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      Lack of spatial structure for phenotypic and genetic variation despite high self-fertilization in Aquilegia canadensis (Ranunculaceae)

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      Springer Nature

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          Abstract

          <p class="first" id="Par1">By reducing genetically effective population size and gene flow, self-fertilization should lead to strong spatial genetic structure (SGS). Although the short-lived plant <i>Aquilegia canadensis</i> produces large, complex, nectar-rich flowers, 75% of seed, on average, are self-fertilized. Previous experimental results are consistent with the fine-scale SGS expected in selfing populations. In contrast, key floral traits show no evidence of SGS, despite a significant genetic basis to phenotypic variation within populations. In this study, we attempt to resolve these contradictory results by hierarchically sampling plants from two plots nested within each of seven rock outcrops distributed over several km, and comparing the spatial pattern of phenotypic variation in four floral traits with neutral genetic variation at 10 microsatellite loci. For both floral and microsatellite variation, we detected only weak hierarchical structuring and no isolation by distance. The spatial pattern of variation in floral traits was on par with microsatellite polymorphisms. These results suggest regular long-distance gene flow via pollen. At much finer spatial scales within plots, estimates of relatedness were higher (albeit very low) between nearest neighbors than random plants, and declined with increasing distance between neighbors, which is consistent with highly localized seed dispersal. High selfing should yield SGS, but strong inbreeding depression in <i>A. canadensis</i> likely erodes SGS so that reproductive plants exhibit weak structure typical of outcrossers, especially given that outcrossing and consequent gene flow in this species are mediated by strong-flying hummingbirds and bumble bees. </p>

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          New insights from fine-scale spatial genetic structure analyses in plant populations.

          Many empirical studies have assessed fine-scale spatial genetic structure (SGS), i.e. the nonrandom spatial distribution of genotypes, within plant populations using genetic markers and spatial autocorrelation techniques. These studies mostly provided qualitative descriptions of SGS, rendering quantitative comparisons among studies difficult. The theory of isolation by distance can predict the pattern of SGS under limited gene dispersal, suggesting new approaches, based on the relationship between pairwise relatedness coefficients and the spatial distance between individuals, to quantify SGS and infer gene dispersal parameters. Here we review the theory underlying such methods and discuss issues about their application to plant populations, such as the choice of the relatedness statistics, the sampling scheme to adopt, the procedure to test SGS, and the interpretation of spatial autocorrelograms. We propose to quantify SGS by an 'Sp' statistic primarily dependent upon the rate of decrease of pairwise kinship coefficients between individuals with the logarithm of the distance in two dimensions. Under certain conditions, this statistic estimates the reciprocal of the neighbourhood size. Reanalysing data from, mostly, published studies, the Sp statistic was assessed for 47 plant species. It was found to be significantly related to the mating system (higher in selfing species) and to the life form (higher in herbs than trees), as well as to the population density (higher under low density). We discuss the necessity for comparing SGS with direct estimates of gene dispersal distances, and show how the approach presented can be extended to assess (i) the level of biparental inbreeding, and (ii) the kurtosis of the gene dispersal distribution.
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            Comparative organization of chloroplast, mitochondrial and nuclear diversity in plant populations.

            Plants offer excellent models to investigate how gene flow shapes the organization of genetic diversity. Their three genomes can have different modes of transmission and will hence experience varying levels of gene flow. We have compiled studies of genetic structure based on chloroplast DNA (cpDNA), mitochondrial DNA (mtDNA) and nuclear markers in seed plants. Based on a data set of 183 species belonging to 103 genera and 52 families, we show that the precision of estimates of genetic differentiation (G(ST)) used to infer gene flow is mostly constrained by the sampling of populations. Mode of inheritance appears to have a major effect on G(ST). Maternally inherited genomes experience considerably more subdivision (median value of 0.67) than paternally or biparentally inherited genomes (approximately 0.10). G(ST) at cpDNA and mtDNA markers covary narrowly when both genomes are maternally inherited, whereas G(ST) at paternally and biparentally inherited markers also covary positively but more loosely and G(ST) at maternally inherited markers are largely independent of values based on nuclear markers. A model-based gross estimate suggests that, at the rangewide scale, historical levels of pollen flow are generally at least an order of magnitude larger than levels of seed flow (median of the pollen-to-seed migration ratio: 17) and that pollen and seed gene flow vary independently across species. Finally, we show that measures of subdivision that take into account the degree of similarity between haplotypes (N(ST) or R(ST)) make better use of the information inherent in haplotype data than standard measures based on allele frequencies only.
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              Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study

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                Author and article information

                Journal
                Heredity
                Heredity
                Springer Nature
                0018-067X
                1365-2540
                March 1 2018
                :
                :
                Article
                10.1038/s41437-018-0065-2
                6221887
                29491467
                e71f61c8-9682-4900-a8dd-84809e29b06f
                © 2018

                http://www.springer.com/tdm

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