Using Age Tracers to Estimate Ecological Rates in a Phytoplankton Model

• Published in: Water (Basel) Vol. 15; no. 11; p. 2097
• Main Authors: Gross, Edward, Holleman, Rusty, Kimmerer, Wim, Munger, Sophie, Burdick, Scott, Durand, John
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Age; Analysis; Biogeochemistry; Biomass; Chlorophyll; Compartments; Datasets; Distribution patterns; Estuaries; Grazing; Mollusks; Phytoplankton; Plankton; Productivity; San Francisco Estuary; Simulation methods; Stratification; time scales; Tracers (Chemistry); Turbidity; Variables; water age; Water depth; Water management; Wetlands; Suisun Bay; United States > US; San Francisco California
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w15112097

The phytoplankton distribution in estuaries is influenced by multiple spatially variable growth and loss processes. As phytoplankton are transported by tidal and net flows, they are exposed to changing conditions of turbidity, depth, temperature, stratification, and grazing. Understanding the factors influencing the observed phytoplankton distribution patterns will allow better-informed restoration and water management efforts. We developed a Lagrangian approach driven by three-dimensional hydrodynamic model results and a simple representation of the production and losses of phytoplankton, allowing a highly efficient closed-form solution for phytoplankton biomass. Our analysis used continuous observations of chlorophyll concentration at four stations and a near-synoptic chlorophyll dataset collected underway from a boat in the channels of Suisun Marsh in the San Francisco Estuary. We divided the study region into four compartments defined by the water depth and location. For each observation location, hydrodynamic model simulations calculated the time that water parcels spent in each of these compartments and the mean depth encountered by water parcels in those compartments. Then, using that information and continuous monitoring data, we inferred compartment-specific grazing rates and two additional ecological parameters. The underway chlorophyll dataset was used for model validation. The model predicted patterns of observed spatial and tidal variability in chlorophyll in Suisun Marsh. The modeling indicated that the chlorophyll concentration at a point in space in time depends largely on the relative exposure to shallow areas, with positive net productivity and deep areas having negative net productivity.