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Recent publications have identified the analysis of phycotoxins in sentinel shellfish as a problematic tool for environmental monitoring purposes. Domoic acid (DA), a neurotoxin produced by some species of the diatom Pseudo‐nitzschia, can remain undetected in sentinel shellfish stocks during toxic blooms and subsequent marine bird and mammal mass mortality events. Solid Phase Adsorption Toxin Tracking (SPATT) has previously been described for monitoring of lipophilic toxins, whereas resin‐based sampling methods are routinely employed for many other environmental contaminants. Here, we evaluate the applicability of SPATT for monitoring the hydrophilic phycotoxin DA and demonstrate that the same field sampling methods can be used for the detection of saxitoxins. We present laboratory‐based adsorption profiles characterizing the performance of SPATT with four resin types: (1) HP20, (2) SP700, (3) SP207, and (4) SP207SS. We present results from 17 mo of approximately weekly SPATT deployments in Monterey Bay, California (USA); this period included two significant toxigenic Pseudo‐nitzschia bloom events as well as low‐level saxitoxin events. SPATT signaled the presence of DA 3 and 7 weeks before the recognition of bloom conditions by traditional monitoring techniques (7 and 8 weeks before shellfish toxicity). Under ambient (non‐bloom) conditions, all resins detected DA when its presence was not apparent from traditional monitoring, highlighting the ubiquity of low level or transient toxin events in the environment. This study is the first to evaluate SPATT deployments in U.S. waters, and the first to demonstrate the applicability of SPATT toward detection of hydrophilic phycotoxins in the field.

Blooms of the diatom genus Pseudo-nitzschia have been recognized as a public health issue in California since 1991 when domoic acid, the neurotoxin produced by toxigenic species of Pseudo-nitzschia, was first detected in local shellfish. Although these blooms are recurring and recognized hazards, the factors driving bloom proliferation remain poorly understood. The lack of longterm field studies and/or deficiencies in the scope of environmental data included within them hinders the development of robust forecasting tools. For this study, we successfully developed predictive logistic models of toxigenic Pseudo-nitzschia blooms in Monterey Bay, California, from a multi-project dataset representing 8.3 yr of sampling effort. Models were developed for year-round (annual model) or seasonal use (spring and fall-winter models). The consideration of seasonality was significant: chlorophyll a (chl a) and silicic acid were predictors in all models, but period-specific inclusions of temperature, upwelling index, river discharge, and/or nitrate provided significant model refinement. Predictive power for 'unknown' (future) bloom cases was demonstrated at ≥75% for all models, out-performing a chl a anomaly model, and performing comparably to, or better than, previously described statistical models for Pseudo-nitzschia blooms or toxicity. The models presented here are the first to have been developed from long-term (>1.5 yr) monitoring efforts, and the first to have been developed for bloom prediction of toxigenic Pseudo-nitzschia species. The descriptive capacity of our models places historical and recent observations into greater ecological context, which could help to resolve historical alternation between the implication of freshwater discharge and upwelling processes in bloom dynamics.

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