Daniele Veneziano

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 × 1027 and 7.0 ± 0.3 × 1026 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.

The effect of discontinuity persistence on rock slope stability

Discontinuity persistence has a major effect on rock mass resistance (strength) but, as direct mapping of discontinuities internal to a rock mass is not possible, persistence is a difficult parameter to measure. As a result, the conservative approach of assuming full persistence is often taken. In this paper a method is developed for relating rock mass stability and hence persistence to the geometry and spatial variability of discontinuities. The method is applied to slope stability calculations in which the probability of failure is related to discontinuity data, as obtained in joint surveys. The complete method makes use of dynamic programming and simulation, but a closed form expression satisfactory for most purposes is also presented.

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.

Nonlinearity and self‐similarity of rainfall in time and a stochastic model

We use physical arguments and statistical analysis to formulate stochastic models of rainfall intensity in time during intense convective storms. Special attention is given to the issue of multiplicative versus additive model structure and to the type of self-similarity that can be displayed by rainfall under the constraints of stationarity and nonnegativity. We show that some multiscaling models proposed in the past do not satisfy these constraints. Using a set of six high-resolution records, we find that the best fitting models are multiplicative, with a log (rain rate) spectrum of the segmented power type, i.e., of the form |ω| -β , with β that varies in different frequency ranges. Four spectral regimes are identified between scales from a few seconds to several hours. Nowhere do we find a log (rain rate) spectrum of the |ω| -1 type or scaling of the moments, which would be consistent with a conserved multifractal model. Other moment and spectral analyses as well as theoretical arguments lead us to reject also nonconserved multiscaling representations. The stochastic model we finally propose is lognormal with a segmented log spectrum and is not scaling. Two versions of the model are considered, one stationary for the central portion of the storm and the other nonstationary to include the buildup and decay phases. Compared to existing alternatives, the model is very easy to fit to data and to simulate.

Intensity‐duration‐frequency curves from scaling representations of rainfall

[1] We develop methods to estimate the intensity-duration-frequency (IDF) curves for three rainfall models with local multifractal behavior and varying complexity. The models use the classical notion of exterior and interior process, respectively, for the variation of rainfall intensity at (approximately) storm and substorm scales. The exterior process is nonscaling and differs in the three models, whereas the interior process is stationary multifractal in all cases. The model-based IDF curves are robust, against outliers, and can be obtained from only very few years of rainfall data. In an application to a 24-year rainfall record from Florence, Italy, the models closely reproduce the empirical IDF curves and make similar extrapolations for return periods longer than the historical record.

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.

Self-similarity and multifractality of fluvial erosion topography: 1. Mathematical conditions and physical origin.

It is suggested that the scaling laws satisfied by fluvial erosion topography and river networks reflect a basic self-similarity or multifractality property of the topographic surface within river basins. By analyzing the symmetries of fluvial topography, we conclude that this self-similarity or multifractality condition should be expressed in a particular way in terms of the topographic increments within subbasins. We then analyze whether self-similar or multifractal topographies can be stationary or transient solutions of dynamic evolution models of the type ∂h/∂t = U − ƒ{β, т}, where U is the uplift rate, ƒ is the fluvial erosion rate, β is a vector of erodibility parameters, and т is hydraulic shear stress. The hydraulic stress on a channel bed is assumed to satisfy т ∝ AmSn, where A is contributing area, S is slope, and m and n are parameters. We allow U to vary randomly in time and β to vary randomly in space and determine conditions on these random functions as well as the parameters m and n under which the topography may remain in a self-similar or multifractal state. Simulation shows that self-similar states are attractive also for non-self-similar boundary and initial conditions.

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.

Temperature and CAPE dependence of rainfall extremes in the eastern United States

We analyze how extreme rainfall intensities in the Eastern United States depend on temperature T, dew point temperature Td, and convective available potential energy CAPE, in addition to geographic sub-region, season, and averaging duration. When using data for the entire year, rainfall intensity has a quasi Clausius-Clapeyron (CC) dependence on T, with super-CC slope in a limited temperature range and a maximum around 25°C. While general, these features vary with averaging duration, season, the quantile of rainfall intensity, and to some extent geographic sub-region. By using Td and CAPE as regressors, we separate the effects of temperature on rainfall extremes via increased atmospheric water content and via enhanced atmospheric convection. The two contributions have comparable magnitudes, pointing at the need to consider both Td and atmospheric stability parameters when assessing the impact of climate change on rainfall extremes.

Stochastic model of the width function

A new class of probabilistic models of the width function, based on so-called iterated random pulse (IRP) processes, is proposed. IRP processes reproduce the main characteristics of empirical width functions (nonnegativity, nonstationarity, and power law decay of the spectrum) and require few and easily accessible parameters. IRP models are based on a simple conceptualization of the geometrical structure of river basins and exploit in a natural way the self-similarity of natural channel networks. A result that is derived from the IRP representation is that the exponent α of Hacks law, L ∼ Aα, and the exponent β of the power spectral density of the width function, S(ω) ∼ |ω|−β, are related as α = 1/β. Empirical values of β are typically in the range 1.8–2.0 and are consistent with this theoretical result and the usual range of α.