Predator-prey landscapes of large sharks and game fishes in the Florida Keys.

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.

Application of machine learning algorithms to identify cryptic reproductive habitats using diverse information sources.

Information on ecological systems often comes from diverse sources with varied levels of complexity, bias, and uncertainty. Accordingly, analytical techniques continue to evolve that address these challenges to reveal the characteristics of ecological systems and inform conservation actions. We applied multiple statistical learning algorithms (i.e., machine learning) with a range of information sources including fish tracking data, environmental data, and visual surveys to identify potential spawning aggregation sites for a marine fish species, permit (Trachinotus falcatus), in the Florida Keys. Recognizing the potential complementarity and some level of uncertainty in each information source, we applied supervised (classic and conditional random forests; RF) and unsupervised (fuzzy k-means; FKM) algorithms. The two RF models had similar predictive performance, but generated different predictor variable importance structures and spawning site predictions. Unsupervised clustering using FKM identified unique site groupings that were similar to the likely spawning sites identified with RF. The conservation of aggregate spawning fish species depends heavily on the protection of key spawning sites; many of these potential sites were identified here for permit in the Florida Keys, which consisted of relatively deep-water natural and artificial reefs with high mean permit residency periods. The application of multiple machine learning algorithms enabled the integration of diverse information sources to develop models of an ecological system. Faced with increasingly complex and diverse data sources, ecologists, and conservation practitioners should find increasing value in machine learning algorithms, which we discuss here and provide resources to increase accessibility.

Seasonal occupancy and connectivity amongst nearshore flats and reef habitats by permit Trachinotus falcatus: considerations for fisheries management.

We used acoustic telemetry to quantify permit Trachinotus falcatus habitat use and connectivity in proximity to the Florida Keys, USA, and assessed these patterns relative to current habitat and fisheries management practices. From March 2017 to June 2018, 45 permit tagged within 16 km of the lower Florida Keys were detected at stationary acoustic receivers throughout the south Florida region, the majority of which remained within the Special Permit Zone, where more extensive fisheries harvest regulations are implemented. There was a high level of connectivity between nearshore flats (i.e., <3 m water depth) and the Florida reef tract (FRT; 15-40 m water depth), with 75% of individuals detected in both habitats. These locations probably function primarily as foraging and spawning habitats, respectively. Permit occupancy on the FRT peaked during the months of March-September, with the highest number of individuals occurring there in April and May. Specific sites on the FRT were identified as potentially important spawning locations, as they attracted a high proportion of individuals that exhibited frequent visits with high residency durations. There were also significant positive relationships between seasonal habitat-use metrics on the FRT and an empirical permit gonadosomatic index. Large aggregations of permit at spawning sites on the FRT are potentially vulnerable to the effects of fishing (including predation during catch and release) at a critical point in their life cycle. These data on permit space use and movement, coupled with knowledge of stressors on their ecology, provide insights for implementing science-based strategic management plans.