Phytoecological importance,mutual redundancy and phytological threshold values of certain climatic factors

A combination of methods (intensity of indication, floristic and mesological redundancy analysis, beta-diversity analysis, principal components analysis and Wildi's interactive ranking procedure) were used to evaluate redundancy and relative phytoecological importance among 80 climatic variables in Galicia (N.W. Spain). The information they contained was found to be adequately summarized by just 3 factors thought to play a major role in regulating the distribution of the species considered in the study area and similar areas: Baudiere's QE index, mean minimum temperature in the coldest month and mean temperature range in the coldest month. For these three factors, phytoclimatic thresholds were determined by examining beta-diversity and were used to define phytoclimatic zone types.

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Resumen Se valora la redundancia e importancia fitoecológica relative de 80 variables climáticas en Galicia (N.O. de España) empleando una combinacion de diferentes metodos (intensidad indicadora, análisis de la redundancia florística y mesológica, análisis de beta-diversidad, análisis de componentes principales y el método de ordenación interactiva de Wildi). La información contenida en esta variables es adecuadamente resumida por 3 factores que juegan un papel predominante en regular la distribución de las especies consideradas en el area de estudio y areas similares: el índice QE de Baudiere, la temperatura media de las mínimas del mes más frío y la oscilación térmica del mes más frío. Para estos factores el análisis de la beta-diversidad permitió determinar los umbrales fitoclimáticos utilizados en la definición y cartografía de las zonas fitoclimáticas.

     

Efficient Evaluation of Ranking Procedures when the Number of Units is Large,with Application to SNP Identification

Simulation‐based assessment is a popular and frequently necessary approach for evaluating statistical procedures. Sometimes overlooked is the ability to take advantage of underlying mathematical relations and we focus on this aspect. We show how to take advantage of large‐sample theory when conducting a simulation using the analysis of genomic data as a motivating example. The approach uses convergence results to provide an approximation to smaller‐sample results, results that are available only by simulation. We consider evaluating and comparing various ranking‐based methods for identifying the most highly associated SNPs in a genome‐wide association study, derive integral equation representations of the pre‐posterior distribution of percentiles produced by three ranking methods, and provide examples comparing performance. These results are of interest in their own right and set the framework for a more extensive set of comparisons.     

Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes

Microarray technology provides a powerful tool for the expression profile of thousands of genes simultaneously, which makes it possible to explore the molecular and metabolic etiology of the development of a complex disease under study. However, classical statistical methods and technologies fail to be applicable to microarray data. Therefore, it is necessary and motivating to develop powerful methods for large-scale statistical analyses. In this paper, we described a novel method, called Ranking Analysis of Microarray Data (RAM). RAM, which is a large-scale two-sample t-test method, is based on comparisons between a set of ranked T statistics and a set of ranked Z values (a set of ranked estimated null scores) yielded by a "randomly splitting" approach instead of a "permutation" approach and a two-simulation strategy for estimating the proportion of genes identified by chance, i.e., the false discovery rate (FDR). The results obtained from the simulated and observed microarray data show that RAM is more efficient in identification of genes differentially expressed and estimation of FDR under undesirable conditions such as a large fudge factor, small sample size, or mixture distribution of noises than Significance Analysis of Microarrays.     

Species ordering on a variance criterion

Summary A technique for ordering, to identify diagnostic species, based on variance ratios has been described. The method was illustrated with river terrace data.The author would like to thank Dr. László Orlóci for permission to use the vegetation data employed in the illustration of the algorithm. This work was supported by a grant from the National Research Council of Canada.     

Responses of aquatic macrophyte assemblages to nutrient enrichment in a lowland river basin of western Greece

Abstract

In Greece, as in many other Mediterranean countries, there is a lack of information enabling the use of aquatic macrophytes as biological indicators of the water trophic status. This research aimed to investigate the trophic preferences of the aquatic macrophyte assemblages encountered in selected standing water bodies of a lowland river basin of western Greece. It also aimed to assess the response of the macrophyte species that occur in the studied water bodies to different nutrient concentrations. A total of 56 macrophyte species were identified. TWINSPAN analysis distinguished four main vegetation groups potentially related to three trophic types. The Trophic Ranking Score developed in the UK was applied in order to investigate the trend in aquatic macrophytes in response to the different trophic conditions. The results indicated that TRS_UK was not in accordance to the water trophic indices. After the addition of 14 new species and the re-scoring of the plants from the UK list, the re-calculation of TRS enabled a better classification of the trophic status convergent with the information from the water trophic indices. The current study constitutes a reference document providing basic information, and must be improved by updating the plant list with new data from other sites.     

A Selection and Testing Procedure for Comparing Several Experimental Treatments with a Control

We consider a selection and testing procedure for comparing k experimental treatments with a control treatment where the treatments are assumed to be normally distributed with unknown means and a common, unknown variance. Stein‐type sampling is used in the selection phase to screen for an experimental treatment that exhibits evidence of being better than the control treatment and each of the other experimental treatments, where better is defined in terms of the largest mean. In the testing phase, the best experimental treatment is compared to the control using a hypothesis test. If no experimental treatment indicates that it is an improvement over the control during the selection phase, our procedure allows for early termination. We provide definitions of level and power appropriate for our hybrid procedure and compute procedure parameters required to implement our procedure.     

Probability of detecting disease-associated single nucleotide polymorphisms in case-control genome-wide association studies

Some case-control genome-wide association studies (CCGWASs) select promising single nucleotide polymorphisms (SNPs) by ranking corresponding p-values, rather than by applying the same p-value threshold to each SNP. For such a study, we define the detection probability (DP) for a specific disease-associated SNP as the probability that the SNP will be "T-selected," namely have one of the top T largest chi-square values (or smallest p-values) for trend tests of association. The corresponding proportion positive (PP) is the fraction of selected SNPs that are true disease-associated SNPs. We study DP and PP analytically and via simulations, both for fixed and for random effects models of genetic risk, that allow for heterogeneity in genetic risk. DP increases with genetic effect size and case-control sample size and decreases with the number of nondisease-associated SNPs, mainly through the ratio of T to N, the total number of SNPs. We show that DP increases very slowly with T, and the increment in DP per unit increase in T declines rapidly with T. DP is also diminished if the number of true disease SNPs exceeds T. For a genetic odds ratio per minor disease allele of 1.2 or less, even a CCGWAS with 1000 cases and 1000 controls requires T to be impractically large to achieve an acceptable DP, leading to PP values so low as to make the study futile and misleading. We further calculate the sample size of the initial CCGWAS that is required to minimize the total cost of a research program that also includes follow-up studies to examine the T-selected SNPs. A large initial CCGWAS is desirable if genetic effects are small or if the cost of a follow-up study is large.     

无量山种子植物区系科属的两种不同排序

地处滇中南的景东无量山地区迄今计种了植物２０７科,１０２６属,２５４０种,按照传统的种的绝对数目进行科属的排序可以得到一个较为完整的区系组成,但无法反映这些属在该地区系建成中的相对重要性,本文提出了区系存在度,即某一科（属）在该地再现的次级分类群与它的所有次级分类的比值,按之再去进行排列,可得到一个迥然有异的排序,按绝对种数排序的大科在第二种排序中很少进入,相反,一些东亚特有或有邻近地区共有的科的