Our paper “Stability of Ecological Systems: A Theoretical Review” has been published in Physics Reports. In this review, we provided a systematic and comprehensive review on the theoretical frameworks employed to assess various stability types in ecological systems, including linear stability, sign stability, diagonal stability, D-stability, total stability, sector stability, and structural stability. We examined necessary or sufficient conditions for achieving such stability and demonstrated the interplay of these conditions on the network structures of ecological systems. The article can be downloaded from here.
Our paper “Identifying keystone species in microbial communities using deep learning” has been published in Nature ecology & evolution. In this paper, we proposd a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. The article can be downloaded from here.