TY - JOUR
AU1 - Madigan, Daniel, J
AU2 - Richardson, Andrew, J
AU3 - Carlisle, Aaron, B
AU4 - Weber, Sam, B
AU5 - Brown,, Judith
AU6 - Hussey, Nigel, E
AB - Abstract Quantifying vertical distributions of pelagic predators elucidates pelagic ecosystem structure and informs fisheries management. In the tropical South Atlantic Ocean, the recently designated large-scale marine protected area around Ascension Island hosts diverse pelagic predators for which basin-specific vertical habitat information is minimal or absent. We used pop-up satellite archival tags to analyse vertical habitat use in 12 species (bigeye tuna Thunnus obesus, blue marlin Makaira nigricans, blue shark Prionace glauca, dolphinfish Coryphaena hippurus, Galapagos shark Carcharhinus galapagensis, oceanic whitetip Carcharhinus longimanus, sailfish Istiophorus albicans, silky shark Carcharhinus falciformis, swordfish Xiphias gladius, tiger shark Galeocerdo cuvier, wahoo Acanthocybium solandri, and yellowfin tuna Thunnus albacares) and quantify parameters (temperature, dissolved oxygen, diel cycles, lunar phase) known to constrain vertical movements. Predator depth distributions varied widely, and classification trees grouped predators into four clades: (i) primarily epipelagic; (ii) partial thermocline use; (iii) oscillatory diving with thermocline/sub-thermocline use; and (iv) extensive use of sub-thermocline waters. Vertical habitat differences were linked to thermal physiology and foraging ecology, and species-specific physical constraints from other ocean basins were largely conserved in the South Atlantic. Water column features defined species-specific depth distributions, which can inform fisheries practices and bycatch risk assessments and population estimates. Introduction Large pelagic predators possess unique physiologies (Block, 2005), exert top-down control in open ocean ecosystems (Heithaus et al., 2008; Block et al., 2011), and are targeted or caught incidentally by commercial and recreational fisheries. Understanding the movements of large marine predators has historically been a research priority (Block and Stevens, 2001; Brill and Lutcavage, 2001) that was challenged by remote environments, cryptic behaviours, and difficulty of effective capture, handling, and release. Early acoustic telemetry work (Carey, 1983; Carey et al., 1990) and more recent improvements to electronic tagging technology have expanded our ability to study pelagic predators in their natural environments (Block et al., 2011; Hussey et al., 2015). Electronic tags have revealed fundamental aspects of marine predator movement ecology, including broad-scale horizontal movements and the occurrence of diversity hotspots and common migration corridors across ocean basins for marine mammals, seabirds, turtles, fishes, and invertebrates (Block et al., 2011). In teleosts and sharks, tracking horizontal predator movements has elucidated migration routes, environmental preferences, and species vulnerability to commercial fisheries on global scales (Queiroz et al., 2016, 2019; White et al., 2019). Quantifying vertical habitat use has shown how pelagic species exploit their local environments, relative to prey availability and physiological constraints, and characterized the physical parameters of the water column that largely dictate the region- and species-specific depth preferences of pelagic fishes (Block et al., 2001; Brill and Lutcavage, 2001; Bernal et al., 2017). Temperature, light levels, and dissolved oxygen (DO) directly influence the depth distribution of fishes in the pelagic environment (Brill and Lutcavage, 2001). These variables have predictable site-specific climatological profiles, with a surface mixed layer of relatively consistent temperatures and higher light and DO than deeper waters (Kara et al., 2000). The thermocline generally separates the warm isothermal waters of the mixed layer from the cooler, deeper waters that extend into the mesopelagic. Electronic tagging data have shown that different aspects of this thermal structure generally constrain the vertical distributions of pelagic fish. Some species primarily use the surface mixed layer (dolphinfish Coryphaena hippurus, marlins and sailfish, silky sharks Carcharhinus falciformis) (Hoolihan et al., 2011; Musyl et al., 2011; Merten et al., 2014), while others repeatedly dive into or through the thermocline to mesopelagic waters to feed, then surfacing to re-warm and/or re-oxygenate (yellowfin Thunnus albacares, skipjack Katsuwonus pelamis, oceanic whitetip sharks Carcharhinus longimanus) (Schaefer et al., 2009; Tolotti et al., 2017). Other species spend prolonged periods at sub-thermocline depths, with associated mesopelagic diets associated with the deep scattering layer (DSL), including regional endotherms (e.g. swordfish Xiphias gladius; bigeye tuna Thunnus obesus; opah Lampris spp.) (Carey and Robinson, 1981; Schaefer and Fuller, 2005; Wegner et al., 2015) and ectotherms (e.g. blue sharks Prionace glauca; escolar Lepidocybium flavobrunneum) (Carey et al., 1990; Howey, 2010; Watanabe et al., 2015). Quantification of vertical habitat use in pelagic fishes provides insight into foraging ecology of study species and regional ecosystem structure (Olson et al., 2016), bioenergetic models (Cooke et al., 2016; Olson et al., 2016), and predictive models of depth distribution across ocean basins (Abecassis et al., 2012). Quantifying the physical parameters that may constrain species-specific vertical distributions in different ocean basins informs our understanding of the relative influence of potential physiological limitations and prey availability. Species-specific vertical distributions can then be applied in the context of commercial fisheries, both to assess spatiotemporal species-specific vulnerability and to minimize the bycatch of unwanted or threatened species (Ward and Myers, 2005; Orbesen et al., 2017), though tag-derived depth distributions do not always match observations of catch depth (Ward and Myers, 2005; Horodysky et al., 2016; Bernal et al., 2017). To date, most tag-derived vertical behaviour information for pelagic fish is from the North Pacific, South Pacific, and North Atlantic Oceans, with a dearth of information in the South Atlantic for specific groups (e.g. see billfish synthesis by Braun et al., 2015) and across global databases (e.g. Ocean Biodiversity Information System, OBIS; https://obis.org/). Pelagic ecosystems are thus poorly described in the tropical South Atlantic, with minimal information available to inform commercial fishing practices in the region that primarily target swordfish and bigeye tuna, species with vertical distributions that have been shown to vary by hundreds of metres in other ocean basins (Arrizabalaga et al., 2008; Dewar et al., 2011). Tag-derived depth distributions provide basin-specific, baseline depth limitations for tagged species, which can be combined with CPUE data to more specifically target commercial species while reducing the catch of vulnerable species. While species-specific depths are constrained by water column structure (Bernal et al., 2017), regional intra-specific variability (e.g. bigeye tuna, Abascal et al., 2018) demonstrates the need for measured data to compare to regional predictions of multi-species depth ranges. Such data are applicable to set depths in commercial fisheries, allowing for post hoc standardization of longline CPUE data, especially important in cases where longline catch data are based on frequently erroneous estimates of fishing depth (Berkeley and Edwards, 1998). Here, we use pop-up satellite archival tags (PSATs) to provide vertical distribution data on 12 species of pelagic predatory billfish, tunas, and sharks in the data-limited South Atlantic Ocean. Specifically, time series depth and temperature data across species (bigeye tuna, blue marlin, blue shark, dolphinfish, Galapagos shark, oceanic whitetip shark, sailfish, silky shark, swordfish, tiger shark, wahoo, and yellowfin tuna) were used to quantify time-at-depth (TAD) and time-at-temperature (TAT) profiles, which were then integrated to classify species into depth clades based on intra-clade similarity. The relative influence(s) of temperature, DO, and diel and lunar phase were then explored to examine the factors that influence the vertical habitat use of these species in the South Atlantic Ocean. Material and methods Ethics statement All procedures for animal capture, handling, and tag application used in this study were approved by the Ascension Island Government in accordance with the requirements of the Wildlife Protection Ordinance (2013). Institutional ethical approval was also granted through permits issued to SBW and through the Animal Care Committee at the University of Windsor. Animal capture, handling, and tagging Study animals were captured between November 2014 and February 2018 in waters around Ascension Island and its surrounding seamounts, within the 200 nm Exclusive Economic Zone (EEZ). Tag deployments targeted ecologically important species and/or common targets or bycatch of commercial fisheries in the region. Animals were captured using baited vertical longlines (2 h maximum soak time, <20 circle hooks; swordfish, bigeye tuna, oceanic whitetip), baited drumlines (Galapagos shark, tiger shark), surface handlines (silky sharks, blue shark, Galapagos shark), or recreational gear (trolling artificial lures) on heavy tackle (bigeye tuna, yellowfin tuna, sailfish, blue marlin, wahoo, dolphinfish) to minimize time from capture to vessel. Captured fish were leadered close to the research vessel and were either immobilized in a custom-made tagging sling or secured alongside the vessel while PSATs were affixed (miniPAT, Wildlife Computers, Redmond, USA). In cases where animals were removed from the water, a saltwater hose was used to continuously irrigate the gills during tag application and a damp cloth placed over eyes to minimize stress. For teleosts, titanium tag darts (Wildlife Computers, large slotted titanium [Ti] anchor) were inserted between dorsal fin pterygiophores using a tag dart applicator. For sharks, tags were either tethered to custom acrylic mounts attached to the base of the dorsal fin using plastic bolts and lock nuts or tethered directly to the dorsal fin through a small incision made in the base of the leading edge such that the tag trailed immediately behind the dorsal fin when the animal was in motion. MiniPATs were leadered and/or tethered with 2-mm stainless steel braided wire, with leader/tether length varying depending on the study species (i.e. animal size). In the case of direct tether mounts, leader wire was first encased in heat shrink tubing to reduce the risk of tissue damage due to drag. Fork length was measured directly and recorded to the nearest cm [with the exception of blue marlin, for which fork length (FL) was estimated to the nearest 10 cm by experienced deckhands]. After miniPATs were affixed, the hook was removed from the animal’s mouth and the animal was released. All tag deployment metadata (species, time at tagging, length, location) were recorded during processing or immediately after release. Tags recorded light, temperature, and pressure (depth) data, which were summarized into binned time series data that were transmitted after tag release to the Argos satellite system. Tags were programmed for deployments of 90–365 days and depth and temperature time series sampling rates of 5, 7.5, or 10 min. Overall depth utilization across species To assess overall water column use across species, we generated violin plots to allow the visualization of multi-modality. Violin plots were generated using all depth data for each species; for species where n > 1 (see Table 1), depth data were pooled for all individuals. Pooling data result in the higher relative contribution of depth data from individuals with longer tagging durations (i.e. individuals are not weighted equally in generating species-specific depth distributions). However, because inter-individual track duration varied widely (e.g. weighted data pooling would result in depth distributions from a 2-day deployment and a 100-day deployment having equal weight), and some species were represented by one tagged individual, we chose an unweighted approach to make maximum use of all available data. This recognizes that derived distributions may be biased towards the longest-tracked individuals and should be interpreted as such. Species kernel density forms were coloured by clade post hoc based on similarity tree analysis (see below). For all analyses of intra- and inter-specific vertical habitat use here and below, data from the day of tagging and the day of tag release were removed to eliminate potential bias from tagging stress (day of tagging) and from post-release tag movement through the water column (day of tag release). Table 1. Summary of analysed tag data and depth utilization metrics for 12 predator species tagged within the Exclusive Economic Zone of Ascension Island, South Atlantic Ocean. Predator . Species . n indiv . n days . Mean length ± SD (range) (cm) . Depth thresholds (SD) (m) . % time (SD) . Depth (SD) (m) . 50% . 75% . 90% . 95% . <50 m . <85 m . Mean . Max . Dolphinfish C. hippurus 3 59 103 ± 6 (98–107) 1 (1) 6 (4) 24 (4) 38 (11) 97% (2%) >99% (0%) 8 (1) 417 (322) Sailfish I. albicans 1 88 190 2 39 54 61 87% >99% 18 105 Blue marlin M. nigricans 10 554 265 ± 23 (250–310) 9 (17) 47 (20) 71 (17) 84 (21) 74% (14%) 94% (6%) 27 (10) 253 (124) Silky shark C. falciformis 1 9 100 11 28 56 76 88% 98% 20 135 Wahoo A. solandri 2 5 141 ± 5 (137–144) 38 (22) 53 (22) 66 (23) 70 (26) 68% (33%) 96% (5%) 39 (15) 123 (1) Oceanic whitetip C. longimanus 2 104 142 ± 6 (137–146) 30 (12) 60 (23) 83 (15) 93 (14) 68% (16%) 89% (10%) 38 (12) 436 (315) Yellowfin tuna T. albacares 15 374 121 ± 33 (85–182) 29 (14) 55 (22) 95 (49) 124 (72) 72% (15%) 89% (10%) 39 (18) 260 (130) Tiger shark G. cuvier 1 41 292 84 104 133 151 21% 51% 79 276 Galapagos shark C. galapagensis 8 559 190 ± 52 (123–240) 53 (10) 74 (8) 91 (7) 98 (8) 46% (13%) 85% (6%) 53 (8) 258 (105) Blue shark P. glauca 10 455 182 ± 18 (166–210) 66 (16) 125 (45) 203 (39) 246 (33) 39% (13%) 60% (11%) 86 (19) 687 (331) Bigeye tuna T. obesus 7 600 115 ± 8 (97–120) 94 (46) 232 (106) 309 (119) 335 (124) 32% (18) 51% (23) 137 (56) 547 (141) Swordfish X. gladius 1 54 265 145 439 487 502 33% 46% 234 929 Predator . Species . n indiv . n days . Mean length ± SD (range) (cm) . Depth thresholds (SD) (m) . % time (SD) . Depth (SD) (m) . 50% . 75% . 90% . 95% . <50 m . <85 m . Mean . Max . Dolphinfish C. hippurus 3 59 103 ± 6 (98–107) 1 (1) 6 (4) 24 (4) 38 (11) 97% (2%) >99% (0%) 8 (1) 417 (322) Sailfish I. albicans 1 88 190 2 39 54 61 87% >99% 18 105 Blue marlin M. nigricans 10 554 265 ± 23 (250–310) 9 (17) 47 (20) 71 (17) 84 (21) 74% (14%) 94% (6%) 27 (10) 253 (124) Silky shark C. falciformis 1 9 100 11 28 56 76 88% 98% 20 135 Wahoo A. solandri 2 5 141 ± 5 (137–144) 38 (22) 53 (22) 66 (23) 70 (26) 68% (33%) 96% (5%) 39 (15) 123 (1) Oceanic whitetip C. longimanus 2 104 142 ± 6 (137–146) 30 (12) 60 (23) 83 (15) 93 (14) 68% (16%) 89% (10%) 38 (12) 436 (315) Yellowfin tuna T. albacares 15 374 121 ± 33 (85–182) 29 (14) 55 (22) 95 (49) 124 (72) 72% (15%) 89% (10%) 39 (18) 260 (130) Tiger shark G. cuvier 1 41 292 84 104 133 151 21% 51% 79 276 Galapagos shark C. galapagensis 8 559 190 ± 52 (123–240) 53 (10) 74 (8) 91 (7) 98 (8) 46% (13%) 85% (6%) 53 (8) 258 (105) Blue shark P. glauca 10 455 182 ± 18 (166–210) 66 (16) 125 (45) 203 (39) 246 (33) 39% (13%) 60% (11%) 86 (19) 687 (331) Bigeye tuna T. obesus 7 600 115 ± 8 (97–120) 94 (46) 232 (106) 309 (119) 335 (124) 32% (18) 51% (23) 137 (56) 547 (141) Swordfish X. gladius 1 54 265 145 439 487 502 33% 46% 234 929 Depth thresholds are the mean (SD) depths above which each species spent 50, 75, 90, and 95% of their time. Percent time shows the mean (SD) proportion of time each species spent above 50 m (approximate mixed layer depth) and 85 m (approximate start of thermocline and deep chlorophyll maximum). Depth metrics show mean (SD) overall daily depths and maximum depths for each species. Mean (SD) values were calculated from metrics for each tagged fish; consequently, no SD is reported for species with n = 1. Open in new tab Table 1. Summary of analysed tag data and depth utilization metrics for 12 predator species tagged within the Exclusive Economic Zone of Ascension Island, South Atlantic Ocean. Predator . Species . n indiv . n days . Mean length ± SD (range) (cm) . Depth thresholds (SD) (m) . % time (SD) . Depth (SD) (m) . 50% . 75% . 90% . 95% . <50 m . <85 m . Mean . Max . Dolphinfish C. hippurus 3 59 103 ± 6 (98–107) 1 (1) 6 (4) 24 (4) 38 (11) 97% (2%) >99% (0%) 8 (1) 417 (322) Sailfish I. albicans 1 88 190 2 39 54 61 87% >99% 18 105 Blue marlin M. nigricans 10 554 265 ± 23 (250–310) 9 (17) 47 (20) 71 (17) 84 (21) 74% (14%) 94% (6%) 27 (10) 253 (124) Silky shark C. falciformis 1 9 100 11 28 56 76 88% 98% 20 135 Wahoo A. solandri 2 5 141 ± 5 (137–144) 38 (22) 53 (22) 66 (23) 70 (26) 68% (33%) 96% (5%) 39 (15) 123 (1) Oceanic whitetip C. longimanus 2 104 142 ± 6 (137–146) 30 (12) 60 (23) 83 (15) 93 (14) 68% (16%) 89% (10%) 38 (12) 436 (315) Yellowfin tuna T. albacares 15 374 121 ± 33 (85–182) 29 (14) 55 (22) 95 (49) 124 (72) 72% (15%) 89% (10%) 39 (18) 260 (130) Tiger shark G. cuvier 1 41 292 84 104 133 151 21% 51% 79 276 Galapagos shark C. galapagensis 8 559 190 ± 52 (123–240) 53 (10) 74 (8) 91 (7) 98 (8) 46% (13%) 85% (6%) 53 (8) 258 (105) Blue shark P. glauca 10 455 182 ± 18 (166–210) 66 (16) 125 (45) 203 (39) 246 (33) 39% (13%) 60% (11%) 86 (19) 687 (331) Bigeye tuna T. obesus 7 600 115 ± 8 (97–120) 94 (46) 232 (106) 309 (119) 335 (124) 32% (18) 51% (23) 137 (56) 547 (141) Swordfish X. gladius 1 54 265 145 439 487 502 33% 46% 234 929 Predator . Species . n indiv . n days . Mean length ± SD (range) (cm) . Depth thresholds (SD) (m) . % time (SD) . Depth (SD) (m) . 50% . 75% . 90% . 95% . <50 m . <85 m . Mean . Max . Dolphinfish C. hippurus 3 59 103 ± 6 (98–107) 1 (1) 6 (4) 24 (4) 38 (11) 97% (2%) >99% (0%) 8 (1) 417 (322) Sailfish I. albicans 1 88 190 2 39 54 61 87% >99% 18 105 Blue marlin M. nigricans 10 554 265 ± 23 (250–310) 9 (17) 47 (20) 71 (17) 84 (21) 74% (14%) 94% (6%) 27 (10) 253 (124) Silky shark C. falciformis 1 9 100 11 28 56 76 88% 98% 20 135 Wahoo A. solandri 2 5 141 ± 5 (137–144) 38 (22) 53 (22) 66 (23) 70 (26) 68% (33%) 96% (5%) 39 (15) 123 (1) Oceanic whitetip C. longimanus 2 104 142 ± 6 (137–146) 30 (12) 60 (23) 83 (15) 93 (14) 68% (16%) 89% (10%) 38 (12) 436 (315) Yellowfin tuna T. albacares 15 374 121 ± 33 (85–182) 29 (14) 55 (22) 95 (49) 124 (72) 72% (15%) 89% (10%) 39 (18) 260 (130) Tiger shark G. cuvier 1 41 292 84 104 133 151 21% 51% 79 276 Galapagos shark C. galapagensis 8 559 190 ± 52 (123–240) 53 (10) 74 (8) 91 (7) 98 (8) 46% (13%) 85% (6%) 53 (8) 258 (105) Blue shark P. glauca 10 455 182 ± 18 (166–210) 66 (16) 125 (45) 203 (39) 246 (33) 39% (13%) 60% (11%) 86 (19) 687 (331) Bigeye tuna T. obesus 7 600 115 ± 8 (97–120) 94 (46) 232 (106) 309 (119) 335 (124) 32% (18) 51% (23) 137 (56) 547 (141) Swordfish X. gladius 1 54 265 145 439 487 502 33% 46% 234 929 Depth thresholds are the mean (SD) depths above which each species spent 50, 75, 90, and 95% of their time. Percent time shows the mean (SD) proportion of time each species spent above 50 m (approximate mixed layer depth) and 85 m (approximate start of thermocline and deep chlorophyll maximum). Depth metrics show mean (SD) overall daily depths and maximum depths for each species. Mean (SD) values were calculated from metrics for each tagged fish; consequently, no SD is reported for species with n = 1. Open in new tab We also calculated simple metrics of depth utilization for each species. For species where n > 1, depth metrics were calculated for each tagged individual and metric values are reported as mean (± SD) across individuals for each species. We calculated 50, 75, 90, and 95% depths, which represent estimates of the depths at which each species spent the respective percentage of their day. We also calculated time spent (%) <50 m, which represents the approximate mixed layer depth across all tag data and in the general Ascension region (Weber et al., 2018); we also calculated time spent (%) <85 m, which represents the start of the abrupt thermocline across tag data and also the deep chlorophyll maximum observed in the region (85 ± 12 m) (Weber et al., 2018). Finally, we calculated the mean (± SD) daily depth and the mean (± SD) maximum depth for each species. Species-specific diel TAD and TAT Contour plots of TAD and TAT were generated to visualize diel patterns in vertical behaviour for all tagged species. For contour plots, all days of data that contained depth or temperature were compiled for each species. Time of day and depth/temperature were binned into set intervals: 30 min for time, 5 m for depth, and 0.2°C for temperature. Proportion of time spent in each depth or temperature bin was then calculated (0–100%) across a 24-h time period and plotted on the x–y plane with isolines between 10% intervals. Contour plots showed continuous depth and temperature use across time of day, as well as shifts between day/night and crepuscular diving activity. To further quantify overall depth and temperature use for day and night periods, TAD and TAT histograms were generated for each species. All days of data containing depth and temperature were compiled and day and night data were categorized using local time of sunrise and sunset based on daily geolocation estimates (Weber et al., unpublished data) and sunrise/sunset times available from the NOAA Sunrise/Sunset Calculator (NOAA Solar Calculator, website). Since tags were programmed to record time series data at 5, 7.5, or 10 min intervals, sunrise and sunset times were rounded to the nearest time at which data were recorded for each tag (times were rounded by a maximum of 4 min). Neither day nor night data were truncated to remove crepuscular behaviour. Histograms were then generated using combined data for each species, showing the proportional use of 10 m depth and 0.5°C temperature bins (i.e. proportion of depth/temperature use in all histograms as a sum of 100% of total time). For the visualization of TAD histograms in relation to temperature and DO (see below for DO data acquisition and analysis), temperature-at-depth and DO profiles for each species were overlaid on TAD histograms. For the visualization of TAT histograms, the temperature range of thermocline top and bottom (∼23 to ∼13.5°C) is shown. TAT histograms were calculated for ten species, as temperature data were not available for sailfish or oceanic whitetip sharks due to programming for longer deployments. Classification trees of vertical habitat use To quantitatively group and classify depth and temperature distributions across species and explore potential groupings of predators based on water column use, we calculated the inter-specific differences between depth and temperature distributions (day and night separately and combined) and used these metrics to create dendrograms (“classification trees”). For each species, the proportions of TAD and TAT were calculated for 10 m depth and 0.5°C temperature bins, as above for TAD and TAT histograms, to standardize depth/temperature distribution metrics. Pairwise comparisons were then made across species by measuring Euclidean distance between bin values to generate a similarity tree across all depth data, using hierarchical cluster analysis to classify species into clades of depth utilization. We set cluster number (n = 4) post hoc after evaluating several scenarios, avoiding (i) single-species clusters (n > 4) and (ii) clusters including species with clearly different habitat use, as demonstrated in prior analyses (n < 4). Clades were compared to calculated violin plots and histograms to assess whether defined clades (see “Results” section) were reasonable representatives of intra-clade similarity and inter-clade dissimilarity. Clade assignments from overall depth distribution were then compared in similarity trees for day and night depths independently and overall, night, and day temperatures. Sailfish and oceanic whitetip sharks were excluded from the calculation of temperature classification trees due to unavailable temperature data for these species. DO To investigate the DO structure of the vertical water column used by each species, we used monthly DO climatologies from the World Ocean Atlas 2018 (WOA; website) to generate species-specific oxygen-depth profiles, following Carlisle et al. (2017). Using daily geolocation estimates for each tagged fish, we extracted climatological DO data from the closest location (WOA data are provided for 1° × 1° geographical bins) and appropriate month. Extracted DO data provided monthly means of DO for each month in 5-m bins for the depths of 0–100 m and then in 25-m bins for the depths of 100–600 m. We calculated mean DO for each depth bin for each fish, assigned the calculated mean to corresponding depth and day for each fish, and reconstructed water column DO profiles for each species. We generated the daily histograms of DO levels likely encountered by each species, as described above for TAD and TAT. As DO data are monthly means from the closest location to each fish, DO profiles and histograms do not necessarily represent the exact DO levels encountered daily by each species but are a best estimate using available data for this region of the tropical South Atlantic Ocean. Night-time depth variation with lunar cycle Since variation in predator depth through the lunar cycle is considered to be related to foraging, we assessed the correlation of night-time depth with lunar cycle for each species. We first eliminated the hour of depth data before sunrise and after sunset to remove bias associated with the influence of refractory sunlight and crepuscular foraging movements. We then limited depth data for each night to only the time between moonrise and moonset, which were obtained from Time and Date AS® (website); date-specific percent lunar illumination values ranged from 0 to 100%. The effect of lunar illumination on nocturnal depth in each species was analysed using a generalized additive mixed model (GAMM) of the form: Depthij−1=β0+f1(Illuminationij)+f2(Timeij)+αi,(1) where Depthij is the average depth of the ith individual over time interval j; β0 is a constant; Illuminationij and Timeij are the percent lunar illumination and midpoint time-of-day, respectively, during interval ij ⁠; f1 and f2 are penalized smooth functions with thin plate regression spline and cubic cyclic regression spline basis functions, respectively; and αi is a random effect associated with individual fish. Depth was assumed to be conditionally gamma-distributed and was mapped to the linear predictor using an inverse link function. All models exhibited strong residual temporal autocorrelation, which was effectively removed by incorporating a second order autoregressive error process (AR2) nested within individual and night. GAMMs were fit by penalized quasilikelihood using the gamm function in R package mgcv (Wood, 2013) and the significance of the smooth terms evaluated using Bayesian approximate p-values estimated internally within the function (see Wood, 2013 for details). For each species, the marginal effect of lunar illumination on average depth was predicted by the fixing time of day at zero (i.e. midnight) and averaging across individual fish. Confidence intervals around the mean were estimated assuming normally distributed errors on the scale of the linear predictor. Results Tag duration and data Between November 2014 and February 2018, miniPATs were deployed and data successfully retrieved from 10 bigeye tuna, 10 blue marlin, 11 blue sharks, 3 dolphinfish, 9 Galapagos sharks, 2 oceanic whitetip sharks, 1 sailfish, 3 silky sharks, 1 swordfish, 2 tiger sharks, 4 wahoo, and 18 yellowfin tuna, for a total of 74 tags on 12 species in waters around Ascension Island and surrounding seamounts. Of these, 11 tags released prematurely within one day of tagging, either due to tag shedding, apparent mortality (3 tuna, 1 wahoo), or potential predation (2 bigeye tuna, 1 yellowfin tuna, 1 wahoo). These tags were excluded from further analysis, resulting in 61 tags and 2902 full days of data for analysis (Table 1). Tag numbers and retention times varied within and across species (Table 1). Because we only included tags with >1 day of data for subsequent analyses, only one individual for four species (sailfish, silky shark, swordfish, and tiger shark) was available, with the highest sample size for blue marlin and blue shark (n = 10) and yellowfin tuna (n = 15) (Table 1). Size ranges (FL, cm; mean ± SD and range) of animals with analysed tag data are shown in Table 1. Tag retention times, excluding tag deployments of <1 day, ranged from 2 (wahoo, yellowfin tuna) to 231 days (blue marlin); see Table 1 for number of full days of data for each species. Due to tag programming to facilitate longer deployments, temperature data were not recorded for sailfish and oceanic whitetip sharks; hence these species could not be included in TAT analyses. Species-specific diel patterns of TAD and TAT Movements of most tagged individuals were within the tropical South Atlantic, close to Ascension and surrounding seamounts (Supplementary Figure S1), resulting in similar physical water column structure across individuals (see Figures 1 and 2). All species showed diel patterns in depth and temperature use, concordant with local light levels and local times of sunrise (06:40–07:10) and sunset (18:50–19:13) (Figure 1). Overall daily depth utilization showed typical diel vertical migration (DVM) in eight species (blue marlin, sailfish, wahoo, yellowfin tuna, Galapagos sharks, blue sharks, bigeye tuna, swordfish), using deeper depths during daytime than night-time (Figure 1). Silky shark, dolphinfish, and oceanic whitetips showed reverse DVM (rDVM; deeper night-time depths), though these differences were sometimes slight (see Figure 2). Oceanic whitetips were highly surface-associated during crepuscular periods (Figure 1). Tiger shark depth distributions were similar between day and night, though high surface-association was observed around dusk (Figure 1). Figure 1. Open in new tabDownload slide Proportion of time spent daily at (a) depth and (b) temperature for 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Data shown are for all full days for all tagged individuals of each species. Note the different y-axis scales across rows in (a) and different species-specific y-axis ranges in (b). Figure 1. Open in new tabDownload slide Proportion of time spent daily at (a) depth and (b) temperature for 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Data shown are for all full days for all tagged individuals of each species. Note the different y-axis scales across rows in (a) and different species-specific y-axis ranges in (b). Figure 2. Open in new tabDownload slide Histograms showing proportion of (a) TAD and (b) TAT for 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. For each species, proportion of TAD or TAT is shown for day and night; note different x-axis scales across species due to wide inter-specific differences in distribution of depth use across depth bins (i.e. narrowest in dolphinfish, broadest in swordfish, bigeye tuna, blue sharks). In (a), profiles of temperature-at-depth and DO-at-depth are plotted for each species, using mean values at 10-m bins (error bars ± 1 SD) from tag data (temperature) and World Ocean Atlas Data (DO; see “Material and methods” section) for each species. Note that temperature profiles for sailfish and oceanic whitetip were reconstructed from data from other fish for the appropriate tagging periods of those species, as temperature data were not available for these species. These data are shown for comparison but were not used in temperature utilization analyses. In (b), shaded areas show approximate temperature at thermocline top (∼23°C) and bottom (∼13.5°C), corresponding to depths of ∼85 and 150 m, respectively. For each day of data for each species, day and night were divided by the local time of sunrise and sunset. Figure 2. Open in new tabDownload slide Histograms showing proportion of (a) TAD and (b) TAT for 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. For each species, proportion of TAD or TAT is shown for day and night; note different x-axis scales across species due to wide inter-specific differences in distribution of depth use across depth bins (i.e. narrowest in dolphinfish, broadest in swordfish, bigeye tuna, blue sharks). In (a), profiles of temperature-at-depth and DO-at-depth are plotted for each species, using mean values at 10-m bins (error bars ± 1 SD) from tag data (temperature) and World Ocean Atlas Data (DO; see “Material and methods” section) for each species. Note that temperature profiles for sailfish and oceanic whitetip were reconstructed from data from other fish for the appropriate tagging periods of those species, as temperature data were not available for these species. These data are shown for comparison but were not used in temperature utilization analyses. In (b), shaded areas show approximate temperature at thermocline top (∼23°C) and bottom (∼13.5°C), corresponding to depths of ∼85 and 150 m, respectively. For each day of data for each species, day and night were divided by the local time of sunrise and sunset. Analysis of temperature at depth, and estimated water column thermal structure, was similar across species (see species-specific thermoclines in Figure 2). A surface mixed layer was observed to ∼50 m, with changes in the temperature of <1°C (Figure 2). An abrupt thermocline was observed at ∼85 m, extending to ∼150 m at which point temperatures declined gradually from 13.5 to 6°C from 150 to 600 m (Figure 2). Hereafter, “mixed layer” refers to 0–50 m, “thermocline” to 85–150 m, and “sub-thermocline” to >150 m. The depths used, and the proportion of time spent in the surface mixed layer, thermocline and sub-thermocline, varied considerably across species (see Table 1 for mean ± SD of all calculated dive metrics for each species). In general, four species (dolphinfish, sailfish, blue marlin, and silky sharks) were almost entirely confined to waters above the thermocline; five species (wahoo, oceanic whitetip, yellowfin tuna, tiger, and Galapagos sharks) made more frequent dives to and below the thermocline; and three species (blue sharks, bigeye tuna, and swordfish) spent considerable time below the thermocline during daytime (Table 2; see Supplementary Materials for detailed descriptions of individual species movements). Some of these species summaries were limited by tag number (n = 1 for swordfish, sailfish, silky sharks, and tiger sharks) and days analysed (n = 9, silky sharks; n = 5, wahoo). Depth metrics reported here and all comparisons should be considered in the context of these data limitations (see statistical comparisons below). Table 2. General description of water column features that constrain the vertical movement patterns of 12 electronically tagged predator species in the South Atlantic Ocean. Group . General limits to vertical movements . Use of mesopelagic zone . General temp limitsa . Ecological zones/ likely prey . Clade 1 Dolphinfish, blue marlin, sailfish, silky shark Almost entirely within mixed layer Extremely limited by top of thermocline (day and night) Negligible <0.1% Highly restricted (>99%) to >20°C ΔSST ≤ 2°C Epipelagic Clade 2 Wahoo, oceanic whitetip shark, yellowfin tuna Highly limited by top of thermocline Brief dives into thermocline (day) Infrequent and brief (day and crepuscular) Most >20°C Brief dives to lower temps ΔSST ≤ 8°C Epipelagic Some mesopelagic Clade 3 Tiger shark, Galapagos shark Oscillatory dives through thermocline (day and night) Limited by thermocline floor (95% of movements) Moderate to high (thermocline-associated) Most >20°C Frequent dives to lower temps ΔSST ≤ 8°C Epipelagic Mesopelagic Clade 4 Blue shark, bigeye tuna, swordfish High sub-thermocline use (day) with species-specific floors below thermocline bottom Most night-time movements limited by thermocline top High; sub-thermocline in all species Sustained in swordfish throughout day Species-specific (7–9°C; day) Most >20°C (night) ΔSST > 18°C Mesopelagic Epipelagic Some bathypelagic Group . General limits to vertical movements . Use of mesopelagic zone . General temp limitsa . Ecological zones/ likely prey . Clade 1 Dolphinfish, blue marlin, sailfish, silky shark Almost entirely within mixed layer Extremely limited by top of thermocline (day and night) Negligible <0.1% Highly restricted (>99%) to >20°C ΔSST ≤ 2°C Epipelagic Clade 2 Wahoo, oceanic whitetip shark, yellowfin tuna Highly limited by top of thermocline Brief dives into thermocline (day) Infrequent and brief (day and crepuscular) Most >20°C Brief dives to lower temps ΔSST ≤ 8°C Epipelagic Some mesopelagic Clade 3 Tiger shark, Galapagos shark Oscillatory dives through thermocline (day and night) Limited by thermocline floor (95% of movements) Moderate to high (thermocline-associated) Most >20°C Frequent dives to lower temps ΔSST ≤ 8°C Epipelagic Mesopelagic Clade 4 Blue shark, bigeye tuna, swordfish High sub-thermocline use (day) with species-specific floors below thermocline bottom Most night-time movements limited by thermocline top High; sub-thermocline in all species Sustained in swordfish throughout day Species-specific (7–9°C; day) Most >20°C (night) ΔSST > 18°C Mesopelagic Epipelagic Some bathypelagic Species are grouped into four clades based on the classification tree analysis and inter-specific similarity of vertical movements. a ΔSST refers to the change in ambient temperature from surface to sub-surface temperatures encountered during dives. Open in new tab Table 2. General description of water column features that constrain the vertical movement patterns of 12 electronically tagged predator species in the South Atlantic Ocean. Group . General limits to vertical movements . Use of mesopelagic zone . General temp limitsa . Ecological zones/ likely prey . Clade 1 Dolphinfish, blue marlin, sailfish, silky shark Almost entirely within mixed layer Extremely limited by top of thermocline (day and night) Negligible <0.1% Highly restricted (>99%) to >20°C ΔSST ≤ 2°C Epipelagic Clade 2 Wahoo, oceanic whitetip shark, yellowfin tuna Highly limited by top of thermocline Brief dives into thermocline (day) Infrequent and brief (day and crepuscular) Most >20°C Brief dives to lower temps ΔSST ≤ 8°C Epipelagic Some mesopelagic Clade 3 Tiger shark, Galapagos shark Oscillatory dives through thermocline (day and night) Limited by thermocline floor (95% of movements) Moderate to high (thermocline-associated) Most >20°C Frequent dives to lower temps ΔSST ≤ 8°C Epipelagic Mesopelagic Clade 4 Blue shark, bigeye tuna, swordfish High sub-thermocline use (day) with species-specific floors below thermocline bottom Most night-time movements limited by thermocline top High; sub-thermocline in all species Sustained in swordfish throughout day Species-specific (7–9°C; day) Most >20°C (night) ΔSST > 18°C Mesopelagic Epipelagic Some bathypelagic Group . General limits to vertical movements . Use of mesopelagic zone . General temp limitsa . Ecological zones/ likely prey . Clade 1 Dolphinfish, blue marlin, sailfish, silky shark Almost entirely within mixed layer Extremely limited by top of thermocline (day and night) Negligible <0.1% Highly restricted (>99%) to >20°C ΔSST ≤ 2°C Epipelagic Clade 2 Wahoo, oceanic whitetip shark, yellowfin tuna Highly limited by top of thermocline Brief dives into thermocline (day) Infrequent and brief (day and crepuscular) Most >20°C Brief dives to lower temps ΔSST ≤ 8°C Epipelagic Some mesopelagic Clade 3 Tiger shark, Galapagos shark Oscillatory dives through thermocline (day and night) Limited by thermocline floor (95% of movements) Moderate to high (thermocline-associated) Most >20°C Frequent dives to lower temps ΔSST ≤ 8°C Epipelagic Mesopelagic Clade 4 Blue shark, bigeye tuna, swordfish High sub-thermocline use (day) with species-specific floors below thermocline bottom Most night-time movements limited by thermocline top High; sub-thermocline in all species Sustained in swordfish throughout day Species-specific (7–9°C; day) Most >20°C (night) ΔSST > 18°C Mesopelagic Epipelagic Some bathypelagic Species are grouped into four clades based on the classification tree analysis and inter-specific similarity of vertical movements. a ΔSST refers to the change in ambient temperature from surface to sub-surface temperatures encountered during dives. Open in new tab Effect of thermal structure on overall depth distributions Use of the mixed layer, thermocline, and sub-thermocline waters varied markedly across species. Kernel density estimates and embedded boxplots of depth use showed that dolphinfish, sailfish, blue marlin, and silky shark were highly surface-associated, with median depth values between 0 and 5 m and interquartile ranges within the mixed layer (50 m), though all four of these species showed relatively infrequent dives into and/or below thermocline waters (Figure 3). Wahoo, oceanic whitetip, and yellowfin tuna were also highly associated with the mixed layer, with median depth values <50 m, but interquartile ranges that extended past the mixed layer and 5th percentile values extending into the thermocline range (Figure 3). Tiger and Galapagos sharks showed deeper distributions, with median values at or below the mixed layer and in tiger sharks, interquartile range that extended into the thermocline and 5th percentile values below the thermocline bottom. Blue shark, bigeye tuna, and swordfish had the deepest distributions, with interquartile ranges that extended below the thermocline bottom in all three species, median values near or within thermocline waters, and 5th percentile values that extended from 300 m (blue shark) to >900 m (swordfish) (Figure 3). Kernel density estimates also showed variable inter-specific modalities in depth distribution, with bi-modality in oceanic whitetips, tiger sharks, Galapagos sharks, and swordfish, and tri-modality in bigeye tuna, though the depth and extent of apparent peak depths varied across species (Figure 3). Finally, depth outliers showed inter-specific differences in relatively infrequent, deep “bounce-diving” behaviour, with extensive outliers most apparent in blue marlin, yellowfin tuna, Galapagos sharks, and blue sharks (Figure 3). Figure 3. Open in new tabDownload slide Depth utilization by 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Coloured forms show kernel density estimates of depth data distribution for each species. Boxplots within coloured forms show median depth (white circle), interquartile range (thick black line), 5th and 95th percentiles (thin lines with whiskers), and outliers (black points). Also, shown are the approximate mixed layer depth, as calculated across all temperature-at-depth data (50 m, dashed line) and the top and bottom depths of the thermocline (85–150 m, grey box). Note break in y-axis below 600 m to depths approaching 1000 m to show outlier depths and the 5th percentile for blue shark and swordfish, respectively. Figure 3. Open in new tabDownload slide Depth utilization by 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Coloured forms show kernel density estimates of depth data distribution for each species. Boxplots within coloured forms show median depth (white circle), interquartile range (thick black line), 5th and 95th percentiles (thin lines with whiskers), and outliers (black points). Also, shown are the approximate mixed layer depth, as calculated across all temperature-at-depth data (50 m, dashed line) and the top and bottom depths of the thermocline (85–150 m, grey box). Note break in y-axis below 600 m to depths approaching 1000 m to show outlier depths and the 5th percentile for blue shark and swordfish, respectively. Predator clades of vertical habitat use The classification tree for combined day and night depth distributions across species was used to identify groupings within the 12 tagged species (Figure 4). The depth distributions of sailfish and blue marlin were most similar (lowest linkage distance), and dolphinfish and silky sharks formed a separate cluster with these two species. Wahoo, yellowfin tuna, and oceanic whitetips formed a second cluster, with high similarity between yellowfin tuna and oceanic whitetips. A further cluster contained the three species with the highest distributions at deeper depths; blue sharks, bigeye tuna, and swordfish, with high similarity between bigeye tuna and blue shark and relatively high similarity of swordfish to these two species (Figure 4). Finally, a fourth cluster was formed from two species (tiger sharks and Galapagos sharks) that were outgroups from the deep depth clade but were also clearly separate, both in terms of classification trees and depth metrics, from the other groups (Figure 4). Clades were numbered from most surface associated to most deep associated, informed by results of violin plots and depth metrics, and were classified as primarily epipelagic (clade 1), partial thermocline use (clade 2), intermediate thermocline/sub-thermocline use (clade 3), and extensive use of sub-thermocline waters (clade 4). Figure 4. Open in new tabDownload slide Similarity trees based on (a) depth distribution and (b) temperature distribution for 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Overall (day and night combined) depth distributions were used to distinguish four clades using hierarchical cluster analysis of overall depth use (top left panel), with species labels coloured accordingly. Clade coherence varied by day/night and depth/temperature utilizations. Note that in (b), sailfish and oceanic whitetip sharks are absent from temperature trees due to unavailability of temperature data for those species. Figure 4. Open in new tabDownload slide Similarity trees based on (a) depth distribution and (b) temperature distribution for 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Overall (day and night combined) depth distributions were used to distinguish four clades using hierarchical cluster analysis of overall depth use (top left panel), with species labels coloured accordingly. Clade coherence varied by day/night and depth/temperature utilizations. Note that in (b), sailfish and oceanic whitetip sharks are absent from temperature trees due to unavailability of temperature data for those species. Clades were not fully cohesive when cluster analyses were repeated for day and night depth distributions independently (Figure 4a). For daytime depths, dolphinfish were the outgroup to all other species, and those forming clades 1 and 2 and clades 3 and 4 tended to be closely linked. For night-time depths, cluster 1 species were closely linked, but species from clusters 2, 3, and 4 showed mixed clustering and varying degrees of linkage distance (Figure 4). Clades derived from overall depth were also cohesive across combined and night temperatures, with some mixing of clade 1 and clade 2 species (Figure 4). Analysis by day temperatures led to different species linkages, likely due to the high variability in ambient temperatures encountered during oscillatory diving (Figure 4). DO The species-specific DO vertical profiles, estimated from WOA data, co-varied with water column thermal structure (Figure 3a). DO was highest (4.5–5.0 ml l−1) and generally consistent throughout the mixed layer and then sharply decreased with the start of the thermocline at 85 m (Figure 3a). For most species, the estimated oxygen minimum layer (OML) occurred between 200 and 250 m, though for some species (oceanic whitetip, swordfish, bigeye tuna, tiger sharks) the estimated OML was deeper at 350–400 m (Figure 1), with the DO concentrations of 1.5–2.0 ml l−1. Below the OML, DO concentrations tended to increase gradually with increasing depth from the OML to 600 m, to levels between 2.0 and 3.0 ml l−1 depending on the species-specific estimated DO profile (Figure 1). The estimated DO levels experienced by each species were related to relative time spent within the mixed layer (highest DO concentrations) and overall depth distributions. The most surface-associated species (dolphinfish, sailfish, blue marlin, silky shark, wahoo) spent 93.9–99.7% of their time at the highest DO concentrations (4.5–5.0 ml l−1) (Figure 5). Blue marlin and silky sharks spent <4% in 4.0–4.5 and experienced DO minima of 2.5–3.0 (1.1% of time) and 3.5–4.0 (2.0% of time), respectively (Figure 5). Oceanic whitetips and yellowfin tuna spent 69.3 and 84.0% at the highest DO in the mixed layer, respectively, and whitetips spent 29.4% at 3.5–4.5 ml l−1 and spent 1% of time at minimum DO levels of 3–3.5 ml l−1. During bounce-dive behaviour, yellowfin tuna experienced DO levels as low as 1.5–2.0 ml l−1 (3.9% of time). Due to dives through the thermocline, tiger sharks were well distributed throughout DO intervals from 2 to 5 ml l−1, experiencing the minimum concentrations of 2–2.5 ml l−1 (8.3% of time), while Galapagos sharks spent 77.7% at 4.5–5.0 ml l−1 and experienced minimum concentrations of 3–3.5 ml l−1 (1.1% of time) (Figure 5). Figure 5. Open in new tabDownload slide Estimated time spent at DO concentrations by 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Values of DO at depth were obtained from World Ocean Atlas 2018 and are for the closest location possible to each daily geolocation estimate for each tagged animal. Bars show mean DO across tagged individuals for each species, with error bars ±1 SD. Figure 5. Open in new tabDownload slide Estimated time spent at DO concentrations by 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Values of DO at depth were obtained from World Ocean Atlas 2018 and are for the closest location possible to each daily geolocation estimate for each tagged animal. Bars show mean DO across tagged individuals for each species, with error bars ±1 SD. Blue sharks, bigeye tuna, and swordfish experienced the lowest estimated DO concentrations. Blue sharks did not experience concentrations <2.0 ml l−1, but spent 15.7% of time at 2–2.5 ml l−1, and the majority of their time (57.5%) in the highest DO concentrations of the surface mixed layer. Bigeye tuna spent 21.9% of time at 2–2.5 ml l−1 and 15.4% of time at 1.5–2.0 ml l−1, with less time (34.4%) in mixed layer concentrations of 4.5–5.0 ml l−1. Finally, swordfish spent extended time at the lowest DO concentrations, spending 25.8 and 20.8% of time at 2–2.5 and 1.5–2.0 ml l−1, respectively, and the least amount of time (21.0%) in the highly oxygenated mixed layer (Figure 5). Night-time depth variation with lunar cycle Lunar phase had significant effects on night-time depth in all but three species (dolphinfish, tiger, and silky sharks; Figure 6 and Supplementary Table S1). Higher lunar illumination was associated with deeper depths in sailfish, blue marlin, yellowfin and bigeye tuna, wahoo, swordfish, and blue sharks, though the strength of this relationship varied by species (see p-values in Figure 6 and Supplementary Table S1). Blue marlin and sailfish were highly surface associated throughout the lunar cycle, with only slight, though significant, increases in depth with increasing illumination (Figure 6). Swordfish and yellowfin tuna primarily used mixed layer depths (<50 m) at low illumination, and depths at and below the thermocline at highest illumination. Wahoo shifted from surface waters to depths of ∼25 m between 28 and 71% lunar illumination, though data limitation did not allow for analysis at lowest (near new moon) or highest (near full moon) lunar illumination (Figure 6). Night-time depths of Galapagos and blue sharks were highly associated with the mixed layer bottom across the lunar cycle, with blue sharks using slightly deeper depths with higher lunar illumination. Finally, night-time depths of Galapagos and oceanic whitetip sharks were slightly closer to the surface near the full moon (Figure 6). See Supplementary Table S1 for summary statistics of GAMMs across species. Figure 6. Open in new tabDownload slide Marginal effects of lunar illumination on mean dive depth of 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Solid lines show the predicted means from fitted GAMMs and dashed lines 95% pointwise confidence intervals. Coloured contour plots show predator depth distribution from new to full moon for all days of data recorded. P-values are Bayesian approximate estimates from GAMM analyses, which corrected for temporal autocorrelation. Note that analyses across all moon phases were not possible for wahoo and silky sharks due to limited data. Figure 6. Open in new tabDownload slide Marginal effects of lunar illumination on mean dive depth of 12 predator species tagged within the EEZ of Ascension Island, South Atlantic Ocean. Solid lines show the predicted means from fitted GAMMs and dashed lines 95% pointwise confidence intervals. Coloured contour plots show predator depth distribution from new to full moon for all days of data recorded. P-values are Bayesian approximate estimates from GAMM analyses, which corrected for temporal autocorrelation. Note that analyses across all moon phases were not possible for wahoo and silky sharks due to limited data. Discussion Electronic tags provided new information on depth and temperature distribution in 12 pelagic species in the South Atlantic. For most species, the majority of depth use was constrained by three water column features: the surface mixed layer, thermocline, and sub-thermocline DSL (discussed below), though some species (tiger sharks, Galapagos sharks, yellowfin tuna) were less predictably bound by these features. Observed depth distributions correspond well with observations from other ocean basins, correlate with described species-specific foraging ecology, and can inform species- and depth-specific bycatch risk of vulnerable, non-targeted species by commercial fisheries in the broader South Atlantic. Diel depth and temperature distributions Physical features of the water column clearly structured the vertical distributions of all predators (see Table 2 for depth and thermal bounds across groups). Physical constraints to movements observed here, in the South Atlantic, were generally consistent with decades of electronic tagging data in other ocean basins. Overall, high surface association of clade 1 species has been previously observed in dolphinfish (Furukawa et al., 2011; Merten et al., 2014; Hernández-Tlapale et al., 2015), sailfish (Mourato et al., 2010; Chiang et al., 2011; Hoolihan et al., 2011; Kerstetter et al., 2011), blue marlin (Holland et al., 1990; Block et al., 1992; Saito and Yokawa, 2006; Carlisle et al., 2017), and silky sharks (Filmalter et al., 2011; Musyl et al., 2011; Hutchinson et al., 2015; Hueter et al., 2018). Clade 2 species were largely restricted by the thermocline top but made some dives into thermocline waters, as has been previously observed in wahoo (Sepulveda et al., 2011; Theisen and Baldwin, 2012), oceanic whitetips (Musyl et al., 2011; Howey-Jordan et al., 2013; Andrzejaczek et al., 2018), and yellowfin tuna (Cayré, 1991; Block et al., 1997; Schaefer et al., 2009). Clade 3 use of thermocline waters, limited by the thermocline floor, and oscillatory diving through the thermocline has been previously observed in tiger sharks (Holland et al., 1999; Heithaus et al., 2007; Nakamura et al., 2011; Vaudo et al., 2014) and Galapagos sharks (Meyer et al., 2010). Finally, clade 4 movements within the mixed layer at night-time with high daytime use of sub-thermocline waters have been observed in blue sharks (Carey et al., 1990; Stevens et al., 2010; Musyl et al., 2011), bigeye tuna (Musyl et al., 2003, 2004; Matsumoto, 2005; Schaefer et al., 2009), and swordfish (Carey and Robinson, 1981; Sepulveda et al., 2010; Dewar et al., 2011), likely tracking the DSL (see Supplementary Discussion). Importantly, all of these species occasionally make deep dives to much greater depths and lower temperatures (Supplementary Text), demonstrating that the general physiological “limits” described here are not absolute. Light levels: diel and lunar cycles The most distinct DVM observed here (sailfish, blue marlin, swordfish, bigeye tuna, blue sharks; see Figures 1 and 2) has been reported in other oceans (Carey et al., 1990; Prince et al., 2010; Dewar et al., 2011; Schaefer et al., 2011) and can be most simplistically linked to reported daytime visual foraging (Varghese et al., 2014). However, in most of these species, rDVM has also been reported, including surface-oriented behaviour in fish aggregating device-associated bigeye tuna (Schaefer and Fuller, 2010) and multiple behaviours (DVM, rDVM, or no difference) observed within individual blue sharks (Queiroz et al., 2012). Night-time diving (yellowfin tuna, Galapagos sharks, and tiger sharks) and rDVM (dolphinfish, silky sharks, and oceanic whitetips) was observed, though dolphinfish in particular are considered visual, daytime predators (Varghese et al., 2014). Linking vertical displacement with foraging behaviour has been validated (Sepulveda et al., 2004; Bestley et al., 2008), but this correlation has limits. Accelerometer data-loggers in dolphinfish revealed that limited daytime diving (i.e. surface association) was linked to high acceleration and active foraging, and oscillatory night-time diving within the mixed layer was gliding descents and active ascents linked to energy conservation (Furukawa et al., 2011). Since studies that link vertical behaviour to a more direct foraging metric (e.g. acceleration data; video capture; post-prandial warming in regional endotherms; post-tagging stomach content analysis) are relatively rare, we are currently limited in the extent to which we can correlate active foraging, vs. other behaviours (i.e. thermoregulation, energy conservation, prey searching, navigation, predator avoidance), with vertical displacement data. Recognizing the caveats above, the most substantial and consistent deeper night-time diving with increasing lunar illumination in yellowfin tuna, bigeye tuna, and swordfish can tentatively be linked with night-time foraging on DSL organisms, as nocturnal feeding and depth shifts with the migrating DSL have been reported in some of these species (Musyl et al., 2003; Loefer et al., 2007; Varghese et al., 2014). However, oceanic whitetip and Galapagos sharks had a significant, opposite response; as oceanic whitetips also showed rDVM, it is possible that species that are more surface-oriented for feeding during daylight respond similarly to lunar illumination, feeding near the surface near the full moon. Complementary studies using accelerometry data and/or diet analysis would clarify the effects of observed lunar response on behaviour and foraging. Constraints of DO The effects of DO on vertical distributions of pelagic predators have been increasingly recognized and demonstrated pelagic fish (Prince and Goodyear, 2006; Prince et al., 2010; Koslow et al., 2011; Stramma et al., 2012), with 3.5 ml l−1 proposed as a general constraint for some tuna species, and all billfish species except swordfish (Barkley et al., 1978; Prince et al., 2010; Carlisle et al., 2017). Surface-associated predators (dolphinfish, sailfish, silky shark, and blue marlin) experienced DO levels (4.5–5.0 ml l−1) consistent with concentrations encountered in the well-oxygenated surface layer in other regions. Tiger and blue sharks spent substantial time at 2.0–3.0 ml l−1, suggesting tolerance to lower DO in these ectothermic sharks. However, bigeye tuna and swordfish experienced DO concentrations of 1.5–2.0 ml l−1, associated with deepest depths. In bigeye tuna, this has been partially explained by higher blood oxygen-binding affinity than other tunas that also decreases with warming (Brill et al., 2005); swordfish in other oceans experience lower extremes than observed here (<0.5 ml l−1) for prolonged periods of time (Abascal et al., 2010; Sepulveda et al., 2010). Since vertical DO profiles often co-vary with vertical temperature profiles (see Figure 3a), it is difficult to discern the relative influence of DO and temperature on predator depth limitations. However, results here corroborate the DO thresholds of >3.5 ml l−1 for billfish and tunas, with lower limitations observed (0.5–1.0 ml l−1) for yellowfin tuna, bigeye tuna, and swordfish. DO limitations are less clear in pelagic sharks; while scalloped hammerheads (Sphyrna lewini) have been shown to use deep waters of <0.5 ml l−1 (Jorgensen et al., 2009), shortfin mako sharks (Isurus oxyrinchus), and blue sharks are potentially limited to >3.0 and >1.5 ml l−1 in the North and South Pacific, respectively (Abascal et al., 2011; Banez, 2019), and blue sharks here did not use waters <2.0 ml l−1. While oxygen minimum zone shoaling will likely compress the habitat of pelagic teleosts (Lehodey et al., 2011; Stramma et al., 2012, Mislan et al., 2017), undescribed DO thresholds for pelagic sharks currently make similar projections difficult in these species. Predator physiology Pelagic species have been hypothesized to exploit the water column for foraging to the extent of their physiological capacities (Brill et al., 2005; Bernal et al., 2017), with low temperatures providing physiological challenges including compromised brain, eye, and cardiac function and lowered contractility of red muscle. The evolution of regional endothermy in fishes has been postulated to expand both horizontal and vertical niche (Block et al., 1993; Dickson and Graham, 2004; Madigan et al., 2015). Five species here have varying degrees of regional endothermic capacity: istiophorid billfish (including sailfish and blue marlin) possess brain heater organs and enhanced photoreceptors; swordfish, yellowfin tuna, and bigeye tuna all possess centralized red muscle, countercurrent heat exchangers, and brain/eye heating organs (Dickson and Graham, 2004), while bigeye tuna also possess visceral heat exchangers, and swordfish potentially control heat loss by altering blood flow to and from red muscle (Stoehr et al., 2018). The two deepest diving species (swordfish and bigeye tuna) are thus able to function at low temperatures by maintaining elevated body, brain, and eye temperatures; this likely facilitates yellowfin bounce-diving behaviour, though with demonstrably less capacity than bigeye tuna (Figure 7). Ocular heating organs potentially facilitate blue marlin foraging during bounce dives through the thermocline, but despite similar adaptations, sailfish generally maintain a more surface-associated depth distribution (Hoolihan et al., 2011), potentially due to cardiac limitations during rapid temperature change (Brill and Lutcavage, 2001; Galli et al., 2009a, b). High mixed layer association in ectothermic fish here (dolphinfish, silky sharks, wahoo, oceanic whitetips) was likely due to the physiological challenges of rapidly decreasing temperatures within the thermocline. Figure 7. Open in new tabDownload slide One week of diving behaviour for 12 predator species tagged within the EEZ of Ascension Island, Atlantic Ocean. One full week of data was randomly selected from one tagged individual of each species. Dark lines show tag-recorded predator depths; grey stippled line shows approximate mixed layer bottom (∼50 m) and grey dashed lines show approximate thermocline top (∼85 m) and bottom (∼150 m). Grey vertical bars indicate night-time. Note different y-axis scale for different species. Figure 7. Open in new tabDownload slide One week of diving behaviour for 12 predator species tagged within the EEZ of Ascension Island, Atlantic Ocean. One full week of data was randomly selected from one tagged individual of each species. Dark lines show tag-recorded predator depths; grey stippled line shows approximate mixed layer bottom (∼50 m) and grey dashed lines show approximate thermocline top (∼85 m) and bottom (∼150 m). Grey vertical bars indicate night-time. Note different y-axis scale for different species. However, regional endothermy does not completely explain the observed inter-specific differences, as ectothermic tiger and Galapagos sharks made relatively high use of thermocline and sub-thermocline waters, and the ectothermic blue shark consistently make deep dives below the thermocline here and elsewhere (Carey et al., 1990; Klimley et al., 2002; Howey, 2010; Stevens et al., 2010; Musyl et al., 2011). It is possible that large-bodied ectotherms may be able to out-perform smaller-bodied prey by maintaining thermal inertia during deep dives (Neill et al., 1974, 1976). Overall, the extensive mechanisms that have evolved for large pelagic fish to tolerate low temperatures and DO suggest a high selective advantage for deep-diving behaviour, though this relationship is complex. For example, in a small group of Pacific bluefin tuna, growth rates decreased with increasing mesopelagic foraging (Madigan et al., 2018). Shifting ocean temperatures and abundance of epipelagic and mesopelagic prey call for better understanding of these trade-offs in the foraging success and fitness of large pelagic predators. Species-specific trophic ecology Species-specific diet studies in other ocean basins generally corroborate observed vertical distributions and inferred ecologies (Table 2). Dolphinfish diet across ocean basins is dominated by epipelagic prey (Oxenford and Hunte, 1999; Olson and Galván-Magaña, 2002; Varghese et al., 2014). Silky sharks feed largely on epipelagic fish, cephalopods, and crustaceans; in the tropical Pacific, they have been observed to swim below schools of small surface feeding tuna, attacking from below when an individual tuna is stunned or line-caught (D. Madigan, pers. obs.). Sailfish and wahoo movements were largely within the mixed layer, and associated epipelagic forage fish comprise the majority of sailfish and wahoo diet (Vaske et al., 2004; Rudershausen et al., 2010). Blue marlin, oceanic whitetip sharks, and yellowfin tuna showed variable use of epipelagic and, to a lesser degree, thermocline waters; the diets of these species are comprised by a mix of epipelagic and mesopelagic fish, cephalopods, and crustaceans (Ménard et al., 2000; Camhi et al., 2009; Vaske et al., 2011). Traditional diet studies in this region would demonstrate the degree to which epipelagic distributions align with diet around Ascension and the broader tropical South Atlantic. Tiger shark diet shifts with ontogeny and is highly diverse, including teleosts, elasmobranchs, seabirds, reptiles, and marine mammals (Lowe et al., 1996; Simpfendorfer et al., 2001; Dicken et al., 2017). In pelagic waters around Ascension, yo-yo diving may increase encounters with diverse prey, as prey abundance strongly influences tiger shark diet (Lowe et al., 1996). Oscillatory diving has been associated both with energetic efficiency (Gleiss et al., 2011) and foraging behaviour (Nakamura et al., 2011). Fewer data exist for Galapagos shark ecology, though Galapagos sharks in the Hawaiian Islands were observed to feed largely on teleosts, but also benthic and pelagic cephalopods, crustaceans, and marine mammals (Papastamatiou et al., 2006). While some dietary overlap was observed between tiger and large Galapagos sharks in Hawaii, further work is required to understand these species’ ecological niches in South Atlantic waters. The observed sub-thermocline spatiotemporal envelopes in swordfish, bigeye tuna, and blue sharks correspond well with observations of diet in the South Atlantic (Sabatié et al., 2003; Vaske et al., 2004, 2011) and other ocean basins. Swordfish feed on mesopelagic fish and cephalopods, bigeye tuna on both epipelagic (e.g. crab megalopae) and mesopelagic (e.g. myctophids, lancetfish, pomfrets) fish, cephalopods, and crustaceans, and blue sharks on mesopelagic and DVM fish (e.g. gempylids, alepisaurids) and cephalopods in waters around Ascension Island and other regions of the tropical and sub-tropical Atlantic Ocean (Sabatié et al., 2003; Camhi et al., 2009; Vaske et al., 2009, 2012). Blue sharks, bigeye tuna, and swordfish appear to target (or are limited to) specific depths of the DSL in Ascension waters, which may be related to species-specific prey preferences; swordfish diets have been differentiated from bigeye tuna by the prevalence of large squids (e.g. ommastrephids), while bigeye tuna target smaller squids and forage fish (Moteki et al., 2001; Markaida and Hochberg, 2005). Comparative electronic tagging data in the same spatiotemporal regions will allow for cost/benefit analyses of pelagic species, operating near the extent of physiological limits, that forage along a density and diversity gradient of DSL resources. Application to fisheries Although commercial fishing was recently prohibited (2019) throughout the Ascension Island Exclusive Economic Zone, observed species-specific depth differences could be applied to targeted fisheries in the wider tropical South Atlantic Ocean to assess bycatch risk at depth of the most vulnerable species in the region. Observed floors of predator distributions (i.e. Figures 2 and 3) can be combined to establish fishing depth windows that maximize interactions with target species and minimize bycatch of unwanted and/or vulnerable species (Bigelow et al., 2002; Bigelow and Maunder, 2007). All species here are vulnerable to shallow sets (<100 m), particularly at night-time. Fisheries targeting swordfish using daytime set depths of ≥500 m may avoid bycatch of undesired species; this may also influence swordfish CPUE, as depth distribution peak at ∼500 m and decrease at greater depths. However, tag-inferred and longline catch depths can be incongruous, usually with species captured at deeper depths than tag data would predict (Ward and Myers, 2005); this may increase swordfish CPUE at depth but also limit the effectiveness of deeper sets to avoid vulnerable species [e.g. oceanic whitetips, which are listed as Critically Endangered by the IUCN (Rigby et al., 2019) and dive to depths >1000 m (Howey-Jordan et al., 2013)]. Importantly, the inability of longliners to fish selective depths accurately has been identified as a major driver of bycatch, due to variable branchline shape and consequent hook depths, current shear, and gear movement (Berkeley and Edwards, 1998). Deepest sets (≥500 m) in this region of the South Atlantic would most efficiently target swordfish and bigeye tuna only if vessels are equipped with technology to monitor set depths, which may not be feasible (Bigelow et al., 2006). Tag-measured vertical distribution can also improve the standardization of CPUE analyses, improving population assessments based on catch statistics (Hinton, 1998; Hinton and Deriso, 1998; Goodyear et al., 2003). Finally, deep sets may also result in substantial bycatch of mesopelagic species not tagged here (e.g. pelagic thresher Alopias pelagicus, bigeye thresher A. superciliosus, oilfish Ruvettus pretiosus). These limitations suggest that while tagging data can potentially refine fishing practices, other management tools (e.g. bycatch quotas, no-take zones) may more effectively protect vulnerable species. CPUE analysis across depth in the region would complement and inform depth ranges observed here, and improve region-specific modelling of species-specific bycatch risk (Ward and Myers, 2005). Conclusions These telemetry data provide baseline depth and temperature distributions for 12 pelagic predators of ecological and economic importance in the South Atlantic Ocean. Similarity of predator associations with water column features across ocean basins suggests that predator physiology constrains general vertical behaviour, with variability that is likely driven by spatiotemporal ecological dynamics. Endothermic species regularly accessed deepest, coldest waters; however, ectothermic sharks spanned all four depth clades, ranging from highly epipelagic (silky sharks) to mesopelagic (blue sharks), underpinning the physiological, energetic, and ecological complexity of explaining predator depth distributions. Coarse vertical distributions of these predators can likely be estimated across their ranges by regional water column structure. This provides the opportunity for the modifications in commercial fishing efforts to increase selectivity and decrease the bycatch of vulnerable species. Comparative, multi-species tagging analyses set the stage for improved modelling work to better understand the trade-offs of epi- and mesopelagic foraging strategies and to better understand species-specific responses to changing pelagic oceans. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Data availability statement The data underlying this article will be shared on reasonable request to the corresponding author. Acknowledgements All tags and fieldwork were funded by the Darwin Plus Initiative (to SBW and AJR; projects DPLUS046 and DPLUS063), the Blue Marine Foundation, and the UK government’s Conflict, Security and Sustainability Fund. 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TI - Water column structure defines vertical habitat of twelve pelagic predators in the South Atlantic
JF - ICES Journal of Marine Science
DO - 10.1093/icesjms/fsaa222
DA - 0032-06-11
UR - https://www.deepdyve.com/lp/oxford-university-press/water-column-structure-defines-vertical-habitat-of-twelve-pelagic-6ChJDHII8H
SP - 1
EP - 1
VL - Advance Article
IS - 
DP - DeepDyve
ER -