Dipturus batis) has a patchy distribution across the North‐East Atlantic Ocean, largely restricted to occidental seas around the British Isles following fisheries‐induced population declines and extirpations. The viability of remnant populations remains uncertain and could be impacted by continued fishing and by‐catch pressure, and the projected impacts of climate change. We genotyped 503 samples of D. batis, obtained opportunistically from the widest available geographic range, across 6 350 single nucleotide polymorphisms (SNPs) using a reduced‐representation sequencing approach. Genotypes were used to assess the species’ contemporary population structure, estimate effective population sizes and identify putative signals of selection in relation to environmental variables using a seascape genomics approach. We identified genetic discontinuities between inshore (British Isles) and offshore  populations, with differentiation most pronounced across the deep waters of the Rockall Trough. Effective population sizes were largest in the Celtic Sea and Rockall, but low enough to be of potential conservation concern among Scottish and Faroese sites. Among the 21 candidate SNPs under positive selection was one significantly correlated with environmental variables predicted to be affected by climate change, including bottom temperature, salinity and pH. The paucity of well‐annotated elasmobranch genomes precluded us from identifying a putative function for this SNP. Nevertheless, our findings suggest that climate change could inflict a strong selective force upon remnant populations of D. batis, further constraining its already‐restricted habitat. Furthermore, the results provide fundamental insights on the distribution, behaviour and evolutionary biology of D. batis in the North‐East Atlantic that will be useful for the establishment of conservation actions for this and other critically endangered elasmobranchs.The blue skate ( Ideally, the assembly and annotation of full genomes would provide a functional basis for genomic investigations of a species. However, genome assembly remains prohibitively costly and resource heavy to address urgent conservation questions at the scale of populations, especially given that elasmobranch genomes can be large and complex . D. batis has only recently received species status after morphological and genetic investigations distinguished it from the parapatric flapper skate D. intermedius  estimate their effective population sizes and (iii) identify potential signals of selection in relation to environmental conditions. Assessment of D. batis’ population structure and the potential consequences of environmental change on its ecological niche are necessary to define current and future spatial management units, and may help identify areas that may qualify for additional protection. The threat of continued fishing mortality and uncertain effects of environmental change, combined with the challenge presented by data deficiency, are characteristics that D. batis shares with many endangered elasmobranchs. It is hoped that our primarily molecular approach will effectively address important knowledge gaps for D. batis and that it may be extended to other elasmobranchs of conservation concern.In this study, we used a population and seascape genomics approach to investigate patterns of population structuring, abundance and local adaptation in a critically endangered elasmobranch, the blue skate 22.1Dipturus batis is one of two rajids classified as Critically Endangered by the IUCN . The two common skate species, which co‐occur in parts of their North‐East Atlantic range  and the larger‐bodied flapper skate (D. batis) based on morphological and genetic differences . DNA concentrations were quantified on a Qubit fluorometer (Thermo Fisher Scientific) and adjusted to 10–60 ng/µl in preparation for sequencing. To assess the quality of DNA, we performed a mock digest of the samples in CutSmart® Buffer (New England Biolabs) for 2 h at 37°C, and resolved all samples on a 0.8% TAE electrophoresis gel. In addition, two samples from each locality were visualized on a Genomic DNA ScreenTape® for a more detailed assessment of DNA quality. Genotyping was performed by Diversity Arrays Technology  using DArTseqTM following standard protocols as described in Kilian et al.  on an Illumina® HiSeq® 2500, generating approximately 1.5 million sequences per sample. Sequences were processed using proprietary DArT Pty. Ltd. analytical pipelines. Twelve samples were identified as nontarget species (data not shown) and were removed from the data set before processing of raw sequences was repeated. This generated data for 17,620 sequences of ~69‐bp length, each containing a SNP.Genomic DNA was extracted using a DNeasyn et al. . DArTseq2.4adegenet . Where duplicates were found , the sample with the highest score rate was retained. Next, we tested for conformation of loci to the Hardy–Weinberg proportions using the R package pegas  in at least two sampling locations. We then tested for linkage disequilibrium among loci using the R package snpStats . Monomorphic loci and loci with low minor allele frequencies (MAF < 0.05) across all samples were identified using 2.5CKMRsim, which simulates related pairs of individuals based on observed allele frequencies using a Monte Carlo approach. Using CKMRsim, we calculated the false‐positive and false‐negative rates at different log‐likelihood thresholds for each pairwise hypothesis test involving parent–offspring (PO), full‐sibling (FS), half‐sibling (HS) and unrelated (U) relationship categories. Due to the large number of pairwise comparisons in relationship testing , this approach allowed us to identify appropriate log‐likelihood thresholds when performing the relationship tests. Following CKMRsim recommendations, we aimed for a false‐positive rate threshold of 100 times smaller than the reciprocal of the number of comparisons made (i.e. FPR < 7.92 × 10−8). We identified 10 related pairs and removed one individual from each pair such that population genomic analyses involved 493 individuals  and second‐order  relatives in our data set and removed one individual of each related pair for downstream analyses. Identifying related individuals also allowed us to observe any patterns of family structuring and habitat use. Related individuals were identified using 2.6PopGenReport . In order to avoid impractically long computation times and potential biases resulting from imbalanced sample sizes , the Faroe Shelf (N = 10) and the Faroe Bank (N = 8), such that the total sample size for STRUCTURE analysis was 57. The most likely value of K was estimated using the delta‐K method of Evanno et al.  using a discriminant analysis of principal components (DAPC) in adegenet. Missing data at 37,939 loci  were replaced with the mean allele frequencies across all samples. Cluster identification was performed using the find.clusters function, with the optimal number of clusters evaluated using the Bayesian information criterion (BIC). Two DAPC plots were produced: one in which the prior grouping of individuals was based on the evaluated number of clusters, and one with prior groupings based on the six predefined geographic locations.Second, spatial genomic structure was assessed for all individuals  with the R function FIS, FST) and pairwise FST , using 1 000 dememorizations, 100 batches and 1 000 iterations per batch.Overall F‐statistics (2.7Ne) is a theoretical estimator of population size after accounting for genetic drift that is useful in conservation genetics as it reflects the additive genetic variation, or evolutionary potential, of wild populations  using the linkage disequilibrium (LD) estimator . Information was extracted for the near‐bottom depth layer for each variable  and for the 12 months preceding each individual's sampling date at a spatial resolution of ~7 km; these criteria were selected in order to obtain data representative of the skates’ year‐round environment and to minimize any seasonal effects from when skates were sampled. Overall mean, maximum and minimum values for each of these variables were retained for our seascape genomics analysis. In attempting to characterize the skates’ year‐round environment, such an approach assumed that the skates remained near their sampled locations in the 12 months prior to being sampled. Bottom depths for each sample were obtained from the EMODnet Bathymetry Consortium . To account for population structure, we employed a multivariate model, taking the first two principal components from a PCA performed on the genomic data set as covariates for each individual. We computed P‐values based on G and Wald scores for each test, and corrected for type I error from multiple comparisons using the Bonferroni correction at thresholds of 0.05 and 0.01.As an additional test for outlier loci, we implemented a Bayesian outlier detection method on all samples using BayeScan ), otherwise known as the inbreeding coefficient, was generally low across sites, but was higher at Rockall and West Coast Scotland. Negative values of FIS for individuals from North Scotland, Faroe Bank and Faroe Shelf corresponded to a slightly higher‐than‐expected level of heterozygosity at these sites, whereas the opposite was true of the Celtic Sea, Rockall and West Coast Scotland. There was no evidence for the presence of unique loci at any of the sites, with no private alleles detected.Patterns of genetic diversity were generally comparable across sites; however, samples from Rockall exhibited lower genetic diversity Table . Allelic3.4Overall, the results suggest a clear barrier to gene flow between Rockall and all other sites, with Rockall demonstrating significant genomic differentiation across all analyses. There was high gene flow among British continental shelf sites , whereas gene flow occurred to a more limited extent between the British shelf and the Faroese sites; results of the different analyses led to different conclusions regarding the degree of genomic differentiation among Faroese and British skates.K method of Evanno et al.  suggested an optimal number of two clusters, based on the lowest BIC score after retaining all 493 principal components , and between all pairwise site comparisons with Rockall . The G tests and F‐statistics were repeated after grouping individuals into three putative populations: British Shelf, Rockall and Faroe Islands. A test with this grouping was performed to statistically investigate the suspected isolation of Faroese skates from those on the continental shelf, as indicated by the STRUCTURE and DAPC results; the proximity of closely related individuals, tagging results reported by Bendall et al. , and there was a slight increase in overall FST and FIS (FST = 0.032 and FIS = 0.044).Results of the G test suggested significant genic differentiation across samples overall  were estimated for each sample site and after grouping samples into the three putative populations. For the former, sample sizes were too small and Ne could not be estimated for some sites . The Faroese skates demonstrated the lowest levels of Ne (estimates ranging from ca. 2 300 to 3 500). In some cases, estimates reached infinity, probably as a result of small sample sizes  explained 72.7% of the environmental variation among sites Figure . Overallp < 0.01 for both G‐score and Wald score). These were as follows: latitude, mean current velocity, maximum pH, minimum bottom temperature, and mean, minimum and maximum salinity. This locus was also detected as one of 21 outliers under putative positive selection in BayeScan  and 36 environmental variables (including latitude and longitude as covariates) generated a total of 457,200 tests in Samβada. After the Bonferroni correction, one allele  was significantly associated with seven environmental variables  waters that exceed the species’ reported depth range. Similarly, the Faroe Shelf and Bank are separated from the Scottish shelf by the Rockall Trough to the south and the ~1,200‐m‐deep Faroe–Shetland Channel to the east. Several studies have attributed the genetic isolation of Rockall and Faroe Island populations of benthic and benthopelagic fishes  of the continental shelf  estimates indicated that the British Shelf and Rockall had the highest Ne , though the former were dominated by high population densities in the Celtic Sea. The Faroe Islands had a much lower Ne . The minimum Ne required to maintain the evolutionary potential of a population has been a subject of debate, with proposed conservation thresholds set between 500 and 5,000 individuals   were more sensitive to acidification and warming, as indicated by a lower aerobic capacity and slower recovery following anaerobic activity (Di Santo, D. batis and its relatives, BLAST searches for this and the 20 other putatively adaptive loci did not produce any unambiguous hits. Whereas this locus could be part of a functional sequence, it could also be physically linked to a gene under selection. Future investigations will benefit from more targeted approaches as genomic resources for elasmobranchs improve.Climate change is expected to lead to warmer, fresher and more acidic waters around the British Isles by the end of the century, with an increasing frequency of extreme oceanographic events MCCIP, . These tD. batis’ population structure, regional abundance and movement patterns that will have immediate relevance for the conservation of this critically endangered species, specifically through the delimitation of current and future spatial management units. In view of our findings suggesting that climate change may affect populations of D. batis differentially across their distributional range, it may be necessary to consider the long‐term efficacy of any conservation strategies. The potential impacts of recent and projected oceanographic changes in the North‐East Atlantic Ocean (MCCIP, D. batis cannot adapt, a poleward distribution shift or a move to greater depths as observed in other species (Barton et al., D. batis may address this hypothesis in greater detail. Nonetheless, our study demonstrates the efficacy of using increasingly affordable genome‐wide approaches to address fundamental knowledge gaps for one of many threatened and data‐deficient elasmobranchs, leading to better spatial management.Our results provide an updated assessment of n MCCIP, , which in MCCIP, , are impn MCCIP, , but alsThe authors declare no conflicts of interest.Supplementary MaterialClick here for additional data file.


Pleurobema clava  and Pleurobema oviforme (species of concern) are similar, causing considerable taxonomic confusion between the two species over the last 100 years. While P. clava was historically widespread throughout the Ohio River basin and tributaries to the lower Laurentian Great Lakes, P. oviforme was confined to the Tennessee and the upper Cumberland River basins. We used two mitochondrial DNA (mtDNA) genes, 13 novel nuclear DNA microsatellite markers, and shell morphometrics to help resolve this taxonomic confusion. Evidence for a single species was apparent in phylogenetic analyses of each mtDNA gene, revealing monophyletic relationships with minimal differentiation and shared haplotypes. Analyses of microsatellites showed significant genetic structuring, with four main genetic clusters detected, respectively, in the upper Ohio River basin, the lower Ohio River and Great Lakes, and upper Tennessee River basin, and a fourth genetic cluster, which included geographically intermediate populations in the Ohio and Tennessee river basins. While principal components analysis (PCA) of morphometric variables  showed significant differences in shell shape, only 3% of the variance in shell shape was explained by nominal species. Using Linear Discriminant and Random Forest (RF) analyses, correct classification rates for the two species' shell forms were 65.5% and 83.2%, respectively. Random Forest classification rates for some populations were higher; for example, for North Fork Holston (HOLS), it was >90%. While nuclear DNA and shell morphology indicate that the HOLS population is strongly differentiated, perhaps indicative of cryptic biodiversity, we consider the presence of a single widespread species the most likely biological scenario for many of the investigated populations based on our mtDNA dataset. However, additional sampling of P. oviforme populations at nuclear loci is needed to corroborate this finding.The shell morphologies of the freshwater mussel species  Pleurobema clava and Pleurobema oviforme, are re‐examined using mitochondrial DNA, morphometrics, and nuclear microsatellites. While phylogenetic analysis of mitochondrial DNA suggests a single, widespread species, one population was significantly divergent based upon microsatellites and morphometrics. Sampling of additional populations and analysis with additional nuclear loci are recommended to further define management units.Relationships between two imperiled and morphologically similar freshwater mussel species,  In fact, species belonging to the genus Pleurobema clava , remains uncertain. Past taxonomic confusion between the two species likely centers on the morphological similarity between the majority of P. oviforme populations, and the congener P. clava. Pleurobema clava is elongate or “club‐like” (clava = “club” in Latin) in appearance  in appearance, with umbos more centrally positioned and much less inflated, typically either even with the hinge line or barely extending beyond it  and lower  sections of the Tennessee River basin have inflated umbos that extend well beyond the hinge line, greater width, and tend to be more elongate and have an angular posterior ridge, similar to P. clava from the Great Lakes region and Ohio River system. In the Cumberland River basin, however, P. oviforme is thought to replace P. clava in headwater streams because shells tend to be more compressed and less elongate and have an angular posterior ridge, with umbos that are located anteriorly and extending beyond the hinge line , with large extant populations occurring in the Tippecanoe River, Indiana ; therefore, determining the relationship and resolving taxonomic uncertainties between these taxa has important implications for conservation efforts and decision making regarding listing and protection.Taxonomic assessments have generally recognized P. clava and P. oviforme to test species boundaries and characterize genetic population structure. First, we assessed genetic distinctiveness by testing monophyly of each putative species expected under the Phylogenetic Species Concept . In addition, we used morphometric data collected from museum specimens identified as P. clava and P. oviforme to test whether the two species can be reliably distinguished based on morphological characters. We then used 13 novel nuclear microsatellite markers to further evaluate species boundaries and determine whether population structuring exists across the broad geographic range of the species complex. We discuss important limitations in our genetic and morphological datasets for delineating these taxa, and additional sampling that is needed to refine our understanding of the two species and potential management units (MUs). These findings have important taxonomic and management implications given the conservation status of P. clava and the imminent ESA listing decision for P. oviforme.In this study, we analyze morphological and genetic  variation from samples collected throughout the ranges of both 22.1P. clava and P. oviforme throughout their present ranges in three watersheds , including 18 drainages in 11 states (Table COI and microsatellites) or by drainage (ND1) and will be referred to as “populations” herein  and resuspended in TE buffer . DNA concentrations were determined by fluorescence assay  and the NADH dehydrogenase subunit I (ND1) genes. Mitochondrial genes were amplified from genomic DNA using the polymerase chain reaction (PCR), with COI primers (dgLCO‐1490 and dgHCO‐2198) and thermal cycling conditions of Folmer et al. (ND1 primers (Leu‐uurF and LoGlyR) and conditions of Serb et al. , 3.75 mM MgCl2, 0.3 of each dNTP, 0.2 mM of each primer (from 5 mM stock), 0.5% Tween‐20, 0.08 units/μl of Taq polymerase (Promega), and 10 ng/ul template DNA. Visual inspection for the targeted amplification product was confirmed on 1% agarose gels.We sequenced two mitochondrial DNA (mtDNA) protein‐encoding genes for phylogenetic and phylogeographic analyses: the r et al.  and ND1 b et al. . Amplifihttps://www.geneious.com) with default settings in the Geneious alignment algorithm. The mtDNA alignments were translated into amino acids to ensure the absence of stop codons and indels. Additional sequences  were downloaded from GenBank for phylogenetic and phylogeographic analyses  and then used as templates in sequencing reactions with the ABI Prism BigDye Terminator Cycle Sequencing Ready Reaction Kit (Applied Biosystems) using the amplification primers. Sequencing reactions were run using a PTC‐200 Thermal Cycler (MJ Research) and subjected to electrophoresis on an ABI Prism 3130™ Genetic Analyzer. Products of forward and reverse sequencing reactions were assembled, forming a consensus sequence for each individual and gene using Sequencher 5.4 (GeneCodes Corporation). Multiple alignments were performed for each gene region using Geneious software v. 11.1.5 (S), number of haplotypes (h), haplotype diversity (dH), and nucleotide diversity (π), were calculated per population and gene region using DnaSP version 6.12.03x64  and net (aD) number of nucleotide substitutions per site were calculated between populations for each gene region using DnaSP. Genetic differentiation among populations was estimated by the haplotype‐based method  were ca2.3P. clava and P. oviforme to evaluate morphological variation throughout the range of both species. A single valve from each individual was positioned with ventral margins of anterior and posterior adductor muscle scars on a horizontal plane before taking the measurements to the nearest 0.01 mm using digital calipers. Total length anterior to posterior (length) parallel to the adductor scar plane, width at interdentum (width), and height from intersection of hinge ligament and umbo to the ventral margin of the shell (height) parallel to the adductor scar plane were measured. Valves were weighed to the nearest 0.1 g using a digital scale.We used museum specimens  identified as e‐transformed measurement values to produce a scale‐invariant matrix while preserving information about allometry  and random forest analysis (RF) applied to standardized ratios to examine morphological variation among species. The random forest analysis was conducted using 80%  of the data as the training dataset and 20%  of the data as the validating dataset. After plotting the out‐of‐bag (OOB) error rate and observing how it declined and stabilized, a total of 500 trees was used for the analysis. The random forest analysis was conducted using the “rattle” package  using the lda function in the MASS package , using magnetic bead capture technology , Maumee St. Joseph (MASJ), and Fish Creek (MAFC) populations plus four P. oviforme from the Cumberland drainage.Two microsatellite‐enriched libraries were prepared by Genetic Identification Services, Inc. , 0.5% Tween‐20, 0.08 units/μl of Taq polymerase (Promega), and 20 ng/μl template DNA in a volume of 10 μl. Amplifications were carried out using the following thermal cycler profile: initial denaturing at 94°C for 2 min; 35 cycles of 94°C C for 40 s, 58°C C for 40 sec, 72°C C for 1 min; and a final extension at 72°C C for 5 min. Amplified, labeled PCR products were subjected to capillary electrophoresis on an ABI Prism™ 3130 XL Genetic Analyzer  with Gene Scan 500 LIZ size standards. GeneScan 3.7 analysis software, Genotyper 4.0 (Applied Biosystems), and GeneMapper (ver. 4) (Thermo Fisher Scientific) were used to score, bin, and output allelic (and genotypic) data following the protocols described in King et al. . Loci were amplified individually, and PCR products were multiplexed into four size‐ and dye‐compatible groups. Microsatellite DNA amplification reactions consisted of 1 X PCR Buffer (Promega), 3.75 mM MgClg et al. .PID; Peakall & Smouse, The quality of microsatellite genotyping was checked by several measures. First, duplicate multi‐locus genotypes (MLGs) were located using GenAlEx v. 6.5  within the MLG dataset by minimizing deviation from HWE within clusters. Values of K = 1 to 15 were tested, where K = 1 represents a single panmictic population and K = 15 represents the 15 populations that were sampled in this study. Twenty replicate simulations were run with 200,000 Markov chain Monte Carlo repetitions and a burn‐in of 100,000 with an admixture model, assuming independent allele frequencies across populations as priors   with geographic river distances in adegenet. River distances were calculated as the shortest path between populations along U.S. National Hydrography flowlines (NHDPlus version 2) referenced to the Albers Equal Area North American Datum 1983 Coordinate Reference System (EPSG = 42303) using the packages sf and stplanr . The P. clava/oviforme COI sequence alignment of 224 sequences was 572 bp in length, including 29 variable sites and nucleotide diversity π = 0.0061. The ND1 alignment of 176 sequences was trimmed to 775 bp in length to minimize missing data, contained 55 variable sites, and nucleotide diversity π = 0.0049. No gaps or stop codons were detected in either protein‐encoding gene sequence.We generated 219 COI and ND1 P. clava/oviforme datasets are shown in Table S) was higher for ND1 (55) than for COI (29). Similarly, haplotype diversity (dH), which accounts for different sample sizes, was higher for the ND1 compared with COI datasets . Haplotype diversity (dH) varied substantially among populations for COI, ranging from 0 in the Tygart (TYGT), Scioto (SCIO), and LTEN populations to 0.8228 in the HOLS and was consistently low in the five Allegheny (ALLE) and Elk  populations , with the exception of St. Joseph/Maumee  between populations ranged from 0 to 1.69 (Table xyD) among P. clava populations averaged 0.55% (range = 0–1.34%), with the highest values involving comparisons with the Maumee drainage (range = 1.08–1.34%). Interestingly, the P. clava Maumee populations were more closely related to P. oviforme populations from the Cumberland (CUMB), PROC, HOLS, and LTEN populations (xyD range = 0.32–0.66%) than to more proximate P. clava populations. Divergence was low for populations within the Allegheny drainage, including the TYGT (0.11–0.34%), as well as among the Wabash (WABA), Green (GREN), and SCIO populations (0–0.22%). Differentiation was greater among P. oviforme populations , except for lower divergence between the LTEN and CUMB populations (0.38%). Between‐species comparisons produced a slightly higher average than within P. oviforme populations (0.99%), but a range of divergences similar to that observed between P. oviforme populations (range = 0.32–1.69). The highest pairwise comparisons involved P. clava versus P. oviforme from the PROC drainage (xyD range = 1.43–1.69%).For the 69 Table . SequencP. clava populations (aD range = 0.69–1.05%) yet lower aD in comparisons with P. oviforme from the CUMB, PROC, and HOLS populations (aD range = −0.09–0.23%). No to minimal net nucleotide substitutions were observed among remaining P. clava populations. Greatest aD distances were observed in comparisons with the PROC drainage . Among all collections, genetic differentiation was significant among populations, with Hs = 0.350 (p < .001). Gene flow estimates indicated an Fst = 0.571 and number of migrants (mN) = 0.19. No significant deviations from neutrality were detected by Tajima's D = −0.936 (p > .10) or Fu's Fs = −2.310 (p > .05).Similar patterns were observed when net nucleotide differences for the COI dataset were examined Table , with thxyD) at ND1 ranged from 0 (P. clava from the GREN and TYGT) to 1.2% (P. oviforme from the PROC and Tennessee (TENN); Table P. clava populations, sequence divergence averaged 0.47% (range = 0.14–0.85%) while comparisons among P. oviforme populations were slightly higher, averaging 0.77% (range = 0.34–1.20%). Like the COI analyses, the highest divergence estimates among P. clava populations involved the Maumee populations (0.45–0.85%). It is noteworthy that the MASJ population was more closely related to the P. oviforme Duck (DUCK) and South Chickamauga (SCHK) populations (~0.4%) than to other P. clava populations (0.52–0.85%). Among P. oviforme populations, the PROC/DUCK and the DUCK/SCHK comparisons were lowest (xyD = 0.34%). Pairwise comparisons between putative species were intermediate, ranging from 0.26% (TENN/TYGT) to 1.05% (TENN/MASJ), and averaging 0.66%. Minimal net nucleotide substitutions (aD) were detected between several populations, even those involving the two species . Like the COI dataset, genetic differentiation was significant among all collections, with Hs = 0.7930 (p < .001); however, the fixation index was lower and gene flow estimates were slightly higher for ND1 . While the COI dataset followed expectations under neutrality, the ND1 dataset deviated, with significant values for both Tajima's D  and Fu's Fs , indicating an excess of rare alleles  and one P. clava‐specific (11) haplotypes as well as two of the three shared haplotypes . The second clade  haplotypes, as well as six P. oviforme‐specific  and one shared haplotype  and maximum parsimony (MP) phylogenetic analyses included 29 2; Table .COI TCS haplotype network . As in the COI phylogenetic analysis, P. clava/oviforme haplotypes formed a single, well‐supported clade  surrounding the common haplotypes  differed from the most common haplotypes by one to two substitutions. Haplotypes from the HOLS were intermingled throughout the network, along with species‐specific haplotypes.The s Figure . Thirty‐s Figure , and simk Figure , haplotyk Figure  differed3.2P. clava N = 129; P. oviforme N = 234). The first two PCs described 86.7% of the total variation . Classification to species by the LDA was 58.9% and 72.1% accuracy for P. clava and P. oviforme, respectively, with an overall classification accuracy of 65.5%  error rate estimate. The OOB error rate was 16.8%, indicating that the model had approximately an 83% accuracy for identification of shells to the correct species (Table 3.3α = 0.0006), none were consistent across populations (results not shown). Similarly, only one in 388 tests for departure from HWE was significant  differed at only one locus (PclC211) and had a kinship value of 0.443. Given the low probability that two unrelated individuals sampled from a single population share the same MLG , this high kinship estimate suggests a possible parent–offspring relationship.Of 300 primer pairs tested for amplification, 13 loci were polymorphic and consistently produced alleles within expected size ranges Table . AlthougPclD7) to 63 (PclC211) and averaged 11.5 (Table P. oviforme populations (CUMB and TENN) plus the P. clava GREN population had positive inbreeding coefficients, suggesting heterozygote deficits. A likely explanation for these positive inbreeding coefficients is a Wahlund effect given that these samples originated from more than one location within the drainage.A total of 454 alleles were detected among 235 genotyped samples Table . The num.5 Table . The mea.5 Table . FollowiHx) for either the SMM or TPM mutation models  under one (ALLE Hunter Station (ALHS)) or both (ALLE West Hickory (ALWH)) mutation models, suggesting that these populations are not in mutation‐drift equilibrium  and AMOVA. First, we tested whether genetic structuring was apparent among all 15 populations. An AMOVA with populations as a second hierarchical level produced a weak but significant global Fst value of 0.029, with 3% of variation among populations and 97% within populations  among P. clava populations ranged from 0 /TYGT populations) to 0.056 , with two of three comparisons significant (exception CUMB/TENN). Pairwise G′ST values between species showed little to moderate differentiation, ranging from 0.013 (MAFC/TENN) to 0.101, HOLS/SCIO; average = 0.053). Pairwise estimates of allelic differentiation (Jost's D) showed greater genetic differentiation than fixation measures (G′ST), indicating that the most common alleles were often not shared between populations. The two measures of differentiation were generally concordant, with low or negative values between populations from the mainstem ALLE, highest estimates in comparisons involving the HOLS population (0.456–0.774), and intermediate estimates involving MASJ and MAFC populations (0.224–0.584), with an exception of the WABA population, which were lower . Pairwise D estimates between P. clava populations ranged from slightly negative /TYGT, concordant with G′ST estimates) to 0.584 (MASJ/SCIO) and averaged 0.244. Estimates of D between P. oviforme populations ranged from 0.558 (CUMB/TENN) to 0.774 (CUMB/HOLS) and averaged 0.620. Pairwise D values between species ranged from 0.250 (MAFC/TENN) to 0.771 (MASJ/HOLS) and averaged 0.500.Pairwise fixation indices (K = 2) was suggested as the most likely number of populations by the ΔK method ) . Both methods suggested K = 2 clustering producing the highest FST , providing the additional geographic and genetic sampling suggested by Inoue et al.  must be taken into consideration  bring into question sole reliance on the mtDNA locus to define evolutionary lineages  at several mtDNA genes and even share some of the same haplotypes among the respective populations. There are numerous examples in the scientific literature of closely related species that are distinct morphologically, behaviorally, and ecologically, but are minimally diverged at mtDNA and even share haplotypes, including some mollusks, freshwater and marine fishes, birds, and other taxa  with severe growth scars on the shell disk, which is not considered a diagnostic character for the species , but he was also aware of the considerable variation observed in the headwater versus big‐river forms of P. oviforme in the Tennessee River system  and the respective ratios as response variables to delineate these taxa over a broad geographic range. While results of the PERMANOVA suggested significant differences in shell shape, broad overlap in shell shapes across species was apparent in the PCA and only 3% of the variance in shell shape was explained, implying a high degree of morphological overlap between the two nominal taxa Figure . HoweverP. oviforme from the Duck and Paint Rock river samples to those of P. clava. Additionally, we do not report herein morphological differences or classification rates among the various populations that we examined. For example, the Random Forest analysis classification rate for individuals from the North Fork Holston River is >90%. Beyett et al. , but very high pairwise Dest estimates  cluster.Many previous studies of unionids have detected low levels of mtDNA divergence, with minimal divergence among haplotypes sampled across geographic ranges, while microsatellites provided increased resolution of population structuring , one possible scenario involves ancient drainage connections that occurred as ice sheets covering the northern portion of the range retracted approximately 15,000–8,000 years ago , and a star‐shaped haplotype network , Hiwassee (HIWA), Little River, Nolichucky (NOLI), PROC, SCHK) would form at a minimum a large MU. Alternatively, sampling intermediate populations may erode genetic clusters through the inclusion of genetically admixed individuals, which has been shown in groups characterized by strong patterns of isolation by distance ; Data curation (lead); Formal analysis ; Funding acquisition (lead); Investigation ; Methodology ; Resources ; Visualization ; Writing‐original draft (lead); Writing‐review & editing (lead). Nathan A. Johnson: Conceptualization ; Data curation (supporting); Formal analysis ; Funding acquisition (supporting); Investigation ; Methodology ; Resources ; Visualization ; Writing‐original draft (supporting); Writing‐review & editing . Jess W. Jones: Conceptualization ; Data curation (supporting); Formal analysis ; Funding acquisition (supporting); Investigation ; Methodology ; Resources ; Visualization ; Writing‐original draft (supporting); Writing‐review & editing (supporting). Michael S. Eackles: Conceptualization (supporting); Data curation (supporting); Formal analysis (supporting); Investigation ; Methodology (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Aaron W. Aunins: Formal analysis (supporting); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Daniel B. Fitzgerald: Formal analysis (supporting); Methodology (supporting); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Eric M. Hallerman: Funding acquisition (supporting); Investigation (supporting); Methodology (supporting); Resources (supporting); Visualization (supporting); Writing‐review & editing (supporting). Tim L. King: Conceptualization ; Funding acquisition (supporting); Investigation (supporting); Methodology ; Resources .Figure S1‐S4Click here for additional data file.Table S1‐S9Click here for additional data file.


Lynx pardinus, n = 31) and Eurasian lynx . As expected, overall genomic diversity correlated negatively with bottleneck intensity and/or duration. Correlations of genetic diversity with divergence, chromosome size, gene or functional site content, GC content, or recombination were observed in nonbottlenecked populations, but were weaker in bottlenecked populations. Also, functional features under intense purifying selection and the X chromosome showed an increase in the observed density of variants, even resulting in higher θW diversity than in nonbottlenecked populations. Increased diversity seems to be related to both a higher mutational input in those regions creating a large collection of low‐frequency variants, a few of which increase in frequency during the bottleneck to the point they become detectable with our limited sample, and the reduced efficacy of purifying selection, which affects not only protein structure and function but also the regulation of gene expression. The results of this study alert to the possible reduction of fitness and adaptive potential associated with the genomic erosion in regulatory elements. Further, the detection of a gain of diversity in ultra‐conserved elements can be used as a sensitive and easy‐to‐apply signature of genetic erosion in wild populations.Demographic bottlenecks generally reduce genetic diversity through more intense genetic drift, but their net effect may vary along the genome due to the random nature of genetic drift and to local effects of recombination, mutation, and selection. Here, we analyzed the changes in genetic diversity following a bottleneck by comparing whole‐genome diversity patterns in populations with and without severe recent documented declines of Iberian ( Genetic diversity is intimately related to adaptive potential and the loss of genetic diversity is generally associated with reduced fitness and increased extinction risks, so the importance of genetic variation transcends to species diversity and is also connected to ecosystem resilience  will increase the action of genetic drift and cause a general loss of genetic diversity, in a magnitude determined by its severity and duration  or maintaining it . The effects of selection on diversity may extend to neighboring linked neutral sites especially in regions of low recombination, a process often referred to as hitchhiking or linked selection. Selective sweeps and background selection are the consequence of linked positive and negative selection, respectively  and the broadly distributed Eurasian lynx (Lynx lynx). By taking advantage of the availability of a de novo assembled and annotated Iberian lynx reference draft genome  populations, and Andújar and Kirov as nonbottlenecked (NB) populations, although the latter have also declined in size to a much lower extent.2.2n = 19) and Doñana (n = 12). We used 29 whole‐genome sequences from Eurasian lynx , NE‐Poland  (n = 8), and Norway (n = 8). Samples were digested overnight using proteinase K and DNA was extracted using NucleoMag B‐beads (NucleoMag DNA from tissue kit) in LEM‐EBD facilities . gDNA samples were used for preparing Illumina sequencing compatible paired‐end libraries. The libraries were prepared, quantified, and sequenced in Illumina HiSeq2000 flowcell v3 (Illumina Inc.), in 2 × 101 bp paired‐end mode, following standard Illumina procedures at Centro Nacional de Análisis Genómico (CNAG‐CRG).Whole‐genome resequencing data were obtained from 60 Iberian and Eurasian lynx Figure . We samp2.3https://www.bioinformatics.babraham.ac.uk/projects/fastqc), and adaptors were removed when necessary using SeqPrep (https://github.com/jstjohn/SeqPrep). All short‐read sequence data were mapped to a 2.8 Gb Iberian lynx LYPA1.0 genome assembly  and merged the bam files from the same individual with SAMtools merge  and performed a local realignment and a base quality recalibration of the data using GATK 3.4  into domestic cat Felis_catus_5.0 coordinates using LiftOver (http://rohsdb.cmb.usc.edu/GBshape/cgi‐bin/hgLiftOver), and these to lynx LYPA1.0 coordinates using lynx to cat synteny , lncRNAs, and ncRNAs , and Watterson theta estimator (θW) per site , and features , weighting by the number of informative sites. Then, we used π, and θW weighted means to calculate a measure of the skewness of the SFS toward rare variants (S) as 1−(π/θW), where a positive S value indicates excess, and a negative value deficit, of rare variants over the mutation‐drift equilibrium for neutral alleles under the infinite sites model  was calculated by bootstrapping units using the R package boot  for each population and feature. Dispersion in X/A ratios was calculated from dispersion in X and dispersion in A, using a propagation of the uncertainties formula, where SD refers to the standard deviationWe also computed the ratios of 2.6We quantified relative diversity differences between pairs of one B population and its corresponding NB population in different genomic features. By doing so, we take NB as the closest representation of the ancestral population and assume that observed differences in diversity are mostly due to population contraction in the B population. Comparisons included Norway vs. Kirov and NE‐Poland vs. Kirov for Eurasian lynx, and Doñana vs. Andújar for Iberian lynx. We used fix‐size adjacent windows on a concatenation of units of each feature to avoid distortions caused by differences in unit length across features. Additionally, we subsampled populations within species to the same number of individuals  to avoid biases due to differences in sample size.δ) were computed as:We filtered concatenated sites for each population and feature within each species so that it contained the same positions in the two populations being compared, and computed diversity for each population and feature in nonoverlapping 1 kb windows. We calculated differences in diversity over these nonoverlapping 1 kb windows to be able to compute statistical tests without the bias introduced by different features having different average length. Relative differences in diversity  corresponding to windows with probably no diversity , this expression reduces the occurrence of the denominator being zero, shows a reduced variance, is bound to values between −1 and 1, and shows an approximately symmetrical distribution around the mean within the bounds. A positive y Figure . As the s Figure . We plot2.7We first annotated units for the following genomic variables: average recombination rate, divergence, as a proxy of mutation rate, GC content, and two different scores related to selection strength , and functional site percentage). Secondly, nonoverlapping 1 kb windows used for calculating diversity differences were also annotated for these genomic variables. We did this by averaging the values annotated for the units included in each 1 kb window weighting by the number of overlapping sites.L. rufus), divided by the number of informative sites in each unit, as reported by Lucena‐Perez et al.  as heat maps.Then, for each of the three pairwise NB‐B comparisons we analyzed the linear regression of genetic diversity in the NB population with genetic diversity in the corresponding B population, separately for centromeric, interstitial, and telomeric regions. We report both the predictor  and the proportion of variance explained (δθW > 0.1) and the rest of the windows in the average values of the following genomic variables: NB diversity  as a proxy of the ancestral diversity, recombination rate, RVIS and functional sites percentage as a proxy of selection, divergence as proxy of mutation rate, and GC content. After rejecting normality of these variables of interest, we used a Wilcoxon signed‐rank test for unpaired samples. The significance threshold was adjusted by applying a strict Bonferroni correction. To get an estimation of the effect of these variables, we calculated effect size (r), as implemented in , against those with some θW diversity in the B population (NDNB‐DB windows)  Figure , and res) Figure .33.1π weighted mean (wm) = 3.3*10−4; θWwm = 2.8*10−4); Iberian lynx πwm = 1.9*10−4; θWwm = 1.4*10−4)   in the order: NE‐Poland, Norway, and Doñana. All populations show a negative S    Figure . Within W Figure . In Iber) Figure . Based ox) Table .3.2πX/πA, and from 0.36 to 0.84 for θWX/θWA ; however, the larger (above 1) θWB_X/θWNB_X than θWB_A/θWNB_A for Norway and Doñana, when compared to Kirov and Andújar, respectively, indicates a larger density of variants in the X chromosome in B relative to NB populations. Accordingly, S values are negative in the NB Andújar and Kirov, and also in the B NE‐Poland populations, but positive in the B Doñana and Norway populations  in the B populations, especially so for θW , and recombination, and negatively related to chromosome size in all populations of both species Figure . HoweverW Figure .π and θW diversity, respectively, with lower ratios of θW occurring in the B Norway and Doñana populations, indicating that θW diversity in B pops is still higher in subtelomeric regions than in other chromosomal regions, but not as higher as in NB populations , and divergence , and inversely to functional sites percentage . Also, subtelomeric regions show higher GC content , and higher RVIS  Table .R2 , followed by CDS . With regard to the NB‐B comparisons, regulatory regions such as gene promoters, noncoding RNAs, but also 5' UTR and 3' UTR, and even introns, show smaller reductions of diversity in B populations with respect to NB than intergenic regions. S is negative for all populations and features, except for UCNE in Norway and Doñana, which shows a high positive value , and for CDS in Norway (S = 0.04), suggesting an accumulation of low‐frequency variants in these constrained features in the most bottlenecked populations (Table θW), but not as nucleotide diversity (π) , and ultra‐conserved‐non‐coding‐elements (UCNE), patterns of genetic diversity are negatively correlated with their anticipated selective pressure Figure . Taking ns Table . Notablyδ) shows the lower overall diversity in the B population as a negative global mean of δ for most features, both for π (δπ) and θW (δθW)  in bottlenecked populations . This pattern is particularly notable for UCNE followed by CDS, 3' UTR and 5' UTR. Although with lower δ than intergenic, ncRNA δθW tends to be larger than δπ in the Norway‐Kirov comparison, and to a lesser extent in the Doñana‐Andújar comparison  and larger S, that is, SFS more skewed toward rare alleles, than the rest of windows  being larger for Norway‐Kirov (range: |0.15|–|0.22|), followed by Doñana‐Andújar (range: |0.09|–|0.14|), and finally NE‐Poland‐Kirov comparison (|0.01|–|0.04|) (Table δθW > 0.1 (i. e. which gain diversity after the bottleneck) tend to show lower diversity in the corresponding NB population, but are in regions with higher divergence, GC content, and recombination, which are generally associated to high diversity. Consequently, the magnitude and significance of these contrasts become larger when we limit the set of windows being considered to those showing no diversity in the NB population, that is, when we compare windows that lack θW diversity in both NB and B populations (NDNB‐NDB windows) to those with no diversity in NB but with some diversity in the B population (NDNB‐DB windows) , and Doñana‐Andújar comparisons (intergenic and introns). Regarding selection, coding, but also intronic, NDNB‐DB windows tend to be in genes with higher RVIS, that is, more tolerant to change, in moderate NB‐B pairwise comparisons, but not so in the most extreme Doñana‐Andújar comparison. More generally, differences in genomic variables between NDNB‐NDB and NDNB‐DB windows are smaller overall when the bottleneck is more intense (Norway and Doñana) and the features are selectively constrained (CDS).In order to gain further insights into the processes driving diversity gains in B populations, we compared average values of different genomic variables between windows of the same feature that show net |) Table . They al|) Table . For ins) Figure . Therefo4Here, we present one of the most comprehensive empirical studies of the genomic consequences of bottlenecks in natural populations. By directly comparing genomic diversity in Eurasian and Iberian lynx populations with contrasting recent demography, we assessed how the impact of recent bottlenecks on population genetic diversity varies across chromosomes, chromosomal regions, and genomic features. Also, we explored how different genomic variables, such as mutation and selection, as well as the initial SFS, might be affecting the different outcomes of genetic drift and, in consequence, the changes in diversity following a demographic decline. Our study provides empirical evidences of the conspicuous action of drift across the genome in replicated natural bottleneck scenarios. Comparing three bottlenecks of different intensity allowed us to assess the consistency of the results across replicates, and to account for differences regarding drift intensity.Recent bottlenecks are expected to globally reduce the genomic diversity in a magnitude determined by the intensity and duration of the bottleneck, and our estimates grossly rank according to records and demographic reconstructions from genetic and genomic data  in B populations. The X chromosome differs from the A in their response to a bottleneck because: (a) its effective size is ¾ of A, which predicts a ratio of diversity X/A of 0.75 at mutation‐drift equilibrium; (b) it has a lower recombination rate; (c) it has a lower mutation rate; and (d) recessive deleterious variation is continuously exposed to selection in hemizygous individuals   in the B than in the corresponding NB populations. Such increase in θW in selectively constrained features is, in principle, unexpected from purely random processes, since random genetic drift in bottlenecked populations would result in the preferential loss of alleles in the low‐frequency side of the spectrum, where most selectively constrained alleles will be  and Doñana (Ne = 10) the corresponding range would be 0.025 < s < 0.05, which are rather high values. Moreover, it must be noted that while Doñana has remained at such low size for many decades, Andújar was much larger a few generations ago, so the range of mutations that behave as neutral in Doñana and not in Andújar may be substantially wider (with a lower inferior s limit), so that the actual number of variants involved may be relatively large , and extending beyond coding regions to include UCNE, and to a lesser extent 5' UTR and 3' UTR. The latter two features are essential for efficient transcription and for the post‐transcriptional regulation of gene expression, and variants in both regions have been linked to several diseases (Hindorff et al., Our results call for extending the analyses of genetic variation in endangered species to include functional variation beyond coding sequences. Including regulatory elements will broaden our understanding of the effects of drift on the genome and allow for a more comprehensive assessment of the possible short‐term fitness reductions and the long‐term loss of adaptive potential in endangered species. As the genome‐wide assessment of genetic variation in nonmodel species is becoming increasingly feasible, and more readily facilitated by the increasing availability of reference genomes, the widespread implementation of genetic monitoring in wild species demands simple and cost‐effective methods, and informative and easy to implement indicator variables. In this regard, the accumulation of variants in highly constrained UCNEs may provide an easily detectable and reliable signal of recent demographic declines.Fig S1‐S7Click here for additional data file.Table S1‐S12Click here for additional data file.


Theileria annulata, which causes tropical theileriosis, is a major impediment to improving cattle production in Sudan. Tropical theileriosis disease is prevalent in the north and central regions of Sudan. Outbreaks of the disease have been observed outside the known endemic areas, in east and west regions of the country, due to changes in tick vector distribution and animal movement. A live schizont attenuated vaccination based on tissue culture technology has been developed to control the disease. The parasite in the field as well as the vaccine strain need to be genotyped before the vaccinations are practiced, in order to be able to monitor any breakthrough or breakdown, if any, after the deployment of the vaccine in the field. Nine microsatellite markers were used to genotype 246 field samples positive for T. annulata DNA and the vaccine strain. North and central populations have a higher multiplicity of infection than east and west populations. The examination of principal components showed two sub-structures with a mix of all four populations in both clusters and the vaccine strain used being aligned with left-lower cluster. Only the north population was in linkage equilibrium, while the other populations were in linkage disequilibrium, and linkage equilibrium was found when all samples were regarded as single population. The genetic identity of the vaccine and field samples was 0.62 with the north population and 0.39 with west population. Overall, genetic investigations of four T. annulata populations in Sudan revealed substantial intermixing, with only two groups exhibiting regional origin independence. In the four geographically distant regions analyzed, there was a high level of genetic variation within each population. The findings show that the live schizont attenuated vaccine, Atbara strain may be acceptable for use in all Sudanese regions where tropical theileriosis occurs. Theileria annulata that continues to be a major concern for livestock in tropical countries affecting millions of animals, particularly crossbreed and exotic cattle, and resulting in significant economic loss and mortality , central (n = 195), east (n = 158), and west (n = 108). North and central regions were designated endemic region, while east and west were designated new extension regions. Information on the sampling locations, whether from endemic or new extension regions, total number examined and T. annulata positive samples by PCR are provided in A total of 530 blood samples were collected from cattle in four Sudanese regions using FTA™ cards . The four regions were north . For diagnosis of f 370 bp .T. annulata Ankara strain was used as a positive control.In this study, the primers used were designed by The ABI 3730 Genetic Analyzer (Applied Biosystems-USA) was used to analyze the PCR amplicons at the BecA-ILRI Hub, SegoliP sequencing unit, Nairobi, Kenya. For size fractionation, the Gene Scan 500 LIZ internal lane size standard (Applied Biosystems-USA) was employed. The Gene Mapper tool (Applied Biosystems-USA) was used to score the results, which allowed for the resolution of 1 base pair (bp) changes with many products from a single PCR reaction. The predominant allele was determined as the one with the biggest area under the curve, and amplicons with highest peak height were scored. Allelobin software  was usedhttp://cmpg.unibe.ch/software/arlequin 35/  was calculated in GenAlEx6.5 (D) (http://lewis.eeb.uconn.edu/lewishome/gda.html).Arlequin v. 3.5 quin 35/  was usednAlEx6.5 ; 2012. Ax6.5 (D)  was calcISA) between each group of samples was estimated using the LIAN 3.7 program, as well as, the degree of linkage disequilibrium (LD) within and between populations (The standardized index of association (ulations . After ehttp://pritchardlab.stanford.edu/structure.html) was used to investigate population structure employing Bayesian clustering analysis with sample sites as a basis and the admixture scenario with linked allele polymorphism  to five , triplicates were performed. To identify which K produced the greatest representation of the data, STRUCTUREHARVESTER 0.6.1  samples positive for T. annulata DNA which were subjected to genotyping in addition to the vaccine strain  and new extension regions n = 90  (e strain . Distrib n = 43) .T. annulata populations. The existence of more than one allele at one or more loci confirmed the presence of several genotypes in the samples. For each marker, the number of alleles identified varied from four in TS9 to 22 for TS8 with the mean of 12.44 per marker  of marker TS8 had the highest (0.87), whereas TS9 had the lowest (0.36) . This fir marker . The domr marker .Principal components analysis (PCA) revealed that there is no clustering according to geographical origin . Two subT. annulata populations observed in Sudan were in linkage equilibrium or disequilibrium. When all the four sub-populations were analysed together (as a single population), the (ISA) was positive and greater than zero and the pairwise variance (VD) was more than the 95% critical value (L) suggesting that the merged populations are in linkage equilibrium (LE)  . The anaium (LE) . Only 11ium (LE) .D) between each of the four regionally sampled populations as well as between them and the vaccine strain, was used to evaluate genetic differentiation between the four populations (D = 0.82) was greater than that observed between the east and west populations (D = 0.64). The population with the lowest genetic distance from the vaccine genotype was west (D = 0.39), while the most genetically similar was north (D = 0.62) (Estimating Nei’s genetic distance (ulations . The gen = 0.62) .K = 3 is the optimal number of genetic groups to define the genotypes of Sudanese T. annulata populations as well as in T. annulata vaccine strain  were obtained from four different geographical regions. North and central regions known to be endemic of T. annulata since the eighties, and the remaining two (west and east) witnessed the spreading of the disease in the nineties. In addition, the T. annulata vaccine from Sudan was also included in the study. In order to gain insight into the epidemiology of a parasite, ascertain sources of infection and modes of transmission, it is critical to assess population, genetic diversity and structure  are collectively utilized under the nomadic cattle systems prominent in Sudan is essential to enhance genetic uniformity. This result is supported by PCA analysis as well as STRUCRURE results. AMOVA revealed a high percentage of crossing between various T. annulata samples as well as recombination within the parasite population. Individual samples, rather than groups derived from a specific geographic region, accounted for the majority of genetic variation. In the future, other aspects such as parasite challenge and quantifying the extent of tick infestation should be examined. PCA and AMOVA results figured out no evident link between population genetic structure and the geographical origin of the isolates investigated. However, PCA analysis revealed a close genetic link between the north and T. annulata vaccine genotypes, with the T. annulata vaccine and majority of north genotypes clustered together.The results revealed relatively slight geographical sub-structuring among the four populations of T. annulata in Sudan. It’s possible that gene pool 1 is introgressing into gene pool 2 or vice versa, with the two gene pools will eventually merging into one. This conclusion could be a result of cattle migration being unfettered across the country, due to the lack of trade barriers and policies restricting livestock movement  that was greater than L (1.9591), demonstrating LE. The presence of LE in the combined populations could be due to an epidemic population structure  was identified in the north, with a highest value of 3.22, followed by east, with lowest and maximum values of 0.45 and 2.89, respectively showing a significant degree of variability in the dataset. In the midgut of the tick vector, multiple infections stimulate cross-mating and recombination among distinct parasite genotypes, as well as the formation of unique recombinant genotypes . The higine host . It couline host .T. annulata population in Sudan. Extensive genetic intermixing between the four T. annulata populations studied was indicated, as well as minimal evidence of genetic differentiation and a high level of genetic diversity within each population. The findings show that the vaccine (Atbara strain) could be used in all areas where tropical theileriosis present.In conclusion, the application of polymorphic microsatellite loci has offered preliminary insight into the population genetic diversity and structure of T. annulata populations found in north African countries where tropical theileriosis is currently an economically important disease, should be examined and compared to see how genetically similar they are. Such data can assist veterinary control policy makers in determined if preventative measures, such as immunization, should be deployed at the national, regional or continental level.


Saccharomyces cerevisiae populations created using one of two strategies: pairwise crossing of isogenic strains or simple mixing of strains in equal proportion. We also explore the impact of the varying the number of parental strains. We find that more genetic variation is initially present and maintained when population construction includes a round of pairwise crossing. As perhaps expected, we also observe that increasing the number of parental strains typically increases genetic diversity. In summary, we suggest that when constructing populations for use in evolution experiments, simply mixing founder strains in equal proportion may limit the adaptive potential.“Synthetic recombinant” populations have emerged as a useful tool for dissecting the genetics of complex traits. They can be used to derive inbred lines for fine QTL mapping, or the populations themselves can be sampled for experimental evolution. In the latter application, investigators generally value maximizing genetic variation in constructed populations. This is because in evolution experiments initiated from such populations, adaptation is primarily fueled by standing genetic variation. Despite this reality, little has been done to systematically evaluate how different methods of constructing synthetic populations shape initial patterns of variation. Here we seek to address this issue by comparing outcomes in synthetic recombinant  Alongside traditional QTL mapping and genome-wide association studies, the “evolve-and-resequence” (E&R) approach has emerged as powerful technique that can generate insight into the genetic basis of complex phenotypes, at least in model organisms5. This approach involves subjecting experimental populations to conditions that target a specific trait, and simply monitoring the genotypic and phenotypic changes that occur over generations. As these experiments typically feature replicate populations evolving in parallel, researchers can use the data they generate to make powerful statistical associations between genomic variants and traits of interest.Despite major advances in DNA sequencing technologies, deciphering the genetic basis of complex traits remains a major challenge in modern biology. Efforts to fully characterize the genetic variants underlying traits like human height have had limited success6. However, despite the promise of the E&R approach for dissecting complex traits, there are still unanswered questions regarding what constitutes optimal experimental design. To that end, simulations have assessed the effects of particular experimental parameters on the outcomes of E&R experiments designed to model the evolution of complex traits9. These simulations support intuitive practices such as maximizing replication and experimental duration, and they recommend maximizing levels of starting genetic variation in experimental populations (i.e. by including more founder haplotypes). Our work here aims to determine how to best achieve this recommendation in experiments with outcrossing Saccharomyces cerevisiae.In order to most appropriately model the evolution of complex traits in humans, E&R studies should employ sexually-reproducing eukaryotic organismsDrosophila19. However, outcrossing S. cerevisiae populations have emerged as an attractive alternative due to shorter generation times, ease of replication, the ability to freeze and “resurrect” samples taken from different timepoints, and comparable genetic resources21. As such, we have chosen to focus our efforts on determining how to best construct populations of outcrossing S. cerevisiae for use in E&R studies with respect to maximizing standing genetic variation and haplotype diversity.Most E&R experiments with sexually-reproducing model organisms use S. cerevisiae populations isolated from natural or industrial settings typically lack genetic heterogeneity, generating laboratory populations with standing genetic variation requires crossing distinct isogenic strains. These “synthetic recombinant” or multiparent populations have traditionally been used for QTL mapping23, but they can also serve as the ancestral population in evolution experiments21. Notably, synthetic recombinant populations have also been used for E&R in other model organisms such as D. melanogaster  and Caenorhabditis elegans . In this context, having clearly defined founder genotypes enables the estimation of haplotype frequencies in evolved populations from “Pool-SEQ” data26. And this ability to detect haplotypes responding to selection, as opposed to individual markers, allows for better characterization of linkage and genetic hitchhiking in evolved populations27 than is possible in studies where founder genotypes are unknown . All of this said, to our knowledge little has been done to explicitly assess how to best generate a recombinant population for downstream use in an evolution experiment. While simulations predict that maximizing the number of founding haplotypes and levels of genetic variation will improve experimental outcomes, there are practical and biological limits to the number of haplotypes that can be combined into a single recombinant background and effectively maintained. One of our goals here is to evaluate the costs and benefits of increasing the number of founding haplotypes in a synthetic recombinant population for E&R.As 19). With this strategy, the genetic makeup of the resulting population will depend on mating efficiency between individuals from different founding strains, reproductive output, and chance. However, work with Drosophila has shown there is no significant allele frequency differentiation between independently constructed synthetic populations using the same sets of isofemale lines28. Such a high level of reproducibility could indicate that mating efficiency and differences in reproductive output among founding lines are negligible, or it could indicate that these differences exist and shape the genetic makeup of recombinant populations in a parallel manner. So, while this approach is relatively simple and practical to implement, it may be difficult to maximize the amount of genetic variation and founding haplotype representation in synthetic populations when there are drastic differences in reproductive output and/or mating efficiencies among founding strains. In other words, if there is substantial variation in mating efficiencies among founding strains, this could lead to the over-representation of certain haplotypes belonging to strains that mate most efficiently. By contrast, the second approach for constructing synthetic populations is more complex and involves some level of pairwise crossing between founders29. While this approach is significantly more labor-intensive and time-consuming, it perhaps has advantages in terms of producing populations that have more equal founder haplotype representation and, consequently, higher levels of genetic variation.Another of our goals is to evaluate how different methods of crossing founder strains impact levels of genetic variation in synthetic recombinant populations. There are two general approaches to this task across model organisms. In the more common approach, individuals sampled from various lines are simply mixed in equal proportions and allowed to mate freely prior to the start of the experiment  in 2017. Briefly, these strains were modified so that MATa and MATα strains both contain ho deletions to prevent mating-type switching, but each contain a different drug-resistance marker in a pseudogene (YCR043C) tightly linked to the mating type locus . These genotypes enable haploids of each mating type to be recovered using media supplemented with either hygromycin B or nourseothricin sulfate, and they enable newly mated a/α diploids to be recovered in media supplemented with both drugs.All yeast strains used in this study originated from heterothallic, haploid, barcoded derivatives of the SGRP yeast strain collectionTwo different crossing strategies were used to create genetically diverse populations using 4, 8, and 12 strains as founders (Table 600) before being pooled in equal volumes in a 50 mL conical tube. Populations were given 90 min to mate at room temperature, then were plated on agar plates supplemented with both NTC/hyg/G418 so that only newly mated diploid cells could grow. The resulting lawns of mated diploid cells were collected by scraping with a sterile glass slide into fresh YPD media. This “cell bank” was archived as a frozen stock at − 80º °C for each of the S-type populations made with 4, 8 and 12 founders  were built to mimic more careful crossing designs in which founding lines are crossed in pairs and/or a round-robin. To accomplish this, each haploid strain was paired with a different strain of the opposite mating type and mated as described above. Successful diploid colonies were isolated, grown overnight in 1 mL of YPD, washed and resuspended in sporulation media (1 mL 1% potassium acetate), then cultured for 72 h at 30 °C/200 rpm. Tetrads from these diploid cells were dissected using a SporePlay dissecting microscope (Singer). The four meiotic products (spores) were then collected, allowed to grow for 2 days, and replica plated to plates containing either NTC or hyg to verify the proper segregation of drug resistance markers and thus mating types. Once validated, the meiotic products were grown overnight in 1 mL of YPD. Overnight cultures were standardized to the same optical density  were grown overnight in 10 mL YPD media. These cultures were washed and resuspended in sporulation media, and incubated with shaking for 72 h (30 °C/200 rpm). Cells then underwent a number of methods to disrupt asci and isolate/randomize spores, including incubation with Y-PER yeast protein extraction reagent (Thermo) to kill vegetative diploids, digestion with 1% zymolyase (Zymo Research) to weaken ascus walls, and well as high-speed shaking with 0.5 mm silica beads (BioSpec) to mechanically agitate the asci. After these steps spores were resuspended in 10 mL YPD and allowed to settle and mate for 90 min at room temperature. Diploids were recovered as described above; cultures were transferred to 10 individual YPD agar plates supplemented with NTC/hyg/G418 in 200 μL aliquots and incubated at 30 °C for 48 h. The resulting lawns of mated diploid cells were collected by scraping with a sterile glass slide into fresh YPD media. This “cell bank” was again sampled for archiving at − 80 °C, and used to initiate an overnight culture for the next outcrossing cycle. We estimate that 15–20 asexual generations occurred between every outcrossing cycle of the experiment. Based on counting colonies from dilutions of cultures plated at various benchmarks during the protocol, we expect that 7.5–11 generations elapse during the overnight culture in YPD media, and another 7.5–11 generations elapse during the period of diploid recovery on agar plates. Thus, a minimum of 15*12 = 180 cell doublings likely took place over the 12 cycles of outcrossing in each synthetic recombinant population.After the creation of the 3 “K-type” and 3 “S-type” synthetic recombinant populations described above, all populations were taken through 12 consecutive cycles of intentional outcrossing; in other words, the populations were subjected in parallel to a series of steps that induced regular sporulation, spore isolation, and mating. Detailed methods are described by Burke et al.30 provide barcode and primer sequences). Once validated, single colonies were again isolated for whole-genome sequencing. One milliliter of YPD media was inoculated with single colonies, grown overnight, and the resulting culture was harvested for gDNA extracted using the Qiagen Puregene Yeast/Bact. Kit. Purified gDNA from each haploid founder was then prepared for sequencing using the Nextera DNA Sample Preparation Kit (Illumina). Some minor modifications to the manufacturer’s protocol were implemented to optimize throughput . Genomic DNA libraries were prepared for experimental recombinant populations in the same way and all samples were pooled to generate a single multiplexed library. Because the recombinant  populations require significantly higher coverage to accurately estimate allele frequencies at variable sites, these populations were added to the library at 10X the molarity of each haploid founder sample. The multiplexed library was run on two SE150 lanes on the HiSeq3000 at the OSU Center for Genomic Research and Biocomputing (CGRB). Data for the 4S populations were previously published in Burke et al.31 and raw fastq files are available through NCBI SRA (BioProject ID: PRJNA678990). Raw fastqs for all other populations are available through NCBI SRA (BioProject ID: PRJNA732717).Each of the recombinant K- or S-type population was sequenced at three specific timepoints: initially , after 6 cycles of outcrossing (“cycle 6”), and after 12 cycles of outcrossing (“cycle 12”). We also sequenced each haploid founder strain such that we could estimate the relative contributions of each to the recombinant populations. Each of the founding SGRP strains were plated as haploids on plates containing either NTC or hygromycin to verify the presence of the appropriate drug resistance markers. Individual colonies were isolated from each strain for verification of identifying barcodes at the URA3 locus using Sanger sequencing  and create a single VCF file for all variants identified across all replicate populations, using standard best practices workflows and default filtering options. We also downloaded and indexed a reference VCF file with SNP information for a number of distinct natural isolates of S. cerevisiae35; this is a recommended best practice for calibrating base quality with GATK v4.0. This VCF file was converted into a SNP frequency table by extracting the AD  and DP (unfiltered depth) fields for all SNPs passing quality filters; the former field was used as the minor allele count and the latter was used as the total coverage. The python scripts used to generate and convert VCF files to tables suitable for downstream analyses in R (www.R-project.org) are available through GitHub .We have developed a processing pipeline for estimating allele frequencies in each population directly from our pooled sequence data. We used GATK v4.0Our general SNP analysis strategy involved portioning the data to create three separate SNP tables with each table corresponding to a set of founders and populations derived from them (e.g. a table containing with the S4 and K4 populations and their founders). In each table, we chose to only include sites with a minimum coverage > 20X in the in synthetic populations as a quality control measure. Next, sites were filtered based on data from the founder populations. We excluded all sites that appeared to be polymorphic within a given founder, and sites where a single nucleotide was fixed across all founders. This was done as such occurrences could indicate sequencing error given that our founder strains are haploid and isogenic, and a site is unlikely to be polymorphic in our synthetic populations if it is fixed across all of the founders. After these filters were applied, we retained a collection of high-quality SNPs in each population to subject to further analysis. The total number of SNPs identified in each population is given in Table 36 in R to visualize patterns of overlap between the total number of SNPs possible for a given combination of founder strains, and the SNPs we observed in our actual populations. We define the total number of possible SNPs as all loci for which at least one of the founding strains used has an allele different from the others; this number will therefore differ among the 4-way, 8-way, and 12-way crosses.Our main objective was to evaluate how crossing strategy and the number of founder strains impacts patterns of SNP variation in synthetic recombinant populations. To that end, we assessed SNP-level variation in our recombinant populations using several metrics. First, we simply determined the number of polymorphic sites segregating in each population immediately following their creation (cycle 0), and monitored how that number changed over time ((i.e. after 6 or 12 outcrossing cycles). This approach of tracking the total number of SNPs should reveal whether particular crossing strategies – i.e. using a certain number of founders, and/or one of the two crossing strategies – consistently produced populations with more SNPs, and whether these SNPs were maintained or lost over 12 outcrossing cycles. We also generated UpSet plots using the UpsetR package2pq, the product of the reference  and alternate allele frequency at a given site multiplied by 2. In addition to looking at differences in mean genome-wide heterozygosity between populations, we also generated sliding window plots showing patterns of variation across each chromosome. To define windows, we used the GenWin package37 in R with the following parameters: “smoothness = 6000, method = 3.” GenWin itself uses a smoothing spline technique to define windows based on breakpoints in the data. While we ultimately used “smoothness = 6000”, we did initially try a range of values. Our final selection was made based on what most clearly represented trends in the data. For interested parties, plots with more or less smoothness can be easily generated using data and scripts we have made available through Dryad and Github (See “Data availability” statement for details).In addition to SNP number, we also characterized the distribution of SNP frequencies in each population, which allows more direct comparisons between populations with different numbers of founders but the same crossing strategy, or the same number of founders but different crossing strategy. To do this, we focused on two metrics: the site frequency spectrum (SFS), and genome-wide heterozygosity. Here heterozygosity refers to It is worth noting that our ability to assess levels of genetic variation across our synthetic populations is limited by the fact we have only collected Pool-SEQ data. Given the complex life-history of the yeast populations in this experiment, which involves periods of 7–15 generations of asexual growth punctuated by discrete outcrossing events, it is not possible for the genotypes of all individuals in the population to be shuffled by recombination every generation. Therefore, asexual lineages will evolve by clonal interference for relatively short periods of time, until the next outcrossing event decouples individual adaptive alleles from a particular genetic background. It is possible that during these periods of clonal interference, particular diploid lineages will dominate, and if these lineages are heterozygous at a given locus, that will lead to an artificially elevated heterozygosity value at that SNP. But we do not believe this is a major complication in the understanding of nucleotide diversity in our experiment for several reasons; namely, that our outcrossing protocol includes several measures that maximize outcrossing efficiency , the generally high rate of recombination in yeast, and that the periods of asexual growth are short and unlikely to exceed ~ 20 cell doublings.2 test as implemented in the poolSeq38 package in R to compare SNP frequencies between cycle 0 and cycle 12 in each synthetic population. We chose this particular test based on a benchmarking effort that suggests it is well-suited to detecting selection in E&R experiments lacking replication9. After results were generated for each synthetic population, log transformed p values were plotted for each chromosome across sliding windows. The GenWin package in R  was once again used to define windows based on breakpoints in the data. Plots were then examined to see if there were any genomic regions showing signs of selection based on significance levels relative to the background.Although statistical power in this power is limited due to a lack of replication, we attempted to identify regions of the genome showing obvious responses to selection in each synthetic population. Specifically, we used Pearson’s χhttps://github.com/tdlong/yeast_SNP-HAP. Our results were generated by using the haplotyper.limSolve.code.R script and estimates were made across 30 KB windows with a 1 KB stepsize. This particular haplotype caller was developed specifically to estimate haplotype frequencies in multiparent populations when founder haplotypes are known. A full description of the algorithm being used, and results of empirical validation can be found in Linder et al. (2020). To quantify haplotype variation in each population, we calculated haplotype diversity (H) using the following formula: (1) xi is the frequency of the ith haplotype of the n founders used to create given population39. Though it is worth noting that maximum expected H will vary depending on the number of founders used to create a given population as (2) In addition to describing SNP diversity, we also describe the diversity of founder haplotypes represented in our synthetic populations. We were particularly interested in evaluating whether the S-type strategy might produce populations in which founder haplotypes are more evenly represented (at intermediate frequency) compared to the K-strategy. Given the stochasticity inherent in the K-type strategy, we thought it probable that founder genotypes with especially high sporulation and/or mating efficiencies (i.e. those with the highest reproductive outputs in the outcrossing context) might come to dominate. To this end, we estimated haplotype frequencies in all experimental populations initially, and after 6 and 12 cycles of outcrossing to determine how evenly haplotypes were represented, and how this might have changed over time. We used the sliding-window haplotype caller described in Linder et al. (2020) and software the authors have made available as a community resource: th outcrossing cycle) were revived by plating 1 mL of thawed culture onto a YPD agar plate and incubation at 30 °C for 48 h. In order to sample the genetic diversity of each population, a sterile wooden applicator was scraped in a zig-zag pattern across the lawn of cells on each plate to collect a pinhead-sized clump of yeast. Each clump was mixed in 10 mL YPD in a 50 mL conical tube and vortexed. Tubes were then incubated at 30 °C/200 rpm for ~ 24 h. After confirming that each tube had comparable cell densities – this was done by verifying that the OD600 absorbance value of a 1:100 dilution ranged between 0.095 and 0.2—cell pellets were collected by spinning for 5 min at 5000 rpm. Cell pellets were washed in 1 mL of sterile water, spun down again, and resuspended in 40 mL of minimal sporulation media (1% potassium acetate w/v). Each culture was transferred to sterile 250 mL Erlenmeyer flasks and covered loosely with foil, where they were cultured at 30 °C/200 rpm for ~ 72 h to sporulate. After sporulation, aliquots of each culture were loaded onto a hemacytometer  and visualized under 40 × magnification on a Singer SporePlay microscope. For each culture, ~ 200 cells were counted (specific range: 190–230 cells), and sporulation efficiencies were estimated as the proportion of tetrads observed over the total number of cells in the field of view. Sporulation efficiency for each of the 12 recombinant populations (6 “cycle 0” and 6 “cycle 12”) was assessed by averaging these proportions over 2–3 independent biological replicates.To evaluate the possibility that populations might be phenotypically differentiated, we measured two life-history traits: sporulation efficiency and growth rate. We estimated the 3-day sporulation efficiency for each recombinant population at the beginning and end of the experiment, as this is a life-history trait that might have reasonably responded to the selection imposed by the regular outcrossing protocol. All populations archived at “cycle 0” (i.e. the pool of diploid cells used to initiate each K- or S-type population) and “cycle 12” . 200uL of each culture was aliquoted to separate wells of a 96-well plate, with two technical replicates per biological replicate. The arrangement of technical replicates on the plate was carried out in an attempt to control for possible edge effects. The growth rate assay was carried out in a Tecan Spark Multimode Microplate Reader, set to record the absorbance at 600 nm for each well every 30 min for 48 h at 30 °C, without plate agitation/aeration. The R-package “Growthcurver” (Sprouffske and Wagner40) was used to estimate population growth parameters from the raw data. In order to determine the carrying capacity and doubling time of the culture in each well, the absorbance measurements taken during the assay were fit to the following equation:Nt is the absorbance reading at time t, N0 is the initial absorbance, K is the carrying capacity, and r is the growth rate, or doubling time. Here, doubling time refers to the time necessary for the size of a population to double under non-restricted conditions, while carrying capacity is the maximum population size under the given conditions. The values for each biological replicate were averaged across technical replicates, and the values for each strain/population were determined by averaging across biological replicates.In addition to characterizing sporulation efficiencies for each of the “cycle 0” and “cycle 12” recombinant populations, we also measured growth rate with high-throughput absorbance-based assays in liquid YPD. We also included the 12 founder strains in this assay, for comparison with the recombinant populations. S- and K-type recombinant populations were sampled from each freezer recovery plate as described above. Haploid founder strains were revived from freezer stocks by striking for single colonies onto YPD agar plates. Each population or strain was assayed in two biological replicates; recombinant populations were sampled to inoculate two separate overnight cultures in liquid YPD, and strains were sampled by picking two distinct colonies to initiate two separate overnight cultures (one colony per culture). All biological replicates were incubated for ~ 24 h at 30 °C/200 rpm. The day of the assay, ODTo assess how crossing strategy and number of founder strains impacts SNP variation, we began by simply counting the number of SNPs present in each of our synthetic populations upon their creation and how that changes over several cycles of recombination Table . As expeWe also examined patterns of overlap between our synthetic populations and what is possible given their respective founders using UpSet plots  implicated more candidate regions from the literature than the others, this could provide evidence that a particular crossing strategy might lead to the best outcomes in an E&R study, with respect to identifying regions of interest. We do find instances where peaks in significance align with previously identified candidate regions . Based on their analysis of genomic data taken from each population across three timepoints, Burke et al. (2014) identified five major candidate regions associated with adaptation to forced outcrossing. Four of these regions  were also found to be clearly defined by a given haplotype at the end of the experiment . As the founders used to create the synthetic populations of Burke et al. (2014) are present in all of the populations featured in this study, we can reasonably interpret changes in haplotype frequency in these four candidate regions in our populations as evidence of selection. In other words, as our populations here are unreplicated, and we cannot use strong-inference approaches with them directly, we sought parallels between haplotype frequency differences observed here and those described as adaptive in a very similar experiment.Next, we evaluated whether any of our observed patterns of haplotype representation could be easily explained by selection. To do this we compared our findings to the results of Burke et al.20 study also show substantial increases in frequency in our populations. In candidate regions A  and E , we find clear evidence of the Y12 haplotype frequency increasing across all of our populations as it did in the Burke et al. (2014) study. Given how dramatic some of these changes are from cycle 0 to cycle 12, we think it reasonable to speculate that these haplotypes might also be driving adaptation here. However, this pattern in not corroborated for candidate regions B  and C . Here the haplotypes driving adaptation in the Burke et al.20 study actually decrease in frequency in most instances.As shown in Table Sporulation efficiencies were estimated in all recombinant populations initially, and after 12 cycles of outcrossing .Haplotype frequency estimates for our synthetic populations suggest that a strategy involving pairwise crosses results in better representation and maintenance of founder genotypes Fig. . It is cS. cerevisiae have reported evidence of such “cheater” lineages . Since we observe intermediate levels of haplotype representation in the K4 population mid-way through the experiment . Similarly, YPS128 which has a mean frequency of 0.41 in one of their populations appears at frequencies below 0.11 in all of our populations except S4. That being said, it is worth noting that while there is overlap in the strains used between these studies, maintenance protocols are different, and their populations include several strains absent in our study. So, we can only say that no common founders appear to universally favored when selection for frequent outcrossing is imposed.While we interpret the differences between the different crossing strategies  as being primarily driven by initial differences in sporulating and mating efficiencies between strains and subsequent genetic drift, selection may also play a role. For instance, we do find evidence that two specific regions of the genome exhibit similar dynamics in haplotype frequencies, compared to similar prior work; Burke et al.ns Table  with meaOur analysis comparing initial SNP frequencies to those observed after 12 rounds of outcrossing in each population also yields possible evidence for adaptation, though our interpretation here is cautious. We do observe regions that produce peaks in significance in some populations that overlap regions described by other studies to underlie traits related to outcrossing (e.g. Figure Given that any adaptation we did observe can only be ascribed to the outcrossing process and associated handling protocols, we conclude that there is likely a great deal of value in allowing newly-established synthetic populations to experience several cycles of outcrossing – this can also be thought of as laboratory domestication – before any sort of new selective pressure is imposed. To be explicit, if some other selection pressure was intentionally imposed on our populations immediately following cycle 0, it would be very difficult to dissect the specific genetic changes that might occur due to that pressure, other laboratory handling steps, or general selection for reproduction via outcrossing.We assayed sporulation efficiencies and growth rates in recombinant populations as these are both important fitness characters that may be impacted by crossing strategy, or that might respond to the selection imposed by many cycles of forced outcrossing. Looking at sporulation efficiencies (Supplementary Table Comparing growth rates of founder strains to recombinant populations (Supplementary Table 8. Here, we provide empirical results that also support these recommendations. Across the metrics we examined, we consistently find that a crossing strategy involving careful pairwise crosses leads to populations with more standing genetic variation than those produced by simply mixing founder genotypes in equal proportion. As such, using this sort of strategy would be our primary recommendation for researchers aiming to establish recombinant populations from clonal strains or isogenic lines for use in E&R studies. Using more founders also results in greater total levels of genetic variation but comes at the cost of maximizing representation of all possible alleles from a given set of founders. So, the number of founders to used should be chosen based upon the specific goals of an E&R study, and the questions it hopes to test. Finally, our results should be placed in the context of yeast biology. Meaning, given the high degree of genetic differentiation between our yeast strains and the variation among them for mating and sporulation efficiencies, the patterns we observe are likely more extreme than what might be expected when creating a Drosophila synthetic population using either the K-type or S-type approach. However, it is certainly conceivable that mating preferences and genetic incompatibilities among isogenic Drosophila lines could impact levels of genetic variation and haplotype representation when they are crossed. Our findings therefore perhaps warrant consideration even when creating synthetic populations in non-yeast systems.The results of simulated E&R studies in which populations are sexually reproducing and adaptation is driven by standing genetic variation have led to general experimental design recommendations that maximize genetic variation in the ancestral population, specifically by increasing the number of starting haplotypesSupplementary Information.