The origin of motif families in food webs

Food webs have been found to exhibit remarkable “motif profiles”, patterns in the relative prevalences of all possible three-species subgraphs, and this has been related to ecosystem properties such as stability and robustness. Analysing 46 food webs of various kinds, we find that most food webs fall into one of two distinct motif families. The separation between the families is well predicted by a global measure of hierarchical order in directed networks—trophic coherence. We find that trophic coherence is also a good predictor for the extent of omnivory, defined as the tendency of species to feed on multiple trophic levels. We compare our results to a network assembly model that admits tunable trophic coherence via a single free parameter. The model is able to generate food webs in either of the two families by varying this parameter, and correctly classifies almost all the food webs in our database. This is in contrast with the two most popular food web models, the generalized cascade and niche models, which can only generate food webs within a single motif family. Our findings suggest the importance of trophic coherence in modelling local preying patterns in food webs

The origin of motif families in food webs

Food webs have been found to exhibit remarkable “motif profiles”, patterns in the relative prevalences of all possible three-species subgraphs, and this has been related to ecosystem properties such as stability and robustness. Analysing 46 food webs of various kinds, we find that most food webs fall into one of two distinct motif families. The separation between the families is well predicted by a global measure of hierarchical order in directed networks—trophic coherence. We find that trophic coherence is also a good predictor for the extent of omnivory, defined as the tendency of species to feed on multiple trophic levels. We compare our results to a network assembly model that admits tunable trophic coherence via a single free parameter. The model is able to generate food webs in either of the two families by varying this parameter, and correctly classifies almost all the food webs in our database. This is in contrast with the two most popular food web models, the generalized cascade and niche models, which can only generate food webs within a single motif family. Our findings suggest the importance of trophic coherence in modelling local preying patterns in food webs

Collective decision-making on triadic graphs

Many real-world networks exhibit community structures and non-trivial clustering associated with the occurrence of a considerable number of triangular subgraphs known as triadic motifs. Triads are a set of distinct triangles that do not share an edge with any other triangle in the network. Network motifs are subgraphs that occur significantly more often compared to random topologies. Two prominent examples, the feedforward loop and the feedback loop, occur in various real-world networks such as gene-regulatory networks, food webs or neuronal networks. However, as triangular connections are also prevalent in communication topologies of complex collective systems, it is worthwhile investigating the influence of triadic motifs on the collective decision-making dynamics. To this end, we generate networks called Triadic Graphs (TGs) exclusively from distinct triadic motifs. We then apply TGs as underlying topologies of systems with collective dynamics inspired from locust marching bands. We demonstrate that the motif type constituting the networks can have a paramount influence on group decision-making that cannot be explained solely in terms of the degree distribution. We find that, in contrast to the feedback loop, when the feedforward loop is the dominant subgraph, the resulting network is hierarchical and inhibits coherent behavior

Clone size distributions in networks of genetic similarity

We build networks of genetic similarity in which the nodes are organisms sampled from biological populations. The procedure is illustrated by constructing networks from genetic data of a marine clonal plant. An important feature in the networks is the presence of clone subgraphs, i.e. sets of organisms with identical genotype forming clones. As a first step to understand the dynamics that has shaped these networks, we point up a relationship between a particular degree distribution and the clone size distribution in the populations. We construct a dynamical model for the population dynamics, focussing on the dynamics of the clones, and solve it for the required distributions. Scale free and exponentially decaying forms are obtained depending on parameter values, the first type being obtained when clonal growth is the dominant process. Average distributions are dominated by the power law behavior presented by the fastest replicating populations.Comment: 17 pages, 4 figures. One figure improved and other minor changes. To appear in Physica

Seeking an ‘Ideal Place’ in a Nuosu Origin Epic

The Book of Origins (hnewo tepyy) is a major ritual text of the Nuosu, a subgroup of the official Yi (Yizu) ethnic group of southwest China. The narrative, existing in both written and oral variants, is part of a living tradition, especially among priests (bimo) and folk singers, in the Liangshan Yi autonomous region in Sichuan provinceand nearby Yunnan province. The epic narrates the creation of the sky, earth, and living creatures through the frame of genealogies. After an age of scorching heat, life is re-seeded on earth and a descendant of the snow tribes of flora and fauna finds a bride. Many generations later this union results in the marriage between an earthling and the Sky God’s daughter. The tropes of genealogy and migration intertwine in the storyworld as clans descended from the couple seek out an “ideal place” to settle and prosper in the local environment in a pattern that resonates with other epics from the southwest and the Southeast Asian Massif

The structure and stability of ecological networks

All species interact with other species to form complex networks of connections. Such networks are a powerful way to represent ecological communities because they describe (i) the roles of individual species and (ii) the structure of the community as a whole in a single framework amenable to mathematical and computational analysis. In this thesis I consider a number of outstanding problems in network ecology. In Chapter 1, I examine the consequences for network structure of removing non-mutualistic interactions from plant-frugivore visitation networks. I find that plant-frugivore visitation networks act as a good proxy for mutualistic seed dispersal networks in terms of whole-network topology, but not when considering species- level structures. Chapter 2 deals with whether generalisation drives abundance or vice versa in plant-hummingbird pollination networks. I find evidence that abundance drives generalisation and use a simple model to show that neutral processes can explain broad patterns of species- level generalisation. In Chapter 3, I quantify the importance and vulnerability of mutualistic interactions to understand the risk that interaction extinction poses to communities. I conclude that (i) the interactions most important for community stability are those which are most vulnerable to extinction, and (ii) important and vulnerable interactions tend to be important and vulnerable wherever they occur. In Chapter 4, I consider motifs as an alternative to indices for characterising the structure of bipartite networks. I find that motifs capture significantly more information about network topology than indices and advocate adding bipartite motifs to the suite of analytical tools used by network ecologists. Chapter 5 describes a software package in R, MATLAB and Python for conducting motif analyses of bipartite networks. It uses novel mathematical formulations to dramatically reduce the computational time required for motif calculations compared to competing software.Funded by the Natural Environment Research Council as part of the Cambridge Earth System Science NERC DTP [NE/L002507/1]

Complex networks: the key to systems biology

Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.FAPESPCNP

A molecular insight into algal-oomycete warfare : cDNA analysis of Ectocarpus siliculosus infected with the basal oomycete Eurychasma dicksonii

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