1Ryukoku University, 2National Taiwan University, 3Kyoto University
Microbial communities fundamentally drive ecosystem processes such as primary production and decomposition. However, mainly because of the high diversity and complex interactions of microbial species in nature, understanding and predicting the influences of microbial communities on ecosystem processes are difficult. In this presentation, we would like to introduce a promising approach, nonlinear time series analysis, to investigate the dynamics of microbial communities and its impact on ecosystem processes. High-throughput sequencing provides a huge amount of microbial sequence data, and it is possible to obtain time series data of the diversity and community dynamics of microbes in nature. Recent developments in nonlinear time series analysis allow detecting causality between two species, which enables us to illustrate an interaction network of a biological community in nature. We will show how to reconstruct the interaction network using time series data of a marine fish community as an example. This approach can be applied to the time series data of microbial communities. Furthermore, we will show that the approach is useful to quantify the relationships between microbial communities and ecosystem processes. These new analytical tools would pave the way to practically analyze and predict the influences of microbial communities on ecosystem processes.
keywords:Nonlinear time series analysis,Interaction network,Convergent cross mapping,Causality