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Disease-suppressive soils encompass specific plant−pathogen−microbial interactions and represent a rare example of an agroecosystem where soil conditions and microbiome together prevent the pathogen from causing disease. Such soils have the potential to serve as a model for characterizing soil pathogen-related aspects of soil health, but the mechanisms driving the establishment of suppressive soils vary and are often poorly characterized. Yet, they can serve as a resource for identifying markers for beneficial activities of soil microorganisms concerning pathogen prevention. Many recent studies have focused on the nature of disease-suppressive soils, but it has remained difficult to predict where and when they will occur. This review outlines current knowledge on the distribution of these soils, soil manipulations leading to pathogen suppression, and markers including bacterial and fungal diversity, enzymes, and secondary metabolites. The importance to consider soil legacy in research on the principles that define suppressive soils is also highlighted. The goal is to extend the context in which we understand, study, and use disease-suppressive soils by evaluating the relationships in which they occur and function. Finally, we suggest that disease-suppressive soils are critical not only for the development of indicators of soil health, but also for the exploration of general ecological principles about the surrounding landscape, effects of deeper layers of the soil profile, little studied soil organisms, and their interactions for future use in modern agriculture.

Common scab of potatoes (CS) is influenced by plant-microbe-soil interactions, which are locally specific. The study aims to identify any common trends in such interactions across a landscape differentiated by soil pH and CS severity. The 33 sites and 17 cultivars were characterized using soil and periderm nutrient contents and microbial communities. Quantitative PCR and Illumina amplicon sequencing were used to assess abundance of bacteria, actinobacteria and pathogens, and community composition. Comparisons between bulk and tuberosphere soil compartments as well as potato cultivars divided to three categories of CS susceptibility revealed that nitrogen was elevated in tuberosphere soil and N, Mg and Fe were lowered in periderm of resistant cultivars. The susceptible cultivar Agria grown at 7 sites had higher Ca content in tuberosphere soil, while the resistant cultivar Adela grown at 10 sites had higher S, P and Mg contents in its tuberosphere soil and P and Fe in periderm. That suggests further interactions between plants and bacterial community involving nutrient uptake. Diversity of bacteria was positively correlated with CS severity suggesting interactions between the Streptomyces pathogen populations and the local soil community. Overall, pathogen abundance assessed by quantifying the thaxtomin biosynthetic txtB genes were randomly dispersed among the sites without connections to CS severity or soil pH. Thus, the significant differences between bacterial communities of bulk and tuberosphere soils together with cultivar CS susceptibility showed that the susceptible cultivars select bacterial community relatively similar to the bulk soil, while the resistant cultivars promote more distinct communities.

This paper deals with a situation when one is interested in the dependence structure of a multidimensional response variable in the presence of a multivariate covariate. It is assumed that the covariate affects only the marginal distributions through regression models while the dependence structure, which is described by a copula, is unaffected. A parametric estimation of the copula function is considered with focus on the maximum pseudo-likelihood method. It is proved that under some appropriate regularity assumptions the estimator calculated from the residuals is asymptotically equivalent to the estimator based on the unobserved errors. In such case one can ignore the fact that the response is first adjusted for the effect of the covariate. A Monte Carlo simulation study explores (among others) situations where the regularity assumptions are not satisfied and the claimed result does not hold. It shows that in such situations the maximum pseudo-likelihood estimator may behave poorly and the moment estimation of the copula parameter is of interest. Our results complement the results available for nonparametric estimation of the copula function.

The fundamental knowledge at all levels of decision-making related to waste management is the quantity and composition of waste. Many articles deal with methods for estimating the composition of municipal waste, but most details are given as to how many categories should be chosen and what technical procedure should be followed. In order to obtain a broader view and a reasonable evaluation of the results, it is necessary to select the areas where the analyzes will be performed effectively. Current approaches have insufficiently addressed this issue at the regional and national levels. This paper presents a method that uses multi-level stratification to divide municipalities into similar groups to reduce the number of observations needed to obtain an estimate of the composition of waste in a selected area (region or state level). The method combines expert knowledge with statistical considerations and makes use of cluster analysis. Socio-economic and waste-related parameters are used within the individual steps. Regarding the available financial resources and the required accuracy of the results, the municipalities in which the analyzes should take place are selected. These representative municipalities represent other municipalities in the created groups, and thanks to them, it is possible to estimate the composition of waste in any municipality, region, or larger territory. Waste analysis planning is an essential procedure for waste management, but the respective costs represent a crucial factor at the national level and even more for individual municipalities. Estimating waste composition impacts the transition to sustainable waste management and is thus a key element for further development in this sector. The presented method demonstrates the selection of 10 representative municipalities from the Czech Republic, but an arbitrary number can be set respecting the financial resources. Estimating the composition of the mixed municipal waste for the Czech Republic should cost around 72,000 euros for ten representatives with different distribution of dwelling types. The method is described in general and can be applied to any territory/country in the world, considering local conditions and possibilities.

Rank-based procedures are commonly used for inference in copula models for continuous responses whose behavior does not depend on covariates. This paper describes how these procedures can be adapted to the broader framework in which (possibly non-linear) regression models for the marginal responses are linked by a copula that does not depend on covariates. The validity of many of these techniques can be derived from the asymptotic equivalence between the classical empirical copula process and its analog based on suitable residuals from the marginal models. Moment-based parameter estimation and copula goodness-of-fit tests are shown to remain valid under weak conditions on the marginal error term distributions, even when the residual-based empirical copula process fails to converge weakly. The performance of these procedures is evaluated through simulation in the context of two general insurance applications: micro-level multivariate insurance claims, and dependent loss triangles.

Conditional copulas describe the conditional dependence and the influence that covariates have on the dependence structure between two (or more) variables. Of interest is to test the null hypothesis that the covariates have a specific effect. This paper proposes an omnibus test for testing the null hypothesis of a specified effect of the covariates. The test statistic is designed for having power against many alternatives, and can be used to test for a variety of covariate effects (no effects, linear effects, partial effects, etc.). A special case is the testing problem that the covariates do not affect the dependence structure. In this semiparametric framework the marginal distribution functions are estimated using nonparametric kernel techniques and the parametric dependence model is estimated using maximum likelihood estimation. We establish the asymptotic distribution of the test statistic under the null hypothesis, and evaluate the finite-sample performance of the test via a simulation study, which also includes comparisons with alternative tests. A real data analysis illustrates the practical use of the test.

Soil microorganisms are diverse, although they share functions during the decomposition of organic matter. Thus, preferences for soil conditions and litter quality were explored to understand their niche partitioning. A 1-year-long litterbag transplant experiment evaluated how soil physicochemical traits of contrasting sites combined with chemically distinct litters of sedge (S), milkvetch (M) from a grassland, and beech (B) from forest site decomposition. Litter was assessed by mass loss; C, N, and P contents; and low-molecular-weight compounds. Decomposition was described by the succession of fungi, Actinobacteria, Alphaproteobacteria, and Firmicutes; bacterial diversity; and extracellular enzyme activities. The M litter decomposed faster at the nutrient-poor forest site, where the extracellular enzymes were more active, but microbial decomposers were not more abundant. Actinobacteria abundance was affected by site, while Firmicutes and fungi by litter type and Alphaproteobacteria by both factors. Actinobacteria were characterized as late-stage substrate generalists, while fungi were recognized as substrate specialists and site generalists, particularly in the grassland. Overall, soil conditions determined the decomposition rates in the grassland and forest, but successional patterns of the main decomposers (fungi and Actinobacteria) were determined by litter type. These results suggest that shifts in vegetation mostly affect microbial decomposer community composition. IMPORTANCE Anthropogenic disturbance may cause shifts in vegetation and alter the litter input. We studied the decomposition of different litter types under soil conditions of a nutrient-rich grassland and nutrient-poor forest to identify factors responsible for changes in the community structure and succession of microbial decomposers. This will help to predict the consequences of induced changes on the abundance and activity of microbial decomposers and recognize if the decomposition process and resulting quality and quantity of soil organic matter will be affected at various sites.

Control of common scab disease can be reached by resistant cultivars or suppressive soils. Both mechanisms are likely to translate into particular potato microbiome profiles, but the relative importance of each is not known. Here, microbiomes of bulk and tuberosphere soil and of potato periderm were studied in one resistant and one susceptible cultivar grown in a conducive and a suppressive field. Disease severity was suppressed similarly by both means yet, the copy numbers of txtB gene (coding for a pathogenicity determinant) were similar in both soils but higher in periderms of the susceptible cultivar from conducive soil. Illumina sequencing of 16S rRNA genes for bacteria (completed by 16S rRNA microarray approach) and archaea, and of 18S rRNA genes for micro-eukarytes showed that in bacteria, the more important was the effect of cultivar and diversity decreased from resistant cultivar to bulk soil to susceptible cultivar. The major changes occurred in proportions of Actinobacteria, Chloroflexi, and Proteobacteria. In archaea and micro-eukaryotes, differences were primarily due to the suppressive and conducive soil. The effect of soil suppressiveness × cultivar resistance depended on the microbial community considered, but differed also with respect to soil and plant nutrient contents particularly in N, S and Fe.

International audience; Organic matter sequestration is influenced by both biotic and abiotic factors, which determine the rate of decomposition and result into distinguish soil forming processes and different humus types. Previous observa-tions indicated that podzol soils and mor humus form typically develop under spruce forest, while under beech forest the higher mineralization rate leads to moder humus and the development of dystric cambisol. This study was designed to observe the forming of the different microbial communities participating in diverse decompo-sition processes reflecting the soil types developed under naturally occurring beech and artificially planted spruce forests. A litterbag experiment was performed with beech and spruce litter placed respectively to beech and spruce forests. In the beech forest, the observed litter decomposition rate reached the exponential faze after 15 months, while in the spruce forest a steeper decrease was noted only 29 months after the litter burial. Thus, the study focused on the period between 15 and 29 months to observe the exponential stages of recalcitrant organic matter transformation. In this period, the chemical composition of two litters was distinguished by higher contents of Mn and Ca in beech and higher content of Fe, S, N and P in spruce. On top of that, the beech litter released higher amount of dissolved organic carbon and its associated bacterial community was enriched with r-selected taxa that correlated positively with organic acids and cations. In contrast, the more acidic spruce litter was dominated by K-selected acidophilic community and its turnover rate was slower, resulting in increased carbon sequestration. The higher pH and humus quality of beech cambisol also correlates with its observed higher resilience to disturbances by acidification, pests or climate change in mountainous environments.

The redistribution of soil and associated organic matter across landscape represents a major perturbation to the carbon cycle because the established colluvial soils change the levels of C mineralization and sequestration. In this study, two profiles of a colluvial soil 4 m deep were analyzed to test whether its two layers produced by erosion differ from the organic layer of the original soil and if the microbial characteristics correspond to the soil properties. The structure of microbial communities was assessed by both quantitative PCR and Illumina amplicon sequencing. Microbial activities were determined by hydrolytical enzymes. The bacterial community structure was correlated with vertical gradients of soil chemical properties. The dominating bacterial phyla were the same along the whole profile but their relative abundance changed. The upper horizon determined by tillage and reaching to approx. 75 cm had highest values of dissolved organic carbon, P and K and was characterized by Proteobacteria and Bacteroidetes . Also, the activities of hydrolytical enzymes occurred mostly there. The second horizon of deposited soil reaching to approx. 250 cm was characterized by Acidobacteria and Gemmatimonadetes . The lowest horizon of buried Chernozem was characterized by increased soil organic carbon, manganese, iron and sulfate and characterized by Nitrospirae and Rubrobacteria . The community analysis indicated that chemolithotrophic processes might be important in these buried horizons so the decomposition may be slower and residence times for these deep carbon pools longer than in the original upper horizons. In these colluvial systems, erosion could lead to soil organic C stabilization.

Multivariate tail coefficients are an important tool when investigating dependencies between extreme events for different components of a random vector. Although bivariate tail coefficients are well-studied, this is, to a lesser extent, the case for multivariate tail coefficients. This paper contributes to this research area by (i) providing a thorough study of properties of existing multivariate tail coefficients in the light of a set of desirable properties, (ii) proposing some new multivariate tail measurements, (iii) dealing with estimation of the discussed coefficients and establishing asymptotic consistency, and, (iv) studying the behavior of tail measurements with increasing dimension of the random vector. A set of illustrative examples is given, and practical use of the tail measurements is demonstrated in a data analysis with a focus on dependencies between stocks that are part of the EURO STOXX 50 market index.

Connections between the structure of bacterial communities in suppressive soils and potato resistance to common scab (CS) are not well understood. In this study, one resistant and one susceptible cultivar were grown in a conducive and suppressive field to assess cultivar resistance × soil suppressiveness interactions. The resistant cultivar had a higher Mg content in periderm compare to susceptible cultivar, while suppressive soil had lower pH (5.3 vs 5.9), N, C, P, Ca contents but higher Fe and S compared with the conducive soil. Bacteria and actinobacteria numbers were higher in the conducive soil. Copy numbers oftxtBgene (coding for a pathogenicity determinant) were similar in both soils but were higher in the conducive soil (for periderm samples) and in the susceptible cultivar (for conducive soil samples). Taxonomic microarray analysis and Illumina sequencing of 16S rRNA genes amplicon showed that bacterial community differed between resistant vs susceptible cultivar and to a lesser extend between suppressive vs conducive soil. Bacteria participating in soil suppression belonged toPseudomonadaceae, Bradyrhizobiaceae, AcetobacteraceaeandPaenibacillaceae, while resistant cultivars selected a bacterial community resembling that of the suppressive soil, which was enriched inNitrospiraeandAcidobacteria. Thus, the analysis of soil suppressiveness×cultivar resistance interactions enabled to gain new insight to CS control in the field.IMPORTANCEIt was demonstrated that potato cultivars susceptible and resistant to common scab select differing bacterial community and above that this trait is further modified in suppressive and conducive soil. Common scab severity was diminished by either resistant cultivar or suppressive soil but without additive effect between them. Out of the two factors, potato cultivar had a more significant influence on tuberosphere bacterial community composition than soil. Results highlighted the usefulness of both cultivar resistance and soil suppressiveness traits in understanding and managing disease control of crops.

Common scab (CS) severity was assessed in relationship to mineral contents of potato periderm in 18 cultivars varying in maturity period and CS susceptibility. Two field experiments, one repeated in 3 years, were conducted. The effect of site, cultivar and growing period on the disease severity was always significant but not that of year to year variability. Zn, Mn and Fe periderm contents were related to site, while Ca content to cultivar effects. In both experiments, the CS severity was positively correlated to calcium and negatively to phosphorus periderm content, while correlations of CS severity to other nutrients were dependent on site or year. Since soil pH, Ca and P soil contents were not different between sites, relatively small differences in other soil chemical characteristics combined with specific cultivar nutrient requirements seemed to determine the CS severity.

© 2015, Springer-Verlag Berlin Heidelberg. This paper concerns depth functions suitable for smooth functional data. We suggest a modification of the integrated data depth that takes into account the shape properties of the functions. This is achieved by including a derivative(s) into the definition of the suggested depth measures. We then further investigate the use of integrated data depths in supervised classification problems. The performances of classification rules based on different data depths are investigated, both in simulated and real data sets. As the proposed depth function provides a natural alternative to the depth function based on random projections, the difference in the performances of these two methods are discussed in more detail. ispartof: Computational Statistics vol:30 issue:4 pages:1011-1031 status: published

© 2017 Elsevier Inc. We consider copula modeling of the dependence between two or more random variables in the presence of a multivariate covariate. The dependence parameter of the conditional copula possibly depends on the value of the covariate vector. In this paper we develop a new testing methodology for some important parametric specifications of this dependence parameter: constant, linear, quadratic, etc. in the covariate values, possibly after transformation with a link function. The margins are left unspecified. Our novel methodology opens plenty of new possibilities for testing how the conditional copula depends on the multivariate covariate and also for variable selection in copula model building. The suggested test is based on a Rao-type score statistic and regularity conditions are given under which the test has a limiting chi-square distribution under the null hypothesis. For small and moderate sample sizes, a permutation procedure is suggested to assess significance. In simulations it is shown that the test performs well (even under misspecification of the copula family and/or the dependence parameter structure) in comparison to available tests designed for testing for constancy of the dependence parameter. The test is illustrated on a real data set on concentrations of chemicals in water samples. ispartof: Journal of Multivariate Analysis vol:159 pages:111-133 status: published

© 2016 Informa UK Limited, trading as Taylor & Francis Group. In dependence modelling using conditional copulas, one often imposes the working assumption that the covariate influences the conditional copula solely through the marginal distributions. This so-called (pairwise) simplifying assumption is almost standardly made in vine copula constructions. However, in recent literature evidence was provided that such an assumption might not be justified. Among the first issues is thus to test for its appropriateness. In this paper nonparametric tests for the null hypothesis of the simplifying assumption are proposed, and their asymptotic behaviours, under the null hypothesis and under some local alternatives, are established. The tests are fully nonparametric in nature: not requiring choices of copula families nor knowledge of the marginals. In a simulation study, the finite-sample size and power performances of the tests are investigated, and compared with these of the few available tests. A real data application illustrates the use of the tests. peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=gsta20 ispartof: Statistics vol:51 issue:3 pages:475-509 status: published

This paper is concerned with modeling the dependence structure of two (or more) time series in the presence of a (possibly multivariate) covariate which may include past values of the time series. We assume that the covariate influences only the conditional mean and the conditional variance of each of the time series but the distribution of the standardized innovations is not influenced by the covariate and is stable in time. The joint distribution of the time series is then determined by the conditional means, the conditional variances and the marginal distributions of the innovations, which we estimate nonparametrically, and the copula of the innovations, which represents the dependency structure. We consider a nonparametric and a semiparametric estimator based on the estimated residuals. We show that under suitable assumptions, these copula estimators are asymptotically equivalent to estimators that would be based on the unobserved innovations. The theoretical results are illustrated by simulations and a real data example.

The Mantel test based on a comparison of two distance matrices belongs to standard statistical tools for assessing an association between two sets of variables. Even though it has been widely used in biological, ecological and related studies, several objective criticisms have appeared recently in the methodological literature. In this paper, we compare the Mantel test with a generalisation of a recently introduced concept of distance covariance (dCov). This generalisation, denoted further as dCovG, keeps the simplicity of the Mantel test, but at the same time, it is more flexible and might be advantageous in applications. The dCovG test includes the RV coefficient as well as dCov as two important special cases. Furthermore, we show the link of dCovG test with PROTEST, a multivariate correlation coefficient, canonical correlations and redundancy analysis. A simulation study compares the power of the dCovG test with the Mantel test and PROTEST. The benefits of the dCovG test are illustrated by a real data analysis as well. Copyright © 2013 John Wiley & Sons, Ltd.

This paper is concerned with inference about the dependence or association between two random variables conditionally upon the given value of a covariate. A way to describe such a conditional dependence is via a conditional copula function. Nonparametric estimators for a conditional copula then lead to nonparametric estimates of conditional association measures such as a conditional Kendall's tau. The limiting distributions of nonparametric conditional copula estimators are rather involved. In this paper we propose a bootstrap procedure for approximating these distributions and their characteristics, and establish its consistency. We apply the proposed bootstrap procedure for constructing confidence intervals for conditional association measures, such as a conditional Blomqvist beta and a conditional Kendall's tau. The performances of the proposed methods are investigated via a simulation study involving a variety of models, ranging from models in which the dependence (weak or strong) on the covariate is only through the copula and not through the marginals, to models in which this dependence appears in both the copula and the marginal distributions. As a conclusion we provide practical recommendations for constructing bootstrap-based confidence intervals for the discussed conditional association measures.

© 2016 EDP Sciences, SMAI. Several depths suitable for infinite-dimensional functional data that are available in the literature are of the form of an integral of a finite-dimensional depth function. These functionals are characterized by projecting functions into low-dimensional spaces, taking finite-dimensional depths of the projected quantities, and finally integrating these projected marginal depths over a preset collection of projections. In this paper, a general class of integrated depths for functions is considered. Several depths for functional data proposed in the literature during the last decades are members of this general class. A comprehensive study of its most important theoretical properties, including measurability and consistency, is given. It is shown that many, but not all, properties of the integrated depth are shared with the finite-dimensional depth that constitutes its building block. Some pending measurability issues connected with all integrated depth functionals are resolved, a broad new notion of symmetry for functional data is proposed, and difficulties with respect to consistency results are identified. A general universal consistency result for the sample depth version, and for the generalized median, for integrated depth for functions is derived. ispartof: ESAIM-Probability and Statistics vol:20 pages:95-130 status: published

Summary Testing homogeneity of dispersions may be of its own scientific interest as well as an important auxiliary step verifying assumptions of a main analysis. The problem is that many biological and ecological data are highly skewed and zero-inflated. Also the number of variables often exceeds the sample size. Thus data analysts often do not rely on parametric assumptions, but use a particular dissimilarity measure to calculate a matrix of pairwise differences. This matrix is then the basis for further statistical inference. Anderson (2006) proposed a distance-based test of homogeneity of multivariate dispersions for a one-way ANOVA design, for which a matrix of pairwise dissimilarities can be taken as an input. The key idea, like in Levene's test, is to replace each observation with its distance to an estimated group center. In this paper we suggest an alternative approach that is based on the means of within-group distances and does not require group centre calculations to obtain the test statistic. We show that this approach can have theoretical as well as practical advantages. A permutation procedure that gives type I error close to the prescribed value even in small samples is described.

The present paper investigates the asymptotic behaviour of a studentized permutation test for testing equality of (Pearson) correlation coefficients in two populations. It is shown that this test is asymptotically of exact level and has the same power for contiguous alternatives as the corresponding asymptotic test. As a by-product we specify the assumptions needed for the validity of the permutation test suggested in Sakaori (2002) . A small simulation study compares the finite sample properties of the considered tests.

Plant and microbial community composition in connection with soil chemistry determines soil nutrient cycling. The study aimed at demonstrating links between plant and microbial communities and soil chemistry occurring among and within four sites: two pine forests with contrasting soil pH and two grasslands of dissimilar soil chemistry and vegetation. Soil was characterized by C and N content, particle size, and profiles of low-molecular-weight compounds determined by high-performance liquid chromatography (HPLC) of soil extracts. Bacterial and actinobacterial community composition was assessed by terminal restriction fragment length polymorphism (T-RFLP) and cloning followed by sequencing. Abundances of bacteria, fungi, and actinobacteria were determined by quantitative PCR. In addition, a pool of secondary metabolites was estimated by erm resistance genes coding for rRNA methyltransferases. The sites were characterized by a stable proportion of C/N within each site, while on a larger scale, the grasslands had a significantly lower C/N ratio than the forests. A Spearman's test showed that soil pH was correlated with bacterial community composition not only among sites but also within each site. Bacterial, actinobacterial, and fungal abundances were related to carbon sources while T-RFLP-assessed microbial community composition was correlated with the chemical environment represented by HPLC profiles. Actinobacteria community composition was the only studied microbial characteristic correlated to all measured factors. It was concluded that the microbial communities of our sites were influenced primarily not only by soil abiotic characteristics but also by dominant litter quality, particularly, by percentage of recalcitrant compounds.

Members of the Actinobacteria are among the most important litter decomposers in soil. The site of a waterlogged deciduous forest with acidic soil was explored for actinobacteria because seasonality of litter inputs, temperature, and precipitation provided contrasting environmental conditions, particularly variation of organic matter quantity and quality. We hypothesized that these factors, which are known to influence decomposition, were also likely to affect actinobacterial community composition. The relationship between the actinobacterial community, soil moisture and organic matter content was assessed in two soil horizons in the summer and winter seasons using a 16S rRNA taxonomic microarray and cloning-sequencing of 16S rRNA genes. Both approaches showed that the community differed significantly between horizons and seasons, paralleling the changes in soil moisture and organic matter content. The microarray analysis further indicated that the actinobacterial community of the upper horizon was characterized by high incidence of the genus Mycobacterium. In both horizons and seasons, the actinobacterial clone libraries were dominated (by 80%) by sequences of a separate clade sharing an ancestral node with Streptosporangineae. This relatedness is supported also by some common adaptations, for example, to soil acidity and periodic oxygen deprivation or dryness.

Abstarct. This paper is concerned with studying the dependence structure between two random variables Y1 and Y2 conditionally upon a covariate X. The dependence structure is modelled via a copula function, which depends on the given value of the covariate in a general way. Gijbels et al. (Comput. Statist. Data Anal., 55, 2011, 1919) suggested two non-parametric estimators of the ‘conditional’ copula and investigated their numerical performances. In this paper we establish the asymptotic properties of the proposed estimators as well as conditional association measures derived from them. Practical recommendations for their use are then discussed.

The diversity of environmental mycobacteria was studied in water-logged acidic forest soil. Mycobacteria were assessed in upper and lower soil horizons and summer and winter seasons using T-RFLP and sequencing of 16S rRNA gene produced with Mycobacterium-specific primers. Mycobacteria diversity differed between both the two seasons and soil horizons. Cloning revealed the presence of mycobacteria belonging to three major clusters recognized within the genus, i.e. fast-growing, intermediate, and slow-growing species, with unprecedented abundance and diversity of the latter. Two novel clusters of sequences unrelated to the known mycobacteria were identified. This study raises the possibility that forest wetlands could serve as environmental reservoirs for an unexplored diversity of mycobacteria including those related to pathogenic species.

In this paper the interest is in testing the null hypothesis of positive quadrant dependence between two random variables. Such a testing problem is important since prior knowledge of positive quadrant dependence is a qualitative restriction that should be taken into account in further statistical analysis, e.g. when choosing an appropriate copula function to model the dependence structure. The key methodology of the proposed testing procedures consists of evaluating a 'distance' between a nonparametric estimator of a copula and the independence copula, which serves as a reference case in the whole set of copulas having the positive quadrant dependence property. Choices of appropriate distances and nonparametric estimators of copula are discussed, and the proposed methods are compared with testing procedures based on bootstrap and multiplier techniques. The consistency of the testing procedures is established. In a simulation study the authors investigate the finite sample size and power performances of three types of test statistics, Kolmogorov-Smirnov, Cramer-von-Mises and Anderson-Darling statistics, together with several nonparametric estimators of a copula, including recently developed kernel type estimators. Finally they apply the testing procedures on some real data.

We study asymptotic properties of an estimator of the Pareto tail index, obtained by an inversion of a suitable nonparametric test of tails in the Hodges–Lehmann manner. The estimator is of semiparametric nature, involving an unknown slowly varying function; however, we do not impose any special condition on the latter function. The estimator is strongly consistent and asymptotically normal under mild conditions, with the standardization based on the asymptotic power of the test. Unless we impose some additional conditions on the model, the slowly varying function generally leads to an asymptotic bias. Possible improvements of the finite-sample properties of the estimator are discussed.

Background Residual disease (RD) is an important prognostic factor in acute lymphoblastic leukemia (ALL). Flow cytometry (FC)-based RD detection is easy to perform, but interpretation requires expert analysis due to individual differences among patients. Procedure We focused at the design of standardized and reproducible RD monitoring in ALL. RD was investigated by a uniform gating strategy, which was designed internationally and tested in one center by Ig/TCR rearrangements. Results For each gate, positivity cutoff value was assigned using quantification of non-leukemic background. Comparing to Ig/TCR at 0.1% level, 80 of 103 specimens were correctly diagnosed by FC. The predictive value of FC RD at day 15 was then analyzed. In B lineage ALL, day 15 FC significantly correlated with Ig/TCR results at day 33 and/or week 12 (P

Detailed research of superficial deposits below the northern peak of Huascaran (Cordillera Blanca) provides new information on the limits of a paleo-avalanche originating from this mountain. Geomorphological mapping of the sediments identified glacial deposits, deposits from historical rock-debris avalanches and huge boulders from a paleo-avalanche. Schmidt Hammer rock-hardness tests were used to distinguish between the several generations of rock-debris avalanches, but largely failed to distinguish between the much older moraine and the paleo-avalanche sediments. Thus, only the field geomorphological mapping proved to be reliable for identifying the limits of the paleo-avalanche. The limits identified as granite boulders deposited over volcanic rocks were found to extend 30 m further up the opposite valley slope than previously had been mapped. This larger extent implies a greater volume and/or greater mobility for the prehistoric event.

Omelka [Omelka, M., 2007. Second-order linearity of Wilcoxon rank statistics. Ann. Inst. Statist. Math. 59, 385–402] proposed an alternative way of constructing a confidence interval based on R -estimators for single parameters in linear models. We will compare this confidence interval with a traditional (Wald type) confidence interval theoretically as well as by means of a Monte Carlo experiment. As a by-product we will show the asymptotic normality of the estimator of ∫ f 2 proposed in [Koul, H.L., Sievers, G.L., McKean, J., 1987. An estimator of the scale parameter for the rank analysis of linear models under general score functions. Scand. J. Statist. 14, 131–141] under somewhat different assumptions from those in [Thewarepperuma, P.S., 1987. On estimation of some density functionals under regression and one sample models. Ph.D. Thesis. Michigan State University, Department of Statistics and Probability].

The antibiotic lincomycin an anthropogenic pollutant – may change the bacterial diversity and resistance of soil communities by increasing selection pressure in favour of resistant phenotypes. In a laboratory experiment, two forest soils of contrasting characteristics in both pH and clay content were examined for changes in their bacterial communities after lincomycin treatment. The two soils were incubated in beakers and treated with three dosages of lincomycin. Changes in bacterial community were assessed by (i) cultivation of bacteria on two different media, and percentage of resistant colonies at two lincomycin concentrations; (ii) analysis of putative actinomycete isolates for antibiotic activities and resistance; (iii) T-RFLP diversity profiles of the 16S rDNA and homologues of the resistance gene lmrB; and (iv) cloning-sequencing of PCR amplicons of both 16S rDNA and lmrB homologues. In cultivation, no increase in percentage of resistant strains in lincomycin-treated versus untreated soils was detected. Similar numbers of bacterial cells were determined by DAPI staining in the two, treated or untreated, soils; but significantly higher numbers of both total bacteria and actinomycetes were cultivated from all treatments of the high pH soil. Significantly higher numbers of antibiotic-producing bacteria as well as resistant bacteria were found in the low pH soil. Significant shifts in bacterial diversity (16S rRNA gene) were demonstrated after lincomycin treatment for the high pH soil, while the low pH soil with a higher percentage of actinomycetes and higher diversity of the lmrB homologue genes did not change after treatment. Finally, sequencing of 157 new clones of lmrB homologues revealed that the antibiotic treatment significantly influenced selection for lmrB homologues in both soils.

In this paper we find verifiable regularity conditions to ensure that S-estimators of scale and regression and MM-estimators of regression are uniformly consistent and uniformly asymptotically normally distributed over contamination neighbourhoods. Moreover, we show how to calculate the size of these neighbourhoods. In particular, we find that, for MM-estimators computed with Tukey’s family of bisquare score functions, there is a trade-off between the size of these neighbourhoods and both the breakdown point of the S-estimators and the leverage of the contamination that is allowed in the neighbourhood. These results extend previous work of Salibian-Barrera and Zamar for location-scale to the linear regression model.

The geometry of the phosphodiester backbone was analyzed for 7739 dinucleotides from 447 selected crystal structures of naked and complexed DNA. Ten torsion angles of a near-dinucleotide unit have been studied by combining Fourier averaging and clustering. Besides the known variants of the A-, B- and Z-DNA forms, we have also identified combined A + B backbone-deformed conformers, e.g. with alpha/gamma switches, and a few conformers with a syn orientation of bases occurring e.g. in G-quadruplex structures. A plethora of A- and B-like conformers show a close relationship between the A- and B-form double helices. A comparison of the populations of the conformers occurring in naked and complexed DNA has revealed a significant broadening of the DNA conformational space in the complexes, but the conformers still remain within the limits defined by the A- and B- forms. Possible sequence preferences, important for sequence-dependent recognition, have been assessed for the main A and B conformers by means of statistical goodness-of-fit tests. The structural properties of the backbone in quadruplexes, junctions and histone-core particles are discussed in further detail.

The effect of environmental factors on bacterial and actinobacterial communities was assessed to predict microbial community structure in natural gradients. Bacterial and actinobacterial communities were studied at four sites differing in vegetation and water regime: creek sediment, wet meadow, dry meadow and deciduous forest located in a shallow valley. The vegetation structure was assessed by phytocoenological releves. T-RFLP and quantitative PCR were used to determine community composition and abundances. Significant relationships between bacterial community structure and selected soil traits at sites located relatively close to each other (within 200 m) were demonstrated. Both the quantity and structure of bacterial communities were significantly influenced by organic matter content, soil moisture and pH. Bacterial diversity was higher in summer, while that of actinobacteria increased in winter. The Simpson’s evenness E was significantly correlated with soil organic matter content. Soil pH had the greatest influence on bacterial community structure showing higher within-site variability in summer than in winter.

For a pair (Y1, Y2) of random variables there exist several measures of association that characterize the dependence between Y1 and Y2 by means of one single value. Classical examples are Pearson’s correlation coefficient, Kendall’s tau and Spearman’s rho. For the situation where next to the pair (Y1, Y2) there is also a third variable X present, so-called partial association measures, such as a partial Pearson’s correlation coefficient and a partial Kendall’s tau, have been proposed in the 1940’s. Following criticism on e.g. partial Kendall’s tau, better alternatives to these original partial association measures appeared in the literature: the conditional association measures, e.g. conditional Kendall’s tau, and conditional Spearman’s rho. Both, unconditional and conditional association measures can be expressed in terms of copulas. Even in case the dependence structure between Y1 and Y2 is influenced by a third variable X, we still want to be able to summarize the level of dependence by one single number. In this paper we discuss two different ways to do so, leading to two relatively new concepts: the (new concept of) partial Kendall’s tau, and the average Kendall’s tau. We provide a unifying framework for the diversity of concepts: global (or unconditional) association measures, conditional association measures, and partial and average association measures. The main contribution is that we discuss estimation of the newly-defined concepts: the partial and average copulas and association measures, and establish theoretical results for the estimators. The various concepts of association measures are illustrated on a real data example. This research was supported by the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy). The first author gratefully acknowledges support from the GOA/12/014 project of the Research Fund KU Leuven. The second author gratefully acknowledges support from the grant GACR 15-04774Y. The third author is an extraordinary professor at the North-West University, Potchefstroom, South Africa. The authors are grateful to an Associate Editor and two referees for their valuable comments that led to a considerable improvement of the paper.

© 2015 Board of the Foundation of the Scandinavian Journal of Statistics. This paper is concerned with studying the dependence structure between two random variables Y1 and Y2 in the presence of a covariate X, which affects both marginal distributions but not the dependence structure. This is reflected in the property that the conditional copula of Y1 and Y2 given X, does not depend on the value of X. This latter independence often appears as a simplifying assumption in pair-copula constructions. We introduce a general estimator for the copula in this specific setting and establish its consistency. Moreover, we consider some special cases, such as parametric or nonparametric location-scale models for the effect of the covariate X on the marginals of Y1 and Y2 and show that in these cases, weak convergence of the estimator, at n-rate, holds. The theoretical results are illustrated by simulations and a real data example. ispartof: Scandinavian Journal of Statistics vol:42 issue:4 pages:1109-1126 status: published

The rank statistic $$S_n({\bf t}) = 1 / n \sum^{n}_{i=1} c_i R_{i}({\bf t}) ({\bf t} \in \mathbb{R}^{p})$$ , with R i (t) being the rank of $$e_{i}-{\bf t}^{\hbox{T}}{\bf x}_i, i=1,\ldots,n$$ and e 1 , . . . , e n being the random sample from a distribution with a cdf F, is considered as a random process with t in the role of parameter. Under some assumptions on c i , x i and on the underlying distribution, it is proved that the process $$\{S_n(\frac{{\bf t}}{\sqrt{n}})-S_n(\mathbf{0}) - {\rm E} S_{n}({\bf t}), |{\bf t}|_2 \leq M\}$$ converges weakly to the Gaussian process. This generalizes the existing results where the one-dimensional case $${\bf t} \in \mathbb{R}$$ was considered. We believe our method of the proof can be easily modified for the signed-rank statistics of Wilcoxon type. Finally, we use our results to find the second order asymptotic distribution of the R-estimator based on the Wilcoxon scores and also to investigate the length of the confidence interval for a single parameter β l .