In recent years, harmful algal blooms (HABs) have increased in their severity and extent in many parts of the world and pose serious threats to local aquaculture, fisheries, and public health. In many cases, the mechanisms triggering and regulating HAB events remain poorly understood. Using underwater microscopy and Residual Neural Network (ResNet‐18) to taxonomically classify imaged organisms, we developed a daily abundance record of four potentially harmful algae (Akashiwo sanguinea, Chattonella spp., Dinophysis spp., and Lingulodinium polyedra) and major grazer groups (ciliates, copepod nauplii, and copepods) from August 2017 to November 2020 at Scripps Institution of Oceanography pier, a coastal location in the Southern California Bight. Random Forest algorithms were used to identify the optimal combination of environmental and ecological variables that produced the most accurate abundance predictions for each taxon. We developed models with high prediction accuracy for A. sanguinea (R2=0.79±0.06), Chattonella spp. (R2=0.63±0.06), and L. polyedra (R2=0.72±0.08), whereas models for Dinophysis spp. showed lower prediction accuracy (R2=0.24±0.07). Offshore nutricline depth and indices describing climate variability, including El Niño Southern Oscillation, Pacific Decadal Oscillation, and North Pacific Gyre Oscillation, that influence regional‐scale ocean circulation patterns and environmental conditions, were key predictor variables for these HAB taxa. These metrics of regional‐scale processes were generally better predictors of HAB taxa abundances at this coastal location than the in situ environmental measurements. Ciliate abundance was an important predictor of Chattonella and Dinophysis spp., but not of A. sanguinea and L. polyedra. Our findings indicate that combining regional and local environmental factors with microzooplankton populations dynamics can improve real‐time HAB abundance forecasts.

The record-setting wildfires that ravaged the western United States throughout 2020 released high concentrations of organic carbon (C) into the environment, including the adjacent Pacific Ocean. Yet little is known about the fate of marine wildfire-derived C, solubilized as dissolved organic matter (DOM), despite growing observations of ash deposition in such systems. We sought to quantify and characterize DOM inputs to Pacific surface waters spanning the California coastline from August 1 to October 31, 2020. Across over 290 field samples, dissolved organic C concentrations peaked 2- to 4-fold higher after the eruption of fire systems than immediate pre-wildfire levels. C concentrations were well correlated with atmospheric pyrogenic proxies PM2.5 and ozone, supporting pyrogenic sourcing. Molecular characterization of DOM by ultrahigh-resolution FTICR-MS revealed both a diversity of formulas, supporting a growing consensus of pyrogenic heterogeneity, and temporal shifts conserved across sites. An initial increase in highly aromatic, oxygen-containing compounds aligned with PM2.5 concentrations, burn extent, and C deposition. Over time, transformation to increasingly aliphatic DOM occurred. The latter is hypothesized to be a result of complex interplay between biotic and abiotic processes, warranting further study. Our observations suggest that wildfires are a substantial yet dynamic source of marine surface organic C.

Digital imaging technologies are increasingly used to study life in the ocean. To deal with the large volume of image data collected over space and time, scientists employ various machine learning and deep learning algorithms to perform automated image classification. Training of classifiers requires a large number of expertly curated sets of images, a time‐consuming process that requires taxonomic knowledge and understanding of the local ecosystem. The creation of these labeled training sets is the critical bottleneck for building skillful automated classifiers. Here, we discuss how we overcame this barrier by leveraging taxonomic knowledge from a group of specialists in a workshop setting and suggest best practices for effectively organizing image annotation efforts. In our experience, this 2 day workshop proved very insightful and facilitated classification of over 4 years of plankton images obtained at Scripps Pier (La Jolla, CA), focusing on diatoms and dinoflagellates. We highlight the importance of facilitating a dialog between taxonomists and engineers to better integrate ecological goals with computational constraints, and encourage continuous involvement of taxonomic experts for successful implementation of automated classifiers.