Abstract. Long-term ecological data sets present opportunities for identifying drivers of community dynamics and quantifying their effects through time series analysis. Multivariate autoregressive (MAR) models are well known in many other disciplines, such as econometrics, but widespread adoption of MAR methods in ecology and natural resource management has been much slower despite some widely cited ecological examples. Here we review previous ecological applications of MAR models and highlight their ability to identify abiotic and biotic drivers of population dynamics, as well as community-level stability metrics, from longterm empirical observations. Thus far, MAR models have been used mainly with data from freshwater plankton communities; we examine the obstacles that may be hindering adoption in other systems and suggest practical modifications that will improve MAR models for broader application. Many of these modifications are already well known in other fields in which MAR models are common, although they are frequently described under different names. In an effort to make MAR models more accessible to ecologists, we include a worked example using recently developed R packages (MAR1 and MARSS), freely available and open-access software.

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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.

Multivariate autoregressive (MAR) models have been useful in elucidating food web dynamics and stability from freshwater plankton monitoring data, but their applicability to marine datasets has not been as well explored. Characteristics of marine systems, such as the movement of water masses by tides and currents, may present unique challenges to MAR modeling of data gathered in marine environments. To explore the behavior of MAR models with marine plankton data, in the context of what we know about applying MAR to freshwater data, we applied MARs to each of three freshwater and four marine long-term datasets and compared results among them. We generated sets of replicate MAR models for each dataset and used the consistency of models within each set of replicates as a measure of MAR performance. Overall, replicate MAR models generated from the marine datasets were less consistent than those generated from the freshwater datasets, suggesting that MAR methods need fundamental reconfigurations to be applied to standard marine plankton data. Higher variability observed within the marine MAR results may be attributable to weaker biotic interactions as represented by the data, and to overparameterization when the criteria for lumping freshwater plankton taxa into model variables are directly applied to marine plankton taxa. Adjustments to dataset preparation for MAR application and to the modeling framework itself may address these issues associated with analyzing data from highly dynamic systems.