Diversity in a complex ecological network with two interaction types

Most studies on ecological networks consider only a single interaction type (e.g. competitive, predatory or mutualistic), and try to developrules for system stability based exclusively on properties of this interaction type. However, the stability of ecological networks may be more dependent on the way different interaction types are combined in real communities. To address this issue, we start by compiling an ecological network in the Doñana Biological Reserve, southern Spain, with 390 species and 798 mu-tualistic and antagonistic interactions. We characterize network structure by looking at how mutualistic and antagonistic interactions are combined across all plant species. Both the ratio of mutualistic to antagonistic interactions per plant, and the number of basic modules with an antagonistic and a mutualistic interaction are very heterogeneous across plant species, with a few plant species showing very high values for these parameters. To assess the implications of these network patterns on species diversity, we study analytically and by simulation a model of this ecological network. We find that the observed correlation between strong interaction strengths and high mutualistic to antagonistic ratios in a few plant species significantly increases community diversity. Thus, to predict the persistence of biodiversity we need to understand how interaction strength and the architecture of ecological networks with different interaction types are combined.     

Are Nested Networks More Robust to Disturbance? A Test Using Epiphyte-Tree,Comensalistic Networks

Recent research on ecological networks suggests that mutualistic networks are more nested than antagonistic ones and, as a result, they are more robust against chains of extinctions caused by disturbances. We evaluate whether mutualistic networks are more nested than comensalistic and antagonistic networks, and whether highly nested, host-epiphyte comensalistic networks fit the prediction of high robustness against disturbance. A review of 59 networks including mutualistic, antagonistic and comensalistic relationships showed that comensalistic networks are significantly more nested than antagonistic and mutualistic networks, which did not differ between themselves. Epiphyte-host networks from old-growth forests differed from those from disturbed forest in several topological parameters based on both qualitative and quantitative matrices. Network robustness increased with network size, but the slope of this relationship varied with nestedness and connectance. Our results indicate that interaction networks show complex responses to disturbances, which influence their topology and indirectly affect their robustness against species extinctions.     

Detecting phylogenetic signal in mutualistic interaction networks using a Markov process model

Ecological interaction networks, such as those describing the mutualistic interactions between plants and their pollinators or between plants and their frugivores, exhibit non‐random structural properties that cannot be explained by simple models of network formation. One factor affecting the formation and eventual structure of such a network is its evolutionary history. We argue that this, in many cases, is closely linked to the evolutionary histories of the species involved in the interactions. Indeed, empirical studies of interaction networks along with the phylogenies of the interacting species have demonstrated significant associations between phylogeny and network structure. To date, however, no generative model explaining the way in which the evolution of individual species affects the evolution of interaction networks has been proposed. We present a model describing the evolution of pairwise interactions as a branching Markov process, drawing on phylogenetic models of molecular evolution. Using knowledge of the phylogenies of the interacting species, our model yielded a significantly better fit to 21% of a set of plant–pollinator and plant–frugivore mutualistic networks. This highlights the importance, in a substantial minority of cases, of inheritance of interaction patterns without excluding the potential role of ecological novelties in forming the current network architecture. We suggest that our model can be used as a null model for controlling evolutionary signals when evaluating the role of other factors in shaping the emergence of ecological networks.     

Predicting the effect of competition on secondary plant extinctions in plant–pollinator networks

What are the limitations of models that predict the behavior of an ecological community based on a single type of species interaction? Using plant–pollinator network models as an example, we contrast the predicted vulnerability of a community to secondary extinctions under the assumption of purely mutualistic interactions versus mutualistic and competitive interactions. We find that competition among plant species increases the risk of secondary extinctions and extinction cascades. Simulations over a number of different network structures indicate that this effect is stronger in larger networks, more strongly connected networks and networks with higher plant:pollinator ratios. We conclude that efforts to model plant–pollinator communities will systematically over‐estimate community robustness to species loss if plant competition is ignored. However, because the effect of plant competition depends on network architecture, and because characterization of plant competition is work intensive, we suggest that efforts to account for plant competition in plant–pollinator network models should be focused on large, strongly connected networks with high plant:pollinator ratios.     

Removing interactions,rather than species,casts doubt on the high robustness of pollination networks

In the last 15 years, a complex networks perspective has been increasingly used in the robustness assessment of ecological systems. It is therefore crucial to assess the reliability of such tools. Based on the traditional simulation of node (species) removal, mutualistic pollination networks are considered to be relatively robust because of their 1) truncated power‐law degree distribution, 2) redundancy in the number of pollinators per plant and 3) nested interaction pattern. However, species removal is only one of several possible approaches to network robustness assessment. Empirical evidence suggests a decline in abundance prior to the extinction of interacting species, arguing in favour of an interaction removal‐based approach (i.e. interaction disruption), as opposed to traditional species removal. For simulated networks, these two approaches yield radically different conclusions, but no tests are currently available for empirical mutualistic networks. This study compared this new robustness evaluation approach based on interaction extinction versus the traditional species removal approach for 12 alpine and subalpine pollination networks. In comparison with species removal, interaction removal produced higher robustness in the worst‐case extinction scenario but lower robustness in the best‐case extinction scenario. Our results indicate that: 1) these two approaches yield very different conclusions and 2) existing assessments of ecological network robustness could be overly optimistic, at least those based on a disturbance affecting species at random or beginning with the least connected species. Therefore, further empirical study of plant–pollinator interactions in disturbed ecosystems is imperative to understand how pollination networks are disassembled.     

Interaction type and intimacy structure networks between forest-dwelling organisms and their host trees

Species interact in many ways. Potentially, the type of interaction, e.g. mutualistic, commensalistic or antagonistic, determines the structure of interaction networks, but this remains poorly tested. Here we investigate whether epiphytes and wood decomposers, having different types of interaction with their host trees, show different network properties. We also test whether the traits of host trees affect network architecture. We recorded presence/absence of organisms colonizing trees, and traits of host trees, in 102 forest plots. Epiphytic bryophytes (64 species) and lichens (119 species) were recorded on c. 2300 trees. Similarly, wood-inhabiting fungi (193 species) were recorded on c. 900 dead wood items. We studied the patterns of species aggregation on host trees by comparing network metrics of species specialization, nestedness and modularity. Next, we tested whether the prevalence of interactions was influenced by host tree traits. We found non-random interaction patterns between host trees and the three ecological groups (bryophytes, lichens and fungi), with nested and modular structures associated with high host specificity. A higher modularity and number of modules was found for fungi than for epiphytes, which is likely related to their trophic relationship with the host plant, whilst the stronger nestedness for epiphytes is likely reflecting the commensalistic nature of their interactions. For all three groups, the difference in prevalence of interaction across modules was determined by a gradient in interaction intimacy (i.e. host tree specialization), driven by host tree traits. We conclude that the type of interaction with host trees defines the properties of each network: while autotrophic epiphyte networks show similar properties to mutualistic networks, the heterotrophic wood decomposers show similarity with antagonistic networks.     

The nested structure of marine cleaning symbiosis: is it like flowers and bees?

In a given area, plant-animal mutualistic interactions form complex networks that often display nestedness, a particular type of asymmetry in interactions. Simple ecological and evolutionary factors have been hypothesized to lead to nested networks. Therefore, nestedness is expected to occur in other types of mutualisms as well. We tested the above prediction with the network structure of interactions in cleaning symbiosis at three reef assemblages. In this type of interaction, shrimps and fishes forage on ectoparasites and injured tissues from the body surface of fish species. Cleaning networks show strong patterns of nestedness. In fact, after controlling for species richness, cleaning networks are even more nested than plant-animal mutualisms. Our results support the notion that mutualisms evolve to a predictable community-level structure, be it in terrestrial or marine communities.     

The abundance effect on network nestedness is stronger for parasitic than herbivory interactions

多度对寄生型网络嵌套结构的影响大于食草型网络 因为物种多度显著影响种间互作频率,分析食物网结构对物种多样性和稳定性影响时,应基于能够反映物种真实偏好的网络(即偏好网),而不是直接观察得到的网络(即观察网)。食草网络中(植物为低营养级)的植物资源多度大于寄生网络中(动物为低营养级)寄主资源多度,因此我们假设：寄生网络的结构比食草网络的结构更易受到多度效应的影响。为验证这一假设,我们从已发表的文献中收集了80 个定量观察网络(包括34个植物-食草昆虫网络和46个寄生网络),应用有效多度模型去除物种多度对观察网络的影响,从而得出偏好网络。然后,我们应用weighted NODF和spectral radius两个嵌套系数表征网络嵌套性,分析了观察网和偏好网的物种链接数分布、相互作用均匀度、加权连通度和稳健性的差异。结果表明,在偏好网中,寄生网络的嵌套程度要低于食草网络,这可能是因为去除多度影响增加了种间作用频率的均匀度。偏好网的加权连通度和稳健性显著高于相应的观察网,表明偏好网比观察网具有更高的网络稳定性。未来的食物网研究不仅应关注互惠和拮抗网络的结构差异,还应该关注食草和寄生等不同类型拮抗食物网的结构差异。     

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.     

Phenology drives mutualistic network structure and diversity

Several network properties have been identified as determinants of the stability and complexity of mutualistic networks. However, it is unclear which mechanisms give rise to these network properties. Phenology seems important, because it shapes the topology of mutualistic networks, but its effects on the dynamics of mutualistic networks have scarcely been studied. Here, we study these effects with a general dynamical model of mutualistic and competitive interactions where the interaction strength depends on the temporal overlap between species resulting from their phenologies. We find a negative complexity-stability relationship, where phenologies maximising mutualistic interactions and minimising intraguild competitive interactions generate speciose, nested and poorly connected networks with moderate asymmetry and low resilience. Moreover, lengthening the season increases diversity and resilience. This highlights the fragility of real mutualistic communities with short seasons (e.g. Arctic environments) to drastic environmental changes.     

Trait-mediated interaction leads to structural emergence in mutualistic networks

As asymmetric structures of mutualistic networks can potentially contribute to system resilience, elucidating drivers behind the emergence of particular network architectures remains a major endeavour in ecology. Here, using an eco-evolutionary model for bipartite mutualistic networks with trait-mediated interactions, we explore how particular levels of connectance, nestedness and modularity are affected by three network assembly forces: resource accessibility, tolerance to trait difference between mutualistic pairs and competition intensity. We found that a moderate accessibility to intra-trophic resources and cross-trophic mutualistic support can result in a highly nested web, while low tolerance to trait difference between interacting pairs leads to a high level of modularity. Network-level trait complementarity leads to low connectance and high modularity, while network-level specialization can result in nested structures. Consequently, we argue that the interplay of ecological and evolutionary processes through trait-mediated interactions can explain these widely observed architectures in mutualistic networks.     

The impact of life form on the architecture of orchid mycorrhizal networks in tropical forest

Understanding the processes that determine the architecture of interaction networks represents a major challenge in ecology and evolutionary biology. One of the most important interactions involving plants is the interaction between plants and mycorrhizal fungi. While there is a mounting body of research that has studied the architecture of plant–fungus interaction networks, less is known about the potential factors that drive network architecture. In this study, we described the architecture of the network of interactions between mycorrhizal fungi and 44 orchid species that represented different life forms and co‐occurred in tropical forest and assessed the relative importance of ecological, evolutionary and co‐evolutionary mechanisms determining network architecture. We found 87 different fungal operational taxonomic units (OTUs), most of which were members of the Tulasnellaceae. Most orchid species associated with multiple fungi simultaneously, indicating that extreme host selectivity was rare. However, an increasing specificity towards Tulasnellaceae fungal associates from terrestrial to epiphytic and lithophytic orchids was observed. The network of interactions showed an association pattern that was significantly modular (M = 0.7389, M_random = 0.6998) and nested (NODF = 5.53, p < 0.05). Terrestrial orchids had almost no links to modules containing epiphytic or lithophytic orchids, while modules containing epiphytic orchids also contained lithophytic orchids. Within each life form several modules were observed, suggesting that the processes that organize orchid–fungus interactions are independent of life form. The overall phylogenetic signal for both partners in the interaction network was very weak. Overall, these results indicate that tropical orchids associate with a wide number of mycorrhizal fungi and that ecological rather than phylogenetic constraints determine network architecture.     

Interaction intimacy affects structure and coevolutionary dynamics in mutualistic networks

The structure of mutualistic networks provides clues to processes shaping biodiversity [1-10]. Among them, interaction intimacy, the degree of biological association between partners, leads to differences in specialization patterns [4, 11] and might affect network organization [12]. Here, we investigated potential consequences of interaction intimacy for the structure and coevolution of mutualistic networks. From observed processes of selection on mutualistic interactions, it is expected that symbiotic interactions (high-interaction intimacy) will form species-poor networks characterized by compartmentalization [12, 13], whereas nonsymbiotic interactions (low intimacy) will lead to species-rich, nested networks in which there is a core of generalists and specialists often interact with generalists [3, 5, 7, 12, 14]. We demonstrated an association between interaction intimacy and structure in 19 ant-plant mutualistic networks. Through numerical simulations, we found that network structure of different forms of mutualism affects evolutionary change in distinct ways. Change in one species affects primarily one mutualistic partner in symbiotic interactions but might affect multiple partners in nonsymbiotic interactions. We hypothesize that coevolution in symbiotic interactions is characterized by frequent reciprocal changes between few partners, but coevolution in nonsymbiotic networks might show rare bursts of changes in which many species respond to evolutionary changes in a single species.     

Abiotic factors shape temporal variation in the structure of an ant–plant network

Despite recognition of key biotic processes in shaping the structure of biological communities, few empirical studies have explored the influences of abiotic factors on the structural properties of mutualistic networks. We tested whether temperature and precipitation contribute to temporal variation in the nestedness of mutualistic ant–plant networks. While maintaining their nested structure, nestedness increased with mean monthly precipitation and, particularly, with monthly temperature. Moreover, some species changed their role in network structure, shifting from peripheral to core species within the nested network. We could summarize that abiotic factors affect plant species in the vegetation (e.g., phenology), meaning presence/absence of food sources, consequently an increase/decrease of associations with ants, and finally, these variations to fluctuations in nestedness. While biotic factors are certainly important, greater attention needs to be given to abiotic factors as underlying determinants of the structures of ecological networks.     

Top‐down network analysis characterizes hidden termite–termite interactions

The analysis of ecological networks is generally bottom‐up, where networks are established by observing interactions between individuals. Emergent network properties have been indicated to reflect the dominant mode of interactions in communities that might be mutualistic (e.g., pollination) or antagonistic (e.g., host–parasitoid communities). Many ecological communities, however, comprise species interactions that are difficult to observe directly. Here, we propose that a comparison of the emergent properties from detail‐rich reference communities with known modes of interaction can inform our understanding of detail‐sparse focal communities. With this top‐down approach, we consider patterns of coexistence between termite species that live as guests in mounds built by other host termite species as a case in point. Termite societies are extremely sensitive to perturbations, which precludes determining the nature of their interactions through direct observations. We perform a literature review to construct two networks representing termite mound cohabitation in a Brazilian savanna and in the tropical forest of Cameroon. We contrast the properties of these cohabitation networks with a total of 197 geographically diverse mutualistic plant–pollinator and antagonistic host–parasitoid networks. We analyze network properties for the networks, perform a principal components analysis (PCA), and compute the Mahalanobis distance of the termite networks to the cloud of mutualistic and antagonistic networks to assess the extent to which the termite networks overlap with the properties of the reference networks. Both termite networks overlap more closely with the mutualistic plant–pollinator communities than the antagonistic host–parasitoid communities, although the Brazilian community overlap with mutualistic communities is stronger. The analysis raises the hypothesis that termite–termite cohabitation networks may be overall mutualistic. More broadly, this work provides support for the argument that cryptic communities may be analyzed via comparison to well‐characterized communities.     

Defining invasiveness and invasibility in ecological networks

The success of a biological invasion is context dependent, and yet two key concepts—the invasiveness of species and the invasibility of recipient ecosystems—are often defined and considered separately. We propose a framework that can elucidate the complex relationship between invasibility and invasiveness. It is based on trait-mediated interactions between species and depicts the response of an ecological network to the intrusion of an alien species, drawing on the concept of community saturation. Here, invasiveness of an introduced species with a particular trait is measured by its per capita population growth rate when the initial propagule pressure of the introduced species is very low. The invasibility of the recipient habitat or ecosystem is dependent on the structure of the resident ecological network and is defined as the total width of an opportunity niche in the trait space susceptible to invasion. Invasibility is thus a measure of network instability. We also correlate invasibility with the asymptotic stability of resident ecological network, measured by the leading eigenvalue of the interaction matrix that depicts trait-based interaction intensity multiplied by encounter rate (a pairwise product of propagule pressure of all members in a community). We further examine the relationship between invasibility and network architecture, including network connectance, nestedness and modularity. We exemplify this framework with a trait-based assembly model under perturbations in ways to emulate fluctuating resources and random trait composition in ecological networks. The maximum invasiveness of a potential invader (greatest intrinsic population growth rate) was found to be positively correlated with invasibility of the recipient ecological network. Additionally, ecosystems with high network modularity and high ecological stability tend to exhibit high invasibility. Where quantitative data are lacking we propose using a qualitative interaction matrix of the ecological network perceived by a potential invader so that the structural network stability and invasibility can be estimated from the literature or from expert opinion. This approach links network structure, invasiveness and invasibility in the context of trait-mediated interactions, such as the invasion of insects into mutualistic and antagonistic networks.     

Biology,Methodology or Chance? The Degree Distributions of Bipartite Ecological Networks

The distribution of the number of links per species, or degree distribution, is widely used as a summary of the topology of complex networks. Degree distributions have been studied in a range of ecological networks, including both mutualistic bipartite networks of plants and pollinators or seed dispersers and antagonistic bipartite networks of plants and their consumers. The shape of a degree distribution, for example whether it follows an exponential or power-law form, is typically taken to be indicative of the processes structuring the network. The skewed degree distributions of bipartite mutualistic and antagonistic networks are usually assumed to show that ecological or co-evolutionary processes constrain the relative numbers of specialists and generalists in the network. I show that a simple null model based on the principle of maximum entropy cannot be rejected as a model for the degree distributions in most of the 115 bipartite ecological networks tested here. The model requires knowledge of the number of nodes and links in the network, but needs no other ecological information. The model cannot be rejected for 159 (69%) of the 230 degree distributions of the 115 networks tested. It performed equally well on the plant and animal degree distributions, and cannot be rejected for 81 (70%) of the 115 plant distributions and 78 (68%) of the animal distributions. There are consistent differences between the degree distributions of mutualistic and antagonistic networks, suggesting that different processes are constraining these two classes of networks. Fit to the MaxEnt null model is consistently poor among the largest mutualistic networks. Potential ecological and methodological explanations for deviations from the model suggest that spatial and temporal heterogeneity are important drivers of the structure of these large networks.     

COEVOLUTION AND THE ARCHITECTURE OF MUTUALISTIC NETWORKS

Although coevolution is widely recognized as an important evolutionary process for pairs of reciprocally specialized species, its importance within species‐rich communities of generalized species has been questioned. Here we develop and analyze mathematical models of mutualistic communities, such as those between plants and pollinators or plants and seed‐dispersers to evaluate the importance of coevolutionary selection within complex communities. Our analyses reveal that coevolutionary selection can drive significant changes in trait distributions with important consequences for the network structure of mutualistic communities. One such consequence is greater connectance caused by an almost invariable increase in the rate of mutualistic interaction within the community. Another important consequence is altered patterns of nestedness. Specifically, interactions mediated by a mechanism of phenotype matching tend to be antinested when coevolutionary selection is weak and even more strongly antinested as increasing coevolutionary selection favors the emergence of reciprocal specialization. In contrast, interactions mediated by a mechanism of phenotype differences tend to be nested when coevolutionary selection is weak, but less nested as increasing coevolutionary selection favors greater levels of generalization in both plants and animals. Taken together, our results show that coevolutionary selection can be an important force within mutualistic communities, driving changes in trait distributions, interaction rates, and even network structure.     

Fine‐tuning the nested structure of pollination networks by adaptive interaction switching,biogeography and sampling effect in the Galápagos Islands

The structure of pollination networks, particularly its nestedness, contain important information on network assemblages. However, there is still limited understanding of the mechanisms underlying nested pollination network structures. Here, we investigate the role of adaptive interaction switching (AIS), island area, isolation, age and sampling effort in explaining the nestedness of pollination networks across ten Galápagos Islands. The AIS algorithm is inspired by Wallace's elimination of the unfit, where a species constantly replaces its least profitable mutualistic partner with a new partner selected at random. To explain network structures, we first use a dynamic model that includes functional response of pollination and AIS, with only species richness and binary connectance as input (hereafter the AIS model). Thereafter, other explanatory variables (isolation, area, age and sampling effort) were added to the model. In four out of ten islands, the pollination network was significantly nested, and predictions from the AIS model correlated with observed structures, explaining 69% variation in nestedness. Overall, in terms of independent contribution from hierarchical partitioning of variation in observed nestedness, the AIS model predictions contributed the most (37%), followed by sampling effort (28%) and island area (22%), with only trivial contributions from island isolation and age. Therefore, adaptive switching of biotic interactions seems to be key to ensure network function, with island biogeographic factors being only secondary. Although large islands could harbour more diverse assemblages and thus foster more nested structures, sufficient sampling proves to be essential for detecting non‐random network structures.     

A neutral‐niche theory of nestedness in mutualistic networks

Recently, there has been a vigorous interest in community ecology about the structure of mutualistic networks and its importance for species persistence and coevolution. However, the mechanisms shaping mutualistic networks have been rarely explored. Here we extend for the first time the neutral theory of biodiversity to a multi trophic system. We focus on nestedness, a distinctive pattern of mutualistic community assembly showing two characteristics, namely, asymmetrical specialization (specialists interacting with generalists) and a generalist core (generalists interacting with generalists). We investigate the importance of relative species abundance (RSA) for the nested assembly of plant–animal mutualistic networks. Our results show that neutral mutualistic communities give rise to networks considerably more nested than real communities. RSA explains 60–70% of nested patterns in two real communities studied here, while 30–40% of nestedness is still unexplained. The nested pattern in real communities is better explained when we introduce interaction‐specific species traits such as forbidden links and intensity of dependence (relative importance of fruits for the diet of a frugivore) in our analysis. The fact that neutral mutualistic communities exhibit a perfectly nested structure and do not show a random or compartmentalized structure, underlines the importance of RSA in the assembly of mutualistic networks.