A Bayesian palaeoenvironmental transfer function model for acidified lakes

A Bayesian approach to palaeoecological environmental reconstruction deriving from the unimodal responses generally exhibited by organisms to an environmental gradient is described. The approach uses Bayesian model selection to calculate a collection of probability-weighted, species-specific response curves (SRCs) for each taxon within a training set, with an explicit treatment for zero abundances. These SRCs are used to reconstruct the environmental variable from sub-fossilised assemblages. The approach enables a substantial increase in computational efficiency (several orders of magnitude) over existing Bayesian methodologies. The model is developed from the Surface Water Acidification Programme (SWAP) training set and is demonstrated to exhibit comparable predictive power to existing Weighted Averaging and Maximum Likelihood methodologies, though with improvements in bias; the additional explanatory power of the Bayesian approach lies in an explicit calculation of uncertainty for each individual reconstruction. The model is applied to reconstruct the Holocene acidification history of the Round Loch of Glenhead, including a reconstruction of recent recovery derived from sediment trap data.The Bayesian reconstructions display similar trends to conventional (Weighted Averaging Partial Least Squares) reconstructions but provide a better reconstruction of extreme pH and are more sensitive to small changes in diatom assemblages. The validity of the posteriors as an apparently meaningful representation of assemblage-specific uncertainty and the high computational efficiency of the approach open up the possibility of highly constrained multiproxy reconstructions

The effects of nickel on the structure and functioning of a freshwater plankton community under high dissolved organic carbon conditions : a microcosm experiment

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Climatic variability during the last millennium in Western Iceland from lake sediment records

The aim of this research was to create a decadal-scale terrestrial quantitative palaeoclimate record for NW Iceland from lake sediments for the last millennium. Geochemical, stable isotope and chironomid reconstructions were obtained from a lake sequence constrained by tephra deposits on the Snæfellsnes peninsula, western Iceland. Obtaining a quantitative record proved problematic, but the qualitative chironomid record showed clear trends associated with past summer temperatures, and the sedimentological records provided evidence for past changes in precipitation, mediated through catchment soil in-wash. When the full range of chronological uncertainty is considered, four clear phases of climatic conditions were identified: (1) a relatively warm phase between AD 1020 and 1310; (2) a relatively stable period between AD 1310 and 1510, cooler than the preceding period but still notably warmer than the second half of the millennium; (3) a consistent reduction of temperatures between AD 1560 and 1810, with the coolest period between AD 1680 and 1810; and (4) AD 1840–2000 has temperatures mainly warmer than in the preceding two centuries, with a rising trend and increased variability from c. AD 1900 onwards. The reconstructions show clearly that the first half of the millennium experienced warmer climatic conditions than the second half, with a return to the warmer climate only occurring in the last c. 100 years. Much of the variability of the chironomid record can be linked to changes in the North Atlantic Oscillation (NAO). The reconstructions presented can track low-frequency and long-term trends effectively and consistently but high-resolution and calibrated quantitative records remain more of a challenge – not just in finding optimal sedimentary deposits but also in finding the most reliable proxy. It is this that presents the real challenge for Holocene climate reconstruction from this key area of the North Atlantic. Keywords : iceland, palaeolimnology, chironomids, little ice age, medieval climate anomaly, north atlantic oscillatio

EM algorithm for Bayesian estimation of genomic breeding values

<p>Abstract</p> <p>Background</p> <p>In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model. Two Bayesian methods based on MCMC algorithm, Bayesian shrinkage regression (BSR) method and stochastic search variable selection (SSVS) method, (which are called BayesA and BayesB, respectively, in some literatures), have been so far proposed for the estimation of SNP effects. However, much computational burden is imposed on the MCMC-based Bayesian methods. A method with both high computing efficiency and prediction accuracy is desired to be developed for practical use of genomic selection.</p> <p>Results</p> <p>EM algorithm applicable for BSR is described. Subsequently, we propose a new EM-based Bayesian method, called wBSR (weighted BSR), which is a modification of BSR incorporating a weight for each SNP according to the strength of its association to a trait. Simulation experiments show that the computational time is much reduced with wBSR based on EM algorithm and the accuracy in predicting GBV is improved by wBSR in comparison with BSR based on MCMC algorithm. However, the accuracy of predicted GBV with wBSR is inferior to that with SSVS based on MCMC algorithm which is currently considered to be a method of choice for genomic selection.</p> <p>Conclusions</p> <p>EM-based wBSR method proposed in this study is much advantageous over MCMC-based Bayesian methods in computational time and can predict GBV more accurately than MCMC-based BSR. Therefore, wBSR is considered a practical method for genomic selection with a large number of SNP markers.</p

Analysis with respect to instrumental variables for the exploration of microarray data structures

BACKGROUND: Evaluating the importance of the different sources of variations is essential in microarray data experiments. Complex experimental designs generally include various factors structuring the data which should be taken into account. The objective of these experiments is the exploration of some given factors while controlling other factors. RESULTS: We present here a family of methods, the analyses with respect to instrumental variables, which can be easily applied to the particular case of microarray data. An illustrative example of analysis with instrumental variables is given in the case of microarray data investigating the effect of beverage intake on peripheral blood gene expression. This approach is compared to an ANOVA-based gene-by-gene statistical method. CONCLUSION: Instrumental variables analyses provide a simple way to control several sources of variation in a multivariate analysis of microarray data. Due to their flexibility, these methods can be associated with a large range of ordination techniques combined with one or several qualitative and/or quantitative descriptive variables

Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms

Markov Chain Monte Carlo (MCMC) methods are often used to sample from intractable target distributions. Some MCMC variants aim to improve the performance by running a population of MCMC chains. In this paper, we investigate the use of techniques from Evolutionary Computation (EC) to design population-based MCMC algorithms that exchange useful information between the individual chains. We investigate how one can ensure that the resulting class of algorithms, called Evolutionary MCMC (EMCMC), samples from the target distribution as expected from any MCMC algorithm. We analytically and experimentally show—using examples from discrete search spaces—that the proposed EMCMCs can outperform standard MCMCs by exploiting common partial structures between the more likely individual states. The MCMC chains in the population interact through recombination and selection. We analyze the required properties of recombination operators and acceptance (or selection) rules in EMCMCs. An important issue is how to preserve the detailed balance property which is a sufficient condition for an irreducible and aperiodic EMCMC to converge to a given target distribution. Transferring EC techniques to population-based MCMCs should be done with care. For instance, we prove that EMCMC algorithms with an elitist acceptance rule do not sample the target distribution correctly

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand

Restoration of european habitats in mainland, Portugal, using commercial seed mixtures. Considerations for its management and conservation

Permanent mountain pastures include meadows and other perennial pastures of high ecological, economic, cultural and scenic value. Increasing desertification limits the maintenance and conservation of its biodiversity and the associated landscape mosaic. A restoration experiment in permanent high altitude grasslands in Beira Alta (Centre East (CE) mainland Portugal) was made, by sowing adequate cultivars of existing grass and legume species. The main objectives addressed were: (1) comparison of floristic composition between reference communities included in the previous habitats and the improved communities; (2) evaluation of the success of sowing adequate cultivars of autochthonous species; (3) evaluation of the establishment of target species in terms of the maintenance of floristic composition of reference. The experiment was carried out in 2014 on nine farms situated in Beira Alta (Guarda District) and the phytosociological method was applied in the floristic surveys. The sown species with highest percentage of soil cover were Trifolium subterraneum, Lolium multiflorum, Ornithopus sativus and Trifolium vesiculosum. In the priority habitat 6220 it was observed a re-establishment of many species in their original composition and a high cover of several cultivars of Trifolium subterraneum. These results highlight the importance of using cultivars of autochthonous species in the improvement of altitude pasturesinfo:eu-repo/semantics/publishedVersio