower than those reported by vonHoldt et al. (EH = 0.22). While both estimates are based on genome‐wide SNP data, this discrepancy is likely reflective of the methodological differences of the SNP ascertainment strategies. That is, the canine genotyping array employed in vonHoldt et al.  were investigated extensively in a rodent model. For example, mice heterozygous for a CACNA1C knockout exhibited reduced locomotion bursts and scanning behavior, as well as increased freezing time, relative to their wild type counterparts  The mutation was present in the historical range, but at an extremely low frequency that was not captured by our sampling, (b) a de novo mutation occurred, either convergently along both expansions fronts, or along one front and then was transferred via intraspecific gene flow, or (c) this allele introgressed from a closely related Canis species following a hybridization event and was subsequently transferred via gene flow. As discussed above, interspecies hybridization occurs among Canis species, and there is evidence that this has been adaptive in the context of coyote range expansion . Genotype calls for 394 coyotes at 22,935 SNPs and associated sample metadata have been deposited to Dryad:  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


When closely related individuals mate, they produce inbred offspring, which often have lower fitness than outbred ones. Geographical exogamy, by favouring matings between distant individuals, is thought to be an inbreeding avoidance mechanism; however, no data has clearly tested this prediction. Here, we took advantage of the diversity of matrimonial systems in humans to explore the impact of geographical exogamy on genetic diversity and inbreeding. We collected ethno-demographic data for 1,344 individuals in 16 populations from two Inner Asian cultural groups with contrasting dispersal behaviours (Turko-Mongols and Indo-Iranians) and genotyped genome-wide single nucleotide polymorphisms in 503 individuals. We estimated the population exogamy rate and confirmed the expected dispersal differences: Turko-Mongols are geographically more exogamous than Indo-Iranians. Unexpectedly, across populations, exogamy patterns correlated neither with the proportion of inbred individuals nor with their genetic diversity. Even more surprisingly, among Turko-Mongols, descendants from exogamous couples were significantly more inbred than descendants from endogamous couples, except for large distances (>40 km). Overall, 37% of the descendants from exogamous couples were closely inbred. This suggests that in Inner Asia, geographical exogamy is neither efficient in increasing genetic diversity nor in avoiding inbreeding, which might be due to kinship endogamy despite the occurrence of dispersal. Such consanguineous mating events can occur for different reasons: it can be because of socio-cultural habits, as emblematically described for European royal families2 (mating choice inbreeding), or by chance, for example if there are too many related individuals in the population, as found in isolated groups4 (drift inbreeding). At the individual scale, the offspring of such close relatives are prone to carry a genetic burden5. Indeed, their genomes contain multiple chromosomal segments that are identical by descent6 . These segments, called runs of homozygosity (ROHs), consist of a succession of homozygous sites7 that subsequently increase the risk for deleterious recessive mutations to be phenotypically expressed8 and reduce the fitness at loci under heterozygote advantage9. This reduction in fitness associated with inbreeding can result in reduced fertility11, viability13 and a wide range of other general disorders14. At the population level, inbreeding decreases the effective population size and thus the population genetic variability15. A lack of genetic diversity is often associated with a decrease in the adaptive response of a population16 and can even lead to its extinction18. Moreover, in small populations undergoing strong genetic drift, inbreeding can increase the frequency (or even lead to the fixation) of mildly deleterious mutations20. However, inbreeding can also enable the purge of severely deleterious recessive mutations22 and eventually improve the population fitness23.At least 10% of humans descend from parents that are related as second cousins or closer, based on registers as well as civil and medical surveys24. Most pre-copulatory behaviours involve kin recognition, which evolved both for avoidance and for cooperation26. These can rely on the learning of familiarity, which is called Westermarck’s effect in humans28, on physical and acoustic cues32 , or on the expression of some genes such as the human MHC34 or mouse MUP35. Additionally, in some species, there is a delayed sexual maturation in the presence of relatives from the opposite sex36. Finally, a more straightforward mechanism, even though it is costly37, is to avoid relatives by geographically dispersing39. However, it is unclear if this dispersal is aimed originally at avoiding inbreeding or rather at reducing intra-sexual competition40. In western gorillas, females appear to specifically avoid related males when dispersing41, which supports the former hypothesis. In humans, geographical exogamy, i.e., choosing spouses among geographically distant individuals, has been interpreted as a mechanism to avoid marriages between related individuals42. One line of evidence supporting this hypothesis comes from the negative relationship observed between exogamy and individual inbreeding in inhabitants from the Orkney Islands of Scotland43. Despite this observation, there is a lack of data, whether in humans or in other species, to better understand the general impact of geographical distance between mates on both mating choice inbreeding and drift inbreeding patterns, as well as on population genetic diversity. This is likely because dispersal data are hard to obtain for most species, and combining such data with genetic information is even more challenging44.How do species cope with such inbreeding issues? Several pre- and post-copulatory behaviours are thought to reduce inbreeding in the animal kingdomi.e., in Uzbekistan, Kyrgyzstan, Tajikistan, West Mongolia and South Siberia. Inner Asia, the region located between the Caspian Sea in the West and Lake Baikal in the East, is particularly interesting in this context, as two groups presenting contrasting cultural traits  cohabit. The first group, composed of Indo-Iranian-speaking populations, practices mainly geographically endogamous marriages, while the second group, Turkic- and Mongolic-speaking populations (referred to later as Turko-Mongol populations), practices mostly geographically exogamous marriages45. Our genomic data allowed us to untangle drift inbreeding, which is due to small population sizes, from mating choice inbreeding, which is due to matrimonial preferences. To our knowledge, this is the first time that such quantitative data have been combined in humans or in any other species.To explore the impact of geographical exogamy on genetic diversity at both the population and individual scales, we jointly analysed ethnological, geographical and genome-wide data in 16 populations corresponding to 11 distinct ethnic groups from Inner Asia, p-value < 0.001). Based on the local minimum of the distance densities, we set the limit for geographical exogamy at 4 km and found the percentage of exogamous couples per population to be significantly higher in the 12 Turko-Mongol populations  than in the four Indo-Iranians  . When defining exogamy with other thresholds, we still found significant differences between groups at 10, 20 and 50 km  and a similar trend for 30 and 40 km . As this result could only be due to differences in average exogamous proportions between groups, we also focused exclusively on exogamous couples (>4 km) and found a tendency for larger distances for Turko-Mongol couples than for Indo-Iranians . Therefore, not only are Turko-Mongol couples geographically more exogamous than Indo-Iranians but they are also composed of spouses born at slightly larger distances.To quantify the amount of dispersal among spouses, we collected ethno-demographic questionnaires including spatial information for 1,344 individuals  in 16 populations belonging to 11 distinct ethnic groups from Inner Asia and corresponding to 4 Indo-Iranians and 12 Turko-Mongols populations see Fig. . We calcp-value = 0.001) . Furthermore, the exogamy rates of both generations are significantly correlated , even though we observed lower exogamy rates for the parental couples.In addition to the distance within the sampled couples, for each of the 1,344 sampled individuals, we calculated the distance between the birth places of their parents  . Because genotyping arrays are known to be enriched for Europeans SNPs47 and Indo-Iranians are genetically closer to Europeans than Turko-Mongols49, the lower diversity observed within Turko-Mongols could be an artefact. To correct for this ascertainment bias, we used another measure of population diversity which is less sensitive to ascertainment bias than site-by-site measures51: the averaged haplotypic heterozygosity computed for each autosome over low-recombination rate blocks. For this measure, we similarly found significantly higher mean heterozygosities for Indo-Iranians than for Turko-Mongols   and computed allele-sharing dissimilarity (ASD) distances29) Fig. . Therefo53 , with a similar average among Turko-Mongols and Indo-Iranians . Moreover, for each of the 503 genotyped individuals, we inferred, from a likelihood ratio test performed by FSuite, the most likely parental mating type, i.e., inbred mating between first or second cousins, or avunculars, or outbred mating . Using this likelihood test, we found that 36% of the individuals are inbred across populations with a similar average between Turko-Mongol and Indo-Iranian populations . The majority of inbred individuals have parents that are second-cousins: they represent between 5% and 76% of all individuals, depending on the population . None of the parents have avuncular relationships, but one case of double-first-cousins was inferred. Overall, the Turko-Mongol and Indo-Iranian groups have a similar distribution of these mating type categories: on average, 31% and 27% of second-cousins, 4% and 7% of first-cousins, and 0% versus less than 1% of double-first-cousins , respectively.To investigate inbreeding differences between populations, we used a site-by-site inbreeding coefficient (F-Median)53 Fig.  includedi.e., regions of the genome where the paternal and maternal copies are identical and which are long enough to be considered as identical by descent. Their sizes and numbers are therefore informative for the nature of inbreeding55. We aimed at distinguishing ROHs of an intermediate size, probably resulting from matings between individuals sharing distant ancestry (and therefore mostly due to drift), from long ROHs likely derived from matings between close relatives , even though they could rise from other mechanisms57. First, using classical thresholds from the literature59, we found that Turko-Mongol individuals have statistically more intermediate ROHs  than Indo-Iranians   ROHs range between 885 and 2,647 kb, while class C (long) ROHs are above 2,647 kb , 60. In each population, we found most individuals to be less homozygous than expected: overall, the median differences between observed and expected homozygosity are negative and are between −0.032 and −0.004 (average of −0.019), without any significant difference between Indo-Iranians and Turko-Mongols . Similarly, in terms of genetic diversity, the population exogamy rates are not associated with haplotypic heterozygosities . We further verified these results when taking into account potential confounding factors such as the age and sex ratio within each population, various environmental variables , as well as lifestyle  . However, when using multiple linear regression models and ANOVAs taking into account various combinations of these significant variables, we never found the exogamy rate to be significantly correlated with the percentage of inbred individuals per population nor with the haplotypic heterozygosity .We then tested whether exogamous populations include less inbred individuals than endogamous ones as expected if geographical dispersal is a mechanism of inbreeding avoidance. Surprisingly, in our dataset, the exogamy rate per population (defined with a limit of 4 to 50 km) is significantly correlated neither with the percentage of closely inbred individuals nor with the proportion of first-cousins’ descendants . Then, inbreeding decreases such that descendants from long-range exogamous couples (>40 km) are as inbred as descendants from endogamous couples for class C-ROH total length and F-Median  and significantly less inbred for the number of class C-ROHs . Consistently, we found that the distance between exogamous spouses  had an effect on their descendant’s inbreeding  , 30% have parents that are second-cousins, and 7% are first-cousins, which are proportions that do not differ from those obtained for endogamous couples (26% and 5%)  or village  exogamy. Here, we used a dataset of 16 populations representing 11 different ethnic groups from both cultural groups and we quantified geographical exogamy rates and distances in each population. Using an empirical threshold of 4 km, we confirmed that matrimonial behaviours differ as described in the literature, even though we found some exceptions: three Turko-Mongol populations (out of 14) have less than 50% exogamy, whereas one Indo-Iranian population (out of four) has more than 50% exogamy. Of note, this latter Indo-Iranian exception is a peculiar Tajik population (Tjy) that underwent population displacement in the 1970s from the Yagnob Valley to the Dushanbe area (approximately 60 km apart) in Tajikistan61. In general, the exogamous rates measured for the parental and current generations are highly correlated, even though we observed a trend towards higher values in the current generation, possibly reflecting changes in matrimonial behaviours through time  or some recall bias for information about parental couples.Two cultural groups, which matrimonial systems are reported to differ, coexist in Inner Asia: Turko-Mongols are described as mainly exogamous while Indo-Iranians are thought to be mainly endogamous1 but is still within the range of estimations made for neighbouring regions  from data available in http://consang.net/index.php/Global_prevalence. Interestingly, marriages between first-cousins that are prescribed in some Muslim societies from areas close to Inner Asia, such as Middle East62 or Pakistan63, only represent a small proportion of the Inner Asian marriages despite their Muslim tradition. Furthermore, most of the mating choice inbred alliances in our dataset are between second-cousins, and no avuncular relationship was observed. As inbreeding information is lacking for Inner Asia, our data complete the global picture of mating choice inbreeding prevalence. Interestingly, among the 16 studied populations, we found a large variability of mating choice inbreeding level (from 5% to 83%) and of mating type proportions. In particular, three populations  include more than 50% of inbred individuals . Of all the factors we tested , only the ecosystem had a significant effect on the rate of inbreeding, with the forest environment  being significantly associated with a higher inbreeding than the other environments. Further data would be needed to explore these relationships. Moreover, the population effective population size, as approximated by haplotypic heterozygosity, was not correlated with the level of close inbreeding observed in the populations, based on the proportions of i) inbred individuals per population, ii) first-cousins’ descendants, and on iii) the population median F-Median .We used these contrasted dispersal behaviours to investigate the level of inbreeding in these populations and to test whether geographical exogamy is a mechanism of inbreeding avoidance. First, we showed that inbreeding resulting from matings between relatives that are at least second-cousins indeed occurs in Inner Asia: on average, 36% of the individuals are inbred. This estimation exceeds the worldwide lower bound estimate of 10.4%i.e. between 500 and 1,500 kb and over 1,500 kb, respectively). Indeed, we detected some inconsistencies between the two approaches: the classes B and C-ROHs ranges only partly overlap with the arbitrary thresholds ranges, and the limit between class B and C is about two times larger than the 1,500 kb threshold. Consequently, the results obtained for ROHs longer than 1,500 kb are contradictory with those obtained for class C-ROHs and with the results obtained based on FSuite.From a methodological point of view, our study highlights the necessity to define for each population its own ROH boundaries rather than using arbitrary thresholds originally defined for European populations  that are proscribed in all societies64 or to other pre/post-copulatory mechanisms, and this remains an open question.Another result of our study is that the observed inbreeding levels in all Inner Asian populations deviate from the expectation under random mating, with each population being composed of less inbred offspring than expected by chance. We found the same result in nearly all populations from the HapMap3 worldwide dataset, which suggests that this is a general pattern. Such a deficit of inbreeding could be due to the avoidance of close kin matings  or from long-range exogamous ones (>40 km). As a consequence, close inbreeding exists despite geographical exogamy, and about a third of descendants from exogamous couples are inbred.An additional important result of our study is that geographical distances are not negatively correlated with inbreeding, as could have been expected under an isolation-by-distance model66, highlight the importance of using geographic distances rather than exogamy rates to characterize the impact of exogamy on inbreeding, as already described when studying patrilocality67. Indeed, when we compare mating choice inbreeding patterns for descendants from exogamous and endogamous couples defined for thresholds of 4, 10, 20 and 30 km, we find no significant differences . We only detect significantly lower values in descendants from exogamous couples for larger distances above 40 and 50 km .These results, in addition to those obtained by69. However, under this hypothesis, we would expect inbreeding patterns to be similar between descendants of endogamous and intermediate exogamous couples, yet we observe a significant excess of mating choice inbreeding in the latter (4 to 40 km apart). A second scenario is that geographical exogamous marriages preferentially involve spouses from the same family (kinship endogamy). It is indeed known from previous ethnological work that in patrilineal societies where individuals cannot mate with related spouses from the paternal line, they often choose spouses among relatives from the maternal side70. To properly examine this hypothesis in the particular case of Turko-Mongols, further analyses and sampling are required. Indeed, we would need to analyse genetic data for more exogamous couples as well as for individuals coming from the populations of origin of both spouses. Thereby, we would be able to explore spouses’ kinship by comparison with other potential partners and genetic distances between the populations. Furthermore, based on the kinship estimated from the X chromosome, we could investigate if related partners belong to the same maternal line, as assumed for these patrilineal societies, which would be of great interest to understand matrimonial strategies.Such inbred matings of individuals born at intermediate distances can be explained by two scenarios. First, spouses could be chosen outside the village unit but still within a quite genetically homogeneous unit52, we chose to focus on each group separately. Within each group, we also found that across populations, the exogamy rates are not correlated with haplotypic heterozygosities. Moreover, at the individual scale, the total length or number of class B-ROHs was not significantly different between descendants from endogamous and exogamous Turko-Mongol couples . This result is contrary to what was observed in the Orkney Islands of Scotland43; thus, this correlation might differ depending on the cultural context.Our results also challenge the intuition that exogamy necessarily increases the genetic diversity within a population and therefore reduces drift inbreeding. Indeed, we found that Turko-Mongol populations have a lower genetic diversity (as measured by the mean haplotypic heterozygosity) and more intermediate ROHs associated with drift inbreeding than those of Indo-Iranians despite higher exogamous rates. However, because these groups have different demographic histories  for 1,344 individuals  and their parents in 16 Inner Asian populations, ons Fig. . Among ti.e. current generation) or between the parents of the husband on the one hand, and those of the wife on the other hand . We defined exogamous couples as those with spouses born at more than 4 km . These distances can be calculated between each husband and wife , which resulted in a median of 27 individuals per population . The SNPs data has been deposited at the European Genome-phenome Archive (EGA), under accession number EGAS00001002951. We computed allele-sharing dissimilarity (ASD) distances77 between individuals using the software asd46 . We also calculated the autosomal haplotypic heterozygosity over low-recombination rated blocks for each population based on78 .Among the 907 individuals with blood or saliva sampled, we extracted DNA and genotyped 576 of them on five different Illumina arrays . We chose the individuals randomly to reach an average of approximately 30 individuals per population. Among them, 503 passed quality-control filters 55, and computing the median of these 100 coefficients. Furthermore, a likelihood ratio test was performed to assign each individual as inbred or outbred. For each individual, based on both F-Median and A-Median, it was possible to estimate the probability to be the descendant of avunculars (AV), double first-cousins (2x1C), first-cousins (1C), second-cousins (2C), or unrelated individuals . We kept the most likely inbred mating type for each individual. We also calculated the genomic excess of homozygosity60 relative to an expected baseline of homozygosity for each of the Inner Asian populations and for the 11 populations from the HapMap3 worldwide dataset using plink1.9 . Furthermore, we identified runs of homozygosity, called ROHs3, using plink1.9 . First, to be able to compare with other studies from the literature59, we categorized ROHs based on classical thresholds: 500–1,500 kb for intermediate ROHs, versus > 1,500 kb for long ROHs. However, as Pemberton et al.3 showed that these ROH thresholds can be variable between populations, we also used population-specific categories defined from their ROH size classification method  in R) resulting in what we call intermediate (class B-ROHs) and long ROHs (class C-ROHs). For each of the four ROH classes, we computed the number of ROHs observed within an individual genome and their total length.For each individual, we estimated the inbreeding coefficient (F-Median) which is the median value of multiple estimates of the probability of identity by descent at each marker of the genomeThe datasets generated and analysed during the current study are available from the corresponding author upon reasonable request.Supplementary Information


Opuntia microdasys, a self‐incompatible cactus with three dispersal units . We analyzed dispersal, using experiments at three populations, and assessed multilocus genotypes (ISSR markers) of all individuals in 10 clumps per population with known reproductive origin . Genotype of all samples, population structure, and migration between clumps and populations were assessed with GenAlEx and GenoDive, assuming higher genotypic diversity and migration when sexual reproduction is more frequent. We determined the most likely number of genetic clusters with STRUCTURE and geneland. Dispersal differed among populations; primary dispersal occurred at short distances and was farthest on steep slopes, and dispersal distance increased after secondary dispersal. Clumps had 116 different multilocus genotypes in three spatially explicit genetic clusters. We detected genetic structure at small scale, genotypic diversity among clumps varied between populations; diversity decreased while clonal dominance increased, and the most variation occurred among clumps. Genetic structure was moderate, suggesting gene flow by seed dispersal allows slight differentiation among population at large scales. Genetic diversity within clumps was the lowest because dispersal of clonal propagules was limited and caused genotypic dominance at local scale. However, the combined dispersal pattern of sexual and clonal dispersal units is fine‐tuned by environmental factors, generating a range of genetic diversity among clusters and populations. This pattern suggests that genetic structure of clonal plants is more dynamic than thought, and dispersal of different types of offspring affects genetic structure at many scales.The diverse offspring of clonal species differ in their dispersability, influencing genotypic diversity and clonal structure. Here, we determined dispersal patterns and their impact on genetic structure in  The soils are deep in BH, usually a mixture of gravel and sand, and the vegetation is dominated by Larrea tridentata, F. splendens, and Opuntia rastrera Weber. The IDH has sandy and deep soils with slopes of <1%; the dunes form a network of hills connected by flat interdune plains where the dominant vegetation includes Acacia constricta Benth., Acacia greggii A. Gray, L. tridentata, and Prosopis glandulosa Torr.The study was conducted in three populations of O. microdasys and represented recruitment as a percentage of offspring that were produced either sexually or clonally. IDH has the highest percentage of establishment by cladodes, BH has the highest percentage of establishment of plantlets, and HPH has the highest percentage of sexual recruitment.Palleiro et al.  reported2.3O. microdasys during fruiting. In the first experiment, we measured primary dispersal, the distance traveled by fruits immediately after they had detached from the parent plant. In the second experiment, we measured the combined effect of primary and secondary dispersal during the entire reproductive season by marking and tracking the fruits.Two field experiments were set up to explore fruit dispersal of 2.3.1O. microdasys in relation to landscape and their effect in the spatial distribution of plantlets around parent plants. Ten reproductive focal plants were randomly selected within each population, and the area around the plant crown was split into eight quadrants that corresponded to cardinal and intercardinal directions . An unripe fruit (ca. 2 cm long and 1.7 cm diameter) was taken from a focal plant and painted; the detachment of fruits from the parent plant was simulated by dropping a different painted fruit 40 times in each cardinal direction, from a height of 1 m to mimic the natural fruit detachment. Fruit was released at the tip of the branches to avoid impact with cladodes that could alter their fall strength or trajectory. Once the fruit reached the ground and stopped moving, we measured the distance traveled by the fruit (cm) and recorded the quadrant in which the fruit was located (fate). We analyzed the fruit fate with circular statistics  to assess the process of primary dispersal of fruits of  Fisher, . Correla Fisher, . Finally Fisher,  in JMP 8 Fisher, .2.3.2The fate of fruits was followed over the entire reproductive season (from June to September of 2010) to assess the distances reached by fruits over longer periods. In this experiment, the distance from the parent plant to the point where the fruits were found could have resulted from primary and/or secondary dispersal. Here, we assumed that any fruits found beyond the mean distance from the focal plant measured in the primary dispersal experiment had undergone secondary dispersal.Neotoma nest, when the fruit was located in the nest of the white‐throated wood rat ; and (e) lost, the remaining fruits that were not found after detachment.During the 2010 fruiting season, 10 reproductive focal plants in each population , with at least 200 fruits each, were selected. Using unique plant‐specific colors, we painted all fruits within each focal plant to later identify the parentage. Once a month, we counted all fruits that remained on the parent plant and all painted fruits found on the ground. We estimated the proportion of detached fruits at each count as the ratio of the number of fruits found on the ground to the number of fruits originally painted on the parent. We recorded the distance from the parent plant, the stage of maturity, and the fate for all painted fruits found on the ground. The fruits were assigned to one of four categories of maturity by direct observations of their morphological qualities: (a) ripe, (b) unripe, (c) aborted, and (d) other . In addition, we identified five possible fates for fruits: (a) parent, when fruits were found under the crown of the focal plant; (b) other plants, when the fruit was observed under the plant crown of other plants in the neighborhood; (c) exposed, when a fruit was found in bare areas; (d) With the proportion of detached fruits every month as a response variable and populations  and sampling period (months) as fixed factors, the results were analyzed with a generalized linear model using a binomial distribution and a logit link function in JMP 8.0.2. The distances reached by fruits were evaluated with a two‐way nested ANOVA in JMP 8.0.2, considering population and month as fixed factors and individual nested in population as random effect. The number of fruits at each stage of maturity was evaluated with a generalized linear model with a Poisson distribution of residuals and the log link function in JMP 8.0.2; fruit count was the response variable, with stage of maturity of fruits, populations, and month as factors . DNA was extracted with a Fast‐DNA Kit , and we test the quantity and purity with electrophoresis in a 2% agarose gel to obtain between 10 and 20 ng.We collected samples of fresh tissue from 10 clumps of plants in each population. A clump of plants consisted of a parent plant  and all their putative offspring established under its crown. Offspring were considered to be any established individual with a size of less than three cladodes; when possible, we recorded whether the offspring originated via unripe fruit, cladode, or seed. All clumps of plants sampled at each population were selected within permanent plots that were previously established for a demographic study that began in 2007. We sampled 577 individual ramets distributed among 30 clumps of plants across the three populations  as molecular markers to genotype each sample , 0.2 mM dNTPs (Invitrogen), 1 U Taq polymerase (Amplificasa—BIOGENICA), and 2 μl (10–20 ng/μl) DNA of O. microdasys and purified water (Sigma). The amplification reactions were carried out in a PTC‐100 thermocycler (MJ Research) programmed with an initial denaturation of 4 min at 94°C; followed by 36 cycles of 30 s at 94°C, 45 s at 52°C, 2 min at 72°C, and 2 min at 72°C; and a final extension of 7 min at 72°C. Amplification products were separated on 1.4% agarose gels (0.5× Tris‐borate‐EDTA [TBE] buffer at 120 V for 4.5 h), stained with ethidium bromide (0.01%), and visualized and photographed under UV light. The molecular marker 1Kb Plus (Invitrogen) was used as a molecular weight standard. Digital images of the gels were obtained for each individual using LabWorks software 4.0 . The banding pattern was subsequently transformed into a presence/absence matrix. To test the reproducibility of the band patterns, we re‐extracted and amplified DNA from a sample of 45 individuals taken from all parent plants from all clumps. If the banding pattern of the sampled replicates varied, we adjusted the conditions  and, if necessary, diluted or repeated the extraction and amplification. Following the method of Bonin, Ehrich, and Manel (/N) according to the proposal of Lynch & Milligan  or 0.6 μM (for primer 827), 1× PCR buffer (BIOGENICA), 2.0 mM MgClnd Manel , we estiMilligan  for domi2.4.3When molecular markers are used to define multilocus genotypes (MLGs), individuals can be misallocated in two ways: (a) Genetically similar individuals are assigned as clones but in reality are from different MLGs; (b) dissimilar genetic individuals are assumed to be genets but are actually clones n–1. Further, Pdgen = ∏ pi, where pi is the frequency of each locus in the multilocus genotype  and the number of private bands (PB) with the program GenALEX 6.4 . With geneland, we tested the values of K from 1 to 30, the number of iterations of MCMC was set to 500,000, with thinning of 100, coordinate uncertainty of 0.01, and a burn‐in period of 200 . Consequently, a second GENELAND analysis was performed with K fixed at three, 800,000 MCMC iterations, thinning of 1,000, and coordinate uncertainty of 0.01 and burn‐in of 100; 30 independent runs were done with the same parameters, and we selected the run in which no “ghost” populations occurred and obtained approximately the same estimates of individual population membership, K, and maps.We calculated d Goudet  defined 33.13.1.1(r), and the chi‐squared test for uniformity at which fruits fell in the primary dispersal experiment differed between populations (Table r = 0.273) and the concentration (κ = 0.568) and the lowest standard deviation (SD = 92.32°). Quadrants NW > SW > NE > SE showed the highest frequency of fruits in BH . Finally, the frequency of fruits at IDH was more homogeneous between directions, with a slight increase in the frequencies to the NE, E, SE, and SW .The mean directional angle (μ), mean resultant length of dispersal ns Table . The slons Table a, which ns Table . Three cAppendix b. Nevertr = 0.393, p < 0.0001; BH, r = 0.207, p < 0.0001; IDH, r = 0.107, p < 0.0001). The mean dispersal distance of fruits differed between populations  and quadrants , and the interaction between factors was significant . The interaction reflects the influence of specific quadrants in each habitat; for example, quadrants with west orientations determined the fate of fruits in HPH . The proportion of detached fruits did not differ between populations , but the proportion did differ among months , and an interaction existed between months and population . The proportion of detached fruits increased during the fruiting season, from 0.003 to 0.004 at the first count in June to 0.82 ≅ 1 at the last count . In the IDH, fruits disperse the farthest (average 182 cm), followed by HPH (106 cm), then BH (75 cm). Dispersal distance also differed among months , with the longest distance in the fourth count (273 cm) and the shortest in the first count . The interaction between factors was also significant . Fruits in the IDH moved farthest, and fruits in BH always moved the shortest distance ; the factors population  and stage of fruit maturity  were not statistically significant, but month had a significant effect  as fruits mature , because most fruits fell in the first or second count at BH and IDH and abortion rate was constant over time at HPH. Also, the interaction of the month and the stage of maturity of fruits differed  because the highest proportion of fallen ripe fruits peaked in the second or the third count, unripe fruits peaked in the first count and that proportion decreased toward mid‐season . The proportion of fruits did not differ between populations , but there were differences among months  and fates , with a significant interaction between population and fate  and between months and fate . The fates with the highest proportions of fruits were the parent and lost fruit fates , followed by IDH (R = 0.222) and BH  and the fewest members (only three ramets). In HPH, the highest values of R corresponded to two clumps of plants , which suggests that the assignment of clones is robust.In most clumps of plants, we found clonal individuals. The population with the highest genotypic diversity was HPH (ΦPT = 0.21). The AMOVA that included the clumps and populations also highlighted the fact that almost all the variation was found between clumps , followed by IDH  and BH  was chosen in both analysis, but assignment of individuals differed between the two methods. The three clusters predicted by structure were represented by individuals from all three populations in different proportions. In BH, the proportion of individuals in cluster one . The association of fruits with packrat nests and the greater mobility of fruits as the duration of exposure to secondary vectors increased are evidence of the redistribution of fruits and secondary dispersal. Water streams formed after heavy rain is another vector of secondary dispersal that modifies the distance reached by fruits, water moved fruits 440 m . Propagules of species that undergo secondary dispersal are commonly redistributed by a second agent  move seeds of Opuntia ficus‐indica and may be the most important vector for the range expansion of this Opuntia species.The importance of abiotic factors such as river water flow and the hydrologic regime determines the site of deposition of postrelease propagules of  Eckert, . NonetheOn the other hand, indirect methods that use molecular markers are well developed and are used to evaluate effective dispersals  with traits that we had expected to provide greater mobility and, consequently, longer dispersal distances. This kind of pseudo‐viviparity is a common phenomenon found in other species of Cactaceae such as Opuntia spp. and Cylindropuntia spp.  apparently failed to establish new offspring. The population with the highest genotypic diversity was HPH, for which slope was the main factor influencing primary dispersal, and HPH was the population with the highest percentage of sexual recruitment . In a study with Bromus ircutensis, a clonal grass, found FST values ranged from 0.118 to 0.15% and 87% of the genetic variation within the populations. structure analysis assigned some individuals from all three populations to one genetic cluster , generating monoclonal and intermingled clumps, but with a level of migration over longer distances that allowed some differentiation among populations.None declared.Opuntia spp. of MCM. Other details: EGM; Israel Carrillo‐Angeles (ICA); MCM; and Jordan Golubov did the field work. EGM, ICA, and MCM performed the molecular experiments. MCM and Daniel Piñero edited the MS as advisors and provided financial and laboratory facilities to carry out molecular experiments.All authors contributed with planning, designing, analyzing, and writing of the manuscript. The study is part of the PhD project of GME and is included within a long‐term project of population biology of Data of sampling locations, ID of each plant and population, ISSR genotype of each individual, and UTM locations of each sampled genotype will be available in the Dryad Digital Repository.


Photinus pyralis. We further tested (3) whether selection has acted on these loci by examining their population-level differentiation relative to the distribution of differentiation derived from a genome-wide sample of loci generated by double-digest RADseq.Genes underlying signal production and reception are expected to evolve to maximize signal detection in specific environments. Fireflies vary in their light signal color both within and between species, and thus provide an excellent system in which to study signal production and reception in the context of signaling environments. Differences in signal color have been hypothesized to be due to variation in the sequence of luciferase, the enzyme that catalyzes the light reaction. Similarly, differences in visual sensitivity, which are expected to match signal color, have been hypothesized to be due to variation in the sequence of opsins, the protein component of visual pigments. Here we investigated (1) whether sequence variation in luciferase correlates with variation in signal color and (2) whether sequence variation in opsins correlates with inferred matching visual sensitivity across populations of a widespread North American firefly species, We found virtually no coding variation in luciferase or opsins. However, there was extreme divergence in non-coding variation in luciferase across populations relative to a panel of random genomic loci.The absence of protein variation at both loci challenges the paradigm that variation in signal color and visual sensitivity in fireflies is exclusively due to coding variation in luciferase and opsin genes. Instead, flash color variation within species must involve other mechanisms, such as abdominal pigmentation or regulation of light organ physiology. Evidence for selection at non-coding variation in luciferase suggests that selection is targeting luciferase regulation and may favor differ expression levels across populations.The online version of this article (10.1186/s12862-018-1251-9) contains supplementary material, which is available to authorized users. Communication has been the subject of intense study since Darwin . All comFireflies, in the beetle family Lampyridae, are an established system for the study of mating signal evolution due to their conspicuous and variable light displays (e.g. –16. GeneLUC1) is expressed in the adult light organ and functions to produce flash signals [Here, we undertook a candidate gene population genetics approach to test whether genes that underlie signal production and reception have evolved in the context of population-level variation in signaling environment and signal color. In fireflies, the primary genes underlying both light signal production (luciferase) and visual reception of the signal (opsins) are known. To produce a light signal, the enzyme luciferase interacts with its substrate, luciferin, in the presence of oxygen and ATP to release a photon of light  across populations, and the color range across populations spans ~ 60% of the entire color range of all measured firefly species. Applying the species paradigm, we hypothesized that natural selection on coding variation in adult-expressed luciferase and LW opsin also underlies these expansive differences in emission color among P. pyralis populations and their inferred matching visual sensitivities. We tested this hypothesis by determining whether within-species genetic variation in luciferase and LW opsin is correlated with variation in the color of emitted light across populations of P. pyralis. We predicted that nonsynonymous substitutions in the coding sequences of adult luciferase and LW opsin would be correlated with emission color. We further predicted that molecular variation in these genes would exhibit signatures of divergent selection across populations that differ in light color. Lastly, if there is evidence for selection causing divergence in luciferase and opsins across populations, we predicted that it would be driven by the light environment (habitat) in which signals are produced and received. Specifically, allele frequencies at selected loci were expected to correlate with differences in habitat. As there is no variation in the onset of crepuscular activity across P. pyralis populations, UV opsin amino acid sequence was not expected to be under selection and thus served as a control.Here, we capitalize on documented variation in signal color across populations of a widespread North American species,  pyralis , to inveP. pyralis populations exhibiting different peak emission colors and habitats. We tested for selection at specific sites within these loci by examining their pattern of molecular variation relative to a set of genome-wide single nucleotide polymorphisms (SNPs) generated from double-digest restriction-site associated DNA sequencing . We thP. pyralis North American geographical range. These populations differed in their mean peak emission color (558 to 568 nm) and habitat type (6 open field populations and 6 closed forest populations)  across the cytochrome oxidase I (COI) locus  and average pairwise nucleotide differences (π), but the highest amount of differentiation among populations (Fst) and a signature of excess low frequency polymorphisms (negative Tajima’s D) . Applying the Stacks pipeline with optimized parameters to the data resulted in 2019 variable loci across populations that were suitable for population analysis . All loci were determined to be in Hardy Weinberg equilibrium within populations after Bonferroni correction for multiple testing.To test for selection and examine genetic differentiation at the three signaling loci relative to genome-wide genetic differentiation, we generated double-digest restriction-site associated DNA (ddRAD) libraries for 15 individuals from each of the 12 To generate a set of neutral loci for use in population structure analyses we performed Fst outlier analysis on the 2019 RAD loci. Outliers were required to be in the top (diversifying) or bottom  5% of Fst values across all 10 LOSITAN  and haveTo test signaling-related loci for evidence of selection, we combined the 2019 RAD loci and the variable SNPs from adult luciferase, LW opsin, and UV opsin, and then performed Fst outlier analysis using the same criteria on this combined dataset. This resulted in a slightly larger consensus set of 46 loci under selection  and 2058 neutral  yielded two major components that together accounted for ~ 30% of the variance Fig. . PC1 24. The twoTo understand how genetic variation at the three adult luciferase SNPs with evidence for divergent selection  was geographically distributed, we visualized allele frequencies at these loci across the populations sampled Fig. . The genAll 2019 RAD loci, combined with the signal locus SNPs, were tested for population-level associations in peak light emission color (wavelength of peak emission intensity) and habitat type (closed forest versus open field) using BayeScEnv . This meP. pyralis. These genes are instrumental for light signal production and light detection, respectively. We utilized 12 populations across the range (96 to ~ 3000 km apart) and population structure measures to test whether coding variation in signaling genes could explain differences in light emission color and the expected matching differences in visual sensitivity across different habitats.While firefly signals have been the focus of much study, molecular approaches to investigating the genetic variation underlying this signal diversity have been few, likely limited by the dearth of DNA sequence data in this non-model insect system. Here, we took advantage of reference genome-free population genomic methods  to investigate the genetic variation in luciferase and opsin genes among populations of a widely distributed North American firefly species, P. pyralis is a widespread and abundant species. It is common across the Eastern U.S. and found in a variety of habitats, including urban and disturbed areas, and both males and females are able flyers. Therefore, we expected to observe relatively high levels of gene flow across the range , during which males actively search for sedentary females, foregoing food consumption , similar to other fireflies [ = 0.039 ; pine be = 0.039 ), perhap(but see ), and thireflies , and durP. pyralis populations, one in the South-West and one in the North-East. The division between these clusters roughly corresponds to the Appalachian Mountains, a known biogeographical break in a wide range of North American taxa .SNP analysis identified two large genetic clusters of axa e.g –58). Int. IntP. pP. pyralis was the original source for firefly luciferase used in molecular assays and millions of individuals were harvested each season for scientific use [P. pyralis luciferase in the 1980s [P. pyralis is a widespread, abundant, and robust species, more often associated with human agricultural activities compared to most other firefly species. The presence of high population structure in this species suggests that populations of other species may be even more genetically isolated. Thus, it is essential that firefly monitoring and conservation efforts take population structure into account.It is critical to take population structure into account in evolutionary analyses. In addition, population structure has important implications for firefly conservation. ific use . Followihe 1980s , luciferhe 1980s . The strContrary to our expectations based on prevailing hypotheses of the genetic basis of signal color and visual sensitivity and known differences in light emission color across populations, neither luciferase nor LW opsin showed a pattern of nonsynonymous changes in their coding sequence that was correlated with either their light emission wavelength or habitat type. Furthermore, the single low-frequency nonsynonymous mutation that we identified in luciferase (V182I) does not shift emission wavelength, though thermostability is altered . The sinDespite the lack of amino acid variation, three luciferase SNPs showed a strong signature of selection, based on their high Fst relative to the distribution generated from neutral genome-wide SNPs. This finding suggests that there has been selection-driven differentiation at a region linked to luciferase that has driven the observed differentiation of coding sequence of luciferase at silent sites. This region could be located either in a cis-regulatory sequence upstream of the luciferase coding region, or in another gene that is in strong linkage disequilibrium with luciferase and is the actual target of diversifying selection.P. pyralis luciferase has been examined in relation to related luciferase genes in other species, and a core promoter region has been identified [Luciola lateralis [P. pyralis. Interestingly, in Lampyris noctiluca, the European glowworm, an ancient transposon endonuclease domain is located 686 bp upstream of the start codon [Not much is known about natural variation in luciferase regulation. The 5′ upstream region of entified , 64. In ateralis , though rt codon , though Photuris fireflies that mimic light signals of their firefly prey species also cue in on light signals [Luciferase regulation may be under selection because populations differ in optimal luciferase expression due to abiotic or biotic factors in the environment. For example, mean temperature differences among populations could affect turnover of luciferase, requiring altered expression to achieve optimal levels. In addition, females respond more readily to brighter flashes . If the  signals , so diffP. pyralis. Further, we found no amino acid variation that is likely to affect the color of the light produced by luciferase or the color sensitivity of opsins. While both molecules are expected to be conserved in sequence due to their essential functions in mate signal production and visual reception, the current paradigm for variation in light color, and matched variation in opsins, based on amino acid variation at specific sites within these conserved molecules, does not hold. Our data instead strongly suggest alternative mechanisms for the observed differences in light color across populations, such as physiological mechanisms in the light organ, morphological filtering of the color emitted by luciferase through screening pigments in the light organ [We detected no significant association between light emission color or habitat type and the luciferase and opsin SNP data across divergent populations of ht organ , or throht organ , revieweht organ ). Similaht organ , 11, 72.P. pyralis populations, we found no evidence for selection on the amino acid sequence of UV and LW opsins, the two light-detecting proteins in firefly eyes. In contrast, the luciferase protein, which generates the light signal, exhibits large differences in allele frequency across populations for three SNPs, indicating positive selection for different luciferase haplotypes in different parts of the range. Surprisingly, none of these SNPs result in a change in the luciferase amino acid sequence, strongly suggesting that selection, rather than acting on the amino acid level, acts on variation that alters luciferase expression.Across widely-distributed These findings are in stark contrast to the current paradigm that differences in flash signal color across firefly species are due to amino acid variation in luciferase. This study rejects the paradigm of coding variation in luciferase and opsins underlying signal color variation and visual reception, at least at the population-level. Instead, there is a strong signal of divergent selection in luciferase non-coding variation that points to luciferase regulation as a target of selection across populations, even though it is not correlated with habitat or signal color. This work provides a fascinating perspective on a long-established model for light color evolution, and it highlights the need for further study of mechanisms determining light color in natural populations.P. pyralis is a widespread and abundant firefly species in the United States, ranging from Arizona to New York [P. pyralis adult light color is yellow [P. pyralis luciferase was the first to be cloned [P. pyralis luciferase emits at 560 nm [New York . Adult ls yellow , 73, ands yellow . P. pyrae cloned  and is we cloned , 74). Int 560 nm –77.P. pyralis individuals were collected from 12 populations (locations) during the summer months of 2011–2014  and coP. pyralis individuals from each of six closed (forest) populations and six open (field) populations, that captured most of the range of male peak emission  and luciferase, and performed ddRADseq on individuals from these 12 populations .LUC1 [P. pyralis transcriptome and genome sequences [Adult-expressed luciferase LUC1 ;) and boLUC1 ;) were aLUC1  from flaequences . Both cu ; and boLUC1: M15077 [LUC1, 190; LW, 191; UV, 191).Sequences were assembled in Geneious R7 (Biomatters Ltd.) and manually inspected for errors and heterozygous sites. Full-length contigs were aligned using Muscle  in Genei: M15077 ,). Singl: M15077 ; 10,000 : M15077 ,), pairw: M15077 ,), Fst [: M15077 , and Taj: M15077  calculatSanger-sequenced individuals were assessed for genome-wide molecular variation using ddRAD sequencing , 85. To Library construction followed a 3RAD protocol . 3RAD diTo generate a set of SNP-containing RAD loci that were sequenced across all individuals for use in downstream analyses, we identified reads from each sample in our pool using unique combinatorial barcodes, eliminated potential contaminant reads, and then grouped these into RAD loci bioinformatically. Samples were initially demultiplexed into their respective aliquot libraries (three from each of two plates) by outer Illumina adapters using bcl2fastq v2.16.0.10 . Subsequently, the reads for individual specimens were identified from each of these demultiplexed pools and trimmed for quality using process_radtags in Stacks v1.29 . Readsm = 3, M = 4, n = 3, max_locus_stacks = 3) with the default SNP calling model. In total, 154 samples were used in the final analysis—all the unique samples that passed our “failure” threshold, plus the “best” sample from each replicate pair (i.e. the one with the most reads).High-quality paired-end reads were concatenated and run through the Stacks pipeline using default parameters for initial exploration prior to parameter optimization. We identified 28/192 libraries (15%) that “failed”, meaning that they had data for less than 50% of the loci that were shared across 80% of the samples. All of these libraries had fewer than 100,000 reads and were excluded from downstream analysis. The failed libraries included 3 of the technical replicate samples, leaving 9/12 samples with replicates for optimization. Technical replicates were used to find the optimal Stacks parameters according to the procedure of  the FDIST method  as impleBayeScan decomposes Fst into that shared across all loci (population-specific Fst) and locus-specific Fst. Candidate loci under selection are identified as loci where locus-specific Fst needs to be invoked to explain diversity at that locus. BayeScan was run twice  and convergence of the two runs assessed using Gelman and Rubin’s diagnostic as implemented in the coda package  in R v3.First, all RAD loci were run with both methods to develop a set of neutral loci used to investigate population structure. Loci with evidence for selection across both LOSITAN and Bayescan methods were excluded from further analysis with neutral markers. Then, all RAD loci and variable SNPs identified in luciferase, LW opsin, and UV opsin were combined and run to investigate selection at these three candidate loci.When investigating candidate loci under selection, it is important to place their variation in the context of geographical population structure. Thus, using the set of neutral RAD loci, we examined population structure in several ways: (i) we constructed a neighbor-joining dendrogram of individuals using Nei’s distance  using thTo examine variation in candidate SNPs under selection with respect to population structure, geographical distributions of allele frequencies were visualized using ggmap v2.7  in R.If SNPs in LUC, LW, and UV underlie phenotypic variation in spectra and inferred visual sensitivity that are associated with habitat, SNP genotype should be associated with these variables across populations. We tested for associations of all RAD and signaling loci SNPs with spectra and habitat using BayeScEnv v1.1  with defAdditional file 1:For Lower et al. Contents: Notes 1–5. 1. Methods. 2. COI analysis. 3. Fst outlier analysis. 4. Gene flow among populations. 5. Additional signaling locus SNPs. 6. Files on Figshare. (PDF 6217 kb)


Insertion/deletion (InDel) markers have been treated as a prospective and helpful aid in the fields of forensic human identifications and biogeography origin researches for the past few years. In this study, we analyzed genetic polymorphisms and forensic efficiencies of 35 InDels in a novel multiplex PCR-InDel panel in a Chinese Mongolian group. All these 35 InDel loci were observed to conform to Hardy–Weinberg equilibrium and linkage equilibrium. The mean values of expected heterozygosity and observed heterozygosity were 0.4788 and 0.4852, respectively. Besides, the interpopulation differentiations and genetic distributions based on 35 InDels found that the Chinese Mongolian group might have closer genetic relationships and similar population genetic structures with East Asian populations. InDels are length polymorphisms resulting from the insertion or deletion of one or more nucleotides in the genome . In 2002http://www.paulnoll.com/China/Minorities/min-Mongolian.html). Nowadays, genetic analyses of the Chinese Mongolian group mainly focused on STR loci, such as 19 X-STR loci [Mongolian, in terms of population size, is the tenth largest ethnic group in China, distributed in Gansu, Qinghai Provinces, Xinjiang Uygur, and Inner Mongolia Autonomous Regions. Some Mongolians also dwell in Liaoning, Jilin, Heilongjiang, and other provinces. The language of Mongolian group belongs to the Altaic family. The main religion of the Mongolian people is Buddhism  and Nei's genetic distances (DA distances), principal component analysis (PCA), phylogenetic reconstruction, population clustering analysis of the studied Mongolian ethnic group and other reference populations were also constructed to explore their genetic relationships.Previously, we developed a novel multiplex PCR-InDel panel for forensic individual identifications in the Chinese Kazak group and reference populations from East Asia . Here, gWe collected a total of 110 bloodstain samples from unrelated healthy Mongolian individuals in China. All subjects signed the written informed consent prior to sampling. This study obtained the approval of the Ethics Committees of Xi'an Jiaotong University Health Science Center and Southern Medical University, China.In this study, the PCR amplification of 35 InDel loci was conducted on a GeneAmp PCR system 9700 thermal cycler , following previous descriptions . Then, tThe reference populations of this study were the intercontinental populations  and KazaP values for Hardy–Weinberg equilibrium tests  and linkage disequilibrium (LD) analyses of 35 InDel loci in Mongolian group were calculated by STRAF online program (version 1.0.5) [R software (version 3.4.5) (https://www.r-project.org/). DA distances and Fst values among the studied Mongolian group and other reference populations were calculated by DISPAN program [DA and Fst values of these populations were conducted on pheatmap package (version 1.0.12) by R software (version 3.4.5). The PCA of the studied Mongolian group and 27 compared populations was generated by MVSP software (version 3.1). Moreover, PCA of these populations at individual level was conducted by PLINK software (version 1.9) [R software (version 3.4.5). Beyond that, a multidimensional scaling (MDS) plot [DA distances was established by MEGA software (version 6.06) [K value was assessed by the Structure Harvester online tool (version 0.6.94) (http://taylor0.biology.ucla.edu/structureHarvester/).The allelic frequency distributions and forensic statistical parameters which included the values of observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC), discrimination power (DP), probability of exclusion (PE), match probability (MP), typical paternity index (TPI), and n 1.0.5) . A map s program  and Gene program , respection 1.9) , which wDS) plot  was condon 6.06) . The popon 6.06)  and CLUMP values for Hardy–Weinberg equilibrium tests and forensic statistical parameters of 35 InDels were shown in P values at the 35 InDels were not found after a Bonferroni correction (P = 0.05/35 = 0.0014). The PIC values ranged from 0.1190 (rs3054057) to 0.3750 , with the mean value of 0.3609. The He and Ho values ranged from 0.1276 (rs3054057) to 0.5023 (rs10556291), and from 0.1182 (rs3054057) to 0.6273 (rs3028455), with the mean values of 0.4788 and 0.4852, respectively. The values of MP, DP, PE, and TPI varied from 0.3522 (rs4024564) to 0.7757 (rs3054057), 0.2243 (rs3054057) to 0.6478 (rs4024564), 0.0114 (rs3054057) to 0.3249 (rs3028455), and 0.5670 (rs3054057) to 1.3415 (rs3028455), respectively. Additionally, the frequencies of insertion alleles (+) and deletion alleles (−) ranged from 0.3682 (rs371194629) to 0.9318 (rs3054057), and from 0.0682 (rs3054057) to 0.6318 (rs371194629), respectively. The forensic parameters of cumulative PE and combined DP values of 35 InDel loci were 0.99925 and 0.9999999999999904, respectively.The P = 0.05/595 = 0.000084), indicating that these 35 InDel loci were mutually independent in the studied Mongolian ethnic group.LD tests of these 35 InDel loci in the Chinese Mongolian group were calculated by STRAF online program (version 1.0.5). As shown in δ) between the studied Mongolian group and other reference populations were given in δ values (<0.1) with East Asian populations and Kazak group at most loci in comparisons with other reference populations. And then, genetic distances (DA) of the studied group and other reference populations were calculated using allelic frequencies of 35 InDel loci, as shown in DA distance has been used to measure genetic differences of different populations. It is developed on the assumption that genetic drift and mutation events finally lead to genetic differences [DA values of the paired populations. The different color scaling on the upper right corner of the plot showed the DA values ranged from 0 to 0.06 which varied from orange to red. Different colors stood for different levels of DA values: orange for low DA values and red for high DA values. Orange color blocks between the Chinese Mongolian and the Kazak groups as well as five East Asian populations  were observed, indicating that they had close genetic relationships. However, the colors of the blocks between the Mongolian group and some African populations were nearly red, meaning that there were relatively large DA values between the Chinese Mongolian and these African populations. In addition, we also generated a heat map based on the Fst values of the pairwise populations to further measure population differentiations, as shown in Fst value was, indicating that the genetic differences of pairwise populations were the smaller. We also found that the blocks of the Chinese Mongolian group and five East Asian and Kazak groups showed deep green colors while the Chinese Mongolian group and other intercontinental populations showed light green or blue colors, showing that genetic differentiations between Chinese Mongolian and the five East Asian populations as well as Kazak group were smaller compared to the other reference intercontinental populations.Absolute values of insertion allelic frequency differences (ferences . As showThe genetic relationships between the Chinese Mongolian and the other compared populations were explored using PCA by the MVSP software (version 3.1). The advantage of the PCA is that it allows graphical representation of multidimensional data with reduced number of dimensions . As showFst values of these populations was generated as shown in For further validation, a MDS plot based on pairwise The purpose of phylogenetic analysis is to intuitively infer or evaluate the relationships among different populations . PopulatK) which were assumed from 2 to 7 by using the STRUCTURE software (version 2.3.4.) and CLUMPP software (version 1.1.2). Then the appropriate K value was estimated by Structure Harvester (http://taylor0.biology.ucla.edu/structureHarvester/), as shown in K value was 3 for the population data set used in this study according to the appropriate K value standard in a previous report [K = 2 ‐ 7, which further indicated that the population structure of the Chinese Mongolian and East Asian populations was similar.In this study, a population clustering analysis method was used to reflect ancestral proportion memberships of the Chinese Mongolian and 27 compared populations with the number of hypothetical populations . Wei et al. stated that the Xinjiang Mongolian group had close genetic relationships with Uyghur, Xibe, and other Chinese populations [Genghis Khan established the Mongol Empire in the 13th century, which was a successful nomadic nation. The Mongol Empire's territorial expansion promoted cultural exchanges between Asia and Europe, which had a remarkable influence on the genetic structure of the Eurasian people . Xinjianulations , which wulations ; besidesulations .Our study validated the forensic applicability of these 35 InDel loci in the Xinjiang Mongolian group. In order to further carry out the population study and explain the origin of the Mongolian group, it is necessary to further evaluate the genetic characteristics of the Mongolian group by using other genetic markers like AIM, mitochondrial markers, and so on.This study evaluated forensic efficiencies of a set of novel 35 InDels and assessed the genetic structure of the Chinese Mongolian group based on these InDels. The results of forensic value evaluation indicated that this system of 35 InDels was efficient enough to forensic human identifications in the Mongolian group. And the results of the population genetic analyses indicated that the genetic relationships between the Chinese Mongolian and East Asian populations were relatively close, followed by Kazak group. In a word, these results enrich the Mongolian group data and lay the basis of forensic applications of these 35 InDels in the Mongolian group.


Na). We propose the addition of genetic estimates of the effective number of breeders (Nb) as indirect measures of the risk of loss of genetic diversity to improve the evaluation of demographics and increase the accuracy of trend estimates in breeding colonies. Here, we investigated which methods of the estimation of Nb are more precise under conditions of moderate genetic diversity, limited sample sizes and few microsatellite loci, as often occurs with natural populations. We used the wood stork as a model species and we offered a workflow that researchers can follow for monitoring bird breeding colonies. Our approach started with simulations using five estimators of Nb and the theoretical results were validated with empirical data collected from breeding colonies settled in the Brazilian Pantanal wetland. In parallel, we estimated census size using a corrected method based on counting active nests. Both in simulations and in natural populations, the approximate Bayesian computation (ABC) and sibship assignment (SA) methods yielded more precise estimates than the linkage disequilibrium, heterozygosity excess, and molecular coancestry methods. In particular, the ABC method performed best with few loci and small sample sizes, while the other estimators required larger sample sizes and at least 13 loci to not underestimate Nb. Moreover, according to our Nb/Na estimates , the wood stork colonies evaluated could be facing the loss of genetic diversity. We demonstrate that the combination of genetic and census estimates is a useful approach for monitoring natural breeding bird populations. This methodology has been recommended for populations of rare species or with a known history of population decline to support conservation efforts.Detecting trends in population size fluctuations is a major focus in ecology, evolution, and conservation biology. Populations of colonial waterbirds have been monitored using demographic approaches to determine annual census size ( Characteristics of the wood stork make it a good model for these purposes: It is very sensitive to environmental changes in the wetlands where it inhabits and reproduces  yield estimates proportional to N^a, (b) have the power to yield precise estimates from datasets based on a limited number of genetic markers, and (c) require a sample size that can be collected during the short period of the breeding season at several sites. Furthermore, we apply our findings on the estimation of Nb to natural populations and we test the suitability of a counting method with an adjustment for the heterospecific composition of breeding colonies.Our primary aim was to provide baseline methodological guidelines for monitoring changes in population size of colonial waterbirds by assuming breeding colonies as attractive target units for conservation, using both census and genetic methods. Using simulations, we analyzed different empirical datasets to illustrate the common problems encountered when estimating 22.1n = 48) was sampled in 2000, colony Fazenda Ipiranga , colony Sangradouro 1 , and colony Sangradouro 2  were sampled in 2013  was recorded and adherence to Poisson's distribution (α < 0.05) was tested. The mean and the variance of the recorded values of k  were calculated for each colony and used to determine the index of variability (Vk/k¯) . Samples were amplified at 13 species‐specific microsatellite loci . Genotypes were recorded using Fragment Profiler® v1.2 , checked for evidence of null alleles using Micro‐Checker v2.2.3  with the Holm‐Bonferroni correction , 2 × 105 runs of MCMC and 104 of burn‐in. The Delta K evaluation  were simulated from each sub‐dataset in kingroup v2.08 . Therefore, the genotype frequencies in simulated cohorts are in concordance with HW equilibrium and with the provided allele frequencies . Frequencies of null alleles were adjusted using the Brookfield 1 method , 10% (n = 80), 15% (n = 120), 20% (n = 160) and 50% (n = 400) were taken from the simulated cohorts derived from the original PF dataset. Nb was estimated for all simulations using five single‐sample methods  of colonies FI, SG1, and SG2. At breeding colonies, nests were classified as “with content confirmed visually from the ground” (NCG) or “with content not confirmed visually from the ground” (NNG). NCG were classified as active  or inactive. Active nests were classified as belonging to wood storks (NWD) or other species. To estimate the number of nests with contents that could not be confirmed from the ground, but were indeed active wood stork nests, the status of nests was visually checked from tree tops in randomly selected sub‐areas of the colonies. The frequency of active wood stork nests (FWD) within NNG was estimated using the total number of nests with contents that could not be confirmed from the ground (NSANG) and the number of active wood stork nests confirmed only when checking from tree tops (NWDSA) in the sub‐areas . Using this frequency, the total number of active nests per wood stork population (TNWD) was estimated as TN^WD=NWD+(NNGFWD). N^a was estimated as 2(TN^WD) considering an independent pair of reproductive adults per active nest, and the number of nestlings in cohorts (N^1) was estimated as k¯ (TNWD). Based on Sahai and Khurshid =(95%CI(N^a)k¯/)2. Nb was estimated for cohorts sampled from the FI, SG1, and SG2 colonies as well as the pooled dataset of the three colonies using the genetic estimators described above in item 2.4, and N^b/N^a ratios were calculated. To estimate Nb using the SA method, empirical k¯ values were set as the medium sibship prior for each parent .and the 95% CI for 2.6N^b, when the objective is to provide genetic monitoring guidelines for natural populations, one should prioritize the use of a precise estimator under conditions similar to those encountered in nature . Estimates with infinite values could compromise the monitoring of population sizes, making it impossible to compare estimates in time and space. Therefore, the applicability of the single‐sample methods for monitoring changes in population size was initially evaluated based on the precision of the estimates measured by the mean percentage of outliers (for simulations), the mean percentage of finite N^b and the mean percentage of N^b with narrower 95% CIs.Although simulations under ideal conditions are more appropriate for assessing the accuracy of N^b that differed significantly with an error rate ≤10% from those obtained by sampling all individuals from the simulated cohort. The average error rate of the estimates was determined by computing the percentage of the differences between harmonic means of N^b obtained using different sample sizes, and the entire set of simulated cohorts and the standard errors were computed using 10,000 bootstrap iterations. For real cohorts, correlations between N^b versus N^a and sample sizes (n) were assessed. The precision of N^b was equated to its log root mean squared error (log RMSE) for simulated cohorts and its variance (V) for real cohorts  was contained within populations . Likewise, Bayesian clustering analyses revealed low Delta K values, with all individuals admixed at K ≥ 2, suggesting gene flow and no significant population subdivisions  and locus WSμ 20 in all colonies . However, loci WSμ 03 and WSμ 20 had high informative content (from 0.55 to 0.80 for WSμ 03 and 0.73 to 0.85 for WSμ 20), and these were maintained in the datasets with their allelic frequencies adjusted. After correcting for the presence of null alleles, only loci WSμ 08 and WSμ 23 in the FI colony and locus WSμ 23 in the SG1 colony departed significantly from HW expectations. There was no evidence of significant linkage disequilibrium or deviations from selective neutrality at any loci. Genetic diversity indices (Table k¯) was 2.29 in FI, 2.13 in SG1, 2.12 in SG2, and 2 in PF. The seasonal variance in the number of nestlings (Vk) was 0.48 in FI, 0.35 in SG1, 0.24 in SG2, and 0.51 in PF. Nonrandom variance in breeding success was also found: mean Vk/k¯ ranged from 0.11 to 0.25 and k values did not adhere to a Poisson distribution model in any population (α < 0.05).Evidence of low migration was found among wood stork colonies from the Pantanal wetland, as indicated by the assignment of three individuals sampled in FI and one individual sampled in SG1 as belonging to the SG2 colony. The mean number of nestlings from accessed nests   Figure . By yield Figure , the ABCN^b obtained by sampling 100% and 10% of the cohorts from the simulated populations revealed that the ABC method had the lowest error rate (10%), followed by the MC (23%), HE (58%), SA (90%), and LD (90%) methods , followed by the SA, MC, LD, and HE methods . N^b/N^a ratios ranged from 0.02 to 0.49 . In contrast, the LD method demonstrated the most infinite values and lowest precision, possibly due to large sampling errors or very large Nb  when the sample size increased  estimates that are positively correlated with Na, but not with sample size  in N^b obtained with the SA method than with the ABC method, probably because setting prior limits to N^b in the ABC method leads to an increase in precision. On the other hand, the LD, MC, and HE methods demonstrated lower precision and none of these methods yielded finite estimates for SG1 or the pooled dataset of the three cohorts . Given that natural populations with intrinsic moderate microsatellite variation were studied .When the objective is to determine population trends using  Beebee, . In the od Table , which iod Table . As presod Table  also fouts Table . The ABCN^b/N^a ratios varied widely, even in single colonies   involves performing an extensive literature review, setting up a pilot study, and planning the field and laboratory research. Steps 2 and 3 (field and laboratory research) involve the execution of the planning to obtain the estimates of the census and effective number of breeders, following what was determined in the pilot study  chosen for estimating Nb considering the available information on a given sample size and number of loci).Based on the workflow described in item 2 of this study, the monitoring using A literature review and an empirical enquiry near the location of the breeding colonies can eliminate some time‐consuming steps from the field research itself, which can assist in the planning of the monitoring as a whole. Using the wood stork populations sampled in the present study as an example, personal communications on the onset of the establishment of the colonies were crucial to defining the period in which the breeding colonies would be visited, as the more adequate time for collecting blood from wood stork nestlings is the period between three and six weeks of age. All information on the reproductive biology, genetic population structure, population dynamics, and impact of the human presence in the colonies . Depending on the degree of natal philopatry of individuals to either colonies or regions, it is possible to monitor specific colonies or a geographic region. If birds exhibit high fidelity to the breeding sites, a researcher can monitor focal colonies and, in this case, the target should be larger colonies, which are most likely to persist over time than smaller ones . However, as demonstrated here, the SA method requires a larger sample size to yield precise estimates with a limiting number of loci. Likewise, findings from this study show that the sample size relative to Na is an important and limiting factor to estimating Nb. Therefore, whenever possible, researchers may estimate Na and k¯, in order to reach adequate sample sizes to obtain precise, unbiased N^b. Na and k¯ should be estimated immediately before or concurrently with the blood collection during initial field work, as the number of nestlings in a colony may vary greatly over the breeding season (Borkhataria et al., The present study pointed that the ABC method is suitable for genetically tracking demographic changes in wood stork populations, as it required smaller sample sizes and fewer loci to estimate Na and/or Nb estimates are useful tools for monitoring natural breeding bird populations. However, as the estimates varied widely depending on the method used, researchers can follow the methodological workflow offered herein to explore genotypic datasets and choose the estimator most suited to the conditions of the focal natural populations prior to start the monitoring. Furthermore, this workflow may not provide useful for the effective monitoring of natural populations when it is not feasible in terms of funds and time to collect representative population samples or when monitoring populations of nomadic species. The methodology based on N^a described herein can be used by researchers interested in genetic monitoring and studying populations of birds with colonial breeding where the sampling of discrete generations is possible. In particular, the workflow offered herein can be used by researchers studying colonial birds with a limited number of microsatellites and low levels of polymorphism. Combining demographic and genetic estimates is a feasible strategy for monitoring few small colonies with no major access difficulties and low human impact. Moreover, combining demographic and genetic approaches is highly recommended for populations of rare species or with a known history of population decline to support conservation efforts by enabling the informed, timely, precise monitoring of trends in population size.This study demonstrates that None declared.F.M.S., C.I.M., and S.N.D.L. designed the study and wrote the manuscript. F.M.S. and S.N.D.L. collected samples. F.M.S. conducted the genetic analyses and the census in natural populations. F.M.S. and C.I.M. led the computational work. F.M.S. and R.I. conducted the statistical analyses and created the figures with the results. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.