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Formal and informal Bayesian approaches have found widespread implementation and use in environmental modeling to summarize parameter and predictive uncertainty. Successful implementation of these methods relies heavily on the availability of efficient sampling methods that approximate, as closely and consistently as possible the (evolving) posterior target distribution. Much of this work has focused on continuous variables that can take on any value within their prior defined ranges. Here, we introduce theory and concepts of a discrete sampling method that resolves the parameter space at fixed points. This new code, entitled DREAM((D)) uses the recently developed DREAM algorithm (Vrugt et al., 2008, 2009a,b) as its main building block but implements two novel proposal distributions to help solve discrete and combinatorial optimization problems. This novel MCMC sampler maintains detailed balance and ergodicity, and is especially designed to resolve the emerging class of optimal experimental design problems. Three different case studies involving a Sudoku puzzle, soil water retention curve, and rainfall - runoff model calibration problem are used to benchmark the performance of DREAM((D)). The theory and concepts developed herein can be easily integrated into other (adaptive) MCMC algorithms.

Formal and informal Bayesian approaches have found widespread implementation and use in environmental modeling to summarize parameter and predictive uncertainty. Successful implementation of these methods relies heavily on the availability of efficient sampling methods that approximate, as closely and consistently as possible the (evolving) posterior target distribution. Much of this work has focused on continuous variables that can take on any value within their prior defined ranges. Here, we introduce theory and concepts of a discrete sampling method that resolves the parameter space at fixed points. This new code, entitled DREAM(D) uses the recently developed DREAM algorithm (Vrugt et al., 2008, 2009 a,b) as its main building block but implements two novel proposal distributions to help solve discrete and combinatorial optimization problems. This novel MCMC sampler maintains detailed balance and ergodicity, and is especially designed to resolve the emerging class of optimal experimental design problems. Three different case studies involving a Sudoku puzzle, soil water retention curve, and rainfall -- runoff model calibration problem are used to benchmark the performance of DREAM(D). The theory and concepts developed herein can be easily integrated into other (adaptive) MCMC algorithms. [ABSTRACT FROM AUTHOR]

Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well-constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled Differential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, highdimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems. [ABSTRACT FROM AUTHOR]

Focuses on changes in large-scale patterns of plant biodiversity, predicted from environmental economic scenarios. Measures to preserve biodiversity by improving environmental quality; Impact of various European economic scenarios, on potential plant biodiversity in the Netherlands.

Abstract Background In addition to sequence conservation, protein multiple sequence alignments contain evolutionary signal in the form of correlated variation among amino acid positions. This signal indicates positions in the sequence that influence each other, and can be applied for the prediction of intra- or intermolecular contacts. Although various approaches exist for the detection of such correlated mutations, in general these methods utilize only pairwise correlations. Hence, they tend to conflate direct and indirect dependencies. Results We propose RMRCM, a method for Regularized Multinomial Regression in order to obtain Correlated Mutations from protein multiple sequence alignments. Importantly, our method is not restricted to pairwise (column-column) comparisons only, but takes into account the network nature of relationships between protein residues in order to predict residue-residue contacts. The use of regularization ensures that the number of predicted links between columns in the multiple sequence alignment remains limited, preventing overprediction. Using simulated datasets we analyzed the performance of our approach in predicting residue-residue contacts, and studied how it is influenced by various types of noise. For various biological datasets, validation with protein structure data indicates a good performance of the proposed algorithm for the prediction of residue-residue contacts, in comparison to previous results. RMRCM can also be applied to predict interactions (in addition to only predicting interaction sites or contact sites), as demonstrated by predicting PDZ-peptide interactions. Conclusions A novel method is presented, which uses regularized multinomial regression in order to obtain correlated mutations from protein multiple sequence alignments. Availability R-code of our implementation is available via http://www.ab.wur.nl/rmrcm

A study was conducted to determine the joint effect of gaseous atmospheric pollutants and trace elements on epiphytic lichens. We used our data to test the hypothesis that lichens are generally insensitive to toxic effects of trace elements, and can therefore be used as accumulator organisms to estimate concentrations of these elements in theenvironment. In a field study in The Netherlands the abundance of epiphytic lichen species was estimated, and their supporting bark was collected. Concentrations of a range of trace elements were determinedin the bark, and concentrations of atmospheric trace gases were estimated at the sites of collection. Multivariate statistics were used to determine the relation between the abundance of the species and pollutant concentrations. Atmospheric SO2 and NO2 appeared to be the most important factors determining lichen biodiversity. Nearly all species were sensitive to these compounds. The effect of the other trace elements was very slight; only Sb had a significantly negative effect on the abundance of a few species. It is concluded that lichens can safely be used as accumulator organisms in pollution studies, provided that concentration in lichen thalli reflect atmospheric concentrations. [ABSTRACT FROM AUTHOR]

In this paper a novel multivariate method is proposed for the analysis of community response data from designed experiments repeatedly sampled in time. The long-term effects of the insecticide chlorpyrifos on the invertebrate community and the dissolved oxygen (DO)-pH-alkalinity-conductivity syndrome, in outdoor experimental ditches, are usedas example data. The new method. which we have named the principal response curve method (PRC), is based on redundancy analysis (RDA), adjusted for overall changes in community response over time, as observed in control test systems. This allows the method to focus on the time-dependent treatment effects. The principal component is plotted against time, yielding a principal response curve of the community for each treatment. The PRC method distills the complexity of time-dependent, community-level effects of pollutants into a graphic form that can be appreciated more readily than the results of other currently available multivariate techniques. The PRC method also enables a quantitative interpretation of effects towards the species level. [ABSTRACT FROM AUTHOR]

The possible use of the understorey vegetation as a biomonitor for atmospheric deposition of acid and nutrients was explored by analyzingthe vegetation of two pine forest stands in central and northern Sweden. Percentage cover of vascular plants, bryophytes and lichens was estimated in four factorial experiments with a total of 122 plots. Inthese experiments N, P, K, lime and sulphuric acid had been regularly added over a period of ca. 15 years. Redundancy analysis (RDA) was used to detect treatment effects on the composition of the vegetation, and was compared to analysis of variance (ANOVA) on Ellenberg's indicator values. Of all treatments nitrogen addition had by far the strongest effect, causing a shift in dominance from cryptogams and Ericaceae towards Deschampsia flexuosa and ruderal species. Acidification caused a decrease in the cover of most species, while liming increased the dominance of Ericaceae. There was a fairly good correspondence among the N fertilization, acidification and liming treatments and the indicator scores for nitrogen and acidity. Although the indicator method was not as sensitive as RDA in discriminating between the treatments, it may have a wider applicability in biomonitoring because it yields information on possible causal factors behind differences in vegetation. (c) 1999 Elsevier Science B.V. [ABSTRACT FROM AUTHOR]