The Sacramento-San Joaquin Delta (Delta) is experiencing an increase in the frequency and severity of Cyanobacterial Harmful Algal Blooms (CHABs), which can produce harmful cyanotoxins. This issue is likely to intensify due to climate changes and rising temperatures. The most common CHAB genus in the Delta is Microcystis. Currently, the most extensive dataset for tracking Delta CHABs is the Microcystis Visual Index (MVI), a qualitative assessment of Microcystis colony densities observed in surface water. This index, recorded by natural agency staff across numerous monitoring stations, provides broad spatial coverage but is inherently subjective and not quantitative, thereby limiting its utility.
This project has the following objectives: 1. Develop an MVI image classification model and model algorithm that can identify and quantify Microcystis aggregate presence and coverage level in digital photos. 2. Translate MVI rankings to Microcystis biomass ranges by obtaining data to ground-truth a range of Microcystis biomass that corresponds with MVI rankings 2 through 5. 3. Explore relationship between proportion of toxic Microcystis cells and Microcystis biomass levels by relating each MVI scale (for ranks 2 through 5) and Microcystis biomass range to a) proportion of toxic Microcystis cells (i.e. ratio of mcyE and 16S rDNA genes) and b) microcystin concentration, in surface grab samples.
Pesticide mixtures originating from both legacy and current-use chemicals are widely detected in Delta waters, sediments, and invertebrate prey, posing potential risks to juvenile Chinook Salmon during critical rearing periods. While pesticides have been measured in juvenile salmon and their prey, substantial uncertainty remains regarding how mixtures of contaminants affect fish behavior and physiology, and how these effects vary across space and time within the Delta. This science activity will apply a recently developed response spectrum modeling framework to evaluate spatial and temporal patterns of sublethal pesticide effects on juvenile Chinook Salmon rearing in the Delta. The model integrates pesticide concentrations measured in salmon tissues to predict behavioral and physiological impairment associated with complex pesticide mixtures. Juvenile fall-run Chinook Salmon collected through existing Delta monitoring programs, including archived specimens from prior years, will be analyzed alongside hatchery-origin juveniles deployed in cages at multiple Delta locations. This combined approach will allow assessment of how pesticide bioaccumulation and model-predicted effects vary across habitats, seasons, and hydrologic conditions. Results will provide a predictive assessment of where and when pesticide mixtures are most likely to impair juvenile salmon performance, with implications for growth, survival, and population-level outcomes. The activity will support management decisions related to pesticide regulation, TMDL development, and evaluation of habitat restoration actions, including reconnected floodplain rearing habitats. In addition, spatial patterns of pesticide bioaccumulation may help identify contaminant sources and inform targeted remediation strategies.
