Theo Gibbs, Simon A. Levin, Jonathan M. Levine

A central assumption in most ecological models is that the interactions in a community operate only between pairs of species. However, the interaction between two species may be fundamentally changed by the presence of others. Although interactions among three or more species, called higher-order interactions, have the potential to modify our theoretical understanding of coexistence, ecologists lack clear expectations for how these interactions shape community structure. Here, we analytically predict and numerically confirm how the variability and strength of higher-order interactions affect species coexistence. We found that, as higher-order interaction strengths become more variable across species, fewer species coexist, echoing the behavior of pairwise models. If inter-specific higher-order interactions become too harmful relative to self-regulation, coexistence was destabilized, but coexistence was also lost when these interactions were too weak and mutualistic effects became prevalent. Last, we showed that more species rich communities structured by higher-order interactions lose species more readily than their species poor counterparts, generalizing classic results for community stability. Our work provides needed theoretical expectation for how higher-order interactions impact species coexistence in diverse communities.

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Michael M. Danziger & Albert-László Barabási
Nature Communications volume 13, Article number: 955 (2022)

The increased complexity of infrastructure systems has resulted in critical interdependencies between multiple networks—communication systems require electricity, while the normal functioning of the power grid relies on communication systems. These interdependencies have inspired an extensive literature on coupled multilayer networks, assuming a hard interdependence, where a component failure in one network causes failures in the other network, resulting in a cascade of failures across multiple systems. While empirical evidence of such hard failures is limited, the repair and recovery of a network requires resources typically supplied by other networks, resulting in documented interdependencies induced by the recovery process. In this work, we explore recovery coupling, capturing the dependence of the recovery of one system on the instantaneous functional state of another system. If the support networks are not functional, recovery will be slowed. Here we collected data on the recovery time of millions of power grid failures, finding evidence of universal nonlinear behavior in recovery following large perturbations. We develop a theoretical framework to address recovery coupling, predicting quantitative signatures different from the multilayer cascading failures. We then rely on controlled natural experiments to separate the role of recovery coupling from other effects like resource limitations, offering direct evidence of how recovery coupling affects a system’s functionality.

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Dynamical systems can be chaotic and impossible to predict, but mathematicians have discovered tools to help understand them.

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Jiarong Xie, Xiangrong Wang, Ling Feng, Jin-Hua Zhao, Wenyuan Liu, Yamir Moreno, and Yanqing Hu

PNAS Vol. 119 | No. 9

Increasing empirical evidence in diverse social and ecological systems has shown that indirect interactions play a pivotal role in shaping systems’ dynamical behavior. Our empirical study on collaboration networks of scientists further reveals that an indirect effect can dominate over direct influence in behavioral spreading. However, almost all models in existence focus on direct interactions, and the general impact of indirect interactions has not been studied. We propose a new percolation process, termed induced percolation, to characterize indirect interactions and find that indirect interactions raise a plethora of new phenomena, including the wide range of possible phase transitions. Such an indirect mechanism leads to very different spreading outcomes from that of direct influences.

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Allisandra G. Kummer, Juanjuan Zhang, Maria Litvinova, Alessandro Vespignani, Hongjie Yu, Marco Ajelli

Considerable uncertainties surround the seasonality of respiratory infectious diseases. To which extent the observed seasonality is associated with biological reasons (e.g., virus survival rates, host immune dynamics) or human behavior remains unclear. Here, we investigate the association between temperature and human contact patterns using data collected through a contact diary-based survey between December 24, 2017 and May 30, 2018 in Shanghai, China. We found a significant inverse relationship between number of contacts and temperature seasonal trend (p=0.003) and temperature daily variation (p=0.009), with contacts increasing from 19.6 (95%CI: 14.9-22.2) in December to 24.4 (95%CI: 19.0-28.0) in January and declining to 10.9 (95%CI: 10.1-11.9) in May. This seasonal trend in number of contacts translates into a seasonal trend in the basic reproduction number – mean number of secondary cases generated by a typical infector in a fully susceptible population. By setting the basic reproduction number at 1.4 on December 24, weekly mean estimates showed a clear increasing trend during the fall, beginning at 1.14 (95%CI: 0.78-1.39) in October and reaching 2.02 (95%CI: 1.60-1.35) in February and then remaining below 1 in the summer. Epidemic dynamics comparable with those of seasonal influenza are obtained through model simulations when the infection is seeded during the fall; however, their dynamics become more complex when seeded after February (e.g., double peaks or no epidemic until after the summer). Our findings indicate a distinct seasonal trend among human contact patterns and highlight a behavioral mechanism contributing to the seasonality of respiratory infectious diseases.

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