Sub-monthly prediction of harmful algal blooms based on automated cell imaging

Abstract

Harmful algal blooms (HABs) are an increasing threat to global fisheries and human health. The mitigation of HABs requires management strategies to successfully forecast the abundance and distribution of harmful algal taxa. In this study, we attempt to characterize the dynamics of 2 phytoplankton genera (Pseudo-nitzschia spp. and Dinophysis spp.) in Narragansett Bay, Rhode Island, using empirical dynamic modeling. We utilize a high-resolution Imaging FlowCytobot dataset to generate a daily-resolution time series of phytoplankton images and then characterize the sub-monthly (1-30 days) timescales of univariate and multivariate prediction skill for each taxon. Our results suggest that univariate predictability is low overall, different for each taxon and does not significantly vary over sub-monthly timescales. For all univariate predictions, models can rely on the inherent autocorrelation within each time series. When we incorporated multivariate data based on quantifiable image features, we found that predictability increased for both taxa and that this increase was apparent on timescales >7 days. Pseudo-nitzschia spp. has distinctive predictive dynamics that occur on timescales of around 16 and 25 days. Similarly, Dinophysis spp. is most predictable on timescales of 25 days. The timescales of prediction for Pseudo-nitzschia spp. and Dinophysis spp. could be tied to environmental drivers such as tidal cycles, water temperature, wind speed, community biomass, salinity, and pH in Narragansett Bay. For most drivers, there were consistent effects between the environmental variables and the phytoplankton taxon. Our analysis displays the potential of utilizing data from automated cell imagers to forecast and monitor harmful algal blooms.

Keywords: Dinophysis; Ecological forecasting; Empirical dynamic modeling (EDM); Imaging FlowCytobot (IFCB); Narragansett Bay; Phytoplankton population dynamics; Pseudo-nitzschia.