Using wavelet analyses to examine variability in phytoplankton seasonal succession and annual periodicity

In most north temperate lakes, phytoplankton biomass oscillates on an annual scale. While phytoplankton seasonal succession within a year has been described for many lakes, much less is known about variability in seasonal succession over multiple years. Here, we describe how continuous wavelet transforms can be used to identify variation in the periodicity in phytoplankton time series at multiple timescales. To demonstrate our approach, we analyzed 16 years of biweekly phytoplankton data from eutrophic Lake Mendota, USA, that coincided with substantial variability in climate and nutrient loading. Throughout the time series, the wavelet transforms identified the annual scale as the dominant scale of variation in aggregated phytoplankton, except for a 3-year period when there was no significant dominant scale. This period coincided with drought and decreased nutrient loading. During this time, phytoplankton biomass was markedly lower, and the phytoplankton community exhibited a unimodal, not bimodal, pattern of seasonal succession. Our results highlight the utility of wavelet techniques for identifying changes in seasonal succession in long-term phytoplankton records, which are becoming more available for many lakes. As aquatic ecosystems increasingly experience exogenous forcings at multiple timescales, wavelet analyses provide a powerful tool for determining how phytoplankton communities may respond.