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Bivalve control on nutrients and phytoplankton in Sacramento-San Joaquin Delta

Bivalve control on nutrients and phytoplankton in Sacramento-San Joaquin Delta. By Zhenlin Zhang, David Senn , and Alexandra King. A changing Delta aquatic ecosystem. Low DO. `. The fate of nutrients from the dischargers. Risk of HABS. Regional San DIN Loads (kg d -1 ): NH4 or NO3.

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Bivalve control on nutrients and phytoplankton in Sacramento-San Joaquin Delta

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  1. Bivalve control on nutrients and phytoplankton in Sacramento-San Joaquin Delta By Zhenlin Zhang, David Senn, and Alexandra King

  2. A changing Delta aquatic ecosystem Low DO ` The fate of nutrients from the dischargers Risk of HABS Regional San DIN Loads (kg d-1): NH4 or NO3 PHYTOPLANKTON 15000 Current: NH4 ~14,000 kg d-1 2030 10000 NUTRIENTS 5000 Future: NO3 ~3,500 kg d-1 Points represent Calculated Load = Measured Concentration x Flow Data: pre-2005, Jassby 2008; 2008-2016, Regional San 0 2010 2020 1990 2000

  3. A changing Delta aquatic ecosystem Low DO The impact of primary productivity on fish Risk of HABS PHYTOPLANKTON PHYTOPLANKTON Clams NUTRIENTS GRAZERS GRAZERS Mortality Zooplankton DETRITUS Allochthonous Fish Cloern, 2018

  4. A changing Delta aquatic ecosystem Low DO Light How does the changing turbidity affect primary productivity and biomass? Risk of HABS PHYTOPLANKTON Clams Turbidity NUTRIENTS GRAZERS GRAZERS GRAZERS Mortality 50% Zooplankton DETRITUS Allochthonous Fishe Cloern and Jassby, 2012

  5. A holistic modeling approach to address management needs Low DO Light Risk of HABS PHYTOPLANKTON Clams NUTRIENTS GRAZERS Mortality Zooplankton DETRITUS Allochthonous Fish The currency: nitrogen, phosphorous, and carbon

  6. Approaches + Biogeochemical model (In-situ) Hydrodynamic model (Transport) Light Limitation PHYTOPLANKTON Limitation Grazing NUTRIENTS GRAZERS Mortality DETRITUS Transport = advection + dispersion + mixing Delft3D-Flexible Mesh Delft3D Water Quality (DELWAQ)

  7. The Dynamic Energy Budget Model Phytoplankton Advantages X Energy storage (1-k) BLOOD Storage (E) 2) Growth rate decreases with the size of the organism Large Organism Small Organism Maturity & Reproduction (R) Biovolume (V) Maintenance V, E, and R are model variables

  8. Results

  9. Corbicula biomass

  10. Potamocorbula biomass

  11. Model validation results Email: zhenlinz@sfei.org if you are interested in getting the reports

  12. Scenario 1: no bivalves Cloern (2018) 1 2 3 4 5 6 7 8 9 10 11 12

  13. Scenario 1: no bivalves The presence of the bivalves effectively reduced blooms, particularly during the warmer months of the year

  14. Scenario 2: Increased phytoplankton growth rate by 25% The presence of the bivalves cannot fully prevent a bloom event from happening when other conditions are right

  15. Conclusions • Our model correctly captured the spatial and temporal variability in nutrients and chlorophyll-a for the Suisun Bay/Delta system for WY2011. • Our model captured the magnitude and some seasonal variability of bivalve biomass. • A scenario run without the clams shows that our modeling results explained the observed trend and seasonal shift of Chlorophyll-a in 1987 and clams indeed play an effective role in reducing phytoplankton biomass in the system. • However, due to the low growth rate of clams, their presence cannot fully prevent a bloom from happening when other conditions are right. • The fact that nutrient limitation is observed either when clams are removed or when phytoplankton growth rate is increased in multiple locations emphasized that nutrient source control (such as Sac Regional upgrade) can reduce phytoplankton biomass under these conditions.

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