Biodiversity effects on nitrate concentrations in soil solution: a Bayesian model

Ecosystems are faced with high rates of species loss which has consequences for their functions and services. To assess the effects of plant species diversity on the nitrogen (N) cycle, we developed a model for monthly mean nitrate (NO3-N) concentrations in soil solution in 0-30 cm mineral soil depth using plant species and functional group richness and functional composition as drivers and assessing the effects of conversion of arable land to grassland, spatially heterogeneous soil properties, and climate. We used monthly mean NO3-N concentrations from 62 plots of a grassland plant diversity experiment from 2003 to 2006. Plant species richness (1-60) and functional group composition (1-4 functional groups: legumes, grasses, non-leguminous tall herbs, non-leguminous small herbs) were manipulated in a factorial design. Plant community composition, time since conversion from arable land to grassland, soil texture, and climate data (precipitation, soil moisture, air and soil temperature) were used to develop one general Bayesian multiple regression model for the 62 plots to allow an in-depth evaluation using the experimental design. The model simulated NO3-N concentrations with an overall Bayesian coefficient of determination of 0.48. The temporal course of NO3-N concentrations was simulated differently well for the individual plots with a maximum plot-specific Nash-Sutcliffe Efficiency of 0.57. The model shows that NO3-N concentrations decrease with species richness, but this relation reverses if more than approx. 25 % of legume species are included in the mixture. Presence of legumes increases and presence of grasses decreases NO3-N concentrations compared to mixtures containing only small and tall herbs. Altogether, our model shows that there is a strong influence of plant community composition on NO3-N concentrations.

The new Southeast Asian goblin spider genus Aposphragisma (Araneae, Oonopidae): diversity and phylogeny

The new genus Aposphragisma (Araneae, Oonopidae, Oonopinae) comprising the new species A. baltenspergerae, A. borgulai, A. brunomanseri, A. confluens, A. dayak, A. dentatum, A. draconigenum, A. hausammannae, A. helvetiorum, A. kolleri, A. menzi, A. monoceros, A. nocturnum, A. retifer, A. rimba, A. salewskii, A. scimitar, A. sepilok and A. stannum is described. It is characterised by very hard bodied, strongly sclerotized species with completely armoured prosoma and strongly sclerotized ventral and dorsal abdominal scuta. Aposphragisma gen. nov. is placed within the Gamasomorpha-group sensu Saaristo (2001). Descriptions and illustrations are given for all new species. A phylogenetic analysis based on 40 characters using Prethopalpus fosuma, Gamasomorpha asterobothros, G. cataphracta, G. seximpressa, Xestaspis biflocci, X. kandy and X. paulina as outgroup-taxa and Cortestina thaleri (Oonopidae, Sulsulinae) as the root is presented and discussed. Furthermore it is shown that females of Aposphragisma gen. nov. possess complex internal genitalia. The members of the new genus are ground-dwelling litter inhabitants restricted to Southeast Asian lowland and montane forests, with more than 60% of the species only known from single localities. They are presumed to be negatively affected by the massive destruction of pristine forest habitats within their range. This work has been conducted within the framework of the Planetary Biodiversity Inventory (PBI) of Oonopidae (see http://research.amnh.org/oonopidae).

A new Bayesian approach to the reconstruction of spectral functions

We present a novel approach for the reconstruction of spectra from Euclidean correlator data that makes close contact to modern Bayesian concepts. It is based upon an axiomatically justified dimensionless prior distribution, which in the case of constant prior function m(ω) only imprints smoothness on the reconstructed spectrum. In addition we are able to analytically integrate out the only relevant overall hyper-parameter α in the prior, removing the necessity for Gaussian approximations found e.g. in the Maximum Entropy Method. Using a quasi-Newton minimizer and high-precision arithmetic, we are then able to find the unique global extremum of P[ρ|D] in the full Nω » Nτ dimensional search space. The method actually yields gradually improving reconstruction results if the quality of the supplied input data increases, without introducing artificial peak structures, often encountered in the MEM. To support these statements we present mock data analyses for the case of zero width delta peaks and more realistic scenarios, based on the perturbative Euclidean Wilson Loop as well as the Wilson Line correlator in Coulomb gauge.

Benchmarking the Bayesian reconstruction of the non-perturbative heavy ǪǬ potential

The extraction of the finite temperature heavy quark potential from lattice QCD relies on a spectral analysis of the real-time Wilson loop. Through its position and shape, the lowest lying spectral peak encodes the real and imaginary part of this complex potential. We benchmark this extraction strategy using leading order hard-thermal loop (HTL) calculations. I.e. we analytically calculate the Wilson loop and determine the corresponding spectrum. By fitting its lowest lying peak we obtain the real- and imaginary part and confirm that the knowledge of the lowest peak alone is sufficient for obtaining the potential. We deploy a novel Bayesian approach to the reconstruction of spectral functions to HTL correlators in Euclidean time and observe how well the known spectral function and values for the real and imaginary part are reproduced. Finally we apply the method to quenched lattice QCD data and perform an improved estimate of both real and imaginary part of the non-perturbative heavy ǪǬ potential.

Overview and phylogeny of Mycobacterium tuberculosis complex organisms: Implications for diagnostics and legislation of bovine tuberculosis

Members of the Mycobacterium tuberculosis complex (MTBC) cause a serious disease with similar pathology, tuberculosis; in this review, bovine tuberculosis will be considered as disease caused by any member of the MTBC in bovids. Bovine tuberculosis is responsible for significant economic loss due to costly eradication programs and trade limitations and poses a threat to both endangered and protected species as well as to public health. We here give an overview on all members of the MTBC, focusing on their isolation from different animal hosts. We also review the recent advances made in elucidating the evolutionary and phylogenetic relationships of members of the MTBC. Because the nomenclature of the MTBC is controversial, its members have been considered species, subspecies or ecotypes, this review discusses the possible implications for diagnostics and the legal consequences of naming of new species.

Association of optical tracklets from a geosynchronous belt survey via the direct Bayesian admissible region approach

The direct Bayesian admissible region approach is an a priori state free measurement association and initial orbit determination technique for optical tracks. In this paper, we test a hybrid approach that appends a least squares estimator to the direct Bayesian method on measurements taken at the Zimmerwald Observatory of the Astronomical Institute at the University of Bern. Over half of the association pairs agreed with conventional geometric track correlation and least squares techniques. The remaining pairs cast light on the fundamental limits of conducting tracklet association based solely on dynamical and geometrical information.

Introduction to Bayesian Data Analysis

A workshop providing an introduction to Bayesian data analysis and hypothesis testing using R, Jags and the BayesFactor package.

Taxonomy, phylogeny and molecular epidemiology of Echinococcus multilocularis: From fundamental knowledge to health ecology.

Alveolar echinococcosis, caused by the tapeworm Echinococcus multilocularis, is one of the most severe parasitic diseases in humans and represents one of the 17 neglected diseases prioritised by the World Health Organisation (WHO) in 2012. Considering the major medical and veterinary importance of this parasite, the phylogeny of the genus Echinococcus is of considerable importance; yet, despite numerous efforts with both mitochondrial and nuclear data, it has remained unresolved. The genus is clearly complex, and this is one of the reasons for the incomplete understanding of its taxonomy. Although taxonomic studies have recognised E. multilocularis as a separate entity from the Echinococcus granulosus complex and other members of the genus, it would be premature to draw firm conclusions about the taxonomy of the genus before the phylogeny of the whole genus is fully resolved. The recent sequencing of E. multilocularis and E. granulosus genomes opens new possibilities for performing in-depth phylogenetic analyses. In addition, whole genome data provide the possibility of inferring phylogenies based on a large number of functional genes, i.e. genes that trace the evolutionary history of adaptation in E. multilocularis and other members of the genus. Moreover, genomic data open new avenues for studying the molecular epidemiology of E. multilocularis: genotyping studies with larger panels of genetic markers allow the genetic diversity and spatial dynamics of parasites to be evaluated with greater precision. There is an urgent need for international coordination of genotyping of E. multilocularis isolates from animals and human patients. This could be fundamental for a better understanding of the transmission of alveolar echinococcosis and for designing efficient healthcare strategies.

Bayesian models for the analysis of genetic structure when populations are correlated

Motivation: Population allele frequencies are correlated when populations have a shared history or when they exchange genes. Unfortunately, most models for allele frequency and inference about population structure ignore this correlation. Recent analytical results show that among populations, correlations can be very high, which could affect estimates of population genetic structure. In this study, we propose a mixture beta model to characterize the allele frequency distribution among populations. This formulation incorporates the correlation among populations as well as extending the model to data with different clusters of populations. Results: Using simulated data, we show that in general, the mixture model provides a good approximation of the among-population allele frequency distribution and a good estimate of correlation among populations. Results from fitting the mixture model to a dataset of genotypes at 377 autosomal microsatellite loci from human populations indicate high correlation among populations, which may not be appropriate to neglect. Traditional measures of population structure tend to over-estimate the amount of genetic differentiation when correlation is neglected. Inference is performed in a Bayesian framework.

Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.

Bayesian doubly optimal group sequential designs for randomized clinical trials

When conducting a randomized comparative clinical trial, ethical, scientific or economic considerations often motivate the use of interim decision rules after successive groups of patients have been treated. These decisions may pertain to the comparative efficacy or safety of the treatments under study, cost considerations, the desire to accelerate the drug evaluation process, or the likelihood of therapeutic benefit for future patients. At the time of each interim decision, an important question is whether patient enrollment should continue or be terminated; either due to a high probability that one treatment is superior to the other, or a low probability that the experimental treatment will ultimately prove to be superior. The use of frequentist group sequential decision rules has become routine in the conduct of phase III clinical trials. In this dissertation, we will present a new Bayesian decision-theoretic approach to the problem of designing a randomized group sequential clinical trial, focusing on two-arm trials with time-to-failure outcomes. Forward simulation is used to obtain optimal decision boundaries for each of a set of possible models. At each interim analysis, we use Bayesian model selection to adaptively choose the model having the largest posterior probability of being correct, and we then make the interim decision based on the boundaries that are optimal under the chosen model. We provide a simulation study to compare this method, which we call Bayesian Doubly Optimal Group Sequential (BDOGS), to corresponding frequentist designs using either O'Brien-Fleming (OF) or Pocock boundaries, as obtained from EaSt 2000. Our simulation results show that, over a wide variety of different cases, BDOGS either performs at least as well as both OF and Pocock, or on average provides a much smaller trial. ^