Develop Organism Growth Model of Secondary Production Utilizing Forcing Functions of Biophysical Data from Buoys, Sensors, Water Samples, and Transects

Project Description

Growth of filter-feeding bivalves (mussels, oysters, and clams) in nearshore environments is correlated to fluctuations in temperature, salinity, and food availability. This project utilizes historical data, data from the buoy network, sample data, and FVCOM model results as inputs for shellfish growth simulations using the ShellSIM model (Hawkins et al., 2012). This enables grow studies representative of various culture methods: bottom, rope, pole, and trestle. Additional empirical growth models have been created to validate the pre-calibrated methods in ShellSIM. These models predict production rates within the three target bioregions. ShellSIM is linked to ShellGIS to generate maps of relative growth rates of target species as a function of site specific environmental conditions (currents, temperature, salinity, chlorophyll-α, phytoplankton biomass, SPM, POM, PIM, POC, and PON). 

Results and Accomplishments

Results are now being integrated into species suitability maps for aquacultured species. Through 2017-2018, ShellSIM has been acquired and adjusted to model dynamics on the coast of Maine. ShellSIM requires temperature and a suite of food characterization data (provided through SEANET’s buoy and transect surveys) to predict growth rates of bivalve species. Graduate student, Nicholas Keeney, joined SEANET in Fall 2017, and is combining remote sensing data, including temperature, turbidity (a measure of inorganic food content), and chlorophyll for the coast of Maine, to generate maps of suitability for oyster aquaculture production. Researchers have also developed a protocol for adding other emerging bivalve species (e.g., scallops, mussels, and European oysters) and have applied for additional funding to build off the success of SEANET’s integrated modeling infrastructure (New high-resolution satellite-derived water-quality data informs sustainable aquaculture development. Brady, D., Boss, E., Morse, D., Thomas, A. Submitted to the National Sea Grant 2018 Aquaculture Initiative for $692,200). One clear result of the fine scale feeding and growth trials over the past year has been identifying the potential for protein limitation of bivalve aquaculture. When using Enzyme Hydrolyzable Amino Acid (EHAA) in growth models, researchers can significantly improve the predictive power. However, EHAA is difficult to sample and is certainly outside the laboratory capabilities of a prospective or even experienced aquaculturist. The determination of an optical proxy for EHAA, such as the ratio between chlorophyll and turbidity, is an exciting direction being explored in 2018-2019.

Summary of Data Being Collected

Data	Type	Quantity	Location
See buoy monitoring program and transect sampling project summary for details on LOBO data collection, seston samples, and oyster cage samples	NA	NA	NA
DMR water quality data 20-year data set	Variables: salinity, temperature, fecal coliform scores	300,000	Coast Wide