Revisiting Guerry's data: Introducing spatial constraints in multivariate analysis

Standard multivariate analysis methods aim to identify and summarize the main structures in large data sets containing the description of a number of observations by several variables. In many cases, spatial information is also available for each observation, so that a map can be associated to the multivariate data set. Two main objectives are relevant in the analysis of spatial multivariate data: summarizing covariation structures and identifying spatial patterns. In practice, achieving both goals simultaneously is a statistical challenge, and a range of methods have been developed that offer trade-offs between these two objectives. In an applied context, this methodological question has been and remains a major issue in community ecology, where species assemblages (i.e., covariation between species abundances) are often driven by spatial processes (and thus exhibit spatial patterns). In this paper we review a variety of methods developed in community ecology to investigate multivariate spatial patterns. We present different ways of incorporating spatial constraints in multivariate analysis and illustrate these different approaches using the famous data set on moral statistics in France published by Andr\'{e}-Michel Guerry in 1833. We discuss and compare the properties of these different approaches both from a practical and theoretical viewpoint.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS356 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Interactive Multivariate Data Analysis in R with the ade4 and ade4TkGUI Packages

ade4 is a multivariate data analysis package for the R statistical environment, and ade4TkGUI is a Tcl/Tk graphical user interface for the most essential methods of ade4. Both packages are available on CRAN. An overview of ade4TkGUI is presented, and the pros and cons of this approach are discussed. We conclude that command line interfaces (CLI) and graphical user interfaces (GUI) are complementary. ade4TkGUI can be valuable for biologists and particularly for ecologists who are often occasional users of R. It can spare them having to acquire an in-depth knowledge of R, and it can help first time users in a first approach.

Spatially-constrained clustering of ecological networks

Spatial ecological networks are widely used to model interactions between georeferenced biological entities (e.g., populations or communities). The analysis of such data often leads to a two-step approach where groups containing similar biological entities are firstly identified and the spatial information is used afterwards to improve the ecological interpretation. We develop an integrative approach to retrieve groups of nodes that are geographically close and ecologically similar. Our model-based spatially-constrained method embeds the geographical information within a regularization framework by adding some constraints to the maximum likelihood estimation of parameters. A simulation study and the analysis of real data demonstrate that our approach is able to detect complex spatial patterns that are ecologically meaningful. The model-based framework allows us to consider external information (e.g., geographic proximities, covariates) in the analysis of ecological networks and appears to be an appealing alternative to consider such data

The ade4 Package: Implementing the Duality Diagram for Ecologists

Multivariate analyses are well known and widely used to identify and understand structures of ecological communities. The ade4 package for the R statistical environment proposes a great number of multivariate methods. Its implementation follows the tradition of the French school of "Analyse des Donnees" and is based on the use of the duality diagram. We present the theory of the duality diagram and discuss its implementation in ade4. Classes and main functions are presented. An example is given to illustrate the ade4 philosophy.

Nine Quick Tips for Analyzing Network Data

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

The ade4 Package: Implementing the Duality Diagram for Ecologists

Multivariate analyses are well known and widely used to identify and understand structures of ecological communities. The ade4 package for the R statistical environment proposes a great number of multivariate methods. Its implementation follows the tradition of the French school of "Analyse des Donnees" and is based on the use of the duality diagram. We present the theory of the duality diagram and discuss its implementation in ade4. Classes and main functions are presented. An example is given to illustrate the ade4 philosophy

Supervised Multiblock Analysis in R with the ade4 Package

This paper presents two novel statistical analyses of multiblock data using the R language. It is designed for data organized in (K + 1) blocks (i.e., tables) consisting of a block of response variables to be explained by a large number of explanatory variables which are divided into K meaningful blocks. All the variables - explanatory and dependent - are measured on the same individuals. Two multiblock methods both useful in practice are included, namely multiblock partial least squares regression and multiblock principal component analysis with instrumental variables. The proposed new methods are included within the ade4 package widely used thanks to its great variety of multivariate methods. These methods are available on the one hand for statisticians and on the other hand for users from various fields in the sense that all the values derived from the multiblock processing are available. Some relevant interpretation tools are also developed. Finally the main results are summarized using overall graphical displays. This paper is organized following the different steps of a standard multiblock process, each corresponding to specific R functions. All these steps are illustrated by the analysis of real epidemiological datasets

Visualisation de données multivariées: réimplémentation des fonctionnalités graphiques de la librairie ade4

Visualisation de données multivariées: réimplémentation des fonctionnalités graphiques de la librairie ade

Effects of visuo-spatial working memory load on auditory attention: behavioural and cortical evidence

Working memory (WM) plays an important role in pilots since they have to continuously integrate and dynamically update information within a rapidly changing environment. WM is essential for overcoming response conflict and for optimal selective attention performance. Yet, WM is a capacity-limited system and increasing the demands on WM reduces the ability to ignore irrelevant stimuli and can led decreased performance in dual –tasking. In the present study we used an experimental approach aiming at providing evidence for the sensitivity of the functional near infrared spectroscopy (fNIRS) in providing measures of brain activity within the prefrontal cortex (PFC), with regard to WM-specific task demands combined to an additional different secondary task

Can an herbivore affect where a top predator kills its prey by modifying woody vegetation structure?

International audienceIn large mammal communities, little is known about modification of interspecific interactions through habitat structure changes. We assessed the effects of African elephants (Loxodonta africana) on features of woody habitat structure that can affect predator–prey interactions. We then explored how this can influence where African lions (Panthera leo) kill their prey. Indeed, lions are stalk-and-ambush predators and habitat structure and concealment opportunities are assumed to influence their hunting success. During 2 years, in Hwange National Park, Zimbabwe, kill sites (n = 167) of GPS-collared lions were characterized (visibility distance for large mammals, distance to a potential ambush site and presence of elephant impacts). We compared characteristics of lion kill sites with characteristics of random sites (1) at a large scale (i.e. in areas intensively used by lions, n = 418) and (2) at the microhabitat scale (i.e. in the direct surrounding available habitat, < 150 m, n = 167). Elephant-impacted sites had a slightly higher visibility and a longer distance to a potential ambush site than non-impacted sites, but these relationships were characterized by a high variability. At large scale, kill sites were characterized by higher levels of elephant impacts compared to random sites. At microhabitat scale, compared to the direct nearby available habitat, kill sites were characterized by a reduced distance to a potential ambush site. We suggest a conceptual framework whereby the relative importance of habitat features and prey abundance could change upon the scale considered