Search

Floods affected more than 2 billion people worldwide from 1998 to 2017 and their occurrence is expected to increase due to climate warming, population growth and rapid urbanization. Recent approaches for understanding the resilience of transportation networks when facing floods mostly use the framework of percolation but we show here on a realistic high-resolution flood simulation that it is inadequate. Indeed, the giant connected component is not relevant and instead, we propose to partition the road network in terms of accessibility of local towns and define new measures that characterize the impact of the flooding event. Our analysis allows to identify cities that will be pivotal during the flooding by providing to a large number of individuals critical services such as hospitalization services, food supply, etc. This approach is particularly relevant for practical risk management and will help decision makers for allocating resources in space and time. [ABSTRACT FROM AUTHOR]

Oceanic frontal zones have been shown to deeply influence the distribution of primary producers and, at the other extreme of the trophic web, top predators. However, the relationship between these structures and intermediate trophic levels is much more obscure. In this paper we address this knowledge gap by comparing acoustic measurements of mesopelagic fish concentrations to satellite-derived fine-scale Lagrangian Coherent Structures in the Indian sector of the Southern Ocean. First, we demonstrate that higher fish concentrations occur more frequently in correspondence with strong Lagrangian Coherent Structures. Secondly, we illustrate that, while increased fish densities are more likely to be observed over these structures, the presence of a fine-scale feature does not imply a concomitant fish accumulation, as other factors affect fish distribution. Thirdly, we show that, when only chlorophyll-rich waters are considered, front intensity modulates significantly more the local fish concentration. Finally, we discuss a model representing fish movement along Lagrangian features, specifically built for mid-trophic levels. Its results, obtained with realistic parameters, are qualitatively consistent with the observations and the spatio-temporal scales analysed. Overall, these findings may help to integrate intermediate trophic levels in trophic models, which can ultimately support management and conservation policies. [ABSTRACT FROM AUTHOR]

Understanding how climate change will affect oceanic fluid transport is crucial for environmental applications and human activities. However, a synoptic characterization of the influence of climate change on mesoscale stirring and transport in the surface ocean is missing. To bridge this gap, we exploit a high‐resolution, fully coupled climate model of the Mediterranean basin using a Network Theory approach. We project significant increases of horizontal stirring and kinetic energies in the next century, likely due to increments of available potential energy. The future evolution of basin‐scale transport patterns hints at a rearrangement of the main hydrodynamic provinces, defined as regions of the surface ocean that are well mixed internally but with minimal cross‐flow across their boundaries. This results in increased heterogeneity of province sizes and stronger mixing in their interiors. Our approach can be readily applied to other oceanic regions, providing information for the present and future marine spatial planning. Plain Language Summary: Transport and mixing of water masses driven by ocean currents influence a variety of fundamental processes, including heat redistribution, ecosystem functioning, and pollutants spreading. Therefore, understanding how fluid transport will be affected by climate change is crucial, in particular in the ocean surface, where marine life and human activities are concentrated. Here, we exploit a state‐of‐the‐art climate model over the Mediterranean basin using a novel methodology which integrates Network Theory concepts with Lagrangian modeling. We assess past conditions and future changes at climatic scales of ocean stirring and transport over the entire basin. Our results reveal a significant increment of surface stirring linked to an increase of currents kinetic energy, which in turn could be ascribed to increments of available potential energy. We then provide a regionalization of the ocean surface based on hydrodynamic provinces that are well mixed internally but with little leaking across their boundaries. Our model project an increased heterogeneity of province sizes and a stronger mixing in their interiors, while their mean area and coherence remain unaffected. Our approach could be applied to other oceanic domains and help designing adaptive strategies for marine spatial planning. Key Points: We exploit a coupled climate model over the Mediterranean combining Network Theory with a Lagrangian approachEntropy and kinetic energy analyses project a significant increase of stirring for the next centuryFuture transport patterns result in larger areas variability and stronger internal mixing of hydrodynamic provinces [ABSTRACT FROM AUTHOR]

Abstract Quantifying the mechanisms of tracer dispersion in the ocean remains a central question in oceanography, for problems ranging from nutrient delivery to phytoplankton, to the early detection of contaminants. Until now, most of the analysis has been based on Lagrangian concepts of transport, often focusing on the identification of features that minimize fluid exchange among regions, or more recently, on network tools which focus instead on connectivity and transport pathways. Neither of these approaches, however, allows us to rank the geographical sites of major water passage, and at the same time, to select them so that they monitor waters coming from separate parts of the ocean. These are instead key criteria when deploying an observing network. Here, we address this issue by estimating at any point the extent of the ocean surface which transits through it in a given time window. With such information we are able to rank the sites with major fluxes that intercept waters originating from different regions. We show that this allows us to optimize an observing network, where a set of sampling sites can be chosen for monitoring the largest flux of water dispersing out of a given region. When the analysis is performed backward in time, this method allows us to identify the major sources which feed a target region. The method is first applied to a minimalistic model of a mesoscale eddy field, and then to realistic satellite-derived ocean currents in the Kerguelen area. In this region, we identify the optimal location of fixed stations capable of intercepting the trajectories of 43 surface drifters, along with statistics on the temporal persistence of the stations determined in this way. We then identify possible hotspots of micro-nutrient enrichment for the recurrent spring phytoplanktonic bloom occurring here. Promising applications to other fields, such as larval connectivity, marine spatial planning or contaminant detection, are then discussed. Highlights • We propose a new Lagrangian method for the design of an optimal monitoring network for the detection of a dispersed tracer. • We analyze its properties with a steady model flow. • We validate it with an array of 43 real drifters. • We apply the method to the case of iron dispersal in the Kerguelen region (Southern Ocean). • We discuss other possible applications, like fish larvae dispersal and contaminant retrieval. [ABSTRACT FROM AUTHOR]

Concepts and tools from network theory, the so-called Lagrangian Flow Network framework, have been successfully used to obtain a coarse-grained description of transport by closed fluid flows. Here we explore the application of this methodology to open chaotic flows, and check it with numerical results for a model open flow, namely a jet with a localized wave perturbation. We find that network nodes with high values of out-degree and of finite-time entropy in the forward-in-time direction identify the location of the chaotic saddle and its stable manifold, whereas nodes with high in-degree and backwards finite-time entropy highlight the location of the saddle and its unstable manifold. The cyclic clustering coefficient, associated to the presence of periodic orbits, takes non-vanishing values at the location of the saddle itself. [ABSTRACT FROM AUTHOR]

We show that the clustering coefficient, a standard measure in network theory, when applied to flow networks, i.e., graph representations of fluid flows in which links between nodes represent fluid transport between spatial regions, identifies approximate locations of periodic trajectories in the flow system. This is true for steady flows and for periodic ones in which the time interval τ used to construct the network is the period of the flow or a multiple of it. In other situations, the clustering coefficient still identifies cyclic motion between regions of the fluid. Besides the fluid context, these ideas apply equally well to general dynamical systems. By varying the value of τ used to construct the network, a kind of spectroscopy can be performed so that the observation of high values of mean clustering at a value of τ reveals the presence of periodic orbits of period 3τ, which impact phase space significantly. These results are illustrated with examples of increasing complexity, namely, a steady and a periodically perturbed model two-dimensional fluid flow, the three-dimensional Lorenz system, and the turbulent surface flow obtained from a numerical model of circulation in the Mediterranean sea. [ABSTRACT FROM AUTHOR]

A Lagrangian flow network is constructed for the atmospheric blocking of Eastern Europe and Western Russia in summer 2010. We compute the most probable paths followed by fluid particles, which reveal the Omega-block skeleton of the event. A hierarchy of sets of highly probable paths is introduced to describe transport pathways when the most probable path alone is not representative enough. These sets of paths have the shape of narrow coherent tubes flowing close to the most probable one. Thus, even when the most probable path is not very significant in terms of its probability, it still identifies the geometry of the transport pathways. [ABSTRACT FROM AUTHOR]

We consider paths in weighted and directed temporal networks, introducing tools to compute sets of paths of high probability. We quantify the relative importance of the most probable path between two nodes with respect to the whole set of paths and to a subset of highly probable paths that incorporate most of the connection probability. These concepts are used to provide alternative definitions of betweenness centrality. We apply our formalism to a transport network describing surface flow in the Mediterranean sea. Despite the full transport dynamics is described by a very large number of paths we find that, for realistic time scales, only a very small subset of high probability paths (or even a single most probable one) is enough to characterize global connectivity properties of the network. [ABSTRACT FROM AUTHOR]

We represent transport between different regions of a fluid domain by flow networks, constructed from the discrete representation of the Perron-Frobenius or transfer operator associated to the fluid advection dynamics. The procedure is useful to analyze fluid dynamics in geophysical contexts, as illustrated by the construction of a flow network associated to the surface circulation in the Mediterranean sea. We use network-theory tools to analyze the flow network and gain insights into transport processes. In particular, we quantitatively relate dispersion and mixing characteristics, classically quantified by Lyapunov exponents, to the degree of the network nodes. A family of network entropies is defined from the network adjacency matrix and related to the statistics of stretching in the fluid, in particular, to the Lyapunov exponent field. Finally, we use a network community detection algorithm, Infomap, to partition the Mediterranean network into coherent regions, i.e., areas internally well mixed, but with little fluid interchange between them. [ABSTRACT FROM AUTHOR]

In the context of agent based modeling and network theory, we focus on the problem of recovering behavior-related choice information from origin-destination type data, a topic also known under the name of network tomography. As a basis for predicting agents' choices we emphasize the connection between adaptive intelligent behavior, causal entropy maximization, and self-organized behavior in an open dynamic system. We cast this problem in the form of binary and weighted networks and suggest information theoretic entropy-driven methods to recover estimates of the unknown behavioral flow parameters. Our objective is to recover the unknown behavioral values across the ensemble analytically, without explicitly sampling the configuration space. In order to do so, we consider the Cressie-Read family of entropic functionals, enlarging the set of estimators commonly employed to make optimal use of the available information. More specifically, we explicitly work out two cases of particular interest: Shannon functional and the likelihood functional. We then employ them for the analysis of both univariate and bivariate data sets, comparing their accuracy in reproducing the observed trends. [ABSTRACT FROM AUTHOR]

For more than a decade, high‐throughput sequencing has transformed the study of marine planktonic communities and has highlighted the extent of protist diversity in these ecosystems. Nevertheless, little is known relative to their genomic diversity at the species‐scale as well as their major speciation mechanisms. An increasing number of data obtained from global scale sampling campaigns is becoming publicly available, and we postulate that metagenomic data could contribute to deciphering the processes shaping protist genomic differentiation in the marine realm. As a proof of concept, we developed a findable, accessible, interoperable and reusable (FAIR) pipeline and focused on the Mediterranean Sea to study three a priori abundant protist species: Bathycoccus prasinos, Pelagomonas calceolata and Phaeocystis cordata. We compared the genomic differentiation of each species in light of geographic, environmental and oceanographic distances. We highlighted that isolation‐by‐environment shapes the genomic differentiation of B. prasinos, whereas P. cordata is impacted by geographic distance (i.e. isolation‐by‐distance). At present time, the use of metagenomics to accurately estimate the genomic differentiation of protists remains challenging since coverages are lower compared to traditional population surveys. However, our approach sheds light on ecological and evolutionary processes occurring within natural marine populations and paves the way for future protist population metagenomic studies. [ABSTRACT FROM AUTHOR]

Lagrangian Flow Network (LFN) is a modelling framework in which ocean sub-areas are represented as nodes in a network interconnected by links representing transport of propagules (eggs and larvae) by currents. We asses the sensitivity and robustness of four LFN-derived connectivity metrics measuring retention and exchange. The most relevant parameters are tested over large ranges and a wide region with contrasting hydrodynamics: density of released particles, node size (spatial scale of discretization), Pelagic Larval Duration (PLD) and spawning modality. We find a minimum density of released particles that guarantees reliable values for most of the metrics examined. We also find that node size has a nontrivial influence on them. Connectivity estimates for long PLDs are more robust against biological uncertainties (PLD and spawning date) than for short PLDs. For mass-spawners releasing propagules over short periods (≈ 2-10 days), daily release must be simulated to properly consider connectivity fluctuations due to variable currents. In contrast, average connectivity estimates for species that spawn repeatedly over longer durations (few weeks to few months) remain robust even using longer periodicity (5-10 days). Our results have implications to design connectivity experiments with particle-tracking models and to evaluate the reliability of their results. [ABSTRACT FROM AUTHOR]

During the last few years, complex network approaches have demonstrated their great potentials as versatile tools for exploring the structural as well as dynamical properties of dynamical systems from a variety of different fields. Among others, recent successful examples include (i) functional (correlation) network approaches to infer hidden statistical interrelationships between macroscopic regions of the human brain or the Earth's climate system, (ii) Lagrangian flow networks allowing to trace dynamically relevant fluid-flow structures in atmosphere, ocean or, more general, the phase space of complex systems, and (iii) time series networks unveiling fundamental organization principles of dynamical systems. In this spirit, complex network approaches have proven useful for data-driven learning of dynamical processes (like those acting within and between sub-components of the Earth's climate system) that are hidden to other analysis techniques. This Focus Issue presents a collection of contributions addressing the description of flows and associated transport processes from the network point of view and its relationship to other approaches which deal with fluid transport and mixing and/or use complex network techniques. [ABSTRACT FROM AUTHOR]

Aim Assessing the spatial structure and dynamics of marine populations is still a major challenge in ecology. The need to manage marine resources from ecosystem and large-scale perspectives is recognized, but our partial understanding of oceanic connectivity limits the implementation of globally pertinent conservation planning. Based on a biophysical model for the entire Mediterranean Sea, this study takes an ecosystem approach to connectivity and provides a systematic characterization of broad-scale larval dispersal patterns. It builds on our knowledge of population dynamics and discusses the ecological and management implications. Location The semi-enclosed Mediterranean Sea and its marine ecosystems are used as a case study to investigate broad-scale connectivity patterns and to relate them to oceanography and population dynamics. Methods A flow network is constructed by evenly subdividing the basin into sub-regions which are interconnected through the transport of larvae by ocean currents. It allows for the computation of various connectivity metrics required to evaluate larval retention and exchange. Results Our basin-scale model predicts that retention processes are weak in the open ocean while they are significant in the coastal ocean and are favoured along certain coastlines due to specific oceanographic features. Moreover, we show that wind-driven divergent (convergent, respectively) oceanic regions are systematically characterized by larval sources (sinks, respectively). Finally, although these connectivity metrics have often been studied separately in the literature, we demonstrate they are interrelated under particular conditions. Their integrated analysis facilitates the appraisal of population dynamics, informing both genetic and demographic connectivities. Main conclusions This modelling framework helps ecologists and geneticists to formulate improved hypotheses of population structures and gene flow patterns and to design their sampling strategy accordingly. It is also useful in the implementation and assessment of future protection strategies, such as coastal and offshore marine reserves, by accounting for large-scale dispersal patterns, a missing component of current ecosystem management. [ABSTRACT FROM AUTHOR]