Explore the vast range of titles available.

The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible–Exposed–Infected–Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number (R₀ = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.

All organisms leave traces of DNA in their environment. This environmental DNA (eDNA) is often used to track occurrence patterns of target species. Applications are especially promising in rivers, where eDNA can integrate information about populations upstream. The dispersion of eDNA in rivers is modulated by complex processes of transport and decay through the dendritic river network, and we currently lack a method to extract quantitative information about the location and density of populations contributing to the eDNA signal. Here, we present a general framework to reconstruct the upstream distribution and abundance of a target species across a river network, based on observed eDNA concentrations and hydro-geomorphological features of the network. The model captures well the catchment-wide spatial biomass distribution of two target species: a sessile invertebrate (the bryozoan Fredericella sultana) and its parasite (the myxozoan Tetracapsuloides bryosalmonae). Our method is designed to easily integrate general biological and hydrological data and to enable spatially explicit estimates of the distribution of sessile and mobile species in fluvial ecosystems based on eDNA sampling.

The pressing need to restart socioeconomic activities locked-down to control the spread of SARS-CoV-2 in Italy must be coupled with effective methodologies to selectively relax containment measures. Here we employ a spatially explicit model, properly attentive to the role of inapparent infections, capable of: estimating the expected unfolding of the outbreak under continuous lockdown (baseline trajectory); assessing deviations from the baseline, should lockdown relaxations result in increased disease transmission; calculating the isolation effort required to prevent a resurgence of the outbreak. A 40% increase in effective transmission would yield a rebound of infections. A control effort capable of isolating daily  ~5.5% of the exposed and highly infectious individuals proves necessary to maintain the epidemic curve onto the decreasing baseline trajectory. We finally provide an ex-post assessment based on the epidemiological data that became available after the initial analysis and estimate the actual disease transmission that occurred after weakening the lockdown. Releasing COVID-19 lockdown measures risks increases in transmission. Here, the authors estimate the increase in transmission rate for different regions in Italy and estimate that isolation of 5.5% exposed and highly infectious individuals would be needed to compensate for a 40% increase in transmission.

The majority of tidal channels display marked meandering features. Despite their importance in oil-reservoir formation and tidal landscape morphology, questions remain on whether tidal-meander dynamics could be understood in terms of fluvial processes and theory. Key differences suggest otherwise, like the periodic reversal of landscape-forming tidal flows and the widely accepted empirical notion that tidal meanders are stable landscape features, in stark contrast with their migrating fluvial counterparts. On the contrary, here we show that, once properly normalized, observed migration rates of tidal and fluvial meanders are remarkably similar. Key to normalization is the role of tidal channel width that responds to the strong spatial gradients of landscape-forming flow rates and tidal prisms. We find that migration dynamics of tidal meanders agree with nonlinear theories for river meander evolution. Our results challenge the conventional view of tidal channels as stable landscape features and suggest that meandering tidal channels recapitulate many fluvial counterparts owing to large gradients of tidal prisms across meander wavelengths.

We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statistical testing of the validity of power law distributions of network degrees. Specifically, we analyze by finite size scaling analysis the datasets of real networks to check whether the purported departures from power law behavior are due to the finiteness of sample size. We find that a large number of the networks follows a finite size scaling hypothesis without any self-tuning. This is the case of biological protein interaction networks, technological computer and hyperlink networks, and informational networks in general. Marked deviations appear in other cases, especially involving infrastructure and transportation but also in social networks. We conclude that underlying scale invariance properties of many naturally occurring networks are extant features often clouded by finite size effects due to the nature of the sample data.

Global freshwater resources are under increasing pressure from economic development, population growth, and climate change. The international trade of water-intensive products (e.g., agricultural commodities) or virtual water trade has been suggested as a way to save water globally. We focus on the virtual water trade network associated with international food trade built with annual trade data and annual modeled virtual water content. The evolution of this network from 1986 to 2007 is analyzed and linked to trade policies, socioeconomic circumstances, and agricultural efficiency. We find that the number of trade connections and the volume of water associated with global food trade more than doubled in 22 years. Despite this growth, constant organizational features were observed in the network. However, both regional and national virtual water trade patterns significantly changed. Indeed, Asia increased its virtual water imports by more than 170%, switching from North America to South America as its main partner, whereas North America oriented to a growing intraregional trade. A dramatic rise in China's virtual water imports is associated with its increased soy imports after a domestic policy shift in 2000. Significantly, this shift has led the global soy market to save water on a global scale, but it also relies on expanding soy production in Brazil, which contributes to deforestation in the Amazon. We find that the international food trade has led to enhanced savings in global water resources over time, indicating its growing efficiency in terms of global water use.

Habitat fragmentation and land use changes are causing major biodiversity losses. Connectivity of the landscape or environmental conditions alone can shape biodiversity patterns. In nature, however, local habitat characteristics are often intrinsically linked to a specific connectivity. Such a link is evident in riverine ecosystems, where hierarchical dendritic structures command related scaling on habitat capacity. We experimentally disentangled the effect of local habitat capacity (i.e., the patch size) and dendritic connectivity on biodiversity in aquatic microcosm metacommunities by suitably arranging patch sizes within river-like networks. Overall, more connected communities that occupy a central position in the network exhibited higher species richness, irrespective of patch size arrangement. High regional evenness in community composition was found only in landscapes preserving geomorphological scaling properties of patch sizes. In these landscapes, some of the rarer species sustained regionally more abundant populations better tracking their own niche requirements compared to landscapes with homogeneous patch size or landscapes with spatially uncorrelated patch size. Our analysis suggests that altering the natural link between dendritic connectivity and patch size strongly affects community composition and population persistence at multiple scales. The experimental results are demonstrating a principle that can be tested in theoretical metacommunity models and eventually be projected to real riverine ecosystems.

Biological communities often occur in spatially structured habitats where connectivity directly affects dispersal and metacommunity processes. Recent theoretical work suggests that dispersal constrained by the connectivity of specific habitat structures, such as dendrites like river networks, can explain observed features of biodiversity, but direct evidence is still lacking. We experimentally show that connectivity per se shapes diversity patterns in microcosm metacommunities at different levels. Local dispersal in isotropic lattice landscapes homogenizes local species richness and leads to pronounced spatial persistence. On the contrary, dispersal along dendritic landscapes leads to higher variability in local diversity and among-community composition. Although headwaters exhibit relatively lower species richness, they are crucial for the maintenance of regional biodiversity. Our results establish that spatially constrained dendritic connectivity is a key factor for community composition and population persistence.

Significance The past few decades have seen an intensification of international food trade and the increase in the number of countries that depend on food imports. As an effect of the associated globalization of food, local shocks in food production, combined with the adoption of new national or regional energy and trade policies, have recently led to global food crises. Here we develop a framework to investigate the coupled global food–population dynamics, and evaluate the effect of international trade on global food security. We find that, as the dependency on trade increases, the global food system is losing resilience and is becoming increasingly unstable and susceptible to conditions of crisis. The escalating food demand by a growing and increasingly affluent global population is placing unprecedented pressure on the limited land and water resources of the planet, underpinning concerns over global food security and its sensitivity to shocks arising from environmental fluctuations, trade policies, and market volatility. Here, we use country-specific demographic records along with food production and trade data for the past 25 y to evaluate the stability and reactivity of the relationship between population dynamics and food availability. We develop a framework for the assessment of the resilience and the reactivity of the coupled population–food system and suggest that over the past two decades both its sensitivity to external perturbations and susceptibility to instability have increased.

Landscape and climate alterations foreshadow global-scale shifts of river flow regimes. However, a theory that identifies the range of foreseen impacts on streamflows resulting from inhomogeneous forcings and sensitivity gradients across diverse regimes is lacking. Here, we derive a measurable index embedding climate and landscape attributes (the ratio of the mean interarrival of streamflow-producing rainfall events and the mean catchment response time) that discriminates erratic regimes with enhanced intraseasonal streamflow variability from persistent regimes endowed with regular flow patterns. Theoretical and empirical data show that erratic hydrological regimes typical of rivers with low mean discharges are resilient in that they hold a reduced sensitivity to climate fluctuations. The distinction between erratic and persistent regimes provides a robust framework for characterizing the hydrology of freshwater ecosystems and improving water management strategies in times of global change.