A multivariate analysis framework for interpreting long-term zooplankton community dynamics

Abstract

Multivariate analyses are widely used to characterize plankton community dynamics in long-term datasets, which often exhibit sparse sampling. These datasets typically require multiple multivariate approaches to describe patterns in community structure, relate those patterns to environmental variation, and identify the taxa contributing to observed differences. However, it can be challenging for plankton researchers to know which multivariate approach to use to best analyze their data. In this paper, our goal was to demonstrate how commonly used multivariate approaches that are typically applied individually can be integrated to provide complementary insights into plankton community variability through time. We applied three approaches—unconstrained ordination, constrained ordination and taxon-specific analyses—to a non-consecutive 7-year freshwater zooplankton dataset to evaluate community variability. Unconstrained ordination revealed variable dynamics within and among years, with the importance of environmental drivers differing within versus among years. Constrained ordination identified several drivers of the zooplankton community, including Secchi depth, total phosphorus, and Schmidt stability. Lastly, taxon-specific analyses identified both the taxa associated with individual years and those driving among-year compositional differences. Our results demonstrate that diverse multivariate methods can provide complementary insights, and sporadic plankton time series can yield robust ecological inferences when analyzed in an integrative framework.