Liu Yang

The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well‐being. Inferring microbial community structure and dynamics directly from time‐resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low‐abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.

Graphical AbstractTo mechanistically understand the dynamics of complex ecosystems, Yize Chen et al. employ symbolic regression (SR), a machine learning method that automatically reverse-engineers both model structure and parameters from temporal data. SR randomly assembles candidate models, computes the model fitness, and employs mutation and crossover to build better ones. The Pareto front reflects the trade-off between complexity and fitness of candidate models. More details can be found in article number 1900069 by Yize Chen et al.

Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a “dictionary” of possible ecological functional responses opens the door to correctly reverse-engineering ecosystem dynamics, even in the case of poorly informative data, is shown. This strategy is validated using both synthetic and experimental data, and it is found that this strategy is promising for the systematic modeling of complex ecological systems.

Behavioral psychology is increasingly important in relationship marketing. As key factors of emotional interactions between buyer and seller, psychological contracts and opportunistic behaviors play a critical role in interorganizational relationships which are based on personal relationships of boundary spanners and top management. Most of the existing research mainly focus on positive performance of cooperation but ignoring the dark side of relationships. This study introduces the psychological contract into the exploration of why formal contracts cannot completely avoid opportunistic behaviors. It mainly investigates whether psychological contracts in relationships can reduce the occurrence of opportunistic behaviors. The results show that psychological contract has a significant positive effect on the relationship quality, and negatively affect opportunistic behavior through trust and commitment. The positive relationship between psychological contracts and relationship quality is moderated by dependence. This study enriches and expands the domestic and foreign research on psychological contracts and opportunistic behaviors in relationship marketing.