The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Pierre Legendre - One of the best experts on this subject based on the ideXlab platform.
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beta diversity as the variance of community data Dissimilarity coefficients and partitioning
Ecology Letters, 2013Co-Authors: Pierre Legendre, Miquel De CaceresAbstract:Beta diversity can be measured in different ways. Among these, the total variance of the community data table Y can be used as an estimate of beta diversity. We show how the total variance of Y can be calculated either directly or through a Dissimilarity Matrix obtained using any Dissimilarity index deemed appropriate for pairwise comparisons of community composition data. We addressed the question of which index to use by coding 16 indices using 14 properties that are necessary for beta assessment, comparability among data sets, sampling issues and ordination. Our comparison analysis classified the coefficients under study into five types, three of which are appropriate for beta diversity assessment. Our approach links the concept of beta diversity with the analysis of community data by commonly used methods like ordination and anova. Total beta can be partitioned into Species Contributions (SCBD: degree of variation of individual species across the study area) and Local Contributions (LCBD: comparative indicators of the ecological uniqueness of the sites) to Beta Diversity. Moreover, total beta can be broken up into within- and among-group components by manova, into orthogonal axes by ordination, into spatial scales by eigenfunction analysis or among explanatory data sets by variation partitioning.
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analyzing beta diversity partitioning the spatial variation of community composition data
Ecological Monographs, 2005Co-Authors: Pierre Legendre, Daniel Borcard, Pedro R PeresnetoAbstract:Robert H. Whittaker defined beta diversity as the variation in species com- position among sites in a geographic area. Beta diversity is a key concept for understanding the functioning of ecosystems, for the conservation of biodiversity, and for ecosystem management. This paper explains how hypotheses about the origin of beta diversity can be tested by partitioning the spatial variation of community composition data (presence- absence or abundance data) with respect to environmental variables and spatial base func- tions. We compare two statistical methods to accomplish that. The sum-of-squares of a community composition data table, which is one possible measure of beta diversity, is correctly partitioned by canonical ordination; hence, canonical partitioning produces correct estimates of the different portions of community composition variation. In recent years, several authors interested in the variation in community composition among sites (beta diversity) have used another method, variation partitioning on distance matrices (Mantel approach). Their results led us to compare the two partitioning approaches, using simulated data generated under hypotheses about the variation of community composition among sites. The theoretical developments and simulation results led to the following observations: (1) the variance of a community composition table is a measure of beta diversity. (2) The variance of a Dissimilarity Matrix among sites is not the variance of the community com- position table nor a measure of beta diversity; hence, partitioning on distance matrices should not be used to study the variation in community composition among sites. (3) In all of our simulations, partitioning on distance matrices underestimated the amount of variation in community composition explained by the raw-data approach, and (4) the tests of significance had less power than the tests of canonical ordination. Hence, the proper statistical procedure for partitioning the spatial variation of community composition data among environmental and spatial components, and for testing hypotheses about the origin and maintenance of variation in community composition among sites, is canonical parti- tioning. The Mantel approach is appropriate for testing other hypotheses, such as the var- iation in beta diversity among groups of sites. Regression on distance matrices is also appropriate for fitting models to similarity decay plots.
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improving the additive tree representation of a Dissimilarity Matrix using reticulations
2000Co-Authors: Vladimir Makarenkov, Pierre LegendreAbstract:This paper addresses the problem of approximating a Dissimilarity Matrix by means of a reticulogram. A reticulogram represents an evolutionary structure in which the objects may be related in a non-unique way to a common ancestor. Dendrograms and additive (phylogenetic) trees are particular cases of reticulograms. The reticulogram is obtained by adding edges (reticulations) to an additive tree, gradually improving the approximation of the Dissimilarity Matrix. We constructed a reticulogram representing the evolution of 12 primates. The reticulogram not only improved the data approximation provided by the phylogenetic tree, but also depicted the homoplasy contained in the data, which cannot be expressed bv a tree topology. The algorithm for reconstructing reticulograms is part of the T-Rex software package, available at URL .
Bin Wang - One of the best experts on this subject based on the ideXlab platform.
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a robust wireless sensor network localization algorithm in mixed los nlos scenario
Sensors, 2015Co-Authors: Wei Cui, Bin WangAbstract:Localization algorithms based on received signal strength indication (RSSI) are widely used in the field of target localization due to its advantages of convenient application and independent from hardware devices. Unfortunately, the RSSI values are susceptible to fluctuate under the influence of non-line-of-sight (NLOS) in indoor space. Existing algorithms often produce unreliable estimated distances, leading to low accuracy and low effectiveness in indoor target localization. Moreover, these approaches require extra prior knowledge about the propagation model. As such, we focus on the problem of localization in mixed LOS/NLOS scenario and propose a novel localization algorithm: Gaussian mixed model based non-metric Multidimensional (GMDS). In GMDS, the RSSI is estimated using a Gaussian mixed model (GMM). The Dissimilarity Matrix is built to generate relative coordinates of nodes by a multi-dimensional scaling (MDS) approach. Finally, based on the anchor nodes’ actual coordinates and target’s relative coordinates, the target’s actual coordinates can be computed via coordinate transformation. Our algorithm could perform localization estimation well without being provided with prior knowledge. The experimental verification shows that GMDS effectively reduces NLOS error and is of higher accuracy in indoor mixed LOS/NLOS localization and still remains effective when we extend single NLOS to multiple NLOS.
Costes Evelyne - One of the best experts on this subject based on the ideXlab platform.
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Comparison of tree architecture using tree edit distances: application to 2-year-old apple hybrids
2008Co-Authors: Segura Vincent, Ouangraoua Aida, Ferraro Pascal, Costes EvelyneAbstract:In fruit trees, understanding genetic determinisms of architectural traits is considered as a promising manner to control vegetative development and yield regularity. In this context, our study aimed to classify 2-year-old apple hybrids on the basis of their architectural traits. From a fine phenotyping, trees were described as tree graphs, including topological and geometric information. To evaluate the similarity between trees, comparison methods based on edit operations (substitution, insertion and deletion) were carried out. Distance between two tree graphs was computed by minimising the sum of the costs of the edit operations applied to transform one tree into another. Two algorithms for the comparison of unordered and partially ordered tree graphs were applied to a sub-sample of the population, taking into account several geometric attributes. For each comparison, a Dissimilarity Matrix was computed, and subsequently trees were clustered. A local interpretation of the matched entities was proposed through schematic representations of the trees, and similarities between trees were analysed within and between clusters. The tree graphs, both unordered or partially ordered and whether the attributes were considered or not, were grouped, by clustering, according to the number of entities per tree. The robustness of the unordered comparison was demonstrated by its application to the whole population, since it provided results similar to those obtained on the sub-sample. Further developments towards a higher relative weight of geometric versus topological information are discussed in the perspective to define an architectural ideotype in appl
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Comparison of Tree Architecture Using Tree Edit Distances: Application to Two-Year-Old Apple Hybrids
'Springer Science and Business Media LLC', 2008Co-Authors: Segura Vincent, Ouangraoua Aida, Ferraro Pascal, Costes EvelyneAbstract:UMR DAP, équipe AFEF, Publication Inra prise en compte dans l'analyse bibliométrique des publications scientifiques mondiales sur les Fruits, les Légumes et la Pomme de terre. Période 2000-2012. http://prodinra.inra.fr/record/256699International audienceIn fruit trees, understanding genetic determinisms of architectural traits is considered as a promising manner to control vegetative development and yield regularity. In this context, our study aimed to classify 2-year-old apple hybrids on the basis of their architectural traits. From a fine phenotyping, trees were described as tree graphs, including topological and geometric information. To evaluate the similarity between trees, comparison methods based on edit operations (substitution, insertion and deletion) were carried out. Distance between two tree graphs was computed by minimising the sum of the costs of the edit operations applied to transform one tree into another. Two algorithms for the comparison of unordered and partially ordered tree graphs were applied to a sub-sample of the population, taking into account several geometric attributes. For each comparison, a Dissimilarity Matrix was computed, and subsequently trees were clustered. A local interpretation of the matched entities was proposed through schematic representations of the trees, and similarities between trees were analysed within and betweenclusters. The tree graphs, both unordered or partially ordered and whether the attributes were considered or not, were grouped, by clustering, according to the number of entities per tree. The robustness of the unordered comparison was demonstrated by its application to the whole population, since it provided results similar to those obtained on the sub-sample. Further developments towards a higher relative weight of geometric versus topological information are discussed in the perspective to define an architectural ideotype in apple
Miquel De Caceres - One of the best experts on this subject based on the ideXlab platform.
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beta diversity as the variance of community data Dissimilarity coefficients and partitioning
Ecology Letters, 2013Co-Authors: Pierre Legendre, Miquel De CaceresAbstract:Beta diversity can be measured in different ways. Among these, the total variance of the community data table Y can be used as an estimate of beta diversity. We show how the total variance of Y can be calculated either directly or through a Dissimilarity Matrix obtained using any Dissimilarity index deemed appropriate for pairwise comparisons of community composition data. We addressed the question of which index to use by coding 16 indices using 14 properties that are necessary for beta assessment, comparability among data sets, sampling issues and ordination. Our comparison analysis classified the coefficients under study into five types, three of which are appropriate for beta diversity assessment. Our approach links the concept of beta diversity with the analysis of community data by commonly used methods like ordination and anova. Total beta can be partitioned into Species Contributions (SCBD: degree of variation of individual species across the study area) and Local Contributions (LCBD: comparative indicators of the ecological uniqueness of the sites) to Beta Diversity. Moreover, total beta can be broken up into within- and among-group components by manova, into orthogonal axes by ordination, into spatial scales by eigenfunction analysis or among explanatory data sets by variation partitioning.
Jari Saramäki - One of the best experts on this subject based on the ideXlab platform.
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EDENetworks: A user-friendly software to build and analyse networks in biogeography, ecology and population genetics
Molecular Ecology Resources, 2015Co-Authors: Mikko Kivelä, Sophie Arnaud-haond, Jari SaramäkiAbstract:The recent application of graph-based network theory analysis to biogeography, community ecology and population genetics has created a need for user-friendly software, which would allow a wider accessibility to and adaptation of these methods. EDENetworks aims to fill this void by providing an easy-to-use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically. The user can choose between several different ecological distance metrics, such as Bray-Curtis or Sorensen distance, or population genetic metrics such as FST or Goldstein distances, to turn the raw data into a distance/Dissimilarity Matrix. This Matrix is then transformed into a network by manual or automatic thresholding based on percolation theory or by building the minimum spanning tree. The networks can be visualized along with auxiliary data and analysed with various metrics such as degree, clustering coefficient, assortativity and betweenness centrality. The statistical significance of the results can be estimated either by resampling the original biological data or by null models based on permutations of the data.