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Human body composition is made up of mutually exclusive and exhaustive parts (e.g. %truncal fat, %non-truncal fat and%fat-free mass) which are constrained to sum to the same total (100%). In statistical analyses, individual parts of body composition (e.g. %truncal fat or %fat-free mass) have traditionally been used as proxies for body composition, and have been linked with a range of health outcomes. But analysis of individual parts omits information about the other parts,which are intrinsically co-dependent because of the constant sum constraint of 100%. Further, body mass may be associated with health outcomes. We describe a statistical approach for body composition based on compositional data analysis. The body composition data are expressed as logratios to allow relative information about all the compositional parts to be explored simultaneously in relation to health outcomes. We describe a recent extension to the logratio approach to compositional data analysis which allows absolute information about the total of the compositional parts (body mass) to be considered alongside relative information about body composition. The statistical approach is illustrated by an example that explores the relationships between adults’ body composition, body mass and bone strength. Refereed/Peer-reviewed

Compositional data carry relative information. Hence, their statistical analysis has to be performed on coordinates with respect to a log-ratio basis. Frequently, the modeler is required to back-transform the estimates obtained with the modeling to have them in the original units such as euros, kg or mg/liter. Approaches for recovering original units need to be formally introduced and its properties explored. Here, we formulate and analyze the properties of two procedures: a simple approach consisting of adding a residual part to the composition and an approach based on the use of an auxiliary variable. Both procedures are illustrated using a geochemical data set where the original units are recovered when spatial models are applied.

Association rule mining is a powerful data analytic technique used for extracting information from transaction databases with a collection of itemsets. The aim is to indicate what item goes with what item (ie, an association rule) in a set of collected transactions. It is extensively used in text analytics of text records or social media. Here we use Compositional Data analysis (CoDa) techniques to generate new visualizations and insights from association rule mining. These CoDa methods show the relationship between itemsets, their strength, and direction of dependency. Moreover, after expressing each association rule as a contingency table, we discuss two statistical tests to guide identification of the relevant rules by analyzing the relative importance of the elements of the table. As an example, we use these visualizations and statistical tests for investigating the association of negative mood emotions to various types of headache/migraine events. Data for those analysis comes from N1-HeadacheTM, a digital platform where individual users record attacks and symptoms as well as their daily exposure to a list of potential factors This research has been supported by theSpanish Ministry of Economy, Industry and Competitiveness under the project CODAMET (Ref: RTI2018-095518-B-C21) Open Access funding provided thanks to the CRUE-CSIC agreement with Wiley

Purpose: To describe youth time-use compositions, focusing on time spent in shorter and longer bouts of sedentary behavior and physical activity (PA), and to examine associations of these time-use compositions with cardiometabolic biomarkers. Methods: Accelerometer and cardiometabolic biomarker data from 2 Australian studies involving youths 7–13 years old were pooled (complete cases with accelerometry and adiposity marker data, n = 782). A 9-component time-use composition was formed using compositional data analysis: time in shorter and longer bouts of sedentary behavior; time in shorter and longer bouts of light-, moderate-, or vigorous-intensity PA; and “other time” (i.e., non-wear/sleep). Shorter and longer bouts of sedentary time were defined as

Here we present a high-resolution record of benthic foraminiferal assemblages for the last 74 kyr from the Sicily Channel Ocean Drilling Program Leg 160 Site 963. Benthic foraminiferal results are compared with geochemical (benthic and planktic δ18O and δ13C) and calcareous plankton data, previously acquired on the same marine core sediments. Within the succession, three benthic foraminifera compositional zones were defined. Temporal changes in the assemblages are interpreted in the context of the modification of subtropical and temperate climate systems that affected the Mediterranean thermohaline circulation. A close connection between bottom conditions in the Sicily Channel and eastern Mediterranean Sea is evident in two intervals, characterized in the ODP Site 963 by reduced oxygen conditions. The first one, around 53–51 ka, is tentatively attributed to the ‘missing’ sapropel S2 while the second, between 35 and 29 ka, is marked by short and recurrent episodes of bottom-water oxygen decrease. Both are related to a weakening of the intermediate circulation in the Sicily Channel connected with relatively high northern hemisphere summer insolation and increase in Nile River discharge, which inhibited vertical mixing and intermediate water ventilation in Eastern Mediterranean Sea. Over the last deglaciation, the African humid period (AHP) and the sea level rise, also influenced the water mass structure in the Sicily Channel and a reduction of the bottom ventilation is suggested by a strong reduction of deep-water Miliolids. Decreased bottom oxygen levels, which testifies for a weakening of intermediate circulation in Sicily Strait, also characterized the interval corresponding to S1 deposition. Yet, the strong decrease of benthic foraminiferal abundance related to a low surface water trophic level, appears to be conditioned by the reduction of trophic levels in the western Mediterranean. The very high relative abundance of U. mediterranea recorded during this interval is explained by the availability of organic matter during a limited short year period and/or the availability of more degraded organic matter from river runoff. In general, compositional data analysis highlighted a quite complex response of benthic foraminifera to paleoclimatic changes. However, changes in the benthic foraminiferal assemblages recorded during the last glacial are coherent with surface paleoproductivity dynamics connected with D-O oscillation, and support oligotrophic, meso-eutrophic and oligo-mesotrophic conditions during the early interstadials, late interstadials and stadials, respectively.

A self-organizing map (SOM) is a non-linear projection of a D-dimensional data set, where the distance among observations is approximately preserved on to a lower dimensional space. The SOM arranges multivariate data based on their similarity to each other by allowing pattern recognition leading to easier interpretation of higher dimensional data. The SOM algorithm allows for selection of different map topologies, distances and parameters, which determine how the data will be organized on the map. In the particular case of compositional data (such as elemental, mineralogical, or maceral abundance), the sample space is governed by Aitchison geometry and extra steps are required prior to their SOM analysis. Following the principle of working on log-ratio coordinates, the simplicial operations and the Aitchison distance, which are appropriate elements for the SOM, are presented. With this structure developed, a SOM using Aitchison geometry is applied to properly interpret elemental data from combustion products (bottom ash, fly ash, and economizer fly ash) in a Wyoming coal-fired power plant. Results from this effort provide knowledge about the differences between the ash composition in the coal combustion process.

Chemically, petroleum is an extraordinarily complex mixture of different types of hydrocarbons that are now possible to isolate and identify because of advances in geochemistry. Here, we use biomarkers and carbon isotopes to establish genetic differences and similarities among oil samples. Conventional approaches for evaluating biomarker and carbon isotope relative abundances include statistical techniques such as principal component and cluster analysis. Considering that proportions of the different hydrocarbon molecules are relative parts of a laboratory sample, the data are compositional in nature, thus requiring the use of log-ratio approaches for adequate mathematical modeling. We apply both traditional and compositional modeling approaches to crude oil samples from an onshore area of about 50,000 square miles in southeast Texas. The data comprise 177 crude oil samples from producing oil fields that include key biomarkers, elemental, and isotopic values commonly used in source rock correlation studies. Our results indicate that compositional modeling has higher discriminating power and lower uncertainty than the traditional approach, allowing the identification of up to 16 clusters. Each cluster represents one oil family from a source rock organofacies ranging from Carboniferous to Paleogene. The families provide new insights into important petroleum systems in the Texas onshore region of the Gulf of Mexico sedimentary basin.

How we allocate time to activities impacts our health. Daily times spent in activities are interrelated because they compete for time-shares within a finite 24 h window. If more time is spent in one activity, time must be taken from one or more of the remaining activities to maintain the fixed total of 24 h. Thus, time-use data have a relative nature and can be analysed accordingly using compositional data analysis. In this chapter, we demonstrate exploratory and cross-sectional inferential analyses of an eight-part time-use composition using data from the Longitudinal Study of Australian Children (.n. = 2224, 52% boys, mean age = 34 months, standard deviation = 3). For inferential analyses, time-use compositions are expressed as a specific choice of balance coordinates to separate between types of activities. Considering the balance coordinates as explanatory variables, we explore the relationship between children's time-use composition and their socio-emotional health. Subsequently, we consider the balance coordinates as dependent variables and explore the relationship between parental perception of neighbourhood liveability and their child's time-use composition

The sample space of a manifest random vector is of crucial importance for a latent variable model. Compositional data require an appropriate statistical analysis because they provide the relative importance of the parts of a whole. Any statistical model including variables created using the original parts should be formulated according to the geometry of the simplex. Methods based on log-ratio coordinates give a consistent framework for analyzing this type of data. Here, we introduce an approach that includes both the orthonormal log-ratio coordinates and an auxiliary variable carrying absolute information and illustrate it through the factor analysis of two real datasets.

Background The consequences for youth cardiometabolic risk might depend on whether sedentary time and physical activity are accumulated sporadically (in shorter bouts) or in a sustained pattern (in longer bouts). This study aimed to: 1) describe daily time-use compositions of youth, including time spent in shorter and longer bouts of sedentary behaviour and physical activity; and 2) examine associations between time-use compositions with cardiometabolic biomarkers.Methods Accelerometer and cardiometabolic biomarker data (adiposity, blood pressure, lipids) from 7–13 year olds (mean ± SD: 10.4 ± 1.7) from two Australian studies were pooled (complete cases adiposity n = 772). A time-use composition of nine components was formed using compositional data analysis: time in shorter and longer bouts of sedentary behaviour, light-, moderate- and vigorous-intensity physical activity, and “other time” (i.e., non-wear/sleep). Shorter and longer bouts of sedentary time were defined as Results Confounder-adjusted models showed that the overall time-use composition was associated with zBMI, waist circumference, systolic blood pressure, high-density lipoprotein cholesterol, triglycerides, and a combined cardiometabolic risk score. Specifically, more time in longer relative to shorter bouts of light-intensity physical activity was associated with greater zBMI (β = 1.79, SE = 0.70, p = 0.010) and waist circumference (β = 17.28, SE = 4.87, p Conclusions Accumulating physical activity in frequent short bursts may be beneficial for adiposity compared to engaging in the same amount of these intensities in longer bouts.Trial registration: 'Lifestyle Of Our Kids’ (ACTRN12615000066583 [23/01/2015]) and ‘Transform-Us!’ (ACTRN12609000715279 [19/08/2009], ISRCTN83725066 [30/06/2010]).

Time spent in different glucose ranges indicate the occurrence of adverse events and measure the quality of glucose control in type one diabetes (T1D) patients. This work proposes a Compositional Data (CoDa) approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles limited to 6-h duration were analyzed at four different times of the day These glucose profiles were distributed into time spent in five glucose ranges, which determine the composition. The log-ratio coordinates of the compositions were categorized through a clustering algorithm, which later made possible the obtainment of a linear model that should be used to predict the category of a 6-h period in different times of day. Leave-one-out cross-validation was performed, achieving an average above 90% of correct classification. A probabilistic model of transition between the category of the past 6-h of glucose to the category of the future 6-h period was obtained. Results show that the CoDa approach not only works as new analysis tool and is suitable for the categorization of glucose profiles, but also is a complementary tool for the prediction of different categories of glucose control. This prediction could assist patients to take correction measures in advance to adverse situations.

In recent years, the focus of activity behavior research has shifted away from univariate paradigms (e.g., physical activity, sedentary behavior and sleep) to a 24-hour time-use paradigm that integrates all daily activity behaviors. Behaviors are analyzed relative to each other, rather than as individual entities. Compositional data analysis (CoDA) is increasingly used for the analysis of time-use data because it is intended for data that convey relative information. While CoDA has brought new understanding of how time use is associated with health, it has also raised challenges in how this methodology is applied, and how the findings are interpreted. In this paper we provide a brief overview of CoDA for time-use data, summarize current CoDA research in time-use epidemiology and discuss challenges and future directions. We use 24-hour time-use diary data from Wave 6 of the Longitudinal Study of Australian Children (birth cohort, n = 3228, aged 10.9&plusmn, 0.3 years) to demonstrate descriptive analyses of time-use compositions and how to explore the relationship between daily time use (sleep, sedentary behavior and physical activity) and a health outcome (in this example, adiposity). We illustrate how to comprehensively interpret the CoDA findings in a meaningful way.

A mathematically sound approach for summarizing chemical analyses of feed coal and all its combustion products (bottom ash, economizer fly ash, and fly ash) is presented. The nature of the data requires the application of compositional techniques when conducting statistical analysis, techniques that have not been applied before to the study of partitioning of elements between the coal that enters the boiler and the associated coal combustion products. A collection of descriptive and inferential compositional techniques was used to analyze the coal combustion products from a Wyoming power plant burning Paleocene Wyodak–Anderson coal. The significance of the fluctuation in ash composition is determined by using a Hotelling’s T-squared test and bootstrapping. Tree displays allow for visualization of the progressive effect of filters in removal of chemical species based on their geochemical composition. Results indicate that, in general, as the suspended combustion products entrained in the flue gases move closer to the stack, chemical species are removed from the combustion gas, starting with minerals associated with elements having the lowest volatility.

Background Daily activity data are by nature compositional data. Accordingly, they occupy a specific geometry with unique properties that is different to standard Euclidean geometry. This study aimed to estimate the difference in adiposity associated with isotemporal reallocation between daily activity behaviours, and to compare the findings from compositional isotemporal subsitution to those obtained from traditional isotemporal substitution. Methods We estimated the differences in adiposity (body fat%) associated with reallocating fixed durations of time (isotemporal substitution) between accelerometer-measured daily activity behaviours (sleep, sedentary time and light and moderate-to-vigorous physical activity (MVPA)) among 1728 children aged 9–11 years from Australia, Canada, Finland and the UK (International Study of Childhood Obesity, Lifestyle and the Environment, 2011–2013). We generated estimates from compositional isotemporal substitution models and traditional non-compositional isotemporal substitution models. Results Both compositional and traditional models estimated a positive (unfavourable) difference in body fat% when time was reallocated from MVPA to any other behaviour. Unlike traditional models, compositional models found the differences in estimated adiposity (1) were not necessarily symmetrical when an activity was being displaced, or displacing another (2) were not linearly related to the durations of time reallocated, and (3) varied depending on the starting composition. Conclusion The compositional isotemporal model caters for the constrained and therefore relative nature of activity behaviour data and enables all daily behaviours to be included in a single statistical model. The traditional model treats data as real variables, thus the constrained nature of time is not accounted for, nor reflected in the findings. Findings from compositional isotemporal substitution support the importance of MVPA to children’s health, and suggest that while interventions to increase MVPA may be of benefit, attention should be directed towards strategies to avoid decline in MVPA levels, particularly among already inactive children. Future applications of the compositional model can extend from pair-wise reallocations to other configurations of time-reallocation, for example, increasing MVPA at the expense of multiple other behaviours. Electronic supplementary material The online version of this article (10.1186/s12889-018-5207-1) contains supplementary material, which is available to authorized users.

PURPOSE: Health-related quality of life has been related to physical activity, sedentary behavior, and sleep among children from developed nations. These relationships have rarely been assessed in developing nations, nor have behaviors been considered in their true context, as mutually exclusive and exhaustive parts of the movement behavior composition. This study aimed to explore whether children's health-related quality of life is related to their movement behavior composition and if the relationship differs according to human development index.METHODS: Children aged 9-11 years (n = 5855), from the 12-nation cross-sectional observational International Study of Childhood Obesity, Lifestyle and the Environment 2011-2013, self-reported their health-related quality of life (KIDSCREEN-10). Daily movement behaviors were from 24-h, 7-day accelerometry. Isometric log-ratio mixed-effect linear models were used to calculate estimates for difference in health-related quality of life for the reallocation of time between daily movement behaviors.RESULTS: Children from countries of higher human development index reported stronger positive relationships between health-related quality of life and moderate-to-vigorous physical activity, relative to the remaining behaviors (r = 0.75, p = 0.005) than those from lower human development index countries. In the very high human development index strata alone, health-related quality of life was significantly related to the movement behavior composition (p = 0.005), with moderate-to-vigorous physical activity (relative to remaining behaviors) being positively associated with health-related quality of life.CONCLUSIONS: The relationship between children's health-related quality of life and their movement behaviors is moderated by their country's human development index. This should be considered when 24-h movement behavior guidelines are developed for children around the world.

Compositional data analysis requires selecting an orthonormal basis with which to work on coordinates. In most cases this selection is based on a data driven criterion. Principal component analysis provides bases that are, in general, functions of all the original parts, each with a different weight hindering their interpretation. For interpretative purposes, it would be better to have each basis component as a ratio or balance of the geometric means of two groups of parts, leaving irrelevant parts with a zero weight. This is the role of principal balances, defined as a sequence of orthonormal balances which successively maximize the explained variance in a data set. The new algorithm to compute principal balances requires an exhaustive search along all the possible sets of orthonormal balances. To reduce computational time, the sets of possible partitions for up to 15 parts are stored. Two other suboptimal, but feasible, algorithms are also introduced: (i) a new search for balances following a constrained principal component approach and (ii) the hierarchical cluster analysis of variables. The latter is a new approach based on the relation between the variation matrix and the Aitchison distance. The properties and performance of these three algorithms are illustrated using a typical data set of geochemical compositions and a simulation exercise.

How people use their time has been linked with their health. For example, spending more time being physically active is known to be beneficial for health, whereas long durations of sitting have been associated with unfavourable health outcomes. Accordingly, public health messages have advocated swapping strategies to promote the reallocation of time between parts of the time-use composition, such as ‘‘Move More, Sit Less’’, with the aim of achieving optimal distribution of time for health. However, the majority of research underpinning these public health messages has not considered daily time use as a composition, and has ignored the relative nature of time-use data. We present a way of applying compositional data analysis to estimate change in a health outcome when fixed durations of time are reallocated from one part of a particular time-use composition to another, while the remaining parts are kept constant, based on a multiple linear regression model on isometric log ratio coordinates. In an example, we examine the expected differences in Body Mass Index z-scores for reallocations of time between sleep, physical activity and sedentary behaviour Refereed/Peer-reviewed

The analysis of compositional data (CoDa) consists in the study of the relative importance of parts of a whole rather than the size of the whole because absolute information is either unavailable or not of interest. On the other hand, when absolute and relative information are both relevant, research hypotheses concern both. This article introduces a model including both the logratios used in CoDa and a total variable carrying absolute information as predictors in an otherwise standard statistical model. It shows how logratios can be tailored to the researchers’ hypotheses and alternative ways of computing the total. The interpretational advantages with respect to traditional approaches are presented and the equivalence and invariance properties are proven. A sequence of nested models is presented to test the relevance of relative and absolute information. The approach can be applied to dependent metric, binary, ordinal or count variables. Two illustrations are provided, the first on tourist expenditure and satisfaction and the second on solid waste management and floating population.

The information in modern or fossil foraminifera assemblages is the relative abundance or percentages of species, i.e., they can be considered as compositional data. In this study we deal with CoDa and regression-based methods as tools to estimate past climatic conditions. We tested standard and robust Partial Least Squares and Principal Component Regression, applied to the log-ratio coordinates of percentage data of Atlantic Ocean and Mediterranean Sea planktonic foraminiferal assemblages. Due to the presence of groups, it was preferred to model separately high latitude and mid to low latitude assemblages. This approach implies the application of cluster analysis, MANOVA and discriminant analysis to the logratio transformed fossil assemblage's compositions. The methods were then applied on marine core assemblages to reconstruct past sea surface temperatures. The obtained results were compared with those formerly obtained by means of compositional modern analogue technique and with the information arising from other paleoclimatic proxies This work has been supported by the project “CODAMET” (Ministerio de Ciencia, Innovación y Universidades; Ref: RTI2018-095518-B-C21)

Coal mine gas originates from the gas emission zone (GEZ) of the mine, as well as the longwall face and pillars. Gas emissions are controlled directly at the sources using horizontal or vertical boreholes drilled from surface or from the entries in advance of mining, or it is captured from the fractured and caved zones (gob) using ventholes during mining. The rest of the gas, especially that gas that originates from the longwall face and caved zone, mixes with the ventilation air and travels through bleeder and return entries before being exhausted to atmosphere from air shafts. Although the gas associated with mining mostly focuses on methane, the gas is not pure methane but is a mixture (where both the components and their quantities vary depending on the sampling location). Understanding the evolution of the composition of the gas from source to different sampling and evaluation points in mines using proper statistical analysis and interpretation methods can lead to better designed degasification and ventilation systems and the selection of the most adequate utilization method for generating energy from the gas. In this work, we present the results of compositional data analysis (CoDa) of gases sampled at different locations in a longwall mine operating in Pennsylvanian coal-bearing strata in the Northern Appalachian coal basin. Sampling locations were from accessible parts of the bleeder entries, returns, bleeder evaluation points and shafts within the mine, and also from gob gas ventholes (GGV) and coal degasification boreholes drilled in the panel areas. In addition, desorbed gas samples from seams that are important for the mine were included in the analyses for comparison. The compositional data analysis showed that the gas composition shifts based on the sampling location, putting in-mine and pure coalbed gases on the opposite ends. Removal of air from the samples did not change this observation suggesting that oxygen is depleted especially for in-mine samples, which is due to oxidation. Results also suggested that desorbed gas samples may not represent the composition of the coal seam gas.

The modern analogue technique has been accordingly revised with compositional data analysis framework. The method adopts the Aitchison distance, obtained from isometric log-ratio coordinates of relative abundances, as a natural measure of similarity among assemblages. The number of analogues from which obtain the estimates was determined through leave-oneout verification of modern assemblages. Mean distances and local outlier factor are considered to evaluate the quality of palaeoestimates. The method has been tested on Atlantic Ocean and Mediterranean planktonic foraminiferal assemblages to reconstruct past sea surface temperatures (SST). In comparison with previous planktonic foraminiferal based reconstructions, the Portugal offshore SST record for the last 210 ka shows a higher coherence with other paleoclimate proxies (i.e. the stable isotope and alkenone records). In comparison with raw data analysis, CoDa-MAT yields lower estimates of the western Mediterranean last glacial period SST This work has been supported by the project “CODARETOS” (Spanish Ministry of Economy and Competitiveness; Ref: MTM2015-65016-C2-1-R).

SummaryBackground The relationship between children's adiposity and lifestyle behaviour patterns is an area of growing interest. Objectives The objectives of this study are to identify clusters of children based on lifestyle behaviours and compare children's adiposity among clusters. Methods Cross-sectional data from the International Study of Childhood Obesity, Lifestyle and the Environment were used. Participants: the participants were children (9–11 years) from 12 nations (n = 5710). Measures: 24-h accelerometry and self-reported diet and screen time were clustering input variables. Objectively measured adiposity indicators were waist-to-height ratio, percent body fat and body mass index z-scores. Analysis: sex-stratified analyses were performed on the global sample and repeated on a site-wise basis. Cluster analysis (using isometric log ratios for compositional data) was used to identify common lifestyle behaviour patterns. Site representation and adiposity were compared across clusters using linear models. Results Four clusters emerged: (1) Junk Food Screenies, (2) Actives, (3) Sitters and (4) All-Rounders. Countries were represented differently among clusters. Chinese children were over-represented in Sitters and Colombian children in Actives. Adiposity varied across clusters, being highest in Sitters and lowest in Actives. Conclusions Children from different sites clustered into groups of similar lifestyle behaviours. Cluster membership was linked with differing adiposity. Findings support the implementation of activity interventions in all countries, targeting both physical activity and sedentary time.

Community Level Physiological Profiles (CLPPs) have been used as ecological indicators from long ago as they determine physiologic and metabolic potential of microbial communities and indicate the capability to adapt their metabolism to anthropogenic stressors or to changes in environmental conditions. Also, these profiles are proxies for functional diversity or functional traits of a microbial community, usually linked to the ecosystem functioning. However, statistical data analysis of CLPPs has turned out to be the main handicap in these studies. Specifically, the interference of inoculum density, the need to choose an optimal incubation time and the different profiles obtained depending on the incubation time-point limits its usefulness. To this aim, we have developed a new approach for CLPP data analysis that considers the whole data set and dynamics of colour development along the incubation period. This approach is based on compositional data (CoDa) transformation, which reflects relative rather than absolute information, followed by repeated measures multivariate analysis of variance and data representation in a canonical variates plot. The approach has been tested by using CLPPs from real fluvial biofilm samples affected by seasonality, flow stress and different sediment depth conditions, and it has been compared to the most classic applied approaches until today (incubation fixed point or curve integration, followed by a Principal Components Analysis). Our results highlight that the proposed approach (i) makes ecological interpretation of CLPPs more sensitive, (ii) clarifies differences between CLPPs even in situations of marked seasonality while classical approaches provided confounding results, (iii) produces ecologically meaningful results (i.e. the proposed approach showed significant differences in organic matter use capabilities among flow conditions and depth), and (iv) unifies the analysis methodology for CLPPs data. With this work we provide an R-script to be used in future studies.

Diet choice in marine species is typically derived from indirect methods such as stomach contents and stable isotope analysis, while choice experiments in controlled laboratory settings are used to infer foraging decisions in the wild. However, these methods are limited in their capacity to make inferences about foraging decisions by predators in variable environments or recreate the array of factors (such as prey traits, predator condition, and environmental conditions) present in natural systems which may interact to affect diet decisions by predators. Recent work has provided evidence for selectivity in the invasive Indo-Pacific lionfish (Pterois volitans/miles) despite the predator's apparent opportunistic, generalist feeding behavior. We directly tested diet choice by presenting wild-caught lionfish with multi-species prey assemblages in field enclosures. We offered lionfish equal biomasses of prey species sharing similar prey traits that are both highly abundant on coral reefs and prevalent in the lionfish diet across the invaded range. We then applied compositional analyses to determine relative prey consumption given prey availability. We observed lionfish selectively foraging on prey and manifesting strong consistent preferences for one prey species. Additionally, we observed condition-dependent foraging behavior, as lionfish with higher body conditions were more likely to exhibit selective foraging behavior. Our findings provide direct evidence for diet choice in an invasive generalist species and highlight the importance of preserving the ecological complexity of natural ecosystems in choice experiments, particularly when investigating predator-prey interactions in complex environments.

[EN] The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24h, and the times spent in each of these different ranges are co-dependent and carry only relative information; therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government MINECO through Grants DPI-2016-78831-C2-1-R, DPI2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7, 207688/2014-1 and EU through FEDER funds.

Pollen data from five palaeo-lacustrine basins are used in this study to correlate floral and vegetation patterns along a NW-SE transect in the Southern Apennines (Italy) during Marine Isotope Stage (MIS) 13. Compositional Data Analysis (CoDA) is applied to pollen data in order to better define and explain the main differences in the pollen composition of coeval records. Pollen data belonging to MIS 13, chosen on the basis of a constrained cluster analysis on each entire record, have been analyzed through a non-constrained cluster analysis. The relative variation biplot, the parametric and the non-parametric MANOVA computed for the MIS 13 pollen data, show that samples belonging to the different sites are distinct by their average composition. This means that each site has a distinctive character of forest landscape, which could have been the result of local environmental (physical) factors, such as mesoclimate, topography and edaphic humidity, and/or of historical factors such as species composition of refuges and temporary changes in environmental conditions due to disturbance ecology This research has been supported by the Spanish Ministry of Economy and Competitiveness under the project "METRICS" Ref. MTM2012-33236. The authors wish to thank two referees and the editor for their helpful comments

Received: 31 March 2015 Abstract Accepted: 12 May 2015 This paper introduces the most basic concepts of the compositional analysis of data with a simple but real example form the Management and Production Engineering (MPE) field. Compositional Data (CoDa) are vectors of positive elements that represent parts of a whole and are widely found in MPE, i.e. production times, resource composition, percentage utilization of work stands, waste components. . . The need for an analysis based on ratios of components (or better log-ratios of components) is illustrated step by step, and findings are compared to the corresponding standard methods applied to raw compositions. The paper also exposes the principles of CoDa analysis and presents two basic descriptive tools suitable for CoDa: the clr-biplot and the CoDa dendrogram. The example is a time series, from 1994 to 2013, of motor vehicle production in 8 countries and regions.

Compositional Isotemporal Substitution at Weight Status Subgroups (PDF 100 kb)

Characteristics of Included and Excluded Participants (PDF 91 kb)

Compositional Data Analysis Description and Sample R Code (PDF 2113 kb)

Mean Daily Behaviour Composition Across zBMI Categories (PDF 93 kb)

The efficiency of sputtered refractory elements by H+ and He++ solar wind ions from Mercury's surface and their contribution to the exosphere are studied for various solar wind conditions. A 3D solar wind-planetary interaction hybrid model is used for the evaluation of precipitation maps of the sputter agents on Mercury's surface. By assuming a global mineralogical surface composition, the related sputter yields are calculated by means of the 2013 SRIM code and are coupled with a 3D exosphere model. Because of Mercury's magnetic field, for quiet and nominal solar wind conditions the plasma can only precipitate around the polar areas, while for extreme solar events (fast solar wind, coronal mass ejections, interplanetary magnetic clouds) the solar wind plasma has access to the entire dayside. In that case the release of particles form the planet's surface can result in an exosphere density increase of more than one order of magnitude. The corresponding escape rates are also about an order of magnitude higher. Moreover, the amount of He++ ions in the precipitating solar plasma flow enhances also the release of sputtered elements from the surface in the exosphere. A comparison of our model results with MESSENGER observations of sputtered Mg and Ca elements in the exosphere shows a reasonable quantitative agreement. (C) 2015 Elsevier Ltd. All rights reserved.

OBJECTIVE: To evaluate the relationship between children's lifestyles and health-related quality of life and to explore whether this relationship varies among children from different world regions.STUDY DESIGN: This study used cross-sectional data from the International Study of Childhood Obesity, Lifestyle and the Environment. Children (9-11 years) were recruited from sites in 12 nations (n = 5759). Clustering input variables were 24-hour accelerometry and self-reported diet and screen time. Health-related quality of life was self-reported with KIDSCREEN-10. Cluster analyses (using compositional analysis techniques) were performed on a site-wise basis. Lifestyle behavior cluster characteristics were compared between sites. The relationship between cluster membership and health-related quality of life was assessed with the use of linear models.RESULTS: Lifestyle behavior clusters were similar across the 12 sites, with clusters commonly characterized by (1) high physical activity (actives); (2) high sedentary behavior (sitters); (3) high screen time/unhealthy eating pattern (junk-food screenies); and (4) low screen time/healthy eating pattern and moderate physical activity/sedentary behavior (all-rounders). Health-related quality of life was greatest in the all-rounders cluster.CONCLUSIONS: Children from different world regions clustered into groups of similar lifestyle behaviors. Cluster membership was related to differing health-related quality of life, with children from the all-rounders cluster consistently reporting greatest health-related quality of life at sites around the world. Findings support the importance of a healthy combination of lifestyle behaviors in childhood: low screen time, healthy eating pattern, and balanced daily activity behaviors (physical activity and sedentary behavior).TRIAL REGISTRATION: ClinicalTrials.gov: NCT01722500.