Population dispersal and migration often indicate an expanded habitat and reduced inbreeding probability, and to some extend reflects improvement in the condition of the population. The Amur tiger population in the northern region of the Changbai mountain in China mostly distributes along the Sino–Russian border, next to the population in southwest Primorye in Russia. The successful dispersal westward and transboundary movement are crucial for the persistence of the Amur tiger in this area. This study explored the spatial dispersal of the population, transboundary migration, and the genetic condition of the Amur tiger population within the northern Changbai mountain in China, using occurrence data and fecal samples. Our results from 2003 to 2016 showed that the Amur tiger population in this area was spreading westward at a speed of 12.83 ± 4.41 km every three years. Genetic diversity of the Amur tiger populations in southwest Primorye was slightly different than the population in our study area, and the potential individual migration rate between these two populations was shown to be about 13.04%. Furthermore, the relationships between genetic distances and spatial distances indicated the existence of serious limitations to the dispersal of the Amur tiger in China. This study provided important information about spatial dispersal, transboundary migration, and the genetic diversity of Amur tigers in China, showed the urgent need for Amur tiger habitat restoration, and suggested some important conservation measures, such as corridor construction to eliminate dispersal barriers and joint international conservation to promote trans‐boundary movement. Trizio et al. (Sciurus vulgaris), suggesting that geographical factors limited the dispersal of this population.Microscale genetic differentiation is a good tool for quantifying dispersal and revealing spatial patterns in certain conditions  is an flagship species and before the 20th century was widely distributed in northeastern China, the Korean Peninsula, and the southern part of the Russian Far East . Now, fewer than 400 Amur tigers survive in Russia and the eastern region of northeastern China . Total joint analysis with Russia was permitted by both Chinese and Russian governments.2.22 region in the northern Changbai mountain in China, accounting for 68% of the China tiger distribution area  in China and the local forest bureau. FRC‐CSFA is a specialist agency for Amur tiger and leopard research in China. FRC‐CSFA extracted and recorded all the occurrence information of Amur tigers, such as killings, footprints, and feces as reported by local residents and forestry workers since 2012. Jilin provincial government introduced ecological compensation for ecological conservation in 2001. When livestock was killed by wild animals, the government paid the cattleman for it after professional identification; all these cases were recorded by the local forest bureau. Since 2012, we installed 1,000 camera traps in the study area, all the camera traps were designed at an average density of 1 site/10 kmThe transect survey in winter was conducted primarily to find traces of the tiger and collect fecal samples. We surveyed different areas each year from 2012 to 2016.www.esri.com).All the occurrence information was divided into groups by every 3 years, given that the data were scarce in some years. Totally, five groups were formed, and the fifth group only included 2015 and 2016. The occurrence locations in each group were located on the map based on geographical coordinates. After that, we divided the study area into grids of 14 km × 14 km. The longitudes and latitudes of center points of the grids where Amur tiger occurred were extracted to calculate the geometrical center of each group. The calculated geometrical centers were used to show the population dispersal of the Amur tiger in China. All analysis was conducted using ArcGIS software .Pta‐CbR (5′‐CCGTAAACAATAGCACAATCCCGATA‐3′).et al.), and 10–20 ng of DNA by using QIAGEN Multiplex PCR Plus Kit. PCR amplification was performed on a Model 9700 Thermocycler (PerkinElmer) following the process below: 1 cycle for 5 min at 95°C, 35 cycles for 30 s at 95°C, annealing temperature 60°C for 90 s, 72°C for 3 min, and 1 cycle of 68°C for 30 min. The products were analyzed with an ABI 3730xl Automated DNA Sequencer (Applied Biosystems). We performed genotyping four times, after which the genotype was determined by standard peak map. Meanwhile, correspondingly, the consensus genotype was determined, which is found a homozygote with a minimum of three times and two times for a heterozygote  , as well as observed and expected heterozygosity (2.6K (K = 1–10). The method of Evanno, Regnaut, and Goudet  based on the analysis of STRUCTURE HAVESTER were considered. We identified an individual as admixed by using the criteria of mixed Q and undetermined GeneClass assignment probability and being an F0 migrant with a high probability .Two methods for individual heterozygosity evaluation were used as follows: (a) homozygosity by loci (HL) and (b) internal relatedness (IR). HL was estimated by weighting the contribution of each locus accounting for differences in the number and frequency of alleles between loci . Then, we used ArcGIS 10.3 to measure the Euclidean distance between different individuals in the study area based on feces location and genetic results. In addition, the average distance of each individual to the Sino–Russian boundary was also measured. Finally, we used a model to test the relationship between genetic distance and Euclidean distance.Many methods can be used to calculate genetic distances, mainly verified by the strength of the relationship between pairwise genetic distances and landscape distances among sampled individuals in a population , and 702 other records including killings, footprints, ecological compensation, and other traces left by tigers.R2 = 0.979, p < 0.001)  were discarded because they were monomorphic or there was a low rate of amplification, given that usable combined genotypes must include at least 14 alleles in the whole multilocus genotype. In Russia, nine individuals were identified by the match function of the Microsatellite Toolkit, four individuals were detected once and five were detected 2–4 times. For gender identification by sex‐specific primers, five females and four males were identified. In China, a total of 15 individuals were identified, including nine males, five females, and one sex unknown, and were confirmed after amplification (Table HE ± SD) in Russia (0.61 ± 0.1) was higher than in China (0.57 ± 0.15), while observed heterozygosity (HO) in Russia ranged from 0.43 (FCA32) to 0.78 (FCA161 and FCA220). In China, it ranged from 0.14 (FCA32) to 0.83 (FCA176). The average polymorphism information content (PIC) per locus was 0.51 in Russia and 0.49 in China. Expected heterozygosity, observed heterozygosity, and polymorphism information for the 8 microsatellite loci are presented in Table PID value of samples from Russia was 6.866 × 10−7  and a rate of dispersal of 13.04% (3/23). Two of them  sampled in China were suspected to be originally from Russia, while the other 1  migrated in the opposite direction. The individual called B1 assigned strongly to a particular locality in the exclusion test, indicating a seemingly clear population of origin  were identified as potential migrants with a probability that exceed 0.95 , and the geographical distance values ranged from 77.99 to 274.908 (95.329 ± 92.832) km. The difference in the geographical distance from individual to the boundary ranged from 0.031 to 216.569 (68.391 ± 78.553 km). The correlation analysis of the Gamma and lognormal distributions model showed significant positive correlation between genetic distances and geographical distances, as well as the distances to the boundary  and there was no distinct cluster between populations in China and Russia, as the all individuals identified had Q values between 0.4 and 0.5. There was evidently some individuals crossed the boundary based on molecular analysis. The macroapproach also found evidence of transboundary movement. The tracking of a young female tiger in Russia found that she crossed the national boundary at the Ussuri River and moved into China (Miquelle et al., The Amur tiger habitat in Changbai mountain in China was linked with southwest Primorye in Russia where 1 of the 2 Amur tiger populations lives in Russia. This population was limited in a small area by city and a railroad; thus, the successful dispersal from Russia to China was not only crucial to Amur tiger population recovery in China but also important for the local population to avoid inbreeding and over competition. The southwest Primorye population in Russia, as the source population for population recovery in the Changbai mountain and its genetic composition, is extremely important to the population in China (Henry et al., Genetic distance and geographical distance of Amur tigers in China were significantly positively correlated, meaning that with the increase in geographical distance between individuals, kinship becomes more estranged. Geographical distance could influence population structure. The shortest distance from the individual to the boundary ranged from 0.37 to 219 km. Correlation analysis found no significant relationship between individual heterozygosity calculated by two methods and the distance to the boundary, since only long‐distance dispersal makes the most sense for changing genetic relationships (Trizio et al., Our results comprehensively demonstrated that the migration of the Amur tiger from Russia to China and the dispersal further into China was possible, and it was occurring. But successful migration and dispersal were occasional and faced great challenges. There were two reasons that may contribute to this situation. First, previous research identified that cattle grazing was in competition with ungulates for food and space resources, which ultimately leads to a sharp reduction in prey for the Amur tiger. Second, the distribution of villages, roads, farmland, and other infrastructure has a negative effect on prey and on the Amur tiger itself. These factors also affect tiger movement and their ability to breed successfully in the environment (Li et al., 5The Amur tiger population in China is in a stage of continuous growth. Individuals need to be provided with indispensable opportunities, and conservation managers need enough data concerning habitat and population dynamic assessments. This study combined the explicit spatial genetic analysis to explore Amur tiger dispersal both across the international boundary and into China. This study clarified the dispersal process of the Amur tiger into China and how genetic conditions benefit from dispersal. The potential trans‐boundary movement of Amur tiger was also assessed.The study demonstrated that international cooperation and the continuous monitoring of spatial distribution and genetic information are critical to understand the dispersal limitations and recovery effectiveness of Amur tigers in both countries. Based on these results, we believe that the free movements of Amur tigers from border areas further into China, as well as between China and Russia, were limited by some unknown disturbance factors. Thus, international cooperation between China and Russia should be strengthened, and the habitat restoration in China should continue. Furthermore, ecological corridors between China and Russia and habitat patches in the Laoyeling mountain and the Zhangguangcailing mountain should be constructed to create viable migration opportunities for the Amur tiger, in order to promote individual exchange within inner China, and keep sustainable tiger population in the Sino–Russian border areas. Meanwhile, to keep track of the population and dispersal dynamics of the Amur tiger by different methods were important references to assist with timely adjustments to conservation strategies. This study provided a good example of joint research for the continual monitoring of the recovery of other species in both China and Russia.None declared.G. J., A. K., and J. M. conceived and designed this study. Y. N. and G. J. wrote the manuscript and carried out field and laboratory work with C. M., V. Y. G., J. Q., and M. W. Click here for additional data file.


Phellodendron species  as the materials, collected 59 P. amurense samples from 35 populations greatly to represent the genetic diversity, and analyzed the haplotype, genetic distance, barcoding gap, and Neighbor‐Joining (NJ) trees based on psbA‐trnH and internal transcribed spacer gene sequences. Additionally, a sampling simulation was conducted to assess the correlation between genetic diversity and the number of populations. Finally, analysis of critical geographical populations was performed. Based on analysis of haplotype, genetic distance, barcoding gap, and NJ trees, we found that eight P. amurense samples impacted on the effectiveness of DNA barcoding, which genetic information were very important to identify Phellodendron species. Moreover, the result of the NJ tree analysis performed the small‐scale P. amurense sample size did not completely match the objective phylogenetic relationship in Phellodendron. In simulation sampling analysis, the data showed the genetic diversity indexes at the same population level gradually decreased and stabilized as the number of simulation sampling populations increased. We found that 1–2 samples from over 24 populations based on uniform geographical distribution could represent 80% of the genetic diversity of P. amurense and ensure authenticity and reliability of DNA barcoding. Thus, we proposed it is particularly important adequately samples to cover infraspecific genetic diversity in order to ensure identification accuracy of DNA barcoding.DNA barcoding is widely used in species identification, but there is considerable controversy regarding the extent of sampling in research methods. Some scholars have proposed that this small sample size underestimates the intraspecific genetic diversity, which would impact on the accuracy of DNA barcoding to identify species. In study, we selected all  We proposed adequately covering infraspecific variation should be focused more on establishment databases and application of DNA barcode. Small‐scale samples for each species may be not enough to represent the genetic diversity of the species and had a huge risk in application of DNA barcode. Used a case to prove the genetic diversity impacting on the identification accuracy of DNA barcoding.P. amurense by simulation sampling.Analyzed the relationship between samples size and genetic diversity parameters of Proposed adequately samples covering infraspecific variation of species being the key to DNA barcoding.1COI gene) sequences from the mitochondrial genome can be used for the identification of multiple animal groups, making it an ideal animal barcode sequence  and maturase K (matK) genes as core DNA barcodes for seed plants, as well as the psbA‐trnH intergenic spacer (psbA‐trnH) and internal transcribed spacer (ITS) genes as supplementary DNA barcodes , which is far from sufficient to speculate the number of samples needed to establish a database to represent the genetic diversity of each species. The efficiency and accuracy of DNA barcoding depends on the degree of sampling per species, because a large enough sample size is needed to provide a reliable estimate of genetic polymorphism and for delimiting species  has two species and one variant, including P. amurense Rupr., P. chinense Schneid., and P. chinense var. glabriusculum Schneid., are distributed in China  primers  were rubbed for 2 min at a frequency of 30 r/s. Total genomic DNA was extracted using the Plant Genomic DNA Kit (Tiangen Biotech Co.) according to the manufacturer's instructions. The extracted genomic DNA was amplified by polymerase chain reaction (PCR) using the ITS (ITS5F and ITS4R) and rs Chen, . The PCRrs Chen, . PCR pro2.3P Language based on Kimuraʼs 2‐parameter theory  as well as the base quality was evaluated to avoid technical error. The inter/intraspecific genetic distances and barcoding gap were analyzed with the P. amurense, a sampling simulation was conducted in this study. In total, 3, 6, 9, 12, 15, 18, 23, 28, and 33 populations levels were randomly selected from 35 P. amurense population samples by a computer sampling simulation, with each population sampling being repeated 50 times. The number of haplotypes (H) was used as an important indicator of genetic diversity in studies of populations calculated by the MEGA (4.0 version)  and nucleotide diversity (iP) were also studied at the same time. The haplotype diversity (Hd), the frequencies of two different haplotypes randomly extracted in the sample, is an important indicator of the degree of variation in a population. The nucleotide diversity (iP), the average of all pairwise distances in a sample, is commonly used to estimate genetic polymorphism. The relationship of H with a population's number in the sampling simulation was analyzed by nonlinear regression (curve fit) with GraphPad Prism 5 (https://www.graphpad.com/), while the relationships of numbers of populations with Hd and iP were analyzed with a scatter plot.To assess the relationship between a number of populations and the genetic diversity of 33.1psbA‐trnH contig sequences from all the samples in this study are shown in Table P. amurense had 6 haplotypes, P. chinense had four haplotypes, and P. chinense var. glabriusculum had three haplotypes. P. chinense was different from P. amurense and P. chinense var. glabriusculum at bp 173 with a T; simultaneously P. chinense var. glabriusculum was also different from P. amurense and P. chinense at bp 208 with a T, except in haplotypes A12 and A13.The GenBank accession No. for the ITS and psbA‐trnH sequence (Table P. chinense and P. chinense var. glabriusculum had one haplotype (B10) in common, and that P. amurense had nine haplotypes, with 54.1% of the samples being haplotype B1. Based on the variable sites, haplotypes B2 and B3 in P. amurense were the same as haplotype B10 with 379, 380, 381, and 382 bp.The haplotype and variable sites in the ce Table , which t3.2Phellodendron species.Six parameters were used to analyze intraspecific variation and interspecific divergence with two barcodes Table . In thispsbA‐trnH sequence displayed murky barcoding gaps with overlapping intra‐ and interspecific variation distributions. Through calculation and traceability, we found that eight P. amurense samples had overlapping regions with haplotypes B2 and B3.The barcoding gap presents the remarkable variation between inter‐ and intraspecies and demonstrates the separate or overlapping distributions between intra‐ and interspecific samples. In this study Figure , the ITS3.3P. amurense, P. chinense, and P. chinense var. glabriusculum were short of monophyly. The NJ tree built on the psbA‐trnH sequences clustered into two major branches  = ax/(b + x)] is used to perform nonlinear regression (curve fit), which satisfied the requirements in this study. The haplotype discovery curve (HDC) is presented in Figure f(x) = 7.072x/(5.756 + x) and an r2 = .8082. The results showed that the number of haplotypes (H) index gradually increased with the increase in the simulation sampling populations and had an overall sample level as shown in Figure Hd) and nucleotide diversity (iP) are shown in Figures Since 072x/5.75 + x and 3.5H) was a pivotal criterion for estimating the genetic diversity of P. amurense in simulation sampling. We arrived at the haplotype discovery curve (HDC) in Figure f(x) = 7.072x/(5.756 + x). We focused on the following two theoretical key parameters for sample sizes in this study: (a) the threshold of sample sizes where new haplotypes become considerably more difficult to identify with extra sampling efforts  and (b) the sample size that includes the majority of haplotypes (indicating that 80% of haplotypes are found).The number of haplotypes (P. amurense haplotypes when the theoretical threshold of the sample size was 23.024 populations using simulated sampling with the theoretical equation or sample sizes with no less than 24 populations in an actual sampling. To obtain a higher percentage of P. amurense haplotypes, we should increase the sample size.The theoretical key parameters are presented in Figure 3.6psbA‐trnH performed better than ITS for identifying Phellodendron species, we analyzed the key populations based on psbA‐trnH. Combining the haplotype analysis with the NJ tree analysis, we discovered eight special samples with seven populations existing in all the P. amurense populations. The samples' variable sites and genetic relationship were similar to P. chinense and P. chinense var. glabriusculum based on our data (Table P. amurense sample haplotypes would directly affect the reliability of sampling research results. We defined the eight P. amurense samples as critical samples and their populations as critical populations based on sufficient evidence. The geographical distribution of the seven critical P. amurense populations is shown in Figure P. amurense area. This meant there was no key geographical group of P. amurense. Thus, it is highly meaningful to assess the sampling size from the uniform geographical distribution based on the genetic diversity.Since ta Table . Therefo4psbA‐trnH sequences is natural. It is necessary to assess the intraspecific genetic diversity of species. Furthermore, lack the genetic information of key populations will lead to DNA barcoding gaps, and the number of samples for collection is crucial to establish a reliable reference database for species identification (Meyer & Paulay, It is fact that different plant species will vary in the amount of infraspecific variation they contain, because of variation in length of time since divergence, and variation. Speciation is a gradual process, meaning that the discussion of species based on the evolutionary history of a cross section has significant limitations Hennig, . IntraspP. amurense and collected adequately samples throughout the distribution area to ensure covering infraspecific variation. We conducted a comparative analysis of the overall samples and small‐scale randomly samples. The results showed that small‐scale samples could not demonstrate an objective phylogenetic relationship in Phellodendron (Figures P. amurense (Figure Taxus, and Matz and Nielsen (P. amurense area, which meant there were no key geographical groups (Figure P. amurense clearly underwent genetic variation among the existing populations. It was successful to take large‐scale uniform sampling covering the entire distribution area in study.In study, we paid close attention to the genetic diversity of  Figures , 4 and 5e Figure . It did e Figure  believe  Nielsen  recommen Nielsen  hold tha Nielsen . The B2 s Figure . P. amurThe most important challenge for species identification is DNA barcodes used for closely related species and recently differentiated species (Newmaster, Fazekas, Steeves, & Janovec, In order to ensure identification accuracy of DNA barcoding which is used as tools for species identification, it is particularly important to collect adequately samples covering infraspecific genetic diversity of species.The authors declare no conflicts of interest.Zhi‐peng Zhang: experimental operation, manuscript writing; Xiao‐yue Wang: experimental operation; Zhao Zhang: corresponding author, experimental design; Hui Yao: manuscript modification; Xiao‐mei Zhang: manuscript modification; Yang Zhang: sample Collection; Ben‐gang Zhang: experimental design.


The introduction of proteasome inhibitors , immunomodulators , and other novel agents have greatly improved the prognosis of patients with MM2. However, recurrence or drug resistance occurs frequently. In particular, the bone marrow microenvironment confers protection effect to MM cells by direct cell contact or releasing cytokines such as interleukin-6 (IL-6)3. Finding novel treatment is urgently needed. Signal transducer and activator of transcription 3 (STAT3) is a transcription factor that regulates the expression of many genes, such as Bcl-xL, Mcl-1, and Cyclin D1. STAT3 is involved in variety of biological processes, such as cell proliferation, differentiation, survival, inflammatory response, immunity, and angiogenesis4. In MM cells, STAT3 is aberrant activated by endogenous  or exogenous signals   is the second most common form of blood cancer9. In a recent article published in Cell Death Discovery10, we synthesized a series of sinomenine derivatives and identified YL064 as the most effective one. YL064 is at least 10-fold more potent than sinomenine in exerting cytotoxic effect against MM cells, but is not cytotoxic to normal blood cells even at higher concentrations. Importantly, YL064 can induce MM cell death in the presence of stromal cells or in the addition of exogenous IL-6. Furthermore, in vivo experiments shown that YL064 is well tolerated in mice and it could significantly inhibit tumor growth. These data suggest that YL064 is a promising lead compound for the treatment of MM. Due to the pivotal role of STAT3 in the pathogenesis of MM, we examined the possible effect of YL064 on STAT3 activation. Interestingly, YL064 can inhibit the phosphorylation of Tyr705 but not Ser727, indicating the suppression of STAT3 activity. In support of this, YL064 inhibited the nuclear translocation of STAT3 and the transcription of STAT3 target genes, including cyclin D1, Mcl-1. Intriguingly, both the endogenous activation and exogenous factors-induced activation of STAT3 could be blocked by YL064. Based on these data, we hypothesized that YL064 may directly interacts with STAT3. For this purpose, we synthesized Biotin-labelled or FITC-labelled YL064. The results showed that Biotin-YL064 could pull down STAT3 from cell lysate, which could be competed away by unlabeled YL064, indicating the specific interaction between STAT3 and YL064. And FITC-YL064 co-localized with STAT3 in MM cells. Moreover, using the cellular thermal shift assay12, we further demonstrated the engagement of YL064 with STAT3 in cells. Taken together, our data strongly suggest that YL064 directly interacts with STAT3 in cells. To reveal the mode of action of YL064, molecular docking studies were performed. The results showed that YL064 may prevent the interaction of STAT3 with phosphorylated tyrosine residues on cytoplasmic receptor kinases through targeting the SH2 domain of STAT3. However, a better understanding of the molecular basis for their interaction may be achieved by future co-crystal analysis.Sinomenine is a natural compound isolated from Sinomeniumacutum and has been used clinically for the treatment of rheumatoid arthritis and other inflammatory diseases. However, in order to achieve efficacy, sinomenine must be used at high concentrations, which causes some side effects and limits its applicationIn conclusion, using a chemical biological approach, we demonstrated that YL064 is a novel STAT3 inhibitor with potent anti-MM activity. Our data suggest that YL064 may overcome proteasome inhibitor resistance caused by the bone marrow microenvironment, which warrant further investigation.


Rhizoglyphus robini, that express ‘fighter’ and ‘scrambler’ male morphs mediated by a complex combination of environmental and genetic factors.Alternative reproductive tactics (ARTs) are taxonomically pervasive strategies adopted by individuals to maximize reproductive success within populations. Even for conditionally-dependent traits, consensus postulates most ARTs involve both genetic and environmental interactions (GEIs), but to date, quantifying genetic variation underlying the threshold disposing an individual to switch phenotypes in response to an environmental cue has been a difficult undertaking. Our study aims to investigate the origins and maintenance of ARTs within environmentally disparate populations of the microscopic bulb mite, Using never-before-published individual genetic profiling, we found all individuals across populations are highly inbred with the exception of scrambler males in stressed environments. In fact within the poor environment, scrambler males and females showed no significant difference in genetic differentiation (Fst) compared to all other comparisons, and although fighters were highly divergent from the rest of the population in both poor or rich environments , fighters demonstrated approximately three times less genetic divergence from the population in poor environments. AMOVA analyses further corroborated significant genetic differentiation across subpopulations, between morphs and sexes, and among subpopulations within each environment.Our study provides new insights into the origin of ARTs in the bulb mite, highlighting the importance of GEIs: genetic correlations, epistatic interactions, and sex-specific inbreeding depression across environmental stressors. Asymmetric reproductive output, coupled with the purging of highly inbred individuals during environmental oscillations, also facilitates genetic variation within populations, despite evidence for strong directional selection. This cryptic genetic variation also conceivably facilitates stable population persistence even in the face of spatially or temporally unstable environmental challenges. Ultimately, understanding the genetic context that maintains thresholds, even for conditionally-dependent ARTs, will enhance our understanding of within population variation and our ability to predict responses to selection.The online version of this article (10.1186/s12862-019-1385-4) contains supplementary material, which is available to authorized users. Onthophagus acuminatus; [Philomachus pugnax; [In numerous species, it is common for individuals  to adopt different strategies to increase their reproductive success when intrasexual competition is intense. These strategies can ultimately lead to diversity within populations, comprising of characteristics such as behaviour, physiology, or morphology . Referreminatus; ), wherea pugnax; ), althou pugnax; –6. More  pugnax; .Genotype-by-environment interactions (GEIs) are routinely observed in traits linked to fitness  such thaCurrently, the environmental threshold model, which links condition-dependence and GEIs –23, is t(Rhizoglyphus robini) is a microscopic agricultural pest, which thrives on invading crops and disperses easily when food is deprived (a species familiar with fluctuating environment conditions) [R. robini, individuals express either a ‘fighter’ or ‘scrambler’ mating tactic consisting of the ontogenetic development (or not) of weaponry comprised of a thickened, sharply terminated third pair of legs used to combat and kill rival males   . This spditions) , and is ditions)  that, duly) Fig. . The envHere, we aim to resolve broad evolutionary questions surrounding the origin and maintenance of ARTs by quantifying the cryptic variance underpinning threshold responses to environmental cues. We do this by testing the hypothesis that genetic diversity differs between the two male ARTs in the bulb mite, such that larger fighters with associative high body condition will demonstrate higher levels of genetic diversity compared to their smaller, poorer conditioned scrambler counterparts. Using populations consisting of tens-of-thousands of individual bulb mites reared under different environments, we quantified underlying genetic context in relation to ART expression, using never-before-published genetic markers to quantify individual-level genetic diversity across populations.ad libitum. The rich resource treatment (yeast), in fact, creates a similar rearing environment to that of natural bulb mite populations feeding on garlic bulbs [We used bulb mites from stock populations originating from 10 sampling sites via collecting flower bulbs near Anna Paulowna, North Holland, Netherlands in 2010, that ultimately comprise tens-of-thousands of individuals. Mites were reared and maintained at the Institute for Biodiversity and Ecosystem Dynamics at the University of Amsterdam, Netherlands, in a controlled environmental chamber  under tic bulbs .n = 126) and poor (n = 105) environments, including 72 scrambler males , 76 fighter males , and 83 females . Upon collection, mites were individually preserved in 1.5 mL Eppendorf tubes containing 95% ethanol, and stored at − 20 °C until DNA extraction.From the rich and poor environments, mites were randomly collected and examined with a stereomicroscope for identification. Sexes and ART morphs were identified according to the morphological criteria described by Smallegange , includiBecause we aimed to create near-equal sampling of each subpopulation , the representative sex-ratio from the overall environments did not reflect the female biased operational sex-ratio from either stock or natural populations . However− 1) were added to each tube, after which the samples were homogenized 3 times for 30s at 6500 rpm using a Precellys24 tissue homogenizer (Bertin Corp). Upon homogenization, the samples were incubated for 2 h at 56 °C, and proteinase K was inactivated via incubation for 8 min at 95°C. Samples were centrifuged for 2 min at 14000 rpm and thereafter stored at − 20°C.Prior to extraction, all ethanol within the Eppendorf tubes was evaporated. For the female and fighter male mites, we used a modified protocol from Knegt et al.  in which− 1) was added to each Eppendorf tube, and with a pestle, mites were ground into small pieces after which 30 μL of a 5% chelex solution was added to each tube. The samples were subsequently incubated for 3 h at 56°C, and proteinase K was inactivated via incubation for 8 min at 95°C. Samples were vortexed and centrifuged shortly, and stored at − 20°C prior to DNA amplification.As scrambler male mites are typically much smaller than their fighter or female counterparts, we adjusted the DNA extraction protocol as follows: after the evaporation of all ethanol, 4.5 μL proteinase K  primer pairs designed for our species at Jagiellonian University in Kraków , optimizPrimer pairs were labelled with four different fluorescent tags, allowing them to be multiplexed and analysed simultaneously using capillary electrophoresis . Per two amplicons (differently labelled), 1 μL of PCR product, 0.3 μL of orange dye labelled GeneScan™ size standard 500LIZ™ (Applied Biosystems), and 10 μL formamide was added and denatured before running on the ABI analyzer. Data was visualised, and alleles scored, in GeneMapper™ software (v4.1.1) (Applied Biosystems), after which each automatically scored allele was double-checked by hand. Our nSSRs were defined by a characteristic stutter followed by a peak of at least 450 relative fluorescent units or greater. We further assayed approximately 10% of our samples a second time to check and ensure repeatability of scoring.A), we also quantified observed heterozygotes within a subpopulation  (HO) and expected frequency of heterozygotes (HS) under Hardy-Weinberg equilibrium (HWE) [IS), and determined whether subpopulations departed from HWE . To measure genetic divergence among subpopulations, Wright’s FST was estimated according to F-statistics defined by Weir & Cockerham [With the use of GenoDive v.2.28  that accum (HWE) , both raockerham , wherebyWe also performed an analysis of molecular variance (AMOVA)  in GenoDWe further subsampled 30 random individuals per group and performed the same analyses with the aim to control for possible artefacts or bias stemming from missing data or unequal sampling. Random subsampling and reanalysis was performed 5 times . In the poor environment (19.1%), missing data for females was 5.4%, and for males, 20.5% . In the rich environment (10.2%), missing data for females was 8.65%, and for males, 9.68% . We additionally detected 11 private alleles across 5 loci that differentiated between males and females, and 12 alleles that segregate between the rich (4) or poor (8) environments , with the exception of scramblers in the poor environment.Across all individuals, allelic richness remained low, ranging from 3 to 11 alleles per locus. For four loci, significant deviations from HWE were detected demonstrating an excess of homozygosity present across individuals Table , where rST = 0.109, p < 0.001) between the subpopulations  within their respective environments . These results corroborate the findings that fighters were significantly more genetically divergent compared to scramblers within either environment  differed significantly , between morphs , and between sexes . Subpopulations within environment were also genetically different from one another , but the environments (rich and poor) do not differ from the total population .AMOVA analysis Table  showed sWith the exception of locus Rrms72 demonstrating no significant deviations from HWE  R. robini [Despite previous formative work focusing on sex- and morph-specific population mean transcriptome patterns in . robini , 53, thi. robini , 53) and. robini , 35, 54)R. robini females derived from fighter selection-lines is evidence for intralocus sexual conflict [Counter to our hypothesis for genetic diversity-condition links within our male morphs, we find evidence that large fighters are less genetically variable than their smaller scrambler counterparts. As fighters have been shown to achieve higher reproductive success than scramblers , while aconflict . Our obsconflict .A non-mutually exclusive but more adaptive explanation for the origins of the genetic patterns underlying bulb mite ART expression could be their genetic context, or the relation and interaction of genes underpinning this phenotype (epistasis or genetic correlation). Non-additive, epistatic combinations , 63 are positive epistasis could be responsible for fighter expression, such that many alleles in conjunction work in a way that synergistically outperforms their individual contributions to genetically determine the fighter phenotype. Similarly, if many alleles in coordination lead to a less fit phenotype than expected based on their effects in singularity, the process may give rise to a new/alternative phenotype within a population; certain genetic elements combinations may also mask the effects of others (antagonistic epistasis), functionally suppressing the manifestation of high fitness traits . The laCorrespondingly, genetic correlations among traits could equally link genetic components together causing similar patterns to the ones we see here. ART-specific genetic correlations have been previously shown in another invertebrate taxa , and theR. robini work should aim to assess whether these same GEI patterns are also reflected in natural populations. However, as these broad GEI associations remain consistent between rearing environments, and our rich environment reflects similar natural history responses to that of natural resources , we havConsiderable variation has been observed in the effects and strength of inbreeding depression among environments, populations of the same species, and even within sexes e.g., , 72–74).–74.72–74Previous empirical evidence in bulb mites not only demonstrates that scrambler morphs live longer , but impAcross taxa, processes for the maintenance of genetic diversity are especially significant as they serve as a means for populations to adapt to changing environments and thus play an important role for the survival of a species , includiThe complexity and need for organisms to interact with their environment  implies that genetic context, and thus GEIs, are likely to be pervasive even among plastic phenotypes. Still, the evolution and proximate cause of these phenotypic alternatives are only beginning to be understood. Ultimately, our ability to accurately predict responses to selection based on the genetic variation that maintain thresholds for ARTs, and appreciating the relative genetic and environmental contributions influencing phenotypic expression, is critical to understanding both the breadth and maintenance of within-species variation and a populations capacity to adapt to external adjustments.Additional file 1:Table S2.1. – S2.5. Exemplar (1 from 5) of subsampled analyses performed from 30 randomly sampled individuals per subpopulation  between rich and poor environments. Table S2.1. nSSR information on each locus. Table S2.2. Hardy-Weinberg statistics across environments and subpopulations. Table S2.3. Pairwise FST values for population differentiation. Table S2.4. Summary of hierarchical AMOVA. Table S2.5. Genetic differentiation between strategies (fighters and scramblers) compared to total population within that environment (rich and poor). (DOCX 32 kb)Additional file 2:Table S1.1. Allele frequencies by sex, morph, and environments. (DOCX 39 kb)


Vitis spp. acquired different mechanisms of resistance to leaf and root attack. Its appearance in European vineyards at the beginning of the 1860s, where the phylloxera-susceptible grapevine species V. vinifera L. is majorly cultivated, caused the devastation of a great number of vineyards, generating a deep crisis in the European wine production and trade industries. However, the origin and genetic structure of this pest across European vineyards still remain controversial and uncertain. Herein, we analysed the genetic structure of 1173 grape phylloxera individuals collected from 100 locations across eight European countries. Structure and phylogenetic analyses show that contemporary grape phylloxera populations in Europe are the result of at least two independent introductions from the native range that mirrors the historical records that also suggest two major outbreaks in Europe. The comparative analysis with samples from the native range trace back one of these two genetic groups to plants imported from the North East coast of North America, where the American species V. riparia and V. labrusca dominate. This study clarifies the level of genetic diversity of grape phylloxera in Europe and provides relevant information to resolve previous controversy about its origin.Grape phylloxera is native to North America, where  Daktulosphaira vitifoliae Fitch (commonly known as grape phylloxera) is the most geographically spread pest species, virtually occurring in all viticultural regions around the world1. Grape phylloxera is an obligate biotroph of Vitis spp., capable of feeding on both grapevine roots and leaves2. Its native range extends from southern Canada to northern South America, but it is more common in the eastern and central USA3. The coevolution of grape phylloxera and American grapevine Vitis spp. led to the generation of different grapevine species with different strategies of resistance to grape phylloxera leaf and root attack4. Because grape phylloxera was not present in the centre of origin of cultivated grapes and Europe before the nineteenth century, the cultivated grapevine (Vitis vinifera L.) did not develop any resistance mechanism4. In susceptible V. vinifera cultivars, grape phylloxera forms abundant root nodosities and tuberosities that occlude the vascular system of the vine, resulting in a dramatic loss of leaf surface area and yield. In addition, feeding wounds allow secondary infections by soil-borne pathogens and cause plants to die from secondary infections2. The natural capability of grape phylloxera to move among vineyards to infest healthy plants is limited5, but human-assisted dispersal can inadvertently transport insects between sites through infested plant material, machinery, clothing and footwear, postharvest grape products and winery wastes1. It is generally accepted that the insect was unintentionally introduced into Europe at the end of the nineteenth century through infected plant material from North America to fight oidium (powdery mildew), the fungus that threatened European vineyards in the 1850s6. Grape phylloxera appearance almost destroyed European viticulture, and it provoked the most radical switch in viticultural practices of the last two centuries, when grape growing changed from the use of own-rooted V. vinifera plants to their grafting onto partially-resistant American non-vinifera Vitis spp. or hybrids used as rootstocks7.Phylloxera vastatrix8. Almost simultaneously, the insect was observed in the Bordeaux region, with an outbreak registered in 1869 in the term of Floirac (Gironde)9. Soon after, grape phylloxera become ubiquitous all over southern and central vineyards of France11, where it destroyed about 40% of vines, inducing an important economic shock in a society largely dependent on agriculture12. From this main focus, grape phylloxera advanced southward towards Spain13 and Italy14, and northward towards the wine regions of Geneva and St. Gallen in Switzerland10. Between 1874 and 1900, diverse outbreaks were reported in several locations in the Rhine, Moselle, Neckar and Main river valleys (Germany)10. At the same time, two important additional grape phylloxera outbreaks were registered in very distant regions. During the 1860s, wine producers reported a new pest affecting Douro’s vines in Portugal, with withering vines detected in vineyards of the municipalities of Sabrosa, Santa Marta de Penaguião, Peso da Régua and São João da Pesqueira. In spring of 1863, the initial focus of this grape phylloxera outbreak was found in a vineyard of Gouvinhas, probably as a result of the introduction of some American vine cuttings imported by the owner of the vineyard15. Simultaneously, another major outbreak was observed in 1868 in the city of Klosterneuburg , which was officially registered in 187216. From there, grape phylloxera spread following the Danube river route, first to neighbouring Austrian viticultural areas of Lower Austria and Burgenland, and then to modern Czechia, Slovakia and Hungary10. After 1880, grape phylloxera was observed in viticultural regions of Serbia, Bulgaria and Romania, reaching Moldavia in 1887 and the Crimean Peninsula soon after10.Historical records show that grape phylloxera appeared in Europe at the beginning of the 1860s in the southern Rhône region of France , where vines began to wither and die for no previously-known causes. Nevertheless, it was not until 1868 that the presence of grape phylloxera in grapevine dead roots was certified by a commission of experts, who initially denominated the insect as 19, few studies have dealt with this issue at a multinational scale21. Nevertheless, the use of low resolution genetic markers20 or the screening of a limited number of populations from a reduced number of sampling sites21 hindered obtaining sound conclusions on the genetic structure of this pest in Europe. Better understanding of genetic diversity of grape phylloxera in Europe provides critical information to develop monitoring and control strategies for this pest in European vineyards in near future. The goal of this study is to analyse the spatial genetic structure of grape phylloxera infesting commercial and abandoned vineyards in Europe. For this purpose, we investigated the association between the insect and geography in conjunction with historical information to understand contemporary population structure patterns. We carried out extensive sampling of grape phylloxera populations from 100 locations spread over eight European countries . In addition, we included some grape phylloxera samples from the introduced regions of South Africa and Uruguay. They were jointly analysed with individuals from the native range to evaluate the point of origin of the populations nowadays present in Europe.In spite of being a matter of concern for European grape growers and scientists for more than 150 years, the genetic structure of grape phylloxera in Europe is still unclear. Although several reports dealing with grape phylloxera populations at a national level can be foundV. vinifera L. cultivars, Vitis spp. interspecific rootstocks, Vitis spp. interspecific hybrid direct-producers and Vitis spp. interspecific resistant grape varieties) were genotyped with 7 SSR markers . All the SSR markers were polymorphic among grape phylloxera samples, with a mean number of 9.1 alleles per locus. The number of alleles ranged from 5 (for DV8) to 12 (for PhyIII_55 and DVSSR4). The combination of this genetic information led to the identification of 774 unique MLGs  and Quinten (in Switzerland), separated by ca. 620 km. MLG380 was not the only case in which the same MLG was collected from sites separated by large distances. MLG113 was found in Nuβdorf ob der Traisen (Austria) and in Rudolfingen (Switzerland), separated by ca. 330 km. Similarly, we found MLG475 in Köveskál (Hungary) and in Rafz (Switzerland), locations separated by ca. 690 km. On the other hand, we found seven MLGs  feeding in both leaves and roots. In addition, several examples of the same MLG in Vitis spp. interspecific hybrid direct-producers and Vitis spp. interspecific rootstocks , in Vitis spp. interspecific hybrid direct-producers and V. vinifera L. cultivars (MLG247 and MLG381), and in Vitis spp. interspecific rootstocks and V. vinifera L. cultivars  were found  and two other introduced regions (South Africa and Uruguay) from a diverse variety of hosts . To avoid the impact of uneven sampling for these variables, STRUCTURE output was evaluated considering the MedMeaK, MaxMeaK, MedMedK and MaxMedK statistics to determine the most probable number of genetic groups. MedMedK, MaxMedK and MaxMeaK suggested K = 3 as the optimal level of structuring when using the sampling site (country) as correcting factor, whereas MedMeaK pointed out K = 2. On the other hand, the four metrics indicated K = 2 as the most probable number of genetic groups when using feeding form or host plant as co-factor  over 0.80 for group assignation, 302 MLGs (39.0%) were associated with one of the two genetic groups (denominated Eu1 and Eu2), whereas 472 MLGs (61.0%) were identified as “admixed” , Vitis spp. interspecific rootstocks (54 MLGs), V. vinifera L. cultivars (31 MLGs) and one Vitis spp. interspecific resistant grape variety (1 MLG). On the contrary, MLGs in Eu2 were mostly collected from Vitis spp. interspecific rootstocks (125 MLGs) , Germany (21 MLGs), Uruguay (19 MLGs), Hungary (14 MLGs), Italy (13 MLGs), Croatia (9 MLGs) and Austria (8 MLGs) Fig. . On the Gs) Fig. . More dePsex values (<0.01). The average number of alleles per locus is similar in Eu1 (6.14) and Eu2 (5.71). We detected 10 and 13 private alleles in Eu1 and Eu2, respectively, which were found across all the loci screened. A lower than expected level of heterozygosity was observed in Eu1 and Eu2, suggesting the effect of inbreeding forces on both populations. Lastly, FIS values were positive for most of the loci analysed in this work, but for PhyIII_36 in Eu1 and PhyIII_30 and DVSSR4 in Eu2.Genetic parameters indicated a similar level of genotypic diversity for Eu1 and Eu2 genetic groups, with values rather similar to those calculated for the whole dataset  on grape phylloxera genetic structure, as well as a minor effect of host plant . No significant effect for the feeding behaviour was observed , neither for the two-way or three-way interactions between the factors evaluated. Linear regression modelling results revealed a significant effect (p ≤ 0.05) of longitude and latitude of the sampling sites on grape phylloxera population structure. Attending to the standardised beta coefficients given by the model to each variable to compare their relative relevance on the genetic structure obtained, we observed a major effect of longitude (beta = 0.626) over latitude (beta = 0.171).The three-way ANOVA analysis revealed a major significant effect of sampling site (country)  was formed by 212 MLGs, 211 from the native range of grape phylloxera in the current states of Tennessee (42 MLGs), Missouri (34), Virginia (26), North Carolina (17), New Mexico (17) and Arizona (15) (among others), and one MLG sampled in Germany (assigned to Eu1). Group 2 (Eu) clustered 376 MLGs, mainly from Europe (140 of Eu1 and 156 MLGs of Eu2). The remaining MLGs clustering in this group corresponded to samples isolated from the native range, including, among others, isolates from the states of New York (25 MLGs), Pennsylvania (8 MLGs), Massachusetts (6 MLGs), South Dakota (6 MLGs) and Indiana (5 MLGs), as well as some MLGs from the introduced regions of Austria (7 MLGs), Hungary (5 MLGs), California (4 MLGs), Uruguay (3 MLGs), and Brazil (3 MLGs) retrieved from Lund et al.3 , but the Eu genetic group was divided into two genetic groups (Eu1’ and Eu2’). This division matched with the genetic structuring previously observed when analysing the 774 MLGs from Europe. Eu1’ grouped 188 MLGs, 122 of which were previously assigned to the Eu1 genetic group. It also clustered samples from the states of New York (22 MLGs), South Dakota (10), Pennsylvania (8), Massachusetts (7), Indiana (5), Minnesota (4) and Arkansas (4), as well as from the introduced regions of Uruguay (3 MLGs), Brazil (2) and Hungary (1). Eu2’ grouped 160 MLGs, 151 of which were previously assigned to the Eu2 genetic group. The remaining MLGs corresponded to 1 MLG from Eu1, 2 from the Arizona state, 5 from Austria and 1 from Hungary   showed three distinct groups  and Iași (eastern Romania), which represents a quantitative improvement compared to previous workset al.20, who found a loose correlation with sampling latitude. Unfortunately, authors did not test the effect of longitude. Here, we also found a significant effect of latitude on the genetic structure, but longitude had a major effect. In this regard, Eu1 is mostly formed by grape phylloxera genotypes that dominate in western regions , whereas Eu2 genotypes dominate in eastern areas . This genetic structure observed from nuclear genomic analyses is in accordance with previous mtDNA phylogenetic analyses that indicate a common origin for grape phylloxera in Germany and France, but independent to those from Hungary23. The effect of host plant on the selection of specific grape phylloxera clones has been already suggested24, especially in the native range25 as a result of centuries of plant-insect coevolution4. Here, we observed a significant effect of host plant on the genetic structure, but it can be the result of the non-random distribution of the different plant hosts in the diverse regions analysed in this work . In this line, a fine-scale exploration is needed to separate host plant- and geography-based associations.Our results strongly indicate the presence of at least two independent grape phylloxera introductions from the native range into Europe, which derived into two genetic groups (Eu1 and Eu2). The structuring of grape phylloxera populations into two groups was already suggested by Forneck 8, which spread northwards and southwards to infest Swiss and Italian vineyards, respectively14. In addition, our results indicate that this introduction might have been responsible of the outbreaks reported in the west of Germany between 1874 and 1900 too10, as no additional stratification between Swiss, German and Italian samples was observed. On the other hand, grape phylloxera genotypes of Eu2 might have directly derived from the grape phylloxera outbreak detected in 1868 in the nurseries of the formerly known as the Imperial and Royal Viticulture Research Station at Klosterneuburg (Austria)16, where vine cuttings from different regions (including the USA and France) were received26. According to historical records, vine cuttings were distributed as a solution against oidium before grape phylloxera symptoms were evident, easing the dispersal of the insect across the former Austro-Hungarian Empire26. The independence on the origin of these two outbreaks was already evidenced in the 1870s, as no grape phylloxera injuries were detected when all the plant material received from France was screened at the Klosterneuburg nurseries27.Considering the geographic distribution and historical reports, it seems plausible that genotypes of Eu1 derived from the grape phylloxera population introduced in the South of France in the 1860s23. Nevertheless, we found this statement to be exclusively true for the Eu1 genetic group, which was likely founded by grape phylloxera populations originating from the North East coast of North America, where V. riparia and V. labrusca dominate4. How grape phylloxera was introduced into France is still controversial, although it is generally accepted it might have been introduced on roots of V. labrusca and/or leaf-galled V. riparia plants4, agreeing with our results. Some historical records suggest that the French outbreak probably arose from the cultivation of infested plants of the hybrid grape cultivar Isabella à grains noirs, introduced from Pennsylvania28. Some reports additionally indicate that first grape phylloxera populations could have been introduced to France via United Kingdom, where grape leaf galls were already observed in table grape vines by 186311. The cultivation of Isabella à grains noirs vines  was rather common in the South of France due to its pleasant fragrance and raspberry-like flavour28. Unfortunately, this trend favoured the rapid dissemination of the insect across neighbouring vineyards, and caused the decease of a great number of vines throughout west and central Europe. On the other hand, historical reports indicate that the strains initially introduced into Austria (founder of the Eu2 genetic group) could have been transported within a set of 20 American varieties imported to Klosterneuburg from New Jersey (USA), which were planted in the experimental vineyard to evaluate their fitness against oidium29. Nevertheless, we could not place the point of origin of this genetic group, as genotypes in Eu2 clustered only with samples from other introduced regions and not with those of the native range. In this line, it would be of interest to cross our data with a major number of grape phylloxera samples collected in the native range, as a wide insect genetic diversity exists as the result of plant-insect coevolution4.Our analysis confirmed previous findings that indicated a close relationship between European grape phylloxera populations and north-eastern American populations30.Samples from the introduced regions of Uruguay and South Africa did not form a specific cluster, suggesting that (I) either both regions received infested plant material from Europe, or (II) the introduction came from the same source(s) as those that devastated European vineyards. Accordingly, it has been recently indicated that Uruguayan populations might have resulted from the unintended introduction of infested plant material from Europe2, which presents a low capability of colonizing new vines or vineyards5. The lack of natural resistance in V. vinifera L. and the absence of natural enemies in European vineyards favoured the fast colonization of the new environment. Genetic diversity parameters indicate that these initial populations were subjected to different selection pressures that lead to the selection of different alleles for local adaptation, generating a series of clones mainly by parthenogenesis, as suggested by significant Psex values. In fact, parthenogenesis is acknowledged as the preferable mechanism for grape phylloxera reproduction in different introduced regions33. Clonal reproduction is still capable of generating a high level of genetic variation per generation34. This reproductive mechanism likely facilitated the long-term persistence of early introduced populations until today, as this strategy favours aphid populations persistence in areas with temperate climate35, like European winemaking regions36. Unravelling the mechanisms that drove to the generation of site- and host-adapted grape phylloxera populations in Europe will aid to understand why some of the rootstocks generated more than a century ago are still useful to control grape phylloxera effects, as well as to plan effective measures of control (including the breeding of new more resistant rootstocks) to anticipate to new, more aggressive outbreaks.Our analysis revealed an important role of the first introductions that occurred in the nineteenth century in Europe. Considering historical records, it is likely that the introduced grape phylloxera populations rapidly spread across the continent by the unintended transportation of infested American vine cuttings to fight other disease-causing agents. This human-assisted dispersal counteracted the sessile nature of the insect for most of its life cycleV. vinifera L. cultivars (such as Cabernet Sauvignon and Chardonnay), Vitis spp. interspecific rootstocks , Vitis spp. interspecific hybrid direct-producers , and Vitis spp. interspecific resistant grape varieties  in 100 locations from eight European countries . It corresponded to a latitudinal gradient from 43.0°N to 50.0°N and a longitudinal gradient between 6.2°E and 27.6°E. Samples were collected from 2012 to 2019  have been analysed in this work.Leaf and soil-emerged grape phylloxera individuals were collected from et al.19 and stored at −20 °C for further use. A total of 1173 samples were genotyped with seven fluorescently labelled (6-FAM or HEX) simple sequence repeat (SSR) primers . DNA amplification, fragments selection and allele calling were performed as detailed previously38. In every set of samples, we included six control genotypes with known allele size to keep allele calling consistent between different runs. This analysis revealed the presence of 774 grape phylloxera MLGs.Whole genomic DNA was extracted from grape phylloxera individuals as indicated in Forneck et al.3 explored the population genetic diversity of grape phylloxera in its native range, using samples collected across the USA, as well as some samples obtained from Europe (Austria and Hungary) and South America . These samples were genotyped using 32 SSRs, including the SSR markers Phy_III_55, Phy_III_30, Phy_III_36 and Dvit6. We retrieved the information of these 4 markers to perform a joint analysis between these samples and our genotypes.In a previous work, Lund 39 was used to infer the number of genetic groups and to assign individuals to the inferred populations, using our dataset of 774 MLGs based on 7 SSR markers. The existence of genetic structure was tested in a number of hypothetical genetic groups (K) from 1 to 15, using a cycle of 250.000 burn-in steps followed by 500.000 Markov Chain Monte Carlo iterative steps. Ten replications per K value were run to assess the consistency of the results, and each one was performed considering an admixture model with uncorrelated allele frequencies. Aware of the uneven sampling for the factors likely affecting grape phylloxera population structure , four estimators of the most likely K value (median of means (MedMeaK), maximum of means (MaxMeaK), median of medians (MedMedK) and maximum of medians (MaxMedK)) were determined using a membership coefficient threshold of 0.5, as proposed by Puechmaille40 and implemented in STRUCTURE SELECTOR41. Thus, these four statistics were independently calculated using the different group classes obtained for sampling site , feeding form (leaf or root) or host plant  as co-factors. The statistics of the three scenarios were then compared, and the most likely number of genetic groups was determined as the most repeated K value within the 12 calculations. Once the most probable K value was set, MLGs were assigned to a genetic group considering an ancestry coefficient Q ≥ 0.80; otherwise, a MLG was considered as “admixed”. In parallel, a dissimilarity matrix was calculated between 752 grape phylloxera MLGs (22 were discarded because of the presence of missing data) using the DARwin software package v. 6.0.2142 on the basis of 10.000 bootstrap steps, which was further used for a Principal Coordinate Analysis (PCoA). As indicated by Emanuelli et al.43, a Neighbor-Joining Unweighted (NJUw) dendrogram was constructed for the subset of MLGs clustered in the genetic groups previously determined to support the consistency of STRUCTURE and PCoA clustering methods. The dendrogram was constructed on the basis of a dissimilarity matrix with 10.000 bootstrap steps, and it was drawn considering 1.000 bootstrap replicates. MLGs with missing data were excluded from the analysis. The three outcomes  were used to determine the optimum level of genetic structure of grape phylloxera in Europe.The Bayesian clustering software STRUCTURE v.2.3.4Hobs) and expected (He) heterozygosities under Hardy-Weinberg expected equilibrium, and FIS values, which were independently calculated for each locus. The genotypic diversity index (R) was estimated as R = (MLG-1)/(N-1), and the clonal diversity index (Pd) as Pd = MLGs/N, MLG being the number of multilocus genotypes identified, and N the number of sampled individuals. All calculations were performed using MLGsim 2.044 with 10.000 simulation steps. In addition, the same software was used to calculate the Psex statistic to estimate the likelihood that grape phylloxera individuals with the same MLG are products of distinct sexual reproductive events or truly clonal. Thus, a multiple MLG was considered to be a true clone if Psex < 0.01 .For each genetic group, we calculated the mean number of alleles per locus, the observed (Q ≥ 0.80 in any of the identified genetic groups (n = 302). For this analysis, three variables  were used as independent variables, defining eight levels for country, two for feeding form and four for host plant, as detailed above. As dependent variable we used the Q values given by STRUCTURE to each MLG (Q-matrix). Factors were considered to have a significant effect on the Q-matrix at p ≤ 0.05. To deepen into the effect of geography on the genetic structure, we calculated a linear regression model using the Q-matrix as dependent variable and latitude and longitude values of the sampling sites as independent variables. Factors were considered significant if p ≤ 0.05. Analyses were performed using SPSS v.24.0 .To evaluate the effect of sampling site (country), feeding form and host plant on the genetic structure of grape phylloxera in Europe, a three-way ANOVA was calculated focusing in the European MLGs with an ancestry coefficient 39 to analyse the relationship between the 302 European MLGs clustered in the final genetic groups previously identified and the MLGs reported in Lund et al.3 sampled in the native range and in diverse introduced regions. In total, 621 MLGs have been included in this analysis. To this aim, we used the procedure described above, but using the information available for four SSR loci . The most likely number of genetic groups was assessed by plotting the ΔK value of the data over 10 runs, as implemented in STRUCTURE HARVESTER45, and defined in Evanno et al.46. MLGs were assigned to a genetic group if they have a coefficient of ancestry (Q) over 0.80; otherwise, they were considered as “admixed”. A dissimilarity matrix was calculated considering genetic data on 4 SSRs for 575 MLGs as previously detailed, discarding 46 MLGs for the presence of missing data. The matrix was then used to construct an NJUw dendrogram with 1.000 bootstrap replicates to get insights into the relationship between the genetic groups identified in our dataset and those previously reported from grape phylloxera native range and different introduced regions3.A second population structure analysis was performed with STRUCTURESupplementary information


Limnoria contains algae-eating species. Previous phylogeographic studies have suggested that Limnoria species feeding on buoyant kelp underwent low genetic differentiation on a large spatial scale because rafting on floating host kelps promotes high levels of gene flow. In this paper, we survey the genetic structure of Limnoria nagatai, which bores into the non-buoyant kelps Eisenia bicyclis and E. arborea. We analyze the mitochondrial DNA (cytochrome oxidase subunit I [COI] gene) and morphological traits of L. nagatai, and the host kelps E. bicyclis and E. arborea from 14 populations along the Japanese archipelago of the Pacific Ocean and the Sea of Japan. Four major lineages are recognized within L. nagatai: three lineages in the Pacific Ocean, and one lineage in the Sea of Japan which might be a cryptic species. For L. nagatai, we show high genetic differentiation between geographically separated habitats in the Pacific Ocean, while low differentiation is found among continuous host kelps habitats in the Pacific Ocean as well as the Sea of Japan. L. nagatai in E. bicyclis in the Pacific Ocean has experienced large population expansion after the Last Glacial Maximum (LGM), whereas the lineage in E. bicyclis in the Sea of Japan has not. We suggest that Limnoria feeding on non-buoyant kelps, may attain low genetic differentiation because they might be able to disperse long distance if the habitat of host kelps is continuous. The historical events affecting Limnoria after the LGM may differ between the coasts of the Pacific Ocean and the Sea of Japan.The marine isopod genus  Limnoria Leach  ) species , which mCOI) of L. nagatai Nunomura, 2012, and its hosts the non-buoyant kelp Eisenia bicyclis (Kjellman) Setchell, 1905 and E. arborea Areschoug, 1876 in Japan to: (1) test whether L. nagatai shows the genetic differentiation between spatially separated habitats; (2) show the genetic structure within the continuous populations; and (3) test if the genetic structure of L. nagatai is similar to that of their host kelp, which is expected in small crustaceans [L. nagatai and resulted in genetic divergence between distant L. nagatai populations, leading to IBD patterns.Therefore, we analyzed the patterns of genetic structures using mitochondrial cytochrome oxidase subunit I gene  and along the Sea of Japan (Saga to Kyoto: 600 km), with the exception of a doubtful population of E. bicyclis in the Ehime Prefecture. The distribution of E. arborea, which is located south of E. bicyclis, is separated into two areas along the Pacific coast, except for a recently extinct population of E. arborea in the Shizuoka Prefecture [E. bicyclis from 11 sites and E. arborea from 3 sites   . Kelp saLimnoria were collected at each site  and proteinase K as described by Kim et al. [COI in Limnoria was amplified using the primers LCO1718 (5′- TW GGD GCN CCD GAY ATG GCH TTY CCD CG -3′) and HCO2386 (5′- AA AAT TTT AAT TCC AGT AGG AAC TGC AAT AAT TAT -3′), which were designed based on preliminary sequences obtained by using the Limnoria primers given in Nikula et al. [Limnoria gene was carried out in a thermocycler using the following profile: initial denaturation phase of 2 min at 95°C; 41 cycles of 50 s at 95°C, 1 min 30 s at 45°C, 1 min 30 s at 72°C; final extension step of 10 min at 72°C.To preserve the body of the collected m et al. . To guarCOI gene in kelps. PCR was carried out following McDevit & Saunders [DNA extraction from kelps was performed with the HEPES and CTAB method ,27. SpecSaunders : initialThe PCR products were purified by the enzymatic method with ExoSAP-IT  modified from Dugan et al. . Namely,Limnoria samples were stored in 70% ethanol. The samples were placed in petri dishes filled with glycerin and dissected and observed under an optical microscope . The legs, antennae, and other body parts were observed with a scanning electron microscope, JSM-6010LA  after air-drying the samples for approximately 10–15 min to allow the alcohol and glycerin to evaporate. After the observation, the bodies were returned to the tubes filled with 70% ethanol.After DNA extraction, Limnoria collected from Eisenia, we verified that the treatment with SNET and proteinase K did not alter the form of the exoskeleton by comparing the body of Limnoria sp. collected in wood from Kominato before and after DNA extraction.Before DNA extraction from Limnoria collected from Eisenia were compared with the descriptions of all Limnoria species and between genetically diverged groups that were assigned based on the haplotype network. In Japan, L. rhombipunctata and L. zinovae have been found in the rhizome of a seagrass, Phyllospadix iwatensis [L. nagatai and L. segnoides are morphologically expected to feed on algae or seagrasses [L. nagatai deposited at the Toyama Science Museum and L. segnoides deposited at the Natural History Museum of Denmark.The characters selected according to Bruce  and Cookwatensis ,34, wheragrasses ,35,36. BLimnoria and kelps were aligned using Clustal W [DNA sequences of lustal W , translalustal W . The endLimnoria and kelp sample, the genealogical relationships among the haplotypes were represented by a minimum spanning tree (MST) obtained using the TCS software package [For each  package .Limnoria was conducted using maximum likelihood method employed in PAUP4.0b10 [Ligia occidentalis JQ895008) was used as outgroup. Bootstrapping of 1000 replicates for maximum likelihood and parsimony analyses was done with PAUP4.0b10 [Phylogenetic analysis of UP4.0b10 . The HKYUP4.0b10  as the bUP4.0b10  to assesLimnoria were calculated by the p-distance  method using MEGA5 [h; [π; [Limnoria, we calculated Tajima’s D [Fs neutrality statistic [Fs represents the probability of observing a random sample with a number of alleles equal to or smaller than the observed value, given the observed level of diversity and the assumption that all the alleles are selectively neutral [Fs are expected. Genetic distances for kelp were corrected using a general time reversible model in PAUP 4.0b10 [The genetic distances between sequences of ng MEGA5 . The genng MEGA5  based onEGA5 [h; ); and nu [h; [π; ,43). To jima’s D  and Fu’statistic  using Dntatistic , respect neutral . In popuP 4.0b10 , followiP 4.0b10 .7 with log parameters sampled every 1 × 103 generations and burn-in set to initial 10%.Furthermore, we estimated the demographic history of four groups  by Bayesian skyline plots . BayesiaCOI region between populations was calculated using Arlequin 3.5.2.2 [The relationships between geographic distances and genetic differentiations between populations were investigated within Group A and D. GPS records were used for geographic locations of each population . Pairwis 3.5.2.2 . Statist 3.5.2.2  with 999 3.5.2.2 .COI gene was generated from each of the 106 individuals collected from 14 populations of Limnoria (A 549-bp sequence of the Limnoria . Stop coLimnoria . The topLimnoria . Groups Eisenia bicyclis). The most common haplotype, observed in 79% of our Limnoria specimens, occupied a central position in the network and was observed at highest frequencies in all locations. The other rare haplotypes, except one isolated haplotype found in Miyagi and Choshi, were restricted to single locations and only one mutational step differentiated from the common haplotype. The most isolated haplotype showed nine mutational steps of differentiation from the common haplotype. A population in Group B consisted of three haplotypes, whereas two populations in Group C had only one haplotype. Haplotypes differed by 19 steps between Group B and C, although the host kelp of these groups was E. arborea. Seven haplotypes from three populations from E. bicyclis along the coastline of the Sea of Japan were observed in Group D. All haplotypes of Group D were restricted to single locations and were not shared among the three populations although they had similar sequences.Group A comprised the populations from Miyagi to Shizuoka (substrate: E. bicyclis.The haplotypes from different groups differed by more than 3.5% . GeneticCOI data for L. nagatai are shown in Fs were -4.306 (p = 0.022), 0.406 (p = 0.506), and -1.294 (p = 0.230), respectively. Both negative values for Group A would be expected from a recent population expansion. The value for Group D was small and not significant, indicating that the gene had no evidence of natural selection and was in the mutation–drift equilibrium.The results of the neutrality tests based on groups of Fs.The results of the Bayesian skyline plots are shown in 2 = 0.2584, p = 0.171; Group D: R2 = 0.9828, p = 0.200).Limnoria samples collected from E. bicyclis and E. arborea, we compared the morphology of Limnoria sp. (collected in wood from Kominato) before and after proteinase K treatment to ensure that the digestion treatment with proteinase K had no effect on the body, including details such as dactylus and setae . However, the individuals of Group D collected from the Sea of Japan were obviously separated from the type specimens of L. nagatai by vague and weak carinae on the pleonite 5 and pleotelson and the presence of trifid secondary unguis of pereopods 1  were often reported as rafts around Japan [Eisenia. Second, it may be difficult for the non-buoyant kelp to drift long distance with heavy holdfasts on which Limnoria feed. The non-buoyant kelp Saccharina sculpera was found washed ashore outside its habitat coast, but there were no holdfasts [Eisenia might have drifted along with their holdfasts, the non-buoyancy of the kelp could prevent L. nagatai from surviving by rafting due to the disturbance in seawater. An experiment indicated that Limnoria had low intensity of adhesion to substrates [Limnoria individuals to reach long distances. The dispersal by rafting on algae requires that the animals can survive for long periods of time, until algae can be established successfully in new areas [Limnoria dispersal between populations. Further studies on feeding and temperature-tolerance are needed to elucidate whether the causes of genetic difference among Groups A, B, and C are either adaptation difference or dispersal delimitation or both.Another possible reason in the divergence of Groups A, B, and C might be geographic barrier that is beyond habitats . One of e et al.  inferrednd Japan , that dioldfasts . Third, bstrates , and it ew areas . TherefoE. bicyclis along the Pacific coast extended more than 600 km, from Miyagi in the north to Shizuoka in the south  around Japan [L. nagatai from buoyant substrates and we were unable to find any individuals in Sargassum spp., Zostera spp. and wood.in Japan ,66. It ind Japan  could faFs for Group A indicated a population expansion event . The scale bar represents the number of substitutions per site.Maximum likelihood phylogram of (TIF)Click here for additional data file.S1 TableN), and GenBank accession numbers.Sampling locations, latitude and longitude GPS coordinates, number of analyzed individuals, haplotype names, number of specimens ((DOCX)Click here for additional data file.


Anolis lizards, an iconic example of adaptive radiation. We found that the major axis of morphological evolution was well aligned with a major axis of genetic variance shared by all species despite separation times of 20–40 million years, suggesting that divergence occurred along a conserved genetic line of least resistance. Further, this signal persisted even as G itself evolved, apparently because the largest evolutionary changes in G were themselves aligned with the line of genetic least resistance. Our results demonstrate that the signature of genetic constraint may persist over much longer timescales than previously appreciated, even in the presence of evolving genetic architecture. This pattern may have arisen either because pervasive constraints have biased the course of adaptive evolution or because the G matrix itself has been shaped by selection to conform to the adaptive landscape.On microevolutionary timescales, adaptive evolution depends upon both natural selection and the underlying genetic architecture of traits under selection, which may constrain evolutionary outcomes. Whether such genetic constraints shape phenotypic diversity over macroevolutionary timescales is more controversial, however. One key prediction is that genetic constraints should bias the early stages of species divergence along “genetic lines of least resistance” defined by the genetic (co)variance matrix, G. This bias is expected to erode over time as species means and G matrices diverge, allowing phenotypes to evolve away from the major axis of variation. We tested for evidence of this signal in West Indian  Anolis lizards in the West Indies to show that genetic architecture aligns with phenotypic change for up to 40 million years, about ten times longer than previously predicted. We show that this signature is even maintained when the genetic constraints themselves evolve. Although the pattern we demonstrate is consistent with genetic constraints biasing evolutionary change, it is equally consistent with the action of natural selection simultaneously shaping traits and the genetic variation that underlies them. Depending on what mechanisms are ultimately responsible for these patterns, our results may have one of two equally exciting implications. On the one hand, elaborate adaptive radiations like the one seen in West Indian anoles may be possible even when genetic constraints persist for millions of years. On the other hand, genetic constraints may respond to natural selection in such a way as to facilitate further adaptive evolution.Evolutionary biologists have long debated whether biodiversity is shaped mainly by natural selection or by intrinsic factors, such as genetic variation and the developmental mechanisms that translate genes into phenotype. The importance of selection has been convincingly demonstrated many times, but the extent to which genetic architecture might constrain the long‐term outcomes of selection is poorly understood. In this study, we use the adaptive radiation of Both natural selection and genetic architecture play important roles in determining the direction and magnitude of evolutionary change Lande , 1979. OG, the additive genetic variance‐covariance matrix  for a suite of skeletal traits and the direction and magnitude of evolutionary divergence among Anolis species in size‐corrected morphological space. We then explore whether the major axes of genetic variation for each species share orientation in multivariate space . We find that two axes describe the majority of genetic variation in all seven species, and that these directions are aligned to the major axis of genetic variation in an ancestral G matrix reconstructed to represent the hypothetical pattern of ancestral genetic architecture. To ask whether G itself evolved during the adaptive radiation, we analyze covariance tensors and find that most differentiation of genetic architecture occurs in subspaces that include limb traits. By comparing angles of orientation of these major axes of phenotypic divergence (d), genetic variation (h), and genetic differentiation (e), we reveal that both trait means and genetic covariance structure appear to evolve most rapidly along lines of genetic least resistance.We take a multivariate approach to dissecting patterns of genetic architecture and their relationships with phenotypic divergence among seven G matrices for seven species of West Indian Anolis lizards representing three different ecomorphs  that originated independently on three different islands of the Greater Antilles: A. cristatellus (trunk‐ground), A. evermanni (trunk‐crown), and A. pulchellus (grass‐bush) from Puerto Rico; A. grahami (trunk‐crown) and A. lineatopus (trunk‐ground) from Jamaica; and A. sagrei (trunk‐ground) and A. smaragdinus (trunk‐crown) from South Bimini, the Bahamas  to determine axes of greatest divergence  and the variance explained by each . As described in Additional Methods, we also calculated two alternative estimates of species divergence that accounted for phylogeny and a third from a separate dataset of measurements from wild‐collected adult males of 15 species.Parameters from our animal models were used to quantify species divergence in morphology. Using estimated intercepts, slopes from the regression of ln‐transformed trait values on ln‐transformed SVL, and the grand mean SVL across all seven species  for each G matrix and calculated several descriptive statistics to aid in the interpretation of their overall structure. The trace, or the sum of the eigenvalues of each G matrix , was used as an index of its overall size, which should predict the potential magnitude of a population's overall response to selection. The percent variance explained by gmax (the axis of greatest additive genetic variance) was used as an index of G matrix shape, which indicates a population's potential to respond to selection aligned with gmax relative to other directions. Finally, we calculated the angle between gmax estimates from each species and the vector of greatest species divergence (d1) as an index of orientation. As an additional measure of orientation, we calculated all pairwise angles between species‐specific estimates of gmax. These values indicate the degree to which G matrices vary in the direction of greatest genetic variation.We performed eigenanalysis  describing the greatest similarity across a set of matrices. Eigenanalysis of this subspace provides a set of orthogonal vectors (hi) that represent axes of genetic variance that are shared to some extent across species, and its eigenvalues (p) indicate the extent to which those axes are shared. In our analyses, these eigenvalues could range from 0 to 7, the number of species. An eigenvalue of 7 would indicate that a particular h vector can be reconstructed exactly for all seven species using the eigenvectors of its G matrix, and would suggest that a given eigenvector represented a conserved axis of genetic variance.After estimating H using the first four eigenvectors of each G matrix, the maximum allowed for an 8 × 8 matrix .We calculated subspace es Table . Next, wG matrix (Ganc) using element‐by‐element maximum likelihood reconstruction, a time‐calibrated phylogeny ; however, they do offer the advantage of incorporating phylogenetic structure, which cannot be accomplished using Krzanowski's method. The eigenvectors of Ganc were highly similar to those of H, indicating that they described a similar subspace. Substituting these eigenvectors for hi in our subsequent analyses did not change our results. To visualize species differences in multivariate genetic variance, we projected each of these eigenvectors through each species‐specific G matrix to calculate genetic variance in a common set of orthogonal trait combinations.As an additional way to explore conserved axes of genetic variance, we reconstructed an ancestral G diverged across species variances of (co)variances. Eigenanalysis of this tensor provides genetic covariance eigentensors (Ei), which are square matrices describing independent subspaces in which in G varies across species. Analogous to a first principal component, the first eigentensor describes the subspace in which G varies the most across species. The coordinates of each species within each eigentensor can be calculated to determine the extent to which species differ in a particular subspace , which describe linear combinations of the original traits that contribute to divergence in G. Within each eigentensor, an eigenvector associated with the eigenvalue of the largest absolute value describes the combination of traits whose genetic variance differs the most across species. We used the method described by Hine et al.  to that of conserved axes of genetic variance identified via Krzanowski's common subspace analysis (hi). Because these vectors describe axes of genetic variance across all species, they represent putative genetic lines of least resistance that may have influenced divergence. Alignment of d and h vectors would indicate that evolutionary change was biased toward such lines of least resistance. Second, we asked whether the divergence of G itself occurred in directions predicted by either morphological divergence or conserved axes of genetic variance by calculating the angles between the largest ten eigenvectors (eij) from the tensor analysis and di and hi vectors, respectively. Alignment of eij with di would suggest that G‐matrix evolution was influenced by the same factors that led to divergence in species means. Alignment of eij with hi would show that divergence in G occurred in directions similar to conserved axes of genetic variation, suggesting that evolution of G is itself subject to constraints, or alternatively, that both standing genetic variation and divergence of G across species were influenced in a similar way by a third factor, such as selection or drift.Determining the relationship between evolutionary divergence and Schluter , comparidi, hi, and eij), which may range from 0°  to 90° . All tests involving d vectors were repeated using our alternative measures of species divergence . Because the direction of eigenvectors is arbitrary, we reversed the sign of one of the vectors if the calculated angle was above 90°. To determine whether vectors were significantly aligned, we compared this angle to a null distribution generated from a simulation of 100,000 pairs of randomly generated vectors. We constructed each random vector by drawing its eight elements from a uniform distribution bounded by –1 and 1 and then standardizing the vector to unit length. The critical values from this null distribution were 47.6° (P = 0.05), 35.7° (P = 0.01), and 24.0° (P = 0.001).To perform each of these comparisons, we calculated angles (θ) between different types of vectors . Then we calculated the average of the rescaled Ganc and D and calculated the eigenvectors of the resultant matrix, providing a set of orthogonal vectors representing a subspace common to Ganc and D. Next, these eigenvectors were projected through both of the original matrices to determine the amount of within‐species genetic variance and among‐species variance, respectively, for each trait combination. We then regressed log10‐transformed among‐species variances (log D) on log10‐transformed genetic variances (log G). A positive relationship would indicate an association between greater genetic variance and divergence, and the slope of this regression represents the scaling parameter for the relationship between Ganc and D, the predicted value of which varies across different models of evolution  on log G. For the latter analysis, we used species‐specific genetic variances in the eigenvectors of Ganc to calculate log E and the eigenvalues of Ganc to calculate log G.Analogously, to ask how change in G matrices, size  varied the most across species (coefficient of variation = 27%), followed by orientation , and shape  was strongly associated with genetic variance in limb traits, which consistently showed strong positive loadings , but none were collinear  separated species with long limbs and wide heads from those with shorter limbs and narrow heads  separated species with wider bodies and relatively short hindlimbs from those with narrow bodies and longer hindlimbs  explained between 46 and 61% of genetic variance within each species and was similar  to each species‐specific gmax (Table gmax (θ = 10.7°). These patterns suggest that h1 represents a conserved genetic line of least resistance. Like gmax, this axis was most strongly loaded with limb traits. A second axis (h2) explained between 13 and 30% of genetic variance within species (Tables h3 and h4) were less similar across species  and captured a smaller amount of genetic variance within each species . Together, h1–h4 captured between 83% and 94% of total genetic variance within species.Despite the divergence of ax Table  as well s Tables . This axG could be explained by the first three of six independent subspaces (E1–E3). Species coordinates in the first eigentensor (E1), which explained 48% of divergence in G, were highly correlated with the trace of G , suggesting that the largest changes in G were changes in size . A single combination of traits (e11) within E1 was responsible for 41% of the overall divergence in G  were marginally correlated with the orientation of G .Genetic covariance tensor analysis showed that 84% of divergence in es E1–E. Specieses E1–E. Speciesd1) was closely aligned with the major axis of conserved genetic variance  was nearly orthogonal to h1  was well aligned with the major axes of both morphological divergence (d1) and conserved genetic variance  than with axes of morphological divergence  to the divergence matrix D in a common subspace, we found a strong relationship between within‐species genetic variance (G) in a given direction and divergence in species means (D) in the same direction . In other words, trait combinations with more genetic variance showed greater divergence. The scaling relationship between D and G did not differ significantly from 1 (P = 0.102), a value predicted by various evolutionary models and observed in a recent study of fly wings  was also predicted by within‐species genetic variance , with the trait combinations with the greatest genetic variance also showing the greatest divergence in variance across species . This finding suggests that the evolutionary radiation of anole skeletal morphology proceeds along a genetic line of least resistance defined by gmax. Third, we show that this pattern likely persisted because the evolution of G was proportional to both within‐species genetic variance and divergence in species means. In other words, evolution of G occurred in such a way as to preserve the relationship between axes of genetic variation and morphological divergence. Together, these findings suggest that groups of species may diverge along lines of genetic least resistance for millions of years and that this pattern is unlikely to be disrupted by concomitant changes in underlying genetic architecture.Here, we present three main findings. First, we show that gmax originally demonstrated by Schluter  and lead to the alignment between G and D. The repeated evolution of ecomorphs may resemble the repeated movement of fitness peaks along the same trait axis in response to interspecific competition , which was highly correlated with the overall genetic variance. Further, 40% of divergence in G could be accounted for by change in genetic variance associated with a single combination of characters consisting primarily of limb‐length traits. This trait combination (e11) was highly aligned with both the first axis of divergence (d1) and the first axis of genetic variation (h1). These results suggest that a large portion of change in G can be interpreted as growing and shrinking of the G matrix along conserved axes of variation—including gmax—as species means diverge along a genetic line of least resistance. Changes in G‐matrix shape and orientation also occurred, but did not obscure the relationship between divergence and gmax. A similar pattern has recently been detected for G‐matrix evolution in a much younger (<1 million years) radiation of ecotypes within a plant species variance matrix M, which describes the per‐generation input of new genetic variation in a population. The observed changes in G across species may reflect a deeper constraint on G imposed by the rate and phenotypic effect of mutations. Certain patterns within M, such as the correlation of mutational effects, may have a large effect on G even when opposed by selection , which likely harbor larger populations, were larger than those estimated for species collected from the small Bahamian island of South Bimini (the two species of Cuban origin). Drift should also cause population means to diverge in directions with more genetic variance, resulting in divergence along gmax and thus alignment between d1 and the direction of most change in G. Although this scenario is theoretically plausible, the well‐established role of selection in the evolution of Anolis ecomorphs variance matrices (G) for seven Anolis species.Table S2. Size‐corrected species means and divergence matrix.Table S3. Divergence matrix for 15 Anolis species using traits measured in wild‐caught adult males.Table S4. Krzanowski's common subspace analysis.Table S5. Genetic covariance tensor analysis.Table S6. Tests from Table 4 using alternative estimates of species divergence.Figure S1. Species means of wild‐caught adult males along the first two divergence axes.Figure S2. Relationship between log‐transformed genetic variance (G) and four different estimates of divergence in a set of eight orthogonal trait combinations.Click here for additional data file.