Spatiotemporal Predictive Models for Juvenile Southern Flounder in Texas Estuaries

Southern Flounder Paralichthys lethostigma supports a multimillion dollar commercial and recreational fishery in the Gulf of Mexico. Despite its economic importance, the Southern Flounder population has been declining for decades. To improve the management of this fishery, both population trends and changes in environmental conditions need to be considered. Using two different statistical modeling techniques, boosted regression tree (BRT) and artificial neural network (ANN), a 29-year fisheries-independent record of juvenile Southern Flounder abundance in Texas was examined to illustrate how environmental factors influence the temporal and spatial distribution of juvenile Southern Flounder. Boosted regression trees show the presence of juvenile Southern Flounder is closely associated with relatively low temperatures, low salinity levels, and high dissolved oxygen concentrations. Both ANN and BRT models resulted in high predictive performance with slight spatial differences in predicted distribution. Both models suggested high probability of occurrence in Galveston Bay and East Matagorda Bay. The ANN accurately predicted higher probability of occurrence in Sabine Lake compared with the BRT model. Our results will provide tools for fisheries managers to enhance management and sustainability of the Southern Flounder population. Moreover, these results also identify a predictive framework for proactive approaches to ecosystem management by providing more data to identify essential habitat features and understanding relationships between abiotic and biotic factors within those habitats.