Sea turtle populations are often assessed at the regional to sub-basin scale from discrete indices of nesting abundance. While this may be practical and sometimes effective, widespread in-water surveys may enhance assessments by including additional demographics, locations, and revealing emerging population trends. Here, we describe sea turtle observations from 13 years of towed-diver surveys across 53 coral islands, atolls, and reefs in the Central, West, and South Pacific. These surveys covered more than 7,300 linear km, and observed more than 3,400 green (Chelonia mydas) and hawksbill (Eretmochelys imbricata) sea turtles. From these data, we estimated sea turtle densities, described trends across space and time, and modelled the influence of environmental and anthropogenic drivers. Both species were patchily distributed across spatial scales, and green turtles were 11 times more abundant than hawksbills. The Pacific Remote Island Areas had the highest densities of greens (3.62 turtles km-1 , Jarvis Island), while American Samoa had the most hawksbills (0.12 turtles km-1 , Ta'u Island). The Hawaiian Islands had the lowest turtle densities (island ave = 0.07 turtles km-1) yet the highest annual population growth (μ = 0.08, σ = 0.22), suggesting extensive management protections can yield positive conservation results. Densities peaked at 27.5˚C SST, in areas of high productivity and low human impact, and were consistent with patterns of historic overexploitation. Though such intensive surveys have great value, they are logistically demanding and therefore have an uncertain budget and programmatic future. We hope the methods we described here may be applied to future comparatively low-cost surveys either with autonomous vehicles or with environmental DNA.
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.
Background: Information regarding the movement ecology of horse-eye jack Caranx latus throughout the Caribbean is limited despite their prevalence. Passive acoustic telemetry was used to infer movement patterns of seven adult C. latus within Buck Island Reef National Monument (BIRNM), a no-take marine protected area (MPA) northeast of St. Croix, U.S. Virgin Islands. In addition, a preliminary exploration of detections recorded outside of BIRNM was used to gain knowledge of the potential for larger scale movements. Ascertaining long-term movement patterns, including residency, mobility, and identifying core activity spaces can play a considerable role in how MPAs, like BIRNM, are adapted to meet the needs of mobile species.Results: High residency index values were observed for individual C. latus within the BIRNM array (mean ± SE: 0.913 ± 0.04, range 0.75-1.0) across the 17 months monitored. Most fish were also detected on receivers located outside BIRNM. An observed to expected detection ratio revealed that despite high residency, only 9.6% of expected transmissions were detected based on the average tag transmission rate. Network analysis revealed high individual connectivity with many of the receivers inside BIRNM and a large number of core use receivers (mean: 10.7, range 6-14) within individual networks.Conclusions: Most C. latus were present in BIRNM at least twice per day, but were overall detected below the expected rates, demonstrating mobility, large core activity spaces and wide use of the acoustic array inside BIRNM and greater St. Croix shelf. How residency is inferred from acoustic telemetry detections, and interpreted for species with variable mobility, has important considerations for spatial management planning and telemetry analyses. For MPA development to meet the spatial requirements of species with mixed resident-mobile spatial ecology, detailed long-term movement data are required. Assessing residency in MPAs using acoustic telemetry should be formalized and carefully interpreted based on specific species, environmental conditions, and array configuration.
Climate change projections are central to fisheries and aquatic conservation research, and to planning for a warming world. Such projections include assumptions about future emissions pathways and climate-system sensitivity to emissions. Fisheries and aquatic conservation research typically uses emissions scenarios created for the Intergovernmental Panel on Climate Change (IPCC). However, recent climate research and global development trends have significantly changed our understanding of the ranges of plausible emissions pathways to 2100 and climate sensitivities. Here, we provide a concise review of these updates to our understanding of climate futures, and we make recommendations for best-practice use of climate change scenarios in fisheries and aquatic conservation research. Although emissions pathways are subject to deep uncertainty, recent research suggests that emissions scenarios producing a range of approximately 3.4-4.5W/m2 radiative forcing by 2100 might be most plausible. With median climate sensitivities, this corresponds to approximately 2-3 degrees C global warming by 2100. Climate-sensitivity uncertainties expand this range to approximately 1.5-4 degrees C. In terms of the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), radiative forcing outcomes mostly fall between SSP2-3.4 and SSP2-4.5/RCP4.5, though higher and lower emissions scenarios (e.g., RCP2.6 and RCP6.0) might be plausible and should be explored in research. However, we argue that uses of the highest-emission scenarios (RCP8.5/SSP5-8.5, SSP3-7.0)—which currently predominate the literature—should come with clearly articulated rationales and appropriate caveats to ensure results are not misinterpreted by scholars, policymakers, and media.
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