Re-evaluating the ‘plankton paradox’ using an interlinked empirical data and a food web model

Species coexistence in the plankton community, which is also known as the ‘plankton paradox’, has been extensively discussed in studies of the aquatic ecosystem. Theoretical work also proves that seasonal variation can produce population chaos, which can reduce species extinction. However, there is still a lack of empirical research to analyze how both species interactions and seasonal forcing affect both species coexistence and system stability in the plankton community. In this work, we model plankton system via combining both seasonal temperature and a food web model with prey preference, and further use the Markov chain Monte Carlo (MCMC) algorithms to estimate model parameters. Theoretical predictions show both internal factor (intraspecific competition coefficient of calanoid copepods) and external factor (seasonal temperature) are the key factors, which can produce population chaos. Meanwhile, phase space reconstruction method shows Lyapunov exponents of empirical data are positive. Both theoretical predictions and empirical data show the plankton community presents the chaotic dynamics, which reduce species extinction and may help understand the ‘plankton paradox’.