Diversity and toxicity of Pseudo-nitzschia species in Monterey Bay: Perspectives from targeted and adaptive sampling.

ABSTRACT

Monterey Bay, California experiences near-annual blooms of Pseudo-nitzschia that can affect marine animal health and the economy, including impacts to tourism and commercial/recreational fisheries. One species in particular, P. australis, has been implicated in the most toxic of events, however other species within the genus can contribute to widespread variability in community structure and associated toxicity across years. Current monitoring methods are limited in their spatial coverage as well as their ability to capture the full suite of species present, thereby hindering understanding of HAB events and limiting predictive accuracy. An integrated deployment of multiple in situ platforms, some with autonomous adaptive sampling capabilities, occurred during two divergent bloom years in the bay, and uncovered detailed aspects of population and toxicity dynamics. A bloom in 2013 was characterized by spatial differences in Pseudo-nitzschia populations, with the low-toxin producer P. fraudulenta dominating the inshore community and toxic P. australis dominating the offshore community. An exceptionally toxic bloom in 2015 developed as a diverse Pseudo-nitzschia community abruptly transitioned into a bloom of highly toxic P. australis within the time frame of a week. Increases in cell density and proliferation coincided with strong upwelling of nutrients. High toxicity was driven by silicate limitation of the dense bloom. This temporal shift in species composition mirrored the shift observed further north in the California Current System off Oregon and Washington. The broad scope of sampling and unique platform capabilities employed during these studies revealed important patterns in bloom formation and persistence for Pseudo-nitzschia. Results underscore the benefit of expanded biological observing capabilities and targeted sampling methods to capture more comprehensive spatial and temporal scales for studying and predicting future events.