Predator–prey landscapes of large sharks and game fishes in the Florida Keys. Issue 5 (29th April 2022)

• Record Type: Journal Article
• Authors: Griffin, Lucas P.; Casselberry, Grace A.; Lowerre‐Barbieri, Susan K.; Acosta, Alejandro; Adams, Aaron J.; Cooke, Steven J.; Filous, Alex; Friess, Claudia; Guttridge, Tristan L.; Hammerschlag, Neil; Heim, Vital; Morley, Danielle; Rider, Mitchell J.; Skomal, Gregory B.; Smukall, Matthew J.; Danylchuk, Andy J.; Brownscombe, Jacob W.
• Is Part Of: Ecological applications. Volume 32:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-29
• Subjects: acoustic telemetry -- conservation -- machine learning -- predator–prey dynamics; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2584
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: 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 encounter rates with these game fishes are elevated at specific locations and times, including a prespawning aggregation site in the case of Atlantic tarpon. Further, using machine learning algorithms, we identify environmental variability in overlap between predators and their potential prey, including location, habitat, time of year, lunar cycle, depth, and water temperature. These predator–prey landscapes provide insights into fundamental ecosystem function and biological conservation, especially in the context of emerging fishery‐related depredation issues in coastal marine ecosystems.