Variation In Network Structure Research Articles

High Resource Overlap and a Consistently Generalised Pattern of Interactions in a Bat-Flower Network in a Seasonally Dry Landscape.

Pollination is an ecosystem process that is crucial to maintain biodiversity and ecosystem function. Bats are important pollinators in the tropics and are an integral part of complex plant-pollinator interaction networks. However, network analysis-based approaches are still scarce at the plant species and bat community levels. We used metabarcoding to identify plant taxa present in pollen from fur and faecal samples collected across 1 year from three nectar-feeding bat roosts in central Mexico. We calculated the frequency of occurrence of plant taxa and assembled a zoocentric network of bat-plant interactions. We constructed a year-long network, encompassing the entire period of sampling, two seasonal networks comprising the wet and dry seasons, and six individual networks from sampling at two-month intervals across the year. Four species of nectar-feeding bats interacted with 36 plant species from 16 families. We found highly generalised interaction patterns across networks corresponding with opportunistic feeding behaviour by bats, with little seasonal variation in network structure. There was high resource overlap between bat species, and bats visited a diverse range of plant species even during periods with a high abundance of particular resources in the landscape. The diverse diet of nectar-feeding bats emphasises the importance of floristically rich natural habitats in the landscape to provide reliable foraging resources year-round in a seasonally variable system. While a generalised network structure is thought to increase robustness, further research is necessary to understand how fluctuations in pollinator abundance and diversity in the face of land use and climate change may impact bat-flower networks and the consequences to plant communities.

Key microbial taxa play essential roles in maintaining soil muti-nutrient cycling following an extreme drought event in ecological buffer zones along the Yangtze River.

Climatic extremes, especially extreme droughts, are occurring more frequently and profoundly impacting biogeochemical processes. However, the relative importance of microbial communities on soil nutrient cycling and community maintenance under natural extreme drought events remains elusive. During a record-breaking drought in the Yangtze River Basin (YRB) in the summer of 2022, we collected ambient soils and drought-affected bare and vegetated soils in ecological buffer zones from two sites with similar soil and vegetation characteristics along the YRB, and examined the relative contribution of soil bacterial communities in supporting multi-nutrient cycling index (MNCI) involving carbon-, nitrate- and phosphorus-cycling and their associations with microbial network. Extreme drought decreased (p < 0.05) bacterial α-diversity but increased MNCI in vegetated soils at both sites, while both remained unchanged (p > 0.05) in bare soils, possibly as a result of vegetation releasing rhizodeposits under drought which selectively recruited bacterial communities. Bacterial community compositions were shifted (p < 0.05) only in vegetated soils, and they exerted more influence than α-diversity on soil MNCI. Notably, the Anaerolineae, identified as a biomarker enriched in vegetated soils, had close associations with enzyme activities and soil MNCI at both sites, suggesting their potential recruitment by vegetation to withstand drought. Furthermore, key ecological clusters (Module 1) in bacterial co-occurrence networks at both sites supported (p < 0.05) higher MNCI, despite no substantial variation in network structure due to drought. Specifically, the most important taxa within Module 1 for predicting soil MNCI revealed by random forest modeling analysis (R2=0.44 - 0.63, p < 0.001), such as B1-7BS, SBR1031 and Nocardioides, could be deeply involved in soil nitrogen-cycling, suggesting an essential role of specialized interactions of bacterial communities in maintaining soil multifunctionality. Overall, this study demonstrates that changes in biomarkers and functional taxa under extreme drought may better reflect the biological mechanisms involved in microbial communities impacting ecosystem function, which may aid in forecasting the ecological consequences of ongoing climate change in the ecological buffer zones along the YRB.

Spatially explicit predictions of food web structure from regional-level data.

Knowledge about how ecological networks vary across global scales is currently limited given the complexity of acquiring repeated spatial data for species interactions. Yet, recent developments in metawebs highlight efficient ways to first document possible interactions within regional species pools. Downscaling metawebs towards local network predictions is a promising approach to using the current data to investigate the variation of networks across space. However, issues remain in how to represent the spatial variability and uncertainty of species interactions, especially for large-scale food webs. Here, we present a probabilistic framework to downscale a metaweb based on the Canadian mammal metaweb and species occurrences from global databases. We investigated how our approach can be used to represent the variability of networks and communities between ecoregions in Canada. Species richness and interactions followed a similar latitudinal gradient across ecoregions but simultaneously identified contrasting diversity hotspots. Network motifs revealed additional areas of variation in network structure compared with species richness and number of links. Our method offers the potential to bring global predictions down to a more actionable local scale, and increases the diversity of ecological networks that can be projected in space. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Ant social network structure is highly conserved across species.

The ecological success of social insects makes their colony organization fascinating to scientists studying collective systems. In recent years, the combination of automated behavioural tracking and social network analysis has deepened our understanding of many aspects of colony organization. However, because studies have typically worked with single species, we know little about interspecific variation in network structure. Here, we conduct a comparative network analysis across five ant species from five subfamilies, separated by more than 100 Myr of evolution. We find that social network structure is highly conserved across subfamilies. All species studied form modular networks, with two social communities, a similar distribution of individuals between the two communities, and equivalent mapping of task performance onto the communities. Against this backdrop of organizational similarity, queens of the different species occupied qualitatively distinct network positions. The deep conservation of the two community structure implies that the most fundamental behavioural division of labour in social insects is between workers that stay in the nest to rear brood, and those that leave the nest to forage. This division has parallels across the animal kingdom in systems of biparental care and probably represents the most readily evolvable form of behavioural division of labour.

Refugee Networks, Cooperation, and Resource Access

Without formal avenues for claims-making or political participation, refugees must find their own means of securing services from state and non-state providers. This article asks why some refugee communities are more effective than others in mitigating community problems. I present a framework for understanding how refugees’ social networks shape the constraints and capabilities for collective action. I analyze a field experiment where I organized community meetings with Syrian refugees in Lebanon and Jordan, randomly assigning the recruitment method for meetings to introduce exogenous variation in network structure. During meetings, participants were tasked with resolving collective action problems. I examine the dynamics of subsequent group discussion. Results show that although densely networked refugee groups exhibit more cooperation, they suffer from a resource diversity disadvantage. Group diversity facilitates access to resources that may help refugee communities confront community problems. The novel experimental design allows for separately identifying group-level and individual-level mechanisms.

A case study on variations in network structure and cross-sector alignment in two local systems serving pregnant and parenting women in recovery.

To describe network structure and alignment across organizations in healthcare, public health, and social services sectors that serve pregnant and parenting women with substance use disorder (SUD) in an urban and a rural community. Two community networks, one urban and one rural with each including a residential substance use treatment program, in Kentucky during 2021. Social network analysis measured system collaboration and cross-sector alignment between healthcare, public health, and social services organizations, applying the Framework for Aligning Sectors. To understand the alignment and structure of each network, we measured network density overall and between sectors, network centralization, and each organization's degree centrality and effective size. Computer-assisted telephone interviews were conducted to document alignment around shared purpose, data, financing, and governance. On average, overall and cross-sector network densities in both communities were similar. However, alignment was highest for data sharing and financing in the urban community and for shared purpose and governance in the rural community. Cross-sector partnerships involving healthcare organizations were more prevalent in the rural county (44% vs. 38% for healthcare/public health, 44% vs. 29% for healthcare/social services), but more prevalent for those involving public health/social services organizations in the urban county (42% vs. 24%). A single healthcare organization had the highest degree centrality (Mdn [IQR] = 26 [26-9.5]) and effective size (Mdn [IQR] = 15.9 [20.6-8.7]) within the rural county. Social services organizations held more central positions in the urban county (degree centrality Mdn [IQR] = 13 [14.8-9.5]; effective size Mdn [IQR] = 10.4 [11.4-7.9]). Cross-sector alignment may strengthen local capacity for comprehensive SUD care for pregnant and parenting women. Healthcare organizations are key players in cross-sector partnerships in the rural community, where one healthcare facility holds the central brokerage role. In contrast, public health agencies are key to cross-sector collaboration with social services in the urban community.

Why do some entrepreneurs thrive? A network content perspective

Why do some entrepreneurs in developing environments thrive whereas others fail, even though they all face the same uncertainties? Prior research on entrepreneurial networks has attributed differences in business performance to variations in network structure. However, many entrepreneurs—such as necessity entrepreneurs—have networks with low structural variation. We show that variations in network content may be an alternative explanation for performance differences. Drawing on the resource-based view, we propose that entrepreneurs whose networks are characterized by low structural variation may benefit from variations in network content—namely, greater resource variety and more resource spanning. Our argument is supported strongly by a random sample of 200 Chinese necessity entrepreneurs working in Shanghai. By bringing network content to the analytical fore, we contribute to research on entrepreneurial networks, which has focused mostly on network structures. We further add to research on necessity entrepreneurship by examining how heterogeneous network content may influence business performance.

Terrestrial food web complexity in Amazonian forests decays with habitat loss

The conversion of natural ecosystems into human-modified landscapes (HMLs) is the main driver of biodiversity loss in terrestrial ecosystems.1,2,3 Even when species persist within habitat remnants, populations may become so small that ecological interactions are functionally lost, disrupting local interaction networks.4,5 To uncover the consequences of land use changes toward ecosystem functioning, we need to understand how changes in species richness and abundance in HMLs6,7,8 rearrange ecological networks. We used data from forest vertebrate surveys and combined modeling and network analysis to investigate how the structure of predator-prey networks was affected by habitat insularization induced by a hydroelectric reservoir in the Brazilian Amazonia.9 We found that network complexity, measured by interaction diversity, decayed non-linearly with decreasingly smaller forest area. Although on large forest islands (>100 ha) prey species were linked to 3-4 potential predators, they were linked to one or had no remaining predator on small islands. Using extinction simulations, we show that the variation in network structure cannot be explained by abundance-related extinction risk or prey availability. Our findings show that habitat loss may result in an abrupt disruption of terrestrial predator-prey networks, generating low-complexity ecosystems that may not retain functionality. Release from predation on some small islands may produce cascading effects over plants that accelerate forest degradation, whereas predator spillover on others may result in overexploited prey populations. Our analyses highlight that in addition to maintaining diversity, protecting large continuous forests is required for the persistence of interaction networks and related ecosystem functions.

Navigating uncertainty in networks of social exchange: a relational event study of a community currency system

Abstract This article analyzes the structure of socially embedded exchange under uncertainty in the context of a community currency system in Germany. We discuss three relational and path-dependent mechanisms—experience-based trust, networked reputation and public reputation—which serve as navigation practices to mitigate uncertainty. We furthermore associate these mechanisms with observable structures of exchange, namely repeated transactions and reciprocity, transitivity and provider activity, and discuss differences in product-inherent uncertainty as a source of variation in network structure. Based on original observations of more than 4000 transactions over a period of 8 years, we use relational event models to demonstrate that the history of transactions exhibits structure consistent with the three hypothesized mechanisms, with some variation across different types of transactions. This variation is partly in line with differences in product-inherent uncertainty, but we also discuss alternative sources of variation related to organizational and institutional conditions of the exchange system.

Novel idea generation in social networks is optimized by exposure to a "Goldilocks" level of idea-variability.

Recent works suggest that striking a balance between maximizing idea stimulation and minimizing idea redundancy can elevate novel idea generation performances in self-organizing social networks. We explore whether dispersing the visibility of high-performing idea generators can help achieve such a trade-off. We employ popularity signals (follower counts) of participants as an external source of variation in network structures, which we control across four conditions in a randomized setting. We observe that popularity signals influence inspiration-seeking ties, partly by biasing people's perception of their peers' novel idea-generation performances. Networks that partially disperse the top ideators' visibility using this external signal show reduced idea redundancy and elevated idea-generation performances. However, extreme dispersal leads to inferior performances by narrowing the range of idea stimulation. Our work holds future-of-work implications for elevating idea generation performances of people.

Rapid reconstruction of neural circuits using tissue expansion and light sheet microscopy.

Brain function is mediated by the physiological coordination of a vast, intricately connected network of molecular and cellular components. The physiological properties of neural network components can be quantified with high throughput. The ability to assess many animals per study has been critical in relating physiological properties to behavior. By contrast, the synaptic structure of neural circuits is presently quantifiable only with low throughput. This low throughput hampers efforts to understand how variations in network structure relate to variations in behavior. For neuroanatomical reconstruction, there is a methodological gulf between electron microscopic (EM) methods, which yield dense connectomes at considerable expense and low throughput, and light microscopic (LM) methods, which provide molecular and cell-type specificity at high throughput but without synaptic resolution. To bridge this gulf, we developed a high-throughput analysis pipeline and imaging protocol using tissue expansion and light sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many animals with single-synapse resolution and molecular contrast. Using Drosophila to validate this approach, we demonstrate that it yields synaptic counts similar to those obtained by EM, enables synaptic connectivity to be compared across sex and experience, and can be used to correlate structural connectivity, functional connectivity, and behavior. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species.

Influences of climate change and land use change on the interactions of ecosystem services in China’s Xijiang River Basin

Ecosystem services (ES) emerge from the complex interactions of diverse ecosystem components. In turn, the provision of ES is influenced by how they are put to use and environmental drivers such as climate change and land use change (CC-LUC). Researches into CC-LUC impacts on ES have so far focused on biophysical supply rather than ES interactions, making it difficult to design appropriate strategies for natural capital investment and the consequent ecosystem benefits for human well-being. In this study, we built networks from correlations among climatic forcing, land-use intensity and ES for the Xijiang River Basin in southern China, a rapidly urbanizing region with rich endowments of ecosystem assets. The variations in network structure were evaluated under different climate and land use projections up to 2035. We found that the CC-LUC-ES networks were more sensitive to land use change which affected the network structure either by directly influencing biophysical ES supplies or indirectly through altering ES interactions. Sustainable land-use management (i.e., ecological protection priority scenario) coupled with the RCP4.5 emission scenario showed higher improvements for both water-erosion prevention and regulating services. Whereas increasing land-use intensity (i.e., economic development priority scenario) with RCP8.5 resulted in losses of ES synergies and a more heterogenous network of greater integrity. Our approach provides a comprehensive view of the relationships among multiple ES and how they respond to CC-LUC, which can be applied to advance mechanistic hypotheses for ES variations. The results of this study can inform sustainable management strategies by regulating ES interactions in watersheds.

Stable pollination service in a generalist high Arctic community despite the warming climate.

Insects provide key pollination services in most terrestrial biomes, but this service depends on a multistep interaction between insect and plant. An insect needs to visit a flower, receive pollen from the anthers, move to another conspecific flower, and finally deposit the pollen on a receptive stigma. Each of these steps may be affected by climate change, and focusing on only one of them (e.g., flower visitation) may miss important signals of change in service provision. In this study, we combine data on visitation, pollen transport, and single-visit pollen deposition to estimate functional outcomes in the high Arctic plant-pollinator network of Zackenberg, Northeast Greenland, a model system for global warming-associated impacts in pollination services. Over two decades of rapid climate warming, we sampled the network repeatedly: in 1996, 1997, 2010, 2011, and 2016. Although the flowering plant and insect communities and their interactions varied substantially between years, as expected based on highly variable Arctic weather, there was no detectable directional change in either the structure of flower-visitor networks or estimated pollen deposition. For flower-visitor networks compiled over a single week, species phenologies caused major within-year variation in network structure despite consistency across years. Weekly networks for the middle of the flowering season emerged as especially important because most pollination service can be expected to be provided by these large, highly nested networks. Our findings suggest that pollination ecosystem service in the high Arctic is remarkably resilient. This resilience may reflect the plasticity of Arctic biota as an adaptation to extreme and unpredictable weather. However, most pollination service was contributed by relatively few fly taxa (Diptera: Spilogona sanctipauli and Drymeia segnis [Muscidae] and species of Rhamphomyia [Empididae]). If these key pollinators are negatively affected by climate change, network structure and the pollination service that depends on it would be seriously compromised.

Drivers and consequences of structure in plant-lemur ecological networks.

Species interactions shape the diversity and resilience of ecological networks. Plant and animal traits, as well as phylogeny, affect interaction likelihood, driving variation in network structure and tolerance to disturbance. We investigated how traits and phylogenetic effects influenced network-wide interaction probabilities and examined the consequences of extinction on the structure and robustness of ecological networks. We combined both mutualistic and antagonistic interactions of animals (55 species, Infraorder Lemuriformes, Order Primates) and their food plants (590 genera) throughout Madagascar to generate ecological networks. We tested the effects of both lemur and plant traits, biogeographic factors and phylogenetic relatedness on interaction probability in these networks using exponential random graph models. Next, we simulated animal and plant extinction to analyse the effects of extinction on network structure (connectance, nestedness and modularity) and robustness for mutualistic, antagonistic and combined plant-animal networks. Both animal and plant traits affected their interaction probabilities. Large, frugivorous lemurs with a short gestation length, occurring in arid habitats, and with a Least Concern threat level had a high interaction probability in the network, given all other variables. Closely related plants were more likely to interact with the same lemur species than distantly related plants, but closely related lemurs were not more likely to interact with the same plant genus. Simulated lemur extinction tended to increase connectance and modularity, but decrease nestedness and robustness, compared to pre-extinction networks. Networks were more tolerant to plant than lemur extinctions. Lemur-plant interactions were highly trait structured and the loss of both lemurs and plants threatened the tolerance of mutualistic, antagonistic and combined networks to future disturbance.

Differences in prey availability across space and time lead to interaction rewiring and reshape a predator-prey metaweb.

Space and time promote variation in network structure by affecting the likelihood of potential interactions. However, little is known about the relative roles of ecological and biogeographical processes in determining how species interactions vary across space and time. Here we study the spatiotemporal variation in predator-prey interaction networks formed by anurans and arthropods and test for the effects of prey availability in determining interaction patterns, information that is often absent and limits the understanding of the determinants of network structure. We found that network dissimilarity between ecoregions and seasons was high and primarily driven by interaction rewiring.We also found that species turnover was positively related to geographical distance. Using a null model approach to disentangle the effect of prey availability on the spatial and temporal variation, we show that differences in prey availability were important in determining the variation in network structure between seasons and among areas. Our study reveals that fluctuations in prey abundance, alongside the limited dispersal abilities of anurans and their prey, may be responsible for the spatial patterns that emerged in our predator-prey metaweb. These findings contribute to our understanding of the assembly rules that maintain biotic processes in metacommunities and highlight the importance of prey availability to the structure of these systems.

Effect of (BaO+CaO)/Al2O3 ratio (1.7∼2.0) on the structure and Al-Li association of BaO-CaO-Al2O3-CaF2-Li2O mold flux

Al2O3 possess properties of amphoteric oxide, so it plays different network roles in the traditional CaO-Al2O3-based melts, while, its influence mechanism in atomic scale has not yet been fully understood. Molecular dynamics (MD) simulation and MAS NMR Spectrum was conducted to gain a clear understanding about the variation of network structure and Al-Li association on the non-reactive melts structure in different (BaO+CaO)/Al2O3 ratio and temperature. The results show that [AlO4]− tetrahedra serves as the network framework of BaO-CaO-Al2O3-CaF2-Li2O non-reactive melt, and forms the chain and layered spatial network. As the (BaO+CaO)/Al2O3 ratio decreases from 2.0 to 1.7, Al2O3 converts to alkaline first and then to acidic, and the polymerization degree and viscosity of melt increase first and then decrease. In addition, the strength of Al-Li association decreases with the increase of (BaO+CaO)/Al2O3 ratio and temperature, which is conducive to the hop of Li+ between non-bridging oxygen sites and increases the diffusion rate of Li+ in the spatial network.

Personal Network Inference Unveils Heterogeneous Immune Response Patterns to Viral Infection in Children with Acute Wheezing.

Human rhinovirus (RV)-induced exacerbations of asthma and wheeze are a major cause of emergency room presentations and hospital admissions among children. Previous studies have shown that immune response patterns during these exacerbations are heterogeneous and are characterized by the presence or absence of robust interferon responses. Molecular phenotypes of asthma are usually identified by cluster analysis of gene expression levels. This approach however is limited, since genes do not exist in isolation, but rather work together in networks. Here, we employed personal network inference to characterize exacerbation response patterns and unveil molecular phenotypes based on variations in network structure. We found that personal gene network patterns were dominated by two major network structures, consisting of interferon-response versus FCER1G-associated networks. Cluster analysis of these structures divided children into subgroups, differing in the prevalence of atopy but not RV species. These network structures were also observed in an independent cohort of children with virus-induced asthma exacerbations sampled over a time course, where we showed that the FCER1G-associated networks were mainly observed at late time points (days four–six) during the acute illness. The ratio of interferon- and FCER1G-associated gene network responses was able to predict recurrence, with low interferon being associated with increased risk of readmission. These findings demonstrate the applicability of personal network inference for biomarker discovery and therapeutic target identification in the context of acute asthma which focuses on variations in network structure.

Sampling and asymptotic network properties of spatial multi‐trophic networks

Collecting well‐resolved empirical trophic networks requires significant time, money and expertise, yet we are still lacking knowledge on how sampling effort and bias impact the estimation of network structure. Filling this gap is a critical first step towards creating accurate representations of ecological networks and for teasing apart the impact of sampling compared to ecological and evolutionary processes that are known to create spatio‐temporal variation in network structure. We use a well‐sampled spatial dataset of lake food webs to examine how sample effort influences network structure. Specifically, we predict asymptotic network properties (ANPs) for our dataset by comparing lake‐specific network metrics with increasing sampling effort. We then contrast three sampling strategies – random, smallest lake to largest lake or largest lake to smallest lake – to assess which strategy best captures the regional metaweb (i.e. network of all potential interactions) network properties. We demonstrate metric‐specific relationships between sample effort and network metrics, often diverging from the ANPs. For example, low sample effort can contribute to much lower and poorer estimates of closeness centralization, as compared to approximations of modularity with similar sample efforts. In fact, many network metrics (e.g. connectance) have a quadratic relationship with sample effort indicating a sampling ‘sweet spot', which represents optimal sample effort for a close approximation of the ANP. Further, we find that sampling larger lakes followed by smaller lakes is a more optimal sampling strategy for capturing metaweb properties in this lentic ecosystem. Overall, we provide clear ways to better understand the impacts of sampling bias in food‐web studies which may be particularly critical given the rapid increase in studies comparing food webs across space and time.

Network edge entropy decomposition with spin statistics

In a previous study, we have explored how to decompose the global entropy of a network into edge components using a graph-spectral decomposition technique. Here, we develop this work in more depth to understand the role of edge entropy as an efficient and effective tool in analysing network structure. We use the edge entropy distribution as a network feature or characterisation and combine it with linear discriminant analysis to distinguish different types of network model and structure. Interpreting the normalised Laplacian matrix as the network Hamiltonian (or energy) operator, the network is assumed to be in thermodynamic equilibrium with a heat bath where the energy states correspond to the normalised Laplacian eigenvalues. To model the way in which particles occupy the energy states, we explore the use of three different spin-dependent statistical models to determine the thermodynamic entropy of the network. These are a) the classical spinless Maxwell-Boltzmann distribution, and two models based on quantum mechanical spin-statistics, namely b) the Bose-Einstein model for particles with integer spin, and c) the Fermi-Dirac model for particles with half-integer spin. By using the spectral decomposition of the Laplacian, we illustrate how to project out the edge-entropy components from the global network entropy. In this way, the detailed distribution of entropy across the edges of a network can be constructed. Compared to our previous study of the von Neumann edge entropy, where the edge entropy just depends on the degrees of the nodes forming an edge, in the case of the new statistical mechanical model, there is a more subtle dependence of the edge entropy on the structure of a network. We illustrate how this new edge entropy distribution can be used to more effectively identify variations in network structure, in particular for edges incorporating nodes of large degree. Numerical experiments on synthetic and real-world data-sets are presented to evaluate the qualitative and quantitative differences in performance.

Seeing through the static: the temporal dimension of plant-animal mutualistic interactions.

Most studies of plant-animal mutualistic networks have come from a temporally static perspective. This approach has revealed general patterns in network structure, but limits our ability to understand the ecological and evolutionary processes that shape these networks and to predict the consequences of natural and human-driven disturbance on species interactions. We review the growing literature on temporal dynamics of plant-animal mutualistic networks including pollination, seed dispersal and ant defence mutualisms. We then discuss potential mechanisms underlying such variation in interactions, ranging from behavioural and physiological processes at the finest temporal scales to ecological and evolutionary processes at the broadest. We find that at the finest temporal scales (days, weeks, months) mutualistic interactions are highly dynamic, with considerable variation in network structure. At intermediate scales (years, decades), networks still exhibit high levels of temporal variation, but such variation appears to influence network properties only weakly. At the broadest temporal scales (many decades, centuries and beyond), continued shifts in interactions appear to reshape network structure, leading to dramatic community changes, including loss of species and function. Our review highlights the importance of considering the temporal dimension for understanding the ecology and evolution of complex webs of mutualistic interactions.