The cosmic evolution of the spatially-resolved star formation rate and stellar mass of the CALIFA survey

We investigate the cosmic evolution of the absolute and specific star formation rate (SFR, sSFR) of galaxies as derived from a spatially-resolved study of the stellar populations in a set of 366 nearby galaxies from the CALIFA survey. The analysis combines GALEX and SDSS images with the 4000 break, H_beta, and [MgFe] indices measured from the datacubes, to constrain parametric models for the SFH, which are then used to study the cosmic evolution of the star formation rate density (SFRD), the sSFR, the main sequence of star formation (MSSF), and the stellar mass density (SMD). A delayed-tau model, provides the best results, in good agreement with those obtained from cosmological surveys. Our main results from this model are: a) The time since the onset of the star formation is larger in the inner regions than in the outer ones, while tau is similar or smaller in the inner than in the outer regions. b) The sSFR declines rapidly as the Universe evolves, and faster for early than for late type galaxies, and for the inner than for the outer regions of galaxies. c) SFRD and SMD agree well with results from cosmological surveys. At z< 0.5, most star formation takes place in the outer regions of late spiral galaxies, while at z>2 the inner regions of the progenitors of the current E and S0 are the major contributors to SFRD. d) The inner regions of galaxies are the major contributor to SMD at z> 0.5, growing their mass faster than the outer regions, with a lookback time at 50% SMD of 9 and 6 Gyr for the inner and outer regions. e) The MSSF follows a power-law at high redshift, with the slope evolving with time, but always being sub-linear. f) In agreement with galaxy surveys at different redshifts, the average SFH of CALIFA galaxies indicates that galaxies grow their mass mainly in a mode that is well represented by a delayed-tau model, with the peak at z~2 and an e-folding time of 3.9 Gyr.Comment: 23 pages, 16 figures, 6 tables, accepted for publication in Astronomy & Astrophysics. *Abridged abstract

Census of HII regions in NGC 6754 derived with MUSE: Constraints on the metal mixing scale

We present a study of the HII regions in the galaxy NGC 6754 from a two pointing mosaic comprising 197,637 individual spectra, using Integral Field Spectrocopy (IFS) recently acquired with the MUSE instrument during its Science Verification program. The data cover the entire galaxy out to ~2 effective radii (re ), sampling its morphological structures with unprecedented spatial resolution for a wide-field IFU. A complete census of the H ii regions limited by the atmospheric seeing conditions was derived, comprising 396 individual ionized sources. This is one of the largest and most complete catalogue of H ii regions with spectroscopic information in a single galaxy. We use this catalogue to derive the radial abundance gradient in this SBb galaxy, finding a negative gradient with a slope consistent with the characteristic value for disk galaxies recently reported. The large number of H ii regions allow us to estimate the typical mixing scale-length (rmix ~0.4 re ), which sets strong constraints on the proposed mechanisms for metal mixing in disk galaxies, like radial movements associated with bars and spiral arms, when comparing with simulations. We found evidence for an azimuthal variation of the oxygen abundance, that may be related with the radial migration. These results illustrate the unique capabilities of MUSE for the study of the enrichment mechanisms in Local Universe galaxies.Comment: 13 pages, 7 Figurs, accepted for publishing in A&

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species interactions within an ecology. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for such a methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining approach allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases