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The objective of this study was to determine if fronts sustained up to three days will result in an aggregation of kingfish due to the anticipated accumulation of forage, increasing fishing success at these locations. Automated algorithms to detect frontal features in satellite-derived sea surface temperature, chlorophyll concentration, water clarity, and fluorescence images were successfully adapted for the coastal waters off west-central Florida. The surface ocean fronts were used to study the linkages between environmental conditions and recreational catch statistics of king mackerel (Scomberomorus cavalla) during 19 seasonal tournaments held in April to May and October to November of 2004 and 2005. The local winds estimated from a USF Coastal Ocean Monitoring and Prediction System observing station were analyzed with the frontal data to examine factors that influence oceanic frontal formation and stability. The front detection algorithms were also applied to high-r esolution bathymetry data which serves as a new technique for analyzing bottom topography. The spatial relationships between catch data collected through 415 angler interviews, frontal boundaries and stability, bathymetric gradients, bottom structure, and baitfish presence were identified using ESRI ArcGIS.Fishing success and fishing effort were highly variable regarding the distance of fishing activity to the nearest front. This was attributed to non-persistent winds. Intermediate water clarity (0.7 to 1.0 mW cm-2 microm-1 sr-1), the presence of baitfish, and the side of the front with relatively less chlorophyll showed the greatest influence on the king mackerel catch rates. Fishing success was found to be significantly higher at fishing locations where baitfish were reported present compared to where they were not reported. Concurrent with the 2005 harmful algal bloom event, a significant decrease in king mackerel catch occurred in the fall of 2005 (208 fish) compared to fall 20 04 (818) and spring 2005 (538). Additionally, fishing locations with baitfish present were observed about 15% less often during the fall of 2005 than the preceding seasons. From this, a model can be developed to diagnose the environmental conditions that can be used by resource managers to better understand variations in catch, which result from naturally occurring phenomena or man-induced overfishing.

In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three problems in seismic processing and imaging namely, seismic data regularization from data with large percentages of traces missing; seismic amplitude recovery for subsalt images obtained by reverse-time migration and primary-multiple separation, given an inaccurate multiple prediction. We argue why these nonlinear formulations are beneficial.