Recently, researchers from the Computational Biology Center of the College of Biological Science and Technology published a paper entitled "idopNetwork: A network tool to dissect spatial community ecology" in the internationally renowned journal Methods in Ecology and Evolution (Q1, IF=6.6). PhD candidate Dong Ang is the first author, and Professor Wu Rongling is the corresponding author.

Network models have been used as a tool to characterize internal workings of complex systems. The amount of topological and functional information extracted from a network depend on the method of network inference and the type of network data. An interdisciplinary computational model has been proposed to reconstruct informative, dynamic, omnidirectional and personalized networks (idopNetwork) from any data domains including static data.

The research team implement idopNetwork as an R-based cartographic tool to characterize spatially varying interspecies interaction networks using the abundance data of multiple species from different geographical locations. This tool provides a unified framework for integrating power curve fitting based on allometrical scaling law, functional clustering, LASSO-based variable selection, quasi-dynamic ordinary differential equation solving, species abundance decomposition and network visualization. It coalesces all species from different spaces into location-specific networks.

The team demonstrate the utility of this tool by analysing different organs that are spatially interconnected via microbiomes within the host using two datasets from the gut microbiota and plant microbiota. Given that biodiversity and organization vary biogeographically at different scales, idopNetwork will find its widespread application to modelling and estimating interspecific interactions with differing functions across space.

This work has been supported by the Beijing Forestry University.

Paper link: https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14172