Ecological Interaction Networks Research Articles

Plant and animal functional diversity drive mutualistic network assembly across an elevational gradient

Species’ functional traits set the blueprint for pair-wise interactions in ecological networks. Yet, it is unknown to what extent the functional diversity of plant and animal communities controls network assembly along environmental gradients in real-world ecosystems. Here we address this question with a unique dataset of mutualistic bird–fruit, bird–flower and insect–flower interaction networks and associated functional traits of 200 plant and 282 animal species sampled along broad climate and land-use gradients on Mt. Kilimanjaro. We show that plant functional diversity is mainly limited by precipitation, while animal functional diversity is primarily limited by temperature. Furthermore, shifts in plant and animal functional diversity along the elevational gradient control the niche breadth and partitioning of the respective other trophic level. These findings reveal that climatic constraints on the functional diversity of either plants or animals determine the relative importance of bottom-up and top-down control in plant–animal interaction networks.

Trophic structure alters consequences of environmental warming

Climate warming can directly affect traits and demographic rates of organisms. However, individuals are embedded in complex networks of ecological interactions with other members of the community, allowing for a range of direct and indirect effects that depend on the trophic structure of the community. Here we show that effects of warming (i.e. increase in mean temperature) on a given species can strongly depend on the community context and trophic complexity of the system. Specifically, we manipulated the presence/ absence of two competing tadpole species and their dragonfly predators to simulate different food webs of increasing complexity that were exposed to ambient or warmed conditions. We found that warming dramatically reduced herbivore (tadpole) survival in the absence of strong interspecific competition and predation, but it had no measureable effect on demographic rates on the dominant competitor in more complex communities where it was exposed to interspecific competition and predation. Conversely, our results also indicate that warming reduced the strength of interspecific competition and predation in our system. These results suggest that trophic complexity could potentially buffer climate change effects on populations and emphasize that we often cannot predict the effects of changes in abiotic conditions on a given population without accounting for the community context.

Morphological trait matching shapes plant–frugivore networks across the Andes

Interactions between resource and consumer species are organized in ecological networks. Species interactions in these networks are influenced by the functional traits of the interacting partners, but the generality of trait‐based interaction rules and the relationship between functional traits and a species’ specialization on specific interaction partners are not yet understood. Here we combine data on eight interaction networks between fleshy‐fruited plants and frugivorous birds sampled across the tropical and subtropical Andean range. We test which combinations of morphological plant and animal traits determine trait matching between resource and consumer species in these networks. In addition, we test which of the morphological traits influence functional specialization of plant and bird species. In a meta‐analysis across network‐specific fourth‐corner analyses, we found that plant–animal trait pairs related to size matching (fruit size–beak size) and avian foraging behavior (plant height–wing shape and crop mass–body mass) were positively related in these networks. The degree of functional specialization on specific interaction partners was positively related to crop mass in plants and to the pointedness of the wing in birds. Our findings show that morphological trait matching between fleshy‐fruited plants and frugivorous birds is a general phenomenon in plant–frugivore networks across the Andes and that specific plant and bird traits can be used to approximate the degree of functional specialization. These insights into the generality of interaction rules are the base for predictions of species interactions in ecological networks, for instance in novel communities in the future, and can be applied to identify plant and animal species that fulfill specialized functional roles in ecological communities.

Moving from frugivory to seed dispersal: Incorporating the functional outcomes of interactions in plant-frugivore networks.

There is growing interest in understanding the functional outcomes of species interactions in ecological networks. For many mutualistic networks, including pollination and seed dispersal networks, interactions are generally sampled by recording animal foraging visits to plants. However, these visits may not reflect actual pollination or seed dispersal events, despite these typically being the ecological processes of interest.Frugivorous animals can act as seed dispersers, by swallowing entire fruits and dispersing their seeds, or as pulp peckers or seed predators, by pecking fruits to consume pieces of pulp or seeds. These processes have opposing consequences for plant reproductive success. Therefore, equating visitation with seed dispersal could lead to biased inferences about the ecology, evolution and conservation of seed dispersal mutualisms.Here, we use natural history information on the functional outcomes of pairwise bird–plant interactions to examine changes in the structure of seven European plant–frugivore visitation networks after non‐mutualistic interactions (pulp pecking and seed predation) have been removed. Following existing knowledge of the contrasting structures of mutualistic and antagonistic networks, we hypothesized a number of changes following interaction removal, such as increased nestedness and lower specialization.Non‐mutualistic interactions with pulp peckers and seed predators occurred in all seven networks, accounting for 21%–48% of all interactions and 6%–24% of total interaction frequency. When non‐mutualistic interactions were removed, there were significant increases in network‐level metrics such as connectance and nestedness, while robustness decreased. These changes were generally small, homogenous and driven by decreases in network size. Conversely, changes in species‐level metrics were more variable and sometimes large, with significant decreases in plant degree, interaction frequency, specialization and resilience to animal extinctions and significant increases in frugivore species strength.Visitation data can overestimate the actual frequency of seed dispersal services in plant–frugivore networks. We show here that incorporating natural history information on the functions of species interactions can bring us closer to understanding the processes and functions operating in ecological communities. Our categorical approach lays the foundation for future work quantifying functional interaction outcomes along a mutualism–antagonism continuum, as documented in other frugivore faunas.

Metatranscriptome of human faecal microbial communities in a cohort of adult men.

The gut microbiome is intimately related to human health, but it is not yet known which functional activities are driven by specific microbes’ ecological configurations or transcription. We report a large-scale investigation of 372 human fecal metatranscriptomes and 929 metagenomes from a subset of 308 men in the Health Professionals Follow-up Study. We identified a metatranscriptomic “core” universally transcribed over time and across participants, often by different microbes. In contrast to the housekeeping functions enriched in this core, a “variable” metatranscriptome included specialized pathways that were differentially expressed both across participants and among microbes. Finally, longitudinal metagenomic profiles allowed ecological interaction network reconstruction, which remained stable over the six-month timespan, as did strain tracking within and between participants. These results provide an initial characterization of human fecal microbial ecology into core, subject-specific, microbe-specific, and temporally-variable transcription, and they differentiate metagenomically versus metatranscriptomically informative aspects of the human fecal microbiome.

Fungi stabilize connectivity in the lung and skin microbial ecosystems

BackgroundNo microbe exists in isolation, and few live in environments with only members of their own kingdom or domain. As microbiome studies become increasingly more interested in the interactions between microbes than in cataloging which microbes are present, the variety of microbes in the community should be considered. However, the majority of ecological interaction networks for microbiomes built to date have included only bacteria. Joint association inference across multiple domains of life, e.g., fungal communities (the mycobiome) and bacterial communities, has remained largely elusive.ResultsHere, we present a novel extension of the SParse InversE Covariance estimation for Ecological ASsociation Inference (SPIEC-EASI) framework that allows statistical inference of cross-domain associations from targeted amplicon sequencing data. For human lung and skin micro- and mycobiomes, we show that cross-domain networks exhibit higher connectivity, increased network stability, and similar topological re-organization patterns compared to single-domain networks. We also validate in vitro a small number of cross-domain interactions predicted by the skin association network.ConclusionsFor the human lung and skin micro- and mycobiomes, our findings suggest that fungi play a stabilizing role in ecological network organization. Our study suggests that computational efforts to infer association networks that include all forms of microbial life, paired with large-scale culture-based association validation experiments, will help formulate concrete hypotheses about the underlying biological mechanisms of species interactions and, ultimately, help understand microbial communities as a whole.

Field studies reveal functions of chemical mediators in plant interactions.

Plants are at the trophic base of most ecosystems, embedded in a rich network of ecological interactions in which they evolved. While their limited range and speed of motion precludes animal-typical behavior, plants are accomplished chemists, producing thousands of specialized metabolites which may function to convey information, or even to manipulate the physiology of other organisms. Plants' complex interactions and their underlying mechanisms are typically dissected within the controlled environments of growth chambers and glasshouses, but doing so introduces conditions alien to plants evolved in natural environments, such as being pot-bound, and receiving artificial light with a spectrum very different from sunlight. The mechanistic understanding gained from a reductionist approach provides the tools required to query and manipulate plant interactions in real-world settings. The few tests conducted in natural ecosystems and agricultural fields have highlighted the limitations of studying plant interactions only in artificial environments. Here, we focus on three examples of known or hypothesized chemical mediators of plants' interactions: the volatile phytohormone ethylene (ET), more complex plant volatile blends, and as-yet-unknown mediators transferred by common mycorrhizal networks (CMNs). We highlight how mechanistic knowledge has advanced research in all three areas, and the critical importance of field work if we are to put our understanding of chemical ecology on rigorous experimental and theoretical footing, and demonstrate function.

Evolutionary history as a driver of ecological networks: a case study of plant–hummingbird interactions

Multiple factors drive species interactions in ecological networks, such as morphological barriers, spatio–temporal distribution, abundances and evolutionary histories of species. Novel methods are making it possible to evaluate the relative importance of each of these drivers. However, the lack of appropriate methods has prevented evaluating the extent to which interaction networks are shaped by species’ evolutionary histories. This study includes the evolutionary histories of species among the potential drivers of interactions, allowing the comparative analysis of its importance in structuring ecological networks. We hypothesized different possible phylogenetic scenarios to predict frequencies of interactions between species by combining concepts from the fields of ecological networks and ecophylogenetics. The usage of these scenarios is illustrated in a plant–hummingbird interaction network database from the Atlantic Forest, southeastern Brazil. We first evaluated which phylogenetic hypotheses better predict the observed network; subsequently, we evaluated the relative importance of species evolutionary histories, abundances, and matching on species morphologies and phenologies as drivers of their frequencies of interactions. The results suggest that the evolutionary histories of hummingbirds are more important than the species abundances in structuring the studied plant–hummingbird network but less important than the morphological and phenological matching among species. The approach developed here offers the potential to advance our understanding of the multiple factors structuring ecological networks.

Non-adaptive origins of evolutionary innovations increase network complexity in interacting digital organisms.

The origin of evolutionary innovations is a central problem in evolutionary biology. To what extent such innovations have adaptive or non-adaptive origins is hard to assess in real organisms. This limitation, however, can be overcome using digital organisms, i.e. self-replicating computer programs that mutate, evolve and coevolve within a user-defined computational environment. Here, we quantify the role of the non-adaptive origins of host resistance traits in determining the evolution of ecological interactions among host and parasite digital organisms. We find that host resistance traits arising spontaneously as exaptations increase the complexity of antagonistic host-parasite networks. Specifically, they lead to higher host phenotypic diversification, a larger number of ecological interactions and higher heterogeneity in interaction strengths. Given the potential of network architecture to affect network dynamics, such exaptations may increase the persistence of entire communities. Our in silico approach, therefore, may complement current theoretical advances aimed at disentangling the ecological and evolutionary mechanisms shaping species interaction networks.This article is part of the themed issue 'Process and pattern in innovations from cells to societies'.

Understanding Evolutionary Impacts of Seasonality: An Introduction to the Symposium.

Seasonality is a critically important aspect of environmental variability, and strongly shapes all aspects of life for organisms living in highly seasonal environments. Seasonality has played a key role in generating biodiversity, and has driven the evolution of extreme physiological adaptations and behaviors such as migration and hibernation. Fluctuating selection pressures on survival and fecundity between summer and winter provide a complex selective landscape, which can be met by a combination of three outcomes of adaptive evolution: genetic polymorphism, phenotypic plasticity, and bet-hedging. Here, we have identified four important research questions with the goal of advancing our understanding of evolutionary impacts of seasonality. First, we ask how characteristics of environments and species will determine which adaptive response occurs. Relevant characteristics include costs and limits of plasticity, predictability, and reliability of cues, and grain of environmental variation relative to generation time. A second important question is how phenological shifts will amplify or ameliorate selection on physiological hardiness. Shifts in phenology can preserve the thermal niche despite shifts in climate, but may fail to completely conserve the niche or may even expose life stages to conditions that cause mortality. Considering distinct environmental sensitivities of life history stages will be key to refining models that forecast susceptibility to climate change. Third, we must identify critical physiological phenotypes that underlie seasonal adaptation and work toward understanding the genetic architectures of these responses. These architectures are key for predicting evolutionary responses. Pleiotropic genes that regulate multiple responses to changing seasons may facilitate coordination among functionally related traits, or conversely may constrain the expression of optimal phenotypes. Finally, we must advance our understanding of how changes in seasonal fluctuations are impacting ecological interaction networks. We should move beyond simple dyadic interactions, such as predator prey dynamics, and understand how these interactions scale up to affect ecological interaction networks. As global climate change alters many aspects of seasonal variability, including extreme events and changes in mean conditions, organisms must respond appropriately or go extinct. The outcome of adaptation to seasonality will determine responses to climate change.

A knowledge-based system for generating interaction networks from ecological data

Semantic heterogeneity hampers efforts to find, integrate, analyse and interpret ecological data. An application case-study is described, in which the objective was to automate the integration and interpretation of heterogeneous, flower-visiting ecological data. A prototype knowledge-based system is described and evaluated. The system's semantic architecture uses a combination of ontologies and a Bayesian network to represent and reason with qualitative, uncertain ecological data and knowledge. This allows the high-level context and causal knowledge of behavioural interactions between individual plants and insects, and consequent ecological interactions between plant and insect populations, to be discovered. The system automatically assembles ecological interactions into a semantically consistent interaction network (a new design of a useful, traditional domain model). We discuss the contribution of probabilistic reasoning to knowledge discovery, the limitations of knowledge discovery in the application case-study, the impact of the work and the potential to apply the system design to the study of ecological interaction networks in general.

Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience.

Reorganization of interaction networks modulates the persistence of species in late successional stages.

Ecological interaction networks constantly reorganize as interspecific interactions change across successional stages and environmental gradients. This reorganization can also be associated with the extent to which species change their preference for types of niches available in their local sites. Despite the pervasiveness of these interaction changes, previous studies have revealed that network reorganizations have a minimal or insignificant effect on global descriptors of network architecture, such as connectance, modularity and nestedness. However, little is known about whether these reorganizations may have an effect on community dynamics and composition. To answer the question above, we study the multi-year dynamics and reorganization of plant-herbivore interaction networks across secondary successional stages of a tropical dry forest. We develop new quantitative tools based on a structural stability approach to estimate the potential impact of network reorganization on species persistence. Then, we investigate whether this impact can explain the likelihood of persistence of herbivore species in the observed communities. We find that resident (early-arriving) herbivore species increase their likelihood of persistence across time and successional stages. Importantly, we demonstrate that, in late successional stages, the reorganization of interactions among resident species has a strong inhibitory effect on the likelihood of persistence of colonizing (late-arriving) herbivores. These findings support earlier predictions suggesting that, in mature communities, changes of species interactions can act as community-control mechanisms (also known as priority effects). Furthermore, our results illustrate that the dynamics and composition of ecological communities cannot be fully understood without attention to their reorganization processes, despite the invariability of global network properties.

An equation‐free method reveals the ecological interaction networks within complex microbial ecosystems

Abstract Mapping the network of ecological interactions is a key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time‐series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics. We developed the sparse S‐map method (SSM), which generates a sparse interaction network from a multivariate ecological time series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non‐equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes. We showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM However, the accuracy of a comparative equation‐based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation‐based method when datasets were large and the magnitude of observational errors was small. The results were robust to the magnitude of process noise and the functional forms of interspecific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4–40 week‐old) and middle to old age (36–72 week‐old) mice. The complexity of microbial relationships impedes detailed equation‐based modelling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.

High temporal variability in the occurrence of consumer–resource interactions in ecological networks

Ecological networks have been used to represent interactions between species as fixed linkages despite that populations naturally oscillate over time and space. As such, the influence of the persistence of linkages between species in communities has been overlooked. Unfortunately, empirical analysis of the temporal variation of trophic networks is constrained by the lack of data with high spatial, temporal and taxonomic resolution. Here, we evaluate the spatiotemporal variability of multiple consumer– resource interactions to quantify the relative dominance of highly persistent versus poorly persistent interactions, the commonness of the interaction persistence patterns, and the effect of biotic and abiotic conditions on these patterns. We took advantage of a dataset from four large marine intertidal rocky‐shore networks monitored seasonally for three years along 1000 km of the coast of northern Chile. Our results showed that the communities were characterized by few persistent interactions and a large number of transient trophic interactions, which was well described by a common exponential decay in the rank‐frequency relationship of consumer–resource interactions despite dissimilarities in environmental conditions among sites. These results were independent of the degree of consumer–resource co‐occurrence. Our results stress the need for more long‐term studies that evaluate the temporal variability of ecological networks.

Conflicting selection alters the trajectory of molecular evolution in a tripartite bacteria-plasmid-phage interaction.

Bacteria engage in a complex network of ecological interactions, which includes mobile genetic elements (MGEs) such as phages and plasmids. These elements play a key role in microbial communities as vectors of horizontal gene transfer but can also be important sources of selection for their bacterial hosts. In natural communities, bacteria are likely to encounter multiple MGEs simultaneously and conflicting selection among MGEs could alter the bacterial evolutionary response to each MGE. Here, we test the effect of interactions with multiple MGEs on bacterial molecular evolution in the tripartite interaction between the bacterium, Pseudomonas fluorescens, the lytic bacteriophage, SBW25φ2, and conjugative plasmid, pQBR103, using genome sequencing of experimentally evolved bacteria. We show that individually, both plasmids and phages impose selection leading to bacterial evolutionary responses that are distinct from bacterial populations evolving without MGEs, but that together, plasmids and phages impose conflicting selection on bacteria, constraining the evolutionary responses observed in pairwise interactions. Our findings highlight the likely difficulties of predicting evolutionary responses to multiple selective pressures from the observed evolutionary responses to each selective pressure alone. Understanding evolution in complex microbial communities comprising many species and MGEs will require that we go beyond studies of pairwise interactions.

Paco: implementing Procrustean Approach to Cophylogeny in R

Summary The concordance of evolutionary histories and extant species interactions provides a useful metric for addressing questions of how the structure of ecological communities is influenced by macro‐evolutionary processes. We introduce paco (v0.3.1), an R package to perform Procrustean Approach to Cophylogeny. This method assesses the phylogenetic congruence, or evolutionary dependence, of two groups of interacting species using both ecological interaction networks and their phylogenetic history. We demonstrate the functionality of paco through its application to empirical host‐parasite and plant‐pollinator communities. Although the package is intended to assess the phylogenetic congruence between groups of interacting species, the method is also directly applicable to other scenarios that may show phylogenetic congruence including historical biogeography, molecular systematics, and cultural evolution.

Dissecting the role of transitivity and intransitivity on coexistence in competing species networks

It is well established that intransitively assembled interaction networks can support the coexistence of competing species, while transitively assembled (hierarchical) networks are prone to species loss through competitive exclusion. However, as the number of species grows, the complexity of ecological interaction networks grows disproportionately, and species can get involved simultaneously in transitive and intransitive groups of interactions. In such complex networks, the effects of intransitivity on species persistence are not straightforward. Dissecting networks into intransitive/transitive components can help us to understand the complex role that intransitivity may play in supporting species diversity. We show through simulations that those species participating in the largest group of intransitive interactions (the core of the network) have high probabilities of persisting in the long term. However, participation in a group of intransitive interactions other than the core does not always improve persistence. Likewise, participating in transitive interactions does not always decrease persistence because certain species (the satellites) transitively linked to the core have also a high persistence probability. Therefore, when networks contain transitive and intransitive structures, as it can be expected in real ecological networks, the existence of a large intransitive core of species can have a disproportionate positive effect on species richness.

Using palaeoecology to determine baseline ecological requirements and interaction networks for de‐extinction candidate species

Summary A key rationale for pursuing de‐extinction is the potential to restore lost processes and function to modern ecosystems. However, understanding and providing for the ecological requirements of the candidate species will also play a key role in determining the ultimate success of a de‐extinction. This assessment is challenging for prehistoric extinct species, where empirical studies or observations on their ecology are not available or are incomplete. A healthy, stable and self‐sustaining population of a resurrected species needs to be embedded in an ecological interaction network that consists not only of interactions between a resurrected species and its external environment (e.g. habitat, diet), but also those where the resurrected species is the environment (e.g. microbiota and parasites). Palaeoecology can provide information on all of these interactions for extinct species, and this information can help guide and inform the selection of suitable de‐extinction candidates or traits for resurrection. Ecological interaction network analyses offer a complementary tool to existing frameworks for determining the suitability of de‐extinction candidates, allowing palaeoecological information to be used to identify and quantify the potential implications of removing or adding resurrected species/functions to an ecosystem. Although palaeoecological data and understanding clearly have an important role in informing and modelling the potential ecological functions provided by extinct species, they can only ever provide an incomplete picture and therefore would only complement, rather than replace, observational or experimental data on the resurrected organisms themselves. A lay summary is available for this article.

Sampling networks of ecological interactions

Summary Sampling ecological interactions presents similar challenges, problems, potential biases and constraints as sampling individuals and species in biodiversity inventories. Robust estimates of the actual number of interactions (links) within diversified ecological networks require adequate sampling effort that needs to be explicitly gauged. Yet we still lack a sampling theory explicitly focusing on ecological interactions. While the complete inventory of interactions is likely impossible, a robust characterization of its main patterns and metrics is probably realistic. We must acknowledge that a sizeable fraction of the maximum number of interactions among, say, A animal species and P plant species (i.e. ) is impossible to record due to forbidden links, that is life‐history restrictions. Thus, the number of observed interactions I in robustly sampled networks is typically , resulting in sparse interaction matrices with low connectance. Here I provide a review and outline a conceptual framework for interaction sampling by building an explicit analogue to individuals and species sampling, thus extending diversity‐monitoring approaches to the characterization of complex networks of ecological interactions. Contrary to species inventories, a sizable fraction of non‐observed pairwise interactions cannot be sampled, due to biological constraints that forbid their occurrence. Reasons for forbidden links are multiple but mainly stem from spatial and temporal uncoupling, size mismatches and intrinsically low probabilities of interspecific encounter for most potential interactions of partner species. Adequately assessing the completeness of a network of ecological interactions thus needs knowledge of the natural history details embedded, so that forbidden links can be accounted for as a portion of the unobserved links when addressing sampling effort. Recent implementations of inference methods for unobserved species or for individual‐based data can be combined with the assessment of forbidden links. This can help in estimating their relative importance, simply by the difference between the asymptotic estimate of interaction richness in a robustly sampled assemblage and the maximum richness of interactions. This is crucial to assess the rapid and devastating effects of defaunation‐driven loss of key ecological interactions and the services they provide and the analogous losses related to interaction gains due to invasive species and biotic homogenization. A lay summary is available for this article.