Patterns of life in the ocean wave: The open ocean is not uniform. The seas teem with unseen life that has evolved distinctive patterns of species distribution, much as the land-bound world contains a spectrum of biological provinces. Taking data from genome studies, Hellweger et al. devised a model showing how marine bacteria diversified throughout the world's oceans into stable geographic provinces independently of natural selection (see the Perspective by Giovannoni and Nemergut). All that is needed to create these distributions are mutations and ocean currents. Science , this issue p. 1346 ; see also p. 1246

An important unanswered question in evolutionary genomics is the source of considerable variation of genomic base composition (GC content) even among organisms that share one habitat. Evolution toward GC-poor genomes has been considered a major adaptive pathway in the oligotrophic ocean, but GC-rich bacteria are also prevalent and highly successful in this environment. We quantify the contribution of multiple factors to the change of genomic GC content of Ruegeria pomeroyi DSS-3, a representative and GC-rich member in the globally abundant Roseobacter clade, using an agent-based model. The model simulates 2 × 10⁸ cells, which allows random genetic drift to act in a realistic manner. Each cell has a whole genome subject to base-substitution mutation and recombination, which affect the carbon and nitrogen requirements of DNA and protein pools. Nonsynonymous changes can be functionally deleterious. Together, these factors affect the growth and fitness. Simulations show that experimentally determined mutation bias toward GC is not sufficient to build the GC-rich genome of DSS-3. While nitrogen availability has been repeatedly hypothesized to drive the evolution of GC content in marine bacterioplankton, our model instead predicts that DSS-3 and its ancestors have been evolving in environments primarily limited by carbon.

Model subdivision is used to capture spatial heterogeneity in input parameters and it is well-established that spatial resolution (i.e., degree of aggregation) affects model output. However, a general consensus about the effect does not exist. The objective of this study was to investigate the effects of spatial resolution on model predictions in an urban catchment, and to understand the mechanism(s) responsible for the scale effect. The general approach is to develop models at various spatial resolutions, perform simulations, and compare the predictions of total outflow volume and peak flow. Models were developed on the basis of actual drainage networks, and artificial ones generated on the basis of a fractal algorithm by using the Artificial Network Generator (ANGel). Simulations were performed by using the EPA Storm Water Management Model (SWMM), and model output was compared for 50 storms. There was very little difference in the total annual outflow volumes predicted by the different resolutions. Howe...