Collaborative Research: Spatial Dynamics, Early Warnings and Harmful Algal Blooms

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

Small changes in the environment can have large effects on ecosystems when these changes approach a critical tipping point. For example, harmful algal blooms in lakes can appear suddenly in response to subtle changes in environmental conditions. Knowing when and where they are likely to happen in time to prevent them or to reduce their impact can be quite challenging. Some scientists have suggested that there may be early warnings of algal blooms detectable by frequent measurements of water quality. Recent advances in technology now make it possible to measure water quality in real time using sensors. These high frequency data provide a way to develop early warnings of imminent blooms of harmful algae. An effective early warning system will help scientists understand what causes harmful algal blooms and develop strategies to prevent them. This research will study how to use spatial patterns, in addition to changes over time, to more effectively detect early warnings in lakes where harmful algal blooms are expected to occur. The goal is to create a new approach to predicting when and where large changes to ecosystems will occur. The results will be useful for management as harmful algal blooms are a serious water quality and public health problem. Early warnings before blooms begin could help managers protect beaches, water quality, and other important public resources. This project will also involve outreach to journalists to help educate the public about efforts to prevent harmful algal blooms. Empirical evaluation of spatial pattern in ecosystems requires a means of repeatedly generating spatially rich data. The Fast Limnology Automated Measurement (FLAMe) platform rapidly determines the spatial distribution of a suite of variables including phytoplankton pigments that are diagnostic of algal blooms. The project will generate pigment maps and a variety of spatial statistics using the FLAMe platform. Spatial patterns will be studied for lakes across a natural nutrient loading gradient and for a set of eutrophic lakes. Dynamic spatial models of lake phytoplankton will be developed to evaluate existing indicators and possibly develop new ones. Lakes near thresholds for algal blooms are hypothesized to have higher spatial variance, autocorrelation, and skewness than either more oligotrophic (low nutrient inputs) or more eutrophic lakes (high nutrient inputs). Spatial early warnings will be tested in a whole lake manipulation that compares a nutrient enriched lake to an unenriched reference lake. The enriched lake is hypothesized to increase in spatial variance, autocorrelation, and skewness prior to the development of a bloom. The research will provide new knowledge of spatial patterns in lakes as nutrient status changes, as well as new insights about spatial variability and change indicators. The study will systematically explore spatial indicators for blooms and generate hypotheses testable in other types of ecosystems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.