This research focuses on extending Earth science research results to decision support systems in the Ecological Forecasting national priority area. The activity seeks to improve the existing National Oceanic and Atmospheric Administration¿s (NOAA) National Marine Fisheries Service (NMFS) decision making system for population assessment and management of Atlantic bluefin tuna (Thunnus thynnus thynnus). The research team is a multi-sector and multi-disciplinary team composed of government (NOAA_NMFS), academic (University of South Florida Institute for Marine Remote Sensing) and commercial (Roffer¿s Ocean Fishing Forecasting Service, Inc.). The goal is to reduce the variance in the estimates of adult Atlantic bluefin tuna spawning stock abundance in the Gulf of Mexico (GOM) through the development of spawning site habitat classification and catchability indices of the larvae. These will be derived from the innovative use of several earth orbiting satellites. The estimates of the adult spawning biomass are critical in understanding changes in the population size used to derive international and national fisheries management strategies and allocations. Accurate estimates of adult population size are critical in understanding the population dynamics of an internationally important fish that supports commercial and recreational fisheries. Functional links have been established between climate variability, regional-scale oceanographic processes and recruitment to fisheries. The exact mechanisms driving many of these links remain poorly understood and influences on the transport and survival of pelagic larval stages are likely to be significant. We propose to analyze these data in combination to develop a time-series of enhanced biological and oceanographic indicators for the GOM fisheries. While the catchability indices will be used in developing indices for the population analyses, the habitat classification will aid in developing models forecasting where concentrations of larvae are likely to occur leading to an adaptive sampling strategy.