Local and collective transitions in sparsely-interacting ecological communities

Interactions in natural communities can be highly heterogeneous, with any given species interacting appreciably with only some of the others, a situation commonly represented by sparse interaction networks. We study the consequences of sparse competitive interactions, in a theoretical model of a community assembled from a species pool. We find that communities can be in a number of different regimes, depending on the interaction strength. When interactions are strong, the network of coexisting species breaks up into small subgraphs, while for weaker interactions these graphs are larger and more complex, eventually encompassing all species. This process is driven by emergence of new allowed subgraphs as interaction strength decreases, leading to sharp changes in diversity and other community properties, and at weaker interactions to two distinct collective transitions: a percolation transition, and a transition between having a unique equilibrium and having multiple alternative equilibria. Understanding community structure is thus made up of two parts: first, finding which subgraphs are allowed at a given interaction strength, and secondly, a discrete problem of matching these structures over the entire community. In a shift from the focus of many previous theories, these different regimes can be traversed by modifying the interaction strength alone, without need for heterogeneity in either interaction strengths or the number of competitors per species.

Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack

In this paper we investigate how the modularity of model and real-world social networks affect their robustness and the efficacy of node attack (removal) strategies based on node degree (ID) and node betweenness (IB). We build Barabasi–Albert model networks with different modularity by a new ad hoc algorithm that rewire links forming networks with community structure. We traced the network robustness using the largest connected component (LCC). We find that when model networks present absent or low modular structure ID strategy is more effective than IB to decrease the LCC. Conversely, in the case the model network present higher modularity, the IB strategy becomes the most effective to fragment the LCC. In addition, networks with higher modularity present a signature of a 1st order percolation transition and a decrease of the LCC with one or several abrupt changes when nodes are removed, for both strategies; differently, networks with non-modular structure or low modularity show a 2nd order percolation transition networks when nodes are removed. Last, we investigated how the modularity of the network structure evaluated by the modularity indicator (Q) affect the network robustness and the efficacy of the attack strategies in 12 real-world social networks. We found that the modularity Q is negatively correlated with the robustness of the real-world social networks for both the node attack strategies, especially for the IB strategy (p-value < 0.001). This result indicates how real-world networks with higher modularity (i.e. with higher community structure) may be more fragile to node attack. The results presented in this paper unveil the role of modularity and community structure for the robustness of networks and may be useful to select the best node attack strategies in network.

The influence of the characteristics of polyethylene on thermoelectric properties of their composites with black carbon

The work is devoted to the study of the effect of characteristics (melt flow and density) of various grades of polyethylene on the electrical resistance of polyethylene composites with carbon black at normal and elevated temperatures. Such polyethylene composites are characterized by abnormally high values of the positive temperature coefficient of electrical resistance in the melting temperature range of the polyethylene matrix. This causes the effect of power self-regulation of such heaters (selfregulating polymer heaters). It has been established that the content of carbon black, which provides a stable and clear effect of self-regulation of such heaters, is located in a concentration region approaching the region of the second concentration-structural percolation transition, which for all investigated polyethylene composites was about 12 vol% of carbon black. The growth rate of electrical resistance at these carbon-black contents is influenced by crystallinity of the polyethylene matrix.

Habitat percolation transition undermines sustainability in social-ecological agricultural systems

Steady increases in human population size and resource consumption levels are driving rampant agricultural expansion and intensification in some of the world’s most pristine ecosystems. Habitat loss caused by agriculture puts the integrity of ecosystems at risk, and as a consequence, threatens the persistence of human societies that rely on ecosystem services to produce resources. Here we develop a spatially explicit model describing the coupled dynamics of an agricultural landscape and human population size to study the effect of different land-use management strategies, defined by the levels of agricultural clustering and intensification, on the sustainability of the social-ecological system. We show how gradual agricultural expansion can cause natural habitat to undergo a percolation transition leading to abrupt habitat fragmentation that feedbacks on human’s decision making, causing faster agricultural expansion and aggravating habitat loss and fragmentation. We found that agricultural intensification to spare land from conversion is a successful strategy only in highly natural landscapes and that clustering agricultural land is the most effective measure to preserve landscape connectivity and avoid severe fragmentation. Our work highlights the importance of preserving large connected natural fragments in agricultural landscapes to enhance sustainability.

Habitat percolation transition undermines sustainability in social-ecological agricultural systems

Steady increases in human population size and resource consumption are driving rampant agricultural expansion and intensification. Habitat loss caused by agriculture puts the integrity of ecosystems at risk, and threatens the persistence of human societies that rely on ecosystem services. We develop a spatially explicit model describing the coupled dynamics of an agricultural landscape and human population size to study the effect of different land-use management strategies, defined by agricultural clustering and intensification, on the sustainability of the social-ecological system. We show how agricultural expansion can cause natural habitat to undergo a percolation transition leading to abrupt habitat fragmentation that feedbacks on human's decision making, aggravating landscape degradation. We found that agricultural intensification to spare land from conversion is a successful strategy only in highly natural landscapes, and that clustering agricultural land is the most effective measure to preserve large connected natural fragments, avoid severe fragmentation, and thus, enhance sustainability.