Nestedness across biological scales

Networking nutrients: How nutrition determines the structure of ecological networks

Nutrients can shape ecological interactions but remain poorly integrated into ecological networks. Concepts such as nutrient-specific foraging nevertheless have the potential to expose the mechanisms structuring complex ecological systems. Nutrients also present an opportunity to predict dynamic processes, such as interaction rewiring and extinction cascades, and increase the accuracy of network analyses. Here, we propose the concept of nutritional networks. By integrating nutritional data into ecological networks, we envisage significant advances to our understanding of ecological processes from individual to ecosystem scales. We show that networks can be constructed with nutritional data to illuminate how nutrients structure ecological interactions in natural systems through an empirical example. Throughout, we identify fundamental ecological hypotheses that can be explored in a nutritional network context, alongside methods for resolving those networks. Nutrients influence the structure and complexity of ecological networks through mechanistic processes and concepts including nutritional niche differentiation, functional responses, landscape diversity, ecological invasions and ecosystem robustness. Future research on ecological networks should consider nutrients when investigating the drivers of network structure and function.

Metastatic cells exploit their stoichiometric niche in the network of cancer ecosystems

Metastasis is a nonrandom process with varying degrees of organotropism-specific source-acceptor seeding. Understanding how patterns between source and acceptor tumors emerge remains a challenge in oncology. We hypothesize that organotropism results from the macronutrient niche of cells in source and acceptor organs. To test this, we constructed and analyzed a metastatic network based on 9303 records across 28 tissue types. We found that the topology of the network is nested and modular with scale-free degree distributions, reflecting organotropism along a specificity/generality continuum. The variation in topology is significantly explained by the matching of metastatic cells to their stoichiometric niche. Specifically, successful metastases are associated with higher phosphorus content in the acceptor compared to the source organ, due to metabolic constraints in proliferation crucial to the invasion of new tissues. We conclude that metastases are codetermined by processes at source and acceptor organs, where phosphorus content is a limiting factor orchestrating tumor ecology.

Fission-fusion dynamics in the social networks of a North American pitviper

Many animal species exist in fission-fusion societies, where the size and composition of conspecific groups change spatially and temporally. To help investigate such phenomena, social network analysis (SNA) has emerged as a powerful conceptual and analytical framework for assessing patterns of interconnectedness and quantifying group-level interactions. We leveraged behavioral observations via radiotelemetry and genotypic data from a long-term (>10 years) study on the pitviper Crotalus atrox (western diamondback rattlesnake) and used SNA to quantify the first robust demonstration of social network structures for any free-living snake. Group-level interactions among adults in this population resulted in structurally modular networks (i.e., distinct clusters of interacting individuals) for fidelis use of communal winter dens (denning network), mating behaviors (pairing network), and offspring production (parentage network). Although the structure of each network was similar, the size and composition of groups varied among them. Specifically, adults associated with moderately sized social groups at winter dens but often engaged in reproductive behaviors-both at and away from dens-with different and fewer partners. Additionally, modules formed by individuals in the pairing network were frequently different from those in the parentage network, likely due to multiple mating, long-term sperm storage by females, and resultant multiple paternity. Further evidence for fission-fusion dynamics exhibited by this population-interactions were rare when snakes were dispersing to and traversing their spring-summer home ranges (to which individuals show high fidelity), despite ample opportunities to associate with numerous conspecifics that had highly overlapping ranges. Taken together, we show that long-term datasets incorporating SNA with spatial and genetic information provide robust and unique insights to understanding the social structure of cryptic taxa that are understudied.

Interaction Networks Help to Infer the Vulnerability of the Saproxylic Beetle Communities That Inhabit Tree Hollows in Mediterranean Forests

Insect communities are facing contrasting responses due to global change. However, knowledge on impacts of communities' reorganizations is scarce. Network approaches could help to envision community changes in different environmental scenarios. Saproxylic beetles were selected to examine long-term variations in insect interaction/diversity patterns and their vulnerability to global change. We evaluated interannual differences in network patterns in the tree hollow-saproxylic beetle interaction using absolute samplings over an 11-year interval in three Mediterranean woodland types. We explored saproxylic communities' vulnerability to microhabitat loss via simulated extinctions and by recreating threat scenarios based on decreasing microhabitat suitability. Although temporal diversity patterns varied between woodland types, network descriptors showed an interaction decline. The temporal beta-diversity of interactions depended more on interaction than on species turnover. Interaction and diversity temporal shifts promoted less specialized and more vulnerable networks, which is particularly worrisome in the riparian woodland. Network procedures evidenced that saproxylic communities are more vulnerable today than 11 years ago irrespective of whether species richness increased or decreased, and the situation could worsen in the future depending on tree hollow suitability. Network approaches were useful for predicting saproxylic communities' vulnerability across temporal scenarios and, thus, for providing valuable information for management and conservation programs.

Socioconnectomics: Connectomics Should Be Extended to Societies to Better Understand Evolutionary Processes

Connectomics, which is the network study of connectomes or maps of the nervous system of an organism, should be applied and expanded to human and animal societies, resulting in the birth of the domain of socioconnectomics compared to neuroconnectomics. This new network study framework would open up new perspectives in evolutionary biology and add new elements to theories, such as the social and cultural brain hypotheses. Answering questions about network topology, specialization, and their connections with functionality at one level (i.e., neural or societal) may help in understanding the evolutionary trajectories of these patterns at the other level. Expanding connectomics to societies should be done in comparison and combination with multilevel network studies and the possibility of multiorganization selection processes. The study of neuroconnectomes and socioconnectomes in animals, from simpler to more advanced ones, could lead to a better understanding of social network evolution and the feedback between social complexity and brain complexity.

Blue and green food webs respond differently to elevation and land use

While aquatic (blue) and terrestrial (green) food webs are parts of the same landscape, it remains unclear whether they respond similarly to shared environmental gradients. We use empirical community data from hundreds of sites across Switzerland and a synthesis of interaction information in the form of a metaweb to show that inferred blue and green food webs have different structural and ecological properties along elevation and among various land-use types. Specifically, in green food webs, their modular structure increases with elevation and the overlap of consumers' diet niche decreases, while the opposite pattern is observed in blue food webs. Such differences between blue and green food webs are particularly pronounced in farmland-dominated habitats, indicating that anthropogenic habitat modification modulates the climatic effects on food webs but differently in blue versus green systems. These findings indicate general structural differences between blue and green food webs and suggest their potential divergent future alterations through land-use or climatic changes.

Social foraging can benefit artisanal fishers who interact with wild dolphins

Abstract

Social foraging decisions depend on individual payoffs. However, it is unclear how individual variation in phenotypic and behavioural traits can influence these payoffs, thereby the decisions to forage socially or individually. Here, we studied how individual traits influence foraging tactics of net-casting fishers who interact with wild dolphins. While net-casting is primarily an individual activity, in the traditional fishery with dolphins, fishers can choose between fishing in cooperative groups or solitarily. Our semi-structured interviews with fishers show their social network is mapped onto these foraging tactics. By quantifying the fishers’ catch, we found that fishers in cooperative groups catch more fish per capita than solitary fishers. By quantifying foraging and social traits of fishers, we found that the choice between foraging tactics—and whom to cooperate with—relates to differences in peer reputation and to similarities in number of friends, propensity to fish with relatives, and frequency of interaction with dolphins. These findings indicate different payoffs between foraging tactics and that by choosing the cooperative partner fishers likely access other benefits such as social prestige and embeddedness. These findings reveal the importance of not only material but also non-material benefits of social foraging tactics, which can have implications for the dynamics of this rare fishery. Faced with the current fluctuation in fishing resource availability, the payoffs of both tactics may change, affecting the fishers’ social and foraging decisions, potentially threatening the persistence of this century-old fishery involving humans and wildlife.

Significance statement

Social foraging theory proposes that decisions to forage in groups are primarily driven by cost–benefit trade-offs that individuals experience, but it remains unclear whether, and how much, individual foragers’ characteristics influence these trade-offs and consequently the choice to forage in social groups. We study the artisanal net-casting fishers who choose between cooperating with each other or fishing alone when engaging in a rare interaction with wild dolphins. Our findings suggest that cooperative fishers capture more fish than solitary fishers, and that by choosing cooperative partners based on similarities and differences in key social (peer reputation, kinship, friendships) and foraging (fishing frequency) traits; these fishers also experience higher social prestige and more social embeddedness. These results suggest that material gains from foraging—but also non-material benefits accrued from socializing with like-minded individuals—can influence the dynamics of human social foraging.

Edge disturbance shapes liana diversity and abundance but not liana-tree interaction network patterns in moist semi-deciduous forests, Ghana

Edge disturbance can drive liana community changes and alter liana-tree interaction networks, with ramifications for forest functioning. Understanding edge effects on liana community structure and liana-tree interactions is therefore essential for forest management and conservation. We evaluated the response patterns of liana community structure and liana-tree interaction structure to forest edge in two moist semi-deciduous forests in Ghana (Asenanyo and Suhuma Forest Reserves: AFR and SFR, respectively). Liana community structure and liana-tree interactions were assessed in 24 50 × 50 m randomly located plots in three forest sites (edge, interior and deep-interior) established at 0-50 m, 200 m and 400 m from edge. Edge effects positively and negatively influenced liana diversity in forest edges of AFR and SFR, respectively. There was a positive influence of edge disturbance on liana abundance in both forests. We observed anti-nested structure in all the liana-tree networks in AFR, while no nestedness was observed in the networks in SFR. The networks in both forests were less connected, and thus more modular and specialised than their null models. Many liana and tree species were specialised, with specialisation tending to be symmetrical. The plant species played different roles in relation to modularity. Most of the species acted as peripherals (specialists), with only a few species having structural importance to the networks. The latter species group consisted of connectors (generalists) and hubs (highly connected generalists). Some of the species showed consistency in their roles across the sites, while the roles of other species changed. Generally, liana species co-occurred randomly on tree species in all the forest sites, except edge site in AFR where lianas showed positive co-occurrence. Our findings deepen our understanding of the response of liana communities and liana-tree interactions to forest edge disturbance, which are useful for managing forest edge.

Resolving the Rules of Robustness and Resilience in Biology Across Scales

Why do some biological systems and communities persist while others fail? Robustness, a system's stability, and resilience, the ability to return to a stable state, are key concepts that span multiple disciplines within and outside the biological sciences. Discovering and applying common rules that govern the robustness and resilience of biological systems is a critical step toward creating solutions for species survival in the face of climate change, as well as the for the ever-increasing need for food, health, and energy for human populations. We propose that network theory provides a framework for universal scalable mathematical models to describe robustness and resilience and the relationship between them, and hypothesize that resilience at lower organization levels contribute to robust systems. Insightful models of biological systems can be generated by quantifying the mechanisms of redundancy, diversity, and connectivity of networks, from biochemical processes to ecosystems. These models provide pathways towards understanding how evolvability can both contribute to and result from robustness and resilience under dynamic conditions. We now have an abundance of data from model and non-model systems and the technological and computational advances for studying complex systems. Several conceptual and policy advances will allow the research community to elucidate the rules of robustness and resilience. Conceptually, a common language and data structure that can be applied across levels of biological organization needs to be developed. Policy advances such as cross-disciplinary funding mechanisms, access to affordable computational capacity, and the integration of network theory and computer science within the standard biological science curriculum will provide the needed research environments. This new understanding of biological systems will allow us to derive ever more useful forecasts of biological behaviors and revolutionize the engineering of biological systems that can survive changing environments or disease, navigate the deepest oceans, or sustain life throughout the solar system.

The asymptotic distribution of modularity in weighted signed networks

Modularity is a popular metric for quantifying the degree of community structure within a network. The distribution of the largest eigenvalue of a network's edge weight or adjacency matrix is well studied and is frequently used as a substitute for modularity when performing statistical inference. However, we show that the largest eigenvalue and modularity are asymptotically uncorrelated, which suggests the need for inference directly on modularity itself when the network size is large. To this end, we derive the asymptotic distributions of modularity in the case where the network's edge weight matrix belongs to the Gaussian orthogonal ensemble, and study the statistical power of the corresponding test for community structure under some alternative models. We empirically explore universality extensions of the limiting distribution and demonstrate the accuracy of these asymptotic distributions through Type I error simulations. We also compare the empirical powers of the modularity based tests with some existing methods. Our method is then used to test for the presence of community structure in two real data applications.

Diverse interactions and ecosystem engineering can stabilize community assembly

The complexity of an ecological community can be distilled into a network, where diverse interactions connect species in a web of dependencies. Species interact directly with each other and indirectly through environmental effects, however to our knowledge the role of these ecosystem engineers has not been considered in ecological network models. Here we explore the dynamics of ecosystem assembly, where species colonization and extinction depends on the constraints imposed by trophic, service, and engineering dependencies. We show that our assembly model reproduces many key features of ecological systems, such as the role of generalists during assembly, realistic maximum trophic levels, and increased nestedness with mutualistic interactions. We find that ecosystem engineering has large and nonlinear effects on extinction rates. While small numbers of engineers reduce stability by increasing primary extinctions, larger numbers of engineers increase stability by reducing primary extinctions and extinction cascade magnitude. Our results suggest that ecological engineers may enhance community diversity while increasing persistence by facilitating colonization and limiting competitive exclusion.

Hierarchically embedded interaction networks represent a missing link in the study of behavioral and community ecology

Because genes and phenotypes are embedded within individuals, and individuals within populations, interactions within one level of biological organization are inherently linked to interactors at others. Here, we expand the network paradigm to consider that nodes can be embedded within other nodes, and connections (edges) between nodes at one level of organization form "bridges" for connections between nodes embedded within them. Such hierarchically embedded networks highlight two central properties of biological systems: 1) processes occurring across multiple levels of organization shape connections among biological units at any given level of organization and 2) ecological effects occurring at a given level of organization can propagate up or down to additional levels. Explicitly considering the embedded structure of evolutionary and ecological networks can capture otherwise hidden feedbacks and generate new insights into key biological phenomena, ultimately promoting a broader understanding of interactions in evolutionary theory.

Revealing biases in the sampling of ecological interaction networks

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases with respect to both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These ecological and statistical issues directly affect ecologists’ abilities to accurately construct ecological networks. However, statistical biases introduced by sampling are difficult to quantify in the absence of full knowledge of the underlying ecological network’s structure. To explore properties of large-scale ecological networks, we developed the software EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different mathematical sampling designs that correspond to methods used in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties depends strongly both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, modules with nested structure were the easiest to detect, regardless of the sampling design used. Sampling a network starting with any species that had a high degree (e.g., abundant generalist species) was consistently found to be the most accurate strategy to estimate network structure. Because high-degree species tend to be generalists, abundant in natural communities relative to specialists, and connected to each other, sampling by degree may therefore be common but unintentional in empirical sampling of networks. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. To reduce biases introduced by sampling methods, we recommend that these findings be incorporated into field design considerations for projects aiming to characterize large species interaction networks.

Trait-Based Modeling of Multihost Pathogen Transmission: Plant-Pollinator Networks

Epidemiological models for multihost pathogen systems often classify individuals taxonomically and use species-specific parameter values, but in species-rich communities that approach may require intractably many parameters. Trait-based epidemiological models offer a potential solution but have not accounted for within-species trait variation or between-species trait overlap. Here we propose and study trait-based models with host and vector communities represented as trait distributions without regard to species identity. To illustrate this approach, we develop susceptible-infectious-susceptible models for disease spread in plant-pollinator networks with continuous trait distributions. We model trait-dependent contact rates in two common scenarios: nested networks and specialized plant-pollinator interactions based on trait matching. We find that disease spread in plant-pollinator networks is impacted the most by selective pollinators, universally attractive flowers, and cospecialized plant-pollinator pairs. When extreme pollinator traits are rare, pollinators with common traits are most important for disease spread, whereas when extreme flower traits are rare, flowers with uncommon traits impact disease spread the most. Greater nestedness and specialization both typically promote disease persistence. Given recent pollinator declines caused in part by pathogens, we discuss how trait-based models could inform conservation strategies for wild and managed pollinators. Furthermore, while we have applied our model to pollinators and pathogens, its framework is general and can be transferred to any kind of species interactions in any community.

Interaction paths promote module integration and network-level robustness of spliceosome to cascading effects

The functionality of distinct types of protein networks depends on the patterns of protein-protein interactions. A problem to solve is understanding the fragility of protein networks to predict system malfunctioning due to mutations and other errors. Spectral graph theory provides tools to understand the structural and dynamical properties of a system based on the mathematical properties of matrices associated with the networks. We combined two of such tools to explore the fragility to cascading effects of the network describing protein interactions within a key macromolecular complex, the spliceosome. Using S. cerevisiae as a model system we show that the spliceosome network has more indirect paths connecting proteins than random networks. Such multiplicity of paths may promote routes to cascading effects to propagate across the network. However, the modular network structure concentrates paths within modules, thus constraining the propagation of such cascading effects, as indicated by analytical results from the spectral graph theory and by numerical simulations of a minimal mathematical model parameterized with the spliceosome network. We hypothesize that the concentration of paths within modules favors robustness of the spliceosome against failure, but may lead to a higher vulnerability of functional subunits, which may affect the temporal assembly of the spliceosome. Our results illustrate the utility of spectral graph theory for identifying fragile spots in biological systems and predicting their implications.

The One Health Concept: 10 Years Old and a Long Road Ahead

Over the past decade, a significant increase in the circulation of infectious agents was observed. With the spread and emergence of epizootics, zoonoses, and epidemics, the risks of pandemics became more and more critical. Human and animal health has also been threatened by antimicrobial resistance, environmental pollution, and the development of multifactorial and chronic diseases. This highlighted the increasing globalization of health risks and the importance of the human-animal-ecosystem interface in the evolution and emergence of pathogens. A better knowledge of causes and consequences of certain human activities, lifestyles, and behaviors in ecosystems is crucial for a rigorous interpretation of disease dynamics and to drive public policies. As a global good, health security must be understood on a global scale and from a global and crosscutting perspective, integrating human health, animal health, plant health, ecosystems health, and biodiversity. In this study, we discuss how crucial it is to consider ecological, evolutionary, and environmental sciences in understanding the emergence and re-emergence of infectious diseases and in facing the challenges of antimicrobial resistance. We also discuss the application of the "One Health" concept to non-communicable chronic diseases linked to exposure to multiple stresses, including toxic stress, and new lifestyles. Finally, we draw up a list of barriers that need removing and the ambitions that we must nurture for the effective application of the "One Health" concept. We conclude that the success of this One Health concept now requires breaking down the interdisciplinary barriers that still separate human and veterinary medicine from ecological, evolutionary, and environmental sciences. The development of integrative approaches should be promoted by linking the study of factors underlying stress responses to their consequences on ecosystem functioning and evolution. This knowledge is required for the development of novel control strategies inspired by environmental mechanisms leading to desired equilibrium and dynamics in healthy ecosystems and must provide in the near future a framework for more integrated operational initiatives.