The TimeGeo modeling framework for urban motility without travel surveys.

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.

Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus.

The Cyanobacteria Prochlorococcus and Synechococcus account for a substantial fraction of marine primary production. Here, we present quantitative niche models for these lineages that assess present and future global abundances and distributions. These niche models are the result of neural network, nonparametric, and parametric analyses, and they rely on >35,000 discrete observations from all major ocean regions. The models assess cell abundance based on temperature and photosynthetically active radiation, but the individual responses to these environmental variables differ for each lineage. The models estimate global biogeographic patterns and seasonal variability of cell abundance, with maxima in the warm oligotrophic gyres of the Indian and the western Pacific Oceans and minima at higher latitudes. The annual mean global abundances of Prochlorococcus and Synechococcus are 2.9 ± 0.1 × 10(27) and 7.0 ± 0.3 × 10(26) cells, respectively. Using projections of sea surface temperature as a result of increased concentration of greenhouse gases at the end of the 21st century, our niche models projected increases in cell numbers of 29% and 14% for Prochlorococcus and Synechococcus, respectively. The changes are geographically uneven but include an increase in area. Thus, our global niche models suggest that oceanic microbial communities will experience complex changes as a result of projected future climate conditions. Because of the high abundances and contributions to primary production of Prochlorococcus and Synechococcus, these changes may have large impacts on ocean ecosystems and biogeochemical cycles.

Metapopulation structure of Vibrionaceae among coastal marine invertebrates.

Although animal-associated microbial communities (microbiomes) are increasingly recognized to influence health, the extent to which animals represent highly selective habitats for microbes leading to predominance of high host specificity remains poorly understood. Here, we show that vibrios, which are well-known commensals and opportunistic pathogens of marine animals, overall display little host preference, likely because of efficient dispersal-colonization dynamics mediated by food items. We isolated 1753 strains from water and animal samples, which are linked in a food chain and display different degrees of similarity (respiratory and digestive tract of mussels and crabs, live and dead zooplankton, and whole water samples). Multilocus sequence data served as input for modelling and statistical analysis of spatiotemporal population structure. These data showed that the majority of populations occurred broadly within and among hosts, with the dominant population being a near perfect generalist with regard to seasons, host taxa and body regions. Zooplankton harboured the fewest and most specific populations, while crabs and mussels contained the highest diversity with little evidence for host preferences. Most mussel- and crab-associated populations were detected in water samples at similar frequencies, particularly in filter-feeding mussels where populations were also evenly distributed across host individuals. The higher variation among individuals observed in crabs and zooplankton is consistent with stochastic clonal expansions. These patterns suggest that evolution of a high degree of host specificity is surprisingly rare even though these animals represent long-lived habitats, and vibrios are consistent members of their microbiome. Instead, many of the populations show stronger association with planktonic (micro)habitats while the microbiome may be a fairly open system for vibrios in which high rates of immigration can outpace selection for specialization.

Taxonomic resolution, ecotypes and the biogeography of Prochlorococcus.

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.

Accurately quantifying low-abundant targets amid similar sequences by revealing hidden correlations in oligonucleotide microarray data.

Microarrays have enabled the determination of how thousands of genes are expressed to coordinate function within single organisms. Yet applications to natural or engineered communities where different organisms interact to produce complex properties are hampered by theoretical and technological limitations. Here we describe a general method to accurately identify low-abundant targets in systems containing complex mixtures of homologous targets. We combined an analytical predictor of nonspecific probe-target interactions (cross-hybridization) with an optimization algorithm that iteratively deconvolutes true probe-target signal from raw signal affected by spurious contributions (cross-hybridization, noise, background, and unequal specific hybridization response). The method was capable of quantifying, with unprecedented specificity and accuracy, ribosomal RNA (rRNA) sequences in artificial and natural communities. Controlled experiments with spiked rRNA into artificial and natural communities demonstrated the accuracy of identification and quantitative behavior over different concentration ranges. Finally, we illustrated the power of this methodology for accurate detection of low-abundant targets in natural communities. We accurately identified Vibrio taxa in coastal marine samples at their natural concentrations (<0.05% of total bacteria), despite the high potential for cross-hybridization by hundreds of different coexisting rRNAs, suggesting this methodology should be expandable to any microarray platform and system requiring accurate identification of low-abundant targets amid pools of similar sequences.