It is difficult to make skillful predictions about the future dynamics of marine phytoplankton populations. Here, we use a 22-year time series of monthly average abundances for 198 phytoplankton taxa from Station L4 in the Western English Channel (1992-2014) to test whether and how aggregating phytoplankton into multi-species assemblages can improve predictability of their temporal dynamics. Using a non-parametric framework to assess predictability, we demonstrate that the prediction skill is significantly affected by how species data are grouped into assemblages, the presence of noise, and stochastic behavior within species. Overall, we find that predictability one month into the future increases when species are aggregated together into assemblages with more species, compared with the predictability of individual taxa. However, predictability within dinoflagellates and larger phytoplankton (>12 μm cell radius) is low overall and does not increase by aggregating similar species together. High variability in the data, due to observational error (noise) or stochasticity in population growth rates, reduces the predictability of individual species more than the predictability of assemblages. These findings show that there is greater potential for univariate prediction of species assemblages or whole-community metrics, such as total chlorophyll or biomass, than for the individual dynamics of phytoplankton species.
Keywords: Station L4; ecosystem forecasting; empirical dynamic modeling; functional groups; phytoplankton population dynamics; portfolio effect; predictability.
© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.