Complexity Research in Nature Communications

This web collection showcases the potential of interdisciplinary complexity research by bringing together a selection of recent Nature Communications articles investigating complex systems. Complexity research aims to characterize and understand the behaviour and nature of systems made up of many interacting elements. Such efforts often require interdisciplinary collaboration and expertise from diverse schools of thought. Nature Communications publishes papers across a broad range of topics that span the physical and life sciences, making the journal an ideal home for interdisciplinary studies.

The Ecology and evolution section contains studies that explore the dynamics of networks of genes, individuals and communities using combinations of empirical data and mathematical tools. Other examples of computational modeling in biology include studies on precision medicine and molecular network dynamics, and these are highlighted in Network medicine tab. Neuroscience is another discipline that is effectively leveraging network analysis to better understand how the complex interactions between neuroanatomy and function give rise to equally complex human behaviors. Such behaviors, when combined, give rise to cultures and societies.  The latter are paradigmatic complex systems, and articles presented in the Social systems section examine these paradigmatic complex systems, describing the dynamics of social systems, financial systems and transport networks that affect much of our daily lives.  Finally, the collection under the Network structure and dynamics tab showcases methodological advances in complex system modeling and network analysis. The articles that we felt represented each section particularly well are also available in the Editors' picks section, below.  

Solving some of the most important problems in science may only be possible when scientists with different backgrounds collaborate to address shared questions using complementary techniques. Truly interdisciplinary research that can bridge the natural, physical and social sciences remains challenging, as it requires scientists to share and discuss their views across disciplines, and therefore such research must also be able to reach a diverse audience. Our collection, which has been chosen by editors across the broad spectrum of subjects covered by Nature Communications, has been put together with this specific goal in mind.  


Ecology and evolution

Developmental nonlinearity drives phenotypic robustness
Rebecca M. Green et al., Nat. Commun. 8 1970 (2017)

Developmental processes often involve nonlinearities, but the consequences for translating genotype to phenotype are not well characterized. Here, Green et al. vary Fgf8 signalling across allelic series of mice and show that phenotypic robustness in craniofacial shape is explained by a nonlinear effect of Fgf8 expression.


Predicting metabolic adaptation from networks of mutational paths
Christos Josephides and Peter S. Swain, Nat. Commun. 8 685 (2017)

The structure and dynamics of microbial communities reflect trade-offs in the ability to use different resources. Here, Josephides and Swain incorporate metabolic trade-offs into an eco-evolutionary model to predict networks of mutational paths and the evolutionary outcomes for microbial communities.


Multilayer networks reveal the spatial structure of seed-dispersal interactions across the Great Rift landscapes
Sérgio Timóteo et al., Nat. Commun. 9 140 (2018)

Species interaction networks have been usually delimited by perceived habitat borders. Here, seed-dispersal is analysed as a regional multilayer network of interconnected habitats, highlighting the key role of versatile dispersers for the functional cohesion of the whole Gorongosa landscape. 


Microbial community-level regulation explains soil carbon responses to long-term litter manipulations
Katerina Georgiou et al., Nat. Commun. 8 1223 (2017)

Microbial models of soil organic carbon feed into Earth System Models, but many exhibit unrealistic oscillatory behaviour. Here, the authors propose a density-dependent formulation of microbial turnover that improves microbial models, with large implications for global carbon-concentration feedbacks.


Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium faciparum
Qixin He et al., Nat. Commun. 9 1817 (2018)

Plasmodium has evolved high genetic diversity in var genes, which encode for the major blood-stage antigen. Here, the authors show how immune selection shapes the var gene repertoire in both simulated systems and a population in Ghana, by using neutral models and genetic similarity networks.


Metric clusters in evolutionary games on sclale-free networks
Kaj-Kolja Kleineberg, Nat. Commun. 8 1888 (2017)

Heterogeneous complex networks tend to be a more realistic representation of social networks than homogenous ones. Here Kleineberg investigates the role of network heterogeneity in the emergence of cooperation in social dilemmas and shows that it can sometimes hinder it.


Disentangling the the role of floral sensory stimuli in pollination networks
Aphrodite Kantsa et al., Nat. Commun. 9 1041 (2018)

Can floral phenotype predict the most influential species for maintaining plant–pollinator communities? Here, Kantsa et al. develop a methodology for trait-based analysis, revealing the critical role of floral scent, and floral colour as perceived by insects, in shaping visitation networks.


Predicting the effect of habitat modification on networks of interacting species
Phillip P. A. Staniczenko et al., Nat. Commun. 8 792 (2017)

In a changing world, the ability to predict the impact of environmental change on ecological communities is essential. Here, the authors show that by separating species abundances from interaction preferences, they can predict the effects of habitat modification on the structure of weighted species interaction networks, even with limited data.

The fin to limb transition as the reo-organisation of a Turing pattern
Koh Ominaru et al. Nat. Commun. 7 11582 (2016)

Alan Turing suggested that morphological development could be regulated by a self-organizing, reaction-diffusion mechanism. Indeed, mouse digit patterning has been found to be controlled by a Turing network of Bmp, Sox9, and Wnt. Here, Onimaru and colleagues show that fin patterning in the catshark, Scyliorhinus canicula, is controlled by the same network with a different spatial organization. Thus, they demonstrate that the Turing network is deeply conserved in limb development.

Predicting the stability of large structured food webs
Stefano Allesina et al., Nat. Commun. 6 7842 (2015)

Random matrix theory has been central to the study of the relationship between complexity and stability in ecological networks. However, incorporating variable ecological interaction strengths and other dimensions of ecological complexity into these models has been a key limitation. Here, Allesina and colleagues use the ‘cascade model’, which does not assume a random graph, to develop model food web networks and analytically approximate the stability under various conditions. They find that network stability is determined by variability in interaction strengths, rather than the mean.

Social inheritance can explain the structure of animal social networks
Amiyaal Ilany and Erol Akçay, Nat. Commun. 7 12084 (2016)

Social network position influences many aspects of life not only for humans but also for other animals. Here, Ilany and Akçay develop a social network model in which social contacts can be inherited by offspring. They find that including such social inheritance produces networks with similar degree distribution, modularity, and clustering coefficient distribution as observed in the spotted hyena, rock hyrax, bottlenose dolphin, and sleepy lizard.

Environmental change makes robust ecological networks fragile
Giovanni Strona and Kevin D. Lafferty, Nat. Commun. 7 12462 (2016)

Despite their complexity, ecological networks appear robust to species loss. Here, Strona and Lafferty explore whether that robustness extends to novel environmental conditions. They simulate the dynamics of artificial and real-world host-parasite communities and show that secondary extinctions following the removal of a host species are limited under historical environmental conditions. However, they demonstrate that the same communities are likely to collapse when the environment changes.

Ecological networks are more sensitive to plant than to animal extinction under climate change
Matthias Schleuning et al., Nat. Commun. 7 13965 (2016)

Ecological networks involve numerous interdependencies, such as between plants and their pollinators, and the ability of a species to persist despite changing environmental conditions could depend on the strength and number of its interactions. Here, Schleuning and colleagues couple simulation models with empirical networks (plants and either their pollinators or seed-dispersers). They show that animal persistence under climate change depends more strongly on plant persistence than vice versa.

No complexity–stability relationship in empirical ecosystems
Claire Jacquet et al., Nat. Commun. 7 12573 (2016)

Most studies of the complexity-stability relationship in ecological networks have used randomly-built in silico networks and have supported initial theoretical predictions that more complex ecological networks are, counterintuitively, less stable. However, Jacquet and colleagues demonstrate that this prediction does not stand in a stability analysis of over 100 empirical food webs. This work highlights the need for network theories to better account for the non-random properties of ecological networks.