In order to expand our understanding of the diversity and biogeography of Prochlorococcus ribotypes, we PCR-amplified, cloned and sequenced the 16S/23S rRNA ITS region from sites in the Atlantic and Pacific oceans. Ninety-three per cent of the ITS sequences could be assigned to existing Prochlorococcus clades, although many novel subclades were detected. We assigned the sequences to operational taxonomic units using a graduated scale of sequence identity from 80% to 99.5% and correlated Prochlorococcus diversity with respect to environmental variables and dispersal time between the sites. Dispersal time was estimated using a global ocean circulation model. The significance of specific environmental variables was dependent on the degree of sequence identity used to define a taxon: light correlates with broad-scale diversity (90% cut-off), temperature with intermediate scale (95%) whereas no correlation with phosphate was observed. Community structure was correlated with dispersal time between sample sites only when taxa were defined using the finest sequence similarity cut-off. Surprisingly, the concentration of nitrate, which cannot be used as N source by the Prochlorococcus strains in culture, explains some variation in community structure for some definitions of taxa. This study suggests that the spatial distribution of Prochlorococcus ecotypes is shaped by a hierarchy of environmental factors as well dispersal limitation.

Well-established fine-scale urban mobility models today depend on detailed but cumbersome and expensive travel surveys for their calibration. Not much is known, however, about the set of mechanisms needed to generate complete mobility profiles if only using passive datasets with mostly sparse traces of individuals. In this study, we present a mechanistic modeling framework (TimeGeo) that effectively generates urban mobility patterns with resolution of 10 min and hundreds of meters. It ties together the inference of home and work activity locations from data, with the modeling of flexible activities (e.g., other) in space and time. The temporal choices are captured by only three features: the weekly home-based tour number, the dwell rate, and the burst rate. These combined generate for each individual: (i) stay duration of activities, (ii) number of visited locations per day, and (iii) daily mobility networks. These parameters capture how an individual deviates from the circadian rhythm of the population, and generate the wide spectrum of empirically observed mobility behaviors. The spatial choices of visited locations are modeled by a rank-based exploration and preferential return (r-EPR) mechanism that incorporates space in the EPR model. Finally, we show that a hierarchical multiplicative cascade method can measure the interaction between land use and generation of trips. In this way, urban structure is directly related to the observed distance of travels. This framework allows us to fully embrace the massive amount of individual data generated by information and communication technologies (ICTs) worldwide to comprehensively model urban mobility without travel surveys.