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Phytoplankton exhibit a range of dynamics, from relative stability at high or low biomass to cycles. These dynamics are driven by a variety of factors (e.g. nutrient loading rates, grazing, physical characteristics of the environment). Theory suggests that changes in drivers approaching critical transitions, for example from clear-water to algal-dominated states, manifest in statistical properties of systems in both time and space. Temporal indicators of proximity to thresholds have been well studied in models, lab experiments, and even a few whole ecosystem manipulations. While spatial indicators are also supported by theory, their evaluation has lagged behind that of temporal indicators. A few studies utilizing spatial models of terrestrial vegetation in arid landscapes have demonstrated the promise of these indicators; however it is unclear if aquatic systems will show similar patterns due to differences in the physical environment. We use a spatial phytoplankton model that incorporates both physical and biological processes and captures observed features of phytoplankton blooms to test for spatial indicators of thresholds in bloom drivers. Spatial indicators, such as the standard deviation of phytoplankton biomass, successfully signaled approaching transitions in phytoplankton dynamics across varying levels of physical mixing. These results are promising for potential application of spatial indicators to real aquatic ecosystems, especially considering the increased availability of spatial data from technologies such as remote sensing.


Buelo, C. D., University of Virginia, USA,

Pace, M. L., University of Virginia, USA,

Carpenter, S. R., University of Wisconsin, USA,


Oral presentation

Session #:021
Date: 03/03/2017
Time: 10:00
Location: 323 B

Presentation is given by student: Yes