Intraseasonal predictability of natural phytoplankton population dynamics.

Vitul Agarwal,Claire E Widdicombe,Andrew D Barton,Chase C James

doi:10.1002/ece3.8234

Abstract

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.

Highlights

Primary production by marine phytoplankton fuels marine ecosystems and marine fisheries (Ryther, 1969)
Predictability increased with the number of aggregated taxa, regardless of taxonomy, and the predictability exceeded what would be expected from seasonality alone
Time series length might play an important role in determining the maximum level of predictability of an assemblage or species, as it has been shown that the number of observations can have an impact on the predictability of plankton dynamics (Giron-Nava et al, 2017)

Summary

Introduction

Primary production by marine phytoplankton fuels marine ecosystems and marine fisheries (Ryther, 1969). | 15721 et al, 2003; Chiba et al, 2012; Edwards et al, 2013; Falkowski & Oliver, 2007) These dynamics are affected by changing environmental conditions (Falkowski & Oliver, 2007; Margalef, 1978), and by physiological (i.e., a change in organism traits without a change in genome) and evolutionary (i.e., a change in genome) responses to these changes (Collins et al, 2014; Hunter-Cevera et al, 2014; Irwin et al, 2015; Lohbeck et al, 2012) and interactions within food webs (Di Lorenzo & Ohman, 2013; Ripa & Ives, 2003; Vasseur, 2007; Xu & Li, 2002). Because of the demonstrated empirical links between variations in plankton community structure, detrital flux to the benthos, and trophic efficiency and fisheries production (Stock et al, 2017), the management of living marine resources could be improved with skillful predictions of phytoplankton community structure (Hobday et al, 2016; Marshall et al, 2019; Tommasi et al, 2017)

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