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Though fish populations typical experience spatially varying mortality, abundance, and fishing pressure, stock assessments commonly model a population that is assumed to be well-mixed. When assumptions about population mixing are not met, these models can result in biased estimates. Spatial population estimates are particularly beneficial to the Chesapeake Bay as this region faces unique challenges as a result of climate change, fishing pressure, and land use within the watershed. Though the Chesapeake Bay supports many important commercial and recreational fisheries, few assessments have estimated abundance of fish within the bay. However, use of spatial models for fisheries management relies on the ability of these models to reliably estimate biological parameters. This project will test the performance of spatially-explicit population estimates for the Chesapeake Bay. Objectives of this project are to 1) assess the performance spatially explicit stock assessment approaches for Striped Bass (Morone saxatilis) and determine how assessment model performance changes with data availability; and 2) determine how the performance of spatial stock assessments changes as a results of species range shifts due to climate change. Simulations will be built for estimating population abundance of Striped Bass with different temporal scales, movement rates, and patterns of fishing mortality. Based on model performance, I will identify data needs for reliable population estimates of fish species. Finally, I will explore how parameter estimates within the Chesapeake Bay may be affect by climate change induced range shifts. This project will serve as an evaluating of methods to assess population abundance for other ecologically valuable fish species in the bay and code will be made publicly available to assist in other stock assessments and studies.