The catchability coefficient is used in most marine stock assessment models, and is usually assumed to be stationary and density-independent. However, recent research has shown that these assumptions are violated in most fisheries. Violation of these assumptions will cause underestimation of stock declines or recoveries, leading to inappropriate management policies. This project assesses the soundness of stationarity and density independence assumptions using multi-species data for seven stocks and four gears in the U.S. Gulf of Mexico. This study also develops a multi-species methodology to compensate for failures of either assumption. To evaluate catchability assumptions, abundance-at-age was reconstructed and compared with catch-per-unit-effort data in the Gulf. Mixed-effects, Monte Carlo, and bootstrap analyses were applied to estimate time-varying catchability parameters. Gulf data showed large and significant density dependence (0.71, s.e. 0.07, p<0.001) and increasing trends in catchability (2.0% annually compounding, s.e. 0.6%, p < 0.001). Simulation modeling was also used to evaluate the accuracy and precision of seven different single-species and multi-species estimation procedures. Imputing estimates from similar species provided accurate estimates of catchability parameters. Multi-species estimates also improved catchability estimation when compared with the current assumptions of density independence and stationarity. This study shows that multi-species data in the Gulf of Mexico have sufficient quantity and quality to accurately estimate catchability model parameters. This study also emphasizes the importance of estimating density-dependent and density-independent factors simultaneously. Finally, this study shows that multi-species imputation of catchability estimates decreases errors compared with current assumptions, when applied to single-species stock assessment data. / Master of Science