Intraseasonal predictability of natural phytoplankton population dynamics

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.

Patterns in the temporal complexity of global chlorophyll concentration

“…Errors may decrease due to the greater availability of data and possibility of quality control but may also increase over time as the hardware of individual satellites age. Further improvements in satellite capabilities may allow future studies to increase the precision of their conclusions and enhance the predictability of [chl- a ] time series by quantifying more accurately the impact of stochasticity and the measurement error 59 . While we did not evaluate the chlorophyll- a product uncertainty in this study, future studies may consider accounting for the difference between [chl- a ] algorithms, different uncertainty calculations, and their relationship to time series complexity 60 – 62 .…”

Intermittent instability is widespread in plankton communities

“…Along these lines, non‐seasonal plankton competition models can exhibit chaotic succession of many different species, but near constant aggregate biomass (Huisman & Weissing, 1999). In models with a seasonal environment, aggregate dynamics can show seasonal blooms, while species‐level chaos drives variability in the magnitude and composition of those blooms (Dakos et al, 2009), making species abundance less predictable than aggregate biomass (Agarwal et al, 2021; Cottingham et al, 1998; Tilman, 1995). Indeed, overall biomass behaves relatively predictably in lake ecosystems (Sommer et al, 1986).…”

“…However, the fact that the aggregate is less variable than the constituents does not imply that it is more predictable or that dynamical stability has increased. Thus far, one study using data from a single marine location found that aggregation made phytoplankton time series more predictable, at least for some taxonomic groups (Agarwal et al, 2021). However, it is unclear how widespread this pattern is or whether increased predictability at coarser taxonomic resolution corresponds with increased stability.…”

Constraining nonlinear time series modeling with the metabolic theory of ecology

“…Although we did not see this in our data and have insufficient sample size to draw any general conclusions, we suspect MTE-EDM might work better on aggregated time series, since species-specific temperature dependencies may average out. Aggregation has also been shown to lead to higher forecast accuracy with EDM ( 54 ).…”