146,011 research outputs found
Intraspecific trait variation and coordination: Root and leaf economics spectra in coffee across environmental gradients
Hypotheses on the existence of a universal “Root Economics Spectrum” (RES) have received arguably the least attention of all trait spectra, despite the key role root trait variation plays in resource acquisition potential. There is growing interest in quantifying intraspecific trait variation (ITV) in plants, but there are few studies evaluating (i) the existence of an intraspecific RES within a plant species, or (ii) how a RES may be coordinated with other trait spectra within species, such as a leaf economics spectrum (LES). Using Coffea arabica (Rubiaceae) as a model species, we measured seven morphological and chemical traits of intact lateral roots, which were paired with information on four key LES traits. Field collections were completed across four nested levels of biological organization. The intraspecific trait coefficient of variation (cv) ranged from 25 to 87% with root diameter and specific root tip density showing the lowest and highest cv, respectively. Between 27 and 68% of root ITV was explained by site identity alone for five of the seven traits measured. A single principal component explained 56.2% of root trait covariation, with plants falling along a RES from resource acquiring to conserving traits. Multiple factor analysis revealed significant orthogonal relationships between root and leaf spectra. RES traits were strongly orthogonal with respect to LES traits, suggesting these traits vary independently from one another in response to environmental cues. This study provides among the first evidence that plants from the same species differentiate from one another along an intraspecific RES. We find that in one of the world's most widely cultivated crops, an intraspecific RES is orthogonal to an intraspecific LES, indicating that above and belowground responses of plants to managed (or natural) environmental gradients are likely to occur independently from one another. (Résumé d'auteur
Security and VO management capabilities in a large-scale Grid operating system
This paper presents a number of security and VO management capabilities in a large-scale distributed Grid operating system. The capabilities formed the basis of the design and implementation of a number of security and VO management services in the system. The main aim of the paper is to provide some idea of the various functionality cases that need to be considered when designing similar large-scale systems in the future
Technic and Collaboration Breakdown Structures: Drivers of collaborative problem solving approaches in a supply chain context
Problem Solving Methodologies have been par excellence a cornerstone element of the firms’ strategy on achieving effective continuous improvement. But the enterprise evolution towards an extended environment characterized by network-based organization has radically changed the problem solving paradigms. This paper aims to propose a generic and collaborative methodology addressing more complex and distributed problems, dealing with Supply Chain issues and having a key role as a driver for building global competitive advantages and create superior performances at a Supply Chain level
ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization
ROOT is an object-oriented C++ framework conceived in the high-energy physics
(HEP) community, designed for storing and analyzing petabytes of data in an
efficient way. Any instance of a C++ class can be stored into a ROOT file in a
machine-independent compressed binary format. In ROOT the TTree object
container is optimized for statistical data analysis over very large data sets
by using vertical data storage techniques. These containers can span a large
number of files on local disks, the web, or a number of different shared file
systems. In order to analyze this data, the user can chose out of a wide set of
mathematical and statistical functions, including linear algebra classes,
numerical algorithms such as integration and minimization, and various methods
for performing regression analysis (fitting). In particular, ROOT offers
packages for complex data modeling and fitting, as well as multivariate
classification based on machine learning techniques. A central piece in these
analysis tools are the histogram classes which provide binning of one- and
multi-dimensional data. Results can be saved in high-quality graphical formats
like Postscript and PDF or in bitmap formats like JPG or GIF. The result can
also be stored into ROOT macros that allow a full recreation and rework of the
graphics. Users typically create their analysis macros step by step, making use
of the interactive C++ interpreter CINT, while running over small data samples.
Once the development is finished, they can run these macros at full compiled
speed over large data sets, using on-the-fly compilation, or by creating a
stand-alone batch program. Finally, if processing farms are available, the user
can reduce the execution time of intrinsically parallel tasks - e.g. data
mining in HEP - by using PROOF, which will take care of optimally distributing
the work over the available resources in a transparent way
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