福建山樱花形态多样性分化的研究

以闽、赣、粤、台4省13个种群292份福建山樱花种质为分析材料,对16个形态性状进行聚类分析和主成分分析。结果表明:福建山樱花不同个体间各性状均有明显差异,变异系数较大,优株选择潜力很大。系统聚类分析可将292份参试材料划分为2个表征群4个亚表征群,除重瓣型福建山樱花外,单瓣型福建山樱花的形态特征在种群间总体上无明显的分化规律,不同种群的个体交叉聚在同一个表征群内,但在同一亚表征群内,同一种群的多数个体常能聚在一起,表明种群之间在形态上发生了一定程度的分化,只是这种分化还不能清楚地区分不同的种群。主成分分析结果表明叶形、叶尖、叶缘、分枝能力、60 d苗高、花色和花形是造成福建山樱花形态分化的主要因素。     

Biplots for multifactorial analysis of distance

Many data sets in practice fit a multivariate analysis of variance (MANOVA) structure, but do not accord with MANOVA assumptions for their analysis. One way forward is to calculate the matrix of dissimilarities or distances between every pair of individuals, and then to conduct an analysis of distance on the resulting data. Various metric scaling plots can be used to interpret the results of the analysis. However, developments to date of this approach have focused mainly on the individuals in the sample, and little attention has been paid to the assessment of influence of the original variables on the results. The present article attempts to rectify this omission. We discuss the inclusion of biplots on all forms of metric scaling representations in the analysis of distance. Exact biplots will often be nonlinear so we propose a simple linear approximation, and contrast it with other simple linear possibilities. An example from ecology illustrates the methodology.     

The additive genetic variance after bottlenecks is affected by the number of loci involved in epistatic interactions

Abstract We investigated the role of the number of loci coding for a neutral trait on the release of additive variance for this trait after population bottlenecks. Different bottleneck sizes and durations were tested for various matrices of genotypic values, with initial conditions covering the allele frequency space. We used three different types of matrices. First, we extended Cheverud and Routman's model by defining matrices of "pure" epistasis for three and four independent loci; second, we used genotypic values drawn randomly from uniform, normal, and exponential distributions; and third we used two models of simple metabolic pathways leading to physiological epistasis. For all these matrices of genotypic values except the dominant metabolic pathway, we find that, as the number of loci increases from two to three and four, an increase in the release of additive variance is occurring. The amount of additive variance released for a given set of genotypic values is a function of the inbreeding coefficient, independently of the size and duration of the bottleneck. The level of inbreeding necessary to achieve maximum release in additive variance increases with the number of loci. We find that additive-by-additive epistasis is the type of epistasis most easily converted into additive variance. For a wide range of models, our results show that epistasis, rather than dominance, plays a significant role in the increase of additive variance following bottlenecks.     

Heritability of parental effort in a passerine bird

Abstract The study of the evolution of parental care is central to our understanding of social systems, sexual selection, and interindividual conflict, yet we know virtually nothing about the genetic architecture of parental care traits in natural populations. In this paper, we use data from a long term field study of a passerine bird, the long-tailed tit ( Aegithalos caudatus ), to examine the heritability of the rate at which parents feed offspring. This measure of effort is positively related to offspring survival, is repeatable within individuals, and does not appear to be confounded by environmental effects. Using both parent-offspring regression, and an animal model approach, with a pedigree derived from ringing data, we show that our measure of effort has a significant heritable component.     

A flexible model of foraging by a honey bee colony: the effects of individual behaviour on foraging success

This paper develops and explores a model of foraging in honey bee colonies. The model may be applied to forage sources with various properties, and to colonies with different foraging-related parameters. In particular, we examine the effect of five foraging-related parameters on the foraging response and consequent nectar intake of a homogeneous colony. The parameters investigated affect different quantities critical to the foraging cycle--visit rate (affected by g), probability of dancing (mpd and bpd), duration of dancing (mcirc), or probability of abandonment (A). We show that one parameter, A, affects nectar intake in a nonlinear way. Further, we show that colonies with a midrange value of any foraging parameter perform better than the average of colonies with high- and low-range values, when profitable sources are available. Together these observations suggest that a heterogeneous colony, in which a range of parameter values are present, may perform better than a homogeneous colony. We modify the model to represent heterogeneous colonies and use it to show that the most important effect of heterogeneous foraging behaviour within the colony is to reduce the variance in the average quantity of nectar collected by heterogeneous colonies.     

Factors affecting the robustness of metabolite fingerprinting using 1H NMR spectra

1H NMR spectroscopy is one of the techniques whose potential is currently being explored in the emerging field of metabolomics. It is a non-targeted method, producing signals for all proton-containing chemical species. For crude plant materials the spectra are always complex, with many signals overlapping. Hence a most suitable approach for analysing them is 'metabolite fingerprinting', which is aimed at highlighting compositional similarities and exploring the overall natural variability in a population of samples. The most commonly used method for this is principal component analysis (PCA), as it allows the whole spectral trace to be analysed and the vast quantity of information to be simplified. In this paper we investigate whether there are factors which may affect the NMR spectra in a way that subsequently decreases the robustness of the metabolite fingerprinting by PCA. Imperfections in the signal registration (i.e. inconsistency of the peak position) are generally detrimental to analysing whole traces by multivariate methods. The sources of such problems are illustrated through specially designed repeatability studies using potato and tomato samples, and the analysis of a tea dataset containing many samples. Careful sample preparation can help to limit peak shifts; for instance here by attempting to control the pH of the extracts. In addition, some compounds are susceptible to interactions affecting their chemical shifts and mathematical alignment of peaks may be necessary. Lastly factors such as resolution can also affect analyses and must be carefully adjusted. Our choice of examples aims to raise awareness of potential problems. We do not question the validity of the NMR approach, but point out those areas where special care may need to be taken.     

Assessment of 1H NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arabidopsis thaliana

An approach to metabolite fingerprinting of crude plant extracts that utilizes 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate statistics has been tested. Using ecotypes of Arabidopsis thaliana as experimental material, a method has been developed for the rapid analysis of unfractionated polar plant extracts, enabling the creation of reproducible metabolite fingerprints. These fingerprints could be readily stored and compared by a variety of chemometric methods. Comparison by principal component analysis using SIMCA-P allowed the generation of residual NMR spectra of the compounds that contributed significantly to the differences between samples. From these plots, conclusions were drawn with respect to the identity and relative levels of metabolites differing between samples.     

Clonal diversity and genetic differentiation in Ilex leucoclada M. patches in an old-growth beech forest

We investigated clonal diversity within patches of Ilex leucoclada and genetic variation within and among patches using random amplified polymorphic DNA (RAPD) markers in a 1-ha plot within an old-growth beech forest. We found 38 patches that exhibited a clumped distribution in the middle of the plot. We identified a total of 166 RAPD phenotypes among the 215 stems sampled from 27 patches that were completely within the plot. The population showed high clonal diversity within patches (mean number of genets relative to number of stems = 0.79; mean Simpson's D = 0.89). Variation in RAPD phenotypes among patches was highly significant (PhiST in the molecular variance analysis = 0.316, P < 0.001), indicating genetic differentiation among patches. Pairwise genetic distances, PhiST, among patches did not correlate with geographical distances among patches. The cluster analysis based on the genetic distances showed few clear clusters of patches, indicating no spatial genetic structure among patches. High levels of clonal diversity both within patches and within the population may be explained by multiple founders, seedling recruitment during patch-formation, and somatic mutation. The significant genetic differentiation among patches may be caused by separate founding events and/or kin structuring within patches.     

A general clustering approach with application to the Miyazawa-Jernigan potentials for amino acids

In this article, we address the problem of classification of amino acids. Starting from the Miyazawa-Jernigan matrix obtained from the relative positions of amino acids in the crystal structure of globular proteins, we develop a fully unsupervised method of classification for the amino acids. The method is based in the subdominant ultrametric associated to the distance induced by the Miyazawa-Jernigan matrix and the maximum likelihood principle to determine the cluster structure. We obtain a classification consistent with the five groups used in the literature, although with some peculiarities. We also show the stability of our results against changes of the method used to classify the amino acids. Proteins 2004.