Soil microbial networks play a crucial role in plant community stability. However, we lack knowledge on the network topologies associated with stability and the pathways shaping these networks. In a 13-year mesocosm experiment, we determined links between plant community stability and soil microbial networks. We found that plant communities on soil abandoned from agricultural practices 60 years prior to the experiment promoted destabilising properties and were associated with coupled prokaryote and fungal soil networks. This coupling was mediated by strong interactions of plants and microbiota with soil resource cycling. Conversely, plant communities on natural grassland soil exhibited a high stability, which was associated with decoupled prokaryote and fungal soil networks. This decoupling was mediated by a large variety of past plant community pathways shaping especially fungal networks. We conclude that plant community stability is associated with a decoupling of prokaryote and fungal soil networks and mediated by plant-soil interactions. 

/pdf/plant-community-stability-is-associated-with-a-decoupling-of-3ehxkbde.pdf

Plant community stability is associated with a decoupling of prokaryote and fungal soil networks

Soil microbial networks play a crucial role in plant community stability. However, we lack knowledge on the network topologies associated with stability and the pathways shaping these networks. In a 13-year mesocosm experiment, we determined links between plant community stability and soil microbial networks. We found that plant communities on soil abandoned from agricultural practices 60 years prior to the experiment promoted destabilising properties and were associated with coupled prokaryote and fungal soil networks. This coupling was mediated by strong interactions of plants and microbiota with soil resource cycling. Conversely, plant communities on natural grassland soil exhibited a high stability, which was associated with decoupled prokaryote and fungal soil networks. This decoupling was mediated by a large variety of past plant community pathways shaping especially fungal networks. We conclude that plant community stability is associated with a decoupling of prokaryote and fungal soil networks and mediated by plant-soil interactions.

/pdf/plant-community-stability-is-associated-with-a-decoupling-of-177l7mdq.pdf


 Sex- and age-based social structures have been well documented in animals with visible aggregations. However, very little is known about the social structures of snakes. This is most likely because snakes are often considered non-social animals and are particularly difficult to observe in the wild. Here, we show that wild Butler’s Gartersnakes have an age and sex assorted social structure similar to more commonly studied social animals. To demonstrate this, we use data from a 12-year capture-mark-recapture study to identify social interactions using social network analyses. We find that the social structures of Butler’s Gartersnakes comprise sex- and age-assorted intra-species communities with older females often central and age segregation partially due to patterns of study site use. In addition, we find that females tended to increase in sociability as they aged while the opposite occurred in males. We also present evidence that social interaction may provide fitness benefits, where snakes that were part of a social network were more likely to have improved body condition. We demonstrate that conventional capture data can reveal valuable information on social structures in cryptic species. This is particularly valuable as research has consistently demonstrated that understanding social structure is important for conservation efforts. Additionally, research on the social patterns of animals without obvious social groups provides valuable insight into the evolution of group living.

Social networks reveal sex- and age-patterned social structure in Butler’s gartersnakes (Thamnophis butleri)

Mapping actor networks: shaping the dynamics of economic corridors through the lens of the Bioceanic Road Corridor

Abstract When collecting several data sets and heterogeneous data types on a given phenomenon of interest, the individual analysis of each data set will provide only a particular view of such phenomenon. Instead, integrating all the data may widen and deepen the results, offering a better view of the entire system. In the context of network integration, we propose the algorithm. assumes a similar network structure, representing latent variables in different network layers of the same system. Therefore, by combining individual edge weights and topological network structures, first constructs a that represents the shared information underneath the different layers to provide a global view of the entities that play a fundamental role in the phenomenon of interest. Then, it derives a for each layer containing peculiar information of the single data type not present in all the others. We demonstrated good performance with our method through simulated data and detected new insights by analyzing biological and sociological datasets. 

INet for network integration

In network analysis, many community detection algorithms have been developed, however, their implementation leaves unaddressed the question of the statistical validation of the results. Here we present robin(ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset.

ROBustness In Network (robin): an R package for Comparison and Validation of communities.

/pdf/robustness-in-network-robin-an-r-package-for-comparison-and-4s00g7fpa1.pdf

ROBustness In Network (robin): an R Package for Comparison and Validation of Communities

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ROBustness In Network (robin): an R package for Comparison and Validation of communities.

ROBustness In Network (robin): an R Package for Comparison and Validation of Communities