Developing cyanobacterial bloom indicators from spatiotemporal differences in productivity and water quality across a lake-stream network

Abstract

Cyanobacterial Harmful Algal Blooms (cHABs) are an increasingly common occurrence in inland waters and carry ecological, economic, and public health consequences. It is difficult to predict when a cHAB will occur and there is a need to develop methods (indicators) to accurately predict the development of cHABs Here, we studied planktonic primary production (chlorophyll and phycocyanin) in a lake-stream network that is prone to cHABs in southern New Jersey, during bloom and non-bloom years. Primary productivity was lake-dependent, with productivity patterns interacting across sampling locations and years (p < 0.001 for both chlorophyll and phycocyanin). The lake with recurrent cHABs had higher productivity readings in both years, but the sampling location within this lake had a large influence on the observed primary productivity patterns. Productivity differences among lakes were greater in the bloom year compared to the non-bloom year. The bloom year was characterized by a strong correlation between conductivity and nitrate readings, suggesting that cHABs in our study system are associated with nutrient-laden runoff. The linear progression of primary productivity readings was a better indicator for the onset of cHAB conditions than temporal autocorrelation using weekly samples.

Keywords

• Cyanobacteria
• Productivity
• Spatiotemporal
• Linearity
• Autocorrelation
• Turbidity