Local animal breeds represent a national genetic heritage for every country. Creole free-range chickens have important cultural, historical, genetic, and economic roles in several countries. In Brazil, there is a lack of information regarding the genetic formation of local chicken breeds. These animals were brought to Brazil during colonization in the 16th century. Currently, Brazilian Creole chickens are highly adapted to the edaphoclimatic conditions of the country and are mostly reared by smallholders. In this study, we used microsatellite markers to determine the genetic composition of three chicken breeds from the northeast region of Brazil. Our results confirm the existence of interbreed genetic diversity and high genetic variability within the Brazilian Creole chickens studied. Furthermore, our findings show that the formation of these genetic groups had contributions from different ancestors. Our results will be useful to support the development of conservation programs, as well as the sustainable use and official recognition of these breeds.In this study, we aimed to evaluate the genetic diversity within and among chicken breeds from the northeast region of Brazil (states of Bahia and Piauí) using microsatellite markers. In addition, we assessed the identity and genetic relationships of chickens from Europe, Africa, and South America, as well as their influence on the formation of the Brazilian breeds. A total of 25 microsatellite markers and a panel containing 886 samples from 20 breeds (including the Brazilian chickens) were used in this study. Different statistical parameters were used to estimate the genetic diversity and relationship among the genetic groups studied. Our study indicates that the Brazilian Creole chickens have high genetic variability. The results show that chickens reared in the states of Bahia and Piauí could have originated from different ancestors. The Brazilian breeds studied have an evolutionary relationship with chickens from Portugal, Nigeria, Chile, and Spain. Our results will contribute directly to the conservation and recognition of Brazilian Creole chicken breeds and provide a solid basis for the demonstration of their genetic identity and genetic conservation of American Creole chicken populations. Local animal breeds are considered a national genetic heritage, and thus, they are important resources for the future of every country. There is a great interest in the conservation and sustainable use of those breeds worldwide. The knowledge of the genetic organization of local genetic resources increases their valuation and recognition. Furthermore, this information is valuable for the genetic management and establishment of conservation programs. Thus, the protection of local breeds is the responsibility of each country, as determined by international agreements .In the northeast region of Brazil, there are several genetic groups of free-range chickens, such as the local breeds Canela-Preta (CP), Caneluda do Catolé (CAN), and Peloco (PEL). These animals have a dual purpose (production of meat and eggs) and are mostly raised by smallholders using low or no technological investment, in extensive systems. Birds of the genetic groups mentioned above are less susceptible to diseases, cope better than other chicken breeds in the harsh environment of Northeastern Brazil, and have tolerance to poor nutrition ,3.Chickens of the Canela-Preta breed are docile and easy to handle; the plumage of these animals is predominantly black, but they can also present colored feathers around their necks, i.e., white or golden in females, and white, silver, or red in males ; their eThe genetic structuring and geographical distribution of local breeds can be important to support studies on the dynamics of the human culture, as animals have accompanied human migration for centuries. Thus, knowing the genetic constitution of animal species can provide information that could be historically related to the development of civilizations ,5. It isThe individual genetic evaluation of local breeds, as well as the genetic relationship among them and their contextualization regarding other international genetic influences, can provide evidence of mechanisms and events that contributed to the origin and development of free-range chicken breeds in the northeast region of Brazil. A similar study was conducted by Toalombo Vargas et al.  in EcuadResearchers around the world have conducted studies that aim to shed light on the genetic differentiation of free-range chickens, how the evolution of these animals occurred, and how their diversity can be measured. In this context, microsatellite markers have been widely used to describe the genetic diversity and structure of populations of different domestic species. In general, comparative studies on the genetic diversity and relationship of different breeds consolidate a wide database that can be used for conservation, selection schemes, and understanding of the evolution of species .http://www.uco.es/conbiand/consorcios.html) was created within the RED CONBIAND network. This consortium has participated in studies that aim at the genetic characterization of breeds at the national [In order to support projects for the study of the biodiversity of chickens from Ibero-American countries, the BIOCHIKEN consortium  using microsatellite markers. With this, we aim to provide a basis to support the characterization, national and international recognition, conservation, sustainable use, and valuation of the local chickens. For this purpose, we used a panel containing 25 microsatellite markers to assess the diversity and genetic relationship of three Brazilian chicken breeds. In addition, a panel containing 886 samples from 20 genetic groups (including Brazilian chickens) from various countries previously genotyped was used to determine the genetic identity of breeds and to identify their potential recent influences on the formation of the Brazilian breeds.This study was approved by the Committee on Ethics in the Use of Animals (CEUA) of the Federal University of Piauí, Teresina/ Piauí, Brazil (Nº 399/17).A dataset composed of 100 animals was used to assess the inter- and intrapopulation genetic diversity of free-range chickens from Northeastern Brazil. The local chickens were of three breeds: Canela-Preta (40); Caneluda do Catolé (30); and Peloco (30). The samples were collected randomly from animals raised in conservation nuclei located in Itapetinga/Bahia and Teresina/Piauí, Brazil. Canela-Preta chickens are widely spread throughout the Piauí state, whereas the Caneluda do Catolé and Peloco breeds are widespread in Southwestern Bahia .In this study, we also used 886 samples of 20 breeds (including Brazilian chickens and a dataset of the BIOCHICKEN research consortium) to determine the possible recent influence of Creole chicken breeds from other countries and commercial strains on the genetic identity of the current Brazilian chicken populations . The CreIn addition, Araucana chickens from Chile were used in this study. Moreover, chickens from the north of Africa (Nigeria) represented in the dataset of the BIOCHICKEN research consortium were used. These animals may have some genetic relationship with Brazilian chickens due to the historical period of slavery in Brazil, when people from Nigeria were brought to Brazil and forced to work as slaves. Potential recent influences of the commercial lineages Leghorn and Cornish were also considered, as these strains contributed to the origin of commercial laying and commercial broiler lines, respectively.Fragments of each biological sample were used for extraction of genomic DNA. The procedures used for DNA extraction followed a modification of the method described by Walsh et al. . Three chttp://aviandiv.tzv.fal.de/) and recommended by FAO (Food and Agriculture Organization of the United Nations) [The following 25 microsatellite markers from the AVIANDIV project (Nations)  were useNations) . Genotyp1, Z2, …, Zp into a new set of uncorrelated variables Y1 (1PC), Y2 (2PC), …, Yp (pPC) ordered in decreasing order of variance. The main idea of this procedure is that the first few principal components explain most of the variability in the original data. These estimates were used for the three Brazilian breeds studied.The program GenAlEx 6.5  was usedThe global genetic variance among breeds was estimated using analysis of molecular variance (AMOVA) . For thiDeviation from the Hardy–Weinberg equilibrium (HWE) at each locus within populations was tested using GENEPOP v.4.0.10 .A neighbor-net was constructed as implemented in the SplitsTree4 software, in order to represent the relationships between breeds graphically and to depict any evidence of admixture .The STRUCTURE software v.2.3.4  was usedThe CLUMPAK program  was usedp > 0.05) from the HWE were observed in the Brazilian chickens.The mean number of alleles by breed was 4.960 for Canela-Preta (CP) and Peloco (PEL), and 5.040 for Caneluda do Catolé (CAN) . The aveThe AMOVA indicates that the highest genetic variability (89%) is distributed within breeds/individuals . The varThe results of the principal component (PCoA) analysis  demonstrThe matrix of Nei’s genetic distance considering all genetic groups studied  showed tThe Brazilian breeds were not clustered together with the commercial lines used as control group . FurtherIn the starting point, we proposed two populations (K = 2) . Under tThe mean number of alleles (NA) is an indicator of genetic variability that is essential to establish the long-term evolutionary potential of the population. The NA values found in the present study were higher than those reported by Ceccobelli et al.  in five The average values of genetic variability measured by the heterozygosity (Ho and He) for the Brazilian chickens indicate that there is equilibrium between Ho and He. This can be due to the fact that these animals are raised on conservation nuclei, where the maximum variability among breeds must be maintained.We observed a small variation of Ho and He among the three Brazilian breeds. Several studies in chickens have shown the importance of microsatellite markers using population parameters in analyses of genetic diversity and population structure. Information obtained from those markers allows different direct applications that benefit the genetic management of those birds, as well as their conservation and genetic improvement ,23,24.The negative values observed for Wright’s fixation indices probably indicate that there is a trend of fixation of the heterozygosity in the populations studied; therefore, it is suggested that the breeds have genetic variability. This result corroborates the nonsignificant deviations from the Hardy–Weinberg equilibrium observed in the three Brazilian breeds. These chickens compose large populations where random mating system is used; therefore, the genetic variability of these animals is important, as this indicates that these native resources are genetically conserved.The AMOVA indicates that the highest genetic variability is distributed within breeds, i.e., within individuals, which is relevant as these chickens are native and were not still included in any breeding or selection program. Additionally, this indicates that these breeds have high genetic richness, which makes them potential future donors of genes for the development of new commercial strains and indicates their potential for within-breed selection for genetic improvement practices.Gallus gallus domesticus), and probably have common ancestors. Nevertheless, this value can be considered high when compared to other studies in Brazilian chicken breeds [The genetic richness of local genetic groups allows one to use the wide range of options aforementioned in future focal point studies. The percentage of variation between breeds (8%) indicates some similarity among these animals, as they belong to the same subspecies , moderate (between 0.05 and 0.15), high (from 0.15 to 0.25), and very high (above 0.25) genetic differentiation between populations ,27. Our The FIS value obtained in our study (0.029) indicates the existence of genetic variability within breeds, controlled percentage of homozygosity within populations, and random association of alleles. The FIT value (0.108), in turn, may be indicative of the fact that genes derived from a common ancestor were favored. Knowing the ancestry allows one to use ancestors to rescue the genetic variability in extreme cases in which some breeds are at risk of extinction.Knowledge of the genetic composition of native breeds is essential for conservation and breeding programs of these genetic resources. This study is the first on the origin and genetic differentiation of the three Creole chicken breeds  from the northeast region of Brazil. Thus, our results will contribute directly to conservation of these chickens and encourage the conduction of new genetic studies in other states of Brazil.Results of the first axis obtained in the PCoA indicate that exotic birds may have participated as ancestors during the formation of the Brazilian local chicken populations. The second axis indicates that most of the current Spanish breeds have a small contribution to the formation of the Brazilian chickens.The matrix of Nei’s genetic distance shows that the Brazilian breeds studied have multiple origins. Chickens of the CP, CAN, and PEL breeds produce multicolored eggs, including eggs with bluish shells. This could explain the close relationship of these chickens with ARAU, in which hens produce only blue-colored eggs . The genThe results of the analysis performed using STRUCTURE corroborates the results of the PCoA, which showed similar clusters for the two groups formed in K2. At this point, all Brazilian breeds were clustered together with birds from South America (Chile), Africa (Nigeria), and Europe . This result demonstrates the multiple ancestries of the Brazilian chickens. These birds showed low genetic similarity with most of the breeds from Spain, which indicates that this country had a small contribution to the formation of the chicken genetic groups from Northeastern Brazil.It is important to note that in K5, the genetic composition of the CP breed was similar to that of the Nigerian chickens. This result corroborates the estimate of the matrix of Nei’s genetic distance, which shows that these two breeds are genetically closer. This relationship is probably due to the introduction of African animals in Brazil during the slavery period. Further, this could be explained by the fact that chickens were taken from Brazil to Africa after the abolition of slavery. In this period, several Africans returned to their home countries and some animals accompanied human migration .In most situations, Delta K helps in the identification of the correct number of clusters; however, it should not be used exclusively . We obseGallus gallus in these countries.Note that from K10, none of the Brazilian breeds share genetic combinations with commercial strains. In K13 and K18, we confirm the genetic differentiation of CP from all other breeds in our study. The formation of these two possible groups indicates the genetic richness and exclusivity in the genetic composition of the Brazilian Creole chickens. Additionally, from K20, all breeds are defined in genetically different groups. In this case, the Portuguese chickens had the highest genetic migration. The results show that breeds from each country have a unique genetic structure, which demonstrates the genetic richness of the species Our findings form the basis to justify and support the implementation of conservation programs of Brazilian local chicken breeds. Despite the genetic relationship with genetic groups from other countries, the Creole chickens from Brazil have unique genetic combinations of poultry from this country. Animals of these breeds are adapted to the edaphoclimatic conditions of the region and are spread throughout backyards of smallholders, serving as an example of multifunctionality of small-scale livestock production systems. Thus, promoting the raising of these birds in sustainable production systems will contribute to the development of livestock production in the northeast region of Brazil.This study provides an overall outlook of the genetic diversity of chicken breeds from the northeast region of Brazil. Our findings highlight the efficiency of microsatellite markers for the study of the genetic characterization, differentiation, and relationship of chickens. In general, the populations of Canela-Preta, Caneluda do Catolé, and Peloco chickens are consolidated as defined breeds from a genetic point of view, because they have high genetic variability. In this study, we suggested that Brazilian chicken breeds are genetically stable, and there is high genetic difference between chickens from the Piauí and Bahia states and the other groups considered. The chickens from the northeast region of Brazil used in this study have an evolutionary relationship with breeds from Portugal, Nigeria, and Chile, as well as a weak relationship with Spanish chickens. Our results can be used in future strategies of genetic management of Brazilian free-range chickens as a basis for conservation programs, sustainable use, and genetic improvement through selection.


G). The structure and evolutionary stability of G will thus influence the course of phenotypic evolution. However, there are few studies assessing the stability of G and its relationship with population divergence within foundation tree species. We compared the G-matrices of Mainland and Island population groups of the forest tree Eucalyptus globulus, and determined the extent to which population divergence aligned with within-population genetic (co)variation. Four key wood property traits exhibiting signals of divergent selection were studied—wood density, extractive content, and lignin content and composition. The comparison of G-matrices of the mainland and island populations indicated that the G-eigenstructure was relatively well preserved at an intra-specific level. Population divergence tended to occur along a major direction of genetic variation in G. The observed conservatism of G, the moderate evolutionary timescale, and close relationship between genetic architecture and population trajectories suggest that genetic constraints may have influenced the evolution and diversification of the E. globulus populations for the traits studied. However, alternative scenarios, including selection aligning genetic architecture and population divergence, are discussed.The evolutionary response to selection depends on the distribution of genetic variation in traits under selection within populations, as defined by the additive genetic variance-covariance matrix ( G) within populations has a crucial importance in predicting multivariate responses to selection [G matrix [G (gmax) in the ancestral population. This will cause the orientation of the matrix of divergence among population trait means (D) to be biased toward gmax—the direction in a multivariate trait space that has the most genetic variance—which Schluter [G matrix will determine how a population will respond to selection, implying that, besides gmax, other multivariate axes of higher than average genetic variance can also influence the direction of evolutionary change [G can affect adaptive evolution by biasing responses to selection (and hence evolutionary trends) toward directions of trait combinations with more available genetic variance. In particular, such constraints may reflect high G-matrix eccentricity [G may be indicative that the amount and structure of genetic (co)variances will not limit adaptation, hence supporting rapid adaptive divergence among populations under directional selection [G-eigenstructure to directionally constrain the course of phenotypic evolution of closely-related species or conspecific populations [G in influencing the direction and rate of phenotypic differentiation among populations under natural selection is expected to decay over evolutionary time [gmax  over evolutionary time may reflect some compromise between multivariate patterns imposed by natural selection and mutation, coupled with large effective population size [G becomes eventually an empirical question and, in this sense, comparisons among closely-related species or conspecific populations have found relatively stable G-matrices  selection [G [Ultimately, the predictive significance of genetic constraints in causing deviations from the optimal response to selection may depend on whether interest ,19. Geneinterest ,21,22, ainterest ,24,25. Sion size ,28,29,30ay vary; ,32,33), matrices ,35,36,37d , with uelection ,23. The ction [G .gmax . However, natural selection within populations is expected to directly affect G-matrix evolution and stability, as an alignment of G with the adaptive landscape may be promoted by the landscape’s curvature and the movement of the selective optimum [G with the adaptive landscape can be produced when traits are under stabilizing and correlational selection [G [G over evolutionary time when the adaptive landscape has a persistent configuration producing strong correlational selection , and a multivariate population mean tracks its moving adaptive peak along the landscape’s leading eigenvector (ωmax) [G-matrix evolution and stability via the influence on the (co)variance matrix of mutational effects (M), as M evolves to become aligned with the adaptive landscape [Changing genetic architecture within diversifying populations over evolutionary time may potentially alter or conserve the relationship between major axes of genetic variation and population divergence . Schlute optimum ,25. Thus optimum , selectielection . Additioction [G . In partr (ωmax) . This dir (ωmax) . Selectiandscape ,28.G-eigenstructure within diverging populations, as G will tend to conform its orientation to the adaptive landscape and M over both intermediate and long time scales [gmax axes that are oriented toward ωmax  [G and D. Yet, without knowing the actual direction of selection operating on traits through evolutionary time, and particularly over long timeframes, it may be difficult to verify whether an observed relationship between genetic architecture and population divergence is due to constraints imposed by genetic (co)variance patterns or to the influence of natural selection in potentially shaping both G and D [Together with large effective population size, evolutionary persistent and coordinated patterns of correlational selection, pleiotropic mutation, and peak movement can contribute to eventually stabilizing the e scales . Under tistance; ), and adD (dmax) ,39, henc G and D ,40.Eucalyptus globulus Labill. to address the following objectives: (1) assess the extent to which the G-eigenstructure has been conserved at an intra-specific level, through the comparison of G matrices between two distantly-related population groups (Mainland and Island); and (2) evaluate the influence of within-population genetic (co)variation on the evolutionary divergence among populations. While these evolutionary issues have been addressed in several empirical studies with plants , ,37, Eucas . Trees s . Basic , [sensu . It is o, [sensu ,46,47. T, [sensu ,49,50. W, [sensu  and, as , [sensu ,53,54. L, [sensu . It cont, [sensu ,56. In w, [sensu . Lignin , [sensu ,59. Thes, [sensu . Togethelatitude . While tlatitude , the extE. globulus is native to south-eastern Australia, including the southern islands of Tasmania, where it occurs over a latitudinal range from 38.4° to 43.5° S, and altitudinal range from sea-level to 830 m [E. globulus [to 830 m ,62. It ito 830 m . During globulus . These fglobulus  summarizglobulus . The maiglobulus ,67.E. globulus, as detailed in Stackpole et al. [r > 0.9; [We used wood property data collected from one of the largest base-population field trials of e et al. ,68. In br > 0.9; ) and widr > 0.9; . The curThe following data analyses were undertaken using ASReml , SAS 7070, and RG-eigenstructure has been conserved is the first step to explore the prospect of long-term genetic constraints on phenotypic evolution. Ideally, this evaluation would be pursued within a phylogenetic framework in empirical studies, such that contemporary populations used to compare G matrices are sampled on a phylogeny to assess whether there is a phylogenetically-structured pattern of variation in genetic architecture [G over time based on studies comparing G-estimates from contemporary populations assume that these populations have a common ancestral population, as well as that their genetic architectures reflect the original G-matrix and changes in it through time [G-estimates, which precluded to compare G matrices of individual populations in a phylogenetic context. Thus, to evaluate whether the G-eigenstructure was conserved through the evolutionary divergence of the E. globulus populations, G matrices were compared between the Mainland and Island groups, where populations in the Island group tended to be phylogenetically distant from those in the Mainland group , the joint distribution of the random terms was assumed to be multivariate normal with a zero-mean vector and a block-diagonal variance matrix, specified as a direct sum of variance-covariance matrices related to the effects in Prior to analyses, the traits were mean standardized for each group  to put them on a comparable scale, and thus to avoid interpretation problems due to the effects of different trait measurement scales. All measures of the focal traits are continuous, positive real numbers for which scaling in relation to the mean is both permissible and meaningful . Mean-stolution variances based on OP-progeny [G matrix were thus obtained by multiplying the REML estimates of OP-family (co)variances by 2.5 . For a given trait, the mean-standardized additive genetic variance equals the expected proportional change per generation to directional selection of unit strength (assuming selection on the trait to be as strong as on fitness itself), and thus can be interpreted as a univariate mean-scaled evolvability , and the Delta method was used to calculate approximate standard errors for genetic correlations estimated between the traits. Statistical significance was assessed for variance estimates via one-tailed likelihood-ratio (LR) tests [G matrices differ in regard to the particular REML estimates of (co)variances, this approach does not enable examination of matrix differences in terms of the influence of G-eigenstructure on the response to selection. In this sense, we used the following measures to compare the mean-standardized G matrices of the two groups. progeny . Assumin-progeny ,64, inst-progeny . EstimatR) tests , and forR) tests . AlthougG matrices. In this context, we evaluated the unconditional evolvability . For G matrix, G or by the average of the trait Hansen and Houle  developeg et al. . Given tnd Houle . Mean vand Houle . In partG-matrices in the direction of the response to selection, we quantified the average similarity between the matrices in their response to a set of random selection gradients  of random selection gradients; G matrices subjected to the thj selection gradient; atan, sin, and cos denote the four-quadrant inverse tangent, sine, and cosine functions, respectively. The value of G-matrices share a common orientation . Each oG-eigenstructure on response to selection, the Krzanowski geometric approach [G-matrices in terms of orientation. By finding the closest alignment between two matrix subspaces, each defined by a subset of k eigenvectors within a q-dimensional trait space , whereas a value close to k indicates that the two subspaces are coincident (share a similar structure) , KrzanoG and D matrices in terms of size, shape, and orientation. The procedures used in this matrix comparison are described in D-matrix.The influence of intra-specific genetic (co)variation on the evolutionary trajectory of a population and its divergence from other populations was assessed through the estimation of evolvability in the direction of evolutionary change of a population. As a complementary approach, we also did a comparison of z that defines the direction of divergence of a population, observed unconditional . In addntiation .z as a direction of divergence of a population from the inferred ancestral states , as well as on the estimation of evolvabilities and the amount of divergence along a z-direction. The calculation of G matrix that was estimated by combining all data, and thus to obtain a G-matrix common to all populations across the Mainland and Island groups. This followed from the G-matrix comparison between these groups, which indicated that the G-eigenstructure was reasonably well conserved at an intra-specific level (see Results). Thus, similar to other studies , and it was mean-standardized by using estimates of phylogenetically-weighted trait means , we , we z asnd Roger .G or D matrix estimates, calculated the target measures for each sample, and generated the sampling distribution for each measure across samples. Lower and upper limits of the 95% confidence intervals for the measures of interest were approximated from the 2.5th and 97.5th percentiles, respectively, of the generated sampling distributions. Further details on the REML-MVN approach and on its application in the present study  are given in Variance-covariance matrices have an estimation error and so do functions of them, such as the measures described above for matrix comparison. Assessing uncertainty in these measures followed the restricted maximum-likelihood multivariate normal (REML-MVN) sampling approach suggested by Houle and Meyer . This apEvaluating the statistical support for similarity of two estimated variance-covariance matrices being compared depended on whether :.measures were computed separately for each matrix and then compared between matrices , where we generated the sampling distribution for the difference between matrices in a measure, based on 100,000 REML-MVN matrix samples drawn from either estimated matrix; or.gmax and dmax), where we computed the statistic m, measures were directly calculated from a between-matrix comparison  in )—and dreStatistical support for similarity between estimated matrices in the evaluated measures was examined through the 95% confidence intervals approximated from the generated sampling distributions . G matrices of the Mainland and Island groups, with the variances being equivalent to the p < 0.001) for all traits and ranked similarly in both population groups, with the levels of p < 0.05) different from the other three traits, whereas the difference between S/G and WD was not statistically significant (not show). Both G matrices tended to have negative covariances between S/G and either BD or EX, and a similar pattern of covariances of EX with the other two traits  (t for KL . Under awith BD) .G matrices differed in (co)variance estimates. As shown in G matrices were constrained to have the same estimates for each of the 10 parameters, the 4 variances or the 6 covariances did not lead to a significantly worse fit (at the 5% level) than a full model where all parameters were estimated independently for each matrix. This was also observed when testing a single parameter per se, except for the genetic covariance between S/G and KL which differed significantly (p < 0.05) between matrices (not shown). In general, these results suggest a weak differentiation between the (co)variance patterns of the two G-matrices.In analyses jointly modeling data from both groups under Equation (1), three null hypotheses were tested to evaluate whether the G-eigenstructure on response to selection are given in G had lower levels of mean evolvability  for the difference between matrices in G matrices did not significantly differ in the degree to which evolvability was (on average) affected by genetic covariances between traits. This is better elucidated by the mean evolutionary autonomy over random directions  over raenvector , indicat) values . The modmatrices , providiG matrices to the same selection gradients (“random skewers”) was relatively small , as shown in G matrices defined the subspaces to be used in a two-dimensional matrix comparison. Krzanowski’s index  of a population from the inferred ancestral states, and plotted against the amount of its divergence in this direction. The observed e(max)) and minimum (e(min)) possible evolvability values, based on the mean-standardized G matrix common to all populations. This matrix is presented in G-matrix provided intermediate values in z-vectors of evolutionary change, differed significantly (p < 0.001) for every trait, indicating strong population differentiation. The inferred ancestral states for the traits were: S/G = 1.94 ± 0.04; KL = 20.67% ± 0.30; BD = 546.72 kg/m3 ± 8.18; EX = 5.06% ± 0.36.a¯ or f¯ . The este(max)  than to z tended to be positively related to r = 0.65, p = 0.041) being detected, but this trend was not evident with z was independent of trait genetic covariances in directions other than z (assumed to be under stabilizing selection), as reflected in When the direction of population divergence is not random in regard to evolvability, the observed ith e(z) . This prgmax) of the pooled G-matrix, as most of the angles between gmax and each of the z-vectors were relatively small and less than the critical value  from a null distribution, generated by simulating 100,000 pairs of random vectors uniformly distributed in a four-dimensional space ). Directions of divergence with high evolvability can be different from gmax, since G may include other multivariate axes of higher than average genetic variance beyond gmax. This was not the case here, as genetic variance was concentrated  along the first eigenvector of the pooled G-matrix . A high level of shared orientation between G and D was indicated by Krzanowski’s index of overall similarity . Under a two-dimensional matrix comparison, the index gmax and dmax directions were closely aligned, with an angle of 5.9°  between them. Statistical support for similarity in matrix orientation could not be rejected for either of these two measures . Genomeure 9 in ). The Maulations , suggestulations ,89. The ulations . While tremnants . While tremnants . Given tremnants , with geremnants .G matrix in the ancestral population. For a set of traits, the predictive importance of this hypothesis will depend on whether recent selection mirrors historical selection, and on patterns of genetic (co)variance remaining fairly stable over evolutionary time (and thus be reflected in measured G-matrices of each of the diverging populations). These assumptions could be plausible over moderate time frames as adaptation may take a while following the introduction of populations into new habitats [G, the structure of genetic (co)variances within populations may remain (at least partly) conserved over evolutionary time. In the present case, given the tree life cycle, population divergence times  of the E. globulus populations would be moderate. For the focal wood properties, there appears to have been a weak differentiation in G-eigenstructure following the colonization of Tasmania, as the G-matrices of the Mainland and Island groups showed similarity in average measures that capture the expected potential for evolution over a wide range of selection gradients on the traits. The weak differentiation observed in G-eigenstructure could suggest that the genetic architecture for the studied traits may have been relatively well kept through time. However, it could also reflect that G may have evolved in a consistent manner to become aligned with the adaptive landscape, so that natural selection has shaped similarly genetic (co)variation within populations variation in wood extractives (EX)  the trait relationships on the first eigenvector of the G and D matrices  selection within populations over evolutionary time [G matrices, as well as to the observed alignment of G and D matrices.As indicated in two recent empirical studies ,37, and irection . Such a n [ωmax; ). Correlmatrices  are conssee also ), as mayG-matrix ,26. An eandscape ,28. Thisary time , and sucG-eigenstructure, and lead to an increase in genetic variance along the direction of population divergence. However, the changes caused by migration were found to be slow and to increase over time, and tended to be reduced with increased correlational selection and/or mutation correlation [E. globulus to be pollinated by animals, including a key bird pollinator which annually migrates between the Mainland and Island populations [G-eigenstructure, as well as in inducing an alignment between genetic (co)variation and population divergence [Migration is another factor which could lead to the observed alignment between genetic architecture and population divergence . Using arelation . In the ulations ,99; and ulations . While tulations . Neverthvergence .G by influencing all features describing the G-eigenstructure within individual populations but, on average, it will lead to proportionality of G and D. Thus, when population differentiation is due to genetic drift alone, G and D will have a similar expectation in terms of shape, but also in orientation so that population means will diverge along directions of greatest genetic variance [G and D matrices reflecting matrix similarity in both shape and orientation could be indicative of genetic architecture biasing population divergence that is generated by drift [G and D did not differ significantly in size and orientation, they did in shape . While  of time , as sugg of time ,66.G has been found to be on the same order of magnitude as observed differences in G among species  or conss . Therefh traits ,105,106.G-matrix variation among individual populations within the Mainland and Island groups, there was no evidence for a systematic difference in the G matrices of the two groups as would be expected if natural selection, gene flow or genetic drift during the Island colonization differentially impacted G-matrix evolution. In general, the unconditional evolvability estimates suggested that standing additive genetic variance would not substantially limit the traits to respond to directional selection (but see lignin). However, as indicated by the estimated average level of autonomy of this genetic variance, the reasonably high degree of evolutionary integration of the focal traits may have been important in underlying the observed close relationship between genetic architecture and population trajectories. It is not possible to ascertain whether these results reflect genetic constraints, or an association between evolutionary forces that may have shaped genetic variation and those that have led to population diversification. The latter scenario could arise from genetic (co)variance patterns that may have consistently evolved to become aligned with natural selection, since genetic drift and/or migration may not have played an important role in generating divergence among the studied populations. Yet, as the G-eigenstructure appeared to be relatively well preserved at an intra-specific level, and given the moderate evolutionary timescale involved, the potential effects of genetic constraints in influencing the evolution and diversification of the E. globulus populations for the set of focal traits may not be discounted.The present study indicated that population divergence for the set of wood properties occurred along a genetic line of least resistance. While we did not have sufficient statistical power to warrant testing


Indolent GI T-LPD has recently been provisionally recognized as a distinct entity by the 2016 revision of the WHO classification of lymphoid neoplasms. Appropriate diagnosis of these cases is challenging as they may be misdiagnosed as T cell lymphoma that has an aggressive clinical course. Consequently, aggressive therapeutic approaches were usually chosen to treat these cases with no obvious benefit for most of the patients and potential side effects. Moreover, inflammatory diseases of the GI tract with similar symptoms may lead to misdiagnosis that leads to delays in administration of proper therapeutics against these cases. Therefore, it is of utmost importance to identify prognostic genetic biomarkers at the time of diagnosis for optimal medical care of these patients. TCR clonality analyses may not be useful for distinguishing these benign neoplasms from aggressive gastrointestinal T cell lymphomas; however, molecular genetic tests may prove useful as recurrent STAT3-JAK2 fusions, which may have diagnostic, prognostic or therapeutic value, have recently been identified. However, there is still lack of comprehensive information on the genetic and epigenetic factors associated with pathogenesis of indolent GI T-LPD. In this mini-review, we focus on the so far reported literature on indolent GI T-LPD cases, and discuss future directions for better differential diagnosis, risk stratification, and therapeutic target discovery with a special focus on the genetic and epigenetic alterations.Indolent T-cell lymphoproliferative disease of the gastrointestinal tract (indolent GI T-LPD) is a benign neoplasm of CD4 The preSTAT5B  and lossB  can be highly similar to those of indolent GI T-LPD cases, which in turn may lead to misdiagnosis of the latter ones . Inflamm complex . These g complex . Given tSTAT3 previously reported in lymphomas derived from gamma delta T or NK cells  cells but not STAT3  . This obEdison et al. reported an unusual case of indolent GI T-LPD with resistant Chron's disease, which developed after tumor necrosis factor-α (TNF-α) inhibitor therapy . Interes+ T cells, and results in cytokine independent growth of Ba/F3, an IL3-dependent pro-B cell line cell. Of note, the main oncogenic function of this fusion protein was through activation of STAT5. Using an in vivo xenograft model, authors showed that STAT3-JAK2 promotes tumor growth, and this growth can be inhibited using JAK inhibitors. Altogether these observations imply that STAT3-JAK2 fusion protein is not important only as a diagnostic biomarker but also as a potential therapeutic target in CD4+ indolent GI T-LPD cases having this genetic lesion.Characterization of genetic lesions observed in indolent GI T-LPD as oncogenic drivers have potential to reveal novel therapeutic targets. Hu et al.'s study provided invaluable information on potential oncogenic role of STAT3-JAK2 in GI T-LPD pathogenesis. In that study, they showed that ectopic STAT3-JAK2 promotes growth of CD4Our current knowledge on genetic and epigenetic alterations associated with indolent GI T-LPD is at the very beginning. There are several research areas to be focused on to improve differential diagnosis, predict prognosis, or identify therapeutic targets for better management of these cases . Most imEpigenetic alterations such as promoter methylation-mediated tumor suppressor gene silencing , aberranTo sum up, there are many unknowns regarding the genetic and epigenetic aberrations associated with the development and pathogenesis of indolent GI T-LPD. Future investigations will reveal the genomic and epigenomic landscape that potentially can distinguish this neoplasm from other similar diseases to achieve accurate diagnosis; thereby leading to more effective therapeutic approaches with better disease management. Importantly, the presence of these disease-related aberrations will support distinguished recognition of indolent GI T-LPD as a distinct disease category by the medical authorities.CK, LW, and HY critically evaluated the literature related to the topic, identified references, and wrote the manuscript. HY financially supported the study. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Diplodus bellottii, may be used as a model to understand the population genetics of these invasions. In the last decades, this species has undergone an outstanding range expansion from its African area of origin to the Atlantic coast of the Iberian Peninsula, where now occurs abundantly. Mitochondrial and nuclear markers revealed a striking high haplotypic nucleotide and genetic diversity values, along with significant population differentiation throughout the present-day geographical range of the Senegal seabream. These results are not consistent with the central-marginal hypothesis, nor with the expectations of a leptokurtic distribution of individuals, as D. bellottii seems to be able to retain exceptional levels of diversity in marginal and recently colonised areas. We discuss possible causes for hyperdiversity and lack of geographical structure and subsequent implications for fisheries.The displacement of species from equatorial latitudes to temperate locations following the increase in sea surface temperatures is among the significant reported consequences of climate change. Shifts in the distributional ranges of species result in fish communities tropicalisation, i.e., high latitude colonisations by typically low latitude distribution species. These movements create new interactions between species and new trophic assemblages. The Senegal seabream,  The increase of sea surface temperatures (SSTs) in the last decades has promoted shifts in the distributional ranges of species  with individuals moving into areas best corresponding to their physiological optimum. Additionally, the ability of a species to colonise new habitats is influenced by oceanographic currents, the existence of adequate resource availability  and life-history patterns . These movements lead to the colonization of more poleward habitats by low latitude species, and create new interactions between species and new trophic assemblages. In commercial species, these shifts due to climate change can be magnified by fishing pressures, as reported for the North Sea cod .Tropicalisation, the displacement of species from equatorial latitudes to temperate locations, is one of the major reported consequences of climate changes10). As a general rule, organisms follow a leptokurtic distribution type, in which the majority of individuals stay at or near the original area, and only a fraction disperse to longer distances. This range extension is usually done in a stepping-stone manner, implying that each settlement has fewer individuals compared with the previous one. Theoretically, this process corresponds to multiple successive genetic founder events associated with the corresponding genetic implications of the downsize in the effective population numbers: the erosion of the genetic diversity by genetic drift induces allele loss, the “southern richness and northern purity” paradigm11. Additionally, the “central-marginal hypothesis”12 posits that populations at the centre of the distribution have higher population sizes and gene flow, and peripheral (leading edge) populations, are smaller in size, have lower genetic diversity and will be more genetically differentiated12.Poleward colonization by organisms with a typically equatorial distribution was described almost three decades ago for terrestrial organisms in association with postglacial recolonisation routes  panmictic populations without latitudinal variation in genetic diversity (e.g. Lipophrys pholis15); (2) significant population structure with similar levels of genetic diversity throughout the entire species range ; and (3) sharp decline of genetic diversity from south (west European) to northern (Scandinavian) populations . It is less common the observation of geographical range expansion of marine species without loss of genetic diversity but, for examples, see21. Contemporary and increasingly visible tropicalization will contribute to more records of fast range expansion events.Phylogeographic studies using mitochondrial and nuclear genes revealed that species with similar environmental requirements and life-history traits often present distinct genetic and demographic historical patternsDiplodus bellottii Steindachner, 1882 (Sparidae), displayed an outstanding range expansion from its endemic origin  to the Atlantic coast of the Iberian Peninsula within a decade  estuaries28, and Ria de Aveiro in the north Atlantic coast of Portugal29  coherent with a predictable loss of overall genetic diversity. We hypothesise that similarly to several other marine fish species in this area of the Northeastern Atlantic such as Halobatrachus didactylus33, Symphodus melops34 or Labrus bergylta19, the recent observed range expansion will lead to a substantial loss of diversity. Also, the expanding low-density leading edge is by definition constituted by edge patches, which are most typically affected by founder events35, where demographic and allelic composition stochasticity36 is predictably stronger. If no significant reduction in singleton haplotypes is observed, and no differences among haplotype frequencies are found, there would be no evidence for a founder event or genetic bottleneck and the species may be panmictic. Alternatively, if no significant reduction in singleton haplotypes is observed but there are differences among haplotype frequencies, the species may be genetically structured. This study is the first appraisal on the genetic variability of this species across its range, a key information to understand its astonishing northward expansion. Additionally, we used species distribution modelling with present-day conditions to evaluate suitable areas for potential colonization, and explored whether the sampling coverage allowed an effective representation of the genetic diversity of the species.In this study, we used the documented expansion of CR = 0.997 to 1.000 and hS7 = 0.960 to 1.000) and nucleotide diversities were high, ranging from πCR = 0.4% (Cadiz) to 10.4% (Mauritania) and πS7 = 0.0% (Cadiz) to 2.7% (Mauritania) with πCR = 7.9 and πS7 = 0.1% for the entire data set  and for S7 . The same parameters behaved differently in pairwise comparisons for both markers  involving four pairs of sampling sites in CR and six in S7, including locations that are not the most distant ones, such as Algarve and Cadiz.In the entire data set the three estimates of fixation The haplotype accumulation curves failed to reach the asymptote for any of the markers Fig. , indicatThe main characteristic exhibited by the two markers’ haplotype networks is their bush-like complexity, with no central or otherwise extremely abundant haplotype. The networks displayed a large number of singleton haplotypes and very few shared haplotypes among sampling sites (coloured circles), a pattern characteristic of DNA hyperdiversity Fig. . The CR p = 0.036) and not for the S7 .The neighbour-net network Fig.  showed tOne of the main features of the BIOMOD2 software is the ability to combine predictions made in single models in an ensemble. The species distribution modelling produced six models  that can be considered good to excellent while CTA, Maxent and SRE have a poor fit with TSS and ROC values under 0.7 Fig. . Among tD. bellottii without apparent anomalies  ranged from 65 to 92% considering all models individually, with the highest value for the GBM model and the lowest for SRE . Nevertheless, the diversity levels recorded in D. bellottii were similar to values observed in widely distributed pelagic species such as sardines (e.g.41) or other fish species with larger population sizes (see Table 1 in42). Interestingly, in the congeneric D. vulgaris43 and D. puntazzo13, the overall CR haplotype (0.983 and 0.998 respectively), and nucleotide diversity (1.8% and 4.1%) are also very high. Contrariwise, in another species of Diplodus, D. sargus, diversity measures were much lower (h = 0.700 and π = 0.8%)13. The differences between D. puntazzo and D. sargus genetic diversity values were attributed to a suite of fortuitous occurrences such as extinction and colonisation rather than to ecological differences between the two species13.Benthopelagic fish such as GST, G’ST, θ, and D) were very different with GST; and θ, showing few pairwise significant differences, and G’ST, and D returning all pairwise comparisons significant. It is important to refer that both θ and GST suffer from the fact that this dataset is hypervariable, therefore restricting these estimators to values close to zero and overestimating gene flow (for a review see44). On the other hand, both G’ST and D, because they measure differentiation as the fraction of a deme's population that consists of private or near-private alleles45, will always be significantly different from zero. The hypervariable mtDNA in D. bellottii leads to a lack of shared haplotypes (with rare exceptions) among locations  and very few among-location shared haplotypes, 4% and 13%, for CR and S7, respectively. This fact makes all locations virtually significantly different from one another, with statistically significant values of D ranging between 0.847–1.000 and 0.849–1.000 for CR and S7, respectively. The expanding low-density leading edge is by definition constituted by edge patches, which are typically affected by founder events35, where demographic36 and allelic composition stochasticity are predictably stronger. However, alleles present in the leading edge patches, even if absent from other marginal locations are frequently shared with sites at the centre of the distribution47, which is not observed in our results.The high diversity recorded in D. bellottii and the paucity of shared haplotypes throughout all the sampled locations, including the native southern site, place our findings in apparent conflict with “southern richness and northern purity”11 and the peripheral populations  hypotheses. Non-expected high diversity was also found in the leading edge population of other species  suggesting that marine organisms with a long planktonic stage and high numbers of colonisers can travel long distances and export their diversity to the newly colonised areas16. In these cases, bottlenecks may not occur because populations are not close to the carrying capacity of the habitats. Diplodus bellottii is a prolific species at least in its southern distribution limit48 and it may have sufficiently large numbers of individuals to colonise in significantly large waves of individuals.The extremely high level of genetic diversity found across the entire distribution of 49. The haplotype network . The mitochondrial control region is a high-mutation region organized in hypervariable regions flanking a central conserved region and is considered the most variant non-coding gene in fishes (e.g. for groupers51). This mitochondrial region is a marker of choice in phylogeographic and connectivity studies (but on the use of hyperdiverse mtDNA for population differentiation see46). The gastropod Melarhaphe neritoides revealed hypervariable mitochondrial DNA markers  as a consequence of high mutation rates49. The second hypothesis used to explain high mutation rates in tropical areas , suggests that temperature and UV tend to generally increase mutation rates in the mtDNA of vertebrates52. The third hypothesis, the metabolic rate theory, posits that smaller animals accumulate mutations quicker than large animals54. However, it has been shown in the cold-water blue crab Callinectes sapidus, that in each recruitment event, the genetic diversity could be just as high as the one found in the adult population, although with different composition55. This would be exactly the opposite of a ‘sweepstakes effect,’ in which a small proportion of the gene pool contributes to the recruitment of the population56, therefore promoting reduced genetic diversity of populations. This way, in each seasonal recruitment, a new combination of alleles may constitute the new generation, which could help to explain the high genetic diversity in this blue crab species, even in populations near the limits of the species range55. Indeed, a similar pattern was found in two fish species, Lipophrys pholis and Atherina presbyter, with contrasting life-history patterns in a nursery rocky coastal area in Portugal57. In L. pholis only two out of 171 haplotypes were shared between three sampling periods, and in A. presbyter 25 haplotypes out of 155 were shared, which reveals an increase of private alleles in the gene pool over the years, even without a significant corresponding increase in diversity indices.High genetic diversity in CR may have several explanations. The most plausible hypotheses involve: (1) the mutation rate of the fragment, (2) the evolutionary rates hypothesis, or (3) the metabolic rate theory , while other species present contrasting high values (as the congeneric D. sargus61). While the dispersal distance is often used as a function of the pelagic larval duration (PLD)63, there is ample controversy on the subject, with studies finding evidence for uncorrelated individuals’ PLDs and net distance travelled65. Although the exact PLD for our species is unknown, it is expected to be around 17 days, value reported for other Diplodus species (e.g.66); this value may however vary with latitude (a proxy of sea temperature). Finally, there are no apparent physical barriers between sampled locations, which may promote considerable admixture among individuals from different origins. However, the species distribution modelling evidenced stretches of coast along northwestern Africa that seem to be an unsuitable habitat for the species, which could theoretically foster some level of geographical isolation. Apparently, D. bellottii overreaches those soft barriers keeping its genetic diversity undisturbed.Despite the results of this study in which all sampling sites are significantly different from each other, no geographical associated structure could be inferred from the networks. Lack of geographic genetic structure can be rooted in large effective population sizes, high potential for dispersal and weak physical barriers to gene flow67. Spatial selection would favour dispersal, and since both dispersal and reproductive rate limit invasion (colonisation) speed, high reproductive rates promote faster range expansions. Because D. bellottii is under thermal stress at 30 °C68, a scenario of spatial selection in the most southern range is rather probable. The reduced tolerance to higher temperatures, coupled with the species preference for estuaries with large areas and high volumes but low depth nursery grounds, means that an increase in sea surface temperatures may result in habitat loss for juveniles, as the estuaries of southern Europe may get too warm. During the summer of 2006, D. bellottii was first detected at 40° N  showing the quick continued northward expansion of this species. Although the results from the species distribution modelling do not show a particular affinity of this species for areas north to Lisbon, the continued raising of sea surface temperature will make these areas suitable for continual range expansion of D. bellottii. The impacts of this species in the recently colonised ecosystems, mainly in what concerns interactions with other species occupying the same ecological niche, has not been fully evaluated. The spatial overlap of D. bellottii with other sparid species is not consistent, across the literature, ranging from 47%69 to 94%30. These large differences may be explained by the dynamics of the estuaries, which suffer major changes in their hydrological features across the years. A noteworthy observation is that the spatial niche overlap between D. bellottii and D. vulgaris has been shown to increase with latitude69.The concept of “spatial selection” includes the idea that more available resources coupled with less competition may lead to spatial allele sorting and increased growth at the invasion front, with the corresponding increase in reproduction70. Sometimes it is hard to realize that any particular genealogy is the single outcome of an infinite number of equally possible genealogies resulting from stochastic outcome71.The origin of the two groups Fig.  is debat31, and the existence of more valuable commercial species, has not made the Senegal seabream a main fisheries target. In Lisbon, the species is mostly a bycatch of a traditional shallow artisanal fishery31 and for official statistics, D. bellottii is merely recorded as Diplodus sp. The fact of this species may constitute a future target fishery should prompt an adequate biological survey to assist its proper management. The unexpected pattern of very high diversity in recently-colonised areas within a climate change scenario, deserves further investigation and this species can serve as a useful model for studying the impact of a changing environment on the genetic diversity of a coastal species. Surveys need to be implemented, ecological implications evaluated, and a genomic temporal approach assessed to understand the causes for a successful colonisation without the loss of genetic diversity levels.This species is an important commercial species in Mauritania, but its smaller length in the Lisbon regionD. bellotti were collected from five locations along its distributional range in the northeastern Atlantic  and a final extension at 72 °C for 7′. The same primers were used for the sequencing reaction, and the PCR products were purified and sequenced in STABVIDA .Total genomic DNA was extracted with the REDExtract-N-Amp Kit (Sigma-Aldrich) following the manufacturer's instructions. The mitochondrial control region (CR) and the first intron of the nuclear S7 ribosomal protein gene (S7) were amplified, with the same kit, in a Bio-Rad Mycycler thermal cycler, using L-pro1 and H-DL1https://www.codoncode.com/index.htm) and sequences were aligned with Clustal X 2.174. For the S7 gene forward and reverse strands were obtained and some samples were sequenced twice for error checking. All sequences were deposited in GenBank .Chromatograms were edited with Codon Code Aligner 77 and nucleotide diversity (π)76 and respective standard deviations.The standard measures of genetic diversity were computed for the five location sites separately and after pooling . We used a variety of packages developed for R v.3.6.1FST‐like statistics78, such as: (1) Nei's GST,77, (2) Hedrick's G′ST79, (3) Weir and Cockerham θ80 and (4) Jost's D81 using diveRsity R-package82. While the first three estimators measure nearness to fixation, the last quantifies the relative degree of allelic differentiation45. We report these four estimates to provide complementary information on the genetic structure among the Senegal seabream sampled locations. The diveRsity R-package was used to estimate 95% confidence intervals for all estimators using 10,000 bootstrap iterations.To assess population structure, we estimated spider R-package83 was used to obtain haplotype accumulation curves by 1000 random permutation subsampling using the functions haploAccum and plot.haploAccum supplied in the spider R-package83 and the Chao 1 estimator84, with chaoHaplo function. Haplotype accumulation curves provide a graphical way to assess the extent of haplotype sampling by representing the extent of saturation as a function of the number of individuals sampled and the number of haplotypes accumulated. Curves displaying weak asymptotic behaviour suggest further sampling is necessary to capture the genetic variation of the species, while curves with rapid saturation indicate that much of the intraspecific haplotype diversity was sampled. The Chao1 estimates the appropriate minimum sample size to account for all haplotype diversity based on the sample size and number of detected haplotypes as well as the number of singleton and doubleton sequences (those occurring once and those appearing twice) in the dataset.The D. bellottii was investigated by reconstructing haplotype networks using a statistical parsimony algorithm85, implemented in TCS v.1.2186. The raw output was visualized in the web implementation of tcsBU37. Additionally, an unrooted neighbour-net network was constructed using SplitsTree 487 to investigate the genealogical relationships among haplotypes. The network was built using HKY + I + G nucleotide substitution model for both markers, as the best selected with the modelTest function of the phangorn88 R-package according to the Akaike Information Criterion89 and compatible with the distance algorithms available in SplitsTree 4, and equal-angle split transformation90 with 1000 bootstrap replicates.Population genetic connectivity in Diplodus bellottii was obtained from occurrence records from two databases: OBIS (http:// www.iobis.org/) and GBIF (https://data.gbif.org/). We used 94 records of Diplodus bellottii compiled from the Global Biodiversity Information Facility  and 72 points lifted from the Ocean Biogeographic Information System  and added five own records. Spurious geo-reference errors and duplications were deleted from the initial data. Presence points were limited to within 200 m depth to consider the recorded depth range for this species (between 10 and 90 m depth)91. Finally, the remaining 39 presence cells were georeferenced to a 0.083° × 0.083° (= 5 arc min ~ 9.2 km) grid resolution.The georeferenced locations of the observed distribution of 92 and MARSPEC93 with a spatial resolution of 5 arcmin using the R package sdmpredictors94. All predictors were delimited up to 200 m depth, given the species known bathymetric preference95, which enhances modelling performances by limiting the extent of extrapolation.The occurrence data was linked to 43 environmental variables available from Bio‐ORACLE96 implemented in R package uSDM98. We used a stringent VIF value (< 2) to retain predictors, as recommended for ecological studies see99. The final set of variables was reduced to four  complying with two criteria: being conceptually meaningful variables100 and proportion of occurrences to predictors obeying to the general rule of thumb of 10 to 1101 01 Table .Table 3Ebiomod2 package102 (v. 3.3–7) for R32. One of the main features of the BIOMOD2 software is the ability of producing an ensemble model by combining predictions from single models nine different methods: maximum entropy (MAXENT), general linear models (GLM), general boosted models , classification tree analysis (CTA), artificial neural networks (ANN), surface range envelope (SRE), flexible discriminant analysis (FDA), multiple adaptive regression splines (MARS), random forests (RF), with default settings39. Explicit D. bellottii absence records were not available so we used two sets of pseudo-absences (background data) extracted at random in a proportion of ten pseudo-absences per presence cell103. Data (presences and pseudo-absences) were split at random into a calibration (70%) and a validation set (30%). We evaluated 140 models: 2 pseudo-absence sets × 10 iterations × 7 models. Models’ performance was evaluated using two measures: the true skill statistic (TSS104) and the area under the receiver operating characteristic (ROC) curve (AUC)105. For the ensemble modeling, only those models with a TSS and ROC scores above 0.8, and 0.9, respectively  were considered106.We produced an ensemble approach to obtain predictions for the current distribution implemented in raster107, rgdal108, dismo109, vegan110, sp111, rJava112, performed in R32 using the R2C2 computational facility (https://rcastilho.pt/R2C2/R2C2_cluster.html).All calculations and analyses were performed with R version 3.0.3, with the following R packages Supplementary Table 1.


Boletus edulis, one of the world's most commercially important wild mushrooms. Microsatellite genotyping of fruiting bodies from 14 different sites around Bielefeld in Germany revealed little in the way of population structure over a geographic scale of several kilometres. However, on a more local scale we found evidence for elevated relatedness as well as inbreeding. We also observed a significant negative association between the genetic diversity of fruit and the age of the trees under which they were sampled. Taken together, our results suggest that as genets mature, they compete and potentially create conditions under which further spores struggle to become established. By implication, even though this species is widely picked, propagules remain common enough to create strong competition when new habitats become available.Ectomycorrhizal fungi are key players in terrestrial ecosystems yet their mating systems and population dynamics remain poorly understood. We investigated the fine-scale relatedness structure and genetic diversity of  ECMs alDespite their importance, relatively little is known about the population structure, genetic diversity and mating systems of ECM fungi. Unique genetic individuals, termed ‘genets'  arise frMycetophilidae that lay eggs at the base of the stem into which the resulting larvae move to feed [Russula brevipes appears unstructured over geographical scales of up to a kilometre, despite strong population structure being present over scales of thousands of kilometres [Most ECM fungal spores are thought to be deposited very close to the sporocarp that produced them –19. Howe to feed . During lometres . Such a At a local level, population structure should also reflect a balance between the proportion of spores that establish mycelia, the longevity of the mycelia and competition among different genets. It has been suggested that new stands of trees are colonized by large numbers of propagules and that the resulting genets compete with each other, leading to some either being prevented from fruiting, perhaps diverting resources to vegetative reproduction, or even dying ,8–10. AsBoletus edulis, known variously as the penny bun, cèpe de Bordeaux, porcino or Steinpilz . For example, only six genets were discovered in a sample of 38 sporocarps from site one, whereas a smaller sample of 20 sporocarps from site two yielded 14 genets . The only exceptions were comparisons involving the locality with the highest genetic diversity (site two) and sites one and four. However, spatial autocorrelation analysis of B. edulis genets -corrected pairwise s genets  showed ar) values among all of the genets. Relatedness varied significantly among the sample sites . Plotting pairwise relatedness values as a heatmap revealed a striking pattern in which clusters of highly related individuals were readily apparent within some but not all of the sites  for our dataset. The resulting estimate of 0.055 was high relative to many species but the 95% confidence interval was broad and overlapped zero . Nevertheless, significant differences in individual standardized multilocus heterozygosity (sMLH) were found among the study sites , with the lowest values being found in sites two and ten. Furthermore, mean pairwise r and sMLH were significantly negatively correlated , indicating that sites with high levels of relatedness among genets also tended to have higher levels of inbreeding.High levels of local relatedness among sexually reproducing genets might be expected to be reflected in elevated levels of inbreeding. To investigate this, we quantified the variance in inbreeding among 2.4.B. edulis and woodland age, we fitted a series of generalized linear models. First, we expressed diversity as the number of genets relative to the total number of sporocarps collected from each sampling site and analysed this using a Poisson distribution to account for nonlinearity. Across the full dataset, the number of genets per site was significantly negatively associated with mean woodland age . Including the main tree species present within each site as an additional three-level factor  resulted in a strengthening of the p-value associated with woodland age  and an increase in the proportion of explained variance from 53% to 68%. Similar relationships were found for two alternative diversity measures, Shannon's H and the Simpson index . Mean pairwise relatedness was also significantly higher in younger sites , and sMLH showed an inverse relationship with age, but this was not significant .To investigate the relationship between genetic diversity in 3.B. edulis sporocarps sampled from multiple localities around Bielefeld in northwestern Germany. We uncovered important deviations from the null model of panmixia driven by high levels of wind dispersal. Specifically, at a local level, relatedness can be but is not always high, while genet diversity declines with the age of the trees in the stand. By implication, as new habitat becomes available, large numbers of genets may become established, but as the habitat matures, intra-specific competition reduces the chances of new spores adding to the population and may cause many genets to be lost. Our results open a new window on how B. edulis populations may develop over time.Here we report a genetic analysis of 3.1.et al. [Suillus grevillei and found ratios of 3.7 and 1.8 in larch stands that were 35 and 85 years old, respectively. Similarly, Lee and Koo [Suillus granulatus. These values are broadly similar to ours but direct comparisons are hampered both by observation bias and by the difficulty of controlling for changes in genetic diversity as hosts age, which may differ between species. Observation bias relates to the thoroughness of sampling. Porcini growing under beech can be cryptic and many sporocarps are variously picked by humans and eaten by a variety of animals. By contrast, Suillus species are much less picked by humans and, often growing in grass, can be more visible, making complete sampling more likely.Each genet was on average represented by 2.5 sporocarps (range = 1.43 for the youngest site to 7 for the oldest site). This is comparable but perhaps a little more extreme than reported by Zhou et al.  who stud and Koo  publishe3.2.Fst values. In both cases, one of the populations was the youngest, most genetically diverse site (site two), which also had the second largest sample size of sporocarps, while the other two populations were those with the first and fourth largest sample sizes of sporocarps, respectively. This may suggest that the power to detect significant differences depends on the genetic diversity of the populations in question, and that overall power is quite low with only seven microsatellites. Our results are broadly in line with studies of other species such as Tricholoma matsutake, Russula breviceps and Laccaria amethystina, none of which showed evidence of genetic structure on a geographical scale of around 1 km [We found little evidence of population structure among sites separated by a few kilometres, with only two pairwise comparisons yielding statistically significant und 1 km ,4,12. Hound 1 km .Suillus spraguei where no fine-scale clustering of similar genotypes was detected, indicating that there may be few restrictions to gene flow in this species [Cantherellus formosus at distances up to 400 m [Tricholoma scalpturatum at distances up to 20 m [At odds with the broader scale pattern, spatial autocorrelation analysis uncovered significant fine-scale structuring but only over the smallest distance class (250 m). This contrasts with  species . Howeverto 400 m  and in T to 20 m . These dB. edulis, therefore, our data suggest that while structure can develop over short temporal and spatial scales, the longer term outcome appears to be influenced by factors that act to prevent these patterns extending, such as mycelial competition, selection or even occasional years when long-distance spore dispersal rates are high.High relatedness of sporocarps could arise either though the local recruitment of spores to the same population or if individual mycelia reproduce by interacting with multiple others. These possibilities could be distinguished either by investigating the spatial distribution of pairs of close relatives, with distances above 30–50 m being more likely to reflect wind or insect-dispersed spores, or by sampling the mycelia themselves so as to identify the underlying haploid genotypes. Having said this, a general failure to find evidence of strong population structure over larger scales might argue against the idea that most spores recruit successfully on a scale of tens to hundreds of metres, as this would tend to generate strong local genetic structure that would, over time, extend up to larger spatial scales. For 3.3.Suillus bovinus, S. grevillei and Russula brevipes have shown by comparing stands of trees of different age that fungal clone size increases and the number of clones decreases with increasing forest age [Previous studies of rest age –10. Thiset al. [Hebeloma cylindrosporum are often short-lived relative to outcrossed mycelia. This generates the prediction that heterozygosity should increase as genet diversity decreases. Our data are too limited to test for such a pattern meaningfully, because many of our sites yielded only a single genotype. However, if future research vindicates this prediction, it would provide a simple explanation for why population substructure does not develop: selection for high heterozygosity favours the descendants of long-distance dispersal events so will always act against processes that tend to generate local structure. To quantify inbreeding more precisely will require the development of larger panels of genetic markers from across the genome [In order to better understand the mechanisms driving the association between genet diversity and woodland age, it will also be important to consider genet longevity. For instance, in older forests, when one mycelium dies, does this reset the clock and allow the process to begin again with high diversity recruitment, or do younger mycelia expand to occupy the available space? One potential factor influencing the longevity of individual genets could be heterozygosity, as Gryta et al.  have shoe genome .B. edulis, but they have the potential to cause a reduction in individual heterozygosity as a stand ages. However, the effect does not seem to be large in our sample because, ironically, loss of heterozygosity will tend to increase genotype diversity, the exact opposite trend to the one we observe. Specifically, an event that causes a locus to become homozygous will effectively split that genet into two, one which is homozygous at the effected locus and the other of which is heterozygous. In the future, we aim to analyse much larger sample sizes and to extend our panel of genetic markers, both of which should allow us to document these events should they be present at any appreciable frequency.In addition, fungi often experience genomic changes during vegetative growth, including mitotic recombination and loss of heterozygosity . It is u3.4.Compared with studies of other species, our study benefits from a relatively large number of different, discrete sites and their accessibility, which allows more or less exhaustive sampling throughout the season. The use of highly variable microsatellites also allowed us to resolve individual genets, even when relatedness was high, which is an advantage over the use of less polymorphic markers such as internal transcribed spacers. Indeed, while it would be desirable to increase the number of markers further, we were unable to find previous studies where pairwise relatedness between genets could be or had been quantified. This possibility is likely to prove important in understanding fine-scale patterns of reproduction and resolving the balance between sexual and vegetative reproduction. Increasing the number of genetic markers should also allow us to ask questions about inbreeding and the importance of outcrossing.Our study also has a number of limitations. In particular, we assume that sporocarp diversity provides a reasonable proxy for mycelial diversity. Consequently, we cannot distinguish between an absence of fruit indicating that a particular genet has been outcompeted and died versus an absence of fruit indicating that a genet is focusing its resources on maximizing vegetative growth rather than producing sporocarps. In our experience, in ‘good years', sporocarps appear in a greater diversity of sites compared with ‘poor years', with fruit appearing near certain trees only in exceptional years. Consequently, short-term changes in sporocarp diversity may, in reality, merely reflect differing propensities for fruiting. If so, by sampling only in a single season, we may have missed the opportunity to detect genets that only fruit in unusually good years. It will be interesting in future studies to see whether inter-annual patterns help to resolve questions about how intra-specific competition operates. For example, do good years reveal a higher diversity of genets in older stands of trees, thereby showing that mycelia persist and tend not to fruit rather than dying?3.5.B. edulis reveals a rather dynamic picture in which new stands of trees are colonized by high diversity genets, whose representation progressively diminishes with increasing tree age. As yet, it is unclear whether this pattern reflects actual mycelial mortality or simply a failure of certain genets to garner enough resources in competition with the dominant genets. To understand this process better will require the sampling of mycelia rather than just fruit, coupled with genome-wide sequence data to reveal if and where selection is acting.Our study of 4.4.1.B. edulis sporocarps (Fagus sylvatica) followed by common oak (Quercus robur) and silver birch (Betula pendula). The sample sites (2) in relation to the accuracy of civilian GPS (approximately 5–10 m), we recorded a single GPS location for each site using a Garmin 64 s handheld navigator. As a proxy for the age of the woodland in each sample site, we measured the circumference of each tree greater than 20 cm in diameter and also recorded the species of each tree. The resulting data were then fed into a tree age calculator  to ascertain the approximate age of each tree (to within ±10%) and from there to derive a mean ± s.e. for each site. In order to sample the sporocarps as comprehensively as possible, all of the sites were visited between two and four times a week from mid-August until mid-October 2015. The first and last sporocarps were found on 29 August and 10 October 2015, respectively.A total of 134 orocarps  were samorocarps  lies maile sites  are main4.2.B. edulis genome sequence version 1.0 from https://mycocosm.jgi.doe.gov/Boled1/Boled1.home.html and searched for perfect di- tri- and tetranucleotide motifs using MISA-web [B. edulis tends to have short microsatellites, so in practice this meant more than eight pure repeats for dinucleotides, and more than four pure repeats for tri- and tetranucleotides) and where the flanking regions were not themselves overly repetitive, as judged by eye. Primers were designed using Primer3Plus [B. edulis samples, and primers revealing three or more alleles were combined into multiplexes for genotyping.We downloaded the MISA-web . Primersmer3Plus  with def4.3.2O and 4 µl Qiagen 2X multiplex PCR mastermix to which PCR primers had already been added. All of the primers were multiplexed into two separate multiplexed reactions using the following PCR profile: one cycle of 5 min at 95°C; 36 cycles of 45 s at 95°C, 3 min at 58°C and 1 min at 68°C; and one final cycle of 10 min at 72°C. The resulting PCR products were then resolved by electrophoresis on an ABI 3730xl and allele sizes were called relative to the LIZ size standard (1 : 150 dilution) using GeneMarker version 2.6.2 . Samples with missing genotypes at three or more loci were excluded from further analysis.Total genomic DNA was extracted using a standard Proteinase K digestion  followed by purification with glass milk. Briefly, 50 µl of digestate was added to 100 µl of 6 M NaI and 5 µl glass milk. After 20–30 min adsorption time, the glass milk was spun down and washed twice with ‘new wash solution'  before the DNA was eluted in 150 µl low TE buffer. For speed and efficiency, all operations were carried out in 96-well microtitre plates. For each PCR reaction, 1 µl of DNA was added to 3 µl H4.4.α level of 0.05 were applied to the resulting p-values to account for multiple testing. Genepop was also used to calculate observed and expected heterozygosities as well as to determine the number of alleles amplified at each locus.All of the loci were tested for deviation from Hardy–Weinberg equilibrium (HWE) using Genepop v. 4.2 . For eac4.5.To control for clonal replicates, all identical multilocus genotypes were removed from the full dataset using the clonecorrect function in the R package poppr v. 2.8.3 ,41. To p4.6.He, Nei's gene diversity) were calculated using the poppr function in the R package poppr. F-statistics among mushrooms from the 14 sites were computed using Arlequin v. 3.5.2.2 [The Shannon–Weiner diversity index, the Simpson's diversity index and expected heterozygosity  was constructed with a Poisson distribution and with weights equal to the number of sporocarps at each site. To investigate the relationship between mean tree age and Shannon's H, Simpson's index, mean sMLH and mean pairwise relatedness, we constructed four independent GLMs with Gaussian distributions. All models were built in base R v. 3.5.3 and visualized with the attached packages car version 3.0.3 [To investigate the relationship between the number of on 3.0.3 , ggplot2on 3.0.3  and sjPlon 3.0.3 .