Ecological and biological systems consist of numerous interlinked components that interact and exchange information; such interactions give rise to emergent, collective behaviors that are of interest for physicists and life scientists. The relationship between the dynamics of individual components and the emergent behavior of the system can be used to develop control methods and manipulate the output of the system. These control methods are used for ecological community management or restoration, or for therapeutic medical applications. In this dissertation, the main focus is on mutualistic plant pollinator networks, and specifically on their description by a well-established predictive dynamical model. We use the stable motifs - self-sustaining minimal positive feedback loops in the networks - to identify the system's dynamic repertoire, predict the outcome of specific interventions and suggest management and control methods. In ecological terms, we identify the stable communities that arise as a result of species interacting, analyze their response to species extinction, and suggest prevention and restoration measures based on the knowledge gained from the stable motifs. We show that knowing the stable motifs simplifies and speeds up the process of identification of stable communities, successfully finds the crucial species for community survival, and predicts restoration strategies if catastrophic changes happen in communities.