Stability of competition-antagonism-mutualism hybrid community and the role of community network structure

Will a large complex model ecosystem be viable? The essential role of positive interactions

Ecologists have documented many characteristics of natural systems that foster ecosystem persistence, and it might be deduced that such strategies are essential for counteracting the negative effect of complexity on local stability that was suggested by R.M. May in his influential work of the 1970s. However, we show that the loss of local stability does not necessarily imply total ecosystem extinction. A more general criterion of ecosystem viability is the long-term persistence of any number of surviving species-not necessarily all of them. With this approach, we show that persistence increases with complexity, contrary to previous theoretical findings. In particular, positive interactions (mutualistic or prey-to-predator) play a crucial role in creating ecological niches, which sustain biodiversity with increasing complexity.

Ecosystem engineering and food web stability

Ecosystem engineering, which involves organism-triggered physical modification of the environment, is a widespread phenomenon. Despite this, the role of engineering in ecological communities remains poorly understood. This study employs a food web model to uncover the key roles of ecosystem engineering in maintaining food webs. While engineers facilitating population growth and suppressing consumers' foraging activity can help maintain complex communities with diverse species, engineering effects that suppress population growth and facilitate consumers' foraging activity can largely destabilize community dynamics. Furthermore, in the middle levels of engineering-related species within a community, an increase in species richness can increase community stability, contrary to classical ecological prediction. The study findings suggest that ecosystem engineering can explain biodiversity persistence in nature, but it depends on the proportion of engineering-related species and how engineering affects organisms.

Symbioses between fungi and bacteria: from mechanisms to impacts on biodiversity

Symbiotic interactions between fungi and bacteria range from positive to negative. They are ubiquitous in free-living as well as host-associated microbial communities worldwide. Yet, the impact of fungal-bacterial symbioses on the organization and dynamics of microbial communities is uncertain. There are two reasons for this uncertainty: (1) knowledge gaps in the understanding of the genetic mechanisms underpinning fungal-bacterial symbioses and (2) prevailing interpretations of ecological theory that favor antagonistic interactions as drivers stabilizing biological communities despite the existence of models emphasizing contributions of positive interactions. This review synthesizes information on fungal-bacterial symbioses common in the free-living microbial communities of the soil as well as in host-associated polymicrobial biofilms. The interdomain partnerships are considered in the context of the relevant community ecology models, which are discussed critically.

Interconnection between density-regulation and stability in competitive ecological network

In natural ecosystems, species can be characterized by the nonlinear density-dependent self-regulation of their growth profile. Species of many taxa show a substantial density-dependent reduction for low population size. Nevertheless, many show the opposite trend; density regulation is minimal for small populations and increases significantly when the population size is near the carrying capacity. The theta-logistic growth equation can portray the intraspecific density regulation in the growth profile, theta being the density regulation parameter. In this study, we examine the role of these different growth profiles on the stability of a competitive ecological community with the help of a mathematical model of competitive species interactions. This manuscript deals with the random matrix theory to understand the stability of the classical theta-logistic models of competitive interactions. Our results suggest that having more species with strong density dependence, which self-regulate at low densities, leads to more stable communities. With this, stability also depends on the complexity of the ecological network. Species network connectance (link density) shows a consistent trend of increasing stability, whereas community size (species richness) shows a context-dependent effect. We also interpret our results from the aspect of two different life history strategies: r and K-selection. Our results show that the stability of a competitive network increases with the fraction of r-selected species in the community. Our result is robust, irrespective of different network architectures.

Drivers and consequences of structure in plant-lemur ecological networks

Species interactions shape the diversity and resilience of ecological networks. Plant and animal traits, as well as phylogeny, affect interaction likelihood, driving variation in network structure and tolerance to disturbance. We investigated how traits and phylogenetic effects influenced network-wide interaction probabilities and examined the consequences of extinction on the structure and robustness of ecological networks. We combined both mutualistic and antagonistic interactions of animals (55 species, Infraorder Lemuriformes, Order Primates) and their food plants (590 genera) throughout Madagascar to generate ecological networks. We tested the effects of both lemur and plant traits, biogeographic factors, and phylogenetic relatedness on interaction probability in these networks using exponential random graph models. Next, we simulated animal and plant extinction to analyze the effects of extinction on network structure (connectance, nestedness, and modularity) and robustness for mutualistic, antagonistic, and combined plant-animal networks. Both animal and plant traits affected their interaction probabilities. Large, frugivorous lemurs with a short gestation length, occurring in arid habitats, and with a Least Concern threat level had a high interaction probability in the network, given all other variables. Closely related plants were more likely to interact with the same lemur species than distantly related plants, but closely related lemurs were not more likely to interact with the same plant genus. Simulated lemur extinction tended to increase connectance and modularity, but decrease nestedness and robustness, compared to pre-extinction networks. Networks were more tolerant to plant than lemur extinctions. Lemur-plant interactions were highly trait-structured and the loss of both lemurs and plants threatened the tolerance of mutualistic, antagonistic, and combined networks to future disturbance.

How Multiple Interaction Types Affect Disease Spread and Dilution in Ecological Networks

Ecological communities are composed of different functional guilds that are engaging in multiple types of biotic interactions. We explore how ecological networks fare when confronting infectious diseases according to density-dependent (DD) and frequency-dependent (FD) transmission modes. Our model shows that network compositions can dictate both disease spreading and the relationship between disease and community diversity (including species richness and Shannon’s diversity) as depicted in the dilution effect. The disease becomes more prevalent within communities harboring more mutualistic interactions, generating a positive relationship between disease prevalence and community diversity (i.e., an amplification effect). By contrast, in communities with a fixed proportion of mutualistic interactions, higher diversity from the balance of competition and predation can impede disease prevalence (i.e., the dilution effect). Within-species disease prevalence increases linearly with a species’ degree centrality. These patterns of disease transmission and the diversity-disease relationship hold for both transmission modes. Our analyses highlight the complex effects of interaction compositions in ecological networks on infectious disease dynamics and further advance the debate on the dilution effect of host diversity on disease prevalence.

Predator interference and complexity-stability in food webs

It is predicted that ecological communities will become unstable with increasing species numbers and subsequent interspecific interactions; however, this is contrary to how natural ecosystems with diverse species respond to changes in species numbers. This contradiction has steered ecologists toward exploring what underlying processes allow complex communities to stabilize even through varying pressures. In this study, a food web model is used to show an overlooked role of interference among multiple predator species in solving this complexity–stability problem. Predator interference in large communities weakens species interactions due to a reduction in consumption rates by prey-sharing species in the presence of predators in response to territorial and aggressive behavior, thereby playing a key stabilizing role in communities. Especially when interspecific interference is strong and a community has diverse species and dense species interactions, stabilization is likely to work and creates a positive complexity–stability relationship within a community. The clear positive effect of complexity on community stability is not reflected by/intraspecific interference, emphasizing the key role of interspecific interference among multiple predator species in maintaining larger systems.

Dynamical systems on large networks with predator-prey interactions are stable and exhibit oscillations

We analyze the stability of linear dynamical systems defined on sparse, random graphs with predator-prey, competitive, and mutualistic interactions. These systems are aimed at modeling the stability of fixed points in large systems defined on complex networks, such as ecosystems consisting of a large number of species that interact through a food web. We develop an exact theory for the spectral distribution and the leading eigenvalue of the corresponding sparse Jacobian matrices. This theory reveals that the nature of local interactions has a strong influence on a system's stability. We show that, in general, linear dynamical systems defined on random graphs with a prescribed degree distribution of unbounded support are unstable if they are large enough, implying a tradeoff between stability and diversity. Remarkably, in contrast to the generic case, antagonistic systems that contain only interactions of the predator-prey type can be stable in the infinite size limit. This feature for antagonistic systems is accompanied by a peculiar oscillatory behavior of the dynamical response of the system after a perturbation, when the mean degree of the graph is small enough. Moreover, for antagonistic systems we also find that there exist a dynamical phase transition and critical mean degree above which the response becomes nonoscillatory.

Stability of ecosystems enhanced by species-interaction constraints

Ecosystem stability is a central question both in theoretical and applied biology. Dynamical systems theory can be used to analyze how growth rates, carrying capacities, and patterns of species interactions affect the stability of an ecosystem. The response to increasing complexity has been extensively studied and the general conclusion is that there is a limit. While there is a complexity limit to stability at which global destabilisation occurs, the collapse rarely happens suddenly if a system is fully viable (no species is extinct). In fact, when complexity is successively increased, we find that the generic response is to go through multiple single-species extinctions before a global collapse. In this paper we demonstrate this finding via both numerical simulations and elaborations of theoretical predictions. We explore more biological interaction patterns, and, perhaps most importantly, we show that constrained interaction structures-a constant row sum in the interaction matrix-prevent extinctions from occurring. This makes an ecosystem more robust in terms of allowed complexity, but it also means singles-species extinctions do not precede or signal collapse-a drastically different behavior compared to the generic and commonly assumed case. We further argue that this constrained interaction structure-limiting the total interactions for each species-is biologically plausible.

Predicting collapse of complex ecological systems: quantifying the stability-complexity continuum

Dynamical shifts between the extremes of stability and collapse are hallmarks of ecological systems. These shifts are limited by and change with biodiversity, complexity, and the topology and hierarchy of interactions. Most ecological research has focused on identifying conditions for a system to shift from stability to any degree of instability-species abundances do not return to exact same values after perturbation. Real ecosystems likely have a continuum of shifting between stability and collapse that depends on the specifics of how the interactions are structured, as well as the type and degree of disturbance due to environmental change. Here we map boundaries for the extremes of strict stability and collapse. In between these boundaries, we find an intermediate regime that consists of single-species extinctions, which we call the extinction continuum. We also develop a metric that locates the position of the system within the extinction continuum-thus quantifying proximity to stability or collapse-in terms of ecologically measurable quantities such as growth rates and interaction strengths. Furthermore, we provide analytical and numerical techniques for estimating our new metric. We show that our metric does an excellent job of capturing the system's behaviour in comparison with other existing methods-such as May's stability criteria or critical slowdown. Our metric should thus enable deeper insights about how to classify real systems in terms of their overall dynamics and their limits of stability and collapse.

The balance of interaction types determines the assembly and stability of ecological communities

What determines the assembly and stability of complex communities is a central question in ecology. Past work has suggested that mutualistic interactions are inherently destabilizing. However, this conclusion relies on the assumption that benefits from mutualisms never stop increasing. Furthermore, almost all theoretical work focuses on the internal (asymptotic) stability of communities assembled all at once. Here, we present a model with saturating benefits from mutualisms and sequentially assembled communities. We show that such communities are internally stable for any level of diversity and any combination of species interaction types. External stability, or resistance to invasion, is thus an important but overlooked measure of stability. We demonstrate that the balance of different interaction types governs community dynamics. A higher fraction of mutualistic interactions can increase the external stability and diversity of communities as well as species persistence, if mutualistic interactions tend to provide unique benefits. Ecological selection increases the prevalence of mutualisms, and limits on biodiversity emerge from species interactions. Our results help resolve long-standing debates on the stability, saturation and diversity of communities.

Contact-Dependent Interbacterial Antagonism Mediated by Protein Secretion Machines

To establish and maintain an ecological niche, bacteria employ a wide range of pathways to inhibit the growth of their microbial competitors. Some of these pathways, such as those that produce antibiotics or bacteriocins, exert toxicity on nearby cells in a cell contact-independent manner. More recently, however, several mechanisms of interbacterial antagonism requiring cell-to-cell contact have been identified. This form of microbial competition is mediated by antibacterial protein toxins whose delivery to target bacteria uses protein secretion apparatuses embedded within the cell envelope of toxin-producing bacteria. In this review, we discuss recent work implicating the bacterial Type I, IV, VI, and VII secretion systems in the export of antibacterial 'effector' proteins that mediate contact-dependent interbacterial antagonism.

The effect of multiple biotic interaction types on species persistence

No species can persist in isolation from other species, but how biotic interactions affect species persistence is still a matter of inquiry. Is persistence more likely in communities with higher proportion of competing species, or in communities with more positive interactions? How do different components of community structure mediate this relationship? We address these questions using a novel simulation framework that generates realistic communities with varying numbers of species and different proportions of biotic interaction types within and across trophic levels. We show that when communities have fewer species, persistence is more likely if positive interactions-such as mutualism and commensalism-are prevalent. In species-rich communities, the disproportionate effect of positive interactions on persistence is diluted and different combinations of biotic interaction types can coexist without affecting persistence significantly. We present the first theoretical examination of how multiple-interaction networks with varying architectures relate to local species persistence, and provide insight about the underlying causes of stability in communities.

The joint influence of competition and mutualism on the biodiversity of mutualistic ecosystems

In the past years, there have been many advances –but also many debates– around mutualistic communities, whose structural features appear to facilitate mutually beneficial interactions and increase biodiversity, under some given population dynamics. However, most approaches neglect the structure of inter-species competition by adopting a mean-field perspective that does not deal with competitive interactions properly. Here, we build up a multilayer network that naturally accounts for mutualism and competition and show, through a dynamical population model and numerical simulations, that there is an intricate relation between competition and mutualism. Specifically, the multilayer structure is coupled to a dynamical model in which the intra-guild competitive terms are weighted by the abundance of shared mutualistic relations. We find that mutualism does not have the same consequences on the evolution of specialist and generalist species, and that there is a non-trivial profile of biodiversity in the parameter space of competition and mutualism. Our findings emphasize how the simultaneous consideration of positive and negative interactions derived from the real networks is key to understand the delicate trade-off between topology and biodiversity in ecosystems and call for the need to incorporate more realistic interaction patterns when modeling the structural and dynamical stability of mutualistic systems.

Population cycles emerging through multiple interaction types

Cyclic dynamics of populations are outstanding and widespread phenomena across many taxa. Previous theoretical studies have mainly focused on the consumer-resource interaction as the driving force for such cycling. However, natural ecosystems comprise diverse types of species interactions, but their roles in population dynamics remains unclear. Here, using a four-species hybrid module with antagonistic, mutualistic and competitive interactions, we analytically showed that the system with major interaction types can drive population cycles. Stronger interactions easily cause cycling, and even when sub-modules with possible combinations of two interactions are stabilized by weak interactions, the system with all interaction types can cause unstable population oscillations. Diversity of interaction types allows to add mutualists to the list of drivers of oscillations in a focal species' population size, when they act in conjunction to other drivers.

The roles of amensalistic and commensalistic interactions in large ecological network stability

Ecological communities comprise diverse species and their interactions. Notably, ecological and evolutionary studies have revealed that reciprocal interactions such as predator–prey, competition, and mutualism, are key drivers of community dynamics. However, there is an argument that many species interactions are asymmetric, where one species unilaterally affects another species (amensalism or commensalism). This raises the unanswered question of what is the role of unilateral interactions in community dynamics. Here I use a theoretical approach to demonstrate that unilateral interactions greatly enhance community stability. The results suggested that amensalism and commensalism were more stabilizing than symmetrical interactions, such as competition and mutualism, but they were less stabilizing than an asymmetric antagonistic interaction. A mix of unilateral interactions increased stability. Furthermore, in communities with all interaction types, unilateral interactions tended to increase stability. This study suggests that unilateral interactions play a major role in maintaining communities, underlining the need to further investigate their roles in ecosystem dynamics.

Stability of an adaptive hybrid community

Contrary to stable natural ecosystems, the classical ecological theory predicts that complex ecological communities are fragile. The adaptive switching of interaction partners was proposed as a key factor to resolve the complexity–stability problem. However, this theory is based on the food webs that comprise predator–prey interactions alone; thus, the manner in which adaptive behavior affects the dynamics of hybrid communities with multiple interaction types remains unclear. Here, using a bipartite community network model with antagonistic and mutualistic interactions, I show that adaptive partner shifts by both antagonists and mutualists are crucial to the persistence of communities. The results show that adaptive behavior destabilizes the dynamics of communities with a single interaction type; however, the hybridity of multiple interaction types within a community greatly improves the stability. Moreover, adaptive behavior does not create a positive complexity–stability relationship in communities with a single interaction type but it does in the hybrid community. The diversity of interaction types is predicted to play a crucial role in community maintenance in an adaptive world.

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks

Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network’s stability by adjusting the network’s properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

Red Queen dynamics in multi-host and multi-parasite interaction system

In host-parasite systems, dominant host types are expected to be eventually replaced by other hosts due to the elevated potency of their specific parasites. This leads to changes in the abundance of both hosts and parasites exhibiting cycles of alternating dominance called Red Queen dynamics. Host-parasite models with less than three hosts and parasites have been demonstrated to exhibit Red Queen cycles, but natural host-parasite interactions typically involve many host and parasite types resulting in an intractable system with many parameters. Here we present numerical simulations of Red Queen dynamics with more than ten hosts and specialist parasites under the condition of no super-host nor super-parasite. The parameter region where the Red Queen cycles arise contracts as the number of interacting host and parasite types increases. The interplay between inter-host competition and parasite infectivity influences the condition for the Red Queen dynamics. Relatively large host carrying capacity and intermediate rates of parasite mortality result in never-ending cycles of dominant types.