Seasonal predictability and interannual variability in coastal planktonic communities
Understanding the predictability and variability of coastal planktonic communities is essential for forecasting ecosystem responses to environmental change and managing marine resources. This thesis examines seasonal predictability, temperature-phenology relationships, and interannual bloom variability in coastal plankton using two decades of high-resolution observations from the Martha’s Vineyard Coastal Observatory (MVCO). Through automated flow cytometry, we analyzed community dynamics across multiple taxa representing diverse functional groups and seasonal strategies. The first component investigates seasonal predictability patterns across the planktonic community using wavelet analysis, novel cyclicity indices, and lag-adjusted seasonal models. Wavelet analysis reveals that annual periodicity dominates temporal variability, with 71% of taxa exhibiting consistent annual cycles throughout the observation period. However, substantial variation exists in predictability among taxa, with some species serving as reliable seasonal anchors while others display more erratic dynamics. Critically, we demonstrate that shifts in bloom amplitude contribute more to interannual variability than changes in seasonal timing, and that individual taxa often exhibit higher predictability than their aggregated functional groups, challenging common modeling approaches that rely on broad taxonomic categories. The second component examines the mechanistic basis for seasonal predictability by quantifying relationships between temperature timing and bloom phenology. Using year-day crossing models, we assess how temperature threshold transitions predict bloom onset and decline across four representative taxa: Synechococcus (spring), Pseudo-nitzschia (summer), Akashiwo (fall), and Ditylum brightwellii (winter). Results show that bloom onset timing is more tightly coupled to temperature transitions than decline timing, with positive correlations indicating that earlier warming leads to earlier bloom initiation. This onset-decline asymmetry suggests temperature functions as a reliable seasonal cue for bloom initiation, while termination depends on more complex, multi-factorial processes including nutrient depletion, grazing pressure, and biological interactions. The third component characterizes interannual variability in bloom dynamics through episode-based analyses of frequency, integrated intensity, peak magnitude, and timing. Community-scale patterns reveal distinct ecological strategies for packaging biomass through time, with some taxa favoring infrequent but intense episodes while others maintain consistent moderate-level activity. Temporal trends in bloom characteristics differ markedly among seasonal taxa, with spring bloomers showing declining magnitudes, summer diatoms exhibiting increasing peak concentrations, and winter taxa displaying variable intensity patterns. Event-based temperature-timing analyses demonstrate that only a subset of strongly seasonal taxa exhibit significant phenological coupling, with most community members showing weak or inconsistent temperature relationships when examined at the episode level. These findings have important implications for understanding and predicting coastal ecosystem dynamics. The dominance of amplitude over timing variability suggests that forecasting efforts should prioritize predicting bloom magnitude rather than precise timing. The stronger temperature control on onset versus decline indicates that warming-driven phenological advances are likely, but termination timing will remain more stochastic. The superior predictability of individual taxa compared to functional groups argues for maintaining taxonomic resolution in monitoring and modeling efforts. Together, these results provide a comprehensive framework for understanding how coastal plankton communities respond to environmental variability and change, informing both fundamental ecological theory and practical forecasting applications for marine ecosystem management.
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Massachusetts Institute of Technology and Woods Hole Oceanographic Institution