Fig 1.
Indo-Pacific sampling efforts and frequency distribution of predator attributes.
(A) Map of deployments (n = 1,041) with protection level and numbers of deployments per region, unprotected (outlined in blue), partially protected or small no-take MPAs (outlined and filled in pink), and large no-take MPAs (>1,000 km2, outlined and filled in green). (B) Frequency distributions of vertebrate species richness, (C) mean maximum body size (cm), and (D) shark abundance (sum of MaxN) across all deployments. The numerical values for B, C, and D can be found in S1 Data. (E) Shark abundance (log[SumMaxN + 1]) in each region (same color scale as for A). (F) Key to EEZs within the Indo-Pacific. EEZ from https://rosselkhoznadzor.carto.com/tables/world_maritime_boundaries_v8. Some EEZs are contested. 1, BIOT (UK); 2, Maldives; 3, Sri Lanka; 4, Cocos (Keeling) Island (Aus); 5, Malaysia; 6, Christmas Island (Aus); 7, Indonesia; 8, Australia; 9, Palau; 10, Papua New Guinea; 11, Micronesia; 12, Solomon Island; 13, Nauru; 14, New Caledonia (Fr); 15, Vanuatu; 16, Norfolk Island (Aus); 17, Marshall Islands; 18, Kiribati; 19, Fiji; 20, Tuvalu; 21, Kermadec Island (NZ); 22, Wallis and Futuna (Fr); 23, Samoa; 24, Tonga; 25, Howland and Baker Island (US); 26, Tokelau (NZ); 27, Phoenix Island Group; 28, Niue (NZ); 29, American Samoa (US); 30, Palmyra Atoll (US); 31, Cook Island (NZ); 32, Jarvis Island (US); 33, Line Island Group (US); 34, French Polynesia (Fr); 35, Pitcairn (UK). EEZ, Exclusive Economic Zones; MPA, marine protected area.
Fig 2.
Examples of midwater predators surveyed by the BRUVS.
(A) Blue shark (Prionace glauca). (B) Rainbow runner (Elagatis bipinnulata). (C) Mahi-mahi (Coryphaena hippurus). (D) Black marlin (Istiompax indica). BRUVS, baited remote underwater video system.
Fig 3.
Drivers and patterns of vertebrate species richness in the Indo-Pacific.
(A) Relative contribution of main drivers explaining variations in species richness were generated from 100 iterations of BRTs. (B, C) Partial dependence plot (lines), observed values (dots), and 95% confidence intervals for distance to the coast (B) and SST. (D) Predictions of species richness (top 5% values, >3.8, in red). The numerical values for (A) can be found in S2 Data. BRT, boosted regression tree; dist coast, distance to nearest coast; dist CoralTri, distance to the Coral Triangle; dist seamount, distance to nearest seamount with summit depth <1,500 m; SST, sea surface temperature.
Fig 4.
Drivers and patterns of mean max body size in the Indo-Pacific.
(A) Relative contributions of the main drivers explaining variation in body size were generated from 100 iterations of BRTs. (B,C) Partial dependence plot (lines), observed values (dots), and 95% confidence intervals for SST (B) and distance to nearest market (thresholds represented by breaking point [C]). (D) Prediction values of body size (top 5% values, >108 cm, in red). The numerical values for (A) can be found in S2 Data. BRT, boosted regression tree; Dist market, distance to nearest market; Dist pop, distance to nearest population; Dist seamount, distance to nearest seamount with summit depth of <1,500 m; SST, sea surface temperature.
Fig 5.
Drivers and patterns of shark abundance in the Indo-Pacific.
(A) Relative contributions of drivers explaining variations in shark abundance (log[sumMaxN + 1]) were generated from 100 iterations of BRTs. (B,C) Partial dependence plot (lines), observed values (dots), and 95% confidence intervals for distance to nearest market (B) and seabed depth (C) and thresholds represented by breaking point (C). (D) Predicted values of shark abundance and hotspots (top 5% values, >0.54, in red). The numerical values for (A) can be found in S2 Data. BRT, boosted regression tree; Chla, chlorophyll-a concentration; Dist coast, distance to nearest coast; Dist CoralTri, distance to the Coral Triangle; Dist market, distance to nearest market; Dist seamount, distance to nearest seamount with a summit depth of <1,500 m; SST, sea surface temperature.
Fig 6.
Frequency distributions of predator attribute values predicted to occur under different spatial management regimes in the Indo-Pacific.
(A) Vertebrate species richness, (B) body size, and (C) shark abundance (log[sumMaxN + 1]) across the entire unprotected Indo-Pacific, inside partially protected or small MPAs and inside large no-take MPAs (>1,000 km2). Vertical lines and values are associated medians. MPA, marine protected area.
Fig 7.
Predicted shark abundance and occurrence along a gradient of human pressures (Distance to Market) and habitat suitability (Depth).
Values are segregated according to protection levels and whether they are hotspots (>.95 quantiles) or not (NA).