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Interspecific interactions can play an essential role in shaping wildlife populations and communities. To date, assessments of interspecific interactions, and more specifically predator-prey dynamics, in aquatic systems over broad spatial and temporal scales (i.e., hundreds of kilometers and multiple years) are rare due to constraints on our abilities to measure effectively at those scales. We applied new methods to identify space-use overlap and potential predation risk to Atlantic tarpon (Megalops atlanticus) and permit (Trachinotus falcatus) from two known predators, great hammerhead (Sphyrna mokarran) and bull (Carcharhinus leucas) sharks, over a 3-year period using acoustic telemetry in the coastal region of the Florida Keys (USA). By examining spatiotemporal overlap, as well as the timing and order of arrival at specific locations compared to random chance, we show that potential predation risk from great hammerhead and bull sharks to Atlantic tarpon and permit are heterogeneous across the Florida Keys. Additionally, we find that predator Lucas P. Griffin and Grace A. Casselberry share co-first authorship and contributed equally to this work.

Data from the Integrated Tracking of Aquatic Animals in the Gulf of Mexico (iTAG) network, and sister networks, were used to evaluate fish movements in the Florida Keys-an extensive reef fish ecosystem just north of Cuba connecting the Atlantic Ocean and Gulf of Mexico. We analysed ~2 million detections for 23 species, ranging from reef fish such as Nassau grouper (Epinephelus striatus, Serranidae) to migratory apex predators such as white sharks (Carcharodon carcharias, Lamnidae). To facilitate comparisons across species, we used an eco-evolutionary movement strategy framework that identified measurable movement traits and their proximate and ultimate drivers. Detectability was species-specific and quantified with a detection potential index. Life stages detected in the study area varied by species and residency varied with life stage. Four annual movement types were identified as follows: high site-fidelity residents, range residents, seasonal migrants and general migrants. The endangered smalltooth sawfish (Pristis pectinata, Pristidae), a seasonal migrant, exhibited the greatest within-ecosystem connectivity. Site attachment, stopover and deep-water migration behaviours differed between individuals, species and annual movement types. All apex predators were migratory. General migrants were significantly larger than fish in the other movement types, a life-history and movement trait combination that is common but not exclusive, as many small pelagics also migrate.Most teleosts exhibited movements associated with spawning. As concerns grow over habitat and biodiversity loss, multispecies movescapes, such as presented here, are expected to play an increasingly important role in informing ecosystem-based and non-extractive fisheries management strategies.

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.