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Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and management strategies.

Long-lived species share life history traits such as slow growth, late maturity, and low fecundity, which lead to slow recovery rates and increase a population’s vulnerability to disturbance. The Greenland shark (Somniosus microcephalus) has recently been recognized as the world’s longest-lived vertebrate, but many questions regarding its biology, physiology, and ecology remain unanswered. Here we review how current and future research will fill knowledge gaps about the Greenland shark and provide an overall framework to guide research and management priorities for this species. Key advances include the potential for specialized aging techniques and demographic studies to shed light on the distribution and age-class structure of Greenland shark populations. Advances in population genetics and genomics will reveal key factors contributing to the Greenland shark’s extreme longevity, range and population size, and susceptibility to environmental change. New tagging technologies and improvements in experimental and analytical design will allow detailed monitoring of movement behaviors and interactions among Greenland sharks and other marine species, while shedding light on habitat use and susceptibility to fisheries interactions. Interdisciplinary approaches, such as the combined use of stable isotope analysis and high-tech data-logging devices (i.e., accelerometers and acoustic hydrophones) have the potential to improve knowledge of feeding strategies, predatory capabilities, and the trophic role of Greenland sharks. Measures of physiology, including estimation of metabolic rate, as well as heart rate and function, will advance our understanding of the causes and consequences of long lifespans. Determining the extent and effects of current threats (as well as potential mitigation measures) will assist the development of policies, recommendations, and actions relevant for the management of this potentially vulnerable species. Through an interdisciplinary lens, we propose innovative approaches to direct the future study of Greenland sharks and promote the consideration of longevity as an important factor in research on aquatic and terrestrial predators.

Basking shark (Cetorhinus maximus) populations are considered “vulnerable” globally and “endangered” in the northeast Atlantic by the International Union for the Conservation of Nature (IUCN). Much of our knowledge of this species comes from surface observations in coastal waters, yet recent evidence suggests the majority of their lives may be spent in the deep ocean. Depth preferences of basking sharks have significantly limited movement studies that used pop-up satellite archival transmitting (PSAT) tags as conventional light-based geolocation is impossible for tagged animals that spend significant time below the photic zone. We tagged 57 basking sharks with PSAT tags in the NW Atlantic from 2004 to 2011. Many individuals spent several months at meso- and bathy-pelagic depths where accurate light-level geolocation was impossible during fall, winter and spring. We applied a newly-developed geolocation approach for the PSAT data by comparing three-dimensional depth-temperature profile data recorded by the tags to modeled in situ oceanographic data from the high-resolution HYbrid Coordinate Ocean Model (HYCOM). Observation-based likelihoods were leveraged within a state-space hidden Markov model (HMM). The combined tracks revealed that basking sharks moved from waters around Cape Cod, MA to as far as the SE coast of Brazil (20°S), a total distance of over 17,000 km. Moreover, 59% of tagged individuals with sufficient deployment durations (>250 days) demonstrated seasonal fidelity to Cape Cod and the Gulf of Maine, with one individual returning to within 60 km of its tagging location 1 year later. Tagged sharks spent most of their time at epipelagic depths during summer months around Cape Cod and in the Gulf of Maine. During winter months, sharks spent extended periods at depths of at least 600 m while moving south to the Sargasso Sea, the Caribbean Sea, or the western tropical Atlantic. Our work demonstrates the utility of applying advances in oceanographic modeling to understanding habitat use of highly migratory, often meso- and bathy-pelagic, ocean megafauna. The large-scale movement patterns of tagged sharks highlight the need for international cooperation when designing and implementing conservation strategies to ensure that the species recovers from the historical effects of over-fishing throughout the North Atlantic Ocean.