Complexity Thoughts: Issue #62
Unraveling complexity: building knowledge, one paper at a time
If you find Complexity Thoughts interesting, follow us! Click on the Like button, leave a comment, repost on Substack or share this post. It is the only feedback I can have for this free service. The frequency and quality of this newsletter relies on social interactions. Thank you!
→ Don’t miss the podcast version of this post: click on “Spotify/Apple Podcast” above!
Foundations of network science and complex systems
Ensemble Inequivalence and Phase Transitions in Unlabeled Networks
Does this feature of real-world networks lead to critical phenomena akin to those we have established here?
We discover a first-order phase transition in the canonical ensemble of random unlabeled networks with a prescribed average number of links. The transition is caused by the nonconcavity of microcanonical entropy. Above the critical point coinciding with the graph symmetry phase transition, the canonical and microcanonical ensembles are equivalent and have a well-behaved thermodynamic limit. Below the critical point, the ensemble equivalence is broken, and the canonical ensemble is a mixture of phases: empty networks and networks with average degrees diverging logarithmically with the network size. As a consequence, networks with bounded average degrees do not survive in the thermodynamic limit, decaying into the empty phase. The celebrated percolation transition in labeled networks is thus absent in unlabeled networks. In view of these differences between labeled and unlabeled ensembles, the question of which one should be used as a null model of different real-world networks cannot be ignored.
Assessing the Similarity of Real Matrices with Arbitrary Shape
Assessing the similarity of matrices is valuable for analyzing the extent to which data sets exhibit common features in tasks such as data clustering, dimensionality reduction, pattern recognition, group comparison, and graph analysis. Methods proposed for comparing vectors, such as cosine similarity, can be readily generalized to matrices. However, this approach usually neglects the inherent two-dimensional structure of matrices. Here we propose singular angle similarity (SAS), a measure for evaluating the structural similarity between two arbitrary, real matrices of the same shape based on singular value decomposition. After introducing the measure, we compare SAS with standard measures for matrix comparison and show that only SAS captures the two-dimensional structure of matrices. Further, we characterize the behavior of SAS in the presence of noise, as a function of matrix dimensionality, and when singular values are degenerate. Finally, we apply SAS to two use cases: square nonsymmetric matrices of probabilistic network connectivity, and nonsquare matrices representing neural brain activity. For synthetic data of network connectivity, SAS matches intuitive expectations and allows for a robust assessment of similarities and differences. For experimental data of brain activity, SAS captures differences in the structure of high-dimensional responses to different stimuli. We conclude that SAS is a suitable measure for quantifying the shared structure of matrices with arbitrary shape.
Surfacic networks are structures built upon a 2D manifold. Many systems, including transportation networks and various urban networks, fall into this category. The fluctuations of node elevations imply significant deviations from typical plane networks and require specific tools to understand their impact. Here, we present such tools, including lazy paths that minimize elevation differences, graph arduousness which measures the tiring nature of shortest paths (SPs), and the excess effort, which characterizes positive elevation variations along SPs. We illustrate these measures using toy models of surfacic networks and empirically examine pedestrian networks in selected cities. Specifically, we examine how changes in elevation affect the spatial distribution of betweenness centrality. We also demonstrate that the excess effort follows a nontrivial power law distribution, with an exponent that is not universal, which illustrates that there is a significant probability of encountering steep slopes along SPs, regardless of the elevation difference between the starting point and the destination. These findings highlight the significance of elevation fluctuations in shaping network characteristics. Surfacic networks offer a promising framework for comprehensively analyzing and modeling complex systems that are situated on or constrained to a surface environment.
Evolution
The reach of gene–culture coevolution in animals
Culture (behaviour based on socially transmitted information) is present in diverse animal species, yet how it interacts with genetic evolution remains largely unexplored. Here, we review the evidence for gene–culture coevolution in animals, especially birds, cetaceans and primates. We describe how culture can relax or intensify selection under different circumstances, create new selection pressures by changing ecology or behaviour, and favour adaptations, including in other species. Finally, we illustrate how, through culturally mediated migration and assortative mating, culture can shape population genetic structure and diversity. This evidence suggests strongly that animal culture plays an important evolutionary role, and we encourage explicit analyses of gene–culture coevolution in nature.
Cumulative culture can emerge from collective intelligence in animal groups
Across many animal taxa, individuals form groups that collectively process information and make joint decisions1,2. By pooling information, these groups can generate better decisions than solitary agents—a phenomenon referred to as collective intelligence3.
Studies of collective intelligence in animal groups typically overlook potential improvement through learning. Although knowledge accumulation is recognized as a major advantage of group living within the framework of Cumulative Cultural Evolution (CCE), the interplay between CCE and collective intelligence has remained unexplored. Here, we use homing pigeons to investigate whether the repeated removal and replacement of individuals in experimental groups (a key method in testing for CCE) alters the groups’ solution efficiency over successive generations. Homing performance improves continuously over generations, and later-generation groups eventually outperform both solo individuals and fixed-membership groups. Homing routes are more similar in consecutive generations within the same chains than between chains, indicating cross-generational knowledge transfer. Our findings thus show that collective intelligence in animal groups can accumulate progressive modifications over time. Furthermore, our results satisfy the main criteria for CCE and suggest potential mechanisms for CCE that do not rely on complex cognition.
Knowledge transmission, culture and the consequences of social disruption in wild elephants
Cultural knowledge is widely presumed to be important for elephants. In all three elephant species, individuals tend to congregate around older conspecifics, creating opportunities for social transmission. However, direct evidence of social learning and cultural traditions in elephants is scarce. Here, we briefly outline that evidence then provide a systematic review of how elephant societies respond to the loss of potentially knowledgeable individuals or opportunities for knowledge transfer, which we characterize as social disruption. We consider observations from 95 peer-reviewed, primary research papers that describe disruption to elephant societies or networks via the removal or death of individuals. Natural deaths were mentioned in 14 papers, while 70 detailed human-caused deaths or disruption. Grouping descriptions according to consequences for behaviour and sociality, and demography and fitness, we show that severely disrupted populations are less cohesive, may exhibit reduced fitness or calf survival and respond inappropriately to threats and predators. We suggest that severe social disruption can inhibit or break potential pathways of information transmission, providing indirect evidence for the role of social transmission in elephants. This has implications for elephant conservation amid increasing anthropogenic change across their habitats.
Host use drives convergent evolution in clownfish
Clownfishes (Amphiprioninae) are a fascinating example of a marine radiation. From a central Pacific ancestor, they quickly colonized the coral reefs of the Indo-Pacific and diversified independently on each side of the Indo-Australian Archipelago. Their association with sea anemones has been proposed to be a key innovation that enabled the clownfish radiation. However, this intuition has little empirical or theoretical support given our current knowledge of the group. To date, no ecological variable has been identified to explain clownfish niche partitioning, phenotypic evolution, species co-occurrence, and thus, the adaptive aspect of the group’s radiation. Our study solves this long-standing mystery by testing the influence of sea anemone host use on phenotypic divergence. We provide a major revision of the known clownfish-sea anemone host associations, accounting for the biologically relevant aspects of host associations. We gathered whole-genome data for all 28 clownfish species and reconstructed a fully supported species tree for the Amphiprioninae. Integrating this data into comparative genomic approaches, we demonstrate that the host sea anemones are the drivers of convergent evolution in clownfish color pattern and morphology. During the diversification of this group, clownfishes in different regions that associate with the same hosts have evolved similar phenotypes. Comparative genomics also reveals several genes under convergent positive selection linked to host specialization events. Our findings reveal that the sea anemone host plays a crucial role in driving clownfish diversification. This highlights how a strong mutualistic interaction can promote the diversification of entire clades by influencing their phenotypes, defining their geographic distribution, and ultimately contributing to their evolutionary and ecological success.
Ecosystems
Cross-feeding creates tipping points in microbiome diversity
Understanding how diversity is maintained in microbial communities presents a significant challenge, as cross-feeding networks create complex interdependencies between consumer populations that can be hard to disentangle. We address this problem by using methods from network percolation theory to develop a model that captures the dependence of microbial community diversity on cross-feeding network structure. Our results identify tipping points at which small structural changes can trigger the collapse of cross-feeding networks, leading to catastrophic loss of diversity. Furthermore, we demonstrate how perturbations to cross-feeding networks affect diversity, showing how the difficulty of culturing diverse microbiomes may arise from the structural constraints of their interaction networks. These findings offer insights into the fundamental mechanisms shaping microbiomes and their robustness.
A key unresolved question in microbial ecology is how the extraordinary diversity of microbiomes emerges from the interactions among their many functionally distinct populations. This process is driven in part by the cross-feeding networks that help to structure these systems, in which consumers use resources to fuel their metabolism, creating by-products which can be used by others in the community. Understanding the effects of cross-feeding presents a major challenge, as it creates complex interdependencies between populations which can be hard to untangle. We address this problem using the tools of network science to develop a structural microbial community model. Using methods from percolation theory, we identify feasible community states for cross-feeding network structures in which the needs of consumers are met by metabolite production across the community. We identify tipping points at which small changes in structure can cause the catastrophic collapse of cross-feeding networks and abrupt declines in microbial community diversity. Our results are an example of a well-defined tipping point in a complex ecological system and provide insight into the fundamental processes shaping microbiomes and their robustness. We further demonstrate this by considering how network attacks affect community diversity and apply our results to show how the apparent difficulty in culturing the microbial diversity emerges as an inherent property of their cross-feeding networks.
Multiple targeted grassland restoration interventions enhance ecosystem service multifunctionality
The need to combat widespread degradation of grassland ecosystem services makes grassland restoration a global sustainability priority. However, simultaneously enhancing multiple ecosystem services (i.e. ecosystem service multifunctionality) is a major challenge for grassland restoration due to trade-offs among services. We use a long-term multifactor grassland restoration experiment established in 1989 on agriculturally improved, species-poor grassland in northern England, to assess how increasing the number of restoration treatments, including addition of manure, inorganic fertiliser, a seed mixture, and promotion of a nitrogen-fixing legume (Trifolium pratense), affects ecosystem service multifunctionality, based on 26 ecosystem service indicators measured between 2011 and 2014. We find that single interventions usually lead to trade-offs among services and thus have few positive effects on ecosystem service multifunctionality. However, ecosystem service multifunctionality increases with the number of restoration interventions, as trade-offs are reduced. Our findings highlight the significant potential for combined use of multiple targeted interventions to aid the restoration of ecosystem service multifunctionality in degraded grasslands, and potentially, other ecosystems.
Biomedical Systems
Chemical Complexity of Food and Implications for Therapeutics
Growing evidence further highlights the importance of dietary quality in disease prevention, particularly amid a global surge in early-onset cancers
Food contains more than 139,000 molecules, which influence nearly half the human proteome. Systematic analysis of food–chemical interactions can potentially advance nutrition science and drug discovery.
Human behavior
Extending Minds with Generative AI
As human-AI collaborations become the norm, we should remind ourselves that it is our basic nature to build hybrid thinking systems – ones that fluidly incorporate non-biological resources. Recognizing this invites us to change the way we think about both the threats and promises of the coming age.