Evolutionary Applications

Published by Wiley
Online ISSN: 1752-4571
Print ISSN: 1752-4563
Discipline: Evolution
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Aims and scope

Evolutionary Applications is a fully peer reviewed open access journal. It publishes papers that utilize concepts from evolutionary biology to address biological questions of health, social and economic relevance. Papers are expected to employ evolutionary concepts or methods to make contributions to areas such as (but not limited to): medicine, agriculture, forestry, exploitation and management (fisheries and wildlife), aquaculture, conservation biology, environmental sciences (including climate change and invasion biology), microbiology, and toxicology. All taxonomic groups are covered from microbes, fungi, plants and animals. In order to better serve the community, we also now strongly encourage submissions of papers making use of modern molecular and genetic methods (population and functional genomics, transcriptomics, proteomics, epigenetics, quantitative genetics, association and linkage mapping) to address important questions in any of these disciplines and in an applied evolutionary framework. Theoretical, empirical, synthesis or perspective papers are welcome.



Recent publications
Map of the study area where Malacosoma disstria egg bands and larvae were sampled (n = 21 collection locations). Individuals were collected from four host tree species: Trembling aspen (Populus tremuloides), sugar maple (Acer saccharum), red oak (Quercus rubra), and white birch (Betula papyrifera). Ecological niche models, parameterized using the collection locations of sequenced individuals and M. disstria global biodiversity information facility (GBIF) records, were used to predict habitat suitability across the study area. Predictors included both geographic and environmental/ecological geographic information system (GIS) variables. Within the map of predicted habitat suitability, higher index scores correspond to higher suitability.
Population genetic structure of Malacosoma disstria, using (a) principal component analysis (PCA), (b) discriminant analysis of principal components (DAPC), and (c) model‐based clustering with structure. For PCA and DAPC plots, every point represents a sequenced individual (n = 104), color coded according to the host tree species it was collected from. Our structure analyses addressing K = 1:10 found an optimal value of K = 2; plotted bars show each individual's proportional membership to each cluster. Within the admixture plot, individuals are sorted according to host association and then by increasing latitude.
Pairwise heatmap visualizing results of reciprocal causal modelling (RCM) that assess correlates of genomic differentiation in Malacosoma disstria. Variables included in this analysis were categorized as measures of isolation by distance (IBD), isolation by resistance (IBR), isolation by environment/ecology (IBE), or host‐associated differentiation (HAD as a form of IBE). Euclidean distances were estimated between all sequenced individuals. Least‐cost and resistance distances were estimated using a resistance surface parameterized as the inverse of predicted habitat suitability. Environmental/ecological distances were measured as the absolute difference in the values of environmental variables at the collection location of sequenced individuals. Host association measured whether sequenced individuals were collected on the same (0) or different (1) host tree species. Within the heatmap, values in each cell represent results of RPM‐A–RPM‐B, with red and blue colors indicating positive and negative values, respectively. Rows and columns contain the focal and alternative variables, respectively, for partial Mantel test A within each reciprocal model. This heatmap should be interpreted by rows and not columns; variables on the y‐axis with more positive (red) values in their rows are the strongest correlates of genomic differentiation after partialling out relationships with alternative variables.
Diverse geographic, environmental, and ecological factors affect gene flow and adaptive genomic variation within species. With recent advances in landscape ecological modelling and high‐throughput DNA sequencing it is now possible to effectively quantify and partition their relative contributions. Here we use landscape genomics to identify determinants of genomic differentiation in the forest tent caterpillar, Malacosoma disstria, a widespread and irruptive pest of numerous deciduous tree species in North America. We collected larvae from multiple populations across eastern Canada, where the species experiences a diversity of environmental gradients and feeds on a number of different host tree species, including trembling aspen (Populus tremuloides), sugar maple (Acer saccharum), red oak (Quercus rubra), and white birch (Betula papyrifera). Using a combination of reciprocal causal modelling (RCM) and distance‐based redundancy analyses (dbRDA), we show that differentiation of thousands of genome‐wide single nucleotide polymorphisms (SNPs) among individuals is best explained by a combination of isolation by distance, isolation by environment (differences in summer temperatures and length of the growing season), and differences in host association. Configuration of suitable habitat inferred from ecological niche models was not significantly related to genomic differentiation, suggesting that M. disstria dispersal is agnostic with respect to habitat quality. Although population structure was not discretely related to host association, our modelling framework provides the first molecular evidence of host‐associated differentiation in M. disstria, congruent with previous documentation of reduced growth and survival of larvae moved between natal host species. We conclude that ecologically‐mediated selection is contributing to variation within M. disstria, and that divergent adaptation related to both environmental conditions and host association should be considered in ongoing research and management of this important forest pest.
Global climate change has threatened world crop production and food security. Decoding the adaptive genetic basis of wild relatives provides an invaluable genomic resource for climate-smart crop breedinG. Here, we performed whole-genome sequencing of 185 diverse wild soybean (Glycine soja) accessions collected from three major agro-ecological zones in China to parse the genomic basis of local adaptation in wild soybean. The population genomic diversity pattern exhibited clear agro-ecological zone-based population structure, and multiple environmental factors were observed to contribute to the genetic divergence. Demographic analysis shows that wild soybeans from the three ecological zones diverged about 1 × 105 years ago, and then the effective population sizes have undergone different degrees of expansions. Genome-environment association identified multiple genes involved in the local adaptation, such as flowering time and temperature-related genes. A locus containing two adjacent MADS-box transcription factors on chromosome 19 was identified for multiple environmental factors, and it experienced positive selection that enables the adaptation to high-latitude environment. This study provides insights into the genetic mechanism of ecological adaptation in wild soybean that may facilitate climate-resilient soybean breeding.
Fisher's geometric model in species with complex life cycles: an example involving three life‐history stages and two traits that are shared across stages (the traits are represented by the y‐ and x‐axes, per circle; pictograms show two different larval stages (zoea and megalopa), and the adult stage (crab). The optimal phenotype per stage (O1, O2, O3) occurs at the intersection between the dotted lines, and stage‐specific survival decreases with distance from the optimum. A1, A2, and A3 represent wild‐type phenotypes of the population (the filled circles). Directional selection in each stage is represented by a vector (the solid arrows) that points from the current phenotype to the optimal phenotype. The stage‐specific distances to the optima are represented by z1, z2, and z3. Angles between stage‐specific vectors of directional selection are θ12$$ {\theta}_{12} $$ (i.e., between stages 1 and 2), θ13$$ {\theta}_{13} $$, and θ23$$ {\theta}_{23} $$. An example is shown of a mutation that alters trait expression in all three stages. Vectors show the effect of the mutation on the phenotype with ri (for stage i) representing the magnitude of the change. The example mutation is beneficial for stages 1 and 3 (mutant individuals are closer to the optimum) and deleterious for stage 2.
Trade‐offs between stages. Among mutations that are beneficial in at least one stage, fA is the fraction that exhibits a trade‐off. The solid curves show the lower bound for fA (see Equations [7 and 8]). Circles are simulated values for fA, each based on 10⁶ simulated mutations and different mutation sizes (mutation sizes are presented in Fisher's scale, rn/2z$$ r\sqrt{n}/2z $$, where r is the mutation's absolute magnitude, n is the number of traits, and z is the distance to the optimum; see Orr, 1998). Results show cases in which the mutational variance for survival (i.e., Equation 5) is equal among stages, and there are no carry‐over effects (i.e., cij = 0 in Equation (3)). In the right‐hand panel (three stages), the thin gray line shows the case where two of the three stages are perfectly correlated with each other, and the remaining stage varies in its correlation with the other two. The remaining results show the case where all stages are equally correlated with each other (i.e., ρ12w=ρ13w=ρ23w$$ {\rho}_{12}^w={\rho}_{13}^w={\rho}_{23}^w $$).
Stage‐specific adaptation and orientations of directional selection during adaptive walks toward stage‐specific optima. Results show cases where the optima for a life cycle of three stages are equally divergent from one another (they form an equilateral triangle in multidimensional space) and there are no carry‐over effects (c12 = c13 = c23 = 0). Given symmetry in these results, we present the evolutionary movement of the first stage to its optimum (∆z1$$ \Delta {z}_1 $$, where ∆z1=0$$ \Delta {z}_1=0 $$ corresponds to no adaptation, and ∆z1=1$$ \Delta {z}_1=1 $$ corresponds to a completed adaptive walk), and the orientation of selection in stage 1 relative to stage 2 (−1<cosθ12<1$$ -1<\cos \left({\theta}_{12}\right)<1 $$, where cosθ12$$ \cos \left({\theta}_{12}\right) $$ captures the correlation of directional selection between stages 1 and 2). Means are based on 100 adaptive walks (top panel) or 5000 walks (bottom panel), with gray lines showing the values of cosθ12$$ \cos \left({\theta}_{12}\right) $$ for the first 50 adaptive walks. Initial conditions were z1 = z2 = z3 = 1 and cos(θ12) = cos(θ13) = cos(θ23) = 0.95, with selection parameters ω1 = ω2 = ω3 = ½, and equal marginal distributions of mutant phenotypic effects on each stage. Mutational magnitudes (r1, r2, r3) follow an exponential distribution (gamma with parameters α = 1 and λ = 0.1). Results for intermediate phenotypic effect correlations between stages are presented in Figure S2.
Phenotypic divergence and stage‐specific orientations of selection during adaptive walks with strong carry‐over effects between stages (c12 = c13 = c23 = 2). The simulations are otherwise identical to those in Figure 3. Results for intermediate phenotypic effect correlations between stages are presented in Figure S2.
Most marine organisms have complex life histories, where the individual stages of a life cycle are often morphologically and ecologically distinct. Nevertheless, life history stages share a single genome and are linked phenotypically (by ‘carry‐over effects’). These commonalities across the life history couple the evolutionary dynamics of different stages and provide an arena for evolutionary constraints. The degree to which genetic and phenotypic links among stages hamper adaptation in any one stage remains unclear and yet adaptation is essential if marine organisms will adapt to future climates. Here, we use an extension of Fisher’s geometric model to explore how both carry‐over effects and genetic links among life history stages affect the emergence of pleiotropic trade‐offs between fitness components of different stages. We subsequently explore the evolutionary trajectories of adaptation of each stage to its optimum using a simple model of stage‐specific viability selection with nonoverlapping generations. We show that fitness trade‐offs between stages are likely to be common, and that such trade‐offs naturally emerge through either divergent selection or mutation. We also find that evolutionary conflicts among stages should escalate during adaptation, but carry‐over effects can ameliorate this conflict. Carry‐over effects also tip the evolutionary balance in favour of better survival in earlier life history stages at the expense of poorer survival in later stages. This effect arises in our discrete‐generation framework and is, therefore, unrelated to age‐related declines in the efficacy of selection that arise in models with overlapping generations. Our results imply a vast scope for conflicting selection between life‐history stages, with pervasive evolutionary constraints emerging from initially modest selection differences between stages. Organisms with complex life histories should also be more constrained in their capacity to adapt to global change than those with simple life histories.
Population structure analysis of the 17 populations of lake whitefish (Coregonus clupeaformis) sampled across Lake Michigan. Details about the sampling sites are provided in Table 1. (a) Shows the geographic locations of the 17 populations in Lake Michigan. Populations in the legend were boxed and color coded to represent their geographical subset. Individual‐based principal component analyses (PCA) using all 197,588 SNPs shows the overall population structure with the largest genetic break between northwestern and eastern sides of Lake Michigan (b). Regional population structure in the northwestern (c), eastern (d) and northeastern Lake Michigan (e) were also shown.
Correlation of linearized pairwise FST, FST/(1−FST), with the pairwise in‐water distance (unit not specified) among all population pairs (a), populations within the same side of the lake (b), and population within the same side of the lake but with MSKG removed (c). Isolation by distance (IBD) was assessed using mantel tests with 10,000 permutations.
Genome scan analyses using pcadapt on the whole dataset and each geographic region‐specific dataset (northwestern, eastern, and northeastern Lake Michigan). Orange points are pcadapt outliers with adjusted p values (q values) less than 0.01 (red dashed line). Six candidate regions under selection are highlighted in purple (see Table S4 for details). The y‐axis was restricted to the range 0–10 for visualization purpose.
Pairwise FST heatmap between 17 lake whitefish populations in Lake Michigan using pcadapt outliers in each candidate region shown in Figure 3. Non‐significant pairwise FST values were labeled with an “X”. the number of pcadapt outliers in each candidate region is 12 (chromosome 4), 23 (chromosome 7), 24 (chromosome 10), 16 (chromosome 11), 22 (chromosome 18), and 85 (chromosome 20), respectively.
Putative chromosomal inversion on chromosome 20 (45.4–53.8 Mb). (a) Lostruct identified a series of eight windows with extremely high loading values along MDS1 within this region (purple shade); (b) and (c) PCA using 85 pcadapt outliers within this region showed that individuals were grouped into two clusters using k‐means clustering (cluster 0 and 1) along PC1 and the grouping pattern was not associated with populations. (d) Genotype heatmap using 85 pcadapt outliers showed clear haplotype structure within this region. Rows represent individuals, which are color ordered by PCA clusters (cluster 0 and 1); columns represent SNPs, which are ordered by chromosomal positions.
Understanding patterns of genetic structure and adaptive variation in natural populations is crucial for informing conservation and management. Past genetic research using 11 microsatellite loci identified six genetic stocks of lake whitefish (Coregonus clupeaformis) within Lake Michigan, USA. However, ambiguity in genetic stock assignments suggested those neutral microsatellite markers did not provide adequate power for delineating lake whitefish stocks in this system, prompting calls for a genomics approach to investigate stock structure. Here, we generated a dense genomic dataset to characterize population structure and investigate patterns of neutral and adaptive genetic diversity among lake whitefish populations in Lake Michigan. Using Rapture sequencing, we genotyped 829 individuals collected from 17 baseline populations at 197,588 SNP markers after quality filtering. Although the overall pattern of genetic structure was similar to the previous microsatellite study, our genomic data provided several novel insights. Our results indicated a large genetic break between the northwestern and eastern sides of Lake Michigan, and we found a much greater level of population structure on the eastern side compared to the northwestern side. Collectively, we observed five genomic islands of adaptive divergence on five different chromosomes. Each island displayed a different pattern of population structure, suggesting that combinations of genotypes at these adaptive regions are facilitating local adaptation to spatially heterogenous selection pressures. Additionally, we identified a large linkage disequilibrium block of ~8.5 Mb on chromosome 20 that is suggestive of a putative inversion but with a low frequency of the minor haplotype. Our study provides a comprehensive assessment of population structure and adaptive variation that can help inform management of Lake Michigan’s lake whitefish fishery and highlights the utility of incorporating adaptive loci into fisheries management.
Spike brittleness in wheat. (a) Terminology of the wheat spike organs, depicting the rachis segments and spikelets and a single spikelet in ventral view. Archaeobotanical samples of (b) wild spikelet from the Ohalo II (dated 23,000 years ago) and (c) domesticated spikelet from the A'rugot cave (dated to the second century AD). (d) Wild emmer wheat (Triticum turgidum ssp. dicoccoides) spikelet with smooth wild abscission scar, and (e) durum wheat (T. turgidum ssp. durum) spikelet with a jagged break. (f) The phenotype of introgression line (IL)‐3A with intermediate brittle rachis and an abscission scar (g), an upper (smooth scar similar to wild wheat), and (h) bottom (rough edges torn from the nonshattering rachis similar to domesticated durum wheat). (i) The phenotype of wild emmer chromosome substitution line LDN(DIC)2A with an intermediate brittle rachis and an abscission scar of (j) an upper and (k) bottom parts of the spike. (l) Measures of the A (maximal width of the spikelet base, above the scar), D (scar width), and C (scar length) (based on Snir & Weiss, 2014). p‐Values represent differences between upper and lower spikelets, t‐test (n = 6). (m) A representative photo of mature spikes of domesticated emmer (T. turgidum ssp. dicoccum) cultivars, with quasi‐brittle rachises.
Spike‐shattering patterns in wild emmer. (a) Wild emmer wheat (Triticum turgidum ssp. dicoccoides) plants in their natural habitats in Israel, with mature disarticulating spikes. (b) Examples of wild emmer spikes after shattering, with 2–5 remaining spikelets (photos taken more than 2 months after full maturity). (c) Examples of half‐spike brittleness wild emmer spikes of plants grown under controlled conditions (collected in the Arbel nature reserve and the Mt. Gilboa habitats, Northern Israel), and (d) mature near‐isogenic line (NIL) of wild emmer wheat accession Zavitan with introgressed domesticated alleles of Btr‐A (chromosome 3A), and (e) spikes with a half‐brittle phenotype. SEM image of an abscission scar of an (f) upper and (g) lower spikelets. The upper image (f) confirming that the NILs have smooth scars similar to those of wild emmer Zavitan (i.e., BR phenotype) and lower image (g) confirming that the NILs have rough edges scars similar to those of durum wheat. White arrow points to the smooth or rough edges scar associated with the shattering versus nonshattering phenotype.
Two opposing models currently dominate Near Eastern plant domestication research. The core area‐one event model depicts a knowledge‐based, conscious, geographically centered, rapid single‐event domestication, while the protracted‐autonomous model emphasizes a non‐centered, millennia‐long process based on unconscious dynamics. The latter model relies, in part, on quantitative depictions of diachronic changes (in archaeological remains) in proportions of spikelet shattering to non‐shattering, towards full dominance of the non‐shattering (domesticated) phenotypes in cultivated cereal populations. Recent wild wheat genome assembly suggests that shattering and non‐shattering spikelets may originate from the same (individual) genotype. Therefore, their proportions among archaeobotanical assemblages cannot reliably describe the presumed protracted‐selection dynamics underlying wheat domestication. This calls for a reappraisal of the ‘domestication syndrome’ concept associated with cereal domestication.
Sustainable management of exploited populations benefits from integrating demographic and genetic considerations into assessments, as both play a role in determining harvest yields and population persistence. This is especially important in populations subject to size‐selective harvest, because size selective harvesting has the potential to result in significant demographic, life‐history, and genetic changes. We investigated harvest‐induced changes in the effective number of breeders (N̂b$$ {\hat{N}}_b $$) for introduced brook trout populations (Salvelinus fontinalis) in alpine lakes from western Canada. Three populations were subject to 3 years of size‐selective harvesting, while three control populations experienced no harvest. The N̂c$$ {\hat{N}}_c $$ decreased consistently across all harvested populations (on average 60.8%) but fluctuated in control populations. There were no consistent changes in N̂b$$ {\hat{N}}_b $$ between control or harvest populations, but one harvest population experienced a decrease in N̂b$$ {\hat{N}}_b $$ of 63.2%. The N̂b$$ {\hat{N}}_b $$/N̂c$$ {\hat{N}}_c $$ ratio increased consistently across harvest lakes; however we found no evidence of genetic compensation (where variance in reproductive success decreases at lower abundance) based on changes in family evenness (FÊ$$ \hat{FE} $$) and the number of full‐sibling families (N̂fam$$ {\hat{N}}_{fam} $$). We found no relationship between FÊ$$ \hat{FE} $$ and N̂c$$ {\hat{N}}_c $$ or between N̂fam$$ {\hat{N}}_{fam} $$/N̂c$$ {\hat{N}}_c $$ and FÊ$$ \hat{FE} $$. We posit that change in N̂b$$ {\hat{N}}_b $$ was buffered by constraints on breeding habitat prior to harvest, such that the same number of breeding sites were occupied before and after harvest. These results suggest that effective size in harvested populations may be resilient to considerable changes in Nc in the short‐term, but it is still important to monitor exploited populations to assess the risk of inbreeding and ensure their long‐term survival.
For hundreds of years, the color diversity of Mollusca shells has been a topic of interest for humanity. However, the genetic control underlying color expression is still poorly understood in mollusks. The pearl oyster Pinctada margaritifera is increasingly becoming a biological model to study this process due to its ability to produce a large range of colors. Previous breeding experiments demonstrated that color phenotypes were partly under genetic control, and while a few genes were found in comparative transcriptomics and epigenetic experiments, genetic variants associated to the phenotypes have not yet been investigated. Here, we used a pooled‐sequencing approach on 172 individuals to investigate color‐associated variants on three color phenotypes of economic interest for pearl farming, in three wild and one hatchery populations. While our results uncovered SNPs targeting pigment‐related genes already identified in previous studies, such as PBGD, tyrosinases, GST, or FECH, we also identified new color‐related genes occurring in the same pathways, like CYP4F8, CYP3A4 and CYP2R1. Moreover, we identified new genes involved in novel pathways unknown to be involved in shell coloration for P. margaritifera, like the carotenoid pathway, BCO1. These findings are essential to possibly implement future breeding programs focused on individual selection for specific color production in pearl oysters and improve the footprint of perliculture on Polynesian lagoon by producing less, but with a better quality.
Map of sample sites from LCT populations in Nevada, California, and Oregon, United States, with single (filled circles) and temporal (open circles) samples denoted; samples from several out of basin transplanted populations are also indicated. Bold lines denote three Major Geographic Management Units for LCT (Western, Eastern, Northwestern), and major river basins are noted. Location of a reference sample of naturalized rainbow trout and a previously assessed hybrid sample are also shown.
(a) Principal Components Analysis of full dataset of individual LCT collected in the field as well as naturalized rainbow trout from the Truckee River, California and Nevada (Local_RBT, orange dots); 1406 rainbow trout collected from across the Columbia River basin (Micheletti et al., 2018, Columbia_RBT, purple dots), individuals previously assessed as LCT‐RBT hybrids (MCD, lime green dots); and 58 YCT individuals (YCT, green dots), see text for further description. Lahontan watersheds containing individuals with notable hybridization detailed in the text are colored by major basin (South Fork Humboldt = SFH, gold, and Little Truckee River = LTR, pink); all others are shown in black (Other). (b) Results of admixture analysis using the clustering approach in ngsAdmix; each vertical bar represents an individual fish, with colors within indicating membership proportion in each of three clusters (k = 3). Along the x‐axis, YCT contains the YCT samples, represented in green; LCT contains the LCT samples largely represented in orange, with individuals at right demonstrating some hybridization with mostly RBT (purple); Local_RBT and Columbia_RBT contain the Truckee River naturalized rainbow trout and the 1406 rainbow trout collected from across the Columbia River basin (purple, Micheletti et al., 2018).
Comparison of estimates of various genetic diversity metrics with estimates of abundance and extinction probability generated from MPVA across nonhybridized LCT field populations meeting inclusion criteria (see text). Top 2 rows display results of linear models evaluating correlations between MPVA estimates of log10 30‐year Extinction (a, left three columns) and log10 Harmonic Mean Abundance (b, right three columns) versus nucleotide diversity (π), homozygosity (Ho), Tajima's theta (ΘT), Watterson's theta (ΘW), and theta skew (see text, Θdiff). Bottom row (c) displays regression results from Random Forest Models of 30‐year Extinction (left) and Harmonic Mean Abundance (right) MPVA estimates (left and right panels, respectively) considering the above genetic metrics; observed value from data (x‐axis) versus weighted predicted tree values (y‐axis, see text for details).
Difference in various genetic metrics over time for field nonhybridized populations of LCT that were temporally sampled (twenty populations, with the year span between sample periods ranging from 2 to 20 and averaging 12 years). Genetic metrics are nucleotide diversity (π), homozygosity (ho), Tajima's theta (ΘT), Watterson's theta (ΘW), and theta skew (Θdiff, see text).
Evaluation of the relationship between change in estimates generated from a Multiple Population Viability Model of LCT versus the change in genetic estimates over time (Delta statistic/Year). For populations with temporal genetic sampling, change in harmonic mean abundance (Delta Estimated Harmonic Mean N, top panel) and 30‐year extinction probability (static probability, Estimated PVA Extinction Risk, lower panel) versus per‐year average change for each genetic diversity statistic: nucleotide diversity (π), homozygosity (Ho), Watterson's theta (ΘW), Tajima's theta (ΘT), and theta skew (see text, Θdiff). See Table S1b for associated uncorrected and corrected p values.
The current extinction crisis requires effective assessment and monitoring tools. Genetic approaches are appealing given the relative ease of field sampling required to estimate genetic diversity characteristics assumed related to population size, evolutionary potential, and extinction risk, and to evaluate hybridization with non‐native species simultaneously. However, linkages between population genetic metrics of diversity from survey‐style field collections and demographic estimates of population size and extinction risk are still in need of empirical examples, especially for remotely distributed species of conservation concern where the approach might be most beneficial. We capitalized on an exceptional opportunity to evaluate congruence between genetic diversity metrics and demographic‐based estimates of abundance and extinction risk from a comprehensive Multiple Population Viability Analysis (MPVA) in a threatened fish, the Lahontan cutthroat trout (LCT). We sequenced non‐native trout reference samples and recently collected and archived tissue samples of most remaining populations of LCT (N=60) and estimated common genetic assessment metrics, predicting minimal hybridization with non‐native trout, low diversity, and declining diversity over time. We further hypothesized genetic metrics would correlate positively with MPVA‐estimated abundance and negatively with extinction probability. We uncovered several instances of hybridization that pointed to immediate management needs. After removing hybridized individuals, cautious interpretation of low effective population sizes (2‐63) suggested reduced evolutionary potential for many LCT populations. Other genetic metrics did not decline over time nor correlate with MPVA‐based estimates of harmonic mean abundance or 30‐year extinction probability. Our results demonstrate benefits of genetic monitoring for efficiently detecting hybridization and, though genetic results were disconnected from demographic assessment of conservation status, they suggest reduced evolutionary potential and likely a higher conservation risk than currently recognized for this threatened fish. We emphasize that genetic information provides essential complementary insight, in addition to demographic information, for evaluating species status.
Sampling locations of all Dipturus batis in this study (black points), full‐siblings (red squares), and half‐sibling pairs (orange and yellow triangles). Straight lines are drawn between full‐sibling (red) and half‐sibling (orange) pair capture locations. Half‐siblings captured in the same haul are indicated by yellow triangles. Latitude and longitude are in decimal degrees.
Mean (red line) and 95% credible intervals (black lines) adult breeding abundance of Dipturus batis in the Celtic Sea, estimated using CKMR in a Bayesian MCMC framework. 100 random iterations from the model are shown (grey lines). Estimates for the modelled cohorts (solid lines) and years following the last cohort in the model (dotted lines) are shown. Note that the mean tends upwards towards the beginning and end of the time‐series, which is an artefact occurring from taking averages.
Temporal changes in CPUE of Dipturus batis (left panel: Abundance; right panel: Biomass) for all stations fished (top) and for four stations sampled each year (bottom) during fishery‐dependent common skate surveys in the Celtic Sea by CEFAS in collaboration with fishing industry from 2014 to 2017.
Estimating the demographic parameters of contemporary populations is essential to the success of elasmobranch conservation programmes, and to understanding their recent evolutionary history. For benthic elasmobranchs such as skates, traditional fisheries-independent approaches are often unsuitable as the data may be subject to various sources of bias, whilst low recapture rates can render mark-recapture programmes ineffectual. Close-kin mark-recapture (CKMR), a novel demographic modelling approach based on the genetic identification of close relatives within a sample, represents a promising alternative approach as it does not require physical recaptures. We evaluated the suitability of CKMR as a demographic modelling tool for the critically endangered blue skate (Dipturus batis) in the Celtic Sea using samples collected during fisheries-dependent trammel-net surveys that ran from 2011 to 2017. We identified three full-sibling and 16 half-sibling pairs among 662 skates, which were genotyped across 6,291 genome-wide single nucleotide polymorphisms (SNPs), 15 of which were cross-cohort half-sibling pairs that were included in a CKMR model. Despite limitations owing to a lack of validated life-history trait parameters for the species, we produced the first estimates of adult breeding abundance, population growth rate, and annual adult survival rate for D. batis in the Celtic Sea. The results were compared to estimates of genetic diversity, effective population size (Ne), and to catch per unit effort (CPUE) estimates from the trammel-net survey. Although each method was characterised by wide uncertainty bounds, together they suggested a stable population size across the time-series. Recommendations for the implementation of CKMR as a conservation tool for data-limited elasmobranchs are discussed. In addition, the spatio-temporal distribution of the 19 sibling pairs revealed a pattern of site-fidelity in D. batis, and supported field observations suggesting an area of critical habitat that could qualify for protection might occur near the Isles of Scilly.
Outcrossing can be advantageous in a changing environment because it promotes the purge of deleterious mutations and increases the genetic diversity within a population, which may improve population persistence and evolutionary potential. Some species may therefore switch their reproductive mode from inbreeding to outcrossing when under environmental stress. This switch may have consequences on the demographic dynamics and evolutionary trajectory of populations. For example, it may directly influence the sex ratio of a population. However, much remains to be discovered about the mechanisms and evolutionary implications of sex ratio changes in a population in response to environmental stress. Populations of the androdioecious nematode Caenorhabditis elegans, are composed of selfing hermaphrodites and rare males. Here we investigate the changes in sex ratio of C. elegans populations exposed to radioactive pollution for 60 days or around 20 generations. We experimentally exposed populations to three levels of ionizing radiation (i.e. 0 mGy.h‐1, 1.4 mGy.h‐1, and 50 mGy.h‐1). We then performed reciprocal transplant experiments to evaluate genetic divergence between populations submitted to different treatments. Finally, we used a mathematical model to examine the evolutionary mechanisms that could be responsible for the change in sex ratio. Our results showed an increase in male frequency in irradiated populations, and this effect increased with the dose rate. The model showed that an increase in male fertilization success or a decrease in hermaphrodite self‐fertilization could explain this increase in the frequency of males. Moreover, males persisted in populations after transplant back into the control conditions. These results suggested selection favoring outcrossing under irradiation conditions. This study shows that ionizing radiation can sustainably alter the reproductive strategy of a population, likely impacting its long‐term evolutionary history. This study highlights the need to evaluate the impact of pollutants on the reproductive strategies of populations when assessing the ecological risks.
Assigning individuals to their source populations is crucial for conservation research, especially for endangered species threatened by illegal trade and translocations. Genetic assignment can be achieved with different types of molecular markers, but technical advantages and cost saving are recently promoting the shift from short tandem repeats (STRs) to single nucleotide polymorphisms (SNPs). Here, we designed, developed, and tested a small panel of SNPs for cost‐effective geographic assignment of individuals with unknown origin of the endangered Mediterranean tortoise Testudo hermanni. We started by performing a ddRAD‐seq experiment on 70 wild individuals of T. hermanni from 38 locations. Results obtained using 3,182 SNPs are comparable to those previously obtained using STR markers in terms of genetic structure and power to identify the macro‐area of origin. However, our SNPs revealed further insights into the substructure in Western populations, especially in Southern Italy. A small panel of highly informative SNPs was then selected and tested by genotyping 190 individuals using the KASP genotyping chemistry. All the samples from wild populations of known geographic origin were genetically re‐assigned with high accuracy to the original population. This reduced SNPs panel represents an efficient molecular tool that enables individuals to be genotyped at low cost (less than €15 per sample) for geographical assignment and identification of hybrids. This information is crucial for the management in‐situ of confiscated animals and their possible re‐allocation in the wild. Our methodological pipeline can easily be extended to other species.
Human actions are altering ecosystems worldwide. Among human‐released pollutants, ionizing radiation arises as a rare but potentially devastating threat for natural systems. The Chornobyl accident (1986) represents the largest release of radioactive material to the environment. Our aim was to examine how exposure to radiation from the Chornobyl accident influences dorsal skin coloration of Eastern tree frog (Hyla orientalis) males sampled across a wide gradient of radioactive contamination in northern Ukraine. We assessed the relationship between skin frog coloration (which can act as a protective mechanism against ionising radiation), radiation conditions, and oxidative stress levels. Skin coloration was darker in localities closest to areas with high radiation levels at the time of the accident, whereas current radiation levels seemed not to influence skin coloration in Chornobyl tree frogs. Tree frogs living within the Chornobyl Exclusion Zone had a remarkably darker dorsal skin coloration than frogs from outside the Zone. The maintenance of dark skin coloration was not linked to physiological costs in terms of frog body condition or oxidative status, and we did not detect short‐term changes in frog coloration. Dark coloration is known to protect against different sources of radiation by neutralizing free radicals and reducing DNA damage, and, particularly melanin pigmentation has been proposed as a buffering mechanism against ionizing radiation. Our results suggest that exposure to high levels of ionizing radiation, likely at the time of the accident, may have selected for darker coloration in Chornobyl tree frogs. Further studies are needed to determine the underlying mechanisms and evolutionary consequences of the patterns found here.
(a) Map of the Arabian Peninsula showing the three regions from Saudi Arabia and the three governorates from Yemen where citrus canker samples were collected (including the number of authenticated Xanthomonas citri pv. citri strains and their genetic assignation). Note that the exact isolation place is unknown for Omanese strains from a previous study (Vernière et al., 1998). (b) Close‐up map. Blue solid lines link pairs of subclade 4.2 local populations (blue dots) for which no significant genetic differentiation (p > 0.05 based on RST) was found.
Minimum spanning tree from MLVA‐14 data showing the genetic diversity of Xanthomonas citri pv. citri in the Arabian Peninsula. All strains from distinct networks or singletons differed at ≥6 microsatellite loci. Dots represent haplotypes. Dot diameter and color are representative of the number of strains per haplotype, country, and host of isolation, respectively (light green: Saudi Arabia from Mexican lime; dark green: Saudi Arabia from other citrus; light blue: Yemen from Mexican lime; dark blue: Yemen from other citrus). Oman strains from a previous study (Vernière et al., 1998) are shown as red dots.
Genetic structure of Xanthomonas citri pv. citri subclade 4.2 originating from Saudi Arabia and Yemen based on the discriminant analysis of principal components (DAPC) of microsatellite data. Numbers and colors represent the seven genetic clusters retained from Bayesian information criterion (BIC) values. Clockwise: (a) scatterplot representing axes 1 and 2 of the DAPC; (b) scatterplot representing axes 1 and 3 of the DAPC; (c) scatterplot representing axes 1 and 4 of the DAPC; (d) scatterplot representing axes 1 and 5 of the DAPC.
Molecular epidemiology studies are essential to refine our understanding of migrations of phytopathogenic bacteria, the major determining factor in their emergence, and to understand the factors that shape their population structure. Microsatellite and minisatellite typing are useful techniques for deciphering the population structure of Xanthomonas citri pv. citri, the causal agent of Asiatic citrus canker. This paper presents a molecular epidemiology study, which has improved our understanding of the history of the pathogen’s introductions into the Arabian Peninsula, since it was first reported in the 1980s. An unexpectedly high genetic diversity of the pathogen was revealed. The four distinct genetic lineages within X. citri pv. citri, which have been reported throughout the world, were identified in the Arabian Peninsula, most likely as the result of multiple introductions. No copper‐resistant X. citri pv. citri strains were identified. The pathogen’s population structure on Mexican lime (their shared host species) was closely examined in two countries, Saudi Arabia and Yemen. We highlighted the marked prevalence of specialist pathotype A* strains in both countries, which suggests that specialist strains of X. citri pv. citri may perform better than generalist strains when they occur concomitantly in this environment. Subclade 4.2 was the prevailing lineage identified. Several analyses (genetic structure deciphered by discriminant analysis of principal components, RST‐based genetic differentiation, geographic structure) congruently suggested the role of human activities in the pathogen’s spread. We discuss the implications of these results on the management of Asiatic citrus canker in the region.
(a) Map of Cebu, Bohol, and Leyte showing the location for each sampling site. Sites 13, 14, 15, and 22 (shown with an “X”) were excluded from further analysis due to sample sizes of less than five individuals. The triangle symbol denotes sites that were surveyed but at which no A. biaculeatus were found. The purple oval is referred to as our IBD study region, and the purple line represents the length of the IBD study region. (b) Plots of mean velocity from January 2003 to December 2007 in m/s. The strongest currents in our study region flow in the Bohol Sea from northeast to southwest and are known as the Bohol Jet Current.
Plot of pairwise geographic distance and pairwise linearized genetic distance (FST/[1 − FST]) between sites. Pairwise comparisons involving site 19 (southern Leyte) are shown as unfilled circles and other comparisons as black dots. The line shows a linear regression without site 19, and the shaded region represents the 95% confidence interval around the slope.
Plot of pairwise potential larval connectivity and pairwise linearized genetic distance (FST/[1 − FST]) between sites. Pairs of sites in the IBD study region are shown in black dots; pairs involving site 19 (southern Leyte) are shown in unfilled circles. Pairwise comparisons between sites 8&9, 8&10, and 9&10 (in southern Cebu) are shown as squares. The dotted line shows a linear regression for comparisons involving site 19 and each other site.
Obtaining dispersal estimates for a species is key to understanding local adaptation and population dynamics, and to implementing conservation actions. Genetic isolation‐by‐distance patterns can be used for estimating dispersal, and these patterns are especially useful for marine species in which few other methods are available. In this study, we genotyped coral reef fish (Amphiprion biaculeatus) at 16 microsatellite loci across 8 sites across 210 km in the central Philippines to generate fine‐scale estimates of dispersal. All sites except for one followed isolation‐by‐distance patterns. Using isolation‐by‐distance theory, we estimated a larval dispersal kernel spread of 8.9 km (95% confidence interval of 2.3‐18.4 km). Genetic distance to the remaining site correlated strongly with the inverse probability of larval dispersal from an oceanographic model. Ocean currents were a better explanation for genetic distance at large spatial extents (sites greater than 150 km apart), while geographic distance remained the best explanation for spatial extents less than 150 km. Our study demonstrates the utility of combining isolation‐by‐distance patterns with oceanographic simulations to understand connectivity in marine environments and to guide marine conservation strategies.
Gene flow between wild and domestic populations has been repeatedly demonstrated across a diverse range of taxa. Ultimately, the genetic impacts of gene flow from domestic into wild populations depends both on the degree of domestication and the original source of the domesticated population. Atlantic Salmon, Salmo salar, used in North American aquaculture are ostensibly of North American origin. However, evidence of European introgression into North American aquaculture salmon has accumulated in recent decades, even though the use of diploid European salmon has never been approved in Canada. The full extent of such introgression as well as the potential impacts on wild salmon in the Northwest Atlantic remains uncertain. Here, we extend previous work comparing North American and European wild salmon (n=5799) using a 220K SNP array to quantify levels of recent European introgression into samples of domestic salmon, aquaculture escapees, and wild salmon collected throughout Atlantic Canada. Analysis of North American farmed salmon (n=403) and escapees (n=289) displayed significantly elevated levels of European ancestry by comparison with wild individuals (p<0.001). Of North American farmed salmon sampled between 2011 and 2018, ~17% had more than 10% European ancestry and several individuals exceeded 40% European ancestry. Samples of escaped farmed salmon similarly displayed elevated levels of European ancestry, with two individuals classified as 100% European. Analysis of juvenile salmon collected in rivers proximate to aquaculture locations also revealed evidence of elevated European ancestry and larger admixture tract in comparison to individuals collected at distance from aquaculture. Overall, our results demonstrate that even though diploid European salmon have never been approved for use in Canada, individuals of full and partial European ancestry have been in use over the last decade, and that some of these individuals have escaped and hybridized in the wild.
Genetic structure of populations across the hermit thrush breeding range, demonstrating high genetic structure in western North America and limited genetic structure throughout the boreal and eastern regions. (a) Results from ADMIXTURE illustrating five genetically distinct populations, including cluster names, across the breeding range for the full genomic dataset of 90,439 SNPs. Numbers refer to breeding site locations depicted on the map in panel b and are identified in Table 1. (b) Spatially explicit map of population genetic structure across the breeding range. The colors correspond to the five genetic clusters (K = 5). The density of each color reflects the posterior probability of membership for each pixel to the most probable of the five genetic clusters. Transparent color appears on the map in areas of admixture (i.e., mixed posterior probability and thus uncertain assignment). Due to admixture among the four western clusters, the relatively continuous distribution (see Figure 2a) of hermit thrushes throughout the west is not apparent on this map.
Genotype–environment associations across the hermit thrush breeding range, indicating relatively high turnover of putatively adaptive alleles in western North America. (a) Gradient forest‐based genomic signatures mapped to geography support climate adaptation across the breeding range and higher turnover of putatively adaptive allelic variation in the western region compared to the boreal and eastern regions. Background colors on map are based on modeled gene–environment correlations predicted at 100,000 random points across the breeding range. Circles on map represent sampling locations and are colored according to the corresponding genetic cluster from Figure 1. (b) Principal component analysis of gradient forest predictions of genomic signature. Background color represents environmental space, whereas circles are positioned to reflect PC scores associated with each sampling location (colored according to genetic cluster). The western clusters are separated throughout environmental space, whereas sites associated with the East‐Taiga cluster are tightly grouped together. (c) Plot of relative mean within‐group Euclidean distances (Environmental vs Geographic) for each genetic cluster reveals contrasting patterns. Each western cluster shows high environmental distances across relatively small geographic distances, whereas the East‐Taiga cluster shows low environmental distance across large geographic distances. Numbers in parentheses represent within‐cluster pairwise comparisons.
Variation in patterns of allele frequency changes across the hybrid zone in British Columbia for temperature‐associated top candidate loci identified in the rangewide analysis. (a) Allele frequency for the top candidate locus associated with mean diurnal temperature range (BIO2) is fixed. (b) Allele frequency for the top candidate locus associated with temperature seasonality (BIO4) shows a large shift close to the coast. (c) Allele frequency for the top candidate locus associated with maximum temperature of the warmest month (BIO5) shows a large shift farther inland. The color within each circle represents the frequency of the highest ranked allele (as determined by the rangewide LFMM analyses) across the eight sampling sites, while the underlying map represents the gradient across the hybrid zone of the associated bioclimatic variable.
Geographic cline plots show the relationship between the genomic cline, temperature gradient, and candidate loci across the hybrid zone in British Columbia. The clines for candidate loci associated with temperature seasonality (BIO4) (gray open squares and circles) and Environmental PC1 (black diamonds) are shifted to the left of the genomic cline (RADseq; black triangles), which represents the ancestry estimates from the full genomic dataset (90,439 SNPs) with a K = 2. The Environmental PC1 cline (black diamonds) represents scaled top uncorrelated climatic variables (mean diurnal temperature range (BIO2), temperature seasonality (BIO4), and maximum temperature of the warmest month (BIO5)) identified by the gradient forest analysis. The clines for the candidate loci associated with maximum temperature of the warmest month (BIO5) are closely associated with the genomic cline. Candidate loci associated with mean diurnal temperature range (BIO2) are fixed and are not included here.
Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide‐ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype‐environment associations in the hermit thrush (Catharus guttatus), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype‐environment association analyses indicate higher allelic turnover in the west compared to the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species’ range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature‐associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature‐related barriers to gene flow and/or temperature‐driven ecological selection in maintaining putative local adaptation. This study demonstrates how the patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype‐environment correlations at the local (microgeographic) scale. Further, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population‐level sensitivity to climate change and other stressors.
The western redcedar range and the location of the three polycross progeny tests in the province of British Columbia, Canada. The test sites are located at Jordan River, Port McNeill and Powell River. The climate variables (annual temperature, mean annual precipitation, heat moisture index, and degree days over 0 degrees) for the three sites are: Jordan River (7.7, 2480 mm, 7.1, and 168); Port McNeill (8.2, 2302 mm, 7.9, and 135); and Powell River (9.2, 1400 mm, 13.7, and 132).
Heat‐map of pairwise PX‐pedigree (APX matrix in the lower diagonal) and genomic relationship matrix (G matrix in the diagonal and upper diagonal) for parents and offspring. Parent relationships are on the top and offspring are ordered by corrected maternal families. We observe no relationship between parents (in the first 44 rows and columns) in both matrices. Parent‐offspring relationship can be seen in the first 44 columns (in the APX matrix) and the first 44 rows (in the G matrix). The upper diagonal (G matrix) shows ideal HS relationship within corrected maternal families, which is represented by the squared matrices on the diagonal, and scattered HS and FS relationships in the remaining upper off‐diagonals. The lower diagonal (APX matrix) shows pedigree errors in the form of a lot of unrelated individuals within the squared matrices on the diagonal (corrected maternal families), and incorrect HS‐relationship (in scattered lines) in the remaining lower off‐diagonals.
Histogram of pairwise genomic relationships for one out of the eight maternal families showing two possible genotypes. (a) Parent‐offspring relationship; showing two clusters, the peak at 0 relationship coefficient represents the offspring group not related to the genotyped parent, while the peak, around 0.4 relationship coefficient, represents the offspring group related to the genotyped parent. (b) Offspring‐offspring relationship within the same family; showing two clusters, the peak at 0 relationship coefficient represents the half‐sib offspring group not related to each other, while the peak around 0.2 relationship coefficient represent the HS offspring group related to each other. (c and d) Offspring‐offspring relationship of the two groups separately showing the disappearance of the peak at 0 relationship coefficient, and half‐sib relationship within each new corrected maternal family around 0.2 relationship coefficient.
Unequal male contribution leads to unbalanced FS family sizes. (a) Histogram of unequal male contribution. Number of offspring per pollen donor ranges from 7 to 181. The dashed line represents the expected equal male contribution of 68 offspring per pollen donor. The blue bars represent the 1433 (out of 1510) trees assigned one of the 21 males used in the pollen mix. The red and yellow bars represent pollen contamination; yellow bar represents 8 trees who were not assigned to any male parent; red bars represent trees identified as selfs or assigned foreign males other than the 21 males in the pollen mix, which were identified from sib‐sib analysis or parent‐offspring relationship with other genotyped parents. (b) Histogram of full‐sib (FS) family size distribution showing small and unbalanced sizes ranged from 1 to 15 offspring per family, total of 438 FS families.
Western redcedar (WRC) is an ecologically and economically important forest tree species characterized by low genetic diversity with high self‐compatibility and high heartwood durability. Using sequence capture genotyping of target genic and non‐genic regions, we genotyped 44 parent trees and 1520 offspring trees representing 26 polycross (PX) families collected from three progeny test sites using 45,378 SNPs. Trees were phenotyped for eight traits related to growth, heartwood and foliar chemistry associated with wood durability and deer browse resistance. We used the genomic realized relationship matrix for paternity assignment, maternal pedigree correction, and to estimate genetic parameters. We compared genomics‐based (GBLUP) and two pedigree‐based (ABLUP: polycross and reconstructed full‐sib (FS) pedigrees) models. Models were extended to estimate dominance genetic effects. Pedigree reconstruction revealed significant unequal male contribution and separated the 26 PX families into 438 FS families. Traditional maternal PX pedigree analysis resulted in up to 51% overestimation in genetic gain and 44% in diversity. Genomic analysis resulted in up to 22% improvement in offspring breeding value (BV) theoretical accuracy, 35% increase in expected genetic gain for forward selection, and doubled selection intensity for backward selection. Overall, all traits showed low to moderate heritability (0.09–0.28), moderate genotype by environment interaction (type‐B genetic correlation: 0.51–0.80), low to high expected genetic gain (6.01–55%), and no significant negative genetic correlation reflecting no large trade‐offs for multi‐trait selection. Only three traits showed significant dominance effect. GBLUP resulted in smaller but more accurate heritability estimates for five traits, but larger estimates for the wood traits. Comparison between all, genic‐coding, genic‐non‐coding and intergenic SNPs showed little difference in genetic estimates. In summary, we show that GBLUP overcomes the PX limitations, successfully captures expected historical and hidden relatedness as well as linkage disequilibrium (LD), and results in increased breeding efficiency in WRC.
OE_Roslin_V1 assembly quality evaluation. (a) Omni‐C contact map highlighting the top 10 super‐scaffolds generated by HiRise. The contact map was visualized using Juicebox (Durand, Robinson, et al., 2016). (b) Merqury k‐mer copy number spectrum plot for the curated genome assembly. Nearly half of the single‐copy k‐mers (black region) were missing from the heterozygous peak, indicating efficient purging of haplotigs from the final assembly. K‐mers missing from the assembly (black region in the homozygous peak) indicates bases present in the Illumina data missing from the assembly. (c) BUSCO scores for the final scaffolded OE_Roslin_V1 assembly (mollusca_odb10 database). (d) Circos map highlighting the concordance between the 10 super‐scaffolds (RL1 to RL10) and linkage groups (LG1 to LG10). Blue dotted squares within super‐scaffolds 1 and 2 highlight the manual scaffolding performed on the basis of 3D contact information in the Omni‐C data (Figure S1).
Annotation of the O. edulis OE_Roslin_V1 assembly. (a) Summary of genome repeat classes. (b) Density plot showing gene, exon and intron lengths. (c) Circos plot highlighting annotated features across the ten super‐scaffolds (window size 0.5 Mb except track‐v, which is 0.1 Mb). Tracks as follows: i: 10 super‐scaffolds OE‐1 to OE‐10; ii: GC percentage (33–38%), with red and green bars indicating GC >36.5% and <34.5%, respectively; iii: genic content (sum of annotated gene models) expressed as percentage of total window size, regions with <20% genic content are coloured blue, while 20 to 40% are coloured grey and >40% are coloured red; iv: gene density (0–80); v: mean Illumina sequencing depth, with values <45 and >150 shown as red points; vi: classified repeats expressed as percentage of total window size (0–35%); vii: novel unclassified repeat elements expressed as percentage of total window size (0–35%).
Chromosome‐level synteny between the OE_Roslin_V1 O. edulis assembly and three independent bivalve assemblies. Circos plots are shown comparing the ten super‐scaffolds (OE‐1 to OE‐10) with putative chromosomes of (a) C. gigas, (b) C. virginica, (c) P. maximus chromosomes and (d) an independent O. edulis assembly reported in Boutet et al. (2022) (‘RC’ denotes super‐scaffolds from Boutet et al. (2022); ‘RS’ denotes super‐scaffolds from OE_Roslin_V1).
Classification of gene family expansion during O. edulis evolution. (a) Species tree of bivalve genomes used in the analysis, (b–g) different categories of gene family expansion (classified as described in Methods). Branch annotations: blue circles indicate putative expansion; green circles indicates no expansion; red circle indicates an absence of species along that branch for the affected orthogroups. Full data are provided in Table S7.
Most represented protein domains in expanded O. edulis gene families. (a) Top 20 represented IPR domains. (b and c) Example maximum‐likelihood phylogenetic trees highlighting gene family expansions in O. edulis. Blue squares at nodes indicate bootstrap support value >50%.
The European flat oyster (Ostrea edulis L.) is a bivalve naturally distributed across Europe that was an integral part of human diets for centuries, until anthropogenic activities and disease outbreaks severely reduced wild populations. Despite a growing interest in genetic applications to support population management and aquaculture, a reference genome for this species is lacking to date. Here we report a chromosome‐level assembly and annotation for the European Flat oyster genome, generated using Oxford Nanopore, Illumina, Dovetail OmniCTM proximity ligation and RNA sequencing. A contig assembly (N50: 2.38Mb) was scaffolded into the expected karyotype of 10 pseudo‐chromosomes. The final assembly is 935.13 Mb, with a scaffold‐N50 of 95.56 Mb, with a predicted repeat landscape dominated by unclassified elements specific to O. edulis. The assembly was verified for accuracy and completeness using multiple approaches, including a novel linkage map built with ddRAD‐Seq technology, comprising 4,016 SNPs from four full‐sib families (8 parents and 163 F1 offspring). Annotation of the genome integrating multi‐tissue transcriptome data, comparative protein evidence and ab‐initio gene prediction identified 35,699 protein‐coding genes. Chromosome level synteny was demonstrated against multiple high‐quality bivalve genome assemblies, including an O. edulis genome generated independently for a French O. edulis individual. Comparative genomics was used to characterize gene family expansions during Ostrea evolution that potentially facilitated adaptation. This new reference genome for European flat oyster will enable high‐resolution genomics in support of conservation and aquaculture initiatives, and improves our understanding of bivalve genome evolution.
(a) Global distribution of the lentil diversity panel (LDP). The size of the circles represents the number of accessions from different countries, and the colors indicate the clusters to which they belong. (b) Principal component analysis (PCA) for the first three principal components using the single nucleotide polymorphism (SNP) data set. (c) Ancestry plot of the LDP. The distributions of the lentil genetic clusters are: 1 = the Middle East (Iran, Turkey); 2 = the Middle East (Syria, Turkey); 3 = Iran; 4 = South Asia (India, Pakistan) and Syria; 5 = East African highlands and South Levant (Ethiopia, Jordan, and Egypt); 6 = Central Asia (Afghanistan, Iran); 7 = Mediterranean costs (Spain) and South America (Chile); 8 = Temperate Mediterranean (France) and North America (Canada).
(a) Tajima's D distribution estimated for each of the eight population clusters estimated for the lentil diversity panel (LDP). (b) Principal component analysis (PCA) inferred from the normalized depth of copy number variation (CNV) loci with each population cluster represented by dots in different colors. (c) Site frequency spectrum (SFS) of the individual LDP clusters. The observed SFS constructed from single nucleotide polymorphisms (SNPs) and from CNVs is indicated with different bar colors. Black dots and lines show the expected SFS.
Distribution of copy number variation (CNV) loci along the lentil chromosomes. Green indicates the CNV density, and gray indicates the disease resistance gene frequency, in 1 mb windows.
MapMan classification of the genes in copy number variation (CNV) regions. The first column shows the classification of all lentil genes, while the rest of the columns indicate the classification of genes affected by CNV loci that were presented within each cluster in a frequency higher than 0.20. The “Not assigned” category was not included in the figure. Asterisks show the categories that were overrepresented in the genes affected by CNVs. See Figure S9 for more details about subcategories of enzymes, nucleotide metabolism, external stimuli response, and Cell Wall Organization.
The characterization and preservation of genetic variation in crops is critical to meeting the challenges of breeding in the face of changing climates and markets. In recent years the use of Single Nucleotide Polymorphisms (SNPs) has become routine, allowing to understand the population structure, find divergent lines for crosses, and illuminate the origin of crops. However, the focus on SNPs overlooks other forms of variation, such as Copy Number Variation (CNVs). Lentil is the third most important cold‐season legume and was domesticated in the Fertile Crescent. We genotyped 324 accessions that represent its global diversity and using both SNPs and CNVs we dissected the population structure, genetic variation, and identified candidate genes.k.bett@usask.ca Eight clusters were detected, most of them located in or near the Fertile Crescent, even though different clusters were present in distinct regions. The cluster from South Asia was particularly differentiated and presented low diversity, contrasting with the clusters from the Mediterranean and the northern temperate. Accessions from North America were mainly assigned to one cluster and were highly diverse, reflecting the efforts of breeding programs to integrate variation. Thirty‐three genes were identified as candidates under selection and among their functions were sporopollenin synthesis in pollen, a component of chlorophyll B reductase that partially determines the antenna size, and two genes related to the import system of chloroplasts. Eleven percent of all lentil genes, and 21% of lentil disease resistance genes, were affected by CNVs. The gene categories overrepresented in these genes were ‘Enzymes’, ‘Cell Wall Organization’ and ‘External Stimuli Response’. All the genes found in the latter were associated with pathogen response. CNVs provided information about population structure and might have played a role in adaptation. The incorporation of CNVs in diversity studies is needed for a broader understanding of how they evolve and contribute to domestication.
This volume of Evolutionary Applications sees the publication of two genomes for the European native flat oyster Ostrea edulis, a species of significant evolutionary, ecological and commercial value. Each is a highly contiguous chromosome‐level assembly from individuals of different genetic backgrounds, which have been benchmarked against one another. This situation has resulted from the serendipitous discovery that two independent research groups were both deep into the process of building, annotating and investigating separately produced assemblies. Due to constraints with funder requirements and the need to recognize early career researchers for their work, alongside the technical challenge of integrating assemblies from two very different genomes, there was limited capacity to merge the sequences into one publication at the stage of discovery. This issue is likely to become very common over the next few years until the technologies for working with multiple genomes at once, for example, graph genomes, become commonplace in nonmodel species. Consequently, both of our teams have decided to collaborate rather than compete, recognizing the benefit to copublishing two separate genome resources for the research community, each with distinct scientific investigations, and working collaboratively to benchmark the assemblies.
Overall experimental design and analysis pipeline. (a) Individual pigs (Bama Xiang and Chinese wild boar) and their samples (the brain and liver) investigated in this study. (b) Four modules of analyses were performed in this study: the identification of super‐enhancers, differential peak activity analysis, differential gene expression analysis and integrative analysis of domestication loci.
Analysis of peaks with differential activities. (a,b) Volcano plot showing the differential activity analysis of peaks in brains (a) and livers (b) of BMXs and CWBs. (c,d) The intersection of (c) BMX‐ and (d) CWB‐specific peaks in the brain and liver. (e,f) Unsupervised hierarchical clustering of the top differential peaks in (e) the brain and (f) the liver of BMXs and CWBs.
Diversity of super‐enhancers. (a) Unsupervised hierarchical clustering of 4118 super‐enhancers. (b) The tracks of H3K27ac activity and genes in the representative super‐enhancer that show differential activities between BMXs and CWBs for reference (H3K27ac: chr16:27093775–27396751 Gene: GHR).
Overlap of differentially active H3K27ac peaks with the previously reported differentiated loci and candidate genes. (a) In BMXs, six high‐activity peaks (chr9:86379472–86384542, chr9:86412687–86432455, chr9:86450583–86455826, chr9:86458600–86459756, chr9:86462254–86478890 and chr9:86504316–86506928) were upstream of AHR. (b) In CWBs, two high activity peaks (chr13:92808713–92821750 and chr13:92849261–92850154) were upstream of P2RY1. The yellow shades mark differential H3K27ac regions between BMXs and CWBs.
Dramatic phenotypic differences between domestic pigs and wild boars (Sus scrofa) provide opportunities to investigate molecular mechanisms underlying the formation of complex traits, including morphology, physiology, and behaviour. Most studies comparing domestic pigs and wild boars have focused on variations in DNA sequences and mRNA expression, but not on epigenetic changes. Here, we present a genome‐wide comparative study on H3K27ac enhancer activities and the corresponding mRNA profiling in the brain and liver tissues of adult Bama Xiang pigs (BMXs) and Chinese wild boars (CWBs). We identified a total of 1,29,487 potential regulatory elements, among which 11,241 H3K27ac peaks showed differential activity between CWBs and BMXs in at least one tissue. These peaks were overrepresented by binding motifs of FOXA1, JunB, ATF3, and BATF, and overlapped with differentially expressed genes that are involved in female mating behaviour, response to growth factors and hormones, and lipid metabolism. We also identified 4,118 non‐redundant super‐enhancers from ChIP‐seq data on H3K27ac. Notably, we identified differentially active peaks located close to or within candidate genes, including TBX19, MSTN, AHR, and P2RY1, which were identified in DNA sequence‐based population differentiation studies. This study generates a valuable dataset on H3K27ac profiles of the brain and liver from domestic pigs and wild boars, which helps gain insights into the changes in enhancer activities from wild boars to domestic pigs.
Although all marine ecosystems have experienced global‐scale losses, oyster reefs have shown the greatest. Therefore, substantial efforts have been dedicated to restoration of such ecosystems during the last two decades. In Europe, several pilot projects for the restoration of the native European flat oyster, Ostrea edulis, recently begun and recommendations to preserve genetic diversity and to conduct monitoring protocols have been made. In particular, an initial step is to test for genetic differentiation against homogeneity among the oyster populations potentially involved in such programs. Therefore we conducted a new sampling of wild populations at the European scale and a new genetic analysis with 203 markers to (1) confirm and study in more detail the pattern of genetic differentiation between Atlantic and Mediterranean populations, (2) identify potential translocations that could be due to aquaculture practices, (3) investigate the populations at the fringe of the geographical range, since they seemed related despite their geographic distance. Such information should be useful to enlighten the choice of the animals to be translocated or reproduced in hatcheries for further restocking. After the confirmation of the general geographical pattern of genetic structure and the identification of one potential case of aquaculture transfer at a large scale, we were able to detect genomic islands of differentiation mainly in the form of two groups of linked markers, which could indicate the presence of polymorphic chromosomal rearrangements. Furthermore, we observed a tendency for these two islands and the most differentiated loci to show a parallel pattern of differentiation, grouping the North Sea populations with the Eastern Mediterranean and Black Sea populations, against geography. We discussed the hypothesis that this genetic parallelism could be the sign of a shared evolutionary history of the two groups of populations despite them being at the border of the distribution nowadays.
Sampling locations for Plectropomus leopardus in the Great Barrier Reef (GBR; red reefs) and the Coral Sea (blue reefs). Sampling locations for reference collections of congeneric species; Plectropomus laevis and P. maculatus are shown as Pl and Pm, respectively. All reference samples were collected at locations where P. leopardus do not occur or from samples previously identified to be “pure” individuals (Harrison, Berumen, et al., 2017). The Southern Equatorial Current is considered the dominant oceanographic feature in the region and moves west through the Coral Sea as the North Vanuatu Jet (NVJ) and the New Caledonia Jet (NCJ), before bifurcating on the central GBR as the north flowing Hiri Current (HC) and south flowing East Australia Current (EAC). The solid black line shows the 120 m depth contour indicating the lowest approximate water level in the GBR and the Coral Sea during the last two glacial periods between 190–130 kya and 10–120 kya. Currents are re‐drawn from Burrage (1993) and Ceccarelli et al. (2013).
Regional summaries of (a) the inbreeding coefficient (FIS), (b) genetic diversity (He), and (c) allelic richness (Ar). Horizontal black lines indicate the median, and gray circles indicate the mean. Upper and lower boundaries of the box indicate 75% and 25% quartiles, respectively, while outliers are shown as black circles.
Pairwise FST (Weir & Cockerham, 1984) heat map for Plectropomus leopardus collected in the Great Barrier Reef and the Coral Sea based on 4548 neutral SNP markers. Pairwise comparisons that were significantly different from zero at a corrected alpha threshold of 0.011 (Benjamini & Yekutieli, 2001) are indicated by a black circle. SNP, single nucleotide polymorphism.
(a) Scatterplot of discriminant analyses of principal components (DAPC, Adegenet v2.1.3, Jombart, 2008) for populations of Plectropomus leopardus sampled in the Great Barrier Reef (GBR) and Coral Sea based on 4548 neutral SNP markers, explaining 22% of total variance in the genetic data. Bar plots of admixture coefficients for P. leopardus at (b) K = 2 and (c) K = 3 estimated using sparse non‐negative matrix factorization (SNMF) in the R package LEA (Frichot & François, 2015). Each vertical bar represents an individual, and the color indicates relative admixture coefficients at K ancestral populations. Populations are separated by black vertical lines and labeled according to the legend. GBR populations are ordered north to south from left to right, and Coral Sea populations are ordered by proximity to the GBR, from left (closest) to right (farthest). SNP, single nucleotide polymorphism.
Best‐ranked demographic models describing divergence and changes in population size between (a) Flinders Reef (Coral Sea) and Britomart Reef (Great Barrier Reef) and (b) East Lihou (Coral Sea) and Princess Charlotte Bay (Great Barrier Reef). Demographic models were constructed using the diffusion approximation method (moments; Jouganous et al., 2017) implemented in the program GADMA (Noskova et al., 2020). Joint site frequency spectra for empirical and inferred data are shown in Figure S1.
Many coral reef fishes display remarkable genetic and phenotypic variation across their geographic ranges. Understanding how historical and contemporary processes have shaped these patterns remains a focal question in evolutionary biology, since they reveal how diversity is generated and how it may respond to future environmental change. Here we compare the population genomics and demographic histories of a commercially and ecologically important coral reef fish, the common coral grouper (Plectropomus leopardus [Lacépède 1802]), across two adjoining regions (the Great Barrier Reef; GBR, and the Coral Sea, Australia) spanning approximately 14 degrees of latitude and 9 degrees of longitude. We analysed 4,548 single nucleotide polymorphism (SNP) markers across 11 sites and show that genetic connectivity between regions is low, despite their relative proximity (~ 100 km) and an absence of any obvious geographic barrier. Inferred demographic histories using 10,479 markers suggest that the Coral Sea population was founded by a small number of GBR individuals and that divergence occurred ~ 190 kya under a model of isolation with asymmetric migration. We detected population expansions in both regions, but estimates of contemporary effective population sizes were approximately 50 % smaller in Coral Sea sites, which also had lower genetic diversity. Our results suggest that P. leopardus in the Coral Sea have experienced a long period of isolation that precedes the recent glacial period (~ 10 – 120 kya) and may be vulnerable to localised disturbances due to their relative reliance on local larval replenishment. While it is difficult to determine the underlying events that led to the divergence of Coral Sea and GBR lineages, we show that even geographically proximate populations of a widely dispersed coral reef fish can have vastly different evolutionary histories.
Histograms showing the frequency distributions of (a) initial budding time, (b) 1 cm budding time, (c) male flowering time, (d) female flowering time, and (e) height for the yellow mapping family of common ragweed (n = 336). Gray arrows denote the phenotypic values of the early‐flowering parent from the introduced European range. Black arrows denote the phenotypic values of the late‐flowering parent from the native north American range. Initial budding time was not measured in the parental generation.
Density chart showing linkage group lengths and marker distributions of the integrated linkage map constructed from two mapping families of common ragweed. the 18 linkage groups correspond to the study system's karyotype (2n = 36).
LOD distribution curves for (a) initial budding time, (b) 1 cm budding time, (c) male flowering time, (d) female flowering time, and (e) height, based on single‐QTL scans of an experimental mapping population of common ragweed. dotted lines represent genome‐wide significance thresholds at 0.1% (light‐gray dashes) and 5% (dark‐gray dashes).
Linkage groups 2, 6, and 12 and their corresponding quantitative trait loci (QTL). Central markers where LOD scores were maximized are to the right and their genomic positions in centimorgans (cM) are to the left. combined 95% Bayesian confidence intervals for all five traits are indicated by the colours sea‐green (QTL‐2), pink (QTL‐6), and orange (QTL‐12).
The genetic distance (cM) versus physical distance (bp) along a portion of scaffold 27 containing QTL‐2. Recombination distance was calculated for the pink (top), yellow (middle), and F1 families (bottom), and separately for each sex (male left; female right). The marker closest to QTL‐2 is represented by the black line. The 95% confidence intervals for male flowering time (dot), female flowering time (dash), and height (dash and dot) are shown in gray. The haploblocks are shaded (HB27a blue; HB27b purple).
Biological invasions offer a unique opportunity to investigate evolution over contemporary time‐scales. Rapid adaptation to local climates during range expansion can be a major determinant of invasion success, yet fundamental questions remain about its genetic basis. This study sought to investigate the genetic basis of climate adaptation in invasive common ragweed (Ambrosia artemisiifolia). Flowering time adaptation is key to this annual species’ invasion success, so much so that it has evolved repeated latitudinal clines in size and phenology across its native and introduced ranges despite high gene flow among populations. Here, we produced a high‐density linkage map (4,493 SNPs) and paired this with phenotypic data from an F2 mapping population (n=336) to identify one major and two minor quantitative trait loci (QTL) underlying flowering time and height differentiation in this species. Within each QTL interval, several candidate flowering time genes were also identified. Notably, the major flowering time QTL detected in this study was found to overlap with a previously identified haploblock (putative inversion). Multiple genetic maps of this region identified evidence of suppressed recombination in specific genotypes, consistent with inversions. These discoveries support the expectation that a concentrated genetic architecture with fewer, larger and more tightly‐linked alleles should underlie rapid local adaptation during invasion, particularly when divergently‐adapting populations experience high‐levels of gene flow.
Population structure of cereal cyst nematodes inferred from mtCOI gene. (a) Minimum spanning haplotype network. Each circle corresponds to one haplotype and its size is proportional to its frequency. Each line connecting the haplotypes refers to a mutational step. Different colors indicate different types of host (wheat in yellow or grasses in blue) for each haplotype. The annotation next to the circle donates the geographic origin. CC, Central China; NWC, Northwest China. (b) Bayesian clustering using STRUCTURE program. The studied Heterodera spp. populations can be divided into two or four groups measured by ΔK and maximum posterior probability method. Mixed ancestries are shown by differently colored sectors, corresponding to inferred genetic percentages of the corresponding clusters. The abbreviations stand for the regions where the studied populations were collected (see Table S1 for details).
Haplotype compositions of Chinese cereal cyst nematode populations in different geographic regions of China. (a) Haplotype distribution in the Yellow River Basin, and the sites where cysts were caught from Yellow River, its tributaries, or irrigation ditches. The region abbreviations are given in Table S1. (b) Historical courses of the Yellow River and related river networks. The colors in the pie chart are proportional to the haplotype composition in each region. The colored dash line indicates historical courses of Yellow River, with details given in Figure 5k and c–e for Xinyi River in the Guanyun County, Jiangsu Province. (c) Cross section schematic of the river showing floodplain, seasonal rivers, and the locations where Heterodera spp. were recovered. In the dry season, wheat and wild grass grow on the floodplain between two inner rivers. In wet season, overbank flowing takes place when the inner river is flooded and the floodplain is fully submerged. (d) Google satellite view of the floodplain and seasonal rivers. Arrows point to two inner rivers. (e) Partial map of Shandong Province showing the sampling sites where cysts were observed in the water flow of the Yellow River or in irrigation ditches (two sites in Qingdao and Taian).
The infection process and general morphology of cereal cyst nematodes recovered in this study. (a) Fourth‐stage juvenile feeding from syncytium. (b) Third‐stage juvenile. (c) Swollen egg‐filled white female lodged in root tissue. (d) Eggs showing outlines of the J1 folded within the egg. (e) Head region of second‐stage juvenile. (f) Tail of second‐stage juvenile. (g–n) Fenestration (g, i, k, m) and underneath level view (h, g, l, n) of vulval cone for haplotypes H5 (g, h), H20 (i, j), H1 (k, l) and H13 (m, n); (o–r): Cysts extracted from soil for haplotypes H5 (o), H20 (p), H1 (q), H13 (r). Scale bar: A, b, o–r = 100 μm, c = 1 mm, d = 50 μm, e–l = 10 μm.
Phylogeny (a), divergence dating (b), molecular species‐delimitation (c) and historical biogeographic reconstruction analysis (d) of the cereal cyst nematodes using mtCOI gene marker. (a) mtCOI‐based phylogeny of Heterodera spp. using MrBayes. (b) Chronogram of Heterodera spp. based on BEAST analysis. Blue bars indicate 95% highest posterior density intervals. The terminal colors highlight individuals isolated from the same region. (c) Molecular species‐delimitation using three methods: GMYC, bPTP, and ABGD. For the ABGD analysis, groupings of 10 species and seven species are presented, as recovered based on different prior. (d) Reconstruction of the possible ancestral ranges of Heterodera species. The areas of occurrence were set as seven regions. The proportion of colors in a node circle is the probability of each region to be a historical distribution region. Nodes of interest are marked as (1): Chinese H. avenae, H. pratensis and H. australis, (2): The predominant CHA found in wheat, (3): The populations that parasitizing both grass and wheat, (4): The predominant grass parasitic populations including H. pratensis.
The landscapes and schematics of the Yellow River in its upper, middle and lower reaches. (a) Map of China with major wheat growing areas marked in green shadow. (b) Map of Yellow River Basin and the subdivision of reaches. Colored dashed lines indicate the historical river courses. LR, lower reaches; MR, middle reaches; UR, upper reaches. (c) Altitude distribution along river showing total elevation drops is minor in middle and lower reaches. (d) The typical alpine meadow landscape in upper reaches. (e) Pictures of agro‐pastoral zone in upper reaches showing the alpine meadow and adjacent wheat field. (f) Image and schematic of agro‐pastoral zone. The village and wheat fields are located in valleys next to streams. In the rainy season, surface runoff carries cysts from alpine meadow downward to wheat fields, then to stream, and finally flows to tributaries and Yellow River. (g–i) Typical landscape of loess plateau in the middle reaches, where substantial erosion takes place. The large amount of mud and sand discharged into the river. Since it is an important wheat growing region, this process brings initial cyst source to local wheat and outputs more cysts into the river. Image h is adapted from Google earth. (j) The landscape of wheat growing region in lower reaches. This region is characterized by a large plain that is frequently flooded by Yellow River. (k) Historical courses of Yellow River, with map showing in image b. (l) Schematic of “hanging river” at lower reaches. The silts received from the middle reaches form sediments in lower reaches, elevating the river bed. Excessive sediment deposits have raised the riverbed several meters above the surrounding ground. At Kaifeng of Henan Province, the Yellow River is more than 10 m above the ground level.
Reconstructing the dispersal routes of pathogens can help identify the key drivers of their evolution and provides a basis for disease control. The cereal cyst nematode Heterodera avenae is one of the major nematode pests on cereals that can cause 10–90% crop yield losses worldwide. Through extensive sampling on wheat and grasses, the Chinese population of H. avenae is widely identified in virtually all wheat growing regions in China, with H1 being the predominant haplotype. The monoculture of wheat in north China might have been the key driver for the prevalence of H1 population, which should date no earlier than the Han Dynasty (202 BCE–220 CE). Molecular phylogenetic and biogeographic analyses of Chinese H. avenae suggest a Pleistocene northwest China origin and an ancestral host of grasses. We assume the prosperity of Heterodera in this region is a result of their favor cooler climate and various grass hosts, which only appeared after the uplift of Qinghai‐Tibetan Plateau and aridification of Inner Asia. Nematode samples from the current and historical floodplains show a significant role of the Yellow River in the distribution of Chinese H. avenae. Whereas mechanical harvesters that operate on an inter‐provincial basis suggest the importance in the transmission of this species in eastern China in recent times. This study highlights the role of environmental change, river dynamics, and anthropogenic factors in the origin and long‐distance dissemination of pathogens.
Stock structure is of paramount importance for sustainable management of exploited resources. In that context, genetic markers have been used for more than two decades to resolve spatial structure of marine exploited resources and to fully fathom stock dynamics and interactions. While genetic markers such as allozymes and RFLP dominated the debate in the early era of genetics, technology advances have provided scientists with new tools every decade to better assess stock discrimination and interactions (i.e. gene flow). Here, we provide a review of genetic studies performed to understand stock structure of Atlantic cod in Icelandic waters, from the early allozyme approaches to the genomic work currently carried out. We further highlight the importance of the generation of a chromosome anchored genome assembly together with whole‐genome population data, which drastically changed our perception of the possible management units to consider. After nearly 60 years of genetic investigation of Atlantic cod structure in Icelandic waters, genetic (and later genomic) data combined with behavioural monitoring using Data Storage Tags (DSTs) shifted the attention from geographical population structures to behavioural ecotypes. This review also demonstrates the need for future research to further disentangle the impact of these ecotypes (and gene flow among them) on the population structure of Atlantic cod in Icelandic waters. It also highlights the importance of whole‐genome data to unravel unexpected within‐species diversity related to chromosomal inversions and associated supergenes, which are important to consider for future development of sustainable management programmes of the species within the North Atlantic.
Our experimental work illustrates how microbial ecosystems can be shaped by selective pressures over long‐term ecological time scales. Natural microbial ecosystems generally consist of various co‐existing species, where community composition describes the frequency at which species or types are present. Overall functionality of the system is achieved by interacting species. Upon short‐term selection, for instance by transfer to a novel environment, community composition and functionality may change in a process referred to as species sorting. Various factors, such as initial community composition and selective pressures from the environment, may influence this change. Mabisi is a traditional fermented food from Zambia that naturally contains a bacterial community of around twenty unique bacterial types. We used six comparable but different natural bacterial Mabisi communities, each split into five identical replicates, for 16 propagation cycles in a novel, common laboratory environment. Composition of the bacterial communities changed upon propagation. The influence of four main factors on community composition, i.e. initial composition (history), impact of the environment (adaptation), changes due to interaction between species, and random processes (chance) in species dynamics, was tested using maximum likelihood ratios. Initial community composition seemed to determine the change in community composition, followed by random processes. Interestingly, we observed convergence at the level of ecosystem functionality, which was measured as profiles of metabolic output. This suggests different combinations of species or types can achieve similar eco‐system functionality.
By applying morphological and molecular data on two genera of the nudibranch molluscs it is shown that the tension between taxonomic practice and evolutionary processes persists. A review of the related genera Catriona and Tenellia is used to demonstrate that the fine‐scale taxonomic differentiation is an important tool in the integration of morphological and molecular data. This is highlighted by the hidden species problem and provides strong argument that the genus must be kept as a maximally narrowly‐defined entity. Otherwise, we are forced to compare a highly disparate species under the putatively lumped name “Tenellia”. We demonstrate this in the present study by applying a suite of delimitation methods and describing a new species of Tenellia from the Baltic Sea. The new species possesses fine‐scale morphological distinguishing features, which were not investigated before. The true, narrowly defined genus Tenellia represents a peculiar taxon with a clearly expressed paedomorphic characters and predominantly brackish‐water habitats. The phylogenetically related genus Catriona, of which three new species are described here, clearly demonstrates different features. A lumping decision to call many morphologically and evolutionary different taxa as “Tenellia” will downgrade the taxonomic and phylogenetic resolution of the entire family Trinchesiidae to just a single genus. The dissolution of the dilemma of “lumpers & splitters”, which still significantly affects taxonomy, will further help to make systematics a true evolutionary discipline.
Habitat fragmentation impacts the distribution of genetic diversity and population genetic structure. Therefore protecting the evolutionary potential of species, especially in the context of the current rate of human‐induced environmental change, is an important goal. In riverine ecosystems, migration barriers affect the genetic structure of native species, while also influencing the spread of invasive species. In this study, we compare genetic patterns of two native and one highly invasive riverine fish species in a Belgian river basin, namely the native three‐spined stickleback (Gasterosteus aculeatus) and stone loach (Barbatula barbatula), and the non‐native and invasive topmouth gudgeon (Pseudorasbora parva). We aimed to characterize both natural and anthropogenic determinants of genetic diversity and population genetic connectivity. Genetic diversity was highest in topmouth gudgeon followed by stone loach and three‐spined stickleback. The correlation between downstream distance and genetic diversity, a pattern often observed in riverine systems, was only marginally significant in stone loach and three‐spined stickleback, while genetic diversity strongly declined with increasing number of barriers in topmouth gudgeon. An Isolation‐By‐Distance pattern characterizes the population genetic structure of each species. Population differentiation was only associated with migration barriers in the invasive topmouth gudgeon, while genetic composition of all species seemed at least partially determined by the presence of migration barriers. Among the six barrier types considered (watermills, sluices, tunnels, weirs, riverbed obstructions, and others), the presence of watermills was the strongest driver of genetic structure and composition. Our results indicate that conservation and restoration actions, focusing on conserving genetic patterns, cannot be generalized across species. Moreover, measures might target either on restoring connectivity, while risking a rapid spread of the invasive topmouth gudgeon, or not restoring connectivity, while risking native species extinction in upstream populations.
The European flat oyster (Ostrea edulis L.) is a native bivalve of the European coasts. Harvest of this species has declined during the last decades, because of the appearance of two parasites that have led to the collapse of the stocks and the loss of the natural oyster beds. O. edulis has been the subject of numerous studies and programs in population genetics and on the presence of the parasites Bonamia ostreae and Marteilia refringens. These studies investigated immune responses to these parasites at the molecular and cellular levels. Several genetic improvement programs have been initiated especially for parasite resistance. Within the framework of a European project (PERLE 2) which aims to produce genetic lines of O. edulis with hardiness traits (growth, survival, resistance) for the purpose of repopulating natural oyster beds in Brittany and reviving the culture of this species on the foreshore, obtaining a reference genome becomes essential as done recently in many bivalve species of aquaculture interest. Here, we present a chromosome‐level genome assembly and annotation for the European flat oyster, generated by combining PacBio, Illumina, 10X linked and Hi‐C sequencing. The finished assembly is 887.2 Mb with a scaffold‐N50 of 97.1 Mb scaffolded on the expected 10 pseudo‐chromosomes. Annotation of the genome revealed the presence of 35962 protein‐coding genes. We analyzed in details the transposable element (TE) diversity in the flat oyster genome, highlighted some specificities in tRNA and miRNA composition and provided first insights into the molecular response of O. edulis to M. refringens. This genome provides a reference for genomic studies on O. edulis to better understand its basic physiology and as a useful resource for genetic breeding in support of aquaculture and natural reef restoration.
Microbes can play a prominent role in the evolution of their hosts, facilitating adaptation to various environments and promoting ecological divergence. The Wave and Crab ecotypes of the intertidal snail Littorina saxatilis is an evolutionary model of rapid and repeated adaptation to environmental gradients. While patterns of genomic divergence of the Littorina ecotypes along the shore gradients have been extensively studied, their microbiomes have been so far overlooked. The aim of the present study is to start filling this gap by comparing gut microbiome composition of the Wave and Crab ecotypes using metabarcoding approach. Since Littorina snails are micro‐grazers feeding on the intertidal biofilm, we also compare biofilm composition (i.e. typical snail diet) in the crab and wave habitats. In the results, we found that bacterial and eukaryotic biofilm composition varies between the typical habitats of the ecotypes. Further, the snail gut bacteriome was different from outer environments, being dominated by Gammaproteobacteria, Fusobacteria, Bacteroidia and Alphaproteobacteria. There were clear differences in the gut bacterial communities between the Crab and the Wave ecotypes as well as between the Wave ecotype snails from the low and high shores. These differences were both observed in the abundances and in the presence of different bacteria, as well as at different taxonomic level, from bacterial OTU’s to families. Altogether, our first insights show that Littorina snails and their associated bacteria are a promising marine system to study co‐evolution of the microbes and their hosts, which can help us to predict the future for wild species in the face of rapidly changing marine environments.
European flat oyster (Ostrea edulis) is an ecologically and economically important marine bivalve, that has been severely affected by the intracellular parasite Bonamia ostreae. In this study, a flat oyster SNP array (~14,000 SNPs) was used to validate previously reported outlier loci for divergent selection associated with B. ostreae exposure in the Northeast Atlantic Area. A total of 134 wild and hatchery individuals from the North Sea, collected in naïve (NV) and long‐term affected (LTA) areas, were analysed. Genetic diversity and differentiation were related to the sampling origin (wild vs hatchery) when using neutral markers, and to bonamiosis status (NV vs LTA) when using outlier loci for divergent selection. Two genetic clusters appeared intermingled in all sampling locations when using outlier loci and their frequency was associated with their bonamiosis status. When both clusters were compared, outlier datasets showed high genetic divergence (FST > 0.25) unlike neutral loci (FST not ≠ 0). Moreover, the cluster associated with LTA samples showed much higher genetic diversity and significant heterozygote excess with outlier loci, but not with neutral data. Most outliers mapped on chromosome 8 (OE‐C8) of the flat oyster genome, supporting a main genomic region underlying resilience to bonamiosis. Furthermore, differentially expressed genes previously reported between NV and LTA strains showed higher mapping density on OE‐C8. A range of relevant immune functions were specifically enriched among genes annotated on OE‐C8, providing hypotheses for resilience mechanisms to an intracellular parasite. The results suggest that marker‐assisted selection could be applied to breed resilient strains of O. edulis to bonamiosis, if lower parasite load and / or higher viability of the LTA genetic cluster following B. ostreae infection is demonstrated.
Cnidarian microRNA biogenesis pathways compared with bilaterians, green algae and plants. In cnidarians, pri‐miRNAs are proposed to be processed into pre‐miRNAs by the miRNA microprocessor complex composed of pasha and drosha. The loop structure is then removed by dicer, together with plant‐associated cofactors serrate and HYL1 to form the miRNA duplex. Hen1 methylates the 3′ end of the miRNA after miRNA loading and star strand ejection. 3′ ends methylation of miRNAs is common in both plants and cnidarians (Modepalli et al., 2018), whereas bilaterian miRNAs do not undergo this modification.
A phylogenetic relationship of metazoan Argonuate protein family. Multiple alignments were performed using MUSCLE and the rooted phylogenetic tree was constructed with the LG (G + I) model using the maximum likelihood method (with 1000 replicates). Bootstrap support values above 50% are indicated above branches.
(a) the phylogenetic tree of cnidarian species with small RNA sequencing; (b) list of species of cnidaria small RNA research. The green, yellow and orange grids indicate that the small RNAs in question have already been the subject of study, while the gray grids indicate that no studies have yet been conducted.
siRNA biogenesis. The ribonuclease III dicer dices dsRNA into short interfering RNAs (siRNA), which is often followed by signal amplification by RNA‐dependent RNA polymerases (RdRPs). When siRNAs are generated, they are bound by RISC, a multiprotein component complex (RNA‐induced silencing complex) and final silencing of foreign or invasive nucleic acids by Argonaute proteins.
piRNA biogenesis and TE targeting. Initiator piRNAs guide PIWI‐catalyzed cleavage of a target piRNA precursor (black triangles). The sliced target is then loaded into another PIWI protein (dark ellipses) to produce trailing piRNAs by the piRNA‐independent endonuclease and pre‐piRNA 3′‐to‐5′ trimming exonuclease (dark blue triangles) during the phasing mechanism. Simultaneously, cleavage by initiator piRNAs also generates responder piRNAs which bind to target RNAs to guide cleavage and then in turn act as initiator piRNAs after loading to another PIWI to silence the piRNA precursors. This mechanism of piRNA amplification is called the ping‐pong cycle and is conserved in cnidarians.
As fundamental components of RNA silencing, small RNA (sRNA) molecules ranging from 20‐32 nucleotides in length have been found as potent regulators of gene expression and genome stability in many biological processes of eukaryotes. Three major small RNAs are active in animals, including the microRNA (miRNA), short interfering RNA (siRNA), and PIWI‐interacting RNA (piRNA). Cnidarians, the sister group to bilaterians, are at a critical phylogenetic node to better model eukaryotic small RNA pathway evolution. To date, most of our understanding of sRNA regulation and its potential contribution to evolution has been limited to a few triploblastic bilaterian and plant models. The diploblastic non‐bilaterians, including the cnidarians, are understudied in this regard. Therefore, this review will present the current‐known small RNA information in cnidarians to enhance our understanding of the development of the small RNA pathways in early branch animals.
Anthropogenic translocations open new pathways and connect habitats at different scales. (a–c) represent processes happening at a regional scale. (a) Artificial and offshore structures can act as stepping stones and become springboards for organisms to disperse and colonize other locations. (b) Natural dispersal (in green) depends on the species’ dispersal abilities and is mostly done between close locations. Thus, the further two populations are from each other, the more differentiated they will be. Meanwhile, shipping (in red) sustains both short‐ and long‐distance translocations. Dispersal by human action breaks the isolation‐by‐distance patterns and can bring down the genetic structure of populations or make it more complex. (c) Shipping can be responsible for spillovers from ports to wild populations and help organisms colonize locations where they are not yet established. (d) Transoceanic shipping translocates organisms on a global scale, potentially bringing them into contact with geographically distant lineages; some of them might have evolved in complete allopatry.
Illustration of the biofouling pathways for spreading the Pacific kelp Undaria pinnatifida from port to port. This seaweed native to Asia has been introduced in New Zealand and Europe during the 1970s–1980s. It is a short‐lived species, with a life‐cycle alternating macroscopic diploid sporophytes (left and central picture) and microscopic haploid gametophytes (right picture) that can both be found attached to boat hulls, anchoring systems, or ropes. While natural dispersal by spores or gametes occurs at a very short distance (<10–100 m; Forrest et al., 2000), it can be easily spread over long distance (>100 km) through shipping trade and leisure boating, as evidenced by both field and genetic studies (Epstein & Smale, 2017; Guzinski et al., 2018; South et al., 2017). Ports, and associated shipping and boating, provide major expansion pathways and are responsible for long‐distance dispersal events of this introduced seaweed.
Adaptation in a patchy environment. In this schematic scenario, two lineages of one species (species 1) are separated by a barrier to gene flow. In each location, one population is found in a port habitat (filled circle), another one in a wild habitat (empty circle). Two independent convergent mutations (μ1 and μ2) related to adaptation to the port environment appear in one population of each lineage (adaptation by de novo mutations). These mutations then propagate to close populations found in the same port habitat by gene flow through wild populations (thanks to migration‐selection balance that maintains a low frequency of port‐adapted alleles in wild populations, aka transporter hypothesis) or helped by maritime traffic. This latter anthropogenic pathway may introduce individuals with the mutation to an area where a second species (species 2) is found in port habitats. Introgression from the introduced species to the second species occurs, as this mutation is advantageous in the port environment. This process is called adaptive introgression. On the right of the figure, the upper tree shows the genetic relationships at neutral markers between the different populations involved, while the second tree is obtained with the selected locus.
Biological portuarization and its evolutionary outcomes. Ports are singular habitats due to their particular abiotic and biotic properties, at local and global (seascape) levels. They are the port‐of‐entry of non‐native lineages and species and the nodes of a vast and dense network. Evolutionary outcomes already documented are diverse, including genetic diversity shuffling, rapid adaptation, putative risks associated with gene flow in natural habitats, admixture, and hybridization among others.
Humans have built ports on all the coasts of the world, allowing people to travel, exploit the sea, and develop trade. The proliferation of these artificial habitats and the associated maritime traffic are not predicted to fade in the coming decades. Ports share common characteristics: species find themselves in novel singular environments, with particular abiotic properties ‐e.g., pollutants, shading, protection from wave action‐ within novel communities in a melting‐pot of invasive and native taxa. Here we discuss how this drives evolution, including setting‐up of new connectivity hubs and gateways, adaptive responses to exposure to new chemicals or new biotic communities, and hybridization between lineages that would have never come into contact naturally. There are still important knowledge gaps however, such as the lack of experimental tests to distinguish adaptation from acclimation processes, the lack of studies to understand the putative threats of port lineages to natural populations, or to better understand the outcomes and fitness effects of anthropogenic hybridization. We thus call for further research examining “biological portuarization”, defined as the repeated evolution of marine species in port‐ecosystems under human‐altered selective pressures. Furthermore, we argue that ports act as giant mesocosms often isolated from the open sea by seawalls and locks, and so provide replicated life‐size evolutionary experiments essential to support predictive evolutionary sciences.
Infographic of study design and scenarios investigating the spread of the invasive round goby (Neogobius melanostomus) across the steep salinity gradient of the Port of Gothenburg, Sweden. The three sites yielding genotypic data (green circles 1, 2 and 3), and the two of those sites yielding phenotypic data (1 and 2) are marked with grey info boxes. Numbered circles mark sites where initial sampling occurred in 2016: green shows sites where fish were caught for the study, yellow where one fish was caught and excluded, and red where no fish were caught after a minimum of 6 fishing hours with baited hook and line. Sites 7 and 10 were also fished with 3 baited cages overnight. An eleventh site (off the map 1 km up the river) was also sampled without catch. The purple circle marked X shows the site furthest upstream where N. melanostomus has been found during a fishing survey in 2018 (not part of the present study). Habitats are roughly categorized and separated with dotted lines according to salinity. Striped polygon shows the extent of the Gothenburg international shipping port, Scandinavia's largest port with over 5300 cargo vessels visiting in 2020
Neighbour‐joining tree based on pairwise FST estimates of round gobies (Neogobius melanostomus) sampled from 12 sites and genotyped at 12,937 SNPs. Colours represent the four larger geographic regions targeted
Individual ancestry of 305 round gobies (Neogobius melanostomus) based on 12,937 SNPs for (a) K = 3 and (b) K = 9 estimated using sNMF. Each vertical bar is one individual, and the colour is the proportion of that individual assigned to the different K clusters. Individuals are separated by sampling sites and grouped in the four Baltic Sea regions. Clusters 2–10 can be found in Figure S4
First (x‐axis) and second (y‐axis) component of a principal component analysis (PCA) on 305 round gobies (Neogobius melanostomus) genotyped at 12937 SNPs. The first component explains 10.56% of the total variation and the second 6.41%. Each point represents one individual, colours represent sampling sites, and shape is used for better distinction
Metabolic performance of round goby (Neogobius melanostomus) caught from two sites of different ambient salinities (Outer port or Inner port) and acclimated to salinities of first 15, then either 0 or 30 PSU at 10°C. Bars show mean values, boxes show median, and upper and lower quartile, and error bars show max and min with outliers denoted by dots. Jittered points show individual values. Letters indicate statistical differences outlined in the results. Panels show the following: (a) oxygen uptake (mg O2 kg⁻¹ h⁻¹) shown in top values as maximum metabolic rate (MMR) and shown in bottom values as resting metabolic rate (SMR) (lowest 20% of values measured every 15 min over 48 h). (b) Aerobic scope (MMR – SMR). (c) Factorial aerobic scope (MMR/SMR)
Species invasions are a global problem of increasing concern, especially in highly connected aquatic environments. Despite this, salinity conditions can pose physiological barriers to their spread and understanding them is important for management. In Scandinavia’s largest cargo port, the invasive round goby (Neogobius melanostomus), is established across a steep salinity gradient. We used 12 937 SNPs to identify the genetic origin and diversity of three sites along the salinity gradient and round goby from western, central and northern Baltic Sea, as well as north European rivers. Fish from two sites from the extreme ends of the gradient were also acclimated to freshwater and seawater, and tested for respiratory and osmoregulatory physiology. Fish from the high salinity environment in the outer port showed higher genetic diversity, and closer relatedness to the other regions, compared to fish from lower salinity upstream the river. Fish from the high salinity site also had higher maximum metabolic rate, fewer blood cells and lower blood Ca2+. Despite these genotypic and phenotypic differences, salinity acclimation affected fish from both sites in the same way: seawater increased the blood osmolality and Na+ levels, and freshwater increased the levels of the stress hormone cortisol. Our results show genotypic and phenotypic differences over short spatial scales across this steep salinity gradient. These patterns of the physiologically robust round goby are likely driven by multiple introductions into the high salinity site, and a process of sorting, likely based on behaviour or selection, along the gradient. Since this euryhaline fish risks spreading from this area, seascape genomics and phenotypic characterisation can inform management strategies even within an area as small as a coastal harbour inlet.
The number of unique Zymoseptoria tritici genotypes identified in naturally infected wheat fields versus the total number of wheat leaves sampled in each of the studies (reported in Table 1). Red points show the outcomes of individual locations. The blue line represents the linear regression with zero intercept, where we estimated the slope as 0.97 ± 0.07
STB severity versus incidence measured in the field over 10 consecutive years (2008–2017; Suffert & Sache, 2011; Suffert et al., 2018). Mean values over field assessments in each year are shown as small circles (for dates and values, see Table S1). Different colors correspond to three clusters obtained using K‐mean clustering: Low epidemics (blue), moderate epidemics (green), and high epidemics (orange). Large circles show mean values within each cluster
Pathogen populations differ in the amount of genetic diversity they contain. Populations carrying higher genetic diversity are thought to have a greater evolutionary potential than populations carrying less diversity. We used published studies to estimate the range of values associated with two critical components of genetic diversity, the number of unique pathogen genotypes and the number of spores produced during an epidemic, for the septoria tritici blotch pathogen Zymoseptoria tritici. We found that wheat fields experiencing typical levels of infection are likely to carry between 3.1 and 14.0 million pathogen genotypes per hectare and produce at least 2.1 to 9.9 trillion pycnidiospores per hectare. Given the experimentally derived mutation rate of 3 x 10‐10 substitutions per site per cell division, we estimate that between 27 and 126 million pathogen spores carrying adaptive mutations to counteract fungicides and resistant cultivars will be produced per hectare during a growing season. This suggests that most of the adaptive mutations that have been observed in Z. tritici populations can emerge through local selection from standing genetic variation that already exists within each field. The consequences of these findings for disease management strategies are discussed.
Trade‐offs between host resistance to parasites and host growth or reproduction can occur due to the allocation of limited available resources between competing demands. To predict potential trade‐offs arising from genetic selection for host resistance, a better understanding of the associated nutritional costs is required. Here we studied resistance costs by using sheep from lines divergently selected on their resistance to a common blood‐feeding gastro‐intestinal parasite (Haemonchus contortus). First, we assessed the effects of selection for high or low host resistance on condition traits (body weight, back fat and muscle thickness) and infection traits (parasite fecal egg excretion, loss in blood haematocrit) at various life stages, in particular during the periparturient period when resource allocation to immunity may limit host resistance. Second, we analysed the condition‐infection relationship to detect a possible trade‐off, in particular during the periparturient period. We experimentally infected young females in four stages over their first two years of life, including twice around parturition (at one year and at two years of age). Linear mixed model analyses revealed a large and consistent between‐line difference in infection traits during growth and outside of the periparturient period, whereas this difference was strongly attenuated during the periparturient period. Despite their different responses to infection, lines had similar body condition traits. Using covariance decomposition, we then found that the phenotypic relationship between infection and condition was dominated by direct infection costs arising from parasite development within the host. Accounting for these within‐individual effects, a cost of resistance on body weight was detected among ewes during their first reproduction. Although this cost and the reproductive constraint on resistance are unlikely to represent a major concern for animal breeding in nutrient‐rich environments, this study provides important new insights regarding the nutritional costs of parasite resistance at different lifestages and how these may affect response to selection.
(a) Average maximum growth rate (calculated from fluorescence measurements, N = 3) per strain cultured in estuarine water (salinity 7 PSU) and marine water (salinity 26 PSU) conditions. E1–E6 refer to estuarine strains and M1–M6 to marine strains. Error bars indicate standard deviation of the mean (N = 3)
(a) Measured pH level at different mean cell concentrations of estuarine (light blue) and marine (dark blue) strains grown in marine water. (b) Measured pH level at different mean cell concentrations of estuarine (light blue) and marine (dark blue) strains grown in estuarine water. Dotted lines show linear correlation, equation provided with R² value
Average relative abundance of estuarine strain versus marine strain after 8–10 days (depending on when strain pairs reached early stationary phase), growing together in marine high salinity (H) and estuarine low (L) salinity F/2 medium. Relative abundances were established using AsQ‐PCR. P1–P7 refer to specific strain combinations. Error bars indicate standard deviation of the mean (n = 3)
Marine microorganisms have the potential to disperse widely with few obvious barriers to gene flow. However, among microalgae, several studies have demonstrated that species can be highly genetically structured with limited gene flow among populations, despite hydrographic connectivity. Ecological differentiation and local adaptation have been suggested as drivers of such population structure. Here we tested whether multiple strains from two genetically distinct Baltic Sea populations of the diatom Skeletonema marinoi showed evidence of local adaptation to their local environments; the estuarine Bothnian Sea and the marine Kattegat Sea. We performed reciprocal transplants of multiple strains between culture media based on water from the respective environments, and we also allowed competition between strains of estuarine and marine origin in both salinities. When grown alone, both marine and estuarine strains performed best in the high salinity environment, and estuarine strains always grew faster than marine strains. This result suggests local adaptation through countergradient selection, i.e. genetic effects counteract environmental effects. However, higher growth rate of the estuarine strains appears to have a cost in the marine environment and when strains were allowed to compete, marine strains performed better than estuarine strains in the marine environment. Thus, other traits are likely to also affect fitness. We provide evidence that tolerance to pH could be involved, and that estuarine strains which are adapted to a more fluctuating pH continue growing at higher pH than marine strains.
Genetic structure of Myzus aphids sampled in the air and on different crops. Top two lines, Q plot from the structure analysis of microsatellite multilocus genotypes, with K = 3 and K = 4. A vertical line represents an individual, and the proportion of its assignment to each cluster is represented by the colored segments. The colors of the clusters identified in K = 4 are used throughout the paper to designate the associated genetic clusters (red, green, yellow, and blue). Lower lines, visualization of resistance genotypes identified in each individual, with the following color code: light green, susceptible homozygote, purple, resistant homozygote, orange, heterozygote carrying the susceptible wild‐type allele and a mutant allele, gray, missing data. kdr and skdr loci are linked to pyrethroids target site resistance, MACE locus to pirimicarb target site resistance, and R81T locus to neonicotinoids target site resistance. The sampling sources (different crops and aerial trapping) are indicated at the bottom. The sampling years for the aerial trapping are indicated at the top
Multilocus genotypes network (microsatellite data) with genotypes colored according to their respective genetic clusters (a) and with their resistance genotypes at the three loci of interest kdr (b), skdr (c), and MACE (d). kdr and skdr loci are linked to pyrethroids target site resistance and MACE locus to pirimicarb target site resistance. Note that several resistant alleles were encountered at the skdr locus (b), so resistant homozygotes (purple) and heterozygotes (orange) have various genotypes
Copy number of multilocus genotypes (MLG) per year over the aerial trapping period. Colors refer to the 4‐K cluster assignment of individuals in Figure 1 (unassigned individuals in gray). Solid colors: repeated MLG; shaded colors: unique MLGs. The first recurrent treatment failures with carbamate in France were recorded in 2005
Temporal dynamics of 3‐locus resistotypes (RG) encountered in aphid individuals from the aerial sample. Individuals whose 3‐loci resistotype was not completely characterized (one or more unidentified loci; 12.7% of the aerial sample) were discarded. MLG: multilocus genotype; G:N: ratio between the number of genotypes and the number of individuals. *ss, sensitive homozygous; sr, heterozygous; rr, resistant homozygous, as known from literature in terms of associated phenotype. kdr: s = L, r = F; skdr: s = M, r = T, L, I; MACE: s = S, r = F. **The values on the vertical axis represent the number of genotyped individuals and the horizontal axis the years, shown above the first graph. Colors refer to the 4‐K cluster assignment of individuals in Figure 1 (unassigned individuals in gray). Black arrow, date of first records of recurrent failures with carbamate in oilseed rape in France
Understanding the spatiotemporal dynamics of pesticide resistance at the landscape scale is essential to anticipate the evolution and spread of new resistance phenotypes. In crop mosaics, host plant specialization in pest populations is likely to dampen the spread of pesticide resistance between different crops even in mobile pests such as aphids. Here, we assessed the contribution of host‐based genetic differentiation to the dynamics of resistance alleles in Myzus persicae, a major aphid pest which displays several insecticide resistance mechanisms. We obtained a representative sample of aphids from a crop mosaic through a suction trap for 7 years and from various crops as a reference collection. We genotyped these aphids at 14 microsatellite markers and four insecticide‐resistant loci, analyzed the genetic structure, and assigned host‐based genetic groups from field‐collected aphids. Four well‐defined genetic clusters were found in aerial samples, three of which with strong association with host‐plants. The fourth group was exclusive to aerial samples and highly divergent from the others, suggesting mixture with a closely related taxon of M. persicae associated with unsampled plants. We found a sharp differentiation between individuals from peach and herbaceous plants. Individuals from herbaceous hosts were separated into two genetic clusters, one more strongly associated with tobacco. The 4‐loci resistance genotypes showed a strong association with the four genetic clusters, indicative of barriers to the spread of resistance. However, we found a small number of clones with resistant alleles on multiple host‐plant species, which may spread insecticide resistance between crops. The 7‐year survey revealed a rapid turn‐over of aphid genotypes as well as the emergence, frequency increase and persistence of clones with resistance to several families of insecticides. This study highlights the importance of considering landscape‐scale population structure to identify the risk of emergence and spread of insecticide resistance for a particular crop.
Genomic studies are uncovering extensive cryptic diversity within reef‐building corals, suggesting that evolutionarily and ecologically relevant diversity is highly underestimated in the very organisms that structure coral reefs. Furthermore, endosymbiotic algae within coral host species can confer adaptive responses to environmental stress and may represent additional axes of coral genetic variation that are not constrained by taxonomic divergence of the cnidarian host. Here, we examine genetic variation in a common and widespread, reef‐building coral, Acropora tenuis, and its associated endosymbiotic algae along the entire expanse of the Great Barrier Reef (GBR). We use SNPs derived from genome‐wide sequencing to characterise the cnidarian coral host and organelles from zooxanthellate endosymbionts (genus Cladocopium). We discover three distinct and sympatric genetic clusters of coral hosts, whose distributions appear associated with latitude and inshore‐offshore reef position. Demographic modelling suggests that the divergence history of the three distinct host taxa ranges from 0.5 – 1.5 million years ago, preceding the GBR’s formation, and has been characterised by low to moderate ongoing inter‐taxon gene flow, consistent with occasional hybridisation and introgression typifying coral evolution. Despite this differentiation in the cnidarian host, A. tenuis taxa share a common symbiont pool, dominated by the genus Cladocopium (Clade C). Cladocopium plastid diversity is not strongly associated with host identity but varies with reef location relative to shore: inshore colonies contain lower symbiont diversity on average but have greater differences between colonies as compared to symbiont communities from offshore colonies. Spatial genetic patterns of symbiont communities could reflect local selective pressures maintaining coral holobiont differentiation across an inshore‐offshore environmental gradient. The strong influence of environment (but not host identity) on symbiont community composition supports the notion that symbiont community composition responds to habitat, and may assist in the adaptation of corals to future environmental change.
OXA‐23 is the predominant carbapenemase in carbapenem‐resistant Acinetobacter baumannnii. The co‐evolutionary dynamics of A. baumannii and OXA‐23‐encoding plasmids are poorly understood. Here, we transformed A. baumannnii ATCC 17978 with pAZJ221, a blaOXA‐23‐containing plasmid from clinical A. baumannnii isolate A221, and subjected the transformant to experimental evolution in the presence of a sub‐inhibitory concentration of imipenem for nearly 400 generations. We used population sequencing to track genetic changes at six time‐points and evaluated phenotypic changes. Increased fitness of evolving populations, temporary duplication of blaOXA‐23 in pAZJ221, interfering allele dynamics, and chromosomal locus‐level parallelism were observed. To characterize genotype‐to‐phenotype associations, we focused on six mutations in parallel targets predicted to affect small RNAs and a cyclic dimeric (3’→5’) GMP‐metabolizing protein. Six isogenic mutants with or without pAZJ221 were engineered to test for the effects of these mutations on fitness costs and plasmid kinetics, and the evolved plasmid containing two copies of blaOXA‐23 was transferred to ancestral ATCC 17978. Five of the six mutations contributed to improved fitness in the presence of pAZJ221 under imipenem pressure, and all but one of them impaired plasmid conjugation ability. The duplication of blaOXA‐23 increased host fitness under carbapenem pressure but imposed a burden on the host in antibiotic‐free media relative to the ancestral pAZJ221. Overall, our study provides a framework for the co‐evolution of A. baumannii and a clinical blaOXA‐23‐containing plasmid in the presence of imipenem, involving early blaOXA‐23 duplication followed by chromosomal adaptations that improved the fitness of plasmid‐carrying cells.
(a) Sperm motility (% motile sperm) and (b) velocity (velocity of the curvilinear path, VCL, μm s⁻¹) were measured for sand goby (Pomatoschistus minutus) sperm that were activated and kept in 12°C seawater, which was either seawater that contained sperm‐duct gland content (2021) or not (2012 and 2021). Motility was measured approx. 5 min and 22 h after initial sampling. The graphs show nonparametric bootstrapped mean ± CI values (large circles and error bars) and individual data points (small dots) for breeding‐coloured males (black) and sneaker‐morph males (grey). The sperm were tested with sperm‐duct gland contents (mucus) present or absent. Since sneaker‐morph males have no or very small sperm‐duct gland, all sperm‐duct glands came from breeding‐coloured males
Heatmap of differentially expressed genes between breeding‐coloured (BC) and sneaker‐morph males (SN) of sand goby (Pomatoschistus minutus). The expression value of each gene was standardized to the mean (Z‐Score). Red colour shows genes that are upregulated, and blue indicates genes that are downregulated within one morph in relation to the other morph. Genes and samples are clustered with complete‐linkage clustering using coolmap from the R package limma (Ritchie et al., 2015)
Boxplots of selected differentially expressed genes between breeding‐coloured (BC, in black) and sneaker‐morph males (SN, in grey) of sand goby (Pomatoschistus minutus)
(a) Sperm motility (% motile sperm) and (b) velocity (velocity of the curvilinear path, VCL, μm s⁻¹) of sand goby males (Pomatoschistus minutus) depicted with the 1st principal component (PC1) of 109 transcripts that were differently expressed between breeding‐coloured (black) and sneaker‐morph males (grey). The sperm performance assays and gene expression were collected in a paired design, from one testis each of the same male. The outlier is sneaker‐morph male SN08. This male is visible in Figure 2 as having a large proportion if its genes downregulated
In species with alternative reproductive tactics, there is much empirical support that parasitically spawning males have larger testes and greater sperm numbers as an evolved response to a higher degree of sperm competition, but support for higher sperm performance (motility, longevity, speed) by such males is inconsistent. We used the sand goby (Pomatoschistus minutus) to test whether sperm performance differed between breeding‐coloured males (small testes, large mucus‐filled sperm‐duct glands; build nests lined with sperm‐containing mucus, provide care) and parasitic sneaker‐morph males (no breeding colouration, large testes, rudimentary sperm‐duct glands; no nest, no care). We compared motility (proportion motile sperm), velocity, longevity of sperm, gene expression of testes, and sperm morphometrics between the two morphs. We also tested if sperm‐duct gland contents affected sperm performance. We found a clear difference in gene expression of testes between the male morphs with 109 transcripts differentially expressed between the morphs. Notably, several mucin genes were upregulated in breeding‐coloured males and two ATP‐related genes were upregulated in sneaker‐morph males. There was a partial evidence of higher sperm velocity in sneaker‐morph males, but no difference in sperm motility. Presence of sperm‐duct gland contents significantly increased sperm velocity, and non‐significantly tended to increase sperm motility, but equally so for the two morphs. The sand goby has remarkably long‐lived sperm, with only small or no decline in motility and velocity over time (5 min vs 22 h), but again, this was equally true for both morphs. Sperm length (head, flagella, total, flagella‐to‐head ratio) did not differ between morphs, and did not correlate with sperm velocity for either morph. Thus, other than a clear difference in testes gene expression, we found only modest differences between the two male morphs, confirming previous findings that increased sperm performance as an adaptation to sperm competition is not a primary target of evolution.
While ecological interactions have been identified as determinant for biological control efficiency, the role of evolution remains largely underestimated in biological control programs. With the restrictions on the use of both pesticides and exotic biological control agents (BCAs), the evolutionary optimization of local BCAs becomes central for improving the efficiency and the resilience of biological control. In particular, we need to better account for the natural processes of evolution to fully understand the interactions of pests and BCAs, including in biocontrol strategies integrating human manipulations of evolution (i.e. artificial selection and genetic engineering). In agro‐ecosystems, the evolution of BCAs traits and performance depends on heritable phenotypic variation, trait genetic architecture, selection strength, stochastic processes, and other selective forces. Humans can manipulate these natural processes to increase the likelihood of evolutionary trait improvement, by artificially increasing heritable phenotypic variation, strengthening selection, controlling stochastic processes, or overpassing evolution through genetic engineering. We highlight these facets by reviewing recent studies addressing the importance of natural processes of evolution and human manipulations of these processes in biological control. We then discuss the interactions between the natural processes of evolution occurring in agroecosystems and affecting the artificially improved BCAs after their release. We emphasize that biological control cannot be summarized by interactions between species pairs because pests and biological control agents are entangled in diverse communities and are exposed to a multitude of deterministic and stochastic selective forces that can change rapidly in direction and intensity. We conclude that the combination of different evolutionary approaches can help optimize BCAs to remain efficient under changing environmental conditions and, ultimately, favor agroecosystem sustainability.
Evolutionary developmental biology, the interdisciplinary effort of illuminating the conserved similarities and difference during animal development across all phylogenetic clades, has gained renewed interest in the past decades. As technology (immunohistochemistry, next generation sequencing, advanced imaging, computational resources) has advanced, so has our ability of resolving fundamental hypotheses and overcoming the genotype‐phenotype gap. This rapid progress, however, has also exposed gaps in the collective knowledge around the choice and representation of model organisms. It has become clear that evo‐devo requires a comparative, large‐scale approach including marine invertebrates to resolve some of the most urgent questions about the phylogenetic positioning and character traits of the last common ancestors. Many invertebrates at the base of the tree of life inhabit marine environments and have been used for some years due to their accessibility, husbandry, and morphology. Here, we briefly review the major concepts of evolutionary developmental biology and discuss the suitability of established model organisms to address current research questions, before focussing on the importance, application and state‐of‐the‐art of marine evo‐devo. We highlight novel technical advances that progress evo‐devo as a whole.
Structural variations (SVs) are important DNA polymorphisms that contribute to genetic diversity and evolution in humans, animals and plants. In this study, we present a novel swine SV dataset of 79,919 deletions, 23,638 duplications and 9,333 inversions with average sequence depths of 24.1× from 24 varieties of worldwide pig populations, encompassing 305 individuals. Genotypes of SVs, particularly deletions, can accurately group individuals based on their population identity. We showed that exon‐covering deletions were subject to negative selection. Fixation index and differential allele frequency analysis identified highly differentiated SVs between European and Asian indigenous pigs, including deletions in NR6A1 and PLAG1, which are significantly associated with vertebrate numbers and growth performances, respectively. The growth‐enhancing allele at the deletion in PLAG1 was shared by European commercial pigs and Northern Chinese indigenous pigs including Laiwu and Min pigs, suggesting potential introgression from European commercial breeds into Chinese indigenous breeds. Moreover, we uncovered highly differentiated SVs in 139 genes between domesticated pigs and wild boars in Asia, temperature and altitude‐adaptation associated SVs in 41 genes and population specific SVs in 718 genes. This study provides novel insights into the role of porcine SVs in domestication, environmental adaptation and breed formation.
Understanding genetic structure and diversity within species can uncover associations with environmental and geographic attributes that highlight adaptive potential and inform conservation and management. The California gnatcatcher, Polioptila californica, is a small songbird found in desert and coastal scrub habitats from the southern end of Baja California Sur to Ventura County, California. Lack of congruence among morphological subspecies hypotheses and lack of measurable genetic structure found in a few genetic markers led to questions about the validity of subspecies within P. californica and the listing status of the coastal California gnatcatcher, P. c. californica. As a U.S. federally threatened subspecies, P. c. californica is recognized as a flagship for coastal sage scrub conservation throughout southern California. We used restriction site‐associated DNA sequencing to develop a genomic dataset for the California gnatcatcher. We sampled throughout the species' range, examined genetic structure, gene–environment associations, and demographic history, and tested for concordance between genetic structure and morphological subspecies groups. Our data support two distinct genetic groups with evidence of restricted movement and gene flow near the U.S.‐ Mexico international border. We found that climate‐associated outlier loci were more strongly differentiated than climate neutral loci, suggesting that local climate adaptation may have helped to drive differentiation after Holocene range expansions. Patterns of habitat loss and fragmentation are also concordant with genetic substructure throughout the southern California portion of the range. Finally, our genetic data supported the morphologically defined P. c. californica as a distinct group, but there was little evidence of genetic differentiation among other previously hypothesized subspecies in Baja California. Our data suggest that retaining and restoring connectivity, and protecting populations, particularly at the northern range edge, could help preserve existing adaptive potential to allow for future range expansion and long‐term persistence of the California gnatcatcher.
The efficacy of fisheries management strategies depends on stock assessment and management actions being carried out at appropriate spatial scales. This requires understanding of spatial and temporal population structure and connectivity, which is challenging in weakly structured and highly connected marine populations. We carried out a population genomics study of the heavily exploited snapper (Chrysophrys auratus) along ~2,600 km of the Australian coastline, with a focus on Western Australia (WA). We used 10,903 filtered SNPs in 341 individuals from eight sampling locations to characterise population structure and connectivity in snapper across WA and to assess if current spatial scales of stock assessment and management agree with evidence from population genomics. Our dataset also enabled us to investigate temporal stability in population structure as well as connectivity between WA and its nearest, eastern jurisdictional neighbor. As expected for a species influenced by the extensive ocean boundary current in the region, low genetic differentiation and high connectivity was uncovered across WA. However, we did detect strong isolation by distance and genetic discontinuities in the mid‐west and south‐east. The discontinuities correlate with boundaries between biogeographic regions, influenced by on‐shelf oceanography, and the sites of important spawning aggregations. We also detected temporal instability in genetic structure at one of our sites, possibly due to interannual variability in recruitment in adjacent regions. Our results partly contrast with the current spatial management of snapper in WA, indicating the likely benefits of a review. This study supports the value of population genomic surveys in informing the management of weakly‐structured and wide‐ranging marine fishery resources.
Conceptual illustration of the experimental set‐up. Zosterops silvanus (green birds) occur in montane forest habitats, whereas Zosterops flavilateralis (yellow birds) occur in the more open habitats. They can hybridize in intermediate habitats with a given probability, if there are no available conspecific mates. For the simulation experiments, we varied either the hybridization propensity or the model landscapes. Note that this figure is for illustration purposes, see the main text for a complete study description and Figure S2 for a map of the actual landscape. Inset shows the location of the Taita Hills in Kenya
Development of key variables over 300 years in the hybridization experiment, differentiated by hybridization propensity. (a) Global number of adult Z. silvanus individuals. (b) Mean population heterozygosity of Z. silvanus (i.e. percentage of extraspecific chromosomes in the population gene pool). (c) Mean AGC optimum trait value of all Z. silvanus individuals. (d) Mean AGC tolerance trait value of all Z. silvanus individuals. Solid lines show the mean of 50 replicates, shaded areas are 95% confidence intervals. AGC: above‐ground carbon, in Mg C ha⁻¹ (a proxy for habitat type, see main text). The dashed line in panel (c) denotes the boundary between montane forest habitats (AGC ≧ 90) and other habitat types (AGC < 90)
Spatial distribution of population density of Z. silvanus in the Taita Hills, Kenya, after 300 simulation years in the hybridization experiment. Darker colours denote relatively higher densities, measured as number of individuals per patch. Results shown for select hybridization propensities: (a) 0%; (b) 1%; (c) 10%; (d) 100%
Development of key variables over 300 years in the habitat experiment, differentiated by habitat scenario. (a) Global number of adult Z. silvanus individuals. (b) Mean population heterozygosity of Z. silvanus (i.e. percentage of extraspecific chromosomes in the population gene pool). (c) Mean AGC optimum trait value of all Z. silvanus individuals. (d) Mean AGC tolerance trait value of all Z. silvanus individuals. Solid lines show the mean of 50 replicates, shaded areas are 95% confidence intervals. AGC: above‐ground carbon, in Mg C ha⁻ (a proxy for habitat type, see main text). The dashed line in panel (c) denotes the boundary between montane forest habitats (AGC ≧ 90) and other habitat types (AGC < 90)
Spatial distribution of population heterozygosity of Z. silvanus in the Taita Hills, Kenya, after 300 simulation years in the habitat experiment. Darker colours denote relatively higher heterozygosity values, measured as percentage of extraspecific chromosomes in the patch gene pool. Grids show the four habitat change scenarios (excluding the control): (a) edge depletion; (b) fragment clearing; (c) corridor planting; (d) plantation conversion
Introgressive hybridisation is a process that enables gene flow across species barriers through the backcrossing of hybrids into a parent population. This may make genetic material, potentially including relevant environmental adaptations, rapidly available in a gene pool. Consequently, it has been postulated to be an important mechanism for enabling evolutionary rescue, i.e. the recovery of threatened populations through rapid evolutionary adaptation to novel environments. However, predicting the likelihood of such evolutionary rescue for individual species remains challenging. Here, we use the example of Zosterops silvanus, an endangered East African highland bird species suffering from severe habitat loss and fragmentation, to investigate whether hybridisation with its congener Zosterops flavilateralis might enable evolutionary rescue of its Taita Hills population. To do so, we employ an empirically parameterised individual‐based model to simulate the species’ behaviour, physiology and genetics. We test the population’s response to different assumptions of mating behaviour as well as multiple scenarios of habitat change. We show that as long as hybridisation does take place, evolutionary rescue of Z. silvanus is likely. Intermediate hybridisation rates enable the greatest long‐term population growth, due to trade‐offs between adaptive and maladaptive introgressed alleles. Habitat change did not have a strong effect on population growth rates, as Z. silvanus is a strong disperser and landscape configuration is therefore not the limiting factor for hybridisation. Our results show that targeted gene flow may be a promising avenue to help accelerate the adaptation of endangered species to novel environments, and demonstrate how to combine empirical research and mechanistic modelling to deliver species‐specific predictions for conservation planning.
Population structure defined with PCA of 714 pigs from nine European breeds, 37 pigs from two Asian breeds, and 362 French WB. The first (PC1) and second (PC2) principal components are shown. The WBs outside the WB cluster (WB_Outliers) are represented with their respective identification numbers (GISA‐xxx). Letters A, B/B′ and C represent the Asian pig breeds, European pig breeds, and wild boar groups, respectively
Estimates of WB ancestry and 95% confidence intervals (CI) for all 362 individuals. Individuals are arranged by Q score following admixture analysis for k = 11. The colors represent the chromosome number of each individual. The first vertical line (on the left side) separates the outliers from animals belonging to the WB cluster (based on PCA). Among the WB_cluster, animals were considered as “unadmixed” if the 95% CI overlapped 0.99 (proportion of WB ancestry)
Admixture analysis (K = 11) of wild boar outliers (+ “Vietnamese” DP on the left of the figure). The different colors represent the dominant ancestral proportions of the different breeds (gene pools) considered in this study
Average wild boar ancestry estimated over the 349 WB (without outliers) using ELAI, for each position of each autosome. The bold red line represents the mean ancestry, and dotted red lines represent a deviation of three SD and six SD from the mean
Different categories of wild boars sampled.
The admixture of domestic pig into French wild boar populations has been monitored since the 1980s thanks to the existence of a cytogenetic difference between the two sub‐species. The number of chromosomes is 2n=36 in wild boar and 2n=38 in pig, respectively. This difference makes it possible to assign the “hybrid” status to wild boar individuals controlled with 37 or 38 chromosomes. However, it does not make it possible to determine the timing of the hybridization(s), nor to guarantee the absence of domestic admixture in an animal with 2n=36 chromosomes. In order to analyze hybridization in greater detail and to avoid the inherent limitations of the cytogenetic approach, 362 wild boars recently collected in different French geographical areas and in different environments (farms, free ranging in protected or unprotected areas, animals with 2n=36, 37 or 38 chromosomes) were genotyped on a 70K SNP chip. Principal component analyses allowed the identification of 13 “outliers” (3.6%), for which the proportion of the genome of “domestic” origin was greater than 40% (Admixture analyses). These animals were probably recent hybrids, having Asian domestic pig ancestry for most of them. For the remaining 349 animals studied, the proportion of the genome of “wild” origin varied between 83 and 100% (median: 94%). This proportion varied significantly depending on how the wild boar populations were managed. Local ancestry analyses revealed adaptive introgression from domestic pig, suggesting a critical role of genetic admixture in improving the fitness and population growth of wild boars. Overall, our results show that the methods used to monitor the domestic genetic contributions to wild boar populations should evolve in order to limit the level of admixture between the two gene pools.
Vietnam harnesses a rich diversity of rice landraces adapted to a range of conditions, which constitute a largely untapped source of diversity for the continuous improvement of cultivars. We previously identified a strong population structure in Vietnamese rice, which is captured in five Indica and four Japonica subpopulations, including an outlying Indica-5 group. Here, we leveraged that strong differentiation and 672 native rice genomes to identify genomic regions and genes putatively selected during the breeding of rice in Vietnam. We identified significant distorted patterns in allele frequency (XP-CLR) and population differentiation scores (FST) resulting from differential selective pressures between native subpopulations, and later annotated them with QTLs previously identified by GWAS in the same panel. We particularly focused on the outlying Indica-5 subpopulation because of its likely novelty and differential evolution, where we annotated 52 selected regions, which represented 8.1% of the rice genome. We annotated the 4,576 genes in these regions and selected 65 candidate genes as promising breeding targets, several of which harboured alleles with non-synonymous substitutions. Our results highlight genomic differences between traditional Vietnamese landraces, which are likely the product of adaption to multiple environmental conditions and regional culinary preferences in a very diverse country. We also verified the applicability of this genome scanning approach to identify potential regions harbouring novel loci and alleles to breed a new generation of sustainable and resilient rice.
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4.929 (2021)
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Top-cited authors
Andrew P Hendry
  • McGill University
Sally N Aitken
  • University of British Columbia - Vancouver
Ary Hoffmann
  • University of Melbourne
Tongli Wang
  • University of British Columbia - Vancouver
Sam Yeaman
  • The University of Calgary