Abstract: Social-ecological systems are prototypical complex adaptive systems. Ecosystem services such as water purification, atmospheric regulation, and food production emerge from the interactions of ecological components occurring at smaller scales. At any focal scale, the diversity of ecosystem services available for production is constrained by the number of unique combinations of component aggregation patterns. For example, a patch can be managed to support high rates of denitrification under wetland cover or high outputs of food production as intensive corn cover, but not both simultaneously in time. At a larger time scale, or in an ecosystem with multiple patches, both types of ecosystem services can be accommodated. At increasingly smaller scales, however, the opposite is true. Social processes, such as management interventions intended to optimize certain ecosystem service process rates, interact with ecological components by changing patterns of aggregation and potentially leading to the development of cross-scale feedbacks. These cross-scale feedbacks can eventually contribute to the loss of relationships among ecological components, including those that support the desired ecosystem service. Our complex systems approach to analyzing ecosystem services provides a way to examine ecosystem service tradeoffs at multiple focal scales and potential cross scale interactions that could result in unexpected, non-linear system behavior. Specifically, we show how a surprising, sudden loss of ecosystem services can emerge from the interactions between management decisions and ecological components, and provide a framework for avoiding these losses.

Abstract: In the study of complex systems, one of the main assets of statistical physics consists in the postulation of simple models capable to reproduce one given relevant property of the system under consideration. This approach allows to simplify the study, by focusing on the property under scrutiny, independently of other complicating factors. In the case of static complex networks, the configuration model fulfills this role. In the field of temporal networks, the non-Poissonian activity driven (NoPAD) model fills this niche, providing a simple model characterized by an arbitrary inter-event time distribution, which assumes any form, in particular that dictated by empirical evidence. In this paper, we present a detailed mathematical study of the properties of the time-integrated networks emerging from the dynamics of the NoPAD model. We focus in two main issues: The topological properties of the integrated networks, and their percolation behavior, as determined by the time Tp at which a giant connected component, spanning a finite fraction of the total number of nodes, first emerges. These two properties are determined as functions of the model’s parameters, namely the exponent of the distribution of the waiting time between two consecutive activations of an agent, and the exponent of the agents’ heterogeneity distribution. The topological properties and the percolation dynamics also depend on the time window of the integration process [ta , ta + t], and are determined by applying a mapping of the network’s construction algorithm to the hidden variables class of models. The NoPAD model represents a minimal model of temporal networks with long tailed inter-event time distribution. As such, it has a wide potential to serve as a synthetic controlled environment to check both numerically and analytically several properties of these networks, and in particular their effect on dynamical processes.

Abstract: Here, we analyze the adaptive climate policies that comply with the planetary boundary (Rockström et al., 2009) on climate change in 2100 - recovering a CO2 concentration of 350ppm until 2100 – and the policy implications in terms of CO2 emission reductions and in terms of implementation of geoengineering technologies (negative emissions) under budget constraint. For this purpose, we couple the viability theory and the DICE model for assessing the set of these adaptive climate policies and we analyze the trade-off between increasing CO2 emission reduction and implementing new geoengineering technologies yielding negative emissions. Results show that the objective of 350ppm in 2100 is reached only with carbon neutrality and the effective implementation of innovative geoengineering technologies (10% of negative emissions) before 2060, under the assumption of getting out of the baseline scenario without delay. Then, this trade-off is analyzed according to costs involved in terms of abatement costs and investment in new technologies. The talk will present the main processes of the DICE model as well as viability theory before discussing the main results in terms of adaptive climate policies associated to abatement and investment costs. Reference Rockström J, Steffen W, Noone K, Persson A, Chapin FSIII, Lambin E, Lenton TM, Scheffer M, Folke C, Schellnhuber HJ, Nykvist B, De Wit CA, Hughes T, Van der Leeuw S, Rodhe H, Sörlin S, Snyder PK, Costanza R, Svedin U, Falkenmark M, Karlberg L, Corell RW, Fabry VJ, Hansen J, Walker B, Liverman D, Richardson K, Crutzen P, Foley J (2009) Planetary boundaries: Exploring the safe operating space for humanity. Ecology and Society 14(2):32.

Abstract: Diffusion processes model propagation phenomena on complex networks, such as epidemics, information diffusion, and viral marketing. In many situations, it is critical to suppress an undesired diffusion process by means of dynamic resource allocation, where one needs to decide targeted actions by taking into account the evolving infection state of the network. In the context of continuous-time SIS, and with provided full information regarding the nodes’ state, we consider the scenario where a budget of treatment resources of limited efficiency is available at each time for distribution to infected nodes. Recent results on this particular problem include the Priority Planning approach which computes a linear ordering of the nodes with minimal maxcut, and the optimal greedy approach called Largest Reduction of Infectious Edges (LRIE). The latter is a simple, yet efficient, strategy that computes an intuitive priority score for the infected nodes which combines the notion of node virality (possibility to infect other nodes) and vulnerability (possibility to get reinfected after recovery). In this work we show that the principle of the LRIE score holds for a wide range of SIS-like modeling scenarios. More specifically, we propose the Generalized LRIE (gLRIE) strategy and study the dynamic diffusion control by introducing a two-fold extension to SIS which can model important aspects of social diffusion (e.g. behaviors or habits). The first considers nonlinear functions of infection rates with saturation. On the top that, our second extension considers competition in the sense that the two node states, the infected and the healthy, are both diffusive, though each node can only be in only one of them at a time. In this case, our gLIE control strategy has to align with the healthy diffusion to help it win the competition. Finally, simulations on large-scale real and synthetic networks show the efficiency of gLIE.

Abstract: MERS coronavirus emerged in Arabian Peninsula in 2012 raising great concern for its severity, its international spread, and the many uncertainties characterizing its transmission and ecology. During the first three years after the emergence, the epidemic in the source area showed strong spatiotemporal heterogeneities emerging by the interplay between the zoonotic and the human-to-human transmission routes. Episodes of virus importation in foreign countries had highly variable outcomes, where little if no transmission followed importation except for a large outbreak in South Korea that raised worldwide alert. We aimed at understanding the mechanisms underlying this complex dynamics. We studied MERS spread in Middle East by means of an integrative approach combining dynamical modeling at different spatial scales – regional and international. The resulting multi-scale framework allowed extracting maximal information from the sparse and diverse epidemiological records, thus increasing inference power. It provided estimates of epidemiological parameters and their spatiotemporal variation, showing that human-to-human transmission is more important than expected for the generation of cases, while the observed geographical structure is induced by variations in the zoonotic source. To understand the drivers of global dissemination we modeled imported cases and onward transmission using detailed information on air-travel, along with digital proxies for collective and public health awareness (e.g. Google Trends records). We showed that structure and dynamics of air-transportation network shapes the spatiotemporal pattern of MERS propagation, and we quantified the effect of collective attention on the epidemic response observing that high collective attention is associated to more rapid isolation of imported cases. The study demonstrates the power of dynamical models in interpreting limited epidemiological records in light of the extensive socio-demographic and behavioral information available. Models are thus able to address fundamental questions regarding emerging diseases’ spread, the underling biological mechanisms and the role of human response.