Biodiversity's big wet secret: the global distribution of marine biological records reveals chronic under-exploration of the deep pelagic ocean

Background: Understanding the distribution of marine biodiversity is a crucial first step towards the effective and sustainable management of marine ecosystems. Recent efforts to collate location records from marine surveys enable us to assemble a global picture of recorded marine biodiversity. They also effectively highlight gaps in our knowledge of particular marine regions. In particular, the deep pelagic ocean - the largest biome on Earth - is chronically under-represented in global databases of marine biodiversity. Methodology/Principal Findings: We use data from the Ocean Biogeographic Information System to plot the position in the water column of ca 7 million records of marine species occurrences. Records from relatively shallow waters dominate this global picture of recorded marine biodiversity. In addition, standardising the number of records from regions of the ocean differing in depth reveals that regardless of ocean depth, most records come either from surface waters or the sea bed. Midwater biodiversity is drastically under-represented. Conclusions/Significance: The deep pelagic ocean is the largest habitat by volume on Earth, yet it remains biodiversity's big wet secret, as it is hugely under-represented in global databases of marine biological records. Given both its value in the provision of a range of ecosystem services, and its vulnerability to threats including overfishing and climate change, there is a pressing need to increase our knowledge of Earth's largest ecosystem

Potential utility of chloroplast trnL (UAA) gene intron sequences for inferring phylogeny in Scrophulariaceae

Whereas chloroplast DNA-derived sequence data from protein coding regions have been utilized successfully at many taxonomic levels in recent years, sequences which are variable enough to allow for efficient phylogenetic inference (maximum information with relatively low sequencing costs and effort) at the subfamilial level huve been few. Sequence data were obtained in this study from a noncoding region, the trnL (UAA) gene intron, from a selection of taxa from the Scrophulariaceae and closely related families (representing 41 species in 26 genera). Groups of species from commonly recognized tribes and subtribes were included to determine if these taxa were grouped together by analyses of this sequence. These included seven species from the tribe Cheloneae. 14 species of the tribe Antirrhineae, and four from the tribe Euphraseae or Pediculareae, Also included are representatives of Bignoniaceae (four species, each in a different genus ), and the outgroup Anisacanthus thurberi of the Acanthaceae. These taxa were examined to estimate the potential utility of this sequence data set for subfamilial phylogenetic reconstruction. In the majority rule consensus tree, taxa of the tribe Cheloneae (the North American representatives) form a monophyletic clade and generally conform to previous systematic hypotheses from the literature. Sampled taxa of the tribe Antirrhineae (with the exception of Linaria) also appear as a potentially monophyletic clade, with support for the subtribes Antirrhinae and Maurandynae. Based on these sequence data wc recommend reassessment o f some generic placements at the tribal level. The genera Verbascum and Scrophularia appear on the same, well-supported clade and Veronica and Digitalis occur together on another well-supported clade. The trnL intron sequence comparisons indicate that this relatively short region (about 500 bp) may be useful in studies of phylogeny within Scrophulariaceae and allied taxa at the suprageneric level

Future benefits and applications of intelligent on-board processing to VSAT services

The trends and roles of VSAT services in the year 2010 time frame are examined based on an overall network and service model for that period. An estimate of the VSAT traffic is then made and the service and general network requirements are identified. In order to accommodate these traffic needs, four satellite VSAT architectures based on the use of fixed or scanning multibeam antennas in conjunction with IF switching or onboard regeneration and baseband processing are suggested. The performance of each of these architectures is assessed and the key enabling technologies are identified

\u3cem\u3eXMM-Newton\u3c/em\u3e discovery of the X-ray transient XMMU J181227.8-181234 in the Galactic plane

We report the discovery of an X-ray transient, observed in outburst with XMM-Newton on 2003 March 20, and with position (J2000, approximate positional error 2arcsec). No known source is present at this position and the source was not detected during published ROSAT or ASCA observations of that region. However, the source may be associated with 1H1812-182 detected by HEAO 1, although the error bars on the HEAO 1 position are very large and the two sources could also be unrelated. Therefore, we name the source XMMU J181227.8-181234. Initially, the source was not detected using the All-Sky Monitor (ASM) on-board the Rossi X-ray Timing Explorer, however, reprocessing of the ASM data shows that the source was in fact detected and it was active for about 50d. The X-ray spectrum of this transient is fitted equally well by an absorbed power law (with a spectral index of 2.5) or multicolour disc blackbody model (with kT ~ 2keV), where we find that the source is highly absorbed. We detect an unabsorbed 0.5-10keV flux in the range (2-5) × 10-9ergcm-2s-1, which at a distance of 8kpc corresponds to a 0.5-10keV luminosity of (1-4) × 1037ergs-1. No pulsations were detected by timing analysis. A colour-colour diagram from ASM data of different accreting objects suggests that the transient is a high-mass X-ray binary, as is also suggested by the high absorption compared to the average interstellar value in the direction of the source. However, the power-law spectral index is far more typical of a low-mass X-ray binary. Thus, we are unable to conclusively identify the nature of the transient. We also report on three sources first detected by the ASCA Galactic Plane Survey that are close to this transient

Surface Reflectance Estimation and Natural Illumination Statistics

Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks automatically. Reflectance estimation under unknown, arbitrary illumination proves highly underconstrained due to the variety of potential illumination distributions and surface reflectance properties. We have found that the spatial structure of real-world illumination possesses some of the statistical regularities observed in the natural image statistics literature. A human or computer vision system may be able to exploit this prior information to determine the most likely surface reflectance given an observed image. We develop an algorithm for reflectance classification under unknown real-world illumination, which learns relationships between surface reflectance and certain features (statistics) computed from a single observed image. We also develop an automatic feature selection method

Recognition of Surface Reflectance Properties from a Single Image under Unknown Real-World Illumination

This paper describes a machine vision system that classifies reflectance properties of surfaces such as metal, plastic, or paper, under unknown real-world illumination. We demonstrate performance of our algorithm for surfaces of arbitrary geometry. Reflectance estimation under arbitrary omnidirectional illumination proves highly underconstrained. Our reflectance estimation algorithm succeeds by learning relationships between surface reflectance and certain statistics computed from an observed image, which depend on statistical regularities in the spatial structure of real-world illumination. Although the algorithm assumes known geometry, its statistical nature makes it robust to inaccurate geometry estimates

How do Humans Determine Reflectance Properties under Unknown Illumination?

Under normal viewing conditions, humans find it easy to distinguish between objects made out of different materials such as plastic, metal, or paper. Untextured materials such as these have different surface reflectance properties, including lightness and gloss. With single isolated images and unknown illumination conditions, the task of estimating surface reflectance is highly underconstrained, because many combinations of reflection and illumination are consistent with a given image. In order to work out how humans estimate surface reflectance properties, we asked subjects to match the appearance of isolated spheres taken out of their original contexts. We found that subjects were able to perform the task accurately and reliably without contextual information to specify the illumination. The spheres were rendered under a variety of artificial illuminations, such as a single point light source, and a number of photographically-captured real-world illuminations from both indoor and outdoor scenes. Subjects performed more accurately for stimuli viewed under real-world patterns of illumination than under artificial illuminations, suggesting that subjects use stored assumptions about the regularities of real-world illuminations to solve the ill-posed problem