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Why species have geographically restricted distributions is an unresolved question in ecology and evolutionary biology. Here, we test a new explanation that mutation accumulation due to small population size or a history of range expansion can contribute to restricting distributions by reducing population growth rate at the edge. We examined genomic diversity and mutational load across the entire geographic range of the North American plant Arabidopsis lyrata, including old, isolated populations predominantly at the southern edge and regions of postglacial range expansion at the northern and southern edges. Genomic diversity in intergenic regions declined toward distribution edges and signatures of mutational load in exon regions increased. Genomic signatures of mutational load were highly linked to phenotypically expressed load, measured as reduced performance of individual plants and lower estimated rate of population growth. The geographic pattern of load and the connection between load and population growth demonstrate that mutation accumulation reduces fitness at the edge and helps restrict species’ distributions.

Polyploidy, or whole-genome duplication, is a common speciation mechanism in plants. An important barrier to polyploid establishment is a lack of compatible mates. Because self-compatibility alleviates this problem, it has long been hypothesized that there should be an association between polyploidy and self-compatibility (SC), but empirical support for this prediction is mixed. Here, we investigate whether the molecular makeup of the Brassicaceae self-incompatibility (SI) system, and specifically dominance relationships among S-haplotypes mediated by small RNAs, could facilitate loss of SI in allopolyploid crucifers. We focus on the allotetraploid species Capsella bursa-pastoris, which formed ~300 kya by hybridization and whole-genome duplication involving progenitors from the lineages of Capsella orientalis and Capsella grandiflora. We conduct targeted long-read sequencing to assemble and analyze eight full-length S-locus haplotypes, representing both homeologous subgenomes of C. bursa-pastoris. We further analyze small RNA (sRNA) sequencing data from flower buds to identify candidate dominance modifiers. We find that C. orientalis-derived S-haplotypes of C. bursa-pastoris harbor truncated versions of the male SI specificity gene SCR and express a conserved sRNA-based candidate dominance modifier with a target in the C. grandiflora-derived S-haplotype. These results suggest that pollen-level dominance may have facilitated loss of SI in C. bursa-pastoris. Finally, we demonstrate that spontaneous somatic tetraploidization after a wide cross between C. orientalis and C. grandiflora can result in production of self-compatible tetraploid offspring. We discuss the implications of this finding on the mode of formation of this widespread weed.

Sequencing pooled DNA of multiple individuals from a population instead of sequencing individuals separately has become popular due to its cost-effectiveness and simple wet-lab protocol, although some criticism of this approach remains. Here we validated a protocol for pooled whole-genome re-sequencing (Pool-seq) of Arabidopsis lyrata libraries prepared with low amounts of DNA (1.6 ng per individual). The validation was based on comparing single nucleotide polymorphism (SNP) frequencies obtained by pooling with those obtained by individual-based Genotyping By Sequencing (GBS). Furthermore, we investigated the effect of sample number, sequencing depth per individual and variant caller on population SNP frequency estimates. For Pool-seq data, we compared frequency estimates from two SNP callers, VarScan and Snape; the former employs a frequentist SNP calling approach while the latter uses a Bayesian approach. Results revealed concordance correlation coefficients well above 0.8, confirming that Pool-seq is a valid method for acquiring population-level SNP frequency data. Higher accuracy was achieved by pooling more samples (25 compared to 14) and working with higher sequencing depth (4.1× per individual compared to 1.4× per individual), which increased the concordance correlation coefficient to 0.955. The Bayesian-based SNP caller produced somewhat higher concordance correlation coefficients, particularly at low sequencing depth. We recommend pooling at least 25 individuals combined with sequencing at a depth of 100× to produce satisfactory frequency estimates for common SNPs (minor allele frequency above 0.05).

A sound analysis of DNA sequencing data is important to extract meaningful information and infer quantities of interest. Sequencing and mapping errors coupled with low and variable coverage hamper the identification of genotypes and variants and the estimation of population genetic parameters. Methods and implementations to estimate population genetic parameters from sequencing data available nowadays either are suitable for the analysis of genomes from model organisms only, require moderate sequencing coverage, or are not easily adaptable to specific applications. To address these issues, we introduce ngsJulia, a collection of templates and functions in Julia language to process short-read sequencing data for population genetic analysis. We further describe two implementations, ngsPool and ngsPloidy, for the analysis of pooled sequencing data and polyploid genomes, respectively. Through simulations, we illustrate the performance of estimating various population genetic parameters using these implementations, using both established and novel statistical methods. These results inform on optimal experimental design and demonstrate the applicabil- ity of methods in ngsJulia to estimate parameters of interest even from low coverage sequencing data. ngsJulia provide users with a flexible and efficient framework for ad hoc analysis of sequencing data.ngsJulia is available from: https://github.com/mfumagalli/ngsJulia

A sound analysis of DNA sequencing data is important to extract meaningful information and infer quantities of interest. Sequencing and mapping errors coupled with low and variable coverage hamper the identification of genotypes and variants and the estimation of population genetic parameters. Methods and implementations to estimate population genetic parameters from sequencing data available nowadays either are suitable for the analysis of genomes from model organisms only, require moderate sequencing coverage, or are not easily adaptable to specific applications. To address these issues, we introduce ngsJulia, a collection of templates and functions in Julia language to process short-read sequencing data for population genetic analysis. We further describe two implementations, ngsPool and ngsPloidy, for the analysis of pooled sequencing data and polyploid genomes, respectively. Through simulations, we illustrate the performance of estimating various population genetic parameters using these implementations, using both established and novel statistical methods. These results inform on optimal experimental design and demonstrate the applicability of methods in ngsJulia to estimate parameters of interest even from low coverage sequencing data. ngsJulia provide users with a flexible and efficient framework for ad hoc analysis of sequencing data.ngsJulia is available from: https://github.com/mfumagalli/ngsJulia

Parallel adaptation provides valuable insight into the predictability of evolutionary change through replicated natural experiments. A steadily increasing number of studies have demonstrated genomic parallelism, yet the magnitude of this parallelism varies depending on whether populations, species, or genera are compared. This led us to hypothesize that the magnitude of genomic parallelism scales with genetic divergence between lineages, but whether this is the case and the underlying evolutionary processes remain unknown. Here, we resequenced seven parallel lineages of two Arabidopsis species, which repeatedly adapted to challenging alpine environments. By combining genome-wide divergence scans with model-based approaches, we detected a suite of 151 genes that show parallel signatures of positive selection associated with alpine colonization, involved in response to cold, high radiation, short season, herbivores, and pathogens. We complemented these parallel candidates with published gene lists from five additional alpine Brassicaceae and tested our hypothesis on a broad scale spanning ∼0.02 to 18 My of divergence. Indeed, we found quantitatively variable genomic parallelism whose extent significantly decreased with increasing divergence between the compared lineages. We further modeled parallel evolution over the Arabidopsis candidate genes and showed that a decreasing probability of repeated selection on the same standing or introgressed alleles drives the observed pattern of divergence-dependent parallelism. We therefore conclude that genetic divergence between populations, species, and genera, affecting the pool of shared variants, is an important factor in the predictability of genome evolution.

Understanding the factors that govern the distribution of species is a central goal of evolutionary ecology. It is commonly assumed that geographic range limits reflect ecological niche limits and that species experience increasingly marginal conditions towards the edge of their ranges. Using spatial data and ecological niche models we tested these hypotheses in Arabidopsis lyrata. Specifically, we asked whether range limits coincide with predicted niche limits in this system and whether the suitability of sites declines towards the edge of the species’ range in North America. We further explored patterns of environmental change towards the edge of the range and asked whether genome‐wide patterns of genetic diversity decline with increasing peripherality and environmental marginality. Our results suggest that latitudinal range limits coincide with niche limits. Populations experienced increasingly marginal environments towards these limits – though patterns of environmental change were more complex than most theoretical models for range limits assume. Genomic diversity declined towards the edge of the species’ range and with increasing distance from the estimated centre of the species’ niche in environmental space, but not with the suitability of sites based on niche model predictions. Thus while latitudinal range limits in this system are broadly associated with niche limits, the link between environmental conditions and genetic diversity (and thus the adaptive potential of populations) is less clear.

Abstract Fertilization in angiosperms involves the germination of pollen on the stigma, followed by the extrusion of a pollen tube that elongates through the style and delivers two sperm cells to the embryo sac. Sexual selection could occur throughout this process when male gametophytes compete for fertilization. The strength of sexual selection during pollen competition should be affected by the number of genotypes deposited on the stigma. As increased self-fertilization reduces the number of mating partners, and the genetic diversity and heterozygosity of populations, it should thereby reduce the intensity of sexual selection during pollen competition. Despite the prevalence of mating system shifts, few studies have directly compared the molecular signatures of sexual selection during pollen competition in populations with different mating systems. Here we analyzed whole-genome sequences from natural populations of Arabis alpina, a species showing mating system variation across its distribution, to test whether shifts from cross- to self-fertilization result in molecular signatures consistent with sexual selection on genes involved in pollen competition. We found evidence for efficient purifying selection on genes expressed in vegetative pollen, and overall weaker selection on sperm-expressed genes. This pattern was robust when controlling for gene expression level and specificity. In agreement with the expectation that sexual selection intensifies under cross-fertilization, we found that the efficacy of purifying selection on male gametophyte-expressed genes was significantly stronger in genetically more diverse and outbred populations. Our results show that intra-sexual competition shapes the evolution of pollen-expressed genes, and that its strength fades with increasing self-fertilization rates.