Supernova Enrichment of Dwarf Spheroidal Galaxies

(Abridged) Many dwarf galaxies exhibit sub-Solar metallicities, with some star-to-star variation, despite often containing multiple generations of stars. The total metal content in these systems is much less than expected from the heavy element production of massive stars in each episode of star formation. Such a deficiency implies that a substantial fraction of the enriched material has been lost from these small galaxies. Mass ejection from dwarf galaxies may have important consequences for the evolution of the intergalactic medium and for the evolution of massive galaxies, which themselves may have formed via the merger of smaller systems. We report here the results of three-dimensional simulations of the evolution of supernova-enriched gas within dwarf spheroidal galaxies (dSph's), with the aim of determining the retention efficiency of supernova ejecta. We consider two galaxy models, selected to represent opposite ends of the dSph sequence. For each model galaxy we investigate a number of scenarios, ranging from a single supernova in smooth gas distributions to more complex multiple supernovae in highly disturbed gas distributions. The results of these investigations suggest that, for low star-formation efficiencies, it is difficult to completely expel the enriched material from the galaxy. Most of the enriched gas is, however, lost from the core of the galaxy following multiple supernovae, especially if the interstellar medium is already highly disturbed by processes such as photo-ionization and stellar winds. If subsequent star formation occurs predominantly within the core where most of the residual gas is concentrated, then these results could explain the poor self-enrichment efficiency observed in dwarf galaxies.Comment: 29 pages, 10 figures, to appear in Astrophysical Journa

Spitzer/MIPS Infrared Imaging of M31: Further Evidence for a Spiral/Ring Composite Structure

New images of M31 at 24, 70, and 160 micron taken with the Multiband Imaging Photometer for Spitzer (MIPS) reveal the morphology of the dust in this galaxy. This morphology is well represented by a composite of two logarithmic spiral arms and a circular ring (radius ~10 kpc) of star formation offset from the nucleus. The two spiral arms appear to start at the ends of a bar in the nuclear region and extend beyond the star forming ring. As has been found in previous work, the spiral arms are not continuous but composed of spiral segments. The star forming ring is very circular except for a region near M32 where it splits. The lack of well defined spiral arms and the prominence of the nearly circular ring suggests that M31 has been distorted by interactions with its satellite galaxies. Using new dynamical simulations of M31 interacting with M32 and NGC 205 we find that, qualitatively, such interactions can produce an offset, split ring like that seen in the MIPS images.Comment: 7 pages, 4 figures, ApJ Letters, in press (preprint with full resolution images available at http://dirty.as.arizona.edu/~kgordon/papers/PS_files/m31_mips_letter.pdf

Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment

Land use and related pressures have reduced local terrestrial biodiversity, but it is unclear how the magnitude of change relates to the recently proposed planetary boundary (“safe limit”). We estimate that land use and related pressures have already reduced local biodiversity intactness—the average proportion of natural biodiversity remaining in local ecosystems—beyond its recently proposed planetary boundary across 58.1% of the world’s land surface, where 71.4% of the human population live. Biodiversity intactness within most biomes (especially grassland biomes), most biodiversity hotspots, and even some wilderness areas is inferred to be beyond the boundary. Such widespread transgression of safe limits suggests that biodiversity loss, if unchecked, will undermine efforts toward long-term sustainable development

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost