Developing a fine-scale spatial operating model of eastern oyster population dynamics in Chesapeake Bay, Maryland, U.S.A.

Abstract

Management decisions for the eastern oyster (Crassostrea virginica) fishery in Maryland are made at a finer spatial scale than the spatial data resolution of the current stock assessment. This mismatch creates concerns that the consequences of management actions and fishing activities are not being adequately represented when assessing fishery status. To produce a model that could support a participatory modeling process intended to differentiate results of fine-scale management actions we developed a method for conditioning a down-scaled oyster stock assessment model to produce a spatially-explicit operating model at the scale of individual oyster bars for eastern oysters in Chesapeake Bay, Maryland. To ensure that parameter values of the operating model were consistent with data at multiple scales we fitted the model to bar-specific harvest data during 2004–2020 and regional abundance estimates from the current Maryland Oyster Stock Assessment during 1999–2020. The operating model can then predict effects of different management actions, such as planting hatchery-reared oysters, addition of substrate, or modifying fishing regulations, at a bar-specific scale and compare outcomes among different scenarios. Model outputs included a suite of management performance metrics, including but not limited to oyster abundance and fishery harvest, that were important to stakeholders. These bar-specific scenarios would not have been possible using an operating model with the same spatial resolution as the current Maryland Oyster Stock Assessment. Accounting for fine scale spatial processes can be important to engaging participants and our model provides an expedient option to develop an operating model that downscales stock assessment models.