Ranking of critical species to preserve the functionality of mutualistic networks using thek-core decomposition

Abstract

BackgroundNetwork analysis has become a relevant approach to analyze cascading species extinctions resulting from perturbations on mutualistic interactions as a result of environmental change. In this context, it is essential to be able to point out key species, whose stability would prevent cascading extinctions, and the consequent loss of ecosystem function. In this study, we aim to explain how the k-core decomposition sheds light on the understanding the robustness of bipartite mutualistic networks.MethodsWe defined three k-magnitudes based on the k-core decomposition: k-radius, k-degree, and k-risk. The first one, k-radius, quantifies the distance from a node to the innermost shell of the partner guild, while k-degree provides a measure of centrality in the k-shell based decomposition. k-risk is a way to measure the vulnerability of a network to the loss of a particular species. Using these magnitudes we analyzed 89 mutualistic networks involving plant pollinators or seed dispersers. Two static extinction procedures were implemented in which k-degree and k-risk were compared against other commonly used ranking indexes, as for example MusRank, explained in detail in Material and Methods.ResultsWhen extinctions take place in both guilds, k-risk is the best ranking index if the goal is to identify the key species to preserve the giant component. When species are removed only in the primary class and cascading extinctions are measured in the secondary class, the most effective ranking index to identify the key species to preserve the giant component is k-degree. However, MusRank index was more effective when the goal is to identify the key species to preserve the greatest species richness in the second class.DiscussionThe k-core decomposition offers a new topological view of the structure of mutualistic networks. The new k-radius, k-degree and k-risk magnitudes take advantage of its properties and provide new insight into the structure of mutualistic networks. The k-risk and k-degree ranking indexes are especially effective approaches to identify key species to preserve when conservation practitioners focus on the preservation of ecosystem functionality over species richness.

Temporal switching of species roles in a plant–pollinator network

“…The latter finding has far-reaching practical implications, as the idea of focusing management and conservation efforts on a small subset of species at the network core (19; 20; 21; 22; 23; 36; 37) may be difficult to achieve, given that virtually no species plays that role consistently over time in the long run. Our findings do indicate that a small subset of species is likely to be found playing a key role as part of the network core in many seasons and years, which brings them close to the notion of “core” species and would make them adequate targets for conservation efforts.…”

“…Answering the above questions is essential to improve our understanding of how different species contribute to community stability and to guide management and conservation efforts. For example, the existence of a stable set of species at the network core could represent a reasonable target for biodiversity conservation—a small, manageable set of keystone species on which to focus conservation efforts (19; 20; 21; 22; 23). Conversely, a highly dynamic network core would make that target more elusive, with a larger, variable set of potentially keystone species.…”

Bioinformatics-based Prediction of the mechanism and targets for BushenjieduDecoction in preventing relapse of acute leukemia

“…Although we obtained the PPI network of these 109 proteins, their underly ing connections cannot be directly reflected by this network structure. Theref ore, on the basis of GENE MANIA analysis, we selected 82 proteins with s core above 0.6 for k-core analysis in Cytoscape3.7.1 using MCODE.K-core i s a topological analysis that decomposes the network relation of interaction, and find out the important nodes in the complex network relation structure (25).In this study, we set the parameters of degree>15 and k-core=2 to identi fy key nodes in the co-expression network (Fig.7). This sub network contain sHspb1、Dnmt1、Mmp2、Thbs1、Crebbp、Hmgb1、Acta2、Cdkn1b、Atg7、T sc2 and Icam1.…”

A Structural Approach to Disentangle the Visualization of Bipartite Biological Networks

“…This procedure helps to recognize how the nodes of the -shell are tied to the network. We refer to [41] for further details on the k-core analysis of bipartite networks.…”