Population persistence under advection-diffusion in river networks

An integro-differential equation on a tree graph is used to model the evolution and spatial distribution of a population of organisms in a river network. Individual organisms become mobile at a constant rate, and disperse according to an advection-diffusion process with coefficients that are constant on the edges of the graph. Appropriate boundary conditions are imposed at the outlet and upstream nodes of the river network. The local rates of population growth/decay and that by which the organisms become mobile, are assumed constant in time and space. Imminent extinction of the population is understood as the situation whereby the zero solution to the integro-differential equation is stable. Lower and upper bounds for the eigenvalues of the dispersion operator, and related Sturm-Liouville problems are found, and therefore sufficient conditions for imminent extinction are given in terms of the physical variables of the problem

Mass Exchange Dynamics of Surface and Subsurface Oil in Shallow-Water Transport

We formulate a model for the mass exchange between oil at and below the sea surface. This is a particularly important aspect of modeling oil spills. Surface and subsurface oil have different chemical and transport characteristics and lumping them together would compromise the accuracy of the resulting model. Without observational or computational constraints, it is thus not possible to quantitatively predict oil spills based upon partial field observations of surface and/or sub-surface oil. The primary challenge in capturing the mass exchange is that the principal mechanisms are on the microscale. This is a serious barrier to developing practical models for oil spills that are capable of addressing questions regarding the fate of oil at the large spatio-temporal scales, as demanded by environmental questions. We use upscaling to propose an environmental-scale model which incorporates the mass exchange between surface and subsurface oil due to oil droplet dynamics, buoyancy effects, and sea surface and subsurface mechanics. While the mass exchange mechanism detailed here is generally applicable to oil transport models, it addresses the modeling needs of a particular to an oil spill model [1]. This transport model is designed to capture oil spills at very large spatio-temporal scales. It accomplishes this goal by specializing to shallow-water environments, in which depth averaging is a perfectly good approximation for the flow, while at the same time retaining mass conservation of oil over the whole oceanic domain.Comment: 18 pages, 6 figure

2MASS J18082002-5104378: The brightest (V=11.9) ultra metal-poor star

Context. The most primitive metal-poor stars are important for studying the conditions of the early galaxy and are also relevant to big bang nucleosynthesis. Aims. Our objective is to find the brightest (V<14) most metal-poor stars. Methods. Candidates were selected using a new method, which is based on the mismatch between spectral types derived from colors and observed spectral types. They were observed first at low resolution with EFOSC2 at the NTT/ESO to obtain an initial set of stellar parameters. The most promising candidate, 2MASS J18082002-5104378 (V=11.9), was observed at high resolution (R=50 000) with UVES at the VLT/ESO, and a standard abundance analysis was performed. Results. We found that 2MASS J18082002-5104378 is an ultra metal-poor star with stellar parameters Teff = 5440 K, log g = 3.0 dex, vt = 1.5 km/s, [Fe/H] = -4.1 dex. The star has [C/Fe]<+0.9 in a 1D analysis, or [C/Fe]<=+0.5 if 3D effects are considered; its abundance pattern is typical of normal (non-CEMP) ultra metal-poor stars. Interestingly, the star has a binary companion. Conclusions. 2MASS J1808-5104 is the brightest (V=11.9) metal-poor star of its category, and it could be studied further with even higher S/N spectroscopy to determine additional chemical abundances, thus providing important constraints to the early chemical evolution of our Galaxy.Comment: A&A Letter

Probabilistic Measures for Biological Adaptation and Resilience

The importance of understanding and predicting biological resilience is growing as we become increasingly aware of the consequences of changing climatic conditions. However, various approaches to operationalize resilience have been proposed. Here, we adapt a statistical mechanical framework for the time dependent dynamics of biological systems that offers a powerful conceptualization of systems whose response share similarities with heterogeneous forced/dissipative physical systems. In this framework we are concerned with the dynamics of a probabilistic description of observables. In this study we propose and derive a quantitative measure of adaptive resilience. Unlike more common resilience measures, ours takes into account the variability of the time history of the dynamics and the heterogeneity of the organism. Once a measure of success is proposed it quantifies the degree to which a biological system succeeds to adapt to new conditions after being stressed