Stabilité d'une structure spatiale et compromis d'une analyse statistique multi-tableaux : application à la physico-chimie d'un lac réservoir

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 based on the observation that three-dimensional data matrices (sites x times x variables) often used in limnological investigations require statistical analyses fitted to experimental objectives. Many apparently different statistical tools (3-mode PCA of TUCKER, 1964; KROONENBERG, 1983; projection of variables WILLIAMS and STEPHENSON, 1973; DOLEDEC and CHESSEL, 1987) may be useful to clarify limnological problems such as : 1) the temporal variability of a pattern (elimination of spatial heterogenity) : 2) the spatial structure of a pattern (elimination of temporal effects, mapping of an average situation) : 3) the temporal variability of Lake stratification (stability, modification or inversion) : 4) the spatial structure of temporal variability, and 5) the between variables typology of a spatial and temporal structure. Our methodological approach allowed us to assess the temporal stability of the spatial structure of the Lake waters (question 3) using a multitable analysis known as triadic analysis (THIOULOUSE and CHESSEL, 1987).As part of the limnological study of a reservoir Lake (Sorme reservoir Lake, Saône-et-Loire, France) 10 commonly used physical and chemical variables were studied from July 1980 to October 1931. During this period, 12 water samples were taken near the surface at each of the 10 stations scattered along the Sorme Lake (see figure 1). Main morphometric features of the Sorme Lake are : 1) a surface area equal to 230 ha, 2) a 25 km long perimeter and 3) a volume of 9.5 106 m3 with a maximum depth of 13 meters upstream of the dam and an average depth of about 4 meters. Seasonal tidal range was only a few meters.Only 2 of the 3 concepts of triadic analysis stated by THIOULOUSE and CHESSEL, 1987 are developed here : 1) for each of the 12 tables (stations x variables) coming from the 12 sampling dates, data are first centered (elimination of mean) and standardized (division by standard deviation) (see figure 2). The resulting table Y called interstructure matrix, i.e. interstructure between each of the sampling dates matrix, is organized to have sampling dates as columns and the ten physical and chemical variables at each station successively as fines. Principal Component Analysis (PCA on the variance-covariance matrix) is then applied to the interstructure matrix. In our case it is a one-dimensional matrix, i.e. according to physical and chemical variables, there is only one spatial structure common to each sampling date (figure 3 and 2) compromise matrix are associated with the successive PCA factors of the interstructure (figure 4). According to the previous remark, only the first factor is considered. Data are reorganized to have physical and chemical variables as columns, and stations as fines. This last table defines a compromise matrix labelled Z. The mapping of the numerical values of matrix Z renders a ten-dimensional description of the permanent spatial structure (figure 5). To summarize the multivariate description, matrix Z is processed with a PCA on the variance-covariance matrix producing a three-dimensional compromise (figure 6).The interpretation of the compromise table by mapping the factorial scores of the PCA leads to a functional scheme of the reservoir Lake waters distinguishing five sectors (see figure 7) as a function of water depth, influence of tidal range, influence of tributaries and of the Sorme River. 3 stations are periodically isolated from the reservoir and produce 3 sectors with lower pH and temperature values and higher concentrations in ammonia and sulphate according to the influence of tributaries. The 4th sector is associated with the former submerged valley, i.e. main channel of the Sorme River prior to the dam closure, and demonstrated an ionic gradient concerning mainly nitrate and chloride-concentrations. The 5th sector, opposed to the latter, consists in the deeper area of the Sorme Lake which reveals rather homogeneous waters near the surface

Habitat filtering determines spatial variation of macroinvertebrate community traits in northern headwater streams

Although our knowledge of the spatial distribution of stream organisms has been increasing rapidly in the last decades, there is still little consensus about trait-based variability of macroinvertebrate communities within and between catchments in near-pristine systems. Our aim was to examine the taxonomic and trait based stability vs. variability of stream macroinvertebrates in three high-latitude catchments in Finland. The collected taxa were assigned to unique trait combinations (UTCs) using biological traits. We found that only a single or a highly limited number of taxa formed a single UTC, suggesting a low degree of redundancy. Our analyses revealed significant differences in the environmental conditions of the streams among the three catchments. Linear models, rarefaction curves and beta-diversity measures showed that the catchments differed in both alpha and beta diversity. Taxon- and trait-based multivariate analyses also indicated that the three catchments were significantly different in terms of macroinvertebrate communities. All these findings suggest that habitat filtering, i.e., environmental differences among catchments, determines the variability of macroinvertebrate communities, thereby contributing to the significant biological differences among the catchments. The main implications of our study is that the sensitivity of trait-based analyses to natural environmental variation should be carefully incorporated in the assessment of environmental degradation, and that further studies are needed for a deeper understanding of trait-based community patterns across near-pristine streams

Author correction : a global database for metacommunity ecology, integrating species, traits, environment and space

Correction to: Scientific Data https://doi.org/10.1038/s41597-019-0344-7, published online 08 January 202

Importance of data structure in comparing two dimension reduction methods for classification of microarray gene expression data

BACKGROUND: With the advance of microarray technology, several methods for gene classification and prognosis have been already designed. However, under various denominations, some of these methods have similar approaches. This study evaluates the influence of gene expression variance structure on the performance of methods that describe the relationship between gene expression levels and a given phenotype through projection of data onto discriminant axes. RESULTS: We compared Between-Group Analysis and Discriminant Analysis (with prior dimension reduction through Partial Least Squares or Principal Components Analysis). A geometric approach showed that these two methods are strongly related, but differ in the way they handle data structure. Yet, data structure helps understanding the predictive efficiency of these methods. Three main structure situations may be identified. When the clusters of points are clearly split, both methods perform equally well. When the clusters superpose, both methods fail to give interesting predictions. In intermediate situations, the configuration of the clusters of points has to be handled by the projection to improve prediction. For this, we recommend Discriminant Analysis. Besides, an innovative way of simulation generated the three main structures by modelling different partitions of the whole variance into within-group and between-group variances. These simulated datasets were used in complement to some well-known public datasets to investigate the methods behaviour in a large diversity of structure situations. To examine the structure of a dataset before analysis and preselect an a priori appropriate method for its analysis, we proposed a two-graph preliminary visualization tool: plotting patients on the Between-Group Analysis discriminant axis (x-axis) and on the first and the second within-group Principal Components Analysis component (y-axis), respectively. CONCLUSION: Discriminant Analysis outperformed Between-Group Analysis because it allows for the dataset structure. An a priori knowledge of that structure may guide the choice of the analysis method. Simulated datasets with known properties are valuable to assess and compare the performance of analysis methods, then implementation on real datasets checks and validates the results. Thus, we warn against the use of unchallenging datasets for method comparison, such as the Golub dataset, because their structure is such that any method would be efficient

Author correction : a global database for metacommunity ecology, integrating species, traits, environment and space

Correction to: Scientific Data https://doi.org/10.1038/s41597-019-0344-7, published online 08 January 202

Methods of nutrition surveillance in low-income countries

Background In 1974 a joint FAO/UNICEF/WHO Expert Committee met to develop methods for nutrition surveillance. There has been much interest and activity in this topic since then, however there is a lack of guidance for practitioners and confusion exists around the terminology of nutrition surveillance. In this paper we propose a classification of data collection activities, consider the technical issues for each category, and examine the potential applications and challenges related to information and communication technology. Analysis There are three major approaches used to collect primary data for nutrition surveillance: repeated cross-sectional surveys; community-based sentinel monitoring; and the collection of data in schools. There are three major sources of secondary data for surveillance: from feeding centres, health facilities, and community-based data collection, including mass screening for malnutrition in children. Surveillance systems involving repeated surveys are suitable for monitoring and comparing national trends and for planning and policy development. To plan at a local level, surveys at district level or in programme implementation areas are ideal, but given the usually high cost of primary data collection, data obtained from health systems are more appropriate provided they are interpreted with caution and with contextual information. For early warning, data from health systems and sentinel site assessments may be valuable, if consistent in their methods of collection and any systematic bias is deemed to be steady. For evaluation purposes, surveillance systems can only give plausible evidence of whether a programme is effective. However the implementation of programmes can be monitored as long as data are collected on process indicators such as access to, and use of, services. Surveillance systems also have an important role to provide information that can be used for advocacy and for promoting accountability for actions or lack of actions, including service delivery. Conclusion This paper identifies issues that affect the collection of nutrition surveillance data, and proposes definitions of terms to differentiate between diverse sources of data of variable accuracy and validity. Increased interest in nutrition globally has resulted in high level commitments to reduce and prevent undernutrition. This review helps to address the need for accurate and regular data to convert these commitments into practice

Low diversity Cryptococcus neoformans variety grubii multilocus sequence types from Thailand are consistent with an ancestral African origin.

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The need for eco-efficient landscapes to prevent irreversible degradation of agroecosystems in deforested Amazonia.

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Unpacking ecosystem service bundles: towards predictive mapping of synergies and trade-offs between ecosystem services

Multiple ecosystem services (ES) can respond similarly to social and ecological factors to form bundles. Identifying key social-ecological variables and understanding how they co-vary to produce these consistent sets of ES may ultimately allow the prediction and modelling of ES bundles, and thus, help us understand critical synergies and trade-offs across landscapes. Such an understanding is essential for informing better management of multi-functional landscapes and minimising costly trade-offs. However, the relative importance of different social and biophysical drivers of ES bundles in different types of social-ecological systems remains unclear. As such, a bottom-up understanding of the determinants of ES bundles is a critical research gap in ES and sustainability science. Here, we evaluate the current methods used in ES bundle science and synthesize these into four steps that capture the plurality of methods used to examine predictors of ES bundles. We then apply these four steps to a cross-study comparison (North and South French Alps) of relationships between social-ecological variables and ES bundles, as it is widely advocated that cross-study comparisons are necessary for achieving a general understanding of predictors of ES associations. We use the results of this case study to assess the strengths and limitations of current approaches for understanding distributions of ES bundles. We conclude that inconsistency of spatial scale remains the primary barrier for understanding and predicting ES bundles. We suggest a hypothesis-driven approach is required to predict relationships between ES, and we outline the research required for such an understanding to emerge