Coevolution by different functional mechanisms modulates the structure and dynamics of antagonistic and mutualistic networks

Coevolution increases robustness to extinctions in mutualistic but not exploitative communities

Coextinctions may exacerbate the current biodiversity crisis. Yet, we do not understand all the factors that shape the robustness of communities to the loss of species. Here we analyze how coevolution influences the robustness to secondary extinctions of mutualistic and exploitative communities. We find that coevolution increases robustness in mutualism but reduces it under exploitative interactions. These differences are due to coevolution altering the density of interactions in communities. Coevolution leads to densely connected mutualistic communities and sparsely connected exploitative communities. We find the magnitude of these effects depends on the strength of coevolution and the size of the community. The largest changes to the density of interactions and robustness of communities occur when coevolutionary selection is strong. Moreover, the changes to network robustness are greater for small mutualistic communities and large exploitative communities. Our results broaden our understanding of the suite of mechanisms affecting the resilience of ecological communities. These insights may inform efforts to reduce the risk of species loss in the face of global change.

Macroevolution of the plant-hummingbird pollination system

Plant-hummingbird interactions are considered a classic example of coevolution, a process in which mutually dependent species influence each other's evolution. Plants depend on hummingbirds for pollination, whereas hummingbirds rely on nectar for food. As a step towards understanding coevolution, this review focuses on the macroevolutionary consequences of plant-hummingbird interactions, a relatively underexplored area in the current literature. We synthesize prior studies, illustrating the origins and dynamics of hummingbird pollination across different angiosperm clades previously pollinated by insects (mostly bees), bats, and passerine birds. In some cases, the crown age of hummingbirds pre-dates the plants they pollinate. In other cases, plant groups transitioned to hummingbird pollination early in the establishment of this bird group in the Americas, with the build-up of both diversities coinciding temporally, and hence suggesting co-diversification. Determining what triggers shifts to and away from hummingbird pollination remains a major open challenge. The impact of hummingbirds on plant diversification is complex, with many tropical plant lineages experiencing increased diversification after acquiring flowers that attract hummingbirds, and others experiencing no change or even a decrease in diversification rates. This mixed evidence suggests that other extrinsic or intrinsic factors, such as local climate and isolation, are important covariables driving the diversification of plants adapted to hummingbird pollination. To guide future studies, we discuss the mechanisms and contexts under which hummingbirds, as a clade and as individual species (e.g. traits, foraging behaviour, degree of specialization), could influence plant evolution. We conclude by commenting on how macroevolutionary signals of the mutualism could relate to coevolution, highlighting the unbalanced focus on the plant side of the interaction, and advocating for the use of species-level interaction data in macroevolutionary studies.

The coevolutionary consequences of biodiversity change

Coevolutionary selection is a powerful process shaping species interactions and biodiversity. Anthropogenic global environmental change is reshaping planetary biodiversity, including by altering the structure and intensity of interspecific interactions. However, remarkably little is understood of how coevolutionary selection is changing in the process. Here, we outline three interrelated pathways - change in evolutionary potential, change in community composition, and shifts in interaction trait distributions - that are expected to redirect coevolutionary selection under biodiversity change. Assessing how both ecological and evolutionary rules governing species interactions are disrupted under anthropogenic global change is of paramount importance to understand the past, present, and future of Earth's biodiversity.

Stability, resilience and eco-evolutionary feedbacks of mutualistic networks to rising temperature

Ecological networks comprising of mutualistic interactions can suddenly transition to undesirable states, such as collapse, due to small changes in environmental conditions such as a rise in local environmental temperature. However, little is known about the capacity of such interaction networks to adapt to a rise in temperature and the occurrence of critical transitions. Here, combining quantitative genetics and mutualistic dynamics in an eco-evolutionary framework, we evaluated the stability and resilience of mutualistic networks to critical transitions as environmental temperature increases. Specifically, we modelled the dynamics of an optimum trait that determined the tolerance of species to local environmental temperature as well as to species interaction. We then evaluated the impact of individual trait variation and evolutionary dynamics on the stability of feasible equilibria, the occurrence of threshold temperatures at which community collapses, and the abruptness of such community collapses. We found that mutualistic network architecture, that is the size of the community and the arrangement of species interactions, interacted with evolutionary dynamics to impact the onset of network collapses. Some networks had more capacity to track the rise in temperatures than others and thereby increased the threshold temperature at which the networks collapsed. However, such a result was modulated by the amount of heritable trait variation species exhibited, with high trait variation in the mean optimum phenotypic trait increasing the environmental temperature at which networks collapsed. Furthermore, trait variation not only increased the onset of temperatures at which networks collapsed but also increased the local stability of feasible equilibria. Our study argued that mutualistic network architecture interacts with species evolutionary dynamics and increases the capacity of networks to adapt to changes in temperature and thereby delayed the occurrence of community collapses.

Evolutionary plant-pollinator responses to anthropogenic land-use change: impacts on ecosystem services

Agricultural intensification at field and landscape scales, including increased use of agrochemicals and loss of semi-natural habitats, is a major driver of insect declines and other community changes. Efforts to understand and mitigate these effects have traditionally focused on ecological responses. At the same time, adaptations to pesticide use and habitat fragmentation in both insects and flowering plants show the potential for rapid evolution. Yet we lack an understanding of how such evolutionary responses may propagate within and between trophic levels with ensuing consequences for conservation of species and ecological functions in agroecosystems. Here, we review the literature on the consequences of agricultural intensification on plant and animal evolutionary responses and interactions. We present a novel conceptualization of evolutionary change induced by agricultural intensification at field and landscape scales and emphasize direct and indirect effects of rapid evolution on ecosystem services. We exemplify by focusing on economically and ecologically important interactions between plants and pollinators. We showcase available eco-evolutionary theory and plant-pollinator modelling that can improve predictions of how agricultural intensification affects interaction networks, and highlight available genetic and trait-focused methodological approaches. Specifically, we focus on how spatial genetic structure affects the probability of propagated responses, and how the structure of interaction networks modulates effects of evolutionary change in individual species. Thereby, we highlight how combined trait-based eco-evolutionary modelling, functionally explicit quantitative genetics, and genomic analyses may shed light on conditions where evolutionary responses impact important ecosystem services.

Host- plasmid network structure in wastewater is linked to antimicrobial resistance genes

As mobile genetic elements, plasmids are central for our understanding of antimicrobial resistance spread in microbial communities. Plasmids can have varying fitness effects on their host bacteria, which will markedly impact their role as antimicrobial resistance vectors. Using a plasmid population model, we first show that beneficial plasmids interact with a higher number of hosts than costly plasmids when embedded in a community with multiple hosts and plasmids. We then analyse the network of a natural host-plasmid wastewater community from a Hi-C metagenomics dataset. As predicted by the model, we find that antimicrobial resistance encoding plasmids, which are likely to have positive fitness effects on their hosts in wastewater, interact with more bacterial taxa than non-antimicrobial resistance plasmids and are disproportionally important for connecting the entire network compared to non- antimicrobial resistance plasmids. This highlights the role of antimicrobials in restructuring host-plasmid networks by increasing the benefits of antimicrobial resistance carrying plasmids, which can have consequences for the spread of antimicrobial resistance genes through microbial networks. Furthermore, that antimicrobial resistance encoding plasmids are associated with a broader range of hosts implies that they will be more robust to turnover of bacterial strains.

The Role of Indirect Effects in Coevolution along the Mutualism-Antagonism Continuum

AbstractThe web of interactions in a community drives the coevolution of species. Yet it is unclear how the outcome of species interactions influences the coevolutionary dynamics of communities. This is a pressing matter, as changes to the outcome of interactions may become more common with human-induced global change. Here, we combine network and evolutionary theory to explore coevolutionary outcomes in communities harboring mutualistic and antagonistic interactions. We show that as the ratio of mutualistic to antagonistic interactions decreases, selection imposed by direct partners outweighs that imposed by indirect partners. This weakening of indirect effects results in communities composed of species with dissimilar traits and fast rates of adaptation. These changes are more pronounced when specialist consumers are the first species to engage in antagonistic interactions. Hence, a shift in the outcome of species interactions may reverberate across communities and alter the direction and speed of coevolution.

Ecological network analysis reveals cancer-dependent chaperone-client interaction structure and robustness

Cancer cells alter the expression levels of metabolic enzymes to fuel proliferation. The mitochondrion is a central hub of metabolic reprogramming, where chaperones service hundreds of clients, forming chaperone-client interaction networks. How network structure affects its robustness to chaperone targeting is key to developing cancer-specific drug therapy. However, few studies have assessed how structure and robustness vary across different cancer tissues. Here, using ecological network analysis, we reveal a non-random, hierarchical pattern whereby the cancer type modulates the chaperones' ability to realize their potential client interactions. Despite the low similarity between the chaperone-client interaction networks, we highly accurately predict links in one cancer type based on another. Moreover, we identify groups of chaperones that interact with similar clients. Simulations of network robustness show that this group structure affects cancer-specific response to chaperone removal. Our results open the door for new hypotheses regarding the ecology and evolution of chaperone-client interaction networks and can inform cancer-specific drug development strategies.

The Resilience of Plant-Pollinator Networks

There is growing awareness of pollinator declines worldwide. Conservation efforts have mainly focused on finding the direct causes, while paying less attention to building a systemic understanding of the fragility of these communities of pollinators. To fill this gap, we need operational measures of network resilience that integrate two different approaches in theoretical ecology. First, we should consider the range of conditions compatible with the stable coexistence of all of the species in a community. Second, we should address the rate and shape of network collapse once this safe operational space is exited. In this review, we describe this integrative approach and consider several mechanisms that may enhance the resilience of pollinator communities, chiefly rewiring the network of interactions, increasing heterogeneity, allowing variance, and enhancing coevolution. The most pressing need is to develop ways to reduce the gap between these theoretical recommendations and practical applications. This perspective shifts the emphasis from traditional approaches focusing on the equilibrium states to strategies that allow pollination networks to cope with global environmental change.

Biotic Interactions and the Future of Fishes on Coral Reefs: The Importance of Trait-Based Approaches

Biotic interactions govern the structure and function of coral reef ecosystems. As environmental conditions change, reef-associated fish populations can persist by tracking their preferred niche or adapting to new conditions. Biotic interactions will affect how these responses proceed and whether they are successful. Yet, our understanding of these effects is currently limited. Ecological and evolutionary theories make explicit predictions about the effects of biotic interactions, but many remain untested. Here, we argue that large-scale functional trait datasets enable us to investigate how biotic interactions have shaped the assembly of contemporary reef fish communities and the evolution of species within them, thus improving our ability to predict future changes. Importantly, the effects of biotic interactions on these processes have occurred simultaneously within dynamic environments. Functional traits provide a means to integrate the effects of both ecological and evolutionary processes, as well as a way to overcome some of the challenges of studying biotic interactions. Moreover, functional trait data can enhance predictive modeling of future reef fish distributions and evolvability. We hope that our vision for an integrative approach, focused on quantifying functionally relevant traits and how they mediate biotic interactions in different environmental contexts, will catalyze new research on the future of reef fishes in a changing environment.

Effects of habitat destruction on coevolving metacommunities

Habitat destruction is a growing threat to biodiversity and ecosystem services. The ecological consequences of habitat loss and fragmentation involve reductions in species abundance and even the extinction of species and their interactions. However, we do not yet understand how habitat loss alters the coevolutionary trajectories of the remaining species or how coevolution, in turn, affects their response to habitat loss. To investigate this, we develop a spatially explicit model which couples metacommunity and coevolutionary dynamics. We show that, by changing the size, composition and structure of local networks, habitat destruction increases the diversity of coevolutionary trajectories of mutualists across the landscape. Conversely, in antagonistic communities, some species increase while others reduce their spatial trait heterogeneity. Furthermore, we show that while coevolution dampens the negative effects of habitat destruction in mutualistic networks, its effects on the persistence of antagonistic communities tend to be smaller and less predictable.

Fitness effects of plasmids shape the structure of bacteria-plasmid interaction networks

Antimicrobial resistance (AMR) genes are often carried on broad host range plasmids, and the spread of AMR within microbial communities will therefore depend on the structure of bacteria–plasmid networks. Empirical and theoretical studies of ecological interaction networks suggest that network structure differs between communities that are predominantly mutualistic versus antagonistic, with the former showing more generalized interactions (i.e., species interact with many others to a similar extent). This suggests that mutualistic bacteria–plasmid networks—where antibiotics are present and plasmids carry AMR genes—will be more generalized than antagonistic interactions, where plasmids do not confer benefits to their hosts. We first develop a simple theory to explain this link: fitness benefits of harboring a mutualistic symbiont promote the spread of the symbiont to other species. We find support for this theory using an experimental bacteria–symbiont (plasmid) community, where the same plasmid can be mutualistic or antagonistic depending on the presence of antibiotics. This short-term and parsimonious mechanism complements a longer-term mechanism (coevolution and stability) explaining the link between mutualistic and antagonistic interactions and network structure.

The impact of individual variation on abrupt collapses in mutualistic networks

Individual variation is central to species involved in complex interactions with others in an ecological system. Such ecological systems could exhibit tipping points in response to changes in the environment, consequently leading to abrupt transitions to alternative, often less desirable states. However, little is known about how individual trait variation could influence the timing and occurrence of abrupt transitions. Using 101 empirical mutualistic networks, I model the eco‐evolutionary dynamics of such networks in response to gradual changes in strength of co‐evolutionary interactions. Results indicated that individual variation facilitates the timing of transition in such networks, albeit slightly. In addition, individual variation significantly increases the occurrence of large abrupt transitions. Furthermore, topological network features also positively influence the occurrence of such abrupt transitions. These findings argue for understanding tipping points using an eco‐evolutionary perspective to better forecast abrupt transitions in ecological systems.

The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms

Coevolution can sculpt remarkable trait similarity between mutualistic partners. Yet, it remains unclear which network topologies and selection regimes enhance trait matching. To address this, we simulate coevolution in topologically distinct networks under a gradient of mutualistic selection strength. We describe three main insights. First, trait matching is jointly influenced by the strength of mutualistic selection and the structural properties of the network where coevolution is unfolding. Second, the strength of mutualistic selection determines the network descriptors better correlated with higher trait matching. While network modularity enhances trait matching when coevolution is weak, network connectance does so when coevolution is strong. Third, the structural properties of networks outrank those of modules or species in determining the degree of trait matching. Our findings suggest networks can both enhance or constrain trait matching, depending on the strength of mutualistic selection.

Coevolutionary Arms Races and the Conditions for the Maintenance of Mutualism

AbstractEmpirical evidence suggests that coevolutionary arms races between flowering plants and their pollinators can occur in wild populations. In extreme cases, trait escalation may result in evolutionary switching from mutualism to parasitism. However, theoretical approaches to studying coevolution typically assume fixed types of ecological interactions and ignore the evolution of absolute fitness. Here, we introduce a novel approach to track the evolution of absolute fitness as a framework to determine when escalatory coevolution results in a switch from mutualism to parasitism. We apply our approach to two previously studied mechanisms mediating selection as a function of phenotype. Our results demonstrate that interactions mediated by a "bigger-is-better" mechanism evolve toward parasitism. In contrast, generalizing the classical trait-matching mechanism so that the fitness of each species is optimized when trait values mismatch by a particular amount, we find theoretical support for indefinite trait exaggeration that preserves mutualistic interactions. Building on our results, we discuss the consequences of coevolutionary arms races for the maintenance of cheating. Moving beyond pairwise interactions, we consider the ramifications of coevolution in a South African pollination network for the evolution of parasitism. Future work extending our approach beyond pairwise interactions can lead to a framework for understanding the evolution of parasitism in mutualistic networks and further insights into the structure and dynamic nature of ecological communities in general.

Differential effects of nematode infection on pollinating and non-pollinating fig wasps: Can shared antagonism provide net benefits to a mutualism?

Species pairs that form mutualistic associations are also components of broader organismal community networks. These interaction networks have shaped the evolution of individual mutualisms through interspecific interactions ranging from secondarily mutualistic to intensely antagonistic. Our understanding of this complex context remains limited because characterizing the impacts of species interacting with focal mutualists is often difficult. How is the fitness of mutualists impacted by the co-occurring interactive network of community associates? We investigated this context using a model interaction network comprised of a fig and fig wasp mutualist, eight non-pollinating fig wasp (NPFW) antagonists/commensals and a nematode previously believed to be associated only with the pollinator wasp mutualist. Through repeated sampling and field observations, we characterized the ecological roles of these mutualist-associated organisms to identify key antagonists. We then investigated how potential nematode infection of NPFWs could impact wasp survival across key life stages and, in turn, inferred how this influences the fitness of the fig-pollinator mutualists. Unexpectedly, we found all Ficus petiolaris-associated NPFWs to be the targets for nematode infection, with infection levels sometimes exceeding that of pollinators. Experimental data collected for the most abundant NPFW species suggest that nematode infection significantly reduces their longevity. Further, comparisons of nematode loads for emerging and successfully arriving NPFWs suggest that infection severely limits their dispersal ability. Through these observations, we conclude that this infection could impact NPFWs more severely than either mutualistic partner, suggesting a novel role of density-dependent facultative mutualism between figs, pollinator wasps and the nematode. This antagonist-mediated suppression of other network antagonists may present an ecologically common mechanism through which antagonists can present net benefits for mutualists' fitness.

A white noise approach to evolutionary ecology

Although the evolutionary response to random genetic drift is classically modelled as a sampling process for populations with fixed abundance, the abundances of populations in the wild fluctuate over time. Furthermore, since wild populations exhibit demographic stochasticity and since random genetic drift is in part due to demographic stochasticity, theoretical approaches are needed to understand the role of demographic stochasticity in eco-evolutionary dynamics. Here we close this gap for quantitative characters evolving in continuously reproducing populations by providing a framework to track the stochastic dynamics of abundance density across phenotypic space using stochastic partial differential equations. In the process we develop a set of heuristics to operationalize the powerful, but abstract theory of white noise and diffusion-limits of individual-based models. Applying these heuristics, we obtain stochastic ordinary differential equations that generalize classical expressions of ecological quantitative genetics. In particular, by supplying growth rate and reproductive variance as functions of abundance densities and trait values, these equations track population size, mean trait and additive genetic variance responding to mutation, demographic stochasticity, random genetic drift, deterministic selection and noise-induced selection. We demonstrate the utility of our approach by formulating a model of diffuse coevolution mediated by exploitative competition for a continuum of resources. In addition to trait and abundance distributions, this model predicts interaction networks defined by niche-overlap, competition coefficients, or selection gradients. Using a high-richness approximation, we find linear selection gradients and competition coefficients are uncorrelated, but magnitudes of linear selection gradients and quadratic selection gradients are both positively correlated with competition coefficients. Hence, competing species that strongly affect each other's abundance tend to also impose selection on one another, but the directionality is not predicted. This approach contributes to the development of a synthetic theory of evolutionary ecology by formalizing first principle derivations of stochastic models tracking feedbacks of biological processes and the patterns of diversity they produce.

An individual-based model for the eco-evolutionary emergence of bipartite interaction networks

How ecological interaction networks emerge on evolutionary time scales remains unclear. Here we build an individual-based eco-evolutionary model for the emergence of mutualistic, antagonistic and neutral bipartite interaction networks. Exploring networks evolved under these scenarios, we find three main results. First, antagonistic interactions tend to foster species and trait diversity, while mutualistic interactions reduce diversity. Second, antagonistic interactors evolve higher specialisation, which results in networks that are often more modular than neutral ones; resource species in these networks often display phylogenetic conservatism in interaction partners. Third, mutualistic interactions lead to networks that are more nested than neutral ones, with low phylogenetic conservatism in interaction partners. These results tend to match overall empirical trends, demonstrating that structures of empirical networks that have most often been explained by ecological processes can result from an evolutionary emergence. Our model contributes to the ongoing effort of better integrating ecological interactions and macroevolution.