Nine Quick Tips for Analyzing Network Data

These tips provide a quick and concentrated guide for beginners in the analysis of network data

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

Methods of pattern classification for the design of a NIRS-based brain computer interface.

Brain-Computer Interface (BCI) is a communication system that offers the possibility to act upon the surrounding environment without using our nervous systems efferent pathways. One of the most important parts of a BCI is the pattern classification system which allows to translate mental activities into commands for an external device. This work aims at providing new pattern classification methods for the development of a Brain Computer Interface based on Near Infrared Spectroscopy. To do so, a thorough study of machine learning techniques used for developing BCIs has been conducted

Methods of pattern classification for the design of a NIRS-based brain computer interface.

Brain-Computer Interface (BCI) is a communication system that offers the possibility to act upon the surrounding environment without using our nervous systems efferent pathways. One of the most important parts of a BCI is the pattern classification system which allows to translate mental activities into commands for an external device. This work aims at providing new pattern classification methods for the development of a Brain Computer Interface based on Near Infrared Spectroscopy. To do so, a thorough study of machine learning techniques used for developing BCIs has been conducted

Spatial variation in springtime food resources influences the winter body mass of roe deer fawns. Oecologia 137

Abstract It is well established that the dynamics of mammalian populations vary in time, in relation to density and weather, and often in interaction with phenotypic differences (sex, age and social status). Habitat quality has recently been identified as another significant source of individual variability in vital rates of deer, including roe deer where spatial variations in fawn body mass were found to be only about a tenth of temporal variations. The approach used was to classify the habitat into blocks a priori, and to analyse variation in animal performance among the predefined areas. In a fine-grained approach, here we use data collected over 24 years on 1,235 roe deer fawns captured at known locations and the plant species composition sampled in 2001 at 578 sites in the ChizØ forest to determine the spatial structure at a fine scale of both vegetation and winter body mass of fawns, and then to determine links between the two. Space and time played a nearly equal role in determining fawn body masses of both sexes, each accounting for about 20% of variance and without any interaction between them. The spatial distribution of fawn body mass was perennial over the 24 years considered and predicted values showed a 2 kg range according to location in the reserve, which is much greater than suggested in previous work and is enough to have strong effects on fawn survival. The spatial distribution and the range of predicted body masses were closely similar in males and females. The result of this study is therefore consistent with the view that the life history traits of roe deer are only weakly influenced by sexual selection. The occurrence of three plant species that are known to be important food items in spring/summer roe deer diets, hornbeam (Carpinus betulus), bluebell (Hyacinthoides sp.) and Star of Bethlehem (Ornithogalum sp.) was positively related to winter fawn body mass. The occurrence of species known to be avoided in spring/summer roe deer diets [e.g. butcher's broom (Ruscus aculeatus) and beech (Fagus sylvatica)], was negatively related to fawn body mass. We conclude that the spatial variation in the body mass of fawns in winter in this forest is as important as the temporal variation, and that the distribution of plant species that are actively selected during spring and summer is an important determinant of spatial variation in winter fawn body mass. The availability of these plants is therefore likely to be a key factor in the dynamics of roe deer populations

Statistical ecology comes of age

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.Peer reviewe

ARIA digital anamorphosis : Digital transformation of health and care in airway diseases from research to practice

Digital anamorphosis is used to define a distorted image of health and care that may be viewed correctly using digital tools and strategies. MASK digital anamorphosis represents the process used by MASK to develop the digital transformation of health and care in rhinitis. It strengthens the ARIA change management strategy in the prevention and management of airway disease. The MASK strategy is based on validated digital tools. Using the MASK digital tool and the CARAT online enhanced clinical framework, solutions for practical steps of digital enhancement of care are proposed.Peer reviewe

Nine Quick Tips for Analyzing Network Data

International audienceThese tips provide a quick and concentrated guide for beginners in the analysis of network data