DNA-based networks reveal the ecological determinants of plant-herbivore interactions along environmental gradients

Understanding the ecological rules structuring the organization of species interactions is a prerequisite to predicting how ecosystems respond to environmental changes. While the ecological determinants of single networks have been documented, it remains unclear whether network ecological rules are conserved along spatial and environmental gradients. To address this gap, we reconstructed 48 plant-herbivore interaction networks along six elevation gradients in the Central European Alps in Switzerland, using DNA metabarcoding on orthoptera faeces. We developed hypotheses on the ecological mechanisms expected to structure interaction networks, based on plant phylogeny, plant abundance, leaf toughness, leaf nitrogen content and plant metabolomics. We show that plant phylogenetic relationships and species abundance have the greatest explanatory power regarding the structure of the ecological networks. Moreover, we found that leaf nitrogen content is a key determinant of interactions in warmer environments, while phenolic compounds and tannins are more important in colder environments, suggesting that determinants of species interactions can shift along environmental gradients. With this work, we propose an approach to study the mechanisms that structure the way species interact with each other between bioregions and ecosystems.

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.

Partner Fidelity and Asymmetric Specialization in Ecological Networks

AbstractSpecies are embedded in complex networks of interdependencies that may change across geographic locations. Yet most approaches to investigate the architecture of this entangled web of life have considered exclusively local communities. To quantify to what extent species interactions change at a biogeographic scale, we need to shed light on how among-community variation affects the occurrence of species interactions. Here we quantify the probability for two partners to interact wherever they co-occur (i.e., partner fidelity) by analyzing the most extensive database on species interaction networks worldwide. We found that mutualistic species show more fidelity in their interactions than antagonistic ones when there is asymmetric specialization (i.e., when specialist species interact with generalist partners). Moreover, resources (e.g., plants in plant-pollinator mutualisms or hosts in host-parasite interactions) show a higher partner fidelity in mutualistic interactions than in antagonistic interactions, which can be explained neither by sampling effort nor by phylogenetic constraints developed during their evolutionary histories. In spite of the general belief that mutualistic interactions among free-living species are labile, asymmetric specialization is very much conserved across large geographic areas.

The Role of Evolution in Shaping Ecological Networks

The structure of ecological networks reflects the evolutionary history of their biotic components, and their dynamics are strongly driven by ecoevolutionary processes. Here, we present an appraisal of recent relevant research, in which the pervasive role of evolution within ecological networks is manifest. Although evolutionary processes are most evident at macroevolutionary scales, they are also important drivers of local network structure and dynamics. We propose components of a blueprint for further research, emphasising process-based models, experimental evolution, and phenotypic variation, across a range of distinct spatial and temporal scales. Evolutionary dimensions are required to advance our understanding of foundational properties of community assembly and to enhance our capability of predicting how networks will respond to impending changes.

Structural trade-offs can predict rewiring in shrinking social networks

There is growing evidence that organisms can respond to declining population sizes by adapting their interactions with others. Regulating connections with others could underpin resilience of biological networks spanning from social groups to ecological communities. However, our ability to predict the dynamics of shrinking social networks remains limited. Network regulation involves several trade-offs. Removing nodes (and therefore their connections) from networks reduces the number of connections among remaining nodes. Responding by forming new connections then impacts other network properties. A simple way to minimize the impact of up-regulating network connections is to form new connections or to strengthen connections, between nodes that share a lost connection with a recently removed node. I propose a simple 'second-degree rewiring' rule as a biologically plausible regulatory mechanism in shrinking social networks. I argue that two individuals that have lost a connection with a common removed individual will both be more likely, or more willing, to form a new, or strengthen an existing, connection among themselves. I then show that such second-degree rewiring has less impact on important structural properties of the network than forming random new connections. For example, in a network with phenotypic assortment, second-degree nodes are more likely to be similar than any random pair of nodes, and connecting these will better maintain assortativity. This simple rule can therefore maintain network properties without individuals having any knowledge of the global structure of the network or the relative properties of the nodes within it. In this paper, I outline an algorithm for second-degree rewiring. I demonstrate how second-degree rewiring can have less impact than adding new, or increasing the strength of, random connections on both the individual and whole network properties. That is, relative to randomly adding or strengthening connections, second-degree rewiring has less impact on mean degree, assortativity, clustering and network density. I then demonstrate empirically, using social networks of great tits (Parus major), that individuals that previously shared connections to a removed conspecific were more likely to form a new connection or to strengthen their connection, relative to other individuals in the same population. This study highlights how developing a better mechanistic understanding of the structural properties of networks, and the consequences of adding new connections, can provide useful insights into how organisms are likely to regulate their interactions in shrinking populations.

Specialization in plant-pollinator networks: insights from local-scale interactions in Glenbow Ranch Provincial Park in Alberta, Canada

Background

The occurrence and frequency of plant–pollinator interactions are acknowledged to be a function of multiple factors, including the spatio-temporal distribution of species. The study of pollination specialization by examining network properties and more recently incorporating predictors of pairwise interactions is emerging as a useful framework, yet integrated datasets combining network structure, habitat disturbance, and phylogenetic information are still scarce.

Results

We found that plant–pollinator interactions in a grassland ecosystem in the foothills of the Rocky Mountains are not randomly distributed and that high levels of reciprocal specialization are generated by biological constraints, such as floral symmetry, pollinator size and pollinator sociality, because these traits lead to morphological or phenological mismatching between interacting species. We also detected that landscape degradation was associated with differences in the network topology, but the interaction webs still maintained a consistently higher number of reciprocal specialization cases than expected. Evidence for the reciprocal evolutionary dependence in visitors (e.g., related pollinators visiting related plants) were weak in this study system, however we identified key species joining clustered units.

Conclusions

Our results indicate that the conserved links with keystone species may provide the foundation for generating local reciprocal specialization. From the general topology of the networks, plant–pollinators interactions in sites with disturbance consisted of generalized nodes connecting modules (i.e., hub and numerous connectors). Vice versa, interactions in less disturbed sites consisted of more specialized and symmetrical connections.

Adaptive Networks for Restoration Ecology

The urgent need to restore biodiversity and ecosystem functioning challenges ecology as a predictive science. Restoration ecology would benefit from evolutionary principles embedded within a framework that combines adaptive network models and the phylogenetic structure of ecological interactions. Adaptive network models capture feedbacks between trait evolution, species abundances, and interactions to explain resilience and functional diversity within communities. Phylogenetically-structured network data, increasingly available via next-generation sequencing, inform constraints affecting interaction rewiring. Combined, these approaches can predict eco-evolutionary changes triggered by community manipulation practices, such as translocations and eradications of invasive species. We discuss theoretical and methodological opportunities to bridge network models and data from restoration projects and propose how this can be applied to the functional restoration of ecological interactions.

Comparing species interaction networks along environmental gradients

Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co-variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant-hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis-based metaweb. Overall, our analysis warns against a comparison of studies that rely on distinct forms of standardization, as they are likely to highlight different signals. Fostering a better understanding of the analytical tools available and the signal they detect will help produce deeper insights into how and why ecological networks vary along environmental gradients.

How phylogeny shapes the taxonomic and functional structure of plant-insect networks

Phylogenetically related species share a common evolutionary history and may therefore have similar traits. In terms of interaction networks, where traits are a major determinant, related species should therefore interact with other species which are also related. However, this prediction is challenged by current evidence that there is a weak, albeit significant, phylogenetic signal in species' taxonomic niche, i.e., the identity of interacting species. We studied mutualistic and antagonistic plant-insect interaction networks in species-rich alpine meadows and show that there is instead a very strong phylogenetic signal in species' functional niches-i.e., the mean functional traits of their interactors. This pattern emerges because related species tend to interact with species bearing certain traits that allow biotic interactions (pollination, herbivory) but not necessarily with species from all the same evolutionary lineages. Those traits define a set of potential interactors and show clear patterns of phylogenetic clustering on several portions of plants and insect phylogenies. Thus, this emerging pattern of low phylogenetic signal in taxonomic niches but high phylogenetic signal in functional niches may be driven by the interplay between functional trait convergence across plants' and insects' phylogenies and random sampling of the potential interactors.