Declines of important fish species such as southern flounder, Paralichthys lethostigma, in the Gulf of Mexico underscore the importance of defining critical habitats as well as the processes contributing to habitat value. Southern flounder is a valuable commercial and recreational fishery, distributed from North Carolina to Florida on the Atlantic Coast and from Florida to Northern Mexico on the Gulf Coast. Despite the economic and ecological importance of southern flounder, current management efforts failed to recover a sharp population decline and restore back to a historical level. Therefore, it is important for resource managers to understand and predict the future status of juvenile southern flounder. The overall purpose of this study was to provide research data and decision tools needed for development of a fishery management plan for flounders by using statistical modeling techniques. A long-term fisheries independent data set (1975–2008) from Texas Parks and Wildlife Department fisheries monitoring program was used to assess population trends of juvenile and adult southern flounder along the Texas coast in the northern Gulf of Mexico, USA. These data were examined for age-specific population trends using generalized least squares and extended with non-parametric bootstrapping to obtain interval estimates of regression parameters (juveniles) and linear regression (adults) and showed long-term declines in juvenile southern flounder abundance. For adult southern flounder, rate of decline was much more rapid. Results suggest that survival of post-juvenile flounder have decreased during the time series. This precipitous decline has prompted increasingly stricter harvest restrictions along the Texas coast. However, past management measures have been insufficient to curb declines, and it is too early to assess the recent regulations. To develop a predictive species habitat model delineating critical areas for nursery habitat field collections of juvenile bay whiff and southern flounder were collected from February to May 2010 within the Aransas Bay Complex. To determine the mechanism of habitat selection the "best" species habitat model for both species was identified using BRT. Ten predictors were included in the model: habitat type, dry weight, depth (m), dissolved oxygen (mg O 2/L), temperature (°C), turbidity (cm), salinity, pH, distance to the inlet, and month. Species habitat model for juvenile bay whiff indicated that bay whiff were not associated with any particular habitat type, but were associated with low temperatures (< 15°C), moderate percent dry weight of sediments (25–60%), salinity >10 psu, and moderate to high dissolved oxygen (6–9 mg/L, 10–14 mg/L). Species habitat model for juvenile southern flounder indicated that southern flounder were associated with low temperatures (<15°C), low percent dry weight of sediment (<30 mg/L), seagrass habitats, shallow depths (<1.2 m), and high dissolved oxygen (>8 mg/L). Results suggest EFH within the Aransas Bay Complex needs to occur among all habitat types along the eastern side of Aransas Bay, and the north corner of Copano Bay. The findings will provide a valuable new tool for fisheries managers to aid sustainable management of bay whiff and southern flounder and the Mission-Aransas Reserve ecosystem and provides crucial information needed to prioritize areas for habitat conservation and management in the Gulf of Mexico. Modeling approaches using BRT and ANN were constructed to understand how environmental factors influence the temporal and spatial patterns of juvenile southern flounder throughout all of the major Texas bays. Data were acquired from the Resource and Sport Harvest Monitoring Program conducted by Texas Parks and Wildlife Department. The BRT model indicated juvenile southern flounder were associated with low temperatures, low salinity levels, and high dissolved oxygen. Both spatio-temporal models (BRT and ANN) consisted of high predictive performance with slight spatial differences. Both models suggest high probability of occurrence in Galveston Bay and East Matagorda Bay whereas the Artificial Neural Network also indicated high probability of occurrence in Sabine Lake. (Abstract shortened by UMI.)