Many predator species make regular excursions from near-surface waters to the twilight (200-1,000 m) and midnight (1,000-3,000 m) zones of the deep pelagic ocean. While the occurrence of significant vertical movements into the deep ocean has evolved independently across taxonomic groups, the functional role(s) and ecological significance of these movements remain poorly understood. Here, we integrate results from satellite tagging efforts with model-predictions of deep prey layers in the North Atlantic Ocean to determine if prey distributions are correlated with vertical habitat use across 12 species of predators. Using 3D movement data for 344 individuals that traversed nearly 1.5 million km of pelagic ocean in >42,000 days, we found that nearly every tagged predator frequented the twilight zone and many made regular trips to the midnight zone. Using a predictive model, we found clear alignment of predator depth use with the expected location of deep pelagic prey for at least half of the predator species. We compared high-resolution predator data with shipboard acoustics and selected representative matches that highlight the opportunities and challenges in the analysis and synthesis of these data. While not all observed behavior was consistent with estimated prey availability at depth, our results suggest that deep pelagic biomass likely has high ecological value for a suite of commercially important predators in the open ocean. Careful consideration of the disruption to ecosystem services provided by pelagic food webs is needed before the potential costs and benefits of proceeding with extractive activities in the deep ocean can be evaluated.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.