Absolute contributions of species, modalities of factor A and B in the two case studies.

Link between the sexual segregation and aggregation statistic (SSAS) and the segregation coefficient (SC).

Two species of solitary parasitoïd wasps, Diadromus collaris and D. pulchellus, are able to parasitize simultaneously the same host (Acrolepiopsis assectella). Only one parasitoïd individual can develop per host. The potential of each hymenopteron to produce an adult F1 was studied. Competition therefore occurs between young overabundant larvae. The theoretical and observed numbers of D. pulchellus adults were significantly higher than those of D. collaris. However, the differences observed between the number of D. pulchellus and D. collaris adults did not suggest that one species was more competitive under thermoperiodic conditions which were favorable to D. pulchellus.

En forêts naturelles, la recherche de la rentabilité économique constitue une préoccupation essentielle. Ainsi, les sylviculteurs concentrent-ils le plus souvent leurs interventions en vue de stimuler et d'accroître la dynamique des espèces commerciales au détriment des espèces non commerciales. L'efficacité de ces interventions sylvicoles a déjà été démontrée par de nombreux chercheurs en forêt sempervirente (dispositif permanent d'Irobo) et en forêt semi-décidue (dispositif permanent de Mopri). Les résultats de la présente étude confirment l'impact positif des éclaircies sélectives sur la dynamique de ces deux types de forêts. Le taux d'accroissement de la surface terrière du peuplement commercial est significativement plus élevé dans les placettes éclaircies que dans les placettes témoins. Il est proportionnel à l'intensité d'éclaircie. Il ressort en outre que la productivité des forêts naturelles ne dépend pas uniquement de la compétition interspécifique. Elle dépend aussi de la composition floristique et des propriétés individuelles des espèces au profit desquelles les éclaircies sont appliquées. Dans la forêt classée de Mopri, le taux d'accroissement de la surface terrière du peuplement commercial est dépendant de la dynamique individuelle des espèces les plus abondantes : Celtis mildbraedii (Ulmaceae) et Gambeya africana (Sapotaceae).Mots clés : Forêt sempervirente, forêt semi-décidue, peuplement commercial, éclaircie sélective, taux d'accroissement, surface terrière

L\'impact des éclaircies sélectives sur la variation de la composition floristique des forêts denses a été evalué en Côte d\'Ivoire. Des essais sylvicoles ont été conduits en forêt dense sempervirente (périmètre d\'Irobo) et en forêt semi-décidue (périmètre de Mopri). L\'enrichissement naturel des quadrats a été entièrement déterminé par deux approches opposées, notamment l\'apparition et la disparition des espèces. L\'analyse de la dynamique spatiale des processus d\'apparition et de disparition a révèlé une spatialisation des espèces. Les apparitions et les disparitions d\'espèces dépendent des facteurs environnementaux. Pour les apparitions, les variables les plus déterminantes ont été la surface terrière et la richesse spécifique initiale. Ces variables n\'ont cependant pas eu d\'influence significative sur le processus de disparition. Une corrélation négative entre les apparitions et les disparitions d\'espèces a été obtenue. En effet, les apparitions ont été généralement plus abondantes dans les parcelles, où les disparitions ont été peu fréquentes, et vice-versa. Considéré comme le bilan des apparitions et des disparitions d\'espèces, l\'enrichissement naturel n\'a pas été directement lié à l\'intensité des éclaircies, mais des caractéristiques initiales des quadrats..The effect of selective thinning on species composition in tropical forests was tested in Côte d\'Ivoire. This silvicultural study was conducted in an evergreen forest (test plots at Irobo) and in a semi-deciduous forest (test plots at Mopri). Natural enrichment of the quadrants was entirely determined by two opposite processes : the appearance and the disappearance of the commercial species. Analysis of spatial dynamics of appearance and disappearance processes revealed species spatialisation. The processes were found to be depend ent upon environmental factors. For appearances, the most determining variables were the basal area and the initial species richness. However, the variables did not have any significant influence on disappearance processes. Species disappearances seemed to be negatively related to species appearances. In fact, disappearances were generally more abundant in the quadrants, where appearances were not very frequent, and vice-versa. The natural enrichment was considered as the assessment of species appearances and disappearances. It did not depend directly on the thinning intensity but on initial characteristics in the quadrants. Keywords: Forêts humides, éclaircie, espèces commerciales, enrichissement naturel, disparition d\'espèces.Agronomie Africaine Vol. 19 (3) 2007: pp. 301-322

Abouheif adapted a test for serial independence to detect a phylogenetic signal in phenotypic traits. We provide the exact analytic value of this test, revealing that it uses Moran's I statistic with a new matrix of phylogenetic proximities. We introduce then two new matrices of phylogenetic proximities highlighting their mathematical properties: matrix A which is used in Abouheif test and matrix M which is related to A and biodiversity studies. Matrix A unifies the tests developed by Abouheif, Moran and Geary. We discuss the advantages of matrices A and M over three widely used phylogenetic proximity matrices through simulations evaluating power and type-I error of tests for phylogenetic autocorrelation. We conclude that A enhances the power of Moran's test and is useful for unresolved trees. Data sets and routines are freely available in an online package and explained in an online supplementary file.

The aim of this paper is to tackle the problem that arises from asymmetrical data cubes formed by two crossed factors fixed by the experimenter (factor A and factor B, e.g., sites and dates) and a factor which is not controlled for (the species). The entries of this cube are densities in species. We approach this kind of data by the comparison of patterns, that is to say by analyzing first the effect of factor B on the species-factor A pattern, and second the effect of factor A on the species-factor B pattern. The analysis of patterns instead of individual responses requires a correspondence analysis. We use a method we call Foucart's correspondence analysis to coordinate the correspondence analyses of several independent matrices of species x factor A (respectively B) type, corresponding to each modality of factor B (respectively A). Such coordination makes it possible to evaluate the effect of factor B (respectively A) on the species-factor A (respectively B) pattern. The results obtained by such a procedure are much more insightful than those resulting from a classical single correspondence analysis applied to the global matrix that is obtained by simply unrolling the data cube, juxtaposing for example the individual species x factor A matrices through modalities of factor B. This is because a single global correspondence analysis combines three effects of factors in a way that cannot be determined from factorial maps (factor A, factor B, and factor A x factor B interaction) whereas the applications of Foucart's correspondence analysis clearly discriminate two different issues. Using two data sets, we illustrate that this technique proves to be particularly powerful in the analyses of ecological convergence which include several distinct data sets and in the analyses of spatiotemporal variations of species distributions.

The study of sexual segregation has received increasing attention over the last two decades. Several hypotheses have been proposed to explain the existence of sexual segregation, such as the "predation risk hypothesis," the "forage selection hypothesis," and the "activity budget hypothesis." Testing which hypothesis drives sexual segregation is hampered, however, by the lack of consensus regarding a formal measurement of sexual segregation. By using a derivation of the well-known chi-square (here called the sexual segregation and aggregation statistic [SSAS]) instead of existent segregation coefficients, we offer a reliable way to test for temporal variation in the occurrence of sexual segregation and aggregation, even in cases where a large proportion of animals are observed alone. A randomization procedure provides a test for the null hypothesis of independence of the distributions of males and females among the groups. The usefulness of SSAS in the study of sexual segregation is demonstrated with three case studies on ungulate populations belonging to species with contrasting life histories and annual grouping patterns (isard, red deer, and roe deer). The existent segregation coefficients were unreliable since, for a given value, sexual segregation could or could not occur. Similarly, the existent segregation coefficients performed badly when males and females aggregated. The new SSAS was not prone to such limitations and allowed clear conclusions regarding whether males and females segregate, aggregate, or simply mix at random applicable to all species.

In recent years, there has been an increased interest in studying the variability of a quantitative life-history trait across a set of species sharing a common phylogeny. However, such studies have suffered from an insufficient development of statistical methods aimed at decomposing the trait variance with respect to the topological structure of the tree. Here we propose a new and generic approach that expresses the topological properties of the phylogenetic tree via an orthonormal basis, which is further used to decompose the trait variance. Such a decomposition provides a structure function, referred to as an "orthogram," which is relevant to characterize in both graphical and statistical aspects the dependence of trait values on the topology of the tree ("phylogenetic dependence"). We also propose four complementary test statistics to be computed from orthogram values that help to diagnose both the intensity and the nature of phylogenetic dependence. The relevance of the method is illustrated by the analysis of three phylogenetic data sets, drawn from the literature and typifying contrasted levels and aspects of phylogenetic dependence. Freely available routines which have been programmed in the R framework are also proposed.

This paper is the first of two articles describing theoretical and practical aspects of spatial discrimination of species distributions in a given area. Three multivariate methods used to show species geographical zonation from lists of species occurrence data from herbarium records are discussed. These methods are (i) Correspondence Analysis (CA), (ii) Canonical Correlation Trend Surface Analysis (CCTSA), and (iii) Discriminant Analysis on Eigenvectors of Neighbourhood Operator (DAENO). Their use is illustrated through the analysis of the spatial distribution of three virtual species. This paper shows that these methods are forms of Discriminant Analysis (DA) which use the spatial position of the species occurrences as variables; they just differ in the way they measure this position in space. It is concluded that these three methods are likely to produce the same results from a given data set, providing that the underlying spatial structures are well defined within the study area.

10 paramètres physico-chimiques sont mesurés en surface dans 10 stations à 12 dates sur le lac réservoir de la Sorme (Saône-et-Loire). L'article montre comment une analyse multitableaux peut caractériser la structure spatiale et préciser sa stabilité. Les notions d'interstructure et de compromis sont accessibles par une procédure simple et efficace.

This paper is a short summary of the main classes defined in the ade4 package for one table analysis methods (e.g., principal component analysis). Other papers will detail the classes defined in ade4 for twotables coupling methods (such as canonical correspondence analysis, redundancy analysis, and coinertia analysis), for methods dealing with K-tables analysis (i.e., three-ways tables), and for graphical methods. This package is a complete rewrite of the ADE4 software (Thioulouse et al. (1997), http://pbil.univlyon1.fr/ADE-4/) for the R environment. It contains Data Analysis functions to analyse Ecological and Environmental data in the framework of Euclidean Exploratory methods, hence the name ade4 (i.e., 4 is not a version number but means that there are four E in the acronym). The ade4 package is available in CRAN, but it can also be used directly online, thanks to the Rweb system (http://pbil.univ-lyon1.fr/Rweb/). This possibility is being used to provide multivariate analysis services in the field of bioinformatics, particularly for sequence and genome structure analysis at the PBIL (http://pbil.univ-lyon1.fr/). An example of these services is the automated analysis of the codon usage of a set of DNA sequences by correspondence analysis (http://pbil.univ-lyon1.fr/ mva/coa.php).

In this paper, we present a statistical method called STATICO that can be used to analyze series of pairs of ecological tables. The objective of this method is to find the stable part in the dynamics of the relationships between the species and their environment. The treateddata are a sequence of pairs of ecological tables. Each pair is made of a species abundance table (species in columns) and an environmental variables table (variables in columns). The sampling sites (in rows) must be the same for the two tables of one pair, but they may be different among the pairs. The environmental variables must be the same in all the environmental tables, and the list of species must be the same in all the species tables too, although some species may be absent from some tables (the corresponding columns will contain all zeros). From a statistical point of view, STATICO is a multitable analysis (partial triadic analysis) performed on the series of cross-tables resulting from the co-inertia analysis of each pair of tables. A small ecological example data set is analyzed and the results are discussed to show how this method can be used to extract the stable part of species-environment relationships. All computations and graphical displays can be performed with free software available on Internet.

This paper presents a new ordination method to compare several communities containing species that differ according to their taxonomic, morphological or biological features. The objective is first to find dissimilarities among communities from the knowledge about differences among their species, and second to describe these dissimilarities with regard to the feature diversity within communities. In 1986, Rao initiated a general framework for analysing the extent of the diversity. He defined a diversity coefficient called quadratic entropy and a dissimilarity coefficient and proposed a decomposition of this diversity coefficient in a way similar to ANOVA. Furthermore, Gower and Legendre (1986) built a weighted principal coordinate analysis. Using the previous context, we propose a new method called the double principal coordinate analysis (DPCoA) to analyse the relation between two kinds of data. The first contains differences among species (dissimilarity matrix); the second the species distribution among communities (abundance or presence/absence matrix). A multidimensional space assembling the species points and the community points is built. The species points define the original differences between species and the community points define the deduced differences between communities. Furthermore, this multidimensional space is linked with the diversity decomposition into between-community and within-community diversities. One looks for axes that provide a graphical ordination of the communities and project the species onto them. An illustration is proposed comparing bird communities which live in different areas under mediterranean bioclimates. Compared to some existing methods, the double principal coordinate analysis can provide a typology of communities taking account of an abundance matrix and can include dissimilarities among species. Finally, we show that such an approach generalizes some of these methods and allows us to develop new analyses.

AbstractClassical multivariate analyses are based on matrix algebra and enable the analysis of a table containing measurements of a set of variables for a set of sites. Incomplete mapped data consist of measurements of a set of variables recorded for the same geographical region but for different zonal systems and with only a partial sampling of this zone. This kind of data cannot be analysed with usual multivariate methods because there is no common system of sites for all variables. We propose a new approach using GIS technology and NIPALS, an iterative multivariate method, to analyse the spatial patterns of this kind of data. Moreover, an extension of our method is that it can be used for areal interpolation purposes. We illustrate the method in analysing data concerning the distribution of roe deer weights over several years in a reserve.

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 Chizé 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.

Procrustes analysis is a method for fitting a set of points to another. These two sets of points are often defined by the measurements of two sets of variables for the same individuals (e.g., measurements of species abundances and environmental variables at the same sites). We present a solution for graphical representation of the results of procrustes analysis when the number of variables in each of the two datasets exceeds two. This method is named procrustean co-inertia analysis because it is based on the joint use of procrustes analysis and co-inertia analysis, which is a coupling method for finding linear combinations of two sets of variables of maximal covariance. It provides better graphical representation of the concordance between the two datasets than classical co-inertia analysis. Moreover, distance matrices can be introduced in the analysis to improve its ecological meaning. Lastly, a randomization test equivalent to-PROTEST is proposed as an alternative to the Mantel test. An ecological example is presented to illustrate the method.