Large-scale spatial patterns in the distribution of Collembola (Hexapoda) species in Antarctic terrestrial ecosystems

We tested whether the distribution of three common springtail species (Gressittacantha terranova, Gomphiocephalus hodgsoni and Friesea grisea) in Victoria Land (Antarctica) could be modelled as a function of latitude, longitude, altitude and distance from the sea.

Victoria Land, Ross Dependency, Antarctica.

Generalized linear models were constructed using species presence/absence data relative to geographical features (latitude, longitude, altitude, distance from sea) across the species' entire ranges. Model results were then integrated with the known phylogeography of each species and hypotheses were generated on the role of climate as a major driver of Antarctic springtail distribution.

Based on model selection using Akaike's information criterion, the species' distributions were: hump-shaped relative to longitude and monotonic with altitude for Gressittacantha terranova; hump-shaped relative to latitude and monotonic with altitude for Gomphiocephalus hodgsoni; and hump-shaped relative to longitude and monotonic with latitude, altitude and distance from the sea for Friesea grisea.

No single distributional pattern was shared by the three species. While distributions were partially a response to climatic spatial clines, the patterns observed strongly suggest that past geological events have influenced the observed distributions. Accordingly, present-day spatial patterns are likely to have arisen from the interaction of historical and environmental drivers. Future studies will need to integrate a range of spatial and temporal scales to further quantify their respective roles.

Large-scale density functional calculations of the surface properties of the Wigner crystal

The surface properties of the jellium model have been investigated by large supercell computations in the density functional theory-local spin-density (DFT-LSD) approach for planar slabs with up to 1000 electrons. A wide interval of densities has been explored, extending into the stability range of the Wigner crystal. Most computations have been carried out on nominally paramagnetic samples with an equal number of spin-up and spin-down electrons. The results show that within DFT-LSD spontaneous spin polarization and charge localization start nearly simultaneously at the surface for r(s) similar to 20, then, with decreasing density, they progress toward the center of the slab. Electrons are fully localized and spin polarized at r(s) = 30. At this density the charge distribution is the superposition of disjoint charge blobs, each corresponding to one electron. The distribution of blobs displays both regularities and disorder, the first being represented by well-defined planes and simple in-plane geometries, and the latter by a variety of surface defects. The surface energy, surface dipole, electric polarisability, and magnetization pattern have been determined as a function of density. All these quantities display characteristic anomalies at the density of the localization transition. The analysis of the low-frequency electric conductivity shows that in the fluid paramagnetic regime the in-plane current preferentially flows in the central region of the slab and the two spin channels are equally conducting. In the charge localized, spin-polarized regime, conductivity is primarily a surface effect, and an apparent asymmetry is observed in the two spin currents.

The broad-scale distribution of five jellyfish species across a temperate coastal environment

Jellyfish (medusae) are sometimes the most noticeable and abundant members of coastal planktonic communities, yet ironically, this high conspicuousness is not reflected in our overall understanding of their spatial distributions across large expanses of water. Here, we set out to elucidate the spatial (and temporal) patterns for five jellyfish species (Phylum Cnidaria, Orders Rhizostomeae and Semaeostomeae) across the Irish & Celtic Seas, an extensive shelf-sea area at Europe's northwesterly margin encompassing several thousand square kilometers. Data were gathered using two independent methods: (1) surface-counts of jellyfish from ships of opportunity, and (2) regular shoreline surveys for stranding events over three consecutive years. Jellyfish species displayed distinct species-specific distributions, with an apparent segregation of some species. Furthermore, a different species composition was noticeable between the northern and southern parts of the study area. Most importantly, our data suggests that jellyfish distributions broadly reflect the major hydrographic regimes (and associated physical discontinuities) of the study area, with mixed water masses possibly acting as a trophic barrier or non-favourable environment for the successful growth and reproduction of jellyfish species.

Received signal strength in large-scale wireless relay sensor network: a stochastic ray approach

The authors consider a point percolation lattice representation of a large-scale wireless relay sensor network (WRSN) deployed in a cluttered environment. Each relay sensor corresponds to a grid point in the random lattice and the signal sent by the source is modelled as an ensemble of photons that spread in the space, which may 'hit' other sensors and are 'scattered' around. At each hit, the relay node forwards the received signal to its nearest neighbour through direction-selective relaying. The authors first derive the distribution that a relay path reaches a prescribed location after undergoing certain number of hops. Subsequently, a closed-form expression of the average received signal strength (RSS) at the destination can be computed as the summation of all signal echoes' energy. Finally, the effect of the anomalous diffusion exponent ß on the mean RSS in a WRSN is studied, for which it is found that the RSS scaling exponent e is given by (3ß-1)/ß. The results would provide useful insight into the design and deployment of large-scale WRSNs in future. © 2011 The Institution of Engineering and Technology.

Large‐scale wind deployment, social acceptance

The public is typically in agreement with the renewable energy targets established in many national states and generally supports the idea of increased reliance on wind energy. Nevertheless, many specific wind power projects face significant local opposition. A key question for the wind energy sector is, therefore, how to better engage local people to foster support for specific projects. IEA Wind Task 28 on Social Acceptance of Wind Energy Projects aims to facilitate wind energy development by reviewing current practices, emerging ideas, and exchanging successful practices among the participating countries. It also aims to disseminate the insights of leading research to a nontechnical audience, including project developers, local planning officials, and the general public. The interdisciplinary approach adopted by Task 28 enables an in-depth understanding of the nature of opposition to wind projects and a critical assessment of emerging strategies for social acceptance. Task 28 has analyzed a range of key issues related to social acceptance of wind energy, including the impacts on landscapes and ecosystems, on standard of living and well-being, the implementation of energy policy and spatial planning, the distribution of costs and benefits, and procedural justice. It is clear that although wind energy has many benefits; however, specific projects do impact local communities. As such the concerns of the affected people have to be taken seriously. Moreover, as opposition is rarely without foundation, it is in the interests of developers and advocates to engage local people and to improve projects for the benefit of all.

The analysis of large-scale turbulence characteristics in the Indonesian seas derived from a regional model based on the Princeton Ocean Model

Turbulence characteristics in the Indonesian seas on the horizontal scale of order of 100 km were calculated with a regional model of the Indonesian seas circulation in the area based on the Princeton Ocean Model (POM). As is well known, the POM incorporates the Mellor–Yamada turbulence closure scheme. The calculated characteristics are: twice the turbulence kinetic energy per unit mass, <i>q</i>²; the turbulence master scale, &ell;; mixing coefficients of momentum, <i>K</i>_M; and temperature and salinity, <i>K</i>_H; etc. The analyzed turbulence has been generated essentially by the shear of large-scale ocean currents and by the large-scale wind turbulence. We focused on the analysis of turbulence around important topographic features, such as the Lifamatola Sill, the North Sangihe Ridge, the Dewakang Sill, and the North and South Halmahera Sea Sills. In general, the structure of turbulence characteristics in these regions turned out to be similar. For this reason, we have carried out a detailed analysis of the Lifamatola Sill region because dynamically this region is very important and some estimates of mixing coefficients in this area are available. <br><br> Briefly, the main results are as follows. The distribution of <i>q</i>² is quite adequately reproduced by the model. To the north of the Lifamatola Sill (in the Maluku Sea) and to the south of the Sill (in the Seram Sea), large values of <i>q</i>² occur in the deep layer extending several hundred meters above the bottom. The observed increase of <i>q</i>² near the very bottom is probably due to the increase of velocity shear and the corresponding shear production of <i>q</i>² very close to the bottom. The turbulence master scale, &ell;, was found to be constant in the main depth of the ocean, while &ell; rapidly decreases close to the bottom, as one would expect. However, in deep profiles away from the sill, the effect of topography results in the &ell; structure being unreasonably complicated as one moves towards the bottom. Values of 15 to 20 × 10^&minus;4 m² s^-1 were obtained for <i>K</i>_M and <i>K</i>_H in deep water in the vicinity of the Lifamatola Sill. These estimates agree well with basin-scale averaged values of 13.3 × 10^&minus;4 m² s^-1 found diagnostically for <i>K</i>_H in the deep Banda and Seram Seas (Gordon et al., 2003) and a value of 9.0 × 10^&minus;4 m² s^-1 found diagnostically for <i>K</i>_H for the deep Banda Sea system (van Aken et al., 1988). The somewhat higher simulated values can be explained by the presence of steep topography around the sill.

A Scalable General Purpose System for Large-Scale Graph Processing

Graph analytics is an important and computationally demanding class of data analytics. It is essential to balance scalability, ease-of-use and high performance in large scale graph analytics. As such, it is necessary to hide the complexity of parallelism, data distribution and memory locality behind an abstract interface. The aim of this work is to build a scalable graph analytics framework that does not demand significant parallel programming experience based on NUMA-awareness.
The realization of such a system faces two key problems:
(i)~how to develop a scale-free parallel programming framework that scales efficiently across NUMA domains; (ii)~how to efficiently apply graph partitioning in order to create separate and largely independent work items that can be distributed among threads.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Analysis of Extended Partial Least Squares for Monitoring Large-Scale Processes

This paper theoretically analysis the recently proposed "Extended Partial Least Squares" (EPLS) algorithm. After pointing out some conceptual deficiencies, a revised algorithm is introduced that covers the middle ground between Partial Least Squares and Principal Component Analysis. It maximises a covariance criterion between a cause and an effect variable set (partial least squares) and allows a complete reconstruction of the recorded data (principal component analysis). The new and conceptually simpler EPLS algorithm has successfully been applied in detecting and diagnosing various fault conditions, where the original EPLS algorithm did only offer fault detection.