Predicting oyster populations

Interactive digital models of oyster and reef areas which may be altered and updated in real time to create multivariate predictive models of environmental impacts on oyster population growth

Software & Information Technology Environmental

For counting oysters, you can go out in the field and risk pulling a “mussel” or you can use this modeling software.

Over $10 billion of seafood products are processed in the US each year of which oysters have traditionally been a significant component.

In the 1970’s one-third of all US fisheries produced oyster-related products, employing residents in 48 states. But since the 1990’s, oyster harvests have dropped more than 85 percent, displacing local employees and increasing the US foreign trade deficit as imported food products have been substituted. The US foreign trade deficit for seafood, which is second only to crude oil, has increased dramatically as a result of diminishing oyster harvests.

More than 20 federal agencies and nine state agencies are currently undertaking oyster restoration research projects expected to total more than $500 million dollars. The largest collaborative effort is the Chesapeake Bay Project (CBP). The CBP is a comprehensive study of 10 major oyster reefs and tracks hundreds of variables related to currents, temperature, salinity, and total suspended solids, which impact reefs and the timing of harvests relative to the survival of larvae and juveniles at various critical life stages.

Because the CBP area and other ecosystems are too large and complex for direct monitoring, scientists rely on computer modeling and simulation tools. These systems statistically extrapolate and predict environmental conditions and impacts. Increasingly powerful models simulate current state data and are used to predict future impacts on oyster populations under different scenarios. However, different models use different protocols and may measure different parameters. This makes it difficult to reduce the error associated with models and to apply knowledge gained from previous studies.

To address this, the Army has developed modeling tools that allow researchers to access, adapt, combine and standardize statistical methodologies for future predictive oyster population models. The tools meet the further need of enabling rapid comparison and extrapolation of data and identification of relationships. As a result, researchers using these tools can produce multiple highly complex, multivariate predictive models in real time. The computer architecture utilizes virtual machines, each of which includes a Project Class, Reef Class, and an Oyster Group Demographic Class. The Project Class receives geographical, time and reef association parameters to instantiate a project that includes digital replicas of oyster demographic groups and reefs.

Each Reef Class is configured with processing functions to instantiate Reef Objects defined by Reef Attributes with corresponding values and Reef Object functions which are invoked when attribute values are instantiated or updated.

Each Oyster Group Demographic Class is a virtual machine which has attributes with corresponding values and functions to represent oyster demographic groups associated with Reef Objects. Processing functions are invoked when attribute values are instantiated or updated.

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