Mutualistic Ecological Networks Research Articles

Reviving collapsed plant-pollinator networks from a single species.

Mutualistic ecological networks can suddenly transition to undesirable states due to small changes in environmental conditions. Recovering from such a collapse can be difficult as restoring the original environmental conditions may be infeasible. Additionally, such networks can also exhibit a phenomenon known as hysteresis, whereby the system could exhibit multiple states under the same environmental conditions, implying that ecological networks may not recover. Here, we attempted to revive collapsed mutualistic networks to a high-functioning state from a single species, using concepts from signal propagation theory and an eco-evolutionary model based on network structures of 115 empirical plant-pollinator networks. We found that restoring the environmental conditions rarely aided in recovery of collapsed networks, but a positive relationship between recovering pollinator density and network nestedness emerged, which was qualitatively supported by empirical plant-pollinator restoration data. In contrast, network resurrection from a collapsed state in undesirable environmental conditions where restoration has minimal impacts could be readily achieved by perturbing a single species or a few species that controls the response of the dynamical networks. Additionally, nestedness in networks and a moderate amount of trait variation could aid in the revival of networks even in undesirable environmental conditions. Our work suggests that focus should be applied to a few species whose dynamics could be steered to resurrect entire networks from a collapsed state and that network architecture could play a crucial role in reviving collapsed plant-pollinator networks.

Corrigendum

Corrigendum

Tipping point and noise-induced transients in ecological networks.

A challenging and outstanding problem in interdisciplinary research is to understand the interplay between transients and stochasticity in high-dimensional dynamical systems. Focusing on the tipping-point dynamics in complex mutualistic networks in ecology constructed from empirical data, we investigate the phenomena of noise-induced collapse and noise-induced recovery. Two types of noise are studied: environmental (Gaussian white) noise and state-dependent demographic noise. The dynamical mechanism responsible for both phenomena is a transition from one stable steady state to another driven by stochastic forcing, mediated by an unstable steady state. Exploiting a generic and effective two-dimensional reduced model for real-world mutualistic networks, we find that the average transient lifetime scales algebraically with the noise amplitude, for both environmental and demographic noise. We develop a physical understanding of the scaling laws through an analysis of the mean first passage time from one steady state to another. The phenomena of noise-induced collapse and recovery and the associated scaling laws have implications for managing high-dimensional ecological systems.

Designing eco-industrial parks in a nested structure to mimic mutualistic ecological networks

Industrial Ecology uses ecological systems as a guide for improving the sustainability of complex industrial systems. Eco-industrial Parks (EIPs) have gained support as a solution that seeks to simultaneously reduce environmental burdens and promote economic interests by exchanging materials and energy between industries to their mutual benefit. Recent studies have focused on drawing relations between food webs (FWs) and EIPs to improve the sustainability of the latter using ecological metrics, such as the level of cycling or average connections between actors. This study incorporates a new ecological metric, nestedness, into the discussion of sustainable design for EIPs. The association of nestedness with mutualistic ecological networks supports its application to EIP design. The work here improves the understanding of holistic network structure with the goal of improving future design decisions for EIPs with purposeful placement of material and energy flows.

Architecture of attractor determines dynamics on mutualistic complex networks

A mathematical system of differential equations for the modelization of mutualistic networks in Ecology has been proposed in Bastolla et al. (2007). Basically, it is studied how the complex structure of cooperation interactions between groups of plants and pollinators or seed dispersals affects to the whole network. In this paper we prove existence and characterization of the global attractor associated to the model. The description of the geometrical internal structure of the attractor becomes the proper complex network describing all the possible future scenarios of the phenomena. The arguments show a Morse Decomposition of the attractors, leading to the existence of a global Lyapunov function for the associated gradient semigroup. In particular, we are able to prove topological structural stability of the system, i.e., the associated attracting complex networks are robust under (autonomous and non-autonomous) perturbation of parameters.

Effect of localization on the stability of mutualistic ecological networks.

The relationships between the core–periphery architecture of the species interaction network and the mechanisms ensuring the stability in mutualistic ecological communities are still unclear. In particular, most studies have focused their attention on asymptotic resilience or persistence, neglecting how perturbations propagate through the system. Here we develop a theoretical framework to evaluate the relationship between the architecture of the interaction networks and the impact of perturbations by studying localization, a measure describing the ability of the perturbation to propagate through the network. We show that mutualistic ecological communities are localized, and localization reduces perturbation propagation and attenuates its impact on species abundance. Localization depends on the topology of the interaction networks, and it positively correlates with the variance of the weighted degree distribution, a signature of the network topological heterogeneity. Our results provide a different perspective on the interplay between the architecture of interaction networks in mutualistic communities and their stability.

Randomizing bipartite networks: the case of the World Trade Web.

Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the World Trade Web, comparing the observed values of a number of structural quantities of interest with the expected ones, calculated via our randomization procedure. Interestingly, the behavior of the World Trade Web in this new representation is strongly different from the monopartite analogue, showing highly non-trivial patterns of self-organization.

The more the merrier

The 'nested' pattern of mutualistic interactions between plants and their pollinators is thought to promote species coexistence. But the key determinant may instead be the number of partners that species have. See Letter p.227 The nested structure of mutualistic ecological networks — the tendency for ecological specialists to interact with a subset of species that also interact with more generalist species — is thought to promote biodiversity. This study of 59 empirical data sets for plant-pollinator mutualisms comes to the surprising conclusion that adding mutualisms typically decreases biodiversity by breaking the competition symmetry, and that nestedness is not important in determining ecosystem function. According to this model, it is the simpler measure of the number of mutualistic links a species has that determines its survival chances, with nestedness being just a side effect.

Disentangling nestedness from models of ecological complexity

Complex networks of interactions are ubiquitous and are particularly important in ecological communities, in which large numbers of species exhibit negative (for example, competition or predation) and positive (for example, mutualism) interactions with one another. Nestedness in mutualistic ecological networks is the tendency for ecological specialists to interact with a subset of species that also interact with more generalist species. Recent mathematical and computational analysis has suggested that such nestedness increases species richness. By examining previous results and applying computational approaches to 59 empirical data sets representing mutualistic plant–pollinator networks, we show that this statement is incorrect. A simpler metric—the number of mutualistic partners a species has—is a much better predictor of individual species survival and hence, community persistence. Nestedness is, at best, a secondary covariate rather than a causative factor for biodiversity in mutualistic communities. Analysis of complex networks should be accompanied by analysis of simpler, underpinning mechanisms that drive multiple higher-order network properties.