Predicting phytoplankton community dynamics:  understanding water quality responses to global change

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Virginia Tech


A fundamental focus in ecology is understanding interactions between environmental heterogeneity and ecological community structure, both of which are currently undergoing unprecedented alterations due to global change. In particular, many freshwater phytoplankton communities are experiencing multiple global change stressors, altering phytoplankton community composition, biomass, and spatial distribution. I used multiple approaches to characterize the interactions between spatial distribution and community structure of phytoplankton and quantify uncertainty in predictions of phytoplankton temporal dynamics. First, I analyzed data from 51 lakes to determine the environmental drivers of phytoplankton vertical distributions across the water column for different phytoplankton groups. I show that the relative importance of environmental drivers varies according to the functional traits of each phytoplankton group. Second, I conducted whole-ecosystem experiments in a reservoir to assess phytoplankton responses to surface water mixing events, which may become more prevalent as storms increase under global change. My results demonstrate that aggregated phytoplankton biomass has inconsistent responses to mixing over the short term, but responses of morphology-based functional groups of phytoplankton to mixing are more predictable. Third, I conducted a long-term whole-ecosystem experiment to assess phytoplankton responses to changes in water column thermal gradients which are predicted to increasingly occur under global change. I found that phytoplankton depth distributions responded similarly to thermal gradient disturbance over multiple years, and changes in depth distributions were related to changes in community composition. Fourth, I produced weekly hindcasts of phytoplankton density in a lake for two years to determine the dominant sources of uncertainty in phytoplankton density predictions. I found that better estimation of current phytoplankton density improved representation of error in phytoplankton models, and incorporation of additional life history stages to model structure may improve phytoplankton predictions. Overall, my dissertation chapters demonstrate that the vertical distribution and community structure of phytoplankton are linked, and that the interaction of phytoplankton community structure with environmental heterogeneity is more predictable over longer-term (e.g., months to years) than shorter-term (e.g., days to weeks) scales. My research emphasizes that consideration of phytoplankton community dynamics and the uncertainty associated with phytoplankton predictions are needed for freshwater management under global change.



algae, climate change, deep chlorophyll maximum, ecological forecasting, thermocline deepening, variance partitioning