Assessing marine plankton community structure from long‐term monitoring data with multivariate autoregressive (MAR) models: a comparison of fixed station versus spatially distributed sampling data

Abstract

We examined how marine plankton interaction networks, as inferred by multivariate autoregressive (MAR) analysis of time‐series, differ based on data collected at a fixed sampling location (L4 station in the Western English Channel) and four similar time‐series prepared by averaging Continuous Plankton Recorder (CPR) datapoints in the region surrounding the fixed station. None of the plankton community structures suggested by the MAR models generated from the CPR datasets were well correlated with the MAR model for L4, but of the four CPR models, the one most closely resembling the L4 model was that for the CPR region nearest to L4. We infer that observation error and spatial variation in plankton community dynamics influenced the model performance for the CPR datasets. A modified MAR framework in which observation error and spatial variation are explicitly incorporated could allow the analysis to better handle the diverse time‐series data collected in marine environments.