Stomach-contents data were analyzed using a modified (bagged) classification tree approach, which is being prepared as an R statistical software package. To illustrate the utility of the statistical methods, we show an example using yellowfin tuna in a “test” area in the Pacific Ocean. Specifically, we present a framework for world-wide compilation and analysis of global stomach-contents and stable-isotope data of tunas and other large pelagic predatory fishes. We loosely define top predators not as species at the apex of the food web, but rather a guild of large predators near the top of the food web. Here we review the progress of an international collaboration that compiled regional diet datasets of multiple top predator fishes from the Indian, Pacific and Atlantic Oceans and developed new statistical methods that can be used to obtain a comprehensive ocean-scale understanding of food webs and climate impacts on marine top predators. Global-scale studies of marine food webs are rare, despite their necessity for examining and understanding ecosystem level effects of climate variability.
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