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

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

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

Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale