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ABSTRACT Disparate bodies of literature implicate risk avoidance and energy conservation as important drivers of animal movement decisions. Theory posits that these phenomena interact in ecologically consequential ways, but rigorous empirical tests of this hypothesis have been hampered by data limitations. We fuse fluid dynamics, telemetry, and attack data to reconstruct risk and energy landscapes traversed by migrating juvenile salmon and their predators. We find that migrants primarily use midriver microhabitats that facilitate migration at night. During daylight, predators become more aggressive in the midriver, and prey reduce midriver use in favour of nearshore microhabitats, resulting in increased energy expenditure and decreased migration efficiency. Predators attack most when migrants are not prioritising threat avoidance and during ephemeral periods of low lighting. Our findings suggest that predator–prey interactions result from an interplay between landscapes of fear and energy, which can determine the degree to which predators affect prey through mortality or fear. 

Image-based machine learning methods are becoming among the most widely-used forms of data analysis across science, technology, engineering, and industry. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically required a large amount of human labor. A wide range of recent scientific applications have demonstrated the potential of these methods to change how researchers study the ocean. However, despite their promise, machine learning tools are still under-exploited in many domains including species and environmental monitoring, biodiversity surveys, fisheries abundance and size estimation, rare event and species detection, the study of animal behavior, and citizen science. Our objective in this article is to provide an approachable, end-to-end guide to help researchers apply image-based machine learning methods effectively to their own research problems. Using a case study, we describe how to prepare data, train and deploy models, and overcome common issues that can cause models to underperform. Importantly, we discuss how to diagnose problems that can cause poor model performance on new imagery to build robust tools that can vastly accelerate data acquisition in the marine realm. Code to perform analyses is provided at https://github.com/heinsense2/AIO_CaseStudy .