Interspecific interaction has been a key concept in ecology to understand the structure and dynamics of ecological communities. Important, yet often overlooked, is that an interspecific interaction is a product of multiple biological processes at various temporal and spatial scales, including changes in demographic parameters such as birth and death rates, behavioral responses such as inter-habitat movements, and hiding and evolutionary responses in a longer temporal scale. Each of those mechanisms, according to ecological theory, potentially affects population dynamics and modifies the community-level properties such as community complexity and stability in different manners. Here, a question arises: how does the net interspecific interaction, which is made up with those multiple processes, look like in the real nature? How do changes depend on the temporal or spatial scale? In this chapter we show that a data-driven approach using demographic time series is a powerful tool to answer those questions. According to nonlinear dynamics theory, a time series of a variable contains information about the dynamic system that the variable belongs to. We can use this fact to identify interspecific interactions, quantify their signs and strengths and evaluate its effect to community-level dynamic properties. Some results we got by applying the time-series analysis based on nonlinear dynamics theory (called Empirical Dynamic Modeling) to empirical demographic data, experimental or observational, will be presented, which will demonstrate how fluctuating and condition-dependent the real interactions are and reveal how those interactions give rise to the dynamic properties at higher organization levels.
Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach