Cervus elaphus). We use factor analytic modeling of the genetic variance–covariance matrix (G) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations.Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits  for both males and females in a wild, pedigreed, population of red deer ( Cervus elaphus) on the Isle of Rum, Northwest Scotland, and to consider in particular the relationship between traits expressed in either sex.EVOLUTIONARY theory predicts low equilibrium genetic variation for fitness and fitness-related traits, because alleles that have negative effects on fitness should have been removed by selection, whereas those with positive effects should have reached fixation . The obsBy far the most common approach to studying genetic trade-offs and genetic constraints is to estimate the bivariate genetic correlation between two traits . In partAs well as focusing on bivariate genetic correlations, the majority of previous studies have also focused on genetic correlations among traits expressed in the same sex as a potential cause of constraint  and yet C. elaphus) on the Isle of Rum, Scotland. Previous studies in this population have shown genetic variation for numerous traits  for females, males, and both sexes, with particular focus on characterizing the major multivariate axes of variation; and (2) to assess the degree of constraint imposed by the structure of G relative to the direction of selection using, first, estimates of the angle between the vector of selection and the vector of the predicted response (θ) and, second, the length that the vector of the predicted response travels in the direction of selection  when genetic covariances are fixed to zero vs. not zero, to assess the importance of genetic variance vs. covariances in generating any constraint.Here we extend the multivariate analysis of genetic constraint in the Rum red deer population to include both males and females and to study the effect of both genetic variances and within- and between-sex genetic covariances in generating constraint. We consider four life history traits, which together form a comprehensive set of all life history traits that determine individual fitness: survival to breeding age, age at first reproduction, longevity, and annual reproductive success. Our aims were split into two parts: (1) to quantify the genetic variance–covariance matrix  ]. Part 2We used individual life history information from red deer born between 1971 and 2007 in the study population in the North Block of the Isle of Rum, Inner Hebrides, Scotland  . IndividMaternity was assigned with certainty based on observed associations between mothers and calves . IndividWe analyzed within- and between-sex variances and covariances in four life history traits that represent the major components of fitness in this population, measured separately in each sex to give a total of eight traits. The traits were as follows:Survival to breeding age (SBA): Defined as 0 for all animals known to have died before and 1 for all animals known to have survived to May 1 in the year in which they reached the age of 3 years. Individuals whose fate was unknown or who died as a result of being shot outside the study area were removed from all analyses, resulting in a sample size of 1126 females (born to 462 mothers) and 1114 males (born to 437 mothers).N = 519 females and 149 males; many more males than females fail to breed). Because first breeding at an early age necessarily has a positive direct effect on fitness, AFR was multiplied by −1 in all multivariate analyses so that trade-offs would be represented by negative covariances and correlations.Age at first reproduction (AFR): The age in years at which a female first produced a calf or at which a male was first assigned paternity (L): For both sexes, the age in years at death for individuals that survived to at least 3 years of age . Individuals that emigrated from the study area and thus for whom age at death was unknown were removed from the data set, as were individuals that died as a result of being shot outside the study area . For both sexes, data were limited to individuals that were born before 1995 because for cohorts born from 1995 onward <80% of individuals were dead. This resulted in longevity values for 338 females and 245 males.Adult longevity : Defined as a repeated measure for all adults of breeding age. Females received a 0/1 score, with 1 representing years in which they produced a calf and 0 for years in which they did not. Females were included only if they were ≥3 years old and survived to at least 6 years of age; for each female ABS was recorded for each year until her death (or until 2008 if still living), so that several possible breeding attempts were included. This gave 3859 records from 439 individual females. For males, ABS was defined as the number of calves to which a male was assigned paternity for any given year in which males were ≥3 years old during the mating season (rut) and also were seen in the study area during the rut. Males were assigned an ABS score of 0 for a given year if they were seen in the study area during the corresponding rut, but not assigned paternity of any calves born the following year. Calves born in calendar year For analyses of selection on these traits (see below), we also required estimates of individuals’ lifetime breeding success, defined as the total number of offspring produced across a lifetime, restricted to individuals known to have died a natural death.To prevent differences in scale causing particular life history traits to dominate loadings in the factor analytic models described below, all traits were standardized to unit phenotypic variance by dividing by their standard deviation. In addition, AFR and male ABS were square-root transformed prior to standardization to better approximate normality.A total of 1188 females were measured for at least one of the four life history traits under consideration in this study. The informative pedigree for these traits consisted of 1327 maternal links (including mothers that lacked phenotypic information but were informative for relatedness) and 893 paternal links. For males a total of 1369 individuals were measured for at least one trait and the informative pedigree consisted of 1586 maternal links and 1077 paternal links (of which 141 were dummy sires). When combining data on both sexes, 2557 individuals were scored for at least one trait, with the informative pedigree consisting of 2368 maternal links and 1573 paternal links. For all analyses, the pedigree had a maximum depth of eight generations with a mode of three generations of information for each individual.y is a vector of phenotypic observations, μ is the mean, b is a vector of fixed effects, u is a vector of random effects, X and Z are design matrices linking individual records to the appropriate fixed and random effects, and e is a vector of residual errors.For each trait, variance components and appropriate random effects structures were initially investigated using univariate animal models of the general formVA), year of birth (VBY), and residual (VR) effects were modeled for all traits; maternal effects } or it was not possible to add additional factors to a model (due to failure of convergence). FA modeling allows estimation of i.e., ΛΛT) that contains the maximum possible variance estimable given the data and thus provides the best possible estimate of G with which to subsequently assess its potential to generate evolutionary constraint (see below). Because the leading factors to be estimated are those that contain the most variance, any unestimable factors in our analysis should explain considerably less variance than those that have already been estimated and would thus contribute much less to a predicted response to selection than those that are included.Although FA has been used to assess the rank of G e.g., , doing sG is the multivariate genetic variance–covariance matrix, and β is the vector of directional selection gradients (fG is the additive genetic (co)variance matrix for females, mG is the additive genetic (co)variance matrix for males, B is the genetic covariance matrix between the sexes, T is the transpose of a matrix, mβ is the vector of selection gradients for male traits, and fβ is the vector of selection gradients for female traits , as defined by the vector of selection gradients (β), by G. The degree of genetic constraint can therefore be summarized by comparing the vector of the predicted response to selection (β). Comparison of vectors can be done in two related ways (details below); doing so first required calculation of selection gradients for the traits under study.The majority of methods for estimating multivariate genetic constraint center around radients . When co(βfβm)(4), where Ge traits . The facdifferentials (S) for all traits in this study from the covariance between the trait and relative fitness  to score for all other traits , we analyzed selection as a two-step process, analyzing selection on SBA separately from selection on AFR, L, and ABS. Relative fitness for selection on SBA was defined as the ratio of lifetime breeding success  of an individual to the sex-specific population mean lifetime breeding success of individuals with known SBA. Relative fitness for selection on all other traits  was defined as the ratio of lifetime breeding success of an individual to the sex-specific population mean lifetime breeding success of individuals that survived to breeding age  and had a known phenotype for at least one of AFR, L, and ABS.We initially calculated selection  fitness . Traits S for each trait, bivariate models of the form in Equation 1 were fitted with both relative fitness and the trait of interest  as dependent variables. For the single-measures traits , no random effects  were fitted and the selection differentials were estimated as the phenotypic covariance between the standardized trait and relative fitness. For the repeated-measures trait ABS, the relationship with fitness was calculated by fitting a bivariate model of ABS and fitness with an individual-level term for both traits, similar to the models used above. The residual variance for fitness was then fixed at zero, forcing the residual variance for fitness to be represented by the individual-level term and thus allowing the estimation of the phenotypic covariance between relative fitness and the individual repeatability of ABS  were then calculated from the equationβ is a vector of selection gradients, P is the phenotypic variance–covariance matrix for each sex , and S is a vector of selection differentials (Discussion in File S1). We calculated three vectors of selection gradients: a vector of selection gradients on female traits (fβ); a vector of selection gradients on male traits (mβ); and then, by combining fβ and mβ, a vector of selection gradients for both female and male traits (bsβ). Standard errors for S and P are estimated within ASReml.Sex-specific selection rentials . The pheθ) between the vector of selection (β) and the predicted response to selection (θ provides an estimate of the degree to which G deflects evolutionary trajectories away from the direction of selection and is thus a representation of the degree of genetic constraint. The use of angles between vectors can be extended (nc) when all covariances within G are set to zero . Different angles can then be calculated to assess the effect of various aspects of G on the predicted response to selection β and , the combined effect of unequal genetic variances and nonzero covariances on deflection; θ2, the angle between β and nc, the extent to which unequal variances cause deflection; θ3, the angle between  and nc, the effect of nonzero covariances on the direction of the predicted response to selection; and θ4, the difference between θ1 and θ2 (θ1 − θ2), the amount that genetic covariances alter deflection .To assess the strength of genetic constraints to evolution we calculated the angle (on (Δz¯)  asθ=cos−extended  to assesθ5_bs, the angle between bsβ and i.e., using nbsG in Equation 4 to calculate nbs, where nbs = no between-sex covariances), which represents the combined effect of unequal genetic variances and within-sex covariances on deflection; θ6_bs, the angle between bs and nbs, the effect of nonzero between-sex covariances on the direction of the predicted response to selection; and θ7_bs, the difference between θ1_bs and θ5_bs (θ1_bs − θ5_bs), the amount that between-sex covariances alter deflection ] ]  defined β and thus describes the length of the response in the direction of selection  of the vector of selection (|β|2 in Equation 7). It summarizes the effect of G on β and adjusting the magnitude of the response. For comparison with evolvability, a useful benchmark is the average evolvability (ē) over random selection gradients, defined asiλ are eigenvalues of G and the sum is over all k eignevalues (G independent of the strength and direction of selection (f), males (m), and both sexes (bs) gives 0.111, 0.224, and 0.181, respectively.Evolvability corresponds to the length of the projection of election  and is snevalues . This iselection . CalculaG, where both genetic variances and covariances were estimated . e(fβ), e(mβ), and e(bsβ) provide an estimate of the effect of both genetic variances and covariances on evolvability. We also calculated evolvability fixing genetic covariances to 0 , providing an estimate of the evolvability based on the genetic variances alone . e(xβ) and e(xβ)nc can then be compared to provide an estimate of the degree to which genetic covariances alter evolvability,x = f, m, or bs nbsG giving evolvability e(bsβ)nbs]. e(bsβ)nbs allowed calculation of two additional values of Re: (1) Re_bs_nbs = e(bsβ)/e(bsβ)nbs and (2) Re_bs_nbs.nc = e(bsβ)nbs /e(bsβ)nc. Re_bs_nbs compares evolvability with and without between-sex genetic covariances fixed to 0, with a value of less than one indicating that between-sex genetic covariances reduce predicted evolvability and thus increase the constraint. Re_bs_nbs.nc compares evolvability with and without within-sex genetic covariances fixed to 0, with a value of less than one indicating that within-sex genetic covariances reduce the predicted evolvability and thus increase constraint.For both-sex models we also calculated evolvability fixing between sex covariances to zero . Errors in these estimates were approximated using an MC simulation algorithm  as the mean and the variance covariance matrices of these parameter estimates as the variance (again these are given in ASReml). These 100,000 values were then combined as appropriate in Equations 5, 4, 6, 7, and 9 to produce 100,000 estimates of β, θ, e(β), and Re. The 95% credible interval (CI) around these values was then calculated using the quantile function in R and used as an estimate of the 95% credible interval around each parameter estimate. It should be noted that this method assumes the sampling errors in the estimates of variances and covariances are multivariate normal. For angles θ1, θ2, θ3, θ5_bs, and θ6_bs  and for all values of evolvability, estimates cannot be negative and thus interpreting a lack of overlap of the 95% CI with zero as indicative of the value differing from zero is not valid. As such, statistical hypothesis tests have limited meaning and we therefore assessed statistical support for substantially nonzero values by examining the distribution of MC samples , drawing conclusions equivalent to those associated with failure to reject a null hypothesis.Estimates of ples see . In pracTable S1). Nongenetic random effects followed expected patterns with maternal and birth year effects being significant only for early life history traits although not all early life history traits in males (Table S1).There was evidence of significant additive genetic variance for all female life history traits apart from female longevity and for male ABS but not other male life history traits (Table S2), with all but one significantly greater than zero. In addition, all but one estimable nongenetic covariances among traits were positive (Table S3). Full-rank FA models of the genetic covariance matrix (G) converged for females, but for males and both sexes the maximal-rank models that would converge were 2 (of a maximum of 4) and 4 (of 8), respectively (Table S4). Although statistical comparison provided support only for lower-rank models for all G matrices (Table S4), we used the highest-rank model that converged in all subsequent analyses, for the reasons described in Materials and Methods and File S1. Genetic covariances between traits within and between the sexes were a mix of positive and negative values, although within-sex genetic covariances in males were all positive . A principal component analysis (PCA) of bsG revealed a major axis of genetic variation that loaded positively on all male traits and female L, but negatively although weakly on female SBA, AFR, and ABS (Table S7C). A similar pattern was apparent when examining the sexes separately: negative associations between female longevity and the other female traits compared to positive associations among all male traits .Multivariate models of phenotypic covariance within each sex indicated positive phenotypic correlations among all traits  suggested an aspect of constraint for female traits in the general pattern of negative genetic covariance between survival and reproductive traits (Table S5 and Table S8). In males, an overall pattern of facilitation of adaptive evolution dominated as estimated genetic covariances were all positive (Table S6); however, it is unclear from the multiple imprecise estimates alone whether such an interpretation is really justified. Similarly, interpretation of the multiple modest between-sex genetic correlations (Table S3) is difficult from consideration of the estimates alone, necessitating consideration of metrics that integrate over the implications of all aspects of G and β.All selection differentials and gradients were positive and strong, as life history traits must be by definition . Given sθ1_f was small , but appeared greater than zero  was merely 5.06° ] was 0.0801 (e(fβ) (f) of 0.111. The evolvability of female traits due to genetic variances alone [e(fβ)nc] was 0.0753 /e(fβ)nc, showed no evidence of differing from 1   suggestes 0.0753  and appe753  was small  was only 2.57° ] was 0.0659  = 0.235]. The evolvability of male traits when considering only genetic variances [e(βm)nc] was 0.0274 /e(mβ)nc, was significantly >1  . Instead, evolvability was higher when including genetic covariances than when fixing them to zero  . Howeveret al. do not present explicit estimates of evolvability). In contrast, in our analyses, although a substantial proportion of the estimates of individual genetic covariances were negative , in general we found overall genetic constraints to be relatively mild and to result mainly from genetic variances rather than from genetic covariances among traits. In particular, we found little evidence that genetic covariances among traits in males or between the sexes generate constraint; rather, genetic covariances in males and between the sexes appear to facilitate the predicted response to selection in terms of evolvability and any constraint occurs primarily from the pattern of genetic variances.These results provide a detailed investigation of genetic constraints to life history evolution in a wild population of red deer. Studies that apply multiple measures of genetic constraint, particularly measures of evolvability, to data from a wild animal population are extremely rare. i.e., the direction of fastest adaptation) was small. This was particularly true in females (θ1_f and θ2_f < 20°), while in the male and both-sex models, deflection was of intermediate magnitude . In females the small deflection and thus constraint that was evident appeared to result from a combination of unequal genetic variances and nonzero genetic covariances, since neither one caused significant deflection alone. However, in males and both-sex models, genetic covariances caused limited deflection, while unequal genetic variances caused significant  deflection and thus constraint.In general, the deflection of the predicted response to selection away from the vector of selection (G independent of the direction of selection relative to G (G . However, for males, evolvability was low compared to the average evolvability particularly when genetic covariances were fixed to zero [e(mβ) was 29% of m and e(mβ)nc was 12% of m]. This suggests constraint in the pattern of genetic variances relative to the direction of selection in males when compared to the evolutionary potential of G. The effect of genetic covariances on the predicted evolvability differed slightly between female vs. male and both-sex models. For females, genetic covariances had very little effect on the predicted evolvability, as opposed to the slight increase in deflection and thus constraint as measured by deflection. In male and both-sex models evolvability increased as a result of genetic covariances and thus, while causing only a very slight increase in evolvability in absolute terms , caused a large increase in evolvability in relative terms (Re_m and Re_bs_nc estimates were >2). This suggests that genetic covariances increased the predicted evolvability when considering males and both sexes, by increasing the magnitude of the predicted response in the direction of maximally increasing fitness.Conclusions regarding the extent of constraint were slightly different when considering evolvability. For female and both-sex models, evolvability was similar to the average evolvability, a baseline that indicates the evolutionary potential of ive to G . Thus, tFile S1. However, it should be noted here that despite patterns of genetic covariances between female traits being similar to those of a previous study (File S1 for further discussion).Detailed discussion of the comparison with previous results from the Rum red deer population is given in Drosophila cuticular hydrocarbons  and find substantial angles between β and the first three principal components of G: 117°, 73°, and 103°. Applying the same technique to the results in our study  will give correct selection gradient estimates for S and in the latter case, the bonus of the correct selection gradient for R; in each case correct refers to the value of the selection gradient that provides the correct evolutionary predictions when used in the Lande equation . Thus, while it is typically impossible to be sure that all relevant traits are included, a comprehensive set of life history traits  is a special case where all pathways mediating any effects of traits or environmental variables on fitness are necessarily included, because life history completely determines fitness; there are no missing traits, in the sense that all effects of variables on fitness are mediated by life history.Finally, it is worth clarifying here that multiple regression-based selection analysis is expected to provide robust inference when complete life history data are analyzed. As we and others have discussed in the past, multiple regression-based selection gradient estimates may fail to represent the true direct effects of traits on fitness when unmeasured variables cause trait–fitness covariance . Two altEnter the following in an R console:#simulate data according to hypothetical example in text, true LH betas = 1n<-10000; E<-rnorm; S<-1*E+rnorm; R<-1*E+rnorm; w<-1*R+1*S+rnorm;#missing variable problemsummary(lm(w∼S))#solution 1: include ultimate effectssummary(lm(w∼S+E))#solution 2: include mediating effects (complete life history)summary(lm(w∼S+R)).This study attempts to quantify the multivariate genetic constraint, considering both within- and between-sex genetic (co)variation, in a wild population experiencing a natural environment. Overall we found estimates of genetic constraint were mild and that patterns of genetic variances rather than genetic covariances were the main source of genetic constraint. There was little support for the contention that between-sex genetic covariances caused constraint, indicating that sexual antagonism may not be as strong as previous results from this population suggested. Finally, the degree of genetic constraint in this study was much lower than in previous studies based predominantly on laboratory populations. Given the lack of data on natural populations and the difficulty of estimating these parameters in such populations, we encourage further analysis of this type to assess whether the apparently lower level of genetic constraint in natural populations is a general pattern.


Stylissa carteri in the Red Sea. Furthermore, a small-scale (<1 km) analysis was also conducted to infer reproductive potential in this organism. At the broad scale, we found that sponge connectivity is not structured by geography alone, but rather, genetic isolation in the southern Red Sea correlates strongly with environmental heterogeneity. At the scale of a 50-m transect, spatial autocorrelation analyses and estimates of full-siblings revealed that there is no deviation from random mating. However, at slightly larger scales (100–200 m) encompassing multiple transects at a given site, a greater proportion of full-siblings was found within sites versus among sites in a given location suggesting that mating and/or dispersal are constrained to some extent at this spatial scale. This study adds to the growing body of literature suggesting that environmental and ecological variables play a major role in the genetic structure of marine invertebrate populations.A main goal of population geneticists is to study patterns of gene flow to gain a better understanding of the population structure in a given organism. To date most efforts have been focused on studying gene flow at either broad scales to identify barriers to gene flow and isolation by distance or at fine spatial scales in order to gain inferences regarding reproduction and local dispersal. Few studies have measured connectivity at multiple spatial scales and have utilized novel tools to test the influence of both environment and geography on shaping gene flow in an organism. Here a seascape genetics approach was used to gain insight regarding geographic and ecological barriers to gene flow of a common reef sponge,  Historically, gene flow in the marine realm was thought to generate panmictic populations semiconnected ones following a stepping stone trajectory, or completely isolated groups. However, it is now widely accepted that genetic connectivity can take any combination of the above-mentioned trajectories and can differ given the temporal and spatial scale at which it is investigated   has been utilized to analyze the effect of increasing environmental heterogeneity on genetic structure .Sponge tissue samples (n = 966) were collected by SCUBA between October 2012 and February 2014 from a total of 36 reef sites. A total of 34 of those sites span 1500 km of Saudi Arabian coastline, one is off the coast of Sudan, and one is off the coast of Socotra in the Indian Ocean , transects were sampled in different sites . Sites were separated by no more than 30 km, 38 km, and 52 km at Yanbu, Thuwal, and Al-Lith, respectively. At each of the eight sites  and shaking incubation or overnight proteinase K incubation at 56°C (Qiagen). Following cell lysis, the DNA extraction method proceeded via the Allprep DNA/RNA mini Kit (Qiagen).The DNA was extracted from 200 to 250 mg of sponge tissue with cell lysis immersion in 600 Nine microsatellite markers were used. Marker details and PCR parameters are specified in Giles et al.  and summNA), observed (HO), and unbiased expected heterozygosities (uHE) were estimated for each site using the software Genalex , and tests for linkage disequilibrium (LD) and deviations from Hardy–Weinberg proportions (HWE) were determined using Genepop (Raymond and Rousset Samples were genotyped using the software GENEMAPPER 4.0 (Applied Biosystems). Allelic frequencies, number of alleles . To avoid over-representation of individual genotypes, one of each of the members of a clonal pair was removed from the dataset for subsequent genetic analyses.Bayesian clustering via the program STRUCTURE  using the program Genalex. The AMOVA was run four times, once without regional classification of the samples, once with a two-region classification, once with a three-region classification, and once with a four-region classification. The regional classifications were made based on results from the STRUCTURE  from the rest of the dataset, probably due to its geographic isolation. Thus, the inclusion of this disparate site could confound meaningful results about processes within the Red Sea.Pairwise geographic distance (Euclidean distance in km) was calculated between each of the 35 sample sites following Nanninga et al. FST (FST / (1 − FST)) and geographic distance were compared to determine the degree of isolation by distance (IBD) of S. carteri populations. Pearson correlation coefficients were calculated for the linear relationship between geographic distance and genetic distance. Regressions were made twice, once using raw matrices and secondly on matrices standardized by subtracting the mean and dividing by the standard deviation of the dataset.Using a Mantel test with 9999 permutations, pairwise matrices of linearized http://oceancolor.gsfc.nasa.gov) with standard NASA estimate algorithms. Color radiometry measurements of sea surface temperature (day DSST °C and night NSST °C), colored dissolved organic matter (CDOM), chlorophyll a (CHLA mg/m3), and particulate organic carbon (POC mg/m3) were used as the environmental criteria in this study. All criteria for the 9-year winter season (October 2003 to May 2012) and annual (January 2003 to December 2012) time frame were averaged and downloaded yielding a value of SST, CDOM, CHLA, and POC for the entire Red Sea and northwest Indian Ocean for winter and annual means. Due to seasonal cloud cover/shallow sea level, there was a lack of satellite data for summer months; thus, this time frame was excluded from the study. The data used for the environmental analysis were selected because they are the most robust available measurements of large-scale environmental criteria for the Red Sea. The raw data for the five environmental criteria assessed were averaged because it is thought that processes such as reproduction, dispersal, and ultimately gene flow happen over demographic to evolutionary time scales. Contour maps of the 9-year annual averages of CHLA and POC for the entire Red Sea and northwest Indiana Ocean were made in R using the raw data downloaded from MODIS-Aqua  nearest to the 36 sample site locations in this study (FST (FST / (1 − FST)) and environmental distance were compared to determine the degree of isolation by environment (IBE) of S. carteri populations. Pearson correlation coefficients were calculated for the linear relationship between environmental distance and genetic distance and significance tests were made (α = 0.05).These datasets were then mined for the values that corresponded to the point (4 kmis study . PairwisMultiple matrix regression with randomization (MMRR) was used to determine which model best explained the observed trend in genetic distance Wang . The MMRS. carteri multilocus genotypes were randomly distributed in space (within each transect) or not  within transects vs. among transects (within each site) and (2) within sites vs among sites (within each location) using a χ2 test of proportions conducted in R. We expected that if nonrandom mating or limited dispersal is occurring in a given location, the proportion of full-siblings within transects or within sites would be higher than the proportion of full-siblings among transects or among sites.Genetic structure at the scale of 50 m and at slightly larger scales (100–200 m depending on distance between 50-m transects) was also explored using the same belt transect samples in a hierarchical design. We used a likelihood-based approach implemented in KINGROUP v2  for which loci sc90 would not amplify. As the S. carteri microsatellite markers were developed on Red Sea samples, it is possible that a mutation in the primer-binding region of the more divergent Socotra samples is causing faulty amplification at this locus. The missing data of the Socotra samples did not affect the summary statistic analysis, as results were identical when analysis was made with and without loci sc90 and with and without the Socotra samples. In addition, of the 966 samples, 16 clonal pairs with identical multilocus genotypes were recovered , which was consistent at all sample site locations (TableFIS ranged from 0.1442 to 0.4322) that was significant in 31 of the 36 sites. The allelic diversity ranged from 1 to 10 alleles per locus for any given site. While some deviations from HWE were found, no loci showed significant deviations (using FDR correction) from HWE at all sites tested . When two regions were indicated , the variation among populations was 3% (P = 0.001), while the variation among regions was 6% (P = 0.001). When a three-region classification was used , the variation among populations was 2% (P = 0.001), while the variation among regions was 7% (P = 0.001). Likewise, when a four-region classification was used , the variation among populations was 2% (P = 0.001), while the variation among regions was 7% (P = 0.001). In addition, there were many significant pairwise population comparisons with FST values ranging from 0 to 0.284 and GST values ranging from −0.011 to 0.077  is low. When no regional classification of sites was used, the variation among populations was 4%  but significant  relationship between geographic distance and genetic distance ) and geographic distance (GGD) yielded several significant correlations (α = 0.05). Geographic distance was significantly correlated with all environmental criteria, yet correlations with CHLA, POC, and CDOM were weak, R2 = 0.065, 0.075, and 0.031, respectively. Genetic distance was also strongly correlated with several of the environmental measurements, and these correlations were consistent for the two time frames  studied , annual POC , winter CHLA , and winter POC   with genetic distance (Multiple matrix regression with randomization (MMRR) was used to determine whether ecological isolation (CHLA and POC) or geographic isolation is more likely contributing to the observed genetic structure in this study. The MMRR analysis showed that environmental distance (both CHLA and POC) is a stronger predictor of genetic distance than geographic distance Fig. and in tThe spatial autocorrelation analysis of the 16 belt transects taken at 8 sites revealed no significant genetic autocorrelation at the scale of 50 m Fig.; that isP > 0.05) between the proportion of full-siblings within transects vs. among transects at any of the three sampling locations  and Al-Lith  . Overall, these results indicate that while at Thuwal individuals seem to be distributed randomly regardless of the scale, at Yanbu and Al-Lith, more related individuals are found at slightly larger than 50 m scales (100–200 m within sites) than is to be expected by chance (10–45 km among sites).Overall, full-sibling pairs within transects, within sites, and among sites represented a small proportion of all pairwise comparisons. The proportion of full-sibling pairs ranged from 5.19% to 7.22% within transects, 4.72% to 6.24% among transects, 4.96% to 6.74% within sites, and 3.92% to 4.84% among sites . The reason for the difference in this exact location of this genetic break may be due to a difference in the biology of the two species , a question warranting further investigation. Regardless of the exact location of the boundary, it is notable that the southern Farasan Islands and potentially the Wassalyat Shoals of the Red Sea appear to host populations exhibiting this type of genetic break compared to the rest of the Red Sea. From this, we gather that there could be differential larval survival of S. carteri from the southern Red Sea. It is possible the nutrient poor waters found in the north and central Red Sea could present a boundary to larval survival for those individuals originating from the southern Red Sea . And while in the past local adaptation was thought to be limited in marine invertebrates and restricted to only those species with very short dispersal distances, it has now been found that local adaptation occurs in many marine invertebrates, including those with planktonic dispersal . Furthermore, in the northwest Indian Ocean, Socotra could represent another genetic cluster. The finding of strong genetic structure in the Farasan Islands is interesting in that it is similar to the results recently published in another study  are the consequence, to some extent, of sporadic self-fertilization events. While the presence of null alleles cannot be completely ruled out, self-fertilization or asexual reproduction is relatively common in sponge populations and occurs in all classes within phylum Porifera (see review by Uriz and Turon While we found no evidence for deviations from random mating at the scale of 50 m, the evidence for a greater proportion of full-siblings at scales of 100–200 m in This study supports the growing body of literature supporting the importance of the environmental landscape in shaping the genetic structure of marine invertebrate populations (see review by Sanford and Kelly


Hordeum vulgare subsp. spontaneum (K. Koch) Thell., a primary wild relative of barley, is an important source of genetic diversity for barley improvement and co-occurs with the domesticate within the center of origin. We studied the current distribution of genetic diversity and population structure in H. vulgare subsp. spontaneum in Jordan and investigated whether it is correlated with either spatial or climatic variation inferred from publically available climate layers commonly used in conservation and ecogeographical studies. The genetic structure of 32 populations collected in 2012 was analyzed with 37 SSRs. Three distinct genetic clusters were identified. Populations were characterized by admixture and high allelic richness, and genetic diversity was concentrated in the northern part of the study area. Genetic structure, spatial location and climate were not correlated. This may point out a limitation in using large scale climatic data layers to predict genetic diversity, especially as it is applied to regional genetic resources collections in H. vulgare subsp. spontaneum.Informed collecting, conservation, monitoring and utilization of genetic diversity requires knowledge of the distribution and structure of the variation occurring in a species.  Crop wild relatives (CWR) are vital for food security because they provide novel alleles for crop improvement and adaptation –3. Theirin situ conservation [Ecogeographical data has been used to identify areas and populations for ervation ,10,13, tervation  and to iervation ,16. Habiervation ,17–19. Het al. [Maxted et al.  have cauet al. –23. Of tHordeum vulgare subsp. spontaneum (K. Koch) Thell. (hereafter Spontaneum) is the progenitor of barley and represents an important genetic resource in barley breeding for traits such as powdery mildew, leaf scald or leaf rust resistance [Spontaneum donor accessions to induce genetic variation, was set up and tested to study regulation of flowering time in barley [Barley is the fourth most important cereal crop worldwide in terms of production, yield and area harvested, and is one of the crops in which CWR use in breeding programs is particularly prominent . Hordeumsistance –31, yielsistance , droughtsistance ,34 and asistance ,36. Recen barley . The Fern barley ,39. Othen barley , Tibet [n barley , Ethiopin barley ,43.Spontaneum between Jordan and neighboring countries. Baek et al. [Efforts have been made since the 1970s to characterize wild barley germplasm across its distribution range using morphological characters, isozymes and molecular markers –55. The k et al.  found thk et al. .Spontaneum. They have documented an association between genetic diversity, at single loci, and geography, acrosstemperature or rainfall gradients [et al. [et al. [Spontaneum in Israel and attempted to correlate genetic population structure—as opposed to polymorphism or allele frequencies at individual loci—with climate variables. No studies of the correlation between environment and population structure of Spontaneum in Jordan have yet been published.Past studies have investigated the correlation between genetic diversity and environment in radients ,52,58–61radients ,63. In Jradients  characteradients . The dis [et al.  used 18  [et al.  studied Spontaneum populations across their range in Jordan and analyzed this collection with a set of 37 SSRs. Our aim was to describe the patterns of genetic diversity and population structure of Jordanian Spontaneum and to determine the degree to which the genetic structure estimated with our markers is correlated with spatial and climatic variables derived from global data sources commonly used in conservation and ecogeographical studies [In this study, we sampled  studies ,67–69.Spontaneum populations during a barley collecting mission carried out in 2012, which covered the entire distribution of Spontaneum in Jordan. The collecting had been formalized in a letter of agreement between the Jordanian National Center for Agricultural Research and Extension (NCARE) and Bioversity International, which encompassed the permit to collect Spontaneum from all visited sites. The collecting was carried out with the continuous participation of NCARE staff and no rare or threatened species was collected. Seeds from each spike were germinated to produce leaf tissue for DNA extraction. Up to eight seeds per spike were rolled into germination paper and placed in an incubator at 25°C for germination. 50–100 mg of 3–5 day old leaf tissue was harvested from one germinated seed per spike. 32 populations , a global and freely available source for climate data layers generated through interpolation of average monthly climate data from weather stations [Geographical coordinates, altitude, slope, and aspect of the collecting sites were recorded with a GPS Garmin Emap device (datum: WGS84) and habitat type was recorded. Climate data was obtained from the WorldClim database version 1.4 (stations . Layers ® 96 Plant Kit. Thirty-seven EST-derived SSR primers were used for genotyping [DNA was purified from 3–5 day old leaf tissue with the Qiagen DNeasynotyping –73 (Tablhttp://dyerlab.bio.vcu.edu). Polymorphism information content (PIC) per locus was calculated with PICcalc [Summary statistics of the marker data such as number of alleles, sample adjusted allelic richness, and observed heterozygosity were calculated with GDA  and FSTA PICcalc .ST was used to measure differentiation between populations and was calculated with FSTAT. Inter-individual distances were calculated using a simple matching coefficient with DARwin software version 5.0.158 [Spontaneum is a highly selfing species, the program InStruct [ad hoc measure of change in likelihood between successive K values, ΔK [et al. [5 followed by 105 iterations.F 5.0.158  and usedInStruct  was usedInStruct  and can InStruct  and grapInStruct . The ad lues, ΔK  was calc [et al. , cluster [et al.  and Stru [et al. . STRUCTUSpontaneum with MaxEnt version 3.3.3k [https://www.genesys-pgr.org). Occurrences showing a geographical coordinate quality rank > 70 [We used a procedure developed by Newman and Rissler  to delinn 3.3.3k . Occurreank > 70  were inchttp://www.r-project.org/). Geographic distances were calculated as straight-line distances with the GeographicDistanceMatrixGenerator version 1.2.3 [ST/(1- FST) [5 permutations. To test isolation by environment (IBE), environmental distances between sites were estimated. A principal coordinate analysis (PCO) was performed using data from all Bioclim variables and altitude. Environmental distance was then approximated as the simple Euclidean distance between points on the first principal coordinate axis, which accounted for 49% of the environmental variation across sampling sites. The multivariate measure of environmental distance represented a conservative approach aiming to avoid overfitting, as many of the Bioclim parameters covaried significantly. Two-tailed Mantel tests were carried out to estimate IBE. As environmental and geographical distances were significantly correlated, IBD and IBE were also tested using a partial Mantel test. In addition, correlation of the distance matrix calculated with FPTEST to individual Bioclim variables was examined using appropriate Holm-Bonferroni correction [Correlations between allelic richness, InStruct clustering results and environmental data were tested using JMP 5.1 . Means were compared using the Tukey-Kramer HSD test. Pearson product-moment and Spearman's Rho rank correlation coefficients were calculated. Isolation by distance (IBD) was estimated using R (on 1.2.3  and log (1- FST)  and as p(1- FST) . Two-tairrection  to avoido) ranging between 0–0.025. Spontaneum is a highly self-pollinating species and previous studies on Spontaneum reported similar levels of heterozygosity [A total of 291 alleles were identified. Alleles per locus ranged from 3 to 22, with an average of 7.9. The mean number of alleles per locus averaged across sites was 2.8. PIC varied from 0.34 to 0.914 with a mean of 0.62. Allelic richness per locus varied from 2.2–9.1. All populations showed low observed heterozygosity  than those collected from ruderal areas or field margins . Also population size observed in reserves was significantly smaller . The average habitat suitability, according to the habitat suitability model, was significantly lower in reserves than in the other sites (p = 0.0289).Average values of geographical, geophysical and Bioclim variables, allelic richness, and selfing rates are compared among clusters in ST based distance: r = 0.3, p = 0.0003; FPTEST based distance: r = 0.2, p = 0.02), suggesting isolation by distance, when analyzed over all 32 populations, while the Mantel tests for isolation by environment were not significant. Environmental and geographic distance were strongly correlated , indicating possible confounding effects. These were accounted for using a partial Mantel test which confirmed significant IBD among all studied populations , but did not find significant IBE , with a distinct cluster (cluster 3) in the South. The study area was characterized by a longitudinal distribution of distinct habitat types as shown in Spontaneum prefers disturbed, human-made or influenced habitats [habitats ,22, symphabitats –98. ThesSpontaneum is a highly self-pollinating species. In self-pollinating species much genetic diversity is distributed among populations rather than within populations, population to population variation is greater than in out-crossing species and the genetic structure is more variable [variable . Given tvariable ,100,101.Spontaneum. Thus global climatic data would not be especially useful for predicting existing genetic diversity in Jordan. A ruderal habitat preference and high self-pollination might explain why the general expectation of tight correlation between genetic and ecogeographical diversity does not hold. If collecting and conservation actions are designed without previous knowledge of genetic structure, it will be important to consider species biology and habitat preferences when using ecogeographical diversity to predict genetic diversity.Global climate data such as the Bioclim layers provided by WorldClim climatic data are used in a range of studies and applications ,86,102, Spontaneum populations sampled here, irrespective of cluster assignment, contained many unique multi-locus genotypes. Only three populations showed a single multi-locus genotype twice, and no multi-locus genotype was repeated among populations. Allelic richness, which is a good metric to assess and monitor genetic diversity [et al. [et al. [Spontaneum attributed to sporadic outcrossing events [All iversity , increas [et al.  in Israe [et al.  observedg events  and geneSpontaneum in Jordan prescribes further collecting and monitoring in the northern part of the country, in particular the area occupied by cluster 1.Due to the reduced level of diversity expected within populations of highly selfing species, germplasm collections are often limited to a few samples per population. The heterogeneity found within populations in this study cautions against such sampling strategies. Modeling studies have shown that collections of highly selfing species need substantially more samples than are commonly recommended to capture existing diversity . The disSpontaneum have been reported to harbour large neutral genetic diversity, and also show considerable diversity in disease resistance and quantitative traits of agronomic importance [Spontaneum populations, the number of ex situ barley accessions from Jordan in global collections is lower than those from neighboring countries. Although in general the number of Spontaneum accessions in ex situ collections seems relatively high compared with other CWR samples in genebanks, they are derived from a limited number of populations [Spontaneum is widespread and locally common [et al. [Hordeum should be conserved and suggested the establishment of a network of several reserves in the Israel/Jordan region to more effectively conserve the genetic diversity of wild Hordeum. These assessments describe the obvious need to promote in situ conservation of Spontaneum in Jordan and to enlarge ex situ collections. Our description of the distribution of genetic diversity across the Jordanian landscape provides a tool to evaluate the propriety of existing in situ conservation activities and supports the application of proper sampling techniques for future ex situ acquisitions.Natural populations of portance ,106–108.ulations . Maxted ulations  suggest y common , individ [et al.  identifiS1 Resource(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Although behavioural problems  are known to be heritable both in early childhood and in adolescence, limited work has examined prediction across these ages, and none using a genetically informative sample.n > 3000) population (UK) sample of monozygotic and dizygotic twins.We examined, first, whether parental ratings of behavioural problems (indexed by the Strengths and Difficulties questionnaire) at ages 4, 7, 9, 12, and 16 years were stable across these ages. Second, we examined the extent to which stability reflected genetic or environmental effects through multivariate quantitative genetic analysis on data from a large  showed significant associations with the corresponding behavioural problem at all subsequent ages. Moreover, stable genetic influences were observed across ages, indicating that biological bases underlying behavioural problems in adolescence are underpinned by genetic influences expressed as early as age 4 years. However, genetic and environmental innovations were also observed at each age.These observations indicate that genetic factors are important for understanding stable individual differences in behavioural problems across childhood and adolescence, although novel genetic influences also facilitate change in such behaviours. ResearcSeveral measurement instruments exist to screen for such behaviours  on the conduct problems scale of the SDQ have been reported to be substantially heritable (35–77%)  in adolescence and adulthood can be predicted by measures in early childhood , which is an ongoing longitudinal study following monozygotic (MZ) and dizygotic (DZ) twins born in England and Wales between 1994 and 1996  for conduct  and for peer problems (ages 4 and 7), although in line with previously reported values  are informative as a guiding heuristic to estimate relative magnitudes of genetic and environmental effects  and non-shared-environment factors (E). This design can also be viewed as allowing the genetic and environmental influences explaining SDQ measures at age 4 to also explain SDQ measures at the later ages, while also leaving specific genetic and environmental paths available to explain SDQ variation at the later ages that does not covary with SDQ measures at age 4.The Cholesky decomposition specifies as many factors as there are variables for each source of variance, with each subsequent factor having one fewer pathway than the preceding factor see . In otheet al.The common pathway model is a more restricted model, constraining all genetic and environmental variance though a single pathway  and so we retained the Cholesky as our final model for each of the SDQ measures see  and 4. SWhile levels of innovation were broadly mirrored for non-shared-environment influences, the stability of non-shared-environment influences was considerably more modest for each measure. Finally, shared-environment effects were significant, albeit modest in magnitude, for anxiety, conduct, and prosociality, at age 4. These effects were broadly stable over subsequent ages. Some evidence for novel shared-environment effects was also observed – namely, anxiety at age 7 and conduct at ages 7 and 9 – although these effects were typically modest.v. early adolescence  indicates that aspects of antisociality , as well as broader elements of behavioural problems, are genetically stable from as early as age 4, in line with longitudinal work examining such phenotypes from later in childhood  were predicted by the corresponding SDQ score at age 4. These phenotypic overlaps were mostly accounted for by genetic factors, although evidence for modest shared environmental stability was observed (with the exception of hyperactivity). While research has suggested that genetic influences on antisociality shows distinct genetic underpinnings in early childhood et al.et al.et al.et al.v. later emergent genetic bases of behavioural problems. Given that brain regions governing high-level cognitive control, such as the dorsolateral prefrontal cortex, appear to mature comparatively late in adolescence (Gogtay et al.These findings converge with related work stressing that behavioural measures taken in early childhood predict later life psychopathology (Caspi et al.et al.et al.Specific recommendations for future research are warranted. First, from a methodological standpoint, the degree of genetic innovation observed across the ages suggests that gene discovery should either focus on adult individuals, where genetic innovation is less evident (Johnson In summary, here we report for the first time evidence that genetic influences on behavioural problems in early childhood are stable across childhood and into adolescence. Of importance, we also observed novel genetic influences at each age, with non-shared-environment effects also providing additional novel sources of variance across this important developmental phase. These findings support models of behavioural problems that posit early emerging and enduring individual differences in biology reflect such phenotypes, although also suggesting that the genetic influences underlying behavioural problems unfold across childhood and adolescence.


Leopardus guttulus and L. geoffroyi was prompted after genetic studies identified the occurrence of extensive hybridization between them at their geographic contact zone in southern Brazil. This is a region where two biomes intersect, each of which is associated with one of the hybridizing species (Atlantic Forest with L. guttulus and Pampas with L. geoffroyi). In this study, we conducted in-depth analyses of multiple molecular markers aiming to characterize the magnitude and spatial structure of this hybrid zone. We also performed a morphological assessment of these species, aiming to test their phenotypic differentiation at the contact zone, as well as the correlation between morphological features and the admixture status of the individuals. We found strong evidence for extensive and complex hybridization, with at least 40% of the individuals sampled in Rio Grande do Sul state (southernmost Brazil) identified as hybrids resulting from post-F1 generations. Despite such a high level of hybridization, samples collected in this state still comprised two recognizable clusters . Genetically pure individuals were sampled mainly in regions farther from the contact zone, while hybrids concentrated in a central region (exactly at the interface between the two biomes). The morphological data set also revealed a strong spatial structure, which was correlated with the molecular results but displayed an even more marked separation between the clusters. Hybrids often did not present intermediate body sizes and could not be clearly distinguished morphologically from the parental forms. This observation suggests that some selective pressure may be acting on the hybrids, limiting their dispersal away from the hybrid zone and perhaps favoring genomic combinations that maintain adaptive phenotypic features of one or the other parental species.Increased attention towards the Neotropical cats  Hybridization between species is currently considered to be a natural process that often plays an important role in the evolution of various organisms An initial and important issue in the investigation of a hybrid zone is the accurate identification of hybrids and parental types. The detection of hybrid individuals relied upon morphological characteristics until the mid-1960s. However, the use of morphological features alone to distinguish between pure and admixed individuals is often inappropriate, because not all morphological variation has a genetic basis, and most of the phenotypic characters have multifactorial determinants L. geoffroyi and L. guttulus  in this admixed population, as well to assess the magnitude of hybridization at a local level.Hybridization between two endangered Neotropical small cats, L. guttulus has a more gracile appearance, with a mean total length of 78.5 cm and mean weight of 2.37 kg, while L. geoffroyi is larger and more robust, with a total length and weight varying from 69 to 125 cm and from 2.2 to 7.8 kg, respectively L. guttulus pelage has a yellowish/ochre background with mostly open rosettes, while L. geoffroyi presents a gray/yellowish background pelage color with solid black spots instead of rosettes. In spite of these usual standards for identification, some animals with atypical pelage color, which appeared to be intermediate between the two species, have been documented in RS state since the early 1990s Although previous studies mentioned the existence of some individuals with ambiguous phenotypic characteristics, the great majority of the analyzed animals could be identified to species level on the basis of their morphology. The distinction between the two species was normally based on body size and pelage patterns L. guttulus and L. geoffroyi populations, it is critical to determine the extent and nature of the admixture events occurring between these cats. The genetic study reported in L. guttulus and L. geoffroyi still be recognized as genetically distinct units in this region despite the observed hybridization? and 5) What is the spatial distribution of hybrids in RS state? In addition to these genetic assessments, we also performed morphometric analyses with a subset of the individuals from the contact zone, aiming to address another set of questions: 6) Can the two species still be confidently separated into distinct morphological groups even in the face of possible extensive hybridization? 7) Is the pattern of morphological variation in this area correlated with the genetic evidence of admixture? and finally 8) Is there evidence of selective pressures acting on this hybrid zone?To better understand the role that hybridization plays in i.e. it was accomplished over several years via sporadic collection as well as donation of specimens by collaborating research groups. For all the sampled captive animals , all participating zoos provided consent for use of these individuals in the studies. These samples were collected by the zoo staff following their approved procedures . Animals listed as ‘caught by farmers’ comprise individuals that were captured or killed in agricultural areas due to livestock predation conflicts with local landowners, and subsequently confiscated by environmental law enforcement agencies. All such animals included in our study were directly donated to our research group by the responsible environmental agency  in charge of the case. Animals that were already dead  were donated to research . Live confiscated animals were released into the wild by the government agency in charge of the case after collection of blood samples that were donated to our research group. The focal species are considered threatened taxa, but such listing is not an impediment to research. On the contrary, their status as threatened makes them a priority for research, so that Brazilian wildlife authorities support studies on their biology, ecology and genetics that provide critical information for conservation planning on their behalf.All biological samples used in this study were previously reported in ref. L. guttulus and 74 L. geoffroyi individuals (L. guttulus and 49 L. geoffroyi were from Rio Grande do Sul state (RS), in southernmost Brazil (L. guttulus and 25 L. geoffroyi) originated from areas farther away from their contact zone (where only one of the two species occurs), and were used here as control/parental populations , two Y-linked introns (ZFY and SMCY3) and 10 autosomal microsatellite loci . These analyses comprised only features related to body size, because pelage features were found to be highly variable and thus difficult to quantify and to obtain reproducible categorization in replicated assessments. Therefore, we chose not to perform statistical analyses on them, although our qualitative observations do support the presence of intermediate pelage features in the hybridizing populations of RS.L. guttulus and 25 L. geoffroyi sampled from areas outside RS state, by employing the Bayesian analysis implemented in STRUCTURE 2.3 i.e. species-level) classification. We performed 1,000,000 Markov Chain Monte Carlo (MCMC) iterations after a burn-in period of 500,000 steps. Five independent runs, each of K = 1–5, were conducted and averaged using CLUMPP 1.1.2 We initially analyzed the microsatellite data from the control/parental populations, comprising 49 i.e. ignoring any morphology-based species identification. This sample was tested for deviations from Hardy-Weinberg Equilibrium (HWE) and linkage equilibrium (LE) using the software packages ARLEQUIN and GENEPOP 3.0 i.e. whether discrete clusters could be identified. To visualize patterns of genetic differentiation between the parental populations and with respect to RS specimens, based on the same set of markers, we performed a Factorial Correspondence Analysis (FCA) using GENETIX 4.05 Samples collected in RS state (n = 94) were initially evaluated for microsatellite loci as a single population, L. guttulus, parental L. geoffroyi, RS L. geoffroyi and RS L. guttulus. HWE and LE were also tested for these populations, and the genetic differentiation with both microsatellite and sequence-based data was assessed with an Analysis of Molecular Variance  for the sequence-based data sets. The statistical significance of the observed values was tested using 10,000 permutations. The presence of hybridization in RS was also tested with an allele-based estimate of recent gene flow between the two species using BAYESASS We also performed some analyses considering the sample obtained in RS in two distinct populations according the morphological identification. In this sample arrangement, we consider the existence of four populations: parental ce AMOVA  implemenq, following the same conditions described above, with the objective of distinguishing between pure and hybrid individuals. NEWHYBRIDS was used to compute the posterior probability (Q) that an individual in the sample belonged to each of six genotypic classes: pure I, pure II, F1, F2 (F1×F1), backcross to I and backcross to II. This analysis was performed using the genotypic classes and allele frequency assumptions described in Admixed individuals in the RS sample were initially detected with the microsatellite data set using two different Bayesian clustering methods, implemented in the packages STRUCTURE and NEWHYBRIDS 1.1 q and Q values for each genetic category, and to assess the power of our set of microsatellite loci to distinguish between parental and different hybrid categories, the program HYBRIDLAB 1.0 To determine the range of expected After the microsatellite-based analysis, each of the RS specimens was examined for its sequence-based markers . The presence of any incongruence between the phenotype-based identification and at least one of the three segments was considered to be evidence of potential hybrid origin. This sequence-based hybrid identification was then compared to and integrated with the microsatellite-based hybrid identification, with the aim of assessing the number and proportion of hybrids, and consequently, the extent of hybridization in the RS population.L. guttulus, pure L. geoffroyi and hybrids.After these genetic analyses, we evaluated the spatial distribution of all the individuals identified as pure or hybrid. The genetic status of each sample was combined with its geographic location, allowing each individual to be plotted on a map to assess the spatial distribution of pure L. guttulus and 23 as L. geoffroyi) that were suitable for morphometric analysis (i.e. with minimum damage and thus permitting the precise collection of the predefined measurements)  . All theL. guttulus, and 12 L. geoffroyi) were independently measured by two different researchers. Student's test (t-test) for paired samples was used to assess the existence of significant differences between these paired measures for each species, independently. Only two measurements (ear length and hind toe width) differed significantly between the two independent researchers for both species (p<0.05) and were thus excluded from further analysis, since these data were considered less reliable.Some individuals of our sample (13 t-test) to verify the existence of significant differences between the identified groups. We also performed a Multivariate Analysis of Variance (MANOVA), with a subsequent LSD (Least Significant Difference) post hoc test to assess significant differences among groups defined by sex and morphology-driven species identification. Finally, to evaluate the classification of each individual defined as pure or hybrid according to the genetic data into different morphological groups, we conducted a Discriminant Function Analysis (DFA) with SPSS.After this preliminary test, only the measurements taken by one of the researchers were analyzed subsequently. To investigate the structure of morphological diversity in our sample we performed a Principal Component Analysis (PCA) over the variance-covariance matrix, using the software SPSS . All measurements were standardized by the mean and standard deviation before being subjected to the PCA. For this analysis we chose not to include the phenotype-based identification of each individual, aiming to test whether distinct morphological clusters existed, and if so, whether they correlated with the genetic identification of pure and hybrid individuals. We then employed a univariate analysis  of each sample from a line drawn through the central region of RS, lying on parallel 30°S on its eastern side and shifting towards the north on the western end. This geographic limit was defined based on the study of Eizirik et al. L. geoffroyi and Atlantic Forest with L. guttulus). Samples collected north (or east) of this line (i.e. towards the Atlantic Forest side) received positive values, while those collected south or west (i.e. towards the Pampas), received negative values. For each sample, we used the minimum distance (in km) measured in a straight line perpendicular to this central line dividing the two biomes. The PCA scores and the coefficient of membership q were standardized to a scale ranging from −2 to +2, while the geographic distances from the central line were log-transformed.To construct graphs depicting such relationships, as well as to perform correlation analyses, we used the first principal component (PCI) obtained in the PCA as a measure of morphological variation, and the individual coefficient of membership L. guttulus and L. geoffroyi, with a maximum ΔK observed for two populations. Within this sample, 34 L. guttulus and 20 L. geoffroyi had at least 0.95 assignment of their composite genotype to their sampling population, and were thus included in the respective parental/control group, and representing the purest set of individuals for each species. However, two putatively pure L. guttulus presented a signal of introgression in their mtDNA data, and were thus excluded from the control population. Therefore, the final parental data set included 32 L. guttulus individuals. Private alleles were detected in both parental populations, totaling 13 in L. guttulus and 35 in L. geoffroyi. However, when we excluded low-frequency alleles (frequency <0.05), this number declined to eight in the former species and 14 in the latter.The first analysis performed with STRUCTURE to verify the genetic composition of the parental/control populations based on the microsatellite data revealed a significant genetic differentiation between ST, RSTF and ФST for all molecular markers , we observed a reduced number of locus combinations deviating from LE , but four loci with deviations from HWE. When we assumed the RS sample to comprise two populations (based on the phenotypic identification), these numbers declined to only one and four combinations deviating from LE in L. guttulus and L. geoffroyi, respectively, and only two loci with deviations from HWE for each population. The existence of substantial deviations from LE and HWE in the RS sample (when treated as a single unit) was reflected in the STRUCTURE analysis, which yielded a maximum ΔK for two populations when this set of individuals was assessed.In contrast, when we included the RS samples in the FCA, we observed a clear reduction in this genetic distinction between the two groups. When we considered the e center . When weca. 50% lower than those obtained between parental populations  for L. guttulus, and 2q of 0.928 (0.543–0.974) for L. geoffroyi . However, there were some discrepancies in which one individual was identified as pure with one analysis (q>0.8/0.9 or Q>0.6) and as hybrid with the other one. In those situations, we only considered conclusively those individuals to be hybrids where there was additional evidence of admixture in the sequence-based markers.The analyses performed with the real data set identified 29–43 likely hybrids in RS state with STRUCTURE (depending on the threshold used: 0.8 or 0.9) and 42 with NEWHYBRIDS (using a threshold of 0.6) . In mosti.e. presenting q>0.8/0.9 for STRUCTURE and Q>0.6 for NEWHYBRIDS), showed a signal of genetic introgression in at least one of the sequence-based markers , and may be considered a result of later Fn or backcross generations (L. geoffroyi (bLge13 and bLge72) showed very low values of association to their original phenotype-based population in the microsatellite analyses, and simultaneously presented all haplotypes assigned to L. guttulus.For both phenotype-based RS populations, some individuals identified as pure with all microsatellite analyses  of association to the F2 category, reaching 75% of the hybrids in the RS phenotype-based L. guttulus population, and 66.67% in the RS L. geoffroyi population.Evaluating all the data sets jointly (microsatellite loci and sequence-based markers), and considering the criterion described above with a threshold of 0.8 for STRUCTURE results, we could observe a large number of complex genetic combinations, with 23  species . Interesi.e. Pampas in the south and west and Atlantic Forest in the north-east . Individuals identified as pure predominated outside this central region, with L. guttulus specimens dominating the northern portion of RS (strongly associated with the Atlantic Forest biome) and L. geoffroyi dominating the south and west, in strong association with the Pampas . We found that 17 of the 23 measurements evaluated independently showed significant differences between the sexes, indicating the presence of marked sexual dimorphism in L. geoffroyi, with males being significantly larger than females. Unfortunately, it was not possible to perform the same analysis for L. guttulus due to the small number of females from this species with no evidence of hybridization (n = 2). Nevertheless, we tested the existence of significant differences in body size between samples of L. geoffroyi  and L. guttulus (both sexes pooled) through a MANOVA. The MANOVA indicated a significant difference among the three groups , with the LSD post hoc test showing significant differences for the majority of the body measurements between male and female L. geoffroyi, and between male L. geoffroyi and L. guttulus, but not between female L. geoffroyi and L. guttulus.However, a closer inspection of the L. geoffroyi and the size overlap between female L. geoffroyi and L. guttulus, we decided to conduct the subsequent set of analyses only with the male samples of each species. We initially performed a PCA including only male specimens inferred to be pure (i.e. not admixed) with the molecular data. This analysis revealed two clearly distinct groups, with significant differences between them along PCI . L. guttulus individuals formed a group in the lower area of the plot, and L. geoffroyi in the upper area, clearly due to a larger size in the latter species . These analyses indicated that most hybrid individuals could be assigned to one or the other morphological population, even in face of the extensive introgression affecting this system.We then included the male specimens identified as hybrids with the molecular data, and repeated the same PCA. The resulting diagram revealed that the majority of the  the PCA . The twoL. guttulus and L. geoffroyi individuals. For the genetic data, hybrids generally exhibited intermediate q values, with exceptions being related to our criterion of hybrid identification   . Integrahybrids) . In facthybrids) , possiblL. guttulus were mainly found in the northern portion of RS state, while L. geoffroyi-like individuals were mainly in the south, resulting in high and significant (p<0.001) correlation values for both types of data. The correlation with distance from the central zone was higher for the morphological data (r = −0.843) than for the molecular markers (r = −0.718), showing a clear tendency of body size increase towards the southern end of the state  in distinguishing pure from hybrid individuals under either a 0.9 or a 0.8 threshold. The use of a more conservative threshold (0.8) led to an underestimation of hybrids in the population due to the larger overlap of values found for pure animals and backcrosses. Such a problem has also been reported in other studies that used simulated genotypes in hybrid analyses L. guttulus and L. geoffroyi individuals with q-values even higher than 0.9 (thus being considered pure) presenting some sequence-based evidence of introgression, implying a hybrid ancestry. In the great majority of hybrid studies, the most complex task is considered to be the identification of backcrossed individuals, especially in the case of later-generation backcrosses FST  = 0.21.The difficulty in distinguishing purebred from backcrosses on the basis of microsatellite data alone was clearly seen in both populations, with The assignment of each individual to different hybrid categories using NEWHYBRIDS was largely inconclusive with the simulated data. Therefore, this software was only used in the final analysis as a complement to STRUCTURE in the task of distinguishing pure from hybrid individuals, and for such a purpose it performed well, largely agreeing with the STRUCTURE results. In addition, the inclusion of DNA sequence data from the mtDNA, X-linked and Y-linked segments was important in improving the sensitivity of our hybrid categorization. When we evaluated the entire suite of genetic markers, combining species-specific sequences with microsatellite assignments, we perceived that the identified hybrids were simultaneously compatible with an F2 or backcross status, without a single clear case for a F1 individual. This dominance of post-F1 hybrids with the use of only 10 microsatellite loci without fully diagnostic alleles for each species may underlie the failure of NEWHYBRIDS to distinguish among the different hybrid categories L. guttulus and L. geoffroyi are viable and at least partially fertile. The fertility of hybrids could be directly documented by the example of one road-killed and pregnant female (bLgut79) identified morphologically as L. guttulus and recognized as a hybrid with our molecular data. On the other hand, even the combination of sequences and microsatellite assignments did not allow us to clearly identify the predominant post-F1 category occurring in this system. Therefore, the molecular tools used in this study were still insufficient to accurately determine whether the main crosses occurring in this hybrid zone involve mainly backcrosses to the parental species or mating among F1 hybrids.These findings indicate that hybrids generated from initial crosses between L. guttulus and L. geoffroyi in this region, relative to allopatric populations of the same species. These results indicate that this case comprises one of the most extensive ongoing hybridization process documented for carnivores, being equivalent only to wolves and coyotes in North America The difficulty in distinguishing different hybrid categories in this system might also be associated with the high rate of hybridization we detected. According to the criteria established here, hybrids may account for at least 40% of our total RS sample, which explains our observation of higher genetic similarity between L. guttulus and L. geoffroyi from areas outside RS are still different genetically, supporting their current maintenance as two distinct taxonomic entities. On the other hand, their populations in RS were much more similar and, despite significant differences detected by the fixation indices and STRUCTURE analysis, individuals belonging to the two species did not form completely separated groups. This was especially the case in the central region of RS, where the proportion of admixed individuals was significantly higher than in the northern and southern areas of the state. These results highlight how the extensive introgressive hybridization process identified in this central area is promoting a substantial admixture of these two cat species at their geographic contact zone, at least with respect to neutral markers.Finally, our genetic results indicate that L. guttulus and L. geoffroyi in RS state, with the predominance of post-F1 hybrids, might indicate the absence of selection and post-zygotic barriers against the hybrids, or at least that they are limited. However, the incorporation of the morphometric analyses and the spatial assessment of genetic and morphological variation suggested the existence of some level of selection acting against hybrids, preventing the complete genetic and morphological homogenization of the two species in the regions surrounding their contact zone.The extensive rate of hybridization detected between L. geoffroyi presenting higher PCA scores, reflecting the overall larger size of this species relative to L. guttulus. We thus conclude that the apparent homogeneity observed in the first analysis was likely a consequence of the body size overlap between L. guttulus and female L. geoffroyi, more than of the hybridization process. The observed morphological segregation between the two species in RS was exactly what one would expect based on literature sources reporting their body size range in areas outside this geographic contact zone Although the first PCA results suggested an apparent lack of differentiated clusters within the RS sample, we observed that previously ignored sexual dimorphism in these species was obliterating a clear-cut pattern. Subsequent analyses conducted only with male individuals clearly indicated the occurrence of morphological heterogeneity in this region, with individuals identified as genetically pure L. guttulus individuals tended to have a smaller body size and pure L. geoffroyi a larger size, genetically intermediate individuals (identified as hybrids) did not necessarily show an intermediate size. In fact, the majority of male individuals identified as hybrids did not show intermediate scores in the PCA, and were clearly allocated by the DFA in their respective phenotypic population, in agreement with the a priori species assignments.In addition, we found that the morphological variation was significantly correlated with genetic variation and with the spatial distribution of samples, although different levels of correlation were detected see . The corca. 93% of the individuals (including hybrids) in their a priori phenotypic group is in stark contrast to the level of admixture estimated with the molecular data. The only two misclassified individuals (bLge46 and bLge32) did have intermediate q values , suggesting that in this case a severely admixed genome did lead to morphological misclassification. However, all other hybrid males were strongly allocated to one or the other morphological group . Interestingly, in most cases this allocation did not match their highest q value. For the four hybrids that were morphologically allocated in L. geoffroyi, the q value assigning them to this group were 0.265, 0.483, 0.773 and 0.932, while for those morphologically assigned to L. guttulus, the q values relative to this group were even lower (0.078 and 0.191). Such discordance is not unexpected in such a complex hybrid zone, but the strength of morphological allocation to one or the other group indicates that these clusters are better defined in this area than their molecular counterparts (see below).The observation that the DFA classified vs. pure individuals on the basis of morphology alone. Identification of hybrids in such extensive hybrid zones using only morphological features can be challenging because introgression is often not reflected on morphology L. geoffroyi that presented all molecular data associated with L. guttulus, but clearly showed PCA scores very similar to those obtained for genetically pure L. geoffroyi individuals  strongly predominating in the region north-east of the mid-line drawn in L. geoffroyi-like) predominating south or west of this line. Even genetically identified hybrids seem to maintain the general body size associated with each geographic region, so that the correlation between spatial location and the morphological PCI is very similar with or without the inclusion of hybrids (see Results). These findings indicate that the general body size of these cats in RS may not be strongly influenced by their ongoing hybridization. Taking into account the different phytophysiognomies occurring in the north and southern parts of the state, is reasonable that different morphologies are associated with different landscape attributes favoring the smaller forms in the rugged and forested northern regions, and the larger forms in the more open regions (with less mountainous terrain) of southern RS.Body size in this system seems to be strongly correlated with the spatial distribution of samples, with smaller individuals , where different genetic combinations may be favored in different environments L. geoffroyi was higher than those resembling L. guttulus. In addition, the number of hybrids identified outside the limits of the central area defined here was larger in southern RS than towards the north. These findings indicate that genomic introgression into this population may be higher than in the opposite direction. On the other hand, it is also possible that genomic processes involving dominance and/or epistasis at morphology-related loci lead hybrids to more often resemble L. geoffroyi than L. guttulus, which might also influence their relative success and the direction of their spread.The spatial assessment of the genetic data also revealed a pattern suggesting that the magnitude of introgression between these taxa may be asymmetric. For all of the investigated markers and analyses performed, the number of hybrid individuals morphologically resembling versus the other L. guttulus and L. geoffroyi in the wild, preliminary field data on these species indicate that the latter seems to be quite common in the vicinities of their geographic contact zone L. geoffroyi-like individuals, possibly exhibiting a lower reduction in viability and fertility.Asymmetric introgression seems to be a common pattern in carnivore hybrid zones L. guttulus and L. geoffroyi in RS state is quite high, with our sample being composed of a large proportion of post-F1 hybrids. In spite of the high rates of introgression, which should lead to rapid homogenization between the two cat species around their geographical contact zone, our morphological and spatial evaluations indicate that they remain significantly distinct. In this context, we consider it likely that different selective pressures (possibly related to body size) play a role in maintaining this morphological distinctiveness, as well as in restricting the geographic breadth of the hybrid zone to the vicinities of the contact region between the two species. Investigating this hypothesis with integrated genomic and ecological approaches should shed light onto the historical and current processes influencing the dynamics of this hybrid zone, and open up new avenues for future research focusing on this complex system.In conclusion, this study demonstrated that the rate of hybridization and introgression between Figure S1Species specific-haplotypes described by ref.  (A) mtDNA ND5 gene, (B) X-linked introns of genes PLP1 and BTK, (C) Y-linked introns of genes ZFY and SMCY3. Each unique haplotype is represented by a circle whose size is proportional to its frequency. Colors indicate the frequency of the haplotype in each population group: dark grey for Leopardus guttulus, light grey for Leopardus geoffroyi, and white for L. colocolo. Each haplotype nomination was the same used in L. tigrinus from Northeastern Brazil.(TIF)Click here for additional data file.Table S1Samples analyzed in the present study. The parental populations of both species include only the individuals used in the genotypes simulation.(DOCX)Click here for additional data file.Table S2Haplotypes used in this study from reference (DOCX)Click here for additional data file.


Gasterosteus aculeatus by combining the effects of elevated temperature and infection with a bacterial strain of Vibrio sp. using a common garden experiment.Pathogens are a major regulatory force for host populations, especially under stressful conditions. Elevated temperatures may enhance the development of pathogens, increase the number of transmission stages, and can negatively influence host susceptibility depending on host thermal tolerance. As a net result, this can lead to a higher prevalence of epidemics during summer months. These conditions also apply to marine ecosystems, where possible ecological impacts and the population-specific potential for evolutionary responses to changing environments and increasing disease prevalence are, however, less known. Therefore, we investigated the influence of thermal stress on the evolutionary trajectories of disease resistance in three marine populations of three-spined sticklebacks ST) between populations by releasing cryptic within-population variation. While life history traits displayed positive genetic correlations across environments with relatively weak genotype by environment interactions (GxE), environmental stress led to negative genetic correlations across environments in pathogen resistance. This reversal of genetic effects governing resistance is probably attributable to changing environment-dependent virulence mechanisms of the pathogen interacting differently with host genotypes, i.e. GPathogenxGHostxE or (GPathogenxE)x(GHostxE) interactions, rather than to pure host genetic effects, i.e. GHostxE interactions.We found that thermal stress had an impact on fish weight and especially on survival after infection after only short periods of thermal acclimation. Environmental stress reduced genetic differentiation (QVibrio sp. in both thermal environments. The negative genetic correlation of pathogen resistance between thermal environments, on the other hand, indicates that adaptation to current conditions can be a weak predictor for performance in changing environments. The observed feedback on selective gradients exerted on life history traits may exacerbate this effect, as it can also modify the response to selection for other vital components of fitness.To cope with climatic changes and the associated increase in pathogen virulence, host species require wide thermal tolerances and pathogen-resistant genotypes. The higher resistance we found for some families at elevated temperatures showed that there is evolutionary potential for resistance to  Pathogens are strong selective agents that can influence biodiversity by regulating host population dynamics ]. En]. En2]].Rising temperatures are also likely to stress the host organism, thereby increasing host susceptibility to diseases ]. Th]. Th9]].The ecological and evolutionary consequences of thermal stress-disease interactions will strongly depend on the thermal tolerance and rate of adaptation of populations to climate warming . A highGasterosteus aculeatus and challenged the offspring with an infection by a bacterial strain of Vibrio sp. in two different temperature environments (ambient and stressful). In general, bacteria of the genus Vibrio include many facultative symbionts and pathogenic strains ]. M]. MGastestrains , makingivalves ], b], bGasteivalves ], w], wGasteivalves .The three-spined stickleback represents an ideal model system to study the evolutionary ecology of host-parasite interactions [-30]] es] es30]] With this study, we estimated the effects of thermal stress on pathogen resistance  and its interactions with other life history traits that can affect an individual’s survival and reproductive potential. In a narrower sense, life history traits include gestation time, age to sexual maturity, reproductive life span and number of progeny. In a wider sense, morphological traits such as length and weight can also be seen as life history traits representing key maturational and reproductive characteristics, and we thus chose to also measure size and weight.Short-term fluctuations and extreme events like heat waves that are predicted to increase in the nearer future  could hDuring April and June 2011, adult marine sticklebacks were caught by dip netting in an oyster farm in Yerseke  and in tidal channels on the islands of Texel  and Sylt . Between 50–70 individuals were sampled from each location and transferred in groups of 5–7 fish to 20 l aquaria (38 × 25 × 20 cm) equipped with permanent seawater flow-through at a constant temperature of 17°C. Between July and August, laboratory-bred F1 families were produced for each population (20 families per population) using a modified North Carolina II breeding design . Since not all of the crosses resulted in successful hatchings, we produced additional families (n = 10 and using different parents) to increase sample size and achieve better estimates of within-population variation for the Sylt population. Eggs were kept in aerated 1 l glass beakers until hatching. Hatchlings were then transferred to 10 l flow through tanks  with a water temperature of 17°C and a 16 h: 8 h light: dark cycle. In total, 14 families from Oosterschelde, 11 families from Texel and 24 families from Sylt hatched.ad libitum with live Artemia sp. until 12 weeks of age and afterwards with frozen chironomid larvae until the end of the experiment. During the early rearing phase, we lost several families due to electrical failure of aeration pumps. These affected only newly hatched families from all populations and did not lead to a systematic bias in mortality . In the end, nine families remained from the Oosterschelde population (nfish = 164), nine families from Texel (nfish = 192) and 13 families from Sylt (nfish = 263) that had sufficient family sizes (20 fish per family) for the infection experiment.One month after hatching, the density of each family was reduced to 20 fish per 10 l aquaria. Fish were fed daily Vibrio sp. closely related to a pathogenic strain of Vibrio tubiashii  or with the same amount of phosphate buffered saline (PBS) medium as control . Infected and control fish were then held in ambient or elevated water temperature without flow through and susceptibility was determined by monitoring induced mortality every 12 hours for 10 days. Dead fish as well as fish remaining at the end of the experiment were weighed (wet mass to the nearest 0.01 g) and measured (standard length to the nearest 0.1 cm). All experiments were conducted in accordance with German animal welfare laws (Permission No. V312-72241.123-16).Eight months after hatching, families were separated into smaller groups of 10 fish per 2,6 l aquarium . In line with recent climate change predictions for the North Sea , half o2, 0.1 μl 500 U GoTaq DNA Polymerase , 5 pmol of each fluorescently labelled forward and unlabelled reverse primer and 2 μl DNA template. Thermal cycling for PCR group 1  was genotyped at 15 microsatellite loci. DNA was extracted from the caudal fin using the DNAeasy Blood and Tissue Kit . The 20 μl multiplex PCR reactions consisted of 4 μl of 5x PCR Flexi buffer, 2 μl 10 mM dNTP, 0.6 μl 25 mM MgCl33 NED, ) starte33 NED, ) starteST) was calculated after Weir and Cockerham ), ST = to 0.19 .ST) exceeded the overall neutral expectation , and selection differentials for body length increased in two out of three cases (Oosterschelde and Texel), whereas selection differentials for weight decreased uniformly in all populations.Pathogen-induced mortality also exerted correlated selection on life history traits. At ambient temperature, surviving fish were significantly longer , which led to reduced genetic differentiation (QST) between populations. While life history traits showed positive genetic correlations between temperatures and only weak, non-significant GxE interactions, thermal stress led to negative genetic correlations across environments and strong GxE interactions in pathogen resistance. These trait-specific patterns indicate that evolutionary potential of life history traits (with positive genetic correlations across environments) can be reliably predicted from levels of standing genetic variation. For survival from infection, on the other hand, negative genetic correlations across environments indicate that current genetic variation in resistance may be a weak predictor for future resistance evolution, highlighting the crucial importance of disease in adaptation to new environmental conditions potentially favouring pathogens and parasites.In this study, we tried to connect the effects of elevated temperature on life history traits, pathogen resistance, and population-specific potential for evolutionary responses in marine sticklebacks. After only a short acclimation period at elevated temperature, we found impacts of thermal stress on fish weight and especially survival after infection. Furthermore, thermal stress tended to release cryptic within-population genetic variation . Therefore, weight differences more likely represent effects of our treatment than pure chance. Additionally, we have previously shown that the efficiency of mitochondrial energy metabolism is reduced at 21°C ]. T]. T60]].at 21°C , leadinVibrio tubiashii is reported to be at 25°C ], a], aST -  traits . We obsThe maximal rate of adaptation is of crucial importance for estimating the ecological and evolutionary consequences of climatic warming. Populations need to shift the distribution of phenotypes to maintain optimal fitness in the changed environment . Fast aOur study shows that thermal stress can negatively impact life history traits and pathogen resistance of marine three-spined sticklebacks. Moreover, environmental stress led to negative genetic correlations across environments for pathogen resistance. Although we found evolutionary potential in both thermal environments, it is hard to predict the capacity for adaptation by selection of pathogen resistant genotypes from ambient conditions. While positive genetic correlations of life history traits between environments indicate similar responses to selection, the altered selective gradient imposed by infection shows that responses of vital fitness components can change unpredictably in changing environments.http://doi.org/10.1594/PANGAEA.833937 [[The data sets supporting the results of this article are available in the Pangaea repository .833937 .The authors declare that they have no competing interests.FSM wrote the manuscript, conducted the fish breeding as well as the experiment, dissected the fish, and was involved in designing the project, genotyping and performing statistical analyses. LNSS was involved in designing the project, running pilot studies and writing the manuscript. KMW was involved in designing the project, genotyping, performing statistical analyses and writing the manuscript. All authors read and approved the final manuscript.