The time varying network of urban space uses in Milan

Abstract In a metropolis, people movements design intricate patterns that change on very short temporal scales. Population mobility obviously is not random, but driven by the land uses of the city. Such an urban ecosystem can interestingly be explored by integrating the spatial analysis of land uses (through ecological indicators commonly used to characterize natural environments) with the temporal analysis of human mobility (reconstructed from anonymized mobile phone data). Considering the city of Milan (Italy) as a case study, here we aimed to identify the complex relations occurring between the land-use composition of its neighborhoods and the spatio-temporal patterns of occupation made by citizens. We generated two spatially explicit networks, one static and the other temporal, based on the analysis of land uses and mobile phone data, respectively. The comparison between the results of community detection performed on both networks revealed that neighborhoods that are similar in terms of land-use composition are not necessarily characterized by analogous temporal fluctuations of human activities. In particular, the historical concentric urban structure of Milan is still under play. Our big data driven approach to characterize urban diversity provides outcomes that could be important (i) to better understand how and when urban spaces are actually used, and (ii) to allow policy makers improving strategic development plans that account for the needs of metropolis-like permanently changing cities

The temporal patterns of disease severity and prevalence in schistosomiasis

Schistosomiasis is one of the most widespread public health problems in the world. In this work, we introduce an eco-epidemiological model for its transmission and dynamics with the purpose of explaining both intra-and inter-annual fluctuations of disease severity and prevalence. The model takes the form of a system of nonlinear differential equations that incorporate biological complexity associated with schistosome's life cycle, including a prepatent period in snails (i.e., the time between initial infection and onset of infectiousness). Nonlinear analysis is used to explore the parametric conditions that produce different temporal patterns (stationary, endemic, periodic, and chaotic). For the time-invariant model, we identify a transcritical and a Hopf bifurcation in the space of the human and snail infection parameters. The first corresponds to the occurrence of an endemic equilibrium, while the latter marks the transition to interannual periodic oscillations. We then investigate a more realistic time-varying model in which fertility of the intermediate host population is assumed to seasonally vary. We show that seasonality can give rise to a cascade of period-doubling bifurcations leading to chaos for larger, though realistic, values of the amplitude of the seasonal variation of fertility. (C) 2015 AIP Publishing LLC

Movement Strategies of Seed Predators as Determinants of Plant Recruitment Patterns

Plant recruitment in nature exhibits several distinctive patterns ranging from hump shaped to monotonically decreasing with distance from the seed source. We investigate the role of post-dispersal seed predation in shaping these patterns, introducing a new mechanistic model that explicitly accounts for the movement strategy used by seed eaters. The model consists of two partial differential equations describing the spatiotemporal dynamics of both seed and predator densities. The movement strategy is defined by how predators move in response to the different cues they can use to search for seeds. These cues may be seed density, seed intake, distance from the plant, density of conspecific foragers, or a mixture of these four.The model is able to reproduce all the basic plant recruitment pat-terns found in the field. We compare the results to those of the ideal free distribution (IFD) theory and show that hump-shaped plant recruitment patterns cannot be generated by IFD predators and, in general, by foragers that respond exclusively to seed density. These foragers can produce only non increasing patterns, the shapes of which are determined by the foragers’ navigation capacities. In contrast, hump-shaped patterns can be produced by distance-responsive predators or by foragers that use conspecifics as a cue for seed abundanc

Modeling Plastics Exposure for the Marine Biota: Risk Maps for Fin Whales in the Pelagos Sanctuary (North-Western Mediterranean)

Several anthropogenic stressors threaten the Mediterranean basin, which is currently regarded as one of the most impacted marine ecoregions globally. Among those stressors, marine plastic litter is causing increasing concern about its environmental and biological consequences, the latter being largely unknown. To improve the understanding of these aspects, here we provide a mapped indicator of the risk of plastic ingestion by the fin whale Balaenoptera physalus, an endangered cetacean whose feeding grounds are located within the Pelagos Sanctuary for Mediterranean Marine Mammals, in the north-western Mediterranean Sea. We analyse a decade (2000-2010) of advection patterns of marine plastic litter, modeled as Lagrangian particles and released from the three major sources: untreated waste along coasts, plastic discharged from rivers and along maritime shipping routes. Risk of exposure to microplastics via food ingestion for fin whales is then evaluated by interlacing the plastic litter distribution obtained via particle tracking with maps of habitat suitability based on bathymetry and satellite-derived estimates of chlorophyll-a. Our modeling results locate the highest risk values in the Central Ligurian Sea, and show that all the three main sources of plastic litter taken into account clearly contribute to impacting cetaceans in the Sanctuary, yet with spatial and interannual variability of patterns. The procedure formalized with our approach can be extended to assess the risk caused by ingestion of plastics by other taxa and/or in other MPAs, as we suggest by providing an application on the whole ecosystem of Pelagos, thus informing targeted actions to tackle the complex issue of marine litter

Modelling carbon export mediated by biofouled microplastics in the Mediterranean Sea

Marine microplastics can be colonized by biofouling microbial organisms, leading to a decrease in microplastics' buoyancy. The sinking of biofouled microplastics could therefore represent a novel carbon export pathway within the ocean carbon cycle. Here, we model how microplastics are biofouled by diatoms, their consequent vertical motion due to buoyancy changes, and the interactions between particle-attached diatoms and carbon pools within the water column. We initialize our Lagrangian framework with biogeochemical data from NEMO-MEDUSA-2.0 and estimate the amount of organic carbon exported below 100 m depth starting from different surface concentrations of 1-mm microplastics. We focus on the Mediterranean Sea that is characterized by some of the world's highest microplastics concentrations and is a hotspot for biogeochemical changes induced by rising atmospheric carbon dioxide levels. Our results show that the carbon export caused by sinking biofouled microplastics is proportional to the concentration of microplastics in the sea surface layer, at least at modeled concentrations. We estimate that, while current concentrations of microplastics can modify the natural biological carbon export by < 1%, future concentrations projected under business-as-usual pollution scenarios may lead to carbon exports up to 5% larger than the baseline (1998–2012) by 2050. Areas characterized by high primary productivity, that is, the Western and Central Mediterranean, are those where microplastics-mediated carbon export results to be the highest. While highlighting the potential and quantitatively limited occurrence of this phenomenon in the Mediterranean Sea, our results call for further investigation of a microplastics-related carbon export pathway in the global ocean

Floquet theory for seasonal environmental forcing of spatially explicit waterborne epidemics

The transmission of waterborne pathogens is a complex process that is heavily linked to the spatial characteristics of the underlying environmental matrix as well as to the temporal variability of the relevant hydroclimatological drivers. In this work, we propose a time-varying, spatially explicit network model for the dynamics of waterborne diseases. Applying Floquet theory, which allows to extend results of local stability analysis to periodic dynamical systems, we find conditions for pathogen invasion and establishment in systems characterized by fluctuating environmental forcing, thus extending to time-varying contexts the generalized reproduction numbers recently obtained for spatially explicit epidemiology of waterborne disease. We show that temporal variability may have multifaceted effects on the invasion threshold, as it can either favor pathogen invasion or make it less likely. Moreover, environmental fluctuations characterized by distinctive geographical signatures can produce diversified, highly nontrivial effects on pathogen invasion. Our study is complemented by numerical simulations, which show that pathogen establishment is neither necessary nor sufficient for large epidemic outbreaks to occur in time-varying environments. Finally, we show that our framework can be used to reliably characterize the early geography of epidemic outbreaks triggered by fluctuating environmental conditions

Spatially explicit effective reproduction numbers from incidence and mobility data

Current methods for near real-time estimation of effective reproduction numbers from surveillance data overlook mobility fluxes of infectors and susceptible individuals within a spatially connected network (the metapopulation). Exchanges of infections among different communities may thus be misrepresented unless explicitly measured and accounted for in the renewal equations. Here, we first derive the equations that include spatially explicit effective reproduction numbers, ℛk(t), in an arbitrary community k. These equations embed a suitable connection matrix blending mobility among connected communities and mobility-related containment measures. Then, we propose a tool to estimate, in a Bayesian framework involving particle filtering, the values of ℛk(t) maximizing a suitable likelihood function reproducing observed patterns of infections in space and time. We validate our tools against synthetic data and apply them to real COVID-19 epidemiological records in a severely affected and carefully monitored Italian region. Differences arising between connected and disconnected reproduction numbers (the latter being calculated with existing methods, to which our formulation reduces by setting mobility to zero) suggest that current standards may be improved in their estimation of disease transmission over time

The spatial spread of schistosomiasis: A multidimensional network model applied to Saint-Louis region, Senegal

AbstractSchistosomiasis is a parasitic, water-related disease that is prevalent in tropical and subtropical areas of the world, causing severe and chronic consequences especially among children. Here we study the spatial spread of this disease within a network of connected villages in the endemic region of the Lower Basin of the Senegal River, in Senegal. The analysis is performed by means of a spatially explicit metapopulation model that couples local-scale eco-epidemiological dynamics with spatial mechanisms related to human mobility (estimated from anonymized mobile phone records), snail dispersal and hydrological transport of schistosome larvae along the main water bodies of the region. Results show that the model produces epidemiological patterns consistent with field observations, and point out the key role of spatial connectivity on the spread of the disease. These findings underline the importance of considering different transport pathways in order to elaborate disease control strategies that can be effective within a network of connected populations