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Spatial and Temporal Dynamics of Cyanobacterial Blooms in Two Rhode Island Ponds.
Citation:
Shivers, S., Jeff Hollister, B. Kreakie, AND Bryan Milstead. Spatial and Temporal Dynamics of Cyanobacterial Blooms in Two Rhode Island Ponds. Quarterly Meeting of the Friends Of Warwick Pond, Warwick, Rhode Island, May 17, 2018.
Impact/Purpose:
Most data on productivity in lakes and ponds is collected infrequently and at few locations within a given lake. This presentation summarizes the first year of a study to address this limitation. The impact of this work is that we will now be able to examine spatial and temporal variability of harmful algal blooms at scales that were previously difficult to assess. The first year of data we have collected suggests changes to the dynamics of blooms at these finer scales.
Description:
Cyanobacteria are natural components of freshwater ecosystems. When conditions are favorable (e.g., high nutrient inputs), cyanobacteria can form dense blooms that have negative effects on human and animal health, ecosystem functioning, and aesthetics. When blooms occur they can often develop rapidly requiring frequent sampling to adequately capture bloom dynamics. The purpose of this study was to investigate the spatial and temporal variation of cyanobacterial blooms in two Rhode Island ponds in different landscape settings (urban vs. forested). Seven sites were sampled in each pond weekly or biweekly between June and October 2017. Physical parameters were measured at all sites, and water samples were collected for chlorophyll a and turbidity analyses. The forested pond did not have a bloom and all parameters remained relatively stable during the study. In contrast, the urban pond had an observable bloom resulting in a health advisory. During the bloom, chlorophyll a and turbidity increased while secchi transparency decreased. Our sampling indicated fine scale variability that may be important in better understanding cyanobacterial bloom events. The results of this study will be used to evaluate potential bloom indicators, such as temperature, and eventually develop predictive models based on how the indicators vary through space and time.