The role of indirect effects in coevolution as mutualism transitions into antagonism

Species interactions have evolved from antagonistic to mutualistic and back several times throughout life's history. Yet, it is unclear how changes in the type of interaction between species alter the coevolutionary dynamics of entire communities. This is a pressing matter, as transitions from mutualisms to antagonisms may be becoming more common with human-induced global change. Here, we combine network and evolutionary theory to simulate how shifts in interaction types alter the coevolution of empirical communities. We show that as mutualistic networks shift to antagonistic, selection imposed by direct partners begins to outweigh that imposed by indirect partners. This weakening of indirect effects is associated with communities losing their tight integration of traits and increasing their rate of adaptation. The above changes are more pronounced when specialist consumers are the first species to switch to antagonism. A shift in the outcome of species' interactions may therefore reverberate across communities and alter the direction and speed of coevolution.

A New Methodology to Study Street Accessibility: A Case Study of Avila (Spain)

Taking into account that accessibility is one of the most strategic and determining factors in economic models and that accessibility and tourism affect each other, we can say that the study and improvement of one of them involved the development of the other. Using network analysis, this study presents an algorithm for labeling the difficulty of the streets of a city using different accessibility parameters. We combine network structure and accessibility factors to explore the association between innovative behavior within the street network, and the relationships with the commercial activity in a city. Finally, we present a case study of the city of Avila, locating the most inaccessible areas of the city using centrality measures and analyzing the effects, in terms of accessibility, on the commerce and services of the city.

Cooperation Beyond the Network

It is well known in economics, law, and sociology that reputation costs in a closed network give insiders a feeling of being protected from bad behavior in their relations with one another. A person accustomed to doing business within a closed network is, therefore, likely to feel at unusual risk when asked to cooperate beyond the network because of absent reputation-cost security. It follows that business leaders in more closed networks should be less likely to cooperate beyond their network (Hypothesis 1 ). Success reinforces the status quo. Business leaders successful with a closed network associate their success with the safety of their network, so they should be even less likely to cooperate with a stranger (Hypothesis 2 ). We combine network data from a heterogeneous area probability survey of Chinese CEOs with a behavioral measure of cooperation to show strong empirical support for the two hypotheses. CEOs in more closed networks are less likely to cooperate beyond their network, especially those running successful businesses: successful CEOs in closed networks are particularly likely to defect against people beyond their network. The work contributes to a growing literature linking network structure with behavior: here, the closure that facilitates trust and cooperation within a network simultaneously erodes the probability of cooperation beyond the network, thereby reinforcing a social boundary around the network. Taking our results as a baseline, we close sketching new research on personality, homophily, network dynamics, and variation in the meaning of “beyond the network.”

Complex networks of marine heatwaves reveal abrupt transitions in the global ocean

Understanding how marine heatwaves (MHWs) unfold in space and time under anthropogenic climate change is key to anticipate future impacts on ecosystems and society. Yet, our knowledge of the spatiotemporal dynamics of MHWs is very limited. Here, I combine network theory with topological data analysis and event synchronization to high-resolution satellite data and to a set of Earth System Model simulations to reveal the dynamical organization of complex MHW networks. The analysis reveals that MHWs have already crossed a tipping point separating highly synchronized preindustrial MHWs from the more extreme, but less coherent warming events we experience today. This loose spatiotemporal organization persists under a reduced RCP 2.6 emission scenario, whereas a second abrupt transition towards a permanent state of highly synchronized MHWs is foreseen by 2075 under a business-as-usual RCP 8.5 scenario. These results highlight the risks of abrupt ocean transitions, which may dramatically affect marine life and humanity by eroding valuable time for adaptation to climate change.

International high-tech SMEs innovative foreign knowledge inflows: effects of host country weak network ties and absorptive capacity

Purpose The main purpose of this study is to explain the combined effects of host country weak network ties and absorptive capacity on the innovative foreign knowledge inflows of international high-tech small- and medium-sized enterprises (SMEs). Design/methodology/approach Data are drawn from the two largest and most authoritative German Federal Government census-databases of biotech and nanotech SMEs. A structured survey questionnaire was administered and regression analysis adopted. Findings This study demonstrates weak network ties in the host country and developing absorptive capacity produce a combined effect that positively influences international high-tech SMEs innovative foreign knowledge inflows. Also, host country weak network ties and absorptive capacity when considered separately, each respectively, positively influence innovative foreign knowledge inflows. Practical implications The results help inform key personnel in international high-tech SMEs about the relevance of host country weak network ties and absorptive capacity for foreign knowledge inflows. In addition, the results help policymakers and think-tanks to promote tailored advice and guidance e.g. those policymakers implementing the EU Entrepreneurship 2020 Action Plan. Originality/value There is a recent call in the literature to combine network theory and absorptive capacity theory to better explain knowledge creation in the context of international high-tech SMEs knowledge sourcing. By addressing this call, the study provides a more refined and comprehensive account of international high-tech SMEs innovative foreign knowledge inflows.

IHS: an integrative method for the identification of network hubs

Biological systems are composed of highly complex networks and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points, or hub nodes, within a network. However, none of the current methods correct for inherent positional biases which limits their applicability. In addition, none of the currently available hub detection algorithms effectively combine network centrality measures together. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures, and developed a novel algorithm termed “integrated hubness score” (IHS), which integrates the most important and commonly used network centrality measures, namely degree centrality, betweenness centrality and neighbourhood connectivity, in an unbiased way. When compared against the four most commonly used hub identification methods on four independent validated biological networks, the IHS algorithm outperformed all other assessed methods. Using this novel and universal method, researchers of any discipline can now identify the most influential network nodes.

Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model

Everything is connected in the world. From small groups to global societies, the interactions among people, technology, and policies need sophisticated techniques to be perceived and forecasted. In social network, it has been concluded that the microblog users influence and microblog grade are nonlinearly dependent. However, to the best of our knowledge, the nonlinear influence predication of social network has not been explored in the existing literature. This article proposes a partial autoregression single index model to combine network structure (linear) and static covariates (nonparametric) flexibly. Compared with previous work, our model has fewer limits and more applications. The profile least squares estimation is employed to infer this semiparametric model, and variables selection is performed via the smoothly clipped absolute deviation penalty (SCAD). Simulations are conducted to demonstrate finite sample behaviors.

Scientific networks on Twitter: Analyzing scientists’ interactions in the climate change debate

Scientific issues requiring urgent societal actions—such as climate change—have increased the need for communication and interaction between scientists and other societal actors. Social media platforms facilitate such exchanges. This study investigates who scientists interact with on Twitter, and whether their communication differs when engaging with actors beyond the scientific community. We focus on the climate change debate on Twitter and combine network analysis with automated content analysis. The results show that scientists interact most intensively with their peers, but also communication beyond the scientific community is important. The findings suggest that scientists adjust their communication style to their audience: They use more neutral language when communicating with other scientists, and more words expressing negative emotions when communicating with journalists, civil society, and politicians. Likewise, they stress certainty more when communicating with politicians, indicating that scientists use language strategically when communicating beyond the scientific community.

Boundary conditions for early life converge to an organo-sulfur metabolism

It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals prior to genetically encoded enzymes (1–6). A major challenge in unraveling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms (9, 10) with physicochemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fueled by a thioester- and redox-driven variant of the reductive TCA cycle, capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors seem not to be necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a proto-cell, and find that different combinations of carbon sources and electron acceptors can support the continuous production of a minimal ancient “biomass” composed of putative early biopolymers and fatty acids.

Chronic infections can shape epidemic exposure: Pathogen co-occurrence networks in the Serengeti lions

Pathogens are embedded in a complex network of microparasites that can collectively or individually alter disease dynamics and outcomes. Chronic pathogens, for example, can either facilitate or compete with subsequent pathogens thereby exacerbating morbidity and mortality. Pathogen interactions are ubiquitous in nature, but poorly understood, particularly in wild populations. We report here on ten years of serological and molecular data in African lions, leveraging comprehensive demographic and behavioral data to utilize pathogen networks to test if chronic infections shape infection by acute pathogens. We combine network and community ecology approaches to assess broad network structure and characterize associations between pathogens across spatial and temporal scales. We found significant non-random structure in the lion-pathogen co-occurrence network and identified potential facilitative and competitive interactions between acute and chronic pathogens. Our results provide a novel insight for untangling the complex associations underlying pathogen co-occurrence networks.