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.     

Structure of trophic and mutualistic networks across broad environmental gradients

This study aims to understand how inherent ecological network structures of nestedness and modularity vary over large geographic scales with implications for community stability. Bipartite networks from previous research from 68 locations globally were analyzed. Using a meta‐analysis approach, we examine relationships between the structure of 22 trophic and 46 mutualistic bipartite networks in response to extensive gradients of temperature and precipitation. Network structures varied significantly across temperature gradients. Trophic networks showed decreasing modularity with increasing variation in temperature within years. Nestedness of mutualistic networks decreased with increasing temperature variability between years. Mean annual precipitation and variability of precipitation were not found to have significant influence on the structure of either trophic or mutualistic networks. By examining changes in ecological networks across large‐scale abiotic gradients, this study identifies temperature variability as a potential environmental mediator of community stability. Understanding these relationships contributes to our ability to predict responses of biodiversity to climate change at the community level.     

Intrinsic and extrinsic causes of spatial variability across scales in a metacommunity

The relative importance of extrinsic and intrinsic causes of variability is among the oldest unresolved problems in ecology. However, the interaction between large-scale intrinsic variability in species abundance and environmental heterogeneity is still unknown. We use a metacommunity model with disturbance-recovery dynamics to resolve the interaction between scales of environmental heterogeneity, biotic processes and of intrinsic variability. We explain how population density increases with environmental variability only when its scale matches that of intrinsic patterns of abundance, through their ability to develop in heterogeneous environments. Succession dynamics reveals how the strength of local species interactions, through its control of intrinsic variability, can in turn control the scale of metapopulation response to environmental scales. Our results show that the environment and species density might fail to show any correlation despite their strong causal association. They more generally suggest that the spatial scale of ecological processes might not be sufficient to build a predictive framework for spatially heterogeneous habitats, including marine reserve networks.     

Testing the link between species interactions and species co‐occurrence in a trophic network

Species interactions are dynamic processes that vary across environmental and ecological contexts, and operate across scale boundaries, making them difficult to quantify. Nevertheless, ecologists are increasingly interested in inferring species interactions from observational data using statistical analyses of their spatial co‐occurrence patterns. Trophic interactions present a particular challenge, as predators and prey may frequently or rarely co‐occur, depending on the spatial or temporal scale of observation. In this study, we investigate the accuracy of inferred interactions among species that both compete and trophically interact. We utilized a long‐term dataset of pond‐breeding amphibian co‐occurrences from Mt Rainier National Park (Washington, USA) and compiled a new dataset of their empirical interactions from the literature. We compared the accuracy of four statistical methods in inferring these known species interactions from spatial associations. We then used the best performing statistical method, the Markov network, to further investigate the sensitivity of interaction inference to spatial scale‐dependence and the presence of predators. We show that co‐occurrence methods are generally inaccurate when estimating trophic interactions. Further the strength and sign of inferred interactions were dependent upon the spatial scale of observation and predator presence influenced the detectability of competitive interactions among prey species. However, co‐occurrence analysis revealed new patterns of spatial association among pairs of species with known interactions. Overall, our study highlights a limiting frontier in co‐occurrence theory and the disconnect between widely implemented methodologies and their ability to accurately infer interactions in trophically‐structured communities.     

Reshaping our understanding of species’ roles in landscape‐scale networks

In network ecology, landscape‐scale processes are often overlooked, yet there is increasing evidence that species and interactions spill over between habitats, calling for further study of interhabitat dependencies. Here, we investigate how species connect a mosaic of habitats based on the spatial variation of their mutualistic and antagonistic interactions using two multilayer networks, combining pollination, herbivory and parasitism in the UK and New Zealand. Developing novel methods of network analysis for landscape‐scale ecological networks, we discovered that few plant and pollinator species acted as connectors or hubs, both within and among habitats, whereas herbivores and parasitoids typically have more peripheral network roles. Insect species’ roles depend on factors other than just the abundance of taxa in the lower trophic level, exemplified by larger Hymenoptera connecting networks of different habitats and insects relying on different resources across different habitats. Our findings provide a broader perspective for landscape‐scale management and ecological community conservation.     

Structural asymmetry in biotic interactions as a tool to understand and predict ecological persistence

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species' relative abundances during 7 years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.     

Interactions retain the co‐phylogenetic matching that communities lost

Both species and their interactions are affected by changes that occur at evolutionary time‐scales, and these changes shape both ecological communities and their phylogenetic structure. That said, extant ecological community structure is contingent upon random chance, environmental filters and local effects. It is therefore unclear how much ecological signal local communities should retain. Here we show that, in a host–parasite system where species interactions vary substantially over a continental gradient, the ecological significance of individual interactions is maintained across different scales. Notably, this occurs despite the fact that observed community variation at the local scale frequently tends to weaken or remove community‐wide phylogenetic signal. When considered in terms of the interplay between community ecology and coevolutionary theory, our results demonstrate that individual interactions are capable and indeed likely to show a consistent signature of past evolutionary history even when woven into communities that do not.     

The dimensionality of ecological networks

How many dimensions (trait‐axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high‐quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large‐scale community structure.     

Seasonal consistency of the assembling rules in a bird-fruiting plant network

There is an ongoing debate on whether species' traits or neutrality generate recurrent patterns of mutualistic networks. Although there have been recent advances on the issue, many studies neglect the seasonal dynamics of these drivers. In this study, we investigated how pervasive are the drivers (i.e. species’ size, phenological overlap and species relative abundance) of the frequency of pairwise interactions and the aggregate metrics of seasonal seed dispersal networks in a semideciduous forest from the savannahs of Southeastern Brazil. We used a likelihood approach and built probability matrices based on different drivers to compare how they fit in the frequency and structure of the seasonal observed networks. We found that in both seasons trait-based processes, especially phenological overlap between species, best predicted the frequency of pairwise interactions. However, species relative abundances performed better than species traits in explaining most of aggregate network metrics in both seasons, except for the interaction evenness. These findings suggest that ecological and evolutionary processes are seasonally pervasive and determine the ability of species to interact with their partners. Besides, the general structure of the seasonal networks is less sensitive to species traits and its drivers remain seasonally constant. We conclude that our ability to understand the complexity of plant-frugivore interactions depends on assessing the contribution of species traits and their relative abundances to the structure of seasonal-detailed networks.     

Predicting phenology by integrating ecology,evolution and climate science

Forecasting how species and ecosystems will respond to climate change has been a major aim of ecology in recent years. Much of this research has focused on phenology – the timing of life‐history events. Phenology has well‐demonstrated links to climate, from genetic to landscape scales; yet our ability to explain and predict variation in phenology across species, habitats and time remains poor. Here, we outline how merging approaches from ecology, climate science and evolutionary biology can advance research on phenological responses to climate variability. Using insight into seasonal and interannual climate variability combined with niche theory and community phylogenetics, we develop a predictive approach for species’ reponses to changing climate. Our approach predicts that species occupying higher latitudes or the early growing season should be most sensitive to climate and have the most phylogenetically conserved phenologies. We further predict that temperate species will respond to climate change by shifting in time, while tropical species will respond by shifting space, or by evolving. Although we focus here on plant phenology, our approach is broadly applicable to ecological research of plant responses to climate variability.     

The nested assembly of plant facilitation networks prevents species extinctions

Facilitation is a positive interaction assembling ecological communities and preserving global biodiversity. Although communities acquire emerging properties when many species interact, most of our knowledge about facilitation is based on studies between pairs of species. To understand how plant facilitation preserves biodiversity in complex ecological communities, we propose to move from the study of pairwise interactions to the network approach. We show that facilitation networks behave as mutualistic networks do, characterized by a nonrandom, nested structure of plant-plant interactions in which a few generalist nurses facilitate a large number of species while the rest of the nurses facilitate only a subset of them. Consequently, generalist nurses shape a dense and highly connected network. Interestingly, such generalist nurses are the most abundant species in the community, making facilitation-shaped communities strongly resistant to extinction, as revealed by coextinction simulations. The nested structure of facilitative networks explains why facilitation, by preventing extinction, preserves biodiversity.     

Predicting rates of interspecific interaction from phylogenetic trees

Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction.     

Motifs in bipartite ecological networks: uncovering indirect interactions

Indirect interactions play an essential role in governing population, community and coevolutionary dynamics across a diverse range of ecological communities. Such communities are widely represented as bipartite networks: graphs depicting interactions between two groups of species, such as plants and pollinators or hosts and parasites. For over thirty years, studies have used indices, such as connectance and species degree, to characterise the structure of these networks and the roles of their constituent species. However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. Given the large literature demonstrating the importance and ubiquity of indirect effects, many studies of network structure are likely missing a substantial piece of the ecological puzzle. Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. While this framework is a significant departure from the current way of thinking about bipartite ecological networks, we show that this shift is supported by analyses of simulated and empirical data. We use simulations to show how consideration of indirect interactions can highlight differences missed by the current index paradigm that may be ecologically important. We extend this finding to empirical plant–pollinator communities, showing how two bee species, with similar direct interactions, differ in how specialised their competitors are. These examples underscore the need to not rely solely on network‐ and species‐level indices for characterising the structure of bipartite ecological networks.     

Spatial resolution and location impact group structure in a marine food web

Ecological processes in food webs depend on species interactions. By identifying broad‐scaled interaction patterns, important information on species' ecological roles may be revealed. Here, we use the group model to examine how spatial resolution and proximity influence group structure. We examine a data set from the Barents Sea, with food webs described for both the whole region and 25 subregions. We test how the group structure in the networks differ comparing (1) the regional metaweb to subregions and (2) subregion to subregion. We find that more than half the species in the metaweb change groups when compared to subregions. Between subregions, networks with similar group structure are spatially related. Interestingly, although species overlap is important for similarity in group structure, there are notable exceptions. Our results highlight that species ecological roles vary depending on fine‐scaled differences in the patterns of interactions, and that local network characteristics are important to consider.     

Active colonization dynamics and diversity patterns are influenced by dendritic network connectivity and species interactions

Habitat network connectivity influences colonization dynamics, species invasions, and biodiversity patterns. Recent theoretical work suggests dendritic networks, such as those found in rivers, alter expectations regarding colonization and dispersal dynamics compared with other network types. As many native and non‐native species are spreading along river networks, this may have important ecological implications. However, experimental studies testing the effects of network structure on colonization and diversity patterns are scarce. Up to now, experimental studies have only considered networks where sites are connected with small corridors, or dispersal was experimentally controlled, which eliminates possible effects of species interactions on colonization dynamics. Here, we tested the effect of network connectivity and species interactions on colonization dynamics using continuous linear and dendritic (i.e., river‐like) networks, which allow for active dispersal. We used a set of six protist species and one rotifer species in linear and dendritic microcosm networks. At the start of the experiment, we introduced species, either singularly or as a community within the networks. Species subsequently actively colonized the networks. We periodically measured densities of species throughout the networks over 2 weeks to track community dynamics, colonization, and diversity patterns. We found that colonization of dendritic networks was faster compared with colonization of linear networks, which resulted in higher local mean species richness in dendritic networks. Initially, community similarity was also greater in dendritic networks compared with linear networks, but this effect vanished over time. The presence of species interactions increased community evenness over time, compared with extrapolations from single‐species setups. Our experimental findings confirm previous theoretical work and show that network connectivity, species‐specific dispersal ability, and species interactions greatly influence the dispersal and colonization of dendritic networks. We argue that these factors need to be considered in empirical studies, where effects of network connectivity on colonization patterns have been largely underestimated.     

Environmentally‐induced noise dampens and reddens with increasing trophic level in a complex food web

Stochastic variability of key abiotic factors including temperature, precipitation and the availability of light and nutrients greatly influences species’ ecological function and evolutionary fate. Despite such influence, ecologists have typically ignored the effect of abiotic stochasticity on the structure and dynamics of ecological networks. Here we help to fill that gap by advancing the theory of how abiotic stochasticity, in the form of environmental noise, affects the population dynamics of species within food webs. We do this by analysing an allometric trophic network model of Lake Constance subjected to positive (red), negative (blue), and non‐autocorrelated (white) abiotic temporal variability (noise) introduced into the carrying capacity of basal species. We found that, irrespective of the colour of the introduced noise, the temporal variability of the species biomass within the network both reddens (i.e. its positive autocorrelation increases) and dampens (i.e. the magnitude of variation decreases) as the environmental noise is propagated through the food web by its feeding interactions from the bottom to the top. The reddening reflects a buffering of the noise‐induced population variability by complex food web dynamics such that non‐autocorrelated oscillations of noise‐free deterministic dynamics become positively autocorrelated. Our research helps explain frequently observed red variability of natural populations by suggesting that ecological processing of environmental noise through food webs with a range of species’ body sizes reddens population variability in nature.     

How plants cope with biotic interactions

In their natural environment, plants interact with many different organisms. The nature of these interactions may range from positive, for example interactions with pollinators, to negative, such as interactions with pathogens and herbivores. In this special issue, the contributors provide several examples of how plants manage both positive and negative biotic interactions. This review aims to relate their findings to what we know about the complex natural environments in which plants have evolved. Molecular analyses of plant genomes and expression profiles have shown how intricately plants may regulate responses to single or multiple biotic interactions. Plant responses are fine-tuned by signalling hormone interactions. When multiple organisms interact with a single plant this may result in antagonistic or synergistic effects. The emerging fields of ecogenomics and metabolomics undoubtedly will refine our understanding of the multilayered regulation that plants use to manage relationships with their biotic environment. However, we can only understand why plants have such an intricate regulatory apparatus if we consider the ecological context of plant biotic interactions.     

The macroecology of reef fish agonistic behaviour

Understanding the interplay between processes operating at large and small spatiotemporal scales in shaping biotic interactions remains challenging. Recent studies illustrate how phenotypic specialization, species life-history traits and/or resource partitioning recurrently underlie the structure of mutualistic interactions in terrestrial ecosystems along large latitudinal gradients of biodiversity. However, we know considerably less about how local processes interact with large-scale patterns of biodiversity in modulating biotic interactions in the marine realm. Considering agonistic behaviour as a proxy for contest competition, we empirically investigate whether the structure of reef fish agonistic interactions is conserved across a 34 000-km longitudinal gradient of biodiversity. By sampling coral reefs using standardized remote underwater video, we found recurrent patterns of fish agonistic behaviour in disparate communities distributed across five biogeographic provinces of the Pacific and Atlantic oceans. While the sheer number of species increases with regional richness, the number of aggressive disputes at the habitat scale is similar across communities. We then combined generalized linear models and network theory to reveal that, the emergent structure of local agonistic networks is not modular but instead recurrently display a nested structure, with a core of highly interactive site-attached herbivores of the Pomacentridae family. Therefore, despite the increase in the number of species involved in agonistic interactions toward speciose communities, the network structure is conserved along the longitudinal richness gradient because local disputes are mostly driven by closely-related, functionally-similar species. These findings suggest that evolutionary and local processes interact in modulating reef fish agonistic behaviour and that fine-scale niche-partitioning can structure the ecological networks in marine ecosystems.     

Phylogeography: spanning the ecology‐evolution continuum

Synthesis of ecological and evolutionary concepts and tools has led to improved understanding of how diversification, dispersal, community assembly, long‐term coexistence and extinction shape patterns of biological diversity. Phylogeography, with its focus on Quaternary interactions within and between populations, can help elucidate the processes acting between the evolutionary time‐scales on which species arise and the ecological time‐scales on which members of an assemblage interact with each other and their environment. Still, it has yet to be widely incorporated in that synthesis. Here, we highlight three areas where integration of phylogeography with ecological and evolutionary approaches can provide new insights into key questions. First, phylogeography can help clarify the roles of isolation, niche conservatism and environmental stability in generating patterns of alpha‐ and beta‐diversity. Second, phylogeography can help isolate the effects of Quaternary dispersal limitation from other factors driving community assembly and spatial turnover. Third, phylogeography can help identify key processes leading to and resulting from extinction events, including the population dynamics of species range reduction and its effects on the strength and temporal flexibility of networks of species interactions. We conclude with an outlook on the data‐gathering protocols necessary for this collaborative, interdisciplinary research agenda.