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Increasing occurrence of harmful algal blooms across the land-water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics., (© 2021. The Author(s).)

We compared the mean normalized water-leaving radiances ([L W ¯(λ)] N ) of two in-water optical profilers, a compact optical profiling system (C-OPS) and a HyperPro II Optical Profiler (HP2), with modeled [L W ¯(λ)] N at five stations in Monterey Bay, California. Although C-OPS and HP2 [L W ¯(λ)] N s were mostly within one standard deviation, C-OPS and modeled [L W ¯(λ)] N showed the lowest absolute percent differences (≤25% for most wavelengths) at four of the stations. We attribute this to C-OPS's high vertical resolution (∼1  cm), which is important for detecting changes in optical layers and for measuring the upper 0-0.5 m of the water column. HP2's low vertical resolution (∼50  cm), low signal-to-noise ratio, and inability to measure the upper 0-0.5 m are problematic in Monterey Bay. Although a significant component of the [L W ¯(λ)] N differences between C-OPS and HP2 likely stems from fluctuating water mass's inherent optical properties, the 25% error in [L W (λ)] N s processed from HP2 casts, compared to <1% for C-OPS, is also important.