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Co-inertia Analysis (co-inertia + analysis)

Distribution by Scientific Domains

Life Sciences	25%
Earth and Environmental Science	25%

Selected Abstracts

Common components and specific weight analysis and multiple co-inertia analysis applied to the coupling of several measurement techniques

JOURNAL OF CHEMOMETRICS, Issue 5 2006
M. Hanafi
Abstract The present paper compares two multiblock techniques: the Common Components and Specific Weights Analysis (CCSWA) and the Multiple Co-inertia Analysis (MCoA). Both methods are used to (1) to investigate the relationships among various data tables and (2) to extract latent variables from information of different nature, reflecting different facets of a food product. Our objective is to study the ability of these methods to extract, from a set of data tables, latent characteristics which are representative of the whole modifications brought to a complex system (food product) by a modification of a given process factor. The comparison of these methods is based on the investigation of their conceptual framework by particularly highlighting new properties of CCSWA. Moreover, the two techniques of analysis are compared on the basis of a case study in cheese processing where each cheese sample is described by different kinds of measurements. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Waterlogging and canopy interact to control species recruitment in floodplains

FUNCTIONAL ECOLOGY, Issue 4 2010
Wiktor Kotowski
Summary 1.,The extent to which seedling recruitment contributes to local functional diversity depends on the environmental filters operating in a plant community. Classical community assembly models assume that habitat constraints and competition act like hierarchical filters with habitat filtering as the dominant one. Alternative models assume a synergic interaction since responses to environmental stress and competition may impose physiological trade-offs in plants. 2.,River floodplains are an ideal system to test the relationship between habitat and competition filtering in community (re)assembly, as flooding causes changes in both habitat stress (waterlogging, resulting in anoxia and toxicity) and competition (dieback of vegetation) on one hand and acts as an effective seed dispersal vector on the other hand. 3.,We conducted a mesocosm experiment on early community assembly from a pool of 34 floodplain species covering a wetness gradient. Seed mixtures were sown in a full factorial design with water level, canopy and mowing as controlling factors. We measured the biomass of all species after one growing season and determined germination and seedling growth traits, both outside (response to waterlogging/no waterlogging) and in a growth-chamber (response to light/darkness). 4.,Species recruitment was analysed in relation to the controlling factors and measured functional traits using co-inertia analysis. Furthermore we analysed the effects of the controlling factors on several aspects of functional diversity. 5.,There was no establishment in grass sward, unless mowing was applied. Species-rich communities only developed when germination and early establishment phases occurred on waterlogged bare soil. High water level did not suppress establishment but reduced the total biomass and lowered inter-specific competition. The effect of mowing on species richness depended upon the interplay between waterlogging and canopy. 6.,Establishment success under canopy required seedling strategies to tolerate shade. The elimination of typical wetland specialists from oxic mesocosms was clearly an effect of their poorer and/or slower germination and lower competitive abilities in comparison to non-wetland plants, leading to their disappearance in this low-stress environment. 7.,Our results indicate that single stress factors can enhance species richness and functional diversity through limiting competition but a synergic interaction of different stresses can lead to reduced richness. [source]

Seasonality in adult flight activity of two neuroptera assemblages of southern Mali

AFRICAN JOURNAL OF ECOLOGY, Issue 4 2009
Bruno Michel
Abstract The seasonality of insect assemblages in Africa is poorly investigated. To provide information on the relationships between climate and insect assemblages in the Sudanian region, strongly affected by climate change, we studied Myrmeleontidae and Ascalaphidae assemblages (Insecta: Neuroptera) for 7 and 5 consecutive years respectively in southern Mali. To make the species inventory as exhaustive as possible, we performed weekly sampling by netting and light trapping. For both assemblages, results showed very similar patterns of variation in species diversity throughout the year. Adults of Myrmeleontidae and Ascalaphidae were active all year, and the species succession was influenced by a strong temporal segregation. Species diversity peaked at the end of the rainy season and surprisingly during the dry season. Principal component analysis of the climatic factors followed by co-inertia analysis applied to two data sets, one comprising climatic factors and the other reporting presence/absence of species, showed a good association between the annual trend of climatic factors and the species diversity. But no well defined species grouping was clearly linked to a particular period of the year. This tight association between climate and species composition suggests that even small climate changes could modify significantly species assemblage characteristics. Résumé La saisonnalité des assemblages d'insectes en Afrique est peu étudiée. Pour fournir des informations sur les relations entre le climat et les assemblages d'insectes dans la région soudanienne, très affectée par les changements climatiques, les assemblages de Myrmeleontidae et d'Ascalaphidae (Insectes: Neuroptera) ont étéétudiés respectivement pendant sept et cinq années consécutives dans le sud du Mali. Pour que l'inventaire des espèces soit le plus complet possible, on a réalisé des échantillonnages hebdomadaires au moyen de filets et de pièges lumineux. Pour les deux assemblages, les résultats ont montré des schémas de variation de la diversité des espèces très comparables tout au long de l'année. Il y avait des adultes de Myrmeleontidae et d'Ascalaphidae actifs toute l'année, et la succession des espèces était influencée par une ségrégation temporelle très forte. La diversité des espèces connaissait un pic à la fin de la saison des pluies et, étonnamment, pendant la saison sèche. L'analyse en composantes principales des facteurs climatiques suivie par une analyse de co-inertie appliquée à deux jeux de données, un comprenant des facteurs climatiques, l'autre rapportant la présence/l'absence d'espèces, a montré une bonne association entre la tendance annuelle des facteurs climatiques et la diversité des espèces. Mais aucun groupement bien défini d'espèces n'était clairement liéà une période particulière de l'année. Cette étroite association entre le climat et la composition des espèces suggère que même de petits changements climatiques pourraient modifier significativement les caractéristiques de l'assemblage d'espèces. [source]

Common components and specific weight analysis and multiple co-inertia analysis applied to the coupling of several measurement techniques

Generalized orthogonal multiple co-inertia analysis(,PLS): new multiblock component and regression methods

JOURNAL OF CHEMOMETRICS, Issue 5 2003
Myrtille Vivien
Abstract The purpose of this paper is to develop new component-wise component and regression multiblock methods that overcome some of the difficulties traditionally associated with multiblocks, such as the step-by-step optimization and component orthogonalities. Generalized orthogonal multiple co-inertia analysis (GOMCIA) and generalized orthogonal multiple co-inertia analysis,partial least squares (GOMCIA,PLS) are proposed for modelling two sets of blocks measured on the same observations. We especially emphasize GOMCIA,PLS methods in which we consider one of the sets as predictive. All these methods are based on the step-by-step maximization of the same criterion under normalization constraints and produce orthogonal components or super-components. The solutions of the problem have to be computed with an iterative algorithm (which we prove to be convergent). We also give some interesting special cases and discuss the differences compared with a few other multiblock and/or multiway methods. Finally, short examples of real data are processed to show how GOMCIA,PLS can be used and its properties. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Spatial patterns of the biological traits of freshwater fish communities in south-west France

JOURNAL OF FISH BIOLOGY, Issue 2 2005
F. Santoul
Spatial patterns in the combinations of biological traits of fish communities were studied in the Garonne River system (57 000 km2, south-west France). Fish species assemblages were recorded at 554 sampling sites, and the biological traits of species were described using a fuzzy-coding method. A co-inertia analysis of species distributions and biological traits identified some spatial patterns of species trait combinations. Fish species richness progressively increased from up- to downstream sections, and the longitudinal patterns of fish assemblages partitioned the river into clear biogeographic areas, such as the brown trout Salmo trutta(headwater streams), the grayling Thymallus thymallus, the barbel Barbus barbus and the bream Abramis brama zones (most downstream sections), which fitted with Huet's well-known zonation for western European rivers. Only a few biological traits, chiefly related to life-history attributes, significantly influenced the observed fish distributions. Fecundity, potential size, maximum age and reproductive factor increased from headwater to plain reaches. As a theoretical framework for assessing and predicting the functional organization of stream fish communities, spatial variations in species traits can be related to habitat conditions, thus providing explicit spatial schemes that may be useful to the design of both scientific studies and river management. [source]

Matching data sets from two different spatial samples

JOURNAL OF VEGETATION SCIENCE, Issue 6 2002
Stéphane Dray
Rameau et al. (1989) Abstract. Methods for coupling two data sets (species composition and environmental variables for example) are well known and often used in ecology. All these methods require that variables of the two data sets have been recorded at the same sample stations. But if the two data sets arise from different sample schemes, sample locations can be different. In this case, scientists usually transform one data set to conform with the other one that is chosen as a reference. This inevitably leads to some loss of information. We propose a new ordination method, named spatial-RLQ analysis, for coupling two data sets with different spatial sample techniques. Spatial-RLQ analysis is an extension of co-inertia analysis and is based on neighbourhood graph theory and classical RLQ analysis. This analysis finds linear combinations of variables of the two data sets which maximize the spatial cross-covariance. This provides a co-ordination of the two data sets according to their spatial relationships. A vegetation study concerning the forest of Chizé (western France) is presented to illustrate the method. [source]