Uncovering Phytoplankton Dynamics in the Salish Sea through Novel Statistical Inference

Drs. Matias Salibián-Barrera and Gabriela Cohen Freue (both Stats) have been awarded the DSI Postdoctoral Matching Fund for their project titled "Uncovering Phytoplankton Dynamics in the Salish Sea through Novel Statistical Inference".

Summary

The Salish Sea, located across the international border between the United States and Canada, supports highly productive and diverse marine ecosystems that are critical for sustaining local fisheries, wildlife, and coastal communities. Among the key biological processes in this ecosystem is the annual spring phytoplankton bloom, which forms the foundation of the marine food web. Our scientific goal is to contribute to the understanding of the phytoplankton’s temporal and spatial dynamics in the Salish Sea. Identifying important factors driving the annual spring bloom in the Salish Sea, understanding its relationship with other processes, and predicting its peak is central to support conservation efforts and to promote resource management decisions. The complexity and richness of the collected data requires the development of new statistical methods to draw rigorous inferential conclusions which will help us quantify the uncertainty associated with the resulting estimates, predictions and conclusions. We anticipate a fruitful collaboration to advance knowledge in both fields as well as contributing with new methods to cover an existing gap in data science methodologies.

Background

The Salish Sea includes Puget Sound, Juan de Fuca Strait, and the Strait of Georgia. This region is home to rich and dynamic marine ecosystems that support local fisheries, wildlife, and coastal communities. One of the key biological processes in this ecosystem is the annual spring phytoplankton bloom, which forms the foundation of the marine food web, primarily composed of diatoms. Phytoplankton and zooplankton are key components of marine ecosystems, and their abundance is associated with the health of many other marine populations. In the Salish Sea case, they impact the abundance of salmon and herring, which in turn affect endangered populations such as the Southern Resident Killer Whales (Perry et al. 2021). Our scientific goal is to contribute to the understanding of the phytoplankton dynamics in the Salish Sea, spatially and over time. More specifically, we are interested in identifying important factors driving the annual spring bloom in the Salish Sea, understanding its relationship with other processes, and predicting its peak (Collins et al, 2009; Allen and Wolfe, 2013; Allen et al, 2018). The timing of the spring phytoplankton bloom in the Strait of Georgia can impact juvenile herring abundance, with abundance being larger for blooms with typical timing (Boldt et al. 2018). Extreme shifts of timing have led to poor zooplankton growth (e.g., Sastri and Dower 2009) and late spring blooms are also associated with fewer large and medium copepods (Perry et al. 2021). Zooplankton and juvenile herring are important food items for juvenile salmon.

Challenge

The quality of the data and the technical resources available to characterize biological processes in the Salish Sea region are well recognized. However, the development of tailored statistical and computational methodologies to analyze such complex data has lagged behind. Most existing statistical methods can naturally model either the temporal evolution of the data or the spatial correlation of the observations. However, methods to analyze entire time-trajectories based on large, spatially correlated datasets remain limited and are not yet fully studied.

Research Goals

Developing appropriate inference techniques will provide rigorous statistical tools to evaluate results and deepen our understanding of biological processes in the Salish Sea. More generally, we aim to integrate the existing biological, technological, and experimental wealth of resources with the development and application of specialized statistical and computational methodologies to advance knowledge in the Earth and Ocean Sciences. The proposed methodologies will provide a powerful and reusable framework applicable to a broad range of scientific questions involving spatial-temporal data, thereby accelerating progress in Data Science more broadly.