Factors shaping community assemblages and species co-occurrence of different trophic levels

Species assemblages are the results of various processes, including dispersion and habitat filtering. Disentangling the effects of these different processes is challenging for statistical analysis, especially when biotic interactions should be considered. In this study, we used plants (producers) and leafhoppers (phytophagous) as model organisms, and we investigated the relative importance of abiotic versus biotic factors that shape community assemblages, and we infer on their biotic interactions by applying three-step statistical analysis. We applied a novel statistical analysis, that is, multiblock Redundancy Analysis (mbRA, step 1) and showed that 51.8% and 54.1% of the overall variation in plant and leafhopper assemblages are, respectively, explained by the two multiblock models. The most important blocks of variables to explain the variations in plant and leafhopper assemblages were local topography and biotic factors. Variation partitioning analysis (step 2) showed that pure abiotic filtering and pure biotic processes were relatively less important than their combinations, suggesting that biotic relationships are strongly structured by abiotic conditions. Pairwise co-occurrence analysis (step 3) on generalist leafhoppers and the most common plants identified 40 segregated species pairs (mainly between plant species) and 16 aggregated pairs (mainly between leafhopper species). Pairwise analysis on specialist leafhoppers and potential host plants clearly revealed aggregated patterns. Plant segregation suggests heterogeneous resource availability and competitive interactions, while leafhopper aggregation suggests host feeding differentiation at the local level, different feeding microhabitats on host plants, and similar environmental requirements of the species. Using the novel mbRA, we disentangle for the first time the relative importance of more than five distinct groups of variables shaping local species communities. We highlighted the important role of abiotic processes mediated by bottom-up effects of plants on leafhopper communities. Our results revealed that in-field structure diversification and trophic interactions are the main factors causing the co-occurrence patterns observed.Fil: Trivellone, Valeria. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Bougeard, Stephanie. French Agency for Food, Environmental and Occupational Health Safety; FranciaFil: Giavi, Simone. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Krebs, Patrik. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Balseiro, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones en Ciencias de la Tierra. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones en Ciencias de la Tierra; ArgentinaFil: Dray, Stephane. Université Claude Bernard Lyon 1; FranciaFil: Moretti, Marco. Swiss Federal Institute for Forest, Snow and Landscape Research; Suiz

Multiblock analysis reveals key areas and risk factors for dairy cow losses

Describe the relative contribution of the production areas (thematic blocks) to cow losses characterized by herd on farm mortality risk (MR), culling rates (CR) and mean age of culled cows (MAofCC). Also, the study aimed to identify within each block, the variables mostly contributing to the cow losses.This work was financed by the Estonian Research Council grant (PSG 268).This work was financed by the Estonian Research Council grant (PSG 268

Genome Evolution of Two Genetically Homogeneous Infectious Bursal Disease Virus Strains During Passages in vitro and ex vivo in the Presence of a Mutagenic Nucleoside Analog

The avibirnavirus infectious bursal disease virus (IBDV) is responsible for a highly contagious and sometimes lethal disease of chickens (Gallus gallus). IBDV genetic variation is well-described for both field and live-attenuated vaccine strains, however, the dynamics and selection pressures behind this genetic evolution remain poorly documented. Here, genetically homogeneous virus stocks were generated using reverse genetics for a very virulent strain, rvv, and a vaccine-related strain, rCu-1. These viruses were serially passaged at controlled multiplicities of infection in several biological systems, including primary chickens B cells, the main cell type targeted by IBDV in vivo. Passages were also performed in the absence or presence of a strong selective pressure using the antiviral nucleoside analog 7-deaza-2′-C-methyladenosine (7DMA). Next Generation Sequencing (NGS) of viral genomes after the last passage in each biological system revealed that (i) a higher viral diversity was generated in segment A than in segment B, regardless 7DMA treatment and viral strain, (ii) diversity in segment B was increased by 7DMA treatment in both viruses, (iii) passaging of IBDV in primary chicken B cells, regardless of 7DMA treatment, did not select cell-culture adapted variants of rvv, preserving its capsid protein (VP2) properties, (iv) mutations in coding and non-coding regions of rCu-1 segment A could potentially associate to higher viral fitness, and (v) a specific selection, upon 7DMA addition, of a Thr329Ala substitution occurred in the viral polymerase VP1. The latter change, together with Ala270Thr change in VP2, proved to be associated with viral attenuation in vivo. These results identify genome sequences that are important for IBDV evolution in response to selection pressures. Such information will help tailor better strategies for controlling IBDV infection in chickens

Association of herd BRSV and BHV-1 seroprevalence with respiratory disease and reproductive performance in adult dairy cattle

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to detect the associations between bovine herpesvirus 1 (BHV-1) status of a herd and respiratory disease (BRD) occurrence and reproductive performance in pregnant heifers and cows. The association between management-related factors and higher BRD occurrence was also estimated.</p> <p>Methods</p> <p>Serum samples, collected from cows and youngstock from 103 dairy cattle herds, were analyzed for antibodies against BHV-1, bovine respiratory syncytial virus (BRSV), bovine viral diarrhoea virus (BVDV), and <it>Mycoplasma bovis</it>. A questionnaire was used to collect data concerning herd management factors and reproductive performance, as well as the occurrence of clinical signs of respiratory disease in the last two years, as evaluated by the veterinarian or farm manager. Multiple correspondence analysis (MCA) and logistic regression analysis were performed to identify and quantify the risk factors.</p> <p>Results</p> <p>A low to moderate prevalence (1-49%) of BRSV antibodies among youngstock was associated with a high occurrence of respiratory disease (OR = 6.2, p = 0.010) in cows and in-calf heifers. Employees of the farm may participate in the spread of such disease. Larger herd size, loose-housing of cows, housing youngstock separately from cows until pregnancy, and purchasing new animals were factors possibly related to a high occurrence of respiratory disease symptoms in pregnant heifers and cows. The highest risk of abortions (> 1.3%) and increased insemination index (number of inseminations per pregnancy) (> 1.9) occurred in herds with a moderate prevalence of BHV-1 antibodies (1-49%) in cows.</p> <p>Conclusions</p> <p>BHV-1 was not associated with acute respiratory disease in adult dairy cattle, however was significantly related to reproductive performance. BRSV possesses the main role in respiratory disease complex in adult dairy cattle.</p

A pan-European epidemiological study reveals honey bee colony survival depends on beekeeper education and disease control

Reports of honey bee population decline has spurred many national efforts to understand the extent of the problem and to identify causative or associated factors. However, our collective understanding of the factors has been hampered by a lack of joined up trans-national effort. Moreover, the impacts of beekeeper knowledge and beekeeping management practices have often been overlooked, despite honey bees being a managed pollinator. Here, we established a standardised active monitoring network for 5 798 apiaries over two consecutive years to quantify honey bee colony mortality across 17 European countries. Our data demonstrate that overwinter losses ranged between 2% and 32%, and that high summer losses were likely to follow high winter losses. Multivariate Poisson regression models revealed that hobbyist beekeepers with small apiaries and little experience in beekeeping had double the winter mortality rate when compared to professional beekeepers. Furthermore, honey bees kept by professional beekeepers never showed signs of disease, unlike apiaries from hobbyist beekeepers that had symptoms of bacterial infection and heavy Varroa infestation. Our data highlight beekeeper background and apicultural practices as major drivers of honey bee colony losses. The benefits of conducting trans-national monitoring schemes and improving beekeeper training are discussed

Multiblock modeling for complex preference study. Application to European preferences for smoked salmon

The aim of the paper is to propose an alternative method to external preference mapping for the case of complex data where explanatory variables are organized in meaningful blocks. We propose an innovative method in the multiblock modeling framework, called multiblock Redundancy Analysis. The interest and relevance of this method is illustrated on the basis of a European consumer preference study for cold-smoked salmon. The study aims at explaining six homogeneous clusters of preference with explanatory parameters organized in five thematic blocks related to physico-chemical measurements, microbiological characterization, appearance attributes, odor/flavor characterization and texture descriptors. Overall indexes and graphical displays associated with different interpretation levels are proposed to sort the key drivers of preference by order of priority at the variables and at the block level. On the basis of these data, multiblock Redundancy Analysis is also compared to standard preference mapping in terms of model quality; the best model is here associated with the multiblock method

CHIRURGIE DU TROU MACULAIRE IDIOPATHIQUE AVEC ECHANGE FLUIDE-AIR ET ADJONCTION DE CONCENTRE PLAQUETTAIRE AUTOLOGUE (A PROPOS DE 18 OBSERVATIONS)

LYON1-BU Santé (693882101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Prediction for regularized clusterwise multiblock regression

International audienc

A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology

Metabolomics is a powerful phenotyping tool in nutrition and health research, generating massive and complex data that need dedicated treatments to enrich our knowledge of biological systems. In particular, to deeper investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses performed separately on metabolomic datasets, are often complemented by associations with metadata (anthropometric, clinical, nutritional and physical activity data…). Another relevant strategy is to perform a multi-block partial least squares discriminant analysis (MBPLSDA) that simultaneously analyses data available from different sources, allowing determining the importance of variables and variable blocks in discriminating groups of subjects, taking into account data structure in thematic blocks.In order to propose a full open-source standalone tool, the present objective was to develop an R package allowing all steps of MBPLSDA analysis for the joint analysis of metabolomic and additional data.The tool was based on the mbpls function of the ade4 R package, enriched with different functionalities, including some dedicated to discriminant analysis. Provided indicators help to determine the optimal number of components, to check the MBPLSDA model validity, and to evaluate the variability of its parameters and predictions. To illustrate the potential of the proposed tool and the associated procedure, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort.The availability of the different functionalities in a single R package allowed optimizing parameters for an efficient joint analysis of metabolomics and epidemiological data to obtain new insights into multidimensional phenotypes. In particular, we highlighted the impact of filtering the metabolomic variables beforehand, and the relevance of a MBPLSDA approach in comparison to a standard PLS-discriminant analysis method