Journal of Plant Ecology【🎉Latest accepted article】
#ForestStructure determines the structure and #FunctionalTraits of #Liana community during secondary succession in a temperate oak forest
#CommunityStructure | #SoilProperties | #SecondaryForestSuccession
American NaturalistICYMI: Lertzman-Lepofsky et al. find that elevation interacts with deforestation to shape community structure among Anolis lizards and suggest that this interaction is key to understanding community assembly in the Anthropocene.
Read now!
https://www.journals.uchicago.edu/doi/abs/10.1086/738326
Charo del GenioNew paper!
How can we detect the presence of communities in networks with higher-order interactions? For instance, by maximizing hypermodularity! Also, this formulation will allow you to leverage tensor spectral methods to do it. Additionally, the paper also argues that the "overfitting" of modularity methods is actually just people applying them where they are not supposed to be used. And, as a byproduct, there is an explanation of why higher-order SVD works so well in classification tasks in machine learning. Oh, the code is available to use in your own projects (link in the first comment). And moreover, the code includes an efficient data structure for higher-order networks that is independent from the community detection method and that you can also use in your own work. 😎
https://journals.aps.org/prresearch/abstract/10.1103/58dr-wktc
#networks #complexity #physics #maths #CompSci #graphs #higherorder #hypergraphs #community #detection #algorithm #communitystructure #modularity #hypermodularity
Zhenlu Qiu et al. explore the dynamics of #CommunityStructure, #Diversity, #InterspecificRelationships and stability during naturalized developmental restoration of #PinusTabulaeformis plantation without human’s involvement in North warm temperate zone.
https://doi.org/10.1093/jpe/rtac102
https://doi.org/10.1093/jpe/rtac102
Legumes play critical roles in agroecosystems by modulating nitrogen-fixing microorganisms to enhance #SoilFertility and promote #CropProductivity. Yi-Heng Tao et al. investigated the #CommunityStructure of soil #Nitrogen-fixingMicroorganisms in different soil layers under #Legume and non-legume cultivation .Both #TotalCarbon and #DissolvedOrganicCarbon were identified as key drivers of #Nitrogen-fixingGenes.
Details: https://doi.org/10.1093/jpe/rtae092
Details: https://doi.org/10.1093/jpe/rtae092
Luis M. RochaSo happy to finally see this collaboration with Rion Correia and @alainbarrat out. The distance backbone is a unique, algebraically-principled network subgraph that preserves all shortest paths. We were were excited to find out (with #sociopatterns and other data) that the backbones of #socialnetworks contain large amounts of redundant interactions that can be removed with very little impact on #communitystructure and #epidemic spread.
#complexsystems #dynamics
#Networkscience #Sparsification #complexnetworks
#PLOSCompBio:
Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs
Author summary It is through social networks that contagious diseases spread in human populations, as best illustrated by the current pandemic and efforts to contain it. Measuring such networks from human contact data typically results in noisy and dense graphs that need to be simplified for effective analysis, without removal of their essential features. Thus, the identification of a primary subgraph that maintains the social interaction structure and likely transmission pathways is of relevance for studying epidemic spreading phenomena as well as devising intervention strategies to hinder spread. Here we propose and study the metric backbone as an optimal subgraph for sparsification of social contact networks in the study of simple spreading dynamics. We demonstrate that it is a unique, algebraically-principled network subgraph that preserves all shortest paths. We also discover that nine contact networks obtained from proximity sensors in a variety of social contexts contain large amounts of redundant interactions that can be removed with very little impact on community structure and epidemic spread. This reveals that epidemic spread on social networks is very robust to random interaction removal. However, extraction of the metric backbone subgraph reveals which interventions—strategic removal of specific social interactions—are likely to result in maximum impediment to epidemic spread.
Joshua McNeillThe terminology on #Mastodon is interesting when considering the distinction between physical and #virtual #communities. When I first saw "local timeline", I thought geographically local, but it in fact meant my local #virtualcommunity.