Article

Linking topological structure and dynamics in ecological networks.

Departamento Biología Animal, Biología Vegetal y Ecología, Universidad de Jaén, Spain.
The American Naturalist (Impact Factor: 4.45). 08/2012; 180(2):186-99. DOI: 10.1086/666651
Source: PubMed

ABSTRACT Interaction networks are basic descriptions of ecological communities and are at the core of community dynamics models. Knowledge of their structure should enable us to understand dynamical properties of ecological communities. However, the relationships between dynamical properties of communities and qualitative descriptors of network structure remain unclear. To improve our understanding of such relationships, we develop a framework based on the concept of strongly connected components, which are key structural components of networks necessary to explain stability properties such as persistence and robustness. We illustrate this framework for the analysis of qualitative empirical food webs and plant-plant interaction networks. Both types of networks exhibit high persistence (on average, 99% and 80% of species, respectively, are expected to persist) and robustness (only 0.2% and 2% of species are expected to disappear following the extinction of a species). Each of the networks is structured as a large group of interconnected species accompanied by much smaller groups that most often consist of a single species. This low-modularity configuration can be explained by a negative modularity-stability relationship. Our results suggest that ecological communities are not typically structured in multispecies compartments and that compartmentalization decreases robustness.

2 Followers
 · 
140 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: QuestionsWhen a tree or a shrub dies, the space it occupied can be overtaken by plants that were recruiting beneath it or colonized by new species. Such replacement processes can drive the temporal change in species abundance in a plant community. Can we predict the dynamics of a real plant community using observational data on plant–plant recruitment interactions? What would be the relative importance of recruitment interactions vs life-history traits in determining community dynamics?LocationForest communities dominated by Pinus halepensis and Quercus ilex in SE Spain.Methods We develop a continuous time non-linear model that can be easily parameterized with empirical data for the interactions between adult and juvenile plants recruiting beneath them. These interactions form a complex replacement network (or matrix) that is the backbone of the model. We parameterize the model with life-history data from the literature and from recruitment interactions observed in a successional community 12 yr after a forest fire. We explore the behaviour of the model under different intensities of chronic disturbance, and after modifications of the structure of the replacement network and the values of the parameters.ResultsThe model predicts that the current community of the burned area will develop into a forest very similar quantitatively to the surrounding mature forests, as long as the rates of chronic disturbance remain very low. For increasingly higher levels of chronic disturbance, the community would reach stable states resembling a mixed pine–oak forest, a degraded oak forest, an oak dehesa and, finally, a steppe-like vegetation. All these types of plant assemblages can currently be found throughout the study area. These predictions are less sensitive to variation in the estimates of species' life history (i.e. growth, death and colonization rates) than to variation in the structure of the recruitment matrix.Conclusions The model projects realistic community dynamics. The analysis of the model suggest that understanding the structure of replacement networks and how they are assembled can contribute significantly to our knowledge of the dynamics and stability of forest plant communities.
    Journal of Vegetation Science 01/2015; In press. DOI:10.1111/jvs.12252 · 3.37 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The structure of networks has always been interesting for researchers. Investigating their unique architecture allows to capture insights and to understand the function and evolution of these complex systems. Ecological networks such as food-webs and niche-overlap graphs are considered as complex systems. The main purpose of this work is to compare the topology of 15 real niche-overlap graphs with random ones. Five measures are treated in this study: (1) the clustering coefficient, (2) the between ness centrality, (3) the assortativity coefficient, (4) the modularity and (5) the number of chord less cycles. Significant differences between real and random networks are observed. Firstly, we show that niche-overlap graphs display a higher clustering and a higher modularity compared to random networks. Moreover we find that random networks have barely nodes that belong to a unique sub graph (i.e. between ness centrality equal to 0) and highlight the presence of a small number of chord less cycles compared to real networks. These analyses may provide new insights in the structure of these real niche-overlap graphs and may give important implications on the functional organization of species competing for some resources and on the dynamics of these systems.
    Proceedings of the 2013 International Conference on Signal-Image Technology & Internet-Based Systems; 12/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background/Question/Methods The relationship between complexity and stability is not only a fundamental ecological debate but also an important and current multidisciplinary issue. The network paradigm is ideally suited to inquiry in this area and continues to be widely used to probe the dynamics of complex systems in nature and society. Most ecological network research, however, has so far focused on particular interaction types, dynamics of highly simplified systems or empirical energy flows applicable only to specific systems. Few have tried to overcome these limitations concurrently by constructing realistically complex systems using simple rules and examining their emergent properties under a general theoretical framework. In this study, we first designed a simulation model combining ecological network topology, bioenergetics and population dynamics using fundamental ecological principles and mechanistic equations. We then used the model to investigate how the addition of different types of non-predator-prey interactions (competition, mutualism, parasitism) to food webs affect their resilience. Resilience was quantified in terms of the mean time taken to recover half the energy lost in perturbations. We accounted for parameter uncertainty via Monte Carlo simulations. Results/Conclusions Linear mixed-effects regression, with presence or absence of interaction types as dummy variables and model parameterization as a random effect, associated mutualism with shorter recovery time and parasitism with longer recovery time (n = 212, p << 0.0001). Direct competition among basal species was also associated with longer recovery time but the effect was less significant (p = 0.004). Mutualism had the aforementioned effect regardless of the presence or absence of parasitism. A separate simulation experiment, keeping connectance constant across model configurations with and without mutualism and the number of species constant across configurations with and without parasitism, produced qualitatively identical results for mutualism and parasitism (n = 2102, p << 0.0001). The effects are present despite the large variation across simulated ecosystems that reflects the variety of real ecosystems. We discuss possible ecological mechanisms by which non-predator-prey interactions can affect ecosystem resilience as evidenced by the simulations. Our findings illustrate the potential effects of non-predator-prey interactions and support the cause for accounting for these interactions in investigations of ecological communities. We advocate systems approaches as part of our arsenal of tools for better understanding and anticipation of the trajectories of complex ecosystems under rapid anthropogenic change.
    97th ESA Annual Convention 2012; 08/2012