Article ID: 2309
Abstract: In ecological studies, methods used in other fields to elucidate causal relationships, such as randomized controlled experiments and large-scale computer simulations, may not be feasible or effective. However, the availability of large-scale data generated in ecosystem monitoring is dramatically increasing. Accompanying this recent trend are growing expectations for the development of methods to elucidate causal relationships in a data-driven manner. CCM, proposed by George Sugihara et al. in 2012, has led ecologists to focus on causal estimation by time series. On the other hand, Granger causality and information theoretic approaches have also developed as important methods for dealing with causality in dynamical systems. Time series based causal analysis is based on a wide range of fields, and it is necessary to properly understand the advantages and disadvantages of each method in order to use them appropriately. In this paper, we propose that by introducing a unified perspective through information theory, Granger causality deals with momentary information imbalances, whereas CCM deals with those in the larger temporal structures represented by attractors. From this perspective, technical complementarity is recognized, while a new challenge of the multifaceted nature of causality emerges for the study of ecological dynamics. Thus, the elucidation of causality in ecological systems will progress by developing new approaches from a broad perspective that transcends domain boundaries and confronts real-world complexity.
